OpenRaiser/Intern-Atlas huggingface dataset:
Network Service Dependencies in Commodity Internet-of-Things Devices
Venue: arXiv cs.NI 2019
Year: 2019

We continue to see increasingly widespread deployment of IoT devices, with apparent intent to embed them in our built environment likely to accelerate if smart city and related programmes succeed. In this paper we are concerned with the ways in which current generation IoT devices are being designed in terms of their ill-considered dependencies on network connectivity and services. Our hope is to provide evidence that such dependencies need to be better thought through in design, and better documented in implementation so that those responsible for deploying these devices can be properly informed as to the impact of device deployment (at scale) on infrastructure resilience. We believe this will be particularly relevant as we feel that commodity IoT devices are likely to be commonly used to retrofit `smart' capabilities to existing buildings, particularly domestic buildings. To the existing body of work on network-level behaviour of IoT devices, we add \one~a protocol-level breakdown and analysis of periodicity, \two~an exploration of the service and infrastructure dependencies that will implicitly be taken in `smart' environments when IoT devices are deployed, and \three~examination of the robustness of device operation when connectivity is disrupted. We find that many devices make use of services distributed across the planet and thus appear dependent on the global network infrastructure even when carrying out purely local actions. Some devices cease to operate properly without network connectivity (even where their behaviour appears, on the face of it, to require only local information, e.g.,~the Hive thermostat). Further, they exhibit quite different network behaviours, typically involving significantly more traffic and possibly use of otherwise unobserved protocols, when connectivity is recovered after some disruption.
Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks
Venue: arXiv cs.CV 2019
Year: 2019

Lesions characterized by computed tomography (CT) scans, are arguably often elliptical objects. However, current lesion detection systems are predominantly adopted from the popular Region Proposal Networks (RPNs) that only propose bounding boxes without fully leveraging the elliptical geometry of lesions. In this paper, we present Gaussian Proposal Networks (GPNs), a novel extension to RPNs, to detect lesion bounding ellipses. Instead of directly regressing the rotation angle of the ellipse as the common practice, GPN represents bounding ellipses as 2D Gaussian distributions on the image plain and minimizes the Kullback-Leibler (KL) divergence between the proposed Gaussian and the ground truth Gaussian for object localization. We show the KL divergence loss approximately incarnates the regression loss in the RPN framework when the rotation angle is 0. Experiments on the DeepLesion dataset show that GPN significantly outperforms RPN for lesion bounding ellipse detection thanks to lower localization error. GPN is open sourced at https://github.com/baidu-research/GPN
Maximum Marginal Likelihood Estimation of Phase Connections in Power Distribution Systems
Venue: arXiv cs.SY 2019
Year: 2019

Accurate phase connectivity information is essential for advanced monitoring and control applications in power distribution systems. The existing data-driven approaches for phase identification lack precise physical interpretation and theoretical performance guarantee. Their performance generally deteriorates as the complexity of the network, the number of phase connections, and the level of load balance increase. In this paper, by linearizing the three-phase power flow manifold, we develop a physical model, which links the phase connections to the smart meter measurements. The phase identification problem is first formulated as a maximum likelihood estimation problem and then reformulated as a maximum marginal likelihood estimation problem. We prove that the correct phase connection achieves the highest log likelihood values for both problems. An efficient solution method is proposed by decomposing the original problem into subproblems with a binary least-squares formulation. The numerical tests on a comprehensive set of distribution circuits show that our proposed method yields very high accuracy on both radial and meshed distribution circuits with a combination of single-phase, two-phase, and three-phase loads. The proposed algorithm is robust with respect to inaccurate feeder models and incomplete measurements. It also outperforms the existing methods on complex circuits.
Towards Higher Spectral Efficiency: Spatial Path Index Modulation Improves Millimeter-Wave Hybrid Beamforming
Venue: arXiv cs.IT 2019
Year: 2019

The combination of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems and index modulation (IM) technique has recently constituted a novel form of mmWave hybrid beamforming. Lots of studies have been conducted to show that such system has the potential to outperform conventional mmWave-MIMOs with respect to spectral efficiency (SE). Most of the current works only focused on designing hybrid beamforming structures to empirically achieve higher SE performance. However, a fundamental question that whether IM technique can truly improve the SE of current mmWave hybrid beamforming is still left unanswered. Against such background, in this work we firstly extend the IM-assisted mmWave system to a more generalized version, i.e. a spatial path index modulation aided mmWave (SPIM-mmWave) system, which subsumes both IM-assisted and conventional mmWave-MIMO systems as its special cases. Based on the framework of SPIM-mmWave, a fundamental study is conducted towards a theoretic condition under which SPIM-mmWave guarantees to outperform conventional mmWave-MIMO schemes. It is demonstrated that, under a specific channel condition and noise level (which will be explicitly given by our work), SPIM-mmWave guarantees to outperform conventional mmWave-MIMO systems.
Topological Bayesian Optimization with Persistence Diagrams
Venue: arXiv cs.LG 2019
Year: 2019

Finding an optimal parameter of a black-box function is important for searching stable material structures and finding optimal neural network structures, and Bayesian optimization algorithms are widely used for the purpose. However, most of existing Bayesian optimization algorithms can only handle vector data and cannot handle complex structured data. In this paper, we propose the topological Bayesian optimization, which can efficiently find an optimal solution from structured data using \emph{topological information}. More specifically, in order to apply Bayesian optimization to structured data, we extract useful topological information from a structure and measure the proper similarity between structures. To this end, we utilize persistent homology, which is a topological data analysis method that was recently applied in machine learning. Moreover, we propose the Bayesian optimization algorithm that can handle multiple types of topological information by using a linear combination of kernels for persistence diagrams. Through experiments, we show that topological information extracted by persistent homology contributes to a more efficient search for optimal structures compared to the random search baseline and the graph Bayesian optimization algorithm.
Generative Visual Dialogue System via Adaptive Reasoning and Weighted Likelihood Estimation
Venue: arXiv cs.CV 2019
Year: 2019

The key challenge of generative Visual Dialogue (VD) systems is to respond to human queries with informative answers in natural and contiguous conversation flow. Traditional Maximum Likelihood Estimation (MLE)-based methods only learn from positive responses but ignore the negative responses, and consequently tend to yield safe or generic responses. To address this issue, we propose a novel training scheme in conjunction with weighted likelihood estimation (WLE) method. Furthermore, an adaptive multi-modal reasoning module is designed, to accommodate various dialogue scenarios automatically and select relevant information accordingly. The experimental results on the VisDial benchmark demonstrate the superiority of our proposed algorithm over other state-of-the-art approaches, with an improvement of 5.81% on recall@10.
Adding the Power-Set to Description Logics
Venue: arXiv cs.LO 2019
Year: 2019

We explore the relationships between Description Logics and Set Theory. The study is carried on using, on the set-theoretic side, a very rudimentary axiomatic set theory Omega, consisting of only four axioms characterizing binary union, set difference, inclusion, and the power-set. An extension of ALC, ALC^Omega, is then defined in which concepts are naturally interpreted as sets living in Omega-models. In ALC^Omega not only membership between concepts is allowed---even admitting circularity---but also the power-set construct is exploited to add metamodeling capabilities. We investigate translations of ALC^Omega into standard description logics as well as a set-theoretic translation. A polynomial encoding of ALC^Omega in ALCIO proves the validity of the finite model property as well as an ExpTime upper bound on the complexity of concept satisfiability. We develop a set-theoretic translation of ALC^Omega in the theory Omega, exploiting a technique originally proposed for translating normal modal and polymodal logics into Omega. Finally, we show that the fragment LC^Omega of ALC^Omega, which does not admit roles and individual names, is as expressive as ALC^Omega.
cuSten -- CUDA Finite Difference and Stencil Library
Venue: arXiv cs.DC 2019
Year: 2019

In this paper we present cuSten, a new library of functions to handle the implementation of 2D and batched 1D finite-difference/stencil programs in CUDA. cuSten wraps data handling, kernel calls and streaming into four easy to use functions that speed up development of numerical codes on GPU platforms. The paper also presents an example of this library applied to solve the Cahn-Hilliard equation utilizing an ADI method with periodic boundary conditions, this solver is also used to benchmark the cuSten library performance against a serial implementation.
Epileptic seizure classification using statistical sampling and a novel feature selection algorithm
Venue: arXiv cs.LG 2019
Year: 2019

Epilepsy is a well-known neuronal disorder that can be identified by interpretation of the electroencephalogram (EEG) signal. Usually, the length of an EEG signal is quite long which is challenging to interpret manually. In this work, we propose an automated epileptic seizure detection method by applying a two-step minimization technique: first, we reduce the data points using a statistical sampling technique and then, we minimize the number of features using our novel feature selection algorithm. We then apply different machine learning algorithms for performance measurement of the proposed feature selection algorithm. The experimental results outperform some of the state-of-the-art methods for seizure detection using the reduced data points and the least number of features.
Anomalous Situation Detection in Complex Scenes
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we investigate a robust method to identify anomalies in complex scenes. This task is performed by evaluating the collective behavior by extracting the local binary patterns (LBP) and Laplacian of Gaussian (LoG) features. We fuse both features together which are exploited to train an MLP neural network during the training stage, and the anomaly is identified on the test samples. Considering the challenge of tracking individuals in dense crowded scenes due to multiple occlusions and clutter, in this paper we extract LBP and LoG features and use them as an approximate representation of the anomalous situation. These features well match the appearance of anomaly and their consistency, and accuracy is higher both in regular and irregular areas compared to other descriptors. In this paper, these features are exploited as input prior to train the neural network. The MLP neural network is subsequently explored to consider these features that can detect the anomalous situation. The experimental tests are conducted on a set of benchmark video sequences commonly used for anomaly situation detection.
Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing
Venue: arXiv cs.IT 2019
Year: 2019

With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network edge, is envisioned to reduce computation latency as well as to conserve energy for wireless devices (WDs). This paper studies a novel device-to-device (D2D)-enabled multi-helper MEC system, in which a local user solicits its nearby WDs serving as helpers for cooperative computation. We assume a time division multiple access (TDMA) transmission protocol, under which the local user offloads the tasks to multiple helpers and downloads the results from them over orthogonal pre-scheduled time slots. Under this setup, we minimize the computation latency by optimizing the local user's task assignment jointly with the time and rate for task offloading and results downloading, as well as the computation frequency for task execution, subject to individual energy and computation capacity constraints at the local user and the helpers. However, the formulated problem is a mixed-integer non-linear program (MINLP) that is difficult to solve. To tackle this challenge, we propose an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one. Next, we consider a benchmark scheme that endows the WDs with their maximum computation capacities. To further reduce the implementation complexity, we also develop a heuristic scheme based on the greedy task assignment. Finally, numerical results validate the effectiveness of our proposed algorithm, as compared against the heuristic scheme and other benchmark ones without either joint optimization of radio and computation resources or task assignment design.
A Wireless Sensor Network based approach to monitor and control air Pollution in large urban areas
Venue: arXiv cs.CR 2019
Year: 2019

Air pollution is a major concern in large urban areas. Various studies have been made to monitor and control the pollution level emitted by the vehicles but some main factors like ease of implementation or feasibility of the proposed approaches are not focused in the told studies. In this paper we propose a wireless sensor network solution to monitor vehicles exhaust systems pollution emission. Furthermore, we evaluate the feasibility of our proposed approach with respect to energy consumption and network lifetime by means of simulation. Our results indicate that our proposed scheme is practical for implementation using well known MAC layer protocols.
Chromatic Adaptation Transform by Spectral Reconstruction (Preprint)
Venue: arXiv cs.GR 2019
Year: 2019

A color appearance model (CAM) is an advanced colorimetric tool used to predict color appearance under a wide variety of viewing conditions. A chromatic adaptation transform (CAT) is an integral part of a CAM. Its role is to predict "corresponding colors," that is, a pair of colors that have the same color appearance when viewed under different illuminants, after partial or full adaptation to each illuminant. Modern CATs perform well when applied to a limited range of illuminant pairs and a limited range of source (test) colors. However, they can fail if operated outside these ranges. For imaging applications, it is important to have a CAT that can operate on any real color and illuminant pair without failure. This paper proposes a new CAT that does not operate on the standard von Kries model of adaptation. Instead it relies on spectral reconstruction and how these reconstructions behave with respect to different illuminants. It is demonstrated that the proposed CAT is immune to some of the limitations of existing CATs (such as producing colors with negative tristimulus values). The proposed CAT does not use established empirical corresponding-color datasets to optimize performance, as most modern CATs do, yet it performs as well as or better than the most recent CATs when tested against the corresponding-color datasets. This increase in robustness comes at the expense of additional complexity and computational effort. If robustness is of prime importance, then the proposed method may be justifiable.
Efficient and Secure ECDSA Algorithm and its Applications: A Survey
Venue: arXiv cs.CR 2019
Year: 2019

Public-key cryptography algorithms, especially elliptic curve cryptography (ECC) and elliptic curve digital signature algorithm (ECDSA) have been attracting attention from many researchers in different institutions because these algorithms provide security and high performance when being used in many areas such as electronic-healthcare, electronic-banking, electronic-commerce, electronic-vehicular, and electronic-governance. These algorithms heighten security against various attacks and at the same time improve performance to obtain efficiencies (time, memory, reduced computation complexity, and energy saving) in an environment of the constrained source and large systems. This paper presents detailed and a comprehensive survey of an update of the ECDSA algorithm in terms of performance, security, and applications.
An Editorial Network for Enhanced Document Summarization
Venue: arXiv cs.CL 2019
Year: 2019

We suggest a new idea of Editorial Network - a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences. Our network tries to imitate the decision process of a human editor during summarization. Within such a process, each extracted sentence may be either kept untouched, rephrased or completely rejected. We further suggest an effective way for training the "editor" based on a novel soft-labeling approach. Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractive-only baseline methods.
User-based collaborative filtering approach for content recommendation in OpenCourseWare platforms
Venue: arXiv cs.IR 2019
Year: 2019

A content recommender system or a recommendation system represents a subclass of information filtering systems which seeks to predict the user preferences, i.e. the content that would be most likely positively "rated" by the user. Nowadays, the recommender systems of OpenCourseWare (OCW) platforms typically generate a list of recommendations in one of two ways, i.e. through the content-based filtering, or user-based collaborative filtering (CF). In this paper, the conceptual idea of the content recommendation module was provided, which is capable of proposing the related decks (presentations, educational material, etc.) to the user having in mind past user activities, preferences, type and content similarity, etc. It particularly analyses suitable techniques for implementation of the user-based CF approach and user-related features that are relevant for the content evaluation. The proposed approach also envisages a hybrid recommendation system as a combination of user-based and content-based approaches in order to provide a holistic and efficient solution for content recommendation. Finally, for evaluation and testing purposes, a designated content recommendation module was implemented as part of the SlideWiki authoring OCW platform.
Quantized Polar Code Decoders: Analysis and Design
Venue: arXiv cs.IT 2019
Year: 2019

Applications of massive machine-type communications, such as sensor networks, smart metering, 'internet-of-things', or process and factory automation, are forecast to have great economic impact in the next five to ten years. Low-complexity energy- and cost-efficient communication schemes are essential to enable large-scale deployments. To target these requirements, we study decoding of polar codes with coarse quantization in the short block length regime. In particular, we devise schemes to mitigate the impact of coarse quantization, which has not been adequately explored in prior works. We introduce the 3-level quantized successive cancellation (SC) and SC list (SCL) decoder. Coarse quantization of log-likelihood ratios (LLRs) leads to quantization of path metrics (PMs). Quantized PMs severely impact the list management of SCL decoders, and hence cause performance degradation. Two mitigation strategies are presented: 1.) Selecting the winning codeword from the decoder's list based on maximum-likelihood (ML) rather than PM. 2.) Utilizing statistical knowledge about the reliability of bit estimates in each decoding step to improve list management. We demonstrate the effectiveness of our techniques in simulations. In particular, our enhancements prove useful in the low code rate regime, where theory available in the literature predicts pronounced losses caused by quantization. Furthermore, we put our work into perspective by comparing it to finer quantization and partially unquantized schemes. This yields insights and recommendations as to which quantization schemes offer the best cost-benefit ratio for practical implementation.
Fix Your Features: Stationary and Maximally Discriminative Embeddings using Regular Polytope (Fixed Classifier) Networks
Venue: arXiv cs.CV 2019
Year: 2019

Neural networks are widely used as a model for classification in a large variety of tasks. Typically, a learnable transformation (i.e. the classifier) is placed at the end of such models returning a value for each class used for classification. This transformation plays an important role in determining how the generated features change during the learning process. In this work we argue that this transformation not only can be fixed (i.e. set as non trainable) with no loss of accuracy, but it can also be used to learn stationary and maximally discriminative embeddings. We show that the stationarity of the embedding and its maximal discriminative representation can be theoretically justified by setting the weights of the fixed classifier to values taken from the coordinate vertices of three regular polytopes available in $\mathbb{R}^d$, namely: the $d$-Simplex, the $d$-Cube and the $d$-Orthoplex. These regular polytopes have the maximal amount of symmetry that can be exploited to generate stationary features angularly centered around their corresponding fixed weights. Our approach improves and broadens the concept of a fixed classifier, recently proposed in \cite{hoffer2018fix}, to a larger class of fixed classifier models. Experimental results confirm both the theoretical analysis and the generalization capability of the proposed method.