Proceedings of Fifth International Congress on Information and Communication Technology

This paper presents a cognitive model on electroconvulsive treatment to reduce the stress level in body. The stress reduction is triggered by a cognitive electroconvulsive treatment that uses persistent manipulation of this treatment. The goal of this treatment is to decrease the strength between certain parts of the brain which are in charge of the stress. The proposed adaptive cognitive model aims to illustrate the effect of the therapy on different components of the brain. The model begins with a state of tough, powerful, and consistent stress within a post-traumatic disorder patient, and after following electroconvulsive treatment, the stress level starts to decrease from time to time according to each treatment session. The results show that, in the end, the disorder person will have a declined stress in contrast to not performing electroconvulsive treatment.

Through various approaches such as the eHealth Switzerland 2.0 strategy, the Swiss healthcare system aims to digitally catch up with other industries and drive the industry into the digital future. To enable hospitals to transform their business model and prepare for the future, this paper presents an approach for the implementation of the digital transformation in Swiss hospitals. Thus, a metamodel consisting of nine elements was created as a base. The focus of the metamodel and the later reference model lay on the central activity elements, which are each embedded in a phase and are directly or indirectly connected to all the other elements in the metamodel. For the reference modeling, the metamodel serves as a structural template, while an existing roadmap from the literature on the digital transformation was used as a content-based starting point. The final reference model consists of 30 activities within six different phases.

In the last years, increasing attention has been paid to air pollution, due to its impact on human health and on the environment. Current EU legislation establishes fixed limits for some air components that have been shown to have adverse effects on human health. It is therefore important to identify regions where the probability of exceeding those limits is high. In this paper, we propose a bootstrap scheme to obtain the distribution of the considered air pollutant at a given time point. In particular, the proposed resampling scheme is based on the residuals of a semiparametric model which is able to incorporate some stylized facts usually observed in such kind of data, such as missing data, trends and conditional heteroscedasticity. The estimated bootstrap distribution is then used to estimate the probability that the air pollutant exceeds the fixed legal limits. An application to ( $$PM_{10}$$ P M 10 ) in Torino area in the North-Italian region Piemonte is shown.

Today, new and existing small-scaled gem merchants are finding it difficult to lead the business in a successful way due to poor decision-making, unpredictable market conditions, inexperience, and maintenance of client base. Therefore, it has urged the need of a solution focusing on helping to maintain and improve these areas leading to the profitability and success of small-scaled merchandise. This research focuses on analyzing the difficulties faced by small-scaled gem merchants relating to purchasing gemstones, adding and manipulating records, marketing and sales and to design and develop a software solution giving importance in helping to make corrective decisions leading to the profitability of the company such as purchasing a gemstone for the right price and adequate ways of reaching out to potential clients leading to marketing and sale of colored stones.

Malnutrition is one of the major problems in developing countries including Bangladesh. Stunting is a chronic malnutrition, which indicates low height for age and interrupt the growth. The purpose of this research is to find out the factors associated with the malnutrition status and test the accuracy of the algorithms used to identify the factors. Data from Bangladesh Demographic Health Survey (BDHS), 2014, is used. Factors like demographic, socioeconomic, and environmental have differential influence on stunting. Based on analysis, about 36% of under-five children were suffering from stunting. Decision tree algorithm was applied to find the associated factors with stunting. It is found that mothers’ education, birth order number, and economic status were associated with stunting. Support vector machine (SVM) and artificial neural network (ANN) are also applied with the stunting dataset to test the accuracy. The accuracy of decision tree is 74%, SVM is 76%, and ANN is 73%.

The study aimed to identify the human resources information systems and their impact on the strategy of evaluating the performance of the employees of Jordan Telecom Company. The objectives of the study achieved through a questionnaire were developed for the purpose of data collection. The (SPSS.16.1) statistical software was used to analyze data. The most prominent results were: The level of importance of human resources information systems (the efficiency of human resources information systems, integration with other MIS, responsiveness) in Jordan Telecom was moderate in terms of respondents’ perceptions, as well as the impact of human resources information systems in the performance assessment strategy. Human resource systems accounted for 54% of the variation in the strategy of evaluating the performance of Jordan Telecom employees. In light of the achieved results, the study recommends, the most important of which is the development of human resources information system aimed at tracking the performance of employees to improve their performance and improvement, and recommended the development of human resources management policies, so as to contribute to the detection of the potential and potential of workers.

Complexity management is a well-understood concept in systems engineering with strong theoretical and practical foundations. The complexity of legal systems, however, is mainly considered in trade or tax context and remains largely qualitative in nature. Since the ability to create, develop and follow law is crucial to functioning of a society, a quantitative method for assessing the complexity of a set of laws from both its creation and consumption perspective and development of that complexity over time would be beneficial. For example, such a measure could be used to assess the sustainability of a legal system, develop “complexity budgets” for legislative texts and quantitatively measure the impact of changes. In this paper, the authors utilise a complexity measure for engineering systems in the legal context of the Republic of Estonia. A specific measure of legal complexity is developed based on ideas from systems engineering and morphological analysis. It is then applied to time series of quarterly sets of Estonian legislation from 2002 to 2019. The research shows that systems engineering approach does yield meaningful results in the legal domain and that, assuming limited cognitive capabilities, the existing trend of complexity growth is not sustainable. Policy recommendations are presented to change the trend found.

A multimodal biometric system utilizes more than one biometric modality of a person to relieve some of the shortcomings of a unimodal biometric system and improves its security. In this paper, we propose a novel deep learning approach for fusing the features extracted from the individual’s face and iris (left and right) to get a more secure biometric verification system. Firstly, we extract the facial and iris features separately using various convolutional neural network (CNN) models. Further, the feature vectors of the final CNN layers of both models are fused to achieve classification of individuals with improved performance. The proposed system is tested on the CASIA-Face V5 dataset for faces and IITD iris dataset for left and right irises. The results achieved prove the superiority of the proposed multimodal system. It is efficient, reliable, and robust as compared to unimodal biometric systems.

Asymmetric cryptography algorithms, still considered the most robust tool available in the cryptography domain. It incorporates intensive modular exponentiation calculations, which entail considerable computing power, memory and storage space. Common IoT device is equipped with limited computing resources and so, for security purposes, it can execute symmetric and limited asymmetric cryptography. In most cases, IoT devices do not have the capacity required for executing massive modular multiplications of numbers of magnitude of 4 K bits and more. This leads to the lack of asymmetric cryptography in the IoT domain resulting with a reduction in security means to cope with the raising security challenges. The security problem increases as the number of IoT devices is expected to significantly grow soon. We propose a new implementation of asymmetric cryptography, which splits its heavy calculations into micro-processes, where each micro-process is distributed to an appropriate IoT device, connected to the network. The accepted result is transferred back to the distributing IoT. Once all the results are accumulated, a consolidation process is activated to generate the final calculation result, which then is fed into the cryptography process, to generate the Encrypted/Decrypted string, proving the ability to maintain asymmetric cryptography also at the IoT level without compromising security.

Excessive usage of smartphones and tablets have led to drastic increase of mobile ad fraud in recent years. The fraudulent users can be either human or automated scripts with the intention of making illegal revenue or exhausting the advertiser budget are being engaged with this multimillion industry. The ad fraud referred to any kind of activities that are generated by a fraudulent user is a huge threat to the existence of the online advertising ecosystem. The researchers have proposed various kinds of methodologies and tools in the context of ad fraud detection and prevention. However, the fraudulent users are smart enough to bypass the significant number of existing detection and prevention systems. The combat between fraud users and researchers or solution designers in this field never ends. Thus, the novel solution of ad fraud detection and prevention techniques is needed. The proposed approach of this study to address this problem is called hidden Markov scoring model—HMSM. The model calculates scores for each observe/emission variable of experimental data set towards the hidden states of target variable based on hidden Markov model so that fraud impression can be classified. The experimental results show that the significance of the proposed approach to classify the fraud and non-fraud impression.

This paper introduces a method of selecting the most probable future investment clients of a brokerage company on the capital market. The method consists in using the answers given by the virtual investors to a set of prescribed questions. Namely, the aforementioned answers are fusioned with a data mining procedure using the Choquet integrable. The scores thus obtained are classified using some preassigned thresholds, allowing to select the most probable future investment clients.

Low-power wide area network is a new wireless communication technology designed for low-power consumption together with long-distance communications, and LoRa technology is one of the leading technology solutions. The long-range connection between end-nodes and gateway is achievable by LoRa devices due to star-based network topology and modulation techniques used in wireless communication of the technology. One of the main features of LoRa technology is the ability to scale. Modelling and simulation can interpret the actual network behaviour of LoRa technology as accurate as possible. This paper aims to investigate the performance of the low-power wide area network technology focusing on capability of the network to scale. We model the network system based on the behaviours of the communication between the end-node and gateway. The simulation to study the scalability was done based on several parameters, such as the number of end-nodes, application time and the number of channels used by the end-node. The results show that the amount of successfully received data signal at gateway increased as the application time and channel used increased.

Most of the road accidents can be attributed to human errors. Advanced driver assistance system (ADAS) is an electronic system that guides a vehicle driver while driving. It is designed with a safe human-machine interface that is intended to increase vehicle safety and road safety. ADAS is developed to automate, adapt and enhance vehicle systems for safety and better driving. An increasing number of modern vehicles have ADAS such as collision avoidance, lane departure warning, automotive night vision, driver monitoring system, anti-lock braking system and automatic parking system. ADAS relies on input from multiple data sources like lidar, radar, and camera. This paper describes the implementation of ADAS using machine and deep learning algorithms. We implement a model which has a 360-degree camera (lens on two sides of 170 degrees each), lidar, ultrasonic sensor, and radar that provide the input for ADAS. We implement the ADAS by training this whole model using deep learning (advanced machine learning) by designing a neural network using Python in TensorFlow. Generative adversarial networks (GANs) are used in object detection when a hazed image (foggy, rainy, etc.) is detected. This reduces the sensor complexity and area in the vehicle. Results gained from the study and their implications are presented.

The growing interest and expectations from the blockchain applications attract many analysts to this issue. In what spheres of logistics and supply chain management blockchain is appropriate? What blockchain software solutions are available to companies now? This paper investigates the basic functionality of the existing software solutions on the market and the comparative analysis of blockchain platforms used for developing the solutions for logistics is also carried out. The main trends of blockchain applications are identified, based on the analysis of the project experience on the use of blockchain, in logistics and supply chain management, in different countries. The problems, limitations and conditions of blockchain implementation are also determined.

This study aims to use AR technology to develop an AR-based learning of a digital storybook for preschool children aged 6 years old as to motivate their reading. This application will show digital storytelling based on book titled “The Three Bears” by Emma Bailey focusing on pages 4 and 5. User testing with both teachers and preschool children was conducted to examine the potential of the AR approach in motivating them learning by using both qualitative and quantitative interview in order to measure their learning experience. The findings indicate that the AR-based storybook approach does affect their enjoyment, engagement and motivation. A suggestion for further research is to embed elements of surprise to prevent boredom in an AR-based storybook to preschool children to increase their engagement in reading.

Underwater acoustic sensor network (UASN) marks a new era in ocean exploration, enabling various scientific, military and commercial applications. This paper describes one of the scientific applications named coral reef monitoring in a desired region of interest in the Arabian Sea. The events of coral bleaching that leads to massive destruction of coral reef is a current alarm faced by most of the buildups. UASN can be deployed to monitor the environmental parameters of various such regions. In this paper, a number of nodes are deployed in the form of clusters at various regions. An autonomous underwater vehicle (AUV) is used to collect data by visiting the clusters/cluster-heads periodically. Travelling salesman problem (TSP) is used to find optimum tour for the AUV in the data collection phase. The application of TSP in data gathering helps the data collection by forming minimal tour for the vehicle. This paper provides the implementation of protocol stack along with detailed results and analysis using an underwater network simulator (UnetSim).

The deep learning method has achieved particularly good results in many application fields, such as computer vision, image processing, voice recognition, and signal processing. Recently, this method has also been used in the field of natural language processing and has achieved impressive results. In this field, the problem of categorizing subjective opinions which is an individual’s thinking or judgment of a product or an event or a cultural and social issue. Subjective opinions have received attention from many producers and businesses who are interested in exploiting the opinions of the community and scientists. This paper experiments with the deep learning model convolution neural network (CNN), long short-term memory (LSTM), and the boxed model of CNN and LSTM. Training data sets comprise reviews of cars in Vietnamese. Cars are objects with a significant number of specifications that are provided in user reviews. The Vietnamese opinion set is preprocessed according to the method of aspect analysis based on an ontology of semantic and sentimental approaches. A Vietnamese corpus experiment with CNN, LSTM, and CNN + LSTM models are used to evaluate the effectiveness of the data preprocessing method that was used in this paper. To assess the validity of the test models with the Vietnamese opinion set, the paper also tests the sentiment classification with the English Sentence Collection Stanford Sentiment Treebank (SST).

New agile techniques have brought advantages over traditional techniques. However, agile techniques fail to solve soft skills problems. People with high technical knowledge still find difficulties to adapt to industrial work environments due to the lack of soft skills. At the individual level, specific skills are not perfected, questioned or transmitted. Team members with weaknesses in leadership and communication do not establish a regular channel for dissemination and resolution of disputes. Soft skill problems must be resolved quickly, at least, at the beginning of a project. Thus, people with high potential can lead to having a high impact on development iterations. This paper presents an agile technique taking into account weaknesses in soft skills. The proposed technique is based on rotating the leader team according to an iteration. Experimental evaluation shows that team members reach minimum communication soft skills after a short time. At the same time, teams gain more cohesion and better knowledge of everyone.

An under-representation of women in the Information and Communication Technology (ICT) industry exits. Current research tends to focus on either social aspects (social construction) or physical aspects as cause for this phenomenon. Consequently, there is a lack of a holistic perspective of factors that causes the under-representation of women in ICT. This research provides a holistic perspective of factors that causes the under-representation of women in the ICT industry. This research was performed by conducting a systematic literature review that considered 89 articles to identify factors that cause the under-representation of women in ICT. The identified factors were classified as: organizational, economical and socio-psychobiological. The under-representation of women in ICT can now be better addressed by holistically considering this classification of factors to increase female participation in ICT.

Security is a major concern in Software Defined Cognitive Radio Network (SDCRN). SDCRN is an integration of the Software Defined Network (SDN) with the Cognitive Radio Network (CRN). SDN is an architecture developed to reduce the complexity of the network, whereas CRN is a technology developed to allow radios to learn and adapt to their environment. However, the architecture and technology are susceptible to a number of malicious attacks such as Distributed Denial of Service (DDoS) and Primary User Emulation (PUE), respectively. The DDoS and PUE attacks could be launched onto the SDCRN with the intention of disrupting service. The design of effective security schemes that enhance maximum protection of SDCRN from these malicious attacks is a sought after solution in network security. Hence, this research study proposes a security mechanism that addresses the effects of DDoS and PUE attacks in SDCRN.

Whenever Project Risk (PR) exists, there is complexity. The difficulty to make concerned decisions related to Project Risk Management (PRM) increases project complexity and even its failure. In order to assist practitioners and professionals to better study the potential impacts of their decisions and assess the PR as precisely as possible, this chapter put forward an ontological approach based on OWL ontology with SWRl rules, that provides the project team clear guidelines to effectively manage PR, and then make the appropriate decisions based on the right recommendations. This approach takes advantages of ontology semantic strengths as it represents a unified PRM knowledge relying on PMI’s frameworks. As well, through SWRL reasoning rules, the proposed ontology generates recommendations by which a team member ask for risk-related request more targeted. The proposed ontological approach was evaluated, in term of content and structure, achieving promising results based on the F-measure metric.

Ensuring the integrity and security of an Internet of Things (IoT) system, and maintaining and updating the firmware driving its devices, becomes critical as the number of nodes, sensors, actuators and control loops increases. The security of IoT can be addressed by deploying blockchain technology. The feasibility of building a blockchain-based IoT on a low-cost microcontroller is demonstrated. This is achieved by implementing an Ethereum node on Raspberry Pi 3 controlling an LED. The LED data of turning ON and OFF by the user are collected and stored on a smart contract. The interaction with the smart contract is performed by a DApp called, Status, developed based on Light Ethereum Subprotocol (LES). The feasibility of the concept was successfully demonstrated, showing the potential of blockchain technology in developing more secure IoT systems.

The European General Data Protection Regulation (GDPR) implies a lot of new regulations. The implementation of these new regulations is a major challenge for many small- and medium-sized enterprises (SMEs). Therefore, we investigated which factors influence the implementation of the GDPR in already existing business models for SMEs. Our model is focusing on already existing business models for SMEs. It is based on empirical data from German experts. It has been developed by using clustering and qualitative content analysis. Important influencing factors that are now open for quantitative verification are know-how, expenditure of time, uncertainty, costs, provision of information and process adaption.

The concept of teaching through the flipped classroom technique is always considered as one of the most important in higher education in the Indian context. It enables undergraduates to gain fundamental learning even before they attend the class and also facilitate educators to manage discussions among the undergraduates during the classroom interaction hours. Likewise, the undergraduates share their insights gained during such discussions in the classroom. It is seen that the majority of the Engineering courses are lacking in implementing the blended learning approach into their curriculum. The case study presented in this research paper is an attempt to prepare undergraduates before they attend their class to provide further speculation in the subject matter. The educator disseminated the learning course material for the chosen programming subject for five topics even before the beginning of the regular classes in a semester and interacted with the undergraduates during the hours meant for classroom interaction in the semester. From the observations, it is found that undergraduates have actively participated in the flipped classroom approach and also lead to improved learning outcomes of the undergraduate over the span of time during the semester.

Automation is one of the inventions of this generation, and it is widely used not only here in the Philippines but all over the world. Amid all these technological advancements and innovations, electrical energy plays a vital role in our lives. It powers our devices, machinery, equipment, modes of transportation, and many other things that we need and use. To date, solar energy is one of the most abundant renewable energy sources that have been gaining attention in the past few years. This study generally aims to develop a remote monitoring and control system for the Mariano Marcos State University Industry 4.0 (MMSU i4.0) Platform, particularly for an energy self-sufficient small smart laboratory. By using solar energy, the implementation of the Maximum Power Point Tracking (MPPT) technique needed to supply a small-scale smart laboratory integrating the use of the Internet of Things (IoT) in controlling and monitoring devices. The system controlled through a web application and mobile application to lessen the human effort in manual switching and monitoring tasks. It can print weekly and monthly power consumption of the smart small-scale laboratory. Since it is automated, users can monitor the actual voltage and current, the temperature of the power source, and the total generated power from renewable energy. It will improve the level of comfort by controlling and managing laboratory devices automatically and also applicable to enhance buildings, houses, and other work areas cleanly, thus helping the environment.

This article raises issues of children and young people communication safety in the Internet space. The materials of the article contain the results of an intellectual search conducted by the authors to compile a list of the most common risks of Internet communication faced by Russian children, or teachers and counselors working with children, including using the Internet, note the growth of these risks. Research design combines the following methods: social media analytics (provides content, dynamic, structural and discourse characteristics of relevant social media streams, including automatical metrics: tag cloud, audience activity markers, online opinion leaders, and so on) and eye-tracking for measuring perception of Internet memes by teens and youth. The article presents descriptions of markers that allow to identify these risks, possibilities of neurovisual correction of these risks, as well as makes recommendations for teachers and counselors, schools and universities, departments and agencies for social work with teens and youth to minimize them.

Blockchain technology has been used recently as a secure method for authenticating digital information in many applications. Inspired by the success of the technology, we envision the potential of the blockchain for secured communication in a decentralised Internet of things (IoT). In this paper, we envisage a framework for a secured IoT and describe the infrastructure and mechanism of the entire system. Also, we provide solutions to overcome some of the limitations of blockchain technology including miner selection and reaching consensus, for a decentralised IoT by incorporating a learning-to-rank method for node selection. We also contemplate using hybrid consensus algorithm in the blockchain to detect faulty node and to improve the node convergence.

In the modern world, wireless local area networks (WLANs) have seen major growth and almost become a necessity for organisations regardless of its size due to its flexibility, easy installation and cost-efficiency compared to traditional wired LANs. On the other hand, Wi-Fi cartography is being utilised to enhance wireless network deployment, optimise Wi-Fi infrastructure through performing a radio frequency (RF) spectrum analysis (2.4 and 5 GHz) and identifying crucial points to improve the performance of voice and data communications. This paper presents a methodology to build radio cartography of Wi-Fi coverage. Furthermore, simulations are run and Wi-Fi cartography results performed in several areas at the University are presented. A thorough examination and critical analysis of the simulated areas are discussed. The authors reflect upon propagation environments, access points (APs) detected with respective standards, techniques and speeds supported, latency, packet loss and quality of service parameters.

Market competition today has given a massive advantage for those who can better use data. The main reason is that they understand how to develop medium long-term correct strategies, with short-term pragmatic operational approaches. The challenge is not on how to get or how to produce data, but on how to use it and transform it oriented to get business focus and business value. For that reason, it is fundamental to understand the details on how companies apply the well-known Ackoff’s DIKW hierarchy (data, information, knowledge, and wisdom) in their value chains. This ability to transform data into wisdom in a real-time mode is pressuring companies to transform themselves, and that is an internal process change, commonly known as digital transformation. In reality, companies are turning their value chains since they understand the power and the use of information systems as a strategic value-added decision-making tool. That means it is more than a simple technological support activity like Michael Porter’s value chain defined initially. This paper, being a conceptual paper, intends to present the evolution that enterprise value chains need to face and the need they have to include information systems as a core activity nowadays. Doing so, they will achieve the best data to wisdom transformation in a real-time loop and continuous mode, leveraging their value chains to higher probabilities of optimization of company’s market value. Those who address digital and information systems strategies will be able to get faster optimization of their market value functions.

This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish between various appliance footprints. In this context, a precise and powerful characteristic projection technique depending on fuzzy-neighbors-preserving analysis based QR-decomposition (FNPA-QR) is applied on the extracted energy consumption time-domain features. The FNPA-QR aims to diminish the distance among the between class features and increase the gap among features of dissimilar categories. Following, a novel bagging decision tree (BDT) classifier is also designed to further improve the classification accuracy. The proposed technique is then validated on three appliance energy consumption datasets, which are collected at both low and high frequency. The practical results obtained point out the outstanding classification rate of the time-domain based FNPA-QR and BDT.

The purpose of this study was to use geometric morphometrics (GMM), a technique suitable for the study of complex anatomical objects, to investigate spinal cord injury (SCI). Eight individuals with SCIs who underwent radiologic evaluation of their lumbar column in a lateral seated position prior to recruitment were included in the study. Each individual was assessed with rasterstereography using the Formetric 4D, and the results were compared with an X-ray of the column analysed through two-dimensional landmark-based GMM. A principal component analysis (PCA) was performed to describe shape variation. Subsequently, the correlation between the Formetric 4D indexes and the shape of the first principal component axis (PC1) was measured with Spearman’s rank correlation coefficient. Thin-plate spline deformation grids were used to describe shape changes in the morphospace depicted by the PCA and to describe shape changes predicted by linear regression. Through the analysis of human X-ray plates, we highlighted the ability of GMM to describe the shape of the column and to evaluate spinal and vertebral malformations. This pilot study is the first step for using a GMM approach to investigate human spinal cord abnormalities. These results provide clinicians and researchers a new method to evaluate bone structures that could provide important information about the development and progression of various deformities in the future.

Handling stream data is a tedious task. Recently numerous techniques are presented for analysing stream data. Stream data clustering is one of the important tasks in stream data mining. A number of application programming interfaces (APIs) are available for implementing the stream data clustering. These APIs can handle the stream data of any dimension. The objective of the presented paper is to explore the impact of dimensionality over the existing standard data stream clustering algorithms. Selected standard data stream clustering algorithms are compared for different dimensions of stream using six performance parameters, namely adjusted Rand index, Dunn index, entropy, F1 measure, purity and within cluster sum of square measure.

In recent times, Web application development has become essential for the IT industry. In the past, Web applications were used primarily to provide a graphical view of data stored in backend systems, and business logic execution or any form of data manipulation was programmed in the backend systems. Web applications were treated as dummy client interfaces because client machines were not proficient in performing complex computational operations or executing business operations such as data manipulation and analysis. Now, as client machines have become more advanced and capable of performing complicated business operations, application developers can make client interfaces more intelligent. Enabling client interfaces to handle such complex business processes is of great importance for some business scenarios, like in the case of collaborative user interface designing where data representation and data insertion are not the sole purpose of the Web applications. Adapting design patterns like “Command Pattern” (Long in IEEE, 2017 [1]; Betts et al. in Avail Maintainability Windows Azure, 2013 [2]) can make the Web client smarter and more efficient. This pattern reduces the burden on the backend systems when performing all kinds of business operations [3]. This design pattern provides flexibility to the developers for converting all user actions executed in the user interface (UI) into simple, discrete commands. In this paper, a detailed approach is discussed on how to apply the pattern in more optimized manner by which one can then submit commands individually in an asynchronous manner to the backend system for processing. Handling of these user actions through individual asynchronous commands discussed in the paper makes the UI non-blocking. This also provides flexibility in performing some of the UI changes locally until a response is received from the backend. This paper details the architecture for using “Command Pattern” and design approach for any Web application development and best practices that improve the user experience.

The fast food industry, specifically the service department, has experienced sustainable economic growth and has evolved in the application of substantial methods, however, it has presented a slowdown due to deficient operation level provided in the customer service area. A large number of customers do not make the purchase owing to a series of unmet trends that face the consumer behaviour which leads to significant economic losses and inefficient service. They have therefore focused their efforts on finding impulse mechanisms through allowing them to migrate to less costly processes and/or to achieve better utilisation of available resources without success. This research inquires into the effectiveness of the Lean Thinking Simulation (LTS) model, which consists in the development of a set of methodological phases and the adaptation of the technological support termed as Digital Change to improve the performance of customer service in Peruvian fast food. The main result of this practical study was defined by a Dashboard in real-time, and as a first approximation of the model, a 17.03% improvement can be shown in the performance of customer service on the fast food selected.

This paper explores primary user emulation attack (PUEA) impact on spectrum sensing (SS), sum secondary user (SU) throughput and energy harvesting (EH) in cognitive radio network (CRN). Cognitive radio (CR) system model consists of a set of transmit-receive node pairs, one fusion center (FC) and one PUEA node. At the initial time slot of the frame, simultaneous EH and spectrum sensing (SS) are done through power splitting (PS) mode. Then based on SS decision, at the FC, CR transmit nodes either perform EH or transmit data in time division mode. Closed form expressions of the optimal sensing duration and transmit power for each SU are found while maximizing the sum SU throughput under the constraints of SS reliability, energy causality on each SU, interference at PU and outage probability on individual SU link. Simulation results show that $$\sim $$ ∼ 20% increase in PUEA power enhances the throughput $$\sim $$ ∼ 9.76% and residual energy $$\sim $$ ∼ 0.31% while meeting the detection and false alarm probabilities 0.95 and 0.05, respectively.

As the Hilbert transform is known as the main cause of intersymbol interference (ISI) in single-sideband generation (SSB), the design and performance evaluation of relevant equalizers over Hilbert transform with different numbers of taps is critical to minimize the ISI effect. In this paper, we develop a mathematical model of Hilbert transform with the different number of taps with the relevant equalizers over four single-sideband (4-SSB), given that 4-SSB modulation can carry a double amount of information while using half of the bandwidth compared to the conventional modulation of SSB. As expected, the evaluation results demonstrate that the soft-input soft-output (SISO) Turbo equalizer has degraded the ISI issue with small number of tap since the Hilbert transform is showed to work empirically in the case of small tap number. Similarly, the Shadow equalizer verifies the benefits and robustness of the Hilbert transform effect with the lowest number of taps, particularly Bit Error Rate (BER) performance is converged when the number of taps is greater than 15. This demonstrates a new insight into applying the SISO Turbo and Shadow equalizer using the novel concept of 4-SSB modulation for data transmission in the case of coded and uncoded wireless environments, respectively, toward 5G communication networks and beyond.

The objective of this study is to analyze the results of the e-learning course “Health promotion, prevention, diagnosis and treatment of idiopathic scoliosis,” directed to the Italian healthcare professionals involved in idiopathic scoliosis management. The e-learning course, based on the problem-based learning methodology, was directed to health professionals previously enrolled to the University of Rome’s research project “Prevention, diagnosis and treatment of idiopathic scoliosis: evaluation of skills acquired in health professionals through distance learning.” This study is focused on the analysis of the participant’s data. A significant improvement in knowledge (t = 17.2; p < 0.001) was found in participants’ scores from pre- and post-test. 80.1% of participants passed the final certification test. The final satisfaction questionnaire showed a high level of satisfaction among participants. Health professionals involved in idiopathic scoliosis management need adequate training, based on methods appropriate to their professional context. The e-learning course’s results are positive and in line with the project’s task; moreover, this confirms the validity of an active didactic approach such as the problem-based learning.

Due to demographic change, elderly care is one of the major challenges for society in near future, fostering new services to support and enhance the life quality of the elderly generation. A particular aspect is the desire to live in one’s homes instead of hospitals and retirement homes as long as possible. Therefore, it is essential to monitor the health status, i.e. the activity of the individual. In our data-driven society, data is collected at an increasing rate enabling personalized services for our daily life using machine-learning and data mining technologies. However, the lack of labeled datasets from a realistic environment hampers research for training and evaluating algorithms. In the project BLADL, we use data mining technologies to gauge the health status of elderly people. Within this work, we discuss the challenges and caveats both from a technical and ethical perspectives to create such a dataset.

Database tamper detection is identifying the change in the old state and new state of database systems. There is a change in the state of database mostly with three kinds of operations namely insert, update and delete. Dropping the database or entire table will also affect the state of the database. Data is very precious to any individual or organization and data tampering will have serious ramifications. Now considering data as an asset of the organization, the protection of this data is supremely important. The tamper detection technique for the MongoDB database is proposed here, which identifies the operation that has altered the part of the database. This paper presents the performance analysis of the proposed technique in terms of computational time.

Phonocardiogram (PCG) or heart sound signal administers crucial information for the diagnosis of various cardiovascular affliction. The heart sound classification is a confronting task in the field of modern healthcare. This paper confers the heart sound classification using the Hilbert transform envelope technique. The major constituents in the classification stage are preprocessing of PCG signal, features (temporal, spectral, and statistical) extraction, machine learning, and features-based classification of PCG signal. The accuracy and firmness of the proposed method are evaluated using two different datasets with different classes. The heart sound signals are taken from the standard phonocardiogram databases, i.e., PASCAL and PhysioNet/CinC. Evaluation results manifest that the proposed method for PCG signal classification achieves an overall accuracy (A) of 97.7% for the PASCAL dataset and overall accuracy (A) of 98.8% for PhysioNet/CinC dataset. Comparative results manifest that the proposed method is capable of classification of the PCG signal. Further, the method permits extraction of appropriate features for the classification of the PCG signal.

The chapter presents the review of modern approaches for the domain terminology extraction, concept discovery, concept hierarchy derivation, and learning of non-taxonomic relations steps in the ontology learning task. The chapter presents the review of not only various approaches to solving these NLP tasks but also ready-made tools that implement these approaches.

Being a developing nation, India faces a major issues that is the huge population which is now becoming too large for the spaces available. With increased living standards and affordability, the number of cars owned today has exponentially increased. This has led to the emergence of a major concern of proper management of available spaces while keeping up with the pace of revolutionizing lives by utilizing the SMART way. “SMART PARKING” aims at addressing this plight by providing a mobile-based platform to reserve a parking slot before arriving at desired location. The user is provided with a real-time availability map of the parking space showing occupancy. Depending on the traffic and time required to reach the parking slot, reservation is dynamically altered. It provides the feature of sending alerts based on live tracking to accommodate the unpredictability of reaching the destination due to traffic and adapt the booking timings accordingly by calculation buffer time. In cases where user comes without a booking, slot is provided based on how many vacant parking slots are available. The occupancy of the user’s car in the slot is analyzed and utilized to predict the nature of booking to help for future purposes. Monthly subscriptions are provided and user is notified for renewals or updating.

5G network must support a large number of Vehicle-to-Everything (V2X) connections with high throughput and low latency. The existing resource allocation scheme based on orthogonal multiple access (OMA) seems to be unsuitable for dense networks due to the limitation of the spectral bandwidth and the available resources. This work presents a new algorithm named SAVCN (Scheduling Algorithm for V2X Communication based on NOMA), for 5G network. In non-orthogonal multiple access (NOMA) scheme, the same resource can be shared by several transmitters. The proposed algorithm improves network performances in terms of throughput, fairness, and error rate. In fact, SAVCN assigns the available resource blocks (RBs) in order to maximize the throughput by consideration of the minimal distance between transmitters and receivers. It maximizes also the number of served V2X users and minimizes the bit error rate. Simulation results indicate promising performance for SAVCN.

Nowadays, online social networks (OSNs) are being used as a hosting ground for criminal activities, and the legal enforcement agencies (LEAs) are struggling to process and analyse the huge amount of data coming from these sources. OSNs generate a huge massive volume of unstructured data making it difficult for the LEAs to ‘patrol the facts’ and to gather intelligence in order to provide it to the legal domain. There is no ontology model, among those found in literature, that allows to exhaustively describe all the aspects of crime investigation targeting data integration, information sharing, collection and preservation of digital evidences by using biometric features, and query answering. To bridge this gap, this paper presents an extended version of our earlier SMONT ontology, called CISMO as a semantic tool suitable for gathering digital evidence from OSNs helping LEAs to develop new investigative systems to counter the threat of different crimes. The new version introduces the core concepts related to crime cases in the police repositories, biometric data and digital evidences collected by OSNs, making it possible for LEAs to classify crimes, investigate hidden crime patterns or predict future crime patterns. CISMO is more concise and has a richer concept knowledge-based compared with the previous version SMONT. We prove the effectiveness of CISMO in a case study covering some general aspects in criminal cases in OSNs, demonstrating how this semantic approach can help LEAs to gather knowledge for crime investigation using natural language processing and machine learning to process messages shared in an online platform and also applying reasoning rules, as semantic inferences.

In this chapter, we present an approach to automatize the assessment of attitudes toward violence against women and women’s rights, by analyzing Youtube comments, written in Arabic. More specifically, we propose a context-aware approach to opinion analysis in comments by taking into account the polarity of videos to which comments are associated. We build a training set and use it to train a classifier that predicts videos’ polarity. The accuracy and precision of the produced video classifier are $$98\%$$ 98 % and $$94\%$$ 94 % , respectively.

Ontology is used as knowledge representation of a particular domain that consists of the concepts and the two relations, namely taxonomic relation and non-taxonomic relation. In ontology, both relations are needed to give more knowledge about the domain texts, especially the non-taxonomic components that used to describe more about that domain. Most existing extraction methods extract the non-taxonomic relation component that exists in a same sentence with two concepts. However, there is a possibility of missing or unsure concept in a sentence, known as an incomplete sentence. It is difficult to identify the matching concepts in this situation. Therefore, this paper presents a method, namely similarity extraction method (SEM) to identify a missing concept in a non-taxonomic relation by using a rough set theory. The SEM will calculate the similarity precision and suggest as much as similar or relevant concepts to replace the missing or unclear value in an incomplete sentence. Data from the Tourism Corpus has been used for the experiment and the results were then evaluated by the domain experts. It is believed that this work is able to increase the pair extraction and thus enrich the domain texts.

With the recent trend of artificial intelligence, specifically within machine learning, there are some powerful tools that can be utilized to create video game artificial intelligence bots. Bots that can beat professional players or immerse players within the game to the point where enemies are considered intelligent and react to situations similar to how a real human would. However, some of these processes and tasks to create a bot can be an expensive and time-consuming process. In this research paper, we look at two models to building an AI bot and comparing the two, namely a simple reflex model and a recurrent neural network model. From the results, we can see that the recurrent neural network goes further into the tech tree and is able to produce a more complexed set of units as compared to the simple reflex solution. The simple reflex solution, however, is able to reach the win condition by defeating the enemy bot much quicker than the recurrent neural network solution at 5 min and 39 s and costs less in terms of production and complexity. The recurrent neural network solution was also able to get a higher food supply count and spent the most amount of resources in all areas including technology, economy and army supply.

Background The brain performs a very significant job in our body by processing the information associated with human critical inclinations, intentions, sensory attention and awareness, execution, and mental state sustenance during a specific task. The attention of every human being gets altered while undergoing two or more cognitive tasks collaterally. Methods In this research, subjects were asked to perform two tasks collaterally in which one task was considered as primary task whereas the other task as secondary. The EEG (Electroencephalography) signals of the subjects undergoing those collateral cognitive tasks were recorded using RMS EEG-32 Super Spec machine. Result The relative band powers ratio (Theta to Beta band power ratio) helped in tracing the point of time when the attention devoted to the primary task got deviated by the secondary task. An auditory P300 peak generation validated the deviation in this research. Conclusion This research track could pave the way for designing a battery that can analyze the subject’s performance during multi-tasking. Other than this, such an investigation will help in avoiding the disasters caused by the attention deviation of the operator.

Globally, skin cancer is one of the major health problems. While an early diagnosis with the proper management of the disease can successful help in the treatment of the disease, the assessment of the disease by a medical practitioner is time-consuming, subjective, and prone to bias due to variation in the training and experience of dermatologists. Although different automated methods of the disease’s diagnosis have been proposed, various problems like image noise due to varying illumination, uneven low contrasts and ambiguities in the non-diseased skin and tumours in the different regions of the clinical image alongside with the edges and boundaries have been highlighted to require accurate discrimination during the use of the automated methods. This is due to the fact that they can lead to inaccurate extraction of the melanoma skin cancer in the medical images as the problems plague the performance of an automated approach of detecting the disease. This study implements a multi-stage image segmentation approach that utilises a fuzzy transformation at the image enhancement stage with graph-cuts technique for a more efficient detection of melanoma skin cancer. This experimental study shows that fuzzy enhancement integrated with graph-cuts technique achieve a very good segmentation performance on the overall image (i.e. foreground and background) with an average accuracy rate of 97.42%. This study also shows that the background segmentation using fuzzy enhancement combined with graph-cuts technique achieved the good background segmentation with an average specificity rate of 99.07%.

In Wireless Sensor Networks (WSNs), the real-time data which is highly sensitive, collected by sensor nodes. If anyone acquires this real-time information illegally, then the privacy will be revealed. Therefore, privacy, authentication, and data security are extremely important to access real-time information over an unreliable channel. Authentication is most common security mechanism to protect WSNs data over an untrustworthy network. Recently, Amin-Biswas devised two-factor remote user authentication protocol for WSNs and declared that it is safe from various security threats. However, we identified that their scheme has several security threats like password-guessing, identity-guessing, user-impersonation, session-key temporary information, smart card theft, and lack of forward-secrecy property. The computation cost, smartcard storage cost, and communication cost are also presented in the section of performance comparison.

Recent years have been characterised by a rising interest in using entertainment computing to monitor, maintain, and improve human health. This is observed in many systems and applications that leverage the benefits of a playful and enjoyable experience to provide a technology-enabled health intervention. This paper reviews one decade of papers (679) published at the intersection of health, entertainment and technology to determine trends, studies’ characteristics, type of solutions, domains of application and study purposes. Results show that there is a growing body of research in the area, with the majority of studies providing solutions for rehabilitation and addressing motor conditions related to stroke and/or fitness. Where half of the solutions reported are custom made, the bulk of those studies is performed with the purpose of evaluating the solutions proposed or validating their efficacy. In 80% of the cases, the studies are performed with subjects from the target population with sample sizes that have been steadily increasing over the years.

In the digital age, higher education varies widely from the educational environment, the role of teachers and learners to teaching methods. Currently, Vietnam as well as other countries around the world is facing the great challenges of the shortage of a highly skilled workforce. The quality of workforce educated in university has not met the demands of socio-economic development and international integration. Besides, there is a lack of international experienced researchers in higher education institutions. The connection between universities and businesses is not focused, which makes learners not be able to meet job requirements after graduating. Therefore, it is critical to improve the quality of Vietnam’s higher education in the context of Industry Revolution 4.0.

Advanced Driving Assistant Systems (ADAS) are mature in vehicles. At the same time, automatic driving technology is starting to be applied. But compared with those intelligent cars, old vehicles without any assistance are exposed to the dangers of accidents. And by the fact that, it is unsuitable to install ADAS on old motor vehicles, we are in dilemma to monitor their behaviours of while driving. This paper introduces an intelligent behaviour monitoring and safeguard system with high accuracy, robustness and convenience for drivers in their regular vehicles to guarantee both public’s and their own safety. It merges computer vision,and multiple biosensors to collect both drivers behavioural data and health data. Considering the influence of emotion of drivers while driving, their facial expression and driving aggressiveness will be detected as well. For data from biosensors, we use a Statistical-based Bayes data fusion Method to deal with data from multiple sensors and different kinds of data. Using the cloud and Internet of Things (IoT) technology to concentrate those data, the visualized data can be checked and managed on a smartphone. Drivers in unsafe driving states will be warned by a voice assistant. The system can be connected to smart city system in progress, and drivers behaviour and health data is valuable for future data mining as well.

The advent of Information and Communication Technologies (ICTs) brought a fast development of urban centers, and a debate emerges on how to use ICTs to enhance the development and quality of life in cities and how to make these more efficient. The object of this paper has as the analyze whether or not the traditional regulatory model, based on a system of sanctioning behavior divergent from the normative prescription by the postulates of digital governance. For the elaboration of this paper, we use the method of deductive approach. This way, along with the prominent literature and the experience of good international practices, we must recognize the need for an “intelligent” regulatory modeling thus being, we presented a contribution to building a new legal paradigm toward the enhancement of democratic processes in smart cities, structured on the postulates of Crowdlaw (collective production of the legislative process). Last, we believe that the contributions arising out of this work may fill some of the gaps existing in terms of legal theory production on the regulatory modeling for participative governance.

The research objective measures the muscle lower body of level of fitness included in armed forces cadet commissioner in Malaysia. This research uses standing broad jump as test battery to measure muscle lower body fitness and to gauge the sample power of fitness. 212 male respondents (N = 212) comprising of military cadet commissioner of the NDUM were chosen into research. The standing broad jump test was utilized as an instrument for this research. Quantitative research as a quasi-experiment is the technique embraced for this research. The quasi-experiment technique is utilized to gauge and assess the degree of physical fitness of muscle lower body especially for leg power fitness among the military cadet officers. The research plan is a quasi-experiment study with pre- and post-test where data were gotten through down to testing at the outdoor. The information broke down utilizing the SPSS programming adaptation 20.0 to figure standard deviation, mean and paired sample t-test in measure the lower body of physical fitness of the military cadet officers. The result appears that the standard deviation and mean for measure the muscle lower body fitness is (m = 204.01), (SD = 23.197) for pre-test and (m = 222.21) (SD = 20.716) for post-test. The paired-sample t-test for assessing the level of fitness in muscle lower body for pre- and post-test is significantly different (p < 0.05). The ramifications of research the military cadet officers can recognize the degree of physical fitness of their muscle lower body especially for leg power fitness.

With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 years. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ~28 to ~50% in the European Union and from ~33 to ~45% in Asia [1]. Therefore, the percentage of people who need additional care is also expected to increase. Geriatric health care, which pertains to care for the elderly, has gained a lot of prominence in the recent years, with specific focus on fall and sleep apnea detection systems because of their impact on public lives. In the recent years, there has been widespread application of Internet of things (IoT) and machine learning in the geriatric healthcare domain because of the potential cost reduction such technologies can bring in. In this paper, we present the architecture and design of an end-to-end geriatric healthcare system, with focus on wearable device based fall detection using machine learning. We explain the major components of the system architecture, under a certain deployment scenario, and present the communication protocol between these system components. We also present the salient aspects of the multi-channel variable time-division multiple access (multi-channel V-TDMA) MAC protocol designed to suit the requirements of such a system. This protocol combines the strengths of both standard time-division multiple access (TDMA) that is modified to support flexibility and frequency-division multiple access (FDMA).

Schedule length minimization in TDMA protocols for single-channel raw data aggregation convergecast is a topic that is often studied. Many algorithms that solve this problem have been proposed. However, the impact of interference introduced with parallel transmissions in such networks was not studied in detail so far. Most of the protocols use a simple 2-hop interference model, which considers the interference range to be equal to the transmission range. We show that this model does not provide satisfactory results, and we propose an adaptable interference model, derived from real-world measurements. Using extensive simulations, we prove that the proposed model can significantly increase network throughput when used to calculate conflict graphs for any of the existing parallel scheduling algorithms.

This research focuses on the behavior of isolated payment systems. Our previous analysis of the isolated ‘consumer-to-business’ (C2B) and ‘peer-to-peer’ (P2P) payment systems has shown that their behavior can be analyzed using modifications of the Bass equations. In this paper, this approach is extended to the analysis to `hybrid' payment systems with both C2B and P2P functionality using Ricatti equations. We derived universal solution which contains previously obtained C2B and P2P solutions as particular cases. The results are illustrated by analytical solutions for different system parameters and practical examples.

Current research suggests that using gamification in a pedagogical context can provide a positive learning experience to students. Integrating teachers’ pedagogical input into the early stage of game design is an understudied area, and although the teachers’ role is acknowledged, the way of communicating teachers’ input is still undetermined. The current literature suggests the practice of using a Gamification Design Document (GDD) to illustrate a game requirements plan is a useful approach. In this paper, we discuss the development of a Pedagogical Game Design Document (Ped-GDD) using an Agile Holistic Framework to Support Teachers in Pedagogical Gamification Design. The paper also suggests the benefits of extending the research of the Ped-GDD further to be part of an e-Government scheme in the Kingdom of Saudi Arabia (KSA) to promote resources collaboration among teachers in educational software tool developments.

The thesis development is a combined process that requires the student’s knowledge, skills, and self-determination to complete the thesis and reach the desired degree. Despite the efforts made by students, research supervisor and the abundance of information that exists on research methodologies, a good percentage of the students complete the studies but do not complete the research. This research shows the results of a study directed at sixty-six undergraduate students with advanced abilities and knowledge to develop their thesis. The conclusions of the research show the necessity to supplement the knowledge and need for the reinforcement of a research supervisor.

In the present study, the relationship between hedonic capacity and online shopping is explored through a Swedish nationally representative sample. A survey was distributed to 3000 citizens. The number of respondents was 1591 (response rate: 53%). Ordinal regression analyses were conducted in order to test the association between hedonic capacity and online shopping. The dependent variable was online shopping frequencies. Gender, age, and individual income were control variables. Our findings indicated that hedonic capacity was positively associated with online shopping (p < 0.001). The findings propose that online shopping primarily is triggered by emotions and affect rather than reasoning and cognition. Such insights can be used in strategical marketing and technological decisions by academy and industry, as well as in Web site design and communication.

This work describes a virtual testbed of a heating, ventilation, and air conditioning (HVAC) system that has been developed. The testbed incorporates a programmable logic controller (PLC) and is applicable to Internet of Things (IoT) devices. The PLC’s ladder logic program uses hysteresis control and multiple modes of operation. This form of control was selected because of its common use in industry and even in residential applications. The purpose of this work is to demonstrate that by using modern tools and platforms, such as OpenPLC, a controller for an HVAC system can be prototyped and used to research and explore possible cyberattacks and their effects in various cyberphysical systems. In particular, possible and plausible implementations of the aforementioned hysteresis controller in ladder logic are studied and described to the reader. Also, in the experimental results, this work explores how these implementations can be compromised by an injection attack to change settings and by a malicious ladder logic upload in the virtual testbed. The impacts of cyberattacks in terms of safety and cost are discussed also.

A probability distribution is constructed using a deterministic method by gradually slicing a fuzzy number and incrementally transforming a set of $$\alpha {\text{- }}$$ α - cuts into high density intervals. The test of the proposed method was conducted by artificial examples to make a comparison arbitrarily with other commonly used methods and measured by common statistics. The result showed a good, however not the best, possibility-to-probability transformation.

The Indian subcontinent is a south geographic part of Asia continent which consists of India, Bangladesh, Pakistan, Sri Lanka, Bhutan, Nepal, and Maldives. Different rulers or the empire of different periods have built various buildings and structures in these territories like Taj Mahal (Mughal Period), Sixty Dome Mosque (Sultanate Period), etc. From archaeological perspectives, a computational approach is very essential for identifying the construction period of the old or ancient buildings. This paper represents the construction era or period identification approach for Indian subcontinent old heritage buildings by using deep learning. In this study, it has been focused on the constructional features of British (1858–1947), Sultanate (1206–1526), and Mughal (1526–1540, 1555–1857) periods’ old buildings. Four different feature detection methods (Canny Edge Detector, Hough Line Transform, Find Contours, and Harris Corner Detector) have been used for classifying three types of architectural features of old buildings, such as Minaret, Dome and Front. The different periods’ old buildings contain different characteristics of the above-mentioned three architectural features. Finally, a custom Deep Neural Network (DNN) has been developed to apply in Convolutional Neural Network (CNN) for identifying the construction era of above-mentioned old periods.

Theatre is a control-based actor system currently developed in Java, whose design specifically addresses the development of predictable, time-constrained distributed systems. Theatre, though, can also be used for untimed concurrent applications. The control structure regulating message scheduling and dispatching can be customized by programming. This paper describes a novel implementation pTheatre (Parallel Theatre), whose control structure can exploit the potential of parallel computing offered by nowadays multi-core machines. With respect to the distributed implementation of Theatre, pTheatre is more lightweight because it avoids the use of Java serialization during actor migration, and when transmitting messages from a computing node (theatre/thread) to another one. In addition, no locking mechanism is used both in high-level actor programs and in the underlying runtime support. This way, common pitfalls related to classic multi-threaded programming are naturally avoided, and the possibility of enabling high-performance computing is opened. The paper demonstrates the potential of the achieved realization through a parallel matrix multiplication example.