Reviewerly/exHarmony huggingface dataset:
T-Distributed Stochastic Neighbor Embedding for Co-Representation Learning

Authors:
raw_author_name: Wei Chen
raw_affiliation_string: Southwest Jiaotong University, Chengdu, Sichuan, China
raw_author_name: Hongjun Wang
raw_affiliation_string: Southwest Jiaotong University, Chengdu, Sichuan, China
raw_author_name: Yinghui Zhang
raw_affiliation_string: Chengdu Neusoft University, Chengdu, Sichuan, China
raw_author_name: Ping Deng
raw_affiliation_string: Xihua University, Chengdu, Sichuan, China
raw_author_name: Zhipeng Luo
raw_affiliation_string: Southwest Jiaotong University, Chengdu, Sichuan, China
raw_author_name: Tianrui Li
raw_affiliation_string: Southwest Jiaotong University, Chengdu, Sichuan, China

Abstract: Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute perspective. To solve this problem, in this article, ctSNE , a co-representation learning model based on t-distributed stochastic neighbor embedding, is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters (i.e. co-discrimination). On the basis of t-distributed stochastic neighbor embedding retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced (i.e., row and column hidden relationship information) so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms.
Cooperative Multiagent Reinforcement Learning With Partial Observations

Authors:
raw_author_name: Michael M. Zavlanos
raw_affiliation_string: Department of Mechanical Engineering and Material Science, Duke University, Durham, NC, USA

Abstract: In this paper, we propose a distributed zeroth-order policy optimization method for Multi-Agent Reinforcement Learning (MARL). Existing MARL algorithms often assume that every agent can observe the states and actions of all the other agents in the network. This can be impractical in large-scale problems, where sharing the state and action information with multi-hop neighbors may incur significant communication overhead. The advantage of the proposed zeroth-order policy optimization method is that it allows the agents to compute the local policy gradients needed to update their local policy functions using local estimates of the global accumulated rewards that depend on partial state and action information only and can be obtained using consensus. Specifically, to calculate the local policy gradients, we develop a new distributed zeroth-order policy gradient estimator that relies on one-point residual-feedback which, compared to existing zeroth-order estimators that also rely on one-point feedback, significantly reduces the variance of the policy gradient estimates improving, in this way, the learning performance. We show that the proposed distributed zeroth-order policy optimization method with constant stepsize converges to the neighborhood of a policy that is a stationary point of the global objective function. The size of this neighborhood depends on the agents' learning rates, the exploration parameters, and the number of consensus steps used to calculate the local estimates of the global accumulated rewards. Moreover, we provide numerical experiments that demonstrate that our new zeroth-order policy gradient estimator is more sample-efficient compared to other existing one-point estimators.
Text Document Clustering Using Modified Particle Swarm Optimization with k-means Model

Authors:
raw_author_name: R. Dodda
raw_affiliation_string: Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Ananthapur, India
raw_author_name: A.S. Babu
raw_affiliation_string: Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Ananthapur, India

Abstract: In the present digital era, vast amounts of data are generated by millions of Internet users in the form of unstructured text documents. The clustering and organizing of text documents play a crucial role in the applications of data analysis and market research. In this research manuscript, a new modified version of metaheuristic-based optimization technique is proposed with k-means for clustering the text documents. In the initial phase, the input data are acquired from the three-benchmark databases such as Reuters-21578, 20-Newsgroup and British Broadcasting Corporation (BBC)-sport. Further, the data denoising is accomplished by using the common techniques: stemming, lemmatization, tokenization, and stop word removal. In addition to this, the denoised data are transformed into feature vectors by utilizing Term Frequency (TF)-Inverse Document Frequency (IDF) technique. The computed feature vectors are given to the Modified Particle Swarm Optimization (MPSO) with k-means to group the closely related text documents by minimizing the similarity in different clusters. The experimental examination showed that the proposed MPSO with k-means model achieved accuracy of 0.85, 0.85 and 0.86 on the Reuters-21578, 20-Newsgroup and BBC-sport databases, which are superior to the comparative models.
Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

Authors:
raw_author_name: Wenjie Zhu
raw_affiliation_string: Jinling Wenyun Art Design Co., Ltd.,Zhenjiang,China,212000
raw_author_name: Yongsheng Zhou
raw_affiliation_string: School of Design, Dongseo University,Busan,Republic of Korea,47011

Abstract: With the ever-increasing diversification of people's interests and preferences, artwork has become one of the most popular commodities or investment goods in E-commerce, and it increasingly attracts the attention of the public.Currently, many real-world or virtual artworks can be found in E-commerce, and finding a means to recommend them to appropriate users has become a significant task to alleviate the heavy burden on artwork selection decisions by users.Existing research mainly studies the problem of single-artwork recommendation while neglecting the more practical but more complex composite recommendation of artworks in E-commerce, which considerably influences the quality of experience of potential users, especially when they need to select a set of artworks instead of a single artwork.Inspired by this limitation, we put forward a novel composite recommendation approach to artworks by a user keyword-driven correlation graph search named ART com-rec .Through ART com-rec , the recommender system can output a set of artworks (e.g., an artwork composite solution) in E-commerce by considering the keywords typed by a user to indicate his or her personalized preferences.Finally, we validate the feasibility of the ART com-rec approach by a set of simulated experiments on a real-world PW dataset.
Efficient Barrett Modular Multiplication Based on Toom-Cook Multiplication

Authors:
raw_author_name: Yue Hao
raw_affiliation_string: School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
raw_author_name: Weijiang Wang
raw_affiliation_string: School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
raw_author_name: Hua Dang
raw_affiliation_string: School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China

Abstract: The Toom-Cook multiplier (TCM) is the most suitable method for multiplying large integers with key widths typically employed in ECC or RSA due to its computational efficiency. In this brief, we improve the accuracy of hardware TCM algorithm, and propose a TCM-based Barrett modular multiplication algorithm. We provide detailed mathematical proofs for the determinantion of parameters, error ranges, and input-output bounds, which serve to validate the robustness and reliability of our proposed algorithm. Further, the hardware architecture and the ASIC implementation results under 40nm, 65nm, 90nm process are presented. Compared with existing works, the proposed modular multiplier exhibits superior performance in terms of Area-Time-Product.
Cerise: Program Verification on a Capability Machine in the Presence of Untrusted Code

Authors:
raw_author_name: Aïna Linn Georges
raw_affiliation_string: MPI-SWS, Germany
raw_author_name: Armaël Guéneau
raw_affiliation_string: Université Paris-Saclay, CNRS, ENS Paris-Saclay, Inria, Laboratoire Méthodes Formelles, France
raw_author_name: Thomas Van Strydonck
raw_affiliation_string: imec-Distrinet, KU Leuven, Belgium
raw_author_name: Amin Timany
raw_affiliation_string: Aarhus University, Denmark
raw_author_name: Alix Trieu
raw_affiliation_string: ANSSI, France
raw_author_name: Dominique Devriese
raw_affiliation_string: imec-Distrinet, KU Leuven, Belgium
raw_author_name: Lars Birkedal
raw_affiliation_string: Aarhus University, Denmark

Abstract: A capability machine is a type of CPU allowing fine-grained privilege separation using capabilities , machine words that represent certain kinds of authority. We present a mathematical model and accompanying proof methods that can be used for formal verification of functional correctness of programs running on a capability machine, even when they invoke and are invoked by unknown (and possibly malicious) code. We use a program logic called Cerise for reasoning about known code, and an associated logical relation, for reasoning about unknown code. The logical relation formally captures the capability safety guarantees provided by the capability machine. The Cerise program logic, logical relation, and all the examples considered in the paper have been mechanized using the Iris program logic framework in the Coq proof assistant. The methodology we present underlies recent work of the authors on formal reasoning about capability machines [Georges et al. 2021 ; Skorstengaard et al. 2019a ; Van Strydonck et al. 2022 ], but was left somewhat implicit in those publications. In this paper we present a pedagogical introduction to the methodology, in a simpler setting (no exotic capabilities), and starting from minimal examples. We work our way up to new results about a heap-based calling convention and implementations of sophisticated object-capability patterns of the kind previously studied for high-level languages with object-capabilities, demonstrating that the methodology scales to such reasoning.
A novel fixed-time error-monitoring neural network for solving dynamic quaternion-valued Sylvester equations

Authors:
raw_author_name: Lin Xiao
raw_affiliation_string: Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, Hunan 410081, China
raw_author_name: Zidong Wang
raw_affiliation_string: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom

Abstract: This paper addresses the dynamic quaternion-valued Sylvester equation (DQSE) using the quaternion real representation and the neural network method. To transform the Sylvester equation in the quaternion field into an equivalent equation in the real field, three different real representation modes for the quaternion are adopted by considering the non-commutativity of quaternion multiplication. Based on the equivalent Sylvester equation in the real field, a novel recurrent neural network model with an integral design formula is proposed to solve the DQSE. The proposed model, referred to as the fixed-time error-monitoring neural network (FTEMNN), achieves fixed-time convergence through the action of a state-of-the-art nonlinear activation function. The fixed-time convergence of the FTEMNN model is theoretically analyzed. Two examples are presented to verify the performance of the FTEMNN model with a specific focus on fixed-time convergence. Furthermore, the chattering phenomenon of the FTEMNN model is discussed, and a saturation function scheme is designed. Finally, the practical value of the FTEMNN model is demonstrated through its application to image fusion denoising.
Adaptive Shape Servoing of Elastic Rods Using Parameterized Regression Features and Auto-Tuning Motion Controls

Authors:
raw_author_name: Jiaming Qi
raw_affiliation_string: Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
raw_author_name: Guangtao Ran
raw_affiliation_string: Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
raw_author_name: Jian Liu
raw_affiliation_string: School of Automation, Southeast University, Nanjing, China
raw_author_name: Wanyu Ma
raw_affiliation_string: Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
raw_author_name: Peng Zhou
raw_affiliation_string: Department of Computer Science, University of Hong Kong, Pok Fu Lam, Hong Kong
raw_author_name: David Navarro-Alarcon
raw_affiliation_string: Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Abstract: The robotic manipulation of deformable linear objects has shown great potential in a wide range of real-world applications. However, it presents many challenges due to the objects' non-linear properties and high-dimensional geometric configuration. In this paper, we propose an efficient shape servoing framework to manipulate elastic objects through real-time visual feedback automatically. The proposed parameterized regression features are used to construct a compact (low-dimensional) feature vector (Bézier and NURBS) that quantifies the object's shape, thus enabling the establishment of an explicit shape servo-loop. To automatically manipulate the object into a desired configuration, our adaptive controller can iteratively estimate the sensorimotor model that relates the robot's motion and shape changes. This valuable capability enables the effective deformation of objects with unknown mechanical models. An auto-tuning algorithm is introduced to adjust the controller's gain and, thus, modulate the shaping motions based on optimal performance criteria. To validate the proposed framework, a detailed experimental study with vision-guided robot manipulators is presented.
Adapting COVID-19 Contact Tracing Protocols to Accommodate Resource Constraints, Philadelphia, Pennsylvania, USA, 2021

Authors:
raw_author_name: Seonghye Jeon
raw_author_name: Lydia Watson-Lewis
raw_author_name: Gabriel Rainisch
raw_author_name: Patrick K. Moonan
raw_author_name: John E. Oeltmann
raw_author_name: Bishwa B. Adhikari
raw_author_name: Hannah Lawman
raw_author_name: Martin I. Meltzer

Abstract: Because of constrained personnel time, the Philadelphia Department of Public Health (Philadelphia, PA, USA) adjusted its COVID-19 contact tracing protocol in summer 2021 by prioritizing recent cases and limiting staff time per case. This action reduced required staff hours to prevent each case from 21-30 to 8-11 hours, while maintaining program effectiveness.
Quantum Key Distribution Based on the Quantum Eraser

Authors:
raw_author_name: Tarek A. Elsayed

Abstract: Quantum information and quantum foundations are becoming popular topics for advanced undergraduate courses. Many of the fundamental concepts and applications in these two fields, such as delayed choice experiments and quantum encryption, are comprehensible to undergraduates with basic knowledge of quantum mechanics. In this paper, we show that the quantum eraser, usually used to study the duality between wave and particle properties, can also serve as a generic platform for quantum key distribution. We present a pedagogical example of an algorithm to securely share random keys using the quantum eraser platform and propose its implementation with quantum circuits.
Numerical Composition of Differential Privacy

Authors:
raw_author_name: Sivakanth Gopi
raw_affiliation_string: Microsoft Research
raw_author_name: Yin Tat Lee
raw_affiliation_string: University of Washington and Microsoft Research
raw_author_name: Lukas Wutschitz
raw_affiliation_string: Microsoft

Abstract: We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of \emph{privacy loss random variables} to quantify the privacy loss of DP algorithms.The running time and memory needed for our algorithm to approximate the privacy curve of a DP algorithm composed with itself $k$ times is $\tilde{O}(\sqrt{k})$. This improves over the best prior method by Koskela et al. (2021) which requires $\tilde{\Omega}(k^{1.5})$ running time. We demonstrate the utility of our algorithm by accurately computing the privacy loss of DP-SGD algorithm of Abadi et al. (2016) and showing that our algorithm speeds up the privacy computations by a few orders of magnitude compared to prior work, while maintaining similar accuracy.
Grounded language acquisition through the eyes and ears of a single child

Authors:
raw_author_name: Wai Keen Vong
raw_affiliation_string: Center for Data Science, New York University, New York, NY, USA.
raw_author_name: A. Emin Orhan
raw_affiliation_string: Center for Data Science, New York University, New York, NY, USA.
raw_author_name: Brenden M. Lake
raw_affiliation_string: Center for Data Science, New York University, New York, NY, USA.; Department of Psychology, New York University, New York, NY, USA.

Abstract: Starting around 6 to 9 months of age, children begin acquiring their first words, linking spoken words to their visual counterparts. How much of this knowledge is learnable from sensory input with relatively generic learning mechanisms, and how much requires stronger inductive biases? Using longitudinal head-mounted camera recordings from one child aged 6 to 25 months, we trained a relatively generic neural network on 61 hours of correlated visual-linguistic data streams, learning feature-based representations and cross-modal associations. Our model acquires many word-referent mappings present in the child’s everyday experience, enables zero-shot generalization to new visual referents, and aligns its visual and linguistic conceptual systems. These results show how critical aspects of grounded word meaning are learnable through joint representation and associative learning from one child’s input.
Who Wants to Shop With You: Joint Product–Participant Recommendation for Group-Buying Service

Authors:
raw_author_name: Xiao Sha
raw_affiliation_string: School of Computer Science and Engineering, Hebei University of Water Resources and Electric Engineering, Cangzhou, China
raw_author_name: Zhu Sun
raw_affiliation_string: Centre for Frontier AI Research and the Institute of High Performance Computing, Agency for Science, Technology and Research (A&#x22C6;STAR), Fusionopolis, Singapore
raw_author_name: Jie Zhang
raw_affiliation_string: School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore
raw_author_name: Yew-Soon Ong
raw_affiliation_string: School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore

Abstract: Recent years have witnessed the great success of group buying (GB) in social e-commerce, opening up a new way of online shopping. In this business model, a user can launch a GB as an initiator to share her interested product with social friends. The GB is clinched once enough friends join in as participants to copurchase the shared product. As such, a successful GB depends on not only whether the initiator can find her interested product but also whether the friends are willing to join in as participants. Most existing recommenders are incompetent in such complex scenario, as they merely seek to help users find their preferred products and cannot help identify potential participants to join in a GB. To this end, we propose a novel joint product-participant recommendation (J2PRec) framework, which recommends both candidate products and participants for maximizing the success rate of a GB. Specifically, J2PRec first designs a relational graph embedding module, which effectively encodes the various relations in GB for learning enhanced user and product embeddings. It then jointly learns the product and participant recommendation tasks under a probabilistic framework to maximize the GB likelihood, i.e., boost the success rate of a GB. Extensive experiments on three real-world datasets demonstrate the superiority of J2PRec for GB recommendation.
Proposal for sequential Stern–Gerlach experiment with programmable quantum processors

Authors:
raw_author_name: Meng-Jun Hu
raw_affiliation_string: Beijing Academy of Quantum Information Sciences, Beijing, 100193, CHINA
raw_author_name: Haixing Miao
raw_affiliation_string: State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, CHINA
raw_author_name: Yong-Sheng Zhang
raw_affiliation_string: Laboratory of Quantum Information, University of Science and Technology of China, Hefei, 230026, CHINA

Abstract: The historical significance of the Stern–Gerlach (SG) experiment lies in its provision of the initial evidence for space quantization. Over time, its sequential form has evolved into an elegant paradigm that effectively illustrates the fundamental principles of quantum theory. To date, the practical implementation of the sequential SG experiment has not been fully achieved. In this study, we demonstrate the capability of programmable quantum processors to simulate the sequential SG experiment. The specific parametric shallow quantum circuits, which are suitable for the limitations of current noisy quantum hardware, are given to replicate the functionality of SG devices with the ability to perform measurements in different directions. Surprisingly, it has been demonstrated that Wigner’s SG interferometer can be readily implemented in our sequential quantum circuit. With the utilization of the identical circuits, it is also feasible to implement Wheeler’s delayed-choice experiment. We propose the utilization of cross-shaped programmable quantum processors to showcase sequential experiments, and the simulation results demonstrate a strong alignment with theoretical predictions. With the rapid advancement of cloud-based quantum computing, such as BAQIS Quafu, it is our belief that the proposed solution is well-suited for deployment on the cloud, allowing for public accessibility. Our findings not only expand the potential applications of quantum computers, but also contribute to a deeper comprehension of the fundamental principles underlying quantum theory.