Reviewerly/exHarmony huggingface dataset:
Modern Load Balancing Techniques and Their Effects on Cloud Computing

Cloud computing is a new and advanced viewpoint for large-scale parallel and distributed computing systems. Cloud computing is growing quickly, and users are demanding more services and better results, so cloud-computing load balancing has become a very thought-provoking and important research area. Load on the cloud is growing extremely with the expansion of new applications. Load balancing is a major area of the cloud computing environment, which guarantees that all connected devices or processors simultaneously perform the same amount of work. Hence, an efficient load-balancing scheme is needed to improve the performance of cloud computing. Different researchers in the past years have proposed several load-balancing algorithms. This paper examines the important necessities and concerns for designing and implementing a suitable load balancer for cloud environments. In addition, we constitute an entire survey of recently proposed cloud load balancing solutions; finally, we propose evaluating these solutions based on suitable metrics and discuss their advantages and disadvantages.
地球表层特征参量反演与模拟的机理&amp;lt;bold&amp;gt;-&amp;lt;/bold&amp;gt;学习耦合范式

构建物理驱动的机理模型一直是估算地球表层特征参量的核心科学范式, 发展数据驱动的机器学习模型是地学研究范式转换的重要途径, 而耦合机理模型与学习模型则可以实现“理性主义”与“经验主义”的结合, 是当前最受关注的研究热点之一. 文章针对参量估算的遥感反演与动力学模拟方法, 深入分析了机理模型与学习模型的内在瓶颈及其互补性, 搭建了以机理级联学习、学习嵌入机理、机理融进学习为核心的耦合范式框架, 归纳了预处理与初始化、中间变量传递、后置精化处理、模型替代、模型调整、模型求解、输入变量约束、目标函数约束、模型结构约束、混合应用等十种具体耦合方式, 剖析了当前的主要问题与未来的挑战方向. 研究为深入理解、应用机理-学习耦合模型提供了新视角, 为提升地球表层特征参量反演与模拟能力、服务地球系统科学发展提供理论与技术支撑.
Spatial characteristics and complexity of the urban economic network structure based on the secure Internet of Things

In the era of network economic development, the Internet of Things has become a key factor in economic growth and urban competitiveness. Inter-regional interconnection promotes economic growth, and urban economic growth provides innovative elements for regional innovation such as technology, capital, talent, and information. These elements are indispensable for the development and optimization of the innovation environment. On this basis, establish an urban economic network structure system based on the Internet of things, analyze the three relationships between the city’s industrial structure and urban economy, urban economy and urban spatial characteristics, and the Internet of Things and urban network structure systems. The system framework, the construction process and the specific content of the framework are checked. The description method is suitable for the similarity analysis of complex network topologies and the evaluation of the effect of complex network scanning algorithms. The samples generated by the evolutionary network model are used to analyze the similarity of network topologies. Several different sampling algorithms are used to compare the evolution model samples and the empirical network samples to evaluate the sample results. The experimental results confirm the practicability and effectiveness of the description method. The data of the article is downloaded in the city's commercial network module through the Internet of Things. According to the relevant information of the city's commercial logistics, calculate the ratio of each interval to the overall to measure the economic development status. According to different development situations, set up indicators to measure traffic conditions, determine the causal relationship between quantities, and observe the relationship between data and urban economic management. The urban business network system based on the Internet of Things constructed in this paper provides reports on the transformation and upgrading of urban industries, enriches the urban distribution theory and the application of the Internet of Things, and provides Internet of Things reports and foundations for organization and management, surpassing the national bourgeoisie In the economic system, the efficiency of urban economic management has increased by more than 30%. • The samples generated by the evolutionary network model are used to analyze the similarity of network topologies. • Several different sampling algorithms are used to compare the evolution model samples and the empirical network samples. • The efficiency of urban economic management has increased by more than 30%.
Automorphism Shuffles for Graphs and Hypergraphs and Its Applications

In card-based cryptography, a deck of physical cards is used to achieve secure computation. A shuffle, which randomly permutes a card-sequence along with some probability distribution, ensures the security of a card-based protocol. The authors proposed a new class of shuffles called graph shuffles, which randomly permutes a card-sequence by an automorphism of a directed graph (New Generation Computing 2022). For a directed graph $G$ with $n$ vertices and $m$ edges, such a shuffle could be implemented with pile-scramble shuffles with $2(n+m)$ cards. In this paper, we study graph shuffles and give an implementation, an application, and a slight generalization of them. First, we propose a new protocol for graph shuffles with $2n+m$ cards. Second, as a new application of graph shuffles, we show that any cyclic group shuffle, which is a shuffle over a cyclic group, is a graph shuffle associated with some graph. Third, we define a hypergraph shuffle, which is a shuffle by an automorphism of a hypergraph, and show that any hypergraph shuffle can also be implemented with pile-scramble shuffles.
Automating Vehicles by Risk-Averse Preview-based Q-Learning Algorithm

A risk-averse preview-based Q-learning planner is presented for navigation of autonomous vehicles. To this end, the multi-lane road ahead of a vehicle is represented by a finite-state non-stationary Markov decision process (MDP). A sampling-based risk-averse preview-based Q-learning algorithm is finally developed that generates samples using the preview information and reward function to learn risk-averse optimal planning strategies without actual interaction with the environment. The risk factor is imposed on the objective function to avoid fluctuation of the Q values, which can jeopardize the vehicle's safety and/or performance. Theoretical results are provided to bound the number of samples required to guarantee ϵ-optimal planning with a high probability. Finally, to verify the efficiency of the presented algorithm, its implementation on highway driving of an autonomous vehicle in a varying traffic density is considered.
BigGAN evaluation for the generation of vehicle interior images

The number of shared autonomous vehicles (SAV) tends to increase in the coming years, highlighting the need to create monitoring systems that safeguard the integrity of the SAV and the safety of passengers. For the creation of monitoring systems, it is necessary to develop algorithms capable of detecting and classifying a multitude of objects (i.e. dangerous, forgotten, damaged), in different types of vehicles. Currently, deep learning (DL) algorithms present themselves as the best option to solve this problem, but require a large amount of data for training. This article focuses on the use of Generative Adversarial Networks (GAN) for the automatic generation of artificial images of vehicle interiors. Specifically, we propose to employ the BigGAN arquitecture, with the combined implementation of two recent techniques that aim to improve training stability and GAN generalization, namely consistency regularization and differential augmentation. With an expanded version of MoLa-VI dataset (made publicly available), satisfactory results were obtained with the proposed. Moreover, CR+BigGAN combination presented the best results, achieving a Fréchet Inception Distance of 28.23 and an Inception Score of 17.19.
Detecting fraudulent online Yelp reviews using K-L divergence and linguistic features

Nearly all businesses that sell a product or service directly to consumers will be reviewed online. The ubiquity and importance of these online reviews to business success has woefully produced numerous opportunities for fraud. In this paper, we introduce and combine two tactics to distinguish fake Yelp restaurant reviews from real ones. Using genuine Yelp reviews, we employ two tactics. First, we examine 12 features across 7 linguistic categories and exploit the difference between the two datasets, achieving an average precision of 0.854. Next, we draw upon the Zipf distribution of ranked terms in the real and fake reviews and employ the Kullback–Leibler (K-L) divergence technique to augment our ability to discriminate between real and fake reviews. We obtain a final average precision of 0.867, which is an improvement over other techniques on comparable datasets.
Investigation on Human Activity Recognition using Deep Learning

The goal of human activity recognition (HAR) is to describe a people action constructed on a set of sensor readings. Human activity recognition can be classified into two types economic and non-economic. Economic type is used to generate revenue. Non-economic type is used for mental satisfaction. Human activity recognition can be applied in the area of people work evaluations, elderly people care, convalescence, thief detections in a public place, intelligent homes and intelligent traffic. This paper discusses the deep learning model, merits, demerits and dataset used in human activity recognition. Finally, we have summarized essential challenges in HAR using deep supervised and deep unsupervised learning models.
Dynamic Path Planning for Mobile Robots with Deep Reinforcement Learning

Traditional path planning algorithms for mobile robots are not effective to solve high-dimensional problems, and suffer from slow convergence and complex modelling. Therefore, it is highly essential to design a more efficient algorithm to realize intelligent path planning of mobile robots. This work proposes an improved path planning algorithm, which is based on the algorithm of Soft Actor-Critic (SAC). It attempts to solve a problem of poor robot performance in complicated environments with static and dynamic obstacles. This work designs an improved reward function to enable mobile robots to quickly avoid obstacles and reach targets by using state dynamic normalization and priority replay buffer techniques. To evaluate its performance, a Pygame-based simulation environment is constructed. The proposed method is compared with a Proximal Policy Optimization (PPO) algorithm in the simulation environment. Experimental results demonstrate that the cumulative reward of the proposed method is much higher than that of PPO, and it is also more robust than PPO.
Blockchain-based smart microgrid power transaction model

As a decentralized database technology, blockchain is increasingly being applied to smart grids. This paper proposes a blockchain-based smart microgrid power transaction model. The model realizes the power dispatching between users and agents in the microgrid through two-way auctions and point-to-point transactions and optimizes energy allocation through market regulation. And based on the Ethereum private chain, it ensures the openness, transparency, safety, and reliability of transactions. The simulation results show that the open and reliable features of the blockchain improve the efficiency of power dispatching, and verify the feasibility and effectiveness of the model.
Synthetic Data Generation: A Comparative Study

Generating synthetic data similar to realistic data is a crucial task in data augmentation and data production. Due to the preservation of authentic data distribution, synthetic data provide concealment of sensitive information and therefore enable Big Data acquisition for model training without facing privacy challenges. Nevertheless, the obstacles arise starting with acquiring real-world open-source data to effectively synthesizing new samples as genuine as possible. In this paper, a comparative study is conducted by considering the efficacy of different generative models like Generative Adversarial Networks (GAN), Variational Autoencoder (VAE), Synthetic Minority Oversampling Technique (SMOTE), Data Synthesizer (DS), Synthetic Data Vault with Gaussian Copula (SDV-G), Conditional Generative Adversarial Networks (SDV-GAN), and SynthPop Non-Parametric (SP-NP) approach to synthesize data with regard to various datasets. We used the pairwise correlation and Synthetic Data (SD) metrics as utility measures respectively between real data and generated data for evaluation. Accordingly, this paper investigates the effects of various data generation models, and the processing time of every model is included as one of the evaluation metrics.
Programmable On-chip Photonic Machine Learning System Based on Joint Transform Correlator

We present a programmable on-chip photonic machine learning system based on the joint transform correlator. Testing the system on MNIST dataset shows 96.9% accuracy with even 10% signal delay.
Ship Detection Based on LiDAR and Visual Information Fusion

A multi-modal information fusion algorithm with LiDAR point cloud and camera images for ship target detection is proposed. It can effectively solve the limitations of single sensor environment perception and has higher precision and robustness.
Experimental Demonstration of Quantum Deliberate Signal Randomization for Y-00 Quantum Noise Stream Cipher

Deliberate signal randomization (DSR) driven by quantum random numbers is proposed for enhancing security. PSK Y-00 cipher with the quantum DSR is demonstrated at 5 Gbit/s, indicating secure transmission is achievable at high optical powers.
Adversarial Sample Attack and Defense Method for Encrypted Traffic Data

Resisting the adversarial sample attack on encrypted traffic is a challenging task in the Intelligent Transportation System. This paper focuses on the classification, adversarial samples attack and defense method for the encrypted traffic. To be more specific, the one-dimensional encrypted traffic data is firstly translated into the two-dimensional images for further utilization. Then different classification networks based on the deep learning algorithm are adopted to classify the encrypted traffic data. Moreover, various adversarial sample generation methods are employed to generate the adversarial sample to implement the attacking process on the classification network. Furthermore, the passive and active defense method are proposed to resist the adversarial sample attack: 1) the passive defense is used to denoise the perturbation in the adversarial sample and to restore to the original image; and 2) the active defense is used to resist the adversarial sample attack by leveraging the adversarial training method, which can improve the robustness of the classification network. We conduct the extensive experiments on the ISCXVPN2016 dataset to evaluate the effectiveness of classification, adversarial sample attacking and defending.
Chinese Named Entity Recognition based on BERTbased-BiLSTM-CRF Model

This paper utilizes the BERTbased-BiLSTM-CRF models to complete Chinese named entity recognition tasks, including finetuned and unfinetuned BERT models. Use the pre-training model BERT(Bidirectional Encoder Representations from Transformers), a BiLSTM(Bi-directional Long Short-Term Memory) network and CRF(Conditional Random Field) to perform NER(Named Entity Recognition) on Chinese. Tested on the people-daily-ner-pretreatment corpus, compared with other models, the BERTbased models can effectively identify entity information, and the evaluation metrics on the dataset have been improved.
Chinese Named Entity Recognition based on BERT-CRF Model

Named entity recognition (NER) is an important research direction in natural language processing (NLP). Traditional machine learning algorithms in NER have problems such as low accuracy, highly dependent feature design, poor domain adaptability, and inability to handle the different contexts of multiple meanings of the term in recognizing Chinese entities. Based on these problems, this paper adopts a method based on the BERT-CRF model in Chinese NER. The BERT preprocessing language model generates word vectors that represent contextual semantic information, automatically extract numerous word-level features and semantic features in text, and decodes through the CRF layer generates entity tag sequences. In this paper, the BERT model has been fine-tuned to make the model perform better on NER tasks, and the experimental verification is carried out on the People’s Daily dataset, and the F1 value reaches 94.5%.
Performance Evaluation of Text Embeddings with Online Consumer Reviews in Retail Sectors

Analyzing online consumer reviews is one of many popular applications of natural language processing in retail sectors. Text embedding models can transform the textual content of reviews into numerical representations for downstream analytic tasks and therefore they play an essential role in review analytics. As review analytics is increasingly used in the industries, more empirical research is needed to investigate how text embeddings perform in understanding the thoughts and attitudes of customers. In this study, we examined four commonly used text embeddings: namely TF-IDF, word2vec, sent2vec and BERT, to evaluate their performance in predicting the ratings and the sentiments of online consumer reviews. Drawn on the results, we highlight the strengths of these text embeddings and their desirable use cases. Our findings reveal that BERT and sent2vec can produce stable results in predicting the ratings of retail reviews in general. Besides, word2vec is more suitable for identifying negative sentiment within reviews. From a practical perspective, it is worth analyzing reviews from different product categories separately to achieve better results.
Geographically weighted regression in mineral exploration: A new application to investigate mineralization

ABSTRACT Geographically weighted regression (GWR) is an effective model for the investigation of spatially nonstationary relations among variables in the geographical and social sciences. GWR was introduced to the field of mineral exploration to further understanding of the location, controlling factors, and coupling mechanisms related to the triggering of mineralization—in other words, the where, what, and how. Previous studies reported that Cu and Au in a porphyry system present a paragenetic relation at different stages of mineralization, which can be an informative indicator in mineral exploration. As a successor, the current study further applies the GWR model to characterize the paragenetic relation between the ore-forming elements Cu and Au in the Duolong mineral district of Tibet, China, in a spatial scenario. Unlike the spatially varied ore-forming mechanism quantified by the regression coefficients of GWR, the coefficient of determination (R2) is discussed to verify the existence and to evaluate the strength of the paragenetic relation between Cu and Au, because regression coefficients can only inform the mutual influence between one and the other. Furthermore, the fractal and multifractal-based spectrum–area method is adopted to separate the GWR results into anomaly and background. Areas with GWR results that indicate the existence and intensity of a paragenetic relation are mapped as target areas for mineral exploration. The current quantitative recognition of mineralization represents a meaningful and useful extension to the application and interpretation of the GWR model.
Logic of Visibility in Social Networks

Social media is not a neutral channel for news consumption. How visible information posted online is, depends on many factors such as the network structure, the emotional volatility of the content and the design of the social media platform. In this paper, we use formal methods to study the visibility of agents and information in a social network. We introduce a modal logic to reason about a social network of agents that can follow each other, post and share information. We show that by imposing some simple rules on the system, a potentially malicious agent can take advantage of the network construction to post an unpopular opinion that may reach many agents. The network is presented both in a static and dynamic form. We prove completeness, expressivity and model checking problem complexity results for the corresponding logical systems.
Enhancing BIM through Mixed Reality for Facility Management

Implementation of processes in facility management asks for coordination and collaboration among several factors, each implementing its own sub-process. This goal must cope with several challenges caused by the fragmentation of the AEC (architectural, engineering, construction) industry. In the specific case of facility recommissioning, further constraints are determined by limitations posed by the current status of the existing facility whose knowledge is often limited to coarse preliminary surveys. In this chapter, the benefits determined by the integration between BIM and mixed reality will be presented, along with a prototypical platform that realizes an efficient, distributed collaborative workflow enabling asynchronous collaboration among members of the facility management office, the owner, the design team and technical specialists that may be appointed in recommissioning workflows. Technically, this approach provides an immersive mixed-reality environment capable of seamlessly displaying project information, through which specialists can evaluate and refine different recommissioning options. In addition, the platform supports on-site enrichment of BIM models for a facilitated, yet asynchronous, collaboration between remote and on-site users. This technology was validated by means of real-life experiments regarding a hypothetical recommissioning project of the Construction Division of the DICEA Department at Università Politecnica delle Marche (Ancona, Italy).
Expanding UlyssesNER-Br Named Entity Recognition Corpus with Informal User-Generated Text

Named Entity Recognition (NER) is a challenging Natural Language Processing task for a language as rich as Portuguese. When applied in a scenario appropriate to informal language and short texts, the task acquires a new layer of complexity, handling a particular lexicon to the domain in question. In this paper, we expanded the UlyssesNER-Br corpus for NER task with Brazilian Portuguese comments about bills. Additionally, we enriched the annotated set with a formal corpora, in order to analyze whether the combination of formal and informal texts from the same domain could improve NER. Finally, we carry out experiments with a Conditional Random Fields (CRF) model, a Bidirectional LSTM-CRF (BiLSTM-CRF) model, and subsequently, we realized fine-tuning of a language model BERT on NER task with our dataset. We concluded that formal texts helped identification of entities in informal texts. The best model was the fine-tuned BERT which achieved an F1-score of 73.90%, beating the benchmark of related works.