Reviewerly/exHarmony huggingface dataset:
Preventing COVID-19 Infection in a University Using Office 365

Preventing COVID-19 infection in a university using a cloud computing service, Microsoft Office 365, is discussed. In order to start in-person classes safely, our university decided to survey health status of every student and staff member, every day. The student or the staff member is asked to not to come to campus if there is a symptom of COVID-19 infection or if they did not fill out the survey sheet daily for two weeks. If there are no symptom of COVID-19 for two weeks, there is almost no possibility of infection of COVID-19 or the person had recovered from the infection. The survey data is collected and analyzed with Office 365 to minimize time and effort. In addition to the survey, we made the class seat assignment sheets using our academic affair system and Excel. The assigned seats were used in order to coduct contuct tracing if a student tested positive with COVID-19.
Sequential Extraction and Mobility Factor of Metals in the Urban Soil of Warri-Nigeria (A Case Study of the Environment of Esisi Open Dump)

Sequential fractionation of Cr, Zn, Pb and Mn in soil samples collected 5 m -104 m away from Esisi open dump was determined to evaluate the potential health danger posed by the metals.Spatial test soil samples were collected at various increasing distances (5 m -104 m) from the epicentre of the dump using a stainless steel auger.Tessier's sequential extraction protocols were employed.The concentrations of the metals in the various fractions were determined, the fractions are exchangeable fraction, carbonate fraction, Fe-Mn oxide fraction, organic fraction and residual fraction.The metals were found mostly concentrated in the Fe-Mn oxide fraction.This study has provided information on the mobility factors and chemical forms of Cr, Zn, Pb and Mn in the soil of the environment of Esisi open dump and the environmental risk of the metals in the environment of Esisi open dump.
Formal Derivation of Mesh Neural Networks with Their Forward-Only Gradient Propagation

This paper proposes the Mesh Neural Network (MNN), a novel architecture which allows neurons to be connected in any topology, to efficiently route information. In MNNs, information is propagated between neurons throughout a state transition function. State and error gradients are then directly computed from state updates without backward computation. The MNN architecture and the error propagation schema is formalized and derived in tensor algebra. The proposed computational model can fully supply a gradient descent process, and is potentially suitable for very large scale sparse NNs, due to its expressivity and training efficiency, with respect to NNs based on back-propagation and computational graphs.
Digital analysis of a form

Purpose This paper aims to show how a sociological description – a swarm analysis of the Nazi dictatorship – initially made with the means borrowed from George Spencer-Brown’s Calculus of Indications, can be transformed into a digital circuit and with which methods and tools of digital mathematics this digital circuit can be analyzed and described in its behavior. Thus, the paper also aims to contribute to a better understanding of Chapter 11 of “Laws of Form.” Design/methodology/approach The analysis uses methods of automata theory for finite, deterministic automata. Basic set operations of digital mathematics and special set operations of the Boolean Differential Calculus are used to calculate digital circuits. The software used is based on ternary logic, in which the binary Boolean logic of the elements {0, 1} is extended by the third element “Don’t care” to {0, 1, −}. Findings The paper confirms the method of transforming a form into a digital circuit derived from the comparative functional and structural analysis of the Modulator from Chapter 11 of “Laws of Form” and defines general rules for this transformation. It is shown how the indeterminacy of re-entrant forms can be resolved in the medium of time using the methods of automata theory. On this basis, a refined definition of the degree of a form is presented. Originality/value The paper shows the potential of interdisciplinary approaches between sociology and information technology and provides methods and tools of digital mathematics such as ternary logic, Boolean Differential Calculus and automata theory for application in sociology.
Web application vulnerability detection method based on machine learning

Abstract In order to solve the security problems caused by network vulnerabilities, a web application vulnerability detection method based on machine learning is proposed to effectively prevent cross site scripting attacks of web applications and reduce the occurrence of network security incidents. Through the in-depth study of the existing security vulnerability detection technology, combined with the development process of machine learning security vulnerability detection technology, the requirements of security vulnerability detection model are analyzed in detail, and a cross site scripting security vulnerability detection model for web application is designed and implemented. Based on the existing network vulnerability detection technology and tools, the verification code identification function is added, which solves the problem that the data can be submitted to the server only by inputting the verification code. According to the server filtering rules, the network code bypassing the server filtering is constructed. Experimental results show that the model has a low rate of missed detection and false alarm, and the improved model is more efficient.
Professor’s Image as Viewed by Engineering University Students

The higher education image contributes to the attitude towards the country in the globalized world. This image is formed due to different factors, among them is a university professor’s image which is shaped by a variety of expectations from different social groups referring to the rights, obligations and standards such a professor is to follow. Although there is a lot of research done as for the professor’s image and expectations of different stakeholders, there is a limited number of works analyzing the students’ expectations. To bridge this gap, the paper aims at investigating the students’ views in regard of a professor’s image, comparing them with those of the other stakeholders and providing recommendations to follow. The paper uses both theoretical research methods based on the analysis of recent publications and discussions in the academic community and empirical methods in the form of a survey among students and its content analysis. The results show a mismatch between expectations of employers, community, professors and students as for the role of a professor in society in regard of their behavioral characteristics, research performance and leadership in teaching. The authors recommend that the professors prioritize the expectations from the students and teaching is the primary mission of the university. Further research could imply surveys among the employing university administrators and different societal groups.
Real-Time Hands-Free Mouse Control for Disabled

In this paper, a human–computer interface system using eye motion is implemented. In traditional methods, human–computer interfaces use keyboard, mouse as input devices. A hand-free interface between computer and human is represented in this paper. The system is developed using template matching and is a real time, fast and affordable technique for tracking facial features and eye gestures. The traditional computer screen pointing devices can be replaced by this technology, for the use of disabled people. The paper presents computer mouse cursor movement with human eyes. Wherever the eyesight focuses, accordingly the mouse is controlled. The proposed vision-based virtual interface controls the system by various eye movements such as eye blinking, winking of eye.
DFCN: An Effective Feature Interactions Learning Model for Recommender Systems

Data features in real industrial recommendation scenarios are diverse, high-dimensional and sparse. Effective feature crossing can improve the performance of recommendation, which is of great significance. Manual feature engineering is no longer applicable due to its high cost and low efficiency. Factorization machines introduce the second-order feature interactions to enhance learning ability. Deep neural networks (DNNs) have good nonlinear combination ability and can learn high-order feature interactions. However, DNNs implicitly learn feature interactions at the bit-wise level is not always effective. In this paper, we propose a novel factorization cross network (FCN), which is based on factorization to learn explicit feature crossing through neural network. FCN can learn low- and high-order feature interactions at the vector-wise level with linear time complexity. We introduce deep residual network (DRN) to learn implicit feature interactions. We further use learnable parameters to combine FCN and DRN, and name the new model as deep factorization cross network (DFCN). DFCN can automatically learn low- and high-order explicit and implicit feature interaction information. We have carried out comprehensive experiments on three real-world datasets. Experimental results demonstrate the effectiveness of DFCN, which performs best compared with other competitive models.
Acquaintance with Digital Etiquette: Email Correspondence for Distance Learning

The article is devoted to the analysis of the rules of behaviour of communicants in the process of distance learning. The purpose of the study is to consider the main difficulties that arise when students and teachers communicate in the virtual space, to identify their causes and ways to overcome them. The author focuses on the rules of email etiquette. The data obtained during a survey of students and teachers of the Perm State National Research University is used by the author. The relevance of the topic raised in this article is due to the fact that at present it is an urgent need to teach students and schoolchildren the basics of digital etiquette, namely, polite communication in social networks, compliance with the etiquette of official email correspondence, etc. The methods used for research include analysis, description, observation, comparison. The results of the study showed that in the moving to distance learning, when students ' communication with the teacher becomes mediated, many students experience certain difficulties, because they do not know the rules of behaviour in cyberspace. In this regard, it is concluded that teachers should include the module " Acquaintance with digital etiquette " in the course of training in order to make communication with students more effective and develop the necessary communicative competence.
Bilingual Machine Translation: Bengali to English

The present work proposes a novel architecture of machine translation from Bengali language to English Language. After taking the Bengali sentences as input, syntactical analysis has been done. After that it is analyzed semantically, and the sentence is rearranged as per English grammar. A dictionary has been prepared and used for mapping from Bengali to English words. The proposed system is based on the concept of syntax-based and meaning-based approach. Proper grammatical rules are used for translating the sentences from Bengali to English language. In some cases, the proposed system performed better compared to the Google translator output.
Signal Integrity Analysis Based on SVR Improved Algorithm

Compared with the traditional support vector machine regression (SVR), the SVR hyperparameter fast optimization algorithm can improve the accuracy of the prediction results. However, the data shows that when the training sample is too large, it will increase the complexity of model learning, resulting in too long modeling time. Therefore, we refer to the most effective support vector set search method in the variable selection and sparse support vector machine (VS-SSVM) algorithm, and appropriately fit the “advantages” of these two algorithms to construct a fast optimization hyperparameter and sparse support vector machine (FOH-SSVM) algorithm. In this work, we use the algorithm to solve the problem of signal integrity. The experimental results show that the modeling time required by the FOH-SSVM algorithm is 1%, which greatly reduces the modeling time. At the same time, the prediction accuracy of the algorithm is increased by 8%, ensuring good prediction performance.
Selection of the type of cooling for an overclocked Raspberry Pi 4B minicomputer processor operating at maximum load conditions

The Raspberry Pi is a computer platform that is widely used in education, has a very large community and extensive documentation. Therefore, it can be a good and cheap alternative to a traditional computer, a TV streaming device or a console for less demanding games. In the case of observing a lower efficiency of the microcomputer, one of many possibilities of improvement, which this device offers is overclocking the processor. It is associated with a proper selection of parameters (voltage, clocking) and software in order to achieve the highest possible performance of the dedicated Raspbian system. However, increasing the work efficiency causes the temperature rise up to the limit values. Therefore, an appropriate, i.e. effective, kind of cooling should be applied. Taking all these circumstances into account, an experiment was developed in which temperature measurements were taken during the maximum processor load on all cores at the clock setting that enabled reaching the highest performance. During the research three cases were considered: without the use of cooling, with passive cooling and with active cooling. The results showed that only the use of active cooling clearly improves the operating conditions of the device, due to lowering the temperature by about 16°C compared to the situation without cooling or with the use of a passive radiator.
Object Identification and Tracking Using YOLO Model: A CNN-Based Approach

The object identification, detection and tracking them in the individual video frames are an expensive and highly recommended task for security and surveillance. This work expects to consolidate the procedure for object recognition with the objective of accomplishing high precision with a real-time performance. A significant test in huge numbers of the object detection frameworks is the reliance on other computer vision systems for helping the profound learning-based methodology, which prompts moderate and nonideal execution. A deep learning-based approach will be used to solve the problem of object identification in an end-to-end fashion. The framework is set up on the most testing straightforwardly open dataset (PASCAL VOC), on which an article revelation challenge is driven each year. The subsequent framework is quick and exact, along these lines helping those applications which require object detection. This work also demonstrates an appropriate study for well popular methods.
Multilevel User Verification in Cloud Banking System

The recent incidents of sky high fraudulent banking transactions,Biswas, Ankur ponzi monetary schemes and its corresponding corruption charges have shaken the economic backbone of India.Roy, Abhishek Its impact is so high that Government has to opt for legal remedies to bring back the imposters under justice. This scenario clearly depicts that existing banking system is incapable to handle and crack down these fraudulent activities and hence it requires extensive research work to prevent similar incidents in near future. Information and communication technology (ICT)-based electronic message communication system may be utilized under controlled environment to monitor and maintain these financial transaction from its initial stage. Since cyber space is an open forum, which is easily accessible to all (including the adversaries), multilevel verification of user will help to reduce risk factors of banking transactions to significant level, if not eliminate it completely. This monitoring system will be more cohesive in nature if a single window-based integrated environment streamlines all the parties engaged in a specific banking transaction. To achieve this objective, in this paper, authors have proposed a Multipurpose Electronic Card (MEC)-based Cloud Banking System (CBS) as a part of broader Integrated Electronic Service Delivery System (IESDS), which will verify its user (i.e. Client) through multilevel architecture.
Application of the multiagent approach for modeling integrated energy systems

Currently, operation control and expansion planning of energy systems occurs separately for local systems and tasks. Traditionally, the considered energy systems unite large energy sources, such as hydro, thermal power plants, combined heat and power plants, boiler plants and electric and pipeline networks distributed over a large area. New trends in the energy sector necessitate a revision of the principles of the construction of energy systems and creating integrated energy systems. THE PURPOSE. Combining existing energy systems into a single integrated system with many interconnected and coordinating elements can contribute to the implementation of new functionalities, the use of more advanced technologies in operation and the active participation of consumers with distributed generation in the energy supply process. METHODS. To study integrated energy supply systems, it is proposed to use a multiagent approach, which is one of the promising areas for the study of complex systems. This approach is used in many subject areas to study systems involving a large number of elements with complex behavior. Such systems include integrated energy systems, the modeling and analysis of which on the basis of a multiagent approach is formed by a multitude of interconnected agents that exchange various data with each other. RESULTS. Based on the research results, the active structure of a multiagent system is proposed for the calculation and optimization of integrated energy systems and, taking into account their main features and properties, in the framework of which the agents of the multiagent system, their goals and objectives are determined. CONCLUSIONS. Based on this structure, a model has been developed that allows modeling integrated energy systems. The experiments carried out using the developed model showed its efficiency, practical applicability and prospects for further development.
Hand Gesture Recognition Under Multi-view Cameras Using Local Image Descriptors

Hand gesture recognition has various applications in recent years such as robotics, e-commerce, human–machine interaction, e-sport, and assisting people with hearing-impaired humans. The latter is the most useful and interesting application in our daily life. Nowadays, cameras can be installed easily and everywhere. So, gesture recognition faces the most challenging issue under image acquisition by multiple cameras. This paper introduces an approach for hand gesture recognition under multi-views cameras. The proposed approach is evaluated on the HGM-4 benchmark dataset by using local binary patterns.
Deneysel Yazılım Mühendisliğindeki Araştırma Eğilimleri için Metin Madenciliği

This paper intends to examine the research trends in Empirical Software Engineering domain within the last two decades using text mining. It studies published articles in the relevant literature with an emphasis on abstracts of 10658 articles published in the literature on Experimental Software Engineering domain. Using a probabilistic topic modelling technique (Latent Dirichlet Allocation), it brings forward the main topics of research within this domain. By further analysis, the paper evaluates the changes of focus in published works in the last two decades and depicts the recent trends in research content wise. Through a timely comparison, it portrays the alteration of interest within empirical software engineering research and proposes a future research agenda to develop an advanced field, beneficial both for academics and practitioners.
General Decidability Results for Asynchronous Shared-Memory Programs: Higher-Order and Beyond

Abstract The model of asynchronous programming arises in many contexts, from low-level systems software to high-level web programming. We take a language-theoretic perspective and show general decidability and undecidability results for asynchronous programs that capture all known results as well as show decidability of new and important classes. As a main consequence, we show decidability of safety, termination and boundedness verification for higher-order asynchronous programs—such as OCaml programs using Lwt—and undecidability of liveness verification already for order-2 asynchronous programs. We show that under mild assumptions, surprisingly, safety and termination verification of asynchronous programs with handlers from a language class are decidable iff emptiness is decidable for the underlying language class. Moreover, we show that configuration reachability and liveness (fair termination) verification are equivalent, and decidability of these problems implies decidability of the well-known “equal-letters” problem on languages. Our results close the decidability frontier for asynchronous programs.
Cross-cultural effects on graphical password memorability and design

Purpose This study aims to examine the impact of cultural familiarity with images on the memorability of recognition-based graphical password (RBG-P). Design/methodology/approach The researchers used a between-group design with two groups of 50 participants from China and the Kingdom of Saudi Arabia, using a webtool and two questionnaires to test two hypotheses in a four-week long study. Findings The results showed that culture has significant effects on RBG-P memorability, including both recognition and recall of images. It was also found that the login success rate depreciated quickly as time progressed, which indicates the memory decay and its effects on the visual memory. Research limitations/implications Collectively, these results can be used to design universal RBG-Ps with maximal password deflection points. For better cross-cultural designs, designers must allow users from different cultures to personalize their image selections based on their own cultures. Practical implications The RBG-P interfaces developed without consideration for users’ cultures may lead to the construction of passwords that are difficult to memorize and easy to attack. Thus, the incorporation of cultural images is indispensable for improving the authentication posture. Social implications The development of RBG-P with cultural considerations will make it easy for the user population to remember the password and make it more expensive for the intruder to attack. Originality/value This study provides an insight for RBG-P developers to produce a graphical password platform that increases the memorability factor.
A General Semantic Construction of Dependent Refinement Type Systems, Categorically

Abstract Dependent refinement types are types equipped with predicates that specify preconditions and postconditions of underlying functional languages. We propose a general semantic construction of dependent refinement type systems from underlying type systems and predicate logic, that is, a construction of liftings of closed comprehension categories from given (underlying) closed comprehension categories and posetal fibrations for predicate logic. We give sufficient conditions to lift structures such as dependent products, dependent sums, computational effects, and recursion from the underlying type systems to dependent refinement type systems. We demonstrate the usage of our construction by giving semantics to a dependent refinement type system and proving soundness.