Reviewerly/exHarmony huggingface dataset:
Analysis of the Maximal a Posteriori Partition in the Gaussian Dirichlet Process Mixture Model

Mixture models are a natural choice in many applications, but it can be difficult to place an a priori upper bound on the number of components.To circumvent this, investigators are turning increasingly to Dirichlet process mixture models (DPMMs).It is therefore important to develop an understanding of the strengths and weaknesses of this approach.This work considers the MAP (maximum a posteriori) clustering for the Gaussian DPMM (where the cluster means have Gaussian distribution and, for each cluster, the observations within the cluster have Gaussian distribution).Some desirable properties of the MAP partition are proved: 'almost disjointness' of the convex hulls of clusters (they may have at most one point in common) and (with natural assumptions) the comparability of sizes of those clusters that intersect any fixed ball with the number of observations (as the latter goes to infinity).Consequently, the number of such clusters remains bounded.Furthermore, if the data arises from independent identically distributed sampling from a given distribution with bounded support then the asymptotic MAP partition of the observation space maximises a function which has a straightforward expression, which depends only on the within-group covariance parameter.As the operator norm of this covariance parameter decreases, the number of clusters in the MAP partition becomes arbitrarily large, which may lead to the overestimation of the number of mixture components.
Novel Memory Efficient Hardware Architecture for 5/3 Lifting-Based 2D Inverse DWT

This paper presents a novel memory efficient hardware architecture for 5/3 lifting-based two-dimensional (2D) inverse discrete wavelet transform (IDWT). The proposed architecture processes multiple levels of composition simultaneously using only one one-dimensional (1D) 5/3 lifting-based inverse vertical filter and only one 1D 5/3 lifting-based inverse horizontal filter. In case of [Formula: see text] levels of composition for [Formula: see text] image, the proposed 5/3 2D IDWT architecture requires the total memory of size less than [Formula: see text], which is lower memory size than memory size required in any other previously published architecture. In terms of total number of adders, total number of multipliers (shifters), total computing time and output latency, presented solution is comparable with other state-of-the-art solutions. Proposed hardware architecture is suitable for implementation in JPEG 2000 decoder, since default inverse filter for reversible transformation in JPEG 2000 standard is 5/3 IDWT filter.
Single-phase and correlated-phase estimation with multiphoton annihilated squeezed vacuum states: An energy-balancing scenario

In the last years, several works have demonstrated the advantage of photon subtracted Gaussian states for various quantum optics and information protocols. In most of these works, it was not clearly investigated the relation between the advantages and the usual increasing energy of the quantum state related to photon subtraction. In this paper, we study the performance of an interferometer injected with multi photon annihilated squeezed vacuum states mixed with coherent states for both single and correlated phase estimation. For single phase estimation, albeit the use of multi-photon annihilated squeezed vacuum states at low mean photons per mode provide advantage compared to classical strategy, when the total input energies is held fixed, the advantage due to photon subtraction is completely lost. However, for the correlated case in analogous scenario, some advantage appears to come from both the energy rise and improvement in photon statistics. In particular quantum enhanced sensitivity with photon subtracted states appears more robust to losses, showing an advantage of about 30% with respect to squeezed vacuum state in case of realistic value of the detection efficiency.
Automated mutual induction proof in separation logic

Abstract We present a deductive proof system to automatically prove separation logic entailments by mathematical induction. Our technique is called the mutual induction proof . It is an instance of the well-founded induction, a.k.a., Noetherian induction. More specifically, we propose a novel induction principle based on a well-founded relation of separation logic models. We implement this principle explicitly as inference rules so that it can be easily integrated into a deductive proof system. Our induction principle allows a goal entailment and other entailments derived during the proof search to be used as hypotheses to mutually prove each other. This feature increases the success chance of proving the goal entailment. We have implemented this mutual induction proof technique in a prototype prover and evaluated it on two entailment benchmarks collected from the literature as well as a synthetic benchmark. The experimental results are promising since our prover can prove most of the valid entailments in these benchmarks, and achieves a better performance than other state-of-the-art separation logic provers.
Exact Legendre–Fourier moments in improved polar pixels configuration for image analysis

This study presents the exact Legendre–Fourier moments and a novel arrangement of polar pixels that allows calculating orthogonal moments defined in a unit radius more accurately than traditional methods. This arrangement simplifies implementation and preserves the values of the pixels of the image during the calculation of the moments. Moreover, the exact Legendre–Fourier moments use the weighted substituted radial shifted Legendre polynomials as kernel, which has the ability to accurately calculate the circular moments. Finally, the author presents a comparative analysis of the reconstruction error with existing configurations and other families of circular moments. The results indicate that the mehod provides a significant advantage.
Application of remote sensing method for geological interpretation of Sokoto Plain, Nigeria

Landsat-8 OLI imagery of Sokoto, Nigeria, was processed to emphasize the geology features and mineral potential of the area.Band ratios � 4 2 6 7 was replaced with 7 5 in order to accentuate clay minerals with high reflectance within band 7. The last technique evaluated in this study used spectral information from minimum noise fraction image to map surface geology.Supervised classification training sites were selected using five classes (clay, ironstone, alteration zone, water and vegetation).The band ratio classification using maximum likelihood classification was fairly accurate and matched the geologic map of the area, also showing an alteration zone that coincided with the migmatite-quartz/mica schist contact.The classified image was finally passed through a filtering effect for generalization of the data.This filtering effect was helpful in discriminating the pixels of ironstone and those of the alteration zone on the classified map.This study shows the distribution of classified earth-surface materials in Sokoto plain with the aid of supervised classification of Landsat-8 multispectral bands interpreted to reflect in-situ features.
Agent-Based Modeling and Simulation of Biological Systems

Agent-based modeling and simulation is a powerful technique in simulating and exploring phenomena that includes a large set of active components represented by agents. The agents are actors operating in a real system, influencing the simulated environment and influenced by the simulated environment. The agents are included in the simulation model as model components performing actions autonomously and interacting with other agents and the simulated environment to represent behaviors in the real system. In this chapter, we describe how to develop an agent-based model and simulation for biological systems in Repast Simphony platform, which is a Java-based modeling system. Repast Simphony helps developers to create a scenario tree including displays of agents, grid and continuous space, data sets, data loaders, histogram, and time charts. At the end of this chapter, we present case studies developed by our research group with references to demonstrate local behavior of biological system.
Adversarial Dropout for Recurrent Neural Networks

Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs). Dropout techniques for RNNs were introduced to respond to these demands, but we conjecture that the dropout on RNNs could have been improved by adopting the adversarial concept. This paper investigates ways to improve the dropout for RNNs by utilizing intentionally generated dropout masks. Specifically, the guided dropout used in this research is called as adversarial dropout, which adversarially disconnects neurons that are dominantly used to predict correct targets over time. Our analysis showed that our regularizer, which consists of a gap between the original and the reconfigured RNNs, was the upper bound of the gap between the training and the inference phases of the random dropout. We demonstrated that minimizing our regularizer improved the effectiveness of the dropout for RNNs on sequential MNIST tasks, semi-supervised text classification tasks, and language modeling tasks.
Random Feature Maps for the Itemset Kernel

Although kernel methods efficiently use feature combinations without computing them directly, they do not scale well with the size of the training dataset. Factorization machines (FMs) and related models, on the other hand, enable feature combinations efficiently, but their optimization generally requires solving a non-convex problem. We present random feature maps for the itemset kernel, which uses feature combinations, and includes the ANOVA kernel, the all-subsets kernel, and the standard dot product. Linear models using one of our proposed maps can be used as an alternative to kernel methods and FMs, resulting in better scalability during both training and evaluation. We also present theoretical results for a proposed map, discuss the relationship between factorization machines and linear models using a proposed map for the ANOVA kernel, and relate the proposed feature maps to prior work. Furthermore, we show that the maps can be calculated more efficiently by using a signed circulant matrix projection technique. Finally, we demonstrate the effectiveness of using the proposed maps for real-world datasets..
Face recognition using depth and infrared pictures

This paper proposes the face recognition method using both the depth and infrared pictures. The conventional face recognition methods based on color picture recognize the human faces accurately, but are easily affected by the illumination and are vulnerable to the attempts to steal user's face information through fake face such as the photograph or the sculpture. On the other hand, the methods based on the depth or infrared picture are less affected by the illumination, and prevent an attempt to recognize a false face. This paper utilizes the depth picture to reduce the recognition time and the infrared picture to increase the recognition performance. In the face detection, this paper finds the nose of a person using the captured depth picture for reducing detection time, and it detects the region of the face. In the feature extraction, it extracts the feature pictures from the infrared picture by 3D Local Binary Pattern. In the face identification, this paper compares between the features of the captured face and features of faces that are pre-stored in the DB, and obtains the face similarity. If the similarity of the face is larger than the certain threshold, the face recognition succeeds. Simulation results show that the face recognition performance is good not only in the normal environment but also in the little illumination.
Bayesian estimation and model averaging of convolutional neural networks by hypernetwork

Neural networks have a rich ability to learn complex representations and have achieved remarkable results in various tasks. However, they are prone to overfitting owing to the limited number of training samples and regularizing the learning process of neural networks is essential. In this paper, we propose a regularization method that estimates the parameters of a large convolutional neural network as probabilistic distributions using a hypernetwork, which generates the parameters of another network. Additionally, we perform model averaging to improve the network performance. Then, we apply the proposed method to a large model such as wide residual networks. The experimental results demonstrate that our method and its model averaging outperform the commonly used maximum a posteriori estimation with L2 regularization.
No-Dictionary Searchable Symmetric Encryption

In the model of no-dictionary searchable symmetric encryption (SSE) schemes, the client does not need to keep the list of keywords W. In this paper, we first show a generic method to transform any passively secure SSE scheme to a no-dictionary SSE scheme such that the client can verify search results even if w ∉ W. In particular, it takes only O(1) time for the server to prove that w ∉ W. We next present a no-dictionary SSE scheme such that the client can hide even the search pattern from the server.
Pattern Recognition using a Neural Network on a Microcontroller with I2C Ultrasonic Sensors

Ultrasonic sensors have been used in a variety of applications to measure ranges to objects. Hand gestures via ultrasonic sensors form unique motion patterns for controls. In this research, patterns formed by placing a set of objects in a grid of cells are used for control purposes. A neural network algorithm is implemented on a microcontroller which takes in range signals as inputs read from ultrasonic sensors and classifies them in one of four classes. The neural network is then trained to classify patterns based on objects’ locations in real-time. The testing of the neural network for pattern recognition is performed on a testbed consisting of Inter-Integrated Circuit (I2C) ultrasonic sensors and a microcontroller. The performance of the proposed model is presented and it is observed the model is highly scalable, accurate, robust and reliable for applications requiring high accuracy such as in robotics and artificial intelligence.
A verified ground confluence tool for linear variable-separated rewrite systems in Isabelle/HOL

It is well known that (ground) confluence is a decidable property of ground term rewrite systems, and that this extends to larger classes. Here we present a formally verified ground confluence checker for linear, variable-separated rewrite systems. To this end, we formalize procedures for ground tree transducers and so-called RRn relations. The ground confluence checker is an important milestone on the way to formalizing the decidability of the first-order theory of ground rewriting for linear, variable-separated rewrite systems. It forms the basis for a formalized confluence checker for left-linear, right-ground systems.
High Utility Association Rule Mining

Most businesses focus on the profits. For example, supermarkets often analyze sale activities to investigate which products bring the most revenue, as well as find out customer trends based on their carts. To achieve this, a number of studies have examined high utility itemsets (HUIs). Traditional association rule mining algorithms only generate a set of highly frequent rules, but these rules do not provide useful answers for what the high utility association rules are. This chapter provides overview current approaches to mine high utility association rules.
Financial Development and Income Inequality: Evidence from African Countries in the Franc Zone

The aim of this article is to evaluate the effect of financial development on income inequality in a sample of African countries of the Franc Zone.Using data from Worldwide Governance indicators, UNESCO, COBAC, the Banking Commission of West African States and the World Bank, based on cylindrical dynamic panel whose instrumentalisation and stationarity of variables enabled us to use GMM in system, the results show that financial development through its components which are credit to the private sector, network development as well as the monetary mass significantly reduce income inequality among households.This result is robust by alternative or dual use of the components as well as when several control variables are integrated.
Assessment of the diffuse component of the global horizontal solar radiation for different solar altitude ranges. Case study: Oran (Algeria)

The design of a solar energy system in a region where the solar potential is restricted to the measurement of the global horizontal solar radiation and the diffuse component is not supported requires using models for this estimation with radiometric parameters such as sunshine duration, clearness index, temperature, and relative humidity. The present study deals with a qualitative and quantitative study that was established on the diffuse component of horizontal global radiation, which has been applied for the Oran site, a coastal region located in northwestern Algeria. The results obtained from this analysis permitted to find mathematical correlations between the diffuse and the clearness index for different levels of solar altitude and for different seasons. A comparative study was carried out; first, to see the impact of the season on the accuracy of the models established by using statistical indicators namely mean bias error, root-mean-squared error, and the t-stat. Secondly, another comparative study was carried out using other works previously published in the scientific literature. The introduction of the solar altitude as a predictor for establishing correlations of the diffuse component has significantly reduced the standard error compared to other models, which considered the clearness index as the only independent variable.
A Scientific Approach to the Shakespeare Authorship Question

In the field of science, it is widely accepted that all hypotheses and theories can, and should, be tested. Here, we treat the authorship of the works attributed to William Shakespeare from Stratford-upon-Avon as a hypothesis (rather than received truth) and review the current methods available for testing this hypothesis. Justification for this investigation arises from the recent identification of several of Shakespeare’s coauthors. To illustrate potential approaches, we compare the widely accepted Stratfordian hypothesis with other competing hypotheses (authorship candidates), mainly referring to the case for Henry Neville (1562-1615). First, we identify important components of the scientific method as applied to the Shakespeare authorship issue: evaluation of evidence, formulation of a hypothesis, testing the hypothesis, and rejecting or revising the hypothesis. Referring to historical examples, we show how the scientific method has produced precise, dependable advances, even though the way in which it proceeds is often messy. Crucial for science’s progress is confirmation of experimental results, and discussion (with peer review) by the community of scientists before any hypothesis gains general acceptance. Second, using the example of Neville as a candidate, we provide specific examples of application of these principles to factors such as a candidate’s social networks, access to privileged knowledge, and textual analysis; we comment on the strengths and weakness of each approach, and how they might be applied in future studies. Throughout, we stress how doubt is an essential ingredient of progress not only in science but also in knowledge generally, including the Shakespeare authorship debate.
The Impact of Strategic Fit on the Marketing Performance of the Industrial Corporations in Jordan

The present study aimed at identifying the impact of strategic fit on the marketing performance of the industrial corporations in Jordan. The population consists from all the Jordanian industrial corporations that are listed on Amman Stock Exchange. To be specific, the population consists from 63 companies. A sample was chosen through using the simple random sampling method. It was selected from 54 companies. Questionnaire forms were distributed to marketing, and administrative directors (or the ones holding equivalent positions).&amp;nbsp; 76 questionnaire forms were retrieved (out of 108 questionnaires). All of the retrieved forms are valid for statistical analysis. The response rate is 70.37%. The latter rate is considered a good rate.&#x0D; &#x0D; Several results were concluded. For instance, it was found that the strategic fit has a statistically significant positive impact on the marketing performance of the Jordanian industrial corporations. The researchers recommend providing more attention to the achievement of strategic fit in the industrial corporations. That shall enable those companies to improve their marketing performance.
A Fleet-Wide Approach for Condition Monitoring of Similar Machines Using Time-Series Clustering

The application of machine learning to fault diagnosis allows automated condition monitoring of machines, leading to reduced maintenance costs and increased machine availability. Traditional approaches train a machine learning algorithm to identify specific faults or operational settings. Therefore, these approaches cannot always cope with a dynamic industrial environment. However, an industrial installation often contains multiple machines of the same type, which enables a fleet-based analysis. This type of analysis compares machines to tackle the challenges of a dynamic environment. In this paper a novel method is proposed for analyzing a fleet of machines operating under similar conditions in the same area by using inter-machine comparisons. The proposed methodology consists of two steps. First, the inter-machine difference is calculated using dynamic time warping by using the amount of warping as measure. This method allows comparing the measured signals even when fluctuations are present. Second, a clustering method uses the inter-machine similarity to identify groups of machines that operate in a similar manner. The generation of a fault usually causes a change in the raw signals and diagnostic features. As a result, the inter-machine difference between the faulty machine and the rest of the fleet will increase, leading to the creation of a separate group that contains the faulty machine. The methodology is evaluated and validated on phase current signals measured on a fleet of electrical drivetrains, where a phase unbalance fault is introduced in some of the drivetrains for a specific time duration.