OpenRaiser/Intern-Atlas huggingface dataset:
Equilibria, information and frustration in heterogeneous network games with conflicting preferences
Venue: arXiv cs.GT 2017
Year: 2017

Interactions between people are the basis on which the structure of our society arises as a complex system and, at the same time, are the starting point of any physical description of it. In the last few years, much theoretical research has addressed this issue by combining the physics of complex networks with a description of interactions in terms of evolutionary game theory. We here take this research a step further by introducing a most salient societal factor such as the individuals' preferences, a characteristic that is key to understand much of the social phenomenology these days. We consider a heterogeneous, agent-based model in which agents interact strategically with their neighbors but their preferences and payoffs for the possible actions differ. We study how such a heterogeneous network behaves under evolutionary dynamics and different strategic interactions, namely coordination games and best shot games. With this model we study the emergence of the equilibria predicted analytically in random graphs under best response dynamics, and we extend this test to unexplored contexts like proportional imitation and scale free networks. We show that some theoretically predicted equilibria do not arise in simulations with incomplete Information, and we demonstrate the importance of the graph topology and the payoff function parameters for some games. Finally, we discuss our results with available experimental evidence on coordination games, showing that our model agrees better with the experiment that standard economic theories, and draw hints as to how to maximize social efficiency in situations of conflicting preferences.
Pay-with-a-Selfie, a human-centred digital payment system
Venue: arXiv cs.ET 2017
Year: 2017

Mobile payment systems are increasingly used to simplify the way in which money transfers and transactions can be performed. We argue that, to achieve their full potential as economic boosters in developing countries, mobile payment systems need to rely on new metaphors suitable for the business models, lifestyle, and technology availability conditions of the targeted communities. The Pay-with-a-Group-Selfie (PGS) project, funded by the Melinda & Bill Gates Foundation, has developed a micro-payment system that supports everyday small transactions by extending the reach of, rather than substituting, existing payment frameworks. PGS is based on a simple gesture and a readily understandable metaphor. The gesture - taking a selfie - has become part of the lifestyle of mobile phone users worldwide, including non-technology-savvy ones. The metaphor likens computing two visual shares of the selfie to ripping a banknote in two, a technique used for decades for delayed payment in cash-only markets. PGS is designed to work with devices with limited computational power and when connectivity is patchy or not always available. Thanks to visual cryptography techniques PGS uses for computing the shares, the original selfie can be recomposed simply by stacking the shares, preserving the analogy with re-joining the two parts of the banknote.
A Survey on Workflow Satisfiability, Resiliency, and Related Problems
Venue: arXiv cs.CR 2017
Year: 2017

Workflows specify collections of tasks that must be executed under the responsibility or supervision of human users. Workflow management systems and workflow-driven applications need to enforce security policies in the form of access control, specifying which users can execute which tasks, and authorization constraints, such as Separation of Duty, further restricting the execution of tasks at run-time. Enforcing these policies is crucial to avoid frauds and malicious use, but it may lead to situations where a workflow instance cannot be completed without the violation of the policy. The Workflow Satisfiability Problem (WSP) asks whether there exists an assignment of users to tasks in a workflow such that every task is executed and the policy is not violated. The WSP is inherently hard, but solutions to this problem have a practical application in reconciling business compliance and business continuity. Solutions to related problems, such as workflow resiliency (i.e., whether a workflow instance is still satisfiable even in the absence of users), are important to help in policy design. Several variations of the WSP and similar problems have been defined in the literature and there are many solution methods available. In this paper, we survey the work done on these problems in the past 20 years.
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Venue: arXiv cs.CL 2017
Year: 2017

Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.
Personalization in Goal-Oriented Dialog
Venue: arXiv cs.CL 2017
Year: 2017

The main goal of modeling human conversation is to create agents which can interact with people in both open-ended and goal-oriented scenarios. End-to-end trained neural dialog systems are an important line of research for such generalized dialog models as they do not resort to any situation-specific handcrafting of rules. However, incorporating personalization into such systems is a largely unexplored topic as there are no existing corpora to facilitate such work. In this paper, we present a new dataset of goal-oriented dialogs which are influenced by speaker profiles attached to them. We analyze the shortcomings of an existing end-to-end dialog system based on Memory Networks and propose modifications to the architecture which enable personalization. We also investigate personalization in dialog as a multi-task learning problem, and show that a single model which shares features among various profiles outperforms separate models for each profile.
Design Choices for Data Governance in Platform Ecosystems: A Contingency Model
Venue: arXiv cs.SE 2017
Year: 2017

As platform ecosystems are growing by platform users' data, the importance of data governance has been highlighted. In particular, how to share control and decision rights with platform users are regarded as significant design issues since the role of them is increasing. Platform context should be considered when designing data governance in platform ecosystems (i.e. centralized/decentralized governance). However, there is limited research on this issue. Existing models focus on characteristics for enterprises. This results in limited support for platform ecosystems where there are different types of business context such as open strategies or platform maturity. This paper develops a contingency model for platform ecosystems including distinctive contingency factors. The study then discusses which data governance factors should be carefully considered and strengthened for each contingency in order to succeed in governance and to win market. A case study is performed to validate our model and to show its practical implications.
A Churn for the Better: Localizing Censorship using Network-level Path Churn and Network Tomography
Venue: arXiv cs.CR 2017
Year: 2017

Recent years have seen the Internet become a key vehicle for citizens around the globe to express political opinions and organize protests. This fact has not gone unnoticed, with countries around the world repurposing network management tools (e.g., URL filtering products) and protocols (e.g., BGP, DNS) for censorship. However, repurposing these products can have unintended international impact, which we refer to as "censorship leakage". While there have been anecdotal reports of censorship leakage, there has yet to be a systematic study of censorship leakage at a global scale. In this paper, we combine a global censorship measurement platform (ICLab) with a general-purpose technique -- boolean network tomography -- to identify which AS on a network path is performing censorship. At a high-level, our approach exploits BGP churn to narrow down the set of potential censoring ASes by over 95%. We exactly identify 65 censoring ASes and find that the anomalies introduced by 24 of the 65 censoring ASes have an impact on users located in regions outside the jurisdiction of the censoring AS, resulting in the leaking of regional censorship policies.
A $(2 + ε)$-approximation for precedence constrained single machine scheduling with release dates and total weighted completion time objective
Venue: arXiv cs.DS 2017
Year: 2017

We give a $(2 + ε)$-approximation algorithm for minimizing total weighted completion time on a single machine under release time and precedence constraints. This settles a recent conjecture made in [18]
D-STREAMON: from middlebox to distributed NFV framework for network monitoring
Venue: arXiv cs.NI 2017
Year: 2017

Many reasons make NFV an attractive paradigm for IT security: lowers costs, agile operations and better isolation as well as fast security updates, improved incident responses and better level of automation. On the other side, the network threats tend to be increasingly complex and distributed, implying huge traffic scale to be monitored and increasingly strict mitigation delay requirements. Considering the current trend of the net- working and the requirements to counteract to the evolution of cyber-threats, it is expected that also network monitoring will move towards NFV based solutions. In this paper, we present D- StreaMon an NFV-capable distributed framework for network monitoring realized to face the above described challenges. It relies on the StreaMon platform, a solution for network monitoring originally designed for traditional middleboxes. An evolution path which migrates StreaMon from middleboxes to Virtual Network Functions (VNFs) has been realized.
Comparison of Modified Kneser-Ney and Witten-Bell Smoothing Techniques in Statistical Language Model of Bahasa Indonesia
Venue: arXiv cs.CL 2017
Year: 2017

Smoothing is one technique to overcome data sparsity in statistical language model. Although in its mathematical definition there is no explicit dependency upon specific natural language, different natures of natural languages result in different effects of smoothing techniques. This is true for Russian language as shown by Whittaker (1998). In this paper, We compared Modified Kneser-Ney and Witten-Bell smoothing techniques in statistical language model of Bahasa Indonesia. We used train sets of totally 22M words that we extracted from Indonesian version of Wikipedia. As far as we know, this is the largest train set used to build statistical language model for Bahasa Indonesia. The experiments with 3-gram, 5-gram, and 7-gram showed that Modified Kneser-Ney consistently outperforms Witten-Bell smoothing technique in term of perplexity values. It is interesting to note that our experiments showed 5-gram model for Modified Kneser-Ney smoothing technique outperforms that of 7-gram. Meanwhile, Witten-Bell smoothing is consistently improving over the increase of n-gram order.
Inferring Structural Characteristics of Networks with Strong and Weak Ties from Fixed-Choice Surveys
Venue: arXiv cs.SI 2017
Year: 2017

Knowing the structure of an offline social network facilitates a variety of analyses, including studying the rate at which infectious diseases may spread and identifying a subset of actors to immunize in order to reduce, as much as possible, the rate of spread. Offline social network topologies are typically estimated by surveying actors and asking them to list their neighbours. While identifying close friends and family (i.e., strong ties) can typically be done reliably, listing all of one's acquaintances (i.e., weak ties) is subject to error due to respondent fatigue. This issue is commonly circumvented through the use of so-called "fixed choice" surveys where respondents are asked to name a fixed, small number of their weak ties (e.g., two or ten). Of course, the resulting crude observed network will omit many ties, and using this crude network to infer properties of the network, such as its degree distribution or clustering coefficient, will lead to biased estimates. This paper develops estimators, based on the method of moments, for a number of network characteristics including those related to the first and second moments of the degree distribution as well as the network size, using fixed-choice survey data. Experiments with simulated data illustrate that the proposed estimators perform well across a variety of network topologies and measurement scenarios, and the resulting estimates are significantly more accurate than those obtained directly using the crude observed network, which are commonly used in the literature. We also describe a variation of the Jackknife procedure that can be used to obtain an estimate of the estimator variance.
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task spaces), e.g., such base deep CNNs are trained to recognize different subsets of tens of thousands of atomic object classes. First, a two-layer (category layer and object class layer) ontology is constructed to achieve more effective solution for task group generation, e.g., assigning the semantically-related atomic object classes at the sibling leaf nodes into the same task group because they may share similar learning complexities. Second, one particular base deep CNNs with $M+1$ ($M \leq 1,000$) outputs is learned for each task group to recognize its $M$ atomic object classes effectively and identify one special class of "not-in-group" automatically, and the network structure (numbers of layers and units in each layer) of the well-designed AlexNet is directly used to configure such base deep CNNs. A deep multi-task learning algorithm is developed to leverage the inter-class visual similarities to learn more discriminative base deep CNNs and multi-task softmax for enhancing the separability of the atomic object classes in the same task group. Finally, all these base deep CNNs with diverse outputs (task spaces) are seamlessly combined to form a deep mixture of diverse experts for recognizing tens of thousands of atomic object classes. Our experimental results have demonstrated that our deep mixture of diverse experts algorithm can achieve very competitive results on large-scale visual recognition.
Semi-supervised Text Categorization Using Recursive K-means Clustering
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we present a semi-supervised learning algorithm for classification of text documents. A method of labeling unlabeled text documents is presented. The presented method is based on the principle of divide and conquer strategy. It uses recursive K-means algorithm for partitioning both labeled and unlabeled data collection. The K-means algorithm is applied recursively on each partition till a desired level partition is achieved such that each partition contains labeled documents of a single class. Once the desired clusters are obtained, the respective cluster centroids are considered as representatives of the clusters and the nearest neighbor rule is used for classifying an unknown text document. Series of experiments have been conducted to bring out the superiority of the proposed model over other recent state of the art models on 20Newsgroups dataset.
The Semantic Information Method for Maximum Mutual Information and Maximum Likelihood of Tests, Estimations, and Mixture Models
Venue: arXiv cs.IT 2017
Year: 2017

It is very difficult to solve the Maximum Mutual Information (MMI) or Maximum Likelihood (ML) for all possible Shannon Channels or uncertain rules of choosing hypotheses, so that we have to use iterative methods. According to the Semantic Mutual Information (SMI) and R(G) function proposed by Chenguang Lu (1993) (where R(G) is an extension of information rate distortion function R(D), and G is the lower limit of the SMI), we can obtain a new iterative algorithm of solving the MMI and ML for tests, estimations, and mixture models. The SMI is defined by the average log normalized likelihood. The likelihood function is produced from the truth function and the prior by semantic Bayesian inference. A group of truth functions constitute a semantic channel. Letting the semantic channel and Shannon channel mutually match and iterate, we can obtain the Shannon channel that maximizes the Shannon mutual information and the average log likelihood. This iterative algorithm is called Channels' Matching algorithm or the CM algorithm. The convergence can be intuitively explained and proved by the R(G) function. Several iterative examples for tests, estimations, and mixture models show that the computation of the CM algorithm is simple (which can be demonstrated in excel files). For most random examples, the numbers of iterations for convergence are close to 5. For mixture models, the CM algorithm is similar to the EM algorithm; however, the CM algorithm has better convergence and more potential applications in comparison with the standard EM algorithm.
Challenges of facet analysis and concept placement in universal classifications: the example of architecture in UDC
Venue: arXiv cs.DL 2017
Year: 2017

The paper discusses the challenges of faceted vocabulary organization in universal classifications which treat the universe of knowledge as a coherent whole and in which the concepts and subjects in different disciplines are shared, related and combined. The authors illustrate the challenges of the facet analytical approach using, as an example, the revision of class 72 in UDC. The paper reports on the research undertaken in 2013 as preparation for the revision. This consisted of analysis of concept organization in the UDC schedules in comparison with the Art & Architecture Thesaurus and class W of the Bliss Bibliographic Classification. The paper illustrates how such research can contribute to a better understanding of the field and may lead to improvements in the facet structure of this segment of the UDC vocabulary.
OS Fingerprinting: New Techniques and a Study of Information Gain and Obfuscation
Venue: arXiv cs.CR 2017
Year: 2017

Passive operating system fingerprinting reveals valuable information to the defenders of heterogeneous private networks; at the same time, attackers can use fingerprinting to reconnoiter networks, so defenders need obfuscation techniques to foil them. We present an effective approach for passive fingerprinting that uses data features from TLS as well as the TCP/IP and HTTP protocols in a multi-session model, which is applicable whenever several sessions can be observed within a time window. In experiments on a real-world private network, our approach identified operating system major and minor versions with accuracies of 99.4% and 97.5%, respectively, and provided significant information gain. We also show that obfuscation strategies can often be defeated due to the difficulty of manipulating data features from all protocols, especially TLS, by studying how obfuscation affects our fingerprinting system. Because devices running unpatched operating systems on private networks create significant vulnerabilities, their detection is critical; our approach achieved over 98% accuracy at this important goal.
A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking
Venue: arXiv cs.CL 2017
Year: 2017

This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory Network (Bi-LSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.
Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance
Venue: arXiv cs.CV 2017
Year: 2017

Wireless Multimedia Sensor Network (WMSN) is a promising technology capturing rich multimedia data like audio and video, which can be useful to monitor an environment under surveillance. However, many scenarios in real time monitoring requires 3D depth information. In this research work, we propose to use the disparity map that is computed from two or multiple images, in order to monitor the depth information in an object or event under surveillance using WMSN. Our system is based on distributed wireless sensors allowing us to notably reduce the computational time needed for 3D depth reconstruction, thus permitting the success of real time solutions. Each pair of sensors will capture images for a targeted place/object and will operate a Stereo Matching in order to create a Disparity Map. Disparity maps will give us the ability to decrease traffic on the bandwidth, because they are of low size. This will increase WMSN lifetime. Any event can be detected after computing the depth value for the target object in the scene, and also 3D scene reconstruction can be achieved with a disparity map and some reference(s) image(s) taken by the node(s).
Steiner Point Removal with Distortion $O(\log k)$
Venue: arXiv cs.DS 2017
Year: 2017

In the Steiner point removal (SPR) problem, we are given a weighted graph $G=(V,E)$ and a set of terminals $K\subset V$ of size $k$. The objective is to find a minor $M$ of $G$ with only the terminals as its vertex set, such that the distance between the terminals will be preserved up to a small multiplicative distortion. Kamma, Krauthgamer and Nguyen [KKN15] used a ball-growing algorithm with exponential distributions to show that the distortion is at most $O(\log^5 k)$. Cheung [Che17] improved the analysis of the same algorithm, bounding the distortion by $O(\log^2 k)$. We improve the analysis of this ball-growing algorithm even further, bounding the distortion by $O(\log k)$.
Scalable multimodal convolutional networks for brain tumour segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.