OpenRaiser/Intern-Atlas huggingface dataset:
Ordered Level Planarity, Geodesic Planarity and Bi-Monotonicity
Venue: arXiv cs.CG 2017
Year: 2017

We introduce and study the problem Ordered Level Planarity which asks for a planar drawing of a graph such that vertices are placed at prescribed positions in the plane and such that every edge is realized as a y-monotone curve. This can be interpreted as a variant of Level Planarity in which the vertices on each level appear in a prescribed total order. We establish a complexity dichotomy with respect to both the maximum degree and the level-width, that is, the maximum number of vertices that share a level. Our study of Ordered Level Planarity is motivated by connections to several other graph drawing problems. Geodesic Planarity asks for a planar drawing of a graph such that vertices are placed at prescribed positions in the plane and such that every edge is realized as a polygonal path composed of line segments with two adjacent directions from a given set $S$ of directions symmetric with respect to the origin. Our results on Ordered Level Planarity imply $NP$-hardness for any $S$ with $|S|\ge 4$ even if the given graph is a matching. Katz, Krug, Rutter and Wolff claimed that for matchings Manhattan Geodesic Planarity, the case where $S$ contains precisely the horizontal and vertical directions, can be solved in polynomial time [GD'09]. Our results imply that this is incorrect unless $P=NP$. Our reduction extends to settle the complexity of the Bi-Monotonicity problem, which was proposed by Fulek, Pelsmajer, Schaefer and Štefankovič. Ordered Level Planarity turns out to be a special case of T-Level Planarity, Clustered Level Planarity and Constrained Level Planarity. Thus, our results strengthen previous hardness results. In particular, our reduction to Clustered Level Planarity generates instances with only two non-trivial clusters. This answers a question posed by Angelini, Da Lozzo, Di Battista, Frati and Roselli.
Voltage Control Using Eigen Value Decomposition of Fast Decoupled Load Flow Jacobian
Venue: arXiv cs.SY 2017
Year: 2017

Voltage deviations occur frequently in power systems. If the violation at some buses falls outside the prescribed range, it will be necessary to correct the problem by controlling reactive power resources. In this paper, an optimal algorithm is proposed to solve this problem by identifying the voltage buses, that will have a maximum effect on the affected buses, and setting their new set-points. This algorithm is based on the Eigen-Value Decomposition of the fast decoupled load flow Jacobian matrix. Different Case studies including IEEE 9, 14, 30 and 57 bus systems have been used to verify the method.
Unified Host and Network Data Set
Venue: arXiv cs.CR 2017
Year: 2017

The lack of data sets derived from operational enterprise networks continues to be a critical deficiency in the cyber security research community. Unfortunately, releasing viable data sets to the larger com- munity is challenging for a number of reasons, primarily the difficulty of balancing security and privacy concerns against the fidelity and utility of the data. This chapter discusses the importance of cyber secu- rity research data sets and introduces a large data set derived from the operational network environment at Los Alamos National Laboratory. The hope is that this data set and associated discussion will act as a catalyst for both new research in cyber security as well as motivation for other organizations to release similar data sets to the community.
Utility Maximization Framework for Opportunistic Wireless Electric Vehicle Charging
Venue: arXiv cs.NI 2017
Year: 2017

This is an extended abstract, it has no separate abstract section
A Robust Indoor Scene Recognition Method based on Sparse Representation
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we present a robust method for scene recognition, which leverages Convolutional Neural Networks (CNNs) features and Sparse Coding setting by creating a new representation of indoor scenes. Although CNNs highly benefited the fields of computer vision and pattern recognition, convolutional layers adjust weights on a global-approach, which might lead to losing important local details such as objects and small structures. Our proposed scene representation relies on both: global features that mostly refers to environment's structure, and local features that are sparsely combined to capture characteristics of common objects of a given scene. This new representation is based on fragments of the scene and leverages features extracted by CNNs. The experimental evaluation shows that the resulting representation outperforms previous scene recognition methods on Scene15 and MIT67 datasets, and performs competitively on SUN397, while being highly robust to perturbations in the input image such as noise and occlusion.
Active Preference Learning for Personalized Portfolio Construction
Venue: arXiv cs.CE 2017
Year: 2017

In financial asset management, choosing a portfolio requires balancing returns, risk, exposure, liquidity, volatility and other factors. These concerns are difficult to compare explicitly, with many asset managers using an intuitive or implicit sense of their interaction. We propose a mechanism for learning someone's sense of distinctness between portfolios with the goal of being able to identify portfolios which are predicted to perform well but are distinct from the perspective of the user. This identification occurs, e.g., in the context of Bayesian optimization of a backtested performance metric. Numerical experiments are presented which show the impact of personal beliefs in informing the development of a diverse and high-performing portfolio.
Secure by default - the case of TLS
Venue: arXiv cs.CR 2017
Year: 2017

Default configuration of various software applications often neglects security objectives. We tested the default configuration of TLS in dozen web and application servers. The results show that "secure by default" principle should be adopted more broadly by developers and package maintainers. In addition, system administrators cannot rely blindly on default security options.
Learning the Enigma with Recurrent Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

Recurrent neural networks (RNNs) represent the state of the art in translation, image captioning, and speech recognition. They are also capable of learning algorithmic tasks such as long addition, copying, and sorting from a set of training examples. We demonstrate that RNNs can learn decryption algorithms -- the mappings from plaintext to ciphertext -- for three polyalphabetic ciphers (Vigenère, Autokey, and Enigma). Most notably, we demonstrate that an RNN with a 3000-unit Long Short-Term Memory (LSTM) cell can learn the decryption function of the Enigma machine. We argue that our model learns efficient internal representations of these ciphers 1) by exploring activations of individual memory neurons and 2) by comparing memory usage across the three ciphers. To be clear, our work is not aimed at 'cracking' the Enigma cipher. However, we do show that our model can perform elementary cryptanalysis by running known-plaintext attacks on the Vigenère and Autokey ciphers. Our results indicate that RNNs can learn algorithmic representations of black box polyalphabetic ciphers and that these representations are useful for cryptanalysis.
Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations
Venue: arXiv cs.SY 2017
Year: 2017

Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEVs' arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. The present paper develops a model predictive control (MPC)- based approach to address the joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, the known PEVs' future demand, or the unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this approach.
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids
Venue: arXiv cs.SY 2017
Year: 2017

We consider the problem of minimizing the difference in the demand and the supply of power using microgrids. We setup multiple microgrids, that provide electricity to a village. They have access to the batteries that can store renewable power and also the electrical lines from the main grid. During each time period, these microgrids need to take decision on the amount of renewable power to be used from the batteries as well as the amount of power needed from the main grid. We formulate this problem in the framework of Markov Decision Process (MDP), similar to the one discussed in [1]. The power allotment to the village from the main grid is fixed and bounded, whereas the renewable energy generation is uncertain in nature. Therefore we adapt a distributed version of the popular Reinforcement learning technique, Multi-Agent Q-Learning to the problem. Finally, we also consider a variant of this problem where the cost of power production at the main site is taken into consideration. In this scenario the microgrids need to minimize the demand-supply deficit, while maintaining the desired average cost of the power production.
Subspace Approximation for Approximate Nearest Neighbor Search in NLP
Venue: arXiv cs.AI 2017
Year: 2017

Most natural language processing tasks can be formulated as the approximated nearest neighbor search problem, such as word analogy, document similarity, machine translation. Take the question-answering task as an example, given a question as the query, the goal is to search its nearest neighbor in the training dataset as the answer. However, existing methods for approximate nearest neighbor search problem may not perform well owing to the following practical challenges: 1) there are noise in the data; 2) the large scale dataset yields a huge retrieval space and high search time complexity. In order to solve these problems, we propose a novel approximate nearest neighbor search framework which i) projects the data to a subspace based spectral analysis which eliminates the influence of noise; ii) partitions the training dataset to different groups in order to reduce the search space. Specifically, the retrieval space is reduced from $O(n)$ to $O(\log n)$ (where $n$ is the number of data points in the training dataset). We prove that the retrieved nearest neighbor in the projected subspace is the same as the one in the original feature space. We demonstrate the outstanding performance of our framework on real-world natural language processing tasks.
Shape Registration with Directional Data
Venue: arXiv cs.CV 2017
Year: 2017

We propose several cost functions for registration of shapes encoded with Euclidean and/or non-Euclidean information (unit vectors). Our framework is assessed for estimation of both rigid and non-rigid transformations between the target and model shapes corresponding to 2D contours and 3D surfaces. The experimental results obtained confirm that using the combination of a point's position and unit normal vector in a cost function can enhance the registration results compared to state of the art methods.
Inverse of a Special Matrix and Application
Venue: arXiv cs.DM 2017
Year: 2017

The matrix inversion is an interesting topic in algebra mathematics. However, to determine an inverse matrix from a given matrix is required many computation tools and time resource if the size of matrix is huge. In this paper, we have shown an inverse closed form for an interesting matrix which has much applications in communication system. Base on this inverse closed form, the channel capacity closed form of a communication system can be determined via the error rate parameter alpha
Channel reciprocity calibration in TDD hybrid beamforming massive MIMO systems
Venue: arXiv cs.IT 2017
Year: 2017

Hybrid analog-digital (AD) beamforming structure is a very attractive solution to build low cost massive multiple-input multiple-output (MIMO) systems. Typically these systems use a set of fixed beams for transmission and reception to avoid the need to obtain channel state information at transmitter (CSIT) for each antenna element individually. However, such a method can not fully exploit the potential of hybrid AD beamforming systems. Alternatively, CSIT can be estimated by assuming a model for the propagation channel, whereas this model is only validated in millimeter-wave (mmWave) band thanks to its poor scattering nature. In this paper, we focus on time division duplex (TDD) systems with hybrid beamforming structure and propose a reciprocity calibration scheme that allows to acquire full CSIT. Different to existing CSIT acquisition methods, our approach does not require any assumption on the channel model and can, in theory, estimate the CSIT up to an arbitrary small error.
LevelHeaded: Making Worst-Case Optimal Joins Work in the Common Case
Venue: arXiv cs.DB 2017
Year: 2017

Pipelines combining SQL-style business intelligence (BI) queries and linear algebra (LA) are becoming increasingly common in industry. As a result, there is a growing need to unify these workloads in a single framework. Unfortunately, existing solutions either sacrifice the inherent benefits of exclusively using a relational database (e.g. logical and physical independence) or incur orders of magnitude performance gaps compared to specialized engines (or both). In this work we study applying a new type of query processing architecture to standard BI and LA benchmarks. To do this we present a new in-memory query processing engine called LevelHeaded. LevelHeaded uses worst-case optimal joins as its core execution mechanism for both BI and LA queries. With LevelHeaded, we show how crucial optimizations for BI and LA queries can be captured in a worst-case optimal query architecture. Using these optimizations, LevelHeaded outperforms other relational database engines (LogicBlox, MonetDB, and HyPer) by orders of magnitude on standard LA benchmarks, while performing on average within 31% of the best-of-breed BI (HyPer) and LA (Intel MKL) solutions on their own benchmarks. Our results show that such a single query processing architecture is capable of delivering competitive performance on both BI and LA queries.
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer
Venue: arXiv cs.AI 2017
Year: 2017

Dependency graph, as a heterogeneous graph representing the intrinsic relationships between different pairs of system entities, is essential to many data analysis applications, such as root cause diagnosis, intrusion detection, etc. Given a well-trained dependency graph from a source domain and an immature dependency graph from a target domain, how can we extract the entity and dependency knowledge from the source to enhance the target? One way is to directly apply a mature dependency graph learned from a source domain to the target domain. But due to the domain variety problem, directly using the source dependency graph often can not achieve good performance. Traditional transfer learning methods mainly focus on numerical data and are not applicable. In this paper, we propose ACRET, a knowledge transfer based model for accelerating dependency graph learning from heterogeneous categorical event streams. In particular, we first propose an entity estimation model to filter out irrelevant entities from the source domain based on entity embedding and manifold learning. Only the entities with statistically high correlations are transferred to the target domain. On the surviving entities, we propose a dependency construction model for constructing the unbiased dependency relationships by solving a two-constraint optimization problem. The experimental results on synthetic and real-world datasets demonstrate the effectiveness and efficiency of ACRET. We also apply ACRET to a real enterprise security system for intrusion detection. Our method is able to achieve superior detection performance at least 20 days lead lag time in advance with more than 70% accuracy.
Batch-Based Activity Recognition from Egocentric Photo-Streams
Venue: arXiv cs.CV 2017
Year: 2017

Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries.
Some variations on the standard theoretical models for the h-index: A comparative analysis
Venue: arXiv cs.DL 2017
Year: 2017

There are various mathematical models proposed in the recent literature for estimating the h-index through bibliometric measures, such as number of articles (P) and citations received (C). These models have been previously empirically tested assuming a mathematical model and predetermining the models parameter values at some fixed constant. Here, by adopting a statistical modelling view I investigate alternative distributions commonly used for this type of point data. I also show that the typical assumptions for the parameters of the h-index mathematical models in such representations are not always realistic, with more suitable specifications being favorable. Prediction of the hindex is also demonstrated.
Comparison of the h-index for Different Fields of Research Using Bootstrap Methodology
Venue: arXiv cs.DL 2017
Year: 2017

An important disadvantage of the h-index is that typically it cannot take into account the specific field of research of a researcher. Usually sample point estimates of the average and median h-index values for the various fields are reported that are highly variable and dependent of the specific samples and it would be useful to provide confidence intervals of prediction accuracy. In this paper we apply the non-parametric bootstrap technique for constructing confidence intervals for the h-index for different fields of research. In this way no specific assumptions about the distribution of the empirical hindex are required as well as no large samples since that the methodology is based on resampling from the initial sample. The results of the analysis showed important differences between the various fields. The performance of the bootstrap intervals for the mean and median h-index for most fields seems to be rather satisfactory as revealed by the performed simulation.
The Unfolding Semantics of Functional Programs
Venue: arXiv cs.PL 2017
Year: 2017

The idea of using unfolding as a way of computing a program semantics has been applied successfully to logic programs and has shown itself a powerful tool that provides concrete, implementable results, as its outcome is actually source code. Thus, it can be used for characterizing not-so-declarative constructs in mostly declarative languages, or for static analysis. However, unfolding-based semantics has not yet been applied to higher-order, lazy functional programs, perhaps because some functional features absent in logic programs make the correspondence between execution and unfolding not as straightforward. This work presents an unfolding semantics for higher-order, lazy functional programs and proves its adequacy with respect to a given operational semantics. Finally, we introduce some applications of our semantics.
Carnot Efficiency of Publication
Venue: arXiv cs.DL 2017
Year: 2017

This paper analyzes publication efficiency in terms of Hirsch-index or h-index and total citations, with an analogy to the Carnot efficiency used in thermodynamics. Such publication efficiency, with typical value of 30%, can be utilized to normalize the research output judgment, favoring quality outputs in reduced quantity, which is currently lacking in many discipline.
Impact of Feature Selection on Micro-Text Classification
Venue: arXiv cs.IR 2017
Year: 2017

Social media datasets, especially Twitter tweets, are popular in the field of text classification. Tweets are a valuable source of micro-text (sometimes referred to as "micro-blogs"), and have been studied in domains such as sentiment analysis, recommendation systems, spam detection, clustering, among others. Tweets often include keywords referred to as "Hashtags" that can be used as labels for the tweet. Using tweets encompassing 50 labels, we studied the impact of word versus character-level feature selection and extraction on different learners to solve a multi-class classification task. We show that feature extraction of simple character-level groups performs better than simple word groups and pre-processing methods like normalizing using Porter's Stemming and Part-of-Speech ("POS")-Lemmatization.
An IoT Real-Time Biometric Authentication System Based on ECG Fiducial Extracted Features Using Discrete Cosine Transform
Venue: arXiv cs.CV 2017
Year: 2017

The conventional authentication technologies, like RFID tags and authentication cards/badges, suffer from different weaknesses, therefore a prompt replacement to use biometric method of authentication should be applied instead. Biometrics, such as fingerprints, voices, and ECG signals, are unique human characters that can be used for authentication processing. In this work, we present an IoT real-time authentication system based on using extracted ECG features to identify the unknown persons. The Discrete Cosine Transform (DCT) is used as an ECG feature extraction, where it has better characteristics for real-time system implementations. There are a substantial number of researches with a high accuracy of authentication, but most of them ignore the real-time capability of authenticating individuals. With the accuracy rate of 97.78% at around 1.21 seconds of processing time, the proposed system is more suitable for use in many applications that require fast and reliable authentication processing demands.
T-DB: Toward Fully Functional Transparent Encrypted Databases in DBaaS Framework
Venue: arXiv cs.DB 2017
Year: 2017

Individuals and organizations tend to migrate their data to clouds, especially in a DataBase as a Service (DBaaS) pattern. The major obstacle is the conflict between secrecy and utilization of the relational database to be outsourced. We address this obstacle with a Transparent DataBase (T-DB) system strictly following the unmodified DBaaS framework. A database owner outsources an encrypted database to a cloud platform, needing only to store the secret keys for encryption and an empty table header for the database; the database users can make almost all types of queries on the encrypted database as usual; and the cloud can process ciphertext queries as if the database were not encrypted. Experimentations in realistic cloud environments demonstrate that T-DB has perfect query answer precision and outstanding performance.