Reviewerly/exHarmony huggingface dataset:
CBC and DBC Counter Using Image Processing and Analysis

In this study, the researchers developed a cost-effective way of performing Complete Blood Count (CBC) and Differential Blood Count (DBC) by implementing image processing, analysis, and machine learning algorithms using a Raspberry pi B+ microcontroller in conjunction with a microscope that has Short Message Service (SMS) feature to be used in rural areas for remote transmission of results to a health professional. Blood image input from the raspberry pi's camera will be used for the counting and classification needed by the system. The capturing and analysis of the images will be integrated on a graphical user interface (GUI) to provide user-friendly access. HSV color space conversion, thresholding, morphological operations, and Connected Component Labeling (CCL) are used for CBC. For DBC, an additional process of Convolutional Neural Network (CNN) classification is done. The study shows a remarkable result by having no significant difference from conventional method which is manual counting.
Collection of Online and Offline Handwritten Japanese Charcters and Handwriting Classification Using the Data

This paper describes the establishment of an online and offline Japanese handwritten character dataset and experiments on handwriting identification. Handwriting data were collected for forensic writer identification with 5-time repetition to examine intra-individual constancy, which was also as important as inter-individual difference. Handwritten samples were collected from four hundred and one participants. Online data included the data on the pen-tip location, the pen pressure and the time at each measuring point. The dataset had 702 characters which included uppercase and lowercase Latin alphabet letters, numerals, Hiragana, Katakana and Kanji characters. Three kinds of writer classification experiments were done using the offline data in the dataset. Image data used were 26 uppercase and 26 lowercase Latin alphabet letters, 26 Hiragana characters and 26 Kanji characters written by 10 writers. In each experiment, four samples out of 5 samples per writer and per letter or character were used for training and rest of one sample was used for test. LeNet was applied to the classification. Correct classification ratio was about 80% except lowercase letters. Lowercase letters showed 70%. Correct ratio improved by the increase in the number of the training data. Some participants showed high correct ratio and their samples were unique regarding the size, the position or the line quality. These results suggested the importance of the sample size in the classification using a Convolutional Neural Network rather than the equality of writing conditions and that the existence of the characteristics consistent to all handwriting of a writer.
Forecasting of Electricity Generation by Solar Panels Using Neural Networks with Incomplete Initial Data

An increase in the number of alternative energy sources, in particular solar power stations in the generation structure of the modern energy systems, makes it difficult to control such energy systems due to the inconsistency in the amount of electricity production that depends on external factors. It also leads to an increase in risks when concluding electricity supply agreements in a modern energy market. This problem may be solved by forecasting electricity by modern means, such as neural networks. In this context, due to the flexibility and nonlinearity of neural networks there is no need for source data that directly affects the generation value, and there is a possibility of using indirect data available to most alternative energy producers. This has resulted in the creation of the solution based on neural networks that allows forecasting energy production using solar panels with high accuracy on the basis of conventional meteorological data.
An Efficient Group Feature Fusion Residual Network for Image Super-Resolution

Convolutional neural networks (CNNs) have made great breakthrough in the field of image super-resolution (SR). However, most current methods are usually to improve their performance by simply increasing the depth of their network. Although this strategy can get promising results, it is inefficient in many real-world scenarios because of the high computational cost. In this paper, we propose an efficient group feature fusion residual network (GFFRN) for image super-resolution. In detail, we design a novel group feature fusion residual block (GFFRB) to group and fuse the features of the intermediate layers. In this way, GFFRB can enjoy the merits of the lightweight of the group convolution and the high-efficiency of the skip connections, thus achieving better performance compared with most current residual blocks. Experiments on the benchmark test sets show that our models are more efficient than most of the state-of-the-art methods.
Mitigating Matching Scarcity in Recruitment Recommendation Domains

A major concern when building Recommender Systems (RSs) for recruitment domains is ensuring actual matching opportunities for all candidates and job vacancies on the system as fast as possible. Indeed, long periods waiting for matchings imply losses of business opportunities, causing financial damages for both candidates and companies. We refer to these scenarios where candidates or job vacancies suffer from the absence of matching opportunities as the Matching Scarcity Problem (MaSP). In this paper, first, we formalize the MaSP itself. Second, we proposed and evaluated strategies to identify automatically candidates and job vacancies suffering from MaSP. Finally, we proposed five heuristic strategies to mitigate MaSP. Our strategies consist of introducing changes in curricula and job descriptions to approximate candidates suffering from MaSP to jobs semantically related to them, and vice versa. The best strategy was able to reduce up to 50% the number of both CVs and jobs suffering from MaSP before applying it in our sample.
Forecasting Election Data Using Regression Models and Sentimental Analysis

Predictive Analytics is emerging as one of the popular branch of Machine Learning with huge amount of money spent on to research, build and test models to improve the accuracy of the outcome. In our paper we have implemented different regression models such as Logistic Regression, Support Vector Machines, Naive Bayes Classifier, and Neural Networks to forecast the result of the US Presidential election 2016. Social media websites have become a very popular communication platform among users. Millions of users share their opinions, extend their support, and vent their anger on various government policies and on different aspects of their life. Hence, social media websites contain huge amount of data for sentimental analysis. We have narrowed our attention toward Twitter, the most popular microblogging website for performing sentimental analysis using the opinions shared by the users on twitter.
Self-supervised Sparse to Dense Motion Segmentation

Observable motion in videos can give rise to the definition of objects moving with respect to the scene. The task of segmenting such moving objects is referred to as motion segmentation and is usually tackled either by aggregating motion information in long, sparse point trajectories, or by directly producing per frame dense segmentations relying on large amounts of training data. In this paper, we propose a self supervised method to learn the densification of sparse motion segmentations from single video frames. While previous approaches towards motion segmentation build upon pre-training on large surrogate datasets and use dense motion information as an essential cue for the pixelwise segmentation, our model does not require pre-training and operates at test time on single frames. It can be trained in a sequence specific way to produce high quality dense segmentations from sparse and noisy input. We evaluate our method on the well-known motion segmentation datasets \(\mathrm {FBMS59}\) and \(\mathrm {DAVIS}_{16}\).
Success and Failure Factors for Adopting a Combined Approach: A Case Study of Two Software Development Teams

The combination of Agile, User-Centered Design and Lean Startup has emerged as a solution for teams that are struggling with lack of user involvement and delivering products that fulfill stakeholder needs. Adopting such a development approach involves several factors, some of which can assist or hinder the adoption process. Currently, the literature reports on studies on such factors, but only for agile-only methods. Motivated by this knowledge gap, our goal is to map the success and failure factors of a combined approach adoption. We conduct a case study with two software development teams from a large organization transitioning to the combined approach. We used semi-structured interviews and focus group sessions to collect data. Our findings show five success factors categories (e.g., team engagement, technical aspects) and one failure factor category (team autonomy at risk), along with several argumentation points suggested by the teams to argue against a company policy perceived to be a very impactful failure factor. This study contributes to academic literature by reporting on success and failure factors of a combined approach transformation, and could be used as a starting point in defining tools (e.g., maturity models) to aid organizations in transitioning to the combined approach.
Lazy Build of Acceleration Structures with Traversal Shaders

Modern ray tracing APIs allow developers to easily build acceleration structures (AS) with various optimization techniques. However, the visibility-driven on-demand build can not be implemented with the current APIs due to the lack of flexibility during ray traversal. In this paper, we propose a new algorithm to lazily build ASes for real-time ray tracing with an extended programming model supporting flexible ray traversal. The core idea of our approach is a multi-pass build-traversal, which computes instance visibility and builds the visible ASes in different passes. This allows us to lazily build the entire AS only when necessary without hardware implication. Applying our algorithm to dynamic scenes, we demonstrate that the build cost is significantly reduced with minimal overhead.
Classification of Seismic Signals Using Scalogram and Wavelet Based Features

In this paper we propose a method to extract image-based features from the scalogram which represents the energy percentage of each coefficient obtained after applying the Wavelet Transform to seismic signals and then identify the most significant energy levels, which are synonymous of a seismic event. The scalogram graphs were worked by digital image processing, to treat the event as a geometric figure and to extract characteristics of it. In addition, the energy coefficient of each wavelet decomposition level was calculated, as well as the energy contained in each image. These coefficients were also used as features from the seismic event. Finally, a bank of 16 features was obtained, which was evaluated by using three different Machine Learning classifiers, with and without feature selection stage. The results obtained corroborated that the selected scalogram and wavelet based features provide enough discriminating guidelines to classify seismic events with low error rates.
Cough Detection System Based on ASR-HMM

In this paper, we propose a new approach to design a cough detection system based on speech-recognition algorithms. Our system is implemented with Kaldi opensource platform by Gaussian Mixture Model-based Hidden Markov Model (GMM-HMM) hybrid system through a simple Monophone training model. Also, a comparison between the Perceptual Linear Prediction (PLP) and Mel Frequency Cepstral Coefficient (MFCC) feature extraction methods is presented. Our proposed system can be used as a collection platform to collect naturally and spontaneous cough data from conversation or continuous speech. The system achieved the best performance when trained using the MFCC feature.
Measuring inferred gaze direction to support analysis of people in a meeting

This paper introduces a method to infer gaze direction and the point of focus of participants involved in a collaborative activity, such as a meeting. It uses a single depth-based sensor placed overhead to capture the meeting, which has the benefit of avoiding occlusion and is unobtrusive, minimising possible changes in behaviour that might arise if people are aware of the sensor. The inferred gaze direction of each participant is estimated in the horizontal plane from the orientation of the head (yaw), derived from a segmentation of the depth image to generate an outline of the head. A common focus of attention is inferred by intersecting the gaze directions of each participant. Performance evaluation using a depth camera to record a meeting achieved a head detection performance of 99.6% and a valid gaze detection of 96.9%.
Principles and patterns of JastAdd-style reference attribute grammars

Reference attribute grammars (RAGs) have reached a level of maturity where they are supported by several tools, and have gained traction in both academic and industrial language tool development. However, despite a lot of accumulated knowledge of how to best develop RAGs in practice, there is limited support to guide practitioners.
A Small amount of Labeled Data Chinese Online Course Review Target Extraction via ALBERT-IDCNN-CRF Model

Abstract Aspect sentiment analysis of online course reviews is of great significance in helping users choose courses and improve course quality. Review target extraction is particularly important as the basis of aspect sentiment analysis. Because the current models mostly rely on a large amount of annotation data, there is fewer relevant research on the extraction of online course review targets with higher annotation costs. This paper proposes an ALBERT-IDCNN-CRF review target extraction model for a small amount of labeled data. First, using ALBERT pre-trained sentences obtained dynamic model Chinese word vector coding; Simultaneously, using ALBERT pre-trained model of Transformer obtain sentence abstract features. Then, abstract features are input into the dilated convolutional neural network (IDCNN) to reduce the number of neuron layers and parameters. Finally, conditional random field (CRF) is used to decode and annotate the review sentences to extract the appropriate review objectives. The experimental results on the school online real Chinese online course review data set show that our model has achieved better results than existing models.
Design of Chatbots Using Node-RED

Over the past decades, there has been a tremendous technological advancement in the field of human computer speech system. Emerging as an effective medium for computer interaction, the last decade has witnessed the growth of various speech-based search engines like Siri, Google Chrome, and Cortana. Use of various language processing techniques like NLTK which mainly works in Python language has been done in speech analysis and design of an intelligent response engine to generate responses as close as human nature. The last couple of years has seen a growth, although in the initial phase, of conversational agents based on text messages. They basically suffer from a low usage factor as more than 85% of the Internet population do not rely and use the conversational agents (CAs). In spite of this shortcoming, proper investigation of the usage pattern of CA users can pave the path for the future development of the chatbots. This paper briefly discusses the primary stage of designing a chatbot with the help of Node-RED, a lightweight platform for the application of Internet of things.
Why are Generative Adversarial Networks so Fascinating and Annoying?

This paper focuses on one of the most fascinating and successful, but challenging generative models in the literature: the Generative Adversarial Networks (GAN). Recently, GAN has attracted much attention by the scientific community and the entertainment industry due to its effectiveness in generating complex and high-dimension data, which makes it a superior model for producing new samples, compared with other types of generative models. The traditional GAN (referred to as the Vanilla GAN) is composed of two neural networks, a generator and a discriminator, which are modeled using a minimax optimization. The generator creates samples to fool the discriminator that in turn tries to distinguish between the original and created samples. This optimization aims to train a model that can generate samples from the training set distribution. In addition to defining and explaining the Vanilla GAN and its main variations (e.g., DCGAN, WGAN, and SAGAN), this paper will present several applications that make GAN an extremely exciting method for the entertainment industry (e.g., style-transfer and image-to-image translation). Finally, the following measures to assess the quality of generated images are presented: Inception Search (IS), and Frechet Inception Distance (FID).
RETRACTED: Human Muscle Measurement and Big Data Health Based on Wireless Sensors

Research in computer science and medical applications such as wireless sensor technology is considered one of the most important fields in improving life quality. The purpose of this article is to provide an image of current trends and future directions for the study of continuous injection monitoring of patient wear and human lane systems. The human muscles' size to assess by ultrasound and Magnetic Resonance Imaging (MRI) in 6 subjects throughout the body. In both proposed methods (preprocessing, feature extraction, classification), 11 axial scans were taken with the abdominal muscles, and the cross-sectional area of each scan muscle digitized. Muscle mass is calculated by processing the muscles into continuous cones. Explain the important role of this program as a big data human health analysis in wireless sensor, not only in providing high-quality care to people but also in reducing the living needs of the elderly independently from the nursing staff and helping patients with chronic illness. In introducing to propose a comprehensive survey based on the Sensitivity Enhancement based Classification Algorithm (SEBCA) proposed in the most advanced research activities in the country to address this gap and recommended wireless sensor for the large data system. Potential applications, networks and infrastructure technology challenges work together and are explained in terms of research areas and objectives.
Generic Image Segmentation in Fully Convolutional Networks by Superpixel Merging Map

Recently, the Fully Convolutional Network (FCN) has been adopted in image segmentation. However, existing FCN-based segmentation algorithms were designed for semantic segmentation. Before learning-based algorithms were developed, many advanced generic segmentation algorithms are superpixel-based. However, due to the irregular shape and size of superpixels, it is hard to apply deep learning to superpixel-based image segmentation directly. In this paper, we combined the merits of the FCN and superpixels and proposed a highly accurate and extremely fast generic image segmentation algorithm. We treated image segmentation as multiple superpixel merging decision problems and determined whether the boundary between two adjacent superpixels should be kept. In other words, if the boundary of two adjacent superpixels should be deleted, then the two superpixels will be merged. The network applies the colors, the edge map, and the superpixel information to make decision about merging suprepixels. By solving all the superpixel-merging subproblems with just one forward pass, the FCN facilitates the speed of the whole segmentation process by a wide margin meanwhile gaining higher accuracy. Simulations show that the proposed algorithm has favorable runtime, meanwhile achieving highly accurate segmentation results. It outperforms state-of-the-art image segmentation methods, including feature-based and learning-based methods, in all metrics.
A Cloud Platform-based Micro-Power Microseismic Monitoring IoT System for Underground Mine

In view of the challenging problems of low reliability, small coverage, high cost, poor maintenance and inadequate early warning, which have been the bottleneck in microseismic monitoring of underground mines, a new cloud platform-based micro-power microseismic monitoring IoT system is proposed, which can be used as an effective technical feedback for real-time mining operation optimization. The proposed system has broken through key technologies such as smart sensing, micro-power acquisition, fault diagnosis and online intelligent analysis, has been successfully applied in mining enterprises. Site test shows that the proposed system is more reliable in harsh mining conditions than traditional systems. The microseismic sensor's SNR is increased by 1.4 times, the comprehensive energy consumption is reduced obviously and the online analysis and warning services can be achieved via safety monitoring and analysis cloud platform.
Parallel global convolutional network for semantic image segmentation

In this paper, a novel convolutional neural network for fast semantic segmentation is presented. Deep convolutional neural networks have achieved great progress in the task of vision scene understanding. While the increase of the accuracy mainly depends on the increase of depth and width. This slows down large networks and consumes power. A fast and efficient convolutional neural network, PGCNet, aiming at segmenting high-resolution images with a high speed is introduced. Compared with the competitive methods, the generated model has high performance with fewer parameters and floating point operations. First, a lightweight general architecture pre-trained on ImageNet is relied on as the main encoder. Then, a novel lateral connection module to better transmit features from encoder to decoder. Third, a powerful method termed as PGCN block to extract features of each block in the encoder is proposed and an edge decoder is applied as a supervision for pixels on the edge of stuff and things during training. Experiments show that this method has great advantages. Based on the proposed PGCNet, 75.8% mean IoU is achieved on the cityscapes test set and 35.4 Hz on a standard Cityscapes image on GTX1080Ti.
A Study of BPE-based Language Modeling for Open Vocabulary Latin Language OCR

We present a study of byte pair encoding (BPE) based language modeling for open vocabulary Latin language OCR. On a large-scale handwritten English OCR task, we demonstrate that a simple BPE-based n-gram language model (LM) can deal with out-of-vocabulary word problem effectively and achieve better accuracy-footprint tradeoff than a state-of-the-art hybrid word/subword n-gram LM interpolated by a standard hybrid LM, a word-based LM, and a subword-based LM. On another large-scale printed OCR task for six Latin languages, namely English, Spanish, French, German, Italian, and Portuguese, we discover that a unified OCR system with a single character-based optical model and a single BPE-based n-gram LM shared by six languages performs better than language-dependent OCR systems. BPE-based LM offers a good product solution for both monolingual and multilingual open-vocabulary Latin language OCR.
A Database for Speech Processing Based Qur'anic Recitation Correction

In this paper, we present a database of Qur'anic recitations for the purpose of automatic speech processing based tajweed correction. To the best of our knowledge, this is the first such database specifically designed to assist the researchers in Qur'anic recitations. A total of 54 different errors made by people while reciting Surah Al-Fatihah are identified. 17 volunteer reciters participated in recording the erroneous recitations for these 54 cases, along with the correct recitations. The resultant data contains over 1000 unique audio samples. All the data samples are labelled for ease of access by the researchers. In addition, some speech level feature analysis is discussed for the database. An interactive recitation learning application is presented to demonstrate the use of the developed database.