OpenRaiser/Intern-Atlas huggingface dataset:
Dempster-Shafer Belief Function - A New Interpretation
Venue: arXiv cs.AI 2017
Year: 2017

We develop our interpretation of the joint belief distribution and of evidential updating that matches the following basic requirements: * there must exist an efficient method for reasoning within this framework * there must exist a clear correspondence between the contents of the knowledge base and the real world * there must be a clear correspondence between the reasoning method and some real world process * there must exist a clear correspondence between the results of the reasoning process and the results of the real world process corresponding to the reasoning process.
Constructions of optimal LCD codes over large finite fields
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we prove existence of optimal complementary dual codes (LCD codes) over large finite fields. We also give methods to generate orthogonal matrices over finite fields and then apply them to construct LCD codes. Construction methods include random sampling in the orthogonal group, code extension, matrix product codes and projection over a self-dual basis.
A Search for Improved Performance in Regular Expressions
Venue: arXiv cs.NE 2017
Year: 2017

The primary aim of automated performance improvement is to reduce the running time of programs while maintaining (or improving on) functionality. In this paper, Genetic Programming is used to find performance improvements in regular expressions for an array of target programs, representing the first application of automated software improvement for run-time performance in the Regular Expression language. This particular problem is interesting as there may be many possible alternative regular expressions which perform the same task while exhibiting subtle differences in performance. A benchmark suite of candidate regular expressions is proposed for improvement. We show that the application of Genetic Programming techniques can result in performance improvements in all cases. As we start evolution from a known good regular expression, diversity is critical in escaping the local optima of the seed expression. In order to understand diversity during evolution we compare an initial population consisting of only seed programs with a population initialised using a combination of a single seed individual with individuals generated using PI Grow and Ramped-half-and-half initialisation mechanisms.
Single Image Super-Resolution based on Wiener Filter in Similarity Domain
Venue: arXiv cs.CV 2017
Year: 2017

Single image super resolution (SISR) is an ill-posed problem aiming at estimating a plausible high resolution (HR) image from a single low resolution (LR) image. Current state-of-the-art SISR methods are patch-based. They use either external data or internal self-similarity to learn a prior for a HR image. External data based methods utilize large number of patches from the training data, while self-similarity based approaches leverage one or more similar patches from the input image. In this paper we propose a self-similarity based approach that is able to use large groups of similar patches extracted from the input image to solve the SISR problem. We introduce a novel prior leading to collaborative filtering of patch groups in 1D similarity domain and couple it with an iterative back-projection framework. The performance of the proposed algorithm is evaluated on a number of SISR benchmark datasets. Without using any external data, the proposed approach outperforms the current non-CNN based methods on the tested datasets for various scaling factors. On certain datasets, the gain is over 1 dB, when compared to the recent method A+. For high sampling rate (x4) the proposed method performs similarly to very recent state-of-the-art deep convolutional network based approaches.
Joint Transfer of Energy and Information in a Two-hop Relay Channel
Venue: arXiv cs.IT 2017
Year: 2017

We study the problem of joint information and energy transfer in a two-hop channel with a Radio frequency (RF) energy harvesting relay. We consider a finite battery size at the relay and deterministic energy loss in transmitting energy. In other words, to be able to send an energy-contained symbol, the relay must receive multiple energy-contained symbols. Thus, we face a kind of channel with memory. We model the energy saved in battery as channel state with the challenge that the receiver does not know the channel state. First, we consider the problem without any channel noise and derive an achievable rate. Next, we extend the results to the case with an independent and identically distributed noise in the second hop (the relay-receiver link).
Video Acceleration Magnification
Venue: arXiv cs.CV 2017
Year: 2017

The ability to amplify or reduce subtle image changes over time is useful in contexts such as video editing, medical video analysis, product quality control and sports. In these contexts there is often large motion present which severely distorts current video amplification methods that magnify change linearly. In this work we propose a method to cope with large motions while still magnifying small changes. We make the following two observations: i) large motions are linear on the temporal scale of the small changes; ii) small changes deviate from this linearity. We ignore linear motion and propose to magnify acceleration. Our method is pure Eulerian and does not require any optical flow, temporal alignment or region annotations. We link temporal second-order derivative filtering to spatial acceleration magnification. We apply our method to moving objects where we show motion magnification and color magnification. We provide quantitative as well as qualitative evidence for our method while comparing to the state-of-the-art.
Close Yet Distinctive Domain Adaptation
Venue: arXiv cs.LG 2017
Year: 2017

Domain adaptation is transfer learning which aims to generalize a learning model across training and testing data with different distributions. Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions. In this paper, we propose a close yet discriminative domain adaptation method, namely CDDA, which generates a latent feature representation with two interesting properties. First, the discrepancy between the source and target domain, measured in terms of both marginal and conditional probability distribution via Maximum Mean Discrepancy is minimized so as to attract two domains close to each other. More importantly, we also design a repulsive force term, which maximizes the distances between each label dependent sub-domain to all others so as to drag different class dependent sub-domains far away from each other and thereby increase the discriminative power of the adapted domain. Moreover, given the fact that the underlying data manifold could have complex geometric structure, we further propose the constraints of label smoothness and geometric structure consistency for label propagation. Extensive experiments are conducted on 36 cross-domain image classification tasks over four public datasets. The comprehensive results show that the proposed method consistently outperforms the state-of-the-art methods with significant margins.
On a Distributed Approach for Density-based Clustering
Venue: arXiv cs.DB 2017
Year: 2017

Efficient extraction of useful knowledge from these data is still a challenge, mainly when the data is distributed, heterogeneous and of different quality depending on its corresponding local infrastructure. To reduce the overhead cost, most of the existing distributed clustering approaches generate global models by aggregating local results obtained on each individual node. The complexity and quality of solutions depend highly on the quality of the aggregation. In this respect, we proposed for distributed density-based clustering that both reduces the communication overheads due to the data exchange and improves the quality of the global models by considering the shapes of local clusters. From preliminary results we show that this algorithm is very promising.
Dataset Augmentation for Pose and Lighting Invariant Face Recognition
Venue: arXiv cs.CV 2017
Year: 2017

The performance of modern face recognition systems is a function of the dataset on which they are trained. Most datasets are largely biased toward "near-frontal" views with benign lighting conditions, negatively effecting recognition performance on images that do not meet these criteria. The proposed approach demonstrates how a baseline training set can be augmented to increase pose and lighting variability using semi-synthetic images with simulated pose and lighting conditions. The semi-synthetic images are generated using a fast and robust 3-d shape estimation and rendering pipeline which includes the full head and background. Various methods of incorporating the semi-synthetic renderings into the training procedure of a state of the art deep neural network-based recognition system without modifying the structure of the network itself are investigated. Quantitative results are presented on the challenging IJB-A identification dataset using a state of the art recognition pipeline as a baseline.
Deep API Programmer: Learning to Program with APIs
Venue: arXiv cs.AI 2017
Year: 2017

We present DAPIP, a Programming-By-Example system that learns to program with APIs to perform data transformation tasks. We design a domain-specific language (DSL) that allows for arbitrary concatenations of API outputs and constant strings. The DSL consists of three family of APIs: regular expression-based APIs, lookup APIs, and transformation APIs. We then present a novel neural synthesis algorithm to search for programs in the DSL that are consistent with a given set of examples. The search algorithm uses recently introduced neural architectures to encode input-output examples and to model the program search in the DSL. We show that synthesis algorithm outperforms baseline methods for synthesizing programs on both synthetic and real-world benchmarks.
Point Sweep Coverage on Path
Venue: arXiv cs.DS 2017
Year: 2017

An important application of wireless sensor networks is the deployment of mobile sensors to periodically monitor (cover) a set of points of interest (PoIs). The problem of Point Sweep Coverage is to deploy fewest sensors to periodically cover the set of PoIs. For PoIs in a Eulerian graph, this problem is known NP-Hard even if all sensors are with uniform velocity. In this paper, we study the problem when PoIs are on a line and prove that the decision version of the problem is NP-Complete if the sensors are with different velocities. We first formulate the problem of Max-PoI sweep coverage on path (MPSCP) to find the maximum number of PoIs covered by a given set of sensors, and then show it is NP-Hard. We also extend it to the weighted case, Max-Weight sweep coverage on path (MWSCP) problem to maximum the sum of the weight of PoIs covered. For sensors with uniform velocity, we give a polynomial-time optimal solution to MWSCP. For sensors with constant kinds of velocities, we present a $\frac{1}{2}$-approximation algorithm. For the general case of arbitrary velocities, we propose two algorithms. One is a $\frac{1}{2α}$-approximation algorithm family scheme, where integer $α\ge2$ is the tradeoff factor to balance the time complexity and approximation ratio. The other is a $\frac{1}{2}(1-1/e)$-approximation algorithm by randomized analysis.
Encoding Cardinality Constraints using Generalized Selection Networks
Venue: arXiv cs.DS 2017
Year: 2017

Boolean cardinality constraints state that at most (at least, or exactly) $k$ out of $n$ propositional literals can be true. We propose a new class of selection networks that can be used for an efficient encoding of them. Several comparator networks have been proposed recently for encoding cardinality constraints and experiments have proved their efficiency. Those were based mainly on the odd-even or pairwise comparator networks. We use similar ideas, but we extend the model of comparator networks so that the basic components are not only comparators (2-sorters) but more general $m$-sorters, for $m \geq 2$. The inputs are organized into $m$ columns, in which elements are recursively selected and, after that, columns are merged using an idea of multi-way merging. We present two algorithms parametrized by $m \geq 2$. We call those networks $m$-Wise Selection Network and $m$-Odd-Even Selection Network. We give detailed construction of the mergers when $m=4$. The construction can be directly applied to any values of $k$ and $n$. The proposed encoding of sorters is standard, therefore the arc-consistency is preserved. We prove correctness of the constructions and present the theoretical and experimental evaluation, which show that the new encodings are competitive to the other state-of-art encodings.
A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints
Venue: arXiv cs.NI 2017
Year: 2017

We consider caching in cellular networks in which each base station is equipped with a cache that can store a limited number of files. The popularity of the files is known and the goal is to place files in the caches such that the probability that a user at an arbitrary location in the plane will find the file that she requires in one of the covering caches is maximized. We develop distributed asynchronous algorithms for deciding which contents to store in which cache. Such cooperative algorithms require communication only between caches with overlapping coverage areas and can operate in asynchronous manner. The development of the algorithms is principally based on an observation that the problem can be viewed as a potential game. Our basic algorithm is derived from the best response dynamics. We demonstrate that the complexity of each best response step is independent of the number of files, linear in the cache capacity and linear in the maximum number of base stations that cover a certain area. Then, we show that the overall algorithm complexity for a discrete cache placement is polynomial in both network size and catalog size. In practical examples, the algorithm converges in just a few iterations. Also, in most cases of interest, the basic algorithm finds the best Nash equilibrium corresponding to the global optimum. We provide two extensions of our basic algorithm based on stochastic and deterministic simulated annealing which find the global optimum. Finally, we demonstrate the hit probability evolution on real and synthetic networks numerically and show that our distributed caching algorithm performs significantly better than storing the most popular content, probabilistic content placement policy and Multi-LRU caching policies.
Parity Objectives in Countable MDPs
Venue: arXiv cs.LO 2017
Year: 2017

We study countably infinite MDPs with parity objectives, and special cases with a bounded number of colors in the Mostowski hierarchy (including reachability, safety, Buchi and co-Buchi). In finite MDPs there always exist optimal memoryless deterministic (MD) strategies for parity objectives, but this does not generally hold for countably infinite MDPs. In particular, optimal strategies need not exist. For countable infinite MDPs, we provide a complete picture of the memory requirements of optimal (resp., $ε$-optimal) strategies for all objectives in the Mostowski hierarchy. In particular, there is a strong dichotomy between two different types of objectives. For the first type, optimal strategies, if they exist, can be chosen MD, while for the second type optimal strategies require infinite memory. (I.e., for all objectives in the Mostowski hierarchy, if finite-memory randomized strategies suffice then also MD strategies suffice.) Similarly, some objectives admit $ε$-optimal MD-strategies, while for others $ε$-optimal strategies require infinite memory. Such a dichotomy also holds for the subclass of countably infinite MDPs that are finitely branching, though more objectives admit MD-strategies here.
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
Venue: arXiv cs.CV 2017
Year: 2017

The discriminative power of modern deep learning models for 3D human action recognition is growing ever so potent. In conjunction with the recent resurgence of 3D human action representation with 3D skeletons, the quality and the pace of recent progress have been significant. However, the inner workings of state-of-the-art learning based methods in 3D human action recognition still remain mostly black-box. In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition. Compared to popular LSTM-based Recurrent Neural Network models, given interpretable input such as 3D skeletons, TCN provides us a way to explicitly learn readily interpretable spatio-temporal representations for 3D human action recognition. We provide our strategy in re-designing the TCN with interpretability in mind and how such characteristics of the model is leveraged to construct a powerful 3D activity recognition method. Through this work, we wish to take a step towards a spatio-temporal model that is easier to understand, explain and interpret. The resulting model, Res-TCN, achieves state-of-the-art results on the largest 3D human action recognition dataset, NTU-RGBD.
RaPro: A Novel 5G Rapid Prototyping System Architecture
Venue: arXiv cs.IT 2017
Year: 2017

We propose a novel fifth-generation (5G) rapid prototyping (RaPro) system architecture by combining FPGA-privileged modules from a software defined radio (or FPGA-coprocessor) and high-level programming language for advanced algorithms from multi-core general purpose processors. The proposed system architecture exhibits excellent flexibility and scalability in the development of a 5G prototyping system. As a proof of concept, a multi-user full-dimension multiple-input and multiple-output system is established based on the proposed architecture. Experimental results demonstrate the superiority of the proposed architecture in large-scale antenna and wideband communication systems.
Generic LSH Families for the Angular Distance Based on Johnson-Lindenstrauss Projections and Feature Hashing LSH
Venue: arXiv cs.DS 2017
Year: 2017

In this paper we propose the creation of generic LSH families for the angular distance based on Johnson-Lindenstrauss projections. We show that feature hashing is a valid J-L projection and propose two new LSH families based on feature hashing. These new LSH families are tested on both synthetic and real datasets with very good results and a considerable performance improvement over other LSH families. While the theoretical analysis is done for the angular distance, these families can also be used in practice for the euclidean distance with excellent results [2]. Our tests using real datasets show that the proposed LSH functions work well for the euclidean distance.
FML-based Prediction Agent and Its Application to Game of Go
Venue: arXiv cs.AI 2017
Year: 2017

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.
Bounded Distributed Flocking Control of Nonholonomic Mobile Robots
Venue: arXiv cs.RO 2017
Year: 2017

There have been numerous studies on the problem of flocking control for multiagent systems whose simplified models are presented in terms of point-mass elements. Meanwhile, full dynamic models pose some challenging problems in addressing the flocking control problem of mobile robots due to their nonholonomic dynamic properties. Taking practical constraints into consideration, we propose a novel approach to distributed flocking control of nonholonomic mobile robots by bounded feedback. The flocking control objectives consist of velocity consensus, collision avoidance, and cohesion maintenance among mobile robots. A flocking control protocol which is based on the information of neighbor mobile robots is constructed. The theoretical analysis is conducted with the help of a Lyapunov-like function and graph theory. Simulation results are shown to demonstrate the efficacy of the proposed distributed flocking control scheme.
Angle-Domain Doppler Pre-Compensation for High-Mobility OFDM Uplink with a Massive ULA
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a Doppler pre-compensation scheme for high-mobility orthogonal frequency division multiplexing (OFDM) uplink, where a high-speed terminal transmits signals to the base station (BS). Considering that the time-varying multipath channel consists of multiple Doppler frequency offsets (DFOs) with different angle of departures (AoDs), we propose to perform DFO pre-compensation at the transmitter with a large-scale uniform linear array (ULA). The transmitted signal passes through a beamforming network with high-spatial resolution to produce multiple parallel branches. Each branch transmits signal towards one direction thus it is affected by one dominant DFO when passing over the time-varying channel. Therefore, we can compensate the DFO for each branch at the transmitter previously. Theoretical analysis for the Doppler spread of the equivalent uplink channel is also conducted. It is found that when the number of transmit antennas is sufficiently large, the time-variation of channel can be efficiently suppressed. Therefore, the performance will not degrade significantly if applying the conventional time-invariant channel estimation and equalization methods at the receiver. Simulation results are provided to verify the proposed scheme.
Automatic Bug Triage using Semi-Supervised Text Classification
Venue: arXiv cs.SE 2017
Year: 2017

In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectation-maximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the approach iteratively labels numerous unlabeled bug reports and trains a new classifier with labels of all the bug reports. We also employ a weighted recommendation list to boost the performance by imposing the weights of multiple developers in training the classifier. Experimental results on bug reports of Eclipse show that our new approach outperforms existing supervised approaches in terms of classification accuracy.