OpenRaiser/Intern-Atlas huggingface dataset:
Optimized Energy Aware 5G Network Function Virtualization
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, network function virtualization (NFV) is identified as a promising key technology that can contribute to energy-efficiency improvement in 5G networks. an optical network supported architecture is proposed and investigated in this work to provide the wired infrastructure needed in 5G networks and to support NFV towards an energy efficient 5G networks. In this architecture the mobile core network functions as well as baseband function are virtualized and provided as VMs. the impact of the total number of active users in the network, backhaul/fronthaul configurations and VM inter-traffic are investigated. A mixed integer linear programming (MILP) optimising model is developed with the objective of minimising the total power consumption by optimizing the VMs location and VMs servers' utilisation. The MILP model results show that virtualization can result in up to 38% (average 34) energy saving. The results also reveal how the total number of active users affects the baseband VMs optimal distribution whilst the core network VMs distribution is affected mainly by the inter-traffic between the VMs. For real-time implementation, two heuristics are developed an Energy Efficient NFV without CNVMs inter-traffic (EENFVnoITr) heuristic and an Energy Efficient NFV with CNVMs inter-traffic (EENFVwithITr) heuristic, both produce comparable results to the optimal MILP results
Living IoT: A Flying Wireless Platform on Live Insects
Venue: arXiv cs.NI 2018
Year: 2018

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.
An Industrial Case Study on Shrinking Code Review Changesets through Remark Prediction
Venue: arXiv cs.SE 2018
Year: 2018

Change-based code review is used widely in industrial software development. Thus, research on tools that help the reviewer to achieve better review performance can have a high impact. We analyze one possibility to provide cognitive support for the reviewer: Determining the importance of change parts for review, specifically determining which parts of the code change can be left out from the review without harm. To determine the importance of change parts, we extract data from software repositories and build prediction models for review remarks based on this data. The approach is discussed in detail. To gather the input data, we propose a novel algorithm to trace review remarks to their triggers. We apply our approach in a medium-sized software company. In this company, we can avoid the review of 25% of the change parts and of 23% of the changed Java source code lines, while missing only about 1% of the review remarks. Still, we also observe severe limitations of the tried approach: Much of the savings are due to simple syntactic rules, noise in the data hampers the search for better prediction models, and some developers in the case company oppose the taken approach. Besides the main results on the mining and prediction of triggers for review remarks, we contribute experiences with a novel, multi-objective and interactive rule mining approach. The anonymized dataset from the company is made available, as are the implementations for the devised algorithms.
Bioinformatics Computational Cluster Batch Task Profiling with Machine Learning for Failure Prediction
Venue: arXiv cs.DC 2018
Year: 2018

Motivation: Traditional computational cluster schedulers are based on user inputs and run time needs request for memory and CPU, not IO. Heavily IO bound task run times, like ones seen in many big data and bioinformatics problems, are dependent on the IO subsystems scheduling and are problematic for cluster resource scheduling. The problematic rescheduling of IO intensive and errant tasks is a lost resource. Understanding the conditions in both successful and failed tasks and differentiating them could provide knowledge to enhancing cluster scheduling and intelligent resource optimization. Results: We analyze a production computational cluster contributing 6.7 thousand CPU hours to research over two years. Through this analysis we develop a machine learning task profiling agent for clusters that attempts to predict failures between identically provision requested tasks.
Learning Dynamical Demand Response Model in Real-Time Pricing Program
Venue: arXiv cs.LG 2018
Year: 2018

Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent temporal correlation of the EUC behaviors, and may result in large errors when predicting the actual responses of EUCs in real-time pricing (RTP) programs. In this paper, we propose a dynamical DR model so as to capture the temporal behavior of the EUCs. The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. In both cases, the dynamical DR model can be learned from historical price and energy consumption data. Numerical simulation illustrated how the states are chosen and also showed the proposed dynamical DR model significantly outperforms the static ones.
Estimating Rationally Inattentive Utility Functions with Deep Clustering for Framing - Applications in YouTube Engagement Dynamics
Venue: arXiv cs.LG 2018
Year: 2018

We consider a framework involving behavioral economics and machine learning. Rationally inattentive Bayesian agents make decisions based on their posterior distribution, utility function and information acquisition cost Renyi divergence which generalizes Shannon mutual information). By observing these decisions, how can an observer estimate the utility function and information acquisition cost? Using deep learning, we estimate framing information (essential extrinsic features) that determines the agent's attention strategy. Then we present a preference based inverse reinforcement learning algorithm to test for rational inattention: is the agent an utility maximizer, attention maximizer, and does an information cost function exist that rationalizes the data? The test imposes a Renyi mutual information constraint which impacts how the agent can select attention strategies to maximize their expected utility. The test provides constructive estimates of the utility function and information acquisition cost of the agent. We illustrate these methods on a massive YouTube dataset for characterizing the commenting behavior of users.
A Cross-Architecture Instruction Embedding Model for Natural Language Processing-Inspired Binary Code Analysis
Venue: arXiv cs.CR 2018
Year: 2018

Given a closed-source program, such as most of proprietary software and viruses, binary code analysis is indispensable for many tasks, such as code plagiarism detection and malware analysis. Today, source code is very often compiled for various architectures, making cross-architecture binary code analysis increasingly important. A binary, after being disassembled, is expressed in an assembly languages. Thus, recent work starts exploring Natural Language Processing (NLP) inspired binary code analysis. In NLP, words are usually represented in high-dimensional vectors (i.e., embeddings) to facilitate further processing, which is one of the most common and critical steps in many NLP tasks. We regard instructions as words in NLP-inspired binary code analysis, and aim to represent instructions as embeddings as well. To facilitate cross-architecture binary code analysis, our goal is that similar instructions, regardless of their architectures, have embeddings close to each other. To this end, we propose a joint learning approach to generating instruction embeddings that capture not only the semantics of instructions within an architecture, but also their semantic relationships across architectures. To the best of our knowledge, this is the first work on building cross-architecture instruction embedding model. As a showcase, we apply the model to resolving one of the most fundamental problems for binary code similarity comparison---semantics-based basic block comparison, and the solution outperforms the code statistics based approach. It demonstrates that it is promising to apply the model to other cross-architecture binary code analysis tasks.
A Comprehensive View on Quality Characteristics of the IoT Solutions
Venue: arXiv cs.NI 2018
Year: 2018

Categorization of quality characteristics helps in a more effective structuring of the testing process and in the determination of properties, which can be verified in the system under test. In the emerging area of Internet of Things (IoT) systems, several individual attempts have been made to summarize these aspects, but the previous work is rather heterogenic and focuses on specific subareas. Hence, we consolidated the quality characteristics into one unified view, which specifically emphasizes the aspects of security, privacy, reliability and usability, as these aspects are of often quoted as major challenges in the quality of contemporary IoT systems. The consolidated view also covers other areas of system quality, which are relevant for IoT system testing and quality assurance. In the paper, we also discuss relevant synonyms of particular quality characteristics as presented in the literature or being used in the current industry praxis. The consolidated view uses two levels of characteristics to maintain a suitable level of granularity and specificity in the discussed quality characteristics.
Computations in Stochastic Acceptors
Venue: arXiv cs.LG 2018
Year: 2018

Machine learning provides algorithms that can learn from data and make inferences or predictions on data. Stochastic acceptors or probabilistic automata are stochastic automata without output that can model components in machine learning scenarios. In this paper, we provide dynamic programming algorithms for the computation of input marginals and the acceptance probabilities in stochastic acceptors. Furthermore, we specify an algorithm for the parameter estimation of the conditional probabilities using the expectation-maximization technique and a more efficient implementation related to the Baum-Welch algorithm.
Nonlinear Robust Filtering of Sampled-Data Dynamical Systems
Venue: arXiv cs.SY 2018
Year: 2018

This work is concerned with robust filtering of nonlinear sampled-data systems with and without exact discrete-time models. A linear matrix inequality (LMI) based approach is proposed for the design of robust $H_{\infty}$ observers for a class of Lipschitz nonlinear systems. Two type of systems are considered, Lipschitz nonlinear discrete-time systems and Lipschitz nonlinear sampled-data systems with Euler approximate discrete-time models. Observer convergence when the exact discrete-time model of the system is available is shown. Then, practical convergence of the proposed observer is proved using the Euler approximate discrete-time model. As an additional feature, maximizing the admissible Lipschitz constant, the solution of the proposed LMI optimization problem guaranties robustness against some nonlinear uncertainty. The robust H_infty observer synthesis problem is solved for both cases. The maximum disturbance attenuation level is achieved through LMI optimization. At the end, a path to extending the results to higher-order approximate discretizations is provided.
The Equivalent Conversions of the Role-Based Access Control Model
Venue: arXiv cs.CR 2018
Year: 2018

The problems which are important for the effective functioning of an access control policy in a large information system (LIS) are selected. The general concept of a local optimization of a role-based access control (RBAC) model is formulated. The optimization criteria are proposed. The algorithms of a local optimization of the RBAC model are defined and justified. The developed algorithms are used in the methods of the solution of the following problems: the assessment of risks of the leakage of permissions in the RBAC policy, the access control in the distributed hierarchical systems, the combining of role-based and mandatory access control models. In the first problem the question of the permissions distribution in the role hierarchy is researched. The analytic hierarchy process (AHP) is applied to creation of the estimates. The method is based on the hierarchical structure of a role set. The offered technique can order the permissions according to the value of the risks of their leakage. In the second problem the algorithm of the distribution of the cryptographic keys in the system with a hierarchical arrangement of the objects is offered. The cryptography protocols for the practical use of this algorithm are defined. The conditions of the implementation of the discretionary and mandatory principles of the access control on the basis of the developed algorithm are formulated.
The ShareGrid Portal: an easy way to submit jobs on computational Grids
Venue: arXiv cs.DC 2018
Year: 2018

Grid computing is a distributed computing paradigm which aims to aggregate several heterogeneous and distributed resources, belonging to different and independent organizations, in a dynamic, transparent and coordinated way. Since its introduction, Grid computing has been successfully applied to solve several scientific challenging applications. Despite of the consolidation of many of its aspects, there are some issues that are still open. One of them is the transparency: in many real Grid systems, users still need to be aware of Grid computing, either for adapting their applications to this paradigm or for wrapping them in a suitable software framework. In this paper we present the ShareGrid Portal, a Web portal and a portal framework, built on top of the ShareGrid project infrastructure. Its intent is both to ease the execution of user applications in a Grid system and to allow developers to flexibly add new portal functionalities. In this work, we compare it with other well-known Grid portals and we show its user interface and its architecture. Finally we discuss user experiences and future extensions.
The TAAROA Project Specification
Venue: arXiv cs.DC 2018
Year: 2018

Since its introduction, the Grid computing paradigm has been widely adopted both in scientific and also in industrial areas. The main advantage of the Grid computing paradigm is the ability to enable, in a transparent way, the sharing and the coordination of several heterogeneous and large-scale distributed resources belonging to different institutional domains. One of its limitation is the lack of facilities for executing services. In fact, Grid computing has been traditionally used and improved for running computational-intensive or data-intensive applications. A service differs from this kind of applications in that it usually waits for requests from clients and replies with useful information; moreover, a service is typically subjected to some predefined constraints, called Service Level Agreement (SLA), including both temporal and performance restrictions. In this paper we present the TAAROA middleware, a software system that tries to extend the traditional target of the Grid computing paradigm to include the service concept. It attempts to accomplish its goal by using the virtualization technology. By abstracting the hardware and software resources of a computer, virtualization brings to TAAROA two important benefits: (1) the encapsulation of the service runtime environment, and (2) the possibility, through the migration facility, to move a service from the computer where it is running to another one that hopefully reduces the risk of violating some of the SLA constraints. In the current version of TAAROA middleware there is no explicit mechanism for achieving the level of a service as defined by the related SLA; this means that actually TAAROA is only able to provide a best-effort service.
Markov chain aggregation and its application to rule-based modelling
Venue: arXiv cs.OH 2018
Year: 2018

Rule-based modelling allows to represent molecular interactions in a compact and natural way. The underlying molecular dynamics, by the laws of stochastic chemical kinetics, behaves as a continuous-time Markov chain. However, this Markov chain enumerates all possible reaction mixtures, rendering the analysis of the chain computationally demanding and often prohibitive in practice. We here describe how it is possible to efficiently find a smaller, aggregate chain, which preserves certain properties of the original one. Formal methods and lumpability notions are used to define algorithms for automated and efficient construction of such smaller chains (without ever constructing the original ones). We here illustrate the method on an example and we discuss the applicability of the method in the context of modelling large signalling pathways.
Deep Learning for Inferring the Surface Solar Irradiance from Sky Imagery
Venue: arXiv cs.CV 2018
Year: 2018

We present a novel approach to perform ground-based estimation and prediction of the surface solar irradiance with the view to predicting photovoltaic energy production. We propose the use of mini-batch k-means clustering to extract features, referred to as per cluster number of pixels (PCNP), from sky images taken by a low-cost fish eye camera. These features are first used to classify the sky as clear or cloudy using a single hidden layer neural network; the classification accuracy achieves 99.7%. If the sky is classified as cloudy, we propose to use a deep neural network having as input features the PCNP to predict intra-hour variability of the solar irradiance. Toward this objective, in this paper, we focus on estimating the deep neural network model relating the PCNP features and the solar irradiance, which is an important step before performing the prediction task. The proposed deep learning-based estimation approach is shown to have an accuracy of 95%.
Moment Matching Training for Neural Machine Translation: A Preliminary Study
Venue: arXiv cs.CL 2018
Year: 2018

In previous works, neural sequence models have been shown to improve significantly if external prior knowledge can be provided, for instance by allowing the model to access the embeddings of explicit features during both training and inference. In this work, we propose a different point of view on how to incorporate prior knowledge in a principled way, using a moment matching framework. In this approach, the standard local cross-entropy training of the sequential model is combined with a moment matching training mode that encourages the equality of the expectations of certain predefined features between the model distribution and the empirical distribution. In particular, we show how to derive unbiased estimates of some stochastic gradients that are central to the training, and compare our framework with a formally related one: policy gradient training in reinforcement learning, pointing out some important differences in terms of the kinds of prior assumptions in both approaches. Our initial results are promising, showing the effectiveness of our proposed framework.
Sherali--Adams Strikes Back
Venue: arXiv cs.DS 2018
Year: 2018

Let $G$ be any $n$-vertex graph whose random walk matrix has its nontrivial eigenvalues bounded in magnitude by $1/\sqrtΔ$ (for example, a random graph $G$ of average degree~$Θ(Δ)$ typically has this property). We show that the $\exp\Big(c \frac{\log n}{\log Δ}\Big)$-round Sherali--Adams linear programming hierarchy certifies that the maximum cut in such a~$G$ is at most $50.1\%$ (in fact, at most $\tfrac12 + 2^{-Ω(c)}$). For example, in random graphs with $n^{1.01}$ edges, $O(1)$ rounds suffice; in random graphs with $n \cdot \text{polylog}(n)$ edges, $n^{O(1/\log \log n)} = n^{o(1)}$ rounds suffice. Our results stand in contrast to the conventional beliefs that linear programming hierarchies perform poorly for \maxcut and other CSPs, and that eigenvalue/SDP methods are needed for effective refutation. Indeed, our results imply that constant-round Sherali--Adams can strongly refute random Boolean $k$-CSP instances with $n^{\lceil k/2 \rceil + δ}$ constraints; previously this had only been done with spectral algorithms or the SOS SDP hierarchy.
VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control
Venue: arXiv cs.LG 2018
Year: 2018

Recent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bottlenecks are also encountered when we generalize this paradigm to universal complex tasks. Among them, the low efficiency of data utilization in model-free reinforcement algorithms is of great concern. In contrast, the model-based reinforcement learning algorithms can reveal underlying dynamics in learning environments and seldom suffer the data utilization problem. To address the problem, a model-based reinforcement learning algorithm with attention mechanism embedded is proposed as an extension of World Models in this paper. We learn the environment model through Mixture Density Network Recurrent Network(MDN-RNN) for agents to interact, with combinations of variational auto-encoder(VAE) and attention incorporated in state value estimates during the process of learning policy. In this way, agent can learn optimal policies through less interactions with actual environment, and final experiments demonstrate the effectiveness of our model in control problem.