OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Observation Elicitation
Venue: arXiv cs.LG 2017
Year: 2017

We study loss functions that measure the accuracy of a prediction based on multiple data points simultaneously. To our knowledge, such loss functions have not been studied before in the area of property elicitation or in machine learning more broadly. As compared to traditional loss functions that take only a single data point, these multi-observation loss functions can in some cases drastically reduce the dimensionality of the hypothesis required. In elicitation, this corresponds to requiring many fewer reports; in empirical risk minimization, it corresponds to algorithms on a hypothesis space of much smaller dimension. We explore some examples of the tradeoff between dimensionality and number of observations, give some geometric characterizations and intuition for relating loss functions and the properties that they elicit, and discuss some implications for both elicitation and machine-learning contexts.
Towards a Flow- and Path-Sensitive Information Flow Analysis: Technical Report
Venue: arXiv cs.PL 2017
Year: 2017

This paper investigates a flow- and path-sensitive static information flow analysis. Compared with security type systems with fixed labels, it has been shown that flow-sensitive type systems accept more secure programs. We show that an information flow analysis with fixed labels can be both flow- and path-sensitive. The novel analysis has two major components: 1) a general-purpose program transformation that removes false dataflow dependencies in a program that confuse a fixed-label type system, and 2) a fixed-label type system that allows security types to depend on path conditions. We formally prove that the proposed analysis enforces a rigorous security property: noninterference. Moreover, we show that the analysis is strictly more precise than a classic flow-sensitive type system, and it allows sound control of information flow in the presence of mutable variables without resorting to run-time mechanisms.
A Joint Model for Question Answering and Question Generation
Venue: arXiv cs.CL 2017
Year: 2017

We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework that encodes the document and generates a question (answer) given an answer (question). Significant improvement in model performance is observed empirically on the SQuAD corpus, confirming our hypothesis that the model benefits from jointly learning to perform both tasks. We believe the joint model's novelty offers a new perspective on machine comprehension beyond architectural engineering, and serves as a first step towards autonomous information seeking.
Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units
Venue: arXiv cs.CV 2017
Year: 2017

Emotion being a subjective thing, leveraging knowledge and science behind labeled data and extracting the components that constitute it, has been a challenging problem in the industry for many years. With the evolution of deep learning in computer vision, emotion recognition has become a widely-tackled research problem. In this work, we propose two independent methods for this very task. The first method uses autoencoders to construct a unique representation of each emotion, while the second method is an 8-layer convolutional neural network (CNN). These methods were trained on the posed-emotion dataset (JAFFE), and to test their robustness, both the models were also tested on 100 random images from the Labeled Faces in the Wild (LFW) dataset, which consists of images that are candid than posed. The results show that with more fine-tuning and depth, our CNN model can outperform the state-of-the-art methods for emotion recognition. We also propose some exciting ideas for expanding the concept of representational autoencoders to improve their performance.
QuickCSG: Fast Arbitrary Boolean Combinations of N Solids
Venue: arXiv cs.GR 2017
Year: 2017

QuickCSG computes the result for general N-polyhedron boolean expressions without an intermediate tree of solids. We propose a vertex-centric view of the problem, which simplifies the identification of final geometric contributions, and facilitates its spatial decomposition. The problem is then cast in a single KD-tree exploration, geared toward the result by early pruning of any region of space not contributing to the final surface. We assume strong regularity properties on the input meshes and that they are in general position. This simplifying assumption, in combination with our vertex-centric approach, improves the speed of the approach. Complemented with a task-stealing parallelization, the algorithm achieves breakthrough performance, one to two orders of magnitude speedups with respect to state-of-the-art CPU algorithms, on boolean operations over two to dozens of polyhedra. The algorithm also outperforms GPU implementations with approximate discretizations, while producing an output without redundant facets. Despite the restrictive assumptions on the input, we show the usefulness of QuickCSG for applications with large CSG problems and strong temporal constraints, e.g. modeling for 3D printers, reconstruction from visual hulls and collision detection.
Stateless Puzzles for Real Time Online Fraud Preemption
Venue: arXiv cs.SI 2017
Year: 2017

The profitability of fraud in online systems such as app markets and social networks marks the failure of existing defense mechanisms. In this paper, we propose FraudSys, a real-time fraud preemption approach that imposes Bitcoin-inspired computational puzzles on the devices that post online system activities, such as reviews and likes. We introduce and leverage several novel concepts that include (i) stateless, verifiable computational puzzles, that impose minimal performance overhead, but enable the efficient verification of their authenticity, (ii) a real-time, graph-based solution to assign fraud scores to user activities, and (iii) mechanisms to dynamically adjust puzzle difficulty levels based on fraud scores and the computational capabilities of devices. FraudSys does not alter the experience of users in online systems, but delays fraudulent actions and consumes significant computational resources of the fraudsters. Using real datasets from Google Play and Facebook, we demonstrate the feasibility of FraudSys by showing that the devices of honest users are minimally impacted, while fraudster controlled devices receive daily computational penalties of up to 3,079 hours. In addition, we show that with FraudSys, fraud does not pay off, as a user equipped with mining hardware (e.g., AntMiner S7) will earn less than half through fraud than from honest Bitcoin mining.
An Upper Bound of 7n/6 for the Minimum Size 2EC on Cubic 3-Edge Connected Graphs
Venue: arXiv cs.DM 2017
Year: 2017

In this paper, we study the minimum size 2-edge connected spanning subgraph problem (henceforth 2EC) and show that every 3-edge connected cubic graph G=(V, E), with n=|V| allows a 2EC solution for G of size at most 7n/6, which improves upon Boyd, Iwata and Takazawa's guarantee of 6n/5.
Sparse Iterative Learning Control with Application to a Wafer Stage: Achieving Performance, Resource Efficiency, and Task Flexibility
Venue: arXiv cs.SY 2017
Year: 2017

Trial-varying disturbances are a key concern in Iterative Learning Control (ILC) and may lead to inefficient and expensive implementations and severe performance deterioration. The aim of this paper is to develop a general framework for optimization-based ILC that allows for enforcing additional structure, including sparsity. The proposed method enforces sparsity in a generalized setting through convex relaxations using $\ell_1$ norms. The proposed ILC framework is applied to the optimization of sampling sequences for resource efficient implementation, trial-varying disturbance attenuation, and basis function selection. The framework has a large potential in control applications such as mechatronics, as is confirmed through an application on a wafer stage.
On the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems Using Multivariate Statistical Process Control
Venue: arXiv cs.CR 2017
Year: 2017

Process Control Systems (PCSs) are the operating core of Critical Infrastructures (CIs). As such, anomaly detection has been an active research field to ensure CI normal operation. Previous approaches have leveraged network level data for anomaly detection, or have disregarded the existence of process disturbances, thus opening the possibility of mislabelling disturbances as attacks and vice versa. In this paper we present an anomaly detection and diagnostic system based on Multivariate Statistical Process Control (MSPC), that aims to distinguish between attacks and disturbances. For this end, we expand traditional MSPC to monitor process level and controller level data. We evaluate our approach using the Tennessee-Eastman process. Results show that our approach can be used to distinguish disturbances from intrusions to a certain extent and we conclude that the proposed approach can be extended with other sources of data for improving results.
Label-Dependencies Aware Recurrent Neural Networks
Venue: arXiv cs.CL 2017
Year: 2017

In the last few years, Recurrent Neural Networks (RNNs) have proved effective on several NLP tasks. Despite such great success, their ability to model \emph{sequence labeling} is still limited. This lead research toward solutions where RNNs are combined with models which already proved effective in this domain, such as CRFs. In this work we propose a solution far simpler but very effective: an evolution of the simple Jordan RNN, where labels are re-injected as input into the network, and converted into embeddings, in the same way as words. We compare this RNN variant to all the other RNN models, Elman and Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language Understanding (SLU). Thanks to label embeddings and their combination at the hidden layer, the proposed variant, which uses more parameters than Elman and Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other RNNs, but also outperforms sophisticated CRF models.
Precoder Design for Signal Superposition in MIMO-NOMA Multicell Networks
Venue: arXiv cs.IT 2017
Year: 2017

The throughput of users with poor channel conditions, such as those at a cell edge, is a bottleneck in wireless systems. A major part of the power budget must be allocated to serve these users in guaranteeing their quality-of-service (QoS) requirement, hampering QoS for other users and thus compromising the system reliability. In nonorthogonal multiple access (NOMA), the message intended for a user with a poor channel condition is decoded by itself and by another user with a better channel condition. The message intended for the latter is then successively decoded by itself after canceling the interference of the former. The overall information throughput is thus improved by this particular successive decoding and interference cancellation. This paper aims to design linear precoders/beamformers for signal superposition at the base stations of NOMA multi-input multi-output multi-cellular systems to maximize the overall sum throughput subject to the users' QoS requirements, which are imposed independently on the users' channel condition. This design problem is formulated as the maximization of a highly nonlinear and nonsmooth function subject to nonconvex constraints, which is very computationally challenging. Path-following algorithms for its solution, which invoke only a simple convex problem of moderate dimension at each iteration are developed. Generating a sequence of improved points, these algorithms converge at least to a local optimum. Extensive numerical simulations are then provided to demonstrate their merit.
Deep Factorization for Speech Signal
Venue: arXiv cs.SD 2017
Year: 2017

Speech signals are complex intermingling of various informative factors, and this information blending makes decoding any of the individual factors extremely difficult. A natural idea is to factorize each speech frame into independent factors, though it turns out to be even more difficult than decoding each individual factor. A major encumbrance is that the speaker trait, a major factor in speech signals, has been suspected to be a long-term distributional pattern and so not identifiable at the frame level. In this paper, we demonstrated that the speaker factor is also a short-time spectral pattern and can be largely identified with just a few frames using a simple deep neural network (DNN). This discovery motivated a cascade deep factorization (CDF) framework that infers speech factors in a sequential way, and factors previously inferred are used as conditional variables when inferring other factors. Our experiment on an automatic emotion recognition (AER) task demonstrated that this approach can effectively factorize speech signals, and using these factors, the original speech spectrum can be recovered with high accuracy. This factorization and reconstruction approach provides a novel tool for many speech processing tasks.
StreetStyle: Exploring world-wide clothing styles from millions of photos
Venue: arXiv cs.CV 2017
Year: 2017

Each day billions of photographs are uploaded to photo-sharing services and social media platforms. These images are packed with information about how people live around the world. In this paper we exploit this rich trove of data to understand fashion and style trends worldwide. We present a framework for visual discovery at scale, analyzing clothing and fashion across millions of images of people around the world and spanning several years. We introduce a large-scale dataset of photos of people annotated with clothing attributes, and use this dataset to train attribute classifiers via deep learning. We also present a method for discovering visually consistent style clusters that capture useful visual correlations in this massive dataset. Using these tools, we analyze millions of photos to derive visual insight, producing a first-of-its-kind analysis of global and per-city fashion choices and spatio-temporal trends.
Measuring Offensive Speech in Online Political Discourse
Venue: arXiv cs.CL 2017
Year: 2017

The Internet and online forums such as Reddit have become an increasingly popular medium for citizens to engage in political conversations. However, the online disinhibition effect resulting from the ability to use pseudonymous identities may manifest in the form of offensive speech, consequently making political discussions more aggressive and polarizing than they already are. Such environments may result in harassment and self-censorship from its targets. In this paper, we present preliminary results from a large-scale temporal measurement aimed at quantifying offensiveness in online political discussions. To enable our measurements, we develop and evaluate an offensive speech classifier. We then use this classifier to quantify and compare offensiveness in the political and general contexts. We perform our study using a database of over 168M Reddit comments made by over 7M pseudonyms between January 2015 and January 2017 -- a period covering several divisive political events including the 2016 US presidential elections.
Resource Allocation Strategies for Secure WPCN Multiantenna Multicasting Systems
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates a secure wireless-powered multiantenna multicasting system, where multiple power beacons (PBs) supply power to a transmitter in order to establish a reliable communication link with multiple legitimate users in the presence of multiple eavesdroppers. The transmitter has to harvest radio frequency (RF) energy from multiple PBs due to the shortage of embedded power supply before establishing its secure communication. We consider two different scenarios. In the first, the PBs and the transmitter belong to the same operator, where we formulate the resource allocation problem as the minimization of the total transmit power subject to the target secure rate constraint. The solution of this problem yields both the optimal power and energy transfer time allocation. Due to the non-convexity of this problem, we propose a two-level approach, where the inner level problem can be recast as a convex optimization framework via conic convex reformulation, while the outer level problem can be handled by using one-dimensional (1D) search. The second scenario considers the case where the transmitter and the PBs belong to different service suppliers. Hence, we formulate the resource allocation problem where we consider incentives for the PBs to assist the transmitter. This leads to the formulation of a Stackelberg game for the secure wireless-powered multiantenna multicasting system. The transmitter has to pay for the energy services from these multiple PBs in order to facilitate secure communications. In this game, the transmitter and the PB are modelled as leader and follower, respectively, in which both of them try to maximize their own utility function. The closed-form Stackelberg equilibrium of the formulated game is then derived. Finally, numerical results are provided to validate our proposed schemes.
An Opportunistic-Bit Scheme with IP Styled Communication
Venue: arXiv cs.IT 2017
Year: 2017

This work is motivated by the need for the fundamental increase of spectral efficiency with the transmissions on the Transmission Control Protocol and the Internet Protocol (TCP/IP). To emphasize the work in physical layer, we define a bit-unit (BU) that is conceptually similar to an IP packet that contains sufficient information for its destination node to identify the address and interpret the contents in performing the message communication. Armed with these functions, we divide one BU into two parts, which are defined as opportunistic bit (OB) and conventional bit (CB), respectively. In addition, we design the sequential time-slots (TSs) in such a way that the OB can be mapped to the index of a TS, and the CB can be carried by the corresponding TS. To enable the communication, we pre-store a bit-to-TS mapping table at both of the transmitter and the receiver. As result, we can save time resource and gain spectral efficiency as shown in the theoretical analysis confirmed by the simulations.
Fast Eigen Decomposition for Low-Rank Matrix Approximation
Venue: arXiv cs.NA 2017
Year: 2017

In this paper we present an efficient algorithm to compute the eigen decomposition of a matrix that is a weighted sum of the self outer products of vectors such as a covariance matrix of data. A well known algorithm to compute the eigen decomposition of such matrices is though the singular value decomposition, which is available only if all the weights are nonnegative. Our proposed algorithm accepts both positive and negative weights.
Preliminary Performance Estimations and Benchmark Results for a Software-based Fault-Tolerance Approach aboard Miniaturized Satellite Computers
Venue: arXiv cs.DC 2017
Year: 2017

Modern embedded technology is a driving factor in satellite miniaturization, contributing to a massive boom in satellite launches and a rapidly evolving new space industry. Miniaturized satellites however suffer from low reliability, as traditional hardware-based fault-tolerance (FT) concepts are ineffective for on-board computers (OBCs) utilizing modern systems-on-a-chip (SoC). Larger satellites therefore continue to rely on proven processors with large feature sizes. Software-based concepts have largely been ignored by the space industry as they were researched only in theory, and have not yet reached the level of maturity necessary for implementation. In related work, we presented the first integral, real-world solution to enable fault-tolerant general-purpose computing with modern multiprocessor-SoCs (MPSoCs) for spaceflight, thereby enabling their use in future high-priority space missions. The presented multi-stage approach consists of three FT stages, combining coarse-grained thread-level distributed self-validation, FPGA reconfiguration, and mixed criticality to assure long-term FT and excellent scalability for both resource constrained and critical high-priority space missions. As part of the ongoing implementation effort towards a hardware prototype, several software implementations were achieved and tested. This document contains an outline of the conducted tests, performance evaluation results, and supplementary information not included in the actual paper. It is being continuously expanded and updated.
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis
Venue: arXiv cs.CL 2017
Year: 2017

Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful. In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art rule-based sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources. The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.
A Pragmatic Approach for Measuring Maintainability of DPRA Models
Venue: arXiv cs.SE 2017
Year: 2017

Dynamic Probabilistic Risk Assessment (DPRA) is a powerful concept that is used to evaluate design and safety of complex industrial systems. A DPRA model uses a conceptual system representation as a formal basis for simulation and analysis. In this paper we consider an adaptive maintenance of DPRA models that consist in modifying and extending a simplified model to a real-size DPRA model. We propose an approach for quantitative maintainability assessment of DPRA models created with an industrial modeling tool called PyCATSHOO. We review and adopt some metrics from conceptual modeling, software engineering and OO design for assessing maintainability of PyCATSHOO models. On the example of well-known "Heated Room" test case, we illustrate how the selected metrics can serve as early indicators of model modifiability and complexity. These indicators would allow experts to make better decisions early in the DPRA model development life cycle.