OpenRaiser/Intern-Atlas huggingface dataset:
Ask Me Anything: A Conversational Interface to Augment Information Security Workers
Venue: arXiv cs.HC 2017
Year: 2017

Security products often create more problems than they solve, drowning users in alerts without providing the context required to remediate threats. This challenge is compounded by a lack of experienced personnel and security tools with complex interfaces. These interfaces require users to become domain experts or rely on repetitive, time consuming tasks to turn this data deluge into actionable intelligence. In this paper we present Artemis, a conversational interface to endpoint detection and response (EDR) event data. Artemis leverages dialog to drive the automation of complex tasks and reduce the need to learn a structured query language. Designed to empower inexperienced and junior security workers to better understand their security environment, Artemis provides an intuitive platform to ask questions of alert data as users are guided through triage and hunt workflows. In this paper, we will discuss our user-centric design methodology, feedback from user interviews, and the design requirements generated upon completion of our study. We will also present core functionality, findings from scenario-based testing, and future research for the Artemis platform.
A Note on Unconditional Subexponential-time Pseudo-deterministic Algorithms for BPP Search Problems
Venue: arXiv cs.CC 2017
Year: 2017

We show the first unconditional pseudo-determinism result for all of search-BPP. Specifically, we show that every BPP search problem can be computed pseudo-deterministically on average for infinitely many input lengths. In other words, for infinitely many input lengths and for any polynomial-time samplable distribution our algorithm succeeds in producing a unique answer (if one exists) with high probability over the distribution and the coins tossed.
The Value of Information Concealment
Venue: arXiv cs.GT 2017
Year: 2017

We consider a revenue optimizing seller selling a single item to a buyer, on whose private value the seller has a noisy signal. We show that, when the signal is kept private, arbitrarily more revenue could potentially be extracted than if the signal is leaked or revealed. We then show that, if the seller is not allowed to make payments to the buyer, the gap between the two is bounded by a multiplicative factor of 3, if the value distribution conditioning on each signal is regular. We give examples showing that both conditions are necessary for a constant bound to hold. We connect this scenario to multi-bidder single-item auctions where bidders' values are correlated. Similarly to the setting above, we show that the revenue of a Bayesian incentive compatible, ex post individually rational auction can be arbitrarily larger than that of a dominant strategy incentive compatible auction, whereas the two are no more than a factor of 5 apart if the auctioneer never pays the bidders and if each bidder's value conditioning on the others' is drawn according to a regular distribution. The upper bounds in both settings degrade gracefully when the distribution is a mixture of a small number of regular distributions.
Learning Unified Embedding for Apparel Recognition
Venue: arXiv cs.CV 2017
Year: 2017

In apparel recognition, specialized models (e.g. models trained for a particular vertical like dresses) can significantly outperform general models (i.e. models that cover a wide range of verticals). Therefore, deep neural network models are often trained separately for different verticals. However, using specialized models for different verticals is not scalable and expensive to deploy. This paper addresses the problem of learning one unified embedding model for multiple object verticals (e.g. all apparel classes) without sacrificing accuracy. The problem is tackled from two aspects: training data and training difficulty. On the training data aspect, we figure out that for a single model trained with triplet loss, there is an accuracy sweet spot in terms of how many verticals are trained together. To ease the training difficulty, a novel learning scheme is proposed by using the output from specialized models as learning targets so that L2 loss can be used instead of triplet loss. This new loss makes the training easier and make it possible for more efficient use of the feature space. The end result is a unified model which can achieve the same retrieval accuracy as a number of separate specialized models, while having the model complexity as one. The effectiveness of our approach is shown in experiments.
Achieving both positive secrecy rates of the users in two-way wiretap channel by individual secrecy
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, the individual secrecy of two-way wiretap channel is investigated, where two legitimate users' messages are separately guaranteed secure against an external eavesdropper. For one thing, in some communication scenarios, the joint secrecy is impossible to achieve both positive secrecy rates of two users. For another, the individual secrecy satisfies the secrecy demand of many practical communication systems. Thus, firstly, an achievable secrecy rate region is derived for the general two-way wiretap channel with individual secrecy. In a deterministic channel, the region with individual secrecy is shown to be larger than that with joint secrecy.Secondly, outer bounds on the secrecy capacity region are obtained for the general two-way wiretap channel and for two classes of special two-way wiretap channels.The gap between inner and outer bounds on the secrecy capacity region is explored via the binary input two-way wiretap channels and the degraded Gaussian two-way wiretap. Most notably, the secrecy capacity regions are established for the XOR channel and the degraded Gaussian two-way wiretap channel.Furthermore, the secure sum-rate of the degraded Gaussian two-way wiretap channel under the individual secrecy constraint is demonstrated to be strictly larger than that under the joint secrecy constraint.
Effects of Feedback on the One-sided Secrecy of Two-way Wiretap through Multiple Transmissions
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, the one-sided secrecy of two-way wiretap channel with feedback is investigated, where the confidential messages of one user through multiple transmissions is guaranteed secure against an external eavesdropper. For one thing, one-sided secrecy satisfies the secure demand of many practical scenarios. For another, the secrecy is measured over many blocks since the correlation between eavesdropper's observation and the confidential messages in successive blocks, instead of secrecy measurement of one block in previous works. Thus, firstly, an achievable secrecy rate region is derived for the general two-way wiretap channel with feedback through multiple transmissions under one-sided secrecy. Secondly, outer bounds on the secrecy capacity region are also obtained. The gap between inner and outer bounds on the secrecy capacity region is explored via the binary input two-way wiretap channels. Most notably, the secrecy capacity regions are established for the XOR channel. Furthermore, the result shows that the achievable rate region with feedback is larger than that without feedback. Therefore, the benefit role of feedback is precisely characterized for two-way wiretap channel with feedback under one-sided secrecy.
First-Order Query Evaluation with Cardinality Conditions
Venue: arXiv cs.LO 2017
Year: 2017

We study an extension of first-order logic that allows to express cardinality conditions in a similar way as SQL's COUNT operator. The corresponding logic FOC(P) was introduced by Kuske and Schweikardt (LICS'17), who showed that query evaluation for this logic is fixed-parameter tractable on classes of structures (or databases) of bounded degree. In the present paper, we first show that the fixed-parameter tractability of FOC(P) cannot even be generalised to very simple classes of structures of unbounded degree such as unranked trees or strings with a linear order relation. Then we identify a fragment FOC1(P) of FOC(P) which is still sufficiently strong to express standard applications of SQL's COUNT operator. Our main result shows that query evaluation for FOC1(P) is fixed-parameter tractable with almost linear running time on nowhere dense classes of structures. As a corollary, we also obtain a fixed-parameter tractable algorithm for counting the number of tuples satisfying a query over nowhere dense classes of structures.
MISO in Ultra-Dense Networks: Balancing the Tradeoff between User and System Performance
Venue: arXiv cs.IT 2017
Year: 2017

With over-deployed network infrastructures, network densification is shown to hinder the improvement of user experience and system performance. In this paper, we adopt multi-antenna techniques to overcome the bottleneck and investigate the performance of single-user beamforming, an effective method to enhance desired signal power, in small cell networks from the perspective of user coverage probability (CP) and network spatial throughput (ST). Pessimistically, it is proved that, even when multi-antenna techniques are applied, both CP and ST would be degraded and even asymptotically diminish to zero with the increasing base station (BS) density. Moreover, the results also reveal that the increase of ST is at the expense of the degradation of CP. Therefore, to balance the tradeoff between user and system performance, we further study the critical density, under which ST could be maximized under the CP constraint. Accordingly, the impact of key system parameters on critical density is quantified via the derived closed-form expression. Especially, the critical density is shown to be inversely proportional to the square of antenna height difference between BSs and users. Meanwhile, single-user beamforming, albeit incapable of improving CP and ST scaling laws, is shown to significantly increase the critical density, compared to the single-antenna regime.
Modeling Target-Side Inflection in Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

NMT systems have problems with large vocabulary sizes. Byte-pair encoding (BPE) is a popular approach to solving this problem, but while BPE allows the system to generate any target-side word, it does not enable effective generalization over the rich vocabulary in morphologically rich languages with strong inflectional phenomena. We introduce a simple approach to overcome this problem by training a system to produce the lemma of a word and its morphologically rich POS tag, which is then followed by a deterministic generation step. We apply this strategy for English-Czech and English-German translation scenarios, obtaining improvements in both settings. We furthermore show that the improvement is not due to only adding explicit morphological information.
Discovering topics in text datasets by visualizing relevant words
Venue: arXiv cs.CL 2017
Year: 2017

When dealing with large collections of documents, it is imperative to quickly get an overview of the texts' contents. In this paper we show how this can be achieved by using a clustering algorithm to identify topics in the dataset and then selecting and visualizing relevant words, which distinguish a group of documents from the rest of the texts, to summarize the contents of the documents belonging to each topic. We demonstrate our approach by discovering trending topics in a collection of New York Times article snippets.
KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis
Venue: arXiv cs.HC 2017
Year: 2017

In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient's gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.
Twitter Activity Timeline as a Signature of Urban Neighborhood
Venue: arXiv cs.SI 2017
Year: 2017

Modern cities are complex systems, evolving at a fast pace. Thus, many urban planning, political, and economic decisions require a deep and up-to-date understanding of the local context of urban neighborhoods. This study shows that the structure of openly available social media records, such as Twitter, offers a possibility for building a unique dynamic signature of urban neighborhood function, and, therefore, might be used as an efficient and simple decision support tool. Considering New York City as an example, we investigate how Twitter data can be used to decompose the urban landscape into self-defining zones, aligned with the functional properties of individual neighborhoods and their social and economic characteristics. We further explore the potential of these data for detecting events and evaluating their impact over time and space. This approach paves a way to a methodology for immediate quantification of the impact of urban development programs and the estimation of socioeconomic statistics at a finer spatial-temporal scale, thus allowing urban policy-makers to track neighborhood transformations and foresee undesirable changes in order to take early action before official statistics would be available.
Solving Mixed Model Workplace Time-dependent Assembly Line Balancing Problem with FSS Algorithm
Venue: arXiv cs.NE 2017
Year: 2017

Balancing assembly lines, a family of optimization problems commonly known as Assembly Line Balancing Problem, is notoriously NP-Hard. They comprise a set of problems of enormous practical interest to manufacturing industry due to the relevant frequency of this type of production paradigm. For this reason, many researchers on Computational Intelligence and Industrial Engineering have been conceiving algorithms for tackling different versions of assembly line balancing problems utilizing different methodologies. In this article, it was proposed a problem version referred as Mixed Model Workplace Time-dependent Assembly Line Balancing Problem with the intention of including pressing issues of real assembly lines in the optimization problem, to which four versions were conceived. Heuristic search procedures were used, namely two Swarm Intelligence algorithms from the Fish School Search family: the original version, named "vanilla", and a special variation including a stagnation avoidance routine. Either approaches solved the newly posed problem achieving good results when compared to Particle Swarm Optimization algorithm.
NuCypher KMS: Decentralized key management system
Venue: arXiv cs.CR 2017
Year: 2017

NuCypher KMS is a decentralized Key Management System (KMS) that addresses the limitations of using consensus networks to securely store and manipulate private, encrypted data. It provides encryption and cryptographic access controls, performed by a decentralized network, leveraging proxy re-encryption. Unlike centralized KMS as a service solutions, it doesn't require trusting a service provider. NuCypher KMS enables sharing of sensitive data for both decentralized and centralized applications, providing security infrastructure for applications from healthcare to identity management to decentralized content marketplaces. NuCypher KMS will be an essential part of decentralized applications, just as SSL/TLS is an essential part of every secure web application.
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
Venue: arXiv cs.CV 2017
Year: 2017

Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning to medical image analysis. However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples. Specifically, annotation and labeling of the medical images is much more expensive and time-consuming than other applications and often involves manual labor from multiple domain experts. In this work, we propose a multi-stage, self-paced learning framework utilizing a convolutional neural network (CNN) to classify Computed Tomography (CT) image patches. The key contribution of this approach is that we augment the size of training samples by refining the unlabeled instances with a self-paced learning CNN. By implementing the framework on high performance computing servers including the NVIDIA DGX1 machine, we obtained the experimental result, showing that the self-pace boosted network consistently outperformed the original network even with very scarce manual labels. The performance gain indicates that applications with limited training samples such as medical image analysis can benefit from using the proposed framework.
Learning model-based planning from scratch
Venue: arXiv cs.AI 2017
Year: 2017

Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to construct a plan. Here we introduce the "Imagination-based Planner", the first model-based, sequential decision-making agent that can learn to construct, evaluate, and execute plans. Before any action, it can perform a variable number of imagination steps, which involve proposing an imagined action and evaluating it with its model-based imagination. All imagined actions and outcomes are aggregated, iteratively, into a "plan context" which conditions future real and imagined actions. The agent can even decide how to imagine: testing out alternative imagined actions, chaining sequences of actions together, or building a more complex "imagination tree" by navigating flexibly among the previously imagined states using a learned policy. And our agent can learn to plan economically, jointly optimizing for external rewards and computational costs associated with using its imagination. We show that our architecture can learn to solve a challenging continuous control problem, and also learn elaborate planning strategies in a discrete maze-solving task. Our work opens a new direction toward learning the components of a model-based planning system and how to use them.
Entropy-based Pruning for Learning Bayesian Networks using BIC
Venue: arXiv cs.AI 2017
Year: 2017

For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score. We target the task of constructing the collection of candidate parent sets when the score of choice is the Bayesian Information Criterion (BIC). We provide new non-trivial results that can be used to prune the search space of candidate parent sets of each node. We analyze how these new results relate to previous ideas in the literature both theoretically and empirically. We show in experiments with UCI data sets that gains can be significant. Since the new pruning rules are easy to implement and have low computational costs, they can be promptly integrated into all state-of-the-art methods for structure learning of Bayesian networks.
The Role of Conversation Context for Sarcasm Detection in Online Interactions
Venue: arXiv cs.CL 2017
Year: 2017

Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, speaker's sarcastic intent is not always obvious without additional context. Focusing on social media discussions, we investigate two issues: (1) does modeling of conversation context help in sarcasm detection and (2) can we understand what part of conversation context triggered the sarcastic reply. To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the sarcastic response. We show that the conditional LSTM network (Rocktaschel et al., 2015) and LSTM networks with sentence level attention on context and response outperform the LSTM model that reads only the response. To address the second issue, we present a qualitative analysis of attention weights produced by the LSTM models with attention and discuss the results compared with human performance on the task.
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation
Venue: arXiv cs.CL 2017
Year: 2017

Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are presented, but word transcripts are only available for the source domain speech. We present novel augmentation-based methods that transform speech in a way that does not change the transcripts. Specifically, we first train a variational autoencoder on both source and target domain data (without supervision) to learn a latent representation of speech. We then transform nuisance attributes of speech that are irrelevant to recognition by modifying the latent representations, in order to augment labeled training data with additional data whose distribution is more similar to the target domain. The proposed method is evaluated on the CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as 35% compared to the non-adapted baseline.
Orthogonal Ramanujan Sums, its properties and Applications in Multiresolution Analysis
Venue: arXiv cs.IT 2017
Year: 2017

Signal processing community has recently shown interest in Ramanujan sums which was defined by S.Ramanujan in 1918. In this paper we have proposed Orthog- onal Ramanujan Sums (ORS) based on Ramanujan sums. In this paper we present two novel application of ORS. Firstly a new representation of a finite length signal is given using ORS which is defined as Orthogonal Ramanujan Periodic Transform.Secondly ORS has been applied to multiresolution analysis and it is shown that Haar transform is a spe- cial case.