Reviewerly/exHarmony huggingface dataset:
Multi-User QoE Enhancement: Federated Multi-Agent Reinforcement Learning for Cooperative Edge Intelligence

Federated learning (FL) as a new decentralized learning/computing technique has potential advantages (e.g., accelerating computation task processing and protecting user privacy) for edge intelligence. However, due to limited computing/ caching capacities of network edges and dynamic arrivals of computation tasks, edge intelligence with FL cannot appropriately offload and effectively process computation tasks, which will degrade multi-user quality of experience (QoE). To address these challenges, it is critical to enhance the cooperation of network edges and quantify the multi-user QoE. In this article, we investigate the issue of cooperative edge intelligence by considering federated multi-agent reinforcement learning to enhance the multi-user QoE. Particularly, we present a cooperative edge intelligence architecture with vertical-horizontal cooperation supporting computation offloading. We model a comprehensive system cost to quantify the multi-user QoE and formulate the optimization problem as minimizing the expected long-term system cost. We further propose a decentralized intelligent offloading framework based on soft actor-critic and FL with an attention mechanism. Evaluation results demonstrate that the proposed scheme outperforms existing offloading schemes in terms of convergence and multi-user QoE. Finally, we discuss several open issues and opportunities of edge intelligence with FL.
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian

We propose a framework that can deform an object in a 2D image as it exists in 3D space. Most existing methods for 3D-aware image manipulation are limited to (1) only changing the global scene information or depth, or (2) manipulating an object of specific categories. In this paper, we present a 3D-aware image deformation method with minimal restrictions on shape category and deformation type. While our framework leverages 2D-to-3D reconstruction, we argue that reconstruction is not sufficient for realistic deformations due to the vulnerability to topological errors. Thus, we propose to take a supervised learning-based approach to predict the shape Laplacian of the underlying volume of a 3D reconstruction represented as a point cloud. Given the deformation energy calculated using the predicted shape Laplacian and user-defined deformation handles (e.g., keypoints), we obtain bounded biharmonic weights to model plausible handle-based image deformation. In the experiments, we present our results of deforming 2D character and clothed human images. We also quantitatively show that our approach can produce more accurate deformation weights compared to alternative methods (i.e., mesh reconstruction and point cloud Laplacian methods).
Moving from Human Ratings to Word Vectors to Classify People with Focal Dementias: Are We There Yet?

Fine-grained variables based on semantic proximity of words can provide helpful diagnostic information when applied to the analysis of Verbal Fluency tasks. However, before leaving human-based ratings in favour of measures derived from distributional approaches, it is essential to assess the performance of the latter against that of the former. In this work, we analysed a Verbal Fluency task using measures of semantic proximity derived from Distributional Semantic Models of language, and we show how Machine Learning models based on them are less accurate in classifying patients with focal dementias than the same models built on human-based ratings. We discuss the possible interpretation of these results and the implications for the application of distributional semantics in clinical settings.1
Enhancing Network Security with A Hybrid Cryptography Method

Network and data security is of paramount importance to ISPs and other service providers.In order to ensure the safety of the information being transmitted, cryptographic methods are used throughout the exchange of this information.However, the standard cryptographic methods are well-known to both attackers and defenders, and the answer to these problems is public knowledge.This calls for a fresh approach to cryptography that can boost the safety and complexity of data cypher.In this work, we propose and report on the implementation of a hybrid cryptographic technique for bolstering data security through network transmission.The proposed cryptographic method, which makes use of RSA, DES, and SHA1, is said to be a very secure method of generating cyphers.A JAVA-based implementation of the suggested method is provided, and its performance in terms of space and time complexity is estimated and compared to that of conventional RSA cryptography.While comparing the performance of many cypher texts, the proposed cryptographic method discovered the most effective and superior one.
Empirical Measurement of Client Contribution for Federated Learning With Data Size Diversification

Client contribution evaluation is crucial in federated learning(FL) to effectively select influential clients. Contrary to data valuation in centralized settings, client contribution evaluation in FL faces a lack of data accessibility and consequently challenges stable quantification of the impact of data heterogeneity. To address this instability of client contribution evaluation, we introduce an empirical method, Federated Client Contribution Evaluation through Accuracy Approximation(FedCCEA), which exploits data size as a tool for client contribution evaluation. After several FL simulations, FedCCEA approximates the test accuracy using the sampled data size and extracts the client contribution from the trained accuracy approximator. In addition, FedCCEA grants <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data size diversification</i> , which reduces the massive variation in accuracy resulting from game-theoretic strategies. Several experiments have shown that FedCCEA strengthens the robustness to diverse heterogeneous data environments and the practicality of partial participation.
Human Activity Recognition Using LDA And Stochastic Configuration Networks

Sensor-based Human Activity Recognition (HAR) research has made great strides, Compared with video-based human behavior recognition, sensors have the advantages of better privacy and convenience, making it an important emerging research area. When modeling the information collected by the sensor as the input of the neural network, if the input of the neural network contains too much redundant information, it will not only increase the modeling time, but also affect the classification accuracy of the model, and it is time-consuming to correctly determine the structure and hyperparameters of the neural network. Aiming at this problem, this paper proposes a method of using Linear Discriminant Analysis (LDA) to reduce the number of features of the original data, and then use stochastic Configuration Networks (SCN) to identify and classify human behavior. It was evaluated on the UCI-HAR datasets and achieved a classification accuracy of 98.61%, while reducing the modeling time to 4.03 seconds. Compared with the existing learning methods, the method we propose uses less dimensional feature data, the learning model structure is simpler, the recognition rate is higher, and the calculation process of the HAR system is simplified.
Exploring the direct and indirect effect of trust in leadership and perceived organisational support on turnover intentions

Currently, employee retention is a critical aspect of people management, especially in the context of pandemics or post-pandemics, where job insecurity and job destruction challenge workers' trust in leadership and organisational support. Using survey data treated with structural equation models, this study explores, in the context of the COVID-19 pandemic, the direct and indirect effects on turnover intentions of trust in leadership and perceived organisational support, using the mediating effect of job satisfaction. The results reveal that job satisfaction and perceived organisational support have a negative impact on turnover intention, but less that what was expected. The results obtained in the context of COVID-19 delivers important insights related to changes of employees' perceptions, their relations with the organisation and its impact in the turnover intention.
Frequency-aware Deep Dual-path Feature Enhancement Network for Image Dehazing

Single image dehazing is a challenging task due to the severe degradations caused by the particles in the air. Recently, various CNN-based methods have been proposed and they have achieved promising results on some dehazing tasks. However, the existing end-to-end dehazing networks process high-frequency information and low-frequency information at the same time. Therefore, most dehazing methods cannot restore dehazed image with satisfying high-frequency details. In this paper, we propose a Frequency-aware deep Dual-path Feature enhancement Network (FDF-Net) to better restore the high-frequency information while removing the haze. To achieve this, we introduce a Dual-path Feature Enhancement (DFE) block, which contains two branches: one path is to remedy the missing spatial information from high-resolution features, and the other one is to obtain new features to increase the variety of features. We believe the dual-path architecture can help the first path to focus on the recovering the high-frequency information. Furthermore, to reserve more detailed image information from the features with larger resolution, we adopt a wavelet transform module during the downsampling process of the encoder module to directly pass the high frequency information to the next level. The extensive experiments show the superiority of the proposed model over previous methods on the benchmark datasets as well as real-world hazy images.
Projection of semi-shapes for rotational symmetry detection

A novel method for detecting rotational symmetry is addressed in this paper by introducing a new concept of semi-shapes to overcome the main problem of projection-based approaches for studying rotational symmetric properties of an arbitrary shape. It is due to the fact that in the classical approaches, projection cues are periodical with a period of π preventing exploitation of rotational properties. We then propose the profile of semi-shapes as a signature of the shape together with a simple yet efficient technique to determine the rotation symmetry of an arbitrary shape by considering the correlation of this signature and its circular shift. A new measure is also introduced to determine how good the rotational symmetry would be. Experiments on single/compound-contour shapes have clearly corroborated the efficacy of our proposal.
Machine Learning Approach Based Phishing Email Text Analysis (ML-PE-TA)

Phishing based incidents are the procedure of posting illegal or dishonest communications that seems to be originated from a trustworthy sending node. It is commonly reflected in the case of email transmission. The aim is to rob one’s important personal information which includes credit/ debit card details, login information, account related data etc. In one way or another, Phishing emails are can trick users into revealing crucial technical or personal information. Users needs to be very careful and cautious when working with the emails they receive in their Inbox. In addition to verifying sender information, users must be wise enough in analyzing the body of the email to remain protected against such frauds. To account for this reason, this study have proposed a simple yet effective approach, ML-PE-TA,for analyzing the email text body with Machine learning algorithms. Using the proposed approach, this paper could achieve an accuracy of 96 percentage with a minimum count of vital features only for Phishing Email Text Analysis. At the same time, by the application of dimensionality reduction techniques, the proposed approach is able to reduce the dimensionality of the data considerably, which is better than state-of-the-art existing phishing email detection techniques which have much greater number of features and lesser accuracy rate.
Multi-scale super-pixels-based passive forensics for copy-move forgery using local characteristics

Copy-move forgery is the most common kind of tampering technique for digital images. This paper presents a novel hybrid approach, which uses the Speeded-Up Robust Features (SURF) point and characteristics of Local Feature Regions (LFRs) matching. First, the multi-scale super-pixels algorithm adaptively divides the suspect image into irregular blocks according to the texture level of host images. Then, the improved SURF detector is adopted in extracting feature points from each super-pixel and the feature point threshold is related to the entropy of each super-pixel block. Next, LFRs are defined, and a robust feature descriptor is extracted from each LFR as a vector field. Last, the matching LFRs are found by using Euclidean locality sensitive hashing; the removal of falsely matched pairs is realised by using the Random Sample Consensus (RANSAC) algorithm. Comparing with the leading-edge block-matching methods and point-based methods, our method can produce far better detection results.
SECOM

One way to detect and assess software vulnerabilities is by extracting security-related information from commit messages. Automating the detection and assessment of vulnerabilities upon security commit messages is still challenging due to the lack of structured and clear messages. We created a convention, called SECOM, for security commit messages that structure and include bits of security-related information that are essential for detecting and assessing vulnerabilities for both humans and tools. The full convention and details are available here: https://tqrg.github.io/secom/.
A Comprehensive Insight into Blockchain Technology: Past Development, Present Impact and Future Considerations

Blockchain technology is based on the idea of a distributed, consensus ledger, which it employs to create a secure, immutable data storage and management system.It is a publicly accessible and collectively managed ledger enabling unprecedented levels of trust and transparency between business and individual collaborations.It has both robust cryptographic security and a transparent design.The immutability feature of blockchain data has the potential to transform numerous industries.People have begun to view blockchain as a revolutionary technology capable of identifying "The Best Possible Solution" in various real-world scenarios.This paper provides a comprehensive insight into blockchains, fostering an objectual understanding of this cutting-edge technology by focusing on the theoretical fundamentals, operating principles, evolution, architecture, taxonomy, and diverse application-based manifestations.It investigates the need for decentralisation, smart contracts, permissioned and permissionless consensus mechanisms, and numerous blockchain development frameworks, tools, and platforms.Furthermore, the paper presents a novel compendium of existing and emerging blockchain technologies by examining the most recent advancements and challenges in blockchain-enabled solutions for a variety of application domains.This survey bridges multiple domains and blockchain technology, discussing how embracing blockchain technology is reshaping society's most important sectors.Finally, the paper delves into potential future blockchain ecosystems providing a clear picture of open research challenges and opportunities for academics, researchers, and companies with a strong fundamental and technical grounding.
Transferring AI technology in medical supply chain: a disruptive approach at addressing political, socioeconomic, and environmental dilemma in developing economies

AI has attracted extant literature devoted to different subjects, including healthcare. However, AI studies within healthcare have focused extensively on medical diagnosis, operations, and prescription, to the neglect of healthcare supply chain. To bridge this knowledge gap, we draw on innovation and knowledge transfer theories and an in-depth semi-structured interview to explain how AI-enabled medical drones in healthcare supply chain are transferred to developing countries to solve political, socioeconomics and environmental challenges. Data are collated from gatekeepers' knowledge and innovation transfers of the world's largest medical drone program in Ghana. Our evidence shows that despite the developing economy's peculiarities, a systematic knowledge transfer process was adopted and with the support of enabling factors such as political will, skilled personnel, and institutional cooperation contributed to the transfer of the AI-enabled medical drones. This ensures that there is fluidity in the application of the technology, resulting in political, socio-economic, and environmental benefits.
Building the Software Pillar of Open Science

This conference on open science is covering many aspects of strategic relevance to build the future of Europe, a future based on knowledge leading to more opportunities for younger generations to express their talent in attractive research careers, be it in universities, research organisations or industry.Some sessions will be dedicated to discussing the assessment of research and researchers' performance, which affects their behaviours.This debate is timely and justified.Most researchers are used to measuring in their labs when collecting and processing data.Scientists have to measure with accuracy to produce reliable and verifiable results.The Nobel laureate Richard Feynman started one of his famous lectures challenging his students about the complexity introduced over the centuries by using different measurement units.Measuring research and researchers' performance is of a different nature and more complex in many ways.It has direct impact on people and a far-reaching policy dimension for the European Research Area.We therefore agree on the need to ensure objectiveness and fairness as guiding principles and we can use assessment methodologies to support broader policy objectives, considering that we live in times of change, including in the scientific methods and the public perception of the role of science.Research processes are in constant evolution, accelerated by the current pandemic and the transition to a resilient, green and digital economy and an inclusive society.The variety of activities making increased use of advanced technologies puts us in a situation where research deliverables are richer and not limited to delivering and counting publications.Modern science delivers other outputs, mostly digital, like datasets, software, algorithms or protocols, and it delivers highly skilled people through specialised education and mentoring.Multidisciplinary collaborations are required to address new scientific questions arising from increased complexity and that is another aspect of this evolution.More and more researchers from different domains, from biology and physics to economics, anthropology or humanities, work together in the boundaries of areas of knowledge.One of the actions included in the first Policy Agenda for the European Research Area, for the next three years 2022-2024, is to advance towards the reform of the assessment system for research, researchers and institutions to improve their quality, performance and impact.We consulted a variety of stakeholders in 2021, including organisations representing researchers, public funders, private funders, universities, research centres, academies, national evaluation agencies and policymakers, and we received strong support for the objective of ensuring a combination of qualitative and quantitative indicators to evaluate research and researchers without compromising on objectivity and peer review.
Searching for Network Width With Bilaterally Coupled Network

Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constraints. To fulfil the searching, a one-shot supernet is usually leveraged to efficiently evaluate the performance w.r.t. different network widths. However, current methods mainly follow a unilaterally augmented (UA) principle for the evaluation of each width, which induces the training unfairness of channels in supernet. In this article, we introduce a new supernet called Bilaterally Coupled Network (BCNet) to address this issue. In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately. Besides, we propose to reduce the redundant search space and present the BCNetV2 as the enhanced supernet to ensure rigorous training fairness over channels. Furthermore, we leverage a stochastic complementary strategy for training the BCNet, and propose a prior initial population sampling method to boost the performance of the evolutionary search. We also propose a new open-source width search benchmark on macro structures named Channel-Bench-Macro for the better comparisons of the width search algorithms with MobileNet- and ResNet-like architectures. Extensive experiments on the benchmark datasets demonstrate that our method can achieve state-of-the-art performance.
Multimodal Tree Decoder for Table of Contents Extraction in Document Images

Table of contents (ToC) extraction aims to extract headings of different levels in documents to better understand the outline of the contents, which can be widely used for document understanding and information retrieval. Existing works often use hand-crafted features and predefined rule-based functions to detect headings and resolve the hierarchical relationship between headings. Both the benchmark and research based on deep learning are still limited. Accordingly, in this paper, we first introduce a standard dataset, HierDoc, including image samples from 650 documents of scientific papers with their content labels. Then we propose a novel end-to-end model by using the multimodal tree decoder (MTD) for ToC as a benchmark for HierDoc. The MTD model is mainly composed of three parts, namely encoder, classifier, and decoder. The encoder fuses the multimodality features of vision, text, and layout information for each entity of the document. Then the classifier recognizes and selects the heading entities. Next, to parse the hierarchical relationship between the heading entities, a tree-structured decoder is designed. To evaluate the performance, both the metric of tree-edit-distance similarity (TEDS) and F1-Measure are adopted. Finally, our MTD approach achieves an average TEDS of 87.2% and an average F1-Measure of 88.1% on the test set of HierDoc. The code and dataset will be released at: https://github.com/Pengfei-Hu/MTD.
Threat-driven Risk Assessment for APT Attacks using Risk-Aware Problem Domain Ontology

Cybersecurity attacks, which have many business impacts, continuously become more intelligent and complex. These attacks take the form of a combination of various attack elements. APT attacks reflect this characteristic well. To defend against APT attacks, organizations should sufficiently understand these attacks based on the attack elements and their relations and actively defend against these attacks in multiple dimensions. Most organizations perform risk management to manage their information security. Generally, they use the information system risk assessment (ISRA). However, the method has difficulties supporting sufficiently analyzing security risks and actively responding to these attacks due to the limitations of asset-driven qualitative evaluation activities. In this paper, we propose a threat-driven risk assessment method. This method can evaluate how dangerous APT attacks are for an organization, analyze security risks from multiple perspectives, and support establishing an adaptive security strategy.