Reviewerly/exHarmony huggingface dataset:
Page Boundary Extraction of Bound Historical Herbaria

When digitizing bound historical collections such as herbaria it is important to extract the main page region so that it could be used for automated processing. The thickness of the herbaria books also gives rise to deformations during imaging which reduces the efficiency of automatic detection tasks. In this work we address these problems by proposing an automatic page detection algorithm that estimates all the boundaries of the page and performs morphological corrections in order to reduce deformations. The algorithm extracts features from Hue, Saturation and Value transformations of an RGB image to detect the main page polygon. The algorithm was evaluated on multiple textual and herbaria type historical collections and obtains over 94% mean intersection over union on all these datasets. Additionally, the algorithm was also subjected to an ablation test to demonstrate the importance of morphological corrections.
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database

This paper newly introduces <i>multi-modality</i> loss function for GAN-based super-resolution that can maintain image structure and intensity on unpaired training dataset of clinical CT and micro CT volumes. Precise non- invasive diagnosis of lung cancer mainly utilizes 3D multidetector computed-tomography (CT) data. On the other hand, we can take <i>&mu;</i>CT images of resected lung specimen in 50 <i>&mu;</i>m or higher resolution. However, <i>&mu;</i>CT scanning cannot be applied to living human imaging. For obtaining highly detailed information such as cancer invasion area from pre-operative clinical CT volumes of lung cancer patients, super-resolution (SR) of clinical CT volumes to <i>&mu;</i>CT level might be one of substitutive solutions. While most SR methods require paired low- and high-resolution images for training, it is infeasible to obtain precisely paired clinical CT and <i>&mu;</i>CT volumes. We aim to propose unpaired SR approaches for clincial CT using micro CT images based on unpaired image translation methods such as CycleGAN or UNIT. Since clinical CT and <i>&mu;</i>CT are very different in structure and intensity, direct appliation of GAN-based unpaired image translation methods in super-resolution tends to generate arbitrary images. Aiming to solve this problem, we propose new loss function called <i>multi-modality loss function</i> to maintain the similarity of input images and corresponding output images in super-resolution task. Experimental results demonstrated that the newly proposed loss function made CycleGAN and UNIT to successfully perform SR of clinical CT images of lung cancer patients into <i>&mu;</i>CT level resolution, while original CycleGAN and UNIT failed in super-resolution.
Prediction and Automation of Road Traffic using Internet of Things

Road traffic causes a lot of inconvenience to the society and environment at an unexpected level. The amount of time being wasted at traffic signals could save a lot of human hours which in turn could help the productivity of the country. The consequences of road traffic not just wastes human hours but pollute the environment at a very high level. Thus addressing road traffic issues could save the economy, people's health and the environment. These will bring in changes like better living conditions and sustainable development in society. This paper proposes an Internet of Things based solution to effectively handle the heavy road traffic conditions.
Smart Attendance Based Decision Support System for Mid-day Meal Scheme

The Mid Day Meal Scheme is a flagship program of Govt. of India. It is implemented nationwide level for primary and upper primary school to increase student retention and enrollment. While this scheme has shown great success, it still uses archaic methods of accountability based on register maintenance making it vulnerable to exploitation. This paper proposes the use of computer vision and analytics to bring accountability to the entire system with a reliable facial detection and recognition framework. It aims to maintain student attendance via facial recognition and use it as a blind dual verification method for cross-matching of prevailing attendance monitoring. Anomalies are detected based on the number of students claimed to be present as compared to the number of students detected and schools are notified to look into irregular and specific pattern attendance by students. The algorithm present on the cloud server is run on group images input by the school authority either by logging into a web portal or via an authorized app on their phones. Image metadata is stored in the common database before further analysis for the decision support system.
Research on Convergence of Artificial bee colony algorithm based on crossover and consistency distribution–good point set

Abstract A new artificial bee colony (ABC) algorithm based on crossover and consistency distribution–good point set is proposed. In the iterative search process, we use the consistency distribution and good point set principle of number theory to establish the initialization model of the algorithm, thereby ensuring the uniform distribution of the population in the search space. The location is updated iteratively to maintain the diversity of the population and prevent the algorithm from falling into the local optimum. Finally, the method that the artificial bee colony algorithm improved by consistency distribution-good point sets is proven and it conforms to Markov process from the point of view of number theory, thus proving the convergence of the algorithm.
A convex parametrization of a new class of universal kernel functions

The accuracy and complexity of kernel learning algorithms is determined by the set of kernels over which it is able to optimize. An ideal set of kernels should: admit a linear parameterization (tractability); be dense in the set of all kernels (accuracy); and every member should be universal so that the hypothesis space is infinite-dimensional (scalability). Currently, there is no class of kernel that meets all three criteria - e.g. Gaussians are not tractable or accurate; polynomials are not scalable. We propose a new class that meet all three criteria - the Tessellated Kernel (TK) class. Specifically, the TK class: admits a linear parameterization using positive matrices; is dense in all kernels; and every element in the class is universal. This implies that the use of TK kernels for learning the kernel can obviate the need for selecting candidate kernels in algorithms such as SimpleMKL and parameters such as the bandwidth. Numerical testing on soft margin Support Vector Machine (SVM) problems show that algorithms using TK kernels outperform other kernel learning algorithms and neural networks. Furthermore, our results show that when the ratio of the number of training data to features is high, the improvement of TK over MKL increases significantly.
Diagnostic competence of undergraduates enrolled in "special (defectological) education"

Introduction: the article presents the results of the study of diagnostic competence of undergraduates studying in the master's degree in the specialty of special (defectological) education. The content of diagnostic activity of the modern teacher-defectologist and the structure of diagnostic competence are considered. The variative multi-level readiness of master's students of the first year of study to master the content of diagnostic activities for the examination of persons with disabilities is revealed. Materials and methods: the study of the readiness of undergraduates of the first year of study for diagnostic activities was carried out with the help of special tests developed by a team of teachers of the Department of special pedagogy and special psychology to assess the knowledge and skills of students in the direction of "Special (defectological) education". In addition to the tests, the survey participants were used. The results of the survey and testing were interpreted on the basis of qualitative and quantitative approach. The results of the study: the study identified variable groups of undergraduates with various characteristics. Variability depended on basic education, experience of previous work in correctional institution, presence of persons with various deviations in families. All participants of the experiment had a positive motivational orientation. In assessing and analyzing the level of formation of diagnostic competence, a combination of several approaches to assessing the results of the study was used. Discussion and conclusions: diagnostic competence aimed at solving the problems of examination of children of different ages with various developmental disorders to determine the special conditions of their education and upbringing, occupies an important place in the formation of professional competence of masters of special (defectological) education. Diagnostic competence of the master of special (defectological) education has a multicomponent structure, including motivational, operational and reflexive components and can be represented at several levels of formation.
Abstract P1-02-10: Using deep residual networks for malignant and benign classification of two-dimensional Doppler breast ultrasound imaging

Abstract Background: The most important issue of the overall performance of traditional CAD is effective feature extraction. However, extract a meaningful image feature is a complicated and time-consuming task. This makes fine tuning of the overall performance of traditional CAD more difficult. Currently, the deep learning was considered the most advanced technology in image classification. The main benefit of deep learning is decreasing the feature selection and classification burden of generating a transformation function set and feature of the image from the data directly. We bring up a modified deep learning algorithm in classifying the benign and malignant patients in breast ultrasound image. Methods: There are 150 benign breast tumor patients and 120 malignant patients were enrolled from January 1, 2017 to December 31, 2018. The ultrasound images are captured at the full-view of lesion in US image by experienced physicians, and the tumor size measuring was according to the largest diameter of the tumor. The patients’ ages ranged was from 35 to 75 years, and the classification of benign or malignant were pathologically proven (either by fine needle cytology, core-needle biopsy or open biopsy). A modified deep residual network was developed and utilized to generate the US image feature for classification. The 5-fold cross-validation was used to test error percentage, mean, standard deviation and the 95% level confidence interval for the baseline algorithms. The accuracy of diagnosis was estimated using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with DeLong’s non-parametric test. Results: In this study, after using the 373 ultrasonic image dataset (all images were above BI-RADS C3, according to the report) for training and testing this modified model with 5-fold cross-validation, the AUC = 0.84 (SE = 0.81 to 0.87). The sensitivity is 77.08 and the specificity is 91.07 (p &amp;lt; 1x10e-5). With the accuracy as an indicator, the evaluation for classifying breast tumors in this study presents the similar performance to the subjective categories determined by physicians. Conclusions: Comparing to the previous studies, the significant improvement of this study is the input was the full-view of the image, and waive the preprocess in tumor region selection. It decreases the human effort and make the automated computer aids diagnosis possible. By using the deep residual network, the performance of classification was not inferior previous studies. It represents the deep learning have its merits in benign and malignant classification of breast ultrasound image. Citation Format: Weichung Shia, Darren Chen. Using deep residual networks for malignant and benign classification of two-dimensional Doppler breast ultrasound imaging [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-02-10.
Volumetric medical image compression using inter‐slice correlation switched prediction approach

Abstract With the advancement in medical data acquisition and telemedicine systems, image compression has become an important tool for image handling, as the tremendous amount of data generated in medical field needs to be stored and transmitted effectively. Volumetric MRI and CT images comprise a set of image slices that are correlated to each other. The prediction of the pixels in a slice depends not only upon the spatial information of the slice, but also the inter‐slice information to achieve compression. This article proposes an inter‐slice correlation switched predictor (ICSP) with block adaptive arithmetic encoding (BAAE) technique for 3D medical image data. The proposed ICSP exploits both inter‐slice and intra‐slice redundancies from the volumetric images efficiently. Novelty of the proposed technique is in selecting the correlation coefficient threshold (T ϒ ) for switching of ICSP. Resolution independent gradient edge detector (RIGED) at optimal prediction threshold value is proposed for intra‐slice prediction. Use of RIGED, which is modality and resolution independent, brings the novelty and improved performance for 3D prediction of volumetric images. BAAE is employed for encoding of prediction error image to resulting in higher compression efficiency. The proposed technique is also extended for higher bit depth volumetric medical images (16‐bit depth) presenting significant compression gain of 3D images. The performance of the proposed technique is compared with the state‐of‐the art techniques in terms of bits per pixel (BPP) for 8‐bit depth and was found to be 31.21%, 27.55%, 21.89%, and 2.39% better than the JPEG‐2000, CALIC, JPEG‐LS, M‐CALIC, and 3D‐CALIC respectively. The proposed technique is 11.86%, 8.56%, 7.97%, 6.80%, and 4.86% better than the M‐CALIC, 3D CALIC, JPEG‐2000, JPEG‐LS and CALIC respectively for 16‐bit depth image datasets. The average value of compression ratio for 8‐bit and 16‐bit image dataset is obtained as 3.70 and 3.11 respectively by the proposed technique.
A Simple Parallel Algorithm for Natural Joins on Binary Relations.

In PODS'17, Ketsman and Suciu gave an algorithm in the MPC model for computing the result of any natural join where every input relation has two attributes. Achieving an optimal load O(m/p^{1/ρ}) - where m is the total size of the input relations, p the number of machines, and ρ the fractional edge covering number of the join - their algorithm requires 7 rounds to finish. This paper presents a simpler algorithm that ensures the same load with 3 rounds (in fact, the second round incurs only a load of O(p²) to transmit certain statistics to assist machine allocation in the last round). Our algorithm is made possible by a new theorem that provides fresh insight on the structure of the problem, and brings us closer to understanding the intrinsic reason why joins on binary relations can be settled with load O(m/p^{1/ρ}).
Information Construction of Brand Marketing System in the Internet Era

Abstract Brand marketing is an important marketing strategy. Through marketing, it enables customers to recognize the brand and products of the enterprise, which can greatly improve the brand awareness of the enterprise. At the same time, it improves the customer’s concern and demand for products, so as to achieve good and sustainable profit growth for the enterprise. In order to gain a good competitive advantage, an enterprise must build a high-grade marketing concept. In marketing, the marketing network is not the bigger the better, but to make full use of brand symbols, so as to build an invisible marketing network in the hearts of the public and deliver products to the hearts of consumers. Through brand marketing, consumers are willing to pay for products, and to recognize enterprises and brands. In today’s Internet era, enterprises should pay more attention to the information construction of brand marketing system, and integrate Internet thinking into brand marketing strategy, so as to further improve the marketing effect.
Multi-View Face Recognition Via Well-Advised Pose Normalization Network

Numerous face frontalization methods based on 3D Morphable Model (3DMM) and Generative Adversarial Networks (GAN) have made great progress in multi-view face recognition. However, facial feature analysis and identity discrimination often suffer from failure frontalization results because of monotonous single-domain training and unpredictable input profile faces. To overcome the drawback, we present a novel approach named Well-advised Pose Normalization Network (WAPNN), which leverages multiple domains and extracts features considering their frontalization qualities wisely, to achieve a high accuracy on multi-view face recognition. Through multi-domain datasets, we design an end-to-end facial pose normalization network with adaptive weights on different objectives to exploit potentialities of various profile-front relationships. Meanwhile, the proposed method encourages intra-class compactness and inter-class separability between facial features by introducing quality-aware feature fusion. Experimental analyses show that our method effectively recovers frontal faces with good-quality textures and high identity-preserving, and significantly reduces the impact of various poses on face recognition under both constrained and wild environments.
HLS-Based Acceleration Framework for Deep Convolutional Neural Networks

Deep Neural Networks (DNNs) have been successfully applied in many fields. Considering performance, flexibility, and energy efficiency, Field Programmable Gate Array (FPGA) based accelerator for DNNs is a promising solution. The existing frameworks however lack the possibility of reusability and friendliness to design a new network with minimum efforts. Modern high-level synthesis (HLS) tools greatly reduce the turnaround time of designing and implementing complex FPGA-based accelerators. This paper presents a framework for hardware accelerator for DNNs using high level specification. A novel architecture is introduced that maximizes data reuse and external memory bandwidth. This framework allows to generate a scalable HLS code for a given pre-trained model that can be mapped to different FPGA platforms. Various HLS compiler optimizations have been applied to the code to produce efficient implementation and high resource utilization. The framework achieves a peak performance of 23 frames per second for SqueezeNet on Xilinx Alveo u250 board.
Rule extraction and performance estimation by using variable neighborhoodsearch for solar power plant in Konya

The use of renewable energy sources in the production of electricity has become inevitable in order to reduce the greenhouse gases left in the atmosphere that cause the Earth to warm up. Although countries on a national basis have implemented a number of policies to support electricity generated from renewable energy sources, investments to produce electricity without a license on a local basis are not desirable. Those who want to invest medium and small scale for the most reason expect that this work will be supported by real data. Although the electricity generated by renewable investments is generated by simulation data, these data are not realistic for such investors. In this study, the climatic conditions of the power plant of 1 MW installed in Konya and power plant production data are monitored. The artificial neural network (ANN) can achieve a high value for accuracy, but these values are sometimes complex and unclear. In the literature, a number of studies have been conducted using different methods to overcome such problems. Real-time solar power plant (SPP) data were used to determine the feasibility and success of the proposed method. The variable neighborhood search (VNS) metaheuristic method was used to acquire the optimal values belonging to input vectors, Gh , which were maximized to the value of the fitness function Fs belonging to output class node s. The results obtained by the VNS method showed that the proposed method has the potential to produce the correct rules. Generally, energy investors are curious about the return on their investment. It is very important for energy providers to estimate how much electricity will be generated from existing solar power plants and accordingly determine the measures they will take to meet the electricity demand in the future. In this study, the performance estimation value obtained from the solar power plant depending on the weather conditions was obtained with 95.55% accuracy.
Introduction

The purpose of this chapter is two-fold. First, the chapter lays out the rationale for the Handbook and why it is essential given the conceptual, operational, and analytic difficulties associated with strategic processing in the literature. This is especially salient given the entanglement of metacognition, self-regulation, and the self-regulation learning literature with the strategic processing literature. Second, the chapter outlines how each section, and each chapter within a section, serves to help the reader more deeply understand the historical aspects of research on strategies and strategic processing, contemporary research on the topic, and, finally, how research on strategies and strategic processing can progress to help us better understand how they relate to task and problem-solving outcomes.
STRUCTURAL EQUATION MODELING BASED EMPIRICAL ANALYSIS: DIRECT AND INDIRECT EFFECTS OF JOB SATISFACTION ON JOB PERFORMANCE IN JORDANIAN FIVE-STAR HOTELS

In order to improve and maintain competitiveness in the hotel sector, the key elements of success must be available, which is headed by an efficient employee, so it is essential that the employees' performance be at its best. This study examined the effect of job satisfaction (JS) on job performance (JP), organisational structure (OS) and leadership style (LS) on (JS), and Quality of work-life (QWL) on (JP) among five-star hotels employees in Jordan. The study also examined the moderating role of quality of work-life on the variable’s relationships. A questionnaire was used as the main instrument for data collection from 430 employees of five-star hotels. Data analysis was conducted with the help of Structural Equation Modelling (SEM) to determine the level of relationships among (JS), (OS), (LS), (JP), and (QWL). According to the obtained findings, there is a positive relationship between JS and QL on JP, and QWL has a partial moderating role in the relationship between JS and JP. The study findings motivate future studies to carry out studies of the same caliber in other sectors of Jordan, and also in other contexts based on different perspectives.
Automated Techniques for Creating Speech Corpora from Public Data Sources for ML Training

He is a member of the technical staff in the Applications Platforms and Software Systems of Nokia Bell