Reviewerly/exHarmony huggingface dataset:
Image compression based on 2D Discrete Fourier Transform and matrix minimization algorithm

In the present era of the internet and multimedia, image compression techniques are essential to improve image and video performance in terms of storage space, network bandwidth usage, and secure transmission. A number of image compression methods are available with largely differing compression ratios and coding complexity. In this paper we propose a new method for compressing high-resolution images based on the Discrete Fourier Transform (DFT) and Matrix Minimization (MM) algorithm. The method consists of transforming an image by DFT yielding the real and imaginary components. A quantization process is applied to both components independently aiming at increasing the number of high frequency coefficients. The real component matrix is separated into Low Frequency Coefficients (LFC) and High Frequency Coefficients (HFC). Finally, the MM algorithm followed by arithmetic coding is applied to the LFC and HFC matrices. The decompression algorithm decodes the data in reverse order. A sequential search algorithm is used to decode the data from the MM matrix. Thereafter, all decoded LFC and HFC values are combined into one matrix followed by the inverse DFT. Results demonstrate that the proposed method yields high compression ratios over 98% for structured light images with good image reconstruction. Moreover, it is shown that the proposed method compares favorably with the JPEG technique based on compression ratios and image quality.
4D Light Field Segmentation From Light Field Super-Pixel Hypergraph Representation

Efficient and accurate segmentation of full 4D light fields is an important task in computer vision and computer graphics. The massive volume and the redundancy of light fields make it an open challenge. In this article, we propose a novel light field hypergraph (LFHG) representation using the light field super-pixel (LFSP) for interactive light field segmentation. The LFSPs not only maintain the light field spatio-angular consistency, but also greatly contribute to the hypergraph coarsening. These advantages make LFSPs useful to improve segmentation performance. Based on the LFHG representation, we present an efficient light field segmentation algorithm via graph-cut optimization. Experimental results on both synthetic and real scene data demonstrate that our method outperforms state-of-the-art methods on the light field segmentation task with respect to both accuracy and efficiency.
Utilizing Source Code Embeddings to Identify Correct Patches

The so called Generate-and-Validate approach of Automatic Program Repair consists of two main activities, the generate activity, which produces candidate solutions to the problem, and the validate activity, which checks the correctness of the generated solutions. The latter however might not give a reliable result, since most of the techniques establish the correctness of the solutions by (re-)running the available test cases. A program is marked as a possible fix, if it passes all the available test cases. Although tests can be run automatically, in real life applications the problem of over- and underfitting often occurs, resulting in inadequate patches. At this point manual investigation of repair candidates is needed although they passed the tests. Our goal is to investigate ways to predict correct patches. The core idea is to exploit textual and structural similarity between the original (buggy) program and the generated patches. To do so we apply Doc2vec and Bert embedding methods on source code. So far APR tools generate mostly one-line fixes, leaving most of the original source code intact. Our observation was, that patches which bring in new variables, make larger changes in the code are usually the incorrect ones. The proposed approach was evaluated on the QuixBugs dataset consisting of 40 bugs and fixes belonging to them. Our approach successfully filtered out 45% of the incorrect patches.
Towards Data-Efficient Modeling for Wake Word Spotting

Wake word (WW) spotting is challenging in far-field not only because of the interference in signal transmission but also the complexity in acoustic environment. Traditional WW model training requires large amount of in-domain WW-specific data with substantial human annotations. This prevents the model building in the situation of lacking such data. In this paper we present data-efficient solutions to address the challenges in WW modeling, such as domain-mismatch, noisy conditions, limited annotation, etc. Our proposed system is composed of a multi-condition training pipeline with stratified data augmentation, which improves the model robustness to a variety of predefined acoustic conditions, together with a semi-supervised learning pipeline to extract the WW and adversarial examples from untranscribed speech corpus. Starting from only 10 hours of domain-mismatched WW audio, we are able to enlarge and enrich the training dataset by 20-100 times to capture the complexity in acoustic environments. Our experiments on real user data show that the proposed solutions can achieve comparable performance of a production-grade model by saving 97% of the amount of WW-specific data to collect and 86% of the bandwidth for annotation.
A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems

Recommender systems (RSs) commonly describe its user-item preferences with a high-dimensional and sparse (HiDS) matrix filled with non-negative data. A non-negative latent factor (NLF) model relying on a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to process such an HiDS matrix. However, an NLF model mostly adopts Euclidean distance for its objective function, which is naturally a special case of α-β-divergence. Moreover, it frequently suffers slow convergence. For addressing these issues, this study proposes a generalized and fast-converging non-negative latent factor (GFNLF) model. Its main idea is two-fold: a) adopting α-β-divergence for its objective function, thereby enhancing its representation ability for HiDS data; b) deducing its momentum-incorporated non-negative multiplicative update (MNMU) algorithm, thereby achieving its fast convergence. Empirical studies on two HiDS matrices emerging from real RSs demonstrate that with carefully-tuned hyperparameters, a GFNLF model outperforms state-of-the-art models in both computational efficiency and prediction accuracy for missing data of an HiDS matrix.
CLARA: Clinical Report Auto-completion

Generating clinical reports from raw recordings such as X-rays and electroencephalogram (EEG) is an essential and routine task for doctors. However, it is often time-consuming to write accurate and detailed reports. Most existing methods try to generate the whole reports from the raw input with limited success because 1) generated reports often contain errors that need manual review and correction, 2) it does not save time when doctors want to write additional information into the report, and 3) the generated reports are not customized based on individual doctors' preference. We propose CLinicAl Report Auto-completion (, an interactive method that generates reports in a sentence by sentence fashion based on doctors' anchor words and partially completed sentences. earches for most relevant sentences from existing reports as the template for the current report. The retrieved sentences are sequentially modified by combining with the input feature representations to create the final report. In our experimental evaluation chieved 0.393 CIDEr and 0.248 BLEU-4 on X-ray reports and 0.482 CIDEr and 0.491 BLEU-4 for EEG reports for sentence-level generation, which is up to 35% improvement over the best baseline. Also via our qualitative evaluation, s shown to produce reports which have a significantly higher level of approval by doctors in a user study (3.74 out of 5 for s 2.52 out of 5 for the baseline).
Mutation grey wolf elite PSO balanced XGBoost for radar emitter individual identification based on measured signals

Radar emitter individual identification plays an increasingly important role in electronic support measures (ESM) system. To cope with the problems of low accuracy and poor stability of radar emitter individual identification, a novel method, named MGWEPSO-BXGBoost (Mutation Grey Wolf Elite Particle Swarm Optimization Balanced eXtreme Gradient Boosting), is proposed. In consideration of the fact that the number of radar signals measured from the real environment is usually imbalanced, a novel balance mechanism is designed for XGBoost. MGWEPSO is further proposed to simultaneously optimize the prime parameters, whose values and combinations have a great influence on the identification results, of BXGBoost to improve the identification accuracy. To overcome the local optimal solution problem, leadership mechanism in wolves, elite rule and the idea of mutation are adopted, which is also conducive to improving the ability to find the global optimal solution. Furthermore, experiments based on signals measured in the real environment are carried out to demonstrate the effectiveness of the proposed method. The results verify that MGWEPSO-BXGBoost has high accuracy and strong stability even when the sample size of each individual is limited and imbalanced.
MLPSO: A Filter Multi-label Feature Selection Based on Particle Swarm Optimization

Feature selection is one of the preprocessing steps in data mining and machine learning which aims to cope with redundant and irrelevant features through dimensionality reduction. In this paper, for multi-label classification, a novel filter approach using Particle Swarm Optimizer (PSO) is introduced. According to PSO, at first, a population of particles is generated and divided into two equal groups and compete in pairs, the winners are moved to the next iteration and the losers learn from the winners, and at the end of each iteration, the objective function for all the particles is computed. Finally, based on the best particle, the salient feature subset will be selected. The comparison results between the proposed method and six state-of-the-art multi-label feature selection methods on benchmark datasets using the ML-KNN classifier on various multi-label evaluation measures show the superiority of the proposed method.
Self-tracking while doing sport: Comfort, motivation, attention and lifestyle of athletes using personal informatics tools

The spread of wearable technologies is paving the way for the mass-scale adoption of self-tracking instruments, which are progressively integrating into different social practices. Among these, sport seems to be a promising domain in which Personal Informatics tools can support individuals in performing their activities and in achieving their situated goals. In this article, we conducted semi-structured interviews with amateur and elite athletes to explore what they seek in their trackers, how such instruments may impact on their “mind”, by affecting their motivation and attention during workouts and races, and how sports data are intertwined with other information pertaining to their lifestyle. On the basis of these findings we discussed three themes that may be relevant for Personal Informatics, also proposing a series of implications for design that may help researchers in designing self-tracking tools for sport.
Anomaly Detection Models for Detecting Sensor Faults and Outliers in the IoT - A Survey

Over the past few years, the Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world. The sensors within the Internet of Things are indispensable parts and are the first port to capture the raw data. As the sensors within IoT are usually deployed in environments which are harsh, which inevitably make the sensors venerable to failure and malfunction. Beside sensor faults and malfunctions, the inherent environment where the sensors are usually installed could also make the sensor to fail prematurely. These conditions will make the sensors within the IoT to generate unusual and erroneous data, often known as outliers. Outliers detection is very crucial in IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. Data anomalies, abnormal data or outliers are considered to be the sensor data streams that are significantly distinct from the normal behavioural data. In this paper, we present a comprehensive survey that can be used as a guideline to select which outlier model is adequate for the application in the IoT context.
Adaptive Saliency Biased Loss for Object Detection in Aerial Images

Object detection in aerial images remains a challenging problem due to low image resolutions, complex backgrounds, and variations of sizes and orientations of objects in images. In recent years, several multiscale and rotated box-based deep neural networks (DNNs) have been proposed and have achieved promising results. In this article, a new method for designing loss function, called adaptive saliency biased loss (ASBL), is proposed for training DNN object detectors to achieve improved performance. ASBL can be implemented at the image level, which is called image-based ASBL, or at the anchor level, which is called anchor-based ASBL. The method computes saliency information of input images and anchors generated by DNN object detectors, weights of different training examples, and anchors differently based on their corresponding saliency measurements. It gives complex images and difficult targets more weights during training. In our experiments using two of the largest public benchmark data sets of aerial images, DOTA and NWPU VHR-10, the existing RetinaNet was trained using ASBL to generate an one-stage detector, ASBL-RetinaNet. ASBL-RetinaNet significantly outperformed the original RetinaNet by 3.61 and 12.5 mean average precision (mAP), respectively, on the two data sets. In addition, ASBL-RetinaNet outperformed ten other state-of-the-art object detection methods.
Latency Optimization for Coded Computation Straggled by Wireless Transmission

Although distributed computing is an efficient solution to large-scale computational tasks, its performance is determined by the slowest computing nodes, i.e., the computation stragglers among distributed nodes. To mitigate their impact, coded computation has emerged as a promising technique by introducing clever computational redundancy. Most existing works only considered the computation latency, however, transmission latency is a serious concern in wireless computing networks. In such a case, the performance is straggled by not only local computation but also wireless transmission. In this letter, we consider the wireless distributed computing network where nodes have different transmission rates and different computation capabilities. Unlike computation stragglers always existing, we derive a straggling factor for each node to indicate whether the network is straggled by transmission. To deal with both stragglers, the wireless coded computation (WCC) algorithm is proposed, which generalizes existing methods. By comparing WCC with four benchmark schemes, Uniform Uncoded Allocation, Non-Uniform Uncoded Allocation, Uniform Coded Allocation, and Heterogeneous Coded Matrix Multiplication, it shows that WCC results in significant speedups of up to 72%, 66%, 64% and 47% over the four aforementioned benchmark schemes, respectively.
Evolving Deep Neural Networks via Cooperative Coevolution With Backpropagation

Deep neural networks (DNNs), characterized by sophisticated architectures capable of learning a hierarchy of feature representations, have achieved remarkable successes in various applications. Learning DNN's parameters is a crucial but challenging task that is commonly resolved by using gradient-based backpropagation (BP) methods. However, BP-based methods suffer from severe initialization sensitivity and proneness to getting trapped into inferior local optima. To address these issues, we propose a DNN learning framework that hybridizes CC-based optimization with BP-based gradient descent, called BPCC, and implement it by devising a computationally efficient CC-based optimization technique dedicated to DNN parameter learning. In BPCC, BP will intermittently execute for multiple training epochs. Whenever the execution of BP in a training epoch cannot sufficiently decrease the training objective function value, CC will kick in to execute by using the parameter values derived by BP as the starting point. The best parameter values obtained by CC will act as the starting point of BP in its next training epoch. In CC-based optimization, the overall parameter learning task is decomposed into many subtasks of learning a small portion of parameters. These subtasks are individually addressed in a cooperative manner. In this article, we treat neurons as basic decomposition units. Furthermore, to reduce the computational cost, we devise a maturity-based subtask selection strategy to selectively solve some subtasks of higher priority. Experimental results demonstrate the superiority of the proposed method over common-practice DNN parameter learning techniques.
Machine Learning-Based Sentiment Analysis for Analyzing the Travelers Reviews on Egyptian Hotels

Tourism affects the economy of any country; actually, it is the foundation of the country on the economic side. Egyptian Government is giving a big concern in developing the tourism sector. Hotel companies are using E-commerce technology for online booking and online reviewing. Travelers choose hotels based on their prices, facilities and other traveler's review. Sentiment analysis is a very important topic that can be used to analyze the opinion of online users. Different websites are classifying the traveler reviews such as Tripadvisor, Expedia. The research aims to propose a Traveler Review Sentiment Classifier that will analyze the traveler's reviews on Egyptian Hotels and provide a classification of each sentiment based on hotel features. Travelers Sentiment about five hotels located in Aswan in Egypt with a total of 11458 reviews were collected and analyzed. Sentiment model uses three classification techniques: Support Vector Machine, Naive Bayes and Decision Tree. Results had shown that Naïve Bayes has the highest accuracy level.
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

In the Muslim community, the prayer (i.e. Salat) is the second pillar of Islam, and it is the most essential and fundamental worshiping activity that believers have to perform five times a day. From a gestures' perspective, there are predefined human postures that must be performed in a precise manner. However, for several people, these postures are not correctly performed, due to being new to Salat or even having learned prayers in an incorrect manner. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an artificial intelligence assistive framework that guides worshippers to evaluate the correctness of the postures of their prayers. This paper represents the first step to achieve this objective and addresses the problem of the recognition of the basic gestures of Islamic prayer using Convolutional Neural Networks (CNN). The contribution of this paper lies in building a dataset for the basic Salat positions, and train a YOLOv3 neural network for the recognition of the gestures. Experimental results demonstrate that the mean average precision attains 85% for a training dataset of 764 images of the different postures. To the best of our knowledge, this is the first work that addresses human activity recognition of Salat using deep learning.
Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes

Automated biomedical named entity recognition and normalization serves as the basis for many downstream applications in information management. However, this task is challenging due to name variations and entity ambiguity. A biomedical entity may have multiple variants and a variant could denote several different entity identifiers.To remedy the above issues, we present a novel knowledge-enhanced system for protein/gene named entity recognition (PNER) and normalization (PNEN). On one hand, a large amount of entity name knowledge extracted from biomedical knowledge bases is used to recognize more entity variants. On the other hand, structural knowledge of entities is extracted and encoded as identifier (ID) embeddings, which are then used for better entity normalization. Moreover, deep contextualized word representations generated by pre-trained language models are also incorporated into our knowledge-enhanced system for modeling multi-sense information of entities. Experimental results on the BioCreative VI Bio-ID corpus show that our proposed knowledge-enhanced system achieves 0.871 F1-score for PNER and 0.445 F1-score for PNEN, respectively, leading to a new state-of-the-art performance.We propose a knowledge-enhanced system that combines both entity knowledge and deep contextualized word representations. Comparison results show that entity knowledge is beneficial to the PNER and PNEN task and can be well combined with contextualized information in our system for further improvement.
A Web Services Classification Method Based on GCN

With the development of Web2.0 and Web services, the diversity and amount of Web services are increasing quickly. Finding Web services to meet the needs of users has turned into increasingly difficult. It is a valid way to promote service discovery that classifying Web services with similar functionality. The existing Web services classification technology mainly focuses on exploiting the functional information, such as description texts or tags to achieve Web services classification, but ignores the network structure information implied between the words inside the Web service description text and the Web service description text itself. Therefore, we put forward an approach of Web service classification on top of graph convolutional neural networks. This method, firstly, uses the name, description text, tags of Web service as the basic corpus to construct a heterogeneous graph network of "Words & Web service description documents" according to word co-occurrence and word relationship among the Web service description document. In the heterogeneous graph network, all weights of the edges between the document nodes and the word nodes are calculated by used the term frequency-inverse document frequency, and the point mutual information is utilized to calculate the weights of the edges between different word nodes. Then, it applies the graph convolutional neural network to learn the embedding information of words and Web service description document, and transforms Web service documents classification problem into node classification problem. Finally, the experiment on ProgrammableWeb dataset display that the precision, recall, F-measure, purity and entropy of the proposed approach are greatly improved, compared to those of TF-IDF+LR, LDA, WE-LDA, LSTM, Wide&Deep, Bi-LSTM, Wide&Bi-LSTM.
Status Update on Data Required to Build a Learning Health System

Wide adoption of electronic health records (EHRs) has raised the expectation that data obtained during routine clinical care, termed “real-world” data, will be accumulated across health care systems and analyzed on a large scale to produce improvements in patient outcomes and the use of health care resources. To facilitate a learning health system, EHRs must contain clinically meaningful structured data elements that can be readily exchanged, and the data must be of adequate quality to draw valid inferences. At the present time, the majority of EHR content is unstructured and locked into proprietary systems that pose significant challenges to conducting accurate analyses of many clinical outcomes. This article details the current state of data obtained at the point of care and describes the changes necessary to use the EHR to build a learning health system.