Reviewerly/exHarmony huggingface dataset:
Knowledge representation and intelligent fault diagnosis technology for power grid faults

Aiming at that the automatic fault diagnosis method is difficult to locate the fault causes under uncertain circumstances which include malfunctions of the equipment and wrong alarm messages, a knowledge model was proposed to describe the relationship, status and operation of the equipment. And based on the model, the action logic between equipment after accidents is expressed in the form of rules combined with predicate logic. The corresponding interpretation and checking results of the relative alarm messages are given by reasoning the accident chain under different fault hypothesis. And the optimal judgment result is obtained through the calculation of prior probability. The validity of the method is verified by a practical fault case.
Riemannian Proximal Policy Optimization

In this paper, we propose a general Riemannian proximal optimization algorithm with guaranteed convergence to solve Markov decision process (MDP) problems. To model policy functions in MDP, we employ Gaussian mixture model (GMM) and formulate it as a non-convex optimization problem in the Riemannian space of positive semidefinite matrices. For two given policy functions, we also provide its lower bound on policy improvement by using bounds derived from the Wasserstein distance of GMMs. Preliminary experiments show the efficacy of our proposed Riemannian proximal policy optimization algorithm.
Learning with Educational Robotics through Co-creative Methodologies

STEM education is crucial in successfully shapingfuture technology driven societies. It is important to engage learners from an early age with STEM in order to facilitate successful entry into relevant careers. Towards that, educational robotics are a proven, effective introduction to overall STEM concepts but also to hands-on skills. Co-creation, a marketing method, tailor made to foster participation and engagement, translated successfully in educational contexts. In this work we present a co-creative robotics curriculum for primary school education and its impact on young learners’ perceptions for STEM careers. Learners co-created educational robotics activities to learn from them. A validated questionnaire for assessing the learners’ perceptions for STEM careers was administered before and after the curriculum. All learners improved their perceptions for STEM careers after the co-creative robotics curriculum. This study initiates a greater endeavor for exploring the impact of co-creative robotics in learning efficacy and STEM careers engagement.
The test set for the TransCoder system

The TransCoder system translates source code between Java, C++, and Python 3. The test set that was used to evaluate its quality is missing important features of Java, including the ability to define and use classes and the ability to call user-defined functions other than recursively. Therefore, the accuracy of TransCoder over programs with those features remains unknown.
MEG–MRI CO-REGISTRATION USING 3D GENERALIZED HOUGH TRANSFORM

In this paper, we propose a method to use the three-dimensional (3D) generalized Hough transform (GHT) to co-register magnetoencephalography (MEG) and magnetic resonance imaging (MRI) of a brain automatically, whose results can be used to align MRI images and MEG data accurately and efficiently. Recently, many medical devices have been developed to study the neuronal activity in the human brain. MEG is a high-temporal-resolution measurement tool to study the physiological functions of brain nerves noninvasively; whereas the MRI of the scalp, skull, and cortex of the human brain is a high-spatial-resolution tool. The proposed method combines two tools for investigating the cognitive neuroscience between the human brain structure and weak magnetic fields from two different medical systems. An accurate and automatic registration method is necessitated to improve the brain analysis processes by combining multimodal data. The conventional GHT is a well-known method for detecting two-dimensional (2D) images or locating transformed planar shapes in 2D image processes. To further improve the 2D GHT, we extended it to a 3D GHT, which can co-register MEG and MRI data automatically and accurately. Some experimental results are included to demonstrate and evaluate the error and applicability of MEG–MRI co-registration.
EFEKTIVITAS KEBIJAKAN REDAKSI PROGRAM MATA NAJWA DALAM MEMBONGKAR KASUS KORUPSI DI PSSI

The Mata Najwa talk show program raised the theme of bribery and score-fixing at PSSI (Indonesian Football Association), in shows on 28 November 2018 and 19 December 2018 on Trans 7.Both episodes were titled PSSI Bisa Apa and PSSI Bisa Apa Jilid 2. In the program, Mata Najwa presented information about bribery practices and score-fixing, and revealed figures suspected of being involved.Apparently, this show had an impact on law enforcement policies.The police responded immediately by forming the Antimafia Task Force.Members of PSSI were then arrested.The Mata Najwa talk show that reviewed the PSSI corruption policy are manifestations of editorial policy.This study uses a qualitative descriptive method, to understand the factors that support Mata Najwa's editorial policy in uncovering the problem of corruption in PSSI.These factors are: independence, credibility program, professionalism, research, and exclusivity of the resource persons.
Keyword Recommendation Methods for Earth Science Data Considering Hierarchical Structure of Vocabularies

To understand and properly use scientific data, it is important that the metadata, which describes information related to data, contains sufficient information. Keywords are one of the metadata items, and for assigning keywords to the earth science dataset, it is common to select and assign appropriate keywords from a controlled vocabulary with a hierarchical structure. Keyword information plays an important role in dataset search and classification; however, the cost of selecting appropriate keywords from a controlled vocabulary is high, and in many cases, a sufficient number of keywords are not actually assigned. In this study, we focus on keyword recommendations for earth science datasets using the definition sentences given to the keywords in the controlled vocabulary, and propose content-based recommendation methods considering the hierarchical structure in the controlled vocabulary.
Neural ODE with Temporal Convolution and Time Delay Neural Networks for Small-Footprint Keyword Spotting

In this paper, we propose neural network models based on the neural ordinary differential equation (NODE) for small-footprint keyword spotting (KWS). We present techniques to apply NODE to KWS that make it possible to adopt Batch Normalization to NODE-based network and to reduce the number of computations during inference. Finally, we show that the number of model parameters of the proposed model is smaller by 68% than that of the conventional KWS model.
Stochastic Diffusion Search: Modifications and Application

Stochastic Diffusion Search (SDS), originally published in 1989, is the first Swarm Intelligence metaheuristic. This paper presents a brief description of the standard SDS algorithm. Next, some of SDS applications are briefly summarised based on their domains. Then, some modifications to the algorithm are presented which include recruitment strategies, agents initialisation, process termination criteria and partial function evaluation. Finally the paper is concluded by providing a real-world problem of identifying metastasis in bone scans using SDS. For this purpose, the algorithm is adopted and applied to this medical imaging problem.
Abnormal Activity-Based Video Synopsis by Seam Carving for ATM Surveillance Applications

Criminal activities have been increasing in ATM centers, but the law enforcement authorities become mindful only after the incident have occurred. Viewing the whole video sequence is tedious and also slows down the investigation process. The abnormal activity involved in the proposed work is stabbing. The Lucas Kanade method of optical flow is proposed to analyze the stabbing action by the velocity and direction the knife moves with. The analysis is further enhanced by the facial expression recognition. The proposed method involves abnormal activity analysis followed by video synopsis. Video condensation by a seam carving provides an effective solution. The concept of the seam carving is to associate reliable activity-aware cost with seams and recursively evacuate seams one at a time. The seam cost is equal to the sum of all pixels that make up a seam. To condense a given video the ribbons with minimum cost are removed by a user-defined stopping criterion. The datasets are real-time ATM crimes involving stabbing action with a knife. The experimental outcomes show that the demonstrated framework gives an effective synopsis of the video based on abnormal activities, i.e., a reduction in duration and no frames.
Effectiveness Evaluation Method for Cyber Deception Based on Dynamic Bayesian Attack Graph.

The theoretical study of cyber deception technology often focuses on the analysis of specific defense technologies, and most researches are limited to the single-level qualitative measurement, which cannot be utilized in the complex environment. In this paper, to quantify the effectiveness of cyber deception comprehensively and reasonably, a method is proposed based on a dynamic Bayesian attack graph. By analyzing the capability of attackers, the Bayesian attack graph is established based on the dependence of network resources and vulnerability factors. Then we verify the consistency of host nodes in the project based on network features, device fingerprints, and file attributes, and update the Bayesian attack graph with network attack-defense events. Finally, in combination with the common vulnerability scoring system (CVSS) metrics, the concealment and effectiveness indexes are given to evaluate the cyber deception's performance. Experiments show that our method is feasible for the attack-defense adversarial network environment, which is beneficial for the defender to formulate the optimal deception strategy according to the current situation of attack-defense events.
PICTURE WORDS INDUCTIVE MODEL FOR YOUNG LEARNER IN IMPROVING ENGLISH VOCABULARY FOR INDONESIAN STUDENTS AT SD SWASTA PELANGI MEDAN

The Picture word inductive is an inquiry-oriented language arts strategy that uses pictures containing familiar objects and actions to elicit words from children's listening and speaking vocabularies. This strategy leads the students to identify each object in a picture that they will describe. The aim of this study was to find out the effectiveness of Picture Word Inductive Model in improvement vocabulary skills‟ for Indonesian students at SD Swasta Pelangi Medan. The population of this research is the Second year students at SD Swasta Pelangi. The number of sample is 28 students selected by using cluster random sampling technique. Based on the result of this research, the writer suggest that Picture Word Inductive Model may be used as alternative media in teaching vocabulary, in order that the students can more understand and interested in learning activity especially in learning of English.
Group Data Sharing and Auditing While Securing Sensitive Information

In today’s world, it can be said that cloud computing is a most important research topic nowadays. The reason behind this is the number of services provided by it to its users. Among them, there exists a service which allows a number of users to share their data with each other. This service is very helpful in collaborative environment as it improves its efficiency and has a number of potential applications. But the data shared might include some information which is sensitive and should not be shared with every other member of the group. To tackle the issue, this paper proposes a scheme which ensures the security of such sensitive data to be shared among a group of cloud users. For sharing data among them, block design-based key agreement protocol has been used in the proposed approach along with identity-based blind signature for verifying data’s integrity. Later, a performance analysis in the end shows that the proposed scheme is efficient and stable.
Improving Generative Adversarial Networks with Local Coordinate Coding

Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution (e.g., Gaussian noises). However, such prior distribution is often independent of real data and thus may lose semantic information (e.g., geometric structure or content in images) of data. In practice, the semantic information might be represented by some latent distribution learned from data. However, such latent distribution may incur difficulties in data sampling for GANs. In this paper, rather than sampling from the predefined prior distribution, we propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data. First, we propose an LCC sampling method in LCCGAN to sample meaningful points from the latent manifold. With the LCC sampling method, we can exploit the local information on the latent manifold and thus produce new data with promising quality. Second, we propose an improved version, namely LCCGAN++, by introducing a higher-order term in the generator approximation. This term is able to achieve better approximation and thus further improve the performance. More critically, we derive the generalization bound for both LCCGAN and LCCGAN++ and prove that a low-dimensional input is sufficient to achieve good generalization performance. Extensive experiments on four benchmark datasets demonstrate the superiority of the proposed method over existing GANs.
К ВОПРОСУ О СОЦИАЛЬНОЙ АДАПТАЦИИ БУДУЩИХ ИНЖЕНЕРОВ В УСЛОВИЯХ ПАНДЕМИИ

The article in the framework of the signal sociological study by the method of content analysis of student essays considers the main problems and directions of social adaptation in the assessments of future engineers. The main types and difficulties of adaptation faced by students in the context of the Covid 19 pandemic are highlighted: educational activities, communications, problems of social responsibility, material and financial difficulties, changing the usual place of residence during University studies.
Using Multi-Element Reference Materials for Approximating Concentrations in Soils and Sediments from Micro XRF Spectra

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Tracking of Soccer Players Using Optical Flow

This paper describes and gives an idea of moving objects’ detection and tracking which is useful for sports applications. In sports, the challenging task is to detect and track the motion of players in each video frame. This paper describes a hierarchical clustering algorithm to segment human region (player) based on color and depth followed by optical flow analysis using the Lucas–Kanade algorithm to detect and estimate motions of players. The optical flow used for analyzing large displacement and estimate motions in each frame accurately. For the past few years sports video analysis has received increasing attention, hence player tracking and recognizing trajectories are two major issues which are highly relating. In the proposed system, hierarchical clustering and optical flow together demonstrated on soccer dataset captured at Alfheim stadium to achieve96%of tracking accuracy. In Future, the given system is extended to find the trajectories of each player.
Workshop on Blockchain Use Cases in Digital Health

We present in this chapter discussion of how blockchain can be used in Digital Health. Benefits and risks of using blockchain were presented. Guide questions with sample answers are also presented to deepen the discussion of the topic. At the last section of the chapter, several use cases on how blockchain is used in Digital Health are presented.
Personalized Book Recommendation to Young Readers

Online learning platforms that aim to improve reading interests and proficiency of young readers, particularly students in elementary schools, rarely have automated personalized recommendation services. This study attempts to bridge this gap by developing and evaluating two book recommenders that are integrated into an online learning platform for young readers. A preliminary user experiment was conducted to measure the effectiveness and usability of the recommender prototypes. Results of think-aloud usability testing, post-test questionnaires, and a semi-structured interview verified the feasibility of adding these book recommenders to improve personalization of the online learning platform. Further improvements of the recommenders were also suggested. The user evaluation framework provides a reference for future studies on personalized learning material recommendation.
Optimal Layout Synthesis for Quantum Computing

Recent years have witnessed the fast development of quantum computing. Researchers around the world are eager to run larger and larger quantum algorithms that promise speedups impossible to any classical algorithm. However, the available quantum computers are still volatile and error-prone. Thus, layout synthesis, which transforms quantum programs to meet these hardware limitations, is a crucial step in the realization of quantum computing. In this paper, we present two synthesizers, one optimal and one approximate but nearly optimal. Although a few optimal approaches to this problem have been published, our optimal synthesizer explores a larger solution space, thus is optimal in a stronger sense. In addition, it reduces time and space complexity exponentially compared to some leading optimal approaches. The key to this success is a more efficient spacetime-based variable encoding of the layout synthesis problem as a mathematical programming problem. By slightly changing our formulation, we arrive at an approximate synthesizer that is even more efficient and outperforms some leading heuristic approaches, in terms of additional gate cost, by up to 100%, and also fidelity by up to 10x on a comprehensive set of benchmark programs and architectures. For a specific family of quantum programs named QAOA, which is deemed to be a promising application for near-term quantum computers, we further adjust the approximate synthesizer by taking commutation into consideration, achieving up to 75% reduction in depth and up to 65% reduction in additional cost compared to the tool used in a leading QAOA study.
Anti Cybercrime Strategies as an Integral Part of a Cyber Security Strategy

Internet using tendencies through computer and mobile phone is increasing day by day. But the trend of use of internet through mobile phone is increasing rapidly. The rate of cyber crime is also increasing accordingly. Technology is an endless evolving expression in modern era, which increased security concerns and pushed us to create cyber environment. A National Cyber Security Strategy (NCSS) of a country reflects the state of that country’s cyber strength which represents the aim and vision of the cyber security of a country. So, it is very essential to make robust and strong strategies for the users of internet so that the internet users think several times to commit any cyber crime. Very insignificant attempts had been made to assess the strategic strength of NCSS of Bangladesh by performing cross comparisons on NCSS of different Nations. Therefore, the motive of this research is to evaluate the robustness of the existing cyber security strategy of Bangladesh in comparison with some of the most technologically advanced countries in Asian continent and others developed countries of the world.
Correlated Data in Differential Privacy: Definition and Analysis

Differential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real-world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: 1) using parameters to describe data correlation in differential privacy, 2) using models to describe data correlation in differential privacy, and 3) describing data correlation based on the framework of Pufferfish. Firstly, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.
Uyarlamalı Bilgisayar Testlerinde Bulanık Mantık Kullanılması

In this research, the contribution of expert knowledge in question selection algorithms of adaptive tests is studied and the employment of this approach in computerized adaptive tests is examined. In addition, the effect of multi-dimensional questions on measurement is also addressed. An algorithm utilizing the expert knowledge in computerized adaptive tests is proposed and tested in the experiments. A case study was conducted on seventh-grade science course with the utilization of fuzzy-logic based adaptive test. Consequently, by the help of the statistical studies, it is shown that computerized adaptive testing expresses the differences in student achievement levels in more visible way. Furthermore, 31 questions in maximum and 22 questions in minimum were observed to be effective for accurate assessment or student achievement levels on seven dimensions in adaptive testing. Using fuzzy logic in adaptive tests which were assessed to be effective in self-learning and distance education environments is found to be plausible.
SeqDialN: Sequential Visual Dialog Networks in Joint Visual-Linguistic Representation Space

In this work, we formulate a visual dialog as an information flow in which each piece of information is encoded with the joint visual-linguistic representation of a single dialog round. Based on this formulation, we consider the visual dialog task as a sequence problem consisting of ordered visual-linguistic vectors. For featurization, we use a Dense Symmetric Co-Attention network as a lightweight vison-language joint representation generator to fuse multimodal features (i.e., image and text), yielding better computation and data efficiencies. For inference, we propose two Sequential Dialog Networks (SeqDialN): the first uses LSTM for information propagation (IP) and the second uses a modified Transformer for multi-step reasoning (MR). Our architecture separates the complexity of multimodal feature fusion from that of inference, which allows simpler design of the inference engine. IP based SeqDialN is our baseline with a simple 2-layer LSTM design that achieves decent performance. MR based SeqDialN, on the other hand, recurrently refines the semantic question/history representations through the self-attention stack of Transformer and produces promising results on the visual dialog task. On VisDial v1.0 test-std dataset, our best single generative SeqDialN achieves 62.54% NDCG and 48.63% MRR; our ensemble generative SeqDialN achieves 63.78% NDCG and 49.98% MRR, which set a new state-of-the-art generative visual dialog model. We fine-tune discriminative SeqDialN with dense annotations and boost the performance up to 72.41% NDCG and 55.11% MRR. In this work, we discuss the extensive experiments we have conducted to demonstrate the effectiveness of our model components. We also provide visualization for the reasoning process from the relevant conversation rounds and discuss our fine-tuning methods. Our code is available at https://github.com/xiaoxiaoheimei/SeqDialN