Reviewerly/exHarmony huggingface dataset:
Deep Learning-based Smart Predictive Evaluation for Interactive Multimedia-enabled Smart Healthcare

Two-dimensional 1 arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements. This study aims to enhance the security for people's health, improve the medical level further, and increase the confidentiality of people's privacy information. Under the trend of wide application of deep learning algorithms, the convolutional neural network (CNN) is modified to build an interactive smart healthcare prediction and evaluation model (SHPE model) based on the deep learning model. The model is optimized and standardized for data processing. Then, the constructed model is simulated to analyze its performance. The results show that accuracy of the constructed system reaches 82.4%, which is at least 2.4% higher than other advanced CNN algorithms and 3.3% higher than other classical machine algorithms. It is proved based on comparison that the accuracy, precision, recall, and F1 of the constructed model are the highest. Further analysis on error shows that the constructed model shows the smallest error of 23.34 pixels. Therefore, it is proved that the built SHPE model shows higher prediction accuracy and smaller error while ensuring the safety performance, which provides an experimental reference for the prediction and evaluation of smart healthcare treatment in the later stage.
AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing

Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also an important factor. This paper contributed an adaptive Particle Swarm Optimisation (PSO) based task scheduling approach that reduces the task execution time, and increases throughput and Average Resource Utilization Ratio (ARUR). Moreover, an adaptive inertia weight strategy namely Linearly Descending and Adaptive Inertia Weight (LDAIW) is introduced. The proposed scheduling approach provides a better balance between local and global search leading to an optimized task scheduling. The performance of the proposed approach has been evaluated and compared against five renown PSO based inertia weight strategies concerning makespan and throughput. The experiments are then extended and compared the proposed approach against the other four renowned meta-heuristic scheduling approaches. Analysis of the simulated experimentation reveals that the proposed approach attained up to 10%, 12% and 60% improvement for makespan, throughput and ARUR respectively.
Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives

Abstract Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught with numerous attack surfaces throughout the FL execution. These attacks can not only cause models to fail in specific tasks, but also infer private information. While previous surveys have identified the risks, listed the attack methods available in the literature or provided a basic taxonomy to classify them, they mainly focused on the risks in the training phase of FL. In this work, we survey the threats, attacks and defenses to FL throughout the whole process of FL in three phases, including Data and Behavior Auditing Phase , Training Phase and Predicting Phase . We further provide a comprehensive analysis of these threats, attacks and defenses, and summarize their issues and taxonomy. Our work considers security and privacy of FL based on the viewpoint of the execution process of FL. We highlight that establishing a trusted FL requires adequate measures to mitigate security and privacy threats at each phase. Finally, we discuss the limitations of current attacks and defense approaches and provide an outlook on promising future research directions in FL.
Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study

Convolutional neural networks (CNN) are widely used in computer vision and medical image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly for downsampling the feature maps by aggregating features from local regions. Pooling can help CNN to learn invariant features and reduce computational complexity. Although the max and the average pooling are the widely used ones, various other pooling techniques are also proposed for different purposes, which include techniques to reduce overfitting, to capture higher-order information such as correlation between features, to capture spatial or structural information, etc. As not all of these pooling techniques are well-explored for medical image analysis, this paper provides a comprehensive review of various pooling techniques proposed in the literature of computer vision and medical image analysis. In addition, an extensive set of experiments are conducted to compare a selected set of pooling techniques on two different medical image classification problems, namely HEp-2 cells and diabetic retinopathy image classification. Experiments suggest that the most appropriate pooling mechanism for a particular classification task is related to the scale of the class-specific features with respect to the image size. As this is the first work focusing on pooling techniques for the application of medical image analysis, we believe that this review and the comparative study will provide a guideline to the choice of pooling mechanisms for various medical image analysis tasks. In addition, by carefully choosing the pooling operations with the standard ResNet architecture, we show new state-of-the-art results on both HEp-2 cells and diabetic retinopathy image datasets.
A blockchain‐ and artificial intelligence‐enabled smart IoT framework for sustainable city

Advancements in digital technologies, such as the Internet of Things (IoT), fog/edge/cloud computing, and cyber-physical systems have revolutionized a broad spectrum of smart city applications. The significant contributions and rapid developments of advanced artificial intelligence-based technologies and approaches, like, machine learning and deep learning, which are applied for extracting accurate information from extensive data, perform a potential role in IoT applications. Moreover, blockchain technology's fast adoption also contributes a significant role in the development of the new digital smart city ecosystem. Thus, artificial intelligence and blockchain technology convergence revolutionize smart city infrastructures to establish sustainable ecosystems for IoT applications. Nevertheless, these advancements and technological improvements also provide both opportunities and challenges for developing sustainable IoT applications. This paper aims to examine the convergence of blockchain technology and artificial intelligence, a unique driver towards technological transformation in intelligent and sustainable IoT applications. We mainly discussed the advantages of blockchain technology that might promote the advancement and development of sustainable IoT applications. On the basis of the discussion, we introduced a smart and sustainable conceptual framework that leverages cloud computing, IoT devices, and artificial intelligence to process and obtain necessary information. The system provides digital analytics and saves results in decentralized cloud repositories through blockchain technology to promote various applications. Moreover, the layer-based architecture allows a sustainable incentive structure, which can possibly assist secure and protected smart city applications. We reviewed the enhanced solutions, summing up the key points that can be applied for generating various artificial intelligence and blockchain-based systems. Also, we discussed the issues that still remain open and our future research goals; that can introduce new ideas and future guidelines for sustainable IoT applications.
Co<sup>2</sup>L: Contrastive Continual Learning

Recent breakthroughs in self-supervised learning show that such algorithms learn visual representations that can be transferred better to unseen tasks than cross-entropy based methods which rely on task-specific supervision. In this paper, we found that the similar holds in the continual learning context: contrastively learned representations are more robust against the catastrophic forgetting than ones trained with the cross-entropy objective. Based on this novel observation, we propose a rehearsal-based continual learning algorithm that focuses on continually learning and maintaining transferable representations. More specifically, the proposed scheme (1) learns representations using the contrastive learning objective, and (2) preserves learned representations using a self-supervised distillation step. We conduct extensive experimental validations under popular benchmark image classification datasets, where our method sets the new state-of-the-art performance. Source code is available at https://github.com/chaht01/Co2L.
Research and Application of Deep Learning in Image Recognition

Deep learning is a technical tool with broad application prospects and has an important role in the field of image recognition. In view of the theoretical value and practical significance of image recognition technology in promoting the development of computer vision and artificial intelligence, this paper will review and study the application of deep learning in image recognition. This paper first outlines the development of icon recognition technology, and then introduces three main learning models in deep learning: convolutional neural networks, recurrent neural networks, and generative adversarial networks, and provides a comparative analysis of these three learning models. Finally, the research results of deep learning image recognition application fields, such as face recognition, medical image recognition, and remote sensing image classification, are analyzed and discussed. This paper also analyze the development trend of deep learning in the field of image recognition, and conclude that the future development direction is the effective recognition of video images and the theoretical strengthening of models.
Restoring and attributing ancient texts using deep neural networks

Ancient history relies on disciplines such as epigraphy-the study of inscribed texts known as inscriptions-for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian's workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.
Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject's intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.
Boosting image watermarking authenticity spreading secrecy from counting‐based secret‐sharing

Abstract This study presents enhancing images authentication by securing watermarking hidden data via shares generated from counting‐based secret sharing. The trustfulness of shares utilised secret‐sharing as an applicable privacy creation tool for the authentication of real‐life complex platforms. This research adjusts embedding the watermarking data over the images by innovative redistribution of shares to be embedded spread over all the images. The anticipated watermarking technique guaranteed to scatter the share bits implanting at different least significant bits of image pixels as boosting up the trust overall authentication practicality. The paper experimentation performance analysis shows that this improved image watermarking authentication (capacity) is averagely better by 33%–67% than other related exclusive‐OR oriented and octagon approaches. Interestingly, these measurement improvements did not degrade the robustness and security of the system, inspiring our research for opening novel track of related future counting‐based secret‐sharing authentication progresses to come.
Geographical POI recommendation for Internet of Things: A federated learning approach using matrix factorization

Summary With the popularity of Internet of Things (IoT), Point‐of‐Interest (POI) recommendation has become an important application for location‐based services (LBS). Meanwhile, there is an increasing requirement from IoT devices on the privacy of user sensitive data via wireless communications. In order to provide preferable POI recommendations while protecting user privacy of data communication in a distributed collaborative environment, this paper proposes a federated learning (FL) approach of geographical POI recommendation. The POI recommendation is formulated by an optimization problem of matrix factorization, and singular value decomposition (SVD) technique is applied for matrix decomposition. After proving the nonconvex property of the optimization problem, we further introduce stochastic gradient descent (SGD) into SVD and design an FL framework for solving the POI recommendation problem in a parallel manner. In our FL scheme, only calculated gradient information is uploaded from users to the FL server while all the users manage their rating and geographic preference data on their own devices for privacy protection during communications. Finally, real‐world dataset from large‐scale LBS enterprise is adopted for conducting extensive experiments, whose experimental results validate the efficacy of our approach.
An analytical study of large SPARQL query logs

With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from end users has become more and more common in SPARQL endpoints. In this paper, we conduct an in-depth analytical study of the queries formulated by end users and harvested from large and up-to-date structured query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, spanning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries that exhibits already interesting results on this generalized corpus, we drill deeper in the structural characteristics related to the graph and hypergraph representation of queries. We outline the most common shapes of queries when visually displayed as undirected graphs and characterize their treewidth, length of their cycles, maximal degree of nodes, and more. For queries that cannot be adequately represented as graphs, we investigate their hypergraphs and hypertreewidth. Moreover, we analyze the evolution of queries over time, by introducing the novel concept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users and brings us to draw a number of conclusions and pinpoint future directions for SPARQL query evaluation, query optimization, tuning, and benchmarking.
A Multi-party Protocol for Constructing the Public Parameters of the Pinocchio zk-SNARK

Recent efficient constructions of zero-knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs), require a setup phase in which a common-reference string (CRS) with a certain structure is generated. This CRS is sometimes referred to as the public parameters of the system, and is used for constructing and verifying proofs. A drawback of these constructions is that whomever runs the setup phase subsequently possesses trapdoor information enabling them to produce fraudulent pseudoproofs. Building on a work of Ben-Sasson, Chiesa, Green, Tromer and Virza [BCG+15], we construct a multi-party protocol for generating the CRS of the Pinocchio zk-SNARK [PHGR16], such that as long as at least one participating party is not malicious, no party can later construct fraudulent proofs except with negligible probability. The protocol also provides a strong zero-knowledge guarantee even in the case that all participants are malicious. This method has been used in practice to generate the required CRS for the Zcash cryptocurrency blockchain.
A fair protocol for data trading based on Bitcoin transactions

On-line commercial transactions involve an inherent mistrust between participant parties since, sometimes, no previous relation exists between them. Such mistrust may be a deadlock point in a trade transaction where the buyer does not want to perform the payment until the seller sends the goods and the seller does not want to do so until the buyer pays for the purchase. In this paper we present a fair protocol for data trading where the commercial deal, in terms of delivering the data and performing the payment, is atomic, since the seller cannot redeem the payment unless the buyer obtains the data and the buyer cannot obtain the data without performing the payment. The protocol is based on Bitcoin scripting language and the fairness of the protocol can be probabilistically enforced.
Traceable-then-revocable ciphertext-policy attribute-based encryption scheme

A traceable ciphertext-policy attribute-based encryption (T-CPABE) scheme can trace a malicious user, who may leak her/his decryption privilege to a third party for some benefits. However, even if the malicious user is traced, the existing schemes cannot revoke her/him from the cryptosystems. Thus, it is necessary to embed a revocation mechanism into a T-CPABE scheme in practice. In this paper, we propose a ciphertext-policy attribute-based encryption scheme with white-box traceability and direct user revocation. In the proposed scheme, the ciphertext is related to an access structure and a revocation list R. The secret key is associated with an attribute set and a user’s identity assigned a leaf node in a binary tree. The value of a leaf node is used to trace a malicious user. Once a malicious user is caught, her/his identity is added in the revocation list R. Only the ciphertext components associated with the revocation list R are updated according to the new revocation list R′, and the updated ciphertext can provide forward security. Therefore, a user can decrypt a ciphertext if and only if she/he is not in the revocation list and her/his attribute set satisfies the access policy, simultaneously. Furthermore, our scheme is proved to be secure under selective access policy and chosen-plaintext attacks based on the decisional q-bilinear Diffie–Hellman exponent hardness assumption in the standard model.
Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices

Parameter control is aimed at realizing performance gains through a dynamic choice of the parameters which determine the behavior of the underlying optimization algorithm. In the context of evolutionary algorithms, this research line has for a long time been dominated by empirical approaches. With the significant advances in running-time analysis achieved in the last ten years, the parameter control question has become accessible to theoretical investigations. A number of running-time results for a broad range of different parameter control mechanisms have been obtained in recent years. This chapter surveys these results, and puts them into context by proposing an updated classification scheme for parameter control.
EMPOWERMENT, KNOWLEDGE SHARING AND INNOVATIVE BEHAVIOURS: EXPLORING GENDER DIFFERENCES

We examine the effects of empowerment and knowledge sharing on employee innovative behaviours and explore gender differences. The study draws on a sample of 305 employees from the UAE (United Arab Emirates) service sector. Based on an extensive literature review, we develop a conceptual model and formulate four main hypotheses. Statistical analysis was conducted using structural equation modelling with Smart-Partial Least Squares (PLS). The results clearly show and confirm that feelings of empowerment and knowledge sharing have strong and significant impacts on employee innovative behaviours. Surprising and quite interestingly, females report greater feelings of empowerment but were less inclined to engage in knowledge sharing. The findings also show significant gender differences in relation to the impacts of empowerment and knowledge sharing on innovative behaviours. The above gender disparities seem to be specific to the UAE contexts which are discussed. The implications of the above findings for management practice and future research are also discussed.