Reviewerly/exHarmony huggingface dataset:
Deep Learning in Diverse Intelligent Sensor Based Systems

Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.
Meshfree Digital Image Correlation Using Element Free Galerkin Method: Theory, Algorithm and Validation

Abstract Background The association of advanced digital image correlation (DIC) and numerical simulation has been widely used for inverse parameter identification. Objective It is attractive to develop an accurate DIC method sharing the common features with numerical simulation, which can lead to better synergy between experiments and simulations. Methods A new meshfree digital image correlation (MF-DIC) using element free Galerkin method (EFGM) is proposed for deformation measurement. The EFGM is a classical meshfree method in numerical studies, and it is directly used to construct the shape function in MF-DIC from a set of scattered nodes for image matching. The MF-DIC is principally different from the classical local DIC and global DIC since it does not rely on the concept of a subset or an element. Results In MF-DIC, the $${C}^{1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mn>1</mml:mn> </mml:msup> </mml:math> -continuous displacement for every point is constructed based on a group of scattered nodes in a small support domain surrounding it. The continuous strain map can then be directly derived from the displacement, instead of using an additional smoothing technique as required in classical local DIC or post-processing used in global DIC. A performance assessment based on the Metrological Efficiency Indicator (MEI), as defined in DIC Challenge 2.0, shows that the proposed MF-DIC yields an excellent balance between spatial resolution and measurement resolution for both displacement and strain measurements. Conclusions Given that the proposed MF-DIC shares common features with the classical meshfree method in computational mechanics, it paves the way for an enhanced synergy between experiments and simulations required for robust inverse parameter identification methods.
Coordinate Translator for Learning Deformable Medical Image Registration

The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also understand image coordinate systems. We argue that the latter task is challenging for traditional CNNs, limiting their performance in registration tasks. To tackle this problem, we first introduce Coordinate Translator, a differentiable module that identifies matched features between the fixed and moving image and outputs their coordinate correspondences without the need for training. It unloads the burden of understanding image coordinate systems for CNNs, allowing them to focus on feature extraction. We then propose a novel deformable registration network, im2grid, that uses multiple Coordinate Translator’s with the hierarchical features extracted from a CNN encoder and outputs a deformation field in a coarse-to-fine fashion. We compared im2grid with the state-of-the-art DL and non-DL methods for unsupervised 3D magnetic resonance image registration. Our experiments show that im2grid outperforms these methods both qualitatively and quantitatively.
Learning Quality-aware Dynamic Memory for Video Object Segmentation

Recently, several spatial-temporal memory-based methods have verified that storing intermediate frames and their masks as memory are helpful to segment target objects in videos. However, they mainly focus on better matching between the current frame and the memory frames without explicitly paying attention to the quality of the memory. Therefore, frames with poor segmentation masks are prone to be memorized, which leads to a segmentation mask error accumulation problem and further affect the segmentation performance. In addition, the linear increase of memory frames with the growth of frame number also limits the ability of the models to handle long videos. To this end, we propose a Quality-aware Dynamic Memory Network (QDMN) to evaluate the segmentation quality of each frame, allowing the memory bank to selectively store accurately segmented frames to prevent the error accumulation problem. Then, we combine the segmentation quality with temporal consistency to dynamically update the memory bank to improve the practicability of the models. Without any bells and whistles, our QDMN achieves new state-of-the-art performance on both DAVIS and YouTube-VOS benchmarks. Moreover, extensive experiments demonstrate that the proposed Quality Assessment Module (QAM) can be applied to memory-based methods as generic plugins and significantly improves performance. Our source code is available at https://github.com/workforai/QDMN .
Predicting Academic Performance using a Multiclassification Model: Case Study

Now-a-days predicting the academic performance of students is increasingly possible, thanks to the constant use of computer systems that store a large amount of student information.Machine learning uses this information to achieve big goals, such as predicting whether or not a student will pass a course.The main purpose of the work was to make a multiclassifier model that exceeds the results obtained from the machine learning models used independently.For the development of our proposed predictive model, the methodology was used, which consists of several phases.For the first step, 557 records with 25 characteristics related to academic performance were selected, then the preprocessing was applied to said data set, eliminating the attributes with the lowest correlation and those records with inconsistencies, leaving 500 records and 9 attributes.For the transformation, it was necessary to convert categorical to numerical data of four attributes, being the following: SEX, ESTATUS_lab_padre, ESTATUS_lab_madre and CONDITION.Having the data set clean, we proceeded to balance the data, where 1,167 data were generated, using the 2/3 for training and the remaining 1/3 for validation, then the following techniques were applied: Extra Tree, Random Forest, Decision Tree, Ada Boost and XGBoost, each obtained an accuracy of 57.41%, 61.96%, 91.44%, 59.65% and 83.3% respectively.Then the proposed model was applied, combining the five algorithms mentioned above, which reached an accuracy of 92.86%, concluding that the proposed model provides better accuracy than when the models are used independently meaning that it was the one that obtained the best result.
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation

This paper addresses the problem of cross-dataset generalization of 3D human pose estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a major performance drop. Previous methods have mainly addressed this problem by improving the diversity of the training data. We argue that diversity alone is not sufficient and that the characteristics of the training data need to be adapted to those of the new dataset such as camera view-point, position, human actions, and body size. To this end, we propose AdaptPose, an end-to-end framework that generates synthetic 3D human motions from a source dataset and uses them to fine-tune a 3D pose estimator. AdaptPose follows an adversarial training scheme. From a source 3D pose the generator generates a sequence of 3D poses and a camera orientation that is used to project the generated poses to a novel view. Without any 3D labels or camera information AdaptPose successfully learns to create synthetic 3D poses from the target dataset while only being trained on 2D poses. In experiments on the Human3.6M, MPI-INF-3DHp, 3DPW, and Ski-Pose datasets our method outperforms previous work in cross-dataset evaluations by 14% and previous semi-supervised learning methods that use partial 3D annotations by 16%.
Operon

The past decade has witnessed the rapid development of cloud computing and data-centric applications. While these innovations offer numerous attractive features for data processing, they also bring in new issues about the loss of data ownership. Though some encrypted databases have emerged recently, they can not fully address these concerns for the data owner. In this paper, we propose an ownership-preserving database (OPDB), a new paradigm that characterizes different roles' responsibilities from nowadays applications and preserves data ownership throughout the entire application. We build Operon to follow the OPDB paradigm, which utilizes the trusted execution environment (TEE) and introduces a behavior control list (BCL). Different from access controls that merely handle accessibility permissions, BCL further makes data operation behaviors under control. Besides, we make Operon practical for real-world applications, by extending database capabilities towards flexibility, functionality and ease of use. Operon is the first database framework with which the data owner exclusively controls its data across different roles' subsystems. We have successfully integrated Operon with different TEEs, i.e. , Intel SGX and an FPGA-based implementation, and various database services on Alibaba Cloud, i.e. , PolarDB and RDS PostgreSQL. The evaluation shows that Operon achieves 71% - 97% of the performance of plaintext databases under the TPC-C benchmark while preserving the data ownership.
Secure and Smart Healthcare System using IoT and Deep Learning Models

Patients of Smart Healthcare Systems have access to their medical records through an online portal. Due to the fact that patients do not want their names made public, maintaining data privacy and security is essential to the success of the organisation. Users are required to submit personal information to an authentication server before they can proceed with the login process. The information includes a login ID as well as a password. It is possible that the patient's adversaries will be able to violate their right to privacy if they are able to keep an eye on the patient or get in touch with them. Therefore, in this body of work, we suggest a strategy to protect the privacy of patients and the confidentiality of their medical information from dangers posed by the Authorization Service and other parties. In the course of this research, we utilised a method known as camel-based rotating panel signature. This was done not merely to protect the patients' privacy but also to protect the network itself from potential threats. The theoretical analysis of the performance of the software revealed numerous layers of security that are able to withstand a broad variety of different kinds of attacks.
Machine learning applied to electronic health record data in home healthcare: A scoping review

Despite recent calls for home healthcare (HHC) to integrate informatics, the application of machine learning in HHC is relatively unknown. Thus, this study aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the HHC setting. Our secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model.During March 2022 we conducted a literature search in four databases: PubMed, Embase, CINAHL, and Scopus. Inclusion criteria were 1) describing services provided in the HHC setting, 2) applying machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) using EHR data and 4) focusing on the adult population. Predictors were mapped to the Biopsychosocial Model. A risk of bias analysis was conducted using the Prediction Model Risk Of Bias Assessment Tool.The final sample included 20 studies. Eighteen studies used predictors from standardized assessments integrated in the EHR. The most common outcome of interest was hospitalization (55%), followed by mortality (25%). Psychological predictors were frequently excluded (35%). Tree based algorithms were most frequently applied (75%). Most studies demonstrated high or unclear risk of bias (75%).Future studies in HHC should consider incorporating machine learning algorithms into clinical decision support systems to identify patients at risk. Based on the Biopsychosocial model, psychological and interpersonal characteristics should be used along with biological characteristics to enhance risk prediction. To facilitate the widespread adoption of machine learning, stakeholders should encourage standardization in the HHC setting.
Feasibility analysis of floating photovoltaic power plant in Bangladesh: A case study in Hatirjheel Lake, Dhaka

The installation of large-scale photovoltaic (LSPV) power plants is a solution to mitigate the national energy demand in Bangladesh. However, the land crisis is one of the key challenges for the rapid growth of ground-mounted LSPV plants in Bangladesh. The per unit cost of energy from ground-mounted PV systems is rising as a response to numerous difficulties, particularly for large-scale electricity generation. To overcome the issues with land-based PV, the floating photovoltaic (FPV) could be a viable solution. To the aspirations of the Sustainable and Renewable Energy Development Authority (SREDA), this article has investigated the feasibility of constructing a floating solar plant at Hatirjheel Lake in Dhaka, Bangladesh. The lake is an excellent spot to build an FPV plant due to its geographic location and climatic conditions inside the capital city. In this paper, the design of the plant and tariff are carried out using the PVsyst simulator. It is found that the optimum cost of energy for the plant is $ 0.0959/KWh, which is lesser than the currently operational ground-mounted PV plants in Bangladesh. Additionally, the projected 6.7 MW plant can meet 12.5 % of the local energy demand. Furthermore, the FPV plant is capable to cut off 6685 tons of CO2 annually. A reduction in power costs and environmental protection would assist the government of Bangladesh in achieving the sustainable development goals and electricity generation target of 6000 MW from solar photovoltaics by 2041 as well.
A Communication, Management and Tracking Mobile Application for Enhancing Earthquake Preparedness and Situational Awareness in the Event of an Earthquake

The presented DepApp is an application that provides information about the intensity, time and whereabouts of a recent earthquake. In addition, the presented application is a mobile application that contains emergency phone numbers and can provide general information about an earthquake which people can benefit from before and after the earthquake. With the presented application, people will be able to easily access information about earthquakes. Moreover, safe areas in which to shelter after an earthquake are offered to the user. Information about an earthquake kit is given to user in the application. The earthquake kit should be prepared before the earthquake. The application also includes emergency sounds that can be used in the event of an earthquake. A survey system is designed to determine the intensity of the earthquake as it is felt by the users. After the earthquake, the application presents the intensity, time, and location of the earthquake to the user. Thus, with this application, users will be more knowledgeable and prepared for an earthquake.
The Effectiveness Of Using Quillbot In Improving Writing For Students Of English Education Study Program

Abstract: The aims of this study was to assess students' proficiency with Quillbot to compose error-free original scientific papers in English. This research is a quantitative study. One particular type of data was used for analysis in this study, and that was responses to questionnaires. As a result, it is very important that all students know this. Some students, however, still haven't learned the best method for paraphrasing. These skills can be honed through regular reading or by making use of various online tools, both free and paid. This research consisted of three stages: planning, implementation, and evaluation (through student questionnaires), all of which were carried out sequentially. The aim of this experiment was to determine whether using Quillbot would help students commit less plagiarism; however, students need to reread the tool to ensure that the meaning of the cited articles remains consistent. Students were able to take advantage of the software and find it useful to prephrase text without changing its meaning, according to survey findings submitted via google forms.
PlexusNet: A neural network architectural concept for medical image classification

State-of-the-art (SOTA) convolutional neural network models have been widely adapted in medical imaging and applied to address different clinical problems. However, the complexity and scale of such models may not be justified in medical imaging and subject to the available resource budget. Further increasing the number of representative feature maps for the classification task decreases the model explainability. The current data normalization practice is fixed prior to model development and discounting the specification of the data domain. Acknowledging these issues, the current work proposed a new scalable model family called PlexusNet; the block architecture and model scaling by the network's depth, width, and branch regulate PlexusNet's architecture. The efficient computation costs outlined the dimensions of PlexusNet scaling and design. PlexusNet includes a new learnable data normalization algorithm for better data generalization. We applied a simple yet effective neural architecture search to design PlexusNet tailored to five clinical classification problems that achieve a performance noninferior to the SOTA models ResNet-18 and EfficientNet B0/1. It also does so with lower parameter capacity and representative feature maps in ten-fold ranges than the smallest SOTA models with comparable performance. The visualization of representative features revealed distinguishable clusters associated with categories based on latent features generated by PlexusNet. The package and source code are at https://github.com/oeminaga/PlexusNet.git.
The digital competence of academics in higher education: is the glass half empty or half full?

This paper aims to evaluate and discuss the digital competence of academics at universities, to identify challenges and define recommendations for policy. This study was conducted through collaboration between the Joint Research Centre (JRC) of the European Commission and Metared of the Universia Foundation, surveying 30,407 participants who present the perceptions of their own digital competence levels. These self-reflections took place in universities in seven countries, namely Argentina, Brazil, Colombia, Chile, Peru, Mexico and Portugal, and used the Check-In tool, which consists of 22 questions based on the European Framework for the Digital Competence of Educators-or 'DigCompEdu' framework. A descriptive statistical analysis was performed, followed by a qualitative evaluation. Almost 70% of the academics have an average intermediate level of competence when the data is aggregated, with results varying in each DigCompEdu area according to the specific question asked. There is no significant difference between young and senior academics, as well as between men and women. The results present a discussion of whether the age and gender of educators and their work environment have an impact on their digital competence level, and at the same time highlights the areas in which educators perceive themselves to be most and least competent. It shows how the amount of institutional support that is offered affects the academics' perceptions of their level of digital competence. On the basis of the results, recommendations are presented for higher-education institutions, with the aim of supporting the professional development of their academics.
Improved Security of a Pairing-Free Certificateless Aggregate Signature in Healthcare Wireless Medical Sensor Networks

Recently, Zhan et al. presented a pairing-free certificateless aggregation signature scheme (ZH-CLAS) to address security issues in healthcare wireless medical sensor networks (HWMSNs) and proved that their scheme is secure against adaptive chosen message attacks. In this article, we analyze the security of the ZH-CLAS scheme by utilizing two types of concrete attacks. Unfortunately, we demonstrate that their scheme cannot withstand public key replacement attacks and is not secure against coalition attacks from malicious sensor nodes. To solve these security challenges, we further improve the security of the ZH-CLAS scheme. Our enhanced scheme has a fixed-length aggregate signature, which efficiently minimizes the transmission bandwidth. Additionally, our scheme outperforms related pairing-free certificateless aggregate signature schemes in terms of security and communication performance.
A Survey on Deep-Learning-Based Real-Time SAR Ship Detection

Recently, deep learning has greatly promoted the development of SAR ship detection. But the detectors are usually heavy and computation intensive which hinder the usage on the edge. In order to solve this problem, a lot of lightweight networks and acceleration ideas are proposed. In this survey, we review the papers that about real-time SAR ship detection. We firstly introduce the model compression and acceleration methods. They are pruning, quantization, knowledge distillation, low-rank factorization, lightweight networks and model deployment. They are the source of innovation in real-time SAR ship detection. Then we summarize the real-time object detection methods. They are two-stage, single-stage, anchor free, trained from scratch, model compression and acceleration. Researchers in SAR ship detection usually learn from these ideas. We then spend a lot of content on the review of the 70 real-time SAR ship detection papers. The years, datasets, journals, deep learning frameworks, and hardwares are introduced firstly. After that, the 10 public datasets and the evaluation metrics are shown. Then, we survey the 70 papers according to anchor free, trained from scratch, YOLO series, CFAR+CNN, lightweight backbone, pruning, quantization, knowledge distillation and hardware deployment. The experimental results show that the algorithms have been greatly developed in speed and accuracy. In the end we pointed out the problems of the 70 papers and the directions to be studied in the future. Our work can enable researchers to quickly understand the research status in this field.