Reviewerly/exHarmony huggingface dataset:
Virtual Reality Berbasis Video 360O Sebagai Alternatif Penyampaian Informasi Pelayanan Rumah Sakit

The hospital is a health service institution that provides full range of personal health services providing inpatient, outpatient and emergency care services. Most hospitals, outpatient services are one of the most important services because the number of patients is more significant than inpatient services, requiring more attention in providing services and delivering information to patients. Submission of information on outpatient services, doctor's schedules, types of payments and several other services at the Hospital were developed to provide innovation, one of them using virtual reality technology as an alternative media so that the delivered message will be easily understood by patients and society. This research uses MDLC (Multimedia Development Life Cycle) development method. The results of this study is 360-based virtual reality video as an alternative media to deliver information that is expected to help the public in obtaining information about outpatient services in the Hospital.
Unmanned Vehicle Path Planning for Unknown Off-road Environments with Sparse Waypoints

Unmanned vehicles in urban structured environments have become the main battlefields of related companies all over the world. By relying on high-precision maps established in advance, unmanned vehicles can perform well enough. However, this method is difficult to apply to unknown off-road environment which cannot obtain high-precision maps in advance, such as transportation and target search of dangerous scenes in the wild. In this paper, we introduce a novel and efficient real-time path planning method for autonomous driving in unknown environments with sparse waypoints. The contributions of this paper are as follows. First, we present a goal point extraction algorithm to extract the goal configuration in free space for local path planning. It is extremely suitable for the sparse waypoints situation, in which local perception map may not contain any waypoints. Second, we present a novel path re-planning algorithm for large-scale environment without SLAM(Simultaneous Localization And Mapping) by building a topology map constantly. Our method not only overcame the failure of path planning due to the limited on-board sensing range, but also avoided high computing resource consumption brought by the large-scale construction maintenance of SLAM. A large number of challenging real off-road scenarios show that the proposed method can effectively solve the problem that unmanned vehicles cannot plan to travel due to sparse waypoints, and give real-time effective planning results when unmanned vehicles need to carry out large-scale path re-planning.
Watermarking algorithms for JPEG 2000 lossy compressed images

In the paper, we propose two watermarking algorithms for semi-fragile data hiding in JPEG 2000 lossy compressed images. Both algorithms are based on the concept of quantization index modulation. These methods have a property of semi-fragility to the image quality. It means that the hidden information is preserved after high-quality compression, and is destroyed in the case of significant degradation. Experimental investigations confirm this property for both algorithms. They also show that the introduced embedding distortions in terms of PSNR and PSNR-HVS are in almost linear dependence on the quantization parameter. It allows us to control the quality at an acceptable level when information embedding.
Prevention of Man-In-The-Middle Attack in Diffie-Hellman Key Exchange Algorithm using Proposed Hash Function

The key exchange process is a very important feature for many parts in cryptography. In data encryption, symmetric encryption techniques need to share the same secret key securely between two parties before encryption and this is a challenging task for secure data transmission. Most symmetric key encryptions and key management systems widely use Diffie-Hellman Key Exchange (DHKE) algorithm for the purpose of key distribution because it has simple computation and supports forward security. However, there is no key authentication and Man-In-The-Middle (MITM) attack has occurred during the key generation process. To overcome this problem, a new hash function is proposed to get the public key integrity during the public key sharing process of DHKE algorithm. This hash function is created by using six bitwise operators and operated in a variable length of the rounds depending on message length. Thus, the proposed system improves the security of DHKE and grantees the user authentication requirements.
Modified Vegenere Cipher to Enhance Data Security Using Monoalphabetic Cipher

The rapid progression of exchange data by public networks is important, especially in information security. We need to keep our information safe from attackers or intruders. Furthermore, information security becomes needed for us. Many kind cipher methods of cryptography are improved to secure information such as monoalphabetic cipher and polyalphabetic cipher. Cryptography makes readable messages becoming non-readable messages. One of the popular algorithms of a polyalphabetic cipher is Vigenere cipher. Vigenere cipher has been used for a long time, but this algorithm has weaknesses. The calculation of the encryption process is only involving additive cipher, it makes this algorithm vulnerability to attacker based on frequency analysis of the letter. The proposed method of this research is making Vigenere cipher more complex by combining monoalphabetic cipher and Vigenere cipher. One of the monoalphabetic ciphers is Affine cipher. Affine cipher has two steps in the encryption process that are an additive cipher and a multiplicative cipher. Our proposed method has been simulated with Matlab. We also tested the vulnerability of the result of encryption by Vigenere Analyzer and Analysis Monoalphabetic Substitution. It shows that our method overcomes the weakness of Vigenere Cipher. Vigenere cipher and Affine cipher are classical cryptography that has a simple algorithm of cryptography. By combining Vigenere cipher and Affine cipher will make a new method that more complex algorithm.
CNN-Based Refactoring of Hand-Designed Filters for Texture Analysis: A Classic Revisited

Filtering has been one of the main approaches to texture analysis since early on. Traditionally, the process involved designing the filters essentially by hand based on some prior knowledge (e.g. perceptual models, optimal mathematical properties, etc.) In this work we propose the use of convolutional networks for refactoring traditional, hand-designed filters. Our method consists of initialising the first convolutional layer of the network with some classic banks of filters, training the network on texture images and retrieve the modified filters. Experimenting with five classes of filters and eight datasets of texture images we show that the refactored filters can be conveniently used `off-the-shelf' to achieve better performance than obtained with the original filters, but at the same computational cost.
Based on QoS and Energy Efficiency Virtual Machines Consolidation Techniques in Cloud

Virtual machines (VMs) consolidation is one of the primary methods for improving the energy efficiency of cloud data centers. However, aggressive VMs consolidation methods may cause the hosts overload and produce massive inefficient VM migrations, so that host re-overload and even Quality of Service (QoS) degraded. Therefore, workloads are increasingly being deployed in virtualized cloud data centers to improve energy efficiency by using Virtual machines (VMs) consolidation. In this paper, we propose VMs selection algorithm and VMs placement algorithm for VMs consolidation to meet Quality of Service (QoS) for cloud applications while maximizing the energy efficiency and resource utilization of the data center. The VMs selection algorithm develops the relative CPU capacity gains to select the VMs which are selected to migrate from overloaded hosts. The VMs placement algorithm proposes an adaptive reservation CPU capacity for each physical host and allocates the selected VM to the hosts with idle CPU capacity. We experimented with real workloads, and results show that the algorithms can significantly reduce the energy consumption and improve QoS while decreasing the number of VM migrations.
Modern Optimization Methods for Science, Engineering and Technology

Achieving a better solution or improving the performance of existing system design is an ongoing a process for which scientists, engineers, mathematicians and researchers have been striving for many years. Ever increasingly practical and robust methods have been developed, and every new generation of computers with their increased power and speed allows for the development and wider application of new types of solutions. This book defines the fundamentals, background and theoretical concepts of optimization principles in a comprehensive manner along with their potential applications and implementation strategies. It encompasses linear programming, multivariable methods for risk assessment, nonlinear methods, ant colony optimization, particle swarm optimization, multi-criterion and topology optimization, learning classifier, case studies on six sigma, performance measures and evaluation, multi-objective optimization problems, machine learning approaches, genetic algorithms and quality of service optimizations. The book will be very useful for wide spectrum of target readers including students and researchers in academia and industry.
Query-By-Committee Framework Used for Semi-Automatic Sleep Stages Classification

Active learning is very useful for classification problems where it is hard or time-consuming to acquire classes of data in order to create a subset for training a classifier. The classification of over-night polysomnography records to sleep stages is an example of such application because an expert has to annotate a large number of segments of a record. Active learning methods enable us to iteratively select only the most informative instances for the manual classification so the total expert&rsquo;s effort is reduced. However, the process is able to be insufficiently initialised because of a large dimensionality of polysomnography (PSG) data, so the fast convergence of active learning is at risk. In order to prevent this threat, we have proposed a variant of the query-by-committee active learning scenario which take into account all features of data so it is not necessary to reduce a feature space, but the process is quickly initialised. The proposed method is compared to random sampling and margin uncertainty sampling which is another well-known active learning method. It was shown that, during crucial first iteration of the process, the provided variant of query-by-committee acquired the best results among other strategies in most cases.
Cloud Computing Vulnerabilities Analysis

Nowadays cloud computing technologies are the most widely used tools due to their great flexibility and also to their lower maintenance costs. Many vendors of cloud computing have appeared on the market for each type of cloud. These solutions still pose certain vulnerabilities and work to improve the security of cloud computing technologies. We analyze the main cloud computing solutions, analyze the vulnerabilities identified for these solutions, and also calculate the impact of these vulnerabilities based on the NVD scores. We average the scores for each solution for each cloud computing model. This way, we can see the impact of the vulnerabilities identified so far for each cloud computing model. Also, we analyze the number of identified vulnerabilities for during the 2007-2019. This analysis presents the period when the cloud computing solutions has a big interest to the users and to people who wants to hack these solutions.
A Structural Approach to EnhancingWordNet with Conceptual Frame Semantics

This paper outlines procedures for enhancing WordNet with conceptual information from FrameNet.The mapping of the two resources is non-trivial.We define a number of techniques for the validation of the consistency of the mapping and the extension of its coverage which make use of the structure of both resources and the systematic relations between synsets in WordNet and between frames in FrameNet, as well as between synsets and frames).We present a case study on causativity, a relation which provides enhancement complementary to the one using hierarchical relations, by means of linking in a systematic way large parts of the lexicon.We show how consistency checks and denser relations may be implemented on the basis of this relation.We, then, propose new frames based on causative-inchoative correspondences and in conclusion touch on the possibilities for defining new frames based on the types of specialisation that takes place from parent to child synset.
Development and Evaluation of Three Named Entity Recognition Systems for Serbian - The Case of Personal Names

In this paper we present a rule-and lexicon-based system for the recognition of Named Entities (NE) in Serbian newspaper texts that was used to prepare a gold standard annotated with personal names.It was further used to prepare training sets for four different levels of annotation, which were further used to train two Named Entity Recognition (NER) systems: Stanford and spaCy.All obtained models, together with a rule-and lexiconbased system were evaluated on two sample texts: a part of the gold standard and an independent newspaper text of approximately the same size.The results show that rule-and lexicon-based system outperforms trained models in all four scenarios (measured by F 1 ), while Stanford models have the highest recall.The produced models are incorporated into a Web platform NER&Beyond that provides various NE-related functions.
Emotional Concept Extraction Through Ontology-Enhanced Classification

Capturing emotions affecting human behavior in social media bears strategic importance in many decision-making fields, such as business and public policy, health care, and financial services, or just social events. This paper introduces an emotion-based classification model to analyze the human behavior in reaction to some event described by a tweet trend. From tweets analysis, the model extracts terms expressing emotions, and then, it builds a topological space of emotion-based concepts. These concepts enable the training of the multi-class SVM classifier to identify emotions expressed in the tweets. Classifier results are “softly” interpreted as a blending of several emotional nuances which thoroughly depicts people’s feeling. An ontology model captures the emotional concepts returned by classification, with respect to the tweet trends. The associated knowledge base provides human behavior analysis, in response to an event, by a tweet trend, by SPARQL queries.
Analytical And Critical Thinking Skills Of Cadets And Post-Graduate Students

Cognitive skills such as analytical and critical thinking are important components of professional culture of the military personnel. Clear thinking, reasoning, problem solving and the military decision-making process are a cornerstone of military education. Advanced skills in counterterrorist analysis, threat assessment and preparation of tactics, techniques and procedures - all of these skills may help military professionals bring peace and stability to different groups of people and function effectively during peacekeeping and humanitarian operations. Contemporary military education undergoes significant changes and presupposes developing creative personality of the future officer. Nevertheless, educating a creative thinker takes time and effort. The authors advocate the ideas of R. Paul, L. Elder, and D. Halpern on analytical and critical thinking and focus attention on the learners’ awareness of their cognitive skills. For this purpose a questionnaire, with descriptors of the levels of analytical and critical thinking skills taken from Reference Framework of Competences for Democratic Culture, was offered to cadets and post-graduate students of Ryazan Guard Higher Airborne Command School. The results of the study proved the hypothesis that post-graduate students have a higher level of analytical and critical thinking than the cadets of the 5th year of study. The collected data gave evidence of positive dynamics in the development of the studied skills. Naturally, in the course of study post graduate students have a specially designed curriculum, do their research and develop their cognitive skills and abilities to a higher level.
Evolving Mamdani Fuzzy Rules Using Swarm Algorithms for Accurate Data Classification

This paper proposes and develops a fuzzy evolutionary system based on the Grey Wolf Optimizer (GWO) algorithm to evolve Mamdani fuzzy rules that give a knowledge base for accurate classification of data set. GWO takes inspiration from nature and is modeled after the hunting behavior of the grey wolves as they move around within a pack taking cues from the leader alpha, beta, and delta wolves until they find the best position to encircle and attack the prey. The algorithm is mapped onto the data specific rule base structure of the fuzzy systems. A grammar template in the form of fuzzy rules is designed, and then the GWO algorithm is used to evolve the fuzzy rules which classify the datasets. The algorithm will generate meaningful rules that make sense of data in easy to comprehend fuzzy rules. The algorithm was extensively tested on 15 datasets. GWO was compared with the standard Particle Swarm Optimizer (PSO) algorithm in generating a similar type of rules and comparing the accuracy of these two sets of rules in data classification. It was noted that the GWO algorithm converges in a lesser number of iterations and in a shorter time as compared to PSO and achieves the best accuracy.
Forming Of Professional Culture Of Foreign Students Of Pre-Master Training Program

Article deals with pre-master training of foreign citizens in aspect of forming professional culture of engineering field researchers. Paradigm basis of pre-master training is polytechnic approach. This one focuses on solving innovative problems, breakthrough technologies, and interdisciplinary study. The educational process of foreigners’ pre-master training bases on theory of non-native language teaching synthesizing didactic and ethno-psychological components. Need for individualization follows from age characteristics of students at higher degree of social maturity, and dictates introduction of adults’ education approaches in andragogy manner. Therefore, pre-masters training programs teacher performs the role of mentor, possibly coach. Due to interdisciplinary approach teacher should be a kind of wide range of scientific and technological problems expert. Selection of educational program disciplines and their content is important. We give curriculum of two-semester program. Implementing discipline “Pre-exam training” teacher advises students on exam questions, and leads preparation and demonstration of presentations of main results of undergraduate studies prepared in foreign universities. Presentations are leading tool for professional culture formation and readiness for research work in Russian-speaking scientific community. Important component of graduates professional culture is professional Russian language. Language training aims on formation of skills and abilities necessary for graduation qualification work. We introduce individual component into learning process, and draw up professionally oriented text libraries.
Artificial intelligence, Digital Single Market and the proposal of a right to fair and reasonable inferences: a legal issue between ethics and techniques

The article discusses the ethical and technical consequences of Artificial intelligence (hereinafter, A.I) applications and their usage of the European Union data protection legal framework to enable citizens to defend themselves against them. This goal is under the larger European Union Digital Single Market policy, which has concerns about how this subject correlates with personal data protection. The article has four sections. The first one introduces the main issue by describing the importance of AI applications in the contemporary world scenario. The second one describes some fundamental concepts about AI. The third section has an analysis of the ongoing policies for AI in the European Union and the Council of Europe proposal about ethics applicable to AI in the judicial systems. The fourth section is the conclusion, which debates the current legal mechanisms for citizens protection against fully automated decisions, based on European Union Law and in particular the General Data Protection Regulation. The conclusion will be that European Union Law is still under construction when it comes to providing effective protection to its citizens against automated inferences that are unfair or unreasonable.
Peningkatan Akuntabilitas Publik Melalui Sistem Informasi Akuntansi pada Pondok Pesantren Salafiyah

This study aims to determine the cash out transactions that occur in the Sabilurrosyad Islamic boarding school, analyze the internal control system, then compile the AIS cash outflow chart for the Sabilurrosyad boarding school so that it can increase public accountability. The research method is descriptive qualitative method using case studies. The results of the study indicate that internal control is still not good as evidenced by the existence of multiple tasks. Understanding of risk is still not understood by the leaders of Islamic boarding schools, information and communication is unclear. Monitoring has been carried out with the existence of monthly and annual financial reports, but it has not been compiled according to the accounting standards that should be. The flow chart has not been created so that the results of this study are in the form of a cash flow chart AIS can be used by the Sabilurrosyad boarding school.
A Survey on Sentiment Analysis Algorithms and Datasets

In this paper we present a deep literature review on existing system for sentimental analysis. Basically sentimental analysis (SA) is the measurement of preference of people’s thoughts via natural language processing. The main aim of sentiment analysis is to know the orientation of the sentiment described in script. In recent decades the researcher focuses on the study various algorithms for relevant research results of the sentiment analysis. This research paper provides a comprehensive overview of this field's latest update. In this review, some recent proposed improvements of algorithms and various SA applications are explored and briefly described. The aim of this paper is to provide knowledge about the different method related to sentimental analysis also how they are classified, what the applications of this analysis.