Miracle-dz/UniFAR-FTUs huggingface dataset:
Guiding Next-Step Hint Generation Using Automated Tests

Learning basic programming with Scratch can be hard for novices and tutors alike: Students may not know how to advance when solving a task, teachers may face classrooms with many raised hands at a time, and the problem is exacerbated when novices are on their own in online or virtual lessons. It is therefore desirable to generate next-step hints automatically to provide individual feedback for students who are stuck, but current approaches rely on the availability of multiple hand-crafted or hand-selected sample solutions from which to draw valid hints, and have not been adapted for Scratch. Automated testing provides an opportunity to automatically select suitable candidate solutions for hint generation, even from a pool of student solutions using different solution approaches and varying in quality. In this paper we present Catnip, the first nextstep hint generation approach for Scratch, which extends existing data-driven hint generation approaches with automated testing. Evaluation of Catnip on a dataset of student Scratch programs demonstrates that the generated hints point towards functional improvements, and the use of automated tests allows the hints to be better individualized for the chosen solution path.
How Reliable are Model Diagnostics?

In the pursuit of a deeper understanding of a model's behaviour, there is recent impetus for developing suites of probes aimed at diagnosing models beyond simple metrics like accuracy or BLEU. This paper takes a step back and asks an important and timely question: how reliable are these diagnostics in providing insight into models and training setups? We critically examine three recent diagnostic tests for pre-trained language models, and find that likelihood-based and representation-based model diagnostics are not yet as reliable as previously assumed. Based on our empirical findings, we also formulate recommendations for practitioners and researchers.
Self-citation Analysis using Sentence Embeddings

The purpose of citation indexes and metrics is intended to be a measure for scientific innovation and quality for researchers, journals, and institutions. However, those metrics are often prone to abuse and manipulation by excessive and unethical self-citations induced by authors, reviewers, editors, or journals. Identifying whether there are or not legitimate reasons for self-citations is normally determined during the review process, where the participating parts may have intrinsic incentives, rendering the legitimacy of self-citations, after publication, questionable. In this paper, we conduct a large-scale analysis of journal self-citations while taking into consideration the similarity between a publication and its references. Specifically, we look into PubMed Central articles published since 1990 and compute similarities of article-reference pairs using sentence embeddings. We examine journal self-citations with an aim to distinguish between justifiable and unethical self-citations.
Development of an expected possession value model to analyse team attacking performances in rugby league

This study aimed to evaluate team attacking performances in rugby league via expected possession value (EPV) models. Location data from 59,233 plays in 180 Super League matches across the 2019 Super League season were used. Six EPV models were generated using arbitrary zone sizes (EPV-308 and EPV-77) or aggregated according to the total zone value generated during a match (EPV-37, EPV-19, EPV-13 and EPV-9). Attacking sets were considered as Markov Chains, allowing the value of each zone visited to be estimated based on the outcome of the possession. The Kullback-Leibler Divergence was used to evaluate the reproducibility of the value generated from each zone (the reward distribution) by teams between matches. Decreasing the number of zones improved the reproducibility of reward distributions between matches but reduced the variation in zone values. After six previous matches, the subsequent match’s zones had been visited on 95% or more occasions for EPV-19 (95±4%), EPV-13 (100±0%) and EPV-9 (100±0%). The KL Divergence values were infinity (EPV-308), 0.52±0.05 (EPV-77), 0.37±0.03 (EPV-37), 0.20±0.02 (EPV-19), 0.13±0.02 (EPV-13) and 0.10±0.02 (EPV-9). This study supports the use of EPV-19 and EPV-13, but not EPV-9 (too little variation in zone values), to evaluate team attacking performance in rugby league.
The Greedy and Recursive Search for Morphological Productivity

As children acquire the knowledge of their language's morphology, they invariably discover the productive processes that can generalize to new words. Morphological learning is made challenging by the fact that even fully productive rules have exceptions, as in the well-known case of English past tense verbs, which features the -ed rule against the irregular verbs. The Tolerance Principle is a recent proposal that provides a precise threshold of exceptions that a productive rule can withstand. Its empirical application so far, however, requires the researcher to fully specify rules defined over a set of words. We propose a greedy search model that automatically hypothesizes rules and evaluates their productivity over a vocabulary. When the search for broader productivity fails, the model recursively subdivides the vocabulary and continues the search for productivity over narrower rules. Trained on psychologically realistic data from child-directed input, our model displays developmental patterns observed in child morphology acquisition, including the notoriously complex case of German noun pluralization. It also produces responses to nonce words that, despite receiving only a fraction of the training data, are more similar to those of human subjects than current neural network models' responses are.
Object-Based Augmentation Improves Quality of Remote SensingSemantic Segmentation

Today deep convolutional neural networks (CNNs) push the limits for most computer vision problems, define trends, and set state-of-the-art results. In remote sensing tasks such as object detection and semantic segmentation, CNNs reach the SotA performance. However, for precise performance, CNNs require much high-quality training data. Rare objects and the variability of environmental conditions strongly affect prediction stability and accuracy. To overcome these data restrictions, it is common to consider various approaches including data augmentation techniques. This study focuses on the development and testing of object-based augmentation. The practical usefulness of the developed augmentation technique is shown in the remote sensing domain, being one of the most demanded ineffective augmentation techniques. We propose a novel pipeline for georeferenced image augmentation that enables a significant increase in the number of training samples. The presented pipeline is called object-based augmentation (OBA) and exploits objects' segmentation masks to produce new realistic training scenes using target objects and various label-free backgrounds. We test the approach on the buildings segmentation dataset with six different CNN architectures and show that the proposed method benefits for all the tested models. We also show that further augmentation strategy optimization can improve the results. The proposed method leads to the meaningful improvement of U-Net model predictions from 0.78 to 0.83 F1-score.
Conversational Negation using Worldly Context in Compositional Distributional Semantics

We propose a framework to model an operational conversational negation by applying worldly context (prior knowledge) to logical negation in compositional distributional semantics. Given a word, our framework can create its negation that is similar to how humans perceive negation. The framework corrects logical negation to weight meanings closer in the entailment hierarchy more than meanings further apart. The proposed framework is flexible to accommodate different choices of logical negations, compositions, and worldly context generation. In particular, we propose and motivate a new logical negation using matrix inverse. We validate the sensibility of our conversational negation framework by performing experiments, leveraging density matrices to encode graded entailment information. We conclude that the combination of subtraction negation and phaser in the basis of the negated word yields the highest Pearson correlation of 0.635 with human ratings.
Cinnamomum casia Extract Encapsulated Nanochitosan as Antihypercholesterol

Atherosclerosis vascular disease with clinical manifestations such as cardiovascular disease and stroke are the leading cause of death in Indonesia. One solution to these problems is a natural antihypercholesterol medicine by utilizing Cinnamomum casia extract. However, the use of natural extracts to lower blood cholesterol levels do not provide optimal results because it is possible that the active components of extract have been degraded/damaged during the absorption process. So that, we need to do the research to get a combination of chitosan nanoparticles-Cinnamomum casia. extract as a compound which has an antihypercholesterol activity through the in vitro study. Modification of natural extracts encapsulated nanochitosan be a freshness in this study, which were conducted using the method of inclusion. The combination of both has the dual function of protecting the natural extracts from degradation and deliver the natural extracts to the target site. Analysis of nanochitosan using the Particle Size Analyzer (PSA) shows the particle size of synthesis product that is equal to 64.9 nm. Encapsulation efficiency of Cinnamomum casia extract-Chitosan Nanoparticles known through UV-VIS spectrophotometry test and obtained the efficiency encapsulation percentage of 84.93%. Zeta Potential at 193,3 mv that chitosan appropriate for a delivery drug. Antihypercholesterol activity tested in vitro assay that showed the extract-nanoparticle chitosan in concentration 150 ppm gave the highest cholesterol decreasing level in the amount of 49.66% w/v. So it can be concluded that Cinnamomum casia extract can be encapsulated in nanoparticles of chitosan and proved that it has a cholesterol-lowering effect through the in vitro study.
ALMA Host Galaxy Observation of the Off-axis Gamma-Ray Burst XRF 020903

We investigated the radio properties of the host galaxy of X-ray flash, XRF 020903, which is the best example for investigating the off-axis origin of gamma-ray bursts (GRBs). Dust continuum at 233 GHz and CO are observed using the Atacama Large millimeter/submillimeter array. The molecular gas mass derived by applying the metallicity-dependent CO-to-H2 conversion factor matches the global trend along the redshift and stellar mass of the GRB host galaxies. The estimated gas depletion timescale (pertaining to the potential critical characteristics of GRB host galaxies) is equivalent to those of GRBs and super-luminous supernova hosts in the same redshift range. These properties of the XRF 020903 host galaxy observed in radio resemble those of GRB host galaxies, thereby supporting the identical origin of XRF 020903 and GRBs.
Automatic Classification of Games using Support Vector Machine

Game developers benefit from availability of custom game genres when doing game market analysis. This information can help them to spot opportunities in market and make them more successful in planning a new game. In this paper we find good classifier for predicting category of a game. Prediction is based on description and title of a game. We use 2443 iOS App Store games as data set to generate a document-term matrix. To reduce the curse of dimensionality we use Latent Semantic Indexing, which, reduces the term dimension to approximately 1/9. Support Vector Machine supervised learning model is fit to pre-processed data. Model parameters are optimized using grid search and 20-fold cross validation. Best model yields to 77% mean accuracy or roughly 70% accuracy with 95% confidence. Developed classifier has been used inhouse to assist games market research. Keywords—automatic, classification, games, genres, support vector machine.
An Efficient Matrix Multiplication with Enhanced Privacy Protection in Cloud Computing and Its Applications

As one of the most important basic operations, matrix multiplication computation (MMC) has varieties of applications in the scientific and engineering community such as linear regression, k-nearest neighbor classification and biometric identification. However handling these tasks with large-scale datasets will lead to huge computation beyond resource-constrained client s computation power. With the rapid development of cloud computing, outsourcing intensive tasks to cloud server has become a promising method. While the cloud server is generally out of the control of clients, there are still many challenges concerned with the privacy security of clients sensitive data. Motivated by this, Lei et al. presented an efficient encryption scheme based on random permutation to protect the privacy of client s data in outsourcing MMC task. Nevertheless, there exists inherent security flaws in their scheme, revealing the statistic information of zero elements in the original data thus not satisfying the computational indistinguishability (IND-ZEA). Aiming to enhance the security of the outsourcing MMC task, we propose a new encryption scheme based on subtly designed invertible matrix where the additive perturbation is introduced besides the multiplicative perturbation. Furthermore, we show that the proposed encryption scheme can be applied to not only MMC task but also other kinds of outsourced tasks such as linear regression and principal component analysis. Theoretical analyses and experiments indicate that our methods are more secure in terms of data privacy, with comparable performance to the state-of-the-art scheme based on matrix transformation.
Jet Single Shot Detection

We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of calorimeter cells and using a Single Shot Detection network, called Jet-SSD. The model performs simultaneous localization and classification and additional regression tasks to measure jet features. We investigate TernaryWeight Networks with weights constrained to {-1, 0, 1} times a layer- and channel-dependent scaling factors. We show that the quantized version of the network closely matches the performance of its full-precision equivalent.
Indoor positioning systems: Smart fusion of a variety of sensor readings

Robust and versatile localization techniques are the key to the success of the next industrial revolution. Yet, it is uncertain which combination of sensors will be the most robust and valuable. Thus, we present a versatile and reproducible measurement system incorporating a manifold number of state-of-the art sensors to compare and fuse the raw input data. It is shown that some techniques show very good results on the same scenario and data-set, but fall apart on translating to a slightly different scenario. In general we show that the vanilla approach to fuse the raw data achieves reasonable results in the generalization domain, demonstrating that radio frequency (RF) localization techniques in combination with an inertial measurement unit (IMU) could result in a very robust and promising candidate for solving this challenging task.
Learning a Skill-sequence-dependent Policy for Long-horizon Manipulation Tasks

In recent years, the robotics community has made substantial progress in robotic manipulation using deep reinforcement learning (RL). Effectively learning of long-horizon tasks remains a challenging topic. Typical RL-based methods approximate long-horizon tasks as Markov decision processes and only consider current observation (images or other sensor information) as input state. However, such approximation ignores the fact that skill-sequence also plays a crucial role in long-horizon tasks. In this paper, we take both the observation and skill sequences into account and propose a skill-sequence-dependent hierarchical policy for solving a typical long-horizon task. The proposed policy consists of a high-level skill policy (utilizing skill sequences) and a low-level parameter policy (responding to observation) with corresponding training methods, which makes the learning much more sample-efficient. Experiments in simulation demonstrate that our approach successfully solves a long-horizon task and is significantly faster than Proximal Policy Optimization (PPO) and the task schema methods.
Ultra-intense laser pulses and the High Power Laser System at Extreme Light Infrastructure -- Nuclear Physics

Lasers make possible the production and ultimate control of electromagnetic fields in terms of spectral purity, spatial confinement down to micrometer scale, duration down to a single cycle in the femtosecond domain or shorter and electromagnetic field strengths corresponding to the highest intensities achieved by mankind in the lab. Ultra-intense laser facilities are pushing the limits of the achievable pulse intensity, hence the coined term extreme light. They make possible fundamental and applied investigations in physics and material science with emergent societal impact. Extreme Light Infrastructure is the most advanced project dedicated to the production and use of such extreme fields. The Extreme Light Infrastructure project will be outlined, with emphasis on the extreme light capabilities of the three pillars. The architecture of the first finalized 10PW high power laser system (HPLS) will be highlighted. This dual arm, 10PW each, laser system, at Extreme Light Infrastructure Nuclear Physics (ELI-NP), in Romania, delivers beams in five experimental areas that address research centered on nuclear physics, materials in extreme environments and exotic physics.
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data

Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple delayed updates of recent temporal slices for some locations or diseases. Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem. Existing tensor completion methods usually assume that i) missing elements are randomly distributed and ii) noise for each tensor element is i.i. d. zero-mean. Both assumptions can be violated for spatio-temporal tensor data. We often observe multiple versions of the input tensor with different under-reporting noise levels. The amount of noise can be time- or location-dependent as more updates are progressively introduced to the tensor. We model such dynamic data as a multi-version tensor with an extra tensor mode capturing the data updates. We propose a low-rank tensor model to predict the updates over time. We demonstrate that our method can accurately predict the ground-truth values of many real-world tensors. We obtain up to 27.2% lower root mean-squared-error compared to the best baseline method. Finally, we extend our method to track the tensor data over time, leading to significant computational savings.
SimNet: Computer Architecture Simulation using Machine Learning

While cycle-accurate simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic problems under investigation. This work describes a concerted effort, where machine learning (ML) is used to accelerate discrete-event simulation. First, an ML-based instruction latency prediction framework that accounts for both static instruction/architecture properties and dynamic execution context is constructed. Then, a GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor, and its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator. Leveraging modern GPUs, the ML-based simulator outperforms traditional simulators signiﬁcantly.
ROSEFusion

Online reconstruction based on RGB-D sequences has thus far been restrained to relatively slow camera motions (<1m/s). Under very fast camera motion (e.g., 3m/s), the reconstruction can easily crumble even for the state-of-the-art methods. Fast motion brings two challenges to depth fusion: 1) the high nonlinearity of camera pose optimization due to large inter-frame rotations and 2) the lack of reliably trackable features due to motion blur. We propose to tackle the difficulties of fast-motion camera tracking in the absence of inertial measurements using random optimization, in particular, the Particle Filter Optimization (PFO). To surmount the computation-intensive particle sampling and update in standard PFO, we propose to accelerate the randomized search via updating a particle swarm template (PST). PST is a set of particles pre-sampled uniformly within the unit sphere in the 6D space of camera pose. Through moving and rescaling the pre-sampled PST guided by swarm intelligence, our method is able to drive tens of thousands of particles to locate and cover a good local optimum extremely fast and robustly. The particles, representing candidate poses, are evaluated with a fitness function defined based on depth-model conformance. Therefore, our method, being depth-only and correspondence-free, mitigates the motion blur impediment as (ToF-based) depths are often resilient to motion blur. Thanks to the efficient template-based particle set evolution and the effective fitness function, our method attains good quality pose tracking under fast camera motion (up to 4m/s) in a realtime framerate without including loop closure or global pose optimization. Through extensive evaluations on public datasets of RGB-D sequences, especially on a newly proposed benchmark of fast camera motion, we demonstrate the significant advantage of our method over the state of the arts.
Fairness and Discrimination in Information Access Systems

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems. While fair information access shares many commonalities with fair classication, the multistakeholder nature of information access applications, the rank-based problem seing, the centrality of personalization in many cases, and the role of user response complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant, let alone measuring or promoting them. In this monograph, we present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. We preface this with brief introductions to information access and algorithmic fairness, to facilitate use of this work by scholars with experience in one (or neither) of these elds who wish to learn about their intersection. We conclude with several open problems in fair information access, along with some suggestions for how to approach research in this space.