Miracle-dz/UniFAR-FTUs huggingface dataset:
Shashthosheba: Dissecting Perception of Bangladeshi People towards Telemedicine Apps through the Lens of Features of the Apps

Bangladesh, a developing country with a large and dense population, has recently seen signiﬁcant economic as well as technological developments. The growth of technology has resulted in a dramatic increase in the number of smartphone users in Bangladesh, and as such, mobile apps have become an increasingly important part of peoples’ life, even encompassing healthcare services. However, the apps used in healthcare (telemedicine to be speciﬁc) in Bangladesh are yet to be studied from the perspective of their features as per the voices of the users as well as service providers. Therefore, in this study, we focus on the features of the telemedicine apps used in Bangladesh. First, we evaluated the present status of existing telemedicine apps in Bangladesh, as well as their beneﬁts and draw-backs in the context of HCI. We analyzed publicly accessible reviews of several Bangladeshi telemedicine apps (N = 14) to evaluate the user impressions. Additionally, to ascertain the public opinion of these apps, we performed a survey in which the patients (N = 87) participated willingly. Our analysis of the collected opinions reveals what users experience, what they appreciate, and what they are concerned about when they use telemedicine apps. Additionally, our study demonstrates what users expect from telemedicine apps, independent of their past experience. Finally, we explore how to address the issues we discovered and how telemedicine may be used to eﬀectively oﬀer healthcare services throughout the country. To the best of our knowledge, this study is the ﬁrst to analyze the perception of the people of Bangladesh towards telemedicine apps from the perspective of features of the apps.
Freezing transitions of Brownian particles in confining potentials

We study the mean first passage time (MFPT) to an absorbing target of a one-dimensional Brownian particle subject to an external potential v(x) in a finite domain. We focus on the cases in which the external potential is confining, of the form v(x) = k|x − x 0| n /n, and where the particle’s initial position coincides with x 0. We first consider a particle between an absorbing target at x = 0 and a reflective wall at x = c. At fixed x 0, we show that when the target distance c exceeds a critical value, there exists a nonzero optimal stiffness k opt that minimizes the MFPT to the target. However, when c lies below the critical value, the optimal stiffness k opt vanishes. Hence, for any value of n, the optimal potential stiffness undergoes a continuous ‘freezing’ transition as the domain size is varied. On the other hand, when the reflective wall is replaced by a second absorbing target, the freezing transition in k opt becomes discontinuous. The phase diagram in the (x 0, n)-plane then exhibits three dynamical phases and metastability, with a ‘triple’ point at (x 0/c ≃ 0.171 85, n ≃ 0.395 39). For harmonic or higher order potentials (n ⩾ 2), the MFPT always increases with k at small k, for any x 0 or domain size. These results are contrasted with problems of diffusion under optimal resetting in bounded domains.
Foundations for AI-Assisted Formative Assessment Feedback for Short-Answer Tasks in Large-Enrollment Classes

Research suggests “write-to-learn” tasks improve learning outcomes, yet constructed-response methods of formative assessment become unwieldy with large class sizes. This study evaluates natural language processing algorithms to assist this aim. Six short-answer tasks completed by 1,935 students were scored by several human raters using a detailed rubric and an algorithm. Results indicate substantial inter-rater agreement using quadratic weighted kappa for rater pairs (each QWK > 0.74) and group consensus (Fleiss’ Kappa = 0.68). Additionally, intra-rater agreement was estimated for one rater who had scored 178 responses seven years prior (QWK = 0.88). With compelling rater agreement, the study then pilots cluster analysis of response text toward enabling instructors to ascribe meaning to clusters as a means for scalable formative assessment.
Monitoring AI systems: A Problem Analysis, Framework and Outlook

Knowledge-based systems have been used to monitor machines and pro- cesses in the real world. In this paper we propose the use of knowledge-based systems to monitor other AI systems in operation. We motivate and provide a problem analysis of this novel setting and subsequently propose a framework that allows for structuring future research related to this setting. Several directions for further research are also discussed. We aim to study how to monitor AI systems in such a way that the expectations for the performance are formulated into an interpretable, knowledge-based system for monitoring.
GANimator

We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel and diverse movements. Existing data-driven techniques for motion synthesis require a large motion dataset which contains the desired and specific skeletal structure. By contrast, GANimator only requires training on a single motion sequence, enabling novel motion synthesis for a variety of skeletal structures e.g., bipeds, quadropeds, hexapeds, and more. Our framework contains a series of generative and adversarial neural networks, each responsible for generating motions in a specific frame rate. The framework progressively learns to synthesize motion from random noise, enabling hierarchical control over the generated motion content across varying levels of detail. We show a number of applications, including crowd simulation, key-frame editing, style transfer, and interactive control, which all learn from a single input sequence. Code and data for this paper are at https://peizhuoli.github. io/ganimator.
Proximity-Induced Superconductivity in Scalable Topological Insulator/Graphene/Gallium Heterostructures

In this work, we demonstrate the synthesis of epitaxial (Bi,Sb) 2 Te 3 /graphene/Gallium (BST/Gr/Ga) thin films with atomically sharp hetero-interfaces. We construct clean van der Waals (vdW) tunnel junctions and employ transport tunneling spectroscopy superconducting properties. Tunneling spectra obtained on heterostructures with the BST thickness ranging from 5 to 10 quintuple layers (QLs) exhibit the characteristics of two superconducting gaps. The larger gap originates from the superconducting Ga film 23 . We attribute the smaller gap, which is 30-50% of the gap in Ga, to proximity-induced superconductivity in the Dirac surface states of the BST film. The introduction of magnetic vortex leads to discrete tunneling conductance jumps, with the smallest unit corresponding to the motion of a single flux quantum. Our experiments established a new scalable TI/SC platform for the studies of TSC and Majorana physics. are placed within a single terrace. c, Circular windows of radius ~ 250 nm are etched into the top h-BN sheet to expose the graphite electrodes underneath. The inset shows a magnified image of a few windows. d , A completed device.
Language Models in the Loop: Incorporating Prompting into Weak Supervision

We propose a new strategy for applying large pre-trained language models to novel tasks when labeled training data is limited. Rather than apply the model in a typical zero-shot or few-shot fashion, we treat the model as the basis for labeling functions in a weak supervision framework. To create a classifier, we first prompt the model to answer multiple distinct queries about an example and define how the possible responses should be mapped to votes for labels and abstentions. We then denoise these noisy label sources using the Snorkel system and train an end classifier with the resulting training data. Our experimental evaluation shows that prompting large language models within a weak supervision framework can provide significant gains in accuracy. On the WRENCH weak supervision benchmark, this approach can significantly improve over zero-shot performance, an average 19.5% reduction in errors. We also find that this approach produces classifiers with comparable or superior accuracy to those trained from hand-engineered rules.
Network synthesis and parameter optimization for vehicle suspension with inerter

In order to design a comfortable-oriented vehicle suspension structure, the network synthesis method was utilized to transfer the problem into solving a timing robust control problem and determine the structure of “inerter–spring–damper” suspension. Bilinear Matrix Inequality was utilized to obtain the timing transfer function. Then, the transfer function of suspension system can be physically implemented by passive elements such as spring, damper, and inerter. By analyzing the sensitivity and quantum genetic algorithm, the optimized parameters of inerter–spring–damper suspension were determined. A quarter-car model was established. The performance of the inerter–spring–damper suspension was verified under random input. The simulation results manifested that the dynamic performance of the proposed suspension was enhanced in contrast with traditional suspension. The root mean square of vehicle body acceleration decreases by 18.9%. The inerter–spring–damper suspension can inhibit the vertical vibration within the frequency of 1–3 Hz effectively and enhance the performance of ride comfort significantly.
A rank-two relaxed parallel splitting version of the augmented Lagrangian method with step size in (0,2) for separable convex programming

The augmented Lagrangian method (ALM) is classic for canonical convex programming problems with linear constraints, and it finds many applications in various scientific computing areas. A major advantage of the ALM is that the step for updating the dual variable can be further relaxed with a step size in $(0,2)$, and this advantage can easily lead to numerical acceleration for the ALM. When a separable convex programming problem is discussed and a corresponding splitting version of the classic ALM is considered, convergence may not be guaranteed and thus it is seemingly impossible that a step size in $(0,2)$ can be carried on to the relaxation step for updating the dual variable. We show that for a parallel splitting version of the ALM, a step size in $(0,2)$ can be maintained for further relaxing both the primal and dual variables if the relaxation step is simply corrected by a rank-two matrix. Hence, a rank-two relaxed parallel splitting version of the ALM with a step size in $(0,2)$ is proposed for separable convex programming problems. We validate that the new algorithm can numerically outperform existing algorithms of the same kind significantly by testing some applications.
ActorRL: A Novel Distributed Reinforcement Learning for Autonomous Intersection Management

As an emerging tendency of future transportation, Connected Autonomous Vehicle (CAV) has the potential to improve trafﬁc capacity and safety at intersections. In autonomous intersection management (AIM), distributed scheduling algorithm formulates the interactions among trafﬁc participants as multi-agent problem with information exchange and behavioral cooperation. Deep Reinforcement Learning (DRL), as an approach obtaining satisfying performance in many domains, has been brought in AIM recently. Attempts to overcome the challenges of curse of dimensionality and instability in multi-agent DRL, we propose a novel DRL framework for AIM problem, ActorRL, where actor allocation mechanism attaches multiple roles with different personalities to CAVs under global observation, including radical actor, conservative actor, safety-ﬁrst actor, etc. The actor shares behavioral policies with collective memories from CAVs it is assigned to, playing the role of "navigator" at AIM. In experiments, we compares the proposed method with several widely used scheduling methods and distributed DRL without actor allocation, the results shows better performance over benchmarks.
J-PLUS: Support vector regression to measure stellar parameters

Context. Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. However, time cost and brightness limits restrain the efficiency of spectral observations. The J-PLUS is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools to efficiently analyse large data sets, such as the one from J-PLUS, and enable us to expand the research domain to stellar parameters. Aims. The main goal of this study is to construct a SVR algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. Methods. The training data for the parameters regressions is featured with 12-waveband photometry from J-PLUS, and is cross-identified with spectrum-based catalogs. These catalogs are from the LAMOST, the APOGEE, and the SEGUE. We then label them with the stellar effective temperature, the surface gravity and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach in order to fully take into account the uncertainties of both the magnitudes and stellar parameters. The method utilizes more than two hundred models to apply the uncertainty analysis. Results. We present a catalog of 2,493,424 stars with the Root Mean Square Error of 160K in the effective temperature regression, 0.35 in the surface gravity regression and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods.
Factors of Group Design Decision Making

Nathan Mentzer is an assistant professor in the College of Technology with a joint appointment in the College of Education at Purdue University. Hired as a part of the strategic P12 STEM initiative, he prepares Engineering/Technology candidates for teacher licensure. Dr. Mentzer’s educational efforts in pedagogical content knowledge are guided by a research theme centered in student learning of engineering design thinking on the secondary level. Nathan was a former middle and high school technology educator in Montana prior to pursuing a doctoral degree. He was a National Center for Engineering and Technology Education (NCETE) Fellow at Utah State University while pursuing a Ph.D. in Curriculum and Instruction. After graduation he completed a one year appointment with the Center as a postdoctoral researcher.
pyRDF2Vec: A Python Implementation and Extension of RDF2Vec

This paper introduces pyRDF2Vec, a Python software package that reimplements the well-known RDF2Vec algorithm along with several of its extensions. By making the algorithm available in the most popular data science language, and by bundling all extensions into a single place, the use of RDF2Vec is simplified for data scientists. The package is released under a MIT license and structured in such a way to foster further research into sampling, walking, and embedding strategies, which are vital components of the RDF2Vec algorithm. Several optimisations have been implemented in \texttt{pyRDF2Vec} that allow for more efficient walk extraction than the original algorithm. Furthermore, best practices in terms of code styling, testing, and documentation were applied such that the package is future-proof as well as to facilitate external contributions.
A quantitative variational analysis of the staircasing phenomenon for a second order regularization of the Perona-Malik functional

We consider the Perona-Malik functional in dimension one, namely an integral functional whose Lagrangian is convex-concave with respect to the derivative, with a convexiﬁcation that is identically zero. We approximate and regularize the functional by adding a term that depends on second order derivatives multiplied by a small coeﬃcient. We investigate the asymptotic behavior of minima and minimizers as this small parameter vanishes. In particular, we show that minimizers exhibit the so-called staircasing phenomenon, namely they develop a sort of microstructure that looks like a piecewise constant function at a suitable scale. Our analysis relies on Gamma-convergence results for a rescaled functional, blow-up techniques, and a characterization of local minimizers for the limit problem. This approach can be extended to more general models.
Scheduling Coflows with Precedence Constraints for Minimizing the Total Weighted Completion Time in Identical Parallel Networks

Coﬂow is a recently proposed network abstraction for data-parallel computing applications. This paper considers scheduling coﬂows with precedence constraints in identical parallel networks, such as to minimize the total weighted completion time of coﬂows. The identical parallel network is an architecture based on multiple network cores running in parallel. In the divisible coﬂow scheduling problem, the proposed algorithm achieves (6 − 2 m ) µ and (5 − 2 m ) µ approximate ratios for arbitrary release time and zero release time, respectively, where m is the number of network cores and µ is the coﬂow number of the longest path in the precedence graph. In the indivisible coﬂow scheduling problem, the proposed algorithm achieves (7 − 2 m ) µ and (6 − 2 m ) µ approximate ratios for arbitrary release time and zero release time, respectively. In the single network core scheduling problem, we propose a 5 µ -approximation algorithm with arbitrary release times, and a 4 µ -approximation without release time. Moreover, the proposed algorithm can be modiﬁed to solve the coﬂows of multi-stage jobs scheduling problem. In multi-stage jobs, coﬂow is transferred between servers to enable starting of next stage. This means that there are precedence constraints between coﬂows of job. Our result represents an improvement upon the previous best approximation ratio of O (˜ µ log( N ) / log(log( N ))) where ˜ µ is the maximum number of coﬂows in a job and N is the number of servers.
GitRank: A Framework to Rank GitHub Repositories

Open-source repositories provide wealth of information and are increasingly being used to build artificial intelligence (AI) based systems to solve problems in software engineering. Open-source repositories could be of varying quality levels, and bad-quality repositories could degrade performance of these systems. Evaluating quality of open-source repositories, which is not available directly on code hosting sites such as GitHub, is thus important. In this hackathon, we utilize known code quality measures and GrimoireLab toolkit to implement a framework, named Gi tRank, to rank open-source repositories on three different criteria. We discuss our findings and preliminary evaluation in this hackathon report.
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction

This paper studies the problem of fixing malfunctional 3D objects. While previous works focus on building passive perception models to learn the functionality from static 3D objects, we argue that functionality is reckoned with respect to the physical interactions between the object and the user. Given a malfunctional object, humans can perform mental simulations to reason about its functionality and figure out how to fix it. Inspired by this, we propose FixIt, a dataset that contains about 5k poorly-designed 3D physical objects paired with choices to fix them. To mimic humans' mental simulation process, we present FixNet, a novel framework that seamlessly incorporates perception and physical dynamics. Specifically, FixNet consists of a perception module to extract the structured representation from the 3D point cloud, a physical dynamics prediction module to simulate the results of interactions on 3D objects, and a functionality prediction module to evaluate the functionality and choose the correct fix. Experimental results show that our framework outperforms baseline models by a large margin, and can generalize well to objects with similar interaction types. Code and dataset are publicly available11http://fixing-malfunctional.csail. mit.edu.
Peningkatan Pengetahuan dan Sikap Ibu Hamil Resiko Tinggi dengan Penyuluhan Berbasis Media

Latar belakang : Deteksi dini pada kehamilan dapat dijadikan salah satu upaya untuk mencegah kehamilan resiko tinggi ibu hamil. Masalah utama adalah masih banyaknya ibu hamil resiko tinggi yang tidak mau periksa ke palayanan kesehatan dan belum paham mengenali tentang tanda-tanda ibu hamil yang beresiko tinggi. Peningkatan pengetahuan merupakan salah satu upaya promosi kesehatan dan pendidikan kesehatan. Tujuan penelitian untuk mengetahui peningkatan pengetahuan dan sikap ibu hamil resiko tinggi dengan penyuluhan berbasis media. Metode Penelitian : menggunakan penelitian eksperimen semu. Populasi sebanyak 110 orang wanita hamil. Teknik sampling menggunakan simple random sampling. Sampel 72. Analisis menggunakan Mann Whitney. Hasil penelitian : Ada ada perbedaan pengetahuan antara pra penyuluhan (LCD) dan post penyuluhan (LCD) tentang peningkatan pengetahuan ibu hamil resiko tinggi dengan penyuluhan berbasis media (p-value = 0,000), Ada perbedaan pengetahuan pra penyuluhan dengan post penyuluhan (Leaflet) tentang peningkatan pengetahuan ibu hamil resiko tinggi dengan penyuluhan berbasis media (p-value = 0,000), Tidak ada perbedaan antara sikap pra penyuluhan (LCD) dengan sikap post penyuluhan (LCD) tentang peningkatan sikap ibu hamil resiko tinggi dengan penyuluhan berbasis media (p-value = 0,266), Ada perbedaan sikap pra penyuluhan (Leaflet) dengan sikap post penyuluhan (Leaflet) tentang peningkatan sikap ibu hamil resiko tinggi dengan penyuluhan berbasis media (p-value = 0,000). Simpulan : Ada perbedaan pengetahuan antara pra penyuluhan (LCD) dan post penyuluhan (LCD) tentang peningkatan pengetahuan ibu hamil resiko tinggi dengan penyuluhan berbasis media dan (Leaflet), Tidak ada perbedaan antara sikap pra penyuluhan (LCD) dengan sikap post penyuluhan (LCD) tentang peningkatan sikap ibu hamil resiko tinggi dengan penyuluhan berbasis media, Ada perbedaan sikap pra penyuluhan (Leaflet) dengan sikap post penyuluhan (Leaflet) tentang peningkatan sikap ibu hamil resiko tinggi dengan penyuluhan berbasis media.
Rehabilitation of Abandoned Monumental Hospitals： Tools for Urban Liveability

This proposal is intended to be a contribution toward achieving more liveable cities through the revitalization of inner areas based on the restoration and rehabilitation of historic facilities in order to meet current needs. The research starts by posing the following questions. Can we claim, in a general perspective of improvement of the quality of life in towns and cities, that the recovery of abandoned historic buildings could be a key-factor in conservation and innovation policies of the historical heart of towns? What relationship, if any, is there between the adaptive-reuse design of ancient hospitals and the effects of such action, not only in terms of heritage conservation but also in terms of economic and social regeneration of the surrounding context? The complexity of this issue is addressed by analyzing related cases, relevant for their design solutions and effects, and proposes answers to the opening questions by correlating the main characters of relevant case studies in Europe.