Reviewerly/exHarmony huggingface dataset:
Knowledge graph embedding with self adaptive double-limited loss

Many well-performing embedding models for knowledge graphs employ a negative sampling framework to complete the representation learning in which the loss function is a critical component in distinguishing between positive and negative triplets. One of the most recently proposed loss functions is the double-limited scoring loss, which sets fixed upper and lower bounds respectively for positive and negative triplets. We find that, for all positive and negative triplets, fixed upper and lower bounds are not appropriate since triplets that are difficult to be distinguished usually have changing bounds. In this paper, we propose a self adaptive double-limited loss (ADL) that dynamically adjusts the upper limit of positive triplet scores and the lower limit of negative triplet scores through evaluating the score proportion between positive and negative triplets. Furthermore, based upon ADL, we build several knowledge graph embedding models, including TransE-ADL, TransH-ADL, TransD-ADL, TorusE-ADL, and ComplEx-ADL, in which the gradient descent technique is used to train their parameters. Dynamically adjusted bounds lead to a reasonable partition of positive and negative triplets within embedding space, improving prediction accuracy significantly. The experimental results of link prediction confirm this improvement compared to the state-of-the-art baselines. • A new loss function, named ADL, for Knowledge graph embedding is proposed. • Incorporating ADL into the currently popular embedding models, five extensions, i.e., TransE-ADL, TransH-ADL, TransD-ADL, TorusE-ADL, and ComplEx-ADL, are developed. • The developed models have significantly improved the performance of link prediction in comparison with the baseline model.
Understanding Factors that Shape Children’s Long Term Engagement with an In-Home Learning Companion Robot

Social robots are emerging as learning companions for children, and research shows that they facilitate the development of interest and learning even through brief interactions. However, little is known about how such technologies might support these goals in authentic environments over long-term periods of use and interaction. We designed a learning companion robot capable of supporting children reading popular-science books by expressing social and informational commentaries. We deployed the robot in homes of 14 families with children aged 10-12 for four weeks during the summer. Our analysis revealed critical factors that affected children's long-term engagement and adoption of the robot, including external factors such as vacations, family visits, and extracurricular activities; family/parental involvement; and children's individual interests. We present four in-depth cases that illustrate these factors and demonstrate their impact on children's reading experiences and discuss the implications of our findings for robot design.
Architecture entropy sampling-based evolutionary neural architecture search and its application in osteoporosis diagnosis

Abstract In recent years, neural architecture search (NAS) has achieved unprecedented development because of its ability to automatically achieve high-performance neural networks in various tasks. Among these, the evolutionary neural architecture search (ENAS) has impressed the researchers due to the excellent heuristic exploration capability. However, the evolutionary algorithm-based NAS are prone to the loss of population diversity in the search process, causing that the structure of the surviving individuals is exceedingly similar, which will lead to premature convergence and fail to explore the search space comprehensively and effectively. To address this issue, we propose a novel indicator, named architecture entropy, which is used to measure the architecture diversity of population. Based on this indicator, an effective sampling strategy is proposed to select the candidate individuals with the potential to maintain the population diversity for environmental selection. In addition, an unified encoding scheme of topological structure and computing operation is designed to efficiently express the search space, and the corresponding population update strategies are suggested to promote the convergence. The experimental results on several image classification benchmark datasets CIFAR-10 and CIFAR-100 demonstrate the superiority of our proposed method over the state-of-the-art comparison ones. To further validate the effectiveness of our method in real applications, our proposed NAS method is applied in the identification of lumbar spine X-ray images for osteoporosis diagnosis, and can achieve a better performance than the commonly used methods. Our source codes are available at https://github.com/LabyrinthineLeo/AEMONAS.
Blockchain enabled trusted task offloading scheme for fog computing: A deep reinforcement learning approach

Abstract With the recent advancements in the Internet of Things, cloud computing has emerged as an important industrial technology that assists in various data analysis operations. However, the remote locality of cloud servers and scalability issues of cloud computing make it unsuitable for real‐time computing‐intensive applications. Fog computing strives to support cloud computing in meeting scalability demands by providing location‐sensitive services closer to end devices. With decentralized heterogeneous resource capabilities, fog architecture can handle several computation‐intensive and delay‐sensitive user requests. Although deploying service providers in an untrustworthy environment makes it challenging to assess the trustworthy acquired services. Conspicuously, in this article, we present a trusted task offloading and resource allocation using blockchain technology. To start with, we analyze direct and indirect trust with a subjective logical aggregation approach using a distributed trust assessment approach. Additionally, we examined the various quality of service parameters and constructed a smart contract that utilizes the state‐of‐the‐art deep reinforcement learning algorithm, namely Deep Deterministic Policy Gradient, to maximize fog revenue while serving as many user requests as possible. The entire process from task generation to results calculation is assisted by blockchain and offloading task transactions are stored in the secure, immutable, and tamper‐resistant ledger. To assess the effectiveness of our proposed scheme, we compared the simulation results with other baseline schemes over different performance metrics in terms of reward, service latency, energy consumption, task drop ratio, and transaction success rate. The results suggest that enabling trust computation improves transaction success by 21%.
Distinguishing foreground and background alignment for unsupervised domain adaptative semantic segmentation

Unsupervised domain adaptive semantic segmentation uses the knowledge learned from the labeled source domain dataset to guide the segmentation of the target domain. However, this domain migration method will cause a large inter-domain difference due to the different feature distributions between the source domain and the target domain. We use the self-supervised learning method to generate pseudo labels for the target domain, so that the corresponding pixels are directly aligned with the source domain according to the segmentation loss. Through observation, it is found that the spatial distribution of the background class in the source domain and the target domain has a small difference, and the appearance of the same class of the foreground class will also be quite different. We use the method of distinguishing alignment between foreground and background classes. We understand that acquiring the rich space and channel information on the feature map during the convolution process is essential for fine-grained semantic segmentation. Therefore, in order to obtain the dependency relationship between the channels of the feature map and the spatial position information, we use a channel and spatial parallel attention module. This module enables the network to select and amplify valuable space and channel information from the global information and suppress useless information. In addition, we introduce focal loss to solve the problem of class imbalance in the data set. Experiments show that our method achieves better segmentation performance in unsupervised domain adaptive semantic segmentation.
Breast Tumor Ultrasound Image Segmentation Method Based on Improved Residual U-Net Network

In order to achieve efficient and accurate breast tumor recognition and diagnosis, this paper proposes a breast tumor ultrasound image segmentation method based on U-Net framework, combined with residual block and attention mechanism. In this method, the residual block is introduced into U-Net network for improvement to avoid the degradation of model performance caused by the gradient disappearance and reduce the training difficulty of deep network. At the same time, considering the features of spatial and channel attention, a fusion attention mechanism is proposed to be introduced into the image analysis model to improve the ability to obtain the feature information of ultrasound images and realize the accurate recognition and extraction of breast tumors. The experimental results show that the Dice index value of the proposed method can reach 0.921, which shows excellent image segmentation performance.
<scp>SKMAT‐U‐Net</scp> architecture for breast mass segmentation

Abstract Being a deadly disease, breast cancer provides higher global mortality among women. The challenge in employing ultrasound imaging for breast cancer diagnosis lies in the segmentation part. Thus, the work intends to segment the breast cancer ultrasound images using the deep learning approach. The U‐shaped convolution neural network, U‐Net, has become popular and efficient for biomedical image segmentation. Specifically, the paper proposes an improved version, SKMAT‐U‐Net, where a selective kernel (SK) utilizes an attention mechanism to adjust the receptive fields of the network and combine feature maps extricated with dilated and standard convolution operations. Next, based on the conventional cross‐entropy loss function, four attention loss functions are integrated to form the Mixed Attention Loss Function (MAT) based U‐Net. Thus, the SKMAT‐U‐Net model is proposed to segment the lesions effectively in breast ultrasound images. The results obtained indicated that the proposed U‐Net model provides a better Dice score (0.929) than others.
Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convolution neural network (SSD-CNN) model. Third, landmarks are localized on the hand using the skeleton method. Fourth, features are extracted based on point-based trajectories, frame differencing, orientation histograms, and 3D point clouds. Fifth, features are optimized using fuzzy logic, and last, the H-Hash classifier is used for the classification of hand gestures. The system is tested on two benchmark datasets, namely, the IPN hand dataset and Jester dataset. The recognition accuracy on the IPN hand dataset is 88.46% and on Jester datasets is 87.69%. Users can control their smart home appliances, such as television, radio, air conditioner, and vacuum cleaner, using the proposed system.
Keyword-guided abstractive code summarization via incorporating structural and contextual information

Source code summarization is a crucial yet far from settled task for describing structured code snippets in natural language . High-quality code summaries could effectively facilitate program comprehension and software maintenance. A good code summary is supposed to have the following characteristics: complete information, correct meaning, and consistent description. In recent years, numerous approaches have been proposed for code summarization, but it is still very challenging for developers to automatically learn the complex semantics from the source code and generate complete, correct and consistent code summaries. In this paper, we propose KGCodeSum , a novel keyword-guided abstractive code summarization approach that incorporates structural and contextual information. To improve summaries’ quality, we leverage both the structural information embedded in code itself and the contextual information from related code snippets. Meanwhile, we make use of keywords to guide summaries’ generation to guarantee the code summaries contain key information. Finally, we propose a new dynamic vocabulary strategy which can effectively resolve the UNK problems in code summaries. Through our evaluation on the large-scale benchmark datasets with 2.1 million java method-comment pairs and 1.1 million C/C++ function-summary pairs, We have observed that our approach could generate better code summaries than existing state-of-the-art approaches in terms of completeness, correctness and consistency. In addition, we also find that incorporating the dynamic vocabulary strategy into our approach could significantly save time and space in the model training process. Our KGCodeSum approach could effectively generate code summaries.
DFT-MF: Enhanced deepfake detection using mouth movement and transfer learning

<h2>Abstract</h2> Deepfake technology presents serious cybersecurity challenges that have become more prevalent with the availability of easily accessible applications. An effective method for detection and prevention of this is necessary. This paper introduces a robust approach and software implementation to detect fake videos constructed with Deep Learning technology that depends on utilizing teeth and mouth movement as distinguishing features that remain very difficult to perfect when faking videos. The proposed methodology has a higher efficiency and accuracy of fake video detection than similar approaches. The work in this article is an extension of previous work that introduced the main concepts with additional application of multi-transfer learning approaches including DenseNet121, DenseNet169, EfficientNetB0, EfficientNetB7, InceptionV3, MobileNet, ResNet50, vgg16, vgg19 and Xception to enhance the algorithm's ability to detect and classify Deepfake videos based on features extracted from the teeth and mouth frames as a biological signal.
Neural topic-enhanced cross-lingual word embeddings for CLIR

Cross-lingual information retrieval (CLIR) methods have quickly made the transition from translation-based approaches to semantic-based approaches. In this paper, we examine the limitations of current unsupervised neural CLIR methods, especially those leveraging aligned cross-lingual word embedding (CLWE) spaces. At the moment, CLWEs are normally constructed on the monolingual corpus of bilingual texts through an iterative induction process. Homonymy and polysemy have become major obstacles in this process. On the other hand, contextual text representation methods often fail to outperform static CLWE methods significantly for CLIR. We propose a method utilizing a novel neural generative model with Wasserstein autoencoders to learn neural topic-enhanced CLWEs for CLIR purposes. Our method requires minimal or no supervision at all. On the CLEF test collections, we perform a comparative evaluation of the state-of-the-art semantic CLWE methods along with our proposed method for neural CLIR tasks. We demonstrate that our method outperforms the existing CLWE methods and multilingual contextual text encoders. We also show that our proposed method obtains significant improvements over the CLWE methods based upon representative topical embeddings.
Comfort with and willingness to participate in COVID-19 contact tracing: The role of risk perceptions, trust, and political ideology

Contact tracing (CT) can limit the spread of infectious diseases, however its effectiveness hinges on public participation. We evaluated perceptions of the financial and health risk posed by COVID-19 and trust in information about COVID-19 provided by the state health department that manages CT as predictors of comfort and willingness to comply with CT. We further examined the moderating effect of political ideology on these relationships.We used structural equation modeling to test hypotheses in data from a cross-sectional survey completed by a representative sample of Michigan residents (N = 805) in 2020.Perceptions of the risk of COVID-19 to one's health (but not finances) was negatively related to comfort and willingness to participate in CT. Trust in information about COVID-19 and liberalism were positively related to comfort and willingness. There was also a moderating effect of political ideology, such that conservatives were less comfortable and willing at greater perceptions of health risk.Conservatives and those who perceive a greater health risk may require targeted messaging and more deliberate engagement strategies to increase CT participation.
VSAI: A Multi-View Dataset for Vehicle Detection in Complex Scenarios Using Aerial Images

Arbitrary-oriented vehicle detection via aerial imagery is essential in remote sensing and computer vision, with various applications in traffic management, disaster monitoring, smart cities, etc. In the last decade, we have seen notable progress in object detection in natural imagery; however, such development has been sluggish for airborne imagery, not only due to large-scale variations and various spins/appearances of instances but also due to the scarcity of the high-quality aerial datasets, which could reflect the complexities and challenges of real-world scenarios. To address this and to improve object detection research in remote sensing, we collected high-resolution images using different drone platforms spanning a large geographic area and introduced a multi-view dataset for vehicle detection in complex scenarios using aerial images (VSAI), featuring arbitrary-oriented views in aerial imagery, consisting of different types of complex real-world scenes. The imagery in our dataset was captured with a wide variety of camera angles, flight heights, times, weather conditions, and illuminations. VSAI contained 49,712 vehicle instances annotated with oriented bounding boxes and arbitrary quadrilateral bounding boxes (47,519 small vehicles and 2193 large vehicles); we also annotated the occlusion rate of the objects to further increase the generalization abilities of object detection networks. We conducted experiments to verify several state-of-the-art algorithms in vehicle detection on VSAI to form a baseline. As per our results, the VSAI dataset largely shows the complexity of the real world and poses significant challenges to existing object detection algorithms. The dataset is publicly available.
Benchmarking Audio-based Deep Learning Models for Detection and Identification of Unmanned Aerial Vehicles

Over the last few years, Unmanned Aerial Vehicles (UAVs) have become increasingly popular for both commercial and personal applications. As a result, security concerns in both physical and cyber domains have been raised, as a malicious UAV can be used for the jamming of nearby targets or even for carrying explosive assets. UAV detection and identification is a very important task for safety and security. In this regard, several techniques have been proposed for the detection and identification of UAVs, in general, through image, audio, radar, and RF based approaches. In this paper, we benchmark the detection and identification of UAVs via audio data from [1]. We benchmarked with widely used deep learning algorithms such as Deep Neural Networks (DNN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Convolutional Long Short Term Memory (CLSTM) and Transformer Encoders (TE). In addition to the dataset of [1], we collected our own diverse identification audio dataset and experimented with Deep Neural Networks (DNN). In a UAV detection task, our best model (LSTM) outperformed the best model of [1] (CRNN) by over 4% in accuracy, 2% in precision, 4% in recall and 4% in F1-score. In UAV identification task, our best model (LSTM) outperformed the best model of [1] (CNN) by over 5% in accuracy, 2% in precision, 4% in recall and 3% in F1-score.
A tooth surface design method combining semantic guidance, confidence, and structural coherence

Research on tooth surface design based on deep neural networks has recently achieved progress in terms of both accuracy and execution efficiency. However, unrealistic outputs are still a challenging issue, partially because of (1) the lack of semantic guidance, (2) the inability to discover and rectify false results, and (3) the lack of exploration of structural coherence in intermediate layers. In this paper, we present an approach to predict depth images for designed teeth based on a conditional generative adversarial network (CGAN) by incorporating semantic guidance. Moreover, the uncertainty of semantic inference is employed to improve the model outputs, and a structural coherence loss is proposed for adversarial learning to enhance the discrimination capability of the network in intermediate layers. We evaluate the performance of our approach with the Shining3D tooth dataset. The experimental results show that our method produces better results than the other available approaches in terms of accuracy.
Excavating RoI Attention for Underwater Object Detection

Self-attention is one of the most successful designs in deep learning, which calculates the similarity of different tokens and reconstructs the feature based on the attention matrix. Originally designed for NLP, self-attention is also popular in computer vision, and can be categorized into pixel-level attention and patch-level attention. In object detection, RoI features can be seen as patches from base feature maps. This paper aims to apply the attention module to RoI features to improve performance. Instead of employing an original self-attention module, we choose the external attention module, a modified self-attention with reduced parameters. With the proposed Double Head structure and the Positional Encoding module, our method can achieve promising performance in object detection. The comprehensive experiments show that it achieves promising performance, especially in the under-water object detection dataset. The code will be avaiable in: https://github.com/zsyasd/Excavating-RoI-Attention-for-Underwater-Object-Detection
Here To Stay: A Quantitative Comparison of Virtual Object Stability in Markerless Mobile AR

Mobile augmented reality (AR) has the potential to enable immersive, natural interactions between humans and cyberphysical systems.In particular markerless AR, by not relying on fiducial markers or predefined images, provides great convenience and flexibility for users.However, unwanted virtual object movement frequently occurs in markerless smartphone AR due to inaccurate scene understanding, and resulting errors in device pose tracking.We examine the factors which may affect virtual object stability, design experiments to measure it, and conduct systematic quantitative characterizations across six different user actions and five different smartphone configurations.Our study demonstrates noticeable instances of spatial instability in virtual objects in all but the simplest settings (with position errors of greater than 10cm even on the best-performing smartphones), and underscores the need for further enhancements to pose tracking algorithms for smartphone-based markerless AR.