Reviewerly/exHarmony huggingface dataset:
Industrial Time-Series Signal Anomaly Detection Based on G-LSTM-AE Model

In practical industrial scenarios, efficient anomaly detection is important for the development of industrial system safety and maintenance. In this paper, an industrial time-series signal anomaly detection algorithm based on Gaussian confidence interval and long short-term memory auto-encoder (G-LSTM-AE) is proposed to improve the accuracy and slow detection efficiency of traditional industrial time-series signal anomaly detection. The proposed algorithm is consisted of four steps: time-series data preprocessing, G-LSTM-AE model constructing, Gaussian confidence interval solving, and time-series signal real-time detection. To verify the effectiveness of our proposed algorithm, we conduct the experiments on automatic guided vehicle (AGV) dataset and Skoltech Anomaly Benchmark (SKAB) dataset, respectively. Compared with the existing unsupervised time-series signal anomaly detection algorithm, the accuracy and recall rate of the proposed G-LSTM-AE based algorithm achieves 95% (3–5% improvement). Meanwhile, the Gaussian confidence interval anomaly analysis method improves the reliability and practicality of anomaly detection in industrial scenarios.
The IEEE Blockchain Transactive Energy Demo Initiative

In 2021 and 2022, the IEEE Blockchain Transactive Energy (BCTE) initiative, a volunteer-staffed outreach effort by the IEEE Future Directions committee, issued requests for proposals (RFPs) for innovative demonstrations of blockchain transactive energy technology. Demos selected by the screening committee were awarded $10,000 each. The intent was to foster interest and activity in actually implementing and deploying blockchain transactive energy systems. The RFP process was open to any team worldwide at an academic institution, utility, large company, nonprofit, or startup. Since this is the first time any IEEE group has run such a demo competition, this paper aims to demonstrate the initial results of the competition.
Knowledge Graph Completion Technology Research

In recent years, knowledge graphs have been widely used in the fields of language cognition, search and recommendation, question answering systems, etc. However, with the continuous expansion of the scale of many knowledge graphs constructed by RDF, how to solve the incompleteness of knowledge graphs has become a research hotspot. knowledge graph completion technology is used to complete the missing content in the knowledge graph, mainly including entity completion and relationship completion. This paper mainly focuses on the completion problem, and sorts out completion methods based on deep representation learning. Through the analysis of the research history and latest progress of completion technology, the practical challenges and future development directions of the technology are put forward.
Incentive Mechanism and Simulation of Data Sharing in Blockchain Based on Evolutionary Game

Data sharing plays a vital role in forming a complete blockchain system. This paper focuses on how to incent the enterprises in the blockchain of enterprise alliance to upload offline data to the chain to complete data sharing in the blockchain. To encourage enterprises to actively upload shared data to the blockchain, it is necessary to give reasonable rewards to the enterprises sharing data and punishments to the enterprises not sharing data. This paper builds an incentive model for data sharing of enterprises, and analyzes the evolutionary stable strategies in different conditions using evolutionary game theory. When the evolutionary game reaches a stable state and all enterprises choose to share data, the incentive value in the stable state is a reasonable incentive value. And then, with the automatic execution of smart contract in the blockchain, the incentive mechanism of data sharing can be written into the smart contract. When enterprises upload data, the incentive is automatically triggered to continuously encourage enterprises to upload data. Finally, several evolutionary stable strategies are simulated and verified by using Repast.
3D Data Augmented Person Re-identification and Edge-based Implementation

In recent years, artificial intelligence technology has made breakthrough progress. It is widely used in person re-identification(Re-ID), but its accuracy still needs to be improved. This paper proposes a person re-identification(Re-ID) model based on 3D data augmentation and designs and implements edge computing deployment. The 3D human body model is put into the 3D scene and take automatic virtual photography to obtain the simulation image data. The neural network model is trained through the expanded data set of 3D simulation, and its improvement is significant. Based on this, an onboard scheme of artificial intelligence edge computing based on the Rock chips 3588 was designed to meet the practical needs of person re-identification(Re-ID) applications. This scheme is widely compatible with standard IP cameras based on RTSP/RTMP streams, supports person re-identification of visible and infrared video streams, can control the PTZ through the network interface, and can be updated and iterated remotely through the network.
Introduction to Public-Key Cryptosystems and Hotspot Analysis Based on CiteSpace

This paper first introduces the relationship between classical cryptography and public-key cryptography, presents the public-key cryptosystem, draws out the research status and progress of the public-key cryptography system, and introduces the principle, process, and security analysis of the public-key cryptosystems. Then it explains the principle, key generation, encryption, decryption processes, security analysis, and optimization design, including RSA, ECC, ElGamal, Knapsack, and NTRU ciphers, and discusses the progress of each cipher. To understand RSA more deeply, this paper uses python to implement RSA encryption-decryption system, the automatic key generation, and file dragging encryption and decryption functions. Finally, I used CiteSpace to conduct a visual statistical analysis of relevant papers in Web of Science and concluded that the current hot direction of public-key cryptography research is cloud computing and asymmetric public key cryptosystem direction.
5G Potential Customer Recognition Research Based on Multi-layer Heterogeneous Integration Model

With the rapid development of 5G, telecom operators have begun to deploy and promote 5G services. How to accurately recognize 5G potential customers and increase conversion rate of 5G customer has become a very concerned issue for telecom operators. In this paper, a 5G potential customer recognition model based on telecom big data is constructed. Firstly, a influencing factor system is constructed to fully reflect the relevant characteristics of 5G customers. Secondly, feature selection is performed by summing the importance evaluated by multiple tree ensemble models, and it is found that relatively important features include data ARPU, brand type, DOU and its changes, ARPU and its changes, video APP usage and so on. Thirdly, the SMOTE-ENN combined algorithm is applied to improve the performance of the basic model. Then, various machine learning models are used to identify 5G potential customers, and the comparison results show that XGBoost, LightGBM and DNN have better performance. Finally, based on the three best-performing classifiers, a multi-layer heterogeneous integrated model is constructed, which combines the cascade and parallel structures. Experiments show that the MHI model can achieve better performance than the separate classifiers.
A Multilayer Distributed Ledger Technology Architecture for Immutable Registry of Mobility and Location Information

Movement tracking information can reveal much about the daily operation of companies. Several applications can benefit from recording the physical places a mobile entity visits and how long it stays at each position. This paper discusses the need for an Available, Reliable, Transparent, Immutable, and Irrevocable service for Mobility Records (ARTIIMoR). We present an approach to recording spatio-temporal presence information in a reliable, immutable, and scalable way using a multilayer Distributed Ledger Technology (DLT) architecture for storing location information in variable levels of abstraction and aggregation. We implemented this multilayer solution as a middleware service that uses Complex Event Processing on smartphones to efficiently record nearby place-specific beacons in a DLT. We compare the performance of inserting data in the IOTA, Ethereum, and Hyperledger DLTs, the impact that different data aggregation techniques have on insertion performance, and the effect of the multilayer approach on data query performance.
Utilization of Three.JS and Unity to Make A Webgl API to Perform 3D Immersiveness In Web

Hats off to Prometheus, who stole fire from heaven. From fire to WebGL, it's a long journey. But in today's world, WebGL is said to be the world's greatest invention. This paper is on the inquiry of the Web3D visualization methods in WEB. Three. js is a perfect 3D graphics engine out of many We- bGL frameworks that has a lot ofbuiltin lights and materials. Meanwhile, it provides many convenient functions for this study. In addition, this research can be applied to the customization of LED lights and lanterns, interior de- sign, etc. This article introduces a kind of Web3D technology along with Unity 3D engine, that combines great advantages in GPU acceleration in fields of game designing and visual effects. The basic scene setup is a bit different from Three JS and Babylon JS because Unity has its own editor. All the basic things like creating a scene, adding mesh and lights can be done directly from the editor without coding a single line. This is a very ba- sic Unity feature and anyone with a little experience with the program can easily do this by adding the necessary objects to the scene from the Unity menus. Unlike Three. JS and Babylon. JS, Unity does not require the creation of a rendering loop, which is done automatically by the game engine.
User Experience in Virtual Museums - Evaluating Assassin’s Creed Odyssey: Discovery Tour

This paper investigates the user experience of virtual museums, based on a user study of Ubisoft’s Assassin’s Creed Odyssey: Discovery Tour. The study included 22 participants, who tried the virtual experience in a lab environment and answered a questionnaire. Four main user experience themes were identified—usability, presence, features and content. Issues of accessibility, authenticity, and the possibilities and limitations of virtual museum experiences are discussed.
A Comparative Study of Four 3D Facial Animation Methods: Skeleton, Blendshape, Audio-Driven, and Vision-Based Capture

In this paper, the authors explore different approaches to animating 3D facial emotions, some of which use manual keyframe animation and some of which use machine learning. To compare approaches the authors conducted an experiment consisting of side-by-side comparisons of animation clips generated by skeleton, blendshape, audio-driven, and vision-based capture facial animation techniques. Ninety-five participants viewed twenty face animation clips of characters expressing five distinct emotions (anger, sadness, happiness, fear, neutral), which were created using the four different facial animation techniques. After viewing each clip, the participants were asked to identify the emotions that the characters appeared to be conveying and rate their naturalness. Findings showed that the naturalness ratings of the happy emotion produced by the four methods tended to be consistent, whereas the naturalness ratings of the fear emotion created with skeletal animation were significantly higher than the other methods. Recognition of sad and neutral emotions were very low for all methods as compared to the other emotions. Overall, the skeleton approach had significantly higher ratings for naturalness and higher recognition rate than the other methods.
Touchy Tap: A Slow Technology for Shared Reflections on Water Consumption

Turning on a water tap in one’s home is an easy, simple and typically solitary interaction that masks the huge amounts of resources and efforts behind the collection, processing, and delivery of fresh water. There are many design and design research examples that aim to raise environmental awareness by drawing attention to consumption of domestic utilities. However, less common are designs that aim to provoke reflection on sustainable behaviors through an effortful and shared interaction. In this paper we present ‘Touchy Tap’ - a multi-touch kitchen faucet prototype in the form of a dynamically illuminated 2-m wavy tube that requires control by two people. A slow interaction was designed with the intention to evoke reflections in use on issues related to water consumption. A preliminary evaluation indicates the potential of this ‘slow technology’ approach to highlight water consumption issues, even though the ambiguity of such an unconventional interface may negatively impact on overall user experiences.
A Deep Learning-Based Approach for Generating 3D Models of Fluid Arts

This paper explores a method for creating 3D models of video artwork based on a fluid phenomenon called the Sound of Ikebana artwork. A process of using multiple generative adversarial networks (GANs) to reconstruct and predict the shape of the fluid artworks from two-dimensional reference photos was proposed. This is an extension of our previous efforts with Wasserstein GAN enhancements to predict the shape of the unmapped part and correct the texture. The experiment’s results show that our process can reconstruct 3D arts without having large amount of 3D training data.
Shadow Display Design Concepts for AI Enhanced Environments

In this paper, we focus on the idea of using artificial shadows as information displays. Through scenarios, co-design methods, and low-fi prototyping, we assess and create different scenarios to chart people’s preferences in designing shadow displays. We report on two user studies, which reveal aesthetics, experiental aspects, unobtrusiveness, and ambiguity as key strengths of shadow displays. Our work contributes to the research knowledge in applying co-design methods to create user interfaces for artificial intelligence (AI) enhanced environments.
Application and Technical Analysis of Computer Vision Technology in Natural Resource Survey

Natural resources are the material basis for human survival and national economic and social development. Natural resources survey is related to major national decision-making and deployment, fully supports and serves the economic and social development and ecological civilization construction in the new era, and systematically grasps the quantity, quality, spatial distribution, development and utilization, ecological status, and dynamic changes of national natural resources and landmark substrates. Computer vision technology uses computers to process, analyze and understand images to identify targets and objects in various patterns. The natural resources are photographed and imaged by industrial unmanned cameras or remote sensing satellites, and the characteristics are compared according to image processing, to realize the investigation and analysis of natural resources, improve the efficiency of natural resource management investigation, and provide a reference for planning decision-making and implementation.
Implementation and Playtesting for a World Adventure Game’s Procedural Content Generation System

This paper presents an experimental methodology of procedural content generation (PCG) for natural environments with a focus on game design and visualization. This approach allows us to define a set of instructions to shape the content generation to meet the requirements of game design and level design teams. For the validation process, we have discussed and analyzed a playtesting session.
Designing Virtual Guides’ Characteristics for Remote Tourism in the Arctic

The paper investigates the design of virtual guide characters in the context of remote tourism, focusing on their appearance, characteristics, and effects on the user experience. We created four virtual guide characters for an Arctic tourism context and evaluated them with 15 participants with a design background. In addition, the participants created their own virtual guide character designs by sketching and writing descriptions. The salient findings highlight e.g. that in the virtual guide’s design, the personality, local features, and historical authenticity matter for the user experience. However, local stereotypes provoke mixed feelings depending on the audience, and the diverse aspects of design should be paid attention to. The work contributes towards designing remote and more sustainable tourism experiences in the future.
VR Diet Museum: A Virtual Experience Designed for Better Learning and Reflection on Eating Habits and Its Effects

Inappropriate choices may lead to problems like food shortages, climate change, and sub-health among people. Nowadays, the conditions are becoming more and more serious. However, factors that can affect people’s food choices are complex and varied. Meanwhile, they may give different degrees of attention and have different degrees of understanding of this problem. All these may be the reasons that cause people to make wrong food choices or have wrong eating habits. To better learn about people’s food choices and the reasons behind them, both qualitative and quantitative research consisting of surveys and co-creation workshops have been conducted. Our design is to build a virtual experience with Virtual Reality (VR) technology to help people to better understand the impacts of their food choices in many ways and encourage them to focus and reflect more on their eating habits. Further, to prompt more people to adopt healthy and sustainable eating habits that are good for the well-being of ourselves and the planet. Based on the feedback from the user testing, we also determined the next-step developing plan to further optimize the design.
Acquisition of the Chinese Indefinite Determiner “One + Classifier” and English Articles in Two-Way Learner Corpora

This paper presents findings concerning use of classifiers and articles in learner corpora and the effect of learners’ native languages on their acquisition of a second language. First, we use data from the Learners’ Corpus of Chinese, an error-tagged two-way learner corpus of intermediate and advanced learners’ written production, developed by Tokyo University of Foreign Studies (TUFS) in collaboration with National Taiwan Normal University. The corpus data reveals that English L1 learners of Chinese overuse the “one + classifier” structure for indefinite reference, analogous to English indefinite articles, whereas Japanese L1 learners show underuse of this structure, despite Chinese and Japanese both being regarded as “classifier languages”. Second, data from the TUFS Learners’ Corpus of English reveals that Chinese L1 learners use the definite article in a more native-like way than Japanese L1 learners. Third, analysis of the International Corpus of Japanese as a Second Language reveals that Chinese L1 learners of Japanese use the “one + classifier” structure more frequently than native speakers. Similarities and differences between L1 and L2 can supersede ostensible typological similarities, such as the classification of both Chinese and Japanese as classifier languages.
An Analysis on the Missing of the Adverb 都 Dou by CSL Learners Based on an Error-Tagged Learner Corpus

Learners’ difficulties in correctly using adverbs have long been reported in CSL/CFL research, and findings yielded in previous research have shed some lights on CSL/CFL learners’ patterns and causes of misuse. Many of the investigated adverbs in these studies, however, were subjectively selected by the researchers and might not cover the common errors in learners’ production. To more objectively identify common adverb-based errors in CSL/CFL learners’ writing, this study extracted adverb-based errors from the error-tagged Chinese Learner Written Corpus of National Taiwan Normal University and discovered that missing of adverbs occurred much more frequently than other adverb-based errors. Among the 2,923 tokens of omitted adverbs, missing of the adverb 都 dou ranked first, which accounted for 18% of the same error type. Further analysis of the 526 tokens of omitted 都 dou revealed that 都 dou was mostly misused when serving as a scope adverb (461 tokens). In addition, the omission of 都 dou often occurred when the quantified NPs included 每 mei, 所有的 suoyoude, 任何 renhe, 隨時 suishi, and 到處 daochu, taking up 56.62% (261 tokens) of the 461 tokens. A follow-up examination of how 都 dou is presented in the learners’ textbook indicated that the high percentage of omitting obligatory 都 dou in these sentences might relate to the inadequate explanation of 都 dou’s correct usage in the textbooks. Based on the findings, suggestions for future material writing are offered for CSL/CFL learners’ better acquisition of 都 dou.
Design Principles and Functionality of Chinese Interlanguage Corpora: A Case Study of the HSK Dynamic Composition Corpus 2.0

Since the beginning of the twenty-first century, great progress has been made in terms of the construction of the Chinese Interlanguage Corpora and the essential role that these corpora has played in the study of Chinese second language teaching and research. However, there still exist some technical issues in terms of corpora design and functionality, such as the simplicity of the search function, difficulty searching for a certain interlingual phenomenon, and inconvenience caused by its not-so-user-friendly interface design. Furthermore, especially in current times, network safety has become an increasingly prominent issue and has resulted in a lack of operational corpora that satisfy the needs of the academic community. Under these circumstances, and in order to aim for Generation 2.0 which requires more delicacy and abundance, it has become necessary to adjust the design concepts and motivations behind the corpora. To ensure the uninterrupted operation and accessibility of the corpora, attentiveness and improvements in terms of system security are crucial. Meanwhile, optimizations and improvements of corpora features, especially the search function, are also essential for comprehensively meeting the needs of all users.
On Feature Selection Algorithms for Effective Botnet Detection

The threats posed by botnets are becoming a growing concern as more and more computers are getting infected every day. Although botnets can be detected from their behavioral patterns, the margin in the behavior of the malicious traffic and the legitimate traffic are diminishing with the advancement of the technologies as the malicious traffics have learned to follow the behavioral patterns of benign traffics. The detection of malicious traffic largely depends on the traffic characteristics that are being used to feed the detection algorithm. Selecting the best features for effective botnet detection is still infancy and is the main contribution of this paper. At the very beginning, we iterate different features used for botnet detection process. Then we propose several heuristics to select the best features from this handful set. Some proposed heuristics are flat feature-based and some are group-based yielding different levels of accuracy. We also analyze the time complexity of each heuristic and provide a comprehensive performance comparison. As working with all combinations of a large number of features is infeasible and intractable, some proposed heuristics group the features based on their similarity in patterns and check all combinations within the group of small sizes, eventually improving the time complexity by a large margin. Through experiments, we show the efficacy of the proposed feature selection heuristics. The result shows that some heuristics outperform state-of-the-art feature selection algorithms.