OpenRaiser/Intern-Atlas huggingface dataset:
Arabian Horse Identification Benchmark Dataset
Venue: arXiv cs.CV 2017
Year: 2017

The lack of a standard muzzle print database is a challenge for conducting researches in Arabian horse identification systems. Therefore, collecting a muzzle print images database is a crucial decision. The dataset presented in this paper is an option for the studies that need a dataset for testing and comparing the algorithms under development for Arabian horse identification. Our collected dataset consists of 300 color images that were collected from 50 Arabian horse muzzle species. This dataset has been collected from 50 Arabian horses with 6 muzzle print images each. A special care has been given to the quality of the collected images. The collected images cover different quality levels and degradation factors such as image rotation and image partiality for simulating real time identification operations. This dataset can be used to test the identification of Arabian horse system including the extracted features and the selected classifier.
Stability analysis of a general class of singularly perturbed linear hybrid systems
Venue: arXiv cs.SY 2017
Year: 2017

Motivated by a real problem in steel production, we introduce and analyze a general class of singularly perturbed linear hybrid systems with both switches and impulses, in which the slow or fast nature of the variables can be mode-dependent. This means that, at switching instants, some of the slow variables can become fast and vice-versa. Firstly, we show that using a mode-dependent variable reordering we can rewrite this class of systems in a form in which the variables preserve their nature over time. Secondly, we establish, through singular perturbation techniques, an upper bound on the minimum dwell-time ensuring the overall system's stability. Remarkably, this bound is the sum of two terms. The first term corresponds to an upper bound on the minimum dwell-time ensuring the stability of the reduced order linear hybrid system describing the slow dynamics. The order of magnitude of the second term is determined by that of the parameter defining the ratio between the two time-scales of the singularly perturbed system. We show that the proposed framework can also take into account the change of dimension of the state vector at switching instants. Numerical illustrations complete our study.
Extracting Formal Models from Normative Texts
Venue: arXiv cs.CL 2017
Year: 2017

We are concerned with the analysis of normative texts - documents based on the deontic notions of obligation, permission, and prohibition. Our goal is to make queries about these notions and verify that a text satisfies certain properties concerning causality of actions and timing constraints. This requires taking the original text and building a representation (model) of it in a formal language, in our case the C-O Diagram formalism. We present an experimental, semi-automatic aid that helps to bridge the gap between a normative text in natural language and its C-O Diagram representation. Our approach consists of using dependency structures obtained from the state-of-the-art Stanford Parser, and applying our own rules and heuristics in order to extract the relevant components. The result is a tabular data structure where each sentence is split into suitable fields, which can then be converted into a C-O Diagram. The process is not fully automatic however, and some post-editing is generally required of the user. We apply our tool and perform experiments on documents from different domains, and report an initial evaluation of the accuracy and feasibility of our approach.
DPerm: Assisting the Migration of Android Apps to Runtime Permissions
Venue: arXiv cs.SE 2017
Year: 2017

Android apps require permissions when accessing resources related to privacy or system integrity. Starting from Android 6, these permissions have to be asked at runtime. However, migrating to the new permission model poses multiple challenges for developers. First, developers have to discover where the app uses permissions, which requires a permission specification. To date several such specifications have been built, yet these are either imprecise, incomplete or don't support all types of protected resources. We first present DPSpec, a novel permission specification built from several documentation formats supplied with the Android SDK. Compared with the state the art specification, it contains 2.5x as many entries for protected methods and detects dangerous permission usages in more than twice as many apps. A second challenge for developers is where to insert permission requests, with possible locations restricted by the request mechanism. We also present DPerm, a static analysis for Android apps that recommends locations for permission requests in code. It achieves high precision through context sensitivity and improves recall through a general call graph augmentation algorithm for incomplete code. Our empirical evaluation on 32 apps shows a precision of 96% and recall of 89%.
Asymmetric Unification and Disunification
Venue: arXiv cs.LO 2017
Year: 2017

We compare two kinds of unification problems: Asymmetric Unification and Disunification, which are variants of Equational Unification. Asymmetric Unification is a type of Equational Unification where the right-hand sides of the equations are in normal form with respect to the given term rewriting system. In Disunification we solve equations and disequations with respect to an equational theory for the case with free constants. We contrast the time complexities of both and show that the two problems are incomparable: there are theories where one can be solved in Polynomial time while the other is NP-hard. This goes both ways. The time complexity also varies based on the termination ordering used in the term rewriting system.
Generalization for Adaptively-chosen Estimators via Stable Median
Venue: arXiv cs.LG 2017
Year: 2017

Datasets are often reused to perform multiple statistical analyses in an adaptive way, in which each analysis may depend on the outcomes of previous analyses on the same dataset. Standard statistical guarantees do not account for these dependencies and little is known about how to provably avoid overfitting and false discovery in the adaptive setting. We consider a natural formalization of this problem in which the goal is to design an algorithm that, given a limited number of i.i.d.~samples from an unknown distribution, can answer adaptively-chosen queries about that distribution. We present an algorithm that estimates the expectations of $k$ arbitrary adaptively-chosen real-valued estimators using a number of samples that scales as $\sqrt{k}$. The answers given by our algorithm are essentially as accurate as if fresh samples were used to evaluate each estimator. In contrast, prior work yields error guarantees that scale with the worst-case sensitivity of each estimator. We also give a version of our algorithm that can be used to verify answers to such queries where the sample complexity depends logarithmically on the number of queries $k$ (as in the reusable holdout technique). Our algorithm is based on a simple approximate median algorithm that satisfies the strong stability guarantees of differential privacy. Our techniques provide a new approach for analyzing the generalization guarantees of differentially private algorithms.
Horcrux: A Password Manager for Paranoids
Venue: arXiv cs.CR 2017
Year: 2017

Vulnerabilities in password managers are unremitting because current designs provide large attack surfaces, both at the client and server. We describe and evaluate Horcrux, a password manager that is designed holistically to minimize and decentralize trust, while retaining the usability of a traditional password manager. The prototype Horcrux client, implemented as a Firefox add-on, is split into two components, with code that has access to the user's master's password and any key material isolated into a small auditable component, separate from the complexity of managing the user interface. Instead of exposing actual credentials to the DOM, a dummy username and password are autofilled by the untrusted component. The trusted component intercepts and modifies POST requests before they are encrypted and sent over the network. To avoid trusting a centralized store, stored credentials are secret-shared over multiple servers. To provide domain and username privacy, while maintaining resilience to off-line attacks on a compromised password store, we incorporate cuckoo hashing in a way that ensures an attacker cannot determine if a guessed master password is correct. Our approach only works for websites that do not manipulate entered credentials in the browser client, so we conducted a large-scale experiment that found the technique appears to be compatible with over 98% of tested login forms.
Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder
Venue: arXiv cs.CL 2017
Year: 2017

We investigate the integration of a planning mechanism into an encoder-decoder architecture with an explicit alignment for character-level machine translation. We develop a model that plans ahead when it computes alignments between the source and target sequences, constructing a matrix of proposed future alignments and a commitment vector that governs whether to follow or recompute the plan. This mechanism is inspired by the strategic attentive reader and writer (STRAW) model. Our proposed model is end-to-end trainable with fully differentiable operations. We show that it outperforms a strong baseline on three character-level decoder neural machine translation on WMT'15 corpus. Our analysis demonstrates that our model can compute qualitatively intuitive alignments and achieves superior performance with fewer parameters.
Number game
Venue: arXiv cs.CL 2017
Year: 2017

CLARIN (Common Language Resources and Technology Infrastructure) is regarded as one of the most important European research infrastructures, offering and promoting a wide array of useful services for (digital) research in linguistics and humanities. However, the assessment of the users for its core technical development has been highly limited, therefore, it is unclear if the community is thoroughly aware of the status-quo of the growing infrastructure. In addition, CLARIN does not seem to be fully materialised marketing and business plans and strategies despite its strong technical assets. This article analyses the web traffic of the Virtual Language Observatory, one of the main web applications of CLARIN and a symbol of pan-European re-search cooperation, to evaluate the users and performance of the service in a transparent and scientific way. It is envisaged that the paper can raise awareness of the pressing issues on objective and transparent operation of the infrastructure though Open Evaluation, and the synergy between marketing and technical development. It also investigates the "science of web analytics" in an attempt to document the research process for the purpose of reusability and reproducibility, thus to find universal lessons for the use of a web analytics, rather than to merely produce a statistical report of a particular website which loses its value outside its context.
Spatial Coding Techniques for Molecular MIMO
Venue: arXiv cs.ET 2017
Year: 2017

This paper studies spatial diversity techniques applied to multiple-input multiple-output (MIMO) diffusion-based molecular communications (DBMC). Two types of spatial coding techniques, namely Alamouti-type coding and repetition MIMO coding are suggested and analyzed. In addition, we consider receiver-side equal-gain combining, which is equivalent to maximum-ratio combining in symmetrical scenarios. For numerical analysis, the channel impulse responses of a symmetrical $2 \times 2$ MIMO-DBMC system are acquired by a trained artificial neural network. It is demonstrated that spatial diversity has the potential to improve the system performance and that repetition MIMO coding outperforms Alamouti-type coding.
Creating a Cybersecurity Concept Inventory: A Status Report on the CATS Project
Venue: arXiv cs.CR 2017
Year: 2017

We report on the status of our Cybersecurity Assessment Tools (CATS) project that is creating and validating a concept inventory for cybersecurity, which assesses the quality of instruction of any first course in cybersecurity. In fall 2014, we carried out a Delphi process that identified core concepts of cybersecurity. In spring 2016, we interviewed twenty-six students to uncover their understandings and misconceptions about these concepts. In fall 2016, we generated our first assessment tool--a draft Cybersecurity Concept Inventory (CCI), comprising approximately thirty multiple-choice questions. Each question targets a concept; incorrect answers are based on observed misconceptions from the interviews. This year we are validating the draft CCI using cognitive interviews, expert reviews, and psychometric testing. In this paper, we highlight our progress to date in developing the CCI. The CATS project provides infrastructure for a rigorous evidence-based improvement of cybersecurity education. The CCI permits comparisons of different instructional methods by assessing how well students learned the core concepts of the field (especially adversarial thinking), where instructional methods refer to how material is taught (e.g., lab-based, case-studies, collaborative, competitions, gaming). Specifically, the CCI is a tool that will enable researchers to scientifically quantify and measure the effect of their approaches to, and interventions in, cybersecurity education.
Time-optimal Velocity Tracking Control for Differential Drive Robots
Venue: arXiv cs.SY 2017
Year: 2017

Nonholonomic wheeled mobile robots are often required to implement algorithms designed for holonomic kinematic systems. This creates a velocity tracking problem for the actual wheeled mobile robot. In this paper, we investigate the issue of tracking the desired velocity in the least amount of time, for a differential drive nonholonomic wheeled mobile robot. If the desired velocity is a constant, the Pontryagin Maximum Principle can be used to design a control. A control is designed for the cases when the wheel can be commanded speeds and torques. When the desired velocity is smoothly time-varying, we propose a hybrid structure and study its properties.
AI-Powered Social Bots
Venue: arXiv cs.SI 2017
Year: 2017

This paper gives an overview of impersonation bots that generate output in one, or possibly, multiple modalities. We also discuss rapidly advancing areas of machine learning and artificial intelligence that could lead to frighteningly powerful new multi-modal social bots. Our main conclusion is that most commonly known bots are one dimensional (i.e., chatterbot), and far from deceiving serious interrogators. However, using recent advances in machine learning, it is possible to unleash incredibly powerful, human-like armies of social bots, in potentially well coordinated campaigns of deception and influence.
Collaborative vehicle routing: a survey
Venue: arXiv cs.MA 2017
Year: 2017

In horizontal collaborations, carriers form coalitions in order to perform parts of their logistics operations jointly. By exchanging transportation requests among each other, they can operate more efficiently and in a more sustainable way. Collaborative vehicle routing has been extensively discussed in the literature. We identify three major streams of research: (i) centralized collaborative planning, (ii) decentralized planning without auctions, and (ii) auction-based decentralized planning. For each of them we give a structured overview on the state of knowledge and discuss future research directions.
The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study
Venue: arXiv cs.NE 2017
Year: 2017

A neural network-based chart pattern represents adaptive parametric features, including non-linear transformations, and a template that can be applied in the feature space. The search of neural network-based chart patterns has been unexplored despite its potential expressiveness. In this paper, we formulate a general chart pattern search problem to enable cross-representational quantitative comparison of various search schemes. We suggest a HyperNEAT framework applying state-of-the-art deep neural network techniques to find attractive neural network-based chart patterns; These techniques enable a fast evaluation and search of robust patterns, as well as bringing a performance gain. The proposed framework successfully found attractive patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential.
CD-HOC: Indoor Human Occupancy Counting using Carbon Dioxide Sensor Data
Venue: arXiv cs.HC 2017
Year: 2017

Human occupancy information is crucial for any modern Building Management System (BMS). Implementing pervasive sensing and leveraging Carbon Dioxide data from BMS sensor, we present Carbon Dioxide - Human Occupancy Counter (CD-HOC), a novel way to estimate the number of people within a closed space from a single carbon dioxide sensor. CD-HOC de-noises and pre-processes the carbon dioxide data. We utilise both seasonal-trend decomposition based on Loess and seasonal-trend decomposition with moving average to factorise carbon dioxide data. For each trend, seasonal and irregular component, we model different regression algorithms to predict each respective human occupancy component value. We propose a zero pattern adjustment model to increase the accuracy and finally, we use additive decomposition to reconstruct the prediction value. We run our model in two different locations that have different contexts. The first location is an academic staff room and the second is a cinema theatre. Our results show an average of 4.33% increment in accuracy for the small room with 94.68% indoor human occupancy counting and 8.46% increase for the cinema theatre in comparison to the accuracy of the baseline method, support vector regression.
Value-Decomposition Networks For Cooperative Multi-Agent Learning
Venue: arXiv cs.AI 2017
Year: 2017

We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large combined action and observation spaces. In the fully centralized and decentralized approaches, we find the problem of spurious rewards and a phenomenon we call the "lazy agent" problem, which arises due to partial observability. We address these problems by training individual agents with a novel value decomposition network architecture, which learns to decompose the team value function into agent-wise value functions. We perform an experimental evaluation across a range of partially-observable multi-agent domains and show that learning such value-decompositions leads to superior results, in particular when combined with weight sharing, role information and information channels.
L2 Regularization versus Batch and Weight Normalization
Venue: arXiv cs.LG 2017
Year: 2017

Batch Normalization is a commonly used trick to improve the training of deep neural networks. These neural networks use L2 regularization, also called weight decay, ostensibly to prevent overfitting. However, we show that L2 regularization has no regularizing effect when combined with normalization. Instead, regularization has an influence on the scale of weights, and thereby on the effective learning rate. We investigate this dependence, both in theory, and experimentally. We show that popular optimization methods such as ADAM only partially eliminate the influence of normalization on the learning rate. This leads to a discussion on other ways to mitigate this issue.
Obfuscation in Bitcoin: Techniques and Politics
Venue: arXiv cs.CY 2017
Year: 2017

In the cryptographic currency Bitcoin, all transactions are recorded in the blockchain - a public, global, and immutable ledger. Because transactions are public, Bitcoin and its users employ obfuscation to maintain a degree of financial privacy. Critically, and in contrast to typical uses of obfuscation, in Bitcoin obfuscation is not aimed against the system designer but is instead enabled by design. We map sixteen proposed privacy-preserving techniques for Bitcoin on an obfuscation-vs.-cryptography axis, and find that those that are used in practice tend toward obfuscation. We argue that this has led to a balance between privacy and regulatory acceptance.
Adversarial Network Coding
Venue: arXiv cs.IT 2017
Year: 2017

A combinatorial framework for adversarial network coding is presented. Channels are described by specifying the possible actions that one or more (possibly coordinated) adversaries may take. Upper bounds on three notions of capacity (the one-shot capacity, the zero-error capacity, and the compound zero-error capacity) are obtained for point-to-point channels, and generalized to corresponding capacity regions appropriate for multi-source networks. A key result of this paper is a general method by which bounds on these capacities in point-to-point channels may be ported to networks. This technique is illustrated in detail for Hamming-type channels with multiple adversaries operating on specific coordinates, which correspond, in the context of networks, to multiple adversaries acting on specific network edges. Capacity-achieving coding schemes are described for some of the considered adversarial models.
Bayesian Conditional Generative Adverserial Networks
Venue: arXiv cs.LG 2017
Year: 2017

Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input $y'$ to a sample $\mathbf{x}$. Our BC-GANs extend traditional GANs to a Bayesian framework, and naturally handle unsupervised learning, supervised learning, and semi-supervised learning problems. Experiments show that the proposed BC-GANs outperforms the state-of-the-arts.
Generalized-impedance and Stability Criterion for Grid-connected Converters
Venue: arXiv cs.SY 2017
Year: 2017

The output impedance matrix of a grid-connected converter plays an important role in analyzing system stability. Due to the dynamics of the DC-link control and the phase locked loop (PLL), the output impedance matrices of the converter and grid are difficult to be diagonally decoupled simultaneously, neither in the dq domain nor in the phase domain. It weakens the effectiveness of impedance-based stability criterion (ISC) in system oscillation analysis. To this end, this paper innovatively proposes the generalized-impedance based stability criterion (GISC) to reduce the dimension of the transfer function matrix and simplify system small-signal stability analysis. Firstly, the impedances of the converter and the grid in polar coordinates are formulated, and the concept of generalized-impedance of the converter and the grid is put forward. Secondly, through strict mathematical derivation, the equation that implies the dynamic interaction between the converter and the grid is then extracted from the characteristic equation of the grid-connected converter system. Using the proposed method, the small-signal instability of system can be interpreted as the resonance of the generalized-impedances of the converter and the grid. Besides, the GISC is equivalent to ISC when the dynamics of the outer-loop control and PLL are not considered. Finally, the effectiveness of the proposed method is further verified using the MATLAB based digital simulation and RT-LAB based hardware-in-the-loop (HIL) simulation.