Reviewerly/exHarmony huggingface dataset:
A Logarithmic Function-Based Novel Representation Algorithm for Image Classification

Salient feature extraction is an important task in image classification and recognition. Although classification techniques focus on the bright part of an image, many pixels of the image are of similar saliency. To address the issue, this paper proposes the logarithmic function-based novel representation algorithm (LFNR) to apply a novel representation for each image. The original and novel representations were fused to improve the classification accuracy. Experimental results show that, thanks to the simultaneous use of original and novel representations, the test samples could be better classified. The classification algorithms coupled with the LFNR all witnessed lower error rates than the original algorithms. In particular, the collaboration representation-based classification coupled with the LFNR significantly outperformed the other sparse representation algorithms, such as homotopy, primal augmented Lagrangian method (PALM), and sparse reconstruction by separable approximation algorithm (SpaRSA). The no-parameter property of the LFNR is also noteworthy.
Investigation of text data augmentation for transformer training via translation technique

Data augmentation can improve model’s final accuracy by introducing new data samples to the dataset. In this paper, text data augmentation using translation technique is investigated. Synthetic translations, generated by Opus-MT model are compared to the unique foreign data samples in terms of an impact to the trans- former network-based models’ performance. The experimental results showed that multilingual models like DistilBERT in some cases benefit from the introduction of the addition artificially created data samples presented in a foreign language.
CopyrightChain: Permissioned Blockchain-based Collaboration and Design Right using Hyperledger Composer and IPFS

Information technologies, which have developed in every field in recent years, bring various problems in digital environments.The increase in digital records has increased the importance given to digital identity information in terms of data security and data source.With digital identity, information such as where and when data is shared can be obtained.Data that can be easily reproduced in digital environment puts copyright holders in a difficult situation.This article outlines how copyrights can be secured digitally and executed in a transparent manner.For this purpose, the furniture sector, which is one of the sectors where personalized products increase, has been examined as an example.Reaching customer demands in customized production and ensuring manufacturer-customer integration can be achieved by overcoming various difficulties.For this purpose, blockchain-based in this article proposed to bring together the actors in the manufacturing sector and the customer.In addition, it was emphasized how to secure the copyright of products designed by the customer or other actors.It is known that the data obtained during the protection of copyrights becomes cumbersome over time.For this purpose, it is aimed to increase the speed by using an external database.A distributed database, IPFS, was used for this.
Table detection and cell segmentation in online handwritten documents with graph attention networks

In this paper, we propose a multi-task learning approach for table detection and cell segmentation with densely connected graph attention networks in free form online documents. Each online document is regarded as a graph, where nodes represent strokes and edges represent the relationships between strokes. Then we propose a graph attention network model to classify nodes and edges simultaneously. According to node classification results, tables can be detected in each document. By combining node and edge classification resutls, cells in each table can be segmented. To improve information flow in the network and enable efficient reuse of features among layers, dense connectivity among layers is used. Our proposed model has been experimentally validated on an online handwritten document dataset IAMOnDo and achieved encouraging results.
A Comprehensive Study on IoT security problems based on blockchain

IOT refers to other enhancements that integrate with the virtual and related and interactive, and create new digital organizations that build our self-sufficiency. IOT provides several performance, not to mention, collaborations now presenting different issues with the same areas of appeal, security, security, integrity and direct data. These issues are preventive in anticipation of what might come of the success of IOT applications. This study gives a general summary of IOT integration with blockchain development. An internal and external measurement of the combination of block-chain to IOT model has been introduced, which has the advantages of access and will how blockchain will solve IoT concerns. Blockchain designing can be utilized as a help for different IOT applications. This is trailed by a glance at the effect of coordinating man-made consciousness (AI) into IOT and blockchain. At long last, the effect of blockchain on IOT investigation is tended to.
Young Adult Literature (YAL): An Analytical Study Among Selected EFL Teachers in Jordanian Public Schools

The purpose of this study is to shed light on Young Adult literature (YAL) in Jordan. Eleven English as a foreign language (EFL) teachers and 62 EFL students in Jordanian public schools participated in this research to first measure their awareness of the YAL and to evaluate the academic benefits of integrating English YA literature into the Jordanian public school curricula. A group of EFL instructors and EFL learners were interviewed using a semi-structured guide. The instructors' interview questions focused on demographics, definitions of YA literature, age classification of YAL, benefits, and challenges of using YA novel in Jordanian English curricula. Questions for the learners centered on demographics, definition and age classification of YAL, and the use of YA novels in the EFL classroom. The study revealed that EFL teachers' reliance on the official curriculum tends to remove innovation and creativity from their teaching and limit their opportunities to adapt the curricula to the competence and interest of their EFL students. The research also revealed that EFL students who read English YA novels developed necessary competencies in both the English language and daily life experiences. The results showed that most EFL teachers in this research paper asserted that English YA novels positively affected EFL students in Jordan. This effect led to increased motivation and engagement in EFL classrooms.&#x0D; &#x0D; Received: 27 January 2021 / Accepted: 8 April 2021 / Published: 10 May 2021
Fast Job Recognition and Sorting Based on Image Processing

With the advancement of artificial intelligence (AI) and the upgrading of intelligent manufacturers, the development of intelligent manufacturing is now propelled by the replacement of inefficient traditional assembly machines and operators with machine vision (MV)-based industrial robots. The classic job recognition and positioning algorithm has multiple shortcomings, such as high complexity, manual design of similarity function, and susceptibility to noise disturbance. To solve these shortcomings, this study presents a fast job recognition and sorting method based on image processing. Firstly, the extraction approach for wavelet moment features and wavelet descriptors was introduced, and the feature fusion based on echo state network (ESN) was detailed. Then, the authors explained the idea of job template matching, and described how to measure similarity and terminate the measurement during template matching. Experimental results fully manifest the effectiveness of our strategy for fast job recognition and sorting. Our method offers a new solution to rapid recognition and sorting of objects in other fields.
Algorithm Recommendation for Data Streams

In the last decades, many companies have taken advantage of knowledge discovery to identify valuable information in massive volumes of data generated at high frequency. Machine learning techniques can be employed for knowledge discovery since they can extract patterns from data and induce models to predict future events. However, dynamic and evolving environments usually generate non-stationary data streams. Hence, models trained in these scenarios may perish over time due to seasonality or concept drift. Periodic retraining can help, but a fixed hypothesis space may no longer be appropriate. An alternative solution is to use meta-learning for regular algorithm selection in time-changing environments, choosing the bias that best suits the current data. In this paper, we present an enhanced framework for data stream algorithm selection based on MetaStream. Our approach uses meta-learning and incremental learning to actively select the best algorithm for the current concept in a time-changing environment. Different from previous work, we use a rich set of state-of-the-art meta-features, and an incremental learning approach in the meta-level based on LightGBM. The results show that this new strategy can improve the recommendation accuracy of the best algorithm in time-changing data.
The Lexicogrammar of Hate Speech

This study aims to investigate the hate speech appearing in the comment section of YouTube channel entitled "New Zealand Mass Shooting in the Christchurch mosque" through the lexicogrammar of SFL's interpersonal meaning, i.e., MOOD and modality.This study employed qualitative approach.Using content analysis method, this study analysed as many as 20 comments.The result shows that declarative MOOD is the most frequently used, serving the functions of giving statements, of indirect directive, and of threat.The other MOOD types used are imperative as well as interrogative that functions as a rhetorical question to assert a proposition.In terms of modality, modulated clauses outnumber modalized clause.The use of MOOD types in expressing hate speech responding to the incident indicates that the writers position themselves as superior toward the targeted people, i.e., the victims; whereas the type of modality used shows that the writers play as an authority and deprive others' rights.
Application of E-Management and its Obstacles from Perspectives of Faculty Members at Imam Abdulrahman bin Faisal University

E-Management is very important in the current high-tech word, and it plays a vital role in the world’s progress in general and academic field in particular. This study aimed to determine the degree of application of electronic management at Imam Abdul Rahman bin Faisal University and to identify the obstacles facing its application. The overall sample participated in this study was 285. A quantitative research design was employed in this study in which an adapted questionnaire was administered to the participants of this study. The sample includes faculty members from Imam Abdulrahman bin Faisal University. The results showed that the E-Management is reasonable applied at the university. Also, the results revealed that the organizational obstacles are insignificant, in addition, that the human obstacle is the most important obstacle among other obstacles, followed by the technical and financial obstacles, while the organizational obstacles came in the last rank. Some recommendations were provided based on the results of the present study.&#x0D; &#x0D; Received: 15 February 2021 / Accepted: 19 March 2021 / Published: 10 May 2021
Use of E – Book in Science Learning of Junior High Students in the University of Batangas

The Department of Education Order 105, s. 2009 stated that as this country advances through the 21st - century, the use of technology to research, organize, evaluate and communicate information has grown. In response to this, the use of E-book or Electronic Book is one of the 21st - century educational tools that provide opportunities to practice learning fundamentals in as many as possible. Moreover, this study aimed to assess the use of e-book as an instructional material in science learning among the junior high students of the University of Batangas, Batangas City, Philippines with the end purpose of developing activities that will enhance the teaching-learning process. It determined the usefulness of ebook in improving students’ achievement, study habits and acquired skills including the common problems met by teachers and students in using an e-book. The descriptive method was used with a questionnaire as the main data gathering instrument with an interview. The respondents were composed of two groups, 40 Grade 10 students and 42 teachers are selected through purposive sampling. The statistical tools used were t-test and weighted mean. The results of the study revealed that teachers agreed that the content presents a series of activities in each section or unit, while students agreed that the contents have comprehensive and appropriate topics. The teachers agreed that the graphics use contrasting colors to the background for easy reading, while students agreed that it uses varying fonts and sizes. There are no significant differences between the assessment of teachers and students on the use of e-book as instructional material in learning Science in terms of contents, design, and graphics. The teachers and students agreed that e-book helps in enhancing student’s achievement in the attainment of the content standard. The teachers agreed that students enhanced their acquired skills by demonstrating independent learning, while students agreed that their skills improved because they can transfer specific print-based reading skills to interactive. The common problems met by the respondents are the compatibility and design limitation of the software. The proposed activities have interactive visual features, varying fonts, sizes and color that can sustain the interest of the learners.
Collaborative Filtering-Based Recommendation System Using Time Decay Model

Online collaborative movie recommendation systems attempt to help customers accessing their favourable movies by gathering exactly comparable neighbors between the movies from their chronological identical ratings. Collaborative filtering-based movie recommendation systems require viewer-specific data, and the need for collecting viewer-specific data diminishes the effectiveness of the recommendation. To solve this problem, the authors employ an effective multi-armed bandit called upper confidence bound, which is applied to automatically recommend the movies for the users. In addition, the concept of time decay is provided in a mathematical definition that redefines the dynamic item-to-item similarity. Then, two patterns of time decay are analyzed, namely concave and convex functions, for simulation. The experiment test the MovieLens 100K dataset. The proposed method attains a maximum F-measure of 98.45 whereas the existing method reaches a minimum F-measure of only 95.60. The presented model adaptively responds to new users, can provide a better service, and generate more user engagement.
Information System Security Audit Based on the DSS05 Framework Cobit 5 at Higher Education XX

Currently, information has become a commodity or basic need, it can even be said that we are already in an "information-based social" era. It is undeniable that the ability to access and ensure the availability of information quickly and accurately has become a very essential component for an organization, whether in the form of social or commercial organizations, educational institutions such as universities, government agencies, and individuals. Various channels were created to regulate access rights to information, to prevent unauthorized people from accessing it, to minimize losses for the owner of the information. Based on the results of interviews with the research object of XX college, there are still frequent disruptions to information system security such as attacks on servers that result in server downtime, attacks on institutional e-mails that result in being unable to receive or even send e-mails, and other disturbances. This certainly harms information services at the tertiary institution, therefore an information system security audit is required. This study aims to measure the level of information system security capabilities using the Cobit 5 framework in the APO13 and DSS05 domains. Based on the results of the audit, it was found that the GAP value was 3.6 for the APO13 domain or at level 1 while 3.4 for the GAP DSS05 value or at level 2, it can be concluded that the information system security maturity level is still very low so that it needs improvement. It is recommended to make SOPs and documentation of maintenance, control, and periodic security evaluation, install an antivirus that has high and up to date protection accuracy, and make regular maintenance reports both on software and hardware.
Deteksi Dini Mahasiswa Drop Out Menggunakan C5.0

The decline in the number of active students also occurred at the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This greatly affects the profile of study program graduates. So it is necessary to have a system that is able to detect students who are threatened with dropping out early. In this study, the attributes chosen were the student's GPA and the percentage of attendance . This attribute is used to classify students who are predicted to drop out. The research data uses student data from the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This study uses the C5.0 algorithm to build a decision tree to assist data classification. The decision tree that was built with 304 data as training data resulted a C5.0 decision tree which had an error rate of 5%. The accuracy results obtained from the 76 test data is 93%.
Word-Level ASL Recognition and Trigger Sign Detection with RF Sensors

Current research in the recognition of American Sign Language (ASL) has focused on perception using video or wearable gloves. However, deaf ASL users have expressed concern about the invasion of privacy with video, as well as the interference with daily activity and restrictions on movement presented by wearable gloves. In contrast, RF sensors can mitigate these issues as it is a non-contact ambient sensor that is effective in the dark and can penetrate clothes, while only recording speed and distance. Thus, this paper investigates RF sensing as an alternative sensing modality for ASL recognition to facilitate interactive devices and smart environments for the deaf and hard-of-hearing. In particular, the recognition of up to 20 ASL signs, sequential classification of signing mixed with daily activity, and detection of a trigger sign to initiate human-computer interaction (HCI) via RF sensors is presented. Results yield %91.3 ASL word-level classification accuracy, %92.3 sequential recognition accuracy, 0.93 trigger recognition rate.
IDENTIFICATION OF FEATURES IN PREDICTING PROMINENT MALAY WORDS USING DECISION TREE

Predicting word prominence is a major topic in the field of speech synthesis where predicting prominent words is necessary to produce a natural-sounding speech synthesis. In our previous work, marking prominent words in a speech corpus is required to select the most suitable unit for speech synthesis; however, given that marking is performed manually, building a large speech corpus will be expensive in terms of labor and time-consuming. Thus, predicting prominent words automatically for which features represent an important aspect is required. This study presents an experimental work on identifying features (including part-of-speech (POS) sequence, phrasal break, and word position) in predicting prominent Malay words using decision tree and WEKA feature selection correlation method. Results show that using the decision tree for predicting prominent words (Precision = 85.0%, Recall = 84.2%, and F-measure = 83.5%) is optimal when the phrasal break is omitted as a feature. In addition, the results (Precision = 66.40%, Recall = 67.2%, and F-measure = 66.60%) are poorest when the POS sequence is excluded from the features. Therefore, this study concludes that phrasal break is a weak (noisy) feature, whereas POS sequence is an important feature in predicting prominent Malay words.
A Neutral Rewrite Mutation Operator for Genetic Programming applied to Boolean Domain Problems

The effect of semantically neutral tree rewrites is analyzed in the context of genetic programming applied to Boolean domain problems. Different setups of the proposed Neutral Rewrite Operator are studied from the perspective of improving performance.
A new approach for extracting and scoring aspect using SentiWordNet

Aspect-based online information on social media plays a vital role in influencing people’s opinions when consumers concern with their decisions to make a purchase, or companies intend to pursue opinions on their product or services. Determining aspect-based opinions from the online information is necessary for business intelligence to support users in reaching their objectives. In this study, we propose the new aspect extraction and scoring system which has three procedures. The first procedure is normalizing and tagging part-of-speech for sentences of datasets. The second procedure is extracting aspects with pattern rules. The third procedure is assigning scores for aspects with SentiWordNet. In the experiments, benchmark datasets of customer reviews are used for evaluation. The performance evaluation of our proposed system shows that our proposed system has high accuracy when compared to other systems.
From Hilbert proofs to consecutions and back

Restall set forth a "consecution" calculus in his An Introduction to Substructural Logics. This is a natural deduction type sequent calculus where the structural rules play an important role. This paper looks at different ways of extending Restall's calculus. It is shown that Restall's weak soundness and completeness result with regards to a Hilbert calculus can be extended to a strong one so as to encompass what Restall calls proofs from assumptions. It is also shown how to extend the calculus so as to validate the metainferential rule of reasoning by cases, as well as certain theory-dependent rules.