OpenRaiser/Intern-Atlas huggingface dataset:
LIME: A Method for Low-light IMage Enhancement
Venue: arXiv cs.CV 2016
Year: 2016

When one captures images in low-light conditions, the images often suffer from low visibility. This poor quality may significantly degrade the performance of many computer vision and multimedia algorithms that are primarily designed for high-quality inputs. In this paper, we propose a very simple and effective method, named as LIME, to enhance low-light images. More concretely, the illumination of each pixel is first estimated individually by finding the maximum value in R, G and B channels. Further, we refine the initial illumination map by imposing a structure prior on it, as the final illumination map. Having the well-constructed illumination map, the enhancement can be achieved accordingly. Experiments on a number of challenging real-world low-light images are present to reveal the efficacy of our LIME and show its superiority over several state-of-the-arts.
Growth of Dimension in Complete Simple Games
Venue: arXiv cs.GT 2016
Year: 2016

The concept of dimension in simple games was introduced as a measure of the remoteness of a given game from a weighted game. Taylor and Zwicker (1993) demonstrated that the dimension of a simple game can grow exponentially in the number of players. However, the problem of worst-case growth of the dimension in complete games was left open. Freixas and Puente (2008) showed that complete games of arbitrary dimension exist and, in particular, their examples demonstrate that the worst-case growth of dimension in complete games is at least linear. In this paper, using a novel technique of Kurz and Napel (2016), we demonstrate that the worst-case growth of dimension in complete simple games is exponential in the number of players.
SPAM: Signal Processing to Analyze Malware
Venue: arXiv cs.CR 2016
Year: 2016

In this article, we explored orthogonal methods to analyze malware motivated by signal and image processing. Malware samples are represented as images or signals. Image and signal-based features are extracted to characterize malware. Our extensive experiments demonstrate the efficacy of our methods on malware classification and retrieval. We believe that our techniques will open the scope of signal-and image-based methods to broader fields in computer security.
$d$-imbalance WOM Codes for Reduced Inter-Cell Interference in Multi-Level NVMs
Venue: arXiv cs.IT 2016
Year: 2016

In recent years, due to the spread of multi-level non-volatile memories (NVM), $q$-ary write-once memories (WOM) codes have been extensively studied. By using WOM codes, it is possible to rewrite NVMs $t$ times before erasing the cells. The use of WOM codes enables to improve the performance of the storage device, however, it may also increase errors caused by inter-cell interference (ICI). This work presents WOM codes that restrict the imbalance between code symbols throughout the write sequence, hence decreasing ICI. We first specify the imbalance model as a bound $d$ on the difference between codeword levels. Then a $2$-cell code construction for general $q$ and input size is proposed. An upper bound on the write count is also derived, showing the optimality of the proposed construction. In addition to direct WOM constructions, we derive closed-form optimal write regions for codes constructed with continuous lattices. On the coding side, the proposed codes are shown to be competitive with known codes not adhering to the bounded imbalance constraint. On the memory side, we show how the codes can be deployed within flash wordlines, and quantify their BER advantage using accepted ICI models.
Lossless Intra Coding in HEVC with Integer-to-Integer DST
Venue: arXiv cs.MM 2016
Year: 2016

It is desirable to support efficient lossless coding within video coding standards, which are primarily designed for lossy coding, with as little modification as possible. A simple approach is to skip transform and quantization, and directly entropy code the prediction residual, but this is inefficient for compression. A more efficient and popular approach is to process the residual block with DPCM prior to entropy coding. This paper explores an alternative approach based on processing the residual block with integer-to-integer (i2i) transforms. I2i transforms map integers to integers, however, unlike the integer transforms used in HEVC for lossy coding, they do not increase the dynamic range at the output and can be used in lossless coding. We use both an i2i DCT from the literature and a novel i2i approximation of the DST. Experiments with the HEVC reference software show competitive results.
Improvement of algorithms to identify transportation modes for MobilitApp, an Android Application to anonymously track citizens in Barcelona
Venue: arXiv cs.OH 2016
Year: 2016

MobilitApp is an Android application whose objective is to obtain mobility data from the citizens of the metropolitan area of Barcelona. The current project is based on the research of more trustful and stronger transport decision algorithms using advantages of accelerometry techniques. The developed algorithm reads data from the mobile's accelerometer and gyroscope and writes it in a file that is afterwards sent to the server. This process is executed in background without interfering in the main application activity. Collected data has been processed and used to analyze the behaviour of the mobility pattern of the distinct transport modalities. The obtained result has been parameters which allow us to configure a model for each mean activity and designing a transport mode detection algorithm which would use information obtained from the mobile's own sensors. MobilitApp is still executing its main functionality, monitoring mobility data from the citizens. In future versions the solution proposed to detect the transport systems will be integrated into the application, in this way the app will work with information obtained from the device and will not depend on any other services.
Are Facial Attributes Adversarially Robust?
Venue: arXiv cs.CV 2016
Year: 2016

Facial attributes are emerging soft biometrics that have the potential to reject non-matches, for example, based on mismatching gender. To be usable in stand-alone systems, facial attributes must be extracted from images automatically and reliably. In this paper, we propose a simple yet effective solution for automatic facial attribute extraction by training a deep convolutional neural network (DCNN) for each facial attribute separately, without using any pre-training or dataset augmentation, and we obtain new state-of-the-art facial attribute classification results on the CelebA benchmark. To test the stability of the networks, we generated adversarial images -- formed by adding imperceptible non-random perturbations to original inputs which result in classification errors -- via a novel fast flipping attribute (FFA) technique. We show that FFA generates more adversarial examples than other related algorithms, and that DCNNs for certain attributes are generally robust to adversarial inputs, while DCNNs for other attributes are not. This result is surprising because no DCNNs tested to date have exhibited robustness to adversarial images without explicit augmentation in the training procedure to account for adversarial examples. Finally, we introduce the concept of natural adversarial samples, i.e., images that are misclassified but can be easily turned into correctly classified images by applying small perturbations. We demonstrate that natural adversarial samples commonly occur, even within the training set, and show that many of these images remain misclassified even with additional training epochs. This phenomenon is surprising because correcting the misclassification, particularly when guided by training data, should require only a small adjustment to the DCNN parameters.
Maximally Recoverable Codes for Grid-like Topologies
Venue: arXiv cs.IT 2016
Year: 2016

The explosion in the volumes of data being stored online has resulted in distributed storage systems transitioning to erasure coding based schemes. Yet, the codes being deployed in practice are fairly short. In this work, we address what we view as the main coding theoretic barrier to deploying longer codes in storage: at large lengths, failures are not independent and correlated failures are inevitable. This motivates designing codes that allow quick data recovery even after large correlated failures, and which have efficient encoding and decoding. We propose that code design for distributed storage be viewed as a two-step process. The first step is choose a topology of the code, which incorporates knowledge about the correlated failures that need to be handled, and ensures local recovery from such failures. In the second step one specifies a code with the chosen topology by choosing coefficients from a finite field. In this step, one tries to balance reliability (which is better over larger fields) with encoding and decoding efficiency (which is better over smaller fields). This work initiates an in-depth study of this reliability/efficiency tradeoff. We consider the field-size needed for achieving maximal recoverability: the strongest reliability possible with a given topology. We propose a family of topologies called grid-like topologies which unify a number of topologies considered both in theory and practice, and prove a collection of results about maximally recoverable codes in such topologies including the first super-polynomial lower bound on the field size.
Parallel Algorithms for Summing Floating-Point Numbers
Venue: arXiv cs.DS 2016
Year: 2016

The problem of exactly summing n floating-point numbers is a fundamental problem that has many applications in large-scale simulations and computational geometry. Unfortunately, due to the round-off error in standard floating-point operations, this problem becomes very challenging. Moreover, all existing solutions rely on sequential algorithms which cannot scale to the huge datasets that need to be processed. In this paper, we provide several efficient parallel algorithms for summing n floating point numbers, so as to produce a faithfully rounded floating-point representation of the sum. We present algorithms in PRAM, external-memory, and MapReduce models, and we also provide an experimental analysis of our MapReduce algorithms, due to their simplicity and practical efficiency.
Beyond Caption To Narrative: Video Captioning With Multiple Sentences
Venue: arXiv cs.CV 2016
Year: 2016

Recent advances in image captioning task have led to increasing interests in video captioning task. However, most works on video captioning are focused on generating single input of aggregated features, which hardly deviates from image captioning process and does not fully take advantage of dynamic contents present in videos. We attempt to generate video captions that convey richer contents by temporally segmenting the video with action localization, generating multiple captions from multiple frames, and connecting them with natural language processing techniques, in order to generate a story-like caption. We show that our proposed method can generate captions that are richer in contents and can compete with state-of-the-art method without explicitly using video-level features as input.
Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices
Venue: arXiv cs.IT 2016
Year: 2016

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost, which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm, namely, the Lyapunov optimization-based dynamic computation offloading (LODCO) algorithm is proposed, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the instantaneous side information without requiring distribution information of the computation task request, the wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.
Improving Weakly-Supervised Object Localization By Micro-Annotation
Venue: arXiv cs.CV 2016
Year: 2016

Weakly-supervised object localization methods tend to fail for object classes that consistently co-occur with the same background elements, e.g. trains on tracks. We propose a method to overcome these failures by adding a very small amount of model-specific additional annotation. The main idea is to cluster a deep network's mid-level representations and assign object or distractor labels to each cluster. Experiments show substantially improved localization results on the challenging ILSVC2014 dataset for bounding box detection and the PASCAL VOC2012 dataset for semantic segmentation.
Slotless Protocols for Fast and Energy-Efficient Neighbor Discovery
Venue: arXiv cs.NI 2016
Year: 2016

In mobile ad-hoc networks, neighbor discovery protocols are used to find surrounding devices and to establish a first contact between them. Since the clocks of the devices are not synchronized and their energy-budgets are limited, usually duty-cycled, asynchronous discovery protocols are applied. Only if two devices are awake at the same point in time, they can rendezvous. Currently, time-slotted protocols, which subdivide time into multiple intervals with equal lengths (slots), are considered to be the most efficient discovery schemes. In this paper, we break away from the assumption of slotted time. We propose a novel, continuous-time discovery protocol, which temporally decouples beaconing and listening. Each device periodically sends packets with a certain interval, and periodically listens for a given duration with a different interval. By optimizing these interval lengths, we show that this scheme can, to the best of our knowledge, outperform all known protocols such as DISCO, U-Connect or Searchlight significantly. For example, Searchlight takes up to 740 % longer than our proposed technique to discover a device with the same duty-cycle. Further, our proposed technique can also be applied in widely-used asymmetric purely interval-based protocols such as ANT or Bluetooth Low Energy.
Network Hardware-Accelerated Consensus
Venue: arXiv cs.DC 2016
Year: 2016

Consensus protocols are the foundation for building many fault-tolerant distributed systems and services. This paper posits that there are significant performance benefits to be gained by offering consensus as a network service (CAANS). CAANS leverages recent advances in commodity networking hardware design and programmability to implement consensus protocol logic in network devices. CAANS provides a complete Paxos protocol, is a drop-in replacement for software-based implementations of Paxos, makes no restrictions on network topologies, and is implemented in a higher-level, data-plane programming language, allowing for portability across a range of target devices. At the same time, CAANS significantly increases throughput and reduces latency for consensus operations. Consensus logic executing in hardware can transmit consensus messages at line speed, with latency only slightly higher than simply forwarding packets.
Capacity of the State-Dependent Wiretap Channel: Secure Writing on Dirty Paper
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we consider the State-Dependent Wiretap Channel (SD-WC). As the main idea, we model the SD-WC as a Cognitive Interference Channel (CIC), in which the primary receiver acts as an eavesdropper for the cognitive transmitter's message. By this point of view, the Channel State Information (CSI) in SD-WC plays the role of the primary user's message in CIC which can be decoded at the eavesdropper. This idea enables us to use the main achievability approaches of CIC, i.~e., Gel'fand-Pinsker Coding (GPC) and Superposition Coding (SPC), to find new achievable equivocation-rates for the SD-WC. We show that these approaches meet the capacity under some constraints on the rate of the channel state. Similar to the dirty paper channel, extending the results to the Gaussian case shows that the GPC lead to the capacity of the Gaussian SD-WC which is equal to the capacity of the wiretap channel without channel state. Hence, we achieve the capacity of the Gaussian SD-WC using the dirty paper technique. Moreover, our proposed approaches provide the capacity of the Binary SD-WC. It is shown that the capacity of the Binary SD-WC is equal to the capacity of the Binary wiretap channel without channel state.
Axioms of the Analytic Hierarchy Process (AHP) and its Generalization to Dependence and Feedback: The Analytic Network Process (ANP)
Venue: arXiv cs.DM 2016
Year: 2016

The AHP/ANP are multicriteria decision-making theories that deal with both hierarchic structures when the criteria are independent of the alternatives and with networks when there is any dependence within and between elements of the decision. Both of them have been repeatedly used in practice by various researchers and practitioners. From the perspective of almost 40 years of practice in solving problems using both theories, some of their properties seem to be more important than others. The article indicates four of them as fundamental for understanding AHP/ANP. These are the axioms related to structure, computation, and expectation. The mathematical formulation of the axioms is preceded by an introduction explaining the motivation behind the introduced concepts. The article is expository and it is an improved and refined version of the work [1].
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification
Venue: arXiv cs.CV 2016
Year: 2016

Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification. While extensive studies have focused on developing methods to improve the classification accuracy, experimental setting and design for method evaluation have drawn little attention. In the scope of supervised classification, we find that traditional experimental designs for spectral processing are often improperly used in the spectral-spatial processing context, leading to unfair or biased performance evaluation. This is especially the case when training and testing samples are randomly drawn from the same image - a practice that has been commonly adopted in the experiments. Under such setting, the dependence caused by overlap between the training and testing samples may be artificially enhanced by some spatial information processing methods such as spatial filtering and morphological operation. Such interaction between training and testing sets has violated data independence assumption that is abided by supervised learning theory and performance evaluation mechanism. Therefore, the widely adopted pixel-based random sampling strategy is not always suitable to evaluate spectral-spatial classification algorithms because it is difficult to determine whether the improvement of classification accuracy is caused by incorporating spatial information into classifier or by increasing the overlap between training and testing samples. To partially solve this problem, we propose a novel controlled random sampling strategy for spectral-spatial methods. It can greatly reduce the overlap between training and testing samples and provides more objective and accurate evaluation.
How hard is it to verify flat affine counter systems with the finite monoid property ?
Venue: arXiv cs.CC 2016
Year: 2016

We study several decision problems for counter systems with guards defined by convex polyhedra and updates defined by affine transformations. In general, the reachability problem is undecidable for such systems. Decidability can be achieved by imposing two restrictions: (i) the control structure of the counter system is flat, meaning that nested loops are forbidden, and (ii) the set of matrix powers is finite, for any affine update matrix in the system. We provide tight complexity bounds for several decision problems of such systems, by proving that reachability and model checking for Past Linear Temporal Logic are complete for the second level of the polynomial hierarchy $Σ^P_2$, while model checking for First Order Logic is PSPACE-complete.
Towards Better Internet Citizenship: Reducing the Footprint of Internet-wide Scans by Topology Aware Prefix Selection
Venue: arXiv cs.NI 2016
Year: 2016

Internet service discovery is an emerging topic to study the deployment of protocols. Towards this end, our community periodically scans the entire advertised IPv4 address space. In this paper, we question this principle. Being good Internet citizens means that we should limit scan traffic to what is necessary. We conducted a study of scan data, which shows that several prefixes do not accommodate any host of interest and the network topology is fairly stable. We argue that this allows us to collect representative data by scanning less. In our paper, we explore the idea to scan all prefixes once and then identify prefixes of interest for future scanning. Based on our analysis of the censys.io data set (4.1 TB data encompassing 28 full IPv4 scans within 6 months) we found that we can reduce scan traffic between 25-90% and miss only 1-10% of the hosts, depending on desired trade-offs and protocols.
Parametric and Probabilistic Model Checking of Confidentiality in Data Dispersal Algorithms (Extended Version)
Venue: arXiv cs.CR 2016
Year: 2016

Recent developments in cloud storage architectures have originated new models of online storage as cooperative storage systems and interconnected clouds. Such distributed environments involve many organizations, thus ensuring confidentiality becomes crucial: only legitimate clients should recover the information they distribute among storage nodes. In this work we present a unified framework for verifying confidentiality of dispersal algorithms against probabilistic models of intruders. Two models of intruders are given, corresponding to different types of attackers: one aiming at intercepting as many slices of information as possible, and the other aiming at attacking the storage providers in the network. Both try to recover the original information, given the intercepted slices. By using probabilistic model checking, we can measure the degree of confidentiality of the system exploring exhaustively all possible behaviors. Our experiments suggest that dispersal algorithms ensure a high degree of confidentiality against the slice intruder, no matter the number of storage providers in the system. On the contrary, they show a low level of confidentiality against the provider intruder in networks with few storage providers (e.g. interconnected cloud storage solutions).