Computer Information Systems and Industrial Management

The study assesses the suitability of multi-muscle texture analysis (TA) for the dystrophy development characterization in Golden Retriever Muscular Dystrophy (GRMD) dogs. Textural features, statistical and model-based, are derived from T2-weighted Magnetic Resonance Images (MRI) of canine hindlimb muscles. Features obtained from different types of muscles (EDL, GasLat, GasMed, and TC) are analyzed simultaneously. Four phases of dystrophy progression, including the “zero phase” – the absence of the disease, are differentiated. Two classifiers are applied: Support Vector Machines (SVM) and Adaptive Boosting (AdaBoost). A Monte Carlo-based feature selection enables to find features (and the corresponding muscle types) that are the most useful in identifying the phase of dystrophy. The simultaneous consideration of several muscles improves the classification accuracy by maximum 12.5% in comparison to the best corresponding result achieved with single-muscle TA. A combination of 17 textural features derived from different types of muscles provides a classification accuracy of approximately 82%.

In this paper, we propose an approach in microscopic image classification for histological sections of human tissues. The method is based on image descriptors composed of vectors of accumulated Zernike moments. The goal is to construct a robust and precise method of image recognition and classification that can be applied in the case of histological tissue samples. Thanks to their properties Zernike moments fit these requirements. Additionally, processed Zernike moments can be made scale, translation, and rotation invariant. In a series of experiments, we verify the effectiveness of the method and its application to the presented problem of medical image classification. The results are obtained with the help of predefined classifiers provided by dedicated software. The paper presents a comparison of results and proposes an example method of improving the approach.

Fingerprint identification is an important part of forensic science (e.g. criminal investigations or identity verification). Friction ridge impressions left at the crime scene can be affected by the nonlinear distortion due to elasticity of the skin, pressure changes or finger movement during deposition. These deformations affect relative distances between fingerprint features such as minutiae point, ridge frequency and orientation, which eventually leads to difficulties in establishing a positive match between impressions of the same finger.In this study we present preliminary results of the impact of fingerprint friction ridge distortion on NBIS Bozorth3 fingerprint matching algorithm. For this purpose special fingerprint database was developed. The database contained 5175 prints obtained from 40 volunteers. Experimental results reveal that the some types of fingerprint distortion (especially movement to right and left) impacts the recognition performance. The results of our studies can be used in future work on statistical friction ridge analysis and fingerprint algorithms robust to distortions.

The venous system is similar in all people, but there are some individual variations. The system is well described and compared in the literature but it seems that there is a lack of comparison on the microscopic level. In this paper we present a segmentation framework for histological image segmentation that uses a clustering approach. The described framework can be used for further comparison of the venous system at the microscopic level. For that purpose we adopted a k–means and fuzzy c–means algorithms to classify image pixels to obtain vein segmentation. The presented results are promising and achieved partitioning can further be utilized for quantitative vein comparison.

According to the World Health Organization, each year cardiovascular diseases (CVD) cause the death of 17.5 million people. Equipment used for oscillograms has great inertia and low sensitivity. The authors have suggested morphological, temporal, spectral methods of analysis of arterial oscillograms. These methods were used to study the adaptive capacity of cardiovascular system to compression of blood vessels in arm during rest and application of 26 kinds of external influences.Application of these ICT methods for arterial oscillogram analysis allows the physician expand information on: the state of the autonomic nervous and cardiovascular systems and centralization level of their activities management; activity and interaction of various parts of nervous system, heart and brain rhythms. They increase the information content on functional reserves and body adaptive ability to resist external influences, mechanisms and quality process of homeostasis maintaining, speed and quality of recovery processes herewith, pathological process dynamics and effectiveness of rehabilitation methods application. The suggested methods will be useful for general physicians, pediatricians, cardiologists, neurologists, researchers, in sports medicine.

Semantic segmentation of medical images is an area of active research all over the world. It can dramatically improve accuracy and efficiency of diagnosis if used properly. High reliability of potential solutions is required to support specialists. In this work we introduce a novel solution to perform pixelwise segmentation of vein preparations dyed with movat stain. Our proposed deep convolutional neural network achieves the accuracy of $$89\%$$ 89 % .

Parathyroid disease has a huge impact on overall health and quality of life. Immunohistochemistry (IHC) is a biological technique, which is useful in diagnosis and prognosis of the parathyroid disorders. The use of IHC as a diagnostic tool brings a substantial methodological problem related to evaluation of stain intensity in micrographs. This paper introduces an image processing approach for automatic IHC stain analysis in micrographs of parathyroid tissue. The introduced approach can be used for computer-aided diagnosis of parathyroid disease as well as for medical research studies in this field. The main novelty of this approach lays in the combination of color deconvolution procedure with a parathyroid cell nuclei localization algorithm, which is based on custom image filtering and circular objects recognition. Accuracy of the proposed approach was verified by comparison with results of experts’ evaluation in experiments conducted on micrographs of healthy tissue, adenomas, and hyperplasias with various IHC markers.

Recently more interest in the recognition algorithms based on human veins is observable. In the literature we can find results confirm that this trait provide huge accuracy level. This feature is used for instance in cash machines. In the last years, more financial institutions took into consideration vein-based identification technology. Its popularity is connected with ease of use and analyzed trait uniqueness. A method to extract finger veins features with image processing algorithms is presented in this paper. In the preliminary stage of the research, the device to collect finger veins images was created. The second part of the work is implementation of the algorithm to process input images. The authors used soft computing algorithm that is artificial neural network to find specific structures on the image. The last stage of the work is connected with confirmation of the results obtained with artificial neural network.

The paper addresses the problem of rotation and translation invariant recognition of objects described by many features. A new set of rotation invariants features are introduced. Numerical experiments are performed to test the invariance for coloured images and chemical compounds. A comparisons with the other methods are made. The obtained results suggest it is worth to explore the proposed method.

The paper proposes a method, which utilizes light sensors from wireless nodes, to detect moving objects like vehicles or pedestrians. The method is analyzing light intensity of the general red, green, and blue spectrums of visible light from nodes that are placed on a roadside. The proposed aggregation algorithm, based on justified granulation paradigm, adapts exponential forgetting mechanism to descriptive statistic functions (features). This approach allows to reduce memory utilization of wireless node. The aggregated values are used by lightweight state-of-the-art machine learning methods to build profile of moving objects. The method is tuned using heuristic-based genetic algorithm. Advantages of the introduced method were demonstrated in real-world scenarios. Broad experiments were conducted to test various classification approaches and feature subsets. The experimental results confirm that the introduced method can be adopted for sensor node, which can detect objects independently or in cooperation with other nodes (working as classifier ensemble).

Service Oriented Architecture (SOA) is a widely used architectural style for constructing Service Oriented Systems (SOS). SOS incorporates several crucial features such as, service composition, service discovery; those are related with different behavioral aspects of SOS. Further, effective analysis of these behavioral characteristics depends on suitable specification of events and constraints in SOA. Thus, to achieve precise and correct specification for SOS, there is a serious need of formal conceptualization of SOA and its associated behavioral characteristics. In this context, this paper proposes a conceptual model for SOA using set theoretic approach and its behavioral aspects using Event-B language [2]. Further, correctness of the model is proved through Rodin platform [2]. A case study is also specified for exhibiting the practical usability of the proposed concepts. The novelty of the proposed work is to model SOA, its behavioral characteristics, related phenomenon and constraints in a way that is more feasible, effective and suitable for service-oriented system.

Information is a crucial value nowadays in network digital societies. Therefore, the phenomenon of “fake news” is a serious problem in modern media and communication, e.g. with respect to information spreading within the society about current events and incidents. Fake news are currently a problem for media and broadcasting sector, for citizens, but also for homeland security. In this paper we present and overview the problem of fake news, we show the ideas and solutions for fake news detection, and we present our initial results for one of such approaches based on forged images detection.

Visibility impairment is maximum definitely defined because the formation of haze that obscures the clarity, shade, texture, and form of what’s visible through the atmosphere. It’s far a complex phenomenon inspired via some of the emissions and air pollutants and tormented by some of the herbal factors which include temperature, humidity, meteorology, time and sunlight. The aim of the research is that to estimate weather visibility using machine learning techniques. We use images taken from CCTV cameras as inputs and deep convolutional neural network model to predict results. We implemented Java based GUI application that can flexibly operate all operations in real-time. Users are also able to use a specially built web page to estimate visibility that a built-in machine learning (ML) model gives an opportunity to the user to get results. In this paper, we will detail explain regarding an architecture of the ML model, System Structure, and other essential details.

Our aim is to investigate the robustness of classifiers against the class imbalance. From this point of view, we compare several most widely used classifiers as well as the one recently proposed, which is based on the assumption that the probability densities in classes have the matrix normal distribution. As the base for comparison we take a sequence of images from that laser based additive manufacturing process. It is important that the classifiers are fed by raw images. The classifiers are compared according to several criterions and the methodology of all pair-wise comparisons is used to rank them.

In this paper, we present an easy method of open-set face classification problem with application to access control and an identity verification. We use normal random projections as a method of feature extraction from face images. The image transformation consists of local projections of spatially-organized rectangular blocks of an image. Two classification algorithms are analyzed: the nearest neighbor method with scalar product similarity measure and individual acceptance/rejection thresholds and multinomial logistic regression. The computational complexity of designing the transformation is linear with respect to the size of images and does not depend on the form of image partition. Experiments performed on the ORL Database demonstrate that the proposed technique is simple and suitable not only for an access monitoring system but also for face verification. The contents of an image after RP-based transformation is hidden and will not be stably recoverable. So, this approach can be used in systems where the privacy-preserving property is important.

Cooperation is increasingly been used in the industrial sector because of its benefits. This have motivated companies to establish alliances and agreements with others in order to reduce cost or access new markets, for example. In the literature, we could find many works aimed at cooperation in supply chains. A smaller amount was focused at cluster cooperation and few of them propose methodologies or models to facilitate the development and implementation of cooperation in industrial clusters. This paper provides a methodology for cooperation in clusters, which was applied to the furniture industry of Atlántico region in Colombia. Shapley value was used in order to evaluate the different coalitions and to split the benefits obtained with these coalitions. The methodology is useful for cluster members in order to encourage the formation of alliances within the cluster in order to overcome the prevailing mistrust, strengthening the cluster and gaining competitiveness.

Workflow Petri nets with time stamps (WPNTS) are the newly introduced class of low-level Petri nets, whose definition and the properties are the main topic of this article; they generalize the properties of Petri net processes in the area of design, modeling and verification of generally parallel systems with the discrete time. Property-preserving Petri net process algebras (PPPA) does not need to verify composition of Petri net processes because all their algebraic operators preserve the specified set of the properties. These original PPPA are generalized for the class of the WPNTS in this article. The new JOIN, EBPS, COMP and SYNC algebraic operators are defined for the class of WPNTS and their chosen properties are proved. With the support of these operators the WPNTSs can be extended also to the area of the project management and the determination of the project generalized critical path with the support of the principles of the critical path method (CPM). The new CPWPNTS subclass of WPNTS class is specially designed for the generalization of the CPM activities charts, its properties are proved and then demonstrated on the generalized critical path specification of simple project example in this article.

New passenger cars and light commercial vehicles manufactured after March 31st, 2018 should be obligatorily equipped with an eCall system that automatically calls for assistance in the event of an accident. The idea of our research was to develop a system, that will be possible to integrate into older that date vehicles without modification and connection to existing onboard car systems. The system will be able to detect a road accident and beyond standard calls recognize number of vehicle’s travelers and report their vital functions. This paper focuses on a road accident detection part of the whole system. We need to know if an accident took place actually or not, i.e. it might be just a car shock, not collision. In the beginning the state of the art was briefly presented and our direction regarded to the approach was taken based on the worked out model, sensors, equipment and the way of simulation. The second part was focused on the created physical model of test set lab with the designed machine for collision simulation with impact force analysis using selected sensors. At this stage of the project we have built the machine with pendulum and trolley on which we put three-axial accelerometers. We have done some controlled collision with data gathering and the first data analysis of the results.

The article focuses on the issue of risk management in productive systems through risk assessment and analysis. Different approaches of risk management have been presented. The use of integrated methodology of risk assessment and analysis in productive systems have been discussed. The author describes the matter of performing identification of possible risks from 4M group (machine, material, method, man), their continuous analysis, assessment and mitigation method. This article features a case study by which production efficiency disturbing factors have been identified. Additionally the risk levels have been assessed and process improving actions indicated.

Selective preprocessing, representing data–level approach to the imbalanced data problem, is one of the most successful methods. This paper introduces novel algorithm combining this kind of technique with the filtering phase. The information granules are formed to distinguish specific types of positive examples that should be adequately treated. Three modes of oversampling, dedicated to minority class instances placed in specific areas of the feature space, are available. The rough set theory is applied to filter and remove inconsistencies from the generated positive samples. The experimental study shows that proposed method in most cases obtains better or similar performance of standard classifiers, such as C4.5 decision tree, in comparison with other techniques. Additionally, multiple values of algorithm’s parameters are evaluated. It is experimentally proven that two of the examined parameters values are the most appropriate to various applications. However, the automatic parameters tuning, based on the specific requirements of different data distributions, is recommended.

One of the most important steps in the formation of multiple classifier systems is the integration process also called the base classifiers fusion. The fusion process may be applied either to class labels or confidence levels (discriminant functions). These are the two main methods for combining base classifiers. In this paper, we propose an integration process which takes place in the geometry space. It means that the fusion of base classifiers is done using decision boundaries. In our approach, the final decision boundary is calculated by using the geometric mean. The algorithm presented in the paper concerns the case of 3 basic classifiers and two-dimensional features space. The results of the experiment based on several data sets show that the proposed integration algorithm is a promising method for the development of multiple classifiers systems.

The C–fuzzy random forest is a novel ensemble classifier which uses C-fuzzy decision trees as unit classifiers. The main problem connected with this classifier is a relatively long learning process time. In this paper the method of reducing the C–fuzzy random forest’s learning time is proposed. Authors proposed and described the method of parallelization of this classifier’s learning process by generating trees which are the parts of the forest in separate threads. The experiments which were designed to check the effectiveness of the proposed method were performed and the results were presented and discussed.

Waste companies need to reduce the cost of collection of the municipal waste, to increase the separation rate of different types of waste, or site of waste source. The collection of waste is an important logistic activity within any city. In this paper, we mainly focus on the daily commercial waste collection problem. One of the approaches for how to resolve this problem is to use optimization algorithms. Ant colony optimisation metaheuristic algorithm (ACO) was used to solve the problem in this paper. This algorithm was adapted for a real data set (Waste Collection). The aim of this paper is to adapt the ACO algorithm and run it on HPC infrastructure to resolve the waste collection problem. We used High-End Application Execution Middleware (HEAppE), that provides smart access to the supercomputing infrastructure (in our case Salomon cluster operated by IT4Innovations National Supercomputing Centre in the Czech Republic). The results showed that the paralelisation of the algorithm is beneficial and brings together with the supercomputing power the possibility to solve larger problems of this type.

In the traffic network, the betweenness centrality helps in identification of the most occupied roads and crossroads. Usually, the main roads have the highest betweenness centrality score, given their importance in the traffic flow. The side roads’ score is generally lower and it never takes into account what is happening on the main road. In a case of unusual event happening in the city, the betweenness score of the main road can increase multiplicatively, while the score of the side road is increased only slightly. Thus, we propose an extension to the original betweenness centrality score algorithm that enables the propagation of the betweenness centrality score from the main road to the side roads, allowing us better description of the current traffic situation. This is the continuation of our work on better refinement of the BC score for the purpose of the traffic modelling and the traffic flow control.

ImageJ has become a popular software platform for image processing and its community has developed and made available numerous plugins for scientific audiences. Nevertheless, no platform-wide solution for parallel processing of big data has been created so far. As ImageJ is a part of the SciJava collaboration project, we propose the concept of seamlessly integrating parallelization-providing capability into one of the SciJava libraries. Specifically, this approach strives to make high-performance infrastructure accessible to ImageJ plugin developers whilst remaining extensible and technology-agnostic. Two parallelization approaches were created and experimentally evaluated on an HPC infrastructure. The results indicate good scalability and are promising for prospective integration of the created functionality into the SciJava Common library.

Paper presents our results dealing with qualitative investigation of neural network including discrete and distributed time delays. We use indirect method to get exponential decay rates of the model. Dynamic behavior is also investigated numerically when changing model parameters. As a result we get point attractors which transit to periodic ones when increasing absolute values of parameters.

The size, complexity and dimensionality of data collections are ever increasing from the beginning of the computer era. Clustering methods, such as Growing Neural Gas (GNG) [10] that is based on unsupervised learning, is used to reveal structures and to reduce large amounts of raw data. The growth of computational complexity of such clustering method, caused by growing data dimensionality and the specific similarity measurement in a high-dimensional space, reduces the effectiveness of clustering method in many real applications. The growth of computational complexity can be partially solved using the parallel computation facilities, such as High Performance Computing (HPC) cluster with MPI. An effective parallel implementation of GNG is discussed in this paper, while the main focus is on minimizing of interprocess communication which depends on the number of neurons and edges among neurons in the neural network. A new algorithm of adding neurons depending on data density is proposed in the paper.

Data collection is one of the fundamental operations in Wireless Sensor Networks (WSNs). Sensor nodes can sense the data and forward the sensed data to the sink in multi hop communication. During the process of data collection, nodes consume a significant amount of energy by transmitting and receiving of data. Therefore, the key challenge is to minimize the energy dissipation of the nodes so as the network lifetime is maximized. In this paper, we propose a Switching Policy based Energy Aware Routing algorithm (SPEAR) for WSNs that aims to reduce energy usage of nodes and thus extends the network lifetime. In a data collection round, SPEAR constructs an energy balanced data collection tree considering the residual energy level of nodes. Furthermore, a switching policy is introduced to achieve energy balancing and longer lifetime. The proposed SPEAR directs some sensor nodes to switch to the new parents with higher energy when the energy level of the older parent is below some threshold value. The proposed method is loop less and dynamic in nature. Our algorithm is validated through simulation and compared with the existing routing protocol using some performance metrics. Simulation results demonstrate that SPEAR improves the network lifetime, alive nodes count and average residual energy significantly.

The method of a minimization of the power consumed by the finite state machine (FSM) is discussed in the presented paper. The proposed algorithm uses two performed sequentially methods of equivalent transformations of the FSM, which do not change the function, but the structure of an FSM.One method assigns multiple codes to the internal states of an FSM. Using more than one code gives more opportunities to assign to the neighbor states the codes with lower Hamming distance, which in result lead to decrease a power consumption. The other method reduces the length of the internal state’s code using a special model of an FSM, in which the orthogonal state codes are obtained from a concatenation of the parts of the input and output vectors, and the subcode stored in a memory. This approach gives the possibility to reduce the size of the memory used for storing the internal state’s code, which leads to reducing the power consumption.Proposed algorithm executes methods starting from the state splitting, followed by the implementation the common architectural model. The experimental results show that the proposed method reduces the power consumption compared to NOVA, JEDI, column based and sequential algorithms.

The utilization of concurrent computing has significantly increased in the last three decades for various commercial and scientific applications. However, concurrent systems often have an astronomically large number of possible executions. These executions may proceed in many different ways depending on scheduling of processes, sequence of inputs, etc. Such non-determinism often leads to gaps or malfunctions in the system design. Thus synchronization of resources became a great issue and programmers had to put huge effort solving this. Transactional memory is one of those measures to solve these inconsistencies. The goal of a transactional memory system is to transparently support the definition of regions of code that are considered in a transaction to maintain the ACID properties of transactions. This paper explores the possibility of designing a STM based mutual exclusion algorithm and compares its performance in terms of time and message complexity. A new STM-based mutual exclusion algorithm known as SME has been proposed and the results have been compared with those due to traditional FAPP algorithm. Besides, the proposed SME implementation is on the ring topology that provides a stable structure suitable for increasing the degree of multiprogramming.

A new method for the minimization of finite state machines (FSMs) is proposed. In this method, such optimization criterion as the number of used logic elements is taken into account already at the stage of minimizing internal states. The method is based on sequential merging of two internal states. For this purpose, the set of all pairs of states that can be merged is found, and the pair that best satisfies the optimization criteria is chosen for merging. In addition, the proposed method allows one to minimize the number of transitions and input variables of the FSM. The binary, one-hot and JEDI state assignment methods are used. Experimental results show, that the used FPGA area is less on average by 18% comparing to traditional methods.

In agent-based models, agents are expected to coordinate mutual actions – to cooperate. The cooperation among agents is usually described by tools of game theory. In general, the cooperation of autonomous agents is based on information of perspective gain from cooperation. If the gain from cooperation is at least as high as the gain which agents can receive without cooperation, then this situation can be described by tools of superadditive cooperative games. The information received by agents in the case of real-world systems is not deterministic, and the use of more sophisticated tools is required. Hence, the main aim of this paper is to discuss additivity and superadditivity issues in the case of cooperative games with expectations given as Atanassov intuitionistic numbers.

Nowadays in the area of Internet of Things (IoT), congestion control has become an essential research area because of people and devices are progressively get connected over the network. The idea behind congestion control mechanisms originated from the point of network bandwidth, node processing ability, server capacities, channel capacity, flow of the link, number and size of distinct flow and channel reliability. Here we have used the concept of different RED, AMID and COAP based congestion control mechanisms. We have measure two level of congestion control that is node level and channel trustability. In this paper we have presented literature review of some of existing congestion control mechanisms. A congestion control model has also been proposed, which uses the measure of node level congestion and channel-trust for decision making.

In the paper, we propose a new method for FPGA-based design of high-speed Algorithmic State Machine (ASM) controllers. The method is based on the introduction of additional states of the state machine in order to implement all transition functions in the single-level structures. In this method, such an optimization criterion as a critical path delay is applied already at the stage of converting the ASM chart to the state machine HDL description. The proposed method consists of two steps: determining the place of additional labels on the ASM chart and introducing additional states of FSM. Experimental results show that our approach achieves an average performance gain of 20.43% to 27.41% (for various FPGA devices) compared with the traditional synthesis method. The maximum performance increase achieved is 59.17%. At the same time, the method slightly increases the cost of implementation by an average of 5.13% to 5.19%, but in some cases even reduces the cost.

Embedded multimedia devices need to be more and more energy efficient while dealing with applications of increasing complexity. These applications are characterised by having complex array index manipulation, a large number of data accesses and require high performant specific computation at low energy consumption due to battery life.In many cases, the principal component of such systems is a programmable processor, and often, a Very Large Instruction Word (VLIW) processor (alone or integrated with other processor cores). A VLIW processor seems a good solution providing enough performance at low power with sufficient programmability but optimising the access to the data is a crucial issue for the success of those devices. Some modern embedded architectures include a dedicated unit that works in parallel with the central computing elements ensuring efficient feed and storage of the data from/to the data path: the Address Generation Unit.In this paper, we present an experimental work that shows, on real and complete applications and benchmarks, the impact of address generation in VLIW-like processor architectures. We see how address generation in multimedia embedded systems has a very significant contribution to the energy budget and a careful analysis an optimisation is needed to extend battery life as much as possible while keeping enough performance to satisfy the quality of service requirements. We also present the framework used to create and evaluate the impact of address generation on the overall system.

Several organizations look for improving their business processes in order to enhance their efficiency and competitiveness. The lack of integration between the business process model and its incurred financial cost information hampers for better decision making support allowing business process incurred cost reduction. In previous work, we proposed a solution for business process model extension with cost perspective based on process mining, independently of the business process model notation. The proposed solution provides cost data description and analysis at the process and the activity levels. Cost data analysis allows to extract knowledge about factors influencing on cost at each of the process and the activity levels. The proposed solution also involves cost data analysis through the use of classification algorithms which can be selected by the user. However, the lack of support during this selection may affect the accuracy of the obtained results. Furthermore, the performance of the same classification algorithm may vary from a case to another depending on its context: (1) data features and (2) the considered performance criteria. Thus, in this paper, we propose to adopt a context-based cost data analysis allowing to select and apply the classification algorithm the most suited to the case in hand. This supports improving the accuracy of the obtained results. In order to validate the proposed solution, a case study is conducted on the business process of a maternity department in a Tunisian clinic. The results of this case study confirm the expected goals.

Storing data in the cloud environment becomes more and more popular for users and also for entrepreneurs. It offers high scalability, efficiency and good price. However, it’s not always secure, even if providers ensures about high security of their service. “Arms race” never stops, attackers have sophisticated tools and often specialized knowledge. Combination of these two may result in danger for data stored in clouds. Furthermore, by uploading data on cloud, we’re giving away control about them. Rapid technological progress and popularity of mobile devices results with users increasing awareness about threats. On the other hand, there’s not as many solutions for mobile devices as for desktop devices. Also their quality is not always very high. Proposed model reduces the role of “third-parties”, offering much more control for user, the owner of stored data.

In software management, the upgrade of programming languages may introduce critical issues. This is the case of PHP, the fifth version of which is going towards the end of the support. The new release improves on different aspects, but removes the old deprecated MySQL extensions, and supports only the newer library of functions for the connection to the databases. The software systems already in place need to be renewed to be compliant with respect to the new language version. The conversion of the source code, to be safe against injection attacks, should involve also the transformation of the query code. The purpose of this work is the design of specific tool that automatically applies the required transformation yielding to a precise and efficient conversion procedure. The tool has been applied to different projects to provide evidence of its effectiveness.

The article is devoted to dynamic authentication method of electronic network devices with built-in analog-to-digital converters (ADCs) based on authentication templates. The following results were obtained: the authentication of each electronic device can be carried out uniquely by its internal electrical noise (like biometric authentication of a person). Uniqueness of authentication is provided by the invariants of the noise signal such as the shape of the graph of the autocorrelation function of noise and the set of resonance frequencies of the device. The electronic device authentication template is obtained from the sequence of values of the autocorrelation function of the noise. It consists from the bit template and the amplitude template. The technique of obtaining an authentication template is presented. The required duration of the noise signal is 0.5 s for reliable authentication at a sampling frequency of 44.1 kHz. The results of authentication of several computers are presented.

In this paper, we compared the requirements of previously developed manual and ISO/IEC 29134:2017 and analyzed the changes. As a result, there were no major differences in requirements. It is useful to conduct a privacy impact assessment (PIA) before actually operating the system to appropriately construct and operate a system that handles personal information. A manual (procedure manual) is necessary to implement PIA efficiently. In June 2017, ISO issued the ISO/IEC 29134:2017 as an international standard on PIA. Cause the past PIA manual developed based on ISO 22307:2008, development of a PIA manual conforming to ISO/IEC 29134:2017 was required. By our analysis, as a newly stated matter, ISO/IEC 29134:2017 explicitly indicated Due Diligence, stakeholder engagement, and risk countermeasures. Based on the analysis results, we propose a new PIA manual reflecting the requirements of ISO/IEC 29134:2017.

One of the biggest problems plaguing society today is that of fraudulent elections. The world’s largest democracies still suffer from flawed electoral systems. In current voting systems, we see problems with vote rigging, hacking of the EVM (Electronic voting machine), election manipulation, and polling booth capturing. Looking closely at the current Cambridge Analytica scandal brings the validity of current voting systems into question. In this paper, we propose a novel voting model which can resolve these issues. Using a recently introduced blockchain protocol called PHANTOM, we try to alleviate known problems in voting systems. Furthermore, the advantage of using our model is, it is compatible with all voting schemes. So, one can implement our model using any voting scheme depending on the requirement of different type of elections.

The subject matter of the article is methods and means of the databases (DBs) security ensuring, built on the basis of the database scheme that is invariant to subject domains (SDs). The goal is to develop a substantiated approach that implements the complex use of various mechanisms ensuring the databases security built on the database schema with the universal basis of relations. The task: based on the analysis of existing database protection mechanisms supported by various database management systems (DBMSs), and features of the destination, construction of the database schema with the universal basis of relations, to develop and present in a systematized form the means and methods ensuring the databases security built on this DB schema. The following results were obtained: solving the problem of protecting databases as the most important corporate resource, in the process of creating database schema invariant to subject domains, special means were developed (in the form of implemented schema objects such as triggers, procedures, packages, tables, functions) and rules of their use, ensuring: access control to schema objects; data protection and hiding of objects; data integrity support; recovery of incorrectly modified or lost data; monitoring of the state, changes introduced into the database; logging user actions.