Reviewerly/exHarmony huggingface dataset:
Deep reinforcement learning based trajectory optimization for magnetometer-mounted UAV to landmine detection

Unmanned aerial vehicles (UAVs) have emerged as a viable choice for data collection and landmine (LM) detection. The LM buried under the dirt or sand is detected using a UAV-mounted magnetometer in this paper. A UAV is deployed to gather data along the intended route when the magnetometer receives a signal from the LMs. During a whole round of data collection, we want to reduce the total energy consumption of the UAV-Magnetometer-LM system. To do this, we turn the energy consumption reduction issue into a limited combinatorial optimization problem by concurrently picking time slots and arranging the UAV's visitation sequence to identify the LM. The problem of minimizing energy usage is NP-hard, making it difficult to solve optimally. In order to tackle this challenge, we used the deep reinforcement learning (DRL) based deep deterministic policy gradient (DDPG) scheme. DDPG is used to enhance the convergence speed and eliminate redundant computations. Furthermore, to improve the detection in real-time, we proposed the proximal online policy technique (POPT). Numerical results demonstrate that the proposed scheme consumes 37.14%, 31.25%, and 21.42% better results than synthetic aperture radar (SAR), convolution neural network (CNN), and double deep recurrent Q-network (DDRQN).
Licensing Effect of Pro-Environmental Behavior in Metaverse

The Metaverse is an important virtual venue in people's daily lives. This study examined whether pro-environmental behaviors in the Metaverse with a self-customized avatar lead to licensing effects, thereby reducing pro-environmental intention in reality. In addition, the study examined the mediating effect of self-presence on the relationship between avatar customization and pro-environmental behavior. A laboratory experiment was conducted using ZEPETO, a Metaverse platform. A total of 60 participants were randomly assigned to the self-customized avatar condition or the assigned avatar condition, and they engaged in pro-environmental behaviors on the Metaverse platform. Participants with self-customized avatars had less intention to engage in pro-environmental behavior in reality after engaging in pro-environmental behavior in the Metaverse than those with assigned avatars. Furthermore, self-presence partially mediated the relationship between avatar customization and pro-environmental intention.
Privacy-Preserving British Sign Language Recognition Using Deep Learning

Sign language is a means of communication between the deaf community and normal hearing people who use hand gestures, facial expressions, and body language to communicate. It has the same level of complexity as spoken language, but it does not employ the same sentence structure as English. The motions in sign language comprise a range of distinct hand and finger articulations that are occasionally synchronized with the head, face, and body. Existing sign language recognition systems are mainly camera-based, which have fundamental limitations of poor lighting conditions, potential training challenges with longer video sequence data, and serious privacy concerns. This study presents a first of its kind, contact-less and privacy-preserving British sign language (BSL) Recognition system using Radar and deep learning algorithms. Six most common emotions are considered in this proof of concept study, namely confused, depressed, happy, hate, lonely, and sad. The collected data is represented in the form of spectrograms. Three state-of-the-art deep learning models, namely, InceptionV3, VGG19, and VGG16 models then extract spatiotemporal features from the spectrogram. Finally, BSL emotions are accurately identified by classifying the spectrograms into considered emotion signs. Comparative simulation results demonstrate that a maximum classifying accuracy of 93.33% is obtained on all classes using the VGG16 model.
The logic languages of the TPTP world

Abstract The Thousands of Problems for Theorem Provers (TPTP) World is a well-established infrastructure that supports research, development and deployment of automated theorem proving systems. This paper provides an overview of the logic languages of the TPTP World, from classical first-order form (FOF), through typed FOF, up to typed higher-order form, and beyond to non-classical forms. The logic languages are described in a non-technical way and are illustrated with examples using the TPTP language.
The role of desktop virtual reality as an accessible and equitable strategy to improve career opportunities for women in technology

Abstract Background The conventional classroom does not seem to adequately address the gender disparity in technology education. This could negatively affect the contributions of the female gender to national development. There was a need to advance a setting that enhances the participation of women in technology education. Aims This study evaluated desktop Virtual Reality (VR) to know whether it enhances equal academic achievement and learning interests of female and male students. Materials and Methods A toss of a coin was used to divide the six universities into two major groups; three universities were taught with ElectricVlab, while the other three universities were taught in the conventional classroom. Each of the six intact classes sat in clusters of five students and the whole class was taught the construction of an audio amplifier together using a demonstration method of teaching. A Vocational Interest scale and a researcher‐made achievement test with reliability coefficient=0.89 were used for data collection. Two‐way Analysis of Covariance (ANCOVA) was used to analyse means and test hypotheses at a 0.05 level of significance. Results The study found no significant difference between the mean achievement and interests of female and male students in the VR group. There was a significant interaction effect between gender and teaching environment. This study predicts that VR could bring women up to the same level of interest and achievement as men, but conventional classroom methods could not. Conclusions Curriculum planners should consider piloting virtual reality to enhance gender‐fair education, as it shows initial promise in the teaching of electronic technology education. Virtual reality for teaching and learning can be a part of postgraduate studies in universities, especially in academic fields where virtual reality has been found effective. This study may guide other studies and add to available literature.
Compound adversarial examples in deep neural networks

Although deep learning has made great progress in many fields, they are still vulnerable to adversarial examples. Many methods for generating adversarial examples have been proposed, which either contain adversarial perturbation or patch. In this paper, we explore the method that creates compound adversarial examples including both perturbation and patch. We show that fusing two weak attack modes can produce more powerful adversarial examples, where the patch covers only 1% of the pixels at random location in the image, and the perturbation changes only by 2/255 in the original pixel value (scale to 0–1). For both targeted attack and untargeted attack, compound attack can improve the generative efficiency of adversarial examples, and can attain higher attack success rate with fewer iteration steps. The compound adversarial examples successfully attack the models with defensive mechanisms that previously can defend perturbation attack or patch attack. Furthermore, the compound adversarial examples show good transferability on normal trained classifiers and adversarial trained classifiers. Experimental results on a series of widely used classifiers and defense models show that the proposed compound adversarial examples have strong robustness, high effectiveness, and good transferability.
Fuzzy Cognitive Maps for Interpretable Image-based Classification

Image classification is a fundamental component of intelligent vision systems. Developing classifiers capable of explaining how or why a classification result occurs, in a way compatible with human perception, remains a challenge. Considering the increasing demand for such classifiers, this paper introduces a novel interpretable classification scheme based on a Fuzzy Cognitive Map (FCM), named xFCM. xFCM is a directed graph with nodes representing semantic concepts of the real world, as these are illustrated within different images. These concepts are considered as Semantic Granules (SGs) instantiated as clusters of images sharing common characteristics. The edges of the graph represent similarities between the SGs, linguistically expressed by fuzzy sets. Unlike current FCM-based classification approaches, xFCM embeds a mechanism for automatic determination of its structure from data. In addition, it is simple to implement, and it exploits cause- and-effect relationships between its concepts to derive a classification result that is interpretable by humans. The results of the experiments, using publicly available datasets, prove the effectiveness of the proposed framework, in comparison with other state-of-the-art classifiers.
<scp>Semi‐automatic</scp> coding of <scp>open‐ended</scp> text responses in <scp>large‐scale</scp> assessments

Abstract Background In the context of large‐scale educational assessments, the effort required to code open‐ended text responses is considerably more expensive and time‐consuming than the evaluation of multiple‐choice responses because it requires trained personnel and long manual coding sessions. Aim Our semi‐supervised coding method eco (exploring coding assistant) dynamically supports human raters by automatically coding a subset of the responses. Method We map normalized response texts into a semantic space and cluster response vectors based on their semantic similarity. Assuming that similar codes represent semantically similar responses, we propagate codes to responses in optimally homogeneous clusters. Cluster homogeneity is assessed by strategically querying informative responses and presenting them to a human rater. Following each manual coding, the method estimates the code distribution respecting a certainty interval and assumes a homogeneous distribution if certainty exceeds a predefined threshold. If a cluster is determined to certainly comprise homogeneous responses, all remaining responses are coded accordingly automatically. We evaluated the method in a simulation using different data sets. Results With an average miscoding of about 3%, the method reduced the manual coding effort by an average of about 52%. Conclusion Combining the advantages of automatic and manual coding produces considerable coding accuracy and reduces the required manual effort.
White blood cell detection using saliency detection and CenterNet: A two‐stage approach

Abstract White blood cell (WBC) detection plays a vital role in peripheral blood smear analysis. However, cell detection remains a challenging task due to multi‐cell adhesion, different staining and imaging conditions. Owing to the powerful feature extraction capability of deep learning, object detection methods based on convolutional neural networks (CNNs) have been widely applied in medical image analysis. Nevertheless, the CNN training is time‐consuming and inaccuracy, especially for large‐scale blood smear images, where most of the images are background. To address the problem, we propose a two‐stage approach that treats WBC detection as a small salient object detection task. In the first saliency detection stage, we use the Itti's visual attention model to locate the regions of interest (ROIs), based on the proposed adaptive center‐surround difference (ACSD) operator. In the second WBC detection stage, the modified CenterNet model is performed on ROI sub‐images to obtain a more accurate localization and classification result of each WBC. Experimental results showed that our method exceeds the performance of several existing methods on two different data sets, and achieves a state‐of‐the‐art mAP of over 98.8%.
Physics-AI symbiosis

Abstract The phenomenal success of physics in explaining nature and engineering machines is predicated on low dimensional deterministic models that accurately describe a wide range of natural phenomena. Physics provides computational rules that govern physical systems and the interactions of the constituents therein. Led by deep neural networks, artificial intelligence (AI) has introduced an alternate data-driven computational framework, with astonishing performance in domains that do not lend themselves to deterministic models such as image classification and speech recognition. These gains, however, come at the expense of predictions that are inconsistent with the physical world as well as computational complexity, with the latter placing AI on a collision course with the expected end of the semiconductor scaling known as Moore’s Law. This paper argues how an emerging symbiosis of physics and AI can overcome such formidable challenges, thereby not only extending AI’s spectacular rise but also transforming the direction of engineering and physical science.
Superpixel Image Classification with Graph Convolutional Neural Networks Based on Learnable Positional Embedding

Graph convolutional neural networks (GCNNs) have been successfully applied to a wide range of problems, including low-dimensional Euclidean structural domains representing images, videos, and speech and high-dimensional non-Euclidean domains, such as social networks and chemical molecular structures. However, in computer vision, the existing GCNNs are not provided with positional information to distinguish between graphs of new structures; therefore, the performance of the image classification domain represented by arbitrary graphs is significantly poor. In this work, we introduce how to initialize the positional information through a random walk algorithm and continuously learn the additional position-embedded information of various graph structures represented over the superpixel images we choose for efficiency. We call this method the graph convolutional network with learnable positional embedding applied on images (IMGCN-LPE). We apply IMGCN-LPE to three graph convolutional models (the Chebyshev graph convolutional network, graph convolutional network, and graph attention network) to validate performance on various benchmark image datasets. As a result, although not as impressive as convolutional neural networks, the proposed method outperforms various other conventional convolutional methods and demonstrates its effectiveness among the same tasks in the field of GCNNs.
AAD-KWS: A Sub-<i>μ</i> W Keyword Spotting Chip With an Acoustic Activity Detector Embedded in MFCC and a Tunable Detection Window in 28-nm CMOS

As a widely used speech-triggered interface, deep-learning-based keyword spotting (KWS) chips require both ultra-low power and high detection accuracy. We propose a sub-microwatt KWS chip with an acoustic activity detection (AAD) to achieve the above two requirements, including the following techniques: first, an optimized feature extractor circuit using nonoverlapping-framed serial Mel frequency cepstral coefficient (MFCC) to save half of the computations and data storage; second, a zero-cost AAD by using MFCC’s 1st-order output to clock gate neural network (NN) and postprocessing (PP) unit, with 0 miss rate; third, a tunable detection window to adapt to different keyword lengths for better accuracy; and finally, a true form computation method to decrease data transitions and optimized PP. Implemented in a 28-nm CMOS process, this AAD-KWS chip has a 0.4-V supply, an 8-kHz frequency for MFCC, and a 200-kHz frequency for other parts. It consumes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.36~\mu \text{W}$ </tex-math></inline-formula> in quiet scenarios when AAD is enabled and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.8~\mu \text{W}$ </tex-math></inline-formula> in normal scenarios, where the MFCC circuit consumes only 170 nW. Its accuracy reaches 97.8% for two keywords in the Google Speech Command Dataset (GSCD).
A Novel Analytical Framework Is Developed for Wireless Heterogeneous Networks for Video Streaming Applications

Due to bandwidth and time limits, delivering high-quality video streaming facility offers real-time wireless networks, but assuring quality of experience (QoE) is rather difficult. A network host can deliver its data stream via numerous different network pathways. At the same time, using multipath data transmission, where video streaming may be supplied through Internet protocols (IPs) as well as broadband with bidirectional communication between video sources and consumers, is one way to address this difficult problem. In this work, a novel framework is developed for wireless heterogeneous networks for video streaming applications using concurrent multipath transfer (CMT). The performance of frame-level delay can be enhanced for better video quality using this proposed method. The network congestion on end-to-end path utilised in CMT is not dependent of one another, and we operate under prior assumption that receiver’s announced window does not limit the sender. The analytical results show that the proposed framework outperforms existing methods in terms of performance with minimum retransmission delay.
Facilitating learning in immersive virtual reality: Segmentation, summarizing, both or none?

Abstract Introduction This study investigates the effectiveness of the segmentation principle from the cognitive theory of multimedia learning as well as the effectiveness of the generative learning strategy of summarization in immersive virtual reality (IVR) within a sample of preadolescents. Although previous research has supported the effectiveness of these instructional methods in multimedia learning, it remains unclear whether segmentation, summarization or the combination of both are superior to a stand‐alone IVR lesson in facilitating learning. Methods To address this gap, 190 sixth to seventh grade students learned about the human body in an IVR lesson. Students were randomly assigned to one of four experimental conditions: (a) an IVR lesson divided into four segments (segmentation condition); (b) an IVR lesson, where students summarized after the entire lesson (summarizing condition); (c) an IVR lesson presented in four segments, where students summarized after each segment (combined condition); and (d) an IVR lesson without any manipulation (control condition). Results Results indicated that, compared to the control condition, adding segmentation or summarization to an IVR lesson leads to better transfer, but not to acquiring more factual knowledge. Combining the two methods did not improve learning. Conclusion The findings support the evidence that choosing appropriate instructional methods for IVR lessons can foster transfer.
Evaluating computing performance of deep neural network models with different backbones on IoT-based edge and cloud platforms

This paper focuses on evaluating and predicting the computing performance of different architectures of deep neural network models (DNNs) in cross-platform and cross-inference frameworks. We test nearly 30 typical DNN models for image recognition on Google Colab cloud computing platform and Intel neural compute stick 2 embedded edge computing platform and record the computational performance metrics i.e. the Top-N accuracy, model complexity, computational complexity, inference time, memory usage, and so on. We compare and analyze these performance parameters with the previous workstation equipped with NVIDIA Titan X Pascal and an embedded system based on NVIDIA Jetson TX1 board to evaluate the inference efficiency of different DNN models using different inference frameworks. The methods of ANOVA are adopted to quantify the differences between the models. A combination method of cluster analysis and regression analysis is proposed to find the similar inference time variation processes across models, which can be used to predict the inference results of unknown models. These presented results will contribute to better deployment and application of resource-constrained DNN models on the heterogeneous high-performance computing platform.
Robust unsupervised feature selection via sparse and minimum-redundant subspace learning with dual regularization

Unsupervised feature selection, one of the important dimensionality reduction methods for high-dimensional data, has been paid more and more attention by researchers due to its effectiveness of processing the data without labels. However, most unsupervised feature selection methods do not take into account the fact that real data tends to be mixed with noise or outliers, or only consider the sensitivity of the model to them. In addition, the local manifold structure of data is usually preserved by a simple graph based on fixed k nearest neighbors. On the one hand, the different sample distributions of different data are considered without distinction. On the other hand, simple graph can not well capture the inherent relations between samples when facing multi-modal data. In view of the above problems, a robust unsupervised feature selection algorithm, sparse and minimum-redundant subspace learning with dual regularization (SMRSDR), is proposed in this paper. Specifically, a self-paced learning regularization based on soft weight allocation is introduced into SMRSDR to strictly control the samples’ entry into model learning. Normal samples are allowed to enter model learning in order of their importance, while noise or outliers are strictly blocked out of model learning. Besides, considering the difference in sample distribution and the multimodal nature of data, the traditional simple graph Laplacian regularization is replaced by the hypergraph Laplacian regularization based on adaptive nearest neighbors selection. Furthermore, the sparsity and minimum-redundancy constraints are imposed on the subspace learning framework so that the most representative and minimum-redundant features can be selected. Finally, the objective function of SMRSDR is solved by an alternating iterative optimization algorithm. And a series of experiments are performed to comprehensively prove the effectiveness and superiority of SMRSDR.
Deep Expectation-Maximization for Joint MIMO Channel Estimation and Signal Detection

To overcome the influence of channel estimation error on signal detection, this paper presents a model-driven deep learning method for joint channel estimation and signal detection in multiple-input multiple-output (MIMO) wireless communication systems. To improve the robustness of the method, we use the Student's <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -distribution to model the environment noise, and model the joint problem probabilistically by taking the channel state information (CSI) as a latent variable, then derive a generalized expectation maximization (GEM) algorithm to fit the model. In GEM, the expectation step is used for robust CSI estimation while the maximization step detects the transmitted signal with the estimated CSI. To reduce the computational complexity of GEM, we unfold it into a deep neural network. The network contains only a few trainable parameters, which is easy to train and has low space complexity. Based on our experimental results, we find that the proposed GEM algorithm outperforms a variety of signal detection algorithms which take the CSI estimation and signal detection as independent modules. Further, we find empirically that the proposed GEM network outperforms state-of-the-art deep learning based joint channel estimation and signal detection methods and has a good generalization ability against the number of antennas, the modulation mode, the level of signal-to-noise ratio and channel correlation.