OpenRaiser/Intern-Atlas huggingface dataset:
Let's Dance: Learning From Online Dance Videos
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, deep neural network approaches have naturally extended to the video domain, in their simplest case by aggregating per-frame classifications as a baseline for action recognition. A majority of the work in this area extends from the imaging domain, leading to visual-feature heavy approaches on temporal data. To address this issue we introduce "Let's Dance", a 1000 video dataset (and growing) comprised of 10 visually overlapping dance categories that require motion for their classification. We stress the important of human motion as a key distinguisher in our work given that, as we show in this work, visual information is not sufficient to classify motion-heavy categories. We compare our datasets' performance using imaging techniques with UCF-101 and demonstrate this inherent difficulty. We present a comparison of numerous state-of-the-art techniques on our dataset using three different representations (video, optical flow and multi-person pose data) in order to analyze these approaches. We discuss the motion parameterization of each of them and their value in learning to categorize online dance videos. Lastly, we release this dataset (and its three representations) for the research community to use.
On the Computation of Optimal Control Problems with Terminal Inequality Constraint via Variation Evolution
Venue: arXiv cs.SY 2018
Year: 2018

Studies regarding the computation of Optimal Control Problems (OCPs) with terminal inequality constraint, under the frame of the Variation Evolving Method (VEM), are carried out. The attributes of equality constraints and inequality constraints in the generalized optimization problem is traversed, and the intrinsic relations to the multipliers are uncovered. Upon these preliminaries, the right Evolution Partial Differential Equation (EPDE) is derived, and the costate-free optimality conditions are established. Besides the analytic expression for the costates in the classic treatment, they also reveal the analytic relations between the states, the controls and the (Lagrange and KKT) multipliers, which adjoin the terminal (equality and inequality) constraints. Moreover, in solving the transformed Initial-value Problems (IVPs) with common Ordinary Differential Equation (ODE) integration methods, the numerical soft barrier is proposed to eliminate the numerical error resulting from the suddenly triggered inequality constraint and it is shown to be effective.
Leveraging Edge Caching in NOMA Systems with QoS Requirements
Venue: arXiv cs.IT 2018
Year: 2018

Non-Orthogonal Multiple Access (NOMA) and caching are two proposed approaches to increase the capacity of future 5G wireless systems. Typically in NOMA systems, signals at the receiver are decoded using successive interference cancellation in order to achieve capacity in multi-user systems. The leveraging of caching in the physical layer to further improve on the benefits of NOMA is investigated, which is termed cache-aided NOMA. Specific attention is given to the caching cases where the users with weaker channel conditions possess a cache of the information requested by a user with a stronger channel condition. The probability that any of the users is in outage for any of the rates required for this NOMA system, defined as the "union-outage," is derived for the case of fixed-power allocation, and the power allocation strategy that minimizes the union-outage probability is derived. Simulation results confirm the analytical results, which demonstrate the benefits of cache-aided NOMA on reducing the union-outages probability.
Curiosity-driven reinforcement learning with homeostatic regulation
Venue: arXiv cs.AI 2018
Year: 2018

We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range. Our experimental validation shows the added value of the additional homeostatic drive to enhance the overall information gain of a reinforcement learning agent interacting with a complex environment using continuous actions. Our method builds upon two ideas: i) To take advantage of a new Bellman-like equation of information gain and ii) to simplify the computation of the local rewards by avoiding the approximation of complex distributions over continuous states and actions.
Why and How to Avoid the Flipped Quaternion Multiplication
Venue: arXiv cs.RO 2018
Year: 2018

Over the last decades quaternions have become a crucial and very successful tool for attitude representation in robotics and aerospace. However, there is a major problem that is continuously causing trouble in practice when it comes to exchanging formulas or implementations: there are two quaternion multiplications in common use, Hamilton's original multiplication and its flipped version, which is often associated with NASA's Jet Propulsion Laboratory. We believe that this particular issue is completely avoidable and only exists today due to a lack of understanding. This paper explains the underlying problem for the popular passive world to body usage of rotation quaternions, and derives an alternative solution compatible with Hamilton's multiplication. Furthermore, it argues for entirely discontinuing the flipped multiplication. Additionally, it provides recipes for efficiently detecting relevant conventions and migrating formulas or algorithms between them.
The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering' Language Embeddings
Venue: arXiv cs.CL 2018
Year: 2018

Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyberhate) has been frequently posted and widely circulated viathe World Wide Web. This can be considered as a key risk factor for individual and societal tension linked toregional instability. Automated Web-based cyberhate detection is important for observing and understandingcommunity and regional societal tension - especially in online social networks where posts can be rapidlyand widely viewed and disseminated. While previous work has involved using lexicons, bags-of-words orprobabilistic language parsing approaches, they often suffer from a similar issue which is that cyberhate can besubtle and indirect - thus depending on the occurrence of individual words or phrases can lead to a significantnumber of false negatives, providing inaccurate representation of the trends in cyberhate. This problemmotivated us to challenge thinking around the representation of subtle language use, such as references toperceived threats from "the other" including immigration or job prosperity in a hateful context. We propose anovel framework that utilises language use around the concept of "othering" and intergroup threat theory toidentify these subtleties and we implement a novel classification method using embedding learning to computesemantic distances between parts of speech considered to be part of an "othering" narrative. To validate ourapproach we conduct several experiments on different types of cyberhate, namely religion, disability, race andsexual orientation, with F-measure scores for classifying hateful instances obtained through applying ourmodel of 0.93, 0.86, 0.97 and 0.98 respectively, providing a significant improvement in classifier accuracy overthe state-of-the-art
Towards Understanding Connections between Security/Privacy Attitudes and Unlock Authentication
Venue: arXiv cs.CR 2018
Year: 2018

In this study, we examine the ways in which user attitudes towards privacy and security relating to mobile devices and the data stored thereon may impact the strength of unlock authentication, focusing on Android's graphical unlock patterns. We conducted an online study with Amazon Mechanical Turk ($N=750$) using self-reported unlock authentication choices, as well as Likert scale agreement/disagreement responses to a set of seven privacy/security prompts. We then analyzed the responses in multiple dimensions, including a straight average of the Likert responses as well as using Principle Component Analysis to expose latent factors. We found that responses to two of the seven questions proved relevant and significant. These two questions considered attitudes towards general concern for data stored on mobile devices, and attitudes towards concerns for unauthorized access by known actors. Unfortunately, larger conclusions cannot be drawn on the efficacy of the broader set of questions for exposing connections between unlock authentication strength (Pearson Rank $r=-0.08$, $p<0.1$). However, both of our factor solutions exposed differences in responses for demographics groups, including age, gender, and residence type. The findings of this study suggests that there is likely a link between perceptions of privacy/security on mobile devices and the perceived threats therein, but more research is needed, particularly on developing better survey and measurement techniques of privacy/security attitudes that relate to mobile devices specifically.
Human Activity Recognition for Mobile Robot
Venue: arXiv cs.HC 2018
Year: 2018

Due to the increasing number of mobile robots including domestic robots for cleaning and maintenance in developed countries, human activity recognition is inevitable for congruent human-robot interaction. Needless to say that this is indeed a challenging task for robots, it is expedient to learn human activities for autonomous mobile robots (AMR) for navigating in an uncontrolled environment without any guidance. Building a correct classifier for complex human action is non-trivial since simple actions can be combined to recognize a complex human activity. In this paper, we trained a model for human activity recognition using convolutional neural network. We trained and validated the model using the Vicon physical action dataset and also tested the model on our generated dataset (VMCUHK). Our experiment shows that our method performs with high accuracy, human activity recognition task both on the Vicon physical action dataset and VMCUHK dataset.
Enforcing Programming Guidelines with Region Types and Effects
Venue: arXiv cs.PL 2018
Year: 2018

We present in this paper a new type and effect system for Java which can be used to ensure adherence to guidelines for secure web programming. The system is based on the region and effect system by Beringer, Grabowski, and Hofmann. It improves upon it by being parametrized over an arbitrary guideline supplied in the form of a finite monoid or automaton and a type annotation or mockup code for external methods. Furthermore, we add a powerful type inference based on precise interprocedural analysis and provide an implementation in the Soot framework which has been tested on a number of benchmarks including large parts of the Stanford SecuriBench.
Inverse reinforcement learning in continuous time and space
Venue: arXiv cs.SY 2018
Year: 2018

This paper develops a data-driven inverse reinforcement learning technique for a class of linear systems to estimate the cost function of an agent online, using input-output measurements. A simultaneous state and parameter estimator is utilized to facilitate output-feedback inverse reinforcement learning, and cost function estimation is achieved up to multiplication by a constant.
A Classification Refinement Strategy for Semantic Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Based on the observation that semantic segmentation errors are partially predictable, we propose a compact formulation using confusion statistics of the trained classifier to refine (re-estimate) the initial pixel label hypotheses. The proposed strategy is contingent upon computing the classifier confusion probabilities for a given dataset and estimating a relevant prior on the object classes present in the image to be classified. We provide a procedure to robustly estimate the confusion probabilities and explore multiple prior definitions. Experiments are shown comparing performances on multiple challenging datasets using different priors to improve a state-of-the-art semantic segmentation classifier. This study demonstrates the potential to significantly improve semantic labeling and motivates future work for reliable label prior estimation from images.
Who Killed My Parked Car?
Venue: arXiv cs.CR 2018
Year: 2018

We find that the conventional belief of vehicle cyber attacks and their defenses---attacks are feasible and thus defenses are required only when the vehicle's ignition is turned on---does not hold. We verify this fact by discovering and applying two new practical and important attacks: battery-drain and Denial-of-Body-control (DoB). The former can drain the vehicle battery while the latter can prevent the owner from starting or even opening/entering his car, when either or both attacks are mounted with the ignition off. We first analyze how operation (e.g., normal, sleep, listen) modes of ECUs are defined in various in-vehicle network standards and how they are implemented in the real world. From this analysis, we discover that an adversary can exploit the wakeup function of in-vehicle networks---which was originally designed for enhanced user experience/convenience (e.g., remote diagnosis, remote temperature control)---as an attack vector. Ironically, a core battery-saving feature in in-vehicle networks makes it easier for an attacker to wake up ECUs and, therefore, mount and succeed in battery-drain and/or DoB attacks. Via extensive experimental evaluations on various real vehicles, we show that by mounting the battery-drain attack, the adversary can increase the average battery consumption by at least 12.57x, drain the car battery within a few hours or days, and therefore immobilize/cripple the vehicle. We also demonstrate the proposed DoB attack on a real vehicle, showing that the attacker can cut off communications between the vehicle and the driver's key fob by indefinitely shutting down an ECU, thus making the driver unable to start and/or even enter the car.
The WiLI benchmark dataset for written language identification
Venue: arXiv cs.CV 2018
Year: 2018

This paper describes the WiLI-2018 benchmark dataset for monolingual written natural language identification. WiLI-2018 is a publicly available, free of charge dataset of short text extracts from Wikipedia. It contains 1000 paragraphs of 235 languages, totaling in 23500 paragraphs. WiLI is a classification dataset: Given an unknown paragraph written in one dominant language, it has to be decided which language it is.
The Role of Spreadsheets in Clinical Decision Support: A Survey of the Medical Algorithms Company User Community
Venue: arXiv cs.CY 2018
Year: 2018

This paper presents and discusses the results of a small scoping survey of Clinical Decision Support System (CDSS) users from the Medical Algorithms Company website which hosts 24,000 different CDSS. These results are analysed, discussed, and compared with other similar studies and contribute to the wider understanding of how CDSS impact on clinical practice. The results show that CDSS provided by Medal are being used by clinical professionals in a variety of settings, both as an operational tool and as a research and reference tool. Whilst these tools are implemented and executed in a database, the initial logic is worked out on a spreadsheet. The paper describes that process and examines some of the results of the survey.
A General Definition of Network Communities and the Corresponding Detection Algorithm
Venue: arXiv cs.SI 2018
Year: 2018

Network structures, consisting of nodes and edges, have applications in almost all subjects. A set of nodes is called a community if the nodes have strong interrelations. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper and accurate services. However, all the current detection algorithms are motivated by the practical problems, whose applicabilities in other fields are open to question. Thence, for a new community problem, researchers need to derive algorithms ad hoc, which is arduous and even unnecessary. In this paper, we represent a general procedure to find community structures in practice. We mainly focus on two typical types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a general method to define and detect communities. Readers can specialize our general algorithm to accommodate their problems. In the end, we also give a demonstration to show how the algorithm works.
Winning the Caucus Race: Continuous Leader Election via Public Randomness
Venue: arXiv cs.CR 2018
Year: 2018

Consensus protocols inherently rely on the notion of leader election, in which one or a subset of participants are temporarily elected to authorize and announce the network's latest state. While leader election is a well studied problem, the rise of distributed ledgers (i.e., blockchains) has led to a new perspective on how to perform large-scale leader elections via solving a computationally difficult puzzle (i.e., proof of work). In this paper, we present Caucus, a large-scale leader election protocol with minimal coordination costs that does not require the computational cost of proof-of-work. We evaluate Caucus in terms of its security, using a new model for blockchain-focused leader election, before testing an implementation of Caucus on an Ethereum private network. Our experiments highlight that one variant of Caucus costs only $0.10 per leader election if deployed on Ethereum.
Mitigating CSRF attacks on OAuth 2.0 and OpenID Connect
Venue: arXiv cs.CR 2018
Year: 2018

Many millions of users routinely use their Google, Facebook and Microsoft accounts to log in to websites supporting OAuth 2.0 and/or OpenID Connect-based single sign on. The security of OAuth 2.0 and OpenID Connect is therefore of critical importance, and it has been widely examined both in theory and in practice. Unfortunately, as these studies have shown, real-world implementations of both schemes are often vulnerable to attack, and in particular to cross-site request forgery (CSRF) attacks. In this paper we propose a new technique which can be used to mitigate CSRF attacks against both OAuth 2.0 and OpenID Connect.
On the Gold Standard for Security of Universal Steganography
Venue: arXiv cs.CR 2018
Year: 2018

While symmetric-key steganography is quite well understood both in the information-theoretic and in the computational setting, many fundamental questions about its public-key counterpart resist persistent attempts to solve them. The computational model for public-key steganography was proposed by von Ahn and Hopper in EUROCRYPT 2004. At TCC 2005, Backes and Cachin gave the first universal public-key stegosystem - i.e. one that works on all channels - achieving security against replayable chosen-covertext attacks (SS-RCCA) and asked whether security against non-replayable chosen-covertext attacks (SS-CCA) is achievable. Later, Hopper (ICALP 2005) provided such a stegosystem for every efficiently sampleable channel, but did not achieve universality. He posed the question whether universality and SS-CCA-security can be achieved simultaneously. No progress on this question has been achieved since more than a decade. In our work we solve Hopper's problem in a somehow complete manner: As our main positive result we design an SS-CCA-secure stegosystem that works for every memoryless channel. On the other hand, we prove that this result is the best possible in the context of universal steganography. We provide a family of 0-memoryless channels - where the already sent documents have only marginal influence on the current distribution - and prove that no SS-CCA-secure steganography for this family exists in the standard non-look-ahead model.
Deep Interactive Evolution
Venue: arXiv cs.NE 2018
Year: 2018

This paper describes an approach that combines generative adversarial networks (GANs) with interactive evolutionary computation (IEC). While GANs can be trained to produce lifelike images, they are normally sampled randomly from the learned distribution, providing limited control over the resulting output. On the other hand, interactive evolution has shown promise in creating various artifacts such as images, music and 3D objects, but traditionally relies on a hand-designed evolvable representation of the target domain. The main insight in this paper is that a GAN trained on a specific target domain can act as a compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes do resemble valid domain artifacts). Once such a GAN is trained, the latent vector given as input to the GAN's generator network can be put under evolutionary control, allowing controllable and high-quality image generation. In this paper, we demonstrate the advantage of this novel approach through a user study in which participants were able to evolve images that strongly resemble specific target images.
Optimum Fairness for Non-Orthogonal Multiple Access
Venue: arXiv cs.IT 2018
Year: 2018

This paper focuses on the fairness issue in non-orthogonal multiple access (NOMA) and investigates the optimization problem that maximizes the worst user's achievable rate. Unlike previous studies, we derive a closed-form expression for the optimal value and solution, which are related to Perron-Frobenius eigenvalue and eigenvector of a defined positive matrix. On this basis, we propose an iterative algorithm to compute the optimal solution, which has linear convergence and requires only about half iterations of the classical bisection method.
Using Deep Autoencoders for Facial Expression Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature. Selecting the most important features is a very crucial task for systems like facial expression recognition. This paper investigates the performance of deep autoencoders for feature selection and dimension reduction for facial expression recognition on multiple levels of hidden layers. The features extracted from the stacked autoencoder outperformed when compared to other state-of-the-art feature selection and dimension reduction techniques.
Secure and Privacy-Friendly Local Electricity Trading and Billing in Smart Grid
Venue: arXiv cs.CR 2018
Year: 2018

This paper proposes two decentralised, secure and privacy-friendly protocols for local electricity trading and billing, respectively. The trading protocol employs a bidding algorithm based upon secure multiparty computations and allows users to trade their excess electricity among themselves. The bid selection and calculation of the trading price are performed in a decentralised and oblivious manner. The billing protocol is based on a simple privacy-friendly aggregation technique that allows suppliers to compute their customers' monthly bills without learning their fine-grained electricity consumption data. We also implemented and tested the performance of the trading protocol with realistic data. Our results show that it can be performed for 2500 bids in less than five minutes in the on-line phase, showing its feasibility for a typical electricity trading period of 30 minutes.