Reviewerly/exHarmony huggingface dataset:
Traffic Control Gesture Recognition for Autonomous Vehicles

A car driver knows how to react on the gestures of the traffic officers. Clearly, this is not the case for the autonomous vehicle, unless it has road traffic control gesture recognition functionalities. In this work, we address the limitation of the existing autonomous driving datasets to provide learning data for traffic control gesture recognition. We introduce a dataset that is based on 3D body skeleton input to perform traffic control gesture classification on every time step. Our dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence. To evaluate our dataset, we propose eight sequential processing models based on deep neural networks such as recurrent networks, attention mechanism, temporal convolutional networks and graph convolutional networks. We present an extensive evaluation and analysis of all approaches for our dataset, as well as real-world quantitative evaluation. The code and dataset is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> .
Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology

For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.
Mass Image Synthesis in Mammogram with Contextual Information Based on GANs

Background and Objective: In medical imaging, the scarcity of labeled lesion data has hindered the application of many deep learning algorithms. To overcome this problem, the simulation of diverse lesions in medical images is proposed. However, synthesizing labeled mass images in mammograms is still challenging due to the lack of consistent patterns in shape, margin, and contextual information. Therefore, we aim to generate various labeled medical images based on contextual information in mammograms. Methods:In this paper, we propose a novel approach based on GANs to generate various mass images and then perform contextual infilling by inserting the synthetic lesions into healthy screening mammograms. Through incorporating features of both realistic mass images and corresponding masks into the adversarial learning scheme, the generator can not only learn the distribution of the real mass images but also capture the matching shape, margin and context information. Results:To demonstrate the effectiveness of our proposed method, we conduct experiments on publicly available mammogram database of DDSM and a private database provided by Nanfang Hospital in China. Qualitative and quantitative evaluations validate the effectiveness of our approach. Additionally, through the data augmentation by image generation of the proposed method, an improvement of 5.03% in detection rate can be achieved over the same model trained on original real lesion images. Conclusions:The results show that the data augmentation based on our method increases the diversity of dataset. Our method can be viewed as one of the first steps toward generating labeled breast mass images for precise detection and can be extended in other medical imaging domains to solve similar problems.
Phishing Evolves: Analyzing the Enduring Cybercrime

Phishing, the fraudulent attempt to obtain sensitive information by disguising oneself as a trustworthy entity via electronic communication, has quickly evolved beyond low-skill schemes that relied on casting “a wide net.” Spear phishing attacks target a particular high-value individual utilizing sophisticated techniques. This study aims to describe the current state of phishing, the expected technological advances and developments of the near future, and the best prevention and enforcement strategies. Data comes from interviews with approximately 60 information technology security professionals, “hackers,” and academic researchers. Routine Activity Theory provided an operational framework; while it is an imperfect fit for most crimes, it provides enough explanatory power for cyber-crimes. Interviewees mainly agreed: First, technological advances increase the proliferation of phishing attacks, but also aid in their detection. It has never been easier to conduct a simple attack, but a good attack requires more effort than ever before. Second, phishing is directly responsible financial fraud and, indirectly, as the primary attack vector for ransomware. Third, newer types of attacks utilizing technology, like deepfakes, will make the problem worse in the short-term. Fourth, prevention will come from machine learning and public education akin to WIFI security improvement via the combination of encryption and password awareness.
Spatial-Temporal Multi-Cue Network for Sign Language Recognition and Translation

Despite the recent success of deep learning in video-related tasks, deep models typically focus on the most discriminative features, ignoring other potentially non-trivial and informative contents. Such characteristic heavily constrains their capability to learn implicit visual grammars in sign videos behind the collaboration of different visual cues (i.e., hand shape, facial expression and body posture). To this end, we approach video-based sign language understanding with multi-cue learning and propose a spatial-temporal multi-cue (STMC) network to solve the vision-based sequence learning problem. Our STMC network consists of a spatial multi-cue (SMC) module and a temporal multi-cue (TMC) module. The SMC module learns to spatial representation of different cues with a self-contained pose estimation branch. The TMC module models temporal corrections from intra-cue and inter-cue perspectives to explore the collaboration of multiple cues. A joint optimization strategy and a segmented attention mechanism are designed to make the best of multi-cue sources for SL recognition and translation. To validate the effectiveness, we perform experiments on three large-scale sign language benchmarks: PHOENIX-2014, CSL and PHOENIX-2014-T. Experimental results demonstrate that the proposed method achieves new state-of-the-art performance on all three benchmarks.
A survey of RDF stores &amp; SPARQL engines for querying knowledge graphs

RDF has seen increased adoption in recent years, prompting the standardization of the SPARQL query language for RDF, and the development of local and distributed engines for processing SPARQL queries. This survey paper provides a comprehensive review of techniques and systems for querying RDF knowledge graphs. While other reviews on this topic tend to focus on the distributed setting, the main focus of the work is on providing a comprehensive survey of state-of-the-art storage, indexing and query processing techniques for efficiently evaluating SPARQL queries in a local setting (on one machine). To keep the survey self-contained, we also provide a short discussion on graph partitioning techniques used in the distributed setting. We conclude by discussing contemporary research challenges for further improving SPARQL query engines. An extended version also provides a survey of over one hundred SPARQL query engines and the techniques they use, along with twelve benchmarks and their features.
MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks

This paper proposes a client selection method for federated learning (FL) when the computation and communication resource of clients cannot be estimated; the method trains a machine learning (ML) model using the rich data and computational resources of mobile clients without collecting their data in central systems. Conventional FL with client selection estimates the required time for an FL round from a given clients' computation power and throughput and determines a client set to reduce time consumption in FL rounds. However, it is difficult to obtain accurate resource information for all clients before the FL process is conducted because the available computation and communication resources change easily based on background computation tasks, background traffic, bottleneck links, etc. Consequently, the FL operator must select clients through exploration and exploitation processes. This paper proposes a multi-armed bandit (MAB)-based client selection method to solve the exploration and exploitation trade-off and reduce the time consumption for FL in mobile networks. The proposed method balances the selection of clients for which the amount of resources is uncertain and those known to have a large amount of resources. The simulation evaluation demonstrated that the proposed scheme requires less learning time than the conventional method in the resource fluctuating scenario.
A combined online-learning model with K-means clustering and GRU neural networks for trajectory prediction

An aircraft has an obvious state division in a complete flight mission, the data of the trajectory points also have specific characteristics in each flight state, so the trajectory data with the similar characteristics can improve the performance of neural networks. Therefore, a combined online-learning model with K-means clustering and gated recurrent unit (GRU) neural networks for trajectory prediction is proposed in this paper. In the new model, the K-means clustering algorithm is used to adaptively cluster the trajectory points of the aircraft, and the trajectory points with higher similarity are grouped into a same cluster. Then, the online-learning prediction model based on a GRU neural networks is used to learn from the trajectory points of each cluster separately. Finally, the performance superiority of the new model proposed in this paper is tested and verified with the fused flight data of secondary radar and automatic dependent surveillance-broadcast (ADS-B).
A multi-leader whale optimization algorithm for global optimization and image segmentation

In this paper, a multilevel thresholding image segmentation method base on the enhancement of the performance of the whale optimization algorithm (WOA). The developed method, called the multi-leader whale optimization algorithm (MLWOA), aims to avoid the limitations of traditional WOA during the searching process, such as stagnation at the local optimum. This was achieved by integrating the different tools with WOA, such as memory mechanism, multi-leader method, self-learning strategy, and levy flight method. Each of these techniques has its own task, for example, the memory structure of traditional WOA and add a multi-leader mechanism to enhance the ability of exploration. The superiority of leaders will make more influence in MLWOA by adding a self-learning strategy. Also, it used levy flight trajectory to make the algorithm more robust and avoid premature convergence. To evaluate the performance of the developed MLWOA, a set of experiments are conducted using the CEC2017 benchmark. In addition, it is applied to determine the optimal threshold values to segment a set of images using the Otsu method, fuzzy entropy, and Kapur's entropy as a fitness function. The results of MLWOA are compared with well-known meta-heuristic algorithms inside the experiments. The comparison results indicated that MLWOA provides better performance in CEC2017 benchmark functions and shows high superiority in image segmentation in terms of performance measures. In addition, the MLWOA provides better results using Otsu, followed by the Fuzzy entropy and Kapur in terms of PSNR. In terms of SSIM, fuzzy entropy and Otsu have nearly the same SSIM value, but the fuzzy entropy provides better results.
Greed Works: An Improved Analysis of Sampling Kaczmarz--Motzkin

Stochastic iterative algorithms have gained recent interest in machine learning and signal processing for solving large-scale systems of equations, $A{x}={b}$. One such example is the randomized Kaczmarz (RK) algorithm, which acts only on single rows of the matrix $A$ at a time. While RK randomly selects a row of $A$ to work with, Motzkin's Method (MM) employs a greedy row selection. Connections between the two algorithms resulted in the Sampling Kaczmarz--Motzkin (SKM) algorithm, which samples a random subset of $\beta$ rows of $A$ and then greedily selects the best row of the subset. Despite their variable computational costs, all three algorithms have been proven to have the same theoretical upper bound on the convergence rate. In this work, an improved analysis of the range of random (RK) to greedy (MM) methods is presented. This analysis improves upon previous known convergence bounds for SKM, capturing the benefit of partially greedy selection schemes. This work also further generalizes previous known results, removing the theoretical assumptions that $\beta$ must be fixed at every iteration and that $A$ must have normalized rows.
Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm

Abstract As of January 2021, with 2,066,175 deaths, 95,612,831 confirmed cases have been reported globally. Indonesia’s COVID-19 Task Force report shows that there are currently 27,203 deaths, with reported cases exceeding 951,651, among the highest in Asia. The President of the Republic of Indonesia created a national team to speed up the production of vaccines for COVID-19. It stipulates that the government will arrange the provision, delivery, and vaccination of COVID-19 vaccines. The vaccination scheme would then become the pros and cons of Indonesian society. This research assesses the opinion of the Indonesian people through a social network analysis of the COVID-19 vaccine in January 2021. We used sentiment analysis using Naïve Bayes Algorithm by crawling Twitter data with ‘Vaccine COVID-19’ as keywords. We perform the data crawling process manually using the access token received from the Twitter API using the Rapid miner tools to extract the requested information and data. Data crawling continued with the Drone Emprit Academic Streaming Public Twitter Tool because of limited manual crawling resulting in more than 6000 tweets related to selected keywords on January 15-22, 2021. The result of sentiment measurement with over 3.4 thousand negative tweets (56%), over 2.4 thousand positive tweets (39%), and the remaining 301 tweets (1%) was neutral during the period of the week.
Exploring region relationships implicitly: Image captioning with visual relationship attention

Visual attention mechanism has been widely used by image captioning model in order to dynamically attend to the related visual region based on given language information. Such capability allows a trained model to carry out fine-grained level image understanding and reasoning. However, existing visual attention models only focus on the individual visual region in the image and the alignment between the language representation and related individual visual regions. It does not fully explore the relationships/interactions between visual regions. Furthermore, it does not analyze or explore alignment for related words/phrases (e.g. verb or phrasal verb), which may best describe the relationships/interactions between these visual regions. Thus, it causes the inaccurate or impropriate description to the current image captioning model. Instead of visual region attention commonly addressed by existing visual attention mechanism, this paper proposes the novel visual relationship attention via contextualized embedding for individual regions. It can dynamically explore a related visual relationship existing between multiple regions when generating interaction words. Such relationship exploring process is constrained by spatial relationships and driven by the linguistic context of language decoder. In this work, such new visual relationship attention is designed through a parallel attention mechanism under the learned spatial constraint in order to more precisely map visual relationship information to the semantic description of such relationship in language. Different from existing methods for exploring the visual relationship, it is trained implicitly through an unsupervised approach without using any explicit visual relationship annotations. By integrating the newly proposed visual relationship attention with existing visual region attention, our image captioning model can generate high-quality captions. Solid experiments on the MSCOCO dataset demonstrate the proposed visual relationship attention can effectively boost the captioning performances by capturing related visual relationships for generating accurate interaction descriptions.
Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.
An alternative approach to dimension reduction for pareto distributed data: a case study

Abstract Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many important questions, their utility is still harshly criticized, being extremely challenging to identify which data descriptors are the most adequate to represent a given specific phenomenon of interest. With a recent experience in the development of a deep learning model designed to detect failures in mechanical water meter devices, we have learnt that a sensible deterioration of the prediction accuracy can occur if one tries to train a deep learning model by adding specific device descriptors, based on categorical data. This can happen because of an excessive increase in the dimensions of the data, with a correspondent loss of statistical significance. After several unsuccessful experiments conducted with alternative methodologies that either permit to reduce the data space dimensionality or employ more traditional machine learning algorithms, we changed the training strategy, reconsidering that categorical data, in the light of a Pareto analysis . In essence, we used those categorical descriptors, not as an input on which to train our deep learning model, but as a tool to give a new shape to the dataset, based on the Pareto rule. With this data adjustment, we trained a more performative deep learning model able to detect defective water meter devices with a prediction accuracy in the range 87–90%, even in the presence of categorical descriptors.
Aberrance suppressed spatio-temporal correlation filters for visual object tracking

The objective of the present study is to design a correlation filter-based tracking method for robust visual object tracking. In the literature, numerous tracking methods have been proposed based on discriminative correlation filter (DCF) and obtained impressive performance. However, existing algorithms still face difficulties such as partial occlusion, clutter background, uncertainties, boundary effects (especially when the target search area is small) and other challenging visual factors. Furthermore, during the target detection process, the sudden changes in objects caused by illumination variations and partial/full occlusion degrade the performance. To tackle the drawbacks mentioned earlier, we propose a tracking algorithm concerning the aberrance suppressed correlation filters with spatio-temporal information for visual tracking. Specifically, we introduce a spatial regularization term into the correlation filter to suppresses the boundary effects. Following that, a temporal regularization is adopted into the DCF-based framework to achieve a more robust appearance model and further enhance the tracking performance. In addition, we introduce an approach to suppress the aberrance in response maps caused by the sudden changes. Technically, our proposed method can be directly solved by using the alternating direction method of multipliers (ADMM) technique with a low computational cost. Finally, extensive experimental results on OTB2013, OTB2015, TempleColor128 and UAV123 datasets demonstrate that the proposed method performs favorably against state-of-the-art methods.
Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing.Previous methods usually assume this issue as a jobassignment optimization problem and only consider the current VMHs' loads; however, without considering loads of VMHs after balancing, these methods can only gain limited effectiveness in real environments.In this study, two genetic-based methods are integrated and presented.First, performance models of virtual machines (VMs) are extracted from their creating parameters and corresponding performance measured in a cloud computing environment.The gene expression programming (GEP) is applied for generating symbolic regression models that describe the performance of VMs and are used for predicting loads of VMHs after load-balance.Secondly, with the VMH loads estimated by GEP, the genetic algorithm considers the current and the future loads of VMHs and decides an optimal VM-VMH assignment for migrating VMs and performing load-balance.The performance of the proposed methods is evaluated in a real cloud-computing environment, Jnet, wherein these methods are implemented as a centralized load balancing mechanism.The experimental results show that our method outperforms previous methods, such as heuristics and statistics regression.