Reviewerly/exHarmony huggingface dataset:
Towards Thermal-Aware Workload Distribution in Cloud Data Centers Based on Failure Models

Increasing workload conditions lead to a significant surge in power consumption and computing node failures in data centers. The existing workload distribution strategies focused on either thermal awareness or failure mitigation, overlooking the impact of node failures on the energy efficiency of cloud data centers. To address this issue, a new holistic model is built to characterize the impacts of workloads, computing and cooling costs, heat recirculation, and node failure on the energy efficiency of cloud data centers. Leveraging such a holistic model, we propose a novel thermal-aware workload distribution strategy called <i>HGSA</i> that takes node failure into accountand can improve the energy efficiency of cloud data centers. Our empirical findings confirm that (i) faulty nodes lead to a large rise in power consumption, and (ii) failure locations play a vital role in the power consumption of data centers. Experimental results unveil that HGSA is adroit at making near-optimal decisions in workload distribution strategies. In particular, HGSA cuts down the minimum inlet temperature by 5.2 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> -15 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , improves the maximum air temperature of a Computer Room Air Conditioner (CRAC) model by 4.2 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> -26.5 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , lowers the cooling cost by 15.4 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> -50 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> compared to the existing solutions. Furthermore, HGSA cuts back the total power consumption by 0.65 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> -78 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .
A low-illuminance image enhancement method in YUV color space

Aiming at the problems of low-illuminance images such as low brightness, blurred details, and poor visual effects, a low-illuminance image enhancement algorithm based on YUV color space is proposed. First, convert the image from RGB space to YUV space, perform multi-scale guided filtering on the Y component to obtain the illuminance component, use homomorphic filtering to adjust the illuminance component, and then obtain the reflection component through Retinex theory; then use multi-scale bilateral filtering to obtain the detail image of the reflection component; finally, the detail image is added to the reflection component and adjusted by adaptive gamma correction to obtain an enhanced image. The results of the experiment subjectively obtained images with high brightness, clear details, and conform to human visual effects, and objectively performed well in terms of average brightness, information entropy, and average gradient. The results show that the algorithm can effectively increase image brightness, enhance image details, and improve image visual effects.
Medication Recommendation Based on a Knowledge-enhanced Pre-training Model

More and more attention has been paid to electronic medical record (EMR)-based auxiliary diagnosis and treatment, in which medication recommendation is an important research direction. The existing medication recommendation models mainly depend on the data of patients, diagnosis and medications. However, the insufficient amount of clinical data with temporal dependencies becomes a major obstacle. This paper proposes a new knowledge-enhanced pre-training model for medication recommendation. On the one hand, the classification knowledge in diagnostic codes and drug codes is encoded by Graph Attention Network and fused into the clinical data for expanding the data content. On the other hand, a large number of single visit data of EMR are used to create the pre-trained visit model by a modified BERT for expanding the data scale. The experimental results on EMR data from more than 2,000 medical and health institutions in Hainan, China show that the fusion of classification knowledge and pre-training model can effectively improve the accuracy of medication recommendation.
A bartering double auction resource allocation model in cloud environments

Abstract Resource provisioning and allocation are two essential operations of cloud service providers (CSPs). Due to limited resources, it may sometimes not be cost‐effective for CSPs to obtain them from the market. On the other hand, releasing some less profitable resources may violate the service level agreement (SLA). This article presents an auction‐inspired model for providing/demanding resources without exchanging money among CSPs. The core idea is that CSPs do not need to pay monetary values. Instead, they exchange free resources through an auction mechanism and are encouraged to cooperate. This model, which we call bartering double auction resource allocation, helps CSPs receive the resources they need in the form of virtual machine instances with different types of functionalities. Under this mechanism, if one party violates the SLA, the other takes back its resources to avoid possible loss. The experimental results using the CloudSim simulator and the statistical analyses show that the proposed mechanism can increase CSPs' social welfare and profitability while significantly reducing SLA violations. Also, the proposed model provides four essential economic properties, that is, individual rationality , incentive compatibility , budget balancing , and economic efficiency .
Comparative Analysis of YOLOv4 and YOLOv4-tiny Techniques towards Face Mask Detection

Covid-19 has brought various complications in our day-to-day life leading to a disruption in overall movements across the world. Although still researchers and scientists are working on finding more effective ways to deal with it, wearing a face is one of the most simplistic yet efficient ways to overcome this. Wearing a face mask all the time in public places has become a new normal. Therefore, face mask detection for monitoring of people in public places has become a crucial task. Deep learning has been used to make recent advances in the field of object detection. To accomplish this objective, this research employs three state-of-the-art object identification models, notably YOLOv4 and YOLOv4-tiny. The models were trained using a dataset that included photos of persons wearing and not wearing masks. Considering it for surveillance purposes, it can also be used for detection of face and mask in motion. The models employ an approach that involves drawing bounding boxes (red or green) around people’s faces and determining whether or not they are wearing a face mask. Further, the performance of these models was compared using mAP, recall F1-score and FPS
KPCR: Knowledge Graph Enhanced Personalized Course Recommendation

To handle the limitations of collaborative filtering-based recommender systems, knowledge graphs are getting attention as side information. However, there are several problems to apply the existing KG-based methods to the course recommendations of MOOCs. We propose KPCR, a framework for Knowledge graph enhanced Personalized Course Recommendation. In KPCR, internal information of MOOCs and an external knowledge base are integrated through user and course related keywords. In addition, we add the level embedding module that predicts the level of students and courses. Through the experiments with the real-world datasets, we demonstrate that our knowledge graph boosts recommendation performance as side information. The results also show that the two auxiliary modules improve the recommendation performance. In addition, we evaluate the effectiveness of KPCR through the satisfaction survey of users of the real-world MOOCs platform.
An Efficient Anchor-Free Universal Lesion Detection in Ct-Scans

Existing universal lesion detection (ULD) methods utilize compute-intensive anchor-based architectures which rely on predefined anchor boxes, resulting in unsatisfactory detection performance, especially in small and mid-sized lesions. Further, these default fixed anchor-sizes and ratios do not generalize well to different datasets. Therefore, we propose a robust one-stage anchor-free lesion detection network that can perform well across varying lesions sizes by exploiting the fact that the box predictions can be sorted for relevance based on their center rather than their overlap with the object. Furthermore, we demonstrate that the ULD can be improved by explicitly providing it the domain-specific information in the form of multi-intensity images generated using multiple HU windows, followed by self-attention based feature-fusion and backbone initialization using weights learned via self-supervision over CT-scans. We obtain comparable results to the state-of-the-art methods, achieving an overall sensitivity of 86.05% on the DeepLesion dataset, which comprises of approximately 32K CT-scans with lesions annotated across various body organs.
Analysis and comparison of the main zero-knowledge proof scheme

Thanks to the development in encryption technology and blockchain, cryptocurrency has already proven feasible and applicable. However, blockchain requires massive information including details of every transaction, the information of the dealer and buyer, which makes the privacy of every user on blockchain vulnerable. With the presence of Zcash, a new concept called Zero-Knowledge proof which focuses on proving and verifying identities appears in front of every researcher and engineer working on blockchain. Because of the enormous quantity of the ZKP schemes and the limited time, in this article, we have compared four main ZKP algorithms-ZK-SNARK, ZK-STARK, MPC protocol-based algorithms, and Bulletproof. By comparing these algorithms, we intend to give relevant engineers and researchers a general understanding of the advantages and disadvantages of these algorithms. In addition, we try to make the relevant people aware of the application scenarios of each ZKP.
Supercalifragilisticexpialidocious: Why Using the “Right” Readability Formula in Children’s Web Search Matters

Readability is a core component of information retrieval (IR) tools as the complexity of a resource directly affects its relevance: a resource is only of use if the user can comprehend it. Even so, the link between readability and IR is often overlooked. As a step towards advancing knowledge on the influence of readability on IR, we focus on Web search for children. We explore how traditional formulas–which are simple, efficient, and portable–fare when applied to estimating the readability of Web resources for children written in English. We then present a formula well-suited for readability estimation of child-friendly Web resources. Lastly, we empirically show that readability can sway children's information access. Outcomes from this work reveal that: (i) for Web resources targeting children, a simple formula suffices as long as it considers contemporary terminology and audience requirements, and (ii) instead of turning to Flesch-Kincaid–a popular formula–the use of the "right" formula can shape Web search tools to best serve children. The work we present herein builds on three pillars: Audience, Application, and Expertise. It serves as a blueprint to place readability estimation methods that best apply to and inform IR applications serving varied audiences.
Method of Obtaining Data from Open Scientific Sources and Social Engineering Attack Simulation

Anti-spam software is constantly being improved. User behavior algorithms—the ability to recognize and correctly respond to phishing messages are widely known. The task of our research is to elaborate a way of effective dataset preparation from open scientific sources and test the efficacy of phishing attacks on a sample of respondents who represent the scientific community, as well as cybersecurity specialists. We developed and tested a method of mining data necessary for effective phishing attacks from open scientific sources. Authors suggest automated scripts to check the legitimacy of gathered data before use and to automate mailing bypassing spam detection algorithms. Elaborated scripts can be used not only for simulated attacks but for legitimate datasets cleaning and mass mailing. The experiment results confirm that successful phishing mailing is possible. Both scholars and cybersecurity specialists are vulnerable to this type of phishing attack based solely on open data. The study shows the way of effective testing and bypassing existing spam filters in the "black box" mode without knowledge of their algorithms. Even though these attacks are well-known and studied from the psychological perspective, we show that the scientific community and, in particular, the study demonstrates no difference in the vulnerability level to this type of attack between cybersecurity specialists and other scholars. We conclude that existing spam filters do not prevent phishing messages' mass delivery and require further improvement. The degree of users who still trust emails from an unknown source masquerading as legitimate ones and sending their data in return without caution remains relatively high in the scientific community and, particularly in a community of academic cybersecurity scholars.
Privacy-Preserving Federated Edge Learning: Modeling and Optimization

In this letter, we consider the personalized differential privacy (DP) based federated edge learning system. Each edge device adds DP noise to its local machine learning (ML) model updates to prevent the private information contained in the model updates to be obtained by the edge server. However, the noise perturbation can degrade the ML model performance. We aim to optimize the tradeoff between the ML model performance measured by the global loss and the privacy preservation. The closed-form global loss and privacy leakage are first derived. The loss and leakage are then jointly minimized by optimizing the DP noise scales and the local update numbers of the edge devices. Numerical results show that a better loss-leakage trade-off is reached compared to the conventional methods.
Evolved PoW: Integrating the Matrix Computation in Machine Learning Into Blockchain Mining

Machine learning is an essential technology providing ubiquitous intelligence in Internet of Things (IoT). However, the model training in machine learning demands tremendous computing resource, bringing heavy burden to the IoT devices. Meanwhile, in the Proof-of-Work (PoW)-based blockchains, miners have to devote large amount of computing resource to compete for generating valid blocks, which is frequently disputed for tremendous computing resource waste. To address this dilemma, we propose an Evolved-PoW (E-PoW) consensus that can integrate the matrix computations in machine learning into the process of blockchain mining. The integrated architecture, the elaborated schemes of transferring matrix computations from machine learning to blockchain mining, and the reward adjustment scheme to affect the activity of the miners are, respectively, designed for E-PoW in detail. E-PoW can keep the advantages of PoW in blockchain and simultaneously salvage the computing power of the miners for the model training in machine learning. We conduct experiments to verify the availability and effect of E-PoW. The experimental results show that E-PoW can salvage by up to 80% computing power from pure blockchain mining for parallel model training in machine learning.
Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?

Assessing the personality of software engineers may help to match individual traits with the characteristics of development activities such as code review and testing, as well as support managers in team composition. However, self-assessment questionnaires are not a practical solution for collecting multiple observations on a large scale. Instead, automatic personality detection, while overcoming these limitations, is based on off-the-shelf solutions trained on non-technical corpora, which might not be readily applicable to technical domains like Software Engineering (SE). In this paper, we first assess the performance of general-purpose personality detection tools when applied to a technical corpus of developers' emails retrieved from the public archives of the Apache Software Foundation. We observe a general low accuracy of predictions and an overall disagreement among the tools. Second, we replicate two previous research studies in SE by replacing the personality detection tool used to infer developers' personalities from pull-request discussions and emails. We observe that the original results are not confirmed, i.e., changing the tool used in the original study leads to diverging conclusions. Our results suggest a need for personality detection tools specially targeted for the software engineering domain.
REALY: Rethinking the Evaluation of 3D Face Reconstruction

The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan. We observe that aligning two shapes with different reference points can largely affect the evaluation results. This poses difficulties for precisely diagnosing and improving a 3D face reconstruction method. In this paper, we propose a novel evaluation approach with a new benchmark REALY, consists of 100 globally aligned face scans with accurate facial keypoints, high-quality region masks, and topology-consistent meshes. Our approach performs region-wise shape alignment and leads to more accurate, bidirectional correspondences during computing the shape errors. The fine-grained, region-wise evaluation results provide us detailed understandings about the performance of state-of-the-art 3D face reconstruction methods. For example, our experiments on single-image based reconstruction methods reveal that DECA performs the best on nose regions, while GANFit performs better on cheek regions. Besides, a new and high-quality 3DMM basis, HIFI3D $$^{\pmb {+}\pmb {+}}$$ , is further derived using the same procedure as we construct REALY to align and retopologize several 3D face datasets. We will release REALY, HIFI3D $$^{\pmb {+}\pmb {+}}$$ , and our new evaluation pipeline at https://realy3dface.com .
Classification of histopathological images of breast cancer using an improved convolutional neural network model

The accurately automatic classification of medical pathological images has always been an important problem in the field of deep learning. However, the traditional manual extraction of features and image classification usually requires in-depth knowledge and more professional researchers to extract and calculate high-quality image features. This kind of operation generally takes a lot of time and the classification effect is not ideal. In order to solve these problems, this study proposes and tests an improved network model DenseNet-201-MSD to accomplish the task of classification of medical pathological images of breast cancer. First, the image is preprocessed, and the traditional pooling layer is replaced by multiple scaling decomposition to prevent overfitting due to the large dimension of the image data set. Second, the BN algorithm is added before the activation function Softmax and Adam is used in the optimizer to optimize performance of the network model and improve image recognition accuracy of the network model. By verifying the performance of the model using the BreakHis dataset, the new deep learning model yields image classification accuracy of 99.4%, 98.8%, 98.2%and 99.4%when applying to four different magnifications of pathological images, respectively. The study results demonstrate that this new classification method and deep learning model can effectively improve accuracy of pathological image classification, which indicates its potential value in future clinical application.
An improved Monte Carlo Tree Search approach to workflow scheduling

Workflow computing has become an essential part of many scientific and engineering fields, while workflow scheduling has long been a well-known NP-complete research problem. Major previous works can be classified into two categories: heuristic-based and guided random-search-based workflow scheduling methods. Monte Carlo Tree Search (MCTS) is a recently proposed search methodology with great success in AI research on game playing, such as Computer Go. However, researchers found that MCTS also has potential application in many other domains, including combinatorial optimization, task scheduling, planning, and so on. In this paper, we present a new workflow scheduling approach based on MCTS, which is still a rarely explored direction. Several new mechanisms are developed for the major steps in MCTS to improve workflow execution schedules further. Experimental results show that our approach outperforms previous methods significantly in terms of execution makespan.
Deep Learning Accurately Quantifies Plasma Cell Percentages on CD138-Stained Bone Marrow Samples

The diagnosis of plasma cell neoplasms requires accurate, and ideally precise, percentages. This plasma cell percentage is often determined by visual estimation of CD138-stained bone marrow biopsies and clot sections. While not necessarily inaccurate, estimates are by definition imprecise. For this study, we hypothesized that deep learning can be used to improve precision. We trained a semantic segmentation-based convolutional neural network (CNN) using annotations of CD138+ and CD138- cells provided by one pathologist on small image patches of bone marrow and validated the CNN on an independent test set of image patches using annotations from two pathologists and a non-deep learning commercial software. On validation, we found that the intraclass correlation coefficients for plasma cell percentages between the CNN and pathologist #1, a non-deep learning commercial software and pathologist #1, and pathologists #1 and #2 were 0.975, 0.892, and 0.994, respectively. The overall results show that CNN labels were almost as accurate as pathologist labels at a cell-by-cell level. Once satisfied with performance, we scaled-up the CNN to evaluate whole slide images (WSIs), and deployed the system as a workflow friendly web application to measure plasma cell percentages using snapshots taken from microscope cameras.
High speed fringe projection for dynamic shape measurement using binary phase mask. Part 1: theory and simulation

Projection rates of up to 30,000 greyscale fringe patterns per second have been achieved recently by defocusing binary fringe patterns from a digital micromirror device (DMD) based projector. Part 1 of this two-part paper describes the design of a binary phase mask, based on a virtual scatter plate, for the purpose of enhancing the performance of a binary fringe projector. The phase mask's anisotropic point spread function (PSF) produces a well-defined blur of the fringes parallel to the fringe direction, thereby minimising degradation of fringe contrast. The shape of the PSF is also shown, by means of a polychromatic Fourier optics model, to be insensitive to projection distance over a range of ±10% of the standoff distance. Two new binary fringe design methods are proposed, including extensions to optimize the system performance in the case of a mismatch between camera and projector framing rates. Expressions for the phase noise are derived as a function of the phase mask design parameters, which demonstrate that fringe quality comparable to traditional 8-bit greyscale fringes is achievable at projection rates over two orders of magnitude higher.