Reviewerly/exHarmony huggingface dataset:
Facemask Wearing Detection Based on Deep CNN to Control COVID-19 Transmission

Since the expansion of the COVID-19, almost all countries have advocated their residents to put on facemasks and adopt social distance and hand cleanliness. Due to the complicated attitudes in the settings of real life, besides several socio-behavioral and cultural factors, it is not easy to give a convincing situation for the general public that wearing facemasks is useful and effective. Therefore, facemasks wearing has not been widely embraced by many residents. However, the usage of facemasks has offered the considerable potential to filter or block the transmission of respiratory viruses including COVID-19. In this paper, a model of deep convolutional neural network (CNN) for facemask wearing detection is proposed to control covid-19 transmission. This proposed deep learning model includes two main processes; feature extraction and classification. The CNN classifier provides 99.57% of accuracy for the utilized Real-World Masked Face Dataset (RMFD).
Adversarial Autoencoder and Multi-Armed Bandit for Dynamic Difficulty Adjustment in Immersive Virtual Reality for Rehabilitation: Application to Hand Movement

Motor rehabilitation is used to improve motor control skills to improve the patient's quality of life. Regular adjustments based on the effect of therapy are necessary, but this can be time-consuming for the clinician. This study proposes to use an efficient tool for high-dimensional data by considering a deep learning approach for dimensionality reduction of hand movement recorded using a wireless remote control embedded with the Oculus Rift S. This latent space is created as a visualization tool also for use in a reinforcement learning (RL) algorithm employed to provide a decision-making framework. The data collected consists of motions drawn with wireless remote control in an immersive VR environment for six different motions called "Cube", "Cylinder", "Heart", "Infinity", "Sphere", and "Triangle". From these collected data, different artificial databases were created to simulate variations of the data. A latent space representation is created using an adversarial autoencoder (AAE), taking into account unsupervised (UAAE) and semi-supervised (SSAAE) training. Then, each test point is represented by a distance metric and used as a reward for two classes of Multi-Armed Bandit (MAB) algorithms, namely Boltzmann and Sibling Kalman filters. The results showed that AAE models can represent high-dimensional data in a two-dimensional latent space and that MAB agents can efficiently and quickly learn the distance evolution in the latent space. The results show that Sibling Kalman filter exploration outperforms Boltzmann exploration with an average cumulative weighted probability error of 7.9 versus 19.9 using the UAAE latent space representation and 8.0 versus 20.0 using SSAAE. In conclusion, this approach provides an effective approach to visualize and track current motor control capabilities regarding a target in order to reflect the patient's abilities in VR games in the context of DDA.
mGEMM

The convolution layer is the key building block in many neural network designs. Most high-performance implementations of the convolution operation rely on GEMM (General Matrix Multiplication) to achieve high computational throughput with a large workload size. However, in mobile environments, the user experience priority puts focus on low-latency inferences over a single or limited batch size. This signifies two major problems of current GEMM-based solutions: 1) GEMM-based solutions require mapping the convolution operation to GEMM, causing overheads in both computation and memory, 2) GEMM-based solutions lose large opportunities of data reuse while mapping, leading to under-utilization of the given hardware. Through an in-depth analysis of current GEMM-based solutions, we identify the root cause of these problems, and we propose mGEMM, a convolution solution that overcomes the aforementioned problems, without changes in accuracy. mGEMM expands the structure of GEMM in such a way that it can accommodate the convolution operation without any overhead, while the existing algorithms suffer from inefficiencies in converting the convolution operation to a static GEMM algorithm. Our extensive evaluations done over various neural networks and test devices show that mGEMM outperforms the existing solutions in the aspects of latency, memory overhead, and energy consumption. In running a real-world application, YoloV3-Tiny object detection, mGEMM achieves up to 1.29× and 1.58× speedup in total latency and convolution latency compared to the state-of-the-art, resulting in 15.5% reduction in energy consumption while using only near-minimum heap memory.
Customer Support Chatbot for Electronic Components

Customer satisfaction is a key metric for any company providing products or services. A great deal of customer satisfaction is directly related to the support provided by the company to its customers. This paper describes an automated solution to achieving good customer support. The focus is on an e-commerce industry selling various electronics components such as sensors, micro-controllers and actuators. One of the key challenges is in the large variation in the types of queries and possible solutions. Different NLP based data exploration techniques are employed before moving on to exploring different model architectures. The solution described in this paper is based on a Seq2Seq LSTM model with Attention. The data from a private company is used and an evaluation accuracy of about 0.90 is achieved. However, this could be misleading since the dataset is very small, and additional evaluation metrics need to be measured and compared.
Balance label correction using contrastive loss

The training of accurate deep neural networks using noisy labels is attracting tremendous attention as of late. Several studies have been devoted to noisy-label-based learning using label correction methods. The latest label correction methods provide a theoretical guarantee and avoid the accumulation of irreversible errors. However, these methods often encounter class-biased problems: some classes (“easy”) are easier to correct than others (“hard”). It has been observed that the noisy samples with a lower class confidence are harder to correct, and the class confidence is measured by the average of the model predictions, resulting from the correctly predicted samples across all classes. To address this class-biased problem, we provide a theoretical analysis of the correction procedure by investigating model predictions. Furthermore, we propose a new balance label correction method by employing the statistics of contrastive loss as a dynamic balancing factor that focuses more on the lower confidence hard classes. To demonstrate the efficacy of the proposed method, we performed experiments on synthetic and real-world datasets with various noise patterns and levels. The experimental results demonstrated that the proposed method achieved a superior classification accuracy compared with other state-of-the-art noisy label learning frameworks.
Future teachers confronting extremism and hate speech

Abstract Hate speech has become a social problem that needs to be addressed urgently. In many cases, these discourses and ideologies arrive through the media and the internet, and they are transferred to educational contexts. Debates of this type should be addressed at school and should be channelled into a democratic debate, and into the definition of shared objectives through the development of counterspeeches and alternative narratives based on Human Rights. In this research, we investigate the capacity of future teachers ( n = 114) to identify online hate speech and how they develop counterspeeches. The results show that the majority are able to identify hate speech. However, future teachers present more difficulties developing counterspeeches or complex alternative narratives, which can be transferred to educational practices. We conclude that teacher training needs to be redesigned if we want them to be able to face these problems in their future educational practice.
A deep selective learning network for cross-domain recommendation

In the past two decades, recommendation system has been successfully applied to many e-commerce companies and is a ubiquitous part of today online entertainment. However, many single-domain recommendations suffer from the sparsity problems due to a lack of sufficient interactive data. In fact, user behaviors from different domains are usually relevant. Therefore, cross-domain ideas have been proposed to help alleviate the data sparsity issue in traditional single-domain recommender systems. Motivated by this, we design a deep selective learning network (DSLN) in this paper, for the scenario when domains have minimum or no common users DSLN firstly exploits reviews to profile the preference of users and characteristic of items. Then it selects useful user or item information from the auxiliary domain and transfers it to the target domain to solve the negative transfer problem, even though there may be no overlapping users or items between these two domains. In DSLN model, the selection of useful information is realized by the de-noising auto-encoder (DAE), which is shared between the auxiliary and target domains. By minimizing the reconstruction error of the DAE, on the one hand, only the useful information can be selected from the auxiliary domain; on the other hand, the latent representation of users and items in two domains can be learned. Our experiments on three cross-domain scenarios with different sparsity of Amazon review dataset show that, our proposed model gains 0.58% to 18.16% relative improvement compared to single-domain recommendation models, and from 1.05% to 19.4% relative improvement compared to cross-domain recommendation models.
Grammar-Induced Wavelet Network for Human Parsing

Most existing methods of human parsing still face a challenge: how to extract the accurate foreground from similar or cluttered scenes effectively. In this paper, we propose a Grammar-induced Wavelet Network (GWNet), to deal with the challenge. GWNet mainly consists of two modules, including a blended grammar-induced module and a wavelet prediction module. We design the blended grammar-induced module to exploit the relationship of different human parts and the inherent hierarchical structure of a human body by means of grammar rules in both cascaded and paralleled manner. In this way, conspicuous parts, which are easily distinguished from the background, can amend the segmentation of inconspicuous ones, improving the foreground extraction. We also design a Part-aware Convolutional Recurrent Neural Network (PCRNN) to pass messages which are generated by grammar rules. To further improve the performance, we propose a wavelet prediction module to capture the basic structure and the edge details of a person by decomposing the low-frequency and high-frequency components of features. The low-frequency component can represent the smooth structures and the high-frequency components can describe the fine details. We conduct extensive experiments to evaluate GWNet on PASCAL-Person-Part, LIP, and PPSS datasets. GWNet obtains state-of-the-art performance on these human parsing datasets.
An Improved Oriented Ship Detection Method in High-resolution SAR Image Based on YOLOv5

With the continuous development of space borne synthetic aperture radar imaging technology, massive volumes of high-resolution earth observation images have been used in marine ship monitoring. Aiming at the ship target with large aspect ratio, arbitrary direction and dense arrangement in high-resolution image, an improved oriented ship detection method based onYOLOv5 is proposed in this paper. First, considering the accuracy and effectiveness of model training, we recalibrated the SAR Ship Detection Dataset (SSDD) based on the oriented bounding box, which effectively improved the detection performance of deep learning-based detectors. Secondly, the mosaic method is used to enhance the data set and the loss function is modified to improve the model performance. In addition, the rotation detection algorithm with Circular Smooth Label (CSL) is applied to the YOLOv5 detection network, achieving precise positioning of ship target. Experiments on the recalibrated SSDD show that, compared with the classic deep learning-based detection method, the method proposed in this paper has obvious advantages in detection accuracy and detection efficiency.
Understanding the Overlap of Online Offending and Victimization: Using Cluster Analysis to Examine Group Differences

Criminal offending and victimization often overlap in both the virtual and offline worlds. However, scholars are still unsure how the offending-victimization relationship plays out between the online and offline worlds. Using a sample of 2,491 adults, four clusters are discovered: 1) those unlikely to have offended or been victimized, 2) those who had online victimization and offending experiences, 3) Those who have been victimized offline and online, but who are unlikely to have offended, and 4) individuals who were victims both online and offline and offended online. Thus, the offending-victimization overlap may be common, but it is certainly not exclusive.
STEAM in Primary Education. Impact on Linguistic and Mathematical Competences in a Disadvantaged Context

This research has studied the impact of the use of STEAM (Science, Technology, Engineering, Art, and Mathematics) in education, specifically in the development of linguistic and mathematical skills. The instruments used to extract the learning results were the external evaluation tests carried out by the National Institute of Educational Evaluation INEE over a period of 4 years. The study included 242 third-level primary school students from an educational center in the city of Ceuta (Spain) located in a disadvantaged area. The objectives of the study were to describe the learning results obtained in the respective competencies according to the sex and the migratory history of the students’ parents, to compare the results obtained before and during the use of STEAM and to determine if there is a correlation between the understanding of writing and problem solving. The research method was quantitative descriptive, cross-sectional and correlational. The results of the study showed that the transversal and coordinated use of STEAM improves the learning outcomes of linguistic and mathematical competence of students of the third year of Primary Education, especially speaking, oral comprehension and calculation. The results were, in general, more positive in the case of girls and students whose parents were born in Spain. The study also revealed that there is a positive correlation between reading comprehension and problem solving.
Low Light Image Enhancement Algorithm Based on Detail Prediction and Attention Mechanism

Most LLIE algorithms focus solely on enhancing the brightness of the image and ignore the extraction of image details, leading to losing much of the information that reflects the semantics of the image, losing the edges, textures, and shape features, resulting in image distortion. In this paper, the DELLIE algorithm is proposed, an algorithmic framework with deep learning as the central premise that focuses on the extraction and fusion of image detail features. Unlike existing methods, basic enhancement preprocessing is performed first, and then the detail enhancement components are obtained by using the proposed detail component prediction model. Then, the V-channel is decomposed into a reflectance map and an illumination map by proposed decomposition network, where the enhancement component is used to enhance the reflectance map. Then, the S and H channels are nonlinearly constrained using an improved adaptive loss function, while the attention mechanism is introduced into the algorithm proposed in this paper. Finally, the three channels are fused to obtain the final enhancement effect. The experimental results show that, compared with the current mainstream LLIE algorithm, the DELLIE algorithm proposed in this paper can extract and recover the image detail information well while improving the luminance, and the PSNR, SSIM, and NIQE are optimized by 1.85%, 4.00%, and 2.43% on average on recognized datasets.
Multi-feature deep information bottleneck network for breast cancer classification in contrast enhanced spectral mammography

There is considerable variation in the size, shape and location of tumours, which makes it challenging for radiologists to diagnose breast cancer. Automated diagnosis of breast cancer from Contrast Enhanced Spectral Mammography (CESM) can support clinical decision making. However, existing methods fail to obtain an effective representation of the CESM and ignore the relationships between images. In this paper, we investigated for the first time a novel and flexible multimodal representation learning method, multi-feature deep information bottleneck (MDIB), for breast cancer classification in CESM. Specifically, the method incorporated an information bottleneck (IB)-based module to learn the prominent representation that provide concise input while informative for the classification. In addition, we creatively extended IB theory to multi-feature IB, which facilitates the learning of relevant features for classification between CESM images. To validate our method, experiments were conducted on our private and public datasets. The classification results of our method were also compared with those of state-of-the-art methods. The experiment results proved the effectiveness and the efficiency of the proposed method. We release our code at https://github.com/sjq5263/MDIB-for-CESM-classification.
Information Disclosure Decision for Tourism O2O Supply Chain Based on Blockchain Technology

(1) Background: With the development of blockchain technology and fierce competition between tourism platforms, tourism platforms can adopt blockchain technology to disclose product information to enhance their core competitiveness. As for a tourism O2O, i.e., online to offline, supply chain, the tourism platform sells the product online, and the tour operator provides services offline. (2) Methods: We establish a game theory model and study the optimal strategies of supply chain members in two scenarios (decentralized and centralized) when the online platform does not adopt or adopts blockchain technology. Then, we introduce a two-part tariff contract for coordination. Furthermore, we discuss the impact of the cost of adopting blockchain technology, disclosing information and the proportion of information-sensitive consumers on the optimal strategies. (3) Conclusions: When the costs of adopting blockchain technology and information disclosure are low, if the proportion of information-sensitive consumers is large, adopting blockchain technology is beneficial to supply chain members. Compared with a wholesale price contract, a two-part tariff contract can encourage the platform to improve information disclosure quality, so the tour operator can adjust their cooperation contract to achieve Pareto improvements. Under a two-part tariff contract, the tourism platforms are more likely to disclose information and can effectively regulate the operational performance of the tourism O2O supply chain.
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via a Structure-Specific Generative Method

Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental in building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and reconstructing the 3D volume are challenging. In this study, we propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes, including accurate image segmentation, synthetic high-resolution 3D image, and 3D reconstructed volume. Our method identifies the optimal latent representation of the cine image that contains accurate semantic information for cardiac structures. In particular, our model jointly generates synthetic images with accurate semantic information and segmentation of the cardiac structures using the optimal latent representation. We further explore downstream applications of 3D shape reconstruction and 4D motion pattern adaptation by the different latent-space manipulation strategies. The simultaneously generated high-resolution images present a high interpretable value to assess the cardiac shape and motion. Experimental results demonstrate the effectiveness of our approach on multiple fronts including 2D segmentation, 3D reconstruction, downstream 4D motion pattern adaption performance.
Online Consumer Fraud Victimization and Reporting: A Quantitative Study of the Predictors and Motives

Online consumer fraud is one of the most fast-growing crimes. It corresponds to situations where, for instance, the products that are bought, but are not delivered. The present study (N = 1710) aimed at exploring, on the one hand, the predictors of online fraud victimization and, on the other hand, to examine the reporting to the police. Even though 223 were victims of online consumer fraud, only 20 reported to the police. Moreover, while individual variables were not predictors of victimization, online routines were relevant to its explanation. Finally, monetary loss was the most important predictor of reporting to the police.
Perceptions of the human and social factors that influence the productivity of software development teams in Colombia: A statistical analysis

This research aims to know if software engineering professionals consider that social and human factors (SHF) influence the productivity of a work team. A survey-based study was conducted among 112 members of software development teams. Empirical results show professionals agree with the SHF in the context of software development influence in the productivity of work teams. It was identified that the 13 SHFs have a weak or moderate correlation with each other. Additionally, the results of the exploratory factorial analysis suggest categorizing the factors into those associated with the individual, those associated with team interaction, and those related to capabilities and experience. This categorization reduced the number of items in the original questionnaire while preserving the variability explained in the latent variables, which will require a shorter response time. Our results broaden the understanding of the SHFs that influence software development team productivity and open up new research opportunities. Measuring the perception of these factors can be used to identify which SHFs should be prioritized in a strategy to improve productivity. In addition, this knowledge can help software organizations appropriately manage their development teams and propose innovative work approaches that have a positive impact on the success of their projects.
User Gender Classification Based on Twitter Profile Using Machine Learning

In the recent past, there has been a swift rise in the count of people accessing social media sites like twitter in many countries across the globe. This led to a surge in interest to identify gender based on their profile. This study could be beneficial for many fields such as legal inquisition, analysing election polls, recommendations of e-commerce platforms, survey results, marketing, and forensics. Men and women have unique styles of expressing their views in tweets. Based on these distinctions between both men and women, this research aims to categorise the gender of a Twitter profile. This investigation will deep dive into the usage of various Natural Language Tool Kit (NLTK) libraries that process on textual attributes of the dataset. This study provides a comparative analysis of the accuracies obtained after applying various algorithms of machine learning. The usage of predominant textual attributes is majorly concentrated in this study.