Reviewerly/exHarmony huggingface dataset:
Reparameterizing and dynamically quantizing image features for image generation

Authors:
raw_author_name: Mingzhen Sun
raw_affiliation_string: Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Weining Wang
raw_affiliation_string: Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Xinxin Zhu
raw_affiliation_string: Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Jing Liu
raw_affiliation_string: Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

Abstract: For autoregressive image generation, vector-quantized VAEs (VQ-VAEs) quantize image features with discrete codebook entries and reconstruct images from quantized features. However, they treat each codebook entry separately, which causes losses of image details. In this paper, we propose to reparameterize image features with weight vectors to treat all codebook entries as an entity, and present a novel dynamically vector quantized VAE (DVQ-VAE) to quantize reparameterized image features. Specifically, each image feature corresponds to a weight vector and we sum weighted codebook entries to obtain values of image features. In this way, image features can incorporate information from different codebook entries. Additionally, a novel continuous weight regularization loss is proposed to improve the reconstruction of image details. Our method achieves competitive results with prior state-of-the-art works for image generation and extensive experiments are conducted to take a deep insight into our DVQ-VAE.
Facial expression-enhanced recommendation for virtual fitting rooms

Authors:
raw_author_name: Ying Xue
raw_affiliation_string: School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
raw_author_name: Jianshan Sun
raw_affiliation_string: Anhui Provincial Laboratory of Philosophy and Social Sciences, Hefei, Anhui 230009, China; Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Anhui 230009, China; School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
raw_author_name: Yezheng Liu
raw_affiliation_string: National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai 200436, China; School of Management, Hefei University of Technology, Hefei, Anhui 230009, China

Abstract: With the development of Augmented Reality (AR) technology in the retail industry, virtual fitting room (VFR) are considered promising enhancement of e-commerce by providing users with an immersive environment to try on new products, especially fashion products. While allowing users having more vivid impression of products, virtual fitting rooms also offer sellers more channels to collect information on user preferences, which can be used to enhance recommender systems. This study proposes to leverage facial expression recognition technology together with fine-grained human-computer interactions in virtual fitting rooms to personalize product recommendations. This paper proposes a recommendation algorithm based on confidence setting, negative feedback sampling, and matrix factorization to model user behaviors in virtual fitting rooms. We conduct an experiment on 81 subjects to evaluate the proposed method. Experimental results show the proposed method outperforms existing methods using traditional behavior information. Our study provides a strong support to the value of AR in enhancing e-commerce.
A High-Performance, Conflict-Free Memory-Access Architecture for Modular Polynomial Multiplication

Authors:
raw_author_name: Zeming Cheng
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
raw_author_name: Bo Zhang
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
raw_author_name: Massoud Pedram
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

Abstract: In this paper, we present the HiCoP architecture, a high-performance, conflict-free memory access, modular polynomial multiplication design that accelerates the number theoretic transform (NTT), inverse NTT (INTT), and modular polynomial multiplications. To optimize hardware costs, the HiCoP architecture utilizes a high-radix reconfigurable butterfly unit (RBU) that can be dynamically configured to perform NTT, INTT, and point-wise multiplications, alongside an area-efficient Montgomery modular multiplier (MMM) tailored for NTT-friendly modulus. Moreover, by integrating pre-processing, post-processing, and Montgomery domain transformations into NTT and INTT operations, we effectively minimize the cycle count for modular polynomial multiplication. Additionally, we propose a novel conflict-free memory access algorithm that simplifies the control logic and eliminates the need for ping-pong memory in the HiCoP architecture. Experimental results of modular polynomial multiplications demonstrate significant performance gains for the HiCoP architecture implemented on the Xilinx Virtex-7 field-programmable gate array (FPGA) platform, with up to 8.75×, 4.15×, 10.57×, and 8.50× improvements in throughput-to-hardware-cost ratio for LUT count, FF count, BRAM count, and DSP count, respectively.
Augmented Reality Navigation System for Biliary Interventional Procedures With Dynamic Respiratory Motion Correction

Authors:
raw_author_name: Danni Ai
raw_affiliation_string: School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
raw_author_name: Haixiao Geng
raw_affiliation_string: School of Optics and Photonics, Beijing Institute of Technology, China
raw_author_name: Deqiang Xiao
raw_affiliation_string: School of Optics and Photonics, Beijing Institute of Technology, China
raw_author_name: Hong Song
raw_affiliation_string: School of Computer Science and Technology, Beijing Institute of Technology, China
raw_author_name: Jian Yang
raw_affiliation_string: School of Optics and Photonics, Beijing Institute of Technology, China

Abstract: Biliary interventional procedures require physicians to track the interventional instrument tip (Tip) precisely with X-ray image. However, Tip positioning relies heavily on the physicians' experience due to the limitations of X-ray imaging and the respiratory interference, which leads to biliary damage, prolonged operation time, and increased X-ray radiation.We construct an augmented reality (AR) navigation system for biliary interventional procedures. It includes system calibration, respiratory motion correction and fusion navigation. Firstly, the magnetic and 3D computed tomography (CT) coordinates are aligned through system calibration. Secondly, a respiratory motion correction method based on manifold regularization is proposed to correct the misalignment of the two coordinates caused by respiratory motion. Thirdly, the virtual biliary, liver and Tip from CT are overlapped to the corresponding position of the patient for dynamic virtual-real fusion.Our system is respectively evaluated and achieved an average alignment error of 0.75 ± 0.17 mm and 2.79 ± 0.46 mm on phantoms and patients. The navigation experiments conducted on phantoms achieve an average Tip positioning error of 0.98 ± 0.15 mm and an average fusion error of 1.67 ± 0.34 mm after correction.Our system can automatically register the Tip to the corresponding location in CT, and dynamically overlap the 3D virtual model onto patients to provide accurate and intuitive AR navigation.This study demonstrates the clinical potential of our system by assisting physicians during biliary interventional procedures. Our system enables dynamic visualization of virtual model on patients, reducing the reliance on contrast agents and X-ray usage.
Multi-view fusion for universal translation quality estimation

Authors:
raw_author_name: Hui Huang
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, PR China
raw_author_name: Shuangzhi Wu
raw_affiliation_string: ByteDance AI Lab, Beijing, PR China
raw_author_name: Kehai Chen
raw_affiliation_string: School of Computer Science and Engineering, Harbin Institute of Technology, Shenzhen, PR China
raw_author_name: Xinnian Liang
raw_affiliation_string: State Key Lab of Software Development Environment, Beihang University, Beijing, PR China
raw_author_name: Hui Di
raw_affiliation_string: Research&Development Center, Toshiba Co. Ltd, Beijing, PR China
raw_author_name: Muyun Yang
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, PR China
raw_author_name: Tiejun Zhao
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, PR China

Abstract: Machine translation quality estimation (QE) aims to evaluate the result of translation without reference. Despite the progress it has made, state-of-the-art QE models are proven to be biased. More specifically, they over-rely on spurious statistical features while ignoring the bilingual semantic adequacy, leading to performance degradation. Besides, existing approaches require large amounts of annotation data, restricting their applications in new domains and languages. In this work, we propose a universal framework for quality estimation based on multi-view fusion. We first introduce noise to the target side of the parallel sentence pair, either by pre-trained language model or by large language model. After that, with the clean parallel pairs and the noised pairs as different views, the QE model is trained to distinguish the clean pairs from the noised ones. Our method can improve the accuracy and generalizability in supervised scenario, and can solely perform estimation in zero-shot scenario. We perform experiments on WMT QE evaluation datasets under different scenarios, verifying the effectiveness of our method. We also make an in-depth investigation of the bias of QE model.
SCH: Symmetric Consistent Hashing for cross-modal retrieval

Authors:
raw_author_name: Haomin Ni
raw_affiliation_string: School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Guangzhou, 510000, Guangdong, China
raw_author_name: Xiaozhao Fang
raw_affiliation_string: Department of Mathematics and Theories, Peng Cheng Laboratory, Shenzhen, 518000, Guangdong, China; School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Guangzhou, 510000, Guangdong, China
raw_author_name: Peipei Kang
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, No. 100 Waihuan Xi Road, Guangzhou, 510000, Guangdong, China
raw_author_name: Hongbo Gao
raw_affiliation_string: Department of Automation, School of Information Science, University of Science and Technology of China, No. 96 Jinzhai Road, HeFei, 230026, Anhui, China
raw_author_name: Guoxu Zhou
raw_affiliation_string: School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Guangzhou, 510000, Guangdong, China
raw_author_name: Shengli Xie
raw_affiliation_string: School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Guangzhou, 510000, Guangdong, China

Abstract: When handling large-scale multimodal data, hashing-based retrieval methods have gained significant attention due to their advantages in terms of low storage consumption and quick query speed. Despite the good performance of supervised hashing techniques, there are still certain limitations that need to be addressed. Firstly, they ignore valuable information such as reconstruction residuals when employing matrix factorization to learn the low-dimensional space. Secondly, most methods map heterogeneous data to the same feature space, which is unreasonable because different modalities possess distinct dimensions and distributions. Lastly, some methods relax constraints in order to optimize discrete hash codes, leading to considerable quantization errors. To solve the above problems, a novel two-step hashing-based technology called Symmetric Consistent Hashing (SCH) is proposed. Specifically, in the first step, two separate latent semantic spaces are learned by leveraging Principal Component Analysis to mitigate principal energy loss and Locality Preserving Projections to preserve local similarity. Then, the latent spaces of different modalities are aligned to facilitate the sharing of high-level semantic information by exploiting modality consistency. Next, hash codes are learned from distinct latent spaces, which are embedded in the second step for learning hash functions through Kernel Logistic Regression. To validate the effectiveness of SCH, extensive experiments on three publicly available benchmark datasets are accomplished. The results demonstrate the superiority of SCH over state-of-the-art hashing baselines, thus confirming the potency of the proposed approach.
Gaze estimation with semi-supervised eye landmark detection as an auxiliary task

Authors:
raw_author_name: Yunjia Sun
raw_affiliation_string: Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
raw_author_name: Jiabei Zeng
raw_affiliation_string: Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, China
raw_author_name: Shiguang Shan
raw_affiliation_string: Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China

Abstract: The changes in gaze are often reflected in the movements of eye landmarks, highlighting the relevance of eye landmark learning for accurate gaze estimation. To leverage eye landmarks, we propose a gaze estimation framework that incorporates eye landmark detection as an auxiliary task. However, obtaining eye landmark annotations for real-world gaze datasets is challenging. To address this, we exploit synthetic data, which provides precise eye landmark labels, by jointly training an eye landmark detector using labeled synthetic data and unlabeled real-world data in a semi-supervised manner. To reduce the influence of discrepancy between synthetic and real-world data, we improve the generalization ability of the landmark detector by performing a self-supervised learning strategy on a large scale of unlabeled real-world images. The proposed method outperforms other state-of-the-art gaze estimation methods on three gaze datasets, indicating the effectiveness of leveraging eye landmark detection as an auxiliary task to enhance gaze estimation performance.
DFLNet: Deep Federated Learning Network With Privacy Preserving for Vehicular LoRa Nodes Fingerprinting

Authors:
raw_author_name: Tiantian Zhang
raw_affiliation_string: School of Information and Communication Engineering, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China
raw_author_name: Dongyang Xu
raw_affiliation_string: School of Information and Communication Engineering, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China
raw_author_name: Pinyi Ren
raw_affiliation_string: School of Information and Communication Engineering, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China
raw_author_name: Keping Yu
raw_affiliation_string: Graduate School of Science and Engineering, Hosei University, Tokyo, Japan
raw_author_name: Mohsen Guizani
raw_affiliation_string: Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE

Abstract: The rapid advancement of the Internet of Things (IoT) and intelligent vehicle networks has led to the widespread deployment of massive long-range (LoRa) nodes. These nodes play a crucial role in facilitating the sharing of critical information towards various vehicles. However, a significant challenge that persists is the secure authentication of LoRa. Recently, radio frequency fingerprinting (RFF) emerges as a robust potential solution, offering both rapid and scalable identification capabilities. By harnessing machine learning (ML) techniques, the inherent hardware-level, unique, and resilient features of RFF can be throughly explored. Given the inherent non-identical (heterogeneity) of LoRa networks and the non-stationary characteristics of chirp signals, conventional approaches prove inadequate in effectively capturing the RFF features. To surmount these challenges, a novel scheme termed as “deep federated learning network (DFLNet)” is proposed. DFLNet facilitates distributed RFF learning while ensuring privacy preservation within diverse LoRa networks. Addressing the heterogeneity of datasets, we incorporate an adaptive optimizer that contributes to the efficient convergence improvement. The DFLNet adeptly manages computational demands and privacy concerns through the utilization of mobile edge computing (MEC) servers, thus enabling distributed learning. Empirical evaluations substantiate the efficacy of DFLNet, attaining an impressive identification accuracy of up to approximately 96.7% utilizing merely around 5000 samples per client, while concurrently ensuring a robust privacy preservation.
AGRIC: Artificial-Intelligence-Based Green Routing for Industrial Cyber–Physical System Pertaining to Extreme Environment

Authors:
raw_author_name: Sandeep Verma
raw_affiliation_string: Bharti School of Telecommunications Technology and Management, Indian Institute of Technology Delhi, New Delhi, India
raw_author_name: Satnam Kaur
raw_affiliation_string: Department of Computer Science Engineering, Punjab Engineering College, Chandigarh, India
raw_author_name: Ajay K Sharma
raw_affiliation_string: Department of Computer Science, National Institute of Technology Delhi, New Delhi, India
raw_author_name: Mubarak Alrashoud
raw_affiliation_string: Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

Abstract: Industrial Cyber-Physical Systems (ICPS) can play a crucial role in damage assessment during extreme conditions by leveraging their integration of physical infrastructure, sensing capabilities, and advanced analytics. However, due to the wireless sensing devices that are made to operate in ICPS, there is a dire need to address the green routing (energy-efficient) challenges through an optimized solution. In recent times, Artificial Intelligence (AI) has had a significant impact on Wireless Sensor Networks (WSNs) designed to operate as ICPS components. In this research work, we present AGRIC: AI-based Green Routing for ICPS. While following the cluster-based routing, the election of Cluster Head (CH) is executed using our proposed AI-inspired Extended Spotted Hyena Levy Flight Optimization (ESHLFO) algorithm. Furthermore, to address the energy hole problem, four energy-unlimited data collection nodes are used around the periphery of the network. The results of the experiment demonstrate the fact AGRIC delivers network longevity and supreme performance in the context of stability time, throughput, and energy left over in the network as important performance indicators.
Graph-Based Data Publication via Differentially Structural Inference

Authors:
raw_author_name: Ping Zhao
raw_affiliation_string: College of Information Science and Technology, Donghua University, Shanghai, China
raw_author_name: Ziyi Yang
raw_affiliation_string: College of Information Science and Technology, Donghua University, Shanghai, China
raw_author_name: Jianming Wu
raw_affiliation_string: College of Information Science and Technology, Donghua University, Shanghai, China
raw_author_name: Zhonglin Wang
raw_affiliation_string: School of Finance and Information, Ningbo University of Finance and Economics, Ningbo, China

Abstract: Due to the rapid development of Internet of Things, a large number of data are collected and published. Nevertheless, the process of data publication entails the risk of data privacy disclosure. Most of the related works can be largely classified into data publication based on anonymity, differential privacy, and graph. However, these existing works either cannot provide theoretically provable privacy protection, or only considered one kind of data attribute and thus cannot guarantee the desirable data utility. To this end, we propose a graph-based data publication scheme via differentially structural inference that can provide theoretically provable differential privacy for individuals, and maintain desirable data utility in many practical applications rather than a certain kind of statistics or data mining results. The main idea is to map the dataset to be published into a data graph, and further use the hierarchical random graph model in statistics to encode the structure of the data graph into dendrograms. Then, we use the Markov Chain Monte Carlo to infer an optimal dendrogram, and moreover design threshold strategy to differentially disturb the optimal dendrogram. Finally, we generate the sanitized data graph based on the disturbed optimal dendrogram, and further map the sanitized data graph to the sanitized dataset to be published. Thereafter, we theoretically prove the performance boundaries of both the privacy preservation and the data utility guarantees provided in our work. Furthermore, the extensive experimental results on two real-world datasets demonstrate that the proposed scheme is superior to the existing work and Baseline, guaranteeing the data utility and preserving the data privacy in many practical applications.
Matching Game for Multi-Task Federated Learning in Internet of Vehicles

Authors:
raw_author_name: Zejun Li
raw_affiliation_string: State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China
raw_author_name: Hao Wu
raw_affiliation_string: State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China
raw_author_name: Yunlong Lu
raw_affiliation_string: State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China
raw_author_name: Bo Ai
raw_affiliation_string: State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China
raw_author_name: Zhangdui Zhong
raw_affiliation_string: State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China
raw_author_name: Yan Zhang
raw_affiliation_string: Department of Informatics, University of Oslo, Oslo, Norway

Abstract: To overcome the inherent defects of massive data uploading and processing in traditional machine learning, federated learning is emerged as a promising tool given that it enables to implement privacy-preserved distributed machine learning in Internet of Vehicles (IoV). However, the performance of the federated learning suffers from several challenges, especially ineffective execution of delay-sensitive tasks triggered simultaneously by moving vehicles. To minimize the total execution delay of multiple tasks, we propose a multi-task federated learning framework which improves task completion rate and enables each task to be completed in time. Moreover, we also aim to improve the network utility of the IoV. The algorithm of joint optimization algorithm is proposed to achieve a stable partition of vehicle coalitions based on the block coordinate descent (BCD) method, the matching game-based coalition method, and gradient projection method. The performance of the proposed multi-task federated learning is evaluated through numerical simulations in terms of total latency, network utility, and accuracy of federated learning tasks. The results show that our proposed multi-task federated learning framework and algorithm guarantees the completion of multiple delay-sensitive tasks effectively while improving vehicular network utility.