Reviewerly/exHarmony huggingface dataset:
Expert System for Diseases Diagnosis in Living Things: A Narrative Review

Expert systems are a branch of artificial intelligence, namely by storing expertise from human experts into computers and storing knowledge in computers so that users can consult like human experts. Expert systems are now being applied to diagnosing diseases in humans, animals, and plants. With this system users becoming more practical and knowledgeable in diagnosing natural diseases. a good expert system must provide a good and accurate result so that it can be use. Factors that influence system accuracies such as classification methods, input analysis, and technology are considered for the development of a good expert system to serve it functions as an expert. The purpose of this paper is to analyze these factors for the development of a better system.
Affioramenti di lessico artistico nella letteratura italiana delle Origini

Abstract In the first part of this paper, an overview of the commonly adopted approaches to the study of technical vocabularies in Old Italian is provided: in particular, it is confirmed that traditionally, researchers exploit documents such as treatises, translations, and practical texts – mostly composed from the 15th century to the present – to gather new information on the Italian lexicon of art. In the second section, the author argues that it is possible to expand the research beyond these chronological and typological borders, and find occurrences of technical words in literary texts from the 13th and 14th centuries. Since many of these terms are also part of everyday vocabulary, their artistic meaning is hard – but not impossible – to identify. Thus, the examples of terra bianca (‘white soil’) and marmo cotto (‘cooked marble’) are introduced in order to show an alternative method of verifying the presence of semantic technicisms in old literary texts.
A New Approach for Semi-Automatic Building and Extending a Multilingual Terminology Thesaurus

This paper describes a new system for semi-automatically building, extending and managing a terminological thesaurus---a multilingual terminology dictionary enriched with relationships between the terms themselves to form a thesaurus. The system allows to radically enhance the workflow of current terminology expert groups, where most of the editing decisions still come from introspection. The presented system supplements the lexicographic process with natural language processing techniques, which are seamlessly integrated to the thesaurus editing environment. The system's methodology and the resulting thesaurus are closely connected to new domain corpora in the six languages involved. They are used for term usage examples as well as for the automatic extraction of new candidate terms. The terminological thesaurus is now accessible via a web-based application, which a) presents rich detailed information on each term, b) visualizes term relations, and c) displays real-life usage examples of the term in the domain-related documents and in the context-based similar terms. Furthermore, the specialized corpora are used to detect candidate translations of terms from the central language (Czech) to the other languages (English, French, German, Russian and Slovak) as well as to detect broader Czech terms, which help to place new terms in the actual thesaurus hierarchy. This project has been realized as a terminological thesaurus of land surveying, but the presented tools and methodology are reusable for other terminology domains.
A Collision Modeling Method for Multiple Manipulators Task Planning Using Shadow Space

For the cooperative task planning of multiple manipulators, it is very important to check the collisions among the links and the end-effectors of the manipulators on their movement routes. This paper aims to propose a novel collision check method for multiple mainpulators using the concept of a shadow space. The shadow space is defined as the space that a manipulator sweeps while moving in a 3-dimentional workspace. The workspace is discretized into MxNxL voxels; then, the discretized workspace is explicitly represented as a rank-3 tensor, which is named a shadow space tensor. When a manipulator moves from one task point to another, the shadow space tensor contains the spatial information with respect to the shadow space of the manipulator during the movement. The collision conditions among the manipulators can be simply detected using their shadow space tensors. This paper presents a definition of a problem about the collision detection among multiple manipulators and proposes the collision checking method using the shadow space tensors. Then, the proposed method is validated through 3D robot simulation.
BREAKING: Password Entry Is Fine

In our digital world, we have become well acquainted with the login form—username shown in plaintext, password shown in asterisks or dots. This design dates back to the early days of terminal computing, and despite huge changes in nearly every other area, the humble login form remains largely untouched. When coupled with the ubiquity of smartphones, this means we often find ourselves entering complex passwords on a tiny touchscreen keyboard with little or no visual feedback on what is being typed. This paper explores how password masking on mobile devices affects the error rate for password entry. We created an app where users entered selected passwords into masked and unmasked password fields, measuring various metrics such as typing speed, error rate, and number of backspaces. We then did an exploratory analysis of the data. Our findings show that, perhaps unexpectedly, there is no significant difference between masked and unmasked passwords for any of these metrics.
The Relationship Between Willingness to Communicate in English Inside the Classroom and Achievement in the Omani ELT Context

Willing to Communicate in English (WTC) when choice is given to the learners has been explored in different ELT contexts.The current quantitative study aims at investigating the WTC in the Omani tertiary level in relation to the students' achievement in a public speaking course.Data were gathered by administrating a modified version of Wietelak and Pawlak (2016) WTC inside the class questionnaire.The questionnaire was answered by 50 post foundation students compromising 14 males, and 36 females who registered in the public speaking course in the second academic semester 2018 in Shinas College of Technology, Oman.Additionally, their marks in the progress test in the speaking course were obtained.The results of the correlational analyses revealed that there was no significant relation between students WTC and their achievement in the Public speaking course.Also the results indicated that students avoid communicating in English frequently because of their limited linguistic abilities more than the other cultural and psychological reasons.
Human Emotion Recognition System using Machine Learning for Videos

Emotion is a key element in user-generated video. However, it is difﬁcult to understand emotions conveyed in such videos due to the complex and unstructured nature of user-generated content and the sparsity of video frames expressing emotion. In this paper, for the ﬁrst time, we propose a technique for transferring knowledge from heterogeneous external sources, including image and textual data, to facilitate tasks in understanding video emotion recognition. After that the audio files are played based on the emotion in the video frames. Speciﬁcally, our framework (1) learns a video encoding from an auxiliary emotional image dataset in order to improve supervised video emotion recognition. A comprehensive set of experiments on multiple datasets demonstrate the effectiveness of our framework. At first the video is separated into number of frames. Then the separated frames are taken for the analyzing using Convolutional Neural Network (CNN). The process is used to analysis the video frames to identify the emotion that describe in the videos. After that the audio files are played based on the emotion in the video frames. As a result, after processing the frames through CNN layers it provides a sound clip according to each emotions.
Formation Of Competitive Graduates’ Competencies In Extracurricular Activities

Every year the role of soft skills in the process of professional development of employees and development of the company increases, it is denoted not only by researchers, but also by the managers of large companies. Many organizations include soft skills in the list of corporate competencies. The article deals with the concept of soft skills and its importance for the specialist in professional activity. The types of activities that contribute to the students’ soft skills formation in the learning process at the University are given. It is noted that only educational activities may be not enough for the students’ soft skills formation, but extracurricular activities can be one of the additional means of the formation of these competencies. The article presents the results of employment of graduates from Surgut state University of 2016 and 2017 years involved and not involved in extracurricular activities during the period of study at the University. The authors determined a student as involved in extracurricular activities, who became the winner of the competition "the Evening of outstanding students" which included rewarding of the students who scored the highest number of points compared to other students during the year in the following types of activity: volunteering, science, sport, activity, creativity, allowing students to develop the employers soft skills. The main reasons for non-employment of graduates in the first six months after graduation are denoted. The steps to improve the competitiveness of graduates in the labor market are identified.
Information Disclosure and Security Vulnerability Awareness: A Large-Scale Randomized Field Experiment in Pan-Asia

This paper investigates how the disclosure of a security vulnerability index based on outgoing spams and phishing website hosting, which may serve as an indicator of a firm's inadequate security controls, affects companies' security protection strategy.Our core objective is to study whether firms improve their security when they become aware of their vulnerabilities and such information is publicized.To achieve this goal, we conduct a randomized field experiment on 1,262 firms in six Pan-Asian countries and regions.For the treatment group of 631 firms, we alert them of their security vulnerability index and ranking over time, and their relative performance compared to their peers via emails and a public advisory website.Compared with the control group without being informed of their security vulnerability index, the treatment group improved their security over time, with a significant reduction of outgoing spam volume.A marginally significant improvement in reducing phishing hosting websites is also observed among non-web hosting firms in the treatment group.The security improvement may be attributed to firms' proactive reaction to the security vulnerability information.Our study provides cybersecurity policy makers with useful insights on how to motivate firms to adopt better security measures.
Lattice-based Threshold Ring Signature with Message Block Sharing

Threshold ring signature scheme enables any t entities from N ring members to spontaneously generate a publicly verifiable t-out-of-N signature anonymously.The verifier is convinced that the signature is indeed generated by at least t users from the claimed group, but he cannot tell them apart.Threshold ring signatures are significant for ad-hoc groups such as mobile ad-hoc networks.Based on the lattice-based ring signature proposed by Melchor et al. at AFRICRYPT'13, this work presents a lattice-based threshold ring signature scheme, employing the technique of message block sharing proposed by Choi and Kim.Besides, in order to avoid the system parameter setup problems, we proposed a message processing technique called "pad-then-permute", to pre-process the message before blocking the message, thus making the threshold ring signature scheme more flexible.Our threshold ring signature scheme has several advantages: inherits the quantum immunity from the lattice structure; has considerably short signature and almost no signature size increase with the threshold value; provable to be correct, efficient, indistinguishable source hiding, and unforgeable.
Proposal for the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)

The algorithm selection problem describes the challenge of identifying the best algorithm for a given problem space. In many domains, particularly artificial intelligence, the algorithm selection problem is well studied, and various approaches and tools exist to tackle it in practice. Especially through meta-learning impressive performance improvements have been achieved. The information retrieval (IR) community, however, has paid little attention to the algorithm selection problem, although the problem is highly relevant in information retrieval. This workshop will bring together researchers from the fields of algorithm selection and meta-learning as well as information retrieval. We aim to raise the awareness in the IR community of the algorithm selection problem; identify the potential for automatic algorithm selection in information retrieval; and explore possible solutions for this context. In particular, we will explore to what extent existing solutions to the algorithm selection problem from other domains can be applied in information retrieval, and also how techniques from IR can be used for automated algorithm selection and meta-learning.
Enhancing Access and Use of Climate Information through ICTs

Although the role of ICTs in improving human life in Kenya is acknowledged widely, the focus of much of ICT-related developments has been on human experiences at the level of disease and needs for communication and mobility. Less obvious is how such technological interventions may be used to address seemingly abstract yet grave concerns like climate change and its impact on the quality of human life. This review paper, therefore, shall investigate the different situations where ICTs may be deployed in relaying packaged and relevant localized climate information that can help rural farmers in Kitui County, Kenya to make pertinent and timely decisions to improve their productivity and, ultimately, their livelihoods. We hypothesize that rural communities' use ICT tools such as the mobile phones and the community radios to access localized climate information (weather, seasonal forecasts, and agro-advisories) and that livelihood assets and livelihood strategies positively change with the increasing availability and use of the ICT-based climate information. The idea of the paper presented is to merge theoretical and applied research outcomes to narrow the gap between the theory of ICTs usage and the practice of it, while linking it to climate information and enhanced rural livelihood strategies. The review of this paper shall be captured in social-scientific terms, and shall contribute to knowledge by helping researchers and policymakers to determine climate information needs of rural ASAL communities, knowledge on innovations related to ICTs, among others.
Quick Response Code Binary Research based on Basic Image Processing

The goal of this paper is to binarize quick response (QR) codes with basic image processing methods, so that they can be well recognized in extreme cases.In this paper, we avoid the complex computation that comes from sophisticated algorithms, and employ block truncation coding (BTC) to simplify the computation to make processing fast.The final results show that complex algorithms are not required in QR code binarization, and the basic image processing algorithm can achieve faster and better processing results.
A Motion Free Approach to Dense Depth Estimation in Complex Dynamic Scene.

Recent geometric methods need reliable estimates of 3D motion parameters to procure accurate dense depth map of a complex dynamic scene from monocular images \cite{kumar2017monocular, ranftl2016dense}. Generally, to estimate \textbf{precise} measurements of relative 3D motion parameters and to validate its accuracy using image data is a challenging task. In this work, we propose an alternative approach that circumvents the 3D motion estimation requirement to obtain a dense depth map of a dynamic scene. Given per-pixel optical flow correspondences between two consecutive frames and, the sparse depth prior for the reference frame, we show that, we can effectively recover the dense depth map for the successive frames without solving for 3D motion parameters. Our method assumes a piece-wise planar model of a dynamic scene, which undergoes rigid transformation locally, and as-rigid-as-possible transformation globally between two successive frames. Under our assumption, we can avoid the explicit estimation of 3D rotation and translation to estimate scene depth. In essence, our formulation provides an unconventional way to think and recover the dense depth map of a complex dynamic scene which is incremental and motion free in nature. Our proposed method does not make object level or any other high-level prior assumption about the dynamic scene, as a result, it is applicable to a wide range of scenarios. Experimental results on the benchmarks dataset show the competence of our approach for multiple frames.
Quantum-State Tomography with Photon-Number-Resolving Homodyne Detection

A homodyne-like detection scheme based on photon-number-resolving and low-intensity a local oscillator is exploited to reconstruct optical quantum states by applying the same tomographic reconstruction procedures originally developed for standard homodyne detection based on photodiodes.
Dynamic Collision Avoidance Strategy for Unmanned Aerial Vehicles Formation Flight

Unmanned aerial vehicles (UAV) have been widely used in many fields. UAV formation flight can complete more assignments than single UAV. Collision avoidance is an important part of UAV formation flight. In this paper, we put forward a three-dimensional method of dynamic collision avoidance for UAV formation flight based on the theory of velocity obstacle. The collection of all possible accelerations of the UAV can be obtained by the input of UAV. Considering that the relative velocity between the UAV and obstacle must be out of the collision cone, we can obtain the collection of feasible accelerations next moment. Then we choose the acceleration which is able to let the UAV closest to the target next moment in the collection as the real acceleration. The maximum and minimum velocity should also be set up. Each UAV can determine the best acceleration next time for itself. In addition to formation keeping, this paper also presents how to split the formation on the basis of the direction of each UAV. Numerical simulation is used to test the performance of this method at the end of this paper.
Experiences with the 2013-2016 CLEF interactive information retrieval tracks

This paper describes our experiences with the interactive IR tracks organized at CLEF from 2013-2016 and aggregates the lessons learned with each consecutive instance of the lab. We end with a summary of practical insights and lessons for future collaborative interactive IR evaluation exercises and for potential re-use scenarios.
Integrated Photonics for Counterfactual Communication

In contrast to standard communication, counterfactual communication (CFC) counterintuitively allows information exchange without particle exchange. In this paper we use advances in integrated photonics to implement a CFC protocol that does not rely on post-selection.
METEOROLOGY FOR MARITIME STUDENTS. LITERATURE SEARCH FOR RELEVANT INFORMATION ON METEOROLOGICAL SUBJECT AND PROMOTION OF TEAMWORK

METEOROLOGY FOR MARITIME STUDENTS. LITERATURE SEARCH FOR RELEVANT INFORMATION ON METEOROLOGICAL SUBJECT AND PROMOTION OF TEAMWORK
IMSM: An Interval Migration Based Approach for Skew Mitigation in MapReduce

Background: Extreme growth of data necessitates the need for high-performance computing. MapReduce is among the most sought-after platform for processing large-scale data. Research work and analysis of the existing system has revealed its performance bottlenecks and areas of concern. MapReduce has the problem of skew on its processing nodes. This paper proposes an algorithm for MapReduce to balance the load and eliminate the skew on Map tasks. It reduces the execution time of job by lowering the completion time of the slowest task. Methods: The proposed method performs one-time settlement of load balancing among the Map tasks by analyzing the expected completion time of the Map tasks and redistributes the load. It uses intervals to migrate the overloaded or slows tasks and append them on the under loaded tasks. Results: Experiments revealed an improvement of up to 1.3x by implementing the proposed strategy. Comparison of the proposed technique with other relevant strategies exhibits a better distribution of load among Map tasks and lower level of the skew. Evaluation is done using different workloads. Conclusion: A significant improvement is observed in the performance and reduced completion time of job.
WCSC 2018: The 8th World Chess Software Championship

The 'WCSC 2018' World Chess Software Championship was the ICGA's eighth experimental test of computer chess software in a neutral hardware environment. Held in Stockholm, this event was won by KOMODO after a play-off against BOOOT.
Use of Data Analytics to Incorporate Geophysical Surface Uncertainty into Subsurface Modelling

Abstract Uncertainty is present at every stage of the subsurface modelling workflow and understanding it is an ongoing challenge for the petroleum industry. Quantifying this uncertainty is a rapidly growing field of study as increasingly available high-performance computing enables the application of traditional statistical methods to this problem. However, the extension of these methods to spatial data remains a challenge for which there is no immediate solution. This paper describes the use of data analytics techniques to incorporate spatial uncertainty in reservoir surfaces into subsurface modelling. A metric usually applied in image analytics, the Modified Hausdorff Distance, is adapted for this purpose. The workflow involves sampling the domain of possible surface realisations, characterising them using this metric and determining the most efficient subset to represent the entire data set. The value of this process is that the selected subset captures spatial uncertainty in the surface rather than only gross rock volume. The proposed technique proved to be a simple process that was able to easily select these surfaces from a stochastically generated set and has been successfully applied to the top reservoir surfaces in two fields.
Enhancement of Low-Quality Images using Bi-Histogram Equalization adaptive sigmoid function based on Shifted Gomphertz Distribution

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.