Reviewerly/exHarmony huggingface dataset:
An Optimized Machine Learning and Deep Learning Framework for Facial and Masked Facial Recognition

In this study, we aimed to find an optimized approach to improving facial and masked facial recognition using machine learning and deep learning techniques. Prior studies only used a single machine learning model for classification and did not report optimal parameter values. In contrast, we utilized a grid search with hyperparameter tuning and nested cross-validation to achieve better results during the verification phase. We performed experiments on a large dataset of facial images with and without masks. Our findings showed that the SVM model with hyperparameter tuning had the highest accuracy compared to other models, achieving a recognition accuracy of 0.99912. The precision values for recognition without masks and with masks were 0.99925 and 0.98417, respectively. We tested our approach in real-life scenarios and found that it accurately identified masked individuals through facial recognition. Furthermore, our study stands out from others as it incorporates hyperparameter tuning and nested cross-validation during the verification phase to enhance the model's performance, generalization, and robustness while optimizing data utilization. Our optimized approach has potential implications for improving security systems in various domains, including public safety and healthcare.
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges

Sanjana Ramprasad, Jered Mcinerney, Iain Marshall, Byron Wallace. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. 2023.
G-BERT: An Efficient Method for Identifying Hate Speech in Bengali Texts on Social Media

The rapid increase in Internet users has increased online concerns such as hate speech, abusive texts, and harassment. In Bangladesh, hate text in Bengali is frequently used on various social media platforms to condemn and abuse individuals. However, Research on recognizing hate speech in Bengali texts is lacking. The pervasive negative impact of hate speech on individuals’ well-being and the urgent need for effective measures to address hate speech in Bengali texts have created a significant research gap in the Bengali hate speech detection field. This study suggests a technique for identifying hate speech in Bengali social media posts that may harm individuals’ sentiments. Our approach utilizes the Bidirectional Encoder Representations from Transformers (BERT) architecture to extract Bengali text properties, whereas hate speech is categorized using a Gated Recurrent Units (GRU) model with a Softmax activation function. We propose a new model, G-BERT, that combines both models. We compared our model’s performance with several other algorithms and achieved an accuracy, precision, recall, and F1-score of 95.56%, 95.07%, 93.63%, and 92.15%, respectively. Our proposed model outperformed all other classification algorithms tested. Our findings show that the strategy we have suggested is successful in locating hate speech in Bengali texts posted on social media platforms, which can aid in mitigating online hate speech and promoting a more respectful online environment.
High-order multi-view clustering for generic data

Graph-based multi-view clustering has achieved better performance than most non-graph approaches. However, in many real-world scenarios, the graph structure of data is not given or the quality of initial graph is poor. Additionally, existing methods largely neglect the high-order neighborhood information that characterizes complex intrinsic interactions. To tackle these problems, we introduce an approach called high-order multi-view clustering (HMvC) to explore the long-distance structural information of generic data. Firstly, graph filtering is applied to encode structure information, which unifies the processing of attributed graph data and non-graph data in a single framework. Secondly, up to infinity-order intrinsic relationships are exploited to enrich the learned graph. Thirdly, to explore the consistent and complementary information of various views, an adaptive graph fusion mechanism is proposed to achieve a consensus graph. Comprehensive experimental results on both non-graph and attributed graph data show the superior performance of our method with respect to various state-of-the-art techniques, including some deep learning methods.
Explainability meets uncertainty quantification: Insights from feature-based model fusion on multimodal time series

Feature importance evaluation is one of the prevalent approaches to interpreting Machine Learning (ML) models. A drawback of using these methods for high-dimensional datasets is that they often lead to high-dimensional explanation output that hinders human analysis. This is especially true for explaining multimodal ML models, where the problem’s complexity is further exacerbated by the inclusion of multiple data modalities and an increase in the overall number of features. This work proposes a novel approach to lower the complexity of feature-based explanations. The proposed approach is based on uncertainty quantification techniques, allowing for a principled way of reducing the number of modalities required to explain the model’s predictions. We evaluated our method in three multimodal datasets comprising physiological time series. Results show that the proposed method can reduce the complexity of the explanations while maintaining a high level of accuracy in the predictions. This study illustrates an innovative example of the intersection between the disciplines of uncertainty quantification and explainable AI.
Detecting Abnormal Behaviors in Dementia Patients Using Lifelog Data: A Machine Learning Approach

In this paper, a proof-of-concept method for detecting abnormal behavior in dementia patients based on a single case study is proposed. This method incorporates the collection of lifelog data using affordable sensors and the development of a machine-learning-based system. Such an approach has the potential to enable the prompt detection of abnormal behavior in dementia patients within nursing homes and to send alerts to caregivers, which could potentially reduce their workload and decrease the risk of accidents and injuries. In a proof-of-concept experiment conducted on a single dementia patient in a Korean nursing home, the proposed system, specifically the multilayer perceptron model, demonstrated exceptional performance, achieving an accuracy of 0.99, a precision of 1.00, a recall of 1.00, and an F1 score of 1.00. While being cost-effective and adaptable to various nursing homes, these results should be interpreted as preliminary, being based on a limited sample. Future research is aimed at validating and improving the performance of the abnormal behavior detection system by expanding the experiments to include lifelog data from multiple nursing homes and a larger cohort of dementia patients. The potential application of this system extends beyond healthcare and medical fields, reaching into smart home environments and various other facilities. This study underscores the potential of this system to enhance patient safety, alleviate family concerns, and reduce societal costs, thereby contributing to the improvement of the quality of life for dementia patients.
Risk-based tsunami early warning using random forest

A new risk-based tsunami early warning method is developed using Random Forest (RF) with an extensive tsunami monitoring network (S-net) deployed off the Northeastern region of Japan. To consider a wide range of possible tsunami waves that may occur in the future, the RF model is developed using simulated 4000 tsunami wave time series at the S-net sensors. The response variable is the total aggregate loss of buildings caused by the tsunami, and the explanatory variables include earthquake information (magnitude, epicenter latitude, and epicenter longitude) and tsunami wave amplitudes at the S-net sensors. Unlike the conventional tsunami early warning method for predicting the tsunami wave amplitude along the shoreline, the response variable adopted in this study is a tsunami risk metric that reflects the tsunami impact to people and assets in coastal areas. The RF model is suitable for predicting highly nonlinear and scattered tsunami loss due to its characteristics of nonparametric regression and ensemble learning. The result indicates that compared with conventional linear regression-based algorithms, RF predicts the tsunami loss significantly better, reducing the mean square error by 90%. Furthermore, RF does not rely heavily on earthquake information, making it useful for announcing early warning for tsunamis generated by other sources.
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering

Visual language data such as plots, charts, and infographics are ubiquitous in the human world.However, state-of-the-art visionlanguage models do not perform well on these data.We propose MATCHA (Math reasoning and Chart derendering pretraining) to enhance visual language models' capabilities in jointly modeling charts/plots and language data.Specifically we propose several pretraining tasks that cover plot deconstruction and numerical reasoning which are the key capabilities in visual language modeling.We perform the MATCHA pretraining starting from Pix2Struct, a recently proposed imageto-text visual language model.On standard benchmarks such as PlotQA and ChartQA, the MATCHA model outperforms state-of-the-art methods by as much as nearly 20%.We also examine how well the MATCHA pretraining transfers to domains such as screenshots, textbook diagrams, and document figures and observe overall improvement, verifying the usefulness of MATCHA pretraining on broader visual language tasks.
AVATAR: A Parallel Corpus for Java-Python Program Translation

Program translation refers to migrating source code from one programming language to another.It has tremendous practical value in software development, as porting software across languages is time-consuming and costly.Automating program translation is of paramount importance in software migration, and recently researchers explored unsupervised approaches due to the unavailability of parallel corpora.However, the availability of pre-trained language models for programming languages enables supervised fine-tuning with a small number of labeled examples.Therefore, we present AVATAR, a collection of 9,515 programming problems and their solutions written in two popular languages, Java and Python.AVATAR is collected from competitive programming sites, online platforms, and open-source repositories.Furthermore, AVATAR includes unit tests for 250 examples to facilitate functional correctness evaluation.We benchmark several pretrained language models fine-tuned on AVATAR.Experiment results show that the models lack in generating functionally accurate code.
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning

Two-Tower Vision-Language (VL) models have shown promising improvements on various downstream VL tasks.Although the most advanced work improves performance by building bridges between encoders, it suffers from ineffective layer-by-layer utilization of uni-modal representations and cannot flexibly exploit different levels of uni-modal semantic knowledge.In this work, we propose ManagerTower, a novel VL model architecture that gathers and combines the insights of pre-trained uni-modal experts at different levels.The managers introduced in each cross-modal layer can adaptively aggregate uni-modal semantic knowledge to facilitate more comprehensive cross-modal alignment and fusion.ManagerTower outperforms previous strong baselines both with and without Vision-Language Pre-training (VLP).With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79.15% accuracy on VQAv2 Test-Std, 86.56% IR@1 and 95.64% TR@1 on Flickr30K.Code and checkpoints
Consistent Prototype Learning for Few-Shot Continual Relation Extraction

Few-shot continual relation extraction aims to continually train a model on incrementally fewshot data to learn new relations while avoiding forgetting old ones.However, current memory-based methods are prone to overfitting memory samples, resulting in insufficient activation of old relations and limited ability to handle the confusion of similar classes.In this paper, we design a new N-way-K-shot Continual Relation Extraction (NK-CRE) task and propose a novel few-shot continual relation extraction method with Consistent Prototype Learning (ConPL) to address the aforementioned issues.Our proposed ConPL is mainly composed of three modules: 1) a prototypebased classification module that provides primary relation predictions under few-shot continual learning; 2) a memory-enhanced module designed to select vital samples and refined prototypical representations as a novel multi-information episodic memory; 3) a consistent learning module to reduce catastrophic forgetting by enforcing distribution consistency.To effectively mitigate catastrophic forgetting, ConPL ensures that the samples and prototypes in the episodic memory remain consistent in terms of classification and distribution.Additionally, ConPL uses prompt learning to extract better representations and adopts a focal loss to alleviate the confusion of similar classes.Experimental results on two commonly-used datasets show that our model consistently outperforms other competitive baselines 1 .
Optimizing Naive Bayes for Arabic Dialect Identification

This article describes the language identification system used by the SUKI team in the 2022 Nuanced Arabic Dialect Identification (NADI) shared task.In addition to the system description, we give some details of the dialect identification experiments we conducted while preparing our submissions.In the end, we submitted only one official run.We used a Naive Bayes-based language identifier with character n-grams from one to four, of which we implemented a new version, which automatically optimizes its parameters.We also experimented with clustering the training data according to different topics.With the macro F1 score of 0.1963 on test set A and 0.1058 on test set B, we achieved the 18th position out of the 19 competing teams.
A precision-centric approach to overcoming data imbalance and non-IIDness in federated learning

Federated learning enables decentralized model training, but the distribution of data across devices presents significant challenges to global model convergence. Existing approaches risk losing the representativeness of local models after model aggregation, calling for a more efficient and robust solution. In this study, we address the model aggregation challenge in federated learning by focusing on improving the performance of global model with class imbalance and non-independent and identically distributed data. We aim to train a global model collaboratively that represents all participating nodes, promoting fairness and ensuring adequate representation of all classes in the model. We propose redistributing local model weights based on their precision-based contributions to each class to enhance the performance and communication efficiency of federated thermal comfort prediction. Our proposed method can assist in allocating more resources and attention to nodes with high precision for underrepresented classes, thereby improving the global model overall performance and fairness. Furthermore, our framework leverages the virtualization capability of digital twins to enable dynamic registration and participation of nodes in the federated learning process in real-time. The developed DT framework enables real-time monitoring and control of the decentralized training. Through evaluation on a real data-set, we demonstrate significant improvements in accuracy and communication efficiency compared to existing methods. Our evaluation shows that the proposed Class Precision-Weighted Aggregation technique Fed-CPWA outperforms Federated Averaging, with higher accuracy of 82.85% and lower communication costs by 31.64%. Our contribution provides a valuable step towards sustainable thermal comfort modeling and furthers the development of fair and robust federated learning techniques.
Smartphone-based fluorescence spectroscopic device for cervical precancer diagnosis: a random forest classification of in vitro data

Cervical cancer can be treated and cured if diagnosed at an early stage. Optical devices, developed on smartphone-based platforms, are being tested for this purpose as they are cost-effective, robust, and field portable, showing good efficiency compared to the existing commercial devices. This study reports on the applicability of a 3D printed smartphone-based spectroscopic device (3D-SSD) for the early diagnosis of cervical cancer. The proposed device has the ability to evaluate intrinsic fluorescence (IF) from the collected polarized fluorescence (PF) and elastic-scattering (ES) spectra from cervical tissue samples of different grades. IF spectra of 30 cervical tissue samples have been analyzed and classified using a combination of principal component analysis (PCA) and random forest (RF)-based multi-class classification algorithm with an overall accuracy above 90%. The usage of smartphone for image collection, spectral data analysis, and display makes this device a potential contender for use in clinics as a regular screening tool.
EFE: End-to-end Frame-to-Gaze Estimation

Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher resolution in the eye regions and having fewer confounding factors (such as clothing and hair) is believed to benefit the final model performance. However, this eye/face patch cropping process is expensive, erroneous, and implementation-specific for different methods. In this paper, we propose a frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze direction from the raw frame out of the camera without any face or eye cropping. Our method demonstrates that direct gaze regression from the raw downscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite the challenges of having very few pixels in the eye region. The proposed method achieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG) estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE, and generalizes well to extreme camera view changes.
Optical control of collective states in one-dimensional ordered atomic chains beyond the linear regime

Driven by the need to develop efficient atom-photon interfaces, recent efforts have proposed replacing cavities by large arrays of cold atoms that can support subradiant or superradiant collective states. In practice, subradiant states are decoupled from radiation, which constitutes a hurdle to most applications. In this work, we study theoretically a protocol that bypasses this limit using a one dimensional chain composed of $N$ three-level atoms in a V-shaped configuration. Throughout the protocol, the chain behaves as a time-varying metamaterial: enabling absorption, storage, and on-demand emission in a spectrally and spatially controlled mode. Taking into account the quantum nature of atoms, we establish the boundary between the linear regime and the nonlinear regime. In the nonlinear regime, we demonstrate that doubly excited states can be coherently transferred from superradiant to subradiant states, opening the way to the optical characterization of their entanglement.
Diagnosis of intermittent faults in Multi-Agent Systems: An SFL approach

Multi-Agent Systems (MAS) can be found in a wide variety of applications, including industrial systems, transportation, software systems and more. In such systems, agents may experience faults that affect the performance of the whole system. However, faulty agents might not consistently experience their fault, but rather in certain conditions. For example, a robot with a faulty rotating mechanism will appear healthy if it is tasked to only move in a straight line. Those faults are called Intermittent Faults. Such faults may cause the entire system to fail, but not always. Previous work proposed diagnosis algorithms for MAS, assuming faulty agents persistently behave abnormally. To the best of our knowledge, intermittent faults in MAS have not been concretely explored. In this paper we formally present a novel problem called Diagnosis of Intermittent Faults in Multi-Agent Systems (DIFMAS): a group of agents are observed across multiple runs. In each run, the success/failure of the agents and the system is observed, aiming to explain all the failed runs by diagnosing which agent(s) are faulty. The contributions of this paper are: (1) formalizing DIFMAS as a Model-Based Diagnosis problem, (2) solving it by presenting a Spectrum-Based Fault Localization (SFL) based method, called Multi-Run SFL-based Diagnosis Algorithm (MRSD). Experiments demonstrate that MRSD's outperforms competing SFL-based algorithms. Moreover, the algorithm's performance increases if planned interactions are considered.
Instant Domain Augmentation for LiDAR Semantic Segmentation

Despite the increasing popularity of LiDAR sensors, perception algorithms using 3D LiDAR data struggle with the sensor-bias problem. Specifically, the performance of perception algorithms significantly drops when an unseen specification of the LiDAR sensor is applied at test time due to the domain discrepancy. This paper presents a fast and flexible LiDAR augmentation method for the semantic segmentation task called LiDomAug. It aggregates raw LiDAR scans and creates a LiDAR scan of any configurations with the consideration of dynamic distortion and occlusion, resulting in instant domain augmentation. Our on-demand augmentation module runs at 330 FPS, so it can be seamlessly integrated into the data loader in the learning framework. In our experiments, learning-based approaches aided with the proposed LiDomAug are less affected by the sensor-bias issue and achieve new state-of-the-art domain adaptation performances on SemanticKITTI and nuScenes dataset without the use of the target domain data. We also present a sensor-agnostic model that faithfully works on the various LiDAR configurations.
Implementation of Character Education through Children's Language Development in Elementary Schools

Abstract: The application of character education through language development is very important in basic education. However, the implementation of character education is in fact not optimal. This study aims to explore the implementation of character education through language development in elementary schools. A qualitative approach was used in this study, and data was collected through observation, interviews and documentation. The population of this study consisted of 522 students and 16 teachers in 15 classes at public elementary school Silih Asah II Cirebon City, Indonesia. The research sample was taken from class 2A which consisted of 40 students, and 10 parents also gave their responses regarding this lesson. The teacher sample consisted of 5 male teachers and 5 female teachers including the school principal. The findings show that the implementation of character education through language development can be done by combining language development activities with character learning activities such as storytelling, singing, and group games involving all students. In addition, the results show that through language development, children can learn communication and social skills that help them build good character. Therefore, this study concludes that language development can be an effective means of implementing character education in elementary schools.