Reviewerly/exHarmony huggingface dataset:
Oscillating photonic Bell state from a semiconductor quantum dot for quantum key distribution

Authors:
raw_author_name: Matteo Pennacchietti
raw_affiliation_string: Institute for Quantum Computing and Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
raw_author_name: Shlok Nahar
raw_affiliation_string: Institute for Quantum Computing and Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
raw_author_name: Mohd Zeeshan
raw_affiliation_string: National Research Council of Canada, Ottawa, ON, K1A 0R6, Canada
raw_author_name: Sayan Gangopadhyay
raw_affiliation_string: Institute for Quantum Computing and Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
raw_author_name: Philip J. Poole
raw_affiliation_string: National Research Council of Canada, Ottawa, ON, K1A 0R6, Canada
raw_author_name: Dan Dalacu
raw_affiliation_string: National Research Council of Canada, Ottawa, ON, K1A 0R6, Canada
raw_author_name: Andreas Fognini
raw_affiliation_string: Single Quantum B.V., Delft, 2628 CJ, The Netherlands
raw_author_name: Klaus D. Jöns
raw_affiliation_string: Institute for Photonic Quantum Systems (PhoQS), Center for Optoelectronics and Photonics Paderborn (CeOPP) and Department of Physics, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
raw_author_name: Val Zwiller
raw_affiliation_string: Department of Applied Physics, Royal Institute of Technology, Albanova University Centre, 106 91, Stockholm, Sweden
raw_author_name: Thomas Jennewein
raw_affiliation_string: Institute for Quantum Computing and Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
raw_author_name: Norbert Lütkenhaus
raw_affiliation_string: Institute for Quantum Computing and Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
raw_author_name: Michael E. Reimer
raw_affiliation_string: Institute for Quantum Computing and Department of Physics and Astronomy, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

Abstract: Abstract An on-demand source of bright entangled photon pairs is desirable for quantum key distribution (QKD) and quantum repeaters. The leading candidate to generate such pairs is based on spontaneous parametric down-conversion (SPDC) in non-linear crystals. However, its pair extraction efficiency is limited to 0.1% when operating at near-unity fidelity due to multiphoton emission at high brightness. Quantum dots in photonic nanostructures can in principle overcome this limit, but the devices with high entanglement fidelity (99%) have low pair extraction efficiency (0.01%). Here, we show a measured peak entanglement fidelity of 97.5% ± 0.8% and pair extraction efficiency of 0.65% from an InAsP quantum dot in an InP photonic nanowire waveguide. We show that the generated oscillating two-photon Bell state can establish a secure key for peer-to-peer QKD. Using our time-resolved QKD scheme alleviates the need to remove the quantum dot energy splitting of the intermediate exciton states in the biexciton-exciton cascade.
Blockchain-Based Electronic Voting System: Significance and Requirements

Authors:
raw_author_name: Said El Kafhali
raw_affiliation_string: Faculty of Sciences and Techniques, Computer, Networks, Mobility and Modeling Laboratory (IR2M), Hassan First University of Settat, Settat, Morocco

Abstract: In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology.
Spatio-temporal neural network with handcrafted features for skeleton-based action recognition

Authors:
raw_author_name: Mihai Nan
raw_author_name: Mihai Trăscău
raw_author_name: Adina-Magda Florea

Abstract: Abstract The task of human action recognition (HAR) can be found in many computer vision practical applications. Various data modalities have been considered for solving this task, including joint-based skeletal representations which are suitable for real-time applications on platforms with limited computational resources. We propose a spatio-temporal neural network that uses handcrafted geometric features to classify human actions from video data. The proposed deep neural network architecture combines graph convolutional and temporal convolutional layers. The experiments performed on public HAR datasets show that our model obtains results similar to other state-of-the-art methods but has a lower inference time while offering the possibility to obtain an explanation for the classified action.
Convolution smoothing and non-convex regularization for support vector machine in high dimensions

Authors:
raw_author_name: Kangning Wang
raw_affiliation_string: School of Statistics, Shandong Technology and Business University, Yantai, China
raw_author_name: Junning Yang
raw_affiliation_string: School of Statistics, Shandong Technology and Business University, Yantai, China
raw_author_name: Kemal Polat
raw_affiliation_string: Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey
raw_author_name: Xiaofei Sun
raw_affiliation_string: School of Statistics, Shandong Technology and Business University, Yantai, China

Abstract: The support vector machine (SVM) is a well-known statistical learning tool for binary classification. One serious drawback of SVM is that it can be adversely affected by redundant variables, and research has shown that variable selection is crucial and necessary for achieving good classification accuracy. Hence some SVM variable selection studies have been devoted, and they have an unified “empirical hinge loss plus sparse penalty” formulation. However, a noteworthy issue is the computational complexity of existing methods is high especially for large-scale problems, due to the non-smoothness of the hinge loss. To solve this issue, we first propose a convolution smoothing approach, which turns the non-smooth hinge loss into a smooth surrogate one, and they are asymptotically equivalent. Moreover, we construct computationally more efficient SVM variable selection procedure by implementing non-convex penalized convolution smooth hinge loss. In theory, we prove that the resulting variable selection possesses the oracle property when the number of predictors is diverging. Numerical experiments also confirm the good performance of the new method.
Dual-perspective semantic-aware representation blending for multi-label image recognition with partial labels

Authors:
raw_author_name: Tianshui Chen
raw_affiliation_string: School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
raw_author_name: Hefeng Wu
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, Guangdong, China
raw_author_name: Yukai Shi
raw_affiliation_string: School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
raw_author_name: Zhijing Yang
raw_affiliation_string: School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
raw_author_name: Liang Lin
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, Guangdong, China

Abstract: Recently, multi-label image recognition with partial labels (MLR-PL) has attracted increasing attention, in which only some labels are known while others are unknown for each image. However, current algorithms rely on pre-trained image similarity models or iteratively updating the image classification models to generate pseudo labels for unknown labels. Thus, they depend on a certain amount of annotations and inevitably suffer from obvious performance drops. To address this dilemma, we propose a dual-perspective semantic-aware representation blending (DSRB) framework that blends multi-granularity category-specific semantic representation across different images, from an instance and prototype perspective, respectively, to transfer information of known labels to complement unknown labels. Specifically, an instance-perspective representation blending (IPRB) module is designed to blend the representations of the known labels in an image with the representations of the corresponding unknown labels in another image to complement these unknown labels. Meanwhile, a prototype-perspective representation blending (PPRB) module is introduced to learn more stable representation prototypes for each category and blends the representation of unknown labels with the prototypes of corresponding labels in a location-sensitive manner to complement these unknown labels. Extensive experiments on various datasets show that the proposed DSRB consistently outperforms current state-of-the-art algorithms on all known label proportion settings.
AdaMO: Adaptive Meta-Optimization for cold-start recommendation

Authors:
raw_author_name: Juhua Pu
raw_affiliation_string: Beihang Hangzhou Innovation Institute, Yuhang, Hangzhou, China; State Key Laboratory of Software Development Environment, Beihang University, Beijing, China
raw_author_name: Yuanhong Wang
raw_affiliation_string: Beihang Hangzhou Innovation Institute, Yuhang, Hangzhou, China; State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

Abstract: Meta-learning has been proven effective in tackling the cold-start problem of recommendation systems. Most work in this line adopts the meta-optimization idea that learns global knowledge to initialize the base recommender and adapts it for unseen recommendation tasks. Typically, the process is non-adaptive in the sense that both the initialization and model fitting are not task-specific. Such non-adaptive approaches do not work well for the strict cold-start problem, where historical interaction data of the new users is scarce or not available at all. In this paper, we propose a novel adaptive meta-optimization approach, called Adaptive Meta-Optimization (AdaMO), to address the problem. AdaMO is fully adaptive, namely, it simultaneously customizes the initialization and model fitting according to task-specific information. During the cold-start phase, it aggregates task-specific information with transferred knowledge from all historical tasks to tune the initialization of the base model. In the subsequent warm-up phase, AdaMO mines the adaptive transition information from the behavior patterns and parameter status to effectively fit the model using transitional learning. By making a sophisticated balance between shared knowledge and task-specific information, AdaMO outperforms the state-of-the-art methods in various cold-start scenarios.
A contrastive learning based unsupervised multi-view stereo with multi-stage self-training strategy

Authors:
raw_author_name: Zihang Wang
raw_affiliation_string: School of Computer Science and Engineering, State Key Laboratory of Software Development Environment, Jiangxi Research Institute, Beihang University, Beijing, 100191, China
raw_author_name: Haonan Luo
raw_affiliation_string: School of Computer and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, China
raw_author_name: Xiang Wang
raw_affiliation_string: School of Computer Science and Engineering, State Key Laboratory of Software Development Environment, Jiangxi Research Institute, Beihang University, Beijing, 100191, China
raw_author_name: Jin Zheng
raw_affiliation_string: School of Computer Science and Engineering, State Key Laboratory of Software Development Environment, Jiangxi Research Institute, Beihang University, Beijing, 100191, China
raw_author_name: Xin Ning
raw_affiliation_string: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
raw_author_name: Xiao Bai
raw_affiliation_string: School of Computer Science and Engineering, State Key Laboratory of Software Development Environment, Jiangxi Research Institute, Beihang University, Beijing, 100191, China

Abstract: Recent years, unsupervised multi-view stereo (MVS) methods have achieved excellent success that can produce comparable results to earlier supervised work. However, as unsupervised MVS uses image reconstruction as pretext task, it faces two vital drawbacks: RGB value, which is the measurement of image, is not robust enough across views due to complicated environment like lighting conditions and reconstruction itself cannot reflect quality of depth estimation linearly. These problems cause the actual optimization goal to diverge from the expected optimization goal, thus could impair the training process. To enhance robustness of pretext task, we propose a contrastive learning based constraint. The constraint adds featuremetric consistency across views by forcing the features between matching points similar and the features between unmatched points opposite. To add linear properties to overall training procedure, we propose a multi-stage training strategy that uses pseudo label as supervision after unsupervised training at the beginning. On the other hand, we adopt an iterative optimizer that proven to be quite effective in supervised MVS to accelerate training. Finally, we conduct a series of experiments on the DTU dataset and Tanks and Temples dataset that demonstrate the efficiency and robustness of our method compared with the state-of-the-art methods in terms of accuracy, completeness and speed.
Coal and gas protrusion risk evaluation based on cloud model and improved combination of assignment

Authors:
raw_author_name: Wei Wang
raw_affiliation_string: College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou, 121001, People’s Republic of China
raw_author_name: Xinchao Cui
raw_affiliation_string: School of Coal Engineering, Shanxi Datong University, Datong, 037000, Shanxi, People’s Republic of China
raw_author_name: Ran Liang
raw_affiliation_string: School of Coal Engineering, Shanxi Datong University, Datong, 037000, Shanxi, People’s Republic of China

Abstract: The proposed study presents an enhanced combination weighting cloud model for accurate assessment of coal and gas outburst risks. Firstly, a comprehensive evaluation index system for coal and gas outburst risks is established, consisting of primary indicators such as coal rock properties and secondary indicators including 13 factors. Secondly, the improved Analytic Hierarchy Process (IAHP) based on the 3-scale method and the improved CRITIC based on indicator correlation weight determination method are employed to determine subjective and objective weights of evaluation indicators respectively. Additionally, the Lagrange multiplier method is introduced to fuse these weights in order to obtain optimal weights. Subsequently, a prominent danger assessment model is developed based on cloud theory. Finally, using a mine in Hebei Province as an example, the results obtained from IAHP combined with improved CRITIC weighting method are compared with those from traditional AHP method and AHP-CRITIC combination weighting method. The findings demonstrate that among all methods considered, IAHP combined with improved CRITIC exhibits superior performance in terms of distribution expectation Ex, entropy value En, and super entropy He within cloud digital features; thus indicating that the risk level of coal and gas outbursts in this particular mine can be classified as general risk. These evaluation results align well with actual observations thereby validating the effectiveness of this approach. Consequently, this constructed model enables rapid yet accurate determination of coal and gas outburst risks within mines.
Online course as a means of developing master's professional competencies: results of a pedagogical experiment

Authors:
raw_author_name: Ruslan Yu. Kaluhin
raw_affiliation_string: Kryvyi Rih State Pedagogical University

Abstract: The article proposes a solution to a scientific problem, which is to substantiate the feasibility of using online courses in the professional training of masters in the pedagogical university, to develop an online course and experimentally test its effectiveness as a means of developing the professional competencies of masters in the speciality 014~Secondary Education (Mathematics). The author used the following methods of scientific research: pedagogical experiment, observation (for systematic perception and analysis of learning activities, motivation and personal growth of students in the context of blended learning), testing (to determine the level of logical thinking and development of students’ professional competences), questionnaire (to monitor the ergonomics and efficiency of the online course and the Mathematics teacher’s e-portfolio), expert evaluation of the developed online course, and methods of mathematical statistics (for proper processing of experimental data and verification of the online course’ effectiveness). The results of the control stage of the study indicate a positive trend in the development of mathematical, information and educational, methodological, psychological and pedagogical, and professional and technological competencies of students in the experimental group. The final test of the online course was chosen as a tool for determining the level of mathematical competencies of the masters in the experimental group. The reliability of the assessment of the masters’ professional competencies was confirmed by testing the hypothesis of the normal distribution of test results according to the Shapiro-Wilk test. In addition, a close correlation between students' academic achievement level in an online course and the level of logical thinking development was substantiated using the Student’s t-test. Prospects for further research include the development of online courses on Mathematics teaching methods with an emphasis on methodical, professional, and technological competencies; a detailed study of the impact of the latest information technologies (artificial intelligence, virtual and augmented reality, web programming, etc.) on the development of students’ information and educational professional competences in distance and blended learning.
Programmable photonic system for quantum simulation in arbitrary topologies

Authors:
raw_author_name: Ben Bartlett
raw_affiliation_string: Department of Applied Physics, Stanford University 1 , Stanford, California 94305, USA
raw_author_name: Olivia Y. Long
raw_affiliation_string: Department of Applied Physics, Stanford University 1 , Stanford, California 94305, USA
raw_author_name: Avik Dutt
raw_affiliation_string: Department of Mechanical Engineering, and Institute for Physical Science and Technology (IPST), University of Maryland 2 , College Park, Maryland 20742, USA; National Quantum Laboratory (QLab) at Maryland 3 , College Park, Maryland 20742, USA
raw_author_name: Shanhui Fan
raw_affiliation_string: Department of Electrical Engineering, Stanford University 4 , Stanford, California 94305, USA

Abstract: Synthetic dimensions have generated great interest for studying many types of topological, quantum, and many-body physics, and they offer a flexible platform for simulation of interesting physical systems, especially in high dimensions. In this paper, we describe a programmable photonic device capable of emulating the dynamics of a broad class of Hamiltonians in lattices with arbitrary topologies and dimensions. We derive a correspondence between the physics of the device and the Hamiltonians of interest, and we simulate the physics of the device to observe a wide variety of physical phenomena, including chiral states in a Hall ladder, effective gauge potentials, and oscillations in high-dimensional lattices. Our proposed device opens new possibilities for studying topological and many-body physics in near-term experimental platforms.
Conv-Attention: A Low Computation Attention Calculation Method for Swin Transformer

Authors:
raw_author_name: Suyun Luo
raw_affiliation_string: School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, 333 Longteng Road, Songjiang District, Shanghai, 201620, China
raw_author_name: Xiaoci Huang
raw_affiliation_string: School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, 333 Longteng Road, Songjiang District, Shanghai, 201620, China
raw_author_name: Dan Wei
raw_affiliation_string: School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, 333 Longteng Road, Songjiang District, Shanghai, 201620, China

Abstract: Abstract Transformer networks have excellent performance in various different vision tasks, especially object detection. However, in practical applications, Transformer is difficult to use on-board due to its large computational complexity. In this paper, we propose a new approach for reducing the computation of self-attention, which is called conv-attention. Different from the work of self-attention, conv-attention is inspired by NetVLAD and adopts the probability obtained by soft classification to replace the similarity calculation between query and key. Moreover, we combine the three convolution operations for computing the attention matrix in order to reduce the computational effort. Using the Swin Transformer as a comparison, experiments show that the parameters and FLOPs are reduced by 15% and 16%. Meanwhile, MAP is improved in vehicle object detection, including both FILR and Nuscenses datasets.