Reviewerly/exHarmony huggingface dataset:
Full-text Search for Verifiable Credential Metadata on Distributed Ledgers

Self-sovereign Identity (SSI) powered by distributed ledger technologies enables more flexible and faster digital identification workflows, while at the same time limiting the control and influence of central authorities. However, a global identity solution must be able to handle myriad credential types from millions of issuing organizations. As metadata about types of digital credentials is readable by everyone on the public permissioned ledger with Hyperledger Indy, anyone could find relevant and trusted credential types for their use cases by looking at the records on the blockchain. To this date, no efficient full-text search mechanism exists that would allow users to search for credential types in a simple and efficient fashion tightly integrated into their applications. In this work, we propose a full-text search framework based on the publicly available metadata on the Hyperledger Indy ledger for retrieving matching credential types. The proposed solution is able to find credential types based on textual input from the user by using a full-text search engine and maintaining a local copy of the ledger. Thus, we do not need to rely on information about credentials coming from a very large candidate pool of third parties we would need to trust, such as the website of a company displaying its own identifier and a list of issued credentials. We have also proven the feasiblity of the concept by implementing and evaluating a prototype of the full-text credential metadata search service.
From principal cognitive complexity to teacher intent to leave

Purpose The purpose of this paper is to test an innovative model for exploring the direct and indirect relationships between principals’ cognitive complexity (CC), schools’ absorptive capacity (ACAP), a teacher’s affective commitment and a teacher’s intent to leave. Design/methodology/approach Data were collected from a survey of 1,664 teachers at 107 Arab elementary schools, randomly selected from the database of the Israeli educational system. To test the proposed model, multilevel structural equation modeling was conducted. Findings The analysis confirmed that schools’ ACAP and a teacher’s affective commitment are prominent mediators between principals’ CC and a teacher’s intent to leave. Practical implications Understanding the factors that contribute to a teacher’s intent to leave could help school principals and policy makers retain effective teachers in today’s schools. Originality/value This study adds to the body of research directed at identifying school principals’ characteristics, as well as work-related factors, which may decrease a teacher’s intent to leave and are amenable to leadership intervention.
Hybrid reasoning in knowledge graphs: Combing symbolic reasoning and statistical reasoning

Knowledge graphs (KGs) contain rich resources that represent human knowledge in the world. There are mainly two kinds of reasoning techniques in knowledge graphs, symbolic reasoning and statistical reasoning. However, both of them have their merits and limitations. Therefore, it is desirable to com bine them to provide hybrid reasoning in a knowledge graph. In this paper, we present the first work on the survey of methods for hybrid reasoning in knowledge graphs. We categorize existing methods based on applications of reasoning techniques, and introduce the key ideas of them. Finally, we re-examine the remaining research problems to be solved and provide an outlook to future directions for hybrid reasoning in knowledge graphs.
Optimization of K Value at the K-NN algorithm in clustering using the expectation maximization algorithm

Abstract Data is the most important thing in a study. The quality of the results of the research will be directly proportional to the quality of the data that will be used in the research is concerned. One of the problems that exist in the data set is the absence of a value in the data for a particular attribute or better known as the missing data. One method that is often used by researchers is the k-nearst Neighbor (KNN). However, this method has several drawbacks, one of which is the selection of appropriate values of k not to degrade the performance of the classification. In the process of calculating the parameters k KNN there that can affect the accuracy of the classification results. To use more than one parameter k then used by majority voting to determine the classification results. If the parameter k in KNN classification used 1 then the result was very tight because it will use the nearest neighbor to the results of the classification. Conversely, if the value of the parameter k used KNN is great then the classification results will blur.This research will optimize the parameters k in the UN tax cluster using the algorithm expectation Maximation (EM). The results of the research in the form of clustering information by using the number of clusters k value optimization and the number of clusters without using the optimization of the value k. Then analysis the results after getting data already clustered. Results from the study showed that k obtained from the optimization algorithm can improve the results of the cluster where the 66% error can be reduced to 64%, yet very close to the best result of the measurement accuracy is tested.
Securing Big Data in the Age of AI

Increasingly organizations are collecting ever larger amounts of data to build complex data analytics, machine learning and AI models. Furthermore, the data needed for building such models may be unstructured (e.g., text, image, and video). Hence such data may be stored in different data management systems ranging from relational databases to newer NoSQL databases tailored for storing unstructured data. Furthermore, data scientists are increasingly using programming languages such as Python, R etc. to process data using many existing libraries. In some cases, the developed code will be automatically executed by the NoSQL system on the stored data. These developments indicate the need for a data security and privacy solution that can uniformly protect data stored in many different data management systems and enforce security policies even if sensitive data is processed using a data scientist submitted complex program. In this paper, we introduce our vision for building such a solution for protecting big data. Specifically, our proposed SECUREDL system allows organizations to 1) enforce policies that control access to sensitive data, 2) keep necessary audit logs automatically for data governance and regulatory compliance, 3) sanitize and redact sensitive data on-the-fly based on the data sensitivity and AI model needs, 4) detect potentially unauthorized or anomalous access to sensitive data, 5) automatically create attribute-based access control policies based on data sensitivity and data type.
Playtesting in Match 3 Game Using Strategic Plays via Reinforcement Learning

Playtesting is a lifecycle phase in game development wherein the completeness and smooth progress of planned content are verified before release of a new game. Although studies on playtesting in Match 3 games have attempted to utilize Monte Carlo tree search (MCTS) and convolutional neural networks (CNNs), the applicability of these methods are limited because the associated training is time-consuming and data collection is difficult. To address this problem, game playtesting was performed via learning based on strategic play in Match 3 games. Five strategic plays were defined in the Match 3 game under consideration and game playtesting was performed for each situation via reinforcement learning. The proposed agent performed within a 5% margin of human performance on the most complex mission in the experiment. We demonstrate that it is possible for the level designer to measure the difficulty of the level via playtesting various missions. This study also provides level testing standards for several types of missions in Match 3 games.
Quadratic Function Chaotic System and its Application on Digital Image Encryption

To obtain chaotic systems with good properties to help chaos based image encryption, this paper first study the condition under which quadratic functions can be used as pseudo-random sequence generator. We give a necessary and sufficient condition for quadratic functions topologically conjugate with the most frequently used Logistic map and Tent map. Since topological conjugacy maintains chaotic characteristics, more quadratic functions can be used as pseudo-random sequence generators in the digital image encryption. Then, it analyzes a diffusion algorithm with a good effect but poor in resisting the chosen plaintext attack. It is also verified by simulation. The quadratic function chaotic system is applied in the improvement of the diffusion algorithm. Furthermore, the improved algorithm contains plaintext related and cyclic shift operations. Finally, the performance analysis shows that the improved scheme has good security and high efficiency.
Simultaneous Object Detection and Semantic Segmentation

Both object detection in and semantic segmentation of camera images are important tasks for automated vehicles. Object detection is necessary so that the planning and behavior modules can reason about other road users. Semantic segmentation provides for example free space information and information about static and dynamic parts of the environment. There has been a lot of research to solve both tasks using Convolutional Neural Networks. These approaches give good results but are computationally demanding. In practice, a compromise has to be found between detection performance, detection quality and the number of tasks. Otherwise it is not possible to meet the real-time requirements of automated vehicles. In this work, we propose a neural network architecture to solve both tasks simultaneously. This architecture was designed to run with around 10 Hz on 1 MP images on current hardware. Our approach achieves a mean IoU of 61.2% for the semantic segmentation task on the challenging Cityscapes benchmark. It also achieves an average precision of 69.3% for cars and 67.7% on the moderate difficulty level of the KITTI benchmark.
A GPU-Based Quantum Annealing Simulator for Fully-Connected Ising Models Utilizing Spatial and Temporal Parallelism

Simulated quantum annealing (SQA) is a probabilistic approximation method to find a solution for a combinatorial optimization problem using digital computers. The processing time of SQA increases exponentially with the number of variables. Therefore, acceleration of SQA is regarded as a very important topic. However, parallel implementation is difficult due to the serial nature of the quantum Monte Carlo algorithm used in SQA. In this paper, we propose a method to implement SQA in parallel on a GPU while preserving the data dependency. According to the experimental results, we have achieved over 97 times speed-up while maintaining the same accuracy-level compared to a single-core CPU implementation.
Pedagogical Consciousness Formation of Future University Educators in the Process of Teaching English

The research is devoted to the scientific substantiation of the process of pedagogical consciousness formation of university educators in the process of teaching English, determination of the level of their pedagogical consciousness formation, developing an organization and methodological conditions for pedagogical consciousness formation, determination of the optimal methods of its diagnostics, and further implementation of organization and methodological conditions in the educational process of higher school.The research represents the organization and methodological conditions for the pedagogical consciousness formation of the university educators in the process of teaching English: the training program of theoretical knowledge on the nature and the structure of pedagogical consciousness; ensuring the active learning activities of students to develop the skills of practical application of important pedagogical tools and methodologies; the use of interactive teaching methods to create a learning environment that contributes to the assimilation of moral, ethical and cultural values of pedagogical activity as well as mastering of the self-assessment skills and self-control of the level of pedagogical consciousness formation.The authors define the criteria (content and activity, motivation and value, evaluation and reflection) that provide the effectiveness and optimality of the pedagogical consciousness formation.The positive dynamics of pedagogical consciousness formation of university educators is achieved through the use of the special course "Self-reflection of pedagogical consciousness" which includes pedagogical technologies, interactive and moderation techniques, creative exercises in the process of teaching English.The monitoring analysis of the results of the formative experiment proves that the experimental group, in contrast to the control group, has a higher level of pedagogical consciousness formation.
IDENTIFICATION OF ATTACKERS USING BLOCKCHAIN TRANSACTIONS USING CRYPTOGRAPHY METHODS

Blockchain is inventive approach to manage taking care of information, executing trades, performing limits, and working up trust in an open space. Many consider square chain as an advancement accomplishment for cryptography and digital security, with use cases going from comprehensive sent computerized cash structures like Bit-coin, to sharp understandings, insightful cross sections over the Internet of Things, and so forth. Regardless of the way that Blockchain has gotten creating interests in both academic network and industry in the progressing years, the security and insurance of Blockchains continue being at the point of convergence of the conversation while sending Blockchain in different applications. This paper presents a total layout of the security and insurance of Blockchain. To empower the discussion, we at first present the idea of Blockchains and its utility concerning Bit-coin like on the web trades. By then we portray the essential security properties that are maintained as the fundamental necessities what's more, building discourages for Bit-coin like advanced cash structures, trailed by presenting the additional security what's more, insurance properties that are needed in various Blockchain applications. Finally, we review the security and assurance systems for achieving these security properties in Blockchain-based structures, including delegate accord figurings, has joined limit, mixing shows, puzzling imprints, non-instinctive zero-data check, and so on. We surmise that this investigation can help per clients with increasing an all-around perception of the security and assurance of Blockchain concerning thought, qualities, systems and structures
Learning Constraints from Demonstrations

We extend the learning from demonstration paradigm by providing a method for learning unknown constraints shared across tasks, using demonstrations of the tasks, their cost functions, and knowledge of the system dynamics and control constraints. Given safe demonstrations, our method uses hit-and-run sampling to obtain lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used to obtain a consistent representation of the unsafe set via solving an integer program. Our method generalizes across system dynamics and learns a guaranteed subset of the constraint. We also provide theoretical analysis on what subset of the constraint can be learnable from safe demonstrations. We demonstrate our method on linear and nonlinear system dynamics, show that it can be modified to work with suboptimal demonstrations, and that it can also be used to learn constraints in a feature space.
Toward Interactive Self-Annotation For Video Object Bounding Box: Recurrent Self-Learning And Hierarchical Annotation Based Framework

Amount and variety of training data drastically affect the performance of CNNs. Thus, annotation methods are becoming more and more critical to collect data efficiently. In this paper, we propose a simple yet efficient Interactive Self-Annotation framework to cut down both time and human labor cost for video object bounding box annotation. Our method is based on recurrent self-supervised learning and consists of two processes: automatic process and interactive process, where the automatic process aims to build a supported detector to speed up the interactive process. In the Automatic Recurrent Annotation, we let an off-the-shelf detector watch unlabeled videos repeatedly to reinforce itself automatically. At each iteration, we utilize the trained model from the previous iteration to generate better pseudo ground-truth bounding boxes than those at the previous iteration, recurrently improving self-supervised training the detector. In the Interactive Recurrent Annotation, we tackle the human-in-the-loop annotation scenario where the detector receives feedback from the human annotator. To this end, we propose a novel Hierarchical Correction module, where the annotated frame-distance binarizedly decreases at each time step, to utilize the strength of CNN for neighbor frames. Experimental results on various video datasets demonstrate the advantages of the proposed framework in generating high-quality annotations while reducing annotation time and human labor costs.
Selective Feature Anonymization for Privacy-Preserving Image Data Publishing

There is a strong positive correlation between the development of deep learning and the amount of public data available. Not all data can be released in their raw form because of the risk to the privacy of the related individuals. The main objective of privacy-preserving data publication is to anonymize the data while maintaining their utility. In this paper, we propose a privacy-preserving semi-generative adversarial network (PPSGAN) that selectively adds noise to class-independent features of each image to enable the processed image to maintain its original class label. Our experiments on training classifiers with synthetic datasets anonymized with various methods confirm that PPSGAN shows better utility than other conventional methods, including blurring, noise-adding, filtering, and generation using GANs.
ICT Obstacles and Challenges Faced by English Language Learners During the Coronavirus Outbreak in Jordan

During the Coronavirus outbreak, the integration of Information and Communication Technology (ICT) has become a necessity, as ICT use in education is a systematic and structured process to improve the quality of education in Jordan. This study aimed at investigating the obstacles and challenges faced by English language students at Jadara University in Jordan while using e-learning programs during the school suspension. To achieve the aim of the study, this qualitative investigation has made use of convenience sample. Twelve students were chosen for a focused group interview where they were requested to answer questions concerning the constraining factors that they experienced while learning through ICT platforms. The findings show that students’ responses revealed three themes, namely, lack of effective training, lack of accessibility, and inconsistent teaching styles of instructors. Some of the recommendations suggested are that further research needs to be conducted into the speech acts used by Jordanians on different social networking platforms. Although barriers were significant, this paper recommended additional institutional support for online learning and increased technical and pedagogical training for students.
Improving Self-Attention Networks With Sequential Relations

Recently, self-attention networks show strong advantages of sentence modeling in many NLP tasks. However, self-attention mechanism computes the interactions of every pair of words independently regardless of their positions, which makes it not able to capture the sequential relations between words in different positions in a sentence. In this paper, we improve the self-attention networks by better integrating sequential relations, which is essential for modeling natural languages. Specifically, we 1) propose a position-based attention to model the interaction between two words regarding positions; 2) perform separated attention for the context before and after the current position, respectively; and 3) merge the above two parts with a position-aware gated fusion mechanism. Experiments in natural language inference, machine translation and sentiment analysis tasks show that our sequential relation modeling helps self-attention networks outperform existing approaches. We also provide extensive analyses to shed light on what the models have learned about the sequential relations.
A Symmetric Prior for the Regularisation of Elastic Deformations: Improved anatomical plausibility in nonlinear image registration

Nonlinear registration is critical to many aspects of Neuroimaging research. It facilitates averaging and comparisons across multiple subjects, as well as reporting of data in a common anatomical frame of reference. It is, however, a fundamentally ill-posed problem, with many possible solutions which minimise a given dissimilarity metric equally well. We present a regularisation method capable of selectively driving solutions towards those which would be considered anatomically plausible by penalising unlikely lineal, areal and volumetric deformations. This penalty is symmetric in the sense that geometric expansions and contractions are penalised equally, which encourages inverse-consistency. We demonstrate that this method is able to significantly reduce local volume changes and shape distortions compared to state-of-the-art elastic (FNIRT) and plastic (ANTs) registration frameworks. Crucially, this is achieved whilst simultaneously matching or exceeding the registration quality of these methods, as measured by overlap scores of labelled cortical regions. Extensive leveraging of GPU parallelisation has allowed us to solve this highly computationally intensive optimisation problem while maintaining reasonable run times of under half an hour.
Innovative Deep Neural Network Fusion for Pairwise Translation Evaluation

A language independent deep learning (DL) architecture for machine translation (MT) evaluation is presented. This DL architecture aims at the best choice between two MT (S1, S2) outputs, based on the reference translation (Sr) and the annotation score. The outputs were generated from a statistical machine translation (SMT) system and a neural machine translation (NMT) system. The model applied in two language pairs: English - Greek (EN-EL) and English - Italian (EN-IT). In this paper, a variety of experiments with different parameter configurations is presented. Moreover, linguistic features, embeddings representation and natural language processing (NLP) metrics (BLEU, METEOR, TER, WER) were tested. The best score was achieved when the proposed model used source segments (SSE) information and the NLP metrics set. Classification accuracy has increased up to 5% (compared to previous related work) and reached quite satisfactory results for the Kendall τ score.
F-IVM: Learning over Fast-Evolving Relational Data

F-IVM is a system for real-time analytics such as machine learning applications over training datasets defined by queries over fast-evolving relational databases. We will demonstrate F-IVM for three such applications: model selection, Chow-Liu trees, and ridge linear regression.