Reviewerly/exHarmony huggingface dataset:
Telemedicina e sua relação com comunicação, tecnologia e convergência

This article discusses the role of the professional journalist in promoting health information, especially in telemedicine services.It is questioned, via the convergence culture, how can communication contribute to the telemedicine service?How can collective intelligence through real life experiences collaborate in the convergence of the traditional medicine to telemedicine?In parallel with the view defended by Henry Jenkins (2009) when defining means of communication, there is a change in the protocols of people who seek clarification about health.It is assumed that these protocol changes contribute to the development of treatments and the general health of patients.This work summarizes through bibliographic research some ideas of authors that approach on telemedicine.It is expected, due to the culture of convergence, the patient's autonomy, as for example, in choosing which form he wants to be consulted, which method offers him less risk and greater health benefit.
C2DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation

Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain. Deep Adaptation Network (DAN) is one of these efficient frameworks, it utilizes Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to align the feature distribution in a reproducing kernel Hilbert space. However, DAN does not perform very well in feature level transfer, and the assumption that source and target domain share classifiers is too strict in different adaptation scenarios. In this paper, we further improve the adaptability of DAN by incorporating Domain Confusion (DC) and Classifier Adaptation (CA). To achieve this, we propose a novel domain adaptation method named C2DAN. Our approach first enables Domain Confusion (DC) by using a domain discriminator for adversarial training. For Classifier Adaptation (CA), a residual block is added to the source domain classifier in order to learn the difference between source classifier and target classifier. Beyond validating our framework on the standard domain adaptation dataset office-31, we also introduce and evaluate on the Comprehensive Cars (CompCars) dataset, and the experiment results demonstrate the effectiveness of the proposed framework C2DAN.
The Current State of StarCraft AI Competitions and Bots

Real-Time Strategy (RTS) games have become an increasingly popular test-bed for modern artificial intelligence techniques. With this rise in popularity has come the creation of several annual competitions, in which AI agents (bots) play the full game of StarCraft: Broodwar by Blizzard Entertainment. The three major annual StarCraft AI Competitions are the Student StarCraft AI Tournament (SSCAIT), the Computational Intelligence in Games (CIG) competition, and the Artificial Intelligence and Interactive Digital Entertainment (AIIDE) competition. In this paper we will give an overview of the current state of these competitions, and the bots that compete in them.
Human activities recognition with a single writs IMU via a Variational Autoencoder and android deep recurrent neural nets

Human Activity Recognition (HAR) is an active research field because of its versatility towards various application areas such as healthcare and lifecare. In this study, a novel HAR system is proposed based on an autoencoder for denoising and Recurrent Neural Network (RNN) for classification with a single Inertial Measurement Unit (IMU) located on a dominant wrist. A Variational Autoencoder (VAE) is built to denoise IMU signals which improves HAR by Android Deep RNN. Evaluating our VAE and Android Deep RNN HAR system is done in two ways. First, the system is tested on a PC using discrete epochs of activities of daily living. Our results show that VAE improves Signal-to-Noise Ratio (SNR) of the IMU signal from 8.78 to 17.26 dB. In turn, HAR improves from 89.29% to 95.11% in F1-score and from 90.38% to 95.47% in accuracy. Secondly, the system is tested on an Android device (i.e., smartphone) using continuous activity signals. This is done by transferring the PC HAR system to an Android HAR App (i.e., Android Deep RNN). We have achieved 86.13% and 95.09% in accuracy without and with VAE respectively. Our results demonstrate that HAR can be achieved in real-time on a standalone smart device with a single IMU for lifelogging services.
Variational neural decoder for abstractive text summarization

In the conventional sequence-to-sequence (seq2seq) model for abstractive summarization, the internal transformation structure of recurrent neural networks (RNNs) is completely determined. Therefore, the learned semantic information is far from enough to represent all semantic details and context dependencies, resulting in a redundant summary and poor consistency. In this paper, we propose a variational neural decoder text summarization model (VND). The model introduces a series of implicit variables by combining variational RNN and variational autoencoder, which is used to capture complex semantic representation at each step of decoding. It includes a standard RNN layer and a variational RNN layer [5]. These two network layers respectively generate a deterministic hidden state and a random hidden state. We use these two RNN layers to establish the dependence between implicit variables between adjacent time steps. In this way, the model structure can better capture the complex semantics and the strong dependence between the adjacent time steps when outputting the summary, thereby improving the performance of generating the summary. The experimental results show that, on the text summary LCSTS and English Gigaword dataset, our model has a significant improvement over the baseline model.
Hybrid Enhanced Universal Dependencies Parsing

This paper describes our system to predict enhanced dependencies for Universal Dependencies (UD) treebanks, which ranked 2 nd in the Shared Task on Enhanced Dependency Parsing with an average ELAS of 82.60%.Our system uses a hybrid two-step approach.First, we use a graph-based parser to extract a basic syntactic dependency tree.Then, we use a set of linguistic rules which generate the enhanced dependencies for the syntactic tree.The application of these rules is optimized using a classifier which predicts their suitability in the given context.A key advantage of this approach is its language independence, as rules rely solely on dependency trees and UPOS tags which are shared across all languages.
Wavefront reconstruction based on deep transfer learning for microscopy

The application of machine learning in wavefront reconstruction has brought great benefits to real-time, non-invasive, deep tissue imaging in biomedical research. However, due to the diversity and heterogeneity of biological tissues, it is difficult to train the dataset with a unified model. In general, the utilization of some unified models will result in the specific sample falling outside the training set, leading to low accuracy of the machine learning model in some real applications. This paper proposes a sensorless wavefront reconstruction method based on transfer learning to overcome the domain shift introduced by the difference between the training set and the target test set. We build a weights-sharing two-stream convolutional neural network (CNN) framework for the prediction of Zernike coefficient, in which a large number of labeled randomly generated samples serve as the source-domain data and the unlabeled specific samples serve as the target-domain data at the same time. By training on massive labeled simulated data with domain adaptation to unlabeled target-domain data, the network shows better performance on the target tissue samples. Experimental results show that the accuracy of the proposed method is 18.5% higher than that of conventional CNN-based method and the peak intensities of the point spread function (PSF) are more than 20% higher with almost the same training time and processing time. The better compensation performance on target sample could have more advantages when handling complex aberrations, especially the aberrations caused by various histological characteristics, such as refractive index inhomogeneity and biological motion in biological tissues.
DISL: Deep Isomorphic Substructure Learning for network representations

Abstract The analysis of complex networks based on deep learning has drawn much attention recently. Generally, due to the scale and complexity of modern networks, traditional methods are gradually losing the analytic efficiency and effectiveness. Therefore, it is imperative to design a network analysis model which caters to the massive amount of data and learns more comprehensive information from networks. In this paper, we propose a novel model, namely Deep Isomorphic Substructure Learning (DISL) model, which aims to learn network representations from patterns with isomorphic substructures. Specifically, in DISL, deep learning techniques are used to learn a better network representation for each vertex (node). We provide the method that makes the isomorphic units self-embed into vertex-based subgraphs whose explicit topologies are extracted from raw graph-structured data, and design a Probability-guided Random Walk (PRW) procedure to explore the set of substructures. Sequential samples yielded by PRW provide the information of relational similarity, which integrates the information of correlation and co-occurrence of vertices and the information of substructural isomorphism of subgraphs. We maximize the likelihood of the preserved relationships for learning the implicit similarity knowledge. The architecture of the Convolutional Neural Networks (CNNs) is redesigned for simultaneously processing the explicit and implicit features to learn a more comprehensive representation for networks. The DISL model is applied to several vertex classification tasks for social networks. Our results show that DISL outperforms the challenging state-of-the-art Network Representation Learning (NRL) baselines by a significant margin on accuracy and weighted- F 1 scores over the experimental datasets.
Gossip and Attend: Context-Sensitive Graph Representation Learning

Graph representation learning (GRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and often sparse graphs. Most studies explore the structure and metadata associated with the graph using random walks and employ an unsupervised or semi-supervised learning schemes. Learning in these methods is context-free, resulting in only a single representation per node. Recently studies have argued on the adequacy of a single representation and proposed context-sensitive approaches, which are capable of extracting multiple node representations for different contexts. This proved to be highly effective in applications such as link prediction and ranking.However, most of these methods rely on additional textual features that require complex and expensive RNNs or CNNs to capture high-level features or rely on a community detection algorithm to identify multiple contexts of a node.In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models. We propose Goat, a context-sensitive algorithm inspired by gossip communication and a mutual attention mechanism simply over the structure of the graph. We show the efficacy of Goat using 6 real-world datasets on link prediction and node clustering tasks and compare it against 12 popular and state-of-the-art (SOTA) baselines. Goat consistently outperforms them and achieves up to 12% and 19% gain over the best performing methods on link prediction and clustering tasks, respectively.
Content Based Image Retrieval (CBIR) by Statistical Methods

An image retrieval system is a computer system for browsing, looking and recovering pictures from a huge database of advanced pictures. The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image. The researchers were developing a new mechanism to retrieval systems which is mainly based on two procedures. The first procedure relies on extract the statistical feature of both original, traditional image by using the histogram and statistical characteristics (mean, standard deviation). The second procedure relies on the T- test to measure the independence between more than images, (coefficient of correlate, T- test, Level of significance, find the decision), and, through experimental test, it was found that this proposed method of retrieval technique is powerful than the classical retrieval System.
Neural Simultaneous Speech Translation Using Alignment-Based Chunking

In simultaneous machine translation, the objective is to determine when to produce a partial translation given a continuous stream of source words, with a trade-off between latency and quality.We propose a neural machine translation (NMT) model that makes dynamic decisions when to continue feeding on input or generate output words.The model is composed of two main components: one to dynamically decide on ending a source chunk, and another that translates the consumed chunk.We train the components jointly and in a manner consistent with the inference conditions.To generate chunked training data, we propose a method that utilizes word alignment while also preserving enough context.We compare models with bidirectional and unidirectional encoders of different depths, both on real speech and text input.Our results on the IWSLT 1 2020 English-to-German task outperform a wait-k baseline by 2.6 to 3.7% BLEU absolute.
Multi-Tier Automated Planning for Adaptive Behavior

A planning domain, as any model, is never “complete” and inevitably makes assumptions on the environment's dynamic. By allowing the specification of just one domain model, the knowledge engineer is only able to make one set of assumptions, and to specify a single objective-goal. Borrowing from work in Software Engineering, we propose a multi-tier framework for planning that allows the specification of different sets of assumptions, and of different corresponding objectives. The framework aims to support the synthesis of adaptive behavior so as to mitigate the intrinsic risk in any planning modeling task. After defining the multi-tier planning task and its solution concept, we show how to solve problem instances by a succinct compilation to a form of non-deterministic planning. In doing so, our technique justifies the applicability of planning with both fair and unfair actions, and the need for more efforts in developing planning systems supporting dual fairness assumptions.
Performance Assessment of Transient Signal Detection Methods and Superiority of Energy Criterion (EC) Method

Radio frequency fingerprinting (RFF) based on RF transients is one of the most effective techniques for improving wireless security. For an efficient RFF development, RF transients need to be accurately detected. However, the detection of the transient starting point remains a main challenge due to the channel noise. In the literature, several methods have been presented to detect the starting point of the transient signals. As an alternative to these methods, this study proposes a method that utilizes Energy Criterion (EC) technique for the first time. In order to test its performance, firstly, an extensive dataset consisting of Wi-Fi signals recorded under realistic Signal-to-Noise Ratio (SNR) conditions is created. Using the provided dataset, the proposed method as well as common transient detection methods are employed for transient start detection. Then, the effect of SNR on the performance of transient start detection is evaluated. Moreover, a performance comparison between the methods is provided based on their respective computational speed and complexity. The results prove the feasibility and efficiency of the proposed method to detect the transient starting point for RFF of Wi-Fi device identification. As to the knowledge of the authors, this study is the first report that comparatively assesses the transient detection methods by using extensive data under realistic noise conditions.
Inception LSTM for Next-frame Video Prediction (Student Abstract)

In this paper, we proposed a novel deep-learning method called Inception LSTM for video frame prediction. A standard convolutional LSTM uses a single size kernel for each of its gates. Having multiple kernel sizes within a single gate would provide a richer features that would otherwise not be possible with a single kernel. Our key idea is to introduce inception like kernels within the LSTM gates to capture features from a bigger area of the image while retaining the fine resolution of small information. We implemented the proposed idea of inception LSTM network on PredNet network with both inception version 1 and inception version 2 modules. The proposed idea was evaluated on both KITTI and KTH data. Our results show that the Inception LSTM has better predictive performance compared to convolutional LSTM. We also observe that LSTM with Inception version 1 has better predictive performance compared to Inception version 2, but Inception version 2 has less computational cost.
A General Level Design Editor for Co-Creative Level Design

In this paper we describe a level design editor designed as an interface to allow different AI agents to creatively collaborate on level design problems with human designers. We intend to investigate the comparative impacts of different AI techniques on user experience in this context.
New Results on Gimli: Full-Permutation Distinguishers and Improved Collisions

$$\mathsf {Gimli}$$ is a family of cryptographic primitives (both a hash function and an AEAD scheme) that has been selected for the second round of the NIST competition for standardizing new lightweight designs. The candidate $$\mathsf {Gimli}$$ is based on the permutation $$\mathsf {Gimli}$$ , which was presented at CHES 2017. In this paper, we study the security of both the permutation and the constructions that are based on it. We exploit the slow diffusion in $$\mathsf {Gimli}$$ and its internal symmetries to build, for the first time, a distinguisher on the full permutation of complexity $$2^{64}$$ . We also provide a practical distinguisher on 23 out of the full 24 rounds of $$\mathsf {Gimli}$$ that has been implemented. Next, we give (full state) collision and semi-free-start collision attacks on $$\mathsf {Gimli}$$ -Hash, reaching respectively up to 12 and 18 rounds. On the practical side, we compute a collision on 8-round $$\mathsf {Gimli}$$ -Hash. In the quantum setting, these attacks reach 2 more rounds. Finally, we perform the first study of linear trails in the permutation, and we propose differential-linear cryptanalysis that reach up to 17 rounds of $$\mathsf {Gimli}$$ .
E-Banking services quality and customer loyalty: The moderating effect of customer service satisfaction: Empirical evidence from the UAE banking sector

Despite the importance of the relationship between E-banking service quality and customer loyalty in the banking sector, little research has been devoted to Arabian regions.This paper investigates the relationship between e-banking service quality (i.e., efficiency, requirement fulfillment, accessibility and, Privacy) and customer loyalty by mediating customer satisfaction in UAE.Two steps sampling techniques are used and the researchers rely on cluster sampling in the region of Ras Al Khaimah (RAK) randomly.Again, random sampling techniques are used to choose 28 branches out-off the 34 branches operating within this geographic area.1500 surveys are distributed and 397 valid surveys are collected with a response rate of 26.5%.A partial least squares structural equation modeling (PLS-SEM) is applied to the data and the results show that the direct and indirect path coefficient between E-banking service quality and customer loyalty were statistically significant.Moreover, customer satisfaction mediates the relationship between E-banking service quality and customer loyalty.To sum up, the findings corroborate existing work in Western settings and underscore the importance of E-banking service quality in the Arabian banking context.
Control Your Home With a Smartwatch

With the wide applications of smart devices and mobile computing, smart home becomes a hot issue in the household appliance industry. The controlling and interaction approach plays a key role in users' experience and turns into one of the most important selling points for profit growth. Considering the robustness and privacy protection, wearable devices equipped with MEMS, e.g., smartphones, smartwatches, or smart wristbands, are thought of one of the most feasible commercial solutions for interaction. However, the low-cost built-in MEMS sensors do not perform well in capturing finely grained human activity directly. In this paper, we propose a method that leverages the arm constraint and historical information recorded by MEMS sensors to estimate the maximum likelihood action in a two-phases model. First, in the arm posture estimation phase, we leverage the kinematics model to analyze the maximum likelihood position of users' arms. Second, in the trajectory recognition phase, we leverage the gesture estimation model to identify the key actions and output the instructions to devices by SVM. Our substantial experiments show that the proposed solution can recognize eight kinds of postures defined for man-machine interaction in the smart home application scene, and the solution implements efficient and effective interaction using low-cost smartwatches, and the interaction accuracy is >87%. The experiments also show that the algorithm proposed in this paper can be well applied to the perceptual control of smart household appliances, and has high practical value for the application design of the perceptual interaction function of household appliances.
Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis

Sentiment mining from the textual content on the web can give valuable insights for discernment, strategic decision making, targeted advertisement, and much more.Supervised machine learning (ML) approaches do not capture the sentiment inherent in the individual terms.Whereas the unsupervised sentiment lexicon (SL) based approaches lag behind ML approaches because of a bias they have towards one sentiment than the other.In this paper, we propose a hybrid approach that uses unsupervised sentiment lexicons to transform the term space into a twodimensional sentiment space on which a discriminative classifier is trained in a supervised fashion.This hybrid approach yields higher accuracy, faster training, and lower memory footprint than the ML approaches.It is more suitable for scenarios where training data is scarce.We support our claim by reporting results on six social media datasets using five sentiment lexicons and four ML algorithms.
Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service

Aims to provide flexible, effective and personalized online learning service, micro learning has gained wide attention in recent years as more people turn to use fragment time to grasp fragmented knowledge. Widely available online knowledge sharing is one of the most representative approaches to micro learning, and it is well accepted by online learners. However, information overload challenges such personalized online learning services. In this paper, we propose a deep cross attention recommendation model to provide online users with personalized resources based on users’ profile and historical online behaviours. This model benefits from the deep neural network, feature crossing, and attention mechanism mutually. The experiment result showed that the proposed model outperformed the state-of-the-art baselines.