Reviewerly/exHarmony huggingface dataset:
Experimental research on optical image encryption system based on joint Fresnel transform correlator

Optical image encryption system based on joint Fresnel transform correlator is experimentally researched. Factors affecting decrypted image quality are analyzed, and measures of improving decrypted image quality are taken. Firstly, after a few denoising operations are performed, noise in decrypted image is effectively reduced. Secondly, effects of experimental device parameters on decrypted image quality are also analyzed. After random phase mask in object window and key mask are both optimized by an iteration algorithm, space bandwidth of joint image can match with that of experimental system. Through above-mentioned methods, quality of decrypted image is remarkably improved. Correlation coefficient (0.8346) between experimental decrypted image and original image is close to that (0.8686) between simulational decrypted image and original image. Experimental error-bit rate in decrypted image is also analyzed.
Near infrared nighttime road pedestrians recognition based on convolutional neural network

Pedestrian recognition is the core technology of pedestrian detection in pedestrian protection systems. This paper compares and analyzes, visible and infrared images obtained via visible-spectrum, near-infrared, short-wave infrared, and long-wave infrared cameras. The results show that near-infrared camera was the best for nighttime pedestrian detection when device cost and pedestrian imaging quality were considered. This paper reports on the first time use of a self-learning softmax with a 9-layer Convolutional Neural Network (CNN) model to identify near-infrared nighttime pedestrians. 267,000 samples obtained from the near-infrared images were employed to optimize the CNN recognition model. Collected near-infrared nighttime samples had 3 categories (background, pedestrian, and cyclist or motorcyclist) and will be made publicly available for researchers use. Testing results indicated that the optimized CNN model using self-learning softmax had a competitive accuracy and potential in real-time pedestrian recognition.
Boosting content based image retrieval performance through integration of parametric &amp; nonparametric approaches

The collection of digital images is growing at ever-increasing rate which rises the interest of mining the embedded information. The appropriate representation of an image is inconceivable by a single feature. Thus, the research addresses that point for content based image retrieval (CBIR) by fusing parametric color and shape features with nonparametric texture feature. The color moments, and moment invariants which are parametric methods and applied to describe color distribution and shapes of an image. The nonparametric ranklet transformation is performed to narrate the texture features. Experimentally these parametric and nonparametric features are integrated to propose a robust and effective algorithm. The proposed work is compared with seven existing techniques by determining statistical metrics across five image databases. Finally, a hypothesis test is carried out to establish the significance of the proposed work which, infers evaluated precision and recall values are true and accepted for the all image database.
Fibre direction and stacking sequence measurement in carbon fibre composites using Radon transforms of ultrasonic data

Stacking sequence and, more generally, fibre orientation, are critical parameters in fibrous composite materials since they govern mechanical performance. This paper presents a method, based on the ultrasonic pulse-echo non-destructive technique, that can map the stacking sequence in unidirectional carbon-fibre composites. In-plane fibre orientation is measured using a Radon-transform method applied to local 2D images extracted from the 3D dataset formed from a 2D scan of pulse-echo responses. The ability to align these local 2D images to the plies in the region being assessed makes this technique suitable even in laminates where out-of-plane ply wrinkling is present. The Radon-transform method is shown to provide interpretable stacking-sequence maps, allowing ply lay-up sequence and both in-plane waviness and out-of-plane wrinkling to be visualised and quantified.
Unsupervised word-level affect analysis and propagation in a lexical knowledge graph

Lexical sentiment analysis (LSA) is of central importance in extracting and analyzing user moods and views on the Web. Most existing LSA approaches have utilized supervised learning techniques applied on corpus-based statistics, requiring extensive training data, training time, and large statistical corpora which are not always available. Other studies have utilized unsupervised and lexicon-based approaches to match target words in a lexical knowledge base (KB) with seed words in a sentiment lexicon, usually suffering from the limited coverage or inconsistent connectivity of affective concepts. In this paper, we introduce LISA, an unsupervised word-level knowledge graph-based LSA framework. It uses different variants of shortest path graph navigation techniques to compute and propagate affective scores in a lexical-affective graph (LAG), created by connecting a typical lexical KB like WordNet, with a reliable affect KB like WordNet-Affect Hierarchy (where any other lexical or affective KB can be utilized). LISA was designed in two consecutive iterations, producing two main modules: i) LISA 1.0 for affect navigation, and ii) LISA 2.0 for affect propagation and lookup. LISA 1.0 suffered from the semantic connectivity problem shared by some existing lexicon-based methods, and required polynomial execution time. This led to the development of LISA 2.0, which i) processes affective relationships separately from lexical/semantic connections (solving the semantic connectivity problem of LISA 1.0), and ii) produces a sentiment lexicon which can be searched in logarithmic time (handling LISA 1.0’s efficiency problem). Experimental results on the ANEW dataset show that our approach, namely LISA 2.0, while completely unsupervised, is on a par with existing (semi)supervised solutions, highlighting its quality and potential.
Bibliographic Network Representation Based Personalized Citation Recommendation

With the increasing number of scientific papers, researchers find it more and more difficult to obtain relevant and appropriate papers to cite. Citation recommendation aims to overcome this problem by providing a reference paper list for a given manuscript. In this paper, we propose a bibliographic network representation (BNR) model, which simultaneously incorporates bibliographic network structure and content of different kinds of objects (authors, papers, and venues) for efficient recommendation. The proposed model also makes personalized citation recommendation possible, which is a new issue that a few papers addressed in the past. When conducting experiments on the ACL Anthology Network and DBLP datasets, the results demonstrate that the proposed BNR-based citation recommendation approach is able to achieve considerable improvement over other network representation-based citation recommendation approaches. The performance of the personalized recommendation approach is also competitive with the non-personalized recommendation approach.
Small Object Sensitive Segmentation of Urban Street Scene With Spatial Adjacency Between Object Classes

Recent advancements in deep learning have shown exciting promise in the urban street scene segmentation. However, many objects, such as poles and sign symbols, are relatively small and they usually cannot be accurately segmented since the larger objects usually contribute more to the segmentation loss. In this paper, we propose a new boundary-based metric that measures the level of spatial adjacency between each pair of object classes and find that this metric is robust against object size induced biases. We develop a new method to enforce this metric into the segmentation loss. We propose a network, which starts with a segmentation network, followed by a new encoder to compute the proposed boundary-based metric, and then trains this network in an end-to-end fashion. In deployment, we only use the trained segmentation network, without the encoder, to segment new unseen images. Experimentally, we evaluate the proposed method using CamVid and CityScapes datasets and achieve a favorable overall performance improvement and a substantial improvement in segmenting small objects.
A novel perspective on multiclass classification: Regular simplex support vector machine

Although support vector machine (SVM) and its variants have been combined successfully with partitioning strategies for multiclass classification, a series of individual classifiers have to be considered separately, which significantly limits the sparseness of the solution. In this work, from an absolutely novel perspective, we proposed a regular simplex support vector machine (RSSVM) for the K-class classification. RSSVM maps the K classes to K vertices of a (K−1)-dimensional regular simplex so that the K-class classification becomes a (K−1)-output learning task. We measured the training loss by comparing the square distances between the output point of each sample and the vertices. As a result, we were able to implement RSSVM by constructing a single primal problem with linear inequality constraints, endowing it with excellent sparseness. This method, however, made the computational complexity of RSSVM higher than commonly used K-class classification methods. To overcome this drawback, we added an appropriate regularized term to the primal problem, making the dual problem a quadratic programming problem, and we developed an exclusive sequential minimization optimization–type solver to accelerate our ability to solve it. Numerical experiments indicated that our proposed RSSVM not only demonstrated excellent sparseness but also superior overall accuracy, efficiency, and scalability.
Adaptive Discriminative Deep Correlation Filter for Visual Object Tracking

Correlation filter trackers building on deep convolution neural networks (CNNs) contribute efficient visual object trackers but remain challenged with severe target appearance variations. The reason for this is that CNNs trained for image classification tasks are less discriminative to the dynamic variations of targets and backgrounds. In this paper, we propose an adaptive discriminative deep correlation filter (adaDDCF), which, by incorporating discriminative feature fine-tuning with adaptive appearance modeling, pursues stable object tracking in complex backgrounds. In adaDDCF, a convolutional Fisher discriminative analysis (FDA) layer is implemented for positive and negative instance mining and scene-specific feature learning. A correlation layer is then embedded to learn the correlation response of consecutive frames for target appearance modeling. With an online learning procedure using forward-backward propagation, the FDA layer and the correlation layer are effectively coupled, leading to effective and discriminative fine-tuning for the proposed tracker, which consequently alleviates the target drifting problem. Extensive experiments on the challenging benchmarks OTB2013, OTB2015, and OTB50 demonstrate that the proposed adaDDCF tracker outperforms many state-of-the-art trackers.
Review-Driven Answer Generation for Product-Related Questions in E-Commerce

The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven framework for answer generation for product-related questions in E-commerce, named RAGE. We develope RAGE on the basis of the multi-layer convolutional architecture to facilitate speed-up of answer generation with the parallel computation. For each question, RAGE first extracts the relevant review snippets from the reviews of the corresponding product. Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation. The experiments on two real-world E-Commerce datasets show that the proposed RAGE significantly outperforms the existing alternatives in producing more accurate and informative answers in natural language. Moreover, RAGE takes much less time for both model training and answer generation than the existing RNN based generation models.
Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance

The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact. In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users' environment and support them in their decision making and wayfinding. A novel application of recurrent neural networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them. We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified. From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a usability study leading to the ultimate deployment of the system at a university.
AELA-DLSTMs: Attention-Enabled and Location-Aware Double LSTMs for aspect-level sentiment classification

Aspect-level sentiment classification, as a fine-grained task in sentiment classification, aiming to extract sentiment polarity from opinions towards a specific aspect word, has been made tremendous improvements in recent years. There are three key factors for aspect-level sentiment classification: contextual semantic information towards aspect words, correlations between aspect words and their context words, and location information of context words with regard to aspect words. In this paper, two models named AE-DLSTMs (Attention-Enabled Double LSTMs) and AELA-DLSTMs (Attention-Enabled and Location-Aware Double LSTMs) are proposed for aspect-level sentiment classification. AE-DLSTMs takes full advantage of the DLSTMs (Double LSTMs) which can capture the contextual semantic information in both forward and backward directions towards aspect words. Meanwhile, a novel attention weights generating method that combines aspect words with their contextual semantic information is designed so that those weights can make better use of the correlations between aspect words and their context words. Besides, we observe that context words with different distances or different directions towards aspect words have different contributions in sentiment polarity. Based on AE-DLSTMs, the location information of context words by assigning different weights is incorporated in AELA-DLSTMs to improve the accuracy. Experiments are conducted on two English datasets and one Chinese dataset. The experimental results have confirmed that our models can make remarkable improvements and outperform all the baseline models in all datasets, improving the accuracy of 1.67 percent to 4.77 percent in different datasets compared with baseline models.1
Face analysis through semantic face segmentation

Automatic face analysis, including head pose estimation, gender recognition, and expression classification, strongly benefits from an accurate segmentation of the human face. In this paper we present a multi-feature framework which first segments a face image into six parts, and then performs classification tasks on head pose, gender, and expression. Segmentation is achieved by training a discriminative model on a manually labeled face database, namely FASSEG, which we extend from previous versions, and which we publicly share. Three kinds of features accounting for location, shape, and color are extracted from uniformly sampled square image patches. Facial images are then pixel-wise segmented into six semantic classes – hair, skin, nose, eyes, mouth, and background, – using a Random Forest classifier (RF). Then a linear Support Vector Machine (SVM) is trained for each face analysis task i.e., head pose estimation, gender recognition, and expression classification by using the probability maps obtained during the segmentation step. Performance of the proposed framework is evaluated on four face databases, namely Pointing’04, FEI, FERET, and MPI, with results which outperform the current state-of-the-art.
Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Cancer diagnosis through a tandem of classifiers for digitized histopathological slides

The current research study is concerned with the automated differentiation between histopathological slides from colon tissues with respect to four classes (healthy tissue and cancerous of grades 1, 2 or 3) through an optimized ensemble of predictors. Six distinct classifiers with prediction accuracies ranging from 87% to 95% are considered for the task. The proposed method of combining them takes into account the probabilities of the individual classifiers for each sample to be assigned to any of the four classes, optimizes weights for each technique by differential evolution and attains an accuracy that is significantly better than the individual results. Moreover, a degree of confidence is defined that would allow the pathologists to separate the data into two distinct sets, one that is correctly classified with a high level of confidence and the rest that would need their further attention. The tandem is also validated on other benchmark data sets. The proposed methodology proves to be efficient in improving the classification accuracy of each algorithm taken separately and performs reasonably well on other data sets, even with default weights. In addition, by establishing a degree of confidence the method becomes more viable for use by actual practitioners.
A Survey of High Utility Sequential Pattern Mining

The problem of mining high utility sequences aims at discovering subsequences having a high utility (importance) in a quantitative sequential database. This problem is a natural generalization of several other pattern mining problems such as discovering frequent itemsets in transaction databases, frequent sequences in sequential databases, and high utility itemsets in quantitative transaction databases. To extract high utility sequences from a quantitative sequential database, the sequential ordering between items and their utility (in terms of criteria such as purchase quantities and unit profits) are considered. High utility sequence mining has been applied in numerous applications. It is much more challenging than the aforementioned problems due to the combinatorial explosion of the search space when considering sequences, and because the utility measure of sequences does not satisfy the downward-closure property used in pattern mining to reduce the search space. This chapter introduces the problem of high utility sequence mining, the state-of-art algorithms, applications, present related problems and research opportunities. A key contribution of the chapter is to also provide a theoretical framework for comparing upper-bounds used by high utility sequence mining algorithms. In particular, an interesting result is that an upper-bound used by the popular USpan algorithm is not an upper-bound. The consequence is that USpan is an incomplete algorithm, and potentially other algorithms extending USpan.
App Store Effects on Software Engineering Practices

In this paper, we study the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. Through developer interviews and questionnaires, we uncover findings that highlight and quantify the effects of three high-level app store themes: bridging the gap between developers and users, increasing market transparency and affecting mobile release management. Our findings have implications for testing, requirements engineering and mining software repositories research fields. These findings can help guide future research in supporting mobile app developers through a deeper understanding of the app store-developer interaction.
Massive Open Online Courses in the Initial Training of Social Science Teachers: Experiences, Methodological Conceptions, and Technological Use for Sustainable Development

This article analyzes, on the one hand, the perception of future teachers (n = 162) on the degree of acquisition of the Digital Teaching Competition (CCD) in the teachers’ formative contexts. On the other hand, this article analyzes future teachers’ social representations about the potential contribution of educational technologies, in particular the massive open online courses (MOOCs), to comply with the Sustainable Development Goals (SDGs). A questionnaire validated by three Spanish universities is applied, and a quantitative analysis of the data is used. The results obtained inform the educational need to transfer the progress and generalization of information and communication technologies (ICT) for education for sustainable development in teacher training curricula.
Adversarial Examples Detection in Features Distance Spaces

Maliciously manipulated inputs for attacking machine learning methods – in particular deep neural networks – are emerging as a relevant issue for the security of recent artificial intelligence technologies, especially in computer vision. In this paper, we focus on attacks targeting image classifiers implemented with deep neural networks, and we propose a method for detecting adversarial images which focuses on the trajectory of internal representations (i.e. hidden layers neurons activation, also known as deep features) from the very first, up to the last. We argue that the representations of adversarial inputs follow a different evolution with respect to genuine inputs, and we define a distance-based embedding of features to efficiently encode this information. We train an LSTM network that analyzes the sequence of deep features embedded in a distance space to detect adversarial examples. The results of our preliminary experiments are encouraging: our detection scheme is able to detect adversarial inputs targeted to the ResNet-50 classifier pre-trained on the ILSVRC'12 dataset and generated by a variety of crafting algorithms.
“Freedom of speech requires actions”: Exploring the discourse of politicians convicted of hate‐speech against Muslims

Abstract This study explores how politicians convicted of hate‐speech against Muslims account for their actions in statements on their Facebook‐pages as well as in reported interviews. Taking a critical discursive psychological perspective, the study examines the strategies through which the politicians discursively claim and resist various subject positions, thus managing to construct their hate‐speech as everything from trivial mishaps to acts of virtue. The study examines the multifaceted dynamics of these constructions, and shows how elements from the Five Step Social Identity Model of the Development of Collective Hate are flexibly deployed in the discourse to serve distinct social and political purposes. By allowing the Social Identity and (critical) discursive approaches to challenge and develop each other, the study advances social psychological research on political communication and persuasion, and contributes to the debate on the boundaries between hate‐speech and freedom of speech.