Reviewerly/exHarmony huggingface dataset:
How do Machine Learning Algorithms Effectively Classify Toxic Comments? An Empirical Analysis

Toxic comments on social media platforms, news portals, and online forums are impolite, insulting, or unreasonable that usually make other users leave a conversation. Due to the significant number of comments, it is impractical to moderate them manually. Therefore, online service providers use the automatic detection of toxicity using Machine Learning (ML) algorithms. However, the model's toxicity identification performance relies on the best combination of classifier and feature extraction techniques. In this empirical study, we set up a comparison environment for toxic comment classification using 15 frequently used supervised ML classifiers with the four most prominent feature extraction schemes. We considered the publicly available Jigsaw dataset on toxic comments written by human users. We tested, analyzed and compared with every pair of investigated classifiers and finally reported a conclusion. We used the accuracy and area under the ROC curve as the evaluation metrics. We revealed that Logistic Regression and AdaBoost are the best toxic comment classifiers. The average accuracy of Logistic Regression and AdaBoost is 0.895 and 0.893, respectively, where both achieved the same area under the ROC curve score (i.e., 0.828). Therefore, the primary takeaway of this study is that the Logistic Regression and Adaboost leveraging BoW, TF-IDF, or Hashing features can perform sufficiently for toxic comment classification.
Exercises for Part I

In this chapter we introduce the Bayesian approach to inverse problems in which the unknown parameter and the observed data are viewed as random variables. In this probabilistic formulation, the solution of the inverse problem is the posterior distribution on the parameter given the data. We will show that the Bayesian formulation leads to a form of well-posedness: small perturbations of the forward model or the observed data translate into small perturbations of the posterior distribution. Well-posedness requires a notion of distance between probability measures. We introduce the total variation and Hellinger distances, giving characterizations of them, and bounds relating them, that will be used throughout these notes. We prove well-posedness in the Hellinger distance.
P‐13.3: Crease Improvement Method for Foldable OLED Module by Preloading Mechanism

Folding OLED has promoted new forms of display applications to the consumer market, but the existing folding display generally have serious crease problems, harming consumers' experience and purchase intention. This work proposes a new method to alleviate screen crease based on the preloading mechanism. The results show that the reflective appearance of the panel equipped with a preloading mechanism is obviously improved, and the deformation of the folding area is reduced by more than 50%, which proves the effective crease improvement.
Pre-Trained Model-Based NFR Classification: Overcoming Limited Data Challenges

Machine learning techniques have shown promising results in classifying NFR. However, the lack of annotated training data in the domain of requirement engineering poses challenges to the accuracy, generalization, and reliability of ML-based methods, including overfitting, poor performance, biased models, and out-of-vocabulary issues. This study presents an approach for the classification of non-functional requirements (NFRs) in software requirements specification documents by extracting features from word embedding pre-trained models. The novel algorithms are specifically designed to extract relevant representative features from pre-trained word embedding models. In addition, each pre-trained model is paired with the four tailored neural network architectures for NFR classification including RPCNN, RPBiLSTM, RPLSTM, and RPANN. This combination results in the creation of twelve unique models, each with its unique configuration and characteristics. The results show that the integration of pre-trained GloVe models with RPBiLSTM demonstrates the highest performance, achieving an impressive average Area Under the Curve (AUC) score of 96%, a precision of 85%, and recall of 82%, highlighting its strong ability to accurately classify NFRs. Furthermore, among the integration of pre-trained Word2Vec models, RPLSTM achieved notable results, with an AUC score of 95%, precision of 86%, and recall of 80%. Similarly, integrated fastText-based pre-trained models the RPBiLSTM yield competitive performance, with an AUC score of 95%, precision of 85%, and recall of 80%. This comprehensive and integrated approach provides a practical solution for effectively analyzing and classifying NFRs, thereby facilitating improved software development practices.
Dynamic inference techniques for deep neural networks

With the development of deep neural network, dynamic inference techniques have attracted extensive attention. When the traditional static neural network is faced with complex samples, it will generate a lot of computational redundancy, resulting in a waste of computing resources. In order to solve this problem, on the basis of static network, experts use dynamic reasoning technology to improve the network, so that the network can calculate the samples. Therefore, the network structure has been fundamentally improved by performing operations such as layer skipping and leaving early. Compared with the traditional static networks, the improved model has greatly improved the speed and scale. At the same time, the accuracy of operation has also been greatly improved.
Understanding EdTech's Privacy and Security Issues: Understanding the Perception and Awareness of Education Technologies' Privacy and Security Issues

A clear and well-documented LaTeX document is presented as an article formatted for publication by ACM in a conference proceedings or journal publication. Based on the "acmart" document class, this article presents and explains many of the common variations, as well as many of the formatting elements an author may use in the preparation of the documentation of their work.
Texture and Orientation-based Feature Extraction for Robust Facial Expression Recognition

Facial expressions are the most effective way to characterize people’s motives, emotions, and feelings. Several new methods are proposed each year; however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods.
Kernel-Based Frame Interpolation for Spatio-Temporally Adaptive Rendering

Recently, there has been exciting progress in frame interpolation for rendered content. In this offline rendering setting, additional inputs, such as albedo and depth, can be extracted from a scene at a very low cost and, when integrated in a suitable fashion, can significantly improve the quality of the interpolated frames. Although existing approaches have been able to show good results, most high-quality interpolation methods use a synthesis network for direct color prediction. In complex scenarios, this can result in unpredictable behavior and lead to color artifacts. To mitigate this and to increase robustness, we propose to estimate the interpolated frame by predicting spatially varying kernels that operate on image splats. Kernel prediction ensures a linear mapping from the input images to the output and enables new opportunities, such as consistent and efficient interpolation of alpha values or many other additional channels and render passes that might exist. Additionally, we present an adaptive strategy that allows predicting full or partial keyframes that should be rendered with color samples solely based on the auxiliary features of a shot. This content-based spatio-temporal adaptivity allows rendering significantly fewer color pixels as compared to a fixed-step scheme when wanting to maintain a certain quality. Overall, these contributions lead to a more robust method and significant further reductions of the rendering costs.
CIS Society Officers

Chat, Anyone?H i all, Let's talk about the elephant in the room.ChatGPT and other Large Language Models (LLM) along with their AI cousins focused on images, graphics, math, coding, and audio are dominating our world of generating reports and manuscripts.They can be immensely fun: I asked ChatGPT to write me song lyrics about fuzzy logic in various genres.I wasn't crazy about the folk song, but I really liked ChatGPT's blues lyrics and its bluegrass rendition.And who hasn't chuckled over clever images synthesized by DALL-E?However, these are serious applications with huge potential.Can we get too much of a good thing?We are experiencing, if not the peak, at least the wave of hype, both positive and negative.Many people, including many IEEE members, have signed a letter calling for a sixmonth "AI Pause", worried about the potential for moral and ethical misuse of AI.I didn't sign the letter for several reasons, but it is important to have these conversations to focus on what and how to regulate, as well as our own personal interaction with AI programs.From an IEEE perspective, what are the guidelines and rules to govern our use of AI programs in the creation and reviewing of manuscripts in IEEE publications including, of course, conference proceedings?I chair an AdHoc Committee of the IEEE Publication Services and Products Board (yea, that's a mouthful) on AI in the publication domain: opportunities and threats.One of our charters is to articulate the principles and guidelines for the use of AI in publications.So we've been collecting a lot of information and discussing the Good, the Bad, and the Ugly of AI in publications.There is already a considerable effort to add AI and AI-like (maybe call this "little ai") capabilities into publications.The IEEE Author Portal is one good example of little ai at the moment, though plans are in the works for moving closer to Big AI.Dawn Melley, Senior Director of IEEE Publishing Operations summarizes "The IEEE Author Portal is an article submission system that works seamlessly with Schol-arOne Manuscripts to allow authors to submit more quickly and efficiently.It uses metadata extraction to take what is provided in the author's files and automatically populate many of the submission fields, which saves the authors a lot of time and effort during the submission process."One feature involves machine learning algorithms that are used to extract key submission information from manuscript files so that authors do not have to rekey or copy/paste the information (definitely something not fun about manuscript submission).Enhancements and additions to these features, think grammar checking, etc., are clearly to me part of the Good.Approaches to aid in the review process are being researched and developed.Suggesting relevant referees is a great low hanging fruit.
Two Step Authentication System based on OTP &amp; Gesture Recognition Based on Accelerometer

Abstract: The paper discusses the use of two step authentication system in- stead of traditional single step such as biometric, password etc. The paper discusses the disadvantages of using existing single layer security in everyday security needs. Further the paper proposes the idea how a Two-step authentication system based on pattern recognition and OTP (One Time Password) would create a more robust and secure authentication system while being cost effective and re- quiring less electronic components. The first layer of security is achieved by pattern recognition by pattern recognition we mean a user draws a pattern in air and using Arduino &amp; ADXL35 (3-axis accelerometer) module we can deduce the pattern .The accelerometer is used to track the X,Y values obtained from the module and using these values we ascertain whether the pattern created by the user is accurate. As for the second layer we use Arduino as a random number generator &amp; SIM7100A (4G SIM module) serves the purposes to send data to the user via SMS.
Prelims

Citation (2023), "Prelims", van Tulder, R., Giuliani, E. and Álvarez, I. (Ed.) International Business and Sustainable Development Goals (Progress in International Business Research, Vol. 17), Emerald Publishing Limited, Bingley, pp. i-xxii. https://doi.org/10.1108/S1745-886220230000017019
Uncertainty-based Heterogeneous Privileged Knowledge Distillation for Recommendation System

In industrial recommendation systems, both data sizes and computational resources vary across different scenarios. For scenarios with limited data, data sparsity can lead to a decrease in model performance. Heterogeneous knowledge distillation-based transfer learning can be used to transfer knowledge from models in data-rich domains. However, in recommendation systems, the target domain possesses specific privileged features that significantly contribute to the model. While existing knowledge distillation methods have not taken these features into consideration, leading to suboptimal transfer weights. To overcome this limitation, we propose a novel algorithm called Uncertainty-based Heterogeneous Privileged Knowledge Distillation (UHPKD). Our method aims to quantify the knowledge of both the source and target domains, which represents the uncertainty of the models. This approach allows us to derive transfer weights based on the knowledge gain, which captures the difference in knowledge between the source and target domains. Experiments conducted on both public and industrial datasets demonstrate the superiority of our UHPKD algorithm compared to other state-of-the-art methods.
Image reconstruction with subspace-assisted deep learning

Deep learning-based image reconstruction methods are often sensitive to changes in data acquisition settings (e.g., sampling pattern and number of encodings). This work proposes a novel subspace-assisted deep learning method to effectively address this problem. The proposed method uses a subspace model to capture the global dependence of image features in the training data and a deep network to learn the mapping from a linear vector space to a nonlinear manifold. Significant improvement in reconstruction accuracy and robustness by the proposed method has been demonstrated using the fastMRI dataset.
Data Selection for Deep Learning via diversity visualization and scoring

Data diversity is a key ingredient for robust deep learning models, especially in the medical domain. We present a diversity visualization and quantification scheme which enables decisions on data selection different enough from already existing data. Out experiments amply validate the usefulness of the proposed diversity metric in terms of enhancement in accuracy of models resulting from using them in data selection decision process with accuracy improvement from 3%-&amp;gt;10% across different sites.
Robust Low-rank Denoising of MRSI Data

With the increasing use of MRSI in research and clinical applications, we have seen a surge in the use of low-rank models for denoising MRSI data. This paper presents a novel method for robust denoising of MRSI data corrupted by both Gaussian noise and nuisance signals. The proposed method has been validated using both simulated and in vivo data, producing impressive results. This method will further enhance the practical utility of low-rank denoising.
Automated Brain Extraction from T1-weighted MRI of Rhesus Macaques using a U-Net Deep Learning Framework

Transfer learning of an advanced deep learning framework, utilizing 3D U-Net pre-trained model, and trained on human brain MRI scans is proposed for brain extraction from MRI of other species. The proposed network architecture successfully performed whole brain extraction from rhesus macaque brain MRIs automatically with high accuracy, reduced errors, and lower computational cost. Options for data augmentation and different learning rates were also tested. Successful implementation of automated brain extraction would offer the potential to apply the same strategy on other animal models.
Analysis of Morphological Errors on Speech and Communication Styles of UIN North Sumatra Students

Language errors are caused by deviations from language rules. There are language rules in communication that must be considered. This study is a series of activities related to methods of collecting library data, as well as activities related to reading, storing and processing research materials from journals and books on the internet, as well as textbooks related to the study of morphology.errors. in Student Speeches. and communication style. All data must be matched to the identified research topic to be collected. To adapt to a new area, most students from outside the area use Indonesian to facilitate communication. This is one solution to avoid understanding caused by cultural differences between UIN Sumut Medan students.
A K-space based learning approach to head motion correction for 4D (3D+time) radial sequences

A novel k-space learning based framework is introduced to compensate for bulk motion artifacts in UTE/ZTE radial acquisition schemes. The motion during the scan is modeled as rigid, and is parametrized by time varying translation and rotation parameters. The time varying parameters are used to define a forward model, which transforms the undistorted image to distorted k-t space data. The error between the measured and computed k-t space data is used to optimize the image and the deformation parameters using ADAM optimization. A multi-scale approach is used to minimize the computational complexity.
Visual Security Index Combining CNN and Filter for Perceptually Encrypted Light Field Images

Visual security index (VSI) represents a quantitative index for the visual security evaluation of perceptually encrypted images. Recently, the research on visual security of encrypted light field (LF) images faces two challenges. One is that the existing perceptually encrypted image databases are often too small, which is easy to cause overfitting in convolutional neural network (CNN). The other is that existing VSI models did not take a full account the intrinsic characteristics of the LF images and highly relied on handcrafted feature extraction. In this article, we construct a new database of perceptually encrypted LF images, called the PE-SLF, which is 2.6 times as big as the existing largest perceptual encrypted image database. Moreover, a novel visual security index (VSI) model is proposed by taking into full consideration the intrinsic spatial-angular characteristics of the LF images and the outstanding capabilities of CNN in feature extraction. First, we exploit CNN to detect the texture and structure features of encrypted sub-aperture images in the spatial domain. Second, we apply the Gabor filter to detect the Gabor feature over the epi-polar plane images in angular domain. Last, the spatial and angular similarity measurements are subsequently calculated for jointly yielding the final visual security score. Experimental results on the constructed PE-SLF demonstrate that the proposed VSI model is closer to the perception of HVS in visual security evaluation of encrypted LF images compared to other classical and state-of-the-art models.
Field Survey Mobile Application: Enhancing Spatial Information for Natural Resource Management

This research paper introduces a field survey mobile application designed to address the challenges associated with spatial information gathering. The application enables users, including Ph.D. and PG students of Agriculture, as well as professionals in different organizations, to digitally store crop information with GIS spatial attributes and real-time maps. The paper explores the application's utilization in various fields such as land surveying, environmental studies, urban planning and infrastructure development, natural resource management, archaeological surveys, and real estate surveys. Additionally, it provides an overview of the application's functionalities and explains how it operates in real-time. The paper concludes with a discussion on the results obtained from using the field survey mobile application.
k-Space Interpolation for Accelerated MRI Using Deep Generative Models

k-space deep learning (DL) is emerging as an alternative to the conventional image domain DL for accelerated MRI. Typically, DL requires training on large amounts of data, which is unaccessible in clinical. This paper proposes to present an untrained k-space deep generative model (DGM) to interpolate missing data. Specifically, missing data is interpolated by a carefully designed untrained generator, of which the output layer conforms the MR image multichannel prior, while the architecture of other layers implicitly captures k-space statistics priors. Furthermore, we prove that the proposed method guarantees enough accuracy bounds for interpolated data under commonly used sampling patterns.
Enhancing linguistic research through 2-mm isotropic 3D dynamic speech magnetic resonance imaging

We are able to push the spatial resolution of dynamic speech magnetic resonance imaging to 2-mm near-isotropic level with 64 mm coverage of 32 3D slice locations that are spaced 2-mm apart with 35 fps. We choose to analyze lingual differences of American English voiced lateral [l] and (central) [t]. Several analysing methods are utilized such as magnitude comparison, t-test and deformation map comparison. The results give us detailed observations of lingual articulatory differences such as tongue grooving, twisting and coarticulation. Through this high spatial and temporal resolution, we demonstrate that this method will show great potentials on linguistic research.
Iterative Refinement of Expert Contours for Improved Ground-truth Quality in Intracranial Vessel Segmentation Neural Network Training

With a typical slice-by-slice labeling fashion, the manual contouring process is subject to large intra-observer variation, especially for small-sized intracranial arteries. We propose an iterative refinement approach for ground truth contours with the help of deep neural networks for intracranial lumen and vessel wall segmentations. We demonstrate that the approach improved the smoothness of the predicted contours and feature quantification, which can potentially boost the robustness of a neural network as a consequence of the reduced uncertainty in expert labels.