OpenRaiser/Intern-Atlas huggingface dataset:
Robust LSB Watermarking Optimized for Local Structural Similarity
Venue: arXiv cs.MM 2018
Year: 2018

Growth of the Internet and networked multimedia systems has emphasized the need for copyright protection of the media. Media can be images, audio clips, videos and etc. Digital watermarking is today extensively used for many applications such as authentication of ownership or identification of illegal copies. Digital watermark is an invisible or maybe visible structure added to the original media (known as asset). Images are considered as communication channel when they are subject to a watermark embedding procedure so in the case of embedding a digital watermark in an image, the capacity of the channel should be considered. There is a trade-off between imperceptibility, robustness and capacity for embedding a watermark in an asset. In the case of image watermarks, it is reasonable that the watermarking algorithm should depend on the content and structure of the image. Conventionally, mean squared error (MSE) has been used as a common distortion measure to assess the quality of the images. Newly developed quality metrics proposed some distortion measures that are based on human visual system (HVS). These metrics show that MSE is not based on HVS and it has a lack of accuracy when dealing with perceptually important signals such as images and videos. SSIM or structural similarity is a state of the art HVS based image quality criterion that has recently been of much interest. In this paper we propose a robust least significant bit (LSB) watermarking scheme which is optimized for structural similarity. The watermark is embedded into a host image through an adaptive algorithm. Various attacks examined on the embedding approach and simulation results revealed the fact that the watermarked sequence can be extracted with an acceptable accuracy after all attacks.
Model Visualization in understanding rapid growth of a journal in an emerging area
Venue: arXiv cs.DL 2018
Year: 2018

A recent independent study resulted in a ranking system which ranked Astronomy and Computing (ASCOM) much higher than most of the older journals highlighting the niche prominence of the particular journal. We investigate the remarkable ascendancy in reputation of ASCOM by proposing a novel differential equation based modeling. The Modeling is a consequence of knowledge discovery from big data-centric methods, namely L1-SVD. The inadequacy of the ranking method in explaining the reason behind the growth in reputation of ASCOM is reasonable to understand given that the study was post-facto. Thus, we propose a growth model by accounting for the behavior of parameters that contribute to the growth of a field. It is worthwhile to spend some time in analysing the cause and control variables behind rapid rise in reputation of a journal in a niche area. We intent to probe and bring out parameters responsible for its growing influence. Delay differential equations are used to model the change of influence on a journal's status by exploiting the effects of historical data.
Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a new method called Multimodal RNNs for RGB-D scene semantic segmentation. It is optimized to classify image pixels given two input sources: RGB color channels and Depth maps. It simultaneously performs training of two recurrent neural networks (RNNs) that are crossly connected through information transfer layers, which are learnt to adaptively extract relevant cross-modality features. Each RNN model learns its representations from its own previous hidden states and transferred patterns from the other RNNs previous hidden states; thus, both model-specific and crossmodality features are retained. We exploit the structure of quad-directional 2D-RNNs to model the short and long range contextual information in the 2D input image. We carefully designed various baselines to efficiently examine our proposed model structure. We test our Multimodal RNNs method on popular RGB-D benchmarks and show how it outperforms previous methods significantly and achieves competitive results with other state-of-the-art works.
A Gaze-Assisted Multimodal Approach to Rich and Accessible Human-Computer Interaction
Venue: arXiv cs.HC 2018
Year: 2018

Recent advancements in eye tracking technology are driving the adoption of gaze-assisted interaction as a rich and accessible human-computer interaction paradigm. Gaze-assisted interaction serves as a contextual, non-invasive, and explicit control method for users without disabilities; for users with motor or speech impairments, text entry by gaze serves as the primary means of communication. Despite significant advantages, gaze-assisted interaction is still not widely accepted because of its inherent limitations: 1) Midas touch, 2) low accuracy for mouse-like interactions, 3) need for repeated calibration, 4) visual fatigue with prolonged usage, 5) lower gaze typing speed, and so on. This dissertation research proposes a gaze-assisted, multimodal, interaction paradigm, and related frameworks and their applications that effectively enable gaze-assisted interactions while addressing many of the current limitations. In this regard, we present four systems that leverage gaze-assisted interaction: 1) a gaze- and foot-operated system for precise point-and-click interactions, 2) a dwell-free, foot-operated gaze typing system. 3) a gaze gesture-based authentication system, and 4) a gaze gesture-based interaction toolkit. In addition, we also present the goals to be achieved, technical approach, and overall contributions of this dissertation research.
A pseudo-quasi-polynomial algorithm for solving mean-payoff parity games
Venue: arXiv cs.GT 2018
Year: 2018

In a mean-payoff parity game, one of the two players aims both to achieve a qualitative parity objective and to minimize a quantitative long-term average of payoffs (aka. mean payoff). The game is zero-sum and hence the aim of the other player is to either foil the parity objective or to maximize the mean payoff. Our main technical result is a pseudo-quasi-polynomial algorithm for solving mean-payoff parity games. All algorithms for the problem that have been developed for over a decade have a pseudo-polynomial and an exponential factors in their running times; in the running time of our algorithm the latter is replaced with a quasi-polynomial one. By the results of Chatterjee and Doyen (2012) and of Schewe, Weinert, and Zimmermann (2018), our main technical result implies that there are pseudo-quasi-polynomial algorithms for solving parity energy games and for solving parity games with weights. Our main conceptual contributions are the definitions of strategy decompositions for both players, and a notion of progress measures for mean-payoff parity games that generalizes both parity and energy progress measures. The former provides normal forms for and succinct representations of winning strategies, and the latter enables the application to mean-payoff parity games of the order-theoretic machinery that underpins a recent quasi-polynomial algorithm for solving parity games.
Monitoring Targeted Hate in Online Environments
Venue: arXiv cs.CL 2018
Year: 2018

Hateful comments, swearwords and sometimes even death threats are becoming a reality for many people today in online environments. This is especially true for journalists, politicians, artists, and other public figures. This paper describes how hate directed towards individuals can be measured in online environments using a simple dictionary-based approach. We present a case study on Swedish politicians, and use examples from this study to discuss shortcomings of the proposed dictionary-based approach. We also outline possibilities for potential refinements of the proposed approach.
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks (DNNs) enable innovative applications of machine learning like image recognition, machine translation, or malware detection. However, deep learning is often criticized for its lack of robustness in adversarial settings (e.g., vulnerability to adversarial inputs) and general inability to rationalize its predictions. In this work, we exploit the structure of deep learning to enable new learning-based inference and decision strategies that achieve desirable properties such as robustness and interpretability. We take a first step in this direction and introduce the Deep k-Nearest Neighbors (DkNN). This hybrid classifier combines the k-nearest neighbors algorithm with representations of the data learned by each layer of the DNN: a test input is compared to its neighboring training points according to the distance that separates them in the representations. We show the labels of these neighboring points afford confidence estimates for inputs outside the model's training manifold, including on malicious inputs like adversarial examples--and therein provides protections against inputs that are outside the models understanding. This is because the nearest neighbors can be used to estimate the nonconformity of, i.e., the lack of support for, a prediction in the training data. The neighbors also constitute human-interpretable explanations of predictions. We evaluate the DkNN algorithm on several datasets, and show the confidence estimates accurately identify inputs outside the model, and that the explanations provided by nearest neighbors are intuitive and useful in understanding model failures.
Improved OpenCL-based Implementation of Social Field Pedestrian Model
Venue: arXiv cs.DC 2018
Year: 2018

Two aspects of improvements are proposed for the OpenCL-based implementation of the social field pedestrian model. In the aspect of algorithm, a method based on the idea of divide-and-conquer is devised in order to overcome the problem of global memory depletion when fields are of a larger size. This is of importance for the study of finer pedestrian walking behavior, which usually requires larger fields. In the aspect of computation, the OpenCL heterogeneous framework is thoroughly studied. Factors that may affect the numerical efficiency are evaluated, with regarding to the social field model previously proposed. This includes usage of local memory, deliberate patch of data structures for avoidance of bank conflicts, and so on. Numerical experiments disclose that the numerical efficiency is brought to an even higher level. Compared to the CPU model and the previous GPU model, the current GPU model can be at most 71.56 and 13.3 times faster respectively so that it is more qualified to be a core engine for analysis of super-large scale crowd.
Expert identification of visual primitives used by CNNs during mammogram classification
Venue: arXiv cs.CV 2018
Year: 2018

This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms. We propose an expert-in-the-loop interpretation method to label the behavior of internal units in convolutional neural networks (CNNs). Expert radiologists identify that the visual patterns detected by the units are correlated with meaningful medical phenomena such as mass tissue and calcificated vessels. We demonstrate that several trained CNN models are able to produce explanatory descriptions to support the final classification decisions. We view this as an important first step toward interpreting the internal representations of medical classification CNNs and explaining their predictions.
Reviewing KLEE's Sonar-Search Strategy in Context of Greybox Fuzzing
Venue: arXiv cs.SE 2018
Year: 2018

Automatic test-case generation techniques of symbolic execution and fuzzing are the most widely used methods to discover vulnerabilities in, both, academia and industry. However, both these methods suffer from fundamental drawbacks that stop them from achieving high path coverage that may, consequently, lead to discovering vulnerabilities at the numerical scale of static analysis. In this presentation, we examine systems-under-test (SUTs) at the granularity level of functions and postulate that achieving higher function coverage (execution of functions in a program at least once) than, both, symbolic execution and fuzzing may be a necessary condition for discovering more vulnerabilities than both. We will start this presentation with the design of a targeted search strategy for KLEE, sonar-search, that prioritizes paths leading to a target function, rather than maximizing overall path coverage in the program. Then, we will show that examining SUTs at the level of functions (compositional analysis) leads to discovering more vulnerabilities than symbolic execution from a single entry point. Using this finding, we will, then, demonstrate a greybox fuzzing method that can achieve higher function coverage than symbolic execution. Finally, we will present a framework to effectively manage vulnerabilities and assess their severities.
An agent-based evaluation of impacts of transport developments on the modal shift in Tehran, Iran
Venue: arXiv cs.MA 2018
Year: 2018

Changes in travel modes used by people, particularly reduction of the private car use, is an important determinant of effectiveness of transportation plans. Because of dependencies between the choices of residential location and travel mode, integrated modelling of these choices has been proposed by some researchers. In this paper, an agent-based microsimulation model has been developed to evaluate impacts of different transport development plans on choices of residential location and commuting mode of tenant households in Tehran, the capital of Iran. In the proposed model, households are considered as agents who select their desired residential location using a constrained NSGA-II algorithm and in a competition with other households. In addition, they choose their commuting mode by applying a multi-criteria decision making method. Afterwards, effects of development of a new highway, subway and bus rapid transit (BRT) line on their residential location and commuting mode choices are evaluated. Results show that despite the residential self-selection effects, these plans result in considerable changes in the commuting mode of different socioeconomic categories of households. Development of the new subway line shows promising results by reducing the private car use among the all socio-economic categories of households. But the new highway development unsatisfactorily results in increase in the private car use. In addition, development of the new BRT line does not show significant effects on the commuting mode change, particularly on decrease in the private car use.
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Venue: arXiv cs.LG 2018
Year: 2018

Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap" between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.
A Framework for Video-Driven Crowd Synthesis
Venue: arXiv cs.CV 2018
Year: 2018

We present a framework for video-driven crowd synthesis. Motion vectors extracted from input crowd video are processed to compute global motion paths. These paths encode the dominant motions observed in the input video. These paths are then fed into a behavior-based crowd simulation framework, which is responsible for synthesizing crowd animations that respect the motion patterns observed in the video. Our system synthesizes 3D virtual crowds by animating virtual humans along the trajectories returned by the crowd simulation framework. We also propose a new metric for comparing the "visual similarity" between the synthesized crowd and exemplar crowd. We demonstrate the proposed approach on crowd videos collected under different settings.
Noisy Adaptive Group Testing: Bounds and Algorithms
Venue: arXiv cs.IT 2018
Year: 2018

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of possibly-noisy tests, and is relevant in applications such as medical testing, communication protocols, pattern matching, and many more. One of the defining features of the group testing problem is the distinction between the non-adaptive and adaptive settings: In the non-adaptive case, all tests must be designed in advance, whereas in the adaptive case, each test can be designed based on the previous outcomes. While tight information-theoretic limits and near-optimal practical algorithms are known for the adaptive setting in the absence of noise, surprisingly little is known in the noisy adaptive setting. In this paper, we address this gap by providing information-theoretic achievability and converse bounds under various noise models, as well as a slightly weaker achievability bound for a computationally efficient variant. These bounds are shown to be tight or near-tight in a broad range of scaling regimes, particularly at low noise levels. The algorithms used for the achievability results have the notable feature of only using two or three stages of adaptivity.
Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks
Venue: arXiv cs.CV 2018
Year: 2018

Optical coherence tomography (OCT) is a noninvasive imaging modality which can be used to obtain depth images of the retina. The changing layer thicknesses can thus be quantified by analyzing these OCT images, moreover these changes have been shown to correlate with disease progression in multiple sclerosis. Recent automated retinal layer segmentation tools use machine learning methods to perform pixel-wise labeling and graph methods to guarantee the layer hierarchy or topology. However, graph parameters like distance and smoothness constraints must be experimentally assigned by retinal region and pathology, thus degrading the flexibility and time efficiency of the whole framework. In this paper, we develop cascaded deep networks to provide a topologically correct segmentation of the retinal layers in a single feed forward propagation. The first network (S-Net) performs pixel-wise labeling and the second regression network (R-Net) takes the topologically unconstrained S-Net results and outputs layer thicknesses for each layer and each position. Relu activation is used as the final operation of the R-Net which guarantees non-negativity of the output layer thickness. Since the segmentation boundary position is acquired by summing up the corresponding non-negative layer thicknesses, the layer ordering (i.e., topology) of the reconstructed boundaries is guaranteed even at the fovea where the distances between boundaries can be zero. The R-Net is trained using simulated masks and thus can be generalized to provide topology guaranteed segmentation for other layered structures. This deep network has achieved comparable mean absolute boundary error (2.82 μm) to state-of-the-art graph methods (2.83 μm).
Feature extraction without learning in an analog Spatial Pooler memristive-CMOS circuit design of Hierarchical Temporal Memory
Venue: arXiv cs.ET 2018
Year: 2018

Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates sparsity, hierarchy and modularity resembling the working principles of neocortex. Feature encoding is an important step to create sparse binary patterns. This sparsity is introduced by the binary weights and random weight assignment in the initialization stage of the HTM. We propose the alternative deterministic method for the HTM initialization stage, which connects the HTM weights to the input data and preserves natural sparsity of the input information. Further, we introduce the hardware implementation of the deterministic approach and compare it to the traditional HTM and existing hardware implementation. We test the proposed approach on the face recognition problem and show that it outperforms the conventional HTM approach.
Neuron inspired data encoding memristive multi-level memory cell
Venue: arXiv cs.ET 2018
Year: 2018

Mapping neuro-inspired algorithms to sensor backplanes of on-chip hardware require shifting the signal processing from digital to the analog domain, demanding memory technologies beyond conventional CMOS binary storage units. Using memristors for building analog data storage is one of the promising approaches amongst emerging non-volatile memory technologies. Recently, a memristive multi-level memory (MLM) cell for storing discrete analog values has been developed in which memory system is implemented combining memristors in voltage divider configuration. In given example, the memory cell of 3 sub-cells with a memristor in each was programmed to store ternary bits which overall achieved 10 and 27 discrete voltage levels. However, for further use of proposed memory cell in analog signal processing circuits data encoder is required to generate control voltages for programming memristors to store discrete analog values. In this paper, we present the design and performance analysis of data encoder that generates write pattern signals for 10 level memristive memory.
Multiplicative Updates for Convolutional NMF Under $β$-Divergence
Venue: arXiv cs.LG 2018
Year: 2018

In this letter, we generalize the convolutional NMF by taking the $β$-divergence as the contrast function and present the correct multiplicative updates for its factors in closed form. The new updates unify the $β$-NMF and the convolutional NMF. We state why almost all of the existing updates are inexact and approximative w.r.t. the convolutional data model. We show that our updates are stable and that their convergence performance is consistent across the most common values of $β$.
Enhancing Favorable Propagation in Cell-Free Massive MIMO Through Spatial User Grouping
Venue: arXiv cs.IT 2018
Year: 2018

Cell-Free (CF) Massive multiple-input multiple-output(MIMO) is a distributed antenna system, wherein a large number of back-haul linked access points randomly distributed over a coverage area serve simultaneously a smaller number of users. CF Massive MIMO inherits favorable propagation of Massive MIMO systems. However, the level of favorable propagation which highly depends on the network topology and environment may be hindered by user' spatial correlation. In this paper, we investigate the impact of the network configuration on the level of favorable propagation for a CF Massive MIMO network. We formulate a user grouping and scheduling optimization problem that leverages users' spatial diversity. The formulated design optimization problem is proved to be NP-hard in general. To circumvent the prohibitively high computational cost, we adopt the semidefinite relaxation method to find a sub-optimal solution. The effectiveness of the proposed strategies is then verified through numerical results which demonstrate a non-negligible improvement in the performance of the studied scenario.