Reviewerly/exHarmony huggingface dataset:
Football Player and Ball Tracking System Using Deep Learning

Sports are very popular and watched by a great percentage of the population in the world. As technology progresses, sports as well as the way of playing sports has changed immensely. The technology has been extensively used, be it broadcasting, or detecting the penalties, malpractices, and taking real-time critical decisions of the game. Also, by using the current best technologies in sports, the sportsmen and their teams are able to analyze the game in more depth and hence are able to understand their weaknesses as well as the opponent’s weaknesses. The role of technology also is becoming an integral part of making the strategy of the game. Hence, the paper presents a beginning step in soccer or football match analysis of tracking the player and soccer or football where it can be considered as the first and most crucial step of real-time automation of the analysis of soccer or football matches. The neural network proposed would be able to recognize players and football by giving the video of the match of any arbitrary size and length as input or in simple words our model will be able to work on videos of any size, length and quality. The output of the proposed model would be bounding boxes of players and football with their respective indexes or identity.
A Systematic Review of User Authentication Security in Electronic Payment System

Recently, security is becoming an increasingly crucial component of any financial organization, and as a result, the need for authentication has expanded. To ensure the security of electronic transactions, several authentication mechanisms have been created. The present paper interprets a comprehensive and systematic literature review regarding the articles published in the area of electronic payment systems. This article reviewed the selected papers from 2013 to 2021 to identify the user authentication techniques and threats to the electronic payment methods, which guide the researchers to do in future direction. In addition, this article presented the chosen articles with a comparative table of multifactor security measures and analysis problems. Such findings not only outline the latest research efforts but also indicate possible gaps and future directions for study by suggesting a research agenda. The findings of this study should be used as the basis for potential electronic payment analysis and related topics. Furthermore, all papers are categorized according to the origin of the research, publishing sources and country of affiliation to the author, publishing journal, and year of publication. This paper also provides an exhaustive analysis of various models of attacks on authentication systems.
A Systematic Review on Explicit and Implicit Aspect Based Sentiment Analysis

Opinion mining is also called sentiment analysis is a branch of web mining and text mining that is the process of identifying and determining the orientation of individual web users regarding various products, services, social comments on social media, different e-commerce websites, political issues, hotels, restaurants, emotions or sentiments of individual users about different entities and services of his/her interest on web or Internet. OM or SA interchangeably can be used to monitor and build a recommendation system based on individual user’s reviews that can be document-based, sentence-based, or aspect-based on reviews. Extracting features or aspects of web-based entities or services is a core task of SA. Aspects or features are broadly categorized as implicit and explicit features. The recommendation system-based build with such functionality can enable users to improve buying or selling decisions, also it can improve market intelligence, improve the manufacturing of products, improve services to users, and so on. Most of the time, reviews contain clear information (explicit), but they may also contain information that is not clearly stated or hidden (implicit) that cannot be ignored as may lead to the wrong decision or recommendation to individual users. This area has many challenging issues or problems that are still unaddressed or unsolved.
Genetic Algorithm-Based Clustering with Neural Network Classification for Software Fault Prediction

Early prediction of faults enhances software quality and thereby affects the lower cost. However, early prediction of software faults is a crucial task and may lead to the serious issues like underestimation and overestimation. Such misunderstood situations occur when it comes to the predictions of faulty modules during the early stages of the development life cycle. In this direction, a large community of researchers proposed method-level and object-level metrics to achieve project quality. Uncertainty and lack of data availability allowed researchers to perform diverse experimental explorations. Similar project experience developed in the past has played a vital role in analogy-based approaches. Extensive research is carried out on publicly available datasets from the promise repository. In this paper, the authors propose two different methods using an analogy-based approach for fault prediction at early stages. At first, we developed a comprehensive hybrid model integrating genetic algorithm and regression. The second method employs clustering by fusing neural network features for classification with best-fitted reduced parameters that deal with uncertainty. The proposed model achieves 95% accuracy which is better than other considered methods. Experimental design and result findings show validity and phenomenal outcome of proposed methods.
Fuzzy C-Means and Fuzzy Cheetah Chase Optimization Algorithm

Clustering problems with multiclasses and ambiguities have been handled by fuzzy clustering for decades in real-world applications. Among the most popular algorithms, FCM algorithm used fuzzy clustering techniques, and it is much simple to implement. Due of its initialization sensitivity, the Fuzzy C Means method can readily trap local optimal values. The cheetah chase technique (CCA) is a global optimization algorithm for solving a variety of optimization problems. We presented a hybrid method in this paper, optimization of fuzzy cheetah chase for clustering in fuzzy (FCCO) combines Fuzzy C-Means to provide benefit. While comparing the benefits of FCCO and FCM, recommended hybrid method is capable and motivating findings with the outcomes of our studies.
A Survey on Neural Networks in Cancer Research

The earlier prediction of cancer will be very useful for the future treatment of cancer. An accurate prediction of tumor cells in human beings will increase the chance of survival from cancer. Nowadays, machine learning employs some statistical and probabilistic methods for learning about various types of tumors. Machine learning, a branch of computer science, is having its detection and prediction capabilities to identify the patterns of cancer. Thus, an accurate and efficient model for cancer prediction can be made of machine learning algorithms. Neural networks are the most commonly used branch of machine learning that is used for cancer prognosis and detection. This study focuses on new artificial neural network techniques that are used for cancer prediction. Here it compares the performance of some new techniques and summarizes which model is the best among these techniques. To achieve the maximum throughput using machine learning models, the training and validation phase of that model must be proper. The dataset and the architecture of these techniques may be different but these differences will determine the performance of a particular model. Every neural network model follows the same routine for the training and validation phase, but the difference in their architecture makes them better among others. Machine learning is a day-by-day developing technology and is contributing its maximum to improve the existing technologies in cancer prediction.
Research on Airflow distribution in Internet Data Center

With the rapid growth of IDC (Internet Data Center) construction, the energy consumption is also increasing gradually. It is extremely important to design a reasonable air distribution to reduce the energy consumption of IDC. The research object is an IDC Plant in Beijing. Airpak software is used to conduct DeST ( Building energy consumption analysis software) simulation of the air flow organization in IDC room. Under the same cooling load conditions, through the simulation of the three types of diffuse air supply, closed cold aisle and closed hot aisle in the IDC room, it is concluded that the air flow organization after the closed cold and hot aisle is excellent. The energy consumption analysis of the enclosure structure of the three air flow organizations of IDC room shows that the energy consumption of the closed cold aisle is lower than that of the closed hot aisle air-conditioning system.
Using Data Mining and Mobile Devices to Assist the Interactive Automotive Diagnostics

This work aims to assist the diagnostics of car defects by relating the data obtained through the vehicle telemetry with the driver’s perceptions on a problem when it is perceived. Including the driver in the diagnostic process allows the engineers to identify features to be improved in the car design, even though a possible error/mistake is not apparent. Thus, the driver is seen as a new “sensor” capable of reporting his perceptions. For that, we propose an approach that includes data mining on the automaker knowledge base, the car’s telemetry data obtained through an OBD device, the drivers’ perception captured by a mobile device such as a Smartphone or a Tablet. The proposed Interactive Diagnostic approach enables a more complete preventive diagnostics in comparison with the traditional diagnostic based only on the telemetry data. In addition, the automaker receives the gathered data allowing their engineers to analyze the error/defect and fix the problem or improve the car design. The proposed approach was evaluated through some case studies. Diagnostic engineers answered a questionnaire that shows how the proposed approach influences the diagnostic process, i.e. the solution of the problem was found in fewer steps compared to the current diagnostics process. Therefore, this work advances both the state-of-the-art and the state-of-the-practice in automotive diagnostics by (i) exploring the vehicles’ connectivity in the diagnostics process in an efficient way, and (ii) allowing the automobile industries to obtain more concrete information on the products they offer.
A Comparative Analysis Between SysML and AADL When Modeling a Real-Time System

System Architecture has a primary role in communication between stakeholders, in addition to planning and structuring the whole architectural process. Architecture Description Languages (ADLs) should be helping within architectural activities. However, most ADLs have not yet been widely used in industry. Another limiting factor for the effective use of ADLs is the difficulty of these languages in concretely expressing complex systems architecture. Considering this situation for the effective use of ADLs, UML has been often used in past years for architecture modeling. However, UML itself presents difficulties in representing characteristics which are pertinent to real-time systems, such as security or real-time restrictions. One of the advantages of UML is its extensibility, ability which allows creation of profiles. Thus, this work presents the Systems Modeling Language (SysML), a UML profile used for system architecture modeling. SysML and Architecture Analysis & Design Language (AADL) languages were both applied to a case and compared. As a conclusion, it was noticed that SysML is better than AADL when modeling abstract characteristics, such as decision making and loops functionality, which are not well-described in AADL.
Using of modern technology in performing responsibility Public relation in service institutions

This study aims to know the reality of communications technologies and its processes applied in Sudanese telecommunication company (Sudatel), assessing the impact modern technology on performance of different departments in general and public relations in particular. The researcher applied the descriptive method, data collection ,observation and questionnaire to conduct his study.&#x0D; The study reveals some results such as that senior administrators encourage using modern technology to upgrade performance , they promote electronic application instead of using papers , finally the study draw attention to the necessity of training programs in modern technology to all concerned staff.
Attitudes of communicator towards implementing governance and his relationship to developing the performance of media institutions.

The research aims at identifying the attitudes of communicators towards the principles of governance and its relationship to the development of performance and professional practice in media institution.&#x0D; The based on constructing its variables and interpretation of its results on the system analysis approach through a survey study on a sample of communicators in government media institutions in Emirate of Sharjah. The number of its respondents reaches 50, questionnaire and direct observation were employed to collect concerned data , the researcher has come out with some results , the most important is awareness of communicator which the study sample tackles, the governance concepts and principles , the targeted communicator should aware the importance of applying these principles to achieve quality institutional performance .&#x0D; The results showed that the principles of transparency came at the forefront of the principles of governance applied in the government media institutions Sharjah , the research topic , with percentage of 98.67% , followed by the principle of participation in the second rank , with a percentage of 94.67% followed by the principle of accountability in the third rank with percentage of 93.33% , then the principle of social responsibility in the fifth rank then the principle of integrity in the six last ranking . The results showed the existence of positive repercussions for the application of principles of governance on professional standards and the work environment in the media institutions , the sample of research.&#x0D; The researchers recommended the necessity of spreading the culture of governance among employees and communicators in government media institutions in the Emirate of Sharjah and make governance part of training programs for senior administrative leaders and employees in those institutions.
A contribution to students’ assessment adjusts of multiple choice questionnaires with fuzzy logic

In University environment, it is common to use multiple-choice objective tests with three or four possible answers, of which only one is correct and the rest are erroneous. In this type of tests, usually the wrong answers are penalized in order to avoid the effect of the random answers. However, there are questions that hardly students answer since their difficulty is high. On the other hand, there are also questions that answer virtually all students since their difficulty is simple. While sometimes the course professor chooses to suppress these questions, it is also common to leave them as part of the calculation of the overall score. This communication proposes a way of, without suppressing any question, making a readjustment of the grades based on fuzzy logic techniques. To do this, it is considered, on the one hand, the initial grade obtained by each student and, on the other, the total difficulty index of the test. With these two variables, an approximation can be made to a system of linguistic variables that allows correcting the final grades of each student based on the objective difficulty of the test and a set of rules established by the professor. This will revert to greater “justice” in students’ mark system, since it will be a function of the difficulty of the test.
Prospecting of Free and Open Access Journals by the Intellectual Community of Agricultural Sciences of Karnataka Region: An Analytical Study

Library and Information Centres play pivotal roles to fulfil the thirst of knowledge seekers. We are living in a revolutionary era of Information and Communication Technology (ICTs). The revolution of ICTs emerged as a boon for library and information science professions which opened a door for accessing digital resources i.e. free and open source platforms. The key objective of the study is to explain about prospect of Free and Open Access Journals special reference of Directory of Open Access Journals (DOAJ) by the Intellectual Community (ICs) of Agricultural Sciences of Karnataka Region with special reference of University of Agricultural Sciences, Bangalore. The study used structured questionnaires as a tool of data collection to attain the motto of the study, 255 questionnaires distributed among ICs and 89 per cent respondents given effective responses which have been explained in data and interpretation section. The study revealed that DOAJ resources helped to fulfil the academic and research needs of the intellectual community and suggested that there is a need to promote these resources which provide more accessibility of library resources and set a trend for accessing open and free resources platforms and tools.
Smart Wrist Band Considering Wrist Skin Curvature Variation for Real-Time Hand Gesture Recognition

Interlink Electronics FSR ® 400 Series is part of the single zone Force Sensing Resistor ® family.Force Sensing Resistors, or FSR's, are robust polymer thick film (PTF) devices that exhibit a decrease in resistance with increase in force applied to the surface of the sensor.This force sensitivity is optimized for use in human machine interface devices including automotive electronics, medical systems, industrial controls and robotics.The FSR 400 Series sensors come in seven different models with four different connecting options.A battery operated demo is available.
Interval forecasting of photovoltaic power generation on green ship under Multi-factors coupling

Shipboard photovoltaic power generation is affected by various factors, such as meteorological factors, navigation, and ship rolling. Traditional power prediction methods of the land-based grid do not apply to solar ships. Considering the unavoidable Machine Learning algorithm errors and solar energy fluctuations, an interval prediction framework based on a combination of neural network and kernel density estimation methods is proposed. Its generality is demonstrated by comparing different prediction engines. An improved Extreme Learning Machine approach is used to enhance model robustness, considering the computational speed constraints of online prediction. The improved ELM prediction method is at least 5.26% more accurate than the other forecast engines. Considering the sensitivity of the prediction results to the input data, the K-Means clustering is deployed to cluster the historical data to improve the forecast accuracy. The enhanced prediction framework performs well at different confidence levels (85%-98%). Through experimental verification and comparison with diverse state-of-art benchmarks, the effectiveness and stability of the method are proved. At a confidence level of 98%, the prediction interval average width of the proposed method is at least 26.38% smaller relative to other advanced interval prediction methods. It provides the potential to be applied in a ship’s power system.
High depth resolution wavelength scanning interferometry with narrow scanning bandwidth via sinewaves separation

Wavelength scanning interferometry (WSI) is a promising tomographic imaging technique. However, the depth resolution is fundamentally limited by a narrow wavelength scanning bandwidth, which brings challenges to the frequency extraction for depth information. In this work, we proposed a parameter estimation (PE) paradigm via sinewaves separation, which allows high depth resolution tomographic measurement with narrow-band sources. To obtain accurate depth information, a sinewaves separation model is designed to estimate the frequency of the overlapped signals and it can achieve higher depth resolution without any prior knowledge even under limited scanning bandwidth. Moreover, the estimated frequency is used for phase retrieval and the multi-layer 3D profile measurement can be further implemented. The simulation and experimental results show that the PE method can reach the same depth resolution level as discrete Fourier transform (DFT) even reducing 60% scanning bandwidth when PSNR is higher than 35 dB.
Cancer Detection Based on Medical Image Analysis with the Help of Machine Learning and Deep Learning Techniques: A Systematic Literature Review

Cancer is a deadly disease. It is crucial to diagnose cancer in its early stages. This can be done with medical imaging. Medical imaging helps us scan and view internal organs. The analysis of these images is a very important task in the identification and classification of cancer. Over the past years, the occurrence of cancer has been increasing, so has been the load on the medical fraternity. Fortunately, with the growth of Artificial Intelligence in the past decade, many tools and techniques have emerged which may help doctors in the analysis of medical images.This is a systematic study covering various tools and techniques used for medical image analysis in the field of cancer detection. It focuses on machine learning and deep learning technologies, their performances, and their shortcomings. Also, the various types of imaging techniques and the different datasets used have been discussed extensively. This work also discusses the various preprocessing techniques that have been performed on medical images for better classification.A total of 270 studies from 5 different publications and 5 different conferences have been included and compared on the above-cited parameters.Recommendations for future work have been given towards the end.
Lining Up the Pieces: Coregistration of Digital Pathology Images Is an Underused Technique With Great Promise in Advancing Computational Pathology

Journal Article Lining Up the Pieces: Coregistration of Digital Pathology Images Is an Underused Technique With Great Promise in Advancing Computational Pathology Get access Jeffrey L Fine, MD Jeffrey L Fine, MD UPMC Magee-Womens Hospital and University of Pittsburgh School of Medicine, Pittsburgh, PA, USA finejl@upmc.edu https://orcid.org/0000-0002-2678-8014 Search for other works by this author on: Oxford Academic Google Scholar American Journal of Clinical Pathology, Volume 159, Issue 3, March 2023, Pages 209–210, https://doi.org/10.1093/ajcp/aqac177 Published: 17 February 2023
Expression unleashed in artificial intelligence

The problem of generating generally capable agents is an important frontier in artificial intelligence (AI) research. Such agents may demonstrate open-ended, versatile, and diverse modes of expression, similar to humans. We interpret the work of Heintz & Scott-Phillips as a minimal sufficient set of socio-cognitive biases for the emergence of generally expressive AI, separate yet complementary to existing algorithms.