OpenRaiser/Intern-Atlas huggingface dataset:
An Operational Semantics for the Cognitive Architecture ACT-R and its Translation to Constraint Handling Rules
Venue: arXiv cs.LO 2017
Year: 2017

Computational psychology has the aim to explain human cognition by computational models of cognitive processes. The cognitive architecture ACT-R is popular to develop such models. Although ACT-R has a well-defined psychological theory and has been used to explain many cognitive processes, there are two problems that make it hard to reason formally about its cognitive models: First, ACT-R lacks a formalization of its underlying production rule system and secondly, there are many different implementations and extensions of ACT-R with technical artifacts complicating formal reasoning even more. This paper describes a formal operational semantics - the very abstract semantics - that abstracts from as many technical details as possible keeping it open to extensions and different implementations of the ACT-R theory. In a second step, this semantics is refined to define some of its abstract features that are found in many implementations of ACT-R - the abstract semantics. It concentrates on the procedural core of ACT-R and is suitable for analysis of the transition system since it still abstracts from details like timing, the sub-symbolic layer or conflict resolution. Furthermore, a translation of ACT-R models to the programming language Constraint Handling Rules (CHR) is defined. This makes the abstract semantics an executable specification of ACT-R. CHR has been used successfully to embed other rule-based formalisms like graph transformation systems or functional programming. There are many results and tools that support formal reasoning about and analysis of CHR programs. The translation of ACT-R models to CHR is proven sound and complete w.r.t. the abstract operational semantics of ACT-R. This paves the way to analysis of ACT-R models through CHR. Therefore, to the best of our knowledge, our abstract semantics is the first formulation of ACT-R suitable for both analysis and execution.
Rotten Apples or Bad Harvest? What We Are Measuring When We Are Measuring Abuse
Venue: arXiv cs.CR 2017
Year: 2017

Internet security and technology policy research regularly uses technical indicators of abuse in order to identify culprits and to tailor mitigation strategies. As a major obstacle, readily available data are often misaligned with actual information needs. They are subject to measurement errors relating to observation, aggregation, attribution, and various sources of heterogeneity. More precise indicators such as size estimates are costly to measure at Internet scale. We address these issues for the case of hosting providers with a statistical model of the abuse data generation process, using phishing sites in hosting networks as a case study. We decompose error sources and then estimate key parameters of the model, controlling for heterogeneity in size and business model. We find that 84\,\% of the variation in abuse counts across 45,358 hosting providers can be explained with structural factors alone. Informed by the fitted model, we systematically select and enrich a subset of 105 homogeneous "statistical twins" with additional explanatory variables, unreasonable to collect for \emph{all} hosting providers. We find that abuse is positively associated with the popularity of websites hosted and with the prevalence of popular content management systems. Moreover, hosting providers who charge higher prices (after controlling for level differences between countries) witness less abuse. These factors together explain a further 77\,\% of the remaining variation, calling into question premature inferences from raw abuse indicators on security efforts of actors, and suggesting the adoption of similar analysis frameworks in all domains where network measurement aims at informing technology policy.
Slice-to-volume medical image registration: a survey
Venue: arXiv cs.CV 2017
Year: 2017

During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures. A particular type of image registration problem, known as slice-to-volume registration, played a fundamental role in areas like image guided surgeries and volumetric image reconstruction. However, to date, and despite the extensive literature available on this topic, no survey has been written to discuss this challenging problem. This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category. We draw some general conclusions from this analysis and present our perspectives on the future of the field.
On the Value of Penalties in Time-Inconsistent Planning
Venue: arXiv cs.DS 2017
Year: 2017

People tend to behave inconsistently over time due to an inherent present bias. As this may impair performance, social and economic settings need to be adapted accordingly. Common tools to reduce the impact of time-inconsistent behavior are penalties and prohibition. Such tools are called commitment devices. In recent work Kleinberg and Oren connect the design of prohibition-based commitment devices to a combinatorial problem in which edges are removed from a task graph $G$ with $n$ nodes. However, this problem is NP-hard to approximate within a ratio less than $\sqrt{n}/3$. To address this issue, we propose a penalty-based commitment device that does not delete edges but raises their cost. The benefits of our approach are twofold. On the conceptual side, we show that penalties are up to $1/β$ times more efficient than prohibition, where $β\in (0,1]$ parameterizes the present bias. On the computational side, we significantly improve approximability by presenting a $2$-approximation algorithm for allocating the penalties. To complement this result, we prove that optimal penalties are NP-hard to approximate within a ratio of $1.08192$.
Popular Topics Spread Faster: New Dimension for Influence Propagation in Online Social Networks
Venue: arXiv cs.SI 2017
Year: 2017

Information can propagate among Online Social Network (OSN) users at a high speed, which makes the OSNs become important platforms for viral marketing. Although the viral marketing related problems in OSNs have been extensively studied in the past decade, the existing works all assume known propagation rates and are not able to solve the scenario when the rates may dynamically increase for popular topics. In this paper, we propose a novel model, Dynamic Influence Propagation (DIP), which allows propagation rates to change during the diffusion and can be used for describing information propagation in OSNs more realistically. Based on DIP, we define a new research problem: Threshold Activation Problem under DIP (TAP-DIP). TAP-DIP is more generalized than TAP and can be used for studying the DIP model. However, it adds another layer of complexity over the already \#P-hard TAP problem. Despite it hardness, we are able to approximate TAP-DIP with $O(\log|V|)$ ratio. Our solution consists of two major parts: 1) the Lipschitz optimization technique and 2) a novel solution to the general version of TAP, the Multi-TAP problem. We experimentally test our solution Using various real OSN datasets, and demonstrate that our solution not only generates high-quality yet much smaller seed sets when being aware of the rate increase, but also is scalable. In addition, considering DIP or not has a significant difference in seed set selection.
A New Point-set Registration Algorithm for Fingerprint Matching
Venue: arXiv cs.CV 2017
Year: 2017

A novel minutia-based fingerprint matching algorithm is proposed that employs iterative global alignment on two minutia sets. The matcher considers all possible minutia pairings and iteratively aligns the two sets until the number of minutia pairs does not exceed the maximum number of allowable one-to-one pairings. The optimal alignment parameters are derived analytically via linear least squares. The first alignment establishes a region of overlap between the two minutia sets, which is then (iteratively) refined by each successive alignment. After each alignment, minutia pairs that exhibit weak correspondence are discarded. The process is repeated until the number of remaining pairs no longer exceeds the maximum number of allowable one-to-one pairings. The proposed algorithm is tested on both the FVC2000 and FVC2002 databases, and the results indicate that the proposed matcher is both effective and efficient for fingerprint authentication; it is fast and does not utilize any computationally expensive mathematical functions (e.g. trigonometric, exponential). In addition to the proposed matcher, another contribution of the paper is the analytical derivation of the least squares solution for the optimal alignment parameters for two point-sets lacking exact correspondence.
Continuous-Time User Modeling in the Presence of Badges: A Probabilistic Approach
Venue: arXiv cs.SI 2017
Year: 2017

User modeling plays an important role in delivering customized web services to the users and improving their engagement. However, most user models in the literature do not explicitly consider the temporal behavior of users. More recently, continuous-time user modeling has gained considerable attention and many user behavior models have been proposed based on temporal point processes. However, typical point process based models often considered the impact of peer influence and content on the user participation and neglected other factors. Gamification elements, are among those factors that are neglected, while they have a strong impact on user participation in online services. In this paper, we propose interdependent multi-dimensional temporal point processes that capture the impact of badges on user participation besides the peer influence and content factors. We extend the proposed processes to model user actions over the community based question and answering websites, and propose an inference algorithm based on Variational-EM that can efficiently learn the model parameters. Extensive experiments on both synthetic and real data gathered from Stack Overflow show that our inference algorithm learns the parameters efficiently and the proposed method can better predict the user behavior compared to the alternatives.
Face Aging With Conditional Generative Adversarial Networks
Venue: arXiv cs.CV 2017
Year: 2017

It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the GAN-based method for automatic face aging. Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person's identity in the aged version of his/her face. To this end, we introduce a novel approach for "Identity-Preserving" optimization of GAN's latent vectors. The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.
Preference-based Teaching
Venue: arXiv cs.LG 2017
Year: 2017

We introduce a new model of teaching named "preference-based teaching" and a corresponding complexity parameter---the preference-based teaching dimension (PBTD)---representing the worst-case number of examples needed to teach any concept in a given concept class. Although the PBTD coincides with the well-known recursive teaching dimension (RTD) on finite classes, it is radically different on infinite ones: the RTD becomes infinite already for trivial infinite classes (such as half-intervals) whereas the PBTD evaluates to reasonably small values for a wide collection of infinite classes including classes consisting of so-called closed sets w.r.t. a given closure operator, including various classes related to linear sets over $\mathbb{N}_0$ (whose RTD had been studied quite recently) and including the class of Euclidean half-spaces. On top of presenting these concrete results, we provide the reader with a theoretical framework (of a combinatorial flavor) which helps to derive bounds on the PBTD.
Knowledge Adaptation: Teaching to Adapt
Venue: arXiv cs.CL 2017
Year: 2017

Domain adaptation is crucial in many real-world applications where the distribution of the training data differs from the distribution of the test data. Previous Deep Learning-based approaches to domain adaptation need to be trained jointly on source and target domain data and are therefore unappealing in scenarios where models need to be adapted to a large number of domains or where a domain is evolving, e.g. spam detection where attackers continuously change their tactics. To fill this gap, we propose Knowledge Adaptation, an extension of Knowledge Distillation (Bucilua et al., 2006; Hinton et al., 2015) to the domain adaptation scenario. We show how a student model achieves state-of-the-art results on unsupervised domain adaptation from multiple sources on a standard sentiment analysis benchmark by taking into account the domain-specific expertise of multiple teachers and the similarities between their domains. When learning from a single teacher, using domain similarity to gauge trustworthiness is inadequate. To this end, we propose a simple metric that correlates well with the teacher's accuracy in the target domain. We demonstrate that incorporating high-confidence examples selected by this metric enables the student model to achieve state-of-the-art performance in the single-source scenario.
A Bayesian Game without epsilon equilibria
Venue: arXiv cs.GT 2017
Year: 2017

We present a three player Bayesian game for which there is no epsilon equilibria in Borel measurable strategies for small enough epsilon, however there are non-measurable equilibria.
An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning
Venue: arXiv cs.RO 2017
Year: 2017

Deep learning is an established framework for learning hierarchical data representations. While compute power is in abundance, one of the main challenges in applying this framework to robotic grasping has been obtaining the amount of data needed to learn these representations, and structuring the data to the task at hand. Among contemporary approaches in the literature, we highlight key properties that have encouraged the use of deep learning techniques, and in this paper, detail our experience in developing a simulator for collecting cylindrical precision grasps of a multi-fingered dexterous robotic hand.
Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections
Venue: arXiv cs.CV 2017
Year: 2017

We propose a method to generate multiple diverse and valid human pose hypotheses in 3D all consistent with the 2D detection of joints in a monocular RGB image. We use a novel generative model uniform (unbiased) in the space of anatomically plausible 3D poses. Our model is compositional (produces a pose by combining parts) and since it is restricted only by anatomical constraints it can generalize to every plausible human 3D pose. Removing the model bias intrinsically helps to generate more diverse 3D pose hypotheses. We argue that generating multiple pose hypotheses is more reasonable than generating only a single 3D pose based on the 2D joint detection given the depth ambiguity and the uncertainty due to occlusion and imperfect 2D joint detection. We hope that the idea of generating multiple consistent pose hypotheses can give rise to a new line of future work that has not received much attention in the literature. We used the Human3.6M dataset for empirical evaluation.
Speaker Change Detection Using Features through A Neural Network Speaker Classifier
Venue: arXiv cs.SD 2017
Year: 2017

The mechanism proposed here is for real-time speaker change detection in conversations, which firstly trains a neural network text-independent speaker classifier using in-domain speaker data. Through the network, features of conversational speech from out-of-domain speakers are then converted into likelihood vectors, i.e. similarity scores comparing to the in-domain speakers. These transformed features demonstrate very distinctive patterns, which facilitates differentiating speakers and enable speaker change detection with some straight-forward distance metrics. The speaker classifier and the speaker change detector are trained/tested using speech of the first 200 (in-domain) and the remaining 126 (out-of-domain) male speakers in TIMIT respectively. For the speaker classification, 100% accuracy at a 200 speaker size is achieved on any testing file, given the speech duration is at least 0.97 seconds. For the speaker change detection using speaker classification outputs, performance based on 0.5, 1, and 2 seconds of inspection intervals were evaluated in terms of error rate and F1 score, using synthesized data by concatenating speech from various speakers. It captures close to 97% of the changes by comparing the current second of speech with the previous second, which is very competitive among literature using other methods.
Neural Network Based Speaker Classification and Verification Systems with Enhanced Features
Venue: arXiv cs.SD 2017
Year: 2017

This work presents a novel framework based on feed-forward neural network for text-independent speaker classification and verification, two related systems of speaker recognition. With optimized features and model training, it achieves 100% classification rate in classification and less than 6% Equal Error Rate (ERR), using merely about 1 second and 5 seconds of data respectively. Features with stricter Voice Active Detection (VAD) than the regular one for speech recognition ensure extracting stronger voiced portion for speaker recognition, speaker-level mean and variance normalization helps to eliminate the discrepancy between samples from the same speaker. Both are proven to improve the system performance. In building the neural network speaker classifier, the network structure parameters are optimized with grid search and dynamically reduced regularization parameters are used to avoid training terminated in local minimum. It enables the training goes further with lower cost. In speaker verification, performance is improved with prediction score normalization, which rewards the speaker identity indices with distinct peaks and penalizes the weak ones with high scores but more competitors, and speaker-specific thresholding, which significantly reduces ERR in the ROC curve. TIMIT corpus with 8K sampling rate is used here. First 200 male speakers are used to train and test the classification performance. The testing files of them are used as in-domain registered speakers, while data from the remaining 126 male speakers are used as out-of-domain speakers, i.e. imposters in speaker verification.
The Necessity of Scheduling in Compute-and-Forward
Venue: arXiv cs.IT 2017
Year: 2017

Compute and Forward (CF) is a promising relaying scheme which, instead of decoding single messages or forwarding/amplifying information at the relay, decodes linear combinations of the simultaneously transmitted messages. The current literature includes several coding schemes and results on the degrees of freedom in CF, yet for systems with a fixed number of transmitters and receivers. It is unclear, however, how CF behaves at the limit of a large number of transmitters. In this paper, we investigate the performance of CF in that regime. Specifically, we show that as the number of transmitters grows, CF becomes degenerated, in the sense that a relay prefers to decode only one (strongest) user instead of any other linear combination of the transmitted codewords, treating the other users as noise. Moreover, the sum-rate tends to zero as well. This makes scheduling necessary in order to maintain the superior abilities CF provides. Indeed, under scheduling, we show that non-trivial linear combinations are chosen, and the sum-rate does not decay, even without state information at the transmitters and without interference alignment.
Quasi-reductivity of Logically Constrained Term Rewriting Systems
Venue: arXiv cs.LO 2017
Year: 2017

This paper considers quasi-reductivity - essentially, the property that an evaluation cannot get "stuck" due to a missing case in pattern matching - in the context of term rewriting with logical constraints.
Constructing Receiver Signal Points using Constrained Massive MIMO Arrays
Venue: arXiv cs.IT 2017
Year: 2017

A low cost solution for constructing receiver signal points is investigated that combines a large number of constrained radio frequency (RF) frontends with a limited number of full RF chains. The constrained RF front ends have low cost and are limited to on/off switching of antenna elements and a small number of phases. Severe degradations are typically observed for multi-user MIMO for these simple on/off antenna arrays. A few full RF frontends are shown to compensate for the signal errors of the high number of constrained RF frontends for various scenarios. An algorithm for such a hybrid RF (HRF) system is developed that achieves performance close to that of exhaustive search with respect to the mean square error of the constructed receiver signals for Rayleigh fading and the WINNER 2 Urban Macro channel model.
Parallel implementation of a vehicle rail dynamical model for multi-core systems
Venue: arXiv cs.DC 2017
Year: 2017

This research presents a model of a complex dynamic object running on a multi-core system. Discretization and numerical integration for multibody models of vehicle rail elements in the vertical longitudinal plane fluctuations is considered. The implemented model and solution of the motion differential equations allow estimating the basic processes occurring in the system with various external influences. Hence the developed programming model can be used for performing analysis and comparing new vehicle designs. Keywords-dynamic model; multi-core system; SMP system; rolling stock.
An Executable Sequential Specification for Spark Aggregation
Venue: arXiv cs.DC 2017
Year: 2017

Spark is a new promising platform for scalable data-parallel computation. It provides several high-level application programming interfaces (APIs) to perform parallel data aggregation. Since execution of parallel aggregation in Spark is inherently non-deterministic, a natural requirement for Spark programs is to give the same result for any execution on the same data set. We present PureSpark, an executable formal Haskell specification for Spark aggregate combinators. Our specification allows us to deduce the precise condition for deterministic outcomes from Spark aggregation. We report case studies analyzing deterministic outcomes and correctness of Spark programs.
Scene-adapted plug-and-play algorithm with convergence guarantees
Venue: arXiv cs.CV 2017
Year: 2017

Recent frameworks, such as the so-called plug-and-play, allow us to leverage the developments in image denoising to tackle other, and more involved, problems in image processing. As the name suggests, state-of-the-art denoisers are plugged into an iterative algorithm that alternates between a denoising step and the inversion of the observation operator. While these tools offer flexibility, the convergence of the resulting algorithm may be difficult to analyse. In this paper, we plug a state-of-the-art denoiser, based on a Gaussian mixture model, in the iterations of an alternating direction method of multipliers and prove the algorithm is guaranteed to converge. Moreover, we build upon the concept of scene-adapted priors where we learn a model targeted to a specific scene being imaged, and apply the proposed method to address the hyperspectral sharpening problem.