Miracle-dz/UniFAR-FTUs huggingface dataset:
Aspect-based Sentiment Analysis for Vietnamese Reviews about Beauty Product on E-commerce Websites

Millions of reviews are generated on e-commerce platforms and analyzing them deeply brings a lot of useful information for sellers and buyers. This paper deals with the aspect-based sentiment analysis problem to analyze the aspect and polarity of Vietnamese reviews about beauty products on e-commerce websites. The contribution of this paper is three-fold. Firstly, we introduce a dataset containing 16,227 reviews about lipsticks. There are 32,775 pairs of aspects and sentiments in this dataset. Besides, we conduct some baseline experiments using Deep Learning-based models to build detection and classification models for extracting the aspects of reviews and classifying the sentiment of each aspect. In addition, a comprehensive comparison is also performed to see whether single-task learning or multi-task learning is the better approach to predict the aspect and sentiment of reviews. The experimental results show that the BiGRU+Conv1D model in the single-task learning approach out-performs others with the F1-score in the aspect detection task at 98.09% and 91.01% for the sentiment classification task.
A Deep Learning based Framework for Image Paragraph Generation in Hindi

Generating a paragraph from an image is a complex task that requires object and activity recognition. It is now feasible because of recent advances in image captioning. Sum-marizing an image into a single sentence can only provide a superficial description of the visual information included within the image. This problem can be solved by generating a detailed and coherent description of the input image. Most existing works on image-to-paragraph generation have been accomplished in English. We propose a novel way of generating a paragraph from an image in Hindi. The world’s third most spoken language and one of India’s official languages, Hindi, is extensively spoken throughout India and South Asia. We construct a new dataset for an image-to-paragraph generation in Hindi. We employ a hierarchical recurrent neural network (HRNN) for language modeling and an object detection model to decompose the input image by distinguishing regions of interest and objects. The performance of the proposed methodology is compared with other baselines in terms of BLEU, CIDER, and ME-TEOR scores, and the obtained results show that the proposed method outperforms them.
Effectiveness Analysis of Word Sense Disambiguation Using Example of Word Senses from WordNet

In Word Sense Disambiguation (WSD), several studies have proposed systems that incorporate dictionary glosses. The use of glosses expressed in short sentences can improve the accuracy of the WSD system. However, since many glosses are short sentences, additional lexical information could improve accuracy. In this study, we propose a method to incorporate examples of word senses described in WordNet 3.0 as new lexical information into BEM, a WSD system, and analyze the effectiveness of the examples of word senses. Specifically, examples of word senses are input into BEM, and the [CLS] vector or target word vector is taken from the output word embedding representation sequence and used as the sense embedding representation. In the experiment, out of six evaluation sets, including the development set, the F1 score decreased in five evaluation sets and improved in one evaluation set. Since the F1 score improved in one evaluation set, we expect that the use of examples of word senses would be effective.
Developing and Evaluating a Dataset for How-to Tip Machine Reading at Scale

In this paper, we focus on the task of machine reading at scale within how-to tip machine reading comprehension (MRC). We propose a method for developing a context dataset using how-to tip websites on the Internet as information sources. This shows that the proposed method can easily create a context dataset containing thousands of context sets. Furthermore, this paper uses a method for retrieving the context from the developed context dataset, which contains the answer of the question. It applies to the MRC model. Speciﬁcally, we use three models based on TF-IDF and BERT (TF-IDF, BERT, and TF-IDF+BERT) as our retrieval models. Mean-while, the BERT model served as our MRC model. We apply the retrieval model and the MRC model to the context dataset after combining them. Evaluation results show that the TF-IDF+BERT model outperforms the other two models when tested against the context dataset.
Annotation and Multi-modal Methods for Quality Assessment of Multi-party Discussion

Discussion quality assessment tasks have recently attracted signiﬁcant attention in nat-ural language processing. However, there have been few studies on challenging such tasks, with a focus on synchronous discussions. In this study, we annotate quality scores to each discussion in an existing multi-modal multi-party discussion corpus. Furthermore, we propose some quality assessment meth-ods with multi-modal inputs. As the results show, attention-based long short-term memory (LSTM) with multi-modal inputs produces the best performance for the “Effectiveness” criterion whereas text information has an important role in the “Reasonableness. ”
Impact of Distance Measures on Urdu Document Clustering

Document Clustering aims to group similar documents based on the distance among them. A wide range of distance measures are available in the literature, and selecting an appropriate distance function is a non-trivial task. This paper empirically evaluates four distance measures: Euclidean, Manhattan, Cosine, and Pearson Correlation, on Urdu news headlines. In addition to distance measures, the effect of stemming and lemmatization techniques on clustering is also studied. Unigram-based features and word embedding-based features were used to build a feature matrix. The evaluation results indicate that the frequent unigram features yielded the highest Adjusted Rand Index (ARI) scores on average. Among the four distance measure, the Cosine distance metric was found to be more valuable. Furthermore, the stemming technique was identified to be more useful in contrast to lemmatization for clustering news.
Chat-log Disentanglement via Same-Thread Classification and Direct-Reply Prediction

The early purpose of chatlog (conversation) dis-entanglement is to separate intermingled messages into detached conversations for easier information following and relevant information retrieving from simultaneous messages. Thus, the problem has been modeled as predicting whether two messages come from the same-thread. While the previous study by (Jiang et al., 2018) seems to perform well on same-thread prediction, we ﬁnd that it is because the data are randomly split into training and test sets, resulting overlapping of topics in training and testing sets. When data is split by time order, the performance of existing models drop signiﬁcantly. In this study, we consider the problem of direct reply predication task and study different message pair classiﬁcation models for the task. We argue that independent message encoders could better represent messages to capture their interaction than shared message encoders especially for direct-reply prediction task. We also ﬁnd that BERT model performs well with small datasets, while other models may outperform BERT with large datasets.
Arabic Named Entity Recognition Using Variant Deep Neural Network Architectures and Combinatorial Feature Embedding Based on CNN, LSTM and BERT

One of the most important factors that significantly affects the quality of sequence labeling models is the selection and encoding of input features. The complexity and morphological richness of Arabic language is the main reason why most existing Arabic Named Entity Recognition (NER) systems rely heavily on hand-crafted feature engineering. To overcome such a limitation, we pro-pose a novel neural network architecture to tackle the Arabic NER task. The proposed approach takes advantage of the recent success of deep neural networks in various Natural Language Processing (NLP) applications. We use a variant deep neural network architecture combined with a combinatorial feature embedding based on Convolutional Neural Network (CNN), Long Short-Term Memory networks(LSTM) and BERT to generate rich semantic and syntactic word representation vectors. Without using any external knowledge or hand-crafted feature engineering, the proposed models outperform the state-of-the-art systems on the ANERCorp dataset by yielding an F1-score of 93.34% and 93.68 % using bidirectional LSTM-CRF (BLC) and bidirectional GRU-CRF (BGC) architectures, respectively.
A Graph-Based Approach Leveraging Posts and Reactions for Detecting Rumors on Online Social Media

In this paper, we present a novel graph-based contextual and semantic learning approach for detecting rumors on online social media. The underlying hypothesis is that social media entities are intertwined, and if an event unfolds then similar narratives or user reactions with common interests get circulated. The proposed approach uses tweets and their reactions to understand the underlying interaction patterns and exploits the textual and latent information. Textual data is modeled as a words co-occurrence graph, which produces two prevalent categories of words – substantial words and bridge words . These words serve as building blocks for constructing contextual patterns for rumor detection by computing node-level statistical measures. The contextual patterns are further enriched by identifying negative emotions and inquisitive aspects in the reactions. The patterns are ﬁnally ranked and only top-k check-worthy patterns are used for feature generation. In order to preserve the semantic relations, we use word-level GloVe embedding trained over a Twitter dataset. The proposed approach is evaluated over a publicly available PHEME dataset, and compared with various baselines and SOTA techniques. The experimental results are promising and the proposed approach seems useful for rumor detection on online social media.
Reputation Analysis Using Key Phrases and Sentiment Scores Extracted from Reviews

In recent years, reviews of various products by purchasers have been posted on websites. By browsing these reviews, company representatives intend to collect and analyze opinions from the users' side of the products and use them to improve the products. However, existing analysis methods analyze reviews by dividing them into words, and do not analyze the function names that are phrases. Therefore, we propose a reputation analysis method using a decision tree model based on the frequency of key phrases extracted from reviews and sentiment scores. Experimental results showed that the proposed method using key phrases improved the accuracy by 3 points compared to the existing method that analyzes by words.
Annotating Entity and Causal Relationships on Japanese Vehicle Recall Information

A vehicle recall system is a process of recalling and repairing vehicles with defective designs or potential for accidents and failures. The recall document concisely explains the circumstances and causes of product defects. This paper presents two types of annotations on public vehicle recall reports, part entities and their relations, and causality. We annotated 6,394 car-recall text documents. Named entity and relation annotation suggests a relationship between the elements of an auto-mobile, and causality annotation indicates the cause of a malfunction. The entity and relation annotation and causality annotation allow the system to automatically extract knowledge in the automotive design domain. Subsequently, we present the experimental results for named entity recognition and relation extraction and causality extraction of our annotated corpus to verify the feasibility of building a system for extracting part information and causality. Finally, the experimental results show that employing named entity and relation information as the external knowledge improves causality extraction.
TRPM5+ microvillous tuft cells regulate neuroepithelial intrinsic olfactory stem cell proliferation

The olfactory neuroepithelium serves as a sensory organ for odors and is part of the nasal mucosal barrier. Olfactory sensory neurons are surrounded and supported by epithelial cells. A subset of these, microvillous cells (MVCs), are strategically positioned at the apical surface but their specific functions are still enigmatic and their relationship to the rest of the solitary chemosensory cell family is unclear. Here, we establish that the larger family of MVCs comprises tuft cells and ionocytes in both mice and humans. Olfactory TRPM5+ tuft-MVCs share a core transcriptional profile with the chemosensory tuft family, prominently including the machinery for lipid mediator generation. Integrating analysis of the respiratory and olfactory epithelium, we define the unique receptor expression of TRPM5+ tuft-MVC compared to the Gɑ-gustducin+ respiratory tuft cells and characterize a new population of glandular DCLK1+ tuft cells. To establish how allergen sensing by tuft-MVCs might direct olfactory mucosal responses, we employed an integrated single-cell transcriptional and protein analysis. We defined a remodeling olfactory epithelial switch pathway with induction of Chil4 and a distinct pathway of proliferation of the quiescent olfactory horizontal basal stem cell (HBC), both triggered in the absence of significant olfactory apoptosis. While the Chil4 pathway was dependent on STAT6 signaling and innate lymphocytes, neither were required for HBC proliferation. HBC proliferation was dependent on tuft-MVCs, establishing these specialized epithelial cells as both sensors for allergens and regulators of olfactory stem cell responses. Together our data provide high resolution characterization of the nasal tuft cell heterogeneity and uncover a novel mechanism by which TRPM5+ tuft cells direct the olfactory mucosal response to allergens. One Sentence Summary We identify the enigmatic TRPM5+ olfactory microvillous cells as tuft cells, and show their functional role as regulators of olfactory stem cell proliferation in response to environmental signals.
Distinct Social Behavior and Inter-Brain Connectivity in Dyads with autistic individuals

Autism Spectrum Disorder (ASD) is defined by distinctive socio-cognitive behaviors that deviate from typical patterns. Notably, social imitation skills appear to be particularly impacted, manifesting early on in development. This paper compared the behavior and inter-brain dynamics of dyads made up of two typically developing (TD) participants with mixed dyads made up of ASD and TD participants during social imitation tasks. By combining kinematics and EEG-hyperscanning, we show that individuals with ASD exhibited a preference for the follower rather than the lead role in imitating scenarios. Moreover, the study revealed inter-brain synchrony differences, with low-alpha inter-brain synchrony differentiating control and mixed dyads. The study’s findings suggest the importance of studying interpersonal phenomena in dynamic and ecological settings and using hyperscanning methods to capture inter-brain dynamics during actual social interactions.
To deliberate, remember; to anticipate, forget: Cognitive deliberation profiles underpinning active forgetting-dependent everyday-like memory performance in young and aged mice

Recalling a specific past episode that will enable us to decide which action is suited to a given present situation is a core element of everyday life. A wealth of research has demonstrated that such selective remembering is dependent upon a capacity to inhibit or provisionally ‘forget’ related yet inappropriate memory episodes which could orient behavior in unwilled directions. Everyday-like memory (EdM) refers to this type of common organizational mnemonic capacity, known to deteriorate significantly with age, putatively as a result of decline in the cognitive capacity for selective inhibition or ‘active forgetting’. Moreover, this memory retrieval-concomitant active forgetting comes at the cost of genuine amnesic weakening of the inhibited episodes, a phenomenon referred to as retrieval-induced forgetting (RIF). In the present study, we introduce a novel characterization of our previously validated mouse model of EdM in terms of the existing active forgetting and RIF literature. We also introduce novel behavioral analyses of the deliberation processes elicited by EdM challenge and use detailed multi-factorial explorations to reveal how these processes are impacted by age, temporal retention demand, difficulty of EdM challenge, and anticipation of trial outcome. Our observations indicate that deliberation requires remembering while accurate anticipation—in which a critical age-related deficit is also observed—requires active forgetting. Our results represent a significant advance towards unifying our understanding of the neurocognitive processes underpinning everyday-like memory, RIF, mnemonic deliberation, anticipatory function, and how they all are impacted by the physiological ageing process. In parallel, we present preliminary results using a transgenic mouse model which point to a fundamental role for the endocannabinoid system (eCS) in active forgetting and EdM, thereby demonstrating that deeper investigation of previously characterized age-related decline of the eCS should be a pre-clinical priority with a view to developing treatments for age-related decline of EdM function.
Cultivation of Melilotus officinalis as a source of bioactive compounds in association with soil recovery practices

Melilotus officinalis is a Leguminosae with relevant applications in medicine and soil recovery. This study reports the application of Melilotus officinalis plants in soil recovery and as a source of bioactive compounds. Plants were cultivated in semiarid soil under four different fertilizer treatments, urban waste compost at 10 t/ha and 20 t/ha, inorganic fertilizer and a control (no fertilizer). Agronomic properties of soil (pH, EC, soil respiration, C content, macro- and micro-elements) were analyzed before and after treatment. Also, germination, biomass, element contents, and physiological response were evaluated. Results showed a significant enhancement of the soil microbial activity in planted soils amended with compost, though there were no other clear effects on the soil physicochemical and chemical characteristics during the short experimental period. An improvement in M. officinalis germination and growth was observed in soils with compost amendment. Metabolite composition of plants was analyzed through Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Principal Component and Agglomerative Hierarchical Clustering models suggest that there is a clear separation of the metabolome of four groups of plants grown under different soil treatments. The five most important discriminative metabolites (annotated) were oleamide, palmitic acid, stearic acid, 3-hydroxy-cis-5-octenoylcarnitine, and 6-hydroxynon-7-enoylcarnitine. This study provides information on how the metabolome of Melilotus might be altered by fertilizer application in poor soil regions. These metabolome changes might have repercussions for the application of this plant in medicine and pharmacology. The results support the profitability of Melilotus officinalis cultivation for bioactive compounds production in association with soil recovery practices.
Glutamate Transporters are Involved in Direct Inhibitory Synaptic Transmission in the Vertebrate Retina

In the central nervous system of vertebrates, glutamate serves as the primary excitatory neurotransmitter. However, in the retina, glutamate released from photoreceptors causes hyperpolarization in postsynaptic ON-bipolar cells through a glutamate-gated chloride current, which seems paradoxical. Our research reveals that this current is modulated by two excitatory glutamate transporters, EAAT5b and EAAT7. These transporters are located at the dendritic tips of ON-bipolar cells and interact with all four types of cone photoreceptors. The absence of these transporters leads to a decrease in ON-bipolar cell responses, with eaat5b mutants being more severely affected than eaat5b/eaat7 double mutants, which also exhibit altered response kinetics. Biophysical investigations establish that EAAT7 is an active glutamate transporter with a predominant anion conductance. Our study is the first to demonstrate the direct involvement of postsynaptic glutamate transporters in inhibitory direct synaptic transmission at a central nervous system synapse.
Collective cell migration due to guidance-by-followers is robust to multiple stimuli

Collective cell migration is an important process during biological development and tissue repair, but may turn malignant during tumor invasion. Mathematical and computational models are essential to unravel the mechanisms of self-organization that underlie the emergence of collective migration from the interactions among individual cells. Recently, guidance-by-followers was identified as one such underlying mechanism of collective cell migration in the zebrafish embryo. This poses the question how guidance stimuli are integrated when multiple cells interact simultaneously. Here, we extend a recent individual-based model by an integration step of the vectorial guidance stimuli and compare model predictions obtained for various variants of the mechanism (arithmetic mean of stimuli, dominance of stimulus with largest transmission interface, dominance of most head-on stimulus). Simulations are carried out and quantified within the modeling and simulation framework Morpheus. Collective cell migration is sfound to be robust and qualitatively identical for all considered variants of stimulus integration. Moreover, this study highlights the role of individual-based modeling approaches for understanding collective phenomena at the population scale that emerge from cell-cell interactions.
Dispersal, habitat filtering, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography

Wetlands store 20–30% of the world’s soil carbon, and identifying the microbial controls on these carbon reserves is essential to predicting feedbacks to climate change. Although viral infections likely play important roles in wetland ecosystem dynamics, we lack a basic understanding of wetland viral ecology. Here 63 viral size-fraction metagenomes (viromes) and paired total metagenomes were generated from three time points in 2021 at seven fresh- and saltwater wetlands in the California Bodega Marine Reserve. We recovered 12,826 viral population genomic sequences (vOTUs), only 4.4% of which were detected at the same field site two years prior, indicating a small degree of population stability or recurrence. Viral communities differed most significantly among the seven wetland sites and were also structured by habitat (plant community composition and salinity). Read mapping to a new version of our reference database, PIGEONv2.0 (515,763 vOTUs), revealed 196 vOTUs present over large geographic distances, often reflecting shared habitat characteristics. Wetland vOTU microdiversity was significantly lower locally than globally and lower within than between time points, indicating greater divergence with increasing spatiotemporal distance. Viruses tended to have broad predicted host ranges via CRISPR spacer linkages to metagenome-assembled genomes, and increased SNP frequencies in CRISPR-targeted major tail protein genes suggest potential viral eco-evolutionary dynamics in response to both immune targeting and changes in host cell receptors involved in viral attachment. Together, these results highlight the importance of dispersal, environmental selection, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography.