bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification Morphology of Bamboo, and Traditional Use in Gorontalo
Authors: Febriyanti Febriyanti , Kandowangko Novri Youla , Ahmad Jusna
Published: March 2024

Bamboo is a versatile plant that has many benefits in the lives of the people of Gorontalo. This research aims to provide taxonomic information on bamboo species in Gorontalo Province, including variations in morphological characters and phenetic relationships of bamboo, as well as information on the traditional use of bamboo. The method used in this research is exploration and interviews. Exploration was carried out to collect information on bamboo taxonomy, while interviews were conducted to find out the use of bamboo by the Gorontalo Community. The research data obtained was then analyzed descriptively to describe and interpret data on morphological variations, phenetic relationships and traditional benefits of the various types of bamboo obtained. Based on the research results, it was obtained that 13 types of bamboo are distributed in Gorontalo which show the unique morphological variations of the varieties which are divided into four genera, namely the genus Gigantochloa has distinctive morphological characteristics on the waxy surface of the stem, the Bambusa genus with characteristic shiny, smooth stems, the Dendrocalamus genus has the largest diameter and stem wall thickness, the genus Schizostachyum has the same morphological characteristics of stem segments and branch lengths. Based on the analysis of phenetic relationships, two main clusters were formed with a similarity value of 61.2%. In terms of traditional uses, it was recorded in this research that most bamboo is used as building construction materials, food sources, traditional medicine, materials for traditional ceremonies, handicrafts, and ornamental plants, as a tool designedto help people's livelihoods and ecological functions. This research also provides the first record of the morphological characteristics and use of each type of bamboo in Gorontalo which can be used for future bamboo breeding and conservation projects.
Learners’ preferred L2 vocabulary learning modalities
Authors: Kamiya Nobuhiro
Published: March 2024

Under the framework of the cognitive theory of multimedia learning, this study examined how four modalities (iconic gestures, beat gestures, no gesture, audio) and learner modality preferences affect L2 vocabulary learning coupled with their relation to language aptitude. Sixty native or bilingual Japanese speakers watched video clips of sixty Spanish words under the above-mentioned four conditions six times. Their retention rate was measured immediately following the learning phase and after two weeks. The primary findings were (a) modalities had no relationship with the test scores; (b) learners preferred iconic gestures most; (c) modality preferences had a strong relationship to test scores; (d) the scores of their most preferred modality were significantly higher than those of other less-preferred modalities, and (e) the language aptitude score was correlated with modalities but not with modality preferences. This study suggests that learners may learn L2 vocabulary most efficiently when the modality matches their preferences.
“Música Paraense” e Terruá Pará: identidade regional, indústria cultural e psicologia das massas
Authors: Lopes Ciro Cesar da Silva , Souza Mauricio Rodrigues de
Published: March 2024

Resumo Desde a consolidação de emissoras de rádio e TV no Pará, a mídia local propaga a ideia de uma “música paraense”, título utilizado com frequência em uma série de eventos recentemente produzidos dentro e fora do estado. Ao contestar essa singularização, a presente pesquisa apresenta uma análise psicossociológica dos mecanismos que operam na inter-relação entre indústria cultural e formação de massas, utilizando como objeto privilegiado de estudo o espetáculo Terruá Pará. Visando tal intento, além do recurso à revisão bibliográfica, realizou-se uma pesquisa documental sobre parte do material impresso produzido para a terceira edição do festival. Em termos conclusivos, defende-se que o Terruá Pará, embora pautado por um discurso pretensamente racional em prol da valorização da música regional, torna-se dialeticamente promotor de irracionalidades expressas na sugestão de modos de subjetivação específicos que, desconsiderando diversidades, acabam por reduzir o seu público consumidor à qualidade de multidão abstrata.
“Cê anda igual bandido!”: o que dizem os jovens sobre a construção midiática do criminoso?
Authors: Daher Camila Marques Silva , Paiva Fernando Santana de , Barcellos Luciana Ferreira
Published: March 2024

Resumo A partir do cenário brasileiro, marcado por diferentes expressões da violência, são construídas práticas midiáticas que representam a juventude pauperizada de forma a criminalizar tal grupo. Este artigo apresenta uma discussão a respeito dos sentidos produzidos por jovens de 14 a 16 anos sobre as representações midiáticas expressas por setores da mídia hegemônica. Trata-se de uma pesquisa participante, delineada a partir da perspectiva do sociólogo Orlando Fals-Borda e da ideia de coautoria de Mikhail Bakhtin. A pesquisa foi realizada em uma cidade do estado de Minas Gerais, e foram construídos sete grupos de discussão, em que reportagens nas quais jovens são associados a episódios tidos como criminosos foram discutidas. Por meio dos diálogos construídos, foi possível identificar que o conteúdo midiático pode resultar em uma série de violências nas vidas desses jovens, que resistem através da busca por estratégias criativas, envolvendo a arte e mídias alternativas.
Early selection of resilient progenies to seed yield in soybean populations
Authors: Port Eduarda Donadel , Carvalho Ivan Ricardo , Pradebon Leonardo Cesar , Loro Murilo Vieira , Colet Christiane de Fátima , Silva José Antonio Gonzalez da , Sausen Natalia Hinterholz
Published: March 2024

ABSTRACT: This study carried out early selection of soybean progenies that are productive and resilient to environmental conditions. The experiment took place at the Genetic Breeding Program of UNIJUI (University of the Northwest of the State of Rio Grande do Sul), located in Ijuí - RS, Brazil. The experiment used augmented blocks design with interim checks. The regular treatments correspond to 24 soybean F2 populations and the common treatments were 18 commercial checks, arranged in four replications. At full physiological maturity, in each experimental unit, five plants were randomly collected to obtain seed weight per plant (SWP, g). The Jinks and Pooni methodology was used to calculate the probability of extracting superior lineages from the evaluated populations. The best control and promising cultivars to compose the parent bank are BMX FORÇA RR, FUNDACEP 66 RR and TMG 7062 IPRO. Jinks and Pooni’s methodology identified populations IRC001, IRC002, IRC017, IRC019, IRC028, IRC030, IRC032, IRC033, IRC035, IRC036, IRC039 and IRC040 as having high potential for extraction of superior lineages.
An annotated list of plant viruses described in Paraguay (1920–2023)
Authors: Esquivel-Fariña Arnaldo , González-Segnana Luis R. , Kitajima Elliot W.
Published: March 2024

Abstract Despite an economy based mostly on agriculture, literature on viral diseases of plants is scarce in Paraguay. Only recently, researches on plant viruses took an impulse resulting in a precise identification of many of them affecting plants either cultivated or not. To provide reliable information regarding plant viruses present in Paraguay, an annotated list of them was prepared, covering descriptions from 1920 to present day. There have been some important outbreaks with severe yield losses in crops as cucurbits, citrus, sesame, bean, maize, peanuts and tomato. Many of older descriptions are included for their historical significance, but most identifications made require confirmation. On the other hand, recent descriptions have been completed, based on several assays, especially molecular characterization. This list is organized alphabetically following scientific names of the plant species found naturally infected by viruses, with comments about symptoms, geographical distribution, incidence, identification procedures, and other information, with due literature references. It is based on a compilation of publications made on plant virus diseases in Paraguay. Described virus species, in a total of 38 recognized by ICTV, belonging to 17 different genera (Alphaendornavirus, Ampelovirus, Begomovirus, Benyvirus, Carlavirus, Cilevirus, Closterovirus, Comovirus, Cucumovirus, Dichorhavirus, Fabavirus, Luteovirus, Ophiovirus, Orthotospovirus, Potexvirus, Potyvirus and Tobamovirus), besides two unclassified, and four unidentified. There is also a case of viroid described in Citrus spp. Infections caused by potyviruses are the most numerous. These viruses were described in more than 40 plant species, belonging to 18 botanical families. Because of crop diversity and richness in native flora, many more viruses must be present in Paraguay, which future works will certainly reveal, especially with the increase in manpower involving researches, especially cooperative with foreign centers, on plant viruses, which has been very limited until now. Also, knowledge on existing viruses may have relevance in understanding their epidemiology and provide the basis for their control strategies and quarantine measures, to avoid new variants of existing viruses or new viruses being introduced.
Multiple variation of right renal and gonadal vascularization: report of two cases
Authors: Burihan Marcelo Calil , Garbelotti Junior Silvio Antonio , Cândido Paulo Laino , Souza Rodrigo Barbosa de , Pelozo Junior Osvaldo , Andrade Junior Aluisio , De Angelis Marco Antonio
Published: March 2024

Abstract We present two cases of multiple anatomical variations of the renal and gonadal vessels. The first case presented duplication of the renal vein and the presence of an accessory renal artery. However, the most interesting fact, in this case, was that the right gonadal vein emptied into the inferior right renal vein instead of ending in the inferior vena cava as would typically be the case. In the second case, we also found an accessory renal artery and the right gonadal vein emptied at the exact junction between the right renal vein and the inferior vena cava. Clinicians and surgeons should be familiar with anatomical variations to provide an accurate diagnosis during preoperative studies and to avoid surprises in abdominal surgical procedures.
Lipectomy as an alternative for superficialization of autologous AVF in obese patients: experience of a referral center in Amazon
Authors: Reis José Maciel Caldas dos , Ribeiro Flávio Roberto Cavalleiro de Macêdo , Melo Glauco dos Santos , Reale Neto Humberto Balbi , Andrade Mariseth Carvalho de
Published: March 2024

Abstract Background The preferred vascular access for hemodialysis is a native arteriovenous fistula (AVF) because it offers the best results in the short and long terms, lower morbidity and mortality, and has additional advantages in relation to central venous catheters or arteriovenous grafts. However, obesity can present an additional challenge because of the barrier of subcutaneous cellular tissue covering the surface of the vein to be punctured. Objectives The authors review their experience with excision of subcutaneous tissue (lipectomy) overlying upper arm cephalic vein arteriovenous fistulas in obese patients. Methods Consecutive vascular access patients undergoing lipectomy for cannulation with difficult access because of vein depth were reviewed. Cephalic vein depth was measured by ultrasound in all cases. Results Twenty-two patients were reviewed (15 men and 7 women), with a mean body mass index of 34.0 kg/m2 (range: 28-40 kg/m2). Mean age was 58.4 years. The mean preoperative vein depth of 7.9 mm (range: 7.0-10.0 mm) was reduced to 4.7 mm (range: 3.0-6.0 mm) (P 0.01). The mean follow-up period for patients was 13.2 months. Four patients were lost to follow-up and four died during the period due to causes unrelated to vascular access. Conclusions Obesity should not be a limiting factor to creation of a native AVF, since lipectomy is a relatively simple option for superficialization, enabling functioning native and deep arteriovenous fistulas in obese patients.
Permanências e mudanças na estrutura produtiva da agropecuária paranaense, 2006-2017
Authors: Cunha Marina Silva da
Published: March 2024

Resumo Este trabalho tem como objetivo analisar a dinâmica das mudanças na estrutura produtiva da agropecuária do Estado do Paraná a partir das informações dos Censos Agropecuários de 2006 e 2017. Foram utilizadas as análises multivariadas, fatorial e cluster, para caracterizar as mudanças temporais e espaciais, considerando vinte indicadores da modernização da agropecuária associados ao uso da terra, tecnificação e capitalização, mão de obra e condição do produtor e dos estabelecimentos. Os resultados da pesquisa evidenciam transformações significativas na agropecuária do Estado do Paraná, mas que ainda privilegiam as tradicionais tendências do país, com expansão da agropecuária voltada para exportação e redução da agricultura familiar, a qual ainda absorve a maior parcela da ocupação no meio rural.
Territorialidad campesina y soberanía alimentaria: estudio de caso en el macizo colombiano
Authors: Díaz Avendaño Jorge Eduardo , Pachón Ariza Fabio Alberto
Published: March 2024

Resumen La lucha por la defensa de sus derechos y territorialidad ha sido central en el movimiento campesino colombiano. Dentro de estas luchas se destaca la experiencia histórica del Comité de Integración del Macizo Colombiano (CIMA) en el sur de Colombia. El presente artículo se fundamenta en una investigación cualitativa, descriptiva y empírica. Tiene como objetivo describir, discutir y analizar críticamente la propuesta de territorialidad campesina y Soberanía Alimentaria en el Norte de Nariño y Sur de Cauca - Colombia, a partir de la iniciativa de Territorio Campesino Agroalimentario (TECAM), que representa una alternativa de construcción social del territorio y de resignificación política, económica, cultural y ecológica del movimiento campesino en el Macizo Colombiano, en un contexto de conflictividad socio territorial agudizado por la imposición del modelo neoliberal extractivista con consecuencias sobre el territorio y el modo de vida campesino.
Produção de farinha de mandioca, COVID-19 e Segurança Alimentar: as repercussões para agricultores familiares do Recôncavo Baiano
Authors: Duarte Bianca Ferreira , Pena Lara Conceição Campos , Braga Tereza Cristina Vieira , Cardoso Ryzia de Cássia Vieira
Published: March 2024

Resumo A pandemia gerou uma das maiores crises mundiais, com redução do poder aquisitivo da população e dificuldades no acesso aos alimentos. Neste contexto, este estudo objetivou analisar as condições de produção da farinha e a relação desta com a segurança alimentar de agricultores familiares da cadeia da mandioca, do Recôncavo Baiano, durante a pandemia da COVID-19. Realizou-se estudo transversal, com coleta de dados por meio de questionário, tratamento por estatística descritiva e testes de associação. Do total de participantes (n=86), 64% indicaram alterações no volume de produção de farinha e na renda. Registrou-se redução na produção mensal, no número de trabalhadores e nas horas trabalhadas nas casas de farinha, além da redução na diversidade de compradores. O aumento na renda advinda da farinha foi associado a um pequeno número de produtores, mais experientes (p=0,047). Apesar da manutenção das feiras dos municípios estudados, houve alterações no comportamento dos clientes. O comércio online e por delivery foram pouco representativos. Dos entrevistados, 50% relataram preocupação de que os alimentos acabassem. As medidas governamentais de apoio aos agricultores familiares não tiveram alcance satisfatório junto aos participantes. A maior parte recebeu auxílio emergencial, sendo também beneficiários de programas sociais, o que evidencia vulnerabilidades no setor.
A atuação de forças centrípetas e centrífugas no discurso religioso sobre a pessoa com deficiência: reflexões a partir de Bakhtin e o Círculo
Authors: Costa Dennis Souza da , Teixeira Ivana Siqueira
Published: March 2024

RESUMO Este artigo analisa a atuação das forças centrípetas e centrífugas em enunciados da esfera religiosa que evidenciam cosmovisões do segmento cristão evangélico acerca da deficiência. Para tanto, selecionamos um vídeo disponível na plataforma YouTube contendo enunciados dos apresentadores Tito Rocha e Leandro Quadros relativos à temática da deficiência, bem como a resposta de uma internauta acerca do posicionamento desses sujeitos. A reflexão teórico-metodológica fundamenta-se na orientação dialógica da linguagem, sobretudo nas considerações acerca das relações dialógicas, vozes e forças centrípetas e centrífugas. Também recorremos a estudos sobre cosmovisão, inclusão da pessoa com deficiência, discurso religioso e teologia cristã. Os resultados indicam que os discursos dos apresentadores refletem e refratam uma cosmovisão cristã que unifica o entendimento da deficiência como resultado da condenação divina ocasionada pelo pecado. O comentário da internauta, por sua vez, apresenta movimentos de centralização e dispersão dessa perspectiva cristã.
Meninas que Sangram e Viram Lua: A Menstrunormatividade da Ginecologia Natural na Literatura Infantojuvenil
Authors: Cechin Michelle Brugnera Cruz , Pugens Nat´ália de Borba , Rocha Cristianne Maria Famer da
Published: March 2024

Neste artigo, perscrutamos os modos como os livros de literatura infantojuvenil sobre Ginecologia Natural produzem discursos sobre a menstruação. Compõem o corpus de análise cinco obras publicadas no mercado editorial brasileiro que abordam esse tema. Nos valemos de análises de inspiração foucaultiana sobre os discursos de normalidade e menstruação e em autoras feministas que pesquisam sobre os efeitos do neoliberalismo nos corpos e subjetividades, em especial o conceito de menstrunormatividade , introduzido por Josefin Persdotter (2020). Estabelecemos três eixos de análise das obras: os modos como é narrada a menstruação; os discursos sobre o período pré-menstrual e os modos de subjetivação das pessoas que menstruam; e, como práticas de autocuidado introduzidas pela Ginecologia Natural. Conclui-se que, nas obras comprovadas, as transformações de práticas de autocuidado do ciclo menstrual, propostas pela Ginecologia Natural, formam um conjunto de regras (ou normatividades) menstruais auto-opressivas, pautadas na autogestão, mudança de comportamento e produtividade.
SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter
Authors: Chang Ying-Ying , Wang Wei-Yao , Peng Wen-Chih
Published: March 2024

In the dynamic and rapidly evolving world of social media, detecting anomalous users has become a crucial task to address malicious activities such as misinformation and cyberbullying. As the increasing number of anomalous users improves the ability to mimic normal users and evade detection, existing methods only focusing on bot detection are ineffective in terms of capturing subtle distinctions between users. To address these challenges, we proposed SeGA, preference-aware self-contrastive learning for anomalous user detection, which leverages heterogeneous entities and their relations in the Twittersphere to detect anomalous users with different malicious strategies. SeGA utilizes the knowledge of large language models to summarize user preferences via posts. In addition, integrating user preferences with prompts as pseudo-labels for preference-aware self-contrastive learning enables the model to learn multifaceted aspects for describing the behaviors of users. Extensive experiments on the proposed TwBNT benchmark demonstrate that SeGA significantly outperforms the state-of-the-art methods (+3.5% ∼ 27.6%) and empirically validate the effectiveness of the model design and pre-training strategies. Our code and data are publicly available at https://github.com/ying0409/SeGA.
Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection
Authors: Cui Chaoqun , Jia Caiyan
Published: March 2024

Rumor detection on social media has become increasingly important. Most existing graph-based models presume rumor propagation trees (RPTs) have deep structures and learn sequential stance features along branches. However, through statistical analysis on real-world datasets, we find RPTs exhibit wide structures, with most nodes being shallow 1-level replies. To focus learning on intensive substructures, we propose Rumor Adaptive Graph Contrastive Learning (RAGCL) method with adaptive view augmentation guided by node centralities. We summarize three principles for RPT augmentation: 1) exempt root nodes, 2) retain deep reply nodes, 3) preserve lower-level nodes in deep sections. We employ node dropping, attribute masking and edge dropping with probabilities from centrality-based importance scores to generate views. A graph contrastive objective then learns robust rumor representations. Extensive experiments on four benchmark datasets demonstrate RAGCL outperforms state-of-the-art methods. Our work reveals the wide-structure nature of RPTs and contributes an effective graph contrastive learning approach tailored for rumor detection through principled adaptive augmentation. The proposed principles and augmentation techniques can potentially benefit other applications involving tree-structured graphs.
LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection
Authors: Guo Hongcheng , Yang Jian , Liu Jiaheng , Bai Jiaqi , Wang Boyang , Li Zhoujun , Zheng Tieqiao , Zhang Bo , Peng Junran , Tian Qi
Published: March 2024

Log anomaly detection is a key component in the field of artificial intelligence for IT operations (AIOps). Considering log data of variant domains, retraining the whole network for unknown domains is inefficient in real industrial scenarios. However, previous deep models merely focused on extracting the semantics of log sequences in the same domain, leading to poor generalization on multi-domain logs. To alleviate this issue, we propose a unified Transformer-based framework for Log anomaly detection (LogFormer) to improve the generalization ability across different domains, where we establish a two-stage process including the pre-training and adapter-based tuning stage. Specifically, our model is first pre-trained on the source domain to obtain shared semantic knowledge of log data. Then, we transfer such knowledge to the target domain via shared parameters. Besides, the Log-Attention module is proposed to supplement the information ignored by the log-paring. The proposed method is evaluated on three public datasets and one real-world dataset. Experimental results on multiple benchmarks demonstrate the effectiveness of our LogFormer with fewer trainable parameters and lower training costs.
Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB
Authors: Liu Shengheng , Li Xingkang , Mao Zihuan , Liu Peng , Huang Yongming
Published: March 2024

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.
Molecular Optimization Model with Patentability Constraint
Authors: Turutov Sally , Radinsky Kira
Published: March 2024

In drug development, molecular optimization is a crucial challenge that involves generating novel molecules given a lead molecule as input. The task requires maintaining molecular similarity to the original molecule while simultaneously optimizing multiple chemical attributes. To aid in this process, numerous generative models have been proposed.
However, in practical applications, it is crucial for these models not only to generate novel molecules with the above constraints but also to generate molecules that significantly differ from any existing patented compounds.
In this work, we present a multi-optimization molecular framework to address this challenge.
Our framework trains a model to prioritize both enhanced properties and substantial dissimilarity from patented compounds. By jointly learning continuous representations of optimized and patentable molecules, we ensure that the generated molecules are significantly distant from any patented compounds while improving chemical properties.
Through empirical evaluation, we demonstrate the superior performance of our approach compared to state-of-the-art molecular optimization methods both in chemical property optimization and patentability.
Multilevel Attention Network with Semi-supervised Domain Adaptation for Drug-Target Prediction
Authors: Xie Zhousan , Tu Shikui , Xu Lei
Published: March 2024

Prediction of drug-target interactions (DTIs) is a crucial step in drug discovery, and deep learning methods have shown great promise on various DTI datasets. However, existing approaches still face several challenges, including limited labeled data, hidden bias issue, and a lack of generalization ability to out-of-domain data. These challenges hinder the model's capacity to learn truly informative interaction features, leading to shortcut learning and inferior predictive performance on novel drug-target pairs. To address these issues, we propose MlanDTI, a semi-supervised domain adaptive multilevel attention network (Mlan) for DTI prediction. We utilize two pre-trained BERT models to acquire bidirectional representations enriched with information from unlabeled data. Then, we introduce a multilevel attention mechanism, enabling the model to learn domain-invariant DTIs at different hierarchical levels. Moreover, we present a simple yet effective semi-supervised pseudo-labeling method to further enhance our model's predictive ability in cross-domain scenarios. Experiments on four datasets show that MlanDTI achieves state-of-the-art performances over other methods under intra-domain settings and outperforms all other approaches under cross-domain settings. The source code is available at https://github.com/CMACH508/MlanDTI.
GAMC: An Unsupervised Method for Fake News Detection Using Graph Autoencoder with Masking
Authors: Yin Shu , Zhu Peican , Wu Lianwei , Gao Chao , Wang Zhen
Published: March 2024

With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models like BERT have enhanced fake news detection. However, they primarily focus on content and do not consider social context during news propagation. Graph-based techniques have incorporated the social context but are limited by the need for large labeled datasets. To address these challenges, this paper introduces GAMC, an unsupervised fake news detection technique using the Graph Autoencoder with Masking and Contrastive learning. By leveraging both the context and content of news propagation as self-supervised signals, our method reduces the dependency on labeled datasets. Specifically, GAMC begins by applying data augmentation to the original news propagation graphs. Subsequently, these augmented graphs are encoded using a graph encoder and subsequently reconstructed via a graph decoder. Finally, a composite loss function that encompasses both reconstruction error and contrastive loss is designed. Firstly, it ensures the model can effectively capture the latent features, based on minimizing the discrepancy between reconstructed and original graph representations. Secondly, it aligns the representations of augmented graphs that originate from the same source. Experiments on the real-world dataset validate the effectiveness of our method.
MCSSME: Multi-Task Contrastive Learning for Semi-supervised Singing Melody Extraction from Polyphonic Music
Authors: Yu Shuai
Published: March 2024

Singing melody extraction is an important task in the field of music information retrieval (MIR). The development of data-driven models for this task have achieved great successes. However, the existing models have two major limitations: firstly, most of the existing singing melody extraction models have formulated this task as a pixel-level prediction task. The lack of labeling data has limited the model for further improvements. Secondly, the generalization of the existing models are prone to be disturbed by the music genres. To address the issues mentioned above, in this paper, we propose a multi-Task contrastive learning framework for semi-supervised singing melody extraction, termed as MCSSME.
Specifically, to deal with data scarcity limitation, we propose a self-consistency regularization (SCR) method to train the model on the unlabeled data. Transformations are applied to the raw signal of polyphonic music, which makes the network to improve its representation capability via recognizing the transformations. We further propose a novel multi-task learning (MTL) approach to jointly learn singing melody extraction and classification of transformed data. To deal with generalization limitation, we also propose a contrastive embedding learning, which strengthens the intra-class compactness and inter-class separability. To improve the generalization on different music genres, we also propose a domain classification method to learn task-dependent features by mapping data from different music genres to shared subspace. MCSSME evaluates on a set of well-known public melody extraction datasets with promising performances. The experimental results demonstrate the effectiveness of the MCSSME framework for singing melody extraction from polyphonic music using very limited labeled data scenarios.
NondBREM: Nondeterministic Offline Reinforcement Learning for Large-Scale Order Dispatching
Authors: Zhang Hongbo , Wang Guang , Wang Xu , Zhou Zhengyang , Zhang Chen , Dong Zheng , Wang Yang
Published: March 2024

One of the most important tasks in ride-hailing is order dispatching, i.e., assigning unserved orders to available drivers. Recent order dispatching has achieved a significant improvement due to the advance of reinforcement learning, which has been approved to be able to effectively address sequential decision-making problems like order dispatching. However, most existing reinforcement learning methods require agents to learn the optimal policy by interacting with environments online, which is challenging or impractical for real-world deployment due to high costs or safety concerns. For example, due to the spatiotemporally unbalanced supply and demand, online reinforcement learning-based order dispatching may significantly impact the revenue of the ride-hailing platform and passenger experience during the policy learning period. Hence, in this work, we develop an offline deep reinforcement learning framework called NondBREM for large-scale order dispatching, which learns policy from only the accumulated logged data to avoid costly and unsafe interactions with the environment. In NondBREM, a Nondeterministic Batch-Constrained Q-learning (NondBCQ) module is developed to reduce the algorithm extrapolation error and a Random Ensemble Mixture (REM) module that integrates multiple value networks with multi-head networks is utilized to improve the model generalization and robustness. Extensive experiments on large-scale real-world ride-hailing datasets show the superiority of our design.
Neural Amortized Inference for Nested Multi-Agent Reasoning
Authors: Jha Kunal , Le Tuan Anh , Jin Chuanyang , Kuo Yen-Ling , Tenenbaum Joshua B. , Shu Tianmin
Published: March 2024

Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate reasoning can be effectively modeled through nested multi-agent reasoning. Nonetheless, the computational complexity escalates exponentially with each level of reasoning, posing a significant challenge. However, humans effortlessly perform complex social inferences as part of their daily lives. To bridge the gap between human-like inference capabilities and computational limitations, we propose a novel approach: leveraging neural networks to amortize high-order social inference, thereby expediting nested multi-agent reasoning. We evaluate our method in two challenging multi-agent interaction domains. The experimental results demonstrate that our method is computationally efficient while exhibiting minimal degradation in accuracy.
Music Style Transfer with Time-Varying Inversion of Diffusion Models
Authors: Li Sifei , Zhang Yuxin , Tang Fan , Ma Chongyang , Dong Weiming , Xu Changsheng
Published: March 2024

With the development of diffusion models, text-guided image style transfer has demonstrated great controllable and high-quality results. However, the utilization of text for diverse music style transfer poses significant challenges, primarily due to the limited availability of matched audio-text datasets. Music, being an abstract and complex art form, exhibits variations and intricacies even within the same genre, thereby making accurate textual descriptions challenging. This paper presents a music style transfer approach that effectively captures musical attributes using minimal data. We introduce a novel time-varying textual inversion module to precisely capture mel-spectrogram features at different levels. During inference, we utilize a bias-reduced stylization technique to get stable results. Experimental results demonstrate that our method can transfer the style of specific instruments, as well as incorporate natural sounds to compose melodies. Samples and code are available at https://lsfhuihuiff.github.io/MusicTI/.
Gated Attention Coding for Training High-Performance and Efficient Spiking Neural Networks
Authors: Qiu Xuerui , Zhu Rui-Jie , Chou Yuhong , Wang Zhaorui , Deng Liang-Jian , Li Guoqi
Published: March 2024

Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional artificial neural networks (ANNs) due to their unique spike-based event-driven nature. Coding is crucial in SNNs as it converts external input stimuli into spatio-temporal feature sequences. However, most existing deep SNNs rely on direct coding that generates powerless spike representation and lacks the temporal dynamics inherent in human vision. Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that leverages the multi-dimensional gated attention unit to efficiently encode inputs into powerful representations before feeding them into the SNN architecture. GAC functions as a preprocessing layer that does not disrupt the spike-driven nature of the SNN, making it amenable to efficient neuromorphic hardware implementation with minimal modifications. Through an observer model theoretical analysis, we demonstrate GAC's attention mechanism improves temporal dynamics and coding efficiency. Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency. Notably, we improve top-1 accuracy by 3.10% on CIFAR100 with only 6-time steps and 1.07% on ImageNet while reducing energy usage to 66.9% of the previous works. To our best knowledge, it is the first time to explore the attention-based dynamic coding scheme in deep SNNs, with exceptional effectiveness and efficiency on large-scale datasets. Code is available at https://github.com/bollossom/GAC.
Open-Set Facial Expression Recognition
Authors: Zhang Yuhang , Yao Yue , Liu Xuannan , Qin Lixiong , Wang Wenjing , Deng Weihong
Published: March 2024

Facial expression recognition (FER) models are typically trained on datasets with a fixed number of seven basic classes. However, recent research works (Cowen et al. 2021; Bryant et al. 2022; Kollias 2023) point out that there are far more expressions than the basic ones. Thus, when these models are deployed in the real world, they may encounter unknown classes, such as compound expressions that cannot be classified into existing basic classes. To address this issue, we propose the open-set FER task for the first time. Though there are many existing open-set recognition methods, we argue that they do not work well for open-set FER because FER data are all human faces with very small inter-class distances, which makes the open-set samples very similar to close-set samples. In this paper, we are the first to transform the disadvantage of small inter-class distance into an advantage by proposing a new way for open-set FER. Specifically, we find that small inter-class distance allows for sparsely distributed pseudo labels of open-set samples, which can be viewed as symmetric noisy labels. Based on this novel observation, we convert the open-set FER to a noisy label detection problem. We further propose a novel method that incorporates attention map consistency and cycle training to detect the open-set samples. Extensive experiments on various FER datasets demonstrate that our method clearly outperforms state-of-the-art open-set recognition methods by large margins. Code is available at https://github.com/zyh-uaiaaaa.
Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals
Authors: Altmeyer Patrick , Farmanbar Mojtaba , Van Deursen Arie , Liem Cynthia C. S.
Published: March 2024

Counterfactual explanations offer an intuitive and straightforward way to explain black-box models and offer algorithmic recourse to individuals. To address the need for plausible explanations, existing work has primarily relied on surrogate models to learn how the input data is distributed. This effectively reallocates the task of learning realistic explanations for the data from the model itself to the surrogate. Consequently, the generated explanations may seem plausible to humans but need not necessarily describe the behaviour of the black-box model faithfully. We formalise this notion of faithfulness through the introduction of a tailored evaluation metric and propose a novel algorithmic framework for generating Energy-Constrained Conformal Counterfactuals that are only as plausible as the model permits. Through extensive empirical studies, we demonstrate that ECCCo reconciles the need for faithfulness and plausibility. In particular, we show that for models with gradient access, it is possible to achieve state-of-the-art performance without the need for surrogate models. To do so, our framework relies solely on properties defining the black-box model itself by leveraging recent advances in energy-based modelling and conformal prediction. To our knowledge, this is the first venture in this direction for generating faithful counterfactual explanations. Thus, we anticipate that ECCCo can serve as a baseline for future research. We believe that our work opens avenues for researchers and practitioners seeking tools to better distinguish trustworthy from unreliable models.
Optimal Transport with Tempered Exponential Measures
Authors: Amid Ehsan , Nielsen Frank , Nock Richard , Warmuth Manfred K.
Published: March 2024

In the field of optimal transport, two prominent subfields face each other: (i) unregularized optimal transport, ``a-la-Kantorovich'', which leads to extremely sparse plans but with algorithms that scale poorly, and (ii) entropic-regularized optimal transport, ``a-la-Sinkhorn-Cuturi'', which gets near-linear approximation algorithms but leads to maximally un-sparse plans. In this paper, we show that an extension of the latter to tempered exponential measures, a generalization of exponential families with indirect measure normalization, gets to a very convenient middle ground, with both very fast approximation algorithms and sparsity, which is under control up to sparsity patterns. In addition, our formulation fits naturally in the unbalanced optimal transport problem setting.
Formal Logic Enabled Personalized Federated Learning through Property Inference
Authors: An Ziyan , Johnson Taylor T. , Ma Meiyi
Published: March 2024

Recent advancements in federated learning (FL) have greatly facilitated the development of decentralized collaborative applications, particularly in the domain of Artificial Intelligence of Things (AIoT). However, a critical aspect missing from the current research landscape is the ability to enable data-driven client models with symbolic reasoning capabilities. Specifically, the inherent heterogeneity of participating client devices poses a significant challenge, as each client exhibits unique logic reasoning properties. Failing to consider these device-specific specifications can result in critical properties being missed in the client predictions, leading to suboptimal performance. In this work, we propose a new training paradigm that leverages temporal logic reasoning to address this issue. Our approach involves enhancing the training process by incorporating mechanically generated logic expressions for each FL client. Additionally, we introduce the concept of aggregation clusters and develop a partitioning algorithm to effectively group clients based on the alignment of their temporal reasoning properties. We evaluate the proposed method on two tasks: a real-world traffic volume prediction task consisting of sensory data from fifteen states and a smart city multi-task prediction utilizing synthetic data. The evaluation results exhibit clear improvements, with performance accuracy improved by up to 54% across all sequential prediction models.
Generating Universal Adversarial Perturbations for Quantum Classifiers
Authors: Anil Gautham , Vinod Vishnu , Narayan Apurva
Published: March 2024

Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodologies. Recent studies have revealed that, like their classical counterparts, QML models based on Parametrized Quantum Circuits (PQCs) are also vulnerable to adversarial attacks. Moreover, the existence of Universal Adversarial Perturbations (UAPs) in the quantum domain has been demonstrated theoretically in the context of quantum classifiers. In this work, we introduce QuGAP: a novel framework for generating UAPs for quantum classifiers. We conceptualize the notion of additive UAPs for PQC-based classifiers and theoretically demonstrate their existence. We then utilize generative models (QuGAP-A) to craft additive UAPs and experimentally show that quantum classifiers are susceptible to such attacks. Moreover, we formulate a new method for generating unitary UAPs (QuGAP-U) using quantum generative models and a novel loss function based on fidelity constraints. We evaluate the performance of the proposed framework and show that our method achieves state-of-the-art misclassification rates, while maintaining high fidelity between legitimate and adversarial samples.
Enhancing Training of Spiking Neural Network with Stochastic Latency
Authors: Anumasa Srinivas , Mukhoty Bhaskar , Bojkovic Velibor , De Masi Giulia , Xiong Huan , Gu Bin
Published: March 2024

Spiking neural networks (SNNs) have garnered significant attention for their low power consumption when deployed on neuromorphic hardware that operates in orders of magnitude lower power than general-purpose hardware. Direct training methods for SNNs come with an inherent latency for which the SNNs are optimized, and in general, the higher the latency, the better the predictive powers of the models, but at the same time, the higher the energy consumption during training and inference. Furthermore, an SNN model optimized for one particular latency does not necessarily perform well in lower latencies, which becomes relevant in scenarios where it is necessary to switch to a lower latency because of the depletion of onboard energy or other operational requirements. In this work, we propose Stochastic Latency Training (SLT), a direct training method for SNNs that optimizes the model for the given latency but simultaneously offers a minimum reduction of predictive accuracy when shifted to lower inference latencies. We provide heuristics for our approach with partial theoretical justification and experimental evidence showing the state-of-the-art performance of our models on datasets such as CIFAR-10, DVS-CIFAR-10, CIFAR-100, and DVS-Gesture. Our code is available at https://github.com/srinuvaasu/SLT
Taming Binarized Neural Networks and Mixed-Integer Programs
Authors: Aspman Johannes , Korpas Georgios , Marecek Jakub
Published: March 2024

There has been a great deal of recent interest in binarized neural networks, especially because of their explainability. At the same time, automatic differentiation algorithms such as backpropagation fail for binarized neural networks, which limits their applicability.
We show that binarized neural networks admit a tame representation
by reformulating the problem of training binarized neural networks as a subadditive dual of a mixed-integer program, which we show to have nice properties. This makes it possible to use the framework of Bolte et al. for implicit differentiation, which offers the possibility for practical implementation of backpropagation in the context of binarized neural networks.

This approach could also be used for a broader class of mixed-integer programs, beyond the training of binarized neural networks, as encountered in symbolic approaches to AI and beyond.
MIND: Multi-Task Incremental Network Distillation
Authors: Bonato Jacopo , Pelosin Francesco , Sabetta Luigi , Nicolosi Alessandro
Published: March 2024

The recent surge of pervasive devices that generate dynamic data streams has underscored the necessity for learning systems to adapt continually to data distributional shifts. To tackle this challenge, the research community has put forth a spectrum of methodologies, including the demanding pursuit of class-incremental learning without replay data. In this study, we present MIND, a parameter isolation method that aims to significantly enhance the performance of replay-free solutions and achieve state-of-the-art results on several widely studied datasets. Our approach introduces two main contributions: two alternative distillation procedures that significantly improve the efficiency of MIND increasing the accumulated knowledge of each sub-network, and the optimization of the BachNorm layers across tasks inside the sub-networks. Overall, MIND outperforms all the state-of-the-art methods for rehearsal-free Class-Incremental learning (with an increment in classification accuracy of approx. +6% on CIFAR-100/10 and +10% on TinyImageNet/10) reaching up to approx. +40% accuracy in Domain-Incremental scenarios. Moreover, we ablated each contribution to demonstrate its impact on performance improvement. Our results showcase the superior performance of MIND indicating its potential for addressing the challenges posed by Class-incremental and Domain-Incremental learning in resource-constrained environments.