Innovative Mobile and Internet Services in Ubiquitous Computing

After the introduction of smart grid in power system, two-way communication is now possible which helps in optimizing the energy consumption of consumers. To optimize the energy consumption on the consumer side, demand side management is used. In this paper, we focused on the optimization of smart home appliances with the help of optimization techniques. Cuckoo search algorithm, earthworm optimization and a hybrid technique cuckoo-earthworm optimization are used for scheduling the smart home appliances. Home appliances are classified into three groups and real-time pricing scheme is used. Techniques are evaluated and a performance comparison is performed. Results show that the proposed hybrid technique has decreased the electricity cost by 49% as compared to unscheduled cost and a trade-off exists between electricity cost and user comfort.

In recent years, advanced technology is increasing rapidly, especially in the field of smart grids. A home energy management systems are implemented in homes for scheduling of power for cost minimization. In this paper, for management of home energy we propose a meta-heuristic technique which is hybrid of existing techniques enhanced differential evolution (EDE) and earthworm optimization algorithm (EWA) and it is named as earthworm EWA (EEDE). Simulations show that EWA performed better in term of reducing cost and EDE performed better in reducing peak to average ratio (PAR). However proposed scheme outperformed in terms of both cost and PAR. For evaluating the performance of proposed technique a home energy system proposed by us. In our work we are considering a single home, consists of many appliances. Appliances are categorized into two groups: Interruptible and un-interruptible. Simulations and results show that both algorithms performed well in terms of reducing costs and PAR. We also measured waiting time to find out user comfort and energy consumption.

The Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure because they have many advantages such as low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO, and a simulation system based on Genetic Algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. In this paper, we implement a hybrid simulation system based on PSO and distributed GA (DGA), called WMN-PSODGA. We analyze the performance of WMN-PSODGA by computer simulations considering different client distributions. Simulation results show that the WMN-PSODGA has good performance when the client distribution is Normal compared with the case of Exponential distribution.

Demand side management (DSM) in smart grid (SG) makes users able to take informed decisions according to the power usage pattern of the electricity users and assists the utility in minimizing peak power demand in the duration of high energy demand slots. Where, this ultimately leads to carbon emission reduction, total electricity cost minimization and maximization of grid efficiency and sustainability. Nowadays, many DSM strategies are available in existing literature concentrate on house hold appliances scheduling to decrease electricity cost. However, they ignore peak to average ratio (PAR) and consumer’s delay minimization. In this paper, a load shifting strategy of DSM is considered, to decrease PAR and waiting time. To gain aforementioned objectives, the flower pollination algorithm (FPA), grey wolf optimizer (GWO) and their hybrid i.e., flower grey wolf optimizer (FGWO) are used. Simulations were conducted for a single home consist of 15 appliances and critical peak pricing (CPP) tariff is used for computing user’s electricity payment. The results show and validate that load is successfully transferred to low price rate hours using our proposed FGWO technique, which ultimately leads to 50.425% reduction in PAR, 2.4148 h waiting time and with 54.654% reasonable reduction in cost.

In this work, we present a distributed event-based awareness approach for P2P groupware systems. In our approach, the awareness of collaboration will be achieved by using primitive operations and services that are integrated into the P2P middleware. We propose an abstract model for achieving these requirements and we discuss how this model can support awareness of collaboration in mobile teams. We present a fuzzy-based system for improving peer coordination quality according to three parameters. This model will be implemented in MobilePeerDroid system to give more realistic view of the collaborative activity and better decisions for the groupwork, while encouraging peers to increase their reliability in order to support awareness of collaboration in MobilePeerDroid Mobile System. We evaluated the performance of proposed system by computer simulations. From the simulations results, we conclude that when GS and SCT are high, the peer coordination quality is high. With increasing of AA, the peer coordination quality is increasing.

The OppNets are a subclass of delay-tolerant networks where communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The Internet of Things (IoT) is the network of devices, vehicles, buildings and other items embedded with software, electronics, sensors and network connectivity that enables these objects to collect and exchange data. There are different issues for these networks. One of them is the selection of IoT devices in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT device selection in opportunistic networks. For our system, we use three input parameters: IoT Contact Duration (IDCD), IoT Device Storage (IDST) and IoT Device Remaining Energy (IDRE). The output parameter is IoT Device Selection Decision (IDSD). The simulation results show that the proposed system makes a proper selection decison of IoT-devices in opportunistic networks. The IoT device selection is increased up to 19% and 53% by increasing IDCD and IDRE respectively.

Underwater wireless sensor networks (UWSNs) are capable of providing facilities for the wide range of aquatic applications. However, due to the adverse environment, UWSNs face huge challenges and issues i.e., limited bandwidth, node mobility, higher propagation delay, high manufacturer and deployment costs etc. In this paper, we propose two techniques: the geographic and opportunistic routing via transmission range (T-GEDAR) and the geographic and opportunistic routing via the backward transmission (B-GEDAR). Firstly, in the absence of forwarder node, we increase the transmission range to determine the forwarder node. Because of this, we can send packets to the sink; Secondly, when the forwarder node is unavailable in adjustable transmission range. Then, the B-GEDAR is used for determining the forwarder node so that the packet delivery ratio (PDR) can be increased effectively. This is because, our simulation results perform better network performance in terms of an energy efficiency, PDR, and the fraction of void nodes.

With the fast development of mobile computing and increasing computing capacities of mobile devices, new collaborative applications and platforms are appearing to support collaboration on the move. Indeed, nowadays, members of a team can be not only geographically distributed but they can also work anytime and anywhere thanks to the use of mobile devices. Often, however, team members would like to work either online or offline on a common project; likewise, they may wish to switch among various devices such as laptops, tablets and mobile phones and still work in the same application environment, sharing the same data, etc. In this paper we present a platform that enables application and services migration at runtime between different platforms using the WebRTC (Web Real-Time Communication) framework. We have studied applications migration both through a central server and through a distributed (Peer-to-Peer) model. Various issues that arise in application migration such as profile matching, application context, data synchronisation and consistency are discussed. The efficiency and scalability of the WebRTC framework and mobile devices (peers) under Android in a real computing infrastructure are studied. Some experimental results on the application migration time according to application state data size are reported.

In wireless ad-hoc networks, messages have to be energy-efficiently delivered to destination nodes by exchanging the messages among neighboring nodes. In our previous studies, the reactive type EAO, LEU, and IEAO protocols are proposed to unicast messages. In the EAO protocol, the total electric energy of nodes and delay time from a source node to a destination node can be reduced compared with the ESU and AODV protocols. However, a source-to-destination route may not be found if the communication range of each node is shorter. In the IEAO protocol, a route can be found even in short communication range. In the forwarding phase, a node $$p_i$$ does not necessarily receive an RQ messages which sent by a node $$p_j$$ whose level parameter is bigger than $$p_j$$ and keeps the information which is contained in the RQ messages. Here, by neglecting superfluous RQ messages, the electric energy consumption of the forwarding phase can be reduced. In this paper, we propose an IEAO2 protocol by improving the IEAO protocol so that the electric energy consumption is reduced in the forwarding phase. In the evaluation, we show the information which each node keeps can be reduced in the IEAO2 protocol compared with other protocols.

In this paper, we discuss a virtual machine migration approach to reducing the electric energy consumption of servers. In our previous algorithms, one virtual machine migrates from a host server to a guest server. While the electric energy consumption of servers can be reduced by migrating some number b of processes, there might not be a virtual machine with the same number b of processes on a host server. In this paper, we propose an ISEAM2T algorithm where multiple virtual machines can migrate from a host server to a guest server. Here, multiple virtual machines on a host server are selected so that the total number of processes on the virtual machines can be more easily adjusted to the best number b of processes. In the evaluation, we show the total electric energy consumption and active time of the servers and the average execution time of processes can be reduced in the proposed algorithm.

Using a device for measuring the intensity of a physical activity when a person carries out their daily routines is an important support to monitor their health, especially if this person is overweight or obese since it exists risk for their health when demanding a lot of energy while performing physical activities. To confront this problem, there are new generation devices for measuring physical activity, that can be used to know physical intensity levels and consequently, establish exercise programs if were necessary to lose weight or maintain a certain level of training derived from a medical prescription. This paper evaluates the relationship between values of a motion sensor and heart rate sensor for measuring the intensity of physical activity of overweight or obese people. We propose to use these two sensors to determine the correlation between both so that at a given time, the motion sensor can be a useful alternative to measure the intensity of physical activity. This option makes easier for people to measure physical intensity with a conventional device equipped with an accelerometer, many people that use smartphones might avoid going to an expert to keep track of physical exercises.

If there is an ultra-large scale disaster happened, the important messages such as life safety or rescues would not be transferred because of the serious damages of information networks. It is supposed that the Delay Tolerant Networking (DTN) is one of the effective routing methods for realizing against such robust network conditions, but some problems such as IP configurations or gateway functions have been considered for the realistic mobile networks. Therefore, this paper proposed the layer 2 level DTN routing methods by using WiFi P2P and the gateway functions between the DTN and IP networks. Then, the implementations of the prototype systems by Android smartphones and the field experiments are reported in the paper, and the experimental results are discussed for the effectiveness of the proposed methods and future studies.

For reducing the damage of disaster, it is needed to correct/deliver disaster information rapidly. However, under the disaster occasion, it is not easy to engage the usual communication due to the lack of perfect operation of communication infrastructure. Hence, in this paper, we propose to construct new contents centric data delivery system over the network consisting of DTN nodes. The performance evaluations confirm that our proposal effectively reducing the cache acquisition delay.

In this paper, we propose a method to reduce the difficulties of query caused by lack of information about graph patterns even though the graph pattern is one of the important characteristics of the LOD. To do so, we apply the clustering methodology to find the RDF predicates that have similar patterns. In addition, we identify representative graph patterns that imply its characteristics each cluster. The representative graph patterns are used to extend the users’ query graphs. To show the difficulties of the query on the LOD, we developed an illustrative example. We propose the novel framework to support query extension using predicate clustering-based entity-centered graph patterns. Through the implementation of this framework, the user can easily query the LOD and at the same time collect appropriate query results.

This paper examines an input method for ocular analysis that incorporates eye-motion and eye-blink features to enable an eye-controlled input interface that functions independent of gaze-position measurement. This was achieved by analyzing the visible light in images captured without using special equipment. We propose applying two methods. One method detects eye motions using optical flow. The other method classifies voluntary eye blinks. The experimental evaluations assessed both identification algorithms simultaneously. Both algorithms were also examined for applicability in an input interface. The results have been consolidated and evaluated. This paper concludes by considering of the future of this topic.

In our previous work, we presented an interactive learning process in order to increase the students learning motivation and the self-learning time. We proposed an Active Learning System (ALS) for student’s self-learning. For each level (low, middle and high level) class, we showed that the students could keep concentration using our proposed ALS. However, in the group discussion, some students who understood the lecture could teach other students who did not understand “study points”. But, many students complained that they did not feel like a lecture style. In this paper, to solve this problem, we propose a new method for dynamic group formation. After the system decides the level of lecture understanding, the students who did not understand the lecture make the presentation and show the points that they did not understood. Also, the students who understood the lecture explain by presentation the questions or the points that other students did not understand. So, in the group discussion, each group member presents the points that they understood or did not understand. Thus, most of students can study considering their understanding level using above group discussion and they can keep their learning motivation.

RFID system becomes one of the very useful tools for the management of the library. Using electromagnetic coupling, an RFID tag can get power supplier by a reader and communicate with it for data exchange. Because the RFID system enables non-contact communication, various services and applications including the management of a library catalogue are possible. However, because the system is affected easily by neighboring environment, the communication performance is low. In this paper, by using 13.56 MHz RFID system, we evaluate the resonant frequency of RFID tag and the communication distance between the reader and the target tag when some tags becoming as interference sources come close to each other, and show the possibility to expand the communication distance by using tags near the target tag.

Underwater wireless sensor networks (UWSNs) provide the wide range of aquatic applications. The limited bandwidth, long propagation delay, energy consumption, high manufacturing, and deployment costs are many challenges in the domain of UWSNs. In this paper, we present the two techniques i.e., energy gradation (EG) and depth adjustment (DA) in without the number of coronas. Firstly, the forwarder node determines the higher energy node and it is directly transmitted to sink; secondly, if the forwarder node occurs in transmission void region then the node moves to the new depth so that the data delivery ratio can be ensured effectively. Simulation results define that our proposed schemes show better performance in terms of energy efficiency, packet delivery ratio (PDR) and network lifetime etc.

The mouse is one of the most widely used I/O devices on a computer. Most user authentication methods are password-based through the keyboard, but there exists a vulnerability through which passwords are exposed through data input, such as keyloggers. Thus, image-based authentication, which authenticates through data input from a mouse, has been discovered. Image-based authentication method is widely used in various Web sites and Internet banking services. This paper analyzes the vulnerability of image-based authentication, which is based on the input data through the mouse. This paper also analyzes an experiment where passwords are exposed by taking mouse data through the PS/2 controller, and we also implemented the proof-of-concept tool and confirm the result of mouse data exposure in the image-based authentication applied in the Internet banking service.

This article proposes to construct a spacecraft AIT (Assembly Integration Test) visualization control system based on VR technology, which can further enhance the digitization level of spacecraft manufacturing. The article first outlines the research and development background of the system, and then elaborates on the key technologies and software architecture used in the construction of the system, and gives the actual operation situation of the system, and finally summarizes the article.

In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization.

Micro-grid (MG) is an emerging component of a smart grid and it is increasing the efficiency and reliability of the power system with the passage of time. MGs often need power in order to fulfill its load requirements, which is transmitted form macro station (MS). Transmission of power from MS cause power line losses. To decrease these power line losses, a hierarchical based coordination (HBC) strategy is proposed for efficiently exchanging the power among MGs. HBC aims to decrease power line losses by making hierarchical coalitions. Results are evaluated and compared with conventional non-coordination model (NCM). This comparison shows the effectiveness of proposed HBC strategy. Results indicate that HBC has decreased the power line losses by 40.1% as compared to conventional NCM.

The concept of cloud computing is becoming popular with each passing day. Clouds provide virtual environment for computation and storage. Number of cloud users is increasing drastically which may cause network congestion problem. To avoid such situation, fog computing is used along with cloud computing. Cloud act as a global system and fog works locally. As the requests from users are increasing so load balancing is also required on fog side. In this paper, a three layered cloud and fog based architecture is proposed. Fog computing acts as a middle layer between users and the cloud. Users’ requests are handled at fog layer and filtered data is forwarded to cloud. A single fog has multiple virtual machines (VMs) that are assigned to the users’ requests. The load balancing problem of these requests is managed by proposed weighted cuckoo search (WCS) algorithm. Simulations are carried out to evaluate the performance of proposed model. Results are presented in the form of bar graphs for comparison and detailed values of each parameter are presented in tables. Results show the effectiveness of proposed technique.

In this paper, Smart Grid (SG) efficiency is improved by introducing Cloud-based environment. To access the services and hostage of cloud large number of requests are entertained from Smart Homes (SHs). These SHs exists in clusters of smart buildings. When the number of requests increase, delay, latency and response time also increase. To overcome these issues, Fog is introduced, which act as an intermediate layer between the cloud and consumer. Five Micro Grids (MGs) are attached to each cluster of the smart building to manage its requests. By using Fog base environment, the delay and latency decreases. The response time also increases with less processing time. To handle the load on cloud different load balancing algorithms and service broker policies exist. In order to manage the load, Honey Bee (HB) is implemented. HB is compared with existing algorithm Round Robin (RR). It gives better results than RR.

Short term load forecasting is indispensable for industrial, commercial, and residential smart grid (SG) applications. In this regard, a large variety of short term load forecasting models have been proposed in literature spaning from legacy time series models to contemporary data analytic models. Some of these models have either better performance in terms of accuracy while others perform well in convergence rate. In this paper, a fast and accurate short term load forecasting framework based on stacked factored conditional restricted boltzmann machine (FCRBM) and conditional restricted boltzmann machine (CRBM) is presented. The stacked FCRBM and CRBM are trained using rectified linear unit (RelU) and sigmoid functions, respectively. The proposed framework is applied to offline demand side load data of US utility. Load forecasts decide weather to increase or decrease the generation of an already running generator or to add extra units or exchange power with neighboring systems. Three performance metrics i.e., mean absolute percentage error (MAPE), normalized root mean square (NRMSE), and correlation coefficient are used to validate the proposed framework. The results show that stacked FCRBM and CRBM are accurate and robust as compared to artificial neural network (ANN) and convolutional neural network (CNN).

In this article, a resource allocation model is presented in order to optimize the resources in residential buildings. The whole world is categorized into six regions depending on its continents. The fog helps cloud computing connectivity on the edge network. It also saves data temporarily and sends to the cloud for permanent storage. Each continent has one fog which deals with three clusters having 100 buildings. Microgrids (MGs) are used for the effective electricity distribution among the consumers. The control parameters considered in this paper are: clusters, number of buildings, number of homes and load requests whereas the performance parameters are: cost, Response Time (RT) and Processing Time (PT). Particle Swarm Optimization with Simulated Annealing (PSOSA) is used for load balancing of Virtual Machines (VMs) using multiple service broker policies. Service broker policies in this paper are: new dynamic service proximity, new dynamic response time and enhanced new response time. The results of proposed service broker policies with PSOSA are compared with the existing policy: new dynamic service proximity. New dynamic response time and enhanced new dynamic response time performs better than the existing policy in terms of cost, RT and PT. However, the maximum RT and PT of proposed policies is more than the existing policy. We have used CloudAnalyst for conducting simulations for the proposed scheme.

The most important part of the smart grid (SG) is prediction of electricity price and by this prediction SG becomes cost efficient. To tackle with large amount of data in SG, it is a challenging task for existing techniques to accurately predict the electricity price. So, to handle the above mentioned problem, a framework has been proposed with three different steps: feature selection, feature extraction and classification. The purpose of feature selection is to remove irrelevant data by using extra tree classifier on the basis of pearson correlation coefficient. Feature extraction is performed using t-distributed stochastic neighbor embedding method to reduce redundancy from the selected data. For accurate electricity price forecasting, support vector machine classifier is used. Simulation results show that the proposed framework outperforms than the other methods.

In this paper, we implemented a disaster support expert system for emergency response headquarters. This system consists of the disaster information storage system and the disaster information visualization system. The disaster information storage system stores disaster case, disaster response record, and local disaster prevention plan. And, the disaster visualization system visualizes past disaster information and correspondence records accumulated in the disaster information storage system. By using this system, the emergency response headquarters can promptly and appropriately disaster response through accumulated past disaster cases and disaster response records.

In this paper, we propose collecting personal culture of local residents to collective culture, and returning collective culture to local residents as personal culture by using AR technology and historical map. And, we propose a prototype system of regional knowledge inheritance system in order to support the protection and inheritance of regional knowledge. The prototype system has the function of overlaying the historical map on the basic map and the function of presenting the regional knowledge related to the place as an AR.

In the VR (Virtual Reality) content, the immersion is important sensibility in order to be alive in the VR space. Also, the vision should be improved by following the sight of view and high quality of videos. For this reason, the VR content streaming uses a high-speed network for sending continuously high frame rate and high frame size videos. There are many studies about video streaming technologies and QoS (Quality of Service) control mechanisms. However, they can’t be used for VR streaming technology in case of limited computer network resources. In this paper, we introduce a QoS Management for Virtual Reality Contents by controlling QoS parameters according to user’s requests to keep the immersive experience quality in case of limited computer network resources.

Webshell is a common backdoor program of web applications. After an attacker uploads Webshell successfully by using a vulnerability. Attacker can get a command execution environment to control the web server by access Webshell. In this paper, an attack detection technology based on SVM algorithm is proposed by analyzing the network traffic of attackers accessing Webshell. This technology realizes the detection of Webshell attack traffic in HTTP traffic by means of the method of supervised machine learning model. And this technology achieves high accuracy and recall rate. After detecting abnormal traffic, the system can locate the Webshell according to traffic information. And eliminate the backdoor in time to ensuring the security and stability of the web server. So it also can help to monitor the trend of intrusion and network security.

In recent years, the market demand for smart devices continues to increase. As a widely used communication technology, applications of ZigBee Light Link protocol and its security have received extensive attention in recent years. This article discusses the security of the ZigBee Light Link commissioning protocol. Based on the hash chain technology, this paper proposes a network key update protocol based on the security-enhanced ZLL protocol. In the end, the performance evaluation and comparison are given.

Now people are using the network all the time, but the ensuing network attacks are constantly threatening people’s lives, so information security is becoming more and more important. In this paper, an intrusion detection model based on the MEA-Elman neural network is proposed. Firstly, GA algorithm is used to reduce the dimension of the dataset, and then verified by the MEA-Elman network model. The experiment results show that the detection model has high accuracy, which can meet the basic requirements of intrusion detection.

How to ensure location privacy has become an important issue for VANETs’ security. The more effective mechanism is that the vehicles can not be associated by replacing the pseudonyms to protect their location privacy. This paper proposes a novel location privacy protection scheme for VANETs. When vehicles need to change the pseudonyms, they will cooperate with the neighbor nodes to create the encrypted area through group key encryption, so that the external adversary cannot crack the message in this area. During this period, some vehicles change the pseudonyms jointly so that the external adversary can not associate the pseudonyms before and after. Thus the location privacy protection of the vehicle nodes is achieved.

Radio frequency identification (RFID) technology faces many security issues, and a detailed analysis of these security issues is conducted. It analyzes the existing RFID security authentication protocol and proposes a security authentication protocol that can effectively solve the problems of fraud, retransmission, tracking, and synchronization. In the continuous conversation mode of RFID, it uses the powerful computing power of the reader, the random number identification and comparison between the reader and the tag can resist the denial of service attack. To be able to resist desynchronization attacks, the back-end database stores tag identifiers and dynamically updates the data so that the tags and the background database maintain data synchronization. It utilizes the reader’s computing and storage functions to reduce the cost of tags, makes the agreement meet the requirements of low-cost, ensures two-way authentication, and improves the efficiency of the security authentication protocol.

The past decade has witnessed a rapidly developing Internet, which consequently brings about devastating web attacks of various types. The popularity of automated web attack tools also pushes the need for better methods to proactively detect the huge amounts of evolutionary web attacks. In this work, large quantities of URLs were used for detecting web attacks using machine learning models. Based on the dataset and feature selection methods of [1], multi-classification of six types of URLs was explored using the random forest method, which was later compared against the gated recurrent neural networks. Even without the need of manual feature creation, the gated recurrent neural networks consistently outperformed the random forest method with well-selected features. Therefore, we determine it is an efficient and adaptive proactive detection system, which is more advanced in the ever-changing cyberspace environment.

This paper presents a Compressed PQ Indexing (CPQI) data structure, which realizes the further compression of sparse entries, requires only sub-linear search time, and the sparse entries are no longer stored. The proposed CPQI saves storage space and is suitable for in-memory computing for large-scale data. The CPQI employs the Minimal Perfect Hash to hash the PQ code, preserve non-null entries, and store the structure very compactly; the compressed PQ hash code index no longer stores PQ code. A sub-linear time search is implemented by combining Bloom filtering with a minimum perfect hash function.

Direct Energy trading is a promising approach to simultaneously achieve trading benefits and reduce transmission line losses. Due to the characteristics of selfish requirement and asymmetric information, how to provide proper incentives for the electricity consumer (EC) and small-scale electricity supplier (SES) to take part in direct energy trading is an essential issue. Considering the variable characteristic of requirements and environment in direct energy trading, a two-period dynamic contract incentive mechanism is introduced into the long-term direct energy trading. The optimal contract is designed to obtain the maximum expected utility of the EC based on the individually rational and incentive compatible conditions. Simulation result shows that the optimal dynamic contract is efficient to improve the performance of direct energy trading.

Alzheimer disease is associated with many risks, including the destruction of family morale and the loss of experience of many scientists in different areas. However, little research depending on computer science has been conducted to explore this disease. The purpose of this study is trying to find the possibility of using computer techniques to improve the therapeutic methods of Alzheimer disease. This paper elaborates the approach of using EEG signals on virtual reality environment and introducing them as a patient’s therapeutic program to improve temporary memory. The patient’s memory is rearranging based on a suitable brain signal through the theory of artificial neural network and deep learning technique so that the memory is able to be gradually improved.

Recognizing real world objects based on their 3D shapes is an important problem in robotics, computational medicine, and the internet of things (IoT) applications. In the recent years, deep learning has emerged as the foremost tool for a wide range of recognition and classification problems. However, the main problem of convolutional neural networks, which are the primary deep learning systems for such tasks, lies in the high computational cost required to train and use them, even for 2D problems. For 3D problems, the problem becomes even more pressing, and requires new methods to keep up with the further increase in computational cost. One such method is the use of Fast Fourier Transforms to reduce the computational cost by performing convolution operations in the Fourier domain. Recently, this method has seen widespread use for 2D problems. In this paper, we implement and test the method for 2D and 3D object recognition problems and compare it to the traditional convolution methods. We test our network on the ShapeNet 3D object library, achieving superior performance without any loss in accuracy compared to conventional methods.

In this paper will be described possible applications of cognitive information systems to intelligent and secure information management tasks in Fog and Cloud computing. In particular will be presented the ways of using some semantic descriptors and personal characteristics for creation of protocols dedicated to confidential distribution and secure data management in different distributed environments. The new paradigm of cognitive cryptography will be also described.

Recently, systems for detecting and tracking moving objects from video are gaining research interest in the field of image processing, owing to their applications in fields such as security, observation, and military, and considerable research is being conducted to develop high-accuracy and high-speed processing systems. In particular, as interest in autonomous driving has increased rapidly, various algorithms for lane keeping assistance devices have been developed. This study proposes a lane detection algorithm by comparing color-based lane detection algorithms and using a lane detection algorithm based on representative line extraction. Edge extraction and Gaussian filters are applied for lane detection and a Median filter is applied for image noise reduction. The detection accuracy is improved by extracting the region of interest for the image based on four pointers. Finally, a Hough transform is applied to improve the accuracy of straight line detection, and an algorithm to extract representative lines is applied to increase the detection rate in shadow regions and dark areas. Experimental results show that the proposed algorithm can detect lanes with high accuracy. The application of this algorithm to vehicle black boxes or autonomous driving will help prevent lane departure and reduce accident rates.

This paper mainly introduces the creative design of a children’s cognitive system based on RFID. The electronic tag is affixed to the real learning object, and the card reader is carried by the children. The main functions to be realized are vivid images, which give children quick and vivid knowledge of objects. Through the education of physical objects that can be touched, they can be vividly visualized, and children can be made to know objects vividly. In addition, based on our design concept, which connects cognition with material objects, it can actively mobilize children’s various sensory systems, so that children can have a deeper understanding of the learning objects and reduce the cost of education.

In smart cites, more and more smart mobile devices (SMDs) have many computation-intensive applications to be processed. Mobile cloud computing (MCC) as an effective technology can help SMDs reduce their energy consumption and processing delay by offloading the tasks on the distributed cloudlet. However, due to long transmission delay resulting from the unstable wireless environment, the SMD may be out of the serving area before the cloudlet transmits responses to the user. Thus, delay is a crucial problem for the MCC offloading. In this paper, we consider a multi-SMDs MCC system, where each SMD having an application to be offloaded asks for computation offloading to a cloudlet. In order to minimize the total delay of the SMDs in the system, we jointly take the offloading cloudlet selection, wireless access selection, and computation resource allocation into consideration. We formulate the total delay minimization problem as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. We propose an improved genetic algorithm to obtain a local optimal result. Simulation results demonstrated that our proposal could effectively reduce the system delay.

Fast-growing video services place higher demands on network performance especially in terms of latency, but the traditional networks architecture with congested backhaul link can no longer meet the requirement. Recently, mobile edge computing (MEC) has become a promising paradigm to achieve low latency performance and can provide multi-bitrate video streaming at the edge of radio access networks (RAN) with the ability of caching and transcoding. In this paper, we consider the scenario of multi-cell MEC networks, where each BS deployed with one MEC server is connected to the core network through the limited-capacity backhaul link. Our goal is to minimize the system delay which includes backhaul transmission delay and wireless side transmission delay. To this end, we propose a collaborative optimization of user-BS association and power allocation strategy with the given cache status. This is a mixed-integer nonlinear programming (MINLP) problem which is NP-hard. Thus we propose an improved genetic algorithm to solve this problem based on the traditional genetic algorithm. Simulation results demonstrate that our proposed algorithm performs better in terms of convergence and can get better solution as compared with traditional genetic algorithm.

Cyber-Physical Systems (CPS) are set to radically transform the world we live in. Prototypes for very different domains have been reported, from Industry 4.0 to Ambient Intelligence and the Internet of Things. Several research works have shown the good performance of these systems, which could be useful for everyday living once they become commercial products. However, no complete application for cyber-physical devices has been reported yet. Thus, the large amount of benefits this new paradigm may push remains very difficult to envision by general society and companies. In any case, personalized services rank among the most direct and interesting applications for cyber-physical devices. So far, no work on this topic has been reported, but the implementation of this new generation of services is a key area for the advancement toward the CPS era. Therefore, in this paper we will explore the concept of cyber-physical personalized services and propose a first example of these new services based on cyber-physical sensors and a cyber-physical device: a smart table. Finally, in order to evaluate the performance of the proposed solution, we will carry out an experimental validation.

Expansion of IoT and increasing computing resources provides opportunities in edge computing. There are two types of edge computing: heavy edge computing and lightweight edge computing. The author discusses lightweight edge computing with resource-constraint from real-world use cases from a smart city project. The author proposes device management framework with stand by mechanism and device characteristics from data mining of past device behavior.

In recent years, inter-vehicle communication has attracted attention because it can be applicable not only to alternative networks but also to various communication systems. In this paper, we propose a security-aware Fuzzy-based cluster head selection system for VANETs. We evaluate the proposed system by simulations. From the simulation results, we found that when GS, DC and RA are high, the CHS is high. The simulation result show that the performance of the system is increased when security parameter value is increased.

With the development of society, there are food safety issues. From Sanlu’s “melamine incident”, the chemical composition of American McLean chickens to “false zisha” and “clenbuterol”, these hot topics have made people more and more concerned about food safety issues. In order to improve the quality and safety of food, and also to meet the transparent demand of producers and consumers for food production, it is particularly urgent to build a set of standardized, intelligent anti-counterfeiting traceability systems. Nowadays, thanks to the rapid development of Internet of Things technology, the realization of anti-counterfeiting traceability systems has become possible. This paper researches on RFID technology, combined with anti-counterfeiting technology and Internet technology, puts forward a realization project of food security anti-counterfeiting tracing system based on RFID security authentication and 2-dimensional code. The purpose of this system is to allow consumers to reassure about the products they purchase. The realized system can quickly and efficiently inquire into raw materials or processing problems when there are product quality problems. It can contribute to quality control and recalls products when necessary, so as to improve the competitiveness of the company.

In recent years, lots of web servers increasingly often suffer from Denial of Service (DoS) attacks within application layer. Many approaches provide abnormal traffic detecting in order to prevent any malicious traffic. However, the attack features or patens of the malicious traffics did not addressed clearly. Thus, the aim of this paper is to provide an approach based on the association rule mining technique for traffics appeared in the integrated web services, such as HTTP, HTTPS, and FTP traffic, in order to discover the strong attack features or patens of DoS attacks. Association rule mining is employed in this paper to deal with the DoS patens and then find out the strong relations among features of DoS attacks in large well-known dataset, e.g. NSL-KDD. The strong relations which are determined on when the major attack features are discovered from the open dataset would be considered as the strong patterns of DoS attacks. Finally, the outputted strong patterns could be used in the intrusion detection system (IDS) to enhance the effects of detecting application layer DoS attacks.

This paper reports the current status and records on elliptic curve discrete logarithm problem (ECDLP), which is tightly connected to the security of elliptic curve cryptography (ECC) such as ECDSA and ECDH.

Recently, Android is expanding its application area including vehicle infotainment system, smart TV, AI speaker as IoT devices as well as mobile terminals. To maintain and support these systems, the manufacturer distributes the firmware through the Android firmware build framework and updates after evaluating firmware integrity by a signature verification process. However, attackers potentially falsify the firmware and raise critical security problems on mobile terminals in cases that developers use the public test key or SDK key to sign the firmware for release, due to lack of security readiness of mobile manufacturers. This paper analyzes the firmware signing, verification and update process of the Android platform, introduces vulnerabilities invented when an unsafe key is used and implements an evaluation tool for signature verification to find the firmware features if signed by an unsafe key.

Steganography is a technique used for concealing information by embedding messages or data into other data. However, problems can arise when this technique is used to steal confidential information, for unlawful purposes such as spying, terrorist attacks, and so on. Moreover, when the information is hidden, serious problems are caused if the evidence is not obtained. Therefore, we propose a detection method, based on StegoMagic-a steganography tool-that can be used on hidden information within a JPG file. We also propose an extraction method for the hidden information.

In recent years, social networking platform serves as a new media of news sharing and information diffusion. Social networking platform has become a part of our daily life. As such, social media advertising budgets have explosively expanded worldwide over the past few years. Due to the huge commercial interest, clickbait behaviors are commonly observed, which use attractive headlines and sensationalized textual description to bait users to visit websites. Clickbaits mainly exploit the users’ curiosity’s gap by interesting headlines to entice its readers to click an accompanying link to articles often with poor contents. Clickbaits are bothersome either to social media users or platform site owners. In this paper, we propose an approach called Ontology-based LSTM Model (OLSTM) to detect clickbaits. Compared with the existing solutions for clickbait detection, our approach is characterized by the following three components: word embedding model, Recurrent Neural Networks (RNN), and word ontology information. The observation is that preserving semantic relationships is significantly an important factor to be considered in detecting clickbaits. Therefore, we propose to capture semantic relationships between words by word embedding models. In addition, we adopted RNN as our classification models to consider word orders in a sentence. Furthermore, we consider the word ontology relation as another feature set for clickbait classification, as clickbaits often uses words with generalized concepts to induce curiosity. We conduct experiments with real data from Twitter and news websites to validate the effectiveness of the proposed approach, which demonstrates that the employment of the proposed method improves clickbait detection accuracy from 80% to 90% compared with the existing solutions.

DevOps is an important practice environment and future operation trend for software development and maintenance. DevOps has important features that are continuous integration, continuous delivery, automation and high efficiency to increase enterprise market competition. Micro services architecture is a critical item for keeping the advantages of DevOps environment. In addition, micro services have many advantages than monolithic applications in software development and maintenance. However, many challenges of micro services architecture need to be overcome. Quantified quality characteristics can identify and assist to improve the defects of micro services that affect the efficiency and quality of DevOps operation. In this paper, the authors propose the Micro Services Quality Measurement (MSQM) Model to evaluate and identify work process defects of micro services. Based on MSQM model, the quality improvement procedure is designed for improving micro services work process defects and increasing the overall DevOps operation efficiency and application quality.

Following the fast development of 5G networks and Internet of Things (IoT) techniques, in the future, billions of User Equipment (UE) and IoT devices will connect to networks and send data to backend servers for situation monitoring or required processes. Since mobile devices roam to everywhere in the world, firewalls which are designed to protect fixed-position devices cannot be used to secure mobile users. In this study, we would like to use firewall to protect mobile devices, even though they are roaming in the networks other than their home networks. UEs are secured in data layer with firewalls, distributed topology storage (DT storage) and SDN controller, in which firewalls are installed in the vEPC’s edge computers. We also develop the corresponding processing algorithms.

In this study, we would like to deal with two topics. The first one is that we add a machine, named Mediator, to SDN Controller for managing and keeping track of the data generated by MME during UE authentication. When a VM fails, other MMEs can successfully take over its authentication tasks. The second is that when an MME fails, other MMEs can know this immediately and response properly. The purpose of these is to increase the QoS an UE can receives.

Home energy management systems have been widely used for energy management in smart homes. Management of energy in smart home is a difficult task and requires efficient scheduling for smart appliances in a home. A meta-heuristic optimization technique is proposed in this paper. The proposed Harmony Search Gray Wolf Optimization (HSGWO) is a hybrid of Harmony Search Algorithm (HSA) and Gray Wolf Optimization (GWO). The pricing signal used for the calculation of electricity cost is Real Time Pricing (RTP). The basic aim of this paper is to reduce electricity cost, Peak to Average Ratio (PAR) and maximization of user comfort. Simulation results show that HSGWO performs better as compared to HSA and GWO. The findings demonstrate that there is a trade off between electricity cost and user comfort.

Interconnection networks are emerging as an approach to solving system-level communication problems. An interconnection network is a programmable system that serves to transport data or messages between components/terminals in a network system. The hypercube is one of the most widely studied network structures for interconnecting a huge number of network components so that it is usually considered as the fundamental principle and method of network design. The unidirectional hypercube, which was proposed by Chou and Du (1990), is obtained by orienting the direction of each edge in the hypercube. Routing is crucial for almost all aspects of network functionalities. In this paper, we propose a dimension-ordered shortest-path routing scheme for unidirectional hypercubes and then analyze the incurred channel congestion from a worst-case point of view.

In this article, we design a three test rounds diagnosis algorithm for a 2D-torus network. Suppose that TDT(n, m) is a 2D-torus network with $$n\ge 4$$, $$m\ge 6$$ and m being even. Let F be the faulty set in TDT(n, m). With our algorithm, a diagnosis on TDT(n, m) is completed in three test rounds if $$|F|\le 4$$.

The k-edges fault-tolerance-Hamiltonian graphs have been studied by many researchers. In this paper, we introduce the 2-path-required Hamiltonian graphs. We will show that the complete bipartite graph $$K_{n,n}$$ is $$(n-3)$$-edges fault-tolerance 2-path-required Hamiltonian graphs. We also prove the relationship between hyper-Hamiltonian laceability and 2-path-required Hamiltonian property. Moreover, we present the construction scheme for 2-path-required Hamiltonian graphs, named vertex join. Applying this scheme, we can construct many new 2-path-required Hamiltonian graphs with edges fault-tolerant property.

Traditional GPS location technology cannot work in indoor environment. In order to sum up the positioning theory of RFID positioning method and find an indoor location algorithm suitable for an indoor environment, this paper reviews the composition of RFID indoor positioning system and the location algorithms of RFID indoor positioning system. And more comprehensive study and a systematic summary are carried out. The paper provides an important basis for the selection of RFID location algorithm and positioning system under different conditions.

In this paper, we propose an innovative board game design process to help students to design a cybersecurity board game with a pre-designed board game tool kit, and help them to further learn cybersecurity knowledge by using the design thinking and learning-by-doing strategies. In the process, the board game design course will firstly be given, the CBR-based learning by doing scheme will then be provided for helping the students to develop a similar game by themselves, and finally a preliminary assessment including the questionnaire and the concept map testing will be conducted. The experimental results showed that the given appropriate learning scaffolding can guide students to stimulate the creativity and complete their own cybersecurity board game in a short period of time. Besides, the questionnaire survey result also showed that about 80% of students are very interested in the board game design course, and that they can be able to understand the most frequent attacking techniques.

When using information observed by sensor nodes in a sensor network, it is important in many cases to know the positions of the nodes. One of the well-known methods of estimating node positions is APIT (Approximate Point-in-Triangulation Test). To improve APIT, the authors previously proposed a localization algorithm based on the positions and received signal strengths of neighbor nodes. However, this algorithm leaves the positions of many nodes unknown. To solve this problem, this paper proposes a new localization algorithm which is based on the availability of direct signals from neighbor anchor nodes. Our simulation experiments have confirmed the effectiveness of the proposed algorithm.

The RTB system is a bidding system for advertising in a specific area of on-line page. A typical RTB bidding system is a system provided by Google’s search engine. In this paper, we use the data of the Naver advertisement bidding, a representative Korean search engine operated by a private bidding for the RTB system. Especially, in case of online keyword advertisement, the rank can be important factor the online page when a user enters a certain keyword into a search engine. For example, if a search keyword is ranked at the top of an online page, the probability of bid being directly connected will be increased for the link of related keyword. Therefore, the bid price of the keyword is changed according to the rank of the search keyword. In the end, it is necessary to find an appropriate bid price for registering a keyword in a private bid system. In this paper, we propose a prediction modeling mechanism to predict optimal bid price of the keyword in a specific ranking of search engine. In order to predict the optimal bid price and advertising ranking on the online page, we perform feature engineering on the related data set and define the prediction model using the machine learning algorithms for the corresponding data set.

In this paper, we propose a method for recognizing the complex activity using audio sensors and the machine learning techniques. To do so, we will look for the patterns of combined monophonic sounds to recognize complex activity. At this time, we use only audio sensors and the machine learning techniques like Deep Neural Network (DNN) and Support Vector Machine (SVM) to recognize complex activities. And, we develop the novel framework to support overall procedures. Through the implementation of this framework, the user can support to increase quality of life of elders’.

Cloud system is a new computing technology for dealing with IT services at the Internet server system. In spite of its advantages in various aspects, it is not proper for IoT (Internet of Things) services that require high density and real time data processing. Fog computing is a new paradigm appropriate for decreasing data processing latency, managing the mobility, and increasing the service efficiency of IoT. However, there is a lot of security vulnerabilities for fog computing because of the lack of security related systems. In this work, we propose a group key management for the secure fog computing which can be adjusted dynamically based on the mobility of the IoT devices. Our proposed system can increase the system efficiency by controlling the IoT groups and the group keys management.

The fifth generation of mobile networks (5G) is associated with a wide spectrum of novel use cases that introduce a large number of very diverse requirements, regarding for instance throughput, latency, delay, availability and reliability. End-to-end network slicing is seen as a solution that allows to simultaneously accomplish those manifold requirements in isolated slices running on a shared network infrastructure. However, embedding those virtual end-to-end network slices into a common physical network containing wireless as well as wired network elements, while meeting all the different requirements, is still an unsolved problem. In this paper, a vision of an end-to-end network slice resource allocation process will be presented allowing to give fast feedback to a network operator or tenant on the feasibility of embedding new network slices. The associated research challenges will be discussed, especially focusing on the more complex Radio Access Network (RAN) resource allocation.

Demand side management plays a vital role in load shifting to off peak hours from on peak hours in response to dynamic pricing. In this paper, we propose an optimal stopping rule (OSR) and firefly algorithm (FA) for the demand response based on cost minimization. Each appliance gets the best opportunistic time to start its operation in response to dynamic electricity pricing. The threshold based cost is computed for each appliance where each appliance has its own priority and duty cycle regardless of their energy consumption profile. Numerical simulations show that our proposed scheme performed well in lowering cost, waiting time and peak to average ratio.

In this article, a three layered architecture is proposed for smart buildings. A fog based infrastructure is designed and deployed on the edge of network, where fog processes the private data collected through the smart meters and stores the public data on cloud. Further, end user has facility to schedule and control the home appliances by using a centralized energy management system. Moreover, the electricity and network resources utilization charges can be calculated. We analyze the performance of cloud based centralized system, considering the fog computing as an intermittent layer between system user layer and cloud layer and without considering fog computing. Simulation results prove that fog layer enhances the efficient utilization of network resources and also reduces the bottleneck on the cloud computing.

Indoor positioning technology is the key to further development of LBS system. Based on RFID technology, it is efficient and feasible to combine the indoor navigation management system with LBS technology. The LBS system provides the functions of mobile device location, communication and service, and RFID based indoor positioning provides the function of locating objects indoor environment. The combination of the two technologies can further facilitate the positioning and navigation in our lives. In this paper, a novel indoor navigation system based on RFID and LBS is presented. The implementation of this system shows that it is feasible to support service in indoor environment.

As IoT (Internet of Things) continues to penetrate everyday life, we witness the increase in the number of IoT standardization bodies. As the coverage of standardization bodies overlap, interoperability is threatened despite of good wills of each standardization body. The author analyzes the causes of fragmentation of IoT standardization from the current landscape of IoT standardization. Then, the author presents a broken assumption model that explains the disorganized status of IoT standardization.

In recent years, automatic driving technology and inter-vehicle communication have attracted attention because they can be applicable not only to transportation systems but also to intelligent communication systems. Delay/Disruption/Disconnection Tolerant Networking (DTN) and Vehicular Ad-hoc Networks (VANETs) are used to provide the network services as alternative network. In this work, we develop a DTN testbed for VANETs. The communication components are implemented in Raspberry Pi. We evaluate the performance of our testbed in outdoor environment within the campus. From the experimental results, we found that our implemented testbed has good performance.

In recent years, many researches on automatic exploit generation and automatic patch techniques have been published. Typically, in the CGC (Cyber Grand Challenge) competition hosted by DARPA, a hacking competition was held between machines to find vulnerabilities, automatically generate exploits and automatically patch them. In the CGC competition, they implemented themselves to work on their own platform, allowing only 7 system calls. However, in a real environment, there are much more system calls and the software works on complicated architecture. In order to effectively apply the vulnerability detection and patching process to the actual real environment, it is necessary to identify the point causing the vulnerability. In this paper, we introduce a method to analyze root cause of vulnerabilities divided into three parts, fault localization, code pattern similarity analysis, and taint analysis.

This study focuses on consumers facing controversies due to environmental protection, when they visit a leisure hotel. Specifically, how as a consumer, a teacher’s moral philosophy affects the moral intensity. Two environmental ethic scenarios of leisure hotels are developed. A survey was conducted among 253 teachers from primary schools in Kaohsiung, Taiwan. This study examines and finds that moral intensity of environmental ethics can be divided into two major facets, which are “potential damage” and “validity and the level of influence”. Specifically, the personal idealism in personal philosophy having positive influence on the subject-matter moral intensity’s “potential damage”. The findings also reveal that teachers’ personal philosophy in relativism substantially affecting the “validity and the level of influence” of subject-matter moral intensity.

Ranking first in the top three beverages in the world, tea is widely loved by the public. As the main output of Taiwan’s agricultural products, tea creates many foreign exchange profits for Taiwan. With the changes of the times, changes in Taiwan’s social patterns have led to the gradual decline of the tea-making industry. With the liberalization of agricultural trade, domestic tea is facing fierce competitions from a large number of imported teas. Those hot sale sceneries no longer exist. In order to enhance the advantages of Taiwan’s tea industry, what we must do is to develop new industries with high added value. This study explores the evolution, development, production and sales of the tea industry. Company A is taken as the sample company, its production, sales and future development is discussed. Methods of Porter-five forces analysis and SWOT analysis are adopted in this study to develop marketing strategies for the sample company in the future. The four marketing strategies suggested are utilizing the advantages of geographical location and integrating the characteristics of local industries; developing a tourism innovation experience model; changing tea packaging creatively; increasing opportunities for tea products promotion and exposure.

This study examines the influence of U.S. FED’s interest rate-raising event announcements on the abnormal returns in the Taiwan stock market. The empirical results verified that, when the U.S. FED announced the interest rate decreasing, before the event announced, both the high-tech industries and traditional industries would have the opportunity to obtain significantly negative accumulated abnormal returns (CAR), after the event announced, high-tech industries still have the opportunity to obtain the significantly negative accumulated abnormal returns, while traditional industries just have the opportunity to obtain the significantly positive accumulated abnormal returns on the third day after the announcement. Whereas, when the U.S. FED announced the interest rate increasing, traditional industries have more opportunity than high-tech industries to obtain significantly positive accumulated abnormal returns. The empirical results provide references for investing decisions to the investors.

Investors usually pay more attention to stock dividend payouts and business cycle but less to investment risk. Therefore, volatility and beta, two widely used risk measures of stocks, are used to explore their relationships with dividends, business cycle and institutional ownership. We sampled 200 listed firms which have continuous records of dividend payouts and are held by institutional investors from 2008 to 2014 in Taiwan Stock market. The results show that: (1) dividend and the share ratio of institutional investors have significant positive effect on individual stock risk, (2) the relationship between business cycle and individual stock risk is negative and (3) the effect of dividend, business cycle and share ratio of institutional investor on market risk is insignificant.

Investors of commercial real estate tend to sell their investment property when its price rises to a level high enough to realize capital gains, and they also consider selling the investment when its price declines to a level that triggers stop-loss selling. We assume that the investors of commercial real estate have embedded call and put options in their investment when they are engaged in the transaction of commercial real estate property. By using the real options model with given risk parameters, we derive the valuation model of commercial real estate. The model can provide more insights into the evaluation of commercial real estates by considering risk factors like vacancy rate, interest and tax rate, and transaction cost. Based on this theoretical model, further analysis can also be conducted according to the different classes of commercial real estates.

We propose a model of dynamic share repurchase. The model highlights the central importance of payout for corporate decisions. Our two main results are: (1) free cash flows depends on the operating cash flows changes; (2) optimal share repurchase timing is decided by the relative changes between the free cash flow and dividends.

This paper explores the relationship among the job complexity of senior managers on their compensation and operating performances. The job complexity of senior managers is measured with proxy variables associated with operating activities. Simultaneous equations are then constructed to validate the endogenous relationships among these three factors. The empirical study indicates that the job complexity of senior managers is relevant to the improvement of operating performances and the level of executive compensation. This finding should serve as a reference for the information and electronics industry in the planning of executive compensation programs in order to enhance operating performances.

Nowadays, young generation consumers’ purchasing decision is affected tremendously by online product review, probably far more than other channels of communication. This may imply that strategies of marketing and campaign commercial products very likely need a revolutionary change. Therefore, the purpose of this study is to understand the different impacts of the traditional product endorsement and modern online commentary on young consumer trust and purchase decision. Experimental method was employed to collect data. 120 students participated in the experiment. Results show that youngsters have higher level of trust in online commentary than endorsement. Online commentary than endorsement has a greater impact on youngsters’ purchase intention. As expected, trust has a significant and positive impact on purchase intention. Finally, management implications and future directions are discussed.

The extant literature has shown that dividends have positive valuation implications due to signaling and agency cost effects. However, under the tax systems of most countries, individual investors face a higher tax rate on dividend income than on capital gains. Therefore, individual investors pay a dividend tax penalty, which results in lower equity values. U.S Studies indicate that as the level of institutional ownership increases, the likelihood that a marginal investor is not a high-tax-rate individual increases. Consequently, the negative dividend tax penalty effect on the positive market response to dividend surprises should decrease. This study extends previous research to investigate the tax effect of dividends under Taiwan’s imputation tax system. We find that dividend tax penalty partially offsets the positive effects of dividends on equity values. However, this negative tax effect of dividends can be alleviated by the presence of a marginal investor who represents a tax-exempt institution.

As the changing of population structure, the phenomenon of low-birth rate has been presented globally. We are also encountering a dramatically change in social structure which impacted the education development. The study aimed to discuss the relationship between the factors of business model for school and selection of students. In accordance with the business model generation that proposed by Osterwalder and Pigneur (2010), the study considers the factors as cooperation partner, operation item, resource, value position, customer relationship, channels of student recruiting strategy and the factor of student entrance. The result shows that major cooperation partner, resource, customer relation and student recruiting channels are significant positive factors to impact student’s selection. The contribution of the study is to provide a reference for private educational institution and proposed correspondent strategies to reduce the impact of low-birth rate for school.

The purpose of this study has two folds: (1) to understand whether the audit failure incidents caused by the 2 major corruption cases (Procomp and Rebar) in Taiwan’s history affected the strategy of employee professional training by CPA firms which provide auditing services to the public offering companies, and (2) whether those who invested more on employee professional training had resulted better operational performance. Data were collected for the period of year 1998 to 2013 from “Survey Report of Audit Firms in Taiwan,” published by the Financial Supervisory Commission, Executive Yuan, from. The study used the debacles of Procomp and Rebar to separate the data sets. Results showed after the incidence of Procomp corruption, CPA firm significantly invested in employee professional training more than ever, however, they did not for the periods of before and after the Rebar incidence. This finding indicates that public accountants have different views on these two failed audit cases and come out different professional training strategies. Finally, the study found those CPA firms invested more in continuing professional development had better business performance.

By integrating multiple management systems, this study developed a system for CSR activities, strategic management, daily operations, and sustainable management and control which allows the effective implementation of strategic CSR. The proposed strategic CSR system can help companies solve CSR reporting issues, including a lack of strategic planning, a lack of clarity of expression, and simplicity of reports. On the one hand, the proposed system may help companies comply with CSR reporting regulations introduced by the authorities. On the other hand, it may be used by managers to identify a company’s key strategic areas in economic, environmental, and social aspects and incorporate CSR activities in daily operations to gain competitive advantage and create a long-term value.

The objective of this study is to develop and test a conceptual framework that explains and analyses the factors influencing tablet PC users to become loyal to a particular brand in Indonesia. The study utilizes a survey approach to gather data regarding factors that influence brand loyalty toward the use of tablets. The investigations of the factors that influence tablet PC users to become loyal to a particular tablet brand are anticipated to contribute toward a better understanding of consumer loyalty in tablet PC users and provide related factors that influence tablet PC users to become loyal to a particular brand. The findings supply valuable insights into which factors practitioners should focus their attention on to better tailor their approaches toward a tablet brand. This study strongly endorses the view that the loyalty intentions of tablet users are linked to the extent of their satisfaction with the brand they are using or have used.

Many of Taiwan’s industries are still deeply influenced by traditional customs. The characteristics of the five elements of each day should have a considerable relationship with their stock prices. This study adopts reliable quantitative indicators. Through statistical analysis of differences, correlations, regressions, etc., we observe whether the extent to which Taiwanese firms are affected by the five elements varies with different factors such as industry, company life cycle, education level of the operators, and the location of the company. So far, the relevant research on the five elements and the stock market is still rare. This topic has research value.

Sustainability is of utmost priority to most companies. The key to long-lived company success and sustainability lies in effective professional management. This paper aims to identify the characteristics of asset allocation in the long-lived company of Taiwan and observe the impact on the company’s stock price, asset structure, and operating performance.

For perishable products, the seller usually asks for the buyer to prepay a fraction of the purchasing cost as a good-faith deposit, to pay some cash upon the receipt of the order, and then a permissible delay is granted on the remaining of the purchasing cost. In addition, it is evident that the deterioration rate ages to 100% as time reaches the expiration date. In this paper, we incorporate the above two important and relevant facts to find the optimal cycle time and the fraction of no shortages such that the total profit is maximized. Several managerial insights are presented.

Sustainable development contains three aspects of development including social, economic, and environmental objectives. For the sustainable social issues, the aging society has considered as the major issue and challenging problem facing by the countries of the world. In addition to the diseases and functional decline, the interpersonal interaction, social participation, and mental health would affect the lifestyle of elderly. Given the increasing numbers of aging population, the mature market is developed globally, which changes the structure of elderly consumption and commercial opportunity. With the help of Taichung City Government, a total of 600 effective samples of the survey of social and living status of the 65–75 years old elderly in Taichung City from the project of Global Research & Education on Environment and Society (GREEnS) of Tunghai University were used to study the lifestyle of elderly and their values in life. The current study used cluster analysis to determine the homogenous characteristics of elderly. Furthermore, influencing factors of daily life conditions, diet, and health status of the elderly were investigated to discuss the differences of consumption patterns among elderly. Measurement of the AIO (activities, interests and opinions) was used as a basis to access the status of lifestyle of elderly in Taiwan, and further to demonstrate the economic activities and market segmentation within the different characteristics of elderly.

The Blockchain is the world’s leading software platform for digital assets. The development of Blockchain technology is now growing very rapidly. Blockchain technology could be also deployed in the Internet of Things (IoT) Networks during their transaction processes. However, safe methods for different types of transactions still have major problems. A good trust management system (TMS) is essential for success between IoT devices and Blockchain node during transaction processes. This paper illustrates how IoT devices could be evaluated by the sink nodes acted as a blockchain nodes in order to give the contribution for cooperative evaluation in the blockchain for the integration IoT application. The cooperative evaluation method is required while executing transaction process in Blockchain network, which could validate IoT devices by these collaboration blockchain agent nodes. Finally, the scheme we proposed cooperative evaluation for private blockchain IoT application, which could give trust evaluation for IoT devices by the blockchain nodes during the blockcahin transaction processes.

With the development of internet technology, more and more browsers have introduced third-party plug-ins to add additional features to attract users, which bring more potential risks to browsers. This paper presents a vulnerability-oriented security detection methods for IE browser ActiveX plug-ins. By using technologies such as dynamic binary instrumentation and vulnerability-oriented reverse analysis, the framework can assign different risk factors to each function and parameter inside the ActiveX plug-ins. In this way, this paper build an automated fuzz framework which can quickly generate fuzzing samples focusing on the fragile functions and fragile parameters. The experimental results show that our framework makes the efficiency of ActiveX plug-ins vulnerability detection significantly improved.

Homology detection technology plays a very important role in the copyright protection of computer software. Homology detection technology mainly includes text based technology token, based technology and abstract syntax tree based technology. This paper introduces a method of defect detection based on homology detection technology for open source software. This detection method will collect the code fragments with vulnerabilities and the source code in open source software to compare, through three levels of comparison, to find because of plagiarism code introduced by the vulnerability fragment. After that, the vulnerability fragment is compared with the trigger condition of the vulnerability, and the judgment result is obtained. Finally, the superiority of this technique is verified by experiments.

In recent years, with the rapid development of mobile Internet technology, and the increasing popularity of mobile payment, the security of mobile payment terminal is becoming more and more significant. Even if mobile payment has gained popularity worldwide due to its convenience, it is also facing many threats and security challenges. This paper is targeted at mobile payment security on Android platforms. In detail, we first introduce the current development of mobile payment application, and then we analyze two main threats of mobile payment security, namely near-field payment security and remote payment security. We also focus on phishing attacks on mobile platforms, by analyzing the behavior, characteristics, common techniques and attack methods of these attacks. Finally, we develop a defense strategy based on monitoring running applications, aimed at alerting users when malicious applications are leaking payment information, and we test the feasibility of this strategy on a dataset of Alipay application.