Reviewerly/exHarmony huggingface dataset:
Conditional Threshold Autoregression (CoTAR)

We propose a new time series model where the threshold is specified as an empirical quantile of recent observations of a threshold variable. The resulting conditional threshold traces the fluctuation of the threshold variable, which can enhance the fit and interpretation of the model. In the proposed conditional threshold autoregressive (CoTAR) model, the existence of threshold effects can be tested by wild-bootstrap tests which incorporate all possible values of nuisance parameters. The estimation and hypothesis testing of the CoTAR model satisfy desired statistical properties in both large and small samples. We fit the CoTAR model to new confirmed COVID-19 cases in the U.S. and Japan. Significant conditional threshold effects are detected for both countries, and the implied persistence structures are consistent with the fact that the number of new confirmed cases in the U.S. is larger than in Japan.
Cost-sensitive Heterogeneous Integration for Credit Card Fraud Detection

Credit card fraudulent activities cause huge financial losses around the world every year. In recent years, data-driven methods are increasingly becoming fraud detection methods for financial institutions. Some related studies based on machine learning and data mining have been proposed. However, most of them do not consider the actual financial losses associated with the fraud detection process. Or some related cost sensitive methods focus on minimizing cost loss but the accuracy of detection is low. This paper presents a cost-sensitive heterogeneous integration model, CSHIM, for credit card fraud detection. CSHIM considers the different misclassification costs of each transaction and integrates the superior performance of different individual classifiers through the cost-sensitive weighted Dempster-Shafer fusion theory to achieve better fraud detection results. The goal is to achieve good performance not only in reducing monetary losses but also improving detection accuracy. We have done experiments on public data sets, the experiments show that the method proposed in this paper can save up to 74.69% of the cost, which not only can achieve good results in cost savings, but also has better performance of other standard metrics compared with other methods.
Review of Various types of Spam and Spammer Detection Techniques

With the advancement of technology, the way of social connectivity is also changing. In this modern era most of the people are connected to each other through digital medium of communications. There are various platforms which facilitates social connectivity. The platforms like twitter, Facebook, WhatsApp, and google meet, zoom app etc. provides facilities like tweets, chats, video chats, video conferencing, personal conversation, group conversation etc. As the popularity of this social media platforms are increasing, threats to the privacy, online frauds, spamming activities and vulnerability is also increasing on this platforms. These platforms are becoming prime targets for attackers. There are various spam and spammer detection techniques that are developed for securing social media users from such attackers. A review of recent spam and spammer detection techniques is provided in this paper.
Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights

High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOℝ, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOℝ to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOℝ has better test set generalization than R-Prop, though not to a statistically significant extent.
Text Classification for Marketing Research Using Pre-Trained General Language Models

Marketing research increasingly involves the use of unstructured text data, such as user-generated content collected from social media platforms. A common task in rendering these data useful for analysis is classification of the text units, for example by topic or their expressed sentiment towards brands and products. Pre-trained general language models based on the transformer architecture have recently shown very promising results in these problem domains, but have so far not seen extensive use by marketing researchers. The goal of this short study is to evaluate these models on common marketing research data such as microblog postings and product reviews and compare them to other methods commonly encountered in the marketing literature. Overall, transformer models show impressive performance, warranting further exploration of their capabilities and use cases.
Preliminary Discussion on Research Progress of Intelligent Robots

This paper studies the development history of intelligent robots. First, the various key technologies of intelligent robots are analyzed, including autonomous positioning and navigation, human-computer interaction and multi-sensor information coupling. Furthermore, the main problems existing in intelligent robots are discussed, and corresponding solutions are proposed for the application of intelligent robots in the fields of industry, service industry, and agriculture. Finally, the research shows that intelligent robots have broad application prospects in the future.
Can Computers Create Comics and Animations?

The objective of this chapter is to develop a method for generating and analyzing creative work by using computers. In this chapter, novels, comics, and animations are considered representative creative work because the main method for representing creative work is the use of natural language and pictures. These works focus on interesting issues, which can be explained by computational approaches to narratology. In this chapter, the authors discuss the following three topics. First, a method of semi-automatic story generation by agent-based simulation is presented. Second, methods of generating and analyzing comics on the basis of the features of pictures and stories used are described. Third, the authors introduce a method of estimating the difficulties of translating a storyboard into original pictures in the process of creating animations.
Soyuz-FG and Soyuz-2 1a space rockets trajectory parameters in comparative analysis based on external trajectory measurement data

In order to identify the distinctive features of using analog and digital control systems, a comparative analysis of kinematic parameters of movement of two space rockets was carried out. Relying on the external trajectory measurement data, we developed a method for calculating the estimates of statistical characteristics of isochronous variations in the parameters of the state vector of the space rockets. Within the study, we compared and analyzed the kinematic parameters of the movement of Soyuz-FG and Soyuz-2 1a space rockets. Using the external trajectory measurement data, we calculated the spacecraft initial orbit parameters formed by the considered launch vehicles, and estimated their accuracy. Findings of research show that the parameters of the initial orbit formed by Soyuz-2 have smaller deviations from their nominal values, unlike the launch trajectory of the previous generation launch vehicle. Experimental data have confirmed the positive contribution of terminal control to reducing deviations of the implemented trajectory from the nominal one.
Techland

This work describes Techland, an OpenSim-based VW developed and owned by the author for educational purposes for middle school students. Techland is a group of islands focused on math, and science, where explore various approaches to VW, in different and mutually not exclusive learning sets and degrees of immersion. The main objectives are to simplify abstract concepts by using scripted 3D learning objects and to engage students offering a way to go beyond the classroom as the only learning environment. So Techland can be considered a giant 3D book. Activities range from viewing the contents of the world on an IWB, as a support for the teacher's lesson, to logging on the student's avatars into the world for exploration, producing machinima videos and collaborating together. Most islands are public access and reachable from other similar virtual worlds by the HG system, so other schools could take advantage from its contents or they could share projects. Techland has got the proper requirements to will become a meeting place between students and teachers who want to find new approaches to scientific matters.
A Data-Driven Framework for Transferring the Ground-View Image into the Aerial-View Scene

research-article A Data-Driven Framework for Transferring the Ground-View Image into the Aerial-View Scene Share on Authors: Wei Hu Nanjing Research Institute of Electronics Technology, China Nanjing Research Institute of Electronics Technology, ChinaSearch about this author , Wei Li Nanjing Research Institute of Electronics Technology, China Nanjing Research Institute of Electronics Technology, ChinaSearch about this author , Yongzhen Huang Nanjing Research Institute of Electronics Technology, China Nanjing Research Institute of Electronics Technology, ChinaSearch about this author , Guowei Liu Nanjing Research Institute of Electronics Technology, China Nanjing Research Institute of Electronics Technology, ChinaSearch about this author , Duodong Wu Nanjing University of Posts and Telecommunications, China Nanjing University of Posts and Telecommunications, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 147–153https://doi.org/10.1145/3495018.3495043Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Spear Phishing

The ubiquitous use of the Internet has made it possible for terrorist groups to remotely foment attacks with little risk of capture. Among the newest forms of attacks is cyber hacking, which has seen increased use by terrorist groups for acts ranging from pinpointing targets for assassination to holding organisations hostage and embarrassing governments. In almost all these attacks, spear phishing is the vector used to gain access to a computer network – making it imperative that policymakers find ways to stop it. This chapter provides an overview of the different types of spear phishing attacks and the reasons they succeed. The chapter then provides an overview of the different strategies being used to combat it and their relative effectiveness. Drawing from the latest social science research and from initiatives that have worked around the world, the chapter culminates with six policy suggestions, which could significantly reduce the effectiveness of spear phishing and protect nations from a major cyber attack.
Dealing with the Domain Name Abuse: Issues and Approaches

To facilitate their worldwide access, online resources such as websites, mobile apps as well as email addresses would generally utilize domain names to label their logical locations on the Internet. Meanwhile, people who want to visit somewhere over the Internet would also tend to resort to a specific domain name first. Therefore, domain names are virtually playing an essential role during the interacting procedure between humans and online resources. However, domain names could sometimes be exploited maliciously and involved into some unexpected scenarios such as phishing, spamming, and harmful content distributing. We refer to these kinds of exploitation here as domain name abuse. Due to the explosive growth over the past years, domain name abuse has been drawing plenty of attentions both from academic and industrial areas. However, we note that there still exist a range of fundamental yet vital issues that have not been well addressed so far. In this paper, we conduct a comprehensive reexamination towards some of these major issues as well as relevant approaches regarding domain name abuse handling, and try further to set forth some of our own responses and corresponding updates towards them. We believe that these issues and relevant approaches could lead to extensive impact towards further progress in this area, and our work would help the community to deal with domain name abuse in a more accountable and practical manner.
Introduction

This chapter introduces the job satisfaction problem, why it is important, when the term job satisfaction appeared for the first time in the literature, what are the relationships between employer and employee, what are the benefits of job satisfaction to the employee but also to the company etc. Moreover, terms such as data modeling, simulation and decision-making are familiarized. Finally, the scope, the research objectives and the outline of this thesis are described.
The Library Big Data Research

Libraries are widely used by government, universities, research institutes, and the public since they are storing and managing intellectual assets. The library information directly stored in libraries and about the people interaction with libraries can be transformed into accessible data which then will be used by researchers to help library better serve users. Librarians need to understand how to transform, analyze, and present data in order to facilitate such knowledge creation. For example, the challenges they face include how to make big datasets more useful, visible and accessible. Fortunately, with new and powerful analytics of big data, such as information visualization tools, researchers/users can look at data in new ways and mine it for information they intend to have. Moreover, interaction of users and stored information has been taken into librarian's consideration to improve library service quality. In this work, the authors discuss the characteristics of datasets in library and argue against a popular confusion that data involved in library research is not big enough, conduct a review for the research work on library big data and then summarize the applications and research directions in this field. The status of big data research in library in China is discussed. The challenges associated with it are also discussed and explored.
Role of Cyber Security and Cyber Forensics in India

This chapter describes cyber forensics, also known as computer forensics, which is a subdivision of digital forensic science, relating to evidence detection in computers and digital storage media. The purpose of cyber forensics is the forensically-sound investigation of digital media with the intent to: identify, preserve, recover, analyze, present facts, and opinions; concerning the digital information. Even though it is generally allied with the analysis of cyber-based crimes, computer forensics may also be used in civil proceedings. Evidence composed from cyber forensic analysis is typically subjected to similar procedures and performs as supplementary digital evidence. With these advancements, it was desired that cyber forensics be to protect users and remain citizen-centric. This chapter shows that there is additional research needed to understand the implications of cyber forensic research to improve detection of cyber crimes.
A Semi-supervised Deep Learning-Based Solver for Breaking Text-Based CAPTCHAs

Text-based CAPTCHAs are still the most widely used CAPTCHA mode. Many researchers have proposed attack methods to break them. In previous attacks, segmentation-based methods require at least three steps: preprocessing, segmentation, and recognition, which means that different modes of CAPTCHA require various preprocessing and segmentation algorithms. In recent years, a series of deep learning (DL) models have been designed for cracking text-based CAPTCHAs. However, these methods require annotating numerous images, which are time-consuming and labor-intensive. In this paper, we propose a semi-supervised DL-based solver for breaking text-based CAPTCHAs, which can use a small number of labeled CAPTCHAs to achieve a high-performance attack model. The CNN module and the attention-based Seq2Seq module are two key components for effective feature extraction and character recognition. The experimental results show that our solver successfully attacked 9 types of most popular text-based CAPTCHAs, and the attack success rate is better than the four latest attack models. In addition, our model does not perform any data preprocessing and has a fast attack speed, making it more suitable for real-time attacks. The code and dataset are available on the github.
Visualization

Information visualization is a visual data science technique that amplifies human cognition and perception for the discovery of interested and unexpected patterns in data. Information visualization can support both unsupervised and supervised learning—visualizing some class information or labels in the latter case. This chapter will introduce the concept of information visualization, present multiple examples with smaller and larger datasets, and discuss potential applications of information visualization to cryptocurrency blockchain data.
A Contemplator on Topical Image Encryption Measures

Images unduly assist digital communication in this aeon of multimedia. During times a person transmits confidential images over a flabby communication network, sheer protection is an accost contention to preserve the privacy of images. Encryption is one of the practice to clutch the reticence of images. Image encryption contributes a preeminent bite to charter security for secure sight data communication over the internet. Our work illustrates a survey on image encryption in different domains providing concise exordium to cryptography, moreover, furnishing the review of sundry image encryption techniques.
Introduction

The purpose of this chapter is to lead the readers from applications to research problems. We start the book by briefly reviewing the history of VC, in order to give the reader a general idea of this topic. Then, we identify some important applications of VC. Here, we also discuss in which applications visual cryptography is suitable and in which applications classical cryptography is preferable, in order to guide the reader to select appropriate cryptographic tools according to their intended security services. From these applications, the existing algorithms can be classified into several categories.
Apparatus

An ion trap experiment consists of the trap and vacuum system, as well as systems to control the ions and the environment. The ion trap is housed in a vacuum chamber, surrounded by coils to produce and orient the desired magnetic field. For controlling the ions we use multiple lasers as well as microwaves and RF. Information is obtained from the ions by measuring their fluorescence, detected by either an EMCCD camera, or an APD detector.
Software-Defined Cloud Infrastructure

Cloud computing suggests that the applications conveyed as services over the internet and frameworks programming in the server that give various services and offers in “pay as you go” trend which means pay only for what you use. The information and services are managed as software as a service (SaaS). Some sellers utilize terms, for example, IaaS (infrastructure as a service) and PaaS (platform as a service). The purpose of cloud computing is quickly expanding in everyday life. Today the use of cloud computing is widespread to the point that it is being utilized even in the medicinal services industry. As the development of cloud computing in healthcare is happening at a fast rate, we can expect a noteworthy piece of the healthcare administrations to move onto the Cloud and along these lines more focus is laid on giving cost-effective and efficient services to the general population all around the world. Cloud these days are turning into the new building pieces of significant organizations spread the world over. They offer assistance in servicing to offer different frameworks. Cloud computing has enhanced its technique and technologies in a better way to provide better services. Existing e-healthcare has many difficulties from advancement to usage. In this chapter, the authors discuss how cloud computing is utilized and the services provided by the Cloud and their models and its infrastructure.
FORENSIC ANALYSIS OF DUPLICATE IMEI’S-A CASE STUDY

Mobile forensics is a sub-set of digital forensics which includes the investigation and acquisition of artifacts of mobile phones. With the advancement of technology threats to mobile phones made forensic science a challenging Endeavour. Number of mobile users is increasing worldwide and creates tremendous problems and challenges. As the technology is advancing the criminals are also getting advanced day by day. Investigation agencies come across various crimes committed through different modes, forgery of IMEI number(s) of mobile device being one of them. This paper presents a case study in which mobile phones of duplicate IMEI’s, hardware tools capable of duplicating IMEI along with other evidences have been received and examined in the laboratory.
Open Portable Trusted Execution Environment framework for RISC-V

A Trusted Execution Environment (TEE) is a relatively new technology that provides hardware-enforced isolation within a processor allowing an application to run in a separate execution area called an enclave. It aims to increase the protection level and defenses against the exploitation of software flaws. This way, if the system gets compromised, the attacker cannot access the user's important assets. A recent trend in TEE development is the transition from vendor-controlled, single-purpose TEEs to open TEEs that host Trusted Applications (TAs) from multiple sources with various use-cases in mind. This transition has created a TA ecosystem that provides more robust and customized security to applications and rich operating systems such as Linux and Android. TEEs are widely deployed, especially on consumer devices whose processors are based on the ARM architecture. As an increasing number of vendors now plans to adopt the RISC-V architecture in their products, defining an approach to reuse the existing TAs is needed. This paper aims to port the OP-TEE framework to RISC-V to incorporate various software components, including middle-ware, security stacks, tools, and community support, with the final goal of moving RISC-V forward.
Introduction

In this book, we provide a new mathematical formalism for proving properties about the behavior of systems. A system is a collection of interacting components, each of which may have some internal implementation that is reflected in some external behavior. This external behavior is what other neighboring systems interact with, through a shared environment. Properties of a behavior can be established over a given duration (sometimes called frame or window) window of time, and we propose a mathematical language for working with these behavioral properties.