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关键词： Feature extraction   Transformers   Convolution   Computational modeling   Data mining   Sun   Mixers   Computer science   Support vector machines   Redundancy   Hyperspectral image classification   hierarchical Transformer   multi-feature   cluster attention  

摘要： Vision Transformer (ViT), known for capturing non-local features, is an effective tool for hyperspectral image classification (HSIC). However, ViT's multi-head self-attention (MHSA) mechanism often struggles to balance local details and long-range relationships for complex high-dimensional data, leading to a loss in spectral-spatial information representation. To address this issue, we propose a deformable convolution-enhanced hierarchical Transformer with spectral-spatial cluster attention (SClusterFormer) for HSIC. The model incorporates a unique cluster attention mechanism that utilizes spectral angle similarity and Euclidean distance metrics to enhance the representation of fine-grained homogenous local details and improve discrimination of non-local structures in 3D HSI and 2D morphological data, respectively. Additionally, a dual-branch multiscale deformable convolution framework augmented with frequency-based spectral attention is designed to capture both the discrepancy patterns in high-frequency and overall trend of the spectral profile in low-frequency. Finally, we utilize a cross-feature pixel-level fusion module for collaborative cross-learning and fusion of the results from the dual-branch framework. Comprehensive experiments conducted on multiple HSIC datasets validate the superiority of our proposed SClusterFormer model, which outperforms existing methods. The source code of SClusterFormer is available at https://***/Fang666666/HSIC_SClusterFormer.

关键词： Phishing   Electronic mail   Incremental learning   Neural networks   Companies   Data models   Computational modeling   Predictive models   Frequency modulation   Computer science   Ensemble classifiers   incremental learning   on-line incremental learning   online incremental learning   online learning   neural networks   aggregation functions   uninorms   phishing link detection  

摘要： This contribution proposes a classifier designed to reduce the number of false positive detections. It is a self-tuning model, tested in the context of phishing link detection. In this prediction model diverse types of aggregation functions and time-series data periods are used. Aggregation functions are employed to integrate the prediction values of classification models applied in the email phishing problem. Dividing a dataset into subsets allows for incremental learning strategies. This makes it possible to gradually improve the model by using previously acquired knowledge when training on new data. The aim of the contribution is to discuss the problem of obtaining minimal value of FPR while simultaneously getting maximal value of TPR. We applied the proposed ensemble model and neural networks models which were adjusted to the incremental learning strategy (and as base models were applied typical examples of incremental learning models). The study analyzes the dataset provided by FreshMail company. The reason to consider this problem arose with the real-life problem of the Freshamil Company and the data provided by this Company. This dataset uniquely fulfills the criteria essential for our experiments. Unlike other phishing datasets, this data provides dates which is important for the incremental learning approach. The proposed approach of ensemble learning models, based on aggregation functions, is compared to the well-known neural network models which may be treated as state of the art models in recognizing phishing attacks. The main advantage of the proposed algorithm is achieving high numbers of true positives while simultaneously achieving relatively small number of false positives. According to the statistical tests, for some of the desired TPR levels, the proposed model obtained significantly better by a few percentage points results than neural network models. It mitigates the cost arising from the manual analysis of these cases by domain experts.

关键词： Databases   Systematic literature review   Machine learning   Art   Visualization   Photonics   Machine learning algorithms   Libraries   Cryptography   Computer science   Golden ratio   phi   phi in computing   Fibonacci number   Golden Section   Phi applications  

摘要： The Golden Ratio (Phi ) has intrigued researchers for centuries due to its widespread presence in both nature and science. This article systematically examines Phi 's diverse applications in computing, focusing on its role in enhancing visual appeal, optimizing algorithms, system design, and influencing fields such as biometrics, machine intelligence, and engineering. Through a systematic review of recent studies, this work aims to identify how Phi can improve computational efficiency across various computing domains. The methodology involves a comprehensive analysis of cutting-edge research sourced from 12 reputable digital platforms to explore Phi 's potential. This study highlights the versatility of Golden Ratio, as its applications and relationships were found to account for 51% and 49%, respectively, across a total of 51 selected studies from 27 countries spanning six continents. The review also categorizes findings by their specific applications and contexts, providing valuable insights into Phi 's impact on mathematics, cryptography, search algorithms, machine learning, artificial intelligence, photonics, natural sciences, system design, power engineering, robotics, and practical human life. This study opens promising avenues for future research on the applications of Phi in diverse fields of science, engineering, and computing.

关键词： Games   Usability   Education   Serious games   Decision making   Proposals   Mobile handsets   Training   Three-dimensional displays   Sustainable development   Computer science   digital game-based learning   science   serious games   usability  

摘要： In recent times, the use of serious games has significantly advanced in science education, with notable contributions in the design and implementation of game mechanics in immersive environments. This research aims to assess the usability and experience of the serious game ABCCI, identifying both the positive and negative effects generated by its interaction from the perspective of pre-service teachers, considering levels of competence, immersion, tension, flow, and challenge. To evaluate usability, the System Usability Scale (SUS) was adapted, and the Game Experience Questionnaire (GEQ) was used to assess the overall experience. The results indicate a moderate positive correlation between the experience and usability of the serious game. Most pre-service teachers expressed positive views about the frequent use of the ABCCI serious game in science education, which successfully created an engaging and positive experience characterized by a sense of competence, immersion, and positive emotions, with an appropriate level of challenge and low levels of negative affect. Further studies are needed to develop a framework for designing games in science education. ABCCI proves to be a promising tool for managing learning.

关键词： Training   Games   Data models   Computer science   Servers   Resource management   Federated learning   Optimization   Privacy   Energy consumption   Stackelberg game   resource allocation   incentive mechanism   federated learning  

摘要： Federated learning (FL) is an emerging distributed-learning technology that allows a requester to hire a set of independent FL workers for a model training task. The fact that FL workers are not obliged to do the training task calls for an incentive mechanism to motivate workers. However, existing incentive mechanisms for FL mainly were designed for single requester. A few studies considered multiple requesters but constrained a worker's potential to train multiple models simultaneously. In this paper, we remove the constraint to investigate the interplays among requesters in addition to inherent interplays among workers and between requesters and workers. Two crucial issues in this setting are whether the interplays lead to a stable (i.e., attainable) outcome and how to find it. We model the problem as seeking an equilibrium in a multi-leader multi-follower Stackelberg game and propose an exact method based on the derivation and backward induction to identify the Stackelberg equilibrium. We show how to adapt the proposed method to scenarios with capacity/budget constraints and privacy concerns. We have done a series of analyses to study the interplays of workers and requesters in Stackelberg equilibria. The results confirm the first-mover advantage of the requesters and reveal the possibility of mutual benefits between the requesters and workers.

关键词： Perturbation methods   Measurement   Observers   Predictive models   Distortion   Noise   Feature extraction   Computer science   Visualization   Visual systems   Adversarial stealthiness assessment   adversarial attack   classification  

摘要： Assessing the stealthiness of adversarial perturbations is challenging due to the lack of appropriate evaluation metrics. Existing evaluation metrics, e.g., L-p norms or Image Quality Assessment (IQA), fall short of assessing the pixel-level stealthiness of subtle adversarial perturbations since these metrics are primarily designed for traditional distortions. To bridge this gap, we present the first comprehensive study on the subjective and objective assessment of the stealthiness of adversarial perturbations from a visual perspective at a pixel level. Specifically, we propose new subjective assessment criteria for human observers to score adversarial stealthiness in a fine-grained manner. Then, we create a large-scale adversarial example dataset comprising 10586 pairs of clean and adversarial samples encompassing twelve state-of-the-art adversarial attacks. To obtain the subjective scores according to the proposed criterion, we recruit 60 human observers, and each adversarial example is evaluated by at least 15 observers. The mean opinion score of each adversarial example is utilized for labeling. Finally, we develop a three-stage objective scoring model that mimics human scoring habits to predict adversarial perturbation's stealthiness. Experimental results demonstrate that our objective model exhibits superior consistency with the human visual system, surpassing commonly employed metrics like PSNR and SSIM.

关键词： Artificial intelligence   scientific research   scientific research   science ethics   science ethics   computer science   computer science   physics   physics   medicine   medicine   psychology   psychology   mathematics   mathematics   geography   geography   agriculture   agriculture  

摘要： The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in the foreseeable future. In this review, we first attempt to establish clear definitions of intelligence and consciousness, then provide an overview of AI's state of the art and its applications. A discussion of the implications, opportunities, and liabilities of the diffusion of AI for research in a few representative fields of science follows this. Finally, we address the potential risks of AI to modern society, suggest strategies for mitigating those risks, and present our conclusions and recommendations.

关键词： Knowledge engineering   Semantics   Data models   Predictive models   Attention mechanisms   Self-supervised learning   Analytical models   Deep learning   Computer science   Computational modeling   Intelligent systems   self-supervised learning   attention mechanisms   graph convolutional networks   knowledge tracing  

摘要： As intelligent education advances and online learning becomes more prevalent, Knowledge Tracing (KT) has become increasingly important. KT assesses students' learning progress by analysing their historical performance in related exercises. Despite significant advances in the field, there are still shortcomings in two aspects: first, a lack of effective integration between exercises and knowledge points;second, an overemphasis on nodal information, neglecting deep semantic relationships. To address these, we propose a self-supervised learning approach that uses an enhanced heterogeneous graph attention network to represent and analyse complex relationships between exercises and knowledge points. We introduce an innovative surrogate view generation method to optimise the integration of local structural information and global semantics within the graph, addressing relational inductive bias. In addition, we incorporate the improved representation algorithm into the loss function to handle data sparsity, thereby improving prediction accuracy. Experiments on three real-world datasets show that our model outperforms baseline models.

关键词： Chatbots   Artificial intelligence   Measurement   Education   Computer science   Writing   Object recognition   Electrical engineering   Translation   Robot sensing systems   ChatGPT   education   LLM   computer science and engineering   electrical engineering  

摘要： Large Language Models (LLMs) like ChatGPT have become the most popular regenerative AI applications, used for obtaining responses for queries in different domains. The responses of ChatGPT are already becoming mainstream and are challenging conventional methods of learning. This article focuses on the application of ChatGPT for academic instructional purposes in the field of computer engineering and related majors. The capability of ChatGPT for instructional purposes is evaluated based on the responses to different questions about these engineering streams. This article explores different opportunities (with use cases), that ChatGPT can provide in augmenting the learning experience. It also provides scenarios of limitations and modifying the evaluation process to prevent the use of ChatGPT, which may lead to an inaccurate dissemination of accepted facts. In this paper, common classroom problems and their respective responses from ChatGPT in the domains of Computer Science, Cyber Security, Data Science, and Electrical Engineering are analyzed to determine the categories of queries for which ChatGPT offers reliable responses and those for which it may be factually incorrect. A student survey is performed to demonstrate that students must be made aware that ChatGPT may not be suitable for certain types of queries and means of upgrading the evaluation process.

关键词： Stock markets   Portfolios   Data models   Prediction algorithms   Profitability   Indexes   Heuristic algorithms   Deep reinforcement learning   Computer science   Supervised learning   Deep reinforcement learning (DRL)   stock market trading   portfolio management   max drawdown rewards   advantage actor-critic (A2C)   deep deterministic policy gradient (DDPG)   proximal policy optimization (PPO)  

摘要： Algorithmic stock trading has improved tremendously, with Reinforcement Learning (RL) algorithms being more adaptable than classic approaches like mean reversion and momentum. However, challenges remain in adequately depicting market events and generating suitable rewards to influence the trading decisions of an agent in a dynamic environment. This study proposed an improved stock market trading framework termed the RMS-Driven Deep Reinforcement Learning (DRL) model for optimal portfolio management. The research attempts to give a more comprehensive view of the market and, as a result, enhance trading decisions by including consumer information and incorporating news sentiments into the model, in addition to data from typical earnings reports. More specifically, three kinds of DRL models are presented, combined with data from stock earnings reports, Max Drawdown rewards, and sentiment indicators (RMS), known as PPO_RMS, A2C_RMS, and DDPG_RMS, respectively. The findings of this research indicate that the integrated model, mainly the DDPG_RMS effectively outperforms the baseline <^>DJI index in many risk-return analyses in ratios showing better risk management, and profitability. The proposed stock trading model generates a maximum cumulative return of 27% with a Sharpe ratio of 0.66, showing an appropriate trade-off between risk and return. This approach, which incorporates sentiment analysis and Max Drawdown rewards, significantly enhances the model's performance in adapting to changing market conditions. Therefore, the results emphasize the appropriateness of integrating sentiment indices with traditional financial data to enhance a trader's performance while also offering essential information to aid in the development of improved trading tactics in continuously changing financial markets.