Reviewerly/exHarmony huggingface dataset:
Efficient Bag-of-Features using Improved Whale Optimization Algorithm for Histopathological Image Classification

Background: The whale optimization algorithm is one of the popular meta-heuristic algorithms which has successfully been applied in various application areas such as image analysis and data clustering. However, the slow convergence rate and chances of sticking into the local optima due to improper balance of its exploration and exploitation phases are some of its pitfalls. Therefore, in this paper, a new improved whale optimization algorithm has been proposed. Moreover, the proposed method has been used in bag-of-features method for histopathological image classification. Methods: The new algorithm, improved whale optimization algorithm, modifies the encircling phase of original whale optimization algorithm. The proposed algorithm has been used to cluster the extracted features for finding the relevant codewords to be used in the bag-of-features method for histopathological image classification. Results: The efficiency of proposed algorithm has been analyzed on 23 benchmark functions in terms of mean fitness, standard deviation values, and convergence behavior. The performance of the improved whale optimization algorithm based histopathological image classification method has been analyzed on blue histology image dataset and compared with other meta-heuristic based bagof- features methods in terms of recall, precision, F-measure, and accuracy. The experimental results validate that the proposed method outperforms the considered state-of-the-art methods and attains 12% increase in the histopathological image classification accuracy. Conclusion: In this paper, a new improved whale optimization algorithm has been proposed and applied in bag-of-features method for histopathological image classification. The results of proposed method outperform the other existing meta-heuristic methods over standard benchmark functions and histopathological image dataset.
Applications of Deep Learning in Biomedicine

The growing computing power offered by technical advances has allowed deep learning models to be applied in various fields of healthcare and medical research, including imaging analysis, text mining, and omics studies. This article explores the basic mechanism of artificial neural networks (ANNs), and upon which explains the architecture of popular supervised learning methods such as deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), as well as unsupervised learning methods including variations of autoencoders, restricted Boltzmann machines (RBM), and Deep Belief Nets (DBN). Important features of each architecture are explained, such as the convolution layer and pooling layers of CNNs and the recurrent structure of RNNs. These features have optimized their corresponding models to perform different tasks ranging from image processing to textual analysis. The unique advantages of different forms of neural networks have allowed deep learning tools to become the state-of-the-art in various research studies and medical fields. The construction of various architectures is often completed through deep learning frameworks, which provide powerful built-in libraries and ease of use. Several popular deep learning frameworks including TensorFlow, PyTorch, and others are introduced and compared. Further, the article reviews the current obstacles met by deep learning approaches, including computation and interpretability limitations. Nevertheless, improvements and hardware optimization intending to overcome these challenges are emerging rapidly, offering a promising path to more effective implementation of deep learning in the healthcare industry.
Digital Image Enhancement Techniques for Dental Radiographs

Dental radiographs suffer frequently from issues such as low contrast and non-uniform illumination. The first step, indeed a significant step, is to enhance these digital images to prepare them for successful post-processing. This pre-processing stage assists to increase the contrast between the foreground that is the teeth and bone from the background regions. In this chapter, image enhancement methods based on spatial, frequency, and spatial-frequency are implemented. The dental radiographs used are available in the public database. The performance of the enhancement methods is validated using qualitative and quantitative measures. It is observed from the results that the enhancement method aids in improving dental features such as crowns, fillings, and bridges. This enables human identification and diagnostic purpose in the way it is possible to identify a variety of diseases. It also prevents the need for a remake, saving the patient from an additional treatment.
AGs: Local descriptors derived from the dependent effects model

We present a novel local descriptor based on the dependent effects model. Different types of effects are computed in a local region and properly normalized to form a descriptor, which is designed to be robust to rotation, scale changes, and skew. Two specific instances of descriptor are presented and evaluated. The first, named AG, is real-valued, and uses 126 floating-point values to represent 126 effects. The second version, named AGS, is binarized, and uses 60 bytes to represent 240 effects, each with two bits. The first bit represents the sign of an effect value, while the second denotes whether the value is near or far from zero. We experimentally evaluate the proposed descriptors in combination with popular keypoint detectors on standard feature matching datasets. The extensive evaluation shows that AG and AGS achieve high score in several performance measures, and as such, they represent an attractive alternative to popular local descriptors.
Information-gain computation in the Fifth system

Despite large incentives, correctness in software remains an elusive goal. Declarative programming techniques, where algorithms are derived from a specification of the desired behavior, offer hope to address this problem, since there is a combinatorial reduction in complexity in programming in terms of specifications instead of algorithms, and arbitrary desired properties can be expressed and enforced in specifications directly. However, limitations on performance have prevented programming with declarative specifications from becoming a mainstream technique for general-purpose programming, because a strategy which is both efficient and fully general to derive algorithms from specifications does not yet exist. To address this bottleneck, I propose information-gain computation, a framework where an adaptive evaluation strategy is used to efficiently perform a search which derives algorithms that provide information about a query via the most efficient routes. Within this framework, opportunities to compress the search space present themselves, which suggest that information-theoretic bounds on the performance of such a system might be articulated and a system might be designed to achieve them. The computation of the information measures that are the basis of this strategy crucially depends on a probabilistic semantics for the relations represented by predicates, which may either already be present in a probabilistic logic language, or may be superimposed on a pure logic language. I describe a prototype implementation of Fifth, a system that implements these techniques, and a preliminary empirical study of adaptive evaluation for a simple test program. In the test, the evaluation strategy adapts successfully to efficiently evaluate a query with pathological features that would prevent its evaluation by standard general-purpose strategies.
Bayesian Monte Carlo testing with one-dimensional measures of evidence

Bayesian hypothesis testing procedures are constructed by means of test statistics which are functions of the posterior distribution. Usually, the whole sample vector is selected to form the sufficient empirical part of the posterior distribution. But, in certain problems, one may prefer to use well-established one-dimensional sufficient statistics in place of the sample vector. This paper introduces a Bayesian Monte Carlo procedure specially designed for such cases. It is shown that the performance of this new approach is arbitrarily close to the exact Bayesian test. In addition, for arbitrary desired precisions, we develop a theoretical rule of thumb for choosing the minimum number m0 of Monte Carlo simulations. Surprisingly, m0 does not depend on the shape of loss/cost functions when those are used to compound the test statistic. The method is illustrated for testing mean vectors in high-dimension and for detecting spatial clusters of diseases in aggregated maps.
Visual attention for behavioral cloning in autonomous driving

The goal of our work is to use visual attention to enhance autonomous driving performance. We present two methods of predicting visual attention maps. The first method is a supervised learning approach in which we collect eye-gaze data for the task of driving and use this to train a model for predicting the attention map. The second method is a novel unsupervised approach where we train a model to learn to predict attention as it learns to drive a car. Finally, we present a comparative study of our results and show that the supervised approach for predicting attention when incorporated performs better than other approaches.
Private Message Franking with After Opening Privacy

Grubbs et al. [11] initiated the formal study of message franking protocols. This new type of service launched by Facebook, allows the receiver in a secure messaging application to verifiably report to a third party an abusive message some sender has sent. A novel cryptographic primitive: committing AEAD has been initiated, whose functionality apart from confidentiality and authenticity asks for a compact commitment over the message, which is delivered to the receiver as part of the ciphertext. A new construction $$\textsf{CEP}$$ (Committing Encrypt and $$\textsf{PRF}$$ ) has then been proposed, which is multi-opening secure and reduces the computational costs for the sender and the receiver. In this paper we provide a formal treatment of message franking protocols with minimum leakage whereby only the abusive blocks are opened, while the rest non-abusive blocks of the message remain private.
Research Data Management in Kenya's Agricultural Research Institutes

The purpose of this study was to examine research data management (RDM) in Kenya's agricultural research institutes with the view to proposing interventions to improve management, sharing, and reuse of agricultural research output. The objectives of the study were twofold: to assess the status of research data management in Kenya's agricultural research institutes and to determine the legal and policy framework, ICT infrastructure, and human capital available to facilitate RDM in Kenya's agricultural research institutes. The findings of the study revealed that RDM legal framework did not exist in the institutes surveyed; the RDM policies and regulations were not updated as necessary; the institutes suffered from inadequacies of technical infrastructure, skills, and collaborative partnerships. The study recommended, among others, the establishment of a formal data governance structure to address RDM issues, institutional and policy framework for RDM, capacity building programs, incentives for researchers, and a sound technical infrastructure.
PFF-RVM: A new no reference image quality measure

In this paper, a new no-reference image quality metric is proposed and developed. A set of ten perceptual features are extracted from each distorted image, then Relevance Vector Machine (RVM) is employed to learn the mapping between the combined features and human opinion scores. Validation tests and simulations are conducted on the LIVE II and MDID 2013 databases. The predictive performances of this new metric (we called PFF-RVM for perceptual features fusion using relevance vector machine based metric) are compared to the most recent no-reference metrics in terms of correlation and monotonicity. Results show that the proposed metric has satisfactory and comparable performances to the most sophisticated and commonly used no-reference quality metrics in the state-of-the-art.
Edit Distance Kernelization of NP Theorem Proving For Polynomial-Time Machine Learning of Proof Heuristics

We outline a general strategy for the application of edit-distance based kernels to NP Theorem Proving in order to allow for polynomial-time machine learning of proof heuristics without the loss of sequential structural information associated with conventional feature-based machine learning. We provide a general short introduction to logic and proof considering a few important complexity results to set the scene and highlight the relevance of our findings.
The predictable degree property and minimality in multidimensional convolutional coding

Convolutional codes inherit from polynomials a natural structure of a filtered module, which is a fundamental structure and therefore should be taken into account. Pursuing this idea, we define higher-dimensional analogs of the predictable degree property, Forney's indices and overall constraint length; also, we address the important issue of minimality.
Secure Joins with MapReduce

MapReduce is one of the most popular programming paradigms that allows a user to process Big data sets. Our goal is to add privacy guarantees to the two standard algorithms of join computation for MapReduce: the cascade algorithm and the hypercube algorithm. We assume that the data is externalized in an honest-but-curious server and a user is allowed to query the join result. We design, implement, and prove the security of two approaches: (i) Secure-Private, assuming that the public cloud and the user do not collude, (ii) Collision-Resistant-Secure-Private, which resists to collusions between the public cloud and the user i.e., when the public cloud knows the secret key of the user.
Computer-assisted puzzle design

One approach to game design is to start with existing ideas and to modify them in interesting new ways. This process of “creative plagiarism” is applied to the Japanese logic puzzle Sudoku to create a new variant called Ludoku, which simplifies some aspects of the original puzzle while adding new strategies without adding undue complexity. This paper describes the design process from initial conception to final product, and how computer analysis was crucial to its success.
An Optimal Configuration of Sensitive Parameters of PSO Applied to Textual Clustering

The clustering aims to minimize intra-class distance in the cluster and maximize extra-classes distances between clusters. The text clustering is a very hard task; it is solved generally by metaheuristic. The current literature offers two major metaheuristic approaches: neighborhood metaheuristics and population metaheuristics. In this chapter, the authors seek to find the optimal configuration of sensitive parameters of the PSO algorithm applied to textual clustering. The study will go through in dissociable steps, namely the representation and indexing textual documents, clustering by biomimetic approach, optimized by PSO, the study of parameter sensitivity of the optimization technique, and improvement of clustering. The authors will test several parameters and keep the best configurations that return the best results of clustering. They will use the most widely used evaluation measures like index of Davies and Bouldin (internal) and two external: the F-measure and entropy, which are based on recall and precision.
Subtleties of extrinsic calibration of cameras with non-overlapping fields of view

Abstract The calibration of the relative pose between rigidly connected cameras with non-overlapping fields of view (FOV) is a prerequisite for many applications. In this paper, the subtleties of the experimental realization of such calibration optimization methods like in (Z. Liu, et al., Measurement Science and Technology , 2011, Z. Li, V. Willert, Intelligent Transportation Systems (ITSC) , 2018) are presented. Two strategies that could be adapted to certain optimization processes to find better local minima are evaluated. The first strategy is a careful measurement acquisition of pose pairs for solving the calibration problem, which improves the accuracy of the initial value for the following non-linear refinement. The second strategy is the introduction of a quality measure for the image data used for the calibration, which is based on the projection size of the known planar calibration patterns on the image. We show that introducing an additional weighting to the optimization objective chosen as a function of that quality measure improves calibration accuracy and increases robustness against noise. The above strategies are integrated into different setups and their improvement is demonstrated both in simulation and real-world experiment.
Asymptotic properties of nonparametric estimation and quantile regression in Bayesian structural equation models

We study the asymptotic properties of nonparametric Bayesian structural equation models (SEMs). Under mild conditions, when adjusting nonparametric error distributions, the posteriors of Bayesian SEMs achieve the optimal convergence rate up to logn terms in the nonparametric means and nonlinear relationships of the latent variables. Furthermore, we consider quantile regressions of the error and latent variables in Bayesian SEMs, and we show posterior consistency in Bayesian quantile regression. The theoretical results are validated using simulation studies.
Relativistic Effects Can Be Used to Achieve a Universal Square-Root (Or Even Faster) Computation Speedup

In this paper, we show that special relativity phenomenon can be used to reduce computation time of any algorithm from T to $$\sqrt{T}$$. For this purpose, we keep computers where they are, but the whole civilization starts moving around the computers – at an increasing speed, reaching speeds close to the speed of light. A similar square-root speedup can be achieved if we place ourselves near a growing black hole. Combining the two schemes can lead to an even faster speedup: from time T to the 4-th order root $$\root 4 \of {T}$$.
Textual Data in Transportation Research: Techniques and Opportunities

Transportation is rich in human communication. Travellers often use information services, interact with each other and with transport providers through natural language. And the transport providers and regulators (traffic managers, police, transit operators, etc.) need to constantly communicate to coordinate, plan and inform each other. This generates substantial amounts of structured, semistructured or unstructured data, generated within or outside of an organization's boundary. Such data is often rich in information that becomes absent, or oversimplified e.g., aggregated into dummy variables, in actual research or practical applications. If a researcher could transform such richer data into meaningful variables in her models, new opportunities would arise for e.g., better coping with heterogeneity or random effects. Due to recent advances in computational text analysis, textual data have become utilizable to a much higher degree. The objective of this chapter is to introduce techniques for textual data processing, and provide some recent application examples in transportation research.
LBP-Based Edge Detection Method for Depth Images With Low Resolutions

Depth edge represents contours of depth images. However, captured images often have low resolution. Furthermore, conventional edge detection methods fail to handle depth images with low resolution. To cater depth images with low resolution, we propose a novel edge detection method with high accuracy and computational speed. The proposed method modifies the local binary pattern and selects an adaptive threshold to obtain a rough edge map. Then, it removes irrelevant edges to obtain the result. Experimental results show that the proposed method can rapidly acquire accurate depth edge maps and is applicable to depth images with low resolution. The proposed method is compared with and outperforms other renowned edge detection methods.
Improving Landmark Recognition Using Saliency Detection and Feature Classification

With increasing tourism and democratization of data, image landmark recognition has been one of the most sought-after classification challenges in the field of vision and perception. After so many years of generic classification of buildings and monuments from images, people are now focusing on fine-grained problems—recognizing the category of each building or monument and indexing information to it. In this chapter, we propose an ensemble network for the classification of Indian landmark images. To this end, the proposed method gives a robust classification by ensembling the predictions from the Graph-Based Visual Saliency (GBVS) network along with supervised feature-based classification algorithms such as K-nearest neighbor (kNN) and Random Forest. The final architecture is an adaptive learning of all the mentioned networks. The proposed network produces a decent score to eliminate false category cases. Evaluation of the proposed model was done on a new dataset, which involves challenges such as landmark clutter, variable scaling, and partial occlusion.
Complex Event Processing for Health Monitoring

The increase of the life expectancy has become a challenge in regions with a low population density. This fact is caused by the existence of small towns all far from one another and with the peculiarity of many elders with special health care living there. This situation increases in a high percentage the health costs of the region having to attend daily all these elders who need a close monitoring. We live in a IoT era with a huge quantity of new connected devices with lots of sensors. Taking advantage of this, it is possible to monitor these elders from the distance without having to cover the complete area of the region every day. This way, our approach is using a mobile centric architecture that permits the elders having a device which infers a health virtual profile of them with data from its sensors and from other smart devices like bands with pulsometers. At this point we propose using Complex Event Processing techniques to combine the data coming from all sources and analyze it to extract meaningful information for the doctors and caregivers and even detect important events like falls in real time.
A Framework of Supporting System for Optimizing Information Flow in International Trade Transaction

The current international trade transaction processes among ASEAN countries are facing high-costs structure because of the redundant paperworks. Furthermore, multiple languages used in these processes make the problems more difficult. In this paper, a framework of system, which helps to resolve the problems is proposed. The system can generate an optimized process of paperworks from a complex process. First, an international trade transaction model, which is an underlying basis of the framework is defined. Second, an international logistics ontology, which has been developed for describing the model is shown. Then, an algorithm for optimizing information flows is proposed. Furthermore, a case study is shown in order to explain the proposed framework. This framework contributes to reduce the paperwork costs of international trade transaction processes.