Reviewerly/exHarmony huggingface dataset:
Parental Competencies’ Development In Higher Education System

Modern society imposes high requirements to human capital. The analysis of the modern young parents need for parenthood knowledge and justification of the key role of education social institution in the parental competencies’ development is the paper objective. The Russian higher education standards were analyzed and the content analysis of social media (parents’ thematic communities) and parents’ web requests concerning parenting and children development was carried out. On the basis of performed studies the following conclusions were drawn: 1) a number of social institutions is responsible for the human capital formation; 2) competencies’ formation is not an objective of the education social institution; 3) an efficiency of social institutions of the family, the culture, the religion and the state dealing with the parental competencies’ formation is poor – it is evidenced by the fact that nowadays the young people feel the lack of knowledge concerning parenthood and parental functions implementation methods. The authors suggest to include the parental competencies’ development problem in the education system. The development of an educational environment with flexible modules for the formation of parental competencies can be a solution. Strategically, it will allow for human capital improvement, which in its turn will help to solve a number of population and economic Russian problems.
Designing for Privacy and Confidentiality on Distributed Ledgers for Enterprise (Industry Track)

Distributed ledger technology offers numerous desirable attributes to applications in the enterprise context. However, with distributed data and decentralized computation on a shared platform, privacy and confidentiality challenges arise. Any design for an enterprise system needs to carefully cater for use case specific privacy and confidentiality needs. With the goal to facilitate the design of enterprise solutions, this paper aims to provide a guide to navigate and aid in decisions around common requirements and mechanisms that prevent the leakage of private and confidential information. To further contextualize key concepts, the design guide is then applied to three enterprise DLT protocols: Hyperledger Fabric, Corda, and Quorum.
Analysis of Ciphertext Policy Hidden Attribute-Based Encryption and Its Improved Method

With people paying more attention to personal privacy protection, how to achieve fine-grained access control of data while protecting users’ privacy has become a hot research at present. A Ciphertext Policy Attribute-based Encryption (CP-ABE) with hiding policy is regarded as one of the most effective methods to solve above problem. Although many policy hidden CP-ABE schemes have been proposed, in this paper, we will show some of them fail to achieve the complete privacy-preserving. Hence two effective attacks are introduced at first, namely, the attack of attribute testing and the guessing attack of access policy. Then we show several known schemes can not resist these two attacks. Finally, an effective policy hidden CP-ABE scheme that can resist the above two attacks is proposed. And we also show it achieves full security in the standard model under static assumptions.
Worst case short lattice vector enumeration on block reduced bases of arbitrary blocksizes

In ICITS 2015, Walter studied the worst case computational cost to enumerate short lattice vectors on two well-known block reduced bases, i.e., BKZ reduced bases and slide reduced bases. Until then, existing works analyzed only extreme preprocessed bases, e.g., LLL reduced bases that are the weakest ones and quasi-HKZ reduced bases that are the strongest ones; hence, Walter tried to interpolate these results. For this purpose, Walter tried to calculate enumeration cost on block reduced bases of arbitrary blocksizes. The topic should be theoretically interesting since hardness of the lattice problem relates to the security of lattice-based cryptography. In this paper, we revisit the problem with refined analyses. For both BKZ and slide reduced bases, we show that the worst case enumeration costs are smaller than Walter’s analyses. In particular, we show that our results are the best possible ones when we follow Walter’s approach. Furthermore, we extend Walter’s result for slide reduced bases. Since Walter only studied the original slide reduced bases proposed by Gama and Nguyen, he did not analyze arbitrary blocksizes. To extend the analyses to arbitrary blocksizes, we use the generalized slide reduction that was defined by Li and Wei. As a side contribution, we also show that the worst case behaviors of the generalized slide reduced bases are better than Li and Wei’s analyses. We obtain all these results by exploiting better geometric properties of block reduced bases.
Application of artificial neural network techniques to speaker verification

Recurrent Neural Networks (RNN) have shown promise in the area of automatic speech recognition. In this tutorial we examine the application of RNN architectures to the problem of text dependent automatic speaker identity verification.
Applying Data Mining Techniques for Predicting Diseases

The techniques of data mining are very popular of Diseases.The advancement in health analysis has been improved by technical advances in computation, automation and data mining.Nowadays, data mining is getting used in a vast area .The Nature of the medical field is made with the knowledge wherever there's a spread of data but untapped during a correct.and thus, the foremost serious challenge facing this area is the quality of service provided which suggests to create the diagnose during a correct manner in a timely manner and supply acceptable medications to patients.Thus Health information technology has emerged as a replacement technology within the health care sector in a short amount by utilizing Business Intelligence 'BI' that could be a data-driven Decision Support System.The various techniques of data mining are used and compared during this analysis.
Differential cryptanalysis on DES cryptosystem up to eight rounds

Differential cryptanalysis is considered as a powerful technique in the field of cryptanalysis, applied to symmetric-key block ciphers. It is a kind of chosen plaintext attack in which the cryptanalyst has some sets of the plaintext and the corresponding ciphertext pairs of his choice. These pairs of the plaintext are related by a constant difference. Generally, it is the analysis of how differences in input information can affect the resultant difference at the output. In this paper, we present step by step implementation of differential cryptanalysis of data encryption standard (DES) up to 8-rounds.
Region-Based Image-Key-Element Decomposition for Large-Scale SAR Images

A large-scale SAR image covers various land covers and targets with different complexity and statistical characteristics. Single algorithm always fails to deal with the interpretation tasks of large-scale remote sensing images, due to the mismatch between local background conditions and the algorithm requirements. This paper proposes a novel processing method named Image-Key-Element Decomposition, aiming at decomposing a SAR image into several regions under a certain criterion. Every decomposed key element is the subset of the whole image and represents a pixel set with a particular characteristic. Considering that many algorithms have certain requirements about the complexity status of backgrounds, homogeneous is used as the criterion in this paper and the SAR images will be decomposed into homogeneous and heterogeneous elements. The experimental result shows that homogeneous and heterogeneous regions are separated by the proposed model and the following processing may take advantage of the result to improve the performance.
From Simplified Kripke-Style Semantics to Simplified Analytic Tableaux for Some Normal Modal Logics

Modal logics $$\mathsf{K45}$$, $$\mathsf{KB4}$$, $$\mathsf{KD45}$$ and $$\mathsf{S5}$$ are of particular interest in knowledge representation, especially in the context of knowledge and belief modelling. Pietruszczak showed that these logics are curious for another reason, namely for the fact that their Kripke-style semantics can be simplified. A simplified frame has the form $$\langle W,A\rangle $$, where $$A\subseteq W$$. A reachability relation R may be defined as $$R=W\times A$$, which, however, makes it superfluous to explicitly refer to it. It is well-known that $$\mathsf{S5}$$ is determined by Kripke frames with $$R=W\times W$$, i.e., $$A=W$$. Pietruszczak showed what classes of simplified frames determine $$\mathsf{K45}$$, $$\mathsf{KD45}$$, and $$\mathsf{KB4}$$. These results were generalized to the extensions of these logics by Segerberg’s formulas. In this paper, we devise sound, complete and terminating prefixed tableau algorithms based on simplified semantics for these logics. Since no separate rules are needed to handle the reachability relation and prefixes do not store any extra information, the calculi are accessible and conceptually simple and the process of countermodel-construction out of an open tableau branch is straightforward. Moreover, we obtain a nice explanation of why these logics are computationally easier than most modal logics, in particular $$\textsc {NP}$$-complete.
Learning to request guidance in emergent language

Previous research into agent communication has shown that a pre-trained guide can speed up the learning process of an imitation learning agent.The guide achieves this by providing the agent with discrete messages in an emerged language about how to solve the task.We extend this one-directional communication by a one-bit communication channel from the learner back to the guide: It is able to ask the guide for help, and we limit the guidance by penalizing the learner for these requests.During training, the agent learns to control this gate based on its current observation.We find that the amount of requested guidance decreases over time and guidance is requested in situations of high uncertainty.We investigate the agent's performance in cases of open and closed gates and discuss potential motives for the observed gating behavior.
Language-Parametric Methods for Developing Interactive Programming Systems

All computers run software, such as operating systems, web browsers, and video games, which are used by billions of people around the world. Therefore, it is important to develop high-quality software, which is only possible through interactive programming systems that involve programmers in the exchange of correct and responsive feedback. Fortunately, for many general-purpose programming languages, integrated development environments provide interactive programming systems through code editors and editor services. On the other hand, Domain-Specific Languages (DSLs) are programming languages that are specialized towards a specific problem domain, enabling better software through direct expression of problems and solutions in terms of the domain. However, because DSLs are specialized to a specific domain, and there are many problem domains, we need to develop many new DSLs, including their interactive programming systems! Ad-hoc development of an interactive programming system for a DSL is infeasible, as developing one requires a huge development effort. Therefore, our vision is to create and improve language-parametric methods for developing interactive programming systems. A language-parametric method takes as input a description of a DSL, and automatically implements (parts of) an interactive programming system, reducing development effort, thereby making DSL development feasible. In this dissertation, we develop three language-parametric methods throughout the five core chapters. We develop a language-parametric method for incremental name and type analysis, in which language developers specify the name and type rules of their DSL in meta-languages (languages specialized towards the domain of language development). From such a specification, we automatically derive an incremental name and type analysis, including editor services such as code completion and inline error messages. We develop a language-parametric method for interactively bootstrapping the meta-language compilers of language workbenches. We version metalanguage compilers, explicitly denote dependencies between them, and perform fixpoint bootstrapping, where we iteratively self-apply meta-language compilers to derive new versions until no change occurs, or until a defect is found. These bootstrapping operations can be started and rolled back (when defect) in the interactive programming system of the language workbench. Finally, we develop PIE, a parametric method for developing interactive software development pipelines, a superset of interactive programming environments. With PIE, pipeline developers can concisely write pipeline programs in terms of tasks and dependencies between tasks and files, which the PIE runtime then incrementally executes. PIE scales down to many low-impact changes and up to large dependency graphs through a change-driven incremental build algorithm.
Data Analysis on Powered Two Wheelers Riders’ Behaviour using Machine Learning

Analyzing powered two-wheeler rider behavior, i.e. classification of riding patterns based on 3-D accelerometer/gyroscope sensors mounted on motorcycles is challenging. This paper presents machine ...
Students' Reading Performance as Measured by PISA Reading Literacy Test

Abstract: This study investigated students’ reading literacy performance based on gender. The sample of this study was 285 tenth graders of three public schools in one of the Districts in Palembang who were selected by using a purposive sampling technique. To collect the data, PISA 2009 reading literacy test was distributed to the sample. The data were statistically analyzed. The findings showed that both male and female students’ reading literacy were at Level 3 and there were no gender differences in the students’ reading literacy performance. Key w ords : tenth graders, gender differences in reading, PISA reading literacy test
The Effect of Contextual Cueing Effect for Configuration of Numbers on Users’s Search Time of Target Number : Based on PIN Authentication

모바일 환경에서 PIN(personal identification number) 인증 방식을 사용할 경우, 보안에서 발생할 수 있는 여러 취약점이 있다. 이 취약점을 보완하기 위해 PIN 입력 시 나타나는 숫자 배열에 맥락단서효과(contextual cueing effect)를 활용하여 사용자 인증 방법으로 사용될 수 있는지 알아보았다. 연구 1에서는 8×4 행렬에 0~9까지의 10개의 숫자가 무선적으로 배열될 때 맥락단서효과가 나타날 수 있는지 확인하였다. 연구 2에서는 훈련 단계를 통해 숫자 배열의 맥락을 학습한 후, 검사 단계에서 반복 화면과 새로운 화면이 무선적으로 제시될 때, 표적숫자를 찾을 때 탐색 시간의 차이가 있는지 살펴보았다. 그 후 재인 단계에서는 참가자들이 표적숫자의 위치에 의식적으로 접근할 수 있는지 알아보았다. 그 결과, 연구 1과 2를 통해 숫자 배열에 대한 맥락단서효과가 나타날 수 있음을 확인하였다. 또한, 연구 2를 통해 반복 화면과 새로운 화면이 무선적으로 제시되는 경우에도 탐색 시간의 차이가 확인되었으며, 참가자들이 반복 화면의 표적숫자의 위치에 외현적으로 접근이 가능한 것으로 나타났다. 이에 본 연구는, 맥락단서효과를 적용한 PIN입력이 사용자를 인증하는 추가 요인으로 사용될 가능성이 있음을 제안한다.When using PIN (personal identification number) authentication method in a mobile environment, several security vulnerabilities might occur. To remedy this vulnerabilities, we investigated whether users can be authenticated by PIN authentication adopting contextual cueing effect. In the study 1, we tested whether the contextual cueing effect would takes place when numbers from 0 to 9 randomly appeared in the 8 x 4 matrix. In the study 2, after the training phase, we compared search reaction times on repeated display with that on nonrepeated displays when repeated and nonrepeated displays randomly appeared in the test phase. The results showed that contextual cueing effect for configuration of numbers was found in the study 1,2. Further, participants found the target numbers more faster on repeated displays than on the nonrepeated displays when repeated and nonrepeated displays randomly appeared in test phase. In recognition phase, participants were able to access the location of target numbers on repeated displays explicitly. The present results suggest that the contextual cueing effect can be found in the PIN input display where the numbers were randomly presented.
The Study on Trafficability Assessment of Tracked Vehicle on Soft Seabed

Recently, the underwater robots in the ocean have been used more frequently. Not only the tracked heavy duty underwater vehicles for underwater pipeline or cabling but also tracked vehicles for the shallow water level area(0~5m water depth) where on-land working vehicles and ships can not work. Tracked vehicles secure trafficability by pushing the ground in the track, and if the ground is weak, the soil thrust sufficient to move the equipment cannot be ensured, making the vehicle impossible to move. In the previous study to evaluate the trafficability of tracked vehicle on the soft ground, vehicle can move when the ground thrust is greater by calculating the soil thrust exerted by the ground and the resistance to the vehicle s movement. However, if the seabed soil strength is very small and the ground is not sufficiently supported, the method of assessment by settlement may overestimate the vehicle’s trafficability. In this paper, the depth of destruction beyond the supporting force of the ground due to the heavy tracked vehicle s self weight placed on the weak ground was assessed as the vehicle s sinking load, and the trafficability of the tracked vehicle was evaluated by comparing the resistance and thrust of the ground at this time. In addition, if soft ground exists thinly on hard soil or rocky condition of the seabed, the trafficability of the tracked vehicle was finally assessed, taking into account the increased resistance of the ground due to the settlement of the track vehicle on the soft ground.
Pose and covariance matrix propagation issues in cooperative localization with LiDAR perception.

This work describes a cooperative pose estimation solution where several vehicles can perceive each other and share a geometrical model of their shape via wireless communication. We describe two formulations of the cooperation. In one case, a vehicle estimates its global pose from the one of a neighbor vehicle by localizing it in its body frame. In the other case, a vehicle uses its own pose and its perception to help localizing another one. An iterative minimization approach is used to compute the relative pose between the two vehicles by using a LiDAR-based perception method and a shared polygonal geometric model of the vehicles. This study shows how to obtain an observation of the pose of one vehicle given the perception and the pose communicated by another one without any filtering to properly characterize the cooperative problem independently of any other sensor. Accuracy and consistency of the proposed approaches are evaluated on real data from on-road experiments. It is shown that this kind of strategy for cooperative pose estimation can be accurate. We also analyze the advantages and drawbacks of the two approaches on a simple case study.
devsim/symdiff v1.0.0

SYMDIFF Symbolic differentation of algebraic expressions with Python and Tcl interfaces. Official Website: https://devsim.org Binary Releases: https://github.com/devsim/devsim/releases Latest DOI: https://doi.org/10.5281/zenodo.3544846
Analysis of Lightning Overvoltage According to Grounding Method and Corona Effect of Lightning Arrester in Wind farm

It is important to reduce the impact of lightning strikes because lightning can create overvoltage in the grid of the wind turbine and affect power quality. One of the ways to improve the stability of power facilities used in power systems is to use power surge arresters and to protect against transient overvoltage and surges in normal operation. So This paper analyzes the influence of overvoltage caused by lightning when the ground impedance is changed considering the grounding method of the lightning arrester and the corona effect in the wind farm.
Classification of Tweets Using Dictionary and Lexicon Based Approaches

Online social media is pervasive in nature. It allows people to use short text messages, images, audios and videos to express their opinions and sentiments about products, events and other people. For example, Twitter is an online social networking and news service where users post and interact with small and short messages, called “tweets”. Therefore, nowadays social media become a potential source for business and celebrities to find people’s sentiments and opinions about a particular event or product or themselves.Social media analysis is the process of gathering enormous amount of digital contents generated online from blogs sites and social media networks and examining them to find the insights.This paper focuses on discovering public opinions and sentiments he on the results of Indian election results declared recently. This Paper also deals Dictionary based Approach and Affective Lexicon based approaches which were used to find the public opinion about election results.
A DSL for elastic component-based cloud application

The deployment of component-based applications in cloud systems environment is becoming more and more complex. It is expected to provide elasticity in order to allow a deployed application to scale dynamically and meet variation in demand while ensuring a certain level of quality of service (QoS). However, there are still some open issues associated with the elasticity management. A conceptual model of elasticity management enabling the description of deployment and application constraints, properties and elasticity strategies at different levels (depending on the internal application architecture or on the cloud infrastructure and platform) in an automatic way is needed. In this paper, we propose a domain specific language (DSL) based on a metamodel, which precisely specifies three main views: the cloud service models, the automatic elasticity management strategies and the internal cloud application architecture. We illustrate through a case study, the MAPE-K-based approach using different scenarios of automatic elasticity management.
Product Recognition Algorithm Based on HOG and Bag of Words Model

The rapid detection and identification of products based on computer vision has important applications in the fields of unmanned retail and goods sorting. At present, the recognition rate of traditional product identification methods is not high, and the deep learning recognition method requires large-scale training and cannot meet real-time requirements. This paper proposes a product identification algorithm that combines traditional HOG detection with the SIFT feature-based bag of words model for the needs of product identification. Compared with the traditional product identification method for feature matching, the algorithm has the advantages of higher recognition rate and shorter time. The test results show that the real-time recognition rate can reach 98%. At the same time, the algorithm has the advantages of light weight and easy portability, and can be applied to many occasions such as unmanned retail or express picking.