Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach

Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.
Me, My Health, and My Watch: How Children with ADHD Understand Smartwatch Health Data

Children with ADHD can experience a wide variety of challenges related to self-regulation, which can lead to poor educational, health, and wellness outcomes. Technological interventions, such as mobile and wearable health systems, can support data collection and reflection about health status. However, little is known about how ADHD children interpret such data. We conducted a deployment study with 10 children, aged 10 to 15, for six weeks, during which they used a smartwatch in their homes. Results from observations and interviews during this study indicate that children with ADHD can interpret their own health data, particularly at the moment. However, as ADHD children develop more autonomy, smartwatch systems may require alternatives for data reflection that are interpretable and actionable for them. This work contributes to the scholarly discourse around health data visualization, particularly in considering implications for the design of health technologies for children with ADHD.
A Real-Time Tracking Algorithm for Multi-Target UAV Based on Deep Learning

UAV technology is a basic technology aiming to help realize smart living and the construction of smart cities. Its vigorous development in recent years has also increased the presence of unmanned aerial vehicles (UAVs) in people’s lives, and it has been increasingly used in logistics, transportation, photography and other fields. However, the rise in the number of drones has also put pressure on city regulation. Using traditional methods to monitor small objects flying slowly at low altitudes would be costly and ineffective. This study proposed a real-time UAV tracking scheme that uses the 5G network to transmit UAV monitoring images to the cloud and adopted a machine learning algorithm to detect and track multiple targets. Aiming at the difficulties in UAV detection and tracking, we optimized the network structure of the target detector yolo4 (You Only Look Once V4) and improved the target tracker DeepSORT, adopting the detection-tracking mode. In order to verify the reliability of the algorithm, we built a data set containing 3200 pictures of four UAVs in different environments, conducted training and testing on the model, and achieved 94.35% tracking accuracy and 69FPS detection speed under the GPU environment. The model was then deployed on ZCU104 to prove the feasibility of the scheme.
Developing Digital Literacy for Teaching and Learning

Abstract Digital literacy is a critical competence for empowering citizenship in a digital world. It has become a key element in teaching and learning across the different educational stages that has been addressed since the last decade of the twentieth century within the field of open, distance, and digital education. The literature so far has not agreed on a common definition, but multiple international, national, and even local, frameworks exist to foster digital literacy and to evaluate and certificate it, especially with a focus on educators and students in different educational levels, but also with the citizen perspective. These frameworks are reviewed in this chapter, along with the evolution and conceptualization of digital literacy and some strategies to foster digital literacy in different educational sectors, with a focus on the educator as a key player in this fostering action. The most remarkable challenges for developing digital literacy for teaching and learning include the same conception of digital literacy, which is multiple and situated, the digital divide and the actual consideration of digital literacy as a social practice. Being digital literacy a transversal competency nowadays, clear implications for education can be drawn, such as reshaping organizations to the digital conditions, thinking on digital literacy as a collective effort, and enriching the global discourse through diversity in debates.
Battle of Deep Fakes: Artificial Intelligence Set to Become a Major Threat to the Individual and National Security

The article discusses the possibility of political organizations utilizing deepfake technologies. It is observed that deepfakes can affect all levels of public and political life and contribute to the development of several problems, including reputational risks for celebrities and ordinary citizens, the growth of organized crime, and social stability and national security concerns. The sophistication of deepfake technology (DT) has increased significantly. Cybercriminals can now modify sounds, images, and movies to scam individuals and organizations. This growing threat to international institutions and individuals demands our attention. This article discusses deepfakes, their societal benefits, and how deception technology works. The hazards deepfakes pose to enterprises, governments, and legal systems worldwide are highlighted. In addition, the paper will examine potential solutions for deepfakes and end with future research goals. The authors conclude by discussing potential threats, prospects, and key pathsfor state regulation of this content within the framework of broader political and legal instruments to combat the spread of disinformation and fake news.
Achieving Scalability and Load Balance across Blockchain Shards for State Sharding

Sharding technique is viewed as the most promising solution to improving blockchain scalability. However, to implement a sharded blockchain, developers have to address two major challenges. The first challenge is that the ratio of cross-shard transactions (TXs) across blockchain shards is very high. This issue significantly degrades the throughput of a blockchain. The second challenge is that the workloads across blockchain shards are largely imbalanced. If workloads are imbalanced, some shards have to handle an overwhelming number of TXs and become congested very possibly. Facing these two challenges, a dilemma is that it is difficult to guarantee a low cross-shard TX ratio and maintain the workload balance across all shards, simultaneously. We believe that a fine-grained account-allocation strategy can address this dilemma. To this end, we first formulate the tradeoff between such two metrics as a network-partition problem. We then solve this problem using a community-aware account partition algorithm. Furthermore, we also propose a sharding protocol, named Transformers, to apply the proposed algorithm into the sharded blockchain system. Finally, trace-driven evaluation results demonstrate that the proposed protocol outperforms other baselines in terms of throughput, latency, cross-shard TX ratio, and the queue size of transaction pool.
Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head

The different imaging conditions of high spatial resolution remote sensing images (HSRRSIs) tend to cause large differences in the background information of bridges from the images, including problems of difficult detection of multiscale bridges, leakage of small bridges and insufficient detection accuracy for their detection. To address these problems, a YOLOv5 network with a decoupled head for the automatic detection of bridges in HSRRIs is proposed in this paper. First, the problem of inconsistent scale of information fusion of each feature in the feature pyramid network is solved using a weighted bi-directional feature pyramid network (BiFPN). Then, the convolutional block attention module (CBAM) is fused into the three effective feature layers after feature pyramid network processing. The bridge feature information is effectively extracted from the channel and spatial dimensions. Next, the decoupled head is fused in the YOLO Head to separate the classifier and regressor to speed up the network convergence and improve the network detection accuracy simultaneously. Finally, the practical effect is evaluated by calculating the average precision (AP). According to the experimental results, the AP of the proposed method is 98.1%, which is improved by 4.1%∼23.5% compared with other models.
Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing

Fall detection and physical activity (PA) classification are important health maintenance issues for the elderly and people with mobility dysfunctions. The literature review showed that most studies concerning fall detection and PA classification addressed these issues individually, and many were based on inertial sensing from the trunk and upper extremities. While shoes are common footwear in daily off-bed activities, most of the aforementioned studies did not focus much on shoe-based measurements. In this paper, we propose a novel footwear approach to detect falls and classify various types of PAs based on a convolutional neural network and recurrent neural network hybrid. The footwear-based detections using deep-learning technology were demonstrated to be efficient based on the data collected from 32 participants, each performing simulated falls and various types of PAs: fall detection with inertial measures had a higher F1-score than detection using foot pressures; the detections of dynamic PAs (jump, jog, walks) had higher F1-scores while using inertial measures, whereas the detections of static PAs (sit, stand) had higher F1-scores while using foot pressures; the combination of foot pressures and inertial measures was most efficient in detecting fall, static, and dynamic PAs.
A Multi-Stage Approach to Breast Cancer Classification Using Histopathology Images

Breast cancer is one of the deadliest diseases worldwide among women. Early diagnosis and proper treatment can save many lives. Breast image analysis is a popular method for detecting breast cancer. Computer-aided diagnosis of breast images helps radiologists do the task more efficiently and appropriately. Histopathological image analysis is an important diagnostic method for breast cancer, which is basically microscopic imaging of breast tissue. In this work, we developed a deep learning-based method to classify breast cancer using histopathological images. We propose a patch-classification model to classify the image patches, where we divide the images into patches and pre-process these patches with stain normalization, regularization, and augmentation methods. We use machine-learning-based classifiers and ensembling methods to classify the image patches into four categories: normal, benign, in situ, and invasive. Next, we use the patch information from this model to classify the images into two classes (cancerous and non-cancerous) and four other classes (normal, benign, in situ, and invasive). We introduce a model to utilize the 2-class classification probabilities and classify the images into a 4-class classification. The proposed method yields promising results and achieves a classification accuracy of 97.50% for 4-class image classification and 98.6% for 2-class image classification on the ICIAR BACH dataset.
$kt$-Safety: Graph Release via $k$-Anonymity and $t$-Closeness

In a wide spectrum of real-world applications, it is very important to analyze and mine graph data such as social networks, communication networks, citation networks, and so on. However, the release of such graph data often raises privacy issue, and the graph privacy preservation has recently drawn much attention from the database community. While prior works on graph privacy preservation mainly focused on protecting the privacy of either the graph structure only or vertex attributes only, in this paper, we propose a novel mechanism for graph privacy preservation by considering attacks from both graph structures and vertex attributes, which transforms the original graph to a so-called <inline-formula><tex-math notation="LaTeX">$kt$</tex-math></inline-formula> -safe graph, via <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity and <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> -closeness. We prove that the generation of a <inline-formula><tex-math notation="LaTeX">$kt$</tex-math></inline-formula> -safe graph is NP-hard, therefore, we propose a feasible framework for effectively and efficiently anonymizing a graph with low anonymization cost. In particular, we design a cost-model-based graph partitioning approach to enable our proposed divide-and-conquer strategy for the graph anonymization, and propose effective optimization techniques such as pruning method and a tree synopsis to improve the anonymization efficiency over large-scale graphs. Extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed <inline-formula><tex-math notation="LaTeX">$kt$</tex-math></inline-formula> -safe graph generation approach on both real and synthetic data sets.
An efficient real-time accelerator for high-accuracy DNN-based optical flow estimation in FPGA

Recently, accelerator architectures of deep neural networks (DNNs) have been designed to accelerate computer vision tasks, gaining the advantages of both accuracy and speed. However, DNNs-based optical flow estimation accelerators have not been successfully deployed due to the resource constraint. Existing hardware accelerators for optical flow estimation are all designed for non-DNN methods and generally perform poorly in estimated accuracy. In this paper, we present FlowAcc, a dedicated hardware accelerator for DNN-based optical flow estimation, adopting a pipelined hardware design for the real-time processing of image streams. We design an efficient multiplexing binary neural network (BNN) architecture for pyramidal feature extraction to significantly reduce the hardware cost and make it independent of the pyramid-level number. Furthermore, efficient designs such as hamming distance calculation and competent flow regularization are used for hierarchical optical flow estimation to greatly improve the system's efficiency. Comprehensive experimental results demonstrate that FlowAcc achieves state-of-the-art estimation accuracy and real-time performance on the Middlebury dataset when compared with the existing optical flow accelerators.
Silenced on social media: the gatekeeping functions of shadowbans in the American Twitterverse

Abstract Algorithms play a critical role in steering online attention on social media. Many have alleged that algorithms can perpetuate bias. This study audited shadowbanning, where a user or their content is temporarily hidden on Twitter. We repeatedly tested whether a stratified random sample of American Twitter accounts (n ≈ 25,000) had been subject to various forms of shadowbans. We then identified the type of user and tweet characteristics that predict a shadowban. In general, shadowbans are rare. We found that accounts with bot-like behavior were more likely to face shadowbans, while verified accounts were less likely to be shadowbanned. The replies by Twitter accounts that posted offensive tweets and tweets about politics (from both the left and the right) were more likely to be downtiered. The findings have implications for algorithmic accountability and the design of future audit studies of social media platforms.
The integration of forensic accounting and big data technology frameworks for internal fraud mitigation in the banking industry

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A multi-strategy contrastive learning framework for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) has gained significant popularity as it relies only on weak labels such as image level annotations rather than the pixel level annotations required by supervised semantic segmentation (SSS) methods. Despite drastically reduced annotation costs, typical feature representations learned from WSSS are only representative of some salient parts of objects and less reliable compared to SSS due to the weak guidance during training. In this paper, we propose a novel Multi-Strategy Contrastive Learning (MuSCLe) framework to obtain enhanced feature representations and improve WSSS performance by exploiting similarity and dissimilarity of contrastive sample pairs at image, region, pixel and object boundary levels. Extensive experiments demonstrate the effectiveness of our method and show that MuSCLe outperforms current state-of-the-art methods on the widely used PASCAL VOC 2012 dataset.
A scoping review of personalized user experiences on social media: The interplay between algorithms and human factors

No social media user sees the same feed. These platforms are personalized to the individual with the aid of algorithms that filter and prioritize content based on users' demographic profiles and personal data. On the one hand, this personalization aids the user by making the service more relevant, for instance by curating information of interest. On the other hand, personalization introduces potential risks associated with privacy concerns, lack of autonomy and control, as well as limited diversity of information. This scoping review presents an overview of the current state of knowledge of social media personalization from different research domains, providing insight on social media users’ algorithmic awareness, their customization habits, their interactions with curated content, and the debate on how algorithms may create closed information outlets. It also provides a condensed overview of the different terminology used across domains, in the form of a glossary.
Machine learning in software defect prediction: A business-driven systematic mapping study

Machine learning is a valuable tool in software engineering allowing fair defect prediction capabilities at a relatively small expense. However, although the practical usage of machine learning in defect prediction has been studied over many years, there is not sufficient systematic effort to analyse its potential for business application. The following systematic mapping study aims to analyse the current state-of-the-art in terms of machine learning software defect prediction modelling and to identify and classify the emerging new trends. Notably, the analysis is done from a business perspective, evaluating the opportunities to adopt the latest techniques and methods in commercial settings to improve software quality and lower the cost of development life cycle. We created a broad search universe to answer our research questions, performing an automated query through the Scopus database to identify relevant primary studies. Next, we evaluated all found studies using a classification scheme to map the extent of business adoption of machine learning software defect prediction based on the keywords used in the publications. Additionally, we use PRISMA 2020 guideline to validate reporting. After the application of the selection criteria, the remaining 742 primary studies included in Scopus until February 23, 2022 were mapped to classify and structure the research area. The results confirm that the usage of commercial datasets is significantly smaller than the established datasets from NASA and open-source projects. However, we have also found meaningful emerging trends considering business needs in analysed studies. There is still a considerable amount of work to fully internalise business applicability in the field. Performed analysis has shown that purely academic considerations dominate in published research; however, there are also traces of in vivo results becoming more available. Notably, the created maps offer insight into future machine learning software defect prediction research opportunities.
Side-Channel Analysis of Saber KEM Using Amplitude-Modulated EM Emanations

In the ongoing last round of NIST's post-quantum cryptography standardization competition, side-channel analysis of finalists is a main focus of attention. While their resistance to timing, power and near field electromagnetic (EM) side-channels has been thoroughly investigated, amplitude-modulated EM emanations has not been considered so far. The attacks based on amplitude-modulated EM emanations are more stealthy because they exploit side-channels intertwined into the signal transmitted by the on-board antenna. Thus, they can be mounted on a distance from the device under attack. In this paper, we present the first results of an amplitude-modulated EM side-channel analysis of one of the NIST PQ finalists, Saber key encapsulation mechanism (KEM), implemented on the nRF52832 (ARM Cortex-M4) system-on-chip supporting Bluetooth 5. By capturing amplitude-modulated EM emanations during decapsulation, we can recover each bit of the session key with 0.91 probability on average.