OpenRaiser/Intern-Atlas huggingface dataset:
More new classes of permutation trinomials over $\mathbb{F}_{2^n}$
Venue: arXiv cs.IT 2017
Year: 2017

Permutation polynomials over finite fields have wide applications in many areas of science and engineering. In this paper, we present six new classes of permutation trinomials over $\mathbb{F}_{2^n}$ which have explicit forms by determining the solutions of some equations.
Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling
Venue: arXiv cs.LG 2017
Year: 2017

Conventional methods of estimating latent behaviour generally use attitudinal questions which are subjective and these survey questions may not always be available. We hypothesize that an alternative approach can be used for latent variable estimation through an undirected graphical models. For instance, non-parametric artificial neural networks. In this study, we explore the use of generative non-parametric modelling methods to estimate latent variables from prior choice distribution without the conventional use of measurement indicators. A restricted Boltzmann machine is used to represent latent behaviour factors by analyzing the relationship information between the observed choices and explanatory variables. The algorithm is adapted for latent behaviour analysis in discrete choice scenario and we use a graphical approach to evaluate and understand the semantic meaning from estimated parameter vector values. We illustrate our methodology on a financial instrument choice dataset and perform statistical analysis on parameter sensitivity and stability. Our findings show that through non-parametric statistical tests, we can extract useful latent information on the behaviour of latent constructs through machine learning methods and present strong and significant influence on the choice process. Furthermore, our modelling framework shows robustness in input variability through sampling and validation.
Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages
Venue: arXiv cs.CL 2017
Year: 2017

In this work, we present new state-of-the-art results of 93.59,% and 79.59,% for Turkish and Czech named entity recognition based on the model of (Lample et al., 2016). We contribute by proposing several schemes for representing the morphological analysis of a word in the context of named entity recognition. We show that a concatenation of this representation with the word and character embeddings improves the performance. The effect of these representation schemes on the tagging performance is also investigated.
Temporal correlation detection using computational phase-change memory
Venue: arXiv cs.ET 2017
Year: 2017

For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units, which are physically separated. As computation becomes increasingly data-centric and as the scalability limits in terms of performance and power are being reached, alternative computing paradigms are searched for in which computation and storage are collocated. A fascinating new approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. Here we present a large-scale experimental demonstration using one million phase-change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Also presented is an application of such a computational memory to process real-world data-sets. The results show that this co-existence of computation and storage at the nanometer scale could be the enabler for new, ultra-dense, low power, and massively parallel computing systems.
Ride Sharing and Dynamic Networks Analysis
Venue: arXiv cs.SI 2017
Year: 2017

The potential of an efficient ride-sharing scheme to significantly reduce traffic congestion, lower emission level, as well as facilitating the introduction of smart cities has been widely demonstrated. This positive thrust however is faced with several delaying factors, one of which is the volatility and unpredictability of the potential benefit (or utilization) of ride-sharing at different times, and in different places. In this work the following research questions are posed: (a) Is ride-sharing utilization stable over time or does it undergo significant changes? (b) If ride-sharing utilization is dynamic, can it be correlated with some traceable features of the traffic? and (c) If ride-sharing utilization is dynamic, can it be predicted ahead of time? We analyze a dataset of over 14 Million taxi trips taken in New York City. We propose a dynamic travel network approach for modeling and forecasting the potential ride-sharing utilization over time, showing it to be highly volatile. In order to model the utilization's dynamics we propose a network-centric approach, projecting the aggregated traffic taken from continuous time periods into a feature space comprised of topological features of the network implied by this traffic. This feature space is then used to model the dynamics of ride-sharing utilization over time. The results of our analysis demonstrate the significant volatility of ride-sharing utilization over time, indicating that any policy, design or plan that would disregard this aspect and chose a static paradigm would undoubtably be either highly inefficient or provide insufficient resources. We show that using our suggested approach it is possible to model the potential utilization of ride sharing based on the topological properties of the rides network. We also show that using this method the potential utilization can be forecasting a few hours ahead of time.
Real-time Shared Energy Storage Management for Renewable Energy Integration in Smart Grid
Venue: arXiv cs.SY 2017
Year: 2017

Energy storage systems (ESSs) are essential components of the future smart grids with high penetration of renewable energy sources. However, deploying individual ESSs for all energy consumers, especially in large systems, may not be practically feasible mainly due to high upfront cost of purchasing many ESSs and space limitation. As a result, the concept of shared ESS enabling all users charge/discharge to/from a common ESS has become appealing. In this paper, we study the energy management problem of a group of users with renewable energy sources and controllable (i.e., demand responsive) loads that all share a common ESS so as to minimize their sum weighted energy cost. Specifically, we propose a distributed algorithm to solve the formulated problem, which iteratively derives the optimal values of charging/discharging to/from the shared ESS, while only limited information is exchanged between users and a central controller; hence, the privacy of users is preserved. With the optimal charging and discharging values obtained, each user needs to independently solve a simple linear programming (LP) problem to derive the optimal energy consumption of its controllable loads over time as well as that of purchased from the grid. Using simulations, we show that the shared ESS can achieve lower energy cost compared to the case of distributed ESSs, where each user owns its ESS and does not share it with others. Next, we propose online algorithms for the real-time energy management, under non-zero prediction errors of load and renewable energy. The proposed algorithms differ in complexity and the information required to be shared between the users and central controller, where their performance is also compared via simulations.
Dual-reference Face Retrieval
Venue: arXiv cs.CV 2017
Year: 2017

Face retrieval has received much attention over the past few decades, and many efforts have been made in retrieving face images against pose, illumination, and expression variations. However, the conventional works fail to meet the requirements of a potential and novel task --- retrieving a person's face image at a specific age, especially when the specific 'age' is not given as a numeral, i.e. 'retrieving someone's image at the similar age period shown by another person's image'. To tackle this problem, we propose a dual reference face retrieval framework in this paper, where the system takes two inputs: an identity reference image which indicates the target identity and an age reference image which reflects the target age. In our framework, the raw images are first projected on a joint manifold, which preserves both the age and identity locality. Then two similarity metrics of age and identity are exploited and optimized by utilizing our proposed quartet-based model. The experiments show promising results, outperforming hierarchical methods.
Joint Matrix-Tensor Factorization for Knowledge Base Inference
Venue: arXiv cs.AI 2017
Year: 2017

While several matrix factorization (MF) and tensor factorization (TF) models have been proposed for knowledge base (KB) inference, they have rarely been compared across various datasets. Is there a single model that performs well across datasets? If not, what characteristics of a dataset determine the performance of MF and TF models? Is there a joint TF+MF model that performs robustly on all datasets? We perform an extensive evaluation to compare popular KB inference models across popular datasets in the literature. In addition to answering the questions above, we remove a limitation in the standard evaluation protocol for MF models, propose an extension to MF models so that they can better handle out-of-vocabulary (OOV) entity pairs, and develop a novel combination of TF and MF models. We also analyze and explain the results based on models and dataset characteristics. Our best model is robust, and obtains strong results across all datasets.
The role of asymptotic functions in network optimization and feasibility studies
Venue: arXiv cs.IT 2017
Year: 2017

Solutions to network optimization problems have greatly benefited from developments in nonlinear analysis, and, in particular, from developments in convex optimization. A key concept that has made convex and nonconvex analysis an important tool in science and engineering is the notion of asymptotic function, which is often hidden in many influential studies on nonlinear analysis and related fields. Therefore, we can also expect that asymptotic functions are deeply connected to many results in the wireless domain, even though they are rarely mentioned in the wireless literature. In this study, we show connections of this type. By doing so, we explain many properties of centralized and distributed solutions to wireless resource allocation problems within a unified framework, and we also generalize and unify existing approaches to feasibility analysis of network designs. In particular, we show sufficient and necessary conditions for mappings widely used in wireless communication problems (more precisely, the class of standard interference mappings) to have a fixed point. Furthermore, we derive fundamental bounds on the utility and the energy efficiency that can be achieved by solving a large family of max-min utility optimization problems in wireless networks.
Understanding the Inefficiency of Security-Constrained Economic Dispatch
Venue: arXiv cs.SY 2017
Year: 2017

The security-constrained economic dispatch (SCED) problem tries to maintain the reliability of a power network by ensuring that a single failure does not lead to a global outage. The previous research has mainly investigated SCED by formulating the problem in different modalities, e.g. preventive or corrective, and devising efficient solutions for SCED. In this paper, we tackle a novel and important direction, and analyze the economic cost of incorporating security constraints in economic dispatch. Inspired by existing inefficiency metrics in game theory and computer science, we introduce notion of price of security as a metric that formally characterizes the economic inefficiency of security-constrained economic dispatch as compared to the original problem without security constraints. Then, we focus on the preventive approach in a simple topology comprising two buses and two lines, and investigate the impact of generation availability and demand distribution on the price of security. Moreover, we explicitly derive the worst-case input instance that leads to the maximum price of security. By extensive experimental study on two test-cases, we verify the analytical results and provide insights for characterizing the price of security in general networks.
Representing Nonterminating Rewriting with $\mathbf{F}_2^μ$
Venue: arXiv cs.LO 2017
Year: 2017

We specify a second-order type system $\mathbf{F}_2^μ$ that is tailored for representing nonterminations. The nonterminating trace of a term $t$ in a rewrite system $\mathcal{R}$ corresponds to a productive inhabitant $e$ such that $Γ_{\mathcal{R}} \vdash e : t$ in $\mathbf{F}_2^μ$, where $Γ_{\mathcal{R}}$ is the environment representing the rewrite system. We prove that the productivity checking in $\mathbf{F}_2^μ$ is decidable via a mapping to the $λ$-Y calculus. We develop a type checking algorithm for $\mathbf{F}_2^μ$ based on second-order matching. We implement the type checking algorithm in a proof-of-concept type checker.
Double-Edge Factor Graphs: Definition, Properties, and Examples
Venue: arXiv cs.IT 2017
Year: 2017

Some of the most interesting quantities associated with a factor graph are its marginals and its partition sum. For factor graphs \emph{without cycles} and moderate message update complexities, the sum-product algorithm (SPA) can be used to efficiently compute these quantities exactly. Moreover, for various classes of factor graphs \emph{with cycles}, the SPA has been successfully applied to efficiently compute good approximations to these quantities. Note that in the case of factor graphs with cycles, the local functions are usually non-negative real-valued functions. In this paper we introduce a class of factor graphs, called double-edge factor graphs (DE-FGs), which allow local functions to be complex-valued and only require them, in some suitable sense, to be positive semi-definite. We discuss various properties of the SPA when running it on DE-FGs and we show promising numerical results for various example DE-FGs, some of which have connections to quantum information processing.
Capri: A Control System for Approximate Programs
Venue: arXiv cs.PL 2017
Year: 2017

Approximate computing trades off accuracy of results for resources such as energy or computing time. There is a large and rapidly growing literature on approximate computing that has focused mostly on showing the benefits of approximation. However, we know relatively little about how to control approximation in a disciplined way. This document briefly describes our published work of controlling approximation for non-streaming programs that have a set of "knobs" that can be dialed up or down to control the level of approximation of different components in the program. The proposed system, Capri, solves this control problem as a constrained optimization problem. Capri uses machine learning to learn cost and error models for the program, and uses these models to determine, for a desired level of approximation, knob settings that optimize metrics such as running time or energy usage. Experimental results with complex benchmarks from different problem domains demonstrate the effectiveness of this approach. This report outlines improvements and extensions to the existing Capri system to address its limitations, including a complete rewrite of the software, and discusses directions for follow up work. The document also includes instructions and guidelines for using the new Capri infrastructure.
Wikipedia Vandal Early Detection: from User Behavior to User Embedding
Venue: arXiv cs.CR 2017
Year: 2017

Wikipedia is the largest online encyclopedia that allows anyone to edit articles. In this paper, we propose the use of deep learning to detect vandals based on their edit history. In particular, we develop a multi-source long-short term memory network (M-LSTM) to model user behaviors by using a variety of user edit aspects as inputs, including the history of edit reversion information, edit page titles and categories. With M-LSTM, we can encode each user into a low dimensional real vector, called user embedding. Meanwhile, as a sequential model, M-LSTM updates the user embedding each time after the user commits a new edit. Thus, we can predict whether a user is benign or vandal dynamically based on the up-to-date user embedding. Furthermore, those user embeddings are crucial to discover collaborative vandals.
X-TCP: A Cross Layer Approach for TCP Uplink Flows in mmWave Networks
Venue: arXiv cs.NI 2017
Year: 2017

Millimeter wave frequencies will likely be part of the fifth generation of mobile networks and of the 3GPP New Radio (NR) standard. MmWave communication indeed provides a very large bandwidth, thus an increased cell throughput, but how to exploit these resources at the higher layers is still an open research question. A very relevant issue is the high variability of the channel, caused by the blockage from obstacles and the human body. This affects the design of congestion control mechanisms at the transport layer, and state-of-the-art TCP schemes such as TCP CUBIC present suboptimal performance. In this paper, we present a cross layer approach for uplink flows that adjusts the congestion window of TCP at the mobile equipment side using an estimation of the available data rate at the mmWave physical layer, based on the actual resource allocation and on the Signal to Interference plus Noise Ratio. We show that this approach reduces the latency, avoiding to fill the buffers in the cellular stack, and has a quicker recovery time after RTO events than several other TCP congestion control algorithms.
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
Venue: arXiv cs.CV 2017
Year: 2017

Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.
Literature Survey on Interplay of Topics, Information Diffusion and Connections on Social Networks
Venue: arXiv cs.SI 2017
Year: 2017

Researchers have attempted to model information diffusion and topic trends and lifecycle on online social networks. They have investigated the role of content, social connections and communities, familiarity and behavioral similarity in this context. The current article presents a survey of representative models that perform topic analysis, capture information diffusion, and explore the properties of social connections in the context of online social networks. The article concludes with a set of outlines of open problems and possible directions of future research interest. This article is intended for researchers to identify the current literature, and explore possibilities to improve the art.
Simultaneous Inference of User Representations and Trust
Venue: arXiv cs.IR 2017
Year: 2017

Inferring trust relations between social media users is critical for a number of applications wherein users seek credible information. The fact that available trust relations are scarce and skewed makes trust prediction a challenging task. To the best of our knowledge, this is the first work on exploring representation learning for trust prediction. We propose an approach that uses only a small amount of binary user-user trust relations to simultaneously learn user embeddings and a model to predict trust between user pairs. We empirically demonstrate that for trust prediction, our approach outperforms classifier-based approaches which use state-of-the-art representation learning methods like DeepWalk and LINE as features. We also conduct experiments which use embeddings pre-trained with DeepWalk and LINE each as an input to our model, resulting in further performance improvement. Experiments with a dataset of $\sim$356K user pairs show that the proposed method can obtain an high F-score of 92.65%.
See, Hear, and Read: Deep Aligned Representations
Venue: arXiv cs.CV 2017
Year: 2017

We capitalize on large amounts of readily-available, synchronous data to learn a deep discriminative representations shared across three major natural modalities: vision, sound and language. By leveraging over a year of sound from video and millions of sentences paired with images, we jointly train a deep convolutional network for aligned representation learning. Our experiments suggest that this representation is useful for several tasks, such as cross-modal retrieval or transferring classifiers between modalities. Moreover, although our network is only trained with image+text and image+sound pairs, it can transfer between text and sound as well, a transfer the network never observed during training. Visualizations of our representation reveal many hidden units which automatically emerge to detect concepts, independent of the modality.
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)
Venue: arXiv cs.CR 2017
Year: 2017

It is well-known that Android malware constantly evolves so as to evade detection. This causes the entire malware population to be non-stationary. Contrary to this fact, most of the prior works on Machine Learning based Android malware detection have assumed that the distribution of the observed malware characteristics (i.e., features) does not change over time. In this work, we address the problem of malware population drift and propose a novel online learning based framework to detect malware, named CASANDRA (Contextaware, Adaptive and Scalable ANDRoid mAlware detector). In order to perform accurate detection, a novel graph kernel that facilitates capturing apps' security-sensitive behaviors along with their context information from dependency graphs is proposed. Besides being accurate and scalable, CASANDRA has specific advantages: i) being adaptive to the evolution in malware features over time ii) explaining the significant features that led to an app's classification as being malicious or benign. In a large-scale comparative analysis, CASANDRA outperforms two state-of-the-art techniques on a benchmark dataset achieving 99.23% F-measure. When evaluated with more than 87,000 apps collected in-the-wild, CASANDRA achieves 89.92% accuracy, outperforming existing techniques by more than 25% in their typical batch learning setting and more than 7% when they are continuously retained, while maintaining comparable efficiency.