OpenRaiser/Intern-Atlas huggingface dataset:
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean
Venue: arXiv cs.CV 2017
Year: 2017

Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, automatic and consistent early detection and quantification of disease symptoms which are important in breeding programs for the development of improved cultivars and in crop production for the implementation of disease control measures for yield protection. Current methods of plant disease phenotyping are predominantly visual and hence are slow and prone to human error and variation. There has been increasing interest in hyperspectral imaging applications for early detection of disease symptoms. However, the high dimensionality of hyperspectral data makes it very important to have an efficient analysis pipeline in place for the identification of disease so that effective crop management decisions can be made. The focus of this work is to determine the minimal number of most effective hyperspectral bands that can distinguish between healthy and diseased specimens early in the growing season. Healthy and diseased hyperspectral data cubes were captured at 3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control specimens from 4 different genotypes. Each hyperspectral image was captured at 240 different wavelengths in the range of 383 to 1032 nm. We used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for identification of maximally effective band combinations. A binary classification between healthy and infected samples using six selected band combinations obtained a classification accuracy of 97% and a F1 score of 0.97 for the infected class. The results demonstrated that these carefully chosen bands are more informative than RGB images, and could be used in a multispectral camera for remote identification of charcoal rot infection in soybean.
Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey
Venue: arXiv cs.GT 2017
Year: 2017

This paper presents a comprehensive literature review on applications of economic and pricing theory for resource management in the evolving fifth generation (5G) wireless networks. The 5G wireless networks are envisioned to overcome existing limitations of cellular networks in terms of data rate, capacity, latency, energy efficiency, spectrum efficiency, coverage, reliability, and cost per information transfer. To achieve the goals, the 5G systems will adopt emerging technologies such as massive Multiple-Input Multiple-Output (MIMO), mmWave communications, and dense Heterogeneous Networks (HetNets). However, 5G involves multiple entities and stakeholders that may have different objectives, e.g., high data rate, low latency, utility maximization, and revenue/profit maximization. This poses a number of challenges to resource management designs of 5G. While the traditional solutions may neither efficient nor applicable, economic and pricing models have been recently developed and adopted as useful tools to achieve the objectives. In this paper, we review economic and pricing approaches proposed to address resource management issues in the 5G wireless networks including user association, spectrum allocation, and interference and power management. Furthermore, we present applications of economic and pricing models for wireless caching and mobile data offloading. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to the 5G wireless networks.
Compact Formulae in Sparse Elimination
Venue: arXiv cs.CC 2017
Year: 2017

It has by now become a standard approach to use the theory of sparse (or toric) elimination, based on the Newton polytope of a polynomial, in order to reveal and exploit the structure of algebraic systems. This talk surveys compact formulae, including older and recent results, in sparse elimination. We start with root bounds and juxtapose two recent formulae: a generating function of the m-B{é}zout bound and a closed-form expression for the mixed volume by means of a matrix permanent. For the sparse resultant, a bevy of results have established determinantal or rational formulae for a large class of systems, starting with Macaulay. The discriminant is closely related to the resultant but admits no compact formula except for very simple cases. We offer a new determinantal formula for the discriminant of a sparse multilinear system arising in computing Nash equilibria. We introduce an alternative notion of compact formula, namely the Newton polytope of the unknown polynomial. It is possible to compute it efficiently for sparse resultants, discriminants, as well as the implicit equation of a parameterized variety. This leads us to consider implicit matrix representations of geometric objects.
A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes
Venue: arXiv cs.NE 2017
Year: 2017

Neural networks with random hidden nodes have gained increasing interest from researchers and practical applications. This is due to their unique features such as very fast training and universal approximation property. In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned. Appropriate selection of the intervals from which weights and biases are selected is extremely important. This topic has not yet been sufficiently explored in the literature. In this work a method of generating random weights and biases is proposed. This method generates the parameters of the hidden nodes in such a way that nonlinear fragments of the activation functions are located in the input space regions with data and can be used to construct the surface approximating a nonlinear target function. The weights and biases are dependent on the input data range and activation function type. The proposed methods allows us to control the generalization degree of the model. These all lead to improvement in approximation performance of the network. Several experiments show very promising results.
On Parallel Solution of Sparse Triangular Linear Systems in CUDA
Venue: arXiv cs.MS 2017
Year: 2017

The acceleration of sparse matrix computations on modern many-core processors, such as the graphics processing units (GPUs), has been recognized and studied over a decade. Significant performance enhancements have been achieved for many sparse matrix computational kernels such as sparse matrix-vector products and sparse matrix-matrix products. Solving linear systems with sparse triangular structured matrices is another important sparse kernel as demanded by a variety of scientific and engineering applications such as sparse linear solvers. However, the development of efficient parallel algorithms in CUDA for solving sparse triangular linear systems remains a challenging task due to the inherently sequential nature of the computation. In this paper, we will revisit this problem by reviewing the existing level-scheduling methods and proposing algorithms with self-scheduling techniques. Numerical results have indicated that the CUDA implementations of the proposed algorithms can outperform the state-of-the-art solvers in cuSPARSE by a factor of up to $2.6$ for structured model problems and general sparse matrices.
An NFV and Microservice Based Architecture for On-the-fly Component Provisioning in Content Delivery Networks
Venue: arXiv cs.NI 2017
Year: 2017

Content Delivery Networks (CDNs) deliver content (e.g. Web pages, videos) to geographically distributed end-users over the Internet. Some contents do sometimes attract the attention of a large group of end-users. This often leads to flash crowds which can cause major issues such as outage in the CDN. Microservice architectural style aims at decomposing monolithic systems into smaller components which can be independently deployed, upgraded and disposed. Network Function Virtualization (NFV) is an emerging technology that aims to reduce costs and bring agility by decoupling network functions from the underlying hardware. This paper leverages the NFV and microservice architectural style to propose an architecture for on-the-fly CDN component provisioning to tackle issues such as flash crowds. In the proposed architecture, CDN components are designed as sets of microservices which interact via RESTFul Web services and are provisioned as Virtual Network Functions (VNFs), which are deployed and orchestrated on-the-fly. We have built a prototype in which a CDN surrogate server, designed as a set of microservices, is deployed on-the-fly. The prototype is deployed on SAVI, a Canadian distributed test bed for future Internet applications. The performance is also evaluated.
A Distributed Auction Policy for User Association in Device-to-Device Caching Networks
Venue: arXiv cs.IT 2017
Year: 2017

We propose a distributed bidding-aided Matern carrier sense multiple access (CSMA) policy for device-to-device (D2D) content distribution. The network is composed of D2D receivers and potential D2D transmitters, i.e., transmitters are turned on or off by the scheduling algorithm. Each D2D receiver determines the value of its request, by bidding on the set of potential transmitters in its communication range. Given a medium access probability, a fraction of the potential transmitters are jointly scheduled, i.e., turned on, determined jointly by the auction policy and the power control scheme. The bidding-aided scheduling algorithm exploits (i) the local demand distribution, (ii) spatial distribution of D2D node locations, and (iii) the cache configurations of the potential transmitters. We contrast the performance of the bidding-aided CSMA policy with other well-known CSMA schemes that do not take into account (i)-(iii), demonstrate that our algorithm achieves a higher spectral efficiency in terms of the number of bits transmitted per unit time per unit bandwidth per user. The gain becomes even more visible under randomized configurations and requests rather than more skewed placement configurations and deterministic demand distributions.
Improving Shadow Suppression for Illumination Robust Face Recognition
Venue: arXiv cs.CV 2017
Year: 2017

2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world. An illumination robust preprocessing method thus remains a significant challenge in reliable face analysis. In this paper we propose a novel approach for improving lighting normalization through building the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborates the formation of face color appearance. Specifically, the proposed illumination processing pipeline enables the generation of Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust to illumination variations. Moreover, as an advantage over most prevailing methods, a photo-realistic color face image is subsequently reconstructed which eliminates a wide variety of shadows whilst retaining the color information and identity details. Experimental results under different scenarios and using various face databases show the effectiveness of the proposed approach to deal with lighting variations, including both soft and hard shadows, in face recognition.
Burn-In Demonstrations for Multi-Modal Imitation Learning
Venue: arXiv cs.LG 2017
Year: 2017

Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data. However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learning techniques that produce unstable policies. This work extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce behavior over an extended period of time. Our approach involves reformulating the typical imitation learning setting to include "burn-in demonstrations" upon which policies are conditioned at test time. We demonstrate that our approach outperforms standard InfoGAIL in maximizing the mutual information between predicted and unseen style labels in road scene simulations, and we show that our method leads to policies that imitate expert autonomous driving systems over long time horizons.
A simple data discretizer
Venue: arXiv cs.LG 2017
Year: 2017

Data discretization is an important step in the process of machine learning, since it is easier for classifiers to deal with discrete attributes rather than continuous attributes. Over the years, several methods of performing discretization such as Boolean Reasoning, Equal Frequency Binning, Entropy have been proposed, explored, and implemented. In this article, a simple supervised discretization approach is introduced. The prime goal of MIL is to maximize classification accuracy of classifier, minimizing loss of information while discretization of continuous attributes. The performance of the suggested approach is compared with the supervised discretization algorithm Minimum Information Loss (MIL), using the state-of-the-art rule inductive algorithms- J48 (Java implementation of C4.5 classifier). The presented approach is, indeed, the modified version of MIL. The empirical results show that the modified approach performs better in several cases in comparison to the original MIL algorithm and Minimum Description Length Principle (MDLP) .
On complexity of mutlidistance graph recognition in $\mathbb{R}^1$
Venue: arXiv cs.CC 2017
Year: 2017

Let $\mathcal{A}$ be a set of positive numbers. A graph $G$ is called an $\mathcal{A}$-embeddable graph in $\mathbb{R}^d$ if the vertices of $G$ can be positioned in $\mathbb{R}^d$ so that the distance between endpoints of any edge is an element of $\mathcal{A}$. We consider the computational problem of recognizing $\mathcal{A}$-embeddable graphs in $\mathbb{R}^1$ and classify all finite sets $\mathcal{A}$ by complexity of this problem in several natural variations.
Learning Infinite RBMs with Frank-Wolfe
Venue: arXiv cs.LG 2017
Year: 2017

In this work, we propose an infinite restricted Boltzmann machine~(RBM), whose maximum likelihood estimation~(MLE) corresponds to a constrained convex optimization. We consider the Frank-Wolfe algorithm to solve the program, which provides a sparse solution that can be interpreted as inserting a hidden unit at each iteration, so that the optimization process takes the form of a sequence of finite models of increasing complexity. As a side benefit, this can be used to easily and efficiently identify an appropriate number of hidden units during the optimization. The resulting model can also be used as an initialization for typical state-of-the-art RBM training algorithms such as contrastive divergence, leading to models with consistently higher test likelihood than random initialization.
CNNComparator: Comparative Analytics of Convolutional Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

Convolutional neural networks (CNNs) are widely used in many image recognition tasks due to their extraordinary performance. However, training a good CNN model can still be a challenging task. In a training process, a CNN model typically learns a large number of parameters over time, which usually results in different performance. Often, it is difficult to explore the relationships between the learned parameters and the model performance due to a large number of parameters and different random initializations. In this paper, we present a visual analytics approach to compare two different snapshots of a trained CNN model taken after different numbers of epochs, so as to provide some insight into the design or the training of a better CNN model. Our system compares snapshots by exploring the differences in operation parameters and the corresponding blob data at different levels. A case study has been conducted to demonstrate the effectiveness of our system.
On the Fundamental Characteristics of Ultra-Dense Small Cell Networks
Venue: arXiv cs.NI 2017
Year: 2017

In order to cope with the forecasted 1000x increase in wireless capacity demands by 2030, network operators will aggressively densify their network infrastructure to reuse the spectrum as much as possible. However, it is important to realise that these new ultra-dense small cell networks are fundamentally different from the traditional macrocell or sparse small cell networks, and thus ultra-dense networks (UDNs) cannot be deployed and operated in the same way as in the last 25 years. In this paper, we systematically investigate and visualise the performance impacts of several fundamental characteristics of UDNs, that mobile operators and vendors should consider when deploying UDNs. Moreover, we also provide new deployment and management guidelines to address the main challenges brought by UDNs in the future.
Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection
Venue: arXiv cs.CR 2017
Year: 2017

The advance of smartphones and cellular networks boosts the need of mobile advertising and targeted marketing. However, it also triggers the unseen security threats. We found that the phone scams with fake calling numbers of very short lifetime are increasingly popular and have been used to trick the users. The harm is worldwide. On the other hand, deceptive advertising (deceptive ads), the fake ads that tricks users to install unnecessary apps via either alluring or daunting texts and pictures, is an emerging threat that seriously harms the reputation of the advertiser. To counter against these two new threats, the conventional blacklist (or whitelist) approach and the machine learning approach with predefined features have been proven useless. Nevertheless, due to the success of deep learning in developing the highly intelligent program, our system can efficiently and effectively detect phone scams and deceptive ads by taking advantage of our unified framework on deep neural network (DNN) and convolutional neural network (CNN). The proposed system has been deployed for operational use and the experimental results proved the effectiveness of our proposed system. Furthermore, we keep our research results and release experiment material on http://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is any update.
Synthesis in Distributed Environments
Venue: arXiv cs.LO 2017
Year: 2017

Most approaches to the synthesis of reactive systems study the problem in terms of a two-player game with complete observation. In many applications, however, the system's environment consists of several distinct entities, and the system must actively communicate with these entities in order to obtain information available in the environment. In this paper, we model such environments as a team of players and keep track of the information known to each individual player. This allows us to synthesize programs that interact with a distributed environment and leverage multiple interacting sources of information. The synthesis problem in distributed environments corresponds to solving a special class of Petri games, i.e., multi-player games played over Petri nets, where the net has a distinguished token representing the system and an arbitrary number of tokens representing the environment. While, in general, even the decidability of Petri games is an open question, we show that the synthesis problem in distributed environments can be solved in polynomial time for nets with up to two environment tokens. For an arbitrary but fixed number of three or more environment tokens, the problem is NP-complete. If the number of environment tokens grows with the size of the net, the problem is EXPTIME-complete.
Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network
Venue: arXiv cs.CV 2017
Year: 2017

Automatic multi-organ segmentation of the dual energy computed tomography (DECT) data can be beneficial for biomedical research and clinical applications. However, it is a challenging task. Recent advances in deep learning showed the feasibility to use 3-D fully convolutional networks (FCN) for voxel-wise dense predictions in single energy computed tomography (SECT). In this paper, we proposed a 3D FCN based method for automatic multi-organ segmentation in DECT. The work was based on a cascaded FCN and a general model for the major organs trained on a large set of SECT data. We preprocessed the DECT data by using linear weighting and fine-tuned the model for the DECT data. The method was evaluated using 42 torso DECT data acquired with a clinical dual-source CT system. Four abdominal organs (liver, spleen, left and right kidneys) were evaluated. Cross-validation was tested. Effect of the weight on the accuracy was researched. In all the tests, we achieved an average Dice coefficient of 93% for the liver, 90% for the spleen, 91% for the right kidney and 89% for the left kidney, respectively. The results show our method is feasible and promising.
Evolution of the Global Risk Network Mean-Field Stability Point
Venue: arXiv cs.SI 2017
Year: 2017

With a steadily growing human population and rapid advancements in technology, the global human network is increasing in size and connection density. This growth exacerbates networked global threats and can lead to unexpected consequences such as global epidemics mediated by air travel, threats in cyberspace, global governance, etc. A quantitative understanding of the mechanisms guiding this global network is necessary for proper operation and maintenance of the global infrastructure. Each year the World Economic Forum publishes an authoritative report on global risks, and applying this data to a CARP model, we answer critical questions such as how the network evolves over time. In the evolution, we compare not the current states of the global risk network at different time points, but its steady state at those points, which would be reached if the risk were left unabated. Looking at the steady states show more drastically the differences in the challenges to the global economy and stability the world community had faced at each point of the time. Finally, we investigate the influence between risks in the global network, using a method successful in distinguishing between correlation and causation. All results presented in the paper were obtained using detailed mathematical analysis with simulations to support our findings.
Asynchronous Early Output Section-Carry Based Carry Lookahead Adder with Alias Carry Logic
Venue: arXiv cs.AR 2017
Year: 2017

A new asynchronous early output section-carry based carry lookahead adder (SCBCLA) with alias carry output logic is presented in this paper. To evaluate the proposed SCBCLA with alias carry logic and to make a comparison with other CLAs, a 32-bit addition operation is considered. Compared to the weak-indication SCBCLA with alias logic, the proposed early output SCBCLA with alias logic reports a 13% reduction in area without any increases in latency and power dissipation. On the other hand, in comparison with the early output recursive CLA (RCLA), the proposed early output SCBCLA with alias logic reports a 16% reduction in latency while occupying almost the same area and dissipating almost the same average power. All the asynchronous CLAs are quasi-delay-insensitive designs which incorporate the delay-insensitive dual-rail data encoding and adhere to the 4-phase return-to-zero handshaking. The adders were realized and the simulations were performed based on a 32/28nm CMOS process.