OpenRaiser/Intern-Atlas huggingface dataset:
On the economics of knowledge creation and sharing
Venue: arXiv cs.CY 2017
Year: 2017

This work bridges the technical concepts underlying distributed computing and blockchain technologies with their profound socioeconomic and sociopolitical implications, particularly on academic research and the healthcare industry. Several examples from academia, industry, and healthcare are explored throughout this paper. The limiting factor in contemporary life sciences research is often funding: for example, to purchase expensive laboratory equipment and materials, to hire skilled researchers and technicians, and to acquire and disseminate data through established academic channels. In the case of the U.S. healthcare system, hospitals generate massive amounts of data, only a small minority of which is utilized to inform current and future medical practice. Similarly, corporations too expend large amounts of money to collect, secure and transmit data from one centralized source to another. In all three scenarios, data moves under the traditional paradigm of centralization, in which data is hosted and curated by individuals and organizations and of benefit to only a small subset of people.
EXPOSE the Line Failures following a Cyber-Physical Attack on the Power Grid
Venue: arXiv cs.SY 2017
Year: 2017

Recent attacks on power grids demonstrated the vulnerability of the grids to cyber and physical attacks. To analyze this vulnerability, we study cyber-physical attacks that affect both the power grid physical infrastructure and its underlying Supervisory Control And Data Acquisition (SCADA) system. We assume that an adversary attacks an area by: (i) disconnecting some lines within that area, and (ii) obstructing the information (e.g., status of the lines and voltage measurements) from within the area to reach the control center. We leverage the algebraic properties of the AC power flows to introduce the efficient EXPOSE Algorithm for detecting line failures and recovering voltages inside that attacked area after such an attack. The EXPOSE Algorithm outperforms the state-of-the-art algorithm for detecting line failures using partial information under the AC power flow model in terms of scalability and accuracy. The main advantages of the EXPOSE Algorithm are that its running time is independent of the size of the grid and number of line failures, and that it provides accurate information recovery under some conditions on the attacked area. Moreover, it approximately recovers the information and provides the confidence of the solution when these conditions do not hold.
ComSens: Exploiting Pilot Diversity for Pervasive Integration of Communication and Sensing in MIMO-TDD-Frameworks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a fully-integrated radar and communication system -- named ComSens. We utilize two different pilot sequences (one for uplink and one for downlink) with the condition that they must be uncorrelated to each other. Within such a framework, the signal received from end-user and the back-scattered signal from the desired objects have uncorrelated pilots. Thus, the base-station is able to distinguish data signal from user and back-scattered signal from object. We assume a time division duplex (TDD) framework. The pilot sequences are designed for MIMO channels. We evaluate channel MSE as a figure of merit for communication system. We also show that the designed pilots are uncorrelated for a range of time lags. Moreover, designed uplink pilot has negligible autocorrelation for a range of time lags leading to an impulse-like autocorrelation for radar sensing.
Analyzing users' sentiment towards popular consumer industries and brands on Twitter
Venue: arXiv cs.CL 2017
Year: 2017

Social media serves as a unified platform for users to express their thoughts on subjects ranging from their daily lives to their opinion on consumer brands and products. These users wield an enormous influence in shaping the opinions of other consumers and influence brand perception, brand loyalty and brand advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries, encompassing 12,898 enterprise and consumer brands, as well as associated subject matter topics, via sentiment analysis of 330M tweets over a period spanning a month. We find that users tend to be most positive towards manufacturing and most negative towards service industries. In addition, they tend to be more positive or negative when interacting with brands than generally on Twitter. We also find that sentiment towards brands within an industry varies greatly and we demonstrate this using two industries as use cases. In addition, we discover that there is no strong correlation between topic sentiments of different industries, demonstrating that topic sentiments are highly dependent on the context of the industry that they are mentioned in. We demonstrate the value of such an analysis in order to assess the impact of brands on social media. We hope that this initial study will prove valuable for both researchers and companies in understanding users' perception of industries, brands and associated topics and encourage more research in this field.
Promises and Challenges in Continuous Tracking Utilizing Amino Acids in Skin Secretions for Active Multi-Factor Biometric Authentication for Cybersecurity
Venue: arXiv cs.CR 2017
Year: 2017

We consider a new concept of biometric-based cybersecurity systems for active authentication by continuous tracking, which utilizes biochemical processing of metabolites present in skin secretions. Skin secretions contain a large number of metabolites and small molecules that can be targeted for analysis. Here we argue that amino acids found in sweat can be exploited for the establishment of an amino acid profile capable of identifying an individual user of a mobile or wearable device. Individual and combinations of amino acids processed by biocatalytic cascades yield physical (optical or electronic) signals, providing a time-series of several outputs that, in their entirety, should suffice to authenticate a specific user based on standard statistical criteria. Initial results, motivated by biometrics, indicate that single amino acid levels can provide analog signals that vary according to the individual donor, albeit with limited resolution versus noise. However, some such assays offer digital separation (into well-defined ranges of values) according to groups such as age, biological sex, race, and physiological state of the individual. Multi-input biocatalytic cascades that handle several amino acid signals to yield a single digital-type output, as well as continuous-tracking time-series data rather than a single-instance sample, should enable active authentication at the level of an individual.
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts
Venue: arXiv cs.CL 2017
Year: 2017

Word embedding is a Natural Language Processing (NLP) technique that automatically maps words from a vocabulary to vectors of real numbers in an embedding space. It has been widely used in recent years to boost the performance of a vari-ety of NLP tasks such as Named Entity Recognition, Syntac-tic Parsing and Sentiment Analysis. Classic word embedding methods such as Word2Vec and GloVe work well when they are given a large text corpus. When the input texts are sparse as in many specialized domains (e.g., cybersecurity), these methods often fail to produce high-quality vectors. In this pa-per, we describe a novel method to train domain-specificword embeddings from sparse texts. In addition to domain texts, our method also leverages diverse types of domain knowledge such as domain vocabulary and semantic relations. Specifi-cally, we first propose a general framework to encode diverse types of domain knowledge as text annotations. Then we de-velop a novel Word Annotation Embedding (WAE) algorithm to incorporate diverse types of text annotations in word em-bedding. We have evaluated our method on two cybersecurity text corpora: a malware description corpus and a Common Vulnerability and Exposure (CVE) corpus. Our evaluation re-sults have demonstrated the effectiveness of our method in learning domain-specific word embeddings.
WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition
Venue: arXiv cs.CL 2017
Year: 2017

We study the problem of evaluating automatic speech recognition (ASR) systems that target dialectal speech input. A major challenge in this case is that the orthography of dialects is typically not standardized. From an ASR evaluation perspective, this means that there is no clear gold standard for the expected output, and several possible outputs could be considered correct according to different human annotators, which makes standard word error rate (WER) inadequate as an evaluation metric. Such a situation is typical for machine translation (MT), and thus we borrow ideas from an MT evaluation metric, namely TERp, an extension of translation error rate which is closely-related to WER. In particular, in the process of comparing a hypothesis to a reference, we make use of spelling variants for words and phrases, which we mine from Twitter in an unsupervised fashion. Our experiments with evaluating ASR output for Egyptian Arabic, and further manual analysis, show that the resulting WERd (i.e., WER for dialects) metric, a variant of TERp, is more adequate than WER for evaluating dialectal ASR.
Hijacking .NET to Defend PowerShell
Venue: arXiv cs.CR 2017
Year: 2017

With the rise of attacks using PowerShell in the recent months, there has not been a comprehensive solution for monitoring or prevention. Microsoft recently released the AMSI solution for PowerShell v5, however this can also be bypassed. This paper focuses on repurposing various stealthy runtime .NET hijacking techniques implemented for PowerShell attacks for defensive monitoring of PowerShell. It begins with a brief introduction to .NET and PowerShell, followed by a deeper explanation of various attacker techniques, which is explained from the perspective of the defender, including assembly modification, class and method injection, compiler profiling, and C based function hooking. Of the four attacker techniques that are repurposed for defensive real-time monitoring of PowerShell execution, intermediate language binary modification, JIT hooking, and machine code manipulation provide the best results for stealthy run-time interfaces for PowerShell scripting analysis.
Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances
Venue: arXiv cs.SY 2017
Year: 2017

Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical systems; it has been applied to many small-scale systems in the past decade. Its advantages include compatibility with general nonlinear system dynamics, formal treatment of bounded disturbances, and the availability of well-developed numerical tools. The main challenge is addressing its exponential computational complexity with respect to the number of state variables. In this tutorial, we present an overview of basic HJ reachability theory and provide instructions for using the most recent numerical tools, including an efficient GPU-parallelized implementation of a Level Set Toolbox for computing reachable sets. In addition, we review some of the current work in high-dimensional HJ reachability to show how the dimensionality challenge can be alleviated via various general theoretical and application-specific insights.
Defining a Lingua Franca to Open the Black Box of a Naïve Bayes Recommender
Venue: arXiv cs.IR 2017
Year: 2017

Many AI systems have a black box nature that makes it difficult to understand how they make their recommendations. This can be unsettling, as the designer cannot be certain how the system will respond to novelty. To penetrate our Naïve Bayes recommender's black box, we first asked, what do we want to know from our system, and how can it be obtained? The answers led us to recursively define a common lexicon with the AI, a lingua franca, using the very items that the system ranks to create meta-symbols recognized by the system, and enabling us to understand the system's knowledge in plain terms and at different levels of abstraction. As one bonus, using its existing knowledge, the lingua franca can enable the system to extend recommendations to related, but entirely new areas, ameliorating the cold start problem. We also supplement the lingua franca with techniques for visualizing the system's knowledge state, develop metrics for evaluating the meaningfulness of terms in the lingua franca, and generalize the requirements for developing a similar lingua franca in other applications.
Recovery of Architecture Module Views using an Optimized Algorithm Based on Design Structure Matrices
Venue: arXiv cs.SE 2017
Year: 2017

Design structure matrices (DSMs) are useful to represent high-level system structure, modeling interactions between design entities. DSMs are used for many visualization and abstraction activities. In this work, we propose the use of an existing DSM clustering algorithm to recover software architecture module views. To make it suitable to this domain, optimization has proved necessary. It was achieved through performance analysis and parameter tuning on the original algorithm. Results show that DSM clustering can be an alternative to other clustering algorithms.
Oblivious Stash Shuffle
Venue: arXiv cs.CR 2017
Year: 2017

This is a companion report to Bittau et al. We restate and prove security of the Stash Shuffle.
FogStore: Toward a Distributed Data Store for Fog Computing
Venue: arXiv cs.DC 2017
Year: 2017

Stateful applications and virtualized network functions (VNFs) can benefit from state externalization to increase their reliability, scalability, and inter-operability. To keep and share the externalized state, distributed data stores (DDSs) are a powerful tool allowing for the management of classical trade-offs in consistency, availability and partitioning tolerance. With the advent of Fog and Edge Computing, stateful applications and VNFs are pushed from the data centers toward the network edge. This poses new challenges on DDSs that are tailored to a deployment in Cloud data centers. In this paper, we propose two novel design goals for DDSs that are tailored to Fog Computing: (1) Fog-aware replica placement, and (2) context-sensitive differential consistency. To realize those design goals on top of existing DDSs, we propose the FogStore system. FogStore manages the needed adaptations in replica placement and consistency management transparently, so that existing DDSs can be plugged into the system. To show the benefits of FogStore, we perform a set of evaluations using the Yahoo Cloud Serving Benchmark.
UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization
Venue: arXiv cs.IT 2017
Year: 2017

This paper studies a new unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system, where a UAV-mounted mobile energy transmitter (ET) is dispatched to deliver wireless energy to a set of on-ground energy receivers (ERs). We investigate how the UAV should optimally exploit its mobility via trajectory design to maximize the energy transferred to all ERs during a finite period. First, we consider the maximization of the sum energy received by all ERs by optimizing the UAV's trajectory subject to its maximum speed constraint. We obtain its optimal solution, which shows that the UAV should hover at one single fixed location during the whole period. However, the sum-energy maximization incurs a "near-far" fairness issue. To overcome this issue, we consider a different problem to maximize the minimum received energy among all ERs. We first consider an ideal case by ignoring the UAV's maximum speed constraint, and show that the relaxed problem can be optimally solved via the Lagrange dual method. Then, for the general case with the UAV's maximum speed constraint considered, we propose a new successive hover-and-fly trajectory motivated by the optimal trajectory in the ideal case, and obtain efficient trajectory designs by applying the successive convex programing (SCP).
Quantified Derandomization of Linear Threshold Circuits
Venue: arXiv cs.CC 2017
Year: 2017

One of the prominent current challenges in complexity theory is the attempt to prove lower bounds for $TC^0$, the class of constant-depth, polynomial-size circuits with majority gates. Relying on the results of Williams (2013), an appealing approach to prove such lower bounds is to construct a non-trivial derandomization algorithm for $TC^0$. In this work we take a first step towards the latter goal, by proving the first positive results regarding the derandomization of $TC^0$ circuits of depth $d>2$. Our first main result is a quantified derandomization algorithm for $TC^0$ circuits with a super-linear number of wires. Specifically, we construct an algorithm that gets as input a $TC^0$ circuit $C$ over $n$ input bits with depth $d$ and $n^{1+\exp(-d)}$ wires, runs in almost-polynomial-time, and distinguishes between the case that $C$ rejects at most $2^{n^{1-1/5d}}$ inputs and the case that $C$ accepts at most $2^{n^{1-1/5d}}$ inputs. In fact, our algorithm works even when the circuit $C$ is a linear threshold circuit, rather than just a $TC^0$ circuit (i.e., $C$ is a circuit with linear threshold gates, which are stronger than majority gates). Our second main result is that even a modest improvement of our quantified derandomization algorithm would yield a non-trivial algorithm for standard derandomization of all of $TC^0$, and would consequently imply that $NEXP\not\subseteq TC^0$. Specifically, if there exists a quantified derandomization algorithm that gets as input a $TC^0$ circuit with depth $d$ and $n^{1+O(1/d)}$ wires (rather than $n^{1+\exp(-d)}$ wires), runs in time at most $2^{n^{\exp(-d)}}$, and distinguishes between the case that $C$ rejects at most $2^{n^{1-1/5d}}$ inputs and the case that $C$ accepts at most $2^{n^{1-1/5d}}$ inputs, then there exists an algorithm with running time $2^{n^{1-Ω(1)}}$ for standard derandomization of $TC^0$.
Code Attention: Translating Code to Comments by Exploiting Domain Features
Venue: arXiv cs.AI 2017
Year: 2017

Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension. However, due to the great cost of authoring with the comments, many code projects do not contain adequate comments. Automatic comment generation techniques have been proposed to generate comments from pieces of code in order to alleviate the human efforts in annotating the code. Most existing approaches attempt to exploit certain correlations (usually manually given) between code and generated comments, which could be easily violated if the coding patterns change and hence the performance of comment generation declines. In this paper, we first build C2CGit, a large dataset from open projects in GitHub, which is more than 20$\times$ larger than existing datasets. Then we propose a new attention module called Code Attention to translate code to comments, which is able to utilize the domain features of code snippets, such as symbols and identifiers. We make ablation studies to determine effects of different parts in Code Attention. Experimental results demonstrate that the proposed module has better performance over existing approaches in both BLEU and METEOR.
Optimization of the Waiting Time for H-R Coordination
Venue: arXiv cs.RO 2017
Year: 2017

An analytical model of Human-Robot (H-R) coordination is presented for a Human-Robot system executing a collaborative task in which a high level of synchronization among the agents is desired. The influencing parameters and decision variables that affect the waiting time of the collaborating agents were analyzed. The performance of the model was evaluated based on the costs of the waiting times of each of the agents at the pre-defined spatial point of handover. The model was tested for two cases of dynamic H-R coordination scenarios. Results indicate that this analytical model can be used as a tool for designing an H-R system that optimizes the agent waiting time thereby increasing the joint-efficiency of the system and making coordination fluent and natural.
On the Existence and Structure of Mixed Nash Equilibria for In-Band Full-Duplex Wireless Networks
Venue: arXiv cs.NI 2017
Year: 2017

This article offers the first characterisation of mixed Nash equilibria (MNE) for a wireless system with full-duplex capable terminals that share a common channel via Aloha-based contention. Focussing on a simple grid topology, we prove that mixed MNE exist only if proportionality, driven by the accuracy of self-interference cancellation, is granted between the cost undergone for full- and half-duplex operations. The analysis shows how a proper selection of such costs allows MNE that are also optimal from a network viewpoint in terms of aggregate throughput. The sensitivity of system performance to costs is tackled and discussed considering the price of anarchy.
Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design
Venue: arXiv cs.LG 2017
Year: 2017

The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution neural network on embedded platforms. As CNN is attributed to the strong endurance to computation errors, employing block floating point (BFP) arithmetics in CNN accelerators could save the hardware cost and data traffics efficiently, while maintaining the classification accuracy. In this paper, we verify the effects of word width definitions in BFP to the CNN performance without retraining. Several typical CNN models, including VGG16, ResNet-18, ResNet-50 and GoogLeNet, were tested in this paper. Experiments revealed that 8-bit mantissa, including sign bit, in BFP representation merely induced less than 0.3% accuracy loss. In addition, we investigate the computational errors in theory and develop the noise-to-signal ratio (NSR) upper bound, which provides the promising guidance for BFP based CNN engine design.
Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena
Venue: arXiv cs.CL 2017
Year: 2017

Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis; and it contains many sorts of disfluency such as mid-utterance/sentence hesitations, interruptions, and self-corrections. But training data for machine learning approaches to dialogue processing is often either cleaned-up or wholly synthetic in order to avoid such phenomena. The question then arises of how well systems trained on such clean data generalise to real spontaneous dialogue, or indeed whether they are trainable at all on naturally occurring dialogue data. To answer this question, we created a new corpus called bAbI+ by systematically adding natural spontaneous incremental dialogue phenomena such as restarts and self-corrections to the Facebook AI Research's bAbI dialogues dataset. We then explore the performance of a state-of-the-art retrieval model, MemN2N, on this more natural dataset. Results show that the semantic accuracy of the MemN2N model drops drastically; and that although it is in principle able to learn to process the constructions in bAbI+, it needs an impractical amount of training data to do so. Finally, we go on to show that an incremental, semantic parser -- DyLan -- shows 100% semantic accuracy on both bAbI and bAbI+, highlighting the generalisation properties of linguistically informed dialogue models.