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关键词： Optimization   Feature extraction   Text summarization   Particle swarm optimization   Genetic algorithms   Clustering algorithms   Support vector machines   Deep learning   Data mining   Computer science   single document summarization   pre-processing   feature extraction   vectorization   hybrid CSO-HHO algorithm  

摘要： Presently, the exponential growth of unstructured data on the web and social networks has made it increasingly challenging for individuals to retrieve relevant information efficiently. Over the years, various text summarization techniques have been developed to address this issue. However, traditional approaches that rely on directly extracting words often lead to redundancies and fail to establish a strong connection between the summary and the original document. This paper presents a novel Deep Learning (DL)-based text summarization approach incorporating the following phases: pre-processing, feature extraction, vectorization, and summarization using a hybrid Cat Swarm Optimization (CSO) and Harris Hawk Optimization (HHO) algorithm. Initially, input documents undergo pre-processing steps, including sentence segmentation, word tokenization, stop word removal, and lemmatization, to enhance text quality. Features are then extracted using a Restricted Boltzmann Machine (RBM) to obtain nine key attributes. Vectorization is performed using Term Frequency-Inverse Document Frequency (TF-IDF) to represent sentences in vector form. The hybrid CSO-HHO algorithm is subsequently applied to generate summaries. The proposed method's efficiency was evaluated using datasets from the Document Understanding Conference (DUC), specifically DUC-2002, DUC-2003, and DUC-2005. Metrics such as sensitivity, readability, coherence, precision, BLEU score, ROUGE score, and F-score were analyzed to assess performance. The proposed approach's results were compared with existing methods, including CSO, QABC, PSO, GJO, FF, and machine learning techniques like SVM and RF. The hybrid CSO-HHO algorithm achieved an accuracy of 99.56%, demonstrating its superiority in text summarization tasks.

关键词： Logic   Databases   Data mining   Systematic literature review   Hardware   Formal specifications   Specification languages   Software systems   Computer science   Taxonomy   Automata learning   linear temporal logic   specification mining   SVA generation   API rule inference   software reliability   assertion mining   behavior detection   specification   property discovery  

摘要： Linear Temporal Logic (LTL) specifications play a crucial role in the verification process of cyber-physical systems, increasing the guarantees of their correctness. These specifications are vital for ensuring that both hardware and software components behave as expected, especially in complex real-world scenarios. In the last decades, researchers have developed several methodologies and tools to automatically generate LTL specifications, creating an urgent need to organize and synthesize existing literature to ease entry into this field and guide future research efforts. Therefore, starting from a pool of over 3000 papers extracted from the Scopus database in the temporal range 2000-2024, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to produce a systematic review of mining LTL specifications of hardware and software systems. In particular, we provide a taxonomy of the methods and describe with significant detail all the relevant techniques present at the state of the art. Finally, we discuss the challenges of mining LTL specifications and explore potential directions and opportunities for future research.

关键词： Vectors   Codes   Upper bound   Training   Electronic mail   Data mining   Artificial intelligence   Reed-Solomon codes   Computer science   Polynomials  

摘要： The purpose of this note is to correct an error made by Con et al. (2023), specifically in the proof of Theorem 9. Here we correct the proof but as a consequence we get a slightly weaker result. In Theorem9, we claimed that for integers k and n such that k q RS code that can decode from n - 2k + 1 insdel errors where (Formula presented). Here we prove the following. Theorem 1: For integers n and k q RS-code, where (Formula presented) is a prime power, that can decode from n - 2k + 1 adversarial insdel errors. Note that the exponent of n is 4k - 3 whereas in Theorem 9 it is 4k - 4. For constant dimensional codes, the field size is of order O(n4k-3) , and in particular, for k = 2 the field size is of order O(n5) . © 1963-2012 IEEE.

关键词： Emotion recognition   Computational modeling   Weak supervision   Multilingual   Benchmark testing   Text recognition   Speech recognition   Context modeling   Computer science   Complexity theory   deep learning   bidirectional encoder representations from transformers   weak supervision   neural machine translation   multi-view learning   German language  

摘要： Text-based emotion recognition plays a crucial role in various domains and applications due to its significance in understanding human behavior and improving communication systems. Although extensive research has been conducted for texts in English, only few studies deal with non-English languages. Especially for the German language, previous work is limited due to the focus on specific text domains and the usage of lexicon-based approaches, which lack the ability to consider contextual information. In this paper, an approach for emotion recognition on German text corpora is presented, which addresses these challenges with domain-generalized and context-sensitive methodologies. To achieve domain generalization, neural machine translation and weak supervision techniques are combined with multi-view learning, in which various data sources from different domains are utilized to improve the generalizability and to overcome the problem of lack of data. Using the BERT model as the primary architecture to capture contextual information, an overall F1-score of 65.5% and an accuracy of 68.1% are achieved while maintaining well-balanced results on the metrics over all considered domains and emotions. To ensure the legitimacy and reliability of the findings, further comprehensive evaluations and comparisons are conducted. Given the absence of an established benchmark specifically for emotion recognition on German text corpora, the used datasets, evaluations, and results can serve for benchmarking purposes.

关键词： Decoding   Image segmentation   Medical diagnostic imaging   Feature extraction   Computer architecture   Semantics   Accuracy   Computer science   Training   Deep learning   Biomedical image processing   Medical image segmentation   medical imaging   deep learning  

摘要： Medical image segmentation entails assigning each pixel in an image to its corresponding class label, a challenging task given the considerable anatomical variations in different cases. The encoder-decoder approach, exemplified by architectures such as U-Net, has emerged as the predominant framework for medical imaging segmentation tasks. In recent years, diverse modifications to the U-Net architecture have been explored, giving rise to distinct models that showcase noteworthy results in comparison to the conventional U-Net design. In this paper, we introduce a novel architectural framework, which we refer to as the Polyhierarchical Residual Network (PolyRes-Net). Each encoder step comprises a Multi-Level Residual Block (MLR-block) designed to extract local and global feature maps. Furthermore, each decoder step is preceded by an attention gate, aiding in the extraction of the most salient features from the preceding layer, while skip connections correspond to the respective encoder steps. Lastly, the multi-scale feature aggregation (MSFA) block consolidates features from various decoder steps. Four benchamar datasets are used for evaluating our model: Krusir-SEG, CVC ClinicDB, 2018 Data Science Bowl, and ISIC-2018 skin lesion segmentation challenge dataset based on two metrics: the Mean Dice Similarity Coefficient (mDSC) and the Mean Intersection Over Union (mIOU). The results of the proposed PolyRes-Net outperformed the state-of-the-art segmentation methods. Specifically, PolyRes-Net achieves the highest mDSC scores of 91.02%, 91.80%, and 89.25% on CVC ClinicDB, 2018 Data Science Bowl, and ISIC-2018 skin lesion segmentation challenge dataset, respectively. Additionally, the highest mIOU scores are 85.60%, 85.32%, and 82.14% for the same datasets, further underscoring the efficacy of the proposed model.

关键词： Internet of Things   Training   Security   Biological system modeling   Data privacy   Data models   Botnet   Accuracy   Computer science   Servers   Consumer Internet of Things   federated learning   attack detection   marine predator algorithm   DDoS attacks  

摘要： The rising adoption of Consumer Internet of Things (CIoT) devices brings heightened concerns about security vulnerabilities and privacy breaches. Given their connection to daily routine and data collection abilities, it is vital to protect individual data against unauthorized entry and possible leaks proactively. Data protection's maximum communication and storage charge will gradually challenge the typical ecology of integrated over-the-cloud learning and handling for IoT environments. Federated learning (FL) is developed as the most reasonable method for this challenge. In FL, training data-driven machine learning (ML) approaches is a form of association among several users with no need for data carried to a fundamental point;therefore, improving communication and storage charge offers a great degree of consumer-level secrecy. FL is an advanced ML that performs appropriately by inherently prioritizing confidentiality, circumventing requirements for centralized data collection, and supporting the defence. FL improves security in IoT by enabling decentralized model training without centralizing sensitive data, preserving privacy, and reducing communication costs. Integrating FL into attack detection models ensures zero-trust security, minimizing vulnerabilities and enhancing protection against cyber threats while keeping data on-device. This study develops an enhanced artificial gorilla troop optimizer with an FL-based attack detection and classification (EGTO-FLADC) approach on the CIoT environment. The EGTO-FLADC approach aims to improve security in the CIoT environment via FL and the attack detection process. Primarily, the EGTO-FLADC technique utilizes the FL model for collaborative training of combined models with many devices with no data-sharing process. In addition, the EGTO-FLADC technique uses a min-max scalar for data normalization. For attack detection, the EGTO-FLADC technique employs the temporal convolution networks with a gated recurrent unit (TCN-GR

关键词： Fake news   Social networking (online)   Surveys   Reviews   Computer science   Feature extraction   Economics   Blogs   Voting   Source coding   Text-based fake news detection   image-based fake news detection   large language models   GPT4   systematic review   embeddings  

摘要： Social media platforms have transformed the world into a global village by providing a unique platform for unrestricted communication and opinion sharing. However, this freedom is used to spread misinformation and disrupt societal harmony. To combat misinformation and fake news on social media platforms, multifarious AI applications have been developed to detect such content in various languages and data modalities, including text, images, and videos. To establish a distinctive platform that fosters the rapid development of AI-driven fake news detectors, researchers have published several review articles in recent years. However, many of these articles are outdated, and lack comprehensiveness, particularly regarding recent trends, public datasets, representation learning methods, and classifiers details. The limited scope of articles hindered their ability to provide in-depth information on predictors' language specificity, data modality focus, and state-of-the-art performance across diverse languages. This paper offers a unique platform with detailed information on these aspects to address this gap. It offers a detailed roadmap for understanding the scope, strengths, and limitations of existing review articles on the subject. To support the development of new benchmark datasets and more accurate predictors, it conducts an in-depth analysis of the definitions and perspectives of fake news within the context of existing literature. This analysis provides a more comprehensive definition and basic concepts of fake news. In addition, 310 fake news detection articles published in the last 8 years have been thoroughly investigated. Within the landscape of these articles, the study presents details of languages, datasets, data modalities, predictor architecture designs, and their performance metrics.

关键词： Author impact assessment   H index   Parameter ranking   Scientometrics   Statistical models  

摘要： Evaluating researcher impact is a critical aspect of scientometrics, influencing key academic decisions such as promotions, funding allocations, and prestigious awards. While traditional bibliometric indicators such as the h-index and its various extensions are widely adopted, ongoing debates persist regarding their accuracy in capturing true research impact. This study introduces a novel index aimed at improving researcher evaluation in the field of computer science. Using a dataset of 1200 researchers, evenly split between 600 award recipients and 600 non-recipients, we systematically evaluate individual bibliometric indices to identify the most effective parameters. These top-performing parameters are then combined using advanced statistical models including the geometric mean, harmonic mean, and contra-harmonic mean, etc., to determine the optimal aggregation method for impact assessment. The outcome of this study indicate that the h2-upper index and the AWCR index demonstrate the highest predictive accuracy among individual indicators. Moreover, the contra-harmonic mean proves to be the most effective statistical model for combining parameters. Based on these insights, we propose a new index derived by applying the contra-harmonic mean to the best-performing parameter pair. This enhanced framework offers a more robust and reliable approach to researcher evaluation, supporting improved academic assessment and recognition in computer science. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Quality assessment   Accuracy   Minimization   Mathematical models   Videos   Training   Target recognition   Computer science   Computational modeling   Artificial intelligence   Action quality assessment   long-term action quality assessment   domain shift   flow matching  

摘要： Long-term Action Quality Assessment (AQA) aims to evaluate the quantitative performance of actions in long videos. However, existing methods face challenges due to domain shifts between the pre-trained large-scale action recognition backbones and the specific AQA task, thereby hindering their performance. This arises since fine-tuning resource-intensive backbones on small AQA datasets is impractical. We address this by identifying two levels of domain shift: task-level, regarding differences in task objectives, and feature-level, regarding differences in important features. For feature-level shifts, which are more detrimental, we propose Progressive Hierarchical Instruction (PHI) with two strategies. First, Gap Minimization Flow (GMF) leverages flow matching to progressively learn a fast flow path that reduces the domain gap between initial and desired features across shallow to deep layers. Additionally, a temporally-enhanced attention module captures long-range dependencies essential for AQA. Second, List-wise Contrastive Regularization (LCR) facilitates coarse-to-fine alignment by comprehensively comparing batch pairs to learn fine-grained cues while mitigating domain shift. Integrating these modules, PHI offers an effective solution. Experiments demonstrate that PHI achieves state-of-the-art performance on three representative long-term AQA datasets, proving its superiority in addressing the domain shift for long-term AQA.

关键词： Genomics   Bioinformatics   Sequential analysis   Computer security   Security   Data privacy   DNA   Computer science   Biotechnology   Information systems   Next generation sequencing (NGS)   cyber-biosecurity   genomic data security   malware attack   genomic data privacy   cyber attacks on NGS  

摘要： Next-generation sequencing (NGS) has transformed genomic research and healthcare by enabling the rapid and cost-effective sequencing of DNA and RNA, surpassing traditional techniques such as Sanger sequencing. This technological leap has had a profound impact on fields including biomedical research, personalised medicine, cancer genomics, agriculture, and forensic sciences. With its widespread adoption, NGS has made genomic information more accessible, facilitating the sequencing of millions of genomes. However, the growing reliance on NGS has also brought significant challenges related to cyber-biosecurity, particularly the protection of genomic data against cyber threats such as unauthorised access, data breaches, and exploitation. Genomic data is inherently sensitive, and vulnerabilities in NGS technologies, software, data-sharing practices, and open-access databases expose it to risks concerning data confidentiality, integrity, and privacy. While NGS data plays an indispensable role across numerous sectors, research addressing the cyber-biosecurity of these technologies remains fragmented. Most existing studies focus narrowly on specific areas, such as microbial sequencing or system architecture, and fail to provide a holistic perspective on the security challenges that span the entire NGS workflow. Additionally, the lack of interdisciplinary collaboration between the biotechnology and cybersecurity communities further exacerbates these gaps. This paper seeks to bridge these gaps by thoroughly examining cyber-biosecurity threats throughout the NGS workflow. It introduces a tailored taxonomy specifically designed for NGS, aimed at increasing stakeholder awareness of potential vulnerabilities and threats. Key insights include identifying vulnerabilities at various stages of the NGS process-from data generation to analysis and storage-and categorising these threats systematically. The study highlights critical gaps in current research, underscoring the need for int