Reviewerly/exHarmony huggingface dataset:
A Structured Analysis and Design of a Mobile Repair Development System using a Grady Booch Approach

A mobile phone is an electronic device used for communication and messaging. Nowadays it has become a very popular and essential need for people. Damages are caused to mobile phones on a regular basis. These damages may include, screen breakage, battery problems or camera problems. Therefore, repairing and servicing of mobile phones is required. We have proposed an idea of an online mobile repair system that permits a customer to submit repair services that serve customers online. The online mobile repair and shopping system presents an online display of mobile products and an associated delivery window for items selected by the customer. For repairing the mobile, the customer can select the problem he is facing out of the general problems stated and the repair shop person will be sent to his house for picking up the mobile and the mobile will be repaired and given back to him. Though branded companies have their own service centre the demand for repairing of mobile phones is very high therefore more service centres are required.
Generate a Meta-Model Content for Sharing Space of Learning Management System Compatible with IMS-LD

The context of this work is that of designing an IMS-LD meta-model of the sharing space of a Learning Management System (LMS). Our approach is to first think about the conditions for creating a real sharing space for LMS between learners, and designing the IT environment that supports this space. In this paper, we try to adapt the IMS-LD model with a sharing meta-model for LMS based on the social constructivism. This adaptation will go through three stages. Firstly, the development of a sharing meta-model of LMS. Secondly, the study of correspondence between the developed meta-model and IMS-LD model. Finally, their transformation to IMS-LD meta-model.
Biological Bias of Saliency Model in VR

Limited by human structure, the viewer in VR environment could only get a part of the scenes. VR saliency model is extended from flat saliency model, and it should have a bias to represent this physiological constraint. In this paper, we design two objective experiments to collect the distribution data of viewer in both free-viewing situation and the saliency area moving situation. We use the random generation and abstraction to avoid the inference from the participants' knowledge. As the result, we find that for the free-viewing situation, the participants gaze point shown cross-shaped distribution, and the participants would rather see the front area than always turning around.
Research on Mixed Text Feature Selection Algorithm Based on CHI and ECE

As the number of electronic documents continues to increase, automatic text categorization has become a research hot spot in data mining. Feature selection is a crucial part of automatic text categorization. The selection of feature subset has an essential influence on the classification result. However, the traditional single feature selection algorithm often has different feature subsets due to different calculation principles. The calculation of feature values is also one-sided, which leads to the reduction of classification accuracy. Therefore, a mixed algorithm CHIECE is proposed in this paper, combining advantages of the two algorithms to select a more representative feature subset. The performance of the mixed text feature selection algorithm is evaluated in three widely used classifiers and compared with the traditional feature selection algorithms IG, CHI and ECE. From the results, we can see that the accuracy of mixed algorithm CHIECE is higher than that of traditional algorithms.
Detection of Small-Sized Radar Objects Using Multi-Scanning Observation Method

In this paper we consider a surveillance radar with an additional multi-scanning observation device. In each channel of the multi-scanning observation device the well- known procedure “track-before-detect” is implemented. Combined processing of the results of several scannings is based on Hough transform. In conditions of high probability of false alarms due to the low threshold of primary detection, an excessive number of potential candidate tracks is formed. We analyse the functioning of the device for these conditions, assume variants of signal processing of several space scannings and estimate their efficiency.
A Survey on Context-Aware Computing Frameworks for Resource-Bounded Devices

Internet of Things (IoT) provides ubiquitous computing at any place, at any time and in any data format to any user across a network. Context-awareness is a phenomenon where an entity can portray its behavior in a particular time based on facts, rules, and axioms to form a system that is formally called context-aware computing framework. Several frameworks exist for context-awareness either ported from other platforms to android or explicitly build for android platform. Resource-bounded devices like tablet, smart TV, the smartphone and the wireless sensors nodes have several constraints like memory, power, and time that must be considered while designing a framework for resource-bounded devices. This paper surveys various resource-bounded context-aware computing frameworks that are either ported from desktop to android platform or explicitly build for android platform. The key challenges associated with these frameworks and portability issues from desktop to Android platform have also been discussed in detail.
Guarantees on nearest-neighbor condensation heuristics

The problem of nearest-neighbor condensation aims to reduce the size of a training set of a nearest-neighbor classifier while maintaining its classification accuracy. Although many condensation techniques have been proposed, few bounds have been proved on the amount of reduction achieved. In this paper, we present one of the first theoretical results for practical nearest-neighbor condensation algorithms. We propose two condensation algorithms, called RSS and VSS, along with provable upper-bounds on the size of their selected subsets. Additionally, we shed light on the selection size of two well known condensation algorithms, called MSS and FCNN, and compare them to the new algorithms.
End to End Parts of Speech Tagging and Named Entity Recognition in Bangla Language

Automatic Parts of Speech(POS) tagging is one of the most fundamental tasks for a language in Natural Language Processing(NLP), which acts as a feature for solving advanced NLP tasks. Named Entity Recognition(NER) is another essential task of NLP for information retrieval. Researchers could not find up to the mark solution yet on these two tasks for Bangla language compared to other languages, for instance, English, Ger-man. Moreover, many solutions heavily depend on handcrafted features that require strong linguistic expertise. As these two sequence labeling tasks are similar, In this work, two different datasets of POS tagging and NER were prepared, and different deep neural network approaches studied for solving these two tasks separately. All of the approaches were end to end and did not need any handcrafted feature like word suffixes or affixes, gazetteers, dictionary. This study came up with an end to end solution using deep neural network-based model consisting of Bi-directional Long short-term memory(BLSTM), Convolutional Neural Network(CNN) and Conditional Random Field(CRF). The proposed model trained on respected datasets achieved an accuracy of 93.86% on POS tagging and a strict f1 score of 0.6285 on NER on prepared datasets, respectively.
Collaborating Like Professionals: Integrating NetLogo and GitHub

We are developing an “Online Models” dialogue for NetLogo. It works like NetLogo’s “Models Library” dialogue but shows every NetLogo model publicly available on GitHub instead. Our goal is to encourage the sharing of NetLogo models in a way that brings the advantages of version control systems and open-source style collaboration to the world of agent-based modelling.
Cooperative Collision Avoidance Method for Large Teams of Unmanned Aerial Vehicles

This work presents a novel cooperative method of collision avoidance for a large group of unmanned vehicles involved in forest fire fighting operations. The proposed method is based on the collision detection algorithm that uses information captured from the sensors of passive-type and relies on the method of cooperative distance evaluation based on polygonometry. It allows avoiding collisions using opportunities of cooperation between participants in a joint motion, which is implemented based on the team coordination/communication framework. This method has been designed for navigation and motion control systems of the forest fire-fighting UAVs that use the path planning method based on the potential fields approach transferred into discrete 3D space. The proposed method has satisfactory performance and does not require the intervention of a human or command center.
Clozapp

Abstract This paper introduces a freely available and easy to use Java application for the collection and recording of Cloze probability ratings. Clozapp presents participants with text fragments of the researchers’ choice and collects guesses regarding upcoming words. It can also collect basic demographic information about participants. Available modes of data collection include elicitation of responses to a limited number of omitted words in a text or to all words in a text. Clozeapp can be customized to present instructions and experimental stimuli in any given language and to collect multiple types of demographic data. This paper presents the application by detailing the states and actions available, as well as descriptions of how to customize the app to fit different experimental needs including possible input and output details. The application manual is provided. As a proof of concept, we used Clozapp to conduct a replication study of two existing collections of Cloze probability norms. The Clozapp norms showed strong reliable correlations ( r &gt; 0.7) with both existing data sets, suggesting a high convergence between modes of data collection. The application provides an efficient and customizable way of collecting predictability norms for language research.
Methodology development to study the effect of solar load on an electric vehicle battery pack

In recent times, electric vehicles are rising to popularity in many of the countries as there are zero tailpipe emissions. It is known that battery needs to be operated at defined operating temperature range for maximum performance and hence battery thermal management plays a vital role. In a tropical country like India, the major source of heat is the ambient, which in general remains hot (>35°C) throughout the year, summer being exceptionally hot (>45°C) at few locations. To mitigate thermal issues or to design a thermal management strategy, it becomes really important to understand the effect of heat load due to direct sun exposure of electric vehicle battery packs. To evaluate the effects, experiments are done. Further, a methodology is developed to model this solar heating on electric vehicles using StarCCM+ simulation software.
BGSGA

Distant supervision Relation Extraction(RE) aligns entities in a Knowledge Base (KB) with text to automatically construct large-labeled corpus, which alleviates the need for manual annotation in traditional RE. However, most existing models can't take full advantage of the syntactic structure information of each word in the dependency tree. In this paper, we propose BGSGA, a novel distant supervision RE model, to capture both semantic information and syntactic structure in the bag. BGSGA constructs the syntactic graph by combining the dependency trees of the sentences in the bag and then employs a syntactic structure attention mechanism to update the word embedding obtained from Bi-GRU. The syntactic structure attention mechanism captures the cross-sentence information with different weight by implementing the special attention operation between the target word and its neighbors of first-order and second-order in the graph. The experiments show that BGSGA outperforms our baseline models on benchmark datasets.
Big Data Thinning: Knowledge Discovery from Relevant Data

Using statistical learning theory and machine learning techniques surrounding the principles of Rival Penalised Competitive Learning (RPCL), this chapter proposes a novel approach aiming to aid Big Data Thinning, i.e., analysing only the potential data sub-spaces and not the entire extensive data space. Data scientists, data analysts, IoT applications and Edge-centric services are in need for predictive modelling and analytics. This is achieved by learning from past issued analytics queries and exploiting the analytics query access patterns over the large distributed data-sets revealing the most interested and important sub-spaces for further exploratory analysis. By analysing user queries and respectively mapping them into relatively small-scale predictive local regression models, we can yield higher predictive accuracy. This is done by thinning the data space and freeing it of irrelevant and non-popular data sub-spaces; thus, making use of less training data instances. Experimental results and statistical analysis support the research idea proposed in this work.
Can we automate diagrammatic reasoning?

Diagrammatic reasoning (DR) problems are well known. However, solving DR problems represented in 4 × 1 Raven’s Progressive Matrix (RPM) form using computer vision and pattern recognition has not yet been tried. Emergence of deep learning techniques aided by advanced computing can be exploited to solve such DR problems. In this paper, we propose a new learning framework by combining LSTM and Convolutional LSTM to solve 4 × 1 DR problems. Initially, the elementary geometrical shapes in such problems are detected using a typical CNN-based detector. Next, relations of various shapes are analyzed and a high-level feature set is produced and processed in the LSTM framework. A new 4 × 1 DR dataset has been prepared and made available to the research community. We believe, it will be helpful in advancing this research further. We have compared our method with some of the existing frameworks that can be used for solving RPM-guided DR problems. We have recorded 18–20% increase in the average prediction accuracy as compared to the prior frameworks when applied to RPM-guided DR problems. We believe the CV research community will be interested to carry out similar research, particularly to investigate the feasibility of solving other types of known DR problems.
Construction of a Preliminary Educational System for Fuel Cell Using Hydrogen

Global warming problems, local energy problems, etc. are expected to become serious problems as the aging society in Japan progresses. In addition, the introduction of renewable energy is important not only for measures against global warming, but also from the viewpoints of securing energy security, building independent and distributed energy systems, creating new industries and jobs. In this research, we constructed a preliminary educational system that can confirm the operation of hydrogen sensor, water electrolysis, LED lighting using fuel cell and so on. In addition, examples of fuel cells, microgrids, and future hydrogen-related facilities were introduced.
Autonomous Navigation Based on Proportional Controller with GPS Setpoint for UAV in External Environments

In this paper, we propose the implementation of an autonomous navigation system for quadcopter UAVs through a trajectory established by GPS as a setpoint, where the control system, in this case, we will use the proportional one, will guide the UAV through said plotted trajectory, achieving the objective precisely, which is a very useful application in military operations since a system of this nature is required to carry out observation and recognition missions for decision making and to have clear knowledge of the scenario that is lived. To control a trajectory, the system communicates by means of a computer, acquires data by means of GPS on board and transfers them to the PC to be processed by means of calculations of conversion of distance and angle systems using global positioning data and from there, sends orders and flight plans by means of a proportional controller that allows corrections to be made with tolerances established by the controller to follow your flight plan and reach the exact point. Once reached the point, the system is validated for its automatic return, following the path of return drawn and then concludes with the landing that will be validated by artificial vision; even though the GPS system has a certain degree of accuracy, it is required to recognize a base and that you make your descent at the base where you started your flight plan, through the UAV camera vision, keeping in the system an image pattern that when identified by a certain number of points, will fulfill a landing action, ending the mission.
Cyber Mission Operations: A Literature Review

Military commanders follow various principles of war aiming at reducing material and human losses as well as maximizing the advantages in military missions. Military mission operations are increasingly complex due to the intensive use of information technology, requiring the development of a more accurate view of mission assurance, particularly in cyber missions. In this survey, we aim to unveil the state of art on Cyber Mission Operations. In a quasi-systematic research we selected 51 papers from the main scientific databases and we summarized, described, and compared 18 papers. Research problems, application domains, and main concepts have been obtained from the papers. Gaps in the literature on cyber mission operations were also revealed. In addition, we used a new process to perform this survey which has some advantages over other processes: objective classification of papers, significant reduction in execution time, fast visualization of results, fast refinement of research, and validation of relevance of the papers. The main contribution is a quasi-systematic and updated literature review of cyber mission operations, pointing out gaps in the literature and research problems to be addressed by researchers. This work is meant to be useful for researchers who need to identify research problems and challenges on cyber mission operations.
Machine Learning Approach for Detecting News Agencies' Linguistic Style in Arabic

Recently, Machine Learning has been increasingly applied in journalism in different aspects like automate fact checking, discover hidden facts, and automate workflows. In this paper, we utilize Machine Learning and Natural Language Processing to develop a binary classification model that detects news agency's linguistic style in Arabic. During training phase, we explored different features, it is found that term frequency-inverse document frequency (TF-IDF) correlates more with agency's ideology. The average accuracy of our model was 90%.
Graph-Spectrum Analysis on Shape Relations of Spatial Targets

This paper reviews the existing algorithms for extracting the shape features of spatial objects and defines the circumscribed circle of the space targets as the uniform contrast standard of the shape. The object-oriented shape interpolation algorithm is discussed based on mathematical morphology. Based on the existing graph-spectrum, a complete mathematical framework (including complete datum, complete structure, eigenvalue spectral density distribution, etc.) is constructed. According to the selected information granularity, a shape-interpolation method is used to generate a continuously changing sequence of Alpha-shaped sets from the original image to its circumscribed circle, and a positive/inverse spectral density distribution of the shape is generated. Based on the set eigenvalues (such as mean, variance, entropy, and similarity, etc.), the analysis method of the shape Spectrum is described. Finally, an example is given to verify the correctness and feasibility of the method described in this paper.
Ontology Learning Approach Based on Analysis of the Context and Metadata of a Weakly Structured Content

This article describes ontology learning approach based on the analysis of metadata and the context of weakly structured content. Today, there is a paradigm shift in ontological engineering. It consists of the transition from manual to automatic or semi-automatic design. This approach is called ontology learning. When an author creates a document, one holds in one’s head a model of a certain subject area. Then, analyzing the document, it is possible to restore the model of this subject area. This process is called reverse engineering. Current articles describe ontology learning approaches based on content analysis. We propose to use not only the content, but, if it is possible, its metadata and the context for ontology learning purposes. As the main results of the work, we can introduce the model for the joint presentation of content and its metadata in a content management system. To extract the terms, the ensemble method was used, combining the algorithms for extracting terms both with and without contrast corpus. Metadata was used to expand candidates attribute space. In addition, methods for constructing taxonomic relations based on the vector representation of words and non-taxonomic relations by analyzing universal dependencies are described.
Insights on visual aid and study design for gesture interaction in limited sensor range Augmented Reality devices

Augmented reality(AR) is becoming increasingly available in consumer markets. Most AR devices ship with some form of gesture interactions enabled. Gesture interactions are sometimes made difficult by limitations in the device’s sensors and visual field of view. A series of pilot studies comparing different on-screen aids geared towards alerting a user if their gestures were within the range of recognition for a device are presented here. Users have a stated preference for having no borders present after learning the limitations of the system. Before that learning has occurred users prefer to have a solid border shown.
Automated Railway Track Changing Model with Real-Time Centralized Monitoring Interface

The aim of this study is to make an automated exchangeable railway track at junction crossing and centralized monitoring system to detect the exact position of the train for safety surveillance as well as efficient railway scheduling and changeable railway routes. In this study, the proposed model is based on Arduino microcontroller to reduce complexity and execution cost. Infrared sensors have been employed to track down the position of the moving train. DC motor along with manually-made linear actuator has been used to control the switch gate movement. Total system can be monitored and visualized by a software that shows train position. There is also a scope for statistical data collection and implementation. This system can work both automatically and manually from the control room. This model works for multi-junction railway tracks and one or more train moving along the track simultaneously.
Peculiarities of reliability provision for complex systems

Reliability is one of the comprehensive indicators of system quality. The issue of security is very complex and responsible throughout the life cycle of the system. The complexity of understanding and evaluation creates quite tangible problems when creating new products due to their specificity. Therefore, in our view, understanding the nature of the processes that determine and ensure reliability is important. The point is that reliability cannot be directly measured and estimated in a real object. The most important indicator of reliability is functionality – the performance by the object of its functions in full. Functioning is ensured by the organization of the work of all effector processes to perform basic functions. The summary makes an attempt to outline the problem and suggests to do so on the basis of decision-making theory. The information may be of interest to designers, technologists, and reliability specialists.