Reviewerly/exHarmony huggingface dataset:
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples

Autonomous construction of deep neural network (DNNs) is desired for data streams because it potentially offers two advantages: proper model's capacity and quick reaction to drift and shift. While self-organizing mechanism of DNNs remains an open issue, this task is even more challenging to be developed for standard multi-layer DNNs than that using the different-depth structures, because addition of a new layer results in information loss of previously trained knowledge. A Neural Network with Dynamically Evolved Capacity (NADINE) is proposed in this paper. NADINE features a fully open structure where its network structure, depth and width, can be automatically evolved from scratch in the online manner and without the use of problem-specific thresholds. NADINE is structured under a standard MLP architecture and the catastrophic forgetting issue during the hidden layer addition phase is resolved using the proposal of soft-forgetting and adaptive memory methods. The advantage of NADINE, namely elastic structure and online learning trait, is numerically validated using nine data stream classification and regression problems where it demonstrates performance's improvement over prominent algorithms in all problems. In addition, it is capable of dealing with data stream regression and classification problems equally well.
Hardware-Backed Heist

Trusted Execution Environments (TEEs) such as ARM TrustZone are in widespread use in both mobile and embedded devices, and they are used to protect sensitive secrets while often sharing the same computational hardware as untrusted code. Although there has been limited research in the area, the threat of microarchitectural attacks against ARM TrustZone has not been thoroughly studied. This is not the case for other TEEs, such as Intel SGX, where the security promises of the TEE have been violated numerous times by the academic community, showing that it is possible to use side-channel attacks to gain detailed insight into the microarchitectural behavior of trusted code. In this work, we show that TrustZone is susceptible to similar attacks, and we demonstrate the ability to achieve cache attacks with high temporal precision, high spatial precision, and low noise. These tools make it easy to monitor the data flow and code flow of TrustZone code with great resolution, and we apply our techniques to investigate the security of a real-world application. We examine ECDSA signing in Qualcomm's implementation of Android's hardware-backed keystore and identify a series of vulnerabilities that leak sensitive cryptographic information through shared microarchitectural structures. By using the powerful attacks developed in this paper, we are able to successfully extract this sensitive information and fully recover a 256-bit private key from Qualcomm's version of the hardware-backed keystore.
Soma Design and Politics of the Body

Human reasoning often revolves around dichotomies: male-female, rational-irrational, emotion-thinking, body-mind, white-black, and so on. Through our design processes, we often repeat and reinforce these patterns. We argue that a stronger somatic engagement with the digital materials might open the design space in different manners, thereby bypassing some of these ready-made conceptualizations. Through a soma design stance we have attempted to address: dualism; feminist qualities such as pluralism and participation; addressing and counteracting privilege; and how to get closer to our bodies and our selves instead of letting data and interaction distance ourselves from our bodies, emotion, and experience.
Discriminative deep metric learning for asymmetric discrete hashing

Thanks to its storage and computation efficiency, hashing as a kind of nearest neighbor search method can facilitate massive data processing in recent vision and learning studies. Particularly, deep supervised hashing methods have significantly improved the retrieval performance compared with non-deep supervised hashing methods. However, most existing deep supervised hashing methods approximate the similarity between two images with the hamming distance between the outputs of the same hash function, i.e., the symmetric strategy. Consequently, it is typically time-consuming to train these symmetric hashing methods, and these methods can hardly take full advantage of the supervised information in the large-scale database. In this paper, we propose a novel discriminative deep metric learning approach for asymmetric discrete hashing (ADMH) approach for supervised hashing learning. ADMH integrates an asymmetric strategy with a deep metric learning method to learn the hash function for the query images and the discrete hash codes for database images directly. More specifically, we train a deep neural network to extract the features of the query images. Subsequently, we introduce a metric learning scheme for learning the discrete discriminative hash codes of the database images directly. Finally, the feature learning procedure for generating the hash codes of the query images and the discrete coding procedure for generating the hash codes of the database images are integrated into an end-to-end learning framework. Extensive experiments on various benchmark datasets show that the proposed asymmetric deep hashing method outperforms the existing hashing methods.
A Modular Adversarial Approach to Social Recommendation

This paper proposes a novel framework to incorporate social regularization for item recommendation. Social regularization grounded in ideas of homophily and influence appears to capture latent user preferences. However, there are two key challenges: first, the importance of a specific social link depends on the context and second, a fundamental result states that we cannot disentangle homophily and influence from observational data to determine the effect of social inference. Thus we view the attribution problem as inherently adversarial where we examine two competing hypothesis---social influence and latent interests---to explain each purchase decision. We make two contributions. First, we propose a modular, adversarial framework that decouples the architectural choices for the recommender and social representation models, for social regularization. Second, we overcome degenerate solutions through an intuitive contextual weighting strategy, that supports an expressive attribution, to ensure informative social associations play a larger role in regularizing the learned user interest space. Our results indicate significant gains (5-10% relative [email protected]) over state-of-the-art baselines across multiple publicly available datasets.
Framing collaborative processes of digital transformation in cultural organisations: from literary archives to augmented reality

At a time when it is particularly urgent to identify models of intersection across the digital and cultural sector to respond to an emergent funding and policy environment, this article contributes to a body of scholarly work around designing digital interventions for museums by identifying the role of cultural content in shaping design spaces for collaboration. The context of the article is a research project that brought together magical realist literature and the development of an Augmented Reality smartphone application realised through a public programme held at a museum of children’s literature. This process created an open-ended design space within the organisation embedded into the development of public engagement workshops around magical realism and place making. It investigates how the cultural content (from archival material) occupied a key role in shaping technological development and suggests strategies that could grant autonomy and sustainability to cultural organisations in engaging in digital transformation.
A Content Analysis System That Supports Sentiment Analysis for Subjectivity and Polarity Detection in Online Courses

Given the current and increasing relevance of research aimed towards the optimization of teaching and learning experiences in online Education, a plethora of studies regarding the application of different technologies to this purpose have been developed. Specifically, Natural Language Processing (NLP) has been used to detect potential sentiments and opinions in texts, enabling a broader scope for making inferences. At Universidad Autónoma of Madrid, Spain, we have designed and developed a tool for the application of NLP techniques to analyse the contents of online courses and the contributions of their learners (video transcriptions, readings, questions and answers of the evaluation activities and learner’s posts in forums, among others) to improve the teaching material and the teaching-learning processes of these courses. This tool is called edX-CAS (“Content Analyser System for edX MOOCs”). In this paper, we provide a detailed description of the tool, its functionalities and its NPL processes that support Sentiment Analysis for Subjectivity and Polarity detection. Moreover, we present a review of current research in the field of application of NLP in the improvement of teaching and learning experiences in MOOCs.
A machine learning-based approach to exploration targeting of porphyry Cu-Au deposits in the Dehsalm district, eastern Iran

The Dehsalm district in the central part of the Lut block, is an important polymetallic belt of eastern Iran. This study identifies the prospective areas for Cu-Au porphyry deposits within the Dehsalm district by integrating the outputs of the Restricted Boltzmann Machine (RBM) and Random Forest (RF) models. Based on geological, metallogenic and statistical analyses, seven predictor maps, namely proximity to faults and intrusive rocks, to aeromagnetics anomalies, to hydrothermal alterations (argillic and Fe-oxide) and two multivariate geochemical signatures of the stream sediments, were generated to train the models. To identify anomalies indicative of Cu-Au porphyry mineralization from the RBM results, the RF model was applied as a supervised machine-learning algorithm. The target areas delineated in the combined model occupy 4.2% of the study region and contain 82% of the known Cu-Au occurrences. The most prospective areas identified in the combined model extend along with structures that are spatially associated with known Cu-Au occurrences, and provide clear targets for future exploration.
Person Search by Text Attribute Query As Zero-Shot Learning

Existing person search methods predominantly assume the availability of at least one-shot imagery sample of the queried person. This assumption is limited in circumstances where only a brief textual (or verbal) description of the target person is available. In this work, we present a deep learning method for attribute text description based person search without any query imagery. Whilst conventional cross-modality matching methods, such as global visual-textual embedding based zero-shot learning and local individual attribute recognition, are functionally applicable, they are limited by several assumptions invalid to person search in deployment scale, data quality, and/or category name semantics. We overcome these issues by formulating an Attribute-Image Hierarchical Matching (AIHM) model. It is able to more reliably match text attribute descriptions with noisy surveillance person images by jointly learning global category-level and local attribute-level textual-visual embedding as well as matching. Extensive evaluations demonstrate the superiority of our AIHM model over a wide variety of state-of-the-art methods on three publicly available attribute labelled surveillance person search benchmarks: Market-1501, DukeMTMC, and PA100K.
Delaunay triangulation based text detection from multi-view images of natural scene

Text detection in the wild is still considered as a challenging issue to the researchers because of its several real time applications like forensic application, where CCTV camera captures images at different angles of the same scene. Unlike the existing methods that consider a single view captured orthogonally for text detection, this paper considers multi-view (view-1 and view-2 of the same spot) of the same scene captured at different angles or different height distances for text detection. For each pair of the same scene, the proposed method extracts features that describe characteristics of text components based on Delaunay Triangulation (DT), namely corner points, area and cavity of the DT. The features of corresponding DT in view-1 and view-2 are compared through cosine distance measure to estimate the similarity between two components of respective view-1 and view-2. If the pair satisfies the similarity condition, the components are considered as Candidate Text Components (CTC). In other words, these are the common components for view-1 and view-2 that satisfy the similarity condition. From each CTC of view-1 and view-2, the proposed method finds nearest neighbor components to restore the components of the same text line based on estimating degree of similarly between CTC and neighbor components using Chi-square and cosine distance measures. Furthermore, the proposed method uses a recognition step to detect correct texts by comparing recognition results of view-1 and view-2. The same recognition step is used for removing false positives to improve the performance of the proposed method. Experimental results on our own dataset, which contains pair of images of different situations, and the standard datasets, namely, ICDAR 2013, MSRATD-500, CTW1500, Total-text, ICDAR 2017 MLT and COCO-text, show that the proposed method outperforms the existing methods.
Multiobjective-Based Approach for Microblog Summarization

In recent years, social networking sites such as Twitter have become the primary sources for real-time information of ongoing events such as political rallies, natural disasters, and so on. At the time of occurrence of natural disasters, it has been seen that relevant information collected from tweets can help in different ways. Therefore, there is a need to develop an automated microblog/tweet summarization system to automatically select relevant tweets. In this article, we employ the concept of multiobjective optimization in microblog summarization to produce good quality summaries. Different statistical quality measures namely, length, tf-idf score of the tweets, antiredundancy, measuring different aspects of summary, are optimized simultaneously using the search capability of a multiobjective differential evolution technique. Different types of genetic operators including recently developed self-organizing map (a type of neural network) based operator, are explored in the proposed framework. To measure the similarity between tweets, word mover distance is utilized which is capable of capturing the semantic similarity between tweets. For evaluation, four benchmark data sets related to disaster events are used, and the results obtained are compared with various state-of-the-art techniques using ROUGE measures. It has been found that our algorithm improves by 62.37% and 5.65% in terms of ROUGE-2 and ROUGE-L scores, respectively, over the state-of-the-art techniques. Results are also validated using statistical significance t-test. At the end of the article, extension of the proposed approach to solve the multidocument summarization task is also illustrated.
Evolutionary multitasking fuzzy cognitive map learning

In real-world applications, there exist multiple fuzzy cognitive maps (FCMs) learning tasks with similar attributes that have to be optimized simultaneously, however, all existing algorithms were designed to learn single FCM without considering the valuable patterns that can share with each other. For the purpose of making use of similar structure patterns among different tasks, we introduce the evolutionary multitasking framework to learn different FCMs at one time by taking each FCM learning problem as a task. Most proposed evolutionary-based algorithms learn FCMs from time series by minimizing data error which evaluates the difference between generated response sequences and available response sequences, which did not take the sparsity of the weight matrix into consideration. To learn large-scale FCMs for each task, in this paper we adopt a decomposition strategy based multiobjective optimization algorithm considering both the measure error and sparsity of FCMs. Moreover, the memetic algorithm and LASSO initialization operator are incorporated into the multitasking framework to improve the performance and accelerate the convergence. Through the whole process, we find that multitasking optimization can not only learn various FCMs in a population but also improve the accuracy of similar tasks by taking the advantage of gene transfer for similar patterns. Extensive experiments on two-task FCM learning problems with varying number of nodes, densities and activation functions and the application for the problem of reconstructing gene regulatory networks have been conducted to illustrate that the proposal can learn large-scale FCMs with low errors in a fast convergence speed.
Code generation using model driven architecture: A systematic mapping study

Model Driven Architecture (MDA) has earned a prominent place in Software Engineering. However, while the application of MDA in software industry has grown, its impact in industry is not being as fast and broad as predicted. Moreover, the emerging use of agile methods have had an effect on the interest of industry and academics on developing software using metamodels. As a result, it is difficult to establish the true interest of practicioners about code generation using model driven architectures. This study investigates the scientific evidence of model driven architecture by conducting a systematic mapping of MDA literature in software engineering between 2008 and 2018. The search strategy resulted in 2.145 studies, of which 50 were identified as primary articles relevant to this study. We have verified that there is a considerable number of research articles that make use of MDA, published in a variety of journals, congresses and workshops, referred to very varied fields of application, and this number of publications seems to be increasing. The study also shows that, while there are few relevant publications that specifically deal with code generation using MDA, most of them show how this technology can be applied successfully to enhance software development.
Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Currently, with the satisfaction of people's material life, sports, like yoga and tai chi, have become essential activities in people's daily life. For most yoga amateurs, they could only learn yoga by self-study, like mechanically imitating from yoga video. They could not know whether they performed standardly without feedback and guidance. In this paper, we proposed a full-body posture modeling and quantitative evaluation method to recognize and evaluate yoga postures to provide guidance to the learner. Back propagation artificial neural network (BP-ANN) was adopted as the first classifier to divide yoga postures into different categories, and fuzzy C-means (FCM) was utilized as the second classifier to classify the postures in a category. The posture data on each body part was regarded as a multidimensional Gaussian variable to build a Bayesian network. The conditional probability of the Gaussian variable corresponding to each body part relative to the Gaussian variable corresponding to the connected body part was used as criterion to quantitatively evaluate the standard degree of body parts. The angular differences between nonstandard parts and the standard model could be calculated to provide guidance with an easily-accepted language, such as "lift up your left arm", "straighten your right forearm". To evaluate our method, a wearable device with 11 inertial measurement units (IMUs) fixed onto the body was designed to measure yoga posture data with quaternion format, and the posture database with a total of 211,643 data frames and 1831 posture instances was collected from 11 subjects. Both the posture recognition test and evaluation test were conducted. In the recognition test, 30% data was randomly picked from the database to train BP-ANN and FCM classifiers, and the recognition accuracy of the remaining 70% data was 95.39%, which is highly competitive with previous posture recognition approaches. In the evaluation test, 30% data were picked randomly from subject three, subject four, and subject six, to train the Bayesian network. The probabilities of nonstandard parts were almost all smaller than 0.3, while the probabilities of standard parts were almost all greater than 0.5, and thus the nonstandard parts of body posture could be effectively separated and picked for guidance. We also tested separately the trainers' yoga posture performance in the condition of without and with guidance provided by our proposed method. The results showed that with guidance, the joint angle errors significantly decreased.
Facial Expression Recognition Based on Random Forest and Convolutional Neural Network

As an important part of emotion research, facial expression recognition is a necessary requirement in human–machine interface. Generally, a face expression recognition system includes face detection, feature extraction, and feature classification. Although great success has been made by the traditional machine learning methods, most of them have complex computational problems and lack the ability to extract comprehensive and abstract features. Deep learning-based methods can realize a higher recognition rate for facial expressions, but a large number of training samples and tuning parameters are needed, and the hardware requirement is very high. For the above problems, this paper proposes a method combining features that extracted by the convolutional neural network (CNN) with the C4.5 classifier to recognize facial expressions, which not only can address the incompleteness of handcrafted features but also can avoid the high hardware configuration in the deep learning model. Considering some problems of overfitting and weak generalization ability of the single classifier, random forest is applied in this paper. Meanwhile, this paper makes some improvements for C4.5 classifier and the traditional random forest in the process of experiments. A large number of experiments have proved the effectiveness and feasibility of the proposed method.