OpenRaiser/Intern-Atlas huggingface dataset:
eBPF-based Content and Computation-aware Communication for Real-time Edge Computing
Venue: arXiv cs.NI 2018
Year: 2018

By placing computation resources within a one-hop wireless topology, the recent edge computing paradigm is a key enabler of real-time Internet of Things (IoT) applications. In the context of IoT scenarios where the same information from a sensor is used by multiple applications at different locations, the data stream needs to be replicated. However, the transportation of parallel streams might not be feasible due to limitations in the capacity of the network transporting the data. To address this issue, a content and computation-aware communication control framework is proposed based on the Software Defined Network (SDN) paradigm. The framework supports multi-streaming using the extended Berkeley Packet Filter (eBPF), where the traffic flow and packet replication for each specific computation process is controlled by a program running inside an in-kernel Virtual Ma- chine (VM). The proposed framework is instantiated to address a case-study scenario where video streams from multiple cameras are transmitted to the edge processor for real-time analysis. Numerical results demonstrate the advantage of the proposed framework in terms of programmability, network bandwidth and system resource savings.
N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients
Venue: arXiv cs.CV 2018
Year: 2018

Foot is a vital part of human, and lots of valuable information is embedded. Plantar pressure is one of which contains this information and it describes human walking features. It is proved that once one has trouble with lower limb, the distribution of plantar pressure will change to some degree. Plantar pressure can be converted into images according to some simple standards. In this paper, we take full advantage of these plantar pressure images for medical usage. We present N2RPP, a generative adversarial network (GAN) based method to rebuild plantar pressure images of anterior cruciate ligament deficiency (ACLD) patients from low dimension features, which are extracted from an autoencoder. Through the result of experiments, the extracted features are a useful representation to describe and rebuild plantar pressure images. According to N2RPP's results, we find out that there are several noteworthy differences between normal people and patients. This can provide doctors a rough direction of adjusting plantar pressure to a better distribution to reduce patients' sore and pain during the rehabilitation treatment for ACLD.
Visual Attribute-augmented Three-dimensional Convolutional Neural Network for Enhanced Human Action Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Visual attributes in individual video frames, such as the presence of characteristic objects and scenes, offer substantial information for action recognition in videos. With individual 2D video frame as input, visual attributes extraction could be achieved effectively and efficiently with more sophisticated convolutional neural network than current 3D CNNs with spatio-temporal filters, thanks to fewer parameters in 2D CNNs. In this paper, the integration of visual attributes (including detection, encoding and classification) into multi-stream 3D CNN is proposed for action recognition in trimmed videos, with the proposed visual Attribute-augmented 3D CNN (A3D) framework. The visual attribute pipeline includes an object detection network, an attributes encoding network and a classification network. Our proposed A3D framework achieves state-of-the-art performance on both the HMDB51 and the UCF101 datasets.
Synthesizing Efficient Solutions for Patrolling Problems in the Internet Environment
Venue: arXiv cs.AI 2018
Year: 2018

We propose an algorithm for constructing efficient patrolling strategies in the Internet environment, where the protected targets are nodes connected to the network and the patrollers are software agents capable of detecting/preventing undesirable activities on the nodes. The algorithm is based on a novel compositional principle designed for a special class of strategies, and it can quickly construct (sub)optimal solutions even if the number of targets reaches hundreds of millions.
Low-Latency Human Action Recognition with Weighted Multi-Region Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Spatio-temporal contexts are crucial in understanding human actions in videos. Recent state-of-the-art Convolutional Neural Network (ConvNet) based action recognition systems frequently involve 3D spatio-temporal ConvNet filters, chunking videos into fixed length clips and Long Short Term Memory (LSTM) networks. Such architectures are designed to take advantage of both short term and long term temporal contexts, but also requires the accumulation of a predefined number of video frames (e.g., to construct video clips for 3D ConvNet filters, to generate enough inputs for LSTMs). For applications that require low-latency online predictions of fast-changing action scenes, a new action recognition system is proposed in this paper. Termed "Weighted Multi-Region Convolutional Neural Network" (WMR ConvNet), the proposed system is LSTM-free, and is based on 2D ConvNet that does not require the accumulation of video frames for 3D ConvNet filtering. Unlike early 2D ConvNets that are based purely on RGB frames and optical flow frames, the WMR ConvNet is designed to simultaneously capture multiple spatial and short term temporal cues (e.g., human poses, occurrences of objects in the background) with both the primary region (foreground) and secondary regions (mostly background). On both the UCF101 and HMDB51 datasets, the proposed WMR ConvNet achieves the state-of-the-art performance among competing low-latency algorithms. Furthermore, WMR ConvNet even outperforms the 3D ConvNet based C3D algorithm that requires video frame accumulation. In an ablation study with the optical flow ConvNet stream removed, the ablated WMR ConvNet nevertheless outperforms competing algorithms.
Driving maneuvers prediction based on cognition-driven and data-driven method
Venue: arXiv cs.AI 2018
Year: 2018

Advanced Driver Assistance Systems (ADAS) improve driving safety significantly. They alert drivers from unsafe traffic conditions when a dangerous maneuver appears. Traditional methods to predict driving maneuvers are mostly based on data-driven models alone. However, existing methods to understand the driver's intention remain an ongoing challenge due to a lack of intersection of human cognition and data analysis. To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN) which fuses the data inside the vehicle and the data outside the vehicle in a cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM) branches regulated by human reaction time. Experiments on the Brain4Cars benchmark dataset demonstrate that the proposed method outperforms previous methods and achieves state-of-the-art performance.
Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring
Venue: arXiv cs.AI 2018
Year: 2018

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g. shifting resources from one case onto another to ensure this latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 16 real-life datasets originating from different industry domains.
Learning Short-Cut Connections for Object Counting
Venue: arXiv cs.CV 2018
Year: 2018

Object counting is an important task in computer vision due to its growing demand in applications such as traffic monitoring or surveillance. In this paper, we consider object counting as a learning problem of a joint feature extraction and pixel-wise object density estimation with Convolutional-Deconvolutional networks. We introduce a novel counting model, named Gated U-Net (GU-Net). Specifically, we propose to enrich the U-Net architecture with the concept of learnable short-cut connections. Standard short-cut connections are connections between layers in deep neural networks which skip at least one intermediate layer. Instead of simply setting short-cut connections, we propose to learn these connections from data. Therefore, our short-cuts can work as gating units, which optimize the flow of information between convolutional and deconvolutional layers in the U-Net architecture. We evaluate the introduced GU-Net architecture on three commonly used benchmark data sets for object counting. GU-Nets consistently outperform the base U-Net architecture, and achieve state-of-the-art performance.
Comparing phonemes and visemes with DNN-based lipreading
Venue: arXiv cs.CV 2018
Year: 2018

There is debate if phoneme or viseme units are the most effective for a lipreading system. Some studies use phoneme units even though phonemes describe unique short sounds; other studies tried to improve lipreading accuracy by focusing on visemes with varying results. We compare the performance of a lipreading system by modeling visual speech using either 13 viseme or 38 phoneme units. We report the accuracy of our system at both word and unit levels. The evaluation task is large vocabulary continuous speech using the TCD-TIMIT corpus. We complete our visual speech modeling via hybrid DNN-HMMs and our visual speech decoder is a Weighted Finite-State Transducer (WFST). We use DCT and Eigenlips as a representation of mouth ROI image. The phoneme lipreading system word accuracy outperforms the viseme based system word accuracy. However, the phoneme system achieved lower accuracy at the unit level which shows the importance of the dictionary for decoding classification outputs into words.
Conditional Value-at-Risk for Reachability and Mean Payoff in Markov Decision Processes
Venue: arXiv cs.LO 2018
Year: 2018

We present the conditional value-at-risk (CVaR) in the context of Markov chains and Markov decision processes with reachability and mean-payoff objectives. CVaR quantifies risk by means of the expectation of the worst p-quantile. As such it can be used to design risk-averse systems. We consider not only CVaR constraints, but also introduce their conjunction with expectation constraints and quantile constraints (value-at-risk, VaR). We derive lower and upper bounds on the computational complexity of the respective decision problems and characterize the structure of the strategies in terms of memory and randomization.
Machine Learning Guidance and Proof Certification for Connection Tableaux
Venue: arXiv cs.LO 2018
Year: 2018

Connection calculi allow for very compact implementations of goal-directed proof search. We give an overview of our work related to connection tableaux calculi: First, we show optimised functional implementations of clausal and nonclausal proof search, including a consistent Skolemisation procedure for machine learning. Then, we show two guidance methods based on machine learning, namely reordering of proof steps with Naive Bayesian probablities, and expansion of a proof search tree with Monte Carlo Tree Search. Finally, we give a translation of connection proofs to LK, enabling proof certification and automatic proof search in interactive theorem provers.
A Language Hierarchy of Binary Relations
Venue: arXiv cs.FL 2018
Year: 2018

Motivated by the study of word problems of monoids, we explore two ways of viewing binary relations on $A^*$ as languages. We exhibit a hierarchy of classes of binary relations on $A^*$, according to the class of languages the relation belongs to and the chosen viewpoint. We give examples of word problems of monoids distinguishing the various classes.
Image Retrieval with Mixed Initiative and Multimodal Feedback
Venue: arXiv cs.CV 2018
Year: 2018

How would you search for a unique, fashionable shoe that a friend wore and you want to buy, but you didn't take a picture? Existing approaches propose interactive image search as a promising venue. However, they either entrust the user with taking the initiative to provide informative feedback, or give all control to the system which determines informative questions to ask. Instead, we propose a mixed-initiative framework where both the user and system can be active participants, depending on whose initiative will be more beneficial for obtaining high-quality search results. We develop a reinforcement learning approach which dynamically decides which of three interaction opportunities to give to the user: drawing a sketch, providing free-form attribute feedback, or answering attribute-based questions. By allowing these three options, our system optimizes both the informativeness and exploration capabilities allowing faster image retrieval. We outperform three baselines on three datasets and extensive experimental settings.
A review of neuro-fuzzy systems based on intelligent control
Venue: arXiv cs.AI 2018
Year: 2018

The system's ability to adapt and self-organize are two key factors when it comes to how well the system can survive the changes to the environment and the plant they work within. Intelligent control improves these two factors in controllers. Considering the increasing complexity of dynamic systems along with their need for feedback controls, using more complicated controls has become necessary and intelligent control can be a suitable response to this necessity. This paper briefly describes the structure of intelligent control and provides a review on fuzzy logic and neural networks which are some of the base methods for intelligent control. The different aspects of these two methods are then compared together and an example of a combined method is presented.
Parallel Computation of PDFs on Big Spatial Data Using Spark
Venue: arXiv cs.DC 2018
Year: 2018

We consider big spatial data, which is typically produced in scientific areas such as geological or seismic interpretation. The spatial data can be produced by observation (e.g. using sensors or soil instrument) or numerical simulation programs and correspond to points that represent a 3D soil cube area. However, errors in signal processing and modeling create some uncertainty, and thus a lack of accuracy in identifying geological or seismic phenomenons. Such uncertainty must be carefully analyzed. To analyze uncertainty, the main solution is to compute a Probability Density Function (PDF) of each point in the spatial cube area. However, computing PDFs on big spatial data can be very time consuming (from several hours to even months on a parallel computer). In this paper, we propose a new solution to efficiently compute such PDFs in parallel using Spark, with three methods: data grouping, machine learning prediction and sampling. We evaluate our solution by extensive experiments on different computer clusters using big data ranging from hundreds of GB to several TB. The experimental results show that our solution scales up very well and can reduce the execution time by a factor of 33 (in the order of seconds or minutes) compared with a baseline method.
PAD-Net: A Perception-Aided Single Image Dehazing Network
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we investigate the possibility of replacing the $\ell_2$ loss with perceptually derived loss functions (SSIM, MS-SSIM, etc.) in training an end-to-end dehazing neural network. Objective experimental results suggest that by merely changing the loss function we can obtain significantly higher PSNR and SSIM scores on the SOTS set in the RESIDE dataset, compared with a state-of-the-art end-to-end dehazing neural network (AOD-Net) that uses the $\ell_2$ loss. The best PSNR we obtained was 23.50 (4.2% relative improvement), and the best SSIM we obtained was 0.8747 (2.3% relative improvement.)
A Weakness Measure for GR(1) Formulae
Venue: arXiv cs.LO 2018
Year: 2018

In spite of the theoretical and algorithmic developments for system synthesis in recent years, little effort has been dedicated to quantifying the quality of the specifications used for synthesis. When dealing with unrealizable specifications, finding the weakest environment assumptions that would ensure realizability is typically a desirable property; in such context the weakness of the assumptions is a major quality parameter. The question of whether one assumption is weaker than another is commonly interpreted using implication or, equivalently, language inclusion. However, this interpretation does not provide any further insight into the weakness of assumptions when implication does not hold. To our knowledge, the only measure that is capable of comparing two formulae in this case is entropy, but even it fails to provide a sufficiently refined notion of weakness in case of GR(1) formulae, a subset of linear temporal logic formulae which is of particular interest in controller synthesis. In this paper we propose a more refined measure of weakness based on the Hausdorff dimension, a concept that captures the notion of size of the omega-language satisfying a linear temporal logic formula. We identify the conditions under which this measure is guaranteed to distinguish between weaker and stronger GR(1) formulae. We evaluate our proposed weakness measure in the context of computing GR(1) assumptions refinements.
Superresolution method for data deconvolution by superposition of point sources
Venue: arXiv cs.CV 2018
Year: 2018

In this work we present a new algorithm for data deconvolution that allows the retrieval of the target function with super-resolution with a simple approach that after a precis e measurement of the instrument response function (IRF), the measured data are fit by a superposition of point sources (SUPPOSe) of equal intensity. In this manner only the positions of the sources need to be determined by an algorithm that minimizes the norm of the difference between the measured data and the convolution of the superposed point sources with the IRF. An upper bound for the uncertainty in the position of the sources was derived and two very different experimental situations were used for the test (an optical spectrum and fluorescent microscopy images) showing excellent reconstructions and agreement with the predicted uncertainties, achieving λ/10 resolution for the microscope and a fivefold improvement in the spectral resolution for the spectrometer. The method also provides a way to determine the optimum number of sources to be used for the fit.
Voltron: Understanding and Exploiting the Voltage-Latency-Reliability Trade-Offs in Modern DRAM Chips to Improve Energy Efficiency
Venue: arXiv cs.AR 2018
Year: 2018

This paper summarizes our work on experimental characterization and analysis of reduced-voltage operation in modern DRAM chips, which was published in SIGMETRICS 2017, and examines the work's significance and future potential. We take a comprehensive approach to understanding and exploiting the latency and reliability characteristics of modern DRAM when the DRAM supply voltage is lowered below the nominal voltage level specified by DRAM standards. We perform an experimental study of 124 real DDR3L (low-voltage) DRAM chips manufactured recently by three major DRAM vendors. We find that reducing the supply voltage below a certain point introduces bit errors in the data, and we comprehensively characterize the behavior of these errors. We discover that these errors can be avoided by increasing the latency of three major DRAM operations (activation, restoration, and precharge). We perform detailed DRAM circuit simulations to validate and explain our experimental findings. We also characterize the various relationships between reduced supply voltage and error locations, stored data patterns, DRAM temperature, and data retention. Based on our observations, we propose a new DRAM energy reduction mechanism, called Voltron. The key idea of Voltron is to use a performance model to determine by how much we can reduce the supply voltage without introducing errors and without exceeding a user-specified threshold for performance loss. Our evaluations show that Voltron reduces the average DRAM and system energy consumption by 10.5% and 7.3%, respectively, while limiting the average system performance loss to only 1.8%, for a variety of memory-intensive quad-core workloads. We also show that Voltron significantly outperforms prior dynamic voltage and frequency scaling mechanisms for DRAM.
Geometric Rounding and Feature Separation in Meshes
Venue: arXiv cs.CG 2018
Year: 2018

Geometric rounding of a mesh is the task of approximating its vertex coordinates by floating point numbers while preserving mesh structure. Geometric rounding allows algorithms of computational geometry to interface with numerical algorithms. We present a practical geometric rounding algorithm for 3D triangle meshes that preserves the topology of the mesh. The basis of the algorithm is a novel strategy: 1) modify the mesh to achieve a feature separation that prevents topology changes when the coordinates change by the rounding unit; and 2) round each vertex coordinate to the closest floating point number. Feature separation is also useful on its own, for example for satisfying minimum separation rules in CAD models. We demonstrate a robust, accurate implementation.
Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
Venue: arXiv cs.CV 2018
Year: 2018

The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amounts of data and the corresponding study of disease characteristics. The presence of hyperreflective foci (HRF) is related to disease progression in various retinal diseases. Manual identification of HRF in spectral-domain optical coherence tomography (SD-OCT) scans is error-prone and tedious. We present a fully automated machine learning approach for segmenting HRF in SD-OCT scans. Evaluation on annotated OCT images of the retina demonstrates that a residual U-Net allows to segment HRF with high accuracy. As our dataset comprised data from different retinal diseases including age-related macular degeneration, diabetic macular edema and retinal vein occlusion, the algorithm can safely be applied in all of them though different pathophysiological origins are known.
On the Optimal Achievable Rates for Linear Computation With Random Homologous Codes
Venue: arXiv cs.IT 2018
Year: 2018

The problem of computing a linear combination of sources over a multiple access channel is studied. Inner and outer bounds on the optimal tradeoff between the communication rates are established when encoding is restricted to random ensembles of homologous codes, namely, structured nested coset codes from the same generator matrix and individual shaping functions, but when decoding is optimized with respect to the realization of the encoders. For the special case in which the desired linear combination is "matched" to the structure of the multiple access channel in a natural sense, these inner and outer bounds coincide. This result indicates that most, if not all, coding schemes for computation in the literature that rely on random construction of nested coset codes cannot be improved by using more powerful decoders, such as the maximum likelihood decoder. The proof techniques are adapted to characterize the rate region for broadcast channels achieved by Marton's (random) coding scheme under maximum likelihood decoding.