OpenRaiser/Intern-Atlas huggingface dataset:
LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming
Venue: arXiv cs.NI 2018
Year: 2018

Video streaming today accounts for up to 55\% of mobile traffic. In this paper, we explore streaming videos encoded using Scalable Video Coding scheme (SVC) over highly variable bandwidth conditions such as cellular networks. SVC's unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop Layered Bin Packing (LBP) Adaptation Algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed including handling bandwidth prediction errors, and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared to the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today's mobile devices.
Investigating Power Outage Effects on Reliability of Solid-State Drives
Venue: arXiv cs.OH 2018
Year: 2018

Solid-State Drives (SSDs) are recently employed in enterprise servers and high-end storage systems in order to enhance performance of storage subsystem. Although employing high speed SSDs in the storage subsystems can significantly improve system performance, it comes with significant reliability threat for write operations upon power failures. In this paper, we present a comprehensive analysis investigating the impact of workload dependent parameters on the reliability of SSDs under power failure for variety of SSDs (from top manufacturers). To this end, we first develop a platform to perform two important features required for study: a) a realistic fault injection into the SSD in the computing systems and b) data loss detection mechanism on the SSD upon power failure. In the proposed physical fault injection platform, SSDs experience a real discharge phase of Power Supply Unit (PSU) that occurs during power failure in data centers which was neglected in previous studies. The impact of workload dependent parameters such as workload Working Set Size (WSS), request size, request type, access pattern, and sequence of accesses on the failure of SSDs is carefully studied in the presence of realistic power failures. Experimental results over thousands number of fault injections show that data loss occurs even after completion of the request (up to 700ms) where the failure rate is influenced by the type, size, access pattern, and sequence of IO accesses while other parameters such as workload WSS has no impact on the failure of SSDs.
Robust Multi-Path Communications for UAVs in the Urban IoT
Venue: arXiv cs.NI 2018
Year: 2018

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of scenarios and applications. However, their deployment in urban areas poses important technical challenges. One of the most prominent concerns is the robustness of communications between the ground stations and the UAVs in a highly dynamic and crowded spectrum. Indeed, competing data streams may create local or temporary congestion impairing the ground stations to control the UAVs. The main contribution of this paper is a robust multi-path communication framework for UAV systems. The framework continuously probes the performance of multiple wireless multi-hop paths from the ground stations to each UAV, and dynamically selects the path providing the best performance to support timely control. Numerical results, based on a real-world implementation and extensive field experimentation, demonstrate the ability of the proposed framework to provide robust control against exogenous interference and network congestion.
On the Equivalence of Generative and Discriminative Formulations of the Sequential Dependence Model
Venue: arXiv cs.IR 2018
Year: 2018

The sequential dependence model (SDM) is a popular retrieval model which is based on the theory of probabilistic graphical models. While it was originally introduced by Metzler and Croft as a Markov Random Field (aka discriminative probabilistic model), in this paper we demonstrate that it is equivalent to a generative probabilistic model. To build an foundation for future retrieval models, this paper details the axiomatic underpinning of the SDM model as discriminative and generative probabilistic model. The only difference arises whether model parameters are estimated in log-space or Multinomial-space. We demonstrate that parameter-estimation with grid-tuning is negatively impacting the generative formulation, an effect that vanishes when parameters are estimated with coordinate-gradient descent. This is concerning, since empirical differences may be falsely attributed to improved models.
Generic Single Edge Fault Tolerant Exact Distance Oracle
Venue: arXiv cs.DS 2018
Year: 2018

Given an undirected unweighted graph $G$ and a source set $S$ of $|S| = σ$ sources, we want to build a data structure which can process the following query {\sc Q}$(s,t,e):$ find the shortest distance from $s$ to $t$ avoiding an edge $e$, where $s \in S$ and $t \in V$. When $σ=n$, Demetrescu, Thorup, Chowdhury and Ramachandran (SIAM Journal of Computing, 2008) designed an algorithm with $\tilde O(n^2)$ space ($\tilde O(\cdot)$ hides poly $\log n$ factor.) and $O(1)$ query time. A natural open question is to generalize this result to any number of sources. Recently, Bil{ò} et. al. (STACS 2018) designed a data-structure of size $\tilde O(σ^{1/2}n^{3/2})$ with the query time of $O(\sqrt{nσ})$ for the above problem. We improve their result by designing a data-structure of size $\tilde O(σ^{1/2} n^{3/2})$ that can answer queries in $\tilde O(1)$ time. In a related problem of finding fault tolerant subgraph, Parter and Peleg (ESA 2013) showed that if detours of the {\em replacement} paths ending at a vertex $t$ are disjoint, then the number of such paths is $O(\sqrt{nσ})$. This eventually gives a bound of $O( n \sqrt{n σ}) = O(σ^{1/2}n^{3/2})$ for their problem. {\em Disjointness of detours} is a very crucial property used in the above result. We show a similar result for a subset of replacement path which \textbf{may not} be disjoint. This result is the crux of our paper and may be of independent interest.?
Fast and Efficient Depth Map Estimation from Light Fields
Venue: arXiv cs.CV 2018
Year: 2018

The paper presents an algorithm for depth map estimation from the light field images in relatively small amount of time, using only single thread on CPU. The proposed method improves existing principle of line fitting in 4-dimensional light field space. Line fitting is based on color values comparison using kernel density estimation. Our method utilizes result of Semi-Global Matching (SGM) with Census transform-based matching cost as a border initialization for line fitting. It provides a significant reduction of computations needed to find the best depth match. With the suggested evaluation metric we show that proposed method is applicable for efficient depth map estimation while preserving low computational time compared to others.
Predicting resonant properties of plasmonic structures by deep learning
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning can be used to extract meaningful results from images. In this paper, we used convolutional neural networks combined with recurrent neural networks on images of plasmonic structures and extract absorption data form them. To provide the required data for the model we did 100,000 simulations with similar setups and random structures. By designing a deep network we could find a model that could predict the absorption of any structure with similar setup. We used convolutional neural networks to get the spatial information from the images and we used recurrent neural networks to help the model find the relationship between the spatial information obtained from convolutional neural network model. With this design we could reach a very low loss in predicting the absorption compared to the results obtained from numerical simulation in a very short time.
Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN
Venue: arXiv cs.CV 2018
Year: 2018

Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. Today many delay sensitive, mission-critical applications can leverage these edge devices to reduce the time delay or even to enable real time, online decision making thanks to their onsite presence. Human objects detection, behavior recognition and prediction in smart surveillance fall into that category, where a transition of a huge volume of video streaming data can take valuable time and place heavy pressure on communication networks. It is widely recognized that video processing and object detection are computing intensive and too expensive to be handled by resource limited edge devices. Inspired by the depthwise separable convolution and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural Network (LCNN) is introduced in this paper. By narrowing down the classifier's searching space to focus on human objects in surveillance video frames, the proposed LCNN algorithm is able to detect pedestrians with an affordable computation workload to an edge device. A prototype has been implemented on an edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance is achieved using real world surveillance video streams. The experimental study has validated the design of LCNN and shown it is a promising approach to computing intensive applications at the edge.
Smart Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight CNN
Venue: arXiv cs.CV 2018
Year: 2018

Edge computing efficiently extends the realm of information technology beyond the boundary defined by cloud computing paradigm. Performing computation near the source and destination, edge computing is promising to address the challenges in many delay-sensitive applications, like real-time human surveillance. Leveraging the ubiquitously connected cameras and smart mobile devices, it enables video analytics at the edge. In recent years, many smart video surveillance approaches are proposed for object detection and tracking by using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This work explores the feasibility of two popular human-objects detection schemes, Harr-Cascade and HOG feature extraction and SVM classifier, at the edge and introduces a lightweight Convolutional Neural Network (L-CNN) leveraging the depthwise separable convolution for less computation, for human detection. Single Board computers (SBC) are used as edge devices for tests and algorithms are validated using real-world campus surveillance video streams and open data sets. The experimental results are promising that the final algorithm is able to track humans with a decent accuracy at a resource consumption affordable by edge devices in real-time manner.
Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries
Venue: arXiv cs.CL 2018
Year: 2018

In this study, we explored application of Word2Vec and Doc2Vec for sentiment analysis of clinical discharge summaries. We applied unsupervised learning since the data sets did not have sentiment annotations. Note that unsupervised learning is a more realistic scenario than supervised learning which requires an access to a training set of sentiment-annotated data. We aim to detect if there exists any underlying bias towards or against a certain disease. We used SentiWordNet to establish a gold sentiment standard for the data sets and evaluate performance of Word2Vec and Doc2Vec methods. We have shown that the Word2vec and Doc2Vec methods complement each other results in sentiment analysis of the data sets.
Analysis of Problem Tokens to Rank Factors Impacting Quality in VoIP Applications
Venue: arXiv cs.MM 2018
Year: 2018

User-perceived quality-of-experience (QoE) in internet telephony systems is commonly evaluated using subjective ratings computed as a Mean Opinion Score (MOS). In such systems, while user MOS can be tracked on an ongoing basis, it does not give insight into which factors of a call induced any perceived degradation in QoE -- it does not tell us what caused a user to have a sub-optimal experience. For effective planning of product improvements, we are interested in understanding the impact of each of these degrading factors, allowing the estimation of the return (i.e., the improvement in user QoE) for a given investment. To obtain such insights, we advocate the use of an end-of-call "problem token questionnaire" (PTQ) which probes the user about common call quality issues (e.g., distorted audio or frozen video) which they may have experienced. In this paper, we show the efficacy of this questionnaire using data gathered from over 700,000 end-of-call surveys gathered from Skype (a large commercial VoIP application). We present a method to rank call quality and reliability issues and address the challenge of isolating independent factors impacting the QoE. Finally, we present representative examples of how these problem tokens have proven to be useful in practice.
iSTRICT: An Interdependent Strategic Trust Mechanism for the Cloud-Enabled Internet of Controlled Things
Venue: arXiv cs.CR 2018
Year: 2018

The cloud-enabled Internet of controlled things (IoCT) envisions a network of sensors, controllers, and actuators connected through a local cloud in order to intelligently control physical devices. Because cloud services are vulnerable to advanced persistent threats (APTs), each device in the IoCT must strategically decide whether to trust cloud services that may be compromised. In this paper, we present iSTRICT, an interdependent strategic trust mechanism for the cloud-enabled IoCT. iSTRICT is composed of three interdependent layers. In the cloud layer, iSTRICT uses FlipIt games to conceptualize APTs. In the communication layer, it captures the interaction between devices and the cloud using signaling games. In the physical layer, iSTRICT uses optimal control to quantify the utilities in the higher level games. Best response dynamics link the three layers in an overall "game-of-games," for which the outcome is captured by a concept called Gestalt Nash equilibrium (GNE). We prove the existence of a GNE under a set of natural assumptions and develop an adaptive algorithm to iteratively compute the equilibrium. Finally, we apply iSTRICT to trust management for autonomous vehicles that rely on measurements from remote sources. We show that strategic trust in the communication layer achieves a worst-case probability of compromise for any attack and defense costs in the cyber layer.
An open-source job management framework for parameter-space exploration: OACIS
Venue: arXiv cs.CY 2018
Year: 2018

We present an open-source software framework for parameter-space exploration, named OACIS, which is useful to manage vast amount of simulation jobs and results in a systematic way. Recent development of high-performance computers enabled us to explore parameter spaces comprehensively, however, in such cases, manual management of the workflow is practically impossible. OACIS is developed aiming at reducing the cost of these repetitive tasks when conducting simulations by automating job submissions and data management. In this article, an overview of OACIS as well as a getting started guide are presented.
Computable decision making on the reals and other spaces via partiality and nondeterminism
Venue: arXiv cs.LO 2018
Year: 2018

Though many safety-critical software systems use floating point to represent real-world input and output, programmers usually have idealized versions in mind that compute with real numbers. Significant deviations from the ideal can cause errors and jeopardize safety. Some programming systems implement exact real arithmetic, which resolves this matter but complicates others, such as decision making. In these systems, it is impossible to compute (total and deterministic) discrete decisions based on connected spaces such as $\mathbb{R}$. We present programming-language semantics based on constructive topology with variants allowing nondeterminism and/or partiality. Either nondeterminism or partiality suffices to allow computable decision making on connected spaces such as $\mathbb{R}$. We then introduce pattern matching on spaces, a language construct for creating programs on spaces, generalizing pattern matching in functional programming, where patterns need not represent decidable predicates and also may overlap or be inexhaustive, giving rise to nondeterminism or partiality, respectively. Nondeterminism and/or partiality also yield formal logics for constructing approximate decision procedures. We implemented these constructs in the Marshall language for exact real arithmetic.
Greedy Bipartite Matching in Random Type Poisson Arrival Model
Venue: arXiv cs.DS 2018
Year: 2018

We introduce a new random input model for bipartite matching which we call the Random Type Poisson Arrival Model. Just like in the known i.i.d. model (introduced by Feldman et al. 2009), online nodes have types in our model. In contrast to the adversarial types studied in the known i.i.d. model, following the random graphs studied in Mastin and Jaillet 2016, in our model each type graph is generated randomly by including each offline node in the neighborhood of an online node with probability $c/n$ independently. In our model, nodes of the same type appear consecutively in the input and the number of times each type node appears is distributed according to the Poisson distribution with parameter 1. We analyze the performance of the simple greedy algorithm under this input model. The performance is controlled by the parameter $c$ and we are able to exactly characterize the competitive ratio for the regimes $c = o(1)$ and $c = ω(1)$. We also provide a precise bound on the expected size of the matching in the remaining regime of constant $c$. We compare our results to the previous work of Mastin and Jaillet who analyzed the simple greedy algorithm in the $G_{n,n,p}$ model where each online node type occurs exactly once. We essentially show that the approach of Mastin and Jaillet can be extended to work for the Random Type Poisson Arrival Model, although several nontrivial technical challenges need to be overcome. Intuitively, one can view the Random Type Poisson Arrival Model as the $G_{n,n,p}$ model with less randomness; that is, instead of each online node having a new type, each online node has a chance of repeating the previous type.
Dynamically Improving Branch Prediction Accuracy Between Contexts
Venue: arXiv cs.AR 2018
Year: 2018

Branch prediction is a standard feature in most processors, significantly improving the run time of programs by allowing a processor to predict the direction of a branch before it has been evaluated. Current branch prediction methods can achieve excellent prediction accuracy through global tables, various hashing methods, and even machine learning techniques such as SVMs or neural networks. Such designs, however, may lose effectiveness when attempting to predict across context switches in the operating system. Such a scenario may lead to destructive interference between contexts, therefore reducing overall predictor accuracy. To solve this problem, we propose a novel scheme for deciding whether a context switch produces destructive or constructive interference. First, we present evidence that shows that destructive interference can have a significant negative impact on prediction accuracy. Second, we present an extensible framework that keeps track of context switches and prediction accuracy to improve overall accuracy. Experimental results show that this framework effectively reduces the effect of destructive interference on branch prediction.
On and Off-Blockchain Enforcement Of Smart Contracts
Venue: arXiv cs.CY 2018
Year: 2018

In this paper we discuss how conventional business contracts can be converted into smart contracts---their electronic equivalents that can be used to systematically monitor and enforce contractual rights, obligations and prohibitions at run time. We explain that emerging blockchain technology is certainly a promising platform for implementing smart contracts but argue that there is a large class of applications, where blockchain is inadequate due to performance, scalability, and consistency requirements, and also due to language expressiveness and cost issues that are hard to solve. We explain that in some situations a centralised approach that does not rely on blockchain is a better alternative due to its simplicity, scalability, and performance. We suggest that in applications where decentralisation and transparency are essential, developers can advantageously combine the two approaches into hybrid solutions where some operations are enforced by enforcers deployed on--blockchains and the rest by enforcers deployed on trusted third parties.
BUDAMAF: Data Management in Cloud Federations
Venue: arXiv cs.DB 2018
Year: 2018

Data management has always been a multi-domain problem even in the simplest cases. It involves, quality of service, security, resource management, cost management, incident identification, disaster avoidance and/or recovery, as well as many other concerns. In our case, this situation gets ever more complicated because of the divergent nature of a cloud federation like BASMATI. In this federation, the BASMATI Unified Data Management Framework (BUDaMaF), tries to create an automated uniform way of managing all the data transactions, as well as the data stores themselves, in a polyglot multi-cloud, consisting of a plethora of different machines and data store systems.
From the Periphery to the Center: Information Brokerage in an Evolving Network
Venue: arXiv cs.AI 2018
Year: 2018

Interpersonal ties are pivotal to individual efficacy, status and performance in an agent society. This paper explores three important and interrelated themes in social network theory: the center/periphery partition of the network; network dynamics; and social integration of newcomers. We tackle the question: How would a newcomer harness information brokerage to integrate into a dynamic network going from periphery to center? We model integration as the interplay between the newcomer and the dynamics network and capture information brokerage using a process of relationship building. We analyze theoretical guarantees for the newcomer to reach the center through tactics; proving that a winning tactic always exists for certain types of network dynamics. We then propose three tactics and show their superior performance over alternative methods on four real-world datasets and four network models. In general, our tactics place the newcomer to the center by adding very few new edges on dynamic networks with approximately 14000 nodes.