Reviewerly/exHarmony huggingface dataset:
Performance-based active learning for skewed data with nonparametric logistic regression

Abstract Real-world data often exhibit skewed distribution with a long tail, where certain target values have significantly fewer observations rather than preserving an ideal uniform distribution over each category, which substantially affects model performance for classification problems. Furthermore, parametric logistic regression provides a fundamental classification model with ease of interpretation; however, it is doubtful that the logit function of classification is truly linear in covariates. This research proposes the performance-based active learning (PbAL) scheme with nonparametric logistic regression to address the imbalance problem considering the nonlinear decision boundary. The PbAL is applied to choose the most informative samples in a sequential manner with an imbalanced dataset by directly evaluating a performance metric on a pool set. The nonparametric logistic regression model with smoothing splines is used to achieve a flexible classification boundary. The experiments show that PbAL outperforms traditional active learning approaches based on D-optimality and A-optimality. It is also shown that the proposed method provides superior outcomes compared to the other resampling techniques used for imbalanced classification problems, such as Tomek Link and SMOTE, even with a smaller sample size. This result suggests that PbAL effectively mitigates the bias, which severely influences the model performance with small amounts of initial training data.
Integrally Private Model Selection for Deep Neural Networks

Abstract Deep neural networks (DNNs) are one of the most widely used machine learning algorithm. In the literature, most of the privacy related work to DNNs focus on adding perturbations to avoid attacks in the output which can lead to significant utility loss. Large number of weights and biases in DNNs can result in a unique model for each set of training data. In this case, an adversary can perform model comparison attacks which lead to the disclosure of the training data. In our work, we first introduce the model comparison attack for DNNs which accounts for the permutation of nodes in a layer. To overcome this, we introduce a relaxed notion of integral privacy called $\epsilon$-integral privacy. We further provide a methodology for recommending $\epsilon$-Integrally private models. We use a data-centric approach to generate subsamples which have the same class-distribution as the original data. We have experimented with 6 datasets of varied sizes (10k to 7 million instances) and our experimental results show that our recommended private models achieve benchmark comparable utility. We also achieve benchmark comparable test accuracy for 4 different DNN architectures. The results from our methodology show superiority under comparison with three different levels of differential privacy.
IDLIQ: An Incremental <i>Deterministic Finite Automaton</i> Learning Algorithm Through Inverse Queries for Regular Grammar Inference

We present an efficient incremental learning algorithm for Deterministic Finite Automaton (DFA) with the help of inverse query (IQ) and membership query (MQ). This algorithm is an extension of the Identification of Regular Languages (ID) algorithm from a complete to an incremental learning setup. The learning algorithm learns by making use of a set of labeled examples and by posing queries to a knowledgeable teacher, which is equipped to answer IQs along with MQs and equivalence query. Based on the examples (elements of the live complete set) and responses against IQs from the minimally adequate teacher (MAT), the learning algorithm constructs the hypothesis automaton, consistent with all observed examples. The Incremental DFA Learning algorithm through Inverse Queries (IDLIQ) takes O(|Σ|N+|Pc||F|) time complexity in the presence of a MAT and ensures convergence to a minimal representation of the target DFA with finite number of labeled examples. Existing incremental learning algorithms; the Incremental ID, the Incremental Distinguishing Strings have polynomial (cubic) time complexity in the presence of a MAT. Therefore, sometimes, these algorithms even fail to learn large complex software systems. In this research work, we have reduced the complexity (from cubic to square form) of the DFA learning in an incremental setup. Finally, we prove the correctness and termination of the IDLIQ algorithm.
PO-03-201 VIRTUAL REALITY-BASED METHOD FOR TEACHING COMPLEX CONGENITAL HEART DISEASE ANATOMY TO EP TRAINEES – A PILOT STUDY

Developing an accurate and detailed 3D mental model of cardiac anatomy is critical for electrophysiology (EP) trainees. This can be challenging in certain forms of palliated congenital heart disease. Surgical repair of d-TGA via a Mustard or Senning procedure requires a complex baffling technique that is particularly difficult to visualize from a 3D perspective and creates a propensity for arrhythmias. Traditional teaching methods may be sub-optimal for visualizing the interaction between the conduction system and this unusual anatomic substrate. Due to its immersive, stereoscopic nature, virtual reality (VR) may provide a better learning platform in a collaborative environment. The purpose of this pilot study is to compare the experience of a collaborative, immersive VR-based educational environment with a traditional PowerPoint (PPT) method for teaching the 3D cardiac anatomy of patients who have undergone Mustard and Senning procedures to EP trainees. In collaboration with the Visible Heart Lab at the University of Minnesota and DOPL Technologies, LLC, we created an immersive VR environment that allows remotely shared viewing, manipulation, and virtual dissection of high-resolution digital 3D models of wax-infiltrated heart specimens (d-TGA s/p Mustard and Senning procedures) that were provided by Boston Children’s Hospital. Participants viewed the environment using Oculus Quest2 head-mounted displays (Meta, Menlow Park, CA, USA). Three EP trainees were recruited for the pilot study, which entailed a traditional PPT lecture followed by an interactive viewing of the models in the VR learning environment. Participants were asked to complete a pre/post-test based on the material taught then a Likert satisfaction survey adapted from previously validated usability instruments. The study participants’ mean pre-test score was 46.7% and post-test score was 76.7%, a 64.3% improvement. They globally rated the VR learning environment favorably on the Likert satisfaction survey and reported that they would like to use the VR learning environment in both didactic lecture series and for self-directed study (Figure 1). We demonstrate the feasibility of using a remote, shared, immersive VR-based education platform for teaching EP trainees cardiac anatomy related to Mustard and Senning procedures. This learning environment was favorably received. Further efforts are needed to expand the library of CHD defects and provide additional features to the learning environment.
The Human Touch

The challenge of providing a safe communications environment in the face of such social engineering attacks isn't just the technology; it's also people. As anyone who has done serious work in computer security knows, the biggest problems are between the keyboard and the chair. Most people by default trust other people and are willing to give them the benefit of the doubt.
Tradeoffs within Representational Behaviors: Multi-Objective Optimization Strategies for Simulated Military Units

Generating representative behaviors for opposing and adjacent forces is critical for training military units in a simulated environment. Humans are often required in-the-loop because automated systems struggle to provide intelligent and adaptive behaviors that represent appropriate military doctrine and can be explained in the After Action Review. Automated solutions often fail to maintain enough diversity to be replayed in successive iterations of the simulated mission without becoming easily predictable. This paper describes the decision-making approach for automated hierarchical planning agents designed to overcome these challenges. Our Mission Command Agents (MCAs) combine constraint optimization for multi-agent goal reasoning and state-space planning for route planning to generate Courses of Action (COAs) for simulated units. We detail extensions in both steps to produce a set of COAs on the Pareto front for multiple practical (e.g., distance), doctrinal (e.g., cover and concealment), and behavioral (e.g., risk-tolerance or aggressiveness) objectives.&#x0D; We compare COAs generated using this new approach to results of our previous MCA that uses a more traditional approach of aggregating objectives or preferences into a single weighted utility function and optimizing with respect to that joint utility to find multiple optimal and near-optimal solutions. In contrast to previously emitted behaviors, which often reflected many small differences in utility or positioning, analyzing the trade-offs among multiple objectives produces more meaningfully diverse solutions. In an experiential training context, this diversity supports replaying scenarios with automated units that have distinct preferences and priorities. We summarize our results with evaluation metrics showing a set of COAs can appropriately represent doctrinal constraints with diverse, reasonable, and explainable trade-offs between objectives.
DanfeNER - Named Entity Recognition in Nepali Tweets

Twitter allows users to easily post tweets on any subject or event anytime, generating massive amounts of rich text content on diverse topics. Automated methods such as Named Entity Recognition (NER) are required to process the massive tweet data. Processing tweets, however, poses a special challenge as they are informal posts with incomplete context and often contain acronyms, hashtags, misspellings, abbreviations, and URLs due to length constraints. This paper presents the first systematic study of NER in Nepali tweets corresponding to five different entity types: Person Name (PER), Location (LOC), Organization (ORG), Date (DAT), and Event (EVT). We develop DanfeNER, the first human-labeled high-quality NER benchmark data sets for the low-resource language Nepali. DanfeNER contains 5,366 records and 3,463 entities in its train set and 2,301 records and 1,503 entities in its test set. Using this data set, we benchmark several state-of-the-art Nepali monolingual and multilingual transformer models, obtaining micro-averaged F1 scores up to 81%.
DynamicG2B: Dynamic Node Classification with Layered Graph Neural Networks and BiLSTM

Most studies in graph theory assume that graphs are static, but in reality, graph structures and features change over time, leading to the concept of dynamic graphs, which is an under-researched area. Contemporary research in dynamic graph representation learning typically treats different snapshots of the graph as separate entities, disregarding the benefits of incorporating temporal information. While some techniques try to solve this problem using recurrent neural network-based solutions, these approaches still face the challenge of the vanishing or exploding gradient problem and complicated training procedures. To address these issues, we propose DynamicG2B, a BiLSTM-based graph neural architecture that computes node representations guided by attention using neighborhood aggregation. Our method applies relevant attention weights at different time steps to classify nodes in a supervised manner, utilizing dynamic edges and node feature information. Our evaluation of two benchmark datasets shows that DynamicG2B outperforms seven state-of-the-art baseline models in node classification in dynamic graphs. Additionally, our analysis of attention weights opens up opportunities for further research into exploring the importance of relationships among graph nodes.
Improving Word Embedding Using Variational Dropout

Pre-trained word embeddings are essential in natural language processing (NLP). In recent years, many post-processing algorithms have been proposed to improve the pre-trained word embeddings. We present a novel method - Orthogonal Auto Encoder with Variational Dropout (OAEVD) for improving word embeddings based on orthogonal autoencoders and variational dropout. Specifically, the orthogonality constraint encourages more diversity in the latent space and increases semantic similarities between similar words, and variational dropout makes it more robust to overfitting. Empirical evaluation on a range of downstream NLP tasks, including semantic similarity, text classification, and concept categorization shows that our proposed method effectively improves the quality of pre-trained word embeddings. Moreover, the proposed method successfully reduces the dimensionality of pre-trained word embeddings while maintaining high performance.
A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data

The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values. Therefore, finding the optimal values of these hyperparameters is integral in improving the prediction accuracy of an ML algorithm and the model selection. However, manually searching for the best configuration is a tedious task, and many AutoML (Automated Machine Learning) frameworks have been proposed recently to help practitioners solve this problem. Hyperparameters are the values or configurations that control the algorithm’s behavior while building the model. Hyperparameter optimization (HPO) is the guided process of finding the best combination of hyperparameters that delivers the best performance on the data and task at hand in a reasonable amount of time. In this work, we compare the performance of two frequently used AutoML HPO frameworks, Optuna and HyperOpt, on popular OpenML tabular datasets to identify the best framework for tabular data. The results of the experiments show that Optuna performs better than HyperOpt, whereas HyperOpt is the fastest for hyperparameter optimization.
Evaluation of Techniques for Sim2Real Reinforcement Learning

Reinforcement learning (RL) has demonstrated promising results in transferring learned policies from simulation to real-world environments. However, inconsistencies and discrepancies between the two environments cause a negative transfer. The phenomenon is commonly known as the “reality gap.” The reality gap prevents learned policies from generalizing to the physical environment. This paper aims to evaluate techniques to improve sim2real learning and bridge the reality gap using RL. For this research, a 3-DOF Stewart Platform was built virtually and physically. The goal of the platform was to guide and balance the marble towards the center of the Stewart platform. Custom API was created to induce noise, manipulate in-game physics, dynamics, and lighting conditions, and perform domain randomization to improve generalization. Two RL algorithms; Q-Learning and Actor-Critic were implemented to train the agent and to evaluate the performance in bridging the reality gap. This paper outlines the techniques utilized to create noise, domain randomization, perform training, results, and observations. Overall, the obtained results show the effectiveness of domain randomization and inducing noise during the agents' learning process. Additionally, the findings provide valuable insights into implementing sim2real RL algorithms to bridge the reality gap.
An Introduction to Neural Networks

Neural networks ares an important type of machine learning that have generated significant interest due to their success on difficult problems.. This tutorial will provide an introduction and a high-level overview of artificial neural networks. It will include the biological basis and the inspiration for these methods, and their colorful history. An explanation of deep learning and deep belief networks, hardware acceleration, and case studies will be used to illustrate how the technology works and the direction in which it is developing.
Visual Episodic Memory-based Exploration

In humans, intrinsic motivation is an important mechanism for open-ended cognitive development; in robots, it has been shown to be valuable for exploration. An important aspect of human cognitive development is episodic memory which enables both the recollection of events from the past and the projection of subjective future. This paper explores the use of visual episodic memory as a source of intrinsic motivation for robotic exploration problems. Using a convolutional recurrent neural network autoencoder, the agent learns an efficient representation for spatiotemporal features such that accurate sequence prediction can only happen once spatiotemporal features have been learned. Structural similarity between ground truth and autoencoder generated images is used as an intrinsic motivation signal to guide exploration. Our proposed episodic memory model also implicitly accounts for the agent's actions, motivating the robot to seek new interactive experiences rather than just areas that are visually dissimilar. When guiding robotic exploration, our proposed method outperforms the Curiosity-driven Variational Autoencoder (CVAE) at finding dynamic anomalies.
MOD3NN: A Framework for Automatic Signal Modulation Detection Using 3D CNN

In this work, we present an application of a three-dimensional convolutional neural network for the task of automatic modulation recognition from raw I/Q signal data. Raw I/Q signal data exhibits a special “helical” structure that can be exploited with three-dimensional convolutions (3D convolutions) to learn spatio-temporal features from the signal for the problem of modulation recognition. By tweaking the convolutional filters to learn the helical symmetry of the data, we can design a shallow network for automatic modulation recognition (AMR). We present the results of our experiments with raw I/Q signal data collected in an uncalibrated radio frequency (RF) environment using several different modulation schemes. We show that with our methods and implementation, we can achieve around 99 % accuracy for automatic modulation recognition, for a variety of practical modulation techniques without the need for explicit feature engineering.
Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems

Fashion industry is driven by fashion cycles, in which a fashion item is launched, rises to mainstream appeal and becomes a trend, then diminishes and eventually becomes obsolete. These properties make it critical to incorporate temporal information when adapting a recommendation framework to be employed in the fashion domain. However, an industry standard real-world recommendation architecture entails numerous phases, including data preparation, establishing and training recommender models, filtering and fulfilling revenue-based user needs. The contributions of the presented work are twofold. We first formalise the multi-stage recommendation pipeline by including the time dimension intrinsically present in the fashion data. We then present a study to incorporate explicit fashion domain characteristics into the presented pipeline. Finally, we conduct comprehensive experimentation on a real-world web-scale fashion dataset released by H\&amp;M, illustrating how including domain knowledge in the multi-stage framework can lead to significantly improvement on the final recommendation performance.
Comparative Study of Information Security Risk Assessment Model

Analysis of security risks is crucial to the management of information systems.The same risks brought on by information assets, their potential threats, and vulnerabilities, as well as security measures, are to be prevented by security risk analysis models.Today, the majority of these models are utilized to assess risk value without recognizing the organization's security issues.As a result, decision-makers are unable to choose the best methodology for addressing security concerns.In this research paper, we have developed a Comparative Framework to carry out a thorough comparative analysis of the various models that underpin the information risk assessment process.Next, we have evaluated existing information security risk assessment models through this framework.
Phishing Detection Implementation using Databricks and Artificial Intelligence

Phishing is a fraudulent activity that includes tricking people into disclosing personal or financial information by impersonating a legitimate company or individual.The increasingly complex nature of phishing has drawn the attention of criminals, who see it as a profitable and simple way to get sensitive information.As a result of the negative impact of phishing assaults on both individuals and companies, efficient detection and prevention measures have been developed.This document overviews numerous approaches for detecting and thwarting phishing attacks.The research introduces the Phishcatch algorithm, which has shown substantial success in identifying phishing emails and alerting consumers to fraudulent attempts.Phishcatch studies user behavior on websites and limits access if any suspicious behavior is found.Phishcatch is a vital instrument in the battle against phishing attempts, with an accuracy and detection rate of 90%.Furthermore, this article explains the steps in developing, testing and implementing successful anti-phishing algorithms.
ICT Omnipresence and the Rights to Privacy

The development of technology and online communication has also led to the appearance of what seem to be certain new types of criminal activity. Cyber security faced difficulties as a result of the rise in criminal behavior and the potential for the creation of new types of criminal activity online. This study evaluates the value of cyber security in an effort to stop cybercrimes based on this assumption. The research methodology used in this work is triangulation, which entails using many approaches to corroborate findings. The goal of this essay is to strike a balance between the requirement for cybersecurity and cybercrimes prevention and the right to privacy.
Keefektifan Pelatihan Teknik Parafrase dan Teknik Sitasi Menggunakan Mendeley Bagi Mahasiswa PGSD untuk Menghindari Plagiasi

This study aims to determine the effectiveness of training in paraphrasing and citation techniques using Mendeley paraphrasing and citation skills for PGSD students, Mataram University. Type of this research is a quantitative research using a pre-experimental design method type one group pretest-posttest. This research is included in the population study because all participants are the research subjects. The instruments used were 10 multiple choice questions and 5 short questions which were used as pretest and posttest questions. The data analysis technique used a normality test as a prerequisite for analysis, paired sample t-test, an effect size test, and an N-Gain test. All data are normally distributed as indicated by the normality test. The paired sample t-test test shows a difference between the pretest and posttest values ​​where the posttest value is higher than the pretest value. Test effect size d = -1.90521 is in the small category because &lt;0.05. This means that training in paraphrasing and citation techniques using Mendeley has little effect on students' ability to paraphrase and cite. N-Gain test with SPSS 18.0, the average N-Gain score was 54.9271 or 55% or included in the less effective category. The conclusion is that training in paraphrasing and citation techniques using Mendeley to avoid plagiarism is less effective in improving paraphrasing and citation skills for 7th-semester students of PGSD, University of Mataram.