Miracle-dz/UniFAR-FTUs huggingface dataset:
A Speech Act Classifier for Persian Texts and its Application in Identifying Rumors

Speech Acts (SAs) are one of the important areas of pragmatics, which give us a better understanding of the state of mind of the people and convey an intended language function. Knowledge of the SA of a text can be helpful in analyzing that text in natural language processing applications. This study presents a dictionary-based statistical technique for Persian SA recognition. The proposed technique classifies a text into seven classes of SA based on four criteria: lexical, syntactic, semantic, and surface features. WordNet as the tool for extracting synonym and enriching features dictionary is utilized. To evaluate the proposed technique, we utilized four classification methods including Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN). The experimental results demonstrate that the proposed method using RF and SVM as the best classifiers achieved a state-of-the-art performance with an accuracy of 0.95 for classification of Persian SAs. Our original vision of this work is introducing an application of SA recognition on social media content, especially the common SA in rumors. Therefore, the proposed system utilized to determine the common SAs in rumors. The results showed that Persian rumors are often expressed in three SA classes including narrative, question, and threat, and in some cases with the request SA.
Cloning and differential expression of 1- aminocyclopropane-1-carboxylate synthase gene in different floral tissues of Dendrobium ‘anna’ flowers

Cloning of the DenACS from Dendrobium hybrid cultivar Anna was performed by RT-PCR and nucleotide sequence analysis revealed that the open reading frame of this gene was 1,308 bp in length, encoded for a protein of 435 amino acid residues. The calculated molecular mass of the deduced polypeptide is 48.5 kDa and the predicted isoelectric point is 5.84. The deduced amino acid sequence of the DenACS-encoded protein showed a high degree of identity to those of the ACS from petunia, geranium, carnation and rose. The expression of DenACS was examined by RT-PCR and the results revealed that this gene was highly expressed in flower stage 2 (partially opened flower) and stage 3 (full opened flower) during flower development. It was also expressed in all floral tissues and organs including petal, sepal, pedicel, labellum, stigma, young leaves and roots, but the maximum expression was observed in petal, sepal and pedicel. These results indicated that the orchid DenACS is involved in the flower opening, flower senescence as well as in vegetative cell growth and development.
Relational-Grid-World: A Novel Relational Reasoning Environment and An Agent Model for Relational Information Extraction

Reinforcement learning (RL) agents are often designed specifically for a particular problem and they generally have uninterpretable working processes. Statistical methods-based agent algorithms can be improved in terms of generalizability and interpretability using symbolic Artificial Intelligence (AI) tools such as logic programming. In this study, we present a model-free RL architecture that is supported with explicit relational representations of the environmental objects. For the first time, we use the PrediNet network architecture in a dynamic decision-making problem rather than image-based tasks, and Multi-Head Dot-Product Attention Network (MHDPA) as a baseline for performance comparisons. We tested two networks in two environments ---i.e., the baseline Box-World environment and our novel environment, Relational-Grid-World (RGW). With the procedurally generated RGW environment, which is complex in terms of visual perceptions and combinatorial selections, it is easy to measure the relational representation performance of the RL agents. The experiments were carried out using different configurations of the environment so that the presented module and the environment were compared with the baselines. We reached similar policy optimization performance results with the PrediNet architecture and MHDPA; additionally, we achieved to extract the propositional representation explicitly ---which makes the agent's statistical policy logic more interpretable and tractable. This flexibility in the agent's policy provides convenience for designing non-task-specific agent architectures. The main contributions of this study are two-fold ---an RL agent that can explicitly perform relational reasoning, and a new environment that measures the relational reasoning capabilities of RL agents.
Fast Real-time Counterfactual Explanations

Counterfactual explanations are considered, which is to answer {\it why the prediction is class A but not B. } Different from previous optimization based methods, an optimization-free Fast ReAl-time Counterfactual Explanation (FRACE) algorithm is proposed benefiting from the development of multi-domain image to image translation algorithms. Built from starGAN, a transformer is trained as a residual generator conditional on a classifier constrained under a proposal perturbation loss which maintains the content information of the query image, but just the class-specific semantic information is changed. The transformer can transfer the query image to any counterfactual class, and during inference, our explanation can be generated by it only within a forward time. It is fast and can satisfy the real-time practical application. Because of the adversarial training of GAN, our explanation is also more realistic compared to other counterparts. The experimental results demonstrate that our proposal is better than the existing state of the art in terms of quality and speed.
Enzymatically hydrolyzable amino acids in North Sea sediments and their possible implication for sediment nutritional values

The nutritional value of peptidic material in e ve North Sea sediments that differ in organic matter quality has been investigated with an enzymatic approach measuring the digestibility (rate constant k of proteolysis by protease) and the proportion of bioavailable fraction of amino acids (%EHAA-i: THAA). The bioavailable fraction in sediments varied between 14‐ 50% EHAA-i:THAA and was generally lower than in potential source organisms such as algae (40‐ 43%), plankton ( , 80%) and bacteria (57‐ 72%). The rate of proteolysis ofthe amino acids varied among stations ( k 5 0.3‐3.1h 2 1 ) with a systematic decrease when going from labile to more refractory organic matter. The concentration of both the total and the enzymatically hydrolyzable amino acid pool increased toward the north where e ne-grained sediments accumulate whereas the relative contribution of the labile fraction (%EHAA-i:THAA) decreased. Differences were found between the amino acid composition of the EHAAand the THAApool in sedimentary organic matter, but these differences were minor in source organisms. Reasons for the selective liberation behavior of protease-k toward peptidic material in sediments include (A) specie c cleavage pattern of protease-k preferring certain bounds (B) selection against amino acids protected by structural matrixes (holds for glycine and methionine) (C) protection by adsorptive binding to sediment particles (basic amino acids arginine, histidine, methionine). It is argued that arginine and histidine play an important role in deposit feeder nutrition since they are dee cient in the sedimentary food source and therefore may be limiting growth. Their strong adsorption affinity, low susceptibility toward cleavage and occurrence in structural organic matter make them poorly available to deposit feeders. The relative content of basic amino acids also co-varies systematically with increasing nutritional quality.
The Influence of Spatio-Temporal Variation in Food Availability and Nest-Predation Risk on Clutch-Size Decisions

Abstract. The role of both temporal and spatial variability in nest predation and food availability in influencing birds' decisions about clutch size has not been studied. I examine patterns in nest-predation risk and food availability across a range of severity of wildfire to investigate their relationship to clutch size in the Dark-eyed Junco (Junco hyemalis), which breeds commonly in burned forests. Spatial variation in burn severity led to lower risk of nest predation in patches of intermediate severity in the first two years after the fire, while food availability was inversely related to burn severity during only the first year post-fire. Spatial variation in risk of nest predation explained variation in clutch size only during the first year post-fire when food was limited, consistent with parents investing less in risky nest locations during periods of nutritional stress. Nest-predation risk increased seasonally during both year 1 and year 2 post-fire. Clutch size rose dramatically over the first breeding season post-fire, paralleling a unique seasonal increase in food availability in year 1, consistent with juncos tracking temporal variation in food availability by investing more in eggs. Results are consistent with parents balancing spatio-temporal variation in resource limitation with predation risk in their investment in eggs. Disagreement between existing studies as to the relative importance of food and nest predation further highlights the need for carefully designed experimental approaches that integrate explanations for both temporal and spatial trends in sources of selection likely to shape the evolution of clutch-size decisions.
Contextual Bandit with Missing Rewards

We consider a novel variant of the contextual bandit problem (i.e., the multi-armed bandit with side-information, or context, available to a decision-maker) where the reward associated with each context-based decision may not always be observed("missing rewards"). This new problem is motivated by certain online settings including clinical trial and ad recommendation applications. In order to address the missing rewards setting, we propose to combine the standard contextual bandit approach with an unsupervised learning mechanism such as clustering. Unlike standard contextual bandit methods, by leveraging clustering to estimate missing reward, we are able to learn from each incoming event, even those with missing rewards. Promising empirical results are obtained on several real-life datasets.
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning

As a concrete application of multi-view learning, multi-view classification improves the traditional classification methods significantly by integrating various views optimally. Although most of the previous efforts have been demonstrated the superiority of multi-view learning, it can be further improved by comprehensively embedding more powerful cross-view interactive information and a more reliable multi-view fusion strategy in intensive studies. To fulfill this goal, we propose a novel multi-view learning framework to make the multi-view classification better aimed at the above-mentioned two aspects. That is, we seamlessly embed various intra-view information, cross-view multi-dimension bilinear interactive information, and a new view ensemble mechanism into a unified framework to make a decision via the optimization. In particular, we train different deep neural networks to learn various intra-view representations, and then dynamically learn multi-dimension bilinear interactive information from different bilinear similarities via the bilinear function between views. After that, we adaptively fuse the representations of multiple views by flexibly tuning the parameters of the view-weight, which not only avoids the trivial solution of weight but also provides a new way to select a few discriminative views that are beneficial to make a decision for the multi-view classification. Extensive experiments on six publicly available datasets demonstrate the effectiveness of the proposed method.
Universal-to-Specific Framework for Complex Action Recognition

Video-based action recognition has recently attracted much attention in the field of computer vision. To solve more complex recognition tasks, it has become necessary to distinguish different levels of interclass variations. Inspired by a common flowchart based on the human decision-making process that first narrows down the probable classes, and then applies a “rethinking” process for finer-level recognition, we propose an effective universal-to-specific (U2S) framework for complex action recognition. The U2S framework is composed of three subnetworks: a universal network, a category-specific network, and a mask network. The universal network first learns universal feature representations. The mask network then generates attention masks for confusing classes through category regularization based on the output of the universal network. The mask is further used to guide the category-specific network for class-specific feature representations. The entire framework is optimized in an end-to-end manner. Experiments on a variety of benchmark datasets, e.g., the Something-Something, UCF101, and HMDB51 datasets, demonstrate the effectiveness of the U2S framework; i.e., U2S can focus on discriminative spatiotemporal regions for confusing categories. We further visualize the relationship between different classes, showing that U2S indeed improves the discriminability of learned features. Moreover, the proposed U2S model is a general framework, and may adopt any base recognition network.
Lightweight Modules for Efficient Deep Learning Based Image Restoration

Low level image restoration is an integral component of modern artificial intelligence (AI) driven camera pipelines. Most of these frameworks are based on deep neural networks which present a massive computational overhead on resource constrained platform like a mobile phone. In this paper, we propose several lightweight low-level modules which can be used to create a computationally low cost variant of a given baseline model. Recent works for efficient neural networks design have mainly focused on classification. However, low-level image processing falls under the ‘image-to-image’ translation genre which requires some additional computational modules not present in classification. This paper seeks to bridge this gap by designing generic efficient modules which can replace essential components used in contemporary deep learning based image restoration networks. We also present and analyse our results highlighting the drawbacks of applying depthwise separable convolutional kernel (a popular method for efficient classification network) for sub-pixel convolution based upsampling (a popular upsampling strategy for low-level vision applications). This shows that concepts from domain of classification cannot always be seamlessly integrated into ‘image-to-image’ translation tasks. We extensively validate our findings on three popular tasks of image inpainting, denoising and super-resolution. Our results show that proposed networks consistently output visually similar reconstructions compared to full capacity baselines with significant reduction of parameters, memory footprint and execution speeds on contemporary mobile devices.
Settling the Price of Fairness for Indivisible Goods

In the allocation of resources to a set of agents, how do fairness guarantees impact the social welfare? A quantitative measure of this impact is the price of fairness, which measures the worst-case loss of social welfare due to fairness constraints. While initially studied for divisible goods, recent work on the price of fairness also studies the setting of indivisible goods. In this paper, we resolve the price of two well-studied fairness notions for the allocation of indivisible goods: envy-freeness up to one good (EF1), and approximate maximin share (MMS). For both EF1 and 1/2-MMS guarantees, we show, via different techniques, that the price of fairness is $O(\sqrt{n})$, where $n$ is the number of agents. From previous work, it follows that our bounds are tight. Our bounds are obtained via efficient algorithms. For 1/2-MMS, our bound holds for additive valuations, whereas for EF1, our bound holds for the more general class of subadditive valuations. This resolves an open problem posed by Bei et al. (2019).
On Estimating Machine-Zero Residual

In this paper, we propose two techniques to estimate the magnitude of a machine-zero residual for a given problem, which is the smallest possible residual that can be achieved when we solve a system of discretized equations. We estimate the magnitude of the machine-zero residual by a norm of residuals computed with a randomly-perturbed approximate solution that is considered as close in magnitude to an exactly-converged solution. One method uses free-stream values as the approximate solution, and the other uses a current solution during an iterative solve as the approximate solution via the method of manufactured solutions. Numerical results show that these estimates predict the levels of machine-zero residuals very accurately for all equations of the Euler and Navier-Stokes equations in a transonic flow over an airfoil and viscous flows over a cylinder and a flat plate.
Electron energization in quasi-parallel shocks

In situ observations of energetic particles at the Earth's bow-shock that are attainable by the satellite missions have long created the opinion that electrons are most efficiently accelerated in a quasi-perpendicular shock geometry. However, shocks that are deemed to be responsible for the production of cosmic ray electrons and their radiation from sources such as supernova remnants are much more powerful and larger than the Earth's bow-shock. Their remote observations and in situ measurements at Saturn's bow shock, suggest that electrons are accelerated very efficiently in the quasi-parallel shocks as well. In this paper we investigate the possibility that protons that are accelerated to high energies create sufficient wave turbulence, which is necessary for the electron preheating and subsequent injection into the diffusive shock acceleration in a quasi-parallel shock geometry. An additional test-particle-electron population, which is meant to be a low-density addition to the electron core-distribution on which the hybrid simulation operates, is introduced. We investigate how these electrons are energized by the hybrid electromagnetic field. The reduced spatial dimensionality allowed us to dramatically increase the number of macro-ions per numerical cell and achieve the converged results for the velocity distributions of test electrons. We discuss the electron preheating mechanisms, which can make a significant part of thermal electrons accessible to the ion-driven waves observed in hybrid simulations. We find that the precursor wave field supplied by ions has a considerable potential to preheat the electrons before they are shocked at the subshock.
OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

OpenStreetMap (OSM) is a community-based, freely available, editable map service created as an alternative to authoritative sources. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations are heterogeneous across different geographical locations. Despite that, OSM has been widely used in several applications in geosciences, Earth observation, and environmental sciences. In this article, we review recent methods based on machine learning to improve and use OSM data. Such methods aim to either 1) improve the coverage and quality of OSM layers, typically by using geographic information systems (GISs) and remote sensing technologies, or 2) use the existing OSM layers to train models based on image data to serve applications such as navigation and land use classification. We believe that OSM (as well as other sources of open land maps) can change the way we interpret remote sensing data and that the synergy with machine learning can scale participatory mapmaking and its quality to the level needed for global and up-to-date land mapping. A preliminary version of this manuscript was presented in [120].
Class LM and word mapping for contextual biasing in End-to-End ASR

In recent years, all-neural, end-to-end (E2E) ASR systems gained rapid interest in the speech recognition community. They convert speech input to text units in a single trainable Neural Network model. In ASR, many utterances contain rich named entities. Such named entities may be user or location specific and they are not seen during training. A single model makes it inflexible to utilize dynamic contextual information during inference. In this paper, we propose to train a context aware E2E model and allow the beam search to traverse into the context FST during inference. We also propose a simple method to adjust the cost discrepancy between the context FST and the base model. This algorithm is able to reduce the named entity utterance WER by 57% with little accuracy degradation on regular utterances. Although an E2E model does not need pronunciation dictionary, it's interesting to make use of existing pronunciation knowledge to improve accuracy. In this paper, we propose an algorithm to map the rare entity words to common words via pronunciation and treat the mapped words as an alternative form to the original word during recognition. This algorithm further reduces the WER on the named entity utterances by another 31%.
A simple defense against adversarial attacks on heatmap explanations

With machine learning models being used for more sensitive applications, we rely on interpretability methods to prove that no discriminating attributes were used for classification. A potential concern is the so-called "fair-washing" - manipulating a model such that the features used in reality are hidden and more innocuous features are shown to be important instead. In our work we present an effective defence against such adversarial attacks on neural networks. By a simple aggregation of multiple explanation methods, the network becomes robust against manipulation. This holds even when the attacker has exact knowledge of the model weights and the explanation methods used.
Optimizing Prediction Serving on Low-Latency Serverless Dataflow

Prediction serving systems are designed to provide large volumes of low-latency inferences machine learning models. These systems mix data processing and computationally intensive model inference and benefit from multiple heterogeneous processors and distributed computing resources. In this paper, we argue that a familiar dataflow API is well-suited to this latency-sensitive task, and amenable to optimization even with unmodified black-box ML models. We present the design of Cloudflow, a system that provides this API and realizes it on an autoscaling serverless backend. Cloudflow transparently implements performance-critical optimizations including operator fusion and competitive execution. Our evaluation shows that Cloudflow's optimizations yield significant performance improvements on synthetic workloads and that Cloudflow outperforms state-of-the-art prediction serving systems by as much as 2x on real-world prediction pipelines, meeting latency goals of demanding applications like real-time video analysis.