Miracle-dz/UniFAR-FTUs huggingface dataset:
Dolos 2.0: Towards Seamless Source Code Plagiarism Detection in Online Learning Environments

With the increasing demand for programming skills comes a trend towards more online programming courses and assessments. While this allows educators to teach larger groups of students, it also opens the door to dishonest student behaviour, such as copying code from other students. When teachers use assignments where all students write code for the same problem, source code similarity tools can help to combat plagiarism. Unfortunately, teachers often do not use these tools to prevent such behaviour. In response to this challenge, we have developed a new source code plagiarism detection tool named Dolos. Dolos is open-source, supports a wide range of programming languages, and is designed to be user-friendly. It enables teachers to detect, prove and prevent plagiarism in programming courses by using fast algorithms and powerful visualisations. We present further enhancements to Dolos and discuss how it can be integrated into modern computing education courses to meet the challenges of online learning and assessment. By lowering the barriers for teachers to detect, prove and prevent plagiarism in programming courses, Dolos can help protect academic integrity and ensure that students earn their grades honestly.
Conducting Multi-Institutional Studies of Parsons Problems

Many novice programmers struggle to write code from scratch and get frustrated when their code does not work. Parsons problems can reduce the difficulty of a coding problem by providing mixed-up blocks that the learner assembles in the correct order. Parsons problems can also include distractor blocks that are not needed in a correct solution, but which may help students learn to recognize and fix errors. Evidence indicates that students find Parsons problems engaging, easier than writing code from scratch, useful for learning patterns, and typically faster to solve than writing code from scratch with equivalent learning gains. This working group leverages the work of the 2022 ITiCSE working group which published an extensive literature review of Parsons problems and designed and piloted several studies based on the gaps identified by the literature review. The 2023 working group is revising, conducting, and creating new studies. We will analyze the data from these multi-institutional and multi-national studies and publish the results as well as recommendations for future working groups.
The Impact of Non-Formal Computer Science Outreach on Computational Thinking in Young Women

The role of non-formal education in increasing female participation in Computer Science (CS) is a hot topic. Short-term interventions, including programming skill outreach activities, have been reported to increase self-efficacy and willingness to pursue computing careers in young women. We explored the impact of a programming outreach activity on three types of measures for 30 female pupils: computing self-efficacy, social participation, and understanding of basic computing concepts. Preliminary results revealed a significant increase in participants' self-efficacy and sense of belonging in computing after the informal learning activity. Students were more focused on tasks when engaging socially with their peers and teachers. A decrease in misconception was observed in uni-structural knowledge but no significant difference was found in multi-structural computing knowledge acquisition. These data provide a baseline for study of the long term impact of outreach activities.
An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices

In recent years, we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state-of-the-art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This article aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.
A Computational Thinking Test for Senior High School Students

We have developed a quantitative tool for assessing senior high school students' computational thinking skills and administered it in the Taipei metropolitan area of Taiwan. Our CT test is language/tool/knowledge-independent and focuses on four key CT concepts: decomposition, data representation, algorithms, and pattern generalization. Our paper outlines the development process of the CT test, including its constructs, reliability, and validity, and discusses its educational implications for computing courses. This CT test provides a valuable resource for computing teachers who wish to assess their students' CT abilities and tailor their teaching methods accordingly.
Fostering Upper Elementary AI Education: Iteratively Refining a Use-Modify-Create Scaffolding Progression for AI Planning

The growing ubiquity of artificial intelligence (AI) is reshaping much of daily life. This in turn is raising awareness of the need to introduce AI education throughout the K-12 curriculum so that students can better understand and utilize AI. A particularly promising approach for engaging young learners in AI education is game-based learning. In this work, we present our efforts to embed a unit on AI planning within an immersive game-based learning environment for upper elementary students (ages 8 to 11) that utilizes a scaffolding progression based on the Use-Modify-Create framework. Further, we present how the scaffolding progression is being refined based on findings from piloting the game with students.
The interaction of income inequality and urbanization in affecting environmental quality: International evidence

ABSTRACT This research examines how income inequality interacts with urbanization in its influence on carbon dioxide emissions. We analyze national level data of 95 countries between 1961 and 2015 using the system generalized method of moments to explore the nature of interactions. The results indicate that the environmental impact of income inequality varies with the rise of urbanization. Environmental performance of developing communities improves initially, but when the urbanization ratio is above 35.2 per cent, inequality suppresses progress in environmental performance until the urbanization ratio is greater than 55.7 per cent. This inverted U shape relationship adds further evidence on the conflicting views on the interactions between the two social justice and environmental quality goals of development. While aggregate data often masks within-country variations, these analyses provide valuable insights for policymakers about the forces influencing these aggregate measures and the importance of local context in tackling environmental issues of development.
Dinkelbach Type Approximation Algorithms for Nonlinear Fractional Optimization Problems

Abstract In this paper we establish some approximation versions of the classical Dinkelbach algorithm for nonlinear fractional optimization problems in Banach spaces. We start by examining what occurs if at any step of the algorithm, the generated point desired to be a minimizer can only be determined with a given error. Next we assume that the step error tends to zero as the algorithm advances. The last version of the algorithm we present is making use of Ekeland’s variational principle for generating the sequence of minimizer-like points. In the final part of the article we deliver some results in order to achieve a Palais-Smale type compactness condition that guarantees the convergence of our Dinkelbach-Ekeland algorithm.
Development and validation of NAD+ metabolism-related gene signature in prognosis prediction of hepatocellular carcinoma

Liver cancer is a prevalent and deadly malignancy worldwide, with a rising incidence rate. In this study, we focused on the role of NAD+ metabolism-related genes in liver cancer prognosis prediction. We identified key genes among NAD+ metabolism-related genes and explored their relationship with cancer staging and prognosis using gene expression data. A risk score model was constructed to assess cancer risk and survival status. The model demonstrated significant predictive potential for survival outcomes. Furthermore, we analyzed the risk scores in different populations and performed functional enrichment analyses to gain insights into the biological processes involved. Our study highlights the clinical value and significance of NAD+ metabolism-related genes in liver cancer. The findings provide a foundation for personalized treatment strategies and open new avenues for further research in liver cancer management.
How reliable are latitudinal energy balance models for habitability calculations when using Earth's radiative properties?

Exoplanet habitability studies are often performed using simple energy balance models (EBMs), which rely on an understanding of a planet’s radiative properties. However, knowledge of these properties is currently limited. Here, I use Earth as a testbed for examining habitability and model uncertainty in a diffusive latitudinal EBM. Using an empirical approach, I parametrize Earth’s radiative properties – top of atmosphere albedo and outgoing infrared radiation – as a function of surface temperature, using measurements from the NASA Clouds and Earth’s Radiant Energy System instruments. When using surface temperature to construct these functions, a bias analysis shows only small global biases of 3 and − 0 . 3 W m − 2 are introduced for the albedo and infrared functions, respectively. I also show there is a very ﬁne balance between Earth’s incoming solar and outgoing infrared radiation in the observational data, which is substantially smaller than previously assumed in this model. Using observationally derived radiative functions for an Earth-like planet, the snowball transition can be determined within a semimajor axis of ∼ 3 per cent, with a structural uncertainty of ∼ 2 per cent in the radiative function construction. This work shows that diffusive EBMs can be successfully used to identify habitable exo-Earths within ∼ 3 per cent if the radiative properties are known.
Using a Model of Fraudulent Trader for Fraud Detection

The technological revolution brought by the internet, high performance computing, and artificial intelligence has fundamentally changed and continues to alter the landscape of finance. These innovations, if used with a malicious intent, can seriously destabilize the financial market. For this reason, counter-measures in the form of new detection methods are needed. In this study, we propose a novel detection framework that uses a model of fraudulent behavior to detect fraud from observed data. A similarity measure is defined to decide if the recorded actions of a monitored trader are matching actions of the fraudulent agent. We illustrate the framework on a simple form of manipulative trading in a simulation environment of a market consisting of two exchanges. This demonstrative case study is inspired by a price manipulation scheme that occurred on the Bitcoin market in 2017/18, where such simple forms of manipulation were observed. Simulation results outline vulnerabilities in markets, where uneven distribution of liquidity is present, as this can be exploited by pump-and-dump scheme.
A Numerical Study of Duct Geometry Effect on Radiation of Engine Internal Noisef

The radiation of engine internal noise to the ambient from engine open ends with various geometries is studied in the present paper via numerical methods. The Euler's equations in generalized curvilinear coordinates are solved by the DRP finite difference scheme. The grids are generated numerically to fit the various geometries. A number of accurate boundary conditions are used at boundaries of the computational domain to minimize the non-physical reflections. A multi-domain method is utilized to avoid the unnecessarily dense grid inside and near the engine duct due to the use of the geometry-fitting grids. The far field sound pressure levels are computed by the Kirchhoff integration method.
Discovering the neuronal dynamics in major depressive disorder using Hidden Markov Model

Introduction Major Depressive Disorder (MDD) is a leading cause of worldwide disability, and standard clinical treatments have limitations due to the absence of neurological evidence. Electroencephalography (EEG) monitoring is an effective method for recording neural activities and can provide electroneurophysiological evidence of MDD. Methods In this work, we proposed a probabilistic graphical model for neural dynamics decoding on MDD patients and healthy controls (HC), utilizing the Hidden Markov Model with Multivariate Autoregressive observation (HMM-MAR). We testified the model on the MODMA dataset, which contains resting-state and task-state EEG data from 53 participants, including 24 individuals with MDD and 29 HC. Results The experimental results suggest that the state time courses generated by the proposed model could regress the Patient Health Questionnaire-9 (PHQ-9) score of the participants and reveal differences between the MDD and HC groups. Meanwhile, the Markov property was observed in the neuronal dynamics of participants presented with sad face stimuli. Coherence analysis and power spectrum estimation demonstrate consistent results with the previous studies on MDD. Discussion In conclusion, the proposed HMM-MAR model has revealed its potential capability to capture the neuronal dynamics from EEG signals and interpret brain disease pathogenesis from the perspective of state transition. Compared with the previous machine-learning or deep-learning-based studies, which regarded the decoding model as a black box, this work has its superiority in the spatiotemporal pattern interpretability by utilizing the Hidden Markov Model.
Animal models of focal ischemic stroke: brain size matters

Stroke remains the second leading cause of death worldwide and the third cause of disability-adjusted life-years. Most strokes are ischemic in nature, meaning they are caused by the disruption of cerebral blood flow resulting from obstructed blood vessels. Reperfusion therapies such as thrombolysis with tissue plasminogen activator and endovascular mechanical thrombectomy are very effective and are becoming game changers for eligible patients. Despite these advances, the achieved effects are insufficient from the perspective of the entire population of stroke patients. Therefore, there is an urgent need to expand eligibility for reperfusion therapies and implement adjuvant therapeutic measures. Animal stroke models are at the forefront of these efforts, helping to untangle complex pathophysiology and providing valuable preclinical data to guide further clinical trials. Various stroke models are available, including direct blocking of cerebral arteries or using other means to recapitulate stroke pathophysiology. International advisory boards recommend initial in vivo experiments be performed in smaller animals, such as rodents. However, second testing would be more desirable in larger animals such as cats, pigs, dogs, and non-human primates. Due to larger cerebral volume, gyrencephalization, and higher white/gray matter ratio, large animals are crucial in translational stroke research. Animal stroke models differ in the time and complexity of the stroke induction procedure, the reproducibility rate, the level of similarity to the human condition, and the possibilities for analysis, imaging, and follow-up studies. The choice of the most appropriate stroke model may translate to better bench-to-bedside translation of preclinical stroke research; ideally, this choice should be based solely on scientific merit.
Conflict and control in cortical responses to inconsistent emotional signals in a face-word Stroop

Social communication is fraught with ambiguity. Negotiating the social world requires interpreting the affective signals we receive and often selecting between channels of conflicting affective information. The affective face-word Stroop (AFWS) provides an experimental paradigm which may identify cognitive-affective control mechanisms underpinning essential social-affective skills. Initial functional magnetic resonance imaging (fMRI) study of the AFWS identified right amygdala as driving this affective conflict and left rostral anterior cingulate cortex (rACC) as the locus of conflict control. We employed electroencephalogram (EEG) and eLORETA source localization to investigate the timing, location, and sequence of control processes when responding to affective conflict generated during the AFWS. However we designated affective word as the response target and affective face as the distractor to maximize conflict and control effects. Reaction times showed slowed responses in high vs. low control conditions, corresponding to a Rabbitt type control effect rather than the previously observed Grattan effect. Control related activation occurred in right rACC 96–118 ms post-stimulus, corresponding to the resolution of the P1 peak in the Visual Evoked Potential (VEP). Face distractors elicit right hemisphere control, while word distractors elicit left hemisphere control. Low control trials require rapid “booting up” control resources observable through VEPs. Incongruent trial activity in right fusiform face area is suppressed 118–156 ms post stimulus corresponding to onset and development of the N170 VEP component. Results are consistent with a predicted sequence of rapid early amygdala activation by affective conflict, then rACC inhibition of amygdala decreasing facilitation of affective face processing (however, amygdala activity is not observable with EEG).
Awakening the sleeping giant of urban green in times of crisis—coverage, co-creation and practical guidelines for optimizing biodiversity-friendly and health-promoting residential greenery

As multiple crises deepen existing inequalities in urban societies within and between neighborhoods, strategically integrating nature-based solutions into the living environment can help reduce negative impacts and improve public health, social cohesion, and well-being. Compared to public green such as parks, semi-public residential greenery is rarely studied, is regularly overlooked by planners, and often receives step-motherly treatment from architects and housing companies. We approximated the area of residential greenery of modernist multi-story apartment complexes in Berlin, Germany. We surveyed residents’ suggestions for improving their living environments in vulnerable neighborhoods, report on co-creation experiences, and provide a practical guideline for optimizing health-promoting residential green spaces. The semi-public open space on the doorstep of two-thirds of Berlin’s population is highly fragmented and, in total, has a similar area as the public green spaces and a great potential for qualitative development. Just as the suitability of different nature-based solutions to be integrated into the residential greenery depends on building types, resident demands differ between neighborhoods. Residents called for more involvement in design, implementation, and maintenance, frequently proposing that biodiversity-friendly measures be included. As there is no universal solution even for neighborhoods sharing similar structural and socioeconomic parameters, we propose, and have tested, an optimization loop for health-promoting residential greening that involves exploring residents’ needs and co-creating local solutions for urban regeneration processes that can be initiated by different actors using bottom-up and/or top-down approaches in order to unlock this potential for healthy, livable and biodiversity friendly cities.
Effects of substituting wheat by rye in diets for young fattening pigs on nutrient digestibility, performance, products of intestinal fermentation, and fecal characteristics

Climate change and increasing demands to reduce the environmental impact of feed production are major challenges for animal nutritionists. Compared to wheat, which is commonly used in high levels in European piglet diets, rye is more efficient in using limited resources, most importantly, water and phosphorus. As a result, its cultivation has a relatively low carbon footprint. The high amounts of non-starch polysaccharides of rye might lead to an increased intestinal fermentation with potential beneficial effects on gut health. However, the high levels of non-starch polysaccharides in rye, which have a major impact on the physico-chemical conditions of the digesta, might affect digestibility and performance especially in young animals. It was therefore of interest to compare the effects of isoenergetic diets with increasing levels of rye as a replacement for wheat fed to young fattening pigs (bodyweight: 16–40 kg). The control diet contained 69% of wheat, while in the other three experimental diets, the amount of wheat was gradually replaced (by a third in each case) with rye. Thus, the experimental diets contained 23, 46, and 69% of rye. A total of 40 young pigs were housed individually in four dietary treatment groups. During a 4 week trial, effects on performance, digestibility, products of intestinal fermentation, and fecal characteristics were evaluated. There were no negative effects on feed intake and gains, even though the feed conversion ratio increased with the highest dietary rye level (69%). Digestibility rates of organic matter and crude protein did not differ significantly. Without affecting the characteristics of the feces, numerically higher amounts of intestinal fermentation products and higher colonic digesta mass were observed.
Feedback in augmented and virtual reality piano tutoring systems: a mini review

Researchers in music education are exploring the use of virtual reality (VR) and augmented reality (AR) to support piano instruction. Beginner piano students tend to receive short, infrequent lessons, which they practice on their own. This lack of instructor feedback creates opportunities for students to develop improper technique. Current strategies for using AR and VR to guide solo practice use moving shapes to help students to identify what notes to play. Improvements in commercial AR/VR technology will be needed to provide more detailed real-time feedback.