Reviewerly/exHarmony huggingface dataset:
Agile Team Measurement to Review the Performance in Global Software Development

This chapter is aimed at studying the key performance indicators of team members working in an agile project environment and in an extreme programming software development. Practitioners from six different XP projects were selected to respond to the survey measuring the performance indicators, namely, escaped defects, team member's velocity, deliverables, and extra efforts. The chapter presents a comparative view of Scrum and XP, the two renowned agile methods with their processes, methodologies, development cycles, and artifacts, while assessing the base performance indicators in XP setup. These indicators are key to any agile project in a global software development environment. The observed performance indicators were compared against the gold standard industry benchmarks along with best, average, and worst-case scenarios. Practitioners from six agile XP projects were asked to participate in the survey. Observed results best serve the practitioners to take necessary course corrections to stay in the best-case scenarios of their respective projects.
Trustworthy and Context-Aware Detection of Social Isolation in the Elderly from Daily Activity Sequences

We propose a novel trustworthy self-supervisedly-trained anomaly detection model for multi-sensor time series signals, based on Hyperbolic uncertainty for Anomaly Detection, which we dub HypAD. We leverage hyperbolic neural networks and map the input and the reconstructed signals into an end-to-end self-supervisedly-trained hyperbolic space, where the signals additionally have an uncertainty score. This hyperbolic uncertainty is then used to discern whether the reconstruction error is large because the signal is anomalous, or simply because the model cannot reconstruct it well. Trustworthiness is further strengthened by explainability: a visual explanation is provided for each patient and anomaly to highlight the patient-dependent and contextual features that contributed the most to the detected anomaly.The proposed method has been tested and compared with other systems on existing datasets of the same type, and in addition, it has been integrated into a fully implemented end-to-end AAL solution developed in the context of a funded regional project, E-Linus, aimed at detecting early signs of social isolation of patients in residences for the elderly, and leveraging an interdisciplinary consortium of computer scientists, engineers and geriatricians.
Poster: Safe Gate – IoT system to support sanitary measures to combat COVID-19

The COVID-19 pandemic crisis raised public health attention closer to our global society’s demands. The disease proliferation occurs typically by droplet transmission, by being close to an infected person. Social distancing, a natural solution, is not always applicable to everyday needs, such as in the public transportation system, which is a space highly susceptible to viral proliferation. A set of ways to reduce proliferation in these infrastructures is by reinforcing facial masks usage, restraining symptomatic users, and reducing physical contact with public devices. Safe Gate, an Internet of Things (IoT) solution to enforce containment measures for disease proliferation, is proposed in this paper. This IoT solution is based on a network of edge computing devices used to control access to the entrance gate of the stations. The edge devices service samples an user’s temperature and facial image to verify that body temperature is within normal bounds and the user is correctly wearing a face mask. The system is contact-free and does not require an active operator, with no personal data stored, preserving privacy. Additionally, it minimizes personnel involvement with passengers, ensuring staff protection. The research question is whether the solution with two levels of facial recognition using cognitive edge computing will meet the requirements of a real system. In addition to this question, a queuing model to verify the feasibility of the solution is presented and evaluates the operational impact on a real transportation system.
Bayesian Network Monte Carlo Simulation of Impact of Abnormalities on Event Time Estimation at Clinical Sites

We explored a novel approach to determining the extent of monitoring sites participating in clinical studies. Bayesian networks and Monte Carlo simulations were used to generate survival data and associated readouts of three reference trials: a phase 2 study, a phase 3 study, and a platform study. We then substituted subsets of these data with poor-quality data and evaluated the impact on the readouts. Because the impact on phase 2 and 3 models became statistically significant only after the prevalence of poor-quality data reached implausible levels, we concluded that existing monitoring practices that include extensive validation of survival data may be excessive. In contrast, the platform study model was more sensitive to poor-quality data. We demonstrated a conceivable scenario in which a readout from such a platform study may be significantly affected by poor-quality data and may result in extreme outcomes in which a positive study reads out as negative (or vice versa) solely due to poor-quality data. We argue that in this setting existing monitoring practices should be upheld or their intensity increased.
Network attack detection model based on Linux memory forensics

With the rapid development of information science and technology, the role of the Internet in daily life is becoming more and more important, but while bringing speed and convenience to the experience, network security issues are endless, and fighting cybercrime will be an eternal topic. In recent years, new types of cyberattacks have made defense and analysis difficult. For example, the memory of network attacks makes some key array evidence only temporarily exist in physical memory, which puts forward higher requirements for attack detection. The traditional memory forensic analysis method for persistent data is no longer suitable for a new type of network attack analysis. The continuous development of memory forensics gives people hope. This paper proposes a network attack detection model based on memory forensic analysis to detect whether the system is under attack. Through experimental analysis, this model can effectively detect network attacks with low overhead and easy deployment, providing a new idea for network attack detection.
Teaching performance evaluation model based on machine learning

The accurate teaching performance evaluation model can effectively promote the teaching effect. This paper proposes a teaching effect evaluation model based on machine learning. First of all, through the analysis of the characteristics of teaching impact factors, the first-class index system of teaching effect evaluation is established. Then, based on the decision tree method, the teaching effect evaluation model of machine learning is established. Finally, this paper takes the school enterprise as an example to carry on the experimental research, the experimental results show that the model has higher accuracy, and can effectively assist the school-enterprise cooperative education program optimization design.
Variation of Privacy Loss (V)

Differential privacy offers the same protection for all records. In practice, some records might require a higher level of protection. This chapter of the Brief gives an overview of the corresponding notions.
Effective Candidate Selection and Interpretable Interest Extraction for Follower Prediction on Social Media

We address the problem of predicting new followers for a company account given that the number of social network API calls is limited, in order to enhance the marketing communication effectiveness on social media. The contributions of this paper are three-fold: 1) filtering methods that select promising candidate accounts with high precision while effectively reducing the number of API calls, 2) a new method for extracting interpretable feature vectors (interest vectors) from each account by utilizing standardized categories for marketing communication, and 3) a follower prediction model by utilizing the above candidate selection methods and interpretable interest vectors. Experiments on Twitter data confirm that our follower prediction model performs well with a small number of API calls and clarify which dimension of interest vectors (interest category) contributes to prediction performance.
AsTRA – An Assessment Tool for Recognition and Adaptation of Prior Professional Experience and Vocational Training

Educational systems in most countries are differentiated by level and entail transitions between different school types. The different school types do not always support a transition without gaps or repetitions. For students, this can be very frustrating, demotivating and time-consuming. For example, many graduated IT specialists bring work experience, a number of relevant competencies and previous knowledge from their vocational education and training into a degree programme in computer science – yet they still have to take the associated courses and pass examinations. To improve this situation and value the competencies brought along, we implemented a project to develop a comprehensive system to honour the prior knowledge of these students in various ways. One of our goals is to promote permeability between vocational and higher education, not only in computer science. The core element of this System is AsTRA, a comprehensive assessment tool which we developed to evaluate the common learning content and its difficult level. This system is designed to credit previous knowledge and to adapt the students’ learning paths, based on reliable decisions. In this paper, we present our tool AsTRA that supports these decisions as well as a brief overview of our resulting recognition system.
Modified Kernelized Correlation Filter Tracker Based on Saliency Detection and Reliability Judgment

With the rapid development of the correlation filter and deep learning technology, object tracking has been applied widely in the field of autonomous driving and video surveillance. It is challenging to achieve robust and efficient object tracking due to the variability of scenes and the complexity of the background. Considering real-time and computation limitations, correlation filter based tracker is still a good solution. In this paper, we proposed a modified tracking algorithm based on kernelized correlation filter. To distinguish the object from the background noise and eliminate unreasonably high energy values of the correlation filter in the boundary region, the saliency detection of object candidate regions is adopted. Besides, a compound discrimination method is proposed considering both the maximum correlation peak and the average peak-to-correlation energy to judge the reliability of tracking results accurately. Our approach is evaluated on OTB-2015 dataset and the experimental results show that our approach achieves outstanding performance than the classical algorithm KCF. Moreover, it is robust and efficient enough for occlusion and illumination change.
Evaluating the Efficiency of Several Machine Learning Algorithms for Fall Detection

Elderly falls are a growing phenomenon observed within the world. According to World Health Organization (WHO), it is the second leading cause of unintentional or accidental deaths among the elderly. Thus, the need for research regarding the development of fall detection systems is imperative. Researchers have utilized various approaches to develop fall detection systems, significant number of which have employed Machine Leaning (ML) algorithms for fall detection. In this study, we evaluated the efficiency of six ML algorithms on a public fall detection dataset. A robust deep neural network for fall detection (FD-DNN) is identified to be the current state-of-the-art, it detects falls by using a self-built sensor that consumes low power. By evaluating the efficiency of six machine learning algorithms on a publicly available joint fall detection dataset, the accuracy of the fall detection was increased from 99.17% to 99.88% by using the K-nearest Neighbor indicating that common machine learning algorithms can achieve identical or higher accuracy rendering the complex and expensive deep neural network-based fall detection systems inefficient.
A Brief Study on Smart Medicine Dispensers

The article’s purpose is to throw light on the presentation of an enhanced idea of the usage of a growing technology of Internet of Things. The proposed system SPEC 2.0 (Smart Pill Expert System) is used to automate the capability of dispensing the right dosage of medicine pills at the given interval. The proposed system has been designed to be used at your home, your workplace, at hospitals by a user in any age group and then possibly expanding the functionalities to the visually impaired. The system focuses on providing access control and monitoring management through a mobile app with no monthly subscriptions to the service being offered. The user has been given control of the system through the application to help set the time interval for dispensing the medication. There will also be alerts and notifications that are sent if the pills haven’t been removed from the final container box. The system is tested, and the results are determined by growing the modules for dispensing the pills at certain predefined time intervals.
An open-source low-cost wireless sensor system for acquisition of human movement data

Several fields of research such as medicine, robotics, sports, informatics, etc., require the analysis of human movement. Traditional systems for acquisition and analysis of human movement data are based on video cameras or active sensors. However, those systems are limited to high-resource settings. Wearable devices allow monitoring subjects outside typical clinical or research environments. Here, we present an open source low-cost wireless sensor system for acquisition of human movement data. Our system consists of two main parts: a server that stores data and, one or more wearable sensor modules that collect movement data through Inertial Measurement Units (IMUs) and transmit them wirelessly to the server. As a proof of concept, we measured human gait activity. Our results show that our system with IMUs can acquire quantifiable movement data. Characteristics such as open source code and its low-cost, make our system a viable alternative for clinical or research.
Resilient and Verifiable Federated Learning against Byzantine Colluding Attacks

Federated Learning (FL) is a multiparty learning computing approach that can aid privacy-preservation machine learning. However, FL has several potential security and privacy threats. First, the existing FL requires a central coordinator for the learning process which brings a single point of failure and trust issues for the shared trained model. Second, during the learning process, intentionally unreliable model updates performed by Byzantine colluding parties can lower the quality and convergence of the shared ML models. Therefore, discovering verifiable local model updates (i.e., integrity or correctness) and trusted parties in FL becomes crucial. In this paper, we propose a resilient and verifiable FL algorithm based on a reputation scheme to cope with unreliable parties. We develop a selection algorithm for task publisher and blockchain-based multiparty learning architecture approach where local model updates are securely exchanged and verified without the central party. We also proposed a novel auditing scheme to ensure our proposed approach is resilient up to 50% Byzantine colluding attack in a malicious scenario.
High-Performance and Customizable Vector Retrieval Service Based on Faiss in Power Grid Scenarios

With the rapid development of machine learning and deep learning, more and more services in the power grid have introduced machine learning and deep learning technologies, and related application scenarios and services have become more and more, especially vector retrieval services. With the increase of the amount of data and the increase of the demand, higher requirements are put forward for the vector retrieval service performance and service management. In order to solve this problem, this paper designs a high-performance and customizable vector retrieval service, referred to as HCFRS. The HCVRS service uses Fiass as the underlying framework of the vector retrieval service, supporting functions such as service registration, service unloading, resource allocation, load balancing, and data management. This paper verifies whether the HVCRS service meets the service design requirements from the three aspects of functional testing, accuracy testing and performance testing. The experimental results show that the HVCRS service has complete functions and good performance, which basically solves the difficulties encountered by the State Grid in vector retrieval services.
Scope and Related Work

This chapter details the criteria for inclusion of different data privacy definitions from this Brief and lists related works and existing surveys in the field of data privacy.
Hidden Markov Model for Gesture Recognition

In this chapter, hidden Markov model (HMM) is used to apply for gesture recognition. The study comprises the design, implementation, and experimentation of a system for making gestures, training HMMs, and identifying gestures with HMMs in order to better understand the behaviour of hidden Markov models (HMMs). One person extends his flattened, vertical palm toward the other, as though to reassure the other that his hands are safe. The other individual smiles and responds in kind. This wave gesture has been associated with friendship from childhood. Human motions can be thought of as a pattern recognition challenge. A computer can deduce the sender's message and reply appropriately if it can detect and recognise a set of gestures. By creating and implementing a gesture detection system based on the semi-continuous hidden Markov model, this chapter aims to bridge the visual communication gap between computers and humans.
Toward Task Scheduling Approaches to Reduce Energy Consumption in Cloud Computing Environment

As the emerging cloud computing technologies become more widespread and intensive, the attention to energy consumption to the underpinning network and resources grows rapidly. This happens because the transmission and switching networks are the key for user connectivity to the cloud and give unlimited infrastructures to the users without sharing obligations to manage them. Hence, cloud computing service providers are facing challenges to exploit resources wisely and get the best revenues accordingly. This leads to task scheduling approaches with their associated algorithms that aim to minimize energy consumptions in a cloud environment. This chapter reviews the concept of task scheduling approaches and focuses on the strategies to improve energy efficiency that is meant for cloud computing data centers. Extensive experiments were conducted by researchers to compare the earlier scheduling approaches such as earlier deadline first (EDF), round robin (RR), and greedy with the enhanced algorithms that equipped with strategies to customize resources based on workload and total utility. The results show that the processing time of the enhanced algorithm is significantly improved and reduce the total energy consumption in cloud computing data center.
Research on Intelligent Ticketing Method Based on Text Word Segmentation of Scheduling Operation

In order to realize the intelligent invoice of power grid maintenance application form and speed up the efficiency of power grid dispatching and maintenance, this paper will carry out relevant research. The research mainly focuses on the text word segmentation processing system of scheduling operation, discusses the algorithm logic support of the system, then carries on the system design, and finally introduces the application test of intelligent ticketing of the system. Through the analysis of this paper, text word segmentation processing system can be realized, the system can intelligently complete intelligent ticket, indirectly achieve the ultimate goal of comprehensive overhaul - scheduling integrated security process.
Optimization Strategies for Bayesian Source Localization Algorithms

Target search via Bayesian estimation is a commonly studied problem in which the goal is to locate and possibly characterize a lost “target” within a given space using noisy sensors. Example applications include locating gas leaks, radio sources, ships at sea, etc, using appropriate sensors for the specific phenomena. The motivating application for this effort is detecting and locating nearby radioactive material in nuclear facilities. Past work has produced several implementations for addressing this problem, with varying levels of accuracy and sophistication. Here we present both new and previously developed solution methods to the radioactive source localization problem. The solutions have diverse strategies for selecting advantageous positions at which to collect measurements. The objective is to minimize the number of measurements needed to complete the target localization. We empirically compare the effectiveness of various strategies, three of which are novel and prove superior over prior methods of measurement selection. The relative advantages of the novel strategies are discussed as well as their broader applicability to a general class of target search problems where measurement intensity correlates with target proximity. Note to Practitioners—This paper was motivated by the desire to automate tasks in radiation work spaces of nuclear facilities, particularly laboratory facilities such as those at Los Alamos National Laboratory. Prior work by the authors studied the problem of autonomously locating and characterizing radiation sources in a survey region. This paper presents enhancements to the prior method, which reduce the number of radiation measurements needed to produce the solution. This increases the efficiency and practicality of the method for end users.
Design and Realization of Network Course Virtual Simulation System Based on Digital Media Technology

In view of the current situation where English teaching is limited to the introduction of popular science concepts, single teaching methods, lack of information visualization learning tools, and difficulty in meeting the needs of learners for the construction of learning situations, we propose the application of virtual reality technology to the three-dimensional construction of college English teaching process Model and optimization. Finally, a virtual reality simulation and control system for English teaching was designed and developed. It supports three-dimensional interaction. Learners can master English immersively, aiming to increase the sense of immersion and entertainment in the learning process, reduce the cognitive load of the learners, and can improve the quality of English teaching.
Augmenting Differentiable Neural Computer with Read Network and Key-Value Memory

A Differential Neural Computer (DNC) is a type of neural network architecture that can make use of external memory. A DNC model can represent complex data sequences and reason about them. However, training a complex DNC with large memory matrices was slow, hence this study focused on improving a DNC model to learn faster and perform bAbi question-answering tasks more accurately. The attempted improvements were to use key-value pairs for memory locations instead of arrays and to obtain a read vector from the memory matrix with a neural network. Evaluation of the improved DNC models on the bAbi dataset showed that their compute time was 13&#x0025; shorter and their error rate was 6.6&#x0025; lower. To conclude, the attempted improvements were successful, and the training speed of the key-value memory model also became shorter.