Reviewerly/exHarmony huggingface dataset:
VR/MR systems integrated with heat transfer simulation for training of thermoforming: A multicriteria decision-making user study

Authors:
raw_author_name: Iman Jalilvand
raw_affiliation_string: Materials and Manufacturing Research Institute, The University of British Columbia, Canada
raw_author_name: Jiyoung Jang
raw_affiliation_string: Materials and Manufacturing Research Institute, The University of British Columbia, Canada
raw_author_name: Bhushan Gopaluni
raw_affiliation_string: Materials and Manufacturing Research Institute, The University of British Columbia, Canada
raw_author_name: Abbas S. Milani
raw_affiliation_string: Materials and Manufacturing Research Institute, The University of British Columbia, Canada

Abstract: The emerging Industry 5.0 paradigm emphasizes the importance of collaboration between machines and human operators in smart factories, thereby preventing full automation and elimination of the skilled workforce. One technology that has been proposed recently as a means of achieving this collaboration, is the Extended Reality (XR), particularly for operators training and upskilling. However, current XR applications in advanced manufacturing sector are rather limited, mainly focusing on maintenance or sequential training. Additionally, most of earlier studies in the field have examined the use of one type of XR environment solely, such as Virtual Reality (VR), Augmented Reality (AR), or Mixed Reality (MR) in a respective application. The present study aims to develop and compare the performance of both MR and VR environments for training operators during the monitor and control of a given thermoforming manufacturing process. To achieve this, Meta Quest 2 (VR headset) and Microsoft HoloLens 2 (MR headset) were utilized in combination with heat transfer numerical simulations of the process. A video training tool was also created and employed as a reference/conventional training method, for comparisons. A user study involving 26 participants (with a mix of prior experience) was conducted, assessing different performance criteria including the usefulness, trust, reliability, user satisfaction, training effectiveness, and overall user preference over each training tool. Results, using statistical analysis along with five Multiple Criteria Decision Making (MCDM) algorithms, collectively indicated that the users preferred MR over VR and video training in the present case study; particularly as MR provided greater presence experience and satisfaction, higher user-friendliness and reliability. A demonstration of the developed applications can be found in the Supplementary Material.
Learnable convolutional attention network for knowledge graph completion

Authors:
raw_author_name: Bin Shang
raw_affiliation_string: School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
raw_author_name: Yinliang Zhao
raw_affiliation_string: National Engineering Lab for Big Data Analytics, Xi'an, 710049, Shaanxi, China; Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D, Xi'an, 710049, Shaanxi, China
raw_author_name: Jun Liu
raw_affiliation_string: National Engineering Lab for Big Data Analytics, Xi'an, 710049, Shaanxi, China; Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D, Xi'an, 710049, Shaanxi, China

Abstract: Recently, graph convolutional networks (GCNs) and graph attention networks (GATs) have been used extensively in knowledge graph completion (KGC), which aims to solve the incompleteness of knowledge graphs (KGs). However, both GCNs and GATs have limitations in the KGC task, and the best method is analyzing the neighbors of each entity (pre-validating), while this process is prohibitively expensive. Furthermore, relations in KGs have specific semantics and should be considered when aggregating neighbor information (message passing). To address the above limitations, we propose a learnable convolutional attention network for knowledge graph completion named LCA-KGC. LCA-KGC introduces a knowledge graph convolutional attention network using a convolution operation before the attention mechanism to ensure structural information acquisition and avoid redundant information stacking. Furthermore, to complete the autonomous switching of GNNs types and eliminate the necessity of pre-validating the local structure of KGs, LCA-KGC designs a learnable knowledge graph convolutional attention network by comprising three types of GNNs in one learnable formulation. Moreover, a learnable message function is proposed to emphasize relational semantics when aggregating neighbor information. Extensive experiments on standard KG datasets validate the effectiveness of the proposed innovations, and LCA-KGC achieves state-of-the-art (SOTA) performance compared to existing approaches (e.g., compared to SOTA approaches, LCA-KGC improves MRR from 0.360 to 0.372 on FB15k-237 dataset, and Hits@3 from 0.561 to 0.581 on YAGO3-10 dataset).
HSCNet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer

Authors:
raw_author_name: Zakaria Laskar
raw_affiliation_string: Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague 6, Czechia
raw_author_name: Iaroslav Melekhov
raw_affiliation_string: Aalto University, Espoo, Finland
raw_author_name: Xiaotian Li
raw_affiliation_string: Aalto University, Espoo, Finland
raw_author_name: Yi Zhao
raw_affiliation_string: Aalto University, Espoo, Finland
raw_author_name: Giorgos Tolias
raw_affiliation_string: Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague 6, Czechia
raw_author_name: Juho Kannala
raw_affiliation_string: Aalto University, Espoo, Finland

Abstract: Abstract Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between raw pixels and 3D coordinates in the scene, and thus the matching is implicitly performed by the forward pass through the network. However, in a large and ambiguous environment, learning such a regression task directly can be difficult for a single network. In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image. The proposed method, which is an extension of HSCNet, allows us to train compact models which scale robustly to large environments. It sets a new state-of-the-art for single-image localization on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and the combined indoor scenes.
A New Approach Based on Collective Intelligence to Solve Traveling Salesman Problems

Authors:
raw_author_name: Mustafa Servet Kiran
raw_affiliation_string: Department of Computer Engineering, Konya Technical University, 42250 Konya, Türkiye
raw_author_name: Mehmet Beskirli
raw_affiliation_string: Department of Computer Engineering, Karamanoğlu Mehmetbey University, 70100 Karaman, Türkiye

Abstract: This paper presents a novel approach based on the ant system algorithm for solving discrete optimization problems. The proposed method is based on path construction, path improvement techniques, and the footprint mechanism. Some information about the optimization problem and collective intelligence is used in order to create solutions in the path construction phase. In the path improvement phase, neighborhood operations are applied to the solution, which is the best of the population and is obtained from the path construction phase. The collective intelligence in the path construction phase is based on a footprint mechanism, and more footprints on the arc improve the selection chance of this arc. A selection probability is also balanced by using information about the problem (e.g., the distance between nodes for a traveling salesman problem). The performance of the proposed method has been investigated on 25 traveling salesman problems and compared with state-of-the-art algorithms. The experimental comparisons show that the proposed method produced comparable results for the problems dealt with in this study.
Relation fusion propagation network for transductive few-shot learning

Authors:
raw_author_name: Hongyu Hao
raw_affiliation_string: Megvii Technology, Beijing, China
raw_author_name: Weichao Ge
raw_affiliation_string: Megvii Technology, Beijing, China
raw_author_name: Yang Cao
raw_affiliation_string: Megvii Technology, Beijing, China
raw_author_name: Ming Wu
raw_affiliation_string: Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Chuang Zhang
raw_affiliation_string: Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Jun Guo
raw_affiliation_string: Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China

Abstract: Previous graph-based meta-learning approaches have explored pairwise feature similarity to learn instance-level relations of samples, however, the gap between the sample relations in feature and label spaces is often ignored. It is empirically observed that instances with different labels may display considerable similarity in visual characteristics, making it challenging to distinguish between them in the feature space. To this end, we propose a dual-branch Relation Fusion Propagation Network (RFPN) for transductive few-shot learning, which explicitly models both feature and label relations across support-query pairs. Specifically, we design a Relation Fusion Block (RFB) to fuse instance-level and class-level relations, thus obtaining more robust fusion-level relations to guide feature propagation. In addition to the feature propagation branch, we encode the label relations and construct a label shortcut branch for label propagation. To alleviate the error accumulation during propagation, which is caused by uncertain pseudo-labels for query samples, we propose a Label Shortcut Mechanism (LSM) to progressively update the sample relations with the initial labels. Our full method is plug-and-play and can be easily applied in existing graph-based approaches for transductive few-shot learning. Extensive experiments demonstrate that our proposed RFPN yields significant improvements over the baselines and achieves promising performance on four popular few-shot classification benchmarks.
Hierarchical variable clustering via copula-based divergence measures between random vectors

Authors:
raw_author_name: Irène Gijbels
raw_affiliation_string: Department of Mathematics, KU Leuven, Celestijnenlaan 200B, Leuven, 3001, Belgium

Abstract: This article considers rank-invariant clustering of continuous data via copula-based Φ-dependence measures. The general theoretical framework establishes dependence quantification between random vectors (groups of variables), which is used for measuring the similarity between variable clusters in an agglomerative hierarchical procedure afterwards. Special attention is devoted to meta-elliptical copulas, where we present an improved kernel estimator for the density generator and a corresponding bandwidth selector. This allows for non-Gaussian similarities also capturing e.g. tail dependence. Further, a fully non-parametric estimator is considered, enabling cluster detection in contexts where other measures fail. The theory is supported by simulations and a real data example, focusing on cluster analysis of continuous variables.
A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices

Authors:
raw_author_name: Liang Huang
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Senjie Liang
raw_affiliation_string: College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Feiyang Ye
raw_affiliation_string: College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Nan Gao
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

Abstract: Intent detection and slot filling are two main tasks in natural language understanding and play an essential role in task-oriented dialogue systems. The joint learning of both tasks can improve inference accuracy and is popular in recent works. However, most joint models ignore the inference latency and cannot meet the need to deploy dialogue systems at the edge. In this paper, we propose a Fast Attention Network (FAN) for joint intent detection and slot filling tasks, guaranteeing both accuracy and latency. Specifically, we introduce a clean and parameter-refined attention module to enhance the information exchange between intent and slot, improving semantic accuracy by more than 2%. FAN can be implemented on different encoders and delivers more accurate models at every speed level. Our experiments on the Jetson Nano platform show that FAN inferences fifteen utterances per second with a small accuracy drop, showing its effectiveness and efficiency on edge devices.
Multi-view class incremental learning

Authors:
raw_author_name: Depeng Li
raw_affiliation_string: Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China; School of Computing, National University of Singapore, 117417, Singapore
raw_author_name: Tianqi Wang
raw_affiliation_string: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
raw_author_name: Junwei Chen
raw_affiliation_string: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
raw_author_name: Kenji Kawaguchi
raw_affiliation_string: School of Computing, National University of Singapore, 117417, Singapore
raw_author_name: Cheng Lian
raw_affiliation_string: School of Automation, Wuhan University of Technology, Wuhan, 430070, China
raw_author_name: Zhigang Zeng
raw_affiliation_string: Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China

Abstract: Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods more practical in an open-ended environment, this paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incrementally classifies new classes from a continual stream of views, requiring no access to earlier views of data. However, MVCIL is challenged by the catastrophic forgetting of old information and the interference with learning new concepts. To address this, we first develop a randomization-based representation learning technique serving for feature extraction to guarantee their separate view-optimal working states, during which multiple views belonging to a class are presented sequentially; Then, we integrate them one by one in the orthogonality fusion subspace spanned by the extracted features; Finally, we introduce selective weight consolidation for learning-without-forgetting decision-making while encountering new classes. Extensive experiments on synthetic and real-world datasets validate the effectiveness of our approach.
Semantic-Preserving Surgical Video Retrieval With Phase and Behavior Coordinated Hashing

Authors:
raw_author_name: Yuxuan Yang
raw_affiliation_string: School of Management, Hefei University of Technology, Hefei, China
raw_author_name: Jizhou Wang
raw_affiliation_string: The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
raw_author_name: Kai Dong
raw_affiliation_string: The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
raw_author_name: Shuai Ding
raw_affiliation_string: School of Management, Hefei University of Technology, Hefei, China

Abstract: Medical professionals rely on surgical video retrieval to discover relevant content within large numbers of videos for surgical education and knowledge transfer. However, the existing retrieval techniques often fail to obtain user-expected results since they ignore valuable semantics in surgical videos. The incorporation of rich semantics into video retrieval is challenging in terms of the hierarchical relationship modeling and coordination between coarse- and fine-grained semantics. To address these issues, this paper proposes a novel semantic-preserving surgical video retrieval (SPSVR) framework, which incorporates surgical phase and behavior semantics using a dual-level hashing module to capture their hierarchical relationship. This module preserves the semantics in binary hash codes by transforming the phase and behavior similarities into high- and low-level similarities in a shared Hamming space. The binary codes are optimized by performing a reconstruction task, a high-level similarity preservation task, and a low-level similarity preservation task, using a coordinated optimization strategy for efficient learning. A self-supervised learning scheme is adopted to capture behavior semantics from video clips so that the indexing of behaviors is unencumbered by fine-grained annotation and recognition. Experiments on four surgical video datasets for two different disciplines demonstrate the robust performance of the proposed framework. In addition, the results of the clinical validation experiments indicate the ability of the proposed method to retrieve the results expected by surgeons. The code can be found at https://github.com/trigger26/SPSVR.
Inter-domain mixup for semi-supervised domain adaptation

Authors:
raw_author_name: Jichang Li
raw_affiliation_string: Department of Computer Science, The University of Hong Kong, Hong Kong; School of Computer Science and Engineering, Research Institute of Sun Yat-sen University in Shenzhen, Sun Yat-sen University, Guangzhou, China
raw_author_name: Guanbin Li
raw_affiliation_string: School of Computer Science and Engineering, Research Institute of Sun Yat-sen University in Shenzhen, Sun Yat-sen University, Guangzhou, China
raw_author_name: Yizhou Yu
raw_affiliation_string: Department of Computer Science, The University of Hong Kong, Hong Kong

Abstract: Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain distributions, with a small number of target labels available, achieving better classification performance than unsupervised domain adaptation (UDA). However, existing SSDA work fails to make full use of label information from both source and target domains for feature alignment across domains, resulting in label mismatch in the label space during model testing. This paper presents a novel SSDA approach, Inter-domain Mixup with Neighborhood Expansion (IDMNE), to tackle this issue. Firstly, we introduce a cross-domain feature alignment strategy, Inter-domain Mixup, that incorporates label information into model adaptation. Specifically, we employ sample-level and manifold-level data mixing to generate compatible training samples. These newly established samples, combined with reliable and actual label information, display diversity and compatibility across domains, while such extra supervision thus facilitates cross-domain feature alignment and mitigates label mismatch. Additionally, we utilize Neighborhood Expansion to leverage high-confidence pseudo-labeled samples in the target domain, diversifying the label information of the target domain and thereby further increasing the performance of the adaptation model. Accordingly, the proposed approach outperforms existing state-of-the-art methods, achieving significant accuracy improvements on popular SSDA benchmarks, including DomainNet, Office-Home, and Office-31.
Pseudo-label estimation via unsupervised Identity Link Prediction for one-shot person Re-Identification

Authors:
raw_author_name: Yulin Zhang
raw_affiliation_string: School of Computer Science, Beijing Institute of Technology, Beijing, China
raw_author_name: Bo Ma
raw_affiliation_string: School of Computer Science, Beijing Institute of Technology, Beijing, China
raw_author_name: Meng Li
raw_affiliation_string: School of Computer Science, Beijing Institute of Technology, Beijing, China
raw_author_name: Ying Liu
raw_affiliation_string: School of Computer Science, Beijing Institute of Technology, Beijing, China
raw_author_name: Feng Chen
raw_affiliation_string: Anhui University of Technology, Ma'anshan, China
raw_author_name: Junyu Hou
raw_affiliation_string: Xidian University, Xi'an, China

Abstract: In this paper, we propose an unsupervised identity link prediction (ILP) method for label estimation in one-shot person Re-ID. ILP aims to relax the constraints of labeled samples and group a set of unlabeled pedestrians by their potential identities. The main idea is that the category relationships between pedestrians (nodes) can be inferred from their local context in the feature space. Specifically, an Identity Link Subgraph (ILS) describes the link relationship between nodes and their nearest neighbors, which is constructed by a two-step procedure. Meanwhile, a Dynamic Penalty Module (DPM) is introduced at each ILS construction step to infer which linkage between pairs in the ILS should be pruned to assign higher-quality classification labels. To fully use the accurate identity information in initial labeled samples, we jointly use identity pseudo-labels (which are estimated by adopting the Nearest Neighbors classifier) with classification pseudo-labels for model training. Moreover, we design a Dual-Branch Fusion network (DBF-Net) to optimize the CNN model simultaneously through all (pseudo-)labeled samples. Results on multiple datasets prove that DBF-Net outperforms traditional one-shot Re-ID methods by a large margin.
Simion Zoo: A training workbench for reinforcement learning allowing distributed experimentation

Authors:
raw_author_name: Borja Fernandez-Gauna
raw_affiliation_string: Group of Computational Intelligence, University of the Basque Country (UPV/EHU), Manuel Lardizabal 1, 20018, Donostia-San Sebastian, Gipuzkoa, Spain
raw_author_name: Manuel Graña
raw_affiliation_string: Group of Computational Intelligence, University of the Basque Country (UPV/EHU), Manuel Lardizabal 1, 20018, Donostia-San Sebastian, Gipuzkoa, Spain

Abstract: Simion Zoo is a Reinforcement Learning (RL) workbench developed for training of novel users that can be deployed over computer farms allowing extensive experimentation over distributed resources. In this paper, we present this software platform and share some of the insights gained during the development. This workbench provides a complete set of tools to design, run, carry out statistical analysis of the results, report preparation, and have qualitative visual assessment of the simulation evolution of continuous RL control experiments. The main features that set apart Simion Zoo from other software packages for introduction to RL experimentation are its easy-to-use GUI, its support for distributed execution including deployment over graphics processing units (GPUs), and the possibility to explore concurrently the RL hyper-parameter space, which is key to successful RL experimentation.