Reviewerly/exHarmony huggingface dataset:
Understanding the perceptions of UK COVID-19 contact tracing app in the BAME community in Leicester

Purpose Digital contact tracing technologies are critical to the fight against COVID-19 in many countries including the UK. However, a number of ethical, legal and socio-economic concerns that can affect uptake of the app have been raised. The purpose of this research is to explore the perceptions of the UK digital contact tracing app in the Black, Asian and Minority Ethnic (BAME) community in Leicester and how this can affect its deployment and implementation. Design/methodology/approach Data was collected through virtual focus groups in Leicester, UK. A total of 28 participants were recruited for the study. All participants are members of the BAME community, and data was thematically analysed with NVivo 11. Findings A majority of the participants were unwilling to download and use the app owing to legal and ethical concerns. A minority were willing to use the app based on the need to protect public health. There was a general understanding that lack of uptake will negatively affect the fight against COVID-19 in BAME communities and an acknowledgement of the need for the government to rebuild trust through transparency and development of regulatory safeguards to enhance privacy and prevent misuse. Originality/value To the best of the authors’ knowledge, the research makes original contributions being the first robust study conducted to explore perceptions of marginalised communities, particularly BAME which may be adversely impacted by the deployment of the app. By exploring community-based perceptions, this study further contributes to the emerging citizens’ perceptions on digital contact tracing which is crucial to the effectiveness and the development of an efficient, community-specific response to public attitudes towards the app. The findings can also help the development of responsible innovation approaches that balances the competing interests of digital health interventions with the needs and expectations of the BAME community in the UK.
Reinforcement Learning-Based Anomaly Detection for Internet of Things Distributed Ledger Technology

Distributed Ledger Technologies (DLT) are based on the Blockchain concept and have been specifically designed for enterprise-level devices with acceptable computing powers and network bandwidth. Direct Acyclic Graph (DAG) ledger(s) is a new form of DLT technology designed for Internet-of- Things (IoT) devices due to the nature of its disadvantages of the computing powers and limited network bandwidth. IOTA is a DAG-based Blockchain implementation for IoT applications that has gained an increased attention in recent years. One of the major concerns that is hindering for its wide adaptation is the security concerns. Many security attack occurrences against the IOTA such as parasite attacks, double spending, and DDoS to disrupt availability resources of the new ledger can become both widespread and disruptive. Existing security studies are ad-hoc and typically address a solution scheme for a specific security threat. In this paper, we present an adaptive Reinforcement-Learning (RL) approach to best classify the monitored resource consumption parameters of the DAG-based nodes or devices for any potential security anomaly detection. The aim is to create high accuracy security threat index that can be used to proactively defend the decentralized IOTA infrastructure and individual nodes against compromises. The performance evaluation results of this solution against DoS attacks are promising. The framework implementation derives a stochastic interpretation and output and the same time it converges deterministically.
An improved YOLO v3 algorithm for remote Sensing image target detection

Abstract With more and more in-depth research on deep learning algorithms in recent years, how to use deep learning method to detect remote sensing images is the key to improving the utilization efficiency of remote sensing data and realizing the transformation from data to knowledge. In this paper, an improved YOLO V3 algorithm is proposed to solve the problems of missed detection and false detection of the original YOLOv3 algorithm in remote sensing image target detection with different size and wide disparity in length and width ratio. first of all, K-means algorithm is used for clustering analysis of data set to obtain the position of anchor box; Secondly, the dilated convolution with expansion rate of 2 is used to replace the general convolution in the feature extraction part; Then four scales are used for prediction; Finally, the improved algorithm is applied to the recognition of bridges, harbors and airports. The results show that the detection performance of the algorithm is improved by about 2% compared with the original algorithm.
Two N-of-1 self-trials on readability differences between anonymous inner classes (AICs) and lambda expressions (LEs) on Java code snippets

Abstract In Java, lambda expressions (LEs) were introduced at a time where the similar language construct anonymous inner class (AIC) already existed for years. But while LEs became quite popular in mainstream programming languages in general, their usability is hardly studied. From the Java perspective the need to study the relationship between LEs and AICs was and is quite obvious, because both language constructs co-exist. However, it is quite usual that new language constructs are introduced although they are not or hardly studied using scientific methods – and an often heard argument from programming language designers is that the effort or the costs for the application of the scientific method on language constructs is too high. The present paper contributes in two different ways. First, with respect to LEs in comparison to AICs, this paper presents two N-of-1 studies (i.e. randomized control trials executed on a single subject) where LEs and AICs are used as listeners in Java code. Both experiments had two similar and rather simple tasks (“count the number of parameters”, respectively “count the number of used parameters”) with the dependent variable being reaction time. The first experiment used the number of parameters, the second the number of used parameters as the controlled, independent variable (in addition to the technique LE and AIC). Other variables (LOC, etc.) were randomly generated within given boundaries. The main result of both experiments is that LEs without type annotations require less reading time ( p hs .2, reduction of reaction time of at most 35%). The results are based on 9,600 observations (one N-of-1 trial with eight replications). This gives evidence that the readability of LEs without type annotations improves the readability of code. However, the effect seems to be so small, that we do not expect this to have a larger impact on daily programming. Second, we see the contribution of this paper in the application of N-of-1 trials. Such experiments require relatively low effort in the data selection but still permit to analyze results in a non-subjective way using commonly accepted analysis techniques. Additionally, they permit to increase the number of selected data points in comparison to traditional multi–subject experiments. We think that researchers should take such experiments into account before planning and executing larger experiments.
Authentication of Underwater Acoustic Transmissions via Machine Learning Techniques

We consider the problem of discriminating a legitimate transmitter from an impersonating attacker in an underwater acoustic network under a physical layer security framework.In particular, we utilize features of the underwater acoustic channel such as the number of taps, the delay spread, and the received power.In the absence of a reliable statistical model of the underwater channel, we turn to a machine learning technique to extract the feature statistics and utilize them to distinguish between legitimate and fake transmissions.Numerical results show how, using only four channel features as input of either a neural network or an autoencoder, we achieve a good trade off between false alarm and detection rates.Moreover, cooperative techniques fusing soft decision statistics from multiple trusted nodes further outperform the discrimination capability of each separate node.Data from a sea trial carried out in Israeli eastern Mediterranean waters demonstrate the applicability of our approach.
DFECsiNet: Exploiting Diverse Channel Features for Massive MIMO CSI Feedback

To enhance the performance of a massive multiple-input multiple-output (MIMO) system, downlink channel state information (CSI) should be fed back to the base station (BS) in frequency-division duplexing (FDD) mode. However, large numbers of antennas in massive MIMO systems will lead to an excessive feedback overhead, which poses a large challenge to practical systems. This paper proposes deep learning (DL) based CSI feedback method called DFECsiNet. It is designed based on a simple structure called DFEBlock, which uses two parallel feature extraction paths with different respective fields to extract diverse CSI matrices. Moreover, two specially designed refinement structures are used to progressively recover the initially recovered channel. Experiment results show that DFECsiNet outperforms CsiNet with similar complexities and achieves comparable performances to DS-NLCsiNet with much lower complexities.
GOURMET: A Methodology for Realizing Goal-Driven Self-Adaptation

The re-use of requirements captured during design-time for run-time decision-making represents a popular paradigm for developing self-adaptive systems, generally termed requirements reflection or requirements@runtime. While a significant body of work exists on the topic, at present researchers, architects and developers need to sift through the relevant literature that spans more than one technical domain in order to leverage existing engineering knowledge to guide the realization of concrete, end-to-end requirements reflection approaches. In this position paper, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> we aim to contribute towards filling this gap by outlining a generic, reusable methodology that provides guidance on how to realize one class of requirements reflection approaches: those that capture requirements as goals.
LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective Recommendation

Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item knowledge graph (KG) in low-dimensional space. In this article, we propose a lightweight end-to-end joint learning framework for fusing the tasks of KGE and RSs at the model level. Our method proposes a lightweight KG embedding method by using bidirectional bijection relation-type modeling to enable scalability for large graphs while using self-adaptive negative sampling to optimize negative sample generating. Our method further generates the integrated views for users and items based on relation-types to explicitly model users’ preferences and items’ features, respectively. Finally, we add virtual “recommendation” relations between the integrated views of users and items to model the preferences of users on items, seamlessly integrating RS with user-item KG over a unified graph. Experimental results on multiple datasets and benchmarks show that our method can achieve a better accuracy of recommendation compared with existing state-of-the-art methods. Complexity and runtime analysis suggests that our method can gain a lower time and space complexity than most of existing methods and improve scalability.
Transaction search engine of distributed electricity market trading platform based on blockchain technology

Abstract Currently, it is difficult for China's electricity trading platform to handle large amounts of data during centralized clearing, achieve cross‐regional sharing of transaction information, and realize the optimized allocation of resources. The previous research has proposed establishing a new distributed electricity market trading platform based on blockchain technology to solve the aforementioned problems; however, transaction nodes on the platform have difficulty determining the final delisting plan in the face of numerous choices. Therefore, in this study, a search engine was designed according to the demands of the delisting node. The developed algorithm is run with a programmed script through a smart contract and can provide the best delisting plan for delisting nodes, ensuring that the delisting plan is selected intelligently and publicly. Overall, seven delisting strategies were designed. Additionally, the improved Critic‐G1 method based on the coefficient of variation was used to establish a smart contract for the comprehensive evaluation of listing nodes. Finally, the example analysis verified that calling the smart contract can help the delisting node quickly obtain the best delisting strategy, increase the enthusiasm of the transaction subject, and facilitate the promotion and application of the transaction platform based on blockchain technology, confirming the effectiveness of the proposed method.
A New Method for Identifying Kinetic Parameters of Industrial Robots

Identifying the kinetic parameters of an industrial robot is the basis for designing a controller for it. To solve the problems of the poor accuracy and easy premature convergence of common bionic algorithms for identifying the dynamic parameters of such robots, this study proposed simulated annealing with similar exponential changes based on the beetle swarm optimization (SEDSABSO) algorithm. Expressions for the dynamics of the industrial robot were first obtained through the SymPyBotics toolkit in Python, and the required trajectories of excitation were then designed to identify its dynamic parameters. Following this, the search pattern of the global optimal solution for the beetle swarm optimization algorithm was improved in the context of solving for these parameters. The global convergence of the algorithm was improved by improving the iterative form of the number N of skinks in it by considering random perturbations and the simulated annealing algorithm, whereas its accuracy of convergence was improved through the class exponential change model. The improved beetle swarm optimization algorithm was used to identify the kinetic parameters of the Zhichang Kawasaki RS010N industrial robot. The results of experiments showed that the proposed algorithm was fast and highly accurate in identifying the kinetic parameters of the industrial robot.
TexRGAN

Free form handwritten document images commonly contains deformed text images such as struck-out and underlined words. The deformed text images drastically degrades the performance of intensely used document processing applications like optical character recognition (OCR). Here we propose an end-to-end text image restoration system based on generative adversarial network (GAN). The proposed model TexRGAN is perhaps the first attempt to restore deformed handwritten texts like struck-out and underlined text images using GAN model and it simultaneously handles strikeout and underline words both with a single deep network model. The proposed GAN model uses spatial as well as structural loss to generate restored text images from a given deformed text image input as condition. The proposed network is trained in weakly supervised approach to avoid unavailability of training data and the cost and error of manual annotations. We evaluate the performance of the proposed TexRGAN on various types of deformation and shapes of strike-through-strokes such as slanted strokes, nearly straight strokes, multiple strokes, underlines, crossed strokes etc. The TexRGAN is also evaluated directly in terms the OCR performance. The evaluation metrics show robustness and applicability in real-world scenario.
Evaluating the learning outcomes of a fuzzy-based Intelligent Tutoring System

In this paper, we present a full evaluation of the learning outcomes of an Intelligent Tutoring Systems, which teaches the logic of computer programming and the programming language ‘C’. The adaptation of the evaluated ITS is based on a fuzzy mechanism, which is responsible to identify the learner’s current knowledge level and misconceptions. The system takes into consideration the knowledge dependencies that exist among the domain concept of the learning material and applying fuzzy rules decides about the learning material that has to be delivered to the learner and the lesson sequence. The system was used in real conditions by 70 students of a postgraduate program in Informatics of the University of Piraeus in Greece. After the period of system’s experimental usage, the following four metrics- characteristics were calculated: (1) the learners’ performance, (2) the number of the learners that dropped out from the usage of the system, (3) the number of learners that succeeded to reach the target knowledge by completing, successfully, the lessons of the system, and (4) the mean number of interactions with the system that are needed until the learner reaches the target knowledge. The calculated numbers were compared with the corresponding results after the usage of a similar Intelligent Tutoring System from which the fuzzy mechanism was absent. For ensuring the validity of the evaluation results, t-tests were conducted. The evaluation results show that the presented fuzzy-based Intelligent Tutoring system increases the learners’ knowledge level and offers a personalized learning experience that promotes the active participation of students in the learning process and decreases the number of dropouts.
DNN inference offloading for object detection in 5G multi-access edge computing

In this paper, we present experimental results of a Deep Neural Network (DNN) inference offloading in a realworld 5G MEC testbed to meet requirements for high detection accuracy and low end-to-end latency for object detection. Central Unit (CU), Distributed Unit (DU), Radio Unit (RU), and MEC servers are located in the 5G MEC testbed such that the performance of latency sensitive MEC applications could be tested. We implemented the DNN offloading by applying task pipeline parallelism and DNN task decoupling scheme from edge to MEC server. We verified the effectiveness of the DNN offloading using YOLOv3 in terms of Frames Per Second (FPS) and power consumption in the 5G MEC testbed.
Predicting Big Five Personality Traits Based on Twitter User U sing Random Forest Method

Social media's ever-increasing use makes data about human behavior readily available. Twitter is one of these social media platforms. The tweets and profile information of users can be used to predict their personalities. The Big Five personality traits are Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Big Five personality predictions on Twitter are significant because traditional personality detectors are expensive and time-consuming, and the results of personality predictions can be used to assist recruiters in placing the best employees based on the Big Five personality traits. Numerous previous studies have been conducted on Big Five personality prediction using a machine learning approach and a variety of methods, but predictions using Indonesian data are still scarce and have low accuracy. As a result, this study employs the Random Forest method to develop a Big Five personality prediction system. Random Forest performs well with less accurate data and has a large number of adjustable parameters. The system makes use of tweet data to provide a sentiment and emotion feature. Additionally, account information is utilized as a social feature. The system achieves the highest accuracy of 69.23% when all features are combined.
Augmented Reality based Training of Surgical Staff to Operate a Laparoscopic Instrument

Simulation-based training is used in various fields. One of these fields is medical education. This study researches the use of Augmented Reality (AR) for laparoscopic surgeries and the settings for surgical training. AR adds an overlay of computer-generated content on a camera live image. AR helps medical staff to improve diagnostics and surgical treatment of patients. This paper presents an approach to teaching medical students to use augmented reality. Two software applications MOSAR ICTPro 1.0 and MOSAR ICTPro 2.0 have been developed, which are designed as upgrades to the multifunctional operating station for laparoscopic manipulations, developed on Tcl/Tk. Applications run under Android®. The experimental results take into account the force interaction of the instrument-obj ect. This work is a continuation of previous research in the field of surgical robotics.
Spatio-Temporal Approach using CNN-RNN in Hand Gesture Recognition

One of the ways of communication in human- computer interaction is by hand gesturing through video, a collection of sequential images, and has a frame per second (fps) configuration so that the existing image could change at any time. Recognize hand gesture videos through the pattern of each frame and its connection. Therefore the recognition views them as images in time sequences. There are several approaches—the single spatial approach by collecting image sequences in large images. Even though it has good accuracy, it will have problems with a less responsive background and fast movement because it captures less information on image pattern changes from adjacent frames. Others take memory. The temporal approach focuses on comparing image patterns between frames but requires spatial information or patterns for each frame. It is not the only initial frame. Hence, it is appropriate to combine the two approaches simultaneously in motion recognition or movement called Spatio-Temporal. Convolution Neural Network (CNN) is good in image recognition. Recurrent Neural Networks (RNN) are usually suitable for recognizing sequences and their relationships. Therefore, for hand gesture recognition, this research used a Spatio-Temporal approach with the CNN-RNN method. CNN with Spatial-Streams get image patterns, and Temporal- Streams use RNN to get connected patterns. The results showed that the combination of CNN and RNN for the Spatio- Temporal approach could recognize one of the four-hand gestures by 96.43%. The experiments resulted in eight CNN convolution layers and two Dense layers in RNN with GRU and LSTM architectures.
The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too.