Reviewerly/exHarmony huggingface dataset:
UI Design Pattern-driven Rapid Prototyping for Agile Development of Mobile Applications

In agile development, lean UX designers perform rapid prototyping and quick evaluation of prototypes to ensure fast releases. To understand designers' workflow during rapid prototyping, we interviewed 15 lean UX designers. We identified the following pain points in the workflow: 1) Compromise on quality of UI design due to time constraint 2) UI design knowledge being scattered among numerous sources such as websites and books 3) Inability of developers to reproduce the same quality of UI design due to lack of UI design knowledge. To address these issues, we propose a UI design pattern-driven approach for rapid prototyping. To realize this approach, we introduce Kiwi, a library for UI design patterns and guidelines that aims to consolidate UI design knowledge for mobile applications. Each UI design pattern consists of a problem statement, context, rationale, and a proposed solution. Additionally, Kiwi provides downloadable and customizable GUI examples, layout blueprints and front-end code for each pattern. Usability evaluation (SUS) of Kiwi with 21 lean UX designers depict good usability and high learnability.
BinarySwipes

Smartwatches and other wearables generally have small screens, thereby complicating touch-based interaction. Selection from a long list, e.g. to locate a contact or a music track, is particularly cumbersome due to the limited interaction space. We present BinarySwipes, an interaction technique based on binary search which is designed to speed up list search tasks on space-constrained screens. We evaluate a prototypical implementation of BinarySwipes on a smartwatch. Results from our evaluation with 21 participants show improved performance over a plain linear search on lists with 100, 200 and 500 entries, but also increased mental load on the users.
Comparative Analysis of Alpha Power Spectral Density in Real and Virtual Environments

The virtual reality (VR) technology has been able to emulate physical places with realistic details, useful for multiple areas such as rehabilitation, education, entertainment, etc. This is possible because virtual environments can emulate the sense of “presence” that the user undergoes in the real world. The present study aimed to compare the typical videogame “Power Solitaire” in both scenarios, real and virtual one, by monitoring presence by estimating the alpha power spectral density on 14 electroencephalographic channels. As well, it was used three questionnaires (“Immersive Tendencies”, “Presence”, and “Slater Usoh-Steed”) to check the level of Presence. For this study, seven volunteers were recruited, and an Occulus Rift Headset and a headset “Emotiv epoc” were used. The study showed that “Power Solitaire” videogame rendered in VR mode induced the similar sense of presence experienced in the real world in accordance with (1) a good movement and auditory response of VR, (2) high quality of user-system interaction, and (3) similar level of alpha neural synchrony over the frontal lobe, which reflects sensory decoding, level of attention, and readiness to perform a task.
Anomaly Detection for Visual Quality Control of 3D-Printed Products

We present a method for detection of surface defects in images of 3D-printed products that enables automated visual quality control. The data characterising this problem is typically high-dimensional (high-resolution images), imbalanced (defects are relatively rare), and has few labelled examples. We approach these challenges by formulating the problem as probabilistic anomaly detection, where we use Variational Autoencoders (VAE) to estimate the probability density of non-faulty products. We train the VAE in an unsupervised manner on images of non-faulty products only. A successful model will then assign high likelihood to unseen images of non-faulty products, and lower likelihood to images displaying defects. We test this method on anomaly detection scenarios using the MNIST dataset, as well as on images of 3D-printed products. The demonstrated performance is related to the capability of the model to closely estimate the density distribution of the non-faulty (expected) data. For both datasets we present empirical results that the likelihood estimated with a convolutional VAE can separate the normal and anomalous data. Moreover we show how the reconstruction capabilities of VAEs are highly informative for human observers towards localising potential anomalies, which can aid the quality control process.
Deep Convolutional Neural Networks for Recognition of Historical Handwritten Kannada Characters

Handwritten character recognition is an important step toward automatic transliteration of the valuable historical documents archived in digital libraries. This is a challenging task as it requires a labor-intensive handcrafting of features from a huge set of character classes. Moreover, the intra-class variability of handwritten characters is high causing a major bottleneck for recognition accuracy. A deep convolutional neural networks (DCNN) approach for character recognition of handwritten historical Kannada manuscripts is presented in this paper. DCNN is a model that unifies feature extraction and classification. It inherently learns the most discriminative features from the given data, thus subverting the usage of handcrafted features. In this work, the features extracted from the characters using DCNN are fed to SGDM and SVM classification algorithms for recognition. This approach is experimented on the digitized estampages of historical Kannada stone inscriptions belonging to eleventh century and promising results are observed.
Attention-based Approach for Efficient Moving Vehicle Classification

In recent years, the convolutional neural network (CNN) has shown great advantages in object classification. In the context of smart transportation, an essential task is to correctly detect vehicles from videos and classify them into different types (e.g., car, truck, bus, and etc.). The classified vehicles can be further analyzed for surveillance, monitoring, and counting purposes. However, at least, there are two main challenges remain; excluding the un-interesting region (e.g., swaying motion, noise, etc.) and designing an efficient and accurate system. Therefore, we introduce a novel attention-based approach in order to clearly distinguish the interesting region (moving vehicle) with the un-interesting region (the rest of the region). Finally, we feed the deep CNN with the corresponding interesting region to boost the classification performance considerably. We evaluate our proposed idea using several challenging outdoor sequences from the CDNET 2014 and our own dataset. Experimental results show that it costs around ~85 fps to classify moving vehicles and keep a highly accurate rate. In addition, compared with other state-of-the-art object detection approaches, our method obtains a competitive f-measure score.
Character-Aware Convolutional Recurrent Networks with Self-Attention for Emotion Detection on Twitter

Despite myriad efforts in the literature designing neural representation system for emotion detection, very few works consider constructing effective model for apperceiving various emotion intensity on social media because of the informal expression and lack of context. In this paper, we proposed a character-aware convolutional recurrent networks with self-attention for emotion detection on user-generated content. The proposed model contains three parts: the character-level convolutional layer is designed to learn word representation based on character n-grams for capturing subword information and independent on pre-trained word embedding. The recurrent neural networks learn the sequential context information used both forward and backward recurrent neural network. And the self-attention module is used to extract different emotion aspects of the sentence into multiple vector representations. The attention module performs on top of recurrent networks which enables attention to be used in special domain or task when there are no extra inputs. We evaluate the proposed model on two public emotion datasets including both emotion intensity detection and emotion classification. We compare our model with the state-of-the-art methods on these datasets and the experimental results demonstrate that the proposed model outperforms several baselines on most emotion types detection and indicates the effectiveness of the designed model. In addition, the training of the proposed model for these tasks relies exclusively on initialized character vector which can be used for morphologically rich languages with long-tailed frequency distributions or domains with dynamic vocabuaries.
Elicitation of Candidate Subspaces in High-Dimensional Data

Anomaly detection is an important research area in data mining and has been studied intensively in recent years. The increasing number of features, non-informative noises and other irrelevant features, makes it challenging to detect anomalies in high-dimensional data. When analyzing high-dimensional data, anomalies are difficult to identify due to the sparsity caused by the curse of dimensionality. One most commonly used algorithm for reducing dimensionality is Principal Component Analysis (PCA), however, it is known to be sensitive in identifying anomalies. Furthermore, anomalies are rare and only can be found in low-dimensional subspaces. To effectively find anomalies in the high-dimensional space, we propose a technique that explores locally relevant and low-dimensional subspaces where anomalies may be possibly hidden due to the sparsity caused by the curse of dimensionality, and we call these subspaces as candidate subspaces for anomalies. In particular, the proposed technique integrates a Pearson Correlation Coefficient (PCC) and PCA, thereby combining the highest variances. Our experimental results showed that the technique gives good results when anomalies are synthetically introduced.
Feature-Dependent Graph Convolutional Autoencoders with Adversarial Training Methods

Graphs are ubiquitous for describing and modeling complicated data structures, and graph embedding is an effective solution to learn a mapping from a graph to a low-dimensional vector space while preserving relevant graph characteristics. Most existing graph embedding approaches either embed the topological information and node features separately or learn one regularized embedding with both sources of information, however, they mostly overlook the interdependency between structural characteristics and node features when processing the graph data into the models. Moreover, existing methods only reconstruct the structural characteristics, which are unable to fully leverage the interaction between the topology and the features associated with its nodes during the encoding-decoding procedure. To address the problem, we propose a framework using autoencoder for graph embedding (GED) and its variational version (VEGD). The contribution of our work is two-fold: 1) the proposed frameworks exploit a feature-dependent graph matrix (FGM) to naturally merge the structural characteristics and node features according to their interdependency; and 2) the Graph Convolutional Network (GCN) decoder of the proposed framework reconstructs both structural characteristics and node features, which naturally possesses the interaction between these two sources of information while learning the embedding. We conducted the experiments on three real-world graph datasets such as Cora, Citeseer and PubMed to evaluate our framework and algorithms, and the results outperform baseline methods on both link prediction and graph clustering tasks.
Comparing different techniques of satellite imagery classification to mineral mapping pegmatite of Muiane and Naipa: Mozambique)

Several scientific studies with different concept on the mapping of pegmatites have been done in Muiane and Naipa (Mozambique) region. However, none of the studies compare different satellite data and different remote sensing classification algorithms. This study aims to compare the land cover/use classification maps and their accuracies considered sentinel-2, aster, and Landsat OLI imagery. The algorithms employed to evaluate the pegmatites location at Naipa and muiane in alto ligonha pegmatite district were minimum distance (MinD), spectral angle mapper (SAM), and maximum likelihood (ML). The identified features of landscape characteristics selected includes 8 class (kaolinite; montmorillonite; water; built up; bare soil; grasslands; shrubs; isolated bush). The results showed that SAM and MinD algorithms are appropriate for mineralogical mapping validated with ground truth data and geological maps. A kappa index of 0.85 and an overall accuracy (OA) of 80% was obtained for SAM algorithm, and a kappa of 0,80 and OA of 90% for the MinD algorithm. The classification of the images using SAM and mind showed better results for the clays (kaolinite, montmorillonite) visible in both classifications, has also been tested unsupervised classifications or criteria determined by the geologist using an input training dataset in the case of supervised classifications.
An Empirical Evaluation of dot NET-Based Tools for OWL/RDF Ontologies Processing

Many studies have been carried out on the comparison and performance evaluation of ontology development tools. However, most of these studies evaluated open source ontology tools while only few studies have been presented in dot NET environment. This paper mainly focuses on evaluation and comparison analysis of two popular dot NET-based tools, namely, dotNetRDF and SemWeb. The criterions for comparison are based on general description, architecture, knowledge representation of the tools etc. OWL/RDF ontology files with different sizes are loaded in both tools and SPARQL query are executed against those ontology files. Therefore, various evaluation metrics including (1) Loading Time (LT), (2) Query Execution Time (QET), (3) Query Response Time (QRT) and (4) Storage Capacity in both tools are computed and measured. The results and discussions from experiment are presented based on the performance of each tool. The experimental analysis shows that (1) both tools can parse and process ontologies only at RDF level (2) both tools provide in-memory storage capacity for small and medium ontologies (3) dotNetRDF runs faster on small ontologies while SemWeb performs better on medium ontologies and (4) SemWeb provides big memory storage compared to dotNetRDF.
Approaches for Collaborative Security Defences in Multi Network Environments

Resolving distributed attacks benefits from collaboration between networks. We present three approaches for the same multi-domain defensive action that can be applied in such an alliance: 1) Counteract Everywhere, 2) Minimize Countermeasures, and 3) Minimize Propagation. First, we provide a formula to compute efficiency of a defense; then we use this formula to compute the efficiency of the approaches under various circumstances. Finally, we discuss how task execution order and timing influence defense efficiency. Our results show that the Minimize Propagation approach is the most efficient method when defending against the chosen attack.
Finding the key length of a Vigenère cipher: How to improve the twist algorithm

A Vigenère cipher applies a single short key repeatedly to encrypt a plaintext. If a cryptanalyst correctly finds out the key length, the ciphertext can be divided into multiple instances of shift cipher and be broken by frequency analysis. To determine the key length, the twist algorithm, an alternate method to the standard Kasiski and Friedman tests, was recently proposed. In this article, we propose the twist+ algorithm, an improved twist algorithm, which can estimate the key length more accurately than the original twist algorithm.
Fake Reviews Detection Based on Text Feature and Behavior Feature

Considering the limitations of comment text in the study of fake reviews recognition, this paper proposes to build a classification model by integrating the features of comment text and user behavior. However, the comment data obtained in reality are mostly unlabeled. Therefore, this paper proposes an MPINPUL (Mixing Population and Individual Nature PU Learning) model based on multiple features to build a fake reviews classification model. In this paper, the MPINPUL model is divided into four steps. Firstly, the constrained k-means algorithm is proposed to calculate a negative example of trust. The advantage of constrained k-means is that it can expand the set of positive examples while identifying the trusted negative examples. Then, LDA and k-means are used to calculate multiple representative samples for positive and negative examples respectively. Then we use the idea of population and individuality to determine the category label of the sample. Finally, the classifier is established. Experimental results on real data sets show that the recognition rate of the MPINPUL model proposed in this paper is higher than that of other single features under fusion feature conditions.
Neural Morphological Segmentation Model for Mongolian

Morphological segmentation is useful for processing Mongolian. In this paper, we manually build a morphological segmentation data set for Mongolian. We then present a character-based encoder-decoder model with attention mechanism to perform the morphological segmentation task. We further investigate the influence of analogy features extracted from scratch and improve the performance of our model using multi languages setting. Experimental results show that our encoder-decoder model with attention mechanism provides a strong baseline for Mongolian morphological segmentation. The analogy features provide useful information to the model and improve the performance of the system. The use of multi languages data set shows the capability of our model to acquire knowledge through different languages and delivers the best result.
Physical Activity Recognition Using Multi-Sensor Fusion and Extreme Learning Machines

Sensor based Physical Activity (PA) recognition is an imperative research topic due to its wide applications in many areas such as mobile healthcare, elderly care and personalized recommendation. However, PA recognition based on single sensor suffers from issues such as limited spatial coverage, imprecision and uncertainty. In contrast, fusion of heterogeneous sensor sources brings improved resolution, precision and robustness. In this paper, a multi-sensor fusion method based on variants of Extreme Learning Machine (ELM) is presented to improve the recognition performance in terms of speed and accuracy. The Kernel ELM, Weighted ELM, Regularized ELM are tasked directly to handle the multi-activity classification problem using the real-world test data with 46 test subjects. Their performance has been evaluated against previously published methods based on Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) using a two-step subject-wise cross validation scheme. The multi-sensor fusion based Kernel ELM has shown favorable performance with 88.4% average testing accuracy, outperforming SVM by 1.1%. And for real-time PA recognition, Kernel ELM is 30% faster than SVM. The ELMs trained with the two-step, subject-wise cross validation approach, combined with feature level data fusion also led to a better cross-person generalization than SVM and KNN.
A Framework for Integrating Domain Knowledge in Logistic Regression with Application to Hospital Readmission Prediction

It is commonly understood that machine learning algorithms discover and extract knowledge based on data at hand. However, a huge amount of knowledge is available which is in machine-readable format and ready for inclusion in machine learning algorithms and models. In this paper, we propose a framework that integrates domain knowledge in form of ontologies/hierarchies into logistic regression using stacked generalization. Namely, relations from ontology/hierarchy are used in stacking manner in order to obtain higher, more abstract concepts. Obtained concepts are further used for prediction. The problem we solved is unplanned 30-days hospital readmission, which is considered as one of the major problems in healthcare. Proposed framework yields better results compared to Ridge, Lasso, and Tree Lasso Logistic Regression. Results suggest that the proposed framework improves AUC by up to 9.5% on pediatric datasets and up to 4% on morbidly obese patients’ datasets and also improves AUPRC by up to 5.7% on pediatric datasets and up to 2.6% on morbidly obese patients’ datasets on average. This indicates that the inclusion of domain knowledge improves the predictive performance of Logistic Regression.
Deep Learning for Visual Indonesian Place Classification with Convolutional Neural Networks

Places classification is one of the points of discussion in the computer vision and robotics community. Some renowned techniques such as local-invariant feature extractors (e.g. Scale-invariant feature transform SIFT, Speeded Up Robust Features SURF), as well as Visual BoW approach were used in place classification problems. Nowadays, deep learning methods such as Convolutional Neural Networks (CNNs) have the advantages towards computer vision problems including place classification problem. Albeit, there are several renowned datasets existed to help the community to learn the models, there is no publicly exists in places dataset for specifically places in Indonesia. This paper presents methodology to collect data of visual places in Indonesia, learn deep features from the data, and classify visual places in Indonesia. We aims to contribute a large dataset as well as deep learning models of places in Indonesian. There are more than 16K images collected and augmented to build the places (specifically places in Indonesia) dataset. The highest accuracy score achieved by the models is 92%.
Detecting long-range cause-effect relationships in game provenance graphs with graph-based representation learning

Game Analytics comprises a set of techniques to analyze both the game quality and player behavior. To succeed in Game Analytics, it is essential to identify what is happening in a game (an effect) and track its causes. Thus, game provenance graph tools have been proposed to capture cause-and-effect relationships occurring in a gameplay session to assist the game design process. However, since game provenance data capture is guided by a set of strict predefined rules established by the game developers, the detection of long-range cause-and-effect relationships may demand huge coding efforts. In this paper, we contribute with a framework named PingUMiL that leverages the recently proposed graph embeddings to represent game provenance graphs in a latent space. The embeddings learned from the data pose as the features of a machine learning task tailored towards detecting long-range cause-and-effect relationships. We evaluate the generalization capacity of PingUMiL when learning from similar games and compare its performance to classical machine learning methods. The experiments conducted on two racing games show that (1) PingUMiL outperforms classical machine learning methods and (2) representation learning can be used to detect long-range cause-and-effect relationships in only partially observed game data provenance graphs.