OpenRaiser/Intern-Atlas huggingface dataset:
Inception Recurrent Convolutional Neural Network for Object Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields. In this paper, we introduce a new deep learning model called an Inception- Recurrent Convolutional Neural Network (IRCNN), which utilizes the power of an inception network combined with recurrent layers in DCNN architecture. We have empirically evaluated the recognition performance of the proposed IRCNN model using different benchmark datasets such as MNIST, CIFAR-10, CIFAR- 100, and SVHN. Experimental results show similar or higher recognition accuracy when compared to most of the popular DCNNs including the RCNN. Furthermore, we have investigated IRCNN performance against equivalent Inception Networks and Inception-Residual Networks using the CIFAR-100 dataset. We report about 3.5%, 3.47% and 2.54% improvement in classification accuracy when compared to the RCNN, equivalent Inception Networks, and Inception- Residual Networks on the augmented CIFAR- 100 dataset respectively.
SfM-Net: Learning of Structure and Motion from Video
Venue: arXiv cs.CV 2017
Year: 2017

We propose SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates. The model can be trained with various degrees of supervision: 1) self-supervised by the re-projection photometric error (completely unsupervised), 2) supervised by ego-motion (camera motion), or 3) supervised by depth (e.g., as provided by RGBD sensors). SfM-Net extracts meaningful depth estimates and successfully estimates frame-to-frame camera rotations and translations. It often successfully segments the moving objects in the scene, even though such supervision is never provided.
Separability by Piecewise Testable Languages is PTime-Complete
Venue: arXiv cs.FL 2017
Year: 2017

Piecewise testable languages form the first level of the Straubing-Thérien hierarchy. The membership problem for this level is decidable and testing if the language of a DFA is piecewise testable is NL-complete. The question has not yet been addressed for NFAs. We fill in this gap by showing that it is PSpace-complete. The main result is then the lower-bound complexity of separability of regular languages by piecewise testable languages. Two regular languages are separable by a piecewise testable language if the piecewise testable language includes one of them and is disjoint from the other. For languages represented by NFAs, separability by piecewise testable languages is known to be decidable in PTime. We show that it is PTime-hard and that it remains PTime-hard even for minimal DFAs.
Security Analysis of Tunnel Field-Effect Transistor for Low Power Hardware
Venue: arXiv cs.CR 2017
Year: 2017

Security and energy are considered as the most important parameters for designing and building a computing system nowadays. Today's attacks target different layers of the computing system (i.e. software and hardware). Introduction of new transistor technologies to the integrated circuits design is beneficial, especially for low energy requirements. The new devices have unique features and properties that provide security advantages. However, these properties may come to the aid of an adversary to design stronger attacks. Therefore, the advantages as well as the disadvantages of these technologies need to be well studied. This paper demonstrates the area and power efficiency of the tunnel field-effect transistor (TFET) technology along with analyzing its security aspects.
Models of fault-tolerant distributed computation via dynamic epistemic logic
Venue: arXiv cs.DC 2017
Year: 2017

The computability power of a distributed computing model is determined by the communication media available to the processes, the timing assumptions about processes and communication, and the nature of failures that processes can suffer. In a companion paper we showed how dynamic epistemic logic can be used to give a formal semantics to a given distributed computing model, to capture precisely the knowledge needed to solve a distributed task, such as consensus. Furthermore, by moving to a dual model of epistemic logic defined by simplicial complexes, topological invariants are exposed, which determine task solvability. In this paper we show how to extend the setting above to include in the knowledge of the processes, knowledge about the model of computation itself. The extension describes the knowledge processes gain about the current execution, in problems where processes have no input values at all.
Information Scientific and Educational Resource "Electrophysics"
Venue: arXiv cs.CY 2017
Year: 2017

The paper discusses an advanced level information system to support educational, research and scientific activities of the Department "Electrophysical Facilities" (DEF) of the National Research Nuclear University "MEPhI" (NRNU MEPhI), which is used for training of specialists of the course "Physics of Charged Particle Beams and Accelerator Technology".
A New Integrated FQFD Approach for Improving Quality and Reliability of Solar Drying Systems
Venue: arXiv cs.CY 2017
Year: 2017

Saffron is the most expensive spice and is significantly valuable in non-oil export. Drying process of saffron is considered as a critical control point with major effects on quality and safety parameters. A suitable drying method covering standards and market requirements while it is costlty benefitial and saves energy is desirable. Solar drying could be introduced as an appropriate procedure in rural and collecting sites of saffron since major micorobial and chemical factors of saffron can be preserved and achieved by using a renewable energy source. So, a precise system taking advantage of management, engineering and food technology sciences could be developed. Since there was no published record of integrated methods of Analytical Hierarchy Process (AHP) and Fuzzy Quality Function Deployment (FQFD) applied to solar energy drying systems, in this paper, Fuzzy Quality Function Deployment as a quality management tool by emphasizing technical and customer requirements has been implemented in order to improve quality parameters, optimizing technological expenses and market expansion strategy. Subsequently, Analytical Hierarchy Process based on survey from customers and logical pair-wise comparison are employed to decrease costs and increase the efficiency and the effectiveness of economic indicators. Using the integrated approach of AHP and FQFD in solar drying systems in saffron industry will result in cost benefit, quality improvement, the customer satisfaction enhancement, and the increase in saffron exports.
An ensemble-based online learning algorithm for streaming data
Venue: arXiv cs.LG 2017
Year: 2017

In this study, we introduce an ensemble-based approach for online machine learning. The ensemble of base classifiers in our approach is obtained by learning Naive Bayes classifiers on different training sets which are generated by projecting the original training set to lower dimensional space. We propose a mechanism to learn sequences of data using data chunks paradigm. The experiments conducted on a number of UCI datasets and one synthetic dataset demonstrate that the proposed approach performs significantly better than some well-known online learning algorithms.
POMDPs for Robotic Arm Search and Reach to Known Objects
Venue: arXiv cs.RO 2017
Year: 2017

We propose an approach based on probabilistic models, in particular POMDPs, to plan optimized search processes of known objects by intelligent eye in hand robotic arms. Searching and reaching for a known object (a pen, a book, or a hammer) in one's office is an operation that humans perform frequently in their daily activities. There is no reason why intelligent robotic arms would not encounter this problem frequently in the various applications in which they are expected to serve. The problem suffers from uncertainties coming both from the lack of information about the position of the object, from noisy sensors, imperfect models of the target object, of imperfect models of the environment, and from approximations in computations. The use of probabilistic models helps us to mitigate at least a few of these challenges, approaching optimality for this important task.
Iterative Hybrid Precoder and Combiner Design for mmWave MIMO-OFDM Systems
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates the problem of hybrid precoder and combiner design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems operating in millimeter-wave (mmWave) bands. We propose a novel iterative scheme to design the codebook-based analog precoder and combiner in forward and reverse channels. During each iteration, we apply compressive sensing (CS) technology to efficiently estimate the equivalent MIMO-OFDM mmWave channel. Then, the analog precoder or combiner is obtained based on the orthogonal matching pursuit (OMP) algorithm to alleviate the interference between different data streams as well as maximize the spectral efficiency. The digital precoder and combiner are finally obtained based on the effective baseband channel to further enhance the spectral efficiency. Simulation results demonstrate the proposed iterative hybrid precoder and combiner algorithm has significant performance advantages.
Transmit Filter and Artificial Noise Design for Secure MIMO-OFDM Systems
Venue: arXiv cs.IT 2017
Year: 2017

Physical layer security has been considered as an important security approach in wireless communications to protect legitimate transmission from passive eavesdroppers. This paper investigates the physical layer security of a wireless multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system in the presence of a multiple-antenna eavesdropper. We first propose a transmit-filter-assisted secure MIMO-OFDM system which can destroy the orthogonality of eavesdropper's signals. Our proposed transmit filter can disturb the reception of eavesdropper while maintaining the quality of legitimate transmission. Then, we propose another artificial noise (AN)-assisted secure MIMO-OFDM system to further improve the security of the legitimate transmission. The time-domain AN signal is designed to disturb the reception of eavesdropper while the legitimate transmission will not be affected. Simulation results are presented to demonstrate the security performance of the proposed transmit filter design and AN-assisted scheme in the MIMO-OFDM system.
Optimal Decentralized Economical-sharing Scheme in Islanded AC Microgrids with Cascaded Inverters
Venue: arXiv cs.SY 2017
Year: 2017

To address the economical dispatch problem without communications in islanded AC microgrids consisting of cascaded inverters, this paper proposes an optimal decentralized economical-sharing scheme. In proposed scheme, optimal sharing function of the current is applied to generate the reference voltages. And the frequency is used to drive all distributed generators (DGs) synchronize operation in microgrids. When the microgrid is in steady state, DGs share a single common frequency and current in terms of the proposed scheme. Thus the potential advantages of simplicity and decentralized manner are retained. The AC microgrid model has been developed through simulations and experiments to verify the effectiveness and performance of the proposed scheme.
Microservices: a Language-based Approach
Venue: arXiv cs.PL 2017
Year: 2017

Microservices is an emerging development paradigm where software is obtained by composing autonomous entities, called (micro)services. However, microservice systems are currently developed using general-purpose programming languages that do not provide dedicated abstractions for service composition. Instead, current practice is focused on the deployment aspects of microservices, in particular by using containerization. In this chapter, we make the case for a language-based approach to the engineering of microservice architectures, which we believe is complementary to current practice. We discuss the approach in general, and then we instantiate it in terms of the Jolie programming language.
A Rate Adaptation Algorithm for Tile-based 360-degree Video Streaming
Venue: arXiv cs.MM 2017
Year: 2017

In the 360-degree immersive video, a user only views a part of the entire raw video frame based on her viewing direction. However, today's 360-degree video players always fetch the entire panoramic view regardless of users' head movement, leading to significant bandwidth waste that can be potentially avoided. In this paper, we propose a novel adaptive streaming scheme for 360-degree videos. The basic idea is to fetch the invisible portion of a video at the lowest quality based on users' head movement prediction and to adaptively decide the video playback quality for the visible portion based on bandwidth prediction. Doing both in a robust manner requires overcome a series of challenges, such as jointly considering the spatial and temporal domains, tolerating prediction errors, and achieving low complexity. To overcome these challenges, we first define quality of experience (QoE) metrics for adaptive 360-degree video streaming. We then formulate an optimization problem and solve it at a low complexity. The algorithm strategically leverages both future bandwidth and the distribution of users' head positions to determine the quality level of each tile (i.e., a sub-area of a raw frame). We further provide theoretical proof showing that our algorithm achieves optimality under practical assumptions. Numerical results show that our proposed algorithms significantly boost the user QoE by at least 20\% compared to baseline algorithms.
Hypothesis Testing under Mutual Information Privacy Constraints in the High Privacy Regime
Venue: arXiv cs.IT 2017
Year: 2017

Hypothesis testing is a statistical inference framework for determining the true distribution among a set of possible distributions for a given dataset. Privacy restrictions may require the curator of the data or the respondents themselves to share data with the test only after applying a randomizing privacy mechanism. This work considers mutual information (MI) as the privacy metric for measuring leakage. In addition, motivated by the Chernoff-Stein lemma, the relative entropy between pairs of distributions of the output (generated by the privacy mechanism) is chosen as the utility metric. For these metrics, the goal is to find the optimal privacy-utility trade-off (PUT) and the corresponding optimal privacy mechanism for both binary and m-ary hypothesis testing. Focusing on the high privacy regime, Euclidean information-theoretic approximations of the binary and m-ary PUT problems are developed. The solutions for the approximation problems clarify that an MI-based privacy metric preserves the privacy of the source symbols in inverse proportion to their likelihoods.
Identifying Similarities in Epileptic Patients for Drug Resistance Prediction
Venue: arXiv cs.LG 2017
Year: 2017

Currently, approximately 30% of epileptic patients treated with antiepileptic drugs (AEDs) remain resistant to treatment (known as refractory patients). This project seeks to understand the underlying similarities in refractory patients vs. other epileptic patients, identify features contributing to drug resistance across underlying phenotypes for refractory patients, and develop predictive models for drug resistance in epileptic patients. In this study, epileptic patient data was examined to attempt to observe discernable similarities or differences in refractory patients (case) and other non-refractory patients (control) to map underlying mechanisms in causality. For the first part of the study, unsupervised algorithms such as Kmeans, Spectral Clustering, and Gaussian Mixture Models were used to examine patient features projected into a lower dimensional space. Results from this study showed a high degree of non-linearity in the underlying feature space. For the second part of this study, classification algorithms such as Logistic Regression, Gradient Boosted Decision Trees, and SVMs, were tested on the reduced-dimensionality features, with accuracy results of 0.83(+/-0.3) testing using 7 fold cross validation. Observations of test results indicate using a radial basis function kernel PCA to reduce features ingested by a Gradient Boosted Decision Tree Ensemble lead to gains in improved accuracy in mapping a binary decision to highly non-linear features collected from epileptic patients.
Low-complexity Distributed Tomographic Backprojection for large datasets
Venue: arXiv cs.DC 2017
Year: 2017

In this manuscript we present a fast GPU implementation for tomographic reconstruction of large datasets using data obtained at the Brazilian synchrotron light source. The algorithm is distributed in a cluster with 4 GPUs through a fast pipeline implemented in C programming language. Our algorithm is theoretically based on a recently discovered low complexity formula, computing the total volume within O(N3logN) floating point operations; much less than traditional algorithms that operates with O(N4) flops over an input data of size O(N3). The results obtained with real data indicate that a reconstruction can be achieved within 1 second provided the data is transferred completely to the memory.
A wearable general-purpose solution for Human-Swarm Interaction
Venue: arXiv cs.RO 2017
Year: 2017

Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. Controlling the motion and behavior of these swarms presents unique challenges for human operators, who cannot yet effectively convey their high-level intentions to a group of robots in application. This work proposes a new human-swarm interface based on novel wearable gesture-control and haptic-feedback devices. This work seeks to combine a wearable gesture recognition device that can detect high-level intentions, a portable device that can detect Cartesian information and finger movements, and a wearable advanced haptic device that can provide real-time feedback. This project is the first to envisage a wearable Human-Swarm Interaction (HSI) interface that separates the input and feedback components of the classical control loop (input, output, feedback), as well as being the first of its kind suitable for both indoor and outdoor environments.
Source File Set Search for Clone-and-Own Reuse Analysis
Venue: arXiv cs.SE 2017
Year: 2017

Clone-and-own approach is a natural way of source code reuse for software developers. To assess how known bugs and security vulnerabilities of a cloned component affect an application, developers and security analysts need to identify an original version of the component and understand how the cloned component is different from the original one. Although developers may record the original version information in a version control system and/or directory names, such information is often either unavailable or incomplete. In this research, we propose a code search method that takes as input a set of source files and extracts all the components including similar files from a software ecosystem (i.e., a collection of existing versions of software packages). Our method employs an efficient file similarity computation using b-bit minwise hashing technique. We use an aggregated file similarity for ranking components. To evaluate the effectiveness of this tool, we analyzed 75 cloned components in Firefox and Android source code. The tool took about two hours to report the original components from 10 million files in Debian GNU/Linux packages. Recall of the top-five components in the extracted lists is 0.907, while recall of a baseline using SHA-1 file hash is 0.773, according to the ground truth recorded in the source code repositories.
Improved Oracles for Time-Dependent Road Networks
Venue: arXiv cs.DS 2017
Year: 2017

A novel landmark-based oracle (CFLAT) is presented, which provides earliest-arrival-time route plans in time-dependent road networks. To our knowledge, this is the first oracle that preprocesses combinatorial structures (collections of time-stamped min-travel-time-path trees) rather than travel-time functions. The preprocessed data structure is exploited by a new query algorithm (CFCA) which computes (and pays for it), apart from earliest-arrival-time estimations, the actual connecting path that preserves the theoretical approximation guarantees. To make it practical and tackle the main burden of landmark-based oracles (the large preprocessing requirements), CFLAT is extensively engineered. A thorough experimental evaluation on two real-world benchmark instances shows that CFLAT achieves a significant improvement on preprocessing, approximation guarantees and query-times, in comparison to previous landmark-based oracles, whose query algorithms do not account for the path construction. It also achieves competitive query-time performance and approximation guarantees compared to state-of-art speedup heuristics for time-dependent road networks, whose query-times in most cases do not account for path construction.
Combinatorial Cost Sharing
Venue: arXiv cs.GT 2017
Year: 2017

We introduce a combinatorial variant of the cost sharing problem: several services can be provided to each player and each player values every combination of services differently. A publicly known cost function specifies the cost of providing every possible combination of services. A combinatorial cost sharing mechanism is a protocol that decides which services each player gets and at what price. We look for dominant strategy mechanisms that are (economically) efficient and cover the cost, ideally without overcharging (i.e., budget balanced). Note that unlike the standard cost sharing setting, combinatorial cost sharing is a multi-parameter domain. This makes designing dominant strategy mechanisms with good guarantees a challenging task. We present the Potential Mechanism -- a combination of the VCG mechanism and a well-known tool from the theory of cooperative games: Hart and Mas-Colell's potential function. The potential mechanism is a dominant strategy mechanism that always covers the incurred cost. When the cost function is subadditive the same mechanism is also approximately efficient. Our main technical contribution shows that when the cost function is submodular the potential mechanism is approximately budget balanced in three settings: supermodular valuations, symmetric cost function and general symmetric valuations, and two players with general valuations.