Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming.
Accreting Neutron Stars in 3D GRMHD Simulations: Jets, Magnetic Polarity, and the Interchange Slingshot

Accreting neutron stars differ from black holes by the presence of the star's own magnetic field, whose interaction with the accretion flow is a central component in understanding these systems' disk structure, outflows, jets, and spin evolution. It also introduces an additional degree of freedom, as the stellar dipole can have any orientation relative to the inner disk's magnetic field. We present a suite of 3D general-relativistic magnetohydrodynamic (GRMHD) simulations in which we investigate the two extreme polarities, with the dipole field being either parallel or antiparallel to the initial disk field, in both the accreting and propeller states. When the magnetosphere truncates the disk near or beyond the corotation radius, most of the system's properties, including the relativistic jet power, are independent of the star-disk relative polarity. However, when the disk extends well inside the corotation radius, in the parallel orientation the jet power is suppressed and the inner disk is less dense and more strongly magnetized. We suggest a physical mechanism that may account for this behavior - the interchange slingshot - and discuss its astrophysical implications.
Learning Quantum Phase Estimation by Variational Quantum Circuits

Quantum Phase Estimation (QPE) stands as a pivotal quantum computing subroutine that necessitates an inverse Quantum Fourier Transform (QFT). However, it is imperative to recognize that enhancing the precision of the estimation inevitably results in a significantly deeper circuit. We developed a variational quantum circuit (VQC) approximation to reduce the depth of the QPE circuit, yielding enhanced performance in noisy simulations and real hardware. Our experiments demonstrated that the VQC outperformed both Noisy QPE and standard QPE on real hardware by reducing circuit noise. This VQC integration into quantum compilers as an intermediate step between input and transpiled circuits holds significant promise for quantum algorithms with deep circuits. Future research will explore its potential applicability across various quantum computing hardware architectures.
CLearViD: Curriculum Learning for Video Description

Video description entails automatically generating coherent natural language sentences that narrate the content of a given video. We introduce CLearViD, a transformer-based model for video description generation that leverages curriculum learning to accomplish this task. In particular, we investigate two curriculum strategies: (1) progressively exposing the model to more challenging samples by gradually applying a Gaussian noise to the video data, and (2) gradually reducing the capacity of the network through dropout during the training process. These methods enable the model to learn more robust and generalizable features. Moreover, CLearViD leverages the Mish activation function, which provides non-linearity and non-monotonicity and helps alleviate the issue of vanishing gradients. Our extensive experiments and ablation studies demonstrate the effectiveness of the proposed model. The results on two datasets, namely ActivityNet Captions and YouCook2, show that CLearViD significantly outperforms existing state-of-the-art models in terms of both accuracy and diversity metrics.
On the Multiple Roles of Ontologies in Explainable AI

This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness.
Bilevel Relations and Their Applications to Data Insights

Many data-insight analytic tasks in anomaly detection, metric attribution, and experimentation analysis can be modeled as searching in a large space of tables and finding important ones, where the notion of importance is defined in some adhoc manner. While various frameworks have been proposed (e.g., DIFF, VLDB 2019), a systematic and general treatment is lacking. This paper describes bilevel relations and operators. While a relation (i.e., table) models a set of tuples, a bilevel relation is a dictionary that explicitly models a set of tables, where each ``value'' table is identified by a ``key'' of a (region, features) pair, where region specifies key attributes of the table, and features specify columns of the table. Bilevel relational operators are BilevelRelation-to-BilevelRelation transformations and directly analyze a set of tables. Bilevel relations and operators provide higher level abstractions for creating and manipulating a set of tables, and are compatible with the classic relational algebra. Together, they allow us to construct bilevel queries, which can express succinctly a range of insight-analytical questions with ``search+eval'' character. We have implemented and deployed a query engine for bilevel queries as a service, which is a first of its kind. Bilevel queries pose a rich algorithm and system design space, such as query optimization and data format, in order to evaluate them efficiently. We describe our current designs and lessons, and report empirical evaluations. Bilevel queries have found many useful applications, and have attracted more than 30 internal teams to build data-insight applications with it.
Clonemator: Composing Spatiotemporal Clones to Create Interactive Automators in Virtual Reality

Clonemator is a virtual reality (VR) system allowing users to create their avatar clones and configure them spatially and temporally, forming automators to accomplish complex tasks. In particular, clones can (1) freeze at a user's body pose as static objects, (2) synchronously mimic the user's movement, and (3) replay a sequence of the user's actions in a period of time later. Combined with traditional techniques such as scaling, positional rearrangement, group selection, and duplication, Clonemator enables users to iteratively develop customized and reusable solutions by breaking down complex tasks into a sequence of collaborations with clones. This bypasses implementing dedicated interaction techniques or scripts while allowing flexible interactions in VR applications. We demonstrate the flexibility of Clonemator with several examples and validate its usability and effectiveness through a preliminary user study. Finally, we discuss the potential of Clonemator in VR applications such as gaming mechanisms, spatial interaction techniques, and multi-robot control and provide our insights for future research.
Reusing Convolutional Neural Network Models through Modularization and Composition

With the widespread success of deep learning technologies, many trained deep neural network (DNN) models are now publicly available. However, directly reusing the public DNN models for new tasks often fails due to mismatching functionality or performance. Inspired by the notion of modularization and composition in software reuse, we investigate the possibility of improving the reusability of DNN models in a more fine-grained manner. Specifically, we propose two modularization approaches named CNNSplitter and GradSplitter, which can decompose a trained convolutional neural network (CNN) model for N-class classification into N small reusable modules. Each module recognizes one of the N classes and contains a part of the convolution kernels of the trained CNN model. Then, the resulting modules can be reused to patch existing CNN models or build new CNN models through composition. The main difference between CNNSplitter and GradSplitter lies in their search methods: the former relies on a genetic algorithm to explore search space, while the latter utilizes a gradient-based search method. Our experiments with three representative CNNs on three widely-used public datasets demonstrate the effectiveness of the proposed approaches. Compared with CNNSplitter, GradSplitter incurs less accuracy loss, produces much smaller modules (19.88% fewer kernels), and achieves better results on patching weak models. In particular, experiments on GradSplitter show that (1) by patching weak models, the average improvement in terms of precision, recall, and F1-score is 17.13%, 4.95%, and 11.47%, respectively, and (2) for a new task, compared with the models trained from scratch, reusing modules achieves similar accuracy (the average loss of accuracy is only 2.46%) without a costly training process. Our approaches provide a viable solution to the rapid development and improvement of CNN models.
Investigating the Nature of Disagreements on Mid-Scale Ratings: A Case Study on the Abstractness-Concreteness Continuum

Humans tend to strongly agree on ratings on a scale for extreme cases (e.g., a CAT is judged as very concrete), but judgements on mid-scale words exhibit more disagreement. Yet, collected rating norms are heavily exploited across disciplines. Our study focuses on concreteness ratings and (i) implements correlations and supervised classification to identify salient multi-modal characteristics of mid-scale words, and (ii) applies a hard clustering to identify patterns of systematic disagreement across raters. Our results suggest to either fine-tune or filter mid-scale target words before utilising them.
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments

Online learning to rank (ONL2R) is a foundational problem for recommender systems and has received increasing attention in recent years. Among the existing approaches for ONL2R, a natural modeling architecture is the multi-armed bandit framework coupled with the position-based click model. However, developing efficient online learning policies for MAB-based ONL2R with position-based click models is highly challenging due to the combinatorial nature of the problem, and partial observability in the position-based click model. To date, results in MAB-based ONL2R with position-based click models remain rather limited, which motivates us to fill this gap in this work. Our main contributions in this work are threefold: i) We propose the first general MAB framework that captures all key ingredients of ONL2R with position-based click models. Our model considers personalized and equal treatments in ONL2R ranking recommendations, both of which are widely used in practice; ii) Based on the above analytical framework, we develop two unified greed- and UCB-based policies called GreedyRank and UCBRank, each of which can be applied to personalized and equal ranking treatments; and iii) We show that both GreedyRank and UCBRank enjoy $O(\sqrt{t}\ln t)$ and $O(\sqrt{t\ln t})$ anytime sublinear regret for personalized and equal treatment, respectively. For the fundamentally hard equal ranking treatment, we identify classes of collective utility functions and their associated sufficient conditions under which $O(\sqrt{t}\ln t)$ and $O(\sqrt{t\ln t})$ anytime sublinear regrets are still achievable for GreedyRank and UCBRank, respectively. Our numerical experiments also verify our theoretical results and demonstrate the efficiency of GreedyRank and UCBRank in seeking the optimal action under various problem settings.
ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations

Electronic theses and dissertations (ETDs) have been proposed, advocated, and generated for more than 25 years. Although ETDs are hosted by commercial or institutional digital library repositories, they are still an understudied type of scholarly big data, partially because they are usually longer than conference and journal papers. Segmenting ETDs will allow researchers to study sectional content. Readers can navigate to particular pages of interest, to discover and explore the content buried in these long documents. Most existing frameworks on document page classification are designed for classifying general documents, and perform poorly on ETDs. In this paper, we propose ETDPC. Its backbone is a two-stream multimodal model with a cross-attention network to classify ETD pages into 13 categories. To overcome the challenge of imbalanced labeled samples, we augmented data for minority categories and employed a hierarchical classifier. ETDPC outperforms the state-of-the-art models in all categories, achieving an F1 of 0.84 -- 0.96 for 9 out of 13 categories. We also demonstrated its data efficiency. The code and data can be found on GitHub (https://github.com/lamps-lab/ETDMiner/tree/master/etd_segmentation).
Individual and gender inequality in computer science: A career study of cohorts from 1970 to 2000

Inequality prevails in science. Individual inequality means that most perish quickly and only a few are successful, while gender inequality implies that there are differences in achievements for women and men. Using large-scale bibliographic data and following a computational approach, we study the evolution of individual and gender inequality for cohorts from 1970 to 2000 in the whole field of computer science as it grows and becomes a team-based science. We find that individual inequality in productivity (publications) increases over a scholar's career but is historically invariant, while individual inequality in impact (citations), albeit larger, is stable across cohorts and careers. Gender inequality prevails regarding productivity, but there is no evidence for differences in impact. The Matthew Effect is shown to accumulate advantages to early achievements and to become stronger over the decades, indicating the rise of a"publish or perish"imperative. Only some authors manage to reap the benefits that publishing in teams promises. The Matthew Effect then amplifies initial differences and propagates the gender gap. Women continue to fall behind because they continue to be at a higher risk of dropping out for reasons that have nothing to do with early-career achievements or social support. Our findings suggest that mentoring programs for women to improve their social-networking skills can help to reduce gender inequality.
Where Do We Meet? Key Factors Influencing Collaboration Across Meeting Spaces

Over the past years, there has been a shift towards online and hybrid meeting forms in workplace environments, partly as a consequence of various COVID-19 restrictions. However, the decision-making process on how to best collaborate with team members is predominantly driven by practical concerns. While there is a significant body of literature about where to best meet, this knowledge is fragmented across various disciplines and hard to use in novel meeting solutions. We present the Cross-Space Collaboration model which identifies the main factors that drive the features of in-person collaboration and the meeting aspects that influence these factors such as cognitive load. We designed the model to give guidance to teams and individuals on how to meet in order to have a higher collaboration effectiveness. Finally, we outline how the model can bring added value within new meeting solutions, next generation virtual reality meeting spaces and educational settings.
Node-Binded Communities for Interpolation on Graphs

Partition of unity methods (PUMs) on graphs represent straightforward and remarkably adaptable auxiliary techniques for graph signal processing. By relying solely on the intrinsic graph structure, we propose the generation of a partition of unity through centrality measures and modularity. Subsequently, we integrate PUMs with a local graph basis function (GBF) approximation approach to achieve low-cost global interpolation schemes.
Limit theorems for fixed point biased permutations avoiding a pattern of length three

We prove limit theorems for the number of fixed points occurring in a random pattern avoiding permutation distributed according to a one-parameter family of biased distributions. The bias parameter tilts the distribution towards favoring permutations with more or fewer fixed points than is typical under the uniform distribution. One case we study features a phase transition where the limiting distribution changes abruptly from negative binomial to Rayleigh to normal depending on the bias parameter.
Evaluating Uncertainty Quantification approaches for Neural PDEs in scientific applications

The accessibility of spatially distributed data, enabled by affordable sensors, field, and numerical experiments, has facilitated the development of data-driven solutions for scientific problems, including climate change, weather prediction, and urban planning. Neural Partial Differential Equations (Neural PDEs), which combine deep learning (DL) techniques with domain expertise (e.g., governing equations) for parameterization, have proven to be effective in capturing valuable correlations within spatiotemporal datasets. However, sparse and noisy measurements coupled with modeling approximation introduce aleatoric and epistemic uncertainties. Therefore, quantifying uncertainties propagated from model inputs to outputs remains a challenge and an essential goal for establishing the trustworthiness of Neural PDEs. This work evaluates various Uncertainty Quantification (UQ) approaches for both Forward and Inverse Problems in scientific applications. Specifically, we investigate the effectiveness of Bayesian methods, such as Hamiltonian Monte Carlo (HMC) and Monte-Carlo Dropout (MCD), and a more conventional approach, Deep Ensembles (DE). To illustrate their performance, we take two canonical PDEs: Burger's equation and the Navier-Stokes equation. Our results indicate that Neural PDEs can effectively reconstruct flow systems and predict the associated unknown parameters. However, it is noteworthy that the results derived from Bayesian methods, based on our observations, tend to display a higher degree of certainty in their predictions as compared to those obtained using the DE. This elevated certainty in predictions suggests that Bayesian techniques might underestimate the true underlying uncertainty, thereby appearing more confident in their predictions than the DE approach.
Software Architecture Recovery with Information Fusion

Understanding the architecture is vital for effectively maintaining and managing large software systems. However, as software systems evolve over time, their architectures inevitably change. To keep up with the change, architects need to track the implementation-level changes and update the architectural documentation accordingly, which is time-consuming and error-prone. Therefore, many automatic architecture recovery techniques have been proposed to ease this process. Despite efforts have been made to improve the accuracy of architecture recovery, existing solutions still suffer from two limitations. First, most of them only use one or two type of information for the recovery, ignoring the potential usefulness of other sources. Second, they tend to use the information in a coarse-grained manner, overlooking important details within it. To address these limitations, we propose SARIF, a fully automated architecture recovery technique, which incorporates three types of comprehensive information, including dependencies, code text and folder structure. SARIF can recover architecture more accurately by thoroughly analyzing the details of each type of information and adaptively fusing them based on their relevance and quality. To evaluate SARIF, we collected six projects with published ground-truth architectures and three open-source projects labeled by our industrial collaborators. We compared SARIF with nine state-of-the-art techniques using three commonly-used architecture similarity metrics and two new metrics. The experimental results show that SARIF is 36.1% more accurate than the best of the previous techniques on average. By providing comprehensive architecture, SARIF can help users understand systems effectively and reduce the manual effort of obtaining ground-truth architectures.
Quantum automorphism groups of trees

We give a characterisation of quantum automorphism groups of trees. In particular, for every tree, we show how to iteratively construct its quantum automorphism group using free products and free wreath products. This can be considered a quantum version of Jordan's theorem for the automorphism groups of trees. This is one of the first characterisations of quantum automorphism groups of a natural class of graphs with quantum symmetry.