Reviewerly/exHarmony huggingface dataset:
Penerapan Model Pembelajaran dengan Metode Reinforcement Learning Menggunakan Simulator Carla

Artificial Intelligence is the study of how to make machines or computer programs have the intelligence or ability to do things that humans can do. The application of AI is currently in various ways, one of which is for self-driving cars. To be able to do a self-driving car, the AI that is implanted in a car must applied to the method to be able to walk on its path and be able to adapt to its environment. Reinforcement learning is one type of machine learning where agents learn something by doing certain actions and the results of those actions and try to maximize the gifts received through interactions with the environment that are reward negative or positive. In this research, we applied of the reinforcement learning method on the Carla Car simulator. The simulator is used to collect data using an RGB sensor, then modeling experiments which produce several models to be used in simulation experiments. The model is obtained by using the Convolutional Neural Network (CNN) algorithm with the NVIDIA architectural model. From the results of research based on experiments conducted obtained the best model obtained from the experimental model by comparing the maximum reward value, high accuracy and low loss is model 1 in the experimental model A with 100 episodes and model 4 in model B experiment with 150 episodes
Impact of commodities and global stock prices on the idiosyncratic risk of Bitcoin during the COVID-19 pandemic

In times of exogenous systemic shocks, such as the COVID-19 pandemic, it is important to identify hedge or safe haven assets. Therefore, this paper analyzes changes in the idiosyncratic risk of Bitcoin in a portfolio of commodities and global stocks. For this purpose, the M-GARCH model employed considers the interdependence among all the portfolio assets by using a time-varying asset pricing framework. This framework measures the impact of commodities and global stock prices as sources of systemic risk for Bitcoin returns before and after the COVID-19 pandemic. The evidence suggests that during the COVID-19 pandemic, the effects of changes in commodities and global prices on the idiosyncratic risk of Bitcoin were statistically significant. The idiosyncratic risk of Bitcoin measured as a percentage of total variance not accounted for by the proposed model rose from 86.06% to 95.05% during the pandemic. These results are in line with previous studies regarding the properties of Bitcoin as a hedge or safe haven asset for a portfolio composed of commodities and global stocks.
Experimental Teaching of Information Security Based on Virtual Simulation

In information security technology, virtual simulation technology has been applied in many fields, and its value has been proven and has attracted people’s attention. Especially today, as a new type of computer model, virtual simulation covers the Internet and plays an important role in the information security research of big data platforms. This thesis aims to study and solve the problem of lack of experimental environment for information security technology in schools and to bring new teaching methods to the experimental teaching of information security technology in schools. At the same time, it emphasizes the teaching characteristics of the platform based on technology implementation and combines the cognition of high school students in the design and implementation of the platform. After understanding the main points, a research teaching design was carried out, which made up for the current virtual simulation experiment platform that pays attention to technology but despises teaching design. As a result of the empirical analysis, the virtual experiment simulation platform can solve the problems in the school’s information security technology experiment teaching, increase students’ interest in information security technology, and achieve the educational goal of cultivating students’ independent innovation spirit and practical ability. From 2014 to 2016, the Ministry of Education started the construction of virtual simulation information security experiment teaching. Currently, 290 virtual simulation information security experiment teaching has been implemented and distributed in 26 provinces and cities, of which 199 are engineering, 81 are finance, and 10 are education.
Combinatorial optimization with variational approaches on noisy quantum devices

Variational quantum eigensolver (VQE), a kind of quantum-classical hybrid method, has latent powers to leverage noisy quantum devices by effectively managing a limited number of qubits with finite coherent times. We implemented VQE with real quantum processors through IBM Quantum Experience and applied it to combinatorial optimizations. Notably, a short-depth quantum circuit consisting of a lower number of quantum gate than that in the quantum approximate optimization algorithm can search the ground states of diagonal Hamiltonians encoding the problems. Since the simplest case of the quantum circuit for VQE requires only single-qubit gates, it can be realized using current quantum computers.
An adaptive authentication and authorization model for service oriented enterprise computing

Service oriented enterprise computing is an integration architectural style aimed to expose and consume coarse grained and fine grained modularization of business functionalities as services that are being deployed in the loosely coupled organizational environment. The web service is the implementation technology of service oriented architecture (SOA) where it is built on the existing networking and web interfacing standards as it has to use the web as a medium of communication and does not have any specialized in-built layer for security. The majority of the vendor security products in the market need specialized hardware/software components, eventually, they break the standards and principles of service oriented architecture. The traditional way of problem solving is not effective for developing security solutions for service oriented computing, as its boundaries keep expanding beyond a single organiza-tional environment due to the advent of communication and business technologies such as the Internet of Things (IoT), hyper-personalization, and edge computing. Hence, it is a mandatory entity in this digital age of enterprise computing to have a specialized authentication and authorization solution exclusively for addressing the existing security gaps in SOA in an adaptive way forward approach. In this paper, the security gaps in the existing Identity and Access Management (IDAM) solutions for service oriented enterprise computing are analyzed, and a novel intelligent security engine which is packed with extended authentication and authorization solution model for service consumption is presented. The authentication and authorization security requirements are considered as cross cutting concerns of SOA implementation and the solution is constructed as Aspect-Oriented Programming (AOP) advices, which enables the solution can be attached as a ‘plug &amp; play’ component without changing the underlying source code of the service implementation. For Proof-of-Concept (PoC), the proposed authentication and authorization security model is tested in a large scale service oriented enterprise computing environment and the results have been analyzed statistically. It is evident from the results that the proposed security model addresses security issues comparatively better than existing security solutions.
Efficient Retrieval of Data Using Semantic Search Engine Based on NLP and RDF

With the evolution of Web 3.0, the traditional algorithm of searching Web 2.0 would become obsolete and underperform in retrieving the precise and accurate information from the growing semantic web. It is very reasonable to presume that common users might not possess any understanding of the ontology used in the knowledge base or SPARQL query. Therefore, providing easy access of this enormous knowledge base to all level of users is challenging. The ability for all level of users to effortlessly formulate structure query such as SPARQL is very diverse. In this paper, semantic web based search methodology is proposed which converts user query in natural language into SPARQL query, which could be directed to domain ontology based knowledge base. Each query word is further mapped to the relevant concept or relations in ontology. Score is assigned to each mapping to find out the best possible mapping for the query generation. Mapping with highest score are taken into consideration along with interrogative or other function to finally formulate the user query into SPARQL query. If there is no search result retrieved from the knowledge base, then instead of returning null to the user, the query is further directed to the Web 3.0. The top “k” documents are considered to further converting them into RDF format using Text2Onto tool and the corpus of semantically structured web documents is build. Alongside, semantic crawl agent is used to get &lt;Subject-Predicate-Object&gt; set from the semantic wiki. The Term Frequency Matrix and Co-occurrence Matrix are applied on the corpus following by singular Value decomposition (SVD) to find the results relevant for the user query. The result evaluations proved that the proposed system is efficient in terms of execution time, precision, recall and f-measures.
Perception of Network and Protection Concerns In Cloud Computing

With the advancement of virtualization technologies and the benefit of economies of scale, industries are seeking scalable IT solutions, such as data centers hosted either in-house or by a third party. Data center availability, often via a cloud setting, is pervasive. In the history of computers, Cloud computing is one of the most significant milestones in recent times especially in IT industry. Users of Cloud Computing gain freedom, comfort design and simplicity. Cloud computing progress organizations work by employing slightest resources and management support, with a mutual network, expensive resources, software’s and hardware’s in a cost efficient manner and limited service provider dealings. Cloud computing offers services in terms of performance solution, elasticity and cost-efficiency. It’s a new concept of providing virtualized resources to the consumers. However Cloud computing is not only full of advantages. Certainly, it is still subject to several hazards related to security which is now must be implemented at a large scale, so security and privacy issues present a strong boundary for users to adapt into Cloud Computing systems. In this paper, we are exploring several network and security issues and attacks in Cloud Computing.
Improvement of Message Processing-based Steganographic Algorithm using Convolutional Neural Networks

Steganography is the art and science of hiding secret messages without being discovered by a potential attacker. In this paper we propose an application that uses a Message Processing-based Steganographic Algorithm and an Artificial Intelligence algorithm to colorize the recovered message using Convolutional Neural Networks. This solution gives the opportunity to hide a higher quantity of message data in the cover image. The structure of the application and an analysis of test results are presented in this paper.
Mining subgraph coverage patterns from graph transactions

Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns (SCPs). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the Subgraph ID-based Flat Transactional (SIFT) framework for the efficient extraction of SCPs from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed SIFT framework is indeed capable of efficiently extracting SCPs from GTD. Furthermore, we demonstrate the effectiveness of SIFT through a case study in computer-aided drug design.
Three-dimensional reconstruction technique based on triangular phase shift and complementary gray code

Structured light 3D reconstruction technology has the advantages of non-contact, low cost and high reliability, and is widely used in industrial parts defect detection, cultural relics 3D digitization and other fields. As the core part of structured light, coding method is the key element to obtain 3D scene information, which directly affects the accuracy of 3D reconstruction. The combination of gray coding and phase shifting is a typical method for 3D shape measurement. However, due to the complexity of sinusoidal fringe calculation, and the fringe pattern boundary cannot be strictly aligned, there is a step change problem, which limits the application of this method in the field of real-time high-speed three-dimensional measurement. In this paper, the phase shift method of triangular wave and gray coding are combined to measure the 3D morphology. The triangular wave function only uses two raster images and the phase information can be obtained by simple intensity ratio calculation. The binary defocus technique can shorten the projection time, but it blurs the edge boundary of gray code and aggravates the step problem of reconstruction of object surface. In order to solve this problem, the complementary gray code whose fringe width is half of the sinusoidal fringe period is used to correct the period deviation. The experimental results show that the proposed method can reduce the number of fringe projection, simplify the calculation steps and shorten the time of data processing, so it is feasible.
An Innovative Hybrid Biologically Inspired Method for Traffic Optimization Problem

The transport network and road services are the foundation for the development of human civilization. It is immensely essential to manage network congestion as well as to minimize the travel time of the growing traffic load on the road network. Traffic signals may play an important role in managing the mounting traffic. This work relies on reducing the total time lag at the traffic signals, thus reducing the overall travel period. The model is designed on a bi-level framework. The overall wait time is optimized at the traffic signals by the upper level while the User Equilibrium (UE) is estimated by the lower level. Biologically inspired metaheuristic methods like Bat Algorithm (BA), Genetic algorithms (GA), Ant Colony Optimization (ACO), and many others demonstrated optimized outcomes for bi-level problems. To improve the desirability of the metaheuristic techniques an innovative method encapsulating the desirability of both BA and GA is proposed to evaluate the traffic optimization problem (TOP). While BA helps in faster convergence GA diversifies the search space. A comparative analysis has been carried out with the parent algorithms as well as an existing ACO-GA-based model. It was observed that the proposed BA-GA method performs better than the rest of the techniques.
Towards an Insertion of Technologies: The Need to Train in Digital Teaching Competence

The purpose of this study is to analyze the perception of the digital teaching competences of the students of the Degree in Pre-School Education, Degree in Primary Education and Master in Teacher Training for Secondary Education. Additionally, this paper includes the students’ perception of their teachers’ digital competence. A mixed non-experimental methodology has been used with 428 the University of Alicante students participating. The results show a positive perception of the students about their digital teaching competence with contrasts with a deficient evaluation in relation to the digital teaching training of their teaching staff. Based on these results, the need to apply an improvement in the manipulative and didactic technological training of university teachers is corroborated as well as an adaptation of the teaching skills to the needs of the ICS in order to be able to carry out the correct preparation of the teaching staff in training.
Six Software Engineering Principles for Smarter Cyber-Physical Systems

Cyber-Physical Systems (CPS) integrate computational and physical components. With the digitisation of society and industry and the progressing integration of systems, CPS need to become “smarter” in the sense that they can adapt and learn to handle new and unexpected conditions, and improve over time. Smarter CPS present a combination of challenges that existing engineering methods have difficulties addressing: intertwined digital, physical and social spaces, need for heterogeneous modelling formalisms, demand for context-tied cooperation to achieve system goals, widespread uncertainty and disruptions in changing contexts, inherent human constituents, and continuous encounter with new situations. While approaches have been put forward to deal with some of these challenges, a coherent perspective on engineering smarter CPS is lacking. In this paper, we present six engineering principles for addressing the challenges of smarter CPS. As smarter CPS are software-intensive systems, we approach them from a software engineering perspective with the angle of self-adaptation that offers an effective approach to deal with run-time change. The six principles create an integrated landscape for the engineering and operation of smarter CPS.
An Improved SVDD Algorithm for Fast Real-time Response

Support Vector Data Description (SVDD) is a one-class classification algorithm. It can distinguish target samples from non-target samples, and is usually used in anomaly detection and fault detection. However, by expanding the kernel of the traditional SVDD decision function, it is found that the running time complexity is linearly related to the number of support vectors. Therefore, in order to achieve fast real-time response, it is necessary to speed up the running speed of its decision function. In this paper, by using the direct image searching method we can find the pre-image of the spherical center which is no longer an expansion expression of support vectors. Finally, the decision function of quick SVDD (QSVDD) is obtained, so that the running time complexity is a constant in the support number. Therefore, regardless of the size of the training set, the kernel extension in the traditional SVDD decision function can be naturally reduced obviously, which solves the problem of reducing the SVDD test time complexity to a great extent. We believe that QSVDD can be applied in the process of product quality inspection, which will greatly improve the speed of this process.
Detecting Paraphrases in Marathi Language

Paraphrasing refers to the sentences that either differs in their textual content or dissimilar in rearrangement of words but convey the same meaning. Identifying a paraphrase is exceptionally important in various real life applications such as Information Retrieval, Plagiarism Detection, Text Summarization and Question Answering. A large amount of work in Paraphrase Detection has been done in English and many Indian Languages. However, there is no existing system to identify paraphrases in Marathi. This is the first such endeavor in Marathi Language. A paraphrase has different structured sentences and Marathi being semantically strong language hence this system is designed for checking both statistical and semantic similarity of Marathi sentences. Statistical similarity measure does not need any prior knowledge as it is only based on the factual data of sentences. The factual data is calculated on the basis of the degree of closeness between the word-set, word-order, word-vector and word-distance. Universal Networking Language (UNL) speaks about the semantic significance in the sentence without any syntacticpointofinterest.Hence, these mantic similarity calculated on the basis of generated UNL graphs for two Marathi sentences renders semantic equality of two Marathi sentences. The total para phrases core was calculated after joining statistical and semantic similarity scores which gives the judgement of being paraphrase or non-paraphrase about the Marathi sentences.
Sentence Classification Using N-Grams in Urdu Language Text

The usage of local languages is being common in social media and news channels. The people share the worthy insights about various topics related to their lives in different languages. A bulk of text in various local languages exists on the Internet that contains invaluable information. The analysis of such type of stuff (local language’s text) will certainly help improve a number of Natural Language Processing (NLP) tasks. The information extracted from local languages can be used to develop various applications to add new milestone in the field of NLP. In this paper, we presented an applied research task, “multiclass sentence classification for Urdu language text at sentence level existing on the social networks, i.e., Twitter, Facebook, and news channels by using N-grams features.” Our dataset consists of more than 1,00000 instances of twelve (12) different types of topics. A famous machine learning classifier Random Forest is used to classify the sentences. It showed 80.15%, 76.88%, and 64.41% accuracy for unigram, bigram, and trigram features, respectively.
A GPU-Oriented Application Programming Interface for Digital Audio Workstations

A Digital Audio Workstation (DAW) is a hardware and/or software device aiming to ease those operations required for music production, such as arranging, recording, editing, mixing, and, more in general, modifying sounds creatively. A peculiarity of a DAW environment is that most of the work is highly parallelizable, since the basic architecture of a DAW consists in the simultaneous processing of different audio tracks, mainly independent from each other. In order to exploit such a feature, this paper proposes an interface that lets the DAW interact with the Graphics Processing Unit (GPU) in a standardized way. Despite some academic research and experimentation, the professional audio software industry almost never exploited GPUs when implementing entire DAWs, but only when realising very specific tools or third party extensions (plugins). This work also presents and discusses the outcomes of a number of tests conducted in order to choose the optimal architecture. As a result, a GPU-based approach turned to be a valid alternative to the use of CPUs in the computation of audio effects, such as the rendering of audio tracks after mixing and mastering operations, both in real time and offline.