OpenRaiser/Intern-Atlas huggingface dataset:
Fast rates for online learning in Linearly Solvable Markov Decision Processes
Venue: arXiv cs.LG 2017
Year: 2017

We study the problem of online learning in a class of Markov decision processes known as linearly solvable MDPs. In the stationary version of this problem, a learner interacts with its environment by directly controlling the state transitions, attempting to balance a fixed state-dependent cost and a certain smooth cost penalizing extreme control inputs. In the current paper, we consider an online setting where the state costs may change arbitrarily between consecutive rounds, and the learner only observes the costs at the end of each respective round. We are interested in constructing algorithms for the learner that guarantee small regret against the best stationary control policy chosen in full knowledge of the cost sequence. Our main result is showing that the smoothness of the control cost enables the simple algorithm of following the leader to achieve a regret of order $\log^2 T$ after $T$ rounds, vastly improving on the best known regret bound of order $T^{3/4}$ for this setting.
Finite Horizon Energy-Efficient Scheduling with Energy Harvesting Transmitters over Fading Channels
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, energy-efficient transmission schemes achieving maximal throughput over a finite time interval are studied in a problem setting including energy harvests, data arrivals and channel variation. The goal is to express the offline optimal policy in a way that facilitates a good online solution. We express any throughput maximizing energy efficient offline schedule (EE-TM-OFF) explicitly in terms of water levels. This allows per-slot real-time evaluation of transmit power and rate decisions, using estimates of the associated offline water levels. To compute the online power level, we construct a stochastic dynamic program that incorporates the offline optimal solution as a stochastic process. We introduce the "Immediate Fill" metric which provides a lower bound on the efficiency of any online policy with respect to the corresponding optimal offline solution. The online algorithms obtained this way exhibit performance close to the offline optimal, not only in the long run but also in short problem horizons, deeming them suitable for practical implementations.
Phaseless Sampling and Reconstruction of Real-Valued Signals in Shift-Invariant Spaces
Venue: arXiv cs.IT 2017
Year: 2017

Sampling in shift-invariant spaces is a realistic model for signals with smooth spectrum. In this paper, we consider phaseless sampling and reconstruction of real-valued signals in a shift-invariant space from their magnitude measurements on the whole Euclidean space and from their phaseless samples taken on a discrete set with finite sampling density. We introduce an undirected graph to a signal and use connectivity of the graph to characterize whether the signal can be determined, up to a sign, from its magnitude measurements on the whole Euclidean space. Under the local complement property assumption on a shift-invariant space, we find a discrete set with finite sampling density such that signals in the shift-invariant space, that are determined from their magnitude measurements on the whole Euclidean space, can be reconstructed in a stable way from their phaseless samples taken on that discrete set. In this paper, we also propose a reconstruction algorithm which provides a suboptimal approximation to the original signal when its noisy phaseless samples are available only. Finally, numerical simulations are performed to demonstrate the robust reconstruction of box spline signals from their noisy phaseless samples.
Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we focus on fully automatic traffic surveillance camera calibration, which we use for speed measurement of passing vehicles. We improve over a recent state-of-the-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference method. The method is based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from arbitrary viewpoints, since it has no constraints on camera placement. We evaluate our method on the recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration method by detection of two vanishing points reduces error by 50% (mean distance ratio error reduced from 0.18 to 0.09) compared to the previous state-of-the-art method. We also show that our scene scale inference method is more precise, outperforming both state-of-the-art automatic calibration method for speed measurement (error reduction by 86% -- 7.98km/h to 1.10km/h) and manual calibration (error reduction by 19% -- 1.35km/h to 1.10km/h). We also present qualitative results of the proposed automatic camera calibration method on video sequences obtained from real surveillance cameras in various places, and under different lighting conditions (night, dawn, day).
Efficient Social Network Multilingual Classification using Character, POS n-grams and Dynamic Normalization
Venue: arXiv cs.IR 2017
Year: 2017

In this paper we describe a dynamic normalization process applied to social network multilingual documents (Facebook and Twitter) to improve the performance of the Author profiling task for short texts. After the normalization process, $n$-grams of characters and n-grams of POS tags are obtained to extract all the possible stylistic information encoded in the documents (emoticons, character flooding, capital letters, references to other users, hyperlinks, hashtags, etc.). Experiments with SVM showed up to 90% of performance.
Framework for an Innovative Perceptive Mobile Network Using Joint Communication and Sensing
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we develop a framework for an innovative perceptive mobile (i.e. cellular) network that integrates sensing with communication, and supports new applications widely in transportation, surveillance and environmental sensing. Three types of sensing methods implemented in the base-stations are proposed, using either uplink or downlink multiuser communication signals. The required changes to system hardware and major technical challenges are briefly discussed. We also demonstrate the feasibility of estimating sensing parameters via developing a compressive sensing based scheme and providing simulation results to validate its effectiveness.
Resource Sharing Among mmWave Cellular Service Providers in a Vertically Differentiated Duopoly
Venue: arXiv cs.NI 2017
Year: 2017

With the increasing interest in the use of millimeter wave bands for 5G cellular systems comes renewed interest in resource sharing. Properties of millimeter wave bands such as massive bandwidth, highly directional antennas, high penetration loss, and susceptibility to shadowing, suggest technical advantages to spectrum and infrastructure sharing in millimeter wave cellular networks. However, technical advantages do not necessarily translate to increased profit for service providers, or increased consumer surplus. In this paper, detailed network simulations are used to better understand the economic implications of resource sharing in a vertically differentiated duopoly market for cellular service. The results suggest that resource sharing is less often profitable for millimeter wave service providers compared to microwave cellular service providers, and does not necessarily increase consumer surplus.
Counterfactual Control for Free from Generative Models
Venue: arXiv cs.LG 2017
Year: 2017

We introduce a method by which a generative model learning the joint distribution between actions and future states can be used to automatically infer a control scheme for any desired reward function, which may be altered on the fly without retraining the model. In this method, the problem of action selection is reduced to one of gradient descent on the latent space of the generative model, with the model itself providing the means of evaluating outcomes and finding the gradient, much like how the reward network in Deep Q-Networks (DQN) provides gradient information for the action generator. Unlike DQN or Actor-Critic, which are conditional models for a specific reward, using a generative model of the full joint distribution permits the reward to be changed on the fly. In addition, the generated futures can be inspected to gain insight in to what the network 'thinks' will happen, and to what went wrong when the outcomes deviate from prediction.
A Retrofitting-based Supplementary Controller Design for Enhancing Damping Performance of Wind Power Systems
Venue: arXiv cs.SY 2017
Year: 2017

In this paper we address the growing concerns of wind power integration from the perspective of power system dynamics and stability. We propose a new retrofit control technique where an additional controller is designed at the doubly-fed induction generator site inside the wind power plant. This controller cancels the adverse impacts of the power flow from the wind side to the grid side on the dynamics of the overall power system. The main advantage of this controller is that it can be implemented by feeding back only the wind states and wind bus voltage without depending on any of the other synchronous machines in the rest of the system. Through simulations of a 4-machine Kundur power system model we show that the retrofit can efficiently enhance the damping performance of the system variable despite very high values of wind penetration.
Task-driven Visual Saliency and Attention-based Visual Question Answering
Venue: arXiv cs.CV 2017
Year: 2017

Visual question answering (VQA) has witnessed great progress since May, 2015 as a classic problem unifying visual and textual data into a system. Many enlightening VQA works explore deep into the image and question encodings and fusing methods, of which attention is the most effective and infusive mechanism. Current attention based methods focus on adequate fusion of visual and textual features, but lack the attention to where people focus to ask questions about the image. Traditional attention based methods attach a single value to the feature at each spatial location, which losses many useful information. To remedy these problems, we propose a general method to perform saliency-like pre-selection on overlapped region features by the interrelation of bidirectional LSTM (BiLSTM), and use a novel element-wise multiplication based attention method to capture more competent correlation information between visual and textual features. We conduct experiments on the large-scale COCO-VQA dataset and analyze the effectiveness of our model demonstrated by strong empirical results.
Scene Recognition by Combining Local and Global Image Descriptors
Venue: arXiv cs.CV 2017
Year: 2017

Object recognition is an important problem in computer vision, having diverse applications. In this work, we construct an end-to-end scene recognition pipeline consisting of feature extraction, encoding, pooling and classification. Our approach simultaneously utilize global feature descriptors as well as local feature descriptors from images, to form a hybrid feature descriptor corresponding to each image. We utilize DAISY features associated with key points within images as our local feature descriptor and histogram of oriented gradients (HOG) corresponding to an entire image as a global descriptor. We make use of a bag-of-visual-words encoding and apply Mini- Batch K-Means algorithm to reduce the complexity of our feature encoding scheme. A 2-level pooling procedure is used to combine DAISY and HOG features corresponding to each image. Finally, we experiment with a multi-class SVM classifier with several kernels, in a cross-validation setting, and tabulate our results on the fifteen scene categories dataset. The average accuracy of our model was 76.4% in the case of a 40%-60% random split of images into training and testing datasets respectively. The primary objective of this work is to clearly outline the practical implementation of a basic screne-recognition pipeline having a reasonable accuracy, in python, using open-source libraries. A full implementation of the proposed model is available in our github repository.
Emptiness of zero automata is decidable
Venue: arXiv cs.FL 2017
Year: 2017

Zero automata are a probabilistic extension of parity automata on infinite trees. The satisfiability of a certain probabilistic variant of mso, called tmso + zero, reduces to the emptiness problem for zero automata. We introduce a variant of zero automata called nonzero automata. We prove that for every zero automaton there is an equivalent nonzero automaton of quadratic size and the emptiness problem of nonzero automata is decidable and both in NP and in coNP. These results imply that tmso + zero has decidable satisfiability.
Beyond Talagrand Functions: New Lower Bounds for Testing Monotonicity and Unateness
Venue: arXiv cs.CC 2017
Year: 2017

We prove a lower bound of $\tildeΩ(n^{1/3})$ for the query complexity of any two-sided and adaptive algorithm that tests whether an unknown Boolean function $f:\{0,1\}^n\rightarrow \{0,1\}$ is monotone or far from monotone. This improves the recent bound of $\tildeΩ(n^{1/4})$ for the same problem by Belovs and Blais [BB15]. Our result builds on a new family of random Boolean functions that can be viewed as a two-level extension of Talagrand's random DNFs. Beyond monotonicity, we also prove a lower bound of $\tildeΩ(n^{2/3})$ for any two-sided and adaptive algorithm, and a lower bound of $\tildeΩ(n)$ for any one-sided and non-adaptive algorithm for testing unateness, a natural generalization of monotonicity. The latter matches the recent linear upper bounds by Khot and Shinkar [KS15] and by Chakrabarty and Seshadhri [CS16].
Large-Scale Stochastic Learning using GPUs
Venue: arXiv cs.LG 2017
Year: 2017

In this work we propose an accelerated stochastic learning system for very large-scale applications. Acceleration is achieved by mapping the training algorithm onto massively parallel processors: we demonstrate a parallel, asynchronous GPU implementation of the widely used stochastic coordinate descent/ascent algorithm that can provide up to 35x speed-up over a sequential CPU implementation. In order to train on very large datasets that do not fit inside the memory of a single GPU, we then consider techniques for distributed stochastic learning. We propose a novel method for optimally aggregating model updates from worker nodes when the training data is distributed either by example or by feature. Using this technique, we demonstrate that one can scale out stochastic learning across up to 8 worker nodes without any significant loss of training time. Finally, we combine GPU acceleration with the optimized distributed method to train on a dataset consisting of 200 million training examples and 75 million features. We show by scaling out across 4 GPUs, one can attain a high degree of training accuracy in around 4 seconds: a 20x speed-up in training time compared to a multi-threaded, distributed implementation across 4 CPUs.
Synthesising Dynamic Textures using Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Here we present a parametric model for dynamic textures. The model is based on spatiotemporal summary statistics computed from the feature representations of a Convolutional Neural Network (CNN) trained on object recognition. We demonstrate how the model can be used to synthesise new samples of dynamic textures and to predict motion in simple movies.
FM Backscatter: Enabling Connected Cities and Smart Fabrics
Venue: arXiv cs.NI 2017
Year: 2017

This paper enables connectivity on everyday objects by transforming them into FM radio stations. To do this, we show for the first time that ambient FM radio signals can be used as a signal source for backscatter communication. Our design creates backscatter transmissions that can be decoded on any FM receiver including those in cars and smartphones. This enables us to achieve a previously infeasible capability: backscattering information to cars and smartphones in outdoor environments. Our key innovation is a modulation technique that transforms backscatter, which is a multiplication operation on RF signals, into an addition operation on the audio signals output by FM receivers. This enables us to embed both digital data as well as arbitrary audio into ambient analog FM radio signals. We build prototype hardware of our design and successfully embed audio transmissions over ambient FM signals. Further, we achieve data rates of up to 3.2 kbps and ranges of 5-60 feet, while consuming as little as 11.07μW of power. To demonstrate the potential of our design, we also fabricate our prototype on a cotton t-shirt by machine sewing patterns of a conductive thread to create a smart fabric that can transmit data to a smartphone. We also embed FM antennas into posters and billboards and show that they can communicate with FM receivers in cars and smartphones.
Flipping a Graduate-Level Software Engineering Foundations Course
Venue: arXiv cs.SE 2017
Year: 2017

Creating a graduate-level software engineering breadth course is challenging. The scope is wide. Students prefer hands-on work over theory. Industry increasingly values soft skills. Changing software technology requires the syllabus to be technology-agnostic, yet abstracting away technology compromises realism. Instructors must balance scope with depth of learning. At Carnegie Mellon University, we designed a flipped-classroom course that tackles these tradeoffs. The course has been offered since Fall 2014 in the Silicon Valley campus. In this paper, we describe the course's key features and summarize our experiences and lessons learned while designing, teaching, and maintaining it. We found that the pure flipped-classroom format was not optimal in ensuring sufficient transfer of knowledge, especially in remote settings. We initially underestimated teaching assistantship resources. We gradually complemented video lectures and hands-on live sessions with additional live components: easily replaceable recitations that focus on current technology and mini lectures that address application of theory and common wisdom. We also provided the students with more opportunities to share their successes and experiments with their peers. We achieved scalability by increasing the number of teaching assistants, paying attention to teaching assistant recruitment, and fostering a culture of mentoring among the teaching team.
A Survey of Active Attacks on Wireless Sensor Networks and their Countermeasures
Venue: arXiv cs.NI 2017
Year: 2017

Lately, Wireless Sensor Networks (WSNs) have become an emerging technology and can be utilized in some crucial circumstances like battlegrounds, commercial applications, habitat observing, buildings, smart homes, traffic surveillance and other different places. One of the foremost difficulties that WSN faces nowadays is protection from serious attacks. While organizing the sensor nodes in an abandoned environment makes network systems helpless against an assortment of strong assaults, intrinsic memory and power restrictions of sensor nodes make the traditional security arrangements impractical. The sensing knowledge combined with the wireless communication and processing power makes it lucrative for being abused. The wireless sensor network technology also obtains a big variety of security intimidations. This paper describes four basic security threats and many active attacks on WSN with their possible countermeasures proposed by different research scholars.
An Efficient Framework for Information Security in Cloud Computing Using Auditing Algorithm Shell (AAS)
Venue: arXiv cs.CR 2017
Year: 2017

There is a dynamic escalation and extension in the new infrastructure, educating personnel and licensing new computer programs in the field of IT, due to the emergence of Cloud Computing (CC) paradigm. It has become a quick growing segment of IT business in last couple of years. However, due to the rapid growth of data, people and IT firms, the issue of information security is getting more complex. One of the major concerns of the user is, at what degree the data is safe on Cloud? In spite of all promotional material encompassing the cloud, consortium customers are not willing to shift their business on the cloud. Data security is the major problem which has limited the scope of cloud computing. In new cloud computing infrastructure, the techniques such as the Strong Secure Shell and Encryption are deployed to guarantee the authenticity of the user through logs systems. The vendors utilize these logs to analyze and view their data. Therefore, this implementation is not enough to ensure security, privacy and authoritative use of the data. This paper introduces quad layered framework for data security, data privacy, data breaches and process associated aspects. Using this layered architecture we have preserved the secrecy of confidential information and tried to build the trust of user on cloud computing. This layered framework prevents the confidential information by multiple means i.e. Secure Transmission of Data, Encrypted Data and its Processing, Database Secure Shell and Internal/external log Auditing.
A DIKW Paradigm to Cognitive Engineering
Venue: arXiv cs.AI 2017
Year: 2017

Though the word cognitive has a wide range of meanings we define cognitive engineering as learning from brain to bolster engineering solutions. However, giving an achievable framework to the process towards this has been a difficult task. In this work we take the classic data information knowledge wisdom (DIKW) framework to set some achievable goals and sub-goals towards cognitive engineering. A layered framework like DIKW aligns nicely with the layered structure of pre-frontal cortex. And breaking the task into sub-tasks based on the layers also makes it easier to start developmental endeavours towards achieving the final goal of a brain-inspired system.
Analyzing Learned Convnet Features with Dirichlet Process Gaussian Mixture Models
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional Neural Networks (Convnets) have achieved good results in a range of computer vision tasks the recent years. Though given a lot of attention, visualizing the learned representations to interpret Convnets, still remains a challenging task. The high dimensionality of internal representations and the high abstractions of deep layers are the main challenges when visualizing Convnet functionality. We present in this paper a technique based on clustering internal Convnet representations with a Dirichlet Process Gaussian Mixture Model, for visualization of learned representations in Convnets. Our method copes with the high dimensionality of a Convnet by clustering representations across all nodes of each layer. We will discuss how this application is useful when considering transfer learning, i.e.\ transferring a model trained on one dataset to solve a task on a different one.
Fast generalized Bruhat decomposition
Venue: arXiv cs.SC 2017
Year: 2017

The deterministic recursive pivot-free algorithms for the computation of generalized Bruhat decomposition of the matrix in the field and for the computation of the inverse matrix are presented. This method has the same complexity as algorithm of matrix multiplication and it is suitable for the parallel computer systems.