Reviewerly/exHarmony huggingface dataset:
RETRACTED: Design and development of embedded system of portable bicycle exerciser

A study of Parkinson's disease (P.D.) recovery shows that couples with high accelerations cycling can reduce infection's side effects. In this study, the method planned and designed a sophisticated mechanized bicycle to help patients suffering from Parkinson's disease improve engine work. A sportbike rider can be accurately controlled by increasing the acceleration speed's participation while obtaining constant test information. Here will find a plan and schedule gadgets and devices, like introducing products and maintaining the same calculation. Two control calculations have been created for the bike. They provide a valid load (static mode), providing a speed reference (dynamic way). In the static method, the cycle, just like a common pneumatic bicycle obstacle with programmable (load), can capture and record the signals required, such as pulse, rhythm and strength. In the strong mode, the bicycle at speed selected by the customer (rhythm) is running. It allows programmable speed changes, the fact that this has become the basis to achieve the desired operational benefits of the engine for P.D. patients. Portable bicycle adaptability and scalability planning allow to quickly change the control of computing and integrate different I / O to accommodate various riding experiences. Besides, the organization will not authorize regulators to provide information on bicycle riding distance during the meeting.
Panel: Incorporating Cloud Computing Competences into Computing Curriculum: Challenges &amp; Prospects

Cloud computing has emerged to be one of the leading professional competences desired by modern day employers, especially in the computing profession. We use computing as an umbrella term that includes Computer Science, Information Systems, Software Engineering, and other related courses. The benefits and value proposition of cloud computing have made it a desirable course to be taught across computing curricular. However, teaching and learning cloud computing comes with daunting challenges that often discourage educators. As a result, cloud computing is yet to be part of the computing curriculum in a vast majority of higher institutions in the USA; thereby denying students the myriad career and employment opportunities peculiar to their peers with cloud computing skills. The aim of this panel therefore is to analyze the challenges of teaching cloud computing, proffer solutions to those challenges, and develop an approach that can be adopted by computing instructors who desire to incorporate cloud computing competences into their curriculum. In addition, participants will have the opportunity to learn and access resources provided by 'AWS Educate' for teaching and learning cloud computing.
Recommendation of sustainable economic learning course based on text vector model and support vector machine

The sustainable economic learning course recommendation can quickly find the knowledge information that the user really needs from the massive information space and realize the personalized recommendation to the user. However, the occurrence of trust attacks seriously affects the normal recommendation function of the recommendation system, resulting in its failure to provide users with reliable and reliable recommendation results. In order to solve the vulnerability of the recommendation system to the support attack, based on text vector model and support vector machine, this paper makes a comprehensive analysis of the current research status of the robust recommendation technology. Moreover, based on the idea of suspicious user metrics, this paper has conducts in-depth research on how to design highly robust recommendation algorithms, and constructs a highly reliable sustainable economic learning course recommendation model. In addition to this, this research tests the performance of the system from two perspectives of course recommendation satisfaction and system retrieval accuracy. The experiment proves that the model constructed in this paper performs well in the recommendation of sustainable economic learning courses.
ABX-LEX: An Argument-Driven Approach for the Digital Facilitation of Efficient Group Decisions

The quality of a group decision depends on its members sharing and adopting their various perspectives. Multi-criteria models provide an appropriate vehicle for managing this information sharing because they break down the decision into small, single-issue questions that can be treated independently. We present a new lexicographic decision method with three equivalence classes for use by a group. Our algorithm is the first to combine the hidden profile and multi-criteria paradigms and thereby improving the consensus by identifying critical disagreements and inviting the decision makers to share their mental models of them. A computer simulation demonstrates the algorithm’s effectiveness and efficiency. Unless the problem parameters are very unfavorable, the method achieves the desired choice set or a close approximation to it significantly faster than the commonly used compensatory methods. Our algorithm can either provide step-by-step instructions to a human facilitator, or it can be implemented as a software agent acting as a fully automated digital facilitator, allowing decision makers to participate “on the go” using their internet-connected mobile devices.
Peg solitaire in three colors on graphs

Peg solitaire is a classical one-person game that has been played in various countries on different types of boards. Numerous studies have focused on the solvability of the games on these traditional boards and more recently on mathematical graphs. In this paper, we go beyond traditional peg solitaire and explore the solvability on graphs with pegs of more than one color and arrive at results that differ from previous works on the subject. This paper focuses on classifying the solvability of peg solitaire in three colors on several different types of common mathematical graphs, including the path, complete bipartite, and star. We also consider the solvability of peg solitaire on the Cartesian products of graphs.
Automatic Detection and Repair of Transition- Based Leakage in Software Binaries

The effectiveness of masking as a countermeasure against information leakage in cryptographic ciphers is contingent upon individual instructions leaking information independently . An example of dependent leakage is transition-based leakage: side-channels can leak data proportional to the exclusive-or of the old and the new value during a register write, despite proper first-order masking. In this paper we present a technique to detect and repair transition-based leakage. Our detection technique symbolically executes a binary to relate the old and new values during a register write. We then combine existing analyses to check for, and quantify, any leakage caused by the write. Our repair module closes a leak by flushing the affected register before writing it. We also present a fully automated detection and repair tool called Battl, which is the first to our knowledge to operate at the binary level and is thus sensitive to decisions at any compilation stage that may affect security. We evaluate Battl against first-order secure implementations of the AES block cipher, and of a secure multiplicative inversion algorithm. Battl identified a number of transition-based leakages, some with high leakage amounts. Our countermeasure removes all first-order leakages with only moderate runtime overhead.
Object Motion Deblurring in Single Image Under Static Background

When shooting a moving object, as the object moves too fast or the camera's exposure time is too long, smears may occur in the image, which would result in motion blur. The blind restoration of object motion blur is a challenging inversive problem. To effectively extract useful information from blurred images, this paper proposes a new method to remove motion blur, which is based on the maximum a posterior (MAP) framework. Firstly, the framework combines guided filtering and automatic GrabCut image segmentation algorithm in order to divide the image into different layers. Afterwards, it uses the image gradient to estimate the blur kernel through an alternating iterative optimization strategy. The iteratively reweighted least squares algorithm (IRLS) is used to optimize the solution of the model. Finally, we use the unsharp masking algorithm to improve the high-frequency components of the image and enhance the edge and details of the image. Therefore, the algorithm can effectively remove the blur caused by the motion of the object, suppress the noise and ringing effect, and recover a higher quality clear image, which can be demonstrated on benchmark problems.
Cheating Detectable Ramp Secret Sharing with Optimal Cheating Resiliency

A (k, L, n) ramp secret sharing scheme allows a dealer to share a secret vector with a lesser share size compared to threshold secret sharing schemes. In this work, we formalize the definition of cheating in ramp secret sharing schemes and propose two constructions. The proposed constructions of ramp secret sharing scheme are capable of cheating detection even if $$n-1$$ out of n participants conspire against the single honest participant and try to convince him of a valid but incorrect secret. This is the strongest possible adversarial setup known as the CDV $${}^{n-1}$$ model of cheating. Moreover, we consider arbitrary secret distribution on the space of secrets. To the best of our knowledge, we are the first to address cheating in ramp setup against $$n-1$$ cheaters. Both the constructions proposed in this work are optimal cheating resilient against a centralized adversary with unbounded computational resources.
Bayesian Surprise in Linear Gaussian Dynamic Systems: Revisiting State Estimation

This article proposes a Bayesian surprise minimization scheme to perform adaptive estimation for a family of linear Gaussian dynamic models. It is shown that the redefined Bayesian surprise in linear Gaussian dynamic systems is a function of the Kalman filter parameters and plays a key role in the state-estimation process. The proposed representation of the Kalman filter illustrates that the information from the Bayesian surprise and the innovation process contributes to the estimation of the state vector and its covariance matrix. This unique approach yields a new set of linear estimation algorithms, where filtering is purely performed with respect to the Bayesian surprise. Simulation results confirm that the information in Bayesian surprise can be sufficient to achieve optimal estimation. In addition, an alternative approach is proposed to test filter consistency based on Bayesian surprise.
Information Security Risk Management Analysis Using ISO 27005: 2011 For The Telecommunication Company

The implementation of information security risk management in the telecommunications company is carried out to systematically manage potential losses arising from opportunities and consequences in the information security business process. This implementation uses ISO 27005 in relation to the requirements of ISO 27001 which has control objectives that can be used as a basis for controlling risk. The maintenance and inspection program implemented in the telecommunications company has not considered the risk aspects in the decision-making process so that the problem has resulted in not optimal risk handling and control. The purpose of this study is to determine the selection of the right control object for the basis of a suitable risk control program to support the requirements of the ISO 27001 information security management system standard. This study begins with context establishment, followed by risk identification, risk analysis, risk evaluation, and risk treatment. The sampling technique used purposive sampling and snowball sampling. Data collection was obtained from the management and processed with computer-assisted qualitative data analysis software NVivo 10. This study produces 26 impact scenarios for the highest rank category and 12 impact scenarios as the top priority. Based on the results of risk evaluation, eight ISO 27001 control objectives need to be considered in order to support information security. The results of this study can be used for planning decisions and risk control work programs.
A Knowledge Graph Approach to Mashup Tag Recommendation

Tags have been extensively used to organize and index mashup services. However, the selection of relevant tags that depict functionality of mashups has remained a daunting task. This is because mashups have different functionalities than their constituent web APIs. Some existing tag recommendation methods usually follow a manual approach, which is time consuming and prone to errors. Others propose some means of automatic tag recommendation that use a similarity measure which has to be re-computed for every new mashup against the entire mashup and web API database. Such methods are also time consuming, inefficient and therefore not practical. In this paper, we present an automatic tag recommendation method for mashups, using knowledge graphs (KG). The method uses as entry points (seeds) into the KG, topics from mashup description, its primary category, and its constituent web APIs. From the seeds, we walk the graph to extract candidate tags based on node cosine similarity. We finally employ word similarity as a scoring function to explore and rank the candidate tags. Top-ranked candidate tags are subsequently recommended. We conduct experiments, with a real world dataset from programmable web <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , and compare our results to existing baselines. Our results show that our model outperforms the baselines in all cases.
Machine Translation Systems Analysis and Development Prospects

In the age of data and reactive knowledge sharing, when information generation time has been reduced from months and weeks to days and hours and the volume of information is growing at a blistering pace, the process of obtaining and systematizing relevant knowledge is rapidly becoming more complicated. Modern tools are required for searching, selecting and processing relevant data as well as for systematizing the acquired knowledge. The authors describe the TRIZ-evolutionary approach as a tool for describing and revealing trends of artificial systems development on the example of machine translation systems. Development of any system is headed to the increase of ideality, i.e. increase of utility while reducing costs. At that, contradictions in the system prevent the ideality from increasing. Development occurs after such contradictions are eliminated. As per the TRIZ-evolutionary approach, the evolution of systems is being described from contradiction to contradiction and indicates TRIZ tools that allowed eliminating the contradictions. Such a description allows not only to systematize knowledge on relevant systems, but also to offer new high-performance solutions. The article covers main generations and paradigms of machine translation such as word-for-word machine translation, rule-based translation, statistical and neural machine translation. The analysis by means of the TRIZ-evolutionary approach allowed to trace the evolution of these systems, define main performance parameters, reveal trends and prospects of development taking into account ideality increase. The process of obtaining new effective solutions by eliminating subsequent contradictions is shown. Tasks for further research into machine translation systems have been set.
Comparison of Document Generation Algorithms Using the Docs-as-Code Approach and Using a Text Editor

This article describes the process of generating requirements and the problems that system engineers face during documenting requirements. A comparison is made of two approaches to documentation with the formulation of a computational experiment and the calculation of the index of the complexity of documenting an equivalent document using different approaches #CSOC1120. The process of generating requirements imposes many problems during document phase for system engineers. We compare two approaches to documentation using a computational experiment in order to calculate the complexity index #CSOC1120.
Intercultural Communication and Values-Based Orientations of Youth: Social Mobility and Problems of Self-Realisation

The ability to interact at the level of interpersonal communication allows to say that young people not only perceive society as an environment for their self-realisation, but also use it to gain new experience, that can determine the development of an individual. The relevance of the study is determined by the fact that each act of communication must be implemented for the purposes of development under certain conditions that affect not only the communication environment itself, but also other forms of interaction at the level of the individual, communality or community. The novelty of the subject matter is determined by the fact that the formation of intercultural communication is possible only if programs of social mobility and, accordingly, cultural exchange are implemented. The authors show that such progress is most likely to be achieved in the external environment, which is determined by the presence of external influences. The authors of the paper primarily refer to this effect as the environment of an educational institution. The leading research method is a sociological survey and the method of analysis, which make it possible to comprehensively consider the value priorities and preferences that are characteristic of modern student youth. The practical significance of the study is determined by structuring and factor analysis, which allows to distribute the cultural layer into various social groups and ensure youth intercultural exchange both in the context of local student communication and in the context of an increase in the level of academic mobility.
An Improved Chinese Named Entity Recognition Method with TB-LSTM-CRF

Owing to the lack of natural delimiters, Chinese named entity recognition (NER) is more challenging than it in English. While Chinese word segmentation (CWS) is generally regarded as key and open problem for Chinese NER, its accuracy is critical for the downstream models trainings and it also often suffers from out-of-vocabulary (OOV). In this paper, we propose an improved Chinese NER model called TB-LSTM-CRF, which introduces a Transformer Block on top of LSTM-CRF. The proposed model with Transformer Block exploits the self-attention mechanism to capture the information from adjacent characters and sentence contexts. It is more practical with using small-size character embeddings. Compared with the baseline using LSTM-CRF, experiment results show our method TB-LSTM-CRF is competitive without the need of any external resources, for instance other dictionaries.
Multi-granularity and Multi-semantic Model for Person Re-identification in Variable Illumination

Person re-identification (Re-ID) with deep neural networks has made tremendous improvements recently. The existing person Re-ID methods can do well in viewpoint, occlusion, resolution, etc. However, they are short of robustness under varying lighting conditions. The variable illumination will result in the inconsistency of color, contrast, and SNR (signal-noise ratio), which will cause lots of difficulties to identify the right person. This paper presents a multi-granularity and multi-semantic Re-ID model combined with image enhancement method to minimize the impact of illumination variations and optimize the feature extraction. The Retinex-based image enhancement method is used to balance the variable illumination and enhance the contour information of images. Furthermore, we add multi-granularity and multi-semantic layers in the network to extract powerful feature representation. The proposed model is evaluated on the Market-1501, DukeMTMC-reID and CUHK03 datasets. Extensive experiments show that the new deep neural network model can extract more robustness features from the enhanced images, and verify the effectiveness of our method under changing illumination conditions.
Stochastic Schemata Exploiter-Based Optimization of Convolutional Neural Network

Stochastic Schemata Exploiter (SSE), which is one of Evolutionary Computations, is designed to find the optimal solution of the function. When comparing it with Genetic Algorithm (GA), which is a population evolutionary computation, SSE has interesting features; quick convergence and smaller number of control parameters. In this study, SSE is applied for designing hyperparameters and structure of Convolutional Neural Network (CNN). The validity of the proposal algorithm is discussed for determining CNN in experiments using CIFAR-10. The results show that SSE can find the better parameters and structure of CNN than GA.
Service Pattern Modeling and Simulation: A Case Study of Rural Taobao

In recent years, various innovative service patterns have been practiced with the blooming of modern service industry (MSI). However, its theoretical construction lags far behind practice. The existing service models in concept-level or process languages in practice-level cannot fully express the connotation of service pattern, which covers concept, modeling, and practice, bringing about the difficulties on pattern modeling and simulation. This paper proposes a service pattern description model which decouples the service pattern into two layers: the element layer and the relation layer. Meanwhile, a process-oriented closed loop simulation framework is developed and applied on the Rural Taobao, a case of Alibaba. The simulation framework contains four parts, namely market environment, process calculate, value output and regulatory environment. In case study, we illustrate that our methods can help business analyst identify bottlenecks and estimate the performance of service pattern. At last, the ability of our methods are discussed by comparing with other models and simulation approaches.
Using logic programming for theory representation and scientific inference

The aim of this paper is to show that logic programming is a powerful tool for representing scientific theories and for scientific inference. In a logic program it is possible to encode the qualitative and quantitative components of a theory in first order predicate logic, which is a highly expressive formal language. A theory program can then be handed to an algorithm that reasons about the theory. We discuss the advantages of logic programming with regard to building formal theories and present a novel software package for scientific inference: Theory Toolbox. Theory Toolbox can derive any conclusions that are entailed by a theory, explain why a certain conclusion follows from a theory, and evaluate a theory with regard to its internal coherence and generalizability. Because logic is, or should be, a cornerstone of scientific practice, we believe that our paper can make an important contribution to scientific psychology.
Analysis of the methods for assessing the quality of entrepreneurial education

Improving the quality of education is becoming one of the main goals of the Ministry of Education of the Russian Federation, any reform in education is aimed at improving quality. To date, it has been proven that there is a direct relationship between entrepreneurship activity and entrepreneurship education. Every year the Ministry of Education creates new programs aimed at training enterprisers. The paper aims to study possible methods of independent quality assessment of entrepreneurship education. In the course of the research, mechanisms for monitoring the quality of entrepreneurship education were developed and proposed.