Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers

Automated short answer grading (ASAG) has gained attention in education as a means to scale educational tasks to the growing number of students. Recent progress in Natural Language Processing and Machine Learning has largely influenced the field of ASAG, of which we survey the recent research advancements. We complement previous surveys by providing a comprehensive analysis of recently published methods that deploy deep learning approaches. In particular, we focus our analysis on the transition from hand-engineered features to representation learning approaches, which learn representative features for the task at hand automatically from large corpora of data. We structure our analysis of deep learning methods along three categories: word embeddings, sequential models, and attention-based methods. Deep learning impacted ASAG differently than other fields of NLP, as we noticed that the learned representations alone do not contribute to achieve the best results, but they rather show to work in a complementary way with hand-engineered features. The best performance are indeed achieved by methods that combine the carefully hand-engineered features with the power of the semantic descriptions provided by the latest models, like transformers architectures. We identify challenges and provide an outlook on research direction that can be addressed in the future. a reference answer. The final score was predicted by a logistic regression classifier. In addition, domain adaption was used, where different features have individual weights for each domain.
Arabic Text-To-Speech (TTS) Data Preparation

People may be puzzled by the fact that voice over recordings data sets exist in addition to Text-to-Speech (TTS), Synthesis system advancements, albeit this is not the case. The goal of this study is to explain the relevance of TTS as well as the data preparation procedures. TTS relies heavily on recorded data since it can have a substantial inﬂuence on the outcomes of TTS modules. Furthermore, whether the domain is specialized or general, appropriate data should be developed to address all predicted language variants and domains. Different recording methodologies, taking into account quality and behavior, may also be advantageous in the development of the module. In light of the lack of Arabic language in present synthesizing systems, numerous variables that impact the ﬂow of recorded utterances are being considered in order to manipulate an Arabic TTS module. In this study, two viewpoints will be discussed: linguistics and the creation of high-quality recordings for TTS. The purpose of this work is to offer light on how ground-truth utterances may inﬂuence the evolution of speech systems in terms of naturalness, intelligibility, and understanding. Well provide voice actor specs as well as data specs that will assist both voice actors and voice coaches in the studio as well as the annotators who will be evaluating the audios.
Deep Visual Geo-localization Benchmark

In this paper, we propose a new open-source benchmarkingframeworkfor Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used ar-chitectures, with the flexibility to change individual components of a geo-localization pipeline. The purpose of this framework is twofold: i) gaining insights into how differ-ent components and design choices in a VG pipeline im-pact the final results, both in terms of performance (re-call@N metric) and system requirements (such as execution time and memory consumption); ii) establish a system-atic evaluation protocol for comparing different methods. Using the proposed framework, we perform a large suite of experiments which provide criteria for choosing back-bone, aggregation and negative mining depending on the use-case and requirements. We also assess the impact of engineering techniques like pre/post-processing, data aug-mentation and image resizing, showing that better performance can be obtained through somewhat simple procedures: for example, downscaling the images' resolution to 80% can lead to similar results with a 36% savings in ex-traction time and dataset storage requirement. Code and trained models are available at dataset storage requirement. https://deep-vg-bench.herokuapp. com/.
Vertical Allocation-based Fair Exposure Amortizing in Ranking

Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services. Myopically maximizing customer satisfaction by ranking items only according to relevance will lead to unfair distribution of exposure for items, followed by unfair opportunities and economic gains for item producers/providers. Such unfairness will force providers to leave the system and discourage new providers from coming in. Eventually, fewer purchase options would be left for consumers, and the utilities of both consumers and providers would be harmed. Thus, to maintain a balance between ranking relevance and fairness is crucial for both parties. In this paper, we focus on the exposure fairness in ranking services. We demonstrate that existing methods for amortized fairness optimization could be suboptimal in terms of fairness-relevance tradeoff because they fail to utilize the prior knowledge of consumers. We further propose a novel algorithm named Vertical Allocation-based Fair Exposure Amortizing in Ranking, or VerFair, to reach a better balance between exposure fairness and ranking performance. Extensive experiments on three real-world datasets show that VerFair significantly outperforms state-of-the-art fair ranking algorithms in fairness-performance trade-offs from both the individual level and the group level.
The impact of the uncertainties in the 12C(α, γ)16O reaction rate on the evolution of low– to intermediate–mass stars

One of the largest uncertainties in stellar evolutionary computations is the accuracy of the considered reaction rates. The 12C(α, γ)16O reaction is particularly important for the study of low- and intermediate-mass stars as it determines the final C/O ratio in the core which influences the white dwarf cooling evolution. Thus, there is a need for a study of how the computations of white dwarfs and their progenitors that are made to date may be affected by the uncertainties of the 12C(α, γ)16O reaction rates. In this work we compute fully evolutionary sequences using the mesa code with initial masses in the range of 0.90 ≤ Mi/M⊙ ≤ 3.05. We consider different adopted reaction rates, obtained from the literature, as well as the extreme limits within their uncertainties. As expected, we find that previous to the core helium burning stage, there are no changes to the evolution of the stars. However, the subsequent stages are all affected by the uncertainties of the considered reaction rate. In particular, we find differences to the convective core mass during the core helium burning stage which may affect pulsation properties of subdwarfs, the number of thermal pulses during the asymptotic giant branch and trends between final oxygen abundance in the core and the progenitor masses of the remnant white dwarfs.
Perception-Aware Receding Horizon Trajectory Planning for Multicopters With Visual-Inertial Odometry

Visual inertial odometry (VIO) is widely used for the state estimation of multicopters, but it may function poorly in environments with few visual features or in overly aggressive flights. In this work, we propose a perception-aware collision avoidance trajectory planner for multicopters, that may be used with any feature-based VIO algorithm. Our approach can fly the vehicle to a goal position at high speed, avoiding obstacles in an unknown stationary environment while achieving good VIO state estimation accuracy. The proposed planner samples a group of minimum jerk trajectories and finds collision-free trajectories among them, which are then evaluated based on their speed to the goal and perception quality. Both the motion blur of features and their locations are considered for the perception quality. Our novel consideration of the motion blur of features enables automatic adaptation of the trajectory’s aggressiveness under environments with different light levels. The best trajectory from the evaluation is tracked by the vehicle and is updated in a receding horizon manner when new images are received from the camera. Only generic assumptions about the VIO are made, so that the planner may be used with various existing systems. The proposed method can run in real-time on a small embedded computer on board. We validated the effectiveness of our proposed approach through experiments in both indoor and outdoor environments. Compared to a perception-agnostic planner, the proposed planner kept more features in the camera’s view and made the flight less aggressive, making the VIO more accurate. It also reduced VIO failures, which occurred for the perception-agnostic planner but not for the proposed planner. The ability of the proposed planner to fly through dense obstacles was also validated. The experiment video can be found at https://youtu.be/qO3LZIrpwtQ.
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning

We investigate and leverage a connection be-tween Differential Privacy (DP) and the recently proposed notion of Distributional Generalization (DG). Applying this connection, we introduce new conceptual tools for designing deep-learning methods that bypass “pathologies” of standard stochastic gradient descent (SGD). First, we prove that differentially private methods satisfy a “What You See Is What You Get (WYSIWYG)” generalization guarantee: whatever a model does on its train data is almost exactly what it will do at test time. This guarantee is formally captured by distributional generalization. WYSIWYG enables principled algorithm design in deep learning by reducing generalization concerns to optimization ones: in order to mitigate unwanted behavior at test time, it is provably sufﬁcient to mitigate this behavior on the train data. This is notably false for standard (non-DP) methods, hence this observation has applications even when privacy is not required. For example, importance sampling is known to fail for standard SGD, but we show that it has exactly the intended effect for DP-trained models. Thus, with DP-SGD, unlike with SGD, we can inﬂuence test-time behavior by making principled train-time interventions. We use these insights to construct simple algorithms which match or outperform SOTA in several distributional robustness applications, and to significantly improve the privacy vs. disparate impact tradeoff of DP-SGD. Finally, we also improve on known theoretical bounds relating differential privacy, stability, and distributional generalization.
Comparative analysis of the three- and six-phase fractional slot concentrated winding permanent magnet machines

Purpose The purpose of this paper is to analyse and compare the functional parameters of three- and six-phase permanent magnet synchronous motors (PMSM) with fractional-slot concentrated windings (FSCW). Design/methodology/approach The investigations are focused on the comparison of the distortions of back electromotive force (emf) and magnetomotive force (mmf) waveforms, as well as torque ripples, radial force spatial harmonics and motor performance studies. The finite element models of the test machine and a personally developed computer code have been used to calculate motor characteristics and analyse and synthesise multiphase winding layouts, respectively. Findings Compared with the traditional three-phase PMSM designs, the proposed six-phase machines are characterized by a significantly lower content of sub-harmonics in mmf waveform distribution. Moreover, the investigated six-phase machines exhibited a higher average value of electromagnetic torque, significantly lower torque ripples and a reduced value of low-order harmonics of the radial component of the electromagnetic force in the air-gap of the machine. Originality/value The analyses presented in this paper show that six-phase PMSM with FSCWs are advantageous to their counterpart three-phase machines. Specifically, they are more suited to working with multiple drives supplying a segmented winding system while simultaneously offering higher performance. This suitability to the use of a multi-drive supply for one motor offers flexibility and cost reduction while increasing the fault tolerance of a power train system.
2022 Super-Penrose process with charged particles near naked singularity

We consider the Penrose process near the naked singularity in the Reissner-Nordstr¨om metric. Particle 0 falls from inﬁnity and decays to two fragments at some point r 0 . We show that the energy extraction due to this process can be indeﬁnitely large in the limit r 0 → 0. In doing so, the value of the particle charge can remain bounded, in contrast to the previously known examples of the Penrose process in the electric ﬁeld with unbounded energy extraction. The eﬀect persists even in the limit of the ﬂat pace-time.
Explicit Feature Interaction-aware Graph Neural Networks

Graph neural networks are powerful methods to handle graph-structured data. However, existing graph neural networks only learn higher-order feature interactions implicitly. Thus, they cannot capture information that occurred in low-order feature interactions. To overcome this problem, we propose Explicit Feature Interaction-aware Graph Neural Network (EFI-GNN), which explicitly learns arbitrary-order feature interactions. EFI-GNN can jointly learn with any other graph neural network. We demonstrate that the joint learning method always enhances performance on the various node classification tasks. Furthermore, since EFI-GNN is inherently a linear model, we can interpret the prediction result of EFI-GNN. With the computation rule, we can obtain an any-order feature's effect on the decision. By that, we visualize the effects of the first-order and second-order features as a form of a heatmap.
Cosmic rays and thermal instability in self-regulating cooling flows of massive galaxy clusters

One of the key physical processes that helps prevent strong cooling flows in galaxy clusters is the continued energy input from the central active galactic nucleus (AGN) of the cluster. However, it remains unclear how this energy is thermalised so that it can effectively prevent global thermal instability. One possible option is that a fraction of the AGN energy is converted into cosmic rays (CRs), which provide non-thermal pressure support, and can retain energy even as thermal energy is radiated away. By means of magneto-hydrodynamical simulations, we investigate how CR injected by the AGN jet influence cooling flows of a massive galaxy cluster. We conclude that converting a fraction of the AGN luminosity as low as 10\% into CR energy prevents cooling flows on timescales of billion years, without significant changes in the structure of the multi-phase intra-cluster medium. CR-dominated jets, by contrast, lead to the formation of an extended, warm central nebula that is supported by CR pressure. We report that the presence of CRs is not able to suppress the onset of thermal instability in massive galaxy clusters, but CR-dominated jets do significantly change the continued evolution of gas as it continues to cool from isobaric to isochoric. The CR redistribution in the cluster is dominated by advection rather than diffusion or streaming, but the heating by CR streaming helps maintain gas in the hot and warm phase. Observationally, self-regulating, CR-dominated jets produce a \gammaray~ flux in excess of current observational limits, but low CR fractions in the jet are not ruled out.
The Distressing Ads That Persist: Uncovering The Harms of Targeted Weight-Loss Ads Among Users with Histories of Disordered Eating

Targeted advertising can harm vulnerable groups when it targets individuals' personal and psychological vulnerabilities. We focus on how targeted weight-loss advertisements harm people with histories of disordered eating. We identify three features of targeted advertising that cause harm: the persistence of personal data that can expose vulnerabilities, over-simplifying algorithmic relevancy models, and design patterns encouraging engagement that can facilitate unhealthy behavior. Through a series of semi-structured interviews with individuals with histories of unhealthy body stigma, dieting, and disordered eating, we found that targeted weight-loss ads reinforced low self-esteem and deepened pre-existing anxieties around food and exercise. At the same time, we observed that targeted individuals demonstrated agency and resistance against distressing ads. Drawing on scholarship in postcolonial environmental studies, we use the concept of slow violence to articulate how online targeted advertising inflicts harms that may not be immediately identifiable. CAUTION: This paper includes media that could be triggering, particularly to people with an eating disorder. Please use caution when reading, printing, or disseminating this paper.
DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores

Mean opinion score (MOS) is a typical subjective evaluation metric for speech synthesis systems. Since collecting MOS is time-consuming, it would be desirable if there are accurate MOS prediction models for automatic evaluation. In this work, we propose DDOS, a novel MOS prediction model. DDOS utilizes domain-adaptive pre-training to further pre-train self-supervised learning models on synthetic speech. And a proposed module is added to model the opinion score distribution of each utterance. With the proposed components, DDOS outperforms previous works on BVCC dataset. And the zero-shot transfer result on BC2019 dataset is significantly improved. DDOS also wins second place in Interspeech 2022 VoiceMOS challenge in terms of system-level score.
Around Van den Bergh’s double brackets for different bimodule structures

Abstract A double Poisson bracket, in the sense of M. Van den Bergh, is an operation on an associative algebra A which induces a Poisson bracket on each representation space in an explicit way. In this note, we study the impact of changing the Leibniz rules underlying a double bracket. This change amounts to make a suitable choice of A-bimodule structure on . In the most important cases, we describe how the choice of A-bimodule structure fixes an analogue to Jacobi identity, and we obtain induced Poisson brackets on representation spaces. The present theory also encodes a formalization of the widespread tensor notation used to write Poisson brackets of matrices in mathematical physics. Communicated by P. Kolesnikov
Position-based Prompting for Health Outcome Generation

Probing factual knowledge in Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomenon has been effective, especially when these LMs are fine-tuned towards not just data, but also to the style or linguistic pattern of the prompts themselves. We observe that satisfying a particular linguistic pattern in prompts is an unsustainable, time-consuming constraint in the probing task, especially because they are often manually designed and the range of possible prompt template patterns can vary depending on the prompting task. To alleviate this constraint, we propose using a position-attention mechanism to capture positional information of each word in a prompt relative to the mask to be filled, hence avoiding the need to re-construct prompts when the prompts’ linguistic pattern changes. Using our approach, we demonstrate the ability of eliciting answers (in a case study on health outcome generation) to not only common prompt templates like Cloze and Prefix but also rare ones too, such as Postfix and Mixed patterns whose masks are respectively at the start and in multiple random places of the prompt. More so, using various biomedical PLMs, our approach consistently outperforms a baseline in which the default PLMs representation is used to predict masked tokens.
Exploring Example Selection for Few-shot Text-to-SQL Semantic Parsing

We study example selection methods for few-001 shot text-to-SQL tasks with unseen databases. 002 Annotating natural language questions with cor-003 responding SQL queries is expensive, but we 004 can use abundant unlabeled questions to effi-005 ciently select examples to annotate and then 006 use them to adapt models. Many previous 007 works only randomly sample a few instances 008 for few-shot learning, but this random selec-009 tion is not sufficient to select representative and 010 informative examples that provide specific do-011 main knowledge. We thus explore methods to 012 efficiently choose annotation examples. We 013 identify two important factors: the diversity of 014 selected instances and the dissimilarity to the 015 source training data if any. A diverse training 016 set contains more domain knowledge, while 017 dissimilar examples are selected to fill in the 018 domain gap between the source and target. We 019 show that our best example selection approach 020 substantially improves few-shot text-to-SQL 021 performance in both finetuning using T5 and 022 in-context learning with Codex: average execu-023 tion accuracy gains of 8.7% and 4.3% over ran-024 dom selection. Our extensive analysis demon-025 strates the importance of the similarity metric 026 and the embedding method for example repre-027 sentations. We also find that effective example 028 selection reduces syntax errors on the target 029 domains. Our results encourage future work to 030 further explore example selection for efficient 031 adaptation of text-to-SQL models. 1 032
Limited Attention: Investigating Transformer Models’ Zero-Shot Cross Lingual Transfer Learning Capability with Urdu Named Entity Recognition

The growing number of models pretrained on multilingual corpora has motivated a new challenge of handling low resource languages and the morphological differences across various languages. One such category of low resource languages has been Indic languages, which, often require large masses of data due to their morphological richness and structural ambiguity. This project uses an analytical perspective leveraging named entity recognition (NER), an NLP task with particularly low baseline metrics. Recognizing the challenges of transfer learning, we investigate the attention layers and efficacy of pretrained, few-shot, and fine-tuned mBERT and IndicBERT language models using Urdu as a target language to determine the zero-shot cross lingual capability of multilingual models. We find that models perform significantly better when fully fine-tuning and that IndicBERT’s architecture lends poorly to cross-lingual transfer, with 26.4 and 37.1 point deficits comparing Hindi and Arabic transfer textbff1 scores to mBERT. We also yield important analysis on attention schemes where mBERT intermediate layers have more next, previous, or related word attention patterns as opposed to the increased frequency of attention to delimiter tokens for sample inputs with IndicBERT. We attribute these advantages to WordPiece tokenization structures, dropout, and the importance of typological embeddings which IndicBERT lacked for Perso-Arabic script.