Reviewerly/exHarmony huggingface dataset:
HMM-based phonetic engine for continuous speech of a regional language

A Phonetic Engine (PE), phonetic-level speech recognition system for continuous speech of a regional language named Punjabi, has been proposed in this paper. Punjabi is a highly prosodic language and a very small amount of work has been done in this direction on this language. As a first step towards the development of PE, 25 hrs of data is collected in three different modes, namely, Read Speech, Conversational Speech and Lecture Speech. The 10 hrs of collected data is then manually transcribed using International Phonetic Alphabet (IPA) chart. The architecture of the PE includes three different phases: data preparation, system training and system testing. Initially, the vocabulary of 49 phones is chosen by carefully analyzing the symbol frequency in IPA transcription and data files are prepared to train the system accordingly. The prepared data files and speech files are then used for modeling and feature extraction. In the development of PE, Mel Frequency Cepstral Coefficient (MFCC) is used as a feature extraction technique and Hidden Markov Model (HMM) as a classifier. The PE is developed using HTK Toolkit. The performance of PE is evaluated using three different approaches: (i) by increasing the amount of data from 3 hrs to 5 hrs, (ii) by decreasing the number of symbols from 49 to 29 and (iii) by increasing MFCC dimensions from 12 to 36. An accuracy of 72.3% has been achieved in this work when 5 hrs data with 29 symbols and 12 MFCCs was employed.
Legenda Tokoh Jokotole sebagai Representsi Pendidikan Karakter dalam Pengembangan Media Aplikasi Android pada Pembelajaran Bahasa Indonesia Aspek Kemampuan Membaca Kelas VII Di SMP Negeri se-Kabupaten Bangkalan

These studies are research on the development of research and development (R &amp; D) using the Borg and Gall model. However, this development research takes several stages, namely, problem identification, information gathering, product design, and product testing. The results of the study found that Jokotole figures were legendary figures in the Madura region from Bangkalan, Sampang, Pamekasan, and Sumenep. The legend of Jokotole is found in every legend in the villages of Madura, there are 38 stories that tell the character of Jokotole both in his journey (stop) so as to create his own legend or narrate the battle against his enemies, while in the Bangkalan region there are 12 legends which tell Jokotole namely in the area of talk, keleyen, tip plate, and so forth. In this development research, the Jokotole story was made the main character in its emergence as a learning media based on andorid, because the tendency of the majority of Madurese people to accept Jokotole figures as positive figures starting from Jokotole's birth, journey, chivalry, spirituality, and death. Furthermore, in the development research this took the legend of the villages that gave rise to the character of Jokotole. Andorid media based on learning media applications ranging from stories, places (the area adopted from google map maps, questions, and reading ability tests using the ability formula to read elementary school children, namely junior high school. Jokotole figures in the andorid learning media application created using multimedia unity and design Corel Draw with Story Board Jokotole character journey when visiting the western Madura area until the trip in Bangkalan area, namely in the village of Telang, Socah, Ujung Plate, Bancaran, and Jambu Village.
SilverChunk: An Efficient In-Memory Parallel Graph Processing System

One of the main constructs of graph processing is the two-level nested loop structure. Parallelizing nested loops is notoriously unfriendly to both CPU and memory access when dealing with real graph data due to its skewed distribution. To address this problem, we present SilverChunk, a high performance graph processing system. SilverChunk builds edge chunks of equal size from original graphs and unfolds nested loops statically in pull-based executions (VR-Chunk) and dynamically in push-based executions (D-Chunk). VR-Chunk slices the entire graph into several chunks. A virtual vertex is generated pointing to the first half of each sliced edge list so that no edge list lives in more than one chunk. D-Chunk builds its chunk list via binary searching over the prefix degree sum array of the active vertices. Each chunk has a local buffer for conflict-free maintenance of the next frontier. By changing the units of scheduling from edges to chunks, SilverChunk achieves better CPU and memory utilization. SilverChunk provides a high level programming interface combined with multiple optimization techniques to help developing efficient graph processing applications. Our evaluation results reveal that SilverChunk outperforms state-of-the-art shared-memory graph processing systems by up to $$4{\times }$$, including Gemini, Grazelle, etc. Moreover, it has lower memory overheads and nearly zero pre-processing time.
A Nonlinear Telegraph Equation for Edge-Preserving Image Restoration

Poisson noise suppression is a challengingJain, Subit K and important preprocessing stage for higher level image analysis. Therefore, in this paper, a new attempt has been made using telegraph equation and variational theory for Poisson noise suppression. The proposed approach enjoys the benefits of both telegraph-diffusion equation and Hessian edge detector, which is not only robust to noise but also preservesYadav, Jyoti image structural details. The Hessian function has been used to distinguish between edges and noise. However, to the best of author’s knowledge, the HessianRao, Manisha edge detector driven telegraph-diffusion scheme has not been used before for Poisson noise suppression. With the proposedSharma, Monika model, restoration is carried out on several natural images. The experimentalRay, Rajendra K results of proposed model are found better in terms of noise suppression and detail/edge preservation, with respect to the existing approaches.
LaserTank is NP-Complete

We show that the classical game LaserTank is $\mathrm{NP}$-complete, even when the tank movement is restricted to a single column and the only blocks appearing on the board are mirrors and solid blocks. We show this by reducing $3$-SAT instances to LaserTank puzzles.
Efficient Analysis of Clinical Genomic Database

Clinical genomic database is a database consisting of human genomic data which can be analysed to get information about diagnosis of a particular disease. Data mining is the process of extracting meaningful information from data stored in huge database. Among several data mining algorithms Association rule mining algorithm is used for frequent pattern analysis. In case of Clinical genomic database, Association rule mining algorithm can be used for determination of genes which are responsible for a particular disease. However sequential implementation will consume a lot of processing time. In order to increase the speed of processing parallel implementation Association Rule mining algorithm can be used. In this paper parallel implementation of Association Rule mining algorithm using analytical big data technology have been proposed for efficient analysis of Clinical genomic database.
Phishing Website Detection Algorithm Based on Link Structure

Abstract Starting from the relationship of link structure, this paper analyses the differences between phishing websites and regular websites. The fingerprints of websites in the collection of suspected target websites are compared and the maximum fingerprint difference is obtained. The threshold of maximum fingerprint difference is set to distinguish phishing websites from regular websites.
The Control of Transfer Pricing of Corporations by Blockchain Technology

The globalization movements that had emerged in the last century have not only influenced the world's social, political, and cultural structure, but also have influenced its economic structure as well. The globalization of the world economy makes locations and economic units between countries or regions interdependent on one another. The primary driving forces of this process include technological change and multinational enterprises. Thus, intercompany transactions are important for multinational enterprises. In recent years, discussions regarding the applicability of blockchain technology, or more commonly referred to as crypto coins, for the public sector have emerged. This study therefore attempts to explain the applicability of blockchain technology in relation to transfer pricing, and it clarifies (1) how blockchain technology represents a new approach to transfer pricing, (2) how blockchain technology reduces transfer pricing evasion, and (3) how blockchain technology increases both transparency and accountability.
The application of mathematical modeling to diagnose diseases of the digestive system

This paper presents the results of applying the neural network approach in the diagnosis of inflammatory bowel diseases. The trained neural network can be used as auxiliary tool in the differential diagnosis of ulcerative colitis and Crohn’s disease based on the classification of endoscopic images.
“Shock-Eye” - A Beidou-Based Earthquake Monitoring and Early Warning System

Abstract China’s earthquake activity is high in frequency, high in intensity, shallow in source and wide in distribution. It is a country with severe earthquake disasters. Earthquake disasters have brought huge losses to people’s lives and property safety in China. Based on this situation, combined with Beidou’s high-precision positioning and two-way communication technology covering millimeter level, supplemented by remote sensing and GIS technology, this paper aims to establish a new Beidou system-based seismic monitoring and early warning system to monitor the crust of earthquake-prone areas. The micro-deformation and motion situation, through the data exchange processing, construct a statistical analysis model, effectively predict the earthquake condition in time, automatically issue an early warning to the estimated area before the earthquake arrives, gain valuable time for personnel and property transfer, and form a simulated earthquake. The post-disaster scene provides technical support for the emergency rescue command after the earthquake. We call it the “shock-eye” system. At the same time, the system can provide services for medium and long-term earthquake prediction through observation and processing of massive continuity data.
Content-based power allocation for perception-friendly SoftCast

Gradient based image SoftCast (G-Cast) reconstructs the image by transmitting gradients and uses structural similarity (SSIM) as the image quality assessment standard. The reconstructed image is more consistent with the perception of human vision system, however, it is a little too smoothed. In order to overcome this disadvantage, a perception-friendly SoftCast scheme using content-based power allocation is put forward in this paper. Firstly, an image block classification algorithm is applied to divide the image into edge blocks, detail blocks and smooth blocks. Secondly, gradients in different blocks are allocated to different power factors according to their importance. As a result, the scheme improves the subjective quality of reconstructed images significantly. At the same time, the objective quality of the scheme is also improved.
The impact of technology on user-centric discovery

The amount of resources and information available to users seems infinite, but what is actually discoverable and accessible is much less in comparison.Among the many longstanding roles of libraries is guiding users to the resources that they need, and although technology has expanded the possibilities of this service, it has also exposed new risks and challenges.Libraries also have a unique position within the technology-driven marketplace, as both a content consumer and a content provider.They also have conflicting objectives -protecting users while competing for users.This paper explores these dynamics that result from the impact of technology on library user-centric strategy and solutions.
Sequence image matching using adaptive SIFT under complex environmental conditions

The scale invariant feature transform (SIFT) is one of effective methods for sequence image matching, but under complex environmental conditions such as illumination and blur, the matching rate is low, and the matching process becomes difficult. It is mainly because of the fixed threshold which results in the particular scenes are not considered. A new method with adaptive threshold is proposed for sequence image matching in this paper. Firstly, the statistical features of the sequence images are analyzed, then the comprehensive indicators of each statistical feature are calculated by the principal component analysis method, and finally, based on main influence factor and statistics features, the adaptive threshold prediction model is established. To test the efficiency of the proposed method, it is used for sequence image matching. The experimental results show that the adaptive threshold prediction model can be applied to many cases and improves the matching performance for sequence images under complex environmental conditions, especially for the sequence images under poor illumination.
Empirical Analysis of Proximity Measures in Machine Learning

Availability of abundant and various types of proximity measures often projects a challenge in both supervised and unsupervised learning processes. There are various similarity and dissimilarity measures proposed in the literature of machine learning. These measures differ with respect to various issues imposed by different application domains such as ability to handle noise, ability to detect various types of correlation, and copingHoque, Nazrul with large number of dimensions. In this work, we pick-up eighteen proximity measures and apply them on two well known distance-based learningAhmed, Hasin frameworks. One framework uses a widely used supervisedBhattacharyya, Dhruba K learning method, i.e., KNN classifier and the other uses an unsupervised learning method called k-means clustering.
Applying Graph Theory to Detect Cases of Money Laundering and Terrorism Financing

A technique to automate the generation of criminal cases for money laundering and financing of terrorism (ML/FT) based on typologies is proposed. That will help an automated system from making a decision about the exact coincidence when comparing the case objects and their links with those in the typologies. Several types of subgraph changes (mutations) are examined. The main goal to apply these mutations is to consider other possible ML/FT variants that do not correspond explicitly to the typologies but have a similar scenario. Visualization methods like the graph theory are used to order perception of data and to reduce its volumes. This work also uses the foundations of information and financial security. The research demonstrates possibilities of applying the graph theory and big data tools in investigating information security incidents. A program has been written to verify the technique proposed. It was tested on case graphs built on the typologies under consideration.
Wellness Indexes to Assess Quality of Life

Assessing the quality of life of people means to evaluate their physical, mental, and social well-being. The first part of this work summarizes the most accredited definitions of wellness, the related models, and the tools that can be found in the literature to assess quality of life. Then, it introduces three indicators that can be easily obtained from localization data to monitor some aspects of wellness. The advent of technology in this sector allows continuous and low-cost patient monitoring. This scenario applies particularly well in the context of health-care facilities, where doctors can thus concentrate on interpreting the data, resulting in a reduction in their workload.
Deep Key Clips-Video Feature Fusion Framework for Action Recognition

Action recognition is crucial for many computer vision applications. Recently, deep learning has made breakthrough in recognition performance of action. However, there are a large number of redundant video frames which contain similar information making it difficult to capture discriminative spatio-temporal features for long-term actions. In this paper, we propose a novel framework for action recognition: Deep Key Clips-Video feature fusion framework. First, we propose a key clip selection algorithm based on background subtraction, which utilizes image average gradient and select key clips for training. Then, we further superimpose the key frames to generate historical contour images, effectively aggregating long-term information of the actions. Key video clips and historical contour images are inputted to the 3D convolutional network and the 2D convolutional network respectively, which extract the clip level and long term video level feature. Finally, we fuse these two sub-networks to improve the accuracy of recognition. We conduct experiments on two current mainstream action recognition datasets UCF-101 and HMDB-51. Compared with the state-of-the-art methods, the experimental results demonstrate the effectiveness of our proposed network for action recognition.
On-Line State Estimation of 2-qubit Quantum Systems

In this paper, the online estimation of 2-qubit states is studied based on the quantum continuous weak measurement and compressed sensing (CS) theory. The stochastic master equation is used to describe the dynamic evolution of the system. The weak measurement operator acting on the system and the dynamic equation of the measurement operator over time are derived. The number of measurements is reduced based on the CS theory. The non-negative LS algorithm is used to reconstruct the quantum system state in free evolution. At last, the numerical simulations of the on-line state estimation of 2-qubit quantum systems are carried out in MATLAB environment, and the influences of the parameters on the estimation results are analyzed by the performance comparisons.
Selection of subsea surface horizontal displacement monitors in offshore deep-water channel project

This paper studied the performance of subsea ground horizontal displacement monitors based on a 12.5 meters deep offshore channel project in the ChangJiang River. The feather of monitoring instruments were analyzed and compared. A suitable set of instruments were chose according to the feather of deep operation water depth, large flow and no advection period.
Triplet-CSSVM: Integrating Triplet-Sampling CNN and Cost-Sensitive Classification for Imbalanced Image Detection

In real-world applications, image classes are often imbalanced, which result in detection performance decline and quite different misclassification costs. In order to deal with these issues, cost-sensitive learning based on manually designed features has been studied for many years. With the rapid development of Deep Learning, more comprehensive methods, such as CNN and RNN, have proven their strength on feature extraction and classification. In this paper, we develop triplet-sampling CNN to automatically obtain a great many in-depth features from images. Cost-sensitive SVM (CSSVM) is applied to deal with the classification performance degradation caused by imbalanced image dataset. Furthermore, two techniques are integrated as Triplet-CSSVM for classifying images accurately even over imbalanced image set. This approach can overcome the disadvantages of the conventional features extraction and improve the overall classification performance comparing with several other related schemes.
The Representation of Speech and Its Processing in the Human Brain and Deep Neural Networks

For most languages in the world and for speech that deviates from the standard pronunciation, not enough (annotated) speech data is available to train an automatic speech recognition (ASR) system. Moreover, human intervention is needed to adapt an ASR system to a new language or type of speech. Human listeners, on the other hand, are able to quickly adapt to nonstandard speech and can learn the sound categories of a new language without having been explicitly taught to do so. In this paper, I will present comparisons between human speech processing and deep neural network (DNN)-based ASR and will argue that the cross-fertilisation of the two research fields can provide valuable information for the development of ASR systems that can flexibly adapt to any type of speech in any language. Specifically, I present results of several experiments carried out on both human listeners and DNN-based ASR systems on the representation of speech and lexically-guided perceptual learning, i.e., the ability to adapt a sound category on the basis of new incoming information resulting in improved processing of subsequent speech. The results showed that DNNs appear to learn structures that humans use to process speech without being explicitly trained to do so, and that, similar to humans, DNN systems learn speaker-adapted phone category boundaries from a few labelled examples. These results are the first steps towards building human-speech processing inspired ASR systems that, similar to human listeners, can adjust flexibly and fast to all kinds of new speech.
KFBin: Kalman Filter-Based Approach for Document Image Binarization

In this paper, we propose a novel two-step approach, called KFBin, for the binarization of document images based on the Kalman filtering (KF) technique. In the first step, a state space model is developed as a new document image representation, and then the Kalman filter is applied to track the positions of the foreground and background information and generate two corresponding outputs, which allows the enhancement of the foreground content leading to better legibility of text. Standard thresholding algorithms were used in the second step to generate binary images from the enhanced foreground components. The performance of the proposed approach is validated on a well-known dataset and evaluated using common image binarization quality metrics. Outstanding improvement of the binarization performances of several state-of-the-art binarization methods has been achieved by using the proposed approach. Experimental results point that the poor binarization results of egraded document images can be greatly improved by enhancing their quality.
Enhance Data Security and Privacy in Cloud

With the advancement of communication and information technology, the internet has become used as a platform for computing and not only a way of communications networks. Accordingly, the large spread of cloud computing led to the emergence of different privacy implications and data security complexities. In order to enhance data security in the cloud, the authors propose in this chapter the use of an encryption box, which includes different cryptosystems. In fact, this step gives the user the opportunities to encrypt data with an unknown algorithm and makes a private key before the storage of data in the host company servers. Moreover, to manage the encryption database, the authors propose a quantum approach in search based on Grover's algorithm.