Reviewerly/exHarmony huggingface dataset:
COMPARATIVE ANALYSIS OF SUCCESS IN HIGHER EDUCATION IN UKRAINE AND THE USA

The article is devoted to the study of the success in the system of higher education in the USA and Ukraine. This concept in higher education has interested scholars in recent decades and has caused many debates about determining student progress. At the end of the twentieth century in the United States, student achievement was measured by quantitative indicators that demonstrated the effectiveness of higher education. But the gradual change in the higher education priorities, the characteristics of the students, the need to improve the educational process has led to a revision of the definition of this phenomenon and provoked a number of studies to expand this concept, creating models of success. The modern definition of student success in US higher education establishments involves academic success and the development of the necessary personality traits, skills and abilities for further self-realization. Ukrainian researchers consider success as a category of pedagogical psychology, as this concept concerns, first of all, the personal development of the student, the skills and abilities of useful interaction. At the same time, a number of studies made by Ukrainian educators are devoted to the issue of academic success, which is also considered as a qualitative development of the student`s personality. Unfortunately, compared to the United States, Ukraine does not have sufficient data on the quality of higher education, there are no experimental studies of success models for students. There is a lack of information on further self-realization of students, their employment. Thus comparing student success in the two countries, it can be concluded that there is a difference in understanding of this concept by American and Ukrainian scholars, the issue of retention, persistence and graduation is not sufficiently developed by Ukrainian educators. The problem to find the ways to improve higher education is common, but its development, as well as the creation of models for achieving success is not sufficiently represented in Ukrainian scientific discourses.
Chatbot Designing Information Service for New Student Registration Based on AIML and Machine Learning

One of the efforts made by universities to serve prospective students is by providing consulting services and information that is usually carried out directly at the booth provided, through phone service or live chat support available on the college website. Increased visitors will result in waiting times due to limited availability of officers, which results in decreased satisfaction of prospective new students, moreover this service is only available during campus operating hours. One alternative solution to overcome this problem is to use Chatbot, able to answer questions raised by prospective new students which can be categorized as Frequently Asked Questions abbreviated as FAQ. Chatbot technology can be developed with a variety of AI (Artificial Intelligence) techniques. One of them is the AIML (Artificial Intelligence Markup Language) technique. One of the main drawbacks of AIML is that there is no reasoning ability so a learning system that is focused on supervised learning is needed. In the chatbot that will be built the learning process uses a selective neural conversational model or commonly called the Deep Semantic Similarity Model (DSSM) developed by Microsoft. Meanwhile, the measurement of chatbot performance will be done using Confusion Matrix which is a method of evaluating the performance of the algorithm from Machine Learning (ML). The results of the study stated that the chatbot system that was built was able to answer questions posed by prospective students properly and correctly while the questions were available in the chatbot knowledge base.
Empirical models for predicting global solar radiation using meteorological parameters for Sokoto, Nigeria

The performances of sunshine, temperature and multivariate models for the estimation of global solar radiation for Sokoto (Latitude 13.020N, Longitude 05.250E and 350.8 m asl) located in the Sahelian region in Nigeria were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperatures, sunshine hours, rainfall, wind speed, cloud cover and relative humidity meteorological data during the period of thirty one years (1980-2010). The comparison assessment of the models was carried out using statistical indices of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t â€“ test, Nash â€“ Sutcliffe Equation (NSE) and Index of Agreement (IA). For the sunshine based models, a total of ten (10) models were developed, nine (9) existing and one authorâ€™s sunshine based model. For the temperature based models, a total of four (4) models were developed, three (3) existing and one authorâ€™s temperature based model. The results of the existing and newly developed authorâ€™s sunshine and temperature based models were compared and the best empirical model was identified and recommended. The results indicated that the authorâ€™s quadratic sunshine based model involving the latitude and the exponent temperature based models are found more suitable for global solar radiation estimation in Sokoto. The evaluated existing â„«ngstrÃ¶m type sunshine based model for the location was compared with those available in literature from other studies and was found more suitable for estimating global solar radiation. Comparing the most suitable sunshine and temperature based models revealed that the temperature based models is more appropriate in the location. The developed multivariate regression models are found suitable as evaluation depends on the available combination of the meteorological parameters based on two to six variable correlations. The recommended models are found suitable for estimating global solar radiation in Sokoto and regions with similar climatic information with higher accuracy and climatic variability.Â Â Â
Finding the Skeleton of 2D Shape and Contours: Implementation of Hamilton-Jacobi Skeleton

This paper presents the details of the flux-ordered thinning algorithm, which we refer to as the Hamilton-Jacobi Skeleton (HJS).It computes the skeleton of any binary 2D shape.It is based on the observation that the skeleton points have low average outward flux of the gradient of the distance transform.The algorithm starts by computing the distance function and approximating the flux values for all pixels inside the shape.Then a procedure called homotopy preserving thinning iteratively removes points with high flux while preserving the homotopy of the shape.In this paper, we implement the distance transform using a fast sweeping algorithm.We present numerical experiments to show the performance of HJS applied to various shapes.We point out that HJS serves as a multi-scale shape representation, a homotopy classifier, and a deficiency detector for binary 2D shapes.We also quantitatively evaluate the shape reconstructed from the medial axis obtained by HJS.
Z-Net: an Anisotropic 3D DCNN for Medical CT Volume Segmentation

Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS). 3D Deep Convolutional Neural Network (DCNN) is a viable solution for this task, but is memory intensive. Small isotropic patches are cropped from the original and large CT volume to mitigate this issue in practice, but it may cause discontinuities between the adjacent patches and severe class-imbalances within individual sub-volumes. This paper presents a new 3D DCNN framework, namely Z-Net, to tackle the discontinuity and class-imbalance issue by preserving a full field-of-view of the objects in the XY planes using anisotropic spatial separable convolutions. The proposed Z-Net can be seamlessly integrated into existing 3D DCNNs with isotropic convolutions such as 3D U-Net and V-Net, with improved volume segmentation Intersection over Union (IoU) - up to 12.6%. Detailed validation of Z-Net is provided for CT aortic, liver and lung segmentation, demonstrating the effectiveness and practical value of Z-Net for intra-operative 3D navigation in robot-assisted MIS.
Human action recognition based on mixed gaussian hidden markov model

Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field. This paper uses a hidden Markov model based on mixed Gaussian to solve the problem of human action recognition. The model treats the observed human actions as samples which conform to the Gaussian mixture model, and each Gaussian mixture model is determined by a state variable. The training of the model is the process that obtain the model parameters through the expectation maximization algorithm. The simulation results show that the Hidden Markov Model based on the mixed Gaussian distribution can perform well in human action recognition.
GENDER VIEWS ON 75CRIMES:A Survey of Mexican Opinion

This study reports the results of a surveyof students in a Mexican universitywho were asked to rate the seriousness of 75 crimes on a scale of 1 to 100. Results are reported overall as well as by gender. In 46 of 75 cases, the male and female mean scores were not significantly different. In the other 29 cases, women usually rated particularcrimes as more serious than the men in the sample. Buying a pirated CD/DVD was considered to bethe least serious crime, overall, while various kinds of murderand rape ranked as most serious. Some kinds of murder were found to be more serious than others. Women thought that murdering a prostitute would result in a greater loss to society than would murdering a local politician, whereas the male sample valued the lossesequally.
Emotional Tools Design: A Project to Increase Empathy, Engagement and Interest in Industrial Design Students

Abstract Empathy is the ability of people to identify emotional aspects of others. A fundamental aspect to teaching in design education must be empathy. This paper presents the design process of an emotional tools using emoticons or emojis for evaluated products as educational exercise. The dynamics behind of the tool is the empathy experimented during the develop of the emojis characters. The project was implemented in the second period of the second year, with an execution time of 5 weeks. A series of surveys were conducted to assess perception of aspects such as utility, novelty, sensitivity and relevance of the project, in addition to knowing the progress of empathy evoked by the students dynamic. The results revealed the following findings: The opinion of 25 students describe their wide acceptance of this project methodology. Students considered the relevance of assessment processes, their recommendation to use those processes, and invited other students to develop it. Also, student’s positive perception about utility, novelty, sensibility and relevance of project dynamics are not determined by acceptance of this type of project. The preliminary results suggest that this educational exercise has the potential to cultivate or train empathy and other skills in design and engineering students.
Algoritma Artificial Neural Network pada Text-based Chatbot Frequently Asked Question (FAQ) Web Kuliah Universitas Nasional

The technology development increase the number of automation system in indudustry. One of them is Chatbot application in education industry. This automation technology is able to lessen university's service in order to facilitate the students' need of information whenever and wherever they are. Lack of student literacy regarding the functions and use of the web in conducting online lectures causes the same number of questions repeatedly to the university, which are actually frequently asked questions that have been written in a list of frequently asked questions (Faq), such as: assignment submission, forget passwords, lectures online, video conference lectures and lecture web applications on android. Chatbot will automatically answer students' question in university web page by providing information and suggesting a proper answer suit to the question. This research will develop Chatbot type based on text by applying Artificial Neural Network (ANN) algorithm . The applied data set while conducting the Chatbot coaching is the questions data which frequently being asked (FAQ) in the study web, 25 questions with its answer which is divided into 16 labels or classes. The testing is conducted by using 110 different conversations from the dataset but have the same intention. From those 110 conversation, the Chatbot succed in answering 107 questions precisely and made 3 wrong conversation. The testing result shows a good result by having 97,27% accuracy and 2,72% error.Keywords:Chatbot, Artificial Neural Network, ANN, Text-based.
Impact of Different Types of Head-Centric Rest-Frames on VRISE and User Experience in Virtual Environments

This paper presents the results of a user study of the effects of different head-centric rest-frames on Virtual Reality-Induced Symptoms and Effects (VRISE) and the user experience in virtual environments (VE). Participants played the custom-designed 3D game in two different game modes (high action and low action). For assessing VRISE levels, we used the Simulator Sickness Questionnaire (SSQ) and Fast Motion Sickness Score (FMS). The presence was evaluated by SPES (Spatial Presence Experience Scale), and for the user experience, the short version of the User Experience Questionnaire (UEQ-S) was used. The results indicate that the usage of head-centric rest-frames negatively affected VRISE levels (more sickness) in the low action mode of the game. However, for the users experienced with VR technology, the VRISE disorientation symptoms were alleviated in a high action mode of the game with rest-frame glasses. We found no negative effect of rest-frames on the user experience and presence, except for some negative impact when using rest-frame glasses in the low action mode of the game. No negative impact on the performance itself was observed. That means that the usage of head-centric rest-frames is suitable for usage in VR applications. In terms of VRISE levels, we found out that rest-frame glasses are more suitable for the wearers of the distance spectacles, and a baseball hat is more suitable for non-wearers of distance spectacles.
Minimum Description Length Recurrent Neural Networks

Abstract We train neural networks to optimize a Minimum Description Length score, that is, to balance between the complexity of the network and its accuracy at a task. We show that networks optimizing this objective function master tasks involving memory challenges and go beyond context-free languages. These learners master languages such as anbn, anbncn, anb2n, anbmcn +m, and they perform addition. Moreover, they often do so with 100% accuracy. The networks are small, and their inner workings are transparent. We thus provide formal proofs that their perfect accuracy holds not only on a given test set, but for any input sequence. To our knowledge, no other connectionist model has been shown to capture the underlying grammars for these languages in full generality.
REVIEW OF FSCM WITH BLOCKCHAIN AND BIG DATA INTEGRATION

Distributed ledger technology is an immutable data storage and transparent system, which is a constituent component that empowers the FSCM (Food supply Chain Management).Due to the autonomous and immutable data feature, the scalability of blockchain technology is quite a challenge.A supply chain is a network that flows product from suppliers to the final consumer, which produces a high volume of data.In the blockchain, processing high volume of data is quite a significant issue and may affect business profit.To overcome the issue, Big Data and User Controllable Code for Smart Contracts (UCCSC) will be integrated with the blockchain-based FSCM system.Big data technology is typically used to analyze high volume of both structured and unstructured data, which is very difficult to process using habitual database and software techniques.The UCCSC integrates the blockchain and big data and manage the user in the FSCM system.This paper mainly discusses the integration of blockchain-based FSCM and Big Data, and the mechanism that supports such system.
Am I My Cousin's Keeper? A Proposal to Protect Relatives of Genetic Database Subjects

We live in a brave new world full of exciting possibilities for medical and human advancement.Mapping of the genome has taken our ability to understand individual humans to a substantially more detailed and intimate level than has ever before been possible.At the same time, advances in computer science, artificial intelligence, and the universality of internet connectedness have ushered in the era of "big data" and enabled us to examine and understand as never before what all of us have in common and what makes each of us unique. 1 By aggregating data from and about large numbers of people and getting a broader view of the human collective, we can understand better and in greater detail what makes for the best health and health care of an individual human, especially when we can take it down to the intimate genetic level.These advances have spawned the era of "precision medicine," in which we can diagnose and treat human conditions and ailments with much greater effectiveness.It is a time of great possibilities but also of great perils, particularly as regards the confidentiality of each individual's medical status and human
A Meta-Analysis of Studies on Groupware for Collaborative Work Environments

This meta-analysis examines the studies on groupware published between the years 2010 and 2020. Descriptive analysis was conducted to determine the distribution of studies in terms of publication year, time–space matrix category, targeted sectors, research methods, and the academic field of the journals that published these studies. Although groupware played a significant role in communication, collaboration, and coordination of users in various collaborative work conditions and sectors, the majority of studies focused on asynchronous and distributed collaborative work environments in the software engineering field, and the research method preferred was design science.
Particle Physics Simulator for Scientific Education using Augmented Reality

In this era of fourth industrial revolution, young learners need to be equipped with 21 st century skills, such as critical thinking, creativity, communication, collaboration, innovation and problem solving.Augmented Reality (AR) based learning systems are an effective tool to embed these skills.This paper presents a detailed review of latest research on an AR-based learning systems.Furthermore, an AR-based learning system is proposed to demonstrate the particle physics experiments i.e. proton-proton collision and Higgs field.The proposed learning system algorithms are developed using particle system of unity 3D software.Then, Microsoft Kinect sensor is interfaced with unity 3D to create an immersive experience.Then, the qualitative analysis of the proposed system and latest AR-based learning systems is presented.Finally, the quantitative analysis of the proposed system is conducted.Overall, the results suggest that 85% of the participants recommended the proposed learning system.
On application of artificial neural networks for modeling of water consumption.

The article shows the capabilities of artificial neural networks built on radial basis functions for the study of water consumption by various branches of the Don River basin water system. The use of mathematical models in the form of a system of differentiated equations is hampered by the uncertainty of the coefficients in their right-hand sides, which describe the intensities of processes of different natures: precipitation, water consumption by various sectors of the water management complex, water runoff during snow melting, transpiration, infiltration, etc. As a rule, these parameters are random, and the mathematical models describing the water balance are stochastic. The use of neural networks is very fruitful here. Without going into the physical essence of the processes, they can be used to approximate and make reliable predictions, which is a prerequisite for the development of dynamic-stochastic concepts in the management of water resources.
RASSLE: Return Address Stack based Side-channel LEakage

Microarchitectural attacks on computing systems often stem from simple artefacts in the underlying architecture. In this paper, we focus on the Return Address Stack (RAS), a small hardware stack present in modern processors to reduce the branch miss penalty by storing the return addresses of each function call. The RAS is useful to handle specifically the branch predictions for the RET instructions which are not accurately predicted by the typical branch prediction units. In particular, we envisage a spy process who crafts an overflow condition in the RAS by filling it with arbitrary return addresses, and wrestles with a concurrent process to establish a timing side channel between them. We call this attack principle, RASSLE 1 (Return Address Stack based Side-channel Leakage), which an adversary can launch on modern processors by first reverse engineering the RAS using a generic methodology exploiting the established timing channel. Subsequently, we show three concrete attack scenarios: i) How a spy can establish a covert channel with another co-residing process? ii) How RASSLE can be utilized to determine the secret key of the P-384 curves in OpenSSL (v1.1.1 library)? iii) How an Elliptic Curve Digital Signature Algorithm (ECDSA) secret key on P-256 curve of OpenSSL can be revealed using Lattice Attack on partially leaked nonces with the aid of RASSLE? In this attack, we show that the OpenSSL implementation of scalar multiplication on this curve has varying number of add-and-sub function calls, which depends on the secret scalar bits. We demonstrate through several experiments that the number of add-and-sub function calls can be used to template the secret bit, which can be picked up by the spy using the principles of RASSLE. Finally, we demonstrate a full end-to-end attack on OpenSSL ECDSA using curve parameters of curve P-256. In this part of our experiments with RASSLE, we abuse the deadline scheduler policy to attain perfect synchronization between the spy and victim, without any aid of induced synchronization from the victim code. This synchronization and timing leakage through RASSLE is sufficient to retrieve the Most Significant Bits (MSB) of the ephemeral nonces used while signature generation, from which we subsequently retrieve the secret signing key of the sender applying the Hidden Number Problem.&#x0D; 1RASSLE is a non-standard spelling for wrestle.