Reviewerly/exHarmony huggingface dataset:
Improved Jaya Optimization Algorithm for Feature Selection on Cancer Diagnosis Data using Evolutionary Binary Coded Approach

Cancer Diagnostic Decision Support System may mislead the classification algorithms bymultiplicity of features. Hence, feature selection becomes an essential step in data mining of cancerdiagnosis data. Feature selection is a multi-objective optimization problem that systematically selectsa subset of most informative features for model building. Recently, randomization basedEvolutionary Algorithms (EAs) are becoming popular for feature selection than traditional filter andwrapper methods. EAs explore the entire search space using some heuristic techniques in less time.Jaya Optimization Algorithm (JOA) is a competitive Swarm Intelligence (SI) based EA, yet is simpleand easy to implement for solving optimization problems. The present work proposes BinJOA-SAlgorithm, an improved binary variant of JOA for solving feature selection problem on cancerdiagnosis data using Sigmoidal function. BinJOA-S algorithm employs the scalarization method tosolve feature selection as a multi-objective problem. The experimental results show that thealgorithm can obtain competitive performance when evaluated for parameters such as best, averageand worst fitness, average classification accuracy, best classification accuracy, the average number offeatures selected and, CPU computational time. It is observed that the proposed algorithm producesthe effective performance of the cancer diagnosis system
A Modulation Recognition Algorithm via Hybrid Feature Analysis in Aeronautical Wireless Channel

When communicating in aeronautical wireless channels, the difficulty of radio modulation recognition increases due to the loss of information caused by noise; particularly in circumstances with low signal-to-noise ratios (SNRs), it is difficult to achieve recognition rates exceeding 90.0%. To improve the radio modulation recognition performances of networks at low SNRs in complex electromagnetic environments, a modulation recognition method based on multidimensional feature analysis is proposed in this paper. It is realized through a cascaded structure including a Deep Cross Network (DCN) and an improved Visual Geometry Group Network 16 (VGG16). Our network framework is divided into two modules. In the one-dimensional data analysis module, we take the high-order cumulant of a transmitted signal as the one-dimensional feature input of the DCN. In the two-dimensional data analysis module, the color constellation density of the signal is extracted as the feature map input of the improved VGG16. Finally, we build a cascaded neural network with hybrid feature inputs for modulation recognition. Experimental results show that the recognition rate of our method is higher than 90.0% at an SNR of -4 dB. Compared with other methods, the proposed method has better recognition performance at low SNRs in aeronautical wireless channels.
Methodology for the evaluation of the levels of attention and meditation in the development of virtual online classes of mathematics, through the use of brain-computer interface

Online classes are causing many changes, from the behavior of students to the way they are understanding the classes. In a normal situation, students are in school classrooms, where the environment is suitable for students to pay the most attention, in these times, where online classes are developed, classes are developed at home, where they were adapted environments to carry out these activities, in these situations they cause the appearance of distracting agents, causing students to be distracted when they are in class, these distracting agents can be the same house, the toys, the television and in some cases the brother who is in classes and found in the same environment. The proposed methodology proposes the analysis of the environment where the online classes are held, in a particular case, with the use of the BCI device, measurements of concentration and meditation levels are carried out, on a scale from 0% to 100 %, measurements are carried out in various conventional situations that are carried out in class, with the intention of being able to determine the best strategies so that students can have the best concentration when taking mathematics classes; The results determine that in a conventional situation the student has a concentration level of 20%, which indicates that he is not very attentive to the classes, but when we try to eliminate most of the distracting agents and concentrating on the class, we achieve a level of attention that reaches 90 ”with minimum values ​​of meditation, which indicates that the students can achieve better concentration in the classes, in order to take advantage of the class and solve math problems, we finished the work recommending the best conditions that They must be presented in order to present high levels of attention when taking online classes.
IMPLEMENTASI KEPEMIMPINAN STRATEGIS GUNA MENGHADAPI ANCAMAN BENCANA BANJIR DAN TSUNAMI DI PROVINSI KALIMANTAN TIMUR

This study discusses the implementation of strategic leadership in dealing with the threat of tsunamis and floods in East Kalimantan Province. The research method used in this study is a qualitative research method with a library approach. The findings of this study are in the concept of strategic leadership, BPBD in regency or city level is a strategic leader who organizes disaster management in East Kalimantan Province. East Kalimantan Province consists of 7 regencies and 3 cities and according to the conditions in the field there are already 9 regencies/ cities that have BPBDs and one district does not yet have BPBDs, namely Mahakam Ulu Regency and assisted by BPBD Samarinda City. In implementing strategic leadership, what strategic leaders need to deal with floods and tsunamis is coordination between various stakeholders in disaster management and collaboration as a form of competence that must be possessed by strategic leaders. In a concrete sense, the activities carried out in dealing with floods in regencies/ cities in East Kalimantan Province have been running and tsunami monitoring is being carried out in collaboration with the Geophysics Station of BMKG of Balikpapan City.
Paradeduction in Axiomatic Formal Systems

This paper presents the concept of paradeduction in order to justify that we can overlook contradictory information taking into account only what is consistent. Besides that, it uses paradeduction to show that there is a way to transform any logic, introduced as an axiomatic formal system, into a paraconsistent one.
VERITAS: Mind-Mapping in Virtual Reality

Inquiry based learning is a modern and innovative learning strategy that aims to stimulate students' interest in a topic and target Bloom's higher order cognitive process. Reflective tasks, such as mind mapping, support inquiry-based learning. Virtual Reality (VR) presents novel opportunities to help scaffold reflective tasks in inquiry-based learning by supporting use of the 3D space which is not available via existing 2D mind mapping applications. In this paper we present VERITAS, a VR application for mind-mapping based reflective tasks operating on the low-cost Oculus Go device. We discuss the interaction design for the mind-mapping task and evaluate the system from a usability perspective. Our results show that novice participants are able to learn how to use the interactions quickly and utilize them effectively to build mind-maps in 3D. VERITAS establishes the usability of VR and essential interactions to successfully perform abstract and complex reflective tasks like mind-mapping.
Understanding the ‘Competency-based Education' Conceptual Framework and Presenting Challenges through Conditional Exploration

기존의 ‘역량기반 교육’ 연구는 ‘역량’에 대해서는 상대적으로 깊은 이해를 보여주지만 ‘교육’에 대해서는 논의의 흔적을 찾아보기 어렵다. ‘역량기반 교육’을 그대로 나열하면 ‘역량의 개념을 기반으로 한 교육’이라 할 수 있다. 따라서 ‘역량기반 교육’을 올바로 이해하려면 ‘역량’의 탐색과 더불어 ‘교육’의 의미도 균형 있게 탐색하는 과정이 반드시 필요하다. 본 연구는 이러한 ‘역량기반 교육’ 개념의 부실화 문제를 지적하고 내실화를 위한 조건 제시를 연구의 궁극적 목적으로 삼고, 그 대장정의 과정을 크게 두 단계로 나누었다. 첫 번째 단계는, ‘역량’을 중심으로 한 기존의 ‘역량기반 교육’의 개념체제 분석과 그에 대한 교육관의 비판적 고찰이고, 그 결과를 논의의 기초로 삼아 두 번째 단계에서 ‘교육’을 중심에 둔 균형 있는 ‘역량기반 교육’의 본질을 탐구하여 최종적으로 ‘역량기반 교육’의 내실화를 위한 조건을 제시하는 것을 목표로 한다. 따라서 본고는 서막에 해당하는 첫 번째 연구결과를 소개하는 글이다. 직업현장에서 출발한 다소 산발적인 ‘역량기반 교육’의 개념 정의 다섯 가지-지식기반사회, 행동주의 목표, 핵심역량, 커리큘럼, 직업세계의 요구-를 간결·명료하게 제시하였고, 난맥상의 교육관을 비판적으로 고찰하였다는 점에서 의의가 있으며 향후 연구과제인 ‘역량기반 교육’에 내재된 ‘교육’의 본질 탐구의 필요성을 시사하였다.Existing competency-based education studies show a relatively deep understanding of competence, but there is no trace of discussion about education. If competency-based education is listed as it is, it is education based on the concept of competence . Therefore, to understand competency-based education correctly, it is essential to explore the meaning of education in a balanced manner as well as the search for competence . This study pointed out the problem of insolvency of this concept of competency-based education and made conditions for internalization the ultimate purpose of the study, and divided the process of the long journey into two main stages. The first phase is a critical consideration of the existing conceptual framework analysis of competency-based education and its outcome as a basis for discussion, which aims to explore the nature of balanced competency-based education. Therefore, this article introduces the results of the first study corresponding to the prelude. It is meaningful in that it concisely and clearly presented the conceptual definition of a somewhat sporadic competency-based education starting from the vocational field, and critically considered the chaotic view of education, suggesting the need to explore the essence of competence-based education.
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion

A comprehensive knowledge graph (KG) contains an instance-level entity graph and an ontology-level concept graph.The two-view KG provides a testbed for models to "simulate" human's abilities on knowledge abstraction, concretization, and completion (KACC), which are crucial for human to recognize the world and manage learned knowledge.Existing studies mainly focus on partial aspects of KACC.In order to promote thorough analyses for KACC abilities of models, we propose a unified KG benchmark by improving existing benchmarks in terms of dataset scale, task coverage, and difficulty.Specifically, we collect new datasets that contain larger concept graphs, abundant cross-view links as well as dense entity graphs.Based on the datasets, we propose novel tasks such as multihop knowledge abstraction (MKA), multi-hop knowledge concretization (MKC) and then design a comprehensive benchmark.For MKA and MKC tasks, we further annotate multi-hop hierarchical triples as harder samples.The experimental results of existing methods demonstrate the challenges of our benchmark.The resource is available at
Developing Interactive Learning Multimedia Based on Simulation Model

The purpose of this research is to develop interactive multimedia namely Smile English that can be used as the media for learning reading especially for self-preparation on the Test of English for International Communication (TOEIC) material. This reading interactive media used a simulation model. This idea exploded after observing and collecting the data in the internal environment of Politeknik Negeri Batam Based on the placement test, it was found that about 540 (49,23%) of Polibatam students' test were categorized into the intermediate level and the rest were elementary and novice level. It means that the students need to improve their preparation before taking the test to an accomplished better score. Smile English was developed by using R and D research. In this case, the researcher followed the step of the Luther-Sutopo method which was started from concept, design, material collecting, assembly, testing, and distribution. It was designed using Adobe Flash software which in this software combined text, sound, and animation. The research result showed that the implementation of Smile English was successful to help the students in preparing themselves for the TOEIC test. Therefore, 60 students from Politeknik Negeri Batam had a good result in joining the TOEIC test, especially for the reading section.
Social Distancing Alarming Through Proximity Sensors for COVID-19

The implementation of an accurate face recognition system in a hardware device is a very important aspect of various security applications. In a security system, any type of password is used to access private and confidential data, some are hard to remember and can be stolen or guessed, misplaced, forgotten, purloined or duplicated, or become corrupted and unreadable. While there have been significant improvements in the algorithms with increasing recognition accuracy, only a few types of research were conducted on implementing these face recognitions in hardware devices. Door access control systems based on face recognition is geared towards simplifying much-difficult face recognition problems in uncontrolled environments. This report comprised mainly of three sub-phases: face detection, face recognition, and automatic door access control. The face is detected by using the Viola-Jones method and face recognition is implemented by using the Principal Component Analysis (PCA). If a face is recognized, it is known, else it is unknown. The door will open automatically for the known person due to the command of the microcontroller (Atmega328P) received from the MATLAB program. On the other hand, the alarm will ring for an unknown person.
A New Approach for Paraphrasing and Rewording a Challenging Text

This article aims to propose a practical model for intra-lingual translation or paraphrase in another term. Paraphrase is a restatement of a text, rewording something written or spoken, especially to achieve greater clarity. This approach could help a troubled translator who is having issues translating a complex text into a receptor language by assessing the source text and reconstructing the contents in a simpler semantic structure. (Larson, 2012) Noam Chomsky’s generative–transformational model (1957, 1965) and Larson’s (2012) methodology have been followed to analyze sentences into a series of related levels governed by the help of several other techniques. To achieve this, firstly, the concepts; Intralingual translation, rewording, paraphrasing, and restatement are identified and explained. Secondly, methods of rewording are unpacked, then other elements that play an essential role in paraphrasing are presented. Thirdly, steps of paraphrasing are applied to the text Taj Mahal where skewings between semantic structure and grammatical features are studied and unskewed. Lastly, a conclusion is drawn from the findings to verify the hypothesis of the paraphrase. The findings and results of rewording are also briefly discussed.
THE LANGUAGE OF MOTHERS’ INTERNET-FORUMS AS A DISPLAY OF FEMININE SUBCULTURE IN XXI CENTURY

The article analyzes peculiar features of communication between users of specialized online forums devoted to motherhood. The point is considered within the framework of two approaches - linguistic (implying the analysis of the language specificity of virtual communication on the example of the mentioned Internet resources) and sociocultural (implying the analysis of the social and cultural factors of mother’s Internet forums’ emergence, predominant difference between “real” and “virtual” motherhood and the role of mentioned Internet resources in the formation of special feminine virtual subculture that has a significant impact on the perception of the modern concept of “motherhood” both in the Internet and beyond.
Improving AlphaZero Using Monte-Carlo Graph Search

The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It utilizes a neural network that learns a value and policy function to guide the exploration in a Monte-Carlo Tree Search. Although many search improvements such as graph search have been proposed for Monte-Carlo Tree Search in the past, most of them refer to an older variant of the Upper Confidence bounds for Trees algorithm that does not use a policy for planning. We improve the search algorithm for AlphaZero by generalizing the search tree to a directed acyclic graph. This enables information flow across different subtrees and greatly reduces memory consumption. Along with Monte-Carlo Graph Search, we propose a number of further extensions, such as the inclusion of Epsilon-Greedy exploration, a revised terminal solver and the integration of domain knowledge as constraints. In our empirical evaluations, we use the CrazyAra engine on chess and crazyhouse as examples to show that these changes bring significant improvements to AlphaZero.
Text Embedding Bank for Detailed Image Paragraph Captioning

Existing deep learning-based models for image captioning typically consist of an image encoder to extract visual features and a language model decoder, an architecture that has shown promising results in single high-level sentence generation. However, only the word-level guiding signal is available when the image encoder is optimized to extract visual features. The inconsistency between the parallel extraction of visual features and sequential text supervision limits its success when the length of the generated text is long (more than 50 words). We propose a new module, called the Text Embedding Bank (TEB), to address this problem for image paragraph captioning. This module uses the paragraph vector model to learn fixed-length feature representations from a variable-length paragraph. We refer to the fixed-length feature as the TEB. This TEB module plays two roles to benefit paragraph captioning performance. First, it acts as a form of global and coherent deep supervision to regularize visual feature extraction in the image encoder. Second, it acts as a distributed memory to provide features of the whole paragraph to the language model, which alleviates the long-term dependency problem. Adding this module to two existing state-of-the-art methods achieves a new state-of-the-art result on the paragraph captioning Stanford Visual Genome dataset.
COMPARISON OF LINEAR AND VECTOR DATA NORMALIZATION TECHNIQUES IN DECISION MAKING FOR LEARNING QUOTA ASSISTANCE

Data normalization is essential for all kinds of decision-making problems, and a lot of effort has been spent on the development of normalization models in multi-criteria decision making (MCDM), but despite all this, there is no definite answer to the question: Which is the most appropriate technique?. This paper compares the popular normalization techniques: Linear Normalization (LN) and Vector Normalization (VN) using VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) Method. The beneficiaries dataset of learning quota was collected of 399 students sample through observation (drive-test measurements and online questionnaires) to obtain information on criteria data including attributes in online learning during the Covid-19 pandemic. The ranking results for vector vs linear normalization show how ranking is affected. The difference in the selection of the best alternative (rank) shows that there are differences in vector and linear assessments that are influenced by the max-min criterion value which has an impact on the rank- sum results (benefit/cost). This test clearly shows how important it is to use an appropriate (normalized) representation of the model because there will often be a criterion where "the higher the better" while for others (cost) "the lower the better".
Mini Nurse Lite, Medical Tool Quiz and More: Smartphone Apps as Interactive English Medical Materials for Nursing Students

Gaining medical knowledge in English is challenging for nursing students due to the various challenging necessities such as possessing specialized vocabulary, diagnosing symptoms clearly, and writing health reports. To overcome these classical and practical language barriers, this study tries to establish the effects of using selected smartphone apps, i.e., Mini Nurse Lite, Disease Dictionary, Surgical &amp; Medical Instruments, and Medical Tool Quiz, to increase nursing students’ ability in understanding English medical materials. This research employed a quasi-experimental pre-test and post-test design. The participants were 76 first-year nursing students of a selected university in Solok. The experimental treatment was teaching English medical materials through smartphone applications followed by discussion class, while the control group did not receive training in nursing with smartphone applications as only lecture-based education was performed. The results of the study showed that there was a higher mean score in levels of satisfaction by students in the experimental group (88.53) compared to the control group (82.27). It indicates that smartphone applications were effective for improving nursing students’ ability in understanding English medical materials. The findings suggest optimal conditions for designing smartphone-based learning to be applied in a nursing class and contributed to the lack of studies on smartphone-based instruction in nursing within the context of overcoming English language barriers.
Argus CNN Accelerator Based on Kernel Clustering and Resource-Aware Pruning

Paper proposes a two-step Convolutional Neural Network (CNN) pruning algorithm and resource-efficient Field-programmable gate array (FPGA) CNN accelerator named “Argus”. The proposed CNN pruning algorithm first combines similar kernels into clusters, which are then pruned using the same regular pruning pattern. The pruning algorithm is carefully tailored for FPGAs, considering their resource characteristics. Regular sparsity results in high Multiply-accumulate (MAC) efficiency, reducing the amount of logic required to balance workloads among different MAC units. As a result, the Argus accelerator requires about 170 Look-up tables (LUTs) per Digital Signal Processor (DSP) block. This number is close to the average LUT/DPS ratio for various FPGA families, enabling balanced resource utilization when implementing Argus. Benchmarks conducted using Xilinx Zynq Ultrascale + Multi-Processor System-on-Chip (MPSoC) indicate that Argus is achieving up to 25 times higher frames per second than NullHop, 2 and 2.5 times higher than NEURAghe and Snowflake, respectively, and 2 times higher than NVDLA. Argus shows comparable performance to MIT’s Eyeriss v2 and Caffeine, requiring up to 3 times less memory bandwidth and utilizing 4 times fewer DSP blocks, respectively. Besides the absolute performance, Argus has at least 1.3 and 2 times better GOP/s/DSP and GOP/s/Block-RAM (BRAM) ratios, while being competitive in terms of GOP/s/LUT, compared to some of the state-of-the-art solutions.
A user interface design of the health QR code system in China

In the pandemic era, smart medical technology has also been applied to prevent and control diseases such as COVID-19. Health QR code system shave sparked recent discussion in the hopes of improving its convenience and safety. The many types of current health QR code systems in China exists causes inconvenience to those that are vulnerable, as well as raising the issue of personal information protection. In order to solve this problem, this paper proposes transparent integration of the health QR code systems and the process of protecting personal information, as well as designs an interface for user convenience. Through the method presented in this paper and the newly designed interface, users can enjoy the smart pharmaceutical system more conveniently and safely, and ensure effective results in the prevention and control of diseases that are likely to spread.