bluuebunny/crossref_metadata_2025 huggingface dataset:
Galectin-3 is able to differentiate dogs with myxomatous mitral valve disease from healthy control dogs
Authors: Kim Yoon-Mi , Kim Sang-Won , Kim Jung-Hyun
Published: August 2023

OBJECTIVES

Galectin-3 is a cardiac biomarker for heart failure in humans. However, it has not been investigated in dogs with naturally occurring heart disease. This study aimed to compare plasma galectin-3 concentration in healthy dogs and those with myxomatous mitral valve disease (MMVD) and explore the potential association of galectin-3 with other cardiac biomarkers, inflammatory cytokines, echocardiographic estimates, and dog characteristics.

ANIMALS

10 healthy dogs and 30 dogs with MMVD were prospectively recruited.

PROCEDURES

In this case-control study, plasma galectin-3, inflammatory cytokines, echocardiographic estimates, and other cardiac biomarkers were measured, and dog characteristics were recorded.

RESULTS

Plasma galectin-3 concentration was significantly higher in dogs with MMVD (2.94 [interquartile range, 1.61 to 5.20] ng/mL) than in healthy controls (1.56 [0.69 to 1.84] ng/mL, P = .009). Logistic regression analysis revealed that galectin-3 concentration and age predicted the presence of MMVD (predictive accuracy = 90.0%, P < .05). A cut-off value ≥ 1.9 ng/mL for galectin-3 differentiated healthy dogs from dogs with MMVD (70% sensitivity; 90% specificity AUC, 0.77; P = .01).

CLINICAL RELEVANCE

Plasma galectin-3 concentration was higher in dogs with MMVD than in healthy dogs, indicating that it is a novel cardiac biomarker in dogs with MMVD although there was no significant difference between MMVD stages.
Analysis of the sustainable development path model of resource-based enterprises based on big data technology
Authors: Ma Fangjing
Published: August 2023

With the booming of big data technology, a sustainable development path is an important strategic resource for enterprises. In this paper, a vector is set up by introducing a multidimensional algorithm. Then connect state paths and define an intergenerational criterion. A very large and very small function is generated according to the criterion. The welfare function is set up to obtain the usual expression for the discount rate. Finally, a bivariate is set up to obtain the modified rule, and the implied interest rate is used to define the return rate, based on which the sustainability model is constructed. The experimental results show that the data are collected from enterprises using big data technology, the research is conducted based on the measured objectives, and the non-standardized coefficients are calculated statistically. Among them, the standard error of management resources is 61%, which has the smallest error value compared to other groups.
SWAT: Spatial Structure Within and Among Tokens
Authors: Kahatapitiya Kumara , Ryoo Michael S.
Published: August 2023

Modeling visual data as tokens (i.e., image patches) using attention mechanisms, feed-forward networks or convolutions has been highly effective in recent years. Such methods usually have a common pipeline: a tokenization method, followed by a set of layers/blocks for information mixing, both within and among tokens. When image patches are converted into tokens, they are often flattened, discarding the spatial structure within each patch. As a result, any processing that follows (eg: multi-head self-attention) may fail to recover and/or benefit from such information. In this paper, we argue that models can have significant gains when spatial structure is preserved during tokenization, and is explicitly used during the mixing stage. We propose two key contributions: (1) Structure-aware Tokenization and, (2) Structure-aware Mixing, both of which can be combined with existing models with minimal effort. We introduce a family of models (SWAT), showing improvements over the likes of DeiT, MLP-Mixer and Swin Transformer, across multiple benchmarks including ImageNet classification and ADE20K segmentation. Our code is available at github.com/kkahatapitiya/SWAT.
APR: Online Distant Point Cloud Registration through Aggregated Point Cloud Reconstruction
Authors: Liu Quan , Zhou Yunsong , Zhu Hongzi , Chang Shan , Guo Minyi
Published: August 2023

For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such point clouds have extremely different point density and sensor perspective on the same object, making registration on such point clouds very hard. In this paper, we propose a novel feature extraction framework, called APR, for online distant point cloud registration. Specifically, APR leverages an autoencoder design, where the autoencoder reconstructs a denser aggregated point cloud with several frames instead of the original single input point cloud. Our design forces the encoder to extract features with rich local geometry information based on one single input point cloud. Such features are then used for online distant point cloud registration. We conduct extensive experiments against state-of-the-art (SOTA) feature extractors on KITTI and nuScenes datasets. Results show that APR outperforms all other extractors by a large margin, increasing average registration recall of SOTA extractors by 7.1% on LoKITTI and 4.6% on LoNuScenes. Code is available at https://github.com/liuQuan98/APR.
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering
Authors: Penamakuri Abhirama Subramanyam , Gupta Manish , Gupta Mithun Das , Mishra Anand
Published: August 2023

We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must first retrieve relevant images from the pool and answer the question from these retrieved images. We refer to this problem as retrieval-based visual question answering (or RETVQA in short). The RETVQA is distinctively different and more challenging than the traditionally-studied Visual Question Answering (VQA), where a given question has to be answered with a single relevant image in context. Towards solving the RETVQA task, we propose a unified Multi Image BART (MI-BART) that takes a question and retrieved images using our relevance encoder for free-form fluent answer generation. Further, we introduce the largest dataset in this space, namely RETVQA, which has the following salient features: multi-image and retrieval requirement for VQA, metadata-independent questions over a pool of heterogeneous images, expecting a mix of classification-oriented and open-ended generative answers. Our proposed framework achieves an accuracy of 76.5% and a fluency of 79.3% on the proposed dataset, namely RETVQA and also outperforms state-of-the-art methods by 4.9% and 11.8% on the image segment of the publicly available WebQA dataset on the accuracy and fluency metrics, respectively.
Divide Rows and Conquer Cells: Towards Structure Recognition for Large Tables
Authors: Shen Huawen , Gao Xiang , Wei Jin , Qiao Liang , Zhou Yu , Li Qiang , Cheng Zhanzhan
Published: August 2023

Recent advanced Table Structure Recognition (TSR) models adopt image-to-text solutions to parse table structure. These methods can be formulated as image caption problem, i.e., input a single-table image and output table structure description in a specific text format, e.g., HTML. With the impressive success of Transformer in text generation tasks, these methods use Transformer architecture to predict HTML table text in an autoregressive manner. However, tables always emerge with a large variety of shapes and sizes. Autoregressive models usually suffer from the error accumulation problem as the length of predicted text increases, which results in unsatisfactory performance for large tables. In this paper, we propose a novel image-to-text based TSR method that relieves error accumulation problems and improves performance noticeably. At the core of our method is a cascaded two-step decoder architecture with the former decoder predicting HTML table row tags non-autoregressively and the latter predicting HTML table cell tags of each row in a semi-autoregressive manner. Compared with existing methods that predict HTML text autoregressively, the superiority of our row-to-cell progressive table parsing is twofold: (1) it generates an HTML tag sequence with a vertical-and-horizontal two-step `scanning', which better fits the inherent 2D structure of image data, (2) it performs substantially better for large tables (long sequence prediction) since it alleviates error accumulation problem specific to autoregressive models. Extensive experiments demonstrate that our method achieves competitive performance on three public benchmarks.
Few-shot Classification via Ensemble Learning with Multi-Order Statistics
Authors: Yang Sai , Liu Fan , Chen Delong , Zhou Jun
Published: August 2023

Transfer learning has been widely adopted for few-shot classification. Recent studies reveal that obtaining good generalization representation of images on novel classes is the key to improving the few-shot classification accuracy. To address this need, we prove theoretically that leveraging ensemble learning on the base classes can correspondingly reduce the true error in the novel classes. Following this principle, a novel method named Ensemble Learning with Multi-Order Statistics (ELMOS) is proposed in this paper. In this method, after the backbone network, we use multiple branches to create the individual learners in the ensemble learning, with the goal to reduce the storage cost. We then introduce different order statistics pooling in each branch to increase the diversity of the individual learners. The learners are optimized with supervised losses during the pre-training phase. After pre-training, features from different branches are concatenated for classifier evaluation. Extensive experiments demonstrate that each branch can complement the others and our method can produce a state-of-the-art performance on multiple few-shot classification benchmark datasets.
CADParser: A Learning Approach of Sequence Modeling for B-Rep CAD
Authors: Zhou Shengdi , Tang Tianyi , Zhou Bin
Published: August 2023

Computer-Aided Design (CAD) plays a crucial role in industrial manufacturing by providing geometry information and the construction workflow for manufactured objects. The construction information enables effective re-editing of parametric CAD models. While boundary representation (B-Rep) is the standard format for representing geometry structures, JSON format is an alternative due to the lack of uniform criteria for storing the construction workflow. Regrettably, most CAD models available on the Internet only offer geometry information, omitting the construction procedure and hampering creation efficiency. This paper proposes a learning approach CADParser to infer the underlying modeling sequences given a B-Rep CAD model. It achieves this by treating the CAD geometry structure as a graph and the construction workflow as a sequence. Since the existing CAD dataset only contains two operations (i.e., Sketch and Extrusion), limiting the diversity of the CAD model creation, we also introduce a large-scale dataset incorporating a more comprehensive range of operations such as Revolution, Fillet, and Chamfer. Each model includes both the geometry structure and the construction sequences. Extensive experiments demonstrate that our method can compete with the existing state-of-the-art methods quantitatively and qualitatively. Data is available at https://drive.google.com/CADParserData.
Basket Representation Learning by Hypergraph Convolution on Repeated Items for Next-basket Recommendation
Authors: Yu Yalin , Yang Enneng , Guo Guibing , Jiang Linying , Wang Xingwei
Published: August 2023

Basket representation plays an important role in the task of next-basket recommendation. However, existing methods generally adopts pooling operations to learn a basket's representation, from which two critical issues can be identified.

First, they treat a basket as a set of items independent and identically distributed. We find that items occurring in the same basket have much higher correlations than those randomly selected by conducting data analysis on a real dataset.

Second, although some works have recognized the importance of items repeatedly purchased in multiple baskets, they ignore the correlations among the repeated items in a same basket, whose importance is shown by our data analysis. In this paper, we propose a novel Basket Representation Learning (BRL) model by leveraging the correlations among intra-basket items. Specifically, we first connect all the items (in a basket) as a hyperedge, where the correlations among different items can be well exploited by hypergraph convolution operations. Meanwhile, we also connect all the repeated items in the same basket as a hyperedge, whereby their correlations can be further strengthened. We generate a negative (positive) view of the basket by data augmentation on repeated (non-repeated) items, and apply contrastive learning to force more agreements on repeated items. Finally, experimental results on three real datasets show that our approach performs better than eight baselines in ranking accuracy.
Boosting Few-Shot Open-Set Recognition with Multi-Relation Margin Loss
Authors: Che Yongjuan , An Yuexuan , Xue Hui
Published: August 2023

Few-shot open-set recognition (FSOSR) has become a great challenge, which requires classifying known classes and rejecting the unknown ones with only limited samples. Existing FSOSR methods mainly construct an ambiguous distribution of known classes from scarce known samples without considering the latent distribution information of unknowns, which degrades the performance of open-set recognition. To address this issue, we propose a novel loss function called multi-relation margin (MRM) loss that can plug in few-shot methods to boost the performance of FSOSR. MRM enlarges the margin between different classes by extracting the multi-relationship of paired samples to dynamically refine the decision boundary for known classes and implicitly delineate the distribution of unknowns. Specifically, MRM separates the classes by enforcing a margin while concentrating samples of the same class on a hypersphere with a learnable radius. In order to better capture the distribution information of each class, MRM extracts the similarity and correlations among paired samples, ameliorating the optimization of the margin and radius. Experiments on public benchmarks reveal that methods with MRM loss can improve the unknown detection of AUROC by a significant margin while correctly classifying the known classes.
Neuro-Symbolic Class Expression Learning
Authors: Demir Caglar , Ngonga Ngomo Axel-Cyrille
Published: August 2023

Models computed using deep learning have been effectively applied to tackle various problems in many disciplines. Yet, the predictions of these models are often at most post-hoc and locally explainable.

In contrast, class expressions in description logics are ante-hoc and globally explainable. Although state-of-the-art symbolic machine learning approaches are being successfully applied to learn class expressions, their application at large scale has been hindered by their impractical runtimes. Arguably, the reliance on myopic heuristic functions contributes to this limitation. We propose a novel neuro-symbolic class expression learning model, DRILL, to mitigate this limitation. By learning non-myopic heuristic functions with deep Q-learning, DRILL efficiently steers the standard search procedure in a quasi-ordered search space towards goal states. Our extensive experiments on 4 benchmark datasets and 390 learning problems suggest that DRILL converges to goal states at least 2.7 times faster than state-of-the-art models on all learning problems. The results of our statistical significance test confirms that DRILL converges to goal states significantly faster (p-value <1%) than state-of-the-art models on all benchmark datasets. We provide an open-source implementation of DRILL, including pre-trained models, training and evaluation scripts.
CTW: Confident Time-Warping for Time-Series Label-Noise Learning
Authors: Ma Peitian , Liu Zhen , Zheng Junhao , Wang Linghao , Ma Qianli
Published: August 2023

Noisy labels seriously degrade the generalization ability of Deep Neural Networks (DNNs) in various classification tasks. Existing studies on label-noise learning mainly focus on computer vision, while time series also suffer from the same issue. Directly applying the methods from computer vision to time series may reduce the temporal dependency due to different data characteristics. How to make use of the properties of time series to enable DNNs to learn robust representations in the presence of noisy labels has not been fully explored. To this end, this paper proposes a method that expands the distribution of Confident instances by Time-Warping (CTW) to learn robust representations of time series. Specifically, since applying the augmentation method to all data may introduce extra mislabeled data, we select confident instances to implement Time-Warping. In addition, we normalize the distribution of the training loss of each class to eliminate the model's selection preference for instances of different classes, alleviating the class imbalance caused by sample selection. Extensive experimental results show that CTW achieves state-of-the-art performance on the UCR datasets when dealing with different types of noise. Besides, the t-SNE visualization of our method verifies that augmenting confident data improves the generalization ability. Our code is available at https://github.com/qianlima-lab/CTW.
FedSampling: A Better Sampling Strategy for Federated Learning
Authors: Qi Tao , Wu Fangzhao , Lyu Lingjuan , Huang Yongfeng , Xie Xing
Published: August 2023

Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for local model learning in each round. However, different clients may have significantly different data sizes, and the clients with more data cannot have more opportunities to contribute to model training, which may lead to inferior performance. In this paper, instead of client uniform sampling, we propose a novel data uniform sampling strategy for federated learning (FedSampling), which can effectively improve the performance of federated learning especially when client data size distribution is highly imbalanced across clients. In each federated learning round, local data on each client is randomly sampled for local model learning according to a probability based on the server desired sample size and the total sample size on all available clients. Since the data size on each client is privacy-sensitive, we propose a privacy-preserving way to estimate the total sample size with a differential privacy guarantee. Experiments on four benchmark datasets show that FedSampling can effectively improve the performance of federated learning.
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces
Authors: Watanabe Shuhei , Bansal Archit , Hutter Frank
Published: August 2023

The recent rise in popularity of Hyperparameter Optimization (HPO) for deep learning has highlighted the role that good hyperparameter (HP) space design can play in training strong models. In turn, designing a good HP space is critically dependent on understanding the role of different HPs. This motivates research on HP Importance (HPI), e.g., with the popular method of functional ANOVA (f-ANOVA). However, the original f-ANOVA formulation is inapplicable to the subspaces most relevant to algorithm designers, such as those defined by top performance. To overcome this issue, we derive a novel formulation of f-ANOVA for arbitrary subspaces and propose an algorithm that uses Pearson divergence (PED) to enable a closed-form calculation of HPI. We demonstrate that this new algorithm, dubbed PED-ANOVA, is able to successfully identify important HPs in different subspaces while also being extremely computationally efficient. See https://arxiv.org/abs/2304.10255 for the latest version with Appendix.
LGPConv: Learnable Gaussian Perturbation Convolution for Lightweight Pansharpening
Authors: Zhao Chen-Yu , Zhang Tian-Jing , Ran Ran , Chen Zhi-Xuan , Deng Liang-Jian
Published: August 2023

Pansharpening is a crucial and challenging task that aims to obtain a high spatial resolution image by merging a multispectral (MS) image and a panchromatic (PAN) image. Current methods use CNNs with standard convolution, but we've observed strong correlation among channel dimensions in the kernel, leading to computational burden and redundancy. To address this, we propose Learnable Gaussian Perturbation Convolution (LGPConv), surpassing standard convolution. LGPConv leverages two properties of standard convolution kernels: 1) correlations within channels, learning a premier kernel as a base to reduce parameters and training difficulties caused by redundancy; 2) introducing Gaussian noise perturbations to simulate randomness and enhance nonlinear representation within channels. We incorporate LGPConv into a well-designed pansharpening network and demonstrate its superiority through extensive experiments, achieving state-of-the-art performance with minimal parameters (27K). Code is available on the GitHub page of the authors.
Don't Ignore Alienation and Marginalization: Correlating Fraud Detection
Authors: Zang Yilong , Hu Ruimin , Wang Zheng , Xu Danni , Wu Jia , Li Dengshi , Wu Junhang , Ren Lingfei
Published: August 2023

The anonymity of online networks makes tackling fraud increasingly costly. Thanks to the superiority of graph representation learning, graph-based fraud detection has made significant progress in recent years. However, upgrading fraudulent strategies produces more advanced and difficult scams. One common strategy is synergistic camouflage —— combining multiple means to deceive others. Existing methods mostly investigate the differences between relations on individual frauds, that neglect the correlation among multi-relation fraudulent behaviors. In this paper, we design several statistics to validate the existence of synergistic camouflage of fraudsters by exploring the correlation among multi-relation interactions. From the perspective of multi-relation, we find two distinctive features of fraudulent behaviors, i.e., alienation and marginalization. Based on the finding, we propose COFRAUD, a correlation-aware fraud detection model, which innovatively incorporates synergistic camouflage into fraud detection. It captures the correlation among multi-relation fraudulent behaviors. Experimental results on two public datasets demonstrate that COFRAUD achieves significant improvements over state-of-the-art methods.
TITAN : Task-oriented Dialogues with Mixed-Initiative Interactions
Authors: Yan Sitong , Song Shengli , Li Jingyang , Meng Shiqi , Hu Guangneng
Published: August 2023

In multi-domain task-oriented dialogue systems, users proactively propose a series of domain-specific requests that can often be under-or over-specified, sometimes with ambiguous and cross-domain demands. System-sided initiative would be necessary to identify certain situations and appropriately interact with users to resolve them. However, most existing task-oriented dialogue systems fail to consider such mixed-initiative interaction strategies, performing low efficiency and poor collaboration ability in human-computer conversation. In this paper, we construct a multi-domain task-oriented dialogue dataset with mixed-initiative strategies named TITAN from the large-scale dialogue corpus MultiWOZ 2.1. It contains a total of 1,800 human-human conversations where the system can either ask clarification questions actively or provides relevant information to address failure situations and implicit user requests. We report the results of several baseline models on system response generation and dialogue act prediction to assess the performance of SOTA methods on TITAN. These models can capture mixed-initiative dialogue acts, while remaining the deficiency to actively generate implicit requests and accurately provide alternative information, suggesting ample room for improvement in future studies.
Learning to Act for Perceiving in Partially Unknown Environments
Authors: Lamanna Leonardo , Faridghasemnia Mohamadreza , Gerevini Alfonso , Saetti Alessandro , Saffiotti Alessandro , Serafini Luciano , Traverso Paolo
Published: August 2023

Autonomous agents embedded in a physical environment need the ability to correctly perceive the state of the environment from sensory data. In partially observable environments, certain properties can be perceived only in specific situations and from certain viewpoints that can be reached by the agent by planning and executing actions. For instance, to understand whether a cup is full of coffee, an agent, equipped with a camera, needs to turn on the light and look at the cup from the top. When the proper situations to perceive the desired properties are unknown, an agent needs to learn them and plan to get in such situations. In this paper, we devise a general method to solve this problem by evaluating the confidence of a neural network online and by using symbolic planning. We experimentally evaluate the proposed approach on several synthetic datasets, and show the feasibility of our approach in a real-world scenario that involves noisy perceptions and noisy actions on a real robot.
Optimizing Crop Management with Reinforcement Learning and Imitation Learning
Authors: Tao Ran , Zhao Pan , Wu Jing , Martin Nicolas , Harrison Matthew T. , Ferreira Carla , Kalantari Zahra , Hovakimyan Naira
Published: August 2023

Crop management has a significant impact on crop yield, economic profit, and the environment. Although management guidelines exist, finding the optimal management practices is challenging. Previous work used reinforcement learning (RL) and crop simulators to solve the problem, but the trained policies either have limited performance or are not deployable in the real world. In this paper, we present an intelligent crop management system that optimizes nitrogen fertilization and irrigation simultaneously via RL, imitation learning (IL), and crop simulations using the Decision Support System for Agrotechnology Transfer (DSSAT). We first use deep RL, in particular, deep Q-network, to train management policies that require a large number of state variables from the simulator as observations (denoted as full observation). We then invoke IL to train management policies that only need a few state variables that can be easily obtained or measured in the real world (denoted as partial observation) by mimicking the actions of the RL policies trained under full observation. Simulation experiments using the maize crop in Florida (US) and Zaragoza (Spain) demonstrate that the trained policies from both RL and IL techniques achieved more than 45\% improvement in economic profit while causing less environmental impact compared with a baseline method. Most importantly, the IL-trained management policies are directly deployable in the real world as they use readily available information.
Strip Attention for Image Restoration
Authors: Cui Yuning , Tao Yi , Jing Luoxi , Knoll Alois
Published: August 2023

As a long-standing task, image restoration aims to recover the latent sharp image from its degraded counterpart. In recent years, owing to the strong ability of self-attention in capturing long-range dependencies, Transformer based methods have achieved promising performance on multifarious image restoration tasks. However, the canonical self-attention leads to quadratic complexity with respect to input size, hindering its further applications in image restoration. In this paper, we propose a Strip Attention Network (SANet) for image restoration to integrate information in a more efficient and effective manner. Specifically, a strip attention unit is proposed to harvest the contextual information for each pixel from its adjacent pixels in the same row or column. By employing this operation in different directions, each location can perceive information from an expanded region. Furthermore, we apply various receptive fields in different feature groups to enhance representation learning. Incorporating these designs into a U-shaped backbone, our SANet performs favorably against state-of-the-art algorithms on several image restoration tasks. The code is available at https://github.com/c-yn/SANet.
Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract)
Authors: Marsden Annie , Sharan Vatsal , Sidford Aaron , Valiant Gregory
Published: August 2023

Minimizing a convex function with access to a first order oracle---that returns the function evaluation and (sub)gradient at a query point---is a canonical optimization problem and a fundamental primitive in machine learning. Gradient-based methods are the most popular approaches used for solving the problem, owing to their simplicity and computational efficiency. These methods, however, do not achieve the information-theoretically optimal query complexity for minimizing the underlying function to small error, which are achieved by more expensive techniques based on cutting-plane methods. Is it possible to achieve the information-theoretically query complexity without using these more complex and computationally expensive methods? In this work, we use memory as a lens to understand this, and show that is is not possible to achieve optimal query complexity without using significantly more memory than that used by gradient descent.
Good Explanations in Explainable Artificial Intelligence (XAI): Evidence from Human Explanatory Reasoning
Authors: Byrne Ruth M.J.
Published: August 2023

Insights from cognitive science about how people understand explanations can be instructive for the development of robust, user-centred explanations in eXplainable Artificial Intelligence (XAI). I survey key tendencies that people exhibit when they construct explanations and make inferences from them, of relevance to the provision of automated explanations for decisions by AI systems. I first review experimental discoveries of some tendencies people exhibit when they construct explanations, including evidence on the illusion of explanatory depth, intuitive versus reflective explanations, and explanatory stances. I then consider discoveries of how people reason about causal explanations, including evidence on inference suppression, causal discounting, and explanation simplicity. I argue that central to the XAI endeavor is the requirement that automated explanations provided by an AI system should make sense to human users.
Generative Diffusion Models on Graphs: Methods and Applications
Authors: Liu Chengyi , Fan Wenqi , Liu Yunqing , Li Jiatong , Li Hang , Liu Hui , Tang Jiliang , Li Qing
Published: August 2023

Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video generation. Graph generation is a crucial computational task on graphs with numerous real-world applications. It aims to learn the distribution of given graphs and then generate new graphs. Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years. In this paper, we first provide a comprehensive overview of generative diffusion models on graphs, In particular, we review representative algorithms for three variants of graph diffusion models, i.e., Score Matching with Langevin Dynamics (SMLD), Denoising Diffusion Probabilistic Model (DDPM), and Score-based Generative Model (SGM). Then, we summarize the major applications of generative diffusion models on graphs with a specific focus on molecule and protein modeling. Finally, we discuss promising directions in generative diffusion models on graph-structured data.
Complexity Results and Exact Algorithms for Fair Division of Indivisible Items: A Survey
Authors: Nguyen Trung Thanh , Rothe Jörg
Published: August 2023

Fair allocation of indivisible goods is a central topic in many AI applications. Unfortunately, the corresponding problems are known to be NP-hard for many fairness concepts, so unless P = NP, exact polynomial-time algorithms cannot exist for them. In practical applications, however, it would be highly desirable to find exact solutions as quickly as possible. This motivates the study of algorithms that—even though they only run in exponential time—are as fast as possible and exactly solve such problems. We present known complexity results for them and give a survey of important techniques for designing such algorithms, mainly focusing on four common fairness notions: max-min fairness, maximin share, maximizing Nash social welfare, and envy-freeness. We also highlight the most challenging open problems for future work.
Uncovering the Deceptions: An Analysis on Audio Spoofing Detection and Future Prospects
Authors: Ranjan Rishabh , Vatsa Mayank , Singh Richa
Published: August 2023

Audio has become an increasingly crucial biometric modality due to its ability to provide an intuitive way for humans to interact with machines. It is currently being used for a range of applications including person authentication to banking to virtual assistants. Research has shown that these systems are also susceptible to spoofing and attacks. Therefore, protecting audio processing systems against fraudulent activities such as identity theft, financial fraud, and spreading misinformation, is of paramount importance. This paper reviews the current state-of-the-art techniques for detecting audio spoofing and discusses the current challenges along with open research problems. The paper further highlights the importance of considering the ethical and privacy implications of audio spoofing detection systems. Lastly, the work aims to accentuate the need for building more robust and generalizable methods, the integration of automatic speaker verification and countermeasure systems, and better evaluation protocols.
A Survey of Federated Evaluation in Federated Learning
Authors: Soltani Behnaz , Zhou Yipeng , Haghighi Venus , Lui John C. S.
Published: August 2023

In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), which is called federated evaluation in this work. This is because clients do not expose their original data to preserve data privacy. Federated evaluation plays a vital role in client selection, incentive mechanism design, malicious attack detection, etc. In this paper, we provide the first comprehensive survey of existing federated evaluation methods. Moreover, we explore various applications of federated evaluation for enhancing FL performance and finally present future research directions by envisioning some challenges.
A Survey on Efficient Training of Transformers
Authors: Zhuang Bohan , Liu Jing , Pan Zizheng , He Haoyu , Weng Yuetian , Shen Chunhua
Published: August 2023

Recent advances in Transformers have come with a huge requirement on computing resources, highlighting the importance of developing efficient training techniques to make Transformer training faster, at lower cost, and to higher accuracy by the efficient use of computation and memory resources. This survey provides the first systematic overview of the efficient training of Transformers, covering the recent progress in acceleration arithmetic and hardware, with a focus on the former. We analyze and compare methods that save computation and memory costs for intermediate tensors during training, together with techniques on hardware/algorithm co-design. We finally discuss challenges and promising areas for future research.
A Human-in-the-Loop Tool for Annotating Passive Acoustic Monitoring Datasets
Authors: Kath Hannes , S. Gouvêa Thiago , Sonntag Daniel
Published: August 2023

Deep learning methods are well suited for data analysis in several domains, but application is often limited by technical entry barriers and the availability of large annotated datasets. We present an interactive machine learning tool for annotating passive acoustic monitoring datasets created for wildlife monitoring, which are time-consuming and costly to annotate manually. The tool, designed as a web application, consists of an interactive user interface implementing a human-in-the-loop workflow. Class label annotations provided manually as bounding boxes drawn over a spectrogram are consumed by a deep generative model (DGM) that learns a low-dimensional representation of the input data, as well as the available class labels. The learned low-dimensional representation is displayed as an interactive interface element, where new bounding boxes can be efficiently generated by the user with lasso-selection; alternatively, the DGM can propose new, automatically generated bounding boxes on demand. The user can accept, edit, or reject annotations suggested by the model, thus owning final judgement. Generated annotations can be used to fine-tune the underlying model, thus closing the loop. Investigations of the prediction accuracy and first empirical experiments show promising results on an artificial data set, laying the ground for application to a real life scenario.
Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement
Authors: Guo Hang , Dai Tao , Meng Guanghao , Xia Shu-Tao
Published: August 2023

Scene text image super-resolution (STISR), aiming to improve image quality while boosting downstream scene text recognition accuracy, has recently achieved great success. However, most existing methods treat the foreground (character regions) and background (non-character regions) equally in the forward process, and neglect the disturbance from the complex background, thus limiting the performance. To address these issues, in this paper, we propose a novel method LEMMA that explicitly models character regions to produce high-level text-specific guidance for super-resolution. To model the location of characters effectively, we propose the location enhancement module to extract character region features based on the attention map sequence. Besides, we propose the multi-modal alignment module to perform bidirectional visual-semantic alignment to generate high-quality prior guidance, which is then incorporated into the super-resolution branch in an adaptive manner using the proposed adaptive fusion module. Experiments on TextZoom and four scene text recognition benchmarks demonstrate the superiority of our method over other state-of-the-art methods. Code is available at https://github.com/csguoh/LEMMA.
A Blind Image Steganography Algorithm Based on Knight Tour Algorithm and QR Codes
Authors: Mohammed Younis Younis , J. Mstafa Ramadhan , I. Hussein Haval , L. Alyousify Ahmed
Published: August 2023

Internet proliferation and technological progress have made multimedia information quickly accessible, but they have also posed a threat to privacy and security. Researchers have been interested in digital images due to their capacity to store large amounts of data due to the possibility of protecting sensitive information through digital steganography. Despite their visual imperceptibility, robustness, and ability to embed information, existing image steganography techniques face several challenges. To overcome these challenges, a novel image steganography approach based on a blind model strategy has been proposed for hiding covert messages. The model consists of two stages: embedding and extracting. In the embedding stage, a suitable cover image is selected using the FAST feature point detector. A text message is then converted to a QR code and embedded in the feature points' neighbors using knight tour steps in a chess game. The result is a stego image that appears identical to the original cover image but contains the secret message in its feature points' neighbors. The extracting stage involves finding the feature points and extracting the QR code to obtain the original text message. Since the feature points were not altered during the embedding process, the proposed model is known as a blind model. This approach eliminates the need for the original cover image during the extracting stage. The proposed model was evaluated using several metrics, including the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The results demonstrate that the proposed algorithm can effectively embed and extract secret messages with high accuracy while maintaining the visual quality of the cover image with 100% of (SSIM), and 73.48 as an average of (PSNR).
Energy-Efficient Massive MIMO Network
Authors: Hilme Israa , Abdulkafi Ayad Atiyah
Published: August 2023

Massive Multiple-Input Multiple-Output (Massive MIMO) is widely regarded as a highly promising technology for the forthcoming generation of wireless systems. The massive MIMO implementation involves the integration of a substantial number of antenna elements into base stations (BSs) to enhance spectral efficiency (SE) and energy efficiency (EE). The energy efficiency (EE) of base stations (BSs) has become an increasingly important issue for telecommunications network operators due to the need to take care of profitability while simultaneously minimizing their detrimental effects on the environment and addressing economic challenges faced by wireless communication operators. In this paper, the EE of massive MIMO networks and the relationship between EE, SE, and other parameters like bandwidth (B), number of antennas (M), circuit power, and number of users’ equipment (K) are discussed and investigated. For a fixed circuit power (PFIX), simulation results showed that the EE could be increased by about 1.12 as the number of antennas was doubled. The findings in this work also indicated an almost linear relationship between maximum EE and optimal SE, with a massive increase in the number of antennas when the power consumed by each antenna (PBS) was included in circuit power. In addition, when considering the power consumed per user’s equipment (PUE) impact, the SE increased with the ratio (M/K), in which SE showed a cubic relationship against M/K. On the other hand, the EE increased with M/K ratio until M/K reached a specific value. The maximum EE (and hence optimum SE) was achieved by massive MIMO, where the number of antennas was three times the number of users. However, EE started degrading after this value, as the number of antennas was considered larger than the users’ and consumed more energy, resulting in EE degradation.
PENERAPAN METODE DISTRIBUTION REQUIREMENT PLANNING (DRP) DALAM PENJADWALAN DISTRIBUSI PRODUK DI UKM SB JAYA CIAMIS
Authors: Kusuma Ningrat Nugraha , Aristriyana Eky
Published: August 2023

Metode Distribution Requirement Planning (DRP) untuk perencanaan penjadwalan distribusi yang bertujuan untuk merencanakan kegiatan distribusi produk yang optimal agar jumlah permintaan di setiap distribution center terpenuhi berdasarkan ukuran lot distribusi yang tetap dan mengetahui bagaimana perencanaan penjadwalan distribusi kripik singkong dengan menggunakan metode Distribution Requirement Planning (DRP).

Pemilihan peramalan permintaan berdasarkan nilai SEE terkecil dari dua alternatif yang dipergunakan dalam perhitungan peramalan. Metode peramalan yang terpilih untuk DC Tasikmalaya dan Cikoneng adalah metode Konstan, sedangkan DC Ciamis terpilih metode Linier. perhitungan jumlah order quantity untuk semua daerah distribusi memiliki jumlah order quantity setiap pemesanan yang cukup tinggi yaitu 219 kg, 190 kg dan 222 kg. Hasil perhitungan safety stock untuk DC adalah 17 kg untuk Tasikmalaya, 26 Kg untuk Ciamis, dan 17 Kg untuk Cikoneng. sistem Distribution Requirement Planning dapat mengurangi salah satu masalah di setiap distribution centre yang berupa stock out dengan merencanakan jadwal pendistribusian yang memproyeksi kebutuhan ke depannya.
O Pacto Educativo Global, a educação e o projeto vital ético como ferramenta de consolidação da Democracia
Authors: Eufrásio Marcelo Alves Pereira , Lima Arthur Rodrigues de
Published: August 2023

O presente trabalho é fruto de uma revisão bibliográfica desenvolvida em torno da teoria do reconhecimento. A partir das contribuições de Taylor (1998) e Honneth (2013), propõe refletir sobre as motivações do Pacto Educativo Global, apresentado pelo Papa Francisco em 2020, e os desafios para a democracia atualmente. Como a educação pode se configurar em ferramenta para a consolidação de uma sociedade dialógica, que, por meio do reconhecimento, atue na promoção da cultura de paz e na redução das desigualdades, principalmente motivada através dos desafios colocados em destaque pela pandemia. Nesse sentido, com base em pesquisa qualitativa, a partir de um estudo exploratório e amparado pelo método dedutivo para abordagem, o texto traça uma trilha relacionada à educação, que deixa de exercer sua função social, atendendo unicamente aos interesses do mercado e à necessidade de uma reestruturação do modelo educacional e do espaço da escola. Que modelo de educação é este, que não nos torna pessoas, mas sim sujeitos dependentes e sem atitudes autênticas diante da vida e suas escolhas? Desse modo, o desenvolvimento de um projeto vital ético (DAIMON, 2009) auxilia na produção de valores para a cultura democrática.
O ódio e a morte em nome de Deus
Authors: Passos João Décio
Published: August 2023

As narrativas de ódio ganharam visibilidade pública na conjuntura política recente. A luta entre os dois presidenciáveis foi o gatilho que acionou e acirrou de modo cada vez mais polarizado uma luta ente o bem e o mal. A reflexão se propõe a examinar as relações entre o político e o religioso, concretamente, as relações entre as batalhas narradas pela Bíblia e as disputas políticas protagonizadas pelas frentes que compuseram a disputa eleitoral em nosso país. A relação entre medo-ódio-violência expõe a dinâmica de fundo que conta com Deus como seu último fundamento. A autoridade divina instaura o tempo da luta que autoriza, legitima e alimenta o ódio e a violência contra os inimigos do bem.
Religião em Rubem Alves
Authors: Amaro Flávia Ribeiro
Published: August 2023

Rubem Alves relaciona o surgimento das religiões à percepção das ausências e dos desejos, esperanças e utopias por uma realidade ideal, livre da opressão. A religião, estaria, assim, relacionada à ânsia por liberdade tanto quanto seria motivada pela angústia gerada por sua inalcançável concreção. Trata-se, na sua concepção, de um construto cultural humano, capaz de forjar realidades e construir mundos simbólicos. Com o avanço da racionalidade científica moderna, um sentido utilitarista passou a reger a lógica da produção e da organização social. Nesse ínterim, o sagrado foi colonizado, perdendo a inteireza de uma presença irrestrita. A colonialidade do sagrado demonstrou o aparelhamento da religião com a pauta da secularização e com as demandas por poder das classes dominantes. Contudo, o sagrado, ainda que retraído, não desapareceu, demonstrando sua resistência a partir de reminiscências significativas que perduraram na religião dos oprimidos. O objetivo desta comunicação é discutir como se dá a interação entre o sagrado, a religião e a cultura na abordagem alvesiana. Para tanto, algumas obras do autor foram revisitadas a partir de uma leitura inscrita sob o viés epistemológico das ciências da religião.