Reviewerly/exHarmony huggingface dataset:
Enhanced and Effective Ensemble Model for Breast Cancer Detection and Prediction in Modern Medical System in Machine Learning

Humanoid fitness, is the wide-ranging state-run of corporal, societal, and intellectual well-being and not just the absenteeism of infection, sickness, or disability, is as vibrant a source as H2O, foodstuff, or liveliness. Human has undergone phenomenal transformations during last six decades. This has affected health a very large extent. As technology has enhanced and cancer has increased in recent years. Cancer is most widely detected in both men and women. But the female are widely affected by cancer. In Existing system for breast cancer there are many repositories available with cancer data, many online sources, many hospital information etc. As all are separate entity predictions of cancer is very less. All the medical resources available are underutilized. There are many Artificial Intelligence and Machine Learning approaches Logistic Regression, KNeighbors, Random Forest and Decision Tree Classifier, are available for prediction and the effectiveness needs to be improved. In proposed work breast cancer dataset is taken as input and all the best possibilities of the predicted output using various classifications algorithms are combined and a ensemble model is generated. The proposed ensemble model achieves high accuracy, precision and recall for breast cancer detection and prediction, effectiveness outperforms all existing classifiers.
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-Based Policy Learning

Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers. With MIP branching data collected during the previous solution process, learning to branch methods have recently become superior over heuristics. As branch-and-bound is naturally a sequential decision making task, one should learn to optimize the utility of the whole MIP solving process instead of being myopic on each step. In this work, we formulate learning to branch as an offline reinforcement learning (RL) problem, and propose a long-sighted hybrid search scheme to construct the offline MIP dataset, which values the long-term utilities of branching decisions. During the policy training phase, we deploy a ranking-based reward assignment scheme to distinguish the promising samples from the long-term or short-term view, and train the branching model named Branch Ranking via offline policy learning. Experiments on synthetic MIP benchmarks and real-world tasks demonstrate that Branch Ranking is more efficient and robust, and can better generalize to large scales of MIP instances compared to the widely used heuristics and state-of-the-art learning-based branching models.
Improving Emotional Confusions in SNS Sentiment Analysis by Partial Redistribution of BERT Discrimination Results

Most of previous research works of sentiment analysis on SNS mainly focused on polarity analysis to probe into user tendencies. However, human emotions are complex and changeable. It is difficult to use the results of traditional polarity analysis in the real-time application services. Although finer-grained sentiment analysis may provide more detailed results, it has the problem of ambiguity in the definition of features between emotions. In this study, we propose a partial redistribution method based on BERT to tackle this problem. It improves emotional confusion in sentiment analysis through the confusion matrix, and further uses binary classification models to re-train the data of the confused emotional group through the redistribution process. In addition, the model makes it possible to re-extract and define features for specific emotions. Finally, F1 score is used to judge whether each feature correction process exerts positive impact on the model. Experimental results demonstrate that our proposed approach is effective in improving emotional confusion issues in SNS sentiment analysis.
Introducing Federated Learning into Internet of Things Ecosystems – Maintaining Cooperation Between Competing Parties

In practical realizations of a Federated Learning ecosystems, the parties cooperating during the training process, and that later use the trained/global model may consist of competing institutions. This can result in incentives for malicious behavior, which can infringe on the safety and data privacy of other participants. Additionally, even in cases devoid of foul play, the format of the data stored locally, and the equipment available for training, may differ between participating institutions. This necessitates creation of a flexible and adaptable preprocessing pipeline, including a comprehensive registration and data preparation process. Among others, it should identify the affiliation of the joining device(s), maintain appropriate data privacy mechanisms, and compensate for the heterogeneity of the devices that are to participate in model training. In this context, the practical aspects of deploying federated learning solutions, in real-life production environments, are discussed.
A Camera-Based System to Detect Driver Hands on the Steering Wheel in Semi-autonomous Vehicles

Semi-autonomous vehicles require monitoring the driver to check if he is supervising the system and/or ready to take over. Most cars rely on steering-wheel sensors to detect hands and do not monitor the non-driving related task the driver might be performing. We present a camera-based system with a multi-branch architecture, which provides the number of hands on the steering wheel, on a tablet representing a secondary task and the tablet position. It also tackles a common issue with other camera-based systems: a free hand in front of the steering wheel can be classified as grasping it. Moreover, our system deals with cases when the driver might use a tablet on the steering wheel, as he is allowed to do in autonomous mode. These two points are critical to assess the time the driver will need to take over. Finally, combining both steering wheel and camera systems would also make vehicles harder to trick and therefore safer. Video available at: https://www.youtube.com/watch?v=qfYOM4sdWr4
On the Generalization of Neural Combinatorial Optimization Heuristics

Neural Combinatorial Optimization approaches have recently leveraged the expressiveness and flexibility of deep neural networks to learn efficient heuristics for hard Combinatorial Optimization (CO) problems. However, most of the current methods lack generalization: for a given CO problem, heuristics which are trained on instances with certain characteristics underperform when tested on instances with different characteristics. While some previous works have focused on varying the training instances properties, we postulate that a one-size-fit-all model is out of reach. Instead, we formalize solving a CO problem over a given instance distribution as a separate learning task and investigate meta-learning techniques to learn a model on a variety of tasks, in order to optimize its capacity to adapt to new tasks. Through extensive experiments, on two CO problems, using both synthetic and realistic instances, we show that our proposed meta-learning approach significantly improves the generalization of two state-of-the-art models.
CoviBand - Wearable Social Distancing and Contact Tracing Device Using Signal Strength Indicator Metrics

The Covid-19 pandemic has grown to be a highly hazardous threat to the survival of most of the human race. It has not only caused prolonged stay-at-home or lockdown policies in many countries but has also been eating away from the global economy. Staying at home for long durations has affected the lives of daily wage workers tremendously and has also had negative consequences on the mental health of many. This paper aims to reduce the risk of contracting the disease when people leave their homes for essential services and during the gradual lift of the lockdown restrictions. This is achieved through a wearable device (wristband) which constantly looks for other wristbands in the vicinity using a WiFi module. This WiFi module is inbuilt into a NodeMCU Amica board and the setup is used in addition to a buzzer which sounds an alarm when two wristbands are dangerously close. In addition to the warning feature using the buzzer, the device would also store the contact history and the duration of contact on a remote server which can then be used for contact tracing in case a person is found to test positive for Covid-19. The interface of the remote server would be such that it gives a detailed list of the other wristbands that came into contact with any particular wristband. This device would also have an edge over some of the contact tracing apps as many people fear that these apps are an invasion of privacy and drain their mobile batteries quickly.
Fully densely linked and strongly correlated instance segmentation for street-level images

Instance segmentation of street-level images is an important part of road perception for autonomous vehicles. Different from traditional indoor or outdoor scenes, the segmentation of street-level images needs to ensure the segmentation speed and improve the accuracy as much as possible. The deep learning-based street-level images’ segmentation can achieve high accuracy. However, the complexity and speed of the model are difficult to accept for the development and implementation of autonomous driving system. It is a challenge to design neural networks with higher accuracy while maintaining processing speed. To address this issue, we analyze the advantages and disadvantages of Polar-Mask algorithm, and propose a Polar-Mask-based fully densely linked and strongly correlated instance segmentation network (FCSIS-Polar) which achieves a good balance between precision and speed. Specifically, the original cascaded convolutional layers in Polar-Mask are densely connected to enhance the multi-scale feature extraction. In addition, the category features are co-encoded with the original mask prediction results as a priori information to establish a contour-category correlation. We test our algorithm on the challenging Cityscapes dataset (resolution of 1028 × 728). Experimental results confirm its performance; even in small images, it can achieve 26.4% Mask-mAP segmentation accuracy, which is 2.1% higher than the original model and the mean FPS (Frames Per Second) is 14.2. The proposed algorithm significantly improves the Mask-mAP while ensuring the segmentation speed, which is suitable for the environment perception of vehicles at low speed. At the same time, it can be used in auxiliary driving functions such as automatic parking and collision warning.
Benchmarking GNNs with GenCAT Workbench

We present GenCAT Workbench, an end-to-end framework with which users can generate synthetic attributed graphs with node labels and evaluate their graph analytic methods, e.g., graph neural networks (GNNs), on the generated graphs. GenCAT Workbench supports various types of graphs with controlled node attributes and graph topology. We demonstrate the GenCAT Workbench and how it clarifies the strong and weak points of GNN models. Our code base is available on Github ( https://github.com/seijimaekawa/GenCAT/tree/main/GenCAT_Workbench ).
Comprehensive study of micro-seismicity by using an automatic monitoring platform

A modern digital seismic network, with many stations optimally distributed on the earthquake causative seismic zone, enables detection of very low magnitude earthquakes and determination of their source parameters. It is essential to associate to such kind of networks procedures to analyze the huge amount of continuously recorded data for monitoring the space-time-magnitude evolution of natural and/or induced seismicity. Hence, the demand for near-real-time, automated data collection and analysis procedures for assisting seismic network operators in carrying out microearthquake monitoring is growing. In response to this need, we designed a computational software platform, TREMOR, for fast and reliable detection and characterization of seismicity recorded by a dense local seismic network. TREMOR integrates different open-source seismological algorithms for earthquake signal detection, location, and source characterizations in a fully automatic workflow. We applied the platform in play-back mode to the continuous waveform data recorded during 1 month at the Japanese Hi-net seismic network in the Nagano region (Japan) and compared the resulting catalog with the Japan Meteorological Agency bulletin in terms of number of detections, location pattern and magnitudes. The results show that the completeness magnitude of the new seismic catalog decreased by 0.35 units of the local magnitude scale and consequently the number of events increased by about 60% with respect to the available catalog. Moreover, the fault plane solutions resulted coherent with the stress regime of the region, and the Vp/Vs ratio well delineated the main structural features of the area. According to our results, TREMOR has shown to be a valid tool for investigating and studying earthquakes, especially to identify and monitor natural or induced micro-seismicity.
An Improved Approach of Image Steganography Based on Least Significant Bit Technique for Secure Communication in Cloud

Cloud computing allows for a simple, distributed pool of reconfigurable computers with widespread network connectivity. But maintaining data security and privacy is very difficult. In order to guarantee the privacy and security of data in cloud computing, this study developed an efficient and enhanced method that involves concealing data inside pictures. The authors have proposed Multi Level Encryption Algorithm (MLEA) and Two-Level Encryption Algorithm (TLEA) and also processed the image in various steps to achieve better security. The objective is to design and develop an image steganography method based on the Least Significant Bit (LSB) technique that can secure the data during sharing or storing. The authors used the LSB technique of image steganography and encrypted data using MLEA and TLEA. The authors compared different methods and proved their research provides better security than the existing ones. This research demonstrates that the proposed method has performance with an average of 19.7814% better than other existing schemes.
What the Graph

Abstract This chapter is dedicated to the fourth and last use case of the book, the visualisation of a digital object. Specifically, I analyse two cases of graphical display: user interface (UI) and visualisation of digital material. Following the discussions in the previous chapters, I first present work done towards developing an interface for topic modelling that facilitates critical engagement with the method. I then discuss the conceptual challenges of building UI elements that reflect the adoption of a critical approach to visual display, for instance, by visualising the ambiguities and uncertainties of digital methods. Throughout the chapter, I exemplify how the post-authentic framework can be applied to achieve a mutualistic, symbiotic connection between human and technological processes in visual interactions; this, I argue, helps create a digital conscience and keeps the work honest and accountable. Through the examples, I show how it is in fact through the visual display of the ambiguities and uncertainties of computational methods that the active and critical participation of the researcher is acknowledged as required.
OME ISSUES OF IMPROVING THE MECHANISM OF PROVIDING THE RULE-MAKING PROCESS WITH SCIENTIFIC INFORMATION

This article analyzes the issues of improving the mechanism of providing the rule-making process with scientific information. The purpose of the research is to develop relevant proposals, recommendations and conclusions on improving the legislation of the Republic of Uzbekistan and the practice of law enforcement in this regard by researching the issue of providing scientific information in the rule-making process. The subject matter of the research is normative legal acts related to the rule-making process in the Republic of Uzbekistan, the practice of applying the law, as well as scientific-theoretical conceptual approaches and legal categories. Methods such as historical, systematic-structural, logical, induction, deduction, comparative-legal, comprehensive research of scientific sources and statistical data analysis have been applied in the research. As a result of this research work, the concept of providing the rule-making process with scientific information was developed, it is suggested to introduce the concept of “evidence-based lawmaking” into the national legislation, the mechanism of attaching the scientific conclusions of experts or scientists to the draft laws and the obligatory mechanism of declaration of proposals developed by applicants for obtaining scientific degrees was developed, suggestions to create a national scientific and technical base of “Ilmiy axborot – Scientific Information” and to create a mechanism for determining the annual ranking of scientists and specialists were put forward.
Virtual Reality to Improve Technical Teacher Presenting and Speaking Skills

ContextFear of public speaking (glossophobia) is one form of anxiety. People may struggle to speak in a classroom, meeting, and in other group settings. This can make it very difficult for sufferers to communicate verbally to express their knowledge and thoughts. As a result, glossophobia may hamper the sufferer’s ability to further own academic, social or career opportunities.Purpose/approachThe aim of our study was to share experience with a teaching program based on VR to help future teachers improve their presentation and speaking skills. Our study followed a descriptive mixed research design. Participants of 5 groups, each of 5 members went through the regular course of speaking and presentation skills or through a course based on VR. Several characteristics of an individual speech were measured by VR: eye contact, speed of speech, use of crutch words, speech pausing, clarity, loudness, monotony and word repetition. Reduction of glossophobia was measured by a questionnaire and through personal interviews.Actual research outcomes -Interviews with teachers (6 trainers)Pros: high motivation, visible progress, more self-assurance, better performanceCons: cost of equipment, need of technical support-Interviews with course participants (25 future teachers)Pros: felt like being in „real“ classroom and with „real“ students, enjoyment, new experience, emotions, enthusiasm, fun, loss of fear,Cons: made me tired, had to share the equipment with other students, lost my concentration-QuestionnairesClear reduction of glossophobia as measured by self-report of future teachersConclusions VR is eligible to be used in different environments, and many applications successfully apply it to improve learning. Our findings indicate that VR applications are more likely to lead to certain benefits such as increased motivation of users.
The Road Ahead

Abstract This chapter concludes the book, discussing possible future developments in the quantification arena.
A Model for Prediction of Understandability and Modifiability of Object-Oriented Software

Software quality measurement at an early stage helps in improving the product and process of the development. A good-quality software product is easy to maintain. Maintainability of software refers to the ease with which software can be understood, repaired, and improved. The longevity of software depends on the ability of developers to meet new customer requirements and address the problems faced by them. Software maintenance is the most expensive phase of development and consumes more than half of the development budget. This work is carried out to find the early factors of object-oriented software (OOS) that help developers to refine the quality. The proposed model predicts two major attributes of maintainability, namely understandability and modifiability, of OOS through UML class diagrams on the basis of size and structural complexity metrics. The model is validated by establishing a high correlation with the existing prediction model for both the factors. In comparison with an existing model, the proposed model gives a correlation of 0.9603 and 0.9424 for understandability and modifiability, respectively.
The Quantification Landscape

Abstract This chapter looks at other aspects of the “quantification landscape” that have not been covered in the previous chapters, and discusses the evolution of quantification research, from its beginnings to the most recent quantification-based “shared tasks”; the landscape of quantification-based, publicly available software libraries; visualization tools specifically oriented to displaying the results of quantification-based experiments; and other tasks in data science that present important similarities with quantification. This chapter also presents the results of experiments, that we have carried out ourselves, in which we compare many of the methods discussed in Chapter 2 on a common testing infrastructure.
Integration of ORACLE APEX Environment in Database Courses of Computer, Informatics and Telecommunications Engineering Department of International Hellenic University

The aim of this work is to present the integration actions of ORACLE APEX in the curriculum of the undergraduate course "Databases" of the 5th semester of studies at the Department of Computer, Informatics and Telecommunications Engineering (CITE) of the International Hellenic University (IHU). Particularly in the laboratory part of the course, students get acquainted with using different relational database management systems. The tools and capabilities offered by the ORACLE APEX information system had to be integrated into the laboratory part of the course. Students acquire knowledge and skills in developing applications in the APEX environment. For this there were developed a) a quick guide for using the ORACLE APEX environment in Greek, b) laboratory exercises with a case study where for four weeks the students are invited to complete the parts of the laboratory exercise with SQL commands as well as with the low coding tools provided has ORACLE APEX and c) a questionnaire was designed for the students to evaluate their experience in the different database management environments taught. In the laboratory part of the course, students learn how to create a complex database, develop forms, reports, statistics in a web-based environment of APEX. The first impressions of the students show a full acceptance and adoption of ORACLE APEX over the popular RDBMS taught
Applications of Quantification

Abstract This chapter provides the motivation for what is to come in the rest of the book by describing the applications that quantification has been put at, ranging from improving classification accuracy in domain adaptation, to measuring and improving the fairness of classification systems with respect to a sensitive attribute, to supporting research and development in fields that are usually more concerned with aggregate data than with individual data, such as the social sciences, political science, epidemiology, market research, ecological modelling, and others.
Experimental Teaching Design of Software Testing Process Management

The experimental teaching of software testing course attaches great importance to technical practice. This paper mainly discusses the teaching content design of software testing process management experiment. Through this experimental teaching content, students can grasp all aspects of software testing in general, further understand the software testing activities to be completed in all aspects, and connect the scattered knowledge points of the course to form an organic whole, which is conducive to the further digestion and absorption of the course knowledge.