Reviewerly/exHarmony huggingface dataset:
Hardware Response and Performance Analysis of Multicore Computing Systems for Deep Learning Algorithms

Abstract With the advancement in technological world, the technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are gaining more popularity in many applications of computer vision like object classification, object detection, Human detection, etc., ML and DL approaches are highly compute-intensive and require advanced computational resources for implementation. Multicore CPUs and GPUs with a large number of dedicated processor cores are typically the more prevailing and effective solutions for the high computational need. In this manuscript, we have come up with an analysis of how these multicore hardware technologies respond to DL algorithms. A Convolutional Neural Network (CNN) model have been trained for three different classification problems using three different datasets. All these experimentations have been performed on three different computational resources, i.e., Raspberry Pi, Nvidia Jetson Nano Board, &amp; desktop computer. Results are derived for performance analysis in terms of classification accuracy and hardware response for each hardware configuration.
Thermal pain and detection threshold modulation in augmented reality

Augmented Reality (AR) overlays computer-generated visual, auditory or other sensory information onto the real world. Due to recent technological advancement in the field, it can become increasingly difficult for the user to differentiate between sensory information coming from real and virtual objects, leading to interesting perceptual phenomena. For example, an AR experience in which users can experience their own hands in flames has been shown to elicit heat illusions on the affected hands. In this study, we investigate the potential that AR has for top-down modulation of pain and thermal perception. We assessed thermal pain and detection thresholds on the participant’s right hand while covering it with realistic virtual flames. We compared this experience to a baseline condition with no additional stimuli. We also report on a condition in which the hand is covered by a blue fluid not instantly associated with fire. We found that experiencing a virtual burning hand induces analgesic as well hyperalgesic effects as participants begin to feel heat related pain at lower temperatures and cold related pain at higher temperatures. The experience also impacts significantly on the lowest temperature at which participants starts perceiving warmth. The blue fluid do not affect the thresholds corresponding to the baseline condition. Our research thus confirms previous experiments showing that pain and thermal perception can be manipulated by by AR, while providing quantitative results on the magnitude of this effect.
Learning capability of parametrized quantum circuits

Variational quantum algorithms (VQAs) and their applications in the field of quantum machine learning through parametrized quantum circuits (PQCs) are thought to be one major way of leveraging noisy intermediate-scale quantum computing devices. However, differences in the performance of certain VQA architectures are often unclear since established best practices as well as detailed studies are missing. In this paper, we build upon the work by Schuld et al. and Vidal et al. and compare popular ans\"atze for PQCs through the new measure of learning capability. We also examine dissipative quantum neural networks (dQNN) as introduced by Beer et al. and propose a data re-upload structure for dQNNs to increase their learning capability. Comparing the results for the different PQC architectures, we can provide guidelines for designing efficient PQCs.
Teaching Quality Evaluation of Animal Science Specialty Based on IPSO-BP Neural Network Model

Teaching quality evaluation is one of the most commonly used educational evaluation methods, which is used to evaluate teachers’ teaching ability and teaching effect. In order to improve the effectiveness and accuracy of teaching quality evaluation, a BP neural network model based on improved particle swarm optimization (IPSO) is proposed. Firstly, the evaluation index system of teaching quality is constructed with teaching attitude, teaching content, teaching method, and teaching effect as indicators. Then, IPSO algorithm is used to optimize the weight and threshold of neural network to improve the performance of BP algorithm. Secondly, IPSO-BP algorithm is used for sample training to optimize the model structure. Finally, the model is used to evaluate the teaching quality of animal science-related courses in Inner Mongolia University for Nationalities. The results show that compared with the ordinary BP neural network model, the IPSO-BP model has fast convergence speed, good robustness, and strong global search ability, and the evaluation accuracy rate is 96.7%. It is feasible in the evaluation of teaching quality.
Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks

Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly.
A Reviwe of Arabic Intelligent Chatbots: Developments and Challenges

Chatbots, or machine based conversational systems, have become popular in recent years in a variety of applications.Chatbots are intelligent technologies that employ Artificial Intelligence (AI) to converse with humans in their natural languages.Chatbot's primary function is to interpret user's questions and respond with the most convenient intelligent and natural responses.In a number of the world's most generally spoken languages, chatbots appear to be quite successful; however, Arabic chatbots have not yet attained the predicted degree of success.Many academics have recently sought to bridge the gap in the implementation of Arabic chatbots by overcoming the complicated linguistic Arabic language features.This survey reviews the chatbots, their categories and implementation techniques.It also, presents some challenges that face the developer of Arabic chatbots due to rich complex properties of Arabic language.Then, most of published chatbot researches that deal with Arabic language are highlighted.Finally, a table of comparison among the mentioned Arabic chatbots is presented.The study concludes that, all the present AI based Arabic chatbots are developed in retrieval mode, and that Arabic chatbot literature is scarce in comparison to that in many other languages.
A secured compression technique based on encoding for sharing electronic patient data in slow-speed networks

Healthcare systems capture patients' data using different medical equipment and store it in the databases with a continual increase in data volume. The continuous processing and sharing of this massive data are rising concerns in live data transferring over the networks. Sending patient data to the distant remote user without proper compressing format requires high latency in the communication channels. Any alternation of data transmitted via the communication medium may also cause issues in assuring data authentication and integrity. For solving the problems, watermarking method is being applied to ensure such security, which has a cheaper computational cost. Various watermarking mechanisms are available for ensuring health data security, especially for medical images. Watermarking on the text was not used yet due to the lack of efficient technique. This paper proposes a secured compression technique for patient live-text data while sharing them remotely over a bandwidth-deficient channel. To test the proposed system, we use patient data. The result indicates that the proposed strategy outperforms the existing compression methods and is robust enough to provide data integrity and authentication.
STEM PROJECT AS A MEANS OF LEARNING MODELING FOR PRE-SERVICE MATHEMATICS AND COMPUTER SCIENCE TEACHERS

Modern science operates with various methods, among which modeling is one of the most popular. The development of information technology allows the study of analogues (models) with the most significant characteristics of the real object. Modeling activities have been considered as useful teaching method in STEM education. Cloud services (like GeoGebra) are effective means for STEM education. The paper features a methodology of forming modeling skills based on STEM projects, which is grounded on modeling interesting curves of Analytic Geometry course. The content of the methodology is a course in Computer Modeling, which includes a module "STEM education and modeling". The module idea is based on the formation of skills required to model interesting curves (ellipse, hyperbola, parabola, conchoid of Nicomedes, limaçon of Pascal, strophoid, cissoid of Diocles, lemniscate of Bernoulli, Cassini oval, cycloidal curves, folium of Descartes, witch of Agnesi, logarithmic spiral). The methodology provides 4 steps (Step 1 – the teacher offers an example of a STEM project, which is discussed in class and solved by the teacher using GeoGebra; Step 2 – students are divided into groups of 3-4 people; Step 3 – the teacher offers a short STEM project (7-10 days), in which students model the curve; Step 4 – students offer their own STEM project (15-20 days), the solution of which is based on the modeling of an interesting curve). To test the effectiveness of the developed methodology, a pedagogical experiment was organized (2019-2021), which was joined by Master's students majoring in "Secondary Education (Mathematics)" and "Secondary Education (Computer Science)". Makarenko Sumy State Pedagogical University (Ukraine) was the experimental base. The effectiveness of the proposed methodology is proved by the sign test at the significant level of 0.05.
Low-Light Image Enhancement and Target Detection Based on Deep Learning

Most computer vision applications demand input images to meet their specific requirements. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects. The existing image enhancement methods, which are based on non-physical imaging models, and image generation methods, which are based on deep learning, are not ideal for low-light image processing. To solve the problem, this paper explores low-light image enhancement and target detection based on deep learning. Firstly, a simplified expression was constructed for the optical imaging model of low-light images, and a Haze-line was proposed for color correction of low-light images, which can effectively enhance low-light images based on the global background light and medium transmission rate of the optical imaging model of such images. Next, network framework adopted by the proposed low-light image enhancement model was introduced in detail: the framework includes two deep domain adaptation modules that realize domain transformation and image enhancement, respectively, and the loss functions of the model were presented. To detect targets based on the output enhanced image, a joint enhancement and target detection method was proposed for low-light images. The effectiveness of the constructed model was demonstrated through experiments.
An Approach for Enhancement of MR Images of Brain Tumor

Magnetic Resonance Imaging plays an important role in diagnosing the brain tumor accurately, but it requires the approach to enhance the magnetic resonance images to assist physicians in brain tumor detection and making the treatment plan precisely to reduce the mortality rate. Therefore, in this proposed work, a comprehensive learning-based elephant herding optimization technique has been introduced to select the optimal value of smoothness factor in Bi-Histogram Equalization with Adaptive Sigmoid Function that enhances the visual quality as well as the appearance of the suspicious regions in magnetic resonance images. Further, the enhancement performance has been evaluated by the enhancement quality metrics. The metrics used include mean square error, peak signal to noise ratio, mean absolute error, structural similarity index metric, feature similarity index metric, Riesz transformed based feature similarity index metric, spectral residual-based similarity index metric, and absolute mean brightness error. The outcomes of this proposed work have a remarkable impact on enhancing magnetic resonance images and providing visual assistance for diagnosing brain tumors. The performance of the evaluation metrics is verified with Friedman's mean rank test, which strongly indicates a statistical difference between the proposed method and state-of-the-art methods.
Differentiable Collision Detection: a Randomized Smoothing Approach

Collision detection is an important component of many robotics applications, from robot control to simulation, including motion planning and estimation. While the seminal works on the topic date back to the 80s, it is only recently that the question of properly differentiating collision detection has emerged as a central issue, thanks notably to the ongoing and various efforts made by the scientific community around the topic of differentiable physics. Yet, very few solutions have been suggested so far, and only with a strong assumption on the nature of the shapes involved. In this work, we introduce a generic and efficient approach to compute the derivatives of collision detection for any pair of convex shapes, by notably leveraging randomized smoothing techniques which have shown to be particularly adapted to capture the derivatives of non-smooth problems. This approach is implemented in the HPP-FCL and Pinocchio ecosystems, and evaluated on classic datasets and problems of the robotics literature, demonstrating few micro-second timings to compute informative derivatives directly exploitable by many real robotic applications, including differentiable simulation.
Solar Power Generation in Smart Cities Using an Integrated Machine Learning and Statistical Analysis Methods

Presently, photovoltaic systems are an essential part of the development of renewable energy. Due to the inherent dependence of solar energy production on climate variations, forecasting power production using weather data has a number of financial advantages, including dependable proactive power trading and operation planning. Megacity electricity generation is regarded as a current research problem in the modern features of urban administration, particularly in developing nations such as Iran. Machine learning could be used to identify renewable resources like transformational participation (TP) and photovoltaic (PV) technology; based on resident motivational strategies, the smart city concept offers a revolutionary suggestion for supplying power in a metropolitan region. The sustainable development agenda is introduced at the same time as this approach. Therefore, the article’s goals are to estimate Mashhad, Iran’s electrical power needs using machine learning technologies and to make innovative suggestions for motivating people to generate renewable energy based on the expertise of experts. The potential of solar power over the course of a year is then assessed in our research study in Mashhad, Iran, using the solar photovoltaic modelling tool. The present idea in this research uses linear regression techniques to forecast utilising artificial neural networks (ANN). The most important factor in sizing the installation of solar power producing units is the daily mean sun irradiation. The amount of power that will be produced by solar panels can be estimated using the mean sun irradiance at a particular spot. A precise prediction can also be used to determine the complexity of the system, return on investment (ROI), and system load metrics. Several regression techniques and solar irradiance-related metrics have been combined to forecast the mean sun irradiation in terms of kilowatt hours per square metre. Azimuth and zenith factors considerably enhance the performance of the model, as demonstrated by the proposed method. The results of this study demonstrate 99.9% reliability rate for ANN model prediction of the electrical power usage during the summer and winter seasons. Thus, the maximum of power requirement during the hottest and coolest periods can be managed by using the photovoltaic system’s renewable power projections.
Automatic code optimization for computing the McCaskill partition functions

In this paper, we present the application of three automatic source-to-source compilers to code implementing Mc-Caskill's bioinformatics algorithm.It computes probabilities of various substructures for RNA prediction.McCaskill's algorithm is compute and data intensive and it is within dynamic programming.A corresponding programming code exposes nonuniform dependences that complicate tiling of that code.The corresponding code is represented within the polyhedral model.Its optimization is still a challenging task for optimizing compilers employing multi-threaded loop tiling.To generate optimized code, we used the popular PLuTo compiler that finds and applies affine transformations, the TRACO compiler based on calculating the transitive closure of loop dependence graphs, and the newest polyhedral tool DAPT implementing space-time tiling.An experimental study fulfilled on two multi-core machines: an AMD Epyc with 64 threads and a 2x Intel Xeon Platinum 9242 with 192 threads demonstrates considerable speedup, high locality, and scalability for various problem sizes and the number of threads of generated codes by means of space-time tiling.
Centrality Measures in multi-layer Knowledge Graphs

Knowledge graphs play a central role for linking different data which leads to multiple layers.Thus, they are widely used in big data integration, especially for connecting data from different domains.Few studies have investigated the questions how multiple layers within graphs impact methods and algorithms developed for single-purpose networks, for example social networks.This manuscript investigates the impact on the centrality measures of graphs with multiple layers compared to a those measures in single-purpose graphs.In particular, (a) we develop an experimental environment to (b) evaluate two different centrality measures -degree and betweenness centrality -on random graphs inspired by social network analysis: small-world and scale-free networks.The presented approach (c) shows that the graph structures and topology has a great impact on its robustness for additional data stored.Although the experimental analysis of random graphs allows us to make some basic observations we will (d) make suggestions for additional research on particular graph structures that have a great impact on the stability of networks.
Discovering interactions between applications with log analysis

Abstract4Application logs record the behavior of a system during its runtime and their analysis can provide useful information.In this article, we propose a method of automated log analysis to discover interactions taking place between applications in an enterprise.We believe that such an automated approach can greatly support enterprise architects in building an up-to-date view of a governed system in a modern, fast-paced development environment.Our contribution is the following: we propose a new method for log template generation called SLT (Simple Log Template), we propose a method of extracting knowledge about application interactions from logs, and we validate the proposed methods on a real system running at Nordea Bank.Additionally, we collect statistical information about application logs from the real-life system, based on which we formulate some observations that support our method.
Forensic characteristics of the creation and distribution of content containing child pornography using information and telecommunications systems or technologies

The article is devoted to the forensic characteristics of the creation and distribution of content containing child pornography using information and telecommunication systems or technologies. This type of criminal offenses is classified as complex methods. The components of criminal technology are: obtaining access to child pornography, its acquisition, storage, importation, transportation or other movement, production, sale and distribution (Article 301-1 of the Criminal Code of Ukraine); rape (Article 152 of the Criminal Code of Ukraine); committing acts of a sexual nature with a person who has not reached the age of sixteen (Article 155 of the Criminal Code of Ukraine); sexual violence (Article 153 of the Criminal Code of Ukraine); corruption of minors (Article 156 of the Criminal Code of Ukraine); human trafficking (Article 149 of the Criminal Code of Ukraine).&#x0D; Girls aged 9 to 13 were most often involved in the creation of sexual content. Criminals take advantage of the vulnerable state of children, caused by the fact that they have not yet formed sexual identification, hierarchy of motives, moral consciousness and self-awareness. In more than 90% of cases, the investigated crimes were committed with the participation of persons close to the child. Such criminal offenses are committed for sexual and selfish motives. Consumers of pornographic content have a sexual motive. The creation and distribution of content containing child pornography using information and telecommunication systems or technologies is most often committed by organized criminal groups for selfish motives, which have a clear hierarchical structure: the organizer; recruiters; directors; performers; distributors of pornographic content.&#x0D; Criminal technologies include two groups of offenses: 1) actions related to violence against a child; 2) actions related to the creation and distribution of pornographic content using information and telecommunication systems or technologies.&#x0D; The peculiarity of the circumstances of the commission of the investigated group of criminal offenses is that the place of sexual violence (that is, the creation of content containing child pornography) has a specific (physical) address, and the distribution of such content is carried out using information and telecommunication systems or technologies.&#x0D; Criminal technologies include two groups of offenses: 1) actions related to violence against a child; 2) actions related to the creation and distribution of pornographic content using information and telecommunication systems or technologies.&#x0D; The peculiarity of the circumstances of the commission of the investigated group of criminal offenses is that the place of sexual violence (that is, the creation of content containing child pornography) has a specific (physical) address, and the distribution of such content is carried out using information and telecommunication systems or technologies.