OpenRaiser/Intern-Atlas huggingface dataset:
An Automated CNN Recommendation System for Image Classification Tasks
Venue: arXiv cs.CV 2016
Year: 2016

Nowadays the CNN is widely used in practical applications for image classification task. However the design of the CNN model is very professional work and which is very difficult for ordinary users. Besides, even for experts of CNN, to select an optimal model for specific task may still need a lot of time (to train many different models). In order to solve this problem, we proposed an automated CNN recommendation system for image classification task. Our system is able to evaluate the complexity of the classification task and the classification ability of the CNN model precisely. By using the evaluation results, the system can recommend the optimal CNN model and which can match the task perfectly. The recommendation process of the system is very fast since we don't need any model training. The experiment results proved that the evaluation methods are very accurate and reliable.
Steerable CNNs
Venue: arXiv cs.LG 2016
Year: 2016

It has long been recognized that the invariance and equivariance properties of a representation are critically important for success in many vision tasks. In this paper we present Steerable Convolutional Neural Networks, an efficient and flexible class of equivariant convolutional networks. We show that steerable CNNs achieve state of the art results on the CIFAR image classification benchmark. The mathematical theory of steerable representations reveals a type system in which any steerable representation is a composition of elementary feature types, each one associated with a particular kind of symmetry. We show how the parameter cost of a steerable filter bank depends on the types of the input and output features, and show how to use this knowledge to construct CNNs that utilize parameters effectively.
Learning Non-Lambertian Object Intrinsics across ShapeNet Categories
Venue: arXiv cs.CV 2016
Year: 2016

We consider the non-Lambertian object intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. We build a large-scale object intrinsics database based on existing 3D models in the ShapeNet database. Rendered with realistic environment maps, millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth images are used to train an encoder-decoder CNN. Once trained, the network can decompose an image into the product of albedo and shading components, along with an additive specular component. Our CNN delivers accurate and sharp results in this classical inverse problem of computer vision, sharp details attributed to skip layer connections at corresponding resolutions from the encoder to the decoder. Benchmarked on our ShapeNet and MIT intrinsics datasets, our model consistently outperforms the state-of-the-art by a large margin. We train and test our CNN on different object categories. Perhaps surprising especially from the CNN classification perspective, our intrinsics CNN generalizes very well across categories. Our analysis shows that feature learning at the encoder stage is more crucial for developing a universal representation across categories. We apply our synthetic data trained model to images and videos downloaded from the internet, and observe robust and realistic intrinsics results. Quality non-Lambertian intrinsics could open up many interesting applications such as image-based albedo and specular editing.
FADEWICH: Fast Deauthentication over the Wireless Channel
Venue: arXiv cs.CR 2016
Year: 2016

Both authentication and deauthentication are instrumental for preventing unauthorized access to computer and data assets. While there are obvious motivating factors for using strong authentication mechanisms, convincing users to deauthenticate is not straight-forward, since deauthentication is not considered mandatory. A user who leaves a logged-in workstation unattended (especially for a short time) is typically not inconvenienced in any way; in fact, the other way around: no annoying reauthentication is needed upon return. However, an unattended workstation is trivially susceptible to the well-known "lunchtime attack" by any nearby adversary who simply takes over the departed user's log-in session. At the same time, since deathentication does not intrinsically require user secrets, it can, in principle, be made unobtrusive. To this end, this paper designs the first automatic user deauthentication system, FADEWICH, that does not rely on biometric- or behavior-based techniques (e.g., keystroke dynamics) and does not require users to carry any devices. It uses physical properties of wireless signals and the effect of human bodies on their propagation. To assess FADEWICH's feasibility and performance, extensive experiments were conducted with its prototype. Results show that it suffices to have nine inexpensive wireless sensors deployed in a shared office setting to correctly deauthenticate all users within six seconds (90% within four seconds) after they leave their workstation's vicinity. We considered two realistic scenarios where the adversary attempts to subvert FADEWICH and showed that lunchtime attacks fail.
A Peta-Scale Data Movement and Analysis in Data Warehouse (APSDMADW)
Venue: arXiv cs.DC 2016
Year: 2016

In this research paper so as to handle Information warehousing as well as online synthetic dispensation OLAP are necessary aspects of conclusion support which takes more and more turn into a focal point of the data source business.This paper offers an outline of information warehousing also OLAP systems with a highlighting on their latest necessities.All of us explain backside end tackle for extract clean-up and load information into an Data warehouse multi dimensional data model usual of OLAP frontend user tools for query and facts evaluation server extension for useful query dispensation and apparatus for metadata managing and for supervision the stockroom. Insights centered on complete data on customer actions manufactured goods act and souk performance are powerful advance and opposition in the internet gap .In this research conclude the company inspiration and the program and efficiency of servers working in a data warehouse through use of some new techniques and get better and efficient results. Data in petabyte scale. This test shows the data dropping rate in data warehouse. The locomotive is in creation at Yahoo! since 2007 and presently manages more than half a dozen peta bytes of data.
Symbolic Representation and Classification of Logos
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, a model for classification of logos based on symbolic representation of features is presented. The proposed model makes use of global features of logo images such as color, texture, and shape features for classification. The logo images are broadly classified into three different classes, viz., logo image containing only text, an image with only symbol, and an image with both text and a symbol. In each class, the similar looking logo images are clustered using K-means clustering algorithm. The intra-cluster variations present in each cluster corresponding to each class are then preserved using symbolic interval data. Thus referenced logo images are represented in the form of interval data. A sample logo image is then classified using suitable symbolic classifier. For experimentation purpose, relatively large amount of color logo images is created consisting of 5044 logo images. The classification results are validated with the help of accuracy, precision, recall, F-measure, and time. To check the efficacy of the proposed model, the comparative analyses are given against the other models. The results show that the proposed model outperforms the other models with respect to time and F-measure.
Optimization of Test Case Generation using Genetic Algorithm (GA)
Venue: arXiv cs.SE 2016
Year: 2016

Testing provides means pertaining to assuring software performance. The total aim of software industry is actually to make a certain start associated with high quality software for the end user. However, associated with software testing has quite a few underlying concerns, which are very important and need to pay attention on these issues. These issues are effectively generating, prioritization of test cases, etc. These issues can be overcome by paying attention and focus. Solitary of the greatest Problems in the software testing area is usually how to acquire a great proper set associated with cases to confirm software. Some other strategies and also methodologies are proposed pertaining to shipping care of most of these issues. Genetic Algorithm (GA) belongs to evolutionary algorithms. Evolutionary algorithms have a significant role in the automatic test generation and many researchers are focusing on it. In this study explored software testing related issues by using the GA approach. In addition to right after applying some analysis, better solution produced, that is feasible and reliable. The particular research presents the implementation of GAs because of its generation of optimized test cases. Along these lines, this paper gives proficient system for the optimization of test case generation using genetic algorithm.
Several Classes of Permutation Trinomials From Niho Exponents
Venue: arXiv cs.IT 2016
Year: 2016

Motivated by recent results on the constructions of permutation polynomials with few terms over the finite field $\mathbb{F}_{2^n}$, where $n$ is a positive even integer, we focus on the construction of permutation trinomials over $\mathbb{F}_{2^n}$ from Niho exponents. As a consequence, several new classes of permutation trinomials over $\mathbb{F}_{2^n}$ are constructed from Niho exponents based on some subtle manipulation of solving equations with low degrees over finite fields.
Fast color transfer from multiple images
Venue: arXiv cs.CV 2016
Year: 2016

Color transfer between images uses the statistics information of image effectively. We present a novel approach of local color transfer between images based on the simple statistics and locally linear embedding. A sketching interface is proposed for quickly and easily specifying the color correspondences between target and source image. The user can specify the correspondences of local region using scribes, which more accurately transfers the target color to the source image while smoothly preserving the boundaries, and exhibits more natural output results. Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different regions in the source image. Moreover, our algorithm does not require to choose the same color style and image size between source and target images. We propose the sub-sampling to reduce the computational load. Comparing with other approaches, our algorithm is much better in color blending in the input data. Our approach preserves the other color details in the source image. Various experimental results show that our approach specifies the correspondences of local color region in source and target images. And it expresses the intention of users and generates more actual and natural results of visual effect.
Efficient iterative policy optimization
Venue: arXiv cs.AI 2016
Year: 2016

We tackle the issue of finding a good policy when the number of policy updates is limited. This is done by approximating the expected policy reward as a sequence of concave lower bounds which can be efficiently maximized, drastically reducing the number of policy updates required to achieve good performance. We also extend existing methods to negative rewards, enabling the use of control variates.
On Covert Communication with Noise Uncertainty
Venue: arXiv cs.IT 2016
Year: 2016

Prior studies on covert communication with noise uncertainty adopted a worst-case approach from the warden's perspective. That is, the worst-case detection performance of the warden is used to assess covertness, which is overly optimistic. Instead of simply considering the worst limit, in this work, we take the distribution of noise uncertainty into account to evaluate the overall covertness in a statistical sense. Specifically, we define new metrics for measuring the covertness, which are then adopted to analyze the maximum achievable rate for a given covertness requirement under both bounded and unbounded noise uncertainty models.
Set-based Control for Autonomous Spray Painting
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, a method is presented for lowering the energy consumption and/or increasing the speed of a standard manipulator spray painting a surface. The approach is based on the observation that a small angle between the spray direction and the surface normal does not affect the quality of the paint job. Recent results in set-based kinematic control are utilized to develop a switched control system, where this angle is defined as a set-based task with a maximum allowed limit. Four different set-based methods are implemented and tested on a UR5 manipulator from Universal Robots. Experimental results verify the correctness of the method, and demonstrate that the set-based approaches can substantially lower the paint time and energy consumption compared to the current standard solution.
Phase-incorporating Speech Enhancement Based on Complex-valued Gaussian Process Latent Variable Model
Venue: arXiv cs.SD 2016
Year: 2016

Traditional speech enhancement techniques modify the magnitude of a speech in time-frequency domain, and use the phase of a noisy speech to resynthesize a time domain speech. This work proposes a complex-valued Gaussian process latent variable model (CGPLVM) to enhance directly the complex-valued noisy spectrum, modifying not only the magnitude but also the phase. The main idea that underlies the developed method is the modeling of short-time Fourier transform (STFT) coefficients across the time frames of a speech as a proper complex Gaussian process (GP) with noise added. The proposed method is based on projecting the spectrum into a low-dimensional subspace. The likelihood criterion is used to optimize the hyperparameters of the model. Experiments were carried out on the CHTTL database, which contains the digits zero to nine in Mandarin. Several standard measures are used to demonstrate that the proposed method outperforms baseline methods.
Connectivity based technique for localization of nodes in wireless sensor networks
Venue: arXiv cs.NI 2016
Year: 2016

We propose a localization algorithm for wireless sensor networks, which is simple in design, does not involve significant overhead and yet provides acceptable position estimates of sensor nodes. The algorithm uses settled nodes as beacon nodes so as to increase the number of beacon nodes. The algorithm is range free and does not need any additional piece of hardware for ranging. It also does not involve any significant communication overhead for localization. The simulation and results show that good localization accuracy is achieved for outdoor environments.
Unique Sense: Smart Computing Prototype for Industry 4.0 Revolution with IOT and Bigdata Implementation Model
Venue: arXiv cs.CY 2016
Year: 2016

Today, The Computing architectures are one of the most complex constrained developing area in the research field. Which delivers solution for different domains computation problem from its stack above. The architectural integration constrains makes difficulties to customize and modify the system for dynamic industrial and business needs. This model is the initiation towards the solution for findings of Industry 4.0 and Bigdata needs. This Unique sense smart computing implementation model for Industry 4.0 holds the innovative Smart computing prototype is a part of UNIQUE SENSE computing architecture which can delivers alternate solution for today's computing architecture to satisfy the future generation needs of diversified technologies and techniques, which brings extended support to the ubiquitous environment. Primitively the industrial 4.0 having a lots of chained interlinked process which also holds valuable information. So it is especially designed for fault tolerance data processing integrated system. This implementation model constructed in the way that smart control and selfaccessible system for next generation cyber physical machine and automation controlling system. Also that focusing towards dynamic customization, reusability, eco friendliness for next generation controlling and computation power.
A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning
Venue: arXiv cs.IT 2016
Year: 2016

Unlike the time-division duplexing (TDD) systems, the downlink (DL) and uplink (UL) channels are not reciprocal anymore in the case of frequency-division duplexing (FDD). However, some long-term parameters, e.g. the time delays and angles of arrival (AoAs) of the channel paths, still enjoy reciprocity. In this paper, by efficiently exploiting the aforementioned limited reciprocity, we address the DL channel state information (CSI) feedback in a practical wideband massive multiple-input multiple-output (MIMO) system operating in the FDD mode. With orthogonal frequency-division multiplexing (OFDM) waveform and assuming frequency-selective fading channels, we propose a scalable framework for the DL pilots design, DL CSI acquisition, and the corresponding CSI feedback in the UL. In particular, the base station (BS) can transmit the FFT-based pilots with the carefully-selected phase shifts. Then the user can rely on the so-called time-domain aggregate channel (TAC) to derive the feedback of reduced imensionality according to either its own knowledge about the statistics of the DL channels or the instruction from the serving BS. We demonstrate that each user can just feed back one scalar number per DL channel path for the BS to recover the DL CSIs. Comprehensive numerical results further corroborate our designs.
When the map is better than the territory
Venue: arXiv cs.IT 2016
Year: 2016

The causal structure of any system can be analyzed at a multitude of spatial and temporal scales. It has long been thought that while higher scale (macro) descriptions of causal structure may be useful to observers, they are at best a compressed description and at worse leave out critical information. However, recent research applying information theory to causal analysis has shown that the causal structure of some systems can actually come into focus (be more informative) at a macroscale (Hoel et al. 2013). That is, a macro model of a system (a map) can be more informative than a fully detailed model of the system (the territory). This has been called causal emergence. While causal emergence may at first glance seem counterintuitive, this paper grounds the phenomenon in a classic concept from information theory: Shannon's discovery of the channel capacity. I argue that systems have a particular causal capacity, and that different causal models of those systems take advantage of that capacity to various degrees. For some systems, only macroscale causal models use the full causal capacity. Such macroscale causal models can either be coarse-grains, or may leave variables and states out of the model (exogenous) in various ways, which can improve the model's efficacy and its informativeness via the same mathematical principles of how error-correcting codes take advantage of an information channel's capacity. As model choice increase, the causal capacity of a system approaches the channel capacity. Ultimately, this provides a general framework for understanding how the causal structure of some systems cannot be fully captured by even the most detailed microscopic model.
Fuzzy Constraints Linear Discriminant Analysis
Venue: arXiv cs.AI 2016
Year: 2016

In this paper we introduce a fuzzy constraint linear discriminant analysis (FC-LDA). The FC-LDA tries to minimize misclassification error based on modified perceptron criterion that benefits handling the uncertainty near the decision boundary by means of a fuzzy linear programming approach with fuzzy resources. The method proposed has low computational complexity because of its linear characteristics and the ability to deal with noisy data with different degrees of tolerance. Obtained results verify the success of the algorithm when dealing with different problems. Comparing FC-LDA and LDA shows superiority in classification task.
Parichayana: An Eclipse Plugin for Detecting Exception Handling Anti-Patterns and Code Smells in Java Programs
Venue: arXiv cs.SE 2016
Year: 2016

Anti-patterns and code-smells are signs in the source code which are not defects (does not prevent the program from functioning and does not cause compile errors) and are rather indicators of deeper and bigger problems. Exception handling is a programming construct de- signed to handle the occurrence of anomalous or exceptional conditions (that changes the normal flow of program execution). In this paper, we present an Eclipse plug-in (called as Parichayana) for detecting exception handling anti-patterns and code smells in Java programs. Parichayana is capable of automatically detecting several commonly occurring excep- tion handling programming mistakes. We extend the Eclipse IDE and create new menu entries and associated action via the Parichayana plug- in (free and open-source hosted on GitHub). We compare and contrast Parichayana with several code smell detection tools and demonstrate that our tool provides unique capabilities in context to existing tools. We have created an update site and developers can use the Eclipse up- date manager to install Parichayana from our site. We used Parichyana on several large open-source Java based projects and detected presence of exception handling anti-patterns
Social Media Argumentation Mining: The Quest for Deliberateness in Raucousness
Venue: arXiv cs.CL 2016
Year: 2016

Argumentation mining from social media content has attracted increasing attention. The task is both challenging and rewarding. The informal nature of user-generated content makes the task dauntingly difficult. On the other hand, the insights that could be gained by a large-scale analysis of social media argumentation make it a very worthwhile task. In this position paper I discuss the motivation for social media argumentation mining, as well as the tasks and challenges involved.
Complete DFM Model for High-Performance Computing SoCs with Guard Ring and Dummy Fill Effect
Venue: arXiv cs.ET 2016
Year: 2016

For nanotechnology, the semiconductor device is scaled down dramatically with additional strain engineering for device enhancement, the overall device characteristic is no longer dominated by the device size but also circuit layout. The higher order layout effects, such as well proximity effect (WPE), oxide spacing effect (OSE) and poly spacing effect (PSE), play an important role for the device performance, it is critical to understand Design for Manufacturability (DFM) impacts with various layout topology toward the overall circuit performance. Currently, the layout effects (WPE, OSE and PSE) are validated through digital standard cell and analog differential pair test structure. However, two analog layout structures: the guard ring and dummy fill impact are not well studied yet, then, this paper describes the current mirror test circuit to examine the guard ring and dummy fills DFM impacts using TSMC 28nm HPM process.