Reviewerly/exHarmony huggingface dataset:
Exploring the determinants of victimization and fear of online identity theft: an empirical study

The present study aims at understanding what factors contribute to the explanation of online identity theft (OIT) victimization and fear, using the Routine Activity Theory (RAT). Additionally, it tries to uncover the influence of factors such as sociodemographic variables, offline fear of crime, and computer perception skills. Data for the present study were collected from a self-reported online survey administered to a sample of university students and staff (N = 832, 66% female). Concerning the OIT victimization, binary logistic regression analysis showed that those who do not used credit card had lower odds of becoming an OIT victim, and those who reported visiting risky contents presented higher odds of becoming an OIT victim. Moreover, males were less likely than females of being an OIT victim. In turn, fear of OIT was explained by socioeconomic status (negatively associated), education (positively associated) and by fear of crime in general (positively associated). In addition, subjects who reported more online interaction with strangers were less fearful, and those reported more avoiding behaviors reported higher levels of fear of OIT. Finally, subjects with higher computer skills are less fearful. These results will be discussed in the line of routine activities approach and implications for online preventive behaviors will be outlined.
Learning knowledge graph embeddings by deep relational roto-reflection

Embedding methods map entities and relations to low-dimensional vectors and then use a scoring function to predict missing links for knowledge graph completion. Most of the existing deep embedding methods are primarily based on planner convolution, which generates less expressive feature maps that limit the predictive performance. In this paper, we propose a group convolution and hypernetwork-based neural embedding model named, DeepER for knowledge graph completion. DeepER utilizes rotation and reflection transformations of group convolution to produce more expressive feature maps for entities and relations. Furthermore, it introduces a relation-specific roto-reflection of head entities via hypernetwork architecture to preserve the relation-specific information in the embeddings while keeping the rotation and reflection properties of the relations. In addition, we introduce a multimodal extension of DeepER that includes visual and structured information in the embedding vectors of entities. Experimental results demonstrate that DeepER outperforms 20 existing methods on knowledge graph completion benchmarks, consisting of both structured and multimodal datasets.
Efficient Cryptographic Protocol Design for Secure Sharing of Personal Health Records in the Cloud

The sharing of Personal Health Records (PHR) in the cloud is the most promising platform to exchange health-related information. However, personal medical and health information storage is outsourced to third parties, potentially exposing patients' privacy to unauthorized individuals or groups. This paper intends to propose a new SSPHR (Secure Sharing PHR) method in the cloud. The proposed SSPHR method can preserve the privacy of PHRs and also ensures patient-centric control on PHRs. The encrypted PHRs are stored on the untrusted cloud servers and it grants selective access to different users on different PHRs portions. In addition, a semi-trusted proxy known as Setup and Re-encryption Server (SRS) is presented to arrange the public/private key pairs and to generate the re-encryption keys. This paper introduces a new Modified El-Gamal encryption for preserving health records and it includes key generation, encryption, and decryption process. Finally, the proposed model performance is compared and proven against other existing methods.
Modeling Adoption, Security, and Privacy of COVID-19 Apps: Findings and Recommendations From an Empirical Study Using the Unified Theory of Acceptance and Use of Technology

The global health crisis caused by COVID-19 has drastically changed human society in a relatively short time. However, this crisis has offered insights into the different roles that such a worldwide virus plays in the lives of people and how those have been affected, as well as eventually proposing new solutions. From the beginning of the pandemic, technology solutions have featured prominently in virus control and in the frame of reference for international travel, especially contact tracing and passenger locator applications.The objective of this paper is to study specific areas of technology acceptance and adoption following a unified theory of acceptance and use of technology (UTAUT) research model.We presented a research model based on UTAUT constructs to study the determinants for adoption of COVID-19-related apps using a questionnaire. We tested the model via confirmatory factor analysis (CFA) and structural equation modeling (SEM) using travelers' data from an insular tourist region.Our model explained 90.3% of the intention to use (N=9555) and showed an increased understanding of the vital role of safety, security, privacy, and trust in the usage intention of safety apps. Results also showed how the impact of COVID-19 is not a strong predictor of adoption, while age, education level, and social capital are essential moderators of behavioral intention.In terms of scientific impact, the results described here provide important insights and contributions not only for researchers but also for policy and decision makers by explaining the reasons behind the adoption and usage of apps designed for COVID-19.
Colour balance and contrast stretching for sand‐dust image enhancement

The increasingly frequent sand-dust weather in the inland areas seriously affects outdoor vision applications, especially autonomous vehicles and security monitoring. To moderate the image's colour cast and poor contrast caused by sand-dust weather, an effective approach is proposed in this study to enhance the sand-dust images. First, the original degraded image's colour cast is corrected by a new colour balance and compensation formula, which compensates the blue and green channel information through numerous yellow channel information caused by sand-dust scattering before white balance. Next, in order to avoid the new colour deviation, the corrected image is converted from the RGB colour space to the HSV colour space and use the CLAHE to enhance the V component to improve the contrast. Then, a nonlinear gain function is defined to further adaptively sharpen the V component to enhance image details. Finally, the S component is stretched to improve image saturation. The extensive qualitative and quantitative evaluation shows that this method can improve the image edge clarity and contrast, restore good colour fidelity for all sand-dust images tested. The verification also proves that this method is of much significance in improving the feature point extraction and the target detection results in the sand-dust weather.
Deep learning of longitudinal mammogram examinations for breast cancer risk prediction

Information in digital mammogram images has been shown to be associated with the risk of developing breast cancer. Longitudinal breast cancer screening mammogram examinations may carry spatiotemporal information that can enhance breast cancer risk prediction. No deep learning models have been designed to capture such spatiotemporal information over multiple examinations to predict the risk. In this study, we propose a novel deep learning structure, LRP-NET, to capture the spatiotemporal changes of breast tissue over multiple negative/benign screening mammogram examinations to predict near-term breast cancer risk in a case-control setting. Specifically, LRP-NET is designed based on clinical knowledge to capture the imaging changes of bilateral breast tissue over four sequential mammogram examinations. We evaluate our proposed model with two ablation studies and compare it to three models/settings, including 1) a "loose" model without explicitly capturing the spatiotemporal changes over longitudinal examinations, 2) LRP-NET but using a varying number (i.e., 1 and 3) of sequential examinations, and 3) a previous model that uses only a single mammogram examination. On a case-control cohort of 200 patients, each with four examinations, our experiments on a total of 3200 images show that the LRP-NET model outperforms the compared models/settings.
A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm

We present a deep neural network (DNN) accelerator designed for efficient execution of transformer-based DNNs, which have become ubiquitous for natural language processing tasks. DNN inference accelerators often employ specialized hardware techniques such as reduced precision to improve energy efficiency, but many of these techniques result in catastrophic accuracy loss on transformers. The proposed accelerator supports per-vector scaled quantization and approximate softmax to enable the use of 4-bit arithmetic with little accuracy loss. The 5nm prototype achieves 95.6 TOPS/W in benchmarking and 1711 inferences/s/W with only 0.7% accuracy loss on BERT, demonstrating a practical accelerator design for energy-efficient inference with transformers.
A TinyML approach to non-repudiable anomaly detection in extreme industrial environments

Unforeseen failures of industrial assets may lead to unexpected downtime with a huge impact on critical business processes. Therefore, modern assets usually include several embedded sensors and processing units, allowing to monitor certain operational parameters, i.e., Condition Monitoring. The sensed data can be later analyzed using Machine Learning (ML) approaches to detect anomalies and anticipate failures. Furthermore, the Internet of Things can provide the tools to extend Condition Monitoring to legacy assets that do not have onboard sensing capabilities. In general, these IoT devices offer the opportunity to move ML processing closer to the monitored asset, thus reducing costs and simplifying the anomaly detection system. However, extreme industrial environments present harsh operating conditions and limited resources, further exacerbated by the reduced computation capabilities of most IoT devices. This paper proposes an ML-based anomaly detection system that uses a retrofitting kit based on a constrained and cost-effective IoT device. Despite its limited resources, the latter executes a state-of-the-art unsupervised anomaly detection algorithm locally, autonomously learning the normality behavior of the monitored asset. Furthermore, to improve the transparency of the monitoring process, we propose to leverage blockchain technology as a non-repudiable repository of information, also assessing the impact of such implementation choice in terms of costs and overhead.
Local anatomically-constrained facial performance retargeting

Generating realistic facial animation for CG characters and digital doubles is one of the hardest tasks in animation. A typical production workflow involves capturing the performance of a real actor using mo-cap technology, and transferring the captured motion to the target digital character. This process, known as retargeting , has been used for over a decade, and typically relies on either large blendshape rigs that are expensive to create, or direct deformation transfer algorithms that operate on individual geometric elements and are prone to artifacts. We present a new method for high-fidelity offline facial performance retargeting that is neither expensive nor artifact-prone. Our two step method first transfers local expression details to the target, and is followed by a global face surface prediction that uses anatomical constraints in order to stay in the feasible shape space of the target character. Our method also offers artists with familiar blendshape controls to perform fine adjustments to the retargeted animation. As such, our method is ideally suited for the complex task of human-to-human 3D facial performance retargeting, where the quality bar is extremely high in order to avoid the uncanny valley, while also being applicable for more common human-to-creature settings. We demonstrate the superior performance of our method over traditional deformation transfer algorithms, while achieving a quality comparable to current blendshape-based techniques used in production while requiring significantly fewer input shapes at setup time. A detailed user study corroborates the realistic and artifact free animations generated by our method in comparison to existing techniques.
Analyzing the Spatio-Temporal Characteristics and Influencing Factors of “AI + Education” Network Attention in China

The Internet is a tool for free expression of will, primarily reflecting the public’s willingness to pay attention. Therefore, it is of great significance to use network attention to guide the implementation of “Artificial Intelligence (AI) + Education.” First, this study takes the “AI + Education” network attention of 31 provinces and cities in China as the research object and selects the relevant data from the Baidu Index and the National Bureau of statistics from 2012 to 2020. Then, the study uses the methods of elasticity coefficient, geographical concentration index, and panel model to analyze the spatiotemporal characteristics and influencing factors of “AI + Education.” Finally, the future development trends in “AI + Education” is predicted. The results show that the time characteristics of “AI + Education” are apparent, and there are specific interannual differences. The spatial difference between “AI + Education” attention is narrowing, and the spatial balance is gradually improving. The Internet, level of economic development, education funding, and vocational education are the main factors influencing the attention of “AI + Education.” According to the forecast results, the attention to “AI + Education” in eastern and central China will generally rise in the next 2 years, while some parts of western China will slightly decline. Therefore, in the future development, national and regional governments should pay attention to the policy guidance of regional differences, strengthen the promotion of new teaching methods, and attach importance to the intelligent construction of vocational education, to promote the integrated development of AI and Education.
Learning an enhanced consensus representation for multi-view clustering via latent representation correlation preserving

Multi-view clustering has attracted significant attention due to its sufficiency of exploiting information available from multi-view data to generate a consensus representation across multiple views to improve clustering performance. However, most existing methods merely focus on learning the consistency or complementarity of multi-view data in the original space or latent embedding space alone, which limits their ability to learn an informative consensus representation. To solve this, we propose a novel multi-view clustering method, namely, learning an enhanced consensus representation via latent representation correlation preserving (LRCP). LRCP employs the adaptive neighbors graph approach to construct the initial affinity matrices as input. By introducing a latent representation correlation preserving model, the consensus representation of the initial affinity matrices in the original space and the shared latent representation of the initial affinity matrices in the latent embedding space are integrated into a unified optimization framework to learn an enhanced consensus representation with enriched information and improved representation capability. The learned consensus representation can not only incorporate consensus affinity structure information that is admitted by all views in the original space but also reconstruct the complementary affinity information that is learned by the shared latent representation in the latent space. An efficient optimization algorithm is developed to tackle the resultant optimization problem. Experiments on benchmark datasets have validated the efficacy of the proposed method.
Context-guided entropy minimization for semi-supervised domain adaptation

Semi-Supervised Domain Adaptation has been widely studied with various approaches to address domain shift with labeled source-domain data combined with scarcely labeled target-domain data. Model adaptation is becoming promising with a paradigm of source pre-training and target fine-tuning, which eliminates the simultaneous availability of data from both domains and makes for data privacy. Among the model adaptation methods, Entropy Minimization (EM) is popularly incorporated to encourage a low-density separation on target samples. However, EM tends to brutally force models to make over-confident predictions, which could make the models collapse with deteriorated performance. In this paper, we first study the over-confidence of EM with a quantitative analysis, which shows the importance of capturing the dependency among labels. To address this issue, we propose to guide EM via longitudinal self-distillation. Specifically, we produce a dynamic "teacher" label distribution during training by constructing a graph on target data and perform pseudo-label propagation to encourage the "teacher" distribution to capture context category dependency based on a global data structure. Then EM is guided longitudinally by distilling the learned label distribution to combat the brute-force over-confidence. Extensive experiments demonstrate the effectiveness of our methods.
Research on Path Planning Algorithm for Driverless Vehicles

In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve the oscillation phenomenon existing in the improved artificial potential field method planning path. This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. First, the improved artificial potential field method is combined with the RRT algorithm, and the obstacle avoidance method of the RRT algorithm is used to solve the path oscillation; The vehicle kinematics model is then established, and under the premise of ensuring the safety of the vehicle, a model predictive control (MPC) trajectory tracking controller with constraints is designed to verify whether the path planned by the combination of the two algorithms is optimal and conforms to the vehicle motion. Finally, the feasibility of the method is verified in simulation. The simulation results show that the method can effectively solve the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion.
Weakly-Supervised 3D Human Pose Estimation With Cross-View U-Shaped Graph Convolutional Network

Although monocular 3D human pose estimation methods have made significant progress, it is far from being solved due to the inherent depth ambiguity. Instead, exploiting multi-view information is a practical way to achieve absolute 3D human pose estimation. In this paper, we propose a simple yet effective pipeline for weakly-supervised cross-view 3D human pose estimation. By only using two camera views, our method can achieve state-of-the-art performance in a weakly-supervised manner, requiring no 3D ground truth but only 2D annotations. Specifically, our method contains two steps: triangulation and refinement. First, given the 2D keypoints that can be obtained through any classic 2D detection methods, triangulation is performed across two views to lift the 2D keypoints into coarse 3D poses. Then, a novel cross-view U-shaped graph convolutional network (CV-UGCN), which can explore the spatial configurations and cross-view correlations, is designed to refine the coarse 3D poses. In particular, the refinement progress is achieved through weakly-supervised learning, in which geometric and structure-aware consistency checks are performed. We evaluate our method on the standard benchmark dataset, Human3.6M. The Mean Per Joint Position Error on the benchmark dataset is 27.4 mm, which outperforms existing state-of-the-art methods remarkably (27.4 mm vs 30.2 mm).
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching

Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound progress in stereo matching. However, most of these successes are limited to a specific dataset and cannot generalize well to other datasets. The main difficulties lie in the large domain differences and unbalanced disparity distribution across a variety of datasets, which greatly limit the real-world applicability of current deep stereo matching models. In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network. First, we propose a fused cost volume representation to deal with the large domain difference. By fusing multiple low-resolution dense cost volumes to enlarge the receptive field, we can extract robust structural representations for initial disparity estimation. Second, we propose a cascade cost volume representation to alleviate the unbalanced disparity distribution. Specifically, we employ a variance-based uncertainty estimation to adaptively adjust the next stage disparity search space, in this way driving the network progressively prune out the space of unlikely correspondences. By iteratively narrowing down the disparity search space and improving the cost volume resolution, the disparity estimation is gradually refined in a coarse-to-fine manner. When trained on the same training images and evaluated on KITTI, ETH3D, and Middlebury datasets with the fixed model parameters and hyperparameters, our proposed method achieves the state-of-the-art overall performance and obtains the 1st place on the stereo task of Robust Vision Challenge 2020. The code will be available at https://github.com/gallenszl/CFNet.
DFAEN: Double-order knowledge fusion and attentional encoding network for texture recognition

Recent studies have shown that deep convolutional neural networks (CNNs) have been successfully used for texture representation and recognition. One of the most successful texture recognition methods is the deep texture encoding network (DeepTEN), which has been shown to be effective. However, this network directly uses redundant CNN features with generality and ignores the role of multiorder information during the encoding and learning processes. To address these issues, this paper proposes a double-order knowledge fusion and attentional encoding network for texture recognition (DFAEN). First, crucial texture features are encoded by an embedded attention mechanism. Second, double-order modeling is implemented in the encoding and learning stage to make full use of convolution feature information with different orders, enabling the network to focus on and learn more texture domain information. Our method can stably and effectively perform end-to-end optimization. Evaluation experiments conducted on several widely used benchmark datasets (e.g., the FMD, MINC-2500, the DTD, KTH-TISP-2b, and GTOS-mobile) show that our method clearly demonstrates superior performance to that of competing approaches. • A deep multi-order texture encoding end-to-end network is proposed. • Attention-based texture encoding mechanism for texture representation learning. • The double orders information is modeled effectively in encoding learning stage. • Our approach achieves excellent experimental results in five challenging datasets.