OpenRaiser/Intern-Atlas huggingface dataset:
Deep Neural Network Ensembles for Time Series Classification
Venue: arXiv cs.LG 2019
Year: 2019

Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are currently composed of ensembles of 37 non deep learning based classifiers. We attribute this gap in performance due to the lack of neural network ensembles for TSC. Therefore in this paper, we show how an ensemble of 60 deep learning models can significantly improve upon the current state-of-the-art performance of neural networks for TSC, when evaluated over the UCR/UEA archive: the largest publicly available benchmark for time series analysis. Finally, we show how our proposed Neural Network Ensemble (NNE) is the first time series classifier to outperform COTE while reaching similar performance to the current state-of-the-art ensemble HIVE-COTE.
Efficient Memory Management for GPU-based Deep Learning Systems
Venue: arXiv cs.DC 2019
Year: 2019

GPU (graphics processing unit) has been used for many data-intensive applications. Among them, deep learning systems are one of the most important consumer systems for GPU nowadays. As deep learning applications impose deeper and larger models in order to achieve higher accuracy, memory management becomes an important research topic for deep learning systems, given that GPU has limited memory size. Many approaches have been proposed towards this issue, e.g., model compression and memory swapping. However, they either degrade the model accuracy or require a lot of manual intervention. In this paper, we propose two orthogonal approaches to reduce the memory cost from the system perspective. Our approaches are transparent to the models, and thus do not affect the model accuracy. They are achieved by exploiting the iterative nature of the training algorithm of deep learning to derive the lifetime and read/write order of all variables. With the lifetime semantics, we are able to implement a memory pool with minimal fragments. However, the optimization problem is NP-complete. We propose a heuristic algorithm that reduces up to 13.3% of memory compared with Nvidia's default memory pool with equal time complexity. With the read/write semantics, the variables that are not in use can be swapped out from GPU to CPU to reduce the memory footprint. We propose multiple swapping strategies to automatically decide which variable to swap and when to swap out (in), which reduces the memory cost by up to 34.2% without communication overhead.
TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents
Venue: arXiv cs.CR 2019
Year: 2019

Recent work has identified that classification models implemented as neural networks are vulnerable to data-poisoning and Trojan attacks at training time. In this work, we show that these training-time vulnerabilities extend to deep reinforcement learning (DRL) agents and can be exploited by an adversary with access to the training process. In particular, we focus on Trojan attacks that augment the function of reinforcement learning policies with hidden behaviors. We demonstrate that such attacks can be implemented through minuscule data poisoning (as little as 0.025% of the training data) and in-band reward modification that does not affect the reward on normal inputs. The policies learned with our proposed attack approach perform imperceptibly similar to benign policies but deteriorate drastically when the Trojan is triggered in both targeted and untargeted settings. Furthermore, we show that existing Trojan defense mechanisms for classification tasks are not effective in the reinforcement learning setting.
On Energy Efficiency and Performance Evaluation of SBC based Clusters: A Hadoop case study
Venue: arXiv cs.DC 2019
Year: 2019

Energy efficiency in a data center is a challenge and has garnered researchers interest. In this paper we address the energy efficiency issue of a small scale data center by utilizing Single Board Computer (SBC) based clusters. A compact design layout is presented to build two clusters using 20 nodes each. Extensive testing was carried out to analyze the performance of these clusters using popular performance benchmarks for task execution time, memory/storage utilization, network throughput and energy consumption. Further, we investigate the cost of operating SBC based clusters by correlating energy utilization for the execution time of various benchmarks using workloads of different sizes. Results show that, although the low-cost benefit of a cluster built with ARM-based SBCs is desirable, these clusters yield low comparable performance and energy efficiency due to limited onboard capabilities. It is possible to tweak Hadoop configuration parameters for an ARM-based SBC cluster to efficiently utilize resources. We present, a discussion on the effectiveness of the SBC-based clusters as a testbed for inexpensive and green cloud computing research.
Effects of Self-Avatar and Gaze on Avoidance Movement Behavior
Venue: arXiv cs.HC 2019
Year: 2019

The present study investigates users' movement behavior in a virtual environment when they attempted to avoid a virtual character. At each iteration of the experiment, four conditions (Self-Avatar LookAt, No Self-Avatar LookAt, Self-Avatar No LookAt, and No Self-Avatar No LookAt) were applied to examine users' movement behavior based on kinematic measures. During the experiment, 52 participants were asked to walk from a starting position to a target position. A virtual character was placed at the midpoint. Participants were asked to wear a head-mounted display throughout the task, and their locomotion was captured using a motion capture suit. We analyzed the captured trajectories of the participants' routes on four kinematic measures to explore whether the four experimental conditions influenced the paths they took. The results indicated that the Self-Avatar LookAt condition affected the path the participants chose more significantly than the other three conditions in terms of length, duration, and deviation, but not in terms of speed. Overall, the length and duration of the task, as well as the deviation of the trajectory from the straight line, were greater when a self-avatar represented participants. An additional effect on kinematic measures was found in the LookAt (Gaze) conditions. Implications for future research are discussed.
On Sample Complexity of Projection-Free Primal-Dual Methods for Learning Mixture Policies in Markov Decision Processes
Venue: arXiv cs.LG 2019
Year: 2019

We study the problem of learning policy of an infinite-horizon, discounted cost, Markov decision process (MDP) with a large number of states. We compute the actions of a policy that is nearly as good as a policy chosen by a suitable oracle from a given mixture policy class characterized by the convex hull of a set of known base policies. To learn the coefficients of the mixture model, we recast the problem as an approximate linear programming (ALP) formulation for MDPs, where the feature vectors correspond to the occupation measures of the base policies defined on the state-action space. We then propose a projection-free stochastic primal-dual method with the Bregman divergence to solve the characterized ALP. Furthermore, we analyze the probably approximately correct (PAC) sample complexity of the proposed stochastic algorithm, namely the number of queries required to achieve near optimal objective value. We also propose a modification of our proposed algorithm with the polytope constraint sampling for the smoothed ALP, where the restriction to lower bounding approximations are relaxed. In addition, we apply the proposed algorithms to a queuing problem, and compare their performance with a penalty function algorithm. The numerical results illustrates that the primal-dual achieves better efficiency and low variance across different trials compared to the penalty function method.
Data-driven Neural Architecture Learning For Financial Time-series Forecasting
Venue: arXiv cs.LG 2019
Year: 2019

Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies. In addition, different data modalities often embed different nonlinear relationships which are difficult to capture by human-designed models. To tackle the supervised learning task in financial time-series prediction, we propose the application of a recently formulated algorithm that adaptively learns a mapping function, realized by a heterogeneous neural architecture composing of Generalized Operational Perceptron, given a set of labeled data. With a modified objective function, the proposed algorithm can accommodate the frequently observed imbalanced data distribution problem. Experiments on a large-scale Limit Order Book dataset demonstrate that the proposed algorithm outperforms related algorithms, including tensor-based methods which have access to a broader set of input information.
Lambda Congruences and Extensionality
Venue: arXiv cs.LO 2019
Year: 2019

In this work we provide alternative formulations of the concepts of lambda theory and extensional theory without introducing the notion of substitution and the sets of all, free and bound variables occurring in a term. We also clarify the actual role of $α$-renaming and $η$-extensionality in the lambda calculus: both of them can be described as properties of extensionality for certain classes of terms.
Securing Accelerators with Dynamic Information Flow Tracking
Venue: arXiv cs.DC 2019
Year: 2019

Systems-on-chip (SoCs) are becoming heterogeneous: they combine general-purpose processor cores with application-specific hardware components, also known as accelerators, to improve performance and energy efficiency. The advantages of heterogeneity, however, come at a price of threatening security. The architectural dissimilarities of processors and accelerators require revisiting the current security techniques. With this hardware demo, we show how accelerators can break dynamic information flow tracking (DIFT), a well-known security technique that protects systems against software-based attacks. We also describe how the security guarantees of DIFT can be re-established with a hardware solution that has low performance and area penalties.
Generate What You Can't See - a View-dependent Image Generation
Venue: arXiv cs.CV 2019
Year: 2019

In order to operate autonomously, a robot should explore the environment and build a model of each of the surrounding objects. A common approach is to carefully scan the whole workspace. This is time-consuming. It is also often impossible to reach all the viewpoints required to acquire full knowledge about the environment. Humans can perform shape completion of occluded objects by relying on past experience. Therefore, we propose a method that generates images of an object from various viewpoints using a single input RGB image. A deep neural network is trained to imagine the object appearance from many viewpoints. We present the whole pipeline, which takes a single RGB image as input and returns a sequence of RGB and depth images of the object. The method utilizes a CNN-based object detector to extract the object from the natural scene. Then, the proposed network generates a set of RGB and depth images. We show the results both on a synthetic dataset and on real images.
Does Homophily Make Socialbots More Influential? Exploring Infiltration Strategies
Venue: arXiv cs.SI 2019
Year: 2019

Socialbots are intelligent software controlling all the behavior of fake accounts in an online social network. They use artificial intelligence techniques to pass themselves off as human social media users. Socialbots exploit user trust to achieve their malicious goals, such as astroturfing, performing Sybil attacks, spamming, and harvesting private data. The first phase to countermeasure the malicious activities of the socialbots is studying their characteristics and revealing strategies they can employ to successfully infiltrate stealthily into target online social network. In this paper,we investigate the success of using different infiltration strategies in terms of infiltration performance and being stealthy. Every strategy is characterized by socialbots profile and behavioral characteristics. The findings from this study illustrate that assuming a specific taste for the tweets a socialbot retweets and/or likes make it more influential. Furthermore, the experimental results indicate that considering the presence of common characteristics and similarity increase the probability of being followed by other users. This is in complete agreement with homophily concept which is the tendency of individuals to associate and bond with similar others in social networks.
Formulas for Data-driven Control: Stabilization, Optimality and Robustness
Venue: arXiv cs.SY 2019
Year: 2019

In a paper by Willems and coauthors it was shown that persistently exciting data can be used to represent the input-output behavior of a linear system. Based on this fundamental result, we derive a parametrization of linear feedback systems that paves the way to solve important control problems using data-dependent Linear Matrix Inequalities only. The result is remarkable in that no explicit system's matrices identification is required. The examples of control problems we solve include the state and output feedback stabilization, and the linear quadratic regulation problem. We also discuss robustness to noise-corrupted measurements and show how the approach can be used to stabilize unstable equilibria of nonlinear systems.
Energy and performance evaluation of reactive, proactive, and hybrid routing protocols in wireless mesh network
Venue: arXiv cs.NI 2019
Year: 2019

This paper evaluates the energy consumption of well-known routing protocols, along with other metrics such as throughput, packet delivery ratio (PDR), and delay in different scenarios. We consider two other metrics in order to capture the efficiency of the energy consumption: e-throughput which is the ratio between the consumed energy and the throughput; and the e-PDR which is the ratio between the consumed energy and the PDR. We compare four routing protocols: AODV, OLSR, and HWMP in Reactive and Proactive modes. The number of nodes is varying between 25 and 81 nodes, with different mobility models. Simulations are conducted using NS3 and the parameters of a real network interface card. From the results, AODV presents the lowest energy consumption and a better e-Throughput. OLSR provides a better e-PDR in mobile scenarios. With a smaller e-PDR and e-Throughput, the proactive mode of HWMP is more energy efficient than the reactive mode.
A Deep Look into Neural Ranking Models for Information Retrieval
Venue: arXiv cs.IR 2019
Year: 2019

Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions.
A new Capacity-Achieving Private Information Retrieval Scheme with (Almost) Optimal File Length for Coded Servers
Venue: arXiv cs.IT 2019
Year: 2019

In a distributed storage system, private information retrieval (PIR) guarantees that a user retrieves one file from the system without revealing any information about the identity of its interested file to any individual server. In this paper, we investigate $(N,K,M)$ coded sever model of PIR, where each of $M$ files is distributed to the $N$ servers in the form of $(N,K)$ maximum distance separable (MDS) code for some $N>K$ and $M>1$. As a result, we propose a new capacity-achieving $(N,K,M)$ coded linear PIR scheme such that it can be implemented with file length $\frac{K(N-K)}{\gcd(N,K)}$, which is much smaller than the previous best result $K\big(\frac{N}{\gcd(N,K)}\big)^{M-1}$. Notably, among all the capacity-achieving coded linear PIR schemes, we show that the file length is optimal if $M>\big\lfloor \frac{K}{\gcd(N,K)}-\frac{K}{N-K}\big\rfloor+1$, and within a multiplicative gap $\frac{K}{\gcd(N,K)}$ of a lower bound on the minimum file length otherwise.
Feather: A Feature Model Transformation Language
Venue: arXiv cs.SE 2019
Year: 2019

Feature modeling has been a very popular approach for variability management in software product lines. Building a feature model requires substantial domain expertise, however, even experts cannot foresee all future possibilities. Changing requirements can force a feature model to evolve in order to adapt to the new conditions. Feather is a language to describe model transformations that will evolve a feature model. This article presents the structure and foundations of Feather. First, the language elements, which consist of declarations to characterize the model to evolve and commands to manipulate its structure, are introduced. Then, semantics grounding in feature model properties are given for the commands in order to provide precise command definitions. Next, an interpreter that can realize the transformations described by the commands in a Feather script is presented. Finally, effectiveness of the language is discussed using two realistic examples, where one of the examples includes a system from a dynamic environment and the other employs a system that has a large feature model containing 1,227 features.
Leveling the Playing Field -- Fairness in AI Versus Human Game Benchmarks
Venue: arXiv cs.AI 2019
Year: 2019

From the beginning if the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. Current research focus has shifted to electronic games, which provide unique challenges. As is often the case with AI research, these results are liable to be exaggerated or misrepresented by either authors or third parties. The extent to which these games benchmark consist of fair competition between human and AI is also a matter of debate. In this work, we review the statements made by authors and third parties in the general media and academic circle about these game benchmark results and discuss factors that can impact the perception of fairness in the contest between humans and machines
BRTSim, a general-purpose computational solver for hydrological, biogeochemical, and ecosystem dynamics
Venue: arXiv cs.CE 2019
Year: 2019

This paper introduces the recent release v3.1a of BRTSim (BioReactive Transport Simulator), a general-purpose multiphase and multi-species liquid, gas and heat flow solver for reaction-advection-dispersion processes in porous and non-porous media with application in hydrology and biogeochemistry. Within the philosophy of the BRTSim platform, the user can define (1) arbitrary chemical and biological species; (2) arbitrary chemical and biological reactions; (3) arbitrary equilibrium reactions; and (4) combine solvers for phases and heat flows as well as for specialized biological processes such as bioclogging and chemotaxis. These capabilities complement a suite of processes and process-feedback solvers not currently available in other general-purpose codes. Along with the flexibility to design arbitrarily complex reaction networks, and setup and synchronize solvers through one input text file, BRTSim can communicate with third-party software with ease. Here, four cases study that combine experimental observations and modeling with BRTSim are reported: (i) water table dynamics in a heterogeneous aquifer for variable hydrometeorological conditions; (ii) soil biological clogging by cells and exopolymers; (iii) biotic degradation and isotopic fractionation of nitrate; and (iv) dispersion and biodegradation of atrazine herbicide in agricultural crops.
Compiler-assisted Adaptive Program Scheduling in big.LITTLE Systems
Venue: arXiv cs.PL 2019
Year: 2019

Energy-aware architectures provide applications with a mix of low (LITTLE) and high (big) frequency cores. Choosing the best hardware configuration for a program running on such an architecture is difficult, because program parts benefit differently from the same hardware configuration. State-of-the-art techniques to solve this problem adapt the program's execution to dynamic characteristics of the runtime environment, such as energy consumption and throughput. We claim that these purely dynamic techniques can be improved if they are aware of the program's syntactic structure. To support this claim, we show how to use the compiler to partition source code into program phases: regions whose syntactic characteristics lead to similar runtime behavior. We use reinforcement learning to map pairs formed by a program phase and a hardware state to the configuration that best fit this setup. To demonstrate the effectiveness of our ideas, we have implemented the Astro system. Astro uses Q-learning to associate syntactic features of programs with hardware configurations. As a proof of concept, we provide evidence that Astro outperforms GTS, the ARM-based Linux scheduler tailored for heterogeneous architectures, on the parallel benchmarks from Rodinia and Parsec.
Deep Feature Selection using a Teacher-Student Network
Venue: arXiv cs.LG 2019
Year: 2019

High-dimensional data in many machine learning applications leads to computational and analytical complexities. Feature selection provides an effective way for solving these problems by removing irrelevant and redundant features, thus reducing model complexity and improving accuracy and generalization capability of the model. In this paper, we present a novel teacher-student feature selection (TSFS) method in which a 'teacher' (a deep neural network or a complicated dimension reduction method) is first employed to learn the best representation of data in low dimension. Then a 'student' network (a simple neural network) is used to perform feature selection by minimizing the reconstruction error of low dimensional representation. Although the teacher-student scheme is not new, to the best of our knowledge, it is the first time that this scheme is employed for feature selection. The proposed TSFS can be used for both supervised and unsupervised feature selection. This method is evaluated on different datasets and is compared with state-of-the-art existing feature selection methods. The results show that TSFS performs better in terms of classification and clustering accuracies and reconstruction error. Moreover, experimental evaluations demonstrate a low degree of sensitivity to parameter selection in the proposed method.