Reviewerly/exHarmony huggingface dataset:
<scp>GraphZeppelin</scp> : How to Find Connected Components (Even When Graphs Are Dense, Dynamic, and Massive)

Authors:
raw_author_name: David Tench
raw_affiliation_string: Rutgers University, New Brunswick, USA
raw_author_name: Evan West
raw_affiliation_string: Stony Brook University, Stony Brook, USA
raw_author_name: Victor Zhang
raw_affiliation_string: Rutgers University, New Brunswick, USA
raw_author_name: Michael A. Bender
raw_affiliation_string: Stony Brook University, Stony Brook, USA
raw_author_name: Abiyaz Chowdhury
raw_affiliation_string: Stony Brook University, Stony Brook, USA
raw_author_name: J. Ahmed Dellas
raw_affiliation_string: Rutgers University, New Brunswick, USA
raw_author_name: Martín Farach-Colton
raw_affiliation_string: Rutgers University, New Brunswick, USA
raw_author_name: Tyler Seip
raw_affiliation_string: MongoDB, New York, USA
raw_author_name: Kenny Zhang
raw_affiliation_string: Stony Brook University, Stony Brook, USA

Abstract: Finding the connected components of a graph is a fundamental problem with uses throughout computer science and engineering. The task of computing connected components becomes more difficult when graphs are very large, or when they are dynamic, meaning the edge set changes over time subject to a stream of edge insertions and deletions. A natural approach to computing the connected components problem on a large, dynamic graph stream is to buy enough RAM to store the entire graph. However, the requirement that the graph fit in RAM is an inherent limitation of this approach and is prohibitive for very large graphs. Thus, there is an unmet need for systems that can process dense dynamic graphs, especially when those graphs are larger than available RAM. We present a new high-performance streaming graph-processing system for computing the connected components of a graph. This system, which we call GraphZeppelin , uses new linear sketching data structures ( CubeSketch ) to solve the streaming connected components problem and as a result requires space asymptotically smaller than the space required for an lossless representation of the graph. GraphZeppelin is optimized for massive dense graphs: GraphZeppelin can process millions of edge updates (both insertions and deletions) per second, even when the underlying graph is far too large to fit in available RAM. As a result GraphZeppelin vastly increases the scale of graphs that can be processed.
Strategic Content Generation and Monetization in Financial Social Media

Authors:
raw_author_name: Khim-Yong Goh
raw_affiliation_string: School of Computing, National University of Singapore, Singapore 117418

Abstract: Strategic Content Generation and Monetization in Financial Social Media Abstract Financial social media, which relies on social media analysts (SMAs) to contribute content to investors, is a crucial channel for investors to gain financial information and for SMAs to monetize their content. The interactive nature of financial social media has given SMAs the opportunity to gain access to the investor preferences of their own audience base for financial content. Our study documents that SMAs would exploit this opportunity to strategically generate and monetize content by catering to investor preferences. Specifically, SMAs would increase the (negative) sentiment of the content if paid subscribers’ preferences for (negative) sentiment grow. Additionally, an SMA is more likely to produce paid content when the expected free readership increases and is less likely to do so when the expected paid subscriptions increase. Our findings suggest that the sentiment of financial social media content is not a mere reflection or prediction of stock market movements but also a result of SMAs’ reaction to investor preferences. We thus illustrate an approach to identify the SMAs who may amplify the investors’ confirmation biases because of such catering behaviors so that platform managers and regulators alike can utilize this method to improve the content quality of financial social media.
An Efficient Privacy Protection Mechanism for Blockchain-Based Federated Learning System in UAV-MEC Networks

Authors:
raw_author_name: Chaoyang Zhu
raw_affiliation_string: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China; School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
raw_author_name: Xiao Zhu
raw_affiliation_string: School of Electronic Information Engineering, Guangxi Vocational Technical Institute of Industry, Nanning 530001, China
raw_author_name: Tuanfa Qin
raw_affiliation_string: Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China; School of Computer and Electronic Information, Guangxi University, Nanning 530004, China

Abstract: The widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to the transmission of sensitive data. Traditional UAV-MEC systems with centralized data processing expose this data to risks like breaches and manipulation, potentially hindering the adoption of these valuable technologies. To address this critical challenge, we propose UBFL, a novel privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy (DP), UBFL employs an adaptive nonlinear encryption function to safeguard the privacy of UAV model updates while maintaining data integrity and accuracy. This innovative approach enables rapid convergence, allowing the base station to efficiently identify and filter out severely compromised UAVs attempting to inject malicious data. Additionally, UBFL incorporates the Random Cut Forest (RCF) anomaly detection algorithm to actively identify and mitigate poisoning data attacks. Extensive comparative experiments on benchmark datasets CIFAR10 and Mnist demonstrably showcase UBFL’s effectiveness. Compared to DP-based methods, UBFL achieves accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) in privacy preservation while maintaining superior accuracy. Notably, under data pollution scenarios with varying attack sample rates (10%, 20%, and 30%), UBFL exhibits exceptional resilience, highlighting its robust capabilities in securing UAV gradients within MEC environments.
Online Learning State Evaluation Method Based on Face Detection and Head Pose Estimation

Authors:
raw_author_name: Bin Li
raw_affiliation_string: School of Computer Science, Northeast Electric Power University, Jilin 132011, China
raw_author_name: Peng Liu
raw_affiliation_string: School of Computer Science, Northeast Electric Power University, Jilin 132011, China

Abstract: In this paper, we propose a learning state evaluation method based on face detection and head pose estimation. This method is suitable for mobile devices with weak computing power, so it is necessary to control the parameter quantity of the face detection and head pose estimation network. Firstly, we propose a ghost and attention module (GA) base face detection network (GA-Face). GA-Face reduces the number of parameters and computation in the feature extraction network through the ghost module, and focuses the network on important features through a parameter-free attention mechanism. We also propose a lightweight dual-branch (DB) head pose estimation network: DB-Net. Finally, we propose a student learning state evaluation algorithm. This algorithm can evaluate the learning status of students based on the distance between their faces and the screen, as well as their head posture. We validate the effectiveness of the proposed GA-Face and DB-Net on several standard face detection datasets and standard head pose estimation datasets. Finally, we validate, through practical cases, that the proposed online learning state assessment method can effectively assess the level of student attention and concentration, and, due to its low computational complexity, will not interfere with the student’s learning process.
An Iteratively Extended Target Tracking by Using Decorrelated Unbiased Conversion of Nonlinear Measurements

Authors:
raw_author_name: Yuemei Qin
raw_affiliation_string: School of Automation, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
raw_author_name: Yang Han
raw_affiliation_string: School of Automation, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
raw_author_name: Shuying Li
raw_affiliation_string: School of Automation, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
raw_author_name: Jun Li
raw_affiliation_string: School of Automation, Xi’an University of Posts & Telecommunications, Xi’an 710121, China

Abstract: Extended target tracking (ETT) based on random matrices typically assumes that the measurement model is linear. However, nonlinear measurements (such as range and azimuth) depending on locations of a series of unknown scattering centers always exist in many practical tracking applications. To address this issue, this paper proposes an iteratively extended target tracking based on random matrices by using decorrelated unbiased conversion of nonlinear measurements (ETT-IDUCM). First, we utilize a decorrelated unbiased converted measurement (DUCM) method to convert nonlinear measurements depending on unknown scatters of target extent in polar coordinates into the ones in Cartesian coordinates with equivalent measurement noise covariances. Subsequently, a novel method, combining iteratively extended Kalman filter (IEKF) updates with variational Bayesian (VB) cycles is developed for precise estimation of the target’s kinematic state and extension. This method leverages the synergy between external IEKF iterations, which use the estimated state as a new prediction and input for DUCM, and internal VB iterations, which realize a closed-form approximation of the joint posterior probability. This approach progressively enhances estimation accuracy. Simulation results demonstrate the ETT-IDUCM algorithm’s superior precision in estimating the target’s kinematic state and extension compared to existing methods.
UFCC: A Unified Forensic Approach to Locating Tampered Areas in Still Images and Detecting Deepfake Videos by Evaluating Content Consistency

Authors:
raw_author_name: Po-Chyi Su
raw_affiliation_string: Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
raw_author_name: Bo-Hong Huang
raw_affiliation_string: Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
raw_author_name: Tien-Ying Kuo
raw_affiliation_string: Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan

Abstract: Image inpainting and Deepfake techniques have the potential to drastically alter the meaning of visual content, posing a serious threat to the integrity of both images and videos. Addressing this challenge requires the development of effective methods to verify the authenticity of investigated visual data. This research introduces UFCC (Unified Forensic Scheme by Content Consistency), a novel forensic approach based on deep learning. UFCC can identify tampered areas in images and detect Deepfake videos by examining content consistency, assuming that manipulations can create dissimilarity between tampered and intact portions of visual data. The term “Unified” signifies that the same methodology is applicable to both still images and videos. Recognizing the challenge of collecting a diverse dataset for supervised learning due to various tampering methods, we overcome this limitation by incorporating information from original or unaltered content in the training process rather than relying solely on tampered data. A neural network for feature extraction is trained to classify imagery patches, and a Siamese network measures the similarity between pairs of patches. For still images, tampered areas are identified as patches that deviate from the majority of the investigated image. In the case of Deepfake video detection, the proposed scheme involves locating facial regions and determining authenticity by comparing facial region similarity across consecutive frames. Extensive testing is conducted on publicly available image forensic datasets and Deepfake datasets with various manipulation operations. The experimental results highlight the superior accuracy and stability of the UFCC scheme compared to existing methods.
The importance of context in teacher educators’ professional digital competence

Authors:
raw_author_name: Jeffrey P. Carpenter
raw_affiliation_string: Department of Education &amp; Wellness, Elon University, Elon, USA
raw_author_name: Joshua M. Rosenberg
raw_affiliation_string: Department of Theory &amp; Practice in Teacher Education, University of Tennessee, Knoxville, USA
raw_author_name: Aaron Kessler
raw_affiliation_string: Learning Science &amp; Teaching, Massachusetts Institute of Technology, Cambridge, USA
raw_author_name: Enilda Romero-Hall
raw_affiliation_string: Department of Theory &amp; Practice in Teacher Education, University of Tennessee, Knoxville, USA
raw_author_name: Christian Fischer
raw_affiliation_string: Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Tübingen, Germany

Abstract: Digital technologies permeate modern life, and schools are accordingly expected to help students develop related knowledge and skills. As a result, educators’ professional digital competence (PDC) has received substantial attention from school leaders, policymakers, teacher educators, and researchers. Theory and prior research suggest that educators’ PDC does not solely determine their technology use, with contextual factors, such as competing curricular demands and access to technology, influencing technology implementation and ongoing PDC development. While some prior research has addressed how PK-12 teachers’ contexts shape their digital technology use, few studies have explored similar matters with respect to teacher educators. Drawing on an established set of teacher educator technology competencies, we report results from an international survey of 336 teacher educators regarding their self-reported PDC and its development and enactment. Using qualitative coding, exploratory and confirmatory factor analysis, and quantitative analysis, we find that teacher educators develop and enact their competencies in various ways including through informal endeavours as well as formal, institutionally supported, and/or led activities. Three types of assets and barriers (access, leadership, personal characteristics) present differently for participants in different contexts. These findings have implications for how institutions support teacher educators’ PDC development and technology integration in various settings.
A Fast Optimal Coordination Method for Multiagent in Complex Environment

Authors:
raw_author_name: Suyu Wang
raw_affiliation_string: School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
raw_author_name: Quan Yue
raw_affiliation_string: School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
raw_author_name: Mengyu Zhao
raw_affiliation_string: School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
raw_author_name: Huazhi Zhang
raw_affiliation_string: School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

Abstract: Facing the implementation problems such like low growth reward, long training time, and poor stability of the multiagent learning methods when dealing with complex environment and more agents, this paper proposes a fast optimal coordination method for multiagent in complex environment (FOC-MACE). Firstly, the environment exploration strategy is introduced into the policy network based on the MADDPG method for higher growth rewards. Then, the parallel computing technology is adopted in the critic network, in purpose to effectively reduce the training time. These tactics together are beneficial to enhance the stability of multiagent learning. Lastly, the optimal resource allocation is carried out to realize optimal coevolution of the multiagents and further improve the learning ability of the agents’ group. To verify the effectiveness of our proposal, the FOC-MACE is compared with several advanced methods at current stage in the MPE environment. Three different experiments prove that by using our method, the growth reward is increased by up to 37.1%, the training is speed up significantly, and the stability of the method, which represented by standardized variance, is also improved. In addition, this paper validated the fast optimal coordination method for multiagent systems in the context of UAV scenarios, demonstrating the practical performance of the approach. Through comprehensive experiments and scenario validations, the study successfully confirmed the effectiveness of the proposed fast optimal coordination method for multiagent systems in complex environments.
RadiantVisions: Illuminating Low-Light Imagery with a Multi-Scale Branch Network

Authors:
raw_author_name: Yu Zhang
raw_affiliation_string: Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China; School of Information Engineering, Minzu University of China, Beijing 100081, China
raw_author_name: Shan Jiang
raw_affiliation_string: Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China; School of Information Engineering, Minzu University of China, Beijing 100081, China
raw_author_name: Xiangyun Tang
raw_affiliation_string: Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China; School of Information Engineering, Minzu University of China, Beijing 100081, China

Abstract: In the realms of the Internet of Things (IoT) and artificial intelligence (AI) security, ensuring the integrity and quality of visual data becomes paramount, especially under low-light conditions, where low-light image enhancement emerges as a crucial technology. However, the current methods for enhancing images under low-light conditions still face some challenging issues, including the inability to effectively handle uneven illumination distribution, suboptimal denoising performance, and insufficient correlation among a branch network. Addressing these issues, the Multi-Scale Branch Network is proposed. It utilizes multi-scale feature extraction to handle uneven illumination distribution, introduces denoising functions to mitigate noise issues arising from image enhancement, and establishes correlations between network branches to enhance information exchange. Additionally, our approach incorporates a vision transformer to enhance feature extraction and context understanding. The process begins with capturing raw RGB data, which are then optimized through sophisticated image signal processor (ISP) techniques, resulting in a refined visual output. This method significantly improves image brightness and reduces noise, achieving remarkable improvements in low-light image enhancement compared to similar methods. Using the LOL-V2-real dataset, we achieved improvements of 0.255 in PSNR and 0.23 in SSIM, with decreases of 0.003 in MAE and 0.009 in LPIPS, compared to the state-of-the-art methods. Rigorous experimentation confirmed the reliability of this approach in enhancing image quality under low-light conditions.
Learning Universal Trajectory Representation via a Siamese Geography-Aware Transformer

Authors:
raw_author_name: Chenhao Wu
raw_affiliation_string: College of Civil Engineering, Fuzhou University, Fuzhou 350000, China; Fujian Expressway Science & Technology Innovation Research Institute Co., Ltd., Fuzhou 350000, China
raw_author_name: Xiongwei Wu
raw_affiliation_string: College of Civil Engineering, Fuzhou University, Fuzhou 350000, China; Fujian Expressway Science & Technology Innovation Research Institute Co., Ltd., Fuzhou 350000, China

Abstract: With the development of location-based services and data collection equipment, the volume of trajectory data has been growing at a phenomenal rate. Raw trajectory data come in the form of sequences of “coordinate-time-attribute” triplets, which require complicated manual processing before they can be used in data mining algorithms. Current works have started to explore the emerging deep representation learning method, which maps trajectory sequences to vector space and applies them to various downstream applications for boosting accuracy and efficiency. In this work, we propose a universal trajectory representation learning method based on a Siamese geography-aware transformer (TRT for short). Specifically, we first propose a geography-aware encoder to model geographical information of trajectory points. Then, we apply a transformer encoder to embed trajectory sequences and use a Siamese network to facilitate representation learning. Furthermore, a joint training strategy is designed for TRT. One of the training objectives is to predict the masked trajectory point, which makes the trajectory representation robust to low sampling rates and noises. The other is to distinguish the difference between trajectories by means of contrastive learning, which makes the trajectory representation more uniformly distributed over the hypersphere. Last, we design a benchmark containing four typical traffic-related tasks to evaluate the performance of TRT. Comprehensive experiments demonstrate that TRT consistently outperforms the state-of-the-art baselines across all tasks.