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32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Li, Xiao Xu, Yang Yang, Linyi Zhang, Yue Dong, Ruihai Insight Centre for Data Analytics School of Computer Science University College Dublin Ireland School of Economics and Management Beihang University Beijing China Zhejiang University Zhejiang China School of Engineering Westlake University Zhejiang China

Leveraging recent developments in natural language processing (NLP), we constructed a prediction model using corporate financial annual reports to forecast the stock volatility indicator Beta (β), by analyzing risk discussions. The predicted Beta values were used to construct investment portfolios, whose market performance was then evaluated. Our research demonstrates that the Hierarchical Transformer-based model effectively captures complex risk information from annual reports, leading to improved returns in portfolio simulations. Our motivation arises from the need to better understand and process long, unstructured financial texts like annual reports, which contain crucial yet nuanced risk factors. By utilizing the hierarchical model, we aim to overcome traditional models’ limitations in handling such long documents, thereby improving the model’s understanding of both sentence-level and document-level contexts. The results highlight the potential of deep learning, particularly hierarchical models, in financial text prediction, and provide a novel perspective on asset management strategies. Compared to the S&P 500 benchmark, portfolios constructed using the predicted Beta values from our model achieved an average return increase of 21% over the same period. © 2024 Copyright for this paper by its authors.

关键词： Prediction models

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Routing Algorithm for Wireless Sensor Network Based on GA-LEACH 11th

Routing Algorithm for Wireless Sensor Network Based on GA-LE...

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11th International Conference on computer Engineering and Networks, CENet2021

作者： Zhixun, Liang Yuanyuan, Fan Yunfei, Yi School of Data and Computer Science Hechi University Yizhou Guangxi Hechi546300 China

ISBN: (纸本)9789811665530

The problem of low energy consumption is currently a principal research issue in wireless sensor networks. By optimizing network topology and routing, the problem of excessive energy consumption is effectively solved, and the survival time of the entire network is effectively improved. Traditional Low Energy Adaptive Clustering Hierarchy (LEACH) and other algorithms are effective for sensor network topology control and network Optimization algorithm. Nevertheless, these algorithm has the enigmas of equal probability cluster head selection and slow convergence speed. This paper combines the characteristics of GA algorithm with fast convergence speed and designs the GA-LEACH algorithm to solve these problems. First, LEACH clustering is performed by the Chameleon algorithm. And then adopt a genetic algorithm (GA) to select cluster heads. The simulation results show that the problem of unreasonable topological structure and slow algorithm convergence in wireless sensor networks caused by the selection of equal-probability cluster heads are effectively solved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Towards Robust Token Embeddings for Extractive Question Answering 1

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24th International Conference on Web Information Systems Engineering, WISE 2023

作者： Yao, Xun Ma, Junlong Hu, Xinrong Yang, Jie Guo, Yi Liu, Junping School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Computing and Information Technology University of Wollongong Wollongong Australia School of Computer Data and Mathematical Sciences Western Sydney University Penrith Australia

ISBN: (数字)9789819972548

ISBN: (纸本)9789819972531

Extractive Question Answering (EQA) tasks have gained intensive attention in recent years, while Pre-trained Language Models (PLMs) have been widely adopted for encoding purposes. Yet, PLMs typically take as initial input token embeddings and rely on attention mechanisms to extract contextual representations. In this paper, a simple yet comprehensive framework, termed perturbation for alignment (PFA), is proposed to investigate variations towards token embeddings. A robust encoder is further formed being tolerant against the embedding variation and hence beneficial to subsequent EQA tasks. Specifically, PFA consists of two general modules, including the embedding perturbation (a transformation to produce embedding variations) and the semantic alignment (to ensure the representation similarity from original and perturbed embeddings). Furthermore, the framework is flexible to allow several alignment strategies with different interpretations. Our framework is evaluated on four highly-competitive EQA benchmarks, and PFA consistently improves state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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Cosmetics Recommendation System Based on Ingredients Using Cosine Similarity

Cosmetics Recommendation System Based on Ingredients Using C...

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data science and Its Applications (ICoDSA), International Conference on

作者： Sekar Alisha Firdaus Laksmi Dwi Nurmuthia Raina Imtiyaz Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350365351

ISBN: (纸本)9798350365368

In the rapidly evolving beauty industry, consumers are often bombarded with an overwhelming array of skincare brands and products, making the quest for the perfect skincare regimen a daunting task. This saturation of the market not only confuses consumers but also poses the risk of resource wastage and potential skin damage due to incompatible ingredient combinations. To mitigate these challenges, our research presents an innovative recommendation system designed to streamline the product selection process. Utilizing the principle of cosine similarity, our methodology involves a detailed analysis of the ingredients contained in various skincare products. A quantitative foundation for evaluating ingredient lists of various skincare products is provided by cosine similarity, a mathematical metric that evaluates the similarity between two non-zero vectors by computing the cosine of the angle between them. Our algorithm generates customized product recommendations by thoroughly comprehending the intricate interactions among different constituents. This bespoke approach simplifies the decision-making process for consumers, enabling them to make well-informed choices that cater to their unique skin health needs. The effectiveness of our recommendation system is validated through comprehensive user feedback, demonstrating its potential to redefine the paradigm of personalized skincare recommendations within the beauty industry. Through providing customers with critical information and encouraging a culture of knowledgeable choice, we see a time when customized skincare products will not only increase customer satisfaction but also brand loyalty, which will be a big step toward the democratization of customized skincare.

关键词： Industries Measurement Deep learning Diversity methods Skin Vectors Software

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Deep U-Net Architecture with Curriculum Learning for Left Atrial Segmentation 1

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1st Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2022

作者： Jiang, Lei Li, Yan Wang, Yifan Cui, Hengfei Xia, Yong Zhang, Yanning National Engineering Laboratory for Integrated Aero -Space -Ground -Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

ISBN: (数字)9783031317781

ISBN: (纸本)9783031317774

Segmentation of the late-stage gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is a critical step in the ablation therapy for atrial fibrillation (AF). In this work, we propose an end-to-end deep learning-based segmentation method for delineating 3D left atrial (LA) structures in multiple domains. The proposed method uses the 6 layers deep U-Net architecture as the segmentation backbone. Curriculum learning is integrated into the deep U-Net architecture, helping the network learn step by step from easy to difficult scene. We have tested normal and strong version of data augmentation methods, to verify the effect of reducing domain shifts. Other techniques like Fourier-based data augmentation and Swin Transformer Block have also been explored to further improve the segmentation performance. The experimental results demonstrate that the strong version of data augmentation method can reduce the domain shifts and achieve more accurate result, with mean Dice score of 0.881 on the validation set of LAScarQS 2022 challenge. The evaluation results demonstrate our method’s effectiveness on left atrial segmentation in multi-sequence cardiac magnetic resonance (CMR) data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Curricula

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Natural Language Query and Control Interface for database Using Afghan Language 16

Natural Language Query and Control Interface for Database Us...

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16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022

作者： Karimi, Sadullah Rasel, Annajiat Alim Abdullah, Matin Saad Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (数字)9781665498104

ISBN: (纸本)9781665498104

The Afghan language, or Persian language, is one of the most widely used languages, with up to 110 million speakers worldwide. It is used in countries like Afghanistan, Azerbaijan, Iran, Iraq, Russia, Tajikistan, Turkmenistan, Uzbekistan, etc. The Persian language has insufficient resources and needs higher quality lexicon translation. The proposed Afghan Language Query and Control Interface (ALQCI) is based on NLIDB Approach. The Afghan language is used in this system to work with user-chosen data from a database. Retrieving information from the database requires knowledge of SQL Query Language or a well-designed interface. It is convenient for domain-expert users to retrieve data from the database. However, it may be challenging for non-expert users to access the database through SQL Query in the absence of a flexible user interface. This system helps overcome the challenge for those who speak Persian worldwide. The system may supplement or substitute less friendly user interfaces (UI). The system generates Structured Query Language (SQL) when a user inputs a spoken natural language query in the Persian language. The system works by allowing a user to enter a query in Persian, which the system then translates into a SQL query using the word mapping technique. The SQL is generated through semantic analysis. Afterwards, the generated SQL is used to extract knowledge using the Afghan language from a database. © 2022 IEEE.

关键词： Semantics

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Real-Time Traffic Flow Optimization using Adaptive IoT and data Analytics: A Novel DeepStreamNet Model 4

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Saranya, V.S. Subbarao, Garimidi Balakotaiah, Dega Bhavsingh, Maloth Babu, K. Suresh Dhanikonda, Srinivasa Rao School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai603 203 India India Department of Computer Science and Engineering India Ashok: Womens Engineering College Department of Computer Science and Engineering Andhra Pradesh Kurnool India Department of Computer Science and Engineering A.P Narasaraopet India Department of Artificial Intelligence and Data Science Telangana Hyderabad India

ISBN: (纸本)9798331540364

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts © 2024 IEEE.

关键词： Convolutional neural networks

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Efficiently Answering Why-Not Questions on Radius-Bounded k-Core Searches 28th

Efficiently Answering Why-Not Questions on Radius-Bounded k...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Zong, Chuanyu Dong, Zefang Yang, Xiaochun Wang, Bin Qiu, Tao Zhu, Huaijie School of Computer Science Shenyang Aerospace University Liaoning110136 China School of Computer Science and Engineering Northeastern University Liaoning110819 China Laboratory of Big Data Analysis and Processing Sun Yat-sen University Guangdong510275 China

ISBN: (纸本)9783031306747

The problem of searching radius-bounded k-cores (RB-k-cores) for a given query vertex is to find cohesive subgraphs satisfying both social and spatial constraints on geo-social networks. However, the search results are effected by two parameters: the cohesive constraint k and the query radius r. Furthermore, as the users lack of enough professional knowledge, it is very hard to provide reasonable parameters for RB-k-core searches. In this case, some users will confuse that why some expected vertices are not included in the search results, which called why-not questions. Therefore, we will investigate the problem of answering why-not questions on radius-bound k-core searches in this paper, which is to find why some expected vertices are missing in the search results and how to make the expected vertices appear in the same RB-k-cores with query vertices. To tackle this problem, we firstly analyze the effect of the two parameters on the search results of RB-k-core searches. Then, we explore two effective algorithms by refining the initial search parameters k and r of RB-k-core searches. Finally, we conduct comprehensive experimental studies on four real geo-social networks to evaluate both the effectiveness and efficiency of our proposed explanation algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Constraint satisfaction problems

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Graph decision transformer for offline reinforcement learning

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science China(Information sciences) 2025年第6期68卷 395-396页

作者： Shengchao HU Li SHEN Ya ZHANG Dacheng TAO School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Shanghai AI Laboratory School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University School of Computer and Data Science Nanyang Technological University

Recent advances [1, 2] in offline reinforcement learning(RL)have taken a new perspective on the problem, departing from conventional methods that concentrate on learning value functions or policy gradients. Instead, the problem is viewed as a generic sequence modeling task, where past experiences consisting of state-action-reward triplets are input to the Transformer.

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Overview of Mobile Attack Detection and Prevention Techniques Using Machine Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第10期18卷 125-157页

作者： Al Hwaitat, Ahmad K. Fakhouri, Hussam N. Alawida, Moatsum Atoum, Mohammed S. Abu-Salih, Bilal Salah, Imad K. M. Al-Sharaeh, Saleh Alassaf, Nabil King Abdullah II School of Information Technology The University of Jordan Amman1142 Jordan Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Computer Sciences Abu Dhabi University Abu Dhabi59911 United Arab Emirates

In light of the increasing sophistication and frequency of mobile attacks, there is a growing demand for advanced intelligent techniques capable of offering comprehensive mobile attack detection and prevention. This paper aims to critically evaluate the landscape of mobile security, outlining the evolution of mobile attack vectors and pinpointing the deficiencies in traditional security methods. The text embarks on a journey to understand the connection between machine learning (ML) and its promising applications in enhancing mobile security. First, we outline the current state of mobile attacks and the traditional methods used for their detection, emphasizing the clear limitations and the necessity for an innovative approach. Following this, we will elucidate the fundamentals of ML and its implications in cybersecurity, exploring the benefits it can provide to mobile attack detection frameworks. We delve into discussing various ML algorithms, such as decision trees, random forests, and support vector machines, highlighting their effectiveness and the metrics used to evaluate ML models in security tasks. Moreover, the paper sheds light on novel approaches such as semi-supervised and unsupervised learning in anomaly detection, as well as the applications of transfer learning in security. Addressing the pressing challenges faced in artificial intelligence (AI)-driven mobile attack detection, we delve deep into the intricacies of data collection, labeling, and the prevailing issues of imbalance and overfitting. Furthermore, we explore contemporary adversarial attacks and defenses, scrutinizing the real-world adaptability of AI models and the pivotal role of human-AI collaboration in enhancing attack detection mechanisms. © 2024 by the authors of this article.

关键词： Cybersecurity

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