Reviewerly/exHarmony huggingface dataset:
Journal of the Relationship Between Social Support and Achievement Motivation in High School Students

Achievement motivation is a determinant of student achievement, students should have high achievement motivation regardless of the circumstances, when motivation for achievement is low, it will affect student achievement. Therefore it is necessary to look for factors that influence achievement motivation. This study aims to determine the relationship between social support and achievement motivation and how much influence social support has on achievement motivation on students at the AL-Islam Krian High School Sidoarjo. This research was conducted with a quantitative correlational research method, using proportional sampling. with a sample of 382 students. The measurement tool uses two scales, namely social support scale and achievement motivation scale using the Spearman product moment correlation technique with the help of IBM Statistics 20 Version for Windows. The results of the analysis show that there is a relationship between social support and achievement motivation. The implication of this research is to develop student achievement and provide interventions to improve the quality of education. Increasing the quality of education to the maximum can increase social support and achievement motivation in students.
Compact and low-cost deep-ultraviolet microscope for point-of-care complete blood count analysis

Deep Ultraviolet (UV) Microscopy enables high-resolution, quantitative, and label-free imaging of biomolecules. Recently, we have shown that UV microscopy can be used as a tool for simple and fast hematology analysis by providing diagnostically relevant information on morphological and cytogenic properties of various blood cells. We have demonstrated the ability to classify and segment red blood cells, white blood cell subtypes, and platelets via deep learning frameworks and have applied this technique for diagnosis of blood disorders. In this work, we present a compact, low-cost deep-UV microscope capable of performing a rapid complete blood count (CBC). CBCs, one of the most commonly performed medical tests in the United States, provide critical counts of blood components used to monitor and diagnose blood disorders. This device can serve as a point-of-care alternative to modern hematology analyzers by leveraging endogenous biomolecular contrast from UV light to perform label-free hematology analysis. Here we discuss our approach of using simple, low-cost optics and hardware to enable fast and accurate imaging of blood samples. We demonstrate the capability of this system to scan and capture images of whole blood on blood smears and in custom microfluidic devices. We also show that these images can be used to segment, classify, and colorize blood cell subtypes. Lastly, we evaluate the efficacy of our stage translation system by assessing its positioning and translation accuracy.
Utilization and Verification of Inverse Distance Weighting (IDW) Interpolation Technology for Predicting Solar Radiation of Photovoltaic System

Purpose: Weather conditions around solar photovoltaic (PV) systems have a direct effect on the amount of generated power. Therefore, highly correlated meteorological environmental variables are used to accurately predict the power generation capacity of PV systems. However, it is difficult to install observation equipment due to high installation costs and maintenance. Therefore, public data from the Korea Meteorological Administration are often used. However, uncertainty may arise due to the large distance between the stations of the Korea Meteorological Administration. Therefore, in this study, inverse distance weighting (IDW) was used to predict the solar radiation of an arbitrary area to improve the uncertainty caused by the large distance between the meteorological stations. Method: In this study, IDW was used as a spatial statistical technique to predict the monthly average of daily accumulation solar radiation in South Korea. Moreover, four cases of the automated synoptic observing system (ASOS) were used as study cases to evaluate the prediction technology. The actual solar radiation observed and the predicted solar radiation was compared and analyzed in each study case, and the accuracy was evaluated as an indicator of the mean absolute percentage error (MAPE). Additionally, the MAPE was compared with the prediction results of the multiple regression model. Result: Although the results differ for each case, the average MAPE was approximately 11.06% using the inverse distance weighting interpolation method, and the predicted value by the multiple regression method yielded an average MAPE of approximately 16.12%.
Sentiment Analysis of IMDb Movie Reviews: A Comparative Analysis of Feature Selection and Feature Extraction Techniques

Humans are social animals who are dependent on the opinions and experiences of others when it comes to choosing a product for themselves. Most people need to seek the reviews of products like movies, web series, and video games before trying them out themselves. It becomes difficult for an average person to scour the correct information because of the large number of reviews present on the internet. Sentiment analysis is often used to obtain helpful information about a review and classify it into positive or negative sentiment. Our main goal in this paper is to construct sentiment analysis models using different feature extraction (count vectorization, TF-IDF, and Word2Vec) and feature selection (mutual information gain and Chi-square) techniques on textual movie reviews. We also study the performance of various classification algorithms for constructing sentiment analysis models over several metrics. We obtained the highest accuracy of $$90\%$$ with TF-IDF Vectorization, Chi2 feature selection, and SVM classification algorithm. We also found that feature selection drastically reduces the train test time for almost all the classification models without severely impacting other performance metrics.
Siamese Feedback Network for Visual Object Tracking

Visual object tracking, one of the main topics in computer vision, aims to chase a target object in every frame of the video sequences. In particular, Siamese-based network architectures have been adopted widely for visual object tracking due to their correlation-based nature. On the other hand, the features encoded from the target template and the search image in Siamese branches still suffer from ambiguities, which are driven by complicated real-world environments, e.g., occlusions and rotations. This paper proposes the Siamese feedback network for robust object tracking. The key idea of the proposed method is to encode target-relevant features accurately via the feedback block, which is defined by a combination of attention and refinement modules. Specifically, interdependent features are extracted through self- and cross-attention operations. Subsequently, such re-calibrated features are refined in both spatial and channel-wise manner. Those are fed back to the input of the feedback block again via the feedback loop. This is desirable because the high-level semantic information guides the feedback block to learn more meaningful properties of the target object and its surroundings. The experimental results show that the proposed method outperforms the state-of-the-art Siamese-based methods with a gain of 0.72% and 1.69% for the expected average overlap on the VOT2016 and VOT2018 datasets, respectively. Overall, the proposed method is effective for visual object tracking, even with complicated real-world scenarios.
Proof Theory of Riesz Spaces and Modal Riesz Spaces

We design hypersequent calculus proof systems for the theories of Riesz spaces and modal Riesz spaces and prove the key theorems: soundness, completeness and cut elimination. These are then used to obtain completely syntactic proofs of some interesting results concerning the two theories. Most notably, we prove a novel result: the theory of modal Riesz spaces is decidable. This work has applications in the field of logics of probabilistic programs since modal Riesz spaces provide the algebraic semantics of the Riesz modal logic underlying the probabilistic mu-calculus.
Benchmarking Conventional Vision Models on Neuromorphic Fall Detection and Action Recognition Dataset

Neuromorphic vision-based sensors are gaining popularity in recent years with their ability to capture Spatio-temporal events with low power sensing. These sensors record events or spikes over traditional cameras which helps in preserving the privacy of the subject being recorded. These events are captured as per-pixel brightness changes and the output data stream is encoded with time, location, and pixel intensity change information. This paper proposes and bench-marks the performance of fine-tuned conventional vision models on neuromorphic human action recognition and fall detection datasets. The Spatio-temporal event streams from the Dynamic Vision Sensing cameras are encoded into a standard sequence image frames. These video frames are used for benchmarking conventional deep learning-based architectures. In this proposed approach, we fine-tuned the state-of-the-art vision models for this Dynamic Vision Sensing (DVS) application and named these models as DVS-R2+1D, DVS-CSN, DVS-C2D, DVS-SlowFast, DVS-X3D, and DVS-MViT. Upon comparing the performance of these models, we see the current state-of-the-art MViT based architecture DVS-MViT outperforms all the other models with an accuracy of 0.958 and an F-1 score of 0.958. The second best is the DVS-C2D with an accuracy of 0.916 and an F-1 score of 0.916. Third and Fourth are DVS-R2+1D and DVS-SlowFast with an accuracy of 0.875 and 0.833 and F-1 score of 0.875 and 0.861 respectively. DVS-CSN and DVS-X3D were the least performing models with an accuracy of 0.708 and 0.625 and an F1 score of 0.722 and 0.625 respectively.
Software Quality Prediction Using Machine Learning

In today's fast-changing environment, in order to create much more stable and complex software programs. With the emergence of Machine Learning, many companies are increasingly embracing this revolutionary approach, both in terms of growth and maintenance, to reduce software costs. As the size of applications increases in terms of functionality, when designing test cases, Software Quality Prediction is becoming more complex. Since the software measurement mechanism in a constant cycle has several benefits, namely reliable project cost estimation, process improvement and product quality compliance, it is vital to try further analysis of software metrics in order to implement the use of machine learning in software quality prediction. This research aimed at building two models which is Software Defect Prediction Model (SDPM) which will be used to predict defects in software and Software Maintainability Prediction Model (SMPM) which will be used for Software Maintainability. Different classifiers, namely Random Forest, Decision Tree, Naïve Bayes and Artificial Neural Networks have been considered and then evaluated using different metrics such as Accuracy, Precision, Recall and Area Under the Curve (AUC). The two models have successfully been evaluated and Decision Tree has been chosen as compared to other classifiers which tends to perform much better for both models. These models have been eventually been deployed as web services. Finally a framework based on a set of guidelines that can be used to improve software quality has been devised.
Insights into DevOps automation tools employed at different stages of software development

Speedy releases of software \f “p” are always a prime concern of every organization. Earlier companies were developing the software based on traditional waterfall and iterative models. Afterward, they turned to the Agile methodology in contemplation of good quality and increased productivity. But nowadays, DevOps \f “k” is considered to be the most efficient approach for software development. DevOps is the inculcation of a set of automation tools for automating the tasks of software development and operation teams. It gives the environment to deliver faster and reliable software by adopting automation at all stages of software development. This chapter presents the review of different automation tools \f “a” adopted by DevOps at each and every stage of software development.
A characterization of finite groups in terms of the codegrees of their characters

Abstract For a character 𝜒 of a finite group 𝐺, the number <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>cod</m:mi> <m:mrow> <m:mo stretchy="false">(</m:mo> <m:mi>χ</m:mi> <m:mo stretchy="false">)</m:mo> </m:mrow> <m:mo>:=</m:mo> <m:mrow> <m:mo fence="true" stretchy="false">|</m:mo> <m:mi>G</m:mi> <m:mo>:</m:mo> <m:mi>ker</m:mi> <m:mi>χ</m:mi> <m:mo fence="true" stretchy="false">|</m:mo> </m:mrow> <m:mo>/</m:mo> <m:mi>χ</m:mi> <m:mrow> <m:mo stretchy="false">(</m:mo> <m:mn>1</m:mn> <m:mo stretchy="false">)</m:mo> </m:mrow> </m:mrow> </m:math> \operatorname{cod}(\chi):=\lvert G:\operatorname{ker}\chi\rvert/\chi(1) is called the codegree of 𝜒. The object of this paper is to classify the finite groups 𝐺 which have exactly one irreducible character 𝜒 such that <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mrow> <m:mi>χ</m:mi> <m:mo>⁢</m:mo> <m:mrow> <m:mo stretchy="false">(</m:mo> <m:mn>1</m:mn> <m:mo stretchy="false">)</m:mo> </m:mrow> </m:mrow> <m:mo>∤</m:mo> <m:mrow> <m:mi>cod</m:mi> <m:mo>⁡</m:mo> <m:mrow> <m:mo stretchy="false">(</m:mo> <m:mi>χ</m:mi> <m:mo stretchy="false">)</m:mo> </m:mrow> </m:mrow> </m:mrow> </m:math> \chi(1)\nmid\operatorname{cod}(\chi) .
Sine Fourier Transform-based Frequency Estimation

This paper presents an approach for frequency estimation based on applying a sine Fourier transform and observations of particularly selected characteristic points of sidelobes. The bias of frequency estimation caused by the refusal to complement the developed algorithm by a corresponding cosine transform is eliminated by a specific selection of frequencies, for which a Fourier transform is calculated, and consecutive iteration of calculations is done for various durations of the analyzed signal. This paper also presents approaches for the recognition of a frequency interval with the exact value of frequency for a case of an analyzed signal with noise contamination.
gym-saturation: an OpenAI Gym environment for saturation provers

environment for reinforcement learning (RL) agents capable of proving theorems.Currently, only theorems written in a formal language of the Thousands of Problems for Theorem Provers (TPTP) library (Sutcliffe, 2017) in clausal normal form (CNF) are supported.gym-saturation implements the 'given clause' algorithm (similar to the one used in Vampire (Kovács & Voronkov, 2013) and E Prover (Schulz et al., 2019)).Being written in Python, gym-saturation was inspired by PyRes (Schulz & Pease, 2020).In contrast to the monolithic architecture of a typical Automated Theorem Prover (ATP), gym-saturation gives different agents opportunities to select clauses themselves and train from their experience.Combined with a particular agent, gym-saturation can work as an ATP.Even with a non trained agent based on heuristics, gym-saturation can find refutations for 688 (of 8257) CNF problems from TPTP v7.5.0.
Role of Blockchain in the Healthcare Sector: Challenges, Opportunities and Its Uses in Covid-19 Pandemic

As the world grapples with the Covid-19 pandemic and major populations are getting vaccinated, increasing realisation processes healthcare industry needs to be augmented. It includes managing supply chains, healthcare records, and patient care. With a scarcity of time and resources, adaptation of blockchain technology will help mitigate the pressures on existing infrastructure. A blockchain distributed ledger helps to exchange health information securely without complex intermediation of trust with secure access. The organisations and persons in the blockchain network can verify and authorise the data, thus protecting patient identity, privacy, medical information system, and reducing transaction costs. The paper examines managing and protecting electronic medical records and personal health records data using blockchain. It also analyses issues in healthcare, blockchain implementation, and its uses in the Covid-19 pandemic.
On Neural Network Kernels and the Storage Capacity Problem

In this short note, we reify the connection between work on the storage capacity problem in wide two-layer treelike neural networks and the rapidly growing body of literature on kernel limits of wide neural networks. Concretely, we observe that the "effective order parameter" studied in the statistical mechanics literature is exactly equivalent to the infinite-width neural network gaussian process kernel. This correspondence connects the expressivity and trainability of wide two-layer neural networks.
Mobile time banking on blockchain system development for community elderly care

Abstract This study aims to develop a mobile time-banking system on blockchain (MTBB), which can track service transaction records for community elderly care via mutual service exchange. The MTBB was developed to enable organizations, either corporate-social-responsibility organizations or nonprofit organizations to issue proprietary time tokens to members who participate in the organizations’ volunteer activities. Database applications with smartphone apps integrated with MultiChain blockchain technology were developed. Metadata with the service transaction information are stored in the MultiChain blocks so that the transaction records are immutable and can be analyzed in the future. Cahn’s time-banking guidelines were applied in developing this MTBB with MultiChain blockchain technology integrated for tracking service transaction records. The study also combines one-to-one mutual service exchange with organizations which offer volunteer activities and issue proprietary time tokens. With the blockchain transaction tracking mechanism, all elderly care service records via or within organizations can be tracked and analyzed to show their alignment with some of the Sustainable Development Goals of the United Nations.
An Android Based E-Commerce Application for Farmers

We have developed a mobile application for farmers, which will help them to sell their products directly to the consumers through this application. This mobile application helps the farmers sell as well as buy products through its easy and convenient interface. Farmers face a lot of challenges when it comes to selling their products and therefore the primary goal of our app is to provide a means for farmers to market their commodities at fair prices. Updated market prices and filters are also provided in the application to make it more convenient and efficient for farmers. This system has a simple interface that also provides certain filters that lets the consumer choose from a large variety of products from which they can select and purchase products according to their requirements. The main objective of this application is to take into concern the needs of the farmers as well as the buyers and fulfil their requirements accordingly. The fast and updated delivery system is one of the priorities of our android application.
Face Recognition based Automatic Attendance Updating System

The current methods for taking attendance are inconvenient and time-consuming. Manual recording allows for easy manipulation of attendance records. A smart and validated attendance system is needed to tackle this problem. In smart attendance systems, face identification, fingerprints, Genetic material, retina, eye retina, gesture recognition, and other biometrics are often used. Humans can be identified by their faces, which have distinguishing traits. Face recognition is one of the most useful image processing applications, and it plays an important part in the technical world. The recognition of a human face is a hot topic for verification reasons, particularly in the context of school attendance. A face detection and recognition attendance system uses face biostatistics known for high monitoring and other computing technologies to identify students. The purpose of creating this system is to replace the traditional technique of documenting attendance by calling names and storing pen-and-paper records with a digital version. An innovative solution for a face recognition-based automatic attendance system is presented in this study. The findings are tabulated in an excel file, and students' attendance is processed depending on the date from the excel file, and relevant conclusions are drawn from this.
Rethinking Shared Features and Re-ranking for Cross-Modality Person Re-identification

Cross-Modality Re-Identification (CM-ReID) between infrared images (IR) and color image is an extended task of person ReID, which is mainly responsible for retrieving the specified object at night or in dim environment. It remains challenging due to inter-camera and cross-modality. Targeting to solve these problems, this paper proposed a cross-modality ReID via shared features and re-ranking. It consists of a multi-branch network with attention mechanism and rethinking re-ranking strategy. The network explicitly leverages the global context block and cross-modality constraint to learn distinguished representations, which builds a retrieved bridge between IR and RGB by mining cross-modality shared space. The global context block can capture more context information from original image, and the cross-modality constraint (CrMC) can reduce the possibility of high-dimensional shared feature space. Besides, the improved re-ranking strategy is introduced to further optimize the initial results. Although the proposed method is simple, extensive experimental results demonstrate that it significantly outperforms the state-of-the-art approaches on CM-ReID datasets.
ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming

As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms.
Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge

With the KiTS21 Grand Challenge, I propose the automatic segmentation model between the kidney and the mass of the kidney which includes tumor and cyst. Convolutional Neural Network is trained in patches of three-dimensional abdominal CT imaging. For the segmentation of the 3D image, a variant of U-Net which consists of 3D Encoder-Decoder CNN architecture with additional Skip Connection is used. Lastly, there is a loss function to resolve the class imbalance problem frequently occurring in the task of medical imaging. Sørensen-Dice Score and Surface Dice Score on the test set are 80.13 and 68.61.