Reviewerly/exHarmony huggingface dataset:
Neural machine translation with Gumbel Tree-LSTM based encoder

Neural machine translation has improved the translation accuracy greatly and received great attention of the machine translation community. Tree-based translation models aim to model the syntactic or semantic relation among long-distance words or phrases in a sentence. However, it faces the difficulties of expensive manual annotation cost and poor automatic annotation accuracy. In this paper, we focus on how to encode a source sentence into a vector in a unsupervised-tree way and then decode it into a target sentence. Our model incorporates Gumbel Tree-LSTM, which can learn how to compose tree structures from plain text without any tree annotation. We evaluate the proposed model on both spoken and news corpora, and show that the performance of our proposed model outperforms the attentional seq2seq model and the Transformer base model.
Learning an Attention Model for Robust 2-D/3-D Registration Using Point-To-Plane Correspondences

Minimally invasive procedures rely on image guidance for navigation at the operation site to avoid large surgical incisions. X-ray images are often used for guidance, but important structures may be not well visible. These structures can be overlaid from pre-operative 3-D images and accurate alignment can be established using 2-D/3-D registration. Registration based on the point-to-plane correspondence model was recently proposed and shown to achieve state-of-the-art performance. However, registration may still fail in challenging cases due to a large portion of outliers. In this paper, we describe a learning-based correspondence weighting scheme to improve the registration performance. By learning an attention model, inlier correspondences get higher attention in the motion estimation while the outlier correspondences are suppressed. Instead of using per-correspondence labels, our objective function allows to train the model directly by minimizing the registration error. We demonstrate a highly increased robustness, e.g. increasing the success rate from 84.9% to 97.0% for spine registration. In contrast to previously proposed learning-based methods, we also achieve a high accuracy of around 0.5mm mean re-projection distance. In addition, our method requires a relatively small amount of training data, is able to learn from simulated data, and generalizes to images with additional structures which are not present during training. Furthermore, a single model can be trained for both, different views and different anatomical structures.
Detecting Phishing Website Using Machine Learning

Trying to gather personal information through deceptive ways is becoming more common nowadays. In order to assist the user to be aware of the access to such websites, the implemented system notifies the user through email and also pop-up, when trying to access a phishing site. This paper proposes an approach of phishing detection system to detect blacklisted URL also known as phishing websites, so that individual can be alerted while browsing or accessing a particular website. Therefore, it can be utilized for identification and authentication and become a legitimate tool to prevent an individual from getting tricked.
Multi-Scale Shape Index-Based Local Binary Patterns for Texture Classification

To enhance the weakness of Local Binary Pattern (LBP) and its state-of-the-art variants, this letter presents a new variant of the local concave microstructure pattern (LCvMSP). The proposed multi-scale shape index based texture descriptor is named as SI-LCvMSP. Contrarily to the original LBP and LCvMSP, SI-LCvMSP uses the shape index instead of the original texture image in the kernel function. The shape index is a differential calculation and it can be calculated from local second-order derivatives of texture images. It captures microstructure and macrostructure texture information mathematically. As textural features, we use multi-scale and multi-resolution shape index information as well as rotation-invariant uniform LBP. Thus, we obtain the discriminative feature representation schema to construct cross-scale joint coding. The proposed method has a high discriminability and is less sensitive to image transforms such as rotation and illumination. Experimental results show that the SI-LCvMSP descriptor can improve classification accuracy.
Detecting Hate Speech in Tweets Using Different Deep Neural Network Architectures

One of the major problems, apparent in online social media, is the toxic online content. This has continued unabated, as people from diverse cultural backgrounds access the Internet, concealing their identity under the cloud of anonymity. Deep neural networks have been employed to detect hate speech from online content. This paper describes three different Deep Neural Network (DNN) Architectures for detection of hate words in Twitter - Gated Recurrent Unit (GRU), useful in capturing sequence orders, Convolution Neural Network (CNN), good for feature extraction, and Universal Language Model Fine-tuning (ULMFiT) model, which is based on transfer learning technique. ULMFiT model uses the DNN Architecture called Average-SGD Weight-Dropped Long Short Term Memory (AWD-LSTM). AWD -LSTM model was pre-trained using WikiText103 dataset. This method significantly outperformed the other Architectures.
Prediction of intensity and location of seismic events using deep learning

The object of this work is to predict the seismic rate in Chile by using two Deep Neural Network (DNN) architectures, Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). For this, we propose a methodology based on a three-module approach: a pre-processing module, a spatial and temporal estimation module, and a prediction module. The first module considers the Epidemic-Type Aftershock Sequences (ETAS) model for estimating the intensity function, which will be used for estimating the seismic rate on a 1 × 1 degree grid providing a sequence of daily images covering all the seismic area of Chile. The spatial and temporal estimation module uses the LSTM and CNN for predicting the intensity and the location of earthquakes. The last module integrates the information provided by the DNNs for predicting future values of the maximum seismic rate and their location. In particular, the LSTM will be trained using the maximum intensity of the last 30 days as input for predicting the maximum intensity of the next day, and the CNN will be trained on the last 30 images provided by the application of the ETAS model for predicting the probability that the next day the maximum event will be in certain area of Chile. Some performance indexes (such as R2 and accuracy) will be used for validating the proposed models.
Hybrid Wolf-Bat Algorithm for Optimization of Connection Weights in Multi-layer Perceptron

In a neural network, the weights act as parameters to determine the output(s) from a set of inputs. The weights are used to find the activation values of nodes of a layer from the values of the previous layer. Finding the ideal set of these weights for training a Multi-layer Perceptron neural network such that it minimizes the classification error is a widely known optimization problem. The presented article proposes a Hybrid Wolf-Bat algorithm, a novel optimization algorithm, as a solution to solve the discussed problem. The proposed algorithm is a hybrid of two already existing nature-inspired algorithms, Grey Wolf Optimization algorithm and Bat algorithm. The novel introduced approach is tested on ten different datasets of the medical field, obtained from the UCI machine learning repository. The performance of the proposed algorithm is compared with the recently developed nature-inspired algorithms: Grey Wolf Optimization algorithm, Cuckoo Search, Bat Algorithm, and Whale Optimization Algorithm, along with the standard Back-propagation training method available in the literature. The obtained results demonstrate that the proposed method outperforms other bio-inspired algorithms in terms of both speed of convergence and accuracy.
A Psychologically Inspired Fuzzy Cognitive Deep Learning Framework to Predict Crowd Behavior

In an intelligent surveillance system, detecting and predicting diverse collective crowd behaviors has emerged as a challenging problem for efficient crowd management. In real-world scenarios, potential disasters and hazards can be averted by considering crowd psychology for predicting crowd behaviors. This article proposes an approach that exploits the psychological and cognitive aspects of human behavior in determining nine diverse crowd behaviors. The proposed approach is a combination of two cognitive deep learning frameworks and a psychological fuzzy computational model that utilizes OCC theory of emotions, OCEAN five-factor model of personality and visual attention for detecting crowd behaviors. Experiments are performed on different datasets and the results prove that our approach is successful in detecting and predicting crowd behavior in confronting situations and also outperforms the state-of-the-art methods. In particular, considering psychological aspects and cognition in determining crowd behavior is beneficial for rectifying the semantic ambiguity in identifying crowd behaviors.
Warp to the Future: Joint Forecasting of Features and Feature Motion

We address anticipation of scene development by forecasting semantic segmentation of future frames. Several previous works approach this problem by F2F (feature-to-feature) forecasting where future features are regressed from observed features. Different from previous work, we consider a novel F2M (feature-to-motion) formulation, which performs the forecast by warping observed features according to regressed feature flow. This formulation models a causal relationship between the past and the future, and regularizes inference by reducing dimensionality of the forecasting target. However, emergence of future scenery which was not visible in observed frames can not be explained by warping. We propose to address this issue by complementing F2M forecasting with the classic F2F approach. We realize this idea as a multi-head F2MF model built atop shared features. Experiments show that the F2M head prevails in static parts of the scene while the F2F head kicks-in to fill-in the novel regions. The proposed F2MF model operates in synergy with correlation features and outperforms all previous approaches both in short-term and mid-term forecast on the Cityscapes dataset.
ReFind: Design, Lived Experience and Ongoingness in Bereavement

We describe the design and use of ReFind, a handheld artefact made for people who are bereaved and are ready to re-explore their relationship to the deceased person. ReFind was made within a project seeking to develop new ways to curate and create digital media to support ongoingness - an active, dynamic component of continuing bonds. We draw on bereavement theory and care championing practices that enable a continued sense of connection between someone bereaved and a person who has died. We present the design development of ReFind and the lived experience of the piece by the first author. We discuss our wider methodology which includes autobiographical design and reflections on if and how the piece supported ongoing connections, the challenges faced, and insights gained.
Classification with imperfect training labels

Summary We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we derive detailed asymptotic properties for the popular $k$-nearest neighbour, support vector machine and linear discriminant analysis classifiers. One consequence of these results is that the $k$-nearest neighbour and support vector machine classifiers are robust to imperfect training labels, in the sense that the rate of convergence of the excess risk of these classifiers remains unchanged; in fact, our theoretical and empirical results even show that in some cases, imperfect labels may improve the performance of these methods. The linear discriminant analysis classifier is shown to be typically inconsistent in the presence of label noise unless the prior probabilities of the classes are equal. Our theoretical results are supported by a simulation study.
Linear regression with factor analysis in fault prediction of software

Abstract Abstract In software fault prediction, before beginning with the real software testing process, faultprone software modules are identified with the help of various properties related to the software project. This leads to the achievement of minimal cost apart from the desired software quality. In this study, Object oriented metrics were used to find the main factors. The technique used to find the important predictors is Factor analysis (FA) and regression is used to analyze the goodness of fit of different models drawn from previous studies. Following this, identification of various factors associated with the software fault prediction process is done. In addition, a robust model is developed on the basis of the distinct identified factors. The research work is the extension of our previous work. This research provides a novel approach of different aspects of the software fault prediction process and also assists in discovering the different types of problems associated with the term i.e. software fault prediction. Towards the end, the statistical comparison and the challenges of the paper are discussed along with the future guidelines of the study. Subject Classification: (2010) 94A2094A1268T0168N0168N30Keywords: RegressionFault PredictionFactor Analysissoftware Metrics
Multilingual Opinion Mining Movie Recommendation System Using RNN

Twitter is a news and social networking site where people around the world post their blogs and share their feeling, point of view, and comments regarding any communication or about any latest movie, etc. Thus, Twitter generates a massive quantity of Twitter data every day. This data is real time, which is being used in the proposed work for implementing a “movie recommendation system.” To enhance the performance of the framework, sentimental analysis is also being applied to the data. Nowadays, the recommendation system is also an essential tool for online businesses and used by various e-commerce sites, music applications, entertainment sites, etc. This work proposed a movie recommendation system for the movie domain which is developed using real-time multilingual tweets. These tweets are obtained from Twitter API using the LinqToTwitter Library. Sentimental analysis is also being performed on tweets. In this work, multilingual and real-time tweets are considered. These tweets are translated into the target language using Google Translate API. The proposed work used the Stanford library for preprocessing, and RNN is used for classifying the tweets. The tweets are classified as positive, negative, and neutral tweets. Preprocessing of the tweets is done to remove unwanted words, URLs, emoticons, etc. Finally, based on the classification, the movie is suggested to the user. This proposed work is better than the current practices as the implementation is being done on real-time tweets, and sentimental analysis is also being performed to get better results. This system is achieving 91.67% accuracy, 92% precision, 90.2% recall, and 90.98% f-measure.
An event-driven behavior trees extension to facilitate non-player multi-agent coordination in video games

In this paper, we extend behavior trees (BTs), a behavior creation method that is popular in the video game industry, with three new types of nodes that facilitate the design and implementation of non-player characters (NPCs) that need to coordinate with each other. We provide an implementation and a methodology to use the coordination nodes of our extension appropriately, and we show how to use them to develop an application scenario. In the last years, coordination in multi-agent systems has been a very active research field, both from theoretical and practical points of view. Something similar has happened with the development of new tools for the video game industry. Our approach contributes to both areas by providing a novel extension that facilitates the design and implementation of agents that need to coordinate with each other. In video games, agents or NPCs are—as their name implies—characters that are not controlled by the player but by the game through an algorithmic, predetermined, or responsive behavior, or a more sophisticated AI technique. Some video games require NPCs with dynamic, credible, and intelligently unpredictable behaviors to keep players engaged and immersed. Instead of endowing NPCs with very complex individual behaviors, a feasible way to improve their unpredictability in an intelligent and credible manner is allowing them to coordinate with each other. Since BTs focus on the creation of individual behaviors, coordinated behaviors nowadays tend to be achieved by hard-coding the coordination itself. However, that ad hoc solution partially drives away some of the benefits that popularized BTs: Being visually intuitive, scalable, and reusable. For this reason, we propose an extension to BTs that developers can use to coordinate NPCs without going against the development paradigm: creating complex behaviors by designing an intuitive tree structure.
A Modernistic way for KEY Generation for Highly Secure Data Transfer in ASIC Design Flow

Present day's data security plays a vital role in digital human life. Data is a valuable asset to any organization and hence its security from external attacks is very important. Information security is not only an important aspect but essential, to secure data from unapproved access. Data encryption, decryption and key management are the key factors in data protection. It is very important to have the right data security solution to meet the challenging threats. Cryptosystem implementation and random number generators are crucial for Cryptosystem applications such as security applications, space applications, military applications and smart cards et al. In this paper, we present the implementation of hybrid cryptosystem based on the True Random number Generator, pseudo Random number Generator and whitening the data by using the ASIC design flow.
Performance analysis of hyperparameter optimization methods for ensemble learning with small and medium sized medical datasets

AbstractAbstractThe accuracy of the various classifiers depends mainly on good hyper-parameter and consequently on the scheme (hyper-parameter tuning algorithm) adopted to estimate these values. Currently, the hyper-parameter tuning for Ensemble classifiers (which involves a number of hyper-parameters) is receiving a lot of attention. This paper aims to analyze the effectiveness of three optimization methods: Grid Search (GS), Random Search (RS) and Bayesian Optimization (BO) on small and medium sized medical datasets to select a set of optimal hyper parameters for an ensemble classifier (here we used decision tree ensemble using AdaBoost). 5 fold CV wasemployed to evaluate the generalization performance.Subject Classification: Primary 62H30Keywords: Bayesian OptimizationGrid SearchRandom SearchEnsemble classifierHyper-parameter Optimization
Opposition-based Harris Hawks optimization algorithm for feature selection in breast mass classification

AbstractAbstractOne of the very common forms of cancer found in women all over the world these days is Breast cancer. Mammography is regarded as the general way to detect this form of cancer at an earlier stage. The radiologist uses the mammogram images to search for the abnormalities present in the breasts via visual perception only. Masses are considered as one of the most common abnormalities, which are a collection of cells bounded together in a more dense way than the tissues surrounding it, which may be of benign or malignant nature. These days Machine learning-based Computer-aided diagnosis Systems assist the radiologist to make the right prediction, however there still lays a scope of improvement in the techniques for selecting the optimal number of features with maximum accuracy. Selecting the optimal size of feature subset from a larger number of features is considered as an optimization problem. This research proposes the Opposition based variant of one of the most recent optimization algorithm known as Harris Hawks Optimization algorithm. Results indicate that the proposed algorithm performs better than other algorithms on various benchmark functions and on various criteria’s considered for feature selection in breast mass classification.Subject Classification: 68T0168T1068T3568U10Keywords: Breast massesComputer-aided diagnosisFeature selectionMetaheuristic AlgorithmsOpposition-Based strategy
An Experiential Learning Approach to Learning Manual Communication Through a Virtual Reality Environment

There is a pressing need for effective pedagogical methods of manual languages, as evident in the decline of manual languages, such as New Zealand Sign Language (NZSL). Despite being recognized as one of New Zealand's official languages, recent censuses have shown that fluent NZSL signers have been steadily decreasing. There is a cultural responsibility to preserve such languages, yet the combination of barriers to acquisition and the limited availability of effective teaching methods are standing in the way. In light of this, this article proposes a computer-assisted sign language learning system that incorporates virtual reality and validation-based feedback as tools to implement the experiential learning model. An implementation in the form of virtual reality NZSL is presented, targeting the set of NZSL alphabet. Results show that a vision-based classification method, using the Leap Motion controller, is a scalable, accurate, and usable solution for feedback-assisted manual language learning. A formative usability evaluation of n=10 participants showed promising results for engagement, confidence, and memory retention. The results indicate that virtual reality technology is uniquely situated as an innovative medium for the self-directed acquisition of manual languages. It is hoped this article inspires technology researchers to pursue collaborations with the deaf/Deaf community to design and develop pedagogical technology solutions for manual communication.
Energy clustering for unsupervised person re-identification

Due to the high cost of data annotation in supervised person re-identification (re-ID) methods, unsupervised methods become more attractive in the real world. Recently, the hierarchical clustering serves as a promising unsupervised method. One key factor of hierarchical clustering is the distance measurement strategy. Ideally, a good distance measurement should consider both inter-cluster and intra-cluster distance of all samples. To solve this problem, we propose to use the energy distance to measure inter-cluster distance in hierarchical clustering (E-cluster), and use the sum of squares of deviations (SSD) as a regularization term to measure intra-cluster distance for further performance promotion. We evaluate our method on Market-1501 and DukeMTMC-reID. Extensive experiments show that E-cluster obtains significant improvements over the state-of-the-arts fully unsupervised methods.