Reviewerly/exHarmony huggingface dataset:
Research on the Effect of Online Service by Logistic Regression Model and Remote Video Technology

Abstract At present, online teaching is developing rapidly, and with it is how to improve the effectiveness of online teaching. This research constructs learning behavior indicators from the online learning process, and uses logistic regression to analyze the learning data in online teaching, and explores the impact of learners’ online learning behavior on academic performance. The correlation between indicators such as the time lag of course registration, the number of login courses, the number of submitted homework tests, the average of the number of saved exercises, and the degree of completion of video viewing and other indicators and scores were discussed. The study found that the submission of homework tests can be used as key indicators for online teaching and learning performance prediction.
API Topic Issues Indexing, Exploration and Discovery for API Community Knowledge

Application Programming Interface (API) is a core technology that facilitates developers' productivity by enabling the reuse of software components. Understanding APIs and gaining knowledge about their usage are therefore fundamental needs for developers that impact a wide range of software development activities. This paper presents an approach to enable API users to explore, discover and learn about APIs through API topic issues discussed in Stack Overflow (SO), a widely used programming, community question-answering (CQA) site. Our work proposes an integrated API Knowledge Base (KB) and indexing technique that combines both SO API-related posts as well as other API learning resources collected from the Web (e.g., API video-tutorials from Youtube). The resulting indexed and enriched API community knowledge can be queried in a API-topic-issue driven manner using a simple yet powerful domain-specific language (DSL). We demonstrate the feasibility of our approach through Scout-bot, our tool for exploration and discovery of API topic issues.
Missing Phone Activity Detection Using LSTM Classifier

We propose a smart phone application that aids a user to find his lost phone. In this application, we try to find the cases, where a mobile can be separated from the user, by training a classifier. The application identifies specific events, where a mobile phone goes away from the user, thus recording cumulative sensor data. The obtained sensor data can be used for further analysis of mobile phone surroundings which could narrow down the search domain. A more trivial example would be that GPS couldn’t help when you forgot, where the phone has been placed. So, the application can gather the information of surroundings at the last recognized event and make the search more effective.
Automatic Bus Ticket System For Public Transporation Using QR Code

Today transportation is the cheapest, so it has always been popular with the masses. The progress in the transport system has increased in everyday life. The current manual ticket system has many problems. One of the main problems is that passengers lose their purchased printed tickets. This prompts the passenger to buy another bus ticket. In this project, we propose QR readers for the bus ticket system .The QR code (Quick Response Code) is also becoming popular outside of the automotive industry due to its quick readability and greater storage capacity compared to plain barcodes. In this system, after registration profile and generating ID of a user, this will generate a QR code after we register a particular user which will have a user ID, we can add money to the wallet/ Add balance to a particular user ID. Whenever we go by bus, we have to scan the QR code. So directly money will debit from our wallet. QR code which supports bus integrated E-ticket and to improve the management (Transport corporation) process to generate reports quickly and significantly.
LSSA: A Protective Shared Data Communication Mechanism in Cloud Environment

Users can access shared data warehouses using a cloud infrastructure. It is important to verify the data successfully to ensure mutual data integrity. The accuracy checking of the shared data is carried out by an examination system that encourages Group members to alter data, but this method leads to complicated estimates as per the Group members. The monitoring method of the assigned agent estimates the group members lightly, but lacks the safety threats among the group members and their agents. With the implementation of Hash graph technology and the development of a management Third Party Medium (TPM) approach, a Lightweight Safe Cloud Storage Audition System (LSSA) is suggested, achieving group security protection and a lightweight group measurement. In the meantime, the TCP Sliding Fan Technology incorporates a simulated TPM pool with interconnected features to enhance support for the handler. We test our method in numerical analysis and tests, which prove that our system provides the groups with lightweight computing and ensures the safety data evaluation process.
Deploying Risk Access Models in a Cloud Environment: Possibilities and Challenges

The importance of the cloud has increased due to its capabilities and features, which could facilitate its users. Cloud computing is a full package of virtual servers that can be utilized by customers and cloud service providers to store and manipulate their data. It is a new computer model that provides computing infrastructure resources as on-demand electronic services. The cloud’s bounties need to be protected in terms of the ways they can be accessed. An access control model could control access to such a dynamic environment. Although cloud computing provides unique features for users, it brings new challenges to data security and access control such as unauthorized access with possibilities of data leakage. Since traditional access control models were designed for particular environments and could not be deployed directly in the cloud without security investigations, risk-monitoring models are quite new models that trying to overcome the cloud security constraints. Risk-based access control models provide many security solutions to risk management in dynamic environments such as cloud computing. This paper illustrates an extensive investigation to access control models and displays several risk access control models. Moreover, it looks at the possibility and challenges that make a risk-based access control deployed in a cloud computing environment.
PEDAGOGICAL POTENTIAL OF MASS MEDIA IN THE EDUCATION OF EMPLOYEES OF THE INTERNAL AFFAIRS BODIES OF THE RUSSIAN FEDERATION

The introduction actualizes the urgent need to use the pedagogical potential of the mass media in the education of employees of the internal affairs bodies of the Russian Federation in continuing education with the use of hermeneutical and situational approaches.&#x0D; The purpose of this article is to substantiate the need to organize the education of employees of the internal affairs bodies of the Russian Federation using the pedagogical potential of the mass media. &#x0D; In the main part of the article, approaches to the definition of "pedagogical potential of the media" are considered. The works of Soviet-Russian and foreign teachers who investigated the phenomenon of "pedagogical potential of the media" and its influence on a person, including an employee of the internal affairs bodies of the Russian Federation, are studied. &#x0D; The article considers the aspect of the information and propaganda work of the Department of Internal Affairs of the Russian Federation in the context of the organization of educational work on the development of the personality of a police officer using hermeneutical and situational approaches.
Spatially Supervised Text Mining for Social Media Cleaning and Preprocessing

In this paper, we show a framework for partial bot rejection based on spatially supervised text mining from social media messages.We show qualitative results towards the reduction of known bots and give hints on how this cleaning technique can help us in filling gaps of current signals related to human life on Earth based on social media.The bot rejection framework is based on using a spatial signal for supervising a machine learning model with extreme label noise still being able to reject some of the unwanted components of the social media stream.Furthermore, we comment that such models show significant biases and can, therefore, not be used responsibly without bias analysis and mitigation per application.
Highlighting the Importance of Reducing Research Bias and Carbon Emissions in CNNs

Convolutional neural networks (CNNs) have become commonplace in addressing major challenges in computer vision. Researchers are not only coming up with new CNN architectures but are also researching different techniques to improve the performance of existing architectures. However, there is a tendency to over-emphasize performance improvement while neglecting certain important variables such as simplicity, versatility, the fairness of comparisons, and energy efficiency. Overlooking these variables in architectural design and evaluation has led to research bias and a significantly negative environmental impact. Furthermore, this can undermine the positive impact of research in using deep learning models to tackle climate change. Here, we perform an extensive and fair empirical study of a number of proposed techniques to gauge the utility of each technique for segmentation and classification. Our findings restate the importance of favoring simplicity over complexity in model design (Occam’s Razor). Furthermore, our results indicate that simple standardized practices can lead to a significant reduction in environmental impact with little drop in performance. We highlight that there is a need to rethink the design and evaluation of CNNs to alleviate the issue of research bias and carbon emissions.
Best Typing Mechanical Keyboard

A blissful typing experience can be easily gotten from a mechanical keyboard. It employs durable and high-quality switches with anti-ghosting properties ...
A color image steganography scheme based on texture features

Image information hiding is one of the hotspots in the field of information security. A steganography scheme based on LTP texture features is proposed. Firstly, the carrier image is divided into three channels. The carrier image is transformed into local ternary map (LTP) in green channel and blue channel, and the LTP information of green channel and blue channel is stored in red channel. According to the LTP to determine the green channel and the blue channel, the carrier image needs the amount of embedded secret information. In embedding, a special scheme is adopted to hide the secret information between the selected pixel and the next value of the selected pixel (that is, pixel + 1). The scheme has been tested on a set of color images, and the results are satisfactory in terms of embedding capacity and steganographic image quality (PSNR).
Revisiting security properties in Single Secret Leader Election

In a Single Secret Leader Election (SSLE) protocol, a group of participants aim to randomly choose exactly one leader such that the identity of the leader will be only known by the chosen leader. Later, the elected leader can reveal her identity while proving that she indeed won the election. In this paper, we revisit the security model of the SSLE proposed by Boneh et al. and more specifically we add the liveness property to the security model and revisit the fairness and unpredictability properties.
Reviewer Assignment Using Weighted Matching and Hungarian Algorithm

After closing submission system of the conference, the organizing committees’ duties are huge and among these tasks are assigning at least three reviewers (at least) for each paper. This task involves matching a set of papers against reviewers in order to assign most suitable group of reviewers to specific paper to be reviewed. In this paper, a proposed weighted similarity measure is computed between each pair of (reviewer, author) work. This can be done by keeping a sample of published papers for each reviewer in order to extract the field of expertise of the reviewer and measure the relevance with the author's paper. The proposed weighted similarity measure is based on dividing each paper of authors and reviewers into five sections (i.e. Title, Abstract, Keywords, References, and the rest text in the paper) to compute the matching degree; each paper part is based on its importance thus TF-IDF and cosine similarity techniques are adopted to calculate the degree of correspondence between each pair of coincidence parts. Hereafter, Hungarian optimization algorithm is employed to assign each paper to the most relevance reviewers using computed similarity measure. The experimental results on NTICT 2018 dataset showed that the proposed method achieved its goal.
Work-in-Progress–—Specific Heat of Water Experiment: Augmented Reality Chemistry Lab

Augmented Reality (AR) is becoming readily more available as the number of AR capable smartphones and tablets increase in popularity. With its exponential development, augmented reality offers an oppurtunity to facilitate education in online chemistry. In hopes of furthering the advancement of augmented reality in online chemistry education, we developed a boiling water experiment to show the effects of heat capacity and to create an interactive lab experiment for online learning. Our work-in-progress paper explores how the utilization of augmented reality can improve the learning process and better exhibit chemistry lab concepts.
ASSESSMENT OF CULINARY CLASS DEMONSTRATION METHODS ON STUDENTS’ PERFORMANCE: VIDEO VERSUS LIVE STREAMING

The temporarily close of educational institutions due to the Covid-19 lockout situation affected educators at every level of education to conduct classes online. This study explored whether the use of video demonstration significantly improves students' performance of learning culinary compared with control group counterparts who were exposed to the live streaming demonstration. In addition, students' perceptions of the instructions are assessed. A quasi-experimental study design was used for this study. The study sample consisted of 36 undergraduate Hospitality students enrolled in a culinary course. Sixteen students in the experimental group and 20 students in the control group were exposed to video and live streaming demonstrations, respectively. Both groups were tested about the same target content, ‘Understanding Vegetables and Basic Cuttings’. Students’ performance grades were computed and analyzed to compare students’ learning outcomes between the two groups. Students’ perceptions were assessed based on their opinions of instruction, their self-reported level of understanding of vegetables and basic cuttings, and their level of satisfaction. Results of independent samples t-tests showed; students in the experimental group had a significantly higher performance score and express positive perceptions of the instruction than the students in the control group. The findings from this study would shed light on the instructional strategy suitable for culinary students.
Asynchronous Compatible D-ML System for Edge Devices

The parameter server system (Parameter Server, PS) is the most widely used distributed machine learning system. Its core module is the communication module, which mainly includes three parts: communication content, communication frequency, and communication pace. The communication scheme of the existing PS system will have problems such as waste of computing power resources and a large proportion of communication overhead. This paper hopes to design and implement a distributed set that saves computing power and has a smaller communication overhead based on the communication module of the original PS system. Machine learning system, this article mainly has the following contributions: First, through the analysis and experiment of Bulk Synchronous Parallel (BSP) communication pace, based on the superiority of BSP algorithm level, it overcomes the waste of computing power of BSP at the system level, and designs and implements a new set of Communication pace, the new communication pace under the same training task can save more computing power and achieve higher model accuracy. Secondly, this article combines the quantization compression technology with the parameter server system, and uses the quantization technology to compress and decompress the communication content in the parameter server system, so that the volume of the communication content during the operation of the parameter server system is significantly reduced, thereby reducing the communication overhead and improving Speedup ratio of the system. Finally, based on the analysis and experiment of the change trend of the model accuracy rate, this paper designs and implements a dynamic communication frequency adjustment scheme based on the change trend of the model accuracy rate, which reduces the overall communication frequency during the operation of the parameter server system and reduces the parameter server. T Experiments show that the system designed in this paper is superior to the existing mainstream solutions in terms of communication pace, communication content, and communication frequency. When training the same model, this system compared with the existing parameter server system in model convergence time, model There is a noticeable improvement in the highest accuracy.
Innovative Training of Mechanical Talents Based on Tutorial System under the Background of Big Data

Over the past few years, big data technology has advanced by leaps and bounds. This paper analyzes and studies the inadequacy of the existing teaching system using big data technology, and proposes an innovative tutorial system based on big data. Using big data technology, this paper analyzes and evaluates the knowledge structure under the existing teaching system, and finds out the actual factors that affect the teaching system. Then a teaching system is established based on big data to lay a solid foundation for the future development of the university. Taking into account various factors, a flexible training model is created using big data processing technology. On this basis, a diversified teaching evaluation system based on big data is established. It can not only evaluate the actual application effect of the system in the teaching implementation process, but also effectively evaluate the comprehensive effect of the system on the school and society. Then based on big data, refer to factors such as training, motivation, system and practice, an effective safety system based on the tutorial system is established. All these have provided universities with new educational ideas, improved the existing system and cultivated innovative talents.
Time Fractionalized Lattice Boltzmann Model-Based Image Denoising

In this paper, we have proposed the Caputo sense time fractionalized lattice Boltzmann (LB) model for time sub-diffusion equation in Caputo sense recently proposed by Rui Du et al. We have applied this model to denoise white Gaussian noise corrupted images with different noise levels.Upadhyay, P. Rai, K. N. The denoising results have been compared with some of the recently reported state-of-the-art methods in terms of PSNR and SSIM values. The results found show considerable improvement over other recently reported state-of-the-art methods in terms of both PSNR and SSIM values.
“A Study on Employee’s perception on Employee Monitoring”

Employee monitoring is a long-debated topic. Employers argue that monitoring is their way to protect business interests while employees would say that it’s a breach of privacy. On the whole, it involves many legal and ethical issues at the core of which lies technology and its use. There are many articles in view of both sides and some looking into the resulting effects. In this paper, we focus on the perception of employees about employee monitoring and to what extent is it ethical for employers to monitor their employees. Our study of 122 sample respondents found that the majority of employees are fine with being monitored with technology, and even prefer it to physical monitoring, but employees are overall wary about the security of their private data in the hands of the employer. Thus, this paper attempts to find the balance between employee monitoring and ethics.
Is there a missing on-balance premise in conductive argument?

In this essay I critically examine the standard logical approach to analyzing conductive arguments by adding a missing on-balance premise. From a linguistic perspective, I argue that current efforts to justify adding a missing on-balance premise rest on a misconception of the contrastive import introduced by indicators like even though. I clarify that using these words to mention a counter-consideration can neither imply nor implicate an on-balance claim. Then I demonstrate that counter-considerations can be included in an argument in several different ways, and further argue that the concept of conductive argument needs to be recast as clearly a type of argument by weighing and balancing in which counter-considerations must be taken seriously and truly weighed against the supportive reasons.
An Exploratory Analysis and Prediction of Factors Influencing the Debugging Behavior of Computer Science Students

In recent years, most of the students registered in computer science courses are confronted with the difficulty in computer programming. This is a serious concern for educators and the students. So there is a critical need to analyze the factors influencing the same. Meanwhile, many investigations have been conducted in analyzing the programming skill, and more importantly, their studies focused on marks secured in various science subjects, prior programming experience, reasoning ability, etc. But the analysis of programming behavior in the current study is focused mainly on patterns generated by the students after the compilation. Furthermore, the relationship between debugging capability and hit rate with academic performance was analyzed. Methods like correlation and regression are adopted for analyzing these factors. It is observed that the debugging skill of the students and their hit rate has an upper hand in determining the programming skill compared to the marks secured in the examinations.