Reviewerly/exHarmony huggingface dataset:
Joint Participation Incentive and Network Pricing Design for Federated Learning

Federated learning protects users’ data privacy though sharing users’ local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users’ contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users’ private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server’s cost by up to 24.87% comparing with the state-of-the-art benchmarks.
OmniSense: Towards Edge-Assisted Online Analytics for 360-Degree Videos

With the reduced hardware costs of omnidirectional cameras and the proliferation of various extended reality applications, more and more 360° videos are being captured. To fully unleash their potential, advanced video analytics is expected to extract actionable insights and situational knowledge without blind spots from the videos. In this paper, we present OmniSense, a novel edge-assisted framework for online immersive video analytics. OmniSense achieves both low latency and high accuracy, combating the significant computation and network resource challenges of analyzing 360° videos. Motivated by our measurement insights into 360° videos, OmniSense introduces a lightweight spherical region of interest (SRoI) prediction algorithm to prune redundant information in 360° frames. Incorporating the video content and network dynamics, it then smartly scales vision models to analyze the predicted SRoIs with optimized resource utilization. We implement a prototype of OmniSense with commodity devices and evaluate it on diverse real-world collected 360° videos. Extensive evaluation results show that compared to resource-agnostic baselines, it improves the accuracy by 19.8% – 114.6% with similar end-to-end latencies. Meanwhile, it hits 2.0× – 2.4× speedups while keeping the accuracy on par with the highest accuracy of baselines.
Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence

The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks (SHNs). To enhance the precision of diagnosis, different participants in SHNs share health data that contain sensitive information. Therefore, the data exchange process raises privacy concerns, especially when the integration of health data from multiple sources (linkage attack) results in further leakage. Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining. Furthermore, adversaries launch poisoning attacks to falsify the health data, which leads to misdiagnosing or even physical damage. To protect private health data, we propose a personalized differential privacy model based on the trust levels among users. The trust is evaluated by a defined community density, while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy. To avoid linkage attacks in personalized differential privacy, we design a noise correlation decoupling mechanism using a Markov stochastic process. In addition, we build the community model on a blockchain, which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs. Extensive experiments and analysis on real-world datasets have testified the proposed model, and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.
Past Impact and Future Implications of the World Wide Web on the Accounting Industry

With the initial development of Web 1.0 in 1989, the groundwork was laid out for radical and rapid transformation of an accounting industry which had been slowly evolving over thousands of years. In the late 1990s, with Web 2.0, simple websites and email were eclipsed by innovative accounting software and collaborative online tools. Accountants have taken advantage of these features as many of the lower-level functions are now replaced by value-adding analytics and ongoing communication. The newest version of the World Wide Web, Web 3.0, strives to progress business further with decentralized, blockchain-based applications. Distributed ledger technology, securely storing data in multiple locations with immutable transaction records, is increasing reliability of accounting functions. With smart contracts, business agreements can be executed via blockchain, adding another layer of security. However, the accounting industry is not without challenges as technology advances- it must stand with its ethical principles as the business world around it evolves with Web 3.0.
The Future of Agile Coaches: Do Large Companies Need a Standardized Agile Coach Certification and What Are the Alternatives?

Agility has become a necessary mindset for companies facing new challenges, and the role of the agile coach is becoming increasingly important in the agile transformation. But how can companies train and find the right agile coaches? Two decades after the introduction of agile, this study employs a survey of DACH30 companies to examine how modern organizations can identify and access the most suitable agile coaches for their business. The research investigates the present state of agile coach training and offers insights from the agile community how companies could get more agile by adapting the training concept. This study contributes to the practice of large corporations by recommending to adapt the experience of the DACH30 agile community, which suggests that external agile industry certifications in an isolated manner are not an effective approach to fostering a strong agile culture within an organization. Furthermore, this research advances current knowledge by proposing that a new phase in agile education has been reached, and industries using agile methodologies must explore alternative approaches to attain the next level of agility.
Hybrid Recommendation System with Graph Neural Collaborative Filtering and Local Self-attention Mechanism

As the problem of information overload becomes increasingly serious, traditional recommendation algorithms are difficult to efficiently provide assistance to users. In this context, more and more scholars have applied deep learning methods to recommendation systems, such as deep reinforcement learning, self-attention mechanisms, graph neural collaborative filtering and so on. Experimental results have shown that these methods significantly improve the performance of systems. However, most studies directly apply existing models to recommendation algorithms, wasting a lot of potential features. In recent years, the latest studies have optimized model performance through method fusion. In this paper, we propose a hybrid model NGCF-A that combines graph neural collaborative filtering and self-attention mechanisms. In a system based on graph neural collaborative filtering, local self-attention layers are added to embedding propagation layer, so that we can capturing high level associations between users and items. In addition, we add an implicit feedback transformation method and a dataset splitting strategy that conforms to change in interest, aiming to make better use of features. The experimental results show that this model achieves better performance than traditional graph neural collaborative filtering model, and the new implicit feedback transformation method further optimizes the model.
Elastic Optimization for Stragglers in Edge Federated Learning

To fully exploit enormous data generated by intelligent devices in edge computing, edge federated learning (EFL) is envisioned as a promising solution. The distributed collaborative training in EFL deals with delay and privacy issues compared to traditional centralized model training. However, the existence of straggling devices, responding slow to servers, degrades model performance. We consider data heterogeneity from two aspects: high dimensional data generated at edge devices where the number of features is greater than that of observations and the heterogeneity caused by partial device participation. With large number of features, computation overhead on the devices increases, causing edge devices to become stragglers. And incorporation of partial training results causes gradients to be diverged which further exaggerates when more training is performed to reach local optima. In this paper, we introduce elastic optimization methods for stragglers due to data heterogeneity in edge federated learning. Specifically, we define the problem of stragglers in EFL. Then, we formulate an optimization problem to be solved at edge devices. We customize a benchmark algorithm, FedAvg, to obtain a new elastic optimization algorithm (FedEN) which is applied in local training of edge devices. FedEN mitigates stragglers by having a balance between lasso and ridge penalization thereby generating sparse model updates and enforcing parameters as close as to local optima. We have evaluated the proposed model on MNIST and CIFAR-10 datasets. Simulated experiments demonstrate that our approach improves run time training performance by achieving average accuracy with less communication rounds. The results confirm the improved performance of our approach over benchmark algorithms.
Communication-Efficient Federated Learning for Resource-Constrained Edge Devices

Federated learning (FL) is an emerging paradigm to train a global deep neural network (DNN) model by collaborative clients that store their private data locally through the coordination of a central server. A major challenge is a high communication overhead during the training stage, especially when the clients are edge devices that are linked wirelessly to the central server. In this paper, we propose efficient techniques to reduce the communication overhead of FL from three perspectives. First, to reduce the amount of data being exchanged between clients and the central server, we propose employing low-rank tensor models to represent neural networks to substantially reduce the model parameter size, leading to significant reductions in both computational complexity and communication overhead. Then, we consider two edge scenarios and propose the corresponding FL schemes over wireless channels. The first scenario is that the edge devices barely have sufficient computing and communication capabilities, and we propose a lattice-coded over-the-air computation scheme for the clients to transmit their local model parameters to the server. Compared with the traditional repetition transmission, this scheme significantly reduces the distortion. The second scenario is that the edge devices have very limited computing and communication power, and we propose natural gradient-based FL, that involves forward pass only, and each client transmits only one scalar to the server at each training iteration. Numerical results on the MNIST data set and the CIFAR-10 data set are provided to demonstrate the effectiveness of the proposed communication-efficient FL techniques, in that they significantly reduce the communication overhead while maintaining high learning performance.
Smart Contracts and Web 3

This chapter delves into the burgeoning world of smart contracts and their critical role in powering decentralized autonomous organizations (DAOs). Beginning with an exploration of the logical foundations of smart contracts, it underscores the parallels between smart contract creation and traditional contract formulation, with a keen emphasis on the concept of conditional execution. It further elucidates the integral role of smart contracts in underpinning DAOs, illuminating how these automated, transparent, and immutable constructs facilitate the decentralization and autonomy of such organizations. A rigorous examination of the complex interplay between technology, law, and economy in DAO operations is also undertaken. The chapter additionally navigates the challenging regulatory landscape, drawing on international legal frameworks and proposed regulations. Throughout, it maintains a balanced perspective on the transformative potential of smart contracts and DAOs in reshaping societal structures, alongside the profound challenges their implementation presents.
CYBERENG - Training Cybersecurity Engineer and Manager Skills in Automotive - Experience

As cybersecurity becomes an integral part of car homologation, the importance of cybersecurity skills in automotive project development teams becomes crucial. It is not just about the experts in automotive security themselves, but also about the whole system development team that needs to have the skills to be able to understand, cope with and make a cybersecurity integral part of the system. In this paper, we are giving an overview and experience of the content of training, the skill sets defined as the main needed skills/competence and knowledge of the automotive cybersecurity managers and engineers and present the pilot course implementation experience.
Collaborative Contrastive Refining for Weakly Supervised Person Search

Weakly supervised person search involves training a model with only bounding box annotations, without human-annotated identities. Clustering algorithms are commonly used to assign pseudo-labels to facilitate this task. However, inaccurate pseudo-labels and imbalanced identity distributions can result in severe label and sample noise. In this work, we propose a novel Collaborative Contrastive Refining (CCR) weakly-supervised framework for person search that jointly refines pseudo-labels and the sample-learning process with different contrastive strategies. Specifically, we adopt a hybrid contrastive strategy that leverages both visual and context clues to refine pseudo-labels, and leverage the sample-mining and noise-contrastive strategy to reduce the negative impact of imbalanced distributions by distinguishing positive samples and noise samples. Our method brings two main advantages: 1) it facilitates better clustering results for refining pseudo-labels by exploring the hybrid similarity; 2) it is better at distinguishing query samples and noise samples for refining the sample-learning process. Extensive experiments demonstrate the superiority of our approach over the state-of-the-art weakly supervised methods by a large margin (more than 3% mAP on CUHK-SYSU). Moreover, by leveraging more diverse unlabeled data, our method achieves comparable or even better performance than the state-of-the-art supervised methods.
Fractional decoding of r-Hermitian codes

In this paper, we present a fractional decoding algorithm for a new family of codes which are constructed from the Hermitian curve, called r-Hermitian codes. These codes of length n are defined over an extension field Fq2l of Fq2 and the fractional decoding algorithms that we present are algorithms for error correction that use only αln symbols of a subfield of size q2 as input into the decoding algorithm, where α<1, meaning a fraction of the subsymbols that are typically utilized. We demonstrate that collaborative decoding of interleaved codes supports fractional decoding of the r-Hermitian codes, allowing for improved bounds on the fractional decoding radius.
Building Blocks for the Metaverse

The term ‘metaverse,' was first coined in Neal Stephenson's 1992 novel “Snow Crash,” and has since been used to describe various types of virtual and augmented reality experiences. . The metaverse holds varying interpretations for different individuals. From its origins in science fiction literature to its recent rise to prominence in popular culture and the technology industry, the metaverse has captured the imagination of many. For some, it represents a virtual arena for socializing with friends, while others perceive it as a potential commercial arena for businesses and their clients. This chapter explores the concept of metaverse and its core building blocks. The chapter has three major sections: the first deals with an understanding of the emergence and concept of metaverse with a focus on virtual worlds, the second presents insight into the economy and commercial value generation by this new meta-era, and the third delves into the techniques and tools that enable the metaverse.
Zero-Shot Camouflaged Object Detection

The goal of Camouflaged object detection (COD) is to detect objects that are visually embedded in their surroundings. Existing COD methods only focus on detecting camouflaged objects from seen classes, while they suffer from performance degradation to detect unseen classes. However, in a real-world scenario, collecting sufficient data for seen classes is extremely difficult and labeling them requires high professional skills, thereby making these COD methods not applicable. In this paper, we propose a new zero-shot COD framework (termed as ZSCOD), which can effectively detect the never unseen classes. Specifically, our framework includes a Dynamic Graph Searching Network (DGSNet) and a Camouflaged Visual Reasoning Generator (CVRG). In details, DGSNet is proposed to adaptively capture more edge details for boosting the COD performance. CVRG is utilized to produce pseudo-features that are closer to the real features of the seen camouflaged objects, which can transfer knowledge from seen classes to unseen classes to help detect unseen objects. Besides, our graph reasoning is built on a dynamic searching strategy, which can pay more attention to the boundaries of objects for reducing the influences of background. More importantly, we construct the first zero-shot COD benchmark based on the COD10K dataset. Experimental results on public datasets show that our ZSCOD not only detects the camouflaged object of unseen classes but also achieves state-of-the-art performance in detecting seen classes.
A Global-to-Local Algorithm for High-Resolution Optical and SAR Image Registration

Multi-sensor remote sensing applications require the registration of optical and Synthetic Aperture Radar (SAR) images, which presents challenges due to significant radiometric and geometric differences resulting from distinct imaging mechanisms. Although various algorithms have been proposed, including hand-crafted features and deep learning networks, most of them focus on matching radiometric-invariant features while ignoring geometric differences. Furthermore, these algorithms often achieve promising results on datasets that use manually labeled ground truths that may be less reliable for high-resolution SAR images affected by speckle noise. To address these issues, we propose a robust global-to-local registration algorithm consisting of four modules: geocoding, global matching, local matching, and refinement. We generate a geometry-invariant mask in the geocoding module to help the local matching module focus on valid areas, introduce a fast global matching method to solve large offsets, and use matching confidence to guide subsequent local matching based on the accuracy of global matching. We propose a feature based on multi-directional anisotropic Gaussian derivatives (MAGD) and embed it into the confidence-aware local matching with the geometry-invariant mask to reduce the effect of geometric differences. Finally, we refine correspondence positions and remove outliers. We also build a high-accuracy evaluation dataset with hundreds of image pairs, where the ground truth is obtained by meta poles, which have clear and reliable structures in both optical and SAR images. Experimental results on this dataset demonstrate the superiority of our proposed algorithm compared to several state-of-the-art methods.
Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methods

Multiple-choice questions with item-writing flaws can negatively impact student learning and skew analytics. These flaws are often present in student-generated questions, making it difficult to assess their quality and suitability for classroom usage. Existing methods for evaluating multiple-choice questions often focus on machine readability metrics, without considering their intended use within course materials and their pedagogical implications. In this study, we compared the performance of a rule-based method we developed to a machine-learning based method utilizing GPT-4 for the task of automatically assessing multiple-choice questions based on 19 common item-writing flaws. By analyzing 200 student-generated questions from four different subject areas, we found that the rule-based method correctly detected 91% of the flaws identified by human annotators, as compared to 79% by GPT-4. We demonstrated the effectiveness of the two methods in identifying common item-writing flaws present in the student-generated questions across different subject areas. The rule-based method can accurately and efficiently evaluate multiple-choice questions from multiple domains, outperforming GPT-4 and going beyond existing metrics that do not account for the educational use of such questions. Finally, we discuss the potential for using these automated methods to improve the quality of questions based on the identified flaws.
A Lightweight Blockchain Framework for Visual Homing and Navigation Robots

Visual homing is a lightweight approach to robot visual navigation. Based upon stored visual information of a home location, the navigation back to this location can be accomplished from any other location in which this location is visible by comparing home to the current image. However, a key challenge of visual homing is that the target home location must be within the robot's field of view (FOV) to start homing. Therefore, this chapter addresses such a challenge by integrating blockchain technology into the visual homing navigation system. Based on the decentralized feature of blockchain, the proposed solution enables visual homing robots to share their visual homing information and synchronously access the stored data (visual homing information) in the decentralized ledger to establish the navigation path. The navigation path represents a per-robot sequence of views stored in the ledger. If the home location is not in the FOV, the proposed solution permits a robot to find another robot that can see the home location and travel toward that desired location. The evaluation results demonstrate the efficiency of the proposed framework in terms of end-to-end latency, throughput, and scalability.