Reviewerly/exHarmony huggingface dataset:
Classification Using Decision Tree Ensembles

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientistâ€™s methodological toolbox.
Gödel justification logics and realization

Abstract We study the topic of realization from classical justification logics in the context of the recently introduced Gödel justification logics. We show that the standard Gödel modal logics of Caicedo and Rodriguez are not realized by the Gödel justification logics and moreover, we study possible extensions of the Gödel justification logics, which are strong enough to realize the standard Gödel modal logics. On the other hand, we study the fragments of the standard Gödel modal logics, which are realized by the usual Gödel justification logics. We prove the corresponding realization theorem by using Fitting’s merging of realizations as well as appropriate hypersequent calculi on the modal side, adapting the work by Metcalfe and Olivetti. For these hypersequent calculi, we also show a cut-elimination theorem. We provide natural semantical characterizations for all of these newly introduced logics.
Sentiment Analysis Based Product Selection for Enhancing E-Commerce

One of the fast growing, developing and highly used technology in various computing industries is data mining. Sentiment or opinion mining is a kind of data mining, where it follows the major processes of natural language processing. Nowadays, sentiment analysis meets a high demand. In this paper, it is aimed to consider the problems of sentiment analysis such as classification on opinion and attribute words, because it is the basic problem of sentiment analysis. This paper aimed to use one of the popular machine learning algorithms as MultiClass Support Machine algorithm for classifying sentiment polarity with detailed description. The proposed method is implemented in Python software and experimented on onlineproduct-reviews data taken from Amazon.com. Sentence level and opinion level classification is obtained with promised outcomes. From the results it is noted that the proposed method outperforms than the existing method such as Naïve Bayes and Random Forest algorithms
Bayesian Learning: Approximate Inference and Nonparametric Models

This chapter is the second one dedicated to Bayesian learning. The emphasis here is on more advanced topics, dealing with approximate inference methods. Two paths for approximate inference, known as variational techniques, are discussed. One is based on the mean field approximation and the lower bound interpretation of the EM, and the other on convex duality and variational bounds. Regression and mixture modeling are discussed in this framework. Emphasis is given to sparse Bayesian modeling techniques and hierarchical Bayesian models. The relevance vector machine framework is presented. Expectation propagation is also discussed as an alternative to variational methods for approximate inference. At the end of the chapter, Bayesian learning in the context of nonparametric models is presented, including Dirichlet processes, the Chinese restaurant process (CRP), the Indian buffet process (IPB), and Gaussian processes. Finally, a case study concerning hyperspectral imaging is presented.
ReLF: Scalable Remote Live Forensics for Android

The world has witnessed the proliferation of mobile technologies as well as smartphone-related cybercrimes in recent years. However, due to high mobility of smartphones and tablets and transient nature of those attacks, previous forensic approaches become inadequate to retrieve forensic data and respond to cybersecurity incidents in time, especially when the investigation involves a large number of mobile devices. In this paper, we propose ReLF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Source code available at https://github.com/nexus-lab?q=relf, a remote live forensics system for Android smartphones and tablets. ReLF enables forensic investigators to effectively triage operating Android devices and acquire a wide range of forensic artifacts at scale. Compared to existing Android forensic tools that are publicly available, ReLF provides a much more comprehensive set of collectible artifacts and better OS compatibility. Our evaluation results demonstrate that the ReLF client only introduces minor energy overhead to Android devices and that the ReLF server can well handle a large number of Android devices with increasing workload. We also showcase how ReLF can be used in real-world forensic investigation through case studies.
Real time counting system of glass bottle based on Multi Objects tracking

Glass bottle counting is a common work in the field of intelligent manufacturing, but there are also some problems, such as the reflection of glass bottles, high similarity, which makes it difficult to track and so on. Accurate and stable target detection and target tracking is the key technology of glass bottle counting. In order to improve the problems of wrong tracking, missing tracking and low efficiency in the existing glass bottle real-time counting system. In this paper, a real-time tracking and counting system of glass bottle is presented, and we propose the feature pyramid network (FPN) and pyramid attention network (PAN) network, and uses the cross stage local network (CSP) structure for multi-scale feature map to realize the accurate detection of glass bottle mouth. At the same time, the cascade matching module and the union intersection distance (DIOU) module are fused to further effectively distinguish the matched tracking target from the unmatched tracking target and the detection results, so as to improve the speed and accuracy of glass bottle mouth tracking.
Algorithmic Governance Policy and Implementation Approaches in the United States

This article seeks to illustrate various ways in which governmental and nongovernmental entities in the United States are “governing” the development and deployment of algorithms in different industries and contexts. “Algorithm” here refers to the use of a digital process or set of rules embedded in software designed, but not always executed by, human beings to accomplish a task. Due to advances in computing power and the collection of large amounts of data, a growing number of government and private sector actors are deploying algorithms to make decisions, accomplish tasks, and understand complex systems. The intent of this paper is not to provide an exhaustive review of legal, regulatory, and policy mechanisms that apply to the broad category of artificial intelligence (AI), but instead to present a series of examples that demonstrate the distributed and dynamic nature of algorithmic governance in the United States.
Human Brain Hippocampus Segmentation Based on Improved U-net Model

The hippocampus segmentation in MRI is of great significance for the diagnosis, treatment decision and research of neuropsychiatric diseases. Manual segmentation of the hippocampus is very time-consuming and has low repeatability. With the development of deep learning, great progress has been brought about in this regard. In this paper, the U-net model is selected to realize the automatic segmentation of the hippocampus, and the residual module is added to the U-net segmentation network to speed up the network convergence. Aiming at the characteristics of the hippocampus in the brain MRI image such as blurry edges, irregular shapes, and small size, the Laplacian algorithm is used to sharpen and filter the original image to make the details and edges of the brain image clearer. The enhanced picture can effectively improve the segmentation effect. Finally, the Dice coefficient on the test set reached 90.14%.The experimental results show that the pre-processed images use this segmentation model to achieve accurate segmentation of the hippocampus in the brain MRI, which can assist doctors in better diagnosis.
Particle Filtering

In this chapter, sequential sampling techniques are considered. Kalman filtering is viewed in terms of probabilistic arguments as a special case of a linear dynamic system, where the involved variables follow Gaussian distributions. Particle filtering techniques are then considered as a vehicle to treat more general nonlinear models and/or non-Gaussian random variables. They are introduced as a special instance of the more general family of sequential sampling methods. Different schemes are discussed, such as the generic particle and the auxiliary particle filtering algorithms.
Improving Haskell

Lazy evaluation is a key feature of Haskell, but can make it difficult to reason about the efficiency of programs. Improvement theory addresses this problem by providing a foundation for proofs of program improvement in a call-by-need setting, and has recently been the subject of renewed interest. However, proofs of improvement are intricate and require an inequational style of reasoning that is unfamiliar to most Haskell programmers. In this article, we present the design and implementation of an inequational reasoning assistant that provides mechanical support for improvement proofs, and demonstrate its utility by verifying a range of improvement results from the literature.
A Framework for Protecting Users' Privacy in Cloud

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge. The main focus is to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.
SpeakQL

Speech-based inputs have become popular in many applications on constrained device environments such as smartphones and tablets, and even personal conversational assistants such as Siri, Alexa, and Cortana. Inspired by this recent success of speech-driven interfaces, in this work, we consider an important fundamental question: How should one design a speech-driven system to query structured data? Recent works have studied new querying modalities like visual [4, 8], touch-based [3, 7], and natural language interfaces (NLIs) [5, 6], especially for constrained querying environments such as tablets, smartphones, and conversational assistants. The commands given by the user are then translated to the Structured Query Language (SQL). But conspicuous by its absence is a speech-driven interface for regular SQL or other structured querying. One might wonder: Why dictate structured queries and not just use NLIs or visual interfaces? From a practical standpoint, many users, including in the C-suite, enterprise, Web, and other domains are already familiar with SQL (even if only a subset of it) and use it routinely. A spoken SQL interface could help them speed up query specification, especially in constrained settings such as smartphones and tablets, where typing SQL would be painful. More fundamentally, there is a trade-off inherent in any query interface, as illustrated in Figure 1(A).
Augmented reality on the Internet of Things

The directions of augmented reality development in the sphere of production are considered. The advantage of this technology over virtual reality is given. Security is indicated as the main problem of the Internet of Things concept. MQTT protocol is indicated as an opportunity to correct this problem globally for application at companies. When considering the visualization of large amounts of data obtained through the Internet of things, it is indicated that augmented reality is well suited. Thanks to it, a person, directly looking at the desired object, is able to see all the necessary information about its work in real time.
Retooling GSM-based Services to Link Rural Farmers with Knowledge Experts

Information is one of the most important elements that support the agriculture industry today. ICT-based information systems such as social media, websites, multimedia, mobile apps and other technology driven initiatives were initiated by organizations to disseminate knowledge products. However, with most third world country’s poor ICT infrastructure furthered by the involvement of technology deprived users, an ICT tool to share information has to be chosen well noting its availability, applicability and practicality for the common end-users. This quantitative research explores and establishes the use of GSM-based services for its potential role as an alternative communication and information sharing/access tool for both rural farmers and knowledge experts such as the academe. It is a fact that basic or traditional way of texting and voice calls have been in use in the past decade as communication tool. However, this study’s aim is to establish a theoretical GSM-based model framework that integrates new technological and management features to upscale this service thus, the term “retooling”. The result of this study will be the basis of the actual implementation of the framework as the succeeding part of this study. This paper’s output can be used as guide for researchers, academicians, extension service workers, project managers as well as organizations planning to offer similar technology-based services in various fields.
Improving the Effect of Frequent Itemset Mining with Hadamard Response under Local Differential Privacy

Frequent itemset mining is a basic data mining task and has many applications in other data mining tasks. However, it is likely that the user's personal privacy information may be leaked in the mining process. In recent years, applying the local differential privacy protection model to mine all frequent itemsets is a relatively reliable and secure protection method. In local differential privacy, users first perturb the original data then send it to the aggregator, which prevents the aggregator leaking user's private information throughout the process. There are two major problems with data mining using local differential privacy, one is that the accuracy of the results is relatively low after mining, and the other is that the user transmits too much data to the server, which results in higher communication costs. In this paper, we use Hadamard response algorithm to improve the accuracy of the results while reduce the communication cost. Finally, we use FP-tree for frequent itemset mining to compare the Hadamard response with previous algorithms.
Multi-task Deep Reinforcement Learning with Evolutionary Algorithm and Policy Gradients Method in 3D Control Tasks

In deepImai, Shota reinforcementSei, Yuichi learning, it is difficultTahara, Yasuyuki to converge whenOrihara, Ryohei the explorationOhsuga, Akihiko is insufficient or a reward is sparse. Besides, on specific tasks, the amount of exploration may be limited. Therefore, it is considered effective to learn on source tasks that were previously for promoting learning on the target tasks. Existing researches have proposed pretraining methods for learning parameters that enable fast learning on multiple tasks. However, these methods are still limited by several problems, such as sparse reward, deviation of samples, dependence on initial parameters. In this research, we propose a pretraining method to train a model that can work well on variety of target tasks and solve the above problems with an evolutionary algorithm and policy gradients method. In this method, agents explore multiple environments with a diverse set of neural networks to train a general model with evolutionary algorithm and policy gradients method. In the experiments, we assume multiple 3D control source tasks. After the model training with our method on the source tasks, we show how effective the model is for the 3D control tasks of the target tasks.
Introduction

This chapter introduces the reader into the problem of image segmentation using Metaheuristic Algorithms and presents the intuition of the principal concepts relevant to this book, starting with the segmentation of digital images which is a family of methodologies whose objective is to obtain underlying structures present on pictures to facilitate the interpretation of the content at a high level; for example, to obtain borders or grouping of pixels that form regions with some common property. Within the family of segmentation methods, there is a process known as thresholding. The goal of image thresholding is to separate groups of pixels through the optimal selection of specific pixel values (thresholds) to partition the image. To avoid an exhaustive search, the selection of threshold values can be reformulated as the minimization or maximization of a function, where optimization tools are essential. From all the optimization methods available on the literature, Metaheuristic Algorithms have been widely used for image thresholding with important results.
A Generative Adversarial Network with Attention Module for Unpaired Image-to-Image Translation

The Image-to-Image Translation (I2IT) is a challenging image processing task that can be applied to many aspects, such as super-resolution and style transfer. Although several image translation algorithms based on Generative Adversarial Network (GAN) have been proposed, achieving better translation effects still remains a problem worthy of attention. This work proposes a model that fuses an attention module and ResNet-based generator to enhance the performance of I2IT. Using an attention module after the first downsampling, our model can focus more on important low-level semantic features. After the downsampling, the residual blocks provide contextual supplementary information of the photos. The qualitative and quantitative experimental results on unpaired datasets show that our model is better than the SOTA methods, which further confirms the robustness and effectiveness of the proposed model.
A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.
Atom counting

In this chapter, a statistical model-based method to count the number of atoms of monotype crystalline nanostructures from high-resolution annular dark-field (ADF) scanning transmission electron microscopy (STEM) images is discussed in detail together with a thorough study on the possibilities and inherent limitations. We show that this method can be applied to nanocrystals of arbitrary shape, size, and atom type. The validity of the atom-counting results is confirmed by means of detailed image simulations and it is shown that the high sensitivity of our method enables us to count atoms with single atom sensitivity.
Generation and Control of Color Flame Image Based on GAN

In order to improve the shortcomings of current flame image generation methods based on coding control, this paper proposes to describe the flame shape by the flame height and width, and generate corresponding color rectangles in advance according to the flame height and width. This approach converts the encoding into a more informative graph as a conditional input to the generator. The experiment shows that the flame height and width can be improved effectively by controlling the generated flame in graphic form. The IOU of flame achieves 0.906 and the authenticity achieves 91.4%.
SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure

This paper proposes the use of Sequential Monte Carlo samplers (SMCs) in Bayesian Optimisation and the discovery of an additive kernel structure. We present a comparison between Markov chain Monte Carlo (MCMC) and SMCs in both settings. SMCs are capable of finding the structure of an unknown function well. SMC samplers show comparable results to MCMC in this application and have the advantage of not always requiring a burn-in period.
GPS and IMU Fusion for Human Gait Estimation

This paper proposes a framework for fusing information coming from an independent inertial measurement unit (IMU) and global positioning system (GPS) to deliver robust estimation of human gait. Because these two sensors provide very different kinds of data at different scales and frequencies, a novel approach which fuses global trajectory estimates and back-propagates this information to correct step vectors is put forth here. In several high-fidelity simulations, the proposed technique is shown to improve step estimation error up to 40% in comparison with an IMU-only approach. This work has implications for not only in-the-field biomechanics research, but also cooperative field robotic systems where it may be critical to accurately monitor a person’s position and state in real-time.
Polyteam semantics

Abstract Team semantics is the mathematical framework of modern logics of dependence and independence in which formulae are interpreted by sets of assignments (teams) instead of single assignments as in first-order logic. In order to deepen the fruitful interplay between team semantics and database dependency theory, we define Polyteam Semantics in which formulae are evaluated over a family of teams. We begin by defining a novel polyteam variant of dependence atoms and give a finite axiomatization for the associated implication problem. We relate polyteam semantics to team semantics and investigate in which cases logics over the former can be simulated by logics over the latter. We also characterize the expressive power of poly-dependence logic by properties of polyteams that are downwards closed and definable in existential second-order logic ($\textsf{ESO}$). The analogous result is shown to hold for poly-independence logic and all $\textsf{ESO}$-definable properties. We also relate poly-inclusion logic to greatest fixed point logic.