Reviewerly/exHarmony huggingface dataset:
Method for Obtaining Better Traffic Survey Data

Road traffic surveys determine the number and type of vehicles passing by a specific point over a certain period of time. The manual estimation of the number and type of vehicles from images captured by a camera is the most commonly used method. However, this method has the disadvantage of requiring high amounts of manpower and cost. Recently, methods of automating traffic volume surveys using sensors or deep learning have been widely attempted, but there is the disadvantage that a person must finally manually verify the data in order to ensure that they are reliable. In order to address these shortcomings, we propose a method for efficiently conducting road traffic volume surveys and obtaining highly reliable data. The proposed method detects vehicles on the road from CCTV (Closed-circuit television) images and classifies vehicle types using deep learning or a similar method. After that, it automatically informs the user of candidates with a high probability of error and provides a method for efficient verification. The performance of the proposed method was tested using a data set collected by an actual road traffic survey company. As a result, we proved that our method shows better accuracy than the previous method. The proposed method can reduce the labor and cost in road traffic volume surveys, and increase the reliability of the data due to more accurate results.
DevOps Certification in IT Industry: Preliminary Findings

DevOps is a methodology which pursues the improvement of software delivery time and quality. IT companies lack models or standards that help them through the adoption process. Furthermore, there is no consensus of what DevOps is composed of, and many researchers have identified several challenges in the adoption process. In this paper we describe our attempts to analyze the approaches of industry for DevOps adoption. A survey aimed for DevOps certifying organizations was carried out. The responses of the survey were scarce, and the preliminary findings have been reported.
Ethical Principles for Designing Responsible Offensive Cyber Security Training

In this paper we present five principles for designing ethically responsible offensive cyber security training. The principles can be implemented in existing or new study plans and target both academic and non-academic courses. Subject matter experts within various cyber security domains were consulted to validate and fine tune the principles, together with a literature review of ethical studies in related domains. The background for designing the principles is the continuous popularity of offensive cyber security (penetration testing, ethical hacking). Offensive cyber security means actively trying to break or compromise a system in order to find its vulnerabilities. If this expertise is placed in the wrong hands, the person can cause severe damage to organizations, civilians and society at large. The proposed ethical principles are created in order to mitigate these risks while maintaining the upsides of offensive cyber security. This is achieved by incorporating the ethical principles in offensive cyber security training, in order to facilitate the practitioners with ethical knowledge of how and when to use their acquired expertise.
Getting Your Conversation on Track: Estimation of Residual Life for Conversations

This paper presents a predictive study on the progress of conversations. Specifically, we estimate the residual life for conversations, which is defined as the count of new turns to occur in a conversation thread. While most previous work focus on coarse-grained estimation that classifies the number of coming turns into two categories, we study fine-grained categorization for varying lengths of residual life. To this end, we propose a hierarchical neural model that jointly explores indicative representations from the content in turns and the structure of conversations in an end-to-end manner. Extensive experiments on both human-human and human-machine conversations demonstrate the superiority of our proposed model and its potential helpfulness in chatbot response selection.
Sculpture Inspired Musical Composition

In this paper, we present an inspirational system that takes a 3D model of a sculpture as starting point to compose music. It is considered that cross-domain mapping can be an approach to model inspiration. Our approach does not consider the interpretation of the sculpture but rather looks at it abstractly. The results were promising: the majority of the participants gave a classification of 4 out of 5 to the preferred interpretations of the compositions and related them to the respective sculpture. This is a step to a possible model for inspiration.
Towards a Theory of Factors that Influence Text Comprehension of Code Documents

The design of domain-specific software systems can benefit from participatory design practices making domain experts and programmers equal, collaborating partners. The source code of such a system might be a viable communication artifact to mediate the perspectives of the two groups. However, source code written in a general-purpose programming language is often considered too difficult to comprehend for untrained readers. At the same time, it is yet unclear what makes general-purpose programming languages difficult to understand. Based on our previous study and related work from programming pedagogy and cognitive psychology, we develop an initial theory of factors that might influence the comprehensibility of source code documents by untrained readers. This theory covers factors stemming from the features of source code, factors related to the visual appearance of source code, and factors concerned with aspects independent of code documents. This chapter discusses and illustrates these potential factors and points out initial hypotheses about how these factors can influence comprehensibility.
Elliptic curves over a nonlocal ring 𝔽2d[𝜀],𝜀2 = 𝜀

In this work, we came to present the elliptic curves over a nonlocal ring [Formula: see text] where [Formula: see text] is a finite field of characteristic 2, and [Formula: see text] is a prime integer. We consider an elliptic curve given by the Weierstrass equation of the form [Formula: see text] such that [Formula: see text] are in the ring [Formula: see text] and b is invertible in [Formula: see text] see Tadmori et al. [Normal form of the elliptic curves over the finite ring, J. Math. Syst. Sci. 4 (2014) 194–196]. More precisely, we give a classification of elements of the elliptic curve over this ring, and we construct the group law over it, which allows us to prove same properties of the elliptic group [Formula: see text] by showing the isomorphism [Formula: see text]
An assessment of students’ perception and self-efficacy towards mobile learning: A case of University of Zululand

Background: Mobile learning (m-learning) has been recognised as an essential educational technology component in higher education. The use of mobile devices and the prospect to learn irrespective of time and place make mobile learning an important tool for students. Many studies have been conducted because of the importance of m-learning in advancing teaching and learning but few published scholarly studies have focused on perception and mobile self-efficacy. Objectives: The study’s objective is to assess students’ perception and self-efficacy towards mobile learning at the University of Zululand. Method: The study adopted a descriptive survey method and a stratified random sampling technique was used to select 200 undergraduate students from the four faculties for the study. Data were collected using questionnaire. Statistical tools such as frequency count and simple percentage were employed in analysing the collected data. Results: WhatsApp, Facebook and YouTube are the top three social media applications mostly used by the students. Results showed that the students have positive perceptions towards m-learning and possess the confidence needed in using m-learning solutions. Conclusion: Perception and mobile self-efficacy towards m-learning technology are important factors that help in determining whether or not students are ready to use m-learning. Hence, this study focuses on investigating the perception and self-efficacy of undergraduate students at the University of Zululand towards m-learning. Therefore, the study concludes that the University management fully integrates m-learning solutions to its academic programmes to enhance the quality of teaching and learning.
Rule Based Part of Speech Tagger for Arabic Question Answering System

Part of Speech (POS) Arabic wording is difficult to read in detail and its functionality affects many programs and activities in the Natural Language Processing (NLP) area. POS tagging is a process to assign POS such as a verb, adjective, adverb, noun in each word for any sentence. Farasa is an active and reliable text processing toolkit for Arabic documents. It is an assortment of Java libraries and CLIs for MSA.2. These incorporate a separate tool for Arabic text Diacritizer, segmentation/tokenization module, POS tagger, Named Entity Recognition (NER), and parsing. One of the limitations over the Farasa affects the post-processing results due to the presence of inappropriate tags. For our application on question answering system(QAS) correct POS, tagging is essential for better accuracy. The POS tagger is developed using a rule-based approach which is based on domain-specific. The corpus (database) on which the rule-based POS tagger is built is centered on the core subject of the Arabic language 4th standard textbook of Arabic Medium state board of Yemen. During the development of QAS, the POS tagger is a very essential stage in which the answers for the framed questions are obtained from the paragraphs of a given lesson. The present article provides insights into the complete process of linguistic rule-based POS tagger development for QAS. Sentence segmentation, word tokenization, to stemmer development which becomes an important part of proper morphological analysis is explained. As a result, the morphological analyzer is the input to the rule-based POS tagger. Ultimately, in this article, a comparison of marking based on our POS rule with Farasa is presented and for QAS, our rule-based POS tagger gave better results than Farasa.
Knowledge Transfer using Model-Based Deep Reinforcement Learning

Deep reinforcement learning has recently been adopted for robot behavior learning, where robot skills are acquired and adapted from data generated by the robot while interacting with its environment through a trial-and-error process. Despite this success, most model-free deep reinforcement learning algorithms learn a task-specific policy from a clean slate and thus suffer from high sample complexity (i.e., they require a significant amount of interaction with the environment to learn reasonable policies and even more to reach convergence). They also suffer from poor initial performance due to executing a randomly initialized policy in the early stages of learning to obtain experience used to train a policy or value function. Model based deep reinforcement learning mitigates these shortcomings. However, it suffers from poor asymptotic performance in contrast to a model-free approach. In this work, we investigate knowledge transfer from a model-based teacher to a task-specific model-free learner to alleviate executing a randomly initialized policy in the early stages of learning. Our experiments show that this approach results in better asymptotic performance, enhanced initial performance, improved safety, better action effectiveness, and reduced sample complexity.
Exploring the Incorporation of Opinion Polarity for Abstractive Multi-document Summarisation

Abstractive multi-document summarisation (MDS) remains a challenging task. Part of the problem is the question as to how to preserve a document’s polarity in the summary. We propose an opinion polarity attention model for MDS, which incorporates a polarity estimator based on a BERT-GRU sentiment analysis network. It captures the impact of opinions expressed in the source documents and integrates it in the attention mechanism. Experimental results using a state-of-the-art MDS approach and a common benchmark test collection demonstrate that this model has a measurable positive effect using a range of metrics.
USING LMS MOODLE WHEN CONDUCTING OF TRAINING PRACTICE IN BOTANY

Modern higher education (including pharmaceutical) is inextricably linked with the use of new information and communication technologies in the educational process. In the current conditions, the question arose about the implementation of educational programs in accordance with the federal state educational programs of higher education with the help of mixed (hybrid) training, which involves a combination of traditional (full-time) and distance learning. One of the forms of e-learning used as part of hybrid learning is Learning Management Systems (LMS), created on the basis of Internet technologies. LMS MOODLE is most often used in Russian universities. At Kursk State Medical University, the LMS MOODLE system has been actively used for many years as a platform for distance learning in the organization of continuing medical education courses. At the Faculty of Pharmacy, LMS MOODLE was used in organizing and conducting training practices in botany. To organize and conduct practical training in the LMS MOODLE system, a training course «Educational field practice in botany» was created. For each day, separate sections were organized, in which the necessary information for students was placed. In general, the following LMS MOODLE elements were used to create a training course on educational practice: «SCORM Package», «Test», «Task», «Hyperlink», «File», «Folder», «Forum», «Chat». Analysis of the use of LMS MOODLE showed that this system can be used quite successfully for the organization of the educational process as an element of e-learning and allows you to control the assimilation of educational material.
Social Science Learning In Covid 19 Pandemic By Using Internet Media

Abstract During the current covid 19 pandemic, fourth grade students of SDN Sumbermulyo 02 Winong District Pati Regency are learning online. Initially during the covid 19 pandemic, grade IV SDN Sumbermulyo 02 Winong District Pati Regency students had a little difficulty learning social studies online. Because the fourth grade students of SDN Sumbermulyo 02 Winong District Pati Regency before covid 19 were used to learning face-to-face (outside the network). Based on these problems, researchers conducted research on the use of internet media in social studies learning for grade IV SDN Sumbermulyo 02 Winong District Pati Regency during the covid 19 pandemic. This study aims to describe the use of internet media in social studies learning for grade IV SDN Sumbermulyo 02 Winong District Pati Regency during the covid 19 pandemic. Therefore, the method used is a qualitative method with data collection techniques of observation, interviews, and documentation. Based on data and analysis, there are two kinds of impact of internet media in social studies learning for class IV SDN Sumbermulyo 02 Winong District Pati Regency during the covid 19 pandemic, namely positive and negative. The positive impact of internet media for social studies learning for grade IV SDN Sumbermulyo 02 Winong District Pati Regency for the covid 19 pandemic is that children quickly find out information about social studies and children can quickly complete assignments. Then, the impact of internet media on social studies learning for fourth grade students of SDN Sumbermulyo Winong District Pati Regency in the covid 19 pandemic. Currently, students’ eye health is reduced because they often stare at the screen of a smartphone or tab or notebook or laptop or computer when looking for information about social science. Social studies learning using internet media for class IV SDN Sumbermulyo 02 Winong District Pati Regency during the covid 19 pandemic became effective and efficient.
Data Science Techniques to Detect Fraudulent Resource Consumption in the Cloud

Fraudulent resource consumption (FRC) attacks threaten the economic viability of cloud consumers. Detection of these attacks is difficult as they often blend in with normal traffic patterns although they can still cause dramatic financial consequences. We employ a variety of data science techniques to detect FRC attacks in a cloud environment. Statistical, time series, and machine learning methods all achieve various levels of success and, in some cases, failure at detection. Unfortunately, none of these techniques is independently sufficient for detection for our experiments due to characteristics of the data set we used, but we summarize lessons learned from our research.
Co-refining User and Item Representations with Feature-level Self-attention for Enhanced Recommendation

Self-attention mechanism is primarily designed to capture the correlation (interaction) between any two objects in a sequence. Inspired by self-attention's success in many NLP tasks, some researchers have employed self-attention in sequential recommendation to refine user representations by capturing the correlations between the historical interacted items of a user. However, the user representations in previous self-attention based models are not flexible enough since the self-attention is only applied on user side, restricting performance improvement. In this paper, we propose a deep recommendation model with feature-level self-attention, namely SAFrec, which exhibits enhanced recommendation performance mainly due to its two advantages. The first one is that SAFrec employs self-attention mechanism on user side and item side simultaneously, to co-refine user representations and item representations. The second one is that, SAFrec leverages item features distilled from open knowledge graphs or websites, to represent users and items on fine-grained level (feature-level). Thus the correlations between users and items are discovered sufficiently. The extensive experiments conducted over two real datasets (NetEase music and Book-Crossing) not only demonstrate SAFrec's superiority on top-n recommendation over the state-of-the-art deep recommendation models, but also validate the significance of incorporating self-attention mechanism and feature-level representations.
A Simple Embedding for Classifying Networks with a few Graphlets

Complex networks are a key analytical tool for complex systems. However if one wants to apply this tool in machine learning applications where the data is non-relational data one must find an appropriate network embedding. The embedding represents a network in a vector space, while preserving information about network structure. In this paper, we propose a simple network embedding technique that avoids the need for graph kernels or convolutional networks, as have previously been advocated. Our embedding is based on 3-node and 4-node graphlet counts combined with some feature extraction based on a Principal Component Analysis (PCA). We show that it is competitive with some state-of-the-art methods on a downstream classification task. We then show how to reduce the computational effort in the method by transforming extraction into a feature selection procedure. We claim that this selection procedure, a generalisation of PCA, is more meaningful than a popular alternative.
Temporal Parallelization of Dynamic Programming and Linear Quadratic Control

This article proposes a general formulation for temporal parallelization of dynamic programming for optimal control problems. We derive the elements and associative operators to be able to use parallel scans to solve these problems with logarithmic time complexity rather than linear time complexity. We apply this methodology to problems with finite state and control spaces, linear quadratic tracking control problems, and to a class of nonlinear control problems. The computational benefits of the parallel methods are demonstrated via numerical simulations run on a graphics processing unit.
WITHDRAWN: A lightweight hybrid scheme for security of big data

The source of big data in the contemporary era led to the increased usage of cloud infrastructure. Plenty of Internet based applications are using cloud infrastructure to reap benefits like scalability, availability and fault tolerance. One of the sources of big data is Internet of Things (IoT) use case that exploits sensor networks and plethora of connected devices that are resource constrained. In such typical scenario, there is need for lightweight security schemes to deal with big data storage and dissemination. The traditional cryptography based schemes are not efficient to deal with big data in the aforementioned use case. Therefore, it is indispensable to have an efficient and lightweight scheme that leverages the state of the art in terms of security attributes such as data integrity and confidentiality. In this paper we proposed a security scheme known as Lightweight Hybrid Scheme (LHS) that is based on Diffie-Hellman key exchange and Elliptic Curve Cryptography. The LHS scheme works with any encryption technique and provide support for highly secure key exchange mechanism that results in smaller key sizes. The proposed scheme has optimized method for obtaining modulus of fractional number. LHS is evaluatedwithaprototype application and compared with start of the art algorithms like Diffie-Hellman (DH), Elliptic Curve-Diffie Hellman (ECDH) and RSA. The results revealed that LHS provides improvement in security besides reducing time complexity while dealing with big data operations.
Design of gallery web-based space booking system as media service

Abstract Aim of this study is 1) to create a web-based booking system as a service media at Studio and Gallery B9 Department of Fine Arts, Semarang State University. 2) to implementing a trial-based studio and gallery lending system as a supporting facility for students of the Department of Fine Arts, Semarang State University. Methodology that used is SDLC, which is used as an appraiser on the website. After using a questionnaire for 22 students. The results obtained that the website-based lending system has good communication effectiveness, this is evident from the results of the questionnaire showing 86% communication efficiency. The problem of the model and visual appearance is vital as a representation of the Department and University, it is considered to have an aesthetic level that is 89% efficient and easy to use. The features on the website are also interesting so they can show 89% results. After testing the loan, 82% said they were satisfied using this feature. The average input provided is giving a checklist about the facilities that are owned in each studio.