bluuebunny/crossref_metadata_2025 huggingface dataset:
From Support Propagation to Belief Propagation in Constraint Programming (Extended Abstract)
Authors: Pesant Gilles
Published: July 2020

The distinctive driving force of constraint programming (CP) to solve combinatorial problems has been a privileged access to problem structure through the high-level models it uses. We investigate a richer propagation medium for CP made possible by recent work on counting solutions inside constraints. Beliefs about individual variable-value assignments are exchanged between contraints and iteratively adjusted. Its advantage over standard belief propagation is that the higher-level models do not tend to create as many cycles, which are known to be problematic for convergence. We find that it significantly improves search guidance.
The Computational Complexity of Angry Birds (Extended Abstract)
Authors: Stephenson Matthew , Renz Jochen , Ge Xiaoyu
Published: July 2020

In this paper we present several proofs for the computational complexity of the physics-based video game Angry Birds. We are able to demonstrate that solving levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-hard, depending on the maximum number of birds available and whether the game engine is deterministic or stochastic. We believe that this is the first time that a single-player video game has been proven EXPTIME-hard.
Knowing-How under Uncertainty (Extended Abstract)
Authors: Naumov Pavel , Tao Jia
Published: July 2020

Logical systems containing knowledge and know-how modalities have been investigated in several recent works. Independently, epistemic modal logics in which every knowledge modality is labeled with a degree of uncertainty have been proposed. This article combines these two research lines by introducing a bimodal logic containing knowledge and know-how modalities, both labeled with a degree of uncertainty. The main technical results are soundness, completeness, and incompleteness of the proposed logical system with respect to two classes of semantics.
OptStream: Releasing Time Series Privately (Extended Abstract)
Authors: Fioretto Ferdinando , Van Hentenryck Pascal
Published: July 2020

Many applications of machine learning and optimization operate on sensitive data streams, posing significant privacy risks for individuals whose data appear in the stream. Motivated by an application in energy systems, this paper presents OptStream, a novel algorithm for releasing differentially private data streams under the w-event model of privacy. The procedure ensures privacy while guaranteeing bounded error on the released data stream. OptStream is evaluated on a test case involving the release of a real data stream from the largest European transmission operator. Experimental results show that OptStream may not only improve the accuracy of state-of-the-art methods by at least one order of magnitude but also support accurate load forecasting on the privacy-preserving data.
Incentivizing Evaluation with Peer Prediction and Limited Access to Ground Truth (Extended Abstract)
Authors: Gao Alice , Wright James , Leyton-Brown Kevin
Published: July 2020

In many settings, an effective way of evaluating objects of interest is to collect evaluations from dispersed individuals and to aggregate these evaluations together. Some examples are categorizing online content and evaluating student assignments via peer grading. For this data science problem, one challenge is to motivate participants to conduct such evaluations carefully and to report them honestly, particularly when doing so is costly. Existing approaches, notably peer-prediction mechanisms, can incentivize truth telling in equilibrium. However, they also give rise to equilibria in which agents do not pay the costs required to evaluate accurately, and hence fail to elicit useful information. We show that this problem is unavoidable whenever agents are able to coordinate using low-cost signals about the items being evaluated (e.g., text labels or pictures). We then consider ways of circumventing this problem by comparing agents' reports to ground truth, which is available in practice when there exist trusted evaluators---such as teaching assistants in the peer grading scenario---who can perform a limited number of unbiased (but noisy) evaluations. Of course, when such ground truth is available, a simpler approach is also possible: rewarding each agent based on agreement with ground truth with some probability, and unconditionally rewarding the agent otherwise. Surprisingly, we show that the simpler mechanism achieves stronger incentive guarantees given less access to ground truth than a large set of peer-prediction mechanisms.
SelectScale: Mining More Patterns from Images via Selective and Soft Dropout
Authors: Chen Zhengsu , Niu Jianwei , Liu Xuefeng , Tang Shaojie
Published: July 2020

Convolutional neural networks (CNNs) have achieved remarkable success in image recognition. Although the internal patterns of the input images are effectively learned by the CNNs, these patterns only constitute a small proportion of useful patterns contained in the input images. This can be attributed to the fact that the CNNs will stop learning if the learned patterns are enough to make a correct classification. Network regularization methods like dropout and SpatialDropout can ease this problem. During training, they randomly drop the features. These dropout methods, in essence, change the patterns learned by the networks, and in turn, forces the networks to learn other patterns to make the correct classification. However, the above methods have an important drawback. Randomly dropping features is generally inefficient and can introduce unnecessary noise. To tackle this problem, we propose SelectScale. Instead of randomly dropping units, SelectScale selects the important features in networks and adjusts them during training. Using SelectScale, we improve the performance of CNNs on CIFAR and ImageNet.
Transparent Intent for Explainable Shared Control in Assistive Robotics
Authors: Zolotas Mark , Demiris Yiannis
Published: July 2020

Robots supplied with the ability to infer human intent have many applications in assistive robotics. In these applications, robots rely on accurate models of human intent to administer appropriate assistance. However, the effectiveness of this assistance also heavily depends on whether the human can form accurate mental models of robot behaviour. The research problem is to therefore establish a transparent interaction, such that both the robot and human understand each other’s underlying "intent". We situate this problem in our Explainable Shared Control paradigm and present ongoing efforts to achieve transparency in human-robot collaboration.
Towards High-Level Intrinsic Exploration in Reinforcement Learning
Authors: Bougie Nicolas , Ichise Ryutaro
Published: July 2020

Deep reinforcement learning (DRL) methods traditionally struggle with tasks where environment rewards are sparse or delayed, which entails that exploration remains one of the key challenges of DRL. Instead of solely relying on extrinsic rewards, many state-of-the-art methods use intrinsic curiosity as exploration signal. While they hold promise of better local exploration, discovering global exploration strategies is beyond the reach of current methods. We propose a novel end-to-end intrinsic reward formulation that introduces high-level exploration in reinforcement learning. Our curiosity signal is driven by a fast reward that deals with local exploration and a slow reward that incentivizes long-time horizon exploration strategies. We formulate curiosity as the error in an agent’s ability to reconstruct the observations given their contexts. Experimental results show that this high-level exploration enables our agents to outperform prior work in several Atari games.
Learning Sparse Neural Networks for Better Generalization
Authors: Liu Shiwei
Published: July 2020

Deep neural networks perform well on test data when they are highly overparameterized, which, however, also leads to large cost to train and deploy them. As a leading approach to address this problem, sparse neural networks have been widely used to significantly reduce the size of networks, making them more efficient during training and deployment, without compromising performance. Recently, sparse neural networks, either compressed from a pre-trained model or obtained by training from scratch, have been observed to be able to generalize as well as or even better than their dense counterparts. However, conventional techniques to find well fitted sparse sub-networks are expensive and the mechanisms underlying this phenomenon are far from clear. To tackle these problems, this Ph.D. research aims to study the generalization of sparse neural networks, and to propose more efficient approaches that can yield sparse neural networks with generalization bounds.
Design Adaptive AI for RTS Game by Learning Player's Build Order
Authors: Lorthioir Guillaume , Inoue Katsumi
Published: July 2020

Digital games have proven to be valuable simulation environments for plan and goal recognition. Though, goal recognition is a hard problem, especially in the field of digital games where players unintentionally achieve goals through exploratory actions, abandon goals with little warning, or adopt new goals based upon recent or prior events.

In this paper, a method using simulation and bayesian programming to infer the player's strategy in a Real-Time-Strategy game (RTS) is described, as well as how we could use it to make more adaptive AI for this kind of game and thus make more challenging and entertaining games for the players.
Strategies for Cooperative UAVs Using Model Predictive Control
Authors: Manoharan Amith
Published: July 2020

Unmanned aerial vehicles (UAVs) have reached significant

maturity over several years for safe civilian operations like

mapping, search and rescue. The operation performance can

be significantly improved by deploying multiple cooperating

UAVs and optimal decision making. In this work, we present

the use of nonlinear model predictive control (NMPC) for

two different applications involving cooperative UAVs.
Social Network Analysis using RLVECN: Representation Learning via Knowledge-Graph Embeddings and Convolutional Neural-Network
Authors: Molokwu Bonaventure C. , Kobti Ziad
Published: July 2020

Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.
On Building an Interpretable Topic Modeling Approach for the Urdu Language
Authors: Nasim Zarmeen
Published: July 2020

This research is an endeavor to combine deep-learning-based language modeling with classical topic modeling techniques to produce interpretable topics for a given set of documents in Urdu, a low resource language. The existing topic modeling techniques produce a collection of words, often un-interpretable, as suggested topics without integrat-ing them into a semantically correct phrase/sentence. The proposed approach would first build an accurate Part of Speech (POS) tagger for the Urdu Language using a publicly available corpus of many million sentences. Using semanti-cally rich feature extraction approaches including Word2Vec and BERT, the proposed approach, in the next step, would experiment with different clus-tering and topic modeling techniques to produce a list of potential topics for a given set of documents. Finally, this list of topics would be sent to a labeler module to produce syntactically correct phrases that will represent interpretable topics.
Spatio-Temporal Change Detection Using Granger Sequence Pattern
Authors: Pavasant Nat , Numao Masayuki , Fukui Ken-ichi
Published: July 2020

This paper proposed a method to detect changes in causal relations over a multi-dimensional sequence of events. Cluster Sequence Mining algorithm was modified to extract causal relations in the form of g-patterns: a pair of clusters of events that have their occurrence time determined by Granger causality. This paper also proposed the pattern time signature, a probabilistic density function of the cluster sequence occurring at any given time. Synthetic data were used for validation. The result shows that the proposed algorithm can correctly identify the changes in causal relations even under noisy data.
Generating Natural Counterfactual Visual Explanations
Authors: Zhao Wenqi , Oyama Satoshi , Kurihara Masahito
Published: July 2020

Counterfactual explanations help users to understand the behaviors of machine learning models by changing the inputs for the existing outputs. For an image classification task, an example counterfactual visual explanation explains: "for an example that belongs to class A, what changes do we need to make to the input so that the output is more inclined to class B." Our research considers changing the attribute description text of class A on the basis of the attributes of class B and generating counterfactual images on the basis of the modified text. We can use the prediction results of the model on counterfactual images to find the attributes that have the greatest effect when the model is predicting classes A and B. We applied our method to a fine-grained image classification dataset and used the generative adversarial network to generate natural counterfactual visual explanations. To evaluate these explanations, we used them to assist crowdsourcing workers in an image classification task. We found that, within a specific range, they improved classification accuracy.
Predictive Uncertainty Estimation for Tractable Deep Probabilistic Models
Authors: Villanueva Llerena Julissa
Published: July 2020

Tractable Deep Probabilistic Models (TPMs) are generative models based on arithmetic circuits that allow for exact marginal inference in linear time. These models have obtained promising results in several machine learning tasks. Like many other models, TPMs can produce over-confident incorrect inferences, especially on regions with small statistical support. In this work, we will develop efficient estimators of the predictive uncertainty that are robust to data scarcity and outliers. We investigate two approaches. The first approach measures the variability of the output to perturbations of the model weights. The second approach captures the variability of the prediction to changes in the model architecture. We will evaluate the approaches on challenging tasks such as image completion and multilabel classification.
End-to-End Signal Factorization for Speech: Identity, Content, and Style
Authors: Williams Jennifer
Published: July 2020

Preliminary experiments in this dissertation show that it is possible to factorize specific types of information from the speech signal in an abstract embedding space using machine learning. This information includes characteristics of the recording environment, speaking style, and speech quality. Based on these findings, a new technique is proposed to factorize multiple types of information from the speech signal simultaneously using a combination of state-of-the-art machine learning methods for speech processing. Successful speech signal factorization will lead to advances across many speech technologies, including improved speaker identification, detection of speech audio deep fakes, and controllable expression in speech synthesis.
Beyond Labels: Knowledge Elicitation using Deep Metric Learning and Psychometric Testing
Authors: YIn Lu
Published: July 2020

Knowledge present in a domain is well expressed as relationships between corresponding concepts. For example, in zoology, animal species form complex hierarchies; in genomics, the different (parts of) molecules are organized in groups and subgroups based on their functions; plants, molecules, and astronomical objects all form complex taxonomies. Nevertheless, when applying supervised machine learning (ML) in such domains, we commonly reduce the complex and rich knowledge to a fixed set of labels. This oversimplifies and limits the potential impact that the ML solution can deliver. The main reason for such a reductionist approach is the difficulty in eliciting the domain knowledge from the experts. Developing a label structure with sufficient fidelity and providing comprehensive multi-label annotation can be exceedingly labor-intensive in many real-world applications. Here, we provide a method for efficient hierarchical knowledge elicitation (HKE) from experts working with high-dimensional data such as images or videos. Our method is based on psychometric testing and active deep metric learning. The developed models embed the high-dimensional data in a metric space where distances are semantically meaningful, and the data can be organized in a hierarchical structure.
Generalized Representation Learning Methods for Deep Reinforcement Learning
Authors: Zhu Hanhua
Published: July 2020

Deep reinforcement learning (DRL) increases the successful applications of reinforcement learning (RL) techniques but also brings challenges such as low sample efficiency. In this work, I propose generalized representation learning methods to obtain compact state space suitable for RL from a raw observation state. I expect my new methods will increase sample efficiency of RL by understandable representations of state and therefore improve the performance of RL.
GenC: A Fast Tool for Applications Involving Belief Revision
Authors: Hunter Aaron , Agapeyev John
Published: July 2020

The process of belief revision occurs in many applications where agents may have incorrect or incomplete information. One important theoretical model of belief revision is the well-known AGM approach. Unfortunately, there are few tools available for solving AGM revision problems quickly; this has limited the use of AGM operators for practical applications. In this demonstration paper, we describe GenC, a tool that is able to quickly calculate the result of AGM belief revision for formulas with hundreds of variables and millions of clauses. GenC uses an AllSAT solver and parallel processing to solve revision problems at a rate much faster than existing systems. The solver works for the class of parametrised difference operators, which is an extensive class of revision operators that use a weighted Hamming distance to measure the similarity between states. We demonstrate how GenC can be used as a stand-alone tool or as a component of a reasoning system for a variety of applications.
Ddo, a Generic and Efficient Framework for MDD-Based Optimization
Authors: Gillard Xavier , Schaus Pierre , Coppé Vianney
Published: July 2020

This paper presents ddo, a generic and efficient library to solve constraint optimization problems with decision diagrams. To that end, our framework implements the branch-and-bound approach which has recently been introduced by Bergman et al., (2016) to solve dynamic programs to optimality. Our library allowed us to successfully reproduce the results of Bergman et al. for MISP, MCP and MAX2SAT while using a single generic library. As an additional benefit, our ddo library is able to exploit parallel computing for its purpose without imposing any constraint on the user (apart from memory safety). Ddo is released as an open source rust library (crate) alongside with its companion example programs to solve the aforementioned problems. To the best of our knowledge, this is the first public implementation of a generic library to solve combinatorial optimization problems with branch-and-bound MDD.
Keep It Real: a Window to Real Reality in Virtual Reality
Authors: Lin Baihan
Published: July 2020

This paper proposed a new interaction paradigm in the virtual reality (VR) environments, which consists of a virtual mirror or window projected onto a virtual surface, representing the correct perspective geometry of a mirror or window reflecting the real world. This technique can be applied to various videos, live streaming apps, augmented and virtual reality settings to provide an interactive and immersive user experience. To support such a perspective-accurate representation, we implemented computer vision algorithms for feature detection and correspondence matching. To constrain the solutions, we incorporated an automatically tuning scaling factor upon the homography transform matrix such that each image frame follows a smooth transition with the user in sight. The system is a real-time rendering framework where users can engage their real-life presence with the virtual space.
FlowSynth: Simplifying Complex Audio Generation Through Explorable Latent Spaces with Normalizing Flows
Authors: Esling Philippe , Masuda Naotake , Chemla--Romeu-Santos Axel
Published: July 2020

Audio synthesizers are pervasive in modern music production. These highly complex audio generation functions provide a unique diversity through their large sets of parameters. However, this feature also can make them extremely hard and obfuscated to use, especially for non-expert users with no formal knowledge on signal processing.

We recently introduced a novel formalization of the problem of synthesizer control as learning an invertible mapping between an audio latent space, extracted from the audio signal, and a target parameter latent space, extracted from the synthesizer's presets, using normalizing flows. In addition to model a continuous representation allowing to ease the intuitive exploration of the synthesizer, it also provides a ground-breaking method for audio-based parameter inference, vocal control and macro-control learning.

Here, we discuss the details of integrating these high-level features to develop new interaction schemes between a human user and the generating device: parameters inference from audio, high-level preset visualization and interpolation, that can be used both in off-time and real-time situations. Moreover, we also leverage LeapMotion devices to allow the control of hundreds of parameters simply by moving one hand across space to explore the low-dimensional latent space, allowing to both empower and facilitate the user's interaction with the synthesizer.
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization
Authors: Niu Wei , Zhao Pu , Zhan Zheng , Lin Xue , Wang Yanzhi , Ren Bin
Published: July 2020

High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications. However, the constrained computation and storage resources on these devices still pose significant challenges for real-time DNN inference executions. To address this problem, we propose a set of hardware-friendly structured model pruning and compiler optimization techniques to accelerate DNN executions on mobile devices. This demo shows that these optimizations can enable real-time mobile execution of multiple DNN applications, including style transfer, DNN coloring and super resolution.
JPEG Artifacts Removal via Compression Quality Ranker-Guided Networks
Authors: Wang Menglu , Fu Xueyang , Sun Zepei , Zha Zheng-Jun
Published: July 2020

Existing deep learning-based image de-blocking methods use only pixel-level loss functions to guide network training. The JPEG compression factor, which reflects the degradation degree, has not been fully utilized. However, due to the non-differentiability, the compression factor cannot be directly utilized to train deep networks. To solve this problem, we propose compression quality ranker-guided networks for this specific JPEG artifacts removal. We first design a quality ranker to measure the compression degree, which is highly correlated with the JPEG quality. Based on this differentiable ranker, we then propose one quality-related loss and one feature matching loss to guide de-blocking and perceptual quality optimization. In addition, we utilize dilated convolutions to extract multi-scale features, which enables our single model to handle multiple compression quality factors. Our method can implicitly use the information contained in the compression factors to produce better results. Experiments demonstrate that our model can achieve comparable or even better performance in both quantitative and qualitative measurements.
Biased Opinion Dynamics: When the Devil is in the Details
Authors: Anagnostopoulos Aris , Becchetti Luca , Cruciani Emilio , Pasquale Francesco , Rizzo Sara
Published: July 2020

We investigate opinion dynamics in multi-agent networks when there

exists a bias toward one of two possible opinions; for example, reflecting a status quo vs a

superior alternative.

Starting with all agents sharing an initial opinion representing the status

quo, the system evolves in steps. In each step, one agent selected uniformly at

random adopts with some probability a the superior opinion, and with

probability 1 - a it follows an underlying update rule to revise its

opinion on the basis of those held by its neighbors.

We analyze the convergence of the resulting process under two well-known update

rules, namely majority and voter.

The framework we propose exhibits a rich structure, with a nonobvious

interplay between topology and underlying update rule.

For example, for the voter rule we show that the speed of convergence

bears no significant dependence on the underlying topology,

whereas the picture changes completely under the majority rule,

where network density negatively affects convergence.

We believe that the model we propose is at the same time simple, rich, and modular,

affording mathematical characterization of the interplay between bias,

underlying opinion dynamics, and social structure in a unified setting.
Multi-graph Fusion for Functional Neuroimaging Biomarker Detection
Authors: Gan Jiangzhang , Zhu Xiaofeng , Hu Rongyao , Zhu Yonghua , Ma Junbo , Peng Ziwen , Wu Guorong
Published: July 2020

Brain functional connectivity analysis on fMRI data could improve the understanding of human brain function. However, due to the influence of the inter-subject variability and the heterogeneity across subjects, previous methods of functional connectivity analysis are often insufficient in capturing disease-related representation so that decreasing disease diagnosis performance.

In this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ L1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data. The multi-graph fusion framework automatically learns the connectivity number for every node (i.e., brain region) and integrates all subjects in a unified framework to output homogenous and discriminative representations of all subjects. Experimental results on two real data sets, i.e., fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods.
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation
Authors: He Tao , Gao Lianli , Song Jingkuan , Cai Jianfei , Li Yuan-Fang
Published: July 2020

Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue. Existing methods largely rely on either external knowledge or statistical bias information to alleviate this problem. In this paper, we tackle this issue from another two aspects: (1) scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism; and (2) long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail. Extensive experiments on the benchmark dataset Visual Genome on three tasks demonstrate that our method outperforms current state-of-the-art competitors. Our source code is available at https://github.com/htlsn/issg.
SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor
Authors: Xu Jiachen , Gong Jingyu , Zhou Jie , Tan Xin , Xie Yuan , Ma Lizhuang
Published: July 2020

Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information and make full use of it. In this paper, we propose a SceneEncoder module to impose a scene-aware guidance to enhance the effect of global information. The module predicts a scene descriptor, which learns to represent the categories of objects existing in the scene and directly guides the point-level semantic segmentation through filtering out categories not belonging to this scene. Additionally, to alleviate segmentation noise in local region, we design a region similarity loss to propagate distinguishing features to their own neighboring points with the same label, leading to the enhancement of the distinguishing ability of point-wise features. We integrate our methods into several prevailing networks and conduct extensive experiments on benchmark datasets ScanNet and ShapeNet. Results show that our methods greatly improve the performance of baselines and achieve state-of-the-art performance.
Spatiotemporal Super-Resolution with Cross-Task Consistency and Its Semi-supervised Extension
Authors: Lin Han-Yi , Hsiu Pi-Cheng , Kuo Tei-Wei , Lin Yen-Yu
Published: July 2020

Spatiotemporal super-resolution (SR) aims to upscale both the spatial and temporal dimensions of input videos, and produces videos with higher frame resolutions and rates. It involves two essential sub-tasks: spatial SR and temporal SR. We design a two-stream network for spatiotemporal SR in this work. One stream contains a temporal SR module followed by a spatial SR module, while the other stream has the same two modules in the reverse order. Based on the interchangeability of performing the two sub-tasks, the two network streams are supposed to produce consistent spatiotemporal SR results. Thus, we present a cross-stream consistency to enforce the similarity between the outputs of the two streams. In this way, the training of the two streams is correlated, which allows the two SR modules to share their supervisory signals and improve each other. In addition, the proposed cross-stream consistency does not consume labeled training data and can guide network training in an unsupervised manner. We leverage this property to carry out semi-supervised spatiotemporal SR. It turns out that our method makes the most of training data, and can derive an effective model with few high-resolution and high-frame-rate videos, achieving the state-of-the-art performance. The source code of this work is available at https://hankweb.github.io/STSRwithCrossTask/.
Super-Resolution and Inpainting with Degraded and Upgraded Generative Adversarial Networks
Authors: Huang Yawen , Zheng Feng , Wang Danyang , Jiang Junyu , Wang Xiaoqian , Shao Ling
Published: July 2020

Image super-resolution (SR) and image inpainting are two topical problems in medical image processing. Existing methods for solving the problems are either tailored to recovering a high-resolution version of the low-resolution image or focus on filling missing values, thus inevitably giving rise to poor performance when the acquisitions suffer from multiple degradations. In this paper, we explore the possibility of super-resolving and inpainting images to handle multiple degradations and therefore improve their usability. We construct a unified and scalable framework to overcome the drawbacks of propagated errors caused by independent learning. We additionally provide improvements over previously proposed super-resolution approaches by modeling image degradation directly from data observations rather than bicubic downsampling. To this end, we propose HLH-GAN, which includes a high-to-low (H-L) GAN together with a low-to-high (L-H) GAN in a cyclic pipeline for solving the medical image degradation problem. Our comparative evaluation demonstrates that the effectiveness of the proposed method on different brain MRI datasets. In addition, our method outperforms many existing super-resolution and inpainting approaches.
Real-World Automatic Makeup via Identity Preservation Makeup Net
Authors: Huang Zhikun , Zheng Zhedong , Yan Chenggang , Xie Hongtao , Sun Yaoqi , Wang Jianzhong , Zhang Jiyong
Published: July 2020

This paper focuses on the real-world automatic makeup problem. Given one non-makeup target image and one reference image, the automatic makeup is to generate one face image, which maintains the original identity with the makeup style in the reference image. In the real-world scenario, face makeup task demands a robust system against the environmental variants. The two main challenges in real-world face makeup could be summarized as follow: first, the background in real-world images is complicated. The previous methods are prone to change the style of background as well; second, the foreground faces are also easy to be affected. For instance, the ``heavy'' makeup may lose the discriminative information of the original identity. To address these two challenges, we introduce a new makeup model, called Identity Preservation Makeup Net (IPM-Net), which preserves not only the background but the critical patterns of the original identity. Specifically, we disentangle the face images to two different information codes, i.e., identity content code and makeup style code. When inference, we only need to change the makeup style code to generate various makeup images of the target person. In the experiment, we show the proposed method achieves not only better accuracy in both realism (FID) and diversity (LPIPS) in the test set, but also works well on the real-world images collected from the Internet.
Human Consensus-Oriented Image Captioning
Authors: Wang Ziwei , Huang Zi , Luo Yadan
Published: July 2020

Image captioning aims to describe an image with a concise, accurate, and interesting sentence. To build such an automatic neural captioner, the traditional models align the generated words with a number of human-annotated sentences to mimic human-like captions. However, the crowd-sourced annotations inevitably come with data quality issues such as grammatical errors, wrong identification of visual objects and sub-optimal sentence focus. During the model training, existing methods treat all the annotations equally regardless of the data quality. In this work, we explicitly engage human consensus to measure the quality of ground truth captions in advance, and directly encourage the model to learn high quality captions with high priority. Therefore, the proposed consensus-oriented method can accelerate the training process and achieve superior performance with only supervised objective without time-consuming reinforcement learning. The novel consensus loss can be implemented into most of the existing state-of-the-art methods, boosting the BLEU-4 performance by maximum relative 12.47% comparing to the conventional cross-entropy loss. Extensive experiments are conducted on MS-COCO Image Captioning dataset demonstrating the proposed human consensus-oriented training method can significantly improve the training efficiency and model effectiveness.
Biased Feature Learning for Occlusion Invariant Face Recognition
Authors: Shao Changbin , Huo Jing , Qi Lei , Feng Zhen-Hua , Li Wenbin , Dong Chuanqi , Gao Yang
Published: July 2020

To address the challenges posed by unknown occlusions, we propose a Biased Feature Learning (BFL) framework for occlusion-invariant face recognition. We first construct an extended dataset using a multi-scale data augmentation method. For model training, we modify the label loss to adjust the impact of normal and occluded samples. Further, we propose a biased guidance strategy to manipulate the optimization of a network so that the feature embedding space is dominated by non-occluded faces. BFL not only enhances the robustness of a network to unknown occlusions but also maintains or even improves its performance for normal faces. Experimental results demonstrate its superiority as well as the generalization capability with different network architectures and loss functions.
GestureDet: Real-time Student Gesture Analysis with Multi-dimensional Attention-based Detector
Authors: Zheng Rui , Jiang Fei , Shen Ruimin
Published: July 2020

Students’ gestures, hand-raising, stand-up, and sleeping, indicates the engagement of students in classrooms and partially reflects teaching quality. Therefore, fast and automatically recognizing these gestures are of great importance. Due to limited computational resources in primary and secondary schools, we propose a real-time student behavior detector based on light-weight MobileNetV2-SSD to reduce the dependency of GPUs. Firstly, we build a large-scale corpus from real schools to capture various behavior gestures. Based on such a corpus, we transfer the gesture recognition task into object detections. Secondly, we design a multi-dimensional attention-based detector, named GestureDet, for real-time and accurate gesture analysis. The multi-dimensional attention mechanisms simultaneously consider all the dimensions of the training set, aiming to pay more attention to discriminative features and samples that are important for the final performance. Specifically, the spatial attention is constructed with stacked dilated convolution layers to generate a soft and learnable mask for re-weighting foreground and background features; the channel attention introduces the context modeling and squeeze-and-excitation module to focus on discriminative features; the batch attention discriminates important samples with a new designed reweight strategy. Experimental results demonstrate the effectiveness and versatility of GestureDet, which achieves 75.2% mAP on real student behavior dataset, and 74.5% on public PASCAL VOC dataset at 20fps on embedding device Nvidia Jetson TX2. Code will be made publicly available.
DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue
Authors: Jiang Xiaoze , Yu Jing , Sun Yajing , Qin Zengchang , Zhu Zihao , Hu Yue , Wu Qi
Published: July 2020

Visual Dialogue task requires an agent to be engaged in a conversation with human about an image. The ability of generating detailed and non-repetitive responses is crucial for the agent to achieve human-like conversation. In this paper, we propose a novel generative decoding architecture to generate high-quality responses, which moves away from decoding the whole encoded semantics towards the design that advocates both transparency and flexibility. In this architecture, word generation is decomposed into a series of attention-based information selection steps, performed by the novel recurrent Deliberation, Abandon and Memory (DAM) module. Each DAM module performs an adaptive combination of the response-level semantics captured from the encoder and the word-level semantics specifically selected for generating each word. Therefore, the responses contain more detailed and non-repetitive descriptions while maintaining the semantic accuracy. Furthermore, DAM is flexible to cooperate with existing visual dialogue encoders and adaptive to the encoder structures by constraining the information selection mode in DAM. We apply DAM to three typical encoders and verify the performance on the VisDial v1.0 dataset. Experimental results show that the proposed models achieve new state-of-the-art performance with high-quality responses. The code is available at https://github.com/JXZe/DAM.
TLPG-Tracker: Joint Learning of Target Localization and Proposal Generation for Visual Tracking
Authors: Li Siyuan , Zhang Zhi , Liu Ziyu , Wang Anna , Qiu Linglong , Du Feng
Published: July 2020

Target localization and proposal generation are two essential subtasks in generic visual tracking, and it is a challenge to address both the two efficiently. In this paper, we propose an efficient two-stage architecture which makes full use of the complementarity of two subtasks to achieve robust localization and high-quality proposals generation of the target jointly. Specifically, our model performs a novel deformable central correlation operation by an online learning model in both two stages to locate new target centers while generating target proposals in the vicinity of these centers. The proposals are refined in the refinement stage to further improve accuracy and robustness. Moreover, the model benefits from multi-level features aggregation in a neck module and a feature enhancement module. We conduct extensive ablation studies to demonstrate the effectiveness of our proposed methods. Our tracker runs at over 30 FPS and sets a new state-of-the-art on five tracking benchmarks, including LaSOT, VOT2018, TrackingNet, GOT10k, OTB2015.
The Potential of Arbuscular Mycorrhizal Fungi (AMF) as Biocontrol Agent Against Stem Rot Diseases Caused Sclerotium rolfsii of peanut (Arachis hypogaea L)
Authors: Sulyanti Eri
Published: July 2020

This study was conducted to assess the biocontrol efficacy of arbuscular mycorrhizae fungi (AM Fungi) against stem rot disease caused by Sclerotium rolfsii Sacc. in peanut. The AM Fungi can be associated with almost all types of plants. The purpose of this study was to obtain isolates of arbuscular mycorrhiza fungi (AMF) as a potential biofungisida against Sclerotium rolfsii and can characterize the mechanism of the FMA in controlling S.rolfsii (salicylic acid) on peanut plants. The AM Fungi inoculant (40 spores g-1 in concentration) was introduced to peanut seedling (25 g plant-1 ) at planting time where as Sclerotium rolfsii  inoculated 30 days after planting time. The experiment was arranged in the completely randomized design (CRD), which is 7 treatment sand  repeated 10 times in the greenhouse  experiment. The data were analyzed using analysis of variance (ANOVA) using STAT program 8 and  the Tukey test at 5% significance level. The AM Fungi treatments showed significantly redused the percentage of disease severity in infected peanut plants around 34.28% - 57.15%  and longer incubation period, respectively. They increased root colonization  (20,00 - 46.67%) with a middle to high category. The AM Fungi C isolate (isolated from Solok county), and  the A isolate (isolated from Payakumbuh city)  were the best as a biocontrol against S rolfsii (57.15%), followed by isolate D (isolated from Padang Pariaman county) 54,30 %. They also increased Salicylic acid content 1,4 times (70.72 ppm) compared to control (49,59 ppm). It can be concluded that the application of AM Fungi as a biocontrol agent played an important role in plant resistance and exhibit greater potential to protect peanut plants against S. rolfsii.
Efficacy of bioinoculants on biomass, nutritional status and yield of lemon grass (Cymbopogon citratus (DC.) Stapf.).
Authors: Kumar Ashish , Gupta A. , Aggarwal A. , Bhargav V.
Published: July 2020

In the present investigation, different bioinoculants were analysed to test their efficacy as bio-fertilizer value for medicinal plant i.e. Lemongrass (Cymbopogon citratus (DC.) Stapf.) under controlled conditions in a poly house. Bioinoculants including dominant Arbuscular Mycorhizal Fungi (Glomus mosseae and Acaulospora laevis) and Phosphate solubilizing Bacteria (Pseudomonas fluorescens) were taken in present experimentation. The independent roles of these bioinoculants or with different combinations were assessed on one month old saplings of C. citratus in pot conditions.The results showed that application of these bio-inoculants were very effective for growth responses, biomass, biochemical, yield parameters. A consortium of G. mosseae, A. laevis and P. fluorescens showed best results for plant height, root length and in increasing the biomass. Mycorrhization status was recovered highest in the consortium treatment. The biochemical and yield (biomass) parameters were also better with consortium. The findings of the present study are informative for herbalist, to use these efficient bioinoculants as bio-fertilizer for the better yield of C. citratus along with least adverse effects on microbial diversity and stability of soil as compared to commercially used inorganic fertilizers
A LOGÍSTICA REVERSA E A RESPONSABILIDADE AMBIENTAL FRENTE A POLÍTICA NACIONAL DE RESÍDUOS SÓLIDOS
Authors: Picolli Ícaro Roberto Azevedo , Marcon Gabriela Almeida , Burlani Neves Rafael
Published: July 2020

O presente artigo tem como objetivo geral analisar a responsabilidade civil das empresas e fábricas em face da Política Nacional de Resíduos Sólidos (PNRS). Para que este objetivo seja alcançado, são propostos os seguintes objetivos específicos: a) analisar a lei da PNRS; b) conhecer os conceitos de responsabilidade socioambiental e c) abordar a temática da logística reversa. Com o presente estudo, foi possível observar aspectos da responsabilidade socioambiental das organizações ante a PNRS. As empresas voltaram suas atenções para o bem-estar da comunidade onde estão inseridas. Passaram a compartilhar da responsabilidade pelo ciclo de vida dos bens de consumo que fornecem, sendo chamado esse processo de logística reversa.
PERBANDINGAN NOVEL CANTIK ITU LUKA KARYA EKA KURNIAWAN DENGAN MIDAH SIMANIS BERGIGI EMAS KARYA PRAMOEDYA ANANTA TOER
Authors: Fazalani Runi
Published: July 2020

ABSTRAK: Tujuan dari penelitian ini adalah untuk mengetahui betapa berpengaruhnya kecanti-kan yang dimiliki oleh seorang perempuan tokoh utama dalam novel Cantik Itu Luka karya Eka Kurniawan dan novel Midah Simanis Bergigi Emas karya Pramoedya Ananta Toer. Kecantikan dapat mengakibatkan keperibadian seseorang merasa terancam. Adapun masalah yang dibahas dalam penelitian ini adalah bagaimana perbandingan pengaruh cantik terhadap tokoh utama perempuan dalam novel Cantik Itu Luka karya Eka Kurniawan dan novel Midah Simanis Bergigi Emas karya Pramoedya Ananta Toer. Penelitian ini menggunakan metode deskriptif kualitatif dengan pemahaman secara mendalam dengan menggunakan pendekatan psikologi sastra. Sumber datanya novel Cantik Itu Luka karya Eka Kurniawan dan novel Midah Simanis Bergigi Emas karya Pramoedya Ananta Toer. Data yang bersumber dari satuan-satuan bahasa yang berupa kutipan cerita. Hasil penelitian menunjukkan pengaruh kecantikan yang dimiliki oleh tokoh utama dalam novel Cantik Itu Luka karya Eka Kurniawan.KATA KUNCI: psikologi sastra, kecantikan dan perempuan.��ABSTRACT: The objectives of this reseach are how the influence owned by beauty a women the main character in Cantik Itu Luka novel by Eka Kurniawan and Midah Simanis Bergigi Emas by Pramoedya Ananta Toer . The beauty can lead the personality of a feeling person threatened. The as for problem to in this research is how the comparison of the beautiful influence of the main character womwns in the Cantik Itu Luka novel by Eka Kurniawan and Midah Simanis Bergigi Emas� by Pramoedya Ananta Toer. This reseach using qualitative descriptive method with deep understanding and the using literature psychology the approach. The source of the data are Cantik Itu Luka novel by Eka Kurniawan dan Midah Simanis Bergigi Emas novel by Pramoedya Ananta Toer. The data source from the language units in the from of story quotes. The research of results show the influence of a beauty possessed by the main character in the Cantik Itu Luka novel by Eka Kurniawan and Midah Simanis Bergigi Emas novel by Pramoedya Ananta Toer.KEY WORDS: literature psychology, beauty and womens.