Miracle-dz/UniFAR-FTUs huggingface dataset:
Socio-Semantic Network Motifs Framework for Discourse Analysis

Effective collaborative discourse requires both cognitive and social engagement of students. To investigate complex socio-cognitive dynamics in collaborative discourse, this paper proposes to model collaborative discourse as a socio-semantic network (SSN) and then use network motifs – defined as recurring, significant subgraphs – to characterize the network and hence the discourse. To demonstrate the utility of our SSN motifs framework, we applied it to a sample dataset. While more work needs to be done, the SSN motifs framework shows promise as a novel, theoretically informed approach to discourse analysis.
Participatory and Co-Design of Learning Analytics: An Initial Review of the Literature

Participatory Design (PD), and Co-design (Co-D), can be effective ways to improve technological innovation and to incorporate users’ needs in the development of learning analytics (LA). However, these methods can be difficult to implement and there has yet to be a synopsis of how its and techniques have been applied to the specific needs of LA. This study reviewed 90 papers that described 52 cases of PD of LA between 2010 and 2020 to address the research question “How is participatory design (PD) being used within LA? ”. It focuses on examining which groups of participants are normally included in PD for LA, in what phases of the design process it is used, and what specific tools and techniques have LA designers adapted or developed to co-create with design partners. Findings show that there is a growing number of researchers using these methods in recent years, particularly in higher education and with instructor stakeholders. However, it was also found that often the literature would describe the PD activities only superficially, and that some aspects of PD, such as recruitment, were seldom considered overtly in the descriptions of these processes.
A First Approximation to Linear CRF classifiers for Finger Movement Classification

Finger flexion decoding and classification has gained attention to understand the relationship between finger movements and the brain activity. Initially focused on EEG signals, it was moved quickly to electrocorticography (ECoG) signals because of the advantages provided by the latter signals. The present paper proposes two based-CRF discriminative classifiers as approaches to the task: a CRF classifier and a Latent Dynamic CRF (LDCRF) model. Proposed classifiers have the advantage to take into account time dependencies without implementing generative models. Results show that proposed classifiers work better with high gamma (HG) band (accuracy: CRF: 0.742, LD-CRF: 0.737; Cohen's kappa: CRF: 0.682, LD-CRF: 0.681) than low-frequency components (LFC) (accuracy: CRF: 0.433, LDCRF: 0.430; Cohen's kappa: CRF: 0.322, LDCRF: 0.329). Compared with other classifiers and using HG data, based-CRF classifiers have higher performances (accuracy: CRF: 0.742, LDCRF: 0.737; Cohen's kappa: CRF: 0.674, LDCRF: 0.669) than three other classifiers: Linear Discriminant Analysis (accuracy: 0.224, Cohen's kappa: 0.024), Quadratic Discriminant Analysis (accuracy: 0.224, Cohen's kappa: 0.026), and a Linear Support Vector Machine (accuracy: 0.280, Cohen's kappa: -0.003), with a significance level of 0.05.
Two Deep Learning Approaches for Automated Segmentation of Left Ventricle in Cine Cardiac MRI

Left ventricle (LV) segmentation is critical for clinical quantification and diagnosis of cardiac images. In this work, we propose two novel deep learning architectures called LNU-Net and IBU-Net for left ventricle segmentation from short-axis cine MRI images. LNU-Net is derived from layer normalization (LN) U-Net architecture, while IBU-Net is derived from the instance-batch normalized (IB) U-Net for medical image segmentation. The architectures of LNU-Net and IBU-Net have a down-sampling path for feature extraction and an up-sampling path for precise localization. We use the original U-Net as the basic segmentation approach and compared it with our proposed architectures. Both LNU-Net and IBU-Net have left ventricle segmentation methods: LNU-Net applies layer normalization in each convolutional block, while IBU-Net incorporates instance and batch normalization together in the first convolutional block and passes its result to the next layer. Our method incorporates affine transformations and elastic deformations for image data processing. Our dataset that contains 805 MRI images regarding the left ventricle from 45 patients is used for evaluation. We experimentally evaluate the results of the proposed approaches outperforming the dice coefficient and the average perpendicular distance than other state-of-the-art approaches.
Nitric oxide level modification on antioxidant enzyme activity, antioxidant capacity and pericarp browning of postharvest longan fruit

Overproduction of nitric oxide free radical (•NO) potentially causes the reduction in antioxidant defense system, damaging cell components and leading to physiological disorders in postharvest horticultural crops. In this study, the effects of sodium nitroprusside (SNP; NO donor) or 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (cPTIO; NO scavenger) on antioxidant enzyme activities, antioxidant capacity and pericarp browning development of harvested longan fruit were investigated. Longan fruits were dipped in distilled water (control), SNP (50 and 100 mM) or cPTIO (100 and 500 μM) for 10 min, then stored at 25 ± 1 °C for 7 d. It was shown that antioxidant enzyme activities including superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX) and glutathione peroxidase (GPX) and total antioxidant capacity including ABTS and DPPH decreased during storage. These reductions coincided with an increase in pericarp browning (browning index). Enzyme activity and antioxidant capacity assays confirmed our results that NO involved in longan pericarp browning. The activity of SOD, CAT, APX and GPX were reduced by 2.5-71.5%, 30.6-66.1%, 26.7-75.5% and 34.3-68.7%, respectively, after SNP treatment for 7 d but were increased by 2.4-109.6%, 3.8-153.8%, 6.3-47.1% and 31.9-156.5%, respectively, after treatment with cPTIO. In addition, the total antioxidant capacity ABTS and DPPH were reduced by 13.3-66.8% and 17.6-49.4%, respectively, after SNP treatment for 7 d but were increased by 3.6-58.8% and 2.0-128.7%, respectively, after treatment with cPTIO. Treatment with 100 mM of SNP was more efficient in inhibited the activity and capacity of antioxidant than that of 50 mM SNP. In contrast, the cPTIO-treated fruit exhibited opposite results by maintaining antioxidant enzyme activities and antioxidant capacity, corresponding to the reduction in pericarp browning. Treatment with 500 μM of cPTIO was more efficient in increased the activity and capacity of antioxidant than that of 100 μM SNP. The antioxidant potential and development of pericarp browning depended on the applied concentration of SNP or cPTIO. These results revealed that exposure to excessive NO resulted in the reduction in antioxidant potential and the increment of pericarp browning of harvested longan fruit.
Towards Multi-modal Evaluation of Eye-tracked Virtual Heritage Environment

In times of pandemic-induced challenges, virtual reality (VR) allows audience to learn about cultural heritage sites without temporal and spatial constraints. The design of VR content is largely determined by professionals, while evaluations of content often rely on learners’ self-report data. Learners’ attentional focus and understanding of VR content might be affected by the presence or absence of different multimedia elements including text and audio-visuals. It remains an open question which design variations are more conducive for learning about heritage sites. Leveraging eye-tracking, a technology often adopted in recent multimodal learning analytics (MmLA) research, we conducted an experiment to collect and analyze 40 learners’ eye movement and self-reported data. Results of statistical tests and heatmap elicitation interviews indicate that 1) text in the VR environment helped learners better understand the presented heritage sites, regardless of having audio narration or not, 2) text diverted learners’ attention away from other visual elements that contextualized the heritage sites, 3) exclusively having audio narration best simulated the experience of a real-world heritage tour, 4) narration accompanying text prompted learners to read the text faster. We make recommendations for improving the design of VR learning materials and discuss the implications for MmLA research.
PeerBERT: Automated Characterization of Peer Review Comments across Courses

Writing-to-learn pedagogies are an evidence-based practice known to aid students in constructing knowledge. Barriers exist for the implementation of such assignments; namely, instructors feel they do not have time to provide each student with feedback. To ease implementation of writing-to-learn assignments at scale, we have incorporated automated peer review, which facilitates peer review without input from the instructor. Participating in peer review can positively impact students’ learning and allow students to receive feedback on their writing. Instructors may want to monitor these peer interactions and gain insight into their students’ understanding using the feedback generated by their peers. To facilitate instructors’ use of the content from students’ peer review comments, we pre-trained a transformer model called PeerBERT. PeerBERT was fine-tuned on several downstream tasks to categorize students’ peer review comments as praise, problem/solution, or verification/summary. The model exhibits high accuracy, even across different peer review prompts, assignments, and courses. Additional downstream tasks label problem/solution peer review comments as one or more types: writing/formatting, missing content/needs elaboration, and incorrect content. This approach can help instructors pinpoint common issues in student writing by parsing out which comments are problem/solution and which type of problem/solution students identify.
Thinking with causal models: A visual formalism for collaboratively crafting assumptions

Learning Analytics (LA) is a bricolage field that requires a concerted effort to ensure that all stakeholders it affects are able to contribute to its development in a meaningful manner. We need mechanisms that support collaborative sense-making. This paper argues that graphical causal models can help us to span the disciplinary divide, providing a new apparatus to help educators understand, and potentially challenge, the technical models developed by LA practitioners as they form. We briefly introduce causal modelling, highlighting its potential benefits in helping the field to move from associations to causal claims, and illustrate how graphical causal models can help us to reason about complex statistical models. The approach is illustrated by applying it to the well known problem of at-risk modelling.
Resolving in situ structure of Trypanosoma brucei and insights into the functions of structural microtubules

The flagellar Trypanosoma brucei (T. brucei) is a kinetoplastid protist that causes African sleeping sickness and threats the livestock life. The parasite has a life cycle when it parasitizes in tsetse flies and humans. These parasites have a flagellum that is attached to the cell body that is crucial for its motility. The flagellum consists of a canonical 9 + 2 axoneme and a paraflagellar rod (PFR) and filament attached zone (FAZ). Besides, the cytoskeleton is covered with microtubules. In this paper we describe results from cryo-electron tomography of T. brucei. This method reveals novel structures of the parasite. We show two datasets in situ, revealing organelles, the microtubule array and the flagellum. Since the structures of T. brucei are essential for the pathogenesis, understanding the entire structure can fulfill the knowledge gap and provides a method for treating sleeping sickness.
Patch Tracking-based Online Tensor Ring Completion for Streaming Visual Data

—Tensor completion is the problem of estimating the missing entries of a partially observed tensor by exploiting its low-rank structure. In streaming applications where the frames arrive sequentially such as video completion, the missing entries of the tensor need to be dynamically recovered in an online fashion. Traditional online completion algorithms treat the entire visual data as a tensor, which may not work satisfactorily when there is a big change in the tensor subspace along the temporal dimension, such as due to strong motion across the video frames. In this paper, we develop a novel patch tracking-based online tensor completion framework for streaming data. Each incoming tensor is extracted into small patches, and similar patches are tracked along the temporal domain. We propose a new patch tracking strategy that can accurately and efﬁciently track the patches with missing data. Further, a new online tensor ring completion method is proposed which can efﬁciently and accurately update the latent core tensors and complete the missing entries of the tracked patches. Extensive experimental results demonstrate the superior performance of the proposed algorithms compared with both online and ofﬂine state-of-the-art tensor completion methods.
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings

A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is focused on capturing the word co-occurrences in a document and hence often suffers from poor performance in analyzing short documents. In addition, its parameter estimation often relies on approximate posterior inference that is either not scalable or suffers from large approximation error. This paper introduces a new topic-modeling framework where each document is viewed as a set of word embedding vectors and each topic is modeled as an embedding vector in the same embedding space. Embedding the words and topics in the same vector space, we define a method to measure the semantic difference between the embedding vectors of the words of a document and these of the topics, and optimize the topic embeddings to minimize the expected difference over all documents. Experiments on text analysis demonstrate that the proposed method, which is amenable to mini-batch stochastic gradient descent based optimization and hence scalable to big corpora, provides competitive performance in discovering more coherent and diverse topics and extracting better document representations.
HR 6819 is a binary system with no black hole

Context. Two scenarios have been proposed to match the existing observational constraints of the object HR 6819. The system could consist of a close inner B-type giant plus a black hole (BH) binary with an additional Be companion in a wide orbit. Alternatively, it could be a binary composed of a stripped B star and a Be star in a close orbit. Either scenario makes HR 6819 a cornerstone object as the stellar BH closest to Earth, or as an example of an important transitional, non-equilibrium phase for Be stars with solid evidence for its nature. Aims. We aim to distinguish between the two scenarios for HR 6819. Both models predict two luminous stars but with very different angular separations and orbital motions. Therefore, the presence of bright sources in the 1−100 milliarcsec (mas) regime is a key diagnostic for determining the nature of the HR 6819 system. Methods. We obtained new high-angular resolution data with VLT/MUSE and VLTI/GRAVITY of HR 6819. The MUSE data are sensitive to bright companions at large scales, whilst the interferometric GRAVITY data are sensitive down to separations on mas scales and large magnitude differences. Results. The MUSE observations reveal no bright companion at large separations and the GRAVITY observations indicate the presence of a stellar companion at an angular separation of ∼1.2 mas that moves on the plane of the sky over a timescale compatible with the known spectroscopic 40-day period. Conclusions. We conclude that HR 6819 is a binary system and that no BH is present in the system. The unique nature of HR 6819, and its proximity to Earth make it an ideal system for quantitatively characterising the immediate outcome of binary interaction and probing how Be stars form.
Cubic Lovelock Gravity in Lower Dimensions

In this paper, we obtain the lower-dimensional limits ( p = 2,3,4,5,6 ) of cubic Lovelock gravity through a regularized Kaluza-Klein reduction. By taking a ﬂat internal space for simplicity, we also study the static black hole solutions in the resulting theories. We show that the solutions match with the ones obtained from the “naive limit” of D -dimensional equation for the metric function, which is obtained by ﬁrst scaling the relevant couplings by a factor of 1 D − p and then taking the limit D → p , with one important exception: In 4D, one obtains the expected solution only for the black hole with a planar horizon.
Skew-Symmetric Adjacency Matrices for Clustering Directed Graphs

Cut-based directed graph (digraph) clustering often focuses on finding dense within-cluster or sparse between-cluster connections, similar to cut-based undirected graph clustering. In contrast, for flow-based clusterings the edges between clusters tend to be oriented in one direction and have been found in migration data, food webs, and trade data. In this paper we introduce a spectral algorithm for finding flow-based clusterings. The proposed algorithm is based on recent work which uses complex-valued Hermitian matrices to represent digraphs. By establishing an algebraic relationship between a complex-valued Hermitian representation and an associated real-valued, skew-symmetric matrix the proposed algorithm produces clusterings while remaining completely in the real field. Our algorithm is more memory efficient, requires less computation, and provably preserves solution quality. We also show the algorithm can be easily implemented using standard computational building blocks, possesses better numerical properties, and loans itself to a natural interpretation via an objective function relaxation argument.
A Fully Memristive Spiking Neural Network with Unsupervised Learning

We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spike Timing Dependent Plasticity (STDP) learning rule. The system is fully memristive in that both neuronal and synaptic dynamics can be realized by using memristors. The neuron is implemented using the SPICE-level memristive integrate-and-fire (MIF) model, which consists of a minimal number of circuit elements necessary to achieve distinct depolarization, hyperpolarization, and repolarization voltage waveforms. The proposed MSNN uniquely implements STDP learning by using cumulative weight changes in memristive synapses from the voltage waveform changes across the synapses, which arise from the presynaptic and postsynaptic spiking voltage signals during the training process. Two types of MSNN architectures are investigated: 1) a biologically plausible memory retrieval system, and 2) a multi-class classification system. Our circuit simulation results verify the MSNN’s unsupervised learning efficacy by replicating biological memory retrieval mechanisms, and achieving 97.5% accuracy in a 4-pattern recognition problem in a large scale discriminative MSNN.
Label-Only Model Inversion Attacks via Boundary Repulsion

Recent studies show that the state-of-the-art deep neural networks are vulnerable to model inversion attacks, in which access to a model is abused to reconstruct private training data of any given target class. Existing attacks rely on having access to either the complete target model (whitebox) or the model's soft-labels (blackbox). However, no prior work has been done in the harder but more practical scenario, in which the attacker only has access to the model's predicted label, without a confidence measure. In this paper, we introduce an algorithm, Boundary-Repelling Model Inversion (BREP-MI), to invert private training data using only the target model's predicted labels. The key idea of our algorithm is to evaluate the model's predicted labels over a sphere and then estimate the direction to reach the target class's centroid. Using the example of face recognition, we show that the images reconstructed by BREP-MI successfully reproduce the semantics of the private training data for various datasets and target model architectures. We compare BREP-MI with the state-of-the-art white-box and blackbox model inversion attacks, and the results show that despite assuming less knowledge about the target model, BREP-MI outperforms the blackbox attack and achieves comparable results to the whitebox attack. Our code is available online.11https://github.com/m-kahla/Label-Only-Model-Inversion-Attacks-via-Boundary-Repulsion
Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding

Automatic ICD coding is defined as assigning disease codes to electronic medical records (EMRs). Existing methods usually apply label attention with code representations to match related text snippets.Unlike these works that model the label with the code hierarchy or description, we argue that the code synonyms can provide more comprehensive knowledge based on the observation that the code expressions in EMRs vary from their descriptions in ICD. By aligning codes to concepts in UMLS, we collect synonyms of every code. Then, we propose a multiple synonyms matching network to leverage synonyms for better code representation learning, and finally help the code classification. Experiments on the MIMIC-III dataset show that our proposed method outperforms previous state-of-the-art methods.
Fail-Safe Generative Adversarial Imitation Learning

For ﬂexible yet safe imitation learning (IL), we propose a modular approach that uses a generative imitator policy with a safety layer, has an overall explicit density/gradient, can therefore be end-to-end trained using generative adversarial IL (GAIL), and comes with theoretical worst-case safety/robustness guarantees. The safety layer’s exact density comes from using a countable non-injective gluing of piecewise differentiable injections and the change-of-variables formula. The safe set (into which the safety layer maps) is inferred by sampling actions and their potential future fail-safe fallback continuations, together with Lipschitz continuity and convexity arguments. We also provide theoretical bounds showing the advantage of using the safety layer already during training (imitation error linear in the horizon) compared to only using it at test time (quadratic error). In an experiment on challenging real-world driver interaction data, we empirically demonstrate tractability, safety and imitation performance of our approach.