Reviewerly/exHarmony huggingface dataset:
Comparison Study of Lightweight Hash Functions for an Authentication Solution on IoT

Nowadays, a gigantic number of constrained devices are connected to the internet. The devices interact with each other through the wireless sensor network (WSN) and provide the users with new experiences. The pervasive of devices on the internet has hugely increased which makes paramount the need for securing devices and data against threats. These devices communicate on a public channel which makes them a convenient target to be accessed by unauthorized users and break through the privacy of veritable users. Therefore, the security of constrained end nodes is important to protect data against passive or active attacks. If one single node is attacked, the network might suffer severe damage. However, many researchers find it immensely difficult to implement efficient cryptographic algorithms on constrained devices due to the limitation of their resources; the requirements on less memory and energy consumption led cryptographs to build lightweight cryptography solutions targeting confidentiality, authentication, and integrity. In this paper, we focus on authentication solutions based on lightweight hash functions. We present and compare various lightweight hash functions in terms of conception, security, and performance. According to the obtained results, we will be able to select the most appropriate lightweight hash function suitable for an authentication mechanism in IoT environments.
Super-resolution using Deep Networks for Chest X-Ray Images

Super-resolution imaging is extensively deliberated in medical imaging modalities nowadays, there being a wide panic on the effect of COVID-19 virus impression. Generally, spatial resolutions of CXR are insufficient due to the constraints such as image acquisition time, hardware limits and physical limits. It is a clinically challenging task to recover the high resolution CXR images. A significant concern in CXR imaging is X-Ray contrast disparity and the demand to attain high quality images with adequate structural and imaging details. To address these problems, we propose an effective deep network for the super-resolution reconstruction method to recover high-resolution CXR images while retaining diagnostic capabilities. Specifically, the reinforcement subnetwork is hosted to generate sharp and informative qualitative features. The quantitative and qualitative assessments found that the proposed model based on the evaluation index improves the CXR super-resolution. In addition, the PSNR index of the proposed model has 0.30 higher than that of the SRCNN network.
Erdős-Ko-Rado theorems for ovoidal circle geometries and polynomials over finite fields

In this paper we investigate Erdős-Ko-Rado theorems in ovoidal circle geometries. We prove that in Möbius planes of even order greater than 2, and ovoidal Laguerre planes of odd order, the largest families of circles which pairwise intersect in at least one point, consist of all circles through a fixed point. In ovoidal Laguerre planes of even order, a similar result holds, but there is one other type of largest family of pairwise intersecting circles. As a corollary, we prove that the largest families of polynomials over Fq of degree at most k, with 2≤k<q, which pairwise take the same value on at least one point, consist of all polynomials f of degree at most k such that f(x)=y for some fixed x and y in Fq. We also discuss this problem for ovoidal Minkowski planes, and we investigate the largest families of circles pairwise intersecting in two points in circle geometries.
Analysis of the Effect of System, Content, and Interaction on the Satisfaction and Intention to Use Video Lectures - Focused on the case of K University -

본 연구에서는 비대면 환경에서 학습자의 강의 만족과 지속이용의도 간의 영향 관계를 탐색하는 데 그 목적이있다. 이를 위해 충북 일반 4년제 사립대 학습자를 대상으로 2021년 3월 21일부터 6월 30일까지 632건의 자료를바탕으로 실시간 화상 강의에 대한 확인요인 분석, 정규성, 적합성 검증을 통해 문항 적합성을 검증하였다. 또한, 시스템 품질, 콘텐츠 품질, 상호작용이 강의 만족과 지속이용의도에 미치는 영향 관계를 확인하였다. 연구 결과 첫째, 연구모델의 총 7개의 가설경로 중 6개가 유의한 것으로 밝혀졌다. 중요한 가설경로는 시스템 품질 → 강의 만족(.117), 콘텐츠 품질 → 강의 만족(.455), 상호작용 → 강의 만족(.354), 시스템 → 지속이용의도(.108), 상호작용 → 지속이용의도(.133), 강의 만족 → 지속이용의도(.431)에서 모두 직접 효과가 나타났다. 둘째, 시스템 품질과 상호작용은 지속이용의도에 영향을 미치는 데 있어서 강의 만족이 부분 매개 효과를 가지는 것으로 나타났다, 향후, 온라인 환경에서 교수자와 학습자의 교수 및 학습만족도를 높이기 위해 온라인 교수 및 학습 방법에 대한 방향을 모색하고자 한다.The purpose of this study is to explore the influence relationship between learners’ lecture satisfaction and intention to use in a non-face-to-face environment. To this end, based on 632 data from March 21 to June 30, 2021, the suitability of the questions was verified through confirmation factor analysis, normality, and suitability verification for real-time video lectures for general four-year private university learners in Chungbuk province. In addition, the relationship between system and content interaction and lecture satisfaction and intention to use was confirmed. As a result of the study, first, it was found that 6 out of a total of 7 hypothesis paths of the research model were significant. Important hypothesis pathways were all directly effective in system lecture satisfaction (.117), content lecture satisfaction (.455), interaction lecture satisfaction (.354), system intention to use (.108), interaction intention to use (.133), and lecture satisfaction intention to use (.431). Second, it was found that lecture satisfaction had a partial mediating effect in influencing the intention to use the system. In the future, in order to increase the teaching and learning satisfaction of instructors and learners in an online environment, we will seek directions for online teaching and learning methods.
A Knowledge-Based Recommender System for Mortgage Lending

Recommendation processes are periodic, repetitive activities and time consuming. This encourages experts to store their knowledge and past experiences. The development of a knowledge base clearly facilitates the actions of similar subsequent recommendations and reduces the time necessary to make a choice, especially in financial management. In this article, we develop a recommendation system called BL-KBRS which uses the rules and procedures employed by the experts in the past and stored in the knowledge base. To demonstrate feasibility, we are experimenting with our approach to mortgage lending, a semi-structured, multi-attribute recommendation problem. BL-KBRS is a knowledge-based recommending system that will compensate for the lack of experience of young bank officers and will disseminate the experience available on various banking agencies.
Analysis of Code Mixing Used by Teacher

ABSTRACT &#x0D; This thesis was aimed to find the type of code mixing used by teacher during classroom interaction. The study used descriptive qualitative method. Each sentence from the teacher and students conversation was identified to determine its code mixing type according to Hoffman’s theory (1991). The data of the study is utterances which code mixing is included in it. The technique of the data collections used is documentary technique. It is found that there were three types of code mixing used during the class according to Hoffman theory namely : (1) intra-sentential, (2) intra-lexical, (3) involving change of pronunciation. From those types, intra-sentential was most frequently used by the teacher during the process of teaching and learning. The teacher`s reasons for applying code mixing were to overcome the communication barriers that occur between the teacher and the students to help the students understand and memorize the material more accessible. The result of the interview also showed that the students had a good perception of the implementation of code mixing used by the teacher in the classroom.&#x0D; Keyword : code mixing, intra-sentential, classroom interaction, teacher
IMPROVING STUDENTS’ SPEAKNG SKILL IN THE ASPECT OF GRAMMAR AND VOCABULARY ABOUT DESCRIBING ANIMALS BY USING CUE CARDS MEDIA

This research aimed at improving the students’ speaking skill at grade VII of SMP Islamiyah Pontianak in the academic year of 2019/2020 through cue cards media. This classroom action research was conducted in three cycles; on November 14th, 21st and 28th respectively. It involved 22 seventh grader students as well as the English teacher as the collaborator. The data were taken from the observation, field notes and speaking task. The data of speaking was obtained by speaking task which was assessed through scoring rubric. The result showed that students’ problems in grammar and vocabulary had been solved by using Cue Cards media. The most interesting part of this research was, the cue cards could break the ice among the students as there are pictures and questions which stimulate students’ prior knowledge and could be used as handy clues for them during the learning process. Furthermore, the students’ speaking improved constantly from cycle 1 (the highest score was 48, 3 students got score 48), cycle 2 (the highest score was 64, 2 students got score 64) and cycle 3 (the highest score was 84, 6 students had not achieved the minimum score which is 75). Not only their individual speaking achievement got improved, but also their interest in learning. It was shown by the students’ enthusiasm to pay attention and involve actively in doing every activity during teaching and learning process by using cue cards. Keywords: Speaking, Cue Cards, Media
The Relationship between Professional Identity and Learning Input of Primary School Undergraduate Normal Students: The mediating role of academic self-efficacy

This study conducted a questionnaire survey of 483 primary education undergraduate normal students from Y University to explore the professional identity (cognitive identity, emotional identity, behavioral identity, appropriateness identity) and academic self-efficacy ( efficacy of learning ability, efficacy of learning behavior) and the learning input (vitality, dedication, professionalism). The research found that: the professional identity of undergraduate teachers in elementary education can significantly positively predict their academic self-efficacy; The professional identity and academic self-efficacy of primary education undergraduate normal students can significantly positively predict their learning input; The academic self-efficacy of elementary education undergraduate normal students plays a part of the mediating role between professional identification and learning input.
Governing Cyber Espionage Threats via the Integration of the Risk Society-Cyber Securitisation Theory

In the last decade, virtual attacks in the form of cyber espionage has become more and more rampant. The commission of such crime is through state and non-state backed actors against government websites, public and private networks, individuals and also economic giants. Cyber espionage is seen as a developing crime due to the unlimited connectivity of these cybercriminals to cyberspace which translates it as a visible threat to national security. The risk of cyber espionage is more prevalent and viable as the cyberspace environment facilitates anonymity. The predominant theories adopted to understand cyber espionage is the Risk Society Theory (RST) and the Cyber-Securitisation Theory (CST). However, on their own, these two theories are deficient in that RST relies on the risks and insecurities that come with modernisation, and CST only focuses on the transformation of a domain into a matter of national security. Given these problems, this paper aims at examining each model and expanding and integrating the RST and CST, which would mitigate the understanding of cyber espionage and modalities to prevent such crime. This paper employs a qualitative methodology, with primary data derived from semi-structured interviews with appropriate respondents. The data is triangulated using specialists from two different ministries. Secondary data sources include applicable cyber law, the Penal Code, books, academic publications, online databases, and library-based sources. The findings suggest that focusing on securitisation alone will not be effective in governing such crime, instead, understanding the risks and how to manage and mitigate such risks would be more effective. The integration of both theories would be significant to stakeholders especially government entities that focused on cybersecurity and also endorses the concept of individual responsibility for corporate entities and computer users in protecting themselves against such crime.
Firefly Competitive Swarm Optimization Based Hierarchical Attention Network for Lung Cancer Detection

Lung cancer is a severe disease, which causes high deaths in the world. Earlier discovery of lung cancer is useful to enhance the rate of survival in patients. Computed Tomography (CT) is utilized for determining the tumor and identifying the cancer level in the body. However, the issues of CT images cause less tumor visibility areas and unconstructive rates in tumor regions. This paper devises an optimization-driven technique for classifying lung cancer. The CT image is utilized for determining the position of the tumor. Here, the CT image undergoes segmentation, which is performed using the DeepJoint model. Furthermore, the feature extraction is carried out, wherein features such as local ternary pattern-based features, Histogram of Gradients (HoG) features, and statistical features, like variance, mean, kurtosis, energy, entropy, and skewness. The categorization of lung cancer is performed using Hierarchical Attention Network (HAN). The training of HAN is carried out using proposed Firefly Competitive Swarm Optimization (FCSO), which is devised by combining firefly algorithm (FA), and Competitive Swarm Optimization (CSO). The proposed FCSO-based HAN provided effective performance with high accuracy of 91.3%, sensitivity of 88%, and specificity of 89.1%.
Decision Transformer: Reinforcement Learning via Sequence Modelling - Paper Review (Presentation Slides)

Recent studies have shown that transformers can model high-dimensional distributions of semantic concepts at scale, opening up the intriguing possibility of formalizing sequential decision-making problems as reinforcement learning (RL). New research from a UC Berkeley, Facebook AI Research and Google Brain explores whether generative trajectory modelling — i.e. modelling the joint distribution of a sequence of states, actions, and rewards — could serve as a replacement for conventional RL algorithms. We present a paper review of this research
An Overview About Privacy Protection of Facebook Social Network Users Data

Graph anonymization is a necessary process before any publication of data from a social network in order to preserve the privacy of individuals, especially when these networks contain sensitive or personally identifiable information, such as social contacts, personal opinions and recordings of private communications. So, this publication can therefore constitute a threat to privacy. Generally, this simple procedure is not sufficient, some inference attacks on the published graph can lead to de-anonymize certain nodes, to learn the existence of a social relation between two nodes or to use the structure of the graph itself to deduce the value of certain sensitive attributes. For this reason, several anonymization techniques were proposed in the literature to combat this problem, they modify the structure of the social graph by adding and/or removing nodes and/or edges, these methods can be classified as Graph Modification, Generalization or Clustering and Differential Privacy Models. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. We identify the best graph anonymization method that ensure a high confidentiality level after making a clear comparison between them. Finally, we present our perspective or our proposition that still under development which is based on differential privacy method and attacking the graph processing in different ways at the same time, graph structure and node attributes.
TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light System

We introduced Temporally Incremental Disparity Estimation Network (TIDE-Net), a learning-based technique for disparity computation in mono-camera structured light systems. In our hardware setting, a static pattern is projected onto a dynamic scene and captured by a monocular camera. Different from most former disparity estimation methods that operate in a frame-wise manner, our network acquires disparity maps in a temporally incremental way. Specifically, We exploit the deformation of projected patterns (named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pattern flow</i> ) on captured image sequences, to model the temporal information. Notably, this newly proposed pattern flow formulation reflects the disparity changes along the epipolar line, which is a special form of optical flow. Tailored for pattern flow, the TIDE-Net, a recurrent architecture, is proposed and implemented. For each incoming frame, our model fuses correlation volumes (from current frame) and disparity (from former frame) warped by pattern flow. From fused features, the final stage of TIDE-Net estimates the residual disparity rather than the full disparity, as conducted by many previous methods. Interestingly, this design brings clear empirical advantages in terms of efficiency and generalization ability. Using only synthetic data for training, our extensitve evaluation results (w.r.t. both accuracy and efficienty metrics) show superior performance than several SOTA models on unseen real data. The code will be available on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/CodePointer/TIDENet</uri></i> soon.
Design Safety Training Using Extended Reality Tracking Tools in Semiconductor Fabrication Laboratory Furnace

Due to the possibilities provided by such technologies to provide people with live immersive virtual worlds, Extended Reality (XR) technologies such as virtual (VR), augmented (AR), and mixed reality (MR) have grown. The role of XR has a highly effective and efficient technology in virtual laboratories (VL) as experiments can be performed almost anywhere. During the laboratory activities, students are facing the issue of dealing with dangerous equipment such as Semiconductor fabrication furnace, which has a high temperature. In recent years, modern sensors such as Leap Motion Controller can track human movements and analyses the accuracy. This work presents a novelty approach of design training that can tracks body movements. Moreover, it provides warring alerts to trainer when it comes closer to dangerous places. While she/he is making wrong hand/body movement. When user do the experiment a semiconductor furnace training is operation in a VL. To illustrate more, this training enables the trainee to prevent his/her burning during the experiment according to safety and social distancing standards as well. Thus, Unity 3D has the capability to measure the following: accuracy, quality and efficiency of the system. XR training is significantly considering as safe technology to avoid user’s accidents. To conclude, in the field of Human-computer interaction (HCI), the analysis has been improved the training movement in terms of accuracy and interactivity respectively.
Trends in Web APIs Classification, Selection, and Composition

The number of web services and web APIs is still growing. Due to that, the manual selection and possible manual composition of the services is difficult, which is why the research of automatic classification, selection and composition of the services is still actual. This paper reviews recent research on this topic, with the goal of defining the main topics and directions of future research. From the set of 80 read recent conference and journal articles, 30 most relevant papers were singled out. A review of the literature shows that the greatest potential lies in the topic of new services and web APIs (for example, Internet of Things services, machine and deep learning services and APIs) and approaches to classify, select and compose these types of services. The mentioned services have many specific features, and there are still no things, machine or deep learning as a services specific approaches in the existing literature.
Editing assistance tool validation for English language learners

Purpose Editing assistance software programs are computer-based tools that check and make suggestions for the grammar, spelling and style of a piece of writing. These tools are becoming more popular as recommendations for students who struggle with written expression, such as English language learners (ELLs). The purpose of the present study is to compare the performance of four different programs with embedded editing assistance tools in their ability to identify errors in the writing of ELLs. Design/methodology/approach Repeated measures ANOVAs were conducted to determine whether there were differences in the number of errors (i.e. spelling, grammar, punctuation and errors that change the meaning of the text) identified by editing assistance programs (i.e. Grammarly, Ginger, Microsoft Word, Google Docs and human raters) for writing by ELLs. Findings The results of the present study indicate that the four programs did not differ in their identification of spelling errors. None of the editing assistance programs identified as many errors as the human raters; therefore, editing assistance cannot yet replace effective human editing for ELLs. Research limitations/implications Limitations with the present study include manual verification of errors flagged by editing programs, multiple raters, a small sample size and a young sample of students. Practical implications The paper includes practical factors to consider when integrating editing assistance software into the classroom, including the development needs of students, the impact of students' first language and student training on the technology. Originality/value This paper provides school psychologists, teachers and other professionals working with students with specific, evidence-based recommendations for implementation of editing assistance AT.
Stakeholder-Centric Clustering Methods for Conflict Resolution in the Requirements Engineering Process

This paper describes the development and evaluation of a framework that combines expert-based and clustering methods for resolving conflicts in requirements elicited from stakeholders. The purpose of the framework was to identify and resolve conflicts among expectations by multiple stakeholders that often arise during the requirements elicitation phase. By means of qualitative and quantitative research approaches, face-to-face oral interviews, quantitative surveys, brainstorming sessions, and focus groups, scenarios were generated with stakeholders of a given problem domain. Our approach was implemented within an interactive system that empirically supports the adequacy of our framework with the involvements of experts and other stakeholders of the chosen problem domain. In addition, we presented a dataset of requirements with their weight scales that formed the basis for resolving conflicting views by stakeholders by applying scientific criteria. The framework was validated in a real-life case study. The results demonstrated 85.71% of correctly clustered instances of requirements, based on which the experts agreed that the interactive system was good enough for resolving conflicting subjective views in requirements analysis. The research performed has the two-fold threat to validity, which suggests (i) the need to adequately capture and harmoniously represent the views by different stakeholders in a multicultural and multidisciplinary domain and (ii) the need to validate the framework in other real life case studies in different domains. The research performed has a high potential for reducing software development costs and saving time at the early stage of the development of software products.