OpenRaiser/Intern-Atlas huggingface dataset:
Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning
Venue: arXiv cs.AI 2018
Year: 2018

To solve the text-based question and answering task that requires relational reasoning, it is necessary to memorize a large amount of information and find out the question relevant information from the memory. Most approaches were based on external memory and four components proposed by Memory Network. The distinctive component among them was the way of finding the necessary information and it contributes to the performance. Recently, a simple but powerful neural network module for reasoning called Relation Network (RN) has been introduced. We analyzed RN from the view of Memory Network, and realized that its MLP component is able to reveal the complicate relation between question and object pair. Motivated from it, we introduce which uses MLP to find out relevant information on Memory Network architecture. It shows new state-of-the-art results in jointly trained bAbI-10k story-based question answering tasks and bAbI dialog-based question answering tasks.
A Theory of Traffic Regulators for Deterministic Networks with Application to Interleaved Regulators
Venue: arXiv cs.NI 2018
Year: 2018

We define the minimal interleaved regulator, which generalizes the Urgency Based Shaper that was recently proposed by Specht and Samii as a simpler alternative to per-flow reshaping in deterministic networks with aggregate scheduling. With this regulator, packets of multiple flows are processed in one FIFO queue; the packet at the head of the queue is examined against the regulation constraints of its flow; it is released at the earliest time at which this is possible without violating the constraints. Packets that are not at the head of the queue are not examined until they reach the head of the queue. This regulator thus possibly delays the packet at the head of the queue but also all following packets, which typically belong to other flows. However, we show that, when it is placed after an arbitrary FIFO system, the worst case delay of the combination is not increased. This shaping-for-free property is well-known with per-flow shapers; surprisingly, it continues to hold here. To derive this property, we introduce a new definition of traffic regulator, the minimal Pi-regulator, which extends both the greedy shaper of network calculus and Chang's max-plus regulator and also includes new types of regulators such as packet rate limiters. Incidentally, we provide a new insight on the equivalence between min-plus and max-plus formulations of regulators and shapers.
An Integrated Soft Computing Approach to a Multi-biometric Security Model
Venue: arXiv cs.CV 2018
Year: 2018

The abstract of the thesis consists of three sections, videlicet, Motivation Chapter Organization Salient Contributions. The complete abstract is included with the thesis. The final section on Salient Contributions is reproduced below. Salient Contributions The research presents the following salient contributions: i. A novel technique has been developed for comparing biographical information, by combining the average impact of Levenshtein, Damerau-Levenshtein, and editor distances. The impact is calculated as the ratio of the edit distance to the maximum possible edit distance between two strings of the same lengths as the given pair of strings. This impact lies in the range [0, 1] and can easily be converted to a similarity (matching) score by subtracting the impact from unity. ii. A universal soft computing framework is proposed for adaptively fusing biometric and biographical information by making real-time decisions to determine after consideration of each individual identifier whether computation of matching scores and subsequent fusion of additional identifiers, including biographical information is required. This proposed framework not only improves the accuracy of the system by fusing less reliable information (e.g. biographical information) only for instances where such a fusion is required, but also improves the efficiency of the system by computing matching scores for various available identifiers only when this computation is considered necessary. iii. A scientific method for comparing efficiency of fusion strategies through a predicted effort to error trade-off curve.
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
Venue: arXiv cs.CR 2018
Year: 2018

The popularity of ASR (automatic speech recognition) systems, like Google Voice, Cortana, brings in security concerns, as demonstrated by recent attacks. The impacts of such threats, however, are less clear, since they are either less stealthy (producing noise-like voice commands) or requiring the physical presence of an attack device (using ultrasound). In this paper, we demonstrate that not only are more practical and surreptitious attacks feasible but they can even be automatically constructed. Specifically, we find that the voice commands can be stealthily embedded into songs, which, when played, can effectively control the target system through ASR without being noticed. For this purpose, we developed novel techniques that address a key technical challenge: integrating the commands into a song in a way that can be effectively recognized by ASR through the air, in the presence of background noise, while not being detected by a human listener. Our research shows that this can be done automatically against real world ASR applications. We also demonstrate that such CommanderSongs can be spread through Internet (e.g., YouTube) and radio, potentially affecting millions of ASR users. We further present a new mitigation technique that controls this threat.
A Tractable Analysis of the Blind-spot Probability in Localization Networks under Correlated Blocking
Venue: arXiv cs.NI 2018
Year: 2018

In localization applications, the line-of-sight between anchors and targets may be blocked by obstacles in the environment. A target that is invisible (i.e., without line-of-sight) to a sufficient number of anchors cannot be unambiguously localized and is, therefore, said to be in a blind spot. In this paper, we analyze the blind spot probability of a typical target by using stochastic geometry to model the randomness in the obstacle and anchor locations. In doing so, we handle correlated anchor blocking induced by the obstacles, unlike previous works that assume independent anchor blocking. We first characterize the regime over which the independent blocking assumption underestimates the blind spot probability of the typical target, which in turn, is characterized as a function of the distribution of the visible area, surrounding the target location. Since this distribution is difficult to characterize exactly, we formulate the nearest two-obstacle approximation, which is equivalent to considering correlated blocking for only the nearest two obstacles from the target and assuming independent blocking for the remaining obstacles. Based on this, we derive an approximate expression for the blind spot probability, which helps determine the anchor deployment intensity needed for the blind spot probability of a typical target to be at most a threshold, $μ$.
Reconstructing a cascade from temporal observations
Venue: arXiv cs.SI 2018
Year: 2018

Given a subset of active nodes in a network can we re- construct the cascade that has generated these observa- tions? This is a problem that has been studied in the literature, but here we focus in the case that tempo- ral information is available about the active nodes. In particular, we assume that in addition to the subset of active nodes we also know their activation time. We formulate this cascade-reconstruction problem as a variant of a Steiner-tree problem: we ask to find a tree that spans all reported active nodes while satisfying temporal-consistency constraints. We present three approximation algorithms. The best algorithm in terms of quality achieves a O(\sqrt{k})-approximation guarantee, where k is the number of active nodes, while the most efficient algorithm has linearithmic running time, making it scalable to very large graphs. We evaluate our algorithms on real-world networks with both simulated and real cascades. Our results in- dicate that utilizing the available temporal information allows for more accurate cascade reconstruction. Fur- thermore, our objective leads to finding the "backbone" of the cascade and it gives solutions of high precision.
Individual testing is optimal for nonadaptive group testing in the linear regime
Venue: arXiv cs.IT 2018
Year: 2018

We consider nonadaptive probabilistic group testing in the linear regime, where each of n items is defective independently with probability p in (0,1), and p is a constant independent of n. We show that testing each item individually is optimal, in the sense that with fewer than n tests the error probability is bounded away from zero.
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans
Venue: arXiv cs.CV 2018
Year: 2018

This paper reports Deep LOGISMOS approach to 3D tumor segmentation by incorporating boundary information derived from deep contextual learning to LOGISMOS - layered optimal graph image segmentation of multiple objects and surfaces. Accurate and reliable tumor segmentation is essential to tumor growth analysis and treatment selection. A fully convolutional network (FCN), UNet, is first trained using three adjacent 2D patches centered at the tumor, providing contextual UNet segmentation and probability map for each 2D patch. The UNet segmentation is then refined by Gaussian Mixture Model (GMM) and morphological operations. The refined UNet segmentation is used to provide the initial shape boundary to build a segmentation graph. The cost for each node of the graph is determined by the UNet probability maps. Finally, a max-flow algorithm is employed to find the globally optimal solution thus obtaining the final segmentation. For evaluation, we applied the method to pancreatic tumor segmentation on a dataset of 51 CT scans, among which 30 scans were used for training and 21 for testing. With Deep LOGISMOS, DICE Similarity Coefficient (DSC) and Relative Volume Difference (RVD) reached 83.2+-7.8% and 18.6+-17.4% respectively, both are significantly improved (p<0.05) compared with contextual UNet and/or LOGISMOS alone.
A method for assessing the success and failure of community-level interventions in the presence of network diffusion, social reinforcement, and related social effects
Venue: arXiv cs.SI 2018
Year: 2018

Prevention and intervention work done within community settings often face unique analytic challenges for rigorous evaluations. Since community prevention work (often geographically isolated) cannot be controlled in the same way other prevention programs and these communities have an increased level of interpersonal interactions, rigorous evaluations are needed. Even when the `gold standard' randomized control trials are implemented within community intervention work, the threats to internal validity can be called into question given informal social spread of information in closed network settings. A new prevention evaluation method is presented here to disentangle the social influences assumed to influence prevention effects within communities. We formally introduce the method and it's utility for a suicide prevention program implemented in several Alaska Native villages. The results show promise to explore eight sociological measures of intervention effects in the face of social diffusion, social reinforcement, and direct treatment. Policy and research implication are discussed.
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST
Venue: arXiv cs.CV 2018
Year: 2018

Volumetric lesion segmentation via medical imaging is a powerful means to precisely assess multiple time-point lesion/tumor changes. Because manual 3D segmentation is prohibitively time consuming and requires radiological experience, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Despite their coarseness, RECIST marks are commonly found in current hospital picture and archiving systems (PACS), meaning they can provide a potentially powerful, yet extraordinarily challenging, source of weak supervision for full 3D segmentation. Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation. In addition, we explore how super-resolution images (2~5 times beyond the physical CT imaging), generated from a proposed stacked generative adversarial network, can aid the WSSS performance. We employ the DeepLesion dataset, a comprehensive CT-image lesion dataset of 32,735 PACS-bookmarked findings, which include lesions, tumors, and lymph nodes of varying sizes, categories, body regions and surrounding contexts. These are drawn from 10,594 studies of 4,459 patients. We also validate on a lymph-node dataset, where 3D ground truth masks are available for all images. For the DeepLesion dataset, we report mean Dice coefficients of 93% on RECIST-slices and 76% in 3D lesion volumes. We further validate using a subjective user study, where an experienced radiologist accepted our WSSS-generated lesion segmentation results with a high probability of 92.4%.
Context Models for OOV Word Translation in Low-Resource Languages
Venue: arXiv cs.CL 2018
Year: 2018

Out-of-vocabulary word translation is a major problem for the translation of low-resource languages that suffer from a lack of parallel training data. This paper evaluates the contributions of target-language context models towards the translation of OOV words, specifically in those cases where OOV translations are derived from external knowledge sources, such as dictionaries. We develop both neural and non-neural context models and evaluate them within both phrase-based and self-attention based neural machine translation systems. Our results show that neural language models that integrate additional context beyond the current sentence are the most effective in disambiguating possible OOV word translations. We present an efficient second-pass lattice-rescoring method for wide-context neural language models and demonstrate performance improvements over state-of-the-art self-attention based neural MT systems in five out of six low-resource language pairs.
Stop talking to me -- a communication-avoiding ADER-DG realisation
Venue: arXiv cs.MS 2018
Year: 2018

We present a communication- and data-sensitive formulation of ADER-DG for hyperbolic differential equation systems. Sensitive here has multiple flavours: First, the formulation reduces the persistent memory footprint. This reduces pressure on the memory subsystem. Second, the formulation realises the underlying predictor-corrector scheme with single-touch semantics, i.e., each degree of freedom is read on average only once per time step from the main memory. This reduces communication through the memory controllers. Third, the formulation breaks up the tight coupling of the explicit time stepping's algorithmic steps to mesh traversals. This averages out data access peaks. Different operations and algorithmic steps are ran on different grid entities. Finally, the formulation hides distributed memory data transfer behind the computation aligned with the mesh traversal. This reduces pressure on the machine interconnects. All techniques applied by our formulation are elaborated by means of a rigorous task formalism. They break up ADER-DG's tight causal coupling of compute steps and can be generalised to other predictor-corrector schemes.
Improved Finite Blocklength Converses for Slepian-Wolf Coding via Linear Programming
Venue: arXiv cs.IT 2018
Year: 2018

A new finite blocklength converse for the Slepian- Wolf coding problem is presented which significantly improves on the best known converse for this problem, due to Miyake and Kanaya [2]. To obtain this converse, an extension of the linear programming (LP) based framework for finite blocklength point- to-point coding problems from [3] is employed. However, a direct application of this framework demands a complicated analysis for the Slepian-Wolf problem. An analytically simpler approach is presented wherein LP-based finite blocklength converses for this problem are synthesized from point-to-point lossless source coding problems with perfect side-information at the decoder. New finite blocklength metaconverses for these point-to-point problems are derived by employing the LP-based framework, and the new converse for Slepian-Wolf coding is obtained by an appropriate combination of these converses.
Lattice-Based Group Signatures: Achieving Full Dynamicity (and Deniability) with Ease
Venue: arXiv cs.CR 2018
Year: 2018

In this work, we provide the first lattice-based group signature that offers full dynamicity (i.e., users have the flexibility in joining and leaving the group), and thus, resolve a prominent open problem posed by previous works. Moreover, we achieve this non-trivial feat in a relatively simple manner. Starting with Libert et al.'s fully static construction (Eurocrypt 2016) - which is arguably the most efficient lattice-based group signature to date, we introduce simple-but-insightful tweaks that allow to upgrade it directly into the fully dynamic setting. More startlingly, our scheme even produces slightly shorter signatures than the former, thanks to an adaptation of a technique proposed by Ling et al. (PKC 2013), allowing to prove inequalities in zero-knowledge. Our design approach consists of upgrading Libert et al.'s static construction (EUROCRYPT 2016) - which is arguably the most efficient lattice-based group signature to date - into the fully dynamic setting. Somewhat surprisingly, our scheme produces slightly shorter signatures than the former, thanks to a new technique for proving inequality in zero-knowledge without relying on any inequality check. The scheme satisfies the strong security requirements of Bootle et al.'s model (ACNS 2016), under the Short Integer Solution (SIS) and the Learning With Errors (LWE) assumptions. Furthermore, we demonstrate how to equip the obtained group signature scheme with the deniability functionality in a simple way. This attractive functionality, put forward by Ishida et al. (CANS 2016), enables the tracing authority to provide an evidence that a given user is not the owner of a signature in question. In the process, we design a zero-knowledge protocol for proving that a given LWE ciphertext does not decrypt to a particular message.
For Whom the Bell Trolls: Troll Behaviour in the Twitter Brexit Debate
Venue: arXiv cs.SI 2018
Year: 2018

In a review into automated and malicious activity Twitter released a list of accounts that they believed were connected to state sponsored manipulation of the 2016 American Election. This list details 2,752 accounts Twitter believed to be controlled by Russian operatives. In the absence of a similar list of operatives active within the debate on the 2016 UK referendum on membership of the European Union (Brexit) we investigated the behaviour of the same American Election focused accounts in the production of content related to the UK-EU referendum. We found that within our dataset we had Brexit-related content from 419 of these accounts, leading to 3,485 identified tweets gathered between the 29th August 2015 and 3rd October 2017. The behaviour of the accounts altered radically on the day of the referendum, shifting from generalised disruptive tweeting to retweeting each other in order to amplify content produced by other troll accounts. We also demonstrate that, while these accounts are, in general, designed to resemble American citizens, accounts created in 2016 often contained German locations and terms in the user profiles.
A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction
Venue: arXiv cs.RO 2018
Year: 2018

Picking up objects requested by a human user is a common task in human-robot interaction. When multiple objects match the user's verbal description, the robot needs to clarify which object the user is referring to before executing the action. Previous research has focused on perceiving user's multimodal behaviour to complement verbal commands or minimising the number of follow up questions to reduce task time. In this paper, we propose a system for reference disambiguation based on visualisation and compare three methods to disambiguate natural language instructions. In a controlled experiment with a YuMi robot, we investigated real-time augmentations of the workspace in three conditions -- mixed reality, augmented reality, and a monitor as the baseline -- using objective measures such as time and accuracy, and subjective measures like engagement, immersion, and display interference. Significant differences were found in accuracy and engagement between the conditions, but no differences were found in task time. Despite the higher error rates in the mixed reality condition, participants found that modality more engaging than the other two, but overall showed preference for the augmented reality condition over the monitor and mixed reality conditions.
D2.2 White-box methodologies, programming abstractions and libraries
Venue: arXiv cs.DC 2018
Year: 2018

This deliverable reports the results of white-box methodologies and early results of the first prototype of libraries and programming abstractions as available by project month 18 by Work Package 2 (WP2). It reports i) the latest results of Task 2.2 on white-box methodologies, programming abstractions and libraries for developing energy-efficient data structures and algorithms and ii) the improved results of Task 2.1 on investigating and modeling the trade-off between energy and performance of concurrent data structures and algorithms. The work has been conducted on two main EXCESS platforms: Intel platforms with recent Intel multicore CPUs and Movidius Myriad1 platform. Regarding white-box methodologies, we have devised new relaxed cache-oblivious models and proposed a new power model for Myriad1 platform and an energy model for lock-free queues on CPU platforms. For Myriad1 platform, the im- proved model now considers both computation and data movement cost as well as architecture and application properties. The model has been evaluated with a set of micro-benchmarks and application benchmarks. For Intel platforms, we have generalized the model for concurrent queues on CPU platforms to offer more flexibility according to the workers calling the data structure (parallel section sizes of enqueuers and dequeuers are decoupled). Regarding programming abstractions and libraries, we have continued investigat- ing the trade-offs between energy consumption and performance of data structures such as concurrent queues and concurrent search trees based on the early results of Task 2.1.The preliminary results show that our concurrent trees are faster and more energy efficient than the state-of-the-art on commodity HPC and embedded platforms.