OpenRaiser/Intern-Atlas huggingface dataset:
Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs
Venue: arXiv cs.AI 2017
Year: 2017

Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses. Vector space models excel at determining similarity between concepts, but are severely constrained when evaluating complex dependency relations and other logic-based operations that are a strength of knowledge graphs. We describe the VKG structure that helps unify knowledge graphs and vector representation of entities, and enables powerful inference methods and search capabilities that combine their complementary strengths. We analogize this to thinking `fast' in vector space along with thinking 'slow' and `deeply' by reasoning over the knowledge graph. We have created a query processing engine that takes complex queries and decomposes them into subqueries optimized to run on the respective knowledge graph or vector view of a VKG. We show that the VKG structure can process specific queries that are not efficiently handled by vector spaces or knowledge graphs alone. We also demonstrate and evaluate the VKG structure and the query processing engine by developing a system called Cyber-All-Intel for knowledge extraction, representation and querying in an end-to-end pipeline grounded in the cybersecurity informatics domain.
A discrete event system specification (DEVS)-based model of consanguinity
Venue: arXiv cs.SI 2017
Year: 2017

Consanguinity or inter-cousin marriage is a phenomenon quite prevalent in certain regions around the globe. Consanguineous parents have a higher risk of having offspring with congenital disorders. It is difficult to model large scale consanguineous parental populations because of disparate cultural issues unique to regions and cultures across the globe. Although consanguinity, as a social problem has been studied previously, consanguinity from a biological perspective has yet to be modeled. Discrete Event System Specification (DEVS) formalism is a powerful modeling formalism for the study of intricate details of real-world complex systems. In this article, we develop a DEVS model to get an insight into the role of consanguineous marriages in the evolution of congenital disorders in a population. As proof-of-concept, we develop a consanguinity simulation model in Simio simulation software. Our results show the effectiveness of DEVS in the modeling of consanguinity effects in causing congenital defects.
Making Sense of Word Embeddings
Venue: arXiv cs.CL 2017
Year: 2017

We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling of words in context with learned sense vectors, which gives rise to downstream applications. Experiments show that the performance of our method is comparable to state-of-the-art unsupervised WSD systems.
An Ensemble Classification Algorithm Based on Information Entropy for Data Streams
Venue: arXiv cs.DS 2017
Year: 2017

Data stream mining problem has caused widely concerns in the area of machine learning and data mining. In some recent studies, ensemble classification has been widely used in concept drift detection, however, most of them regard classification accuracy as a criterion for judging whether concept drift happening or not. Information entropy is an important and effective method for measuring uncertainty. Based on the information entropy theory, a new algorithm using information entropy to evaluate a classification result is developed. It uses ensemble classification techniques, and the weight of each classifier is decided through the entropy of the result produced by an ensemble classifiers system. When the concept in data streams changing, the classifiers' weight below a threshold value will be abandoned to adapt to a new concept in one time. In the experimental analysis section, six databases and four proposed algorithms are executed. The results show that the proposed method can not only handle concept drift effectively, but also have a better classification accuracy and time performance than the contrastive algorithms.
Quantify resilience enhancement of UTS through exploiting connect community and internet of everything emerging technologies
Venue: arXiv cs.CY 2017
Year: 2017

This work aims at investigating and quantifying the Urban Transport System (UTS) resilience enhancement enabled by the adoption of emerging technology such as Internet of Everything (IoE) and the new trend of the Connected Community (CC). A conceptual extension of Functional Resonance Analysis Method (FRAM) and its formalization have been proposed and used to model UTS complexity. The scope is to identify the system functions and their interdependencies with a particular focus on those that have a relation and impact on people and communities. Network analysis techniques have been applied to the FRAM model to identify and estimate the most critical community-related functions. The notion of Variability Rate (VR) has been defined as the amount of output variability generated by an upstream function that can be tolerated/absorbed by a downstream function, without significantly increasing of its subsequent output variability. A fuzzy based quantification of the VR on expert judgment has been developed when quantitative data are not available. Our approach has been applied to a critical scenario (water bomb/flash flooding) considering two cases: when UTS has CC and IoE implemented or not. The results show a remarkable VR enhancement if CC and IoE are deployed
Formation Control and Network Localization via Distributed Global Orientation Estimation in $3$-D
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we propose a novel distributed formation control strategy, which is based on the measurements of relative position of neighbors, with global orientation estimation in 3-dimensional space. Since agents do not share a common reference frame, orientations of the local reference frame are not aligned with each other. Under the orientation estimation law, a rotation matrix that identifies orientation of local frame with respect to a common frame is obtained by auxiliary variables. The proposed strategy includes a combination of global orientation estimation and formation control law. Since orientation of each agent is estimated in the global sense, formation control strategy ensures that the formation globally exponentially converges to the desired formation in 3-dimensional space.
Porting of the DBCSR library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi systems
Venue: arXiv cs.DC 2017
Year: 2017

Multiplication of two sparse matrices is a key operation in the simulation of the electronic structure of systems containing thousands of atoms and electrons. The highly optimized sparse linear algebra library DBCSR (Distributed Block Compressed Sparse Row) has been specifically designed to efficiently perform such sparse matrix-matrix multiplications. This library is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. It is parallelized using MPI and OpenMP, and can exploit GPU accelerators by means of CUDA. We describe a performance comparison of DBCSR on systems with Intel Xeon Phi Knights Landing (KNL) processors, with respect to systems with Intel Xeon CPUs (including systems with GPUs). We find that the DBCSR on Cray XC40 KNL-based systems is 11%-14% slower than on a hybrid Cray XC50 with Nvidia P100 cards, at the same number of nodes. When compared to a Cray XC40 system equipped with dual-socket Intel Xeon CPUs, the KNL is up to 24% faster.
iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering
Venue: arXiv cs.IR 2017
Year: 2017

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A collaborative filtering (CF) algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error (MAE).
Visualizing Time-Varying Particle Flows with Diffusion Geometry
Venue: arXiv cs.GR 2017
Year: 2017

The tasks of identifying separation structures and clusters in flow data are fundamental to flow visualization. Significant work has been devoted to these tasks in flow represented by vector fields, but there are unique challenges in addressing these tasks for time-varying particle data. The unstructured nature of particle data, nonuniform and sparse sampling, and the inability to access arbitrary particles in space-time make it difficult to define separation and clustering for particle data. We observe that weaker notions of separation and clustering through continuous measures of these structures are meaningful when coupled with user exploration. We achieve this goal by defining a measure of particle similarity between pairs of particles. More specifically, separation occurs when spatially-localized particles are dissimilar, while clustering is characterized by sets of particles that are similar to one another. To be robust to imperfections in sampling we use diffusion geometry to compute particle similarity. Diffusion geometry is parameterized by a scale that allows a user to explore separation and clustering in a continuous manner. We illustrate the benefits of our technique on a variety of 2D and 3D flow datasets, from particles integrated in fluid simulations based on time-varying vector fields, to particle-based simulations in astrophysics.
Learning Rotation for Kernel Correlation Filter
Venue: arXiv cs.CV 2017
Year: 2017

Kernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate rotation from one frame to another and enhance the detection of KCF. Hence it gains boost in overall accuracy in many of OBT50 detest videos with minimal additional computation.
The generalized vertex cover problem and some variations
Venue: arXiv cs.CC 2017
Year: 2017

In this paper we study the generalized vertex cover problem (GVC), which is a generalization of various well studied combinatorial optimization problems. GVC is shown to be equivalent to the unconstrained binary quadratic programming problem and also equivalent to some other variations of the general GVC. Some solvable cases are identified and approximation algorithms are suggested for special cases. We also study GVC on bipartite graphs and identify some polynomially solvable cases. We show that GVC on bipartite graphs is equivalent to the bipartite unconstrained 0-1 quadratic programming problem. Integer programming formulations of GVC and related problems are presented and establish half-integrality property on some variables for the corresponding linear programming relaxations. We also discuss special cases of GVC where all feasible solutions are independent sets or vertex covers. These problems are observed to be equivalent to the maximum weight independent set problem or minimum weight vertex cover problem along with some algorithmic results.
Sparse Coding and Autoencoders
Venue: arXiv cs.LG 2017
Year: 2017

In "Dictionary Learning" one tries to recover incoherent matrices $A^* \in \mathbb{R}^{n \times h}$ (typically overcomplete and whose columns are assumed to be normalized) and sparse vectors $x^* \in \mathbb{R}^h$ with a small support of size $h^p$ for some $0 <p < 1$ while having access to observations $y \in \mathbb{R}^n$ where $y = A^*x^*$. In this work we undertake a rigorous analysis of whether gradient descent on the squared loss of an autoencoder can solve the dictionary learning problem. The "Autoencoder" architecture we consider is a $\mathbb{R}^n \rightarrow \mathbb{R}^n$ mapping with a single ReLU activation layer of size $h$. Under very mild distributional assumptions on $x^*$, we prove that the norm of the expected gradient of the standard squared loss function is asymptotically (in sparse code dimension) negligible for all points in a small neighborhood of $A^*$. This is supported with experimental evidence using synthetic data. We also conduct experiments to suggest that $A^*$ is a local minimum. Along the way we prove that a layer of ReLU gates can be set up to automatically recover the support of the sparse codes. This property holds independent of the loss function. We believe that it could be of independent interest.
Flower Categorization using Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

We have developed a deep learning network for classification of different flowers. For this, we have used Visual Geometry Group's 102 category flower dataset having 8189 images of 102 different flowers from University of Oxford. The method is basically divided into two parts; Image segmentation and classification. We have compared the performance of two different Convolutional Neural Network architectures GoogLeNet and AlexNet for classification purpose. By keeping the hyper parameters same for both architectures, we have found that the top 1 and top 5 accuracies of GoogLeNet are 47.15% and 69.17% respectively whereas the top 1 and top 5 accuracies of AlexNet are 43.39% and 68.68% respectively. These results are extremely good when compared to random classification accuracy of 0.98%. This method for classification of flowers can be implemented in real time applications and can be used to help botanists for their research as well as camping enthusiasts.
Evacuating Two Robots from Two Unknown Exits on the Perimeter of a Disk
Venue: arXiv cs.DC 2017
Year: 2017

Distributed evacuation of mobile robots is a recent development. We consider the evacuation problem of two robots which are initially located at the center of a unit disk. Both the robots have to evacuate the disk through the exits situated on the perimeter of the disk at an unknown location. The distance between two exits along the perimeter $d$ is given. We consider two different communication models. First, in the wireless model, the robots can send a message to each other over a long distance. Second, in face-to-face communication model, the robots can exchange information with each other only when they touch each other. The objective of the evacuation problem is to design an algorithm which minimizes the evacuation time of both the robots. For the wireless communication model, we propose a generic algorithm for two robots moving to two points on the perimeter with an initial separation of $ζ\leq d$. We also investigate evacuation problem for both unlabeled and labeled exits in the wireless communication model. For the face-to-face communication model, we propose two different algorithms for $ζ=0$ and $ζ=d$ for unlabeled exits. We also propose a generic algorithm for $ζ\leq d$ for labeled exits. We provide lower bounds corresponding to different $d$ values in the face-to-face communication model. We evaluate the performance our algorithms with simulation for both of the communication models.
Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation
Venue: arXiv cs.IR 2017
Year: 2017

Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web. To alleviate the cold start problem, previous approaches have incorporated various additional sources of information into traditional matrix factorization models. These upgraded models, however, achieve only "marginal" enhancements on the performance of personalized recommendation. Therefore, inspired by the recent development of deep-semantic modeling, we propose a hybrid deep-semantic matrix factorization (HDMF) model to further improve the performance of tag-aware personalized recommendation by integrating the techniques of deep-semantic modeling, hybrid learning, and matrix factorization. Experimental results show that HDMF significantly outperforms the state-of-the-art baselines in tag-aware personalized recommendation, in terms of all evaluation metrics, e.g., its mean reciprocal rank (resp., mean average precision) is 1.52 (resp., 1.66) times as high as that of the best baseline.
Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues
Venue: arXiv cs.CV 2017
Year: 2017

In recent years, autonomous driving algorithms using low-cost vehicle-mounted cameras have attracted increasing endeavors from both academia and industry. There are multiple fronts to these endeavors, including object detection on roads, 3-D reconstruction etc., but in this work we focus on a vision-based model that directly maps raw input images to steering angles using deep networks. This represents a nascent research topic in computer vision. The technical contributions of this work are three-fold. First, the model is learned and evaluated on real human driving videos that are time-synchronized with other vehicle sensors. This differs from many prior models trained from synthetic data in racing games. Second, state-of-the-art models, such as PilotNet, mostly predict the wheel angles independently on each video frame, which contradicts common understanding of driving as a stateful process. Instead, our proposed model strikes a combination of spatial and temporal cues, jointly investigating instantaneous monocular camera observations and vehicle's historical states. This is in practice accomplished by inserting carefully-designed recurrent units (e.g., LSTM and Conv-LSTM) at proper network layers. Third, to facilitate the interpretability of the learned model, we utilize a visual back-propagation scheme for discovering and visualizing image regions crucially influencing the final steering prediction. Our experimental study is based on about 6 hours of human driving data provided by Udacity. Comprehensive quantitative evaluations demonstrate the effectiveness and robustness of our model, even under scenarios like drastic lighting changes and abrupt turning. The comparison with other state-of-the-art models clearly reveals its superior performance in predicting the due wheel angle for a self-driving car.
Full-duplex Multi-Antenna Relay Assisted Cooperative Non-Orthogonal Multiple Access
Venue: arXiv cs.IT 2017
Year: 2017

We consider a cooperative non-orthogonal multiple access (NOMA) network in which a full-duplex (FD) multi-antenna relay assists transmission from a base station (BS) to a set of far users with poor channel conditions, while at the same time the BS transmits to a set of near users with strong channel conditions. We assume imperfect self-interference (SI) cancellation at the FD relay and imperfect inter-user interference cancellation at the near users. In order to cancel the SI at the relay a zero-forcing based beamforming scheme is used and the corresponding outage probability analysis of two user selection strategies, namely random near user and random far user (RNRF), and nearest near user and nearest far user (NNNF), are derived. Our finding suggests that significant performance improvement can be achieved by using the FD multi-antenna relay compared to the counterpart system with a half-duplex relay. The achieved performance gain depends on network parameters such as the user density, user zones, path loss and the strength of the inter-user interference in case of near users. We also show that the NNNF strategy exhibits a superior outage performance compared to the RNRF strategy, especially in the case of near
Cooperation in 5G HetNets: Advanced Spectrum Access and D2D Assisted Communications
Venue: arXiv cs.NI 2017
Year: 2017

The evolution of conventional wireless communication networks to the fifth generation (5G) is driven by an explosive increase in the number of wireless mobile devices and services, as well as their demand for all-time and everywhere connectivity, high data rates, low latency, high energy-efficiency and improved quality of service. To address these challenges, 5G relies on key technologies, such as full duplex (FD), device-to-device (D2D) communications, and network densification. In this article, a heterogeneous networking architecture is envisioned, where cells of different sizes and radio access technologies coexist. Specifically, collaboration for spectrum access is explored for both FD- and cognitive-based approaches, and cooperation among devices is discussed in the context of the state-of-the-art D2D assisted communication paradigm. The presented cooperative framework is expected to advance the understandings of the critical technical issues towards dynamic spectrum management for 5G heterogeneous networks.
Optimizing Gross Merchandise Volume via DNN-MAB Dynamic Ranking Paradigm
Venue: arXiv cs.AI 2017
Year: 2017

With the transition from people's traditional `brick-and-mortar' shopping to online mobile shopping patterns in web 2.0 $\mathit{era}$, the recommender system plays a critical role in E-Commerce and E-Retails. This is especially true when designing this system for more than $\mathbf{236~million}$ daily active users. Ranking strategy, the key module of the recommender system, needs to be precise, accurate, and responsive for estimating customers' intents. We propose a dynamic ranking paradigm, named as DNN-MAB, that is composed of a pairwise deep neural network (DNN) $\mathit{pre}$-ranker connecting a revised multi-armed bandit (MAB) dynamic $\mathit{post}$-ranker. By taking into account of explicit and implicit user feedbacks such as impressions, clicks, conversions, etc. DNN-MAB is able to adjust DNN $\mathit{pre}$-ranking scores to assist customers locating items they are interested in most so that they can converge quickly and frequently. To the best of our knowledge, frameworks like DNN-MAB have not been discussed in the previous literature to either E-Commerce or machine learning audiences. In practice, DNN-MAB has been deployed to production and it easily outperforms against other state-of-the-art models by significantly lifting the gross merchandise volume (GMV) which is the objective metrics at JD.