Reviewerly/exHarmony huggingface dataset:
Generalized Representation Learning Methods for Deep Reinforcement Learning

Deep reinforcement learning (DRL) increases the successful applications of reinforcement learning (RL) techniques but also brings challenges such as low sample efficiency. In this work, I propose generalized representation learning methods to obtain compact state space suitable for RL from a raw observation state. I expect my new methods will increase sample efficiency of RL by understandable representations of state and therefore improve the performance of RL.
Secure IoT Systems Monitor Framework using Probabilistic Image Encryption

In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest.Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets.Many systems include detection, storage of video information, and human-computer interfaces.Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications.The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
MUSICHUB: A Web and Android Based Rehearsal Studio Locator and Reservation System in Davao City Utilizing Geolocation API and Rabin-Karp Algorithm

This study aims to provide a search platform for band rehearsal studios where interested customers can locate and reserve the nearest studio.In the proposed system, the band rehearsal studio owners can advertise their studios along with the other services they offer.Through this, the search and reservation process is made more accessible.Android Studio, Java Programming Language, Google Geolocation API, Google Maps Android API, Google Directions API, Photoshop CS6, and PHP, among others, were utilized in the development of the system.Further, the Rabin Karp Algorithm is instrumental in generating the category and list of rehearsal studios.Furthermore, the Agile methodology was observed throughout the development of the system.
Tri Hita Karana dalam Teks Agastya Parwa

&lt;em&gt;Agastya Parwa is an Old Javanese manuscript in the form of prose and is of relatively old age (Century IX-XI M). The Agastya Parwa text is closely related to the name Ṛṣi Agastya as a Hindu Mahaṛṣi who contributed to the spread of Hinduism from the Sindhu river valley throughout India, Central Asia, China, Japan, and Indonesia. According to the literature, the Agastya Parwa text belongs to the parwa group, the Agastya Parwa text is also used as a source for yajña, this means this text belongs to the ceremonial group, but there are also those who declare this text included in the ethics group because the text This contains teachings about ethics, but the text of Agastya Parwa also contains the teachings of God. Tri Hita Karana which means three causes of happiness, consists of parhyangan, pawongan, and palemahan. Parhyangan in this text is taught that devotion to God must be based on a sincere, unconditional sincerity that can cause good for someone in this life and in the life to come. The form of pawongan in the Agastya Parwa text is self-control, this is very important for someone, people who are able to control themselves will get enlightenment. Palemahan explains that animals and plants are human siblings because animals and plants are descended from the ṛṣi.&lt;/em&gt;
Determining Inference Semantics for Disjunctive Logic Programs (Extended Abstract)

[Gelfond and Lifschitz, 1991] introduced simple disjunctive logic programs and defined the answer set semantics called GL-semantics. We observed that the requirement of GL-semantics, i.e., an answer set should be a minimal model of the GL-reduct may be too strong and exclude some answer sets that would be reasonably acceptable. To address this, we present a novel and more permissive semantics, called determining inference semantics.
The Technology for Semantic Interoperability based on a Cognitive Approach

The article presents the results of research conducted to select the means of software implementation (construction) of an ontology used to ensure semantic interoperability of information systems (IS).Previously, the authors of the article have identified several specific features of this type of ontology.Based on the analysis of structures and methods for constructing ontologies, it has been concluded that it is advisable to include concepts that allow describing both the static condition and dynamic changes in the state of objects in the data domain, into the ontology used to ensure semantic interoperability of the IS.The authors have identified the main types of concepts used and have determined a set of mandatory attributes (properties) that characterize each of the objects and possible types of relationships with other concepts.The present article analyzes the possibilities of using existing ontology description languages, metadata representation models, system design languages, algorithms, and software implementations of ontology editors, as well as the possibilities of using relational and graph database management system (DBMS) to build ontologies used to ensure semantic interoperability.The article substantiates the choice of a tool for software implementation (construction) of an ontology used to ensure semantic interoperability of the IS.Using the selected software tools, a fragment of ontology is constructed, which can be applied to ensure semantic interoperability of the IS.In conclusion, the authors present a unified algorithm for constructing ontology to ensure semantic interoperability.The practical significance of the obtained scientific results lies in the development of approaches to the creation of the IS.The results obtained will allow creating IS that would implement the cognitive functions of understanding the content of requests when processing them in the means of the interaction of the IS.The research results in the field of semantic interoperability will allow improving the intelligent IS performance quality that will enable IS to solve a wider range of intelligent tasks.
BLENDED LEARNING TECHNOLOGY IN MODERN PARADIGM OF EDUCATION

The impact of digital technologies on the society, economy and education increases every day. Children are using digital gadgets from preschool age, and the younger generation who cannot imagine their lives without the Internet starts school. The virtual technologies penetrating modern school, teachers must be prepared not only for the awareness of the fact, but to be ready to use them. Before higher education there is the task of training specialists who can use a computer for the organization of interactive training and testing, video conferencing, participation in telecommunication projects, webinars, forums, etc. Although the popularity of blended learning increases in modern educational institutions, not all teachers understand the essence of this concept. The conducted analysis of the publications of foreign scientists showed that blended learning being a new approach in the organization of distance education began to be introduced at the end of the twentieth century in order to optimize the retraining of employees of enterprises. The development of computer technologies with which pupils and students can freely use the Internet services has contributed to the implementation of blended learning for the organization of educational activities of general and higher education institutions. A generalized analysis of works by domestic scholars has shown that the notion of “blended learning” relates to such concepts as “learning process”, “learning system” “educational technology”, “educational methodology”, “combination of methods”, “modernization tool”, etc. On the basis of the analysis of literature on the concept of “blended learning” by foreign and domestic researchers, the author presents her own definition, showing the main components of blended learning and its benefits in organizing educational activities in institutions of general secondary education. A scheme of combining traditional and e-learning in a mixed one is suggested. Digital technologies have a significant impact on the nature and the character of training and students’ activities. It allows the implementation of fundamentally new forms and methods of learning, and consequently, increases the efficiency of educational process in a digital society. It is noted that modern teacher has to develop independent educational content in text, video, multimedia and hypermedia format, to use digital and educational resources, to set up educational content management systems and to organize training with the use of the Internet and its services. A computer science teacher at school should be a specialist who can effectively use the new technologies and teach others.
The Professional Profile of a Post-editor according to LSCs and Linguists: a Survey-Based Research

The boundaries between translation technologies are fading and language professionals are heading towards a pluri- and transdisciplinary job description, for which the use of CAT tools, translation management systems, and machine translation (MT) are compulsory. “Language paraprofessionals”, “paralinguists”, “language consultants”, “digital linguists”, and a long list of other titles is emerging to refer to the professionals who master a number of features of several tools, while remaining attentive to linguistics (see Bond 2018). According to TAUS DQF Dashboard data presented in TAUS Newsletter the 1st of May of 2019, the industry averages show that 9.7% of the translation output origin comes from MT and that 1,057 words per hour are post-edited on average. This has clear repercussions on the profession from the employability perspective.With 66 submissions by LSCs and industry stakeholders, and 142 answers from individuals (in-house or freelance translators), we present the most salient subject matters from and for the translation industry regarding MT post-editing. Some represent gaps to be filled; others represent common ground already found. Thanks to this up-to-date knowledge of the globalization landscape, clear goals can be set, and the way is paved for evolution.
OptStream: Releasing Time Series Privately (Extended Abstract)

Many applications of machine learning and optimization operate on sensitive data streams, posing significant privacy risks for individuals whose data appear in the stream. Motivated by an application in energy systems, this paper presents OptStream, a novel algorithm for releasing differentially private data streams under the w-event model of privacy. The procedure ensures privacy while guaranteeing bounded error on the released data stream. OptStream is evaluated on a test case involving the release of a real data stream from the largest European transmission operator. Experimental results show that OptStream may not only improve the accuracy of state-of-the-art methods by at least one order of magnitude but also support accurate load forecasting on the privacy-preserving data.
Automated Knowledge Management through E-Testing

With the increase in the pace of technological progress in modern professional education, the organization of automated control of educational achievements becomes popular.The purpose of the work is to analyze computer testing software.The article presents software systems designed to develop test tasks and conduct control and assessment work using computer technology.The process of design tests taking into account the instrumental capabilities of electronic systems is disclosed, comparative characteristics of the main functions of the programs under consideration are presented.Software packages, including My TestX Pro, IREN, SunRav Test OfficePro and others provide a wide range of electronic tools for creating tests that meet modern educational needs.They allow you to organize both local and network tests of different levels of complexity.Tests include several types of questions, including music files, images, and videos.
Virtual CPU Scaling for Efficient Server Power Consumption in Cloud Data Center

Cloud computing is a fast-growing technology used by major corporations these days because of the flexibility framework it provides to consumers. Cloud technology requires large data centers consisting of multiple IT equipment and servers. One main problem with these data centers is the vast amount of power consumed during servers operation. This reduces financial benefit and increases the need to produce more energy to cover the needs of operating the cloud infrastructure. This paper proposes an approach for managing the virtual central processing unit (vCPU) of a virtual machine to improve server power efficiency. A framework is used to study the proposed approach while processing different types of workloads widely found in most general-purpose cloud computing applications. Results indicate an improvement in server power saving.
Bridging Causality and Learning: How Do They Benefit from Each Other?

Modern machine learning techniques can discover complicated statistical dependencies between ran- dom variables, usually in the form a statistical model, and make use of these dependencies to per- form predictions on future observations. How- ever, many real problems involve causal inference, which aims to infer how the data generating sys- tem should behave under changing conditions. To perform causal inference, we need not only statisti- cal dependencies but also causal structures to deter- mine the system’s behavior under external interven- tions. In this paper, I will be focusing on two essen- tial problems that bridge causality and learning and investigate how they can benefit from each other. On the one hand, since conducting randomized controlled experiments for causal structure discov- ery is often expensive or infeasible, it would be valuable to investigate how we can explore modern machine learning algorithms to search for causal structures from observational data. On the other hand, since causal structure provides information about the distribution changing properties, it can be used as a fundamental tool to tackle a major chal- lenge for machine learning: the capability of gener- alization to new distributions and prediction in non- stationary environment.
Lossless Semantic Round-Tripping in PENG ASP

The PENG ASP system supports the writing of textual specifications with the help of a smart text editor that possesses knowledge about the structure of the specification language. Specifications written in PENG ASP are incrementally translated into executable answer set programs and vice versa. That means the system allows for lossless semantic round-tripping between a human-readable specification and an answer set program. This functionality is achieved by a single bi-directional logic grammar that serves at the same time as a text processor and a text generator. We demonstrate that the PENG ASP system can be used to bridge the gap between a (seemingly) informal specification and an executable answer set program.
A Tutorial on Single-solution Simulated Kalman Filter

&#x0D; &#x0D; &#x0D; &#x0D; Simulated Kalman Filter (SKF) is an estimation-based optimization algorithm which is established based on the Kalman filtering framework. A variant of SKF which operates using one agent is called single-solution simulated Kalman filter (ssSKF). At present, there is no tutorial been published on ssSKF. One may find that the equations and flowchart of the algorithm is not easy to understand. Hence, this paper provides a tutorial on ssSKF algorithm that emphasizes on a numerical example for easy and intuitive explanations. This tutorial would be important to those who work on the fundamentals and applications of ssSKF as well as to students who are new to optimization research.&#x0D; &#x0D; &#x0D; &#x0D;
Improvement in Convolutional Neural Network for CIFAR-10 Dataset Image Classification

Image classification requires the generation of features capable of detecting image patterns informative of group identity.The objective of this study was to classify images from the public CIFAR10 image dataset by leveraging combinations of disparate image feature sources from deep learning approaches.The majority of regular convolutional neural networks (CNN) are based on the same structure: modification of convolution and the process of max-pooling layers connected with a number of entirely linked layers.In this paper, the prime objective is to improve the effectiveness of simple convolutional neural network models.The Artificial Neural Network (ANN) algorithm is applied on a Canadian Institute For Advanced Research dataset (CIFAR-10) using two different CNN structures.The result of the improved model achieves 88% classification accuracy rate by running for 10 hours.The deep learning models are implemented with the use of Keras library available for Python programming language.
Extraction with Map-Reduce Framework and Correlation-based Feature Selection in Lung Cancer Towards Big Data

Background/objectives: To extract nucleus and cytoplasm that intend to optimize features in high-dimensional images such as all types of raw sputum cells.To calculate following features efficiently: Area, Perimeter, Intensity, NC Ratio, and Circularity.Methods/Statistical analysis: To take results in proposed stride, we introduced map-reduce framework for separating similar cells from sputum cell images that have been collected from Microscope lab images with intended magnification and staining.To avoid model learn from irrelevant features, feature selection methods with correlation-based feature selection contributes appropriate features that are then fed for classification.Features here converted to vectors for the estimation of symmetric uncertainty, correlationbased approach.Findings: Performance evaluation metrics checks into the contribution to measure it's out coming performance.Even though lot of works relied on feature extraction, our work combines feature extraction with map-reduce framework which improves accuracy for classification.Our proposed method makes extraction of nucleus and cytoplasm easier than other methods.Optimized performance assured in proposed feature selection.Novelty/ applications: Eventual accuracy for every feature in proposed stride improves than other existing works.In addition, ROC curves proves higher true positive rate even in increased datasets.Another significant innovation in our work is map-reduce framework applies in images to sort cells with respect to staining.
Offering Informal Education in Public Libraries through Exhibit Design

The American Anthropological Association's forthcoming traveling exhibit on the subject of migration and mobility is designed to be hosted by public libraries.By recruiting libraries as host institutions, we make scholarship accessible to general audiences and provide a focal point for programming and community engagement.This essay outlines our approach to designing with libraries in mind, aiming to influence public discourse about a topical issue.
Chapter 7 Scalable Knowledge Graph Processing Using SANSA

The size and number of knowledge graphs have increased tremendously in recent years. In the meantime, the distributed data processing technologies have also advanced to deal with big data and large scale knowledge graphs. This chapter introduces Scalable Semantic Analytics Stack (SANSA), that addresses the challenge of dealing with large scale RDF data and provides a unified framework for applications like link prediction, knowledge base completion, querying, and reasoning. We discuss the motivation, background and the architecture of SANSA. SANSA is built using general-purpose processing engines Apache Spark and Apache Flink. After reading this chapter, the reader should have an understanding of the different layers and corresponding APIs available to handle Knowledge Graphs at scale using SANSA.
The need for Internet of Things digital forensic black‐boxes

Abstract In our interconnected cyber‐physical world, the types and number of Internet of Things (IoT) will also increase. Such devices are also generally capable of capturing a broad range of information, including digital artifacts that can facilitate a digital investigation during a cyber security incident (e.g., data breach). In other words, IoT devices are potential evidence acquisition sources. We posit the importance of having a digital forensic black‐box, conceptually similar to the cockpit voice recorder (also known as a flight recorder) on aircrafts, to facilitate digital investigations. Using a smart home comprising many different IoT devices (e.g., smart home devices, smart vehicles, and smart wearables) as an example, we discuss where such a black‐box can reside and what sort of artifacts can be collected. This black‐box can also complement other existing digital forensic readiness strategies, such as those described in ISO/IEC 27043:2015. We also explore the associated design requirements such as data provenance. There are changes required to the organization's current computing architecture in order to deploy our proposed black‐box, as explained in this paper. In addition, we will explore the potential privacy implications and potential research opportunities (e.g., blockchain‐based digital forensic black‐box). This article is categorized under: Digital and Multimedia Science &gt; Cloud Forensics Digital and Multimedia Science &gt; IoT Forensics
PENERAPAN MODEL PEMBELAJARAN PAILKEM UNTUK MENINGKATKAN KEMAMPUAN MENULIS TEKS EKSPLANASI

The study aimed to know (1) student’s achievements after applying PAILKEM is learning model in the learning of writing explanation text, (2) the steps of implementing the PAILKEM learning model to write the explanation text, and (3) student response after implementing PAILKEM learning model in writing the explanation text in class XI MM students SMK PGRI 3 Badung. Classroom action research these two cycles consist of five components, namely planning, implementation, observation, evaluation, and reflection. Subject this research is a teacher and students 38 class XI MM SMK PGRI 3 Badung. The data collection methods used are test, observations, and poll/questionnaire. The data analysis techniques used are quantitative. The three data analysis result obtained, namely (1) the test result of the prasilklus achieved on average 69, next on the one cycle occurred an increase of 7 points to 76, and two cycles increased 9 points to 85. The value has reached the KKM school is 80, (2) learning model implementation steps the main, preliminary, and closing, and (3) the study of PAILKEM can be increase in student response, the cycle averages on 43 (positive) and increased by 2% in cycles of two 45 (very positive). Besed on the description, model of PAILKEM in writing the explanation text of class XI students MM SMK PGRI 3 Badung years lesson 2019/2020 increasing. In addition, suggestions in this study are teachers constantly using sustainable PAILKEM is learning model in producing the explanation text.