OpenRaiser/Intern-Atlas huggingface dataset:
Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders
Venue: arXiv cs.CV 2016
Year: 2016

There are many forms of feature information present in video data. Principle among them are object identity information which is largely static across multiple video frames, and object pose and style information which continuously transforms from frame to frame. Most existing models confound these two types of representation by mapping them to a shared feature space. In this paper we propose a probabilistic approach for learning separable representations of object identity and pose information using unsupervised video data. Our approach leverages a deep generative model with a factored prior distribution that encodes properties of temporal invariances in the hidden feature set. Learning is achieved via variational inference. We present results of learning identity and pose information on a dataset of moving characters as well as a dataset of rotating 3D objects. Our experimental results demonstrate our model's success in factoring its representation, and demonstrate that the model achieves improved performance in transfer learning tasks.
Mining Compatible/Incompatible Entities from Question and Answering via Yes/No Answer Classification using Distant Label Expansion
Venue: arXiv cs.CL 2016
Year: 2016

Product Community Question Answering (PCQA) provides useful information about products and their features (aspects) that may not be well addressed by product descriptions and reviews. We observe that a product's compatibility issues with other products are frequently discussed in PCQA and such issues are more frequently addressed in accessories, i.e., via a yes/no question "Does this mouse work with windows 10?". In this paper, we address the problem of extracting compatible and incompatible products from yes/no questions in PCQA. This problem can naturally have a two-stage framework: first, we perform Complementary Entity (product) Recognition (CER) on yes/no questions; second, we identify the polarities of yes/no answers to assign the complementary entities a compatibility label (compatible, incompatible or unknown). We leverage an existing unsupervised method for the first stage and a 3-class classifier by combining a distant PU-learning method (learning from positive and unlabeled examples) together with a binary classifier for the second stage. The benefit of using distant PU-learning is that it can help to expand more implicit yes/no answers without using any human annotated data. We conduct experiments on 4 products to show that the proposed method is effective.
Data allocation on disks with solution reconfiguration (problems, heuristics)
Venue: arXiv cs.DC 2016
Year: 2016

The paper addresses problem of data allocation in two-layer computer storage while taking into account dynamic digraph(s) over computing tasks. The basic version of data file allocation on parallel hard magnetic disks is considered as special bin packing model. Two problems of the allocation solution reconfiguration (restructuring) are suggested: (i) one-stage restructuring model, (ii) multistage restructuring models. Solving schemes are based on simplified heuristics. Numerical examples illustrate problems and solving schemes.
The Innovative Behaviour of Software Engineers: Findings from a Pilot Case Study
Venue: arXiv cs.SE 2016
Year: 2016

Context: In the workplace, some individuals engage in the voluntary and intentional generation, promotion, and realization of new ideas for the benefit of individual performance, group effectiveness, or the organization. The literature classifies this phenomenon as innovative behaviour. Despite its importance to the development of innovation, innovative behaviour has not been fully investigated in software engineering. Objective: To understand the factors that support or inhibit innovative behaviour in software engineering practice. Method: We conducted a pilot case study in a Canadian software company using interviews and observations as data collection techniques. Using qualitative analysis, we identified relevant factors and relationships not addressed by studies from other areas. Results: Individual innovative behaviour is influenced by individual attitudes and also by situational factors such as relationships in the workplace, organizational characteristics, and project type. We built a model to express the interacting effects of these factors. Conclusions: Innovative behaviour is dependent on individual and contextual factors. Our results contribute to relevant impacts on research and practice, and to topics that deserve further study.
Anomaly Detection Using the Knowledge-based Temporal Abstraction Method
Venue: arXiv cs.LG 2016
Year: 2016

The rapid growth in stored time-oriented data necessitates the development of new methods for handling, processing, and interpreting large amounts of temporal data. One important example of such processing is detecting anomalies in time-oriented data. The Knowledge-Based Temporal Abstraction method was previously proposed for intelligent interpretation of temporal data based on predefined domain knowledge. In this study we propose a framework that integrates the KBTA method with a temporal pattern mining process for anomaly detection. According to the proposed method a temporal pattern mining process is applied on a dataset of basic temporal abstraction database in order to extract patterns representing normal behavior. These patterns are then analyzed in order to identify abnormal time periods characterized by a significantly small number of normal patterns. The proposed approach was demonstrated using a dataset collected from a real server.
Spectral video construction from RGB video: Application to Image Guided Neurosurgery
Venue: arXiv cs.CV 2016
Year: 2016

Spectral imaging has received enormous interest in the field of medical imaging modalities. It provides a powerful tool for the analysis of different organs and non-invasive tissues. Therefore, significant amount of research has been conducted to explore the possibility of using spectral imaging in biomedical applications. To observe spectral image information in real time during surgery and monitor the temporal changes in the organs and tissues is a demanding task. Available spectral imaging devices are not sufficient to accomplish this task with an acceptable spatial and spectral resolution. A solution to this problem is to estimate the spectral video from RGB video and perform visualization with the most prominent spectral bands. In this research, we propose a framework to generate neurosurgery spectral video from RGB video. A spectral estimation technique is applied on each RGB video frames. The RGB video is captured using a digital camera connected with an operational microscope dedicated to neurosurgery. A database of neurosurgery spectral images is used to collect training data and evaluate the estimation accuracy. A searching technique is used to identify the best training set. Five different spectrum estimation techniques are experimented to indentify the best method. Although this framework is established for neurosurgery spectral video generation, however, the methodology outlined here would also be applicable to other similar research.
Stochastic closest-pair problem and most-likely nearest-neighbor search in tree spaces
Venue: arXiv cs.CG 2016
Year: 2016

Let $T$ be a tree space (or tree network) represented by a weighted tree with $t$ vertices, and $S$ be a set of $n$ stochastic points in $T$, each of which has a fixed location with an independent existence probability. We investigate two fundamental problems under such a stochastic setting, the closest-pair problem and the nearest-neighbor search. For the former, we study the computation of the $\ell$-threshold probability and the expectation of the closest-pair distance of a realization of $S$. We propose the first algorithm to compute the $\ell$-threshold probability in $O(t+n\log n+ \min\{tn,n^2\})$ time for any given threshold $\ell$, which immediately results in an $O(t+\min\{tn^3,n^4\})$-time algorithm for computing the expected closest-pair distance. Based on this, we further show that one can compute a $(1+\varepsilon)$-approximation for the expected closest-pair distance in $O(t+\varepsilon^{-1}\min\{tn^2,n^3\})$ time, by arguing that the expected closest-pair distance can be approximated via $O(\varepsilon^{-1}n)$ threshold probability queries. For the latter, we study the $k$ most-likely nearest-neighbor search ($k$-LNN) via a notion called $k$ most-likely Voronoi Diagram ($k$-LVD). We show that the size of the $k$-LVD $\varPsi_T^S$ of $S$ on $T$ is bounded by $O(kn)$ if the existence probabilities of the points in $S$ are constant-far from 0. Furthermore, we establish an $O(kn)$ average-case upper bound for the size of $\varPsi_T^S$, by regarding the existence probabilities as i.i.d. random variables drawn from some fixed distribution. Our results imply the existence of an LVD data structure which answers $k$-LNN queries in $O(\log n+k)$ time using average-case $O(t+k^2n)$ space, and worst-case $O(t+kn^2)$ space if the existence probabilities are constant-far from 0. Finally, we also give an $O(t+ n^2\log n+n^2k)$-time algorithm to construct the LVD data structure.
Recurrent Image Captioner: Describing Images with Spatial-Invariant Transformation and Attention Filtering
Venue: arXiv cs.CV 2016
Year: 2016

Along with the prosperity of recurrent neural network in modelling sequential data and the power of attention mechanism in automatically identify salient information, image captioning, a.k.a., image description, has been remarkably advanced in recent years. Nonetheless, most existing paradigms may suffer from the deficiency of invariance to images with different scaling, rotation, etc.; and effective integration of standalone attention to form a holistic end-to-end system. In this paper, we propose a novel image captioning architecture, termed Recurrent Image Captioner (\textbf{RIC}), which allows visual encoder and language decoder to coherently cooperate in a recurrent manner. Specifically, we first equip CNN-based visual encoder with a differentiable layer to enable spatially invariant transformation of visual signals. Moreover, we deploy an attention filter module (differentiable) between encoder and decoder to dynamically determine salient visual parts. We also employ bidirectional LSTM to preprocess sentences for generating better textual representations. Besides, we propose to exploit variational inference to optimize the whole architecture. Extensive experimental results on three benchmark datasets (i.e., Flickr8k, Flickr30k and MS COCO) demonstrate the superiority of our proposed architecture as compared to most of the state-of-the-art methods.
A Light-Weight Approach for Verifying Multi-Threaded Programs with CPAchecker
Venue: arXiv cs.LO 2016
Year: 2016

Verifying multi-threaded programs is becoming more and more important, because of the strong trend to increase the number of processing units per CPU socket. We introduce a new configurable program analysis for verifying multi-threaded programs with a bounded number of threads. We present a simple and yet efficient implementation as component of the existing program-verification framework CPAchecker. While CPAchecker is already competitive on a large benchmark set of sequential verification tasks, our extension enhances the overall applicability of the framework. Our implementation of handling multiple threads is orthogonal to the abstract domain of the data-flow analysis, and thus, can be combined with several existing analyses in CPAchecker, like value analysis, interval analysis, and BDD analysis. The new analysis is modular and can be used, for example, to verify reachability properties as well as to detect deadlocks in the program. This paper includes an evaluation of the benefit of some optimization steps (e.g., changing the iteration order of the reachability algorithm or applying partial-order reduction) as well as the comparison with other state-of-the-art tools for verifying multi-threaded programs.
TeKnowbase: Towards Construction of a Knowledge-base of Technical Concepts
Venue: arXiv cs.CL 2016
Year: 2016

In this paper, we describe the construction of TeKnowbase, a knowledge-base of technical concepts in computer science. Our main information sources are technical websites such as Webopedia and Techtarget as well as Wikipedia and online textbooks. We divide the knowledge-base construction problem into two parts -- the acquisition of entities and the extraction of relationships among these entities. Our knowledge-base consists of approximately 100,000 triples. We conducted an evaluation on a sample of triples and report an accuracy of a little over 90\%. We additionally conducted classification experiments on StackOverflow data with features from TeKnowbase and achieved improved classification accuracy.
Research performance of UNU - A bibliometric analysis of the United Nations University
Venue: arXiv cs.DL 2016
Year: 2016

The scientific paper output of the United Nations University (UNU) was bibliometrically analysed. It was found that (i) a noticeable continous paper output starts in 1995, (ii) about 65% of the research papers have been published as international cooperations and 18% as single-authored papers, (iv) the research papers rank above world average according to Pudovkin-Garfield Percentile Rank Index, and (v) paper content indicate the wide variety of scientific topics UNU has been and is working on.
Runtime enforcement of reactive systems using synchronous enforcers
Venue: arXiv cs.FL 2016
Year: 2016

Synchronous programming is a paradigm of choice for the design of safety-critical reactive systems. Runtime enforcement is a technique to ensure that the output of a black-box system satisfies some desired properties. This paper deals with the problem of runtime enforcement in the context of synchronous programs. We propose a framework where an enforcer monitors both the inputs and the outputs of a synchronous program and (minimally) edits erroneous inputs/outputs in order to guarantee that a given property holds. We define enforceability conditions, develop an online enforcement algorithm, and prove its correctness. We also report on an implementation of the algorithm on top of the KIELER framework for the SCCharts synchronous language. Experimental results show that enforcement has minimal execution time overhead, which decreases proportionally with larger benchmarks.
Virtual Breathalyzer
Venue: arXiv cs.HC 2016
Year: 2016

Driving under the influence of alcohol is a widespread phenomenon in the US where it is considered a major cause of fatal accidents. In this research we present a novel approach and concept for detecting intoxication from motion differences obtained by the sensors of wearable devices. We formalize the problem of drunkenness detection as a supervised machine learning task, both as a binary classification problem (drunk or sober) and a regression problem (the breath alcohol content level). In order to test our approach, we collected data from 30 different subjects (patrons at three bars) using Google Glass and the LG G-watch, Microsoft Band, and Samsung Galaxy S4. We validated our results against an admissible breathalyzer used by the police. A system based on this concept, successfully detected intoxication and achieved the following results: 0.95 AUC and 0.05 FPR, given a fixed TPR of 1.0. Applications based on our system can be used to analyze the free gait of drinkers when they walk from the car to the bar and vice-versa, in order to alert people, or even a connected car and prevent people from driving under the influence of alcohol.
Computational Intelligence: are you crazy? Since when has intelligence become computational?
Venue: arXiv cs.OH 2016
Year: 2016

Computational Intelligence is a dead-end attempt to recreate human-like intelligence in a computing machine. The goal is unattainable because the means chosen for its accomplishment are mutually inconsistent and contradictory: "Computational" implies data processing ability while "Intelligence" implies the ability to process information. In the research community, there is a lack of interest in data versus information divergence. The cause of this indifference is the Shannon's Information theory, which has dominated the scientific community since the early 1950s. However, today it is clear that Shannon's theory is applicable only to a specific case of data communication and is inapplicable to the majority of other occasions, where information about semantic properties of a message must be taken into account. The paper will try to explain the devastating results of overlooking some of these very important issues - what is intelligence, what is semantic information, how they are interrelated and what happens when the relationship is disregarded.
On the Potential of Simple Framewise Approaches to Piano Transcription
Venue: arXiv cs.SD 2016
Year: 2016

In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-based framewise transcription. We systematically compare different popular input representations for transcription systems to determine the ones most suitable for use with neural networks. Exploiting recent advances in training techniques and new regularizers, and taking into account hyper-parameter tuning, we show that it is possible, by simple bottom-up frame-wise processing, to obtain a piano transcriber that outperforms the current published state of the art on the publicly available MAPS dataset -- without any complex post-processing steps. Thus, we propose this simple approach as a new baseline for this dataset, for future transcription research to build on and improve.
The CENDARI Infrastructure
Venue: arXiv cs.DL 2016
Year: 2016

The CENDARI infrastructure is a research supporting platform designed to provide tools for transnational historical research, focusing on two topics: Medieval culture and World War I. It exposes to the end users modern web-based tools relying on a sophisticated infrastructure to collect, enrich, annotate, and search through large document corpora. Supporting researchers in their daily work is a novel concern for infrastructures. We describe how we gathered requirements through multiple methods to understand the historians' needs and derive an abstract workflow to support them. We then outline the tools we have built, tying their technical descriptions to the user requirements. The main tools are the Note Taking Environment and its faceted search capabilities, the Data Integration platform including the Data API, supporting semantic enrichment through entity recognition, and the environment supporting the software development processes throughout the project to keep both technical partners and researchers in the loop. The outcomes are technical together with new resources developed and gathered, and the research workflow that has been described and documented.
Beampattern-Based Tracking for Millimeter Wave Communication Systems
Venue: arXiv cs.IT 2016
Year: 2016

We present a tracking algorithm to maintain the communication link between a base station (BS) and a mobile station (MS) in a millimeter wave (mmWave) communication system, where antenna arrays are used for beamforming in both the BS and MS. Downlink transmission is considered, and the tracking is performed at the MS as it moves relative to the BS. Specifically, we consider the case that the MS rotates quickly due to hand movement. The algorithm estimates the angle of arrival (AoA) by using variations in the radiation pattern of the beam as a function of this angle. Numerical results show that the algorithm achieves accurate beam alignment when the MS rotates in a wide range of angular speeds. For example, the algorithm can support angular speeds up to 800 degrees per second when tracking updates are available every 10 ms.
Multi-Agent Path Finding with Delay Probabilities
Venue: arXiv cs.AI 2016
Year: 2016

Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search. We make the following contributions to solve the MAPF problem with imperfect plan execution with small average makespans: First, we formalize the MAPF Problem with Delay Probabilities (MAPF-DP), define valid MAPF-DP plans and propose the use of robust plan-execution policies for valid MAPF-DP plans to control how each agent proceeds along its path. Second, we discuss 2 classes of decentralized robust plan-execution policies (called Fully Synchronized Policies and Minimal Communication Policies) that prevent collisions during plan execution for valid MAPF-DP plans. Third, we present a 2-level MAPF-DP solver (called Approximate Minimization in Expectation) that generates valid MAPF-DP plans.
Towards the Formalization of a Factory Demonstrator in BeSpaceD
Venue: arXiv cs.SE 2016
Year: 2016

This report gives an overview of our efforts towards a formalization for a food processing demonstrator plant. Our BeSpaceD framework is used for the formalization. The formalization comprises properties of components and relations between components. We present domain-specific constructs for the formalization of industrial automation facilities and provide some insights into the concrete food processing formalization. We are particularly interested in spatio-temporal and other physical characteristics. Relation- ships are formalized as graphs with annotated edges and components are represented as nodes.
Machine Reading with Background Knowledge
Venue: arXiv cs.AI 2016
Year: 2016

Intelligent systems capable of automatically understanding natural language text are important for many artificial intelligence applications including mobile phone voice assistants, computer vision, and robotics. Understanding language often constitutes fitting new information into a previously acquired view of the world. However, many machine reading systems rely on the text alone to infer its meaning. In this paper, we pursue a different approach; machine reading methods that make use of background knowledge to facilitate language understanding. To this end, we have developed two methods: The first method addresses prepositional phrase attachment ambiguity. It uses background knowledge within a semi-supervised machine learning algorithm that learns from both labeled and unlabeled data. This approach yields state-of-the-art results on two datasets against strong baselines; The second method extracts relationships from compound nouns. Our knowledge-aware method for compound noun analysis accurately extracts relationships and significantly outperforms a baseline that does not make use of background knowledge.
Analyzing Web Archives Through Topic and Event Focused Sub-collections
Venue: arXiv cs.DL 2016
Year: 2016

Web archives capture the history of the Web and are therefore an important source to study how societal developments have been reflected on the Web. However, the large size of Web archives and their temporal nature pose many challenges to researchers interested in working with these collections. In this work, we describe the challenges of working with Web archives and propose the research methodology of extracting and studying sub-collections of the archive focused on specific topics and events. We discuss the opportunities and challenges of this approach and suggest a framework for creating sub-collections.