OpenRaiser/Intern-Atlas huggingface dataset:
Maximum Value Matters: Finding Hot Topics in Scholarly Fields
Venue: arXiv cs.SI 2017
Year: 2017

Finding hot topics in scholarly fields can help researchers to keep up with the latest concepts, trends, and inventions in their field of interest. Due to the rarity of complete large-scale scholarly data, earlier studies target this problem based on manual topic extraction from a limited number of domains, with their focus solely on a single feature such as coauthorship, citation relations, and etc. Given the compromised effectiveness of such predictions, in this paper we use a real scholarly dataset from Microsoft Academic Graph, which provides more than 12000 topics in the field of Computer Science (CS), including 1200 venues, 14.4 million authors, 30 million papers and their citation relations over the period of 1950 till now. Aiming to find the topics that will trend in CS area, we innovatively formalize a hot topic prediction problem where, with joint consideration of both inter- and intra-topical influence, 17 different scientific features are extracted for comprehensive description of topic status. By leveraging all those 17 features, we observe good accuracy of topic scale forecasting after 5 and 10 years with R2 values of 0.9893 and 0.9646, respectively. Interestingly, our prediction suggests that the maximum value matters in finding hot topics in scholarly fields, primarily from three aspects: (1) the maximum value of each factor, such as authors' maximum h-index and largest citation number, provides three times the amount of information than the average value in prediction; (2) the mutual influence between the most correlated topics serve as the most telling factor in long-term topic trend prediction, interpreting that those currently exhibiting the maximum growth rates will drive the correlated topics to be hot in the future; (3) we predict in the next 5 years the top 100 fastest growing (maximum growth rate) topics that will potentially get the major attention in CS area.
Universally Weakly Secure Coset Coding Schemes for Minimum Storage Regenerating (MSR) Codes
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of designing codes for distributed storage that protect user data against eavesdroppers that can gain access to network links as well as individual nodes. Our goal is to achieve weak security (also known as block security) that requires that the eavesdroppers would not be able to decode individual files or combinations of a small number of files. The standard approach for achieving block security is to use a joint design scheme that consists of (inner) storage code and the (outer) coset code. However, jointly designing the codes requires that the user, who pre-processes and stores the files, should know the underlying storage code in order to design the (outer) linear transformation for achieving weak security. In many practical scenarios, such as storing the files on the third party cloud storage system, it may not be possible for the user to know the underlying storage code. In this work, we present universal schemes that separate the outer code design from the storage code design for minimum storage regenerating codes (MSR). Our schemes allow the independent design of the storage code and the outer code. Our schemes use small field size and can be used in a broad range of practical settings.
Photo-Guided Exploration of Volume Data Features
Venue: arXiv cs.GR 2017
Year: 2017

In this work, we pose the question of whether, by considering qualitative information such as a sample target image as input, one can produce a rendered image of scientific data that is similar to the target. The algorithm resulting from our research allows one to ask the question of whether features like those in the target image exists in a given dataset. In that way, our method is one of imagery query or reverse engineering, as opposed to manual parameter tweaking of the full visualization pipeline. For target images, we can use real-world photographs of physical phenomena. Our method leverages deep neural networks and evolutionary optimization. Using a trained similarity function that measures the difference between renderings of a phenomenon and real-world photographs, our method optimizes rendering parameters. We demonstrate the efficacy of our method using a superstorm simulation dataset and images found online. We also discuss a parallel implementation of our method, which was run on NCSA's Blue Waters.
Typesafe Abstractions for Tensor Operations
Venue: arXiv cs.PL 2017
Year: 2017

We propose a typesafe abstraction to tensors (i.e. multidimensional arrays) exploiting the type-level programming capabilities of Scala through heterogeneous lists (HList), and showcase typesafe abstractions of common tensor operations and various neural layers such as convolution or recurrent neural networks. This abstraction could lay the foundation of future typesafe deep learning frameworks that runs on Scala/JVM.
VisDA: The Visual Domain Adaptation Challenge
Venue: arXiv cs.CV 2017
Year: 2017

We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains. Unsupervised domain adaptation aims to solve the real-world problem of domain shift, where machine learning models trained on one domain must be transferred and adapted to a novel visual domain without additional supervision. The VisDA2017 challenge is focused on the simulation-to-reality shift and has two associated tasks: image classification and image segmentation. The goal in both tracks is to first train a model on simulated, synthetic data in the source domain and then adapt it to perform well on real image data in the unlabeled test domain. Our dataset is the largest one to date for cross-domain object classification, with over 280K images across 12 categories in the combined training, validation and testing domains. The image segmentation dataset is also large-scale with over 30K images across 18 categories in the three domains. We compare VisDA to existing cross-domain adaptation datasets and provide a baseline performance analysis using various domain adaptation models that are currently popular in the field.
Visualizing Sensor Network Coverage with Location Uncertainty
Venue: arXiv cs.HC 2017
Year: 2017

We present an interactive visualization system for exploring the coverage in sensor networks with uncertain sensor locations. We consider a simple case of uncertainty where the location of each sensor is confined to a discrete number of points sampled uniformly at random from a region with a fixed radius. Employing techniques from topological data analysis, we model and visualize network coverage by quantifying the uncertainty defined on its simplicial complex representations. We demonstrate the capabilities and effectiveness of our tool via the exploration of randomly distributed sensor networks.
Unsupervised Object Discovery and Segmentation of RGBD-images
Venue: arXiv cs.RO 2017
Year: 2017

In this paper we introduce a system for unsupervised object discovery and segmentation of RGBD-images. The system models the sensor noise directly from data, allowing accurate segmentation without sensor specific hand tuning of measurement noise models making use of the recently introduced Statistical Inlier Estimation (SIE) method. Through a fully probabilistic formulation, the system is able to apply probabilistic inference, enabling reliable segmentation in previously challenging scenarios. In addition, we introduce new methods for filtering out false positives, significantly improving the signal to noise ratio. We show that the system significantly outperform state-of-the-art in on a challenging real-world dataset.
OhioState at IJCNLP-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis
Venue: arXiv cs.CL 2017
Year: 2017

This paper describes our systems for IJCNLP 2017 Shared Task on Customer Feedback Analysis. We experimented with simple neural architectures that gave competitive performance on certain tasks. This includes shallow CNN and Bi-Directional LSTM architectures with Facebook's Fasttext as a baseline model. Our best performing model was in the Top 5 systems using the Exact-Accuracy and Micro-Average-F1 metrics for the Spanish (85.28% for both) and French (70% and 73.17% respectively) task, and outperformed all the other models on comment (87.28%) and meaningless (51.85%) tags using Micro Average F1 by Tags metric for the French task.
On Reachable Sets of Hidden CPS Sensor Attacks
Venue: arXiv cs.SY 2017
Year: 2017

For given system dynamics, observer structure, and observer-based fault/attack detection procedure, we provide mathematical tools -- in terms of Linear Matrix Inequalities (LMIs) -- for computing outer ellipsoidal bounds on the set of estimation errors that attacks can induce while maintaining the alarm rate of the detector equal to its attack-free false alarm rate. We refer to these sets to as hidden reachable sets. The obtained ellipsoidal bounds on hidden reachable sets quantify the attacker's potential impact when it is constrained to stay hidden from the detector. We provide tools for minimizing the volume of these ellipsoidal bounds (minimizing thus the reachable sets) by redesigning the observer gains. Simulation results are presented to illustrate the performance of our tools.
Second Order Asymptotics for Communication under Strong Asynchronism
Venue: arXiv cs.IT 2017
Year: 2017

The capacity under strong asynchronism was recently shown to be essentially unaffected by the imposed output sampling rate $ρ$ and decoding delay $d$---the elapsed time between when information is available at the transmitter and when it is decoded. This paper examines this result in the finite blocklength regime and shows that, by contrast with capacity, the second order term in the rate expansion is sensitive to both parameters. When the receiver must exactly locate the sent codeword, that is $d=n$ where $n$ denotes blocklength, the second order term in the rate expansion is of order $Θ(1/ρ)$ for any $ρ=O(1/\sqrt{n})$---and $ρ=ω(1/n)$ for otherwise reliable communication is impossible. However, if $ρ=ω(1/\sqrt{n})$ then the second order term is the same as under full sampling and is given by a standard $O(\sqrt{n})$ term whose dispersion constant only depends on the level of asynchronism. This second order term also corresponds to the case of the slightly relaxed delay constraint $d\leq n(1+o(1))$ for any $ρ=ω(1/n)$.
Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species
Venue: arXiv cs.HC 2017
Year: 2017

Invasive species are a major cause of ecological damage and commercial losses. A current problem spreading in North America and Europe is the vinegar fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and healthy fruits and is therefore of concern to fruit growers, such as vintners. Consequently, large amounts of data about infestations have been collected in recent years. However, there is a lack of interactive methods to investigate this data. We employ ensemble-based classification to predict areas susceptible to infestation by D. suzukii and bring them into a spatio-temporal context using maps and glyph-based visualizations. Following the information-seeking mantra, we provide a visual analysis system Drosophigator for spatio-temporal event prediction, enabling the investigation of the spread dynamics of invasive species. We demonstrate the usefulness of this approach in two use cases.
Generative Adversarial Networks: An Overview
Venue: arXiv cs.CV 2017
Year: 2017

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.
Safe Pointers in SPARK 2014
Venue: arXiv cs.PL 2017
Year: 2017

In the context of deductive software verification, programs with pointers present a major challenge due to pointer aliasing. In this paper, we introduce pointers to SPARK, a well-defined subset of the Ada language, intended for formal verification of mission-critical software. Our solution is based on static alias analysis inspired by Rust's borrow-checker and affine types, and enforces the Concurrent Read, Exclusive Write principle. This analysis has been implemented in the GNAT Ada compiler and tested against a number of challenging examples including parts of real-life applications. Our tests show that only minor changes in the source code are required to fit the idiomatic Ada code into SPARK extended with pointers, which is a significant improvement upon the previous state of the art. The proposed extension has been approved by the Language Design Committee for SPARK for inclusion in a future version of SPARK, and is being discussed by the Ada Rapporteur Group for inclusion in the next version of Ada. In the report, we give a formal presentation of the analysis rules for a miniature version of SPARK and prove their soundness. We discuss the implementation and the case studies, and compare our solution with Rust.
On vertex coloring without monochromatic triangles
Venue: arXiv cs.DS 2017
Year: 2017

We study a certain relaxation of the classic vertex coloring problem, namely, a coloring of vertices of undirected, simple graphs, such that there are no monochromatic triangles. We give the first classification of the problem in terms of classic and parametrized algorithms. Several computational complexity results are also presented, which improve on the previous results found in the literature. We propose the new structural parameter for undirected, simple graphs -- the triangle-free chromatic number $χ_3$. We bound $χ_3$ by other known structural parameters. We also present two classes of graphs with interesting coloring properties, that play pivotal role in proving useful observation about our problem. We give/ask several conjectures/questions throughout this paper to encourage new research in the area of graph coloring.
UniWalk: Explainable and Accurate Recommendation for Rating and Network Data
Venue: arXiv cs.IR 2017
Year: 2017

How can we leverage social network data and observed ratings to correctly recommend proper items and provide a persuasive explanation for the recommendations? Many online services provide social networks among users, and it is crucial to utilize social information since recommendation by a friend is more likely to grab attention than the one from a random user. Also, explaining why items are recommended is very important in encouraging the users' actions such as actual purchases. Exploiting both ratings and social graph for recommendation, however, is not trivial because of the heterogeneity of the data. In this paper, we propose UniWalk, an explainable and accurate recommender system that exploits both social network and rating data. UniWalk combines both data into a unified graph, learns latent features of users and items, and recommends items to each user through the features. Importantly, it explains why items are recommended together with the recommendation results. Extensive experiments show that UniWalk provides the best explainability and achieves the state-of-the-art-accuracy.
Reaching a Target in the Plane with no Information
Venue: arXiv cs.DS 2017
Year: 2017

A mobile agent has to reach a target in the Euclidean plane. Both the agent and the target are modeled as points. In the beginning, the agent is at distance at most $D>0$ from the target. Reaching the target means that the agent gets at a {\em sensing distance} at most $r>0$ from it. The agent has a measure of length and a compass. We consider two scenarios: in the {\em static} scenario the target is inert, and in the {\em dynamic} scenario it may move arbitrarily at any (possibly varying) speed bounded by $v$. The agent has no information about the parameters of the problem, in particular it does not know $D$, $r$ or $v$. The goal is to reach the target at lowest possible cost, measured by the total length of the trajectory of the agent. Our main result is establishing the minimum cost (up to multiplicative constants) of reaching the target under both scenarios, and providing the optimal algorithm for the agent. For the static scenario the minimum cost is $Θ((\log D + \log \frac{1}{r}) D^2/r)$, and for the dynamic scenario it is $Θ((\log M + \log \frac{1}{r}) M^2/r)$, where $M=\max(D,v)$. Under the latter scenario, the speed of the agent in our algorithm grows exponentially with time, and we prove that for an agent whose speed grows only polynomially with time, this cost is impossible to achieve.
Block DCT filtering using vector processing
Venue: arXiv cs.CV 2017
Year: 2017

Filtering is an important issue in signals and images processing. Many images and videos are compressed using discrete cosine transform (DCT). For reducing the computation complexity, we are interested in filtering block and images directly in DCT domain. This article proposed an efficient and yet very simple filtering method directly in DCT domain for any symmetric, asymmetric, separable, inseparable and one or two dimensional filter. The proposed method is achieved by mathematical relations using vector processing for image filtering which it is equivalent to the spatial domain zero padding filtering. Also to avoid the zero padding artifacts around the edge of the block, we prepare preliminary matrices in DCT domain by implementation elements of selected mask which satisfies border replication for a block in the spatial domain. To evaluate the performance of the proposed algorithm, we compared the spatial domain filtering results with the results of the proposed method in DCT domain. The experiments show that the results of our proposed method in DCT are exactly the same as the spatial domain filtering.
Repairing Reed-Solomon codes: Universally achieving the cut-set bound for any number of erasures
Venue: arXiv cs.IT 2017
Year: 2017

The repair bandwidth of a code is the minimum amount of data required to repair one or several failed nodes (erasures). For MDS codes, the repair bandwidth is bounded below by the so-called cut-set bound, and codes that meet this bound with equality are said to support optimal repair of one or multiple failed nodes. We consider the problem of repairing multiple failed nodes of Reed-Solomon (RS) codes. In a recent work with I. Tamo (Proc. IEEE FOCS 2017), we gave the first explicit construction of RS codes with optimal repair of any single failed node from any subset of helper nodes. In this paper, we construct explicit RS codes that universally achieve the cut-set bound for the repair of any number of failed nodes from any set of helper nodes. Moreover, the node size of our codes is close to the optimal (smallest possible) node size of codes with such property.
Machine Learning as Statistical Data Assimilation
Venue: arXiv cs.LG 2017
Year: 2017

We identify a strong equivalence between neural network based machine learning (ML) methods and the formulation of statistical data assimilation (DA), known to be a problem in statistical physics. DA, as used widely in physical and biological sciences, systematically transfers information in observations to a model of the processes producing the observations. The correspondence is that layer label in the ML setting is the analog of time in the data assimilation setting. Utilizing aspects of this equivalence we discuss how to establish the global minimum of the cost functions in the ML context, using a variational annealing method from DA. This provides a design method for optimal networks for ML applications and may serve as the basis for understanding the success of "deep learning". Results from an ML example are presented. When the layer label is taken to be continuous, the Euler-Lagrange equation for the ML optimization problem is an ordinary differential equation, and we see that the problem being solved is a two point boundary value problem. The use of continuous layers is denoted "deepest learning". The Hamiltonian version provides a direct rationale for back propagation as a solution method for the canonical momentum; however, it suggests other solution methods are to be preferred.
Atypicality for Heart Rate Variability Using a Pattern-Tree Weighting Method
Venue: arXiv cs.LG 2017
Year: 2017

Heart rate variability (HRV) is a vital measure of the autonomic nervous system functionality and a key indicator of cardiovascular condition. This paper proposes a novel method, called pattern tree which is an extension of Willem's context tree to real-valued data, to investigate HRV via an atypicality framework. In a previous paper atypicality was developed as method for mining and discovery in "Big Data," which requires a universal approach. Using the proposed pattern tree as a universal source coder in this framework led to discovery of arrhythmias and unknown patterns in HRV Holter Monitoring.
Evaluation Process for an Introductory Programming Course Using Blended Learning
Venue: arXiv cs.CY 2017
Year: 2017

For an offer of the same course for thousands of students, for face-to-face or distance learning, some uniformities must be adopted to allow a comparison of performance in the teaching-learning processes. The Evaluation Unified Process (EUP) was designed and used in an introductory programming course called the Information Process (IP) for students of the Bachelor of Science and Technology of the Federal University of ABC. Initially, the face-to-face scenario (IP-Class) is presented, pointing out the problems identified, describing the EUP designed and applied in mixed learning environments (IP-BL), including the use of software tools developed for this purpose. The two scenarios (IP-Class and IP-BL) are compared with the use of data from all classes of the last 9 years of course application.
A Fast and Generic GPU-Based Parallel Reduction Implementation
Venue: arXiv cs.DC 2017
Year: 2017

Reduction operations are extensively employed in many computational problems. A reduction consists of, given a finite set of numeric elements, combining into a single value all elements in that set, using for this a combiner function. A parallel reduction, in turn, is the reduction operation concurrently performed when multiple execution units are available. The current work reports an investigation on this subject and depicts a GPU-based parallel approach for it. Employing techniques like Loop Unrolling, Persistent Threads and Algebraic Expressions to avoid thread divergence, the presented approach was able to achieve a 2.8x speedup when compared to the work of Catanzaro, using a generic, simple and easily portable code. Experiments conducted to evaluate the approach show that the strategy is able to perform efficiently in AMD and NVidia's hardware, as well as in OpenCL and CUDA.