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Sinhala Hate Speech Detection in Social Media Using Machine Learning and Deep Learning

Sinhala Hate Speech Detection in Social Media Using Machine ...

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international conference on Advances in ICT for Emerging Regions

作者： W.S.S. Fernando Ruvan Weerasinghe E.R.A.D. Bandara Department of Statistics Faculty of Science University of Colombo Colombo Sri Lanka School of Computing 35 Reid Avenue University of Colombo Colombo Sri Lanka

Communication and presentation of beliefs became easier than in previous decades due to the rapid rise of information technology and computer science. Because social media is accessible worldwide via the internet, anyone can simply target someone or a group who adheres to a different culture or belief. While everyone has the freedom to express their own opinions, it should not be destructive, and everyone has the right to be free of hate speech. Because there are no automatic mechanisms for detecting hate speech on social media, anyone can be readily targeted. Because social media service providers do not have extensive linguistic expertise of some languages, such as Sinhala, it may take a few days for them to delete hate-related comments from the material after they become aware of them. As a result, detecting hate speech in the Sinhala language is an urgent and crucial task. Machine learning and deep learning based algorithms were employed in this study to automatically recognize Sinhala hate speeches broadcast on social media. Bag of words, Tf-idf, Word2Vec, and FastText feature extraction methods were used to extract features from the comments. Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine, XGBoost, Random Forest machine learning models and CNN, RNN, LSTM deep learning models were trained using two pre-collected datasets with different sizes. The best six models were then chosen and test set performances were shown. According to this study, FastText with RNN has the greatest AUC ROC 0.71 with 70% accuracy for the test set.

关键词： Deep learning Support vector machines Social networking (online) Hate speech Machine learning Speech recognition Feature extraction

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PrivOff: Secure and Privacy-Preserving Data Management for distributed Off-Chain Networks

PrivOff: Secure and Privacy-Preserving Data Management for D...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Htet Htet Hlaing Hitoshi Asaeda National Institute of Information and Communications Technology (NICT) Japan

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In today’s healthcare landscape, emerging technologies serve as crucial foundations, and the integration of blockchain technology into this rapidly evolving digital framework is invaluable. However, the healthcare industry has long struggled with managing the immense and ever-expanding amount of big data electronic health records (EHRs) collected from various sources, including the Internet of Things (IoT), wearable devices, and mobile applications. The storage of all these big data EHRs on the chain creates blockchain bloat, leading to slow transaction speed and high storage costs. More importantly, it incurs security and privacy leakage problems while sharing patients’ sensitive data transparently with a wide range of users in a distributed network. To address these challenges, we propose a secure and privacy-preserving data management system for distributed off-chain networks called PrivOff based on blockchain technology, which stores big EHRs separately in the decentralized file system with efficient access control enforcement and flexible revocation to prevent data breaches. In addition, patients can securely share data without revealing their unique identities to ensure privacy. The evaluation results and security analysis reveal that PrivOff can notably reduce the blockchain storage burden while offering data security, patient privacy, and high data availability.

关键词： Industries Data privacy File systems Information sharing Medical services Big Data Blockchains

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In-Network Compute Extensions for Rate-Adaptive Content Delivery in Mobile Networks

In-Network Compute Extensions for Rate-Adaptive Content Deli...

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international conference on Network Protocols

作者： Francesco Bronzino Chao Han Yang Chen Kiran Nagaraja Xiaowei Yang Ivan Seskar Dipankar Raychaudhuri WINLAB Rutgers University North Brunswick NJ USA Department of Computer Science Duke University Durham NC USA

Traffic from mobile wireless networks has been growing at a fast pace in recent years and is expected to surpass wired traffic very soon. Service providers face significant challenges at such scales including providing seamless mobility, efficient data delivery, security, and provisioning capacity at the wireless edge. In the Mobility First project, we have been exploring clean slate enhancements to the network protocols that can inherently provide support for at-scale mobility and trustworthiness in the Internet. An extensible data plane using pluggable compute-layer services is a key component of this architecture. We believe these extensions can be used to implement in-network services to enhance mobile end-user experience by either off-loading work and/or traffic from mobile devices, or by enabling en-route service-adaptation through context-awareness (e.g., Knowing contemporary access bandwidth). In this work we present details of the architectural support for in-network services within Mobility First, and propose protocol and service-API extensions to flexibly address these pluggable services from end-points. As a demonstrative example, we implement an in network service that does rate adaptation when delivering video streams to mobile devices that experience variable connection quality. We present details of our deployment and evaluation of the non-IP protocols along with compute-layer extensions on the GENI test bed, where we used a set of programmable nodes across 7 distributed sites to configure a Mobility First network with hosts, routers, and in-network compute services.

关键词： Streaming media Mobile communication Protocols Bit rate Computer architecture Servers Mobile computing

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Multiple distributed energy storage scheduling using constructive evolutionary programming

Multiple distributed energy storage scheduling using constru...

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Power Industry Computer Application conference (PICA)

作者： Thai Doan Hoang Cau R.J. Kaye Australian Graduate School of Management University of New South Wales Sydney Australia School of Electrical Engineering & Telecommunication University of New South Wales Sydney Australia

ISBN: (纸本)0780366816

Deregulation of the electricity supply industry is promoting the increased use of electrical energy storage. However, to achieve the system-wide benefits of competition, techniques for optimal scheduling of distributed storage resources are required. In this paper, we use constructive evolutionary programming to minimise the cost of operating a power system with multiple distributed energy storage resources. The evolutionary technique combines the advantages of both dynamic and evolutionary programming by evolving piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage scheduling problem is thus decomposed into many smaller one-stage sub-problems with evolved cost-to-go functions. Evolutionary programming is shown to be suitable for both decentralised computing and for market applications. Case studies demonstrate that the technique is robust and efficient for this type of scheduling problem.

关键词： Energy storage Optimal scheduling Job shop scheduling Genetic programming Power system dynamics Dynamic programming Processor scheduling Electricity supply industry deregulation Electricity supply industry Costs

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distributed Dependent Task Offloading in CPU-GPU Heterogenous MEC: A Federated Reinforcement Learning Approach

Distributed Dependent Task Offloading in CPU-GPU Heterogenou...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Hualong Huang Zhekai Duan Wenhan Zhan Yichen Liu Zhi Wang Zitian Zhao University of Electronic Science and Technology of China Chengdu China University of Edinburgh Edinburgh UK

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Mobile edge computing (MEC) has emerged as a promising paradigm to enable computation-intensive and latency-sensitive mobile applications by offloading tasks to proximal edge servers. This paper proposes a novel federated reinforcement learning framework called Transformer-based Federated Soft Actor-Critic (TFSAC) to address a joint computation offloading and resource scheduling problem in a CPU-GPU heterogeneous MEC network while preserving privacy. Specifically, a graph attention network (GAT) extracts high-dimensional features from the task dependency graph. Rather than simply averaging weights, TFSAC applies transformer encoders to learn contextual relationships between agents and enable selective aggregation of relevant knowledge during federated model training to preserve agents’ privacy. Experiments on real-world trace data demonstrate TFSAC’s superiority over benchmarks in maximizing quality-of-service (QoS) across configurations.

关键词： Training Privacy Multi-access edge computing Processor scheduling Quality of service Transformers Feature extraction

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Analysis of the Applicability of Cloud computing Scaling Techniques for Real-Time Workloads of Industrial Control Systems

Analysis of the Applicability of Cloud Computing Scaling Tec...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Rebekka Neumann Florian Frick Armin Lechler Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Stuttgart Germany

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

A key feature of cloud computing is elasticity, which enables resources to be allocated and de-allocated according to dynamic application demands. The evolution of Industrial Control Systems (ICS) is moving towards a distributed converged infrastructure that incorporates computing and communication resources as well as software, replacing dedicated hardware running control applications. While modern ICS and cloud environments share similarities, the corresponding applications differ significantly in their execution models and timing requirements. To enhance the flexibility of ICS, control applications must adapt to changing requirements throughout operation, resulting in varying workloads. The potential for dynamic scaling in ICS is considerable, but is not yet being fully exploited. The objective of this paper is to examine the extent to which state-of-the-art cloud autoscaling concepts are applicable to the scaling of real-time workloads and to propose enhancements to these concepts in order to enable the implementation of dynamic auto scaling of real-time workloads. In conclusion, the fundamental concept of cloud autoscaling can be applied, but must be extended, in particular to ensure deterministic resource provisioning and to improve task isolation in order to guarantee predictable task timing behaviour.

关键词： Cloud computing Industrial control Elasticity Dynamic scheduling Real-time systems Software Hardware Timing Integrated circuit modeling Informatics

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Let's wait awhile: how temporal workload shifting can reduce carbon emissions in the cloud 21

Let's wait awhile: how temporal workload shifting can reduce...

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proceedings of the 22nd international Middleware conference

作者： Philipp Wiesner Ilja Behnke Dominik Scheinert Kordian Gontarska Lauritz Thamsen Technische Universität Berlin Berlin Germany University of Potsdam Potsdam Germany

ISBN: (纸本)9781450385343

Depending on energy sources and demand, the carbon intensity of the public power grid fluctuates over time. Exploiting this variability is an important factor in reducing the emissions caused by data centers. However, regional differences in the availability of low-carbon energy sources make it hard to provide general best practices for when to consume electricity. Moreover, existing research in this domain focuses mostly on carbon-aware workload migration across geo-distributed data centers, or addresses demand response purely from the perspective of power grid stability and *** this paper, we examine the potential impact of shifting computational workloads towards times where the energy supply is expected to be less carbon-intensive. To this end, we identify characteristics of delay-tolerant workloads and analyze the potential for temporal workload shifting in Germany, Great Britain, France, and California over the year 2020. Furthermore, we experimentally evaluate two workload shifting scenarios in a simulation to investigate the influence of time constraints, scheduling strategies, and the accuracy of carbon intensity forecasts. To accelerate research in the domain of carbon-aware computing and to support the evaluation of novel scheduling algorithms, our simulation framework and datasets are publicly available.

关键词： carbon-aware scheduling data center resource management green computing temporal workload shifting

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Constellation Autonomy Modeling for Agile on-Orbit Communication and computing

Constellation Autonomy Modeling for Agile on-Orbit Communica...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Shoufeng Wang Hua-Min Chen Ye Ouyang Fan Li Xuan Chen Jianchao Guo Yun Li Sen Bian Xidong Wang Zhidong Ren AsiaInfo Technologies (China) INC. Beijing China Beijing University of Technology Beijing China China Unicom Beijing Branch Beijing China China Unicom Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the growing demand for mobile communication services and continuous advances in communication technologies, ubiquitous coverage and Internet of Everything have become basic capabilities required for 5G, 6G and future networks. On-orbit autonomy enables satellites to perform tasks, process data, adjust parameters, update software, diagnose faults and recover functions autonomously in orbit. This enhances intelligence, efficiency and reduces operation costs and risks, especially for large low earth orbit (LEO) constellations. On-orbit autonomy is an important trend with great significance and value. Although some research and tests have been conducted as well as application requests and standardization gathered, modeling constellation autonomy from an on-orbit communication and computing perspective has received little attention. This paper reviews related standards, research, and tests as a foundation for modeling. Key technologies, networking and management are studied as major concerns in modeling. The Constellation Autonomy Modeling (CAM) framework for agile on-orbit communication and computing is proposed as a reference model and method for on-orbit autonomy.

关键词： Satellites Costs Reviews Computational modeling Low earth orbit satellites Market research Orbits

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Building multicast trees in ad-hoc networks 08

Building multicast trees in ad-hoc networks

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proceedings of the 2nd international conference on Autonomic computing and Communication Systems

作者： Raphaël Kummer Peter Kropf Pascal Felber University of Neuchâtel Emile-Argand Neuchâtel Switzerland

ISBN: (纸本)9789639799349

Multicast trees are used in a variety of applications, such as publish/subscribe systems or content distribution networks. Existing algorithms for ad-hoc networks typically produce multicast trees requiring many nodes to act as relays even though they are not part of the multicast group. In this paper, we propose an algorithm for building efficient multicast trees that strives to minimize the number of non-member relay nodes and the number of transmissions required to reach all the group members, and to balance the degree of members when acting as internal nodes of the multicast tree. Our algorithm relies upon a lightweight distributed hash table (DHT) to construct and optimize the multicast trees. We evaluate the efficiency and scalability of our algorithm by simulations with various network configurations and sizes.

关键词： wireless networks protocol ad-hoc wireless networks mobile ad-hoc computing mobile networking MANETs multicast trees

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LiPI: Lightweight Privacy-Preserving Data Aggregation in IoT

LiPI: Lightweight Privacy-Preserving Data Aggregation in IoT

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Himanshu Goyal Krishna Kodali Sudipta Saha School of Electrical Sciences Indian Institute of Technology Bhubaneswar India

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The ability of IoT devices to sense and share various physical parameters plays a key role in a smart system. Along side benefits, it also bears the potential to cause a breach of privacy for the users of the smart-systems. Existing solutions for privacy-preserving computation for distributed systems either extensively use highly complex cryptographic techniques or exploit an extremely high degree of message passing among the devices through secure channels. However, for the resource-constrained IoT devices, which compose a significant fraction of the smart-systems, the existing solutions for privacy-preserving computation strategies does not fulfill the current requirements. To address this issue, in this work, we propose a novel real-time lightweight strategy LiPI for Privacy-Preserving Data Aggregation in low-power IoT systems. LiPI uses lightweight distributed and collaborative data obfuscation, which substantially minimizes the computation requirements. In addition, it exploits the recent advances in Synchronous-Transmission (ST)-based protocols to efficiently fulfill the communication requirements too, making it efficient to work in real-time. Furthermore, LiPI also avoids dependency on any trusted third party. Extensive evaluation based on comprehensive experiments in both simulation platforms and publicly available WSN/IoT testbeds demonstrates that our strategy achieves the goal at least 51.7% faster and consumes 50.5% lesser energy compared to the existing state-of-the-art strategies.

关键词： Data privacy Wireless sensor networks Privacy Protocols distributed databases Collaboration Data aggregation

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