OpenRaiser/Intern-Atlas huggingface dataset:
Abstract Attribute Exploration with Partial Object Descriptions
Venue: arXiv cs.AI 2015
Year: 2015

Attribute exploration has been investigated in several studies, with particular emphasis on the algorithmic aspects of this knowledge acquisition method. In its basic version the method itself is rather simple and transparent. But when background knowledge and partially described counter-examples are admitted, it gets more difficult. Here we discuss this case in an abstract, somewhat "axiomatic" setting, providing a terminology that clarifies the abstract strategy of the method rather than its algorithmic implementation.
Adjustable Bounded Rectifiers: Towards Deep Binary Representations
Venue: arXiv cs.LG 2015
Year: 2015

Binary representation is desirable for its memory efficiency, computation speed and robustness. In this paper, we propose adjustable bounded rectifiers to learn binary representations for deep neural networks. While hard constraining representations across layers to be binary makes training unreasonably difficult, we softly encourage activations to diverge from real values to binary by approximating step functions. Our final representation is completely binary. We test our approach on MNIST, CIFAR10, and ILSVRC2012 dataset, and systematically study the training dynamics of the binarization process. Our approach can binarize the last layer representation without loss of performance and binarize all the layers with reasonably small degradations. The memory space that it saves may allow more sophisticated models to be deployed, thus compensating the loss. To the best of our knowledge, this is the first work to report results on current deep network architectures using complete binary middle representations. Given the learned representations, we find that the firing or inhibition of a binary neuron is usually associated with a meaningful interpretation across different classes. This suggests that the semantic structure of a neural network may be manifested through a guided binarization process.
Network-based recommendation algorithms: A review
Venue: arXiv cs.IR 2015
Year: 2015

Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use - such as the possible influence of recommendation on the evolution of systems that use it - and finally discuss open research directions and challenges.
Preprint WebVRGIS Based Traffic Analysis and Visualization System
Venue: arXiv cs.OH 2015
Year: 2015

This is the preprint version of our paper on Advances in Engineering Software. With several characteristics, such as large scale, diverse predictability and timeliness, the city traffic data falls in the range of definition of Big Data. A Virtual Reality GIS based traffic analysis and visualization system is proposed as a promising and inspiring approach to manage and develop traffic big data. In addition to the basic GIS interaction functions, the proposed system also includes some intelligent visual analysis and forecasting functions. The passenger flow forecasting algorithm is introduced in detail.
sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings
Venue: arXiv cs.CL 2015
Year: 2015

Neural word representations have proven useful in Natural Language Processing (NLP) tasks due to their ability to efficiently model complex semantic and syntactic word relationships. However, most techniques model only one representation per word, despite the fact that a single word can have multiple meanings or "senses". Some techniques model words by using multiple vectors that are clustered based on context. However, recent neural approaches rarely focus on the application to a consuming NLP algorithm. Furthermore, the training process of recent word-sense models is expensive relative to single-sense embedding processes. This paper presents a novel approach which addresses these concerns by modeling multiple embeddings for each word based on supervised disambiguation, which provides a fast and accurate way for a consuming NLP model to select a sense-disambiguated embedding. We demonstrate that these embeddings can disambiguate both contrastive senses such as nominal and verbal senses as well as nuanced senses such as sarcasm. We further evaluate Part-of-Speech disambiguated embeddings on neural dependency parsing, yielding a greater than 8% average error reduction in unlabeled attachment scores across 6 languages.
Bayesian inference via rejection filtering
Venue: arXiv cs.LG 2015
Year: 2015

We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative to conventional methods by combining rejection sampling with particle filtering. We also provide an approximate form of rejection sampling that makes rejection filtering tractable in cases where exact rejection sampling is not efficient. Finally, we present several numerical examples of rejection filtering that show its ability to track time dependent parameters in online settings and also benchmark its performance on MNIST classification problems.
On Cooperative Behavior of Open Homogeneous Chemical Reaction Systems in the Extent Domain
Venue: arXiv cs.SY 2015
Year: 2015

Material balance equations describe the dynamics of the species in open reaction systems and contain information regarding reaction topology, kinetics and operation mode. For reaction systems, the state variables (the numbers of moles, or concentrations) have recently been transformed into decoupled reaction variants (extents of reaction), and reaction invariants (extents of flow) (Amrhein et al., AIChE Journal, 2010). This paper analyses the conditions under which an open homogeneous reaction system is cooperative in the extents domain. Further, it is shown that the dynamics of the extents of flow exhibit cooperative behavior. Further, we provide the conditions under which the dynamics of the extents of reaction exhibit cooperative behavior. Our results provide physical insights into cooperative and competitive nature of the underlying reaction system in the presence of material exchange with surrounding (i.e., inlet and outlet flows). The results of the article are demonstrated via examples.
An Investigation into the Use of Common Libraries in Android Apps
Venue: arXiv cs.SE 2015
Year: 2015

The packaging model of Android apps requires the entire code necessary for the execution of an app to be shipped into one single apk file. Thus, an analysis of Android apps often visits code which is not part of the functionality delivered by the app. Such code is often contributed by the common libraries which are used pervasively by all apps. Unfortunately, Android analyses, e.g., for piggybacking detection and malware detection, can produce inaccurate results if they do not take into account the case of library code, which constitute noise in app features. Despite some efforts on investigating Android libraries, the momentum of Android research has not yet produced a complete set of common libraries to further support in-depth analysis of Android apps. In this paper, we leverage a dataset of about 1.5 million apps from Google Play to harvest potential common libraries, including advertisement libraries. With several steps of refinements, we finally collect by far the largest set of 1,113 libraries supporting common functionalities and 240 libraries for advertisement. We use the dataset to investigates several aspects of Android libraries, including their popularity and their proportion in Android app code. Based on these datasets, we have further performed several empirical investigations to confirm the motivations behind our work.
Crowd Behavior Analysis: A Review where Physics meets Biology
Venue: arXiv cs.CV 2015
Year: 2015

Although the traits emerged in a mass gathering are often non-deliberative, the act of mass impulse may lead to irre- vocable crowd disasters. The two-fold increase of carnage in crowd since the past two decades has spurred significant advances in the field of computer vision, towards effective and proactive crowd surveillance. Computer vision stud- ies related to crowd are observed to resonate with the understanding of the emergent behavior in physics (complex systems) and biology (animal swarm). These studies, which are inspired by biology and physics, share surprisingly common insights, and interesting contradictions. However, this aspect of discussion has not been fully explored. Therefore, this survey provides the readers with a review of the state-of-the-art methods in crowd behavior analysis from the physics and biologically inspired perspectives. We provide insights and comprehensive discussions for a broader understanding of the underlying prospect of blending physics and biology studies in computer vision.
A Study of Age and Gender seen through Mobile Phone Usage Patterns in Mexico
Venue: arXiv cs.SI 2015
Year: 2015

Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper we focus on the population of Mexican mobile phone users. Our first contribution is an observational study of mobile phone usage according to gender and age groups. We were able to detect significant differences in phone usage among different subgroups of the population. Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We provide details of the methodology and show experimental results on a real world dataset that involves millions of users.
Learning visual groups from co-occurrences in space and time
Venue: arXiv cs.LG 2015
Year: 2015

We propose a self-supervised framework that learns to group visual entities based on their rate of co-occurrence in space and time. To model statistical dependencies between the entities, we set up a simple binary classification problem in which the goal is to predict if two visual primitives occur in the same spatial or temporal context. We apply this framework to three domains: learning patch affinities from spatial adjacency in images, learning frame affinities from temporal adjacency in videos, and learning photo affinities from geospatial proximity in image collections. We demonstrate that in each case the learned affinities uncover meaningful semantic groupings. From patch affinities we generate object proposals that are competitive with state-of-the-art supervised methods. From frame affinities we generate movie scene segmentations that correlate well with DVD chapter structure. Finally, from geospatial affinities we learn groups that relate well to semantic place categories.
ICU Patient Deterioration prediction: a Data-Mining Approach
Venue: arXiv cs.CY 2015
Year: 2015

A huge amount of medical data is generated every day, which presents a challenge in analysing these data. The obvious solution to this challenge is to reduce the amount of data without information loss. Dimension reduction is considered the most popular approach for reducing data size and also to reduce noise and redundancies in data. In this paper, we investigate the effect of feature selection in improving the prediction of patient deterioration in ICUs. We consider lab tests as features. Thus, choosing a subset of features would mean choosing the most important lab tests to perform. If the number of tests can be reduced by identifying the most important tests, then we could also identify the redundant tests. By omitting the redundant tests, observation time could be reduced and early treatment could be provided to avoid the risk. Additionally, unnecessary monetary cost would be avoided. Our approach uses state-ofthe- art feature selection for predicting ICU patient deterioration using the medical lab results. We apply our technique on the publicly available MIMIC-II database and show the effectiveness of the feature selection. We also provide a detailed analysis of the best features identified by our approach.
Analysis of a Play by Means of CHAPLIN, the Characters and Places Interaction Network Software
Venue: arXiv cs.CY 2015
Year: 2015

Recently, we have developed a software able of gathering information on social networks from written texts. This software, the CHAracters and PLaces Interaction Network (CHAPLIN) tool, is implemented in Visual Basic. By means of it, characters and places of a literary work can be extracted from a list of raw words. The software interface helps users to select their names out of this list. Setting some parameters, CHAPLIN creates a network where nodes represent characters/places and edges give their interactions. Nodes and edges are labelled by performances. In this paper, we propose to use CHAPLIN for the analysis a William Shakespeare's play, the famous 'Tragedy of Hamlet, Prince of Denmark'. Performances of characters in the play as a whole and in each act of it are given by graphs.
Designing a mobile game to thwarts malicious IT threats: A phishing threat avoidance perspective
Venue: arXiv cs.CY 2015
Year: 2015

Phishing is an online identity theft, which aims to steal sensitive information such as username, password and online banking details from victims. To prevent this, phishing education needs to be considered. Game based education is becoming more and more popular. This paper introduces a mobile game prototype for the android platform based on a story, which simplifies and exaggerates real life. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues and game design principles were used as a set of guidelines for structuring and presenting information. The overall mobile game design was aimed to enhance the user's avoidance behaviour through motivation to protect themselves against phishing threats. The prototype mobile game design was presented on MIT App Inventor Emulator.
NearBucket-LSH: Efficient Similarity Search in P2P Networks
Venue: arXiv cs.DC 2015
Year: 2015

We present NearBucket-LSH, an effective algorithm for similarity search in large-scale distributed online social networks organized as peer-to-peer overlays. As communication is a dominant consideration in distributed systems, we focus on minimizing the network cost while guaranteeing good search quality. Our algorithm is based on Locality Sensitive Hashing (LSH), which limits the search to collections of objects, called buckets, that have a high probability to be similar to the query. More specifically, NearBucket-LSH employs an LSH extension that searches in near buckets, and improves search quality but also significantly increases the network cost. We decrease the network cost by considering the internals of both LSH and the P2P overlay, and harnessing their properties to our needs. We show that our NearBucket-LSH increases search quality for a given network cost compared to previous art. In many cases, the search quality increases by more than 50%.
Medusa: An Efficient Cloud Fault-Tolerant MapReduce
Venue: arXiv cs.DC 2015
Year: 2015

Applications such as web search and social networking have been moving from centralized to decentralized cloud architectures to improve their scalability. MapReduce, a programming framework for processing large amounts of data using thousands of machines in a single cloud, also needs to be scaled out to multiple clouds to adapt to this evolution. The challenge of building a multi-cloud distributed architecture is substantial. Notwithstanding, the ability to deal with the new types of faults introduced by such setting, such as the outage of a whole datacenter or an arbitrary fault caused by a malicious cloud insider, increases the endeavor considerably. In this paper we propose Medusa, a platform that allows MapReduce computations to scale out to multiple clouds and tolerate several types of faults. Our solution fulfills four objectives. First, it is transparent to the user, who writes her typical MapReduce application without modification. Second, it does not require any modification to the widely used Hadoop framework. Third, the proposed system goes well beyond the fault-tolerance offered by MapReduce to tolerate arbitrary faults, cloud outages, and even malicious faults caused by corrupt cloud insiders. Fourth, it achieves this increased level of fault tolerance at reasonable cost. We performed an extensive experimental evaluation in the ExoGENI testbed, demonstrating that our solution significantly reduces execution time when compared to traditional methods that achieve the same level of resilience.
Improving the performance of the linear systems solvers using CUDA
Venue: arXiv cs.DC 2015
Year: 2015

Parallel computing can offer an enormous advantage regarding the performance for very large applications in almost any field: scientific computing, computer vision, databases, data mining, and economics. GPUs are high performance many-core processors that can obtain very high FLOP rates. Since the first idea of using GPU for general purpose computing, things have evolved and now there are several approaches to GPU programming: CUDA from NVIDIA and Stream from AMD. CUDA is now a popular programming model for general purpose computations on GPU for C/C++ programmers. A great number of applications were ported to CUDA programming model and they obtain speedups of orders of magnitude comparing to optimized CPU implementations. In this paper we present an implementation of a library for solving linear systems using the CCUDA framework. We present the results of performance tests and show that using GPU one can obtain speedups of about of approximately 80 times comparing with a CPU implementation.
A Python Extension for the Massively Parallel Multiphysics Simulation Framework waLBerla
Venue: arXiv cs.DC 2015
Year: 2015

We present a Python extension to the massively parallel HPC simulation toolkit waLBerla. waLBerla is a framework for stencil based algorithms operating on block-structured grids, with the main application field being fluid simulations in complex geometries using the lattice Boltzmann method. Careful performance engineering results in excellent node performance and good scalability to over 400,000 cores. To increase the usability and flexibility of the framework, a Python interface was developed. Python extensions are used at all stages of the simulation pipeline: They simplify and automate scenario setup, evaluation, and plotting. We show how our Python interface outperforms the existing text-file-based configuration mechanism, providing features like automatic nondimensionalization of physical quantities and handling of complex parameter dependencies. Furthermore, Python is used to process and evaluate results while the simulation is running, leading to smaller output files and the possibility to adjust parameters dependent on the current simulation state. C++ data structures are exported such that a seamless interfacing to other numerical Python libraries is possible. The expressive power of Python and the performance of C++ make development of efficient code with low time effort possible.
Modular Autoencoders for Ensemble Feature Extraction
Venue: arXiv cs.LG 2015
Year: 2015

We introduce the concept of a Modular Autoencoder (MAE), capable of learning a set of diverse but complementary representations from unlabelled data, that can later be used for supervised tasks. The learning of the representations is controlled by a trade off parameter, and we show on six benchmark datasets the optimum lies between two extremes: a set of smaller, independent autoencoders each with low capacity, versus a single monolithic encoding, outperforming an appropriate baseline. In the present paper we explore the special case of linear MAE, and derive an SVD-based algorithm which converges several orders of magnitude faster than gradient descent.
What is the plausibility of probability?(revised 2003, 2015)
Venue: arXiv cs.AI 2015
Year: 2015

We present and examine a result related to uncertainty reasoning, namely that a certain plausibility space of Cox's type can be uniquely embedded in a minimal ordered field. This, although a purely mathematical result, can be claimed to imply that every rational method to reason with uncertainty must be based on sets of extended probability distributions, where extended probability is standard probability extended with infinitesimals. This claim must be supported by some argumentation of non-mathematical type, however, since pure mathematics does not tell us anything about the world. We propose one such argumentation, and relate it to results from the literature of uncertainty and statistics. In an added retrospective section we discuss some developments in the area regarding countable additivity, partially ordered domains and robustness, and philosophical stances on the Cox/Jaynes approach since 2003. We also show that the most general partially ordered plausibility calculus embeddable in a ring can be represented as a set of extended probability distributions or, in algebraic terms, is a subdirect sum of ordered fields. In other words, the robust Bayesian approach is universal. This result is exemplified by relating Dempster-Shafer's evidence theory to robust Bayesian analysis.
Planning in the Wild: Modeling Tools for PDDL
Venue: arXiv cs.HC 2015
Year: 2015

Even though there are sophisticated AI planning algorithms, many integrated, large-scale projects do not use planning. One reason seems to be the missing support by engineering tools such as syntax highlighting and visualization. We propose myPDDL - a modular toolbox for efficiently creating PDDL domains and problems. To evaluate myPDDL, we compare it to existing knowledge engineering tools for PDDL and experimentally assess its usefulness for novice PDDL users.
Does the Internet deserve everybody?
Venue: arXiv cs.CY 2015
Year: 2015

There has been a long standing tradition amongst developed nations of influencing, both directly and indirectly, the activities of developing economies. Behind this is one of a range of aims: building/improving living standards, bettering the social status of recipient communities, etc. In some cases, this has resulted in prosperous relations, yet often this has been seen as the exploitation of a power position or a veneer for other activities (e.g. to tap into new emerging markets). In this paper, we explore whether initiatives to improve Internet connectivity in developing regions are always ethical. We draw a list of issues that would aid in formulating Internet initiatives that are ethical, effective, and sustainable.