Reviewerly/exHarmony huggingface dataset:
Arabic Sign Language Recognition System Using 2D Hands and Body Skeleton Data

This paper presents a novel Arabic Sign Language (ArSL) recognition system, using selected 2D hands and body key points from successive video frames.The system recognizes the recorded video signs, for both signer dependent and signer independent modes, using the concatenation of a 3D CNN skeleton network and a 2D point convolution network.To accomplish this, we built a new ArSL video-based sign database.We will present the detailed methodology of recording the new dataset, which comprises 80 static and dynamic signs that were repeated five times by 40 signers.The signs include Arabic alphabet, numbers, and some daily use signs.To facilitate building an online sign recognition system, we introduce the inverse efficiency score to find a sufficient optimal number of successive frames for the recognition decision, in order to cope with a near real-time automatic ArSL system, where tradeoff between accuracy and speed is crucial to avoid delayed sign classification.For the dependent mode, best results were obtained for dynamic signs with an accuracy of 98.39%, and 88.89% for the static signs, and for the independent mode, we obtained for the dynamic signs an accuracy of 96.69%, and 86.34% for the static signs.When both the static and dynamic signs were mixed and the system trained with all the signs, accuracies of 89.62% and 88.09% were obtained in the signer dependent and signer independent modes respectively.
A unified position-aware convolutional neural network for aspect based sentiment analysis

Aspect based sentiment analysis (ABSA) can be divided into aspect category sentiment analysis (ACSA) and aspect term sentiment analysis (ATSA). To detect the sentiment polarity in a context given an aspect, both ACSA and ATSA highly rely on aspect-centroid sentiment information. However, aspect terms in ATSA appear in the context, but aspect categories in ACSA may not. This make it difficult to use important aspect position information and further design effective aspect injection strategy when modeling ACSA and ATSA in a unified framework. To address this problem, we propose a novel Unified Position-aware Convolutional Neural Network (UP-CNN). There are two major modifications in UP-CNN. Firstly, to handle the absence of aspect position in ACSA, we propose an aspect detection network with prior knowledge. Thus ABSA can be solved in a unified view with the important aspect position. Secondly, to fit CNN in ABSA, an aspect mask is proposed to construct aspect-aware context representation. Experimental results on seven datasets demonstrate that our model performs effectively and efficiently on both ACSA and ATSA tasks.
Use of offensive language in human-artificial intelligence chatbot interaction: The effects of ethical ideology, social competence, and perceived humanlikeness

This study examined the factors that affect artificial intelligence (AI) chatbot users' use of profanity and offensive words, employing the concepts of ethical ideology, social competence, and perceived humanlikeness of chatbot. The study also looked into users' liking of chatbots' responses to the users' utterance of profanity and offensive words. Using a national survey (N = 645), the study found that users' idealism orientation was a significant factor in explaining use of such offensive language. In addition, users with high idealism revealed liking of chatbots' active intervention, whereas those with high relativism displayed liking of chatbots' reactive responses. Moreover, users’ perceived humanlikeness of chatbot increased their likelihood of using offensive words targeting dislikable acquaintances, racial/ethnic groups, and political parties. These findings are expected to fill the gap between the current use of AI chatbots and the lack of empirical studies examining language use.
Ensemble of classification models with weighted functional link network

Ensemble classifiers with random vector functional link network have shown improved performance in classification problems. In this paper, we propose two approaches to solve the classification problems. In the first approach, the original input space’s data points are mapped explicitly into a randomized feature space via neural network wherein the weights of the hidden layer are generated randomly. After feature projection, classification models twin bounded support vector machines (SVM), least squares twin SVM, twin k-class SVM, least squares twin k-class SVM and robust energy based least squares twin SVM are trained on the extended features (original features and randomized features). In the second approach, twin bounded support vector machines (SVM), least squares twin SVM, twin k-class SVM, least squares twin k-class SVM and robust energy based least squares twin SVM models are used to generate the weights of the hidden layer architecture and the weights of output layer are optimized via closed form solution. The performance of both the proposed architectures is evaluated on 33 datasets — including datasets from the UCI repository and fisheries data (not in UCI). Both the experimental results and statistical tests conducted demonstrate that the proposed approaches perform significantly better than the other baseline models. We also analyze the effect of the number of enhanced features on the performance of the given models.
PGRide: Privacy-Preserving Group Ridesharing Matching in Online Ride Hailing Services

An online ride hailing (ORH) service creates a typical supply-and-demand two-sided market, which enables riders and drivers to establish optimized rides conveniently via mobile applications. Group ridesharing is a novel form of ridesharing, which allows a group of riders to share a vehicle that holds the minimum aggregate distance to the whole group. Accompanied by the advantage of ORH services, there comes some vital privacy concerns. In this article, we propose a privacy-preserving online group ridesharing matching scheme for ORH services, called PGRide. PGRide can select the nearest driver to serve a group of riders, without leaking the location privacy of both riders and drivers. In PGRide, we propose an encrypted aggregate distance computation approach by using somewhat homomorphic encryption with ciphertexts packing, which efficiently computes the aggregate distances from a group of riders to large-scale dynamic drivers in encrypted form. Meanwhile, we design a secure minimum selection protocol by using ciphertexts packing and blinding, which efficiently finds the minimum element from a set of encrypted integers without leaking any actual element value. Theoretical analysis and performance evaluations prove that PGRide is secure, accurate, and efficient.
vis–NIR and XRF Data Fusion and Feature Selection to Estimate Potentially Toxic Elements in Soil

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models’ accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.
Pedestrian Detection Using Multispectral Images and a Deep Neural Network

Pedestrian fatalities and injuries most likely occur in vehicle-pedestrian crashes. Meanwhile, engineers have tried to reduce the problems by developing a pedestrian detection function in Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, the system is still not perfect. A remaining problem in pedestrian detection is the performance reduction at nighttime, although pedestrian detection should work well regardless of lighting conditions. This study presents an evaluation of pedestrian detection performance in different lighting conditions, then proposes to adopt multispectral image and deep neural network to improve the detection accuracy. In the evaluation, different image sources including RGB, thermal, and multispectral format are compared for the performance of the pedestrian detection. In addition, the optimizations of the architecture of the deep neural network are performed to achieve high accuracy and short processing time in the pedestrian detection task. The result implies that using multispectral images is the best solution for pedestrian detection at different lighting conditions. The proposed deep neural network accomplishes a 6.9% improvement in pedestrian detection accuracy compared to the baseline method. Moreover, the optimization for processing time indicates that it is possible to reduce 22.76% processing time by only sacrificing 2% detection accuracy.
Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review

Image super-resolution has become an important technology recently, especially in the medical and industrial fields. As such, much effort has been given to develop image super-resolution algorithms. A recent method used was convolutional neural network (CNN) based algorithms. super-resolution convolutional neural network (SRCNN) was the pioneer of CNN-based algorithms, and it continued being improved till today through different techniques. The techniques included the type of loss functions used, upsampling module deployed, and the adopted network design strategies. In this paper, a total of 18 articles were selected through the PRISMA standard. A total of 19 algorithms were found in the selected articles and were reviewed. A few aspects are reviewed and compared, including datasets used, loss functions used, evaluation metrics applied, upsampling module deployed, and adopted design techniques. For each upsampling module and design techniques, their respective advantages and disadvantages were also summarized.
A CP-ABE scheme based on multi-authority in hybrid clouds for mobile devices

With the rapid development of cloud computing, the ensuing security issues have become increasingly prominent. In this context, attribute-based encryption (ABE) has gradually attracted widespread attentions due to its unique attribute matching mechanism. At the same time, mobile devices have put forward higher requirements for the design and deployment of ABE schemes. Therefore, in this paper, we propose an original hybrid cloud multi-authority ciphertext-policy attribute-based encryption (HCMACP-ABE) scheme. Specifically, we utilize the LSSS (Linear Secret-Sharing Schemes) access structure to realize secure access control, while the private cloud is responsible for maintaining the user’s authorization list and verifying the user. Finally, we prove that our proposal achieves IND-CCA secure and is efficient in a mobile hybrid cloud environment.
GAN-Based Data Augmentation Strategy for Sensor Anomaly Detection in Industrial Robots

In the current industry world, the industrial robot has emerged as a critical device to make the manufacturing process more efficient through automation. However, abnormal operation of industrial robots caused by sensor failures may interrupt the entire manufacturing process, thereby increasing production costs. In this paper, we first propose a domain-specific framework consisting of offline training and online inference to effectively detect anomalies in the scenario of industrial robotic sensors. In a nutshell, the framework can be identified in three folds: (1) the offline training obtaining historical data from the database to train the time series and anomaly detection models; (2) the online inference deploying the offline trained models for online anomaly detection in real-time; (3) the incremental learning updating the online model for a new type of anomalies. We then propose an improved Generative Adversarial Networks (GANs) named MSGAN with the adaptive update strategy mechanism based on WGAN-GP to generate fake anomaly samples, improving anomaly detection accuracy. Specifically, the Wasserstein distance with the gradient penalty is introduced to improve training stability and sample quality. Moreover, adapting the generator’s complexity in robotic sensors, an adaptive update strategy based on the loss change ratio is adopted to speed up training convergence. Extensive experiments based on real robotic sensor datasets demonstrate the effectiveness of the proposed framework. Moreover, the results demonstrate that the detection accuracy can be improved by the synthetic samples based on the proposed MSGAN algorithm.
Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing

Robust face reconstruction from monocular image in general lighting conditions is challenging. Methods combining deep neural network encoders with differentiable rendering have opened up the path for very fast monocular reconstruction of geometry, lighting and reflectance. They can also be trained in self-supervised manner for increased robustness and better generalization. However, their differentiable rasterization-based image formation models, as well as underlying scene parameterization, limit them to Lambertian face reflectance and to poor shape details. More recently, ray tracing was introduced for monocular face reconstruction within a classic optimization-based framework and enables state-of-the art results. However, optimization-based approaches are inherently slow and lack robustness. In this paper, we build our work on the afore-mentioned approaches and propose a new method that greatly improves reconstruction quality and robustness in general scenes. We achieve this by combining a CNN encoder with a differentiable ray tracer, which enables us to base the reconstruction on much more advanced personalized diffuse and specular albedos, a more sophisticated illumination model and a plausible representation of self-shadows. This enables to take a big leap forward in reconstruction quality of shape, appearance and lighting even in scenes with difficult illumination. With consistent face attributes reconstruction, our method leads to practical applications such as relighting and self-shadows removal. Compared to state-of-the-art methods, our results show improved accuracy and validity of the approach.
Efficient Identity-Based Public Integrity Auditing of Shared Data in Cloud Storage With User Privacy Preserving

Provable Data Possession (PDP) model provides an efficient means for people to audit the integrity of data stored in cloud storage. When sensitive data is shared among multiple users based on cloud storage, it is critical to preserve the anonymity of the data uploader against the auditor. That is, the auditor should not get data uploader's identity through the data audition. To address this problem, many PDP schemes with user identity privacy-persevering are proposed. However, most proposed schemes are designed based on PKI technique which suffers from big burden of certificate management. Moreover, data auditors in most proposed schemes bear heavy computation cost which results to the lower efficiency of the scheme. To overcome the shortcomings, we present a novel identity-based PDP protocol to audit efficiently the integrity of group shared data with uploader's privacy-preserving. Due to the inherent structural advantage of identity-based crypto mechanism, our PDP scheme is able to avoid the problem of certificate management. Different from previous works, our scheme ensures the relationship of the data and the data uploader in the phase of proof generation not the phase of integrity audition. Therefore, the data auditor does not know the relationship at all as well as the extract data uploader of the challenged data. At the same time, establishing the relationship by cloud server in proof generation step can reduce the computational cost of data auditor greatly. Furthermore, the relationship of data uploader and challenged data in the proof is randomized so as to strength the security of the scheme. All these efforts are made in our scheme to efficiently realize the anonymity protection of the data uploader. We give the detailed security proof of our scheme under the computational Diffie-Hellman assumption. Many experiments are performed to evaluate the efficiency of our scheme, the results show that our new scheme is efficient and feasible.
Building enclave-native storage engines for practical encrypted databases

Data confidentiality is one of the biggest concerns that hinders enterprise customers from moving their workloads to the cloud. Thanks to the trusted execution environment (TEE), it is now feasible to build encrypted databases in the enclave that can process customers' data while keeping it confidential to the cloud. Though some enclave-based encrypted databases emerge recently, there remains a large unexplored area in between about how confidentiality can be achieved in different ways and what influences are implied by them. In this paper, we first provide a broad exploration of possible design choices in building encrypted database storage engines, rendering trade-offs in security, performance and functionality. We observe that choices on different dimensions can be independent and their combination determines the overall trade-off of the entire storage. We then propose Enclage , an encrypted storage engine that makes practical trade-offs. It adopts many enclave-native designs, such as page-level encryption, reduced enclave interaction, and hierarchical memory buffer, which offer high-level security guarantee and high performance at the same time. To make better use of the limited enclave memory, we derive the optimal page size in enclave and adopt delta decryption to access large data pages with low cost. Our experiments show that Enclage outperforms the baseline, a common storage design in many encrypted databases, by over 13x in throughput and about 5x in storage savings.
Short-Term Prediction of Global Solar Radiation Energy Using Weather Data and Machine Learning Ensembles: A Comparative Study

Abstract The ability to predict solar radiation one-day-ahead is critical for the best management of renewable energy tied-grids. Several machine learning ensemble techniques have been proposed to enhance the short-term prediction of solar radiation strength. In general, finding an optimal ensemble model that consists of combining individual predictors is not trivial due to the need for tuning and other related issues. Few comparative studies have been presented to obtain optimal structures of machine learning ensemble that deal with predicting solar radiation. The contribution of the present research consists of a comparative study of various structures of stacking-based ensembles of data-driven machine learning predictors that are widely used nowadays to conclude the best stacking strategies in terms of performance to combine predictors of solar radiation. The base individual predictors are arranged to predict solar radiation intensity using historical weather and solar radiation records. Three stacking techniques, namely, feed-forward neural networks, support vector regressors, and k-nearest neighbor regressors, have been examined and compared to combine the prediction outputs of base learners. Most of the examined stacking models have been found capable to predict the solar radiation, but those related to combining heterogeneous models using neural meta-models have shown superior performance. Furthermore, we have compared the performance of combined models against recurrent models. The solar radiation predictions of the surveyed models have been evaluated and compared over an entire year. The performance enhancements provided by each alternative ensemble have been discussed.
Ransomware activity and blockchain congestion

I examine blockchain congestion episodes caused by more than 4,400 triggers for ransomware attacks over a two-year period. When demand for settlement exceeds blockchain capacity, blockchain users engage in fee competition to prioritize their transaction settlement. A typical surge in ransomware activity causes transaction fees to increase by 2.1% and up to 28% in extreme cases. Consistent with theory literature, some users forgo blockchain settlement when transaction fees increase. An event study around an extreme spike in ransomware activity supports the findings of the main analysis.
Unsupervised Image-to-Image Translation via Pre-Trained StyleGAN2 Network

Image-to-Image (I2I) translation is an emerging topic in academia, and it also has been applied in real-world industry for tasks like image synthesis, super-resolution, and colorization. Traditional I2I translation methods usually train data in two or more domains together. This requires lots of computation resources. The results are of lower quality, and contain more artifacts. The training process could be unstable when the data in different domains are not balanced, and modal collapse is more likely to happen. In this paper, we propose a new I2I translation method that generates a new model in the target domain via a series of model transformations on a pre-trained StyleGAN2 model in the source domain. After that, we develop an inversion method to achieve the conversion between an image and its latent vector. By feeding the latent vector into the generated model, we can perform I2I translation between the source domain and target domain. Both qualitative and quantitative evaluations were conducted to verify that the proposed method can achieve better performance in terms of image quality, diversity and semantic similarity to the input and reference images compared to state-of-the-art works.
Product Review Sentiment Analysis by Using NLP and Machine Learning in Bangla Language

In this era of internet technology, in Bangladesh, online marketing or e-commerce businesses were already thriving. Due to the COVID-19 pandemic, as people are in lockdown, online shopping became the main platform for shopping as it is the safest way. It accelerated the businesses to come online. More online product service providers makes it better for people but also raises the question of product quality and services. So it is easy for new customers to get scammed while shopping online. Our goal is to make a system that will analyze the customer's feedback from online shopping and provide a ratio of the positive and negative feedback written in Bangla from the previous customers using Natural Language Processing (NLP). We have collected over 1000 feedback and comments on the product to conduct the research. We used sentiment analysis along with some classification algorithms like KNN, Decision Tree, Support Vector Machine (SVM), Random Forest and Logistic Regression. With the highest accuracy of 88.81%, SVM outperformed all the other algorithms.