Reviewerly/exHarmony huggingface dataset:
Le Mans: Dynamic and Fluid MPC for Dishonest Majority

Most MPC protocols require the set of parties to be active for the entire duration of the computation. Deploying MPC for use cases such as complex and resource-intensive scientific computations increases the barrier of entry for potential participants. The model of Fluid MPC (Crypto 2021) tackles this issue by giving parties the flexibility to participate in the protocol only when their resources are free. As such, the set of parties is dynamically changing over time. In this work, we extend Fluid MPC, which only considered an honest majority, to the setting where the majority of participants at any point in the computation may be corrupt. We do this by presenting variants of the SPDZ protocol, which support dynamic participants. Firstly, we describe a universal preprocessing for SPDZ, which allows a set of n parties to compute some correlated randomness, such that later on, any subset of the parties can use this to take part in an online secure computation. We complement this with a Dynamic SPDZ online phase, designed to work with our universal preprocessing, as well as a protocol for securely realising the preprocessing. Our preprocessing protocol is designed to efficiently use pseudorandom correlation generators, thus, the parties' storage and communication costs can be almost independent of the function being evaluated. We then extend this to support a fluid online phase, where the set of parties can dynamically evolve during the online phase. Our protocol achieves maximal fluidity and security with abort, similarly to the previous, honest majority construction. Achieving this requires a careful design and techniques to guarantee a small state complexity, allowing us to switch between committees efficiently.
Reliable Fuzzy Neural Networks for Systems Identification and Control

Fuzzy neural networks (FNNs) are synergistic structures that aim to benefit from the properties of fuzzy logic in neural network structures. Yet, the traditional FNNs do not explicitly address the reliability aspect of uncertain real-world applications. Here we propose a reliable fuzzy neural networks (ReFNNs) in which an information reliability measure is employed for rule training and robust decision making of the uncertain input data. The universal approximation property of the proposed structure is proved using the Stone-Weierstrass theorem. Furthermore, the resulting structure is continuous and differentiable. Hence, a backpropagation training algorithm is developed to optimize the proposed ReFNN's parameters. Additionally, asymptotic stability analysis based on the Lyapunov theorem is shown for ReFNNs. Finally, this structure is first evaluated with several basic benchmark examples in function approximation (sine, increasing sinusoid, quadratic Hermite, and nonlinear functions). We then apply it to modeling several benchmark nonlinear systems (including a 3rd order nonlinear dynamical system, a continuous stirred tank reactor, a two-cascaded tank problem, a Wiener-Hammerstein system, and wind speed prediction) and the adaptive control of nonlinear systems in both direct and indirect frameworks. Results confirm the superiority of the proposed structure over traditional FNNs in terms of error and sensitivity in the presence of noise.
vCLIMB: A Novel Video Class Incremental Learning Benchmark

Continual learning (CL) is under-explored in the video domain. The few existing works contain splits with imbalanced class distributions over the tasks, or study the problem in unsuitable datasets. We introduce vCLIMB, a novel video continual learning benchmark. vCLIMB is a standardized test-bed to analyze catastrophic forgetting of deep models in video continual learning. In contrast to previous work, we focus on class incremental continual learning with models trained on a sequence of disjoint tasks, and distribute the number of classes uniformly across the tasks. We perform in-depth evaluations of existing CL methods in vCLIMB, and observe two unique challenges in video data. The selection of instances to store in episodic memory is performed at the frame level. Second, untrimmed training data influences the effectiveness of frame sampling strategies. We address these two challenges by proposing a temporal consistency regularization that can be applied on top of memory-based continual learning methods. Our approach significantly improves the baseline, by up to 24% on the untrimmed continual learning task. The code of our benchmark can be found at: https://vclimb.netlify.app/.
Reliability, Rental-Cost and Energy-Aware Multi-Workflow Scheduling on Multi-Cloud Systems

Computationally intensive applications with a wide range of requirements are advancing to cloud computing platforms. However, with the growing demands from users, cloud providers are not always able to provide all the prerequisites of the application. Hence, flexible computation and storage systems, such as multi-cloud systems, emerged as a suitable solution. Different charging mechanisms, vast resource configuration, different energy consumption, and reliability are the key issues for multi-cloud systems. To address these issues, we propose a multi-workflow scheduling framework for multi-cloud systems, intending to lower the monetary cost and energy consumption while enhancing the reliability of application execution. Our proposed platform presents different methods (utilizing resource gaps, the DVFS utilized method, and a task duplication mechanism) to ensure each application's requirement. The Weibull distribution is used to model task reliability at different resource fault rates and fault behavior. Various synthetic workflow applications are used to perform simulation experiments. The results of the performance evaluation demonstrated that our proposed algorithms outperform (in the terms of resource rental cost, efficient energy consumption, and improved reliability) state-of-the-art algorithms for multi-cloud systems.
IF3: An Interpretable Feature Fusion Framework for Lesion Risk Assessment Based on Auto-constructed Fuzzy Cognitive Maps

The detection of abnormalities in the gastrointestinal (GI) tract, including precancerous lesions, is substantially subject to expert knowledge and experience. To address the challenge of automated lesion risk assessment, based on Wireless Capsule Endoscopy (WCE) images, this paper introduces a novel Artificial Intelligence (AI) framework based on Fuzzy Cognitive Maps (FCMs). Specifically, FCMs are fuzzy graph structures used to model knowledge spaces using cause-and-effect relationships, enabling uncertainty-aware reasoning and inference. The novel proposed Interpretable FCM-based Feature Fusion (IF3) framework, includes the following contributions: a) it automatically constructs an FCM based on similarities discovered in training data; b) it enables the fusion of different features extracted using different methods. The proposed framework is generic, domain-independent and it can be integrated into any classifier. To demonstrate its performance, experiments were conducted using real datasets, which include a variety of GI abnormalities, and different feature extractors. The results show that the automatically constructed FCM outperforms state-of-the-art methods, while providing interpretable results, in an easily understandable way.
Quantum-Enhanced Transmittance Sensing

We consider the problem of estimating unknown transmittance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> of a target bathed in thermal background light. As quantum estimation theory yields the fundamental limits, we employ the lossy thermal-noise bosonic channel model, which describes sensor-target interaction quantum mechanically in many practical active-illumination systems (e.g., using emissions at optical, microwave, or radio frequencies). We prove that quantum illumination using two-mode squeezed vacuum (TMSV) states asymptotically achieves minimal quantum Cramér-Rao bound (CRB) over all quantum states (not necessarily Gaussian) in the limit of low transmitted power. We characterize the optimal receiver structure for TMSV input, and show its advantage over other receivers using both analysis and Monte Carlo simulation.
Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation

Many real-world applications can be formulated as multi-agent cooperation problems, such as network packet routing and coordination of autonomous vehicles. The emergence of deep reinforcement learning (DRL) provides a promising approach for multi-agent cooperation through the interaction of the agents and environments. However, traditional DRL solutions suffer from the high dimensions of multiple agents with continuous action space during policy search. Besides, the dynamicity of agents’ policies makes the training non-stationary. To tackle the issues, we propose a hierarchical reinforcement learning approach with high-level decision-making and low-level individual control for efficient policy search. In particular, the cooperation of multiple agents can be learned in high-level discrete action space efficiently. At the same time, the low-level individual control can be reduced to single-agent reinforcement learning. In addition to hierarchical reinforcement learning, we propose an opponent modeling network to model other agents’ policies during the learning process. In contrast to end-to-end DRL approaches, our approach reduces the learning complexity by decomposing the overall task into sub-tasks in a hierarchical way. To evaluate the efficiency of our approach, we conduct a real-world case study in the cooperative lane change scenario. Both simulation and real-world experiments show the superiority of our approach in the collision rate and convergence speed.
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking Over Wireless Sensor Networks

Uncertainty quantification plays a key role in the development of autonomous systems, decision-making, and tracking over wireless sensor networks (WSNs). However, there is a need of providing uncertainty confidence bounds, especially for distributed machine learning-based tracking, dealing with different volumes of data collected by sensors. This paper aims to fill in this gap and proposes a distributed Gaussian process (DGP) approach for point target tracking and derives upper confidence bounds (UCBs) of the state estimates. A unique contribution of this paper includes the derived theoretical guarantees on the proposed approach and its maximum accuracy for tracking with and without clutter measurements. Particularly, the developed approaches with uncertainty bounds are generic and can provide trustworthy solutions with an increased level of reliability. A novel hybrid Bayesian filtering method is proposed to enhance the DGP approach by adopting a Poisson measurement likelihood model. The proposed approaches are validated over a WSN case study, where sensors have limited sensing ranges. Numerical results demonstrate the tracking accuracy and robustness of the proposed approaches. The derived UCBs constitute a tool for trustworthiness evaluation of DGP approaches. The simulation results reveal that the proposed UCBs successfully encompass the true target states with 88% and 42% higher probability in X and Y coordinates, respectively, when compared to the confidence interval-based method.
Demographic and Behavioral Correlates of Cybersickness: A Large Lab-in-the-Field Study of 837 Participants

ybersickness has been one of the main impediments to the widespread adoption of Virtual Reality for decades. It has been argued that several factors can influence the occurrence of cybersickness, such as technical factors, interaction design, but also users’ demographics and their perceived presence. Yet, previous studies had comparably small sample sizes and demographically homogeneous samples; comparisons across studies (e.g., regarding demographic factors) are challenging due to the large variation in the studied virtual environments. In this paper, we address these limitations and report the results of a lab-in-the-field experiment on cybersickness with a large and heterogeneous sample of $N =837$ participants who navigated and interacted inside a virtual environment (ages 18–80, $M = 29.34, SD = 9.50$, 431 males, 400 females, 6 non-binaries and other). We found that female participants and participants with lower VR experience were more susceptible to experiencing higher levels of cybersickness. Participants’ cybersickness levels increased with the time spent in VR and with the distance traversed in the virtual world up to a point, above which reported levels declined. We also found a link between higher levels of cybersickness and reduced head motion, as well as between lower levels of cybersickness and more head motion, which led them to explore more of the virtual environment. In contrast to past studies, we did not find any evidence suggesting an effect of age on cybersickness, nor a negative correlation between presence and cybersickness. Based on our results, we derived a model that achieves a mean classification accuracy of 67.1% for two levels of cybersickness using demographic, user experience, and behavioral data in VR.
A Taxonomy of Functional Augmented Reality for Human-Robot Interaction

Augmented reality (AR) technologies are today more frequently being introduced to Human-Robot Interaction (HRI) to mediate the interaction between human and robot. Indeed, better technical support and improved framework integration allow the design and study of novel scenarios augmenting interaction with AR. While some literature reviews have been published, so far no classifications have been devised for the role of AR in HRI. AR constitutes a vast field of research in HCI, and as it is picking up in HRI, it is timely to articulate the current knowledge and information about the functionalities of AR in HRI. Here we propose a multidimensional taxonomy for AR in HRI that distinguishes the type of perception augmentation, the functional role of AR, and the augmentation artifact type. We place sample publications within the taxonomy to demonstrate its utility. Lastly, we derive from the taxonomy some research gaps in current AR-for-HRI research and provide suggestions for exploration beyond the current state-of-the-art.
Fake Online Reviews: A Unified Detection Model Using Deception Theories

Online reviews influence consumers' purchasing decisions.However, identifying fake reviews online automatically remains a complex problem, and current detection approaches are inefficient in preventing the spread of fake reviews.The literature on fake reviews detection lacks a comprehensive and interpretable theory-based model with high performance, which enables us to understand the phenomenon from a psychological perspective and analyze reviews based on user-generated content as well as consumer behavior.In this research, we synthesized ten well-founded deception theories from psychology, namely leakage theory, four-factor theory, interpersonal deception theory, self-presentational theory, reality monitoring theory, criteria-based content analysis, scientific content analysis, verifiability approach, truthdefault theory, and information manipulation theory, and selected nine relevant constructs to develop a unified model for detecting fake online reviews.These constructs include specificity, quantity, nonimmediacy, affect, uncertainty, informality, consistency, source credibility, and deviation in behavior.We characterized the selected constructs using verbal and non-verbal features to validate the proposed model empirically.Subsequently, we extracted features from the Yelp datasets and used them to train four machine learning algorithms, specifically Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest.We demonstrated that quantity, non-immediacy, affect, informality, consistency, source credibility, and deviation in behavior are essential constructs for detecting fake reviews.To our surprise, we discovered that non-verbal features are more important than verbal features and that combining features from both types improves the prediction performance.Our theory-based model outperformed most of the state-of-the-art fake review detection models and yielded high interpretability and low complexity.
Developing BDI-Based Robotic Systems with ROS2

Robots are very effective in automatizing repetitive operations leveraging hard-coded control, actuation, and sensing algorithms. Current industrial automation trends demand combining low-level reactive primitives with high-level autonomy capabilities (e.g., reasoning and planning). Recent robotic reactive architectures provide capabilities to reliably sense the environment and promptly react to stimuli, but their autonomy capabilities are in an early stage and present several limitations. The BDI model has been proposed as a reference model to build autonomous agents, but it does not provide any kind of conceptual and developing framework to connect the reasoning and planning capabilities with the lower level reactive functionalities of a robotic system. In this paper, we propose an architecture supporting BDI-based solutions to develop agents with deliberation and priority aware executions of plans, and that consider deadline-aware prioritization of desires. We build our architecture on top of ROS 2 (a standard robotic framework) leveraging and extending state-of-the-art ROS planning infrastructures. We provide a novel development tool-kit that allows for the implementation of autonomous robotic systems. Finally, we show by means of a realistic industrially inspired scenario how to use the developed tool-kit.
Cyber Threat Intelligence Sharing for Co-Operative Defense in Multi-Domain Entities

Cloud-hosted applications are prone to targeted attacks such as DDoS, advanced persistent threats, Cryptojacking which threaten service availability. Recently, methods for threat information sharing and defense require cooperation and trust between multiple domains/entities. There is a need for mechanisms that establish distributed trust to allow for such a collective defense. In this paper, we present a novel threat intelligence sharing and defense system, namely “DefenseChain,” to allow organizations to have incentive-based and trustworthy cooperation to mitigate the impact of cyber attacks. Our solution approach features a consortium Blockchain platform and an economic model to obtain threat data and select suitable peers to help with attack detection and mitigation. We apply DefenseChain in the financial technology industry for an insurance claim processing use case to demonstrate the effectiveness of DefenseChain in a real-world application setting. Our evaluation experiments with DefenseChain implementation are performed on an Open Cloud testbed with Hyperledger Composer and in a simulation environment. Our results show that the DefenseChain system overall performs better than state-of-the-art decision making schemes in choosing the most appropriate detector and mitigator peers. Lastly, we validate how DefenseChain helps mitigate the threat risk of incidents relating to potential fraudulent insurance claims or cyber attacks.
Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance

We study the challenging problem of recovering detailed motion from a single motion-blurred image. Existing solutions to this problem estimate a single image sequence without considering the motion ambiguity for each region. Therefore, the results tend to converge to the mean of the multi-modal possibilities. In this paper, we explicitly account for such motion ambiguity, allowing us to generate multiple plausible solutions all in sharp detail. The key idea is to introduce a motion guidance representation, which is a compact quantization of 2D optical flow with only four discrete motion directions. Conditioned on the motion guidance, the blur decomposition is led to a specific, unambiguous solution by using a novel two-stage decomposition network. We propose a unified framework for blur decomposition, which supports various interfaces for generating our motion guidance, including human input, motion information from adjacent video frames, and learning from a video dataset. Extensive experiments on synthesized datasets and real-world data show that the proposed framework is qualitatively and quantitatively superior to previous methods, and also offers the merit of producing physically plausible and diverse solutions. Code is available at https://github.com/zzh-tech/Animation-from-Blur .
A novel approach for user authentication using keystroke dynamics

One Time Password (OTP) and Text Password are becoming less important in the current age of cybercrime because of the rapid development of new security systems. A user authentication system that is easy, robust, scalable, and cost-effective is a must. For both security and surveillance purposes, keystroke biometrics is a viable option. Behavior biometrics, of which keystroke biometrics is a subset, is used to identify individuals based on the way they type. Typing habits are not set in stone and are subject to change depending on the scenario, the device being used, and the user’s emotional state. As a result, the performance of a keystroke biometrics-based user authentication system is influenced by how well the retrieved information from typing and classification algorithms is processed. Using a keyboard with an array of pressure sensors, this research presents a unique way to keystroke dynamics-based authentication. The goal of this study is to develop user profiles that are unique and different in order to improve the suggested system’s efficiency. A real-world dataset is used to test the suggested method. The outcome is achieved with a 97% success rate in experiments.
Novel Precision Vision Measurement Method Between Area-Array Imaging and Linear-Array Imaging Especially for Dynamic Objects

A novel high-performance vision measurement method is proposed in this article. Via single-direction pixel binning, a traditional area-array camera is converted into an equivalent narrow area-array camera, and then a reasonable combination of these three cameras is used to realize precise spatial position and attitude measurements. This method, whose imaging is between area-array imaging and linear-array imaging, is able to overcome the shortcomings of the binocular stereovision measurement system (BSMS) based on area-array cameras, which has limited acquisition speed, insufficient sensitivity at a high frame rate, and a large amount of data. And it also overcomes the disadvantages of the multiple linear-array-camera-based measurement system (MLMS) using cylindrical lenses, which are easy to be disturbed by ambient light, and has weak multi-target capabilities. Simulation and physical experiments validate that, the measurement accuracy of the method in this article is at the same level as BSMS, when the sampling speed is increased by up to 8 times, and the amount of data is reduced to 3/16.