Reviewerly/exHarmony huggingface dataset:
An online bi-objective scheduling algorithm for service provisioning in cloud computing

To reduce costs and risks, service providers can purchase Infrastructure-as-a-Service (IaaS) instances from public clouds to deploy service applications and deliver job-execution services to users. However, it is challenging to make optimal instance purchasing and job scheduling decisions due to diverse instance billing methods and random service demands. In order to minimize time average service costs while ensuring quality of service (QoS), we consider that service providers purchase combinations of reserved and on-demand instances to meet the workload of their service system. Service providers need to dynamically determine the amount of on-demand and reserved instances to purchase for job executions, and then schedule jobs to run at the appropriate time. In this paper, we first propose a method to dynamically purchase reserved instances based on the historical usage data of on-demand and reserved instances. Then, we use a variable-length cycle scheduling mode for the job processing system and propose an online bi-objective scheduling algorithm based on Lyapunov optimization to schedule jobs. Even without the knowledge about the future, our online algorithm can dynamically purchase on-demand instances in an optimal way under the current number of reserved instances, and schedule jobs in varying time periods according to the arrival of jobs and optimization objectives. Based on both synthetic and real-world data, extensive experimental results show that our algorithm can achieve bi-objective optimization of minimizing time average cost while ensuring QoS.
DSRNet: Depth Super-Resolution Network guided by blurry depth and clear intensity edges

Although high resolution (HR) depth images are required in many applications such as virtual reality and autonomous navigation, their resolution and quality generated by consumer depth cameras fall short of the requirements. Existing depth upsampling methods focus on extracting multiscale features of HR color image to guide low resolution (LR) depth upsampling, thus causing blurry and inaccurate edges in depth. In this paper, we propose a depth super-resolution (SR) network guided by blurry depth and clear intensity edges, called DSRNet. DSRNet differentiates effective edges from a number of HR edges with the guidance of blurry depth and clear intensity edges. First, we perform global residual estimation based on an encoder–decoder architecture to extract edge structure from HR color image for depth SR. Then, we distinguish effective edges from HR edges in the decoder side with the guidance of LR depth upsampling. To maintain edges for depth SR, we use intensity edge guidance that extracts clear intensity edges from HR image. Finally, we use residual loss to generate accurate high frequency (HF) residual and reconstruct HR depth maps. Experimental results show that DSRNet successfully reconstructs depth edges in SR results as well as outperforms the state-of-the-art methods in terms of visual quality and quantitative measurements.1
A Novel Fall Detection Framework With Age Estimation Based on Cloud-Fog Computing Architecture

Falls are one of the main causes of injuries and even fatalities among the elderly. However, few existing research on fall detection focuses on the supervision of the elderly. Furthermore, the face image of the target usually exhibits low resolution in the surveillance videos, posing a challenge in achieving a balance between the response latency and the computational cost. To this end, a novel fall detection framework with age estimation based on cloud-fog computing architecture is proposed in this paper. Specifically, an optimized Soft Stage Regression-Shallow (SSR-S) network is presented to achieve excellent performance on age estimation for the low-resolution images collected by the edge layer. A fall pre-judgment mechanism and a Shallow-Convolutional Neural Networks (S-CNN) are proposed to make a better judgment on fall behaviors among different age groups at the fog layer and the cloud layer respectively. Besides, an age estimation-based priority algorithm is presented to prioritize the people aged 60 or older in fall detection at the cloud layer, with the aim to make a trade-off between the response latency and the computational overhead. Finally, extensive simulations have been conducted to evaluate the performance of our proposal. Experimental results have shown that the minimum Mean Absolute Error (MAE) of SSR-S reaches 7.59. The fall prejudgment mechanism can achieve 0% miss rate, and the accuracy of S-CNN reaches 90.5%. The detection speed of the overall framework is 17.0 frames per second (FPS).
Taking 5 minutes protects you for 5 months: Evaluating an anti-phishing awareness video

Phishing is one of the biggest security threats to organizations. Anti-phishing awareness measures can improve phishing email detection rates. These measures need to be efficient, effective, and have an enduring impact over months, rather than days. Related research provides evidence of their effectiveness in the short term. However, questions remain as to how long this impact endures. We conducted a retention user study in two phases, with almost 200 participants in the first phase and almost 80 in the second phase, to determine whether a five-minute video retains its effectiveness five months after the intervention (similar to related work on more time-intensive measures). Our results suggest that short videos can indeed still exert a positive influence five months later. We also report on the video's influence on phishing detection strategies, as well as on viewers' confidence in this respect. Based on our results, we propose recommendations to inform the content of future awareness refreshment measures.
Breaking the curse of dimensional collapse in graph contrastive learning: A whitening perspective

Dimensional collapse in graph contrastive learning (GCL) confines node embeddings to their lower-dimensional subspace, diminishing their distinguishability. However, the causes and solutions of this curse remain relatively underexplored. In statistics, whitening presents a powerful tool to eliminate correlations among multiple variables. This motivates us to relieve the dimensional collapse of GCL from a whitening perspective. In this paper, we propose an intuitive analysis suggesting that high similarity scores of node embeddings may cause dimensional collapse, providing more evidence for its presence. Considering the success of whitening in statistics, we introduce a new plug-and-play module called the hitening raph ontrastive earning (WGCL) to address the dimensional collapse issue in existing GCL methods. WGCL plugin standardises the covariance matrices of dimensions, eliminating correlations among node embeddings' dimensions. Additionally, we enhance the conventional GCL training objective by introducing a mutual information maximisation loss between input features and node embeddings to maintain information capacity. Our experiments demonstrate that WGCL effectively addresses dimensional collapse, leading to an average improvement of 0.93% (up to 2.0%) in classification accuracy across three GCL backbones on nine widely-used datasets. The code to reproduce the experiments is available at https://github.com/acboyty/WGCL.
Multi-scale split matching three-dimensional reconstruction method of surface microcracks on the silicon nitride bearing roller

Aiming at the characteristics of parametric discrete and non-significant depth of silicon nitride bearing roller surface microcracks. A multi-scale split matching method is proposed. To realize the full reconstruction of the characteristics of the three-dimensional morphology of the surface microcracks. Combined with the parametric discretization characteristics of the surface microcracks, a box filter is defined to do second-order Gaussian differentiation on the image. Construct a multiscale Hessian matrix in the scale space of the surface microcrack image. Determine the covariance-discrete feature points, and obtain the global covariance-discrete sparse point cloud of silicon nitride bearing roller surface microcracks by non-maximum value suppression. For the non-significant depth features of silicon nitride bearing roller surface microcracks. Dissect the surface microcrack distortion images. Divide each image into multiple regional pixel blocks and construct an aggregation function to determine the exact depth value of each pixel block at the cost of matching each pixel block with its surrounding pixel blocks. Dense point cloud reconstruction of non-significant depth features of silicon nitride bearing roller surface microcracks is realized. The average distribution of dense clusters of reprojection errors of the dense point cloud features is less than 0.4pix, the median residual of the field of view of the surface microcrack image is 0.57pix, and the total number of effectively extracted feature points is about 92.9 %. The detailed lossless recovery reconstruction of the parametric discrete, non-significant depth features of silicon nitride bearing roller surface microcracks is realized. A new idea is provided for the establishment of molecular dynamics geometric modeling of the surface microcrack extension process on the silicon nitride bearings roller.
Parametric Lie group structures on the probabilistic simplex and generalized Compositional Data

In this paper we build a set of parametric quotient Lie group structures on the probabilistic simplex that can be extended to real vector space structures. In particular, we rediscover the main mathematical objects generally used when treating compositional data as elements associated to the quotient Lie group with respect to the equivalence relation induced by the scale invariance principle. This perspective facilitates the adaptation of the statistical methods used for classical compositional data to data that follows a more general equivalence relation.
Cost-sensitive thresholding over a two-dimensional decision region for fraud detection

Credit fraud poses a challenging task in terms of detection. It can result in significant losses depending on the amount, so a cost-sensitive perspective needs to be taken. Classical approaches focus on estimating the probability of fraud and selecting a decision threshold, but they often fail to consider the transaction amount or account for the cumulative losses incurred within the sample. Consequently, these approaches can result in sub-optimal strategies. A new thresholding approach is proposed, based on the construction of a two-dimensional decision space with an estimated probability and the credit amount. This expansion allows more freedom for the optimal classification rule search, which is performed with a new algorithm. The proposed method generalizes previous approaches, so an improvement is consistently achieved. In addition, it allows a restricted search. This is shown in a study of two real data sets, comparing the results obtained by a wide range of classifiers.
Outlier detection in a multiset-valued information system based on rough set theory and granular computing

Outlier detection on data with missing information values is especially tricky because the uncertainty caused by missing information values may contribute to an object being an outlier. A multiset-valued information system (MSVIS) is an information system (IS) in which information values are multisets. This kind of IS is a useful way of handling datasets with missing information values. In this paper, we study outlier detection in an MSVIS based on rough set theory and granular computing. First, some concepts of multisets and probability distribution sets are reviewed, and the fact that a weak one-to-one correspondence exists between multisets and rational probability distribution sets is illustrated. In this way, multisets may be treated as rational probability distribution sets. Then, an MSVIS can be induced by an incomplete information system (IIS) and viewed as the result of information fusion of multiple categorical ISs. Next, a tolerance relation in an MSVIS is constructed with the induced rational probability distribution sets. Then, the outlier factor in an MSVIS is formulated, and the corresponding outlier detection algorithm is proposed. Finally, the performance evaluation by AUC (area under the curve) and F1-score shows the superiority of the proposed algorithm over some existing algorithms.
Fast two-party signature for upgrading ECDSA to two-party scenario easily

ECDSA is a standardized signature scheme and is widely used in many fields. However, most two-party ECDSA require a complicated multi-party computation technique to compute the multiplication of many shared secrets and necessitate expensive zero-knowledge proofs to deal with malicious adversaries. In this paper, we propose a simple yet fast two-party signing protocol based on ECDSA security, whose signing operation of each party is similar to the original ECDSA signing algorithm. Our protocol can enjoy the same efficiency as ECDSA and upgrades the existing ECDSA application to two-party scenario friendly. For this purpose, we first introduce a variant of ECDSA called combinatorial ECDSA, which is as secure as the standard ECDSA and can be easily divided into two parts. Then, we devise a two-party signing protocol without complicated multi-party computation from the combinatorial ECDSA and give the security proof based on ECDSA. Compared with the state-of-the-art two-party ECDSA, the signing result of each party in our protocol maintains the structure of ECDSA signature, which can be verified easily and avoids expensive zero-knowledge proofs. Last, we conduct an experimental evaluation, demonstrating that the performance of our combinatorial ECDSA and two-party signature is similar to ECDSA and the experimental results show that our proposed schemes are practical.
A game-theory-based scheme to facilitate consensus latency minimization in sharding blockchain

Unbalanced consensus latency in reaching a consensus on transactions occurs due to differences in the ability of different committees to process transactions. As the consensus latency accumulates, the transaction throughput of the sharding blockchain significantly decreases. This article proposes a consensus latency minimization scheme to improve the transaction throughput. According to a time-varying directed graph and a non-cooperative game model solved by a distributed algorithm, we show that the generalized Nash equilibrium for all committees are to balance the differences in consensus latency among various committees and to minimize the consensus latency throughout the whole period. The security of the proposed scheme is analyzed by considering the peaceful equilibrium, and the committee actions asymptotically converge to a generalized Nash equilibrium. Through the comparisons with other baseline algorithms via simulation experiments, we demonstrate that our proposed scheme could result in the optimal consensus latency and throughput. Therefore, the proposed consensus latency minimization scheme effectively avoids the existing problem of the transaction throughput in sharding blockchain and provides a viable and security instrument for the sharding blockchain operator to improve the transaction throughput performance throughout the whole period.
Intelligent Deep-Learning Tutoring System to Assist Instructors in Programming Courses

Artificial intelligence (AI) in programming teaching is something that still has to be explored, since in this area assessment tools that allow grading the students work are the most common ones, but there are not many tools aimed toward providing feedback to the students in the process of creating their program. In this work a small sized AI-based intelligent tutor that answers students programming questions and provides them with examples is presented. AI is becoming more and more popular as time passes, allowing to perform tasks automatically in a way that could not be done before. From predictions to customization, AI is being used in many areas, not being educational environments outside this situation. AI is being used in educational settings to customize contents or to provide personalized feedback to the students, among others. In this scenario, The tool has been tested by university students at the Universidad Rey Juan Carlos during the course Programming Fundamentals in the first course of their Biomedical Engineering degree to evaluate if it helps the students in the process of learning programming skills. One of the main goals was to provide guidance to the students without needing the instructor to be physically by their side. Even if the tool is still in its preliminary phase, it helped the students with their questions, providing accurate answers and examples. The students were able to use the intelligent tutor easily and they thought that it could be a useful tool to use in other courses.
Differentially private trajectory event streams publishing under data dependence constraints

Differential Privacy (DP), as a de facto privacy-preserving paradigm, can be widely applied in numerous event streams publishing schemes to protect private events (i.e., trajectory points of users). However, most existing schemes assume these events are independent, which is a vulnerable assumption that can lead to unforeseeable privacy degradation. The privacy-preserving for event streams publishing under data dependence is still challenging because existing dependence quantify models cannot dynamically measure the degree of dependent relationships between events. In this paper, we investigate the privacy degradation issue of trajectory event streams publishing and propose (Θ,ϵ)-dependent privacy mechanism to mitigate this issue. Specifically, we introduce spatial dependence to model the dependent relationship between events in trajectory data and extend it to quantify the influence of multiple dependent events on the target event. We define the notion of max dissimilarity to formalize the privacy level of DP mechanisms in trajectory event streams publishing. The proposed notion of global dependent coefficient helps the private mechanism to provide the expected privacy level by calibrating the added noise. We further achieve ω-event privacy protection over trajectory event streams under data dependence constraints with (Θ,ϵ)-dependent privacy mechanism. Theoretical analysis proves our scheme provides the expected privacy guarantee under data dependence constraints. Extensive experiments validate our scheme is efficient and effective.
Indoor semantic segmentation based on Swin-Transformer

In recent years, with the rapid development of Transformer in the field of natural language processing, many researchers have realized its potential and gradually applied it to the field of computer vision, with a proliferation of theoretical approaches represented by Vision Transformer (ViT) and Data-efficient image Transformer (DeiT). On the basis of ViT, the famous Swin-Transformer was proposed as one of the best computer vision neural network backbones, which can be widely used in tasks such as image classification, target detection and video recognition. However, in the field of image segmentation, the semantic segmentation of indoor scenes is still very challenging due to the wide variety of objects, large differences in object sizes, and a large number of overlapping objects with occlusion. Aiming at the problem that the existing semantic segmentation of RGB-D indoor scenes cannot effectively fuse multimodal features, in this paper, we propose a novel indoor semantic segmentation algorithm based on Swin-Transformer. It attempts to apply Swin-Transformer to the field of indoor RGBD semantic segmentation, and tests the performance of the model by conducting extensive experiments on the mainstream indoor semantic segmentation datasets NYU-Depth V2 and SUN RGB-D. The experimental results show that the Swin-L RGB+Depth setting achieves 52.44% MIoU on the NYU-Depth V2 data and 51.15% MIoU on the SUN RGB-D data set, which reflects an excellent performance in the field of indoor semantic segmentation. The improved performance of the Depth features on the indoor semantic segmentation model has also been demonstrated in the experiments by controlling the type of input features. Our source code is publicly available at https://github.com/YunpingZheng/ISSSW.
Residual attention fusion network for video action recognition

Human action recognition in videos is a fundamental and important topic in computer vision, and modeling spatial–temporal dynamics in a video is crucial for action classification. In this paper, a novel attention module named Channel-wise Non-local Attention Module (CNAM) is proposed to highlight the important features both spatially and temporally. Besides, another new attention module named Channel-wise Attention Recalibration Module (CARM) is developed to focus on capturing discriminative features at channel level. Based on these two attention modules, a novel convolutional neural network named Residual Attention Fusion Network (RAFN) is proposed to model long-range temporal structure and capture more discriminative action features at the same time. More specifically, first, a sparse temporal sampling strategy is adopted to uniformly sample video data as input to RAFN along the temporal dimension. Secondly, the attention modules CNAM and CARM are plugged into residual network for highlighting important action regions around actors. Finally, the classification scores of four streams of RAFN are combined by late fusion. The experimental results on HMDB51 and UCF101 demonstrate the effectiveness and excellent recognition performance of our proposed method.
Bidirectional utilization of blockchain and privacy computing: Issues, progress, and challenges

With the rapid development of information technology and the increasing popularity of personalized services, the flow of users' personal information in the Internet is inevitable between different platforms and applications, which greatly affects the trust relationship in the digital society. In recent years, an increasing number of researchers have been dedicated to exploring the applications of blockchain and privacy computing technologies in the process of data value release. Scholars have discovered that the combination of the two technologies can alleviate many pain points of each other. Specifically, privacy computing is a computational theory and method for protecting privacy of information throughout its lifecycle. Privacy computing enables the processing, analysis, and computation of data while preserving the confidentiality of the original information. Nevertheless, challenges related to data ownership and benefit distribution impede the establishment of a fair and effective collaborative environment among multiple parties, thereby restricting its further practical development. On the other hand, blockchain is a technology for establishing a trusted platform among mutually untrusted parties, characterized by decentralization, immutability, traceability, and incentive mechanisms. However, blockchain also faces security issues such as transparency of on-chain data and lack of protection in smart contract execution environments. Therefore, this paper aims to comprehensively analyze and explore the integration of blockchain and privacy computing from both perspectives, focusing on bidirectional utilization. We systematically categorize and summarize relevant literature in this field, employing problem-oriented approaches. Finally, we identify the existing challenges and propose potential improvement methods in this field.