Reviewerly/exHarmony huggingface dataset:
Sampling-Based Reactive Synthesis for Nondeterministic Hybrid Systems

Authors:
raw_author_name: Qi Heng Ho
raw_affiliation_string: Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO, USA
raw_author_name: Zachary N. Sunberg
raw_affiliation_string: Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO, USA
raw_author_name: Morteza Lahijanian
raw_affiliation_string: Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO, USA

Abstract: This letter introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We model the evolution of the hybrid system as a two-player game, where the nondeterminism is an adversarial player whose objective is to prevent achieving temporal and reachability goals. The aim is to synthesize a winning strategy – a reactive (robust) strategy that guarantees the satisfaction of the goals under all possible moves of the adversarial player. Our proposed approach involves growing a (search) game-tree in the hybrid space by combining sampling-based motion planning with a novel bandit-based technique to select and improve on partial strategies. We show that the algorithm is probabilistically complete, i.e., the algorithm will asymptotically almost surely find a winning strategy, if one exists. The case studies and benchmark results show that our algorithm is general and effective, and consistently outperforms state of the art algorithms.
Measuring gaze angle changes to maintain fixation upon a small target during motion: 3D motion tracking versus wearable eye-tracker

Authors:
raw_author_name: Alejandro Rubio Barañano
raw_affiliation_string: School of Optometry and Vision Science, University of Bradford, Bradford, UK
raw_author_name: Brendan T. Barrett
raw_affiliation_string: School of Optometry and Vision Science, University of Bradford, Bradford, UK
raw_author_name: John G. Buckley
raw_affiliation_string: School of Engineering, University of Bradford, Bradford, UK

Abstract: Recently we demonstrated how changes in gaze angle can be determined without an eye-tracker. The approach uses 3D motion-capture, to track the viewed target in the head’s reference frame and assumes head or target movement causes a gaze-angle change. This study determined the validity of this “assumed-gaze” method. Participants read information presented on a smartphone whilst walking. Changes in gaze angles were simultaneously assessed with an eye-tracker and our assumed-gaze method. The spatial and temporal agreement of the assumed-gaze approach with the eye-tracker were ∼1deg and ∼0.02s, respectively, and spatial congruence indicated the direction of changes in the assumed-gaze angle were in accordance with those determined with the eye tracker for ∼81% of the time. Findings indicate that when the head is moving and gaze is continually directed to a small target, our assumed-gaze approach can determine changes in gaze angle with comparable precision to a wearable eye-tracker.
FDNet: Feature decoupling for single-stage pose estimation in complex scenes

Authors:
raw_author_name: Qianqian Wang
raw_affiliation_string: School of Software Engineering, South China University of Technology, Guangzhou, 510006, China
raw_author_name: Qiong Liu
raw_affiliation_string: School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

Abstract: Severe error accumulation is a key reason that makes existing multi-person pose estimation challenging in complex scenes. Single-stage pose estimation methods detect human and keypoints in parallel, avoiding serial error accumulation compared to two-stage methods. However, two distinct tasks share identical features in the existing method, which causes error accumulation. Therefore, we propose a Feature Decoupling Network (FDNet) to further extend the pipeline of single-stage methods and reconstruct task-specific features, including Feature Decoupling Module (FDM), Human Aware Loss (HAL) and Keypoint Aware Loss (KAL). FDM can adaptively perceive spatial and channel features and allocate separate feature domains for various tasks. To learn distinctive feature representation for compact human bodies, HAL and KAL measure and suppress feature similarities and distances for different human and keypoints, thus alienating feature interference. Experiments on crowded datasets show that our method is superior, outperforming the state-of-the-art method CID by 1.5%, 0.8% on OCHuman and CrowdPose.
LL-WSOD: Weakly supervised object detection in low-light

Authors:
raw_author_name: Han Zhang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
raw_author_name: Yongfang Wang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
raw_author_name: Yingjie Yang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China

Abstract: Weakly supervised detection performs significantly lower than the fully supervised methods due to the lack of detailed and precise annotations. Especially, its performance deteriorates more severely in low-light conditions with the lack of low-light datasets. To overcome these issues, we propose a new Low-Light Weakly Supervised Object Detection (LL-WSOD) framework. First, we propose a Progressive Low-light NoiseModule (PLNM) to train the model progressively with low light using the common datasets with normal light, greatly reducing the training difficulty. Next, a Residual Self-refinement Low-light Rebuild Module (ResSLRM) is proposed to allow convolutional neural networks to learn sharper features by rebuilding low-light features into normal-light images. Finally, a Pseudo Boundingbox Assisted Learning Module (PBALM) is designed to perform better low-light training using salient priors. The results show that the proposed LL-WSOD algorithm effectively detects objects under low-light conditions and achieves great results on the real low-light dataset ExDark.
Codes and pseudo-geometric designs from the ternary m-sequences with Welch-type decimation d = 2 ⋅ 3(−1)/2 + 1

Authors:
raw_author_name: Can Xiang
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong 510642, China
raw_author_name: Chunming Tang
raw_affiliation_string: School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China
raw_author_name: Haode Yan
raw_affiliation_string: School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China
raw_author_name: Min Guo
raw_affiliation_string: School of Mathematics and Information, China West Normal University, Nanchong, Sichuan 637002, China

Abstract: Pseudo-geometric designs are combinatorial designs which share the same parameters as a finite geometry design, but are not isomorphic to that design. As far as we know, many pseudo-geometric designs have been constructed by the methods of finite geometries and combinatorics. However, none of pseudo-geometric designs with the parameters S(2,q+1,(qn−1)/(q−1)) is constructed by the approach of coding theory. In this paper, we use cyclic codes to construct pseudo-geometric designs. We firstly present a family of ternary cyclic codes from the m-sequences with Welch-type decimation d=2⋅3(n−1)/2+1, and obtain some infinite family of 2-designs and a family of Steiner systems S(2,4,(3n−1)/2) using these cyclic codes and their duals. Moreover, the parameters of these cyclic codes and their shortened codes are also determined. Some of those ternary codes are optimal or almost optimal. Finally, we show that one of these obtained Steiner systems is inequivalent to the point-line design of the projective space PG(n−1,3) and thus is a pseudo-geometric design.
Secret image sharing in the encrypted domain

Authors:
raw_author_name: Rui Wang
raw_affiliation_string: Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei, 230037, China; National University of Defense Technology, Hefei, 230037, China
raw_author_name: Guozheng Yang
raw_affiliation_string: Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei, 230037, China; National University of Defense Technology, Hefei, 230037, China
raw_author_name: Xuehu Yan
raw_affiliation_string: Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei, 230037, China; National University of Defense Technology, Hefei, 230037, China
raw_author_name: Shengyang Luo
raw_affiliation_string: Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei, 230037, China; National University of Defense Technology, Hefei, 230037, China
raw_author_name: Qiang Han
raw_affiliation_string: Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei, 230037, China; National University of Defense Technology, Hefei, 230037, China

Abstract: Nowadays, digital images have a greatly widespread and profound influence on human life, and its security protection problem is increasingly prominent. Image encryption is one of the most effective solutions for image protection but may result in secret concentration or single point of failure. Secret image sharing (SIS) with loss tolerance provides a feasible method to protect secret images in a distributed way. However, existing SIS schemes are most utilized in natural images, and the application of SIS to encrypted images which are widely used in practical scenarios is rarely discussed. Thus, this paper proposes a novel concept of SIS in the encrypted domain (SIS-ED) for the first time on the basis of synthesizing the merits of SIS and image encryption. SIS-ED integrating the encryption and SIS means applying general or user-defined encryption algorithms to encrypt the secret image in the plaintext domain, and in the encrypted domain using appropriate SIS schemes on the encrypted image to share secrets. SIS-ED can prevent secret images from being reconstructed illegally and enhance security against malicious attacks such as differential attacks, which can be widely applied in practical scenarios with high-security requirements such as authentication, blockchain, and cloud systems. In addition, we present a concrete SIS-ED scheme to instantiate the SIS-ED model. The proposed SIS-ED scheme with high security can realize lossless recovery without pixel expansion, and reduced-size shares. Theoretical analysis, experiments, and comparisons are conducted to show the effectiveness and advantages of SIS-ED.
Multisensor Multitarget Tracking Arithmetic Average Fusion Method Based on Probabilistic Time Window

Authors:
raw_author_name: Kuiwu Wang
raw_affiliation_string: School of Air Defense and Missile Defense, Air Force Engineering University, Xi&#x2019;an, China
raw_author_name: Xiaolong Hu
raw_affiliation_string: School of Air Defense and Missile Defense, Air Force Engineering University, Xi&#x2019;an, China

Abstract: The deployment of sensors in multisensor network systems needs to measure the actual state of the target. There may be significant errors in transmitting information to each sensor node through the sensor network system, which cannot accurately reflect the actual target state, resulting in the sensor network generating wrong measurements and instructions. The accuracy of target state estimation is affected by two factors: random noise in the process of sensor measurement and random delay of sensor data information in the network transmission process. Because of the above problems, this article designs a probabilistic time-window multisensor arithmetic average (AA) fusion multitarget tracking algorithm that can resist these interference factors, compensate for the random delay, and calculate the correct probability cumulative value of different sensor nodes in the time window, and then adjusts the probability hypothesis density filter update value as the multisensor fusion weight to reduce the negative impact of random interference on the sensor measurement. The simulation results show that under the condition of accurate probability information of random noise and random delay, compared with the traditional AA fusion and generalized covariance intersection (GCI) fusion algorithms, the proposed probabilistic time-window multisensor network AA fusion algorithm has the most minor tracking error and is more suitable for complex tracking scenarios with clutter and detection probability changes.
Detect malicious websites by building a neural network to capture global and local features of websites

Authors:
raw_author_name: Longwen Zhang
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
raw_author_name: Qiao Yan
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Abstract: With the development of the digital age, the Internet has become an integral part of our daily lives. However, it has also brought about a series of security challenges, among which malicious websites are particularly prominent. These websites often lure ignorant users by disguising themselves as legitimate services or through various fraudulent means to commit identity theft, distribute malware, or launch other forms of cyberattacks. Therefore the detection of malicious websites is very necessary. Traditionally, many malicious website detection methods rely on machine learning techniques, some of which require manual extraction of features, which may result in a time-consuming prediction process. Despite the existence of machine learning models that can automatically extract features, including unsupervised ones, capturing the subtleties of malicious website features is still a challenge. In recent years, deep learning has been gaining attention as a method for automated feature learning. It is capable of capturing and understanding the content of a website in greater depth, thus making classification and detection more accurate and efficient. Although deep learning shows its potential in capturing advanced features, its performance depends on the input data and the chosen model architecture. Both efficiently constructing feature representations of input data and building efficient model architectures to capture features are currently major challenges. For this reason, we propose a new approach for malicious website detection. This method uses wordpiece-level features to represent the information of malicious websites. Combination of multi-filter text convolutional neural network and multi-head self-attention mechanism is used for model construction. This enables the model to capture both global and local features of the input data. Compared to common deep learning methods, our approach captures the features of malicious websites better.
Augmented reality training for improved learnability

Authors:
raw_author_name: Dedy Ariansyah
raw_affiliation_string: Bioinformatics & Data Science Research Center, Bina Nusantara University, Jakarta 11480, Indonesia
raw_author_name: Bens Pardamean
raw_affiliation_string: Bioinformatics & Data Science Research Center, Bina Nusantara University, Jakarta 11480, Indonesia; Computer Science Department, BINUS Graduate Program - Master of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia
raw_author_name: John Ahmet Erkoyuncu
raw_affiliation_string: School of Aerospace, Transport and Manufacturing, Cranfield, Bedfordshire MK43 0AL, UK

Abstract: In the current era of Industry 4.0, many new technologies offer manufacturing industries to achieve high productivity. Augmented Reality (AR) is one of the emerging technologies that has been adopted in industries to aid users in acquiring complex skills and carrying out many complicated tasks such product assembly and maintenance. Nevertheless, most AR applications have been developed without clear understanding of how such technology can facilitate improved learnability in terms of knowledge reusability. This paper proposed an enhanced AR-based training system that provides multimodal information with a contextualized information to improve task comprehension and knowledge reusability compared with traditional AR that presents unimodal and decontextualized information. An empirical test was carried out to assess the task performance and the task learnability aspects of this enhanced AR compared to the traditional AR and the paper-based document. The experiment consisted of a training phase where participants carried out an electrical connection task of a sensor followed by a knowledge reuse phase where participants had to wire a second sensor using their previous training. A pre-test quiz was given before the experiment followed by the post-tests phase after the training. Post-tests consist of one post-test given directly after the experiment (short-term retention test) and a second post-test quiz given one week later (long-term retention test) to measure information retention. The results indicated that AR-based approaches could enhance knowledge acquisition by around 18 % for traditional AR and almost 25 % for enhanced AR as compared to paper-based approach. While all training systems achieved relatively equivalent well for short-term retention test, trainees who used the enhanced AR training systems statistically outperformed those in the paper-based group for long term retention test. Furthermore, there was a positive correlation between the score of short-term retention test and the score in the knowledge reusability which was also shown by the higher scores in knowledge reusability for the enhanced AR training system compared to the other two approaches. These findings are discussed in relation to the Industry 5.0′s human centric core value.
AdvEWM: Generating image adversarial examples by embedding digital watermarks

Authors:
raw_author_name: Yuexin Xiang
raw_affiliation_string: Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University, Wuzhou 543002, China; School of Computer Science, China University of Geosciences, Wuhan, 430074, China
raw_author_name: Tiantian Li
raw_affiliation_string: Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Australia; School of Computer Science, China University of Geosciences, Wuhan, 430074, China
raw_author_name: Wei Ren
raw_affiliation_string: Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University, Wuzhou 543002, China; School of Computer Science, China University of Geosciences, Wuhan, 430074, China
raw_author_name: Jie He
raw_affiliation_string: Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University, Wuzhou 543002, China
raw_author_name: Tianqing Zhu
raw_affiliation_string: School of Computer Science, China University of Geosciences, Wuhan, 430074, China
raw_author_name: Kim-Kwang Raymond Choo
raw_affiliation_string: Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX 78249-0631, USA

Abstract: With the increasing attention to deep neural network (DNN) models, attacks are also upcoming for such models. For example, an attacker may carefully construct images in specific ways (also referred to as adversarial examples) aiming to mislead the DNN models to output incorrect classification results. Similarly, many efforts are proposed to detect and mitigate adversarial examples, usually for certain dedicated attacks. In this paper, we propose a novel framework called AdvEWM based on embedding digital watermark to generate image adversarial examples for fooling DNN models. Specifically, partial main features of the watermark image are embedded into the host image almost invisibly, aiming to tamper with and damage the recognition capabilities of the DNN models. We devise an efficient mechanism to select host images and watermark images and utilize the improved discrete wavelet transform (DWT) based Patchwork watermarking algorithm with a set of valid hyperparameters to embed digital watermarks from the watermark image dataset into original images for generating image adversarial examples. The experimental results illustrate that the attack success rate on common DNN models can reach an average of 95.47% on the CIFAR-10 dataset and the highest at 98.71%. Besides, our scheme is able to generate a large number of adversarial examples efficiently, concretely, an average of 1.17 s for completing the attacks on each image on the CIFAR-10 dataset. In addition, we design a baseline experiment using the watermark images generated by Gaussian noise as the watermark image dataset that also displays the effectiveness of our scheme. Similarly, we also propose the modified discrete cosine transform (DCT) based Patchwork watermarking algorithm. To ensure repeatability and reproducibility, the source code is available on GitHub.
Decidim.viz: A data dashboard prototype for a digital democracy platform

Authors:
raw_author_name: Jorge Chamorro-Padial
raw_affiliation_string: Internet Interdisciplinary Institute, Open University of Catalonia, Barcelona, Spain

Abstract: In this work, we present Decidim.viz, a tool to display metrics and statistical information about Decidim instances. Decidim is a digital platform for political participation. We have created a Proof of Concept that analyses a participatory process launched by the European Commission in 2022. Decidim.viz was co-designed with the community of users and researchers of Decidim to guarantee the usefulness of the tool. Decidim.viz is designed as a client-server application that has a web application as a user interface. In addition, the tool provides a RESTful API to make its data reproducible and available to the community.