Reviewerly/exHarmony huggingface dataset:
Applying Machine Learning to Risk Assessment in Software Projects

Risk management is one of the ten knowledge areas discussed in the Project Management Body of Knowledge (PMBOK), which serves as a guide that should be followed to increase the chances of project success. The popularity of research regarding the application of risk management in software projects has been consistently growing in recent years, especially with the application of machine learning techniques to help identify risk levels of risk factors of a project before its development begins, with the goal of improving the likelihood of success of these projects. This paper presents the results of the application of machine learning techniques for risk assessment in software projects. A Python application was developed and, using Scikit-learn, two machine learning models, trained using software project risk data shared by a partner company of this project, were created to predict risk impact and likelihood levels on a scale of 1 to 3.Different algorithms were tested to compare the results obtained by high performance but non-interpretable algorithms (e.g., Support Vector Machine) and the ones obtained by interpretable algorithms (e.g., Random Forest), whose performance tends to be lower than their non-interpretable counterparts. The results showed that Support Vector Machine and Naive Bayes were the best performing algorithms. Support Vector Machine had an accuracy of 69% in predicting impact levels, and Naive Bayes had an accuracy of 63% in predicting likelihood levels, but the results presented in other evaluation metrics (e.g., AUC, Precision) show the potential of the approach presented in this use case.
An adaptive median filtering denoising algorithm for pepper and salt noised image

There are different degrees of “pepper &gamma; salt” noise in most acquired images. To overcome the shortcomings of traditional median filtering algorithm, an adaptive median filtering algorithm is proposed. Firstly, the appropriate filter window size is automatically selected according to the intensity of noise in the image. Secondly, whether each pixel is a noise point is judged. The noise point outputs the median value and the non-noise point outputs the original value. Thirdly, the denoising effect of the two algorithms was compared by simulation experiments, and the influence of different “pepper & salt” noise density on denoising effect was tested. From the perspective of subjective visual effect and objective PSNR value, the denoising effect of adaptive median filtering is better than that of traditional median filtering.
Access to Information Two Years After an ICT4D Project in Bangladesh: New Digital Skills and Traditional Practices

The continuity of access to information is rarely considered in the design of information and communication technology for development (ICT4D) projects. In this paper, the authors report on the results of a study conducted two years after the end of an ICT4D project which provided 300 Bangladeshi women with smartphones and support services to enable them to access information on agricultural topics. The study shows that although few of the women were still using a smartphone and accessing online information, they still had access to part of the information supplied to them during the project because they had taken the initiative to write it down in notebooks and they had memorised the information they used frequently. However, when they needed to access new information to address new problems, they had to rely on advice from the local agricultural supplies shop. Although the women had developed their literacy skills during the course of the project, and some of them had started new business ventures, most of them did not feel confident enough to select reliable information by themselves and preferred to rely on local “experts”. The authors argue that for an ICT4D project to be successful and sustainable, continued access to the information provided during the project and to up-to-date information after the end of the project should be planned for in the design of the project.
Image Processing Technology Based on Machine Learning

Machine learning is a relatively new field. With the deepening of people's research in this field, the application of machine learning is increasingly extensive. On the other hand, with the development of science and technology, image has become an indispensable medium of information transmission, and image processing technology is also booming. This paper introduces machine learning into image processing, and studies the image processing technology based on machine learning. This paper summarizes the current popular image processing technology, compares various image technology in detail, and explains the limitations of each image processing method. In addition, on the basis of image processing, this paper introduces machine learning algorithm, applies convolution neural network to feature extraction of image processing, and carries out simulation test. In the test, we select voc2007 dataset for image segmentation, Imagenet dataset for target detection, cifar100 dataset for image classification, and ROC curve for performance evaluation. The results show that the algorithm based on deep learning can achieve high accuracy in image segmentation, classification and target detection. The accuracy of image segmentation is 0.984, the accuracy of image classification is 0.987, and the accuracy of target detection is 0.986. Image processing based on machine learning has great advantages.
Projection Pursuit Method Based on Connection Cloud Model for Assessment of Debris Flow Disasters

A rational evaluation of the danger of debris flow disasters at the regional scale is essential for developing effective disas-ter prevention measures and economic planning in debris flow-prone areas. A novel projection pursuit method based on the connection cloud model and fruit fly optimization algorithm is addressed to analyze the dangerous degree of debris flow disasters at the regional scale, considering the random and fuzzy uncertainties of the projection direction vector. In this method, the connection cloud model gen-erates the candidate projection directions around the latest optimization; these candidate projection direction vectors are screened based on set pair analysis to advance the convergence rate. Case studies and comparisons with other algorithms are further carried out to verify the validity and reliability of the proposed method. Results demonstrate that the proposed method does not require existing evaluation criteria compared to the conventional evaluation methods. It can describe the randomness and fuzziness of the projection direction vector and better find the structural characteristics of fuzzy indicators randomly distributed in the finite intervals with a quicker convergence rate.
Blockchain for Electronic Voting System

With the expansion of democracy across the world together, various kinds of election frauds like vote buying, booth rigging, booth polling, and so on have become quite prevalent. The neo concept of E-voting system emerged some three decades back. It deploys voting computers connected to a public network for conducting the election process as contrasted to the manual voting system of paper ballots. Despite this, it has been widely unsuccessful in curbing election frauds. Therefore, with the exponentially elevating demand of a secure and transparent E-voting system, it has become the need of the hour to introduce some radical improvisations in the current E-voting system rendering it easily accessible across the masses along with providing consistent voting results. This chapter provides an insight on blockchain technology as an important service provider to the modern society and elaborates the development and implementation of a distributed ledger technology-based application to optimize the current E-voting system. The decentralized architecture of blockchain technology and its role in distributing digital information synchronously across a network to maintain the integrity of polled and un-polled votes across all EVMs in the network will be the highlight of the chapter. Conclusively, the importance of a blockchain-deployed E-voting system to curb the limitations of the existing E-voting system in the light of hosting a cost-friendly, transparent, and secured nationwide election will be analyzed.
A Bounded Measure for Estimating the Benefit of Visualization (Part II): Case Studies and Empirical Evaluation

Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss due to a variety of many-to-one mappings. At a glance, there seem to be numerous opportunities for viewers to misinterpret the data being visualized, hence, undermining the benefits of these visual representations. In practice, there is little doubt that these visual representations are useful. The recently-proposed information-theoretic measure for analyzing the cost-benefit ratio of visualization processes can explain such usefulness experienced in practice and postulate that the viewers' knowledge can reduce the potential distortion (e.g., misinterpretation) due to information loss. This suggests that viewers' knowledge can be estimated by comparing the potential distortion without any knowledge and the actual distortion with some knowledge. However, the existing cost-benefit measure consists of an unbounded divergence term, making the numerical measurements difficult to interpret. This is the second part of a two-part paper, which aims to improve the existing cost-benefit measure. Part I of the paper provided a theoretical discourse about the problem of unboundedness, reported a conceptual analysis of nine candidate divergence measures for resolving the problem, and eliminated three from further consideration. In this Part II, we describe two groups of case studies for evaluating the remaining six candidate measures empirically. In particular, we obtained instance data for (i) supporting the evaluation of the remaining candidate measures and (ii) demonstrating their applicability in practical scenarios for estimating the cost-benefit of visualization processes as well as the impact of human knowledge in the processes. The real world data about visualization provides practical evidence for evaluating the usability and intuitiveness of the candidate measures. The combination of the conceptual analysis in Part I and the empirical evaluation in this part allows us to select the most appropriate bounded divergence measure for improving the existing cost-benefit measure.
Gesture recognition real-time control system based on YOLOV4

Abstract With the development of industrial information technology in recent years, gesture control has attracted wide attention from scholars. Various gesture control methods have emerged, such as visual control, wearable device control, magnetic field feature extraction control. Based on one of the visual gesture control methods, this paper proposes a visual gesture control method applied to music box control by combining YOLOv4 object detection network. We design seven main gestures, reconstruct gesture datasets, and retrain the YOLOv4 object detection network by the means of the self-built datasets, further build a music box gesture control system. In this paper, we obtain the recognition accuracy of 97.8% for the object detection network in the gesture control system after a series of experiments, and recruit eight volunteers to conduct experimental tests on the self-built gesture-controlled music box system, mainly to quantify the time of executing a single command, attention concentration, etc. The results show that compared with the traditional control method, the visual gesture control method ensures the accuracy while has a faster response speed and takes up less of the user’s attention.
HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

The reviews posted online by the end-users can help the business owners obtain a fair evaluation of their products/services and take the necessary steps. However, due to the large volume of online reviews being generated from time to time, it becomes challenging for business owners to track each review. The Customer Review Summarization (CRS) model that can present the summarized information and offer businesses with significant acumens to understand the reason behind customers' choices and behavior, would therefore be desirable. We propose the Hybrid Analysis of Sentiments (HAS) for the perspective of effective CRS in this paper. The HAS consists of steps like pre-processing, feature extraction, and review classification. The pre-processing phase removes the unwanted data from the text reviews using Natural Language Processing (NLP) based on different pre-processing functions. For efficient feature extraction, the hybrid mechanism consisting of aspect-related features and review-related features is proposed to build the unique feature vector for each customer review. Review classification is performed using different supervised classifiers like Support Vector Machine (SVM), Naïve Bayes, and Random Forest. The experimental results show that HAS efficiently performed the sentiment analysis and outperformed the existing state-of-the-art techniques with an F1 score of 92.2%.
Predictive Analysis of COVID-19 Symptoms in Social Networks through Machine Learning

Social media is a great source of data for analyses, since they provide ways for people to share emotions, feelings, ideas, and even symptoms of diseases. By the end of 2019, a global pandemic alert was raised, relative to a virus that had a high contamination rate and could cause respiratory complications. To help identify those who may have the symptoms of this disease or to detect who is already infected, this paper analyzed the performance of eight machine learning algorithms (KNN, Naive Bayes, Decision Tree, Random Forest, SVM, simple Multilayer Perceptron, Convolutional Neural Networks and BERT) in the search and classification of tweets that mention self-report of COVID-19 symptoms. The dataset was labeled using a set of disease symptom keywords provided by the World Health Organization. The tests showed that Random Forest algorithm had the best results, closely followed by BERT and Convolution Neural Network, although traditional machine learning algorithms also have can also provide good results. This work could also aid in the selection of algorithms in the identification of diseases symptoms in social media content.
Phi: A Generic Microservices-Based Big Data Architecture

We present in this paper Phi, a generic microservices-based Big Data architecture dedicated to complex multi-layered systems, that rallies multiple machine learning jobs, stream and batch processing. We show how to apply our architecture to an adaptive e-learning application that adjusts its recommendation to the emotions of the learner on the spot. We deploy our application on the cloud using AWS services, and perform some performance tests to show its feasibility in a realistic environment.
Data Exfiltration: Preventive and Detective Countermeasures

Owing to the COVID-19 pandemic that has forced several businesses into the notion of work from home, theft of sensitive and private data has become a more prominent threat. Given that the exfiltrated data closely resembles the normal network traffic, detecting data hoarding and exfiltration attempts has become much more challenging since data flows in and out of the enterprise network on a routine basis. The threat actors to data exfiltration exist both. outside and inside the network. The prime reason of concern relating to data exfiltration is the potential risk of misuse of sensitive information and reputational and financial damage to the enterprise. In consideration to this, it is essential to provide an in-depth review of existing countermeasures to pave the path for future research in this field and enhance development efforts towards devising, evaluating, and deploying effective and feasible countermeasures that protect against data exfiltration attacks. Our review provides a structured overview and in-depth analysis of common preventive and detective countermeasures taken against data exfiltration. We have identified text classification and access control as the most commonly adopted preventive measures against data exfiltration. For detection, however, there is a vast domain of research amongst which we have provided a thorough review of the advanced persistent threat detection (APT), insider threat detection, deep packet inspection (DPI) and Domain Name System (DNS) exfiltration and tunneling techniques. Along with reporting the status of current state of art it determines and highlights various challenges and issues that are left untouched and require research attention.
Local approximation of multipartite quantum measurements

We provide a necessary condition that a quantum measurement can be implemented by the class of protocols known as Local Operations and Classical Communication, or LOCC, including when an error is allowed but must vanish in the limit of an infinite number of rounds, a case referred to as asymptotic LOCC. Our condition unifies, extends, and provides an intuitive, geometric justification for previous results on asymptotic LOCC. We use our condition to answer a variety of long-standing, unsolved problems, including for distinguishability of certain sets of states by LOCC. These include various classes of unextendible product bases, for which we prove they cannot be distinguished by LOCC even when infinite resources are available and asymptotically vanishing error is allowed.
An effective color correction method for underwater image

Due to the light scattering and absorption phenomenon in underwater medium, underwater images always suffer from contrast degradation, detail loss, and color distortion. Color correction is an important process in dehazing underwater images, while current techniques could not remove color bias accurately. This paper introduces an effective color correction method for underwater image, which mainly contains three steps. First, a novel algorithm is proposed to choose the dominant and degraded channel(s) of input. Then a parameter adaptive-adjusted color compensation algorithm is proposed to compensate the degraded channels locally. Last, the traditional gray-world assumption and the famous histogram equalization operation is conducted on the color-compensated image to get the final result. Our experimental results demonstrate the superior performance of the proposed method in both subjective and objective evaluations.
Differential and Access Policy Based Privacy-Preserving Model in Cloud Environment

Cloud computing has multiple benefits in terms of minimum cost, maximum efficiency, and high scalability, which prompts shifting a large amount of data from the local machine to the cloud environment for storage, computation, and data sharing among various parties stakeholders. However, owners do not fully trust the cloud platform operated by a third party. Therefore, security and privacy emerge as critical issues while sharing data among different parties. In this paper, a novel privacy-preserving model is proposed by utilizing encryption, differential privacy, and machine learning approaches. It facilitates data owners to share their data securely in the cloud environment. The model defines access policy and communication protocol among the involved untrusted parties for data processing and privacy preservation. The proposed model is evaluated by executing experiments using distinct datasets. The achieved results reveal that the proposed model provides high accuracy, precision, recall, and f1-score up to 98%, 98%, 97%, and 97%, respectively, over the state of the art methods.
A Practical Guide to Robust Multimodal Machine Learning and Its Application in Education

Recently we have seen a rapid rise in the amount of education data available through the digitization of education. This huge amount of education data usually exhibits in a mixture form of images, videos, speech, texts, etc. It is crucial to consider data from different modalities to build successful applications in AI in education (AIED). This talk targets AI researchers and practitioners who are interested in applying state-of-the-art multimodal machine learning techniques to tackle some of the hard-core AIED tasks. These include tasks such as automatic short answer grading, student assessment, class quality assurance, knowledge tracing, etc.
Application of Deep Learning in Wireless Networks for Channel Estimation: A Survey

Abstract The quality of channel estimation (CE) is critical to the performence of wireless communication systems. In addition to the conventional model based CE methods appeared in the past few decades, deep learning (DL) based CE methods, which is introduced to learn the statistical characteristics of wireless channels, has emerged as a promising methods in recently years. In this survey, different DL models applied to solve CE problems are discussed firstly, then DL-based CE problems in different wireless communication systems are investigated.
“I Wanted to be Part of the Solution”: Motivations, Preparation, and Mental Health of COVID-19 Contact Tracers in the New York Metropolitan Area

Contact tracing is a method of controlling the spread of infectious disease and limiting transmission. The sudden and pressing need for COVID-19 contact tracers has emerged due to the growing spread of the pandemic. Twenty interviews were conducted with contact tracers from the New York metropolitan area. The data was analyzed using an inductive method involving constant comparison. The six major themes included: primary motivators, perception of role, challenges of contact tracing, strengths of contact tracing, and perceptions contacts under investigation, and perceptions of mental health. Understanding the experiences of COVID-19 contact tracers has helped to identify implications to recruit, train and retain them and ensure they are satisfied and productive members of the disease investigation workfo
Why Augmented Reality?

Every now and then, there comes a special time in history where things really change humanity for the better. One day, when you look back, I believe augmented reality will be a special time in history.
Computational Argumentation for Medical Device Regulatory Classification

This work implements argumentation as the basis for modeling the relevant EU legislation concerning medical devices classification. Stakeholders can consult a web application for determining the risk-based class of a medical device based on the relevant legislation. The described approach is generally applicable to any other analogous cases of decision-making based on legislative regulations. One of the main advantages of using argumentation is the explainability and the high modularity of software permitting the extension and/or modification of the code when new relevant regulations become available.
A quantum key injection scheme for mobile terminals based on commercial quantum key distribution

In order to satisfy the demand of mobile secure communication for quantum keys, this study proposes a commercial quantum key distribution (QKD) based quantum key injection scheme for mobile terminals. It integrates a quantum network with a classic network, employs a quantum key injection device, and applies an encryption strategy to securely transmit the quantum key from the quantum network to the classic network for injection into a mobile terminal.Experimental results in a real quantum key distribution environment show that the fusion network runs stably, and the injection of the quantum key at the mobile terminal is realised effectively, which can meet the demands of mobile secure communication for quantum keys.