Reviewerly/exHarmony huggingface dataset:
Unsupervised Anomaly Detection of Class Imbalanced Cognition Data Using an Iterative Cleaning Method

The presence of class imbalance in machine learning datasets is a pervasive challenge that often hampers the effectiveness of traditional machine learning models. In the context of anomaly detection, the instances in the minority class are the ones of most interest. To address this issue, we evaluate an unsupervised approach that uses an iterative cleaning process for anomaly detection on cognition data. We conduct experiments on two cognition datasets, one has a large degree of class imbalance and the other is balanced. Our findings show that the unsupervised iterative cleaning approach outperforms two other unsupervised models, namely Isolation Forest and Copula-Based Outlier Detector, in the class-imbalanced dataset. The approach does not outperform both the other two models on the balanced dataset, making the approach presented particularly well-suited when there is a large class imbalance in cognition data.
Attention-Based Residual BiLSTM Networks for Human Activity Recognition

Human activity recognition (HAR) commonly employs wearable sensors to identify and analyze the time series data collected by them, enabling the recognition of specific actions. However, the current fusion of convolutional and recurrent neural networks in existing approaches encounters difficulties when it comes to differentiating between similar actions. To enhance the recognition accuracy of similar actions, we suggest integrating the residual structure and layer normalization into a bidirectional long short-term memory network (BLSTM). This integration enhances the network’s feature extraction capabilities, introduces an attention mechanism to optimize the final feature information, and ultimately improves the accuracy and stability of activity recognition. To validate the effectiveness of our approach, we extensively tested it on three public datasets: UCI-HAR, WISDM, and KU-HAR. The results were highly encouraging, achieving remarkable overall recognition accuracies of 98.37%, 99.01%, and 97.89% for the respective datasets. The experimental results demonstrate that this method effectively enhances the recognition accuracy of similar behaviors. A codebase implementing the described framework is available at: https://github.com/lyh0625/1DCNN-ResBLSTM-Attention.
A Blockchain-Based Multi-Users Oblivious Data Sharing Scheme for Digital Twin System in Industrial Internet of Things

Digital twin (DT) constructs virtual counterparts of physical devices to monitor and optimize their life cycle processes. With the emergence of industry 4.0, Industrial Internet of Things (IIoT) has became the backbone of the DT by providing a fundamental way to transform physical devices to their virtual counterparts. With the deployment of IIoT, built-in sensors enable real-time collection of critical DT data involving various physical parameters associated with devices during their life cycle. However, traditional data sharing services rely on a centralized infrastructure, which inevitably brings severe security threats to share large volume of sensitive DT data derived from numerous sensors. To address the above issue, this paper presents a blockchain based Multi-users Oblivious Data Sharing scheme (MODS) for the digital twin system in the context of IIoT. MODS supports a broad range of security properties including confidentiality, obliviousness, and access control for the DT data stored on the blockchain. MODS adopts a hybrid design approach by combing trusted hardware and cryptography to achieve well balances between security and efficiency. To demonstrate the design advantages of MODS, we explore the design space of a multi-users oblivious data sharing scheme by using pure cryptographic approach, which incurs several design tradeoffs that must be addressed. We show that MODS performs well in these tradeoffs. A comprehensive evaluation has been conducted to demonstrate that MODS is practical to support secure data sharing via blockchain for IIoT.
Digital Twins-Enabled Federated Learning in Mobile Networks: From the Perspective of Communication-Assisted Sensing

With the continuous evolution of emerging technologies such as mobile network, machine learning (ML), 5G, etc., digital twins (DT) bursts out great potential by its capacity of data analysis, data tracking, data prediction, etc, building a bridge between the physical and information world. Meanwhile, mobile network is moving towards data-driven paradigm, the issue of data privacy and data security seem to be a bottleneck. As a result, federated learning (FL) and mobile network are deeply converging. However, the mobile network is time-varying and the parameters of FL-empowered mobile network is huge and continue to increase with exponential growth of wireless terminals, result in the failure of traditional modeling. In the mobile networks, DT is conducive to prototyping, testing, and optimization, enabling mobile networks to be modelled more efficiently in a virtual environment and thus providing guidance for practical application. To this end, a communication-assisted sensing scenario is considered in this paper with FL in DT-empowered mobile networks. More specifically, two communication-assisted sensing architectures are proposed to improve communication efficiency of mobile network, namely, centralized architecture of federated transfer learning (FTL) and decentralized architecture of FTL. For centralized architecture of FTL, feature extraction of sensing information is conducted by FL between partial nodes and central server while the remaining nodes are used to train the fully connected layers at the central server. Considering data safety during the communication between sensing nodes, a decentralized architecture is designed based on FTL and Blockchain, where the feature extraction module is obtained by the fusion of sharing model (by Blockchain) and local model. The performance of proposed schemes is evaluated and demonstrated by the simulations.
FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images

Kidney tumors are a significant health concern. Early detection and accurate segmentation of kidney tumors are crucial for timely and effective treatment, which can improve patient outcomes. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown great promise in medical image analysis, including identifying and segmenting kidney tumors. Computed tomography (CT) scans of kidneys aid in tumor assessment and morphology studies, employing semantic segmentation techniques for precise pixel-level identification of kidneys and surrounding anatomical structures. This paper proposes a Squeeze-and-Excitation-ResNet (SE-ResNet) model for segmentation by combining the encoder stage of SE-ResNet with the Feature Pyramid Network (FPN). The performance of the proposed SE-ResNet model is evaluated using the Intersection over Union (IoU) and F1-score metrics. Experimental results demonstrate that the SE-ResNet models achieve impressive IoU scores for background, kidney, and tumor segmentation, with mean IoU scores ranging from 0.988 to 0.981 for Seresnet50 and Seresnet18, respectively. Notably, Seresnet50 exhibits the highest IoU score for kidney segmentation. These findings suggest that SE-ResNet models accurately identify and segment regions of interest in CT images of renal carcinoma, with higher model versions generally exhibiting superior performance. The proposed Seresnet50 model is a good tool for accurate tumor detection and image classification, aiding medical professionals in early diagnosis and timely intervention.
Transformative practice and its interactional challenges in COVID-19 telephone contact tracing in Flanders

This article focuses on transformative interactional practice in COVID-19 contact tracing telephone calls in Flanders (Belgium). It is based on a large corpus of recorded telephone conversations conducted by COVID-19 contact tracers with index patients in the period mid-2020 to mid-2022. The calls were conducted through government-contracted commercial call centers. For nearly 2 years and applied country-wide, this was the most prominent strategy in Belgium for breaking transmission chains. COVID-19 telephone contact tracing with infected patients counts as transformative professional work in two ways. First, in addition to the registration of recent contacts in a relevant time window, the work is oriented to awareness-raising about how patients and their co-dwellers can and should adjust their behavior by attending actively to critical aspects of the pandemic during an individual period of (potential) infection. This is the terrain of advice, interdictions and recommendations about quarantine, isolation, personal hygiene, etc. In addition, the focus on interactional attention indexes patients' affect and emotions (e.g., anxiety, worry, or anger) in a period of health uncertainty and social isolation. The transformative work thus depends on successfully established rapport and empathetic, responsive behavior. Our analysis of the recorded conversational sequences focuses on the complexities of client-sensitive and responsive transformative sequences and highlights the constraints and affordances which surround the interactional task of 'instructional awareness raising' which is central to telephone contact tracing. Specifically, we detail the following dimensions of transformative sequences: (i) how do contact tracers deal with the knowledge status of clients, (ii) their use of upgrading/downgrading formulations, (iii) the use of humor and other mitigating strategies, and (iv) how contact tracers attend to interactional displays of affect and emotion. In a final section, we tie together our observations about the communication of particularized advice in a context of general measures through the twin notions of categorization/particularization-work. The findings in this paper are limited to the first step in the chain of contact tracing, i.e., telephone calls with tested and infected citizens.
How Good is Google Bard’s Visual Understanding? An Empirical Study on Open Challenges

Google’s Bard has emerged as a formidable competitor to OpenAI’s ChatGPT in the field of conversational AI. Notably, Bard has recently been updated to handle visual inputs alongside text prompts during conversations. Given Bard’s impressive track record in handling textual inputs, we explore its capabilities in understanding and interpreting visual data (images) conditioned by text questions. This exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal Generative models, especially in addressing complex computer vision problems that demand accurate visual and language understanding. Specifically, in this study, we focus on 15 diverse task scenarios encompassing regular, camouflaged, medical, under-water and remote sensing data to comprehensively evaluate Bard’s performance. Our primary finding indicates that Bard still struggles in these vision scenarios, highlighting the significant gap in vision-based understanding that needs to be bridged in future developments. We expect that this empirical study will prove valuable in advancing future models, leading to enhanced capabilities in comprehending and interpreting fine-grained visual data. Our project is released on https://github.com/htqin/GoogleBard-VisUnderstand .
Time-feature attention-based convolutional auto-encoder for flight feature extraction

Quick Access Recorders (QARs) provide an important data source for Flight Operation Quality Assurance (FOQA) and flight safety. It is generally characterized by large volume, high-dimensionality and high frequency, and these features result in extreme complexities and uncertainties in its usage and comprehension. In this study, we proposed a Time-Feature Attention (TFA)-based Convolutional Auto-Encoder (TFA-CAE) network model to extract essential flight features from QAR data. As a case study, we used the QAR data landing at the Kunming Changshui International Airport and Lhasa Gonggar International Airport as the experimental data. The results show that (1) the TFA-CAE model performs the best in extracting representative flight features in comparison to some traditional or similar approaches, such as Principal Component Analysis (PCA), Convolutional Auto-Encoder (CAE), Self-Attention-based CAE (SA-CAE), Gate Recurrent Unit based Auto-Encoder (GRU-AE) and TFA-GRU-AE models; (2) flight patterns corresponding to different runways can be recognized; and (3) anomalous flights can effectively deviate from many observations. Overall, the TFA-CAE model provides a well-established technique for further usage of QAR data, such as flight risk detection or FOQA.
Entropy and improved k‐nearest neighbor search based under‐sampling (ENU) method to handle class overlap in imbalanced datasets

Summary Many real‐world application datasets such as medical diagnostics, fraud detection, biological classification, risk analysis and so forth are facing class imbalance and overlapping problems. It seriously affects the learning of the classification model on these datasets because minority instances are not visible to the learner in the overlapped region and the performance of learners is biased towards the majority. Undersampling‐based methods are the most commonly used techniques to handle the above‐mentioned problems. The major problem with these methods is excessive elimination and information loss, that is, unable to retain potential informative majority instances. We propose a novel entropy and neighborhood‐based undersampling (ENU) that removed only those majority instances from the overlapped region which are having less informativeness (entropy) score than the threshold entropy. Most of such existing methods improved sensitivity scores significantly but not in many other performance contexts. ENU first computes entropy and threshold score for majority instances and, a local density‐based improved KNN search is used to identify overlapped majority instances. To tackle the problem effectively ENU defined four improved KNN‐based procedures (ENUB, ENUT, ENUC, and ENUR) for effective undersampling. ENU outperformed in sensitivity, G‐mean, and F1‐score average ranking with reduced information loss as compared to the existing state‐of‐the‐art methods.
Land suitability evaluation of large-scale photovoltaic plants using structural equation models

Land suitability evaluation is vital for large-scale photovoltaic (PV) plant construction. However, subjectivity in previous methods affects result reliability. We propose a new method using structural equation models to develop PV suitability indices, and demonstrate its validity in Zhejiang Province, China. By combining theoretic design with data mining technique, our method measures the structured paths between the overall index and evaluation indicators, exhibiting good model fit, flexible and objective weighting. Our results suggest that the spatial distribution of targeted PV plants based on suitability indices largely differs from that of observed ones. Most suitable areas for PV construction in Zhejiang Province are situated along the eastern coast, northern plain and river networks. The estimated PV technical potential for areas with and above moderate suitability is approximately 1,600 TWh/year, equivalent to 830 million tons/year of carbon emission reduction. Our study provides PV development strategies for similar areas in China and beyond.
Beyond data transactions: a framework for meaningfully informed data donation

Abstract As we navigate physical (e.g., supermarket) and digital (e.g., social media) systems, we generate personal data about our behavior. Researchers and designers increasingly rely on this data and appeal to several approaches to collect it. One of these is data donation, which encourages people to voluntarily transfer their (personal) data collected by external parties to a specific cause. One of the central pillars of data donation is informed consent, meaning people should be adequately informed about what and how their data will be used. However, can we be adequately informed when it comes to donating our data when many times we don’t even know it is being collected and, even more so, what exactly is being collected? In this paper, we investigate how to foster (personal) data literacy and increase donors’ understanding of their data. We introduce a Research through Design approach where we define a data donation journey in the context of speech records, data collected by Google Assistant. Based on the data donation experiences of 22 donors, we propose a data donation framework that understands and approaches data donation as an encompassing process with mutual benefit for donors and researchers. Our framework supports a donation process that dynamically and iteratively engages donors in exploring and understanding their data and invites them to (re)evaluate and (re)assess their participation. Through this process, donors increase their data literacy and are empowered to give meaningfully informed consent.
Relationship between technology acceptance model, self-regulation strategies, and academic self-efficacy with academic performance and perceived learning among college students during remote education

The aim of this study was to examine the relationship between the technology acceptance model, self-regulation strategies, and academic self-efficacy with academic performance and perceived learning among college students during remote education.The participants were 301 university students from Lima. Structural equation model was used to test the proposed theoretical relationships between the variables. On the one hand, the study sought to explore the relationship between academic self-efficacy and self-regulation strategies with the technology acceptance model. On the other hand, it sought to determine whether the three dimensions of the technology acceptance model are positively related to perceived learning and academic performance.The results suggest the importance of improving psychological variables such as self-efficacy and self-regulation strategies to improve the acceptance of technology, which would also improve the academic performance and perceived learning of students in a virtual environment.The discussion highlights the significance of self-efficacy and metacognitive strategies in influencing technology perception and attitudes, ultimately impacting perceived learning and academic performance in virtual education.
The Devil’s in the Details: How Countries’ Defamation Laws Can (and Can’t) Combat Hate Speech

ABSTRACTDespite the multitude of efforts to combat hate speech, it persists within our global media discourse. It appears in legacy news media content, right-wing media outlets, and social media. In some countries, defamation laws are used to punish those who publish false, defamatory statements about a group defined by its racial, religious, or national origin, gender identity, or sexual orientation. Recognizing the potential role civil defamation laws can play in mitigating the problem of hate speech in media, this article offers a comparative legal analysis of existing defamation laws in Brazil, Germany, and the United States. The purpose of this inquiry is to determine whether and to what extent victims of hate speech might be able to employ these laws to seek justice. Our analysis indicates that criminal libel laws are often abused by political actors, and thus civil laws are preferred. Based on Germany's experience, we also find that social media platform compliance with this process is essential. Therefore, we propose a solution that expands U.S. libel law to include group defamation and uses carveouts to CDA Section 230 to motivate platform participation in identifying and removing group libel.KEYWORDS: Hate speechdefamationlibelcomparative analysisGermanyBrazilUnited Statesgroup libel Disclosure StatementNo potential conflict of interest was reported by the author(s).
An analysis of Watson vs. BARD vs. ChatGPT: The Jeopardy! Challenge

Abstract The recently released BARD and ChatGPT have generated substantial interest from a range of researchers and institutions concerned about the impact on education, medicine, law and more. This paper uses questions from the Watson Jeopardy! Challenge to compare BARD, ChatGPT, and Watson. Using those, Jeopardy! questions, we find that for high confidence Watson questions the three systems perform with similar accuracy as Watson. We also find that both BARD and ChatGPT perform with the accuracy of a human expert and that the sets of their correct answers are rated highly similar using a Tanimoto similarity score. However, in addition, we find that both systems can change their solutions to the same input information on subsequent uses. When given the same Jeopardy! category and question multiple times, both BARD and ChatGPT can generate different and conflicting answers. As a result, the paper examines the characteristics of some of those questions that generate different answers to the same inputs. Finally, the paper discusses some of the implications of finding the different answers and the impact of the lack of reproducibility on testing such systems.
A novel hybrid particle swarm optimization with marine predators

Particle swarm optimization algorithms are often applied to solve optimization problems. However, the traditional particle swarm optimization algorithm has a single search method and is less capable of exploration and exploitation when solving high-dimensional and complex problems. In this paper, a hybrid particle swarm optimization algorithm for marine predators (HMPPSO) is proposed by combining the multi-stage search strategy of marine predators algorithm (MPA). We divide the search process of HMPPSO into three stages: the first stage uses Brownian motion for exploration; the middle stage divides the population into two parts, the first half remains responsible for exploration through Brownian motion. Besides, in the second half of the population, a random wandering strategy is proposed to randomly select five particles so that it can control the update of particles to prevent falling into the local optimal solution; the learning strategy is improved in the later stage. The control parameters are used to adjust the strategy, while making the single case learning method and the example averaging method alternate. This strategy accelerates the convergence of the algorithm while enhancing the diversity of the algorithm in the later stage and improving the development ability of the algorithm. In addition, the population diversity is enhanced using chaotic initialization and opposition-based learning strategies. The algorithm in this paper is applied to different types of CEC2017 benchmark test functions and four multi-dimensional non-linear structural design optimization problems. Compared with other recent algorithms, the results show that the performance of HMPPSO is significantly better than other algorithms.