Reviewerly/exHarmony huggingface dataset:
Discriminative object tracking by domain contrast

Authors:
raw_author_name: Huayue Cai
raw_affiliation_string: Science and Technology on Parallel and Distributed Laboratory, College of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Xiang Zhang
raw_affiliation_string: Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China
raw_author_name: Long Lan
raw_affiliation_string: Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China
raw_author_name: Changcheng Xiao
raw_affiliation_string: Science and Technology on Parallel and Distributed Laboratory, College of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Chuanfu Xu
raw_affiliation_string: Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China
raw_author_name: Jie Liu
raw_affiliation_string: Laboratory of Software Engineering for Complex Systems, College of Computer, National University of Defense Technology, Changsha, China; Science and Technology on Parallel and Distributed Laboratory, College of Computer, National University of Defense Technology, Changsha, China
raw_author_name: Zhigang Luo
raw_affiliation_string: Science and Technology on Parallel and Distributed Laboratory, College of Computer, National University of Defense Technology, Changsha, China

Abstract: Multi-domain tracking method as a novel learning paradigm improves object tracking by sharing domain information with a common backbone whilst learning private information with domain-specific layers. In that context, each individual video sequence as a specific domain serves for a domain-specific layer. In this paper, we observe an intriguing finding that target features from different domains are highly confused with each other, thus having weak discriminative ability, for lack of domain interaction. To this end, we propose a simple yet effective domain interaction training paradigm called domain contrast to boost discriminative object features by effectively using amounts of instances from all the domains in two novel aspects: (1) a light-weight memory-saving training algorithm is proposed to solve the “out-of-the-memory” problem, which paves the way to couple with previous multi-domain trackers, and (2) a composite class-balanced loss is explored to tackle a more practical imbalanced problem, which not only involves the usual class imbalance problem but also accounts for the case of the totally mere negative instances. Experiments on multiple popular tracking benchmark datasets show that our mechanism consistently achieves the tracking performance gain of both base multi-domain tracker and its real-time variant thereof, without any other changes made on the original network.
R2: Optimal vector-based and any-angle 2D path planning with non-convex obstacles

Authors:
raw_author_name: Yan Kai Lai
raw_affiliation_string: National University of Singapore, Department of Electrical and Computer Engineering, Singapore
raw_author_name: Prahlad Vadakkepat
raw_affiliation_string: National University of Singapore, Department of Electrical and Computer Engineering, Singapore
raw_author_name: Cheng Xiang
raw_affiliation_string: National University of Singapore, Department of Electrical and Computer Engineering, Singapore

Abstract: A novel vector-based path planner, R2 (R in two dimensions), is introduced in this paper. R2 is optimal and online, returning any-angle paths by applying heuristic costs to vector-based searches. R2 delays line-of-sight checks to expand the most promising path that has the least detours from the start and goal points. As delayed checks can cause severe path cost underestimates, R2 infers the smallest known convex hull, the best hull, of obstacles while moving around them. To construct the best hull, phantom points are introduced, which are imaginary turning points lying on non-convex corners to guide future searches. Tracing rules are introduced to ensure that the estimated path cost from the best hull increases admissibly and monotonically between queues to the open-list. The distance between the start and goal points have little impact on R2’s performance when compared to the number of line-of-sight checks that collide with obstacles. While having an exponential search time in the worst case with respect to the number of collided line-of-sight checks, R2 is much faster than state-of-the-art when the optimal path is expected to turn around few obstacles, especially on large maps with few disjoint and non-convex obstacles.
Constrained continuous-action reinforcement learning for supply chain inventory management

Authors:
raw_author_name: Calvin Tsay
raw_affiliation_string: Department of Computing, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom

Abstract: Reinforcement learning (RL) is a promising solution for difficult decision-making problems, such as inventory management in chemical supply chains. However, enabling RL to explicitly consider known environment constraints is crucial for safe deployment in practical applications. This work incorporates recent tools for optimization over trained neural networks to introduce two algorithms for safe training and deployment of RL, with a focus on supply chains. Specifically, we use optimization over trained neural-network state–action value functions (i.e., a critic function) to directly incorporate constraints when computing actions in a continuous action space. Furthermore, we introduce a second algorithm that guarantees constraint satisfaction during deployment by directly implementing actions from constrained optimization of a trained value function. The algorithms are compared against state-of-the-art algorithms TRPO, CPO, and RCPO using a computational supply chain case study.
Improved zeroing neural models based on two novel activation functions with exponential behavior

Authors:
raw_author_name: Dimitrios Gerontitis
raw_affiliation_string: Department of Information and Electronic Engineering, International Hellenic University, Thessaloniki, Greece
raw_author_name: Predrag S. Stanimirović
raw_affiliation_string: University of Niš, Faculty of Sciences and Mathematics, 18000 Niš, Serbia
raw_author_name: Vasilios N. Katsikis
raw_affiliation_string: Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, 10559 Athens, Greece

Abstract: A family of zeroing neural networks based on new nonlinear activation functions is proposed for solving various time-varying linear matrix equations (TVLME). The proposed neural network dynamical systems, symbolized as Li-VPZNN1 and Li-VPZNN2, include an exponential parameter in nonlinear activation function (AF) that leads to faster convergence to the theoretical result compared to previous categories of nonlinearly activated neural networks. Theoretical analysis as well as numerical tests in MATLAB's environment confirm the efficiency and accelerated convergence property of the novel dynamics.
Improving rare relation inferring for scene graph generation using bipartite graph network

Authors:
raw_author_name: Jiale Lu
raw_affiliation_string: Chongqing Key Laboratory of Precision Optics, Chongqing Instrtute of East China Normal University, Chongqing, 401120, China; School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China
raw_author_name: Lianggangxu Chen
raw_affiliation_string: School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China
raw_author_name: Haoyue Guan
raw_affiliation_string: Johns Hopkins University, Baltimore, 21218, USA
raw_author_name: Shaohui Lin
raw_affiliation_string: School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China
raw_author_name: Chunhua Gu
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
raw_author_name: Changbo Wang
raw_affiliation_string: School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China
raw_author_name: Gaoqi He
raw_affiliation_string: Chongqing Key Laboratory of Precision Optics, Chongqing Instrtute of East China Normal University, Chongqing, 401120, China; School of Computer Science and Technology, East China Normal University, Shanghai, 200062, China

Abstract: Scene graph generation (SGG) task is plagued by insufficient and long-tailed training samples. Thus external text knowledge has been introduced into SGG task, which augments the training dataset. However, rare relations or low-frequency events, such as <dog, standing on, surfboard>, are still hard to be discovered since they also rarely appear in external texts. Addressed these issues, this paper proposes a model-agnostic Bipartite Graph Network with Dual-Group Message Passing (DG-BGN). It extends the relation space of each object pair in training dataset based on the similarity of different object pairs, which cooperates external text knowledge and internal visual information. Specifically, the main framework of DG-BGN consists of three parts, that is, bipartite graph construction (BGC), dual-group message propagation (DG-MP), and pseudo label generation (PLG). For BGC, alignment between external texts and visual dataset is performed to mine information from multimodal resources. Taking object pair as the index of the grouping, dual bipartite graph containing intra/inter-group graph is established to represent predicate label probability distribution of object pair instances. Among them, inter-group bipartite graph is built based on the similarity between object pair instance. For DG-MP, efficient message passing is running on both kinds of bipartite graph through Graph Convolution Neural Networks to refine the graphs, in order to let each object pair instance learn from its similar instances. Finally in PLG, pseudo labels, which are used to train SGG models in fully supervised way, are obtained by aggregating these refined graphs. Systematic experiments on Visual Genome dataset and Conceptual Captions show that our method performs better on discovering rare relations.
Quantum sub-phase sensitivity of a Mach–Zehnder interferometer with the superposition of Schrödinger’s cat-like state with vacuum state as an input under product detection scheme

Authors:
raw_author_name: Gaurav Shukla
raw_affiliation_string: Department of Physics, Institute of Science, Banaras Hindu University, Varanasi 221005, India
raw_author_name: Dhiraj Yadav
raw_affiliation_string: Department of Physics, Institute of Science, Banaras Hindu University, Varanasi 221005, India

Abstract: We study the phase sensitivity of a Mach–Zehnder interferometer (MZI) with product detection (PD) scheme by using the superposition of Schrödinger’s cat-like state with the vacuum state (SCVS) and the coherent state as the inputs. We compare the results derived from PD with the parity detection scheme’s results. We found that PD performs better than parity detection and approaches the single parameter quantum Cramér-Rao bound in some cases. Therefore, we expect that SCVS may play as an alternative quantum resource with PD for the improvement in the phase sensitivity of an MZI having potential application in quantum metrology and quantum sensing.
Predicting new hackers’ criminal careers: A group-based trajectory approach

Authors:
raw_author_name: C. Jordan Howell
raw_affiliation_string: University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620, USA
raw_author_name: Robert C. Perkins
raw_affiliation_string: Georgia State University, 55 Park Pl NE, Atlanta, GA 30303, USA
raw_author_name: David Maimon
raw_affiliation_string: Georgia State University, 55 Park Pl NE, Atlanta, GA 30303, USA
raw_author_name: Olena Antonaccio
raw_affiliation_string: University of Miami, P.O. Box 248026 Coral Gables, FL 33124-6914, USA

Abstract: The current study employs group-based trajectory modeling to assess the longitudinal attack patterns of new hackers involved in website defacement. Specifically, we track the activity of 241 emergent hackers for one year following their first verified website defacement. In doing so, we find four distinct criminal trajectories: low threat (29.0%), naturally desisting (26.5%), increasingly prolific (22.3%), and persistent threat (22.1%). Hackers classified as low threats engage in few defacements, whereas persistent threats engage in high-frequency attacks. Those labeled as naturally desisting begin their careers with velocity but become less prolific with time. Conversely, those classified as increasingly prolific engage in more attacks as they advance in their criminal careers. Using a series of regression models, we find that digital artifacts and open-source intelligence are predictive of group involvement. The findings presented in this study contribute to our theoretical understanding of the developmental trajectories of hackers, while providing valuable insights in fostering targeted intervention strategies aimed at effectively mitigating and preventing cyber-attacks.
Quantum simulation for partial differential equations with physical boundary or interface conditions

Authors:
raw_author_name: Shi Jin
raw_affiliation_string: School of Mathematical Sciences, Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China
raw_author_name: Nana Liu
raw_affiliation_string: School of Mathematical Sciences, Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China
raw_author_name: Yue Yu
raw_affiliation_string: School of Mathematical Sciences, Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University, Shanghai, 200240, China

Abstract: This paper explores the feasibility of quantum simulation for partial differential equations (PDEs) with physical boundary or interface conditions. Semi-discretization of such problems does not necessarily yield Hamiltonian dynamics and even alters the Hamiltonian structure of the dynamics when boundary and interface conditions are included. This seemingly intractable issue can be resolved by using a recently introduced Schrödingerisation method [1], [2] – it converts any linear PDEs and ODEs with non-Hermitian dynamics to a system of Schrödinger equations, via the so-called warped phase transformation that maps the equation into one higher dimension. We implement this method for several typical problems, including the linear convection equation with inflow boundary conditions and the heat equation with Dirichlet and Neumann boundary conditions. For interface problems we study the (parabolic) Stefan problem, linear convection, and linear Liouville equations with discontinuous and even measure-valued coefficients. We perform numerical experiments to demonstrate the validity of this approach, which helps to bridge the gap between available quantum algorithms and computational models for classical and quantum dynamics with boundary and interface conditions.
An executable domain-specific modeling language for simulating organizational auction-based coordination strategies for crisis response

Authors:
raw_author_name: Samaneh Hoseindoost
raw_affiliation_string: MDSE Research Group, Department of Software Engineering, University of Isfahan, Isfahan, Iran
raw_author_name: Afsaneh Fatemi
raw_affiliation_string: MDSE Research Group, Department of Software Engineering, University of Isfahan, Isfahan, Iran
raw_author_name: Bahman Zamani
raw_affiliation_string: MDSE Research Group, Department of Software Engineering, University of Isfahan, Isfahan, Iran

Abstract: Emergency response environments in which the crisis occurs include many homogenous and heterogeneous entities, e.g., organizations and persons. These entities need to collaborate to address a crisis. However, most of the time, the organizations that are involved in a crisis suffer from communication and coordination problems. This may cause unfair resource distribution. To mitigate the problem, there is a pressing need to construct models of emergency response environments and simulate the coordination strategies proactively, before the onset of any crisis. This paper highlights the complexity of the system formed by interacting organizations during a crisis and the difficulty in programming for the simulation of such complex systems. To cope with this issue, this paper presents an executable domain-specific modeling language called CoorERE. The language facilitates modeling emergency response environments for the simulation of one of the most common coordination strategies, i.e., auction-based coordination strategies in crisis response. CoorERE is used for the graphical modeling of emergency response environments and simulation of both inter and intra-organizational auction-based coordination strategies. The paper presents two case studies with different complexities to show the applicability of CoorERE. An empirical study is conducted to compare CoorERE with MDD4ABMS, which is one of the most recent tools for modeling and simulation of multi-agent systems. The evaluation includes both objective and subjective assessments. The objective assessment considers the development effort required for modeling a complete case study, while the subjective assessment compares the tools in terms of satisfying modeling method requirements and quality metrics such as usability, productivity, and expressiveness. The results of the evaluation indicate that CoorERE reduces the development effort by about 47%. In terms of quality metrics, CoorERE obtained higher scores than MDD4ABMS for all the measurements considered, indicating its superior usability, productivity, and expressiveness.
A comprehensive knowledge map for AI improving security management of cyber-physical system enabled smart manufacturing

Authors:
raw_author_name: Yu Cao
raw_affiliation_string: School of Maritime Economics and Management, Dalian Maritime University, Dalian, China
raw_author_name: Qingcheng Zeng
raw_affiliation_string: School of Maritime Economics and Management, Dalian Maritime University, Dalian, China

Abstract: The enhancement of security management for smart manufacturing has garnered considerable attention, prompting extensive research efforts that explore AI-based secure Cyber-Physical System (CPS) methods. While previous studies have made valuable contributions to this field, the current state of literature reviews still requires a comprehensive examination to construct the knowledge map for future research. This study employed scientometric analyses, including collaboration network analysis, co-occurrence network analysis, and co-citation network analysis, to examine 1133 previous articles conducted between 2015 and 2023 concerning the research topic of AI application in secure CPS-enabled smart manufacturing. The findings reveal that the academic communities in this field comprise influential authors affiliated with leading academic institutions in China, India, and the USA. The key research topics of AI application in secure CPS-enabled smart manufacturing show evolutionary trends during different research periods. Existing research on AI application in secure CPS-enabled smart manufacturing is supported by classical references, which can be divided into four significant co-citation clusters, including the theory of security and privacy in CPS, DL-based IDS, DDoS attacks on CPS by botnet, and FL method for privacy-preserved data sharing. Finally, this study predicted four potential future research directions for the research field of AI application in secure CPS-enabled smart manufacturing: the well-structured and representative dataset for model training, methods for resisting PUF attacks against ML techniques, DL-based methods for detecting and resisting botnets, and lightweight FL-based methods for privacy-preserved data sharing. The findings of this study can guide researchers in this field for future collaboration and work.
SABDTM: Security-first architecture-based dynamic trusted measurement scheme for operating system of the virtual computing node

Authors:
raw_author_name: Haoxiang Huang
raw_affiliation_string: Beijing Key Laboratory of Trusted Computing, Beijing 100124, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Jianbiao Zhang
raw_affiliation_string: Beijing Key Laboratory of Trusted Computing, Beijing 100124, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Lei Zhang
raw_affiliation_string: Beijing Key Laboratory of Trusted Computing, Beijing 100124, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Jun Hu
raw_affiliation_string: Beijing Key Laboratory of Trusted Computing, Beijing 100124, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: YiHao Cao
raw_affiliation_string: Beijing Key Laboratory of Trusted Computing, Beijing 100124, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Abstract: In the cloud environment, the virtual computing node has become the dominant form of cloud services used by users. Hence, it is increasingly critical to guarantee the trusted operation of the virtual computing node's operating system (VCNOS). However, previous schemes suffer a lot, such as insufficient consideration of the comprehensive of the measured objects, ignoring the dynamic of trusted, and the trusted measurement mechanisms rarely consider their security. Thus, a three-dimensional dynamic trusted measurement model SABDTM, which integrates the integrity measurement of kernel static data, trusted evaluation of operating system behavior (OSB), and the feedback trust of interacting nodes, is proposed. First, SABDTM divided OSB into multiple atomic behaviors and introduced the Bayesian decision theory to predict trusted expectations of OSBs. Second, the feedback trust of interacting nodes is considered to improve the comprehensiveness of the trusted measurement and evaluate its value based on the Euclidean distance function to reduce the impact of inaccurate feedback from malicious nodes. Subsequently, we set the appropriate weight for trusted measurement values of different moments based on Induced Ordered Weighted Averaging to accurately portray the actual state of VCNOS. Moreover, we designed a lightweight and independent subsystem to perform the trusted measurement, which guarantees the security of the measurement service. The security of our model is proved rigorously based on the non-interference theory. Finally, the experiments and comparative analysis demonstrated our model has better functionality and superiority.