Reviewerly/exHarmony huggingface dataset:
Case Study of Underwater Robot Contest for Young Students Under an Outbreak of Infection Diseases

This paper reports on the Junior Class at the "Underwater Robot Convention in JAMSTEC '20", a robot contest held in Japan. This event is an annual robot convention with AUV, Free, Junior, and AI Challenge classes. In 2020, the outbreak of Covid-19 restricted the use of the swimming pool, so we had to organize the Junior Class without the pool. Therefore, the authors changed the focus of the convention to presentations, which were held online. As a result, 6 teams with 32 members participated in the Junior Class. The participating teams were not restricted by travel restrictions and came from far away. Two teams built their own robots without using the kits distributed until last year among the participating teams. 65 people attended the presentations. 40% of the participants and visitors actively joined the convention by submitting comments. In addition, 32% of the comments were from participants and observers outside the junior class, which realized a fruitful discussion from a wide point of view. Because of mutual evaluation policy, these comments facilitated communication between not only the organizers and the participants but also the teams themselves.
Retraction: Sustainable Intelligent Prediction and Price Recommendation System (J. Phys.: Conf. Ser. 1916 012086)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Discrete Wavelet-Based Multi-Classifier Approach for Recognition of Offline Handwritten Hindi Numerals

The challenges and broad application fields related to handwritten numeral recognition, attract the research communities for further development. The major challenges one has to face for developing such systems are writing practices of individuals, degree of similarity in various digit shapes, and typical structure of digits written in Hindi script. The proposed model is designed to face these challenges by implementing effective feature extraction and classification methods. The model exploited bi-orthogonal Discrete Wavelet Transform (DWT) for important feature extraction and multiple classifiers, namely, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the classification task. The proposed model not only recognized the handwritten numerals quite accurately, but was also successful in reducing the size of original features to release computational loads of classifiers. The scheme has managed to attain recognition accuracies of 96.64, 99.84, and 97.04% by the mentioned classifiers, respectively.
Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Including the expanding fame of the cloud model and quick multiplication of cloud frameworks there are expanding concerns about energy utilization and subsequent effect of cloud as a supporter of worldwide CO2 discharges. Until now, little is thought about how to fuse energy utilization and CO2 worries into cloud application. Energy consumption has become an important cost factor for computing resources. In this research article, we proposed an algorithm to VM Allocation and VM migrations in the context of power utilisation in the data centers. This mechanism is to minimize the energy utilization in the cloud computing environment. We validate our results with the help of prediction based faster energy efficient VMs approach and modified Best Fit approach which shows the faster assignments and increase the performance when consumption of the energy is optimised. As well as we also simulate our results in the cloudsim in the multiple numbers of host and virtual machine to reduce the energy consumptions.
Collaborative Web Search Using Group Conceptual Index

Collaboration is defined as two or more people or entities work together in a joint activity. The Collaborative Web search approach enhances search activities which provides valuable search knowledge and sharing knowledge among the group. The main objective of the system is to provide search results based on the user&#x0027;s conceptual preferences and the group oriented. The system supports more number of users can interact simultaneously in the web search environment. It includes collaborative user interface (UI), data retrieval from meta data, tracking every users&#x0027; behavior such as query term, visited url, title of the page, index words, time spent on it, feedback of the page and comments about the page. These behaviors termed as a user profile. Then the user profile merge with the concept relevant group profile by using domain ontology. The users&#x0027; concept and ID updated in the Group Conceptual Index table. Group Conceptual Index is a search aiding index and it can be designed as ideational association between search query and the appropriate pages visited by the search community. Using Group Conceptual Index identify the user interested page and recommend it to the user. And rerank the recommended pages with the traditional search engine results. The reranked result shows the interest of the user community.
Clustering of Multidimensional Big Data using Enhanced K-Mean Algorithm

One of the basic issues with K-means clustering is that it just merges to nearby optimum which is simpler than comprehending for worldwide optima however can prompt less ideal union. This is especially valid for enormous information as the underlying focuses assume a significant job on the exhibition of this calculation. The paper proposes a novel K-means clustering algorithm which presents a technique to discover advanced area of beginning focuses and introductory number of bunches. This outcome in getting last arrangement of bunches to meet internationally, encouraging quick and exact grouping over enormous datasets. Distributed computing executes huge scope and complex processing. A lot of information are economically and proficiently broke down by utilizing parallelism method. To get parallelism and versatile registering, using Amazon Web Services with R Studio Flexible Process Cloud occasion which partitions the activity among different hubs. The proposed system presents an exceptionally serious exhibition taking significant less calculation time and financially savvy. It very well may be contrasted with complex Hadoop Disseminated Record Framework and MapReduce A significant disadvantage with Apache Hadoop is its MapReduce worldview that is exceptionally open when a procedure emphasizes number of times. R performs execution inside memory which is quicker and less mind boggling when contrasted with Read/Keep in touch with the circle over and again in MapReduce. The examination work is mimicked on some well known genuine datasets from UCI AI storehouse. The outcomes affirm that the proposed work models a vigorous and adaptable procedure for grouping huge datasets.
Topological Photonics

I will present an overview on the field of Topological Photonics, with an emphasis on the Foundations, on Topological Insulator Lasers, and on current challenges.
The Pitfalls of Neglecting the Integration of Young Women in Business: The Case of the Democratic Republic of Congo

Given the patriarchal nature of the African society, females are often secondary. In patriarchal societies, men are viewed as forebearers of knowledge and hence dominate other considerations. A direct consequence therefore is structural discrimination of women, and small businesses are owned and managed by men. Youth unemployment is notably high in the Democratic Republic of the Congo, and especially among females. Notably too, one critical factor that drives entrepreneurship start-ups is self-employment often induced by unemployment. In patriarchal societies, not only are there few opportunities for women but also women face more constraints than their male counterparts. Inspired by the gap in the literature, this study utilizes the descriptive literature review approach to understand the problems faced by young women when starting a business in Kinshasa, the Democratic Republic of Congo. Understanding this theoretical premise and from the perspective of the explored population is important given the criticality of entrepreneurship to economic growth (including unemployment and poverty reduction). In addition, this study would contribute to strategic policy change for achieving productive entrepreneurship. Finally, this study flags research directions for steering necessary research towards productive entrepreneurship goals.
QoS Aware Resource Scheduling and Performance Assessment of Heuristics for Processing Jobs on Cloud

Cloud computing or in other words, shared computing is a unique way of sharing resources via the Internet. It combines and extends features of parallel processing, grid computing, and distributed computing. Cloud Computing environments provide a competent way to schedule and process various jobs on remote machines. Rather than relying on local machines, Cloud users access services remotely via high-speed networks. Various users submitting jobs to be processed to Cloud would expect Quality of Service (QoS). So, currently, many researchers are proposing various heuristics that provide QoS to cloud users. The job scheduler is responsible for scheduling various jobs to its best-matched resource to achieve desired QoS. There are Service Level Agreements (SLAs) between Cloud Service Providers (CSPs) and Cloud users, which need to be followed by both the parties. Benefits would be affected in case of not complying with SLAs. In this paper various SLAs like Hard SLA, Best Effort SLA and Soft SLA are proposed. Jobs with required QoS parameters like Reliability, Execution Time and Priority are submitted to the scheduler. QoS of resources is determined by parameters like Reliability, Job Completion Time and the Cost of the resource. Schedulers then assign the Job to the best-matched resource according to specified SLA. Simulation is performed for First Fit and Best Fit heuristic approaches. Performances of both the heuristic approaches are evaluated with performance parameters like Average Resource Utilization (ARU), Success Rate of Jobs (SR) and Total Completion Time (TCT). This research work is useful for various organizations that provide various Cloud services to users who seek different levels of QoS for various applications.
Determining VCKM and Quark Masses

The Standard Model of particle physics is an amazingly successful theory describing the fundamental particles and forces of nature. This text, written for a two-semester graduate course on the Standard Model, develops a practical understanding of the theoretical concepts it's built upon, to prepare students to enter research. The author takes a historical approach to demonstrate to students the process of discovery which is often overlooked in other textbooks, presenting quantum field theory and symmetries as the necessary tools for describing and understanding the Standard Model. He develops these tools using a basic understanding of quantum mechanics and classical field theory, such as Maxwell's electrodynamics, before discussing the important role that Noether's theorem and conserved charges play in the theory. Worked examples feature throughout the text, while homework exercises are included for the first five parts, with solutions available online for instructors. Inspired by the author's own teaching experience, suggestions for independent research topics have been provided for the second-half of the course, which students can then present to the rest of the class.
Summary and Skeptic’s Corner

Abstract Developers often discuss four pairs of design metaphors: (1) intelligent agents and supertools, (2) teammates and tele-bots, (3) assured autonomy and control centers, and (4) social robots and active appliances. Successful designs seem to come from a combined strategy: employing automation for tasks for which machines produce reliable results, while leaving users the creative or safety choices that are important to them. Digital cameras offer a good example of supertools by using automated methods to choose the aperture, shutter speed, and reduce jitter, while allowing users to frame the photo, set the zoom, and click when they are pleased with the image preview. Tele-bots, such as drones, control centers for safety-first traffic management, and active appliances for home uses are all promising metaphors to guide developers of new consumer products and services.
In-depth Analysis of Unsupervised Clustering for Female Breast Shape

Female breast shape is significantly essential for female healthcare, bra design, etc.However, there is no authoritative standard for breast shape classification.In this paper, we analysis the female breast category by unsupervised clustering the horizontal female breast contours.Specifically, the Elliptic Fourier Descriptors (EFDs), extracted from breast contour, are employed as the contour features.Subsequently, we use PCA to reduce the feature into lower dimensions.Experiments demonstrate that the lower dimensions are enough to present the original features.Then, we employ two widely used clustering algorithms, K-Means++ and FCM, to cluster the female breast contours, and deeply analyze and compare the results of two clustering results in terms of effectiveness.Experimental results demonstrate that the K-Means++ is more suitable for female breast contour clustering, and the results are more reasonable than FCM.
A Three Stage Summarization Framework for Product Recommendation from Opinion Tweets

This paper presents a three stage framework for feature -based rank of opinions around target product and features in a collection of product review tweets as twitter corpus. The main stages of the framework are discussed and presented the analysis of the sentiment with respect to features of the digital camera sales in ecommerce sites.
Application of 5E model with the virtual experiment in teaching “Rate of reaction” - Grade 10 Chemistry to develop the competence to inquiry the Natural World from a chemical perspective

The article analyzed the process of implementing the 5E model, the methods of using experiments to form new knowledge and the manifestations of the competence to inquiry the natural world from a chemical perspective. Thereby (1) showing the suitability of virtual experiments with the 5E model in teaching chemistry to develop the ability to understand the natural world from a chemical perspective; (2) Propose the application process and activities of teachers and students in applying the 5E model and using virtual experiments in teaching chemistry; (3) Building an illustrative example through the topic "Rate of reaction" - Chemistry 10. To evaluate and confirm the feasibility and effectiveness of the proposed content, the article was conducted the pedagogical experiment with the 10th grader at Tran Van Lan High School, Nam Dinh province.
Crime Factor Anaysis and Prediction Using Machine Learning

A crime, often known as a criminal offence, is an act that harms not just people but also a community, society, or state. Every second, a large number of crimes occur in various locations, patterns, and times, and the number continues to rise. Murder, retaliation, theft, rape, kidnapping, and burglary are only a few examples of many sorts of crimes. As a result, it’s critical to recognise various reasons and criminal patterns. This study examines how machine learning algorithms may be created and analysed to classify crimes as high, medium, or low based on real-time parameters such as single parent crime, health, and crime rate, punished crime.
Peer review declaration for IC-MAME 2021

All papers published in this volume of IOP Conference Series: Materials Science and Engineering have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Single-blind • Conference submission management system : Easy Chair (https://easychair.org/cfp/icmame2021) • Number of submissions received : 150 (One hundred fifty) • Number of submissions sent for review : 140 (One hundred fourty) • Number of submissions accepted : 67 (Sixtyseven) • Number of submissions accepted for IOP : 67 (Sixty seven) • Acceptance Rate : 44.66% • Average number of reviews per paper : 02 (Two) • Total number of reviewers involved : 197 (One ninety seven) • Any additional info on review process: Manuscripts submitted to the IC-MAME 2021 conference were initially screened by the editorial team. After initial screening, manuscripts were sent to at least two experts for peer review. All manuscripts submitted to the conference were single-blind peer-reviewed and were carried out to meet the scientific criteria of novelty and academic excellence. The review process was conducted according to the review policy of IOP conference series. Peer reviewers independently made a recommendation to the conference editors as to whether the manuscript should be rejected or accepted (with or without revisions). Manuscripts with revision were submitted again, accompanied by a file describing the changes wherever needed, and finally decision were made by the editors. The editors informed decision to accept or reject the manuscript. • Contact person for queries: Dr. Rajesh Kumar (+91 9501019371; rajeshmadan@gmail.com.) Dr. Gaurav Sapra (+919815858051; gaurav.sapra@pu.ac.in)
Representative Case-Based Retrieval to Support Case-Based Reasoning for Prediction

Case-based reasoning (CBR) is used to resolve a new problem by searching in past similar situations. It is widely used for prediction. However, CBR induces many shortcomings, particularly in its retrieval phase. Many methods and techniques were provided, but have influenced differently the effectiveness of the deduced results. Also, hybridizing different methods emerged to get a better information retrieval and this reasoning manner becomes a ubiquitous issue. The present study has as an objective lending support to CBR to enable enhanced retrieving valid prediction. For this purpose, the study proposes a methodology based on hybridizing data mining with CBR. Thereby, a data mining model is used and a reduced search space solution before processing a CBR's prediction retrieval is proposed. To assess the approach, a clustering was applied to a car safety data set to generate a reduced case base. Then CBR uses it for predicting the car safety. The results show a precision over 80% and an accuracy over 82%, which are well over the classical CBR and indicate the relevance of the approach.
Design Flaws: Evolution and Impact on Software Quality Aspects

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
Development of Background Ontology for Weather Systems through Ontology Learning

Background or reference ontology is a common vocabulary for a system to share knowledge and support information integration. Weather system has more domain specific words, which are not fully covered by generic knowledge source like web and WordNet. For example, Temp is a word related with temperature in weather system, this kind meaning is not available in WordNet. Secondly, many new technical and scientific words are used and existing words also carry different senses. Thesauri usually cannot capture these new senses and words in time. Available background knowledge is insufficient to overcome the existing challenges and issues. This paper focuses on developing background ontology for weather system by enhancing existing knowledge bases. Finally the comparison is made between manually developed ontology and semi automatically developed ontology.
Design and Development of Lung Disease and Damage Analyzer

Chest X-ray are generated in less time but report based on chest X-ray takes time which is not feasible during the pandemic situations like Covid-19 as delay in report can cause severe consequences. As a result, it is observed that ML can help in medical field to reduce the time to generate a report. With the help of ML and Computer Vision models can be built that can be used to predict Lungs Cancer as well as various diseases of Lungs and not only this several models have been created that are helping in predictions of various ailment as well as fractures. Using ML and Computer Vision can create models that once trained properly can give accurate predictions and that too in less time. In medical field time taken to generate report is very crucial as in some situations it becomes the matter of time for a patient to survive.
A FUSION MEASUREMENT METHOD FOR NANO-DISPLACEMENT BASED ON KALMAN FILTER AND NEURAL NETWORK

ACTA Press publishes numerous proceedings like technical publications, scientific publications for power and energy systems and research papers for international conferences in the general areas of engineering and computer science.
Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-Based SVM

A crucial step to assure drug safety is predicting off-target binding. For oligonucleotide drugs this requires learning the relevant thermodynamics from often large-scale data distributed across different organisations. This process will respect data privacy if distributed and private learning under limited and private communication between local nodes is used. We propose an ADMM-based SVM with differential privacy for this purpose. We empirically show that this approach achieves accuracy comparable to the non-private one, i.e. \({\sim }86\%\), while yielding tight empirical privacy guarantees even after convergence.
An Empirical Analysis of the Satisfaction of College Innovation and Entrepreneurship Education Based on Big Data Analysis

This paper selects students from colleges in Heilongjiang as the research object, and uses the Internet to issue questionnaires and collect data. On the basis of big data, SPSS analysis software is used to conduct an empirical analysis of satisfaction of innovation and entrepreneurship education. It is found that the overall satisfaction of innovation and entrepreneurship education in colleges is low. Finally, according to the calculation formula of priority, the item&#x0027;s priority for improvement is analyzed and the suggestions for improvement are put forward.
Verbal Communication

Verbal communication refers to all aspects of speech including linguistic and paralinguistic elements. Linguistic communication refers to the selection and integration of words into sentences and their literal or implied meaning. Paralinguistic communication encompasses all qualitative characteristics of speech. These qualitative elements often grant context and can exert a significant if not a dominant influence in dictating a recipient’s interpretation and response. Qualitative elements include the tone, volume, tonal quality and cadence of speech.