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the Impact of ChatGPT on Streaming Media: A Crowdsourced and Data-Driven Analysis using Twitter and Reddit

The Impact of ChatGPT on Streaming Media: A Crowdsourced and...

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IEEE international conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Yunhe Feng Pradhyumna Poralla Swagatika Dash Kaicheng Li Vrushabh Desai Meikang Qiu University of North Texas Ohio State University Dakota State University

ChatGPT, a general-purpose text generation AI model, is reshaping various domains ranging from education and software development to legal defense and novel writing. Despite its potential impact, there is a lack of research on how ChatGPT might influence streaming media, which is an essential part of everyday entertainment. As a result, it remains unclear how ChatGPT is changing the future of streaming media. To bridge such a research gap, in this paper, we propose a crowdsourced, data-driven framework that leverages two social media platforms, Twitter and Reddit, to explore the impact of ChatGPT on streaming media. through extensive analysis of social media data collected from Twitter and Reddit, we reveal how ChatGPT is transforming streaming media from diverse perspectives. Our data analytics demonstrates that ChatGPT is sparking both fear and excitement in the context of the streaming media and enhancing the downstream visual generative models, such as DALLE-2 and Stable Diffusion Videos. To the best of our knowledge, this study is the first large-scale and systematical investigation into the effects of ChatGPT on streaming media. Hope our findings will inspire further research and discussions on this topic across academia and industry.

关键词： Analytical models Visualization Social networking (online) Blogs Streaming media Media Writing

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ParaFuzz: Grey-Box Fuzzing Based on distributed Parallelization

ParaFuzz: Grey-Box Fuzzing Based on Distributed Parallelizat...

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international conference on Computer and Communications (ICCC)

作者： Enbo Fang Genzuo Ye Peng Jia Southwest Institute of Communication (30) Co. Ltd Chengdu China School of Cyber Science and Engineering Sichuan University Chengdu China

In recent years, fuzzing has become the most popular and effective vulnerability mining technique due to high degree of automation and versatility. In order to improve the characteristics of fuzz testing such as blindness and inefficiency, a large number of studies have been conducted to optimize the design of each step. Since fuzzing is typically a computationally intensive process, and the performance improved by algorithm optimization is always limited on a single machine, parallel computing to improve the performance of fuzzing is of great research value. However, parallelization of fuzzing must face and overcome challenges such as task conflicts, scalability in distributed environments, data synchronization overhead and workload imbalance. In this paper, we set out to solve each challenge in parallelized fuzzing and propose ParaFuzz, a new parallelized fuzzing tool. ParaFuzz manage and distribute seeds centrally in a client/server architecture to avoid task conflicts, solve workload balancing problems through a request/response model. A unique global sharing mechanism is designed for different information characteristics, and a variant strategy selection mechanism is proposed to improve the efficiency of fuzzing at the task scheduling level. Results from ParaFuzz tests on the LAVA-M test set and two real-world applications show up to 66% improvement in code path discovery compared to AFL native parallel mode at 8-node parallel scale.

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Message from the Technical Program Committee Co-Chairs

ACM International Conference Proceeding Series

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ACM international conference Proceeding Series 2022年 V页

作者： Akbar, Md Mostofa Zulkernine, Mohammad Department of CSE BUET Bangladesh School of Computing Queen’s University Canada

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Deadline-Aware and Energy-Efficient distributed Scheduling Algorithm in Ground-Air Cooperative Networks

Deadline-Aware and Energy-Efficient Distributed Scheduling A...

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Hairui Xu Yanling Shao Liming Liu Pingping Shan Junying Guo Wenxuan Xu School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China School of Computer and software Nanyang Institute of Technology Nanyang China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

the scarcity of energy resources and computational capabilities poses significant challenges for Unmanned Aerial Vehicles (UAVs) when tasked with executing time-sensitive and complex demands, such as those necessary for Artificial Intelligence (AI)-enabled applications. Integrating Unmanned Ground Vehicles (UGVs) in Ground-Air Cooperative Systems offers a promising solution by providing additional computational and storage capacities near the point of need. However, these systems' distributed, dynamic, and resource-constrained nature presents a critical challenge for efficient task scheduling. this study introduces a novel two-tiered distributed online task scheduling approach aimed at optimizing the resource utilization of UGVs in edge computing environments. Our methodology encompasses two algorithms: a task dispatching algorithm designed to identify a UGV server that meets the deadline requirements while minimizing energy consumption and a computing scheduling algorithm that employs the Earliest Deadline First (EDF) strategy to refine the sequence in which tasks are processed, thereby reducing the average task completion time. We thoroughly assess the performance of the suggested algorithm with state-of-the-art methods using the OMNeT++ network simulator. the outcomes indicate that the proposed method significantly reduces the average task latency and energy consumption while meeting task deadline requirements.

关键词： Energy consumption Scheduling algorithms Heuristic algorithms Cooperative systems Quality of service Dynamic scheduling Autonomous aerial vehicles

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A distributed State Management Approach for FaaS Platforms

A Distributed State Management Approach for FaaS Platforms

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Yang Xu Jiazhao Yu Hairui Guo Jialun Li Weigang Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Function-as-a-Service (FaaS) has become increasingly popular due to its ease-of-use operations, scalability, and low management overhead. However, the stateless nature makes it inappropriate for deploying applications that require maintaining state data across series execution. this motivates state management for FaaS platforms, which is currently satisfied by integrating additional storage services or a central coordinator. In this paper, we propose a new approach for FaaS platforms to manage function states in a distributed way. Specifically, we utilize function-level checkpoint to maintain function state and enable state to scale with the number of function instances. To synchronize state among multiple instances of the same function, we employ a synchronization mechanism supported by consensus protocols. Compared to existing works, our new approach distributes the burden of state management across all nodes and does not rely on additional services. In evaluation, we compare our approach to baseline system with a micro-benchmark and three representative applications. the results show that our approach can keep strong consistency of state between functions and reduce function execution time significantly.

关键词： Cloud computing Communication systems Scalability Consensus protocol Synchronization

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5G Network Requirement Analysis and Slice Dimensioning for Sustainable Vehicular Services 17

5G Network Requirement Analysis and Slice Dimensioning for S...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Kakkavas, Grigorios Diamanti, Maria Stamou, Adamantia Karyotis, Vasileios Papavassiliou, Symeon Bouali, Faouzi Moessner, Klaus Natl Tech Univ Athens Sch Elect & Comp Engn Iroon Polytech 9 Athens 15780 Greece Univ Surrey Inst Commun Syst 5G Innovat Ctr Guildford GU2 7XH Surrey England Tech Univ Chemnitz Professorship Commun Engn Str Nationen 62 D-09111 Chemnitz Germany Coventry Univ Inst Future Transport & Cities Coventry CV1 5FB W Midlands England

ISBN: (纸本)9781665439299

the Fifth Generation (5G) mobile communications together with software defined networking (SDN) and network function virtualization (NFV) are expected to enable a wide range of vertical use-cases. Different vertical industries with diverse service streams and sets of requirements should leverage the advanced capabilities of 5G networks through a single infrastructure to support the desired Quality of Service/Experience (QoS/QoE). In this paper, we focus on the Transport vertical and we study four novel service categories, each one consisting of one or more related scenarios, within the framework of the 5G Health, Aquaculture and Transport (5G-HEART) 5G PPP Phase 3 project. the first pass analysis of the envisioned vehicular services and their underlying operation, combined with the mapping of the mostly high-level functional user requirements to quantitative network Key Performance Indicators (KPIs) via a thorough and concise methodology, is essential for future testing with real pilots. Furthermore, our work paves the way towards efficient network slicing by exploring the interrelations between the identified KPIs and the respective target values that must be simultaneously satisfied over the same physical network infrastructure, in the context of the three 5G generic services.

关键词： 5G mobile communications network requirements key performance indicators transport vertical network slicing

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Digital Twin Enhanced Data Protection Based on Cloud-Edge Collaboration in Healthcare System

Digital Twin Enhanced Data Protection Based on Cloud-Edge Co...

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IEEE international conference on Smart Cloud (SmartCloud)

作者： Xinzheng Feng Jun Wu Qianqian Pan Jianhua Li School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Engineering The University of Tokyo Japan

ISBN: (数字)9798350389500

ISBN: (纸本)9798350389517

Empowered with sensing networks and edge computing, the Healthcare system represented by the health monitoring system (HMS) gradually becomes an important part of future medical technology. HMS has been gradually applied to practice scenarios, occurring that the resource-restricted Internet of things (IoT) nodes and heterogeneous networks’ structure make data protection more difficult. Since anomalies of HMS commonly cause extensive loss even human safety, timely anomaly prediction schemes are regarded as valuable to ensure the data protection of HMS. Most of the existing solutions are based on historical logs, which makes it difficult to identify new faults and easily causes extensive security threats. To solve this problem, we proposed a distributed digital twins (DT) enhanced abnormal prediction scheme based on cloud-edge collaboration to improve the availability and data protection of the HMS. this work proposed an HMS framework based on distributed DT to take full advantage of the parallel simulating capabilities of DT. In addition, considering the limitation of user demand and marginal server resources, we further proposed customized DT simulation operating mechanisms for medical devices in the HMS environment. the feasibility and efficiency of this work were discussed and proved in the analysis and performance evaluation.

关键词： Cloud computing Medical devices Data protection Collaboration Predictive models Digital twins Servers

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Towards a Federated Intrusion Detection System based on Neuromorphic computing

Towards a Federated Intrusion Detection System based on Neur...

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international Multidisciplinary conference on Computer and Energy Science (SpliTech)

作者： Domenico Lofù Paolo Sorino Tommaso Di Noia Eugenio Di Sciascio Dept. of Electrical and Information Engineering (DEI) Politecnico di Bari Bari (Italy) Bari Italy

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

Intrusion Detection Systems (IDSs) have emerged as essential tools for detecting cyber attacks and safeguarding sensitive data. Over time, there has been a shift towards designing IDSs that leverage Federated Learning (FL) methods, enabling them to detect attacks across distributed environments while upholding privacy-preserving manner. Concurrently, selecting the appropriate algorithm for Host execution, ensuring data privacy, low power consumption, and swift execution, has become a promising challenge. Recently, there has been a growing interest in Spike Neural Networks (SNNs) due to their ability to directly generate spikes and closely emulate human brain functions. SNN-based models are optimized to achieve energy efficiency by representing computations through asynchronously generated spikes. To tackle these challenges, we propose a theoretical approach for implementing a Federated Intrusion Detection System (IDS) that gathers data from different geographical locations, based on Neuromorphic computing principles.

关键词： Neuromorphic engineering Federated learning Computational modeling Software algorithms Intrusion detection Computer architecture Software

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Energy-Efficient Scheduling of Moldable Streaming Computations for the Edge-Cloud Continuum

Energy-Efficient Scheduling of Moldable Streaming Computatio...

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international conference on Fog and Mobile Edge computing (FMEC)

作者： Sajad Khosravi Christoph Kessler Sebastian Litzinger Jörg Keller Linköping University Linköping Sweden FernUniversität in Hagen Hagen Germany

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

We consider the problem of cost-effectively mapping a swarm of soft real-time stream processing applications with moldable-parallel tasks to multicore resources in the device-edge-cloud continuum, consisting of mobile devices, edge resources and cloud resources. We leverage flexibility from different parallelization degrees and frequency levels (DVFS) for the tasks, keeping application throughput constraints and communication bandwidth limitations while minimizing overall cost (including device/edge resource energy and cloud resource renting). We present two offline algorithmic solutions with a global view of the environment: an integer linear program (ILP) extending the crown scheduling approach for multi-layer distributed systems and a greedy heuristic algorithm. Our experimental evaluation for several real-world and synthetic scenarios shows that the time required for solving the scheduling problem to cost-optimality by the ILP is feasible for nontrivial scenarios. the heuristic achieves about 12% worse cost efficiency on average, yet operates much faster (by 1-2 orders of magnitude), allowing to scale up the problem size more than the ILP approach.

关键词： Costs Multi-access edge computing Processor scheduling Multicore processing Heuristic algorithms throughput Scheduling Real-time systems Mobile handsets Energy efficiency

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Integrative Day Trading Stock Trend Prediction using Web Scraping and Sentiment Analysis

Integrative Day Trading Stock Trend Prediction using Web Scr...

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international conference for Convergence of Technology (I2CT)

作者： Anjali Chennupati Bhamidipati Prahas Bharadwaj Aaditya Ghali Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the digital era, data is pivotal across disciplines like business, marketing, engineering, and social sciences. this research proposes a new method of acquiring intraday stock data from the National Stock Exchangeand sentiment-laden news articles, via the web-scrapping technology. Spark’s distributed computing framework that provides powerful processing capabilities has been employed to perform sentiment analysis and quantify sentiment scores for the chosen stock over the past month. Concurrently, the XGBoost algorithm processes the historical stock data to predict future values along with the cross-validation process. With the integration between the XGBoostpredictions and the sentiment score, this model offers stock recommendations to the user to either buy or sell a certain stock. the proposed model performance is evaluated for future value predictionsbased on the average RMSE score reported as *** extremely systematic take reaches across financial prediction, natural language processing, and machine learning to supply recommendations for investors.

关键词： Sentiment analysis Systematics Machine learning algorithms Social sciences Machine learning Predictive models Prediction algorithms

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