Reviewerly/exHarmony huggingface dataset:
Contrastive fine-tuning for low-resource graph-level transfer learning

Authors:
raw_author_name: Yutai Duan
raw_affiliation_string: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, College of Artificial Intelligence, Nankai University, China
raw_author_name: Jie Liu
raw_affiliation_string: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, College of Artificial Intelligence, Nankai University, China
raw_author_name: Jianhua Wu
raw_affiliation_string: National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, College of Artificial Intelligence, Nankai University, China

Abstract: Due to insufficient supervision and the gap between pre-training pretext tasks and downstream tasks, transferring pre-trained graph neural networks (GNNs) to downstream tasks in low-resource scenarios remains challenging. In this paper, a Contrastive Fine-tuning (Con-tuning) framework is proposed for low-resource graph-level transfer learning, and a graph-level supervised contrastive learning (SCL) task is designed within the framework as the first attempt to introduce SCL for fine-tuning processes of pre-trained GNNs. The SCL task compensates for the insufficient supervision problem in low-resource scenarios and narrows the gap between pretext tasks and downstream tasks. To further reinforce the supervision signal in the SCL task, we devise a graphon theory based labeled graph generator to extract the generalized knowledge of a specific class of graphs. Based on this knowledge, graph-level templates are generated for each class and used as contrastive samples in the SCL task. Then, the proposed Con-tuning framework jointly learns the SCL task and downstream tasks to effectively fine-tune the pre-trained GNNs for downstream tasks. Extensive experiments with eight real-world datasets show that Con-tuning framework enables pre-trained GNNs to achieve better performance on graph-level downstream tasks in low-resource settings.
Using outlier elimination to assess learning-based correspondence matching methods

Authors:
raw_author_name: Xintao Ding
raw_affiliation_string: Anhui Engineering Research Center of Medical Big Data Intelligent System, Wuhu, 241002, Anhui, China; Anhui Province Key Laboratory of Network and Information Security, Wuhu, 241002, Anhui, China; School of Computer and Information, Anhui Normal University, Wuhu, 241002, Anhui, China
raw_author_name: Yonglong Luo
raw_affiliation_string: Anhui Engineering Research Center of Medical Big Data Intelligent System, Wuhu, 241002, Anhui, China; Anhui Province Key Laboratory of Network and Information Security, Wuhu, 241002, Anhui, China; School of Computer and Information, Anhui Normal University, Wuhu, 241002, Anhui, China
raw_author_name: Biao Jie
raw_affiliation_string: Anhui Engineering Research Center of Medical Big Data Intelligent System, Wuhu, 241002, Anhui, China; Anhui Province Key Laboratory of Network and Information Security, Wuhu, 241002, Anhui, China; School of Computer and Information, Anhui Normal University, Wuhu, 241002, Anhui, China
raw_author_name: Qingde Li
raw_affiliation_string: School of Computer Science, University of Hull, Hull, HU67RX, UK
raw_author_name: Yongqiang Cheng
raw_affiliation_string: Faculty of Technology, University of Sunderland, Sunderland, SD60DD, UK

Abstract: Recently, deep learning (DL) technology has been widely used in correspondence matching. The learning-based models are usually trained on benign image pairs with partial overlaps. Since DL model is usually data-dependent, non-overlapping images may be used as poison samples to fool the model and produce false registrations. In this study, we propose an outlier elimination-based assessment method (OEAM) to assess the registrations of learning-based correspondence matching method on partially overlapping and non-overlapping images. OEAM first eliminates outliers based on spatial paradox. Then OEAM implements registration assessment in two streams using the obtained core correspondence set. If the cardinality of the core set is sufficiently small, the input registration is assessed as a low-quality registration. Otherwise, it is assessed to be of high quality, and OEAM improves its registration performance using the core set. OEAM is a post-processing technique imposed on learning-based method. The comparison experiments are implemented on outdoor (YFCC100M) and indoor (SUN3D) datasets using four deep learning-based methods. The experimental results on registrations of partially overlapping images show that OEAM can reliably infer low-quality registrations and improve performance on high-quality registrations. The experiments on registrations of non-overlapping images demonstrate that learning-based methods are vulnerable to poisoning attacks launched by non-overlapping images, and OEAM is robust against poisoning attacks crafted by non-overlapping images.
Combined effects of temperature and confinement on the Shannon entropy of two-dimensional hydrogenic impurity states in the GaAs semiconductor quantum well

Authors:
raw_author_name: Xue Liu
raw_affiliation_string: School of Physics and Optoelectronic Engineering, Ludong University, Yantai, 264025, China

Abstract: Understanding the quantum properties of two-dimensional (2-D) hydrogenic impurity states in the GaAs quantum well is essential for their applications in the semiconductor devices. Here, we report the combined effects of temperature and quantum confienment on the Shannon entropy of 2-D hydrogenic impurity states in the GaAs quantum well. First, by calculating the position and momentum space Shannon entropy (Sr and Sp), as well as the Shannon entropy sum (St), for the hydrogenic impurity states in the GaAs quantum well at room temperature, we show fascinating quantum confinement properties in the two conjugated spaces. Then, the effects of temperature on the Shannon entropy for different hydrogenic impurity states are examined and compared. Intriguingly, we discover that the Shannon entropy sum exhibits the property of translation invariance under the influence of temperature. Finally, with the radius of the quantum well fixed at a constant, we observe that the value of the Shannon entropy for a given quantum state is highly sensitive to the variations in temperature. While the size of circular quantum well is constrained by design parameters, such as the fabrication process and intended application, temperature adjustment emerges as a viable means to control the quantum properties of 2-D hydrogenic impurities in the quantum well. This research presents a pioneering method that employs temperature as a versatile tool for modulating the Shannon entropy of hydrogenic impurity states in the quantum well, and has potential applications in the field of semiconductor physics, material physics, chemical physics, etc.
Siamese capsule network with position correlation and integrating articles of law for Chinese similar case matching

Authors:
raw_author_name: Zhe Chen
raw_affiliation_string: Harbin Institute of Technology, Harbin, China
raw_author_name: Lin Ye
raw_affiliation_string: Harbin Institute of Technology, Harbin, China
raw_author_name: Hongli Zhang
raw_affiliation_string: Harbin Institute of Technology, Harbin, China
raw_author_name: Yunting Zhang
raw_affiliation_string: Harbin Institute of Technology, Harbin, China

Abstract: The purpose of the Chinese similar case matching task is to compare the similarity of two case texts with a given anchor text and find out which text is more similar to the anchor text. In the area of law, it plays an important role and has been of interest to many researchers. Previous approaches have compared legal texts only at the text semantic level, without incorporating article information of law. In addition, the position correlation of words in case texts is often important, but it has not been considered in previous approaches. This paper proposes a method which extracts features from the semantic similarity level and from the level of related articles of law, respectively, to enable similarity comparisons of legal case texts. When similarity comparisons are made at the semantic similarity level, a novel capsule network method is proposed based on siamese structure that introduces the position correlation and the routing mechanism within the capsule network is improved so that deep text features between case pairs can be learned. When similarity comparisons are made at the level of related articles of law, related articles of law are selected and coded and interacted with the case text features to generate legal features. Experiment is conducted with a real-world legal text dataset, and the proposed model outperformed all baseline models, demonstrating effectiveness of the proposed model. Further, to confirm the generality of the improved capsule network proposed in the paper on long text datasets, this paper also carried out experiments on two long text datasets, demonstrating effectiveness of the improved capsule network proposed in the model.
LCEMH: Label Correlation Enhanced Multi-modal Hashing for efficient multi-modal retrieval

Authors:
raw_author_name: Chaoqun Zheng
raw_affiliation_string: Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China; Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
raw_author_name: Lei Zhu
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, China
raw_author_name: Zheng Zhang
raw_affiliation_string: Bio-Computing Research Center, Harbin Institute of Technology, China
raw_author_name: Wenjun Duan
raw_affiliation_string: Shandong Jianzhu University, Jinan, China
raw_author_name: Wenpeng Lu
raw_affiliation_string: Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China; Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China

Abstract: Supervised multi-modal hashing can effectively improve the discriminative power of hash codes by leveraging semantic label information. However, most existing supervised multi-modal hashing models generally rely on discrete labels for supervision. They only provide limited guidance and may not fully capture the potential semantic similarity between multi-modal data. Moreover, the binary constraints of hash codes further restrict their ability to fully capture the rich semantic information embedded in the labels. To address these limitations, we propose a Label Correlation Enhanced Multi-modal Hashing (LCEMH) approach for efficient multi-modal retrieval. Specifically, our proposed model can effectively expand the inter-class boundaries of discrete labels to further enrich the supervision of similarity. Additionally, we directly transform the acquired richer label information into hash codes simply and effectively, thus retaining more comprehensive information in the labels with minimal quantization loss, and enhancing the discriminant ability of the generated hash codes. Experimental results on three public multimedia retrieval datasets demonstrate the superiority of LCEMH in performance. The source code of LCEMH is available online at: https://github.com/ChaoqunZheng/LCEMH.
Lackadaisical discrete-time quantum walk on Johnson graph

Authors:
raw_author_name: Meng Li
raw_affiliation_string: School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
raw_author_name: Xiaoming Sun
raw_affiliation_string: School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Shanghai Innovation Center for Processor Technologies, Shanghai, 200234, China; State Key Laboratory of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

Abstract: Quantum walk is widely used in search problems, and it has an ideal acceleration effect compared with classical algorithm. Suppose there is a marked vertex w on Johnson graph J(n,k), we hope to find w using quantum walk. We adopt the coined quantum walk model, and we have proved that, for fixed order k, lackadaisical discrete-time quantum walk (DTQW) can find w with success probability 1−o(1), keeping a quadratic speedup. Before this paper, the best result of DTQW is that discrete-time quantum walk finds w with success probability 12−o(1). We improve the success probability to 1−o(1) by adding self-loops with proper weights and choose the appropriate oracle operator. Our result also matches the result of continuous-time quantum walk.
PTCAS: Prompt tuning with continuous answer search for relation extraction

Authors:
raw_author_name: Yang Chen
raw_affiliation_string: State Key Lab of Software Development Environment, Beihang University, Beijing, 100191, Beijing, China
raw_author_name: Bowen Shi
raw_affiliation_string: School of Journalism, Communication University of China, Beijing, 100024, Beijing, China
raw_author_name: Ke Xu
raw_affiliation_string: State Key Lab of Software Development Environment, Beihang University, Beijing, 100191, Beijing, China

Abstract: Tremendous progress has been made in the development of fine-tuned pretrained language models (PLMs) that achieve outstanding results on almost all natural language processing (NLP) tasks. Further stimulation of rich knowledge distribution within PLMs can be achieved through additional prompts for fine-tuning, namely, prompt tuning. Generally, prompt engineering involves prompt template engineering, which is the process of searching for an appropriate template for a specific task, and answer engineering, whose objective is to seek an answer space and map it to the original task label set. Existing prompt-based methods are primarily designed manually and search for appropriate verbalization in a discrete answer space, which is insufficient and always results in suboptimal performance for complex NLP tasks such as relation extraction (RE). Therefore, we propose a novel prompt-tuning method with a continuous answer search for RE, which enables the model to find optimal answer word representations in a continuous space through gradient descent and thus fully exploit the relation semantics. To further exploit entity-type information and integrate structured knowledge into our approach, we designed and added an additional TransH-based structured knowledge constraint to the optimization procedure. We conducted comprehensive experiments on four RE benchmarks to evaluate the effectiveness of the proposed approach. The experimental results show that our approach achieves competitive or superior performance without manual answer engineering compared to existing baselines under both fully supervised and low-resource scenarios.
Overlapping cytoplasms segmentation via constrained multi-shape evolution for cervical cancer screening

Authors:
raw_author_name: Ao Zhang
raw_affiliation_string: School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
raw_author_name: Jinglin Zhou
raw_affiliation_string: School of Philosophy, Fudan University, Shanghai, 200433, China
raw_author_name: Yu Luo
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China
raw_author_name: Zhizhe Lin
raw_affiliation_string: School of Information and Communication Engineering, Hainan University, Haikou, 570228, China
raw_author_name: Teng Zhou
raw_affiliation_string: School of Cyberspace Security, Hainan University, Haikou, 570228, China

Abstract: Segmenting overlapping cytoplasms in cervical smear images is a clinically essential task for quantitatively measuring cell-level features to screen cervical cancer This task, however, remains rather challenging, mainly due to the deficiency of intensity (or color) information in the overlapping region Although shape prior-based models that compensate intensity deficiency by introducing prior shape information about cytoplasm are firmly established, they often yield visually implausible results, as they model shape priors only by limited shape hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and impose no shape constraint on the resulting shape of the cytoplasm In this paper, we present an effective shape prior-based approach, called constrained multi-shape evolution, that segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors We model local shape priors (cytoplasm-level) by an infinitely large shape hypothesis set which contains all possible shapes of the cytoplasm In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump-level) modeled by considering mutual shape constraints of cytoplasms in the clump We also constrain the resulting shape in each evolution to be in the built shape hypothesis set for further reducing implausible segmentation results We evaluated the proposed method in two typical cervical smear datasets, and the extensive experimental results confirm its effectiveness.
Learning dynamic embeddings for temporal attributed networks

Authors:
raw_author_name: Luodi Xie
raw_affiliation_string: School of Electronics and Information Engineering, Wuyi University, China
raw_author_name: Hui Tian
raw_affiliation_string: School of Information and Communication Technology, Griffith University, Australia
raw_author_name: Hong Shen
raw_affiliation_string: School of Engineering and Technology, Central Queensland University, Australia

Abstract: Learning low-dimensional representations for networks has attracted considerable research interest due to the ever-increasing volume and variety of network-based information. It becomes especially important and challenging for temporal networks whose nodes and attributes evolve as time changes. To address the problem of temporal network embedding, we propose a novel Dynamic Bayesian Temporal Network Embedding (DBTNE) method that adopts a probabilistic framework and represents network embeddings as Gaussian distributions for learning effective embeddings for temporal networks. Unlike the existing temporal network embedding methods that learn dynamic embeddings only for nodes, our DBTNE jointly learns the dynamic embeddings of both nodes and attributes in temporal attributed networks for effectively capturing the similarities among them, which greatly facilitates attribute-related learning tasks such as link prediction, object classification and user profiling. In addition, we model the temporal dynamics of both nodes and attributes with neural ordinary differential equations, which supports effective and efficient capture of long-range dependencies among embeddings at different timestamps. Extensive experimental results on real-world temporal networks demonstrate that our DBTNE achieves better performance than popular state-of-the-art methods, including DeepWalk, Node2Vec, CAN, HTNE, M2DNE, MTSN, Dane-ATT, for typical graph learning tasks, such as dynamic link prediction and node classification.
A novel compression-based 2D-chaotic sine map for enhancing privacy and security of biometric identification systems

Authors:
raw_author_name: Mobashshirur Rahman
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, 800005, Bihar, India
raw_author_name: Anita Murmu
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, 800005, Bihar, India
raw_author_name: Piyush Kumar
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, 800005, Bihar, India
raw_author_name: Nageswara Rao Moparthi
raw_affiliation_string: Department of Computer Science and Engineering, Koneru Lashmaiah Education Foundation, Vaddeswaram, 522302, Andra Pradesh, India
raw_author_name: Suyel Namasudra
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India

Abstract: Human biometric images are utilized for cell phone authentication, airport security, and biometric passports. To improve the biometric identification process, this should be protected from cyber attackers because it is sensitive to any minor changes. Thus, security is a major concern in biometric images. Traditional encryption and compression methods are ineffective for encrypting biometric images due to their high execution time and algorithm complexity. In this paper, a novel 2D chaotic sine map is proposed for generating encryption keys and improving security for biometric identification systems. The proposed scheme uses the 2D chaotic map to generate the private key and diffusion process. Here, the Hénon-Sine Map (HSM-512), Secure Hash Algorithm (SHA-256), and DNA computing are also used in the key generation process. The pixel values of the original images in the proposed schemes are shuffled using Mersenne Twister (MT) to improve the security of biometric images. Moreover, the Differential Huffman Compression (DHC) method is used for lossless compression while performing the XOR-based encryption process. The proposed model has been tested on different RGB biometric images, namely the SOCOFing dataset and the COVID-19 chest X-ray dataset. The experiment results have been evaluated using many performance metrics, such as entropy, key space, histogram analysis, key sensitivity, robustness analysis, correlation, and similarity analysis. The outcomes demonstrate that the proposed scheme is more effective than the state-of-the-art schemes.
A multi-scale contextual attention network for remote sensing visual question answering

Authors:
raw_author_name: Jiangfan Feng
raw_affiliation_string: School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

Abstract: Remote sensing visual question answering (RSVQA) is a user-friendly method used for analyzing remote sensing images (RSIs) in various tasks. However, current methods often overlook geospatial objects, which possess a multi-scale representation and require contextual information. Furthermore, limited research has been conducted on modeling and reasoning the long-distance dependencies between entities, resulting in one-sided and inaccurate answer predictions. To overcome these limitations, we propose the Scale-Aware Multi-level Feature Pyramid Network (SAMFPN), which integrates contextual and multi-scale information using a Feature Pyramid Network (FPN) and Co-Attention mechanisms. The SAMFPN module incorporates a multi-level FPN to capture both global and local contextual information. Additionally, it introduces a Visual-Question Collaboration Fusion (VQCF) module that simultaneously embeds and learns visual and textual information. Our experimental results demonstrate the superior accuracy and robustness of our proposed model compared to existing models. These outcomes indicate that SAMFPN effectively captures multi-scale contextual information, making it a reliable solution for RSVQA tasks.