Reviewerly/exHarmony huggingface dataset:
Investigating the facilitating conditions, perceived ease of use and usefulness of mobile-assisted language learning

Abstract Mobile technologies and factors influencing English as a foreign language (EFL) learners’ willingness to integrate them into their learning processes are significant. This study aims at investigating the role of facilitating conditions, perceived ease of use, and perceived usefulness as factors in predicting Iranian EFL learners’ perceptions toward mobile-assisted language learning (MALL). Accordingly, data were collected from 223 Iranian EFL learners who had their English language courses in the two major educational contexts (i.e., public schools and private institutes). Considering the first phase of the study, a statistically significant relationship was found between the three hypothesized factors (i.e., facilitating conditions, perceived ease of use, and perceived usefulness) and learners’ perceptions toward MALL. Furthermore, the facilitating conditions were associated with learners’ perceived ease of use. Regarding the second phase of the study, the analysis of the path coefficients indicated a significant predictive power of perceived usefulness on perceptions toward MALL. Also indicated in this study is the significant effect of facilitating conditions on learners’ perceived ease of use. The results are discussed in the light of the literature, and suggestions for further research are provided.
Objective-hierarchy based large-scale evolutionary algorithm for improving joint sparsity-compression of neural network

Network training error and sparsity are two critical factors in optimizing the model parameters of existing neuro-evolution algorithms. Alleviating the curse of dimensionality in optimizing large-scale neuro-evolution algorithms is one of the most serious challenges. Furthermore, different from existing algorithms, compressibility is considered and combined with the sparsity as the joint sparsity-compressibility to indicate the network's complexity in the paper. An objective-hierarchy-based neuro-evolutionary algorithm is proposed to comprehensively improve training error performance and joint sparsity-compressibility. First, a multi-directional neuro-evolution is constructed using a Tucker-based tensor decomposition to greatly compress the decision space and generate the subordinates of the decision variables. Afterwards, each subordinate is circularly updated by a multi-linear neuron-spired search in the compressed decision sub-space. Finally, the superior is constructed by concatenating all subordinates and updated by the sparse evolutionary scheme. Compared with other eight state-of-the-art algorithms on seven classification problems in the fields of image/video processing, industrial engineering, biomedical signal, and smart grid, the experimental results demonstrate the effectiveness of the proposed approach. Moreover, other outstanding large-scale neuro-evolutionary algorithms can be embedded into the tensor decomposition-based objective-hierarchy model of the proposed algorithm.
Securing Distributed SGD Against Gradient Leakage Threats

This paper presents a holistic approach to gradient leakage resilient distributed Stochastic Gradient Descent (SGD). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , we analyze two types of strategies for privacy-enhanced federated learning: (i) gradient pruning with random selection or low-rank filtering and (ii) gradient perturbation with additive random noise or differential privacy noise. We analyze the inherent limitations of these approaches and their underlying impact on privacy guarantee, model accuracy, and attack resilience. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Next</i> , we present a gradient leakage resilient approach to securing distributed SGD in federated learning, with differential privacy controlled noise as the tool. Unlike conventional methods with the per-client federated noise injection and fixed noise parameter strategy, our approach keeps track of the trend of per-example gradient updates. It makes adaptive noise injection closely aligned throughout the federated model training. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Finally</i> , we provide an empirical privacy analysis on the privacy guarantee, model utility, and attack resilience of the proposed approach. Extensive evaluation using five benchmark datasets demonstrates that our gradient leakage resilient approach can outperform the state-of-the-art methods with competitive accuracy performance, strong differential privacy guarantee, and high resilience against gradient leakage attacks.
CFANet: Efficient Detection of UAV Image Based on Cross-Layer Feature Aggregation

With the rapid development of the unmanned aerial vehicle (UAV) industry, UAV image object detection technology has become a hotspot. However, due to a large number of dense small objects in UAV images, quickly and effectively detecting objects and achieving accurate classification is still a challenge. With this observation, we propose an efficient object detection network for UAV images based on cross-layer feature aggregation (CFANet). Firstly, we design a novel cross-feature aggregation module (CFA) to aggregate features at different scales on the basis of avoiding semantic gaps, so as to replace common features for feature fusion and achieve accurate detection. This method makes up for the defect that the layer-by-layer feature transfer method only focuses on the features of the previous layer and cannot fully integrate spatial and semantic information. Secondly, a layered associative spatial pyramid pooling module (LASPP) is proposed to capture context information while maintaining the sensitivity of feature maps at different layers to detail information. Thirdly, the alpha-IoU loss function is introduced to accelerate the convergence speed of the model and improve the detection accuracy. Finally, an adaptive overlapping slice (AOS) for high-resolution images is proposed to protect the integrity of the object when slicing. To verify the effectiveness of the proposed method, extensive experiments on challenge datasets for object detection in UAV images VisDrone2021 and UAVDT datasets are carried out. The results show that, compared with the other most advanced detectors, the proposed method can achieve significant performance on the basis of ensuring real-time detection.
Another use of SMOTE for interpretable data collaboration analysis

Recently, data collaboration (DC) analysis has been developed for privacy-preserving integrated analysis across multiple institutions. DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes integrated analysis via collaboration representations without sharing the original data. To construct the collaboration representations, each institution generates and shares a shareable anchor dataset and centralizes its intermediate representation. Although, random anchor dataset functions well for DC analysis in general, using an anchor dataset whose distribution is close to that of the raw dataset is expected to improve the recognition performance, particularly for the interpretable DC analysis. Based on an extension of the synthetic minority over-sampling technique (SMOTE), this study proposes an anchor data construction technique to improve the recognition performance without increasing the risk of data leakage. Numerical results demonstrate the efficiency of the proposed SMOTE-based method over the existing anchor data constructions for artificial and real-world datasets. Specifically, the proposed method achieves 6, 4, and 36 percentage point performance improvements regarding NMI, ACC and essential feature selection, respectively, over existing methods for an income dataset. The proposed method provides another use of SMOTE not for imbalanced data classifications but for a key technology of privacy-preserving integrated analysis.
A comprehensive survey on blockchain-based C-ITS applications: Classification, challenges, and open issues

Cooperative Intelligent Transport Systems (C-ITS) aim to improve road safety and provide comfort to both drivers and passengers by enabling vehicles, infrastructure, and other road users to exchange environmental and driving data. Therefore, the accuracy of the information exchanged as well as the authenticity of the entities, effectively affect the efficiency and performance of the system. Blockchain is a new distributed ledger technology that enables secure and transparent information storage and transmission without the control of a central entity. Its inherent features of trust, anonymity, immutability, and data availability offer great potential to solve current C-ITS issues. Recently, several research works are carried out to explore the potential of the blockchain technology in a vehicular environment. In this paper, we present a comprehensive review of the literature related to the application of the blockchain technology in C-ITS. We provide background knowledge on blockchain and the motivations for applying this technology in vehicular networks. Then, we discuss the major challenges that can bring the application of blockchains in a vehicular environment, such as mobility, latency, etc. Next, we review current research and classify it into four main categories. Moreover, a qualitative comparison of the discussed solutions is presented to identify the strengths and the weaknesses of each proposal. Finally, we highlight some open research issues that should be considered in future studies to improve the performance of blockchain-based vehicular systems.
Communicating health information with visual displays

Well-designed visual displays have the power to convey health messages in clear, effective ways to non-experts, including journalists, patients and policymakers. Poorly designed visual displays, however, can confuse and alienate recipients, undermining health messages. In this Perspective, we propose a structured framework for effective visual communication of health information, using case examples of three common communication tasks: comparing treatment options, interpreting test results, and evaluating risk scenarios. We also show simple, practical ways to evaluate a design's success and guide improvements. The proposed framework is grounded in research on health risk communication, visualization and decision science, as well as our experience in communicating health data.
Boosting the transferability of adversarial attacks with adaptive points selecting in temporal neighborhood

Deep neural networks are highly susceptible to imperceptible noise, even to the human eye. While high attack success rate has been achieved in white-box setting, the attack performance tends to decline in black-box environments. To address this challenge, this study introduces a novel adaptive points selecting iterative fast gradient sign method, named AI-FGSM, which leverages temporal neighborhoods to enhance transferability performance in black-box environments. AI-FGSM is unique in that it extracts gradients from additional points in the temporal neighborhood and adjusts the current gradient using previous gradients to maintain the updated trend's stability. This gradient correction enables faster optimization and restricts falling into local optima. Experimental results on the ImageNet dataset show that AI-FGSM outperforms advanced gradient-based attack methods such as MI-FGSM and NI-FGSM in terms of attack success rate and transferability. Moreover, in black-box environments, AI-FGSM achieves an average attack performance improvement of 44.2% for some models with adversarial training and 32.9% for several models without adversarial training. These results confirm the performance and effectiveness of AI-FGSM, underscoring its potential as a powerful tool for improving the transferability of adversarial attacks.
Anomaly-GAN: A data augmentation method for train surface anomaly detection

Train surface anomaly detection is an essential task in vision-based railway safety inspection. Although existing deep learning methods show great potential, their anomaly detection accuracy is affected by the lack of abnormal images. The number of abnormal images on the train surface is far less than that of normal images. One effective way to solve this problem is to expand the abnormal sample. However, most of train surface anomaly image generation methods faces difficulties in producing images with high quality and rich diversity at the same time. In addition, generating small-area anomalies is not ideal. An Anomaly-GAN based on mask pool, abnormal aware loss, and local versus global discriminators is thus proposed in this paper to solve these problems. The mask pool consists of prior-knowledge-based masks and expert-experience-based masks that guide model in generating anomalies with different shapes, rotation angles, spatial locations, and part numbers. The anomaly aware loss focuses on small-area anomalies, thereby promoting the generated anomalies with more detailed textures and richer semantics. The local versus global consistency discriminators combine local and global feature expressions that lead to the generation of more realistic and natural abnormal samples. The experiments show that, compared with other advanced data augmentation algorithms, the images generated by Anomaly-GAN achieve the best FID and LPIPS scores in all anomaly categories. In addition, compared with the case without data augmentaion, the proposed data enhancement method improves the performance of CNN on mAP and mIOU by 25.6% and 24.2%, respectively. Test code is available in https://github.com/AI-Dream-Chaser/Anomaly-GAN.
Non-transferable blockchain-based identity authentication

Due to the identification functionality, identity authentication is the first and primary security step in many information systems. There exist many works dedicated to giving secure identity authentication. However, most of the existing schemes suffer from at least one of the following problems: heavy account management, single point of failure, and privacy leakage. To tackle these challenges, we propose two blockchain-based identity authentication schemes in this paper. One is based on the famous Diffie-Hellman key exchange protocol and is efficient but with user-verifier interaction. The other utilizes the ring signature, which is non-interactive with a small computational cost. Besides the traditional security properties, such as unforgeability and identity anonymity, our proposed schemes can hold non-transferability, i.e., the verifier cannot prove the user’s identity authentication to any third party. At last, the extensive experimental results demonstrate that our proposals are practical and efficient.
How to incorporate artificial intelligence (AI) into your library workflow

Purpose This paper aims to highlight the recent developments in artificially intelligent chatbots and how the resulting tools can be incorporated into the daily workflow of library work. Design/methodology/approach Recent literature is examined, parallels to librarian reactions to the birth of the original internet search engines are drawn and suggestions for the use of specific tools for specific tasks are given. Findings Although effectively less than 6 months old, the field of artificial intelligence (AI) chatbots is already fulsome enough to be able to be usefully incorporated into the profession. More tools are imminent, though each of them does and will continue to have shortcomings of which informational professionals need to be aware. Practical implications This paper provides practical suggestions and specific tools to incorporate into the workflow of different library specialties, along with important caveats for quality and bias. Social implications The public has adopted the use of AI chatbots faster than any previously introduced technology. Librarians have a history of moving more slowly when it comes to the core values of the profession, such as information searching. It is vital for information professionals, such as librarians, to understand both the value and the pitfalls of these tools to be able to work with patrons and stay relevant in the eyes of the public and institutional funders. Originality/value This paper fills a need for practical advice in using AI to perform daily library work.
A Decentralized and Automated Contracting System Using a Blockchain-Enabled Network of Stakeholders in Construction Megaprojects

With recent advances in information and communication technologies, trust and collaboration have become imperative in the construction industry. The increasing exchange of digital information and assets without much regard to contractual requirements has resulted in various data corruption, privacy, and security issues escalating to claims and disputes. Hence, there is a need to institute reforms in the existing contracting system to achieve a more efficient, collaborative, and transparent system. However, little research has investigated the digitalization of the contracting process, which continues to be traditional. This study is one of the first to develop a digital and automated contract management system leveraging blockchain technology and smart contracts. Design science research methodology was adopted for the research work. The proposed system aims to achieve the following: (1) a decentralized permissioned network of stakeholders with well-defined roles and approval workflow, (2) automated contract execution based on predefined conditions using smart contracts, and (3) transaction history integrity with time stamps via a blockchain-based secured data structure. To exemplify these points, a use case is presented for implementing blockchain-based contract management in a construction megaproject. A conceptual model was developed illustrating the decentralized network of the key project stakeholders based in a Hyperledger Fabric blockchain environment. The roles and access controls of the stakeholders within the network are defined using the responsibility assignment [responsible, accountable, consulted, and informed (RACI) matrix]. A computable and automated smart contract clause is formulated for the price variation clause used in construction contracts. A prototype of the proposed system was developed and demonstrated to industry experts for evaluation and validation. Positive feedback from the experts concerning the system characteristics and user-friendliness of the prototype showed that it possesses the potential to improve trust and transparency in the existing system. Whereas the decentralized stakeholder network would add trust and accountability to the system, the underlying blockchain data structure would enhance security and traceability by providing an immutable and real-time record of the transactions. The study provides a workable solution that serves as a valuable reference for future research in the field of contract management across multiple sectors. Although the system is shown to be applicable and feasible in this study, it may not be economically viable. It could be optimized in the future through cost–benefit analysis for different projects.
A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding

Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios. However, existing approaches (1) focus on identifying implicit correlations between utterances and one-hot encoded labels in both tasks while ignoring explicit label characteristics; (2) directly incorporate multi-intent information for each token, which could lead to incorrect slot prediction due to the introduction of irrelevant intent. In this paper, we propose a framework termed DGIF, which first leverages the semantic information of labels to give the model additional signals and enriched priors. Then, a multi-grain interactive graph is constructed to model correlations between intents and slots. Specifically, we propose a novel approach to construct the interactive graph based on the injection of label semantics, which can automatically update the graph to better alleviate error propagation. Experimental results <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> show that our framework significantly outperforms existing approaches, obtaining a relative improvement of 6.5% over the previous best model on the MixATIS dataset in overall accuracy.
Phase-Aware Spoof Speech Detection Based On Res2net with Phase Network

For automatic speaker verification systems, spoof speech detection (SSD) is an essential countermeasure. Although SSD with magnitude features in the frequency domain has shown promising results, phase information can also be useful in capturing the artefacts of certain spoofing attacks. Thus, both magnitude and phase features must be considered to ensure the ability to generalize diverse types of spoofing attacks. In this study, we discovered that the randomness difference between magnitude and phase features is large, which can interrupt the feature-level fusion via backend neural network. In this regard, we propose a phase network to reduce that difference, which makes the Res2Net-based feature-level fusion feasible. To validate our SSD system for practical environment, both known- and unknown-type SSD scenarios are considered. As a result, our SSD system delivers competitive results compared to other state-of-the-art SSD systems in all scenarios.
LOGER: A Learned Optimizer Towards Generating Efficient and Robust Query Execution Plans

Query optimization based on deep reinforcement learning (DRL) has become a hot research topic recently. Despite the achieved promising progress, DRL optimizers still face great challenges of robustly producing efficient plans, due to the vast search space for both join order and operator selection and the highly varying execution latency taken as the feedback signal. In this paper, we propose LOGER, a learned optimizer towards generating efficient and robust plans, aiming at producing both efficient join orders and operators. LOGER first utilizes Graph Transformer to capture relationships between tables and predicates. Then, the search space is reorganized, in which LOGER learns to restrict specific operators instead of directly selecting one for each join, while utilizing DBMS built-in optimizer to select physical operators under the restrictions. Such a strategy exploits expert knowledge to improve the robustness of plan generation while offering sufficient plan search flexibility. Furthermore, LOGER introduces ε -beam search, which keeps multiple search paths that preserve promising plans while performing guided exploration. Finally, LOGER introduces a loss function with reward weighting to further enhance performance robustness by reducing the fluctuation caused by poor operators, and log transformation to compress the range of rewards. We conduct experiments on Join Order Benchmark (JOB), TPC-DS and Stack Overflow, and demonstrate that LOGER can achieve a performance better than existing learned query optimizers, with a 2.07x speedup on JOB compared with PostgreSQL.