Reviewerly/exHarmony huggingface dataset:
Smart Energy Platform for Large-space Stadium Construction Based on Internet of Things

A smart energy platform for the large-space stadium based on Internet of Things (IoT) is proposed. The platform could realize the safe and stable operation of the energy system in various scenarios and promote low-carbon, efficient and sustainable development. In this paper, the smart energy platform is constructed based on IoT device data collection, time series data storage, front-end smart energy platform, and back-end optimization modules. The architecture and framework of the platform are explained in details. This study takes a large-space building in Hangzhou as an example to explain how the smart energy platform works in the real site. The real-time monitoring map of carbon emissions module and load prediction module of air-conditioning system are presented. The developed smart energy platform based on IoT could support the digital twin-based operation management of various types of low-carbon buildings in the future.
Practical privacy-preserving mixing protocol for Bitcoin

The privacy of Cryptocurrencies are of great concern in various fields. Researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanonymization attacks. The exisiting schemes typically require a trusted-third party to achieve anonymity, however this usually faces a single-point fault. In addition, existing schemes suffer from high communication complexity and impracticality. This paper proposes a practical privacy-preserving mixing protocol for Bitcoin to achieve unlink-ability of input and output address of transactions. Compared to existing schemes, our protocol improves practicality. The communication complexity of our protocol is linearly related to the number of peers. Moreover, our protocol is scalable as it works not only for Bitcoin, but also for other cryptocurrencies.
A Recurrent Self-learning Labeler for Building Network Traffic Ground Truth

With the increasing number of traffic category, machine learning-based methods have gradually become the mainstream way of traffic classification to support network security and management. Machine learning-based methods require a large amount of high quality labelled data to learn network behavior patterns to achieve better recognition results. In the field of network traffic labeling, manual labeling can achieve more accurate labeling results, but the labeling efficiency is low and the labor cost is high. Deep packet inspection (DPI) technology can greatly improve labeling efficiency and reduce labor costs, but DPI labeling suffers from the problem of inaccurate and incomplete labeling. In this paper, we propose a recurrent self-learning framework (RSL-Labeler) for traffic labeling, which can solve the problem of inaccurate and incomplete DPI labeling. This framework consists of three components: high-quality data generation, class behavior pattern learning, and confidence filtering. Based on high-quality data labeled by multiple DPIs, we build three classifiers to learn the behavior patterns of DPI labeling intelligently from three perspectives. Then, we propose the idea of confidence filtering, which combines the pseudo-labeled data and the confidence values of three learning models to filter the credible samples by combined voting. These samples are added to the self-learning model for recurrent training. Experiments show that our method is able to label application traffic with accuracy of 99%, which is at least 8% better than single DPI.
An Anomaly Detection Method for Multivariate Time Series Based on Cross Window

With the rapid development of cloud-network integration, in order to ensure the normal operation of devices, networks, and services in the cloud, anomaly detection algorithms have received increasing attention. The existing anomaly detection methods for multivariate time series, on the one hand, tend to identify situations where multiple metrics deviate significantly from the normal patterns as anomalies, while ignoring anomalies with small deviations from a small number of metrics but very critical to the system, which has a certain tendency. On the other hand, when selecting the window length to extract time series features, it is difficult to take into account both the local and the global data when extracting the data features in the sliding window. Therefore, this paper proposes an anomaly detection method for multivariate time series based on the cross window and probability balance by combining VAE and AE to weaken the tendency of the existing classical algorithm, and can extract the global information and detailed information of the data in the window at the same time. According to the experimental results, the method in this paper has better performance than the best recently baseline, and the F1-score is improved by about 6%.
Privacy Protection Based on Packet Filtering for Home Internet-of-Things

The development of home internet of things (H-IoT) devices brings convenience but poses significant privacy and security risks. Existing research minimizes data uploaded to the cloud but fails to process data locally, resulting in a trade-off between privacy and functionality. In this paper, we propose a privacy-preserving method that identifies and processes sensitive data sent from H-IoT devices at the edge side, ensuring functionality while preserving privacy. Our method applies different identification strategies to packets with different features, making it applicable to most H-IoT devices and scenarios. We validate our approach through experiments on a prototype system that monitors multiple cameras, demonstrating its effectiveness in preserving privacy while maintaining functionality.
Building Efficient Parallel Endorsement Node on Blockchain Systems

With blockchain technology taking the world by storm, Hyperledger Fabric has large potential for application, but low throughput hinders its development. Especially Fabric endorsement phase serves as the upper process of transaction lifestyle, and its low throughput can diminish the performance of the entire system. In this paper, we focus on performance optimization in the endorsement phase. We design an efficient parallel endorsement node (EPEN) and implement the optimization in two steps. The first is parallel endorsement architecture, which improves the concurrency of endorsements and increases the scalability of peer nodes. Then we propose a load balancing algorithm based on consistent hash and design multilayer consistent hash load minimization (MCHLM) to load balance the chaincode containers and improve its utilization. In addition, we implement these optimizations scheme in Hyperledger Fabric 2.2 LTS and conduct a series of evaluation experiments. The experimental results demonstrated that EPEN improves Fabric system throughput by 204.3% and reduces transaction latency by 77.8%, with a 7.9% increase in CPU overhead.
Lightweight Gesture Based Trigger-Action Programming for Home Internet-of-Things

IFTTT is one of the most popular Trigger-Action Programming platforms. The rules generated in IFTTT are named IoT Applets. Despite the powerful programming interface provided by IFTTT, establishing an Applet requires technical skills and is not convenient enough for most users. To address this problem, we propose a gesture based programming method to help end users establish and manage IoT Applets in a convenient way. It requires employment of an RGB-D camera, and recognizes users’ pointing rays and hand actions. The obtained information is interpreted to certain devices and device events for Applet management. An experiment involving 20 participants validates the performance of our proposed method.
FreezePipe: An Efficient Dynamic Pipeline Parallel Approach Based on Freezing Mechanism for Distributed DNN Training

Deep Neural Network (DNN) training on a large scale is extremely time-consuming and computationally intensive, which is accelerated by distributed training. In recent years, pipeline parallelism has been developed, which enables partitioning the model across several devices, e.g. GPU, and training efficiency is improved by dividing data batches into micro-batches, with each of them processed by a different stage of the model. Currently, parallel training assumes pipeline placement and partitioning are static, with parameters updating each iteration, without accounting for freezing. This results in computational resources not being fully utilized. In this paper, we propose FreezePipe, a novel method for optimizing deep learning training that combines the freezing mechanism with pipeline parallel training. In FreezePipe, a lightweight method for determining the freezing strategy based on gradient changes is employed. Considering that resources need to be released based on the frozen layer, a lightweight model partitioning algorithm was designed to determine the optimal strategy for pipeline partitioning. Experimental results show that FreezePipe can reduce the training time by 64.5% compared to Torchgpipe on CIFAR-10 dataset without compromising any model performance.
PTC: Prompt-based Continual Encrypted Traffic Classification

Encrypted traffic classification (ETC) is necessary for network security, which is the process of identifying encrypted network traffic into a specific class, thus there are numerous applications in the security of network. The rapid development of network web services (applications) makes it attractive to tackle classification of encrypted traffic in a continual learning environment. However, the traffic ambiguity and privacy leakage, restrict existing incremental approaches from achieving satisfactory results in the traffic. We introduce a prompt-based continual encrypted traffic classification method (PTC) in this research to progressively learn tasks under multiple process transitions. Prompts are tiny, learnable parameters that are stored in ram according to our suggested structure. The objective is to find the best way to use prompts to help models make predictions, keep both task-peculiar and task-constant knowledge in model, and prevent catastrophic forgetting. We carry out extensive tests using both real-world and open datasets. PTC method can strengthen the existing offline traffic classification works, make them adapt to online scenarios, and outperforms the SOTA online traffic classification method in three datasets. (by 4.54 %, 7.28 % and 11.68 % on three datasets, respectively)
CoMoRea 2023: 19th Workshop on Context and Activity Modeling and Recognition - Welcome and Committees

The aim of the 19th IEEE Workshop on Context and Activity Modeling and Recognition (CoMoRea'23) is on the one hand to advance the state of the art in the area of activity modeling and recognition; on the other hand, to investigate the research area of context modeling and reasoning and to address fundamental issues in context processing and management.The goal is to identify concepts, theories and methods applicable to context modeling, context reasoning, and activity recognition, as well as systemoriented issues related to the design and implementation of context-aware systems.CoMoRea illustrates the growing interest in activity recognition and the relationship between context models and context/ activity recognition.The workshop's international program committee refereed the submitted papers based on both their quality and their relevance to the workshop theme.In total, this year's workshop program includes 6 high quality papers that were carefully selected out of 12 submissions, as well as a keynote by Thomas Plötz.We would like to thank the members of the program committee for providing detailed and thoughtful reviews, and thus making it possible to assemble an interesting program of high quality papers.We would also like to thank the PerCom organizers and the PerCom workshop co-chairs Carlo Vallati and Qi Han for supporting the workshop and assisting with its organization.Finally, we thank all of the authors and workshop attendees for their contributions and participation.
STARLESS'23: 2nd Workshop on Serverless computing for pervasive cloud-edge-device systems and services - Welcome and Committees *LESS 2023 welcome message

STARLESS'23: 2nd Workshop on Serverless computing for pervasive cloud-edge-device systems and services -Welcome and Committees *LESS 2023 welcome messageAfter the successful kick-off of the *LESS workshop series in 2022 as a virtual event due to the lingering travel restrictions for the COVID-19 pandemic, we are very excited to host the first in-person workshop on serverless computing for pervasive cloud-edge-device systems and services.Serverless technologies have remained so far confined within the realm of cloud computing, but this is changing fast, with serverless/edgeless/deviceless paradigms being adopted more and more in decentralized and distributed systems thanks to the flexible Function-as-a-Service (FaaS) programming model and their ability to efficiently adapt to changing environments.Our ambition through *LESS 2023 is to raise awareness of serverless opportunities within pervasive computing and their associated open research challenges.We aim to lead the way for further research activities in this emerging area, with potentially high impact on the market of Internet of Things (IoT), smart cities, industrial automation, mobile health, and so on.We thank the TPC members for their timely and professional efforts in reviewing the submissions received, which resulted in a technical program with three very high quality papers covering the topics of performance evaluation, open source contributions, and Artificial Intelligence.Moreover, we would like to thank Gregor Schiele for his inspiring keynote speech on adaptive AI in holistic pervasive systems, as well as Gabriele Russo Russo for anticipating his main conference talk on decentralized edge-cloud FaaS in a meet-the-author session.We also thank the PerCom organizers and the PerCom workshop co-chairs, Carlo Vallati and Qi Han, for their continued support and assistance in all the organization phases.Finally, we thank all the authors and workshop attendees for their contributions and participation.
A Decentralized Approach to Award Game Achievements

Blockchain technology allows players to own in-game assets and to be rewarded with NFTs or tokens for their game achievements, thus can be a game changer for all the gaming industry. One central issue is how to check that conditions for achievements are met (e.g., that the player completed level 10). Current approaches open cheating backdoors (e.g. if the client checks the conditions) or introduce centralization points (if a backend checks the condition). Ideally, we would like to “run” games on chain, but so far that has not been possible due to the high computational cost, especially on Ethereum; however, the development of technologies like proofs of computation can solve this problem. Being able to run games on the blockchain, new decentralized rewarding systems can be built to ensure the fair and transparent rewarding of game achievements.
Deep Learning-Based Weight Initialization on Multi-layer Perceptron for Image Recognition

The performance of the multi-layer perceptron (MLP) degrades while working with high-resolution images due to the issues of vanishing gradient and overfitting. However, the performance of an MLP can be improved with efficient weight initialization techniques. This paper puts forward a systematic deep neural network (DNN)-based weight initialization strategy for an MLP to enhance its classification accuracy. Moreover, the training of an MLP may not converge due to the presence of many local minima. It is feasible to avoid local minima by properly initializing weights as an alternative to random weight initialization. A restrictive Boltzmann machine (RBM) has been used in this paper to pre-train the MLP. An MLP is trained layer-by-layer, with weights between each neighboring layer up to the penultimate layer. The whole network is then fine-tuned after the pre-training, by the reduction of the mean square error (MSE). To compare the performance between the proposed initialization of weights and random weight initialization, two standard image classification data sets, (i) CIFER-10 and (ii) STL-10 are used. The outcomes of the simulation showcase the enhancement of the performance of MLP with the proposed weight initialization. Furthermore, the proposed method yields a better convergence speed than standard MLP.
Cybersecurity Imminent Threats with Solutions in Higher Education

With better education, there would be a greater need for factual preservation. Without adequate threat management, serious record breaches have already happened and are probably going to happen again. By reviewing the recent literature on acknowledged assets, risk occurrences, risk actors, and vulnerabilities in better training, it is possible to deduce the effect of cyberattacks in society. The review included studies as well as projects to increase our understanding of the primary cybersecurity threat domains. The top spot has shifted due to the paucity of empirical research on cybersecurity threats in higher education as well as large gaps in the literature despite this issue, some of the publications which are examined spent too much time explaining cybersecurity-related concepts. The top-level view of mission-critical assets, regular risk events, a widely accepted risk model, and an overview of not uncommon cybersecurity vulnerabilities are all combined in this document. Strategic cyber risks are summarized in this report with descriptions of frequency distributions and starting points for protection researchers in higher education.
Text Mining Online Reviews: What Makes a Helpful Online Review?

One of the main strategies to facilitate the innovation diffusion of a technology product is displaying positive customer reviews. However, not all reviews matter to people. We examine more than 100 000 smartphone reviews on Amazon.com to investigate how sentiment, emotions, and length of online reviews might influence online review helpfulness. We perform a lexicon-based sentiment analysis to extract sentimental and emotional polarity. The findings of this study indicate certain patterns in online reviews that receive the most “helpful” votes. First, negative reviews leave a bigger impact on people in terms of “helpful” votes. Second, long reviews might leave a bigger impact on people compared to short reviews; however, the effect is moderated by both positive and negative sentiment polarity. People find long reviews helpful when they are “unsentimental.” Emotional words, in general, are not helpful for improving the impact of online reviews; however, trust-triggering words improve online review helpfulness. To best introduce a technology, give practical information, let people be the judge, and use many trust-triggering words, such as “assure” and “believe.”
Towards Effective and Robust Graph Contrastive Learning with Graph Autoencoding

Graph contrastive learning (GCL) has become the de-facto approach to conducting self-supervised learning on graphs for its superior performance. However, non-semantic graph augmentation methods prevent it from achieving better performance, and it suffers from vulnerability to graph attacks. To deal with these problems, we propose AEGCL to leverage graph AutoEncoder in Graph Contrastive Learning which directly targets graph property reconstruction to boost GCL effectiveness and robustness. Specifically, AEGCL has two distinctive characteristics, (1) a novel adaptive augmentation strategy based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">motif</i> centrality is proposed, which leverages semantic significant higher-order graph property; (2) the original attributed graph is decoupled into feature graph and topology graph to extract their dedicated information, and a simple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AttnFuse</i> is proposed to combine the two augmented graphs and the two decoupled graphs. Graph autoencoder can thus be applied to the topology domain and raw attribute domain. Empirically, extensive experiments on benchmark graph datasets show that AEGCL outperforms existing baseline methods in terms of classification accuracy and robustness.
Live Bidding Application: Predicting Shill Bidding Using Machine Learning

Online bidding systems are a well-known method of satisfying online buyers’ and sellers’ expectations since they allow both parties to buy and sell goods at competitive prices. A live auction is used to implement the online bidding system, allowing multiple bidders to participate at once. Together, these bidders may place a bid on any item. You can sell anything on the website with this app from your home or a store. It is being created with the intention of making the system dependable, simple, and quick. Everyone can now take part in an auction while relaxing in their own homes. Despite the popularity of internet auctions, there are numerous dishonest buying or selling practises that might take place. One of the trickiest forms of auction fraud to spot among all of them is shill bidding. Shill bidding is when a seller participates in his or her own auction while purposefully placing a false bid to raise the price at the end. The seller may do this on his or her own, or a third party may work along with the seller to submit fictitious bids on the seller’s behalf.
Parallel and Distributed Query Processing in Attributed Networks

Graph Analytics (GA) is a rapidly growing field that holds significant importance in numerous applications that focuses on analysing the complex relations in a network. One of the crucial concepts in GA is Query Reachability which refers to the ability to find a path connecting two nodes (u, v) in graph G. The multiple attributes in reachability query processing play a significant role to discover some interesting patterns. Existing studies did not deal with processing complex queries in attributed graphs. To fulfill some of the challenges, We contributed to this paper with three ideas for solving complex queries, finding paths, and processing multiple queries in parallel and distributed paradigms. Therefore, 1) Proposed Distributed Set Reachability Query in Attributed Networks (DSRQ_AN) algorithm using distributed computing through the Neo4j graph data platform and Cypher Query Language, 2) Finding the path to the given complex queries, and 3) Proposed a Multiple Query Processing in Parallel and Distributed (MQP_PD) algorithm to execute in a parallel environment. This paper assessed the performance of the proposed approaches through a parallel paradigm on Neo4j slaves. Furthermore, the demonstrated results of parallel execution outperform sequential execution for multiple queries on attributed graphs.
Impact of mass communication on markets of emerging economies

A free press is essential, not a perk.It is essential to achieving sustainable and fair growth.When the media gets involved, wrongdoing becomes public knowledge.By shining a light on government activity, they may serve as a check on public policy.They provide citizens a forum for expressing their views on governance and reform, and they facilitate the development of a shared understanding of the need for change.Such media improve the efficiency of marketplaces.Facilitating international commerce and the spread of new ideas is one of their many potential benefits.The media plays a significant role in human development by disseminating information on health and education to rural areas in countries as diverse as Uganda and Nicaragua.However, past events have shown how vulnerable the media's impartiality may be.It's obvious that the media can't aid in growth without the proper setting, complete with safeguards and liberties.