Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Assessment of Conceptual Models in Education A Systematic Literature Review

In Computer Science, Software Engineering, Business Informatics or Information Systems, conceptual modeling is an important tool and as such also contained in the respective curricular recommendations. Especially in large university courses, an automated assessment of models can improve the quality of teaching and learning. While there are many different approaches to automatically assess conceptual models, these approaches, however, often only tackle a single aspect or a single type of conceptual model. In this paper, we aim to take a comprehensive perspective on the topic and shed light on the current state of the art and technique. Furthermore, as assessment approaches have to be developed in accordance with appropriate teaching or learning activities and desired learning outcomes, we inquire in which settings automated assessment approaches are included and to which extent didactic aspects are taken into account. To this end, we have conducted a systematic literature review in which we identified 110 relevant publications on the topic which we have analyzed in a structured way. The results provide answers to five relevant research questions and pinpoint open issues which should be inquired in further research.
A Light- and Heat-Seeking Vine-Inspired Robot With Material-Level Responsiveness

The fields of soft and bio-inspired robotics promise to imbue synthetic systems with capabilities found in the natural world. However, many of these biological capabilities are yet to be realized. For example, vines in nature direct growth via localized responses embedded in the cells of the vine body, allowing an organism without a central brain to successfully search for resources (e.g., light). Yet to date, vine-inspired robots have yet to show such localized embedded responsiveness. Here we present a vine-inspired robotic device with material-level responses embedded in its skin and capable of “growing” and steering toward either a light or heat stimulus. We present basic modeling of the concept, design details, and experimental results showing its behavior in response to infrared (IR) and visible light. Our simple design concept advances the capabilities of bio-inspired robots and lays the foundation for future “growing” robots that are capable of seeking light or heat, yet are extremely simple and low-cost. Potential applications include solar tracking, and in the future, fighting smoldering fires. We envision using similar robots to find hot spots in hard-to-access environments, allowing us to put out potentially long-burning fires faster.
Measuring the Dust Attenuation Curves of SINGS/KINGFISH Galaxies Using Swift/UVOT Photometry

We present Swift/Ultraviolet Optical Telescope (UVOT) integrated light photometry of the Spitzer Infrared Nearby Galaxies Survey (SINGS) and the Key Insights on Nearby Galaxies: A Far-Infrared Survey with Herschel (KINGFISH) samples of nearby galaxies. Combining the Swift/UVOT data with archival photometry, we investigate a variety of dust attenuation curves derived using MCSED, a flexible spectral energy distribution (SED) fitting code. We fit the panchromatic data using three different star formation history (SFH) parameterizations: a decaying exponential, a double power law, and a piecewise function with breaks at physically motivated ages. We find that the average attenuation law of the sample changes slightly based on the SFH assumed. Specifically, the exponential SFH leads to the shallowest attenuation curves. Using simulated data, we also find the exponential SFH fails to outperform the more complex SFHs. Finally, we find a systematic offset in the derived bump strength between SED fits with and without UVOT data, where the inclusion of UVOT data leads to smaller bump strengths, highlighting the importance of the UVOT data. This discrepancy is not seen in fits to mock photometry. Understanding dust attenuation in the local universe is key to understanding high-redshift objects where rest-frame far-infrared data are unavailable.
Audioslots: A Slot-Centric Generative Model For Audio Separation

In a range of recent works, object-centric architectures have been shown to be suitable for unsupervised scene decomposition in the vision domain. Inspired by these methods we present AudioSlots, a slot-centric generative model for blind source separation in the audio domain. AudioSlots is built using permutation-equivariant encoder and decoder networks. The encoder network based on the Transformer architecture learns to map a mixed audio spectrogram to an unordered set of independent source embeddings. The spatial broadcast decoder network learns to generate the source spectrograms from the source embeddings. We train the model in an end-to-end manner using a permutation invariant loss function. Our results on Libri2Mix speech separation constitute a proof of concept that this approach shows promise. We discuss the results and limitations of our approach in detail, and further outline potential ways to overcome the limitations and directions for future work.
Integrated Super-Resolution Sensing and Communication with 5G NR Waveform: Signal Processing with Uneven CPs and Experiments: (Invited Paper)

Integrated sensing and communication (ISAC) is a promising technology to simultaneously provide high performance wireless communication and radar sensing services in future networks. In this paper, we propose the concept of integrated super-resolution sensing and communication (ISSAC), which uses super-resolution algorithms in ISAC systems to achieve extreme sensing performance for those critical parameters, such as delay, Doppler, and angle of the sensing targets. Based on practical fifth generation (5G) New Radio (NR) wave forms, the signal processing techniques of ISSAC are investigated and prototyping experiments are performed to verify the achievable performance. To this end, we first study the effect of uneven cyclic prefix (CP) lengths of 5G NR orthogonal frequency division multiplexing (OFDM) waveforms on various sensing algorithms. Specifically, the performance of the standard Periodogram based radar processing method, together with the two classical super resolution algorithms, namely, MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) are analyzed in terms of the delay and Doppler estimation. To resolve the uneven CP issue, a new structure of steering vector for MUSIC and a new selection of submatrices for ESPRIT are proposed. Furthermore, an ISSAC experimental platform is setup to validate the theoretical analysis, and the experimental results show that the performance degradation caused by unequal CP length is insignificant and high-resolution delay and Doppler estimation of the target can be achieved with 5G NR waveforms.
Crop Identification Using Deep Learning on LUCAS Crop Cover Photos

Massive and high-quality in situ data are essential for Earth-observation-based agricultural monitoring. However, field surveying requires considerable organizational effort and money. Using computer vision to recognize crop types on geo-tagged photos could be a game changer allowing for the provision of timely and accurate crop-specific information. This study presents the first use of the largest multi-year set of labelled close-up in situ photos systematically collected across the European Union from the Land Use Cover Area frame Survey (LUCAS). Benefiting from this unique in situ dataset, this study aims to benchmark and test computer vision models to recognize major crops on close-up photos statistically distributed spatially and through time between 2006 and 2018 in a practical agricultural policy relevant context. The methodology makes use of crop calendars from various sources to ascertain the mature stage of the crop, of an extensive paradigm for the hyper-parameterization of MobileNet from random parameter initialization, and of various techniques from information theory in order to carry out more accurate post-processing filtering on results. The work has produced a dataset of 169,460 images of mature crops for the 12 classes, out of which 15,876 were manually selected as representing a clean sample without any foreign objects or unfavorable conditions. The best-performing model achieved a macro F1 (M-F1) of 0.75 on an imbalanced test dataset of 8642 photos. Using metrics from information theory, namely the equivalence reference probability, resulted in an increase of 6%. The most unfavorable conditions for taking such images, across all crop classes, were found to be too early or late in the season. The proposed methodology shows the possibility of using minimal auxiliary data outside the images themselves in order to achieve an M-F1 of 0.82 for labelling between 12 major European crops.
Group Activity Recognition via Dynamic Composition and Interaction

Previous group activity recognition approaches were limited to reasoning using human relations or finding important subgroups and tended to ignore indispensable group composition and human-object interactions. This absence makes a partial interpretation of the scene and increases the interference of irrelevant actions on the results. Therefore, we propose our DynamicFormer with Dynamic composition Module (DcM) and Dynamic interaction Module (DiM) to model relations and locations of persons and discriminate the contribution of participants, respectively. Our findings on group composition and human-object interaction inspire our core idea. Group composition tells us the location of people and their relations inside the group, while interaction reflects the relation between humans and objects outside the group. We utilize spatial and temporal encoders in DcM to model our dynamic composition and build DiM to explore interaction with a novel GCN, which has a transformer inside to consider the temporal neighbors of human/object. Also, a Multi-level Dynamic Integration is employed to integrate features from different levels. We conduct extensive experiments on two public datasets and show that our method achieves state-of-the-art.
Subspace Analysis of Spatial Time-Frequency

Abstract— Spatial time–frequency distributions (STFDs) havebeen recently introduced as the natural means to deal with sourcesignals that are localizable in the time–frequency domain.Previouswork in the area has not provided the eigenanalysis of STFD ma-trices, which is key to understanding their role in solving direc-tion finding and blind source separation problems in multisensorarray receivers. The aim of this paper is to examine the eigenstruc-ture of the STFDs matrices. We develop the analysis and statisticalproperties of the subspace estimates based on STFDs for frequencymodulated (FM) sources. It is shown that improved estimates areachieved by constructing the subspaces from the time–frequencysignatures of the signal arrivals rather than from the data covari-ance matrices, which are commonly used in conventional subspaceestimationmethods.Thisimprovementisevidentinalowsignal-to-noise ratio (SNR) environment and in the cases of closely spacedsources.ThepaperconsiderstheMUSICtechniquetodemonstratethe advantages of STFDs and uses it as grounds for comparison be-tween time–frequency and conventional subspace estimates.Index Terms— Array signal processing, spatial time–frequencydistribution, subspace analysis, time–frequency distribution,time–frequency MUSIC.
GNN S ,Y OU C AN B E S TRONGER ,DEEPER AND F ASTER

Graph neural networks (GNNs), a type of neural network that can learn from graph-structured data and learn the representation of nodes by aggregating their neighbors, have shown excellent performance in downstream tasks.However, it is known that the performance of graph neural networks (GNNs) degrades gradually as the number of layers increases. Based on k-hop subgraph aggregation, which is a new concept, we propose a new perspective to understand the expressive power of GNN.From this perspective, we reveal the potential causes of the performance degradation of the deep traditional GNN - aggregated subgraph overlap, and the fact that the residual-based graph neural networks in fact exploit the aggregation results of 1 to k hop subgraphs to improve the effectiveness.Further, we propose a new sampling-based node-level residual module named SDF, which is shown by theoretical derivation to obtain a superior expressive power compared to previous residual methods by using information from 1 to k hop subgraphs more ﬂexibly. Extensive experiments show that the performance and efﬁciency of GNN with the SDF module outperform other methods. Our code is publicly available at https://github.com/JingboZhou-JLU/SDF-GNN.
Turning Privacy-preserving Mechanisms against Federated Learning

Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build enhanced recommender systems due to their capability to learn patterns from the interaction between involved entities. In addition, previous studies have investigated federated learning as the main solution to enable a native privacy-preserving mechanism for the construction of global GNN models without collecting sensitive data into a single computation unit. Still, privacy issues may arise as the analysis of local model updates produced by the federated clients can return information related to sensitive local data. For this reason, researchers proposed solutions that combine federated learning with Differential Privacy strategies and community-driven approaches, which involve combining data from neighbor clients to make the individual local updates less dependent on local sensitive data. In this paper, we identify a crucial security flaw in such a configuration and design an attack capable of deceiving state-of-the-art defenses for federated learning. The proposed attack includes two operating modes, the first one focusing on convergence inhibition (Adversarial Mode), and the second one aiming at building a deceptive rating injection on the global federated model (Backdoor Mode). The experimental results show the effectiveness of our attack in both its modes, returning on average 60% performance detriment in all the tests on Adversarial Mode and fully effective backdoors in 93% of cases for the tests performed on Backdoor Mode.
Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model

The widespread adoption of Image Processing has propelled Object Recognition (OR) models into essential roles across various applications, demonstrating the power of AI and enabling crucial services. Among the applications, traffic sign recognition stands out as a popular research topic, given its critical significance in the development of autonomous vehicles. Despite their significance, real-world challenges, such as alterations to traffic signs, can negatively impact the performance of OR models. This study investigates the influence of altered traffic signs on the accuracy and effectiveness of object recognition, employing a publicly available dataset to introduce alterations in shape, color, content, visibility, angles and background. Focusing on the YOLOv7 (You Only Look Once) model, the study demonstrates a notable decline in detection and classification accuracy when confronted with traffic signs in unusual conditions including the altered traffic signs. Notably, the alterations explored in this study are benign examples and do not involve algorithms used for generating adversarial machine learning samples. This study highlights the significance of enhancing the robustness of object detection models in real-life scenarios and the need for further investigation in this area to improve their accuracy and reliability.
Microscopic Theory of Vibrational Polariton Chemistry

We present a microscopic theory that aims to explain the vibrational strong coupling (VSC) modified reaction rate constant. The analytic theory is based on a mechanistic conjecture that cavity modes promote the transition from the ground state to the vibrational excited state of the reactant, which is the rate-limiting step of the reaction. The theory explains the observed resonance effect at the normal incident angle. Assuming the coherent vibrational energy transfer picture, the theory can also explain the collective effect and makes several predictions that are experimentally verifiable.
CAMIL: Context-Aware Multiple Instance Learning for Whole Slide Image Classification

Cancer diagnoses typically involve human pathologists examining whole slide images (WSIs) of tissue section biopsies to identify tumor cells and their subtypes. However, artiﬁcial intelligence (AI)-based models, particularly weakly supervised approaches, have recently emerged as viable alternatives. Weakly supervised approaches often use image subsections or tiles as input, with the overall classiﬁcation of the WSI based on attention scores assigned to each tile. However, this method overlooks the potential for false positives/negatives because tumors can be heterogeneous, with cancer and normal cells growing in patterns larger than a single tile. Such errors at the tile level could lead to misclassiﬁcation at the tumor level. To address this limitation, we developed a novel deep learning pooling operator called CHARM (Contrastive Histopathology Attention Resolved Models). CHARM leverages the dependencies among single tiles within a WSI and imposes contextual constraints as prior knowledge to multiple instance learning models. We tested CHARM on the subtyping of non-small cell lung cancer (NSLC) and lymph node (LN) metastasis, and the results demonstrated its superiority over other state-of-the-art weakly supervised classiﬁcation algorithms. Furthermore, CHARM facilitates interpretability by visualizing regions of attention.
Egocentric Hierarchical Visual Semantics

We are interested in aligning how people think about objects and what machines perceive, meaning by this the fact that object recognition, as performed by a machine, should follow a process which resembles that followed by humans when thinking of an object associated with a certain concept. The ultimate goal is to build systems which can meaningfully interact with their users, describing what they perceive in the users' own terms. As from the field of Lexical Semantics, humans organize the meaning of words in hierarchies where the meaning of, e.g., a noun, is defined in terms of the meaning of a more general noun, its genus, and of one or more differentiating properties, its differentia. The main tenet of this paper is that object recognition should implement a hierarchical process which follows the hierarchical semantic structure used to define the meaning of words. We achieve this goal by implementing an algorithm which, for any object, recursively recognizes its visual genus and its visual differentia. In other words, the recognition of an object is decomposed in a sequence of steps where the locally relevant visual features are recognized. This paper presents the algorithm and a first evaluation.
Autumn: A Scalable Read Optimized LSM-tree based Key-Value Stores with Fast Point and Range Read Speed

The Log Structured Merge Trees (LSM-tree) based key-value stores are widely used in many storage systems to support a variety of operations such as updates, point reads, and range reads. Traditionally, LSM-tree's merge policy organizes data into multiple levels of exponentially increasing capacity to support high-speed writes. However, we contend that the traditional merge policies are not optimized for reads. In this work, we present Autumn, a scalable and read optimized LSM-tree based key-value stores with minimal point and range read cost. The key idea in improving the read performance is to dynamically adjust the capacity ratio between two adjacent levels as more data are stored. As a result, smaller levels gradually increase their capacities and merge more often. In particular, the point and range read cost improves from the previous best known $O(logN)$ complexity to $O(\sqrt{logN})$ in Autumn by applying the new novel Garnering merge policy. While Garnering merge policy optimizes for both point reads and range reads, it maintains high performance for updates. Moreover, to further improve the update costs, Autumn uses a small amount of bounded space of DRAM to pin/keep the first level of LSM-tree. We implemented Autumn on top of LevelDB and experimentally showcases the gain in performance for real world workloads.
Discovery of Radial Spectral Hardening in the Hot Bubble of Planetary Nebula BD+30°3639 with Median Energy Imaging

We apply an imaging analysis technique to study the spatial distribution of the X-ray spectral hardness across the hot bubble of planetary nebula BD+30°3639. Hot bubble emission is typically photon starved, thus limiting the methods for spatial–spectral analysis; however, this technique uses the statistics of photon energies across the nebula to identify spatial variations in the X-ray spectral hardness. Using the median energy value of the X-ray photons, we identified a rise in median energy values toward the projected edge of the nebula, which we refer to as radial spectral hardening. We explored the origin of this radial spectral hardening with an X-ray spectral analysis of distinct regions of high and low median energy values. Given the results of the X-ray spectral fits and the fact that the hot bubble is embedded within a young, dense, planetary nebula, we conclude that the radial spectral hardening is due to an increased column density at the projected nebular edge.
What is the best recipe for character-level encoder-only modelling?

This paper aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. Many such modelling architectures and methods to train those architectures have been proposed, but it is currently unclear what the relative contributions of the architecture vs. the pretraining objective are to final model performance. We explore the design space of such models, comparing architectural innovations (Clark et al., 2022, Jaegle et al., 2022, Tay et al., 2021) and a variety of different pretraining objectives on a suite of evaluation tasks with a fixed training procedure in order to find the currently optimal way to build and train character-level BERT-like models. We find that our best performing character-level model exceeds the performance of a token-based model trained with the same settings on the same data, suggesting that character-level models are ready for more widespread adoption. Unfortunately, the best method to train character-level models still relies on a subword-level tokeniser during pretraining, and final model performance is highly dependent on tokeniser quality. We believe our results demonstrate the readiness of character-level models for multilingual language representation, and encourage NLP practitioners to try them as drop-in replacements for token-based models.