bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of the Evaluation of Performance Preconditions in Tennis with the Use of Equal and Expertly Judged Criteria Weights
Authors: Zháněl J. , Holeček P. , Zderčík A.
Published: August 2021

Tennis performance is influenced by various factors, among which physical performance factors play an important role. The aim of the study was an analysis of possibilities of the use of Saaty’s method for assessing the level of performance prerequisites and comparing the results obtained using equal weights and various weights. The research on Czech female players (U12; n = 211) was based on the results of the TENDIAG1 test battery (9 items) and the results were processed by FuzzME software and relevant statistical methods (correlation coefficient r, Student´s t-test, effect size index d). The results of Saaty’s method show that the most important athletic performance criteria for tennis coaches are the leg reaction time and the running speed, while the least important are endurance and strength. The evaluation using various criteria weights offers a finer scale for assessing athletes’ performance prerequisites despite the proven high degree of association between the results obtained with equal and various weights and the insignificant difference of mean values. The results have shown possibilities for the use of a fuzzy approach in sports practice and motivate further research towards broadening the structure or the number of evaluation criteria.
Digital Technologies and Music Digitisation: Challenges and Opportunities for the Nepalese Music Industry
Authors: Giri Subash
Published: August 2021

This paper investigates the current legitimate digital music business trends and models created by the innovation of new digital technologies and examines their pertinence in the Nepalese music industry. Further, it scrutinises neighbouring music markets and juxtaposes the Nepalese music market against their current market trends. Based on eight in-depth semi-structured interviews with executives and stakeholders of different major, medium and independent Nepalese record labels, the paper examines two questions: what is preventing Nepalese recorded music from being found digitally and accessible legally; and what are the opportunities, gaps and requirements that confront the search for a commercially viable route for the optimal digital music business model to make Nepalese music digitally and legally accessible, both locally and globally?
How Has Covid-19 Affected Published Academic Research? A Content Analysis of Journal Articles Mentioning the Virus
Authors: Thelwall Mike , Thelwall Saheeda
Published: August 2021

Purpose

Methods to tackle Covid-19 have been developed by a wave of biomedical research but the pandemic has also influenced many aspects of society, generating a need for research into its consequences, and potentially changing the way existing topics are investigated. This article investigates the nature of this influence on the wider academic research mission.

Design/methodology/approach

This article reports an inductive content analysis of 500 randomly selected journal articles mentioning Covid-19, as recorded by the Dimensions scholarly database on 19 March 2021. Covid-19 mentions were coded for the influence of the disease on the research.

Findings

Whilst two thirds of these articles were about biomedicine (e.g. treatments, vaccines, virology), or health services in response to Covid-19, others covered the pandemic economy, society, safety, or education. In addition, some articles were not about the pandemic but stated that Covid-19 had increased or decreased the value of the reported research or changed the context in which it was conducted.

Research limitations

The findings relate only to Covid-19 influences declared in published journal articles.

Practical implications

Research managers and funders should consider whether their current procedures are effective in supporting researchers to address the evolving demands of pandemic societies, particularly in terms of timeliness.

Originality/value

The results show that although health research dominates the academic response to Covid-19, it is more widely disrupting academic research with new demands and challenges.
Sustainable Business Solutions For Traditional Handicraft Product In The Northwestern Provinces Of Vietnam
Authors: Hai Nguyen , Duong Nguyen , Huy Dinh , Hien Dinh
Published: August 2021

Local tourism development requires us to consider many solutions, among them is sustainable solutions for business development of traditional handicraft products. This study is done based on surveys of northwestern provinces of Vietnam, in which authors use statistics, questionnaires and charts. Research findings show us that despite of potential chances, tourism activities still mainly exploit the value of available natural resources, are heavily spontaneous, lack of planning, small-scale tourism business, lack of creativity and diversity in tourism activities. services, infrastructure and quality of tourism service are limited. Our study also indicated that macro factors affect tourism activities.
Reforming specialized inspection procedures to improve business environment in vietnam for trade facilitation implementation
Authors: Nga Le , Huy Dinh , Hang Nguyen , Lan Leng , Thach Nguyen
Published: August 2021

our study aims to analyze and assess the process of reforming the specialized inspection procedures in Vietnam which is included in Vietnam’s trade facilitation implementation program, and suggesting some recommendations for Vietnam to do the reform to improve business environment and enhance Vietnam’s national competitiveness. By synthesizing and analyzing secondary data from creditable sources such as World Bank, UNESCAPE, UNCTAD, WTO, VCCI, General Department of Vietnam Customs, as well as interviewing officials from Ministry of Industry and Trade of Socialist Republic of Vietnam, etc.

Ha and Lan (2021) stated Economic regulatory reform in Vietnam started in the early 1990s and accelerated when the country shifted its focus to economic integration policy by acceding to the World Trade Organization (WTO) in 1995 and engaging in regional free trade agreements in the Association of Southeast Asian Nations (ASEAN).After analyzing and assessing the real situation of Vietnam’s trade facilitation and specialized inspection reforms, this paper would like to suggest some recommendations to continuously implement specialized inspection reforms, implement trade facilitation, improve Vietnam’s business environment and enhance Vietnam’s national competitiveness.
Efficiency of ventilated facades in terms of airflow in the air gap
Authors: Schabowicz Krzysztof , Zawiślak Łukasz , Staniów Paweł
Published: August 2021

The gradual exploitation of the natural environment has forced most developed countries to promote ecological solutions and the development of sustainable construction. Ventilated facades perfectly match into this trend, and with their appropriate design, they bring real energy savings. This paper analyzes numerically the influence of the inflowing air, mimicking the wind, on the efficiency of heat removal from the ventilated space and heat transmission by thermal radiation and conduction through the consecutive layers of the external wall. For the purpose of comparison, two variants of ventilated facade were adopted: open and closed joints, at different wind speeds prevailing outside. The results obtained show that in windless weather, the ventilated facade with open joints shows higher heat removal efficiency and thus lower heat transmission to the building interior. At higher wind speeds of 5 m/s, the open-joint and closed-joint ventilated facades achieve similar heat transfer efficiency, and the prevailing temperature inside the building for the two technologies is almost identical. Subsequent increments of incoming wind on the building result in minimal differences in the heat transmission to the building interior, representing changes of about 0.1°C at increments of another 5 m/s of incoming wind. Conscious use of this facade technology, along with appropriate urban design of cities, can help reduce the energy needed to cool buildings during the summer period.
К вопросу организации дизайн-среды Спортивной набережной г. Владивостока
Authors: Месенева Н. В.
Published: August 2021

В настоящее время необходима разработка новых подходов к проектированию и реконструкции территории Спортивной набережной города Владивостока. Организация территории набережной должна отражать современные мировые тенденции в архитектуре и дизайне, формирование ландшафта, создание цветового и светового решений, изменения в образе жизни горожан; включать современные объекты и элементы рекламы, декоративно-монументального искусства; соответствовать современному уровню технологий. Дизайн-проекты должны включать высокотехнологичные экологичные решения, учитывать исторические традиции, культуру, экологию, климат. При проектировании набережной очень важно создавать гибкую структуру ее территории, способную к изменениям. Дизайн-среда набережной включает: архитектурные объекты, естественный и искусственный рельеф, малые архитектурные формы, объекты благо-устройства и городского оборудования, объекты монументального искусства, арт-объекты, объекты и элементы рекламы, зеленые зоны, живописные морские панорамы города. Задача дизайнеров и архитекторов состоит в создании яркой, выразительной, гармоничной, концептуальной, функциональной, комфортной, эстетической среды набережной, безопасной, безбарьерной среды прибрежных территорий. При проектировании Спортивной набережной, внедряя передовые экологические технологии, необходимо: формировать художественную концепцию образа города на Тихом океане, подчеркивать приморские панорамы города у моря; сохранять и подчеркивать ландшафт набережной, живописно амфитеатром спускающийся к морю; формировать гармоничную, комфортную визуальную среду с учетом природно-климатических условий, санитарно-гигиенических и микроклиматических требований; сохранять составляющие исторической пространственной среды: памятники архитектуры и культуры; выполнять комплексное проектирование цветовой и световой среды на-бережной; восстанавливать неухоженные участки территории набережной и элементы городской среды, требующие ремонта.
Private Car, Public Oversight: Municipal Regulation of Ride-hailing Platforms in Toronto and the Greater Golden Horseshoe
Authors: Woodside Jonathan , Moos Markus , Vinodrai Tara
Published: August 2021

Municipalities in many regions of Canada have regulated vehicle-for-hire services. With the rise of ride-hailing platforms, such as Uber and Lyft, this responsibility to produce a reliable vehicle-for-hire service has largely been transferred to private platforms. Using a case study of the City of Toronto and surrounding Greater Golden Horseshoe, this article examines how local regulation of this critical urban mobility service has changed. Drawing upon an analysis of 27 interviews with municipal staff, councilors and industry experts, a review of written local media, and a review of government documents, the study finds that municipalities are withdrawing from direct control of the industry due to a lack of tools of oversight and a prioritization of private industry over public service. The study discusses ongoing challenges that may be addressed by greater oversight of the service.  It concludes by highlighting examples of municipalities growing their capacity for oversight and provides recommendations for further growth.
Dynamic Inconsistency-aware DeepFake Video Detection
Authors: Hu Ziheng , Xie Hongtao , Wang YuXin , Li Jiahong , Wang Zhongyuan , Zhang Yongdong
Published: August 2021

The spread of DeepFake videos causes a serious threat to information security, calling for effective detection methods to distinguish them. However, the performance of recent frame-based detection methods become limited due to their ignorance of the inter-frame inconsistency of fake videos. In this paper, we propose a novel Dynamic Inconsistency-aware Network to handle the inconsistent problem, which uses a Cross-Reference module (CRM) to capture both the global and local inter-frame inconsistencies. The CRM contains two parallel branches. The first branch takes faces from adjacent frames as input, and calculates a structure similarity map for a global inconsistency representation. The second branch only focuses on the inter-frame variation of independent critical regions, which captures the local inconsistency. To the best of our knowledge, this is the first work to totally use the inter-frame inconsistency information from the global and local perspectives. Compared with existing methods, our model provides a more accurate and robust detection on FaceForensics++, DFDC-preview and Celeb-DFv2 datasets.
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw
Authors: Huo Yuqi , Ding Mingyu , Lu Haoyu , Huang Ziyuan , Tang Mingqian , Lu Zhiwu , Xiang Tao
Published: August 2021

This paper proposes a novel pretext task for self-supervised video representation learning by exploiting spatiotemporal continuity in videos. It is motivated by the fact that videos are spatiotemporal by nature and a representation learned by detecting spatiotemporal continuity/discontinuity is thus beneficial for downstream video content analysis tasks. A natural choice of such a pretext task is to construct spatiotemporal (3D) jigsaw puzzles and learn to solve them. However, as we demonstrate in the experiments, this task turns out to be intractable. We thus propose Constrained Spatiotemporal Jigsaw (CSJ) whereby the 3D jigsaws are formed in a constrained manner to ensure that large continuous spatiotemporal cuboids exist. This provides sufficient cues for the model to reason about the continuity. Instead of solving them directly, which could still be extremely hard, we carefully design four surrogate tasks that are more solvable. The four tasks aim to learn representations sensitive to spatiotemporal continuity at both the local and global levels. Extensive experiments show that our CSJ achieves state-of-the-art on various benchmarks.
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
Authors: Liu Xiaofeng , Hu Bo , Jin Linghao , Han Xu , Xing Fangxu , Ouyang Jinsong , Lu Jun , El Fakhri Georges , Woo Jonghye
Published: August 2021

In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training. Considering the inherent conditional and label shifts, we would expect the alignment of p(x|y) and p(y). However, the widely used domain invariant feature learning (IFL) methods relies on aligning the marginal concept shift w.r.t. p(x), which rests on an unrealistic assumption that p(y) is invariant across domains. We thereby propose a novel variational Bayesian inference framework to enforce the conditional distribution alignment w.r.t. p(x|y) via the prior distribution matching in a latent space, which also takes the marginal label shift w.r.t. p(y) into consideration with the posterior alignment. Extensive experiments on various benchmarks demonstrate that our framework is robust to the label shift and the cross-domain accuracy is significantly improved, thereby achieving superior performance over the conventional IFL counterparts.
Learn from Concepts: Towards the Purified Memory for Few-shot Learning
Authors: Liu Xuncheng , Tian Xudong , Lin Shaohui , Qu Yanyun , Ma Lizhuang , Yuan Wang , Zhang Zhizhong , Xie Yuan
Published: August 2021

Human beings have a great generalization ability to recognize a novel category by only seeing a few number of samples. This is because humans possess the ability to learn from the concepts that already exist in our minds. However, many existing few-shot approaches fail in addressing such a fundamental problem, {\it i.e.,} how to utilize the knowledge learned in the past to improve the prediction for the new task. In this paper, we present a novel purified memory mechanism that simulates the recognition process of human beings. This new memory updating scheme enables the model to purify the information from semantic labels and progressively learn consistent, stable, and expressive concepts when episodes are trained one by one. On its basis, a Graph Augmentation Module (GAM) is introduced to aggregate these concepts and knowledge learned from new tasks via a graph neural network, making the prediction more accurate. Generally, our approach is model-agnostic and computing efficient with negligible memory cost. Extensive experiments performed on several benchmarks demonstrate the proposed method can consistently outperform a vast number of state-of-the-art few-shot learning methods.
Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following Tasks
Authors: Nguyen Van-Quang , Suganuma Masanori , Okatani Takayuki
Published: August 2021

There is a growing interest in the community in making an embodied AI agent perform a complicated task while interacting with an environment following natural language directives. Recent studies have tackled the problem using ALFRED, a well-designed dataset for the task, but achieved only very low accuracy. This paper proposes a new method, which outperforms the previous methods by a large margin. It is based on a combination of several new ideas. One is a two-stage interpretation of the provided instructions. The method first selects and interprets an instruction without using visual information, yielding a tentative action sequence prediction. It then integrates the prediction with the visual information etc., yielding the final prediction of an action and an object. As the object's class to interact is identified in the first stage, it can accurately select the correct object from the input image. Moreover, our method considers multiple egocentric views of the environment and extracts essential information by applying hierarchical attention conditioned on the current instruction. This contributes to the accurate prediction of actions for navigation. A preliminary version of the method won the ALFRED Challenge 2020. The current version achieves the unseen environment's success rate of 4.45% with a single view, which is further improved to 8.37% with multiple views.
Unsupervised Hashing with Contrastive Information Bottleneck
Authors: Qiu Zexuan , Su Qinliang , Ou Zijing , Yu Jianxing , Chen Changyou
Published: August 2021

Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much information of original data as possible. However, this requirement may force the models spending lots of their effort on reconstructing the unuseful background information, while ignoring to preserve the discriminative semantic information that is more important for the hashing task. To tackle this problem, inspired by the recent success of contrastive learning in learning continuous representations, we propose to adapt this framework to learn binary hashing codes. Specifically, we first propose to modify the objective function to meet the specific requirement of hashing and then introduce a probabilistic binary representation layer into the model to facilitate end-to-end training of the entire model. We further prove the strong connection between the proposed contrastive-learning-based hashing method and the mutual information, and show that the proposed model can be considered under the broader framework of the information bottleneck (IB). Under this perspective, a more general hashing model is naturally obtained. Extensive experimental results on three benchmark image datasets demonstrate that the proposed hashing method significantly outperforms existing baselines.
Speech2Talking-Face: Inferring and Driving a Face with Synchronized Audio-Visual Representation
Authors: Sun Yasheng , Zhou Hang , Liu Ziwei , Koike Hideki
Published: August 2021

What can we picture solely from a clip of speech? Previous research has shown the possibility of directly inferring the appearance of a person's face by listening to a voice. However, within human speech lies not only the biometric identity signal but also the identity-irrelevant information such as the talking content. Our goal is to extract as much information from a clip of speech as possible. In particular, we aim at not only inferring the face of a person but also animating it. Our key insight is to synchronize audio and visual representations from two perspectives in a style-based generative framework. Specifically, contrastive learning is leveraged to map both the identity and speech content information within the speech to visual representation spaces. Furthermore, the identity space is strengthened with class centroids. Through curriculum learning, the style-based generator is capable of automatically balancing the information from the two latent spaces. Extensive experiments show that our approach encourages better speech-identity correlation learning while generating vivid faces whose identities are consistent with given speech samples. Moreover, by leveraging the same model, these inferred faces can be driven to talk by the audio.
AVA: Adversarial Vignetting Attack against Visual Recognition
Authors: Tian Binyu , Juefei-Xu Felix , Guo Qing , Xie Xiaofei , Li Xiaohong , Liu Yang
Published: August 2021

Vignetting is an inherent imaging phenomenon within almost all optical systems, showing as a radial intensity darkening toward the corners of an image. Since it is a common effect for photography and usually appears as a slight intensity variation, people usually regard it as a part of a photo and would not even want to post-process it. Due to this natural advantage, in this work, we study the vignetting from a new viewpoint, i.e., adversarial vignetting attack (AVA), which aims to embed intentionally misleading information into the vignetting and produce a natural adversarial example without noise patterns. This example can fool the state-of-the-art deep convolutional neural networks (CNNs) but is imperceptible to human. To this end, we first propose the radial-isotropic adversarial vignetting attack (RI-AVA) based on the physical model of vignetting, where the physical parameters (e.g., illumination factor and focal length) are tuned through the guidance of target CNN models. To achieve higher transferability across different CNNs, we further propose radial-anisotropic adversarial vignetting attack (RA-AVA) by allowing the effective regions of vignetting to be radial-anisotropic and shape-free. Moreover, we propose the geometry-aware level-set optimization method to solve the adversarial vignetting regions and physical parameters jointly. We validate the proposed methods on three popular datasets, i.e., DEV, CIFAR10, and Tiny ImageNet, by attacking four CNNs, e.g., ResNet50, EfficientNet-B0, DenseNet121, and MobileNet-V2, demonstrating the advantages of our methods over baseline methods on both transferability and image quality.
Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
Authors: Wang Haoqing , Deng Zhi-Hong
Published: August 2021

Few-shot classification aims to recognize unseen classes with few labeled samples from each class. Many meta-learning models for few-shot classification elaborately design various task-shared inductive bias (meta-knowledge) to solve such tasks, and achieve impressive performance. However, when there exists the domain shift between the training tasks and the test tasks, the obtained inductive bias fails to generalize across domains, which degrades the performance of the meta-learning models. In this work, we aim to improve the robustness of the inductive bias through task augmentation. Concretely, we consider the worst-case problem around the source task distribution, and propose the adversarial task augmentation method which can generate the inductive bias-adaptive 'challenging' tasks. Our method can be used as a simple plug-and-play module for various meta-learning models, and improve their cross-domain generalization capability. We conduct extensive experiments under the cross-domain setting, using nine few-shot classification datasets: mini-ImageNet, CUB, Cars, Places, Plantae, CropDiseases, EuroSAT, ISIC and ChestX. Experimental results show that our method can effectively improve the few-shot classification performance of the meta-learning models under domain shift, and outperforms the existing works. Our code is available at https://github.com/Haoqing-Wang/CDFSL-ATA.
Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video
Authors: Wu Jie , Zhang Wei , Li Guanbin , Wu Wenhao , Tan Xiao , Li Yingying , Ding Errui , Lin Liang
Published: August 2021

In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video. Specifically, given an untrimmed video, WSSTAD aims to localize a spatio-temporal tube (i.e., a sequence of bounding boxes at consecutive times) that encloses the abnormal event, with only coarse video-level annotations as supervision during training. To address this challenging task, we propose a dual-branch network which takes as input the proposals with multi-granularities in both spatial-temporal domains. Each branch employs a relationship reasoning module to capture the correlation between tubes/videolets, which can provide rich contextual information and complex entity relationships for the concept learning of abnormal behaviors. Mutually-guided Progressive Refinement framework is set up to employ dual-path mutual guidance in a recurrent manner, iteratively sharing auxiliary supervision information across branches. It impels the learned concepts of each branch to serve as a guide for its counterpart, which progressively refines the corresponding branch and the whole framework. Furthermore, we contribute two datasets, i.e., ST-UCF-Crime and STRA, consisting of videos containing spatio-temporal abnormal annotations to serve as the benchmarks for WSSTAD. We conduct extensive qualitative and quantitative evaluations to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to handle this task.
Hierarchical Self-supervised Augmented Knowledge Distillation
Authors: Yang Chuanguang , An Zhulin , Cai Linhang , Xu Yongjun
Published: August 2021

Knowledge distillation often involves how to define and transfer knowledge from teacher to student effectively. Although recent self-supervised contrastive knowledge achieves the best performance, forcing the network to learn such knowledge may damage the representation learning of the original class recognition task. We therefore adopt an alternative self-supervised augmented task to guide the network to learn the joint distribution of the original recognition task and self-supervised auxiliary task. It is demonstrated as a richer knowledge to improve the representation power without losing the normal classification capability. Moreover, it is incomplete that previous methods only transfer the probabilistic knowledge between the final layers. We propose to append several auxiliary classifiers to hierarchical intermediate feature maps to generate diverse self-supervised knowledge and perform the one-to-one transfer to teach the student network thoroughly. Our method significantly surpasses the previous SOTA SSKD with an average improvement of 2.56% on CIFAR-100 and an improvement of 0.77% on ImageNet across widely used network pairs. Codes are available at https://github.com/winycg/HSAKD.
EmbedMask: Embedding Coupling for Instance Segmentation
Authors: Ying Hui , Huang Zhaojin , Liu Shu , Shao Tianjia , Zhou Kun
Published: August 2021

Current instance segmentation methods can be categorized into segmentation-based methods and proposal-based methods. The former performs segmentation first and then does clustering, while the latter detects objects first and then predicts the mask for each object proposal. In this work, we propose a single-stage method, named EmbedMask, that unifies both methods by taking their advantages, so it can achieve good performance in instance segmentation and produce high-resolution masks in a high speed. EmbedMask introduces two newly defined embeddings for mask prediction, which are pixel embedding and proposal embedding. During training, we enforce the pixel embedding to be close to its coupled proposal embedding if they belong to the same instance. During inference, pixels are assigned to the mask of the proposal if their embeddings are similar. This mechanism brings several benefits. First, the pixel-level clustering enables EmbedMask to generate high-resolution masks and avoids the complicated two-stage mask prediction. Second, the existence of proposal embedding simplifies and strengthens the clustering procedure, so our method can achieve high speed and better performance than segmentation-based methods. Without any bell or whistle, EmbedMask outperforms the state-of-the-art instance segmentation method Mask R-CNN on the challenging COCO dataset, obtaining more detailed masks at a higher speed.
Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy
Authors: Zhang Zihao , Hu Lei , Deng Xiaoming , Xia Shihong
Published: August 2021

3D human pose estimation is a fundamental problem in artificial intelligence, and it has wide applications in AR/VR, HCI and robotics. However, human pose estimation from point clouds still suffers from noisy points and estimated jittery artifacts because of handcrafted-based point cloud sampling and single-frame-based estimation strategies. In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences. To sample effective point clouds from input, we design a differentiable point cloud sampling method built on density-guided attention mechanism. To avoid the jitter caused by previous 3D human pose estimation problems, we adopt temporal information to obtain more stable results. Experiments on the ITOP dataset and the NTU-RGBD dataset demonstrate that all of our contributed components are effective, and our method can achieve state-of-the-art performance.
A Sketch-Transformer Network for Face Photo-Sketch Synthesis
Authors: Zhu Mingrui , Liang Changcheng , Wang Nannan , Wang Xiaoyu , Li Zhifeng , Gao Xinbo
Published: August 2021

We present a face photo-sketch synthesis model, which converts a face photo into an artistic face sketch or recover a photo-realistic facial image from a sketch portrait. Recent progress has been made by convolutional neural networks (CNNs) and generative adversarial networks (GANs), so that promising results can be obtained through real-time end-to-end architectures. However, convolutional architectures tend to focus on local information and neglect long-range spatial dependency, which limits the ability of existing approaches in keeping global structural information. In this paper, we propose a Sketch-Transformer network for face photo-sketch synthesis, which consists of three closely-related modules, including a multi-scale feature and position encoder for patch-level feature and position embedding, a self-attention module for capturing long-range spatial dependency, and a multi-scale spatially-adaptive de-normalization decoder for image reconstruction. Such a design enables the model to generate reasonable detail texture while maintaining global structural information. Extensive experiments show that the proposed method achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations.
Computing Optimal Hypertree Decompositions with SAT
Authors: Schidler Andre , Szeider Stefan
Published: August 2021

Hypertree width is a prominent hypergraph invariant with many

algorithmic applications in constraint satisfaction and

databases. We propose a novel characterization for hypertree width

in terms of linear elimination orderings. We utilize this

characterization to generate a new SAT encoding that we evaluate on

an extensive set of benchmark instances. We compare it to

state-of-the-art exact methods for computing optimal hypertree

width. Our results show that the encoding based on the new

characterization is not only significantly more compact than known

encodings but also outperforms the other methods.
Diversity in Kemeny Rank Aggregation: A Parameterized Approach
Authors: Arrighi Emmanuel , Fernau Henning , Lokshtanov Daniel , de Oliveira Oliveira Mateus , Wolf Petra
Published: August 2021

In its most traditional setting, the main concern of optimization theory is the search for optimal solutions for instances of a given computational problem. A recent trend of research in artificial intelligence, called solution diversity, has focused on the development of notions of optimality that may be more appropriate in settings where subjectivity is essential. The idea is that instead of aiming at the development of algorithms that output a single optimal solution, the goal is to investigate algorithms that output a small set of sufficiently good solutions that are sufficiently diverse from one another. In this way, the user has the opportunity to choose the solution that is most appropriate to the context at hand. It also displays the richness of the solution space.

When combined with techniques from parameterized complexity theory, the paradigm of diversity of solutions offers a powerful algorithmic framework to address problems of practical relevance. In this work, we investigate the impact of this combination in the field of Kemeny Rank Aggregation, a well-studied class of problems lying in the intersection of order theory and social choice theory and also in the field of order theory itself. In particular, we show that KRA is fixed-parameter tractable with respect to natural parameters providing natural formalizations of the notions of diversity and of the notion of a sufficiently good solution. Our main results work both when considering the traditional setting of aggregation over linearly ordered votes, and in the more general setting where votes are partially ordered.
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Authors: Jin Ming , Zheng Yizhen , Li Yuan-Fang , Gong Chen , Zhou Chuan , Pan Shirui
Published: August 2021

Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning. Specifically, we first generate two augmented views from the input graph based on local and global perspectives. Then, we employ two objectives called cross-view and cross-network contrastiveness to maximize the agreement between node representations across different views and networks. To demonstrate the effectiveness of our approach, we perform empirical experiments on five real-world datasets. Our method not only achieves new state-of-the-art results but also surpasses some semi-supervised counterparts by large margins. Code is made available at https://github.com/GRAND-Lab/MERIT
RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection
Authors: Liu Boyang , Wang Ding , Lin Kaixiang , Tan Pang-Ning , Zhou Jiayu
Published: August 2021

Unsupervised anomaly detection plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interests in applying deep neural networks (DNNs) to anomaly detection problems. A common approach is using autoencoders to learn a feature representation for the normal observations in the data. The reconstruction error of the autoencoder is then used as outlier scores to detect the anomalies. However, due to the high complexity brought upon by the over-parameterization of DNNs, the reconstruction error of the anomalies could also be small, which hampers the effectiveness of these methods. To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. We investigate the theoretical properties of the framework and empirically show its outstanding performance as compared to other DNN-based methods. Our experimental results also show the resiliency of the framework to missing values compared to other baseline methods.
MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning
Authors: Liu Chen , Li Bo , Zhao Jun , Su Ming , Liu Xu-Dong
Published: August 2021

Detecting the newly emerging malware variants in real time is crucial for mitigating cyber risks and proactively blocking intrusions. In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time. Particularly, MG-DVD first models the fine-grained execution event streams of malware variants into dynamic heterogeneous graphs and investigates real-world meta-graphs between malware objects, which can effectively characterize more discriminative malicious evolutionary patterns between malware and their variants. Then, MG-DVD presents two dynamic walk-based heterogeneous graph learning methods to learn more comprehensive representations of malware variants, which significantly reduces the cost of the entire graph retraining. As a result, MG-DVD is equipped with the ability to detect malware variants in real time, and it presents better interpretability by introducing meaningful meta-graphs. Comprehensive experiments on large-scale samples prove that our proposed MG-DVD outperforms state-of-the-art methods in detecting malware variants in terms of effectiveness and efficiency.
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy
Authors: Sun Lichao , Qian Jianwei , Chen Xun
Published: August 2021

Training deep learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that collects the local gradient information instead of raw data. One way to achieve a strict privacy guarantee is to apply local differential privacy into federated learning. However, previous works do not give a practical solution due to two issues. First, the range difference of weights in different deep learning model layers has not been explicitly considered when applying local differential privacy mechanism. Second, the privacy budget explodes due to the high dimensionality of weights in deep learning models and many query iterations of federated learning. In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues. It makes the local weights update differentially private by adapting to the varying ranges at different layers of a deep neural network, which introduces a smaller variance of the estimated model weights, especially for deeper models. Moreover, the proposed mechanism bypasses the curse of dimensionality by parameter shuffling aggregation. A series of empirical evaluations on three commonly used datasets in prior differential privacy works, MNIST, Fashion-MNIST and CIFAR-10, demonstrate that our solution can not only achieve superior deep learning performance but also provide a strong privacy guarantee at the same time.
Graphical Cake Cutting via Maximin Share
Authors: Elkind Edith , Segal-Halevi Erel , Suksompong Warut
Published: August 2021

We study the recently introduced cake-cutting setting in which the cake is represented by an undirected graph. This generalizes the canonical interval cake and allows for modeling the division of road networks. We show that when the graph is a forest, an allocation satisfying the well-known criterion of maximin share fairness always exists. Our result holds even when separation constraints are imposed; however, in the latter case no multiplicative approximation of proportionality can be guaranteed. Furthermore, while maximin share fairness is not always achievable for general graphs, we prove that ordinal relaxations can be attained.
UIBert: Learning Generic Multimodal Representations for UI Understanding
Authors: Bai Chongyang , Zang Xiaoxue , Xu Ying , Sunkara Srinivas , Rastogi Abhinav , Chen Jindong , Agüera y Arcas Blaise
Published: August 2021

To improve the accessibility of smart devices and to simplify their usage, building models which understand user interfaces (UIs) and assist users to complete their tasks is critical. However, unique challenges are proposed by UI-specific characteristics, such as how to effectively leverage multimodal UI features that involve image, text, and structural metadata and how to achieve good performance when high-quality labeled data is unavailable. To address such challenges we introduce UIBert, a transformer-based joint image-text model trained through novel pre-training tasks on large-scale unlabeled UI data to learn generic feature representations for a UI and its components. Our key intuition is that the heterogeneous features in a UI are self-aligned, i.e., the image and text features of UI components, are predictive of each other. We propose five pretraining tasks utilizing this self-alignment among different features of a UI component and across various components in the same UI. We evaluate our method on nine real-world downstream UI tasks where UIBert outperforms strong multimodal baselines by up to 9.26% accuracy.
Pruning of Deep Spiking Neural Networks through Gradient Rewiring
Authors: Chen Yanqi , Yu Zhaofei , Fang Wei , Huang Tiejun , Tian Yonghong
Published: August 2021

Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are usually resource-constrained, the compression of SNNs is thus crucial along the road of practical use of SNNs. Most existing methods directly apply pruning approaches in artificial neural networks (ANNs) to SNNs, which ignore the difference between ANNs and SNNs, thus limiting the performance of the pruned SNNs. Besides, these methods are only suitable for shallow SNNs. In this paper, inspired by synaptogenesis and synapse elimination in the neural system, we propose gradient rewiring (Grad R), a joint learning algorithm of connectivity and weight for SNNs, that enables us to seamlessly optimize network structure without retraining. Our key innovation is to redefine the gradient to a new synaptic parameter, allowing better exploration of network structures by taking full advantage of the competition between pruning and regrowth of connections. The experimental results show that the proposed method achieves minimal loss of SNNs' performance on MNIST and CIFAR-10 datasets so far. Moreover, it reaches a ~3.5% accuracy loss under unprecedented 0.73% connectivity, which reveals remarkable structure refining capability in SNNs. Our work suggests that there exists extremely high redundancy in deep SNNs. Our codes are available at https://github.com/Yanqi-Chen/Gradient-Rewiring.
Type Anywhere You Want: An Introduction to Invisible Mobile Keyboard
Authors: Yoo Sahng-Min , Kim Ue-Hwan , Hwang Yewon , Kim Jong-Hwan
Published: August 2021

Contemporary soft keyboards possess limitations: the lack of physical feedback results in an increase of typos, and the interface of soft keyboards degrades the utility of the screen. To overcome these limitations, we propose an Invisible Mobile Keyboard (IMK), which lets users freely type on the desired area without any constraints. To facilitate a data-driven IMK decoding task, we have collected the most extensive text-entry dataset (approximately 2M pairs of typing positions and the corresponding characters). Additionally, we propose our baseline decoder along with a semantic typo correction mechanism based on self-attention, which decodes such unconstrained inputs with high accuracy (96.0%). Moreover, the user study reveals that the users could type faster and feel convenience and satisfaction to IMK with our decoder. Lastly, we make the source code and the dataset public to contribute to the research community.
Choice Logics and Their Computational Properties
Authors: Bernreiter Michael , Maly Jan , Woltran Stefan
Published: August 2021

Qualitative Choice Logic (QCL) and Conjunctive Choice Logic (CCL) are formalisms for preference handling, with especially QCL being well established in the field of AI. So far, analyses of these logics need to be done on a case-by-case basis, albeit they share several common features. This calls for a more general choice logic framework, with QCL and CCL as well as some of their derivatives being particular instantiations. We provide such a framework, which allows us, on the one hand, to easily define new choice logics and, on the other hand, to examine properties of different choice logics in a uniform setting. In particular, we investigate strong equivalence, a core concept in non-classical logics for understanding formula simplification, and computational complexity. Our analysis also yields new results for QCL and CCL. For example, we show that the main reasoning task regarding preferred models is ϴ₂P-complete for QCL and CCL, while being Δ₂P-complete for a newly introduced choice logic.
Scalable Non-observational Predicate Learning in ASP
Authors: Law Mark , Russo Alessandra , Broda Krysia , Bertino Elisa
Published: August 2021

Recently, novel ILP systems under the answer set semantics have been proposed, some of which are robust to noise and scalable over large hypothesis spaces. One such system is FastLAS, which is significantly faster than other state-of-the-art ASP-based ILP systems. FastLAS is, however, only capable of Observational Predicate Learning (OPL), where the learned hypothesis defines predicates that are directly observed in the examples. It cannot learn knowledge that is indirectly observable, such as learning causes of observed events. This class of problems, known as non-OPL, is known to be difficult to handle in the context of non-monotonic semantics. Solving non-OPL learning tasks whilst preserving scalability is a challenging open problem.

We address this problem with a new abductive method for translating examples of a non-OPL task to a set of examples, called possibilities, such that the original example is covered iff at least one of the possibilities is covered. This new method allows an ILP system capable of performing OPL tasks to be "upgraded" to solve non-OPL tasks. In particular, we present our new FastNonOPL system, which upgrades FastLAS with the new possibility generation. We compare it to other state-of-the-art ASP-based ILP systems capable of solving non-OPL tasks, showing that FastNonOPL is significantly faster, and in many cases more accurate, than these other systems.
Likelihood-free Out-of-Distribution Detection with Invertible Generative Models
Authors: Ahmadian Amirhossein , Lindsten Fredrik
Published: August 2021

Likelihood of generative models has been used traditionally as a score to detect atypical (Out-of-Distribution, OOD) inputs. However, several recent studies have found this approach to be highly unreliable, even with invertible generative models, where computing the likelihood is feasible. In this paper, we present a different framework for generative model--based OOD detection that employs the model in constructing a new representation space, instead of using it directly in computing typicality scores, where it is emphasized that the score function should be interpretable as the similarity between the input and training data in the new space. In practice, with a focus on invertible models, we propose to extract low-dimensional features (statistics) based on the model encoder and complexity of input images, and then use a One-Class SVM to score the data. Contrary to recently proposed OOD detection methods for generative models, our method does not require computing likelihood values. Consequently, it is much faster when using invertible models with iteratively approximated likelihood (e.g. iResNet), while it still has a performance competitive with other related methods.