Reviewerly/exHarmony huggingface dataset:
Blockchain-based Marketplace for Software Testing

Department of Computing and Software at Mc-Master University and Highmark Global are collaborating on a research project to develop an efficient blockchain-based marketplace for software testing. In this paper, we propose a consensus protocol for the blockchain platform in medical related technologies. The proposed consensus protocol incorporates a software testing algorithm that takes into account the capability and credibility of software testers and temporally adjusts these qualities based on the performance of testing jobs completed by the testers. In addition, the protocol maintains a quantifiable measure of testers' capabilities based on the past jobs they have completed. We expect to implement this protocol and deploy on a blockchain platform to automate the software testing validation process and resolve discrepancies that arise between testing results from different testers.
Inter-Component Transform for Color Video Coding

In natural digital images and videos, correlations between color components can be observed. These correlations can be exploited to achieve additional coding gain in modern block-based hybrid video coding. To this end, we propose the use of a block-wise, rotational inter-component transform (ICT) applied to the two residual chroma signals that result from conventional intra or inter-picture prediction. Different ICT parameterizations in terms of number and quantization of the rotational angles as well as resulting components signaled in the coded bitstream are investigated. An implementation into the currently developed Versatile Video Coding (VVC) reference software provides average bitrate savings of up to 0.7% (All Intra configuration) with negligible increases in implementation complexity and runtime. Our proposal has been adopted into the VVC draft specification text.
Fine Tactile Representation of Materials for Virtual Reality

The most important aspect of virtual reality (VR) is the degree by which a user can feel and experience virtual space as though it is reality. Until recently, the experience of VR had to be satisfied with operations using a separate controller along with the visual and auditory elements. However, for a far more realistic VR environment, users should be able to experience the delicacy of tactile materials. This study proposes tactile technology, which is inexpensive and easy to use. To achieve this, we analyzed the unique patterns of materials through image filtering and designed a computing model to deliver realistic vibrations to the user. In addition, we developed and tested a haptic glove so that the texture of the material can be sensed in a VR environment.
Multitaper Based MFCC Feature Extraction for Robust Speaker Recognition System

In present scenario, speaker identification under different types of noise conditions is the most challenging task in the area of speech processing. Due to adverse effect of noise on the speech samples, there will be substantial degradation in the system accuracy. In order to overcome this problem, we proposed a novel spectrum estimation approach using multitaper windowing function. Using frequency domain averaging and multiple windowing functions, the multitaper forms a better spectrum estimation when compare to traditional hamming window function. The multitaper spectrum estimation is used in the computation of MFCC and the proposed novel approach is compared with the other feature extraction techniques. The simulation results were conducted using i-vector based model with mixture of PLDA (mPLDA) scorings. In this work the simulation results are conducted using TIMIT database and also different types of noises like car, babble and factory noises are used with different signal to noise ratio.
Analysis of Feature Extraction Algorithm Using Two Dimensional Discrete Wavelet Transforms in Mammograms to Detect Microcalcifications

The paper focusing on the analysis of the feature extraction (FE) algorithm using a two-dimensional discrete wavelet transform (DWT) in mammogram to detect microcalcifications. We are extracting nine features namely Mean(M), Standard deviation(SD), Variance(V), Covariance(Co_V), Entropy(E), Energy(En), Kurtosis(K), Area(A) and Sum(S). FE is a tool for dimensionality reduction. Instead of too much information the data should be reduced representation of the input size. The extraction has been done to construct the dataset in the proper format of the segmented tumor, then only it can be given to the classifier tools and achieving high classification accuracy. The nine statistical features are extracted from the mammogram is determined the effectiveness of the proposed technique. This experiment has been conducted for 322 mammogram images and in this paper, we listed only a few. Actually, the detection of microcalcifications has been done with various algorithms discussed below. The analysis of the FE algorithm using two-dimensional discrete wavelet transform in mammogram to detect macrocalcification technique has been analyzed using MATLAB.
Adolescent age estimation using voice features

Abstract In this paper, a method for evaluating the chronological age of adolescents on the basis of their voice signal is presented. For every examined child, the vowels a, e, i, o and u were recorded in extended phonation. Sixty voice parameters were extracted from each recording. Voice recordings were supplemented with height measurement in order to check if it could improve the accuracy of the proposed solution. Predictor selection was performed using the LASSO (least absolute shrinkage and selection operator) algorithm. For age estimation, the random forest (RF) for regression method was employed and it was tested using a 10-fold cross-validation. The lowest absolute error (0.37 year ± 0.28) was obtained for boys only when all selected features were included into prediction. In all cases, the achieved accuracy was higher for boys than for girls, which results from the fact that the change of voice with age is larger for men than for women. The achieved results suggest that the presented approach can be employed for accurate age estimation during rapid development in children.
Fuzzy AHP Based Optimal Design Building-Attached Photovoltaic System for Academic Campus

Several algorithms have been developed for building-attached photovoltaic system (BAPV) planning in educational institute based on PV capacity. Fewer studies on optimization algorithms for BAPV system planing on campus have been reported which considers a technoeconomic assessment. Therefore, a well-known robust algorithm is used as an optimization technique of BAPV system and considers technoeconomic assessment on campus. This paper presents a combination of analytical hierarchy process (AHP) with fuzzy theory (fuzzy AHP) for selecting a suitable and optimal design of BAPV system on academic campus. The BAPV system design is based on roof area and load profile at the project site. Five BAPV systems have been designed using five different types of PV. The design was comprehensively assessed by experts through a questionnaire with pairwise comparison model. Fuzzy AHP used to consider the qualitative and quantitative assessments that can affect the selection process. The comprehensive assessment in criteria consists of sizing systems, technical, economic, and environmental perspectives as criteria. The perspective is divided into 13 subcriteria. The results show degree of importance from the criteria-based fuzzy AHP as follows: technical &gt; economic &gt; environment &gt; sizing system. Based on the assessment of criteria and subcriteria, design with monocrystalline is most suitable and polycrystalline as the least suitable design for BAPV system connected to grid and battery energy storage system in case study.
The Automatic Generation of NooJ Dictionaries from Lexicon-Grammar Tables

The syntactic and semantic analyses constitute an important part of the automatic natural language processing field. Indeed, the complexity and the richness of the language make these tasks more difficult since they require the description of all the grammatical, syntactic and semantic features of the language lexicon. It is in this context that the lexicon-grammar approach has been introduced: It consists of describing the lexicon of the language through readable and intuitive tables for manual human editing. On the other hand, NooJ is an automatic natural language processing platform that includes different levels of analysis: lexical, morphological, syntactic and semantic. In order to integrate the lexicon-grammar approach into this platform, the tables must be transformed into dictionaries and syntactic grammars. However, setting up the dictionaries in NooJ through these tables is done manually right now, making it very time-consuming and error-prone. Hence, this work aimed at developing dictionaries automatic generation tool in NooJ from lexicon-grammar tables, ensures time saving and fully exploiting the potentialities of the lexicon-grammar approach.
Curriculum Labeling: Self-paced Pseudo-Labeling for Semi-Supervised Learning.

Semi-supervised learning aims to take advantage of a large amount of unlabeled data to improve the accuracy of a model that only has access to a small number of labeled examples. We propose curriculum labeling, an approach that exploits pseudo-labeling for propagating labels to unlabeled samples in an iterative and self-paced fashion. This approach is surprisingly simple and effective and surpasses or is comparable with the best methods proposed in the recent literature across all the standard benchmarks for image classification. Notably, we obtain 94.91% accuracy on CIFAR-10 using only 4,000 labeled samples, and 88.56% top-5 accuracy on Imagenet-ILSVRC using 128,000 labeled samples. In contrast to prior works, our approach shows improvements even in a more realistic scenario that leverages out-of-distribution unlabeled data samples.
Training Data Sets Construction from Large Data Set for PCB Character Recognition

Deep learning has become increasingly popular in both academic and industrial areas nowadays.Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks.However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks.We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model.Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives.Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information.We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.
Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha

Automatic speech recognition (ASR) is vital for very low-resource languages for mitigating the extinction trouble.Chaha is one of the low-resource languages, which suffers from the problem of resource insufficiency and some of its phonological, morphological, and orthographic features challenge the development and initiatives in the area of ASR.By considering these challenges, this study is the first endeavor, which analyzed the characteristics of the language, prepared speech corpus, and developed different ASR systems.A small 3-hour read speech corpus was prepared and transcribed.Different basic and rounded phone unit-based speech recognizers were explored using multilingual deep neural network (DNN) modeling methods.The experimental results demonstrated that all the basic phone and rounded phone unit-based multilingual models outperformed the corresponding unilingual models with the relative performance improvements of 5.47% to 19.87% and 5.74% to 16.77%, respectively.The rounded phone unit-based multilingual models outperformed the equivalent basic phone unit-based models with relative performance improvements of 0.95% to 4.98%.Overall, we discovered that multilingual DNN modeling methods are profoundly effective to develop Chaha speech recognizers.Both the basic and rounded phone acoustic units are convenient to build Chaha ASR system.However, the rounded phone unit-based models are superior in performance and faster in recognition speed over the corresponding basic phone unit-based models.Hence, the rounded phone units are the most suitable acoustic units to develop Chaha ASR systems.
Support Vector Machine Optimized by Fireworks Algorithm for Handwritten Digit Recognition

Handwritten digit recognition is an important subarea in the object recognition research area. Support vector machines represent a very successful recent binary classifier. Basic support vector machines have to be improved in order to deal with real-world problems. The introduction of soft margin for outliers and misclassified samples as well as kernel function for non linearly separably data leads to the hard optimization problem of selecting parameters for these two modifications. Grid search which is often used is rather inefficient. In this paper we propose the use of one of the latest swarm intelligence algorithms, the fireworks algorithm, for the support vector machine parameters tuning. We tested our approach on standard MNIST base of handwritten images and with selected set of simple features we obtained better results compared to other approaches from literature.
Reducing the dimensionality of feature space in pattern recognition tasks

Abstract The definition of an informative set of features is one of the important tasks in pattern recognition. Typically, the determination of informative features is carried out using two types of methods. The first type of methods is “direct methods”, they are directly aimed at identifying informative sets of attributes. And the second type is called "inverse methods", these methods serve to build informative sets of signs by eliminating uninformative signs from the attribute space. This article is devoted specifically to the development of the second type of method; it proposes an accelerated method and an algorithm for determining non-informative features based on the selected non-informative criterion.
Computer Vision for Fashion

The fashion domain is a magnet for computer vision. New vision problems are emerging in step with the fashion industry's rapid evolution towards an online, social, and personalized business. Style models, trend forecasting, interactive search, and recommendation all require visual understanding with rich detail and subtlety. As a result, research in this area is poised to have great influence on how people shop, how the fashion industry analyzes its enterprise, and how we model the cultural trends revealed by what people wear.
Generative Adversarial Nets Enhanced Continual Data Release Using Differential Privacy

In the era of big data, increasing massive volume of data is generated and published consecutively for both research and commercial purposes. The potential value of sensitive information also attracts interest from adversaries and thereby arises public concern. Current research mostly focuses on privacy-preserving data release in a statistic manner rather than taking the dynamics and correlation of context into consideration. Motivated by this, a novel idea is proposed by combining differential privacy and generative adversarial nets. Generative adversarial nets and its extensions are used to generate a synthetic data set with indistinguishable statistic features while differential privacy guarantees a trade-off between the privacy protection and data utility. Extensive simulation results on real-world data set testify the superiority of the proposed model in terms of privacy protection and improved data utility.
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation

Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently genetic algorithms and other stochastic methods. These methods suffer from frequent costly hardware measurements rendering them not only too time consuming but also suboptimal. As such, we devise a solution that can learn to quickly adapt to a previously unseen design space for code optimization, both accelerating the search and improving the output performance. This solution dubbed Chameleon leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain-knowledge inspired logic to improve the samples itself. Experimentation with real hardware shows that Chameleon provides 4.45x speed up in optimization time over AutoTVM, while also improving inference time of the modern deep networks by 5.6%.
&lt;b&gt;Imbalanced data classification procedure based on SMOTE&lt;/b&gt;

Now that it is possible to handle the enormous amounts of data available on the Internet and generated by corporate systems, automatic classification technology has assumed greater importance. Various learning methods have been proposed for solving two-class classification problem. Furthermore, an ensemble learning method that uses a combination of a plurality of classifiers can deliver high accuracy. However, the imbalance of class labels, that is, the sample size between classes to be classified is greatly different, often occurs. It is reported that the ensemble learning method has poor accuracy in regard to such imbalanced data. Therefore, in this research, we propose a novel analysis procedure based on ensemble learning for such imbalanced data. Our proposed procedure involves dividing data into several legions by clustering and uses the over-sampling technique to make data ease imbalanced state and learns classification rule based on the random forest method proposed by Breiman (2001). Through simulations, we show the effectiveness of the proposed procedure.
Semi-supervised ASR by End-to-end Self-training

While deep learning based end-to-end automatic speech recognition (ASR) systems have greatly simplified modeling pipelines, they suffer from the data sparsity issue. In this work, we propose a self-training method with an end-to-end system for semi-supervised ASR. Starting from a Connectionist Temporal Classification (CTC) system trained on the supervised data, we iteratively generate pseudo-labels on a mini-batch of unsupervised utterances with the current model, and use the pseudo-labels to augment the supervised data for immediate model update. Our method retains the simplicity of end-to-end ASR systems, and can be seen as performing alternating optimization over a well-defined learning objective. We also perform empirical investigations of our method, regarding the effect of data augmentation, decoding beamsize for pseudo-label generation, and freshness of pseudo-labels. On a commonly used semi-supervised ASR setting with the WSJ corpus, our method gives 14.4% relative WER improvement over a carefully-trained base system with data augmentation, reducing the performance gap between the base system and the oracle system by 50%.
Applications of remote sensing and geographic information system (GIS) in regional lineament mapping and structural analysis in Ikare Area, Southwestern Nigeria

An attempt was made to use remote sensing technique to analyze some structural components of the metamorphic basement complex of the Ikare area in the SW Nigeria.Multiple data sets coupled with application of directional filtering techniques were used to extract linear features from Landsat ETM+ image with a view to improving on already produced regional structural map of the area and also determine a fast and cost effective approach to lineament mapping when handling multiple datasets.Band 7 (2.080 -2.35) µm of the ETM+ image was used as a single band to visually extract linear feature while Principal Component Analysis (PCA) technique was employed in generating a multi-band enhanced image used to extract linear features.Four standard directional filters, Sobel (NS, EW, NW_SE and NE_SW) were further applied to enhance visual interpretation.The result shows that the area was affected by both ductile and brittle deformations at a regional scale, at least two episodes of deformations.A first episode of deformation (D1) produced the NW -SE tight to isoclinal fold in style (F1) and a second episode of deformation (D2) which affected the axial trace of the first episode to produce a N -S gentle to open fold (F2).Furthermore, the result shows that spatial domain filtering significantly improves the amount of lineaments that can be mapped and single band analysis is a rather fast and cost effective method of lineament extraction.
Group recommendation based on hybrid trust metric

Group recommendation is a special service type which has the ability to satisfy a group’s common interest and find the preferred items for group users. Deep mining of trust relationship between group members can contribute to the improvement of accuracy during group recommendation. Most of the existing trust-based group recommendation methods pay little attention to the diversity of trust sources, resulting in poor recommendation accuracy. To address the problem above, this paper proposes a group recommendation method based on a hybrid trust metric (GR-HTM). Firstly, GR-HTM creates an attribute trust matrix and a social trust matrix based on user attributes and social relationships, respectively. Secondly, GR-HTM accomplishes a hybrid trust matrix based on the integration of these two matrices with the employment of the Tanimoto coefficient. Finally, GR-HTM calculates weights for each item in the hybrid trust matrix based on weighted-meanlist and proceeds to group recommendation with a given trust threshold. Simulation experiments demonstrate that the proposed GR-HTM has better performance for group recommendation in accuracy and effectiveness.
BCSolid: A Blockchain-Based Decentralized Data Storage and Authentication Scheme for Solid

Solid (Social Linked Data) aims to radically change the way web applications work today, giving users true data ownership and improved privacy. However, it is facing two challenges, one is that data in centralized repositories needs to be separated from social web applications that force users to share their information. In addition, a decentralized authentication that guarantees who can operate on user’s data with a secure privacy protection is another significant issue. In this paper, we address these challenges by proposing a blockchain-based decentralized data storage and authentication scheme for Solid, termed BCSolid, in which a user’s data can be independent of multiple web applications and can switch data storage service easily without relying on a trusted third party. Meanwhile, our scheme gurantees data ownership and user’s privacy by leveraging the blockchain miners to perform authentication with the help of certificateless cryptography. Additionally, we present a possible instantiation to illustrate how “transactions” in BCSolid are processed. To our knowledge this is the first work to promote the Solid project using blockchain. The evaluation results show that our scheme can gurantee a low latency network and is a promising solution to Solid.