Reviewerly/exHarmony huggingface dataset:
Learning Cross-lingual Mappings for Data Augmentation to Improve Low-Resource Speech Recognition

augmentation to improve low-resource speech recognition.
An Integrated Support System for People with Intellectual Disability

People with Intellectual Disability (ID) encounter several problems in their daily living regarding their needs, activities, interrelationships, and communication. On this concept, an interactive platform is proposed, aiming to provide personalized recommendations for information and entertainment, including creative and educational activities, tailored to the special users’ needs of this population. Furthermore, the proposed platform integrates capabilities of automatic recognition of health related emergencies, such as fever, oxygen saturation decline and tachycardia, as well as location tracking and detection of wandering behavior based on smartwatch/smartphone sensors, while providing automated assistance and appropriate alerts and notifications to the caregivers.
Consistency of the LSE for Chirp Signal Parameters in the Models with Strongly and Weakly Dependent Noise

A time continuous statistical model of multiple chirp signal observed against the background of strongly-dependent stationary Gaussian noise is considered in the paper. Strong consistency of the least squares estimates for such a trigonometric regression model parameters is proved.
Analyzing Cyberbullying Negative Content on Twitter Social Media with the RoBERTa Method

Social media is an online-based communication tool that can make it easier for users to interact among fellow users without any area and time restrictions. Indonesia has the highest number of social media users in the world. The Twitter social media platform is a place where users can pour out their whole hearts in the form of tweets. Free and diverse interactions on Twitter have a considerable influence on the psychological condition of its users. Cyberbullying or online bullying is an act of humiliating or hurting other people's feelings intentionally and repeatedly on social media, messages, or in other ways. In this study, the RoBERTa classification method was used to detect cyberbullying tweets with a best accuracy score of 86.9% and an f1-score of 77.5%.
Sentiment Analysis on Twitter Social Media towards Shopee E-Commerce through Support Vector Machine (SVM) Method

Shopee is e-commerce widely accessed and used in this era. Many people use Shopee because the products offered are cheaper and more affordable. Despite the fact that Shopee is a well-known e-commerce, it still requires responses and suggestions from the public to maintain or improve the features required. In this study, public sentiment analysis was carried out on Twitter social media related to the Shopee marketplace. This study collected data that contained tweets from predetermined keywords and used Word2Vec and Support Vector Machine classification methods. The use of Word2Vec influenced the level of accuracy so that it increased for each SVM kernel. Meanwhile, the best hyperparameter tuning was found in the polynomial kernel, with an accuracy rate of 93.20%.
IoT Based Cubicle Occupancy Indicator for Public Toilets

The COVID-19 pandemic has subjected everyone to an unprecedented shift in the way of their everyday life in which fundamental social and work etiquette evolved with taking extra safety measures to contain the virus.A new normal has been introduced to prevent the virus's further spread, such as wearing a mask, observing proper hand hygiene, and most importantly, physical distancing by staying 6 feet away from others.Public bathrooms are inevitable to long lines that would be deemed unsafe in the pandemic since a person's exposure time is a significant factor with the transmission.The system design in this study would help improve user experience in public restrooms by eliminating long waiting lines caused by poor communication on a cubicle's vacancy status.With utilizing the technology of IoT (Internet of Things), the public would be able to use restrooms with ease without worrying about long queues.It would also help reduce interaction with other people, which is functional in the ongoing pandemic and could also promote a safe way of using public restrooms by encouraging the public to observe social distancing.
Tagalog Sentiment Analysis Using Deep Learning Approach With Backward Slang Inclusion

As the internet users in the Philippines have become increasingly rampant, Tagalog Sentiment Analysis is essential when determining general sentiments correlated to a topic.Among Tagalog tweets, backward slang has gained increased popularity on social media.Deep learning algorithms such as Long-Short Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) have been proven more effective than traditional machine learning algorithms.This study explored and evaluated the effects of backward slang on two deep learning models using performance metrics for classification models.Results show that backward slang helps in the expressivity of textual data, given the improvement in overall sentiment scores.
Reading-Literacy: Characteristics Of Students In The "Teaching Campus" Program Batch 2

Various gaps exist in every education system. This will impact students' literacy skills, as evidenced by the PISA ranking every year indonesia is still in the lower class. The purpose of this study was to describe the reading literacy ability of the students of SD N 1 Brajan towards the class 2 teaching campus program. This study was qualitative research with a phenomenological design. The subjects taken were fifth-grade students of SD N 1 Brajan for the academic year 2021/2022, with the technique of taking the subject using Snowball Sampling. The time used by the researcher was 30 days at SD N 1 Brajan, Klaten district, Central Java province. The auxiliary instrument used is the class 2 teaching campus program: wall magazines, literacy trees, and literacy tips. The data obtained were drawn using interviews and observations. The validity of the data is required using triangulation methods with inductive data analysis techniques. The results showed that students' reading literacy skills were high, with various characteristics that appeared for each student in each indicator. Hopefully, this research can illustrate that campus teaching activities have a good impact on schools, especially schools with the 3T predicate.
Utilizing Artificial Intelligence (AI) Technology to support MSMEs businesses: ChatGPT

In the digital age, MSMEs must continuously innovate and devise impactful marketing strategies to stay competitive in the midst of intense business competition. Embracing digital marketing is crucial, but it comes with challenges, particularly in creating compelling promotional texts. To address this, the author assisted “Kedai Uni Im”, a MSMEs specializing in Minang Kabau delicacies, by utilizing ChatGPT for marketing content creation. Training and mentoring were provided as part of the science and technology diffusion method. The study's results indicate positive outcomes, including successful implementation of promotional text designs using ChatGPT, increased confidence in creating social media content, and more regular updates on social media platforms. Going forward, it is recommended to explore further benefits of ChatGPT for supporting MSMEs. Additionally, considering the significance and benefits of using ChatGPT for MSMEs, these materials can be replicated and shared broadly with other MSMEs players.
A Converse for Fault-tolerant Quantum Computation

As techniques for fault-tolerant quantum computation keep improving, it is natural to ask: what is the fundamental lower bound on space overhead? In this paper, we obtain a lower bound on the space overhead required for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>&amp;#x03F5;</mml:mi></mml:math>-accurate implementation of a large class of operations that includes unitary operators. For the practically relevant case of sub-exponential depth and sub-linear gate size, our bound on space overhead is tighter than the known lower bounds. We obtain this bound by connecting fault-tolerant computation with a set of finite blocklength quantum communication problems whose accuracy requirements satisfy a joint constraint. The lower bound on space overhead obtained here leads to a strictly smaller upper bound on the noise threshold for noise that are not degradable. Our bound directly extends to the case where noise at the outputs of a gate are non-i.i.d. but noise across gates are i.i.d.
Bird’s Eye View feature selection for high-dimensional data

Abstract In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird’s Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.
Machine Learning Techniques for Analyzing Survival Data of Breast Cancer Patients in Baghdad

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal component algorithm, The second kernel principal component algorithm, and The last is the faster ICA algorithm. Then the important features extracted in the three algorithms for features extraction will be entered into machine learning algorithms: The first K nearest neighbor algorithm, The second survival tree algorithm (or regression tree), and the last random survival forests algorithm.&#x0D; Two criteria for comparing the best models to estimate survival have relied on the MSE and the C-Index. The best model for estimating and predicting survival is the use of the fastest ICA algorithm with the random survival forest algorithm that gave the lowest amount to MSE and the highest value to the C-Index. Accordingly, we recommend doctors and medical professionals in Iraq adopt this model to estimate survival for patients with breast cancer.
Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images

Abstract Deep learning shows promise for medical image analysis but lacks interpretability, hindering adoption in healthcare. Attribution techniques that explain model reasoning may increase trust in deep learning among clinical stakeholders. This paper aimed to evaluate attribution methods for illuminating how deep neural networks analyze medical images. Using adaptive path-based gradient integration, we attributed predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models. The technique highlighted possible biomarkers, exposed model biases, and offered insights into the links between input and prediction. Our analysis demonstrates the method's ability to elucidate model reasoning on these datasets. The resulting attributions show promise for improving deep learning transparency for domain experts by revealing the rationale behind predictions. This study advances model interpretability to increase trust in deep learning among healthcare stakeholders.
Optimal time for sensing in open quantum systems

We study the time-dependent quantum Fisher information (QFI) in an open quantum system satisfying the Gorini-Kossakowski-Sudarshan-Lindblad master equation. Most of our work concerns a simple three-level model for a single probe coupled to a sink, although some results for multiple probes are also given. We also study the dynamics of the system from an effective non-Hermitian dynamics standpoint and use it to understand the scaling of the QFI when multiple probes are used. A focus of our work is how the QFI is maximized at certain times, suggesting that the best precision in parameter estimation can be achieved by focusing on these times. The propagation of errors analysis allows us to confirm and better understand this idea. We also propose an optimum-time-based parameter estimation procedure involving relatively low-resource-consuming measurements followed by higher-resource-consuming measurements and demonstrate it in simulation.
Fake Product Identification System Using Blockchain Technology

The Fake Product Identification System (FPIS) is a cutting-edge solution that tackles the global issue of counterfeit products by leveraging blockchain technology. In this system, each genuine product receives a unique digital identifier and is recorded on the blockchain during its production phase, ensuring a tamper-proof and traceable record. When consumers purchase a product, they can easily verify its authenticity through a mobile application that interfaces with the blockchain to retrieve relevant information. By eliminating the need for centralized databases and intermediaries, the FPIS provides a transparent and secure environment, protecting sensitive data and preventing potential fraud. With its decentralized consensus mechanism, the system maintains the integrity and immutability of product information, ensuring consumers can make informed choices and trust the authenticity of their purchases. Ultimately, the Fake Product Identification System aims to enhance consumer trust and revolutionize supply chain management, promoting a safer marketplace for all stakeholders. Keywords-Consumer Protection, Product Authenticity, Decentralization, Immutability.
Agent-Based Simulation And Analysis Of Infrastructure-As-Code Process To Build And Manage Cloud Environment

Widespread cloud systems present new challenges time and time again. An essential element of such environments is their management. The Infrastructure as Code model has been gaining popularity for some time. In work presented here, we have proposed an agent-based approach to process execution within the Infrastructure as Code approach and have performed several numerical experiments. The work also includes an original formal agent model of the system. The results obtained allow us to develop trade-offs regarding computational demand and utilization.
Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction

Document-level relation extraction (DocRE) attracts more research interest recently.While models achieve consistent performance gains in DocRE, their underlying decision rules are still understudied: Do they make the right predictions according to rationales?In this paper, we take the first step toward answering this question and then introduce a new perspective on comprehensively evaluating a model.Specifically, we first conduct annotations to provide the rationales considered by humans in DocRE.Then, we conduct investigations and reveal the fact that: In contrast to humans, the representative state-of-the-art (SOTA) models in DocRE exhibit different decision rules.Through our proposed RE-specific attacks, we next demonstrate that the significant discrepancy in decision rules between models and humans severely damages the robustness of models and renders them inapplicable to real-world RE scenarios.After that, we introduce mean average precision (MAP) to evaluate the understanding and reasoning capabilities of models.According to the extensive experimental results, we finally appeal to future work to consider evaluating both performance and the understanding ability of models for the development of their applications.We make our annotations and code publicly available 1 .
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning

A number of recent benchmarks seek to assess how well models handle natural language negation.However, these benchmarks lack the controlled example paradigms that would allow us to infer whether a model had learned how negation morphemes semantically scope.To fill these analytical gaps, we present the Scoped Negation NLI (ScoNe-NLI) benchmark, which contains contrast sets of six examples with up to two negations where either zero, one, or both negative morphemes affect the NLI label.We use ScoNe-NLI to assess fine-tuning and in-context learning strategies.We find that RoBERTa and DeBERTa models solve ScoNe-NLI after many shot fine-tuning.For in-context learning, we test InstructGPT models and find that most prompt strategies are not successful, including those using step-by-step reasoning.To better understand this result, we extend ScoNe with ScoNe-NLG, a sentence completion test set that embeds negation reasoning in short narratives.Here, InstructGPT is successful, which reveals the model can correctly reason about negation, but struggles to do so on prompt-adapted NLI examples outside of its core pretraining regime.
Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction

In clinical and other specialized domains, data are scarce due to their confidential nature.This lack of data is a major problem when finetuning language models.Nevertheless, very large language models (LLMs) are promising for the medical domain but cannot be used directly in healthcare facilities due to data confidentiality issues.We explore an approach of annotating training data with LLMs to train smaller models more adapted to our problem.We show that this method yields promising results for information extraction tasks.
A Better Way to Do Masked Language Model Scoring

Estimating the log-likelihood of a given sentence under an autoregressive language model is straightforward: one can simply apply the chain rule and sum the log-likelihood values for each successive token.However, for masked language models (MLMs), there is no direct way to estimate the log-likelihood of a sentence.To address this issue, Salazar et al. (2020) propose to estimate sentence pseudolog-likelihood (PLL) scores, computed by successively masking each sentence token, retrieving its score using the rest of the sentence as context, and summing the resulting values.Here, we demonstrate that the original PLL method yields inflated scores for out-ofvocabulary words and propose an adapted metric, in which we mask not only the target token, but also all within-word tokens to the right of the target.We show that our adapted metric (PLL-word-l2r) outperforms both the original PLL metric and a PLL metric in which all within-word tokens are masked.In particular, it better satisfies theoretical desiderata and better correlates with scores from autoregressive models.Finally, we show that the choice of metric affects even tightly controlled, minimal pair evaluation benchmarks (such as BLiMP), underscoring the importance of selecting an appropriate scoring metric for evaluating MLM properties. 1
Improving Multi-task Stance Detection with Multi-task Interaction Network

Stance detection aims to identify people's standpoints expressed in the text towards a target, which can provide powerful information for various downstream tasks.Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection.However, they neglect to explore capturing the fine-grained task-specific interaction between stance detection and sentiment tasks, thus degrading performance.To address this issue, this paper proposes a novel multi-task interaction network (MTIN) for improving the performance of stance detection and sentiment analysis tasks simultaneously.Specifically, we construct heterogeneous taskrelated graphs to automatically identify and adapt the roles that a word plays with respect to a specific task.Also, a multi-task interaction module is designed to capture the wordlevel interaction between tasks, so as to obtain richer task representations.Extensive experiments on two real-world datasets show that our proposed approach outperforms state-ofthe-art methods in both stance detection and sentiment analysis tasks.
UAV Tracking Based on Correlation Filters With Dynamic Aberrance-Repressed Temporal Regularizations

As a significant research direction in remote sensing fields, unmanned aerial vehicles (UAVs) tracking has achieved rapid development in recent years.However, due to limited power and computation resources on aerial platforms, the tracking methods deployed on UAVs usually require high computational efficiency and performance.In addition, various challenges (i.e., similar object, background clutter and occlusion) have inevitably occurred during the UAV tracking phase.Therefore, considering the above issues comprehensively, this paper proposes a dynamic aberrance-repressed temporal regularized correlation filter to achieve stable tracking in UAV remote sensing videos.First, we have introduced the aberrance-repressed temporal regularizations into the discriminative correlation filter (DCF) framework.Second, a novel objective loss function is constructed to adjust the strength of each regularization for training the filter.Then, a new judgment mechanism based on the response variation is exploited to reflect the response fluctuation and applied to tune parameters of both regularizations.Finally, comprehensive experiments are done on three different UAV benchmarks, i.e., UAV123@10fps, UAVDT and VisDrone2018, to verify the performance of our tracker and have demonstrated that our tracker achieves superior performance against other total 25 state-of-the-art trackers while reaching ∼35 FPS on a single CPU.
Impact of H&amp;E Stain Normalization on Deep Learning Models in Cancer Image Classification: Performance, Complexity, and Trade-Offs

Accurate classification of cancer images plays a crucial role in diagnosis and treatment planning. Deep learning (DL) models have shown promise in achieving high accuracy, but their performance can be influenced by variations in Hematoxylin and Eosin (H&amp;E) staining techniques. In this study, we investigate the impact of H&amp;E stain normalization on the performance of DL models in cancer image classification. We evaluate the performance of VGG19, VGG16, ResNet50, MobileNet, Xception, and InceptionV3 on a dataset of H&amp;E-stained cancer images. Our findings reveal that while VGG16 exhibits strong performance, VGG19 and ResNet50 demonstrate limitations in this context. Notably, stain normalization techniques significantly improve the performance of less complex models such as MobileNet and Xception. These models emerge as competitive alternatives with lower computational complexity and resource requirements and high computational efficiency. The results highlight the importance of optimizing less complex models through stain normalization to achieve accurate and reliable cancer image classification. This research holds tremendous potential for advancing the development of computationally efficient cancer classification systems, ultimately benefiting cancer diagnosis and treatment.