Reviewerly/exHarmony huggingface dataset:
Construction and application of biological visual nerve computing model in robot

Biological vision is very effective and accurate in scene classification and recognition. Based on this, this paper proposes a biological visual neural computing model based on the anatomical structure of rat brain, which is characterized by: constructing a biological visual scene memory model (visual word bag), imitating the biological brain’s storage of environmental scene information, and calculating the similarity between the current scene information and the visual template; designing and constructing the object details located in the lateral entorhinal cortex and the peripheral olfactory cortex Cell discharge model. Experimental results show that the proposed model can effectively extract image features and generate visual word bag model based on image features. Compared with ratslam scan line strength model, the retrieval time of this model is greatly shortened; The object cell discharge model with image similarity information as input can show similar expression of discharge rate as physiological research, which verifies the effectiveness and efficiency of the proposed model. The research results lay a foundation for the research of robot environment cognition method based on brain cognitive mechanism.
Application of Artificial Intelligence Technology in Short Video Industry and Related Thinking

At present, artificial intelligence is developing rapidly in the short video industry. Artificial intelligence can almost replace people to complete many complicated tasks which are time-consuming and laborious, and can complete the production, editing, shooting and even the performance of actors in short video. These powerful functions, while serving human beings, have many disadvantages. We should keep rational thinking about them to make artificial intelligence technology better empower the creation.
“Smart” Discourses, the Limits of Representation, and New Regimes of Spatial Data

As “smart” urbanism becomes more influential, spaces and places are increasingly represented through numeric and categorical data that have been gathered by sensors, devices, and people. Such systems purportedly provide access to always visible, measurable, and knowable spaces, facilitating ever more rational management and planning. Smart city spaces are thus governed through the algorithmic administration and categorization of difference and structured through particular discourses of smartness, both of which shape the production of space and place on a local and general level. Valorization of data and its analysis naturalizes constructions of space, place, and individual that elide the political and surveillant forms of technocractic governance on which they are built. This article argues that it is through processes of measurement, calculation, and classification that “smart” emerges along distinct axes of power and knowledge. Using examples drawn from the British Home Office’s repurposing of charity outreach maps for homeless population deportation and the more recent EU EXIT document checking application for European citizens and family members living in the United Kingdom, we demonstrate the significance of Gunnar Olsson’s thought for understanding the ideological and material power of smartness via his work on the very limits of representation. The discussion further opens a bridge toward a more relational consideration of the construction of space, place, and individual through the thinking of Doreen Massey.
Building Your Community Cybersecurity Program

Communities and states are targets of cyber-attacks. Cities are popular because of generally lax cybersecurity postures and the fact that they have money. States and communities also have personal information on citizens, which can be used for identity theft. With the realization they are becoming frequent targets, communities are looking to enhance their cybersecurity programs, but many do not know where or how to start. The community cyber security maturity model is designed for this purpose – to help states and communities to develop their own viable and sustainable cybersecurity programs. There has also been considerable media attention on the NIST Cyber Security Framework. This is a program designed for organizations, and it contains a lot of good information organizations can use to enhance their cybersecurity posture. From a whole community perspective, however, it is not as useful though there are parts of it that are applicable to a community.
Analogical Reasoning

In law, the process of analogical reasoning appears to work in five simple steps. (1) Some fact pattern A—the “source” case—has certain characteristics; call them x, y, and z. (2) Fact pattern B—the “target” case—has characteristics x, y, and q, or characteristics x, y, z, and q. (3) A is treated a certain way in law. (4) Some principle or rule, announced, created, or discovered in the process of thinking through A, B, and their interrelations, explains why A is treated the way that it is. (5) Because of what it shares in common with A, B should be treated the same way. It is covered by the same principle. It should be clear that the crucial step, and the most difficult, is (4). Often analogical reasoning works through the use of incompletely theorized agreements, making (4) tractable. Some of the disputes about analogical reasoning reflect contests between Burkean and Benthamite conceptions of law.
Virus and Content Filtering

As the size of global computer networks expands and the use of the Internet skyrockets, security issues manifest themselves not only in the security of computer networks but also in individual user security on individual devices connected to the Internet either via an organization’s gateway or an Internet Service Provider (ISP). The security of every user, therefore, is paramount whether the user is a member of an organization network or a user of a connected device. In either case, the effort is focused on protecting not only the data but also the user. The most effective way to protect such a user and the data is through content filtering. Content filtering is a process of removing unwanted, objectionable, and harmful content before it enters the user network or the user device. The filtering process can be located in several locations, including on a user’s device, on a server within an organization, as a service provided by an ISP, or by means of a third-party site that provides the basis of a closed community. This chapter discusses these issues.
Semantic Technologies and Big Data Analytics for Cyber Defence

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.
A DAE-based Approach for Improving the Grammaticality of Summaries

While recent neural sequence-to-sequence models have achieved better and better Rouge performance in summarization task, there is little work emphasizing the grammaticality of the generated summaries. This paper proposes a simple method of pre-training the summarizer as a Denoising Autoencoder (DAE) to reduce the grammar errors. We apply two types of DAE: one is to simply reconstruct the input sentences where randomly sampled tokens are replaced with [MASK] elements, the other is to recover the words or phrases that are replaced with wrong expressions, namely designed for the grammatical error correction task. We evaluate the experiment outputs with three automatic metrics, and the result reveals better grammatical performance while having ROUGE scores higher than that of the baseline model.
Control Signal Generator Based on Ultra-High Frequency Polynomial and Trigonometric Higher Order Neural Networks

This chapter develops a new nonlinear model, ultra high frequency polynomial and trigonometric higher order neural networks (UPT-HONN) for control signal generator. UPT-HONN includes UPS-HONN (ultra high frequency polynomial and sine function higher order neural networks) and UPC-HONN (ultra high frequency polynomial and cosine function higher order neural networks). UPS-HONN and UPC-HONN model learning algorithms are developed in this chapter. UPS-HONN and UPC-HONN models are used to build nonlinear control signal generator. Test results show that UPS-HONN and UPC-HONN models are better than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models, since UPS-HONN and UPC-HONN models can generate control signals with error approaching 10-6.
Real-time Electronic Image Stabilization for Spherical Robot Based on Inertial Measurement Unit and Gray Projection Method

As a new kind of autonomous mobile robot, spherical robot will inevitably sway left and right during walking, and the amplitude will be large sometimes, which brings serious interference to visual systems on the robot, thus real-time electronic image stabilization (EIS) is required. Spherical robot can be used to patrol in complex environments with multiple foreground and background objects, commonly used image-based EIS algorithms often fail under these circumstances. Therefore, this paper proposes a real-time EIS algorithm suitable for spherical robot, according to the characteristics of it. The algorithm divides the task into three subtasks: rotation correction, vertical translation correction and horizontal translation correction. Information from inertial measurement unit (IMU) is used to correct the rotation. Vertical translation correction is based on IMU information, and supplemented by gray projection method. Motion estimation of horizontal translation uses IMU information combining with gray projection, and then mean filter is applied to smooth the translation. Experiments show that the algorithm can effectively stabilize the greatly oscillating images taken by spherical robot, so as to provide a solid foundation for subsequent vision-related algorithms.
Technical Background

Video compression standard is designed with the help of different techniques. The techniques such as color space conversion, motion estimation, and compensation, transform coding, and entropy coding are widely used in video compression. So, in this chapter, discussion on these techniques is taken place with the help of figures.
HAC Covariance Matrix Estimation in Quantile Regression

This study considers an estimator for the asymptotic variance-covariance matrix in time-series quantile regression models which is robust to the presence of heteroskedasticity and autocorrelation. When regression errors are serially correlated, the conventional quantile regression standard errors are invalid. The proposed solution is a quantile analogue of the Newey-West robust standard errors. We establish the asymptotic properties of the heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimator and provide an optimal bandwidth selection rule. The quantile sample autocorrelation coefficient is biased toward zero in finite sample which adversely affects the optimal bandwidth estimation. We propose a simple alternative estimator that effectively reduces the finite sample bias. Numerical simulations provide evidence that the proposed HAC covariance matrix estimator significantly improves the size distortion problem. We examine the impacts of the expansion of renewable energy resources on electricity prices to illustrate the usefulness of the proposed robust standard error.
Multifactorial Bare Bones Sine Cosine algorithm with Chaotic-based elite learning for Multi-tasking Optimization

The problem of multi-task optimization is a new research topic in the field of evolutionary computing in recent years, and it has attracted more and more attention from the academic community. Compared with single-objective optimization and multi-objective optimization, multi-task optimization can use the similarity and complementarity between tasks to solve different optimization tasks at the same time. However, as the population evolves to the later stage, the ability of the two tasks to learn from each other gradually declines. In order to solve this problem and enhance the effectiveness of knowledge transfer between different tasks, this paper combines the bare bone sine cosine algorithm (BBSCA) and the elite learning strategy based on chaos mapping (ELM) into MFEA, and proposes the MFBBSCA-ELM algorithm. Since BBSCA and ELM have different search neighborhoods and are highly complementary to the analog binary crossover used in the classic MFEA algorithm, this article combines BBSCA and ELM, which is also the motivation of this article. In addition, the integration of BBSCA and ELM can reduce the probability of MFEA falling into a local optimum. Finally, this article verifies the effectiveness of integrating BBSCA and ELM into MFEA on the classic MTO benchmark problem. The experimental results show that compared with the classic MFEA, the performance of the MFBBSCA-ELM proposed in this paper has been significantly improved.
Introduction

This is the era of communication and data sharing, which requires secure and confidential transactions. Cryptography is the science that concerns the maintenance and analysis of security, which allows authorized access to information. Cloud computing is one of the new applications, where cryptography is expected to unveil its power to provide desired security solutions. In particular, fully homomorphic encryption (FHE) is one of the main pillars to exploit the complete advantages of cloud computing.
Finding and Discovering Information

This chapter focuses on the finding and discovery stage in the information lifecycle. The chapter identifies the key questions to answer in formulating an information strategy. Concepts associated with these issues and the consequences of ignoring them are explained. The issues are also interpreted in different internal operating environments and in the context of future business environments.
Subscription Cancellation Projects: How to Quiet Some of the Roar

SUMMARY. When faced with yet another subscription cancellation project at Trinity University, a closer look at certain aspects of the budget was decided on by Caraway and Chamberlain as a way to determine price increases and possible cuts. A new way of deciding the amount to cut for non-United States titles was taken in this study, using projected inflation figures. During the project, an even more interesting discovery was made in working and communicating with faculty. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com &gt; Website: &lt; https://www.HaworthPress.com &gt;]
Lossless Compression Algorithms

In this chapter, data compression as it relates to multimedia information is studied from the point of view of lossless algorithms, where the input data is essentially exactly recoverable from the compressed data. Lossy algorithms, for which this is not the case, are presented in Chap. 8. Here we introduce the fundamentals of information theory and algorithms whose goal is savings in bitrate given the entropy, especially Huffman Coding and its adaptive version. We then go on to the introduction of Dictionary-based Coding (as in GIF and WinZip), as well as a detailed discussion of Arithmetic Coding including its binary and adaptive versions. Finally, Lossless Image Compression is examined specifically.
How to Train No Reference Video Quality Measures for New Coding Standards using Existing Annotated Datasets?

Subjective experiments are important for developing objective Video Quality Measures (VQMs). However, they are time-consuming and resource-demanding. In this context, being able to reuse existing subjective data on previous video coding standards to train models capable of predicting the perceptual quality of video content processed with newer codecs acquires significant importance. This paper investigates the possibility of generating an HEVC encoded Processed Video Sequence (PVS) in such a way that its perceptual quality is as similar as possible to that of an AVC encoded PVS whose quality has already been assessed by human subjects. In this way, the perceptual quality of the newly generated HEVC encoded PVS may be annotated approximately with the Mean Opinion Score (MOS) of the related AVC encoded PVS. To show the effectiveness of our approach, we compared the performance of a simple and low complexity but yet effective no reference hybrid model trained on the data generated with our approach with the same model trained on data collected in the context of a pristine subjective experiment. In addition, we merged seven subjective experiments such that they can be used as one aligned dataset containing either original HEVC bitstreams or the newly generated data explained in our proposed approach. The merging process accounts for the differences in terms of quality scale, chosen assessment method and context influence factors. This yields a large annotated dataset of HEVC sequences that is made publicly available for the design and training of no reference hybrid VQMs for HEVC encoded content.
Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network (CNN)-based method to accurately localize the tampered regions, which combines color filter array (CFA) features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity.
Social Media in Higher Education

This chapter discusses the current use of social media for professional growth, focusing on a case study that uses social media to increase instructional design graduate students' awareness and participation in professional growth opportunities. Social media metrics were analyzed from three social networking tools (Facebook Page, Twitter account, and/or Google+ community) that are used to communicate with the students in the program. Additional data was collected using an electronic questionnaire with open and closed-ended questions. The results show that graduate students' participation in the social media initiatives for professional growth provided awareness of self-directed, voluntary, and informal learning opportunities; engaged students in conversations with their peers and the instructors; and allowed the learners to expand their learning experience outside the traditional classroom format.
Multi-Dimensional Feature Selection and Combination Method of Aerospace Target Based on K-means Clustering and Information Entropy

Aiming at the shortcomings of K-means clustering analysis algorithm, such as unable to effectively suppress noise features in high-dimensional data and difficult to solve irregular shape clustering, a classification selection method based on K-means clustering and information entropy for multi-dimensional target feature information is proposed. In this method, the information content of characteristic samples contained in information entropy is used as the evaluation distance instead of the traditional Euclidean distance. And combined with the time domain, the aggregation degree and diversity degree are defined to objectively reflect and measure the initial value of feature sample sets and the similarity measure among feature sets, so as to get rid of the serious dependence on the initial value problem and the computational redundancy problem. The simulation results show that the calculation process is more intuitive and clearer, and because the calculation process is relatively simple, the calculation amount is small, and the timeliness is good.
Information Security Governance in Large Organizations

There is a dearth of academic research literature on the practices and commitments of information security governance in organizations. Despite the existence of referential and standards of the security governance, the research literature remains limited regarding the practices of organizations and, on the other hand, the lack of a strategy and practical model to follow in adopting an effective information security governance. This chapter aims to discuss the information security governance and to address the weaknesses identified in the literature. Based on practices of information security management and governance, the authors propose ISGO, a practical maturity framework for the information security governance and management in organizations. The findings will help organizations to assess their capability maturity state and to address the procedural, technical, and human aspects of information security governance and management process.
Application of Digital Signal Processing Technology in Electronic Information Engineering

Due to the rapid development of modern technology, digital signal processing technology has been widely used in many fields. Among them, the application of digital signal processing technology to the field of electronic information industry has a significant impact on its development. This article mainly focuses on the application of digital signal processing technology in electronic information engineering. On the basis of a large number of documents, it understands the relevant theoretical issues of digital signal processing technology and electronic information engineering, and then analyzes the PSD using digital signal processing technology. The synchronous measurement system was designed to illustrate the practical application of digital signal processing technology in electronic information engineering. Finally, the designed system was tested, and the test conclusions confirmed that the measurement accuracy of the system in this paper is percent within four.