Reviewerly/exHarmony huggingface dataset:
The Readiness of Teachers of Institutions for Orphaned Children and Children Left Without Parental Care to Ensure the Process of Forming the Skills of Pupils’ Volitional Self-Regulation

The paper is devoted to the issue of increasing the readiness of teachers of educational organizations to ensure the process of forming the skills of voli-tional self-regulation of orphans and children left without parental care. The components of teachers’ readiness to ensure the process of formation of stu-dents’ volitional self-regulation skills are deter-mined. A diagnostic toolkit for assessing the readi-ness of teachers to ensure the process of forming the skills of volitional self-regulation of orphans and children left without parental care is proposed. It is revealed that specialists of institutions for orphans and children left without parental care have different levels of readiness to effectively carry out the pro-cess of formation of volitional self-regulation skills in students. To solve the identified problem, a phased work was implemented with teachers of in-stitutions for orphans and children left without pa-rental care. It is proved that the step-by-step work with teachers provides an increase in the level of all components of their readiness to ensure the pro-cess of formation of students’ volitional self-regulation skills.
Dynamic Knowledge Graph Completion with Jointly Structural and Textual Dependency

Knowledge Graph Completion (KGC) aims to fill the missing facts in Knowledge Graphs (KGs). Due to the most real-world KGs evolve quickly with new entities and relations being added by the minute, the dynamic KGC task is more practical than static KGC task because it can be easily to scale up the KGs by add new entities and relations. Most existing dynamic KGC models are ignore the dependency between multi-source information and topology-structure so that they lose very much semantic information in KGs. In this paper, we proposed a novel dynamic KGC model with jointly structural and textual dependency based on deep recurrent neural network (DKGC-JSTD). This model learns embedding of entity’s name and parts of its text-description to connect unseen entities to KGs. In order to establish the relevance between text description information and topology information, DKGC-JSTD uses deep memory network and association matching mechanism to extract relevant semantic feature information between entity and relations from entity text-description. And then using deep recurrent neural network to model the dependency between topology-structure and text-description. Experiments on large data sets, both old and new, show that DKGC-JSTD performs well in the dynamic KGC task.
Feature-Embedded Evolutionary Algorithm for Network Optimization

Network optimization problems are usually NP-hard and evolutionary algorithms (EAs) are good at these problems. However, direct implementation of EA, ignoring the network properties or problem features, is common which usually results in a low-efficient algorithm. In this paper, a framework of feature-embedded evolutionary algorithm is outlined and the idea is demonstrated by embedding the nodal centralities in a genetic algorithm (GA) for solving the node selection problem in pinning control. Simulation results confirm that the new design outperforms existing deterministic schemes in terms of solution quality, and significant improvements are also noticed as compared to classical GA. The results also shed light on high efficient feature-embedded EAs in solving other challenging optimization problems.
Cost- and Time-Based Data Deployment for Improving Scheduling Efficiency in Distributed Clouds

Abstract In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the efficiency of task scheduling. In order to reduce data access cost and data deployment time, an optimal data deployment algorithm is proposed in this paper. By modeling and analyzing the data deployment problem, the problem is solved by using the improved genetic algorithm. After the data are well deployed, aiming at improving the efficiency of task scheduling, a task progress aware scheduling algorithm is proposed in this paper in order to make the speculative execution mechanism more accurate. Firstly, the threshold to detect the slow tasks and fast nodes are set. Then, the slow tasks and fast nodes are detected by calculating the remaining time of the tasks and the real-time processing ability of the nodes, respectively. Finally, the backup execution of the slow tasks is performed on the fast nodes. While satisfying the load balancing of the system, the experimental results show that the proposed algorithms can obviously reduce data access cost, service-level agreement (SLA) default rate and the execution time of the system and optimize data deployment for improving scheduling efficiency in distributed clouds.
A Modular Deep Learning Architecture for Anomaly Detection in HRI

Considering humans as a non-deterministic factor makes anomaly detection in Human-Robot Interaction scenarios rather a challenging problem. Anomalous events like unexpected user interaction or unforeseen environment changes are unknown before they happen. On the other hand, the work process or user intentions could evolve in time. To address this issue, a modular deep learning approach is presented that is able to learn normal behavior patterns in an unsupervised manner. We combined the unsupervised feature extraction learning ability of an autoencoder with a sequence modeling neural network. Both models were firstly evaluated on benchmark video datasets, revealing adequate performance comparable to the state-of-the-art methods. For HRI application, a continuous training approach for real-time anomaly detection was developed and evaluated in an HRI-experiment with a collaborative robot, ToF camera, and proximity sensors. In the user study with 10 subjects irregular interactions and misplaced objects were the most common anomalies, which system was able to detect reliably.
A Methodological Assistant for UML and SysML Use Case Diagrams

Use case driven analysis is the corner stone of software and systems modeling in UML and SysML, respectively. A use case diagram identifies the main functions to be offered by the system and showcases the interactions between in-system use cases and out-system users. Identifying and organizing use cases requires good abstraction skills. Therefore, many students and industry practitioners face methodological problems in writing good use cases. Many books and tutorials have addressed the subject. Nevertheless, integration of use case elaboration principles into a UML or SysML tool still remains an open issue. This paper proposes solutions and discusses implementation in a methodological assistant named UCCheck. The latter helps use case diagrams designers to rely on formalized rules and reuse of previous diagrams to create and review their use case diagrams. Implemented in Python, UCCheck is interfaced with the free SysML software TTool and with Cameo Systems Modeler, leaving doors open for other UML or SysML tools.
Designing a Semi-automatic Map Construction Process for the Effective Visualisation of Business Geodata

This paper proposes a map construction process for the semi-automatic construction of thematic maps from business information data. Addressing a non-specialist user audience, the map construction process will allow a correct, at the same time effective cartographic visualisation for further (geo)visual analysis. Utilising the frequently disregarded geospatial component of existing business mass data, quality map representations facilitate the visual exploration, detection, and analysis of relevant spatial data distributions and structures hitherto unseen in the data. Presently, neither operational procedures nor appropriate software systems, such as BIS, DDS or GIS, are available in the industry for an effective map representation of the geocoded data. To put economic and business experts into a position to make full use of the geo coordinates present in the data, an easy-to-handle map construction process is required to exploit the full semantic and spatial potential of business data. Exemplified for an area diagram map, the map construction process discussed here provides the relevant tools and methods for the targeted audience.
Image smoothing using regularized entropy minimization and self-similarity for the quantitative analysis of drug diffusion

Targetable drug delivery is an important method for the treatment of liver tumors. For the quantitative analysis of drug diffusion, the establishment of a method for information collection and characterization of extracellular space is developed by imaging analysis of magnetic resonance imaging (MRI) sequences. In this paper, we smoothed out interferential part in scanned digital MRI images.Making full use of priors of low rank, nonlocal self-similarity, and regularized sparsity-promoting entropy, a block-matching regularized entropy minimization algorithm is proposed. Sparsity-promoting entropy function produces much sparser representation of grouped nonlocal similar blocks of image by solving a nonconvex minimization problem. Moreover, an alternating direction method of multipliers algorithm is proposed to iteratively solve the problem above.Experiments on simulated and real images reveal that the proposed method obtains better image restorations compared with some state-of-the-art methods, where most information is recovered and few artifacts are produced.
An Efficient approach of NeuroHash and its Comparision with Cryptograhic Hash

Hash characteristic is one of the fantastic thing in cryptology which may be applied to message authentication and Digital Signature. There are numerous cryptographic Hash functions, for example, MD4, MD5, SHA-I SHA-2 mentioned by complicated techniques of multi-spherical cycles. As of overdue a portion of the shortcomings of those hash functions has been located as: 1. MD5 can be cracked thru collision attack. 2. SHA -1 is also now not secure to collision attack.In the proposed Scheme, A newly created hash function has been applied the XOR characteristic at the plaintext block previously preparing it into neural structures for computing message convolution and use of grey code concept on produced hash code-Both together give extra protection to the cryptographic framework. So studies and description of Secure and powerful hash function is required. For age of any other hash feature the wonder of neural synchronization and mutual gaining knowledge of is applied. A tenderfoot hash calculation has been evolved in mild of four layer feed forward system piecewise linear chaotic map and broke down towards the cryptographic hash capabilities.
Incompatibilities Between Iterated and Relevance-Sensitive Belief Revision

The AGM paradigm for belief change, as originally introduced by Alchourron, Gärdenfors and Makinson, lacks any guidelines for the process of iterated revision. One of the most influential work addressing this problem is Darwiche and Pearl's approach (DP approach, for short), which, despite its well-documented shortcomings, remains to this date the most dominant. In this article, we make further observations on the DP approach. In particular, we prove that the DP postulates are, in a strong sense, inconsistent with Parikh's relevance-sensitive axiom (P), extending previous initial conflicts. Immediate consequences of this result are that an entire class of intuitive revision operators, which includes Dalal's operator, violates the DP postulates, as well as that the Independence postulate and Spohn's conditionalization are inconsistent with axiom (P). The whole study, essentially, indicates that two fundamental aspects of the revision process, namely, iteration and relevance, are in deep conflict, and opens the discussion for a potential reconciliation towards a comprehensive formal framework for knowledge dynamics.
A Parallelized Variant of Junker’s QuickXPlain Algorithm

Conflict detection is used in many scenarios ranging from interactive decision making to the diagnosis of potentially faulty hardware components or models. In these scenarios, the efficient identification of conflicts is crucial. Junker's QuickXPlain is a divide-and-conquer based algorithm for the determination of preferred minimal conflicts. Motivated by the increasing size and complexity of knowledge bases, we propose a parallelization of the original algorithm that helps to significantly improve runtime performance especially in complex knowledge bases. In this paper, we introduce a parallelized version of QuickXPlain that is based on the idea of predicting and executing parallel consistency checks needed by QuickXPlain.
Performance of the Secret Electronic Voting Scheme Using Hyperledger Fabric Permissioned Blockchain

Issues related to reliable electronic voting are still very relevant and not fully resolved. The use of distributed ledger technologies for these purposes has great potential, but even private blockchain solutions often have insufficient bandwidth to satisfy the needs of at least corporate voting, not to mention the public elections. The article studies one of such voting systems, which is based on Hyperledger Fabric permissioned blockchain, where the Identity Mixer technology is used as the anonymizing part.
Multi-Domain Dialogue State Tracking with Hierarchical Task Graph

Multi-domain dialogue state tracking (DST), which tracks user goals and intentions across multiple domains, is a core task for multi-domain task-oriented dialogue system. Previous works in multi-domain DST focus on the open-vocabulary setting to alleviate the over-dependence on pre-defined ontology. However, they come up short of modeling the relationships among domains and slots in an explicit and efficient way. In this paper, we propose a multi-domain dialogue state tracker with hierarchical task graph (DST-HTG) to address the above issues. DST-HTG uses a copy mechanism to perform DST under the open-vocabulary setting, which makes our model eliminate the dependence on pre-defined full ontology. Moreover, we extend our DST model with a hierarchical task graph which has simple structure and rich semantic information to incorporate the relationships among domains and slots into DST process explicitly and efficiently. Empirical results show that DST-HTG achieves the state-of-the-art joint goal accuracy and slot accuracy in MultiWOZ 2.0, a recently proposed multi-domain task-oriented dialogue dataset, which indicates the effectiveness of our proposed model.
Writing for Human and Machine Translation

The Red Hat Localization Services team translates technical product documentation from English to other languages required for Red Hat customers. Translators from Localization Services collaborate with Red Hat technical writers to clarify statements in the documentation and answer technical questions related to the documentation as they translate content. This industry insight report examines the team's translation workflow and explores how the machine translation technology used by the Localization Services team impacts the translation progress, for better and for worse. We take a closer look at how this intersection of technology and design both limits and enhances the documentation design and translation processes. This report also examines the role of the technical writer in the translation process, how the writer's documentation design decisions impact the machine translation process, and how the writer should be influenced by the translation process as a whole. This report discusses several methods that can be used by technical writers to write clear and concise content for both human and machine translation. Taking a proactive approach to writing for a global audience makes the translations process more efficient and increases content quality.
OvNMTF Algorithm: an Overlapping Non-Negative Matrix Tri-Factorization for Coclustering

Coclustering algorithms are an alternative to classic one-sided clustering algorithms. Because of its ability to simultaneously cluster rows and columns of a dyadic data matrix, coclustering offers a higher value-added information: it offers column clusters besides row clusters, and the relationship between them in terms of coclusters. Different structures of coclusters are possible, and those that overlap in terms of rows or columns still represent an open question with room for improvements. In addition, while most related literature cites coclustering as a means of producing better results from one-side clustering, few initiatives study it as a tool capable of providing higher quality descriptive information about this clustering. In this paper, we present a new coclustering algorithm - OvNMTF, based on triple matrix factorization, which properly handle overlapped coclusters, by adding degrees of freedom for matrix factorization that enable the discovery of specialized column clusters for each row cluster. As a proof of concept, we modeled text analysis as a coclustering problem with column overlaps, assuming that given words (data matrix columns) are associated with over one document cluster (row cluster) because they can assume different semantic relationships in each association. Experiments on synthetic data sets show the OvNMTF algorithm reasonableness; experiments on real-world text data show its power for extracting high quality information.
Block Chain Model for Regional Elections in Indonesia

PILKADA or regional head election is the activity of the regional government to elect regional heads in Indonesia which are carried out directly by voters. So far, the election has been done manually where the election documents before and after the election are distributed from the polling station to the central office and vice versa about the election results so that the selection process is less effective and efficient. This study aims to create a block chain model for regional elections in Indonesia that will ensure the accuracy of regional head election data. The research method uses observations about the mechanism of regional head election, followed by design using the block chain theory. The resulting model is tested for validity to get input from political observers.
Designs for an Expressive Mechatronic Chordophone

Plucked strings are an exciting sound generation model for technical and timbral exploration. Mechatronic chordophones take advantage of this model and have been the focus of extensive research and exploration in musical robotics, often used as stand-alone instruments or as part of sound art installations. However, no existing chordophone designs have utilised the expressive potential of plucked strings to their full extent.In this paper, we introduce an expressive mechatronic mono-chord that serves as a prototyping platform for the construction of a polystring chordophone. This new chordophone has been developed to offer enhanced dynamic range, fast picking speeds, fast pitch shifter displacement, and additional expressive techniques compared to existing systems.
Hardware implementation of an α - level based binary search and shifting fuzzifier (α - BSSF)

Fuzzy processors are used for control actions in nonlinear mechatronic systems where high processing speed is required. The Field Programmable Gate Arrays (FPGA) are a good option to implement low cost fuzzy hardware in a short development time. A very important block in fuzzy hardware is the fuzzifier, since it affects directly in the accuracy of the result and in the processing time for obtaining a fuzzy number. There have been many design methodologies intended for enhancing the performance of this block. This paper presents a parallel fuzzifier circuit called α-BSSF. Its main design characteristics are the use of α-levels for membership representation, usage of integer numbers, and avoiding time-consuming operations. As result, we obtained a fuzzifier that shows advantages in the reduction of the response time and computational resources against the existing sequential fuzzification methods. This proposal is targeted not only for T1FS, but also for T2FS, since the membership calculation through fuzzifier is applied in the same way but twice.
An Ensemble Classification Framework Based on Latent Factor Analysis

Classification, which is one of the most powerful approaches to filter valuable information from big data, is a typical supervised learning method of machine learning. In many real applications, the collected data may contain many redundant features and have some missing entries. If a classifier is learned directly on such data, it cannot obtain a satisfactory classification performance. In this paper, we propose an ensemble classification framework based on latent factor analysis (ECF-LFA). Its main idea includes two parts: 1) employing the latent factor analysis (LFA) to extract the latent factors (LFs) from original data, which can avoid the influence of redundant features and handle the data with many missing entries, and 2) using these extracted LFs as the input for base classifiers to conduct the ensemble learning, which can boost a base classifier's classification accuracy. Experimental results on four benchmark datasets and three well-known classification algorithms verify that ECF-LFA can effectively improve a classifier's performances.
A Lightweight Network Model For Video Frame Interpolation Using Spatial Pyramids

In recent years, deep learning based video frame interpolation methods have shown impressive results in handling occlusion, blur and large motion. However, they are usually very heavy in terms of model size, and they hardly to be employed in i.e. mobile phones or other portable devices with limited computing power. To address the problem, we propose light-weighted Spatial Pyramid Frame Interpolation Network (SPFIN), a hierarchical network in a coarse-to-fine approach to reconstruct frames. At each pyramid level, we apply two light sub-networks to model optical flow and visibility mask instead of commonly used U-Net architecture. The flow and mask are up-sampled and optimized progressively. Finally, the intermediate frame is formed by linearly blending warped frames and masks. Experimental results on two benchmark problems show that our model has the smallest size, but better or comparable performance comparing to existing state-of-the art models.
A Survey on Student Attendance System Using Face Recognition

The manual attendance system is a time- consuming method which creates many difficulties for teachers to record student's attendance. Therefore, there is a need for a more convenient biometric student attendance system (SAS) to minimize these difficulties. As face recognition is considered among the best biometric technique, hence face recognition technique is inferential to be used in SAS. The main purpose of this study is to understand the existing works on SAS using face recognition. The current study conducted a survey on previous literature on student attendance system using face recognition. This paper describes the face recognition processes, approaches, and challenges involved in face recognition technique. Illumination in face recognition process has been a challenge in many previous studies. To address this challenge, this study proposed a mobile based SAS by using face recognition technique. The study comes up with a conclusion that face recognition is the most time saving method in SAS that used the mobile camera to resolve the issue of illumination and pose invariance for face recognition in SAS. It was also found that the use of hybrid approach of Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) algorithm, increases the accuracy of face recognition in SAS.