bluuebunny/crossref_metadata_2025 huggingface dataset:
FISH-MML: Fisher-HSIC Multi-View Metric Learning
Authors: Zhang Changqing , Liu Yeqinq , Liu Yue , Hu Qinghua , Liu Xinwang , Zhu Pengfei
Published: July 2018

This work presents a simple yet effective model for multi-view metric learning, which aims to improve the classification of data with multiple views, e.g., multiple modalities or multiple types of features. The intrinsic correlation, different views describing same set of instances, makes it possible and necessary to jointly learn multiple metrics of different views, accordingly, we propose a multi-view metric learning method based on Fisher discriminant analysis (FDA) and Hilbert-Schmidt Independence Criteria (HSIC), termed as Fisher-HSIC Multi-View Metric Learning (FISH-MML). In our approach, the class separability is enforced in the spirit of FDA within each single view, while the consistence among different views is enhanced based on HSIC. Accordingly, both intra-view class separability and inter-view correlation are well addressed in a unified framework. The learned metrics can improve multi-view classification, and experimental results on real-world datasets demonstrate the effectiveness of the proposed method.
On the Convergence Properties of a K-step Averaging Stochastic Gradient Descent Algorithm for Nonconvex Optimization
Authors: Zhou Fan , Cong Guojing
Published: July 2018

We adopt and analyze a synchronous K-step averaging stochastic gradient descent algorithm which we call K-AVG  for solving large scale machine learning problems. We establish the convergence results of K-AVG for nonconvex objectives. Our analysis of K-AVG applies to many existing variants of synchronous SGD.  We explain why the K-step delay is necessary and leads to better performance than traditional parallel stochastic gradient descent which is equivalent to K-AVG with $K=1$. We also show that K-AVG scales better with the number of learners than asynchronous stochastic gradient descent (ASGD). Another advantage of K-AVG over ASGD is that it allows larger stepsizes and facilitates faster convergence. On a cluster of $128$ GPUs, K-AVG is faster than ASGD implementations and achieves better accuracies and faster convergence for training with the CIFAR-10 dataset.
Towards Generalized and Efficient Metric Learning on Riemannian Manifold
Authors: Zhu Pengfei , Cheng Hao , Hu Qinghua , Wang Qilong , Zhang Changqing
Published: July 2018

Modeling data as points on non-linear Riemannian manifold has attracted increasing attentions in many computer vision tasks, especially visual recognition. Learning an appropriate metric on Riemannian manifold plays a key role in achieving promising performance. For widely used symmetric positive definite (SPD) manifold and Grassmann manifold, most of existing metric learning methods are designed for one manifold, and are not straightforward for the other one. Furthermore, optimizations in previous methods usually rely on computationally expensive iterations. To address above limitations, this paper makes an attempt to propose a generalized and efficient Riemannian manifold metric learning (RMML) method, which can be flexibly adopted to both SPD and Grassmann manifolds. By minimizing the geodesic distance of similar pairs and the interpoint geodesic distance of dissimilar ones on nonlinear manifolds, the proposed RMML is optimized by computing the geodesic mean between inverse of similarity matrix and dissimilarity matrix, benefiting a global closed-form solution and high efficiency. The experiments are conducted on various visual recognition tasks, and the results demonstrate our RMML performs favorably against its counterparts in terms of both accuracy and efficiency.
Deep Attributed Network Embedding
Authors: Gao Hongchang , Huang Heng
Published: July 2018

Network embedding has attracted a surge of attention in recent years. It is to learn the low-dimensional representation for nodes in a network, which benefits downstream tasks such as node classification and link prediction. Most of the existing approaches learn node representations only based on the topological structure, yet nodes are often associated with rich attributes in many real-world applications. Thus, it is important and necessary to learn node representations based on both the topological structure and node attributes. In this paper, we propose a novel deep attributed network embedding approach, which can capture the high non-linearity and preserve various proximities in both topological structure and node attributes. At the same time, a novel strategy is proposed to guarantee the learned node representation can encode the consistent and complementary information from the topological structure and node attributes. Extensive experiments on benchmark datasets have verified the effectiveness of our proposed approach.
GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction
Authors: Liang Yuxuan , Ke Songyu , Zhang Junbo , Yi Xiuwen , Zheng Yu
Published: July 2018

Numerous sensors have been deployed in different geospatial locations to continuously and cooperatively monitor the surrounding environment, such as the air quality. These sensors generate multiple geo-sensory time series, with spatial correlations between their readings. Forecasting geo-sensory time series is of great importance yet very challenging as it is affected by many complex factors, i.e., dynamic spatio-temporal correlations and external factors. In this paper, we predict the readings of a geo-sensor over several future hours by using a multi-level attention-based recurrent neural network that considers multiple sensors' readings, meteorological data, and spatial data. More specifically, our model consists of two major parts: 1) a multi-level attention mechanism to model the dynamic spatio-temporal dependencies. 2) a general fusion module to incorporate the external factors from different domains. Experiments on two types of real-world datasets, viz., air quality data and water quality data, demonstrate that our method outperforms nine baseline methods.
Pairwise-Ranking based Collaborative Recurrent Neural Networks for Clinical Event Prediction
Authors: Qiao Zhi , Zhao Shiwan , Xiao Cao , Li Xiang , Qin Yong , Wang Fei
Published: July 2018

Patient Electronic Health Records (EHR) data consist of sequences of patient visits over time. Sequential prediction of patients' future clinical events (e.g., diagnoses) from their historical EHR data is a core research task and motives a series of predictive models including deep learning. The existing research mainly adopts a classification framework, which treats the observed and unobserved events as positive and negative classes. However, this may not be true in real clinical setting considering the high rate of missed diagnoses and human errors. In this paper, we propose to formulate the clinical event prediction problem as an events recommendation problem. An end-to-end pairwise-ranking based collaborative recurrent neural networks (PacRNN) is proposed to solve it, which firstly embeds patient clinical contexts with attention RNN, then uses Bayesian Personalized Ranking (BPR) regularized by disease co-occurrence to rank probabilities of patient-specific diseases, as well as use point process to provide simultaneous prediction of the occurring time of these diagnoses. Experimental results on two real world EHR datasets demonstrate the robust performance, interpretability, and efficacy of PacRNN.
Where Have You Been? Inferring Career Trajectory from Academic Social Network
Authors: Wu Kan , Tang Jie , Zhang Chenhui
Published: July 2018

A person’s career trajectory is composed of her/his past work or educational affiliations (institutions) at different points of times. Knowing people’s, especially scholars’, career trajectories can help the government make more scientific strategies to allocate resources and attract talent and help companies make smart recruiting plans. It could also support individuals find appropriate co-researchers or job opportunities. The paper focuses on inferring career trajectories in the academic social network. For about 1/3 of authors not having any affiliations in the dataset, we need to infer the missings at various years. Traditional affiliation/location inferring methods focus on inferring a stationary location (one and only) for a person. Nevertheless, people won’t stay at a place all their lives. We propose a Space-Time Factor Graph Model (STFGM) incorporating spatial and temporal correlations to fulfill the challenging and new task of inferring temporal locations. Experiments show our approach significantly outperforms baselines. At last, as case study, we develop several applications based on our approach which demonstrate the effectiveness further.
CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering
Authors: Zhang Quangui , Cao Longbing , Zhu Chengzhang , Li Zhiqiang , Sun Jinguang
Published: July 2018

Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as in- dependent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better ex- plain how and why a user has personalized pref- erence on an item. This work builds on non- IID learning to propose a neural user-item cou- pling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recom- menders: neural matrix factorization and Google’s Wide&Deep network.
Fact Checking via Evidence Patterns
Authors: Fionda Valeria , Pirrò Giuseppe
Published: July 2018

We tackle fact checking using Knowledge Graphs (KGs) as a source of background knowledge. Our approach leverages the KG schema to generate candidate evidence patterns, that is, schema-level paths that capture the semantics of a target fact in alternative ways. Patterns verified in the data are used to both assemble semantic evidence for a fact and provide a numerical assessment of its truthfulness. We present efficient algorithms to generate and verify evidence patterns, and assemble evidence. We also provide a translation of the core of our algorithms into the SPARQL query language. Not only our approach is faster than the state of the art and offers comparable accuracy, but it can also use any SPARQL-enabled KG.
Weakly Learning to Match Experts in Online Community
Authors: Qian Yujie , Tang Jie , Wu Kan
Published: July 2018

In online question-and-answer (QA) websites like Quora, one central issue is to find (invite) users who are able to provide answers to a given question and at the same time would be unlikely to say "no" to the invitation. The challenge is how to trade off the matching degree between users’ expertise and the question topic, and the likelihood of positive response from the invited users. In this paper, we formally formulate the problem and develop a weakly supervised factor graph (WeakFG) model to address the problem. The model explicitly captures expertise matching degree between questions and users. To model the likelihood that an invited user is willing to answer a specific question, we incorporate a set of correlations based on social identity theory into the WeakFG model. We use two different genres of datasets: QA-Expert and Paper-Reviewer, to validate the proposed model. Our experimental results show that the proposed model can significantly outperform (+1.5-10.7% by MAP) the state-of-the-art algorithms for matching users (experts) with community questions. We have also developed an online system to further demonstrate the advantages of the proposed method.
Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification
Authors: Wang Zheng , Ye Mang , Yang Fan , Bai Xiang , Satoh Shin'ichi
Published: July 2018

Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SR-GAN is one of the most competitive image super-resolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.
Generating Adversarial Examples with Adversarial Networks
Authors: Xiao Chaowei , Li Bo , Zhu Jun-yan , He Warren , Liu Mingyan , Song Dawn
Published: July 2018

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial exam- ples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply Adv- GAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.
A Question Type Driven Framework to Diversify Visual Question Generation
Authors: Fan Zhihao , Wei Zhongyu , Li Piji , Lan Yanyan , Huang Xuanjing
Published: July 2018

Visual question generation aims at asking questions about an image automatically. Existing research works on this topic usually generate a single question for each given image without considering the issue of diversity. In this paper, we propose a question type driven framework to produce multiple questions for a given image with different focuses. In our framework, each question is constructed following the guidance of a sampled question type in a sequence-to-sequence fashion. To diversify the generated questions, a novel conditional variational auto-encoder is introduced to generate multiple questions with a specific question type. Moreover, we design a strategy to conduct the question type distribution learning for each image to select the final questions. Experimental results on three benchmark datasets show that our framework outperforms the state-of-the-art approaches in terms of both relevance and diversity.
Approximating Word Ranking and Negative Sampling for Word Embedding
Authors: Guo Guibing , Ouyang Shichang , Yuan Fajie , Wang Xingwei
Published: July 2018

CBOW (Continuous Bag-Of-Words) is one of the most commonly used

techniques to generate word embeddings in various NLP tasks. However, it fails to reach the optimal performance due to uniform involvements of positive words and a simple sampling distribution of negative words. To resolve these issues, we propose OptRank to optimize word ranking and approximate negative sampling for bettering word embedding. Specifically, we first formalize word embedding as a ranking problem. Then, we weigh the positive words by their ranks such that highly ranked words have more importance, and adopt a dynamic sampling strategy to select informative negative words. In addition, an approximation method is designed to efficiently compute word ranks. Empirical experiments show that OptRank consistently outperforms its counterparts on a benchmark dataset with different sampling scales, especially when the sampled subset is small. The code and datasets can be obtained from https://github.com/ouououououou/OptRank.
A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification
Authors: Ma Shuming , Sun Xu , Lin Junyang , Ren Xuancheng
Published: July 2018

Text summarization and sentiment classification both aim to capture the main ideas of the text but at different levels. Text summarization is to describe the text within a few sentences, while sentiment classification can be regarded as a special type of summarization which ``summarizes'' the text into a even more abstract fashion, i.e., a sentiment class. Based on this idea, we propose a hierarchical end-to-end model for joint learning of text summarization and sentiment classification, where the sentiment classification label is treated as the further ``summarization'' of the text summarization output. Hence, the sentiment classification layer is put upon the text summarization layer, and a hierarchical structure is derived. Experimental results on Amazon online reviews datasets show that our model achieves better performance than the strong baseline systems on both abstractive summarization and sentiment classification.
Interpretable Adversarial Perturbation in Input Embedding Space for Text
Authors: Sato Motoki , Suzuki Jun , Shindo Hiroyuki , Matsumoto Yuji
Published: July 2018

Following great success in the image processing field, the idea of adversarial training has been applied to tasks in the natural language processing (NLP) field.

One promising approach directly applies adversarial training developed in the image processing field to the input word embedding space instead of the discrete input space of texts.

However, this approach abandons such interpretability as generating adversarial texts to significantly improve the performance of NLP tasks.

This paper restores interpretability to such methods by restricting the directions of perturbations toward the existing words in the input embedding space.

As a result, we can straightforwardly reconstruct each input with perturbations to an actual text by considering the perturbations to be the replacement of words in the sentence while maintaining or even improving the task performance.
Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks
Authors: Wang Jingjing , Li Jie , Li Shoushan , Kang Yangyang , Zhang Min , Si Luo , Zhou Guodong
Published: July 2018

Aspect sentiment classification, a challenging task in sentiment analysis, has been attracting more and more attention in recent years. In this paper, we highlight the need for incorporating the importance degrees of both words and clauses inside a sentence and propose a hierarchical network with both word-level and clause-level attentions to aspect sentiment classification. Specifically, we first adopt sentence-level discourse segmentation to segment a sentence into several clauses. Then, we leverage multiple Bi-directional LSTM layers to encode all clauses and propose a word-level attention layer to capture the importance degrees of words in each clause. Third and finally, we leverage another Bi-directional LSTM layer to encode the outputs from the former layers and propose a clause-level attention layer to capture the importance degrees of all the clauses inside a sentence. Experimental results on the laptop and restaurant datasets from SemEval-2015 demonstrate the effectiveness of our proposed approach to aspect sentiment classification.
Densely Connected CNN with Multi-scale Feature Attention for Text Classification
Authors: Wang Shiyao , Huang Minlie , Deng Zhidong
Published: July 2018

Text classification is a fundamental problem in natural language processing. As a popular deep learning model, convolutional neural network (CNN) has demonstrated great success in this task. However, most existing CNN models apply convolution filters of fixed window size, thereby unable to learn variable n-gram features flexibly. In this paper, we present a densely connected CNN with multi-scale feature attention for text classification. The dense connections build short-cut paths between upstream and downstream convolutional blocks, which enable the model to compose features of larger scale from those of smaller scale, and thus produce variable n-gram features. Furthermore, a multi-scale feature attention is developed to adaptively select multi-scale features for classification. Extensive experiments demonstrate that our model obtains competitive performance against state-of-the-art baselines on five benchmark datasets. Attention visualization further reveals the model's ability to select proper n-gram features for text classification.
Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation
Authors: Zhang Hainan , Lan Yanyan , Guo Jiafeng , Xu Jun , Cheng Xueqi
Published: July 2018

Sequence to sequence (Seq2Seq) approach has gained great attention in the field of single-turn dialogue generation. However, one serious problem is that most existing Seq2Seq based models tend to generate common responses lacking specific meanings. Our analysis show that the underlying reason is that Seq2Seq is equivalent to optimizing Kullback–Leibler (KL) divergence, thus does not penalize the case whose generated probability is high while the true probability is low. However, the true probability is unknown, which poses challenges for tackling this problem. Inspired by the fact that the coherence (i.e. similarity) between post and response is consistent with human evaluation, we hypothesize that the true probability of a response is proportional to the coherence degree. The coherence scores are then used as the reward function in a reinforcement learning framework to penalize the case whose generated probability is high while the true probability is low. Three different types of coherence models, including an unlearned similarity function, a pretrained semantic matching function, and an end-to-end dual learning architecture, are proposed in this paper. Experimental results on both Chinese Weibo dataset and English Subtitle dataset show that the proposed models produce more specific and meaningful responses, yielding better performances against Seq2Seq models in terms of both metric-based and human evaluations.
Computational Approaches for Stochastic Shortest Path on Succinct MDPs
Authors: Chatterjee Krishnendu , Fu Hongfei , Goharshady Amir , Okati Nastaran
Published: July 2018

We consider the stochastic shortest path (SSP) problem for succinct Markov decision processes (MDPs), where the MDP consists of a set of variables, and a set of nondeterministic rules that update the variables. First, we show that several examples from the AI literature can be modeled as succinct MDPs. Then we present computational approaches for upper and lower bounds for the SSP problem: (a) for computing upper bounds, our method is polynomial-time in the implicit description of the MDP; (b) for lower bounds, we present a polynomial-time (in the size of the implicit description) reduction to quadratic programming. Our approach is applicable even to infinite-state MDPs. Finally, we present experimental results to demonstrate the effectiveness of our approach on several classical examples from the AI literature.
PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making
Authors: Yang Fangkai , Lyu Daoming , Liu Bo , Gustafson Steven
Published: July 2018

Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may not be robust to domain uncertainties and changes. In this paper we present a unified framework PEORL that integrates symbolic planning with hierarchical reinforcement learning (HRL) to cope with decision-making in dynamic environment with uncertainties. Symbolic plans are used to guide the agent's task execution and learning, and the learned experience is fed back to symbolic knowledge to improve planning. This method leads to rapid policy search and robust symbolic plans in complex domains. The framework is tested on benchmark domains of HRL.
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
Authors: Li Siyi , Liu Tianbo , Zhang Chi , Yeung Dit-Yan , Shen Shaojie
Published: July 2018

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative (PID) controller to enable stable learning without catastrophic failure. The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control.
Behavioral Cloning from Observation
Authors: Torabi Faraz , Warnell Garrett , Stone Peter
Published: July 2018

Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations. While extending this paradigm to autonomous agents is a well-studied problem in general, there are two particular aspects that have largely been overlooked: (1) that the learning is done from observation only (i.e., without explicit action information), and (2) that the learning is typically done very quickly. In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects. First, we allow the agent to acquire experience in a self-supervised fashion. This experience is used to develop a model which is then utilized to learn a particular task by observing an expert perform that task without the knowledge of the specific actions taken. We experimentally compare BCO to imitation learning methods, including the state-of-the-art, generative adversarial imitation learning (GAIL) technique, and we show comparable task performance in several different simulation domains while exhibiting increased learning speed after expert trajectories become available.
Reasoning about Consensus when Opinions Diffuse through Majority Dynamics
Authors: Auletta Vincenzo , Ferraioli Diodato , Greco Gianluigi
Published: July 2018

Opinion diffusion is studied on social graphs where agents hold binary opinions and where social pressure leads them to conform to the opinion manifested by their neighbors. Within this setting, questions related to whether a minority/majority can spread the opinion it supports to all the other agents are considered.It is shown that, no matter of the graph given at hand, there always exists a group formed by a half of the agents that can annihilate the opposite opinion. Instead, the influence power of minorities depends on certain features of the underlying graphs, which are NP-hard to be identified. Deciding whether the two opinions can coexist in some stable configuration is NP-hard, too.
Evaluating and Complementing Vision-to-Language Technology for People who are Blind with Conversational Crowdsourcing
Authors: Salisbury Elliot , Kamar Ece , Ringel Morris Meredith
Published: July 2018

We study how real-time crowdsourcing can be used both for evaluating the value provided by existing automated approaches and for enabling workflows that provide scalable and useful alt text to blind users. We show that the shortcomings of existing AI image captioning systems frequently hinder a user's understanding of an image they cannot see to a degree that even clarifying conversations with sighted assistants cannot correct. Based on analysis of clarifying conversations collected from our studies, we design experiences that can effectively assist users in a scalable way without the need for real-time interaction. Our results provide lessons and guidelines that the designers of future AI captioning systems can use to improve labeling of social media imagery for blind users.
Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth
Authors: Yadav Amulya , Wilder Bryan , Rice Eric , Petering Robin , Craddock Jaih , Yoshioka-Maxwell Amanda , Hemler Mary , Onasch-Vera Laura , Tambe Milind , Woo Darlene
Published: July 2018

This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key "seed" nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ~160% in terms of information spread about HIV among homeless youth.
Evaluation Techniques and Systems for Answer Set Programming: a Survey
Authors: Gebser Martin , Leone Nicola , Maratea Marco , Perri Simona , Ricca Francesco , Schaub Torsten
Published: July 2018

Answer set programming (ASP) is a prominent knowledge representation and reasoning paradigm that found both industrial and scientific applications. The success of ASP is due to the combination of two factors: a rich modeling language and the availability of efficient ASP implementations. In this paper we trace the history of ASP systems, describing the key evaluation techniques and their implementation in actual tools.
Stackelberg Security Games: Looking Beyond a Decade of Success
Authors: Sinha Arunesh , Fang Fei , An Bo , Kiekintveld Christopher , Tambe Milind
Published: July 2018

The Stackelberg Security Game (SSG) model has been immensely influential in security research since it was introduced roughly a decade ago. Furthermore, deployed SSG-based applications are one of most successful examples of game theory applications in the real world. We present a broad survey of recent technical advances in SSG and related literature, and then look to the future by highlighting the new potential applications and open research problems in SSG.
Building Ethics into Artificial Intelligence
Authors: Yu Han , Shen Zhiqi , Miao Chunyan , Leung Cyril , Lesser Victor R. , Yang Qiang
Published: July 2018

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.
Affective Image Content Analysis: A Comprehensive Survey
Authors: Zhao Sicheng , Ding Guiguang , Huang Qingming , Chua Tat-Seng , Schuller Björn W. , Keutzer Kurt
Published: July 2018

Images can convey rich semantics and induce strong emotions in viewers. Recently, with the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this paper, we review the state-of-the-art methods comprehensively with respect to two main challenges -- affective gap and perception subjectivity. We begin with an introduction to the key emotion representation models that have been widely employed in AICA. Available existing datasets for performing evaluation are briefly described. We then summarize and compare the representative approaches on emotion feature extraction, personalized emotion prediction, and emotion distribution learning. Finally, we discuss some future research directions.
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition
Authors: Chen Tianshui , Lin Liang , Chen Riquan , Wu Yang , Luo Xiaonan
Published: July 2018

Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition (e.g., categorizing hundreds of subordinate categories of birds) usually requires a comprehensive visual concept organization including category labels and part-level attributes. In this work, we investigate how to unify rich professional knowledge with deep neural network architectures and propose a Knowledge-Embedded Representation Learning (KERL) framework for handling the problem of fine-grained image recognition. Specifically, we organize the rich visual concepts in the form of knowledge graph and employ a Gated Graph Neural Network to propagate node message through the graph for generating the knowledge representation. By introducing a novel gated mechanism, our KERL framework incorporates this knowledge representation into the discriminative image feature learning, i.e., implicitly associating the specific attributes with the feature maps. Compared with existing methods of fine-grained image classification, our KERL framework has several appealing properties: i) The embedded high-level knowledge enhances the feature representation, thus facilitating distinguishing the subtle differences among subordinate categories. ii) Our framework can learn feature maps with a meaningful configuration that the highlighted regions finely accord with the nodes (specific attributes) of the knowledge graph. Extensive experiments on the widely used Caltech-UCSD bird dataset demonstrate the superiority of our KERL framework over existing state-of-the-art methods.
Watching a Small Portion could be as Good as Watching All: Towards Efficient Video Classification
Authors: Fan Hehe , Xu Zhongwen , Zhu Linchao , Yan Chenggang , Ge Jianjun , Yang Yi
Published: July 2018

We aim to significantly reduce the computational cost for classification of temporally untrimmed videos while retaining similar accuracy. Existing video classification methods sample frames with a predefined frequency over entire video. Differently, we propose an end-to-end deep reinforcement approach which enables an agent to classify videos by watching a very small portion of frames like what we do. We make two main contributions. First, information is not equally distributed in video frames along time. An agent needs to watch more carefully when a clip is informative and skip the frames if they are redundant or irrelevant. The proposed approach enables the agent to adapt sampling rate to video content and skip most of the frames without the loss of information. Second, in order to have a confident decision, the number of frames that should be watched by an agent varies greatly from one video to another. We incorporate an adaptive stop network to measure confidence score and generate timely trigger to stop the agent watching videos, which improves efficiency without loss of accuracy. Our approach reduces the computational cost significantly for the large-scale YouTube-8M dataset, while the accuracy remains the same.
Age Estimation Using Expectation of Label Distribution Learning
Authors: Gao Bin-Bin , Zhou Hong-Yu , Wu Jianxin , Geng Xin
Published: July 2018

Age estimation performance has been greatly improved by using convolutional neural network. However, existing methods have an inconsistency between the training objectives and evaluation metric, so they may be suboptimal. In addition, these methods always adopt image classification or face recognition models with a large amount of parameters, which bring expensive computation cost and storage overhead. To alleviate these issues, we design a lightweight network architecture and propose a unified framework which can jointly learn age distribution and regress age. The effectiveness of our approach has been demonstrated on apparent and real age estimation tasks. Our method achieves new state-of-the-art results using the single model with 36$\times$ fewer parameters and 2.6$\times$ reduction in inference time. Moreover, our method can achieve comparable results as the state-of-the-art even though model parameters are further reduced to 0.9M~(3.8MB disk storage). We also analyze that Ranking methods are implicitly learning label distributions.
THE INVERSE-SCATTERING PROBLEM SOLUTION AND SHAPE FROM THE REFLECTED ELECTROMAGNETIC WAVE FIELD STRUCTURE
Authors: Kozlov A. I. , Maslov V. Yu.
Published: July 2018

The reflected field calculation from the object can be described with the set of point reflectors with the coordinates in electromagnetic wave plane of incidence corresponding to two-dimensional grid nodes with rather small-sized step. At the same time, the single scattering model which does not consider the re-reflection and point elements cross impact is used in the reflected field calculations. The rapid direct and inverse transformation algorithm is used. The numerical solution algorithms of the direct and inverse scattering problems on the object are offered. The method uses the ray representations scattering fields which are based on the Huygens-Fresnel principle. The graphic diagram of the reciprocal object positioning and the observation plane from the reflected electromagnetic field object is represented. The double reflecting Gaussian surface is graphically figured. The figures of the module and the complex amplitude electric field strength of the reflected wave from a double Gaussian surface argument are provided. To shape the surface of the unknown object the recovery shape algorithm is used, by means of reflected wave phase. This algorithm is based on finding the complex matrix elements in dependence on absolute phase, which is proportional to the appropriate point object distance.
ABOUT TWO-STAR GBAS
Authors: Zavalishin O. I.
Published: July 2018

The problem of accurate navigation support for landing systems is of great importance in our time in connection with the constantly increasing intensity of air traffic in major airports. At present, there is a trend towards a transition to navigational identification of aircraft by satellite radio navigation systems. Currently, two global navigation satellite systems, composed of navigational spacecraft – the Russian GLONASS system and the USA GPS system – operate in full. Moreover, to provide the necessary accuracy of positioning and data integrity the additional means are used – differential corrections. The article gives evidence of increasing the accuracy of positioning using the GBAS system. It is shown that the positioning with using GBAS ensures data integrity, corresponding to the category of «critical data» in accordance with ICAO requirements. The technical advantages of the Russian GBAS station are given. A comparative analysis of GBAS and the ILS landing system has been carried out. The article proves the urgency of the functional augmentation development of multi-frequency multi-system terrestrial systems. To calculate the characteristics of the maintenance continuity of the GBAS system, the complex technical systems effectiveness method of evaluation was used. Numerical data are presented on the probability of solving the navigation problem in the differential mode for the nominal mode. The calculation of the maintenance continuity characteristics of the GBAS system based on the complex technical systems effectiveness method of evaluation was carried out. The advantages of using the mobile version of the GBAS LKKS-A-2000 station are substantiated to provide the helicopters with an instrument approach for landing on unprepared sites. The figure shows the implementation of coordinates estimation errors in the differential mode in solving the navigation problem using 5 navigation satellites of the GPS system. The figure shows the implementation of estimation errors for the same record in using all visible and navigational satellites. The figure shows the number of visible navigation satellites.
Comparing the results of four widely used automated bat identification software programs to identify nine bat species in coastal Western Europe
Authors: Brabant Robin , Laurent Yves , Dolap Umit , Degraer Steven , Poerink Bob Jonge
Published: July 2018

Commercially available automated bat identification software packages are widely used in environmental studies to identify bat species from recordings of bat echolocation calls. Caution is, however, needed if the results are used without further verification, as the programs do not guarantee that the results are correct, and wrong species identifications often happen. Taking automated species identifications for granted might hence lead to erroneous conclusions in environmental studies.The goal of our study was to objectively assess the performance of four commercially available and commonly used automated identification software programs by processing an identical reference dataset with all four programs. The reference dataset consisted of nine species selected based on their preference for open habitats in Western Europe or because they occur as vagrants at sea and therefore are vulnerable to the development of onshore and offshore wind farms. Offshore areas are being increasingly examined, as recent studies have identified possible conflicts of offshore wind farms and certain bat species.In our test, we included two automated identification programs that have not yet been tested in other studies, and a reference dataset from a different geographical region (Western-Europe) with a different species composition compared to other studies. Our data hence add to the knowledge base needed for an appropriate assessment of the reliability of analytical software.In general, BatIdent (77% correct species identifications) and Kaleidoscope (71%) seem to be relatively reliable while the performance of BatExplorer (31%) is relatively poor. SonoChiro correctly identified 65% of the sequences to species level. While the tested programs may be considered valuable tools to detect bat calls from the recordings, a trained bat expert needs to cross-check the automated species identifications to avoid erroneous conclusions. Our test hence affirms the conclusions of previous studies in Northern Europe and the USA.
浅谈水利工程施工档案案卷的实物操作
Authors: 陈 敏
Published: July 2018

水利工程档案是一个总类， 它包括工程的规划、 设计档案， 建设管理、 监理档案， 工程施工、 安装档案和工程的竣工档案等。水利工程中施工文件材料的归档一直以来值得我们每个水利人研究。如何做出一套可行并完整的竣工档案， 在整个水利建设施工过程中， 需要准备什么材料？如何把文件材料 “有机联系” 在一起？如何更好地保持和正确地反映文件材料之间的有机联系， 便于今后的管理和查找利用？按照怎么样的顺序对卷内内容进行排列呢？ 这是一个重要的研究课题。 通过对水利工程档案资料的分类组卷、 案卷命名、 卷内文件材料的系统排列的研究， 在做竣工资料时也可以作为参考， 不但积累了水利工程档案资料管理的经验， 也有利于提高施工企业综合实力和经济效益。
No immediate effects of highly cushioned shoes on basic running biomechanics
Authors: Aminaka Naoko , Arthur Kevin
Published: July 2018

The aim of the study was to investigate the effects of highly cushioned shoes on running biomechanics. Sixteen recreational runners (8 males, 8 females) participated and ran at a self-selected pace across the force platform in the research laboratory wearing either the standard or highly cushioned shoes, in randomized order. Impact peak (IP), loading rate to IP (LR), active peak (AP), contact time (CT), strike index (SI), running velocity, and knee and ankle kinematics at initial contact (IC) and AP were recorded during the running trials. Overall, there was no effect of footwear on IP, LR, AP, CT and velocity (p>.05) with small
effect sizes (ES<0.2). The highly cushioned shoes resulted in a more anterior foot strike pattern, based on the slightly higher SI (p=.03, ES=0.5), although the runners demonstrated a rearfoot strike pattern regardless of shoe condition. No kinematic differences were observed at IC or AP, across shoe conditions (p>.05). Our results indicate that the highly cushioned shoes did not show immediate changes in running biomechanics.
Exploring Routley's Nuclear Meinongianism and Beyond
Authors: Fujikawa Naoya
Published: July 2018

Even though Richard Routley is known as a proponent of so-callednuclear Meinongianism (in his terminology, noneism), the details of hisview in Exploring Meinong’s Jungle and Beyond (1980) have not attractedmuch attention. In this paper, I critically examine two notablebut less known features of Meinongianism in Routley (1980) which particularlyconcern a core claim of nuclear Meinongianism that so-calledCharacterization Principle (CP) is applicable to nuclear properties(in his terminology, characterizing properties) but not to extranuclearproperties (in his terminology, noncharacterizing properties): The firstone is his ‘double determination’ argument for restricting the class ofproperties to which CP is applicable; and the second is his expansionof the class of nuclear properties in terms of the supposition operator s.Firstly, I will show that the double determination argument fails, since,for some extranuclear properties, the argument fails to justify that CPis not applicable to them, and, moreover, the argument unintendedlyshows that CP is not applicable to some nuclear properties. Secondly,based on the idea that the supposition operator, combined with anyproperty f, forms the intensional property of being assumed to havef, I will give a formal treatment of the supposition operator withinthe framework of world semantics. We will see that this interpretationgoes beyond Routley’s original nuclear Meinongianism and assimilateshis view with modal Meinongianism proposed by Priest (2005, 2016).