Reviewerly/exHarmony huggingface dataset:
Linear time manageable edge-aware filtering on complementary tree structures

Authors:
raw_author_name: Penghui Bu
raw_affiliation_string: School of Mechanical Engineering, Xi'an Shiyou University, No. 18, Electronic Second Road, Xi'an, 710065, Shaanxi, China
raw_author_name: Hang Wang
raw_affiliation_string: School of Mechanical Engineering, Xi'an Shiyou University, No. 18, Electronic Second Road, Xi'an, 710065, Shaanxi, China
raw_author_name: Hong Zhao
raw_affiliation_string: State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, Shaanxi, China

Abstract: Typical non-local edge-aware filtering methods build long-range connections by deriving a minimum spanning tree (MST) from the input image. Each pixel on the MST only connects to a sub-set of pixels in the 8-connected neighborhood, resulting in piece-wise constant output with fake edges among sub-trees for the unbalanced information propagation along eight directions. In this paper, we propose two complementary spatial trees to incorporate information from the entire image. The structure of each tree depends on the spatial relationships of neighboring pixels. The distances between any two pixels in both spatial space and intensity space are the shortest distances on each tree. We introduce an efficient algorithm to recursively compute the output and the normalization constant on each tree with linear time complexity. For each pixel, we first calculate the outputs from eight subtrees and then fuse them to obtain the result on each tree structure. The final filtering output of our method is the weighted average of the results from two complementary spatial trees. Moreover, we present a distance mapping scheme to adjust the intensity distance between neighboring pixels, enabling our method to filter out a manageable degree of low-amplitude structures while sharpening major edges. Extensive experiments in graphic applications, such as image denoising, JPEG artifact removal, tone mapping, detail enhancement, and colorization, demonstrate the effectiveness and versatility of our method.
Multimodal fusion hierarchical self-attention network for dynamic hand gesture recognition

Authors:
raw_author_name: Pranav Balaji
raw_affiliation_string: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai India
raw_author_name: Manas Ranjan Prusty
raw_affiliation_string: Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India

Abstract: Recent improvements in dynamic hand gesture recognition have seen a shift from traditional convolutional architectures to attention-based networks. These attention networks have been proven to outclass CNN + LSTM architectures, showing higher accuracy as well as reduced model parameters. Especially, skeleton-based attention networks have been shown to outperform visual-based networks due to the rich information from skeleton-based hand features. However, there is an opportunity to introduce complementary features from other modalities like RGB, depth, and optical flow images to enhance the recognition capability of skeleton-based networks. This paper aims to explore the addition of a multimodal fusion network to a skeleton-based Hierarchical Self-Attention Network (MF-HAN) and test for increased model effectiveness. Unlike traditional fusion techniques, this fusion network uses features derived from other sources of multimodal data in a reduced feature space using a cross-attention layer. The model outperforms its root model and other state-of-the-art models on the SHREC’17 track dataset, especially in the 28 gestures setting by more than 1 % in gesture classification accuracy. The experimentation was tested on the DHG dataset as well.
Adv-4-Adv: Thwarting changing adversarial perturbations via adversarial domain adaptation

Authors:
raw_author_name: Tianyue Zheng
raw_affiliation_string: School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
raw_author_name: Zhe Chen
raw_affiliation_string: School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
raw_author_name: Shuya Ding
raw_affiliation_string: School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
raw_author_name: Chao Cai
raw_affiliation_string: School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore
raw_author_name: Jun Luo
raw_affiliation_string: School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore

Abstract: Whereas adversarial training can be useful against specific adversarial perturbations, they have also been proven ineffective in generalizing towards attacks deviating from those used for training. However, we observe that this ineffectiveness is intrinsically connected to domain adaptability, another crucial issue in deep learning for which adversarial domain adaptation appears to be a promising solution. Consequently, we propose Adv-4-Adv as a novel adversarial training method that aims to retain robustness against unseen adversarial perturbations. Essentially, Adv-4-Adv treats attacks incurring different perturbations as distinct domains, and by leveraging the power of adversarial domain adaptation, it aims to remove the domain/attack-specific features. This forces a trained model to learn a robust domain-invariant representation, which in turn enhances its generalizability. Extensive evaluations on Fashion-MNIST, SVHN, CIFAR-10, and CIFAR-100 demonstrate that a model trained by Adv-4-Adv based on examples crafted by simple attacks (e.g., FGSM) can be generalized to more advanced attacks (e.g., PGD), and the performance exceeds state-of-the-art proposals on these datasets.
A multi-message passing framework based on heterogeneous graphs in conversational emotion recognition

Authors:
raw_author_name: Tao Meng
raw_affiliation_string: College of Computer and Information Engineering, Central South University of Forestry and Technology, Hunan, China
raw_author_name: Yuntao Shou
raw_affiliation_string: College of Computer and Information Engineering, Central South University of Forestry and Technology, Hunan, China
raw_author_name: Wei Ai
raw_affiliation_string: College of Computer and Information Engineering, Central South University of Forestry and Technology, Hunan, China
raw_author_name: Jiayi Du
raw_affiliation_string: College of Computer and Information Engineering, Central South University of Forestry and Technology, Hunan, China
raw_author_name: Keqin Li
raw_affiliation_string: Department of Computer Science, State University of New York, New Paltz, NY 12561, USA

Abstract: As an important development direction of natural language processing, emotion recognition in conversation (ERC) remains a challenge in sentiment analysis. Given the large-scale dialogue datasets and their wide application in the fields of recommendation systems and human–machine dialogue systems, researchers have begun to pay more attention to the issue of ERC. In recent research, the task of ERC has been largely based on the graph structure to model the speaker level. However, most existing studies simply splice multimodal features, and the heterogeneity of multimodal features tends to be overlooked. Hence, this paper proposes a multivariate messaging framework to embed heterogeneous information into multimodal relational graphs. In the process of aggregating graph node information, we take into account the homogeneity of nodes and assign different weights to different nodes so as to better aggregate semantic information. In order to improve the robustness of the model, we utilize the mechanism of sharing weights among neighbors to reduce the number of network parameters and improve the generalization ability of the model. In so doing, the node information is aggregated through the constructed graph network, and the final semantic vector representation is obtained. Experiments over two benchmark datasets for ERC show that our proposed model achieves improved performance in accuracy and F1 value.
A novel and efficient model pruning method for deep convolutional neural networks by evaluating the direct and indirect effects of filters

Authors:
raw_author_name: Yongbin Zheng
raw_affiliation_string: College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
raw_author_name: Peng Sun
raw_affiliation_string: College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
raw_author_name: Qiang Ren
raw_affiliation_string: China Mobile (Zhejiang) Research & Innovation Institute, Hangzhou 310030, China; College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
raw_author_name: Wanying Xu
raw_affiliation_string: College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
raw_author_name: Di Zhu
raw_affiliation_string: China Electronic Device System Engineering Corporation, Beijing 100080, China; College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China

Abstract: Deploying deep convolutional neural networks (DCNNs) on devices with low memory resources or in applications with strict latency requirements remains a challenge. The weight-based filter pruning is an effective technique that has been widely applied to DCNNs compression and acceleration due to its lighter computation consumption and better flexibility. However, many existing methods select the filter to be pruned by evaluating the direct effect of the filter on the current pruning layer, resulting in insufficient performance of the pruned model. In this paper, we point out that the key issue for the filter level pruning criteria to improve performance is how to evaluate the importance of filters and propose a new weight-based filter pruning method. The proposed method comprehensively considers the direct and indirect effects of filters, which can better reflect the filter importance, allowing the safe removal of unimportant filters. Extensive experiments demonstrate that the proposed weight-based method achieves a better performance than previous works, which are reaching the level of data-based methods.
Performance enhancement of solar PV system introducing semi-continuous tracking algorithm based solar tracker

Authors:
raw_author_name: Md Aftab Ali Shaikh
raw_affiliation_string: Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, 1205, Dhaka, Bangladesh; University of Dhaka, Bangladesh, Department of Chemistry, Faculty of Science, Kazi Nazrul Islam avenue, 1000, Dhaka, Bangladesh

Abstract: One common method of harnessing solar energy is via a Photovoltaic (PV) system. When sunlight strikes a PV panel surface at around ninety-degree angle, the system produces the most energy. The electricity generated by a PV system may be increased by using a solar tracker (ST). However, the ST has its own energy requirements and additional expenses. A semi-continuous single-axis ST is developed for this purpose. The “track and wait algorithm” is used to monitor the sun rather than constant tracking in the developed ST. While comparing the performance of a fixed PV system, a continuous single-axis solar tracker-based PV system, and a semi-continuous single-axis solar tracker-based PV system, the developed semi-continuous system is only slightly less efficient than the continuous system (by 1.12 %). Nevertheless compared to a PV system with continuous tracking, the semi-continuous tracking system reduces self-energy consumption by 34%.
Multi-level neural prompt for zero-shot weakly supervised group activity recognition

Authors:
raw_author_name: Yinghui Zhang
raw_affiliation_string: College of Education for the Future, Beijing Normal University, Zhuhai, 519087, China
raw_author_name: Bo Sun
raw_affiliation_string: School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
raw_author_name: Jun He
raw_affiliation_string: School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
raw_author_name: Lejun Yu
raw_affiliation_string: School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
raw_author_name: Xiaochong Zhao
raw_affiliation_string: School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China

Abstract: Group activity recognition aims at understanding the overall activity involved multi-person interaction with each other as a whole in the complex variations in both spatial and temporal transition in untrimmed video. While remarkably significance in applications, existing methods are often impractical for the following two reasons: (1) they rely on heavy annotations and off-the-shelf object detectors even in test stage; (2) they are trained to predict a closed-set predetermined group categories, which limits their generality to wider unseen categories. Motivated by this, we propose a novel zero-shot weakly supervised group activity recognition neural model, ZSTGroupCLIP, which is not only free from the restriction of heavy annotations, but also captures the generalizable elements that are vital for open-set exploration. Specifically, our model based on visual–textual joint representation learning seeks discriminative visual cues for aligning both the group vision and category text branches by multi-level neural prompt. Moreover, to further learn group-related contextual representations under weakly supervision, we learn layer-wise prompts across early layer of group encoder for progressive context modeling. Through extensive experimental validations and comparisons to several baseline methods on two benchmarks, Volleyball and NBA datasets, our method achieves outstanding performance.
Heterogeneous graph knowledge distillation neural network incorporating multiple relations and cross-semantic interactions

Authors:
raw_author_name: Jinhu Fu
raw_affiliation_string: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
raw_author_name: Chao Li
raw_affiliation_string: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
raw_author_name: Zhongying Zhao
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
raw_author_name: Qingtian Zeng
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China

Abstract: In recent years, the study of real-world graphs has revealed their inherent heterogeneity, prompting growing research interest in heterogeneous graphs. Characterized by diverse node and relation types, heterogeneous graphs have led to the development of heterogeneous graph neural networks, which possess the remarkable ability of modeling such heterogeneity. Consequently, researchers have embraced these networks, applying them in various domains. A prevalent approach is using meta-path based methods in heterogeneous graph neural networks. However, a significant limitation arises from the fact that such methods tend to overlook vital attribute information within intermediate nodes and disregard relevant semantics across various meta-paths. To address the above limitations, we propose a new model named HGNN-MRCS. Specifically, HGNN-MRCS incorporates three key components, i.e., a relation aware module to encapsulate the attribute information of the intermediate nodes; a meta-path aware technique to facilitate learning of semantic information of each meta-path and enable higher-order representation learning; and a knowledge distillation strategy to learn relevant semantics across meta-paths and fuse them. Experimental results on four real-world datasets demonstrate the superior performance of this work over the SOAT methods. The source codes of this work are available at https://github.com/ZZY-GraphMiningLab/HGNN-MRCS.
Chalcophile element degassing at an active continental arc volcano

Authors:
raw_author_name: Samantha Hammond
raw_affiliation_string: School of Environment, Earth and Ecosystem Sciences, Open University, Milton Keynes MK7 6AA, UK
raw_author_name: Evgenia Ilyinskaya
raw_affiliation_string: School of Earth and Environment, University of Leeds, Leeds, UK
raw_author_name: Frances Jenner
raw_affiliation_string: School of Environment, Earth and Ecosystem Sciences, Open University, Milton Keynes MK7 6AA, UK
raw_author_name: Barbara Kunz
raw_affiliation_string: School of Environment, Earth and Ecosystem Sciences, Open University, Milton Keynes MK7 6AA, UK

Abstract: Arc volcanoes are significant natural sources of trace chalcophile elements to the atmosphere via gas and aerosol plumes. Villarrica volcano, part of the Andean arc, erupts basaltic magmas and is characterised by a persistent volcanic gas plume and therefore presents an opportunity to quantify volcanic chalcophile processing in a subduction zone from slab to surface. Here we present geochemical data for olivine-hosted melt inclusions, as well as for the gas and aerosol plume. We show that melts erupted at Villarrica are enriched (over mid-ocean ridge basalts) in a suite of fluid-mobile elements comprising the large ion lithophiles, including Cs, and chalcophile elements W, Tl, Pb and Sb. Volcanic gas and aerosol samples show that the chalcophile elements, and Cs, are strongly enriched in the gas phase over the silicate melt, 103 to 106 times more so than the non-volatile Rare Earth Elements. Volatilities (the percentage of an element that degasses from a melt on eruption) reach ∼ 45 % for Tl, with Pb, Sn and Mo exhibiting volatilities of up to 0.3 % and Cu up to 0.08 %. Many of the chalcophile elements (e.g. Cu, Ag, Zn) have an affinity for chloride in the gas phase and we observe that the volatility of chloride-speciating trace metals is linked strongly to the availability of chlorine in volcanic plumes globally. Overall, we show that the trace element composition of the volcanic gas—and hence probably also the deeper, denser and more saline fluids in the subsurface—is sensitive to both the availability of chloride in the gas phase and the composition of the melt, which is controlled by the slab flux and may be variable between subduction zones.
Deep Learning-based POS Tagger and Chunker for Odia Language Using Pre-trained Transformers

Authors:
raw_author_name: Tusarkanta Dalai
raw_affiliation_string: Department of Computer Science and Engineering, NIT Rourkela, India
raw_author_name: Tapas Kumar Mishra
raw_affiliation_string: Department of Computer Science and Engineering, NIT Rourkela, India
raw_author_name: Pankaj K Sa
raw_affiliation_string: Department of Computer Science and Engineering, NIT Rourkela, India

Abstract: Developing effective natural language processing (NLP) tools for low-resourced languages poses significant challenges. This article centers its attention on the task of Part-of-speech (POS) tagging and chunking, which pertains to the identification and categorization of linguistic units within sentences. POS tagging and Chunking have already produced positive results in English and other European languages. However, in Indian languages, particularly in Odia language, it is not yet well explored because of the lack of supporting tools, resources, and its complex linguistic morphology. This study presents the building of a manually annotated dataset for Odia phrase chunking task and the development of a deep learning-based model specifically tailored to accommodate the distinctive properties of the language. The process of annotating the Odia chunking corpus involved the utilization of inside-outside-begin labels, which were tagged by using designed Odia chunking tagset. We utilize the constructed Odia chunking dataset to build Odia chunker based on deep learning techniques, employing state-of-the-art architectures. Various techniques, such as Recurrent Neural Networks, Convolutional Neural Networks, and transformer-based models, are investigated to determine the most effective approach for Odia POS tagging and chunking. In addition, we conduct experiments utilizing diverse input representations, including Odia word embeddings, character-level representations, and sub-word units, to effectively capture the complex linguistic characteristics of the Odia language. Numerous experiments are conducted that evaluate the performance of our Odia POS tagger and chunker, employing standard evaluation metrics and making comparisons with existing approaches. The results demonstrate that our transformer-based tagger and chunker achieves superior accuracy and robustness in identifying and categorizing linguistic POS tags and chunks within Odia sentences. It outperforms existing work and exhibits consistent performance across diverse linguistic contexts and sentence structures. The developed Odia POS tagger and chunker have enormous potential for a variety of NLP applications, including information extraction, syntactic parsing, and machine translation, all of which are tailored to the low-resource Odia language. This work contributes to developing NLP tools and technologies for low-resource languages, thereby facilitating enhanced language processing capabilities in various linguistic contexts.
Efficient public auditing scheme for non-administrator group with secure user revocation

Authors:
raw_author_name: Tianqi Zhou
raw_affiliation_string: School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, China
raw_author_name: Sai Ji
raw_affiliation_string: College of Information Engineering, Taizhou University, Taizhou, 225300, Jiangsu, China; School of Computer Science, Nanjing University of Information Science Technology, Nanjing, 210044, Jiangsu, China
raw_author_name: Haowen Tan
raw_affiliation_string: School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, China
raw_author_name: Wenying Zheng
raw_affiliation_string: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, China

Abstract: Cloud auditing, as an important part of cloud computing, ensures file security for users. To this end, many academic authors have conducted an enormous amount of research on cloud auditing. Unfortunately, existing schemes usually involve administrators who have greater power, and malicious administrators may abuse their authority. Thus, a large number of schemes have been designed to ensure users’ authority, such as implementing non-administrator groups. Whereas, it is difficult to achieve efficient user revocation in groups without administrators. This paper aims at solving the aforementioned problem. In particular, an efficient public auditing scheme is proposed with malicious users revocation and re-signature generation under the non-administrators model. First, the signature generation is executed by t users based on the threshold secret technique. Then, the proxy is responsible for aggregating t users’ partial keys and signatures. Moreover, the cloud, proxy and qualified users will collaborate to generate a new partial signature through proxy re-signature when a malicious user appears. Last, we give formal security analyses and conduct a number of experiments.