Reviewerly/exHarmony huggingface dataset:
3-D Reconstruction and Measurement of Blade Profiles With Laser-Scanning Sensor via Multiview Registration Based on Dynamic Encoding of Feature-Coordinate Information

Authors:
raw_author_name: Zongping Wang
raw_affiliation_string: School of Mechanical Engineering, Sichuan University, Chengdu, China
raw_author_name: Jie Dong
raw_affiliation_string: School of Aeronautics and Astronautics, School of Mechanical Engineering, Sichuan University, Chengdu, China
raw_author_name: Ming Yin
raw_affiliation_string: School of Mechanical Engineering, Sichuan University, Chengdu, China
raw_author_name: Yangyang Zhu
raw_affiliation_string: School of Mechanical Engineering, Sichuan University, Chengdu, China
raw_author_name: Haotian Zheng
raw_affiliation_string: School of Mechanical Engineering, Sichuan University, Chengdu, China
raw_author_name: Luofeng Xie
raw_affiliation_string: School of Mechanical Engineering, Sichuan University, Chengdu, China
raw_author_name: Guofu Yin
raw_affiliation_string: School of Mechanical Engineering, Sichuan University, Chengdu, China

Abstract: Currently, optical-based method provides a feasible means for blade reconstruction and measurement. However, as the key step, multi-view registration is still heavily reliant on the measurement system's motion stability and geometric accuracy. Moreover, the complex and high-reflective freeform surface of blades may result in both insufficient overlaps and less prominent overlap-area features between adjacent views, making accurate multi-view data alignment difficult. Thus, we propose a method to realize accurate 3-D reconstruction and measurement of blade profiles based on a scanning system with a laser-sensor and a coarse-to-fine registration strategy. Firstly, coarse alignment of the multi-view data is achieved by calibrating the system's rotational axis using the blade datum plane feature, which can provide a good initial value for the fine registration and improve the reconstruction efficiency. Then, a fine registration algorithm based on the dynamic encoding of feature-coordinate fusion information is proposed to refine the coarsely aligned data and reduce the effect of system motion error on registration accuracy. Here, the introduction of fusion information can effectively eliminate the redundant correspondences to continuously optimize the matching probability between multi-view data in each iteration, improving registration accuracy and efficiency. Finally, experiments on typical blades and comparison with the other fine registration algorithms demonstrate the accuracy and robustness of the proposed method.
Higher-Order Process Matrix Tomography of a Passively-Stable Quantum Switch

Authors:
raw_author_name: Antesberger, Michael
raw_affiliation_string: University of Vienna [Vienna]
raw_author_name: Quintino, Marco Túlio
raw_affiliation_string: Information Quantique [LIP6]
raw_author_name: Walther, Philip
raw_affiliation_string: University of Vienna [Vienna]
raw_author_name: Rozema, Lee A.
raw_affiliation_string: University of Vienna [Vienna]

Abstract: The field of indefinite causal order (ICO) has seen a recent surge in interest. Much of this research has focused on the quantum switch, wherein multiple parties act in a superposition of different orders in a manner transcending the quantum circuit model. This results in a new resource for quantum protocols, and is exciting for its relation to issues in foundational physics. The quantum switch is also an example of a higher-order quantum operation, in that it transforms not only quantum states but also other quantum operations. To date, no quantum process without a definite causal order has been completely experimentally characterized. Indeed, past work on the quantum switch has confirmed its ICO by measuring causal witnesses or demonstrating resource advantages, but the complete process matrix has been described only theoretically. Here we report our performing higher-order quantum process tomography. However, doing so requires exponentially many measurements with a scaling worse than that of standard process tomography. We overcome this challenge by creating a new passively stable fiber-based quantum switch using active optical elements to deterministically generate and manipulate time-bin encoded qubits. Moreover, our new architecture for the quantum switch can be readily scaled to multiple parties. By reconstructing the process matrix, we estimate its fidelity and tailor different causal witnesses directly for our experiment. To achieve this, we measure a set of tomographically complete settings, which also spans the input operation space. Our tomography protocol allows the characterization and debugging of higher-order quantum operations with and without an ICO, while our experimental time-bin techniques could enable the creation of a new realm of higher-order quantum operations with an ICO.1 MoreReceived 1 June 2023Accepted 22 December 2023DOI:https://doi.org/10.1103/PRXQuantum.5.010325Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptical quantum information processingQuantum foundationsQuantum tomographyQuantum Information, Science & Technology
Joint Request Updating and Elastic Resource Provisioning With QoS Guarantee in Clouds

Authors:
raw_author_name: Gongming Zhao
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Jingzhou Wang
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Hongli Xu
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Yangming Zhao
raw_affiliation_string: School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
raw_author_name: Xuwei Yang
raw_affiliation_string: Huawei Cloud Computing Technology Company Ltd., Beijing, China
raw_author_name: He Huang
raw_affiliation_string: School of Computer Science and Technology, Soochow University, Suzhou, China

Abstract: In a commercial cloud, service providers (e.g., video streaming service provider) rent resources from cloud vendors (e.g., Google Cloud Platform) and provide services to cloud users, making a profit from the price gap. Cloud users acquire services by forwarding their requests to corresponding servers. In practice, as a common scenario, traffic dynamics will cause server overload or load-unbalancing. Existing works mainly deal with the problem by two methods: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">elastic resource provisioning</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">request updating</i> . Elastic resource provisioning is a fast and agile solution but may cost too much since service providers need to buy extra resources from cloud vendors. Though request updating is a free solution, it will cause a significant delay, resulting in a bad users’ QoS. In this paper, we present a new scheme, called real-time request updating with elastic resource provisioning (TRUST), to help service providers pay less cost with users’ QoS guarantee in clouds. In addition, we propose an efficient algorithm for TRUST with a bounded approximation factor based on progressive-rounding. Both small-scale experiment results and large-scale simulation results show the superior performance of our proposed algorithm compared with state-of-the-art benchmarks.
Interface-Based Side Channel in TEE-Assisted Networked Services

Authors:
raw_author_name: Xiaohan Zhang
raw_affiliation_string: State Key Laboratory of Integrated Service Networks and the School of Cyber Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Jinwen Wang
raw_affiliation_string: Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
raw_author_name: Yueqiang Cheng
raw_affiliation_string: NIO Security Research, Shanghai, China
raw_author_name: Qi Li
raw_affiliation_string: Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China
raw_author_name: Kun Sun
raw_affiliation_string: Department of Information Sciences and Technology, George Mason University, Fairfax, VA, USA
raw_author_name: Yao Zheng
raw_affiliation_string: Department of Electrical Engineering, University of Hawaii, Manoa, HI, USA
raw_author_name: Ning Zhang
raw_affiliation_string: Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
raw_author_name: Xinghua Li
raw_affiliation_string: State Key Laboratory of Integrated Service Networks and the School of Cyber Engineering, Xidian University, Xi&#x2019;an, China

Abstract: With the accelerating adaption of Cloud and Edge computing, cloud-based networked deployment emerges to enable providers to deliver services in a cost-effective and elastic manner. However, security concern remains one of the major obstacles to its wider adaption. Trusted Execution Environment (TEE) has been advocated to protect cloud services in an isolated execution environment. In this paper, we present a new genre of side-channel attack called interface-based side-channel attack and demonstrate its effectiveness on the TEE-assisted networked service system. The root cause of this attack is the input-dependent interface invocation (e.g., interface information and invocation patterns) that can be observed by untrusted software to reveal the control flows inside the enclave. Our evaluation demonstrates that the attack can effectively re-identify encrypted web pages processed in the SGX enclave with an accuracy of 87.6% and a recall of 76.6%, and can reduce the search domain of the 1024 bits RSA private keys to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.69 \times 10^{-6}$</tex-math> </inline-formula> of the original search domain. As countermeasures, we propose, implement and evaluate a set of static analysis tools to mitigate the newly discovered threats. The key idea is to use inter-procedural dataflow analysis to identify potential leakage via the interface, and then mitigate them during compilation using techniques including branch obfuscation, loop obfuscation, and constant size wrapper.
Multi-objective Feature Attribution Explanation For Explainable Machine Learning

Authors:
raw_author_name: Ziming Wang
raw_affiliation_string: Research Institute of Trustworthy Autonomous Systems, and Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Departmentof Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
raw_author_name: Changwu Huang
raw_affiliation_string: Research Institute of Trustworthy Autonomous Systems, and Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Departmentof Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
raw_author_name: Yun Li
raw_affiliation_string: The Advanced Cognitive Technology Lab, Huawei Technologies Co., Ltd., Shanghai, China
raw_author_name: Xin Yao
raw_affiliation_string: Research Institute of Trustworthy Autonomous Systems, and Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, China and School of Computer Science, University of Birmingham, Birmingham, UK

Abstract: The feature attribution-based explanation (FAE) methods, which indicate how much each input feature contributes to the model’s output for a given data point, are one of the most popular categories of explainable machine learning techniques. Although various metrics have been proposed to evaluate the explanation quality, no single metric could capture different aspects of the explanations. Different conclusions might be drawn using different metrics. Moreover, during the processes of generating explanations, existing FAE methods either do not consider any evaluation metric or only consider the faithfulness of the explanation, failing to consider multiple metrics simultaneously. To address this issue, we formulate the problem of creating FAE explainable models as a multi-objective learning problem that considers multiple explanation quality metrics simultaneously. We first reveal conflicts between various explanation quality metrics, including faithfulness, sensitivity, and complexity. Then, we define the considered multi-objective explanation problem and propose a multi-objective feature attribution explanation (MOFAE) framework to address this newly defined problem. Subsequently, we instantiate the framework by simultaneously considering the explanation’s faithfulness, sensitivity, and complexity. Experimental results comparing with six state-of-the-art FAE methods on eight datasets demonstrate that our method can optimize multiple conflicting metrics simultaneously and can provide explanations with higher faithfulness, lower sensitivity, and lower complexity than the compared methods. Moreover, the results have shown that our method has better diversity, i.e., it provides various explanations that achieve different tradeoffs between multiple conflicting explanation quality metrics. Therefore, it can provide tailored explanations to different stakeholders based on their specific requirements.
Sparse Mutual Granularity-Based Feature Selection and its Application of Schizophrenia Patients

Authors:
raw_author_name: Hengrong Ju
raw_affiliation_string: School of Information Science and Technology, Nantong University, Nantong, China
raw_author_name: Jiashuang Huang
raw_affiliation_string: School of Information Science and Technology, Nantong University, Nantong, China
raw_author_name: Weiping Ding
raw_affiliation_string: School of Information Science and Technology, Nantong University, Nantong, China
raw_author_name: Xibei Yang
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, China

Abstract: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -nearest neighborhood information granularity-based feature selection is derived from the well-known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN) classification technique, which is widely employed in data mining. However, the current <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighborhood-based information granules cannot effectively handle data with different density distributions. To address this problem, a sparse mutual granularity-based feature selection approach is developed. First, a personalized information granule is constructed based on the optimal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> values for each sample. The optimal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> value is obtained through the number of correlated samples, where the correlations between the samples are learned by the sparse constraint function. The achieved optimal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> can define the size of the granularity-based model and increase the classification accuracy. Second, a mutual-information strategy is introduced in the granularity process. Irrelevant samples in the granularity-based model are removed, which improves the classification performance. Third, an improved heuristic feature selection algorithm is developed to address the nonmonotonic problem. Compared with the classical heuristic method, the proposed feature selection method can improve the performance of the obtained subset and avoid degradation caused by non-monotonicity. The experimental results on the UCI datasets show that the sparse mutual granularity-based feature selection approach is effective for managing data with different density distributions. Finally, the proposed feature selection approach is applied to select significant brain regions in several schizophrenia datasets. It contributes to the prediction of schizophrenia and also provides a new direction for the improvement of medical-image analysis.
Quantum algorithm for minimum dominating set problem with circuit design

Authors:
raw_author_name: Haoying Zhang
raw_affiliation_string: Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum, Beijing, 102249, CHINA
raw_author_name: Shaoxuan Wang
raw_affiliation_string: Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum, Beijing, 102249, CHINA
raw_author_name: Xinjian Liu
raw_affiliation_string: Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum, Beijing, 102249, CHINA
raw_author_name: Yukun Wang
raw_affiliation_string: Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum, Beijing, 102249, CHINA

Abstract: Using quantum algorithms to solve various problems has attracted widespread attention with the development of quantum computing. Researchers are particularly interested in using the acceleration properties of quantum algorithms to solve NP-complete problems. This paper focuses on the well-known NP-complete problem of finding the minimum dominating set in undirected graphs. To expedite the search process, a quantum algorithm employing Grover’s search is proposed. However, a challenge arises from the unknown number of solutions for the minimum dominating set, rendering direct usage of original Grover’s search impossible. Thus, a swap test method is introduced to ascertain the number of iterations required. The oracle, diffusion operators, and swap test are designed with achievable quantum gates. The query complexity is O (1.414 n ) and the space complexity is O ( n ). To validate the proposed approach, qiskit software package is employed to simulate the quantum circuit, yielding the anticipated results.
Nonmyopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation

Authors:
raw_author_name: Marcel Hernandez
raw_affiliation_string: Department of Electrical Engineering, Electronics, Computer Science, The University of Liverpool, Liverpool, U.K.
raw_author_name: Ángel F. García-Fernández
raw_affiliation_string: Department of Electrical Engineering, Electronics, Computer Science, The University of Liverpool, Liverpool, U.K.
raw_author_name: Simon Maskell
raw_affiliation_string: Department of Electrical Engineering, Electronics, Computer Science, The University of Liverpool, Liverpool, U.K.

Abstract: This paper is concerned with sensor management for target search and track using the generalised optimal subpattern assignment (GOSPA) metric. Utilising the GOSPA metric to predict future system performance is computationally challenging, because of the need to account for uncertainties within the scenario, notably the number of targets, the locations of targets, and the measurements generated by the targets subsequent to performing sensing actions. In this paper, efficient sample-based techniques are developed to calculate the predicted mean square GOSPA metric. These techniques allow for missed detections and false alarms, and thereby enable the metric to be exploited in scenarios more complex than those previously considered. Furthermore, the GOSPA methodology is extended to perform non-myopic (i.e. multi-step) sensor management via the development of a Bellman-type recursion that optimises a conditional GOSPA-based metric. Simulations for scenarios with missed detections, false alarms, and planning horizons of up to three time steps demonstrate the approach, in particular showing that optimal plans align with an intuitive understanding of how taking into account the opportunity to make future observations should influence the current action. It is concluded that the GOSPA-based, non-myopic search and track algorithm offers a powerful mechanism for sensor management.
Coded Distributed Multiplication for Matrices of Different Sparsity Levels

Authors:
raw_author_name: Yu-Chih Huang
raw_affiliation_string: Institute of Communications Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
raw_author_name: Ming-Chun Lee
raw_affiliation_string: Institute of Communications Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
raw_author_name: Po-Ning Chen
raw_affiliation_string: Institute of Communications Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Abstract: The problem of computing batches of matrix multiplications in distributed computing systems with stragglers is studied. Unlike existing works in the literature, the matrices in a batch are assumed to be sparse, and the sparsity levels for matrices in different batches can be different. A novel coding scheme, called generalized sparse code (GSC), is proposed, in which the matrices are partitioned into smaller chunks that are re-grouped and encoded by respective sparse codes. The expected runtime of the proposed GSC scheme is analyzed, based on which a task assignment problem associated with the proposed GSC is formulated and solved. The solution follows the reverse water-filling principle, by which an efficient worker assignment algorithm whose worst-case time complexity equal to the total number of workers can be developed. Simulation results validate the advantage of the proposed GSC over four existing schemes, including entangled polynomial codes (EP), generalized cross-subspace alignment (GCSA), Lagrange coded computing (LCC) codes and factored Luby transform (FLT) codes at all sparsity levels. As a potential application of the proposed GSC, the problem of computing a batch of matrix multiplications with similarity is discussed.
Identifiability in robust estimation of tree structured models

Authors:
raw_author_name: Piotr Zwiernik
raw_affiliation_string: Department of Statistical Sciences, University of Toronto, Toronto, Canada

Abstract: Consider the problem of learning undirected graphical models on trees from corrupted data. Recently Katiyar, Shah, and Caramanis showed that it is possible to recover trees from noisy binary data up to a small equivalence class of possible trees. Another paper by Katiyar, Hoffmann, and Caramanis follows a similar pattern for the Gaussian case. By framing this as a special phylogenetic recovery problem we largely generalize these two settings. Using the framework of linear latent tree models we discuss tree identifiability for binary data under a continuous corruption model (e.g. black/white images with greyscale corruption). For the Ising and the Gaussian tree model we also provide a characterisation of when the Chow-Liu algorithm consistently learns the underlying tree from the noisy data.