Reviewerly/exHarmony huggingface dataset:
A Cascaded LiDAR-Camera Fusion Network for Road Detection

Most of the existing road detection methods are either single-modal based, e.g., based on LiDAR or camera, or multi-modal based with LiDAR-camera fusion. The algorithms are designed for a specific data type, and cannot cope with input data changes. In addition, the LiDAR-camera based methods can only work in day time with enough light. In this paper, we develop a novel LiDAR-camera fusion strategy, which combines the LiDAR point clouds and the camera images in a cascaded way. The proposed network has two working modes, the single-modal mode with LiDAR point clouds only and the multimodal mode with both LiDAR and camera data, so it can be used in all day scenes. The whole network consists of three parts: 1) LiDAR segmentation module, which segments road points in the LiDAR&#x2019;s imagery view. 2) Sparse-to-dense module, which upsamples the sparse LiDAR feature maps to dense road detection results. 3) LiDAR-camera fusion module, which fuses the dense LiDAR feature maps with the dense camera images to obtain accurate road estimations. Experiments on the KITTI-Road dataset show that the proposed cascaded LiDAR-camera fusion network can obtain very competitive road detection performance, with a MaxF value of 96.38%, and achieve the state-of-the-art in the single-modal mode among all LiDAR-only methods.
Bitcoin mining activity and volatility dynamics in the power market

Utilizing a measure of the Bitcoin network’s daily electricity load, we document a significant volatility effect of Bitcoin mining activity in three prominent electricity markets in the U.S. The volatility effect is found to be increasing over time, particularly with the widespread lockdowns enforced due to the COVID-19 pandemic. The findings provide novel insight to the non-virtual side of mining and trading of cryptocurrencies and underscore the need for establishing mechanisms to prevent possible destabilizing effects of this growing industry, both from a power consumption and carbon emissions perspective.
Dynamic impact for ant colony optimization algorithm

This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Dynamic Impact is designed to improve convergence and solution quality solving challenging optimization problems that have a non-linear relationship between resource consumption and fitness. This proposed method is tested against the real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem and the theoretical benchmark Multidimensional Knapsack problem (MKP). Using Dynamic Impact on single-objective optimization the fitness value is improved by 33.2% over the ACO algorithm without Dynamic Impact. Furthermore, MKP benchmark instances of low complexity have been solved to a 100% success rate even when a high degree of solution sparseness is observed. Large complexity instances have shown the average gap improved by 4.26 times.
A Blockchain-Enabled Federated Learning Model for Privacy Preservation: System Design

Information Silo is a common problem in most industries, while Federated Learning (FL) as an emerging privacy-preservation technique aims to facilitate data sharing to solve the problem. It avoids data leakage by sharing the model gradient instead of the raw data. However, there are some challenges of FL, such as Single Point of Failure (SPoF), gradient privacy, and trust issues. This paper proposes a Homomorphic-integrated and blockchain-based FL model to address the above issues. It provides gradient privacy protection by employing Homomorphic, and uses a smart contract-based reputation scheme and an on/off-chain storage strategy to respectively solve FL trust and blockchain storage issues. In the end, it evaluates the proposed model by providing a qualitative privacy analysis and conducting preliminary experiments on model performance.
Dispersed Federated Learning: Vision, Taxonomy, and Future Directions

The ongoing deployments of the Internet of Things (IoT)-based smart applications are spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there has been significant recent interest in the concept of federated learning. Federated learning offers on-device machine learning without the need to transfer end-device data to a third party location. However, federated learning has robustness concerns because it might stop working due to a failure of the aggregation server (e.g., due to a malicious attack or physical defect). Furthermore, federated learning over IoT networks requires a significant amount of communication resources for training. To cope with these issues, we propose a novel framework of dispersed federated learning (DFL) that is based on true decentralization. We opine that DFL will serve as a practical implementation of federated learning for various IoT-based smart applications such as smart industries and intelligent transportation systems. First, the fundamentals of the DFL are presented. Second, a taxonomy is devised with a qualitative analysis of various DFL schemes. Third, a DFL framework for IoT networks is proposed with a matching theory-based solution. Finally, an outlook on future research directions is presented.
Variance‐aware attention U‐Net for multi‐organ segmentation

With the continuous development of deep learning based medical image segmentation technology, it is expected to attain more robust and accurate performance for more challenging tasks, such as multi-organs, small/irregular areas, and ambiguous boundary issues.We propose a variance-aware attention U-Net to solve the problem of multi-organ segmentation. Specifically, a simple yet effective variance-based uncertainty mechanism is devised to evaluate the discrimination of each voxel via its prediction probability. The proposed variance uncertainty is further embedded into an attention architecture, which not only aggregates multi-level deep features in a global-level but also enforces the network to pay extra attention to voxels with uncertain predictions during training.Extensive experiments on challenging abdominal multi-organ CT dataset show that our proposed method consistently outperforms cutting-edge attention networks with respect to the evaluation metrics of Dice index (DSC), 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD).The proposed network provides an accurate and robust solution for multi-organ segmentation and has the potential to be used for improving other segmentation applications.
3D High-Fidelity Mask Face Presentation Attack Detection Challenge

The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers. To facilitate the study of the algorithms, a largescale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of a total amount of 54,600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors [21]. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask-based attack detection. It attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranking algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition.
A Survey of Self-Supervised and Few-Shot Object Detection

Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github.io/fsod-survey/.
A stochastic algorithm for scheduling bag-of-tasks applications on hybrid clouds under task duration variations

Hybrid cloud computing, which typically involves a hybrid architecture of public and private clouds, is an ideal platform for executing bag-of-tasks (BoT) applications. Most existing BoT scheduling algorithms ignore the uncertainty of task execution times in practical scenarios and schedule tasks by assuming that the task durations can be determined accurately in advance, often leading to the violation of the deadline constraint. In view of this fact, this paper devotes to maximizing the profit of the private cloud provider while guaranteeing the quality-of-service provided by the cloud platform, through designing an effective stochastic BoT scheduling algorithm based on the distribution of task duration variations. With the varying task execution times modeled as random variables, we formulate a stochastic scheduling framework that incorporates a probabilistic constraint upon the makespans of BoT applications. The resultant stochastic optimization model is capable of characterizing the complete distribution information of makespan variations and satisfying the deadline constraint in a probabilistic sense. We further design an immune algorithm-based metaheuristic to solve this stochastic optimization problem. Simulations results justify that our proposed algorithm outperforms several competing algorithms in maximizing the cloud provider's profit while satisfying the user-specified deadline constraint under the impact of uncertain task durations.
A Novel Hybrid K-Means and GMM Machine Learning Model for Breast Cancer Detection

Breast cancer is the second leading cause of death among a large number of women worldwide. It may be challenging for radiologists to diagnose and treat breast cancer. Consequently, primary care improves disease prevention and death. Early detection increases treatment options and saves life, which is the major target of this research. This research indicates the versatility of the methodology by integrating contemporary segmentation approaches with machine learning methods, which are developing areas of research. In the pre-processing process, an adaptive median filter is utilized for noise removal, enhancement of image quality, conservation of edges, and smoothing. This research makes a significant contribution by proposing a new parameter for evaluating K-means and a Gaussian mixture model (GMM) performance. A hybrid combination of segmentation and detection was applied to breast cancer. The proposed technique is significant for classifying benign and malignant tumors. The simulated results are discussed and evaluated to determine the competence of this method for the early diagnosis of breast cancer. This method allows medical experts to recognize breast cancer at a faster rate and provide higher accuracy. An ANOVA test was used to determine the multi-variant analysis and prediction rate for the proposed method.
Deep learning based crowd counting model for drone assisted systems

Recent advances in deep learning make it possible to implement neural network architecture fitted to the task. In this paper we present new deep neural network model developed for drone assisted systems, in which image from drone camera is processed for smart crowd counting operation. Our proposed architecture works to estimate the crowd in the image by using derivative of ResNet conception model. We have used RMSprop algorithm to train it. Research results from our experiments show 98% of Accuracy, Precision and Recall which is very high efficiency in such systems. Proposed model is easy to configure and has high potential for further development.
When a Robot Makes Your Dinner: A Comparative Analysis of Product Level and Customer Experience Between the U.S. and Chinese Robotic Restaurants

Integrating two theoretical frameworks, the product level theory and the experience economy model, this research analyzed and compared robotic technology applications and customer experiences in selected case robot restaurants in the United States and China. Guided by the product level theory, we first analyzed in which product/service levels were robots applied in each case restaurant in Study 1. Then in study 2, guided by the experience economy model, we further explored customers’ dining experiences and compared if customers’ experience differs due to variations in product/service levels that robot applied. The study first contributes to the product level theory by extending its application to the context of robotic restaurants. It also contributes to the experience economy literature, and in particularly, whether applications of robotic technologies at different product levels matter in customers’ dining experience. The study included case restaurants both from the United States and China, presenting findings with cultural implications. Given the challenges presented by COVID-19 and the industry is exploring alternative ways for service delivery and food production, such a study is particularly meaningful.
Adaptive Explicit Kernel Minkowski Weighted K-means

The K-means algorithm is among the most commonly used data clustering methods. However, the regular K-means can only be applied in the input space, and it is applicable when clusters are linearly separable. The kernel K-means, which extends K-means into the kernel space, is able to capture nonlinear structures and identify arbitrarily shaped clusters. However, kernel methods often operate on the kernel matrix of the data, which scale poorly with the size of the matrix, or suffer from the high clustering cost due to the repetitive calculations of kernel values. Another issue is that algorithms access the data only through evaluation of K(xi,xj), which limits many processes that can be done on data through the clustering task. This paper proposes a method to combine the advantages of the linear and nonlinear approaches by using derived corresponding approximate finite-dimensional feature maps based on spectral analysis. Applying approximate finite-dimensional feature maps have been discussed before only in the context of Support Vector Machines (SVM) problems. We suggest using this method in the kernel K-means context as it does not require storing a huge kernel matrix in memory, calculates cluster centers more efficiently, and accesses the data explicitly in the feature space; thus taking advantage of K-means extensions in that space. We demonstrate that our Explicit Kernel Minkowski Weighted K-means (Explicit KMWK-means) method is able to achieve high accuracy in terms of cluster recovery in the new space by applying additional Minkowski exponent and feature weights. The proposed method is evaluated by four benchmark data sets, and its performance is compared with the commonly used kernel clustering approaches. Experiments show the proposed method consistently achieves superior clustering performances while reducing the memory consumption.
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer’s Disease Prediction

Multimodal neuroimage can provide complementary information about the dementia, but small size of complete multimodal data limits the ability in representation learning. Moreover, the data distribution inconsistency from different modalities may lead to ineffective fusion, which fails to sufficiently explore the intra-modal and inter-modal interactions and compromises the disease diagnosis performance. To solve these problems, we proposed a novel multimodal representation learning and adversarial hypergraph fusion (MRL-AHF) framework for Alzheimer's disease diagnosis using complete trimodal images. First, adversarial strategy and pre-trained model are incorporated into the MRL to extract latent representations from multimodal data. Then two hypergraphs are constructed from the latent representations and the adversarial network based on graph convolution is employed to narrow the distribution difference of hyperedge features. Finally, the hyperedge-invariant features are fused for disease prediction by hyperedge convolution. Experiments on the public Alzheimer's Disease Neuroimaging Initiative(ADNI) database demonstrate that our model achieves superior performance on Alzheimer's disease detection compared with other related models and provides a possible way to understand the underlying mechanisms of disorder's progression by analyzing the abnormal brain connections.
Robustness of Object Detectors in Degrading Weather Conditions

State-of-the-art object detection systems for autonomous driving achieve promising results in clear weather conditions. However, such autonomous safety critical systems also need to work in degrading weather conditions, such as rain, fog and snow. Unfortunately, most approaches evaluate only on the KITTI dataset, which consists only of clear weather scenes. In this paper we address this issue and perform one of the most detailed evaluation on single and dual modality architectures on data captured in real weather conditions. We analyze the performance degradation of these architectures in degrading weather conditions. We demonstrate that an object detection architecture performing well in clear weather might not be able to handle degrading weather conditions. We also perform ablation studies on the dual modality architectures and show their limitations.
Raman Nanotags‐Guided Intraoperative Sentinel Lymph Nodes Precise Location with Minimal Invasion

The accurate positioning of sentinel lymph node (SLN) by tracers during surgery is an important prerequisite for SLN biopsy. A major problem of traditional tracers in SLN biopsy is the short surgery window due to the fast diffusion of tracers through the lymphatics, resulting in a misjudgment between SLN and second echelon lymph node (2nd LN). Here, a nontoxic Raman nanoparticle tracer, termed gap-enhanced Raman tags (GERTs), for the accurate intraoperative positioning of SLNs with a sufficient surgical time window is designed. In white New Zealand rabbit models, GERTs enable precise identification of SLNs within 10 min, as well as provide the surgeon with a more than 4 h time window to differentiate SLN and 2nd LN. In addition, the ultrahigh sensitivity of GERTs (detection limit is 0.5 × 10-12 m) allows detection of labeled SLNs before surgery, thereby providing preoperative positioning information for minimally invasive surgery. Comprehensive biosafety evaluations carried out in the context of the Food and Drug Administration and International Standard Organization demonstrate no significant toxicity of GERTs, which supports a promising clinical translation opportunity of GERTs for precise SLN identification in breast cancer.
AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling

Pooling layers are essential building blocks of convolutional neural networks (CNNs), to reduce computational overhead and increase the receptive fields of proceeding convolutional operations. Their goal is to produce downsampled volumes that closely resemble the input volume while, ideally, also being computationally and memory efficient. Meeting both these requirements remains a challenge. To this end, we propose an adaptive and exponentially weighted pooling method: adaPool. Our method learns a regional-specific fusion of two sets of pooling kernels that are based on the exponent of the Dice-Sørensen coefficient and the exponential maximum, respectively. AdaPool improves the preservation of detail on a range of tasks including image and video classification and object detection. A key property of adaPool is its bidirectional nature. In contrast to common pooling methods, the learned weights can also be used to upsample activation maps. We term this method adaUnPool. We evaluate adaUnPool on image and video super-resolution and frame interpolation. For benchmarking, we introduce Inter4K, a novel high-quality, high frame-rate video dataset. Our experiments demonstrate that adaPool systematically achieves better results across tasks and backbones, while introducing a minor additional computational and memory overhead.
Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network

The rationalization of human resource management is helpful for enterprises to efficiently train talents in the field, improve the management mode, and increase the overall resource utilization rate of enterprises. The current computational models applied in the field of human resources are usually based on statistical computation, which can no longer meet the processing needs of massive data and do not take into account the hidden characteristics of data, which can easily lead to the problem of information scarcity. The paper combines recurrent convolutional neural network and traditional human resource allocation algorithm and designs a double recurrent neural network job matching recommendation algorithm applicable to the human resource field, which can improve the traditional algorithm data training quality problem. In the experimental part of the algorithm, the arithmetic F1 value in the paper is 0.823, which is 20.1% and 7.4% higher than the other two algorithms, respectively, indicating that the algorithm can improve the hidden layer features of the data and then improve the training quality of the data and improve the job matching and recommendation accuracy.
Federated Unlearning via Class-Discriminative Pruning

We explore the problem of selectively forgetting categories from trained CNN classification models in federated learning (FL). Given that the data used for training cannot be accessed globally in FL, our insights probe deep into the internal influence of each channel. Through the visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification.
Contrastive Curriculum Learning for Sequential User Behavior Modeling via Data Augmentation

Within online platforms, it is critical to capture the semantics of sequential user behaviors for accurately modeling user interests. However, dynamic characteristics and sparse behaviors make it difficult to train effective user representations for sequential user behavior modeling.
A systematic review of detection and prevention techniques of SQL injection attacks

SQL injection is a type of database-targeted attack for data-driven applications. It is performed by inserting malicious code in the SQL query to alter and modify its meaning, enabling the attacker to retrieve sensitive data or to access the database. Many techniques have been improved and proposed to detect and mitigate these types of attacks. This paper provides a systematic review for a pool of 60 papers on web applications' SQL injection detection methods. The pool was selected using a developed searching and filtering methodology for the existing literature based on scholar databases (IEEE, ScienceDirect, and Springer) with the aim to provide specific answering for several research questions in the area of SQL injection detection. This provides a basis for the design and use of effective SQL injection detection methods.