Reviewerly/exHarmony huggingface dataset:
Exploring Misclassification Information for Fine-Grained Image Classification

Fine-grained image classification is a hot topic that has been widely studied recently. Many fine-grained image classification methods ignore misclassification information, which is important to improve classification accuracy. To make use of misclassification information, in this paper, we propose a novel fine-grained image classification method by exploring the misclassification information (FGMI) of prelearned models. For each class, we harvest the confusion information from several prelearned fine-grained image classification models. For one particular class, we select a number of classes which are likely to be misclassified with this class. The images of selected classes are then used to train classifiers. In this way, we can reduce the influence of irrelevant images to some extent. We use the misclassification information for all the classes by training a number of confusion classifiers. The outputs of these trained classifiers are combined to represent images and produce classifications. To evaluate the effectiveness of the proposed FGMI method, we conduct fine-grained classification experiments on several public image datasets. Experimental results prove the usefulness of the proposed method.
A Mediated Approach to the Virtual Learning Environment (VLE)

This paper examines the Virtual Learning Environment (VLE) in relation to its implication for academic libraries in the 21st century. It sheds light on how technological development has had major impacts in the academic library, with regards to how information is being constructed, transmitted and accessed by library users. In the course of this discourse it was deduced that students constitute a significant percentage of the academic library users. The technological development in the academic sphere has enabled ubiquitous interactions between students and teachers, in which students are enabled to learn and communicate with their teachers from remote locations, thus inducing the notion of VLE. The VLE is regarded as technological-based environment where learners are enabled to access learning outside the classroom. With the aid of VLE students are granted ownership of their learning process, thus ensuring positive learning outcomes. Finally, the Technology Acceptance Model (TAM) was employed to serve as the theoretical basis of this discourse.
Development of a computer-based learning environment in the conditions of multicultural education in the European Union

In the article, based on the results of the analysis of scientific and pedagogical works of domestic and foreign scientists, the concept of multicultural competence of the student and the concept of computer-oriented learning environment in the conditions of multicultural education of students are specified. The scientific search on the basis of the analysis of government documents, programs and projects of informatization of the educational environment of the European Union and the Council of Europe made it possible to identify the main stages of the development of a computer-oriented learning environment in the conditions of multicultural education of students in the EU countries: information and preparation (1997–2000); infrastructural and provisional (2001–2004), pedagogically communicative (2004–2006), project provisional (2007–2010), competently oriented (2010–2013), project activity (2014 — present and time).
Android-Based Child Monitoring Application Using A Smartwatch and Geofence Service

This article discusses implementing an application to monitor children's movements using virtual borders. This virtual boundary is known as a geofence. This geofence is used to mark which areas can be occupied by the child. This child monitoring application with geofence facilities was built using the unified software development process (USDP) method. USDP is divided into 4 stages, namely Inception, Elaboration, Construction, and Transition. This application is designed to assist parents in monitoring children's movements in realtime. This child monitoring application with geofence facilities begins with an investigation of system requirements, followed by a system architecture design and a use case diagram design. Data needs in this system include the actors involved, namely children and parents. Children use smartwatches and parents use Android smartphones. The latitude and longitude positions of the child and parent are stored in the Firebase database. Geofences are set by parents and are used to restrict children's movement in certain areas. In designing this system, the haversine formula is used to calculate the distance between the child's position and geofence to find out whether the child is still in the permitted area. When the child leaves geofencing, a response will appear to the parents.
An Enhanced Twitter Sentiment Analysis Model using Negation Scope Identification Methods

Sentiment Analysis (SA), which is also known as Opinion Mining, is a hot-fastest growing research area, making it challenging to follow all its activities. It intends to study peoples' thoughts, feelings, and attitudes about topics, events, issues, entities, individuals, and attributes in social media (e.g., social networking sites, forums, blogs, etc.) expressed by either text comments or tweets. Twitter is one of the world's largest online microblogging platforms that allows its users to freely post texts called tweets. It offers a wealth of information, therefore utilizing SA to analyze this information into positive or negative will assist organizations' and customers' decision-making that will have a significant impact on daily life. SA draws the attention of scientific research in the Natural Language Processing community due to the text structure challenges that may contain negation. Negation is a widespread linguistic structure that changes the text meaning to the opposite and affects text polarity. Therefore, it needs to be considered in sentiment analysis systems. In this paper, supervised machine learning models have been used as a baseline to categorize the sentiment of a Twitter dataset using (Bag of Words) and (Term Frequency Inverse Document Frequency) feature representation methods. Then we applied negation scope identification methods to find negated tokens and investigate how embedding these tokens can raise SA classifiers' accuracy. The results of the sentiment classification task show an improvement once considering these tokens.
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators

We study the problem of estimating the derivatives of a regression function, which has a wide range of applications as a key nonparametric functional of unknown functions. Standard analysis may be tailored to specific derivative orders, and parameter tuning remains a daunting challenge particularly for high-order derivatives. In this article, we propose a simple plug-in kernel ridge regression (KRR) estimator in nonparametric regression with random design that is broadly applicable for multi-dimensional support and arbitrary mixed-partial derivatives. We provide a non-asymptotic analysis to study the behavior of the proposed estimator in a unified manner that encompasses the regression function and its derivatives, leading to two error bounds for a general class of kernels under the strong $L_\infty$ norm. In a concrete example specialized to kernels with polynomially decaying eigenvalues, the proposed estimator recovers the minimax optimal rate up to a logarithmic factor for estimating derivatives of functions in H\"older and Sobolev classes. Interestingly, the proposed estimator achieves the optimal rate of convergence with the same choice of tuning parameter for any order of derivatives. Hence, the proposed estimator enjoys a \textit{plug-in property} for derivatives in that it automatically adapts to the order of derivatives to be estimated, enabling easy tuning in practice. Our simulation studies show favorable finite sample performance of the proposed method relative to several existing methods and corroborate the theoretical findings on its minimax optimality.
Novel Geometries for Stereotactic Localizers

Introduction:The N-localizer is generally utilized in a 3-panel or, more rarely, a 4-panel system for computing stereotactic positions.However, a stereotactic frame that incorporates a 2-panel (bipanel) Nlocalizer system with panels affixed to only the left and right sides of the frame offers several advantages: improved ergonomics to attach the panels, reduced claustrophobia for the patient, mitigation of posterior panel contact with imaging systems, and reduced complexity.A bipanel system that comprises two standard N-localizer panels yields only two three-dimensional (3D) coordinates, which are insufficient to solve for the stereotactic matrix without further information.While additional information to determine the stereotactic positions could include scalar distances from Digital Imaging and Communications in Medicine (DICOM) metadata or 3D regression across the imaging volume, both have risks related to noise and error propagation.Therefore, we sought to develop new stereotactic localizers that comprise only lateral fiducials (bipanel) that leave the front and back regions of the patient accessible but that contain enough information to solve for the stereotactic matrix using each image independently.Methods: To solve the stereotactic matrix, we assumed the need to compute three or more 3D points from a single image.Several localizer options were studied using Monte Carlo simulations to understand the effect of errors on the computed target location.The simulations included millions of possible combinations for computing the stereotactic matrix in the presence of random errors of 1mm magnitude.The matrix then transformed coordinates for a target that was placed 50mm anterior, 50mm posterior, 50mm lateral, or 50mm anterior and 50mm lateral to the centre of the image.Simulated cross-sectional axial images of the novel localizer systems were created and converted into DICOM images representing computed tomography (CT) images.Results: Three novel models include the M-localizer, F-localizer, and Z-localizer.For each of these localizer systems, optimized results were obtained using an overdetermined system of equations made possible by more than three diagonal bars.In each case, the diagonal bar position was computed using standard Nlocalizer mathematics.Additionally, the M-localizer allowed adding a computation using the Sturm-Pastyr method.Monte Carlo simulation demonstrated that the Z-localizer provided optimal results. Conclusion:The three proposed novel models meet our design objectives.Of the three, the Z-localizer produced the least propagation of error.The M-localizer was simpler and had slightly more error than the Zlocalizer.The F-localizer produced more error than either the Z-localizer or M-localizer.Further study is needed to determine optimizations using these novel models.
Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift

While recent computer vision algorithms achieve impressive performance on many benchmarks, they lack robustness - presented with an image from a different distribution, (e.g. weather or lighting conditions not considered during training), they may produce an erroneous prediction. Therefore, it is desired that such a model will be able to reliably predict its confidence measure. In this work, uncertainty estimation for the task of semantic segmentation is evaluated under a varying level of domain shift: in a cross-dataset setting and when adapting a model trained on data from the simulation. It was shown that simple color transformations already provide a strong baseline, comparable to using more sophisticated style-transfer data augmentation. Further, by constructing an ensemble consisting of models using different backbones and/or augmentation methods, it was possible to improve significantly model performance in terms of overall accuracy and uncertainty estimation under the domain shift setting. The Expected Calibration Error (ECE) on challenging GTA to Cityscapes adaptation was reduced from 4.05 to the competitive value of 1.1. Further, an ensemble of models was utilized in the self-training setting to improve the pseudo-labels generation, which resulted in a significant gain in the final model accuracy, compared to the standard fine-tuning (without ensemble).
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?

Understanding classifier decision under novel environments is central to the community, and a common practice is evaluating it on labeled test sets. However, in real-world testing, image annotations are difficult and expensive to obtain, especially when the test environment is changing. A natural question then arises: given a trained classifier, can we evaluate its accuracy on varying unlabeled test sets? In this work, we train semantic classification and rotation prediction in a multi-task way. On a series of datasets, we report an interesting finding, i.e., the semantic classification accuracy exhibits a strong linear relationship with the accuracy of the rotation prediction task (Pearson's Correlation r > 0.88). This finding allows us to utilize linear regression to estimate classifier performance from the accuracy of rotation prediction which can be obtained on the test set through the freely generated rotation labels.
Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells.
Navigation control of Drone using Hand Gesture based on Complementary Filter Algorithm

Abstract One of the most important things to use UAV is navigation control. Navigation control is a way to adjust the direction of the quadcopter movements according to the command of the pilot. Natural User Interface (NUI) is a new way to interact with a system as navigation control. In this study, a wearable device was made that can detect hand gestures and gives instructions to the Dji Tello drone. The MPU6050 sensor is used to provide response in two dimensional axes. The complementary filter implements low pass filter on the accelerometer and integrates the value of the gyroscope with the previous angle output. After that, the value will be fed to the high pass filter. The results of the two filters will obtain stable angle, by adjusting the filter coefficient and the sampling time. The aim of complementary filter method is to reduce noise in angular transformation when the pilot makes hand gestures. Based on the experiments, the results show that hand gestures could give command for Dji Tello drone movements successfully. Therefore, it has been proven that the hand gesture can be used for the navigation control system on the Dji Tello drone.
NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data

Recent advances in protecting node privacy on graph data and attacking graph neural networks (GNNs) gain much attention. The eye does not bring these two essential tasks together yet. Imagine an adversary can utilize the powerful GNNs to infer users’ private labels in a social network. How can we adversarially defend against such privacy attacks while maintaining the utility of perturbed graphs? In this work, we propose a novel research task, adversarial defenses against GNN-based privacy attacks, and present a graph perturbation-based approach, NetFense, to achieve the goal. NetFense can simultaneously keep graph data unnoticeability (i.e., having limited changes on the graph structure), maintain the prediction confidence of targeted label classification (i.e., preserving data utility), and reduce the prediction confidence of private label classification (i.e., protecting the privacy of nodes). Experiments conducted on single- and multiple-target perturbations using three real graph data exhibit that the perturbed graphs by NetFense can effectively maintain data utility (i.e., model unnoticeability) on targeted label classification and significantly decrease the prediction confidence of private label classification (i.e., privacy protection). Extensive studies also bring several insights, such as the flexibility of NetFense, preserving local neighborhoods in data unnoticeability, and better privacy protection for high-degree nodes.
Some Like It Hoax : Lessons In Regulating Fake News In Malaysia

The growth of digital technology promotes the positive impact of the usage of digital based media as the main source of information.Although the development has a significant positive impact to the digital economy, it is unfortunate that the online media has become a breeding ground for dissemination of false information.Worse, news from unverified sources has often been carelessly quoted by the mainstream press which shows a remarkably questionable practices and consequently shows possible regulatory gap in the enforcement of press ethical conduct.Yet, existing laws on dissemination of false information had often being criticized as draconian, anti-democratic, and viewed as the instrument of the state control over free speech.This sentiment later contributed to the abolishment of the Anti-Fake News Act 2018 -just a year after it was enacted.Nevertheless, controlling propagation of fake news has proven to be challenging to the authorities.The episodes of the spread of fake news in regard to the Covid-19 pandemic in Malaysia had stretched the authorities to choose a stricter offence under the Penal Code rather than the usual anti-fake news legislations such as the Printing Presses and Publication Act 1984 and the Communications and Multimedia Act 1998.Thus, the primary objective of this paper is to revisit the development of fake news regulatory mechanism in Malaysia, and to recommend any possible improvement to curb it in the future.The paper adopts the method of textual analysis of the legislation as well as semi-structured interviews.
Evolutionary Rear-Lamp Tracking at Nighttime

Rear-lamp tracking at nighttime plays a momentous role in the advanced driver assistance system (ADAS), involving collision mitigation, automatic cruise control, automatic headlamp dimming, etc. Most of the existing tracking methods based on monocular camera leverage on color features. However, such tracking methods can be easily influenced by background clutter, illumination change, distance variation, and occlusion. In this paper, we propose an evolutionary adaptive rear-lamp tracking method at nighttime, in which a novel genetic algorithm powered by the probabilistic bitwise operation (PBO) is utilized. Also, to improve the robustness against various environments, a balanced fitness function is proposed by taking color information, symmetry, spatial relationship, and rigidity into account. Especially, a series of adaptive thresholds based on rear data in HSV color space is proposed to exploit color information reasonably with respect to our task. A strategy to deal with occlusion is also proposed, which relies on color information and rigidity. Moreover, to our knowledge, there is no publicly available dataset for rear-lamp tracking at nighttime. To fill the gap between the real-world application and the theoretical research, we create a novel dataset, which contains diverse traffic conditions at nighttime. The experimental results indicate that our method outperforms comparative online tracking methods in terms of success rate and center location error.
Improved supersingularity testing of elliptic curves

In protocols of isogeny-based cryptosystems, we send data of elliptic curves. Then it is necessary to identify supersingularity of the elliptic curves to guarantee the correctness of protocol. Among deterministic algorithms for the purpose, Sutherland proposed an efficient algorithm that utilizes sequences of $2$-isogenies computed by using modular polynomials. In this paper, we improve the efficiency of the algorithm by using some properties of $x$-coordinates of $2$-torsion points instead of modular polynomials. Our experimental result shows that our algorithm succeeded in reducing the computation time by 14 to 32 percent compared to Sutherland's algorithm.
Normalization effects on shallow neural networks and related asymptotic expansions

<p style='text-indent:20px;'>We consider shallow (single hidden layer) neural networks and characterize their performance when trained with stochastic gradient descent as the number of hidden units <inline-formula><tex-math id="M1">\begin{document}$ N $\end{document}</tex-math></inline-formula> and gradient descent steps grow to infinity. In particular, we investigate the effect of different scaling schemes, which lead to different normalizations of the neural network, on the network's statistical output, closing the gap between the <inline-formula><tex-math id="M2">\begin{document}$ 1/\sqrt{N} $\end{document}</tex-math></inline-formula> and the mean-field <inline-formula><tex-math id="M3">\begin{document}$ 1/N $\end{document}</tex-math></inline-formula> normalization. We develop an asymptotic expansion for the neural network's statistical output pointwise with respect to the scaling parameter as the number of hidden units grows to infinity. Based on this expansion, we demonstrate mathematically that to leading order in <inline-formula><tex-math id="M4">\begin{document}$ N $\end{document}</tex-math></inline-formula>, there is no bias-variance trade off, in that both bias and variance (both explicitly characterized) decrease as the number of hidden units increases and time grows. In addition, we show that to leading order in <inline-formula><tex-math id="M5">\begin{document}$ N $\end{document}</tex-math></inline-formula>, the variance of the neural network's statistical output decays as the implied normalization by the scaling parameter approaches the mean field normalization. Numerical studies on the MNIST and CIFAR10 datasets show that test and train accuracy monotonically improve as the neural network's normalization gets closer to the mean field normalization.