Reviewerly/exHarmony huggingface dataset:
Enhanced Lightweight Cloud-assisted Mutual Authentication Scheme for Wearable Devices

With the emergence of IoT, wearable devices are drawing attention and becoming part of our daily life. These wearable devices collect private information about their wearers. Mostly, a secure authentication process is used to verify a legitimate user that relies on the mobile terminal. Similarly, remote cloud services are used for verification and authentication of both wearable devices and wearers. Security is necessary to preserve the privacy of users. Some traditional authentication protocols are proposed which have vulnerabilities and are prone to different attacks like forgery, de-synchronization, and un-traceability issues. To address these vulnerabilities, recently, Wu et al. (2017) proposed a cloud-assisted authentication scheme which is costly in terms of computations required. Therefore this paper proposed an improved, lightweight and computationally efficient authentication scheme for wearable devices. The proposed scheme provides similar level of security as compared to Wu's (2017) scheme but requires 41.2% lesser computations.
IdentifyNet for Non-Maximum Suppression

Two-stage object detectors have achieved great success in recent years. However, recent work mostly focuses on optimizing loss functions or learning multi-level feature representation, while introducing additional homogeneous task to improve detection has been under-explored. In this paper, a novel framework named as IdentifyNet is proposed, which incorporates an additional identification task to enhance the feature learning of region proposals. Specifically, besides classification and bounding box regression, the proposed IdentifyNet further learns to predict whether two different region proposals belong to the same object, thus forcing the network to learn more informative and representative features for different proposals, especially for those from the same object class. Moreover, current detectors apply greedy non-maximum suppression to remove duplicated boxes whenever their Intersaction-over-Union (IoU) exceeds a preset threshold, which would fail when two boxes largely overlap with each other while belonging to two different objects of the same class. To overcome this, we further propose a novel decode non-maximum suppression algorithm by taking advantage of the predicted identity information of different proposals from the identification task. Extensive experiments on PASCAL VOC 2007, VOC 2012 well demonstrate the proposed method can greatly improve detection performance.
Detecting Errors in Convolutional Neural Networks Using Inter Frame Spatio-Temporal Correlation

Object detection, a critical feature for autonomous vehicles, is performed today using Convolutional Neural Networks (CNNs). Errors in a CNN execution can modify the way the vehicle sense the surrounding environment, potentially causing accidents or unexpected behaviors. The high computational requirements of CNNs combined with the need to perform detection in real-time allow little margin for implementing error detection. In this paper, we present an extremely efficient error detection solution for CNN based on the observation that, in the absence of errors, the differences between the input frames and the detection provided by the CNN should be strictly correlated. In other words, if the image between two subsequent frames does not change significantly, the detection should also be very similar. Similarly, if the detection varies considerably from a frame to the next, then the input image should also have been different. Whenever input images and output detection don't correlate we can detect a error. After formalizing and evaluating the inter-frame and output correlation thresholds, we implement and validate the detection strategy, utilizing data from previous radiation experiments. Exploiting the intrinsic efficiency in processing images of devices used to execute CNNs, we can detect up to 80% of errors while adding low overhead.
Strength Characteristics of Heat-Affected Zones in Welded Aluminum Connections

This study proposes a methodology to predict the capacity of aluminum welded connections. In order to evaluate the material characteristics within the heat-affected zone, an inverse analysis methodology, using full-field measurements of the strain field using digital image correlation, was developed during uniaxial tensile tests on specimens extracted from gas metal arc welded 6061-T6 aluminum alloy plates. The identification of the constitutive law problem was formulated within the Virtual Fields Method. The inverse analysis methodology was compared with an identification process of the material in the vicinity of the weld using a fully coupled multiphysics simulation considering thermal, metallurgical, and mechanical mechanisms during heating and cooling. The simulation accounts for the nonhomogeneous hardening properties within the heat-affected zone to extract the constitutive material laws of a welded join. The proposed simulation methodology was used to analyze the structural response of a plate–square hollow structural section (SHSS) joint subjected to tensile loading. The predicted capacity of the specimens was compared with the experimental findings as well as analyses using Canadian code recommendations. It is shown that it is possible to improve the prediction of the capacity of welded aluminum connection using the Canadian recommendations if the width of the heat-affected zone is reduced to 15 mm instead of the original 25 mm.
<i>MarVeLScaler</i>: A Multi-View Learning-Based Auto-Scaling System for MapReduce

To promote cloud computing from current pay-per-request model to truly pay-per-use, tenants are crying for automatic tools to auto-estimate the amount of resources for MapReduce jobs. Such tools call for accurately quantifying the relationship among workload, resources and completion time. Various prediction models have been proposed. However, none of these models takes virtual machines&#x2019; (VMs) performance variance during a job&#x0027;s execution into consideration, leading to underestimate the required resources and exceed the job&#x0027;s deadline. To address this problem, we propose a multi-view deep learning model to capture real-time performance variance and automatically scale out the cloud cluster whenever necessary. We implement <i>MarVeLScaler</i>, a prototype system including two useful modules, namely, <i>Scale Estimator</i> and <i>Scale Controller</i>. Scale Estimator preliminarily estimates the required cluster size for a MapReduce job with given a concrete workload and deadline. During the runtime, Scale Controller adjusts the scale of the cluster according to its real-time running status to guarantee the job finished on time. We evaluate the performance of MarVeLScaler based on Hadoop in Alibaba Cloud. Experiments show that MarVeLScaler can provide 98.4 percent accuracy of prediction in determining initial cluster size, and save 30.8 percent of expense while still guaranteeing similar performance compared with the state-of-the-art methods.
Distributed Active Learning Strategies on Edge Computing

Fog platform brings the computing power from the remote cloud-side closer to the edge devices to reduce latency, as the unprecedented generation of data causes ineligible latency to process the data in a centralized fashion at the Cloud. In this new setting, edge devices with distributed computing capability, such as sensors, surveillance camera, can communicate with fog nodes with less latency. Furthermore, local computing (at edge side) may improve privacy and trust. In this paper, we present a new method, in which, we decompose the data processing, by dividing them between edge devices and fog nodes, intelligently. We apply active learning on edge devices; and federated learning on the fog node which significantly reduces the data samples to train the model as well as the communication cost. To show the effectiveness of the proposed method, we implemented and evaluated its performance on a benchmark images data set.
Been there, seen that, done that: Modification of visual exploration across repeated exposures

The underlying factors that determine gaze position are a central topic in visual cognitive research. Traditionally, studies emphasized the interaction between the low-level properties of an image and gaze position. Later studies examined the influence of the semantic properties of an image. These studies explored gaze behavior during a single presentation, thus ignoring the impact of familiarity. Sparse evidence suggested that across repetitive exposures, gaze exploration attenuates but the correlation between gaze position and the low-level features of the image remains stable. However, these studies neglected two fundamental issues: (a) repeated scenes are displayed later in the testing session, such that exploration attenuation could be a result of lethargy, and (b) even if these effects are related to familiarity, are they based on a verbatim familiarity with the image, or on high-level familiarity with the gist of the scene? We investigated these issues by exposing participants to a sequence of images, some of them repeated across blocks. We found fewer, longer fixations as familiarity increased, along with shorter saccades and decreased gaze allocation towards semantically meaningful regions. These effects could not be ascribed to tonic fatigue, since they did not manifest for images that changed across blocks. Moreover, there was no attenuation of gaze behavior when participants observed a flipped version of the familiar images, suggesting that gist familiarity is not sufficient for eliciting these effects. These findings contribute to the literature on memory-guided gaze behavior and provide novel insights into the mechanism underlying the visual exploration of familiar environments.
A Parallel Crime Activity Clustering Algorithm Based on Apache Spark Cloud Computing Platform

Nowadays, there are more and more criminal behaviors experiencing around the world, and crime spiking has become one of the most critical security and social issues in almost every country. It is critical to seek effective ways to discover these criminal behaviors and patterns and to carry out the prevention for the target place. In this paper, we formulate the problem of criminal behaviors and propose a Criminal Activity Clustering (CAC) algorithm. We introduce the fuzzy clustering method to detect potential criminal patterns in large-scale spatiotemporal datasets. In addition, for the improvement of the the proposed CAC algorithm performance, we implement a parallel solution for the algorithm in the Apache Spark cloud computing platform. The results of the experiment show that the proposed CAC algorithm can effectively detect accurate criminal patterns from large-scale spatiotemporal data.
Automatically Design Convolutional Neural Networks by Optimization With Submodularity and Supermodularity

The architecture of convolutional neural networks (CNNs) is a key factor of influencing their performance. Although deep CNNs perform well in many difficult problems, how to intelligently design the architecture is still a challenging problem. Focusing on two practical architectural design problems: to maximize the accuracy with a given forward running time and to minimize the forward running time with a given accuracy requirement, we innovatively utilize prior knowledge to convert architecture optimization problems into submodular optimization problems. We propose efficient Greedy algorithms to solve them and give theoretical bounds of our algorithms. Specifically, we employ the techniques on some public data sets and compare our algorithms with some other hyperparameter optimization methods. Experiments show our algorithms' efficiency.
NARMAX Model as a Sparse, Interpretable and Transparent Machine Learning Approach for Big Medical and Healthcare Data Analysis

Influenza and influenza-like illnesses are one of the leading causes of death in the world, resulting in heavy losses to individual families and nations. Accurate and timely forecasts of seasonal influenza would therefore crucially important to inform and facilitate public health decision-making for presenting and intervening influenza epidemics. System identification and data-driven modelling approaches play an indispensable role in analyzing and understanding complex processes including medical, healthcare and environmental time series. This paper aims to present a type of sparse, interpretable and transparent (SIT) model, which cannot only be used for future behavior prediction but more importantly for understanding the dependent relationship between the response variables of a system on potential independent variables (also known as input variables or predictors). An ideal candidate for such a SIT representation is the well-known NARMAX (nonlinear autoregressive moving average with exogenous inputs) model, which can be established based on input and output data of the system of interest, and the final refined model is usually simple, parsimonious and easy to interpret. The general framework of the NARMAX model is presented, and the state-of-the-art algorithms for such a SIT model estimation are described. Two case studies are provided to illustrate how well the SIT-NARMAX model can work for medical, healthcare and related data.
Curious Meta-Controller: Adaptive Alternation between Model-Based and Model-Free Control in Deep Reinforcement Learning

Recent success in deep reinforcement learning for continuous control has been dominated by model-free approaches which, unlike model-based approaches, do not suffer from representational limitations in making assumptions about the world dynamics and model errors inevitable in complex domains. However, they require a lot of experiences compared to model-based approaches that are typically more sample-efficient. We propose to combine the benefits of the two approaches by presenting an integrated approach called Curious Meta-Controller. Our approach alternates adaptively between model-based and model-free control using a curiosity feedback based on the learning progress of a neural model of the dynamics in a learned latent space. We demonstrate that our approach can significantly improve the sample efficiency and achieve near-optimal performance on learning robotic reaching and grasping tasks from raw-pixel input in both dense and sparse reward settings.
Towards A Deep Learning Question-Answering Specialized Chatbot for Objective Structured Clinical Examinations

Medical students undergo exams, called "Objective Structured Clinical Examinations" (OSCEs), to assess their medical competence in clinical tasks. In these OSCEs, a medical student interacts with a standardized patient, asking questions to complete a clinical assessment of the patient's medical case. In real OSCEs, standardized patients or "Actors" are recruited and trained to answer questions about symptoms mentioned in a script designed by the medical examiner. Developing a virtual conversational patient for OSCEs would lead to significant logistical savings. In this work, we develop a deep learning framework to improve the virtual patient's conversational skills. First, deep neural networks learned domain specific word embeddings. Then, long short-term memory networks derived sentence embeddings before a convolutional neural network model selected an answer to a given question from a script. Empirical results on a homegrown corpus showed that this framework outperformed other approaches, and reached an accuracy of 81%.
An Optimization Framework for Task Sequencing in Curriculum Learning

Curriculum learning in reinforcement learning is used to shape exploration by presenting the agent with increasingly complex tasks. The idea of curriculum learning has been largely applied in both animal training and pedagogy. In reinforcement learning, all previous task sequencing methods have shaped exploration with the objective of reducing the time to reach a given performance level. We propose novel uses of curriculum learning, which arise from choosing different objective functions. Furthermore, we define a general optimization framework for task sequencing and evaluate the performance of popular metaheuristic search methods on several tasks. We show that curriculum learning can be successfully used to: improve the initial performance, take fewer suboptimal actions during exploration, and discover better policies.
AFSSE: An Interpretable Classifier With Axiomatic Fuzzy Set and Semantic Entropy

In this article, a novel interpretable classifier is proposed on the basis of axiomatic fuzzy set (AFS) theory and semantic entropy. AFS theory provides a unified and coherent way to deal with uncertainty of randomness and imprecision of fuzziness in data mining and knowledge discovery, which support many investigations in classification area. However, the existing AFS-based classifiers are weak in obtaining the optimal semantic description. To address this drawback, a new measure, named semantic entropy extended in Shannon's entropy, is developed to evaluate the discriminatory capabilities of semantic descriptions for each category. Moreover, the semantic entropy is utilized to design a classifier in the framework of AFS theory, called axiomatic fuzzy set and semantic entropy (AFSSE), which is capable of achieving sound classification performance and interpretability. Meanwhile, it provides a new framework of classifier design that can adapt more human-oriented recognition mechanisms. Furthermore, an evaluation index is used to prune descriptions to deliver a promising performance. Compared to the previous AFS-based classifiers, the proposed approach offers a semantic entropy to measure the information that is derived from semantic descriptions of data, so that the optimal semantic descriptions of each class can be obtained. For the purpose of illustrating the effectiveness of the classifier, several datasets are utilized to facilitate a comparative analysis of the proposed approach and other state-of-the-art classifiers. The experimental studies demonstrate that the proposed approach can achieve the semantic descriptions of each class and the performance of AFSSE is comparable with the performance of other approaches.
A cross-media heterogeneous transfer learning for preventing over-adaption

Cross-media heterogeneous transfer learning aims to transfer knowledge from the source media domain to the target media domain, which promotes the performance of the learned model for the target media domain. Existing cross-media heterogeneous transfer learning methods usually attempt to learn the latent feature space with a large amount of co-occurrence data. However, there is a significant challenge: domain over-adaption. In this paper, we propose a Cross-Media Heterogeneous Transfer Learning for Preventing Over-adaption (CMHTL-PO) to address this challenge. The divergence between the different media feature spaces is very large. Each media space has some weak correlation features which have no semantic corresponding features in other media. When the co-occurrence data are not enough, if the weak correlation features are compulsively mapped into the common features in the latent space, it will lead to over-adaption. CMHTL-PO divides the features into the strong correlation features and the weak correlation features, which are respectively mapped into the common features and the peculiar features in the latent space. Extensive experiments are conducted on two benchmark datasets widely adopted in transfer learning to verify the superiority of our proposed CMHTL-PO over existing state-of-the-art Heterogeneous Transfer Learning methods.
Deep Learning with Big Data: An Emerging Trend

Big data is an emerging trend of information technology. Large amount of data are growing very fast these days. To handle this huge amount of data, we need a technology which can extract the complex representation of data. Several approaches exist nowadays; one of the latest paradigm is deep learning. To excerpt difficult representation automatically, deep learning algorithms are used widely for unsupervised data. To resolve an extremely complex problem we used deep learning algorithm which is inspired by field of Artificial intelligence, whose goal is match human brain's potential to perceive, evaluate, grasp and decision making. The real motivation of the deep learning algorithm is to handle complex problem effectively. The unsupervised data representation is directly extracted without the participation of human. The main goal of deep learning is to make a machine which is independent of human intervention. It generates some patterns and their relationship, the algorithms perform an effective task based on that data.
Fuzzy logic and Genetic Algorithm based Text Classification Twitter

Social media are a modern web-based application for communication and interaction between humans through audio messages, written messages, and video messages. These devices build and activate living communities around the world. People share their interests and activities with these Applications. Twitter is a social media site, where people communicate through tweets. A service that enables users to communicate in touch through the exchange of frequent tweets and quick. People publish their tweets on their profile and send their followers to express their thoughts and opinions about events in this world. It is crucial to study and categorize these tweets. This work is based on fuzzy logic and genetic algorithm to solve the problem of text classification based on the relevance degree. The Inputs for this classification system are a set of features extracted from a tweet, and the output of this system is a decision of classification for a tweet, which is a degree of relevance for each tweet to an appointed event where the degree of relevance to the desired event if tweet irrelevant or relevant. The results are compared with a method of keyword search and fuzzy logic, which is based on the method incremental rate and correction rate. In the incremental rate, the proposed system is able to extract tweets more than this method, wherein dataset 1, the number of the tweets that are extracted by the proposed system is 160 tweets, but the number of the tweets that extracted by the other one are 98 and 141. The correction rate of the proposed system is (98.75), but the correction rates of this method are (97.9) and (95.7).
Shape Decision-Making in Map-Like Visualization Design Using the Simulated Annealing Algorithm

Semantic information without spatial characteristics can also be visualised in the form of a map. This map type is usually called map-like visualisation; it is not a representation of a real geo-entity but borrows the map metaphor. In current map-like visualisation works, the manipulation of the cartographic process is poor, and there is a considerable amount of randomness and uncertainty in the map results. For instance, in the process of converting semantic objects to map objects, the map shape is usually uncertain and has many possibilities. Frequently, however, we need shapes to follow certain principles, such as the principles of being close to a certain shape and having a simple geometry, to facilitate recognition and mapping. To achieve a balance between uncertain shapes and design needs, this study enhances the realisation of the cartographer’s design idea by improving the controllability in the cartographic process. An optimisation technology, the simulated annealing algorithm, is introduced to control the determination of shape to improve mapping efficiency. Experiments based on real data show that based on this method, the map-like representation not only creates a whole outline close to the pre-defined shape but also realises internal regions with a simple shape.