A dynamic state sharding blockchain architecture for scalable and secure crowdsourcing systems

Abstract: Currently, the crowdsourcing system has serious problems such as single point of failure of the server, leakage of user privacy, unfair arbitration, etc. By storing the interactions between workers, requesters, and crowdsourcing platforms in the form of transactions on the blockchain, these problems can be effectively addressed. However, the improvement in total computing power on the blockchain is difficult to provide positive feedback to the efficiency of transaction confirmation, thereby limiting the performance of crowdsourcing systems. On the other hand, the increasing amount of data in blockchain further increases the difficulty of nodes participating in consensus, affecting the security of crowdsourcing systems. To address the above problems, in this paper we design a blockchain architecture based on dynamic state sharding, called DSSBD. Firstly, we solve the problems caused by cross sharding transactions and reconfiguration in blockchain state sharding through graph segmentation and relay transactions. Then, we model the optimal block generation problem as a Markov decision process. By utilizing deep reinforcement learning, we can dynamically adjust the number of shards, block spacing, and block size. This approach helps improve both the throughput of the blockchain and the proportion of non-malicious nodes. Security analysis has proven that the proposed DSSBD can effectively resist attacks such as transaction atomic attacks, double spending attacks, sybil attacks, replay attacks, etc. The experimental results show that the crowdsourcing system with the proposed DSSBD has better performance in throughput, latency, balancing, cross-shard transaction proportion, and node reconfiguration proportion, etc., while ensuring security.


Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho

Abstract: The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry. The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks. Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.


APGVAE: Adaptive disentangled representation learning with the graph-based structure information

Abstract: Neural networks are used to learn task-oriented high-level representations in an end-to-end manner by building a multi-layer neural network. Generation models have developed rapidly with the emergence of deep neural networks. But it still has problems with the insufficient authenticity of generated images, the deficiency of diversity, consistency, and unexplainability in the generation process. Disentangled representation is an effective method to learn a high-level feature representation and realize the interpretability of deep neural networks. We propose a general disentangled representation learning network with variational autoencoder network as the basic framework for the image generation process. The graph-based structure of the priors is embedded in the last module of the deep encoder network to build the feature spaces by the class, task-oriented, and task-unrelated information respectively. Meanwhile the priors should be adaptively modified with the task relevance of a generated image. And the semi-supervised learning is further involved in the disentangled representation network framework to reduce the requirements of label and extend the majority of feature space under the task-unrelated feature assumption. Experimental results show that the proposed method is efficient for various types of images and has a good potential for further research and development.


OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning

Abstract: Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy. The success of FL hinges on the efficiency of participating models and their ability to handle the unique challenges of distributed learning. While several variants of Vision Transformer (ViT) have shown great potential as alternatives to modern convolutional neural networks (CNNs) for centralized training, the unprecedented size and higher computational demands hinder their deployment on resource-constrained edge devices, challenging their widespread application in FL. Since client devices in FL typically have limited computing resources and communication bandwidth, models intended for such devices must strike a balance between model size, computational efficiency, and the ability to adapt to the diverse and non-IID data distributions encountered in FL. To address these challenges, we propose OnDev-LCT: Lightweight Convolutional Transformers for On-Device vision tasks with limited training data and resources. Our models incorporate image-specific inductive biases through the LCT tokenizer by leveraging efficient depthwise separable convolutions in residual linear bottleneck blocks to extract local features, while the multi-head self-attention (MHSA) mechanism in the LCT encoder implicitly facilitates capturing global representations of images. Extensive experiments on benchmark image datasets indicate that our models outperform existing lightweight vision models while having fewer parameters and lower computational demands, making them suitable for FL scenarios with data heterogeneity and communication bottlenecks.


An intelligent correlation learning system for person Re-identification

Abstract: Person re-identification (PRe-id) aims to retrieve a target person's images captured across multiple/single non-overlapping cameras. To this end, significant techniques have been implemented that extract handcrafted, deep, part-based, and ensemble features to get more refined patterns for matching. But due to the limited focus on the multi-grained, view-consistent, and semantic correlation among different views, these approaches show low performance. Therefore, we present an attention-based multi-view correlation learning framework named (ACLS), which enables to learn multi-grained spatiotemporal features from individuals. The ACLS is mainly composed of three key steps: First, multi-view correlated visual features of pedestrians are extracted using a correlation vision transformer (CVIT) and a pyramid dilated network (PDN), followed by the person attention mechanism. Next, we employ convolutional long short-term memory (ConvLSTM) to extract spatiotemporal information from pedestrian images captured in different time frames. Finally, a deep fusion strategy is employed, which intelligently integrates features for the final matching task. Extensive evaluations are conducted over three famous datasets: Market-1501, DukeMCMT-reID, and CUHK03, results show tremendous ranking performance including 93.7%, 90.4%, and 85.7%. Thus, concluded remarks that our learning mechanism beats the current state-of-the-art (SOTA) methods.


Evaluation of solar PV-based microgrids viability utilizing single and multi-criteria decision analysis

Abstract: Feasibility analysis is crucial for solar projects to minimize risk and ensure goal achievement. Essential factors include the PV system's location, elevation, land slope, energy yield, soil texture, mean annual cloud frequency map, and distance from urban areas. This study examines the performance of a 3 MW PV system across five remote Egyptian regions characterized by desert weather conditions. The influence of available weather data sources and different yield estimation methods on the feasibility of the PV system is treated as a Single-Criteria Decision Making (SCDM) scheme. Meteorological data from Meteonorm 8.1, PVGIS, and NASA-SSE satellite data are utilized as input data for the designed PVsyst and ANSYS-based energy yield estimation models and for empirical formulas. Significant variations in estimated energy yield are observed, depending on the weather data source. The study also underscores the importance of diligently evaluating and comparing the accuracy and reliability of different weather data sources and estimation methods for simulating PV-based microgrid system performance. The yield estimation-based SCDM indicates that the New Valley PV system consistently outperformed the other systems. However, a Multi-Criteria Decision Making (MCDM) indicates that the Red Sea performs well in technical criteria evaluation, while alternative Aswan City excels in practical and feasibility criteria assessment. The study employs the MCDM approach to provide a more detailed evaluation. Specifically, the technical criteria focus on energy yield, system efficiency, and reliability. In contrast, a decision-maker prioritizing long-term sustainability might lean towards a system that excels in technical criteria, even if it is economically less attractive in the short term. Conversely, a decision-maker constrained by budget limitations might opt for a system that, while not the most technically efficient, offers the most cost-effective solution. This dual-criteria approach reveals that while one region may excel in technical performance, another is more financially viable. The choice between these two regions hinges on the specific priorities of the decision-maker.


Large language models (LLMs) in the evaluation of emergency radiology reports: performance of ChatGPT-4, Perplexity, and Bard

Abstract: Large language models (LLMs), especially those based on the Generative Pre-trained Transformer (GPT) architecture, have become widely popular and have been applied in various fields due to their ability to provide written responses to a diverse range of queries swiftly and accurately. LLMs have demonstrated a transformative and potentially revolutionary capacity in multiple medical subfields, including radiology.1


Secret sharing: A comprehensive survey, taxonomy and applications

Abstract: The emergence of ubiquitous computing and different disruptive technologies caused magnificent development in information and communication technology. Likewise, cybercriminals are also carefully considering different newer ways of attacks. Protecting the confidentiality, integrity, and authentication of sensitive information is the day's major challenge. Secret sharing is a method that allows a trusted authority (the dealer) to distribute a secret or a number of secrets among some target participants with the intention that certain predetermined groups of participants can collaborate to recover the secret or secrets. Any other group formed by the participants cannot do so. Threshold secret sharing (TSS) is a particular form of secret sharing. It permits any group consisting of at least a specific number (called the threshold) of participants to reconstruct the secret or secrets. However, any group with fewer than the specified number of participants is forbidden to do so. It provides tolerance against single point of failure (SPOF), which has attracted a large number of researchers to contribute in this field. It has the potential to be implemented in numerous practical and secure applications. In this paper, we present a comprehensive survey of a variety of existing threshold secret sharing schemes. We have identified various aspects of developing secure and efficient secret sharing schemes. We have also highlighted some of the applications based on secret sharing. Finally, the open challenges and future research directions in the field of secret sharing are identified and discussed.


The role of perceived risk and information security on customers' acceptance of service robots in the hotel industry

Abstract: This study proposed and tested a theoretical framework that investigated the influences of perceived risk and information security on hotel customers’ intention to use service robots. In addition, the impacts of self-efficacy, innovativeness, and facilitating conditions on perceived risk and information security were examined. Structural equation modeling (SEM) was used to test the proposed model by utilizing data collected from eleven countries including the United States, United Kingdom, Turkey, Spain, Romania, Japan, Israel, India, Greece, Canada, and Brazil. The study results demonstrated that perceived risk had a negative impact on customers’ intention to use service robots while information security had a positive impact. In addition, the study results indicated that self-efficacy negatively influenced perceived risk, and positively influenced perceived information security; and innovativeness and facilitating condition positively influenced information security. The study findings offer several important contributions to the hospitality robotics technology adoption literature and present valuable implications for hospitality practitioners and service robot vendors.


Effects of resource orchestration, strategic information exchange capabilities, and digital orientation on innovation and performance of hotel supply chains

Abstract: Supply chain (SC) innovation has become a competitive source for hotels to enhance performance in the turbulent business environment. Drawing on the resource orchestration (RO) and information sharing (IS) theories, we propose an integrated theoretical framework delineating how strategic information exchange (SIE) and RO capabilities and digital orientation of a hotel foster innovation and enhance the performance of its SC. Based on PLS-SEM analysis of data collected from 281 hotels in the United Arab Emirates, our findings indicate that RO and SIE capabilities of hotels influence the performance of SCs, whereas SC innovation mediates the said relationships. However, contrary to the expectations, digital orientation did not moderate the relationships between RO and SIE capabilities and SC innovation of hotels.


SEAM: An integrated activation-coupled model of sentence processing and eye movements in reading

Abstract: Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, lexical, and motor processes but neglect post-lexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on post-lexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Seelig et al., 2023) with key components of the Lewis and Vasishth sentence processing model (Lewis and Vasishth, 2005). This integration becomes possible, for the first time, due in part to recent advances in successful parameter identification in dynamical models, which allows us to investigate profile log-likelihoods for individual model parameters. We present a fully implemented proof-of-concept model demonstrating how such an integrated model can be achieved; our approach includes Bayesian model inference with Markov Chain Monte Carlo (MCMC) sampling as a key computational tool. The integrated Sentence-Processing and Eye-Movement Activation-Coupled Model (SEAM) can successfully reproduce eye movement patterns that arise due to similarity-based interference in reading. To our knowledge, this is the first-ever integration of a complete process model of eye-movement control with linguistic dependency completion processes in sentence comprehension. In future work, this proof of concept model will need to be evaluated using a comprehensive set of benchmark data.


Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems

Abstract: The Bayesian inference approach is widely used to solve inverse problems due to its versatile and natural ability to handle ill-posedness. However, there are often challenges when dealing with situations involving continuous fields or parameters with large-resolution discrete representations. Furthermore, the prior distribution of the unknown parameters is also commonly difficult to determine. Therefore, in this study, an operator learning-based generative adversarial network (OL-GAN) is proposed and integrated into the Bayesian inference framework to address these issues. Compared with classical Bayesian approaches, the distinctive characteristic of the proposed method is that it learns the joint distributions of parameters and responses. By using the trained generative model to handle the prior in Bayes' rule, the posteriors of the unknown parameters can theoretically be approximated by any sampling algorithms (e.g., Markov chain Monte Carlo, MCMC) under the proposed framework. More importantly, efficient sampling can be implemented in a low-dimensional latent space shared by the components of the joint distribution. The latent space is typically a simple and easy-to-sample distribution (e.g., Gaussian, uniform), which significantly reduces the computational cost associated with the Bayesian inference while avoiding prior selection. Furthermore, the generator is resolution-independent due to the incorporation of operator learning. Predictions can thus be obtained at desired coordinates, and inversions can be performed even if the observation data are misaligned with the training data. Finally, the effectiveness of the proposed method is validated through several numerical experiments.


Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

Abstract: Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses. These provide empirical evidence of the strides made in this journal version over the initial work, solidifying our framework’s standing as a substantial contribution to the field of automated feature transformation. To improve the reproducibility, we have released the associated code and data by the Github link https://github.com/coco11563/TKDD2023_code.


DeepSecDrive: An explainable deep learning framework for real-time detection of cyberattack in in-vehicle networks

Abstract: Autonomous driving (AD) technologies are becoming increasingly popular, promising significant enhancements in road safety and efficiency. However, the susceptibility of autonomous vehicles to cyberattacks highlights the critical need for protecting the security and dependability of in-vehicular networks (IVNs). This study proposes a lightweight, efficient, and explainable deep learning framework, DeepSecDrive, for early detection of security threats against IVN. DeepSecDrive presents robust feature extractor units designed with deformable convolutions and a lightweight non-local network (LNLN). The former promotes dynamic change of receptive field to enhance extraction of complex features from the traffics of in-vehicle networks, while the latter enables learning contextual representations and non-local relationships in vehicular data. DeepSecDrive integrates Shapley Additive exPlanations to construct multi-level interpretability units, providing local and global explanations for model decisions in terms of feature contributions to the overall performance. Proof-of-concept experiments are conducted to evaluate the efficacy and durability of DeepSecDrive on real-world IVN attacks from Car-Hacking dataset, and the experimental results demonstrate the ability of our model to outperform state-of-the-art detection methods.


Recovering sign bits of DCT coefficients in digital images as an optimization problem

Abstract: Recovering unknown, missing, damaged, distorted, or lost information in DCT coefficients is a common task in multiple applications of digital image processing, including image compression, selective image encryption, and image communication. This paper investigates the recovery of sign bits in DCT coefficients of digital images, by proposing two different approximation methods to solve a mixed integer linear programming (MILP) problem, which is NP-hard in general. One method is a relaxation of the MILP problem to a linear programming (LP) problem, and the other splits the original MILP problem into some smaller MILP problems and an LP problem. We considered how the proposed methods can be applied to JPEG-encoded images and conducted extensive experiments to validate their performances. The experimental results showed that the proposed methods outperformed other existing methods by a substantial margin, both according to objective quality metrics and our subjective evaluation.


Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Abstract: Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.


Non-fungible tokens: The missing ingredient for sustainable supply chains in the metaverse age?

Abstract: Non-fungible tokens (NFTs), unique digital assets on a blockchain that can represent ownership of digital or physical items, are potentially ground-breaking in their capacity for transforming the sustainability of supply chains. This paper explores and discusses how NFTs, analysed through the lens of the Technology Organisation Environment (TOE) framework, can drive supply chain sustainability and overcome the barriers to Blockchain Technology (BCT) adoption. We elucidate the unique value proposition of NFTs and explore the potential of 'phygital products' for linking digital NFTs to physical goods. Based on the existing literature and a review of real-world case studies, we propose that NFTs can (1) incentivise supply chain stakeholders to engage in more sustainable practices; (2) enhance customer willingness to pay for sustainable products; (3) provide anti-counterfeit measures; (4) support circular business model growth. A sustainable 'Mint-to-Order' production strategy starting with the creation (minting) of an NFT is proposed based on a synthesis of the findings. The use of NFTs has significant implications for both digital and physical product offerings and sustainable supply chain management (SSCM) practice making a compelling case for adoption. This paper is one of the first to explore the potential of using NFTs to drive sustainability in supply chains forming an essential component of a metaverse nexus and delineating a future research agenda.


A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques

Abstract: Falls among older adults substantially affect mobility, health, and mortality. However, advancements in wireless and internet-of-things technologies have led to the development of fall detection and rescue systems. These systems offer a solution to mitigate the impact of falls by swiftly delivering emergency services to individuals, thereby reducing the risk of loss of life, injuries, and associated healthcare expenses. This paper aims to review current elderly fall detection systems (FDSs) comprehensively. The assessment examines FDSs explicitly designed for older individuals, considering fall detection methods, system architecture, wireless communications, sensor types, performance metrics, challenges, limitations, and more. In addition, a taxonomy and comprehensive review, accompanied by comparative analysis, have been conducted to categorize FDSs into traditional and artificial intelligence-based methods. The artificial intelligence techniques containing machine learning and deep learning methods for detecting elderly falls were critically reviewed and compared. Moreover, the deep learning-based systems have shown high accuracy in fall detection during the review. By conducting a comparative analysis among various FDSs, this review aims to aid researchers in identifying the most accurate and appropriate method for detecting falls in elderly individuals. In conclusion, this review assists researchers in making informed decisions and enhances the reliability and usability of elderly fall detection systems by addressing significant challenges and limitations.


CPT: Color-based Prompt Tuning for pre-trained vision-language models

Abstract: Vision-Language Pre-training (VLP) models have shown promising capabilities in grounding natural language in image data, facilitating a broad range of cross-modal tasks. However, we note that there exists a significant gap between the objective forms of model pre-training and fine-tuning, resulting in a need for large amounts of labeled data to stimulate the visual grounding capability of VLP models for downstream tasks. To address the challenge, we present Color-based Prompt Tuning (CPT), a novel paradigm for tuning VLP models, which reformulates visual grounding into a fill-in-the-blank problem with color-based co-referential markers in image and text, maximally mitigating the gap. In this way, CPT enables strong few-shot and even zero-shot visual grounding capabilities of VLP models. Comprehensive experimental results show that CPT achieves state-of-the-art performance on zero/few-shot visual grounding (e.g., 75.1 zero-shot accuracy in RefCOCO evaluation), outperforming fine-tuned and other prompt-tuned models by a large margin. Moreover, CPT can also be easily extended to achieve promising zero/few-shot performance on other vision-language tasks, such as visual relation detection, visual commonsense reasoning and visual question answering. We make the data and codes publicly available at https://github.com/thunlp/CPT.


Surface codes, quantum circuits, and entanglement phases

Abstract: Surface codes---leading candidates for quantum error correction (QEC)---and entanglement phases---a key notion for many-body quantum dynamics---have heretofore been unrelated. Here we establish a link between the two. We map two-dimensional (2D) surface codes under a class of incoherent or coherent errors (bit flips or uniaxial rotations) to $(1+1)\mathrm{D}$ free-fermion quantum circuits via Ising models. We show that the error-correcting phase implies a topologically nontrivial area law for the circuit's 1D long-time state $|{\mathrm{\ensuremath{\Psi}}}_{\ensuremath{\infty}}\ensuremath{\rangle}$. Above the error threshold, we find a topologically trivial area law for incoherent errors and logarithmic entanglement in the coherent case. In establishing our results, we formulate 1D parent Hamiltonians for $|{\mathrm{\ensuremath{\Psi}}}_{\ensuremath{\infty}}\ensuremath{\rangle}$ via linking Ising models and 2D scattering networks, the latter displaying respective insulating and metallic phases and setting the 1D fermion gap and topology via their localization length and topological invariant. We expect our results to generalize to a duality between the error-correcting phase of ($d+1$)D topological codes and $d$-dimensional area laws; this can facilitate assessing code performance under various errors. The approach of combining Ising models, scattering networks, and parent Hamiltonians can be generalized to other fermionic circuits and may be of independent interest.


Securing Cloud Infrastructure in IaaS and PaaS Environments

Abstract: Cloud computing has revolutionized IT infrastructure deployment and management, but it also presents security and resilience challenges. The study delves into the principles and strategies of cloud security to safeguard cloud environments and guarantee business continuity. It explains the concepts of infrastructure as a service (IaaS) and platform as a service (PaaS), their benefits and challenges, and the complex web of security principles within the cloud, including the shared responsibility model, best practices, and identity and access management. The guide explores cloud threats, focusing on common threats and emerging trends. It covers data security, network security measures, and security monitoring. It emphasizes integrating security into DevOps, securing CI/CD pipelines, and infrastructure as code (IaC) security. It covers disaster recovery, business continuity, cloud backup strategies, high availability, and cloud-based solutions, enabling organizations to effectively manage cloud security and resilience.


Improved artificial potential field method for mobile robot path planning

Abstract: Path planning has already been used in areas like robots and unmanned vehicles to prevent collisions in certain environments. A Path planning algorithm is needed to achieve such tasks and Artificial Potential Field (APF) method is one of the methods. However, APF has limitations facing various situations like being stuck in a local minimum such as a dead-end or a narrow path. To solve the problem, First, a side force is added to the algorithm along with two types of definitions of the force direction. Then a variable is proposed to prevent the dead-end situation. Finally, the variable step size is used to improve the efficiency of the algorithm. The simulation results demonstrate the effectiveness of the method. In comparison to APF, the improved APF could prevent the local minimum and reach the target position with fewer processing steps and better performance.


Non-Uniform Illumination Underwater Image Restoration via Illumination Channel Sparsity Prior

Abstract: Underwater image quality is seriously degraded due to the insufficient light in water. Although artificial illumination can assist imaging, it often brings non-uniform illumination phenomenon. To this end, we develop an illumination channel sparsity prior (ICSP) guided variational framework for non-uniform Illumination underwater image restoration. Technically, the illumination channel sparsity prior is built on the observation that the illumination channel of a uniform-light underwater image in HSI color space contains few pixels whose intensity is very low. Then according to the Retinex theory, we design a variational model with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm term, constraint term, and gradient term, by integrating the proposed ICSP into an extended underwater image formation model. Such three regularizations are effective in enhancing the brightness, correcting color distortion, and revealing structures and fine-scale details. Meanwhile, we exploit a fast numerical algorithm on the base of the alternating direction method of multipliers (ADMM) to accelerate solving this optimization problem. We also collect a benchmark dataset, namely NUID that contains 925 real underwater images of different non-uniform illumination. Extensive experiments demonstrate that our proposed method is effective in terms of quantitative and qualitative comparisons, ablation studies, convergence analysis, and applications. The code and dataset are available at https://github.com/Hou-Guojia/ICSP.


A Constrained Competitive Swarm Optimizer With an SVM-Based Surrogate Model for Feature Selection

Abstract: Feature selection (FS) is an important data preprocessing technique that selects a small subset of relevant features to improve learning performance. However, it is also challenging due to its large search space. Recently, a competitive swarm optimizer (CSO) has shown promising results in FS because of its potential global search ability. The main idea of CSO is to select two solutions randomly and then let the loser (worse fitness) learn from the winner (better fitness). Although such a search mechanism provides a high population diversity, it is at risk of generating unqualified solutions since the winner’s quality is not guaranteed. In this work, we propose a constrained evolutionary mechanism for CSO, which verifies the quality of all the particles and lets the infeasible (unqualified) solutions learn from the feasible (qualified) ones. We also propose a novel local search and a size-change operator that guide the population to search for smaller feature subsets with similar or better classification performance. A surrogate model, based on support vector machines, is proposed to assist both local search and the size-change operator to explore a massive number of potential feature subsets without requiring excessive computational resource. Results on 24 real-world datasets show that the proposed algorithm can select smaller feature subsets with higher classification performance than state-of-the-art evolutionary computation (EC) and non-EC benchmark algorithms.


Exploration of challenges to deployment of blockchain in small construction projects

Abstract: Blockchain technology provides a decentralized way of handling the data and supports the construction project's sustainability and performance. Small construction projects in Malaysia always need help adopting new technology like blockchain. This research aimed to investigate the challenges affecting the implementation of blockchain technology in small construction projects in Malaysia. The study examined Malaysian minor construction project blockchain implementation issues. A total of 17 challenges were found significant in affecting the implementation of Blockchain technology in small construction projects in Malaysia. Five constructs were seen: technological barriers, work environment barriers, economic and planning barriers, operational barriers, and privacy and regulation barriers. Structural equation modeling and these structures create a blockchain implementation model for modest building projects. The model's validity, dependability, and forecast accuracy for all obstacles and their potential influence on blockchain deployment in small construction projects in Malaysia are substantial. The model also has good convergent and discriminant validity, supporting it. Blockchain use in Malaysian small building projects could be improved mainly through economic and planning issues. The present study needs to highlight the Malaysian small construction sector's essential obstacles in integrating blocks and technology that are the basis for more creative technologies. This research will help the Malaysian construction sector concentrate on improvements and assess the effect of blockchain on modest projects.


Perceived conversational ability of task-based chatbots – Which conversational elements influence the success of text-based dialogues?

Abstract: The use of text-based chatbots offering individual support to customers has increased steadily in recent years. However, thus far, research has focused on comparing text-based chatbots with either each other or with humans, whilst the investigation of task-based dialogues has been scarce. This paper aims to identify the characteristics of dialogues – that is, conversational elements – that lead to a successful task-based conversation. For this purpose, the chatbot, KIM, by MAGGI Kochstudio was used. It was designed to help customers find a recipe tailored to their individual needs. In order to investigate which conversational elements contribute to successful communication between the user and the chatbot KIM, a usability study collecting 123 unstructured dialogues and a scenario-based experiment using four dialogues with 627 respondents was conducted. The quantitative analysis demonstrates that task completion is characterized by a higher perception of the chatbot's conversational ability and user satisfaction. The chatbot should propose correct recipe suggestions following a short dialogue, without the user needing to provide too much input. Based on these findings, we recommended equipping the skillset of task-based chatbots with elements that will complement their assistive qualities – for example, improved use of standard phrases, and reactions to similar domains and non-requests. Gender-specific differences in task completion should be considered.


JND-aware Two-pass Per-title Encoding Scheme for Adaptive Live Streaming

Abstract: Adaptive live video streaming applications utilize a predefined collection of bitrate-resolution pairs, known as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bitrate ladder , for simplicity and efficiency, eliminating the need for additional run-time to determine the optimal pairs during the live streaming session. These applications do not incorporate two-pass encoding methods due to increased latency. However, an optimized bitrate ladder could result in lower storage and delivery costs and improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quality of Experience (QoE). This paper presents a Just Noticeable Difference (JND)-aware constrained Variable Bitrate (cVBR) Two-pass Per-title encoding Scheme (JTPS) designed specifically for live video streaming. JTPS predicts a content- and JND-aware bitrate ladder using low-complexity features based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Discrete Cosine Transform (DCT) energy and optimizes the constant rate factor (CRF) for each representation using random forest-based models. The effectiveness of JTPS is demonstrated using the open source video encoder x265, with an average bitrate reduction of 18.80% and 32.59% for the same PSNR and VMAF, respectively, compared to the standard <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HTTP Live Streaming (HLS) bitrate ladder using Constant Bitrate (CBR) encoding. The implementation of JTPS also resulted in a 68.96% reduction in storage space and an 18.58% reduction in encoding time for a JND of six VMAF points.


When Deep Learning-Based Soft Sensors Encounter Reliability Challenges: A Practical Knowledge-Guided Adversarial Attack and Its Defense

Abstract: Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly improved sensing accuracy; however, their vulnerability to adversarial attacks affects their reliability, thus hindering their widespread application. To improve the reliability of DLSSs, in this article, we conducted a systematic investigation of the adversarial attack and defense of DLSSs. By considering the task requirements of DLSSs and the actual scenarios that attackers may encounter, a framework based on black-box attack and proactive defense was proposed to realize the adversarial attack and defense of soft sensors. The adversarial attack was implemented through the proposed knowledge-guided adversarial attack (KGAA) method. By reconstructing the optimization model and introducing the mechanism knowledge into the objective function, the KGAA method could overcome the ill-posed problem of adversarial attack optimization when attacking a regression model. Moreover, based on the KGAA, a corresponding KGAA adversarial training defense method was proposed to achieve proactive defense. The attack and defense methods were verified in terms of the thermal deformation sensing of an air preheater rotor. Compared to other attacks, the KGAA exhibited higher imperceptibility, rationality, and stability; it can thus be considered a practical attack. The implementation of KGAA adversarial training enhances the adversarial robustness of DLSSs, thus aiding the defense of DLSSs to various attacks and improving their reliability.


Privacy-Preserving TPE-Based JPEG Image Retrieval in Cloud-Assisted Internet of Things

Abstract: With the large-scale deployment of the Internet of Things, lots of images are generated and outsourced to the cloud to alleviate storage burdens. Encrypted image retrieval has been widely studied as a promising technique for balancing privacy and usability. However, existing schemes have primarily focused on retrieval usability but neglected visual usability, resulting in limitations in image management, even in scenarios where preserved visual information poses no hazard to privacy. In this paper, we are thus inspired to propose a privacy-preserving JPEG image retrieval scheme that effectively enhances retrieval efficiency and accuracy while ensuring low file expansion and excellent thumbnail-preserving accuracy for ciphertext images. Specifically, the well-designed thumbnail-preserving encryption (TPE) enables accurate thumbnail-preserving and high-quality decryption of encrypted images by integrating Huffman coding and information embedding techniques. Moreover, the proposed TPE method fully considers JPEG compression to decrease the ciphertext file expansion and gain good format compatibility. Additionally, adaptive encryption key generation is designed to minimize key storage and considerably bolster security. Also, the cloud can generate preview thumbnails for uploaded TPE-encrypted images, and then extract the Hue-Saturation-Value (HSV) and uniform Local Binary Pattern (ULBP) features from thumbnails instead of encrypted images to boost retrieval efficiency and accuracy. Experimental results show that the peak signal-to-noise ratio (PSNR) of thumbnail-preserving accuracy and decrypted image quality reaches 52dB and 61dB, respectively, the file expansion is minimally limited to 6%, and the mean average precision (mAP) of retrieval attains 64%, indicating that our scheme significantly outperforms the state-of-the-art ones.


Quadratic interpolation and a new local search approach to improve particle swarm optimization: Solar photovoltaic parameter estimation

Abstract: The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters.


An Efficient Differential Grouping Algorithm for Large-Scale Global Optimization

Abstract: Cooperative co-evolution (CC) is a practical and efficient evolutionary framework for solving large-scale global optimization problems (LSGOPs). The performance of CC depends on how variables are being grouped and can be improved through guided variable decomposition for various optimization problems. However, achieving a proper variable decomposition is computationally expensive. This paper proposes an effective yet efficient differential grouping (EDG) method to reduce the associated computational cost. Our method exploits the historical interrelationship information of previous variable groups to examine interactions between the remnant variable groups. This allows us to spend less computing resources without compromising the accuracy of the final grouping result. Our proposal utilizes the covariance matrix adaptation evolution strategy (CMA-ES) algorithm, in conjunction with EDG, to solve LSGOPs. Further, to reduce time complexity and improve the stability of CMA-ES, we substitute the complex matrix decomposition step with simpler matrix operations to compute the square root of the covariance matrix. Results from our experiments and analysis indicate that EDG is a competitive method to solve LSGOPs and improve the performance of CC. The proposed schemes significantly enhance the searchability of CMA-ES compared to the other large-scale variants of CMA-ES and state-of-the-art large-scale optimizers. Moreover, our EDG could be integrated with evolutionary optimizers of different flavors like Differential Evolution (DE).


A Similarity Measurement Method of Normal Cloud Models for the Operational Status Perception and Computing of Urban Rail Transit

Abstract: The similarity measurement method is the key part of the normal cloud model as well as its applications. Too much attention has been paid on geometric and numerical features, leading to the weak interpretability and unreasonable results in the similarity measurement method of the normal cloud models. A bidirectional and weighted similarity measurement (BWSM) method is proposed by the number distribution and membership of cloud droplets on a normal cloud model. First, the cloud droplets are analyzed, and their characteristics of number distribution and membership that belong to different cloud models are obtained. Second, the similarity measurement strategy of the normal cloud models is analyzed and used to propose a BWSM method, which is compared with two commonly used similarity measurement methods of normal cloud models. The influence of expectation, entropy, and hyper entropy on the similarity measurement method of the normal cloud models is analyzed. Finally, the method is applied on the operational status perception and computing of urban rail transit. Results show the method has high rationality, validity, and applicability for perceiving and computing the operational status of urban rail transit. The method improves the interpretability of the similarity measurement of the normal cloud models and the credibility of the research based on the similarity measurement of the normal cloud models.


Multimodal Hierarchical Graph Collaborative Filtering for Multimedia-Based Recommendation

Abstract: Multimedia-based recommendation (MMRec) is a challenging task, which goes beyond the collaborative filtering (CF) schema that only captures collaborative signals from interactions and explores multimodal user preference cues hidden in complex multimedia content. Despite the significant progress of current solutions for MMRec, we argue that they are limited by multimodal noise contamination. Specifically, a considerable amount of preference-irrelevant multimodal noise (e.g., the background, layout, and brightness in the image of the product) is incorporated into the representation learning of items, which contaminates the modeling of multimodal user preferences. Moreover, most of the latest researches are based on graph convolution networks (GCNs), which means that multimodal noise contamination is further amplified because noisy information is continuously propagated over the user–item interaction graph as recursive neighbor aggregations are performed. To address this problem, instead of the common MMRec paradigm which learns user preferences in an integrated manner, we propose a hierarchical framework to separately learn collaborative signals and multimodal preferences cues, thus preventing multimodal noise from flowing into collaborative signals. Then, to alleviate the noise contamination for multimodal user preference modeling, we propose to extract semantic entities from multimodal content that are more relevant to user interests, which can model semantic-level multimodal preferences and thus remove a large fraction of noise. Furthermore, we use the full multimodal features to model content-level multimodal preferences like the existing MMRec solutions, which ensures the sufficient utilization of multimodal information. Overall, we develop a novel model, multimodal hierarchical graph CF (MHGCF), which consists of three types of GCN modules tailored to capture collaborative signals, semantic-level preferences, and content-level preferences, respectively. We conduct extensive experiments to demonstrate the effectiveness of MHGCF and its components. The complete data and codes of MHGCF are available at.


TFC: Transformer Fused Convolution for Adversarial Domain Adaptation

Abstract: In unsupervised domain adaptation (UDA), a classifier is applied to the target domain without or with limited labels, when the target domain has no or few labels. Recently, inspired by their capabilities of long-distance feature dependencies, vision transformer (ViT)-based methods have been used in UDA, however, they ignore the fact that ViT lacks strength in extracting local feature details. To handle the above problems, the purpose of this article is to demonstrate how to take advantage of both convolutional operations and transformer mechanisms for adversarial UDA by using a hybrid network structure called transformer fused convolution (TFC). TFC integrates local features with global features to boost the representation capacity for UDA which can enhance the discrimination between foreground and background. Moreover, to improve the robustness of the TFC, we leverage an uncertainty penalty loss to make incorrect classes have consistently lower scores. Extensive experiments validate the significant performance gains compared to conditional adversarial domain adaptation (CDAN) on all five datasets including DomainNet ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 8.5%), VisDA-2017 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 14.9%), Office-Home ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 18.9%), Office-31 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 11.5%), and ImageCLEF-DA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 5.5%).


An Overview of Advanced Deep Graph Node Clustering

Abstract: Graph data have become increasingly important, and graph node clustering has emerged as a fundamental task in data analysis. In recent years, graph node clustering has gradually moved from traditional shallow methods to deep neural networks due to the powerful representation capabilities of deep learning. In this article, we review some representatives of the latest graph node clustering methods, which are classified into three categories depending on their principles. Extensive experiments are conducted on real-world graph datasets to evaluate the performance of these methods. Four mainstream evaluation performance metrics are used, including clustering accuracy, normalized mutual information, adjusted rand index, and F1-score. Based on the experimental results, several potential research challenges and directions in the field of deep graph node clustering are pointed out. This work is expected to facilitate researchers interested in this field to provide some insights and further promote the development of deep graph node clustering.


Ethical leadership and employee performance: The role of Islamic work ethics and knowledge sharing

Abstract: BACKGROUND: Many recent studies recommend ethical leadership as a key antecedent to improving employee performance in organizations. OBJECTIVE: Therefore, this study aims to examine how ethical leadership affects employee performance by focusing on the role of Islamic work ethics and knowledge sharing. METHODS: Based on data obtained from 196 Muslim employees who work in the three stone milling companies located in Central Java - Indonesia, this study uses a survey in the form of a questionnaire and Structural Equation Modeling (SEM) based on Amos 24.0 software to test hypotheses in the proposed research model. RESULTS: The results of this study prove that ethical leadership has a significant effect on employee performance, Islamic work ethics, and knowledge sharing. Furthermore, Islamic work ethics and knowledge sharing significantly improve employee performance. CONCLUSION: The findings in this study further confirm that Islamic work ethics and knowledge sharing play an important role in linking ethical leadership and employee performance.


A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework

Abstract: We propose a hand gesture-operated system as an AI application to relieve discomfort and restore function in hand and arm movements caused by injuries and nerve and muscle complications. The system trains patients with hand exercises, such as performing hand gestures accurately, traversing within specified bounds, and operating a hand gesture calculator. However, the system requires accurate hand gesture detection, which is impeded by background clutter and variations in illumination and in the hand's size and angle. To address this, we developed a robust hand detection module that uses a single-stage transformer deep network. The transformer network encodes global information and uses bipartite matching to reduce the frequency of spurious detections. It drives a regression head and a classification head to localize the hand gesture in a bounding box and assign it a class label. Hand keypoints are also detected to support drawing, path traversal, and calculator use. The approach is evaluated on two benchmark datasets: OUHANDS and NUS. The method yields 89.6% accuracy for OUHANDS and 100% for NUS. These results indicate that precise hand detection can support a robust system for rehabilitation through hand exercises. Our experiments confirm that the users' hand function progressively improved.


Design and Practical Implementation of a High Efficiency Two-Layer Trajectory Planning Method for AGV

Abstract: This paper proposes a two-layer trajectory optimization method for the autonomous ground vehicle (AGV). This twolayer strategy includes an efficient path planning layer and a fast trajectory planning layer. In the first layer, a novel target area adaptive rapidly exploring random tree algorithm (TAA-RRT*) is proposed to search the shortest path. This layer mainly includes a preprocessing and a sampling planning process. In the preprocessing process, the generalized voronoi diagram (GVD) is used to construct the environment information and find the initial path. Then, the sampled target area (TA) is constructed based on this initial path to provide non-uniform sampling. In the sampling planning process, the improved adaptive RRT* algorithm is used to carry out sampling planning in the TA, and the direct connection strategy (DCS) is combined to quickly locate the optimal solution. In the trajectory planning layer, combined with the constraints of the unmanned vehicle and the path constraints obtained in the first layer, the speed planning and the trajectory optimization are addressed by solving the optimal control problem (OCP). After performing a large number of experiments, the feasibility and effectiveness of the proposed method is verified.


Detecting Offensive Language Based on Graph Attention Networks and Fusion Features

Abstract: The pervasiveness of offensive language on social networks has caused adverse effects on society, such as abusive behavior online. It is urgent to detect offensive language and curb its spread. In the popular datasets, the distribution of users and tweets is imbalanced, which limits the generalization ability of the model. In addition, existing research shows that methods with community information extracted from the social graphs effectively improve the performance of offensive language detection. However, the existing models deal with social graphs independently, which seriously affects the effectiveness of detection models. In this article, we release a new dataset with users and social relationships. To encode community information, we construct the social graphs based on the user historical behavior information and social relationships. Moreover, we propose a model based on graph attention networks (GATs) and fusion features for offensive language detection (GF-OLD). Specifically, the community information is directly captured by the GAT module, and the text embeddings are taken from the last hidden layer of bidirectional encoder representation from transformer (BERT). Attention mechanisms and position encoding are used to fuse these features. Our method outperforms baselines with the F1-score of 89.94%. The results show that our model effectively learns the potential information of social graphs and text, and user historical behavior information is more suitable for user attribute in the social graphs.


An Adaptive and Dynamical Neural Network for Machine Remaining Useful Life Prediction

Abstract: Recently, many neural networks have been proposed for machine remaining useful life (RUL) prediction. However, most network architectures of existing approaches are fixed. Since the sequential information depends on the input data and distributes differently, these fixed networks which cannot be dynamically adjusted according to the input data, may not be able to capture these sequential information well, resulting sub-optimal performances. To mitigate this issue, we propose an adaptive and dynamical neural network (AdaNet), which can dynamically adjust its architecture according to the input data. Neural network is generally determined by kernel size, depth and channel size. In this paper, we aim to enable our proposed AdaNet to adjust its kernel size and channel size dynamically. Firstly, we explore to adapt the deformable convolution to time series data, which allows the convolutional kernel to change according to the feature map. With this deformable convolution, the convolutional kernels in AdaNet become adjustable, which is beneficial to fully exploit the sequential information in time series data, leading to accurate RUL prediction. Additionally, a channel selection module is devised, which can selectively activate the feature channel according to the input, further improving the performance of our AdaNet. Extensive experiments have been carried out on the C-MAPSS dataset, demonstrating that our proposed AdaNet achieves state-of-the-art performances.


Apatite trace element composition as an indicator of ore deposit types: a machine learning approach

Abstract: Abstract The diverse suite of trace elements incorporated into apatite in ore-forming systems has important applications in petrogenesis studies of mineral deposits. Trace element variations in apatite can be used to distinguish between fertile and barren environments, and thus have potential as mineral exploration tools. Such classification approaches commonly employ two-variable scatterplots of apatite trace element compositional data. While such diagrams offer accessible visualization of compositional trends, they often struggle to effectively distinguish ore deposit types because they do not employ all the high-dimensional (i.e., multi-element) information accessible from high-quality apatite trace element analysis. To address this issue, we use a supervised machine-learning-based approach (eXtreme Gradient Boosting, XGBoost) to correlate apatite compositions with ore deposit type, utilizing such high-dimensional information. We evaluated 8629 apatite trace element data from five ore deposit types (porphyry, skarn, orogenic Au, iron oxide copper gold, and iron oxide-apatite) along with unmineralized magmatic and metamorphic apatite to identify discriminating parameters for the individual deposit types, as well as for mineralized systems. According to feature selection, eight elements (Th, U, Sr, Eu, Dy, Y, Nd, and La) improve the model performance. We show that the XGBoost classifier efficiently and accurately classifies high-dimensional apatite trace element data according to the ore deposit type (overall accuracy: 94% and F1 score: 89%). Interpretation of the model using the SHAPley Additive exPlanations (SHAP) tool shows that Th, U, Eu, and Nd are the most indicative elements for classifying deposit types using apatite trace element chemistry. Our approach has broad implications for the better understanding of the sources, chemistry, and evolution of melts and hydrothermal fluids resulting in ore deposit formation.


DYnet++: A Deep Learning Based Single-Shot Phase-Measuring Deflectometry for the 3-D Measurement of Complex Free-Form Surfaces

Abstract: Deflectometry is a three-dimensional optical technique based on structured light projection for measuring specular free-form surfaces. Much research on single-shot deflectometry has been conducted to develop reliable measurement techniques, even for harsh industrial environments. But the measurement of complex surfaces with low reflectivity is still a big challenge in single-shot deflectometry since it is very tricky to obtain the phase from a poor quality single complex pattern. Inspired by recent applications of deep learning in the optical metrology field, we propose a novel single-shot deflectometry that utilizes deep learning to measure complex surfaces with low quality in just a single shot. A deformable mirror with 9 actuators was developed to generate big data for various surface shapes to train the deep learning model. We developed a deep learning network model, DYnet++, to retrieve the phase from single composite patterns even where both closed- and opened- loops are presented. We verified the feasibility of the proposed deep learning based single-shot deflectometry by comparing measurement results with those from 16 step phase-shifting method.


Towards Efficient Cross-Modal Anomaly Detection Using Triple-Adaptive Network and Bi-Quintuple Contrastive Learning

Abstract: Cross-modal anomaly detection is a relatively new and challenging research topic in machine learning field, which aims at identifying the anomalies whose patterns are disparate across different modalities. As far as we know, this topic has yet to be well studied, and existing works often suffer from the incomplete anomalous data detection and low data utilization problems. To alleviate these limitations, this paper proposes an efficient deep cross-modal anomaly detection approach via Triple-adaptive Network and Bi-quintuple Contrastive Learning (TN-BCL), which lies among the earliest attempt to detect various cross-modal anomalies within the heterogeneous multi-modal data. To be specific, a triple-adaptive network is explicitly designed to identify various anomalies, whose patterns are disparate in both single-modal scenario and cross-modal scenario. On the one hand, the top branch network is utilized to adaptively detect the attribute anomalies and part of mixed anomalies in multi-modal data samples. On the other hand, the bottom two-branch network, with shared residual blocks, is leveraged to learn the discriminative cross-modal embeddings. At the same time, an efficient bi-quintuple contrastive learning method is designed to enhance the feature correlation between the same attribute data, while maximally enlarging the feature difference between different attribute data. Besides that, the bidirectional learning scheme is employed to significantly improve the data utilization. Through the joint exploitation of the above, different kinds of anomalous samples can be well detected across different modalities. Extensive experiments show that the proposed framework outperforms the state-of-the-art competing methods, with a large improvement margin.


Hierarchical Patch Selection: An Improved Patch Sampling for No Reference Image Quality Assessment

Abstract: Quality degradation due to compression and transmission of images is a significant threat for multimedia applications. Blind image quality assessment (BIQA) is a principal technique to measure the distortion and dynamically set the optimal parameters for developing image compression standards, image restoration algorithms, etc. Insufficient training data with quality scores is a challenge for image quality assessment (IQA) tasks. The existing solutions to deep convolution neural network (DCNN) based BIQA which rely on patch-wise training, struggle to find an ideal set of patches consistent with the human visual system. To address these issues, HIerarchical paTch Selection (HITS) is proposed. HITS keeps the patches with considerable details in each quadrant according to their intensity variance scatter ratio (IVSR). IVSR identifies the best non-homogeneous patches by computing patch intensity variance (PIV). Extensive trials are conducted, and the performance reveals that the proposed approach achieves excellent performance on synthetic and authentic distortion datasets with less memory and processing power. Moreover, the proposed approach outperforms other BIQA methods that adapt patch-wise training strategies.


FDGNN: Feature-Aware Disentangled Graph Neural Network for Recommendation

Abstract: Collaborative filtering (CF) is dedicated to learning the representations of users and items based on interactive data. Regrettably, the lack of fine-grained modeling of interactive motivation makes the model less interpretable. A feasible solution is to combine the disentangling idea with the graph neural network (GNN) and capture different types of interaction relationships by using a message propagation mechanism on the graph of user–item interaction. However, this process typically relies on the disentangling of users’ hidden intents, ignoring the significance of item features to user engagement. This fact leads to the inadequate interpretability of existing models. To make up for the deficiency, this article proposes a new feature-aware disentangled GNN (FDGNN) for the recommendation. By learning the relationship between user behavior and important features of items, the model aims to achieve better recommendation performance and model interpretability. In the end, we first realize the feature partition based on mutual information and then design an attention-based graph disentangling model to realize the fine-grained disentangling of user intents. In addition, to further ensure the independence of the disentangled intents, we augment the model with disagreement regularization. Through multilayer embedding propagation, FDGNN can display a capture CF effect in feature semantics. The interpretability and efficiency of our proposed approach are demonstrated by numerous pertinent experiments.


Deep Image Feature Learning With Fuzzy Rules

Abstract: Feature extraction methods are key to many image processing tasks. At present, the most popular method is to use a deep neural network to automatically extract features through end-to-end training instead of the traditional hand-crafted feature extraction. However, the training of deep neural network relies heavily on data quality and quantity, and the network is a black-box model that has poor interpretability. Human intelligence can be leveraged here to improve the deep neural network model, where the human decision process can be integrated in feature learning and object classification to enhance robustness and interpretability. In this paper, the method Deep Image Feature Learning with Fuzzy Rules (DIFL-FR) is proposed, where human decision process is embedded in feature extraction by combining fuzzy logic rule-based modeling with deep-stacked learning strategy. The proposed method has the following distinctive characteristics. First, since the method is based on fuzzy sets and fuzzy inference, it can extract more robust features from noisy data scenes. Second, the method progressively learns the image features through a layer-by-layer approach based on fuzzy rules, so that the feature learning process can be better explained by the rules generated. Third, the learning process of the method has a high efficiency since it is only based on forward propagation without back propagation and iterative learning. Finally, while the method is based on unsupervised learning, it can be easily extended to supervised and semi-supervised learning cases. The results of extensive experiments conducted on image datasets of different scales clearly show the effectiveness of the proposed method.


Self-Supervised Deep Clustering Method for Detecting Abnormal Data of Wastewater Treatment Process

Abstract: In wastewater treatment process (WWTP), abnormal data seriously reduce data quality rendering the application techniques impractical. The implementation of abnormal data detection is challenging due to the nonlinear nature of WWTP. Typically, constructing an accurate anomaly detector requires large amounts of labeled data, which is difficult in practice. Thus, a self-supervised memory enhanced deep clustering method (SMEL) is proposed to detect abnormal data without using any labels. First, a self-supervised deep clustering network, combining stacked autoencoders and the clustering algorithm, leverages unlabeled data to extract nonlinear features and capture the normal pattern. Second, an adaptive weight objective function, jointly optimizing the reconstruct error and clustering error, is designed to obtain a robust clustering structure. Third, double memory enhanced modules, consisting of a centroid memory and a score memory, are presented to enhance training stability and detection accuracy. Finally, experiments on three WWTP datasets show that SMEL achieves the highest detection accuracy.


Naive Feature Selection: A Nearly Tight Convex Relaxation for Sparse Naive Bayes

Abstract: Because of its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very large-scale settings. We propose a sparse version of naive Bayes, which can be used for feature selection. This leads to a combinatorial maximum-likelihood problem, for which we provide an exact solution in the case of binary data, or a bound in the multinomial case. We prove that our convex relaxation bounds become tight as the marginal contribution of additional features decreases using a priori duality gap bounds derived from the Shapley–Folkman theorem. We show how to produce primal solutions satisfying these bounds. Both binary and multinomial sparse models are solvable in time almost linear in problem size, representing a very small extra relative cost compared with the classical naive Bayes. Numerical experiments on text data show that the naive Bayes feature selection method is as statistically effective as state-of-the-art feature selection methods such as recursive feature elimination, l 1 -penalized logistic regression, and LASSO while being orders of magnitude faster (a python implementation can be found at https://github.com/aspremon/NaiveFeatureSelection ). Funding: A. d’Aspremont acknowledges support from the Fonds AXA Pour la Recherche and Kamet Ventures [Machine Learning and Optimisation Joint Research Initiative] and a Google-focused award as well as funding from Agence Nationale de la Recherche [Grant ANR-19-P3IA-0001]. L. El Ghaoui acknowledges support from Berkeley Artificial Intelligence Research and the Tsinghua–Berkeley–Shenzhen Institute.


Masked Siamese Prompt Tuning for Few-Shot Natural Language Understanding

Abstract: Recently, prompt-based learning has shown excellent performance on few-shot scenarios. Using frozen language models to tune trainable continuous prompt embeddings has become a popular and powerful methodology. For few-shot natural language understanding, even if we freeze the parameters of the pre-trained language model, the learned pseudo-prompts might still be overfitted. In this paper, we propose a novel masked siamese prompt tuning (MSP-tuning) to improve few-shot natural language understanding. Concretely, MSP-tuning masks randomly out part of the prompt tokens to get a pair of masked siamese prompts for each sample. Each training sample is then fed to the model twice with the masked siamese prompts. Finally, MSP-tuning minimizes the JS-divergence between the two output probability distributions of the pre-trained language model to regularize the model further. Experiment results on the few-shot GLUE benchmark and SuperGLUE benchmark show that MSP-tuning outperforms previous approaches. Numerically, our MSP-tuning achieves an average improvement of 1.79% (BERT-base) and 1.39% (BERT-large) of the GLUE benchmark and 1.90% (RoBERTa-base) and 1.71% (RoBERTa-large) of the SuperGLUE benchmark compared to state-of-the-art method P-tuning. Our method facilitates applying large pre-trained language models in natural language understanding.


Integrating Blockchain and Deep Learning Into Extremely Resource-Constrained IoT: An Energy-Saving Zero-Knowledge PoL Approach

Abstract: The convergence of blockchain and deep learning (DL) drives the intelligence of the Internet of Things (IoT) with security guarantees. However, the soaring resource consumption resulting from blockchain mining and DL model training has overwhelmed the extremely resource-constrained IoT. In this paper, we first build a blockchain and DL-empowered cloud-edge orchestrated framework for an extremely resource-constrained IoT environment. To solve the resource bottleneck of this framework, we then propose a Zero-Knowledge Proof of Learning (ZPoL) consensus approach to channel the meaningless Proof of Work (PoW) mining energy waste to valuable DL model training, while protecting the DL model privacy. Besides, to encourage resource-constrained IoT devices to perform meaningful DL model mining in our ZPoL consensus, we design a model quality-aware incentive mechanism based on a two-stage Stackelberg game. Moreover, we conduct extensive simulations and experiments to evaluate our proposed ZPoL-based framework. The numerical simulation illustrates our proposed incentive mechanism could motivate IoT devices to actively join in DL model mining. Compared with the existing blockchain and DL-enabled IoT system, experimental results demonstrate our proposed ZPoL-based framework could significantly reduce the communication, computation, and storage cost, which is more applicable to a resource-constrained IoT environment.


Secure Video Offloading in MEC-Enabled IIoT Networks: A Multicell Federated Deep Reinforcement Learning Approach

Abstract: Wireless video offloading in mobile-edge-computing (MEC)-enabled Industrial Internet of Things imposes a risk of exposing users' private data to eavesdroppers. It is difficult for existing secure video offloading schemes to simultaneously guarantee security, reduce latency and energy consumption in privacy-sensitive multi-cell scenarios where users are unwilling to offload data to other cells. In this paper, a secure video offloading scheme based on multi-cell federated deep reinforcement learning (DRL) is proposed to facilitate a secure, real-time and efficient MEC network by efficient orchestration of limited resources. We formulate a collaborative optimization problem of video frame resolution and resources to minimize latency and energy consumption while maximizing the security rate subject to analytic accuracy and limited resources. To solve the formulated NP-hard problem, a multi-cell federated DRL algorithm based on the frameworks of multi-cell horizontal federated learning (FL) and hierarchical reward function-based twin delayed deep deterministic policy gradient (TD3) is proposed. First of all, hierarchical reward function-based TD3 is employed to solve the collaborative optimization NP-hard problem formulated for each single cell, where the optimal solution can be efficiently approached by the agent under the guidance of the innovatively designed hierarchical reward function. Then, multi-cell horizontal FL is applied on TD3 to obtain a model with higher model quality by averagely aggregating multiple individual TD3 models. Simulation results reveal that the proposed algorithm outperforms comparison algorithms in terms of utility, cost, latency, energy consumption and security rate.


Dynamic Spatial Focus for Efficient Compressed Video Action Recognition

Abstract: Recent years have witnessed a growing interest in compressed video action recognition due to the rapid growth of online videos. It remarkably reduces the storage by replacing raw videos with sparsely sampled RGB frames and other compressed motion cues (motion vectors and residuals). However, existing compressed video action recognition methods face two main issues: First, the inefficiency caused by the usage of coarse-level information under full resolution, and second, the disturbing due to the noisy dynamics in motion vectors. To address the two issues, this paper proposes a dynamic spatial focus method for efficient compressed video action recognition (CoViFocus). Specifically, we first use a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors. Then the selected patch pair will be processed by a high-capacity two-stream deep model for the final prediction. Such a patch selection strategy crops out the irrelevant motion noise in motion vectors, as well as reduces the spatial redundancy of the inputs, leading to the high efficiency of our method in the compressed domain. Moreover, we found that the motion vectors can help our method to address the possibly happened <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static-issue</i> , which means that the focus patches get stuck at some regions related to static objects rather than target actions, which further improves our method. Extensive results on both the HMDB-51 and UCF-101 datasets demonstrate the effectiveness and efficiency of our method in compressed video action recognition tasks.


Semantic Linear Genetic Programming for Symbolic Regression

Abstract: Symbolic regression (SR) is an important problem with many applications, such as automatic programming tasks and data mining. Genetic programming (GP) is a commonly used technique for SR. In the past decade, a branch of GP that utilizes the program behavior to guide the search, called semantic GP (SGP), has achieved great success in solving SR problems. However, existing SGP methods only focus on the tree-based chromosome representation and usually encounter the bloat issue and unsatisfactory generalization ability. To address these issues, we propose a new semantic linear GP (SLGP) algorithm. In SLGP, we design a new chromosome representation to encode the programs and semantic information in a linear fashion. To utilize the semantic information more effectively, we further propose a novel semantic genetic operator, namely, mutate-and-divide propagation, to recursively propagate the semantic error within the linear program. The empirical results show that the proposed method has better training and test errors than the state-of-the-art algorithms in solving SR problems and can achieve a much smaller program size.


Dual-Level Knowledge Distillation via Knowledge Alignment and Correlation

Abstract: Knowledge distillation (KD) has become a widely used technique for model compression and knowledge transfer. We find that the standard KD method performs the knowledge alignment on an individual sample indirectly via class prototypes and neglects the structural knowledge between different samples, namely, knowledge correlation. Although recent contrastive learning-based distillation methods can be decomposed into knowledge alignment and correlation, their correlation objectives undesirably push apart representations of samples from the same class, leading to inferior distillation results. To improve the distillation performance, in this work, we propose a novel knowledge correlation objective and introduce the dual-level knowledge distillation (DLKD), which explicitly combines knowledge alignment and correlation together instead of using one single contrastive objective. We show that both knowledge alignment and correlation are necessary to improve the distillation performance. In particular, knowledge correlation can serve as an effective regularization to learn generalized representations. The proposed DLKD is task-agnostic and model-agnostic, and enables effective knowledge transfer from supervised or self-supervised pretrained teachers to students. Experiments show that DLKD outperforms other state-of-the-art methods on a large number of experimental settings including: 1) pretraining strategies; 2) network architectures; 3) datasets; and 4) tasks.


IoV environment exploring coordination: A federated learning approach

Abstract: Exploring open fields with coordinated unmanned vehicles is popular in academia and industries, and one of the most impressive applicable approaches is the Internet of Vehicles (IoV). The IoV connects vehicles, road infrastructures, and communication facilities to provide sustainable solutions for task explorations. However, the coordination of acquiring information from others may risk data privacy. To this end, sharing high-quality experiences instead of raw data has become an urgent demand. This paper employs a Deep Reinforcement Learning (DRL) method to let IoV generate training data with prioritized experience and states, which can support the IoV to explore the environment more efficiently. Moreover, a Federated Learning (FL) experience sharing model is established to guarantee the vehicles' privacy. The numerical results show that the proposed method presents a better successful sharing rate and a more stable convergence within the comparison of fundamental methods. The experiments also suggest that the proposed method could support agents with no explicit information to achieve the tasks.


FedGL: Federated graph learning framework with global self-supervision

Abstract: Graph data are ubiquitous in the real world. Graph learning (GL) attempts to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are designed for centralized scenarios. However, in practical scenarios, graph data are usually distributed across different organizations, resulting in isolated data silos. To address this problem, we incorporate federated learning into GL and propose a general Federated Graph Learning framework called FedGL. FedGL is capable of obtaining a high-quality global graph model while protecting data privacy by discovering the global self-supervision information during the federated training. Specifically, we propose uploading prediction results and node embeddings to the server for discovering the global pseudo labels and global pseudo graph. These are then distributed to each client to enrich the training labels and complement the graph structure respectively, thereby improving the quality of each local model. Moreover, the global self-supervision enables information of each client to flow and be shared in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients. Finally, experimental results show that FedGL significantly outperforms baselines on four widely used graph datasets.


Kernel-Based Sparse Representation Learning With Global and Local Low-Rank Label Constraint

Abstract: Due to the large-scale and multiscale natures of social media data, sparse representation (SR) learning methods are widely followed. However, there are three problems associated with the existing SR methods: 1) they neglect the fact that the semantic features of data may change during iterative learning, which leads to weak semantic learning; 2) they often assume that the data are linearly separable, while the data might be nonlinear in many real-world applications; and 3) they cannot ensure the low-rank and discriminative properties of the data at the same time and might neglect the global properties of the data, leading to suboptimal solutions. To solve these problems, we propose a novel method, named kernel-based SR learning with global and local low-rank label (KSR-GL <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^3$</tex-math> </inline-formula> ) constraint, which strengthens the semantic information and ensures the semantic features invariant during learning. First, we map the data into a high-dimensional feature space to learn the linear representation of samples. Second, global and local low-rank label (GL <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^3$</tex-math> </inline-formula> ) constraint is used to ensure the semantic invariance, low-rankness, and discrimination of features during learning. Third, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_{2,1}$</tex-math> </inline-formula> is imposed to explore the sparseness of the subspace. Mathematical analyses show that GL <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^3$</tex-math> </inline-formula> can retain the intrinsic properties of data during learning. By combining the above three components, a generalized power iteration (GPI) approach is applied to build the model and deal with the tricky optimization problem. By KSR-GL <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ ^3$</tex-math> </inline-formula> , a sparse, low-rank, and discriminative subspace is produced from the high-dimensional and orthogonal representation of the data under the guidance of semantics, while the intrinsic properties of data are preserved. Extensive experiments on six datasets compared with five advanced algorithms demonstrate its promising prospects.


Anti-Drugs Chatbot: Chinese BERT-Based Cognitive Intent Analysis

Abstract: Drug abuse has always been a severe issue, but the proportion of drug abuse and addiction is rising. According to research reports, youth are motivated to access drugs mainly due to curiosity and peer influence. Additionally, youth especially lack proper knowledge and education surrounding drug abuse. Analyzing whether potential addicts intend to access drugs is helpful in preventing drug abuse and addiction. We developed an Anti-drug Chatbot for young people on a popular online social platform. We can detect potential risks, obtain warnings from the user-entered query and provide these to professional consultants for help. In this article, we present a hierarchical system with bidirectional encoder representation from transformers (BERT) to efficiently recognize and classify a user’s intent. We use the Chinese BERT-based model to utilize contextual information to perform classification and recognition. We evaluate our proposed system on our conversational dataset.


A Heterogeneous Graph Neural Network With Attribute Enhancement and Structure-Aware Attention

Abstract: Heterogeneous information network (HIN) has been applied in a wide variety of graph analysis tasks. At present, it is a trend of heterogeneous graph neural networks (HGNNs) to cast the meta-paths aside, since it solves the problem of structural information loss caused by artificially designed meta-paths. However, existing meta-path-free HGNNs fail to take into account that most node types in many HINs have no attributes, and they cannot make full use of sparse node attributes when applied to HINs with missing attributes. Furthermore, their computation of attention coefficients explores the correlations of node attributes while almost ignoring structural ones, which may limit the expression ability of the model and cause overfitting in model training. To alleviate these issues, we propose an HGNN with attribute enhancement and structure-aware attention (HGNN-AESA). First, we design an attribute enhancement module (AEM) to connect more useful attributed nodes to the target nodes. Specifically, AEM introduces a random walk with restart (RWR) strategy to obtain structural relevance scores of each node within its specific subgraph. The structural relevance scores are used to capture potentially influential attributed nodes in high-order neighborhood for each target node. Second, we propose heterogeneous structure-aware attention layers (HSALs) to learn node representations. HSALs follow a hierarchical attention framework, including node-level and type-level attention. The node-level attention aggregates feature (attribute) embeddings of same-type neighbors, and the relevant attention coefficients depend on the combination of node attributes and heterogeneous structural interventions. The type-level attention fuses all type-specific vector representations and generates the ultimate node embedding. Finally, extensive experiments on three different real-world HIN datasets demonstrate that our model outperforms state-of-the-art methods.


Circuit Implementation of Proximal Projection Neural Networks for Composite Optimization Problems

Abstract: Existing circuits for composite optimization problems tend to ignore the structure of nonsmooth objectives and lead to less practicability. To this end, with a proximal projection neural network (PPNN) and an inertial proximal projection neural network (IPPNN), this paper presents two novel analog circuit frameworks in presence of a nonsmooth term, where the actions of neuron are simulated via a feedback loop formed by integrator, gradient estimate, proximal operator and other basic operation models. In our circiuts, it shows that the stable output voltages are mapped to optimal solutions. Considering the nonsmooth term as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{l}_{1}}$</tex-math></inline-formula> -norm, we further design an effective analog circuit of soft thresholding operator. Theoretically, it is proved that both PPNN-based and IPPNN-based analog circuit frameworks have stable output voltages in the sense that the value of electronic components meets a certain mild proportion. Finally, two examples are simulated on Multisim 14.0 platform to validate the effectiveness and practicability of the proposed analog circuits.


Blockchain and Federated Reinforcement Learning for Vehicle-to-Everything Energy Trading in Smart Grids

Abstract: The proliferation of electric vehicles (EVs) and the advancement of Vehicle-to-Everything (V2X) energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of EV users. This paper proposes a novel Federated Reinforcement Learning (FRL) system combined with blockchain technology to maximize EV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of Proof of State of Charge (PoSOC) as a consensus mechanism to determine the winning EVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves EV users' utility significantly compared to the existing studies. The obtained simulation results indicate the effectiveness and robustness of the proposed system.


Model Sparsification for Communication-Efficient Multi-Party Learning via Contrastive Distillation in Image Classification

Abstract: Multi-party learning allows all parties to train a joint model under legal and practical constraints without private data transmission. Related research can perform multi-party learning tasks on homogeneous data through deep networks. However, due to the heterogeneity of data from different parties and the limitation of computational resources and costs, traditional approaches may affect the effectiveness of multi-party learning, and cannot provide a personalized network for each party. In addition, to reduce the computational cost and communication bandwidth of local models, there are still challenges in building an adaptive model from the private data of different parties. To address these challenges, we aim to apply a model sparsification strategy in multi-party learning. Model sparsification can not only reduce the computational overhead in local edge devices and the cost of communication and interaction between multi-party models. It can also develop privatized and personalized networks based on the heterogeneity of local data. We use the contrastive distillation method during training to reduce the distance between local and global models. In addition, we maintain the performance of the aggregation model from heterogeneous data. In brief, we developed an adaptive multi-party learning framework based on contrastive distillation, which can significantly reduce the communication cost in the learning process, improve the effectiveness of the aggregation model for local heterogeneous and unbalanced data, and make it easy to deploy in the limited edge devices. Finally, to verify the effectiveness of this framework, we experimented with the Fshion-MNIST, Cifar-10, and Cifar-100 datasets in different scenarios to verify the effectiveness of this framework.