Reviewerly/exHarmony huggingface dataset:
Unsupervised smoke to desmoked laparoscopic surgery images using contrast driven Cyclic-DesmokeGAN

In laparoscopic surgery, energized dissecting devices and laser ablation causes smoke, which degrades the visual quality of the operative field. This paper proposes an unsupervised approach to desmoke laparoscopic images called Cyclic-DesmokeGAN. In the generator, multi-scale residual blocks help to alleviate the smoke component at multiple scales, while refinement module helps to obtain desmoked images with sharper boundaries. As the presence of smoke degrades contrast and fine structure, the proposed method utilizes high boost filtered image at each encoder layer. The contrast loss improves overall contrast, thereby reducing the smoke, while Unsharp Regularization loss helps to stabilize the network. The proposed Cyclic-DesmokeGAN is tested on 200 smoke images obtained from Cholec80 dataset consisting of videos of cholecystectomy surgeries. The results depict effectiveness, as proposed approach achieved 3.47±0.09 Contrast-Distorted Images Quality, 4.15±0.74 Naturalness Image Quality Evaluator, and 0.23±0.00 Fog Aware Density Evaluator, these indexes are best in comparison to other state-of-the-art methods.
Non-Redundant Subspace Clusterings with Nr-Kmeans and Nr-DipMeans

A huge object collection in high-dimensional space can often be clustered in more than one way, for instance, objects could be clustered by their shape or alternatively by their color. Each grouping represents a different view of the dataset. The new research field of non-redundant clustering addresses this class of problems. In this article, we follow the approach that different, non-redundant k -means-like clusterings may exist in different, arbitrarily oriented subspaces of the high-dimensional space. We assume that these subspaces (and optionally a further noise space without any cluster structure) are orthogonal to each other. This assumption enables a particularly rigorous mathematical treatment of the non-redundant clustering problem and thus a particularly efficient algorithm, which we call N r -K means (for non-redundant k -means). The superiority of our algorithm is demonstrated both theoretically, as well as in extensive experiments. Further, we propose an extension of N r -K means that harnesses Hartigan’s dip test to identify the number of clusters for each subspace automatically.
Are n-gram Categories Helpful in Text Classification?

Character n-grams are widely used in text categorization problems and are the single most successful type of feature in authorship attribution. Their primary advantage is language independence, as they can be applied to a new language with no additional effort. Typed character n-grams reflect information about their content and context. According to previous research, typed character n-grams improve the accuracy of authorship attribution. This paper examines their effectiveness in three domains: authorship attribution, author profiling and sentiment analysis. The problem of a very high number of features is tackled with distributed Apache Spark processing.
Slim LSTM NETWORKS: LSTM_6 and LSTM_C6

We show that parameter-reduced slim variants of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) are comparable in performance to the standard LSTM RNN on two diverse benchmark datasets, namely, the review sentiment IMBD and the 20 Newsgroup datasets. We focus on two of the simplest slim variants, namely LSTM_6 (i.e., standard LSTM with three fixed constant gates) and LSTM_C6_(i.e., LSTM_6 with further reduced memory-cell input block). We demonstrate that these two aggressively reduced-parameter variants are competitive with the standard LSTM when hyper-parameters, e.g., the learning rate, number of hidden units and/or gate constants are set properly. These slim architectures enable training speedup and hence these networks would be more suitable for training and inference onto portable devices with relatively limited computational resources.
Curriculum Design Using Artificial Intelligence (AI) Back Propagation Method

With the rapid changes in technologies and industry requirements, goal for the engineering education can be set as the job roles and defining the curriculum of the Program as required to meet the job role. The curriculum design can be done with designing the curriculum and defining the expected outcome of the Program. Then based on the expected outcome, the gap can be identified and the curriculum can be defined to bridge the gap between the Program and the expected outcome. Instead, the Job Roles and the skills required skills can be taken as the as the starting point and the courses required are identified to attain this outcome. In turn, the pre requisite courses can be identified and further prerequisite courses can be identified. The technology of Artificial Intelligence (AI) back propagation method can be effectively used to achieve this. This paper gives an example of such method applied for identifying the curriculum required to meet the job roles in the area of Internet of Things (IoT).
Cluster-Based Membership Function Acquisition Approaches for Mining Fuzzy Temporal Association Rules

In real-world applications, transactions are typically represented by quantitative data.Thus, fuzzy association rule mining algorithms have been proposed to handle these quantitative transactions.In addition, items generally have certain lifespans or temporal periods in which they exist in a database.Therefore, fuzzy temporal association rule mining algorithms have also been proposed in the literature.A key factor in the acquisition of fuzzy temporal association rules (FTARs) is the design of appropriate membership functions.Because current approaches have been designed to generate membership functions for mining fuzzy association rules (FARs) in market-basket analysis, in this paper, we propose a membership function tuning mechanism for a fuzzy temporal association rule mining algorithm.The proposed approach modifies an existing cluster-based method to generate unique membership functions that are specifically tailored to each item in a dataset.Two factors are utilized to decide the appropriate membership functions of each item: (1) the density similarity among intervals corresponding to the density similarity within intervals, and (2) the information closeness within an interval corresponding to the similarity in the number of data points between intervals.A parameter θ is used to indicate the relative importance of these two factors.As a result, the membership functions are generated based on the quantitative ranges of individual items, and the generated membership functions of items are different in terms of the values of each interval and the number of intervals.The generated membership functions are subsequently used in a fuzzy temporal association rule mining algorithm.Computational experiments were conducted on both a synthetic dataset and a real-world one to demonstrate the effectiveness of the proposed approach.
PACL: Piecewise Arc Cotangent Decay Learning Rate for Deep Neural Network Training

Deep neural networks (DNNs) are currently the best-performing method for many classification problems.For training DNNs, the learning rate is the most important hyper-parameter, choice of which affects the performance of the model greatly.In recent years, some learning rate schedulers, such as HTD, CLR, and SGDR, have been proposed.These methods, some of which make use of the cycling mechanism to improve the convergence speed and accuracy of DNN, but performance degradation occurs in the convergence process.Others have good accuracy, but their convergence speed is too slow.This paper proposed a new learning rate schedule called piecewise arc cotangent decay learning rate (PACL), which can not only improve the convergence speed and accuracy of DNN but also significantly reduce performance degradation zone caused by the cycling mechanism.It is easy to implement, but almost at no extra computing expense.Finally, we demonstrate the effectiveness of PACL, on training CIFAR-10, CIFAR-100, and Tiny ImageNet with ResNet, DenseNet, WRN, SEResNet, and MobileNet.
Deep Lighting Environment Map Estimation from Spherical Panoramas

Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and postproduction. In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama. In addition to being a challenging and ill-posed problem, the lighting estimation task also suffers from a lack of facile illumination ground truth data, a fact that hinders the applicability of data-driven methods. We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism. This relies on a global Lambertian assumption that helps us overcome issues related to pre-baked lighting. We relight our training data and complement the model's supervision with a photometric loss, enabled by a differentiable image-based relighting technique. Finally, since we predict spherical spectral coefficients, we show that by imposing a distribution prior on the predicted coefficients, we can greatly boost performance. Code and models available at vcl3d.github.io/DeepPanoramaLighting.
Multi-Robot Coverage and Persistent Monitoring in Sensing-Constrained Environments

This article examines the problems of multi-robot coverage and persistent monitoring of regions of interest with limited sensing robots. A group of robots, each equipped with only contact sensors and a clock, execute a simple trajectory by repeatedly moving straight and then bouncing at perimeter boundaries by rotating in place. We introduce an approach by finding a joint trajectory for multiple robots to cover a given environment and generating cycles for the robots to persistently monitor the target regions in the environment. From a given initial configuration, our approach iteratively finds the joint trajectory of all the robots that covers the entire environment. Our approach also computes periodic trajectories of all the robots for monitoring of some regions, where trajectories overlap but do not involve robot-robot collisions. We present experimental results from multiple simulations and physical experiments demonstrating the practical utility of our approach.
Women and the Sustainability of Rural Community Networks in the Global South

Increased advocacy and support for Community networks (CNs), or locally owned and/or operated telecommunications, reflects hopes that CNs will compensate for telecom companies' failure to serve rural areas of low- and middle-income countries (LMICs). My study of six cases in Africa, Asia and Latin America responds to this political project. Rural populations in LMICs often comprise more women, and increasingly, older women; rural governance and the technoculture of CNs, however, have masculine biases. Discussions, interviews and observations involving 152 women and 172 men champions, operators, users and non-users showed that women are less likely than men to use CN services and be paid for their labour in CNs. Women manage their personal aspirations, identities and expectations about their labour in various ways, and younger women seek alternatives to connect, sometimes setting up independent enterprises. Thus, maintaining the commons ethos that sustains CNs requires attending to different women's approaches.
ASCELLA: Accelerating Sparse Computation by Enabling Stream Accesses to Memory

Sparse computations dominate a wide range of applications from scientific problems to graph analytics. The main characterization of sparse computations, indirect memory accesses, prevents them from effectively achieving high performance on general-purpose processors. Therefore, hardware accelerators have been proposed for sparse problems. For these accelerators, the storage format and the decompression mechanism is crucial but have seen less attention in prior work. To address this gap, we propose Ascella, an accelerator for sparse computations, which besides enabling a smooth stream of data and parallel computation, proposes a fast decompression mechanism. Our implementation on a ZYNQ FPGA shows that on average, Ascella executes sparse problems up to 5.1× as fast as prior work.
Arbitrary Scale Super-Resolution for Brain MRI Images

Recent attempts at Super-Resolution for medical images used deep learning techniques such as Generative Adversarial Networks (GANs) to achieve perceptually realistic single image Super-Resolution. Yet, they are constrained by their inability to generalise to different scale factors. This involves high storage and energy costs as every integer scale factor involves a separate neural network. A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network. In this paper, we propose a new network that combines that technique with SRGAN, a state-of-the-art GAN-based architecture, to achieve arbitrary scale, high fidelity Super-Resolution for medical images. By using this network to perform arbitrary scale magnifications on images from the Multimodal Brain Tumor Segmentation Challenge (BraTS) dataset, we demonstrate that it is able to outperform traditional interpolation methods by up to 20$$\%$$ on SSIM scores whilst retaining generalisability on brain MRI images. We show that performance across scales is not compromised, and that it is able to achieve competitive results with other state-of-the-art methods such as EDSR whilst being fifty times smaller than them. Combining efficiency, performance, and generalisability, this can hopefully become a new foundation for tackling Super-Resolution on medical images.
Systematic Literature Review of Information Security Compliance Behaviour Theories

Abstract The paper aims to identify behavioural theories that influence information security policies compliance behaviour. A systematic review of empirical studies from eleven online databases (ACM digital library, Emerald Insight, IEEE Xplore digital library, Springer link, Science direct, Scopus, Web of Science, Oxford academic journals, SAGE journals, Taylor &amp; Francis and Wiley online library) are conducted. This review identified 29 studies met its criterion for inclusion. The investigated theories were extracted and analysed. Total of 19 theories have been identified and studied concerning to security policy compliance behaviour. The result indicated that the most established theories in information security compliance behaviour studies are the Theory of Planned Behavior and Protection Motivation theory. Meanwhile, General Deterrence Theory, Neutralization theory, Social Bond Theory / Social Control Theory are used moderately in this research area. Less explored theories are namely Self Determination Theory, Knowledge, Attitude, and Behavior, Social Cognitive Theory, Involvement Theory, Health belief model, Theory of Interpersonal Behavior, Extended Parallel Processing Model, Organisational Control Theory, Psychological Reactance Theory, Norm Activation Theory, Organizational Behaviour Theory, Cognitive Evaluation Theory and Extended Job Demands-Resources. The results from this review may guide the development and evaluation of theories promoting information security compliance behaviours. This will further contribute in the development of an integrated theory of information security compliance behaviour.
Fraud Detection in Credit Card Data Using Machine Learning Techniques

Credit cards have become the part of human life these days. It facilitates users in various sectors. Intruders try to steal credit card information in many ways. Hence, security of sensitive information of credit cards is a major concern. We can apply different machine learning techniques to detect such frauds or anomalies. On basis of our survey, we found two outperforming classifiers of machine learning viz., Logistic Regression (LR) and Naïve Bayes (NB). This paper provides a method of fraud detection in credit card system using Random Forest (RF) classifier. The work is compared with the existing classifiers: LR and NB. Their performance is evaluated on various metrics viz., Accuracy, Precision, Recall, F1 Score and Specificity on some datasets of credit card system. It is observed that Random Forest is outperforming others. Random Forest gives 99.95% accuracy while accuracy of LR and NB is 91.16% and 89.35% respectively.
A quality enhancement framework with noise distribution characteristics for high efficiency video coding

Video coding effectively reduces the amount of video data while unavoidably producing compression noise. Compression noise can cause significant artifacts in compressed video, such as blocking, ringing, and blurring, which seriously affects the visual quality of videos and the value of videos for content analysis. In compressed video quality enhancement, few methods based on deep learning fully consider the relationship between video content and compression noise or the possibility of uniting the encoder or the decoder to enhance the quality of compressed video. In an approach different from existing methods, we propose a video quality enhancement framework based on the distribution characteristics of compression noise. The proposed framework consists of two parts: at the encoder, we propose a convolutional neural network (CNN)-based in-loop filtering network combined with noise distribution (IFN-ND) characteristics for the I frame instead of high efficiency video coding (HEVC) standard in-loop filters; at the decoder, we propose a CNN-based quality enhancement network combined with the noise distribution characteristics (PQEN-ND) for the P frames. The noise characteristics are extracted from the code stream to further improve the performance of the proposed networks. The experiments show that the proposed method can significantly improve the quality of HEVC compressed video, achieving an average 12.84% reduction in the BD rate and up to a 1.0476 dB increase in the peak signal-to-noise ratio (PSNR).
Towards integrated dialogue policy learning for multiple domains and intents using Hierarchical Deep Reinforcement Learning

Creation of Expert and Intelligent Dialogue/Virtual Agent (VA) that can serve complicated and intricate tasks (need) of the user related to multiple domains and its various intents is indeed quite challenging as it necessitates the agent to concurrently handle multiple subtasks in different domains. This paper presents an expert, unified and a generic Deep Reinforcement Learning (DRL) framework that creates dialogue managers competent for managing task-oriented conversations embodying multiple domains along with their various intents and provide the user with an expert system which is a one stop for all queries. In order to address these multiple aspects, the dialogue exchange between the user and the VA is split into hierarchies, so as to isolate and identify subtasks belonging to different domains. The notion of Hierarchical Reinforcement Learning (HRL) specifically options is employed to learn optimal policies in these hierarchies that operate at varying time steps to accomplish the user goal. The dialogue manager encompasses a top-level domain meta-policy, intermediate-level intent meta-policies in order to select amongst varied and multiple subtasks or options and low-level controller policies to select primitive actions to complete the subtask given by the higher-level meta-policies in varying intents and domains. Sharing of controller policies among overlapping subtasks enables the meta-policies to be generic. The proposed expert framework has been demonstrated in the domains of “Air Travel” and “Restaurant”. Experiments as compared to several strong baselines and a state of the art model establish the efficiency of the learned policies and the need for such expert models capable of handling complex and composite tasks.
Compositional data analysis of smoke emissions from debris piles with low-density polyethylene

Data describing the composition of smoke are inherently multivariate and always non-negative parts of a whole. The data are relative and the information is contained in the ratios between parts of the composition. A prior analysis of smoke emissions produced from the burning of manzanita wood mixed with low-density polyethylene plastic applied traditional statistical methods to the compositional data and found no effect. The current paper applies compositional data techniques to these smoke emissions to determine if the prior analysis was accurate. Analysis of variance of the isometric log-ratios showed that LDPE significantly affected the CO2 emission ratio for 8 of the 191 trace gases; this analysis showed none of the gases identified in the previous analysis were affected by LDPE. LDPE did not affect the CO2 emission ratios for the alkanes, alkenes, alkynes, aldehydes, cycloalkanes, cycloalkenes, diolefins, ketones, MAHs, and PAHs. Compositional data analysis should be used to analyze smoke emissions data. Burning contaminant-free LDPE should produce emissions like wood.Implications Reanalysis of impact of burning LDPE plastic in silvicultural debris piles using appropriate statistical techniques confirmed previously published results from inappropriate techniques that LDPE did not change the composition of the smoke emissions. Being able to dispose of these LDPE-covered forest debris by burning can save thousands of dollars in labor costs annually. Disposal of pesticide-free agricultural LDPE plastic by burning should only produce wood-like smoke emissions. This applies to LDPE/total mass ratios of 0.25– 2.5% as studied.
Biomedical Corpora and Natural Language Processing on Clinical Text in Languages Other Than English: A Systematic Review

Natural Language Processing (NLP) applications on real-life textual content require a suitable fit for purpose corpora, which can accommodate the ambiguity of the domain. Researchers in the field managed to synthesize gold-standard corpora in many domains and for varying tasks, assisted by domain experts and linguists. The wealth of information buried in free-text electronic documents in healthcare systems presents itself as a leading contender for NLP applications. In this literature review, the efforts to come up and utilize a clinically annotated corpus in a particular healthcare information extraction task are explored. Those efforts can be more pronounced when done on a new language with limited existing gold-standard clinical references. A great number of people around the globe interact with healthcare systems in languages other than English. Advancing the Clinical NLP research in their languages will propel the general progress in the field and potential healthcare advantages considerably. For the purposes of this review, we considered three major world languages: Spanish, Italian, and Chinese. This led to considering the research question to be about the viability of the creation or utilization of a gold-standard clinical corpus in a language other than English and how it can contribute in performing a complex clinical language mining task. The implementations reviewed in these languages considered varying approaches to overcome complexities in biomedical NLP in these languages. This study managed to highlight novel solutions to complex tasks and found that efforts in these languages can be highly successful if a non-English medical corpus is created from scratch, off-the-shelf tools are used or machine translation is considered to bridge the gap in biomedical NLP domain-specific lingual resources in these languages.