Reviewerly/exHarmony huggingface dataset:
SemFlow: Semantic-Driven Interpolation for Large Displacement Optical Flow

This paper presents a semantic-guided interpolation scheme (SemFlow) to handle motion boundaries and occlusions in large displacement optical flow.The basic idea is to segment images into superpixels and estimate their homographies for interpolation.In order to ensure each superpixel can be approximated as a plane, a semantic-guided refinement method is introduced.Moreover, we put forward a homography estimation model weighted by the distance between each superpixel and its K -nearest neighbors.Our newly-proposed distance metric combines the texture and semantic information to find proper neighbors.Our homography model performs better than the original affine model, since it accords with the real world projection relationship.The experiments on KITTI dataset demonstrate that SemFlow outperforms other state-of-the-art methods, especially in solving the problem of large scale motions and occlusions.
Learning Shallow Neural Networks via Provable Gradient Descent with Random Initialization

This paper presents the provable gradient descent algorithm with random initialization for learning a two-layer neural network with quadratic activation functions. Specifically, we focus on the under-parameterized regime where the number of hidden units is smaller than the dimension of the inputs. We reveal that the randomly initialized gradient descent for the nonconvex neural network training problem is able to enter a local region that enjoys strong convexity and strong smoothness within a few iterations, and then provably converges to a globally optimal model at a linear rate.
Interactive spaces for children: gesture elicitation for controlling ground mini-robots

Interactive spaces for education are emerging as a mechanism for fostering children’s natural ways of learning by means of play and exploration in physical spaces. The advanced interactive modalities and devices for such environments need to be both motivating and intuitive for children. Among the wide variety of interactive mechanisms, robots have been a popular research topic in the context of educational tools due to their attractiveness for children. However, few studies have focused on how children would naturally interact and explore interactive environments with robots. While there is abundant research on full-body interaction and intuitive manipulation of robots by adults, no similar research has been done with children. This paper therefore describes a gesture elicitation study that identified the preferred gestures and body language communication used by children to control ground robots. The results of the elicitation study were used to define a gestural language that covers the different preferences of the gestures by age group and gender, with a good acceptance rate in the 6–12 age range. The study also revealed interactive spaces with robots using body gestures as motivating and promising scenarios for collaborative or remote learning activities.
Credit Card Fraud Detection Using Random Forest Algorithm

Credit card fraudulent happens through the account holders card number, card details and personal information. E-commerce payment system is providing the payment for online transaction. The model is used to identify whether a new transaction is fraudulent or not. Aim is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. A standard scalar model is initially trained with the normal behavior of a card holder. If an incoming credit card transaction is not accepted by the trained standard scalar model with sufficiently high probability, it is considered to be fraudulent, which defines a plot of test perception as the y coordinate versus its 1-specificity or false positive rate (FPR) as the x coordinate, is an effective method of estimate the quality or performance of diagnostic tests. The significance of the application technique reviewed in the minimization of credit card fraud. Still some issues when genuine credit card customers are misclassified as fraudulent. SMOTE is a statistical technique for increasing the number of cases in your dataset in a balanced way. Random forest builds multiple decision trees and integrate them together to get stable prediction and accuracy of about 98.6%.
Ad-net: Attention Guided Network for Optical Flow Estimation Using Dilated Convolution

Variational models for optical flow estimation usually define an energy function that contains prior assumptions to explore rudimentary statistics of images. However, such methods cannot learn motion knowledge from the pre-prepared data and have many parameters that need to be set manually. Nowadays, convolutional neural networks (CNNs) have been used in optical flow estimation successfully, which can learn weights from the training dataset and can predict optical flow end-to-end. In this paper, we propose an attention guided network for learning optical flow, named AD-Net, which contains several attention units for modelling the relativities between the channels. Further, we introduce dilated convolution into supervised network for reducing the loss of motion details. In addition, some prior auxiliary constraints are embedded in the supervised network as auxiliary loss terms. Our proposed approach is tested on MPI-Sintel and KITTI2012 datasets and can preserve motion edges and details effectively.
The Missing Requirements Perspective in Large-Scale Agile System Development

Recent developments in agile methods at scale and continuous delivery have successfully removed major bottlenecks that have, so far, limited the speed at which software can be developed, delivered, and evaluated by customers and end users. Now, the ability to manage requirements and related knowledge in continuous software engineering has become a limiting factor.
Encouraging Student Motivation Through Gamification in Engineering Education

At present, there is a wide variety of teaching methods and educational strategies mediated by technology: e-learning, m-learning, b-learning, gamification, flipped classroom, bring your own devices (BYOD), etc. This methodological variety creates an important student-participation space where challenges, problem solving and collaboration are put to the testing. Due to the need for communication, portability and ubiquity, several of these methods use mobile devices to achieve their goal. Laptops, smartphones and tablets, with access to wireless networks, can be used to improve teacher–student interaction and thereby stimulate students' motivation, participation and active learning. Gamification is a teaching-learning strategy, which incorporates knowledge based on games that can support and mostly motivate the work of students and teachers. This document presents the results of a study using the Kahoot Platform, which is a web service of social and gamified education learning, in order to create evaluations changed into games of knowledge and competition among students. When it is used as a questionnaire, it challenges users to answer the questions posed, with a controlled response time, accumulated points, badges and a podium with the first five places. This leads to an active and dynamic learning due to the strong playful component, competitive and teamwork. The results of the research indicate that students improved participation and motivation in classes, besides checking that they had better results when working with mobile devices. These data will allow to analyze future perspectives about the use of the learning platform based on games as an assistance in the teaching-learning process.
Flexible Non-negative Matrix Factorization with Adaptively Learned Graph Regularization

Non-negative matrix factorization (NMF) is an efficient model in learning parts-based data representation. Since the local geometrical structure can be effectively modeled by a nearest neighbor graph, the graph regularized NMF (GNMF) was proposed to make the learned representation more faithfully and better characterize the intrinsic structure of data. However, GNMF shares a similar paradigm with most of existing graph-based learning models which perform learning tasks on a fixed input graph. In this paper, we propose a new Flexible NMF model with adaptively learned Graph regularization (FNMFG) in which the graph is jointly learned with simultaneous performing the matrix factorization. An efficient iterative method with guaranteed convergence and relative low complexity is developed to optimize the FNMFG objective. Experiments compare FNMFG method with state-of-the-art algorithms and demonstrate its improved performance.
A Novel Clustering Framework for Stream Data Un nouveau cadre de classifications pour les données de flux

There is a growing tendency for developing real-time clustering of continuous stream data. In this regard, a few attempts have been made to improve the off-line phase of stream clustering methods, whereas these methods almost use a simple distance function in their online phase. In practice, clusters have complex shapes, and therefore, measuring the distance of incoming samples to the mean of asymmetric microclusters might mislead incoming samples to irrelevant microclusters. In this paper, a novel framework is proposed, which can enhance the online phase of all stream clustering methods. In this manner, for each microcluster for which its population exceeds a threshold, a classifier is exclusively trained to capture its boundary and statistical properties. Thus, incoming samples are assigned to the microclusters according to the classifiersâŁ™ scores. Here, the incremental NaÃve Bayes classifier is chosen, due to its fast learning property. DenStream and CluStream as the state-of-the-art methods were chosen and their performance was assessed over nine synthetic and real data sets, with and without applying the proposed framework. The comparative results in terms of purity, general recall, general precision, concept change traceability, computational complexity, and robustness against noise over the data sets imply the superiority of the modified methods to their original versions.
Automated Classification of Class Role-Stereotypes via Machine Learning

Role stereotypes indicate generic roles that classes play in the design of software systems (e.g. controller, information holder, or interfacer). Knowledge about the role-stereotypes can help in various tasks in software development and maintenance, such as program understanding, program summarization, and quality assurance. This paper presents an automated machine learning-based approach for classifying the role-stereotype of classes in Java. We analyse the performance of this approach against a manually labelled ground truth for a sizable open source project (of 770+ Java classes) for the Android platform. Moreover, we compare our approach to an existing rule-based classification approach. The contributions of this paper include an analysis of which machine learning algorithms and which features provide the best classification performance. This analysis shows that the Random Forest algorithm yields the best classification performance. We find however, that the performance of the ML-classifier varies a lot for classifying different role-stereotypes. In particular its performs degrades for rare role-types. Our ML-classifier improves over the existing rule-based classification method in that the ML-approach classifies all classes, while rule-based approaches leave a significant number of classes unclassified.
Insights into the Feature Selection Problem Using Local Optima Networks

The binary feature selection problem is investigated in this paper. Feature selection fitness landscape analysis is done, which allows for a better understanding of the behaviour of feature selection algorithms. Local optima networks are employed as a tool to visualise and characterise the fitness landscapes of the feature selection problem in the context of classification. An analysis of the fitness landscape global structure is provided, based on seven real-world datasets with up to 17 features. Formation of neutral global optima plateaus are shown to indicate the existence of irrelevant features in the datasets. Removal of irrelevant features resulted in a reduction of neutrality and the ratio of local optima to the size of the search space, resulting in improved performance of genetic algorithm search in finding the global optimum.
Asymptotic Performance of Linear Discriminant Analysis with Random Projections

We investigate random projections in the context of randomly projected linear discriminant analysis (LDA). We consider the case in which the data of dimension p is randomly projected onto a lower dimensional space before being fed to the classifier. Using fundamental results from random matrix theory and relying on some mild assumptions, we show that the asymptotic performance in terms of probability of misclassification approaches a deterministic quantity that only depends on the data statistics and the dimensions involved. Such results permits to reliably predict the performance of projected LDA as a function of the reduced dimension d <; p and thus helps to determine the minimum d to achieve a certain desired performance. Finally, we validate our results with finite-sample settings drawn from both synthetic data and the popular MNIST dataset.
A Variational Adaptive Population Importance Sampler

Adaptive importance sampling (AIS) methods are a family of algorithms which can be used to approximate Bayesian posterior distributions. Many AIS algorithms exist in the literature, where the differences arise in the manner by which the proposal distribution is adapted at each iteration. The adaptive population importance sampler (APIS), for example, deterministically samples from a mixture distribution and uses the local information given by the samples and weights to adapt the location parameter of each proposal. The update rules by nature are heuristic, but effective, especially in the case that the target posterior is multimodal. In this work, we introduce a novel AIS scheme which incorporates modern techniques in stochastic optimization to improve the methodology for higher-dimensional posterior inference. More specifically, we derive update rules for the parameters of each proposal by means of deterministic mixture sampling and show that the method outperforms other state-of-the-art approaches in high-dimensional scenarios.
Local binary pattern based on image gradient for bark image classification

In this work, we present a discriminative and effective local texture descriptor for bark image classification. The proposed descriptor is based on three factors, namely, pixel, magnitude and direction value. Unlike most other descriptors based on original local binary pattern, the proposed descriptor is conducted the changing of local texture of bark image. The performance of the proposed descriptor is evaluated on three benchmark datasets. The experimental results show that our approach is highly effective.
Thoughts on Cyberbullying

Cyberbullying is a cybertrust issue that does not get much attention until after an incident occurs. It is hard to tie it directly to security or privacy. It is its own threat category, fitting between security, safety, and privacy. It is a social and societal issue, a cowardly action attempting to hide behind a virtual shield. Fortunately, digital forensics can usually unmask this.
Securing Smartphone Handwritten Pin Codes with Recurrent Neural Networks

This paper investigates the use of recurrent neural networks to secure PIN code based authentication on smartphones, in a scenario where the user is invited to draw digits on the touchscreen. From the sequence of successive positions of the users finger on the touchscreen, a bidirectional recurrent neural network computes a discriminative embedding in terms of writer traits, carrying the contextual information of the written digit. This allows to reject impostors who would have knowledge of the PIN code. The neural network is trained to recognize both users and digits of a training dataset. Evaluations are run on two datasets of 43 and 33 users, respectively, absent from the training dataset. Results show that when enrolling the users on 4 examples of each digit, the Equal Error Rate reaches 4.9% for a 4-digit PIN code. Including digit value prediction during training is key to achieve good performances.
Chapter 18. Engagement of readers/customers in the discourse of e-tourism promotional genres

Tourism 2.0 involves direct e-communication between travellers and the tourism industry, and needs websites that attract customers. Recent research in this field (Austin 2009; Brodie, Hollebeek et al. 2011) suggests that they must also include the co-creation of values that align with the customer in some way. Their design must be persuasive yet trustworthy. This is partly achieved through discursive strategies that reflect the voice of the institution or business (author), and others that engage the customer (reader). This study discusses engagement as a key concept in promotional websites (hotels, tourist guides, etc.) from interpersonality, based on the cometval corpus. Its conclusions note significant and controversial differences on how these e-genres are construed in English and Spanish, with societal implications.
Evaluation of Performance of VDSR Super Resolution on Real and Synthetic Images

This paper presents an evaluation of the suitability of the Very Deep Super Resolution (VDSR) architecture, to increase the spatial resolution of lower quality images. For this aim, two sets of tests are performed. The former being on real life images to determine the networks ability to improve low resolution images. The second test is performed on images of a resolution chart, and therefore synthetic. This is to analyse the frequency response of the network. For each test, three metrics are used to assess image quality. These are the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Modulation Transfer Function (MTF). Experimental results show that the VDSR network is able to increase the quality of the images within the first test in all three metrics, therefore showing that the network is suitable for super resolution. The second test provides more information on the limitations of the network when given a high contrast image, and the resulting ringing effects it can create. Therefore results in PSNR/SSIM values are not improved over the low resolution images, however they have a higher MTF curve as well as more visually sharp images.
A Study of the UK Undergraduate Computer Science Curriculum: A Vision of Cybersecurity

When the content is not written in the discipline syllabus, it is possible that it will not be taught. In this work, the author has investigated 100 computer science undergraduate courses in the UK, to assess the capability of the software developers in the Country to create secure pieces of software. Besides that, to evaluate to which extent the UK Engineering and System Design students are being taught about the relevance of considering security issues when developing software or if this subject is treated as just an optional element at the end of their professional education.
A Scalable Hierarchical Gaussian Process Classifier

Gaussian process (GP) models are powerful tools for Bayesian classification, but their limitation is the high computational cost. Existing approximation methods to reduce the cost of GP classification can be categorized into either global or local approaches. Global approximations, which summarize training data with inducing points, cannot account for non-stationarity and locality in complex datasets. Local approximations, which fit a GP for each sub-region of the input space, are prone to overfitting. This paper proposes a GP classification method that effectively utilizes both global and local information through a hierarchical model. The upper layer consists of a global sparse GP to coarsely model the entire dataset. The lower layer is composed of a mixture of GP experts, which use local information to learn a fine-grained model. The key idea to avoid overfitting and to enforce correlation among the experts is to incorporate global information into their shared prior mean function. A variational inference algorithm is developed for simultaneous learning of the global GP, the experts, and the gating network by maximizing a lower bound of the log marginal likelihood. We explicitly represent the variational distributions of the global variables so that the model conditioned on these variables factorizes in the observations. This way, stochastic optimization can be employed during learning to cater for large-scale problems. Experiments on a wide range of benchmark datasets demonstrate the advantages of the model, as a stand-alone classifier or as the top layer of a deep neural network, in terms of scalability and predictive power.
Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network

In this paper, the integration of Type-2 fuzzy set theory and recurrent wavelet neural network(WNN) is proposed to allow managing of non-uniform uncertainties for identifying non-linear dynamic system. The proposed Type-2 fuzzy WNN is inherently a recurrent multilayered network which constructed based on a set of Type-2 fuzzy rules and recurrent connections in the second layer of the FWNN. Each rule comprises a wavelet function in the consequent part. The structure has both advantages of recurrent and wavelet neural network which expand the basic ability of fuzzy neural network to deal with temporal problems. Both antecedent and consequent parameters update rules are derived based on the gradient descent method. The structure is applied in the identification of dynamic plants which is commonly used in the literature. Simulation result from the identification of a second-order non-linear plant confirms the better performance and effectiveness of the proposed structure.
Suffix Based Automated Parts of Speech Tagging for Bangla Language

Natural language processing (NLP) is the technique by which we process the human language with the computer. Parts-of-Speech (POS) tagging is one of the fundamental requirements for some NLP applications. It is considered as a solved problem for some foreign languages, such as English, Chinese, due to higher accuracy (97%), where it is still an unsolved problem for Bangla because of its ambiguity. Although making a POS tagger for Bangla is not a new work, but each one of available POS taggers has different kinds of limitations. We choose to develop an unsupervised system rather than a supervised system, because a supervised system needs a huge data resource for training purpose and available resources in Bangla is really poor. Here we develop a POS tagger mainly based on Bangla grammar especially suffixes. Because Bangla is a very inflectional language, where a single word has many variants based on their suffixes. In this POS tagger, we assign 8 base POS tags, where some rules, based on Bangla grammar and suffix, are applied to identify POS tags with the cooperation of verb root dataset. To handle non-suffix words, a dataset of almost 14500 Bangla words, with having their default POS tags, is added with the system, which helps to increase the efficiency of this POS tagger. A modified version of previously used algorithm for suffix analysis is applied, which result in a satisfactory level of about 94.2%.