OpenRaiser/Intern-Atlas huggingface dataset:
On generalized Lyndon words
Venue: arXiv cs.DM 2018
Year: 2018

A generalized lexicographical order on infinite words is defined by choosing for each position a total order on the alphabet. This allows to define generalized Lyndon words. Every word in the free monoid can be factorized in a unique way as a nonincreasing factorization of generalized Lyndon words. We give new characterizations of the first and the last factor in this factorization as well as new characterization of generalized Lyndon words. We also give more specific results on two special cases: the classical one and the one arising from the alternating lexicographical order.
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics
Venue: arXiv cs.CV 2018
Year: 2018

The internal states of most deep neural networks are difficult to interpret, which makes diagnosis and debugging during training challenging. Activation maximization methods are widely used, but lead to multiple optima and are hard to interpret (appear noise-like) for complex neurons. Image-based methods use maximally-activating image regions which are easier to interpret, but do not provide pixel-level insight into why the neuron responds to them. In this work we introduce an MCMC method: Langevin Dynamics Activation Maximization (LDAM), which is designed for diagnostic visualization. LDAM provides two affordances in combination: the ability to explore the set of maximally activating pre-images, and the ability to trade-off interpretability and pixel-level accuracy using a GAN-style discriminator as a regularizer. We present case studies on MNIST, CIFAR and ImageNet datasets exploring these trade-offs. Finally we show that diagnostic visualization using LDAM leads to a novel insight into the parameter averaging method for deep net training.
Unsupervised domain-agnostic identification of product names in social media posts
Venue: arXiv cs.CL 2018
Year: 2018

Product name recognition is a significant practical problem, spurred by the greater availability of platforms for discussing products such as social media and product review functionalities of online marketplaces. Customers, product manufacturers and online marketplaces may want to identify product names in unstructured text to extract important insights, such as sentiment, surrounding a product. Much extant research on product name identification has been domain-specific (e.g., identifying mobile phone models) and used supervised or semi-supervised methods. With massive numbers of new products released to the market every year such methods may require retraining on updated labeled data to stay relevant, and may transfer poorly across domains. This research addresses this challenge and develops a domain-agnostic, unsupervised algorithm for identifying product names based on Facebook posts. The algorithm consists of two general steps: (a) candidate product name identification using an off-the-shelf pretrained conditional random fields (CRF) model, part-of-speech tagging and a set of simple patterns; and (b) filtering of candidate names to remove spurious entries using clustering and word embeddings generated from the data.
Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks
Venue: arXiv cs.CV 2018
Year: 2018

The task in referring expression comprehension is to localise the object instance in an image described by a referring expression phrased in natural language. As a language-to-vision matching task, the key to this problem is to learn a discriminative object feature that can adapt to the expression used. To avoid ambiguity, the expression normally tends to describe not only the properties of the referent itself, but also its relationships to its neighbourhood. To capture and exploit this important information we propose a graph-based, language-guided attention mechanism. Being composed of node attention component and edge attention component, the proposed graph attention mechanism explicitly represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches. Furthermore, the proposed graph attention mechanism enables the comprehension decision to be visualisable and explainable. Experiments on three referring expression comprehension datasets show the advantage of the proposed approach.
A Tensor-based Structural Health Monitoring Approach for Aeroservoelastic Systems
Venue: arXiv cs.LG 2018
Year: 2018

Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is usually from sensor arrays, which results in data which are highly redundant and correlated, an area in which traditional two-way matrix approaches have had difficulty in deconstructing and interpreting. Newer methods involving tensor analysis allow us to analyse this multi-way structural data in a coherent manner. In our approach, we demonstrate the usefulness of tensor-based learning coupled with for damage detection, on a novel $N$-DoF Lagrangian aeroservoelastic model.
Model of a Data Mining System for Personalized Therapy of Speech Disorders
Venue: arXiv cs.CY 2018
Year: 2018

Lately, the children with speech disorder have more and more become object of specialists attention and investment in speech disorder therapy are increasing The development and use of information technology in order to assist and follow speech disorder therapy allowed researchers to collect a considerable volume of data. The aim of this paper is to present a data mining system designed to be associated with TERAPERS system in order to provide information based on which one could improve the process of personalized therapy of speech disorders.
Reconfigurable formations of quadrotors on Lissajous curves for surveillance applications
Venue: arXiv cs.RO 2018
Year: 2018

This paper proposes trajectory planning strategies for online reconfiguration of a multi-agent formation on a Lissajous curve. In our earlier work, a multi-agent formation with constant parametric speed was proposed in order to address multiple objectives such as repeated collision-free surveillance and guaranteed sensor coverage of the area with ability for rogue target detection and trapping. This work addresses the issue of formation reconfiguration within this context. In particular, smooth parametric trajectories are designed for the purpose using calculus of variations. These trajectories have been employed in conjunction with a simple local cooperation scheme so as to achieve collision-free reconfiguration between different Lissajous curves. A detailed theoretical analysis of the proposed scheme is provided. These surveillance and reconfiguration strategies have also been validated through simulations in MATLAB\reg for agents performing parametric motion along the curves, and by Software-In-The-Loop simulation for quadrotors. In addition, they are validated experimentally with a team of quadrotors flying in a motion capture environment.
DoS Attacks at Cooperative MAC
Venue: arXiv cs.NI 2018
Year: 2018

Cooperative networking brings performance improvement to most of the issues in wireless networks, such as fading or delay due to slow stations. However, due to cooperation when data is relayed via other nodes, there network is more prone to attacks. Since, channel access is very important for cooperation, most of the attacks happens at MAC. One of the most critical attack is denial of service, which is reason of cooperation failure. Therefore, the cooperative network as well as simple wireless LAN must be defensive against DOS attacks. In this article we analyzed all possible of DoS attacks that can happen at MAC layer of WLAN. The cooperative protocols must consider defense against these attacks. This article also provided survey of available solutions to these attacks. At the end it described its damages and cost as well as how to handle these attacks while devising cooperative MAC.
Structural accessibility and structural observability of nonlinear networked systems
Venue: arXiv cs.SY 2018
Year: 2018

The classical notions of structural controllability and structural observability are receiving increasing attention in Network Science, since they provide a mathematical basis to answer how the network structure of a dynamic system affects its controllability and observability properties. However, these two notions are formulated assuming systems with linear dynamics, which significantly limit their applicability. To overcome this limitation, here we introduce and fully characterize the notions "structural accessibility" and "structural observability" for systems with nonlinear dynamics. We show how nonlinearities make easier the problem of controlling and observing networked systems, reducing the number of variables that are necessary to directly control and directly measure. Our results contribute to understanding better the role that the network structure and nonlinearities play in our ability to control and observe complex dynamic systems.
Improved Topological Approximations by Digitization
Venue: arXiv cs.CG 2018
Year: 2018

Čech complexes are useful simplicial complexes for computing and analyzing topological features of data that lies in Euclidean space. Unfortunately, computing these complexes becomes prohibitively expensive for large-sized data sets even for medium-to-low dimensional data. We present an approximation scheme for $(1+ε)$-approximating the topological information of the Čech complexes for $n$ points in $\mathbb{R}^d$, for $ε\in(0,1]$. Our approximation has a total size of $n\left(\frac{1}ε\right)^{O(d)}$ for constant dimension $d$, improving all the currently available $(1+ε)$-approximation schemes of simplicial filtrations in Euclidean space. Perhaps counter-intuitively, we arrive at our result by adding additional $n\left(\frac{1}ε\right)^{O(d)}$ sample points to the input. We achieve a bound that is independent of the spread of the point set by pre-identifying the scales at which the Čech complexes changes and sampling accordingly.
STEP : A Distributed Multi-threading Framework Towards Efficient Data Analytics
Venue: arXiv cs.DC 2018
Year: 2018

Various general-purpose distributed systems have been proposed to cope with high-diversity applications in the pipeline of Big Data analytics. Most of them provide simple yet effective primitives to simplify distributed programming. While the rigid primitives offer great ease of use to savvy programmers, they probably compromise efficiency in performance and flexibility in data representation and programming specifications, which are critical properties in real systems. In this paper, we discuss the limitations of coarse-grained primitives and aim to provide an alternative for users to have flexible control over distributed programs and operate globally shared data more efficiently. We develop STEP, a novel distributed framework based on in-memory key-value store. The key idea of STEP is to adapt multi-threading in a single machine to a distributed environment. STEP enables users to take fine-grained control over distributed threads and apply task-specific optimizations in a flexible manner. The underlying key-value store serves as distributed shared memory to keep globally shared data. To ensure ease-of-use, STEP offers plentiful effective interfaces in terms of distributed shared data manipulation, cluster management, distributed thread management and synchronization. We conduct extensive experimental studies to evaluate the performance of STEP using real data sets. The results show that STEP outperforms the state-of-the-art general-purpose distributed systems as well as a specialized ML platform in many real applications.
Time Series Analysis and Forecasting of Internet Congestion Data
Venue: arXiv cs.NI 2018
Year: 2018

There has been a lot of discussion on Net Neutrality and policies that various network service providers and distributors adopt, at times leading to greater network congestion and thus more debates. The aim of this project is to use congestion traffic data to look at correlations between different service providers and message requests for different AS (Autonomous Systems). The RTT (Round Trip Time) from the time-series data of these messages is evaluated, to provide conclusive results of favoring certain websites over others. Lastly, this project attempts time-series prediction to forecast what a the time series will look like a few hours or days from now given the history of what it looked like before.
Bidirectional Type Checking for Relational Properties
Venue: arXiv cs.PL 2018
Year: 2018

Relational type systems have been designed for several applications including information flow, differential privacy, and cost analysis. In order to achieve the best results, these systems often use relational refinements and relational effects to maximally exploit the similarity in the structure of the two programs being compared. Relational type systems are appealing for relational properties because they deliver simpler and more precise verification than what could be derived from typing the two programs separately. However, relational type systems do not yet achieve the practical appeal of their non-relational counterpart, in part because of the lack of a general foundations for implementing them. In this paper, we take a step in this direction by developing bidirectional relational type checking for systems with relational refinements and effects. Our approach achieves the benefits of bidirectional type checking, in a relational setting. In particular, it significantly reduces the need for typing annotations through the combination of type checking and type inference. In order to highlight the foundational nature of our approach, we develop bidirectional versions of several relational type systems which incrementally combine many different components needed for expressive relational analysis.
An Interpretable Produce Price Forecasting System for Small and Marginal Farmers in India using Collaborative Filtering and Adaptive Nearest Neighbors
Venue: arXiv cs.CY 2018
Year: 2018

Small and marginal farmers, who account for over 80% of India's agricultural population, often sell their harvest at low, unfavorable prices before spoilage. These farmers often lack access to either cold storage or market forecasts. In particular, by having access to cold storage, farmers can store their produce for longer and thus have more flexibility as to when they should sell their harvest by. Meanwhile, by having access to market forecasts, farmers can more easily identify which markets to sell at and when. While affordable cold storage solutions have become more widely available, there has been less work on produce price forecasting. A key challenge is that in many regions of India, predominantly in rural and remote areas, we have either very limited or no produce pricing data available from public online sources. In this paper, we present a produce price forecasting system that pulls data from the Indian Ministry of Agriculture and Farmers Welfare's website Agmarknet, trains a model of prices using over a thousand markets, and displays interpretable price forecasts in a web application viewable from a mobile phone. Due to the pricing data being extremely sparse, our method first imputes missing entries using collaborative filtering to obtain a dense dataset. Using this imputed dense dataset, we then train a decision-tree-based classifier to predict produce prices at different markets. In terms of interpretability, we display the most relevant historical pricing data that drive each forecasted price, where we take advantage of the fact that a wide family of decision-tree-based ensemble learning methods are adaptive nearest neighbor methods. We show how to construct heuristic price uncertainty intervals based on nearest neighbors. We validate forecast accuracy on data from Agmarknet and a small field survey of a few markets in Odisha.
Tight Analyses for Non-Smooth Stochastic Gradient Descent
Venue: arXiv cs.LG 2018
Year: 2018

Consider the problem of minimizing functions that are Lipschitz and strongly convex, but not necessarily differentiable. We prove that after $T$ steps of stochastic gradient descent, the error of the final iterate is $O(\log(T)/T)$ with high probability. We also construct a function from this class for which the error of the final iterate of deterministic gradient descent is $Ω(\log(T)/T)$. This shows that the upper bound is tight and that, in this setting, the last iterate of stochastic gradient descent has the same general error rate (with high probability) as deterministic gradient descent. This resolves both open questions posed by Shamir (2012). An intermediate step of our analysis proves that the suffix averaging method achieves error $O(1/T)$ with high probability, which is optimal (for any first-order optimization method). This improves results of Rakhlin (2012) and Hazan and Kale (2014), both of which achieved error $O(1/T)$, but only in expectation, and achieved a high probability error bound of $O(\log \log(T)/T)$, which is suboptimal. We prove analogous results for functions that are Lipschitz and convex, but not necessarily strongly convex or differentiable. After $T$ steps of stochastic gradient descent, the error of the final iterate is $O(\log(T)/\sqrt{T})$ with high probability, and there exists a function for which the error of the final iterate of deterministic gradient descent is $Ω(\log(T)/\sqrt{T})$.
Nrityantar: Pose oblivious Indian classical dance sequence classification system
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we attempt to advance the research work done in human action recognition to a rather specialized application namely Indian Classical Dance (ICD) classification. The variation in such dance forms in terms of hand and body postures, facial expressions or emotions and head orientation makes pose estimation an extremely challenging task. To circumvent this problem, we construct a pose-oblivious shape signature which is fed to a sequence learning framework. The pose signature representation is done in two-fold process. First, we represent person-pose in first frame of a dance video using symmetric Spatial Transformer Networks (STN) to extract good person object proposals and CNN-based parallel single person pose estimator (SPPE). Next, the pose basis are converted to pose flows by assigning a similarity score between successive poses followed by non-maximal suppression. Instead of feeding a simple chain of joints in the sequence learner which generally hinders the network performance we constitute a feature vector of the normalized distance vectors, flow, angles between anchor joints which captures the adjacency configuration in the skeletal pattern. Thus, the kinematic relationship amongst the body joints across the frames using pose estimation helps in better establishing the spatio-temporal dependencies. We present an exhaustive empirical evaluation of state-of-the-art deep network based methods for dance classification on ICD dataset.
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
Venue: arXiv cs.RO 2018
Year: 2018

We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observation. The encoding is useful for sharing local visual observations with other agents under communication resource constraints. The actor-encoder encodes the raw images and chooses an action based on local observations and messages sent by the other agents. The machine learning agent generates not only an actuator command to the physical device, but also a communication message to the other agents. We formulate a reinforcement learning problem, which extends the action space to consider the communication action as well. The feasibility of the reinforcement learning framework is demonstrated using a 3D simulation environment with two collaborating agents. The environment provides realistic visual observations to be used and shared between the two agents.
Aerial Robot Model based design and verification of the single and multi-agent inspection application development
Venue: arXiv cs.MA 2018
Year: 2018

In recent decade, potential application of Unmanned Aerial Vehicles (UAV) has enabled replacement of various operations in hard-to-access areas, such as, inspection, surveillance or search and rescue applications in challenging and complex environments. Furthermore, aerial robotics application with multi-agent systems are anticipated to further extend its potential. However, one of the major difficulties in aerial robotics applications is the testing of the elaborated system within safety concerns, especially when multiple agents are simultaneously applied. Thus, virtual prototyping and simulation-based development can serve in development, assessment and improvement of the aerial robot applications. In this research, two examples of the specific applications are highlighted, harbor structure and facilities inspection with UAV, and development of autonomous positioning of multi-UAVs communication relaying system. In this research, virtual prototype was designed and further simulated in multi-body simulation (MBS) feigning the sensing and actuating equipment behaviors. Simultaneous simulation of the control and application system running with software in the loop (SITL) method is utilized to assess the designed hardware behavior with modular application nodes running in Robot Operating System. Furthermore, prepared simulation environment is assessed with multi-agent system, proposed in previous research with autonomous position control of communication relaying system. Application of the virtual prototype's simulation environment enables further examination of the proposed system within comparison degree with postfield tests. The research aims to contribute through case assessment of the design process to safer, time and cost-efficient development and application design in the field of aerial robotics.
Optimal Algorithm for Profiling Dynamic Arrays with Finite Values
Venue: arXiv cs.DS 2018
Year: 2018

How can one quickly answer the most and top popular objects at any time, given a large log stream in a system of billions of users? It is equivalent to find the mode and top-frequent elements in a dynamic array corresponding to the log stream. However, most existing work either restrain the dynamic array within a sliding window, or do not take advantages of only one element can be added or removed in a log stream. Therefore, we propose a profiling algorithm, named S-Profile, which is of $O(1)$ time complexity for every updating of the dynamic array, and optimal in terms of computational complexity. With the profiling results, answering the queries on the statistics of dynamic array becomes trivial and fast. With the experiments of various settings of dynamic arrays, our accurate S-Profile algorithm outperforms the well-known methods, showing at least 2X speedup to the heap based approach and 13X or larger speedup to the balanced tree based approach.