OpenRaiser/Intern-Atlas huggingface dataset:
A Survey on Spark Ecosystem for Big Data Processing
Venue: arXiv cs.DC 2018
Year: 2018

With the explosive increase of big data in industry and academic fields, it is necessary to apply large-scale data processing systems to analysis Big Data. Arguably, Spark is state of the art in large-scale data computing systems nowadays, due to its good properties including generality, fault tolerance, high performance of in-memory data processing, and scalability. Spark adopts a flexible Resident Distributed Dataset (RDD) programming model with a set of provided transformation and action operators whose operating functions can be customized by users according to their applications. It is originally positioned as a fast and general data processing system. A large body of research efforts have been made to make it more efficient (faster) and general by considering various circumstances since its introduction. In this survey, we aim to have a thorough review of various kinds of optimization techniques on the generality and performance improvement of Spark. We introduce Spark programming model and computing system, discuss the pros and cons of Spark, and have an investigation and classification of various solving techniques in the literature. Moreover, we also introduce various data management and processing systems, machine learning algorithms and applications supported by Spark. Finally, we make a discussion on the open issues and challenges for large-scale in-memory data processing with Spark.
CapsAcc: An Efficient Hardware Accelerator for CapsuleNets with Data Reuse
Venue: arXiv cs.DC 2018
Year: 2018

Deep Neural Networks (DNNs) have been widely deployed for many Machine Learning applications. Recently, CapsuleNets have overtaken traditional DNNs, because of their improved generalization ability due to the multi-dimensional capsules, in contrast to the single-dimensional neurons. Consequently, CapsuleNets also require extremely intense matrix computations, making it a gigantic challenge to achieve high performance. In this paper, we propose CapsAcc, the first specialized CMOS-based hardware architecture to perform CapsuleNets inference with high performance and energy efficiency. State-of-the-art convolutional DNN accelerators would not work efficiently for CapsuleNets, as their designs do not account for key operations involved in CapsuleNets, like squashing and dynamic routing, as well as multi-dimensional matrix processing. Our CapsAcc architecture targets this problem and achieves significant improvements, when compared to an optimized GPU implementation. Our architecture exploits the massive parallelism by flexibly feeding the data to a specialized systolic array according to the operations required in different layers. It also avoids extensive load and store operations on the on-chip memory, by reusing the data when possible. We further optimize the routing algorithm to reduce the computations needed at this stage. We synthesized the complete CapsAcc architecture in a 32nm CMOS technology using Synopsys design tools, and evaluated it for the MNIST benchmark (as also done by the original CapsuleNet paper) to ensure consistent and fair comparisons. This work enables highly-efficient CapsuleNets inference on embedded platforms.
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Venue: arXiv cs.LG 2018
Year: 2018

Estimating the individual treatment effect (ITE) from observational data is essential in medicine. A central challenge in estimating the ITE is handling confounders, which are factors that affect both an intervention and its outcome. Most previous work relies on the unconfoundedness assumption, which posits that all the confounders are measured in the observational data. However, if there are unmeasurable (latent) confounders, then confounding bias is introduced. Fortunately, noisy proxies for the latent confounders are often available and can be used to make an unbiased estimate of the ITE. In this paper, we develop a novel adversarial learning framework to make unbiased estimates of the ITE using noisy proxies.
Validating the Contextual Information of Outdoor Images for Photo Misuse Detection
Venue: arXiv cs.CR 2018
Year: 2018

The contextual information (i.e., the time and location) in which a photo is taken can be easily tampered with or falsely claimed by forgers to achieve malicious purposes, e.g., creating fear among the general public. A rich body of work has focused on detecting photo tampering and manipulation by verifying the integrity of image content. Instead, we aim to detect photo misuse by verifying the capture time and location of photos. This paper is motivated by the law of nature that sun position varies with the time and location, which can be used to determine whether the claimed contextual information corresponds with the sun position that the image content actually indicates. Prior approaches to inferring sun position from images mainly rely on vanishing points associated with at least two shadows, while we propose novel algorithms which utilize only one shadow in the image to infer the sun position. Meanwhile, we compute the sun position by applying astronomical algorithms which take as input the claimed capture time and location. Only when the two estimated sun positions are consistent can the claimed contextual information be genuine. We have developed a prototype called IMAGEGUARD. The experimental results show that our method can successfully estimate sun position and detect the time-location inconsistency with high accuracy. By setting the thresholds to be 9.4 degrees and 5 degrees for the sun position distance and the altitude angle distance, respectively, our system can correctly identify 91.5% of falsified photos with fake contextual information.
Fuzzy Rule Interpolation and SNMP-MIB for Emerging Network Abnormality
Venue: arXiv cs.NI 2018
Year: 2018

It is difficult to implement an efficient detection approach for Intrusion Detection Systems (IDS) and many factors contribute to this challenge. One such challenge concerns establishing adequate boundaries and finding a proper data source. Typical IDS detection approaches deal with raw traffics. These traffics need to be studied in depth and thoroughly investigated in order to extract the required knowledge base. Another challenge involves implementing the binary decision. This is because there are no reasonable limits between normal and attack traffics patterns. In this paper, we introduce a novel idea capable of supporting the proper data source while avoiding the issues associated with the binary decision. This paper aims to introduce a detection approach for defining abnormality by using the Fuzzy Rule Interpolation (FRI) with Simple Network Management Protocol (SNMP) Management Information Base (MIB) parameters. The strength of the proposed detection approach is based on adapting the SNMP-MIB parameters with the FRI. This proposed method eliminates the raw traffic processing component which is time consuming and requires extensive computational measures. It also eliminates the need for a complete fuzzy rule based intrusion definition. The proposed approach was tested and evaluated using an open source SNMP-MIB dataset and obtained a 93% detection rate. Additionally, when compared to other literature in which the same test-bed environment was employed along with the same number of parameters, the proposed detection approach outperformed the support vector machine and neural network. Therefore, combining the SNMP-MIB parameters with the FRI based reasoning could be beneficial for detecting intrusions, even in the case if the fuzzy rule based intrusion definition is incomplete (not fully defined).
Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Crude oil is a major component in most advanced economies of the world. Accurately predicting and understanding the behavior of crude oil prices is important for economists, analysts, forecasters, and traders, to name a few. The price of crude oil has declined in the past decade and is seeing a phase of stability; but will this stability last? This work is an empirical study on how multivariate analysis may be employed to predict crude oil spot prices using neural networks. The concept of using neural networks showed promising potential. A very simple neural network model was able to perform on par with ARIMA models - the state-of-the-art model in time-series forecasting. Advanced neural network models using larger datasets may be used in the future to extend this proof-of-concept to a full scale framework.
Multi-Block Grid deformation Method in 3D
Venue: arXiv cs.CG 2018
Year: 2018

Multi-block grids provide the computational efficiency of structured grids and the flexibility for complex geometry. Thus, Multi-block structured grids are widely used for field simulation on complex domains. In this paper we propose a method which adapts multi-block grids according to a monitor function, which specifies cell volume distribution. The method is an extension of the deformation method on a single block to multi-blocks. Key words: multi-block grids, deformation method, adaptive grids
Mask R-CNN with Pyramid Attention Network for Scene Text Detection
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present a new Mask R-CNN based text detection approach which can robustly detect multi-oriented and curved text from natural scene images in a unified manner. To enhance the feature representation ability of Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention Network (PAN) as a new backbone network of Mask R-CNN. Experiments demonstrate that PAN can suppress false alarms caused by text-like backgrounds more effectively. Our proposed approach has achieved superior performance on both multi-oriented (ICDAR-2015, ICDAR-2017 MLT) and curved (SCUT-CTW1500) text detection benchmark tasks by only using single-scale and single-model testing.
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing
Venue: arXiv cs.CV 2018
Year: 2018

Response of breast cancer to neoadjuvant chemotherapy (NAC) can be monitored using the change in visible tumor on magnetic resonance imaging (MRI). In our current workflow, seed points are manually placed in areas of enhancement likely to contain cancer. A constrained volume growing method uses these manually placed seed points as input and generates a tumor segmentation. This method is rigorously validated using complete pathological embedding. In this study, we propose to exploit deep learning for fast and automatic seed point detection, replacing manual seed point placement in our existing and well-validated workflow. The seed point generator was developed in early breast cancer patients with pathology-proven segmentations (N=100), operated shortly after MRI. It consisted of an ensemble of three independently trained fully convolutional dilated neural networks that classified breast voxels as tumor or non-tumor. Subsequently, local maxima were used as seed points for volume growing in patients receiving NAC (N=10). The percentage of tumor volume change was evaluated against semi-automatic segmentations. The primary cancer was localized in 95% of the tumors at the cost of 0.9 false positive per patient. False positives included focally enhancing regions of unknown origin and parts of the intramammary blood vessels. Volume growing from the seed points showed a median tumor volume decrease of 70% (interquartile range: 50%-77%), comparable to the semi-automatic segmentations (median: 70%, interquartile range 23%-76%). To conclude, a fast and automatic seed point generator was developed, fully automating a well-validated semi-automatic workflow for response monitoring of breast cancer to neoadjuvant chemotherapy.
Online Collective Animal Movement Activity Recognition
Venue: arXiv cs.LG 2018
Year: 2018

Learning the activities of animals is important for the purpose of monitoring their welfare vis a vis their behaviour with respect to their environment and conspecifics. While previous works have largely focused on activity recognition in a single animal, little or no work has been done in learning the collective behaviour of animals. In this work, we address the problem of recognising the collective movement activities of a group of sheep in a flock. We present a discriminative framework that learns to track the positions and velocities of all the animals in the flock in an online manner whilst estimating their collective activity. We investigate the performance of two simple deep network architectures and show that we can learn the collective activities with good accuracy even when the distribution of the activities is skewed.
Verifying C11 Programs Operationally
Venue: arXiv cs.PL 2018
Year: 2018

This paper develops an operational semantics for a release-acquire fragment of the C11 memory model with relaxed accesses. We show that the semantics is both sound and complete with respect to the axiomatic model. The semantics relies on a per-thread notion of observability, which allows one to reason about a weak memory C11 program in program order. On top of this, we develop a proof calculus for invariant-based reasoning, which we use to verify the release-acquire version of Peterson's mutual exclusion algorithm.
Data Context Informed Data Wrangling
Venue: arXiv cs.DB 2018
Year: 2018

The process of preparing potentially large and complex data sets for further analysis or manual examination is often called data wrangling. In classical warehousing environments, the steps in such a process have been carried out using Extract-Transform-Load platforms, with significant manual involvement in specifying, configuring or tuning many of them. Cost-effective data wrangling processes need to ensure that data wrangling steps benefit from automation wherever possible. In this paper, we define a methodology to fully automate an end-to-end data wrangling process incorporating data context, which associates portions of a target schema with potentially spurious extensional data of types that are commonly available. Instance-based evidence together with data profiling paves the way to inform automation in several steps within the wrangling process, specifically, matching, mapping validation, value format transformation, and data repair. The approach is evaluated with real estate data showing substantial improvements in the results of automated wrangling.
Distributed Gradient Descent with Coded Partial Gradient Computations
Venue: arXiv cs.LG 2018
Year: 2018

Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity. Second, they ignore computations carried out by straggling servers; and they are typically designed to recover the full gradient, and thus, cannot provide a balance between the accuracy of the gradient and per-iteration completion time. Here we introduce a hybrid approach, called coded partial gradient computation (CPGC), that benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and decoding complexity.
Fine Grained Classification of Personal Data Entities
Venue: arXiv cs.CL 2018
Year: 2018

Entity Type Classification can be defined as the task of assigning category labels to entity mentions in documents. While neural networks have recently improved the classification of general entity mentions, pattern matching and other systems continue to be used for classifying personal data entities (e.g. classifying an organization as a media company or a government institution for GDPR, and HIPAA compliance). We propose a neural model to expand the class of personal data entities that can be classified at a fine grained level, using the output of existing pattern matching systems as additional contextual features. We introduce new resources, a personal data entities hierarchy with 134 types, and two datasets from the Wikipedia pages of elected representatives and Enron emails. We hope these resource will aid research in the area of personal data discovery, and to that effect, we provide baseline results on these datasets, and compare our method with state of the art models on OntoNotes dataset.
Joint Neural Architecture Search and Quantization
Venue: arXiv cs.CV 2018
Year: 2018

Designing neural architectures is a fundamental step in deep learning applications. As a partner technique, model compression on neural networks has been widely investigated to gear the needs that the deep learning algorithms could be run with the limited computation resources on mobile devices. Currently, both the tasks of architecture design and model compression require expertise tricks and tedious trials. In this paper, we integrate these two tasks into one unified framework, which enables the joint architecture search with quantization (compression) policies for neural networks. This method is named JASQ. Here our goal is to automatically find a compact neural network model with high performance that is suitable for mobile devices. Technically, a multi-objective evolutionary search algorithm is introduced to search the models under the balance between model size and performance accuracy. In experiments, we find that our approach outperforms the methods that search only for architectures or only for quantization policies. 1) Specifically, given existing networks, our approach can provide them with learning-based quantization policies, and outperforms their 2 bits, 4 bits, 8 bits, and 16 bits counterparts. It can yield higher accuracies than the float models, for example, over 1.02% higher accuracy on MobileNet-v1. 2) What is more, under the balance between model size and performance accuracy, two models are obtained with joint search of architectures and quantization policies: a high-accuracy model and a small model, JASQNet and JASQNet-Small that achieves 2.97% error rate with 0.9 MB on CIFAR-10.
Automated Verification of Stand-alone Solar Photovoltaic Systems
Venue: arXiv cs.LO 2018
Year: 2018

With declining costs and increasing performance, the deployment of renewable energy systems is growing faster. Particular attention is given to stand-alone solar photovoltaic systems in rural areas or where grid extension is unfeasible. Tools to evaluate electrification projects are available, but they are based on simulations that do not cover all aspects of the design space. Automated verification using model checking has proven to be an effective technique to program verification. This paper marks the first application of software model checking to formally verify the design of a stand-alone solar photovoltaic system including solar panel, charge controller, battery, inverter, and electric load. Case studies, from real photovoltaic systems deployed in five different sites, ranging from 700W to 1,200W, were used to evaluate this proposed approach and to compare that with specialized simulation tools. Data from practical applications show the effectiveness of our approach, where specific conditions that lead to failures in a photovoltaic solar system are only detected by our automated verification method.
Smart Greybox Fuzzing
Venue: arXiv cs.CR 2018
Year: 2018

Coverage-based greybox fuzzing (CGF) is one of the most successful methods for automated vulnerability detection. Given a seed file (as a sequence of bits), CGF randomly flips, deletes or bits to generate new files. CGF iteratively constructs (and fuzzes) a seed corpus by retaining those generated files which enhance coverage. However, random bitflips are unlikely to produce valid files (or valid chunks in files), for applications processing complex file formats. In this work, we introduce smart greybox fuzzing (SGF) which leverages a high-level structural representation of the seed file to generate new files. We define innovative mutation operators that work on the virtual file structure rather than on the bit level which allows SGF to explore completely new input domains while maintaining file validity. We introduce a novel validity-based power schedule that enables SGF to spend more time generating files that are more likely to pass the parsing stage of the program, which can expose vulnerabilities much deeper in the processing logic. Our evaluation demonstrates the effectiveness of SGF. On several libraries that parse structurally complex files, our tool AFLSmart explores substantially more paths (up to 200%) and exposes more vulnerabilities than baseline AFL. Our tool AFLSmart has discovered 42 zero-day vulnerabilities in widely-used, well-tested tools and libraries; so far 17 CVEs were assigned.
An Adaptive Approach for Automated Grapevine Phenotyping using VGG-based Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

In (grapevine) breeding programs and research, periodic phenotyping and multi-year monitoring of different grapevine traits, like growth or yield, is needed especially in the field. This demand imply objective, precise and automated methods using sensors and adaptive software. This work presents a proof-of-concept analyzing RGB images of different growth stages of grapevines with the aim to detect and quantify promising plant organs which are related to yield. The input images are segmented by a Fully Convolutional Neural Network (FCN) into object and background pixels. The objects are plant organs like young shoots, pedicels, flower buds or grapes, which are principally suitable for yield estimation. In the ground truth of the training images, each object is separately annotated as a connected segment of object pixels, which enables end-to-end learning of the object features. Based on the CNN-based segmentation, the number of objects is determined by detecting and counting connected components of object pixels using region labeling. In an evaluation on six different data sets, the system achieves an IoU of up to 87.3% for the segmentation and an F1 score of up to 88.6% for the object detection.
A Voice Controlled E-Commerce Web Application
Venue: arXiv cs.CY 2018
Year: 2018

Automatic voice-controlled systems have changed the way humans interact with a computer. Voice or speech recognition systems allow a user to make a hands-free request to the computer, which in turn processes the request and serves the user with appropriate responses. After years of research and developments in machine learning and artificial intelligence, today voice-controlled technologies have become more efficient and are widely applied in many domains to enable and improve human-to-human and human-to-computer interactions. The state-of-the-art e-commerce applications with the help of web technologies offer interactive and user-friendly interfaces. However, there are some instances where people, especially with visual disabilities, are not able to fully experience the serviceability of such applications. A voice-controlled system embedded in a web application can enhance user experience and can provide voice as a means to control the functionality of e-commerce websites. In this paper, we propose a taxonomy of speech recognition systems (SRS) and present a voice-controlled commodity purchase e-commerce application using IBM Watson speech-to-text to demonstrate its usability. The prototype can be extended to other application scenarios such as government service kiosks and enable analytics of the converted text data for scenarios such as medical diagnosis at the clinics.