Reviewerly/exHarmony huggingface dataset:
An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models

Computer vision-based identification of different tissue categories in histological images is a critical application of the computer-assisted diagnosis (CAD). Computer-assisted diagnosis systems support to reduce the cost and increase the efficiency of this process. Traditional image classification approaches depend on feature extraction methods designed for a specific problem based on domain information. Deep learning approaches are becoming important alternatives with advance of machine learning technologies to overcome the numerous difficulties of the feature-based approaches. A method for the classification of histological images of human colorectal cancer containing seven different types of tissue using convolutional neural network (CNN) is proposed in this article. The method is evaluated using four different colour models in absence and presence of Gaussian noise. The highest classification accuracies are achieved with HVI colour model, which is 95.8% in nonexistence and 78.5% in existence of noise respectively.
Quantum-Computing-Inspired Algorithms in Machine Learning

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.
Autonomic Execution of Web Service Composition Using AI Planning Method

Web Service Composition is one of the technologies in Service Oriented Architecture which significantly increases the flexibility and reusability of developing service-oriented system. One of the major problems which occurs in web service composition is the difficulties of maintaining the existing running web service composition solutions due to the changes of business requirements, deployment environment, and other dynamic factors. In this proposed work, an automated system had been built to autonomously execute the web service composition. To achieve this objective, the authors had embedded semantic engine and Prolog in C# program to automatically and dynamically discover, compose and execute web service composition, i.e. a web service composition could be self-configured to automatically recover from execution failure and automatically re-generate composition solution due to business protocol changes.
A Study of Format-Preserving Encryption Algorithm Incorporating Robust Chinese Remainder Theorem

Given that the current preservation format structure is too short in terms of the number of input bits in the plaintext, its security cannot be fully guaranteed. In this paper, a Closed-form robust Chinese remainder theorem-based preservation format encryption algorithm is proposed, in which multiple sensitive data fields are first combined into plaintext according to the Closed-form robust Chinese remainder theorem, and the plaintext is encrypted in the preservation format, while the sensitive data is retrieved through the Closed-form robust Chinese remainder theorem for specific data retrieval. The experimental results show that the security performance of the Closed-form robust Chinese Remainder Theorem-based format-preserving encryption algorithm is further improved.
Assessing User Experience via Biometric Sensor Affect Detection

Traditional user experience assessments rely on self-report, human-system performance, and observational data that incompletely capture users' psychological demands, processing, or affect. Specifically, self-report measures require users to identify and articulate subjective responses to product features, yet users may not possess accurate awareness or may be unwilling or unable to express themselves. Similarly, human-system performance and observational measures require analysts to make inferences about hidden psychological states based on observed external patterns. This chapter discusses how biometric sensor-based affect detection technologies (e.g., eye tracking and EEG) may supplement traditional methods. By measuring biometric indicators of psychological states, researchers can gain potentially richer and more accurate insights into user experience. These technologies are gaining traction in educational technology development and functionality, and thus the extension of these tools for usability and user experience evaluation is highly feasible.
Relation-Aware Neighborhood Aggregation for Cross-lingual Entity Alignment

Cross-lingual entity alignment refers to linking entities in different language knowledge graphs if they are of identical meaning. Recent works focus on learning structure information of knowledge graphs and calculate the distance of entity embeddings for entity alignment. However, the GCN-based methods may bring noise from neighbors due to the heterogeneity of knowledge graphs. Besides, relations, as inherent attribute of knowledge graph, should be merged into the structure learning. In this paper, a relation-aware neighborhood aggregation model RANA is proposed to solve cross-lingual entity alignment task. The specific relation semantics are modeled to modify the aggregation weights of neighbors. CSLS and knowledge graph completion are introduced to enhance the alignment metric and structural information respectively. Experiments on real-world datasets demonstrate that RANA significantly outperforms other baselines in alignment accuracy and robustness.
Convolutional Neural Network Based Diagnosis System on Skin and Breast Cancers

Use of artificial intelligence in medicine makes a difference in diagnosis methods. A diagnosis system based on deep neural network can efficiently make predictions for many known diseases. Our study is to construct a cancer diagnosis system using CNN models. The cancer diagnosis system is capable of giving predictions on skin cancer and breast cancer with input images. The diagnosis model for skin cancer is AlexNet, and the model for breast cancer is VGGnet. Based on the two pre-trained CNN models, we use PyQt5 to develop the user interface and construct the diagnosis system. According to the test result, the skin cancer diagnosis model achieves about 80% accuracy, and the breast cancer model achieves about 85% accuracy. As for the diagnosis system, users can upload at most three images, select cancer type, and view the analysis results on the interface. In conclusion, our diagnosis system can accurately and efficiently present skin and breast cancer diagnosis results.
Big Data Processing on Cloud Computing Using Hadoop Mapreduce and Apache Spark

Size of the data used by enterprises has been growing at exponential rates since last few years; handling such huge data from various sources is a challenge for Businesses. In addition, Big Data becomes one of the major areas of research for Cloud Service providers due to a large amount of data produced every day, and the inefficiency of traditional algorithms and technologies to handle these large amounts of data. In order to resolve the aforementioned problems and to meet the increasing demand for high-speed and data-intensive computing, several solutions have been developed by researches and developers. Among these solutions, there are Cloud Computing tools such as Hadoop MapReduce and Apache Spark, which work on the principles of parallel computing. This chapter focuses on how big data processing challenges can be handled by using Cloud Computing frameworks and the importance of using Cloud Computing by businesses
Faculty Perception of Virtual 3-D Learning Environment to Assess Student Learning

This chapter presents some of the methods and approaches that are used in the assessment of student outcomes in 3-D virtual learning environments. Distance learning or eLearning that includes the 3-D virtual learning environments have been increasing dramatically because of the rise in demand by students. Because of this advent in eLearning, assessment of the outcomes is crucial when measuring student learning. In addition to the measurement of student learning, the instructional designers will need assessment of student learning as a means to upgrade the 3-D virtual learning environments and maintain the relevancy of the content for students. Much of the research on measurement of outcomes is based on anecdotal evidence provided by students. A comprehensive examination of assessment of the 3-D virtual learning environment becomes paramount, so educators will have a better idea how to carefully assess outcomes of student learning.
Metrics for Evaluating the Continuity Capabilities of Object Detection Systems

This paper proposes two novel metrics for evaluating the consistency of object detection systems on continuous video sequences. Its aim is to help differentiate between two such systems that have similar results if evaluated with traditional metrics, so two recomputed values for precision and recall are described. They are based on a continuity score, which received its name based on the fact that it favors detections that cover more grouped frames, rather than sparse sets of detections, for true positive detections. The opposite can be said about false positives, where fragmented ones are preferred. It relies on giving bonuses to detections that appear in consecutive frames, it allows a number of interruptions in such a series, before penalties are applied, and it is designed to be highly configurable. A procedure for verifying whether two different detections represent the same object is also described. Having this correlation method, the metrics can be used for simple object detection systems, but it can also be modified such that they are used with systems that assign ids to detected objects.
The Use of Digital Devices in Marketing Library Products in an Inclusively Engaged Academic Library

This chapter scrutinizes and identifies critical issues in the use of digital devices, opportunities and challenges in marketing library products and services in academic libraries. It surveys and examines empirical literature as a review method on the use of digital devices in an engaged academic libraries in marketing library products and services. From the findings, it was revealed that library products and services are underutilized because of lack of marketing strategies and programmes to create awareness of available resources and services in academic libraries. This resulted from negative attitude and behaviours towards reading culture among library users. The implications of this study is in the operations of academic libraries in various academic institutions and the community at large. This made easier accessibility of information and to retain library users to visit and use library products and services. Digital devices transform and widening up the reach, management, storage and delivery. It diversifies library users' demands stimulated by developments of technologies. Also, digital devices make library products and services more interactive hence maximize utilization of library products and services. They influenced attitudes, behaviours and perceptions of library users into a positive reading habits. Therefore, there is a great need to employ digital devices in marketing library products and services in academic libraries. The challenges involve library users' over expectations, diverse needs, wants and choices emanated from technological evolutions, poor ICT infrastructures, insufficient bandwidth, lack of knowledgeable librarians and copyright and intellectual property rights. Also, wide digital split in developing countries compared to developed world and the threats of technologies to librarianship which make librarians to remain redundant and their roles being replaced by digital devices.
Intercloud

Cloud computing is considered the next step in the evolution of technological applications, services and products development, in the Future Internet and Internet of Things context. The permanent connection of users to the Internet has become widespread, so almost all available resources (data, software) can be placed on the Internet, shared between users - sometimes completely independently of others, and brought from the Internet to the user's computer on demand. The chapter highlights the benefits of using hybrid technology with a focus towards entrepreneurs and business innovators. Intercloud systems are built of heterogeneous services, offered by different providers of cloud computing. To emphasize the business opportunities offered by innovative Intercloud approaches, an architecture for the interconnection of cloud services offered by multiple cloud providers, with applications to e-health, is presented. The main concepts and business benefits of using Intercloud systems and an architecture for interconnecting cloud services from multiple cloud providers is described.
Internal Threats and Countermeasures

A threat is a serious, impending, or recurring event that can result in loss and must be dealt with immediately. Internal loss prevention focuses on threats from inside an organization. Crimes, fires, and accidents are major internal loss problems. The insider threat includes the trusted employee who betrays their allegiance to their employer and commits workplace theft, violence, sabotage, espionage, and other harmful acts. The workplace can be subject to infiltration by spies, gangs, organized crime, and terrorists. Losses can result from full-time, part-time, and temporary employees; contractors; vendors; and other groups who have access to the worksite both physically and remotely. Without management support, efforts to prevent losses are doomed. A quality management team sets a foundation for protection strategies through support for budget requests and appropriate policies and procedures. Physical security strategies are being increasingly combined into integrated systems. Convergence of information technology and physical security means that both specializations and related technologies unite for common objectives. Efforts to secure access to organizational databases, email, and intranets are merging with access controls, fire and burglar alarm systems, and video surveillance.
Research On Audio Denoising Algorithm Of Power Generation Equipment In Thermal Power Plant Based On SGN

The power generation equipment of thermal power plants, especially the transfer equipment, will produce special noises when the operation fails. This type of sound can be used as an important basis for identifying the operating status of the equipment. However, the environment in the factory is noisy, and the audio data is seriously interfered by noise. Therefore, an SGN-based audio denoising algorithm is designed to remove the serious noise interference in the factory. First, perform Laplace transform on the audio signal, and extract the signal features in the complex frequency domain; then use the SGN network for denoising processing to remove the energy component of the noise. Experiments show that this algorithm has strong resilience to audio signals mixed into common noise models, and it also has obvious suppression of actual noise signals in the factory.
Imperfect Information Game in Multiplayer No-limit Texas Hold’em Based on Mean Approximation and Deep CFVnet

Imperfect information game in multiplayer no-limit Texas Hold’em is a critical challenge in AI research. Recent advanced solving approaches, such as deep CounterFactual Value networks(CFVnet) combined with continual resolving, provide a way to conduct depth-limited search in imperfect-information games. However, CFVnet has limited deployment in Heads-Up No-Limit Texas Hold’em, and is hard to scale to multiplayer setting. In this paper, we propose a novel algorithm, mean approximation, that effectively converting multi-agent interactions to two-agent interactions, and introduce a useful trick virtual action generation to solve conflicts occur in this conversion. Furthermore, we introduce several improvements to deep CFVnet applied in Texas Hold’em Poker. We combined all above improvements and extensions of CFVnet to create our poker AI MuCFVnet, unlocking the potential of deep CFVnet. Experimental results show that MuCFVnet has strong performance, successfully beats some available public multiplayer poker AI.
Application Analysis of Computer Software Security Vulnerability Detection Based on Web Crawler

With the rapid development of the Internet, the World Wide Web has become a carrier of a large amount of information. In order to effectively extract and use this information, web crawlers that crawl various web resources have emerged. The interconnectedness, openness, and interactivity of information in the World Wide Web bring great convenience for information sharing to the society and they also bring many security risks. To protect resource information, computer software security vulnerabilities have become the focus of attention. This article is based on the method of computer software security detection under a web crawler simply analyzes the basic concepts of computer software security detection and analyzes the precautions in the process of security detection. Finally, combined with the computer software security vulnerability problems in the web crawler environment, its security detection technology Application for further analysis.
A Neuroevolutionary Approach for Opponent Modeling and Exploitation in No-limit Texas Hold’em Poker

In recent years, the research of solving incomplete information games such as Texas Hold’em Poker game promotes the development of Artificial Intelligence(AI), which includes a variety of advanced theories and approaches such as game theory, reinforcement learning, neural network, tree search and so on. As a landmark area in AI, interest in Texas Hold’em Poker game has never dropped off, since the breakthroughs in this area can be potentially applied to many real-world problems varying from financial negotiations to military confrontations. In this paper, we proposed a new framework with population-based neuroevolution for the problem of opponent modeling and exploitation in Texas Hold’em Poker. The framework composes of two core connected components: opponent network and game network. The opponent network is used to capture the opponents’ significant features that can be exploited and the pre-trained game network can thus make decisions accordingly. The game network is an end-to-end recurrent neural network and is evolved with a genetic algorithm in the training process. The feasibility of this scheme is verified through preliminary experiments in specific game scenarios.
IMMUNE-INSPIRED METHOD FOR SELECTING THE OPTIMAL SOLUTION IN SEMANTIC WEB SERVICE COMPOSITION

The increasing interest in developing efficient and effective optimization techniques has conducted researchers to turn their attention towards biology. It has been noticed that biology offers many clues for designing novel optimization techniques, these approaches exhibit self-organizing capabilities and permit the reachability of promising solutions without the existence of a central coordinator. In this paper we handle the problem of dynamic web service composition, by using the clonal selection algorithm. In order to assess the optimality rate of a given composition, we use the QOS attributes of the services involved in the workflow as well as, the semantic similarity between these components. The experimental evaluation shows that the proposed approach has a better performance in comparison with other approaches such as the genetic algorithm
Digital Libraries and the Role of Digital Librarians

This chapter presents a holistic view of digital libraries and the role of digital librarians. Technological changes and the use of electronic storage and retrieval information systems have changed the way students and researchers are able to access, retrieve and use information. Key topics presented in the chapter include the concept of digitization, reasons for digitization, definitions of digital library, features of digital library, characteristics of digital libraries, advantages and disadvantages of digital library, collection development and content creation, digital librarianship, role of librarians in digital libraries, issues of concern to digital librarians, process of digitization of library resources, digital library components, and digital rights management.
More About Entropy

This chapter returns to the subject of the entropy of a training set. It explains the concept of entropy in detail using the idea of coding information using bits. The important result that when using the TDIDT algorithm information gain must be positive or zero is discussed, followed by the use of information gain as a method of feature reduction for classification tasks.
Using Decision Trees for Classification

This chapter introduces the TDIDT (Top-Down Induction of Decision Trees) algorithm for inducing classification rules via the intermediate representation of a decision tree. The algorithm can always be applied provided the ‘adequacy condition’ holds for the instances in the training set. The chapter ends by distinguishing three types of reasoning: deduction, abduction and induction.
Definition of a Pervasive Architecture for a Central Monitoring of Homecare Systems

Every year the queues in hospitals publics and privates grows due to, among others, the increasing of the world population and the delay in the patients service. This is a serious problem faced by administrators of hospitals, which believe that it is increasingly difficult to offer a service of quality to those who search for them. One of the ways to decrease these queues is through the development of homecare systems that allow the patient to receive the clinic treatment directly in his house. The development of these kinds of systems would help to decrease the queues and consequently, would improve the attendance of those who goes to the hospitals looking for assistance. Considering this, this work has as main purpose to present the architecture modeling of a pervasive system to be applied in homecare environments. The pervasive systems developed from this modeling aim to improve the services provided by healthcare professionals in the treatment of patients that are located in their houses. The architecture proposed by the methodology uses concepts of pervasive computing to provide access to information any- time and wherever the user is, once that a homecare environment has a high level of dynamicity. The knowledge representation of the homecare environment needed in the modeling of the architecture is made through ontologies due to the possibility of reuse of the information stored, as well as the interoperability of information among different computational devices. To validate the proposed methodology, we present two use cases, which are also used to demonstrate the workflow of the pervasive system of homecare.