Reviewerly/exHarmony huggingface dataset:
Challenges of Program Synthesis with Grammatical Evolution

Program synthesis is an emerging research topic in the field of EC with the potential to improve real-world software development. Grammar-guided approaches like GE are suitable for program synthesis as they can express common programming languages with their required properties. This work uses common software metrics (lines of code, McCabe metric, size and depth of the abstract syntax tree) for an analysis of GE's search behavior and the resulting problem structure. We find that GE is not able to solve program synthesis problems, where correct solutions have higher values of the McCabe metric (which means they require conditions or loops). Since small mutations of high-quality solutions strongly decrease a solution's fitness and make a high percentage of the solutions non-executable, the resulting problem constitutes a needle-in-a-haystack problem. To us, one of the major challenges of future GP research is to come up with better and more adequate fitness functions and problem specifications to turn the current needle-in-a-haystack problems into problems that can be solved by guided search.
Self-Supervised Adversarial Training

Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in which adversarial training is an effective way which learns robust feature representation so as to resist adversarial attacks. Meanwhile, the self-supervised learning aims to learn robust and semantic embedding from data itself. With these views, we introduce self-supervised learning to against adversarial examples in this paper. Specifically, the self-supervised representation coupled with k-Nearest Neighbour is proposed for classification. To further strengthen the defense ability, self-supervised adversarial training is proposed, which maximizes the mutual information between the representations of original examples and the corresponding adversarial examples. Experimental results show that the self-supervised representation outperforms its supervised version in respect of robustness and self-supervised adversarial training can further improve the defense ability efficiently.
Deep Exposure Fusion with Deghosting via Homography Estimation and Attention Learning

Modern cameras have limited dynamic ranges and often produce images with saturated or dark regions using a single exposure. Although the problem could be addressed by taking multiple images with different exposures, exposure fusion methods need to deal with ghosting artifacts and detail loss caused by camera motion or moving objects. This paper proposes a deep network for exposure fusion. For reducing the potential ghosting problem, our network only takes two images, an underexposed image and an overexposed one. Our network integrates together thew homography estimation for compensating camera motion, the attention mechanism for correcting remaining misalignment and moving pixels, and adversarial learning for alleviating other remaining artifacts. Experiments on real-world photos taken using handheld mobile phones show that the proposed method can generate high-quality images with faithful detail and vivid color rendition in both dark and bright areas.
The imperative of interpretable machines

As artificial intelligence becomes prevalent in society, a framework is needed to connect interpretability and trust in algorithm-assisted decisions, for a range of stakeholders.
High-Accuracy and Low-Latency Speech Recognition with Two-Head Contextual Layer Trajectory LSTM Model

While the community keeps promoting end-to-end models over conventional hybrid models, which usually are long short-term memory (LSTM) models trained with a cross entropy criterion followed by a sequence discriminative training criterion, we argue that such conventional hybrid models can still be significantly improved. In this paper, we detail our recent efforts to improve conventional hybrid LSTM acoustic models for high-accuracy and low-latency automatic speech recognition. To achieve high accuracy, we use a contextual layer trajectory LSTM (cltLSTM), which decouples the temporal modeling and target classification tasks, and incorporates future context frames to get more information for accurate acoustic modeling. We further improve the training strategy with sequence-level teacher-student learning. To obtain low latency, we design a two-head cltLSTM, in which one head has zero latency and the other head has a small latency, compared to an LSTM. When trained with Microsoft's 65 thousand hours of anonymized training data and evaluated with test sets with 1.8 million words, the proposed two-head cltLSTM model with the proposed training strategy yields a 28.2% relative WER reduction over the conventional LSTM acoustic model, with a similar perceived latency.
Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks

Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.
Digital volume correlation technique for characterizing subsurface deformation behavior of a laminated composite

In the present study, the digital volume correlation (DVC) technique is used to study the deformation behavior occurring inside a carbon fibre-reinforced epoxy composite. While a tensile test on a symmetric angle-ply laminated composite specimen is being performed, X-ray images are recorded using synchrotron radiation. The radiographs are processed to reconstruct 3D μCT images. The reconstruction process utilizes a filtered back-projection (FBP) algorithm combined with a phase retrieval method to enhance the contrast of the CT images. Phase contrast is crucial since the natural microstructural pattern is analogous to a speckle pattern for digital image correlation (DIC) analysis. A slight misalignment of the specimen, typically negligible at the macroscopic scale, is corrected in the CT images prior to the DVC analysis. The accuracy of the DVC results is improved by implementing a sub-voxel registration approach to achieve a resolution less than one pixel. The DVC results are compared with finite element analysis (FEA) results. Although the overall results agree well with the FEA results, few subvolumes are prone to fail to find their correct pairs within DVC volumes. Raw 2D tomograms are individually examined and compared to show that a high correlation coefficient value does not always guarantee a correct match. Detailed discussions about the misleading correlations are provided and thereby the validations and limitations of the DVC (and DIC) technique are addressed.
Model-based testing of software product lines: Mapping study and research roadmap

Model-Based Testing (MBT) has been successfully applied to Software Product Lines (SPL). This paper provides a panorama of state-of-the-art on MBT of SPLs. We performed a systematic mapping for answering questions related with domains, approaches, solution types, variability, test case automation, artifacts, and evaluation. We built a roadmap from 44 selected studies. Main obtained results are: Software and Automotive domains are most considered; Black-box testing is widely performed; most studies have fully-automated support; variability is considered in most studies; Finite State Machines is the most used model to test SPLs; Behavioral-based and Scenario-based are the most used models; Case Studies and Experiments are used to evaluate MBT solutions and the majority is performed in industrial environments; traceability is not widely explored for MBT solutions. Furthermore, we provide a roadmap synthesizing studies based on used models, more formal artifacts, supporting tools, variability management, (semi-)automation, and traceability. The roadmap contributes to identify related primary studies based on given artifacts, variability management, tools, automation, and traceability techniques and to identify, from a given primary study, which artifacts, tools, variability management, automation and traceability techniques are related. Therefore, the roadmap serves as a guide to researchers and practitioners on how to model-based test SPLs.
DCT-CompCNN: A Novel Image Classification Network Using JPEG Compressed DCT Coefficients

The popularity of Convolutional Neural Network (CNN) in the field of Image Processing and Computer Vision has motivated researchers and industry experts across the globe to solve different challenging research problems with high accuracy. The simplest way to train a CNN classifier is to directly feed the original RGB pixel images into the network. However, if we intend to classify images directly with its compressed data, the same approach may not work better, like in case of JPEG compressed images. This research paper investigates the issues of modifying the input representation of the JPEG compressed data, and then feeding into the CNN. The architecture is termed as DCT-CompCNN. This novel approach has shown that CNNs can also be trained with JPEG compressed DCT coefficients and subsequently can produce a good performance similar to the conventional CNN approach. The efficiency of the modified input representation is tested with the existing ResNet-50 architecture and the proposed DCT-CompCNN architecture on a public image classification datasets like CIFAR-10, Dogs vs Cats and MNIST datasets, reporting a better performance.
A Blockchain-Based Approach for Optimal and Secure Routing in Wireless Sensor Networks and IoT

The traffic load balance, the interferences reduction, and the security during the routing phase in wireless sensor networks (WSN) and IoT are investigated in this paper. In our work, we suppose that the network's nodes are sensing some events which generate heavy data that must be carried over several packets. We propose a routing protocol that makes use of the Blockchain technology to offer a shared memory between the network's nodes. These nodes are considered as coins in which the ownership transacts between the source nodes and the sink. All the transactions are stored in the Blockchain as a means to share the network's status in real-time. In order to select the optimal path, we introduce a cost function which considers the load density and interferences level at each node. Furthermore, we are taking advantage of the Blockchain security to secure the selected paths in the network. The simulation results have shown that this solution could be applicable and could resolve the issues cited above.
A Randomized Parallel Algorithm for Efficiently Finding Near-Optimal Universal Hitting Sets

As the volume of next generation sequencing data increases, an urgent need for algorithms to efficiently process the data arises. Universal hitting sets (UHS) were recently introduced as an alternative to the central idea of minimizers in sequence analysis with the hopes that they could more efficiently address common tasks such as computing hash functions for read overlap, sparse suffix arrays, and Bloom filters. A UHS is a set of k-mers that hit every sequence of length L, and can thus serve as indices to L-long sequences. Unfortunately, methods for computing small UHSs are not yet practical for real-world sequencing instances due to their serial and deterministic nature, which leads to long runtimes and high memory demands when handling typical values of k (e.g. $$k > 13$$). To address this bottleneck, we present two algorithmic innovations to significantly decrease runtime while keeping memory usage low: (i) we leverage advanced theoretical and architectural techniques to parallelize and decrease memory usage in calculating k-mer hitting numbers; and (ii) we build upon techniques from randomized Set Cover to select universal k-mers much faster. We implemented these innovations in PASHA, the first randomized parallel algorithm for generating near-optimal UHSs, which newly handles $$k > 13$$. We demonstrate empirically that PASHA produces sets only slightly larger than those of serial deterministic algorithms; moreover, the set size is provably guaranteed to be within a small constant factor of the optimal size. PASHA’s runtime and memory-usage improvements are orders of magnitude faster than the current best algorithms. We expect our newly-practical construction of UHSs to be adopted in many high-throughput sequence analysis pipelines.
Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study

Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations.The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively.Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e-Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy.Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively.The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning-based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.
Emotion Detection from Bangla Text Corpus Using Naïve Bayes Classifier

Emotions are an important part of everyday human interaction. Emotions can be expressed by means of written text, verbal speech or facial expressions. In recent years, the practice of expressing emotion in social media or blogs have increased rapidly. People write about their feelings and opinions on any political or global issues. All these social activities have made it essential to gather and analyze human emotion from the text. Although the field of emotion detection has been explored extensively for English language, the investigation of this domain for Bangla language still now in its infant stages. Our paper aims at detecting multi-class emotions from Bangla text using Multinomial Naïve Bayes (NB) classifier along with various features such as stemmer, parts-of-speech (POS) tagger, n-grams, term frequency-inverse document frequency (tf-idf). Our final model was able to classify the text into three emotion classes (happy, sad and angry) with an overall accuracy of 78.6%.
Fast GPU 3D diffeomorphic image registration

3D image registration is one of the most fundamental and computationally expensive operations in medical image analysis. Here, we present a mixed-precision, Gauss–Newton–Krylov solver for diffeomorphic registration of two images. Our work extends the publicly available CLAIRE library to GPU architectures. Despite the importance of image registration, only a few implementations of large deformation diffeomorphic registration packages support GPUs. Our contributions are new algorithms to significantly reduce the run time of the two main computational kernels in CLAIRE: calculation of derivatives and scattered-data interpolation. We deploy (i) highly-optimized, mixed-precision GPU-kernels for the evaluation of scattered-data interpolation, (ii) replace Fast-Fourier-Transform (FFT)-based first-order derivatives with optimized 8th-order finite differences, and (iii) compare with state-of-the-art CPU and GPU implementations. As a highlight, we demonstrate that we can register 2563 clinical images in less than 6 s on a single NVIDIA Tesla V100. This amounts to over 20× speed-up over the current version of CLAIRE and over 30× speed-up over existing GPU implementations.
A Novel and Efficient Blind Image Watermarking In Transform Domain

In today's fast-growing digital world, the fortification of imperceptibility, unambiguity, blindness, and robustness has become vital. This paper proposes a unique and blind digital image watermarking method with discrete cosine transform in order to achieve the security, imperceptibility, robustness, blindness, unambiguity, and simplicity. The aim is accomplished by implanting watermark into DCT coefficients of the image at two different positions. Embedding of same watermark bits at two different positions makes the method unique and helps in maintaining the unambiguity, security, and robustness. The proposed watermark extraction algorithm does not require a host image and a watermark image. In order to validate the proposed watermarking algorithm, the performance of this method is tested on eight different images with distinct quality parameters. Experimental results demonstrate that the proposed watermarking algorithm has good imperceptibility and less computational complexity. The proposed scheme also performs well in terms of robustness.
Deep Learning and Ensemble Approach for Praise or Complaint Classification

Online reviews, social media data contains wealth of information about customer's likes and dislikes about product or service. Extreme opinions in terms of praises and complaints are more informative as compared to plain positive and plain negative sentences and are difficult to find from customer reviews. Existing approaches to sentiment analysis classify the sentences as positive negative or neutral and do not consider informativeness of sentences. This paper compares Machine Learning, Ensemble and Deep Learning based approaches for praise or complaint classification using hybrid features based on linguistic properties of extreme opinions. The performance of the Machine Learning classifiers with proposed hybrid features and Deep Learning based classifiers using Dense Neural Network, CNN, Multichannel CNN were evaluated by training and testing the deep neural network with a set of important words such as nouns, adjectives, intensifiers, verbs, etc present in the sentence. The proposed Ensemble classifier uses SVC, Random Forest, Decision Tree, MLP classifiers. Hotel domain reviews were evaluated using the parameters Accuracy, Precision, Recall, and F1-score. The evaluation results show that the proposed Ensemble classifier and Deep Learning Models provides better classification accuracy as compared to existing methods.
Neural Machine Translation: English to Hindi

Machine Translation (MT) attempts to minimize the communication gap among people from various linguistic backgrounds. Automatic translation between pair of different natural languages is the task of MT mechanism, wherein Neural Machine Translation (NMT) attract attention because it offers reasonable translation accuracy in case of the context analysis and fluent translation. In this paper, two different NMT systems are carried out, namely, NMT-1 relies on the Long Short Term Memory (LSTM) based attention model and NMT-2 depends on the transformer model in the context of English to Hindi translation. System results are evaluated using Bilingual Evaluation Understudy (BLEU) metric. The average BLEU scores of NMT-1 system are 35.89 (Test-Set-1), 19.91 (Test-Set-2) and NMT-2 system are 34.42 (Test-Set-1), 24.74 (Test-Set-2) respectively. The results show better performance than existing NMT systems.
Imports and economic growth in Africa: Testing for granger causality in the frequency domain

In this paper, we test for causality between imports and economic growth in 41 African countries. We differ from earlier studies by testing for causality at high and low frequency levels, that is, in the short and long run, respectively. In doing so, we employ a frequency domain Granger causality test robust to pretest biases relating to unit root and cointegration tests. We document that there is: (i) unidirectional causality running from imports and economic growth in 7 countries in the short run and 5 countries in the long run, (ii) unidirectional causality running from economic growth to imports in 4 countries in the short run and 10 countries in the long run, (iii) bidirectional causality in only one country in the short run and 3 countries in the long run, and (iv) no causality in 29 countries in the short run and 23 countries in the long run. Our findings suggest that, for the most part, the neutrality hypothesis is valid in the short- and long-run periods. We imply from our findings that possible changes in the causality dynamics between imports and economic growth over time should be taken into consideration before designing policies.