Reviewerly/exHarmony huggingface dataset:
PENERAPAN MODEL PEMBELAJARAN LEARNING CYCLE DENGAN MENGGUNAKAN GOOGLE CLASSROOM UNTUK MENINGKATKAN HASIL BELAJAR EKONOMI PESERTA DIDIK KELAS XI

The problems studied in the research are the economic learning outcomes of students, the lack of interest in learning economics for students, the use of inappropriate methods, economic subjects are considered difficult and difficult to understand for some students. With the aim of improving the economic learning outcomes of students in class XI IPS 1 SMA Negeri 8 Bandar Lampung.This research is a classroom action research (CAR) which was conducted in two cycles. Each cycle consists of 4 stages, namely planning, action, observation, and reflection. In the first cycle consists of two meetings and in the second cycle consists of two meetings. The subjects in this study were students of class XI IPS 1 SMA Negeri 8 Bandar Lampung, totaling 31 students. The results showed that by using the learning cycle learning model for class XI IPS 1, students' learning activities increased from 6.89% in the first cycle to 8.02 in the second cycle. The increase in the mastery of students' learning outcomes increased from the first cycle of 15 students (70.48%) to 25 participants (80.32%) in the second cycle. So it can be concluded that learning cycle activities using google classroom can improve learning activities and learning outcomes for students in class XI IPS 1 SMA Negeri 8 Bandar Lampung for the academic year 2021/2022.
Learning by Doing: SMART CLASSROOMS AND e-LEARNING

This paper is about the much growing technology “SMART CLASSROOMS and e-LEARNING”. The Smart Classroom project explores the challenges and potentials of the Intelligent Environment as a new human-computer interaction paradigm. The Smart Classroom could actively observe, listen and serve the teachers, and teachers can write on a wall-size media-board just by their hands, or use speeches and gestures to conduct the class discussion involving of the distant students. This paper discusses the advantages, disadvantages and main uses of the system.
Passive and Causative Serial Verb Constructions in Korean

The purpose of this study is to investigate two patterns of morphological passive and causative serial verb constructions. I am against the approaches in which the two patterns have distinct syntactic structures. Instead, I claim that they have a univocal structure based on the order of two morpho-syntactic applications: serialization precedes morphological passivization/causativization. I claim that verbs must be identical in their category (namely, vDO or vINCH) to form serial verbs. To this successfully serialized verb construction, a higher level of functional categorizer (vPASS or vCAUS) merges to create standard passive/causative SVCs. The consequence of this sequential operation is as if each verb underwent the morphological derivation. This is because the later merged functional head c-commands the entire serialized verb construction, supporting Distributed Morphology (Marantz 1997, 2007; Son 2006). The other structure, where passive/causative morphemes occur on each verb, is a PF variant of the standard SVCs.
Additional file 1: of Virtual reality among the elderly: a usefulness and acceptance study from Taiwan

Acceptance of the Virtual Reality (VR) Experience among the Elderly: Questionnaire. (DOCX 16 kb)
Faculty in the Applied and Pure Sciences May Have Limited Experience with E-books

A Review of: Bierman, J., Ortega, L., &amp; Rupp-Serrano, K. (2010). E-book usage in pure and applied sciences. Science &amp; technology libraries, 29(1-2), 69-91. https://doi.org/10.1080/01942620903579393 Abstract Objective – To determine the usage of and attitudes toward e-books among faculty in the applied and pure sciences. Design – Online survey and in-person interviews. Setting – A large public university in the United States. Subjects – 11 faculty members. Methods – Participants completed an 11-item survey covering demographic data and questions about electronic book experience and preferences. This was followed up by an in-person interview with the researchers. The interviews were structured into three sections: opening questions about e-book usage, an interactive demonstration and discussion of two preselected e-books, and final follow-up questions. Interviews followed a general script of prepared questions, but also encouraged open discussion and dialogue. Main Results – Most participants in the study reported limited experience with e-books and only 3 of the 11 participants reported using library-purchased e-books in their research and instruction. Participants noted ease of access and searchability as key advantages of e-books. Concerns included the belief that reading and learning is more difficult on a desktop computer, as well as concerns about the stability and reliability of e-book access. Participants also felt negatively about the necessity to create a new login profile and password to access e-books. The study found no difference in the way faculty in pure and applied sciences approached e-books. Conclusion – The authors determine that e-books will likely become more commonly used in academia. Users want e-books that are easy to use and customizable. In addition, the authors conclude that librarians need to understand their patrons’ needs as e-book users and proactively promote and market their e-book collections.
STUDY ON BIM MODEL DATA LIGHTWEIGHT PROCESSING METHOD

设计一种轻量化的建筑信息模型(BIM)的文件结构(LBSF),该文件结构能与地理信息系统(GIS)有效集成,流畅展示复杂的BIM几何对象.近年来,基于GIS的城市设施管理研究增多,需使用GIS在园区甚至城市层面有效地可视化展示BIM几何对象,为此设计一种轻量级BIM数据结构并实现了原型系统.LBSF文件开发后,对数据量和加载性能进行比较,取得了较好效果.
Triplet Angular Loss for Pose-Robust Face Recognition

Although face recognition has been widely applied in many areas, pose-robust face recognition is still a challenging topic due to the large pose variations in real scenes. In this paper, we propose to learn the pose-robust face representation by normalizing the profile face in feature level directly and jointly considering both intra-class compactness and inter-class separability. Our approach minimizes the angular distance between the profile face and the positive frontal anchor. And it maximizes the angular distance between the profile face and the negative frontal anchor simultaneously. Furthermore, we modify the Triplet loss and derive the Triplet Angular loss to guarantee the intra-class compactness and the inter-class separability in angular space. In this way, the faces under varying poses can cluster compactly to create a pose-robust feature representation. Extensive experiments on two challenging benchmarks (CFP-FP and IJB-A) illustrate that our approach achieves a competitive performance in the field of pose-robust face recognition.
Toward Explainable Users: Using NLP to Enable AI to Understand Users’ Perceptions of Cyber Attacks

To understand how end-users conceptualize consequences of cyber security attacks, we performed a card sorting study, a well-known technique in Cognitive Sciences, where participants were free to group the given consequences of chosen cyber attacks into as many categories as they wished using rationales they see fit. The results of the open card sorting study showed a large amount of inter-participant variation making the research team wonder how the consequences of security attacks were comprehended by the participants. As an exploration of whether it is possible to explain user’s mental model and behavior through Artificial Intelligence (AI) techniques, the research team compared the card sorting data with the outputs of a number of Natural Language Processing (NLP) techniques with the goal of understanding how participants perceived and interpreted the consequences of cyber attacks written in natural languages. The results of the NLP-based exploration methods revealed an interesting observation implying that participants had mostly employed checking individual keywords in each sentence to group cyber attack consequences together and less considered the semantics behind the description of consequences of cyber attacks. The results reported in this paper are seemingly useful and important for cyber attacks comprehension from user’s perspectives. To the best of our knowledge, this paper is the first introducing the use of AI techniques in explaining and modeling users’ behavior and their perceptions about a context. The novel idea introduced here is about explaining users using AI.
PERBANDINGAN MODEL PEMBELAJARAN PROBLEM BASED LEARNING DAN DISCOVERY LEARNING TERHADAP HASIL BELAJAR SISWA

This study aims to describe student learning outcomes taught using the Problem Based Learning and Discovery Learning learning model and compare student learning outcomes taught using the Problem Based Learning learning model and the Discovery Learning learning model. Research uses experiments of quantitative methods with research design using the actual experimental design (true-ed) with the form of the design of the control group only post-test. The results of the study show that the value is sig. (2-tailed) is 0.045 or smaller than 0.05 meaning rejected and accepted which means comparison of student learning outcomes taught using the Problem Based Learning learning model and the Discovery Learning learning model is not the same. The conclusion of this study is the average learning outcomes of students using the Problem Based Learning learning model of 63.5, the average learning outcomes of students use the Discovery Learning learning model of 49.2, the average student learning outcomes use the BAS problem learning model
Subspace Constraint for Single Image Super-Resolution

Recently, single image super-resolution (SISR) algorithms based on convolutional neural networks (CNN) have proliferated and achieved significant success. However, most of them use the same constraint to both low-frequency and high-frequency features in the loss function. They do not discriminate between high-frequency details and low-frequency information, which limits the representation capacity of high-frequency information. This paper presents a subspace constraint approach for SISR to discriminate between high-frequency information and low-frequency information and enhance the reconstruction of high-frequency features. In our approach, the constraint is introduced in wavelet domain. Meanwhile, our approach adopts the multi-level residual learning to improve the training efficiency. Extensive experimental results on five benchmark datasets show that our model is superior to those state-of-the-art methods for both accuracy and visual comparisons.
Deep Learning Approaches to Determining Optimal Resolution for Scanned Text Documents

Fast track article for IS&amp;T International Symposium on Electronic Imaging 2021: Color Imaging XXVI: Displaying, Processing, Hardcopy, and Applications proceedings.
DESIGN OF DATA COLLECTION SYSTEM OF LECTURER’S RESEARCH AT INSTITUTE INFORMATICS AND BUSINESS DARMAJAYA LAMPUNG

In order to spur the quantity and quality of research is one of the basic tasks of the lecturer, Institute Informatics and Business Darmajaya Lampung facilitate the professors to be able to participate in a range of grants to help financing research lecturer. With a growing number of studies professors proposed then it should be when logging the lecturer research done in a better. Utilization of information technology is the right information to perform data management information can be either information related to it or also as tool of checking data in order to reduce plagiarism practices and publishing redundancy title research
Fuzzy rule-based acceptance criterion in metaheuristic algorithms

Metaheuristic algorithms are solution approaches to solve optimization problems by repeating some algorithmic steps while searching the solution space. The strategy of the metaheuristic includes two basic tactics; exploration for escaping the local optimum and exploitation for the global optimum. The number of solutions while exploring the solution space can be used to classify metaheuristics. If the metaheuristic uses only one solution to generate a new solution, we call it the single-solution-based metaheuristic. Simulated annealing, iterated local search, adaptive large neighborhood search, iterated greedy, local search, and tabu search are examples of single-solution-based metaheuristics. Most of these metaheuristics use an acceptance criterion to whether accept the newly generated solution instead of the incumbent solution to escape from the local optimal. The most used acceptance criterion in the literature is the Metropolis criterion or simulated annealing-like acceptance criterion that decides whether accept the new solution by calculating its acceptance probability. In this study, we propose a fuzzy rule-based acceptance criterion that fuzzies the inputs of the metaheuristic within a fuzzy inference system to create the decision output about the acceptance. The proposed new acceptance criterion is compared with the well-known probabilistic approach in the experimental study with traveling salesman problem, multidimensional knapsack problem, single machine weighted earliness/tardiness problem, linear regression problem, and two continuous optimization problems. The statistical analyses reveal that the number of acceptance solutions while using the proposed acceptance criterion is less than the probabilistic one has but the metaheuristic performance increases with the proposed criterion. Additional analyses are made to explain why the fuzzy criterion convergences better to the global optimum than the probabilistic criterion.
Network embeddings from distributional thesauri for improving static word representations

Word representations obtained from text using the distributional hypothesis have proved to be useful for various natural language processing tasks. To prepare vector representation from the text, some researchers use predictive model (Word2vec) or dense count-based model (GloVe), whereas others attempt to explore network structure obtained from text namely, distributional thesaurus network where the neighborhood of a word is a set of words having adequate context feature overlap. Being inspired by the successful application of network embedding techniques (DeepWalk, LINE, node2vec, etc.) in various tasks, we attempt to apply network embedding techniques to turn a distributional thesaurus network into dense word vectors and investigate the usefulness of distributional thesaurus embedding in improving the overall word vector representation. This is the first attempt where we show that combining the proposed word representation obtained by distributional thesaurus embedding with the state-of-the-art word representations helps in improving the performance by a significant margin when evaluated against several NLP tasks which include intrinsic tasks like word similarity and relatedness, subspace alignment, synonym detection, analogy detection; extrinsic tasks like noun compound interpretation, sentence pair similarity task as well as subconscious intrinsic evaluation methods using neural activation pattern in the brain, etc.
Parsimonious mixture‐of‐experts based on mean mixture of multivariate normal distributions

The mixture‐of‐experts (MoE) paradigm attempts to learn complex models by combining several “experts” via probabilistic mixture models. Each expert in the MoE model handles a small area of the data space in which a gating function controls the data‐to‐expert assignment. The MoE framework has been used extensively in designing non‐linear models in machine learning and statistics to model the heterogeneity in data for the purpose of regression, classification and clustering. The existing MoE of multi‐target regression (MoE‐MTR) models for continuous data is based on multivariate normal distributions. However, in many practical situations, for a set of data, a group or groups of observations may exhibit asymmetric and heavy‐tailed behaviour, and inference based on symmetric distributions in such situations can unduly affect the fit of the regression model. We introduce here a novel robust multivariate non‐normal MoE model by the use of mean mixture of normal distributions. The proposed model can handle the issues of MoE‐MTR models regarding possibly skewed, heavy‐tailed and noisy data. Maximum likelihood estimates of model parameters are developed based on an expectation‐maximization (EM)‐type algorithm. Parsimony is also obtained by imposing suitable constraints on the expert dispersion matrices. The usefulness of the proposed methodology is illustrated using simulated and real data sets.
New Opportunities for Connecting African University Teaching: Open libraries and platforms

Presentation given at SDG Africa Summit 2021, Day 1, Thematic track 6: Building Teaching Capacity: Co-taught curricula; Teaching Hubs; Online Community.
The use of Talking Chips Technique in Cooperative Learning Models to Increase Language Ability for Visual Impairment Students

This research aims to gain information from various written sources related to the cooperative learning model of talking chips types in improving the oral language skills of students with visual impairments. This research was conducted using a content analysis-based literature review, involving 25 articles and 12 books. The research data used is in the form of secondary data taken from printed books, a collection of journals, previous researches, and relevant websites. The results of the literature review show: (1) cooperative learning model of talking chips types can improve oral language skills in composing simple sentences of students with visual impairments; (2) there are differences in the oral language skills between students with visual impairments low vision and blind; (3) the use of cooperative learning model of talking chips types becomes an innovation in the learning process of students with visual impairments. The results of this research implied that the cooperative learning model of talking chips types could serve as an alternative to improve the oral language skills of students with visual impairments.
Priming Effect of Colour on Aiding the Attentional Reorientation in Sequential Presentations of Temporal Data Visualization: Evidence From Eye-Tracking

Abstract In the present study, we focus on the priming effect of colour on mitigating the attentional reorientation cost, which is led by re-constructing the frame of reference for attention shift and visual search in sequential presentations of temporal data visualization. The study involves two experiments using complementary recordings of behavioural performance and eye-tracking events. Two aspects of colour primes are highlighted: the prime validity and the colour perceptual accessibility. A task paradigm integrating the feature search and keeping-track task was adopted in our experiments. In Experiment 1 (with a group of 16 participants), we confirmed the colour priming effect by comparing the priming condition to the neutral baseline. Furthermore, global colours that are with high perceptual accessibility generated more evident priming effects than local colours. However, more interferences in misguiding the attention to task-irrelevant regions were found when the global primes were invalid. In Experiment 2 (with another group of 15 participants), we verify the finding in Experiment 1 that global colours produced more pronounced priming effects in alleviating the attentional reorientation cost by comparing two groups of real-world visualizations with either global or local colours as the prime. Large saccades were initiated much earlier, and the search efficiency got improved when provided with global colours. We conjecture that the facilitatory effect from global colours may stem from its benefit on the pre-attentive processing of the search field. The research findings provide evidence for utilizing colours as the primes in mitigating the attentional reorientation cost and accelerating visual search in sequential presentations.
We Did It! So Can You! Creating "No-Textbook" IT Degrees

One of the largest expenses for university students is the cost of textbooks. This is particularly true in the STEM areas. Data shows that course textbooks cost more and more every year. Textbook costs have risen 812% since 1978 and 73% since 2006 - which is four times the rate of inflation. The College Board recommends that students budget at least $1200 per year for textbook costs. Over 1/3 of students use their financial aid for textbook costs. The average computer science/IT textbook costs $180. In order to help alleviate some of these textbook costs, the State of Georgia has been offering to faculty Affordable Learning Grants across all majors. Several faculty in the Information Technology Department of Kennesaw State University were awarded an ALG grant during the Summer-Fall 2018 for redevelopment of four undergraduate IT courses using no textbook. In the past three years several IT faculty members have been awarded these ALG grants for redevelopment of information security courses, database courses, and an ethics course. The results of the redevelopment of the database courses and information security courses have been very positive for the students. The panel will discuss the current re-development of IT courses, and report on the positive results of prior re-development of no-textbook courses.
Varieties of censorship: Hate speech, pornography, and the First Amendment

Until the 1960s, governmental limits on speech and expression, particularly around issues of pornography and obscenity, were common. These restrictions were enacted to reinforce a set of standards that had broad support among the American people. Since the 1960s, we have seen a great expansion of the right to free speech and expression. Today, however, the libertarian consensus has fractured. The new censorship that favors regulating hate speech confronts the underlying premises of the old censorship. Rather than comparing and contrasting the libertarian position with the censorship of hate speech position, as is so often done, in this article, I will compare and contrast two pro-censorship positions, one group favoring the censorship of hate speech, the other favoring the censorship of pornography and obscenity. My purpose is not to advocate for censorship. Rather, my idea is to examine and explain the different rationales that exist in the two opposing approaches to censorship, to better judge the merits of censorship and free speech.
Analyzing the Security Aspects of Modular Exponentiation Against Power Analysis Attacks

Modular exponentiation has been the primary operation in most of the asymmetric cryptosystems. However, for large exponents which serves as the keys for encryption and decryption, conventional way to compute modular exponentiation is very time consuming. Hence, different binary methods based on bit wise squaring-multiplication operations is more widely used. However, these approaches are vulnerable to sidechannel attacks, specially timing and power analysis attacks. Hence, these approaches are improved so that such attacks can be mitigated. In this paper, we have implemented and analysed some of these binary approaches for computing modular exponentiation. Further, a security analysis of these approaches have also been furnished which shows how these approaches are vulnerable which encourages their improvement so that side-channel attacks specifically power analysis attacks can be resisted.
Analysing online reviews of restaurants in Malaysia: a novel approach to descriptive and predictive analytic

This paper aims to develop a model of restaurant products and services quality based on consumer sentiments shared on social networks. We applied term frequency-inverse document frequency (TF-IDF) ...