OpenRaiser/Intern-Atlas huggingface dataset:
Geospatial Semantics
Venue: arXiv cs.CL 2017
Year: 2017

Geospatial semantics is a broad field that involves a variety of research areas. The term semantics refers to the meaning of things, and is in contrast with the term syntactics. Accordingly, studies on geospatial semantics usually focus on understanding the meaning of geographic entities as well as their counterparts in the cognitive and digital world, such as cognitive geographic concepts and digital gazetteers. Geospatial semantics can also facilitate the design of geographic information systems (GIS) by enhancing the interoperability of distributed systems and developing more intelligent interfaces for user interactions. During the past years, a lot of research has been conducted, approaching geospatial semantics from different perspectives, using a variety of methods, and targeting different problems. Meanwhile, the arrival of big geo data, especially the large amount of unstructured text data on the Web, and the fast development of natural language processing methods enable new research directions in geospatial semantics. This chapter, therefore, provides a systematic review on the existing geospatial semantic research. Six major research areas are identified and discussed, including semantic interoperability, digital gazetteers, geographic information retrieval, geospatial Semantic Web, place semantics, and cognitive geographic concepts.
Using RDF Summary Graph For Keyword-based Semantic Searches
Venue: arXiv cs.AI 2017
Year: 2017

The Semantic Web began to emerge as its standards and technologies developed rapidly in the recent years. The continuing development of Semantic Web technologies has facilitated publishing explicit semantics with data on the Web in RDF data model. This study proposes a semantic search framework to support efficient keyword-based semantic search on RDF data utilizing near neighbor explorations. The framework augments the search results with the resources in close proximity by utilizing the entity type semantics. Along with the search results, the system generates a relevance confidence score measuring the inferred semantic relatedness of returned entities based on the degree of similarity. Furthermore, the evaluations assessing the effectiveness of the framework and the accuracy of the results are presented.
Elephant Search with Deep Learning for Microarray Data Analysis
Venue: arXiv cs.NE 2017
Year: 2017

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.
A Probabilistic Embedding Clustering Method for Urban Structure Detection
Venue: arXiv cs.SI 2017
Year: 2017

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by learning via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.
The detector principle of constructing artificial neural networks as an alternative to the connectionist paradigm
Venue: arXiv cs.NE 2017
Year: 2017

Artificial neural networks (ANN) are inadequate to biological neural networks. This inadequacy is manifested in the use of the obsolete model of the neuron and the connectionist paradigm of constructing ANN. The result of this inadequacy is the existence of many shortcomings of the ANN and the problems of their practical implementation. The alternative principle of ANN construction is proposed in the article. This principle was called the detector principle. The basis of the detector principle is the consideration of the binding property of the input signals of a neuron. A new model of the neuron-detector, a new approach to teaching ANN - counter training and a new approach to the formation of the ANN architecture are used in this principle.
Adversarial Dropout for Supervised and Semi-supervised Learning
Venue: arXiv cs.LG 2017
Year: 2017

Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between the outputs from the network with the dropouts and the training supervisions. The identified adversarial dropout are used to reconfigure the neural network to train, and we demonstrated that training on the reconfigured sub-network improves the generalization performance of supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We analyzed the trained model to reason the performance improvement, and we found that adversarial dropout increases the sparsity of neural networks more than the standard dropout does.
Data preprocessing methods for robust Fourier ptychographic microscopy
Venue: arXiv cs.CV 2017
Year: 2017

Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging technique with both high resolution and wide field-of-view. In current FP experimental setup, the dark-field images with high-angle illuminations are easily submerged by stray light and background noise due to the low signal-to-noise ratio, thus significantly degrading the reconstruction quality and also imposing a major restriction on the synthetic numerical aperture (NA) of the FP approach. To this end, an overall and systematic data preprocessing scheme for noise removal from FP's raw dataset is provided, which involves sampling analysis as well as underexposed/overexposed treatments, then followed by the elimination of unknown stray light and suppression of inevitable background noise, especially Gaussian noise and CCD dark current in our experiments. The reported non-parametric scheme facilitates great enhancements of the FP's performance, which has been demonstrated experimentally that the benefits of noise removal by these methods far outweigh its defects of concomitant signal loss. In addition, it could be flexibly cooperated with the existing state-of-the-art algorithms, producing a stronger robustness of the FP approach in various applications.
Learning Macromanagement in StarCraft from Replays using Deep Learning
Venue: arXiv cs.AI 2017
Year: 2017

The real-time strategy game StarCraft has proven to be a challenging environment for artificial intelligence techniques, and as a result, current state-of-the-art solutions consist of numerous hand-crafted modules. In this paper, we show how macromanagement decisions in StarCraft can be learned directly from game replays using deep learning. Neural networks are trained on 789,571 state-action pairs extracted from 2,005 replays of highly skilled players, achieving top-1 and top-3 error rates of 54.6% and 22.9% in predicting the next build action. By integrating the trained network into UAlbertaBot, an open source StarCraft bot, the system can significantly outperform the game's built-in Terran bot, and play competitively against UAlbertaBot with a fixed rush strategy. To our knowledge, this is the first time macromanagement tasks are learned directly from replays in StarCraft. While the best hand-crafted strategies are still the state-of-the-art, the deep network approach is able to express a wide range of different strategies and thus improving the network's performance further with deep reinforcement learning is an immediately promising avenue for future research. Ultimately this approach could lead to strong StarCraft bots that are less reliant on hard-coded strategies.
Modernized Latvian Ergonomic Keyboard
Venue: arXiv cs.HC 2017
Year: 2017

Increasingly more people use computers and create content using keyboards (even with leading edge touch-screen technology). As in the most part of the world, in Latvia also conventional "Qwerty" keyboard is used. Though for Latvian it is much worse than for English, especially due to enormous load to little fingers. It causes repetitive strain injuries and affects productivity of workers with extensive keyboard usage, especially for data input operators, call centers, inquiry office workers, etc. Improving computer keyboard layout decrease stress to hands and fingers thus minimizing exhaustion and injuries. With analysis of English and Latvian public domain novels and modern texts, letter appearance an sequence distribution for Latvian language was found. Qualities of alternative layouts for English (Dvorak, Colemak, Hallinstad) were investigated and open source carpalx simulation tool was adjusted according to the findings. Then carpalx was used to check more than 25 million keyboard layouts, measuring finger/hand effort, stroke typing convenience etc., to find the best one. It was proved that existing "Šusildatec" (classic Latvian Ergonomic standard) keyboard is only slightly better than "Qwerty" for Latvian, though it is much worse for English. After computer simulation, several best layouts were tried practically for more than 6 months and most convenient one was promoted as a new "Latvian Modern" keyboard. Its typing effort is less than for "Šusildatec", load is distributed according to finger strength, and typing strokes are alternating better between hands and fingers. Comparing to "Qwerty" keyboard new layout is better not only for Latvian but for English also. Keyboard drivers are developed for Microsoft Windows and Linux operating systems and are freely available in the web under permissive license.
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion
Venue: arXiv cs.CV 2017
Year: 2017

Large field-of-view fisheye lens cameras have attracted more and more researchers' attention in the field of robotics. However, there does not exist a convenient off-the-shelf stereo rectification approach which can be applied directly to fisheye stereo rig. One obvious drawback of existing methods is that the resampling distortion (which is defined as the loss of pixels due to under-sampling and the creation of new pixels due to over-sampling during rectification process) is severe if we want to obtain a rectification with epipolar line (not epipolar circle) constraint. To overcome this weakness, we propose a novel pixel-wise local homography technique for stereo rectification. First, we prove that there indeed exist enough degrees of freedom to apply pixel-wise local homography for stereo rectification. Then we present a method to exploit these freedoms and the solution via an optimization framework. Finally, the robustness and effectiveness of the proposed method have been verified on real fisheye lens images. The rectification results show that the proposed approach can effectively reduce the resampling distortion in comparison with existing methods while satisfying the epipolar line constraint. By employing the proposed method, dense stereo matching and 3D reconstruction for fisheye lens camera become as easy as perspective lens cameras.
A Critique of a Critique of Word Similarity Datasets: Sanity Check or Unnecessary Confusion?
Venue: arXiv cs.CL 2017
Year: 2017

Critical evaluation of word similarity datasets is very important for computational lexical semantics. This short report concerns the sanity check proposed in Batchkarov et al. (2016) to evaluate several popular datasets such as MC, RG and MEN -- the first two reportedly failed. I argue that this test is unstable, offers no added insight, and needs major revision in order to fulfill its purported goal.
Process Monitoring on Sequences of System Call Count Vectors
Venue: arXiv cs.CR 2017
Year: 2017

We introduce a methodology for efficient monitoring of processes running on hosts in a corporate network. The methodology is based on collecting streams of system calls produced by all or selected processes on the hosts, and sending them over the network to a monitoring server, where machine learning algorithms are used to identify changes in process behavior due to malicious activity, hardware failures, or software errors. The methodology uses a sequence of system call count vectors as the data format which can handle large and varying volumes of data. Unlike previous approaches, the methodology introduced in this paper is suitable for distributed collection and processing of data in large corporate networks. We evaluate the methodology both in a laboratory setting on a real-life setup and provide statistics characterizing performance and accuracy of the methodology.
Buffer Size for Routing Limited-Rate Adversarial Traffic
Venue: arXiv cs.DC 2017
Year: 2017

We consider the slight variation of the adversarial queuing theory model, in which an adversary injects packets with routes into the network subject to the following constraint: For any link $e$, the total number of packets injected in any time window $[t,t')$ and whose route contains $e$, is at most $ρ(t'-t)+σ$, where $ρ$ and $σ$ are non-negative parameters. Informally, $ρ$ bounds the long-term rate of injections and $σ$ bounds the "burstiness" of injection: $σ=0$ means that the injection is as smooth as it can be. It is known that greedy scheduling of the packets (under which a link is not idle if there is any packet ready to be sent over it) may result in $Ω(n)$ buffer size even on an $n$-line network and very smooth injections ($σ=0$). In this paper we propose a simple non-greedy scheduling policy and show that, in a tree where all packets are destined at the root, no buffer needs to be larger than $σ+2ρ$ to ensure that no overflows occur, which is optimal in our model. The rule of our algorithm is to forward a packet only if its next buffer is completely empty. The policy is centralized: in a single step, a long "train" of packets may progress together. We show that in some sense central coordination is required, by presenting an injection pattern with $σ=0$ for the $n$-node line that results in $Ω(n)$ packets in a buffer if local control is used, even for the more sophisticated "downhill" algorithm, which forwards a packet only if its next buffer is less occupied than its current one.
kIP: a Measured Approach to IPv6 Address Anonymization
Venue: arXiv cs.NI 2017
Year: 2017

Privacy-minded Internet service operators anonymize IPv6 addresses by truncating them to a fixed length, perhaps due to long-standing use of this technique with IPv4 and a belief that it's "good enough." We claim that simple anonymization by truncation is suspect since it does not entail privacy guarantees nor does it take into account some common address assignment practices observed today. To investigate, with standard activity logs as input, we develop a counting method to determine a lower bound on the number of active IPv6 addresses that are simultaneously assigned, such as those of clients that access World-Wide Web services. In many instances, we find that these empirical measurements offer no evidence that truncating IPv6 addresses to a fixed number of bits, e.g., 48 in common practice, protects individuals' privacy. To remedy this problem, we propose kIP anonymization, an aggregation method that ensures a certain level of address privacy. Our method adaptively determines variable truncation lengths using parameter k, the desired number of active (rather than merely potential) addresses, e.g., 32 or 256, that can not be distinguished from each other once anonymized. We describe our implementation and present first results of its application to millions of real IPv6 client addresses active over a week's time, demonstrating both feasibility at large scale and ability to automatically adapt to each network's address assignment practice and synthesize a set of anonymous aggregates (prefixes), each of which is guaranteed to cover (contain) at least k of the active addresses. Each address is anonymized by truncating it to the length of its longest matching prefix in that set.
Query-Aware Sparse Coding for Multi-Video Summarization
Venue: arXiv cs.CV 2017
Year: 2017

Given the explosive growth of online videos, it is becoming increasingly important to relieve the tedious work of browsing and managing the video content of interest. Video summarization aims at providing such a technique by transforming one or multiple videos into a compact one. However, conventional multi-video summarization methods often fail to produce satisfying results as they ignore the user's search intent. To this end, this paper proposes a novel query-aware approach by formulating the multi-video summarization in a sparse coding framework, where the web images searched by the query are taken as the important preference information to reveal the query intent. To provide a user-friendly summarization, this paper also develops an event-keyframe presentation structure to present keyframes in groups of specific events related to the query by using an unsupervised multi-graph fusion method. We release a new public dataset named MVS1K, which contains about 1, 000 videos from 10 queries and their video tags, manual annotations, and associated web images. Extensive experiments on MVS1K dataset validate our approaches produce superior objective and subjective results against several recently proposed approaches.
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL
Venue: arXiv cs.LG 2017
Year: 2017

In various approaches to learning, notably in domain adaptation, active learning, learning under covariate shift, semi-supervised learning, learning with concept drift, and the like, one often wants to compare a baseline classifier to one or more advanced (or at least different) strategies. In this chapter, we basically argue that if such classifiers, in their respective training phases, optimize a so-called surrogate loss that it may also be valuable to compare the behavior of this loss on the test set, next to the regular classification error rate. It can provide us with an additional view on the classifiers' relative performances that error rates cannot capture. As an example, limited but convincing empirical results demonstrates that we may be able to find semi-supervised learning strategies that can guarantee performance improvements with increasing numbers of unlabeled data in terms of log-likelihood. In contrast, the latter may be impossible to guarantee for the classification error rate.
A Note on the Inheritance of the Isometry-Dual Property under Puncturing AG Codes
Venue: arXiv cs.DM 2017
Year: 2017

Consider a sequence of AG codes evaluating at a set of evaluation points $P_1,\dots,P_n$ the functions having only poles at a defining point $Q$, with the sequence of codes satisfying the isometry-dual condition (i.e. containing at the same time primal and their dual codes). We prove a necessary condition under which, after taking out a number of evaluation points (i.e. puncturing), the resulting AG codes can still satisfy the isometry-dual property. The condition has to do with the so-called maximum sparse ideals of the Weierstrass semigroup of $Q$.
Disentangling Motion, Foreground and Background Features in Videos
Venue: arXiv cs.CV 2017
Year: 2017

This paper introduces an unsupervised framework to extract semantically rich features for video representation. Inspired by how the human visual system groups objects based on motion cues, we propose a deep convolutional neural network that disentangles motion, foreground and background information. The proposed architecture consists of a 3D convolutional feature encoder for blocks of 16 frames, which is trained for reconstruction tasks over the first and last frames of the sequence. A preliminary supervised experiment was conducted to verify the feasibility of proposed method by training the model with a fraction of videos from the UCF-101 dataset taking as ground truth the bounding boxes around the activity regions. Qualitative results indicate that the network can successfully segment foreground and background in videos as well as update the foreground appearance based on disentangled motion features. The benefits of these learned features are shown in a discriminative classification task, where initializing the network with the proposed pretraining method outperforms both random initialization and autoencoder pretraining. Our model and source code are publicly available at https://imatge-upc.github.io/unsupervised-2017-cvprw/ .
Armstrong's Axioms and Navigation Strategies
Venue: arXiv cs.AI 2017
Year: 2017

The paper investigates navigability with imperfect information. It shows that the properties of navigability with perfect recall are exactly those captured by Armstrong's axioms from the database theory. If the assumption of perfect recall is omitted, then Armstrong's transitivity axiom is not valid, but it can be replaced by two new weaker principles. The main technical results are soundness and completeness theorems for the logical systems describing properties of navigability with and without perfect recall.