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Combating Bufferbloat in Multi-Bottleneck networks: Equilibrium, Stability, and Algorithms

Combating Bufferbloat in Multi-Bottleneck Networks: Equilibr...

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2018 ieee conference on computer Communications, INFOCOM 2018

作者： Ye, Jiancheng Leung, Ka-Cheong Li, Victor O.K. Low, Steven H. Department of EEE the University of Hong Kong Hong Kong Departments of CMS EE California Institute of Technology PasadenaCA91125 United States

ISBN: (纸本)9781538641286

Bufferbloat is a phenomenon where router buffers are constantly being filled, resulting in high queueing delay and delay variation. Larger buffer size and more delay-sensitive applications on the Internet have made this phenomenon a pressing issue. Active queue management (AQM) algorithms, which play an important role in combating bufferbloat, have not been widely deployed due to complicated manual parameter tuning. Moreover, AQM algorithms are often designed and analyzed based on models with a single bottleneck link, rendering their performance and stability unclear in multi-bottleneck networks. In this paper, we propose a general framework to combat bufferbloat in multi-bottleneck networks. We first conduct an equilibrium analysis for a general multi-bottleneck TCP/ AQM system and develop an algorithm to compute the equilibrium point. We then decompose the system into single-bottleneck subsystems and derive sufficient conditions for the local asymptotic stability of the subsystems. Using the proposed framework, we present a case study to analyze the stability of the recently proposed Controlled Delay (CoDel) in multi-bottleneck networks and devise Self-tuning CoDel to improve the system stability and performance. Extensive simulation results show that Self-tuning CoDel effectively stabilizes queueing delay in multi-bottleneck scenarios, and thus contributes to combating bufferbloat. © 2018 ieee.

关键词： System stability

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Stealthy Malware Detection using RNN-Based Automated localized Feature Extraction and Classifier

Stealthy Malware Detection using RNN-Based Automated Localiz...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Sanket Shukla Gaurav Kolhe Sai Manoj P D Setareh Rafatirad Department of Information Sciences and Technology George Mason University Fairfax VA USA Department of Electrical and Computer Engineering George Mason University Fairfax VA USA George Mason University

Malware analysis, detection and classification has allured a lot of researchers in the past few years. Numerous methods based on machine learning (ML), computer vision and deep learning have been applied to this task and have accomplished some pragmatic results. One of the basic assumption of these works is that malware is spawned as a separate thread and the distinguishing features can be extracted in a "clean" manner irrespective of the malware obfuscation deployed. However, this assumption does not hold true for the advanced malware obfuscation techniques such as code relocation, mutation and polymorphism. Stealthy malware is a malware created by embedding the malware in a benign application through advanced obfuscation strategies to thwart the detection. To perform efficient malware detection for traditional and stealthy malware alike, we propose a two-pronged approach. Firstly, we extract the microarchitectural traces obtained while executing the application, which are fed to the traditional ML classifiers to detect malware spawned as separate thread. In parallel, for an efficient stealthy malware detection, we introduce an automated localized feature extraction technique that will be further processed using the recurrent neural networks (RNNs) for classification. To perform this, we translate the application binaries into images and further convert it into sequences and extract local features for stealthy malware detection. With the proposed two-pronged approach, an accuracy of 94% and nearly 90% is achieved in detecting normal and stealthy malware created through code relocation obfuscation technique. Furthermore, the proposed approach achieves up to 11% higher detection accuracy compared to the CNN-based sequence classification and hidden Markov model (HMM) based approaches in detecting stealthy malware.

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Depth Human Action Recognition Based on Convolution Neural networks and Principal Component Analysis 25

Depth Human Action Recognition Based on Convolution Neural N...

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25th ieee International conference on Image Processing, ICIP 2018

作者： Bui, Manh-Quan Duong, Viet-Hang Tai, Tzu-Chiang Wang, Jia-Ching Dept. of Computer Science and Information Engineering National Central University Jhongli Taiwan Dept. of Computer Science and Information Engineering Providence University Taichung Taiwan

ISBN: (纸本)9781479970612

In this work, we address human action recognition problem under viewpoint variation. The proposed model is formulated by wisely combining convolution neural network (CNN) model with principle component analysis (PCA). In this context, we pass real depth videos through a CNN model in a frame-wise manner. The view invariant features are extracted by employing convolution layers as mid-outputs and considered as 3D nonnegative tensors. The PCA algorithm is separately imposed on view-invariant high-level space of image and video groups to seek both local and holistic hidden dynamics information. To deal with noisy data and temporal misalignment, we utilize the Fourier temporal pyramid to encode temporal and obtain the final descriptors. Our proposed framework supplies a robust discriminative representation with low dimension and computational requirements. We evaluate the proposed method on two standard multiview depth video datasets. The experimental results show that our method has superior performance compared to other approaches. © 2018 ieee.

关键词： Neural network models

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A Deep Reinforcement Learning Method for Self-driving 1

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14th International conference on Intelligent Computing (ICIC)

作者： Fang, Yong Gu, Jianfeng Shanghai Univ Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China

ISBN: (数字)9783319959337

ISBN: (纸本)9783319959320;9783319959337

Self-driving technology is an important issue of artificial intelligence. Basing on the end-to-end architecture, deep reinforcement learning has been applied to research for self-driving. However, self-driving environment yields sparse rewards when using deep reinforcement learning, resulting in local optimum to network training. As a result, the self-driving vehicle does not obtain correct actions from outputs of neural network. This paper proposes a deep reinforcement learning method for self-driving. According to the classification threshold value that is dynamically adjusted by reward distributions, the sparse rewards is divided into three groups. The experience information for different rewards is fully utilized and the local optimum problem in the network training process is avoided. By comparing with the traditional method, simulation results show that the proposed method significantly reduces the training time of network.

关键词： Self-driving Deep reinforcement learning Sparse rewards Reward classification

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Efficient loss-aware uplink scheduling

Efficient loss-aware uplink scheduling

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2018 ieee Wireless Communications and Networking conference, WCNC 2018

作者： Özcan, Yiǧit Rosenberg, Catherine Dept. of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (纸本)9781538617342

Uplink scheduling in cellular networks is challenging due to power and interference management. Typically, each cell performs local scheduling, which requires estimation of inter-cell interference (ICI) to compute the appropriate modulation and coding scheme (MCS) based on the Signal-to-Interference-plus-Noise-Ratio (SINR) for each allocated resource block. Since schedules of neighboring cells are unknown to schedulers, the SINR can be badly estimated, which causes resource losses or under-utilization. The benchmark uplink scheduler we study in this paper produces a high goodput at the cost of significant resource losses, because it does not take the possibility of losses into account. Resource losses imply retransmissions, hence, high variability in delay. Therefore, a scheduler should be evaluated in terms of its goodput/loss trade-off. We propose a novel uplink scheduler that is inspired by Soft Frequency Reuse (SFR) and uses an MCS selection that takes the probability of losses into account, i.e., it selects an MCS that maximizes the effective rates seen by users, while keeping the loss probability below a threshold e. We show that the proposed scheduler yields significantly better goodput/loss trade-off than the benchmark scheduler. © 2018 ieee.

关键词： Signal to noise ratio

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A Fully Wireless Implantable Multi-Channel Muscle Stimulator with Closed-Loop Feedback Control

A Fully Wireless Implantable Multi-Channel Muscle Stimulator...

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2018 ieee Biomedical Circuits and Systems conference, BioCAS 2018

作者： Ong, Li Jing Liu, Shih-Chiang Sheng Wong, Marshal Dian Sibindi, Tafadzwa Lasam Gammad, Gil Gerald Tsai, Chne-Wuen Rusly, Astrid Ng, Kian Ann Libedinsky, Camilo Nag, Sudip Yen, Shih-Cheng Singapore Institute for Neurotechnology National University of Singapore Singapore Department of Psychology National University of Singapore Singapore India Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9781538636039

We have developed an implantable four-channel high-current biphasic stimulator device for controlling the muscles of the hand, and successfully tested it in a non-human primate (NHP). The charge-balanced stimulator features an external unit which connects to a personal computer via WiFi, and provides wireless power and data control commands to the implant across biological tissue. The stimulator obtained realtime grip force from the hand using a force sensor to perform automated closed-loop control of the stimulation amplitude to ensure that we were able to produce sustained gripping force in the hand of the animal in the event of muscle fatigue. The device was encapsulated using a FDA-compliant biocompatible polymer for reliable long-term study. In-vivo experiments performed after the stimulator (with electrodes embedded in different muscles) had been implanted for one month demonstrated that the system was able to evoke, and automatically maintain, a targeted range of gripping force in the animal's hand. Our results demonstrate the utility of our closed-loop muscle stimulator as a neuroprosthesis for restoring functional hand movements in patients with upper-limb peripheral nerve injuries. © 2018 ieee.

关键词： Wireless local area networks (WLAN)

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Performance Optimization of Federated Learning over Wireless networks

Performance Optimization of Federated Learning over Wireless...

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2019 ieee Global Communications conference (GLOBECOM)

作者： Mingzhe Chen Zhaohui Yang Walid Saad Changchuan Yin H. Vincent Poor Shuguang Cui The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Department of Electrical Engineering Princeton University Princeton NJ USA Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Informatics Centre for Telecommunications Research Kings College London UK Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Blacksburg VA USA Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users perform an FL algorithm that trains their local FL models using their own data and send the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL learning algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to 1) an optimal user selection algorithm with random resource allocation and 2) a random user selection and resource allocation algorithm.

关键词： Data models Resource management Wireless networks Training Uplink Prediction algorithms

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Convolutional Neural Network for ASR 2

Convolutional Neural Network for ASR

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2nd International conference on Electronics, Communication and Aerospace Technology, ICECA 2018

作者： Newatia, Sourav Aggarwal, R.K. Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India

ISBN: (纸本)9781538609651

In Automatic speech recognition paradigm has been shifted from statistical model (GMM-HMM) to deep neural network. Among various types of Deep neural networks architecture, CNNs have been most broadly used and considered. There are many advanced features in CNNs like weight sharing, local filters and pooling etc. An ideal pooling method is to be expected to extract only useful information and discards irrelevant details. Pooling is an important component of CNN to be highlighted. Hence, to understand and select the best pooling technique, this paper aims to provide a broad survey on different types of pooling techniques applied in CNNs architecture. This paper will also summarize the improvements of CNNs, including key properties of CNNs, architecture of CNNs. © 2018 ieee.

关键词： Hidden Markov models

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SoftTriple Loss: Deep Metric Learning Without Triplet Sampling

SoftTriple Loss: Deep Metric Learning Without Triplet Sampli...

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International conference on computer Vision (ICCV)

作者： Qi Qian Lei Shang Baigui Sun Juhua Hu Tacoma Tacoma Hao Li Rong Jin Alibaba Group Bellevue WA USA Alibaba Group Hangzhou China School of Engineering and Technology University of Washington Tacoma WA USA NA

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Distance metric learning (DML) is to learn the embeddings where examples from the same class are closer than examples from different classes. It can be cast as an optimization problem with triplet constraints. Due to the vast number of triplet constraints, a sampling strategy is essential for DML. With the tremendous success of deep learning in classifications, it has been applied for DML. When learning embeddings with deep neural networks (DNNs), only a mini-batch of data is available at each iteration. The set of triplet constraints has to be sampled within the mini-batch. Since a mini-batch cannot capture the neighbors in the original set well, it makes the learned embeddings sub-optimal. On the contrary, optimizing SoftMax loss, which is a classification loss, with DNN shows a superior performance in certain DML tasks. It inspires us to investigate the formulation of SoftMax. Our analysis shows that SoftMax loss is equivalent to a smoothed triplet loss where each class has a single center. In real-world data, one class can contain several local clusters rather than a single one, e.g., birds of different poses. Therefore, we propose the SoftTriple loss to extend the SoftMax loss with multiple centers for each class. Compared with conventional deep metric learning algorithms, optimizing SoftTriple loss can learn the embeddings without the sampling phase by mildly increasing the size of the last fully connected layer. Experiments on the benchmark fine-grained data sets demonstrate the effectiveness of the proposed loss function.

关键词： Measurement Task analysis Neural networks Principal component analysis Optimization Benchmark testing Data models

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GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction

GraphCPI: Graph Neural Representation Learning for Compound-...

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ieee International conference on Bioinformatics and Biomedicine

作者： Zhe Quan Yan Guo Xuan Lin Zhi-Jie Wang Xiangxiang Zeng College of Information Science and Engineering Hunan University School of Data and Computer Science Sun Yat-Sen University

ISBN: (纸本)9781728118680

Accurately predicting compound-protein interactions (CPIs) is of great help to increase the efficiency and reduce costs in drug development. Most of existing machine learning models for CPI prediction often represent compounds and proteins in one-dimensional strings, or use the descriptor-based methods. These models might ignore the fact that molecules are essentially structured by the chemical bond of atoms. However, in real-world scenarios, the topological structure information usually provides an overview of how the atoms are connected, and the local chemical context reveals the functionality of the protein sequence in CPI. These two types of information are complementary to each other and they are both important for modeling compounds and proteins. Motivated by this, this paper suggests an end-to-end deep learning framework called GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences for solving the CPI prediction task. Our framework can integrate any popular graph nerual networks for learning compounds, and it combines with a convolutional neural network for embedding sequences. We conduct extensive experiments based on two benchmark CPI datasets. The experimental results demonstrate that our proposed framework is feasible and also competitive, comparing against classic and state-of-the-art methods.

关键词： Compound-Protein Interaction Graph Neural Network Representation Learning Drug Discovery Drug Discovery Chemical process industries Consumer Price Index Amino Acid Sequence information-type Protein-Protein Interaction compounds chemical bond Frameworks Line graph Atoms drug development Proteins Learning

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