OpenRaiser/Intern-Atlas huggingface dataset:
Kubernetes as an Availability Manager for Microservice Applications
Venue: arXiv cs.SE 2019
Year: 2019

The move towards the microservice based architecture is well underway. In this architectural style, small and loosely coupled modules are developed, deployed, and scaled independently to compose cloud-native applications. However, for carrier-grade service providers to migrate to the microservices architectural style, availability remains a concern. Kubernetes is an open source platform that defines a set of building blocks which collectively provide mechanisms for deploying, maintaining, scaling, and healing containerized microservices. Thus, Kubernetes hides the complexity of microservice orchestration while managing their availability. In a preliminary work we evaluated Kubernetes, using its default configuration, from the availability perspective in a private cloud settings. In this paper, we investigate more architectures and conduct more experiments to evaluate the availability that Kubernetes delivers for its managed microservices. We present different architectures for public and private clouds. We evaluate the availability achievable through the healing capability of Kubernetes. We investigate the impact of adding redundancy on the availability of microservice based applications. We conduct experiments under the default configuration of Kubernetes as well as under its most responsive one. We also perform a comparative evaluation with the Availability Management Framework (AMF), which is a proven solution as a middleware service for managing high-availability. The results of our investigations show that in certain cases, the service outage for applications managed with Kubernetes is significantly high.
Automatic Surface Area and Volume Prediction on Ellipsoidal Ham using Deep Learning
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents novel methods to predict the surface and volume of the ham through a camera. This implies that the conventional weight measurement to obtain in the object's volume can be neglected and hence it is economically effective. Both of the measurements are obtained in the following two ways: manually and automatically. The former is assume as the true or exact measurement and the latter is through a computer vision technique with some geometrical analysis that includes mathematical derived functions. For the automatic implementation, most of the existing approaches extract the features of the food material based on handcrafted features and to the best of our knowledge this is the first attempt to estimate the surface area and volume on ham with deep learning features. We address the estimation task with a Mask Region-based CNN (Mask R-CNN) approach, which well performs the ham detection and semantic segmentation from a video. The experimental results demonstrate that the algorithm proposed is robust as promising surface area and volume estimation are obtained for two angles of the ellipsoidal ham (i.e., horizontal and vertical positions). Specifically, in the vertical ham point of view, it achieves an overall accuracy up to 95% whereas the horizontal ham reaches 80% of accuracy.
Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

The Encoder-Decoder architecture is a main stream deep learning model for biomedical image segmentation. The encoder fully compresses the input and generates encoded features, and the decoder then produces dense predictions using encoded features. However, decoders are still under-explored in such architectures. In this paper, we comprehensively study the state-of-the-art Encoder-Decoder architectures, and propose a new universal decoder, called cascade decoder, to improve semantic segmentation accuracy. Our cascade decoder can be embedded into existing networks and trained altogether in an end-to-end fashion. The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders. We replace the decoders of state-of-the-art models with our cascade decoder for several challenging biomedical image segmentation tasks, and the considerable improvements achieved demonstrate the efficacy of our new decoding method.
Identifying and Correcting Label Bias in Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained on such datasets can inherit these biases. In this paper, we provide a mathematical formulation of how this bias can arise. We do so by assuming the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who intends to provide accurate labels but may have biases against certain groups. Despite the fact that we only observe the biased labels, we are able to show that the bias may nevertheless be corrected by re-weighting the data points without changing the labels. We show, with theoretical guarantees, that training on the re-weighted dataset corresponds to training on the unobserved but unbiased labels, thus leading to an unbiased machine learning classifier. Our procedure is fast and robust and can be used with virtually any learning algorithm. We evaluate on a number of standard machine learning fairness datasets and a variety of fairness notions, finding that our method outperforms standard approaches in achieving fair classification.
NNStreamer: Stream Processing Paradigm for Neural Networks, Toward Efficient Development and Execution of On-Device AI Applications
Venue: arXiv cs.DC 2019
Year: 2019

We propose nnstreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to neural network applications. A new trend with the wide-spread of deep neural network applications is on-device AI; i.e., processing neural networks directly on mobile devices or edge/IoT devices instead of cloud servers. Emerging privacy issues, data transmission costs, and operational costs signifies the need for on-device AI especially when a huge number of devices with real-time data processing are deployed. Nnstreamer efficiently handles neural networks with complex data stream pipelines on devices, improving the overall performance significantly with minimal efforts. Besides, nnstreamer simplifies the neural network pipeline implementations and allows reusing off-shelf multimedia stream filters directly; thus it reduces the developmental costs significantly. Nnstreamer is already being deployed with a product releasing soon and is open source software applicable to a wide range of hardware architectures and software platforms.
Tango: A Deep Neural Network Benchmark Suite for Various Accelerators
Venue: arXiv cs.DC 2019
Year: 2019

Deep neural networks (DNNs) have been proving the effectiveness in various computing fields. To provide more efficient computing platforms for DNN applications, it is essential to have evaluation environments that include assorted benchmark workloads. Though a few DNN benchmark suites have been recently released, most of them require to install proprietary DNN libraries or resource-intensive DNN frameworks, which are hard to run on resource-limited mobile platforms or architecture simulators. To provide a more scalable evaluation environment, we propose a new DNN benchmark suite that can run on any platform that supports CUDA and OpenCL. The proposed benchmark suite includes the most widely used five convolution neural networks and two recurrent neural networks. We provide in-depth architectural statistics of these networks while running them on an architecture simulator, a server- and a mobile-GPU, and a mobile FPGA.
Vision Zero: on a Provable Method for Eliminating Roadway Accidents without Compromising Traffic Throughput
Venue: arXiv cs.RO 2019
Year: 2019

We propose an economical, viable, approach to eliminate almost all car accidents. Our method relies on a mathematical model of safety and can be applied to all modern cars at a mild cost.
Answering Comparative Questions: Better than Ten-Blue-Links?
Venue: arXiv cs.CL 2019
Year: 2019

We present CAM (comparative argumentative machine), a novel open-domain IR system to argumentatively compare objects with respect to information extracted from the Common Crawl. In a user study, the participants obtained 15% more accurate answers using CAM compared to a "traditional" keyword-based search and were 20% faster in finding the answer to comparative questions.
Spectrogram Feature Losses for Music Source Separation
Venue: arXiv cs.SD 2019
Year: 2019

In this paper we study deep learning-based music source separation, and explore using an alternative loss to the standard spectrogram pixel-level L2 loss for model training. Our main contribution is in demonstrating that adding a high-level feature loss term, extracted from the spectrograms using a VGG net, can improve separation quality vis-a-vis a pure pixel-level loss. We show this improvement in the context of the MMDenseNet, a State-of-the-Art deep learning model for this task, for the extraction of drums and vocal sounds from songs in the musdb18 database, covering a broad range of western music genres. We believe that this finding can be generalized and applied to broader machine learning-based systems in the audio domain.
Channel Conditions for the Optimality of Interference Decoding Schemes for K-user Gaussian Interference Channels
Venue: arXiv cs.IT 2019
Year: 2019

The Han-Kobayashi (HK) scheme achieves the best known achievable rate region for the K user interference channel (IC). Simple HK schemes are HK schemes with Gaussian signaling, no time sharing, and no private-common power splitting. The class of simple HK schemes includes the treating interference as noise (TIN) scheme and schemes that involve various levels of interference decoding and cancellation at each receiver. We derive conditions under which simple HK schemes achieve sum capacity for general K user Gaussian ICs. These results generalize existing sum capacity results for the TIN scheme to the class of simple HK schemes.
Utility Regions for DF Relay in OFDMA-based Secure Communication with Untrusted Users
Venue: arXiv cs.IT 2019
Year: 2019

This paper investigates the utility of a trusted decode-and-forward relay in OFDMA-based secure communication system with untrusted users. For deciding whether to use the relay or not, we first present optimal subcarrier allocation policies for direct communication (DC) and relayed communication (RC). Next we identify exclusive RC mode, exclusive DC mode, and mixed (RDC) mode subcarriers which can support both the modes. For RDC mode we present optimal mode selection policy and a suboptimal strategy independent of power allocation which is asymptotically optimal at both low and high SNRs. Finally, via numerical results we present insights on relay utility regions.
It's Only Words And Words Are All I Have
Venue: arXiv cs.IR 2019
Year: 2019

The central idea of this paper is to demonstrate the strength of lyrics for music mining and natural language processing (NLP) tasks using the distributed representation paradigm. For music mining, we address two prediction tasks for songs: genre and popularity. Existing works for both these problems have two major bottlenecks. First, they represent lyrics using handcrafted features that require intricate knowledge of language and music. Second, they consider lyrics as a weak indicator of genre and popularity. We overcome both the bottlenecks by representing lyrics using distributed representation. In our work, genre identification is a multi-class classification task whereas popularity prediction is a binary classification task. We achieve an F1 score of around 0.6 for both the tasks using only lyrics. Distributed representation of words is now heavily used for various NLP algorithms. We show that lyrics can be used to improve the quality of this representation.
Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI
Venue: arXiv cs.IT 2019
Year: 2019

Distributed computing platforms typically assume the availability of reliable and dedicated connections among the processors. This work considers an alternative scenario, relevant for wireless data centers and federated learning, in which the distributed processors, operating on generally distinct coded data, are connected via shared wireless channels accessed via full-duplex transmission. The study accounts for both wireless and computing impairments, including interference, imperfect Channel State Information, and straggling processors, and it assumes a Map-Shuffle-Reduce coded computing paradigm. The total latency of the system, obtained as the sum of computing and communication delays, is studied for different shuffling strategies revealing the interplay between distributed computing, coding, and cooperative or coordinated transmission.
Regular Expression Search on Compressed Text
Venue: arXiv cs.FL 2019
Year: 2019

We present an algorithm for searching regular expression matches in compressed text. The algorithm reports the number of matching lines in the uncompressed text in time linear in the size of its compressed version. We define efficient data structures that yield nearly optimal complexity bounds and provide a sequential implementation --zearch-- that requires up to 25% less time than the state of the art.
MRI to CT Translation with GANs
Venue: arXiv cs.CV 2019
Year: 2019

We present a detailed description and reference implementation of preprocessing steps necessary to prepare the public Retrospective Image Registration Evaluation (RIRE) dataset for the task of magnetic resonance imaging (MRI) to X-ray computed tomography (CT) translation. Furthermore we describe and implement three state of the art convolutional neural network (CNN) and generative adversarial network (GAN) models where we report statistics and visual results of two of them.
DAFE-FD: Density Aware Feature Enrichment for Face Detection
Venue: arXiv cs.CV 2019
Year: 2019

Recent research on face detection, which is focused primarily on improving accuracy of detecting smaller faces, attempt to develop new anchor design strategies to facilitate increased overlap between anchor boxes and ground truth faces of smaller sizes. In this work, we approach the problem of small face detection with the motivation of enriching the feature maps using a density map estimation module. This module, inspired by recent crowd counting/density estimation techniques, performs the task of estimating the per pixel density of people/faces present in the image. Output of this module is employed to accentuate the feature maps from the backbone network using a feature enrichment module before being used for detecting smaller faces. The proposed approach can be used to complement recent anchor-design based novel methods to further improve their results. Experiments conducted on different datasets such as WIDER, FDDB and Pascal-Faces demonstrate the effectiveness of the proposed approach.
Deep Holes of Projective Reed-Solomon Codes
Venue: arXiv cs.IT 2019
Year: 2019

Projective Reed-Solomon (PRS) codes are Reed-Solomon codes of the maximum possible length q+1. The classification of deep holes --received words with maximum possible error distance-- for PRS codes is an important and difficult problem. In this paper, we use algebraic methods to explicitly construct three classes of deep holes for PRS codes. We show that these three classes completely classify all deep holes of PRS codes with redundancy at most four. Previously, the deep hole classification was only known for PRS codes with redundancy at most three in work arXiv:1612.05447
Deep learning-based electroencephalography analysis: a systematic review
Venue: arXiv cs.LG 2019
Year: 2019

Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches in order to inform future research and formulate recommendations. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours. As for the model, 40% of the studies used convolutional neural networks (CNNs), while 14% used recurrent neural networks (RNNs), most often with a total of 3 to 10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was 5.4% across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.
Disentangling Video with Independent Prediction
Venue: arXiv cs.LG 2019
Year: 2019

We propose an unsupervised variational model for disentangling video into independent factors, i.e. each factor's future can be predicted from its past without considering the others. We show that our approach often learns factors which are interpretable as objects in a scene.
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
Venue: arXiv cs.CV 2019
Year: 2019

Face anti-spoofing (a.k.a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems. Existing CNN-based approaches usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would drop drastically on testing spoofing faces of unseen scenes. In this paper, we try to boost the generalizability and applicability of these methods by designing a CNN model with two major novelties. First, we propose a simple yet effective Total Pairwise Confusion (TPC) loss for CNN training, which enhances the generalizability of the learned Presentation Attack (PA) representations. Secondly, we incorporate a Fast Domain Adaptation (FDA) component into the CNN model to alleviate negative effects brought by domain changes. Besides, our proposed model, which is named Generalizable Face Authentication CNN (GFA-CNN), works in a multi-task manner, performing face anti-spoofing and face recognition simultaneously. Experimental results show that GFA-CNN outperforms previous face anti-spoofing approaches and also well preserves the identity information of input face images.
Mining Treatment-Outcome Constructs from Sequential Software Engineering Data
Venue: arXiv cs.SE 2019
Year: 2019

Many investigations in empirical software engineering look at sequences of data resulting from development or management processes. In this paper, we propose an analytical approach called the Gandhi-Washington Method (GWM) to investigate the impact of recurring events in software projects. GWM takes an encoding of events and activities provided by a software analyst as input. It uses regular expressions to automatically condense and summarize information and infer treatments. Relating the treatments to the outcome through statistical tests, treatment-outcome constructs are automatically mined from the data. The output of GWM is a set of treatment-outcome constructs. Each treatment in the set of mined constructs is significantly different from the other treatments considering the impact on the outcome and/or is structurally different from other treatments considering the sequence of events. We describe GWM and classes of problems to which GWM can be applied. We demonstrate the applicability of this method for empirical studies on sequences of file editing, code ownership, and release cycle time.
Hand Sign to Bangla Speech: A Deep Learning in Vision based system for Recognizing Hand Sign Digits and Generating Bangla Speech
Venue: arXiv cs.CV 2019
Year: 2019

Recent advancements in the field of computer vision with the help of deep neural networks have led us to explore and develop many existing challenges that were once unattended due to the lack of necessary technologies. Hand Sign/Gesture Recognition is one of the significant areas where the deep neural network is making a substantial impact. In the last few years, a large number of researches has been conducted to recognize hand signs and hand gestures, which we aim to extend to our mother-tongue, Bangla (also known as Bengali). The primary goal of our work is to make an automated tool to aid the people who are unable to speak. We developed a system that automatically detects hand sign based digits and speaks out the result in Bangla language. According to the report of the World Health Organization (WHO), 15% of people in the world live with some kind of disabilities. Among them, individuals with communication impairment such as speech disabilities experience substantial barrier in social interaction. The proposed system can be invaluable to mitigate such a barrier. The core of the system is built with a deep learning model which is based on convolutional neural networks (CNN). The model classifies hand sign based digits with 92% accuracy over validation data which ensures it a highly trustworthy system. Upon classification of the digits, the resulting output is fed to the text to speech engine and the translator unit eventually which generates audio output in Bangla language. A web application to demonstrate our tool is available at http://bit.ly/signdigits2banglaspeech.
Proposition of an implementation framework enabling benchmarking of Holonic Manufacturing Systems
Venue: arXiv cs.MA 2019
Year: 2019

Performing an overview of the benchmarking initiatives oriented towards the performance evaluation of Holonic Manufacturing Systems shows that there are very few of them. However, a comparison between all the isolated emu-lation developments for benchmarking in literature was made, and showed that many common features could be extracted. Several deadlocks for a generic approach of these developments are also exhibited. A global architecture dedicated to a generic performance evaluation platform design is suggested. This architecture integrates a scenario manager, whose main specificities were detailed and justified. Those features are meant to both integrate the best practices encountered in literature and fulfil the missing aspects to respond to the problematics.
H${}^2$CM-based holonic modeling of a gas pipeline
Venue: arXiv cs.MA 2019
Year: 2019

A gas pipeline is a relatively simple physical system, but the optimality of the control is difficult to achieve. When switching from one kind of gas to another , a volume of useless mixture is generated. Therefore, the control needs to both respond to the demand and minimize the volume of lost gas. In case of stable and perfectly known demand, scheduling techniques can be used, but in other cases , calculation times are incompatible with an industrial application. This article introduces the application of H${}^2$CM (Holonic Hybrid Control Model) generic architecture on this specific case. The study case is extensively presented. Then, the defined holonic architecture (H${}^2$CM compatible) is detailed, and the role and functions of each holon are presented. Finally, a tentative general control algorithm is suggested, which gives an insight on the actual algorithms that will be developed in perspective of this work.