Reviewerly/exHarmony huggingface dataset:
An Efficient Elliptic Curve Cryptography-Based Without Pairing KPABE for Internet of Things

The increasing deployment of Internet of Things in diversified fields provides groundwork for smart living. However, at the same time, it brings challenges like security and privacy. Among the numerous proposed security schemes, attribute-based encryption (ABE) is considered as one of the most promising security and privacy preserving scheme in distributed environment (or cloud environment). Many variants of the ABE exist, but most of them employ the expensive bilinear pairing operations. In this article, we have proposed a lightweight elliptic-curve-cryptography-based key-policy ABE without bilinear pairing and having a feature of key refresh/update mechanism. Also, the key distribution is done by the authority, which incorporates direct attribute/user revocation. The scheme is secured under elliptic curve decisional Diffie-Hellman assumption. The performance analysis demonstrates that the proposed scheme is efficient as compared to the existing ABE schemes.
Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning

Purpose To automatically and efficiently segment the lesion area of the colonoscopy polyp image, a polyp segmentation method has been presented. Methods An ensemble model of pretrained convolutional neural networks was proposed, using Unet‐VGG, SegNet‐VGG, and PSPNet. Firstly, the Unet‐VGG is obtained by the first 10 layers of VGG16 as the contraction path of the left half of the Unet. Then, the SegNet‐VGG is acquired by fine‐tuned transfer learning VGG16, using the first 13 layers of VGG16 as the encoder of the SegNet and combined the original decoder of the SegNet. By adjusting the input size of the Unet‐VGG, SegNet‐VGG, and PSPNet, the preprocessed data can be correctly fed to the three network models. The three models are used as the basic trainer to train and segment the datasets. Based on the ensemble learning algorithm, the weight voting method is used to ensemble the segmentation results corresponding to single basic trainer. Results Both IoU and DICE similarity score were used to evaluate the segmentation quality for cvc300 with 300 images, CVC‐ClinicDB with 612 images, and ETIS‐LaribPolypDB with 196 images. From the experimental results, the IoU and DICE obtained by the proposed method for the cvc300 datasets can reach up to 96.16% and 98.04%, respectively, the IoU and DICE for the CVC‐ClinicDB datasets can reach up to 96.66% and 98.30%, respectively, whereas the IoU and DICE for the ETIS‐LaribPolypDB datasets can reach up to 96.95% and 98.45%, respectively. Evaluation of the IoU and DICE in our methods shows higher accuracy than previous methods. Conclusions The experimental results show that the proposed method improved correspondingly in IoU and DICE compared to a single basic trainer. The range of improvement is 1.98%–6.38% . The proposed ensemble learning succeeds in automatic polyp segmentation, which potentially helps to establish more polyp datasets.
Landscape-assisted multi-operator differential evolution for solving constrained optimization problems

Over time, many differential evolution (DE) algorithms have been proposed for solving constrained optimization problems (COPs). However, no single DE algorithm was found to be the best for many types of COPs. Although researchers tried to mitigate this shortcoming by using multiple DE algorithms under a single algorithm structure, while putting more emphasis on the best-performing one, the use of landscape information in such designs has not been fully explored yet. Therefore, in this research, a multi-operator DE algorithm is developed, which uses a landscape-based indicator to choose the best-performing DE operator throughout the evolutionary process. The performance of the proposed algorithm was tested by solving a set of constrained optimization problems, 22 from CEC2006, 36 test problems from CEC2010 (18 with 10D and 18 with 30D), 10 real-application constrained problems from CEC2011 and 84 test problems from CEC2017 (28 with 10D, 28 with 30D and 28 with 50D). Several experiments were designed and carried out, to analyze the effects of different components on the proposed algorithm's performance, and the results from the final variant of the proposed algorithm were compared with different variants of the same algorithm with different selection criteria. Subsequently, the best variant found after analyzing the algorithm's components, was compared to several state-of-the-art algorithms, with the results showing the capability of the proposed algorithm to attain high-quality results.
Aspect Aware Learning for Aspect Category Sentiment Analysis

Aspect category sentiment analysis (ACSA) is an underexploited subtask in aspect level sentiment analysis. It aims to identify the sentiment of predefined aspect categories. The main challenge in ACSA comes from the fact that the aspect category may not occur in the sentence in most of the cases. For example, the review “ they have delicious sandwiches ” positively talks about the aspect category “ food ” in an implicit manner. In this article, we propose a novel aspect aware learning (AAL) framework for ACSA tasks. Our key idea is to exploit the interaction between the aspect category and the contents under the guidance of both sentiment polarity and predefined categories. To this end, we design a two-way memory network for integrating AAL into the framework of sentiment classification. We further present two algorithms to incorporate the potential impacts of aspect categories. One is to capture the correlations between aspect terms and the aspect category like “sandwiches” and “food.” The other is to recognize the aspect category for sentiment representations like “food” for “delicious.” We conduct extensive experiments on four SemEval datasets. The results reveal the essential role of AAL in ACSA by achieving the state-of-the-art performance.
Input Feature Selection Method Based on Feature Set Equivalence and Mutual Information Gain Maximization

Feature selection is the first and essential step for dimension reduction in many application areas, such as data mining and machine learning, due to its computational efficiency and interpretability of the results. This paper focuses on feature selection methods based on information theory. By studying and analyzing the ideas and drawbacks of existing feature selection methods, it finds that in the process of feature selection separately focuses on a candidate feature its individual relationship with the predicted class vector may lead to some problems. And we believe that when the candidate feature is combined with the selected features, its comprehensive discriminative ability should be taken as the evaluation index of the candidate feature. Therefore, we propose a novel feature selection method in this paper. In the proposed method, we introduced the equivalent partition concept and adopted the mutual information gain maximize (MIGM) criterion to evaluate the candidate feature. In order to estimate the performance of MIGM, we conducted experiments on ten benchmark datasets and two different classifiers, k-Nearest Neighbor (KNN) and Naïve-Bayes (NB). Extensive experimental results demonstrate that our method can identify an effective feature subset that leads to better classification results than other methods.
New Method of Image Steganography Based on Particle Swarm Optimization Algorithm in Spatial Domain for High Embedding Capacity

Steganography is a form of technology utilised to safeguard secret data during communication in addition to data repository.Numerous researchers have endeavoured to enhance the performance of steganography techniques through the development of an effective algorithm for the selection of the optimal pixel location within the host image for the concealment of secret bits, for the enhancement of the embedding capacity of the secret data, and for maintaining the visual quality of the host image (stego image) in an accepted rate after the concealment of the secret data.Therefore, steganography is perceived as a challenging task.Thus, the current study proposes a new technique for image steganography based on particle swarm optimisation (PSO) algorithm by using pixel selection for the concealment of a secret image in spatial domain, for the purpose of high embedment capacity.The stego possesses a high level of resistance against a steganalytic attack due to the security provided via image steganography.The function of PSO algorithm is to choose an optimal pixel in grey scale host image for the concealment of secret bits, as the PSO has the ability to achieve an efficient fitness calculation that depends on the cost matrix by dividing the host and secret images into four parts.First of all, the secret bits are modified, which are then embedded within the host image.Several locations in the host image are determined through the order of scanning the host pixels and starting point of the scanning for better least significant bits LSBs of each pixel.The PSO algorithm was utilised to ascertain the ideal initiating point and scanning order.Experimental results show that (1) the average peak signal to noise ratio PSNR value in the benchmark technique based on genetic algorithm for five standard stego images is 45.13%, whereas the result obtained from the recommended technique is 56.60%.(2) The proposed technique has an advantage over the benchmark with a percentage of 33.34%, which encompasses all associated issues within the checklist scenario.Therefore, the performance of the recommended technique is superior over existing techniques.
Path planning for moving target tracking by fixed-wing UAV

For the automatic tracking of unknown moving targets on the ground, most of the commonly used methods involve circling above the target. With such a tracking mode, there is a moving laser spot on the target, which will bring trouble for cooperative manned helicopters. In this paper, we propose a new way of tracking, where an unmanned aerial vehicle (UAV) circles on one side of the tracked target. A circular path algorithm is developed for monitoring the relative position between the UAV and the target considering the real-time range and the bearing angle. This can determine the center of the new circular path if the predicted range between the UAV and the target does not meet the monitoring requirements. A transition path algorithm is presented for planning the transition path between circular paths that constrain the turning radius of the UAV. The transition path algorithm can generate waypoints that meet the flight ability. In this paper, we analyze the entire method and detail the scope of applications. We formulate an observation angle as an evaluation index. A series of simulations and evaluation index comparisons verify the effectiveness of the proposed algorithms.
Virtual exchange for (critical) digital literacy skills development

This article examines the ways in which transcultural and digital literacy skills may be enhanced by telecollaboration, a model of virtual exchange (VE). After an overview of the literature on telecollaboration in language teacher education with a focus on digital literacy, it considers the potential of critical digital literacy development through VE. In particular, it argues for the relevance of critical digital literacy in relation to the characteristics of the twenty-first-century graduate, and the potential of the telecollaborative model of VE to develop skills to this effect in language teachers and subsequently their students. The EVALUATE project is presented, including its rationale and the methods used for data gathering and analysis, followed by the presentation and discussion of the main project findings in terms of the participants' digital pedagogical competence development and evidence of emerging critical digital literacy. Finally, it offers some concluding remarks and points at gaps that remain to be addressed, if we want to draw maximum benefit from VE for promoting a critical and informed approach to the use of technologies first in language teachers and teacher trainees and thereafter also in their students.
RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments

In recent years gaze estimation methods have made substantial progress, driven by the numerous application areas including human-robot interaction, visual attention estimation and foveated rendering for virtual reality headsets. However, many gaze estimation methods typically assume that the subject's eyes are open; for closed eyes, these methods provide irregular gaze estimates. Here, we address this assumption by first introducing a new open-sourced dataset with annotations of the eye-openness of more than 200,000 eye images, including more than 10,000 images where the eyes are closed. We further present baseline methods that allow for blink detection using convolutional neural networks. In extensive experiments, we show that the proposed baselines perform favourably in terms of precision and recall. We further incorporate our proposed RT-BENE baselines in the recently presented RT-GENE gaze estimation framework where it provides a real-time inference of the openness of the eyes. We argue that our work will benefit both gaze estimation and blink estimation methods, and we take steps towards unifying these methods.
Natural Language Processing Methods for Acoustic and Landmark Event-Based Features in Speech-Based Depression Detection

The processing of speech as an explicit sequence of events is common in automatic speech recognition (linguistic events), but has received relatively little attention in paralinguistic speech classification despite its potential for characterizing broad acoustic event sequences. This paper proposes a framework for analyzing speech as a sequence of acoustic events, and investigates its application to depression detection. In this framework, acoustic space regions are tokenized to `words' representing speech events at fixed or irregular intervals. This tokenization allows the exploitation of acoustic word features using proven natural language processing methods. A key advantage of this framework is its ability to accommodate heterogeneous event types: herein we combine acoustic words and speech landmarks, which are articulation-related speech events. Another advantage is the option to fuse such heterogeneous events at various levels, including the embedding level. Evaluation of the proposed framework on both controlled laboratory-grade supervised audio recordings as well as unsupervised self-administered smartphone recordings highlight the merits of the proposed framework across both datasets, with the proposed landmark-dependent acoustic words achieving improvements in F1(depressed) of up to 15% and 13% for SH2-FS and DAIC-WOZ respectively, relative to acoustic speech baseline approaches.
Effective K-nearest neighbor classifications for Wisconsin breast cancer data sets

Using machine learning algorithms for early prediction of the signs and symptoms of breast cancer is in demand nowadays. One of these algorithms is the K-nearest neighbor (KNN), which uses a technique for measuring the distance among data. The performance of KNN depends on the number of neighboring elements known as the K value. This study involves the exploration of KNN performance by using various distance functions and K values to find an effective KNN. Wisconsin breast cancer (WBC) and Wisconsin diagnostic breast cancer (WDBC) datasets from the UC Irvine machine learning repository were used as our main data sources. Experiments with each dataset were composed of three iterations. The first iteration of the experiment was without feature selection. The second one was the L1-norm based selection from the model, which used the linear support vector classifier feature selection, and the third iteration was with Chi-square-based feature selection. Numerous evaluation metrics like accuracy, receiver operating characteristic (ROC) curve with the area under curve (AUC) and sensitivity, etc., were used for the assessment of the implemented techniques. The results indicated that the technique involving the Chi-square-based feature selection achieved the highest accuracy with the Canberra or Manhattan distance functions for both datasets. The optimal K values for these distance functions ranged from 1 to 9. This study indicated that with the appropriate selection of the K value and a distance function in KNN, the Chi-square-based feature selection for the WBC datasets gives the highest accuracy rate as compared with the existing models.Abbreviations: KNN: K-nearest neighbor; Chi2: Chi-square; WBC: Wisconsin breast cancer
Benchmarking deep learning techniques for face recognition

Recent progresses in Convolutional Neural Networks (CNNs) and GPUs have greatly advanced the state-of-the-art performance for face recognition. However, training CNNs for face recognition is complex and time-consuming. Multiple factors need to be considered: deep learning frameworks, GPU platforms, deep network models, training datasets and test datasets. The deep models under different frameworks may perform differently. Based on this concern, we compare three deep learning frameworks and benchmark the performance of different CNN models on five GPU platforms. The scalability issue is also explored. Our findings can help researchers select appropriate face recognition models, deep learning frameworks, GPU platforms, and training datasets for their face recognition tasks.
Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery

Recent work has applied machine learning methods to automatically find and/or assess pedestrian infrastructure in online map imagery (e.g., satellite photos, streetscape panoramas). While promising, these methods have been limited by two interrelated issues: small training sets and the choice of machine learning model. In this paper, aided by the recently released Project Sidewalk dataset of 300,000+ image-based sidewalk accessibility labels, we present the first examination of deep learning to automatically assess sidewalks in Google Street View (GSV) panoramas. Specifically, we investigate two application areas: automatically validating crowdsourced labels and automatically labeling sidewalk accessibility issues. For both tasks, we introduce and use a residual neural network (ResNet) modified to support both image and non-image (contextual) features (e.g., geography). We present an analysis of performance, the effect of our non-image features and training set size, and cross-city generalizability. Our results significantly improve on prior automated methods and, in some cases, meet or exceed human labeling performance.
Reinforcement Learning and Deep Neural Networks for PI Controller Tuning

Reinforcement Learning, using deep neural networks, has recently gained prominence owing to its ability to train autonomous agents that have defeated human players in various complex games. Here, Reinforcement Learning is applied to the challenge of automatically tuning a proportional-integral controller, given only the process variable, set-point, manipulated variable and prior controller gains. The training considers random changes in plant dynamics, disturbances and measurement noise. Two training procedures were tested in this work, one that built up the difficulty of the simulation over time, and another that used the full complexity of the simulation throughout the training. The results show that building up the difficulty of the simulation by introducing greater degrees of randomness as the training progresses, produces an agent that is better able to tune the controller in question. Additional experience gathered in completing this work is also discussed to enable the reader to avoid some of the challenges encountered.
Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning

Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to n target domains (as long as there is paired data covering all domains). Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning. We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains. The proposed method significantly outperforms other domain adaptation baselines.
Predicting Animation Skeletons for 3D Articulated Models via Volumetric Nets

We present a learning method for predicting animation skeletons for input 3D models of articulated characters. In contrast to previous approaches that fit pre-defined skeleton templates or predict fixed sets of joints, our method produces an animation skeleton tailored for the structure and geometry of the input 3D model. Our architecture is based on a stack of hourglass modules trained on a large dataset of 3D rigged characters mined from the web. It operates on the volumetric representation of the input 3D shapes augmented with geometric shape features that provide additional cues for joint and bone locations. Our method also enables intuitive user control of the level-of-detail for the output skeleton. Our evaluation demonstrates that our approach predicts animation skeletons that are much more similar to the ones created by humans compared to several alternatives and baselines.
Detection of Rogue RF Transmitters using Generative Adversarial Nets

Understanding and analyzing the radio frequency (RF) environment have become indispensable for various autonomous wireless deployments. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize a RF environment. However, classical machine learning methods have their limitations and there are situations where such methods become ineffective. One such setting is where active adversaries are present and try to disrupt the RF environment through malicious activities like jamming or spoofing. In this paper we propose an adversarial learning technique for identifying rogue RF transmitters and classifying trusted ones by designing and implementing generative adversarial nets (GAN). The GAN exploits the in-phase (I) and quadrature imbalance (i.e., the IQ imbalance) present in all transmitters to learn the unique high dimensional features that can be used as “fingerprints” for identifying and classifying the transmitters. We implement a generative model that learns the sample space of the IQ values of the known transmitters and use the learned representation to generate fake signals that imitate the transmissions of the known transmitters. We program 8 universal software radio peripheral (USRP) software defined radios as trusted transmitters and collect over-the-air raw IQ data from them using a RTL-SDR in a laboratory setting. We also implement a discriminator model and show that the discriminator is able to discriminate between the trusted transmitters from fake ones with 99.9% accuracy. Finally, the trusted transmitters are classified using convolutional neural network (CNN) and fully connected deep neural networks (DNN). Results reveal that the CNN and DNN are able to correctly discriminate between the 8 trusted transmitters with 81.6% and 96.6% accuracies respectively.