Reviewerly/exHarmony huggingface dataset:
From Cybersecurity Hygiene to Cyber Well-Being

End-user practices are widely recognized as a source of cybersecurity weaknesses, and yet efforts to support related awareness and understanding are often lacking in both the workplace and wider societal contexts. As a result, users are often expected to be cybersecurity-literate and to follow good cyber hygiene practices, without necessarily having an understanding or sufficient guidance on how to do so. This can be down to the fact that mainstream advice surrounding the definition for cyber hygiene is widely used but varies greatly in meaning- therefore can create confusion on what the user should prioritize. This paper investigates and defines the concept of cyber hygiene and uses this foundation as the basis for considering the notion of Cyber Well-being as a positive state that ought to be fostered amongst the user community. Rather than simply adopting the oft-taken stance that the user is a threat to technology, the aim behind Cyber Well-being is to consider the users' feelings as well as what they do to implement cyber hygiene. A total of 165 respondents were involved in order to understand varying interpretations of what good cyber hygiene looks like. A disconnect between theory and practice were apparent from later responses when respondents were asked about the issues that stood in the way of their cyber hygiene. The discussion also proposes how the findings can form the foundation for an accompanying tool that can assist users in practicing and tracking their cyber hygiene and may thereby foster confidence and support the formation of Cyber Well-being in their use of their technologies.
Effect of Environment, Safety, and Health on Employee Performance

This study aims to determine whether there is an influence on the Work Environment, Occupational Safety, Occupational Health, and Employee Performance. Occupational Health and Safety on Employee Performance at the Bekasi City Regional General Hospital (RSUD). The method used in this research is a quantitative approach. The population in this study were all employees as many as 55 respondents with sampling using random sampling technique by calculating the PLS formula on a sample of 55 respondents. The results showed that there was a significant influence between the independent variables, namely the Work Environment, Occupational Safety and Health, on the dependent variable, namely Employee Performance.
First acquaintance with unmanned vehicles

With the rapid advancement of driverless technology in recent years, major vehicle companies and driverless system solution providers, such as Baidu Apollo and Jingchi, have made continuous efforts to commercialize driverless technologies. The field of unmanned vehicle technology includes not only vehicle control, path planning, perception fusion, and other fields but also cutting-edge fields such as artificial intelligence, machine learning, deep learning, and reinforcement learning. Therefore, learning and applying various basic algorithms in unmanned vehicle systems have become important tasks. This chapter presents an overview of unmanned vehicle systems and mainly introduces the concept of unmanned vehicle systems, the significance of unmanned vehicle technology, the development history of unmanned vehicle technology, and the architecture of modern unmanned vehicle systems.
Towards Realistic Privacy-Preserving Deep Learning Inference Over Encrypted Data

Nowadays, the large amounts of sensitive data transmitted and processed in the cyberspace require optimal privacy-preserving processing solutions. Deep Learning (DL) is a robust technique to deal with Big Data in various disciplines, but it requires dedicated high-performance infrastructure. Thus, we often outsource computation workload to server infrastructures, and various Machine-Learning-as-a-Service (MLaaS) platforms have emerged recently. Unfortunately, sending sensitive data (e.g., medical records or personally identifiable information) to third-party servers creates several privacy concerns with legal and ethical implications. Homomorphic Encryption (HE) allows for privacy-preserving computation over encrypted data, thus preserving the privacy of the information. In the scope of Deep Learning, efficient HE requires structural optimizations to perform the complex computation of the internal layers. One such optimization is Packed Homomorphic Encryption (PHE), which encodes multiple elements on a single ciphertext, allowing for efficient Single Instruction over Multiple Data (SIMD) operations. However, its application to DL is not straightforward, and it demands new algorithms and data encoding, which the literature has not accurately addressed. In this work, we elaborate on novel algorithms to adapt the linear algebra operations of DL layers to PHE. Concretely, we focus on Convolutional Neural Networks. We provide detailed descriptions and insights of the different algorithms and efficient inter-layer data format conversion mechanisms. We formally analyze the complexity of the algorithms in terms of performance metrics and provide guidelines and recommendations for the adaption of architectures that deal with private data. Furthermore, we confirm the theoretical analysis with practical experimentation. Among other conclusions, we prove that our new algorithms speed up the processing of convolutional layers compared to the existing proposals.
Interpreting the Development of Information Security Industry from Standards

Information security has been a significant concern in personal life, enterprise development, city construction, and national competitiveness. Information security standards, as an important regulatory and guiding tool, reflects the consensus reached by the industry and therefore are important references to understand an industry. This study explores a new angle to understand the development of the information security industry by analyzing the external and content features of related standards. Based on the available metadata and the possible dimensions, a standards’ features index system and a roadmap of analysis are constructed. The Chinese and ISO information security standards are selected to verify the method. The result shows the evolution of the industry, the status quo, and the future trend of the information security industry.
Blood Sample Image Classification Algorithm Based on SVM and HOG

In the medical field, the classification and analysis of blood samples has always been arduous work. In the previous work of this task, manual classification maneuvers have been used, which are time consuming and laborious. The conventional blood image classification research is mainly focused on the microscopic cell image classification, while the macroscopic reagent processing blood coagulation image classification research is still blank. These blood samples processed with reagents often show some inherent shape characteristics, such as coagulation, attachment, discretization and so on. The shape characteristics of these blood samples also make it possible for us to recognize their classification through computer vision algorithms. Blood sample classification focuses on the texture and shape of the picture. HOG feature is a kind of feature descriptor used for object detection in computer vision and image processing. It can better extract the outline and texture features of the image by calculating and counting the histogram of oriented gradient of the local region of the image. Because the medical machines that need to identify and classify blood samples often lack strong calculation power, the current popular machine-learning classification algorithms cannot play a good role in these machines. In addition, the characteristics of blood samples produced by different types of reagents and processing methods are different, and it is difficult to obtain real samples, so the amount of data that can be used for training is small. Combining the above conditions and the experimental comparison of a variety of classification algorithms, we find that the lightweight SVM model has a better performance on this problem, and the combination of HOG and SVM has also been widely used in other research. The experiment demonstrated that the classification algorithm based on SVM and HOG can give a good result of both performance and accuracy in the classification of blood samples problem.
Clinicomics - Guided Distant Metastasis Prediction in Breast Cancer Via Artificial Intelligence

Purpose: Breast cancer (BC) has become the most common malignant tumour worldwide. Distant metastasis (DM) is one of the leading causes of BC-related death. Clinicomics was theoretically raised to be the potent strategy on diagnosis and prediction in cancer. But it was not put into practice until the present study. The present study aimed to verify the performance of clinicomics-guided DM risk prediction for breast cancer via artificial intelligence (AI) and to investigate the accuracy of the created prediction models for metachronous DM, bone metastasis and visceral metastasis.Results: The areas under the curve (AUCs) and C-index confirmed that the clinicomics-based models had good prognostic accuracy in the training and validation sets. Three clinicomics-guided prediction nomograms on DM, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous DM screening and lead to the implementation of individualized prophylactic therapy for BC patients.Conclusions: Our study is the first to make cliniomics a reality. Such cliniomics strategy possesses the development potential in AI medicine. The established clinicomics-guided distant metastasis prediction nomograms can guide the detection and management of distant metastasis in breast cancer.Funding Information: The present study was sponsored by Natural Science Foundation of China (81801781, 82072004, 81903398, 82011530050).Conflict of Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Ethical Approval: This case-control study protocol was approved by the Ethics Committee of Tianjin Medical University Cancer Institute & Hospital, Tianjin, China (EK2018125).
Encoding Generative Adversarial Networks for Defense Against Image Classification Attacks

Image classification has undergone a revolution in recent years due to the high performance of new deep learning models. However, severe security issues may impact the performance of these systems. In particular, adversarial attacks are based on modifying input images in a way that is imperceptible for human vision, so that deep learning image classifiers are deceived. This work proposes a new deep neural network model composed of an encoder and a Generative Adversarial Network (GAN). The former encodes a possibly malformed input image into a latent vector, while the latter generates a reconstructed image from the latent vector. Then the reconstructed image can be reliably classified because our model removes the deleterious effects of the attack. The experiments carried out were designed to test the proposed approach against the Fast Gradient Signed Method attack. The obtained results demonstrate the suitability of our approach in terms of an excellent balance between classification accuracy and computational cost.
A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches

Binarization of document images is an important pre-processing step in the field of document analysis. Traditional image binarization techniques usually rely on histograms or local statistics to identify a valid threshold to differentiate between different aspects of the image. Deep learning techniques are able to generate binarized versions of the images by learning context-dependent features that are less error-prone to degradation typically occurring in document images. In recent years, many deep learning-based methods have been developed for document binarization. But which one to choose? There have been no studies that compare these methods rigorously. Therefore, this work focuses on the evaluation of different deep learning-based methods under the same evaluation protocol. We evaluate them on different Document Image Binarization Contest (DIBCO) datasets and obtain very heterogeneous results. We show that the DE-GAN model was able to perform better compared to other models when evaluated on the DIBCO2013 dataset while DP-LinkNet performed best on the DIBCO2017 dataset. The 2-StageGAN performed best on the DIBCO2018 dataset while SauvolaNet outperformed the others on the DIBCO2019 challenge. Finally, we make the code, all models and evaluation publicly available (https://github.com/RichSu95/Document_Binarization_Collection) to ensure reproducibility and simplify future binarization evaluations.
A Polynomial Regression-Based Approach to Estimate Relaxation Rate Maps Suitable for Multiparametric Segmentation of Clinical Brain MRI Studies in Multiple Sclerosis

Background and Objective: Relaxation parameter maps (RPMs) calculated from spin-echo data have provided a basis for the segmentation of normal brain tissues and white matter lesions in multiple sclerosis (MS) MRI studies. However, Conventional Spin-Echo (CSE) sequences, once the core of clinical MRI studies, have been now largely replaced by faster ones, whereas the calculation of RPMs cannot be applied a-posteriori on these clinical studies. We developed and validated a method to estimate RPMs (pseudo-RPMs) from routine clinical MRI protocols (including 3D-GrE T1w, FLAIR and fast-T2w sequences), suitable for fully automatic multiparametric segmentation of normal-appearing and pathological brain tissues in MS.Methods: Clinical MRI studies were spatially normalized to a CSE-derived digital template, and further processed through a multistep pipeline, to collect observations matching their signal intensities to the relaxation parameters of the template, and of an MS lesion database derived from CSE studies. A multiple and multivariate 4-th degree polynomial fit was applied to the collected observations to obtain pseudo-RPMs, then segmented through a method originally developed for the CSE-derived RPMs.Results: Age-related changes of normal-appearing brain tissue volumes, as measured in 47 clinical studies, substantially overlapped those measured in a comparable dataset of 47 patients acquired using CSE sequences. In addition, the accuracy of the method in segmenting MS lesions, assessed against manual segmentation carried out by three neuroradiologists, showed a moderate-high spatial overlap, comparable to that of the widely used Lesion Segmentation Tool on FLAIR images, with a greater volumetric agreement.Conclusions: The proposed approach allows calculation from clinical studies of pseudo-RPMs comparable to those obtainable from CSE sequences, avoiding the need for the acquisition of additional, dedicated sequences for segmentation purposes.
Bi-LSTM Sequence Modeling for On-The-Fly Fine-Grained Sketch-Based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo for a given query sketch. However, its widespread applicability is limited by the fact that it is difficult to draw a complete sketch, and the drawing process often takes more time than the text/tag method. On-the-fly FG-SBIR was proposed to address this problem, in which image retrieval is performed after each stroke. The aim is to retrieve the target photo using the least number of strokes. Each photo corresponds to a sketch drawing episode, in which a significant correlation exists between these incomplete sketches. This correlation will allow a more efficient learning embedding space for incomplete sketches, which is considered in this study. First, a triplet network, as used in the classical FG-SBIR framework, was designed to learn the joint embedding space shared between the photo and its corresponding complete sketch. Second, assuming strong time correlation, each sketch drawing episode is considered a sequence, and each incomplete sketch in the drawing episode is extracted as a feature vector. A learnable Bi-LSTM module and triplet loss function map the feature space of incomplete sketches obtained from the base model for efficient representation. In the experiments, we proposed more realistic challenges, and our method achieved superior early retrieval efficiency over the state-of-the-art baseline methods on two publicly available fine-grained sketch retrieval datasets.
Model For The Creation of The Educational Building of Russian As A Foreign Language

English Abstract: The article deals with the creation of the educational building of the Russian as a foreign language. The corpus consists of texts selected from textbooks, teaching aids. Stages of work on the corpus: select textbooks, select academic texts, check for errors and typos, develop meta-markup for the search engine of the Corpus Manager. The text is inside a separate document with an assigned code: language, subject, level, year of study, information about the authors of the textbook, text number, text genre, number of word usages and year of publication of the textbook. To date, the volume of the RFL corpus is 615 texts and 179,863 word usages.
Efficient Segmentation Approach for Different Medical Image Modalities

This paper presents a study of the segmentation of medical images. The paper provides a solid introduction to image enhancement along with image segmentation fundamentals. In the first step, the morphological operations are employed to ensure image detail protection and noise-immunity. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, the Fuzzy C-Means (FCM) clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster centers. It is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition matrix. Simulation results are performed on different medical image modalities. Ultrasonic (Us), X-ray (Mammogram), Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance (MR) images are the main medical image modalities used in this work. The obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image segmentation. Simulation results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%, 99.71%, 99.83%, 99.85%, and 99.74% for Us, Mammogram, CT, PET, and MRI images, respectively.
A Case Study on Blended OSH Education Based on Experiential Learning for Field Trainees: Using the School Broadcast Studio

The purpose of this study is to measure the effectiveness of blended Occupational Safety and Health (OSH) education based on experiential learning for field trainees at specialized high schools, one of the groups that are the most vulnerable to industrial accidents, within the context of the COVID-19 pandemic. In Korea, the number of industrial accidents that occurred to field trainees reached 40 from 2016 to 2018, including two deaths despite the government's sustained efforts. To verify the effectiveness of education, a one-group pretest-posttest design was conducted for each group of students, and the data were analyzed by a paired t-test using the IBM SPSS 21.0. The subjects of this study were 437 third-year students from 6 specialized high schools in Busan metropolitan city and South Gyeongsang Province in Korea, and the survey period was from July 31, 2020, to May 18, 2021. The results indicated that the measured values ​​increased significantly in all three areas after the implementation of safety and health education based on experiential learning: knowledge (t=-5.30, p<.001), skill (t=-6.40, p<.001), attitude (t=-7.32 p<.001). Therefore, it was proven that OSH education based on direct and indirect experiential learning prevents field trainees of specialized high schools from meeting industrial accidents.
Generative Focus Feedback Residual Network for Image Steganalysis and Hidden Information Reconstruction

Image steganalysis is a process of detecting whether there is hidden information in an image. Existing image steganalysis methods cannot fully extract hidden image information even if the presence of the hidden information is detected in success, resulting in either poor performance of detection or inability to reconstruct the hidden information. This paper proposes a generative image steganalysis algorithm based on a focus feedback residual convolutional neural network for simultaneous detection and extraction of the hidden information. First, a possible stego image is processed by a preprocessing network, which consists of several convolutional layers and two fresh focus modules and outputs several enhanced feature maps. Then, the resulting enhanced feature maps are fetched into both a classification network and a reconstruction network. The classification network identifies whether the feature maps are from a stego image or a simple cover image. The reconstruction network, which consists of some layers of convolutional units, pixel shufflers and feedback residual modules, completes the reconstruction of hidden information. The experimental results show that compared to classical rich models and several recent CNN based methods, the proposed image steganalysis algorithm can obtain state-of-the-art results in terms of detection rate and hidden information reconstruction.
Short-term solar irradiance forecasting using ground-based sky images

This chapter describes one of the methods for forecasting solar irradiance based on micro-scale information, that is, sky images captured by Total Sky Imager (TSI). The sky images have been processed to determine the cloud cover and are compared with 1-min ago sky image to analyze the cloud movement by using an optical flow algorithm. The cloud condition of next minute has been predicted and consequently the solar irradiance values were calculated based on the part of cloud covering sun. For more accuracy in forecasting, clearness index Kt has been updated using the Recursive Least Square (RLS) and Variable Leaky Least Mean Square (VLLMS) algorithms. It has been found from the forecasting results that the accuracy of forecasting using the VLLMS algorithm is higher as compared to that of the RLS algorithm. Depending on different locations and applications, the same algorithm can be applied accordingly by calibrating the threshold value for the cloud determination algorithm and Kt value for the solar irradiance calculation.