Reviewerly/exHarmony huggingface dataset:
Transfer learning for domain and environment adaptation in Serbian ASR

In automatic speech recognition systems, the training data used for system development and the data actually obtained from the users of the system sometimes significantly differ in practice. However, other, more similar data may be available. Transfer learning can help to exploit such similar data for training in order to boost the automatic speech recognizer's performance for a certain domain. This paper presents a few applications of transfer learning in the context of speech recognition, specifically for the Serbian language. Several methods are proposed, with the goal of optimizing system performance on a specific part of the existing speech database for Serbian, or in a noisy environment. The experimental results evaluated on a test set from the desired domain show significant improvement in both word error rate and character error rate.
Image Saliency Detection Based on Contrast Feature of Superpixel Patch

Saliency detection based on visual attention mechanism is one of the current researches focuses of computer vision. In this paper, an image saliency detection algorithm based on the contrast feature of superpixel patch is proposed. First, RGB color space image is transformed into HSV color space, and color saliency is calculated. Then, the SLI C algorithm is used to construct the superpixel patch structure of the image. In LAB color space, the contrast saliency is calculated. Finally, the salient images of color feature and contrast feature are fused and multi-scale enhanced. Image saliency detection is carried out on MSRA - 1000 data set. Compared with the existing methods, the algorithm in this paper detects more complete contour of image saliency region, has better precision and recall rate, and can obviously suppress complex texture and noise.
Trust Model for Cloud Computing System

Cloud computing is a distributed architecture for providing pay per use, on demand computing services through the Internet exclusive of actually attaining them. In this way, it holds supervision cost and time for associations. Many associations are going toward the cloud because of the uncomplicated accessibility of administrations gave by the cloud suppliers. In cloud computing, customer information is set up and protected in the data center of a cloud supplier. The limited power over the information may incite diverse safety issues and dangers, including sharing of resources, denial of service, etc. There are diverse investigation difficulties for obtaining cloud computing, such that, well-managed Service Level Agreement (SLA), protection, reliability, and interoperability. This paper spreads out cloud introduction, different cloud models, primary safety dangers, and issues that are correct currently present inside the cloud computing.
No Classifier Left Behind: An In-depth Study of the RBF SVM Classifier's Vulnerability to Image Extraction Attacks via Confidence Information Exploitation

Training image extraction attacks attempt to reverse engineer training images from an already trained machine learning model. Such attacks are concerning because training data can often be sensitive in nature. Recent research has shown that extracting training images is generally much harder than model inversion, which attempts to duplicate the functionality of the model. In this paper, we correct common misperceptions about image extraction attacks and develop a deep understanding ofwhy some trained models are vulnerable to ourattack while others are not. In particular, we use the RBFSVMclassifier to show that we can extract individual training images from models trained on thousands of images., which refutes the notion that these attacks can only extract an “average” of each class. We also show that increasing diversity of the training data set leads to more successful attacks. To the best of our knowledge, our work is the first to show semantically meaningful images extracted from the RBF SVM classifier.
Budget-Aware Performance Optimization of Workflows in Multiple Data Center Clouds

Users pay to use resources in cloud systems which makes them more demanding on performance and costs. Optimizing the response time of the applications and meeting user’s budget needs are therefore critical requirements when scheduling applications.The approach presented in this work is a scheduling based-HEFT algorithm, which aims to optimize the makespan of tasks workflow that is constrained by the budget. For this, we propose a new budget distribution strategy named Estimated task budget that we integrate in our budget-aware HEFT algorithm. We use a multiple datacenters cloud as a real platform model, where data transfer costs are considered. The results obtained by our algorithm relative to recent work, show an improvement of makespan in the case of a restricted budget, without exceeding the given budget.
Implementation of Stemmer and Lemmatizer for a Low-Resource Language—Kannada

Stemming and lemmatization are two basic modules used for text normalization in Natural language processing (NLP) which qualifies text, words, and documents for further processing. Stemming is the process of eliminating the affixes from the inflectional word to generate root word. The extracted stem or root word may not be a valid. Lemmatization is also a process of removing the affixes from the word but returning the word in dictionary form which is known as lemma. This lemma will always be meaningful word. Hence, while developing the Lemmatizer semantic knowledge is considered. In this paper, Unsupervised Stemmer and Rule-Based Lemmatizer have been proposed for Kannada. Experimentation is done by building a dataset of 17,825 root words with the help of Kannada dictionary.
A Multi-Agent System for Color Video Decomposition

Dynamic texture (DT) analysis is an important research area in the field of computer vision. Actually, several methods are used to recognize the dynamic texture, but a good compromise between computational cost and performance is hard to achieve. For color sequences, we propose a fast color decomposition model which splits a video into two components, a first one containing geometrical information, the structure of the sequence and a second one dynamic color texture (DCT). This work presents a combined color video decomposition with multi-agent system (MAS). The combination is used to increase the robustness of the dynamic texture recognition and to decompose a color video in a reasonable time. Experimental results are obtained from Dyntex database which offers a wide variety of natural sequences all presenting dynamic textures.
Peramalan Data dengan Teknik Pemulusan Simple Moving Average (Studi Kasus Harga Saham Harian PT Bank BRI Syariah Tbk)

Time series analysis is used to model time series data and forecast data for future periods. This research was conducted to predict data with a simple smoothing technique, namely the Simple Moving Average of PT Bank BRI Syariah Tbk's stock closing price data. The closing price of shares was analyzed using three average criteria, namely 3, 5, 20, and 100 of the most recent data. Comparison of accuracy with SSE, MSE, and MAPE showed that the best in predicting daily stock closing price data was the Simple Moving Average using the latest 3 data with a prediction result for the future period of Rp. 748, -.
Digest-based Security Protocol for IoT devices

Abstract Short output universal hash functions are widely used in manual authentication protocols. A digest function is one new universal hashing algorithm. The aim of this algorithm is to reduce the CPU processing time, and have the best proven bounds. However, typical manual authentication protocols using digest are based on asymmetric key algorithms, which is not suitable in many Internet of things scenarios. This paper thus proposes a series of digest protocols only use symmetric key algorithms.
Supporting Linguistic Vitality

This chapter reviews efforts at supporting linguistic vitality, including the use of surveys to raise consciousness about the status of language use; catalogs that provide basic facts about low resourced languages; the language archiving movement, which provides state-of-the-art repositories for language materials; funding and other research activities to support language documentation; and language teaching and other language revitalization activities.
MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression

With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace can find clusters in the subspace, which solves the problem of high data dimension. However, their clustering process based on only one single feature, which results in their performance being particularly sensitive to this single view. Affected by the integrated algorithm, a large number of multi-view methods began to emerge. These methods improve the clustering performance by integrating the subspace expressions of multiple views, but the problem is that the complementary information of multiple views cannot be fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high-dimensional data sets. In this paper, based on the multi-view subspace clustering method, a clustering algorithm based on tensor low rank expression is proposed to solve the clustering problem of high-dimensional datasets. On the one hand, this paper solves the problem of noise and data corruption by combining the method of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> norms, which transform the optimization problem of solving multi-view subspace expression into a low-rank expression problem of tensor to fully consider the complementarities between views. On the other hand, the proposed scheme solves the problem of nonuniform distribution in clusters in high-dimensional data by combining with the density peak algorithm based on hierarchical strategy. Experiment results show the effectiveness of the algorithm.
Realities and Perspectives of Software Development in Puerto Rico: A New Analysis

Currently, within the field of Software Engineering (SE), it has been proposed to integrate four variables to develop quality software: i.) A flexible development methodology (traditional, agile, or hybrid) and adapted to the context particular to each application, ii.) a work team (trained, experienced, highly innovative, and with leadership), iii.) cutting-edge technologies, represented in the quality of the equipment and tools (automated or semi-automated) facilitating the planning, design, and manufacturing processes of products and, iv.) the inclusion of usability techniques in the process, as a guarantee to manufacture products according to customer needs. A literature search indicates that in Puerto Rico so far, there are no published articles that allow knowing the state of the art of the SE on the Island and, therefore; the organizational schemes and the type of workgroup that predominates in software development companies are unknown. Almost twenty years after the promulgation of the Agile Manifesto, in Puerto Rico, the degree of adoption of the new paradigms is still unknown. It is necessary to search for information within companies to understand what strategies are used by developers and what benefits report with their use. This new study seeks to evaluate, synthesize and present the empirical findings on software development in Puerto Rico (PR), to contrast or update the previously collected data and to know the type of applications that are carried out on the Island, what methodologies of work are the most used, how agile methods are perceived and if the approach towards usability techniques has changed in recent decades. These aspects are central to proposing to software developers an integration framework according to their needs and the demands of their clients.
Auto Curation on FaceNet Embeddings with Gamma and Gaussian Distribution to Predict Model Performance in Actual Industrial Deployment

Many AI applications, such as face recognition [1] and NLP, rely heavily on data embedding as an intermediate representation on which further processing is made. However few of these applications gain insights to such intermediate representation, and thus have difficulties in data analytic or designing efficient models, or both. The resulting models accordingly designed are thus hard to analyze for performance tuning and optimization. We deeply dived into the embedding of FaceNet in an actual industrial deployed site, and propose a closed-loop solution with data representation, data curation, data modeling on these intermediate data, as to do performance prediction for 1:1 and 1:N scenarios [2]. Our results shows our prediction of the model, in the range of interest of application, achieved 0.4% error in predicting True Positive Rates, and 2.8% error in predicting False Positive Rates.
Wide Area Networks for Both High- and Low-End SMPTE ST 2110 Workflows

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SMPTE ST 2110 supports both uncompressed <sup>1</sup> and compressed video clippings, <sup>2</sup> which allow it to be used in live production workflows for both high- and low-value content. However, high- and low-end live production require different quality and have very different budgets. In addition, although the entire quality spectrum will use the same set of standards, building a wide-area network (WAN) that provides the quality demanded by high-end live production, while also meeting the price pressure from low-end live production, is also challenging. The solution is a WAN that can seamlessly mix both private (dedicated) and public (Internet) infrastructures.</i>
A Reactive Control Algorithm for 3D Navigation of a Non-holonomic Robot in Tunnel-Like Environments with Static Obstacles

The motion of a non-holonomic mobile robot in a tunnel-like environment with obstacles is a tough challenge. We propose a navigation scheme to navigate a robot in a tunnel-like structure without colliding with static obstacles. The mobile robot will maintain a predefined distance with the walls of the tunnel. Computer simulation results validate the performance of the algorithm. The proposed law is also applicable to underwater and aerial autonomous vehicles.
The full separability of three-qubit superposition state and its decayed states in white noise environment

Abstract The research on the separability of quantum states is one of the important research directions in quantum information theory. In this paper, we propose a new entanglement witness to study the full separability of the superposition of three-qubit W and <?CDATA $\bar{{\rm{W}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover accent="true"> <mml:mi mathvariant="normal">W</mml:mi> <mml:mo stretchy="false">¯</mml:mo> </mml:mover> </mml:math> (the qubit inverted W state) states in white noise environment. The witness accounts for most results of separable condition of the state obtained with numerical calculation. The witness also accounts for the decayed state which is the superposition state with and terms decayed by a factor. The separability criterion from our witness is better than that of the further generalized Wootters formula.
Understanding Deep Learning: Case Study Based Approach

Deep learning is a much focused domain of artificial neural networks. Deep learning algorithms try to learn massive amounts of unlabelled data and make a better analysis. With deep learning, all layers learn the input data and transform it into a more abstract and composite format. The word “deep” means higher numbers of hidden layers in which the data from one layer to another is transformed to generate the most accurate outcome. Deep learning architecture has been applied to different fields like medical image analysis, machine translation, bioinformatics, speech recognition, social network filtering, computer vision, audio recognition drug design, natural language processing, and so on. This chapter discusses important deep learning applications across different disciplines, their contribution to the real world, and a study of the architectures and methods used by each application. This chapter also introduces the differences between machine learning and deep learning. Finally, this chapter concludes with future aspects and conclusions.
The Used of E-learning Based on Android in Actuarial Courses

Abstract This research is advance research that built an android application for the lecture process in e-learning for actuarial courses. This research aims to produce the learning designs to apply the applications developed in Actuarial courses. The product developed is a compilation of Semester Lecture Plans, Lecture Program Units, teaching materials, and queries. This research produced an android application program for e-learning in Actuarial lectures at the Mathematics Education Study Program Department of Mathematics, Faculty of Mathematics and Natural Sciences, UNP Padang.
Hybrid Artificial Intelligence Systems for Complex Neural Network Analysis of Abnormal Neurological Movements with Multiple Cognitive-nodes Signal

Research related to feedback-neuro-biosystems regarding the analysis of the status and behavior of T(tremor)-objects under the cognitive influence of neural nodes. They focus on the study of parameters relative to normal states and behavior (normal wave movements of certain parts of the body), which used classical Fourier transform digital processing techniques and the cognitive connections were estimated approximately using neural network techniques and software technologies. In order to decompose the complex abnormal movement path, a scheme of multicomponent decomposition of segments of the trajectory of trajectories is used to further formulate the mathematical model. The mathematical model assumes quantitative characteristics of tremor. In this technique of analyzing spiral test data, an extremely important and up-to-date result is the ability to obtain frequency response, using hybrid Fourier transform and digital signal processing techniques on hybrid spectral functions and spectral values.
Data Deduplication on Multi-domain Big Data to Overcome Communication Overheads

Information deduplication is the procedure for assigning replicas of data and has stood normally applied in dispersed capacity to contract extra room and circulate statistics flow limit. To the degree conceivable, there may be just one reproduction for each report placed away within the cloud, whether or not or now not countless customers request such a report. Likewise, the structure for deduplication expands the usage of belongings in this manner diminishing trustworthy effectiveness. Hence, the safety takes a look at for touchy data regularly happens as clients redistribute them to the cloud. This paper places forth the number one try to formalize the thinking about an appropriated robust deduplication framework to decide the previously mentioned protection challenges. We advocate new structures of dispersed deduplication with higher unwavering quality wherein statistics bumps are taken across distinct cloud servers In expansion, the safety necessities of records protection and name consistency are met through giving a deterministic puzzle sharing technique in coursed power systems in preference to utilizing concurrent encryption as in beyond structures of deduplication. Security audit indicates that our structures for deduplication are secure the extent that the characterizations gave in the anticipated safety model are concerned. We execute the proposed systems as proof of concept to demonstrate that the overhead incited is noticeably constrained underneath sensible conditions.
Computer Vision for Hand Gestures

The model developed here is used to detect specific items from the environment. The desired objects to be detected from the environment is hands(gestures). So, for computer to look at the environment computer vision is the most necessary aspect. Based, on the gesture present in the image captured along with the unnecessary objects, the image is processed and the important message is kept and rest is discarded. After processing the neural networks are introduced for to elevate the standards of computer vision there by allowing computer to know about the gesture provided by the humans. Here the recognition of the image is done by using Convolution neural network (CNN) algorithm, the gesture is predicted and this predicted result is shown on the screen connected or an audio device connected.
A Pattern Recognition Method for Scene Sketch Images Using Deep Learning and Its Evaluation

Many studies on handwritten figure recognition have been published so far. For handwritten graphics in the fields of science and engineering, clear drawing rules are often clear, that is the shapes of the elements are predetermined. On the other hand, there is no drawing rule for sketches such as scene sketches; this makes it difficult to design a feature extractor. Based on this background, in recent years, research on recognition models using deep learning for sketch images has been reported. However, the recognition rate of all the previous research results are about 70% or less. Therefore, in this paper, we propose a new CNN model for recognizing landscape sketch images, and verify its effectiveness by computer experiments. Since there is no benchmark data for landscape sketch images, it cannot be compared with the results of previous studies, but the recognition rate of the CNN model proposed here is about 80%, which is higher than that of previous studies.
A Study on Reinforcement-learning Agents with Personality through the Implementation of Character Parameters

We aim to achieve characteristic action in reinforcement-learning agents. Agents with different personality parameters move through a maze with the aim of reaching a goal while obtaining rewards. Three types of parameters are defined: A: Prioritize additional rewards, S: Prioritize goal, and G: Prioritize search rewards. We trained a reinforcement-learning agent with parameters that correspond to a character. Statistical information of the learning process of agents were obtained by using TensorBoard and compared. As a result, the agents did not show any personality traits in their behavior. However, we confirmed that the learning process changed depending on the parameter setting pattern.
On the Rate Region of Symmetric Multilevel Imperfect Secret Sharing

In this paper, we introduce an n-channel multilevel imperfect secret sharing problem, which can be regarded as a generalization of secret sharing and a variation of multilevel diversity coding. In this model, a discrete memoryless source (DMS) is encoded into n encoded messages. To measure the secrecy of the system, we introduce a security level for each subset of encoded messages. In the paper, we focus on the n-channel symmetric multilevel imperfect secret sharing (SMISS) problem, i.e., the security levels of all the subsets with the same cardinality are identical. First, we explicitly characterize the coding rate regions for 2-channel and 3-channel SMISS problems. However, it is difficult to characterize the explicit rate region for a general n, since the complexity of designing optimal coding schemes can grow with n exponentially. Nevertheless, we put forward an inner bound and an outer bound on the rate region of the general n-channel SMISS problem. Moreover, we consider two special cases, i.e., the Two-SMISS problem and the linear SMISS problem, and prove that the inner and outer bounds are tight for these two problems, respectively.