Reviewerly/exHarmony huggingface dataset:
Review on Unit Selection-Based Concatenation Approach in Text to Speech Synthesis System

Speech synthesis is the technique of artificially generating human speech. A text-to-speech system converts ordinary (raw) text of any language for which it is designed into speech signal. In this era, people are more interested to listen their native language. So, there is need for text-to-speech system (TTS) that will give automatic transformation of raw text into speech that looks like a native speaker of the language reading the raw text. So far, successful research has not been done in generating a speech signal. We have been only able to extract parameters from recorded speech and synthesize the original signal from it. Hence, the speech synthesis term is more appropriate than speech generation. Synthesized speech is generated by many concatenation algorithms. The goal of this paper is to give concise idea about different engineering approaches for talking machines that use sequence of word units that provides flexibility for arbitrary vocabularies as required in many application such as ultimate conversion from written text-to-speech signal.
Bellman's principle of optimality and deep reinforcement learning for time-varying tasks

This paper presents the first framework (up to the authors' knowledge) to address time-varying objectives in finite-horizon Deep Reinforcement Learning (DeepRL), based on a switching control solution developed on the ground of Bellman's principle of optimality. By augmenting the state space of the system with information on its visit time, the DeepRL agent is able to solve problems in which its task dynamically changes within the same episode. To address the scalability problems caused by the state space augmentation, we propose a procedure to partition the episode length to define separate sub-problems that are then solved by specialised DeepRL agents. Contrary to standard solutions, with the proposed approach the DeepRL agents correctly estimate the value function at each time-step and are hence able to solve time-varying tasks. Numerical simulations validate the approach in a classic RL environment.
Computerized Adaptive Test based on Sugeno Fuzzy Inference System

Abstract Along with the development of information and communication technology, assessment of student learning outcomes is no longer carried out in the form of written examinations but instead carried out with a Computerized Adaptive Test (CAT). CAT is adaptive because it allows the items given are selected according to the ability of students. Therefore, the CAT needs a method to estimate student ability. This research aims to design a Sugeno Fuzzy Inference System (SFIS) to estimate student ability. This fuzzy system consists of twelve IF-THEN rules, with four inputs, namely the student’s answer, the probability of students being able to answer correctly, the level of difficulty and discrimination of the questions. The output is an estimated ability, divided into five levels, namely very low, low, average, great, and excellent. Fuzzy system simulation is performed on linear algebra course (consist of six subject) with multiple-choice questions. The simulation results show that the SFIS can estimate the ability of students by working on a maximum of six items each subject. The estimated value of student ability obtained which is not much different from previous research (with twenty four rules). Therefore, this proposed system is more efficient.
A Gravity-Referenced Moving Frame for Vehicle Path Following Applications in 3D

Moving path frames assigned to spatial curves are commonly used in the development of motion control laws for autonomous vehicles. This work presents the Gravity Normal frame, a novel navigation reference frame developed specifically for autonomous vehicle applications. This moving path frame incorporates the knowledge that many autonomous vehicles operate in a gravitational field, and control strategies must account for this. Given a curve in space that represents a desired trajectory, the proposed strategy generates a navigation frame that is well defined regardless of path curvature, while guaranteeing the normal vector is always normal to gravity and hence constrained to the horizontal plane, regardless of path torsion. Due to these characteristics, the Gravity Normal path frame is ideally suited for vehicles with distinct longitudinal and lateral dynamics since the resulting cross-track errors have a precise physical interpretation. The properties of the navigation frame are derived, and its usefulness is showcased through simulation. Finally, its applicability is demonstrated with flight experiments on a fixed-wing unmanned aerial vehicle.
Automated Microservice Code-Smell Detection

Microservice Architecture (MSA) is rapidly taking over modern software engineering and becoming the predominant architecture of new cloud-based applications (apps). There are many advantages to using MSA, but there are many downsides to using a more complex architecture than a typical monolithic enterprise app. Beyond the normal bad coding practices and code-smells of a typical app, MSA specific code-smells are difficult to discover within a distributed app. There are many static code analysis tools for monolithic apps, but no tool exists to offer code-smell detection for MSA-based apps. This paper proposes a new approach to detect code smells in distributed apps based on MSA. We develop an open-source tool, MSANose, which can accurately detect up to eleven different types of MSA specific code smells. We demonstrate our tool through a case study on a benchmark MSA app and verify its accuracy. Our results show that it is possible to detect code-smells within MSA apps using bytecode and or source code analysis throughout the development or before deployment to production.
Sonar image quality evaluation using deep neural network

Sonar technology plays an important role in the development of marine resources and military strategy. Due to the bad quality of underwater acoustics channels, the sonar images collected by sonar technology equipment are easily affected by various kinds of distortions. To obtain high-quality sonar images, the authors devise a novel dual-path deep neural network (DPDNN) to measure the quality of sonar images. In these two paths, the authors use a batch normalization layer to reduce the training time and use the skip operation to speed up the feature extraction . Based on the above two operations, the authors extract the microscopic and macroscopic structures of sonar images, respectively. Finally, a global average pooling layer and a fully connection layer are used to connect the above two paths. Experiments show that the authors' DPDNN achieves significant improvements in prediction performance and efficiency. The source code will be published in the near future.
From Classical to Quantum: Uniform Continuity Bounds on Entropies in Infinite Dimensions

We prove a variety of improved uniform continuity bounds for entropies of both classical random variables on an infinite state space and of quantum states of infinitedimensional systems.We obtain the first tight continuity estimate on the Shannon entropy of random variables with a countably infinite alphabet.The proof relies on a new mean-constrained Fano-type inequality.We then employ this classical result to derive a tight energy-constrained continuity bound for the von Neumann entropy.To deal with more general entropies in infinite dimensions, e.g.α-Rényi and α-Tsallis entropies, we develop a novel approximation scheme based on operator Hölder continuity estimates.Finally, we settle an open problem raised by Shirokov [1], [2] regarding the characterisation of states with finite entropy.
Development Design Technology Comic Literacy Android Based E-book

Abstract This study aims to develop pictorial story literacy valid and practical based on android e-book on the theme of love plant and animals, sub two the benefits of plants of human life, especially in third-class of elementary school in Terangmas Undaan Kudus. This study is a Research and Development study using Borg &amp; Gall’s. The development steps are: (1) analyze the needs of learning in primary school; (2) develop the design of initial products; (3) validation and revisions; (4) small group test; (5) large group test; and (6) diseminations. The research method used was a questionnaire and interview in SD Terangmas and SD 1 Glagahwaru. Date was collected using questionaire of respon students and teachers. The learning is valid looking by the scores obtain from 4 validators, which are 52, 27, 21, and 51 with criteria of valid score. The percentage of positive responses given by the teacher and students respectively is 93.33% and 95% of the minimum criteria 75.01%.
Cross-Domain Error Minimization for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Previous methods focus on learning domain-invariant features to decrease the discrepancy between the feature distributions as well as minimizing the source error and have made remarkable progress. However, a recently proposed theory reveals that such a strategy is not sufficient for a successful domain adaptation. It shows that besides a small source error, both the discrepancy between the feature distributions and the discrepancy between the labeling functions should be small across domains. The discrepancy between the labeling functions is essentially the cross-domain errors which are ignored by existing methods. To overcome this issue, in this paper, a novel method is proposed to integrate all the objectives into a unified optimization framework. Moreover, the incorrect pseudo labels widely used in previous methods can lead to error accumulation during learning. To alleviate this problem, the pseudo labels are obtained by utilizing structural information of the target domain besides source classifier and we propose a curriculum learning based strategy to select the target samples with more accurate pseudo-labels during training. Comprehensive experiments are conducted, and the results validate that our approach outperforms state-of-the-art methods.
BCC NEWS Classification Comparison between Naïve Bayes, Support Vector Machine, Recurrent Neural Network

Data Classification is used to determine the category to which data belongs. In the present era, due to technology and information, NEWS is easily accessible through online sources. In Day to day life Data is accessed by users through many sources like information media, computer media and many more. Usually data will also be available in unorganized form and can be converted to a structure form. In order to convert the data into structured form Data Pre-Processing techniques like punctuation removal, stop word removal, stemming, lemmatizing the words, removing special characters are used and further it is classified. In this research work various data classification models like Naïve Bayes, Support Vector Machine, and Logistic regression are compared to identify best module which gives accurate results in NEWS classification.
Enhanced Word Embedding Method in Text Classification

For the task of natural language processing (NLP), Word embedding technology has a certain impact on the accuracy of deep neural network algorithms. Considering that the current word embedding method cannot realize the coexistence of words and phrases in the same vector space. Therefore, we propose an enhanced word embedding (EWE) method. Before completing the word embedding, this method introduces a unique sentence reorganization technology to rewrite all the sentences in the original training corpus. Then, all the original corpus and the reorganized corpus are merged together as the training corpus of the distributed word embedding model, so as to realize the coexistence problem of words and phrases in the same vector space. We carried out experiment to demonstrate the effectiveness of the EWE algorithm on three classic benchmark datasets. The results show that the EWE method can significantly improve the classification performance of the CNN model.
Robot Path Planning Based on Improved Particle Swarm Optimization

In order to improve the safety and efficiency of the robot in the process of moving, this paper proposes a new hybrid approach combining two meta-heuristic methods, we used particle swarm optimization (PSO)-based grey wolf optimization (GWO) to solve this problem. In this paper, a chaotic random method is used to generate the initial population. For the traditional particle swarm algorithm, it is easy to fall into the local optimal problem. Inspired by the grey wolf optimizer, a speed classification system is introduced into the traditional particle swarm optimization and the C-GWPSO hybrid algorithm is proposed. Then the robot motion environment model is constructed, the path length and the risk are used to construct the evaluation function. Finally, the proposed approach is implemented in MATLAB working platform, the results show that the improved new algorithm is easy for the robot to find a path with the least cost. This algorithm strengthens the global optimization ability, and has stronger optimization accuracy and ability in path planning.
Towards development of a system for automatic assessment of the quality of a question paper

Abstract In this paper, we present a system for automatic evaluation of the quality of a question paper. Question paper plays a major role in educational assessment. The quality of a question paper is crucial to fulfilling the purpose of the assessment. In many education sectors, question papers are prepared manually. A prior analysis of a question paper might help in finding the errors in the question paper, and better achieving the goals of the assessment. In this experiment, we focus on higher education in the technical domain. First, we conducted a student survey to identify the key factors that affect the quality of a question paper. The top factors we identified are question relevance, question difficulty, and time requirement. We explored the strategies to handle these factors and implemented them. We employ various concepts and techniques for the implementation. The system finally assigns a numerical quality score against these factors. The system is evaluated using a set of question papers collected from various sources. The experimental results show that the proposed system is quite promising.
Modulation Type Recognition Algorithm Based on Modulation Instantaneous Structure Difference and Deep Learning

Abstract In order to solve the problems of signal modulation recognition in non-cooperative communication, this paper proposes a modulation type recognition algorithm based on instantaneous difference by neural network. Firstly, the method uses the structural difference of modulation parameters in time domain of modulation signal, and displays the difference in the form of image, so as to transform the modulation recognition problem into image recognition problem; secondly, it uses the advantage of convolution neural network to automatically extract features, and it classify different modulation signals; finally, a hierarchical neural network structure is formed to identify the unknown modulation signals.
A Data-Driven Solar Irradiance Forecasting Model with Minimum Data

An emerging new challenge introduced to solar generation forecasting is the accumulation and effective processing of raw weather data. This paper aims to address this challenge by presenting a hybrid approach to forecasting the solar irradiance, incorporating both clustering and feature extraction techniques. The developed method aims to significantly reduce the amount of data required for forecasting, and at the same time increase the accuracy of the forecast. A clustering and data selection strategy is developed that yields a reduced dataset for prediction. The performance of the forecasting approach is evaluated with real solar irradiance data collected throughout the year. Case studies demonstrate that solar irradiance can be accurately forecasted using only 20% of the full-scale training data, while also improving the forecast error compared to using the entire dataset.
Deformed contour segment matching for multi-source images

Robust and accurate multi-source matching is a difficult task due to significant nonlinear radiometric differences, background clutter, and geometric deformation in corresponding regions. Motivated by these existing problems, a discriminating yet robust combined descriptor for multi-source image matching, called deformed contour segment similarity (DCSS), is proposed in this work. First, the proposed DCSS, which is constructed by histogram of the combined contour features rather than the commonly used corner point and gradient, presents the accurate correspondence between image pairs and improves the descriptive ability to radiometric differences. Second, the deformed curve is presented via a finite-dimensional matrix Lie group to determine the similarity metric with an explicit geodesic solution. The geodesic distance, which indicates the nearest distance between curves in fluid space, is defined as the weight coefficient of the constructed histogram to enhance the robustness of the descriptor. The proposed algorithm utilizes the holistic contour information for the scoring and ranking of the shape similarity hypothesis, which can effectively reduce the influence of partially missing contours. Finally, a precise bilateral matching rule is used to perform the matching between the corresponding contour segments. Some experiments are carried out on various infrared-visible image data sets. The results demonstrate that the proposed DCSS achieves more robust and accurate matching performance than many popular multi-source image matching methods.
Stealing Neural Network Models through the Scan Chain: A New Threat for ML Hardware

Stealing trained machine learning (ML) models is a new and growing concern due to the model's development cost. Existing work on ML model extraction either applies a mathematical attack or exploits hardware vulnerabilities such as side-channel leakage. This paper shows a new style of attack, for the first time, on ML models running on embedded devices by abusing the scan-chain infrastructure. We illustrate that having course-grained scan-chain access to non-linear layer outputs is sufficient to steal ML models. To that end, we propose a novel small-signal analysis inspired attack that applies small perturbations into the input signals, identifies the quiescent operating points and, selectively activates certain neurons. We then couple this with a Linear Constraint Satisfaction based approach to efficiently extract model parameters such as weights and biases. We conduct our attack on neural network inference topologies defined in earlier works, and we automate our attack. The results show that our attack outperforms mathematical model extraction proposed in CRYPTO 2020, USENIX 2020, and ICML 2020 by an increase in accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{20.7}\times, 2^{50.7}\times$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{33.9}\times$</tex> , respectively, and a reduction in queries by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{6.5}\times, 2^{4.6}\times$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{14.2}\times$</tex> , respectively.
SIKE in 32-bit ARM Processors Based on Redundant Number System for NIST Level-II

We present an optimized implementation of the post-quantum Supersingular Isogeny Key Encapsulation (SIKE) for 32-bit ARMv7-A processors supporting NEON engine (i.e., SIMD instruction). Unlike previous SIKE implementations, finite field arithmetic is efficiently implemented in a redundant representation, which avoids carry propagation and pipeline stall. Furthermore, we adopted several state-of-the-art engineering techniques as well as hand-crafted assembly implementation for high performance. Optimized implementations are ported to Microsoft SIKE library written in “a non-redundant representation” and evaluated in high-end 32-bit ARMv7-A processors, such as ARM Cortex-A5, A7, and A15. A full key-exchange execution of SIKEp503 is performed in about 109 million cycles on ARM Cortex-A15 processors (i.e., 54.5 ms @2.0 GHz), which is about 1.58× faster than previous state-of-the-art work presented in CHES’18.
Text-to-Remote-Sensing-Image Generation With Structured Generative Adversarial Networks

Synthesizing high-resolution remote sensing images based on the given text descriptions has great potential in expanding the image data set to release the power of deep learning in the remote sensing image processing field. However, there has been no efficient research carried out on this formidable task yet. Given a remote sensing image, the structural rationality of ground objects is critical to judge it whether real or fake, e.g., real bridges are always straight, while a sinuous one can be easily judged as fake. Inspired by this, we propose a multistage structured generative adversarial network (StrucGAN) to synthesize remote sensing images in a structured way given the text descriptions. StrucGAN utilizes structural information extracted by an unsupervised segmentation module to enable the discriminators to distinguish the image in a structured way. The generators of StrucGAN are, thus, forced to synthesize structural reasonable image contents, which could enhance the image authenticity. The multistage framework enables the StrucGAN to generate remote sensing images with increasing resolution stage by stage. The quantitative and qualitative experiments’ results show that the proposed StrucGAN achieves better performance compared with the baseline, and it could synthesize high resolution, realistic, structural reasonable remote sensing images that are semantically consistent with the given text descriptions.
People counting using visible and infrared images

We propose the use of convolutional neural networks (CNN) for counting and positioning people given aerial shots of visible and infrared images. Our data set is entirely made of semi-artificial images created from real photographs taken from a drone using a dual FLIR camera. We compare the performance between the CNNs using 3 (RGB) and 4 (RGB + IR) channels, both under different lighting conditions. The 4-channel network responds better in all situations, particularly in cases of poor visible illumination that can be found in night scenarios. The proposed methodology could be applied to real situations when an extensive data bank of 4-channel images is available.
Copy-move forgery detection of medical images using golden ball optimization

The medical images can be tampered with by attackers with a malevolent goal of hiding or creating multiple copies of lesions, resulting in wrong treatment, false insurance claim, defame public figures, and so on. Such forgery demands authentication of digital images before starting the diagnosis and treatment of the patients. The existing key-point (KP) based forgery detection methods may not uniformly distribute the KPs on the images, thereby making the detection process to fail for images with forgeries in smooth regions. This paper attempts to employ a minimum Eigen-value algorithm that distributes the KPs in the entire image region and apply speeded up robust features and singular value decomposition for obtaining reduced descriptors at identified KPs. Besides, the method applies golden ball-based optimization, inspired by the behaviour of players in team-based sports games, for optimal clustering of the evaluated features. This paper studies the performances of the suggested method on 300 medical images and compares the results with the existing methods. It exhibits that the precision, specificity, sensitivity, and accuracy of the developed method are superior to the existing methods. Though the suggested method outperforms, it can further be improved by combining it with block-based methods.