bluuebunny/crossref_metadata_2025 huggingface dataset:
Compactness is Not Enough: Development and Trends of a Sustainable Urban Concept
Authors: Poškienė Diana , Dijokienė Dalia
Published: August 2021

The changing concept of sustainable development is changing the practice of designing sustainable urban forms. The article presents a variety of concepts of a sustainable urban form and their ambiguous assessment – the model of a compact city, if applied in all cases, can cost the quality of one’s environment and the quality of life. New bottom-up trends are emerging in theory and in practice of the 21st century, which focus on the urban planning process which is more inclusive in terms of society. The article discusses examples of the creation of a sustainable urban narrative for the development of a relationship with the community.
Fluid-structure interaction simulations with a LES filtering approach in solids4Foam
Authors: Girfoglio Michele , Quaini Annalisa , Rozza Gianluigi
Published: August 2021

The goal of this paper is to test solids4Foam, the fluid-structure interaction (FSI) toolbox developed for foam-extend (a branch of OpenFOAM), and assess its flexibility in handling more complex flows. For this purpose, we consider the interaction of an incompressible fluid described by a Leray model with a hyperelastic structure modeled as a Saint Venant-Kirchho material. We focus on a strongly coupled, partitioned fluid-structure interaction (FSI) solver in a finite volume environment, combined with an arbitrary Lagrangian-Eulerian approach to deal with the motion of the fluid domain. For the implementation of the Leray model, which features a nonlinear differential low-pass filter, we adopt a three-step algorithm called Evolve-Filter-Relax. We validate our approach against numerical data available in the literature for the 3D cross flow past a cantilever beam at Reynolds number 100 and 400.
Comparative study of low dose of capecitabine versus standard dose in metastatic breast cancer: Efficacy and safety
Authors: Aboelnaga Engy M , El-beshbishi Wafaa
Published: August 2021

Background

A lower dose of capecitabine revealed better toxicity profiles and comparable efficacy in treatment of metastatic breast cancer (MBC). We aimed to evaluate the efficacy and toxicity of lower dose of capecitabine in comparison with the standard dose.

Patients and methods

Patients were enrolled in two groups. Group 1 included 21 patients who received the standard dose of capecitabine (1250 mg/m2 twice daily [BID] for 14 days), while the patients in group 2 (19 patients) received lower dose of capecitabine (850 mg/m2 BID for 14 days) every 3 weeks.

Results

In group 1, dose reduction was reported in 12 (57.1%) patients versus 1 patient in group 2 (5.3%; P = 0.0005). Patients in group 1 reported higher toxicity rates without any significant difference between the groups. The median duration of response was 17 weeks in group 1, while it was 19 weeks in group 2. Disease progression was recorded in 10 (47.6%) patients in group 1 versus 8 (42.1%) patients in group 2 (P = 0.81). The mean time to progression was 8.16 ± 0.63 months and the median was 10.1 months in group 1, while the mean was 8.98 ± 0.75 months and the median was 10 months in group 2 (P = 0.66). The overall survival had a mean of 11.94 ± 0.754 and 11.24 ± 0.665 months, while the median was 13.1 and 13 months in groups 1 and 2, respectively (P = 0.9).

Conclusions

A lower dose of capecitabine provides MBC patients with an active therapy that can be continued for prolonged periods to achieve long-term disease control without compromising its antitumor activity.
Risk Management Under Impacts of Macro Economic Factors Ina Big Seafood Ex-Import Firm -Angiang Fisheries Ex-Import Jsc. In Vietnam
Authors: Thach Nguyen , Huy Dinh
Published: August 2021

AnGiang Fisheries Ex-Import Joint Stock Company (AGF) was established in 2001. Agifish has traditionally beena leader in the fields of production and business activities, scientific research in the field of fingerling production, seafood processing technology and the development of value added products processed from Basa fish, tra fish. Agifish cares about buildinga spirit of solidarity between leaders, managers and workers striving for the development of the Company.

Because we figure out there isa research gap in which many previous studies forgot to explore both internal and external macroeconomic elements and their impacts on stock price of Agifish (AGF),a big Vietnam fisheries firm, in the context Viet Nam and the US economies receive impacts from global economic crisis.

With the using of quantitative analysis and statistics, regression OLS, together with qualitative methods including synthesis, comparison and explanatory methods, authors recognize that, ina six factor model, AGF stock price goes up together with effects from increase in GDP growth and lending rate declines.

Last but not least, our research model can be expanded to other markets.
Influence of optimization techniques and nano surface coating materials on microstructure of iron-nickel-chromium alloy using wire EDM process
Authors: Selvabharathi R. , Ganapathy Srinivasan R. , Palani S.
Published: August 2021

This research proposes developing the new hybrid optimization techniques for getting optimized wire EDM machining parameters and analysing the performance and microstructure of hybrid treatment alloy 20 material (high-velocity oxygen fuel spraying and plasma nitriding on iron-nickel-chromium alloy) after machining in wire EDM. The devising optimization is carried out using back-propagation neural networks (BPNN) integrated with fuzzy logic techniques. Taguchi L27 method uses optimized parameters in 3 factors and 3 level methods to BPNN wire EDM processing parameters. Those processing parameter errors are controlled by applying fuzzy logic system in hybrid optimization techniques. The hybrid optimization provides best results (±5% error) while comparing other techniques. This proposal was started with research review of defined factors and BPNN parameters level for hidden layer number, learning algorithm, neurons numbers, and so on. The analysis of variance (ANOVA), analysis of means (ANOM) and signal to noise (S/N) ratio have been used to identify Taguchi results. The BPNN techniques have been employed significantly to tackle hidden layer's uncertain parameter structures. The fuzzy logic controllers in general have been designed engaging the relations between system performance and factor through error method calculation. The microstructure analysis showed that the no evidence was found of recast layer formation on hybrid treated material after machining in wire EDM due to compressive stress and compound layer on material surface.
Представления о работе у молодых сотрудников сети кафе и ресторанов г. Владивостока
Authors: Черемискина И. И.
Published: August 2021

Проблема текучести кадров является универсальной для предприятий разных типов, особые масштабы она приобретает на предприятиях общественного питания, что влечет за собой серьезные экономические траты на поиск, отбор, наем и обучение персонала. Особые сложности в решении этой проблемы связаны с тем, что рядовыми сотрудниками таких предприятий являются преимущественно молодые люди, студенты, которые не связывают свое профессиональное будущее с этой работой. Поиск психологических причин текучести сотрудников предприятий общественного питания не теряет своей актуальности в связи с распространенностью проблемы. В данной статье представлены результаты исследования сотрудников конкретной сети кафе и ресторанов г. Владивостока. За основу было взято предположение о том, что низкая привлекательность работы для сотрудников обусловлена расхождением субъективных представлений об «идеальной» и «реальной» работе. Методическое обеспечение исследования составили методы прямых ассоциаций и психодиагностический. В результате показано, что сотрудники, принявшие участие в исследовании, не удовлетворены возможностями карьерного и профессионального роста, взаимоотношениями с руководством и клиентами, организацией труда. На основании полученных данных сформулированы рекомендации управленческому составу исследуемой сети кафе и ресторанов, хотя в связи со схожестью проблем они могут быть использованы и на других предприятиях общественного питания, что подтверждает практическую значимость работы. Научная новизна исследования заключается в доказательстве связи между удовлетворенностью трудом сотрудников и расхождением их представлений об «идеальной» и «реальной» работе. Этот факт может быть использован в процессе оказания психологической помощи людям при планировании путей профессионализации и индивидуальной карьеры на разных стадиях профессионального становления.
Distance Polymatrix Coordination Games
Authors: Aloisio Alessandro , Flammini Michele , Kodric Bojana , Vinci Cosimo
Published: August 2021

In polymatrix coordination games, each player x is a node of a graph and must select an action in her strategy set. Nodes are playing separate bimatrix games with their neighbors in the graph. Namely, the utility of x is given by the preference she has for her action plus, for each neighbor y, a payoff which strictly depends on the mutual actions played by x and y.

We propose the new class of distance polymatrix coordination games, properly generalizing polymatrix coordination games, in which the overall utility of player x further depends on the payoffs arising by mutual actions of players v,z that are the endpoints of edges at any distance h<d from x, for a fixed threshold value d≤n. In particular, the overall utility of player x is the sum of all the above payoffs, where each payoff is proportionally discounted by a factor depending on the distance h of the corresponding edge.

Under the above framework, which is a natural generalization that is well-suited for capturing positive community interactions, we study the social inefficiency of equilibria resorting to standard measures of Price of Anarchy and Price of Stability. Namely, we provide suitable upper and lower bounds for the aforementioned quantities, both for bounded-degree and general graphs.
Approximating the Shapley Value Using Stratified Empirical Bernstein Sampling
Authors: Burgess Mark A. , Chapman Archie C.
Published: August 2021

The Shapley value is a well recognised method for dividing the value of joint effort in cooperative games. However, computing the Shapley value is known to be computationally hard, so stratified sample-based estimation is sometimes used. For this task, we provide two contributions to the state of the art. First, we derive a novel concentration inequality that is tailored to stratified Shapley value estimation using sample variance information. Second, by sequentially choosing samples to minimize our inequality, we develop a new and more efficient method of sampling to estimate the Shapley value. We evaluate our sampling method on a suite of test cooperative games, and our results demonstrate that it outperforms or is competitive with existing stratified sample-based estimation approaches to computing the Shapley value.
Deep Automatic Natural Image Matting
Authors: Li Jizhizi , Zhang Jing , Tao Dacheng
Published: August 2021

Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap, which is useful for image editing. Prior methods try to learn semantic features to aid the matting process while being limited to images with salient opaque foregrounds such as humans and animals. In this paper, we investigate the difficulties when extending them to natural images with salient transparent/meticulous foregrounds or non-salient foregrounds. To address the problem, a novel end-to-end matting network is proposed, which can predict a generalized trimap for any image of the above types as a unified semantic representation. Simultaneously, the learned semantic features guide the matting network to focus on the transition areas via an attention mechanism. We also construct a test set AIM-500 that contains 500 diverse natural images covering all types along with manually labeled alpha mattes, making it feasible to benchmark the generalization ability of AIM models. Results of the experiments demonstrate that our network trained on available composite matting datasets outperforms existing methods both objectively and subjectively. The source code and dataset are available at https://github.com/JizhiziLi/AIM.
Noise2Grad: Extract Image Noise to Denoise
Authors: Lin Huangxing , Zhuang Yihong , Huang Yue , Ding Xinghao , Liu Xiaoqing , Yu Yizhou
Published: August 2021

In many image denoising tasks, the difficulty of collecting noisy/clean image pairs limits the application of supervised CNNs. We consider such a case in which paired data and noise statistics are not accessible, but unpaired noisy and clean images are easy to collect. To form the necessary supervision, our strategy is to extract the noise from the noisy image to synthesize new data. To ease the interference of the image background, we use a noise removal module to aid noise extraction. The noise removal module first roughly removes noise from the noisy image, which is equivalent to excluding much background information. A noise approximation module can therefore easily extract a new noise map from the removed noise to match the gradient of the noisy input. This noise map is added to a random clean image to synthesize a new data pair, which is then fed back to the noise removal module to correct the noise removal process. These two modules cooperate to extract noise finely. After convergence, the noise removal module can remove noise without damaging other background details, so we use it as our final denoising network. Experiments show that the denoising performance of the proposed method is competitive with other supervised CNNs.
Direct Measure Matching for Crowd Counting
Authors: Lin Hui , Hong Xiaopeng , Ma Zhiheng , Wei Xing , Qiu Yunfeng , Wang Yaowei , Gong Yihong
Published: August 2021

Traditional crowd counting approaches usually use Gaussian assumption to generate pseudo density ground truth, which suffers from problems like inaccurate estimation of the Gaussian kernel sizes. In this paper, we propose a new measure-based counting approach to regress the predicted density maps to the scattered point-annotated ground truth directly. First, crowd counting is formulated as a measure matching problem. Second, we derive a semi-balanced form of Sinkhorn divergence, based on which a Sinkhorn counting loss is designed for measure matching. Third, we propose a self-supervised mechanism by devising a Sinkhorn scale consistency loss to resist scale changes. Finally, an efficient optimization method is provided to minimize the overall loss function. Extensive experiments on four challenging crowd counting datasets namely ShanghaiTech, UCF-QNRF, JHU++ and NWPU have validated the proposed method.
Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification
Authors: Liu Xiaobin , Zhang Shiliang
Published: August 2021

Recent works show that mean-teaching is an effective framework for unsupervised domain adaptive person re-identification. However, existing methods perform contrastive learning on selected samples between teacher and student networks, which is sensitive to noises in pseudo labels and neglects the relationship among most samples. Moreover, these methods are not effective in cooperation of different teacher networks. To handle these issues, this paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with constructing the Graph Consistency Constraint (GCC) between teacher and student networks. Specifically, given unlabeled training images, we apply teacher networks to extract corresponding features and further construct a teacher graph for each teacher network to describe the similarity relationships among training images. To boost the representation learning, different teacher graphs are fused to provide the supervise signal for optimizing student networks. GCMT fuses similarity relationships predicted by different teacher networks as supervision and effectively optimizes student networks with more sample relationships involved. Experiments on three datasets, i.e., Market-1501, DukeMTMCreID, and MSMT17, show that proposed GCMT outperforms state-of-the-art methods by clear margin. Specially, GCMT even outperforms the previous method that uses a deeper backbone. Experimental results also show that GCMT can effectively boost the performance with multiple teacher and student networks. Our code is available at https://github.com/liu-xb/GCMT .
Modality-aware Style Adaptation for RGB-Infrared Person Re-Identification
Authors: Miao Ziling , Liu Hong , Shi Wei , Xu Wanlu , Ye Hanrong
Published: August 2021

RGB-infrared (IR) person re-identification is a challenging task due to the large modality gap between RGB and IR images. Many existing methods bridge the modality gap by style conversion, requiring high-similarity images exchanged by complex CNN structures, like GAN. In this paper, we propose a highly compact modality-aware style adaptation (MSA) framework, which aims to explore more potential relations between RGB and IR modalities by introducing new related modalities. Therefore, the attention is shifted from bridging to filling the modality gap with no requirement on high-quality generated images. To this end, we firstly propose a concise feature-free image generation structure to adapt the original modalities to two new styles that are compatible with both inputs by patch-based pixel redistribution. Secondly, we devise two image style quantification metrics to discriminate styles in image space using luminance and contrast. Thirdly, we design two image-level losses based on the quantified results to guide the style adaptation during an end-to-end four-modality collaborative learning process. Experimental results on two datasets SYSU-MM01 and RegDB show that MSA achieves significant improvements with little extra computation cost and outperforms the state-of-the-art methods.
Adaptive Edge Attention for Graph Matching with Outliers
Authors: Qu Jingwei , Ling Haibin , Zhang Chenrui , Lyu Xiaoqing , Tang Zhi
Published: August 2021

Graph matching aims at establishing correspondence between node sets of given graphs while keeping the consistency between their edge sets. However, outliers in practical scenarios and equivalent learning of edge representations in deep learning methods are still challenging. To address these issues, we present an Edge Attention-adaptive Graph Matching (EAGM) network and a novel description of edge features. EAGM transforms the matching relation between two graphs into a node and edge classification problem over their assignment graph. To explore the potential of edges, EAGM learns edge attention on the assignment graph to 1) reveal the impact of each edge on graph matching, as well as 2) adjust the learning of edge representations adaptively. To alleviate issues caused by the outliers, we describe an edge by aggregating the semantic information over the space spanned by the edge. Such rich information provides clear distinctions between different edges (e.g., inlier-inlier edges vs. inlier-outlier edges), which further distinguishes outliers in the view of their associated edges. Extensive experiments demonstrate that EAGM achieves promising matching quality compared with state-of-the-arts, on cases both with and without outliers. Our source code along with the experiments is available at https://github.com/bestwei/EAGM.
Towards Unsupervised Deformable-Instances Image-to-Image Translation
Authors: Su Sitong , Song Jingkuan , Gao Lianli , Zhu Junchen
Published: August 2021

Replacing objects in images is a practical functionality of Photoshop, e.g., clothes changing. This task is defined as Unsupervised Deformable-Instances Image-to-Image Translation (UDIT), which maps multiple foreground instances of a source domain to a target domain, involving significant changes in shape. In this paper, we propose an effective pipeline named Mask-Guided Deformable-instances GAN (MGD-GAN) which first generates target masks in batch and then utilizes them to synthesize corresponding instances on the background image, with all instances efficiently translated and background well preserved. To promote the quality of synthesized images and stabilize the training, we design an elegant training procedure which transforms the unsupervised mask-to-instance process into a supervised way by creating paired examples. To objectively evaluate the performance of UDIT task, we design new evaluation metrics which are based on the object detection. Extensive experiments on four datasets demonstrate the significant advantages of our MGD-GAN over existing methods both quantitatively and qualitatively. Furthermore, our training time consumption is hugely reduced compared to the state-of-the-art. The code could be available at https://github.com/sitongsu/MGD_GAN.
Towards Cross-View Consistency in Semantic Segmentation While Varying View Direction
Authors: Tong Xin , Ying Xianghua , Shi Yongjie , Zhao He , Wang Ruibin
Published: August 2021

Several images are taken for the same scene with many view directions. Given a pixel in any one image of them, its correspondences may appear in the other images. However, by using existing semantic segmentation methods, we find that the pixel and its correspondences do not always have the same inferred label as expected. Fortunately, from the knowledge of multiple view geometry, if we keep the position of a camera unchanged, and only vary its orientation, there is a homography transformation to describe the relationship of corresponding pixels in such images. Based on this fact, we propose to generate images which are the same as real images of the scene taken in certain novel view directions for training and evaluation. We also introduce gradient guided deformable convolution to alleviate the inconsistency, by learning dynamic proper receptive field from feature gradients. Furthermore, a novel consistency loss is presented to enforce feature consistency. Compared with previous approaches, the proposed method gets significant improvement in both cross-view consistency and semantic segmentation performance on images with abundant view directions, while keeping comparable or better performance on the existing datasets.
Cooperation in Threshold Public Projects with Binary Actions
Authors: Chen Yiling , Tao Biaoshuai , Yu Fang-Yi
Published: August 2021

When can cooperation arise from self-interested decisions in public goods games? And how can we help agents to act cooperatively? We examine these classical questions in a pivotal participation game, a variant of public good games, where heterogeneous agents make binary participation decisions on contributing their endowments, and the public project succeeds when it has enough contributions.

We prove it is NP-complete to decide the existence of a cooperative Nash equilibrium such that the project succeeds. We demonstrate that the decision problem becomes easy if agents are homogeneous enough.

We then propose two algorithms to help cooperation in the game. Our first algorithm adds an external investment to the public project, and our second algorithm uses matching funds. We show the cost to induce a cooperative Nash equilibrium is near-optimal for both algorithms. Finally, the cost of matching funds can always be smaller than the cost of adding an external investment. Intuitively, matching funds provide a greater incentive for cooperation than adding an external investment does.
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
Authors: Wang Yanbo , Lin Shaohui , Qu Yanyun , Wu Haiyan , Zhang Zhizhong , Xie Yuan , Yao Angela
Published: August 2021

Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but often require sophisticated architectures with heavy memory cost and computational overhead significantly restricts their practical deployments on resource-limited devices. In this paper, we proposed a novel contrastive self-distillation (CSD) framework to simultaneously compress and accelerate various off-the-shelf SR models. In particular, a channel-splitting super-resolution network can first be constructed from a target teacher network as a compact student network. Then, we propose a novel contrastive loss to improve the quality of SR images and PSNR/SSIM via explicit knowledge transfer. Extensive experiments demonstrate that the proposed CSD scheme effectively compresses and accelerates several standard SR models such as EDSR, RCAN and CARN. Code is available at https://github.com/Booooooooooo/CSD.
Object Detection in Densely Packed Scenes via Semi-Supervised Learning with Dual Consistency
Authors: Ye Chao , Zhang Huaidong , Xu Xuemiao , Cai Weiwei , Qin Jing , Choi Kup-Sze
Published: August 2021

Deep neural networks have been shown to be very powerful tools for object detection in various scenes. Their remarkable performance, however, heavily depends on the availability of a large number of high quality labeled data, which are time-consuming and costly to acquire for scenes with densely packed objects. We present a novel semi-supervised approach to addressing this problem, which is designed based on a common teacher-student model, integrated with a novel intersection-over-union (IoU) aware consistency loss and a new proposal consistency loss. The IoU-aware consistency loss evaluates the IoU over the prediction pairs of the teacher model and the student model, which enforces the prediction of the student model to approach closely to that of the teacher model. The IoU-aware consistency loss also reweights the importance of different prediction pairs to suppress the low-confident pairs. The proposal consistency loss ensures proposal consistency between the two models, making it possible to involve the region proposal network in the training process with unlabeled data. We also construct a new dataset, namely RebarDSC, containing 2,125 rebar images annotated with 350,348 bounding boxes in total (164.9 annotations per image average), to evaluate the proposed method. Extensive experiments are conducted over both the RebarDSC dataset and the famous large public dataset SKU-110K. Experimental results corroborate that the proposed method is able to improve the object detection performance in densely packed scenes, consistently outperforming state-of-the-art approaches. Dataset is available in https://github.com/Armin1337/RebarDSC.
Learning Implicit Temporal Alignment for Few-shot Video Classification
Authors: Zhang Songyang , Zhou Jiale , He Xuming
Published: August 2021

Few-shot video classification aims to learn new video categories with only a few labeled examples, alleviating the burden of costly annotation in real-world applications. However, it is particularly challenging to learn a class-invariant spatial-temporal representation in such a setting. To address this, we propose a novel matching-based few-shot learning strategy for video sequences in this work. Our main idea is to introduce an implicit temporal alignment for a video pair, capable of estimating the similarity between them in an accurate and robust manner. Moreover, we design an effective context encoding module to incorporate spatial and feature channel context, resulting in better modeling of intra-class variations. To train our model, we develop a multi-task loss for learning video matching, leading to video features with better generalization. Extensive experimental results on two challenging benchmarks, show that our method outperforms the prior arts with a sizable margin on Something-Something-V2 and competitive results on Kinetics.
Multi-Channel Pooling Graph Neural Networks
Authors: Du Jinlong , Wang Senzhang , Miao Hao , Zhang Jiaqiang
Published: August 2021

Graph pooling is a critical operation to downsample a graph in graph neural networks. Existing coarsening pooling methods (e.g. DiffPool) mostly focus on capturing the global topology structure by assigning the nodes into several coarse clusters, while dropping pooling methods (e.g. SAGPool) try to preserve the local topology structure by selecting the top-k representative nodes. However, there lacks an effective method to integrate the two types of methods so that both the local and the global topology structure of a graph can be well captured. To address this issue, we propose a Multi-channel Graph Pooling method named MuchPool, which captures the local structure, the global structure, and node feature simultaneously in graph pooling. Specifically, we use two channels to conduct dropping pooling based on the local topology and node features respectively, and one channel to conduct coarsening pooling. Then a cross-channel convolution operation is designed to refine the graph representations of different channels. Finally, the pooling results are aggregated as the final pooled graph. Extensive experiments on six benchmark datasets present the superior performance of MuchPool. The code of this work is publicly available at Github.
Guided Attention Network for Concept Extraction
Authors: Fang Songtao , Huang Zhenya , He Ming , Tong Shiwei , Huang Xiaoqing , Liu Ye , Huang Jie , Liu Qi
Published: August 2021

Concept extraction aims to find words or phrases describing a concept from massive texts. Recently, researchers propose many neural network-based methods to automatically extract concepts. Although these methods for this task show promising results, they ignore structured information in the raw textual data (e.g., title, topic, and clue words). In this paper, we propose a novel model, named Guided Attention Concept Extraction Network (GACEN), which uses title, topic, and clue words as additional supervision to provide guidance directly. Specifically, GACEN comprises two attention networks, one of them is to gather the relevant title and topic information for each context word in the document. The other one aims to model the implicit connection between informative words (clue words) and concepts. Finally, we aggregate information from two networks as input to Conditional Random Field (CRF) to model dependencies in the output. We collected clue words for three well-studied datasets. Extensive experiments demonstrate that our model outperforms the baseline models with a large margin, especially when the labeled data is insufficient.
Federated Model Distillation with Noise-Free Differential Privacy
Authors: Sun Lichao , Lyu Lingjuan
Published: August 2021

Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures. Sharing prediction instead of weight removes this obstacle and eliminates the risk of white-box inference attacks in conventional federated learning. However, the predictions from local models are sensitive and would leak training data privacy to the public. To address this issue, one naive approach is adding the differentially private random noise to the predictions, which however brings a substantial trade-off between privacy budget and model performance. In this paper, we propose a novel framework called FEDMD-NFDP, which applies a Noise-FreeDifferential Privacy (NFDP) mechanism into a federated model distillation framework. Our extensive experimental results on various datasets validate that FEDMD-NFDP can deliver not only comparable utility and communication efficiency but also provide a noise-free differential privacy guarantee. We also demonstrate the feasibility of our FEDMD-NFDP by considering both IID and Non-IID settings, heterogeneous model architectures, and unlabelled public datasets from a different distribution.
User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation
Authors: Wu Chuhan , Wu Fangzhao , Huang Yongfeng , Xie Xing
Published: August 2021

Accurate user modeling is critical for news recommendation. Existing news recommendation methods usually model users' interest from their behaviors via sequential or attentive models. However, they cannot model the rich relatedness between user behaviors, which can provide useful contexts of these behaviors for user interest modeling. In this paper, we propose a novel user modeling approach for news recommendation, which models each user as a personalized heterogeneous graph built from user behaviors to better capture the fine-grained behavior relatedness. In addition, in order to learn user interest embedding from the personalized heterogeneous graph, we propose a novel heterogeneous graph pooling method, which can summarize both node features and graph topology, and be aware of the varied characteristics of different types of nodes. Experiments on large-scale benchmark dataset show the proposed methods can effectively improve the performance of user modeling for news recommendation.
Graph Deformer Network
Authors: Zhao Wenting , Fang Yuan , Cui Zhen , Zhang Tong , Yang Jian
Published: August 2021

Convolution learning on graphs draws increasing attention recently due to its potential applications to a large amount of irregular data. Most graph convolution methods leverage the plain summation/average aggregation to avoid the discrepancy of responses from isomorphic graphs. However, such an extreme collapsing way would result in a structural loss and signal entanglement of nodes, which further cause the degradation of the learning ability. In this paper, we propose a simple yet effective Graph Deformer Network (GDN) to fulfill anisotropic convolution filtering on graphs, analogous to the standard convolution operation on images. Local neighborhood subgraphs (acting like receptive fields) with different structures are deformed into a unified virtual space, coordinated by several anchor nodes. In the deformation process, we transfer components of nodes therein into affinitive anchors by learning their correlations, and build a multi-granularity feature space calibrated with anchors. Anisotropic convolutional kernels can be further performed over the anchor-coordinated space to well encode local variations of receptive fields. By parameterizing anchors and stacking coarsening layers, we build a graph deformer network in an end-to-end fashion. Theoretical analysis indicates its connection to previous work and shows the promising property of graph isomorphism testing. Extensive experiments on widely-used datasets validate the effectiveness of GDN in graph and node classifications.
Accounting for Confirmation Bias in Crowdsourced Label Aggregation
Authors: Gemalmaz Meric Altug , Yin Ming
Published: August 2021

Collecting large-scale human-annotated datasets via crowdsourcing to train and improve automated models is a prominent human-in-the-loop approach to integrate human and machine intelligence. However, together with their unique intelligence, humans also come with their biases and subjective beliefs, which may influence the quality of the annotated data and negatively impact the effectiveness of the human-in-the-loop systems. One of the most common types of cognitive biases that humans are subject to is the confirmation bias, which is people's tendency to favor information that confirms their existing beliefs and values. In this paper, we present an algorithmic approach to infer the correct answers of tasks by aggregating the annotations from multiple crowd workers, while taking workers' various levels of confirmation bias into consideration. Evaluations on real-world crowd annotations show that the proposed bias-aware label aggregation algorithm outperforms baseline methods in accurately inferring the ground-truth labels of different tasks when crowd workers indeed exhibit some degree of confirmation bias. Through simulations on synthetic data, we further identify the conditions when the proposed algorithm has the largest advantages over baseline methods.
Event-based Action Recognition Using Motion Information and Spiking Neural Networks
Authors: Liu Qianhui , Xing Dong , Tang Huajin , Ma De , Pan Gang
Published: August 2021

Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on the task of object recognition. However, events from the event-based camera are triggered by dynamic changes, which makes it an ideal choice to capture actions in the visual scene. Inspired by the dorsal stream in visual cortex, we propose a hierarchical SNN architecture for event-based action recognition using motion information. Motion features are extracted and utilized from events to local and finally to global perception for action recognition. To the best of the authors’ knowledge, it is the first attempt of SNN to apply motion information to event-based action recognition. We evaluate our proposed SNN on three event-based action recognition datasets, including our newly published DailyAction-DVS dataset comprising 12 actions collected under diverse recording conditions. Extensive experimental results show the effectiveness of motion information and our proposed SNN architecture for event-based action recognition.
On Cycles, Attackers and Supporters --- A Contribution to The Investigation of Dynamics in Abstract Argumentation
Authors: Baumann Ringo , Ulbricht Markus
Published: August 2021

Abstract argumentation as defined by Dung in his seminal 1995 paper is by now a major research area in knowledge representation and reasoning. Dynamics of abstract argumentation frameworks (AFs) as well as syntactical consequences of semantical facts of them are the central issues of this paper. The first main part is engaged with the systematical study of the influence of attackers and supporters regarding the acceptability status of whole sets and/or single arguments. In particular, we investigate the impact of addition or removal of arguments, a line of research that has been around for more than a decade. Apart from entirely new results, we revisit, generalize and sum up similar results from the literature. To gain a comprehensive formal and intuitive understanding of the behavior of AFs we put special effort in comparing different kind of semantics. We concentrate on classical admissibility-based semantics and also give pointers to semantics based on naivity and weak admissibility, a recently introduced mediating approach. In the second main part we show how to infer syntactical information from semantical one. For instance, it is well-known that if a finite AF possesses no stable extension, then it has to contain an odd-cycle. In this paper, we even present a characterization of this issue. Moreover, we show that the change of the number of extensions if adding or removing an argument allows to conclude the existence of certain even or odd cycles in the considered AF without having further information.
Budget-Constrained Coalition Strategies with Discounting
Authors: Bozzone Lia , Naumov Pavel
Published: August 2021

Discounting future costs and rewards is a common practice in accounting, game theory, and machine learning. In spite of this, existing logics for reasoning about strategies with cost and resource constraints do not account for discounting. The paper proposes a sound and complete logical system for reasoning about budget-constrained strategic abilities that incorporates discounting into its semantics.
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies
Authors: Funk Maurice , Jung Jean Christoph , Lutz Carsten
Published: August 2021

We consider the problem to learn a concept or a query in the presence of an ontology formulated in the description logic ELr, in Angluin's framework of active learning that allows the learning algorithm to interactively query an oracle (such as a domain expert). We show that the following can be learned in polynomial time: (1) EL-concepts, (2) symmetry-free ELI-concepts, and (3) conjunctive queries (CQs) that are chordal, symmetry-free, and of bounded arity. In all cases, the learner can pose to the oracle membership queries based on ABoxes and equivalence queries that ask whether a given concept/query from the considered class is equivalent to the target. The restriction to bounded arity in (3) can be removed when we admit unrestricted CQs in equivalence queries. We also show that EL-concepts are not polynomial query learnable in the presence of ELI-ontologies.
HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph
Authors: He Yongquan , Zhang Peng , Liu Luchen , Liang Qi , Zhang Wenyuan , Zhang Chuang
Published: August 2021

In recent years, temporal knowledge graph (TKG) reasoning has received significant attention. Most existing methods assume that all timestamps and corresponding graphs are available during training, which makes it difficult to predict future events. To address this issue, recent works learn to infer future events based on historical information. However, these methods do not comprehensively consider the latent patterns behind temporal changes, to pass historical information selectively, update representations appropriately and predict events accurately. In this paper, we propose the Historical Information Passing (HIP) network to predict future events. HIP network passes information from temporal, structural and repetitive perspectives, which are used to model the temporal evolution of events, the interactions of events at the same time step, and the known events respectively. In particular, our method considers the updating of relation representations and adopts three scoring functions corresponding to the above dimensions. Experimental results on five benchmark datasets show the superiority of HIP network, and the significant improvements on Hits@1 prove that our method can more accurately predict what is going to happen.
Signature-Based Abduction with Fresh Individuals and Complex Concepts for Description Logics
Authors: Koopmann Patrick
Published: August 2021

Given a knowledge base and an observation as a set of facts,

ABox abduction aims at computing a hypothesis that, when added to the

knowledge base, is sufficient to entail the observation. In signature-based

ABox abduction, the hypothesis is further required to use only names from a

given set. This form of abduction has applications such as diagnosis, KB

repair, or explaning missing entailments. It is possible that hypotheses for

a given observation only exist if we admit the use of fresh individuals

and/or complex concepts built from the given signature, something most

approaches for ABox abduction so far do not allow or only allow

with restrictions. In this paper, we investigate the computational complexity

of this form of abduction---allowing either fresh individuals, complex concepts,

or both---for various description logics, and give size bounds on the hypotheses

if they exist.
Reasoning over Argument-Incomplete AAFs in the Presence of Correlations
Authors: Fazzinga Bettina , Flesca Sergio , Furfaro Filippo
Published: August 2021

We introduce "argument-incomplete Abstract Argumentation Frameworks with dependencies",

that extend the traditional

abstract argumentation reasoning to the case where some arguments are uncertain

and correlated through logical dependencies

(such as mutual exclusion, implication, etc.).

We characterize the complexities of the problems DSAT of deciding the satisfiability

of the

dependencies and PDVER of verifying extensions,

and show how they depend on the forms of dependencies and, for PDVER, also on the

semantics of the extensions.
Two Forms of Responsibility in Strategic Games
Authors: Naumov Pavel , Tao Jia
Published: August 2021

The paper studies two forms of responsibility, seeing to it and being blamable, in the setting of strategic games with imperfect information. The paper shows that being blamable is definable through seeing to it, but not the other way around. In addition, it proposes a bimodal logical system that describes the interplay between the seeing to it modality and the individual knowledge modality.
The Surprising Power of Graph Neural Networks with Random Node Initialization
Authors: Abboud Ralph , Ceylan İsmail İlkan , Grohe Martin , Lukasiewicz Thomas
Published: August 2021

Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph isomorphism heuristic. In order to break this expressiveness barrier, GNNs have been enhanced with random node initialization (RNI), where the idea is to train and run the models with randomized initial node features. In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties. This universality result holds even with partially randomized initial node features, and preserves the invariance properties of GNNs in expectation. We then empirically analyze the effect of RNI on GNNs, based on carefully constructed datasets. Our empirical findings support the superior performance of GNNs with RNI over standard GNNs.
Reconciling Rewards with Predictive State Representations
Authors: Baisero Andrea , Amato Christopher
Published: August 2021

Predictive state representations (PSRs) are models of controlled non-Markov

observation sequences which exhibit the same generative process governing

POMDP observations without relying on an underlying latent state. In that

respect, a PSR is indistinguishable from the corresponding POMDP. However,

PSRs notoriously ignore the notion of rewards, which undermines the general

utility of PSR models for control, planning, or reinforcement learning.

Therefore, we describe a sufficient and necessary accuracy condition

which determines whether a PSR is able to accurately model POMDP rewards, we

show that rewards can be approximated even when the accuracy condition is not

satisfied, and we find that a non-trivial number of POMDPs taken from a

well-known third-party repository do not satisfy the accuracy condition.

We propose reward-predictive state representations (R-PSRs), a

generalization of PSRs which accurately models both observations and rewards,

and develop value iteration for R-PSRs. We show that there is a mismatch

between optimal POMDP policies and the optimal PSR policies derived from

approximate rewards. On the other hand, optimal R-PSR policies perfectly

match optimal POMDP policies, reconfirming R-PSRs as accurate state-less

generative models of observations and rewards.
Worst-case Bounds on Power vs. Proportion in Weighted Voting Games with Application to False-name Manipulation
Authors: Gafni Yotam , Lavi Ron , Tennenholtz Moshe
Published: August 2021

Weighted voting games are applicable to a wide variety of multi-agent settings. They

enable the formalization of power indices which quantify the coalitional power of players. We take a novel approach to the study of the power of big vs.~small players in these games. We model small (big) players as having single (multiple) votes. The aggregate relative power of big players is measured w.r.t.~their votes proportion.

For this ratio, we show small constant worst-case bounds for the Shapley-Shubik and the Deegan-Packel indices. In sharp contrast, this ratio is unbounded for the Banzhaf index. As an application, we define a false-name strategic normal form game where each big player may split its votes between false identities, and study its various properties. Together our results provide foundations for the implications of players' size, modeled as their ability to split, on their relative power.