Reviewerly/exHarmony huggingface dataset:
Text summarization using modified generative adversarial network1

Authors:
raw_author_name: Jyoti Srivastava
raw_affiliation_string: Department of Computer Science & Engineering, National Institute of Technology, Hamirpur (H.P.), India
raw_author_name: Ashish Kumar Srivastava
raw_affiliation_string: Department of Computer Science & Engineering, Galgotias University Greater Noida, Uttar Pradesh, India
raw_author_name: B. Muthu Kumar
raw_affiliation_string: School of Computing and Information Technology, REVA University, Bengaluru, India
raw_author_name: S.P. Anandaraj
raw_affiliation_string: HoD-Cyber Security and Internet of Things, Department of Computer Science & Engineering, Presidency University, Bangalore, India

Abstract: Text summarizing (TS) takes key information from a source text and condenses it for the user while retaining the primary material. When it comes to text summaries, the most difficult problem is to provide broad topic coverage and diversity in a single summary. Overall, text summarization addresses the fundamental need to distill large volumes of information into more manageable and digestible forms, making it a crucial technology in the era of information abundance. It benefits individuals, businesses, researchers, and various other stakeholders by enhancing efficiency and comprehension in dealing with textual data. In this paper, proposed a novel Modified Generative adversarial network (MGAN) for summarize the text. The proposed model involves three stages namely pre-processing, Extractive summarization, and summary generation. In the first Phase, the Text similarity dataset is pre-processed using Lowering Casing, Tokenization, Lemmatization, and, Stop Word Removal. In the second Phase, the Extractive summarization is done in three steps Generating similarity metrics, Sentence Ranking, and Sentence Extractive. In the third stage, a generative adversarial network (GAN) employs summary generation to jointly train the discriminative model D and the generative model G. To classify texts and annotate their syntax, Generative Model G employs a convolutional neural network called Bidirectional Gated Recursive Unit (CNN-BiGRU). The performance analysis of the proposed MGAN is calculated based on the parameters like accuracy, specificity, Recall, and Precision metrics. The proposed MGAN achieves an accuracy range of 99% . The result shows that the proposed MGAN improves the overall accuracy better than 9%, 6.5% and 5.4% is DRM, LSTM, and CNN respectively.
Generative adversarial network for car following trajectory generation and anomaly detection

Authors:
raw_author_name: Haotian Shi
raw_affiliation_string: University of Wisconsin-Madison, Madison, WI, USA
raw_author_name: Shuoxuan Dong
raw_affiliation_string: University of Wisconsin-Madison, Madison, WI, USA
raw_author_name: Yuankai Wu
raw_affiliation_string: Sichuan University, Chengdu, China
raw_author_name: Qinghui Nie
raw_affiliation_string: Yangzhou University, Yangzhou, China
raw_author_name: Yang Zhou
raw_affiliation_string: Texas A&amp;M University, College Station, TX, USA
raw_author_name: Bin Ran
raw_affiliation_string: University of Wisconsin-Madison, Madison, WI, USA

Abstract: Car-following trajectory generation and anomaly detection are critical functions in the sensing module of an automated vehicle. However, developing models that capture realistic trajectory data distribution and detect anomalous driving behaviors could be challenging. This paper proposes 'TrajGAN', an unsupervised learning approach based on the Generative Adversarial Network (GAN) to exploit vehicle car following trajectory data for generation and anomaly detection. The proposed TrajGAN consists of two modules, an encoder-decoder Long Short-Term Memory (LSTM)-based generator and an LSTM-multilayer perceptron (MLP) based discriminator, whose former component is used to generate vehicular car following trajectories and the latter one is for trajectory anomaly detection. By letting these two modules game with each other in training, we can simultaneously achieve robust trajectory generators and anomaly detectors. Trained with the Next Generation Simulation (NGSIM) dataset, TrajGAN can generate realistic trajectories with a similar distribution of training data and identify a manifold of anomalous trajectories based on an anomaly scoring scheme. Simulation results indicate that the proposed approach is efficient in reproducing artificial trajectories and identifying anomalous driving behaviors.
Exploring and reflecting on the influences that shape teacher professional digital competence frameworks

Authors:
raw_author_name: Oliver McGarr
raw_affiliation_string: School of Education, University of Limerick, Limerick, Ireland

Abstract: The importance of Digital Competence in teacher education has increased in recent years resulting in a range of digital competency frameworks aimed at guiding national and regional governments in their integration of digital competence in teacher education. The discourses and assumptions underpinning digital competence frameworks are discussed in this paper using two influential supra-national competency frameworks, the UNESCO ICT Competency Framework for Teachers and the DigCompEdu, as examples. The theoretical reflection argues that several influences have shaped the current discourse. These influences include the rise of teacher performance and competency frameworks, the tradition of ranking teachers' levels of technology use and the techno-centric education discourses that align technology use with constructivist practices. Through this theoretical reflection, the paper raises questions about their use in, and implications for, teacher education arguing that, while such frameworks provide helpful guidance for policy makers and teacher educators, they may stifle teachers' professional autonomy and straight-jacket technology use in a linear and deterministic manner.
Synthetic Car Dataset for Vehicle Detection: Integrating Aerial and Satellite Imagery

Authors:
raw_author_name: Filip Novoselnik
raw_affiliation_string: Protostar Labs, HR31551 Belišće, Croatia

Abstract: Vehicle detection is a very important aspect of computer vision application to aerial and satellite imagery, facilitating activities such as instance counting, velocity estimation, traffic predictions, etc. The feasibility of accurate vehicle detection often depends on limited training datasets, requiring a lot of manual work in collection and annotation tasks. Furthermore, there are no known publicly available datasets. Our aim was to construct a pipeline for synthetic dataset generation from aerial imagery and 3D models in Blender software. The dataset generation pipeline consists of seven steps and results in a wished number of images with bounding boxes in YOLO and coco formats. This synthetic dataset has been produced following the steps described in this pipeline. It consists of 5000 2048 × 2048 images with cars inserted into the roads and highways at the images without cars from all over the world. We believe that this dataset and the respective pipeline might be of great importance for vehicle detection, facilitating the customizability of the models to specific needs and context.
Dynamic Transitive Closure-Based Static Analysis through the Lens of Quantum Search

Authors:
raw_author_name: Jiawei Ren
raw_affiliation_string: University of New South Wales, Sydney, Australia
raw_author_name: Yulei Sui
raw_affiliation_string: University of New South Wales, Sydney, Australia
raw_author_name: Xiao Cheng
raw_affiliation_string: University of New South Wales, Sydney, Australia
raw_author_name: Yuan Feng
raw_affiliation_string: University of Technology Sydney, Sydney, Australia
raw_author_name: Jianjun Zhao
raw_affiliation_string: Kyushu University, Fukuoka, Japan

Abstract: Many existing static analysis algorithms suffer from cubic bottlenecks because of the need to compute a dynamic transitive closure (DTC). For the first time, this paper studies the quantum speedups on searching subtasks in DTC-based static analysis algorithms using quantum search (e.g., Grover’s algorithm). We first introduce our oracle implementation in Grover’s algorithm for DTC-based static analysis and illustrate our quantum search subroutine. Then, we take two typical DTC-based analysis algorithms: context-free-language reachability and set constraint-based analysis, and show that our quantum approach can reduce the time complexity of these two algorithms to truly subcubic ( \(O(N^2\sqrt {N}{polylog}(N)) \) ), yielding better results than the upper bound ( O ( N 3 /log N )) of existing classical algorithms. Finally, we conducted a classical simulation of Grover’s search to validate our theoretical approach, due to the current quantum hardware limitation of lacking a practical, large-scale, noise-free quantum machine. We evaluated the correctness and efficiency of our approach using IBM Qiskit on nine open-source projects and randomly generated edge-labeled graphs/constraints. The results demonstrate the effectiveness of our approach and shed light on the promising direction of applying quantum algorithms to address the general challenges in static analysis.
<i>RE-Trace</i> : Re-Identification of Modified GPS Trajectories

Authors:
raw_author_name: Stefan Schestakov
raw_affiliation_string: L3S Research Center, Leibniz Universität Hannover, Hannover, Germany
raw_author_name: Simon Gottschalk
raw_affiliation_string: L3S Research Center, Leibniz Universität Hannover, Hannover, Germany
raw_author_name: Thorben Funke
raw_affiliation_string: L3S Research Center, Leibniz Universität Hannover, Hannover, Germany
raw_author_name: Elena Demidova
raw_affiliation_string: Data Science &amp; Intelligent Systems Group (DSIS), University of Bonn, Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, Germany

Abstract: GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace – a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory’s origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all data sets and identifies modified GPS trajectories effectively and efficiently.
Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks

Authors:
raw_author_name: Jiaxiang Liu
raw_affiliation_string: Shenzhen University, Shenzhen, China
raw_author_name: Xiaomu Shi
raw_affiliation_string: State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
raw_author_name: Fu Song
raw_affiliation_string: State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhiwu Xu
raw_affiliation_string: Shenzhen University, Shenzhen, China
raw_author_name: Zhong Ming
raw_affiliation_string: Shenzhen University &amp; Shenzhen Technology University, Shenzhen, China

Abstract: As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical domains. While there are techniques for verifying DNNs with formal guarantees, they are limited in scalability and accuracy. In this paper, we present a novel counterexample-guided abstraction refinement (CEGAR) approach for scalable and exact verification of DNNs. Specifically, we propose a novel abstraction to break down the size of DNNs by over-approximation. The result of verifying the abstract DNN is conclusive if no spurious counterexample is reported. To eliminate each spurious counterexample introduced by abstraction, we propose a novel counterexample-guided refinement that refines the abstract DNN to exclude the spurious counterexample while still over-approximating the original one, leading to a sound, complete yet efficient CEGAR approach. Our approach is orthogonal to and can be integrated with many existing verification techniques. For demonstration, we implement our approach using two promising tools Marabou and Planet as the underlying verification engines, and evaluate on widely-used benchmarks for three datasets ACAS Xu , MNIST and CIFAR-10 . The results show that our approach can boost their performance by solving more problems in the same time limit, reducing on average 13.4%–86.3% verification time of Marabou on almost all the verification tasks, and reducing on average 8.3%–78.0% verification time of Planet on all the verification tasks. Compared to the most relevant CEGAR-based approach, our approach is 11.6–26.6 times faster.
A novel CNN-based method for localization of temperature anomalies in RDTS system

Authors:
raw_author_name: Shangkun Zeng
raw_affiliation_string: College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, PR China
raw_author_name: Sibo Wang
raw_affiliation_string: College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, PR China
raw_author_name: Yuhang Wang
raw_affiliation_string: College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, PR China

Abstract: In this study, the effect of convolutional neural network combined with cosine similarity approach on the localization of temperature anomalous events was investigated by designing Raman-based Distributed Temperature Sensing (RDTS) experiments. We first constructed convolutional neural network (CNN) models to extract temperature anomaly features. RDTS experiments were designed to obtain the dataset for training the CNN models. Subsequently, the cosine similarity between the anomalous features and the original signal was calculated to overcome the disadvantage of feature offset. Finally, the feature with the highest cosine similarity was used as the final temperature anomaly event feature. Moreover, another experiment was designed to verify the performance of the proposed method. Based on optical fiber data with a resolution of 1 m, we cut the original signal into data segments with a length of 100 sampling points and used them as input to the CNN model. The main results show that the proposed method has an accuracy of 98.37 %, a precision of 93.53 %, a recall of 75.54 %, and an overall evaluation index F1 score of 0.84 (0.81 for the Copula-based outlier detection method, and 0.75 for the quartile method). The proposed method shows excellent generalization performance on datasets with different signal-to-noise ratios, providing a novel and effective solution for locating temperature abnormal events in RDTS systems.
Mixed Graph Contrastive Network for Semi-Supervised Node Classification

Authors:
raw_author_name: Xihong Yang
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: Yiqi Wang
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: Yue Liu
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: Yi Wen
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: Lingyuan Meng
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: Sihang Zhou
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: Xinwang Liu
raw_affiliation_string: National University of Defense Technology, ChangSha, China
raw_author_name: En Zhu
raw_affiliation_string: National University of Defense Technology, ChangSha, China

Abstract: Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the performance of the GNNs in this field. To alleviate the collapse of node representations in semi-supervised scenario, we propose a novel graph contrastive learning method, termed M ixed G raph C ontrastive N etwork (MGCN). In our method, we improve the discriminative capability of the latent embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism. Specifically, we first conduct the interpolation-based augmentation in the latent space and then force the prediction model to change linearly between samples. Second, we enable the learned network to tell apart samples across two interpolation-perturbed views through forcing the correlation matrix across views to approximate an identity matrix. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discriminative representation learning. Extensive experimental results on six datasets demonstrate the effectiveness and the generality of MGCN compared to the existing state-of-the-art methods. The code of MGCN is available at https://github.com/xihongyang1999/MGCN on Github.
A novel technique for image steganography based on maximum energy seam

Authors:
raw_author_name: Ron Shmueli
raw_affiliation_string: Department of Electrical Engineering, AFEKA - College of Engineering, 218 Bnei Efrayim Rd, 69107, Tel-Aviv, Israel
raw_author_name: Divya Mishra
raw_affiliation_string: School of Electrical and Computer Engineering, Ben Gurion University of the Negev, David Ben Gurion Blvd 1, Beer-Sheva, 84105, Israel
raw_author_name: Tal Shmueli
raw_affiliation_string: Department of Computer Science, Bar-Ilan University, Ramat-Gan, 52900, Israel
raw_author_name: Ofer Hadar
raw_affiliation_string: School of Electrical and Computer Engineering, Ben Gurion University of the Negev, David Ben Gurion Blvd 1, Beer-Sheva, 84105, Israel

Abstract: Abstract Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represents the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stego-image, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of images and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1.
Clustering-based attack detection for adversarial reinforcement learning

Authors:
raw_author_name: Rubén Majadas
raw_affiliation_string: Departamento de Informática, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, Leganés, 28911, Madrid, Spain
raw_author_name: Javier García
raw_affiliation_string: Universidad Santiago de Compostela, Rúa Lope Gómez de Marzoa, Santiago de Compostela, 15782, La Coruña, Spain
raw_author_name: Fernando Fernández
raw_affiliation_string: Departamento de Informática, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, Leganés, 28911, Madrid, Spain

Abstract: Abstract Detecting malicious attacks presents a major challenge in the field of reinforcement learning (RL), as such attacks can force the victim to perform abnormal actions, with potentially severe consequences. To mitigate these risks, current research focuses on the enhancement of RL algorithms with efficient detection mechanisms, especially for real-world applications. Adversarial attacks have the potential to alter the environmental dynamics of a Markov Decision Process (MDP) perceived by an RL agent. Leveraging these changes in dynamics, we propose a novel approach to detect attacks. Our contribution can be summarized in two main aspects. Firstly, we propose a novel formalization of the attack detection problem that entails analyzing modifications made by attacks to the transition and reward dynamics within the environment. This problem can be framed as a context change detection problem, where the goal is to identify the transition from a “free-of-attack” situation to an “under-attack” scenario. To solve this problem, we propose a groundbreaking “model-free” clustering-based countermeasure. This approach consists of two essential steps: first, partitioning the transition space into clusters, and then using this partitioning to identify changes in environmental dynamics caused by adversarial attacks. To assess the efficiency of our detection method, we performed experiments on four established RL domains (grid-world, mountain car, carpole, and acrobot) and subjected them to four advanced attack types. Uniform, Strategically-timed, Q-value, and Multi-objective. Our study proves that our technique has a high potential for perturbation detection, even in scenarios where attackers employ more sophisticated strategies.
Who spies on whom? Unravelling the puzzle of state-sponsored cyber economic espionage

Authors:
raw_author_name: William Akoto
raw_affiliation_string: Department of Foreign Policy & Global Security, American University, USA

Abstract: Traditional conceptions of state-sponsored cyber economic espionage suggest that countries with different product profiles should experience high levels of espionage between them. However, this is not what we observe empirically. Incidence of economic espionage tends to be prevalent between countries with similar product and manufacturing profiles. This suggests that we may be missing critical parts of what drives state-sponsored cyber economic espionage. To help unravel this puzzle, I develop a novel theoretical framework that proposes that because attackers seek to maximize the expected utility of stolen information, they target countries that possess similar productive capabilities as themselves. Consequently, countries with dissimilar product profiles should avoid targeting each other for espionage. I test this argument using data on states’ product complexities and cyber economic espionage for a global sample of countries in a dyadic analytical framework. The results robustly show that for any pair of countries, as the complexities of their products diverge, they become significantly less likely to aim espionage attempts at each other. This study thus contributes new insights to explain why cyber economic espionage appears restricted to only a small number of advanced economies. It also illustrates the utility of large-N dyadic approaches in studying state-sponsored cyber espionage.