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ANN Based Estimation of Reputation of Newcomer Web Services in Fog computing

ANN Based Estimation of Reputation of Newcomer Web Services ...

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11th international conference of Computer Communication and Informatics (ICCCI)

作者： Hallappanavar, Vijay L. Bulla, Chetan M. Birje, Mahantesh N. KLE Coll Engn & Technol Chikodi Dept ECE Chikodi India KLE Coll Engn & Technol Chikodi Dept CSE Chikodi India Visvesvaraya Technol Univ Dept CSE Belagavi India

ISBN: (纸本)9781728158754

Fog computing is a distributed computing model which stores and computes at the edge of the network with the reason of reducing the network overload and process the gathered information with low latency. By doing so there is improvement in the response time and the system performance. Fog environment consists of huge number of IoT devices, fog nodes and cloud service providers. the fog nodes are providing services to IoT devices and the selection of required web services in such a heterogeneous environment is a complex issue. the Web Service Recommendation System (WSRS) help IoT devices to choose appropriate service provided by fog nodes based on reputation of the its services. In service based computing environments, reputation of web services is used for recommending services. the performance of recommendation system depends highly on evaluation of the initial reputation values failing which exposes the system to various threats like whitewashing and Sybil attacks. Although the reputation management plays a major role in recommending services of fog nodes to IoT devices, the evaluation of the reputation of newcomer services without feedback ratings is challenging task. Hence this paper presents Artificial Neural Network (ANN) based estimation of reputation of newcomer web services in fog computing. the results obtained from the experiments carried out on a collection of web services in the real world suggest that the approach provided is effective and accurate in estimating the reputation of newcomer services.

关键词： Fog computing Newcomer Web Services Reputation Estimation Artificial Neural Network

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Toward Fast and Reliable Active-Active Geo-Replication for a distributed Data Caching Service in the Mobile Cloud 18

Toward Fast and Reliable Active-Active Geo-Replication for a...

引用

18th international conference on Mobile Systems and Pervasive computing, MobiSPC 2021, the 16th international conference on Future Networks and Communications, FNC 2021 and the 11th international conference on Sustainable Energy Information Technology, SEIT 2021

作者： House, Daniel Kuang, Heng Surendran, Kajaruban Chen, Paul Huawei Research Canada MarkhamON Canada

In this paper, we describe our experiences extending a distributed data caching service in the mobile cloud to support active-active geo-replication. We show how our enhancements guarantee data consistency between regions after a network partition recovery. An approach is presented for keeping geo-distributed replicas synchronized despite the cache data operations replication not ensuring causal delivery in the presence of long network partitions. We use Redis, one of the most popular in-memory databases for the distributed data caching service in the mobile cloud, as a proof of concept to apply our approach in a plug-in way (minimizing the impact on both the server and client side of the cache service). Redis exposes a powerful extension API that allows new abstract data types to be associated with keys but does not provide direct support for adding and managing global dictionary metadata, which we added in our solution. that extension API is used to add the CRDT (Conflict-free Replicated Data Type) to resolve the writing conflicts from multiple regions. © 2021 Elsevier B.V.. All rights reserved.

关键词： active-active geo-replication availability Cloud data caching conflict-free replicated data type

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An Application with Jetson Nano for Plant Stress Detection and On-field Spray Decision

An Application with Jetson Nano for Plant Stress Detection a...

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11th international conference on Sensor Networks, SENSORNETS 2022

作者： de Oliveira, Marcos A. Sedrez, Gregory de Souza, Guilherme Cavalheiro, Gerson Geraldo H. Programa de Pós-Graduação em Computação Universidade Federal de Pelotas RS Pelotas Brazil Instituto Federal de Educação Ciência e Tecnologia Farroupilha RS Santa Maria Brazil

ISBN: (纸本)9789897585517

Increasing field productivity is not just a financial need, but also a social issue. Several technologies converge to promote food production and, in this context, the fog computing paradigm can support the development of solutions for precision agriculture. this paper proposes an application of the Jetson Nano device, embedded in an agricultural spraying implement. this device supports the decision on irrigation activity, based on data collected by sensors distributed in the field. the sensors read information about the plant's stress level from electrical signals and the Jetson Nano enables real-time analysis, through machine learning algorithms, to manage the product spray rate, according to the condition of the crop. Initial studies validated the proposed solution on an experimental basis, showing that the device can be an alternative for this purpose, since it can be used efficiently in machine learning tasks from data collected by the sensors. the experiment also highlighted some limitations of the proposed solution, such as the importance of observing the conditions of the system as a whole, its context and environment, in order to improve performance in spraying process. Copyright © 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Fog computing

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks 18

Machine Learning-based Energy-Aware Offloading in Edge-Cloud...

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作者： Ismail, Leila Materwala, Huned Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. the compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). the underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. the offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. the experimental results show that the proposed algorithm is a promising approach in energy savings. © 2021 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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Cascading Hypergraph Convolution Networks for Multi-Behavior Sequential Recommendation*

Cascading Hypergraph Convolution Networks for Multi-Behavior...

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international conference on Behavior, Economic and Social computing (BESC)

作者： Dan Lu Hao Zhang Lijie Li Shiqing Wu Guandong Xu Harbin Engineering University Harbin China University of Technology Sydney Sydney Australia The Education University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

In current research on recommendation systems, exploring multi-behavior sequence recommendation has become a crucial topic. It is well known that user interactions on online platforms (such as social media websites, news aggregation applications) involve not only singular actions like reading or clicking but also various other behaviors such as commenting, sharing, and bookmarking. these diverse interactions reflect different facets of user preferences in item interaction sequences. therefore, understanding and integrating these diverse behaviors to effectively represent user preferences personalized is essential. To better utilize temporal information, we propose a Cascading Hypergraph Convolution Networks For Multi-Behavior Sequential Recommendation (CHMSR) framework. CHMSR aims to capture the underlying behavioral preferences in user sequence interactions and predict future user behaviors through cascading neural networks. Specifically, CHMSR first independently encodes each behavior sequence to extract user interests from the complex behavior relationships in the sequences. then, it aggregates user preferences for different items through hypergraph convolution to capture global preferences. Furthermore, we employ cascading neural networks in behavior chains to capture directional feature dependencies in multi-behavior sequences, propagating messages from upstream to downstream behaviors. In this way, CHMSR can comprehensively consider user interests in multi-behavior sequences and global preferences for items, thus providing more accurate and personalized recommendations. Our experimental results demonstrate that CHMSR significantly outperforms existing recommendation methods on various datasets, validating its effectiveness and practicality

关键词： Social computing Accuracy Convolution Social networking (online) Aggregates Neural networks Feature extraction Convolutional neural networks Recommender systems

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distributed Ledger Technology for Securing IoT 11

Distributed Ledger Technology for Securing IoT

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11th international conference on computing, Communication and networking Technologies, ICCCNT 2020

作者： Panda, Soumyashree S. Mohanta, Bhabendu Kumar Dey, Meenu Rani Satapathy, Utkalika Jena, Debasish IIIT Department of Computer Science and Engineering Bhubaneshwar Odisha751003 India IIT Department of Computer Science and Engineering Guwahati Assam781039 India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168517

computing and communication are getting increasingly ubiquitous with the inclusion of sophisticated devices like electric vehicles, smart phones and other house hold appliances. Due to the constant evolution in Internet of things (IoT), the process of collaboration of these devices at a mass scale in order to provide improved and better services to the society. Traditional mechanisms which are used to sustain privacy and security become incapable from achieving the same for IoT systems having distributed or decentralized topology. distributed Ledger Technologies (DLT), an emerging digital technology, consists of different kinds of decentralized data structures to ensure immutability by linking blocks using cryptographic measures. DLT has the ability to ensure privacy, security and distributed or decentralized computations with adhering to the constraints of IoT nodes. this study is motivated due to the lack of an in-depth analysis on how the characteristics of DLT can be exploited to secure IoT systems. So, an in depth overview of DLT along with some of the existing solutions to meet security requirements of IoT systems employing DLT have been provided in this paper. With respect to integrating DLT with IoT, this article also highlights the different challenges. © 2020 IEEE.

关键词： Internet of things

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Centered Low-Intelligence Subjects with Appropriate Noise Enhance Coordination in Networks

Centered Low-Intelligence Subjects with Appropriate Noise En...

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international conference on Behavior, Economic and Social computing (BESC)

作者： Jinwei Miao Xiao Sun Jun Qian Yueting Chai Yi Liu Department of Automation National Engineering Laboratory for E-Commerce Technologies Tsinghua University Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

In the era of intelligent interconnection of all things, society gradually forms a system composed of numerous in-telligent entities that collaborate and evolve through division of labor. However, in reality, high-intelligence individuals often fail to achieve high system efficiency after interacting to form intelligent groups. the relationship between interactions among intelligent individuals and the emergence of system efficiency remains to be explored and answered. In this study, we use color coordination game as the basic scenario to investigate the impact of decision error rates of different types of intelligent individuals, the proportion of the same type of intelligent individuals, and the differences in decision error rates within structured groups on problem-solving. through simulation experiments, we find that in high-intelligence groups, adding an appropriate number of relatively low-intelligence individuals can optimize and stabilize problem-solving. Conversely, adding lower-intelligence individuals to a low-intelligence group will decrease overall performance. Notably, when the added lower-intelligence indi-viduals are at the center of the group, their impact on problem-solving efficiency becomes more pronounced. Additionally, we discover that having significant intelligence differences within high-intelligence groups and moderate intelligence differences within low-intelligence groups can also enhance overall group efficiency. this research provides an explanation for the typical phenomenon of mismatch between individual intelligence and system efficiency in collaborative processes and offers guidance for improving system efficiency in real-world social scenarios.

关键词： Error analysis Social networking (online) Robot kinematics Working environment noise Noise Collaboration Games Color Behavioral sciences Problem-solving

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System Aspects of Direct Localization of Multiple Acoustic Sources Using distributed Sensor Networks

System Aspects of Direct Localization of Multiple Acoustic S...

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Electrical, Electronic and computing Engineering (IcETRAN), international conference on

作者： Miljko Erić Nenad Vukmirović Petar Djurić School of Electrical Engineering University of Belgrade Belgrade Serbia School of Electrical Engineering and Innovation Center of the School of Electrical Engineering University of Belgrade Belgrade Serbia Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

When commonly used two-step localization techniques, such as TOA/TDOA (Time-Of-Arrival, Time-Difference-of-Arrival), DOA (Direction-of-Arrival) or RSSI (Received-Signal-Strength-Indication)/Fingerprint-based, are applied to localization in multiple signal scenarios, there is a so called data association problem. this problem is complex and complicated to solve. Data association problem is referred to grouping of localization parameters for a set of sensors (TOAs, TDOAs, DOAs, RSSI/Fingerprints) into subsets, each corresponding to a single source. With single-step (or direct) localization techniques, this problem does not exist. these techniques are of more recent date, and being numerically more demanding and complex compared to the two-step methods, they are used less frequently than two-step methods. However, they significantly outperform the two-step techniques in terms of localization accuracy and resolution properties. We present a comparative study of direct localization of acoustic signal and radio signal sources using distributed sensor networks. this study examines generic system models, generic signal models, direct localization methods, and theoretical Cramér-Rao bound (CRB) limits of location estimation error. We also include two case studies of applying direct localization of acoustic signal sources in indoor environments. these case studies involve detecting two moving e-Puck educational mobile mini-robots and detecting an acoustic signal source using a functional model of an acoustic camera. We also provide theoretical CRB limits of error estimation for these system models.

关键词： Location awareness Analytical models Cameras Acoustic arrays Acoustics Mathematical models Microphone arrays Sensors Numerical models Sensor arrays

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Disentangling Disparate Communication Streams and Recovering Underlying Structure

Disentangling Disparate Communication Streams and Recovering...

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international conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Eisha Nathan Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA

ISBN: (数字)9798331518349

ISBN: (纸本)9798331518356

In this paper we present various approaches for the tasks of disentanglement (or automatic forum identification) and structure recovery (or prediction of message replies) in online discussion forums such as Reddit and Stack Exchange. For the disentanglement task we present a fine-tuned BERT model. By leveraging transfer learning from a pre-trained BERT (Bidi-rectional Encoder Representations from Transformers) model, given the message content, our approach achieves accuracies of 83–92% in predicting where a message originated. For the task of structure recovery, we propose a novel neural network architecture that leverages deep learning techniques to model the sequential relationship between messages to predict if one message follows another with accuracies over 80%. Additionally we present few-shot learning strategies for both tasks of disentanglement and structure recovery. Given the sparse availability of limited samples for training in real data, few-shot learning, or the ability to train without needed large amounts of labeled data, is necessary. Experimental results demonstrate the effectiveness of our proposed architecture and methods in capturing contextual dependencies and improving prediction accuracy in such diverse online forums for both tasks and our few-shot architectures achieve comparable accuracies to the case when large training data is available.

关键词： Context Accuracy Discussion forums Social networking (online) Semantics Neural networks Oral communication Predictive models Encoding Few shot learning

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Social Media Bot Policies: Evaluating Passive and Active Enforcement

Social Media Bot Policies: Evaluating Passive and Active Enf...

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international conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Kristina Radivojevic Christopher McAleer Catrell Conley Cormac Kennedy Paul Brenner Computer Science and Engineering University of Notre Dame Notre Dame USA Mathematics and Statistics Dublin City University Dublin Ireland Center for Research Computing University of Notre Dame Notre Dame USA

ISBN: (数字)9798331518349

ISBN: (纸本)9798331518356

the emergence of Multimodal Foundation Models (MFMs) holds significant promise for transforming social media platforms. However, this advancement also introduces substantial security and ethical concerns, as it may facilitate malicious actors in the exploitation of online users. We aim to evaluate the strength of security protocols on prominent social media platforms in mitigating the deployment of MFM bots. We examined the bot and content policies of eight popular social media platforms: X (formerly Twitter), Instagram, Facebook, threads, TikTok, Mastodon, Reddit, and LinkedIn. Using Selenium, we developed a web bot to test bot deployment and AI -generated content policies and their enforcement mechanisms. Our findings indicate significant vulnerabilities within the current enforcement mechanisms of these platforms. Despite having explicit policies against bot activity, all platforms failed to detect and prevent the operation of our MFM bots. this finding reveals a critical gap in the security measures employed by these social media platforms, underscoring the potential for malicious actors to exploit these weaknesses to disseminate misinformation, commit fraud, or manipulate users.

关键词： Video on demand Protocols Social networking (online) Surveillance Psychology Selenium Security Web sites Fake news Protection

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