Reviewerly/exHarmony huggingface dataset:
Normalization and dropout for stochastic computing-based deep convolutional neural networks

Recently, Deep Convolutional Neural Network (DCNN) has been recognized as the most effective model for pattern recognition and classification tasks. With the fast growing Internet of Things (IoTs) and wearable devices, it becomes attractive to implement DCNNs in embedded and portable systems. However, novel computing paradigms are urgently required to deploy DCNNs that have huge power consumptions and complex topologies in systems with limited area and power supply. Recent works have demonstrated that Stochastic Computing (SC) can radically simplify the hardware implementation of arithmetic units and has the potential to bring the success of DCNNs to embedded systems. This paper introduces normalization and dropout, which are essential techniques for the state-of-the-art DCNNs, to the existing SC-based DCNN frameworks. In this work, the feature extraction block of DCNNs is implemented using an approximate parallel counter, a near-max pooling block and an SC-based rectified linear activation unit. A novel SC-based normalization design is proposed, which includes a square and summation unit, an activation unit and a division unit. The dropout technique is integrated into the training phase and the learned weights are adjusted during the hardware implementation. Experimental results on AlexNet with the ImageNet dataset show that the SC-based DCNN with the proposed normalization and dropout techniques achieves 3.26% top-1 accuracy improvement and 3.05% top-5 accuracy improvement compared with the SC-based DCNN without these two essential techniques, confirming the effectiveness of our normalization and dropout designs.
Semi-Calibrated Photometric Stereo

While conventional calibrated photometric stereo methods assume that light intensities and sensor exposures are known or unknown but identical across observed images, this assumption easily breaks down in practical settings due to individual light bulb's characteristics and limited control over sensors. This paper studies the effect of unknown and possibly non-uniform light intensities and sensor exposures among observed images on the shape recovery based on photometric stereo. This leads to the development of a "semi-calibrated" photometric stereo method, where the light directions are known but light intensities (and sensor exposures) are unknown. We show that the semi-calibrated photometric stereo becomes a bilinear problem, whose general form is difficult to solve, but in the photometric stereo context, there exists a unique solution for the surface normal and light intensities (or sensor exposures). We further show that there exists a linear solution method for the problem, and develop efficient and stable solution methods. The semi-calibrated photometric stereo is advantageous over conventional calibrated photometric stereo in accurate determination of surface normal, because it relaxes the assumption of known light intensity ratios/sensor exposures. The experimental results show superior accuracy of the semi-calibrated photometric stereo in comparison to conventional methods in practical settings.
Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis

Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients. The DWT coefficients of low- or no-noise natural images have leptokurtic, peaky distributions with heavy tails; while noisy images tend to be platykurtic with less peaky distributions and shallower tails. The sample kurtosis is a natural measure of the peakedness and tail weight of the distributions of random variables. Here, we study the efficacy of the sample kurtosis of image wavelet coefficients as a feature driving, an extreme learning machine which learns to map kurtosis values into perceptual quality scores. The model is trained and tested on five types of noisy images, including additive white Gaussian noise, additive Gaussian color noise, impulse noise, masked noise, and high-frequency noise from the LIVE, CSIQ, TID2008, and TID2013 image quality databases. The experimental results show that the trained model has better quality evaluation performance on noisy images than existing blind noise assessment models, while also outperforming general-purpose blind and full-reference image quality assessment methods.
Tsunami risk perception in southern Italy: first evidence from a sample survey

Abstract. The Italian Tsunami Alert Centre of the Istituto Nazionale di Geofisica e Vulcanologia (Centro di Allerta Tsunami, hereinafter CAT-INGV) supported a computer-assisted telephone interview (CATI) survey to investigate tsunami risk perception in two pilot regions of southern Italy. The survey was carried out on a stratified sample of 1021 interviewees representing about 3.2 million people living in 183 coastal municipalities of the two regions, namely Calabria and Apulia. The main goal of this research is to verify whether and how people's perception of tsunami hazard compares to the results of (PTHA) – probabilistic tsunami hazard assessment (TSUMAPS-NEAM project; Basili et al., 2018). As shown by the results of this project, both investigated regions are characterised by high tsunami hazard. Nonetheless, the long return time of such events could lead people to consider the occurrence of a tsunami in the Mediterranean Sea to be very unlikely. The survey results reveal that people's risk perception is low: for almost half of the whole sample the occurrence of a tsunami in the Mediterranean Sea is considered quite unlikely, with a clear difference between Apulia and Calabria. In the latter region the risk perception is much higher than in the former, probably due to the shorter time elapsed since the last event. Also, belonging to different coastal areas1 appears to have a significant influence on the way tsunami hazard is conceived, having a stronger effect on risk characterisation: the interviewees of Tyrrhenian Calabria are indeed more likely to associate tsunami risk with volcanoes than the Ionian citizens. This is coherent considering the presence of active volcanoes and related tsunami precedents in the Tyrrhenian. Television emerged as the most relevant source of knowledge for almost 90 % of the sample, and the influence of media also results in the way tsunami risk is characterised. In particular, the survey showed that people's perception and understanding of tsunamis are affected by media accounts of large events, such as the 2004 Sumatra and the 2011 Japan tsunamis. At the same time, it is evident that the risk posed by smaller events is underrated. Furthermore, the survey's results show that the word “tsunami” occupies a different semantic space in comparison to the Italian traditional headword maremoto, with differences among sample strata. In other words, the same physical phenomenon would be understood in two different ways by younger, educated people and elders with a low education level. The results of this study, although limited to two regions, provide a first assessment of tsunami risk perception in Italy, also entailing important consequences for both risk communication practice and mitigation policies.
Survey on Classic and Latest Textual Sentiment Analysis Articles and Techniques

Text is a typical example of unstructured and heterogeneous data in which massive useful knowledge is embedded. Sentiment analysis is used to analyze and predict sentiment polarities of the text. This paper provides a survey and gives comparative analyses of the latest articles and techniques pertaining to lexicon-based, traditional machine learning-based, deep learning-based, and hybrid sentiment analysis approaches. These approaches have their own superiority and get the state-of-the-art results on diverse sentiment analysis tasks. Besides, a brief sentiment analysis example in the tourism domain is displayed, illustrating the entire process of sentiment analysis. Furthermore, we create a large table to compare the pros and cons of different types of approaches, and discuss some insights with respect to research trends. In addition, a lot of important sentiment analysis datasets are summarized in this survey.
DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction

Recently, collaborative filtering combined with various kinds of deep learning models is appealing to recommender systems, which have shown a strong positive effect in an accuracy improvement.However, many studies related to deep learning model rely heavily on abundant information to improve prediction accuracy, which has stringent data requirements in addition to raw rating data.Furthermore, most of them ignore the interaction effect between users and items when building the recommendation model.To address these issues, we propose DCCR, a deep collaborative conjunctive recommender, for rating prediction tasks that are solely based on the raw ratings.A DCCR is a hybrid architecture that consists of two different kinds of neural network models (i.e., an autoencoder and a multilayered perceptron).The main function of the autoencoder is to extract the latent features from the perspectives of users and items in parallel, while the multilayered perceptron is used to represent the interaction between users and items based on fusing the user and item latent features.To further improve the performance of DCCR, an advanced activation function is proposed, which can be specified with input vectors.The extensive experiments conducted with two well-known real-world datasets and performances of the DCCR with varying settings are analyzed.The results demonstrate that our DCCR model outperforms other state-of-art methods.We also discuss the performance of the DCCR with additional layers to show the extensibility of our model.
Effectiveness of three-dimensional visualisation on undergraduate nursing and midwifery students' knowledge and achievement in pharmacology: A mixed methods study

Historically nursing and midwifery students have reported difficulty understanding the concept-based science underpinning the interactions between drugs and their targets. This knowledge is crucial for the administration and monitoring of the therapeutic and adverse effects of medications. Immersive three-dimensional technology is reported to enhance understanding of complex scientific concepts but the physical effects of motion sickness may limit its use. This project compared the effectiveness of three-dimensional immersive visualisation technology with two-dimensional visualisation technology as a teaching method to improve student understanding of a pharmacological concept, and to assess levels of student discomfort and satisfaction associated with the experience. Traditional lecture content and presentation about drug-receptor binding was followed by exposure to either a two- or three-dimensional artifact visualising β-adrenoceptor binding. Two student groups were compared by type of exposure: Group 1 watched the artifact via a three-dimensional immersive facility and Group 2 on a wide, two-dimensional screen. School of Nursing and Midwifery in a regional university in Southeast Queensland, Australia. Two hundred and two second year undergraduate nursing and midwifery students. The study used mixed methods methodology. Pre- and post- testing of student knowledge was collected using five multiple-choice questions. A post-intervention survey elicited students' self-assessed perceptions of discomfort and satisfaction with the learning experience. The three-dimensional immersive learning experience was comparable to the two-dimensional experience in terms of satisfaction and comfort but resulted in statistically significant improvements in post-test scores. The three-dimensional experience improved understanding when compared to two-dimensional viewing, satisfied students leaning needs, and caused minimal discomfort. The results are encouraging in terms of using three-dimensional technology to enhance student knowledge of pharmacological concepts necessary for competency in medication management.
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

Stochastic Gradient Descent (SGD) based training of neural networks with a large learning rate or a small batch-size typically ends in well-generalizing, flat regions of the weight space, as indicated by small eigenvalues of the Hessian of the training loss. However, the curvature along the SGD trajectory is poorly understood. An empirical investigation shows that initially SGD visits increasingly sharp regions, reaching a maximum sharpness determined by both the learning rate and the batch-size of SGD. When studying the SGD dynamics in relation to the sharpest directions in this initial phase, we find that the SGD step is large compared to the curvature and commonly fails to minimize the loss along the sharpest directions. Furthermore, using a reduced learning rate along these directions can improve training speed while leading to both sharper and better generalizing solutions compared to vanilla SGD. In summary, our analysis of the dynamics of SGD in the subspace of the sharpest directions shows that they influence the regions that SGD steers to (where larger learning rate or smaller batch size result in wider regions visited), the overall training speed, and the generalization ability of the final model.
Closed-Loop Memory GAN for Continual Learning

Sequential learning of tasks using gradient descent leads to an unremitting decline in the accuracy of tasks for which training data is no longer available, termed catastrophic forgetting. Generative models have been explored as a means to approximate the distribution of old tasks and bypass storage of real data. Here we propose a cumulative closed-loop memory replay GAN (CloGAN) provided with external regularization by a small memory unit selected for maximum sample diversity. We evaluate incremental class learning using a notoriously hard paradigm, single-headed learning, in which each task is a disjoint subset of classes in the overall dataset, and performance is evaluated on all previous classes. First, we show that when constructing a dynamic memory unit to preserve sample heterogeneity, model performance asymptotically approaches training on the full dataset. We then show that using a stochastic generator to continuously output fresh new images during training increases performance significantly further meanwhile generating quality images. We compare our approach to several baselines including fine-tuning by gradient descent (FGD), Elastic Weight Consolidation (EWC), Deep Generative Replay (DGR) and Memory Replay GAN (MeRGAN). Our method has very low long-term memory cost, the memory unit, as well as negligible intermediate memory storage.
Interpretability in HealthCare A Comparative Study of Local Machine Learning Interpretability Techniques

Although complex machine learning models (e.g., Random Forest, Neural Networks) are commonly outperforming the traditional simple interpretable models (e.g., Linear Regression, Decision Tree), in the healthcare domain, clinicians find it hard to understand and trust these complex models due to the lack of intuition and explanation of their predictions. With the new General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. To tackle this challenge, recently, several machine learning interpretability techniques have been developed and introduced. In general, the main aim of these interpretability techniques is to shed light and provide insights into the predictions process of the machine learning models and explain how the model predictions have resulted. However, in practice, assessing the quality of the explanations provided by the various interpretability techniques is still questionable. In this paper, we present a comprehensive experimental evaluation of three recent and popular local model agnostic interpretability techniques, namely, LIME, SHAP and Anchors on different types of real-world healthcare data. Our experimental evaluation covers different aspects for its comparison including identity, stability, separability, similarity, execution time and bias detection. The results of our experiments show that LIME achieves the lowest performance for the identity metric and the highest performance for the separability metric across all datasets included in this study. On average, SHAP has the smallest average time to output explanation across all datasets included in this study. For detecting the bias, SHAP enables the participants to better detect the bias.
Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison

Deep neural networks, once considered to be inscrutable black-boxes, are now supplemented with techniques that can explain how these models decide. This raises the question whether the produced explanations are reliable. In this chapter, we consider two popular explanation techniques, one based on gradient computation and one based on a propagation mechanism. We evaluate them using three “axiomatic” properties: conservation, continuity, and implementation invariance. These properties are tested on the overall explanation, but also at intermediate layers, where our analysis brings further insights on how the explanation is being formed.
Industrial Anomaly Detection and Attack Classification Method Based on Convolutional Neural Network

The massive use of information technology has brought certain security risks to the industrial production process. In recent years, cyber-physical attacks against industrial control systems have occurred frequently. Anomaly detection technology is an essential technical means to ensure the safety of industrial control systems. Considering the shortcomings of traditional methods and to facilitate the timely analysis and location of anomalies, this study proposes a solution based on the deep learning method for industrial traffic anomaly detection and attack classification. We use a convolutional neural network deep learning representation model as the detection model. The original one-dimensional data are mapped using the feature mapping method to make them suitable for model processing. The deep learning method can automatically extract critical features and achieve accurate attack classification. We performed a model evaluation using real network attack data from a supervisory control and data acquisition (SCADA) system. The experimental results showed that the proposed method met the anomaly detection and attack classification needs of a SCADA system. The proposed method also promotes the application of deep learning methods in industrial anomaly detection.
Mapping Out Human-Centered Data Science

Social media platforms and social network sites generate a multitude of digital trace behavioral data, the scale of which often necessitates the use of computational data science methods. On the other hand, the socio-behavioral and often relational nature of the social media data requires the attention to context of user activity traditionally associated with qualitative analysis. Human-Centered Data Science (HCDS) attempts to bridge this gap by both harnessing the power of computational techniques and accounting for highly situated and nuanced nature of the social media activity. In this workshop we plan to consider the methods, pitfalls, and approaches of how to do HCDS effectively. Moreover, from collating and organizing these approaches we hope to progress to considering best (or at least common) practices in HCDS.
Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection

This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types.
Multidocument Arabic Text Summarization Based on Clustering and Word2Vec to Reduce Redundancy

Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statistically-based method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fixed-length vectors and, second, to obtain the semantic relationship between each vector based on the dimensions. Similarly, we use a k-means algorithm for two purposes: (1) Selecting the distinctive documents and tokenizing these documents to sentences, and (2) using another iteration of the k-means algorithm to select the key sentences based on the similarity metric to overcome the redundancy problem and generate the initial summary. Lastly, we use weighted principal component analysis (W-PCA) to map the sentences’ encoded weights based on a list of features. This selects the highest set of weights, which relates to important sentences for solving incoherency and readability problems. We adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with state-of-the-art methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE-1 and ROUGE-2 metrics showed promising results in comparison with existing methods.