Reviewerly/exHarmony huggingface dataset:
State of the Art

This chapter discusses the state of the art of Minimum Time Search (MTS) problem, analysing with greater detail several works that have motivated this thesis. The chapter is divided into two sections. The first one discusses, from a general point of view, related probabilistic search problems such as coverage or the Travelling Salesman Problem (TSP), stressing their common characteristics and differences with MTS. The second section analyzes in more detail the state of the art of Probabilistic Search (PS), which aims to find the best Unmanned Vehicles (UV) search trajectories in uncertain environments and which encompasses the MTS problem.
Explainable Artificial Intelligence for Person Identification

There is an increasing demand for explainable models in various applications. We interleave here explanations generated both from black-box models and white-box models. To achieve this we employ (i) a local model-agnostic technique (LIME), (ii) a theoretic game approach (SHAP), (iii) an example-based explanation technique (ExMatchina), and (iv) a model-specific method (KTrain). The running scenario is to identify persons from images and to explain the algorithmic decision. First, we extract features from images in order to train a white-box model and deploy explanation methods that work with tabular data. Second, we take advantage of transfer learning and fine-tuning to train black-box models and deploy image-based explanations methods. We aim to increase the interpretability of the system by providing explanations. We use the results to determine whether or not decisions are valid or if explanations are viable. Although many explanations techniques have been developed, there are no appropriate performance metrics to evaluate them. We propose two metrics to evaluate explanations quality. Based on these metrics, we demonstrate that LIME is unstable as it generates different explanations for same instance at multiple runs. Advantages and disadvantages of explanations techniques are also discussed and a user-grounded evaluation is performed. The evaluation study reveals that several explanation techniques were pre-ferred by participants, followed by features listing, example-based explanations and pixels-based explanations.
The Much Needed Security and Data Reforms of Cloud Computing in Medical Data Storage

Cloud computing has shifted our old documents up into the clouds, with the advancement of technology. Fast-growing virtual document storage platforms provide amenities with minimal expense in the corporate society. Despite living in the 20th century, even the first world countries have issues with the maintenance of document storage. Cloud computing resolves this issue for business and clinic owners as it banishes the requirement of planning, provisioning, and allows corporations to advance their filling system according to service demands. Medical practices heavily, rely on document storage as; almost all information contained in medical files is stored in a printed format. Medical practices urgently need to revolutionize their storage standards, to keep up with the growing population. The traditional method of paper storage in medical practice has completely been obsolete and needs to improve in order to assist patients with faster diagnosis in critical situations. Obtaining Knowledge and sharing it is an important part of medical practice, so it needs immediate attention to reach its full service potential. This chapter has analyzed content from literature that highlights issues regarding data storage and recommends solution. This inquiry has found a useful tool that can be beneficial for the development of this problem which is, ‘data mining' as it gives the option of predictive, and preventative health care options, when medical data is searched. The functionality and worthiness of each algorithm and methods are also determined in this study. By using cloud and big data services to improve the analysis of medical data in network of regional health information system, has huge advancements that assure convenient management, easy extension, flexible investment, and low requirements for low technical based private medical units.
Fuzzy Twin SVM Investment Evaluation Model for Listed Company

With China’s market economy development, more and more investors focus on the analysis of listed company investment value, such as wisdom Agriculture sector, expecting to get good investment reference. Mixed listed companies financial status, coupled with that the stock market is a complex system, it is difficult to accurately predict the future trend. But a healthy company after a long-term stable operation is bound to achieve substantial profit. The fuzzy clustering method is constructed to screen financial indicators systems that affect listed companies’ investment value by avoiding risk philosophy for a sound investment. Financial data from the previous year of 963 companies were made as training samples to establish data mining model of the investment value in the listed companies based on Twin SVM, then predict the investment value of the companies in next year based on the data of this year. And based on the same test samples, traditional SVM and twin SVM were tested and compared. Results show that, our Twin SVM can obtain higher accuracy and faster calculation speed than ordinary SVM.
Better Security and Encryption Within Cloud Computing Systems

Better security and encryption is necessary with regard to all forms of Cloud Computing, Cloud Infrastructure, and Cloud Storage. Areas that are affected the hardest by security breaches include: retail/e-commerce, communications, transportation, and banking. Illustrated within this article are ways that companies such as Walmart, Verizon, Wells-Fargo, and BWM would be affected by a lapse in security and/or a breach in their Cloud Infrastructure. In this article issues that can magnify these breaches and data loss is discussed as it relates to Cloud Structure and Cloud Services based on known vulnerabilities and lack of product testing. This article concludes with why it is necessary to have Public Policies as part of the governing system on Cloud Computing, Cloud Infrastructure, and Cloud Storage
Occluded Pedestrian Attention Network : An occluded pedestrian Detector

Pedestrian detection performance improves greatly with the development of convolution neural network. However, occlusion remains a challenging issue. To solve the problem, in this paper, we proposed a new pedestrian detector called Occlude Pedestrian Attention Network (OPAN). We use anchor settings, which will highlight the features from the pedestrian region. In addition, spatial-wise attention mechanism and channel-wise are used for regression and classification branch separately and guide the detector to pay more attention to the visible part of the pedestrian. Experiments on Caltech and CityPerson datasets show that the proposed algorithm has better performance than RetinaNet. Compared with RetinaNet, the performance is improved 10.65% and 8.44%. Compared with other latest pedestrian detection algorithms, the performance is also great.
Cloud Computing

Cloud computing makes use of many standard technologies in surprisingly novel ways. It leaves many people bewildered, even confused, especially as it is an area of computing that is undergoing rapid change in terms of a diversification of available services and the growth in underlying contingent technologies. This chapter shows how the cloud has changed the user experience and goes on to discuss the definition of cloud computing most commonly used. It describes the three service models of infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) and then discusses the four deployment models of private cloud, public cloud, hybrid cloud, and community cloud. It concludes with how the cloud impacts security and how most organizations will likely make use of multiple cloud providers. This chapter introduces key concepts and characteristics of cloud computing, in this way covering in broad terms what it is and how it essentially works.
Genetic Algorithm (GA) Methodology and Its Internal Working

Many practitioners are shy with implementing GAs. Due to this, a lot of researchers avoid using GAs as problem-solving techniques. It is desirable that an implementer of GA must be familiar in working with high-level computer languages. Implementation of GA involves complex coding and intricate computations which are of a repetitive nature. GAs if not implemented with caution will result in vague or bad solutions. This chapter overcomes the obstacles by implementing and defining various data structures required for implementing a simple GA. They will write various functions of GA code in C ++ programming language. In this chapter, initial string population generation, selection, crossover, and mutation operator used to optimize a simple function (one variable function) coded as unsigned binary integer is implemented using C ++ programming language. Mapping of fitness issue is also discussed in application of GAs.
Advances in Digital Forensics Frameworks and Tools

Digital forensics science is a well-known initiative to unearth computer-assisted crimes. The thriving criminal activities using digital media have changed the typical definition of a traditional crime. Meanwhile, the means and targets of criminal activities have been transformed in a broader context due to the diverse nature of offenses associated with the multiple crime categories, affecting the way of investigations as well. In order to withstand the difficulties caused due to the crime complexity, forensics investigation frameworks are being tuned to adjust with the nature and earnestness of the felonies being committed. This article presents an in-depth comparative survey of fourteen popular and most cited digital forensics process models and various forensics tools associated with different phases of these models. The relationships among these forensics process models and their evolutions are analyzed and a graph-theoretic approach is presented to rank the existing process models to facilitate investigators in selecting an appropriate model for their investigation tasks.
Juror Interpretations of Metadata and Content Information: Implications for the Going Dark Debate

The increasing prevalence of encryption in consumer devices and services has led to a fierce debate over whether the loss of potential evidence due to encryption will be offset by the increase in evidence available from electronic metadata. One major question raised by this debate is how jurors will interpret and value metadata as opposed to content information. Though there are plausible arguments in favor of the persuasive power of each type of evidence, to date no empirical study has examined how ordinary people, potential jurors, view each of these sorts of evidence.We address this issue through a series of survey experiments that present respondents with descriptions of hypothetical criminal trials. Respondents were randomly assigned to case descriptions where the key piece of evidence was either metadata (for example, cell phone location records) or content information (for example, text messages conveying the defendant’s location or state of mind). Data from three studies shows that the relative power of content and metadata information is highly contextual. Content information and metadata can be equally useful when they convey information that is logically equivalent; there is no bias in favor of either. Yet content information conveys additional meaning in circumstances where the defendant’s state of mind is critical. And metadata can more convincingly establish a pattern of behavior. In sum, this suggests that the rise of encryption will have a highly heterogeneous effect on criminal cases, with the direction of the effect depending on the exact nature of the facts that the prosecution must prove.
Recommendation of a Webpage by using Web Mining Technique

In today's world of the Internet, different varieties of content are created in enormous amounts, so to give pertinent outcomes to clients, web suggestions become a significant piece of web applications. Various web proposals are made accessible to clients consistently, including pictures, recordings, Audio, question ideas, and site pages. In our research, we are targeting giving a system to website page proposals. 1) describing the fundamental technique of web mining 2) Web mining technique explanation.3) We propose the engineering for the customized website page suggestion
Differential Evolution Algorithm Optimized ELM Fault Diagnosis of Flight Control System Actuator

The effect of the algorithm based on ELM (Extreme Learning Machine) and improved differential evolution algorithm on fault diagnosis of the flight control system actuator is studied. As a machine learning algorithm, ELM has the characteristics of fast speed and high precision. In order to further improve the generalization performance of ELM training, the differential evolution algorithm is combined with the ELM algorithm to improve its fault diagnosis effect. First, the 3DOF(three-degree-of-freedom) aircraft fault diagnosis experimental platform is used to collect the fault data of the actuator; then the global iterative optimization of the differential evolution algorithm is used to do optimization on the network parameters trained by the ELM with the fault data as well as the threshold, the training results show that the network algorithm has better generalization performance; finally, the trained model is used to detect the same type of actuator fault data. According to the experimental results, the method performs well in fault diagnosis, which shows the effectiveness of the method.
Awareness-Based Recommendation Toward a New Preference

In mechatronics and robotics, one of the important issues is to design human interface. There are two issues on interaction design research. One is the way to education and training to adapt humans for operating the robots or interaction systems. Another one is the way to make interaction design adaptable for humans. This chapter research at the latter issue. This chapter describes the interactive learning system to assist positive change in the preference of a human toward the true preference; then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the system improves the efficiency for deciding the most preferred plan for both heavy users and light users.
Extended Sampled Tree Ensembles for Classification and Regression

Extended Sampled Trees (XSTrees), is a novel tree ensemble machine learning method we introduce in this paper for classification and regression. Instead of learning a single decision tree like CART, or an independent collection of trees like Random Forests or Gradient Boosting methods, XSTrees learns the entire probability distribution over the tree space. This approach results in good theoretical guarantees, but also has a significant edge over other ensemble methods in terms of performance. Analytically, we prove that XSTrees converges to the true underlying tree model with rate O(log(n)/n)Ùè∞â, where n is the number of training observations, through averaging a sequence of local optimums of the CART estimation problem. Experimentally, we show on publicly available datasets, synthetic data, and two real-world case studies - including one with a large online retailer - that XSTrees is very competitive with the state-of-the-art predictive models, with an average accuracy betwee%||| and 50% higher than competitors for classification and an average R2 between 2% and 85% higher for regression.
Handwriting systems

This chapter describes and illustrates by example many handwriting systems and the importance they have in learning to communicate through writing. With increasing frequency, today's schools are not teaching students to write cursively. Both structured systems and the influence of friends and teachers who may have learned to write by different systems influence the student and the formation of their writing. Reduction or absence in the teaching of formal handwriting systems has led to further reliance on teachers and fellow students’ writing styles.
Blind Detection of Partial-Color-Manipulation Based on Self-PRNU Estimation

This article describes how to detect color manipulation which is a commonly used method in the field of digital image forgery. The difficulty that hue forgery does not change the image edges, shapes and gradations brings certain challenge to authenticity detection. Current methods utilize the PRNU from multiple un-tampered images, requiring the camera type to be known. However, the increasing varieties of digital devices greatly complicates the preparation of prior knowledge. This article proposes a blind detection method for partial color manipulation based on self-PRNU of suspicious image, eliminating the necessity of acquiring camera information. The authors estimate the PRNU of suspicious image by removing the regions due to its texture complexity. The tamper region is detected by calculating the correlation between estimated PRNU and residual noise. As to partial manipulation detection, an introduced threshold of connected components is used to reduce the false positive. The experimental results show that the method can effectively detect and locate the partial color manipulation.
More Than a Suspect: An Investigation into the Connection Between Data Breaches, Identity Theft, and Data Breach Notification Laws

Abstract This article investigates the relationship between data breaches and identity theft, including the impact of Data Breach Notification Laws (DBNL) on these incidents (using empirical data and Bayesian modeling). We collected incident data on breaches and identity thefts over a 13-year timespan (2005–2017) in the United States. Our analysis shows that the correlation is driven by the size of a state. Enacting a DBNL still slightly reduces rates of identity theft; while publishing breaches notifications by Attorney Generals helps the broader security community learning about them. We conclude with an in-depth discussion on what the European Union can learn from the US experience.
Improved Checkpoint Using the Effective Management of I/O in a Cloud Environment

One of the most important points for more effective use in the environment of cloud is undoubtedly the study of reliability and robustness of services related to this environment. In this case, fault tolerance is necessary to ensure that reliability and reduce the SLA violation. Checkpointing is a popular fault tolerance technique in large-scale systems. However, its major disadvantage is the overhead caused by the storage time of checkpointing files, which increases the execution time and minimizes the possibility to meet the desired deadlines. In this chapter, the authors propose a checkpointing strategy with lightweight storage. The storage is provided by creating a virtual topology VRbIO and the use of an intelligent and fault tolerant I/O technique CSDS (collective and selective data sieving). The proposal is executed by active and reactive agents and it solves many problems of checkpointing with standard I/O. To evaluate the approach, the authors compare it with a checkpointing with ROMIO as I/O strategy. Experimental results show the effectiveness and reliability of the proposed approach.
A Survey of Computational Intelligence Algorithms and Their Applications

This chapter subscribes in the framework of an analytical study about the computational intelligence algorithms. These algorithms are numerous and can be classified in two great families: evolutionary algorithms (genetic algorithms, genetic programming, evolutionary strategy, differential evolutionary, paddy field algorithm) and swarm optimization algorithms (particle swarm optimisation PSO, ant colony optimization (ACO), bacteria foraging optimisation, wolf colony algorithm, fireworks algorithm, bat algorithm, cockroaches colony algorithm, social spiders algorithm, cuckoo search algorithm, wasp swarm optimisation, mosquito optimisation algorithm). We have detailed each algorithm following a structured organization (the origin of the algorithm, the inspiration source, the summary, and the general process). This paper is the fruit of many years of research in the form of synthesis which groups the contributions proposed by various researchers in this field. It can be the starting point for the designing and modelling new algorithms or improving existing algorithms.
Some criteria for GTA evaluation in business administration and psychology

Many papers claim to have used GTA, but in empirical studies of Organizational Behaviour and Psychology, illustration of cognitive concepts and categories, that give meaning the participant’s experience, is not a theory. Conventionally, the different GTA versions have been conceptually classified by descriptive researchers from the viewpoint of epistemology, but the differences between the generated GTs have not been theoretically evaluated from the viewpoint of social science. Therefore, this paper evaluates each GTA version in terms of ‘social interaction’ and ‘theory’, which form the basis of GTA, by theoretically comparing the differences between representative GTs using conceptual diagrams. As a result, the utility of the original version and M-GTA were found. The following criteria were obtained to enable evaluation of papers that used GTAs: （1） Being an interpersonal interaction that can be abstracted one-to-one and include thought, emotion and behaviour; （2） Examining scope and parsimony in theoretical saturation. In addition, a new criterion for theoretical saturation was proposed for ‘relationships between main categories.’