Reviewerly/exHarmony huggingface dataset:
Dual-stream Reciprocal Disentanglement Learning for domain adaptation person re-identification

Since human-labeled samples are free for the target set, unsupervised person re-identification (Re-ID) has attracted much attention in recent years, by additionally exploiting the source set. However, due to the differences on camera styles, illumination and backgrounds, there exists a large gap between source domain and target domain, introducing a great challenge on cross-domain matching. To tackle this problem, in this paper we propose a novel method named Dual-stream Reciprocal Disentanglement Learning (DRDL), which is quite efficient in learning domain-invariant features. In DRDL, two encoders are first constructed for id-related and id-unrelated feature extractions, which are respectively measured by their associated classifiers. Furthermore, followed by an adversarial learning strategy, both streams reciprocally and positively effect each other, so that the id-related features and id-unrelated features are completely disentangled from a given image, allowing the encoder to be powerful enough to obtain the discriminative but domain-invariant features. In contrast to existing approaches, our proposed method is free from image generation, which not only reduces the computational complexity remarkably, but also removes redundant information from id-related features. Extensive experiments substantiate the superiority of our proposed method compared with the state-of-the-arts. The source code has been released in https://github.com/lhf12278/DRDL.
Autonomous credit card fraud detection using machine learning approach☆

Credit card fraud has risen in vulnerable effects in recent years as more people use credit cards to pay for products. This is owing to advancements in technology and growths in internet transactions, both of which have resulted in massive financial losses due to fraud. To reduce such losses, an effective fraud detection system must be designed and implemented. Machine learning approaches used to detect credit card fraud automatically and do not take into account deception process or behavioral problem, which might lead to alerts. The goal of this study is to figure out how to spot credit card fraud. To detect the occurrence of fraud, a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) is proposed. In addition, an attention mechanism has been included to increase performance even more. In instances like fraud detection, where the information sequence is made up of vectors with complicated interrelated properties, models with this structure have proven to be particularly efficient. LSTM-RNN is compared to other classifiers such as Naive Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN). Experiments reveal that our proposed model produces powerful results and has a high level of accuracy.
An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

Abstract As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial clustering of applications with noise algorithm (DBSCAN), as a common unsupervised learning algorithm, can achieve clusters via finding high-density areas separated by low-density areas based on cluster density. Different from other clustering methods, DBSCAN can work well for any shape clusters in the spatial database and can effectively cluster exceptional data. However, in the employment of DBSCAN, the parameters, EPS and MinPts, need to be preset for different clustering object, which greatly influences the performance of the DBSCAN. To achieve automatic optimization of parameters and improve the performance of DBSCAN, we proposed an improved DBSCAN optimized by arithmetic optimization algorithm (AOA) with opposition-based learning (OBL) named OBLAOA-DBSCAN. In details, the reverse search capability of OBL is added to AOA for obtaining proper parameters for DBSCAN, to achieve adaptive parameter optimization. In addition, our proposed OBLAOA optimizer is compared with standard AOA and several latest meta heuristic algorithms based on 8 benchmark functions from CEC2021, which validates the exploration improvement of OBL. To validate the clustering performance of the OBLAOA-DBSCAN, 5 classical clustering methods with 10 real datasets are chosen as the compare models according to the computational cost and accuracy. Based on the experimental results, we can obtain two conclusions: (1) the proposed OBLAOA-DBSCAN can provide highly accurately clusters more efficiently; and (2) the OBLAOA can significantly improve the exploration ability, which can provide better optimal parameters.
A location-based privacy-preserving oblivious sharing scheme for indoor navigation

In recent years, with the rapid development of wireless communication technology and mobile devices, etc., indoor localization and navigation using WiFi sensing are increasingly being widely employed in life. On this basis, various applications of location-based services have been rapidly developed. However, when users enjoy the location query service of LBS providers, there is a risk of exposing personal information and location privacy simultaneously. In particular, the sensitive information about users based on their location privacy can be inferred by malicious users. Therefore, how to implement location hiding, query privacy protection and query result feedback at the same time is one of the crucial issues of LBS privacy protection. A novel oblivious data sharing scheme employing the designed 1-out-of-n oblivious transfer protocol is proposed to achieve an efficient location-based service for users while effectively hiding location coordinates and protecting the privacy of users and servers. In addition, once a user accesses the same keyword several times, the server is bound to be able to master the accessed frequency of keywords. For this reason, the string length-based transformation algorithm based on private information retrieval is designed and added as a part of OT protocol, which effectively reduces the communication overhead and ensures the privacy of required keywords. The security and performance analysis indicate that the proposed protocol can be instantiated in indoor navigation with high security and efficiency.
Explanations of Machine Learning Models in Repeated Nested Cross-Validation: An Application in Age Prediction Using Brain Complexity Features

SHAP (Shapley additive explanations) is a framework for explainable AI that makes explanations locally and globally. In this work, we propose a general method to obtain representative SHAP values within a repeated nested cross-validation procedure and separately for the training and test sets of the different cross-validation rounds to assess the real generalization abilities of the explanations. We applied this method to predict individual age using brain complexity features extracted from MRI scans of 159 healthy subjects. In particular, we used four implementations of the fractal dimension (FD) of the cerebral cortex—a measurement of brain complexity. Representative SHAP values highlighted that the most recent implementation of the FD had the highest impact over the others and was among the top-ranking features for predicting age. SHAP rankings were not the same in the training and test sets, but the top-ranking features were consistent. In conclusion, we propose a method—and share all the source code—that allows a rigorous assessment of the SHAP explanations of a trained model in a repeated nested cross-validation setting.
A literature review on users' behavioral intention toward chatbots' adoption

Purpose Despite the fact that chatbots have been largely adopted for the last few years, a comprehensive literature review research focusing on the intention of individuals to adopt chatbots is rather scarce. In this respect, the present paper attempts a literature review investigation of empirical studies focused on the specific issue in nine scientific databases during 2017-2021. Specifically, it aims to classify extant empirical studies which focus on the context of individuals' adoption intention toward chatbots. Design/methodology/approach The research is based on PRISMA methodology, which revealed a total of 39 empirical studies examining users' intention to adopt and utilize chatbots. Findings After a thorough investigation, distinct categorization criteria emerged, such as research field, applied theoretical models, research types, methods and statistical measures, factors affecting intention to adopt and further use chatbots, the countries/continents where these surveys took place as well as relevant research citations and year of publication. In addition, the paper highlights research gaps in the examined issue and proposes future research directions in such a promising information technology solution. Originality/value As far as the authors are concerned, there has not been any other comprehensive literature review research to focus on examining previous empirical studies of users' intentions to adopt and use chatbots on the aforementioned period. According to the authors' knowledge, the present paper is the first attempt in the field which demonstrates broad literature review data of relevant empirical studies.
Regression Bias in Using Solar Wind Measurements

Simultaneous solar wind measurements from the solar wind monitors, WIND and ACE, differ due to the spatial and temporal structure of the solar wind. Correlation studies that use these measurements as input may infer an incorrect correlation due to uncertainties arising from this spatial and temporal structure, especially at extreme and rare solar wind values. In particular, regression analysis will lead to a regression function whose slope is biased towards the mean value of the measurement parameter. This article demonstrates this regression bias by comparing simultaneous ACE and WIND solar wind measurements. A non-linear regression analysis between them leads to a perception of underestimation of extreme values of one measurement on average over the other. Using numerical experiments, we show that popular regression analysis techniques such as linear least-squares, orthogonal least-squares, and non-linear regression are not immune to this bias. Hence while using solar wind parameters as an independent variable in a correlation or regression analysis, random uncertainty in the independent variable can create unintended biases in the response of the dependent variable. More generally, the regression to the mean effect can impact both event-based, statistical studies of magnetospheric response to solar wind forcing.
Learning Graph-based Disentangled Representations for Next POI Recommendation

Next Point-of-Interest (POI) recommendation plays a critical role in many location-based applications as it provides personalized suggestions on attractive destinations for users. Since users' next movement is highly related to the historical visits, sequential methods such as recurrent neural networks are widely used in this task for modeling check-in behaviors. However, existing methods mainly focus on modeling the sequential regularity of check-in sequences but pay little attention to the intrinsic characteristics of POIs, neglecting the entanglement of the diverse influence stemming from different aspects of POIs. In this paper, we propose a novel Disentangled Representation-enhanced Attention Network (DRAN) for next POI recommendation, which leverages the disentangled representations to explicitly model different aspects and corresponding influence for representing a POI more precisely. Specifically, we first design a propagation rule to learn graph-based disentangled representations by refining two types of POI relation graphs, making full use of the distance-based and transition-based influence for representation learning. Then, we extend the attention architecture to aggregate personalized spatio-temporal information for modeling dynamic user preferences on the next timestamp, while maintaining the different components of disentangled representations independent. Extensive experiments on two real-world datasets demonstrate the superior performance of our model to state-of-the-art approaches. Further studies confirm the effectiveness of DRAN in representation disentanglement.
Systematic Approach for Detection and Assessment of Dark Web Threat Evolution

Cyber thieves and terrorists use the dark web as one of the most difficult channels to achieve their nefarious goals. There are many similarities between cyber-crimes and real-world crimes taking place on the dark web. However, the dark web's sheer breadth and anonymity are key to tracing the offenders. The first step in finding effective solutions to cybercrime is to assess the different dark web criminal hazards. The investigation of the dark web includes a review of crimes to minimize crime issues. To assist cyber security specialists, the authors used the systematic literature review approach and extracted data from 65 publications from the most relevant internet resources to meet research aims. As a result of an exhaustive investigation, systematic literature review is able to provide a clear picture of how criminal activity on the dark web is expanding and examine the strengths and weaknesses of existing methods for tracking down criminals. This study has showed, to aid law enforcement in the apprehension of criminals, digital evidence must be analyzed as per established standards.
A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation

In this paper, we present a real-time whole-body planner for collision-free legged mobile manipulation. We enforce both self-collision and environment-collision avoidance as soft constraints within a Model Predictive Control (MPC) scheme that solves a multi-contact optimal control problem. By penalizing the signed distances among a set of representative primitive collision bodies, the robot is able to safely execute a variety of dynamic maneuvers while preventing any self-collisions. Moreover, collision-free navigation and manipulation in both static and dynamic environments are made viable through efficient queries of distances and their gradients via a euclidean signed distance field. We demonstrate through a comparative study that our approach only slightly increases the computational complexity of the MPC planning. Finally, we validate the effectiveness of our framework through a set of hardware experiments involving dynamic mobile manipulation tasks with potential collisions, such as locomotion balancing with the swinging arm, weight throwing, and autonomous door opening.
Blockchain-Based Identity Management Systems in Health IoT: A Systematic Review

Identity and Access Management (IAM) systems are crucial for any information system, such as healthcare information systems. Health IoT (HIoT) applications are targeted by attackers due to the high-volume and sensitivity of health data. Thus, IAM systems for HIoT need to be built with high standards and based on reliable frameworks. Blockchain (BC) is an emerging technology widely used for developing decentralized IAM solutions. Although, the integration of BC in HIoT for proposing IAM solutions has gained recent attention, BC is an evolving technology and needs to be studied carefully before using it for IAM solutions in HIoT applications. A systematic literature review was conducted on the BC-based IAM systems in HIoT applications to investigate the security aspect. Twenty-four studies that satisfied the inclusion criteria and passed the quality assessment were included in this review. We studied BC-based solutions in HIoT applications to explore the IAM system architecture, security requirements and threats. We summarized the main components and technologies in typical BC-based IAM systems and the layered architecture of the BC-based IAM system in HIoT. Accordingly, the security threats and requirements were summarized. Our systematic review shows that there is a lack of a comprehensive security framework, risk assessments, and security and functional performance evaluation metrics in BC-based IAM in HIoT applications.
Semisupervised Federated-Learning-Based Intrusion Detection Method for Internet of Things

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based intrusion detection methods, however, suffer from three limitations: 1) model parameters transmitted in each round may be used to recover private data, which leads to security risks; 2) not independent and identically distributed (non-IID) private data seriously adversely affect the training of FL (especially distillation-based FL); and 3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this article develops an intrusion detection method based on a semisupervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a model based on convolutional neural networks (CNNs) for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, the discriminator is designed to improve the quality of each client’s predicted labels, and to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of the hard-label strategy and voting mechanism further reduces communication overhead. The experiments on the real-world traffic data set with three non-IID scenarios show that our proposed method can achieve better detection performance as well as lower communication overhead than state-of-the-art methods.
An Anonymous Pre-Authentication and Post-Authentication Scheme Assisted by Cloud for Medical IoT Environments

The Internet of Things (IoT) environment assisted by a cloud server is an emerging conceptual model which incorporates the benefits of both IoT and cloud. In such an integrated model, IoT gathers information from the sensors, and the cloud server significantly increases the importance of this information by exchanging and processing them. Session key agreement among the user and sensors is practiced nowadays to accelerate the authentication process in Medical IoT (MIoT). However, the use of GateWay Node (GWN) or Trusted Third Party (TTP) for the control of participant registration, session key agreement and data storage was not efficient in IoT because they suffer from increased communication and computation costs. Hence, in this paper, we have proposed a new cloud based session key agreement and data storage scheme which consists of an improved authentication mechanism for MIoT. The proposed scheme achieves anonymous pre-authentication and post-authentication. In this proposed system, with the support of the user data, the cloud server generates the pseudo-identity and this identity is used by a cloud server to anonymously authenticate the legitimacy of a requesting user. After successful login, the cloud server and the user share the session key and data communication occurs. The security analysis of our proposed scheme demonstrates that it is resistant to a variety of security attacks. Moreover, the simulated performance analysis proves that the communication and computation costs associated with our work are acceptable than the existing works.
Multimedia Research Toward the Metaverse

Metaverse, a set of fully immersive digital worlds where people can interact with one another using three-dimensional (3-D) avatars, is poised to become an increasingly popular modality to communicate with others. Many in the space consider it an evolution of the Internet, with augmented and virtual reality headsets providing the hardware to both visualize and interact with it. However, creating custom 3-D content and environments for a digital world can be extremely time-consuming and costly compared to typical web pages. To this end, research in procedural content generation is required to empower the efficient creation of high-quality multimedia contents for the metaverse. One other core concern when creating a fully decentralized metaverse is the need to download relevant environments on demand as people traverse across different worlds. Further research by the multimedia community on efficient multimedia networking, storage, and compression is necessary to ensure that digital worlds remain easily accessible to others.
Hybrid blockchain database systems

With the emergence of hybrid blockchain database systems, we aim to provide an in-depth analysis of the performance and trade-offs among a few representative systems. To achieve this goal, we implement Veritas and BlockchainDB from scratch. For Veritas, we provide two flavors to target the crash fault-tolerant (CFT) and Byzantine fault-tolerant (BFT) application scenarios. Specifically, we implement Veritas with Apache Kafka to target CFT application scenarios, and Veritas with Tendermint to target BFT application scenarios. We compare these three systems with the existing open-source implementation of BigchainDB. BigchainDB uses Tender-mint for consensus and provides two flavors: a default implementation with blockchain pipelining and an optimized version that includes blockchain pipelining and parallel transaction validation. Our experimental analysis confirms that CFT designs, which are typically used by distributed databases, exhibit much higher performance than BFT designs, which are specific to blockchains. On the other hand, our extensive analysis highlights the variety of design choices faced by the developers and sheds some light on the trade-offs that need to be done when designing a hybrid blockchain database system.
A Blockchain Footprint for Authentication of IoT-Enabled Smart Devices in Smart Cities: State-of-the-Art Advancements, Challenges and Future Research Directions

The mechanisms based on the distributed environment have become an obvious choice for solutions, while they have not been limited only to a specific domain (i.e., crypto-currency).Rather, it has influenced other industries to develop robust privacy and security solutions, such as smart houses, smart electrical grids, smart agriculture, smart health care, smart transportation, etc.These Cyber-Physical Systems heavily depend on IoT-based smart devices that constitute a networked system of devices dependent on each other for the smooth operation of the overall system.Hence, security and privacy have become an integral part of all the architectural frameworks they operate in.The adoption of these architectures, such as the Internet of Things (IoT), Internet of Cyber-Physical Things (IoCPT), Cyber-Physical Systems (CPSs), and Internet of Everything (IoE), has reinforced the need to develop solutions based on a distributed environment.Distributed ledger technology, i.e., Blockchain, has taken the lead and may support the development of solutions with robust privacy and security.We provide an updated review of authentication mechanisms developed on blockchain technology that enforce decentralized architectures.We discuss the security issues regarding the authentication of these IoT-enabled smart devices.We evaluate and analyze the study of the proposed literature schemes that pose authentication challenges in terms of computational costs, communication overheads, and models applied to attain robustness.Hence, lightweight solutions for managing, maintaining, processing, and storing authentication data of IoT-enabled assets are a must.From an integration perspective, cloud computing has provided strong support.In contrast, decentralized ledger technology, i.e., Blockchain, and lightweight cryptosystems are the areas for much more to explore.Finally, we discuss the future research challenges, which present an improvement standpoint to help address the ambiguities.