Reviewerly/exHarmony huggingface dataset:
Impacts of a university philosophy outreach program at Kailua High School

The University of Hawai’i at Mānoa’s Uehiro Academy for Philosophy and Ethics in Education (UHM Uehiro Academy) prepares, supports and sustains philosophy for children Hawai‘i (p4cHI) educators, researchers, and students in Hawai‘i and beyond. This paper documents the impact of the Uehiro Academy’s philosophy outreach program at Kailua High School (KHS), a public secondary school on the Hawaiian Island of O’ahu. It describes the twenty year partnership between the University and KHS, which built a foundation for p4cHI to become integrated across the curriculum and within the school culture. To better understand the overall impact of the UHM Uehiro Academy philosophy outreach program on KHS, researchers asked, ‘How has the UHM Uehiro Academy philosophy outreach program impacted the school culture at KHS?’ Qualitative methods were applied to design and conduct a research study, which included interviewing 68 members of the KHS community. Data analysis revealed four main themes: (1) a schoolwide learner-centred pedagogy, (2) a place for learning more than just academics, (3) a community of diverse and connected individuals, (4) a commitment to professional growth for the purpose of a better world. The paper ends with conclusions, implications of the findings, and the researchers reflecting on the findings, including the benefits and challenges of university philosophy outreach programs in the secondary school setting.
PENGEMBANGAN E-LKPD MATA PELAJARAN BAHASA INDONESIA PADA ELEMEN MEMBACA DALAM KURIKULUM MERDEKA DI KELAS IV SEKOLAH DASAR

This research is motivated by problems with student worksheets that still use printed worksheets that are less attractive. Efforts to overcome the problem are developing E-LKPD which is assisted by the liveworksheet application. The research aims to produce E-LKPD products. This type of research is research (R&amp;D) using the 4D development model which consists of several stages: definition, design, development and deployment. The test subjects were grade 4 students at SDN 10 Koto Baru, Dharmasraya Regency. Assessment instruments in the form of validation, practicality, effectiveness. The assessment instrument was validated by three validators, content, language and graphics, then the practicality sheet was determined by the practitioner. And the effectiveness is determined from the learning outcomes of class IV students. The results of the validity of the E-LKPD carried out by three validators obtained an average validity value of 90% categorized as very valid, the average teacher practicality value 90% categorize as very valid. and the average student practicality value - an average of 83% is categorized as very practical and the student effectiveness value of 83.33% is categorized as very effective. It can be concluded that the E-LKPD for Indonesian subjects produced meets the valid category, meaning that it can be applied by students, the category is very practical, it means that it is easy to use by students, and the category is very effective, it means that the E-LKPD for Indonesian subjects can achieve student learning objectives.
Sentiment Analysis on Large-Scale Covid-19 Tweets using Hybrid Convolutional LSTM Based on Naïve Bayes Sentiment Modeling

Millions of lives were affected rapidly throughout the world when the Covid-19 outbreak spread by leaps and bounds. During this catastrophic period, people used to express their condolence as well as emotions through different social networks. In order to analyze the public comments on Twitter, an experimental approach is developed based on popular words regarding this pandemic. In this paper, various NLP-based research works are discussed on sentiment analysis, trend prediction, topic modeling, learning mechanisms, etc. Furthermore, the hybrid deep learning models are developed based on the Naïve Bayes sentiment model to predict the sentiment from the collected huge number of Coronavirus-related tweets. After performing the n-gram analysis, the Covid-19 specific words are extracted based on their popularity. The public sentiment trend has been analyzed using the extracted topics related to Covid-19 and the tweets are classified according to their sentiment scores. The distinguished sentiment ratings are assigned to the collected tweets based on their sentiment class. Then Convo-Sequential and Convo-Bidirectional long-short term networks are trained using fine-grained sentiment-rated tweets to categorize Covid-19 tweets into five different sentiment classes. Finally, our proposed Convo-Sequential and Convo-Bidirectional LSTM models achieved 84.52% and 85.03% of validation accuracy respectively for the first phase dataset whereas using the second phase dataset the models obtained the validation accuracy of 86.58% and 87.22% respectively.
Anomaly Detection with Artificial Intelligence Methods : An Overview

Nowadays, more and more human activities depend on computer-based automated systems. Fully automated (robotized) production lines, energy distribution infrastructures and other urban services or environmental surveillance systems are just some examples of cyber-physical systems that depend entirely on automated control systems. In these cases a significant challenge is to identify abnormal behaviors of the supervised or controlled systems, in order to avoid malfunction or sometimes catastrophic events. Our main research goal was to evaluate the potential of adapting and using AI techniques in the field of anomaly detection. We also developed a platform, called AutomaticAI, which can help specialists in different domains to identify the best approaches to solve a given anomaly detection problem. The platform can select the best AI algorithm and parameter configuration for a given set of data containing normal and abnormal data. The tool was used successfully in a variety of domains, from cyber-physical systems to the medical domain.
Robotic Solutions for the Challenges of Human Labor: Managers Perspective

Managers have to endure some costs in order to develop human resources, which is one of the most significant providers of organizational performance. These costs are expenditures for human resources activities and customer dissatisfaction caused by work accidents and negative personnel behaviors. This study primarily aims to determine the expenditures made by the managers and the behavior of the employees that disturb the guests. Text mining methods is used to identify cost of human labor and employee behaviors referred to by managers' reviews, and association rules is used to find out common cost and behaviors in managers' reviews. The secondary aim of the study is to investigate the managers' intentions to employ robots to solve human-induced challenges. Contrary to the literature, managers believe that robots cannot contribute to the solution of these problems and they don’t think to work with service robots.
Cost-effective Artificial Neural Networks

Deep neural networks (DNNs) have gained huge attention over the last several years due to their promising results in various tasks. However, due to their large model size and over-parameterization, they are recognized as being computationally demanding. Therefore, deep learning models are not well-suited to applications with limited computational resources and battery life. Current solutions to reduce computation costs mainly focus on inference efficiency while being resource-intensive during training. This Ph.D. research aims to address these challenges by developing cost-effective neural networks that can achieve decent performance on various complex tasks using minimum computational resources during training and inference of the network.
A Computational Model of Ostrom's Institutional Analysis and Development Framework (Extended Abstract)

Ostrom's Institutional Analysis and Development (IAD) framework represents a comprehensive theoretical effort to identify and outline the variables that determine the outcome in any social interaction. Taking inspiration from it, we define the Action Situation Language (ASL), a machine-readable logical language to express the components of a multiagent interaction, with a special focus on the rules adopted by the community. The ASL is complemented by a game engine that takes an interaction description as input and automatically grounds its semantics as an Extensive-Form Game (EFG), which can be readily analysed using standard game-theoretical solution concepts. Overall, our model allows a community of agents to perform what-if analysis on a set of rules being considered for adoption, by automatically connecting rule configurations to the outcomes they incentivize.
MFC-Net: A Multiple Feature Complementation Network for Person Re-identification in Aerial Imagery

Person re-identification on Unmanned Aerial Vehicles (UAVs) platforms has received widespread attention, but visual monitoring on UAVs is affected by pixels, angles, and more misalignment, which impairs the discriminative ability of the learning representation and brings new challenges to person re-identification tasks. In order to solve the problem, we propose a Multiple Feature Complementation Network (MFC-Net). MFC-Net consists of two modules, the Parallel Dual Attention Modules (PDAM) and the Multilayer Feature Fusion Module (MFFM). The PDAM consists of two attention branches—Multiscale Channel Attention (MCA) and Weighted Positional Attention (WPA). The PDAM can effectively perceive regional features and better focus the image. The MFFM further fuses two complementary attention features, which effectively solves the problems of angle and misalignment and improves the accuracy of person re-identification. Compared with existing techniques, MFC-Net performs well in the person re-identification of aerial imagery.
Human-AI Collaboration in Recruitment and Selection

My research focuses on using algorithmic systems alongside human collaborators to outperform either humans or machines individually. I specialize in human-machine collaboration in recruiting and selecting talented groups of people.
PathLAD+: An Improved Exact Algorithm for Subgraph Isomorphism Problem

The subgraph isomorphism problem (SIP) is a challenging problem with wide practical applications. In the last decade, despite being a theoretical hard problem, researchers design various algorithms for solving SIP. In this work, we propose three main heuristics and develop an improved exact algorithm for SIP. First, we design a probing search procedure to try whether the search procedure can successfully obtain a solution at first sight. Second, we design a novel matching ordering as a value-ordering heuristic, which uses some useful information obtained from the probing search procedure to preferentially select some promising target vertices. Third, we discuss the characteristics of different propagation methods in the context of SIP and present an adaptive propagation method to make a good balance between these methods. Experimental results on a broad range of real-world benchmarks show that our proposed algorithm performs better than state-of-the-art algorithms for the SIP.
Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges

The goal of Explainable AI (XAI) is to make the reasoning of a machine learning model accessible to humans, such that users of an AI system can evaluate and judge the underlying model. Due to the blackbox nature of XAI methods it is, however, hard to disentangle the contribution of a model and the explanation method to the final output. It might be unclear on whether an unexpected output is caused by the model or the explanation method. Explanation models, therefore, need to be evaluated in technical (e.g. fidelity to the model) and user-facing (correspondence to domain knowledge) terms. A recent survey has identified 29 different automated approaches to quantitatively evaluate explanations. In this work, we take an additional perspective and analyse which toolkits and data sets are available. We investigate which evaluation metrics are implemented in the toolkits and whether they produce the same results. We find that only a few aspects of explanation quality are currently covered, data sets are rare and evaluation results are not comparable across different toolkits. Our survey can serve as a guide for the XAI community for identifying future directions of research, and most notably, standardisation of evaluation.
AutoML for Outlier Detection with Optimal Transport Distances

Automated machine learning (AutoML) has been widely researched and adopted for supervised problems, but progress in unsupervised settings has been limited. We propose `"LOTUS", a novel framework to automate outlier detection based on meta-learning. Our premise is that the selection of the optimal outlier detection technique depends on the inherent properties of the data distribution. We leverage optimal transport to find the dataset with the most similar underlying distribution, and then apply the outlier detection techniques that proved to work best for that data distribution. We evaluate the robustness of our framework and find that it outperforms all state-of-the-art automated outlier detection tools. This approach can also be easily generalized to automate other unsupervised settings.
AudioQR: Deep Neural Audio Watermarks For QR Code

Image-based quick response (QR) code is frequently used, but creates barriers for the visual impaired people. With the goal of ``AI for good", this paper proposes the AudioQR, a barrier-free QR coding mechanism for the visually impaired population via deep neural audio watermarks. Previous audio watermarking approaches are mainly based on handcrafted pipelines, which is less secure and difficult to apply in large-scale scenarios. In contrast, AudioQR is the first comprehensive end-to-end pipeline that hides watermarks in audio imperceptibly and robustly. To achieve this, we jointly train an encoder and decoder, where the encoder is structured as a concatenation of transposed convolutions and multi-receptive field fusion modules. Moreover, we customize the decoder training with a stochastic data augmentation chain to make the watermarked audio robust towards different audio distortions, such as environment background, room impulse response when playing through the air, music surrounding, and Gaussian noise. Experiment results indicate that AudioQR can efficiently hide arbitrary information into audio without introducing significant perceptible difference. Our code is available at https://github.com/xinghua-qu/AudioQR.
Front-to-End Bidirectional Heuristic Search with Consistent Heuristics: Enumerating and Evaluating Algorithms and Bounds

Recent research on bidirectional heuristic search (BiHS) is based on the must-expand pairs theory (MEP theory), which describes which pairs of nodes must be expanded during the search to guarantee the optimality of solutions. A separate line of research in BiHS has proposed algorithms that use lower bounds that are derived from consistent heuristics during search. This paper links these two directions, providing a comprehensive unifying view and showing that both existing and novel algorithms can be derived from the MEP theory. An extended set of bounds is formulated, encompassing both previously discovered bounds and new ones. Finally, the bounds are empirically evaluated by their contribution to the efficiency of the search
On Optimal Strategies for Wordle and General Guessing Games

The recent popularity of Wordle has revived interest in guessing games. We develop a general method for finding optimal strategies for guessing games while avoiding an exhaustive search. Our main contribution are several theorems that build towards a general theory to prove optimality of a strategy for a guessing game. This work is developed to apply to any guessing game, but we use Wordle as an example to present concrete results.
Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Models

Machine learning (ML) technologies are known to be riddled with ethical and operational problems, however, we are witnessing an increasing thrust by businesses to deploy them in sensitive applications. One major issue among many is that ML models do not perform equally well for underrepresented groups. This puts vulnerable populations in an even disadvantaged and unfavorable position. We propose an approach that leverages the power of web search and generative models to alleviate some of the shortcomings of discriminative models. We demonstrate our method on an image classification problem using ImageNet's People Subtree subset, and show that it is effective in enhancing robustness and mitigating bias in certain classes that represent vulnerable populations (e.g., female doctor of color). Our new method is able to (1) identify weak decision boundaries for such classes; (2) construct search queries for Google as well as text for generating images through DALL-E 2 and Stable Diffusion; and (3) show how these newly captured training samples could alleviate population bias issue. While still improving the model's overall performance considerably, we achieve a significant reduction (77.30\%) in the model's gender accuracy disparity. In addition to these improvements, we observed a notable enhancement in the classifier's decision boundary, as it is characterized by fewer weakspots and an increased separation between classes. Although we showcase our method on vulnerable populations in this study, the proposed technique is extendable to a wide range of problems and domains.
IberianVoxel: Automatic Completion of Iberian Ceramics for Cultural Heritage Studies

Accurate completion of archaeological artifacts is a critical aspect in several archaeological studies, including documentation of variations in style, inference of chronological and ethnic groups, and trading routes trends, among many others. However, most available pottery is fragmented, leading to missing textural and morphological cues. Currently, the reassembly and completion of fragmented ceramics is a daunting and time-consuming task, done almost exclusively by hand, which requires the physical manipulation of the fragments. To overcome the challenges of manual reconstruction, reduce the materials' exposure and deterioration, and improve the quality of reconstructed samples, we present IberianVoxel, a novel 3D Autoencoder Generative Adversarial Network (3D AE-GAN) framework tested on an extensive database with complete and fragmented references. We generated a collection of 1001 3D voxelized samples and their fragmented references from Iberian wheel-made pottery profiles. The fragments generated are stratified into different size groups and across multiple pottery classes. Lastly, we provide quantitative and qualitative assessments to measure the quality of the reconstructed voxelized samples by our proposed method and archaeologists' evaluation.
Automated Content Moderation Using Transparent Solutions and Linguistic Expertise

Since the dawn of Transformer-based models, the trade-off between transparency and accuracy has been a topical issue in the NLP community. Working towards ethical and transparent automated content moderation (ACM), my goal is to find where it is still relevant to implement linguistic expertise. I show that transparent statistical models based on linguistic knowledge can still be competitive, while linguistic features have many other useful applications.
Automatic Recognition of the General-Purpose Communicative Functions Defined by the ISO 24617-2 Standard for Dialog Act Annotation (Extended Abstract)

From the perspective of a dialog system, the identification of the intention behind the segments in a dialog is important, as it provides cues regarding the information present in the segments and how they should be interpreted. The ISO 24617-2 standard for dialog act annotation defines a hierarchically organized set of general-purpose communicative functions that correspond to different intentions that are relevant in the context of a dialog. In this paper, we explore the automatic recognition of these functions. To do so, we propose to adapt existing approaches to dialog act recognition, so that they can deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Additionally, we rely on transfer learning processes to address the data scarcity problem. Our experiments on the DialogBank show that this approach outperforms both flat and hierarchical approaches based on multiple classifiers and that each of its components plays an important role in the recognition of general-purpose communicative functions.
Counting and Sampling Models in First-Order Logic

First-order model counting (FOMC) is the task of counting models of a first-order logic sentence over a given set of domain elements. Its weighted variant, WFOMC, generalizes FOMC by assigning weights to the models and has many applications in statistical relational learning. More than ten years of research by various authors has led to identification of non-trivial classes of WFOMC problems that can be solved in time polynomial in the number of domain elements. In this paper, we describe recent works on WFOMC and the related problem of weighted first-order model sampling (WFOMS). We also discuss possible applications of WFOMC and WFOMS within statistical relational learning and beyond, e.g., automated solving of problems from enumerative combinatorics and elementary probability theory. Finally, we mention research problems that still need to be tackled in order to make applications of these methods really practical more broadly.
Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract)

We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series. To do so, after a description of the underlying concepts and modelling assumptions, we present different methods according to the family of approaches they belong to: Granger causality, constraint-based approaches, noise-based approaches, score-based approaches, logic-based approaches, topology-based approaches, and difference-based approaches. We then evaluate several representative methods to illustrate the behaviour of different families of approaches. This illustration is conducted on both artificial and real datasets, with different characteristics. The main conclusions one can draw from this survey is that causal discovery in times series is an active research field in which new methods (in every family of approaches) are regularly proposed, and that no family or method stands out in all situations. Indeed, they all rely on assumptions that may or may not be appropriate for a particular dataset.
Optimizing the Computation of Overriding in DLN (Extended Abstract)

One of the factors that hinder the adoption of nonmonotonic description logics in applications is performance. Even when monotonic and nonmonotonic inferences have the same asymptotic complexity, the implementation of nonmonotonic reasoning may be significantly slower. The family of nonmonotonic logics DLN is no exception to this behavior. We address this issue by introducing two provably correct and complete optimizations for reasoning in DLN. The first optimization is a module extractor that has the purpose of focusing reasoning on a relevant subset of the knowledge base. The second, called optimistic evaluation, aims at exploiting incremental reasoning in a better way. Extensive experimental evaluation shows that the optimized DLN reasoning is often compatible with interactive query answering, thus bringing nonmonotonic description logics closer to practical applications.