Reviewerly/exHarmony huggingface dataset:
Cross-chain Transaction Validation using Lock-and-Key Method for Multi-System Blockchain

Blockchains have profoundly impacted finance and administration, but there are several issues with the current blockchain platforms, including a lack of system interoperability. Currently used blockchain application platforms only work within their networks. Although the underlying concept of all blockchain networks is mainly similar, it involves centralised third-party mediators to transact from other blockchain networks. The current third-party intermediates establish security and trust by keeping track of “account balances” and attesting to the validity of transactions in a centralised ledger. The lack of sufficient inter-blockchain connectivity hinders the mainstream adoption of blockchain. Blockchain technology may be a solid solution for many systems if it grows and works with other systems. For the multi-system blockchain concept to materialise, a mechanism that would connect and communicate with the blockchain systems of various entities in a distributed manner (without any intermediary) while maintaining the property of trust and integrity established by individual blockchains is required. Several methods for verifying cross-chain transactions have been explored in this paper among various blockchains. The efficient verification of cross-chain transactions faces many difficulties, and current research has yet to scratch the surface. In addition to summarising and categorising these strategies, the report also suggests a novel mechanism that gets beyond the existing drawbacks.
Semantic Communication Systems with a Shared Knowledge Base

With the ability to improve transmission efficiency, deep learning-enabled semantic communication is regarded as a promising candidate for future 6G networks. Most existing semantic communication systems are based on the end-to-end architecture that is considered as a black box, leading to the lack of explainability. To tackle this issue, in this paper, a novel semantic communication system with a shared knowledge base is proposed for text transmissions. To be specific, a textual knowledge base for semantic communication is constructed based on a similarity-comparison method, in which a pre-configured threshold can be leveraged to control the size of the knowledge base. The proposed system integrates the message and corresponding knowledge from the shared knowledge base to obtain the residual information, which enables the system to transmit fewer symbols without semantic performance degradation. The simulation results demonstrate that our proposed approach can outperform existing baseline methods in terms of transmitted data size and the sentence similarity.
When Wireless Hierarchical Federated Learning Meets Physical Layer Security: A Finite Blocklength Approach for MIMO Channel

In the authors' previous work, a framework for the wireless hierarchical federated learning (HFL) in the presence of physical layer security (PLS) is established, and a finite blocklength (FBL) approach for the wireless SISO channel is proposed. In this paper, we further propose an FBL approach for the MIMO channel, and show that the proposed scheme almost achieves perfect secrecy without affecting the learning performance of wireless HFL, and the transmission latency of our proposed scheme is significantly shorter than that of classical LDPC code.
Federated Edge Learning via Integrated Sensing, Computation, and Communication

Sensing, computation, and communication (SC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.
Optimizing Data Distribution for Federated Learning Under Bandwidth Constraint

Federated learning, which can distributedly and efficiently train a deep learning model, attracts much attention since a large amount of data generated on a wide network can be utilized. When data generated in a network are collected, how much data is collected on which servers affects the model training time. However, the bandwidths of the links constrain the amount of data collected at each server and transfer routes of data. In this paper, we propose the optimization formula for data destinations and transfer routes in each generating data site under the constraint of the bandwidth of links in a network to minimize the time consumed by training a model. In the experiments, first, we numerically compared the amount of data collected in each server between the proposed algorithm and a naive method. The experiment shows that the proposed method could conduct the federated learning using all data generated in a network even when the bandwidths of links were less than one-third compared to the naive method. Then, we compared the training time of a deep learning model on the basis of the amount of data calculated in the former numerical experiments. We exhibit that the proposed algorithm reduced the training time by 27% to 47% compared to the naive method.
Towards Defending Adaptive Backdoor Attacks in Federated Learning

Federated learning (FL) is an efficient, scalable, and privacy-preserving technology in which clients collaborate on machine learning or deep learning model training. However, malicious clients can send poisoned model updates to the central server without being identified, which makes FL vulnerable to backdoor attacks. In this work, we propose a novel defence approach, FLSec, to mitigate backdoor attacks caused by adversarial local model updates. FLSec utilizes an original measurement, GradScore, computed from the loss gradient norm of the final layer of the local models for backdoor defence. We show that GradScore is efficient and robust in identifying malicious model updates through analysis and experiments. Our extensive evaluation also demonstrates FLSec is highly effective in mitigating three state-of-the-art backdoor attacks on well-known datasets, MNIST, LOAN, and CIFAR-10. The accuracy on a benign dataset with the proposed defence approach is nearly unchanged, with the accuracy on the backdoor dataset being reduced to 0%. In addition, our experiments show that FLSec significantly outperforms existing backdoor defences in multi-round backdoor attacks.
SecMath: An Efficient 2-Party Cryptographic Framework for Math Functions

Complex math functions, such as exponential and tanh, are widely applied in machine learning inference tasks like recurrent neural networks (RNNs). Even though a few works have provided secure implementations of these functions, they still suffer from serious performance bottlenecks, leaving efficiency gaps in practice. To approach this issue, we propose SecMath, an efficient 2-party cryptographic framework for complex math functions. Specifically, SecMath contributes novel communication-efficient protocols for secure exponential, sigmoid and tanh operations. These protocols utilize an advanced underlying primitive, silent oblivious transfer, and employ customized optimizations including lookup table techniques to further improve performance. Extensive evaluations show that our new constructions outperform the counterparts in SIRNN (IEEE S&P'21) by a large margin in terms of both communication and computation overhead. For example, the sigmoid operation of SecMath costs 4.15KB communication and less than 0.2 millisecond, which improves SIRNN up to 7.6× in communication and 2.4× in runtime.
microGEMM: An Effective CNN-Based Inference Acceleration for Edge Computing

Convolutional Neural Networks (CNNs), a widely recognized deep learning algorithm, have been utilized in various domains such as smart cities and healthcare. However, the remarkable performance of CNNs is accompanied by high resource overhead and deployment complexity. To address these challenges, CNN compilers have been developed to simplify convolutional operations for edge device deployment. One of the crucial components in CNN models is the General Matrix Multiply (GEMM) operation, which serves as the main computational kernel. In previous studies, efforts were made to improve the computation speed of GEMM by modifying the matrix calculation sequence, but they did not fully exploit the computing resources of edge devices. In this paper, we propose a novel GEMM-based acceleration algorithm, named microGEMM. The microGEMM algorithm divides convolutional data to reduce the memory access times during the GEMM calculation process. Moreover, the algorithm employs instruction-level optimization in the GEMM calculation unit, decreasing the cache miss rate. To better evaluate the superiority of microGEMM on resource-constrained devices, two edge-oriented metrics are proposed, namely CCPS & CCPoE. The microGEMM algorithm is implemented in C++ and compared with the standard GEMM algorithm (naiveGEMM) and the GEMM of the open-source Basic Linear Algebra Subprograms (BLAS) library (openblasGEMM). The experimental results demonstrate that microGEMM achieves a significant speedup, ranging from 5.67 × to 14.19 ×, compared to naiveGEMM.
Under a False Flag: Literary Hoaxes and the Use of Numerals

The present study pertains to stylometry. There are cases when a writer who has achieved fame, for various reasons, begins to create under a different name, tries to write in a different manner and, at times, again succeeds in a new incarnation. Whether the author is able to significantly change the literary style inherent in him or it is impossible to escape from himself – our work is devoted to the study of this issue. The study is based on the analysis of what numerals are present in the texts of an author. It has been shown by several examples from English-, French- and Russian-language literature, that the use of numerals is an author's feature that manifests itself in all or most of the sufficiently long texts of a given author. We apply our approach to the works of Romain Gary, Boris Akunin (Grigori Chkhartishvili) and some other authors of interest for stylometry. The conclusions are drawn on the basis of hierarchical cluster analysis and supported by the Pearson's chi-squared test.
Modeling and Joint Optimization of Security, Latency, and Computational Cost in Blockchain-Based Healthcare Systems

In the era of the Internet of Things (IoT), blockchain is a promising technology for improving the efficiency of health-care systems, as it enables secure storage, management, and sharing of real-time health data collected by the IoT devices. As the implementations of blockchain-based healthcare systems usually involve multiple conflicting metrics, it is essential to balance them according to the requirements of specific scenarios. In this paper, we formulate a joint optimization model with three metrics, namely latency, security, and computational cost, that are particularly important for IoT-enabled healthcare. However, it is computationally intractable to identify the exact optimal solution of this problem for practical sized systems. Thus, we propose an algorithm called the Adaptive Discrete Particle Swarm Algorithm (ADPSA) to obtain near-optimal solutions in a low-complexity manner. With its roots in the classical Particle Swarm Optimization (PSO) algorithm, our proposed ADPSA can effectively manage the numerous binary and integer variables in the formulation. We demonstrate by extensive numerical experiments that the ADPSA consistently outperforms existing benchmark approaches, including the original PSO, exhaustive search and Simulated Annealing, in a wide range of scenarios.
Investigating the Characteristics and Performance of Augmented Reality Applications on Head-Mounted Displays: A Study of the Hololens Application Store

Augmented Reality (AR) based on Head-Mounted Displays (HMDs) has gained significant traction over the recent years. Nevertheless, it remains unclear what AR HMD-based applications have been developed over the years and what their system performance is when they are run on HMDs. In this paper, we aim to shed light into this direction. Our study focuses on the applications available on the Microsoft Hololens application store given the wide use of the Hololens headset. Our study has two major parts: (i) we collect metadata about the applications available on the Microsoft Hololens application store to understand their characteristics (e.g., categories, pricing, permissions requested, hardware and software compatibility); and (ii) we interact with these applications while running on a Hololens 2 headset and collect data about systems-related metrics (e.g., memory and storage usage, time spent on CPU and GPU related operations) to investigate the systems performance of applications. Our study has resulted in several interesting findings, which we share with the research community.
EdgeC3: Online Management for Edge-Cloud Collaborative Continuous Learning

Deep learning (DL) powered real-time applications usually need continuous training using data streams generated geographically. Enabling data offloading among computation nodes through model training is promising to mitigate the problem that devices generating large datasets may have low computation capability. However, offloading can compromise model convergence and incur communication costs, which must be balanced with the cost spent on computation and model synchronization. Therefore, this paper proposes EdgeC3, a novel framework that can optimize the frequency of model aggregation and dynamic offloading for continuously generated data streams, navigating the trade-off between long-term accuracy and cost. We first provide a new error bound to capture the impacts of data dynamics that are varying over time and heterogeneous across devices. Based on the bound, we design a two-timescale online optimization framework. We periodically learn the synchronization frequency to adapt with uncertain future offloading and network changes. In the finer timescale, we manage online offloading by extending Lyapunov optimization techniques to handle an unconventional setting, where our long-term global constraint can have abruptly changed aggregation frequencies that are decided in the longer timescale. Finally, we theoretically prove the convergence of EdgeC3 by integrating the coupled effects of our two-timescale decisions, and we demonstrate its advantage through extensive experiments.
Measuring the influence of audio on immersive experience in extended reality and digital games: a systematic review

A key goal of extended reality (XR) technologies and digital games is to create immersive experiences for the user. Immersive experience is a multifaceted phenomenon that encompasses perceptual and psychological factors, and audio has been recognized as a key modality that can influence such experiences. Despite extensive research on the subject, there is no consensus regarding the ways in which audio impacts immersive experience. This is also the case in terms of the most appropriate methodology for evaluating this phenomenon in the context of end-user experience in XR and digital games. This paper aims to establish a comprehensive overview of the experimental methodologies associated with this topic through a systematic literature review of the last 20 years of research. The inclusion criteria focuses on research papers that attempt to measure audio-related independent variables (e.g., spatial resolution) on immersion-related dependent variables (e.g., sense of presence), in the context of XR environments and digital games. The results reveal a preference for VR HMD and headphone-based experiments, identifying a lack of AR/MR exploration, as well as the necessity for standardized, audio-specific questionnaires in data collection for a more comprehensive understanding of the field.
Optimizing Program Deployment with libopl in Programmable Networks

Deploying data plane programs on programmable switches involves complex optimization problems that make the optimal deployment decisions. However, existing deployment frameworks only focus on deploying programs in specific domains (i.e., supporting fixed optimization requirements), resulting in poor scalability. To this end, our goal is to simplify program deployment through general high-level abstractions that capture optimization requirements. In this paper, we present libopl, a generic library that enables administrators to express various optimization requirements when deploying programs and further calculates the optimal deployment plans. Existing frameworks can also use libopl to extend their functionalities to fit more deployment scenarios. To evaluate libopl, we build a Tofino-based testbed and a simulator. Our experimental results show that libopl exhibits comparable or better scalability than stateof-the-art frameworks and only introduces negligible overhead.
Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training

Mobile Internet has profoundly reshaped modern lifestyles in various aspects. Encrypted Traffic Classification (ETC) naturally plays a crucial role in managing mobile Internet, especially with the explosive growth of mobile apps using encrypted communication. Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, i) label bias caused by traffic class imbalance, ii) traffic homogeneity caused by component sharing, and iii) training with reliance on sufficient labeled traffic. None of the existing ETC methods can address all these limitations. In this paper, we propose a novel Pre-trAining Semi-Supervised ETC framework, dubbed PASS. Our key insight is to resample the original train dataset and perform contrastive pre-training without using individual app labels directly to avoid label bias issues caused by class imbalance, while obtaining a robust feature representation to differentiate overlapping homogeneous traffic by pulling positive traffic pairs closer and pushing negative pairs away. Meanwhile, PASS designs a semi-supervised optimization strategy based on pseudo-label iteration and dynamic loss weighting algorithms in order to effectively utilize massive unlabeled traffic data and alleviate manual train dataset annotation workload. PASS outperforms state-of-the-art ETC methods and generic sampling approaches on four public datasets with significant class imbalance and traffic homogeneity, remarkably pushing the F1 of Cross-Platform215 with 1.31%$\uparrow$, ISCX-17 with 9.12%$\uparrow$. Furthermore, we validate the generality of the contrastive pre-training and pseudo-label iteration components of PASS, which can adaptively benefit ETC methods with diverse feature extractors.
Over-the-Air Federated Learning with Enhanced Privacy

Federated learning (FL) has emerged as a promising learning paradigm in which only local model parameters (gradients) are shared.Private user data never leaves the local devices thus preserving data privacy.However, recent research has shown that even when local data is never shared by a user, exchanging model parameters without protection can also leak private information.Moreover, in wireless systems, the frequent transmission of model parameters can cause tremendous bandwidth consumption and network congestion when the model is large.To address this problem, we propose a new FL framework with efficient over-the-air parameter aggregation and strong privacy protection of both user data and models.We achieve this by introducing pairwise cancellable random artificial noises (PCR-ANs) on end devices.As compared to existing over-the-air computation (AirComp) based FL schemes, our design provides stronger privacy protection.We analytically show the secrecy capacity and the convergence rate of the proposed wireless FL aggregation algorithm.
A Resource Provisioning Manager for Edge Devices

This paper presents a virtualization-based computing and storage resource provisioning manager, termed RPM, for edge devices. Given a request from an edge device, RPM can perform dynamic volume provisioning and accelerated computation on augmented computing resources. Also, RPM can migrate application execution conducted by edge devices online. We show that RPM is a promising approach through our experiments.
An Online Framework for Joint UAV Trajectory Planning and Intelligent Dependent Task Offloading

Unmanned Aerial Vehicles (UAV)-assisted Mobile Edge Computing (MEC) has recently emerged as a novel and adaptable approach to delivering low-latency services to User Equipments (UEs). Many smart applications, e.g., recognizing illegal buildings based on image processing, involve dependent subtasks where the output from some subtasks serves as input for others and require to respond to UEs in real-time. In UAV-assisted MEC, the latency in task completion can be significantly impacted by the trajectory of the UAV and the offloading order of dependent tasks. How to plan the trajectories of UAVs and offload tasks with dependencies to UAVs is a challenging problem. In this paper, we introduce an online framework, JTDO, that Jointly considers UAV Trajectory and Dependent task Offloading to minimize the response latency. Specifically, to maximize the number of tasks offloaded to UAVs, JTDO adopts an efficient deployment algorithm to dynamically update UAV trajectory. For dependent task offloading, JTDO first models dependent tasks as directed acyclic graphs and then reformulates the dependent task offloading problem as a Markov decision process. Finally, JTDO proposes a proximal policy optimization based deep reinforcement learning algorithm to learn the optimal offloading strategy. Simulation results show that JTDO quickly converges under various scenarios and reduces the latency by up to 37% compared with five state-of-the-art algorithms.
Poster: Analysis of User Uniqueness on LinkedIn Based on Publicly Available Non-PII

The literature has shown combining a few non-Personal Identifiable Information (non-PII) is enough to make a user unique in a dataset including millions of users. In this work, we demonstrate that the combination of the location and 6 rare (14 random) skills in a LinkedIn profile is enough to become unique in a user base of ~800M users with a probability of 75%. The novelty is these attributes are publicly accessible to anyone registered on LinkedIn and could be activated through advertising campaigns.