Reviewerly/exHarmony huggingface dataset:
Siamese capsule network with position correlation and integrating articles of law for Chinese similar case matching

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

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

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

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

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

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

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

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.
Cryptanalysis of cross-coupled chaotic maps multi-image encryption scheme

Recently, Patro et al. proposed a multiple gray-scale image encryption scheme based on cross-coupled chaotic maps. The authors claimed the scheme could withstand known plain-text attacks and chosen-plaintext attacks. In this paper, we have thoroughly analysed the Patro et al. algorithm and reported the crucial loopholes. Patro et al. algorithm uses a row-wise and column-wise scrambling operation based on a secret sequence followed by a feed-forward XOR operation generating the cipher image. Exploiting the loopholes of feed-forward XOR operation and high correlation of pixels in standard images, a cryptanalysis algorithm is developed that deciphers the ciphered images generated by Patro et al. algorithm based on cipher-text only attack. Without using the secret keys, the proposed cryptanalysis algorithm successfully deciphers the cipher image and reveals the secret images.
Semantic information extraction and search of mineral exploration data using text mining and deep learning methods

Large-scale mineral resource reports offer a wealth of information for geological knowledge mining and mineral explorers’ knowledge discovery. The geological conditions in which mineral deposits develop may be learned a great deal from these mineral exploration reports. Mineral exploration data may be queried and aggregated to effectively mitigate future exploration risks and reduce costs. However, due to the reports being presented in unstructured textual format, it becomes challenging to extract valuable geological data without manually scanning through a vast number of reports. This laborious process poses difficulties for geologists. To address this issue, this paper proposes a system that extracts a set of geologically relevant keywords/keyphrases of each chapter from each mineral exploration reposts using latent Dirichlet allocation (LDA), develops a topic graph, recognizes the geological entity and related relations for constructing a knowledge graph, and uses visualization of those graphs (e.g., topic graphs and knowledge graphs) to explore the contents of the report. The text mining and machine learning technique described here serves as the foundation for future research into incorporating semantic analysis into geological information extraction. The findings of this study show how automated text analysis may help with the quick processing of huge quantities of reports in order to identify target mineral systems and their related geological location and rock mineral composition. The suggested approaches can quickly and reliably convert mineral exploration data (e.g., text, figure, and table) into a structured form, which is a hitherto untouched field in geological knowledge mining.
Design of Motor Skill Recognition and Hierarchical Evaluation System for Table Tennis Players

With the rapid development of electronic science and technology, the research on wearable devices is constantly updated. Still, the current wearable devices are not yet comprehensive in recognizing and analyzing the movement patterns of special sports. Based on this, this paper improves wearable devices for table tennis, a single sport, and realizes the recognition and evaluation of table tennis players’ motor skills based on the pattern recognition method and hierarchical evaluation system. First, a table tennis player’s motion information acquisition device was designed and the collected motion data were processed. Secondly, a sliding window was designed to segment the collected motion data and establish a feature database of six table tennis benchmark movements. Secondly, the motion features were constructed based on feature engineering, and after dimensionality reduction, the different models were used for action skill recognition. Finally, a hierarchical evaluation system of movement skills was established and the loss functions of different evaluation indexes were given. The experimental results show that in table tennis players’ action skill recognition, the neural network model based on motor feature extraction proposed in this paper improves the recognition accuracy by 6.5% than the traditional convolutional neural network, and in table tennis players’ action skill evaluation, the ANOVA shows that the hierarchical evaluation system proposed in this paper has a significance level of p<0.05 for the athletes’ professionalism level. The conclusion of the study indicates that the proposed paper table tennis players’ motor skills recognition and hierarchical evaluation assessment system can realize the accurate recognition of athletes’ motor skills and the precise determination of their professional level.
Federated Learning for Software Engineering: A Case Study of Code Clone Detection and Defect Prediction

In various research domains, artificial intelligence (AI) has gained significant prominence, leading to the development of numerous learning-based models in research laboratories, which are evaluated using benchmark datasets. While the models proposed in previous studies may demonstrate satisfactory performance on benchmark datasets, translating academic findings into practical applications for industry practitioners presents challenges. This can entail either the direct adoption of trained academic models into industrial applications, leading to a performance decrease, or retraining models with industrial data, a task often hindered by insufficient data instances or skewed data distributions. Real-world industrial data is typically significantly more intricate than benchmark datasets, frequently exhibiting data-skewing issues, such as label distribution skews and quantity skews. Furthermore, accessing industrial data, particularly source code, can prove challenging for Software Engineering (SE) researchers due to privacy policies. This limitation hinders SE researchers’ ability to gain insights into industry developers’ concerns and subsequently enhance their proposed models. To bridge the divide between academic models and industrial applications, we introduce a federated learning (FL)-based framework called A <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lmity</small> . Our aim is to simplify the process of implementing research findings into practical use for both SE researchers and industry developers. A <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lmity</small> enhances model performance on sensitive skewed data distributions while ensuring data privacy and security. It introduces an innovative aggregation strategy that takes into account three key attributes: data scale, data balance, and minority class learnability. This strategy is employed to refine model parameters, thereby enhancing model performance on sensitive skewed datasets. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In our evaluation, we employ two well-established SE tasks, i.e., code clone detection and defect prediction, as evaluation tasks. We compare the performance of A<small>lmity</small> on both machine learning (ML) and deep learning (DL) models against two mainstream training methods, specifically the Centralized Training Method (CTM) and Vanilla Federated Learning (VFL), to validate the effectiveness and generalizability of A<small>lmity</small>. Our experimental results demonstrate that our framework is not only feasible but also practical in real-world scenarios. A<small>lmity</small> consistently enhances the performance of learning-based models, outperforming baseline training methods across all types of data distributions.
Self-Supervised Monocular Depth Estimation Based on High-Order Spatial Interactions

Depth estimation plays a pivotal role in various applications, including autonomous driving and robot navigation. In contrast to depth estimation using multiple images, such as stereo depth perception, inferring depth relations from a monocular camera is notably more challenging yet highly valuable. Traditionally, convolutional neural networks with residual structures have been extensively employed for this task, but they inherently constrain the model’s feature extraction capabilities. Inspired by HorNet, in this paper, we propose a novel self-supervised monocular depth estimation framework based on high-order spatial interactions, referred to as the Hor-Depth. Furthermore, the Hor-Depth improves feature fusion efficiency in the depth network decoder by incorporating the attentional feature fusion module based on first-order spatial interaction, leading to more refined predicted disparity maps. To address issues of loss fluctuations and training instability, we introduce a progressive scale-weight adjustment strategy-based loss function. This strategy applies varying constraints to the model at different training stages, effectively reducing training fluctuations and mitigating outliers that significantly deviate from the predicted values. The proposed approach demonstrates exceptional performance in self-supervised monocular depth estimation, surpassing certain stereo supervised or monocular supervised methods, as evidenced by its impressive results on the KITTI Eigen split benchmark.
An Adaptive Track Initiation Method Adopting Multiscan Spatiotemporal Information

Trajectory initiation of dense newborn targets is an important research topic in multi-target tracking, however, few reports concerning the related studies can be found. In response to the problem of accurately obtaining spatiotemporal information of densely populated newborn targets, we propose a novel adaptive track initiation method adopting multi-scan spatiotemporal information. Firstly, by combining the spatial information measured from multiple scans, an adaptive track initiation method is proposed to obtain the measurement sequence of newborn targets. Secondly, by establishing an adaptive newborn density model, the newborn state estimation of dense targets is obtained from the measurement sequence of newborn targets. Finally, the state estimation of the targets is input to the labelled random finite set filter to obtain the trajectory information. By quantifying the posterior density error of the labelled random finite set, the truncation error of the newborn state estimation is presented. The error analysis suggests that our proposed method can achieve track initiation with small boundary L1 error. The simulation results verify that the proposed method can effectively cope with the situation of dense newborn targets. Compared with the existing adaptive birth algorithms, our proposal can realize more accurate track initiation when newborn targets occur simultaneously.
Correntropy-Induced Weighted GAN for Bridge Anomaly Detection

Bridges play an essential role in transportation and economics. Since the loss is significant in the event of bridge damage, the monitoring of bridge structure health is important. One of the important tasks of bridge health monitoring is the bridge anomaly detection. The bridge anomaly detection is a challenging problem since the real-world bridge data collected by the sensors is usually contaminated by large non-Gaussian noises. To address this issue, we propose a correntropy-induced weighted generative adversarial network (CWGAN) for bridge anomaly detection. Specifically, we first propose a correntropy-based generator, which has an encoder-decoder structure with a memory structure, and optimize it based on the maximum correntropy criterion. Then, derived from the correntropy of the reconstruction errors, we specially design a weight module to obtain a weight variable that measures the contamination-free degree of the corresponding sample. Afterwards, we take advantage of the weight module and transform the Generator and Discriminator modules of CWGAN into weighted versions to achieve robustness against contamination and obtain smaller reconstruction errors for non-contaminated normal data. We integrate and iteratively perform the reconstruction and weighting process to achieve better anomaly detection performance. After the model optimization, we detect the anomalies with reconstruction errors. We conduct experiments on a model bridge to demonstrate the effectiveness of the proposed CWGAN.