Reviewerly/exHarmony huggingface dataset:
Semantically Enhanced Information Retrieval System

The user gives an input query in classical In-formation Retrieval (IR) system, keywords of the query are extracted and also the matching documents that contain one or more words specified by the user are retrieved. Keyword searches have a tricky time distinguishing between words that are spelled in similar way but mean something different. This often leads to hits that are completely irrelevant to the query. Se-mantic search seeks to enhance search precision by understanding searcher intent and along with the contextual significance of terms, as they seem within the searchable information space, whether on the net or within a closed system, to get more applicable outcomes. Semantically Enhanced Information Retrieval(SEIR) system can overcome the constraints of keyword based search. SEIR can semantically enhance the IR process. Therein way, searching is finished considering the meanings of query in-stead of the literal strings. Such a research automates tasks that need conceptual understanding of objects.
Visualization, Interaction and Simulation Lab at UFRGS

The Visualization, Interaction and Simulation Laboratory (VISLab) is part of the Computer Graphics, Image Processing and Interaction research group, which started its activities in 1978 developing projects mainly on rendering and animation. Along the years, as new researchers joined the group, new research fields such as image acquisition and analysis, virtual reality, nonconventional interaction, and visualization of complex data started to be investigated. Within this group, VISLab is majorly concerned with research on human-computer interaction, with emphasis on non-conventional, 3D interaction and haptics, and immersive visualization in the context of virtual and augmented reality applications. In this paper, we present the VISLab research focus and the strategy we use to achieve its main goal: to enhance the human with computers, extending the perception capabilities, and improving the human power of action in a natural way. We also briefly describe and illustrate some recent works developed in the lab.
Financial statement fraud likelihood determinants in micro financial institutions in Cameroon

The state of health of an organisation is scientifically diagnosed from its financial statements.Where the financial statements' quality is compromised, decisions taken based on such fraud infected financial statements could lead to devastating consequences.Many consumers of micro-financial services, sometimes express worries about the quality of financial reports issued.These worries are further compounded by empirical studies which disclose that financial statement fraud though the least common, is the most costly to an organisation.As a consequence this study sets out to investigate the financial statement fraud likelihood determinants prevalent in micro financial institutions in Cameroon.Data was collected through a survey, analysed and regressed.The empirical results revealed that fraudulent audit confirmations, falsification of financial amounts, alteration of accounting records, misrepresentations, and improper capitalisation of expenses are the main likelihood determinants.Although financial statement fraud cannot be completely eradicated given that they are perpetuated by human beings who themselves are imperfect, it can be significantly mitigated when micro financial institutions become conscious of the events or conditions that motivate the commission of financial statement fraud.Micro financial institutions are encouraged to create enabling environment for the stagnation of these elements by strengthening the internal control systems of assets, liabilities, expenses, and revenues.Independent audits be carried out irregularly, perpetrators of fraud held responsible, and employees treated decently and fairly.Management should introduce financial management ethics to employees and be very sensitive to behavioural red flags.
Fake Note Detection using Machine Learning Techniques

The significance of behavior-based robots and the indoor navigation system has increased significantly over the past few years in its monitoring and wayfinding, taking into account the existence of robot behavior in various fields of social interest.The basic strategy of the overall behavior tasks in a behavior-based system are divided into smaller independent behaviors that focus on the performance of specific tasks such as the behavior of a robot for indoor environments.One behavior will concentrate on the wall, while the other focuses on avoiding obstacles, etc. Indoor navigation is the main concern since almost a lot of individuals spend more time in the indoor climate.In different fields such as hospitals, transportation, marketing, and military purposes, it becomes an essential thought.The main goal of this paper to give a survey of distance estimation in IndoorGML, behaviors system, and the questions of their robotic implementation possibilities.In this essay, we define robot activities and two basic approaches to distance estimation, such as combinatorial planning and sampling-based planning.These two planning strategies are the principles of motion planning.Combinatorial planning is used for finding the path over the continuous configuration space without restoring it to approximations.The most used concept in planning is sampling-based planning, it provides a successful solution in wayfinding path planning, and because of this, it is performed in different robotics fields.Therefore, for distance estimation in IndoorGML maps, we apply combinatorial and sampling-based approaches.
Myanmar News Sentiment Analyzer using Support Vector Machine Algorithm

Sentiment analysis is one of the natural language processing fields that combined computational linguistics and information retrieval.It is one of text classification techniques that extract opinion expressed in a positive and negative.The news, blogs and reviews obtained from social network are become important resources for sentiment analysis.This paper implements sentiment analyzer for Myanmar news.This system create sentiment annotated corpus for Myanmar news that are collected from Myanmar news web sites and ALT Treebank.Feature extraction and transformation are also needed to extract feature and transform to feature vector to get high performance.N-gram and TFIDF weighting methods are used in this system.Machine learning is combination of the techniques and basis from both statistics and computer science.This system is implemented by using machine learning algorithm Support Vector Machine compared with Logistics Regression and Naïve Bayes algorithms.We showed the comparison results of those algorithms and also showed that SVM is the more powerful than other two algorithms.User can easily know how importance about news by using this sentiment analyzer system.
A Novel to Project Customer Feelings via

When buying a product people searches for several options on the internet so that they acquire only the best ones from the market. To assure this, people go to the internet and search for reviews. Based on the reviews the product will be chosen by the customers. When we think about this, there will be a lot of confusion which is the best one. To prevent this, reviews are rated and then they are categorized. Based on the classification the product is named as a good or bad product. To address the problem of classification we use SVM classification. SVM algorithm is chosen since it is easy and user-friendly. The Main aim of this work is finding the opinion of the product on marketing based on their reviews. Online reviews can help them improve the quality of services and products. However, it is an ambitious issue to take on valuable online reviews by realizing the review sentiment. This paper will dynamically determine the customer opinion about the specific product whether the customer will have an optimistic or bad opinion about the product. Online evaluations have transformed the vital resource of data for users prior to making an up-to-date purchase decision. Identifying evaluations is cooperative to check and run early endorsement and also reviewers are likely to be the buyers of a product.
Nd and Sr isotopic compositions and REE geochemistry of alkali basalts from the Massif Central, France

Kimberlites of SiO ,TiO ,A1 O Na 0,K 0,P O and incompatib¬ le trace eldments.The geochemical evolution have been investi¬ gated by molecular ratio diagrams with Zr as the ratio element in order to eliminate defor¬ mation of the data by the closure effect.The kimberlites and lamproites have diffe¬ ring trends, and one rock type cannot have evol¬ ved from the other.The geochemical variation of the lamproites modelled with congruent mel¬ ting of known mantle phases give rich/phl/cpx/ ap/ru = 30/45/20/3/2.This solution yields too high {Fe,Mg)0 values and too low alkali content.A model is proposed where incongruent melting of phlogopite to a melt of sanidine-ilmenite composition leaves a residue of olivine.This model gives rich/phl/cpx/san/ap/ilm = 38/27/ 11/16/3/5.The kimberlite variation can be ex¬ plained by melting of ol/phl/cc/cpx/spi/ap = 65/17/9/5/3/1.REE .distributions of both rock types indica¬ tes that the mantle source was heavily enriched in REE and especially the LREE.C14
The usability of a visual, flow-based programming environment for non-programmers

Living with “Big Data” gives us the advantage of being able to exploit this wealth of data sources and derive useful insights to make better decisions, enhance productivity, and optimize resources. However, this advantage is limited to a small group of professionals, with the rest of the population unable to access this data. Lack of support for non-professionals creates the need for data manipulation tools to support all sectors of society without acquiring complex technical skills. “Kit” is a visual flow-based programming environment that aims to facilitate manipulation and visualization for all citizens, particularly non-programmers, enabling them to have hands on data in an easy manner. This study evaluates Kit’s usability by having non-programmers involved in various evaluation activities to assess their ability to solve data-related problems using a prototype of the environment. The results provided useful insights to improve the design of data manipulation tools aiming to support non-programmers
Structured hexagonal interactive system on Penneys coin game

This paper gives an interesting probability game related to coin arrangements in structured hexagonal interactive system. Motivated by the study on Penney's coin game, hexagonal interactive system and Conway algorithm, structured hexagonal interactive system (SHIS) on Penney's coin game have been constructed. SHIS scenario for computing the probability of winning player B is also presented.
Techniques for Providing Volunteer Feedback

The Volunteer Management ReportVolume 25, Issue 4 p. 6-6 Volunteer Training and Education Techniques for Providing Volunteer Feedback First published: 04 March 2020 https://doi.org/10.1002/vmr.31414Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume25, Issue4April 2020Pages 6-6 RelatedInformation
A Research on Discourse Access

— We are living in the era of intelligent machines where everything is replacing by machine, interpretation of hidden meaning of text/speech becomes necessary to understands by machine. The last few years have seen an emergence of new perspectives in understanding the meaning of the given text. Researchers are working hard for this kind of complex problem. Discourse comes under this theme of understanding hidden information from the text. Syntactical and semantic analysis plays important role in discourse analysis. This paper focuses on the work done by various researchers in understanding discourse using various methods of machine learning and deep learning techniques.
Research on the Security Policies for Cloud Computing

The sizable progressive patterns in customary exchange and the need for non-public statistics to skip global outskirts featured the need to diagram protection pointers and inspire particular guidelines to improve the nicely-being of residents' close to home statistics. An modern jump beforehand, which makes stressful situations to the well being of individual records, is Cloud Computing. the rule of thumb highlight of Cloud Computing is that it allows on-name for arrange get admission to figuring property with least control assignment or company association collaboration. This new innovation gives new measurements to traditional exchanges of private records and due to this it has become easy to installed order a safety affiliation for Cloud Computing administrations. For the easy out of the plastic new age of Cloud Computing, the idea device of a fitness inclusion is to shield humans and data, set proposals for foreseen conduct with the valuable asset of customers, problem threats and help to tune consistence with guideline This investigations paper centered at the development of a Cloud safety inclusion, in admire to facts insurance. basically focused on the model of programming as-an management (SaaS), this paper is meant to focus on a Framework for gatherings, clients, Cloud bearers and supply a gauge to the security inclusion of Cloud Computing. Pointed honestly on the protection requirements which is probably specific to Cloud circumstance, feature how those conditions connect to our Cloud well-being inclusion and endorse, the measures and the relating health regulations. except, it proposes a manner that might be observed with the manual of Cloud corporations for comparing the well being in their structures as, insurance is one of the internal abilties of the Cloud provide
Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling

© 2020, Springer Nature Switzerland AG. Dynamic flexible job shop scheduling (DFJSS) is a very valuable practical application problem that can be applied in many fields such as cloud computing and manufacturing. In DFJSS, machine assignment and operation sequencing decisions need to be made simultaneously in dynamic environments with unpredicted events such as new job arrivals. Scheduling heuristic is an ideal candidate for solving the DFJSS problem due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, GP has a huge search space, and the traditional search algorithms do not utilise effectively the information obtained from the evolutionary process. This paper proposes a new method to make better use of the information during the evolutionary process of GP to further enhance the ability of GP. To be specific, this paper proposes two adaptive search strategies based on the frequency of features in promising individuals to guide GP to evolve effective rules. This paper examines the proposed algorithm on six different DFJSS scenarios. The results show that the proposed GP with adaptive search can converge faster and achieve significantly better performance than the GP without adaptive search in most scenarios while no worse in all other scenarios without increasing the computational cost.
Research on network layout strategy of mobile opportunity perception in coal mines

The underground coal mine monitoring system has high costs of large-scale application, the network is not easy to expand, and it is difficult to achieve complete coverage of coal mines.To solve these problems, this paper presents a new data acquisition and transmission method based on 'wired + wireless', 'A, B, C and D class mobile node'.The method, combining the existing wired network and wireless network in the coal mine, using sparse heterogeneous converged networks centred on opportunistic networks, analyses the composition, function, connection law and historical information of four types of node and the system structure.Experimental results show that this method can further realise the dynamic planning of the node interaction path.Then, it predicts and corrects the data of artificial mobile nodes and fixed interaction information in the next cycle.Finally, it achieves a powerful guarantee for internet of things, interaction of objects, intelligent perception, and intelligent processing in the mine safety management.
Machine Replication of Human Perusing using Optical Character Recognition with Tesseract

Optical Character Recognition is the machine replication of human perusing. Electronic Conversion of examined pictures where picture can be type composed or printed content. It is executed utilizing Google's open source Optical Character Recognition programming called Tesseract. The OCR accepts picture as the information, gets content from that picture and afterward changes over it into whatever other language that the client needed. This framework can be helpful in different applications like banking, legitimate industry, explorers’ different ventures, and home and office robotization. It for the most part intended for individuals who are unfit to peruse any sort of content archives and to diminish the weight of information passage occupations.[4]
Applications of Multi-Label Classification

The absence of labels and the bad quality of data is a prevailing challenge in numerous data mining and machine learning problems. The performance of a model is limited by available data samples with few labels for training. These problems are ultra-critical in multi-label classification, which usually needs clean data. Multi-label classification is a challenging research problem that emerges in several applications such as multi-object recognition, text categorization, music categorization and image classification. This paper presents a literature review on multi-label classification, various evaluation metrics used for analyzing performance and research hchallenges.
On Public Sentiment and Topic Mining during the COVID-19 Pandemic Based on Sina Weibo Comment Data

In the wake of the COVID-19 outbreak, the public resorted to Sina Weibo as a major platform for the trend of the pandemic.Research on public sentiment and topic mining of major public sentiment events based on Sina Weibo's comment data is important for understanding the trend of public opinions during major epidemic outbreaks.Based on classification of the Chinese language into emotion categories in psychology, we use open source tools to build naive Bayesian models to classify Weibo comments.Visualization of comment topics is achieved with word co-occurrence network methods.Commented topics are mined with the help of the latent Dirichlet distribution model.The results show that the psychological sentiment classification combined with the naive Bayesian model can reflect the evolvement of public sentiment during the epidemic, and that the latent Dirichlet distribution model and word co-occurrence network can effectively mine the topics of public concerns.
1<sup>st</sup> International Conference on Computational Intelligence and Energy Advancements (ICCIEA-2020), December 12-13, 2020 Virtual Format

Nishu Gupta 1 , Parvesh Kumar 2 , H. S. Pali 3 1 Electronics and Communication Engineering Department Vaagdevi College of Engineering, Warangal, Telangana, India 2 Mechanical Engineering Department Vaagdevi College of Engineering, Warangal, Telangana, India 3 Mechanical Engineering Department National Institute of Technology, Srinagar Email: gupta_n@vaagdevi.edu.in, p_kumar@vaagdevi.edu.in, hspali@nitsri.ac.in International Conference on Computational Intelligence and Energy Advancements (ICCIEA-2020) is a prestigious event organized with a motivation to provide an excellent platform for the academicians, researchers, engineers, industrial participants and budding students around the world to share their research findings with the global experts. The conference aimed to provide researchers and attendees with prospects for global collaboration and networking among universities and corporate organizations for promoting research. ICCIEA-2020 was conducted in virtual format owing to the pandemic situation during 12 th to 13 th December 2020. It involved all classes of academia having research orientation. The conference was organized jointly by departments of ECE and ME, Vaagdevi College of Engineering and King Abdulaziz University, Rabigh, Saudi Arabia. As it was an event conducted in cyber form, the presentations were delivered using an online platform. Five different and parallel technical sessions were scheduled based upon the tracks of the articles and a Best Paper was awarded in each session. A total of 31 articles were selected for presentation after double blind peer review process. This proceeding issue compiles oral presentations that were presented by the authors and rigorously reviewed by a special committee designated by the editor team of the conference. The organizing and editorial committee of the ICCIEA-2020 hopes you enjoy reading the articles published in this volume of the JPCS. Besides, we wish to thank all the authors and participants for providing their valuable contributions for this proceeding as well as the reviewers for their constructive recommendations and criticism aiding to improve the presented articles. We are confident that this issue will serve as a reference for computational intelligence, industrial automation, energy advancements and sustainable technologies in the world of scientific research, engineering and technology. List of Organizing Committee, Steering Committee, Technical Program Committee, Industry Experts Committee, Editorial Committee, Invited Speakers, Organizing Institutions, Academic Partner are available in the pdf
Performance of Machine Learning for Lane Detection

With the approach of the self supervised cars, the significance and exactness of path discovery has accomplished principal significance in the field of perception and imaging. In this paper, we propose a calculation to accomplish path recognition on streets utilizing the real-time data accumulated by the camera and applying K-means clustering method to report data in a way reasonable to make a feasible guide. The proposed method utilizes the physical way of the data to group the data. Silhouette coefficient is utilized to decide the quantity of groups in which the data ought to be partitioned. Paths are added to get the right markings. We show the adequacy of, the proposed method utilizing real-time activity data to commotion, shadows, and light varieties in the caught street pictures, and its materialness to both stamped and unmarked streets
A Road Map to Nigeria Entrepreneurial Greatness

This paper considers the processes of mastery of entrepreneurial personality, from childhood to the time of retirement from active entrepreneurial practice. It proffers that underpinning construct for the realization of the envisaged economic expansion is entirely developmental. It posits an agenda of developing the entrepreneur from cradle to grace, which involves active participation of all institutions in the society. It contends that the era of relying solely on natural entrepreneurs for economic development is over. To achieve the desired economy, we must develop indigenous entrepreneurs and equip them. This requires the active involvement of all stakeholders in the Nigeria project.