Reviewerly/exHarmony huggingface dataset:
Paraphrase Identification with Lexical, Syntactic and Sentential Encodings

Paraphrase identification has been one of the major topics in Natural Language Processing (NLP). However, how to interpret a diversity of contexts such as lexical and semantic information within a sentence as relevant features is still an open problem. This paper addresses the problem and presents an approach for leveraging contextual features with a neural-based learning model. Our Lexical, Syntactic, and Sentential Encodings (LSSE) learning model incorporates Relational Graph Convolutional Networks (R-GCNs) to make use of different features from local contexts, i.e., word encoding, position encoding, and full dependency structures. By utilizing the hidden states obtained by the R-GCNs as well as lexical and sentential encodings by Bidirectional Encoder Representations from Transformers (BERT), our model learns the contextual similarity between sentences effectively. The experimental results by using the two benchmark datasets, Microsoft Research Paraphrase Corpus (MRPC) and Quora Question Pairs (QQP) show that the improvement compared with the baseline, BERT sentential encodings model, was 1.7% F1-score on MRPC and 1.0% F1-score on QQP. Moreover, we verified that the combination of position encoding and syntactic features contributes to performance improvement.
An interoperable semantic service toolset with domain ontology for automated decision support in the end-of-life domain

In product-diverse, end-of-life (EoL) production lines the relevant markets, competitors and customer bases continuously change as new products are processed. The resale market itself changes with the influx of new products, as well as hardware and software discontinuations. Competitive business decision making is often performed by a human operator and may not be timely or fully informed. These are decisions such as whether to perform a high cost repair or recycle a product or whether to use a batch of parts in repair or sell them on. These decisions can be used to optimise product life-cycle management (PLM) and profit margins. A real-time decision making capability can reduce the risk of performing non-profitable processing. The novel contribution of this work is an interoperable semantic decision support toolset that enables a capability for timely EoL decisions based on complete knowledge on profitability, predicted pricing and cost-of-production. Many decision support systems have been proposed for the EoL domain, but a lack of interoperability and use of unstructured knowledge bases has led to decisions based on knowledge that is not up to date. Using formalised, semantic technologies offers sustainable decision making in this volatile and increasingly competitive domain.
Householders as designers? Generating future energy services with United Kingdom home occupiers

The increasing adoption of smart meters and smart home technologies in domestic dwellings affords new opportunities to collect data about householders’ everyday lives, including their energy use. Current services designed to support householders in reducing their energy use predominantly push ‘feedback’ at householders with limited effect. New services are needed that better engage householders with their energy data and energy saving options in more meaningful ways, and/or facilitate broader energy saving behaviours. After householders had spent a year being immersed in their energy data, this study used a co-design approach with householders, researchers, designers and building energy technologists to generate a set of future energy-related services that would benefit householders. The results present 11 co-designed concepts for future services that support householders in making structural and behavioural changes around energy use, alongside concepts that use energy data to impact positively on future lifestyles. Opportunities, challenges and the implications for the design of future energy services are then discussed. The article closes with reflections on the role of the collaborative design approach used to generate these visions of the future.
Flexible Group-Level Pruning of Deep Neural Networks for On-Device Machine Learning

Network pruning is a promising compression technique to reduce computation and memory access cost of deep neural networks. Pruning techniques are classified into two types: fine-grained pruning and coarse-grained pruning. Fine-grained pruning eliminates individual connections if they are insignificant and thus usually generates irregular networks. Therefore, it is hard to reduce model execution time. Coarse-grained pruning such as filter-level and channel-level techniques can make hardware-friendly networks. However, it can suffer from low accuracy. In this paper, we focus on the group-level pruning method to accelerate deep neural networks on mobile GPUs, where several adjacent weights are pruned in a group to mitigate the irregularity of pruned networks while providing high accuracy. Although several group-level pruning techniques have been proposed, the previous techniques select weight groups to be pruned at group-size-aligned locations. In this paper, we propose a more flexible approach, called unaligned group-level pruning, to improve the accuracy of the compressed model. We can find the optimal solution of the unaligned group selection problem with dynamic programming. Our technique also generates balanced sparse networks to get load balance at parallel computing units. Experiments demonstrate that the 2D unaligned group-level pruning shows 3.12% a lower error rate at ResNet-20 network on CIFAR-10 compared to the previous 2D aligned group-level pruning under 95% of sparsity.
Teaching Data Journalism: Insights for Indian Journalism Education

With emergent subspecialties like data journalism bringing new skillsets and job roles, professionals and journalism educators find it difficult to imbibe the fast-changing industry demands. Such challenges in some countries and media industries put journalism educators in an advantageous position, offering them an agency to actively shape the contours of industry practice than getting shaped by it. From this perspective, the present study tries to understand data journalism practices in India and suggests certain insights to integrate data journalism training in programmes offered by Indian journalism education. By probing insights from the literature on data journalism education and by examining existing data journalism practices in India, the study calls for intervention with a pedagogic strategy to impart better data-sourcing practices, coding skills and critical data literacy among the students as an antidote to the prevalent DIY culture and overdependence on data aggregates. The pedagogic strategy should convey the importance of audience centrality and ethics in data journalism practice. It argues that such an approach can, in effect, improve industry practices as well as the quality of journalism education in India.
Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

In this research, X-ray and MRI images of patients suffering from cervical hyperextension injury are investigated.Also, radiographic images are collected from patients who suffered from trauma but without cervical hyperextension injury.The core engine algorithm of the optimized prediction model is kernel extreme learning machine (KELM), and the input data is 17 factors that may cause cervical hyperextension injury.As the optimization core, we utilized the Butterfly optimization algorithm (BOA).Up to now, few improved variants of BOA have been reported.The original BOA converges slowly and quickly falls into a locally optimal solution.An enhanced BOA based on orthogonal learning, Levy flight, and an exploitation engine is proposed in this paper to relieve these two shortcomings, which is called LBOLBOA.Orthogonal learning is utilized to construct guidance vectors for guiding agents toward the global optimum solution aiming to increase the accuracy of the solutions.Also, Levy flight and Broyden-Fletcher-Goldfarb-Shanno mechanisms are utilized to enrich the intensification propensities of BOA and stagnation avoidance.The proposed LBOLBOA is used to deal with continuous function optimization and machine learning problems, including parameter optimization of KELM.We rigorously verified this variant using a comprehensive set of the benchmark test suite and real-world dataset on cervical hyperextension injury.The results indicate that LBOLBOA can achieve improved performance in dealing with the function optimization and machine learning problems, especially the capability for prediction of cervical hyperextension injury.
Uncertainty-aware integration of local and flat classifiers for food recognition

Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the 'children' classifiers and the prediction from the 'parent' classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen.
Semi-Supervised Texture Filtering With Shallow to Deep Understanding

This work proposed a semi-supervised method for automatic texture filtering. Our method leveraged a limited amount of labeled data and a large amount of unlabeled data to train Generative Adversarial Networks (GANs). Separate loss functions were designed for both labeled and unlabeled datasets. Our main contribution is the introduction of knowledge extracted from shallow and deep layers in neural networks. Loss defined within shallow layers preserves the edge, while loss defined within the deep layers identifies the semantic content and conversely removes the small-scale texture variations. This contribution directly addresses the major challenge for texture filtering, distinguishing the structural content from non-structural textures at the pixel level. The extracted information, in our study, improved the content and color consistency before and after the process of filtering, for unlabeled samples in particular. The proposed method offers twofold benefits: first, significant reductions in the amounts of time and effort expended in reconstructing the labeled dataset, especially given the delicate operations required at the pixel level; second, a reduction in over-fitting, in supervised learning with a small amount of labeled data, by utilizing a large amount of unlabeled data. The results confirm that our method can perform comparably with non-learning-based methods, alleviating the demand for the determination of optimal parameter values.
Review of Apriori based Frequent Itemset Mining Solutions on Big Data

The data being generated today is massive in terms of volume, velocity, and variety. It is a great challenge to derive knowledge from data in this condition. Researchers, therefore have proposed ways to deal with this challenge. frequent itemset mining is one of the proposed ways to distinguish itemsets inside the vast amount of data to aid the operation of a variety of pursuits and businesses, a process termed ‘Association Rule Mining'. However, there are a variety of works done in this area. The introduction of different algorithms, frameworks, and applications throughout the recent decade has produced many interesting approaches. One of the algorithms in this area is the Apriori algorithm. It is a simple yet powerful algorithm. However, the original Apriori is not suitable for big data and due to this reason, researchers have attempted to introduce ways and schemes to adapt it to this new age of data. Because of the number of efforts in this area, having a bird's eye view of the past works is of value. This review aims to present an insight into the works done in the intersection of two matters: big data and the Apriori algorithm. It is concerned with Aprioribased algorithms presented in the recent decade with a focus on the three popular big data platforms: Apache Hadoop, Spark, and Flink. Also, major points of each approach and solution is presented. A conclusion in the end summarizes the points discussed in this paper.
An Introduction to Deep Convolutional Neural Networks With Keras

Deep learning (DL) is the new buzzword for researchers in the research area of computer vision that unlocked the doors to solving complex problems. With the assistance of Keras library, machine learning (ML)-based DL and various complicated or unresolved issues such as face recognition and voice recognition might be resolved easily. This chapter focuses on the basic concept of Keras-based framework DL library to handle the different real-life problems. The authors discuss the codes of previous libraries and same code run on Keras library and assess the performance on Google Colab Cloud Graphics Processing Units (GPUs). The goal of this chapter is to provide you with the newer concept, algorithm, and technology to solve the real-life problems with the help of Keras framework. Moreover, they discuss how to write the code of standard convolutional neural network (CNN) architectures using Keras libraries. Finally, the codes of validation and training data set to start the training procedure are explored.
An enhanced OCT image captioning system to assist ophthalmologists in detecting and classifying eye diseases

Human eye is affected by the different eye diseases including choroidal neovascularization (CNV), diabetic macular edema (DME) and age-related macular degeneration (AMD). This work aims to design an artificial intelligence (AI) based clinical decision support system for eye disease detection and classification to assist the ophthalmologists more effectively detecting and classifying CNV, DME and drusen by using the Optical Coherence Tomography (OCT) images depicting different tissues. The methodology used for designing this system involves different deep learning convolutional neural network (CNN) models and long short-term memory networks (LSTM). The best image captioning model is selected after performance analysis by comparing nine different image captioning systems for assisting ophthalmologists to detect and classify eye diseases. The quantitative data analysis results obtained for the image captioning models designed using DenseNet201 with LSTM have superior performance in terms of overall accuracy of 0.969, positive predictive value of 0.972 and true-positive rate of 0.969using OCT images enhanced by the generative adversarial network (GAN). The corresponding performance values for the Xception with LSTM image captioning models are 0.969, 0.969 and 0.938, respectively. Thus, these two models yield superior performance and have potential to assist ophthalmologists in making optimal diagnostic decision.
HistoMapr™: An Explainable AI (xAI) Platform for Computational Pathology Solutions

Pathologists are adopting whole slide images (WSIs) for diagnostic purposes. While doing so, pathologists should have all the information needed to make best diagnoses rapidly, while supervising computational pathology tools in real-time. Computational pathology has great potential for augmenting pathologists' accuracy and efficiency, but concern exists regarding trust for 'black-box AI' solutions. Explainable AI (xAI) can reveal underlying reasons for its results, to promote safety, reliability, and accountability for critical tasks such as pathology diagnosis. Built on a hierarchy of computational and traditional image analysis algorithms, we present the development of our proprietary xAI software platform, HistoMapr, for pathologists to improve their efficiency and accuracy when viewing WSIs. HistoMapr and xAI represent a powerful and transparent alternative to 'black-box' AI. HistoMapr previews WSIs then presents key diagnostic areas first in an interactive, explainable fashion. Pathologists can access xAI features via a "Why?" button in the interface. Furthermore, two critical early application examples are presented: 1) Intelligent triaging that involves xAI estimation of difficulty for new cases to be forwarded to subspecialists or generalist pathologists; 2) Retrospective quality assurance entails detection of potential discrepancies between finalized results and xAI reviews. Finally, a prototype is presented for atypical ductal hyperplasia, a diagnostic challenge in breast pathology, where xAI descriptions were based on computational pipeline image results.
Lithological correction of chemical weathering proxies based on K, Rb, and Mg contents for isolation of orbital signals in clastic sedimentary archives

The extraction of palaeoenvironmental (palaeoclimatic) signals from the chemical composition of siliciclastic sediments is valuable for the reconstruction of past environments, particularly in continental basins. Here we test novel weathering proxies, which are less sensitive to lithological control than the previously used raw element ratios K/Al, K/Ti, and K/Rb: (1) local enrichment factors of K/Al, Mg/Al, and K/Rb, i.e., the element ratios corrected for grain size- and matrix composition using local background functions (Al/Si, Fe, and Ca as explanatory variables) and ordinary regression and (2) robust regression residuals of those element ratios based on isometric log-ratio coordinates of the most relevant "lithogenic" elements (Ca, Fe, Rb, Si, Zr) in the chemical composition. Chemical weathering proxies can be obtained from departures of chemical composition of sedimentary profiles from relationships with other chemical elements, in particular those with grain-size control. The resulting weathering proxies were examined for the Miocene deposits from the Most Basin, the Czech Republic, which recorded one of the major warm episodes of the Cenozoic time – the Miocene Climatic Optimum. The performance of weathering proxies has been checked by (1) comparison of individual proposed proxies in one drill core HK930, (2) detailed analysis of orbital signals in the relevant compositional functions in HK930; and (3) lateral correlation of three cores HK930, DU7, and DO565 of the same basin. The novel proxies show lateral stability and orbital signatures of short eccentricity, obliquity, and precession, confirming their usefulness in palaeoenvironmental studies. Corrections for grain-size and carbonate contents should help to isolate climatic content from the weathering proxies, although in the studied sediments it weakened the precession component in the orbital signal, as grain-size proxies and other compositional data also carried orbital signals. We propose to consider these proxy ideas in palaeoclimatic reconstructions based on chemical weathering proxies.
Foveated Video Coding for Real-Time Streaming Applications

Vid eo streaming under real-time constraints is an increasingly widespread application.Many recent video eucoders are unsuitablc for lhis sccnario due to theoretical limitations or ruo tim e requ iremcnts.In this paper, we present a framcwork för thc pcrcc1>tual cvaluation of f'oveated video coding schemcs.Foveation describes the process of adapting a visual stimulus according to the acuity of the human eye.In contrast to tradHiooal rcgion-of-interest coding, where certain ar eas arc staticaJly cncodcd at a highcr ttuality, we utilize feedback from an eye-tracker to spatially steer the bit allocation scheme in realtime.We evaluate the performance of an H.264 based foveated coding scheme in a lab environment by compari ng the bitrates at the point of just noticeable d. istortion (J ND).Fur thermore, we identify perceptually optimal codec parameterizations.In our trials, we achieve an ave rage bitrate savings of 63.24% at the J ND in cornparison to thc unfoveated baseline.
SiamFF: Visual Tracking With a Siamese Network Combining Information Fusion With Rectangular Window Filtering

Recently, Siamese trackers have shown excellent performance in both accuracy and speed. However, traditional trackers have poor robustness against similar objects due to the use of single deep features and the limitation of cosine windows. In this paper, a novel Siamese network combining information fusion with rectangular window filtering named SiamFF is introduced. First, a multilevel fusion network is proposed. At feature-level, the shallow and deep features of the network are fused through a layer-hopping connection to obtain complementary feature maps. Then, the score maps generated by the complementary feature maps are further fused at the score-level to improve the robustness. In addition, based on the continuity and stationarity of objects movement in reality, a score map filtering strategy is proposed. The relative displacement of the target can be predicted by obtaining the interframe information, and the moving direction is applied to filter the score map to further eliminate the analog interference. Experimental results on OTB2015 and VOT2016 benchmarks indicate that SiamFF performs favorably against many state-of-the-art trackers in terms of accuracy while maintaining real-time tracking speed.
FOOD SAFETY IN THE SUPPLY CHAIN USING BLOCKCHAIN TECHNOLOGY

The purpose of the article is to determine the importance of blockchain technology in food supply chain management. A practical reference to the adopted research aim was to indicate the usefulness of blockchain technology to build trust between food chain stakeholders. Research shows that the properties of blockchain technology can enable it to solve many problems and shortcomings of the current food production system. Its added value is primarily a significant increase in transparency of operations among all stakeholders using big data in all parts of the food chain.
CCAE: Cross-field categorical attributes embedding for cancer clinical endpoint prediction

Patients with advanced cancer are burdened physically and psychologically, so there is an urgent need to pay more attention to their health-related quality of life (HRQOL). With an expected clinical endpoint prediction, over-treatment can be effectively eliminated by the means of palliative care at the right time. This paper develops a deep learning based approach for cancer clinical endpoint prediction based on patient's electronic health records (EHR). Due to the pervasive existence of categorical information in EHR, it brings unavoidably obstacles to the effective numerical learning algorithms. To address this issue, we propose a novel cross-field categorical attributes embedding (CCAE) model to learn a vectorized representation for cancer patients in attribute-level by orders, in which the strong semantic coupling among categorical variables are well exploited. By transforming the order-dependency modeling into a sequence learning task in an ingenious way, recurrent neural network is adopted to capture the semantic relevance among multi-order representations. Experimental results from the SEER-Medicare EHR dataset have illustrated that the proposed model can achieve competitive prediction performance compared with other baselines.