Reviewerly/exHarmony huggingface dataset:
Analysis of Jordanian University Students Problems Using Data Mining System

Many social media users, especially university students, suffer from some problems that occur in their daily lives, such as anger, stress, depression, sleep problems, physical and psychological fatigue. Most of the students do not express their feelings to their parents and friends. Therefore, social media such as Facebook and Twitter are the best way to express their feelings and opinions because they feel safe and more comfortable there. This paper attempted to collect posts from Facebook and Twitter tweets. In this research, we focused on Twitter tweets and Facebook posts, which were mainly in the Arabic language and belonged to the Jordanian University Students. The huge amount of tweets and posts were not clean, so tweets and posts needed to be analyzed by labeling them to study load, sleep issues, negative feelings and positive feelings. Then, we classified them using many algorithms in data mining, such as a Naive Bayes Multi-Label Classification Algorithm (NB), K-Nearest Neighbor Classification Algorithm (K-NN) and Decision Tree Classification Algorithm. The results were compared based on three parameters which are: accuracy, precision and recall. The NB Algorithm achieved the best performance in terms of accuracy, precision and recall value, in comparison with the K-NN and Decision Tree Algorithm.
Fractional Adaptive Linear Units

This work introduces Fractional Adaptive Linear Units (FALUs), a flexible generalization of adaptive activation functions. Leveraging principles from fractional calculus, FALUs define a diverse family of activation functions (AFs) that encompass many traditional and state-of-the-art activation functions. This family includes the Sigmoid, Gaussian, ReLU, GELU, and Swish functions, as well as a large variety of smooth interpolations between these functions. Our technique requires only a small number of additional trainable parameters, and needs no further specialized optimization or initialization procedures. For this reason, FALUs present a seamless and rich automated solution to the problem of activation function optimization. Through experiments on a variety of conventional tasks and network architectures, we demonstrate the effectiveness of FALUs when compared to traditional and state-of-the-art AFs. To facilitate practical use of this work, we plan to make our code publicly available
Development of Feature Extraction Technique

A problem of personal verification and identification is an actively growing area of research in pattern recognition, computer vision, and digital image processing. A Face acknowledgment system is largely significant move toward in our life. Face acknowledgment method must be proficient to robotically identify images of face. This system mainly used to recognize a person and make available a protection during various features of our life. It is extremely complicated job for investigator to obtain mainly excellent face acknowledgment velocity in a variety of circumstances and benchmarks. Face recognition is grassland of computer visualization to uses faces to recognize or authenticate a human being. Principal Component Analysis (PCA) is accomplished and used for feature extraction and measurement lessening. The feature extraction is used to reduce the dimension of the face space by transforming it into feature representation. Features may be symbolic, numerical or both. The symbolic feature is color and numerical feature is weight. The combined feature extraction of PCA, LDA and Wavelet are used in proposed feature extraction algorithm for human face recognition system. The structure is tested and achieves high recognition rates. Information regarding individuals was stored in a database.
Another model not for the learning of language

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
Development of a Machine Vision System for Image Recognition of Design Estimates

This paper discusses implementation of a machine vision system for pattern recognition of design estimates. The main problem in the development of these systems is the choice of unique features that remain invariant to various kinds of transformations. Angular descriptors were chosen as a dominant feature. The paper presents a comparative analysis of the corner detection methods of the Moravec algorithm, the Harris algorithm, and the Shi-Tomasi algorithm. The authors have developed software in Python language that implements the operation of the Harris detector and the Shih-Tomasi detector. The recognition system is being tested for building 3D models in Blender
CODEC: Complex Document and Entity Collection

CODEC is a document and entity ranking benchmark that focuses on complex research topics. We target essay-style information needs of social science researchers, i.e. "How has the UK's Open Banking Regulation benefited Challenger Banks". CODEC includes 42 topics developed by researchers and a new focused web corpus with semantic annotations including entity links. This resource includes expert judgments on 17,509 documents and entities (416.9 per topic) from diverse automatic and interactive manual runs. The manual runs include 387 query reformulations, providing data for query performance prediction and automatic rewriting evaluation.
Deliberative educational traditions and the post-truth society

This chapter discusses the role of deliberative educational traditions (i.e. Bildung-centred Didaktik and curriculum theory) in the context of the challenges faced in educational and civic practice in the post-truth society. Starting with Bernstein’s work on pedagogic rights, it is argued that in order to sustain democracy, societies require educational systems that foreground inclusion, participation, and individual and collective enhancement. However, our conceptions of enhancement are impoverished if governments choose to focus policy on outcomes-based curriculum frameworks that prioritise standardised testing. The impoverishment of enhancement is accompanied by forms of exclusion and marginalisation that deprives citizens of their pedagogic rights and leads to greater opportunities for those who seek to manipulate citizens through ‘post-truth’ fake news and alternative facts. Unfortunately, empiricist and scientistic trends in educational research and teacher education reforms are exacerbating these developments, by marginalising the deliberative educational traditions and reducing what they can offer in terms of meaningful individual and collective formation within educational institutions. It is only through a reinvigoration of the deliberative educational traditions that an authentic educational practice can be reasserted, and alternatives found to the slide into a post-truth society, and the chapter concludes with some discussion of what that reinvigoration might entail. The argument is briefly illustrated with examples, primarily from the UK and the United States.
Fireworks algorithm based on search space partition

Fireworks algorithm is a novel swarm intelligence optimization framework which focuses on the potential of collaboration among multiple subpopulations with independent search ability. Although it has been proved to perform excellently in many tasks, the collaborative mechanism of fireworks is still quite undeveloped. In this paper, a theoretical model of fireworks algorithm based on search space partition is proposed, analyzed and implemented. The local search of each firework is replaced by covariance matrix adaptation evolution strategy for efficient exploitation. A coordination strategy inspired from Voronoi diagram is proposed to approximate the theoretical model for stable global exploration. Experimental results show that the proposed algorithm not only outperforms previous variants of fireworks algorithm significantly, but also achieves competitive results compared with state-of-the-art evolutionary algorithms, which are intensively fine-tuned on the objective functions.
CFS-crafter: An open-source tool for creating and analyzing images for continuous flash suppression experiments

Continuous flash suppression (CFS) is a popular masking technique used to manipulate visual awareness. By presenting a rapidly changing stimulus to one eye (the 'mask'), a static image viewed by the other (the 'target') may remain invisible for many seconds. This effectiveness affords a means to assess unconscious visual processing, leading to the widespread use of CFS in several basic and clinical sciences. However, the lack of principled stimulus selection has impeded generalization of conclusions across studies, as the strength of interocular suppression is dependent on the spatiotemporal properties of the CFS mask and target. To address this, we created CFS-crafter, a point-and-click, open-source tool for creating carefully controlled CFS stimuli. The CFS-crafter provides a streamlined workflow to create, modify, and analyze mask and target stimuli, requiring only a rudimentary understanding of image processing that is well supported by help files in the application. Users can create CFS masks ranging from classic Mondrian patterns to those comprising objects or faces, or they can create, upload, and analyze their own images. Mask and target images can be custom-designed using image-processing operations performed in the frequency domain, including phase-scrambling and spatial/temporal/orientation filtering. By providing the means for the customization and analysis of CFS stimuli, the CFS-crafter offers controlled creation, analysis, and cross-study comparison. Thus, the CFS-crafter-with its easy-to-use image processing functionality-should facilitate the creation of visual conditions that allow a principled assessment of hypotheses about visual processing outside of awareness.
SDBOSR: Separable Decision Boundary based Open Set Recognition for Manufacturing Equipment Fault Classification

Equipment fault may cause serious losses to environmental, economic, and safety. To reduce losses, the requirements for industrial equipment diagnostic systems are rapidly increasing. However, because, industrial equipment is usually complex and large in number, it is difficult to define all types of fault in advance. To reduces the loss, it is necessary to accurately classify the higher the severity level of the fault. In this paper, we propose an algorithm that can classify a fault type of equipment and classify the unknown type based on the open ser recognition method. The proposed method was applied to the AHU dataset and showed improved results compared to another algorithm.
DBSnap-Eval

Learning to construct database queries can be a challenging task because students need to learn the specific query language syntax as well as properly understand the effect of each query operator and how multiple operators interact in a query. While some previous studies have looked into the types of database query errors students make and how the availability of expected query results can help to increase the success rate, there is very little that is known regarding the patterns that emerge while students are constructing a query. To be able to look into the process of constructing a query, in this paper we introduce DBSnap-Eval, a tool that supports tree-based queries (similar to SQL query plans) and a block-based querying interface to help separate the syntax and semantics of a query. DBSnap-Eval closely monitors the actions students take to construct a query such as adding a dataset or connecting a dataset with an operator. This paper presents an initial set of results about database query construction patterns using DBSnap-Eval. Particularly, it reports identified patterns in the process students follow to answer common database queries.
Performance Analysis of Color Normalization Methods in Histopathology Images

Color normalization in histopathology is a prominent research topic in the image processing field as color in histopathology images plays a crucial role in diagnosis. As computer-aided diagnosis emerged, color normalization is much crucial as it becomes the foundation of medical image processing to assure algorithm precision and accuracy. In this paper, the main objective is to perform an analysis on three commonly used color normalization methods, namely histogram matching, histogram equalization, and stain unmixing methods. 60 breast histopathology images were used for testing purposes. Four statistical metrics were calculated to measure and determine the applicability of the color normalization methods: Structural similarity index measure (SSIM), Pearson’s correlation coefficient (PCC), visual saliency-induced index (VSI), and Gradient similarity (GS). Based on the outputs, it is found that the stain unmixing method demonstrates better than that of the histogram matching and histogram equalization methods with higher values in SSIM and VSI, and comparable values in PCC and GS.
Automated Support Request Categorization using Machine Learning

The automatic categorization of user support requests/tickets for Pennsylvania State University’s high performance computing system is carried out using decision tree and artificial neural network models. We explore estimated model prediction performance across different text embedding techniques (TF-IDF and BERT) and prediction models (Gradient Boosted Decision Trees and Multi-Layer Perceptron Neural Networks) for this multiclass problem. The dataset is comprised of 6213 support tickets categorized using a broad (14 classes) and specific (17 classes) set of labels. The results indicate the optimal prediction accuracy for the ’broad’ and ’specific’ categories are 94.6% and 83.2%, respectfully.
High precision and fast calibration method of fish-eye camera based on two-dimensional turntable

Camera calibration is a prerequisite for spatial position measurement. The fish-eye camera can increase the field of view, but at the same time, the distortion it brings is more serious. It is difficult to obtain high accuracy when using traditional methods to calibrate the fish-eye camera. Therefore, this paper proposes a fast calibration method for fisheye cameras, which makes the calibration process convenient and fast, and improves the calibration accuracy. The camera is fixed on a high-precision two-dimensional turntable, and the turntable rotates according to the set law through a fully automatic program. At the same time, the camera captures the bright spots in the collimator and extracts the pixel coordinates of its centroid. Collect several sets of data, use the bicubic interpolation method to expand the data, find the relationship between the pixel coordinates of the centroid of the bright spot and the rotation angle of the turntable. Finally, the three-dimensional space coordinates can be obtained by binocular vision triangulation to verify the calibration accuracy. The above method bypasses the traditional camera optical imaging model and distortion model, but uses traversal and interpolation. Experiments show that the accuracy of this method can reach the centimeter level in long-distance, large field of view, and severely distorted scenes.
Micro-activity recognition in industrial assembly process with IMU data and deep learning

Automated understanding of work-steps in industrial assembly work is important for assistive guidance technologies in employee-machine collaboration. Our aim is to identify micro activities of employees during the assembly of automated teller machines (ATM) for purposes of assistance in their daily complex tasks using mobile wearable devices and hand-operated tools. Forgotten or incorrectly installed parts, missing or non-tightened screws during assembly, that are expensive and time consuming to repair are some common mistakes that are addressed with this approach. In this paper the focus is at a seamless embedding of non-impeding Inertial Measurement Units (IMUs), worn on body or integrated into tools and devices, allowing for unobstructed monitoring of tools’ usage pattern. Therefore, understanding the activities that occurred and thus recognizing the assembly work steps. The hypothesis is that a system capable of high level detection for micro activities in an assembly line, utilizing IMUs and neural networks, will (i) reduce the error rate in the final product, (ii) assist the workers in real-time scenarios by performing quality control (iii) understand the stages of the assembly workflow. The results for this study are evidenced with empirical observations of work-step executions by (i) hand screwing, (ii) screw driver screwing, (iii) machine screwing, (iv) wrench screwing, with the size of null class being disproportionally dominant in the data set. Deep Learning models including Long Short-term Memory (LSTM) and Convolutional Neural Network (CNN) architectures are evaluated, while presenting the challenges encountered during our research and experiments. The classification performance for our experiments is documented and in the final step a recognition of 91.19% is achieved, using a CNN.
Heterogeneous FPGA-based System for Real-Time Drowsiness Detection

Drowsiness detection is a key feature in modern Advanced Driver Assistance Systems (ADAS). State-of-the-art approaches rely on machine learning techniques and neural networks to monitor unusual movements of the head and eyes activities. Unfortunately, due to their computationally intensive operations, integrating such algorithms in real-time and low-power operating scenarios, like auto-motive applications, is still quite challenging. This paper proposes an efficient hardware architecture for real-time drowsiness detection based on monitoring the driver&#x2019;s eye blinking behaviour through the PERcentage of eye CLOSure (PERCLOS) metric. Experimental results obtained on the Xilinx Zynq XC7Z020 FPGA SoC show that the proposed system is up to 33.3 times faster and 2.6 times less area consuming than state-of-the-art competitors.
COVID-19 Pandemic and Performance: Empirical study of Integrated Remote Site Management services in Dubai, United Arab Emirates

Generally and in the UAE, businesses worldwide are particularly affected by the COVID-19 pandemic. The SME companies in Dubai UAE faced a very tough challenge to deal with the new circumstances of lockdown and curfews in running the international business. The purpose of the current study was to analyze the effect of the COVID-19 pandemic on Business Performance. The theoretical frameworks for this study are Process Theory of Internationalization (PTI), Uppsala-model (the U-model), and internalization theories and the link with IB theories applications nowadays. The quantitative study applied a nonexperimental, deductive approach and explanatory correlation design, and Snowball sampling was used to select stakeholders of integrated remote site management (IRSM) services in Dubai, UAE. Data was collected at a single phase using a cross-sectional survey instrument and analyzed using inferential statistics with the aid of SPSS. The survey results revealed that the COVID-19 pandemic did not significantly change the efficiency performance of the IRSM stakeholders. Stakeholders' confidence in their strategic leaders did not decrease while working under the constraints of the COVID-19 pandemic. The study did not account for any changes in employee or leaders’ behavior over time. This Empirical study findings add value to the limited literature on the correlation between the COVID-19 pandemic and the strategic leadership of international businesses. The paper’s practical implications would be further study on strategic leadership in IB and efficiency performances during global pandemics.
Simultaneous pectoral muscle and nipple location in MLO mammograms, considering image quality assumptions

Feature-based registration algorithms can be used to establish spatial correspondence between two image. Therefore, anatomical landmarks such as the breast boundary, pectoral muscle, nipple, duct and vessels need to be considered. The aim of this paper is to introduce a new approach which combine the pectoral muscle segmentation and nipple location, considering mammography quality assumptions. Pectoral muscle is initialized as a straight line from the top of the image to the nipple level. Afterwards, both pectoral muscle boundary and nipple position are optimized using an iterative approach. The results show that the nipple is localized on the contour of the corresponding area (error smaller than 10 mm) while the Dice’s coefficient of the pectoral muscle segmentation is equal to 0.84 &plusmn; 0.12 using a straight line which is improved using a Chan-Vese active contour approach, reaching 0.87 &plusmn; 0.13. Our algorithm is easily generalized and portable to a different mammographic system since it barely depends on images statistics -i.e. pixel intensity values-, and is just based on geometrical considerations.
A Fast and Efficient Data Augmentation for Sematic Segmentation Based on LQE Head and BAS-DP

Aiming at the problem that it takes too long to manually label numerous semantic segmentation data sets of vehicle images, a fast and effective vehicle image semantic segmentation data augmentation algorithm is proposed. Firstly, to solve the problem that traditional data augmentation algorithms are difficult to generate vehicle images and corresponding labels at the same time, a vehicle image semantic segmentation data augmentation algorithm based on FCN (Fully Convolutional Network) and GCIoU (Generally Contour Intersection over Union) is proposed, which can simultaneously generate vehicle images and corresponding labels. Then, aiming at the problem that some low-quality data exist in the generated dataset, a data set quality discriminator based on LQE (Label Quality Evaluation) head is proposed. The discriminator can distinguish between low-quality and high-quality label files. Finally, aiming at the problem that the excessive weight of the label file causes the calculation speed to decrease, a lightweight algorithm for the label file based on BAS-DP (Beetle Antennae Search Douglas-Peucker Algorithm) is proposed. The lightweight algorithm can greatly decrease parameters of the label file and improve availability of data-augmented results. Experimental results show that the proposed data augmentation algorithm is better than DCGAN (Deep Convolutional Generative Adversarial Networks), WGAN (Wasserstein Generative Adversarial Networks) and other data augmentation algorithms in accuracy. The AP50 and AP75 of the proposed algorithm reach 0.924 and 0.41, respectively. In addition, the proposed data augmentation algorithm still performs well in scenes with single-object, multi-object and ultra-multi-object. Simultaneously, the proposed data augmentation algorithm has three advantages, which are higher accuracy, faster speed, and less training data required.