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关键词： Privacy   Encryption   Data privacy   Servers   Iron   Web services   Electronic mail   Computer science   Proposals   Public key   Multi-input functional encryption   maximum similarity   intermediate result privacy   function privacy   web service  

摘要： As a common metric, maximum similarity between two objects is widely employed by web platforms to provide matching services. However, the calculation of maximum similarity involves numerous sensitive or confidential users' data, and the web platform server is often not trusted who might peep these data out of curiosity, or even worse sell them to unauthorized entities to make profits. Therefore, many research lines on functional encryption have been suggested and studied on how to calculate the maximum similarity while ensure the privacy of users' data. Unfortunately, all of them will divulge some intermediate results to the web platform server when processing this issue. In this paper we present a new functional encryption scheme supporting privacy-preserving maximum similarity, which enables the web service platforms to figure out the maximum similarity without learning anything else about their data. Moreover, we provide a formal analysis to prove the security of the proposed scheme, followed by some experimental evaluations and comprehensive comparisons with the related works. It shows that, our scheme is the first functional encryption realization on maximum similarity without divulging the intermediate result and meanwhile achieve a higher security-function privacy, as well as a traditional data privacy.

关键词： Kalman filters  

摘要： In the field of computer science education, humanistic traits and mental wellness are crucial. However, the role of biomechanics, especially factors like gait and posture, is often overlooked. Biomechanically, proper gait and posture are essential for maintaining physical balance and distributing forces evenly across the body. This study combines DL and biomechanical methods to assess students. Importantly, it delves into how gait and posture, as biomechanical factors, link to educators' mental health. Gait anomalies can signal stress or fatigue. A hesitant or unsteady gait might disrupt teaching focus and flow, inducing anxiety. Poor posture, like prolonged slouching, leads to muscle strain and pain. This physical discomfort can spark negative emotions, reducing teaching enthusiasm and confidence. The questionnaire included 300 computer science professors (180 women and 120 men) from Chinese universities. Data were collected through psychological questionnaires assessing insomnia, mood, fatigue, and depression. Anthropological factors such as social interaction and communication skills. Wearable sensors have been used to collect biomechanical data, including reactions and activities. The proposed Kalman filter algorithm with a modified visual-geometry group (KFA+MVGG) method allows a comprehensive assessment of computer science students’ psychological well-being, personality, and physical activity through activity and by accurately recording mental signals in real-time. Experimental studies show significant increases in classification accuracy and efficiency, outperforming previous benchmarking methods. The findings that sedentary behavior, poor posture, and restricted movement are linked to fatigue and poor mental health emphasize the importance of biomechanics. The KFA+MVGG offers valuable insights. By understanding these biomechanical impacts, teachers can be more aware of their body mechanics. This can potentially enhance their mental health and overall well-be

关键词： Privacy   Protection   Sensitivity   Social networking (online)   Differential privacy   Publishing   Organizations   Data models   Computer science   Training   Social networks   differential privacy   user-perceived sensitivity   personalized privacy protection  

摘要： With the prevalence of online social networks (OSNs), much personal information is collected and maintained by trusted service providers for third-party queries and analyses. Existing works regarding differentially private social network data publication overlook the fact that different users exhibit distinct privacy preferences or sensitivity inclinations. Neglecting these individual nuances may lead to privacy mechanisms that are overly conservative or inadequately protective. Furthermore, the injection of excessive noise into OSN data perceived by users as non-personal or less sensitive can incur additional privacy costs, resulting in lower service quality. This paper introduces a fine-grained, sensitivity-aware personalized edge differential privacy model (SPEDP) for OSNs. Specifically, SPEDP enables each OSN user to individually define the sensitivity level of their social connections, facilitating user-friendly personalized privacy settings. We design a privacy-aware mechanism that operates within a trusted service provider, capable of establishing privacy protection levels based on user-perceived sensitivity settings. Additionally, we propose a sensitivity-aware sampling mechanism to implement SPEDP. To further optimize the privacy mechanism, we explore a privacy threshold optimization strategy aimed at minimizing privacy budget waste. Finally, the personalized privacy protections and utility improvements achieved by the SPEDP mechanism are rigorously validated through theoretical analysis and comprehensive comparative experiments on benchmark datasets.

关键词： Virtual environments  

摘要： The development of the national economy has promoted the development of the tourism industry, and outdoor tourism has gradually become the main leisure and entertainment method for people during holidays. However, the current tourism promotion mechanism only considers tourist preferences, resulting in tourists receiving recommendations. The unique features of the tourist attractions in the destination area are extremely similar to those of previous tourist areas. Therefore, the research is based on the Visual Geometry 19 model, using skip connections and soft threshold functions to improve the Visual Geometry 19 network. A residual network is designed for feature extraction, and panoramic stitching technology is used to construct virtual scenes of tourist attractions. The findings indicated that the residual network designed in the study demonstrated convergence after 50 iterations of training and testing on the tourism map dataset, with an accuracy of scene reconstruction reaching 85%. The designed tourism location image reconstruction could be completed in about 90 seconds, effectively improving the speed of the virtual reconstruction of tourism scenes. The designed tourism attraction promotion mechanism based on virtual reality technology effectively solves the problem of single tourism promotion and improves the experience of national outbound tourism. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Intrusion detection   Feature extraction   Software defined networking   Computer science   Information technology   Deep learning   Classification algorithms   Bidirectional long short term memory   Telecommunication traffic   Prediction algorithms   Software-defined networking   deep learning   feature selection   intrusion detection system   enhanced artificial orcas algorithm  

摘要： Software-defined networking (SDN) is a novel network theory that divides the controller from the network devices such as switches and routers. The integrated SDN structure enables the global network organization and tackles the necessity of present data centers. There are great advantages presented by the architecture of SDN, the hazard of novel assaults is a vital issue and can avert the widespread acceptance of SDNs. The controller of SDN is an essential part, and it is a tempting aim for the invaders. If the attacker effectively acquires the SDN controller, it can transmit the traffic depending upon its desires, causing severe loss to the complete system. Mobile users can access crucial actual services over wireless models such as software-defined networks (SDNs) topologies and the Internet of Things (IoTs). Thus, managing power consumption and system and device congestion turns into a main problem for SDN-based IoT applications. Network intrusion detection systems (NIDSs) are significant devices for identifying and protecting the network landscape from anomalous attacks and malicious activity. Currently, deep Learning (DL) has revealed desired outcomes in a diversity of problems like speech, image, text applications, etc. Whereas numerous works used DL for NIDSs, almost all these methods neglect the outcome of the overfitting issue throughout the execution of DL techniques. This study presents a novel Enhancing Software-Defined Networking Security with Deep Learning and Hybrid Feature Selection (ESDNS-DLHFS) technique for consumer platforms. The proposed ESDNS-DLHFS system primarily focuses on protecting data privacy in SDN-assisted IoT platforms. In the ESDNS-DLHFS method, the initial phase of min-max normalization is executed to scale the input data. For the feature selection process, the hybrid crow search arithmetic optimization algorithm (HCSAOA) is utilized to optimally select feature subsets. Next, the deep bidirection- al long short-term memory (Deep BiL

关键词： 文化出海   国际传播   《黑神话:悟空》  

摘要： 作为一款以中华优秀传统文化为底蕴的现象级游戏产品，《黑神话：悟空》有效提升了中华文化的国际传播力和影响力。从文化出海的角度看，《黑神话：悟空》搭建了“数字再造”的文化传播内容、“具身在场”的文化体验方式、“融通中外”的文化叙事生态、“协同联动”的文化营销格局，彰显出科学技术与文化发展融合创新、用户参与和用户传播共同奏效、本土市场与国际市场联动建设、社会效益与经济效益协同提升的实践特色。这启示我们，在推动文化出海的实践中，要提炼和展示中华文化标识，厚植文化自信；聚焦国际市场需求，加强文化生产；形成媒体联动传播，扩大文化影响；加强科学技术赋能，推进文化创新，从而不断推动中华文化走向世界。

关键词： Layout   Accuracy   Deep learning   Classification algorithms   Cameras   Text analysis   Image analysis   Detectors   Computer science   Complexity theory   DIA   deep learning models   Pashto   SSD  

摘要： The paper explores the layout analysis and classification task of Pashto document images, a field with limited research due to the language's low-resource status. It uses Document Image Analysis (DIA) to detect one-column and two-column text blocks from Pashto documents captured using handheld cameras. A novel dataset containing real-world documents is annotated using bounding boxes to distinguish between one-column and two-column layouts. Layout analysis plays a vital role in Document Image Analysis (DIA) and answers questions about the nature of Document Images integral parts (components). These components are mainly text, graphics, and tabular data. The detection/identification of such elements is initially dependent on the general layout of a document. Textual columns are one of the basic layout structures present in document images. Such textual columns need identification as one-column or two-column before feeding the textual blocks into an Optical Character Recognition (OCR) system. Also, it becomes more crucial if the document images belong to a low-resource language like Pashto. Thus, this work contributes mainly in two aspects. First, it contributes to creating a dataset containing camera-captured document images with one-column and two-column patterns. Second, it proposes a deep learning model to identify/detect one-column and two-column textual blocks in the Pashto text images. The proposed model utilizes a reputed variant of a Faster Region-based Convolutional Neural Network (R-CNN) called single shot detector (SSD). The evaluation was done by examining the test set and a mean average precision (mAP) of 89% is achieved as a baseline.

关键词： Emotion recognition   Oral communication   Commonsense reasoning   History   Information processing   Generators   Computers   Computer science   Visualization   Semantics   Emotion support conversation   strategy prompt reasoning  

摘要： An emotional support conversation (ESC) system aims to reduce users' emotional distress by engaging in conversation using various reply strategies as guidance. To develop instructive reply strategies for an ESC system, it is essential to consider the dynamic transitions of users' emotional states through the conversational turns. However, existing methods for strategy-guided ESC systems struggle to capture these transitions as they overlook the inference of fine-grained user intentions. This oversight poses a significant obstacle, impeding the model's ability to derive pertinent strategy information and, consequently, hindering its capacity to generate emotionally supportive responses. To tackle this limitation, we propose a novel dynamic strategy prompt reasoning model (DSR), which leverages sparse context relation deduction to acquire adaptive representation of reply strategies as prompts for guiding the response generation process. Specifically, we first perform turn-level commonsense reasoning with different approaches to extract auxiliary knowledge, which enhances the comprehension of user intention. Then we design a context relation deduction module to dynamically integrate interdependent dialogue information, capturing granular user intentions and generating effective strategy prompts. Finally, we utilize the strategy prompts to guide the generation of more relevant and supportive responses. DSR model is validated through extensive experiments conducted on a benchmark dataset, demonstrating its superior performance compared to the latest competitive methods in the field.

关键词： Robots   Three-dimensional displays   Programming   Visualization   Programming profession   Mobile applications   Education   Usability   Python   Operating systems   Computational thinking   educational robotics (ER)   pre-university education   programming  

摘要： RoblockLLy is an educational robotics simulator designed for primary and secondary school students, whose goal is to increase their interest in science, technology, engineering, and mathematics. In the particular case of computer science, it allows developing computational thinking skills. It has been designed with ease of use in mind. This free tool is available through a web browser and does not need a complex installation or specific hardware requirements, allowing educational robotics to be introduced to a wide range of users by working on practical projects that will help them understand key concepts of robotics and programming. The effectiveness of RoblockLLy has been validated based on motivation, usability, and user experience criteria. The tool was validated with 212 secondary school students (12-16 years old). Specifically, motivation was measured with the Intrinsic Motivation Inventory, usability with the System Usability Scale, and user experience with the User Experience Questionnaire. Generally speaking, the results demonstrate that students perceived RoblockLLy as a novel and interesting tool. The ratings for usability were predominantly positive, although a few students indicated a preference for expert assistance. The overall rating of the user experience was positive as well, yet notable differences in attitudes toward motivation and usability were observed between genders.

关键词： Deepfakes   Feature extraction   Streaming media   Accuracy   Computer science   Visualization   Petroleum   Systematic literature review   Robustness   Real-time systems   Deepfake   multimedia   audio-video analysis   temporal analysis   media authenticity  

摘要： The rise of deepfake technology poses significant threats to the authenticity of content on social media. This research introduces Sach-AI, a pioneering framework for detecting various deepfakes in video, audio, and image data. Leveraging the power of deep neural networks, Sach-AI utilizes Eulerian Video Magnification combined with the ResNext architecture for enhanced detection. For video deepfakes, Long Short-Term Memory (LSTM) networks are integrated to improve classification tasks. This combination allows Sach-AI to effectively address the evolving, multimodal nature of deepfakes. The framework has been rigorously evaluated using diverse datasets, such as Celeb-DF and FaceForensics++, demonstrating its robustness and accuracy. Sach-AI achieved 97.76% accuracy in video deepfake detection, surpassing Intel's FakeCatcher, 99.13% accuracy in audio deepfake detection, and 93.64% accuracy in image deepfake detection. These results underscore Sach-AI's reliability in safeguarding digital media integrity against deceptive synthetic content in an era increasingly dominated by artificial technologies.