Reviewerly/exHarmony huggingface dataset:
Vertical Federated Learning Over Cloud-RAN: Convergence Analysis and System Optimization

Vertical federated learning (FL) is a collaborative machine learning framework that enables devices to learn a global model from the feature-partition datasets without sharing local raw data. However, as the number of the local intermediate outputs is proportional to the training samples, it is critical to develop communication-efficient techniques for wireless vertical FL to support high-dimensional model aggregation with full device participation. In this paper, we propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation by leveraging over-the-air computation (AirComp) and alleviating communication straggler issue with cooperative model aggregation among geographically distributed edge servers. However, the model aggregation error caused by AirComp and quantization errors caused by the limited fronthaul capacity degrade the learning performance for vertical FL. To address these issues, we characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed. We conduct extensive simulations to demonstrate the effectiveness of the proposed system architecture and optimization framework for vertical FL.
Green, Quantized Federated Learning Over Wireless Networks: An Energy-Efficient Design

The practical deployment of federated learning (FL) over wireless networks requires balancing energy efficiency, convergence rate, and a target accuracy due to the limited available resources of devices. Prior art on FL often trains deep neural networks (DNNs) to achieve high accuracy and fast convergence using 32 bits of precision level. However, such scenarios will be impractical for resource-constrained devices since DNNs typically have high computational complexity and memory requirements. Thus, there is a need to reduce the precision level in DNNs to reduce the energy expenditure. In this paper, a green-quantized FL framework, which represents data with a finite precision level in both local training and uplink transmission, is proposed. Here, the finite precision level is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. In the considered FL model, each device trains its QNN and transmits a quantized training result to the base station. Energy models for the local training and the transmission with quantization are rigorously derived. To minimize the energy consumption and the number of communication rounds simultaneously, a multi-objective optimization problem is formulated with respect to the number of local iterations, the number of selected devices, and the precision levels for both local training and transmission while ensuring convergence under a target accuracy constraint. To solve this problem, the convergence rate of the proposed FL system is analytically derived with respect to the system control variables. Then, the Pareto boundary of the problem is characterized to provide efficient solutions using the normal boundary inspection method. Design insights on balancing the tradeoff between the two objectives while achieving a target accuracy are drawn from using the Nash bargaining solution and analyzing the derived convergence rate. Simulation results show that the proposed FL framework can reduce energy consumption until convergence by up to 70% compared to a baseline FL algorithm that represents data with full precision without damaging the convergence rate.
A Unified Object Counting Network With Object Occupation Prior

The counting task, which plays a fundamental role in numerous applications (e.g., crowd counting, traffic statistics), aims to predict the number of objects with various densities. Existing object counting tasks are designed for a single object class. However, it is inevitable to encounter newly coming data with new classes in our real world. We name this scenario as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evolving object counting</i> . In this paper, we build the first evolving object counting dataset and propose a unified object counting network as the first attempt to address this task. The proposed network consists of two key components: a class-agnostic mask module and a class-incremental module. The class-agnostic mask module learns generic object occupation prior by predicting a class-agnostic binary mask (e.g., 1 denotes there exists an object at the considering position in an image and 0 otherwise). The class-incremental module is used to handle new classes and provides discriminative class guidance for density map prediction. The combined outputs of the class-agnostic mask module and image feature extractor are used to predict the final density map. When new classes arrive, we first add new neural nodes to the last regression and classification layers of the class-incremental module. Then, instead of retraining the model from scratch, we utilize knowledge distillation to help the model retain and consolidate what it has previously learned. We also employ a support sample bank to store a small number of typical training samples for each class, which are used to prevent the model from forgetting key information from old data. With this design, our model can efficiently and effectively adapt to new classes while maintaining good performance on already-seen data without large-scale retraining. Extensive experiments on the collected dataset demonstrate favorable performance. The dataset and code will be available at: https://github.com/Tanyjiang/EOCO.
Complete coverage path planning scheme for autonomous navigation ROS-based robots

In this paper, a new Complete Coverage Path Planning (CCPP) scheme is proposed which combines path planning and dynamic tracking for robot operating system-based robots. For the path planning, firstly a sub-area division algorithm is considered to decompose the occupancy map according to the wall or obstacle position after simultaneous localization and mapping process. For each sub-area, an “S” shape path planning is employed, and then a Bidirectional A-star connects them. Additionally, the dynamic tracking ensures that the robot moves continuously. Simulation results show that the coverage ratio of the planning path is improved as 98% by the proposes scheme.
The digital economy: Challenges and opportunities in the new era of technology and electronic communications

The digital economy has emerged as an innovative economy that utilizes digital technologies and electronic communication to conduct economic and business activities across a wide range of sectors, including e-commerce, digital marketing, digital financial services, software development, computer games, and cloud services. The use of digital technologies and electronic communication has resulted in a significant shift towards online business interactions, leading to improved user experiences, faster processing, and easier access to services and products. The digital economy is recognized as a primary driver of economic growth and development in many advanced countries and is continually expanding due to its significant impact on economic and business activities. In this research, we aim to examine the digital economy and its impact on businesses and users, evaluating the opportunity for easy and quick access to products and services that lead to increased efficiency and productivity. The digital economy has significant impacts on various economic, social, and cultural fields, including changes in the way people work and interact with each other, promoting more flexible and remote work arrangements, and increasing global connectivity. Additionally, the digital economy has impacted education, healthcare, entertainment, and other sectors. Advances in technology and electronic communication have driven the global economy towards digitization, and the role of the digital economy in advancing global digitalization is critical. Its impact is expected to continue to increase in the coming years.
Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence

Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business intelligence.Recently, deep neural network-based sequential recommendation models gained considerable attention.However, existing approaches pay little attention to users' dynamically evolving interests, which are influenced by product attributes, especially product category.To overcome these challenges, we propose a dynamic personalized recommendation model: DynaPR.Specifically, we first embed product information and attribute information into a unified data space.Then, we exploit long short-term memory (LSTM) networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical LSTM networks.Finally, similarity values between users are measured through pairwise interest features, and personalized recommendation lists are generated.A series of experiments reveal the superiority of the proposed method compared with other advanced methods.
A fast density peak clustering based particle swarm optimizer for dynamic optimization

Dynamic optimization problems (DOPs) are optimization problems with time evolution characteristics. In this type of problem, the decision variables and the state variables change over time and produce results that will impact the future. To effectively address DOPs, this paper proposes a fast density peak clustering based particle swarm optimizer for dynamic optimization (DPCPSO). The main innovations of DPCPSO contain three critical components. First, a fast density peak clustering is applied to create multiple sub-populations, which can help the algorithm locate peaks. Second, stagnation detection is used to tackle the loss of diversity. Third, an optimal particle calibration strategy which can find the optimal solution quickly in a changing environment is proposed in response to environmental changes. Moreover, the hill climbing method is applied to help the memory quickly locate new peaks if the environment changes. The performance of our proposed algorithm has been tested on Mobile Peak Benchmark (MPB), Generalized Dynamic Benchmark Generator (GDBG) and Generalized Moving Peaks Benchmark (GMPB) problems and compared with seven state-of-the-art dynamic optimization algorithms. The experimental results validate the proposed algorithm performed competitively while solving DOPs.
ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness.
Role-oriented binary grey wolf optimizer using foraging-following and Lévy flight for feature selection

Feature selection can effectively define the feature subset, remove redundant, irrelevant, and noisy features. In order to adapt the feature selection problem, this paper adopts role-oriented modeling paradigm to make up for the defects of binary grey wolf optimizer such as premature, declining diversity, and insufficient convergence, then comes up with novel V-shaped linear transfer functions. Ultimately, a role-oriented binary grey wolf optimizer is proposed. The top grey wolves combine the foraging-following to enhance convergence, while the ordinary grey wolves integrate Lévy flight to avoid premature and improve diversity. The outstanding ability of role-oriented binary grey wolf optimizer in balance of exploitation and exploration is verified by benchmarks and the effectiveness in solving feature selection is demonstrated through feature selection tasks. The results of benchmark and feature selection task have comprehensively demonstrated the competitiveness of role-oriented modeling paradigm in improving metaheuristic algorithms.
Emotional expression by artificial intelligence chatbots to improve customer satisfaction: Underlying mechanism and boundary conditions

Artificial intelligence chatbots have invaded the tourism industry owing to their low cost and high efficiency. However, the influence of emotional expressions of chatbots on service outcomes has not received much attention from researchers. Drawing upon expectancy violations theory, we explored how emotional expressions of chatbots affect customer satisfaction using three experiments in the context of tourist attraction recommendations. Chatbots' expressions of concern for customers can improve customer satisfaction by reducing expectancy violations. In particular, customer's goal orientation, the human-likeness of chatbot's avatars, and the relationship type between customers and chatbots can moderate the negative relationship between emotional expression and expectancy violation. These findings advance research on the emotional expressions of chatbots and provide critical insights for deploying chatbots in customer service in the tourism industry.
Collaborative contrastive learning for hypergraph node classification

Plenty of models have been presented to handle the hypergraph node classification. However, very few of these methods consider contrastive learning, which is popular due to its great power to represent instances. This paper makes an attempt to leverage contrastive learning to hypergraph representation learning. Specifically, we propose a novel method called Collaborative Contrastive Learning (CCL), which incorporates a generated standard graph with the hypergraph. The main technical contribution here is that we develop a collaborative contrastive schema, which performs contrast between the node views obtained from the standard graph and hypergraph in each network layer, thus making the contrast collaborative. To be precise, in the first layer, the view from the standard graph is used to augment that from the hypergraph. Then, in the next layer, the augmented features are adopted to train a new representation to augment the view from the standard graph conversely. With this setting, the learning procedure is alternated between the standard graph and hypergraph. As a result, the learning on the standard graph and hypergraph is collaborative and leads to the final informative node representation. Experimental results on several widely used datasets validate the effectiveness of the proposed model.
A cross Transformer for image denoising

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://github.com/hellloxiaotian/CTNet.
LFighter: Defending against the label-flipping attack in federated learning

Federated learning (FL) provides autonomy and privacy by design to participating peers, who cooperatively build a machine learning (ML) model while keeping their private data in their devices. However, that same autonomy opens the door for malicious peers to poison the model by conducting either untargeted or targeted poisoning attacks. The label-flipping (LF) attack is a targeted poisoning attack where the attackers poison their training data by flipping the labels of some examples from one class (i.e., the source class) to another (i.e., the target class). Unfortunately, this attack is easy to perform and hard to detect, and it negatively impacts the performance of the global model. Existing defenses against LF are limited by assumptions on the distribution of the peers' data and/or do not perform well with high-dimensional models. In this paper, we deeply investigate the LF attack behavior. We find that the contradicting objectives of attackers and honest peers on the source class examples are reflected on the parameter gradients corresponding to the neurons of the source and target classes in the output layer. This makes those gradients good discriminative features for the attack detection. Accordingly, we propose LFighter, a novel defense against the LF attack that first dynamically extracts those gradients from the peers' local updates and then clusters the extracted gradients, analyzes the resulting clusters, and filters out potential bad updates before model aggregation. Extensive empirical analysis on three data sets shows the effectiveness of the proposed defense regardless of the data distribution or model dimensionality. Also, LFighter outperforms several state-of-the-art defenses by offering lower test error, higher overall accuracy, higher source class accuracy, lower attack success rate, and higher stability of the source class accuracy. Our code and data are available for reproducibility purposes at https://github.com/NajeebJebreel/LFighter.
Metaverse for service industries: Future applications, opportunities, challenges and research directions

Although the metaverse is still in the early stages of development and implementation, it has the potential to revolutionize the way how businesses can interact with customers through both the virtual and real world. In particular, service industries are already exploring the opportunity to utilize the metaverse to provide more immersive, interactive and engaging customer experiences. However, the holistic overview of the future applications, opportunities, and challenges of a metaverse in the context of service industries from academic and expert perspectives is limited. By employing a multi-perspective approach, this study looks into these unexplored aspects of the metaverse in the context of service industries through informed and multifaceted narratives by leading academics and experts from cross-disciplinary backgrounds from media and communication, education, hospitality, financial services, retail, tourism and healthcare. The main opportunities identified include the development of new experiences, the introduction of novel inter-world interactions, and new business-consumer relations within the metaverse. The key challenges covered include current technological boundaries, limitations of the experiences in the metaverse, health issues, and data privacy, security, and legal issues. The paper concludes with formulating future research agendas and presenting contributions to literature and implications for practice.
Big data analytics deep learning techniques and applications: A survey

Deep learning (DL), as one of the most active machine learning research fields, has achieved great success in numerous scientific and technological disciplines, including speech recognition, image classification, language processing, big data analytics, and many more. Big data analytics (BDA), where raw data is often unlabeled or uncategorized, can greatly benefit from DL because of its ability to analyze and learn from enormous amounts of unstructured data. This survey paper tackles a comprehensive overview of state-of-the-art DL techniques applied in BDA. The main target of this survey is intended to illustrate the significance of DL and its taxonomy and detail the basic techniques used in BDA. It also explains the DL techniques used in big IoT data applications as well as their various complexities and challenges. The survey presents various real-world data-intensive applications where DL techniques can be applied. In particular, it concentrates on the DL techniques in accordance with the BDA type for each application domain. Additionally, the survey examines DL benchmarked frameworks used in BDA and reviews the available benchmarked datasets, besides analyzing the strengths and limitations of each DL technique and their suitable applications. Further, a comparative analysis is also presented by comparing existing approaches to the DL methods used in BDA. Finally, the challenges of DL modeling and future directions are discussed.
Learning Flow-Based Disentanglement

Face reenactment aims to generate the talking face images of a target person given by a face image of source person. It is crucial to learn latent disentanglement to tackle such a challenging task through domain mapping between source and target images. The attributes or talking features due to domains or conditions become adjustable to generate target images from source images. This article presents an information-theoretic attribute factorization (AF) where the mixed features are disentangled for flow-based face reenactment. The latent variables with flow model are factorized into the attribute-relevant and attribute-irrelevant components without the need of the paired face images. In particular, the domain knowledge is learned to provide the condition to identify the talking attributes from real face images. The AF is guided in accordance with multiple losses for source structure, target structure, random-pair reconstruction, and sequential classification. The random-pair reconstruction loss is calculated by means of exchanging the attribute-relevant components within a sequence of face images. In addition, a new mutual information flow is constructed for disentanglement toward domain mapping, condition irrelevance, and condition relevance. The disentangled features are learned and controlled to generate image sequence with meaningful interpretation. Experiments on mouth reenactment illustrate the merit of individual and hybrid models for conditional generation and mapping based on the informative AF.