Reviewerly/exHarmony huggingface dataset:
Research on Consensus Mechanism for Anti-mining Concentration

Based on the analysis of the existing consensus mechanism of blockchain, this paper proposes a POWS mechanism based on the adjustment of workload. In the POW consensus mechanism, the concept of coinage is introduced to adjust the mining difficulty of different nodes. The POWS adjusts the mining difficulty through two factors: calculation force and coinage. The POWS is compared with the POW and the POS in two aspects, the basic performance and the ability to resist the mining pool. The experimental results show that the POWS consensus mechanism can meet the performance requirements of normal blockchain system, at the same time it can better diminish the impact of calculation force and coinage on block generate efficiency, narrow the efficiency gap between the mining pool nodes and the non-mineral pool nodes, and reduce the interest of the mining pool to non-mineral pool nodes.
Communication Strategies Used by Students in Indonesia

This research aims at investigating the communication strategies used by the students at some courses in Indonesia. The data is taken from conversations and interview five students in active speaking class. The data were used observed and recorded. After obtaining the data from the dialogue script of the students, the researcher concluded that their communication strategies were very relevant with Tarone's theory. In this theory explained that the communication strategy has two types; verbal and non verbal. Verbal communication strategie,s there are several kinds, such as; Avoidance (topic message, avoidance abandonment), Paraphrase (approximation, word coinage, circumlocution), Borrowing (literal translation, language mix), and the Appeal of Assistance. While the non verbal communication strategy, this is only one category which is Mime, when the student uses non verbal tactics in place of lexical item or action. Effect of the study in this writing form is expected to be an important source of information for teachers and learners because the purpose is to increase education quality, especially related to the problem of mastering speaking and using the communication strategies.
Application of SINS/DGPS integrated navigation technology in unmanned mining vehicles

Abstract The continuous development of driverless technology has provided more possibilities for the expansion of unmanned operation. In this paper, the SINS/DGPS integrated navigation system is used in the unmanned mining vehicle, and its combination mode and working mechanism are expounded. The equations and observation equations of the combined navigation filter system are derived. The measurement residual error checking algorithm is designed to eliminate DGPS Gross error. At the end of the paper, the correctness of the combined system is verified on the simulation platform, and the robustness is better.
Domain bias in distinguishing Flemish and Dutch subtitles

Abstract This paper describes experiments in which I tried to distinguish between Flemish and Netherlandic Dutch subtitles, as originally proposed in the VarDial 2018 Dutch–Flemish Subtitle task. However, rather than using all data as a monolithic block, I divided them into two non-overlapping domains and then investigated how the relation between training and test domains influences the recognition quality. I show that the best estimate of the level of recognizability of the language varieties is derived when training on one domain and testing on another. Apart from the quantitative results, I also present a qualitative analysis, by investigating in detail the most distinguishing features in the various scenarios. Here too, it is with the out-of-domain recognition that some genuine differences between Flemish and Netherlandic Dutch can be found.
An Approach for Video Summarization Using Graph-Based Clustering Algorithm

There has been immense increase in amount of video content over the past few years. This massive growth in the content of video leads to the uncertain outcomes. Processing of these huge chunks of data is demanding plenty of resources, like time, manpower, as well as hardware storage. Video summarization acquires an important remark in this ambiance. It supports in providing efficient storage, fast browsing, and retrieval of huge video data without losing important factors. In this work, video summarization technique has been proposed using graph-based clustering algorithm in three different steps. The presented work has split the video into frames of a predefined time period and computed the similarity between consecutive frames. Based on the similarity values, the frames are grouped into scenes, and the scenes are partitioned separately using video tracks and audio tracks with the help of clustering algorithm. Then the combined clusters of scenes are further analyzed to determine the summary of a video file. This work considers just the video file at hand and attempts to develop a summary file without using any external knowledge from similar videos. As per the authors’ knowledge, no previous research work related to video summarization has been conducted in this field. The quality of the summarized video is a measure to express the effectiveness of the proposed methodology.
Adversarial Imitation Learning between Agents with Different Numbers of State Dimensions

Designing a reward is difficult in reinforcement learning because there is often a tradeoff between the two roles played by the reward, one being to make an agent behave as desired and the other being to allow an agent to learn a policy easily. Meanwhile, adversarial imitation learning can eliminate the design of a reward by estimating the reward that the expert is maximizing from trajectories of the expert. However, previous methods of adversarial imitation learning can be applied only when the number of state dimensions is the same for the agent and expert. We here define the new problem of adversarial imitation learning between agents having different numbers of state dimensions and propose a method of solving the problem. Our method makes the numbers of dimensions equal by inserting an arbitrary constant into the smaller state so that the feature extractor can be shared between agents. In addition, we verify whether domain adaptation can extract a feature independent of the number of dimensions. We find that the imitation rate of our method is at least 80% in a simulation of a reaching task while rates of previous methods are no more than 55%. We also find that our method is robust against the value to be inserted into the state, the dimension into which the value is inserted, and the numbers of joints of the agent and expert.
DGMS: Dataset Generator Based on Malay Stemmer Algorithm

Text mining is an interdisciplinary field of information retrieval, data mining, machine learning, statistics and computational linguistics. Text mining analysis is more complicated than data mining because it involves with unstructured and fuzzy data. On top of that, generation of datasets that are based on the text documents is still not available. Therefore in this study, we proposed a model and finally a tool called Dataset Generator Based on Malay Stemmer Algorithm (DGMS) and experimented based on the news articles from National News Agency of Malaysian (Bernama). The result shows that, the DGMS tool can be used to extract the features and finally generated the desired dataset.
Aerial Path Planning for Multi-Vehicles

Unmanned Aerial Vehicles (UAV) are a potential solution to fast and cost efficient package delivery services. There are two types of UAVs, namely fixed wing (UAV-FW) and rotor wing (UAV-RW), which have their own advantages and drawbacks. In this paper we aim at providing different solutions to a collaborating multi-agent scenario combining both UAVs types. We show the problem can be reduced to the facility location problem (FLP) and propose two local search algorithms to solve it: Tabu search and simulated annealing.
Research on the method of cement bag palletizing system based on machine vision

Abstract Aiming at the problem that the traditional cement bag palletizing robot can only accomplish the simple task of handling operation, but can not meet the production requirements, this paper proposes a kind of cement bag palletizing robot based on machine vision, which applies machine vision technology to the traditional cement bag palletizing robot. Firstly, the camera is used to calibrate the visual system of the target, then the image is collected and preprocessed. Finally, the cement bag is recognized according to the SIFT matching algorithm. This method improves the intelligence level of traditional cement bag palletizing robot, makes it have the ability of detection, analysis and decision-making, improves the operation quality of the cement bag palletizing robot, and expands its application scope.
TSWNN+: Check-in Prediction Based on Deep Learning and Factorization Machine

With the increasing popularity of location-aware social media applications, Point of interest (POI) predictions and POI recommendations have been extensively studied. However, most of the existing research is to predict the next POI for a user. In this paper, we consider a new research problem, which is to predict the number of users visiting the POI during a particular time period. In this work, we extend the TSWNN model structure and propose a new method based on Factorization Machine (FM) and Deep Neural Network (DNN) to learn check-in features—TSWNN+. More specifically, this paper uses the Factorization Machine to learn the latent attributes of user check-in and time features. Then, the DNN is used to bridge time features, space features, and weather features to better mine the latent check-in behavior pattern of the user at the POI. In addition, we design a negative instance algorithm to augment training samples. In order to solve the problem of gradient disappearance caused by DNN, the residual structure is adopted. The experimental results on two classical LBSN datasets—Gowalla and Brightkite show the superior performance of the constructed model.
THE INFLUENCE OF ADVERTISEMENTS IN UKRAINIAN MEDIA ON THE BEHAVIOR OF READERS

The article substantiates the expediency of advertising as a pathogenic phenomenon.The main features of the pathogenicity of the text are analyzed and an attempt is made to highlight the main criteria of the pathogenicity of the advertising text in the Ukrainian press.Tasks of the researching -to consider the work of Ukrainian researchers on the negative impact of advertising on the audience , to make a brief overview of the Ukrainian legislation in the field of advertising and, based on the legislative framework and the theoretical and practical principles of advertising skills , select the criteria unfair advertising.In this article it is also analysed current trends of influence of the press on the readership, also it is focused on advertising in today's print and on its characteristics.It is justified the attitude of newspaper readers to the advertisement on the basis of the results of the sociological survey.The object of our research is the works of Ukrainian researchers in the field of advertising and Ukrainian print media.Subject of the study is unscrupulous advertising in the Ukrainian press.In order to eliminate the phenomenon of low-quality advertising from the Ukrainian media space, it is necessary to exercise clear control over advertising at all levels -from individual (at the level of self-control of a particular journalist) to editorial, regional and national.And this is possible only by creating an appropriate hierarchy of controlling bodies that would work for the good of the state and not their own interests.
The Research of Fast Acquisition Algorithms in GNSS

Recently, GNSS has been applied in various domains deeply and widely. In some of applications such as carbon canyon, GNSS signals degrade severely. The conventional receivers have no ability to deal with such weak signals. The sensitivity performance has already been one of the most important features in modern receivers. Consider the advantages of easy implement and high efficiency, we choose it with coherent integration and differential coherent integration to acquire signals which of power is −145 dBm.
Minimization of Crossover Operator in Genetic Algorithm

Crossover is a genetic operator used in genetic algorithms (GA) for varying the functionalities of chromosomes from one generation to the other. It is the base of any GA approach. It has a vital role in obtaining the optimal solution. The selection of appropriate crossover and how it is used on the individuals is highly significant to get better fitness value. This paper focuses on crossover operator and presents a technique to minimize it in GA such that the optimal solutions are not compensated. In this work, the attempt has been made on reducing the number of crossover operation such that the new generation contains the best traits of the old ones and at the same time the crossover overhead is reduced. As an analysis of the proposed technique, the same has been simulated using MATLAB and the effect has been investigated on the popular traveling salesman problem. The results obtained are satisfactory.
Image Retrieval Evaluation in Specific Domains

Image retrieval was, and still is, a hot topic in research. Image retrieval It comes with many challenges that changed over the years with the emergence of more advanced methods for analysis and enormous growth of images created, shared and consumed. This chapter gives an overview of domain-specific Domain-specific retrieval image retrieval evaluation approaches, which were part of the ImageCLEF evaluation campaign ImageCLEF Evaluation lab Evaluation initiative . Specifically, the robot vision, Robot vision photo retrieval, scalable image annotation and lifelogging tasks are presented. Lifelogging The ImageCLEF medical activity is described in a separate chapter in this volume. Some of the presented tasks have been available for several years, whereas others are quite new (like lifelogging). This mix of new and old topics has been chosen to give the reader an idea about the development and trends within image retrieval. For each of the tasks, the datasets, participants, techniques used and lessons learned are presented and discussed leading to a comprehensive summary.
An efficient algorithm for bounded spectral matching with affine constraint

Summary Graph matching problem appears frequently in the applications of computer vision and machine learning. In this work, based on the spectral matching with affine constraint (SMAC) formulation, we present a new formulation, named bounded SMAC (BSMAC), for the graph matching problem by adding an upper‐bound constraint on the solution norm. We demonstrate the existence of a unique solution with BSMAC, whereas SMAC needs not to have any meaningful solution in general. We develop an effective numerical method to solve the BSMAC formulation as an optimization problem. Numerical experiments are presented to verify feasibility and to show the performance of the proposed numerical method.
Data Hiding in Documents

Malware is any malicious software to cause damage to computer systems. Data hiding is related to hide the secret data without existence of the secret data to the third parties. Recently, data hiding methods are used to intrude anti-virus programs without detection. In this paper, structure of documents are analyzed to embed secret message and the embedding and extracting algorithms are proposed in documents. The experimental results show that the proposed method can hide the secret message into documents and extract the secret message in the receiver side without extra information.
Classification and Analysis of Static Metric Based Antipattern Detection in Service Computing

Background: Design Pattern is regarded as an essential component for enhancement of system design which can further improve the reusability and maintainability whereas antipattern degrades the quality of the program. Antipatterns are sub-optimal implementation choices which initially appears to be a good concept but later on leads to low code maintainability and higher maintenance costs. Objective: The identification of antipatterns which lead to performance degradation plays an important role in the reduction of expensive work and cost involved in maintenance, redesign, reimplementation, and redeployment. The main motivation is to refactor the source code in order to reduce maintenance efforts. Antipatterns impact reliability, testability and maintainability, but they still lack clear identification because of different interpretations and definition of each antipattern. There is a need for a benchmark to analyze the result generated by antipatterns. Methods: Static and dynamic analysis individually do not suffice for antipattern .A hybrid approach is proposed by combining rule based static analysis with dynamic run time analysis. Before applying the hybrid approach a simple manual validation was done to exclude the type of input which had the least probability of containing antipattern. The approach aims at optimizing the results by inclusion of response time metric measure which can be evaluated at runtime execution of the web service. Results and Conclusion: : The paper focusses on detection of antipatterns from web services based on code level and interface level static metrics .Only a brief overview of dynamic approach for detection is proposed. The future scope of this paper will focus on detection of antipattern based on more number of dynamic metrics and also a comparative analysis of the results generated from static, dynamic and hybrid approach.
ISWC 2018: Towards Smarter Wearable Technologies That Blend With Our Bodies

Reports on major events and exhibitions that were part of the 2018 ACM International Symposium on Wearable Computers (ISWC) in Singapore in October 2018.
REKAYASA PERANCANGAN SISTEM INFORMASI POSYANDU (Studi Kasus di Desa Samarang Kec. Samarang)

Posyandu is one form of Community-based Health Efforts (UKBM) that are managed and organized from, by, for and with the community in the implementation of health development. Yandu Post has an important role in providing information about the demographics of the community in a rural area, this is only conveyed through the form of records in tiered reporting so that information that should be absorbed quickly by policy makers requires considerable time. Along with the development of increasingly rapid technology with the development of increasingly sophisticated hardware it is necessary to report for the Yandu post blinded by an application that can accelerate the flow of information for those who are solid and have policies.Engineering and designing an information system for these needs is an absolute thing that must be immediately realized so that reporting can immediately be used by people who have interests and policies in making decisions so that there are no more problems that can occur due to lack of speed in taking action to finish. The results of the engineering design of the system in the form of an Android-based application because Android is already a common item that almost all people have. Information systems can control the implementation of the work and the implementation function by producing information according to predetermined provisions.
Using Frame Similarity for Low Energy Software-Only IoT Video Recognition

Embedded video-processing applications are everywhere, and need to be low-energy in order to extend battery life. Convolutional Neural Networks (CNNs), frequently used for this task, fail to explore the intrinsic redundancy present in videos: similarity between sequential frames means that analyzing all frames can be avoided. On top of that, while several hardware solutions for low-energy execution have been proposed, they require extra or dedicated hardware, which makes them non attractive for low cost applications. In this work we propose a technique that uses frame similarity to identify and process only areas that have a significant difference when comparing two subsequent frames. Our technique reduces energy consumption by discarding unneeded operations, and can also be used in low-cost hardware readily available for IoT applications. We obtain up to 12-80x speedup of CNN execution with software-only modifications that require no network retraining while impacting little on accuracy.
Nonlinear Dynamical System Analysis for Continuous Gesture Recognition

Extracting applicable features from continuous gesture is uneasy since it shows up as a nonlinear dynamic system with a spatial–temporal pattern. This paper introduces a continuous gesture recognition framework that analyzes, models, and classifies the nonlinear dynamics of gestures based on chaotic theory. In this system, the trajectories of finger joints are captured as the discrete observations of nonlinear dynamic system, which defines the feature matrix of gestures by reconstructing a phase space through employing a delay-embedding scheme, the properties of the reconstructed phase space are captured in terms of dynamic and metric invariants that include Lyapunov exponent, correlation integral, and fractal dimension. Finally, we extract a feature matrix for training several classifiers with relatively few samples and get best accuracy of around 96.6% to prove our assumption that the nonlinear dynamics of continuous gesture can be approximated by a particular type of dynamical system for classification.
Event Management Service System

The purpose of the study is to estimate and investigate the advantage of using microservices capability, to develop the system with new functionalities without a relatively large interference in the logic of the individual modules. Similarly using another method as alternative as monolithic architecture which is created as independent on development of an application as a set of small independent services. Creating architecture using microservices brings advantages as freedom in choice of technology and focusing on one service and disadvantages such as delays with remote connections or difficulties related to maintaining transaction security. With the example of Event Watcher system presented it is possible to use completely different technologies in the production of microservices. In the results it was found that the application process of mircorservices were easy to manage and REST communication with service easy to be substituted, testers didn’t had to wait for runnable project, they could work with those micro-services which already they had.
Unsupervised Clustering with Smoothing for Detecting Paratext Boundaries in Scanned Documents

Digital humanities scholars are developing new techniques of literary study using non-consumptive processing of large collections of scanned text. A crucial step in working with such collections is to separate the main text of a work from the surrounding paratext, the content of which may distort word counts, location references, sentiment scores, and other important outputs. Simple heuristic methods have been devised, but are not accurate for some texts and some methodological needs. This study describes a method for paratext detection based on smoothed unsupervised clustering. We show that this method is more accurate than simple heuristics, especially for non-fiction works, and edited works with larger amounts of paratext. We also show that a more accurate detection of paratext boundaries improves the accuracy of subsequent text processing, as exemplified by a readability metric.