Reviewerly/exHarmony huggingface dataset:
A comparative study on SoC embedded low power GPUs for real‐time edge‐based automated traffic surveillance

Summary Achieving real‐time processing for automated traffic surveillance is a major challenge due to the huge amount of data generated by a large number of surveillance cameras. For this, centralized computing has been a standard architecture for many years. But due to the increasing need for real‐time processing and limitations of centralized computing architecture (such as network congestion due to limited bandwidth and the need for costly high‐end servers), the paradigm is shifting toward edge processing. This article compares the suitability of two popular but different types of SoCs (System on Chip) (from NVIDIA and Qualcomm) embedded with low‐power GPUs as processing units for edge devices. These GPUs can be programmed as general‐purpose GPUs (GPGPU) to speed up compute‐intensive tasks, so as to achieve real‐time processing at the edge of the network. The article also discusses the architectural features and differences between these GPUs and recommends optimization techniques to leverage them. For quantitative comparison, we implement a wrong‐way vehicle detection algorithm (using background‐subtraction‐based moving object detection and Kalman‐filter‐based trajectory tracking), and optimized it for these SoCs. The experimental results show that real‐time processing can be achieved for HD videos with implementations optimized for these SoCs.
Emergency Remote Education and Smart Working at Three European Higher Education Institutions

Due to the threat posed by COVID-19, many colleges and universities around the world opted to switch to online courses and smart working to keep their students, professors, and staff safe during the pandemic emergency. Face-to-face classes, including labs and workshops, have been canceled and substituted with online activities. New administrative procedures have also been established to support the emergency remote education. This article analyzes these changes in light of the experiences of three higher education institutions in different countries, namely Latvia, Poland, and Italy. From this analysis, some aspects have emerged that have stimulated a deeper reflection on the use of digital technology in higher education. .
Attention-based Long-term Modeling for Deep Visual Odometry

Visual odometry (VO) aims to determine the positions of a moving camera from an image sequence it acquired. It has been extensively utilized in many applications such as AR/VR, autonomous driving, and robotics. Conventional VO methods largely rely on hand-crafted features and data association that are in fact unreliable and suffering from fast motions. Therefore, learning-based VO utilizes neural networks mapping an image sequence to corresponding camera poses directly. Most existing learning-based methods also integrate with additional Long Short-Term Memory (LSTM) networks to model the temporal context across images, since the camera pose estimation of an image in VO is highly relevant to other images in the same sequence. However, traditional LSTM is limited to model short-term dependency rather than long-term temporal context or global information. To mitigate this issue, we propose an attention based long-term modelling approach by devising a new fusion gate into the LSTM cell. Our method consists of two modules: convolutional motion encoder and recurrent global motion refinement module. Specifically, the convolutional motion encoder extracts from images motion features which are then fused by the refinement module with more long-term temporal information. In the refinement module, the devised fusion gate generates long-term temporal information in two phases: (1) extracting correlated long-term information from previous predictions through a devised attention module; and (2) updating the current hidden state with extracted long-term information. As a result, it enables our model to gather long-term temporal information and further enhance estimation accuracy. We comprehensively evaluate our proposed method on two public datasets, KITTI and Oxford RobotCar. The experimental results demonstrate the effectiveness and superiority of our method over the baseline model.
My adventures with Nick and Hannah: Anthropological explorations into sexdolls and the potential implications for human intimacy

This paper introduces two anthropological case-studies; one in which the (female) author personally rented a male sexdoll to experience, and one in which an owner of a sexdoll was observed in her house, interacting with her doll. Through the first case, in which the anthropological methodology of participation and reflection is used, it is explored what sexdoll-intimacy could look like for a heterosexual female. The second case study uses observation and in-depth interviewing to explore what sexdolls may mean for an atypical group of users: asexuals. Both case-studies serve to reflect on the potential positive impacts for human intimacy, as well as on potential challenges or concerns. It discusses the wider theme of intimacy and human relationships, using sociological literature on former important technological innovations and their societal impact. One of the major conclusions of the paper is that instead of focusing on the ‘humanification’ of robots and sexdolls, as is currently happening in this field of innovation, it is useful for social scientists to turn the topic up side down: focusing on the potential of robotification of humans. This also means a shift from focusing on the (possible) future, to current societal dynamics. Another conclusion is that, while social-scientific scholars have been largely critical on sexdoll innovations; it is equally useful to take a more descriptive approach and experience, rather than moralize, what sexdolls may bring individuals and the larger society.
An eigenvector approach for obtaining scale and orientation invariant classification in convolutional neural networks

The convolution neural networks are well known for their efficiency in detecting and classifying objects once adequately trained. Though they address shift in-variance up to a limit, appreciable rotation and scale in-variances are not guaranteed by many of the existing CNN architectures, making them sensitive towards input image or feature map rotation and scale variations. Many attempts have been made in the past to acquire rotation and scale in-variances in CNNs. In this paper, an efficient approach is proposed for incorporating rotation and scale in-variances in CNN-based classifications, based on eigenvectors and eigenvalues of the image covariance matrix. Without demanding any training data augmentation or CNN architectural change, the proposed method, ‘Scale and Orientation Corrected Networks (SOCN)’, achieves better rotation and scale-invariant performances. SOCN proposes a scale and orientation correction step for images before baseline CNN training and testing. Being a generalized approach, SOCN can be combined with any baseline CNN to improve its rotational and scale in-variance performances. We demonstrate the proposed approach’s scale and orientation invariant classification ability with several real cases ranging from scale and orientation invariant character recognition to orientation invariant image classification, with different suitable baseline architectures. The proposed approach of SOCN, though is simple, outperforms the current state of the art scale and orientation invariant classifiers comparatively with minimal training and testing time.
Blur the Eyes of UAV: Effective Attacks on UAV-based Infrastructure Inspection

Unmanned aerial vehicles (UAVs) are increasingly leveraged to perform infrastructure inspection tasks, especially with the support of rapidly evolving AI algorithms and hardware in recent years. While the integration of UAV and AI techniques enhances the efficiency and effectiveness of infrastructure inspection, it also raises security concerns due to the potential vulnerabilities existing in the underlying AI models. In this paper, we propose to investigate and discover these vulnerabilities with the case study on bridge inspection. In particular, we designed a two-stage approach that can construct effective adversarial perturbations that make the UAV miss the detection of risk-prone regions during the inspection. Spatial constraints, physical limits, as well as dynamic environmental changes are taken into consideration in our approach to make it practical in the physical world. We evaluate our approach using the COCO-Bridge dataset. Our experimental results demonstrate the effectiveness of our approach in both white-box attack and black-box attack settings.
Facial Emotion Recognition Focused on Descriptive Region Segmentation

Facial emotion recognition (FER) is useful in many different applications and could offer significant benefit as part of feedback systems to train children with Autism Spectrum Disorder (ASD) who struggle to recognize facial expressions and emotions. This project explores the potential of real time FER based on the use of local regions of interest combined with a machine learning approach. Histogram of Oriented Gradients (HOG) was implemented for feature extraction, along with 3 different classifiers, 2 based on k-Nearest Neighbor and 1 using Support Vector Machine (SVM) classification. Model performance was compared using accuracy of randomly selected validation sets after training on random training sets of the Oulu-CASIA database. Image classes were distributed evenly, and accuracies of up to 98.44% were observed with small variation depending on data distributions. The region selection methodology provided a compromise between accuracy and number of extracted features, and validated the hypothesis a focus on smaller informative regions performs just as well as the entire image.
Improved Object Detection in Video Surveillance Using Deep Convolutional Neural Network Learning

In this paper, a convolutional neural network is used to improve the functionality of object detection using probabilistic neural network in the analysis of surveillance images. Next, this research work attempts to examine the fundamental principles and some of the underlying theories of perceptual and neural networks are often missed. This helps you understand why, in many areas of usage, deep learning has increased. The processing of surveillance photos is obviously one of the areas most influenced by this exponential progress, particularly in the identification and detection of images. The simulation result shows that the CNN-PNN obtained improved achievements in simulation for the optimal detection of objects in video streaming or video surveillance systems. The results show that the proposed method achieves higher rate of accuracy, f-measure and reduced percentage error than other methods.
Automated test design using swarm and evolutionary intelligence algorithms

Abstract The world's increasing dependence on computer‐assisted education systems has raised significant challenges about student assessment methods, such as automated test design. The exam questions should test the students' potential from various aspects, such as their intellectual and cognitive levels, which can be defined as attributes of the questions to assess student knowledge. Test design is challenging when various question attributes, such as category, learning outcomes, difficulty, and so forth, are considered with the exam constraints, such as exam difficulty and duration. In this paper, four contributions are provided to overcome test design challenges for the student assessment. First, a tool is developed to generate a synthetic question pool. Second, an objective function is designed based on the considered attributes. Third, the popular swarm and evolutionary optimization methods, namely particle swarm optimization, genetic algorithm, artificial bee colony, differential search algorithm are comparatively studied with novel methodologies applied to them. Finally, as the state of the art methods, artificial bee colony, and differential search algorithm are further modified to improve the solution of the test design. To perform the proposed algorithms, a dataset of 1000 questions is built with the proposed question attributes of the test design. Algorithms are evaluated in terms of their successes in both minimizing the objective function and running time. Additionally, Friedman's test and Wilcoxon rank‐sum statistical tests are applied to statistically compare the algorithms' performances. The results show that the improved artificial bee colony and the improved differential search provide better results than others in terms of optimization error and running time.
A Novel Progressive Image Classification Method Based on Hierarchical Convolutional Neural Networks

Deep Neural Networks (DNNs) are commonly used methods in computational intelligence. Most prevalent DNN-based image classification methods are dedicated to promoting the performance by designing complicated network architectures and requiring large amounts of model parameters. These large-scale DNN-based models are performed on all images consistently. However, since there are meaningful differences between images, it is difficult to accurately classify all images by a consistent network architecture. For example, a deeper network is fit for the images that are difficult to be distinguished, but may lead to model overfitting for simple images. Therefore, we should selectively use different models to deal with different images, which is similar to the human cognition mechanism, in which different levels of neurons are activated according to the difficulty of object recognition. To this end, we propose a Hierarchical Convolutional Neural Network (HCNN) for image classification in this paper. HCNNs comprise multiple sub-networks, which can be viewed as different levels of neurons in humans, and these sub-networks are used to classify the images progressively. Specifically, we first initialize the weight of each image and each image category, and these images and initial weights are used for training the first sub-network. Then, according to the predicted results of the first sub-network, the weights of misclassified images are increased, while the weights of correctly classified images are decreased. Furthermore, the images with the updated weights are used for training the next sub-networks. Similar operations are performed on all sub-networks. In the test stage, each image passes through the sub-networks in turn. If the prediction confidences in a sub-network are higher than a given threshold, then the results are output directly. Otherwise, deeper visual features need to be learned successively by the subsequent sub-networks until a reliable image classification result is obtained or the last sub-network is reached. Experimental results show that HCNNs can obtain better results than classical CNNs and the existing models based on ensemble learning. HCNNs have 2.68% higher accuracy than Residual Network 50 (Resnet50) on the ultrasonic image dataset, 1.19% than Resnet50 on the chimpanzee facial image dataset, and 10.86% than Adaboost-CNN on the CIFAR-10 dataset. Furthermore, the HCNN is extensible, since the types of sub-networks and their combinations can be dynamically adjusted.
Cloud Computing Storage Data Access Control Method Based on Dynamic Re-Encryption

In order to improve data security, ensure user privacy, and solve the problems of low data access control accuracy, long time consumption, and high energy consumption in traditional methods, a cloud computing storage data access control method based on dynamic re-encryption is proposed. The principal component analysis method is used to reduce the dimension of the cloud computing storage data, and the random forest algorithm is further used to classify and process the cloud computing storage data according to the processing results. On the basis of data preprocessing, an access control tree is established to obtain the correlation of data nodes. Finally, the dynamic re-encryption method is used for data security state transformation, and the data access control of cloud computing storage is realized through key generation, encryption, re-encryption key generation, and decryption. The experimental results show that the data access control accuracy of the method in this paper is high, time consumption is small, and energy consumption is small, and it is more suitable for cloud computing systems with huge data and information.
Social Arabic Emotion Analysis: A Comparative Study of Multiclass Classification Techniques

The advent of social media platforms (Twitter, Facebook, etc.) has facilitated the development of user generated content, and users tend to express and share their opinions and emotions about political movements, social events, products, and services. Thus, the emotion detection task has emerged and has received significant attention from academia. It is recognized as a part of affective computing to understand and extract the human affective state expressed in a piece of writing (tweet, post, etc.). In this work, we have measured and compared the performance of different traditional machine learning models namely Linear SVC, Logistic Regression, Multinomial and Complement Naive Bayes with the accuracy produced by two of the top performing deep learning techniques namely Convolution Neural Network and Long-Short-Term Memory networks. Two types of social media datasets were utilized in this study. One is the Arabic emotions Twitter dataset (AETD) and the other one is the Iraqi Arabic emotion dataset (IAEDS). The experimental results demonstrate that Convolution Neural Network deep based technique performs better than machine learning-based algorithms on both datasets used in this study. Additionally, the results establish that proposed models outperform the current state-of-the-art Arabic emotion recognition methods, achieving a 2.4% improvement in accuracy on the AETD dataset. Furthermore, the proposed models can significantly distinguish between the different emotion labels.
Inferring the patient’s age from implicit age clues in health forum posts

Broader patient-reported experiences in oncology are largely unknown due to the lack of available information from traditional data sources. Online health community data provide an exploratory way to uncover these experiences at a large scale. Analyzing these data can guide further studies towards understanding patients' needs and experiences. However, analysis of online health data is inherently difficult due to the unstructured nature of these data and the variety of ways information can be expressed over text. Specifically, subscribers may not disclose critical information such as the age of the patient in their posts. In fact, the number of health forum posts that explicitly mention the age of the patient is significantly lower than the number of posts that do not include this information in the Reddit r/Cancer health forum under consideration in the present paper. Health-focused studies often need to consider or control for age as a confounder, hence the importance of having sufficient age data. This paper presents a methodology that can help classify health forum posts according to four age groups (0-17, 18-39, 40-64 and 65 + years) even when the posts do not contain explicit mention of the age of the patient. First, the subset of the posts that include explicit mention of the age of the patient is identified. Second, the explicit age clues are removed from these posts and used to train the proposed age classifier. The resulting classifier is able to infer the age of the patient using only implicit age clues with an average true positive rate (TPR) of 71%. This TPR is comparable to the average TPR of 69% obtained from human annotations for the same set of posts.
KB-Tree: Learnable and Continuous Monte-Carlo Tree Search for Autonomous Driving Planning

In this paper, we present a novel learnable and continuous Monte-Carlo Tree Search method, named as KB-Tree, for motion planning in autonomous driving. The proposed method utilizes an asymptotical PUCB based on Kernel Regression (KR-AUCB) as a novel UCB variant, to improve the exploitation and exploration performance. In addition, we further optimize the sampling in continuous space by adapting Bayesian Optimization (BO) in the selection process of MCTS. Moreover, we use a customized Graph Neural Network (GNN) as our feature extractor to improve the learning performance. To the best of our knowledge, we are the first to apply the continuous MCTS method in autonomous driving. To validate our method, we conduct extensive experiments under several weakly and strongly interactive scenarios. The results show that our proposed method performs well in all tasks, and outperforms the learning-based continuous MCTS method and the state-of-the-art Reinforcement Learning (RL) baseline.
Blockchain: At a glance idea for information science researchers

Blockchain, eventually block chain, (BC) is a distributed ledger system that processes and preserves data and their transactions using time-stamped blocks in decentralized method, which is able to address many traditional issues of information systems design. Since the introduction of the digital currency or cryptocurrency “bitcoin” in 2008, its foundation, the BC technology has gained tremendous interest, both in industry and academia. Over the past few years, many existing review articles generally focus on specific aspects of this rapidly growing disruptive technology, rather than purely investigating the BC to produce a whole landscape illustration, so that researchers may gain comprehensive knowledge on it. This paper presents a unique review on BC technology revealing the concepts, how it works, structure, types, components, characteristics, and applications. Sourcing from open-access online materials and academic outlets, we analyze sample articles concerning BC, associative technologies, their potential application and issues. A total of 56 contents on BC and related technologies were selected for relevant recent documents of the emerging development. The research team scrutinized the contents using established content analysis approach. It is anticipated that the findings would offer benefits in enhancing understanding to innovative solution for development researchers and professionals in industries.
Self-Augmentation with Dual-Cycle Constraint for Unsupervised Image-to-Image Generation

Unsupervised Image-to-Image generation has obtained many studies recently, where generative adversarial networks (GANs) have developed as an effective model. Cycle consistency or dual learning guides the GAN from aligned image pairs to the unpaired training set and can be applied for many applications based on the image-to-image generation. However, for many tasks, error accumulation, which can be produced in the progress of image reconstruction, affects the realism and quality of the generated images. To eliminate error accumulation and guide the adversarial learning, in this paper, we propose dual-cycle consistent GAN (DucGAN), a novel approach for cross-domain image-to-image generation. The key idea is to introduce dual-cycle learning, which restrains error accumulation. In our method, inter-cycle and extra-cycle are based on the cycle consistency, while the output from intercycle is cast as a new augmented input in extra-cycle. On the one hand, reconstruction loss in extra-cycle can constrain and guide the training. On the other hand, dual-cycle learning is self-augmented. Our extensive experiments on image-to-image generation tasks show that DucGAN is effective and superior to several state-of-the-art GANs.