Reviewerly/exHarmony huggingface dataset:
Model and Information Technology for Hourly Water Consumption Interval Forecasting

Theoretical foundation of information technology for short-term water consumption forecasting has been considered. Hourly water consumption has been modelled as a cyclostationary conditional linear random process. Using the model an approach for interval forecasting has been proposed based on periodical autoregressive time series and nonparametric periodic quantile estimations. The corresponding information technology has been developed and analysed. The results of implementation have been presented and can be used for enabling optimal short-term water supply management.
Interest rate liberalization and economic growth nexus: does corruption matter?

Purpose This paper aims to examine the impact of interest rate liberalization on economic growth and the relevance of corruption in the five selected sub-Saharan African countries. Design/methodology/approach The study used the modified version of Driscoll and Kraay’s model by Hoechle, which solved the effects of cross-sectional dependence and heteroscedasticity. Findings The findings reveal a positive impact of the index on economic growth, and it was found that foreign direct investment (FDI) and credit to private sector by banks (CPSB) all stimulate economic growth. The interaction terms of corruption with FDI and CPSB indicate negative effects that show how corruption erodes the benefits of liberalization. Finally, the paper recommends the pursuit of appropriate policies with the sole aim of eradicating corruption and providing a conducive environment for business. Originality/value The paper developed a composite domestic financial liberalization index to capture the timing and essential dimensions of the reform process. The study investigates the effect of interest rate liberalization on economic growth and the relevance of corruption. Most of the recent and past studies only examined the impact of interest rate reforms on growth without investigating the relevance of corruption.
Edge Detection Method Based on Hysteresis Connection and Prediction

Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Based on the analysis of edge gradient changes and the defects of traditional edge detection algorithms, a novel edge detection method is proposed. Firstly, the edge set is initialized by gradient map binaryzation with a given threshold. Then the pixel adjacent to edge pixel will be joined to the edge set, if its gradient is higher than half of threshold or the gradient difference between this pixel and the adjacent strong edge is less than a given value. In order to go over the broken interval, this method predicts the edge extension direction using the local information of edge, and searches for discontinuous weak edges on this direction. The experimental results show that this method not only can extract complete edge segments, but also can detect the valid weak edge segment over the broken interval, and at the same time it can suppress the noise with the same gradient value.
A hybrid two-stage feature selection method based on differential evolution

Feature selection is a crucial data pre-processing step in classification problems. The wrapper approach is widely used due to their good classification performance. However, it is very computational expensive due to the cross validation scheme in the evaluation phase. In order to solve this proble m, this paper proposes a novel hybrid two-stage feature selection method based on differential evolution (HTSDE). In the first stage, a cluster validity index named DB index is employed to evaluate the feature subset and the wrapper approach in used in the second stage to improve the classification accuracy of the feature subsets. In order to find global optimal feature subsets, different trail vector generation strategies of DE are used in the two stages where the first stage focuses on global exploration and the second stage emphasizes fast convergence. The hybrid method is able to combine the advantages of both DB index and wrapper approach and improve the computational efficiency of the wrapper approach while maintaining the classification performance. HTSDE is compared with several state-of-the-art feature selection methods on 12 datasets. Experimental results show the proposed HTSDE achieves higher classification accuracy than both wrapper and filter approaches. Moreover, its computational cost is much less than those wrapper approaches.
Scale balance for prototype-based binary quantization

Nowadays, prototype-based binary quantization (PBQ) is a promising solution for the approximate nearest neighbor search problem, which simultaneously preserves the affinity structures of prototypes in both Euclidean space as well as those of their codes in binary space. To learn longer binary codes, space decomposition based on product quantization is usually adopted. In practice, we find that the scale between Euclidean distance and Hamming distance usually varies across these decomposed subspaces, which degenerates the performance of PBQ based methods. We make an attempt to balance the scale of these subspaces via a joint optimization problem in the classic PBQ model, and present both an iterative and alternate algorithm for optimization. We conducted experiments on 6 public databases, and demonstrated that our scale balancing based methods SKMH and SABQ outperform state-of-the-art hashing methods including popular prototype-based binary quantization methods, with up to 81.62% relative performance gains when learning 256-bit binary codes.
Abstracting probabilistic models: Relations, constraints and beyond

Abstraction is a powerful idea widely used in science, to model, reason and explain the behavior of systems in a more tractable search space, by omitting irrelevant details. While notions of abstraction have matured for deterministic systems, the case for abstracting probabilistic models is not yet fully understood. In this paper, we provide a semantical framework for analyzing such abstractions from first principles. We develop the framework in a general way, allowing for expressive languages, including logic-based ones that admit relational, deterministic and hierarchical constructs with stochastic primitives. We motivate a definition of consistency between a high-level model and its low-level counterpart, but also treat the case when the high-level model is missing critical information present in the low-level model. We go on to prove properties of abstractions, both at the level of the parameter as well as the structure of the models. We conclude with some observations about how abstractions can be derived automatically.
Unsupervised Deep Learning for Stain Separation and Artifact Detection in Histopathology Images

Stain separation is an important pre-processing technique used to aid automated analysis of histopathology images. In this paper, we propose a novel, unsupervised deep learning method for stain separation (Hematoxylin and Eosin). This approach is inspired by Non-Negative Matrix Factorisation (NMF) and decomposes an input image into a stain colour matrix and a stain concentration matrix. In contrast to existing approaches, our method predicts stain colour matrices at the pixel level rather than the image level, thus enabling implicit modelling of tissue-dependant interactions between stains. We demonstrate an 8.81% reduction in mean-squared error on a stain separation task measuring the similarity between predicted and actual hematoxylin images from a publicly available dataset of digitised tissue images. We also present a novel approach to artifact detection in histological images based on a constrained generative adversarial network which we demonstrate is able to detect a variety of artifact types without the use of labels.
Convergence of a Relaxed Variable Splitting Coarse Gradient Descent Method for Learning Sparse Weight Binarized Activation Neural Network

Sparsification of neural networks is one of the effective complexity reduction methods to improve efficiency and generalizability. Binarized activation offers an additional computational saving for inference. Due to vanishing gradient issue in training networks with binarized activation, coarse gradient (a.k.a. straight through estimator) is adopted in practice. In this paper, we study the problem of coarse gradient descent (CGD) learning of a one hidden layer convolutional neural network (CNN) with binarized activation function and sparse weights. It is known that when the input data is Gaussian distributed, no-overlap one hidden layer CNN with ReLU activation and general weight can be learned by GD in polynomial time at high probability in regression problems with ground truth. We propose a relaxed variable splitting method integrating thresholding and coarse gradient descent. The sparsity in network weight is realized through thresholding during the CGD training process. We prove that under threshholding of l_1, l_0, and transformed-l_1 penalties, no-overlap binary activation CNN can be learned with high probability, and the iterative weights converge to a global limit which is a transformation of the true weight under a novel sparsifying operation. We found explicit error estimates of sparse weights from the true weights.
“One-click-dialling” design principles and an intelligent recognition algorithm based on a mobile intelligent terminal

This article focuses on the implementation of “one-click-dialling” (OCD) based on a mobile intelligent terminal. The design principles of OCD are elucidated for the first time and the concepts of multi-layer mapping, polysemy function, and getting value, etc. are proposed. By analysing a bijection between the mobile phone card and the dialling phone, it innovatively studies two different situations about local OCD and remote roaming OCD. It constructs a functional mapping relation of OCD and establishes the functional mapping relationships in local OCD and in remote roaming OCD, respectively. It is used for recognising which city (or region) the user is in. Furthermore, the paper studies an intelligent recognition algorithm of OCD and uses it for implementing intelligent dialling. The innovative method is applied to the emergency rescue calls of existing government, society, enterprises, and individuals in China, such as 110, 119, 120, 122, government emergency office, social rescue organisations, insurance companies, emergency rescue companies starting with 400, other emergency rescue institutions, etc. The research allows users to call a certain country, no matter which city (or region) they are in when dialling, so they do not waste dialling the emergency rescue department on an intelligent mobile using one-by-one for dialling. The user can directly click the call buttons on the OCD system which have pre-designated the telephone numbers of the locate emergency rescue institution for OCD. The research has practical significance for those interested in social security management.
An Autonomous Navigation Algorithm for Monocular Visual Recognition

Aiming at the target identification and navigation problem of quadrotor aircraft, this paper designs a monocular vision recognition and autonomous navigation method. According to the imaging principle of the camera, the linear model without distortion of the lens is selected and its internal parameters are obtained through the camera calibration method. The images captured by the camera and combined with the OpenCV library function are processed in real time to realize the target recognition function of the UVA.
Construction of Cryptographically Secure AES S-Box using Second-order Reversible Cellular Automata

In cryptography the block ciphers are the mostly used symmetric algorithms. In the existing system the standard S-Box of Advanced Encryption Standard(AES) is performed using the irreducible polynomial equation in table form known as look-up tables(LUTs). For more security purposes, second-order reversible cellular automata based S-box is created. The security aspects of the S-Box used in the AES algorithm are evaluated using cryptographic properties like Strict Avalanche Criteria, Non-Linearity, Entropy, and Common Immunity Bias. The design of S-Box using second-order reversible Cellular Automata is better concerning security and dynamic aspect as compared to the classical S-boxes used Advanced Encryption Standard.
Cross-data Automatic Feature Engineering via Meta-learning and Reinforcement Learning

Feature Engineering (FE) is one of the most beneficial, yet most difficult and time-consuming tasks of machine learning projects, and requires strong expert knowledge. It is thus significant to design generalized ways to perform FE. The primary difficulties arise from the multiform information to consider, the potentially infinite number of possible features and the high computational cost of feature generation and evaluation. We present a framework called Cross-data Automatic Feature Engineering Machine (CAFEM), which formalizes the FE problem as an optimization problem over a Feature Transformation Graph (FTG). CAFEM contains two components: a FE learner (FeL) that learns fine-grained FE strategies on one single dataset by Double Deep Q-learning (DDQN) and a Cross-data Component (CdC) that speeds up FE learning on an unseen dataset by the generalized FE policies learned by Meta-Learning on a collection of datasets. We compare the performance of FeL with several existing state-of-the-art automatic FE techniques on a large collection of datasets. It shows that FeL outperforms existing approaches and is robust on the selection of learning algorithms. Further experiments also show that CdC can not only speed up FE learning but also increase learning performance.
Image Clustering and Video Summarization for Efficient 3D Modelling and Reconstruction

Although high quality 3D representations of important cultural landmarks can be obtained via sophisticated photogrammetric techniques, their demands in terms of resources and expertise pose limitations on the scale at which such approaches are used. In parallel, the proliferation of multimedia content posted online creates new possibilities in terms of the ways that such rich content can be leveraged, but only after addressing the significant challenges associated with this content, including its massive volume, unstructured nature, and noise. In this chapter, two strategies are proposed for using multimedia content for 3D reconstruction: an image-based approach that employs clustering techniques to eliminate outliers and a video-based approach that extracts key frames via a summarization technique. In both cases, the reduced and outlier-free image data set is used as input to a structure from motion framework for 3D reconstruction. The presented techniques are evaluated on the basis of the reconstruction of two world-class cultural heritage monuments.
Redefining Trust and Disinter-Mediation With Blockchain in E-Governance

Blockchain technology (BCT) is becoming a common language across diverse geographical domains. BCT attempts to solve the fraud issues involved in financial transactions and government operations. E-governance is a convenient platform providing services to all connected citizens and inter-government sectors. Traditional centralization issues are addressed by decentralizing the stakes, keeping the transactions transparent on an open ledger–BCT. It works on the notion of disintermediation eliding the middle layers with the highest trust level. It can contribute to various e-government sectors, namely agriculture, property registrations, supply chain management, tax calculation, etc. BCT application for agriculture supply change management can provide assistance in crop plantation and delivery records. It can bring transparency in land registrations and tax management by maintaining immutable land records and simplified tax management preserving trust between various parties through smart contracts. This chapter focuses on BCT approaches for secured governance.
Unsupervised Monocular Depth Estimation CNN Robust to Training Data Diversity

We present an unsupervised learning method for the task of monocular depth estimation. In common with many recent works, we leverage convolutional neural network (CNN) training on stereo pair images with view reconstruction as a self-supervisory signal. In contrast to the previous work, we employ a stereo camera parameters estimation network to make our model robust to training data diversity. Another of our contributions is the introduction of self-supervision correction. With it we address one of the serious drawbacks of the stereo pair self-supervision in the unsupervised monocular depth estimation approach: at later training stages, self-supervision by view reconstruction fails to improve predicted depth map due to various ambiguities in the input images. We mitigate this problem by making depth estimation CNN produce both depth map and correction map used to modify the input stereo pair images in the areas of ambiguity. Our contributions allow us to achieve state-of-the-art results on the KITTI driving dataset (among unsupervised methods) by training our model on hybrid city driving dataset.
Social Media and Democracy: Challenges for Election Law and Administration in Canada

This article considers the challenges posed by social media for election law and administration in the Canadian context, particularly in relation to political advertising. I argue that the widespread use of social media by voters, political parties, and interest groups requires policy responses in order to promote the effectiveness of election administration and to further its underlying values. I use the term “social media” primarily in relation to Facebook and Twitter. Social media is already an important component of politics in Canada, including for political advertising. Three of the basic tools evident in Canadian election law include: (1) transparency/disclosure rules, (2) restrictions on spending, and (3) enforcement measures. The emergence of social media has challenged the capacity of each of these pillars of the legal regime to further their intended goals or the underlying values that motivated their introduction. The existing legal rules that regulate electoral activity off-line appear to be largely inadequate for a world in which social media is an important part of political communication and advertising. Either they do not apply to online politics or, if they do, the law has been under-enforced or exposed as out of date. Compounding these problems has been a series of decisions by Elections Canada to narrowly interpret its authority to regulate online politics, so as to leave large gaps in regulatory oversight of social media. In response, I propose several changes to the Canadian legal regime. They aim to promote effective election administration consistent with the values that inform the legislation. These proposals include enhanced disclosure rules for political advertising on social media, a separate social media spending limit for political parties and interest groups, and enhanced regulation of social media platforms, including by treating them as “broadcasters” for specific purposes under the Elections Act.
A New Scaler for the Expanded 4-Moduli Set {2<sup>k</sup> − 1, 2<sup>k</sup> + 1, 2<sup>2k</sup> + 1, 2<sup>2k</sup>}

Residue Number System is a non-weighted representation that is used in signal processing applications and particularly digital filters. The main feature of this arithmetic system is that it is a carry-free representation, which results in fast computations in these applications. This work introduces an arithmetic scaling unit for the 4-moduli residue number system set {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> − 1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> + 1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2k</sup> + 1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2k</sup> }, where k is an integer larger than 1. The scaling factor is the power-of-two modulus 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2k</sup> . The scaling unit consists of four channels, one for each modulus. The newly proposed architecture is built using basically carry-save adders and modular adders. This scaler is the only available scaler for this moduli set.
Ontology of Crop Pest Control

Domain ontology of crop pest control consists of hierarchically structured biological and chemical information and concepts on crops, pests, pest control measures and relations among them. Despite vertical relations in hierarchies, the knowledge about crop protection measures leads to horizontal relations between classes biological and chemical objects. There is analogy between class objects in biological classification of crops, pests and pest control measures from one hand and from the other hand class objects and instances of object oriented programming. The developed domain ontology has characteristics of task ontology because it leads to building analytical models, data analysis and solving practical problems. Classification of tasks and applications based on the domain ontology is in the scope of this work, as well.
Automated category Text identification using Machine Learning

Novice counselors are far more likely to maintain their own therapy interests and tend to use the closed issue effectively to test interpretation with instructor. While experienced therapists teach counseling skills to newbie counselors, they don't forget that a consumer's reaction to the problem of a novice therapist is essential to imagine in a suitable method. To answer the question, we've built a computer to imagine the go with the therapy communiqué flow. Nevertheless, the professional psychiatrist, as the gadget person demands a manual adjustment of the original category outcome and the painting pressure is immense, since the precision of the description of verbal communication results may be very low from the inside of the current system. To improve this issue, we have applied the class approach of text records with SVM (Support Vector System) as a system gaining knowledge of method to visualize the flow of interaction in therapy. However, we contrasted and tested the cutting-edge gadget's initial classification system with consequences. As these consequences indicate, the SVM model's the accuracy rating is below the existing device performance.
Data Augmentation Using GANs for 3D Applications

Modern deep learning techniques have proven that they have the capacity to be successful in a wide area of domains and tasks, including applications related to 3D and 2D images. However, their quality depends on the quality and quantity of the data with which models are trained. As the capacity of deep learning models increases, data availability becomes the most significant. To counter this issue, various techniques are utilized, including data augmentation, which refers to the practice of expanding the original dataset with artificially created samples. One approach that has been found is the generative adversarial networks (GANs), which, unlike other domain-agnostic transformation-based methods, can produce diverse samples that belong to a given data distribution. Taking advantage of this property, a multitude of GAN architectures has been leveraged for data augmentation applications. The subject of this chapter is to review and organize implementations of this approach on 3D and 2D imagery, examine the methods that were used, and survey the areas in which they were applied.