Reviewerly/exHarmony huggingface dataset:
Decision Making Models on the Market of Cloud Services

Introduction. Optimization can be applied in developing profitability management tools for a cloud service broker working according to a certain business model. On behalf of the managing telecommunications holding company (telecommunications operator), this broker integrates, aggregates and configures software and data storage services of third-party Internet software vendors. Such a broker receives only fixed commissions from this company, based on the subscription fee, but does not pay royalties to an Internet software vendor and does not receive payments from the sale of service packages. The purpose. The cloud broker faces the problem of limited human resources required to carry out the relevant legal, technical and economic activities. In addition, the broker faces the problem of uncertainty in sales, service prices, the share of resource use, or the risk of losing operational and financial goals. Results. To run a broker?s business efficiently, one needs to find services and their bundles that increase profitability and reduce financial risk by solving certain optimization problems. Information on such services is needed to support negotiations on fixed and variable commissions, as well as to prioritize services and their packages to be provided. Thus, for the cloud services broker, both profitability management tools and services portfolio development tools are useful. In general, a cloud service broker is an organization that negotiates the relationships between cloud service clients and Internet software vendors. Cloud broker can be created on the basis of different business models regarding the type of service (platform, infrastructure, software), type of clients (enterprise, household), functions performed (identity management, accounting, billing, location, etc.), the degree of rebranding, measures of aggregation of services and other criteria. Conclusions. Different cloud brokers have different attitudes to choice of important solutions for their businesses. Solutions can relate to pricing, capacity planning and utilization in combination with service quality, security, scalability and other issues. Keywords: optimization, portfolio, uncertainty, Boolean variables, revenue generation.
Maneuver Trajectory Identification for Non-cooperative HGVs Using Support Vector Machine and Evidence Theory

The maneuver trajectory identification method of the non-cooperative hypersonic glide vehicle (HGV) is optimized by the support vector machine (SVM) and the Dempster-Shafer (D-S) evidence theory using the unscented Kalman filter (UKF) in this study. Firstly, due to the complexity of penetration maneuvers and the uncertainty of maneuvering modes, the maneuver characteristics of the HGV are analyzed from the point of view of trajectory design. Secondly, the stable tracking of the HGV is achieved by the UKF with an established trajectory tracking model of single ground-based radar. Finally, the HGV maneuver trajectory identification framework is completed using SVM and D-S evidence theory. Simulation results show that this optimized method improves the accuracy of HGV’s trajectory parameter identification.
VR Presentation Training System Using Machine Learning Techniques for Automatic Evaluation

In this paper, the authors build an immersive training space using building-scale VR, a technology that makes a virtual space based on an entire building existing in the real world. The space is used for presentations, allowing students to self-train. The results of a presentation are automatically evaluated by using machine learning or the like and fed back to the user. In this space, users can meet their past selves (more accurately, their avatars), so they can objectively observe their presentations and recognize weak points. The authors developed a mechanism for recording and reproducing activities in virtual space in detail and a mechanism for applying machine learning to activity records. With these mechanisms, a system for recording, reproducing, and automatically evaluating presentations was developed.
Digital Solution to Campus Life: Campus Mate

In everyday campus life both students and staff struggle to keep up with the schedule and tasks assigned to them which are to be completed within the given timeframe. Students often find it difficult to maintain a fine balance between activities of academic nature and co-curricular activities, be it Hackathon, activities of Cultural nature etc., (Tung et al. in Global conference on technical workshops in knowledge networking and technology [4]) Staff often face problems in keeping track of all the student details collected over some time, and often these records are lost. The idea is to create a solution that provides easy access to information collected in an institution by organizing and maintaining the information clean and secure. The other goal is to make campus life easier for active members with any role in an institution. It is very difficult for the students to navigate through the tasks, syllabus, and scoring aspects related to academics. It is also not easy to keep up-to-date with the activities that happen on the campus. To remove these restrictions and to save time for both students and institution staff, a tool must rise which can handle all these tasks efficiently. The tool or application in context should be capable of automating most of this or at least should have an easy to use UI which would make it very simple to do these tasks. Campus Mate solves these problems and provides a custom interface for each user role. E.g.: Student would see a personalized homepage showing his marks, attendance, assignments, etc., whereas staff would see a dashboard on the homepage showing a graph of student’s marks, their active participation in the academic activities, etc., and Principal would have a view where he could view statistics and important higher-level details on the homepage.
$\alpha$ - Rényi based framework for a Robust and Fault-Tolerant Localization

A robust criterion with an adaptive diagnosis for a fail-safe localization system is presented in the paper. To achieve robustness, the Minimum Error Entropy (MEE) is the criterion combined with the Unscented Information Filter (UIF) creating the MEEUIF as a robust estimator in the information theory. As known, the Unscented Transformation (UT) deals well with nonlinearity problems, but the performance decreases significantly under the presence of non-Gaussian noises. The MEE overcomes this problem and shows high robustness dealing with heavy non-Gaussian noises (especially multi-Gaussian noises). For Fault Detection and Isolation (FDI), the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> -Rényi divergence <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\alpha$</tex> -RD) is proposed offering an adaptive diagnosis layer able to interact with the surrounding environment of the system. Then, the decision is executed based on an adaptive threshold determined through the α- Renyi criterion <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\alpha$</tex> - Rc). The proposed approach is tested and validated using real experimental data, for a multi-sensor fusion of Global Navigation Satellite System (GNSS), and Odometer (Odo) data for an autonomous vehicle localization application. The main contributions of the paper are: - The development of a robust multi-sensor fusion using the MEEUIF, - The design of an adaptive diagnosis layer based on - Renyi divergence, and - The validation of the proposed approach using real experimental data.
Does Seamless Learning Translate Seamlessly?: A Decade of Experiences in Adapting Seamless Learning Designs for Various Subjects, Levels and Technological Settings

Seamless learning is “when a person experiences a continuity of learning, and consciously bridges the multifaceted learning efforts, across a combination of locations (such as in and out of classroom), times, technologies or social settings.” It is an opportunity for an integration model of learning to foster deeper connections between multiple learning programs and learning opportunities. The technology-enhanced seamless learning approach was introduced to Singapore schools around 2007. After the initial proof-of-concept on the feasibility of developing a school-based, national curriculum-aligned pedagogy, a series of translation and diffusion efforts ensued over the last decade. Nevertheless, there are still challenges for the learning approach to penetrate the macro layer while keeping the focus on the meso and microlayers of the ecology. Underpinned by the notion of implementation research, this chapter will trace the evolution of the seamless learning practices among participating schools. We discuss the motivations and rationales of developing various translated seamless pedagogical models during different periods of time. In some cases, new translated models were derived not only for the sake of diffusing and spreading the practice to more subjects and levels but also for overcoming the systemic, technological and human (teacher, student and parental) tensions or limitations identified in preceding studies. Lessons drawn from a decade’s worth of implementation research can be useful in informing future translational and scaling up efforts of innovative pedagogical and curricular innovations.
Generalizing QoS-Aware Memory Bandwidth Allocation to Multi-Socket Cloud Servers

Although the problem of QoS-aware resource allocation is not new, novel hardware-based resource allocation mechanisms have recently become available in commodity cloud servers and enabled a new generation of QoS-aware resource allocation approaches. Unfortunately, to the best of our knowledge, existing proposals are by design tailored to single-socket architectures only. In many warehouse scale data centers, dualsocket (or even larger) machines already constitute the largest share of hosts. This paper presents the full design and implementation of BALM, a QoS-aware memory bandwidth allocation technique for multi-socket architectures. BALM combines commodity bandwidth allocation mechanisms originally designed for single-socket with a novel adaptive cross-socket page migration scheme. Our evaluation with a large and dynamic set of real applications co-located on a dual-socket machine shows that BALM can overcome the efficiency limitations of state-of-the-art. BALM delivers substantial throughput gains to bandwidth-intensive best-effort applications, while ensuring marginal SLO violation windows to latency-critical applications.
Strategies for Heterogeneous Multi-Core Processing Based on Graph Programming

In this paper, we explore strategies for automated parallelization and reconfiguration across heterogeneous multi-core processor, based on a programming paradigm and an associated model of computation designed for efficient and automated parallelization across processing elements, efficient reconfiguration (i.e., mapping of computational tasks across processing elements), and combining synchronous and asynchronous I/O handling within the same conceptual programming model. We introduce an analytical model of parallelization, unlocked by graph programming, that can effectively reason about power and performance tradeoffs in heterogeneous multi-core, and inform reconfiguration strategies.
Community Hatred Detection Using Deep Learning on social media in an IoT Environment

Violence detection has always been a key surveillance features in urban environment. Social media can incite hatred resulting in violent crowd triggers. Hateful content against any community practice can give rise to extremism and pungent behavior. Automatic detection of hateful behavior on social media can enable law agencies to prepare for the resulting situations. The paper tries to establish the link between social media hate speeches and crowd behavior which can trigger the violence in cities. The paper proposes a novel solution to integrate the Internet of Tings (IoT) devices with social media analysis system. The proposed approach establishes a clear link between quantitative effects of community hate content on social media and resulting crowd violence in cities. We have utilized conventional deep learning architecture to classify the hate speeches and ensuing violence detection. Effective monitoring of social media speeches can be helpful in analysis of conversion of hate in to violent act.
Brain Image Segmentation based on U-Net Architecture with Adaptive Histogram Equalization

In this paper, a brain tumor segmentation method has been proposed. Image segmentation is required for the detection of brain tumors. For this purpose, brain images are divided into two distinct areas. This is one of the main but also the most complex element of the tumor identification process. A brain tumor is a result of mass of tissue that grows, it is the most important cause of the increased mortality rate among children as well as adults. This paper proposes a brain tumor segmentation method by using a combination of Adaptive Histogram Equalization and U-Net architecture. The performance of U-Net architecture and U-Net Architecture with Adaptive Histogram Equalization Technique are contrast by using PSNR (Peak Signal to Noise Ratio), MSE (Mean Squared Error), Quality and Complexity parameters. Overlap-tile strategy is used to smooth the segmentation of large images. Many MR images were collected, and the observations were carried out for statistical analysis using the Adaptive Histogram Equalization and U-Net architecture.
Research on manipulator control based on deep reinforcement learning

Abstract With the gradual improvement of the influence of intelligent robots in production and life, it has greatly facilitated people's production and life. Therefore, people's requirements for intelligent robots are also increasing, and are developing towards more humanization and intelligence. However, at present, there are still many imperfections in the field of intelligent robot technology in China. In order to solve the problems in work, we must further strengthen the research on artificial intelligence theory and robot technology. Only in this way can we realize the all-round development of intelligent robot system. So this paper will discuss the deep reinforcement learning in the theory of artificial intelligence, and explain its basic theory, research status, existing problems and future development direction. Moreover, under the background of the overall improvement of the current industrial development level, this paper will also talk about the manipulator widely used in the industrial field and the research status of manipulator control based on deep reinforcement learning, hoping to provide effective help for the development of related fields.
Selection System for Poor Student Recipients Using the MFEP Method

Aid for poor students recipients at State elementary school number 060947 Medan Is a government program which helps the community in paying school fees. In processing the provision of assistance to poor students, they still have many weaknesses, including the use of manual processes and subjective assessment, subjective here means that all acceptance decisions are appropriate get help but his friend has not received it, on the other hand those who do not deserve it but he gets it. To overcome this, a decision support system was built to determine the recipients of Aid for poor students so that the results of acceptance are in accordance with the right target and make people who are in the school be helpful and useful for poor student aid recipients, this decision support system using the MFEP method. The criteria set by the school include: Ownership of a KPS (Social Protection Card), Parent's Ownership, Parent's Income, Parent's Dependents, Attendance, Other Accounts. By determining the criteria above the author designed a system that is able to process decisions that are reliable and useful for students who study at school place, as well as being helped by the existence of this decision support system.
Fuzzy Logic Control with PSO Tuning

Several applications of artificial intelligence in the area of control of dynamic systems have proven to be an efficient tool for process improvement. In this context, control systems based on fuzzy logic - Fuzzy Logic Control (FLC) are part of a series of advances in the areas of control systems. Fuzzy control is based on natural language and therefore has the ability to make approximations closer to the real nature of the problems. The use of metaheuristic algorithms such as the particle swarm optimization (PSO) allows it to provide adequate adjustments to the fuzzy controller in an optimized manner. This technique allows to adjust the FLC in a simple way according to the performance desired by the designer, without the need for a long time of conventional tests.
Remote State Preparation in a Reconfigurable Quantum Local Area Network

We demonstrate remote state preparation between three spatially distributed nodes over campus fiber. Utilizing entanglement resources established through flex-grid bandwidth techniques, we nonlocally prepare desired polarization qubit states on demand, showcasing the feasibility of this quantum protocol in a deployed network.
BOOK SELECTION BASED ON CHILDREN'S BOOK COVERS: ANALYSIS MULTIMODALITY

This study discusses the results of investigations into book selection research based on covers in children aged 3-12 years. The aim is to find out how verbal and visual modes are represented in children's book covers and identify whether they influence children's decisions to buy books. This research focuses on the identification of multimodality texts and visual modes in children's books Words of Life and A Dash of Magic. The Multimodal Discourse Analysis Theory (MDA) by Kress and van Lueeween is applied by systematically describing the data found on the covers of children's books. Based on the analysis, it was found that there were three metafunctions found; representational, interactive, and compositional; each plays a certain role in the cover of this child's book. Moreover, research also shows that children aged 3-12 years buy books because they are interested in book covers, with 83.3% of children agreeing to buy books that have engaging titles, images, colors, and designs.
Voiceless Speech Recognition System

Speech recognition is a very important part of the human-machine interaction system. The commonly used methods for speech recognition are invariantly based on the analysis of speech signals. These signals might not be always viable especially in noisy environments or in environments where discretion is of utmost importance. The “Thought Predictor system” allows the user to converse silently with a computing device. It thus enables the user to communicate with devices and AI assistants in a voiceless and discrete manner. The user’s intention to speak and internal speech is characterized by neuromuscular signals by the speech articulating muscles that are captured by this system to reconstruct this speech.
A Novel Autonomous Collision Avoidance Decision Mechanism for UAV in Low Altitude Environment Based on 3D Dynamic Collision Region

Aiming at the autonomous collision avoidance problem for unmanned vehicles (UAVs) in low altitude environment, a novel decision mechanism based on 3D dynamic collision region is proposed in this paper. First, the models of collision avoidance are built, which relates to the UAV kinematic characteristics and the environment. Second, the 3D dynamic collision region is established, which contains the safety information of UAV relates to time and distance simultaneously. Besides, for the design of decision mechanism, the information given by the dynamic collision region is fully exploited and several novel definitions are proposed. Third, based on the safety criterions given by the dynamic collision region, a novel decision mechanism for collision avoidance is proposed. By choosing actions such as path re-planning and emergency maneuvers properly, the decision mechanism allows the UAV to better adapt to the complex environment and enhance the flight safety. Finally, simulations are carried out to verify the effectiveness of our proposed framework.
System Change Controls: A Prioritization Approach Using Analytic Hierarchy Process

Information attacks are a constant threat to every organization. To protect their sensitive information, organizations implement general information technology controls. An example of such controls includes system change controls (or change management controls), which are critical in ensuring the integrity, completeness, and reliability of financial information. The literature points to various evaluation methods of these controls to determine which ones to implement. The literature further shows how traditional assessment methods do not necessarily promote an effective evaluation, prioritization, and, therefore, implementation of system change controls in organizations. Alarming facts within the literature trigger analyses and identification of additional methods to assist organizations in protecting their sensitive and critical information. This research proposes a quantitative approach to assist management in evaluating system change controls using the Analytic Hierarchy Process. Through a case study, the approach is proven successful in providing a way for measuring the quality of system change controls in organizations.
Reducing the dimensionality of data using different techniques

High-dimensional data processing is really the key issue in many systems, including contentbasedextraction, voice signals, fMRI analyses, electroencephalogram object detection,multimedia extraction, market-based technologies, etc. In order to enhance thesystem's effectiveness, the data dimensions must be minimized to a low dimensional space.Inthis paper, we analyzed linearization, nonlinear and network embedding dimensionalityreduction methods. A few of these methods are ideal and used for linear data that have linearrelationship between data points, and many other dimensionality reduction methods are used fornonlinear data that have nonlinear relationship among data points.From an analysis of this paper,we found that Structural Deep Network Embedding (SDNE), LINE (Large-Scale NetworkEmbedding) and Nod2Vec are the best techniques for dimensionality reduct ion in networkdata.Furthermore, every approach has its own characteristics and drawbacks. This studypresents different methods utilized to minimize the high dimensional data into low dimensionalspace.
Efficiently Revocable Identity-Based Broadcast Encryption Using Integer Matrices as Keys

A new Identity-Based Broadcast Encryption (IBBE) scheme is presented that uses integer matrices in finite field as keys. The plain text is encrypted such that only the unrevoked users from a set of registered users can decrypt the ciphertext. The sizes of the ciphertext, public key, and the private keys vary linearly with the total number of registered users. In the proposed scheme, the revocation process is direct. That is a separate access controller does not exist between the users and the storage to filter out the revoked users, because the revoked users anyhow cannot decrypt the ciphertext.
On the Implementation of Access Control in Ethereum Blockchain

Access control is a main component in Blockchain systems. It is usually implemented in smart contracts and defines the security policy, in other words, it determines who can access a protected resource in the network. In this paper, we present a review of the major implementations of access control in Ethereum platform. The latter is based on RBAC model (Role-Based Access Control). Implementations require to take into account the whole RBAC process, that is, user role assignment and permission assignment. Three implementations currently exist and are described and compared in this work according to several features: RBAC-SC, Smart policies and OpenZepplin contracts.