​We present the Utility Maximizing Design (UMD) model​ for optimally redesigning stochastic environments to achieve maximized performance. This model suits well contemporary ​​applications that involve the design of environments where robots and humans co-exist an co-operate, e.g., vacuum cleaning robot. We discuss two special cases of the UMD model. The first is the equi-reward UMD (ER-UMD)​ ​in which the agents and the system share a utility function, such as for the vacuum cleaning robot. The second is the goal​ ​recognition design (GRD) setting, discussed in the literature, in which system and agent utilities are independent. To find the set of optimal​​ modifications to apply to a UMD model, we propose the use of heuristic search, extending previous methods used for GRD settings. After specifying the conditions for optimality in the​ general case, we present an admissible heuristic for the ER-UMD case. We also present a novel compilation that embeds​ the redesign process into a planning problem, allowing use of any off-the-shelf solver to find the best way to modify an environment when a design budget is specified. Our evaluation shows the feasibility of the approach using standard bench​​marks from the probabilistic planning competition.​

The goal of storyboard extraction is to decompose the comic image into several storyboards(or frames), which is the fundamental step of comic image understanding and producing digital comic documents suitable for mobile reading. Most of existing approaches are based on hand crafted low-level visual patters like edge segments and line segments, which do not capture high-level vision. To overcome shortcomings of the existing approaches, we propose a novel architecture based on deep convolutional neural network, namely Shape Regression Network(SReN), to detect storyboards within comic images. Firstly, we use Fast R-CNN to generate rectangle bounding boxes as storyboard proposals. Then we train a deep neural network to predict quadrangles for these propos- als. Unlike existing object detection methods which only output rectangle bounding boxes, SReN can produce more precise quadrangle bounding boxes. Experimental results, evaluating on 7382 comic pages, demonstrate that SReN outperforms the state-of-the-art methods by more than 10% in terms of F1-score and page correction rate.

Latent tree analysis seeks to model the correlations amonga set of random variables using a tree of latent variables. It was proposed as an improvement to latent class analysis—a method widely used in social sciences and medicine to identify homogeneous subgroups in a population. It provides new and fruitful perspectives on a number of machine learningareas, including cluster analysis, topic detection, and deep probabilistic modeling. This paper gives an overview of the research on latent tree analysis and various ways it is used inpractice.

How can individuals and communities protect their privacy against social network analysis tools? How do criminals or terrorists organizations evade detection by such tools? Under which conditions can these tools be made strategy proof? These fundamental questions have attracted little attention in the literature to date, as most social network analysis tools are built around the assumption that individuals or groups in a network do not act strategically to evade such tools. With this in mind, we outline in this paper a new paradigm for social network analysis, whereby the strategic behaviour of network actors is explicitly modeled. Addressing this research challenge has various implications. For instance, it may allow two individuals to keep their relationship secret or private. It may also allow members of an activist group to conceal their membership, or even conceal the existence of their group from authoritarian regimes. Furthermore, it may assist security agencies and counter terrorism units in understanding the strategies that covert organizations use to escape detection, and give rise to new strategy-proof countermeasures.

I present a robotics localization challenge based on the inexpensive Neato XV robotic vacuum cleaner platform. The challenge teaches skills such as computational modeling, probabilistic inference, efficiency vs. accuracy tradeoffs, debugging, parameter tuning, and benchmarking of algorithmic performance. Rather than allowing students to pursue any localization algorithm of their choosing, here, I propose a challenge structured around the particle filter family of algorithms. This additional scaffolding allows students at all levels to successfully implement one approach to the challenge, while providing enough flexibility and richness to enable students to pursue their own creative ideas. Additionally, I provide infrastructure for automatic evaluation of systems through the collection of ground truth robot location data via ceiling-mounted location tags that are automatically scanned using an upward facing camera attached to the robot. The robot and supporting hardware can be purchased for under $400 dollars, and the challenge can even be run without any robots at all using a set of recorded sensor traces.

Automatically describing video content with natural language is a fundamental challenging that has received increasing attention. However, existing techniques restrict the model learning on the pairs of each video and its own sentences, and thus fail to capture more holistically semantic relationships among all sentences. In this paper, we propose to model relative relationships of different video-sentence pairs and present a novel framework, named Long Short-Term Memory with Listwise Supervision (LSTM-LS), for video captioning. Given each video in training data, we obtain a ranking list of sentences w.r.t. a given sentence associated with the video using nearest-neighbor search. The ranking information is represented by a set of rank triplets that can be used to assess the quality of ranking list. The video captioning problem is then solved by learning LSTM model for sentence generation, through maximizing the ranking quality over all the sentences in the list. The experiments on MSVD dataset show that our proposed LSTM-LS produces better performance than the state of the art in generating natural sentences: 51.1% and 32.6% in terms of BLEU@4 and METEOR, respectively. Superior performances are also reported on the movie description M-VAD dataset.

Fine-grained image classification is challenging due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Since two different sub-categories is distinguished only by the subtle differences in some specific parts, semantic part localization is crucial for fine-grained image classification. Most previous works improve the accuracy by looking for the semantic parts, but rely heavily upon the use of the object or part annotations of images whose labeling are costly. Recently, some researchers begin to focus on recognizing sub-categories via weakly supervised part detection instead of using the expensive annotations. However, these works ignore the spatial relationship between the object and its parts as well as the interaction of the parts, both of them are helpful to promote part selection. Therefore, this paper proposes a weakly supervised part selection method with spatial constraints for fine-grained image classification, which is free of using any bounding box or part annotations. We first learn a whole-object detector automatically to localize the object through jointly using saliency extraction and co-segmentation. Then two spatial constraints are proposed to select the distinguished parts. The first spatial constraint, called box constraint, defines the relationship between the object and its parts, and aims to ensure that the selected parts are definitely located in the object region, and have the largest overlap with the object region. The second spatial constraint, called parts constraint, defines the relationship of the object's parts, is to reduce the parts' overlap with each other to avoid the information redundancy and ensure the selected parts are the most distinguishing parts from other categories. Combining two spatial constraints promotes parts selection significantly as well as achieves a notable improvement on fine-grained image classification. Experimental results on CUB-200-2011 dataset demonstrate the superiority of our method even compared with those methods using expensive annotations.

As an interesting and emerging topic, zero-shot recognition (ZSR) makes it possible to train a recognition model by specifying the category's attributes when there are no labeled exemplars available. The fundamental idea for ZSR is to transfer knowledge from the abundant labeled data in different but related source classes via the class attributes. Conventional ZSR approaches adopt a two-step strategy in test stage, where the samples are projected into the attribute space in the first step, and then the recognition is carried out based on considering the relationship between samples and classes in the attribute space. Due to this intermediate transformation, information loss is unavoidable, thus degrading the performance of the overall system. Rather than following this two-step strategy, in this paper, we propose a novel one-step approach that is able to perform ZSR in the original feature space by using directly trained classifiers. To tackle the problem that no labeled samples of target classes are available, we propose to assign pseudo labels to samples based on the reliability and diversity, which in turn will be used to train the classifiers. Moreover, we adopt a robust SVM that accounts for the unreliability of pseudo labels. Extensive experiments on four datasets demonstrate consistent performance gains of our approach over the state-of-the-art two-step ZSR approaches.

Hybrid (mixed discrete and continuous) state and action Markov Decision Processes (HSA-MDPs) provide an expressive formalism for modeling stochastic and concurrent sequential decision-making problems. Existing solvers for HSA-MDPs are either limited to very restricted transition distributions, require knowledge of domain-specific basis functions to achieve good approximations, or do not scale. We explore a domain-independent approach based on the framework of hindsight optimization (HOP) for HSA-MDPs, which uses an upper bound on the finite-horizon action values for action selection. Our main contribution is a linear time reduction to a Mixed Integer Linear Program (MILP) that encodes the HOP objective, when the dynamics are specified as location-scale probability distributions parametrized by Piecewise Linear (PWL) functions of states and actions. In addition, we show how to use the same machinery to select actions based on a lower-bound generated by straight line plans. Our empirical results show that the HSA-HOP approach effectively scales to high-dimensional problems and outperforms baselines that are capable of scaling to such large hybrid MDPs.

Answer selection plays a key role in community question answering (CQA). Previous research on answer selection usually ignores the problems of redundancy and noise prevalent in CQA. In this paper, we propose to treat different text segments differently and design a novel attentive interactive neural network (AI-NN) to focus on those text segments useful to answer selection. The representations of question and answer are first learned by convolutional neural networks (CNNs) or other neural network architectures. Then AI-NN learns interactions of each paired segments of two texts. Row-wise and column-wise pooling are used afterwards to collect the interactions. We adopt attention mechanism to measure the importance of each segment and combine the interactions to obtain fixed-length representations for question and answer. Experimental results on CQA dataset in SemEval-2016 demonstrate that AI-NN outperforms state-of-the-art method.