Reviewerly/exHarmony huggingface dataset:
Transferability of features for neural networks links to adversarial attacks and defences

The reason for the existence of adversarial samples is still barely understood. Here, we explore the transferability of learned features to Out-of-Distribution (OoD) classes. We do this by assessing neural networks' capability to encode the existing features, revealing an intriguing connection with adversarial attacks and defences. The principal idea is that, "if an algorithm learns rich features, such features should represent Out-of-Distribution classes as a combination of previously learned In-Distribution (ID) classes". This is because OoD classes usually share several regular features with ID classes, given that the features learned are general enough. We further introduce two metrics to assess the transferred features representing OoD classes. One is based on inter-cluster validation techniques, while the other captures the influence of a class over learned features. Experiments suggest that several adversarial defences decrease the attack accuracy of some attacks and improve the transferability-of-features as measured by our metrics. Experiments also reveal a relationship between the proposed metrics and adversarial attacks (a high Pearson correlation coefficient and low p-value). Further, statistical tests suggest that several adversarial defences, in general, significantly improve transferability. Our tests suggests that models having a higher transferability-of-features have generally higher robustness against adversarial attacks. Thus, the experiments suggest that the objectives of adversarial machine learning might be much closer to domain transfer learning, as previously thought.
Trust Evaluation Method Based on the Degree of Code Obfuscation

In the power grid environment, equipment certification is the key practice of zero trust security. Through the trust evaluation of equipment, the power grid environment can be effectively protected. This paper introduces identity authentication and behavior authentication to evaluate the security of devices from many aspects, and proposes a trust evaluation scheme based on code confusion for terminal devices. Through the identification and password technology and digital signature mechanism, the identity of terminal equipment is verified to prevent incomplete files caused by virus infection, Trojan horse/back door/man-made tampering, transmission failure and other reasons, and complete identity authentication. Through the improved trust evaluation model based on supervised learning to evaluate the effectiveness of code confusion, we can determine the ability of the code to resist malicious attacks, analyze whether the device will bring threatening behavior, indirectly verify the device behavior trust, and then prove the security of the terminal or software to complete behavior authentication.
Neural Coupled Sequence Labeling for Heterogeneous Annotation Conversion

Supervised statistical models rely on large-scale high-quality labeled data, which is important for model training but expensive to construct. Therefore, instead of constructing new dataset, researchers have attempted to make full use of various existing heterogeneous datasets to boost model performance, considering it is ubiquitous that the same task may have multiple annotated data following different and incompatible annotation guidelines. Representative methods include the guide-feature method which use the knowledge projected from the source-side to the target-side as extra features for target model guidance, and the multi-task learning (MTL) method which simultaneously train on multiple heterogeneous annotations with shared parameters to gain resource-share knowledge. Though effective, the guide-feature method fails to directly use the source-side data as training data, and the MTL method ignores the implicit mappings between heterogeneous datasets. Compared with the above methods, directly converting the heterogeneous datasets into homogeneous datasets for target model training is a more straightforward and effective way to fully exploit heterogeneous resources. In this work, we propose a neural coupled sequence labeling model for heterogeneous annotation conversion. First, for each token, we map a given one-side tag into a set of bundled tags by concatenating the tag with all the possible tags at the other side. Then, we build a neural coupled model over the bundled tag space. Finally, we convert heterogeneous annotations into homogeneous annotations by performing constraint decoding on the coupled model. We also propose a pruning strategy to address the oversize issue of the bundled tag space, which improves efficiency without hurting model performance.Experiments for part-of-speech (POS) tagging, word segmentation (WS), and WS&POS tagging tasks show that our proposed neural coupled model consistently outperforms several benchmark models for all the three tasks by large margin.
Application of Unmanned System in Intelligent Mine

Unmanned mining is of great help to realize efficient and safe mining. However, there is still much development space of unmanned mining, and few scholars integrate the application of unmanned system in mining. This paper summarizes the main equipment and technology of unmanned system in mining and tunneling, and analyzes the development trend of unmanned mining. At present, the key technologies of fully mechanized unmanned mining are shearer positioning, memory cutting technology, automatic follow up of hydraulic support, etc. The main applications of unmanned operation in tunneling face are autonomous positioning and remote control technology of roadheader and mining robot. After the analysis of the existing technology and development direction, the viewpoints of improving the cooperative operation of equipment and the mining information processing speed are put forward.
Usability Analysis for Blockchain-Based Applications

In this digitization phase, several new applications are developed by developers and used by end-users to complete their needs. But very few applications remain popular among users depending on the functional working of an application, such as the mobility of the application, its user-friendliness, pop-up advertisements. The Non-functional requirements such as space required in the memory, security aspects fulfill by application, etc. Blockchain Technology is an emerging trend in the market, and many developers are developing enormous applications in various domains. Human-Computer Interaction (HCI) will play a significant role in designing a graphical user interface. In this paper, we will discuss the opportunities and challenges faced by the developers while working on the different projects.
A New Method of Data Fusion with PMU and SCADA Based on Branch Decomposition

The data fusion with synchro-phasor measurement unit (PMU) and supervisory control and data acquisition (SCADA) is the basis of improving the level of measurement and operational control of distribution network. In order to improve the accuracy of data fusion, a multi-source data fusion method based on multi-time scale is proposed to fuse PMU data and SCADA data. First of all, the high-precision PMU provides a large amount of accurate, high-frequency and multi-type data for the node state, and the topology network structure of open network is reconstructed by using the Depth-First-Search (DFS) algorithm to form an open network with PMU nodes. Secondly, the PMU measurement data is combined with the latest SCADA sampling data based on the sampling time, and the voltage and phase angle without PMU node are derived by power flow calculation. The state estimation based on least square method is used to obtain the state of the whole system. Finally, regularized residual is introduced to identify SCADA data for abnormal data. Numerical results show that the proposed model and method are effective to improve the accuracy of data fusion.
Robust Forecasting-aided State Estimation of Power System Based on Square Root Unscented Particle Filter

In the iterative calculation of the dynamic state estimator, the state prediction error covariance matrix is not positively definite, resulting in poor estimation accuracy or even divergence. To solve this problem, this paper proposes a power system dynamic state estimation method based on square root unscented particle filter (SRUPF). Based on the unscented particle filter (UPF), this method adopts two mathematical methods: matrix QR decomposition and matrix Cholesky factor update, and introduces square root technology to dynamically update the state prediction error covariance matrix, thereby maintaining the state prediction error The positive definiteness of the covariance matrix. Finally, the test and analysis of the IEEE-30 bus system show that compared with the traditional UKF and UPF, the SRUPF proposed in this paper has higher estimation accuracy and robustness.
Identification and Classification of Toxic Statements by Machine Learning Methods

The number of comments left on social media platforms can reach several million per day, so their owners are interested in automatic content filtering. In this paper, the task of identifying offensive statements in texts is considered. When solving the problem, various methods of vector text conversion were considered: TF-IDF, Word2Vec, Glove, etc. The results of the application of classical text classification methods and neural network methods (LSTM, CNN) were also considered and presented.
Quantum Reinforcement Learning Applied to Board Games

Reinforcement learning is a machine learning paradigm where an agent learns how to optimize its behavior solely through its interaction with the environment. It has been extensively studied and successfully applied to complex problems of many different domains in the past decades, i.e., robotics, games, scheduling. However, the performance of these algorithms becomes limited as the complexity and dimension of the state-action space increases. Recent advances in quantum computing and quantum information have sparked interest in possible applications to machine learning. By taking advantage of quantum mechanics, it is possible to efficiently process immense quantities of information and improve computational speed. In this work, we combined quantum computing with reinforcement learning and studied its application to a board game to assess the benefits that it can introduce, namely its impact on the learning efficiency of an agent. From the results, we concluded that the proposed quantum exploration policy improved the convergence rate of the agent and promoted a more efficient exploration of the state space.
Cybersquatting

The mushroom growth of cyberspace and e-commerce has been increasingly threatening to the trademark and trademark statutes like the challenge posed to the domain names in connection with trademarks. Domain names cannot be restricted geographically with regards to access, use, and invasion, and cybersquatting is when trade name is used directly or after registration to invade the rights of the lawful user to whom the domain name belongs. The only objective is to feed upon the goodwill of a lawful owner to earn profits. Unlike other countries, India now does not have specific legislation for the protection of domain names and resorts to the Trademarks Act, 1999. Protection of inventions and enhancing cybersecurity is the need of the hour. To conform in compliance with the WIPO internet treaties to safeguard the copyrighted works, online digital risk management (DRM) schemes were drafted in the Copyright (Amendment) Act, of 2012. Thus, the chapter aims to emphasise the need to designate cybersquatting as a cybercrime while understanding cybercrime, its nature, and judicial intent.
Research on generation algorithm of SOA-oriented integration test order

The generation of integration test order is an important issue in the research of software integration testing. A reasonable test order can improve the efficiency of integration testing and effectively reduce the cost of testing. The architectural complexity of the Service-Oriented Architecture and the complex coupling relationship between services make it difficult for traditional top-down and bottom-up integration test strategies to generate the best integration test order. Therefore, the paper proposes a test priority algorithm oriented to the TDG(test dependency graph) to represent the importance of integration testing between service software, and a genetic algorithm is designed to generate optimal integrated test order with the optimization goal of reducing test costs. Finally, through examples demonstrate the feasibility and validity of the method. The example results show that the method can generate integration test order that allows service software with a relatively high test priority to be integrated first and has a lower test cost.
Construction of Blended Teaching Mode for Chinese Subject Based on Modern Educational Technology

Modern education technology, represented by computer and network technology, plays an important role in deepening the reform of basic education and improving the quality and efficiency of teaching. The educational scenarios expressed by modern educational technology are particularly suitable for teaching Chinese subject. Modern education technology promotes the development of blended teaching mode, and in order to carry out blended teaching in Chinese subject, this paper analyses the components of modern education technology application, summarizes the knowledge system of Chinese subject, and constructs a blended teaching mode consisting of “preparation, online learning, offline teaching, and after-class evaluation”, which complements the advantages of online and offline teaching, emphasizes the shift from knowledge-based to competence-based, and stimulates students' enthusiasm for exploring Chinese knowledge to achieve the maximum teaching effect.
Multi-Task Neural Sequence Labeling for Zero-Shot Cross-Language Boilerplate Removal

Although web pages are rich in resources, they are usually intertwined with advertisements, banners, navigation bars, footer copyrights and other templates, which are often not of interest to users. In this paper, we study the problem of extracting the main content and removing irrelevant information from web pages. The common solution is to classify each web component into boilerplate (noise) or main content. State-of-the-art approaches such as BoilerNet use neural sequence labeling to achieve an impressive score in CleanEval EN dataset. However, the model uses only the top 50 HTML tags as input features, which does not fully utilize the power of tag information. In addition, the most frequent 1,000 words used for text content representation cannot effectively support a real-world environment in which web pages appear in multiple languages. In this paper, we propose a multi-task learning framework based on two auxiliary tasks: depth prediction and position prediction. We explore HTML tag embedding for tag path representation learning. Further, we employ multilingual Bidirectional Encoder Representations from Transformers (BERT) for text content representation to deal with any web pages without language limitations. The experiments show that HTML tag embedding and multi-task learning frameworks achieve much higher scores than using BoilerNet on CleanEval EN datasets. Secondly, the pre-trained text block representation based on multilingual BERT will degrade the performance on EN test sets; however, zero-shot experiments on three languages (Chinese, Japanese, and Thai) have a performance consistent with the five-fold cross-validation of the respective language, which indicates the possibility of providing cross-lingual support in one model.
Research on Online Cloud Experience System of Featured Tourism Products Based on Computer Virtual Reality Technology

Aiming at the difficulty of tourism informatization to meet the ever-changing passenger needs and massive data processing, a smart tourism cloud solution based on cloud computing is proposed. The solution is based on the cloud computing IaaS, PaaS, and SaaS modes to virtualize tourism resources to build a smart tourism cloud platform, that is, tourism resources are integrated and distributed through IaaS, and tourism resources are reused through PaaS. The tourism resources are customized and rentable through SaaS, and the cloud computing open-source software Open stack and Hadoop are used for architectural design. The study found that a tourism service system that combines regional cultural characteristics, technological advantages, and good online and offline interactions can effectively help villagers improve their lives, highlight ethnic characteristics, and improve service experience.
Unsupervised Cross-domain Person re-Identification by Deep Clustering and Instance Learning

Cross-domain unsupervised person re-identification (Re-id) has become more and more popular due to cost of labeled images. However, because of the large differences between two different domains in lighting, background, and so on, cross-domain unsupervised person Re-id is still a very challenging task. Most of the current mainstream methods utilize the labeled source domain data and unlabeled target domain data to train a CNN network, and apply it to the un- labeled target domain. In this paper, we consider the intra-domain variations of the target domain and propose a deep clustering and instance learning (DCIL) approach. Our method considers two factors simultaneously: (1) instance invariance. We use sample memory module to save features of each class, and enforce each person to be close to its corresponding instance; (2) instance similarity. We use clustering to obtain the pseudo-labels for the unlabeled domain instances, and make each person be close to its similar instance so as to minimize the wrong pseudo-labels. We propose a clustering repelled loss to learn discriminative features for the unlabeled data while considering the above two factors. Extensive experiments on benchmark datasets demonstrate the superiority of our method for unsupervised cross-domain person Re-id.
Raising Information Security Awareness in the Field of Urban and Regional Planning

IT is being increasingly used in most areas of life. With the IoT, this technology is set to be in a state of continuous evolution in urban and regional settings. The ongoing development of digitalization processes also increases the possibilities of abuse—both at the technical and interpersonal level. Better information security (IS) awareness (ISA) and knowledge about the dangers that accompany digitalization and the corresponding protective measures are important in private and work life. However, ISA is often overlooked. Training the relevant awareness and skills should also be included in urban and regional planning for citizens. This article thus provides a review of the scientific literature of leading academic journals in the area of IS and the transfer of scientific knowledge for practical purposes. The article presents Serious Games as a way to achieve a deeper understanding of how to promote sustainable ISA using creative methods. Furthermore, ideas of how to apply the Fun Theory and its practice to integrate awareness into modern urban and regional planning will be discussed.
On the Origins of Data Visualization

During the course of his life, Steve Fienberg made important contributions to a remarkably broad range of topics. Because of his desire to communicate effectively and broadly what are sometimes a complex web of facts, he naturally gravitated toward data visualization. In this essay we describe the origins of data visualization and its nineteenth century explosion of use after the rise of empiricism that bloomed during the Scottish Enlightenment. In the course of the essay, we describe the work of some of our heroes of data visualization and the “beautiful evidence” their work yielded.
High assurance software architecture and design

When a software is constructed, it needs to be carefully architected and designed to get benefitted from the inherent features of software quality. In addition to hardware, which is usually taken as the primary source for improving performance, design and architecture has high impact on the performance of a software application. Software design impact various aspects of software quality including reusability, maintainability, flexibility, reliability, and efficiency. Software architecture ensures the presence of quality in a software application by implementing structures like design principles and patterns and by avoiding antipatterns while applying these constructs. Design principles outline the most common scientifically derived approach for constructing a flexible and robust design. Use of design principles provide established guidelines to construct quality solutions. Design patterns relies on proven object-oriented design approaches to construct reliable and reusable solutions for the associated design problems in a system. One of the key purposes of most of the design patterns is to create a simple solution which is extendible and loosely coupled. Hence the design produced as a result of applying design patterns, eventually reduce the inter dependencies between program components and makes them easier to extend and maintain. Antipatterns identify the improper application design which leads to increased development time and can become a bottleneck in the performance of the application. They provide a legitimate way to document the problem scenarios along with their solutions which assist the designers to watch out similar situations in order to avoid bugs and other design constraints that hinders performance. Studies indicate improvements in quality factors using design and architectural refinements. Accordingly, for the application that lacks these quality attributes, it can be redesigned or refactored to incorporate these features of software quality.
Long-Term Visual Localization with Semantic Enhanced Global Retrieval

Visual localization under varying conditions such as changes in illumination, season and weather is a fundamental task for applications such as autonomous navigation. In this paper, we present a novel method of using semantic information for global image retrieval. By exploiting the distribution of different classes in a semantic scene, the discriminative features of the scene’s structure layout is embedded into a normalized vector that can be used for retrieval, i.e. semantic retrieval. Color image retrieval is based on low-level visual features extracted by algorithms or Convolutional Neural Networks (CNNs), while semantic retrieval is based on high-level semantic features which are robust in scene appearance variations. By combining semantic retrieval with color image retrieval in the global retrieval step, we show that these two methods can complement with each other and significantly improve the localization performance. Experiments on the challenging CMU Seasons dataset show that our method is robust across large variations of appearance and achieves state-of-the-art localization performance.
An Integrating Emotional Information and Topic Model Method for Text Topic Mining

With the development of natural language processing technology, the topic mining of the text has become very important, because the hidden topic information in the text can significantly enhance the semantic information of the text. We take the neural topic model CombinedTM as the prototype, modify the input of the original model, combine the emotional information of the text with the contextual representation of the text as the new input of the model, add emotional level semantic information to the topic model, and build an improved topic model CombinedETM. Finally, we discuss the performance and practical value of the model with examples. The simulation results show that the topic model fused with emotional information can generate more coherent topics, and can accurately extract the topic information hidden in real events.