Reviewerly/exHarmony huggingface dataset:
Joint Optimization of QoE and Fairness for Adaptive Video Streaming in Heterogeneous Mobile Environments

Authors:
raw_author_name: Yali Yuan
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing, China
raw_author_name: Weijun Wang
raw_affiliation_string: Institute of Computer Science, University of G&#x00F6;ttingen, G&#x00F6;ttingen, Germany
raw_author_name: Yuhan Wang
raw_affiliation_string: Institute of Computer Science, University of G&#x00F6;ttingen, G&#x00F6;ttingen, Germany
raw_author_name: Sripriya Srikant Adhatarao
raw_affiliation_string: Institute of Computer Science, University of G&#x00F6;ttingen, G&#x00F6;ttingen, Germany
raw_author_name: Bangbang Ren
raw_affiliation_string: Institute of Computer Science, University of G&#x00F6;ttingen, G&#x00F6;ttingen, Germany
raw_author_name: Kai Zheng
raw_affiliation_string: Huawei Technologies Company Ltd., Beijing, China
raw_author_name: Xiaoming Fu
raw_affiliation_string: Institute of Computer Science, University of G&#x00F6;ttingen, G&#x00F6;ttingen, Germany

Abstract: The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.
pFedEff: An Efficient and Personalized Federated Cognitive Learning Framework in Multiagent Systems

Authors:
raw_author_name: Hongjian Shi
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Jianqing Zhang
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Shuming Fan
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Ruhui Ma
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Haibing Guan
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

Abstract: With the increase in data volume and environment complexity, real-world problems require more advanced algorithms to acquire useful information for further analysis or decision-making. Cognitive Learning effectively handles incomplete information, and multi-agent systems can provide enough data for analysis. Inspired by Distributed Machine Learning, Federated Learning has become an efficient framework for implementing Cognitive Learning algorithms in multi-agent systems while preserving user privacy. However, traditional communication optimizations on the federated learning framework suffer from either large communication volumes or large accuracy loss. In this paper, we propose pFedEff, a personalized federated learning framework with efficient communication that can reduce communication volume and preserve training accuracy. pFedEff uses two magnitude masks, two importance masks, and a personalized aggregation method to reduce the model and update size while maintaining the training accuracy. Specifically, we use a pre-training magnitude mask for approximated regularization to reduce the magnitude of ineffective parameters during training. We also use a post-training magnitude mask to eliminate the low-magnitude parameters after training. Furthermore, we use uploading and downloading importance masks to reduce the communication volume in both upload and download streams. Our experimental results show that pFedEff reduces up to 94% communication volume with only a 1% accuracy loss over other state-of-the-art federated learning algorithms. In addition, we conduct multiple ablation studies to evaluate the influence of hyperparameters in pFedEff, which shows the flexibility of pFedEff and its applicability in different scenarios.
Cryptosystem for IoT Devices With Feedback Operation Modes Based on Shared Buffer and Unrolled-Pipeline Techniques

Authors:
raw_author_name: Wen-Long Chin
raw_affiliation_string: Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan
raw_author_name: Pin-Wei Chen
raw_affiliation_string: Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan
raw_author_name: Shih-Hsiang Chou
raw_affiliation_string: Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan
raw_author_name: Yu-Hua Yang
raw_affiliation_string: Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan
raw_author_name: Pei-Yin Chen
raw_affiliation_string: Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan
raw_author_name: Tao Jiang
raw_affiliation_string: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China

Abstract: This paper presents a multichannel cryptosystem based on the proposed shared buffer techniques which enhance security for several Internet of Things (IoT) sensors and devices by using block cipher with feedback operation modes. Information security and its performance are both important characteristics for modern networking devices. In cryptography, the block cipher, for example, advanced encryption standard (AES), using feedback operation modes can enhance the security level but, unfortunately, the encryption cannot be performed in parallel owing to data dependency. Therefore, the pipeline and unrolling techniques are not applicable to increase the throughput of hardware designs. Under this situation, the round-based architecture is popular and regarded as an area-efficient solution. However, this method inherently limits its throughput. The proposed cryptosystem aims to provide many resource-constrained IoT devices with high-speed centralized encryption service to enhance their security levels, which are applicable for various scenarios, such as vehicular network, home network and network function virtualization (NFV)/software-defined networking (SDN) IoT. Beyond that, the proposed design introduces the shared buffer technique based on linked lists and presents a novel queuing structure to enhance the memory utilization so that it can reduce 72.9% memory requirement of the naïve implementation while achieving the same speedup. According to the implementation result, an aggregate throughput of 130.91 Gbps for encrypting 10 IoT devices in cipher block chaining (CBC), cipher feedback (CFB), and output feedback (OFB) modes can be achieved on TSMC 40nm. The area efficiency of this work significantly outperforms the state-of-the-art works.
Does Qubit Connectivity Impact Quantum Circuit Complexity?

Authors:
raw_author_name: Pei Yuan
raw_affiliation_string: Tencent Quantum Laboratory, Tencent, Shenzhen, China
raw_author_name: Jonathan Allcock
raw_affiliation_string: Tencent Quantum Laboratory, Tencent, China
raw_author_name: Shengyu Zhang
raw_affiliation_string: Tencent Quantum Laboratory, Tencent, Shenzhen, China

Abstract: Some physical implementation schemes of quantum computing can apply two-qubit gates only on certain pairs of qubits. These connectivity constraints are commonly viewed as a significant disadvantage. For example, compiling an unrestricted n-qubit quantum circuit to one with poor qubit connectivity, such as a 1D chain, usually results in a blowup of depth by O(n2) and size by O(n). It is appealing to conjecture that this overhead is unavoidable—a random circuit on n qubits has Θ(n) two-qubit gates in each layer and a constant fraction of them act on qubits separated by distance Θ(n). While it is known that almost all n-qubit unitary operations need quantum circuits of Ω(4n/n) depth and Ω(4n) size to realize with all-to-all qubit connectivity, in this paper, we show that all n-qubit unitary operations can be implemented by quantum circuits of O(4n/n) depth and O(4n) size even under 1D chain qubit connectivity constraint. We extend this result and investigate qubit connectivity in three directions. First, we consider more general connectivity graphs and show that the circuit size can always be made O(4n) as long as the graph is connected. For circuit depth, we study d-dimensional grids, complete d-ary trees and expander graphs, and show results similar to the 1D chain. Second, we consider the case when ancillary qubits are available. We show that, with ancilla, the circuit depth can be made polynomial, and the space-depth trade-off is not impaired by connectivity constraints unless we have exponentially many ancillary qubits. Third, we obtain nearly optimal results on special families of unitaries, including diagonal unitaries, 2-by-2 block diagonal unitaries, and Quantum State Preparation (QSP) unitaries, the last being a fundamental task used in many quantum algorithms for machine learning and linear algebra.
ATRC: An Anonymous Traceable and Revocable Credential System Using Blockchain for VANETs

Authors:
raw_author_name: Yang Liu
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Debiao He
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Min Luo
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Huaqun Wang
raw_affiliation_string: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Qin Liu
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China

Abstract: With the number of smart vehicle drivers increasing rapidly, privacy-preserving identity management methods in Vehicular Ad-hoc Networks (VANETs) become more imperative and receive much attention from researchers. Since identity leakage or a single point of failure may result in serious consequences in the VANET, the decentralized anonymous credential (DAC) could be a potential approach to construct a robust network. However, the supervision under decentrilized environment could be a necessary and troublesome in the VANETs. In this paper, we present an anonymous traceable and revocable credential system using blockchain, called ATRC, built over a generalized group signature. The underlying group signature not only meets the anonymity requirement but also makes the users master their identities. What's more, to tackle the privacy leakage in the revocation process, we employ the Merkle tree to construct a whitelist, which trades off the efficiency and the privacy. Finally, we gives the comparison and experiment performance to show our scheme holds lightweight on the user side and has fewer computation costs in the show and revocation phase.
Learning from Incorrectness: Active Learning with Negative Pre-training and Curriculum Querying for Histological Tissue Classification

Authors:
raw_author_name: Wentao Hu
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Lianglun Cheng
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Guoheng Huang
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Xiaochen Yuan
raw_affiliation_string: Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
raw_author_name: Guo Zhong
raw_affiliation_string: School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China
raw_author_name: Chi-Man Pun
raw_affiliation_string: Department of Computer and Information Science, University of Macau, Macao, China
raw_author_name: Muyan Cai
raw_affiliation_string: Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and the Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China

Abstract: Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active learning framework called ICAL, which contains Incorrectness Negative Pre-training (INP) and Category-wise Curriculum Querying (CCQ) to address the above problem from the perspective of category-to-category and from the perspective of categories themselves, respectively. In particular, INP incorporates the unique mechanism of active learning to treat the incorrect prediction results that obtained from CCQ as complementary labels for negative pre-training, in order to better distinguish similar categories during the training process. CCQ adjusts the query weights based on the learning status on each category by the model trained by INP, and utilizes uncertainty to evaluate and compensate for query bias caused by inadequate category performance. Experimental results on two histological tissue classification datasets demonstrate that ICAL achieves performance approaching that of fully supervised learning with less than 16% of the labeled data. In comparison to the state-of-the-art active learning algorithms, ICAL achieved better and more balanced performance in all categories and maintained robustness with extremely low annotation budgets. The source code will be released at https://github.com/LactorHwt/ICAL.
Application of deep learning-based ethnic music therapy for selecting repertoire

Authors:
raw_author_name: Yehua Zhang
raw_affiliation_string: Labour Union, Shunde Polytechnic, Foshan, China
raw_author_name: Yan Zhang
raw_affiliation_string: School of Food and Safety, Hainan University, Haikou, China

Abstract: With the advancement of modern medical concepts, the beneficial effects of music on human health have gradually become accepted, and the corresponding music therapy has gradually become a new research direction that has received much attention in recent years. However, folk music has certain peculiarities that lead to the fact that there is no efficient way of selecting repertoire that can be carried out directly throughout the repertoire selection. This paper combines deep learning theory with ethnomusic therapy based on previous research and proposes a deep learning-based approach to ethnomusic therapy song selection. Since the feature extraction process in the traditional sense has insufficient information on each frame, excessive redundancy, inability to process multiple frames of continuous music signals containing relevant music features and weak noise immunity, it increases the computational effort and reduces the efficiency of the system. To address the above shortcomings, this paper introduces deep learning methods into the feature extraction process, combining the feature extraction process of the Deep Auto-encoder (DAE) with the music classification process of Gaussian mixture model, which forms a new DAE-GMM music classification model. Finally, in terms of music therapy selection, this paper compares the music selection method based on co-matrix and physiological signal with the one in this paper. From the theoretical and simulation plots, it can be seen that the method proposed in this paper can achieve both good music classifications from a large number of music and further optimize the process of music therapy song selection from both subjective and objective aspects by considering the therapeutic effect of music on patients. Through this article research results found that the depth of optimization feature vector to construct double the accuracy of the classifier is higher, in addition, compared with the characteristics of the original optimization classification model, using the gaussian mixture model can more accurately classify music, the original landscape “hometown” score of 0.9487, is preferred, insomnia patients mainly ceramic flute style soft tone, without excitant, low depression, have composed of nourishing the heart function.
An Iterative Method for the Distance Constraints in a Multi-Sensor Positioning System

Authors:
raw_author_name: Tao Liu
raw_affiliation_string: GNSS Research Center, Wuhan University, Wuhan, China

Abstract: The distance constraint can enhance the state estimation performance of a multi-sensor positioning system. However, the existing methods encounter problems such as low state estimation accuracy and high computational complexity. This study proposes an iterative constraint algorithm that effectively solves the distance constraint problem in a multi-sensor positioning system. The proposed algorithm linearizes the distance constraint in each iteration to obtain an approximate linear constraint model. Then, it re-estimates the approximate system state by using the estimation projection algorithm. In the last iteration, the proposed algorithm uses the approximate state estimate as input to estimate a more accurate system state until the iteration is terminated. Three simulations are provided to demonstrate the effectiveness and superiority of the proposed algorithm.
A Theoretical Analysis of Density Peaks Clustering and the Component-Wise Peak-Finding Algorithm

Authors:
raw_author_name: Joshua Tobin
raw_affiliation_string: School of Computer Science &#x0026; Statistics, Trinity College Dublin, Dublin 2, Ireland
raw_author_name: Mimi Zhang
raw_affiliation_string: School of Computer Science &#x0026; Statistics, Trinity College Dublin, Dublin 2, Ireland

Abstract: Density peaks clustering detects modes as points with high density and large distance to points of higher density. Each non-mode point is assigned to the same cluster as its nearest neighbor of higher density. Density peaks clustering has proved capable in applications, yet little work has been done to understand its theoretical properties or the characteristics of the clusterings it produces. Here, we prove that it consistently estimates the modes of the underlying density and correctly clusters the data with high probability. However, noise in the density estimates can lead to erroneous modes and incoherent cluster assignments. A novel clustering algorithm, Component-wise Peak-Finding (CPF), is proposed to remedy these issues. The improvements are twofold: 1) the assignment methodology is improved by applying the density peaks methodology within level sets of the estimated density; 2) the algorithm is not affected by spurious maxima of the density and hence is competent at automatically deciding the correct number of clusters. We present novel theoretical results, proving the consistency of CPF, as well as extensive experimental results demonstrating its exceptional performance. Finally, a semi-supervised version of CPF is presented, integrating clustering constraints to achieve excellent performance for an important problem in computer vision.
CR-TransR: A Knowledge Graph Embedding Model for Cultural Domain

Authors:
raw_author_name: Wenjun Hou
raw_affiliation_string: School of Digital Media and Design Art, Beijing University of Posts and Telecommunications, Beijing Key Laboratory of Internet Culture, Beijing, China
raw_author_name: Bing Bai
raw_affiliation_string: Modern Post College, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Chenyang Cai
raw_affiliation_string: School of Digital Media and Design Art, Beijing University of Posts and Telecommunications, Beijing, China

Abstract: As a combination of information computing technology and the cultural field, cultural computing is gaining more attention. The knowledge graph is also gradually applied as a particular data structure in the cultural area. Based on the domain knowledge graph data of the Beijing Municipal Social Science Project “Mining and Utilization of Cultural Resources in the Ancient Capital of Beijing,” this article proposes a graph representation learning model CR-TransR that integrates cultural attributes. Through the analysis of the data in the cultural field of the ancient capital of Beijing, a cultural feature dictionary is constructed, and a domain-specific feature matrix is constructed in the form of word vector splicing. The feature matrix is used to constrain the embedding graph model TransR, and then the feature matrix and the TransR model are jointly trained to complete the embedded expression of the knowledge graph. Finally, a comparative experiment is carried out on the Beijing ancient capital cultural knowledge graph dataset and the effects of the classic graph embedding algorithms TransE, TransH, and TransR. At the same time, we try to reproduce the embedding method with the core idea of neighbor node information aggregation as the core idea, and CRTransR are compared. The experimental tasks include link prediction and triplet classification, and the experimental results show that the CRTransR model performs better.