Reviewerly/exHarmony huggingface dataset:
Positive and Negative Association Rules Algorithm Based on Improved Interest Measure

There are issues with the mining of association rules of traditional Chinese medicine prescriptions, such as unclear rule strength, incomplete decision analysis, and low practicability. This paper introduces the concept of interest degree and negative association rules, as well as combining positive association rules to analyze traditional Chinese medicine comprehensively. The strength of the rules is determined by the correlation hidden in the prescription. In this paper, an improved correlation interest degree measure is introduced, which effectively improves the uneven value range of commonly used interest measure measures, lacks zero invariance, and is prone to contradictory rules. Experiments reveal that the algorithm can more precisely differentiate between positive and negative rules as well as strong and weak rules, thereby enhancing the efficacy and applicability of prescription association rules.
Analysis of Resources Scheduling for DL Workloads on Multi-tenant GPU Clusters

Deep learning (DL) has already played more and more crucial roles in various fields, such as medicine, service, manufacturing, etc. The high-performance deep learning training tasks mainly rely on the GPU resource. Thus, an increasing number of large-scale technology-centric companies set up multi-tenant GPU clusters to train the DL jobs. Yet, considering the characteristics of DL jobs and GPUs' architecture, the efficiency issues are still the bottleneck for the current multi-tenant GPU clusters and even the improvement of DL: users cannot share resources with the fine-grained standard since GPU is a kind of single resource; moreover, there are the novel and comprehensive requirements with the development of distributed DL jobs. This review introduces the development route of popular GPU cluster schedulers and scheduling frameworks in detail, clarifying how they reduce training time, improve resource utilization, and make sure sharing safety. In addition, based on these analyses, we also propose two focus points as future guidelines: locality constraints and contention for shared resources. In the remaining paper, we point that the elastic and seamless migration strategies for future research.
Cloud Service Credibility Evaluation Based on Double Sliding Window

With the continuous development of cloud computing, the cloud platform carries more and more microservices. Since the cloud platform cannot know the configuration and implementation of cloud services, there will be a problem of lack of trust. In response to this problem, various methods have been proposed at home and abroad. To evaluate the credibility of cloud services, but less consideration is given to the objectivity of evaluation. This paper proposes the concept of environmental indicators, and uses sliding window combined with subjective and objective evaluation to evaluate cloud services. The evaluation method improves the accuracy of cloud service credibility evaluation.
Improving Radiology Report Generation via Object Dropout Strategy and MLP-based Captioner

Recently, automatically generating radiology reports has been addressed since it can not only relieve the pressure on doctors but also avoid misdiagnosis. Radiology report generation is a fundamental and critical step of auxiliary diagnosis. Due to the COVID-19 pandemic, a more accurate and robust structure for radiology report generation is urgently needed. Although radiology report generation is achieving remarkable progress, existing methods still face two main shortcomings. On the one hand, the strong noise in medical images usually interferes with the diagnosis process. On the other hand, these methods usually require complex structure while ignoring that efficiency is also an important metric for this task. To solve the two aforementioned problems, we introduce a novel method for medical report generation, the termed attention-guided object dropout MLP(ODM) model. In brief, ODM first incorporates a tailored pre-trained model to pre-align medical regions and corresponding language reports to capture text-related image features. Then, a fine-grained dropout strategy based on the attention matrix is proposed to relieve training pressure by dropping content-irrelevant information. Finally, inspired by the lightweight structure of Multilayer Perceptron(MLP), ODM adopts an MLP-based structure as an encoder to simplify the entire framework. Extensive experiments demonstrate the effectiveness of our ODM. More remarkably, ODM achieves state-of-the-art performance on IU X-Ray, MIMIC-CXR, and ROCO datasets, with the CIDEr-D score being increased from 26.8% to 41.4%, 21.1% to 30.2%, and 9.1% to 19.3%, respectively.
A Fuzzy Logic-based Expert System for generating SCADA of Caterpillar engine

The development of a fuzzy logic-based expert system module to predict the fault diagnosis of software modules under uncertainty and non-precision of data was proved in this study. The expert system's rule-base and database are tuned using a Fuzzy logic system. A supervision system enabled an intelligent decision-making system to achieve the goals of the degree of variation, failure type, and location. The use of a fuzzy logic-based expert system to generate SCADA for a caterpillar CAT 3126B has been proposed. The main object of a fuzzy knowledge-based system is implemented by simulating the caterpillar's parameters in the SIMULINK/MATLAB environment. An intelligent Scada system uses an expert system to detect events and situations, to reason with the events, and then come up with a response, such as giving advice or taking corrective actions.
Optimal Sizing of Battery/Hydrogen Renewable Energy System With Genetic Algorithm Based on Irradiance Forecasting With LSTM Neural Network

This paper presents a stand-alone renewable energy power generation system supplying an electrical load of Assiut university. The standalone consists of photovoltaic (PV) array, battery, hydrogen system and diesel generator. A comparison between battery and hydrogen system with and without integration of diesel generator is done by comparing both cost of electricity ($/kwh) and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions. The sizing algorithm aims to minimize both cost of electricity (COE) and greenhouse gas emissions (GHG) using a multi-objective genetic algorithm for all system components using MATLAB software. The proposed energy management is based on the predicted data for solar irradiance. The irradiance prediction is done using long-short-term-memory (LSTIM) neural network to forecast hourly time base data for year-ahead of the solar radiation. The results show a better performance for PV/battery and PV/battery/diesel configurations with better loss of power supply probability with diesel integration.
Miniaturization and geometric optimization of SteamVR active optical trackers

Active tracking enables higher precision in tracking the positions, orientations, and states of the virtualized objects. STEAMVR Lighthouse tracking base-stations can be used for tracking specific objects. However, current solutions are bulky and costly. The overall goal of this research work was to reduce the size and cost of active VR trackers to enable their attachment to ever smaller physical tools and objects to be tracked in the real world and displayed in a virtual reality environment.
Direct face pose estimation using multiple camera views and deep learning

Face pose estimation is essential for interactive remote video communication as well as human-computer interaction. In the case of a vision system for video communication using multiple cameras, not only precise but also fast estimation is required for the switching control of the camera views. However, most of the methods based on facial landmarks are not fast enough due to the calculation cost for the detection and alignment of the landmarks. This paper proposes a straightforward method to directly estimate the face pose from input camera images, using multiple camera views and deep learning.
Obstacle detection in front of a railway vehicle using an onboard stereo camera (Evaluation with scaled model experimental vehicle)

In the railway sector, efforts are being made to improve the efficiency of operations. One of these is the automatic operation of trains. To keep the train running safely, it is necessary to make sure that there are no obstacles in front of the railway vehicle. Therefore, automatic train operation requires obstacle detection system in front of the railway vehicle. In recent years, some railway operators have introduced onboard cameras as drive recorders in order to understand the situation at the time of an accident and to investigate the cause. In addition, efforts are being made in the railway sector to improve operational efficiency through the use of onboard cameras. In this paper, we propose an obstacle detection method using an onboard stereo camera. The proposed system can be divided into three parts. First, the center position of the track is estimated using RGB images, and the search area for obstacle detection is set. Second, proposed system checks for the presence of objects within the set search area and creates a candidate area of obstacle. Finally, the area of each neighboring candidate area of obstacle is calculated, and if the area is greater than a certain value, the object is determined to be an obstacle. Furthermore, the proposed system is verified by using a 1/10 scale model experimental vehicle. Validation results show that the proposed system is capable of detecting obstacles in front of railway vehicles.
Retracted: Investigation on the Effect of Graded Emergency Nursing Group under the Assistance of Multidisciplinary First Aid Knowledge Internet-Based Approach on the First Aid of Acute Myocardial Infarction

[This retracts the article DOI: 10.1155/2022/8469930.].
Recognition of handwritten characters from Devanagari, Bangla, and Odia languages using transfer-learning-based VGG-16 networks

Despite promising results in character recognition techniques, research on handwritten characters from Indian regional scripts is still limited. In fact, most character recognizers in Indian languages are far less accurate than those built for English alphanumeric characters. Traditional techniques rely on extracted characteristics that require extensive knowledge of the chosen script, which is never practical. In such a circumstance, automatically extracting features may create interest. This study demonstrates how deep CNN VGG-16 networks can be used to increase character recognition accuracy. We have used nine different datasets from three separate Indian regional languages, Devanagari, Bangla, and Odia, to validate the model's efficacy further. When the samples were very noisy, it was revealed that the VGG model performed exceptionally well. The model recognized up to 3% better accuracy when the input samples were noisy and without applying any preprocessing. Furthermore, the model was implemented via transfer learning rather than being trained from scratch. This accomplishment could pave the way for constructing an automatic character recognition system for Indian regional scripts. The model outperforms recognition accuracy by around 1% for two datasets, cMATERdb 3.1.2 Bangla Basic and NITROHCS_V1.0 Odia Basic, compared to the existing approaches in the literature.
SAO TOME E PRINCIPE: ‘Coup’ Troops Held

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 59, Issue 12 p. 23851B-23852A National Security SAO TOME E PRINCIPE: ‘Coup’ Troops Held First published: 26 January 2023 https://doi.org/10.1111/j.1467-825X.2023.10850.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume59, Issue12January 2023Pages 23851B-23852A RelatedInformation
Defending Evasion Attacks via Adversarially Adaptive Training

Adversarial machine learning has been extensively studied from perspectives of attack settings and defense strategies. However, existing adversarial training models fail to be adaptive and robust against new attacks during test time. In this paper, we propose a novel adversarially adaptive defense (AAD) framework based on adaptive training such that the trained prediction and detection models adapt at test time to new attacks. Our AAD structures the training data into groups and each group represents one attack scenario. Different from empirical risk minimization that trains a single robust model or learns an invariant feature space, our AAD learns a context vector from features of each batch during training and incorporates the learned context vector into both prediction and detection models. Thus, AAD can adapt at test time to new adversarial attacks. We formulate our problem by optimizing a joint loss from prediction, detection, and regularization via a multi-task learning framework. We conduct comprehensive empirical evaluations with popular adversarial attacks and defense strategies on two real-world datasets under different attack settings. Empirical results show that AAD achieves both high prediction and detection accuracy and significantly outperforms baselines.
An interface to view collections of visual art

Abstract Art experts prefer being able to look at the individual images they are working on in the course of their research. However, if one were to look at digitally accessible images in the field of visual art, one would be dealing with billions of images; no one can handle visually examining such huge numbers of images one at a time. Therefore, art experts need special tools to examine and describe artworks in the context of other artworks. We used our experience from previous projects and interviews with members of the target group (art historians, curators, art dealers, and artists) to identify the central issues these experts encounter when working with large image collections and to determine the functionality and properties a system must offer to support their work. The results led to the customized interface LadeCA.View, which is now used in several projects. LadeCA.View enables experts to describe an exhibition or a collection of visual art in such a way that a user can obtain an overview of the intention, content, and structures of the exhibition or collection within a short period of time without looking at each image individually. LadeCA.View can also be used as an interface to probe more deeply into a collection or exhibition. In this paper we show the functions and visualizations of the interface and explain the design decisions. Furthermore, we outline LadeCA.View’s scope of applicability using three case studies
Improved SSD algorithm for mask wearing detection

Since the outbreak of the epidemic, it is extremely important to wear a mask correctly in the natural environment. Therefore, this paper proposes an improved SSD detection method. This paper takes the original SSD algorithm as the overall framework, and uses the lightweight network MobieNet-V2 to replace the original VGG-16 as the backbone network;In order not to compensate for the loss, the attention mechanism senet is added to improve its accuracy;And K-Means algorithm is used to cluster and generate anchor boxes suitable for data sets;Finally, Focal Loss is used to replace the Softmax of the original network as a new loss function. The experimental results show that compared with the original network, The improved network has better mAP, 8.9% higher than the original model.
ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (ReAssigner), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of ReAssigner is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. ReAssigner can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei Cloud, the proposed ReAssigner achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods.
Scalable and Accurate Density-Peaks Clustering on Fully Dynamic Data

Clustering is a primitive and important operator that analyzes a given dataset to discover its hidden patterns and features. Because datasets are usually updated dynamically (i.e., it accepts continuous insertions and arbitrary deletions), analyzing such dynamic data is also an important topic, and dynamic clustering effectively supports it, but is a challenging problem. In this paper, we consider the problem of density-peaks clustering (DPC) on dynamic data. DPC is one of the density-based clustering algorithms and attracts attention for many applications, due to its effectiveness. We investigate the hardness of this problem theoretically to measure the efficiencies of dynamic DPC algorithms. We prove that any exact solutions are costly, and propose an approximation algorithm to enable faster updates. We conduct experiments on real datasets, and the results confirm that our algorithm is much faster and more accurate than state-of-the-art.
FineCloud: Fine-grained Cloud Service Advisory using Machine Learning

Motivated by real customer problems, we investigated utilization of cloud services at different layers including infrastructure (IaaS), application services (PaaS) and databases (DaaS). We found several issues such as forgetting about unused resources, bursty workloads and service dependencies causing under-utilization (a.k.a. over- provisioning) problem. Cloud advisory tools offered by the public providers either lack the fine-grained analysis needed for actionable recommendations or can’t see the correlations among services that are used by the same customers’ resource groups. We proposed an automated, near real-time advisor that utilizes historical usage data and machine learning (ML) models to recommend cost saving opportunities. We demonstrated significant cost savings averaging around 20%, which can accumulate as thousands of Dollars for large and active systems. Since our advisory models depend on time-series data, we compared several forecasting algorithms including ARIMA, LSTM and Prophet. We found LSTM model to deliver the most accurate results for our workloads.
A 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation

We aim to detect the class and orientation of a vehicle by training a model with synthetic data. However, the distribution of the classes in the training data is imbalanced, and the model trained on the synthetic image is difficult to predict in real-world images. We propose a two-stage detection model with photo-realistic image generation to tackle this issue. Our model mainly takes four steps to detect the class and orientation of the vehicle. (1) It builds a table containing the image, class, and location information of objects in the image, (2) transforms the synthetic images into real-world images style, and merges them into the meta table. (3) Classify vehicle class and orientation using images from the meta-table. (4) Finally, the vehicle class and orientation are detected by combining the pre-extracted location information and the predicted classes. We achieved 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> place in IEEE BigData Challenge 2022 Vehicle class and Orientation Detection (VOD) with our approach. Our code and project material will be available at https://github.com/inu-RAISE/VOD_Challenge
Sentiment Analysis of Political Posts on Hong Kong Local Forums Using Fine-Tuned mBERT

Sentiment analysis is an important and challenging task in natural language processing. It has been studied for a few decades. Recently, Bidirectional Encoder Representations from Transformer (BERT) model has been introduced to tackle this task and gain very promising results. However, most existing studies on fine-tuning BERT models for sentiment analysis focus on high-resource language (e.g., En-glish or Mandarin). This paper studies the sentiment analysis of Cantonese political posts on Hong Kong local forums. We first collected and labeled posts related to Anti-Extradition Law Amendment Bill (Anti-ELAB) movement in Hong Kong discussion forums. We then examined the performance of dictionary-based sentiment analysis, traditional machine learning-based, fine-tuned BERT and fine-tuned multilingual BERT (mBERT) models. Our results show that fine-tuned mBERT model achieves the best performance on our collected and labeled Cantonese dataset.
Image reconstruction for the artificial compound eye based on deep learning

The visual system of arthropods, called the compound eye, has distinctive features such as a wide field of view, high-speed motion detection, and infinite depth of field. These features have attracted researchers to build artificial compound eyes. However, the compound eye is limited in spatial resolution by its structural constraints such as the number and size of ommatidia that compose the compound eye. These constraints also can be found in the existing artificial compound eye. In previous work, a design method overcame these limitations and achieved resolution improvements by increasing the acceptance angle of ommatidia and using numerical optimization based on compressive sensing (CS). However, the limitation is that prior information such as a sparsifying basis is needed to solve the numerical optimization problem, and obtaining the solution to this problem is computationally time-consuming. In this paper, we propose a deep learning-based artificial compound eye. The deep learning architecture takes a measurement from the compound eye as input and learns how to reconstruct the original image. The experimental result demonstrates that the proposed deep learning approach provides improved performance in image reconstruction for the artificial compound eye.