Reviewerly/exHarmony huggingface dataset:
Cybersecurity Description and Control Criteria to Strengthen Corporate Governance

The dangers of not properly focusing upon cybersecurity risks were emphasized by recent, notorious attack and hack examples. With guidance from key sources, like the AICPA Cybersecurity Guide, cybersecurity strategies can be developed by corporate executives and Board of Directors to help thwart such attacks. An immediate trend for data security is the General Data Protection Regulation adopted by the European Union in 2018 as well as quantum computing. If all of this is accomplished, the entity’s corporate governance would be strengthened by corporate executives and Boards of Directors who are the key gatekeepers for protecting and enhancing the investments of company stakeholders.
EPSim-C: A Parallel Epoch-Based Cycle-Accurate Microarchitecture Simulator Using Cloud Computing

Recently, computing platforms have been being configured on a large scale to satisfy the diverse requirements of emerging applications like big data and graph processing, neural network, speech recognition and so on. In these computing platforms, each computing node consists of a multicore, an accelerator, and a complex memory hierarchy, which are connected to other nodes using a variety of high-performance networks. Up to now, researchers have been using cycle-accurate simulators to evaluate the performance of computer systems in detail. However, the execution of the simulators, which models modern computing architecture for multi-core, multi-node, datacenter, memory hierarchy, new memory, and new interconnection, is too slow and infeasible; since the architecture has become more complex today, the complexity of the simulator is rapidly increasing. Therefore, it is seriously challenging to employ them in the research and development of next-generation computer systems. To solve this problem, we previously presented EPSim (Epoch-based Simulator), which defines epochs that can be run independently by dividing the simulation run into several sections and executes them in parallel on a multicore platform, resulting in only the limited simulation speedup. In this paper, to overcome the computing resource limitations on multi-core platforms, we propose a novel EPSim-C (EPSim on Cloud) simulator that extends EPSim and achieves higher performance using a cloud computing platform. EPSim-C is designed to perform the epoch-based executions in a massively parallel fashion by using MapReduce on Hadoop-based systems. According to our experiments, we have achieved a maximum speed of 87.0× and an average speed of 46.1× using 256 cores. As far as we know, EPSim-C is the only existing way to accelerate the cycle-accurate simulator on cloud platforms; thus, our significant performance enhancement allows researchers to model and research current and future cutting-edge computing platforms using real workloads.
Sistem Informasi Pencatatan dan Pelaporan Pelayanan Kesehatan Berbasis Android di Kawasan Terpencil dan Sangat Terpencil

Health care is the right of every community to get service without exception, even though it is in an area that is difficult to reach due to geographical access. Mobile Health Services (PKB) is one of the innovations in equitable health services in remote and very remote areas. The process of recording and processing data is generally held also on PKB is still done manually. Recording that is still running in handwriting causes many obstacles in the process of processing, searching, and making reports on health service activities. The purpose of this study is to identify and develop information systems for recording and reporting on maternal and child health service activities at PKB. This research method uses Action Research. This design was chosen because it would delve deeply into the process and management of the development of Android-based applications for PKB. The results of this study present that the use of android-based mobile phones as a tool in data collection for the implementation of recording and reporting of mother and child activities can be carried out offline. This prototype information system made the process more efficient in term of time, especially when the data is uploaded to a properly administered server, makes the data much more secure, more effective to use for mobile health clinic team or health provider for data reporting.
An Adaptive Autonomic Framework for Optimizing Energy Consumption in the Cloud Data Centers

Given the development of the Cloud Computing recently, clients and customers using the Cloud for both individual and business needs have expanded to an uncommon scale.This has normally prompted the expanded deployments of Cloud data centers over the globe.As a result, Cloud data centers are seen to be monstrous energy consumers and natural polluters.They require an extraordinary measure of regular energy which has made an effect on the energy supply and natural conditions of the environment.This is the reason why the vulnerability of persistent energy supply, later on, is being referred to.In this way, there is a need of an energy-aware cloud-based system which automatically and efficiently manages and optimize cloud computing data center resources by considering energy consumption as an essential Quality of Service (QoS) parameter.This paper, focus on the energy utilization of the data centers and how this can be limited so as to make the cloud computing greener.Thus, a new autonomic resource optimization manager has been proposed to avail the most optimum level of resources with reduced server energy consumption.The proposed framework has been verified theoretically and tested experimentally.The experimental analysis has demonstrated that the effectiveness of the proposed solution is greater than the state-ofthe-art methods in terms of the achieved results related to reducing energy consumption and response time in cloud computing data centers.
Career Guidance through TIC-TAC-TOE Game

Many graduates are being qualified year after year in different disciplines that do not have any kind of idea about the career that is most suitable to them.Some of the graduates are suffering due to the bad career choice that they have taken in the absence of proper career guidance system which is based on psychological assessment.The type of career suitable for a graduate student is dependent on the Psychology of a student and therefore the psychology of a graduate must be measured which can be used for determining suitable career that suits the Graduate.In this paper, the design of Tic-Toc-Toe Game that can be played by a student is presented and it is also shown how psychology of student can be measured considering Intelligence, Learning ability, perseverance, patience and Speed of solving problem etc. while the student plays the game.An expert system is also presented that helps predicting a career that is most suitable to a graduating student
Resource Allocation Using Democratic Grey Wolf Optimization in Cloud Computing Environment

Cloud computing is one of the growing environment that outlines an internet-based computing system consisting of a large number of computers and other devices, where a number of things, such as computer infrastructure, access to applications, software, processing power, etc. are shared over the Internet.The allocation of the cloud resources to the user based on their request is a Non-deterministic Polynomial-time (NP) issue, which consumes more time.Therefore, heuristic methods utilize for optimizing resource allocation.In this research work, the Democratic Grey Wolf Optimization (DGWO) is proposed to overcome the limitations of the resource allocation in efficient way.In this method, DGWO performs the following steps; initializing the request size, generating requests, and estimate fitness value of DGWO, sorting, dividing and evaluating the requests of the user.The advantages of DGWO are higher speed convergence, easier implementation, global optimization capacity.The DGWO has high performance in unknown, challenging search spaces which has high local optima avoidance.The efficiency of DGWO is improved in terms of searching optimum resource time where the searching behavior is obtained from GWO.The DGWO method achieved 75% throughput, 96% allocation of resources compared to other meta-heuristic existing techniques.
How Are Design Patterns Concerned by Developers?

In recent years, design pattern has become an accepted concept in software design and many studies have involved various aspects of design patterns. However, it is an open question that how design patterns are discussed by developers. In this study, we conduct an empirical study to answer this question by soliciting Stack Overflow. First we build a new open catalog with 425 design patterns. Then, we extract 187,493 design pattern relevant posts from Stack Overflow. As to these posts, we find that the popularity of design patterns follows a long tail distribution. More surprisingly, nearly half of the posts focus on only five design patterns. We also successfully detect many potential new co-occuring design patterns, which could well complement the deficiency of existing studies.
Population-based ensemble classifier induction for domain adaptation

In classification, the task of domain adaptation is to learn a classifier to classify target data using unlabeled data from the target domain and labeled data from a related, but not identical, source domain. Transfer classifier induction is a common domain adaptation approach that learns an adaptive classifier directly rather than first adapting the source data. However, most existing transfer classifier induction algorithms are gradient-based, so they can easily get stuck at local optima. Moreover, they usually generate only a single classifier which might fit the source data too well, which results in poor target accuracy. In this paper, we propose a population-based algorithm that can address the above two limitations. The proposed algorithm can re-initialize a population member to a promising region when the member is trapped at local optima. The population-based mechanism allows the proposed algorithm to output a set of classifiers which is more reliable than a single classifier. The experimental results show that the proposed algorithm achieves significantly better target accuracy than four state-of-the-art and well-known domain adaptation algorithms on three real-world domain adaptation problems.
FUTURE TEACHERS OF MUSIC ART TRAINING DURING INDIVIDUAL CLASSES IN MAJOR MUSICAL INSTRUMENT

The article highlights the importance of instrumental and performing training for future teachers of musical subjects and teachers of music art, which implies the teacher’s ability to perform “live” music professionally in the process of teaching and during extracurricular activities. Nowadays, higher education pedagogical institutions carry out the training, forming general and professional competencies while teaching course “Major Musical Instrument”. The authors have revealed the meaning, content, and purpose of the course “Major Musical Instrument”. Interdisciplinary connections are highlighted, and an individual form of the practical training is determined as the main one. The structure of the class, which includes work on technical exercises, gamma, etudes, the study of polyphony, works of great form, multi-character plays, etc., is represented in the article. An important aspect of instrumental and performing training of future specialists is an individual and differentiated approach to the organization and conduct of students’ classroom learning and independent work. It involves taking into account their musical preparation for entry into higher education institution, appropriate selection of program material, methods and techniques.
Joint super-resolution and bit depth extension by DNN

Many Super-Resolution algorithm using DNN have been proposed. They simply expand the image size. On the other hand, bit depth expansion has also been proposed independently. In this paper, we consider the case when the image enlargement process is combined with bit depth expansion by Super-Resolution processing. Experimental results show its validity in both SDR and HDR images.
Location and Orientation Based Augmented Reality Mobile Application for Enhancing Heritage Landmark History and Information

Augmented Reality technology in MobileDevice can provide better experience for people to to access and gather information at the location of cultural heritage or tourism landmark point in cultural heritage and historical tourism.Currently, the AR implementation research in cultural aspect hardly explored about the sensing algorithm of the cultural object that ignite the AR and the algorithm's complexity in detail.In this paper, we we propose geodetic location based tracking algorithm that has low computational complexity compare to the object based tracking which required heavy computational complexity for image processing algorithm.Based on the testing and implementation evaluation, our application can mark the cultural heritage or historical landmark and overlay it with the story behind the place to enhance the visitor experience.
Correction to: Gaps in the System of Higher Education in Russia in Terms of Digitalization

The original version of the book was inadvertently published with last author’s name and e-mail address as N.G. Mirzayev and mirzoev@mail.ru, which has been corrected to “N.S. Mirzayev and mirzoev.n@mail.ru” in Chapter 43.
Reverse Engineering Behavioural Models of IoT Devices

This paper addresses the problem of recovering behavioural models from IoT devices in order to help engineers understand how they are functioning and audit them.We present a model learning approach called ASSESS, which takes as inputs execution traces collected from IoT devices and generates models called systems of Labelled Transition Systems (LTSs).ASSESS generates as many LTSs as components integrated and identified into a device.The approach is specialised to IoT devices as it takes into account two architectures often used to integrate components with this kind of system (cyclic functioning, loosely-coupled or decoupled architectures).We experimented the approach on two IoT devices and an IoT gateway to evaluate the model conciseness and the approach efficiency.
Hope at SemEval-2019 Task 6: Mining social media language to discover offensive language

User's content share through social media has reached huge proportions nowadays.However, along with the free expression of thoughts on social media, people risk getting exposed to various aggressive statements.In this paper, we present a system able to identify and classify offensive user-generated content.
Ontology and Enterprise Modelling Driven Software Requirements Development Approach

The aim of the research is to analyze latest methodologies in requirements engineering process, the newest proposals to upgrade the process and to present an approach helping to address challenges in the area, such as lack of quality of requirements specifications and requirements due to system analyst skills and experience.The areas of the research includes ontology engineering practices, as well as part of Model Driven Architecture (MDA) approach, to be more specific -Enterprise Metamodel (EMM).This paper presents Metamodel for knowledge-based subsystem merged with requirements ontology and requirements specification template to cover all the aspects in problematic requirements engineering process.
Examining the Mediating Role of Agile Supply Chain Approach between ERP System implementation and Organizational Performance

The study examine relationship between implementing ERP systems on the boosting of the organizational performance, in addition, to examine the mediating role of agile supply chain approach to achieve the targeted performance enhancements in manufacturing listed firms in Amman Stock Exchange (ASE). The study population entails all manufacturing listed firms in Amman Stock Exchange (ASE), particularly the top retail firms operate in the Jordanian market constitutes of (77) firms. The consent of conducting an interview has been taken with a total of (367) questionnaire were self-distributed. The unit of analysis is at a firm level, the deployed sample is a purposive sample. The study revealed that the ERP solutions are widely implemented in the mid and large sized organizations, due to its contribution in making all sub systems and modules communicate effectively, allowing the access to timely, and accurate information, under the set of authorization levels. Therefore, the Firm performance boosted as a result of implementing an effective ERP system. On the other side the study indicated that the implementing the agile supply chain approach as a method that would help in responding successfully to the customer and market demands, with no risk of having accumulated inventory on the stock. The study fulfils a novel aspect in examining the effect of ERP in the case of implementing the agility approach. The study encourages to consult expertise with the best practices to implement the ERP solutions in parallel with agile supply chain, also the yet to coming research need to focus of such factors rather than ERP effectiveness.
ELiRF-UPV at SemEval-2019 Task 3: Snapshot Ensemble of Hierarchical Convolutional Neural Networks for Contextual Emotion Detection

This paper describes the approach developed by the ELiRF-UPV team at SemEval 2019 Task 3: Contextual Emotion Detection in Text.We have developed a Snapshot Ensemble of 1D Hierarchical Convolutional Neural Networks to extract features from 3-turn conversations in order to perform contextual emotion detection in text.This Snapshot Ensemble is obtained by averaging the models selected by a Genetic Algorithm that optimizes the evaluation measure.The proposed ensemble obtains better results than a single model and it obtains competitive and promising results on Contextual Emotion Detection in Text.
Experiment with Text Summarization as a Positive Hierarchical Fuzzy Logic Ranking Indicator for Domain Specific Retrieval of Malay Translated Hadith

Ranking function acts as a predictive algorithm that is used to establish a simple ordering of documents according to its relevance and this process shows the effectiveness, quality and the accuracy for the variety type of Information Retrieval (IR) such as, Domain Specific Retrieval of Malay Translated Hadith. In this research, a Hierarchical Fuzzy Logic Controller of Mamdani-type Fuzzy Inference System has been built to define the ranking function based on the BM25 Model. The model examines four-inputs which are Ontology BM25 Score, Fabrication Rate of Hadith, Shia Rate of Hadith from the previous works of the researchers and the New additional Positive Rate of Hadith. It also examines four-output values of Final Ranking Score which consist of three triangular membership functions. The new Positive Rate of hadith is based on the score value of the automatic text summarization that was executed in pre-processing phase. The proposed system has outperformed the BM25 original score and the Vector Space Model (VM) on 5 topic of queries and 26 queries in the term of individual queries, while the BM25 original score and Vector Space Model only yielded better result in 3 and 0 queries respectively on the P@10, %no measures and MAP. P@10 represent the values of Precision at Rank 10 P@10), %no measures represent the percentage of queries with no relevant documents in the top ten retrieved and MAP represents Mean Average Precision of the queries. The results show the proposed system have capability to demote negative documents and move up the relevant documents in the ranking list with positive indicator and its capability to recall unseen document with the application of ontology in text retrieval. For the future works, the researcher would like to apply the usage of new ranking indicator such as reliability score from the expert and the lay users of the Domain Specific Retrieval of Malay Translated Hadith.
Anomaly Detection in Smart Homes Using Deep Learning

Smart homes enable many people, especially the elderly and patients, to live alone and maintain their independence and comfort. The realization of this goal depends on monitoring all activities in the house to report any observed anomaly immediately to their relatives or nurses. Anomaly detection in smart homes, just by existing data, is not an easy task. In this work, we train a recurrent network with raw outputs of binary sensors, including motion and door sensors, to predict which sensor will be switched on/off in the next event, and how long this on/off mode will last. Then, using Beam Search, we extend this event into k sequences of consecutive events to determine the possible range of upcoming activities. The error of this prediction i.e., the distance between these possible sequences and the real string of events is evaluated using several innovative methods for measuring the spatio-temporal similarity of the sequences. Modeling this error as a Gaussian distribution allows to assess the likelihood of anomaly scores. The input sequences that are ranked higher than a certain threshold will be considered as abnormal activities. The results of the experiments showed that this method enables the detection of abnormal activities with desirable accuracy.
Homotopic properties of $KA$-digitizations of $n$-dimensional Euclidean spaces

For $X (\subset R^n)$, assume the subspace $(X, E_X^n)$ induced by the $n$-dimensional Euclidean topological space $(R^n, E^n)$. Let $Z$ be the set of integers. Khalimsky topology on $Z$, denoted by $(Z, \kappa)$, is generated by the set $\{\{2m-1, 2m, 2m+1\}\,\vert\, m \in {Z}\}$ as a subbase. Besides, Khalimsky topology on $Z^n, n \in N$, denoted by $(Z^n, \kappa^n)$, is a product topology induced by $({Z}, \kappa)$. Proceeding with a digitization of $(X, E_X^n)$ in terms of the Khalimsky ($K$-, for short) topology, we obtain a $K$-digitized space in ${Z}^n$, denoted by $D_K(X) (\subset {Z}^n$), which is a $K$-topological space. Considering further $D_K(X)$ with $K$-adjacency, we obtain a topological graph related to the $K$-topology (a $KA$-space for short) denoted by $D_{KA}(X)$ (see an algorithm in Section 3). Motivated by an $A$-homotopy between $A$-maps for $KA$-spaces, the present paper establishes a new homotopy, called an $LA$-homotopy, which is suitable for studying homotopic properties of both $(X, E_X^n)$ and $D_{KA}(X)$ because a homotopy for Euclidean topological spaces has some limitations of digitizing $(X, E_X^n)$. The goal of the paper is to study some relationships among an ordinary homotopy equivalence for spaces $(X, E_X^n)$, an $LA$-homotopy equivalence for spaces $(X, E_X^n)$, and an $A$-homotopy equivalence for $KA$-spaces $D_{KA}(X)$. Finally, we classify $KA$-spaces (resp. $(X, E_X^n))$ via an $A$-homotopy equivalence (resp. an $LA$-homotopy equivalence). This approach can facilitate studies of applied topology, approximation theory and digital geometry.