OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Extraction of Representative Concepts from Scientific Literature
Venue: arXiv cs.IR 2017
Year: 2017

This paper studies the automated categorization and extraction of scientific concepts from titles of scientific articles, in order to gain a deeper understanding of their key contributions and facilitate the construction of a generic academic knowledgebase. Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the first phase of our algorithm we propose PhraseType, a probabilistic generative model which exploits textual features and limited POS tags to broadly segment text snippets into aspect-typed phrases. We extend this model to simultaneously learn aspect-specific features and identify academic domains in multi-domain corpora, since the two tasks mutually enhance each other. In the second phase, we propose an approach based on adaptor grammars to extract fine grained concept mentions from the aspect-typed phrases without the need for any external resources or human effort, in a purely data-driven manner. We apply our technique to study literature from diverse scientific domains and show significant gains over state-of-the-art concept extraction techniques. We also present a qualitative analysis of the results obtained.
Rainbow: Combining Improvements in Deep Reinforcement Learning
Venue: arXiv cs.AI 2017
Year: 2017

The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.
Detecting the Moment of Completion: Temporal Models for Localising Action Completion
Venue: arXiv cs.CV 2017
Year: 2017

Action completion detection is the problem of modelling the action's progression towards localising the moment of completion - when the action's goal is confidently considered achieved. In this work, we assess the ability of two temporal models, namely Hidden Markov Models (HMM) and Long-Short Term Memory (LSTM), to localise completion for six object interactions: switch, plug, open, pull, pick and drink. We use a supervised approach, where annotations of pre-completion and post-completion frames are available per action, and fine-tuned CNN features are used to train temporal models. Tested on the Action-Completion-2016 dataset, we detect completion within 10 frames of annotations for ~75% of completed action sequences using both temporal models. Results show that fine-tuned CNN features outperform hand-crafted features for localisation, and that observing incomplete instances is necessary when incomplete sequences are also present in the test set.
Active Attack on User Load Achieving Pilot Design in Massive MIMO Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose an active attacking strategy on a massive multiple-input multiple-output (MIMO) network, where the pilot sequences are obtained using the user load-achieving pilot sequence design. The user load-achieving design ensures that the signal-to-interference-plus-noise ratio (SINR) requirements of all the users in the massive MIMO networks are guaranteed even in the presence of pilot contamination. However, this design has some vulnerabilities, such as one known pilot sequence and the correlation among the pilot sequences, that may be exploited by active attackers. In this work, we first identify the potential vulnerabilities in the user load-achieving pilot sequence design and then, accordingly, develop an active attacking strategy on the network. In the proposed attacking strategy, the active attackers transmit known pilot sequences in the uplink training and artificial noise in the downlink data transmission. Our examination demonstrates that the per-cell user load region is significantly reduced by the proposed attacking strategy. As a result of the reduced per-cell user load region, the SINR requirements of all the users are no longer guaranteed in the presence of the active attackers. Specifically, for the worst affected users the SINR requirements may not be ensured even with infinite antennas at the base station.
Towards a disaster response system based on cognitive radio ad hoc networks
Venue: arXiv cs.NI 2017
Year: 2017

This paper presents an approach towards disaster management based on cognitive radio ad hoc network. Despite the growing interests on cognitive radio ad hoc networks, not much work has been reported on using them for disaster management. This paper discusses opportunities for disaster management based on cognitive radio ad hoc networks. In this direction, the paper presents a novel technique for disaster detection based on Artificial Neural Network (ANN). The ANN is trained using backward propagation algorithm. An ANN-based spectrum sensing scheme is also presented. Finally, a service discovery scheme is presented for coordination during the time of disaster. The simulation of proposed approach has been performed in NS-2 simulator. The proposed approach shows very low false negative alarm rate using the proposed disaster detection system. The spectrum switching time of spectrum sensing scheme is also analyzed along with an analysis of latency of proposed service discovery scheme
Planning High-Quality Grasps using Mean Curvature Object Skeletons
Venue: arXiv cs.RO 2017
Year: 2017

In this work, we present a grasp planner which integrates two sources of information to generate robust grasps for a robotic hand. First, the topological information of the object model is incorporated by building the mean curvature skeleton and segmenting the object accordingly in order to identify object regions which are suitable for applying a grasp. Second, the local surface structure is investigated to construct feasible and robust grasping poses by aligning the hand according to the local object shape. We show how this information can be used to derive different grasping strategies, which also allows to distinguish between precision and power grasps. We applied the approach to a wide variety of object models of the KIT and the YCB real-world object model databases and evaluated the approach with several robotic hands. The results show that the skeleton-based grasp planner is capable to autonomously generate high-quality grasps in an efficient manner. In addition, we evaluate how robust the planned grasps are against hand positioning errors as they occur in real-world applications due to perception and actuation inaccuracies. The evaluation shows that the majority of the generated grasps are of high quality since they can be successfully applied even when the hand is not exactly positioned.
Using data science as a community advocacy tool to promote equity in urban renewal programs: An analysis of Atlanta's Anti-Displacement Tax Fund
Venue: arXiv cs.CY 2017
Year: 2017

Cities across the United States are undergoing great transformation and urban growth. Data and data analysis has become an essential element of urban planning as cities use data to plan land use and development. One great challenge is to use the tools of data science to promote equity along with growth. The city of Atlanta is an example site of large-scale urban renewal that aims to engage in development without displacement. On the Westside of downtown Atlanta, the construction of the new Mercedes-Benz Stadium and the conversion of an underutilized rail-line into a multi-use trail may result in increased property values. In response to community residents' concerns and a commitment to development without displacement, the city and philanthropic partners announced an Anti-Displacement Tax Fund to subsidize future property tax increases of owner occupants for the next twenty years. To achieve greater transparency, accountability, and impact, residents expressed a desire for a tool that would help them determine eligibility and quantify this commitment. In support of this goal, we use machine learning techniques to analyze historical tax assessment and predict future tax assessments. We then apply eligibility estimates to our predictions to estimate the total cost for the first seven years of the program. These forecasts are also incorporated into an interactive tool for community residents to determine their eligibility for the fund and the expected increase in their home value over the next seven years.
Low-resource bilingual lexicon extraction using graph based word embeddings
Venue: arXiv cs.CL 2017
Year: 2017

In this work we focus on the task of automatically extracting bilingual lexicon for the language pair Spanish-Nahuatl. This is a low-resource setting where only a small amount of parallel corpus is available. Most of the downstream methods do not work well under low-resources conditions. This is specially true for the approaches that use vectorial representations like Word2Vec. Our proposal is to construct bilingual word vectors from a graph. This graph is generated using translation pairs obtained from an unsupervised word alignment method. We show that, in a low-resource setting, these type of vectors are successful in representing words in a bilingual semantic space. Moreover, when a linear transformation is applied to translate words from one language to another, our graph based representations considerably outperform the popular setting that uses Word2Vec.
Implicit Decomposition for Write-Efficient Connectivity Algorithms
Venue: arXiv cs.DS 2017
Year: 2017

The future of main memory appears to lie in the direction of new technologies that provide strong capacity-to-performance ratios, but have write operations that are much more expensive than reads in terms of latency, bandwidth, and energy. Motivated by this trend, we propose sequential and parallel algorithms to solve graph connectivity problems using significantly fewer writes than conventional algorithms. Our primary algorithmic tool is the construction of an $o(n)$-sized "implicit decomposition" of a bounded-degree graph $G$ on $n$ nodes, which combined with read-only access to $G$ enables fast answers to connectivity and biconnectivity queries on $G$. The construction breaks the linear-write "barrier", resulting in costs that are asymptotically lower than conventional algorithms while adding only a modest cost to querying time. For general non-sparse graphs on $m$ edges, we also provide the first $o(m)$ writes and $O(m)$ operations parallel algorithms for connectivity and biconnectivity. These algorithms provide insight into how applications can efficiently process computations on large graphs in systems with read-write asymmetry.
Can Machines Think in Radio Language?
Venue: arXiv cs.AI 2017
Year: 2017

People can think in auditory, visual and tactile forms of language, so can machines principally. But is it possible for them to think in radio language? According to a first principle presented for general intelligence, i.e. the principle of language's relativity, the answer may give an exceptional solution for robot astronauts to talk with each other in space exploration.
OSU Multimodal Machine Translation System Report
Venue: arXiv cs.CL 2017
Year: 2017

This paper describes Oregon State University's submissions to the shared WMT'17 task "multimodal translation task I". In this task, all the sentence pairs are image captions in different languages. The key difference between this task and conventional machine translation is that we have corresponding images as additional information for each sentence pair. In this paper, we introduce a simple but effective system which takes an image shared between different languages, feeding it into the both encoding and decoding side. We report our system's performance for English-French and English-German with Flickr30K (in-domain) and MSCOCO (out-of-domain) datasets. Our system achieves the best performance in TER for English-German for MSCOCO dataset.
Rybu: Imperative-style Preprocessor for Verification of Distributed Systems in the Dedan Environment
Venue: arXiv cs.DC 2017
Year: 2017

Integrated Model of Distributed Systems (IMDS) is developed for specification and verification of distributed systems, and verification against deadlocks. On the basis of IMDS, Dedan verification environment was prepared. Universal deadlock detection formulas allow for automatic verification, without any knowledge of a temporal logic, which simplifies the verification process. However, the input language, following the rules of IMDS, seems to be exotic for many users. For this reason Rybu preprocessor was created. Its purpose is to build large models in imperative-style language, on much higher abstraction level.
Smarnet: Teaching Machines to Read and Comprehend Like Human
Venue: arXiv cs.CL 2017
Year: 2017

Machine Comprehension (MC) is a challenging task in Natural Language Processing field, which aims to guide the machine to comprehend a passage and answer the given question. Many existing approaches on MC task are suffering the inefficiency in some bottlenecks, such as insufficient lexical understanding, complex question-passage interaction, incorrect answer extraction and so on. In this paper, we address these problems from the viewpoint of how humans deal with reading tests in a scientific way. Specifically, we first propose a novel lexical gating mechanism to dynamically combine the words and characters representations. We then guide the machines to read in an interactive way with attention mechanism and memory network. Finally we add a checking layer to refine the answer for insurance. The extensive experiments on two popular datasets SQuAD and TriviaQA show that our method exceeds considerable performance than most state-of-the-art solutions at the time of submission.
Vulnerabilities of Massive MIMO Systems Against Pilot Contamination Attacks
Venue: arXiv cs.IT 2017
Year: 2017

We consider a single-cell massive MIMO system in which a base station (BS) with a large number of antennas transmits simultaneously to several single-antenna users in the presence of an attacker.The BS acquires the channel state information (CSI) based on uplink pilot transmissions. In this work, we demonstrate the vulnerability of CSI estimation phase to malicious attacks. For that purpose, we study two attack models. In the first model, the attacker aims at minimizing the sum-rate of downlink transmissions by contaminating the uplink pilots. In the second model, the attacker exploits its in-band full-duplex capabilities to generate jamming signals in both the CSI estimation and data transmission phases. We study these attacks under two downlink power allocation strategies when the attacker knows and does not know the locations of the BS and users. The formulated problems are solved using stochastic optimization, Lagrangian minimization, and game-theoretic methods. A closed-form solution for a special case of the problem is obtained. Furthermore, we analyze the achievable individual secrecy rates under a pilot contamination attack, and provide an upper bound on these rates. Our results indicate that the proposed attacks degrade the throughput of a massive MIMO system by more than half.
Discovery of Paradigm Dependencies
Venue: arXiv cs.DB 2017
Year: 2017

Missing and incorrect values often cause serious consequences. To deal with these data quality problems, a class of common employed tools are dependency rules, such as Functional Dependencies (FDs), Conditional Functional Dependencies (CFDs) and Edition Rules (ERs), etc. The stronger expressing ability a dependency has, data with the better quality can be obtained. To the best of our knowledge, all previous dependencies treat each attribute value as a non-splittable whole. Actually however, in many applications, part of a value may contains meaningful information, indicating that more powerful dependency rules to handle data quality problems are possible. In this paper, we consider of discovering such type of dependencies in which the left hand side is part of a regular-expression-like paradigm, named Paradigm Dependencies (PDs). PDs tell that if a string matches the paradigm, element at the specified position can decides a certain other attribute's value. We propose a framework in which strings with similar coding rules and different lengths are clustered together and aligned vertically, from which PDs can be discovered directly. The aligning problem is the key component of this framework and is proved in NP-Complete. A greedy algorithm is introduced in which the clustering and aligning tasks can be accomplished simultaneously. Because of the greedy algorithm's high time complexity, several pruning strategies are proposed to reduce the running time. In the experimental study, three real datasets as well as several synthetical datasets are employed to verify our methods' effectiveness and efficiency.
Complexity of Detectability, Opacity and A-Diagnosability for Modular Discrete Event Systems
Venue: arXiv cs.SY 2017
Year: 2017

We study the complexity of deciding whether a modular discrete event system is detectable (resp. opaque, A-diagnosable). Detectability arises in the state estimation of discrete event systems, opacity is related to the privacy and security analysis, and A-diagnosability appears in the fault diagnosis of stochastic discrete event systems. Previously, deciding weak detectability (opacity, A-diagnosability) for monolithic systems was shown to be PSPACE-complete. In this paper, we study the complexity of deciding weak detectability (opacity, A-diagnosability) for modular systems. We show that the complexities of these problems are significantly worse than in the monolithic case. Namely, we show that deciding modular weak detectability (opacity, A-diagnosability) is EXPSPACE-complete. We further discuss a special case where all unobservable events are private, and show that in this case the problems are PSPACE-complete. Consequently, if the systems are all fully observable, then deciding weak detectability (opacity) for modular systems is PSPACE-complete.
Visual Servoing of Unmanned Surface Vehicle from Small Tethered Unmanned Aerial Vehicle
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents an algorithm and the implementation of a motor schema to aid the visual localization subsystem of the ongoing EMILY project at Texas A and M University. The EMILY project aims to team an Unmanned Surface Vehicle (USV) with an Unmanned Aerial Vehicle (UAV) to augment the search and rescue of marine casualties during an emergency response phase. The USV is designed to serve as a flotation device once it reaches the victims. A live video feed from the UAV is provided to the casuality responders giving them a visual estimate of the USVs orientation and position to help with its navigation. One of the challenges involved with casualty response using a USV UAV team is to simultaneously control the USV and track it. In this paper, we present an implemented solution to automate the UAV camera movements to keep the USV in view at all times. The motor schema proposed, uses the USVs coordinates from the visual localization subsystem to control the UAVs camera movements and track the USV with minimal camera movements such that the USV is always in the cameras field of view.
Optimal Vertex Fault Tolerant Spanners (for fixed stretch)
Venue: arXiv cs.DS 2017
Year: 2017

A $k$-spanner of a graph $G$ is a sparse subgraph $H$ whose shortest path distances match those of $G$ up to a multiplicative error $k$. In this paper we study spanners that are resistant to faults. A subgraph $H \subseteq G$ is an $f$ vertex fault tolerant (VFT) $k$-spanner if $H \setminus F$ is a $k$-spanner of $G \setminus F$ for any small set $F$ of $f$ vertices that might "fail." One of the main questions in the area is: what is the minimum size of an $f$ fault tolerant $k$-spanner that holds for all $n$ node graphs (as a function of $f$, $k$ and $n$)? This question was first studied in the context of geometric graphs [Levcopoulos et al. STOC '98, Czumaj and Zhao SoCG '03] and has more recently been considered in general undirected graphs [Chechik et al. STOC '09, Dinitz and Krauthgamer PODC '11]. In this paper, we settle the question of the optimal size of a VFT spanner, in the setting where the stretch factor $k$ is fixed. Specifically, we prove that every (undirected, possibly weighted) $n$-node graph $G$ has a $(2k-1)$-spanner resilient to $f$ vertex faults with $O_k(f^{1 - 1/k} n^{1 + 1/k})$ edges, and this is fully optimal (unless the famous Erdos Girth Conjecture is false). Our lower bound even generalizes to imply that no data structure capable of approximating $dist_{G \setminus F}(s, t)$ similarly can beat the space usage of our spanner in the worst case. We also consider the edge fault tolerant (EFT) model, defined analogously with edge failures rather than vertex failures. We show that the same spanner upper bound applies in this setting. Our data structure lower bound extends to the case $k=2$ (and hence we close the EFT problem for $3$-approximations), but it falls to $Ω(f^{1/2 - 1/(2k)} \cdot n^{1 + 1/k})$ for $k \ge 3$. We leave it as an open problem to close this gap.
Checkpoint Ensembles: Ensemble Methods from a Single Training Process
Venue: arXiv cs.LG 2017
Year: 2017

We present the checkpoint ensembles method that can learn ensemble models on a single training process. Although checkpoint ensembles can be applied to any parametric iterative learning technique, here we focus on neural networks. Neural networks' composable and simple neurons make it possible to capture many individual and interaction effects among features. However, small sample sizes and sampling noise may result in patterns in the training data that are not representative of the true relationship between the features and the outcome. As a solution, regularization during training is often used (e.g. dropout). However, regularization is no panacea -- it does not perfectly address overfitting. Even with methods like dropout, two methodologies are commonly used in practice. First is to utilize a validation set independent to the training set as a way to decide when to stop training. Second is to use ensemble methods to further reduce overfitting and take advantage of local optima (i.e. averaging over the predictions of several models). In this paper, we explore checkpoint ensembles -- a simple technique that combines these two ideas in one training process. Checkpoint ensembles improve performance by averaging the predictions from "checkpoints" of the best models within single training process. We use three real-world data sets -- text, image, and electronic health record data -- using three prediction models: a vanilla neural network, a convolutional neural network, and a long short term memory network to show that checkpoint ensembles outperform existing methods: a method that selects a model by minimum validation score, and two methods that average models by weights. Our results also show that checkpoint ensembles capture a portion of the performance gains that traditional ensembles provide.