Reviewerly/exHarmony huggingface dataset:
Fault Prediction in Wireless Sensor Networks using Soft Computing

With a deluge of data from various sources, sensors being resource constrained with less computing power, small memory and battery life leads them into getting compromised with various attacks and malfunction. Failures or Fault tolerance based on a single source of information has inaccuracies. Faults should be identified and described by multiple feature of data collected from different sources over a period of time. Generally, fault tolerance has been carried out by monitoring the health of each device which in itself consumes a lot of battery power. Also we need to know what should be an apt time interval or frequency to consider predictive analysis. Predicting faults in wireless sensor networks is a challenging proposition that requires deep study of the past data available. With advances in Machine learning and data analytics in particular have changed the way organisations can approach this issue to monitor their health and take preventive measures. In this paper, we propose a soft computing-based sensor node fault prediction based on time-series data. Using ARIMA, we predict failures by decomposing time series data into trend, seasonality and residual components and fit a model. A robust forecast estimation model with confidence bounds: 80% and 95% is developed which can help in implementing an intelligent IoT infrastructure in WSN. There is more ambiguity regarding the longer term forecast as the model is supposed to regress future values based on previously recorded predicted values.
Algerian Dialect Translation Applied on COVID-19 Social Media Comments

This work is part of a study on the propagation of misinformation about COVID-19 and its impact on Algerian society. It studies the problem of Algerian dialect translation applied to COVID-19 social media communications. The proposed system begins by filtering messages to identify comments that talk about COVID-19. Then, COVID-19 texts are translated from the Algerian dialect to formal standard Arabic. The filtering process is based on the long short-term memory (LSTM) model. The translation process is based on the embedding-GRU model. Experimental results give precision rates of about 99.98% in the filtering process and about 97.56% in the translation process. The achieved BLUE score is 22.10.
Development of an Educational Mixed Reality Game on Water Desalination Plants

This Work-in-Progress paper reports on the development of a Mixed Reality (MR) game prototype via HoloLens and Unity Engine. We aim to investigate how we can incorporate immersive multimedia technologies such as HoloLens as a potential form of STEM learning for students. This paper also proposes methods for testing the game's effectiveness as a learning tool. In our research, we have developed a prototype of a Desalination Plant in the Mixed Reality model application using the Game Development software called Unity engine. The MR app includes interactions and animations added to the 3D Industrial model, and we crossbuilt the Unity application to the HoloLens device, allowing the user to interact with the model in a real-world environment. The game aims to stimulate competition by introducing time constraints, game levels and a point system to incentivize students by developing their analytical skills. The game will be followed by a survey tool to assess the educational impact of the game and whether it was beneficial for the students in aiding them in the learning process. The research team has drawn upon the experiences of the Petroleum Engineering department at Texas A&M University at Qatar, who implemented a virtual reality application visualizing LNG spill and Dispersion in one of their research projects [1]. However, in our project, we aim to not only simulate the facility for visualization purposes but also to provide more interactive content for the users in STEM courses and increase students' comprehension of specific topics. In the process of designing the MR game, we aim to address the following research questions: 1) Do students' motivation to learn about plant design and process safety procedures improve after implementing MR? & 2) To what extent can a MR game application teach students to synthesize and design processes typically encountered in the desalination plant to meet needs within realistic constraints such as economics, safety, health & the environment?
Generative Oversampling and Deep Forest based Minority-class Sensitive Fault Diagnosis Approach

In the actual industrial production processes, various faults occur at different frequencies and the resulting fault data may be class imbalanced. This means machine learning-driven fault diagnosis methods have to learn from imbalanced data, and accordingly lead to lower diagnostic accuracy or even directly errors in identifying minority class. To solve this problem, we present a novel Minority-class Sensitive Fault Diagnosis approach (MSFD), which can reduce the imbalance of data and enhance the sensitivity of our diagnostic model to minority-class samples. Specifically, we first design a new generative oversampling method by combining Wasserstein Generative Adversarial Network (WGAN) with Synthetic Minority Oversampling Technique (SMOTE) to balance the whole dataset and improve the distribution of the minority-class samples. WGAN is adopted to learn the distribution of minority-class samples and generate some minority-class samples as a supplement to the original dataset, while SMOTE is applied to the resulting dataset to further enhance the diversity of synthetic samples for weakening the influence from WGAN's mode collapse. In addition, a deep forest or multi-Grained Cascade Forest (GcForest) based minority-class aware fault classification model is developed. First, during multi-grained scanning processes, we score the forests and select the corresponding forests with higher scores to generate feature representations for accelerating model convergence. Second, weights are introduced for different forests in cascade levels to further improve the overall performance of our fault diagnostic model. A series of experiments are conducted to testify the effectiveness of our proposed method, and the experimental results show that our approach can synthesize new minority-class samples with higher qualities and improve the diagnosis performance for minority-class samples as well as its overall classification accuracy. Meanwhile, in case of extremely imbalanced datasets, the proposed approach still maintains a relatively high recognition rate for minority-class samples.
Securing Data in Life Sciences—A Plant Food (Edamame) Systems Case Study

Efforts to identify specialty crop genetics and agronomics, such as for edamame (vegetable soybean), that improve crop yields, resilience, and sustainability often fail to account for data on nutritional content, sensory profile, and/or consumer acceptability. Limited exchange of data across agricultural and food sectors challenges the design of specialty crops that meet consumer needs and expectations and the value chain, and can increase cyberbiosecurity risks. Communication and collaboration within the multi-sector system are essential to address cyberbiosecurity issues related to privacy of data producers, ownership of original data, risks of data sharing, security protection for data transfer and storage, and public perceptions of the food supply chain. This paper introduces a new exploration to design domestic (U.S.) edamame, which is based on both our domain knowledge of life science and our information-sharing mechanisms across the agriculture and food sectors. A case study, involving a multidisciplinary team of breeders and non-breeder researchers with expertise in crop/food production, processing, quality, and economics, serves as a model. We introduce the value chain attributed to combining and linking data from different sectors in the research and development phase, and explain why we believe such data-sharing mechanisms can facilitate better analyses that resonate throughout the full system, from seed to consumer. Our perspective illustrates that, by securely sharing and interpreting data across sectors and identifying cyberbiosecurity risks, we can design promising agricultural and food systems to better meet consumers' need and protection of life science data.
Data Science Around the Indexed Literature Perspective

Conceptually, data science burdened with many other terms that refer to various interests about data. The terms calls the subject of learning and study as scientific fields that categorized as the basis of science, implications, and various applications about concepts, theories, or technologies related to data science. The term has a broad impact and affects the scope of work of data scientists. This paper aims to express some of the interests of the three things from the point of that literature for generating consequences.
On the Development of a Resident Monitoring System: Usability, Privacy and Security aspects

Worldwide, the elderly have suffered disproportionately from the effects of the COVID-19 pandemic, both in terms of their prognosis once contracted the disease and in terms of the preventative measures required for this demographic, who are at much higher risk than the rest of the population.In the "new normal", the well-being of older adults (residing either in their own homes or in care homes) will be ideally monitored remotely.These measures would preserve the independence of individuals without compromising on their safety.In this paper we discuss aspects of the design and implementation of a resident monitoring system (RMS) with particular emphasis on overcoming the barriers for adoption among these populations, by addressing the aspects of usability, privacy and security at the core of the development of such a system.We discuss the current challenges of this research and future work on the RMS.
Hierarchical Data Model Choosing in the Information Systems Design in Relational DBMS

Working with hierarchical data is one of the typical tasks in the industrial information systems development. As a repository of this data, relational databases are usually used. The performance and operability of the designed application depend on the correct choice of storage structure and hierarchical data processing mechanisms. To select the most suitable structure, it is necessary to formulate requirements for operations with nodes, on the basis of which a comparative assessment of the various mechanisms operation will be performed. In the future, taking into account the results and specific features, models can be chosen in favor of a particular technology.
AnonFACES

Image data analysis techniques such as facial recognition can threaten individuals' privacy. Whereas privacy risks often can be reduced by adding noise to the data, this approach reduces the utility of the images. For this reason, image de-identification techniques typically replace directly identifying features (e.g., faces, car number plates) present in the data with synthesized features, while still preserving other non-identifying features. As of today, existing techniques mostly focus on improving the naturalness of the generated synthesized images, without quantifying their impact on privacy. In this paper, we propose the first methodology and system design to quantify, improve, and tune the privacy-utility trade-off, while simultaneously also improving the naturalness of the generated images. The system design is broken down into three components that address separate but complementing challenges. This includes a two-step cluster analysis component to extract low-dimensional feature vectors representing the images (embedding) and to cluster the images into fixed-sized clusters. While the importance of good clustering mostly has been neglected in previous work, we find that our novel approach of using low-dimensional feature vectors can improve the privacy-utility trade-off by better clustering similar images. The use of these embeddings has been found particularly useful when wanting to ensure high naturalness and utility of the synthetically generated images. By combining improved clustering and incorporating StyleGAN, a state-of-the-art Generative Neural Network, into our solution, we produce more realistic synthesized faces than prior works, while also better preserving properties such as age, gender, skin tone, or even emotional expressions. Finally, our iterative tuning method exploits non-linear relations between privacy and utility to identify good privacy-utility trade-offs. We note that an example benefit of these improvements is that our solution allows car manufacturers to train their autonomous vehicles while complying with privacy laws.
Classification of Glomeruli with Membranous Nephropathy on Renal Digital Pathological Images with Deep Learning

Membranous nephropathy (MN) is the one of the most common pathological types that cause adult nephrotic syndrome (NS). Recently, the incidence of MN has shown a clear upward trend. Nevertheless, there is no more accurate and fast artificial intelligence algorithm for diagnose of MN which work is laborintensive and time-consuming if it is done manually. In this article, MN-Net, a CNN-based method, is applied to glomeruli detection and classification on whole slide images (WSIs). This work is mainly divided into two parts, a glomerulus detection network and a classification network. The detection network is utilized to locate glomeruli on WSIs. Multiple instance learning (MIL), a weakly supervised classification network following detection network classifies the glomeruli detected earlier. Our network is training on PASM-stained WSIs of 1281 cases collected from multi-centers. Experimental results prove that our method is effective with a high precision of 99.66% for glomeruli detection and 99.53% for MN glomeruli classification on this dataset. In summary, this method has been proved to be an effective method with advantages of speed, high accuracy, strong robustness, and low cost of data annotation that can be applied to the diagnosis of renal pathology. In the future, this method can also be extended to the classification of other glomerular diseases under light microscope (LM). The introduction of glomerular basement membrane (GBM) segmentation and measurement models can further improve the reliability of this model.
Improved Variational Autoencoder Anomaly Detection in Time Series Data

Uncertainty in observations about the state of affairs is unavoidable, and generally undesirable, so we are motivated to try to minimize its effect on data analysis. Detection of anomalies in data has become an important research area. In this paper, we propose a novel approach to anomaly detection based on the Variational Autoencoder method with a Mish activation function and a Negative Log-Likelihood loss function. The proposed method is validated with ten standard datasets, comparing performance on each of the various activation functions and loss functions. Experimental results show that our proposed method offers an improvement over existing methods. Statistical properties (i.e., F1 score, AUC, and ROC) of the method are also examined in light of the experimental results.
Haze Removal of Multispectral Remote Sensing Imagery Using Atmospheric Scattering Model-Based Haze Thickness Map

This paper presents a haze removal method for the multispectral remote sensing imagery. The proposed method estimates the haze thickness map, which represents the amount of the haze. The haze removal is performed using the atmospheric scattering model. Experimental results demonstrate that the proposed method can perform haze removal without over-dehazing and color distortion compared with existing methods.
An Accurate Urine Erythrocytes Detection Model Coupled Faster RCNN with VggNet

In the process of kidney disease diagnosis, it is particularly important to screen the morphology of erythrocytes in urine. Because of the small and similar morphology of erythrocytes in urine, manual labeling and observation is time-consuming and labor-consuming. Deep learning can not only quickly and accurately locate urine erythrocytes, but also effectively identify the types of cells. In this paper, we employ the Faster RCNN combined with VggNet to detect urine erythrocytes. We collected images of urine erythrocytes under optical microscope and used data enhancement method to obtain total 3969 images of urine erythrocytes, which filled in the blank of domestic urine erythrocytes data sets. Experimental results show that the presented method can identify five kinds of urine erythrocytes and the recall rate achieve up to 99.8%, which greatly helped doctors better identify and judge urine erythrocytes.
No-Reference Video Quality Assessment by A Cascade Combination of Neural Networks and Regression Model

In this paper, we propose a general-purpose no-reference (NR) video quality assessment (VQA) metric based on the cascade combination of 2D convolutional neural network (CNN), multi-layer perceptron (MLP), and support vector regression (SVR) model. The features are extracted from both spatial and spatiotemporal domains by using a 2D CNN. These features can capture different aspects of video frames for predicting quality scores, and we take these features as inputs of MLP to obtain a few estimated quality scores on different perspectives. Finally, these estimated scores are combined as a final quality score by an SVR model. The proposed method is evaluated on the well-known LIVE Video database with other state-of-the-art and well-performing VQA metrics. And the experimental result demonstrates that our method is competitive with other full-reference and NR VQA metrics.
Learning multi-granularity features from multi-granularity regions for person re-identification

Part-based methods for person re-identification have been widely studied. In existing part-based methods, although multiple parts are explored, only coarse-grained features of these parts are utilized. Thus, too much fine-grained information is discarded, which limits their ability to extract detailed discriminative features. To tackle this problem, we propose a novel person re-identification network to learn discriminative features across multiple granularities from body regions which are also multi-grained. Specifically, we detect multi-granularity body regions at different stages of a backbone network, and multi-granularity features are learned from body regions with corresponding granularities. To overcome the severe mismatching problem of fine-grained regions and to learn discriminative features, the detection of multi-granularity body regions and the learning of multi-granularity features are jointly optimized. This joint optimization pushes the learned features concentrating on body regions. Moreover, with the body regions well located, the multi-granularity features can be well aligned. Extensive experiments on four popular datasets show that our method is the state-of-the-art in recent years.
Regularization and Sparsity for Adversarial Robustness and Stable Attribution

In recent years, deep neural networks (DNNs) have had great success in machine learning and pattern recognition. It has been shown that these networks can match or exceed human-level performance in difficult image recognition tasks. However, recent research has raised a number of critical questions about the robustness and stability of these deep learning architectures. Specifically, it has been shown that they are prone to adversarial attacks, i.e. perturbations added to input images to fool the classifier, and furthermore, trained models can be highly unstable to hyperparameter changes. In this work, we craft a series of experiments with multiple deep learning architectures, varying adversarial attacks, and different class attribution methods on the CIFAR-10 dataset in order to study the effect of sparse regularization to the robustness (accuracy and stability), in deep neural networks. Our results both qualitatively show and empirically quantify the amount of protection and stability sparse representations lend to machine learning robustness in the context of adversarial examples and class attribution.
Style Evaluation of Chinese Fonts with Applications

Aesthetic evaluation is an important part of Chinese font design and generation, but there is still a lack of scientific calculation model for effective aesthetic and style evaluation of fonts. In this paper, a method of font evaluation based on experimental aesthetics is proposed and applied to the visual evaluation and retrieval development of printed fonts. Firstly, the commonly used computer font database is selected to generate the research font map; secondly, the aesthetic scale is compiled to recruit the subjects to evaluate the aesthetic of the printed characters of the scale; finally, the collected evaluation data are collected to analyze the characteristics of each font. The results showed that there was no significant difference in aesthetic feeling among the printing fonts, but there were significant differences in styles. A retrieval system designed by using style coefficient also has the practicability of font recommendation. This evaluation method can be applied to the research of similar fonts, and further expand the font data onto the recommendation system.
Real-World Objects Are Represented in Visual Long-Term Memory Both as Unbound Features and as Bound Objects

OPINION article Front. Psychol., 07 December 2020 | https://doi.org/10.3389/fpsyg.2020.580667
Comparing Machine-Learning Algorithms for Anticipating the Severity and Non-Severity of a Surveyed Bug

Programming Bug announcing is a vital piece of the product advancement process. When a Bug is accounted for on the Bug detection, their qualities are broken down and along these lines relegated to different fixers for their goals. Bug severity, a characteristic of a product bug survey is the level of effect that imperfection has on the advancement or activity of a segment or framework. In this paper, an endeavour has been made to show the pertinence of AI calculations to be specific SVM, KNN, Naïve Bayes, Naïve Bayes Multinomial, J48 and RIPPER in order to find out which classification algorithm is accurate to find the bug severity. The pertinence of the calculation in deciding the different degrees of bug severity for bug storehouses have been approved utilizing different execution measures by applying 5-overlap cross-approval.
Employee motivational structure as a factor of organizational commitment

The article presents empirical findings of characteristics of the employee motivation structure of a manufacturing plant depending on the commitment level of employees. The authors used a complex approach to assessing the employee motivational structure. The diagnostic complex included research methods of employee satisfaction of needs, job satisfaction, personality focus, type of motivational structure. To study the organizational commitment of employees, the organizational commitment questionnaire of L. Porter was used. The number of respondents was 104 people, 51 people of them had a high level of commitment, 53 people – a low level. The empirical study revealed differences in the employee motivational structure depending on the level of their organizational commitment. Employees with a high organizational commitment level demonstrated a more mature and harmonious motivational structure: focus on the task, creative activity in the labour process and aiming at social usefulness; significantly higher satisfaction rates of social and interpersonal needs, needs for recognition and self-expression (p&lt;0.01).