Reviewerly/exHarmony huggingface dataset:
Preface of ICNISC2020

6th Annual International Conference on Network and Information Systems for Computers [ICNISC2020] has been held on August 14–15, 2020 (Virtual Conference) Due to the COVID-19 situation and travel restrictions via Zoom event platform. Every authors and Committees can be received a Zoom meeting username and password from the organizer who located on Wuhan, China. Every person can attend the conference using these information. The time spent to deliver the talks by each keynote speaker is 30 minutes, 25 min presentation and 5 min question time. The time spent to deliver the talks by each oral speaker is 15 minutes, 10 min presentation and 5 min question time. Every authors can discuss with others about their session after the speech. It was held very successfully. ICNISC2020 aims to bring researchers, engineers, and students to the areas of computer networks and information system. The Technical Program Committee worked very hard to have all manuscripts reviewed before the review deadline. All the accepted papers have been submitted to strict peer-review by 2–3 referees, and selected based on originality, significance and clarity for the purpose of the conference. The conference program is extremely rich, profound and featuring high-impact presentations of selected papers and additional late-breaking contributions. We sincerely hope that the conference would not only show the participants a broad overview of the latest research results on related fields, but also provide them with a significant platform for academic connection and exchange. The keynote speakers are internationally recognized leading experts in their research fields, who have demonstrated outstanding proficiency and have achieved distinction in their profession. The proceedings would be published by IOP Conference Series: Journal of Physics: Conference Series (JPCS) We would like to express our sincere gratitude to all the members of Technical Program Committee and organizers for their enthusiasm, time, and expertise. Our deep thanks also go to many volunteers and staffs for the long hours and hard work they have generously given to ICNISC2020. The last but not least, we would like to thank all the authors, speaker and participants for their great contributions to the success of ICNISC2020. ICNISC2020 Organizing Committee
AidIR: An Interactive Dialog System to Aid Disease Information Retrieval

This paper proposes an interactive dialog system, called AidIR, to aid information retrieval. AidIR allows users to retrieve information on diseases resulting from coronaviruses and diseases transmitted by vector mosquitoes with natural language interaction and Line chat media. In a subjective evaluation, we asked 20 users to rate the intuitiveness, usability, and user experience of AidIR with a range between −2 and 2. Moreover, we also asked these users to answer yes–no questions to evaluate AidIR and provide feedback. The average scores of intuitiveness, usability, and user experience are 0.8, 0.8, and 1.05, respectively. The yes–no questions demonstrated that AidIR is better than systems using the graphical user interface in mobile phones and single-turn dialog systems. According to user feedback, AidIR is more convenient for information retrieval. Moreover, we designed a new loss function to jointly train a BERT model for domain classification and sequence label tasks. The accuracy of both tasks is 92%. Finally, we trained the dialog policy network with supervised learning tasks and deployed the reinforcement learning algorithm to allow AidIR to continue learning the dialog policy.
A Smart Wearable Device for Tracking and Fall Detection for Geriatric People, Alzheimer Patients and Children with Autism

Human tracking and fall detection is one of the major challenges while dealing with geriatric people, Alzheimer patients, and autistic people. Due to muscle and bone weakening geriatric people are prone to fall and this is the major reason for non-fatal injury in their old age. Alzheimer is progressive disease that reduces brain functions like thinking reasoning and most exhibit odd behaviors like wandering. Autism effects the nervous system, emotional and physical health, of the affected child. The symptoms of autism lie in a wide range but wandering is one among them. These three health issues require a constant monitoring of the affected person. The advancement of technology helps in the development of combined solution for all above mentioned health issues. Somnambulism otherwise known as sleep walking is a kind of behavioral disorder seen in autistic children and geriatric people. It originates during sleep and it is seen in the form of wandering or any other complex behaviors while the person is asleep. Person suffering from such a disorder finds it difficult to remember this incident when awake. This paper outlines the implementation of a common smart wearable device for tracking and fall detection based on GPS and GSM technology for geriatric people, Alzheimer patients, those suffering from somnambulism and children with autism.
Implementation of behavioristic theory in online learning of Indonesian lessons in class IV of elementary school

The purpose of this study is to determine changes in behavior (behavioristic) in Indonesian language learning in reading, writing, and listening skills in grade IV at SDN Karang Entang Bangkalan. This research uses qualitative research methods conducted online. The type of data in this research is quantitative and descriptive qualitative obtained from the sample group data, then divided by the number of these samples. Based on the results of the research conducted, the percentage results of listening skills were 72% in very good criteria. In the reading skills material, it is 95% in very good criteria. In the material of writing skills, it is 81% in very good criteria. From these results, it is stated that learning Indonesian on the material of listening, reading and writing skills using behavioristic theory, the teacher is able to change and shape student behavior, including increasing and increasing knowledge, increasing attitudes, and increasing skills and making learning more effective and meaningful.
Fraud Analysis of Financial Statements in the Perspective of Fraud Triangle

This research aims to look at falsified financial statements via the lens of the deep triangle (empirical studies on property and real estate companies listed on the Indonesian stock exchange in 2016-2018).Earnings management is used in this study to commit financial statement fraud.Financial Statement Fraud, which earnings management proxies, is the study's dependent variable.OSHIP is a proxy for personal financial needs; ROA is a proxy for financial objectives, and IND is a proxy for successful monitoring.The study's dependent variables are ACHANGE represents financial stability pressure, whereas FREEC represents external pressure.The purpose sample technique was utilized to choose the research sample, which consisted of 82 professional and real estate businesses from 2016 to 2018.
(1+ϑ)-constacyclic codes over Z8 +ϑZ8

In this paper, (1+ϑ)-constacyclic codes of arbitrary length m over a non-chain finite local frobenious ring Z8 + ϑZ8 are introduced. A new Gray map is constructed from Z8 + ϑZ8 to Z88 and proved that the Z8 - Gray image of (1+ϑ)-constacyclic codes having prescribed length m over the ring Z8 +ϑZ8 is a cyclic code of length 8m over the ring Z8. Moreover, it has been obtained that the binary image of the (1+ϑ)-constacyclic code of length m over Z8 +ϑZ8 is a distance invariant binary quasi-cyclic code of length 32m with index 16.
Supplemental Material for A cross-cultural study of the influence of environmental factors on VR gamers experience of spatial presence, enjoyment, and subjective workload: A preliminary study.

This study is a first step toward identifying some factors influencing spatial presence, enjoyment, and cognitive workload in virtual reality (VR) game play.The study was conducted in the Northeastern U.S. (n = 40) and Southwest Nigeria (n = 40) using a factorial experimental design with three environmental factors (i.e., lighting, flooring, and in-game sound) at two levels per factor.Participants were randomly assigned to one of eight experimental conditions, playing a VR American football game using Oculus Rift S on 3 separate days, under a different condition each time.Following VR game play, measures of spatial presence (α = .86),enjoyment (α = .89),and subjective workload (α = .78)were collected through self-report surveys.A factorial analysis of variance (ANOVA) was used to examine the influence of environmental factors on spatial presence, enjoyment, and subjective workload.The study found main effects of (a) lighting on subjective workload for the Nigerian sample (Day 2 and Day 3), (b) flooring on subjective workload for both cohorts (Day 2), and (c) in-game sound on spatial presence and subjective workload for the Nigerian sample (Day 3) and on spatial presence for the U.S. sample (Day 2 and Day 3).An interaction effect of lighting and flooring on subjective workload was found for the Nigerian sample on Day 1 and Day 2. An interaction effect of lighting, flooring, and in-game sound on enjoyment was found for the Nigerian sample on Day 3. We present implications for theory and practice.
Predicting the Intended Action using Internal Simulation of Perception

This article proposes an architecture, which allows the prediction of intention by internally simulating perceptual states represented by action pattern vectors.To this end, associative self-organising neural networks (A-SOM) is utilised to build a hierarchical cognitive archi-tecture for recognition and simulation of the skeleton based human actions.The abilities of the proposed architecture in recognising and predicting actions is evaluated in experiments using three different datasets of 3D actions.Based on the experiments of this article, apply-ing internally simulated perceptual states represented by action pattern vectors improves the performance of the recognition task in all experiments.Furthermore, internal simulation of perception addresses the problem of having limited access to the sensory input, and also the future prediction of the consecutive perceptual sequences.The performance of the system is compared and discussed with similar architecture using self-organizing neural networks (SOM).
AN EFFICIENT APPROACH FOR MALARIAL EPIDEMIC PROGNOSIS USING MACHINE LEARNING CLASSIFIERS

Malaria is an acute infectious disease, which affects nearly two-thirds of the global population.Annually, the deaths due to malaria have crossed millions, the countries with fewer medical facilities are the ones which are most affected, thus the prediction of its outbreak at early stages will reduce the intense diminishing of human lives.This systematic investigation deals with the analysis and prediction of the malarial epidemic outbreak by investigating several factors such as climate, global warming, human activities, mosquitoes, sewage, etc., with malarial incidence.The first stage includes data collection by the passive surveillance system; the second stage includes establishing relationships among the climatic factors with malarial incidence and finally predicting using machine learning classifiers.In the analysis, the adaptive boosted J48 (AB-J48) decision tree machine learning classifier outperformed other classifiers under the study with an accuracy of 95% in establishing a relationship among climatic factors with malarial incidence.The inferred results from the investigation are found to be stupendous which helps the public health authorities and medical practitioners to take precautionary steps to avoid more deaths.
Web-Based Library Service Information System Design Using Electronic Customer Relationship Management (E-CRM) At SMK YUPPENTEK 1 Tangerang

The utilization of information and communication technology in education is a means of informing quality education. One area that feels the impact of technology in the library. SMK Yuppentek 1 Tangerang currently has a library, but it is not computerized with an information system. In other words, all data is still written in a ledger to recapitulate the transactions that occur so that the data is less accurate in the reporting process. The collection of books that is continually increasing in the library is not followed by the readiness of the officers to update the data so that it becomes difficult for visitors to get the latest information. This study aims to create a website-based library information system at SMK Yuppentek 1 Tangerang city by using Electronic Customer Relationship Management (E-CRM) as a service improvement strategy that is oriented to the needs of members so that it becomes a solution for processing library information data to be practical, accurate and effective. Easy and excellent service can be obtained by visitors well. The methodology of this research analysis is the method of data collection and analysis of Performance, Information, Economic, Control, Efficiency, Service (PIECES), which will present a discussion of the theoretical basis, stages of analyzing and modeling processing systems using Unified Modeling Language (UML), black-box testing that will be used as a testing method.
Fast Implementation of Multiplication on Polynomial Rings

Multiplication on polynomial rings has been widely used in public-key cryptographic schemes based on ideal lattices. It is an important module that significantly affects the efficiency of the schemes. Improved Preprocess-then-NTT (IPtNTT) is an algorithm which can fast realize multiplication on polynomial rings. Compared with the Number Theoretic Transform (NTT), the IPtNTT weakens the parameter restriction of lattice-based public-key cryptographic schemes. By optimizing the IPtNTT with the AVX2 instruction set, we reduce the clock cycles consumed by multiplication on polynomial rings to 15%–22%. According to the experimental results, we give specific suggestions on using AVX2 optimized IPtNTT to realize multiplication on polynomial rings with different parameters chosen in lattice-based public-key cryptosystems.
Human pose estimation based on manifold Gaussian process with depth images

At present, human pose estimation with depth images faces some challenges. Methods based on deep learning perform well but rely on massive amounts of data, while traditional machine learning methods are simple to implement but depend on feature extraction and have low accuracy. To deal with them, this paper proposes a novel method based on the Manifold Gaussian Process, which combines tomographic image denoising and feature fusion to solve human pose estimation with depth images. The experimental prediction accuracy on ITOP datasets outperforms other machine learning methods, achieving 83.3% and 77.9% for full body from the front view and top view respectively, which proves the effectiveness of Manifold Gaussian Process on human pose estimation with depth images.
CLASSIFICATION ON PARKINSON DISEASE USING TRANSFER LEARN

These days, a basic appraisal effort in clinical affiliations biometrics is finding careful biomarkers that permit settling on clinical choice assistance instruments. Parkinson ’s disease (PD) is a chronic and progressive illness that affects hundreds of thousands of people worldwide. Although it is quite easy to identify someone affected by PD when the illness shows itself (e.g. tremors, slowness of movement and freezing-of-gait).Most works have focused on studying the working mechanism and detection of the disease in its very early stages. In such cases, drugs can be administered in order to increase the quality of life of the patients. Since the beginning, it is well-known that PD patients feature the micrography, which is related to muscle rigidity and tremors. As such, most of the approaches to detect Parkinson’s Disease make use of handwritten assessment tools, where the individual is asked to perform some predefined tasks, such as drawing spirals and meanders on a template paper. Later, an expert analyses the drawings in order to classify the progressive of the disease. In this work, we are interested into aiding physicians in such task by means of machine learning techniques, which can learn proper information from digitized versions of the exams, and them recommending a probability of a given individual being affected by PD depending on its handwritten skills. Particularly, we are interested in deep learning techniques (i.e. Convolutional Neural Networks) due to their ability into learning features without human interaction.
Electroweak Symmetry Breaking

The Standard Model of particle physics is an amazingly successful theory describing the fundamental particles and forces of nature. This text, written for a two-semester graduate course on the Standard Model, develops a practical understanding of the theoretical concepts it's built upon, to prepare students to enter research. The author takes a historical approach to demonstrate to students the process of discovery which is often overlooked in other textbooks, presenting quantum field theory and symmetries as the necessary tools for describing and understanding the Standard Model. He develops these tools using a basic understanding of quantum mechanics and classical field theory, such as Maxwell's electrodynamics, before discussing the important role that Noether's theorem and conserved charges play in the theory. Worked examples feature throughout the text, while homework exercises are included for the first five parts, with solutions available online for instructors. Inspired by the author's own teaching experience, suggestions for independent research topics have been provided for the second-half of the course, which students can then present to the rest of the class.
Statistical complexity of the kicked top model considering chaos

The concept of statistical complexity is studied to characterize the classical kicked top model which plays important role in the qbit systems and the chaotic properties of the entanglement. This allows us to understand this driven dynamical system by the probability distribution in phase space to make distinctions among the regular, random and structural complexity on finite simulation. We present the dependence of the kicked top and kicked rotor model through the strength excitation in the framework of statistical complexity.
Personalization Improves Privacy-Accuracy Tradeoffs in Federated  Optimization

Large-scale machine learning systems often involve data distributed across a collection of users. Federated optimization algorithms leverage this structure by communicating model updates to a central server, rather than entire datasets. In this paper, we study stochastic optimization algorithms for a personalized federated learning setting involving local and global models subject to user-level (joint) differential privacy. While learning a private global model induces a cost of privacy, local learning is perfectly private. We show that coordinating local learning with private centralized learning yields a generically useful and improved tradeoff between accuracy and privacy. We illustrate our theoretical results with experiments on synthetic and real-world datasets.
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning

We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning. SYMBOL maintains an adaptive stack of Thompson Sampling bandits, whose size is bounded by the planning horizon and can be automatically adapted according to the underlying domain without any prior domain knowledge beyond a generative model. We empirically test SYMBOL in four large POMDP benchmark problems to demonstrate its effectiveness and robustness w.r.t. the choice of hyperparameters and evaluate its adaptive memory consumption. We also compare its performance with other open-loop planning algorithms and POMCP.
Building a New Tourism Sentiment Lexicon Containing Descriptive Words in Modern Standard and Colloquial Arabic

In tourism industry, sentiment analysis is emerging as a technology that can be used to assess the sentiments of the tourists based on their responses on different social media sites or platforms.Sentiment analysis is an important and helpful technique for decision makers to evaluate services and identify problems and deficiencies.Too many studies have been done on this field in other languages, but in Arabic the number of studies is limited.In addition, such studies on Arabic examine each dialect of Arabic separately and no single study includes sentiment analysis examination of a group or some varieties of Arabic dialects along with Modern Standard Arabic (MSA).The previous studies also do not address the different Arabic dialects, therefore the researches here think there should be a study that includes sentiment analysis of a number of Arabic dialects along with Modern Standard Arabic, the current research paper is an example, as most Arabs (Arab people) express their opinions in their dialects of Arabic and few use Modern Standard Arabic.The main goal of this research paper is to build a new sentiment analysis lexicon based on the opinions of Arab tourists visiting India.This lexicon includes lexemes (words or vocabularies) taken of three Arabic dialects (namely: Gulf, Levantine and Egyptian dialects of Arabic) along with Modern Standard Arabic.The lexicon will be also evaluated by comparing it to the existing one, namely SemEval2016, using a machine learning technique called Support Vector Classifier for obtaining better results.Thus, building a new dictionary will be effective in sentiment analysis in modern Arabic and most Arabic dialects.
Expert System for Staging Breast Cancer

The task of staging breast cancer is very essential in the management of the disease.This is because the stage of the disease determines the most appropriate treatment to be given to patients, and this consequently determines the rate of mortality due to the menace.Despite its importance, staging of breast cancer is cumbersome and requires experiential knowledge for it to be adequately done.New and inexperienced doctors therefore depend on consultant oncologists to carry out this task.This study therefore adopted a traditional rule-based approach to develop an expert system than could serve as a decision support system for inexperienced doctors in staging breast cancer.To achieve this aim, knowledge of the symptoms and the criteria for staging were elicited from the Association of American Cancer Society.The elicited knowledge was represented procedurally using IF-THEN rule.The Breast Cancer Staging System was later implemented using JAVA programming language, The developed system was evaluated for accuracy, reliability and usability using Mean Opinion Score; and the scores gotten were 4.12, 4.12 and 4.08 respectively.This study has therefore developed a computational tool that could reduce the bottlenecks in staging breast cancer and subsequently reduce mortality rate due to the disease.
Hybrid Cryptosystem using Elliptic Curve Cryptography and Caesar Cipher

Security is a global issue, and it is one that requires global attention.Security has many facets.The main aspect of the security problem is privacy.Data privacy and security is not a simple job.So, security is possible with suitable security strategies.Security in addition also involves access control, data veracity, system accessibility, and auditing.In fact, 80% of data thrashing is caused by hackers.Encryption is only one approach to securing data.In this work Caesar cipher combined with Elliptic curve cryptography and product cipher system is developed as one of the solutions for security related problems that global environment is facing.This developed product cipher is quite simple with pattern formula.This combination helps in developing strong system.The role of Elliptic curve is very good in developing applications for protection and digital signatures.It also helps in generating pseudo-random numbers and helps in fast encryption and decryption.