Reviewerly/exHarmony huggingface dataset:
One-shot Video-based Person Re-identification based on SAM Attention and Reciprocal Nearest Neighbors Metric

Pedestrian re-identification (Re-ID) is used to solves the recognition and retrieval of pedestrians across cameras. To solve the difficulties of label annotation in Re-ID, a one-shot video-based Re-ID method is applied. For the problem of large number of neural network parameters and weak feature extraction ability of the model, the SAM attention module is embedded. The module is plug-and-play without any additional parameters, while it can capture the hidden information in samples and improve the discriminative of model. To address the problem of low accuracy of label estimation, a reciprocal nearest neighbours metric is designed. The metric is capable of constructing closer nearest-neighbour relationships between samples, combining the Mahalanobis distance and Jaccard distance to significantly improve the accuracy of label estimation and model performance. The effectiveness of our method in this paper is extensively experimented on two video-based Re-ID datasets, MARS and DukeMTMC-VideoReID.
NeCa: Network Calibration for Class Incremental Learning

Class incremental learning (CIL) aims to continually learn unseen classes in new tasks without forgetting the previous ones. However, deep neural networks are prone to make a biased prediction towards classes in the most recently learned task, dubbed task-recency bias. Most recent studies make a post-training adjustment on the last fully connected layer to alleviate this problem but ignore the feature extractor. This work proposes a novel training framework termed network calibration (NeCa) that simultaneously adjusts the last fully connected layer and the feature extractor. Specifically, we combine the post-training adjustment process with the training process into a balanced learning module, whose loss function is corrected based on the prior probabilities of classes. In this module, the parameters of the whole network are well-calibrated via backpropagation. Additional knowledge transmission and decaying regularization modules further mitigate catastrophic forgetting in CIL. Experiment results manifest that NeCa outperforms the state-of-the-art methods on three mainstream datasets, including MNIST, CIFAR-100, and ImageNet-100, which validates the effectiveness of our framework. Furthermore, we conduct experiments with the prevalent vision transformer backbone, and the consistently excellent performance demonstrates that NeCa is also competently suited for attention-based models.
Frequency Information Matters for Image Matting

Image matting aims to estimate the opacity of foreground objects in order to accurately extract them from the background. Existing methods are only concerned with RGB features to obtain alpha mattes, limiting the perception of local tiny details. To address this issue, we introduce frequency information as an auxiliary clue to accurately distinguish foreground boundaries and propose the Frequency Matting Network (FMN). Specifically, we deploy a Frequency Boosting Module (FBM) in addition to the Discrete Cosine Transform (DCT) to extract frequency information from input images. The proposed FBM is a learnable component that empowers the model to adapt to complex scenarios. Furthermore, we design a Domain Aggregation Module (DAM) to effectively fuse frequency features with RGB features. With the assistance of frequency clues, our proposed FMN achieves significant improvements in matting accuracy and visual quality compared with state-of-the-art methods. Extensive experiments on Composition-1k and Distinctions-646 datasets demonstrate the superiority of introducing frequency information for image matting.
Enhancing Image Quality of Aging Smartphones Using Multi-scale Selective Kernel Feature Fusion Network

Smartphones are now the most popular medium for photography. Smartphones can capture better images than the hardware they use thanks to Machine Learning-based computational photography. Even though modern phones have sophisticated image enhancement applications, older and aged phones lag behind for a variety of reasons. Lens quality degradation, which results in washed out, soft-looking images, is one of the most common issues seen in older devices. We propose a method for improving such images by using an attention-based multi-scale residual neural network trained on a synthetic dataset. We chose two smartphones: an old device that captures degraded images and a modern flagship that provides reference enhanced images. Then we used the bloom filter, contrast, and highlight adjustment to make the reference images appear degraded. Later, we trained the model using synthetically degraded images and tested it on a variety of older devices. We achieved a maximum Peak Signal to Noise Ratio (PSNR) score of 74 throughout our experiments. To evaluate the model's images, we used Blind Image Quality Assessment (BIQA) methods such as HyperIQA. Aside from correcting the lens issue, the model achieves comparatively better sharpness, contrast, and color processing. Our proposed method generalizes well and achieves up to 11.45% improvement on novel devices by utilizing a very limited amount of data.
Designing assessment funds for the bachelor's degree program «art education in visual arts»: problems and solutions

Introduction. The research of tools for assessing the achieved educational results and the effectiveness of the educational process is a necessary mechanism for improving the quality of education and educational systems. Despite the accumulated experience in the formation of funds of assessment tools, the problem of assessing the formation of general professional and professional competencies of students, taking into account the specifics of the educational programme, remains relevant. The aim of the research: the search for procedures, types of tasks and the development of a model of complex FOS, allowing to assess the formation of all levels of competences of professional training of an art-pedagogue, including those reflecting the formation of competence in the field of visual arts. Materials and methods. The research materials were normative documents regulating the design and implementation of higher education programmes; Russian and foreign studies; survey data of 187 teachers of the Herzen State Pedagogical University. Methods: analysis of sources and regulatory documents, survey, quantitative assessment of data (percentages), typical selection. Results. The analysis of normative documents and theoretical sources revealed a contradiction between the need to standardise assessment procedures and the specifics of assessing the competences of an art-pedagogue. The results of the survey of teachers confirm this contradiction: the most acceptable form of objective assessment of competences is an oral test/exam (75.4%), practical assignment (70.1%). Among the main problems of designing assessment funds, teachers highlight the assessment of validity and reliability of the Funds of Assessment Tools (57.2%) and the possibility of assessing the competence as a whole (54%). The analysis of the research materials allowed the authors to propose a comprehensive model of assessment funds, including structural, content and assessment components. The proposed structure, logic of organisation and types of tasks (tests, professional tasks of artistic and creative disciplines, etc.), including invariant and variant parts of tasks for independent work, are the basis for valid assessment of the formation of all levels of competences, including professional ones, reflecting the specificity of training of art-pedagogues. Conclusion. The results of the analysis, the proposed model, procedures, types of assignments, and the described recommendations can be used in the design of funds of assessment tools for educational programmes of higher education in the context of training an art-pedagogue.
The Twenty Sixth International Conference “Information technologies, computer systems and publications for libraries” (“LIBCOM–2022”): The review of events

The Twenty Sixth International Conference “Information technologies, computer systems and publications for libraries” (“LIBCOM–2022”) held on November 20-25, 2022, is reviewed. The key topics are covered, namely, the status of and prospects for modern information, computer and Internet-technologies in library services, in universities and research organizations, educational technologies, legal aspects in the work of libraries and information producers, e-libraries and digital information resources, etc. The authors focus on several events, i. e. the Seventh Industrial Conference “Book publishing and the libraries: Vectors of interaction”, scientific and practical conference of the National Library Association: Libraries of the Future: (NALF), the scientific conference “Scientometrics and bibliometrics in the modern educational environment: The role of libraries”, scientific and educational symposium “University and academic libraries to support educational process in the modern digital environment”, central discussion site “On the way to the new national system of sci-tech information”, presentation of the two-volume publication “Twenty annual reports to the Crimea International Forum” and the publication “Y. L. Shrayberg. The biobibliography”, expert panel “How to bring up inventors and researcher? The role of modern library. New approaches and technologies”, and the 2022 fall session of the permanent IRBIS School “Using and upgrading LIS IRBIS”. The conclusions on the Conference work are made.
DIGITAL ENTITIES COMMUNICATION WITHIN A BLOCKCHAIN-ENABLED INTELLIGENT TRANSPORT SYSTEM

Nowadays the blockchain is considered to be an integral part of intelligent transport systems. Intelligence of a transport systems allows to increase road safety, wisely utilize systems’ resources and provide additional services to participants. However, blockchain implementations for intelligent transport systems must be adopted to the peculiarities of such systems. This work analyzes existing blockchain solutions, their features, advantages, drawbacks, and presents a consortium type blockchain implementation for an intelligent transport system. This implementation includes a two-layered architecture of digital entities interaction, consideration for digital entities distribution over the layers, consensus mechanism selection and its implementation, trust model considerations, and a block structure for the proposed blockchain implementation. The article also brings the solution to the single point of failure vulnerability of the proposed blockchain system. Thus, the paper covers key aspects of a blockchain design for an intelligent transport system.
LAGCL: Towards Stable and Automated Graph Contrastive Learning

Graph Neural Networks (GNNs) have emerged as essential deep learning methods for modeling and analyzing graph structured data. Meanwhile, due to scaling and label sparsity issues, there is an increasing need to develop graph self-supervised representation learning, especially graph contrastive learning. However, existing contrastive method are either based on time-consuming trial-and-error selection of augmentation or employing intense adversarial training to automatically choose augmentation while easily leading to unstable training process and model degradation. In this work, we address above issues by proposing Laplace-smoothing based Automated GraphCL (LAGCL), an automated version of GraphCL. LAGCL leverages laplace-smoothing to smooth the intensity of adversarial training and enriches the diversity of augmentations when performing contrastive learning, further improving downstream task performance over other counterparts. Extensive experiments on various datasets show that LAGCL outperforms most baselines on unsupervised learning task and consistently provide robust representations over different backbones.
FinTech is Impacting the Financial Industry

FinTech is impacting traditional financial businesses. Traditional financial institutions are innovating their business models by applying FinTech, represented by AI, Blockchain, Cloud Computing, and Big Data, for business model optimization. As a result, they simplify their business processes, improve their business efficiency, reduce costs, and increase their profits.
Strategies and Path Analysis of Choral Conducting Teaching in Colleges and Universities in the Context of Multimedia Era

Abstract This paper first presents the O2O teaching strategy for choral conducting and analyzes specific methods and processes for teaching choral conducting using the O2O model. Then, the TFAHP choral conducting teaching evaluation model was constructed, and the judgment matrix was used to calculate the comprehensive degree of fuzzy value of the evaluation criteria and to compare the importance value between the indicators. Lastly, the impact of teaching was evaluated using indicators such as grades, comprehensive performance, and learning emotions. The mean value of the achievement of all knowledge modules is much higher than the mean value before teaching, and the average value of the achievement of each knowledge module is between 0.6-0.85, and the P value of each knowledge module is lower than 0.01, which is an extremely significant difference, indicating that the teaching effect is significant. The innovative development of music education is promoted, and this study greatly impacts choral conducting education.
FAIR EVA: Bringing institutional multidisciplinary repositories into the FAIR picture

The FAIR Principles are a set of good practices to improve the reproducibility and quality of data in an Open Science context. Different sets of indicators have been proposed to evaluate the FAIRness of digital objects, including datasets that are usually stored in repositories or data portals. However, indicators like those proposed by the Research Data Alliance are provided from a high-level perspective that can be interpreted and they are not always realistic to particular environments like multidisciplinary repositories. This paper describes FAIR EVA, a new tool developed within the European Open Science Cloud context that is oriented to particular data management systems like open repositories, which can be customized to a specific case in a scalable and automatic environment. It aims to be adaptive enough to work for different environments, repository software and disciplines, taking into account the flexibility of the FAIR Principles. As an example, we present DIGITAL.CSIC repository as the first target of the tool, gathering the particular needs of a multidisciplinary institution as well as its institutional repository.
An Early Stage Identification of Cryptomining Behavior with DNS Requests

The booming of cryptocurrencies in the last decade brought about the burst of cryptomining for obtaining cryptocurrencies in recent years. Only those users with plenty of computing resources are able to gain profits according to the design of block chain. As a result, this brings out more and more criminal attacks to maliciously plunder private and public computing resources through networks. Consequently, the detection of malicious cryptomining behavior is particularly important for network security and management. In this paper, we designed Mining Vanguard, realizing the recognition of mining behavior through the detection of DNS behavior. By constructing a comprehensive feature set that includes both traditional DNS resolution features and morpheme features, we combine network characteristics with semantic characteristics, aiming to achieve early recognition. Through a large number of targeted experiments, it is verified that Mining Vanguard is promising for detecting mining behaviors on the Internet.
Cardinality Estimation of Subgraph Search Queries with Direction Learner

In recent years, graph data has been widely used in various fields, which has made graph data management a hot topic of research. Subgraph searching is a crucial operation for graph data management, but its NP-complete nature makes exact matching algorithms time-consuming. To address this problem, new methods have begun to employ deep learning techniques to produce approximate results in less time. However, the distinctiveness of representations of different type of nodes are still limited, and edge information are not well used. To address these limitations, a novel graph neural network architecture, Directional Embedding Network ( $$\textsf {DENet}$$ ), is proposed for cardinality estimation of subgraph search queries. It effectively captures node relationships to distinguish different nodes through direction discrepancy, and accurately represents edges with learnable structural information. Experimental results demonstrate that $$\textsf {DENet}$$ could help improve the expressive power of existing approaches, which has a direct impact on producing more accurate cardinality estimation and optimize the subgraph search query procedure during graph data analysis.
LAANet: An Efficient Automatic Modulation Recognition Model Based on LSTM-Autoencoder and Attention Mechanism

This paper focuses on the task of automatic modulation recognition. Existing studies have shown low recognition accuracy at low signal-to-noise ratios, and models with a large number of parameters usually demand substantial computational resources, resulting in slower reasoning processes. In this paper, we propose a novel architecture for efficient recognition based on LSTM-Autoencoder and attention mechanism to address these challenges. Experimental results on benchmark datasets show that the proposed method achieves an average recognition accuracy of 62.43% and 64.49% on the RadioML2016.10a and RadioML2016.10b datasets, respectively. On the RadioML2016.10a dataset, the proposed model outperforms other SOTA models with a 2 $$\sim $$ 6% points improvement in recognition accuracy. The model also demonstrates superior recognition accuracy for both QAM64 and QAM16 modulation schemes and effectively increases average recognition accuracy by 1–2 percentage points in the -8dB to 2( $$\pm 2$$ ) dB lower SNR range, indicating its noise robustness. On the RadioML2016.10b dataset, the proposed method’s recognition accuracy is slightly higher than the SOTA model, demonstrating good performance.
KFEA: Fine-Grained Review Analysis Using BERT with Attention: A Categorical and Rating-Based Approach

User reviews contain many key phrases that are crucial for business understanding, but they are often obscured by the sheer volume of reviews. Extracting key phrases from user reviews could help to understand what users are concerned about and provide timely improvement suggestions. Current pattern-based methods for target phrase extraction usually analyze reviews at a coarse-grained level, making the extracted topics unfocused and useless. Hence, in order to address this issue, we proposed a fine-grained analysis approach (KFEA) to extract, cluster, and visualize key phrases from e-commerce reviews. In order to fully utilize the relevant information from comments, KFEA fuses the information like categories and ratings from a large volume of user reviews, and then extracts key phrases with the help of a pre-trained model. A method is also designed to cluster and visualize the extracted key phrases for business understanding. Our evaluation on 6,088 reviews from 6 products shows that KFEA can effectively extract key phrases and perform clustering and visualization. In particular, KFEA achieved an precision of 76.6% and a recall of 81.8% in extracting key phrases from manually annotated data. KFEA’s cross-categories effectiveness is also validated on 16,772 reviews from products like mobile phones, laptops, and furniture.
TKGAT: Temporal Knowledge Graph Representation Learning Using Attention Network

Temporal knowledge graph representation learning models can capture more comprehensive semantic information, which has higher practical application value and gradually attracts wide attention. However, the existing temporal knowledge graph representation learning models usually have challenges in encoding temporal information and capturing rich structural information. In this paper, we propose a novel temporal knowledge graph representation learning model, named TKGAT, which is based on graph neural networks using Bochner’s theorem to design time encoding function that can flexibly learn relative time information. Furthermore, attention network is adopted to model different relations features and the self-attention mechanism is optimized by the decoupled attention method, so that the attention weight matrix incorporates more extensive temporal and structural information and learns the correlations between entity and temporal features. The extensive experiments have shown that the proposed model can consistently outperform state-of-the-art models over all benchmark datasets.
Read Then Respond: Multi-granularity Grounding Prediction for Knowledge-Grounded Dialogue Generation

Retrieval-augmented generative models have shown promising results in knowledge-grounding dialogue systems. However, identifying and utilizing exact knowledge from multiple passages based on dialogue context remains challenging due to the semantic dependency of the dialogue context. Existing research has observed that increasing the number of retrieved passages promotes the recall of relevant knowledge, but the performance of response generation improvement becomes marginal or even worse when the number reaches a certain threshold. In this paper, we present a multi-grained knowledge grounding identification method, in which the coarse-grained selects the most relevant knowledge from each retrieval passage separately, and the fine-grained refines the coarse-grained and identifies final knowledge as grounding in generation stage. To further guide the response generation with predicted grounding, we introduce a grounding-augmented copy mechanism in the decoding stage of dialogue generation. Empirical results on MultiDoc2Dial and WoW benchmarks show that our method outperforms state-of-the-art methods.
STMC-GCN: A Span Tagging Multi-channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction

Aspect-Based Sentiment Triplet Extraction (ASTE) is a rapidly growing field in sentiment analysis. While most research has focused on processing the ASTE task either in a pipeline or end-to-end manner, both methods have their limitations. Pipeline methods may accumulate errors in practical applications, while sequence labeling methods in end-to-end approaches may overlook important feature information of the three elements themselves. Additionally, various features in sentences and emotional word markers have not been effectively explored in these methods. To address these limitations, we propose a novel solution called Span Tagging Multi-Channel Graph Convolutional Network (STMC-GCN) that explicitly combines multiple prominent features to extract span-level sentiment triplets, where each span may consist of multiple words and play different roles. Specifically, we designed a three-channel graph fusion model that converts sentences into multiple channels of graphs. These channels extract node text features, centrality features, and position features, which are then extracted through cross-channel convolution operations to obtain a common graph representation shared by different channels. To optimize downstream classification with better results, we use consistency and difference constraints to enhance common attributes and independence. Finally, we explore span-level information and constraints to generate more accurate aspect-based sentiment triplet extractions. Experimental results illustrate that STMC-GCN performs well on multiple datasets, proving the effectiveness and robustness of the model.