OpenRaiser/Intern-Atlas huggingface dataset:
Strategies for basing the CS theory course on non-decision problems
Venue: arXiv cs.CY 2017
Year: 2017

Computational and complexity theory are core components of the computer science curriculum, and in the vast majority of cases are taught using decision problems as the main paradigm. For experienced practitioners, decision problems are the best tool. But for undergraduates encountering the material for the first time, we present evidence that non-decision problems (such as optimization problems and search problems) are preferable. In addition, we describe technical definitions and pedagogical strategies that have been used successfully for teaching the theory course using non-decision problems as the central concept.
On the $\mathcal{H}_2$ norm and iterative model order reduction of linear switched systems
Venue: arXiv cs.SY 2017
Year: 2017

A new definition of the $\mathcal{H}_2$ norm for linear switched systems is introduced. It is based on appropriately defined time-domain kernels, or equivalently, on infinite controllability and observability Gramian matrices. Furthermore, an extension of the iterative rational Krylov algorithm to the class of linear switched systems is proposed.
Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image
Venue: arXiv cs.HC 2017
Year: 2017

Articulated hand pose estimation is a challenging task for human-computer interaction. The state-of-the-art hand pose estimation algorithms work only with one or a few subjects for which they have been calibrated or trained. Particularly, the hybrid methods based on learning followed by model fitting or model based deep learning do not explicitly consider varying hand shapes and sizes. In this work, we introduce a novel hybrid algorithm for estimating the 3D hand pose as well as bone-lengths of the hand skeleton at the same time, from a single depth image. The proposed CNN architecture learns hand pose parameters and scale parameters associated with the bone-lengths simultaneously. Subsequently, a new hybrid forward kinematics layer employs both parameters to estimate 3D joint positions of the hand. For end-to-end training, we combine three public datasets NYU, ICVL and MSRA-2015 in one unified format to achieve large variation in hand shapes and sizes. Among hybrid methods, our method shows improved accuracy over the state-of-the-art on the combined dataset and the ICVL dataset that contain multiple subjects. Also, our algorithm is demonstrated to work well with unseen images.
Minimal Solvers for Monocular Rolling Shutter Compensation under Ackermann Motion
Venue: arXiv cs.CV 2017
Year: 2017

Modern automotive vehicles are often equipped with a budget commercial rolling shutter camera. These devices often produce distorted images due to the inter-row delay of the camera while capturing the image. Recent methods for monocular rolling shutter motion compensation utilize blur kernel and the straightness property of line segments. However, these methods are limited to handling rotational motion and also are not fast enough to operate in real time. In this paper, we propose a minimal solver for the rolling shutter motion compensation which assumes known vertical direction of the camera. Thanks to the Ackermann motion model of vehicles which consists of only two motion parameters, and two parameters for the simplified depth assumption that lead to a 4-line algorithm. The proposed minimal solver estimates the rolling shutter camera motion efficiently and accurately. The extensive experiments on real and simulated datasets demonstrate the benefits of our approach in terms of qualitative and quantitative results.
Characterizing the hyper-parameter space of LSTM language models for mixed context applications
Venue: arXiv cs.CL 2017
Year: 2017

Applying state of the art deep learning models to novel real world datasets gives a practical evaluation of the generalizability of these models. Of importance in this process is how sensitive the hyper parameters of such models are to novel datasets as this would affect the reproducibility of a model. We present work to characterize the hyper parameter space of an LSTM for language modeling on a code-mixed corpus. We observe that the evaluated model shows minimal sensitivity to our novel dataset bar a few hyper parameters.
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
Venue: arXiv cs.AI 2017
Year: 2017

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135.
Open Orchestration Cloud Radio Access Network (OOCRAN) Testbed
Venue: arXiv cs.NI 2017
Year: 2017

The Cloud radio access network (C-RAN) offers a revolutionary approach to cellular network deployment, management and evolution. Advances in software-defined radio (SDR) and networking technology, moreover, enable delivering software-defined everything through the Cloud. Resources will be pooled and dynamically allocated leveraging abstraction, virtualization, and consolidation techniques; processes will be automated using common application programming interfaces; and network functions and services will be programmatically provided through an orchestrator. OOCRAN, oocran.dynu.com, is a software framework that is based on the NFV MANO architecture proposed by ETSI. It provides an orchestration layer for the entire wireless infrastructure, including hardware, software, spectrum, fronthaul and backhaul. OOCRAN extends existing NFV management frameworks by incorporating the radio communications layers and their management dependencies. The wireless infrastructure provider can then dynamically provision virtualized wireless networks to wireless service providers. The testbed's physical infrastructure is built around a computing cluster that executes open-source SDR libraries and connects to SDR-based remote radio heads. We demonstrate the operation of OOCRAN and discuss the temporal implications of dynamic LTE small cell network deployments.
Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
Venue: arXiv cs.CV 2017
Year: 2017

Matrix decomposition is a popular and fundamental approach in machine learning and data mining. It has been successfully applied into various fields. Most matrix decomposition methods focus on decomposing a data matrix from one single source. However, it is common that data are from different sources with heterogeneous noise. A few of matrix decomposition methods have been extended for such multi-view data integration and pattern discovery. While only few methods were designed to consider the heterogeneity of noise in such multi-view data for data integration explicitly. To this end, we propose a joint matrix decomposition framework (BJMD), which models the heterogeneity of noise by Gaussian distribution in a Bayesian framework. We develop two algorithms to solve this model: one is a variational Bayesian inference algorithm, which makes full use of the posterior distribution; and another is a maximum a posterior algorithm, which is more scalable and can be easily paralleled. Extensive experiments on synthetic and real-world datasets demonstrate that BJMD considering the heterogeneity of noise is superior or competitive to the state-of-the-art methods.
RHyTHM: A Randomized Hybrid Scheme To Hide in the Mobile Crowd
Venue: arXiv cs.CR 2017
Year: 2017

Any on-demand pseudonym acquisition strategy is problematic should the connectivity to the credential management infrastructure be intermittent. If a vehicle runs out of pseudonyms with no connectivity to refill its pseudonym pool, one solution is the on-the-fly generation of pseudonyms, e.g., leveraging anonymous authentication. However, such a vehicle would stand out in the crowd: one can simply distinguish pseudonyms, thus signed messages, based on the pseudonym issuer signature, link them and track the vehicle. To address this challenge, we propose a randomized hybrid scheme, RHyTHM, to enable vehicles to remain operational when disconnected without compromising privacy: vehicles with valid pseudonyms help others to enhance their privacy by randomly joining them in using on-the-fly self-certified pseudonyms along with aligned lifetimes. This way, the privacy of disconnected users is enhanced with a reasonable computational overhead.
FPGA with Improved Routability and Robustness in 130nm CMOS with Open-Source CAD Targetability
Venue: arXiv cs.AR 2017
Year: 2017

This paper outlines an FPGA VLSI design methodology that was used to realize a fully functioning FPGA chip in 130nm CMOS with improved routability and memory robustness. The architectural design space exploration and synthesis capability were enabled by the Verilog-to-Routing CAD tool. The capabilities of this tool were extended to enable bitstream generation and deployment. To validate the architecture and bitstream implementation, a Chisel (Constructing Hardware in the Embedded Scala Language) model of the FPGA was created to rapidly verify the microarchitectural details of the device prior to schematic design. A custom carrier board and configuration tool were used to verify correct operational characteristics of the FPGA over various resource utilizations and clock frequencies.
A Scalable High-Performance Priority Encoder Using 1D-Array to 2D-Array Conversion
Venue: arXiv cs.AR 2017
Year: 2017

In our prior study of an L-bit priority encoder (PE), a so-called one-directional-array to two-directional-array conversion method is deployed to turn an L-bit input data into an MxN-bit matrix. Following this, an N-bit PE and an M-bit PE are employed to obtain a row index and column index. From those, the highest priority bit of L-bit input data is achieved. This brief extends our previous work to construct a scalable architecture of high-performance large-sized PEs. An optimum pair of (M, N) and look-ahead signal are proposed to improve the overall PE performance significantly. The evaluation is achieved by implementing a variety of PEs whose L varies from 4-bit to 4096-bit in 180-nm CMOS technology. According to post-place-and-route simulation results, at PE size of 64 bits, 256 bits, and 2048 bits the operating frequencies reach 649 MHz, 520 MHz, and 370 MHz, which are 1.2 times, 1.5 times, and 1.4 times, as high as state-of-the-art ones.
Steering the distribution of agents in mean-field and cooperative games
Venue: arXiv cs.SY 2017
Year: 2017

The purpose of this work is to pose and solve the problem to guide a collection of weakly interacting dynamical systems (agents, particles, etc.) to a specified terminal distribution. The framework is that of mean-field and of cooperative games. A terminal cost is used to accomplish the task; we establish that the map between terminal costs and terminal probability distributions is onto. Our approach relies on and extends the theory of optimal mass transport and its generalizations.
Interactions between Health Searchers and Search Engines
Venue: arXiv cs.IR 2017
Year: 2017

The Web is an important resource for understanding and diagnosing medical conditions. Based on exposure to online content, people may develop undue health concerns, believing that common and benign symptoms are explained by serious illnesses. In this paper, we investigate potential strategies to mine queries and searcher histories for clues that could help search engines choose the most appropriate information to present in response to exploratory medical queries. To do this, we performed a longitudinal study of health search behavior using the logs of a popular search engine. We found that query variations which might appear innocuous (e.g. "bad headache" vs "severe headache") may hold valuable information about the searcher which could be used by search engines to improve performance. Furthermore, we investigated how medically concerned users respond differently to search engine result pages (SERPs) and find that their disposition for clicking on concerning pages is pronounced, potentially leading to a self-reinforcement of concern. Finally, we studied to which degree variations in the SERP impact future search and real-world health-seeking behavior and obtained some surprising results (e.g., viewing concerning pages may lead to a short-term reduction of real-world health seeking).
Robust Deep Reinforcement Learning with Adversarial Attacks
Venue: arXiv cs.LG 2017
Year: 2017

This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks. We show that even a naively engineered attack successfully degrades the performance of DRL algorithm. We further improve the attack using gradient information of an engineered loss function which leads to further degradation in performance. These attacks are then leveraged during training to improve the robustness of RL within robust control framework. We show that this adversarial training of DRL algorithms like Deep Double Q learning and Deep Deterministic Policy Gradients leads to significant increase in robustness to parameter variations for RL benchmarks such as Cart-pole, Mountain Car, Hopper and Half Cheetah environment.
The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images
Venue: arXiv cs.CV 2017
Year: 2017

The lack, due to privacy concerns, of large public databases of medical pathologies is a well-known and major problem, substantially hindering the application of deep learning techniques in this field. In this article, we investigate the possibility to supply to the deficiency in the number of data by means of data augmentation techniques, working on the recent Kvasir dataset of endoscopical images of gastrointestinal diseases. The dataset comprises 4,000 colored images labeled and verified by medical endoscopists, covering a few common pathologies at different anatomical landmarks: Z-line, pylorus and cecum. We show how the application of data augmentation techniques allows to achieve sensible improvements of the classification with respect to previous approaches, both in terms of precision and recall.
Faster integer and polynomial multiplication using cyclotomic coefficient rings
Venue: arXiv cs.SC 2017
Year: 2017

We present an algorithm that computes the product of two n-bit integers in O(n log n (4\sqrt 2)^{log^* n}) bit operations. Previously, the best known bound was O(n log n 6^{log^* n}). We also prove that for a fixed prime p, polynomials in F_p[X] of degree n may be multiplied in O(n log n 4^{log^* n}) bit operations; the previous best bound was O(n log n 8^{log^* n}).
I Trust my Zombies: A Trust-enabled Botnet
Venue: arXiv cs.CR 2017
Year: 2017

Defending against botnets has always been a cat and mouse game. Cyber-security researchers and government agencies attempt to detect and take down botnets by playing the role of the cat. In this context, a lot of work has been done towards reverse engineering certain variants of malware families as well as understanding the network protocols of botnets to identify their weaknesses (if any) and exploit them. While this is necessary, such an approach offers the botmasters the ability to quickly counteract the defenders by simply performing small changes in their arsenals. We attempt a different approach by actually taking the role of the Botmaster, to eventually anticipate his behavior. That said, in this paper, we present a novel computational trust mechanism for fully distributed botnets that allows for a resilient and stealthy management of the infected machines (zombies). We exploit the highly researched area of computational trust to create an autonomous mechanism that ensures the avoidance of common botnet tracking mechanisms such as sensors and crawlers. In our futuristic botnet, zombies are both smart and cautious. They are cautious in the sense that they are careful with whom they communicate with. Moreover, they are smart enough to learn from their experiences and infer whether their fellow zombies are indeed who they claim to be and not government agencies' spies. We study different computational trust models, mainly based on Bayesian inference, to evaluate their advantages and disadvantages in the context of a distributed botnet. Furthermore, we show, via our experimental results, that our approach is significantly stronger than any technique that has been seen in botnets to date.
Optimal locally repairable codes via elliptic curves
Venue: arXiv cs.IT 2017
Year: 2017

Constructing locally repairable codes achieving Singleton-type bound (we call them optimal codes in this paper) is a challenging task and has attracted great attention in the last few years. Tamo and Barg \cite{TB14} first gave a breakthrough result in this topic by cleverly considering subcodes of Reed-Solomon codes. Thus, $q$-ary optimal locally repairable codes from subcodes of Reed-Solomon codes given in \cite{TB14} have length upper bounded by $q$. Recently, it was shown through extension of construction in \cite{TB14} that length of $q$-ary optimal locally repairable codes can be $q+1$ in \cite{JMX17}. Surprisingly it was shown in \cite{BHHMV16} that, unlike classical MDS codes, $q$-ary optimal locally repairable codes could have length bigger than $q+1$. Thus, it becomes an interesting and challenging problem to construct $q$-ary optimal locally repairable codes of length bigger than $q+1$. In the present paper, we make use of rich algebraic structures of elliptic curves to construct a family of $q$-ary optimal locally repairable codes of length up to $q+2\sqrt{q}$. It turns out that locality of our codes can be as big as $23$ and distance can be linear in length.
EmLog: Tamper-Resistant System Logging for Constrained Devices with TEEs
Venue: arXiv cs.CR 2017
Year: 2017

Remote mobile and embedded devices are used to deliver increasingly impactful services, such as medical rehabilitation and assistive technologies. Secure system logging is beneficial in these scenarios to aid audit and forensic investigations particularly if devices bring harm to end-users. Logs should be tamper-resistant in storage, during execution, and when retrieved by a trusted remote verifier. In recent years, Trusted Execution Environments (TEEs) have emerged as the go-to root of trust on constrained devices for isolated execution of sensitive applications. Existing TEE-based logging systems, however, focus largely on protecting server-side logs and offer little protection to constrained source devices. In this paper, we introduce EmLog -- a tamper-resistant logging system for constrained devices using the GlobalPlatform TEE. EmLog provides protection against complex software adversaries and offers several additional security properties over past schemes. The system is evaluated across three log datasets using an off-the-shelf ARM development board running an open-source, GlobalPlatform-compliant TEE. On average, EmLog runs with low run-time memory overhead (1MB heap and stack), 430--625 logs/second throughput, and five-times persistent storage overhead versus unprotected logs.
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently
Venue: arXiv cs.LG 2017
Year: 2017

We propose a family of nonconvex optimization algorithms that are able to save gradient and negative curvature computations to a large extent, and are guaranteed to find an approximate local minimum with improved runtime complexity. At the core of our algorithms is the division of the entire domain of the objective function into small and large gradient regions: our algorithms only perform gradient descent based procedure in the large gradient region, and only perform negative curvature descent in the small gradient region. Our novel analysis shows that the proposed algorithms can escape the small gradient region in only one negative curvature descent step whenever they enter it, and thus they only need to perform at most $N_ε$ negative curvature direction computations, where $N_ε$ is the number of times the algorithms enter small gradient regions. For both deterministic and stochastic settings, we show that the proposed algorithms can potentially beat the state-of-the-art local minima finding algorithms. For the finite-sum setting, our algorithm can also outperform the best algorithm in a certain regime.
End-to-end Learning from Spectrum Data: A Deep Learning approach for Wireless Signal Identification in Spectrum Monitoring applications
Venue: arXiv cs.NI 2017
Year: 2017

This paper presents end-to-end learning from spectrum data - an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated (i) modulation recognition and (ii) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation and the frequency domain representation. From our analysis we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.