Reviewerly/exHarmony huggingface dataset:
Increase The Interest In Learning By Implementing Augmented Reality: Case studies studying rail transportation.

Learn a subject, for some people, might be an uninteresting and boring activity, especially when the subject to learn are difficult subjects to understand. Many methods used to change learning activities become more enjoyable and interested. This study proposed a new method in learning activities, by applied augmented reality technology in the learning process. The case study used in this paper are implementation the augmented reality in studied subjects related to train technology. In this study, author implement augmented reality on learning material, combines real and virtual things in one media, in this case a mobile device. The impact of implementation of augmented studied, at the end of experiment, author can conclude when implement augmented reality technology in learning material helps the learning process and increasing the impressive and fun factor in learning process and make the learning process more interested. Implementation of Augmented Reality in learning material gives more information about the object being studied, information about on shapes, textures, and provide more visualization for the object.
Website categorization: A formal approach and robustness analysis in the case of e-commerce detection

Website categorization has recently emerged as a very important task in several contexts. A huge amount of information is freely available through websites, and it could be used for example to accomplish statistical surveys, saving in costs. However, the information of interest for the specific categorization has to be mined among that huge amount. This turns out to be a difficult task in practice. In this work we propose a practically viable procedure to perform website categorization, based on the automatic generation of data records summarizing the content of each entire website. This is obtained by using web scraping and optical character recognition, followed by a number of nontrivial text mining and feature engineering steps. When such records have been produced, we use classification algorithms to categorize the websites according to the aspect of interest. We compare in this task Convolutional Neural Networks, Support Vector Machines, Random Forest and Logistic classifiers. Since in many practical cases the training set labels are physiologically noisy, we analyze the robustness of each technique with respect to the presence of misclassified training records. We present results on real-world data for the problem of the detection of websites providing e-commerce facilities, however our approach is not structurally limited to this case.
On the Efficient Representation of Datasets as Graphs to Mine Maximal Frequent Itemsets

Frequent itemsets mining is an active research problem in the domain of data mining and knowledge discovery. With the advances in database technology and an exponential increase in data to be stored, there is a need for efficient approaches that can quickly extract useful information from such large datasets. Frequent Itemsets (FIs) mining is a data mining task to find itemsets in a transactional database which occur together above a certain frequency. Finding these FIs usually requires multiple passes over the databases; therefore, making efficient algorithms crucial for mining FIs. This work presents a graph-based approach for representing a complete transactional database. The proposed graph-based representation enables the storing of all relevant information (for extracting FIs) of the database in one pass. Later, an algorithm that extracts the FIs from the graph-based structure is presented. Experimental results are reported comparing the proposed approach with 17 related FIs mining methods using six benchmark datasets. Results show that the proposed approach performs better than others in terms of time.
Traversability Assessment and Trajectory Planning of Unmanned Ground Vehicles with Suspension Systems on Rough Terrain

This paper presents a traversability assessment method and a trajectory planning method. They are key features for the navigation of an unmanned ground vehicle (UGV) in a non-planar environment. In this work, a 3D light detection and ranging (LiDAR) sensor is used to obtain the geometric information about a rough terrain surface. For a given SE(2) pose of the vehicle and a specific vehicle model, the SE(3) pose of the vehicle is estimated based on LiDAR points, and then a traversability is computed. The traversability tells the vehicle the effects of its interaction with the rough terrain. Note that the traversability is computed on demand during trajectory planning, so there is not any explicit terrain discretization. The proposed trajectory planner finds an initial path through the non-holonomic A*, which is a modified form of the conventional A* planner. A path is a sequence of poses without timestamps. Then, the initial path is optimized in terms of the traversability, using the method of Lagrange multipliers. The optimization accounts for the model of the vehicle's suspension system. Therefore, the optimized trajectory is dynamically feasible, and the trajectory tracking error is small. The proposed methods were tested in both the simulation and the real-world experiments. The simulation experiments were conducted in a simulator called Gazebo, which uses a physics engine to compute the vehicle motion. The experiments were conducted in various non-planar experiments. The results indicate that the proposed methods could accurately estimate the SE(3) pose of the vehicle. Besides, the trajectory cost of the proposed planner was lower than the trajectory costs of other state-of-the-art trajectory planners.
Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network

Despite remarkable progress, 3D whole brain segmentation of structural magnetic resonance imaging (MRI) into a large number of regions (>100) is still difficult due to the lack of annotated data and the limitation of GPU memory. To address these challenges, we propose a semi-supervised segmentation method based on deep neural networks to exploit the plenty of unlabeled data by extending the self-training method, and improve the U-Net model by designing a novel self-ensemble architecture and a random patch-size training strategy. Further, to reduce the model storage and computational cost, we get a compact model by knowledge distillation. Extensive experiments conducted on the MICCAI 2012 dataset demonstrate that our method dramatically outperforms previous methods and has achieved the state-of-the-art performance. Our compact model segments an MRI image within 3 s on a TITAN X GPU, which is much faster than multi-atlas based methods and previous deep learning methods.
Detailed Human Activity Recognition using Wearable Sensor and Smartphones

Use of Human activity recognition is increasing day by day for smart home, eldercare, remote health monitoring and surveillance purpose. Serving these purposes better, needs detailed recognition of activities, viz. sitting on chair or floor, slow or brisk walk, running with load, etc. Very few works aim to distinguish between intense activities (such as walk carrying weight) from its counterpart (walk) which is essential for effective health monitoring of elder adults and patients recovering from surgery. In this work, solution has been proposed for this purpose with the help of wearable and smartphone-embedded sensors. Accordingly, the contribution of this work is to present a framework for detailing in identification for both static and dynamic activities, as well as their intense counterparts by designing an ensemble of classifiers. The ensemble is designed that applies weighted majority voting for classification of test instances. Weights of the base classifiers are determined by feeding their output performance for training dataset in a neural network. We observed that our work achieves above 94% recognition accuracy.
Light Field Super-Resolution Using Edge-Preserved Graph-Based Regularization

The light field information would be captured through light field cameras and in different directions regarding 3D image view recordings. In this paper, in order to increase the spatio-angular super-resolution quality and to decrease the reconstruction error regarding the light field information, we use a graph-based light field super-resolution strategy. Accordingly, in order to apply the complementary data in the light field views, we use a graph regularizer for the total recovery of the information and an edge-preserving technique that represents an isometry between curves in the 2D manifold and 5D space of the RGB image views. Moreover, the reconstruction of the light field information is based on applying the alternating direction method of multipliers (ADMM) algorithm. Accordingly, a recent enhanced ADMM model has been used in this paper which is denominated as “Plug-and-Play” and permits the user to plug an image reconstruction technique and a denoising methodology as the first and second sub-problems respectively. On that account, we would be able to resolve the light field super-resolution problem considering the graph-based light field structure as the first sub-problem and the edge-preserving technique as the denoising methodology. Consequently, by applying the proposed super-resolution strategy, the super-resolved light field outcome would be more favorable in terms of visual quality and reconstruction errors in comparison with other state-of-the-art methodologies.
Unsupervised Variational Video Hashing With 1D-CNN-LSTM Networks

Most existing unsupervised video hashing methods generate binary codes by using RNNs in a deterministic manner, which fails to capture the dominant latent variation of videos. In addition, RNN-based video hashing methods suffer the content forgetting of early input frames due to the sequential processing inherency of RNNs, which is detrimental to global information capturing. In this work, we propose an unsupervised variational video hashing (UVVH) method for scalable video retrieval. Our UVVH method aims to capture the salient and global information in a video. Specifically, we introduce a variational autoencoder to learn a probabilistic latent representation of the salient factors of video variations. To better exploit the global information of videos, we design a 1D-CNN-LSTM model. The 1D-CNN-LSTM model processes long frame sequences in a parallel and hierarchical way, and exploits the correlations between frames to reconstruct the frame-level features. As a consequence, the learned hash functions can produce reliable binary codes for video retrieval. We conduct extensive experiments on three widely used benchmark datasets, FCVID, ActivityNet and YFCC to validate the effectiveness of our proposed approach.
Enhanced deep convolutional neural network for malarial parasite classification

The standard method for examining malarial disease is performed through the examination of blood smears under the microscope for parasite-infected red blood cells and this is done by qualified technicians. The inadequacy of this traditional method is enhanced using advanced computer vision and deep learning methods to automatically classify the malarial parasite from the microscope’s blood smear image as infected or uninfected. The primary challenge lies in the classification of malarial parasitizes from blood smear data which decide the overall accuracy. The proposed work uses a deep convolutional neural network (DCNN) to detect the malarial parasitizes from smear blood cell images. The primary focus of the work is to (i) compare the validation loss and accuracy by tuning the hyper-parameters in order to classify the images and (ii) compute the Kappa Coefficient and Matthew’s correlation coefficient. The efficiency of the proposed model is analyzed by comparing the optimizers (Adam and Adagrad) at various epochs. The overall accuracy of the proposed DCNN model reached 98.9% compared to the existing state-of-the-art models by focusing on hyper-parameter tuning.
Automatic speech analysis to early detect functional cognitive decline in elderly population

This study aimed at evaluating whether people with a normal cognitive function can be discriminated from subjects with a mild impairment of cognitive function based on a set of acoustic features derived from spontaneous speech. Voice recordings from 90 Italian subjects (age >65 years; group 1: 47 subjects with MMSE>26; group 2: 43 subjects with 20≤ MMSE ≤26) were collected. Voice samples were processed using a MATLAB-based custom software to derive a broad set of known acoustic features. Linear mixed model analyses were performed to select the features able to significantly distinguish between groups. The selected features (% of unvoiced segments, duration of unvoiced segments, % of voice breaks, speech rate, and duration of syllables), alone or in addition to age and years of education, were used to build a learning-based classifier. The leave-one-out cross validation was used for testing and the classifier accuracy was computed. When the voice features were used alone, an overall classification accuracy of 0.73 was achieved. When age and years of education were additionally used, the overall accuracy increased up to 0.80. These performances were lower than the accuracy of 0.86 found in a recent study. However, in that study the classification was based on several tasks, including more cognitive demanding tasks. Our results are encouraging because acoustic features, derived for the first time only from an ecologic continuous speech task, were able to discriminate people with a normal cognitive function from people with a mild cognitive decline. This study poses the basis for the development of a mobile application performing automatic voice analysis on-the-fly during phone calls, which might potentially support the detection of early signs of functional cognitive decline.
Minuet: Multimodal Interaction with an Internet of Things

A large number of Internet-of-Things (IoT) devices will soon populate our physical environments. Yet, IoT devices' reliance on mobile applications and voice-only assistants as the primary interface limits their scalability and expressiveness. Building off of the classic 'Put-That-There' system, we contribute an exploration of the design space of voice + gesture interaction with spatially-distributed IoT devices. Our design space decomposes users' IoT commands into two components—selection and interaction. We articulate how the permutations of voice and freehand gesture for these two components can complementarily afford interaction possibilities that go beyond current approaches. We instantiate this design space as a proof-of-concept sensing platform and demonstrate a series of novel IoT interaction scenarios, such as making 'dumb' objects smart, commanding robotic appliances, and resolving ambiguous pointing at cluttered devices.
Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks

Asynchronous messaging is leading human-machine interaction due to the boom of mobile devices and social networks. The recent release of dedicated APIs from messaging platforms boosted the development of computer programs able to conduct conversations, (i.e., chatbots), which have been adopted in several domain-specific contexts. This paper proposes SMAG: a chatbot framework supporting a smoking cessation program (JDF) deployed on a social network. In particular, it details the single-agent implementation, the campaign results, a multi-agent design for SMAG enabling the modelization of personalized behavior and user profiling, and highlighting of coupling chatbot technology with and multi-agent systems.
Is this Real?

Synner is a tool that helps users generate real-looking synthetic data by visually and declaratively specifying the properties of the dataset such as each field's statistical distribution, its domain, and its relationship to other fields. It provides instant feedback on every user interaction by updating multiple visualizations of the generated dataset and even suggests data generation specifications from a few user examples and interactions. Synner visually communicates the inherent randomness of statistical data generation. Our evaluation of Synner demonstrates its effectiveness at generating realistic data when compared with Mockaroo, a popular data generation tool, and with hired developers who coded data generation scripts for a fee.
TLS Beyond the Browser

The Transport Layer Security (TLS) protocol has evolved in response to different attacks and is increasingly relied on to secure Internet communications. Web browsers have led the adoption of newer and more secure cryptographic algorithms and protocol versions, and thus improved the security of the TLS ecosystem. Other application categories, however, are increasingly using TLS, but too often are relying on obsolete and insecure protocol options.
Smooth Turn-taking by a Robot Using an Online Continuous Model to Generate Turn-taking Cues

Turn-taking in human-robot interaction is a crucial part of spoken dialogue systems, but current models do not allow for human-like turn-taking speed seen in natural conversation. In this work we propose combining two independent prediction models. A continuous model predicts the upcoming end of the turn in order to generate gaze aversion and fillers as turn-taking cues. This prediction is done while the user is speaking, so turn-taking can be done with little silence between turns, or even overlap. Once a speech recognition result has been received at a later time, a second model uses the lexical information to decide if or when the turn should actually be taken. We constructed the continuous model using the speaker's prosodic features as inputs and evaluated its online performance. We then conducted a subjective experiment in which we implemented our model in an android robot and asked participants to compare it to one without turn-taking cues, which produces a response when a speech recognition result is received. We found that using both gaze aversion and a filler was preferred when the continuous model correctly predicted the upcoming end of turn, while using only gaze aversion was better if the prediction was wrong.
A neural Entity Coreference Resolution review

Entity Coreference Resolution is the task of resolving all mentions in a document that refer to the same real world entity and is considered as one of the most difficult tasks in natural language understanding. It is of great importance for downstream natural language processing tasks such as entity linking, machine translation, summarization, chatbots, etc. This work aims to give a detailed review of current progress on solving Coreference Resolution using neural-based approaches. It also provides a detailed appraisal of the datasets and evaluation metrics in the field, as well as the subtask of Pronoun Resolution that has seen various improvements in the recent years. We highlight the advantages and disadvantages of the approaches, the challenges of the task, the lack of agreed-upon standards in the task and propose a way to further expand the boundaries of the field.
Assessing (Social-Ecological) Systems Thinking by Evaluating Cognitive Maps

Systems thinking (ST) skills are often the foundation of sustainability science curricula. Though ST skill sets are used as a basic approach to reasoning about complex environmental problems, there are gaps in our understanding regarding the best ways to promote and assess ST learning in classrooms. Since ST learning provides Science, Technology, Engineering, and Mathematics (STEM) students’ important skills and awareness to participate in environmental problem-solving, addressing these gaps is an important STEM learning contribution. We have created guidelines for teaching and measuring ST skills derived from a hybrid of a literature review and through case study data collection. Our approach is based on semi-quantitative cognitive mapping techniques meant to support deep reasoning about the complexities of social–ecological issues. We begin by arguing that ST should be evaluated on a continuum of understanding rather than a binary of correct/incorrect or present/absent. We then suggest four fundamental dimensions of teaching and evaluating ST which include: (1) system structure, (2) system function, (3) identification of leverage points for change, and (4) trade-off analysis. Finally, we use a case study to show how these ideas can be assessed through cognitive maps to help students develop deep system understanding and the capacity to propose innovative solutions to sustainability problems.
Video-Based Cross-Modal Recipe Retrieval

As a natural extension of image-based cross-modal recipe retrieval, retrieving a specific video given a recipe as the query is seldom explored. There are various temporal and spatial elements hidden in cooking videos. In addition, current image-based cross-modal recipe retrieval approaches mostly emphasize the understanding of textual and visual content independently. Such methods overlook the interaction between textual and visual content. In this work, we innovatively propose a new problem of video-based cross-modal recipe retrieval and thoroughly investigate this issue under the attention paradigm. In particular, we firstly exploit a parallel-attention network to independently learn the representations of videos and recipes. Next, a co-attention network is proposed to explicitly emphasize the cross-modal interactive features between videos and recipes. Meanwhile, a cross-modal fusion sub-network is proposed to learn both the independent and collaborative dynamics, which can enhance the associated representation of videos and recipes. Last but not the least, the embedding vectors of videos and recipes stemming from joint network are optimized with a pairwise ranking loss. Extensive experiments on a self-collected dataset have verified the effectiveness and rationality of our proposed solution.
Sharpen Focus: Learning With Attention Separability and Consistency

Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks. Despite good localization for an individual class of interest, these techniques produce attention maps with substantially overlapping responses among different classes, leading to the problem of visual confusion and the need for discriminative attention. In this paper, we address this problem by means of a new framework that makes class-discriminative attention a principled part of the learning process. Our key innovations include new learning objectives for attention separability and cross-layer consistency, which result in improved attention discriminability and reduced visual confusion. Extensive experiments on image classification benchmarks show the effectiveness of our approach in terms of improved classification accuracy, including CIFAR-100 (+3.33%), Caltech-256 (+1.64%), ImageNet (+0.92%), CUB-200-2011 (+4.8%) and PASCAL VOC2012 (+5.73%).