Reviewerly/exHarmony huggingface dataset:
Coherent Photonic Crossbar Arrays for Large-Scale Matrix-Matrix Multiplication

Advances in deep learning research over the past decade have been enabled by an increasingly unsustainable demand for compute power. This trend has dramatically outpaced the slowing growth in the performance and efficiency of electronic computing hardware. Here, we propose a hybrid photonic-electronic computing architecture which leverages a photonic crossbar array and homodyne detection to perform large-scale coherent matrix-matrix multiplication. This approach bypasses the requirements of high-speed electronic readout and frequent reprogramming of photonic weights which significantly reduces energy consumption and latency in the limit of large matrices—two major factors limiting efficiency for many analog computing approaches.
CEV Framework: A Central Bank Digital Currency Evaluation and Verification Framework With a Focus on Consensus Algorithms and Operating Architectures

We propose a Central Bank Digital Currency Evaluation and Verification (CEV) Framework for recommending and verifying technical solutions in the central bank digital currency (CBDC) system. We demonstrate two sub-frameworks: an evaluation sub-framework that provides consensus algorithm and operating architecture solutions and a verification sub-framework that validates the proposed solutions. Our framework offers a universal CBDC solution that is compatible with different national economic and regulatory regimes. The evaluation sub-framework generates customized solutions by splitting the consensus algorithms into several components and analyzing their impacts on CBDC systems. CBDC design involves a trade-off between system features - the consensus algorithm can not achieve all system features simultaneously. However, we also improve the operating architectures to compensate for the weak system features. The verification sub-framework helps verify our proposed solution through empirical experiments and formal proof. Our framework offers CBDC designers the flexibility to iteratively tune the trade-off between CBDC system features for the desired solution. To the best of our knowledge, we are the first to propose a framework to recommend and verify CBDC technical solutions.
Blockchain-Enabled Multiparty Computation for Privacy Preserving and Public Audit in Industrial IoT

With the rapid increase of the industrial data and the development of the industrial Internet of Things (IIoT) paradigm, the efficiency and the quality of service of the emerging applications have been improved. However, the contradiction between data sharing and privacy preserving is still an obstacle in the IIoT. To this end, in this article, we propose a privacy-preserving and publicly auditable multiparty computation scheme for industrial data sharing and computing, which avoids privacy leakage and computation misbehavior by separating the data ownership, data use, and data verification. Using the blockchain technology, a transparent management platform is provided to recognize and trace the illegal data and computation behavior. Moreover, we integrate the noninteractive zero-knowledge proof in the multiparty interaction mechanism, wherein the verification of data consistency and computation validity is executed publicly on the blockchain. Finally, we implement experiment to evaluate the performance of the computation latency, communication overhead and the influence of encryption parameter, and the numerical results illustrate the efficiency and feasibility of our scheme.
LFG-Net: Low-Level Feature Guided Network for Precise Ship Instance Segmentation in SAR Images

Ship instance segmentation of high-resolution SAR images is a valuable and challenging task due to the complex scattering and noise properties. In this article, we pioneered the construction of the low-level feature to discriminate the ships and complemented the super-resolution denoising techniques in the network modules, termed low-level feature guided network (LFG-Net), for precise ship instance segmentation in SAR images. LFG-Net consists of the low-level feature concerned pyramid (LFCP), the high-resolution interaction module (HR-FIM), and the compression recovery segmentation branch (CRSB). LFCP extends vanilla FPN with the P<sub>1</sub> layer and complements super-resolution techniques to capture the regional and texture information at the image level for small object segmentation. HR-FIM interacts the bounding box region of interest (RoI) feature and mask RoI feature at the instance level with high-resolution techniques to enhance the mask RoI feature. CRSB aims at recovering the high-resolution mask predictions to improve the ship segmentation performance. Comprehensive experiments on HRSID, PSeg-SSDD, and AirSARShip indicate that LFG-Net* achieves 11.7%, 6.3%, and 12.7% AP increments compared with the Mask R-CNN baseline, respectively. Besides, it receives 9.5%, 4.9%, and 7.3% AP increments compared with state-of-the-art method, respectively, which bridges the gap of instance segmentation precision in SAR images. In terms of the visualized instance segmentation results, LFG-Net* is capable of segmenting the complex scenes, e.g, the adjacent distributed ships and ships with strong reflection noise interference, in SAR images. Code is available at: https://github.com/Evarray/LFG-Net.
Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution

This paper considers the inverse problem under hyperspectral images (HSIs) denoising framework. Recently, it has been shown that deep learning is a promising approach to image denoising. However, deep learning to be effective usually needs a massive amount of training data. Moreover, in a practical scenario, HSIs may get contaminated by different kinds of noises such as Gaussian and/or sparse noise. Lately, it has been reported that the convolutional neural network (CNN), the core element used by deep image prior (DIP), is able to capture image statistical characteristics without the need of training, i.e., restore the clean image blindly. Nonetheless, there exists some performance gap between DIP and state-of-the-art methods in HSIs (e.g., low-rank models). By applying the Huber loss function (HLF), which is derived through a least favorable distribution in robust statistics, to DIP, we propose a novel unsupervised denoising algorithm, referred as to the HLF-DIP, free from pre-training and without involving any regularizer. Extensive experimental results are provided to demonstrate that the proposed HLF-DIP algorithm significantly outperforms seven state-of-the-art algorithms in both complexity (thanks to no regularization) and robustness against complex noise (e.g., mixed types of noises).
Enhancing Fan Engagement in a 5G Stadium With AI-Based Technologies and Live Streaming

We participated in Taiwan's National Intercollegiate Athletic Games (NIAG) in early May 2021, and formed a Sport Technology Team of more than 30 scholars, students, and engineers to provide novel systems and solutions that make the athletic games rich in sport technologies. Some of the features could be the first time shown to the Internet audience for large-scale athletic games. The technologies involved include table tennis ball trajectory and bounce distribution, badminton shuttlecock tracking and trajectory, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">augmented-reality</i> enriched video streaming on social networks, real-time three-dimensional broadcasting with free view-angle, in-stadium video stream pushing by a private fifth-generation (5G) network with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiaccess edge computing</i> , AI-based sport data analytics during live streaming, etc. All the technologies and applications are integrated in a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">technology-enhanced broadcasting system</i> (TEBS) that is dedicated to sport events. This article introduces the respective technologies that we have developed, deployed, and demonstrated in the 2021 NIAG. We stress the layered architecture design and integration of the TEBS, as well as experimental results from real games in the smart stadium and swimming pool. We also discuss the technical challenges and our approaches to tackle them, as well as lessons learned.
Product Lifecycle Management (PLM)

Product Lifecycle Management (PLM) is the business activity of managing, in the most effective way, a company's products all the way across their lifecycles; from the very first idea for a product all the way through until it is retired and disposed of. PLM is the management system for a company's products. It doesn't just manage one of its products. It manages, in an integrated way, all of its parts and products, and the product portfolio. PLM manages the whole range, from individual part through individual product to the entire portfolio of products.
Optimizing Energy Efficiency for Data Center via Parameterized Deep Reinforcement Learning

The rapid advancements in cloud computing, Big Data and their related applications have led to a skyrocketing increase in data center energy consumption year by year. The prior approaches for improving data center energy efficiency mostly suffer from high system dynamics or the complexity of data centers. In this paper, we propose an optimization framework based on deep reinforcement learning, named DeepEE, to jointly optimize energy consumption from the perspectives of task scheduling and cooling control. In DeepEE, a PArameterized action space based Deep Q-Network (PADQN) algorithm is proposed to tackle the hybrid action space problem. Then, a dynamic time factor mechanism for adjusting cooling control interval is introduced into PADQN (PADQN-D) to achieve more accurate and efficient coordination of IT and cooling subsystems. Finally, in order to train and evaluate the proposed algorithms safely and quickly, a simulation platform is built to model the dynamics of IT and cooling subsystems. Extensive real-trace based experiments illustrate that: 1) the proposed PADQN algorithm can save up to 15% and 10% energy consumption compared with the baseline siloed and joint optimization approaches respectively; 2) the proposed PADQN-D algorithm with dynamic cooling control interval can better adapt to the change of IT workload; 3) our proposed algorithms achieve more stable performance gain in terms of power consumption by adopting the parameterized action space.
Single-Exposure Optical Measurement of Highly Reflective Surfaces via Deep Sinusoidal Prior for Complex Equipment Production

Three-dimensional (3-D) measurement of metal surfaces is one of the fundamental tasks for product life-cycle management of complex equipment, which is meaningful but challenging due to its optical characteristics of high reflectivity. To reliably reconstruct 3-D metal surfaces, the commonly used techniques heavily rely on multiple exposures for optimal fusion but do not fit to high-efficiency monitoring. To alleviate this reliance, we propose a novel single-exposure method called deep sinusoidal prior (DSP) for damaged phase recovery of highly reflective surfaces. Specifically, the sinusoidal hypothesis is instilled into an untrained deep neural network (DNN) as two-stream information, in order to bypass the problem of brightness enhancement. Utilizing elaborately designed loss functions, this approach enables us to restore the accurate phase encoding by fitting the DNN to two-stream sinusoidal priors. Experimental results demonstrate that the proposed DSP method has superior performances on damaged phase recovery requiring no training samples. For instance, measuring a standard workpiece, absolute errors of the DSP method have been decreased substantially (81.69% and 59.49%) compared with the direct measurement and achieved similar accuracy (0.0754 versus 0.0744 mm) compared with the reference. Most strikingly, the proposed method, for the first time, demonstrates a new perspective of recovering the reliable phase from a degraded one itself, contributing to the superior generalization capability insensitive to fringe frequencies, imaging settings, and variant scenes.
Robust Unsupervised Anomaly Detection With Variational Autoencoder in Multivariate Time Series Data

Accurate detection of anomalies in multivariate time series data has attracted much attention due to its importance in a wide range of applications.Since it is difficult to obtain accurately labeled data, many unsupervised anomaly detection algorithms for multivariate time series data have been developed.However, building such a system is challenging since it requires capturing temporal dependencies in each time series and must also encode the inter-correlations between different pairs of time series.To meet this challenge, we propose a Multi Scale Convolutional Variational Autoencoder (MSCVAE) to detect anomalies in multivariate time series data.Firstly, multi scale attribute matrices are constructed from multivariate time series to characterize multiple levels of the system states at different time steps.Then, given the attribute matrices, a convolutional variational autoencoder is employed to generate reconstructed attribute matrices, and also an attention-based ConvLSTM network is used to capture the temporal patterns.In addition, a new ERR-based threshold setting strategy is developed to optimize anomaly detection performance instead of relying on the traditional ROC-based threshold setting strategy with an imbalanced dataset.Finally, the proposed framework is assessed by means of experiments on four datasets.The experimental results show that our proposed framework is superior to competing algorithms in terms of model performance and robustness, demonstrating that our model is effective in detecting anomalies in multivariate time series.
The Unfairness of Popularity Bias in Book Recommendation

Recent studies have shown that recommendation systems commonly suffer from popularity bias. Popularity bias refers to the problem that popular items (i.e., frequently rated items) are recommended frequently while less popular items are recommended rarely or not at all. Researchers adopted two approaches to examining popularity bias: (i) from the users' perspective, by analyzing how far a recommendation system deviates from user's expectations in receiving popular items, and (ii) by analyzing the amount of exposure that long-tail items receive, measured by overall catalog coverage and novelty. In this paper, we examine the first point of view in the book domain, although the findings may be applied to other domains as well. To this end, we analyze the well-known Book-Crossing dataset and define three user groups based on their tendency towards popular items (i.e., Niche, Diverse, Bestseller-focused). Further, we evaluate the performance of nine state-of-the-art recommendation algorithms and two baselines (i.e., Random, MostPop) from both the accuracy (e.g., NDCG, Precision, Recall) and popularity bias perspectives. Our results indicate that most state-of-the-art recommendation algorithms suffer from popularity bias in the book domain, and fail to meet users' expectations with Niche and Diverse tastes despite having a larger profile size. Conversely, Bestseller-focused users are more likely to receive high-quality recommendations, both in terms of fairness and personalization. Furthermore, our study shows a tradeoff between personalization and unfairness of popularity bias in recommendation algorithms for users belonging to the Diverse and Bestseller groups, that is, algorithms with high capability of personalization suffer from the unfairness of popularity bias. Finally, across the models, our results show that WMF and VAECF can provide a higher quality recommendation when considering both accuracy and fairness perspectives.
Efficient Video Grounding With Which-Where Reading Comprehension

Video grounding aims at localizing the temporal moment related to the given language description, which is very helpful to many cross-modal content understanding applications like visual question answering and sentence-video search. Existing approaches usually directly regress the temporal boundaries of an event described by a query sentence in the video sequence. This direct regression manner often encounters a large decision space due to diverse target events and variable video durations, leading to inaccurate localization as well as inefficient grounding. This paper presents an efficient framework termed from which to where to facilitate video grounding. The core idea is imitating the reading comprehension process to gradually narrow the decision space, in what we decompose the direct regression into two steps. The “which” step first roughly selects a candidate area by evaluating which video segment in the predefined set is closest to the ground truth. To this end, we formulate this step into a multi-choice reading comprehension problem and propose a criterion to select the best-matched segment. In this way, the excessive decision space is effectively reduced. The “where” step aims to precisely regress the temporal boundary of the selected video segment from the shrunk decision space. We thus introduce a triple-span representation for each candidate video segment to use the regional context for better boundary regression. The “which” and “where” steps can be combined into a unified framework and learned end-to-end, leading to an efficient video grounding system. Extensive experiments on Charades-STA, ActivityNet-Captions, and TACoS benchmarks clearly demonstrate the effectiveness of our framework.
<i>K</i>-Means Clustering With Local <i>dᵪ</i>-Privacy for Privacy-Preserving Data Analysis

Privacy-preserving data analysis is an emerging area that addresses the dilemma of performing data analysis on user data while protecting users&#x2019; privacy. In this paper, we consider the problem of constructing privacy-preserving <i>K</i>-means clustering protocol for data analysis that provides local privacy to users&#x2019; data. To enable a desirable degree of local privacy guarantee while maintaining high accuracy of the clustering, we adopt a generalized differential privacy definition, <i>d</i><i>&#x03C7;</i>-privacy, which quantifies the distinguishability level based on the distance between data records defined by the distance function <i>d</i><sub>&#x03C7;</sub>. In our work, we consider the space of data points as a metric space imbued with Euclidean distance and propose a bounded perturbation mechanism (BPM) with bounded sampling space of the perturbed data points, which is formally shown to achieve <i>d</i><sub>&#x03C7;</sub>-privacy. BPM perturbs the data as a whole instead of treating each dimension independently, which is desirable since the privacy budget is no longer required to be split among different dimensions. Bounded output space also means that we will not get into the case where the report or the statistical result is so far out of the data domain that it is hard to interpret. Furthermore, it can also help in limiting the amount of bandwidth needed to send such report to the server. The design of BPM is based on a probability density function which decreases exponentially as the Euclidean distance with respect to the true value grows. It is also designed with the aim of ensuring that BPM produces perturbed data that provides the claimed privacy guarantee while ensuring high utility response. To guarantee the efficiency of the perturbation method, we propose an efficient algorithm to sample from the proposed distribution and apply BPM to the design of <i>d</i><sub>&#x03C7;</sub>-private <i>K</i>-means clustering algorithms. Lastly, we analyse the privacy and utility guarantee provided by the proposed method and provide its experimental results.
Security Protocol Function Using Quantum Elliptic Curve Cryptography Algorithm

Quantum Computing (QC). The content of node or sink nodes is processed using the fundamental principles of quantum mechanics. However, cryptography techniques face several other issues, such as availability, integrity, and vulnerability, to name a few. The researchers have overcome many obstacles, yet security remains a crucial concern in QC. However, experimenters recently discovered that the QC has a lot more data hacking than static networks. Moreover, the bitwise error is still present in implementing the Quantum Computing Cryptography Protocol (QCCP). Because all nodes are mobile and dynamic topology occurs, the proposed research uses the Quantum Elliptical Curve Cryptography (QECC) protocol. To provide the appropriate key generation and key sharing mechanism to convert the fool node to a brawny node and avoid packet loss and energy consumption in the network. By turning the fool node into a vital node, the QECC lowers the network's error rate. The experiment uses Network Simulator 2 to achieve successful outcomes such as decreased packet loss, reduced error rate with increased energy consumption, increased pair key generation for additional nodes, and increased packet size.
CGFuzzer: A Fuzzing Approach Based on Coverage-Guided Generative Adversarial Networks for Industrial IoT Protocols

With the widespread application of the Industrial Internet of Things (IIoT), industrial control systems (ICSs) greatly improve industrial productivity, efficiency, and product quality. However, IIoT protocols as the bridge of different parts of ICSs are vulnerable to be attacked due to their vulnerabilities. To reduce cyberattack threats, we need to find the vulnerabilities of IIoT protocols by using efficient vulnerability mining methods, such as fuzzing. Fuzzing is often used to mine vulnerabilities for IIoT protocols. However, the traditional fuzzing methods for IIoT protocols have a low passing rate and low code coverage. To solve these problems, we propose a generative adversarial network (GAN), here referred to as coverage-guided GANs (CovGAN), which aims to generate test cases with a high passing rate and code coverage by learning IIoT protocol specifications. Based on the CovGAN, we construct a fuzzing framework (CGFuzzer) for IIoT protocols. Finally, we design a protocol simulator to verify the CovGAN performance. Experimental results show that the proposed methodology outperforms approximately 5%, 7%, and 39% of the passing rate of GANFuzz, SeqFuzzer, and Peach, respectively. In addition, CGFuzzer has a significant improvement in code coverage, which is about 17%, 24%, and 31% higher than GANFuzz, SeqFuzzer, and Peach, respectively.