Miracle-dz/UniFAR-FTUs huggingface dataset:
Game-Based Learning: Enhancing Student Experience, Knowledge Gain, and Usability in Higher Education Programming Courses

Contributions: This article presents a large-scale study which investigates students’ reaction to game-based learning as part of programming courses. The study focuses on knowledge acquisition, learner experience, and game usability. Background: Despite the rapid growth of the information and communication technologies (ICTs) sector, the lack of engagement with science, technology, engineering, and mathematics (STEM) subjects and high dropout rates in computer science and engineering majors is linked directly to the large number of unfilled vacancies in the ICT employment market. To tackle one of the underlying causes for this crisis, (i.e., traditional teaching paradigms struggle to attract students to rather abstract and difficult STEM subjects such as programming), innovative technology-enhanced learning solutions are sought. Intended Outcomes: A set of serious games were proposed and designed to promote students’ understanding of programming concepts, improve their confidence, stimulate their interest in STEM and increase engagement with the courses through vivid and appealing scenarios. Application Design: Targeting undergraduate and postgraduate students, the games focused on several key programming topics. They were designed to visualize the programming concepts in illustrative and entertaining scenarios. A comprehensive assessment methodology which includes surveys, observations, and interviews was employed to investigate the impact of the games. Findings: The results show that by using the games in the teaching and learning process all the students have benefited, although differently based on their location, educational backgrounds, and game played. The impact of detailed demographic aspects, such as participants’ use of technology, their initial attitude toward school, and learning STEM on the results needs further study.
Real-Time Condition Monitoring of Filling Machines with Vibration Analysis and Edge AI

This paper presents an embedded sensor system, which allows Condition Monitoring (CM) on filling machines. An auger filler is used as an example, which doses food into bags. Due to a mechanical defect, metal shavings can occur during the operation of an auger filler. Those metal shavings can contaminate the dosed food and cause huge costs due to possible recall action and loss of credibility. The presented CM solution should detect such events by utilizing vibration data. We employ feature extraction and machine learning algorithms to classify the condition of the auger filler. The resulting sensor system consists of an Artificial Neural Network (ANN) for the CM. The feature extraction and the ANN were successfully ported to a microcontroller, allowing retrofitting to existing auger fillers and thus representing an application of Edge AI. On our test set, we achieve a classification accuracy of 100% for the dosing product milk powder, which is the main application of this system. With the ANN for all available dosing products, an accuracy of 96.6% is achieved. Furthermore, the system can monitor each dosing cycle of the auger filler, as the execution time of a condition prediction is lower (< 200 ms) than the cycle time of the auger filler (800 ms). Moreover, it can monitor the auger filler without additional settings for different configurations (e.g., dispensed food and cycle rate), eliminating the need to make individual adjustments, as is the case with systems currently in use.
Autonomous Dynamical System Approach for Inflationary Gauss-Bonnet Modified Gravity

In this paper we shall analyze the $f(\mathcal{G})$ gravity phase space, in the case that the corresponding dynamical system is autonomous. In order to make the dynamical system autonomous, we shall appropriately choose the independent variables, and we shall analyze the evolution of the variables numerically, emphasizing on the inflationary attractors. As we demonstrate, the dynamical system has only one de Sitter fixed point, which is unstable, with the instability being traced in one of the independent variables. This result holds true both in the presence and in the absence of matter and radiation perfect fluids. We argue that this instability could loosely be viewed as an indication of graceful exit in the $f(\mathcal{G})$ theory of gravity.
Smartphone-Based Real-Time Indoor Positioning Using BLE Beacons

To deal with the degraded performance of Global Navigation Satellite Systems (GNSS) in indoor environments, Indoor Positioning Systems (IPS) have been developed. The rapid proliferation of smartphones has led to many IPSs that utilize positioning technologies that are readily available on modern smartphones; including Bluetooth Low Energy (BLE). Using radio signals such as BLE in indoor environments comes with a number of challenges that can limit the reliability of the signal. In dealing with these challenges, most existing BLE-based IPSs introduce undesired drawbacks such as an extensive and fragile calibration phase, strict hardware requirements, and increases in the system’s complexity. In this paper, an IPS is developed and evaluated that requires minimal setup for indoor environments and has a sufficiently low complexity to be run locally on a modern smartphone. An extensive exploration of the IPS’ parameters was performed. The best performing parameter combinations resulted in a median positioning error of 1.48 ± 0.283 meters, while using the log-distance path loss model for distance estimation and Weighted Centroid Localization with a weight exponent be-tween 2.0 and 3.5 for position estimation.
Data-Driven Screening of Network Constraints for Unit Commitment

The transmission-constrained unit commitment (TC-UC) problem is one of the most relevant problems solved by independent system operators for the daily operation of power systems. Given its computational complexity, this problem is usually not solved to global optimality for real-size power systems. In this paper, we propose a data-driven method that leverages historical information to screen out network constraints in the TC-UC problem. First, past data on demand and renewable generation throughout the network are used to learn the congestion status of transmission lines. Then, we infer the lines that will not become congested for upcoming operating conditions based on such learning and disregard their capacity constraints. This way, we formulate a reduced TC-UC problem that is easier to solve. Numerical results on a medium- and a large-size power system show that the proposed approach outperforms existing ones by significantly reducing the computational time while obtaining solutions that are equal or close to the one obtained with the original TC-UC problem. Furthermore, the purely data-driven method we propose can be seamlessly complemented with a constraint generation procedure to guarantee that the optimal solution to the original TC-UC problem is eventually recovered.
Jet-parton assignment in tt̄H events using deep learning

The direct measurement of the top quark-Higgs coupling is one of the important questions in understanding the Higgs boson. The coupling can be obtained through measurement of the top quark pair-associated Higgs boson production cross-section. Of the multiple challenges arising in this cross-section measurement, we investigate the reconstruction of the partons originating from the hard scattering process using the measured jets in simulated tt̄H events. The task corresponds to an assignment challenge of m objects (jets) to n other objects (partons), where m≥ n. We compare several methods with emphasis on a concept based on deep learning techniques which yields the best results with more than 50% of correct jet-parton assignments.
Teaching an HTN Learner

We describe Teachable-HTN-Maker, a modified version of the well-known HTN-Maker algorithm that learns Hierarchical Task Network (HTN) methods. Instead of learning meth-ods from all subsequences of a solution plan as HTN-Maker does, Teachable-HTN-Maker learns from a curriculum consisting of examples that are presented in a meaningful order. We compare Teachable-HTN-Maker against HTN-Maker in two planning domains, and observe that it learns fewer meth-ods and better ones.
Social Diversity, Gender, Equity and Public Policy

This chapter highlights some select issues of importance to policy makers arising from the discourse on social diversity, gender, equity and public policy. In particular, this chapter explains key issues in the discourse on social diversity, gender and equity including vulnerability and exclusion, multiculturalism, social accountability in pursuit of equity and citizen participation in public policy making. It also examines socio-cultural challenges to inclusive policies and legislations and policies on fairness. This chapter draws examples from the African continent as well as global con-texts where necessary to illustrate the existing social diversity, gender and equity issues. The examples are meant to prompt readers of this chapter to reflect on the context of their countries and be able to analyse and seek ways to address discriminatory practices that hinder inclusivity in our very diverse societies.
An ETH-Tight Algorithm for Multi-Team Formation

In the Multi-Team Formation problem, we are given a ground set C of n candidates, each of which is characterized by a d -dimensional attribute vector in R d , and two positive integers α and β satisfying αβ ≤ n . The goal is to form α disjoint teams T 1 , . , T α ⊆ C , each of which consists of β candidates in C , such that the total score of the teams is maximized, where the score of a team T is the sum of the h j maximum values of the j -th attributes of the candidates in T , for all j ∈ { 1 , . , d } . Our main result is an 2 2 O ( d ) n O (1) -time algorithm for Multi-Team Formation . This bound is ETH-tight since a 2 2 d/c n O (1) -time algorithm for any constant c > 12 can be shown to violate the Exponential Time Hypothesis (ETH). Our algorithm runs in polynomial time for all dimensions up to d = c log log n for a sufficiently small constant c > 0. Prior to our work, the existence of a polynomial time algorithm was an open problem even for d = 3.
Contemporary (2016–2020) land cover across West Antarctica and the McMurdo Dry Valleys

Continental-scale land cover information is essential to furthering our understanding of the terrestrial environment, atmosphere and climate change. Several global land cover products have been released in recent years but they typically do 10 not include Antarctica. The lack of land cover data in Antarctica is concerning because mountain glaciers and icecaps there have been losing mass at a rate well above the global average, leading to expansion of proglacial regions. Proglacial regions comprise transient land cover types with high rates of geomorphological activity that delivers sediment into the Southern Ocean and supports its rich biodiversity. With Antarctic mountain glaciers and icecaps projected to lose more mass in the coming decades, and active layer soils expected to increase in thickness, it is timely to establish a baseline land cover dataset 15 for Antarctica with which future classifications can be compared. Here, we use Landsat-8 Operational Land Imager (OLI) images to classify six proglacial regions of Antarctica at 30 m resolution, with an overall accuracy of 77.0 % for proglacial land classes. We conducted this classification using an unsupervised K-means clustering approach, which circumvented the need for training data and was highly effective at picking up key land classes, such as vegetation, water, and different sedimentary surfaces. We have highlighted the spatial pattern in land cover and emphasise a need for more and higher quality 20 field data. The land cover maps produced from this paper are available at: Stringer, C. (2022). Contemporary (2016 - 2020) land cover classification across West Antarctica and the McMurdo Dry Valleys (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/5A5EE38C-E296-48A2-85D2-E29DB66E5E24 are characterised by a polar maritime climate, with air temperatures regularly exceeding 0 °C in summer. The humid environment, due to its maritime location, ice-free seas and regular cyclonic 85 activity, results in liquid precipitation falling regularly in the summer months (Bañón et al., 2013). The James Ross Archipelago, to the north-east of the Antarctic Peninsula, has a MAAT of -7 °C and has a semi-arid polar continental climate (Hrbáček and Uxa, 2020; Martin and Peel, 1978). The two more southerly sites; Alexander Island and The McMurdo Dry Valleys, are distinctly colder and drier than the more northern sites, with continental climates (Harangozo et al., 1997). Alexander Island, specifically Fossil Bluff, has a MAAT of -9 °C and receives approximately 900 mm of precipitation each 90 year (Davies et al., 2017; Harangozo et al., 1997). The McMurdo Dry Valleys are hyper-arid due to katabatic winds and have a MAAT of -17 °C to -20 °C (Doran et al., 1994; Marchant and Head, 2007). normalised snow normalised difference vegetation index normalised difference water index Eq. the classifier in the identification of key land cover types and
Effects of fire danger indexes and land cover on fire growth in Peru

Statistical analyses of wildfire growth are rarely undertaken, particularly in South America. In this study, we describe a simple and intuitive difference equation model of wildfire growth that uses a spread parameter to control the radial speed of the modeled fire and an extinguish parameter to control the rate at which the burning perimeter becomes inactive. Using data from the GlobFire project, we estimate these two parameters for 1003 large, multi-day fires in Peru between 2001 and 2020. For four fire-prone ecoregions within Peru, a set of 18 generalized linear models are fit for each parameter that use 5 fire danger indexes and land cover covariates. Akaike weights are used to identify the best-approximating model and quantify model uncertainty. We find that, in most cases, increased spread rates and extinguish rates are positively associated with fire danger indexes. When fire danger indexes are included in the models, the spread component is usually the best choice. We also find that forest cover is negatively associated with spread rates and extinguish rates in tropical forests, and that anthropogenic cover is negatively associated with spread rates in xeric ecoregions. We explore potential applications of this model to wildfire 10 risk assessment and burned area forecasting. A.M., S.V. , S.B. R. , C.T. C. , and B.P.V. ; visualization, H.R. P. and S.B. R. ; 310 supervision, W.M. J and E.A. ; project administration, H.R. P, B.P.V. , W.M. J, and E.A. ; funding acquisition, W.M. J and E.A. Competing interests. The authors declare no competing interests.
Brane decay of a (4+n)-dimensional rotating black hole. III: spin-1/2 particles

In this work, we study the `scalar channel' of the emission of Hawking radiation from a (4+n)-dimensional, rotating black hole on the brane. We numerically solve both the radial and angular part of the equation of motion for the scalar field, and determine the exact values of the absorption probability and of the spheroidal harmonics, respectively. With these, we calculate the particle, energy and angular momentum emission rates, as well as the angular variation in the flux and power spectra — a distinctive feature of emission during the spin-down phase of the life of the produced black hole. Our analysis is free from any approximations, with our results being valid for arbitrarily large values of the energy of the emitted particle, angular momentum of the black hole and dimensionality of spacetime. We finally compute the total emissivities for the number of particles, energy and angular momentum and compare their relative behaviour for different values of the parameters of the theory.
Vortex dipole in a trapped atomic Bose-Einstein condensate

We calculate the angular momentum and energy of a vortex dipole in a trapped atomic Bose-Einstein condensate. Fully analytic expressions are obtained. We apply the results to understand an interesting phenomenon in the MIT group experiment [Raman et al., Phys. Rev. Lett. 87, 210402 (2001)], an excellent agreement is achieved, and further experimental investigation is proposed to confirm this vortex dipole mechanism. We then suggest an effective generation and detection of vortex dipole for experimental realization. Application of the sum rule to calculate collective mode frequency splitting is also discussed.
Complementary Cues from Audio Help Combat Noise in Weakly-Supervised Object Detection

We tackle the problem of learning object detectors in a noisy environment, which is one of the significant challenges for weakly-supervised learning. We use multimodal learning to help localize objects of interest, but unlike other methods, we treat audio as an auxiliary modality that assists to tackle noise in detection from visual regions. First, we use the audio-visual model to generate new "ground-truth" labels for the training set to remove noise between the visual features and noisy supervision. Second, we propose an "indirect path" between audio and class predictions, which combines the link between visual and audio regions, and the link between visual features and predictions. Third, we propose a sound-based "attention path" which uses the benefit of complementary audio cues to identify important visual regions. We use contrastive learning to perform region-based audio-visual instance discrimination, which serves as an intermediate task and benefits from the complementary cues from audio to boost object classification and detection performance. We show that our methods, which update noisy ground truth and provide indirect and attention paths, greatly boosting performance on the AudioSet and VGGSound datasets compared to single-modality predictions, even ones that use contrastive learning. Our method outperforms previous weakly-supervised detectors for the task of object detection by reaching the state-of-art on AudioSet, and our sound localization module performs better than several state-of-art methods on AudioSet and MUSIC.
On the nature of long-period dwarf novae with rare and low-amplitude outbursts

There are several peculiar long-period dwarf-nova like objects, which show rare, low-amplitude outbursts with highly ionized emission lines. 1SWASP J162117$+$441254, BD Pav, and V364 Lib belong to this kind of objects. Some researchers even doubt whether 1SWASP J1621 and V364 Lib have the same nature as normal dwarf novae. We studied the peculiar outbursts in these three objects via our optical photometry and spectroscopy, and performed numerical modeling of their orbital variations to investigate their properties. We found that their outbursts lasted for a long interval (a few tens of days), and that slow rises in brightness were commonly observed during the early stage of their outbursts. Our analyses and numerical modeling suggest that 1SWASP J1621 has a very high inclination, close to 90 deg, plus a faint hot spot. Although BD Pav seems to have a slightly lower inclination ($\sim$75 deg), the other properties are similar to those in 1SWASP J1621. On the other hand, V364 Lib appears to have a massive white dwarf, a hot companion star, and a low inclination ($\sim$35 deg). In addition, these three objects possibly have low transfer rate and/or large disks originating from the long orbital periods. We find that these properties of the three objects can explain their infrequent and low-amplitude outbursts within the context of the disk instability model in normal dwarf novae without strong magnetic field. In addition, we suggest that the highly-ionized emission lines in outburst are observed due to a high inclination and/or a massive white dwarf. More instances of this class of object may be unrecognized, since their unremarkable outbursts can be easily overlooked.
Evidence for Universality in the Initial Planetesimal Mass Function

Planetesimals may form from the gravitational collapse of dense particle clumps initiated by the streaming instability. We use simulations of aerodynamically coupled gas–particle mixtures to investigate whether the properties of planetesimals formed in this way depend upon the sizes of the particles that participate in the instability. Based on three high-resolution simulations that span a range of dimensionless stopping times , no statistically significant differences in the initial planetesimal mass function are found. The mass functions are fit by a power law, , with p = 1.5–1.7 and errors of . Comparing the particle density fields prior to collapse, we find that the high-wavenumber power spectra are similarly indistinguishable, though the large-scale geometry of structures induced via the streaming instability is significantly different between all three cases. We interpret the results as evidence for a near-universal slope to the mass function, arising from the small-scale structure of streaming-induced turbulence.
EQUITABLE DYNAMIC PRICING OF EXPRESS LANES

Express lanes mitigate traffic congestion by providing a time-reliable alternative and exploiting travelers' willingness to pay to generate revenue for infrastructure projects. Over the last decade, equity and fairness issues for express lanes have been considered; however, there is a lack of guidance on the design of equitable discounts. In this article, we present a modeling framework for the analysis of equity issues with express lanes for tolls optimized for different objectives. Through simulation-based optimization of tolls using reinforcement learning, we show that the choice of dynamic tolls impacts the delay differentials across different groups. We find that higher toll values and higher demand worsen the delay differentials across travel groups. We also prove that discounts proportional to travelers' value of time address delay differentials across the travel groups, where the optimal discounts may be a function of current toll and travel time savings. The analysis of tradeoffs between equity, revenue, and delay reveals that equitable discounts may result in up to 34% loss of revenue and up to 9% increase in delay. Research findings suggest (a) designing discounts proportional to VOT which can be correlated with income groups and (b) balancing the tradeoffs by carefully identifying the agency's priorities.