Reviewerly/exHarmony huggingface dataset:
Three-dimentional reconstruction of underwater side-scan sonar images based on shape-from-shading and monocular depth fusion

Authors:
raw_author_name: Yakun Ju
raw_affiliation_string: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
raw_author_name: Jingchun Zhou
raw_affiliation_string: School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China
raw_author_name: Hao Xie
raw_affiliation_string: Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, 999077, China
raw_author_name: Cong Zhang
raw_affiliation_string: Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, 999077, China
raw_author_name: Jun Xiao
raw_affiliation_string: Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, 999077, China
raw_author_name: Cuixin Yang
raw_affiliation_string: Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, 999077, China

Abstract: Abstract Modern marine research requires high-precision three-dimensional (3D) underwater data. Underwater environments experience severe visible light attenuation, which leads to inferior imaging compared with air. In contrast, sound waves are less affected underwater; hence side-scan sonar is used for underwater 3D reconstruction. Typically, the shape-from-shading algorithm (SfS) is widely used to reconstruct surface normal or heights from side-scan sonar images. However, this approach has challenges because of global information loss and noise. To address these issues, this study introduces a surface-normal fusion method. Specifically, we propose a frequency separation SfS algorithm using a discrete cosine transform, which provides a surface-normal map with less noise. We then fuse the surface-normal map with a novel depth estimation network to achieve high-precision 3D reconstruction of underwater side-scan sonar images. We conducted experiments on synthetic, NYU-depth-v2, and real side-scan sonar datasets to demonstrate the effectiveness of the proposed method.
RSRNeT: a novel multi-modal network framework for named entity recognition and relation extraction

Authors:
raw_author_name: Min Wang
raw_affiliation_string: Key Laboratory of Jiangxi Province for Numerical Simulation and Emulation Techniques, Ganzhou, China; School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China
raw_author_name: Hongbin Chen
raw_affiliation_string: School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China
raw_author_name: Dingcai Shen
raw_affiliation_string: Key Laboratory of Jiangxi Province for Numerical Simulation and Emulation Techniques, Ganzhou, China; School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China
raw_author_name: Shiyu Hu
raw_affiliation_string: School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China

Abstract: Named entity recognition (NER) and relation extraction (RE) are two important technologies employed in knowledge extraction for constructing knowledge graphs. Uni-modal NER and RE approaches solely rely on text information for knowledge extraction, leading to various limitations, such as suboptimal performance and low efficiency in recognizing polysemous words. With the development of multi-modal learning, multi-modal named entity recognition (MNER) and multi-modal relation extraction (MRE) have been introduced to improve recognition performance. However, existing MNER and MRE methods often encounter reduced efficiency when the text includes unrelated images. To address this problem, we propose a novel multi-modal network framework for named entity recognition and relation extraction called RSRNeT. In RSRNeT, we focus on extracting visual features more fully and designing a multi-scale visual feature extraction module based on ResNeSt network. On the other hand, we also emphasize fusing multi-modal features more comprehensively while minimizing interference from irrelevant images. To address this issue, we propose a multi-modal feature fusing module based on RoBERTa network. These two modules enable us to learn superior visual and textual representations, reducing errors caused by irrelevant images. Our approach has undergone extensive evaluation and comparison with various baseline models on MNER and MRE tasks. Experimental results show that our method achieves state-of-the-art performance in recall and F1 score on three public datasets: Twitter2015, Twitter2017 and MNRE.
CSINet: A Cross-Scale Interaction Network for Lightweight Image Super-Resolution

Authors:
raw_author_name: Gang Ke
raw_affiliation_string: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China; School of Electronic Information, Dongguan Polytechnic, Dongguan 523109, China
raw_author_name: Sio-Long Lo
raw_affiliation_string: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
raw_author_name: Hua Zou
raw_affiliation_string: School of Computer Science, Wuhan University, Wuhan 430072, China
raw_author_name: Yi-Feng Liu
raw_affiliation_string: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
raw_author_name: Zhen-Qiang Chen
raw_affiliation_string: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
raw_author_name: Jing-Kai Wang
raw_affiliation_string: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China

Abstract: In recent years, advancements in deep Convolutional Neural Networks (CNNs) have brought about a paradigm shift in the realm of image super-resolution (SR). While augmenting the depth and breadth of CNNs can indeed enhance network performance, it often comes at the expense of heightened computational demands and greater memory usage, which can restrict practical deployment. To mitigate this challenge, we have incorporated a technique called factorized convolution and introduced the efficient Cross-Scale Interaction Block (CSIB). CSIB employs a dual-branch structure, with one branch extracting local features and the other capturing global features. Interaction operations take place in the middle of this dual-branch structure, facilitating the integration of cross-scale contextual information. To further refine the aggregated contextual information, we designed an Efficient Large Kernel Attention (ELKA) using large convolutional kernels and a gating mechanism. By stacking CSIBs, we have created a lightweight cross-scale interaction network for image super-resolution named "CSINet". This innovative approach significantly reduces computational costs while maintaining performance, providing an efficient solution for practical applications. The experimental results convincingly demonstrate that our CSINet surpasses the majority of the state-of-the-art lightweight super-resolution techniques used on widely recognized benchmark datasets. Moreover, our smaller model, CSINet-S, shows an excellent performance record on lightweight super-resolution benchmarks with extremely low parameters and Multi-Adds (e.g., 33.82 dB@Set14 × 2 with only 248 K parameters).
Improving 3D Object Detection with Context-Aware and Dimensional Interaction Attention

Authors:
raw_author_name: Jing Zhou
raw_affiliation_string: School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China
raw_author_name: Zixin Gong
raw_affiliation_string: School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China
raw_author_name: Junchi Zhang
raw_affiliation_string: School of Artificial Intelligence, Jianghan University, Wuhan, 430056, China

Abstract: Abstract Recently, 3D object detection technology based on point clouds has developed rapidly. However, too few points of distant and occluded objects are scanned by the sensor, and thus these objects suffer from too insufficient features to be detected. This case damages the detection accuracy. Therefore, we constitute a novel 3D object detection with Context-aware and dimensional Interaction Attention Network (CIANet) to explore vital geometric cues for enriching the feature representation of the object, thus boosting the overall detection performance. Specifically, in the first stage, we employ the 3D sparse convolution to extract voxel features, and then construct a Channel-Spatial Hybrid Attention (CSHA) module and a Contextual Self-Attention (CSA) module to enhance voxel features for generating proposals. The CSHA module aims to enhance the key information of the channel and spatial domains of 2D Bird’s Eye View (BEV) features, and the CSA module is applied to supplement contextual information to the enhanced BEV features, thus generating accurate proposals. In the second stage, we construct a Dimensional Interaction Attention (DIA) module to refine Region of Interest (RoI) features within the proposals. It enhances the interactions among the channel and spatial dimensions of RoI features to learn accurate boundaries of objects for proposal refinement. Extensive experiments on the KITTI and Waymo benchmarks show the superior detection performance of CIANet compared to recent methods, especially for objects such as pedestrians and cyclists.
A Smart Ad Display System

Authors:
raw_author_name: Min Ding
raw_affiliation_string: Smeal College of Business and College of Information Sciences and Technology, The Pennsylvania State University, University Park, Pennsylvania 16802

Abstract: This paper proposes a smart ad display system to provide personalized delivery of video ads. The proposed system records consumers’ facial expression and eye gaze stream data as they watch an ad and analyzes data at the frame level. The recognized facial expression and detected eye gaze are matched to the corresponding frame of the video ad, thereby linking facial expressions to specific visual objects appearing in the ad. By tracking a consumer’s facial expressions in response to various visual objects in real time, the system learns the consumer’s individual preferences toward different ads, searches the ad pool, and selects and subsequently displays a new ad that is most likely to elicit positive attitudinal and behavioral responses. We demonstrate the feasibility and effectiveness of the proposed system with two empirical studies. The results show that by tracking a consumer’s facial responses to only one ad or even part of an ad, our proposed system is able to make reasonably accurate inferences about a consumer’s ad preferences, with or without using information about other consumers. These inferences are used to make personalized recommendations that help enhance consumers’ ad viewing experiences and elicit favorable responses.
Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak

Authors:
raw_author_name: Zuzana Sokolová
raw_affiliation_string: Department of Electronics and Multimedia Telecommunications, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
raw_author_name: Maroš Harahus
raw_affiliation_string: Department of Electronics and Multimedia Telecommunications, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
raw_author_name: Jozef Juhár
raw_affiliation_string: Department of Electronics and Multimedia Telecommunications, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
raw_author_name: Matúš Pleva
raw_affiliation_string: Department of Electronics and Multimedia Telecommunications, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
raw_author_name: Ján Staš
raw_affiliation_string: Department of Electronics and Multimedia Telecommunications, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
raw_author_name: Daniel Hládek
raw_affiliation_string: Department of Electronics and Multimedia Telecommunications, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia

Abstract: The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the “SentiSK” and “Sentigrade” datasets involved three classes (positive, neutral, and negative). In the second task, testing on the “SentiSK”, “Sentigrade”, and “Slovak dataset for SA” datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%.
Conflicting Marks Archive Dataset: A Dataset of Conflicting Marks from the Brazilian Intellectual Property Office

Authors:
raw_author_name: Francisco José da Silva e Silva
raw_affiliation_string: Postgraduate Program in Computer Science, Federal University of Maranhão, São Luís 65085-580, Brazil
raw_author_name: Ariel Soares Teles
raw_affiliation_string: Federal Institute of Maranhão, Araioses 65570-000, Brazil; Postgraduate Program in Computer Science, Federal University of Maranhão, São Luís 65085-580, Brazil

Abstract: A registered trademark represents one of a company’s most valuable intellectual assets, acting as a safeguard against possible reputational damage and financial losses resulting from infringements of this intellectual property. To be registered, a mark must be unique and distinctive in relation to other trademarks which are already registered. In this paper, we describe the CMAD, an acronym for Conflicting Marks Archive Dataset. This dataset has been meticulously organized into pairs of marks (Number of pairs = 18,355) involved in copyright infringement across word, figurative and mixed marks. Organizations sought to register these marks with the National Institute of Industrial Property (INPI) in Brazil, and had their applications denied after analysis by intellectual property specialists. The robustness of this dataset is ensured by the intrinsic similarity of the conflicting marks, since the decisions were made by INPI specialists. This characteristic provides a reliable basis for the development and testing of tools designed to analyze similarity between marks, thus contributing to the evolution of practices and computer-based solutions in the field of intellectual property.
A Lightweight Context-Aware Feature Transformer Network for Human Pose Estimation

Authors:
raw_author_name: Fan Zhang
raw_affiliation_string: Hebei Machine Vision Engineering Research Center, Hebei University, Baoding 071002, China

Abstract: We propose a Context-aware Feature Transformer Network (CaFTNet), a novel network for human pose estimation. To address the issue of limited modeling of global dependencies in convolutional neural networks, we design the Transformerneck to strengthen the expressive power of features. Transformerneck directly substitutes 3×3 convolution in the bottleneck of HRNet with a Contextual Transformer (CoT) block while reducing the complexity of the network. Specifically, the CoT first produces keys with static contextual information through 3×3 convolution. Then, relying on query and contextualization keys, dynamic contexts are generated through two concatenated 1×1 convolutions. Static and dynamic contexts are eventually fused as an output. Additionally, for multi-scale networks, in order to further refine the features of the fusion output, we propose an Attention Feature Aggregation Module (AFAM). Technically, given an intermediate input, the AFAM successively deduces attention maps along the channel and spatial dimensions. Then, an adaptive refinement module (ARM) is exploited to activate the obtained attention maps. Finally, the input undergoes adaptive feature refinement through multiplication with the activated attention maps. Through the above procedures, our lightweight network provides powerful clues for the detection of keypoints. Experiments are performed on the COCO and MPII datasets. The model achieves a 76.2 AP on the COCO val2017 dataset. Compared to other methods with a CNN as the backbone, CaFTNet has a 72.9% reduced number of parameters. On the MPII dataset, our method uses only 60.7% of the number of parameters, acquiring similar results to other methods with a CNN as the backbone.
Surface trap with adjustable ion couplings for scalable and parallel gates

Authors:
raw_author_name: N. Sterligov
raw_affiliation_string: Russian Quantum Center, Skolkovo, Moscow 121205, Russia
raw_author_name: E. Anikin
raw_affiliation_string: Russian Quantum Center, Skolkovo, Moscow 121205, Russia
raw_author_name: K. Lakhmanskiy
raw_affiliation_string: Russian Quantum Center, Skolkovo, Moscow 121205, Russia

Abstract: We describe the design and operation of a surface-electrode Paul trap for parallel entangling gate implementation. In particular, we demonstrate the possibility of separating or coupling ion motion by adjusting the DC-voltages on a set of electrodes and show the possibility of parallel MS-gate operations for specific voltage configurations. We verify the scalability of this approach and characterize the performance of these gates in the presence of the finite phonon mode occupation and of the finite drift of the phonon frequencies. Additionally, we investigate how the number of ions per individual trapping site and anharmonic potential terms affect the coupling between the wells.
Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning

Authors:
raw_author_name: Yangang Liang
raw_affiliation_string: College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China; Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Mission, Changsha 410073, China
raw_author_name: Kebo Li
raw_affiliation_string: College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China; Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Mission, Changsha 410073, China

Abstract: Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning in dynamic scenarios. Firstly, we establish a task scenario including an obstacle assessment model and model the UAV’s path planning problem using the Markov Decision Process. We translate the MDP model into the framework of reinforcement learning and design the state space, action space, and reward function while incorporating heuristic rules into the action exploration policy. Secondly, we utilize the Q function approximation of an enhanced D3QN with a prioritized experience replay mechanism and design the algorithm’s network structure based on the TensorFlow framework. Through extensive training, we obtain reinforcement learning path planning policies for both static and dynamic scenes and innovatively employ a visualized action field to analyze their planning effectiveness. Simulations demonstrate that the proposed algorithm can accomplish UAV dynamic scene path planning tasks and outperforms classical methods such as A*, RRT, and DQN in terms of planning effectiveness.
Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Authors:
raw_author_name: Wenyu Zang
raw_affiliation_string: Academy of Cyber, China Electronics Technology Group Corporation, Beijing 100085, China
raw_author_name: Zhihao Xiong
raw_affiliation_string: Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing 100876, China

Abstract: Random-walk-based graph representation methods have been widely applied in User Identity Linkage (UIL) tasks, which links overlapping users between two different social networks. It can help us to obtain more comprehensive portraits of criminals, which is helpful for improving cyberspace governance. Yet, random walk generates a large number of repeating sequences, causing unnecessary computation and storage overhead. This paper proposes a novel method called Edge-Removing Walk (ERW) that can replace random walk in random-walk-based models. It removes edges once they are walked in a walk round to capture the l−hop features without repetition, and it walks the whole graph for several rounds to capture the different kinds of paths starting from a specific node. Experiments proved that ERW can exponentially improve the efficiency for random-walk-based UIL models, even maintaining better performance. We finally generalize ERW into a general User Identity Linkage framework called ERW-UIL and verify its performance.