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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Grabska-Gradzińska, Iwona Nowak, Leszek Grabska, Ewa Department of Games Technology Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Department of Information Technologies Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Department of Design and Computer Graphics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland

ISBN: (纸本)9783030614003

This paper deals with storytelling aspects of computer games. A new approach to automatic story generation based on graph modelling is proposed. Godot Engine for visualization of output stories is used. In our research the plot is represented by a specific graph called the layered graph composed of story building blocks, namely layer of locations, layer of characters, layer of items, layer of narration and plot dependencies. An appropriate set of graph rules (productions) stored in JSON format is a generative tool for the building blocks. Graph rules are internal representation of actions which can be taken by the hero of the story. A sequence of the rules from start to end can describe the whole gameplay of one player or can be generated in automatic way as a possible gameplay. All such sequences of the rules create the space of all potential gameplays. Every rule corresponds to a certain animation made in Godot Engine. It allows to visualize every possible story, represented by sequence of rules, by seamless animation. Given a rule, animation is based on graphic assets corresponding to the rule nodes of the layered graph. © 2020, Springer Nature Switzerland AG.

关键词： Animation

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Ultra-Low Energy 5G IoT Devices with Updatable AI Models: A Dynamic AI-IoT Perspectives

Ultra-Low Energy 5G IoT Devices with Updatable AI Models: A ...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Dharmesh Dhabliya K. Yuvaraj Rahul Chauhan Dikshit Sharma Kala Priyadarshini G Shankar Prasad S Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Computer Science and Engineering Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management - UG Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Ultra-Low-Power (ULP) Internet of Things (IoT) devices are becoming more important in the fast developing 5G and possible 6G networks, and this study tackles the problem of incorporating dynamic AI capabilities into these devices. We present the Dynamic AI-IoT design, a new framework that aims to do away with the tedium of firmware upgrades. This design makes use of Narrowband IoT (NB-IoT) to make cloud interactions run smoothly and includes specialized firmware modifications to make dynamic interactions with TinyML models possible. Many sectors rely on deep learning that is based on massive amounts of high-quality data. The information accumulation of the framework relies on real-time acquisitions, which makes it difficult to directly implement deep learning in a real-time system. But such systems' analytical activities can't be done by collecting data for a lengthy period since they need to be executed in real time. This research presents a novel progressive deep-learning architecture and performs experiments on picture identification to address the challenges of high-quality data collection, high-timeliness data processing, and the difficulties of directly integrating deep-learning algorithms in real-time systems. The experimental findings demonstrate the efficacy, performance, and ability to get a conclusion comparable to the deep-learning architecture using large-scale data. An advanced method for managing memory is presented for the effective handling of AI tasks; it is based on memory alignment and the dynamic resolution of AI activities. Through the use of ULP IoT gadgets on a 5G testbed, empirical investigations show that the implementation of a Dynamic AI-IoT network is feasible. With an average inference duration of around 46 ms and model updates averaging less than one second, the findings are impressive.

关键词： Deep learning 5G mobile communication Heuristic algorithms Tiny machine learning Data collection Real-time systems Internet of Things Artificial intelligence Microprogramming Narrowband

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A Method for the Identification of Multiple Labels in Social Media Text using CNN Approach

A Method for the Identification of Multiple Labels in Social...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Dharmesh Dhabliya Vikrant Sharma N. Thangarasu Prakriti Kapoor V. Mahalakshmi Sampangi Rama Reddy B.R Department of Information Technology Vishwakarma Institute of Technology Pune India Computer Science and Engineering Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

People are able to communicate their thoughts and perspectives with one another through various online settings, including social networks, news websites, and blogs. Because so many individuals instantaneously post their thoughts on the internet, the number of submissions that have been submitted has increased dramatically. It is critical for establishments and organizations to do an effective and speedy analysis of the massive amounts of data at their disposal in order to provide summaries that discuss the perspectives or sentiments of individuals. In this article, we offer a scalable framework that makes sentiment classification by assessing the compound probability scores of the most generally used methods in sentiment analysis using a fuzzy inference mechanism in a CNN-style way. This evaluation is carried out in order to make the framework applicable to a broad range of situations. The valance of an individual's attitude may be estimated by the designed fuzzy inference system by calculating the compound scores of the valance aware dictionary, word embedding, and count vectorization procedures. The suggested technique differs from the convolutional neural networks (CNNs) in that it permits weighting of base learners and combines the strengths of each algorithm using fuzzy rules. Another distinction is that the proposed method is more general. The process of estimating sentiment based on textual data can be handled as either a two-class (positive and negative) or a three-class (positive, neutral, and negative) problem. The former has less challenges than the latter. We carried out the experimental study on four different tagged social network data sets, and discovered that the suggested strategy delivers gains in accuracy for both 2-class and 3-class classifications.

关键词： Fuzzy logic Training Sentiment analysis Social networking (online) Standards organizations Organizations Media Information and communication technology Convolutional neural networks Compounds

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Security Analysis for Virtual Machine Allocation in Cloud Computing

Security Analysis for Virtual Machine Allocation in Cloud Co...

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Cyber Resilience (ICCR), International Conference on

作者： Tahir Alyas Karamath Ateeq Mohammed Alqahtani Saigeeta Kukunuru Nadia Tabassum Rukshanda Kamran Department of Computer Science Lahore Garrison University Lahore Pakistan School of Information Technology Skyline University College University City Sharjah Sharjah UAE Department of Computer Information Systems College of Computer Science and Information Technology Abdulrahman Bin Faisal University Dammam Saudi Arabia Assistant professor in management Faculty Swiss Business school. RAK. UAE Department of Computer Science Virtual University of Pakistan Pakistan Faculty of Compter science and Information Technology Universiti Kota Samarahan Sarawak East Malaysia

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

A huge number of cloud users and cloud providers are threatened of security issues by cloud computing adoption. Cloud computing is a hub of virtualization that provides virtualization-based infrastructure over physically connected systems. With the rapid advancement of cloud computing technology, data protection is becoming increasingly necessary. It's important to weigh the advantages and disadvantages of moving to cloud computing when deciding whether to do so. As a result of security and other problems in the cloud, cloud clients need more time to consider transitioning to cloud environments. Cloud computing, like any other technology, faces numerous challenges, especially in terms of cloud security. Many future customers are wary of cloud adoption because of this. Virtualization Technologies facilitates the sharing of recourses among multiple users. Cloud services are protected using various models such as type-I and type-II hypervisors, OS-level, and unikernel virtualization but also offer a variety of security issues. Unfortunately, several attacks have been built in recent years to compromise the hypervisor and take control of all virtual machines running above it. It is extremely difficult to reduce the size of a hypervisor due to the functions it offers. It is not acceptable for a safe device design to include a large hypervisor in the Trusted Computing Base (TCB). Virtualization is used by cloud computing service providers to provide services. However, using these methods entails handing over complete ownership of data to a third party. This paper covers a variety of topics related to virtualization protection, including a summary of various solutions and risk mitigation in VMM (virtual machine monitor). In this paper, we will discuss issues possible with a malicious virtual machine. We will also discuss security precautions that are required to handle malicious behaviors. We notice the issues of investigating malicious behaviors in cloud computing, give t

关键词： Cloud computing Trusted computing Virtual machine monitors Memory Virtual machining Behavioral sciences Security

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Named Entity Recognition in Chinese Judicial Domain Based on Self-attention mechanism and IDCNN 8

Named Entity Recognition in Chinese Judicial Domain Based on...

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8th International Conference on Digital Home, ICDH 2020

作者： Huang, Wenming Zhang, Juan Xiao, Yannan Han, Zheng Deng, Zhenrong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin China Guilin Bank Guilin China

ISBN: (纸本)9781728192345

Chinese named entity recognition (CNER) in the judicial domain is an important basic task for intelligent analysis and processing of massive documents. This domain entity has more complicated structure than the common named entity, and its entity category is more abundant. However, the general method can not solve the problem of domain specific identification. In this paper, we combine self-attention mechanism and iteration dilated convolution neural network (IDCNN) for CNER in judicial domain. The bidirectional gate recurrent unit (BiGRU) model is used to automatically learn the context semantic information of the text and solve the long-distance dependence of the sequence. The model introduce the IDCNN to extract the key features of context semantic information, and capture finer-grained semantic information in underlying texts. The self-attention mechanism is used to analyze the relationship between characters, and the problem of long sequence semantic dilution is effectively solved by means of dynamic weight, and the optimal tag sequence is calculated by integrating conditional random fields (CRF), which further improves the recognition ability of the model. Finally, by analyzing the characteristics of legal documents, the new data set is annotated and the fine-grained named entity recognition is realized. The experimental results on our corpus show that the proposed method can effectively identify the entities in legal documents, and improve performance in the judicial field. © 2020 IEEE.

关键词： Semantics

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The Critical Role Played by Big Data Management in Effectively Addressing the Security and Overall Privacy Concerns Through Correlation Analysis 2

The Critical Role Played by Big Data Management in Effective...

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Ahamad, Shahanawaj Roshan, Amit Lourens, Melanie Shekher, Vineet Joshi, Kapil Alanya-Beltran, Joel College of Computer Science and Engineering University of Hail Saudi Arabia Information Technology Dr Ram Manohar Lohiya Avadh University UP Ayodhya India Durban University of Technology Department of Human Resources Management South Africa Bit Sindri Electrical Engineering Department Dhanbad India UIt Uttaranchal University India Universidad Tecnologica DelPeru Professor. Electronic Department Peru

ISBN: (纸本)9781665437899

Due to the widespread application of big data in a wide range of fields, there has been a significant rise in a diverse range of data assets, and numerous data analysis technologies, such as standardized data mining or quantitative analytical techniques, are speeding up the constant enlargement of the big data industry. The fact that information from several different resources has a life cycle from acquisition to disposal is a key property of big data, as is the fact that new information may be obtained via analysis, combining, and exploitation of existing data. Yet, information security & dependability challenges arise at every stage of the life cycle, rendering the protection of individually identifying data a vital goal throughout the whole process. It is possible to evaluate user behavior using different big data analytics techniques, and the collection of this information results in the violation of personal privacy. This study analyses dangers and security challenges that arise during the administration of large amounts of data by verifying the existing standards produced by international standardization bodies and doing an analysis of relevant research. © 2022 IEEE.

关键词： Big data

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Decoupling Contact for Fine-Grained Motion Style Transfer

arXiv

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arXiv 2024年

作者： Tang, Xiangjun Wu, Linjun Wang, He Wu, Yiqian Hu, Bo Li, Songnan Gong, Xu Liao, Yuchen Kou, Qilong Jin, Xiaogang State Key Lab of CAD&CG Zhejiang University China Department of Computer Science UCL Centre for Artificial Intelligence University College London United Kingdom Tencent Technology Co. Ltd. China State Key Lab of CAD&CG Zhejiang University ZJU-Tencent Game and Intelligent Graphics Innovation Technology Joint Lab China

Motion style transfer changes the style of a motion while retaining its content and is useful in computer animations and games. Contact is an essential component of motion style transfer that should be controlled explicitly in order to express the style vividly while enhancing motion naturalness and quality. However, it is unknown how to decouple and control contact to achieve fine-grained control in motion style transfer. In this paper, we present a novel style transfer method for fine-grained control over contacts while achieving both motion naturalness and spatial-temporal variations of style. Based on our empirical evidence, we propose controlling contact indirectly through the hip velocity, which can be further decomposed into the trajectory and contact timing, respectively. To this end, we propose a new model that explicitly models the correlations between motions and trajectory/contact timing/style, allowing us to decouple and control each separately. Our approach is built around a motion manifold, where hip controls can be easily integrated into a Transformer-based decoder. It is versatile in that it can generate motions directly as well as be used as post-processing for existing methods to improve quality and contact controllability. In addition, we propose a new metric that measures a correlation pattern of motions based on our empirical evidence, aligning well with human perception in terms of motion naturalness. Based on extensive evaluation, our method outperforms existing methods in terms of style expressivity and motion quality. Copyright © 2024, The Authors. All rights reserved.

关键词： computer games

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An Attack on Facial Soft-biometric Privacy Enhancement

arXiv

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arXiv 2021年

作者： Osorio-Roig, Dailé Rathgeb, Christian Drozdowski, Pawel Terhörst, Philipp Štruc, Vitomir Busch, Christoph Department of Computer Science Hochschule Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research Darmstadt Germany Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia

In the recent past, different researchers have proposed privacy-enhancing face recognition systems designed to conceal soft-biometric attributes at feature level. These works have reported impressive results, but generally did not consider specific attacks in their analysis of privacy protection. We introduce an attack on said schemes based on two observations: (1) highly similar facial representations usually originate from face images with similar soft-biometric attributes;(2) to achieve high recognition accuracy, robustness against intra-class variations within facial representations has to be retained in their privacy-enhanced versions. The presented attack only requires the privacy-enhancing algorithm as a black-box and a relatively small database of face images with annotated soft-biometric attributes. Firstly, an intercepted privacy-enhanced face representation is compared against the attacker’s database. Subsequently, the unknown attribute is inferred from the attributes associated with the highest obtained similarity scores. In the experiments, the attack is applied against two state-of-the-art approaches. The attack is shown to circumvent the privacy enhancement to a considerable degree and is able to correctly classify gender with an accuracy of up to approximately 90%. Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Assessing quality of decision reducts 24

Assessing quality of decision reducts

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Stanczyk, Urszula Zielosko, Beata Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bedzinska 39 Sosnowiec41-200 Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution. © 2020 The Authors. Published by Elsevier B.V.

关键词： Rough set theory

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

arXiv

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks. © 2023, CC BY-NC-SA.

关键词： Competition

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