Comput

The integration of a P300-based brain–computer interface (BCI) into virtual reality (VR) environments is promising for the video games industry. However, it faces several limitations, mainly due to hardware constraints and limitations engendered by the stimulation needed by the BCI. The main restriction is still the low transfer rate that can be achieved by current BCI technology, preventing movement while using VR. The goal of this paper is to review current limitations and to provide application creators with design recommendations to overcome them, thus significantly reducing the development time and making the domain of BCI more accessible to developers. We review the design of video games from the perspective of BCI and VR with the objective of enhancing the user experience. An essential recommendation is to use the BCI only for non-complex and non-critical tasks in the game. Also, the BCI should be used to control actions that are naturally integrated into the virtual world. F...

We propose a limited-memory quasi-Newton method using the bad Broyden update and apply it to the nonlinear equations that must be solved to determine the effective Fermi momentum in the weighted density approximation for the exchange energy density functional. This algorithm has advantages for nonlinear systems of equations with diagonally dominant Jacobians, because it is easy to generalize the method to allow for periodic updates of the diagonal of the Jacobian. Systematic tests of the method for atoms show that one can determine the effective Fermi momentum at thousands of points in less than fifteen iterations.

The platform economy denotes a subset of economic activities enabled by platforms such as Amazon, Alibaba, and Uber. Due to their tremendous success, more and more offerings concentrate around platforms increasing platforms’ positional-power, hence leading towards a de-facto centralization of previously decentralized online markets. Furthermore, platform models work well for individual products and services or predefined combinations of these. However, they fall short in supporting complex products (personalized combinations of individual products and services), the combination of which is required to fulfill a particular consumer need, consequently increasing transaction costs for consumers looking for such products. To address these issues, we envision a “post-platform economy”—an economy facilitated by decentralized and self-organized online structures named Distributed Market Spaces. This work proposes a comprehensive model to serve as a guiding framework for the analysis, desig...

This paper describes the objectives of the SEQUOIA 2000 project and the software development that is being done to achieve these objectives. In addition, several lessons relevant to Geographic Information Systems (GIS) that have have been learned from the project are explained.

The Domain Name System (DNS) was created to resolve the IP addresses of web servers to easily remembered names. When it was initially created, security was not a major concern; nowadays, this lack of inherent security and trust has exposed the global DNS infrastructure to malicious actors. The passive DNS data collection process creates a database containing various DNS data elements, some of which are personal and need to be protected to preserve the privacy of the end users. To this end, we propose the use of distributed ledger technology. We use Hyperledger Fabric to create a permissioned blockchain, which only authorized entities can access. The proposed solution supports queries for storing and retrieving data from the blockchain ledger, allowing the use of the passive DNS database for further analysis, e.g., for the identification of malicious domain names. Additionally, it effectively protects the DNS personal data from unauthorized entities, including the administrators that...

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches de...

The aerospace and defense industry is facing an end-of-life production issue with legacy embedded uniprocessor systems. Most, if not all, embedded processor manufacturers have already moved towards system-on-a-chip multicore architectures. Current scheduling arrangements do not consider schedules related to safety and security. The methods are also inefficient because they arbitrarily assign larger-than-necessary windows of execution. This research creates a hierarchical scheduling framework as a model for real-time multicore systems to integrate the scheduling for safe and secure systems. This provides a more efficient approach which automates the migration of embedded systems’ real-time software tasks to multicore architectures. A novel genetic algorithm with a unique objective function and encoding scheme was created and compared to classical bin-packing algorithms. The simulation results show the genetic algorithm had 1.8–2.5 times less error (a 56–71% difference), outperforming...

Virtual Reality (VR) users typically either sit or stand/walk when using VR; however, the impact of this is little researched, and there is a lack of any broad or systematic analysis of how this difference in physical posture might affect user experience and behavior. To address this gap, we propose such a systematic analysis that was refined through discussions and iterations during a dedicated workshop with VR experts. This analysis was complemented by an online survey to integrate the perspectives of a larger and more diverse group of VR experts, including developers and power users. The result is a validated expert assessment of the impact of posture and degree of embodiment on the most relevant aspects of VR experience and behavior. In particular, we posit potential strong effects of posture on user comfort, safety, self-motion perception, engagement, and accessibility. We further argue that the degree of embodiment can strongly impact cybersickness, locomotion precision, safet...

We live in a heavily technologized global society. It is therefore not surprising that efforts are being made to integrate current information technology into the treatment of diabetes mellitus. This paper is dedicated to improving the treatment of this disease through the use of well-designed mobile applications. Our analysis of relevant literature sources and existing solutions has revealed that the current state of mobile applications for diabetics is unsatisfactory. These limitations relate both to the content and the Graphical User Interface (GUI) of existing applications. Following the analysis of relevant studies, there are four key elements that a diabetes mobile application should contain. These elements are: (1) blood glucose levels monitoring; (2) effective treatment; (3) proper eating habits; and (4) physical activity. As the next step in this study, three prototypes of new mobile applications were designed. Each of the prototypes represents one group of applications acc...

COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated an...

In this work, we summarize the recent progress made in constructing time-dependent density-functional theory (TDDFT) exchange-correlation (XC) kernels capable to describe excitonic effects in semiconductors and apply these kernels in two important cases: a “classic” bulk semiconductor, GaAs, with weakly-bound excitons and a novel two-dimensional material, MoS2, with very strongly-bound excitonic states. Namely, after a brief review of the standard many-body semiconductor Bloch and Bethe-Salpether equation (SBE and BSE) and a combined TDDFT+BSE approaches, we proceed with details of the proposed pure TDDFT XC kernels for excitons. We analyze the reasons for successes and failures of these kernels in describing the excitons in bulk GaAs and monolayer MoS2, and conclude with a discussion of possible alternative kernels capable of accurately describing the bound electron-hole states in both bulk and two-dimensional materials.

The use of digital technology as the only communication and relationship channel in work, school and social contexts is bringing out dynamics that are sometimes in contrast with each other. The purpose of this article is to investigate the impact of digital technology on teachers’ school practices in the context of COVID-19. This impact was studied in relation to the constructs of motivation, perceived stress, sense of self-efficacy and resistance to/acceptance of technologies. This study examined the role played by the massive and coercive use of digital technologies (and the relationship with innovation and change) in predicting motivation and perceived stress among teachers. To this end, the impact of digital technologies on motivation and perceived stress were explored in the sample. A questionnaire consisting of three scales was administered to 688 Italian school teachers of all educational levels (from childhood to upper-secondary school), who completed a socio-demographic sec...

The progress of embedded control systems in the last several years has made possible the realization of highly-effective controllers in many domains. It is essential for such systems to provide effective performance at an affordable cost. Furthermore, real-time embedded control systems must have low energy consumption, as well as be reliable and timely. This research investigates primarily the feasibility of implementing an embedded real-time control system, based on a low-cost, commercially off-the-shelf (COTS) microcontroller platform. It explores real-time issues, such as the reliability and timely response, of such a system implementation. This work presents the development and performance evaluation of a novel real-time control architecture, based upon a BeagleBoard microcontroller, and applied into the PWM (pulse width modulation) control of a three-phase induction motor in a suction pump. The approach followed makes minimal use of general-purpose hardware (BeagleBone Black mi...

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achi...

Today, Knowledge Management (KM) is becoming a popular approach for improving organizational innovation, but whether encouraging knowledge sharing will lead to a better innovative working behaviour of employees is still a question. This study aims to identify the factors of KM affecting the innovative working behaviour of Information Technology (IT) employees in Vietnam. The research model involves three elements: attitude, subjective norm and perceived behavioural control affecting knowledge sharing, and then, on innovative working behaviour. The research method is the quantitative method. The survey was conducted with 202 samples via the five-scale questionnaire. The analysis results show that knowledge sharing has a positive impact on the innovative working behaviour of IT employees in Vietnam. Besides, attitude and perceived behavioural control are confirmed to have a strong positive effect on knowledge sharing, but the subjective norm has no significant impact on knowledge shar...

The ability for DFT: B3LYP calculations using the 6-31g and lanl2dz basis sets to predict the electrochemical properties of twenty (20) 3-aryl-quinoxaline-2-carbonitrile 1,4-di-N-oxide derivatives with varying degrees of cytotoxic activity in dimethylformamide (DMF) was investigated. There was a strong correlation for the first reduction and moderate-to-low correlation of the second reduction of the diazine ring between the computational and the experimental data, with the exception of the derivative containing the nitro functionality. The four (4) nitro group derivatives are clear outliers in the overall data sets and the derivative E4 is ill-behaved. The remaining three (3) derivatives containing the nitro groups had a strong correlation between the computational and experimental data; however, the computational data falls substantially outside of the expected range.

Conjugated singlet ground state diradicals have received remarkable attention owing to their potential applications in optoelectronic devices. A distinctive character of these systems is the location of the double exciton state, a low lying excited state dominated by the doubly excited H,H→L,L configuration, which may influence optical and other photophysical properties. In this contribution we investigate this specific excited state, for a series of recently synthesized conjugated diradicals, employing time dependent density functional theory based on the unrestricted parallel spin reference configuration in the spin-flip formulation (SF-TDDFT) and standard TD calculations based on the unrestricted antiparallel spin reference configuration (TDUDFT). The quality of the computed results is assessed considering diradical and multiradical descriptors and the excited state wavefunction composition.

Hand motion tracking plays an important role in virtual reality systems for immersion and interaction purposes. This paper discusses the problem of finger tracking and proposes the application of the extension of the Madgwick filter and a simple switching (motion recognition) algorithm as a comparison. The proposed algorithms utilize the three-link finger model and provide complete information about the position and orientation of the metacarpus. The numerical experiment shows that this approach is feasible and overcomes some of the major limitations of inertial motion tracking. The paper’s proposed solution was created in order to track a user’s pointing and grasping movements during the interaction with the virtual reconstruction of the cultural heritage of historical cities.

In this work, Finite Element Method (FEM) is applied to obtain the condition at the boundary of the interface between a channel and a porous medium. The boundary conditions that should be applied to the inhomogeneous interface zone between the two homogeneous regions of free fluid and porous medium are derived. The comparison has been performed for porous material characterizations to provide the velocity at the inhomogeneous interface zone with variable permeability between the two homogeneous regions of free fluid and porous medium. Also, the dependence of the slip coefficient on the thickness of the transition zone is established and the values of the thickness are so justified that the numerical results and the numerical results of our proposed technique are found to be in good agreement with experimental results in the literature.

Ransomware is a relatively new type of intrusion attack, and is made with the objective of extorting a ransom from its victim. There are several types of ransomware attacks, but the present paper focuses only upon the crypto-ransomware, because it makes data unrecoverable once the victim’s files have been encrypted. Therefore, in this research, it was proposed that machine learning is used to detect crypto-ransomware before it starts its encryption function, or at the pre-encryption stage. Successful detection at this stage is crucial to enable the attack to be stopped from achieving its objective. Once the victim was aware of the presence of crypto-ransomware, valuable data and files can be backed up to another location, and then an attempt can be made to clean the ransomware with minimum risk. Therefore we proposed a pre-encryption detection algorithm (PEDA) that consisted of two phases. In, PEDA-Phase-I, a Windows application programming interface (API) generated by a suspicious ...

Cognitive disorders remain a major cause of disability in Multiple Sclerosis (MS). They lead to unemployment, the need for daily assistance, and a poor quality of life. The understanding of the origin, factors, processes, and consequences of cognitive disfunction is key to its prevention, early diagnosis, and rehabilitation. The neuropsychological testing and continuous monitoring of cognitive status as part of the overall evaluation of patients with MS in parallel with clinical and paraclinical examinations are highly recommended. In order to improve health and disease understanding, a close linkage between fundamental, clinical, epidemiological, and socio-economic research is required. The effective sharing of data, standardized data processing, and the linkage of such data with large-scale cohort studies is a prerequisite for the translation of research findings into the clinical setting. In this context, this paper proposes a software platform for the cognitive assessment and re...

Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ...

The present work highlights the capacity of disparate lattice Boltzmann strategies in simulating natural convection and heat transfer phenomena during the unsteady period of the flow. Within the framework of Bhatnagar-Gross-Krook collision operator, diverse lattice Boltzmann schemes emerged from two different embodiments of discrete Boltzmann expression and three distinct forcing models. Subsequently, computational performance of disparate lattice Boltzmann strategies was tested upon two different thermo-hydrodynamics configurations, namely the natural convection in a differentially-heated cavity and the Rayleigh-Benard convection. For the purposes of exhibition and validation, the steady-state conditions of both physical systems were compared with the established numerical results from the classical computational techniques. Excellent agreements were observed for both thermo-hydrodynamics cases. Numerical results of both physical systems demonstrate the existence of considerable di...

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the ...

In 1947, N. Herlofson proposed a modification to the 1884 Heinrich Hertz’s Emagram with the goal of getting more precise hand-made weather forecasts providing larger angles between isotherms and adiabats. Since then, the Herlofson’s nomogram has been used every day to visualize the results of about 800 radiosonde balloons that, twice a day, are globally released, sounding the atmosphere and reading pressure, altitude, temperature, dew point, and wind velocity. Relevant weather forecasts use such pieces of information to predict fog, cloud height, rain, thunderstorms, etc. However, despite its diffusion, non-technical people (e.g., private gliding pilots) do not use the Herlofson’s nomogram because they often consider it hard to interpret and confusing. This paper copes with this problem presenting a visualization based environment that presents the Herlofson’s nomogram in an easier to interpret way, allowing the selection of the right level of detail and at the same time inspection ...

Mission- and safety-critical circuits and systems employ redundancy in their designs to overcome any faults or failures of constituent circuits and systems during the normal operation. In this aspect, the N-modular redundancy (NMR) is widely used. An NMR system is comprised of N identical systems, the corresponding outputs of which are majority voted to generate the system outputs. To perform majority voting, a majority voter is required, and the sizes of majority voters tend to vary depending on an NMR system. Majority voters corresponding to NMR systems are physically realized by enumerating the majority input clauses corresponding to an NMR system and then synthesizing the majority logic equation. The issue is that the number of majority input clauses corresponding to an NMR system is governed by a mathematical combination, the complexity of which increases substantially with increases in the level of redundancy. In this context, the design of a majority voter of any size corresp...

Explicit expressions are obtained for sensitivity coefficients to separately estimate temperature-dependent thermophysical properties, such as specific heat and thermal conductivity, in two-dimensional inverse transient heat conduction problems for bodies with irregular shape from temperature measurement readings of a single sensor inside the body. The proposed sensitivity analysis scheme allows for the computation of all sensitivity coefficients in only one direct problem solution at each iteration with no need to solve the sensitivity and adjoint problems. In this method, a boundary-fitted grid generation (elliptic) method is used to mesh the irregular shape of the heat conducting body. Explicit expressions are obtained to calculate the sensitivity coefficients efficiently and the conjugate gradient method as an iterative gradient-based optimization method is used to minimize the objective function and reach the solution. A test case with different initial guesses and sensor locat...

IT investment is a crucial issue as it does not only influence the performance in Small-Medium Enterprises (SMEs) but it also helps executives to align business strategy with organizational performance. Admittedly, though, there is ineffective use of Information Systems (IS) due to a lack of strategic planning and of formal processes resulting in executives’ failure to develop IS plans and achieve long-term sustainability. Therefore, the purpose of this paper is to examine the phases of Strategic Information Systems Planning (SISP) process that contribute to a greater extent of success so that guidelines regarding the implementation of the process in SMEs can be provided. Data was collected by 160 IS executives in Greek SMEs during February and May 2017. Multivariate Regression Analysis was applied on the detailed items of the SISP process and success constructs. The results of this survey present that managers should be aware of the strategic use of IS planning so as to increase co...

A computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid AN...

Using molecular dynamics, a comparative study was performed of two pairs of glassy polymers, low permeability polyetherimides (PEIs) and highly permeable Si-containing polytricyclononenes. All calculations were made with 32 independent models for each polymer. In both cases, the accessible free volume (AFV) increases with decreasing probe size. However, for a zero-size probe, the curves for both types of polymers cross the ordinate in the vicinity of 40%. The size distribution of free volume in PEI and highly permeable polymers differ significantly. In the former case, they are represented by relatively narrow peaks, with the maxima in the range of 0.5–1.0 Å for all the probes from H2 to Xe. In the case of highly permeable Si-containing polymers, much broader peaks are observed to extend up to 7–8 Å for all the gaseous probes. The obtained size distributions of free volume and accessible volume explain the differences in the selectivity of the studied polymers. The surface area of A...

Visible Light Communication (VLC) systems use light-emitting diode (LED) technology to provide high-capacity optical links. The advantages they offer, such as the high data rate and the low installation and operational cost, have identified them as a significant solution for modern networks. However, such systems are vulnerable to various exogenous factors, with the background sunlight noise having the greatest impact. In order to reduce the negative influence of the background noise effect, optical filters can be used. In this work, for the first time, a low-cost optical vanadium dioxide (VO2) optical filter has been designed and experimentally implemented based on the requirements of typical and realistic VLC systems in order to significantly increase their performance by reducing the transmittance of background noise. The functionality of the specific filter is investigated by means of its bit error rate (BER) performance estimation, taking into account its experimentally measure...