Reviewerly/exHarmony huggingface dataset:
Using Cryogenic CMOS Control Electronics to Enable a Two-Qubit Cross-Resonance Gate

Authors:
raw_author_name: Devin Underwood
raw_author_name: David J. Frank
raw_author_name: John Timmerwilke
raw_author_name: Emily Pritchett
raw_author_name: Sudipto Chakraborty
raw_author_name: Kevin Tien
raw_author_name: Mark Yeck
raw_author_name: John F. Bulzacchelli
raw_author_name: Raphael Robertazzi
raw_author_name: Rajiv V. Joshi
raw_author_name: Scott Lekuch
raw_author_name: Daniel J. Friedman
raw_author_name: Daniel Ramirez
raw_affiliation_string: IBM Quantum, 2800 37th Street NW, Rochester, Minnesota 55901, USA

Abstract: CMOS chips comprised of a low-power processor and arbitrary waveform generator operating at 4 K are developed and tested, enabling two-qubit randomized benchmarking and highlighting the promise and challenges of cryogenic control.
4-DOF Visual Servoing of a Robotic Flexible Endoscope With a Predefined-Time Convergent and Noise-Immune Adaptive Neural Network

Authors:
raw_author_name: Yisen Huang
raw_affiliation_string: Department of Surgery, The Chinese University of Hong Kong, Hong Kong
raw_author_name: Weibing Li
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Yicong Sun
raw_affiliation_string: Department of Surgery, The Chinese University of Hong Kong, Hong Kong
raw_author_name: Philip Wai Yan Chiu
raw_affiliation_string: Department of Surgery and the Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong
raw_author_name: Zheng Li
raw_affiliation_string: Department of Surgery, Chow Yuk Ho Technology Centre of Innovative Medicine, Li Ka Shing Institute of Health Science, and Multi-scale Medical Robotics, Ltd., The Chinese University of Hong Kong, Hong Kong

Abstract: Endoscopes provide views inside the patient's body during minimally invasive surgery. Although various robotic endoscopes have been developed to reduce surgeons' workload in manual endoscope operations, autonomous endoscope manipulation remains challenging due to the misorientation effect and different disturbances. In this work, we developed an intelligent endoscope system to steer the surgical view automatically. To keep the target (i.e., the tip of an instrument) at the center of the camera view with a suitable size and orientation, an image moment-based 4-degree-of-freedom (DOF) visual servoing method is implemented. We propose an error learning-based sliding mode controller to realize fast and smooth error convergence. It is specially constructed to improve convergence rate without causing undesirable system chattering. Moreover, the kinematic modeling of the endoscope results in a quadratic programming problem, which is solved by a novel adaptive noise-immune zeroing neural network accelerated to predefined-time convergence by a newly constructed activation function. The experiments show that the proposed control strategy guarantees a superior convergence rate and robustness compared with existing methods. Lab tests show the application potential of the proposed endoscope system in clinical practice.
The construction and use of cognitive maps in model-based control.

Authors:
raw_author_name: Wouter Kool
raw_affiliation_string: Department of Psychological and Brain Sciences, Washington University in St. Louis.

Abstract: When making decisions, we sometimes rely on habit and at other times plan toward goals. Planning requires the construction and use of an internal representation of the environment, a cognitive map. How are these maps constructed, and how do they guide goal-directed decisions? We coupled a sequential decision-making task with a behavioral representational similarity analysis approach to examine how relationships between choice options change when people build a cognitive map of the task structure. We found that participants who encoded stronger higher-order relationships among choice options showed increased planning and better performance. These higher-order relationships were more strongly encoded among objects encountered in high-reward contexts, indicating a role for motivation during cognitive map construction. In contrast, lower-order relationships such as simple visual co-occurrence of objects did not predict goal-directed planning. These results show that the construction of cognitive maps is an active process, with motivation dictating the degree to which higher-order relationships are encoded and used for planning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Multivariate Data Explanation by Jumping Emerging Patterns Visualization

Authors:
raw_author_name: Mário Popolin Neto
raw_affiliation_string: Federal Institute of S&#x00E3;o Paulo (IFSP), University of S&#x00E3;o Paulo (USP), Brazil
raw_author_name: Fernando V. Paulovich
raw_affiliation_string: Eindhoven University of Technology (TU/e), the Netherlands

Abstract: Multivariate or multidimensional visualization plays an essential role in exploratory data analysis by allowing users to derive insights and formulate hypotheses. Despite their popularity, it is usually users' responsibility to (visually) discover the data patterns, which can be cumbersome and time-consuming. Visual Analytics (VA) and machine learning techniques can be instrumental in mitigating this problem by automatically discovering and representing such patterns. One example is the integration of classification models with (visual) interpretability strategies, where models are used as surrogates for data patterns so that understanding a model enables understanding the phenomenon represented by the data. Although useful and inspiring, the few proposed solutions are based on visual representations of so-called black-box models, so the interpretation of the patterns captured by the models is not straightforward, requiring mechanisms to transform them into human-understandable pieces of information. This paper presents multiVariate dAta eXplanation (VAX), a new VA method to support identifying and visual interpreting patterns in multivariate datasets. Unlike the existing similar approaches, VAX uses the concept of Jumping Emerging Patterns, inherent interpretable logic statements representing class-variable relationships (patterns) derived from random Decision Trees. The potential of VAX is shown through use cases employing two real-world datasets covering different scenarios where intricate patterns are discovered and represented, something challenging to be done using usual exploratory approaches.
Are there independent effects of constraint and predictability on eye movements during reading?

Authors:
raw_author_name: Roslyn Wong
raw_affiliation_string: School of Psychology, University of Sydney.
raw_author_name: Aaron Veldre
raw_affiliation_string: School of Psychology, University of Sydney.
raw_author_name: Sally Andrews
raw_affiliation_string: School of Psychology, University of Sydney.

Abstract: Evidence of processing costs for unexpected words presented in place of a more expected completion remains elusive in the eye-movement literature. The current study investigated whether such prediction error costs depend on the source of constraint violation provided by the prior context. Participants' eye movements were recorded as they read predictable words and unpredictable alternatives that were either semantically related or unrelated in three-sentence passages. The passages differed in whether the source of constraint originated solely from the global context provided by the first two semantically rich sentences of the passage, from the local context provided by the final sentence of the passage, from both the global and local context, or from none of the three sentences of the passage. The results revealed the expected processing advantage for predictable completions in any constraining context, although the relative contributions of the different sources of constraint varied across the time course of word processing. Unpredictable completions, however, did not yield any processing costs when the context constrained toward a different word, instead producing immediate processing benefits in the presence of any constraining context. Moreover, the initial processing of related unpredictable completions was enhanced further by the provision of a supportive global context. Predictability effects therefore do not appear to be determined by cloze probability alone but also by the nature of the prior contextual constraint especially when they encourage the construction of higher-level discourse representations. The implications of these findings for understanding existing theoretical models of predictive processing are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
CFGN: A Lightweight Context Feature Guided Network for Image Super-Resolution

Authors:
raw_author_name: Tao Dai
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Mengxi Ya
raw_affiliation_string: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
raw_author_name: Jinmin Li
raw_affiliation_string: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
raw_author_name: Xinyi Zhang
raw_affiliation_string: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
raw_author_name: Shu-Tao Xia
raw_affiliation_string: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
raw_author_name: Zexuan Zhu
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

Abstract: Convolutional neural networks (CNNs) have recently been widely and successfully applied in the image computer vision community, and obtained great advances in single image super-resolution (SR). However, most of the existing SR methods focus on designing networks with deeper or wider structures for better performance and suffer from the problem of heavy computational costs. To address this problem, we propose a novel Context Feature Guided Network (CFGN), which is an efficient and effective lightweight SR method. Specifically, To capture semantic features effectively, we propose a novel block, called context feature guided convolution (CFGC), to capture more discriminative features while enlarging the receptive field. Moreover, we design a novel context feature guided group (CFGG) to exploit the multi-scale context information. Extensive experiments demonstrate the effectiveness of our method with a good trade-off between performance and computational efficiency, compared with the proposed method over the state-of-the-art lightweight SR methods.
TD-MDB: A Truth Discovery Based Multi-Dimensional Bidding Strategy for Federated Learning in Industrial IoT Systems

Authors:
raw_author_name: Pengjie Zeng
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China
raw_author_name: Anfeng Liu
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, China
raw_author_name: Neal N. Xiong
raw_affiliation_string: Department of Computer Science and Mathematics, Sul Ross State University, Alpine, TX, USA
raw_author_name: Shaobo Zhang
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Mianxiong Dong
raw_affiliation_string: Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Japan

Abstract: As Industrial Internet of Things (IIoT) systems have superior distributed characteristics, Federated Learning (FL) has gradually become a mainstream distributed computing paradigm in recent years, promoting its intelligent development. A key factor of FL is the model accuracy, which relies on the truth of the participating Industrial Edge Device (IED)’s parameter. However, some untrusted IEDs can contaminate models by fabricating false FL parameters, thereby deceiving rewards, known as Model Poison Attack (MPA). In this paper, a Truth Discovery based Multi-Dimensional Bidding (TD-MDB) strategy is proposed to obtain truthful parameters at low cost for FL. In TD-MDB strategy, Parameter Server (PS) will randomly assign the lightweight trust evaluation model to test the trust of each IED according to the accuracy of IED’s calculation. Then, in order to resist MPA and inference attack, we use an improved clustering algorithm to divide the trusted IEDs into different clusters based on their different trust and stability. Finally, a device selection strategy is proposed to select cost-effective IEDs for model training based on Asynchronous Advantage Actor-Critic (A3C) algorithm. In TD-MDB strategy, the IEDs that provide more resources, high trust and low bid will be selected to train the model and maximize the real revenue of the system with high accuracy. We finally carry out extensive evaluations, where results demonstrate that the TD-MDB strategy can effectively provide efficient offloading strategy, resist MPA and ensure that truth computing for FL, which is better than the state-of-the-art strategies in model accuracy, system revenue and other performance indicators.
Cryptanalysis and Improvement of a Blockchain-Based Certificateless Signature for IIoT Devices

Authors:
raw_author_name: Xiaodong Yang
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
raw_author_name: Wenjia Wang
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
raw_author_name: Tian Tian
raw_affiliation_string: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
raw_author_name: Caifen Wang
raw_affiliation_string: College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China

Abstract: With the rapid development of the Industrial Internet of Things (IIoT), security and privacy issues have become increasingly prominent when the data from IIoT devices is shared across public networks. Very recently, Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">al.</i> presented a blockchain-based certificateless signature (CLS) scheme for IIoT devices in order to achieve secure and lightweight communication (IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2021.3084753). Wang et al. claimed that their CLS scheme is secure under the assumption of the elliptic curve discrete logarithm problem. Unfortunately, by providing two attack methods to analyze the security of Wang et al.'s CLS scheme, we find that it is insecure against universal forgery attacks. Without knowing any information about the target user's private key, the two categories of attackers can successfully forge the valid signature of any message. Hence, Wang et al.'s CLS scheme fails to achieve the claimed security goals. To fix the security flaws, we propose an enhanced blockchain-based and pairing-free CLS scheme, and prove its security in the random oracle model. Furthermore, the analysis results demonstrate that our revised scheme has higher security while keeping the performance of the original scheme.
Preliminary Analysis of Simple Novelty Search

Authors:
raw_author_name: R Paul Wiegand
raw_affiliation_string: Department of Computer Science & Quantitative Methods Winthrop University, Rock Hill, SC, 29733, United States wiegandrp@winthrop.edu

Abstract: Novelty search is a powerful tool for finding diverse sets of objects in complicated spaces. Recent experiments on simplified versions of novelty search introduce the idea that novelty search happens at the level of the archive space, rather than individual points. The sparseness measure and archive update criterion create a process that is driven by a two measures: 1) spread out to cover the space while trying to remain as efficiently packed as possible, and 2) metrics inspired by k Nearest Neighbor theory. In this paper, we generalize previous simplifications of novelty search to include traditional population (μ,λ) dynamics for generating new search points, where the population and the archive are updated separately. We provide some theoretical guidance regarding balancing mutation and sparseness criteria and introduce the concept of saturation as a way of talking about fully covered spaces. We show empirically that claims that novelty search is inherently objectiveless are incorrect. We leverage the understanding of novelty search as an optimizer of archive coverage suggest several ways to improve the search, and we demonstrate one simple improvement-generate some new points directly from the archive rather than the parent population.
Exploring reservoir computing: Implementation via double stochastic nanowire networks

Authors:
raw_author_name: Lei Xia
raw_affiliation_string: Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Chongqing, 400715, CHINA
raw_author_name: Guang-Li Li
raw_affiliation_string: National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology, Chongqing, 400715, CHINA
raw_author_name: Jun Fu
raw_affiliation_string: Chongqing Brain Science Collaborative Innovation Center, Chongqing, 400715, CHINA
raw_author_name: Shukai Duan
raw_affiliation_string: Key Laboratory of Luminescence Analysis and Molecular Sensing, Ministry of Education, Southwest University, Chongqing, 400715, CHINA
raw_author_name: Lidan Wang
raw_affiliation_string: Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Chongqing, 400715, CHINA

Abstract: Neuromorphic computing, inspired by the human brain, uses memristor devices for complex tasks. Recent studies show that self-organizing random nanowires can implement neuromorphic information processing, enabling data analysis. This paper presents a model based on these nanowire networks, with an improved conductance variation profile. We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses. The nanowire network layer generates dynamic behaviors for pulse voltages, allowing time series prediction analysis. Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals, outperforming traditional reservoir computing in terms of fewer nodes, enriched dynamics and improved prediction accuracy. Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets, making neuromorphic nanowire networks promising for physical implementation of reservoir computing.
Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks

Authors:
raw_author_name: Jia Li
raw_affiliation_string: University of Ottawa, Canada
raw_author_name: Shiva Nejati
raw_affiliation_string: University of Ottawa, Canada
raw_author_name: Mehrdad Sabetzadeh
raw_affiliation_string: University of Ottawa, Canada

Abstract: Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.
Distributed Nonlinear Polynomial Adaptive Graph Filter Based on Diffusion Conjugate Gradient Strategy

Authors:
raw_author_name: Kutluyıl Doğançay
raw_affiliation_string: UniSA STEM, University of South Australia, Mawson Lakes, SA, Australia

Abstract: This brief investigates distributed and adaptive estimation of streaming data based nonlinear graph filters. To begin with, a new distributed polynomial filter for nonlinear graphs is proposed based on the Hadamard product, which not only represents the nonlinear relationship between dynamically changing graph input and output signals, but also accounts for the time dimension. A diffusion least mean square algorithm is presented to estimate the nonlinear polynomial graph filter parameters in a distributed manner. The method of diffusion hybrid conjugate gradient is leveraged to further improve the convergence rate of the graph diffusion adaptive filter. The advantages of the proposed algorithms are demonstrated in simulation studies.
Design of a Low-Cost and Device-Free Human Activity Recognition Model for Smart LED Lighting Control

Authors:
raw_author_name: Vijayakumar Krishnasamy
raw_affiliation_string: Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing Kancheepuram, Chennai, India

Abstract: Human activity recognition (HAR) constitutes an integral part of occupant centric smart services, such as health monitoring and building energy management. In this paper, a simple and cost effective solution to HAR through passive sensing of WiFi channel state information (CSI), is proposed. WiFi CSI extracted from ESP32 was utilized to classify four different human activities, using ensemble machine learning models. A mean accuracy of 83.39% was achieved using gradient boosting classifier with Haar wavelet based denoising, in spite of using a single transmission link and in presence of coexisting wireless devices. The proposed model can be employed for the development of an IoT enabled smart LED lighting system, with minimal infrastructure changes. In this strategy, the illuminance level of LED lighting fixtures is adjusted according to the predicted occupant activity, thereby reducing power consumption, without compromising visual comfort. This method offers a potential energy savings of up to 36.42% and 29.45% per month, for a typical office and home scenario, respectively. The possibility of supplementing activity sensing with daylight harvesting, is also explored. An additional energy savings of up to 18.26% may be obtained using this method, during daytime. An evaluation of the annual electricity cost shows an estimated reduction between 29.43% and 62.13% using activity recognition and daylight harvesting.