Reviewerly/exHarmony huggingface dataset:
A Personalized Computer Based Tutor for Vector Algebra

Authors:
raw_author_name: Vasudeva Rao Aravind
raw_affiliation_string: physics at Clarion University.

Abstract: Many of the world's challenges today need to be solved through advances in one or more of the STEM disciplines.This calls for preparing our students to be tomorrow's leading engineers and scientists.It is vital that students in a freshmen class understand how to analyze vectors, and split an arbitrary vector into components along different directions.From a student point of view, concepts in vectors can seem challenging, especially if they do not have strong knowledge background in geometry and trigonometry.We made a computer based personalized tutoring system using state of the art tools provided by Carnegie Mellon University's Cognitive Tutor Authoring Tools (CTAT).In this work, we describe steps that went into building this highly effective tutoring system that challenges the students with a problem, provides immediate feedback, and offers need based hints at different levels, leading the student to solve the problem correctly.
Improved and formal proposal for device-independent quantum private query <sup>*</sup>

Authors:
raw_author_name: Jyotirmoy Basak
raw_affiliation_string: Applied Statistics Unit, Indian Statistical Institute, Kolkata, India
raw_author_name: Kaushik Chakraborty
raw_affiliation_string: School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom
raw_author_name: Arpita Maitra
raw_affiliation_string: TCG Centre for Research and Education in Science and Technology, Kolkata, India
raw_author_name: Subhamoy Maitra
raw_affiliation_string: Applied Statistics Unit, Indian Statistical Institute, Kolkata, India

Abstract: Abstract In this paper, we present a novel quantum private query (QPQ) scheme that is fully device-independent. As far as we know, this is the first QPQ scheme that uses EPR pairs and offers full device independence. Our approach takes into account the self-testing of shared EPR pairs and the self-testing of projective measurement operators in a mistrustful scenario where neither the client nor the server trusts each other. To achieve full device independence, we also exploit self-testing of a specific class of POVM operators used by the client in our scheme. Additionally, we evaluate the performance of our proposal formally and derive an upper limit of the maximum cheating probabilities (when the protocol does not terminate) for both the dishonest client and the dishonest server.
Introducing Emerging Computer Engineering Research to Community College Students through a Summer Internship Project on Development of a Mobile Gesture Recognition System

Authors:
raw_author_name: Xiaorong Zhang
raw_affiliation_string: San Francisco State University
raw_author_name: Amelito G Enriquez
raw_affiliation_string: Canada College
raw_author_name: Cheng Chen
raw_affiliation_string: San Francisco State University

Abstract: Abstract Community colleges play an important role in STEM education because they enroll almost half of the nation's undergraduate students. Undergraduate research experience has been proven effective to engage and retain students in the STEM fields. In order to provide community college students with opportunities and experiences in cutting-edge engineering research, a cooperative internship program between a community college and a public comprehensive university has been developed. In summer 2018, the program engaged 20 community college students in six 10-week engineering research projects conducted at the four-year university. This paper mainly describes the computer engineering internship project, in which four sophomore students from the community college developed a mobile gesture recognition system by integrating bio-signal processing, machine learning, real-time system design, and mobile and cloud computing technologies. The project was conducted in a research lab at the four-year university and was supervised by a faculty advisor and a graduate student mentor. The research outcome of the project as well as the results of the pre- and post-program surveys show that the internship program was a great success in allowing the student interns to gain valuable computer engineering research experience and strengthening their confidence and interest in pursuing a STEM profession. The program also helped the students improve their skills in teamwork, time management, scientific writing, and presentation.
A Minimum Discounted Reward Hamilton–Jacobi Formulation for Computing Reachable Sets

Authors:
raw_author_name: Shromona Ghosh
raw_author_name: Jaime F. Fisac
raw_author_name: Claire J. Tomlin

Abstract: We propose a novel formulation for approximating reachable sets through a minimum discounted reward optimal control problem. The formulation yields a continuous solution that can be obtained by solving a Hamilton-Jacobi equation. Furthermore, the numerical approximation to this solution is the unique fixed-point to a contraction mapping. This allows for more efficient solution methods that are not applicable under traditional formulations for solving reachable sets. Lastly, this formulation provides a link between reinforcement learning and learning reachable sets for systems with unknown dynamics, allowing algorithms from the former to be applied to the latter. We use two benchmark examples, double integrator, and pursuit-evasion games, to show the correctness of the formulation as well as its strengths in comparison to previous work.
Indistinguishability Obfuscation from Well-Founded Assumptions

Authors:
raw_author_name: Aayush Jain
raw_affiliation_string: CMU, Pittsburgh, PA, USA
raw_author_name: Huijia Lin
raw_affiliation_string: UW, Seattle, WA, USA
raw_author_name: Amit Sahai
raw_affiliation_string: UCLA, Los Angeles, CA, USA

Abstract: At least since the initial public proposal of public-key cryptography based on computational hardness conjectures, cryptographers have contemplated the possibility of a "one-way compiler" that translates computer programs into "incomprehensible" but equivalent forms. And yet, the search for such a "one-way compiler" remained elusive for decades. We examine a formalization of this concept with the notion of indistinguishability obfuscation (iO) . Roughly speaking, iO requires that the compiled versions of any two equivalent programs (with the same size and running time) be indistinguishable from any efficient adversary. Finally, we show how to construct iO in such a way that we can prove the security of our iO scheme based on well-studied computational hardness conjectures in cryptography.
Multiview Deep Anomaly Detection: A Systematic Exploration

Authors:
raw_author_name: Siqi Wang
raw_affiliation_string: College of Computer, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: Jiyuan Liu
raw_affiliation_string: College of Computer, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: Guang Yu
raw_affiliation_string: College of Computer, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: Xinwang Liu
raw_affiliation_string: College of Computer, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: Sihang Zhou
raw_affiliation_string: College of Computer, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: En Zhu
raw_affiliation_string: College of Computer, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: Yuexiang Yang
raw_affiliation_string: College of Computer, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: Jianping Yin
raw_affiliation_string: School of Cyberspace Security, Dongguan University of Technology, Dongguan, China
raw_author_name: Wenjing Yang
raw_affiliation_string: College of Computer, National University of Defense Technology (NUDT), Changsha, China

Abstract: Anomaly detection (AD), which models a given normal class and distinguishes it from the rest of abnormal classes, has been a long-standing topic with ubiquitous applications. As modern scenarios often deal with massive high-dimensional complex data spawned by multiple sources, it is natural to consider AD from the perspective of multiview deep learning. However, it has not been formally discussed by the literature and remains underexplored. Motivated by this blank, this article makes fourfold contributions: First, to the best of our knowledge, this is the first work that formally identifies and formulates the multiview deep AD problem. Second, we take recent advances in relevant areas into account and systematically devise various baseline solutions, which lays the foundation for multiview deep AD research. Third, to remedy the problem that limited benchmark datasets are available for multiview deep AD, we extensively collect the existing public data and process them into more than 30 multiview benchmark datasets via multiple means, so as to provide a better evaluation platform for multiview deep AD. Finally, by comprehensively evaluating the devised solutions on different types of multiview deep AD benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines and hopefully provide other researchers with beneficial guidance and insight into the new multiview deep AD topic.
A Pseudoinversion-Free Method for Weight Updating in Broad Learning System

Authors:
raw_author_name: Mei Liu
raw_affiliation_string: Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
raw_author_name: Xiufang Chen
raw_affiliation_string: Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
raw_author_name: Mingsheng Shang
raw_affiliation_string: Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China

Abstract: Neural networks have evolved into one of the most critical tools in the field of artificial intelligence. As a kind of shallow feedforward neural network, the broad learning system (BLS) uses a training process based on random and pseudoinverse methods, and it does not need to go through a complete training cycle to obtain new parameters when adding nodes. Instead, it performs rapid update iterations on the basis of existing parameters through a series of dynamic update algorithms, which enables BLS to combine high efficiency and accuracy flexibly. The training strategy of BLS is completely different from the existing mainstream neural network training strategy based on the gradient descent algorithm, and the superiority of the former has been proven in many experiments. This article applies an ingenious method of pseudoinversion to the weight updating process in BLS and employs it as an alternative strategy for the dynamic update algorithms in the original BLS. Theoretical analyses and numerical experiments demonstrate the efficiency and effectiveness of BLS aided with this method. The research presented in this article can be regarded as an extended study of the BLS theory, providing an innovative idea and direction for future research on BLS.
Subtype-Aware Dynamic Unsupervised Domain Adaptation

Authors:
raw_author_name: Xiaofeng Liu
raw_affiliation_string: Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
raw_author_name: Fangxu Xing
raw_affiliation_string: Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
raw_author_name: Jane You
raw_affiliation_string: Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
raw_author_name: C-C Jay Kuo
raw_affiliation_string: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
raw_author_name: Georges El Fakhri
raw_affiliation_string: Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
raw_author_name: Jonghye Woo
raw_affiliation_string: Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Abstract: Unsupervised domain adaptation (UDA) has been successfully applied to transfer knowledge from a labeled source domain to target domains without their labels. Recently introduced transferable prototypical networks (TPNs) further address class-wise conditional alignment. In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying fine-grained subtype structure and the cross-domain within-class compactness have not been fully investigated. To counter this, we propose a new approach to adaptively perform a fine-grained subtype-aware alignment to improve the performance in the target domain without the subtype label in both domains. The insight of our approach is that the unlabeled subtypes in a class have the local proximity within a subtype while exhibiting disparate characteristics because of different conditional and label shifts. Specifically, we propose to simultaneously enforce subtype-wise compactness and class-wise separation, by utilizing intermediate pseudo-labels. In addition, we systematically investigate various scenarios with and without prior knowledge of subtype numbers and propose to exploit the underlying subtype structure. Furthermore, a dynamic queue framework is developed to evolve the subtype cluster centroids steadily using an alternative processing scheme. Experimental results, carried out with multiview congenital heart disease data and VisDA and DomainNet, show the effectiveness and validity of our subtype-aware UDA, compared with state-of-the-art UDA methods.
Distributed Process Monitoring for Multiagent Systems Through Cognitive Learning

Authors:
raw_author_name: Hongtian Chen
raw_affiliation_string: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada
raw_author_name: Oguzhan Dogru
raw_affiliation_string: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada
raw_author_name: Santhosh Kumar Varanasi
raw_affiliation_string: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada
raw_author_name: Biao Huang
raw_affiliation_string: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada

Abstract: Multi-agent systems are usually large-scaled with a growing degree of intelligence and integration. Direct applications of traditional (centralized) methods will become incompetent for effective process monitoring of multi-agent systems. It necessitates the cognitive learning strategies that determine the effective interactions among subsystems or individuals. Therefore, in order to improve monitoring performance, this paper targets the development of a new distributed process monitoring method that has the cognitive learning ability by embedding an adaptive pickup rule. The proposed cognitive learning-based method can reduce the computation loads in both off-line and online phases because only necessary information exchange (or communication topology) is involved. Furthermore, the threshold used for system monitoring is obtained by developing a fast search algorithm based on statistical learning theory. Case studies on the wastewater treatment system, which can be regarded as a typical multi-agent system, demonstrate the superiority of the proposed distributed process-monitoring method.
Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security

Authors:
raw_author_name: Ahmed S. Almasoud
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
raw_author_name: Bayan Alabduallah
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
raw_author_name: Hamed Alqahtani
raw_affiliation_string: Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi Arabia
raw_author_name: Sumayh S. Aljameel
raw_affiliation_string: SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
raw_author_name: Saud S. Alotaibi
raw_affiliation_string: Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
raw_author_name: Abdullah Mohamed
raw_affiliation_string: Research Centre, Future University in Egypt, New Cairo, 11845, Egypt

Abstract: Image encryption involves applying cryptographic approaches to convert the content of an image into an illegible or encrypted format, reassuring that illegal users cannot simply interpret or access the actual visual details. Commonly employed models comprise symmetric key algorithms for the encryption of the image data, necessitating a secret key for decryption. This study introduces a new Chaotic Image Encryption Algorithm with an Improved Bonobo Optimizer and DNA Coding (CIEAIBO-DNAC) for enhanced security. The presented CIEAIBO-DNAC technique involves different processes such as initial value generation, substitution, diffusion, and decryption. Primarily, the key is related to the input image pixel values by the MD5 hash function, and the hash value produced by the input image can be utilized as a primary value of the chaotic model to boost key sensitivity. Besides, the CIEAIBO-DNAC technique uses the Improved Bonobo Optimizer (IBO) algorithm for scrambling the pixel position in the block and the scrambling process among the blocks takes place. Moreover, in the diffusion stage, DNA encoding, obfuscation, and decoding process were carried out to attain encrypted images. Extensive experimental evaluations and security analyses are conducted to assess the outcome of the CIEAIBO-DNAC technique. The simulation outcome demonstrates excellent security properties, including resistance against several attacks, ensuring it can be applied to real-time image encryption scenarios.
Kernel Path for Semisupervised Support Vector Machine

Authors:
raw_author_name: Zhou Zhai
raw_affiliation_string: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
raw_author_name: Heng Huang
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
raw_author_name: Bin Gu
raw_affiliation_string: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

Abstract: Semisupervised support vector machine (S3VM) is a powerful semisupervised learning model that can use large amounts of unlabeled data to train high-quality classification models. The choice of kernel parameters in the kernel function determines the mapping between the input space and the feature space and is crucial to the performance of the S3VM. Kernel path algorithms have been widely recognized as one of the most efficient tools to trace the solutions with respect to a kernel parameter. However, existing kernel path algorithms are limited to convex problems, while S3VM is nonconvex problem. To address this challenging problem, in this article, we first propose a kernel path algorithm of S3VM (KPS3VM), which can track the solutions of the nonconvex S3VM with respect to a kernel parameter. Specifically, we estimate the position of the breakpoint by monitoring the change of the sample sets. In addition, we also use an incremental and decremental learning algorithm to deal with the Karush-Khun-Tucker violating samples in the process of tracking the solutions. More importantly, we prove the finite convergence of our KPS3VM algorithm. Experimental results on various benchmark datasets not only validate the effectiveness of our KPS3VM algorithm but also show the advantage of choosing the optimal kernel parameters.
Data-Knowledge-Driven Self-Organizing Fuzzy Neural Network

Authors:
raw_author_name: Honggui Han
raw_affiliation_string: Faculty of Information Technology, the Beijing Key Laboratory of Computational Intelligence and Intelligent System, the Engineering Research Center of Digital Community, Ministry of Education, the Beijing Artificial Intelligence Institute, and the Beijing Laboratory for Urban Mass Transit, Beijing University of Technology, Beijing, China
raw_author_name: Hongxu Liu
raw_affiliation_string: Faculty of Information Technology, the Beijing Key Laboratory of Computational Intelligence and Intelligent System, the Engineering Research Center of Digital Community, Ministry of Education, the Beijing Artificial Intelligence Institute, and the Beijing Laboratory for Urban Mass Transit, Beijing University of Technology, Beijing, China
raw_author_name: Junfei Qiao
raw_affiliation_string: Faculty of Information Technology, the Beijing Key Laboratory of Computational Intelligence and Intelligent System, the Engineering Research Center of Digital Community, Ministry of Education, the Beijing Artificial Intelligence Institute, and the Beijing Laboratory for Urban Mass Transit, Beijing University of Technology, Beijing, China

Abstract: Fuzzy neural networks (FNNs) hold the advantages of knowledge leveraging and adaptive learning, which have been widely used in nonlinear system modeling. However, it is difficult for FNNs to obtain the appropriate structure in the situation of insufficient data, which limits its generalization performance. To solve this problem, a data-knowledge-driven self-organizing FNN (DK-SOFNN) with a structure compensation strategy and a parameter reinforcement mechanism is proposed in this article. First, a structure compensation strategy is proposed to mine structural information from empirical knowledge to learn the structure of DK-SOFNN. Then, a complete model structure can be acquired by sufficient structural information. Second, a parameter reinforcement mechanism is developed to determine the parameter evolution direction of DK-SOFNN that is most suitable for the current model structure. Then, a robust model can be obtained by the interaction between parameters and dynamic structure. Finally, the proposed DK-SOFNN is theoretically analyzed on the fixed structure case and dynamic structure case. Then, the convergence conditions can be obtained to guide practical applications. The merits of DK-SOFNN are demonstrated by some benchmark problems and industrial applications.