OpenRaiser/Intern-Atlas huggingface dataset:
Performance Dynamics and Termination Errors in Reinforcement Learning: A Unifying Perspective
Venue: arXiv cs.LG 2019
Year: 2019

In reinforcement learning, a decision needs to be made at some point as to whether it is worthwhile to carry on with the learning process or to terminate it. In many such situations, stochastic elements are often present which govern the occurrence of rewards, with the sequential occurrences of positive rewards randomly interleaved with negative rewards. For most practical learners, the learning is considered useful if the number of positive rewards always exceeds the negative ones. A situation that often calls for learning termination is when the number of negative rewards exceeds the number of positive rewards. However, while this seems reasonable, the error of premature termination, whereby termination is enacted along with the conclusion of learning failure despite the positive rewards eventually far outnumber the negative ones, can be significant. In this paper, using combinatorial analysis we study the error probability in wrongly terminating a reinforcement learning activity which undermines the effectiveness of an optimal policy, and we show that the resultant error can be quite high. Whilst we demonstrate mathematically that such errors can never be eliminated, we propose some practical mechanisms that can effectively reduce such errors. Simulation experiments have been carried out, the results of which are in close agreement with our theoretical findings.
De-identification without losing faces
Venue: arXiv cs.CV 2019
Year: 2019

Training of deep learning models for computer vision requires large image or video datasets from real world. Often, in collecting such datasets, we need to protect the privacy of the people captured in the images or videos, while still preserve the useful attributes such as facial expressions. In this work, we describe a new face de-identification method that can preserve essential facial attributes in the faces while concealing the identities. Our method takes advantage of the recent advances in face attribute transfer models, while maintaining a high visual quality. Instead of changing factors of the original faces or synthesizing faces completely, our method use a trained facial attribute transfer model to map non-identity related facial attributes to the face of donors, who are a small number (usually 2 to 3) of consented subjects. Using the donors' faces ensures that the natural appearance of the synthesized faces, while ensuring the identity of the synthesized faces are changed. On the other hand, the FATM blends the donors' facial attributes to those of the original faces to diversify the appearance of the synthesized faces. Experimental results on several sets of images and videos demonstrate the effectiveness of our face de-ID algorithm.
Improving learnability of neural networks: adding supplementary axes to disentangle data representation
Venue: arXiv cs.LG 2019
Year: 2019

Over-parameterized deep neural networks have proven to be able to learn an arbitrary dataset with 100$\%$ training accuracy. Because of a risk of overfitting and computational cost issues, we cannot afford to increase the number of network nodes if we want achieve better training results for medical images. Previous deep learning research shows that the training ability of a neural network improves dramatically (for the same epoch of training) when a few nodes with supplementary information are added to the network. These few informative nodes allow the network to learn features that are otherwise difficult to learn by generating a disentangled data representation. This paper analyzes how concatenation of additional information as supplementary axes affects the training of the neural networks. This analysis was conducted for a simple multilayer perceptron (MLP) classification model with a rectified linear unit (ReLU) on two-dimensional training data. We compared the networks with and without concatenation of supplementary information to support our analysis. The model with concatenation showed more robust and accurate training results compared to the model without concatenation. We also confirmed that our findings are valid for deeper convolutional neural networks (CNN) using ultrasound images and for a conditional generative adversarial network (cGAN) using the MNIST data.
Effective Network Compression Using Simulation-Guided Iterative Pruning
Venue: arXiv cs.LG 2019
Year: 2019

Existing high-performance deep learning models require very intensive computing. For this reason, it is difficult to embed a deep learning model into a system with limited resources. In this paper, we propose the novel idea of the network compression as a method to solve this limitation. The principle of this idea is to make iterative pruning more effective and sophisticated by simulating the reduced network. A simple experiment was conducted to evaluate the method; the results showed that the proposed method achieved higher performance than existing methods at the same pruning level.
Enhancement Mask for Hippocampus Detection and Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Detection and segmentation of the hippocampal structures in volumetric brain images is a challenging problem in the area of medical imaging. In this paper, we propose a two-stage 3D fully convolutional neural network that efficiently detects and segments the hippocampal structures. In particular, our approach first localizes the hippocampus from the whole volumetric image while obtaining a proposal for a rough segmentation. After localization, we apply the proposal as an enhancement mask to extract the fine structure of the hippocampus. The proposed method has been evaluated on a public dataset and compares with state-of-the-art approaches. Results indicate the effectiveness of the proposed method, which yields mean Dice Similarity Coefficients (i.e. DSC) of $0.897$ and $0.900$ for the left and right hippocampus, respectively. Furthermore, extensive experiments manifest that the proposed enhancement mask layer has remarkable benefits for accelerating training process and obtaining more accurate segmentation results.
PAC-Bayes Analysis of Sentence Representation
Venue: arXiv cs.LG 2019
Year: 2019

Learning sentence vectors from an unlabeled corpus has attracted attention because such vectors can represent sentences in a lower dimensional and continuous space. Simple heuristics using pre-trained word vectors are widely applied to machine learning tasks. However, they are not well understood from a theoretical perspective. We analyze learning sentence vectors from a transfer learning perspective by using a PAC-Bayes bound that enables us to understand existing heuristics. We show that simple heuristics such as averaging and inverse document frequency weighted averaging are derived by our formulation. Moreover, we propose novel sentence vector learning algorithms on the basis of our PAC-Bayes analysis.
A system for generating complex physically accurate sensor images for automotive applications
Venue: arXiv cs.CV 2019
Year: 2019

We describe an open-source simulator that creates sensor irradiance and sensor images of typical automotive scenes in urban settings. The purpose of the system is to support camera design and testing for automotive applications. The user can specify scene parameters (e.g., scene type, road type, traffic density, time of day) to assemble a large number of random scenes from graphics assets stored in a database. The sensor irradiance is generated using quantitative computer graphics methods, and the sensor images are created using image systems sensor simulation. The synthetic sensor images have pixel level annotations; hence, they can be used to train and evaluate neural networks for imaging tasks, such as object detection and classification. The end-to-end simulation system supports quantitative assessment, from scene to camera to network accuracy, for automotive applications.
COP2: Continuously Observing Protocol Performance
Venue: arXiv cs.NI 2019
Year: 2019

As enterprises move to a cloud-first approach, their network becomes crucial to their daily operations and has to be continuously monitored. Although passive monitoring can be convenient from a deployment viewpoint, inferring the state of each connection can cause them to miss important information (e.g., starvation). Furthermore, the increasing usage of fully encrypted protocols (e.g., QUIC encrypts headers), possibly over multiple paths (e.g., MPTCP), keeps diminishing the applicability of such techniques to future networks. We propose a new monitoring framework, Flowcorder, which leverages information already maintained by the end-hosts and records Key Performance Indicators (KPIs) from their transport protocols. More specifically, we present a generic approach which inserts lightweight eBPF probes at runtime in the protocol implementations. These probes extract KPIs from the per-connection states, and eventually export them over IPFIX for analysis. We present an application of this technique to the Linux kernel TCP stack and demonstrate its generality by extending it to support MPTCP. Our performance evaluation confirms that its overhead is negligible. Finally, we present live measurements collected with Flowcorder in a campus network, highlighting some insights provided by our framework.
An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications
Venue: arXiv cs.LG 2019
Year: 2019

In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while the user is driving. A Deep Neural Network (DNN) is trained to extract acoustic features and make a speech classification. Based on the posterior probabilities obtained from DNN, the vehicle information including the speed and direction of vehicle is applied to choose the suitable parameter from a pair of sensitivity values for the KWS system. The experimental results show that the KWS system with the proposed multi-source fusion scheme can achieve better performances in term of precision rate, recall rate, and mean square error compared to the system without it.
Achieving Trust-Based and Privacy-Preserving Customer Selection in Ubiquitous Computing
Venue: arXiv cs.CR 2019
Year: 2019

The recent proliferation of smart devices has given rise to ubiquitous computing, an emerging computing paradigm which allows anytime & anywhere computing possible. In such a ubiquitous computing environment, customers release different computing or sensing tasks, and people, also known as data processors, participate in these tasks and get paid for providing their idle computing and communication resources. Thus, how to select an appropriate and reliable customer while not disclosing processors' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving customer selection scheme in ubiquitous computing, called TPCS, to enable potential processors select the customers with good reputation. The basic concept of TPCS is that each data processor holds a trust value, and the reputation score of the customer is calculated based on processors' trust values and feedbacks via a truth discovery process. To preserve processors' privacy, pseudonyms and Paillier cryptosystem are applied to conceal each processor's real identity. In addition, three authentication protocols are designed to ensure that only the valid data processors (i.e., the processors registering in the system, holding the truthful trust values, and joining the computing tasks) can pass the authentication. A comprehensive security analysis is conducted to prove that our proposed TPCS scheme is secure and can defend against several sophisticated attacks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme.
Guiding Neuroevolution with Structural Objectives
Venue: arXiv cs.NE 2019
Year: 2019

The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be explored. Guiding such neuroevolution with additional objectives related to network structure has been shown to improve performance in some cases, especially when modular neural networks are beneficial. However, apart from objectives aiming to make networks more modular, such structural objectives have not been widely explored. We propose two new structural objectives and test their ability to guide evolving neural networks on two problems which can benefit from decomposition into subtasks. The first structural objective guides evolution to align neural networks with a user-recommended decomposition pattern. Intuitively, this should be a powerful guiding target for problems where human users can easily identify a structure. The second structural objective guides evolution towards a population with a high diversity in decomposition patterns. This results in exploration of many different ways to decompose a problem, allowing evolution to find good decompositions faster. Tests on our target problems reveal that both methods perform well on a problem with a very clear and decomposable structure. However, on a problem where the optimal decomposition is less obvious, the structural diversity objective is found to outcompete other structural objectives -- and this technique can even increase performance on problems without any decomposable structure at all.
Asymptotic Performance Analysis of Blockchain Protocols
Venue: arXiv cs.CR 2019
Year: 2019

In the light of the recent fame of Blockchain technologies, numerous proposals and projects aiming at better practical viability have emerged. However, formally assessing their particularities and benefits has proven to be a difficult task. The aim of this work is to compare the fundamental differences of such protocols to understand how they lead to different practical performances. To reach this goal, we undertake a complexity analysis of a wide range of prominent distributed algorithms proposed for blockchain systems, under the lens of Total Order Broadcast protocols. We sampled protocols designed for very different settings and that use a broad range of techniques, thus giving a good overview of the achievements of state-of-the-art techniques. By analyzing latency and network usage, we are able to discuss each protocol's characteristics and properties in a consistent manner. One corollary result to our work is a more robust criteria to classify protocols as permissioned or permissionless.
An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Today, many public or private institutions provide professional food service for personnels working in their own organizations. Regarding the planning of the said service, there are some obstacles due to the fact that the number of the personnel working in the institutions is generally high and the personnel are out of the institution due to personal or institutional reasons. Because of this, it is difficult to determine the daily food demand, and this causes cost, time and labor loss for the institutions. Statistical or heuristic methods are used to remove or at least minimize these losses. In this study, an artificial intelligence model was proposed, which estimates the daily food demand quantity using artificial neural networks for businesses. The data are obtained from a refectory database of a private institution with a capacity of 110 people serving daily meals and serving at different levels, covering the last two years (2016-2018). The model was created using the MATLAB package program. The performance of the model was determinde by the Regression values, the Mean Absolute Percentage Error (MAPE) and the Mean Squared Error (MSE). In the training of the ANN model, feed forward back propagation network architecture is used. The best model obtained as a result of the experiments is a multi-layer (8-10-10-1) structure with a training R ratio of 0,9948, a testing R ratio of 0,9830 and an error rate of 0,003783, respectively. Experimental results demonstrated that the model has low error rate, high performance and positive effect of using artificial neural networks for demand estimating.
On Conflict Free DNA Codes
Venue: arXiv cs.IT 2019
Year: 2019

DNA storage has emerged as an important area of research. The reliability of DNA storage system depends on designing the DNA strings (called DNA codes) that are sufficiently dissimilar. In this work, we introduce DNA codes that satisfy a special constraint. Each codeword of the DNA code has a specific property that any two consecutive sub-strings of the DNA codeword will not be the same (a generalization of homo-polymers constraint). This is in addition to the usual constraints such as Hamming, reverse, reverse-complement and $GC$-content. We believe that the new constraint will help further in reducing the errors during reading and writing data into the synthetic DNA strings. We also present a construction (based on a variant of stochastic local search algorithm) to calculate the size of the DNA codes with all the above constraints, which improves the lower bounds from the existing literature, for some specific cases. Moreover, a recursive isometric map between binary vectors and DNA strings is proposed. Using the map and the well known binary codes we obtain few classes of DNA codes with all the constraints including the property that the constructed DNA codewords are free from the hairpin-like secondary structures.
QoS-Aware Buffer-Aided Relaying Implant WBAN for Healthcare IoT: Opportunities and Challenges
Venue: arXiv cs.NI 2019
Year: 2019

Internet of Things (IoT) have motivated a paradigm shift in the development of various applications such as mobile health. Wireless body area network (WBAN) comprises many low-power devices in, on, or around the human body, which offers a desirable solution to monitor physiological signals for mobile-health applications. In the implant WBAN, an implant medical device transmits its measured biological parameters to a target hub with the help of at least one on-body device(s) to satisfy its strict requirements on size, quality of service (QoS, e.g., reliability), and power consumption. In this article, we first review the recent advances of conventional cooperative WBAN. Afterwards, to address the drawbacks of the conventional cooperative WBAN, a QoS-aware buffer-aided relaying framework is proposed for the implant WBAN. In the proposed framework, hierarchical modulations are considered to fulfill the different QoS requirements of different sensor data from an implant medical device. We further conceive some new transmission strategies for the buffer-aided signal-relay and multi-relay implant WBANs. Simulation results show that the proposed cooperative WBAN provides better performance than the conventional cooperative counterparts. Finally, some open research challenges regarding the buffer-aided multi-relay implant WBAN are pointed out to inspire more research activities.
Solving All-Pairs Shortest-Paths Problem in Large Graphs Using Apache Spark
Venue: arXiv cs.DC 2019
Year: 2019

Algorithms for computing All-Pairs Shortest-Paths (APSP) are critical building blocks underlying many practical applications. The standard sequential algorithms, such as Floyd-Warshall and Johnson, quickly become infeasible for large input graphs, necessitating parallel approaches. In this work, we provide detailed analysis of parallel APSP performance on distributed memory clusters with Apache Spark. The Spark model allows for a portable and easy to deploy distributed implementation, and hence is attractive from the end-user point of view. We propose four different APSP implementations for large undirected weighted graphs, which differ in complexity and degree of reliance on techniques outside of pure Spark API. We demonstrate that Spark is able to handle APSP problems with over 200,000 vertices on a 1024-core cluster, and can compete with a naive MPI-based solution. However, our best performing solver requires auxiliary shared persistent storage, and is over two times slower than optimized MPI-based solver.
Atomic Cross-chain Swaps: Development, Trajectory and Potential of Non-monetary Digital Token Swap Facilities
Venue: arXiv cs.CR 2019
Year: 2019

Since the introduction of Bitcoin in 2008, many other cryptocurrencies have been introduced and gained popularity. Lack of interoperability and scalability amongst these cryptocurrencies was and still is, acting as a significant impediment to the general adoption of cryptocurrencies and coloured tokens. Atomic Swaps, a smart exchange protocol for cryptocurrencies, is designed to facilitate a wallet-to-wallet transfer enabling direct trades amongst different cryptocurrencies. Since swaps between cryptocurrencies are still relatively unknown, this article will investigate the operation and market development thus far and query the advantages they offer and the future challenges they face. The paper contains detailed literature and technology reviews, followed by the main analysis and findings.
MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

We propose a new approach for 3D instance segmentation based on sparse convolution and point affinity prediction, which indicates the likelihood of two points belonging to the same instance. The proposed network, built upon submanifold sparse convolution [3], processes a voxelized point cloud and predicts semantic scores for each occupied voxel as well as the affinity between neighboring voxels at different scales. A simple yet effective clustering algorithm segments points into instances based on the predicted affinity and the mesh topology. The semantic for each instance is determined by the semantic prediction. Experiments show that our method outperforms the state-of-the-art instance segmentation methods by a large margin on the widely used ScanNet benchmark [2]. We share our code publicly at https://github.com/art-programmer/MASC.
SDN Controllers: Benchmarking & Performance Evaluation
Venue: arXiv cs.NI 2019
Year: 2019

Software Defined Networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The intelligent control plane is responsible for providing flow paths to switches and optimizes network performance. The controller in the control plane is the fundamental element used for all operations of data plane management. Hence, the performance and capabilities of the controller itself are extremely important. Furthermore, the tools used to benchmark their performance must be accurate and effective in measuring different evaluation parameters. There are dozens of controller proposals available in existing literature. However, there is no quantitative comparative analysis for them. In this article, we present a comprehensive qualitative comparison of different SDN controllers, along with a quantitative analysis of their performance in different network scenarios. More specifically, we categorize and classify 34 controllers based on their capabilities, and present a qualitative comparison of their properties. We also discuss in-depth capabilities of benchmarking tools used for SDN controllers, along with best practices for quantitative controller evaluation. This work uses three benchmarking tools to compare nine controllers against multiple criteria. Finally, we discuss detailed research findings on the performance, benchmarking criteria, and evaluation testbeds for SDN controllers.