Reviewerly/exHarmony huggingface dataset:
Futuristic view of the Internet of Quantum Drones: Review, challenges and research agenda

The disruptive technology of unmanned aerial vehicles (UAVs), or drones, is a trend with increasing applications and practical relevance in the current and future society. Despite the common interest in drones for commercial deliveries, the use of this disruptive technology can be examined in the contexts of other world strategic demands such as climate change issues and traffic management. As of very recently, some drone-related futuristic disruptive technologies, including quantum drones (QD), the Internet of Quantum Drones (IoQDs), and a constellation of quantum satellites (CQS), are expected to be a breakthrough technology in strategic areas of society. However, prior research has not adequately examined and explored the potential applications of these drone-related futuristic disruptive technologies for social concern. Therefore, this study investigates how QD, IoQDs, and CQS can be applied in new contexts in real-time applications in strategic areas of societal interest, especially during the quantum age. Overall, our results unveil new potential and advanced applications to drone-related disruptive technologies in recognized and new contexts. Two relevant implications are highlighted. First, this research inaugurates new contexts regarding the use of drone-related technologies enabled by the Internet in themes of economic and social concerns. Second, from a futuristic point of view, the study examines the main challenges, risks, and advantages of the practical use of these technologies. We conclude this research with a summary of the main gaps and scientific challenges to the field and propose opportunities for future research.
Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities

With the adaptation of video surveillance in many areas for object detection, monitoring abnormal behavior in several cameras requires constant human tracking for a single camera operative, which is a tedious task. In multiview cameras, accurately detecting different types of guns and knives and classifying them from other video surveillance objects in real-time scenarios is difficult. Most detecting cameras are resource-constrained devices with limited computational capacities. To mitigate this problem, we proposed a resource-constrained lightweight subclass detection method based on a convolutional neural network to classify, locate, and detect different types of guns and knives effectively and efficiently in a real-time environment. In this paper, the detection classifier is a multiclass subclass detection convolutional neural network used to classify object frames into different sub-classes such as abnormal and normal. The achieved mean average precision by the best state-of-the-art framework to detect either a handgun or a knife is 84.21% or 90.20% on a single camera view. After extensive experiments, the best precision obtained by the proposed method for detecting different types of guns and knives was 97.50% on the ImageNet dataset and IMFDB, 90.50% on the open-image dataset, 93% on the Olmos dataset, and 90.7% precision on the multiview cameras. This resource-constrained device has shown a satisfactory result, with a precision score of 85.5% for detection in a multiview camera.
Productivity assessment of neural code completion

Neural code synthesis has reached a point where snippet generation is accurate enough to be considered for integration into human software development workflows. Commercial products aim to increase programmers’ productivity, without being able to measure it directly. In this case study, we asked users of GitHub Copilot about its impact on their productivity, and sought to find a reflection of their perception in directly measurable user data. We find that the rate with which shown suggestions are accepted, rather than more specific metrics regarding the persistence of completions in the code over time, drives developers’ perception of productivity.
Predictive models aren't for causal inference

Ecologists often rely on observational data to understand causal relationships. Although observational causal inference methodologies exist, predictive techniques such as model selection based on information criterion (e.g. AIC) remains a common approach used to understand ecological relationships. However, predictive approaches are not appropriate for drawing causal conclusions. Here, we highlight the distinction between predictive and causal inference and show how predictive techniques can lead to biased causal estimates. Instead, we encourage ecologists to valid causal inference methods such as the backdoor criterion, a graphical rule that can be used to determine causal relationships across observational studies.
BERT Models for Arabic Text Classification: A Systematic Review

Bidirectional Encoder Representations from Transformers (BERT) has gained increasing attention from researchers and practitioners as it has proven to be an invaluable technique in natural languages processing. This is mainly due to its unique features, including its ability to predict words conditioned on both the left and the right context, and its ability to be pretrained using the plain text corpus that is enormously available on the web. As BERT gained more interest, more BERT models were introduced to support different languages, including Arabic. The current state of knowledge and practice in applying BERT models to Arabic text classification is limited. In an attempt to begin remedying this gap, this review synthesizes the different Arabic BERT models that have been applied to text classification. It investigates the differences between them and compares their performance. It also examines how effective they are compared to the original English BERT models. It concludes by offering insight into aspects that need further improvements and future work.
Securing Multimedia Using a Deep Learning Based Chaotic Logistic Map

Telemedicine and online consultations with doctors has become very popular during the pandemic and involves the transmission of medical data through the internet. Thus this raises concern about the security of the medical data of the patient as the records to contain sensitive and confidential information. A Secure multimedia transformation approach is proposed in this paper using a deep learning-based chaotic logistic map. The proposed work achieves novelty by the integration of a lightweight encryption function using a chaotic logistic map. It also uses the ResNet model to perform classification for identifying the fake medical multimedia data. A linear feedback shift register operations and an interactive user interface facilitate ease of usage to secure the medical multimedia data. The chaotic map provides the security properties such as confusion and diffusion necessary for the encryption ciphers. At the same time, they are highly sensitive to input conditions, thus making the proposed encryption algorithm more secure and robust. The proposed encryption mechanism helps in securing the medical image and video data. On the receiver side, Multilayer perceptions (MLP) of the deep learning approach are used to classify the medical data according to the features required to make other processes. When tested, the proposed work proves efficient in securing medical data against various cyber-attacks and exhibits high entropy levels.
The Impact of Blockchain Technology Adoption on Tourism Industry: A Systematic Literature Review

The current COVID-19 pandemic has led to the acceleration of the digitization process and to a shifting to Industry 4.0. Blockchain (BC) is one of the technologies that has been introduced worldwide in recent years. It has been beneficial for the tourism industry, in addition to many other sectors. This article investigates the advantages and disadvantages of BC adoption in the tourism industry, as well as the possible solutions to overcome the challenges. This paper conducts a systematic literature review (SLR) that consists of the operations for detecting, selecting, categorizing, and analyzing relevant articles on a specified subject. It is evident from the results that the majority of the academic works illustrate the benefits of BC implementation and explain its potential by providing diverse models of BC-based systems. However, since BC is a young technology, numerous challenges have appeared on the path to its full adoption in the tourism industry. Possible solutions are the achievement of a collaborative approach among the stakeholders, the deepening of academic research in the field, testing more models of BC-based systems, and the establishment of relevant policies. Consequently, the implications for the theory, practice, policy, and research of this work are significant.
Blockchain based energy trading scheme for vehicle-to-vehicle using decentralized identifiers

A blockchain-based energy trading system is a new paradigm of grid infrastructure, which allows that energy purchaser and seller can efficiently exchange the energy through two-way communication. However, because energy trading services are provided through public networks, these systems are vulnerable to potential security breaches. This paper proposed a privacy-preserving blockchain-based energy trading scheme for vehicle-to-vehicle to resolve the security issues of contemporary systems and provide secure energy trading services. The proposed scheme has high efficiency by applying decentralized identifiers and verifiable credentials technologies because the records of energy trading are not stored on blockchain and blockchain is only utilized in the validation of users. After completing the energy trading, the vehicle issues a verifiable credential to the counterpart to prove the legitimacy of the transaction. We also perform informal and formal security analysis to demonstrate its security and achieve secure mutual authentication, confidentiality, and session key security. Furthermore, we implement AVISPA simulation to show that our scheme is resistant to man-in-the-middle and replay attacks. As a result, the proposed scheme can be used in distributed smart grid environments.
Using text mining to establish knowledge graph from accident/incident reports in risk assessment

To clarify the risk factors and propagation characteristics affecting railway safety, we learn from historical reports to build a connected network of hazards and accidents, forming a knowledge graph (KG), and apply it to railway hazard identification and risk assessment. First, the open source-British railway accident/incident reports are selected as the data source. The text augmentation algorithm in the text mining technology is introduced and optimized to achieve data enhancement. An ensemble model is constructed based on the hidden Markov model, conditional random field (CRF) algorithm, bidirectional long short-term memory (Bi-LSTM), and Bi-LSTM-CRF deep learning network, completing the named entity recognition of the reports. Then, using the random forest algorithm, the standardized classification of entities is accomplished, and the multi-dimensional knowledge graph network is established. Finally, after defining a series of safety-related feature parameters, the obtained KG is applied to the quantitative assessment of the corresponding risk level of the hazards. The results show that this approach realizes the visualization and quantitative description of the potential relationship among hazards, faults, and accidents by exploring the topological relationship of the railway accident network, further assisting the formulation of railway risk preventive measures.
Collaboration challenges in building ML-enabled systems

The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists. While collaboration can always be challenging, ML introduces additional challenges with its exploratory model development process, additional skills and knowledge needed, difficulties testing ML systems, need for continuous evolution and monitoring, and non-traditional quality requirements such as fairness and explainability. Through interviews with 45 practitioners from 28 organizations, we identified key collaboration challenges that teams face when building and deploying ML systems into production. We report on common collaboration points in the development of production ML systems for requirements, data, and integration, as well as corresponding team patterns and challenges. We find that most of these challenges center around communication, documentation, engineering, and process, and collect recommendations to address these challenges.
Investigation Into Recognition Algorithm of Helmet Violation Based on YOLOv5-CBAM-DCN

Recognition of safety helmets wearing by construction workers is a common target detection topic in applications of deep learning-based image processing. This paper provides a study of an enhanced YOLOv5-based method, in which the challenges caused by complicated construction environment backgrounds, dense targets, and the irregular shape of safety helmets are addressed. In a trunk network, feature extraction is more based on the target shape by using the Deformable Convolution Net instead of the conventional convolution; in the Neck, a Convolutional Block Attention Module is introduced to weaken feature extraction of complex backgrounds by giving weights to enhance the characterization ability of target features; and the original network&#x2019;s Generalized Intersection over Union Loss is replaced by Distance Intersection over Union Loss to overcome the problem of erroneous location when the population is dense. The dataset for the training network is created by mixing open-source datasets with autonomous collecting to evaluate the effectiveness of the algorithm. We observed that the improved model has a detection accuracy of 91.6%, up 2.3% over the original network model, and a detection speed of 29 frames per second, which is compliant with most security cameras&#x2019; capture frame rate.
Path-sensitive code embedding via contrastive learning for software vulnerability detection

Machine learning and its promising branch deep learning have shown success in a wide range of application domains. Recently, much effort has been expended on applying deep learning techniques (e.g., graph neural networks) to static vulnerability detection as an alternative to conventional bug detection methods. To obtain the structural information of code, current learning approaches typically abstract a program in the form of graphs (e.g., data-flow graphs, abstract syntax trees), and then train an underlying classification model based on the (sub)graphs of safe and vulnerable code fragments for vulnerability prediction. However, these models are still insufficient for precise bug detection, because the objective of these models is to produce classification results rather than comprehending the semantics of vulnerabilities, e.g., pinpoint bug triggering paths, which are essential for static bug detection.
Object detection from UAV thermal infrared images and videos using YOLO models

Object detection is one of the most crucial tasks in computer vision and remote sensing to identify specific categories of various objects in images. The unmanned aerial vehicle (UAV)-based thermal infrared (TIR) remote sensing multi-scenario images and videos are two important data sources in public security. However, their object detection process is still challenging because of the complicated scene information, coarse resolution compared with the visible videos and lack of public labelled datasets and training models. This study proposed a UAV TIR object detection framework for images and videos. The You Only Look Once (YOLO) models based on Convolutional Neural Network (CNN) architecture were designed to extract features from ground-based TIR images and videos, which were captured by Forward-looking Infrared (FLIR) cameras. The most effective algorithm was finally identified by evaluation metrics and then applied to detect objects on TIR videos from UAVs. Results showed that the highest mean average precision (mAP) of the person and car instances was 88.69% in the validating task. The fastest detection speed achieved 50 frames per second (FPS), and the smallest model size was observed in YOLOv5-s. In the application, the cross-detection performance on persons and cars in UAV TIR videos under a YOLOv5-s model was discussed in terms of the different UAVs’ observation angles and the effectiveness of the YOLO architecture was revealed. This study provides positive support for the qualitative and quantitative evaluation of objection detection from TIR images and videos using deep-learning models.
Pre-trained language models with domain knowledge for biomedical extractive summarization

Biomedical text summarization is a critical task for comprehension of an ever-growing amount of biomedical literature. Pre-trained language models (PLMs) with transformer-based architectures have been shown to greatly improve performance in biomedical text mining tasks. However, existing methods for text summarization generally fine-tune PLMs on the target corpora directly and do not consider how fine-grained domain knowledge, such as PICO elements used in evidence-based medicine, can help to identify the context needed for generating coherent summaries. To fill the gap, we propose KeBioSum, a novel knowledge infusion training framework, and experiment using a number of PLMs as bases, for the task of extractive summarization on biomedical literature. We investigate generative and discriminative training techniques to fuse domain knowledge (i.e., PICO elements) into knowledge adapters and apply adapter fusion to efficiently inject the knowledge adapters into the basic PLMs for fine-tuning the extractive summarization task. Experimental results from the extractive summarization task on three biomedical literature datasets show that existing PLMs (BERT, RoBERTa, BioBERT, and PubMedBERT) are improved by incorporating the KeBioSum knowledge adapters, and our model outperforms the strong baselines.
Visual object tracking: A survey

Visual object tracking is an important area in computer vision, and many tracking algorithms have been proposed with promising results. Existing object tracking approaches can be categorized into generative trackers, discriminative trackers, and collaborative trackers. Recently, object tracking algorithms based on deep neural networks have emerged and obtained great attention from researchers due to their outstanding tracking performance. To summarize the development of object tracking, a few surveys give analyses on either deep or non-deep trackers. In this paper, we provide a comprehensive overview of state-of-the-art tracking frameworks including both deep and non-deep trackers. We present both quantitative and qualitative tracking results of various trackers on five benchmark datasets and conduct a comparative analysis of their results. We further discuss challenging circumstances such as occlusion, illumination, deformation, and motion blur. Finally, we list the challenges and the future work in this fast-growing field.
Why Developers Refactor Source Code

Refactoring aims at improving code non-functional attributes without modifying its external behavior. Previous studies investigated the motivations behind refactoring by surveying developers. With the aim of generalizing and complementing their findings, we present a large-scale study quantitatively and qualitatively investigating why developers perform refactoring in open source projects. First, we mine 287,813 refactoring operations performed in the history of 150 systems. Using this dataset, we investigate the interplay between refactoring operations and process (e.g., previous changes/fixes) and product (e.g., quality metrics) metrics. Then, we manually analyze 551 merged pull requests implementing refactoring operations and classify the motivations behind the implemented refactorings (e.g., removal of code duplication). Our results led to (i) quantitative evidence of the relationship existing between certain process/product metrics and refactoring operations and (ii) a detailed taxonomy, generalizing and complementing the ones existing in the literature, of motivations pushing developers to refactor source code.
Digital contact tracing

Since the start of the COVID-19 pandemic, technology enthusiasts have pushed for digital contact tracing as a critical tool for breaking the COVID-19 transmission chains. Motivated by this push, many countries and companies have created apps that enable digital contact tracing with the goal to identify the chain of transmission from an infected individual to others and enable early quarantine. Digital contact tracing applications like AarogyaSetu in India, TraceTogether in Singapore, SwissCovid in Switzerland, and others have been downloaded hundreds of millions of times. Yet, this technology hasn't seen the impact that we envisioned at the start of the pandemic. Some countries have rolled back their apps, while others have seen low adoption [12, 17]. Therefore, it is prudent to ask what the technology landscape of contact-tracing looks like and what are the missing pieces. We attempt to undertake this task in this paper. We present a high-level review of technologies underlying digital contact tracing, a set of metrics that are important while evaluating different contact tracing technologies, and evaluate where the different technologies stand today on this set of metrics. Our hope is two fold: (a) Future designers of contact tracing applications can use this review paper to understand the technology landscape, and (b) Researchers can identify and solve the missing pieces of this puzzle, so that we are ready to face the rest of the COVID-19 pandemic and any future pandemics. A majority of this discussion is focused on the ability to identify contact between individuals. The questions of ethics, privacy, and security of such contact tracing are briefly mentioned but not discussed in detail.
Leveraging Blockchain and Smart Contract Technologies to Overcome Circular Economy Implementation Challenges

Adopting a circular economy (CE) has rapidly emerged among policymakers and business community stakeholders to promote material circularization and ensure sustainable development. While the inclination for a paradigm shift away from the linear economy is evident, many challenges have been quoted in the literature regarding its implementation. Lately, it has become common to propose Information and Communication Technologies (ICT)-based approaches to address these challenges. However, they do not question the practicality of the solutions in the context of CE. This paper aims to find an appropriate digital solution for CE implementation, which is not possible without a complete understanding of the existing challenges. A thorough literature review broadly classified the challenges under five barrier categories: Technological, Financial, Infrastructural, Institutional, and Societal, which was followed up with an investigation into the failure of ICT solutions to address CE challenges. Among the various technologies, blockchain and smart contract technologies show some promise as data-driven decision-making tools; however, they are not without their limitations when applied in the context of CE. This perspective explores the role of blockchain smart contract technology-scape in overcoming CE challenges and presents a circular economy blockchain (CEB) architecture development. The findings suggest that CEB may enable CE business models that improve trust and transparency in supply-chain networks, shared and performance economy platforms, stakeholder participation, and governance and management of organizations. Ultimately, this study highlights critical areas for research and development for the blockchainification of CE.