OpenRaiser/Intern-Atlas huggingface dataset:
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
Venue: arXiv cs.IR 2019
Year: 2019

As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features such as word overlap or BM25 scores. Without this combination, these models perform significantly worse than methods based on linguistic feature engineering. In this paper, we propose an attention based neural matching model for ranking short answer text. We adopt value-shared weighting scheme instead of position-shared weighting scheme for combining different matching signals and incorporate question term importance learning using question attention network. Using the popular benchmark TREC QA data, we show that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features. When aNMM is combined with additional features, it outperforms all baselines.
Explicit Constructions of MBR and MSR Codes for Clustered Distributed Storage
Venue: arXiv cs.IT 2019
Year: 2019

This paper considers capacity-achieving coding for the clustered form of distributed storage that reflects practical storage networks. To reflect the clustered structure with limited cross-cluster communication bandwidths, nodes in the same cluster are set to communicate $β_I$ symbols, while nodes in other clusters can communicate $β_c \leq β_I$ symbols with one another. We provide two types of exact regenerating codes which achieve the capacity of clustered distributed storage: the minimum-bandwidth-regenerating (MBR) codes and the minimum-storage-regenerating (MSR) codes. First, we construct MBR codes for general parameter settings of clustered distributed storage. The suggested MBR code is a generalization of an existing code proposed by Rashmi et al., for scenarios where storage nodes are dispersed into L > 1 clusters. The proposed MBR code for the $β_c = 0$ case requires a much smaller field size compared to existing local MBR codes. Secondly, we devise MSR codes for clustered distributed storage. Focus is given on two important cases:$ε=0$ and $ε\in [1/(n-k), 1]$, where $ε=β_c/β_I$ is the ratio of the available cross- to intra-cluster repair bandwidths, n is the total number of distributed nodes and k is the number of contact nodes in data retrieval. The former represents the scenario where cross-cluster communication is not allowed, while the latter corresponds to the case of minimum node storage overhead. For $ε=0$, two existing locally repairable codes are proven to be MSR codes for the clustered model. For $ε\in [1/(n-k), 1]$, existing MSR codes for the non-clustered model are applicable to clustered scenarios with a simple modification.
Optimal Utility-Privacy Trade-off with Total Variation Distance as a Privacy Measure
Venue: arXiv cs.IT 2019
Year: 2019

The total variation distance is proposed as a privacy measure in an information disclosure scenario when the goal is to reveal some information about available data in return of utility, while retaining the privacy of certain sensitive latent variables from the legitimate receiver. The total variation distance is introduced as a measure of privacy-leakage by showing that: i) it satisfies the post-processing and linkage inequalities, which makes it consistent with an intuitive notion of a privacy measure; ii) the optimal utility-privacy trade-off can be solved through a standard linear program when total variation distance is employed as the privacy measure; iii) it provides a bound on the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with a bounded cost function.
Information Theoretic Security for Side-Channel Attacks to the Shannon Cipher System
Venue: arXiv cs.IT 2019
Year: 2019

We study side-channel attacks for the Shannon cipher system. To pose side channel-attacks to the Shannon cipher system, we regard them as a signal estimation via encoded data from two distributed sensors. This can be formulated as the one helper source coding problem posed and investigated by Ahlswede, Körner(1975), and Wyner(1975). We further investigate the posed problem to derive new secrecy bounds. Our results are derived by a coupling of the result Watanabe and Oohama(2012) obtained on bounded storage evasdropper with the exponential strong converse theorem Oohama(2015) established for the one helper source coding problem.
Sequential decomposition of repeated games with asymmetric information and dependent states
Venue: arXiv cs.GT 2019
Year: 2019

We consider a finite horizon repeated game with $N$ selfish players who observe their types privately and take actions, which are publicly observed. Their actions and types jointly determine their instantaneous rewards. In each period, players jointly observe actions of each other with delay 1, and private observations of the state of the system, and get an instantaneous reward which is a function of the state and everyone's actions. The players' types are static and are potentially correlated among players. An appropriate notion of equilibrium for such games is Perfect Bayesian Equilibrium (PBE) which consists of a strategy and a belief profile of the players which is coupled across time and as a result, the complexity of finding such equilibria grows double-exponentially in time. We present a sequential decomposition methodology to compute \emph{structured perfect Bayesian equilibria} (SPBE) of this game, introduced in~\cite{VaAn15arxiv}, where equilibrium policy of a player is a function of a common belief and a private state. This methodology computes SPBE in linear time. In general, the SPBE of the game problem exhibit \textit{signaling} behavior, i.e. players' actions reveal part of their private information that is payoff relevant to other players.
Efficient Local Map Search Algorithms for the Placement of Flying Relays
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies the optimal unmanned aerial vehicle (UAV) placement problem for wireless networking. The UAV operates as a flying wireless relay to provide coverage extension for a base station (BS) and deliver capacity boost to a user shadowed by obstacles. While existing methods rely on statistical models for potential blockage of a direct propagation link, we propose an approach capable of leveraging local terrain information to offer performance guarantees. The proposed method allows to strike the best trade-off between minimizing propagation distances to ground terminals and discovering good propagation conditions. The algorithm only requires several propagation parameters, but it is capable to avoid deep propagation shadowing and is proven to find the globally optimal UAV position. Only a local exploration over the target area is required, and the maximum length of search trajectory is linear to the geographical scale. Hence, it lends itself to online search. Significant throughput gains are found when compared to other positioning approaches based on statistical propagation models.
Content Based Status Updates
Venue: arXiv cs.IT 2019
Year: 2019

Consider a stream of status updates generated by a source, where each update is of one of two types: high priority or ordinary (low priority). These updates are to be transmitted through a network to a monitor. However, the transmission policy of each packet depends on the type of stream it belongs to. For the low priority stream, we analyze and compare the performances of two transmission schemes: (i) Ordinary updates are served in a First-Come-First-Served (FCFS) fashion, whereas, in (ii), the ordinary updates are transmitted according to an M/G/1/1 with preemption policy. In both schemes, high priority updates are transmitted according to an M/G/1/1 with preemption policy and receive preferential treatment. An arriving priority update discards and replaces any currently-in-service high priority update, and preempts (with eventual resume for scheme (i)) any ordinary update. We model the arrival processes of the two kinds of updates, in both schemes, as independent Poisson processes. For scheme (i), we find the arrival and service rates under which the system is stable and give closed-form expressions for average peak age and a lower bound on the average age of the ordinary stream. For scheme (ii), we derive closed-form expressions for the average age and average peak age of the high priority and low priority streams. We finally show that, if the service time is exponentially distributed, the M/M/1/1 with preemption policy leads to an average age of the low priority stream higher than the one achieved using the FCFS scheme. Therefore, the M/M//1/1 with preemption policy, when applied on the low priority stream of updates and in the presence of a higher priority scheme, is not anymore the optimal transmission policy from an age point of view.
A reference model for interaction semantics
Venue: arXiv cs.SE 2019
Year: 2019

In this article, we introduce a reference model for interaction semantics among communicating discrete systems to guide the discourse on interoperability. The necessary set of unifying concepts is small and comprises essentially the notion of discrete systems interacting by exchanging information. It is based on a simple, but nevertheless complete classification of system interactions with respect to information transport and processing. Information transport can only be uni- or bidirectional and information processing is subclassified along the binary dimensions of state, determinism and synchronicity. For interactions with bidirectional information flow we are able to define a criterion for a layered structure of systems: we name a bidirectional interaction "horizontal" if all interacting systems behave the same with respect to state, determinism and synchronicity and we name it "vertical" --- providing a semantic direction --- if there is a behavioral asymmetry between the interacting systems with respect to these properties. It is shown that horizontal interactions are essentially stateful, asynchronous and nondeterministic and are described by protocols. Vertical interactions are essentially top-down-usage, described by object models or operations, and bottom-up-observation, described by anonymous events. The reference model thereby helps us to understand the significant relationships that are created between interacting discrete systems by their interactions and guides us on how to talk about discrete system interoperability. To show its conceptual power, we apply the reference model to assess several other architectural models, communication technologies and so called software design or architectural styles like SOA and REST.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters
Ensemble Reversible Data Hiding
Venue: arXiv cs.MM 2019
Year: 2019

The conventional reversible data hiding (RDH) algorithms often consider the host as a whole to embed a secret payload. In order to achieve satisfactory rate-distortion performance, the secret bits are embedded into the noise-like component of the host such as prediction errors. From the rate-distortion optimization view, it may be not optimal since the data embedding units use the identical parameters. This motivates us to present a segmented data embedding strategy for efficient RDH in this paper, in which the raw host could be partitioned into multiple subhosts such that each one can freely optimize and use the data embedding parameters. Moreover, it enables us to apply different RDH algorithms within different subhosts, which is defined as ensemble. Notice that, the ensemble defined here is different from that in machine learning. Accordingly, the conventional operation corresponds to a special case of the proposed work. Since it is a general strategy, we combine some state-of-the-art algorithms to construct a new system using the proposed embedding strategy to evaluate the rate-distortion performance. Experimental results have shown that, the ensemble RDH system could outperform the original versions in most cases, which has shown the superiority and applicability.
Dependability modeling and optimization of triple modular redundancy partitioning for SRAM-based FPGAs
Venue: arXiv cs.DC 2019
Year: 2019

SRAM-based FPGAs are popular in the aerospace industry for their field programmability and low cost. However, they suffer from cosmic radiation-induced Single Event Upsets (SEUs). Triple Modular Redundancy (TMR) is a well-known technique to mitigate SEUs in FPGAs that is often used with another SEU mitigation technique known as configuration scrubbing. Traditional TMR provides protection against a single fault at a time, while partitioned TMR provides improved reliability and availability. In this paper, we present a methodology to analyze TMR partitioning at early design stage using probabilistic model checking. The proposed formal model can capture both single and multiple-cell upset scenarios, regardless of any assumption of equal partition sizes. Starting with a high-level description of a design, a Markov model is constructed from the Data Flow Graph (DFG) using a specified number of partitions, a component characterization library and a user defined scrub rate. Such a model and exhaustive analysis captures all the considered failures and repairs possible in the system within the radiation environment. Various reliability and availability properties are then verified automatically using the PRISM model checker exploring the relationship between the scrub frequency and the number of TMR partitions required to meet the design requirements. Also, the reported results show that based on a known voter failure rate, it is possible to find an optimal number of partitions at early design stages using our proposed method.
Non-Orthogonal Multiple Access for Mobile VLC Networks with Random Receiver Orientation
Venue: arXiv cs.IT 2019
Year: 2019

We consider a downlink multiuser visible light communications (VLC) network where users randomly change their location and vertical orientation. The non-orthogonal multiple access (NOMA) strategy is adopted to serve multiple users simultaneously, and, hence, to improve spectral efficiency. We propose two novel user scheduling schemes for NOMA, which are referred to as individual and group-based. In order to further reduce the computational complexity and link overhead, novel limited-feedback schemes (on channel quality) are also proposed, which basically involve mean vertical angle (instead of its instantaneous value). Moreover, a two-bit feedback scheme is proposed for group-based user scheduling, which relies on not only distance but also vertical angle (in contrast to conventional one-bit feedback with distance only). The outage probability and sum-rate expressions are derived analytically, which show a very good match with the simulation data. Numerical results verify that the practical feedback scheme with the mean vertical angle achieves a near-optimal sum-rate performance, and the two-bit feedback significantly outperforms the one-bit feedback.
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Venue: arXiv cs.LG 2019
Year: 2019

Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest 3) they inherently allow for richly parameterized policies. A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives. In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties. This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.
Elevating commodity storage with the SALSA host translation layer
Venue: arXiv cs.OS 2019
Year: 2019

To satisfy increasing storage demands in both capacity and performance, industry has turned to multiple storage technologies, including Flash SSDs and SMR disks. These devices employ a translation layer that conceals the idiosyncrasies of their mediums and enables random access. Device translation layers are, however, inherently constrained: resources on the drive are scarce, they cannot be adapted to application requirements, and lack visibility across multiple devices. As a result, performance and durability of many storage devices is severely degraded. In this paper, we present SALSA: a translation layer that executes on the host and allows unmodified applications to better utilize commodity storage. SALSA supports a wide range of single- and multi-device optimizations and, because is implemented in software, can adapt to specific workloads. We describe SALSA's design, and demonstrate its significant benefits using microbenchmarks and case studies based on three applications: MySQL, the Swift object store, and a video server.
Constrained locating arrays for combinatorial interaction testing
Venue: arXiv cs.SE 2019
Year: 2019

This paper introduces the notion of Constrained Locating Arrays (CLAs), mathematical objects which can be used for fault localization in software testing. CLAs extend ordinary locating arrays to make them applicable to testing of systems that have constraints on test parameters. Such constraints are common in real-world systems; thus CLA enhances the applicability of locating arrays to practical testing problems. The paper also proposes an algorithm for constructing CLAs. Experimental results show that the proposed algorithm scales to problems of practical sizes.
Computing Possible and Certain Answers over Order-Incomplete Data
Venue: arXiv cs.DB 2019
Year: 2019

This paper studies the complexity of query evaluation for databases whose relations are partially ordered; the problem commonly arises when combining or transforming ordered data from multiple sources. We focus on queries in a useful fragment of SQL, namely positive relational algebra with aggregates, whose bag semantics we extend to the partially ordered setting. Our semantics leads to the study of two main computational problems: the possibility and certainty of query answers. We show that these problems are respectively NP-complete and coNP-complete, but identify tractable cases depending on the query operators or input partial orders. We further introduce a duplicate elimination operator and study its effect on the complexity results.
Evaluating neural network explanation methods using hybrid documents and morphological agreement
Venue: arXiv cs.CL 2019
Year: 2019

The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP.
A Secure and Privacy-preserving Protocol for Smart Metering Operational Data Collection
Venue: arXiv cs.CR 2019
Year: 2019

In this paper we propose a novel protocol that allows suppliers and grid operators to collect users' aggregate metering data in a secure and privacy-preserving manner. We use secure multiparty computation to ensure privacy protection. In addition, we propose three different data aggregation algorithms that offer different balances between privacy-protection and performance. Our protocol is designed for a realistic scenario in which the data need to be sent to different parties, such as grid operators and suppliers. Furthermore, it facilitates an accurate calculation of transmission, distribution and grid balancing fees in a privacy-preserving manner. We also present a security analysis and a performance evaluation of our protocol based on well known multiparty computation algorithms implemented in C++.
Voltage-driven Building Block for Hardware Belief Networks
Venue: arXiv cs.ET 2019
Year: 2019

Probabilistic spin logic (PSL), based on networks of binary stochastic neurons (or p-bits), has been shown to provide a viable framework for many functionalities including Ising computing, Bayesian inference, invertible Boolean logic and image recognition. This paper presents a hardware building block for the PSL architecture, consisting of an embedded MTJ and a capacitive voltage adder of the type used in neuMOS. We use SPICE simulations to show how identical copies of these building blocks (or weighted p-bits) can be interconnected with wires to design and solve a small instance of the NP-complete Subset Sum Problem fully in hardware.
Virtual Breakpoints for x86/64
Venue: arXiv cs.OS 2019
Year: 2019

Efficient, reliable trapping of execution in a program at the desired location is a linchpin technique for dynamic malware analysis. The progression of debuggers and malware is akin to a game of cat and mouse - each are constantly in a state of trying to thwart one another. At the core of most efficient debuggers today is a combination of virtual machines and traditional binary modification breakpoints (int3). In this paper, we present a design for Virtual Breakpoints. a modification to the x86 MMU which brings breakpoint management into hardware alongside page tables. In this paper we demonstrate the fundamental abstraction failures of current trapping methods, and design a new mechanism from the hardware up. This design incorporates lessons learned from 50 years of virtualization and debugger design to deliver fast, reliable trapping without the pitfalls of traditional binary modification.