OpenRaiser/Intern-Atlas huggingface dataset:
Curie: A method for protecting SVM Classifier from Poisoning Attack
Venue: arXiv cs.CR 2016
Year: 2016

Machine learning is used in a number of security related applications such as biometric user authentication, speaker identification etc. A type of causative integrity attack against machine learning called Poisoning attack works by injecting specially crafted data points in the training data so as to increase the false positive rate of the classifier. In the context of the biometric authentication, this means that more intruders will be classified as valid user, and in case of speaker identification system, user A will be classified user B. In this paper, we examine poisoning attack against SVM and introduce - Curie - a method to protect the SVM classifier from the poisoning attack. The basic idea of our method is to identify the poisoned data points injected by the adversary and filter them out. Our method is light weight and can be easily integrated into existing systems. Experimental results show that it works very well in filtering out the poisoned data.
Generalized Suffix Tree based Multiple Sequence Alignment for Service Virtualization
Venue: arXiv cs.SE 2016
Year: 2016

Assuring quality of contemporary software systems is a very challenging task due to the often large complexity of the deployment environments in which they will operate. Service virtualization is an approach to this challenge where services within the deployment environment are emulated by synthesising service response messages from models or by recording and then replaying service interaction messages with the system. Record-and-replay techniques require an approach where (i) message prototypes can be derived from recorded system interactions (i.e. request-response sequences), (ii) a scheme to match incoming request messages against message prototypes, and (iii) the synthesis of response messages based on similarities between incoming messages and the recorded system interactions. Previous approaches in service virtualization have required a multiple sequence alignment (MSA) algorithm as a means of finding common patterns of similarities and differences between messages required by all three steps. In this paper, we present a novel MSA algorithm based on Generalized Suffix Trees (GSTs). We evaluated the accuracy and efficiency of the proposed algorithm against six enterprise service message trace datasets, with the proposed algorithm performing up to 50 times faster than standard MSA approaches. Furthermore, the algorithm has applicability to other domains beyond service virtualization.
Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification
Venue: arXiv cs.CV 2016
Year: 2016

Person re-identification is to seek a correct match for a person of interest across views among a large number of imposters. It typically involves two procedures of non-linear feature extractions against dramatic appearance changes, and subsequent discriminative analysis in order to reduce intra- personal variations while enlarging inter-personal differences. In this paper, we introduce a hybrid architecture which combines Fisher vectors and deep neural networks to learn non-linear representations of person images to a space where data can be linearly separable. We reinforce a Linear Discriminant Analysis (LDA) on top of the deep neural network such that linearly separable latent representations can be learnt in an end-to-end fashion. By optimizing an objective function modified from LDA, the network is enforced to produce feature distributions which have a low variance within the same class and high variance between classes. The objective is essentially derived from the general LDA eigenvalue problem and allows to train the network with stochastic gradient descent and back-propagate LDA gradients to compute the gradients involved in Fisher vector encoding. For evaluation we test our approach on four benchmark data sets in person re-identification (VIPeR [1], CUHK03 [2], CUHK01 [3], and Market1501 [4]). Extensive experiments on these benchmarks show that our model can achieve state-of-the-art results.
Throughput Optimal Decentralized Scheduling of Multi-Hop Networks with End-to-End Deadline Constraints: Unreliable Links
Venue: arXiv cs.NI 2016
Year: 2016

We consider unreliable multi-hop networks serving multiple flows in which packets not delivered to their destination nodes by their deadlines are dropped. We address the design of policies for routing and scheduling packets that optimize any specified weighted average of the throughputs of the flows. We provide a new approach which directly yields an optimal distributed scheduling policy that attains any desired maximal timely-throughput vector under average-power constraints on the nodes. It pursues a novel intrinsically stochastic decomposition of the Lagrangian of the constrained network-wide MDP rather than of the fluid model. All decisions regarding a packet's transmission scheduling, transmit power level, and routing, are completely distributed, based solely on the age of the packet, not requiring any knowledge of network state or queue lengths at any of the nodes. Global coordination is achieved through a tractably computable "price" for transmission energy. This price is different from that used to derive the backpressure policy where price corresponds to queue lengths. A quantifiably near-optimal policy is provided if nodes have peak-power constraints.
Integrated Transmission & Distribution System Modeling and Analysis: Need & Advantages
Venue: arXiv cs.SY 2016
Year: 2016

The primary objective of this paper is to highlight the need for and benefits of studying the steady state and dynamic response of power systems using three phase integrated transmission and distribution (T&D) system models (hereafter referred to as hybrid models). Modeling and simulation requirements for building and analyzing hybrid models are also discussed. Finally, results from steady state and dynamic simulations of a large hybrid model are presented to demonstrate insights that can be obtained from hybrid models which cannot be obtained from the study of transmission and distribution systems separately.
Mathematical Modeling of General Inaccurate Adders
Venue: arXiv cs.NA 2016
Year: 2016

Inaccurate circuits make possible the conservation of limited resources, such as energy. But effective design of such circuits requires an understanding of resulting tradeoffs between accuracy and design parameters, such as voltages and speed of execution. Although studies of tradeoffs have been done on specific circuits, the applicability of those studies is narrow. This paper presents a comprehensive and mathematically rigorous method for analyzing a large class of inaccurate circuits for addition. Furthermore, it presents new, fast algorithms for the computation of key statistical measures of inaccuracy in such adders, thus helping hardware architects explore the design space with greater confidence.
Performance Analysis of $L$-Branch Scan-and-Wait Combining (SWC) over Arbitrarily Correlated Nakagami-$m$ Fading Channels
Venue: arXiv cs.IT 2016
Year: 2016

The performance of $L$-branch scan-and-wait combining (SWC) reception systems over arbitrarily correlated and not necessarily identically distributed Nakagami-$m$ fading channels is analyzed and evaluated. Firstly, a fast convergent infinite series representation for the SWC output signal-noise ratio (SNR) is presented. This expression is used to obtain analytical expressions in the form of infinite series for the average error probability performance of various modulation schemes for integer values of $m$ as well as the average number of paths estimation and average waiting time (AWT) of $L$-branch SWC receivers for arbitrary values of $m$. The numerically obtained results have shown that the performance expressions converge very fast to their exact analytical values. It was found that the convergence speed depends on the correlation and operating SNR values as well as the Nakagami $m$-parameter. In addition to the analytical results, complementary computer simulated performance evaluation results have been obtained by means of Monte Carlo error counting techniques. The match between these two sets of results has verified the accuracy of the proposed mathematical analysis. Furthermore, it is revealed that, at the expense of a negligible AWT, the average error probability performance of SWC receivers is always superior to that of switched-and-examine combining receivers and in certain cases to that of maximal-ratio combining receivers.
Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research
Venue: arXiv cs.DL 2016
Year: 2016

With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.
Optimal Virtualized Inter-Tenant Resource Sharing for Device-to-Device Communications in 5G Networks
Venue: arXiv cs.NI 2016
Year: 2016

Device-to-Device (D2D) communication is expected to enable a number of new services and applications in future mobile networks and has attracted significant research interest over the last few years. Remarkably, little attention has been placed on the issue of D2D communication for users belonging to different operators. In this paper, we focus on this aspect for D2D users that belong to different tenants (virtual network operators), assuming virtualized and programmable future 5G wireless networks. Under the assumption of a cross-tenant orchestrator, we show that significant gains can be achieved in terms of network performance by optimizing resource sharing from the different tenants, i.e., slices of the substrate physical network topology. To this end, a sum-rate optimization framework is proposed for optimal sharing of the virtualized resources. Via a wide site of numerical investigations, we prove the efficacy of the proposed solution and the achievable gains compared to legacy approaches.
Preliminaries of a Space Situational Awareness Ontology
Venue: arXiv cs.AI 2016
Year: 2016

Space situational awareness (SSA) is vital for international safety and security, and the future of space travel. By improving SSA data-sharing we improve global SSA. Computational ontology may provide one means toward that goal. This paper develops the ontology of the SSA domain and takes steps in the creation of the space situational awareness ontology. Ontology objectives, requirements and desiderata are outlined; and both the SSA domain and the discipline of ontology are described. The purposes of the ontology include: exploring the potential for ontology development and engineering to (i) represent SSA data, general domain knowledge, objects and relationships (ii) annotate and express the meaning of that data, and (iii) foster SSA data-exchange and integration among SSA actors, orbital debris databases, space object catalogs and other SSA data repositories. By improving SSA via data- and knowledge-sharing, we can (iv) expand our scientific knowledge of the space environment, (v) advance our capacity for planetary defense from near-Earth objects, and (vi) ensure the future of safe space flight for generations to come.
Assisted Energy Management in Smart Microgrids
Venue: arXiv cs.AI 2016
Year: 2016

Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources. Pricing is accordingly used to reflect energy availability, to allocate such a limited resource to those loads that value it most. However, the strictly competitive mechanism can result in service interruption in presence of competing demand. To solve this issue we investigate on the use of forward contracts, i.e., service level agreements priced to reflect the expectation of future supply and demand curves. Given the limited resources of microgrids, service interruption is an opposite objective to the one of service availability. We firstly design policy-based brokers and identify then a learning broker based on artificial neural networks. We show the latter being progressively minimizing the reimbursement costs and maximizing the overall profit.
Scalable, Fast Cloud Computing with Execution Templates
Venue: arXiv cs.DC 2016
Year: 2016

Large scale cloud data analytics applications are often CPU bound. Most of these cycles are wasted: benchmarks written in C++ run 10-51 times faster than frameworks such as Naiad and Spark. However, calling faster implementations from those frameworks only sees moderate (3-5x) speedups because their control planes cannot schedule work fast enough. This paper presents execution templates, a control plane abstraction for CPU-bound cloud applications, such as machine learning. Execution templates leverage highly repetitive control flow to cache scheduling decisions as {\it templates}. Rather than reschedule hundreds of thousands of tasks on every loop execution, nodes instantiate these templates. A controller's template specifies the execution across all worker nodes, which it partitions into per-worker templates. To ensure that templates execute correctly, controllers dynamically patch templates to match program control flow. We have implemented execution templates in Nimbus, a C++ cloud computing framework. Running in Nimbus, analytics benchmarks can run 16-43 times faster than in Naiad and Spark. Nimbus's control plane can scale out to run these faster benchmarks on up to 100 nodes (800 cores).
Re-enabling high-speed caching for LSM-trees
Venue: arXiv cs.DS 2016
Year: 2016

LSM-tree has been widely used in cloud computing systems by Google, Facebook, and Amazon, to achieve high performance for write-intensive workloads. However, in LSM-tree, random key-value queries can experience long latency and low throughput due to the interference from the compaction, a basic operation in the algorithm, to caching. LSM-tree relies on frequent compaction operations to merge data into a sorted structure. After a compaction, the original data are reorganized and written to other locations on the disk. As a result, the cached data are invalidated since their referencing addresses are changed, causing serious performance degradations. We propose dLSM in order to re-enable high-speed caching during intensive writes. dLSM is an LSM-tree with a compaction buffer on the disk, working as a cushion to minimize the cache invalidation caused by compactions. The compaction buffer maintains a series of snapshots of the frequently compacted data, which represent a consistent view of the corresponding data in the underlying LSM-tree. Being updated in a much lower rate than that of compactions, data in the compaction buffer are almost stationary. In dLSM, an object is referenced by the disk address of the corresponding block either in the compaction buffer for frequently compacted data, or in the underlying LSM-tree for infrequently compacted data. Thus, hot objects can be effectively kept in the cache without harmful invalidations. With the help of a small on-disk compaction buffer, dLSM achieves a high query performance by enabling effective caching, while retaining all merits of LSM-tree for write-intensive data processing. We have implemented dLSM based on LevelDB. Our evaluations show that with a standard DRAM cache, dLSM can achieve 5--8x performance improvement over LSM with the same cache on HDD storage.
Evidences of the mismatch between industry and academy on modelling language quality evaluation
Venue: arXiv cs.SE 2016
Year: 2016

Quality is an implicit property of models and modelling languages by their condition of engineering artifacts. However, the quality property is affected by the diversity of conceptions around the model-driven paradigm. In this document is presented a report of quality issues on modelling languages and models. These issues result from an analysis about quality evidences obtained from industrial and academic/scientific contexts.
On-board monitoring of 2-D spatially-resolved temperatures in cylindrical lithium-ion batteries: Part I. Low-order thermal modelling
Venue: arXiv cs.SY 2016
Year: 2016

Estimating the temperature distribution within Li-ion batteries during operation is critical for safety and control purposes. Although existing control-oriented thermal models - such as thermal equivalent circuits (TEC) - are computationally efficient, they only predict average temperatures, and are unable to predict the spatially resolved temperature distribution throughout the cell. We present a low-order 2D thermal model of a cylindrical battery based on a Chebyshev spectral-Galerkin (SG) method, capable of predicting the full temperature distribution with a similar efficiency to a TEC. The model accounts for transient heat generation, anisotropic heat conduction, and non-homogeneous convection boundary conditions. The accuracy of the model is validated through comparison with finite element simulations, which show that the 2-D temperature field (r, z) of a large format (64 mm diameter) cell can be accurately modelled with as few as 4 states. Furthermore, the performance of the model for a range of Biot numbers is investigated via frequency analysis. For larger cells or highly transient thermal dynamics, the model order can be increased for improved accuracy. The incorporation of this model in a state estimation scheme with experimental validation against thermocouple measurements is presented in the companion contribution (Part II).
Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)
Venue: arXiv cs.CV 2016
Year: 2016

Supervised learning of convolutional neural networks (CNNs) can require very large amounts of labeled data. Labeling thousands or millions of training examples can be extremely time consuming and costly. One direction towards addressing this problem is to create features from unlabeled data. In this paper we propose a new method for training a CNN, with no need for labeled instances. This method for unsupervised feature learning is then successfully applied to a challenging object recognition task. The proposed algorithm is relatively simple, but attains accuracy comparable to that of more sophisticated methods. The proposed method is significantly easier to train, compared to existing CNN methods, making fewer requirements on manually labeled training data. It is also shown to be resistant to overfitting. We provide results on some well-known datasets, namely STL-10, CIFAR-10, and CIFAR-100. The results show that our method provides competitive performance compared with existing alternative methods. Selective Convolutional Neural Network (S-CNN) is a simple and fast algorithm, it introduces a new way to do unsupervised feature learning, and it provides discriminative features which generalize well.
The Politics of Routing: Investigating the Relationship Between AS Connectivity and Internet Freedom
Venue: arXiv cs.NI 2016
Year: 2016

The Internet's importance for promoting free and open communication has led to widespread crackdowns on its use in countries around the world. In this study, we investigate the relationship between national policies around freedom of speech and Internet topology in countries around the world. We combine techniques from network measurement and machine learning to identify features of Internet structure at the national level that are the best indicators of a country's level of freedom. We find that IP density and path lengths to other countries are the best indicators of a country's freedom. We also find that our methods predict freedom categories for countries with 91% accuracy.
A Formal Semantic for UML 2.0 Activity Diagram based on Institution Theory
Venue: arXiv cs.SE 2016
Year: 2016

Giving a formal semantic to an UML Activity diagram (UML AD) is a hard task. The reason of this difficulty is the ambiguity and the absence of a precise formal semantic of such semi-formal formalism. A variety of semantics exist in the literature having tackled the aspects covered by this language. We can give as example denotational, functional and compositional semantics. To cope with the recent tendency which gave a heterogeneous semantic to UML diagrams, we aim to define an algebraic presentation of the semantic of UML AD. In this work, we define a formal semantic of UML 2.0 AD based on institution theory. For UML AD formalism, which is a graphical language, no precise formal semantic is given to it. We use the institution theory to define the intended semantic. Thus, the UML AD formalism will be defined in its own natural semantic.
How is a data-driven approach better than random choice in label space division for multi-label classification?
Venue: arXiv cs.LG 2016
Year: 2016

We propose using five data-driven community detection approaches from social networks to partition the label space for the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd: modularity-maximizing fastgreedy and leading eigenvector, infomap, walktrap and label propagation algorithms. We construct a label co-occurence graph (both weighted an unweighted versions) based on training data and perform community detection to partition the label set. We include Binary Relevance and Label Powerset classification methods for comparison. We use gini-index based Decision Trees as the base classifier. We compare educated approaches to label space divisions against random baselines on 12 benchmark data sets over five evaluation measures. We show that in almost all cases seven educated guess approaches are more likely to outperform RAkELd than otherwise in all measures, but Hamming Loss. We show that fastgreedy and walktrap community detection methods on weighted label co-occurence graphs are 85-92% more likely to yield better F1 scores than random partitioning. Infomap on the unweighted label co-occurence graphs is on average 90% of the times better than random paritioning in terms of Subset Accuracy and 89% when it comes to Jaccard similarity. Weighted fastgreedy is better on average than RAkELd when it comes to Hamming Loss.
P4QS: A Peer to Peer Privacy Preserving Query Service for Location-Based Mobile Applications
Venue: arXiv cs.CR 2016
Year: 2016

The location-based services provide an interesting combination of cyber and physical worlds. However, they can also threaten the users' privacy. Existing privacy preserving protocols require trusted nodes, with serious security and computational bottlenecks. In this paper, we propose a novel distributed anonymizing protocol based on peer-to-peer architecture. Each mobile node is responsible for anonymizing a specific zone. The mobile nodes collaborate in anonymizing their queries, without the need not get access to any information about each other. In the proposed protocol, each request will be sent with a randomly chosen ticket. The encrypted response produced by the server is sent to a particular mobile node (called broker node) over the network, based on the hash value of this ticket. The user will query the broker to get the response. All parts of the messages are encrypted except the fields required for the anonymizer and the broker. This will secure the packet exchange over the P2P network. The proposed protocol was implemented and tested successfully, and the experimental results showed that it could be deployed efficiently to achieve user privacy in location-based services.