OpenRaiser/Intern-Atlas huggingface dataset:
Research on the Multiple Feature Fusion Image Retrieval Algorithm based on Texture Feature and Rough Set Theory
Venue: arXiv cs.CV 2016
Year: 2016

Recently, we have witnessed the explosive growth of images with complex information and content. In order to effectively and precisely retrieve desired images from a large-scale image database with low time-consuming, we propose the multiple feature fusion image retrieval algorithm based on the texture feature and rough set theory in this paper. In contrast to the conventional approaches that only use the single feature or standard, we fuse the different features with operation of normalization. The rough set theory will assist us to enhance the robustness of retrieval system when facing with incomplete data warehouse. To enhance the texture extraction paradigm, we use the wavelet Gabor function that holds better robustness. In addition, from the perspectives of the internal and external normalization, we re-organize extracted feature with the better combination. The numerical experiment has verified general feasibility of our methodology. We enhance the overall accuracy compared with the other state-of-the-art algorithms.
Towards Information-Seeking Agents
Venue: arXiv cs.LG 2016
Year: 2016

We develop a general problem setting for training and testing the ability of agents to gather information efficiently. Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for fragments of information which can be pieced together to accomplish various goals. We combine deep architectures with techniques from reinforcement learning to develop agents that solve our tasks. We shape the behavior of these agents by combining extrinsic and intrinsic rewards. We empirically demonstrate that these agents learn to search actively and intelligently for new information to reduce their uncertainty, and to exploit information they have already acquired.
Aerial Picking and Delivery of Magnetic Objects with MAVs
Venue: arXiv cs.RO 2016
Year: 2016

Autonomous delivery of goods using a MAV is a difficult problem, as it poses high demand on the MAV's control, perception and manipulation capabilities. This problem is especially challenging if the exact shape, location and configuration of the objects are unknown. In this paper, we report our findings during the development and evaluation of a fully integrated system that is energy efficient and enables MAVs to pick up and deliver objects with partly ferrous surface of varying shapes and weights. This is achieved by using a novel combination of an electro-permanent magnetic gripper with a passively compliant structure and integration with detection, control and servo positioning algorithms. The system's ability to grasp stationary and moving objects was tested, as well as its ability to cope with different shapes of the object and external disturbances. We show that such a system can be successfully deployed in scenarios where an object with partly ferrous parts needs to be gripped and placed in a predetermined location.
Efficient Distinct Heavy Hitters for DNS DDoS Attack Detection
Venue: arXiv cs.CR 2016
Year: 2016

Motivated by a recent new type of randomized Distributed Denial of Service (DDoS) attacks on the Domain Name Service (DNS), we develop novel and efficient distinct heavy hitters algorithms and build an attack identification system that uses our algorithms. Heavy hitter detection in streams is a fundamental problem with many applications, including detecting certain DDoS attacks and anomalies. A (classic) heavy hitter (HH) in a stream of elements is a key (e.g., the domain of a query) which appears in many elements (e.g., requests). When stream elements consist of a <key; subkey> pairs, (<domain; subdomain>) a distinct heavy hitter (dhh) is a key that is paired with a large number of different subkeys. Our dHH algorithms are considerably more practical than previous algorithms. Specifically the new fixed-size algorithms are simple to code and with asymptotically optimal space accuracy tradeoffs. In addition we introduce a new measure, a combined heavy hitter (cHH), which is a key with a large combination of distinct and classic weights. Efficient algorithms are also presented for cHH detection. Finally, we perform extensive experimental evaluation on real DNS attack traces, demonstrating the effectiveness of both our algorithms and our DNS malicious queries identification system.
Predicting Ground-Level Scene Layout from Aerial Imagery
Venue: arXiv cs.CV 2016
Year: 2016

We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (noisy) semantic features automatically extracted from co-located ground imagery. Our network architecture takes an aerial image as input, extracts features using a convolutional neural network, and then applies an adaptive transformation to map these features into the ground-level perspective. We use an end-to-end learning approach to minimize the difference between the semantic segmentation extracted directly from the ground image and the semantic segmentation predicted solely based on the aerial image. We show that a model learned using this strategy, with no additional training, is already capable of rough semantic labeling of aerial imagery. Furthermore, we demonstrate that by finetuning this model we can achieve more accurate semantic segmentation than two baseline initialization strategies. We use our network to address the task of estimating the geolocation and geoorientation of a ground image. Finally, we show how features extracted from an aerial image can be used to hallucinate a plausible ground-level panorama.
TCP-aware Cross Layer Scheduling with Adaptive Modulation in IEEE 802.16 (WiMAX) Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we propose Transmission Control Protocol (TCP)-aware cross layer scheduling algorithms in a multipoint-to-point network such as the uplink of an IEEE 802.16 (WiMAX) network. Inadequate bandwidth allocation to a TCP flow may lead to timeout and since TCP source drops its congestion window ($cwnd$) immediately after a timeout, it may affect the average throughput adversely. On the other hand, since the TCP source increases its $cwnd$ only linearly upon the availability of bandwidth, any excess assignment of bandwidth may remain underutilized. The proposed scheduling algorithms address this by allocating the resources based on $cwnd$ and TCP timeout. Moreover, since we focus on uplink scheduling, we consider that only {\em flow} level resource requirement is communicated to the Base Station ($BS$) instead of per {\em packet} information. The schedulers also take into account the wireless channel characteristics and are thus cross layer in nature. Through exhaustive simulations, we demonstrate that the proposed schedulers exhibit enhanced throughput and fairness properties when compared to that of Round Robin (RR) scheduler under different shadowing. We demonstrate a gain between 3.5 \% to 15 \% in throughput and 15 \% to 25 \% in channel utilization over RR scheduler under different shadowing.
Improved generator objectives for GANs
Venue: arXiv cs.LG 2016
Year: 2016

We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an interpretation for the mismatched GAN generator and discriminator objectives often used in practice, and explains the problem of poor sample diversity. We also derive a family of generator objectives that target arbitrary $f$-divergences without minimizing a lower bound, and use them to train generative image models that target either improved sample quality or greater sample diversity.
In the transmission of information, the great potential of model-based coding with the SP theory of intelligence
Venue: arXiv cs.IT 2016
Year: 2016

Model-based coding, described by John Pierce in 1961, has great potential to reduce the volume of information that needs to be transmitted in moving big data, without loss of information, from one place to another, or in lossless communications via the internet. Compared with ordinary compression methods, this potential advantage of model-based coding in the transmission of data arises from the fact that both the transmitter ("Alice") and the receiver ("Bob") are equipped with a grammar for the kind of data that is to be transmitted, which means that, to achieve lossless transmission of a body of data from Alice and Bob, a relatively small amount of information needs to be sent. Preliminary trials indicate that, with model-based coding, the volume of information to be sent from Alice to Bob to achieve lossless transmission of a given body of data may be less than $6\%$ of the volume of information that needs to be sent when ordinary compression methods are used. Until recently, it has not been feasible to convert John Pierce's vision into something that may be applied in practice. Now, with the development of the "SP theory of intelligence" and its realisation in the "SP computer model", there is clear potential to realise the three main functions that will be needed: unsupervised learning of a grammar for the kind of data that is to be transmitted using a relatively powerful computer that is independent of Alice and Bob; the encoding by Alice of any one example of such data in terms of the grammar; and, with the grammar, decoding of the encoding by Bob to retrieve the given example. It appears now to be feasible, within reasonable timescales, to bring these capabilities to a level where they may be applied to the transmission of realistically large bodies of data.
A Compressive Method for Centralized PSD Map Construction
Venue: arXiv cs.IT 2016
Year: 2016

Spectrum resources are facing huge demands and cognitive radio (CR) can improve the spectrum utilization. Recently, power spectral density (PSD) map is defined to enable the CR to reuse the frequency resources regarding to the area. For this reason, the sensed PSDs are fused by a Fusion Center (FC) which the sensed PSDs are collected by the distributed sensors in the area. But, for a given zone, the sensed PSD by neighbor CR sensors may contain a shared common component for a while. This component can be exploited in the theory of the distributed source coding (DSC) to compress sensing data more. In this paper based on the distributed compressive sensing (DCS) a method is proposed to compress and reconstruct the PSDs of the sensors when the data transmission is slightly imperfect. Simulation results show the advantages of using proposed method in compressing, reducing overhead and also recovering PSDs. % Proposed method can be used to develop a framework when the holding times of the users are large in comparison with the rate of the spectrum sensing.
Randomized Admission Policy for Efficient Top-k and Frequency Estimation
Venue: arXiv cs.DS 2016
Year: 2016

Network management protocols often require timely and meaningful insight about per flow network traffic. This paper introduces Randomized Admission Policy (RAP) - a novel algorithm for the frequency and top-k estimation problems, which are fundamental in network monitoring. We demonstrate space reductions compared to the alternatives by a factor of up to 32 on real packet traces and up to 128 on heavy-tailed workloads. For top-k identification, RAP exhibits memory savings by a factor of between 4 and 64 depending on the skew of the workload. These empirical results are backed by formal analysis, indicating the asymptotic space improvement of our probabilistic admission approach. Additionally, we present d-Way RAP, a hardware friendly variant of RAP that empirically maintains its space and accuracy benefits.
No domain left behind: is Let's Encrypt democratizing encryption?
Venue: arXiv cs.CR 2016
Year: 2016

The 2013 National Security Agency revelations of pervasive monitoring have lead to an "encryption rush" across the computer and Internet industry. To push back against massive surveillance and protect users privacy, vendors, hosting and cloud providers have widely deployed encryption on their hardware, communication links, and applications. As a consequence, the most of web traffic nowadays is encrypted. However, there is still a significant part of Internet traffic that is not encrypted. It has been argued that both costs and complexity associated with obtaining and deploying X.509 certificates are major barriers for widespread encryption, since these certificates are required to established encrypted connections. To address these issues, the Electronic Frontier Foundation, Mozilla Foundation, and the University of Michigan have set up Let's Encrypt (LE), a certificate authority that provides both free X.509 certificates and software that automates the deployment of these certificates. In this paper, we investigate if LE has been successful in democratizing encryption: we analyze certificate issuance in the first year of LE and show from various perspectives that LE adoption has an upward trend and it is in fact being successful in covering the lower-cost end of the hosting market.
Exploring Microtask Crowdsourcing as a Means of Fault Localization
Venue: arXiv cs.SE 2016
Year: 2016

Microtask crowdsourcing is the practice of breaking down an overarching task to be performed into numerous, small, and quick microtasks that are distributed to an unknown, large set of workers. Microtask crowdsourcing has shown potential in other disciplines, but with only a handful of approaches explored to date in software engineering, its potential in our field remains unclear. In this paper, we explore how microtask crowdsourcing might serve as a means of fault localization. We particularly take a first step in assessing whether a crowd of workers can correctly locate known faults in a few lines of code (code fragments) taken from different open source projects. Through Mechanical Turk, we collected the answers of hundreds of workers to a pre-determined set of template questions applied to the code fragments, with a replication factor of twenty answers per question. Our findings show that a crowd can correctly distinguish questions that cover lines of code that contain a fault from those that do not. We also show that various filters can be applied to identify the most effective subcrowds. Our findings also presented serious limitations in terms of the proportion of lines of code selected for inspection and the cost to collect answers. We describe the design of our experiment, discuss the results, and provide an extensive analysis of different filters and their effects in terms of speed, cost, and effectiveness. We conclude with a discussion of limitations and possible future experiments toward more full-fledged fault localization on a large scale involving more complex faults.
Blind Identification of SM and Alamouti STBC-OFDM Signals
Venue: arXiv cs.IT 2016
Year: 2016

This paper proposes an efficient identification algorithm for spatial multiplexing (SM) and Alamouti (AL) coded orthogonal frequency division multiplexing (OFDM) signals. The cross-correlation between the received signals from different antennas is exploited to provide a discriminating feature to identify SM-OFDM and AL-OFDM signals. The proposed algorithm requires neither estimation of the channel coefficients and noise power, nor the modulation of the transmitted signal. Moreover, it does not need space-time block code (STBC) or OFDM block synchronization. The effectiveness of the proposed algorithm is demonstrated through extensive simulation experiments in the presence of diverse transmission impairments, such as time and frequency offsets, Doppler frequency, and spatially correlated fading.
Advancing Bayesian Optimization: The Mixed-Global-Local (MGL) Kernel and Length-Scale Cool Down
Venue: arXiv cs.LG 2016
Year: 2016

Bayesian Optimization (BO) has become a core method for solving expensive black-box optimization problems. While much research focussed on the choice of the acquisition function, we focus on online length-scale adaption and the choice of kernel function. Instead of choosing hyperparameters in view of maximum likelihood on past data, we propose to use the acquisition function to decide on hyperparameter adaptation more robustly and in view of the future optimization progress. Further, we propose a particular kernel function that includes non-stationarity and local anisotropy and thereby implicitly integrates the efficiency of local convex optimization with global Bayesian optimization. Comparisons to state-of-the art BO methods underline the efficiency of these mechanisms on global optimization benchmarks.
Panoptic Studio: A Massively Multiview System for Social Interaction Capture
Venue: arXiv cs.CV 2016
Year: 2016

We present an approach to capture the 3D motion of a group of people engaged in a social interaction. The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions. The Panoptic Studio is a system organized around the thesis that social interactions should be measured through the integration of perceptual analyses over a large variety of view points. We present a modularized system designed around this principle, consisting of integrated structural, hardware, and software innovations. The system takes, as input, 480 synchronized video streams of multiple people engaged in social activities, and produces, as output, the labeled time-varying 3D structure of anatomical landmarks on individuals in the space. Our algorithm is designed to fuse the "weak" perceptual processes in the large number of views by progressively generating skeletal proposals from low-level appearance cues, and a framework for temporal refinement is also presented by associating body parts to reconstructed dense 3D trajectory stream. Our system and method are the first in reconstructing full body motion of more than five people engaged in social interactions without using markers. We also empirically demonstrate the impact of the number of views in achieving this goal.
Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting
Venue: arXiv cs.CL 2016
Year: 2016

Language generation tasks that seek to mimic human ability to use language creatively are difficult to evaluate, since one must consider creativity, style, and other non-trivial aspects of the generated text. The goal of this paper is to develop evaluation methods for one such task, ghostwriting of rap lyrics, and to provide an explicit, quantifiable foundation for the goals and future directions of this task. Ghostwriting must produce text that is similar in style to the emulated artist, yet distinct in content. We develop a novel evaluation methodology that addresses several complementary aspects of this task, and illustrate how such evaluation can be used to meaningfully analyze system performance. We provide a corpus of lyrics for 13 rap artists, annotated for stylistic similarity, which allows us to assess the feasibility of manual evaluation for generated verse.
DeepCancer: Detecting Cancer through Gene Expressions via Deep Generative Learning
Venue: arXiv cs.AI 2016
Year: 2016

Transcriptional profiling on microarrays to obtain gene expressions has been used to facilitate cancer diagnosis. We propose a deep generative machine learning architecture (called DeepCancer) that learn features from unlabeled microarray data. These models have been used in conjunction with conventional classifiers that perform classification of the tissue samples as either being cancerous or non-cancerous. The proposed model has been tested on two different clinical datasets. The evaluation demonstrates that DeepCancer model achieves a very high precision score, while significantly controlling the false positive and false negative scores.
Towards an Automated Image De-fencing Algorithm Using Sparsity
Venue: arXiv cs.CV 2016
Year: 2016

Conventional approaches to image de-fencing suffer from non-robust fence detection and are limited to processing images of static scenes. In this position paper, we propose an automatic de-fencing algorithm for images of dynamic scenes. We divide the problem of image de-fencing into the tasks of automated fence detection, motion estimation and fusion of data from multiple frames of a captured video of the dynamic scene. Fences are detected automatically using two approaches, namely, employing Gabor filter and a machine learning method. We cast the fence removal problem in an optimization framework, by modeling the formation of the degraded observations. The inverse problem is solved using split Bregman technique assuming total variation of the de-fenced image as the regularization constraint.
Limited Feedback in MISO Systems with Finite-Bit ADCs
Venue: arXiv cs.IT 2016
Year: 2016

We analyze limited feedback in systems where a multiple-antenna transmitter sends signals to single-antenna receivers with finite-bit ADCs. If channel state information (CSI) is not available with high resolution at the transmitter and the precoding is not well designed, the inter-user interference is a big decoding challenge for receivers with low-resolution quantization. In this paper, we derive achievable rates with finite-bit ADCs and finite-bit CSI feedback. The performance loss compared to the case with perfect CSI is then analyzed. The results show that the number of bits per feedback should increase linearly with the ADC resolution to restrict the loss.
Community Detection Algorithm Evaluation using Size and Hashtags
Venue: arXiv cs.SI 2016
Year: 2016

Understanding community structure in social media is critical due to its broad applications such as friend recommendations, link predictions and collaborative filtering. However, there is no widely accepted definition of community in literature. Existing work use structure related metrics such as modularity and function related metrics such as ground truth to measure the performance of community detection algorithms, while ignoring an important metric, size of the community. [1] suggests that the size of community with strong ties in social media should be limited to 150. As we discovered in this paper, the majority of the communities obtained by many popular community detection algorithms are either very small or very large. Too small communities don't have practical value and too large communities contain weak connections therefore not stable. In this paper, we compare various community detection algorithms considering the following metrics: size of the communities, coverage of the communities, extended modularity, triangle participation ratio, and user interest in the same community. We also propose a simple clique based algorithm for community detection as a baseline for the comparison. Experimental results show that both our proposed algorithm and the well-accepted disjoint algorithm InfoMap perform well in all the metrics.
Low-complexity Pruned 8-point DCT Approximations for Image Encoding
Venue: arXiv cs.MM 2016
Year: 2016

Two multiplierless pruned 8-point discrete cosine transform (DCT) approximation are presented. Both transforms present lower arithmetic complexity than state-of-the-art methods. The performance of such new methods was assessed in the image compression context. A JPEG-like simulation was performed, demonstrating the adequateness and competitiveness of the introduced methods. Digital VLSI implementation in CMOS technology was also considered. Both presented methods were realized in Berkeley Emulation Engine (BEE3).