OpenRaiser/Intern-Atlas huggingface dataset:
SwipeCut: Interactive Segmentation with Diversified Seed Proposals
Venue: arXiv cs.CV 2018
Year: 2018

Interactive image segmentation algorithms rely on the user to provide annotations as the guidance. When the task of interactive segmentation is performed on a small touchscreen device, the requirement of providing precise annotations could be cumbersome to the user. We design an efficient seed proposal method that actively proposes annotation seeds for the user to label. The user only needs to check which ones of the query seeds are inside the region of interest (ROI). We enforce the sparsity and diversity criteria on the selection of the query seeds. At each round of interaction the user is only presented with a small number of informative query seeds that are far apart from each other. As a result, we are able to derive a user friendly interaction mechanism for annotation on small touchscreen devices. The user merely has to swipe through on the ROI-relevant query seeds, which should be easy since those gestures are commonly used on a touchscreen. The performance of our algorithm is evaluated on six publicly available datasets. The evaluation results show that our algorithm achieves high segmentation accuracy, with short response time and less user feedback.
CPEG: A Typed Tree Construction from Parsing Expression Grammars with Regex-Like Captures
Venue: arXiv cs.PL 2018
Year: 2018

CPEG is an extended parsing expression grammar with regex-like capture annotation. Two annotations (capture and left-folding) allow a flexible construction of syntax trees from arbitrary parsing patterns. More importantly, CPEG is designed to guarantee structural constraints of syntax trees for any input strings. This reduces the amount of user code needed to check whether the intended elements exist. To represent the structural constraints, we focus on regular expression types, a variant formalism of tree automata, which have been intensively studied in the context of XML schemas. Regular expression type is inferred from a given CPEG by the type inference that is formally developed in this paper. We prove the soundness and the uniqueness of the type inference. The type inference enables a CPEG to serve both as a syntactic specification of the input and a schematic specification of the output.
DeepLens: Towards a Visual Data Management System
Venue: arXiv cs.DB 2018
Year: 2018

Advances in deep learning have greatly widened the scope of automatic computer vision algorithms and enable users to ask questions directly about the content in images and video. This paper explores the necessary steps towards a future Visual Data Management System (VDMS), where the predictions of such deep learning models are stored, managed, queried, and indexed. We propose a query and data model that disentangles the neural network models used, the query workload, and the data source semantics from the query processing layer. Our system, DeepLens, is based on dataflow query processing systems and this research prototype presents initial experiments to elicit important open research questions in visual analytics systems. One of our main conclusions is that any future "declarative" VDMS will have to revisit query optimization and automated physical design from a unified perspective of performance and accuracy tradeoffs. Physical design and query optimization choices can not only change performance by orders of magnitude, they can potentially affect the accuracy of results.
Deep Variational Sufficient Dimensionality Reduction
Venue: arXiv cs.LG 2018
Year: 2018

We consider the problem of sufficient dimensionality reduction (SDR), where the high-dimensional observation is transformed to a low-dimensional sub-space in which the information of the observations regarding the label variable is preserved. We propose DVSDR, a deep variational approach for sufficient dimensionality reduction. The deep structure in our model has a bottleneck that represent the low-dimensional embedding of the data. We explain the SDR problem using graphical models and use the framework of variational autoencoders to maximize the lower bound of the log-likelihood of the joint distribution of the observation and label. We show that such a maximization problem can be interpreted as solving the SDR problem. DVSDR can be easily adopted to semi-supervised learning setting. In our experiment we show that DVSDR performs competitively on classification tasks while being able to generate novel data samples.
Demonstration of a Multiresolution Schema Mapping System
Venue: arXiv cs.DB 2018
Year: 2018

Enterprise databases usually contain large and complex schemas. Authoring complete schema mapping queries in this case requires deep knowledge about the source and target schemas and is thereby very challenging to programmers. Sample-driven schema mapping allows the user to describe the schema mapping using data records. However, real data records are still harder to specify than other useful insights about the desired schema mapping the user might have. In this project, we develop a schema mapping system, PRISM, that enables multiresolution schema mapping. The end user is not limited to providing high-resolution constraints like exact data records but may also provide constraints of various resolutions, like incomplete data records, value ranges, and data types. This new interaction paradigm gives the user more flexibility in describing the desired schema mapping. This demonstration showcases how to use PRISM for schema mapping in a real database.
Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews
Venue: arXiv cs.LG 2018
Year: 2018

This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as "critical aspect" analysis altogether in one framework. TSPRA extends sentiment approaches by integrating the key concept "user preference" in collaborative filtering (CF) models into consideration, while it is distinct from current CF models by decoupling "user preference" and "sentiment" as independent factors. Our experiments conducted on 22 Amazon datasets show overwhelming better performance in rating predication against a state-of-art model FLAME (2015) in terms of error, Pearson's Correlation and number of inverted pairs. For sentiment analysis, we compare the derived word sentiments against a public sentiment resource SenticNet3 and our sentiment estimations clearly make more sense in the context of online reviews. Last, as a result of the de-correlation of "user preference" from "sentiment", TSPRA is able to evaluate a new concept "critical aspects", defined as the product aspects seriously concerned by users but negatively commented in reviews. Improvement to such "critical aspects" could be most effective to enhance user experience.
Removing rain streaks by a linear model
Venue: arXiv cs.CV 2018
Year: 2018

Removing rain streaks from a single image continues to draw attentions today in outdoor vision systems. In this paper, we present an efficient method to remove rain streaks. First, the location map of rain pixels needs to be known as precisely as possible, to which we implement a relatively accurate detection of rain streaks by utilizing two characteristics of rain streaks.The key component of our method is to represent the intensity of each detected rain pixel using a linear model: $p=αs + β$, where $p$ is the observed intensity of a rain pixel and $s$ represents the intensity of the background (i.e., before rain-affected). To solve $α$ and $β$ for each detected rain pixel, we concentrate on a window centered around it and form an $L_2$-norm cost function by considering all detected rain pixels within the window, where the corresponding rain-removed intensity of each detected rain pixel is estimated by some neighboring non-rain pixels. By minimizing this cost function, we determine $α$ and $β$ so as to construct the final rain-removed pixel intensity. Compared with several state-of-the-art works, our proposed method can remove rain streaks from a single color image much more efficiently - it offers not only a better visual quality but also a speed-up of several times to one degree of magnitude.
Energy Efficient IoT Virtualization Framework with Peer to Peer Networking and Processing
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, an energy efficient IoT virtualization framework with P2P networking and edge computing is proposed. In this network, the IoT task processing requests are served by peers. The peers in our work are represented by IoT objects and relays that host virtual machines (VMs). We have considered three scenarios to investigate the saving in power consumption and the system capabilities in terms of task processing. The first scenario is the relays only scenario, where the task requests are processed using relays only. The second scenario is the objects only scenario, where the task requests are processed using the IoT objects only. The last scenario is a hybrid scenario, where the task requests are processed using both IoT objects and VMs. We have developed a mixed integer linear programming (MILP) model to maximize the number of processing tasks served by the system and minimize the total power consumed by the IoT network. We investigated our framework under the impact of VMs placement constraints, fairness constraints between the objects, tasks number limitations, uplink and downlink limited capacities, and processing capability limitations. Based on the MILP model principles, we developed an energy efficient virtualized IoT P2P networks heuristic (EEVIPN). The heuristic results were comparable to those of the MILP in terms of energy efficiency and tasks processing. Our results show that the hybrid scenario serves up to 77% (57% on average) processing task requests, but with higher energy consumption compared to the other scenarios. The relays only scenario can serve 74% (57% on average) of the processing task requests with 8% saving in power consumption compared to the hybrid scenario. In contrast, 28% (22% on average) of task requests can be successfully handled by applying the objects only scenario with up to 62% power saving compared to the hybrid scenario.
Model-Checking on Ordered Structures
Venue: arXiv cs.LO 2018
Year: 2018

We study the model-checking problem for first- and monadic second-order logic on finite relational structures. The problem of verifying whether a formula of these logics is true on a given structure is considered intractable in general, but it does become tractable on interesting classes of structures, such as on classes whose Gaifman graphs have bounded treewidth. In this paper we continue this line of research and study model-checking for first- and monadic second-order logic in the presence of an ordering on the input structure. We do so in two settings: the general ordered case, where the input structures are equipped with a fixed order or successor relation, and the order invariant case, where the formulas may resort to an ordering, but their truth must be independent of the particular choice of order. In the first setting we show very strong intractability results for most interesting classes of structures. In contrast, in the order invariant case we obtain tractability results for order-invariant monadic second-order formulas on the same classes of graphs as in the unordered case. For first-order logic, we obtain tractability of successor-invariant formulas on classes whose Gaifman graphs have bounded expansion. Furthermore, we show that model-checking for order-invariant first-order formulas is tractable on coloured posets of bounded width.
Intelligent Autonomous Agents are Key to Cyber Defense of the Future Army Networks
Venue: arXiv cs.CR 2018
Year: 2018

Intelligent autonomous agents will be widely present on the battlefield of the future. The proliferation of intelligent agents is the emerging reality of warfare, and they will form an ever growing fraction of total military assets. By necessity, intelligent autonomous cyber defense agents are likely to become primary cyber fighters on the future battlefield. Initial explorations have identified the key functions, components and their interactions for a potential reference architecture of such an agent. However, it is beyond the current state of AI to support an agent that could operate intelligently in an environment as complex as the real battlefield. A number of difficult challenges are yet to be overcome. At the same time, a growing body of research in Government and academia demonstrates promising steps towards solving some of the challenges. The industry is beginning to embrace approaches that may contribute to technologies of autonomous intelligent agents for cyber defense of the Army networks.
Switch-LSTMs for Multi-Criteria Chinese Word Segmentation
Venue: arXiv cs.CL 2018
Year: 2018

Multi-criteria Chinese word segmentation is a promising but challenging task, which exploits several different segmentation criteria and mines their common underlying knowledge. In this paper, we propose a flexible multi-criteria learning for Chinese word segmentation. Usually, a segmentation criterion could be decomposed into multiple sub-criteria, which are shareable with other segmentation criteria. The process of word segmentation is a routing among these sub-criteria. From this perspective, we present Switch-LSTMs to segment words, which consist of several long short-term memory neural networks (LSTM), and a switcher to automatically switch the routing among these LSTMs. With these auto-switched LSTMs, our model provides a more flexible solution for multi-criteria CWS, which is also easy to transfer the learned knowledge to new criteria. Experiments show that our model obtains significant improvements on eight corpora with heterogeneous segmentation criteria, compared to the previous method and single-criterion learning.
Semantic Frame Parsing for Information Extraction : the CALOR corpus
Venue: arXiv cs.CL 2018
Year: 2018

This paper presents a publicly available corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism. The main difference in our approach compared to previous works on semantic parsing with FrameNet is that we are not interested here in full text parsing but rather on partial parsing. The goal is to select from the FrameNet resources the minimal set of frames that are going to be useful for the applicative framework targeted, in our case Information Extraction from encyclopedic documents. Such an approach leverages the manual annotation of larger corpora than those obtained through full text parsing and therefore opens the door to alternative methods for Frame parsing than those used so far on the FrameNet 1.5 benchmark corpus. The approaches compared in this study rely on an integrated sequence labeling model which jointly optimizes frame identification and semantic role segmentation and identification. The models compared are CRFs and multitasks bi-LSTMs.
Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study
Venue: arXiv cs.CL 2018
Year: 2018

Cyberbullying is a disturbing online misbehaviour with troubling consequences. It appears in different forms, and in most of the social networks, it is in textual format. Automatic detection of such incidents requires intelligent systems. Most of the existing studies have approached this problem with conventional machine learning models and the majority of the developed models in these studies are adaptable to a single social network at a time. In recent studies, deep learning based models have found their way in the detection of cyberbullying incidents, claiming that they can overcome the limitations of the conventional models, and improve the detection performance. In this paper, we investigate the findings of a recent literature in this regard. We successfully reproduced the findings of this literature and validated their findings using the same datasets, namely Wikipedia, Twitter, and Formspring, used by the authors. Then we expanded our work by applying the developed methods on a new YouTube dataset (~54k posts by ~4k users) and investigated the performance of the models in new social media platforms. We also transferred and evaluated the performance of the models trained on one platform to another platform. Our findings show that the deep learning based models outperform the machine learning models previously applied to the same YouTube dataset. We believe that the deep learning based models can also benefit from integrating other sources of information and looking into the impact of profile information of the users in social networks.
Spatial-Spectral Regularized Local Scaling Cut for Dimensionality Reduction in Hyperspectral Image Classification
Venue: arXiv cs.CV 2018
Year: 2018

Dimensionality reduction (DR) methods have attracted extensive attention to provide discriminative information and reduce the computational burden of the hyperspectral image (HSI) classification. However, the DR methods face many challenges due to limited training samples with high dimensional spectra. To address this issue, a graph-based spatial and spectral regularized local scaling cut (SSRLSC) for DR of HSI data is proposed. The underlying idea of the proposed method is to utilize the information from both the spectral and spatial domains to achieve better classification accuracy than its spectral domain counterpart. In SSRLSC, a guided filter is initially used to smoothen and homogenize the pixels of the HSI data in order to preserve the pixel consistency. This is followed by generation of between-class and within-class dissimilarity matrices in both spectral and spatial domains by regularized local scaling cut (RLSC) and neighboring pixel local scaling cut (NPLSC) respectively. Finally, we obtain the projection matrix by optimizing the updated spatial-spectral between-class and total-class dissimilarity. The effectiveness of the proposed DR algorithm is illustrated with two popular real-world HSI datasets.
Log-logarithmic Time Pruned Polar Coding on Binary Erasure Channels
Venue: arXiv cs.IT 2018
Year: 2018

A pruned variant of polar coding is reinvented for all binary erasure channels. For small $\varepsilon>0$, we construct codes with block length $\varepsilon^{-5}$, code rate $\text{Capacity}-\varepsilon$, error probability $\varepsilon$, and encoding and decoding time complexity $O(N\log|\log\varepsilon|)$ per block, equivalently $O(\log|\log\varepsilon|)$ per information bit (Propositions 5 to 8). This result also follows if one applies systematic polar coding [Arıkan 10.1109/LCOMM.2011.061611.110862] with simplified successive cancelation decoding [Alamdar-Yazdi-Kschischang 10.1109/LCOMM.2011.101811.111480], and then analyzes the performance using [Guruswami-Xia arXiv:1304.4321] or [Mondelli-Hassani-Urbanke arXiv:1501.02444].
Polar-like Codes and Asymptotic Tradeoff among Block Length, Code Rate, and Error Probability
Venue: arXiv cs.IT 2018
Year: 2018

A general framework is proposed that includes polar codes over arbitrary channels with arbitrary kernels. The asymptotic tradeoff among block length $N$, code rate $R$, and error probability $P$ is analyzed. Given a tradeoff between $N,P$ and a tradeoff between $N,R$, we return an interpolating tradeoff among $N,R,P$ (Theorem 5). $\def\Capacity{\text{Capacity}}$Quantitatively, if $P=\exp(-N^{β^*})$ is possible for some $β^*$ and if $R=\Capacity-N^{1/μ^*}$ is possible for some $1/μ^*$, then $(P,R)=(\exp(-N^{β'}),\Capacity-N^{-1/μ'})$ is possible for some pair $(β',1/μ')$ determined by $β^*$, $1/μ^*$, and auxiliary information. In fancy words, an error exponent regime tradeoff plus a scaling exponent regime tradeoff implies a moderate deviations regime tradeoff. The current world records are: [arXiv:1304.4321][arXiv:1501.02444][arXiv:1806.02405] analyzing Arıkan's codes over BEC; [arXiv:1706.02458] analyzing Arıkan's codes over AWGN; and [arXiv:1802.02718][arXiv:1810.04298] analyzing general codes over general channels. An attempt is made to generalize all at once (Section IX). As a corollary, a grafted variant of polar coding almost catches up the code rate and error probability of random codes with complexity slightly larger than $N\log N$ over BEC. In particular, $(P,R)=(\exp(-N^{.33}),\Capacity-N^{-.33})$ is possible (Corollary 10). In fact, all points in this triangle are possible $(β',1/μ')$-pairs. $$ \require{enclose} \def\r{\phantom{\Rule{4em}{1em}{1em}}} \enclose{}\r^\llap{(0,1/2)}_\llap{(0,0)} \enclose{left,bottom,downdiagonalstrike}\r_\rlap{(1,0)} \enclose{}\r $$
RankGAN: A Maximum Margin Ranking GAN for Generating Faces
Venue: arXiv cs.CV 2018
Year: 2018

We present a new stage-wise learning paradigm for training generative adversarial networks (GANs). The goal of our work is to progressively strengthen the discriminator and thus, the generators, with each subsequent stage without changing the network architecture. We call this proposed method the RankGAN. We first propose a margin-based loss for the GAN discriminator. We then extend it to a margin-based ranking loss to train the multiple stages of RankGAN. We focus on face images from the CelebA dataset in our work and show visual as well as quantitative improvements in face generation and completion tasks over other GAN approaches, including WGAN and LSGAN.
Learning Unions of k-Testable Languages
Venue: arXiv cs.FL 2018
Year: 2018

A classical problem in grammatical inference is to identify a language from a set of examples. In this paper, we address the problem of identifying a union of languages from examples that belong to several different unknown languages. Indeed, decomposing a language into smaller pieces that are easier to represent should make learning easier than aiming for a too generalized language. In particular, we consider k-testable languages in the strict sense (k-TSS). These are defined by a set of allowed prefixes, infixes (sub-strings) and suffixes that words in the language may contain. We establish a Galois connection between the lattice of all languages over alphabet Σ, and the lattice of k-TSS languages over Σ. We also define a simple metric on k-TSS languages. The Galois connection and the metric allow us to derive an efficient algorithm to learn the union of k-TSS languages. We evaluate our algorithm on an industrial dataset and thus demonstrate the relevance of our approach.