OpenRaiser/Intern-Atlas huggingface dataset:
Quantifying the distribution of editorial power and manuscript decision bias at the mega-journal PLOS ONE
Venue: arXiv cs.DL 2017
Year: 2017

We analyzed the longitudinal activity of nearly 7,000 editors at the mega-journal PLOS ONE over the 10-year period 2006-2015. Using the article-editor associations, we develop editor-specific measures of power, activity, article acceptance time, citation impact, and editorial renumeration (an analogue to self-citation). We observe remarkably high levels of power inequality among the PLOS ONE editors, with the top-10 editors responsible for 3,366 articles -- corresponding to 2.4% of the 141,986 articles we analyzed. Such high inequality levels suggest the presence of unintended incentives, which may reinforce unethical behavior in the form of decision-level biases at the editorial level. Our results indicate that editors may become apathetic in judging the quality of articles and susceptible to modes of power-driven misconduct. We used the longitudinal dimension of editor activity to develop two panel regression models which test and verify the presence of editor-level bias. In the first model we analyzed the citation impact of articles, and in the second model we modeled the decision time between an article being submitted and ultimately accepted by the editor. We focused on two variables that represent social factors that capture potential conflicts-of-interest: (i) we accounted for the social ties between editors and authors by developing a measure of repeat authorship among an editor's article set, and (ii) we accounted for the rate of citations directed towards the editor's own publications in the reference list of each article he/she oversaw. Our results indicate that these two factors play a significant role in the editorial decision process. Moreover, these two effects appear to increase with editor age, which is consistent with behavioral studies concerning the evolution of misbehavior and response to temptation in power-driven environments.
Krivine Machine and Taylor Expansion in a Non-uniform Setting
Venue: arXiv cs.LO 2017
Year: 2017

The Krivine machine is an abstract machine implementing the linear head reduction of lambda-calculus. Ehrhard and Regnier gave a resource sensitive version returning the annotated form of a lambda-term accounting for the resources used by the linear head reduction. These annotations take the form of terms in the resource lambda-calculus. We generalize this resource-driven Krivine machine to the case of the algebraic lambda-calculus. The latter is an extension of the pure lambda-calculus allowing for the linear combination of lambda-terms with coefficients taken from a semiring. Our machine associates a lambda-term M and a resource annotation t with a scalar k in the semiring describing some quantitative properties of the linear head reduction of M. In the particular case of non-negative real numbers and of algebraic terms M representing probability distributions, the coefficient k gives the probability that the linear head reduction actually uses exactly the resources annotated by t. In the general case, we prove that the coefficient k can be recovered from the coefficient of t in the Taylor expansion of M and from the normal form of t.
Surface Proofs for Nonsymmetric Linear Logic (Extended Abstract)
Venue: arXiv cs.LO 2017
Year: 2017

We show that a proof in multiplicative linear logic can be represented as a decorated surface, such that two proofs are logically equivalent just when their surfaces are geometrically equivalent. This is an extended abstract for arXiv:1601.05372.
Non-Blocking Concurrent Imperative Programming with Session Types
Venue: arXiv cs.LO 2017
Year: 2017

Concurrent C0 is an imperative programming language in the C family with session-typed message-passing concurrency. The previously proposed semantics implements asynchronous (non-blocking) output; we extend it here with non-blocking input. A key idea is to postpone message reception as much as possible by interpreting receive commands as a request for a message. We implemented our ideas as a translation from a blocking intermediate language to a non-blocking language. Finally, we evaluated our techniques with several benchmark programs and show the results obtained. While the abstract measure of span always decreases (or remains unchanged), only a few of the examples reap a practical benefit.
Agglomerative Info-Clustering
Venue: arXiv cs.IT 2017
Year: 2017

An agglomerative clustering of random variables is proposed, where clusters of random variables sharing the maximum amount of multivariate mutual information are merged successively to form larger clusters. Compared to the previous info-clustering algorithms, the agglomerative approach allows the computation to stop earlier when clusters of desired size and accuracy are obtained. An efficient algorithm is also derived based on the submodularity of entropy and the duality between the principal sequence of partitions and the principal sequence for submodular functions.
Bringing Impressionism to Life with Neural Style Transfer in Come Swim
Venue: arXiv cs.CV 2017
Year: 2017

Neural Style Transfer is a striking, recently-developed technique that uses neural networks to artistically redraw an image in the style of a source style image. This paper explores the use of this technique in a production setting, applying Neural Style Transfer to redraw key scenes in 'Come Swim' in the style of the impressionistic painting that inspired the film. We document how the technique can be driven within the framework of an iterative creative process to achieve a desired look, and propose a mapping of the broad parameter space to a key set of creative controls. We hope that this mapping can provide insights into priorities for future research.
Breaking the Target: An Analysis of Target Data Breach and Lessons Learned
Venue: arXiv cs.CR 2017
Year: 2017

This paper investigates and examines the events leading up to the second most devastating data breach in history: the attack on the Target Corporation. It includes a thorough step-by-step analysis of this attack and a comprehensive anatomy of the malware named BlackPOS. Also, this paper provides insight into the legal aspect of cybercrimes, along with a prosecution and sentence example of the well-known TJX case. Furthermore, we point out an urgent need for improving security mechanisms in existing systems of merchants and propose three security guidelines and defenses. Credit card security is discussed at the end of the paper with several best practices given to customers to hide their card information in purchase transactions.
A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe
Venue: arXiv cs.NE 2017
Year: 2017

This paper presents the development of several models of a deep convolutional auto-encoder in the Caffe deep learning framework and their experimental evaluation on the example of MNIST dataset. We have created five models of a convolutional auto-encoder which differ architecturally by the presence or absence of pooling and unpooling layers in the auto-encoder's encoder and decoder parts. Our results show that the developed models provide very good results in dimensionality reduction and unsupervised clustering tasks, and small classification errors when we used the learned internal code as an input of a supervised linear classifier and multi-layer perceptron. The best results were provided by a model where the encoder part contains convolutional and pooling layers, followed by an analogous decoder part with deconvolution and unpooling layers without the use of switch variables in the decoder part. The paper also discusses practical details of the creation of a deep convolutional auto-encoder in the very popular Caffe deep learning framework. We believe that our approach and results presented in this paper could help other researchers to build efficient deep neural network architectures in the future.
On the error probability of stochastic decision and stochastic decoding
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates the error probability of a stochastic decision and the way in which it differs from the error probability of an optimal decision, i.e., the maximum a posteriori decision. This paper calls attention to the fact that the error probability of a stochastic decision with the a posteriori distribution is at most twice the error probability of the maximum a posteriori decision. It is shown that, by generating an independent identically distributed random sequence subject to the a posteriori distribution and making a decision that maximizes the a posteriori probability over the sequence, the error probability approaches exponentially the error probability of the maximum a posteriori decision as the sequence length increases. Using these ideas as a basis, we can construct stochastic decoders for source/channel codes.
Bounds on the Size and Asymptotic Rate of Subblock-Constrained Codes
Venue: arXiv cs.IT 2017
Year: 2017

The study of subblock-constrained codes has recently gained attention due to their application in diverse fields. We present bounds on the size and asymptotic rate for two classes of subblock-constrained codes. The first class is binary constant subblock-composition codes (CSCCs), where each codeword is partitioned into equal sized subblocks, and every subblock has the same fixed weight. The second class is binary subblock energy-constrained codes (SECCs), where the weight of every subblock exceeds a given threshold. We present novel upper and lower bounds on the code sizes and asymptotic rates for binary CSCCs and SECCs. For a fixed subblock length and small relative distance, we show that the asymptotic rate for CSCCs (resp. SECCs) is strictly lower than the corresponding rate for constant weight codes (CWCs) (resp. heavy weight codes (HWCs)). Further, for codes with high weight and low relative distance, we show that the asymptotic rates for CSCCs is strictly lower than that of SECCs, which contrasts that the asymptotic rate for CWCs is equal to that of HWCs. We also provide a correction to an earlier result by Chee et al. (2014) on the asymptotic CSCC rate. Additionally, we present several numerical examples comparing the rates for CSCCs and SECCs with those for constant weight codes and heavy weight codes.
MIMO Beamforming for Secure and Energy-Efficient Wireless Communication
Venue: arXiv cs.IT 2017
Year: 2017

Considering a multiple-user multiple-input multiple-output (MIMO) channel with an eavesdropper, this letter develops a beamformer design to optimize the energy efficiency in terms of secrecy bits per Joule under secrecy quality-of-service constraints. This is a very difficult design problem with no available exact solution techniques. A path-following procedure, which iteratively improves its feasible points by using a simple quadratic program of moderate dimension, is proposed. Under any fixed computational tolerance the procedure terminates after finitely many iterations, yielding at least a locally optimal solution. Simulation results show the superior performance of the obtained algorithm over other existing methods.
Control Capacity of Partially Observable Dynamic Systems in Continuous Time
Venue: arXiv cs.IT 2017
Year: 2017

Stochastic dynamic control systems relate in a prob- abilistic fashion the space of control signals to the space of corresponding future states. Consequently, stochastic dynamic systems can be interpreted as an information channel between the control space and the state space. In this work we study this control-to-state informartion capacity of stochastic dynamic systems in continuous-time, when the states are observed only partially. The control-to-state capacity, known as empowerment, was shown in the past to be useful in solving various Artificial Intelligence & Control benchmarks, and was used to replace problem-specific utilities. The higher the value of empowerment is, the more optional future states an agent may reach by using its controls inside a given time horizon. The contribution of this work is that we derive an efficient solution for computing the control-to-state information capacity for a linear, partially-observed Gaussian dynamic control system in continuous time, and discover new relationships between control-theoretic and information-theoretic properties of dynamic systems. Particularly, using the derived method, we demonstrate that the capacity between the control signal and the system output does not grow without limits with the length of the control signal. This means that only the near-past window of the control signal contributes effectively to the control-to-state capacity, while most of the information beyond this window is irrelevant for the future state of the dynamic system. We show that empowerment depends on a time constant of a dynamic system.
Secret Key Agreement under Discussion Rate Constraints
Venue: arXiv cs.IT 2017
Year: 2017

For the multiterminal secret key agreement problem, new single-letter lower bounds are obtained on the public discussion rate required to achieve any given secret key rate below the secrecy capacity. The results apply to general source model without helpers or wiretapper's side information but can be strengthened for hypergraphical sources. In particular, for the pairwise independent network, the results give rise to a complete characterization of the maximum secret key rate achievable under a constraint on the total discussion rate.
Assessing User Expertise in Spoken Dialog System Interactions
Venue: arXiv cs.CL 2017
Year: 2017

Identifying the level of expertise of its users is important for a system since it can lead to a better interaction through adaptation techniques. Furthermore, this information can be used in offline processes of root cause analysis. However, not much effort has been put into automatically identifying the level of expertise of an user, especially in dialog-based interactions. In this paper we present an approach based on a specific set of task related features. Based on the distribution of the features among the two classes - Novice and Expert - we used Random Forests as a classification approach. Furthermore, we used a Support Vector Machine classifier, in order to perform a result comparison. By applying these approaches on data from a real system, Let's Go, we obtained preliminary results that we consider positive, given the difficulty of the task and the lack of competing approaches for comparison.
Transfer learning for multi-center classification of chronic obstructive pulmonary disease
Venue: arXiv cs.CV 2017
Year: 2017

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multi-center dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains, can further improve the results. To encourage further research into transfer learning methods for classification of COPD, upon acceptance of the paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copd
NMODE --- Neuro-MODule Evolution
Venue: arXiv cs.NE 2017
Year: 2017

Modularisation, repetition, and symmetry are structural features shared by almost all biological neural networks. These features are very unlikely to be found by the means of structural evolution of artificial neural networks. This paper introduces NMODE, which is specifically designed to operate on neuro-modules. NMODE addresses a second problem in the context of evolutionary robotics, which is incremental evolution of complex behaviours for complex machines, by offering a way to interface neuro-modules. The scenario in mind is a complex walking machine, for which a locomotion module is evolved first, that is then extended by other modules in later stages. We show that NMODE is able to evolve a locomotion behaviour for a standard six-legged walking machine in approximately 10 generations and show how it can be used for incremental evolution of a complex walking machine. The entire source code used in this paper is publicly available through GitHub.
Lipschitz Properties for Deep Convolutional Networks
Venue: arXiv cs.LG 2017
Year: 2017

In this paper we discuss the stability properties of convolutional neural networks. Convolutional neural networks are widely used in machine learning. In classification they are mainly used as feature extractors. Ideally, we expect similar features when the inputs are from the same class. That is, we hope to see a small change in the feature vector with respect to a deformation on the input signal. This can be established mathematically, and the key step is to derive the Lipschitz properties. Further, we establish that the stability results can be extended for more general networks. We give a formula for computing the Lipschitz bound, and compare it with other methods to show it is closer to the optimal value.
Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples
Venue: arXiv cs.AI 2017
Year: 2017

This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: Logic Programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of Input/Output logic for dealing with uncertainty in dynamic normative contexts.
Implicit Media Tagging and Affect Prediction from video of spontaneous facial expressions, recorded with depth camera
Venue: arXiv cs.HC 2017
Year: 2017

We present a method that automatically evaluates emotional response from spontaneous facial activity recorded by a depth camera. The automatic evaluation of emotional response, or affect, is a fascinating challenge with many applications, including human-computer interaction, media tagging and human affect prediction. Our approach in addressing this problem is based on the inferred activity of facial muscles over time, as captured by a depth camera recording an individual's facial activity. Our contribution is two-fold: First, we constructed a database of publicly available short video clips, which elicit a strong emotional response in a consistent manner across different individuals. Each video was tagged by its characteristic emotional response along 4 scales: \emph{Valence, Arousal, Likability} and \emph{Rewatch} (the desire to watch again). The second contribution is a two-step prediction method, based on learning, which was trained and tested using this database of tagged video clips. Our method was able to successfully predict the aforementioned 4 dimensional representation of affect, as well as to identify the period of strongest emotional response in the viewing recordings, in a method that is blind to the video clip being watch, revealing a significantly high agreement between the recordings of independent viewers.
Combinatorial Algorithms for Control of Biological Regulatory Networks
Venue: arXiv cs.SY 2017
Year: 2017

Biological processes, including cell differentiation, organism development, and disease progression, can be interpreted as attractors (fixed points or limit cycles) of an underlying networked dynamical system. In this paper, we study the problem of computing a minimum-size subset of control nodes that can be used to steer a given biological network towards a desired attractor, when the networked system has Boolean dynamics. We first prove that this problem cannot be approximated to any nontrivial factor unless P=NP. We then formulate a sufficient condition and prove that the sufficient condition is equivalent to a target set selection problem, which can be solved using integer linear programming. Furthermore, we show that structural properties of biological networks can be exploited to reduce the computational complexity. We prove that when the network nodes have threshold dynamics and certain topological structures, such as block cactus topology and hierarchical organization, the input selection problem can be solved or approximated in polynomial time. For networks with nested canalyzing dynamics, we propose polynomial-time algorithms that are within a polylogarithmic bound of the global optimum. We validate our approach through numerical study on real-world gene regulatory networks.
Heterogeneous Information Network Embedding for Meta Path based Proximity
Venue: arXiv cs.AI 2017
Year: 2017

A network embedding is a representation of a large graph in a low-dimensional space, where vertices are modeled as vectors. The objective of a good embedding is to preserve the proximity between vertices in the original graph. This way, typical search and mining methods can be applied in the embedded space with the help of off-the-shelf multidimensional indexing approaches. Existing network embedding techniques focus on homogeneous networks, where all vertices are considered to belong to a single class.
Semantic Evolutionary Concept Distances for Effective Information Retrieval in Query Expansion
Venue: arXiv cs.IR 2017
Year: 2017

In this work several semantic approaches to concept-based query expansion and reranking schemes are studied and compared with different ontology-based expansion methods in web document search and retrieval. In particular, we focus on concept-based query expansion schemes, where, in order to effectively increase the precision of web document retrieval and to decrease the users browsing time, the main goal is to quickly provide users with the most suitable query expansion. Two key tasks for query expansion in web document retrieval are to find the expansion candidates, as the closest concepts in web document domain, and to rank the expanded queries properly. The approach we propose aims at improving the expansion phase for better web document retrieval and precision. The basic idea is to measure the distance between candidate concepts using the PMING distance, a collaborative semantic proximity measure, i.e. a measure which can be computed by using statistical results from web search engine. Experiments show that the proposed technique can provide users with more satisfying expansion results and improve the quality of web document retrieval.