Reviewerly/exHarmony huggingface dataset:
Hybrid Computational Intelligence and the Basic Concepts and Recent Advances

In this chapter, computational intelligence and its major methodologies are introduced, and then hybrid intelligent systems are defined, and the most popular hybrid intelligent approaches are discussed. The increased popularity of hybrid intelligent systems during the last decade is the result of the extensive success of these systems in a wide range of real-world complex problems, but also has to do with the increased capabilities of computational technology. One of the reasons for this success has to do with the synergy derived by the computational intelligent components, such as machine learning, fuzzy logic, neural networks, genetic algorithms, or other intelligent algorithms and techniques. Each of the partial methodologies provides hybrid systems with complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. The chapter includes recent advances and new findings in the area of hybrid computational intelligence.
Analysis of Initial Involvement of Librarians in the Online Virtual World of Second Life

This descriptive case study considers a tech-savvy and geographically distributed group of librarians and information professionals, led by an initiative of the Alliance Library System of Illinois, who gathered together to brainstorm and organize ways to provide online users with real library services. Through document analysis of written conversations in their online discussion group, the researcher has explored the development process of these librarians as they began to provide information services to users in the virtual online world of Second Life®.
The Design and Implementation of the Topic Selection and Guidance System of College Graduation Thesis Based on Web

Graduation design is the main method for colleges and universities to assess students’ learning during college. It is a severe challenge for college students who are about to embark on the society. Whether or not they can successfully complete the challenges will also be related to the graduation of college students. Over the past years, the task of thesis selection for domestic colleges and universities has been more onerous, and there are many problems, such as statistical data, modify data. The content of this design is the topic selection and guidance system of graduation thesis. The system uses the Java language for development, combines the SSH framework optimization code, and the JSP dynamic page development technology. The development tool uses the more comprehensive MyEclipse, and the database uses the more common MySql.The development and research of this system was developed to meet the needs of the development of higher education informatization. Since the topic of graduation thesis is required for each student to complete, the statistical data of the selected topic is also more tedious. After the data statistics are completed, it needs to be summarized to the academic affairs. In the management department, traditional manual topic selection methods are prone to unavoidable errors, resulting in low accuracy of the data. The text data is susceptible to loss due to many factors, resulting in poor data integrity. The educational administration department and students cannot communicate directly. Teachers interact with each other, leading to inefficiency and waste of human and material resources. There will also be missed elections, by-elections, re-elections, etc., and the implementation will be very complicated. Due to the influence of many factors above, it is difficult to select topics for graduation. The system can meet the real-time and effective work of the dissertation without geographical constraints. It is a major breakthrough in time and space. The thesis selection and guidance system It can ensure the fairness and openness of the topics selected, as well as ensure the accuracy and integrity of the data.
Information Need and Seeking Behavior of Farmers in Laduba Community of Kwara State, Nigeria

The chapter present the report of a study that examined information needs and seeking haviour of farmers in Laduba community, Kwara State, Nigeria. This study adopted a survey research design using simple sampling technique to select 28 respondents from the population. Structured questionnaire was designed for the collection of data. It was discovered from the findings that majority of the farmers were illiterate of the middle aged group between 31-40. The farmers plant many crops, but it is noticeable that the farmers plant cassava mostly, the area which the farmers indicated they need information most is on agriculture, it was also observed that the farmers access information from colleagues (co-farmers), friends and relatives, agricultural extension workers. The information needs of the farmers in this study on agriculture includes how to prevent diseases for their crops on the farm and after harvesting, the farmers also stated that they need information on how to seek for loan, where to get the best market to sell their farm produce and how to get the best agro-chemical for effective use. In this study it was discovered that the challenges the farmers face in acquiring information includes, the inability to read and write in English language, lack of constant electricity supply, conflict among members of various associations and lack of access to agricultural extension workers. It is recommended that the farmers should be given effective adult education, provision of information centre with necessary personnel and information media to boost information accessibility. Also the government was encouraged to negotiate with mobile telecommunication operators to subsidize services so that the farmers who form majority in the rural areas can access information and communicate easily through their mobile phones.
Introduction

This chapter sets the context of this book by providing an overview on the considered design tasks in quantum computing, their importance, as well as their complexity. Based on that, the importance of bringing knowledge (on clever data structures and efficient algorithms) from the design automation community into the quantum realm is discussed—to avoid a situation, where we might end up with powerful quantum hardware but without efficient tools to make full use of that power.
Just Keep Your Concerns Private: Guaranteeing Heterogeneous Privacy and Achieving High Availability for ERM Algorithms

Traditional implementations of differential privacy implicitly assume that users have homogeneous privacy requirement for all attributes of data. They provide a uniform level of privacy guarantee for all attributes by using a single privacy budget, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon$</tex> . However, this brings trouble to users in practice applications, where privacy requirements are often heterogeneous. In this case, users have to make a choice: either setting the privacy level high enough to satisfy even the privacy fundamentalists, which often results in poor model utility, or sacrificing the privacy of some attributes for practical model. Both are hard to be accepted by users. In this paper, we offer users what they probably want, an option that could provide a satisfactory privacy guarantee for important attributes with minimal utility loss. Our method, called heterogeneous differentially private ERM (HDP-ERM), allows the private learning algorithms to guarantee heterogeneous privacy for each attribute of training data. The noise injected in each parameter is adaptive according to its individual privacy budget, so that we can control the privacy-utility trade-off more finely. Experimental results show that our method is able to reach a satisfactory utility when only the important attributes need strong privacy guarantee, while the traditional DP-ERM would only get a useless model (one with low test accuracy) when providing the same level of privacy guarantee.
Indonesian Mobile Learning Information System Using Social Media Platforms

As a developing country, Indonesia continues to improve its quality as a state, in which the attempt to optimize all of its potential both in terms of economic, political, social, cultural, technological, educational, health, etc. This modern era, all aspects of life are depending on technology. This makes the technology becomes one of necessary in people's life. The utilization of technology has been used by all people in all aspects of life. Specifically, this paper tries to offer an innovation that has never been designed before, namely a platform of M-Learning in form of social media related to the development of technology for learning. Nowadays, internet users and smartphone ownership in Indonesia increased dramatically, then writers took initiative to design an innovation related to this case. Social media technologies provide the opportunity for teachers to engage students in online classes, thereby supporting the development of skills and learners to achieve competency. In addition to students, the opportunity is also open to outside the community to get information that can add knowledge. This case study provides a platform for M-Learning based learning that facilitate student learning also helps society size to obtain information more easily. The design of this platform using models UML (Unified Modeling Language) to design a visual model of this platform.
CoachGAN

CoachGAN provides an inference time method to improve outputs from GAN generator models. Similar to creating adversarial examples to fool neural network classifiers, CoachGAN exploits gradient information, in this case from a pretrained discriminator model. Unlike the generating adversarial examples, which uses gradient descent to alter outputs directly, CoachGAN alters the inputs of generator models. This allows for output enhancements at test time without additional model training. CoachGAN adapts easily to existing algorithms and is architecture agnostic. In addition to qualitative samples, we quantitatively show that CoachGAN improves IS and FID scores on a variety of GAN architectures and tasks.
Automatic Tracking Method of Unmanned Vehicle Trajectory Based on Reinforcement Learning

Up to now, unmanned driving is still a challenging research field in academia at home and abroad. The vehicle trajectory tracking technology is a very critical and urgent link, because it provides important information for intelligent traffic monitoring. The reinforcement learning method is an important method for learning in an unknown environment. In the field of artificial intelligence machine learning, reinforcement learning research has made great progress in theory, algorithm and application, and has become a current hotspot in research. Unmanned vehicle trajectory tracking is one of the key technologies in the field of unmanned driving research. It uses built-in sensors to perceive the environment, uses trajectory planning algorithms to generate the required path in real time, and the decision system selects the best path. Finally, the built-in path tracking controller implement it. This article mainly adopts the experimental analysis method to discuss how to break through the problem of automatic trajectory tracking technology in the support of enhanced learning by unmanned vehicles, and compare and analyze the expected yaw rate and actual yaw rate and frequency of the target vehicle. According to the experimental research results, the expected yaw rate and actual yaw rate of the unmanned vehicle trajectory automatic tracking test are relatively close, and the test system in this test has a certain tracking effect.
Data for Data Mining

This chapter introduces the standard formulation for the data input to data mining algorithms that will be assumed throughout this book. It goes on to distinguish between different types of variable and to consider issues relating to the preparation of data prior to use, particularly the presence of missing data values and noise. The UCI Repository of datasets is introduced.
Bilingualism

Bilingual early language learning is special in that infants must sort the linguistic input into two languages and learn each of them with a reduced amount of exposure. We review evidence of infants’ abilities in language discrimination, phoneme acquisition, word learning, processing of audiovisual speech and cognitive processing. The general conclusion is that bilinguals keep pace with their monolingual peers by developing specific adaptations.
Deep Learning based Customer Count/Flow Monitoring System for Social Distancing

Despite the COVID-19 vaccination drives, use of preventative measures such as masks and social distancing are still deemed essential. This paper presents an application that will allow businesses/enterprises to monitor the flow of customers by detecting people as objects, counting the number of people, tracking the safe distance between them to maintain the two-meter distance norm. The proposed solution is set up to generate an alarm when the customers reach the allowed limit as per shop dimensions or overcrowding is detected. For the implementation, YOLOv4 and YOLOv3-Tiny were used for the task of object detection and transfer learning is used to set up weights. The models were evaluated using MSCOCO API with 100 image instances per class. The results of the YOLOv4 model are also compared with YOLOv3-Tiny in terms of calculating mean, average precision (AP), frames per second (FPS), and identification of groups (crowd). Experimental results (on several video clips from a shopping center CCTV) show that the YOLOv3-Tiny maintains real-time performance even on modest hardware. It is further demonstrated that if a high-end GPU is available, the overall detection of objects and cluster identification is much more accurate and clearer using YOLOv4.
Mobile Learning

Teachers are encouraged to remain current by embracing and integrating new technologies effectively into their teaching processes. Ubiquitous technologies such as mobile devices are found in all settings including K-12 learning environments. This chapter discusses findings from an action research study. The findings reveal that mobile learning is still in its infancy stage in most K-12 learning environments and there is need for effective integration of mobile technologies into the curriculum. This chapter offers suggestions and strategies on how teachers could integrate mobile technologies into teaching and learning processes. Finally, this chapter provides an insight into some of the critical factors that need to be in place to ensure seamless transition, integration, and sustained implementation of mobile learning.
A New Timestamp Digital Forensic Method Using a Modified Superincreasing Sequence

This paper proposes a new digital forensic method using a modified superincreasing sequence. Timestamp changes by file commands in Windows NTFS file system are used for identifying what commands were executed and are a useful and a logical way for performing digital forensics. A superincreasing sequence is modified for the timestamp change patterns to make each timestamp pattern have a distinct value. The method has two functions; one is a timestamp change check function and the other is a forensic evaluation function. The former checks differences of timestamps between before and after command execution, and the latter produces a characteristic output by applying ten kinds of timestamp change patterns. According to the characteristic output, the kind of command that is executed is identified. By virtue of adopting the modified superincreasing sequence, the evaluation function could produce distinct characteristic output values and thereby provides a way to reconstruct executed file commands.
Statistical Algorithm model of College English Teaching based on artificial Intelligence Technology

This paper mainly introduces the principle, characteristics and practical application of artificial intelligence technology in college English teaching system. The system analysis platform of college English teaching is established by using artificial intelligence technology. The platform system is mainly divided into two parts. The first is the expert system model of teacher English assisted instruction based on expert system theory, which is combined with the generation and framework of data analysis. Artificial intelligence has been widely used in college English teaching. Artificial intelligence technology has the advantages of intelligence, data, networking, multimedia and so on. And teaching information collected through artificial intelligence technology mining at the same time, the data using statistical algorithms accurately grasp the students' learning status, to improve and enhance English software platform operation function, improve the operation speed, efficiently for statistical analysis of students' learning behavior, effectively will artificial intelligence technology and system construction depth fusion, promoting the development of digital college English teaching.
Tracking: B

Abstract In Chapter Tracking: A, we explained the estimation of a random variable based on observations. We also described the Kalman filter and we gave a number of examples. In this chapter, we derive the Kalman filter and explain some of its properties. We also discuss the extended Kalman filter. Section 10.1 explains how to update an estimate as one makes additional observations. Section 10.2 derives the Kalman filter. The properties of the Kalman filter are explained in Sect. 10.3. Section 10.4 shows how the Kalman filter is extended to nonlinear systems.
HyperBench

Virtualization is ubiquitous in modern data centers. By deploying applications on separate virtual machines hosted in a shared physical machine, it brings benefits over traditional systems in resources utilization[5, 10], system security[2, 3], and server management[7]. Despite the adoption and these advantages of virtualization, cloud providers, tenants, and developers face different challenges. The hardware and software options to build a virtualization platform pose a challenge for cloud providers. A variety of virtualization platforms make it difficult for tenants to make a choice. Developers are forced to continually improve the architecture and hypervisor design to better support virtual machines. In this context, evaluating the virtualization capabilities of different platforms is necessary.
Spatial Cross-Attention RGB-D Fusion Module for Object Detection

We investigate different RGB and depth fusion techniques for object detection with the aim to improve the detection accuracy compared to RGB-only systems. We consider recent proposal-free convolutional object detectors which we modify for RGB-D data. We introduce a third mixed branch in our network beside the RGB and depth branches and define a novel attention mechanism which extracts weighted features from the depth branch and applies them to the RGB feature map thus fusing the branches adaptively. Our method, which we call spatial Cross-Attention Fusion network or CAF-Net yields a state-of-the-art mean average precision of 60.3% on the SUN RGB-D dataset outperforming all previous techniques by a significant margin.
Privacy Preserving Data Mining Using Time Series Data Aggregation

This article proposes a mechanism to provide privacy to mined results by assuming that the data is distributed across many nodes. The first objective includes mining the query results by the node in a cluster, communicating it to the cluster head, aggregating the data collected from all the cluster nodes and then communicating it to the group controller. The second objective is to incorporate privacy at each level of the clusters node: cluster head and the group controller level. The final objective is to provide a dynamic network feature, where the nodes can join or leave the distributed network without disturbing the network functionality. The proposed algorithm was implemented and validated in Java for its performance in terms of communication costs computational complexity.
The Cyber Talent Gap and Cybersecurity Professionalizing

Two significant issues loom throughout the cybersecurity domain. The first is the shortage of cybersecurity professionals and the second quandary is the lack of minimum entry standards in cybersecurity. Some organizations' cybersecurity operations are suffering due to the cybersecurity talent gap accompanied by the increasing sophistication and number of cyber-attack attempts. The shortage of cyber talent is rampant in private entities as in public agencies, which highlights the resolve for entry standards into cybersecurity to enhance the professionalization. Researchers and practitioners provide countless recommendations for ameliorating the cybersecurity workforce by addressing the professionalization issue. Professional associations are the nexus of cybersecurity and possess the expertise, leadership, and sustenance to spearhead efforts to develop national-level strategies to resolve the talent gap and establish professionalization standards.
Quantum Stochastic Processes and the Modelling of Quantum Noise

This brief entry gives an overview of quantum mechanics as a quantum probability theory. It begins with a review of the basic operator-algebraic elements that connect probability theory with quantum probability theory. Then quantum stochastic processes are formulated as a generalization of stochastic processes within the framework of quantum probability theory. Quantum Markov models from quantum optics are used to explicitly illustrate the underlying abstract concepts and their connections to the quantum regression theorem from quantum optics.
A Hybrid Kernel Extreme Learning Machine and Improved Cat Swarm Optimization for Microarray Medical Data Classification

This paper presents the pattern classification of the binary microarray gene expression based medical data using extreme learning machine (ELM) and its variants like on-line sequential ELM (OSELM) and kernel based extreme learning machine (KELM). In the KELM category two variants namely the wavelet based kernel (WKELM) extreme learning machine and radial basis kernel extreme learning machine (RKELM) along with support vector machine (SVMRBF) and support vector machine polynomial (SVMPoly) are used to classify microarray medical datasets. Further to reduce the high dimensionality of Microarray medical datasets giving rise to high number of gene expression and small sample sizes, a modified evolutionary cat swarm optimization (MCSO) technique is adopted. The efficiency of the proposed algorithm is verified using a set of performance metrics for four binary medical datasets belonging to breast cancer, prostate cancer, colon tumor, and leukemia, respectively.
The Criminalisation of Tools under the Computer Misuse Act 1990. The Need to Rethink Cybercrime Offences to Effectively Protect Legitimate Activities and Deter Cybercriminals

Fourteen years after its creation in 2006, s3A Computer Misuse Act 1990 remains as problematic, if not more problematic than ever. Established to support the fight against cybercrime, the offence of misuse of tools has not only the paradoxical effect of endangering legitimate security research, as foreseen in 2006, but has also become a threat to established newsgathering practices. Its broad structure, combined with the vagueness of the other CMA offences, and the absence of public interest defences, criminalises the very tools which facilitate the work of respectively security researchers, and whistle-blowers and journalists-, leaving these actors exposed to criminal liability for resorting to dual-use hacking tools and obfuscating tools. Ultimately this pattern of over-criminalisation harms the fight against cybercrime and crime, defeating the very objective of deterrence cybercrime offences harbour. It is time, not just for reforming the CMA and in particular s3ACMA, but also for the legislator, both in the UK and at international level, to properly engage with the security industry and civil society.