OpenRaiser/Intern-Atlas huggingface dataset:
A GA based approach for task scheduling in multi-cloud environment
Venue: arXiv cs.DC 2015
Year: 2015

In multi-cloud environment, task scheduling has attracted a lot of attention due to NP-Complete nature of the problem. Moreover, it is very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. In this paper, we propose a genetic algorithm (GA) based approach for solving task scheduling problem. The algorithm is described with innovative idea of fitness function derivation and mutation. The proposed algorithm is exposed to rigorous testing using various benchmark datasets and its performance is evaluated in terms of total makespan.
Cooperative Communication Using Network Coding
Venue: arXiv cs.IT 2015
Year: 2015

We consider a cognitive radio network scenario where a primary transmitter and a secondary transmitter, respectively, communicate a message to their respective primary receiver and secondary receiver over a packet-based wireless link, using a joint automatic-repeat-request (ARQ) error control scheme. The secondary transmitter assists in the retransmission of the primary message, which improves the primary performance, and is granted limited access to the transmission resources. Conventional ARQ, as well as two network-coding schemes are investigated for application in the retransmission phase; namely the static network-coding (SNC) scheme and the adaptive network-coding (ANC) scheme. For each scheme we analyze the transmission process by investigating the distribution of the number of transmission attempts and approximate it by normal distributions. Considering both the cases of an adaptive frame size and a truncated frame size, we derive analytical results on packet throughput and infer that the ANC scheme outperforms the SNC scheme.
MidRank: Learning to rank based on subsequences
Venue: arXiv cs.CV 2015
Year: 2015

We present a supervised learning to rank algorithm that effectively orders images by exploiting the structure in image sequences. Most often in the supervised learning to rank literature, ranking is approached either by analyzing pairs of images or by optimizing a list-wise surrogate loss function on full sequences. In this work we propose MidRank, which learns from moderately sized sub-sequences instead. These sub-sequences contain useful structural ranking information that leads to better learnability during training and better generalization during testing. By exploiting sub-sequences, the proposed MidRank improves ranking accuracy considerably on an extensive array of image ranking applications and datasets.
How do the naive Bayes classifier and the Support Vector Machine compare in their ability to forecast the Stock Exchange of Thailand?
Venue: arXiv cs.LG 2015
Year: 2015

This essay investigates the question of how the naive Bayes classifier and the support vector machine compare in their ability to forecast the Stock Exchange of Thailand. The theory behind the SVM and the naive Bayes classifier is explored. The algorithms are trained using data from the month of January 2010, extracted from the MarketWatch.com website. Input features are selected based on previous studies of the SET100 Index. The Weka 3 software is used to create models from the labeled training data. Mean squared error and proportion of correctly classified instances, and a number of other error measurements are the used to compare the two algorithms. This essay shows that these two algorithms are currently not advanced enough to accurately model the stock exchange. Nevertheless, the naive Bayes is better than the support vector machine at predicting the Stock Exchange of Thailand.
Multiple-Instance Learning: Radon-Nikodym Approach to Distribution Regression Problem
Venue: arXiv cs.LG 2015
Year: 2015

For distribution regression problem, where a bag of $x$--observations is mapped to a single $y$ value, a one--step solution is proposed. The problem of random distribution to random value is transformed to random vector to random value by taking distribution moments of $x$ observations in a bag as random vector. Then Radon--Nikodym or least squares theory can be applied, what give $y(x)$ estimator. The probability distribution of $y$ is also obtained, what requires solving generalized eigenvalues problem, matrix spectrum (not depending on $x$) give possible $y$ outcomes and depending on $x$ probabilities of outcomes can be obtained by projecting the distribution with fixed $x$ value (delta--function) to corresponding eigenvector. A library providing numerically stable polynomial basis for these calculations is available, what make the proposed approach practical.
Traceability and Provenance in Big Data Medical Systems
Venue: arXiv cs.DB 2015
Year: 2015

Providing an appropriate level of accessibility to and tracking of data or process elements in large volumes of medical data, is an essential requirement in the Big Data era. Researchers require systems that provide traceability of information through provenance data capture and management to support their clinical analyses. We present an approach that has been adopted in the neuGRID and N4U projects, which aimed to provide detailed traceability to support research analysis processes in the study of biomarkers for Alzheimers disease, but is generically applicable across medical systems. To facilitate the orchestration of complex, large-scale analyses in these projects we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of data and workflow usage over time in neuGRID and N4U.
Synchronizing automata and principal eigenvectors of the underlying digraphs
Venue: arXiv cs.FL 2015
Year: 2015

A coloring of a digraph with a fixed out-degree k is a distribution of k labels over the edges resulting in a deterministic finite automaton. An automaton is called synchronizing if there exists a word which sends all states of the automaton to a single state. In the present paper we study connections between spectral and synchronizing properties of digraphs. We show that if a coloring of a digraph is not synchronizing, then the stationary distribution of an associated Markov chain has a partition of coordinates into blocks of equal sum. Moreover, if there exists such a partition, then there exists a non-synchronizing automaton with such stationary distribution. We extend these results to bound the number of non-synchronizing colorings for digraphs with particular eigenvectors. We also demonstrate that the length of the shortest synchronizing word of any coloring is at most $w^2 - 3w + 3$, where $w$ is the sum of the coordinates of the integer principal eigenvector of the digraph.
Energy Efficient and High Performance Current-Mode Neural Network Circuit using Memristors and Digitally Assisted Analog CMOS Neurons
Venue: arXiv cs.ET 2015
Year: 2015

Emerging nano-scale programmable Resistive-RAM (RRAM) has been identified as a promising technology for implementing brain-inspired computing hardware. Several neural network architectures, that essentially involve computation of scalar products between input data vectors and stored network weights can be efficiently implemented using high density cross-bar arrays of RRAM integrated with CMOS. In such a design, the CMOS interface may be responsible for providing input excitations and for processing the RRAM output. In order to achieve high energy efficiency along with high integration density in RRAM based neuromorphic hardware, the design of RRAM-CMOS interface can therefore play a major role. In this work we propose design of high performance, current mode CMOS interface for RRAM based neural network design. The use of current mode excitation for input interface and design of digitally assisted current-mode CMOS neuron circuit for the output interface is presented. The proposed technique achieve 10x energy as well as performance improvement over conventional approaches employed in literature. Network level simulations show that the proposed scheme can achieve 2 orders of magnitude lower energy dissipation as compared to a digital ASIC implementation of a feed-forward neural network.
POPmine: Tracking Political Opinion on the Web
Venue: arXiv cs.SI 2015
Year: 2015

The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of opinion mining. We design and implement the POPmine system which is able to collect texts from web-based conventional media (news items in mainstream media sites) and social media (blogs and Twitter) and to process those texts, recognizing topics and political actors, analyzing relevant linguistic units, and generating indicators of both frequency of mention and polarity (positivity/negativity) of mentions to political actors across sources, types of sources, and across time.
Bayesian and hybrid Cramer-Rao bounds for QAM dynamical phase estimation
Venue: arXiv cs.IT 2015
Year: 2015

-In this paper, we study Bayesian and hybrid Cramer-Rao bounds for the dynamical phase estimation of QAM modulated signals. We present the analytical expressions for the various CRBs. This avoids the calculation of any matrix inversion and thus greatly reduces the computation complexity. Through simulations, we also illustrate the behaviors of the BCRB and of the HCRB with the signal-to-noise ratio. Index Terms-Bayesian Cramer-Rao Bound (BCRB), Hybrid Cramer-Rao Bound (HCRB), Synchronization Performance
Tracking Network Events with Write Optimized Data Structures: The Design and Implementation of TWIAD: The Write-Optimized IP Address Database
Venue: arXiv cs.CR 2015
Year: 2015

Access to network traffic records is an integral part of recognizing and addressing network security breaches. Even with the increasing sophistication of network attacks, basic network events such as connections between two IP addresses play an important role in any network defense. Given the duration of current attacks, long-term data archival is critical but typically very little of the data is ever accessed. Previous work has provided tools and identified the need to trace connections. However, traditional databases raise performance concerns as they are optimized for querying rather than ingestion. The study of write-optimized data structures (WODS) is a new and growing field that provides a novel approach to traditional storage structures (e.g., B-trees). WODS trade minor degradations in query performance for significant gains in the ability to quickly insert more data elements, typically on the order of 10 to 100 times more inserts per second. These efficient, out-of-memory data structures can play a critical role in enabling robust, long-term tracking of network events. In this paper, we present TWIAD, the Write-optimized IP Address Database. TWIAD uses a write-optimized B-tree known as a B ε tree to track all IP address connections in a network traffic stream. Our initial implementation focuses on utilizing lower cost hardware, demonstrating that basic long-term tracking can be done without advanced equipment. We tested TWIAD on a modest desktop system and showed a sustained ingestion rate of about 20,000 inserts per second.
A Short Survey on Data Clustering Algorithms
Venue: arXiv cs.DS 2015
Year: 2015

With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial analysis. Formally speaking, given a set of data instances, a clustering algorithm is expected to divide the set of data instances into the subsets which maximize the intra-subset similarity and inter-subset dissimilarity, where a similarity measure is defined beforehand. In this work, the state-of-the-arts clustering algorithms are reviewed from design concept to methodology; Different clustering paradigms are discussed. Advanced clustering algorithms are also discussed. After that, the existing clustering evaluation metrics are reviewed. A summary with future insights is provided at the end.
Scaling POMDPs For Selecting Sellers in E-markets-Extended Version
Venue: arXiv cs.AI 2015
Year: 2015

In multiagent e-marketplaces, buying agents need to select good sellers by querying other buyers (called advisors). Partially Observable Markov Decision Processes (POMDPs) have shown to be an effective framework for optimally selecting sellers by selectively querying advisors. However, current solution methods do not scale to hundreds or even tens of agents operating in the e-market. In this paper, we propose the Mixture of POMDP Experts (MOPE) technique, which exploits the inherent structure of trust-based domains, such as the seller selection problem in e-markets, by aggregating the solutions of smaller sub-POMDPs. We propose a number of variants of the MOPE approach that we analyze theoretically and empirically. Experiments show that MOPE can scale up to a hundred agents thereby leveraging the presence of more advisors to significantly improve buyer satisfaction.
Hierarchical Invariant Feature Learning with Marginalization for Person Re-Identification
Venue: arXiv cs.CV 2015
Year: 2015

This paper addresses the problem of matching pedestrians across multiple camera views, known as person re-identification. Variations in lighting conditions, environment and pose changes across camera views make re-identification a challenging problem. Previous methods address these challenges by designing specific features or by learning a distance function. We propose a hierarchical feature learning framework that learns invariant representations from labeled image pairs. A mapping is learned such that the extracted features are invariant for images belonging to same individual across views. To learn robust representations and to achieve better generalization to unseen data, the system has to be trained with a large amount of data. Critically, most of the person re-identification datasets are small. Manually augmenting the dataset by partial corruption of input data introduces additional computational burden as it requires several training epochs to converge. We propose a hierarchical network which incorporates a marginalization technique that can reap the benefits of training on large datasets without explicit augmentation. We compare our approach with several baseline algorithms as well as popular linear and non-linear metric learning algorithms and demonstrate improved performance on challenging publicly available datasets, VIPeR, CUHK01, CAVIAR4REID and iLIDS. Our approach also achieves the stateof-the-art results on these datasets.
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
Venue: arXiv cs.AI 2015
Year: 2015

The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expressed by a subject in a given short piece of text was fulfilled. We propose various unstructured and structured models that capture fulfillment cues such as the subject's emotional state and actions. Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task.
Non-profit Organizations' Need to Address Security for Effective Government Contacting
Venue: arXiv cs.CR 2015
Year: 2015

The need for information security within small to mid-size companies is increasing. The risks of information security breach, data loss, and disaster are growing. The impact of IT outages and issues on the company are unacceptable to any size business and their clients. There are many ways to address the security for IT departments. The need to address risks of attacks as well as disasters is important to the IT security policies and procedures. The IT departments of small to medium companies have to address these security concerns within their budgets and other limited resources.Security planning, design, and employee training that is needed requires input and agreement from all levels of the company and management. This paper will discuss security needs and methods to implement them into a corporate infrastructure.
Survey of robust and resilient social media tools on Android
Venue: arXiv cs.CR 2015
Year: 2015

We present an overview of robust and resilient social media tools to overcome natural disasters, censorship and Internet kill switches. These social media tools use Android devices to communicate during disasters and aim to overcome attacks on freedom of expression. There is an abundance of projects that aim to provide resilient communication, enhance privacy, and provide anonymity. We focus specifically on the limited set of mature tools with a healthy development community and Internet-deployment.
Exploring Historical and Emerging Phishing Techniques and Mitigating the Associated Security Risks
Venue: arXiv cs.CR 2015
Year: 2015

Organizations invest heavily in technical controls for their Information Assurance (IA) infrastructure. These technical controls mitigate and reduce the risk of damage caused by outsider attacks. Most organizations rely on training to mitigate and reduce risk of non-technical attacks such as social engineering. Organizations lump IA training into small modules that personnel typically rush through because the training programs lack enough depth and creativity to keep a trainee engaged. The key to retaining knowledge is making the information memorable. This paper describes common and emerging attack vectors and how to lower and mitigate the associated risks.
Unlabeled Sensing with Random Linear Measurements
Venue: arXiv cs.IT 2015
Year: 2015

We study the problem of solving a linear sensing system when the observations are unlabeled. Specifically we seek a solution to a linear system of equations y = Ax when the order of the observations in the vector y is unknown. Focusing on the setting in which A is a random matrix with i.i.d. entries, we show that if the sensing matrix A admits an oversampling ratio of 2 or higher, then with probability 1 it is possible to recover x exactly without the knowledge of the order of the observations in y. Furthermore, if x is of dimension K, then any 2K entries of y are sufficient to recover x. This result implies the existence of deterministic unlabeled sensing matrices with an oversampling factor of 2 that admit perfect reconstruction. The result is universal in that recovery is guaranteed for all possible choices of x. While the proof is constructive, it uses a combinatorial algorithm which is not practical, leaving the question of complexity open. We also analyze a noisy version of the problem and show that local stability is guaranteed by the solution. In particular, for every x, the recovery error tends to zero as the signal-to-noise-ratio tends to infinity. The question of universal stability is unclear. We also obtain a converse of the result in the noiseless case: If the number of observations in y is less than 2K, then with probability 1, universal recovery fails, i.e., with probability 1, there exists distinct choices of x which lead to the same unordered list of observations in y. In terms of applications, the unlabeled sensing problem is related to data association problems encountered in different domains including robotics where it is appears in a method called "simultaneous localization and mapping" (SLAM), multi-target tracking applications, and in sampling signals in the presence of jitter.
Covariance-domain Dictionary Learning for Overcomplete EEG Source Identification
Venue: arXiv cs.IT 2015
Year: 2015

We propose an algorithm targeting the identification of more sources than channels for electroencephalography (EEG). Our overcomplete source identification algorithm, Cov-DL, leverages dictionary learning methods applied in the covariance-domain. Assuming that EEG sources are uncorrelated within moving time-windows and the scalp mixing is linear, the forward problem can be transferred to the covariance domain which has higher dimensionality than the original EEG channel domain. This allows for learning the overcomplete mixing matrix that generates the scalp EEG even when there may be more sources than sensors active at any time segment, i.e. when there are non-sparse sources. This is contrary to straight-forward dictionary learning methods that are based on the assumption of sparsity, which is not a satisfied condition in the case of low-density EEG systems. We present two different learning strategies for Cov-DL, determined by the size of the target mixing matrix. We demonstrate that Cov-DL outperforms existing overcomplete ICA algorithms under various scenarios of EEG simulations and real EEG experiments.
Security and Privacy Policy Languages: A Survey, Categorization and Gap Identification
Venue: arXiv cs.CR 2015
Year: 2015

For security and privacy management and enforcement purposes, various policy languages have been presented. We give an overview on 27 security and privacy policy languages and present a categorization framework for policy languages. We show how the current policy languages are represented in the framework and summarize our interpretation. We show up identified gaps and motivate for the adoption of policy languages for the specification of privacy-utility trade-off policies.
Taxonomy grounded aggregation of classifiers with different label sets
Venue: arXiv cs.AI 2015
Year: 2015

We describe the problem of aggregating the label predictions of diverse classifiers using a class taxonomy. Such a taxonomy may not have been available or referenced when the individual classifiers were designed and trained, yet mapping the output labels into the taxonomy is desirable to integrate the effort spent in training the constituent classifiers. A hierarchical taxonomy representing some domain knowledge may be different from, but partially mappable to, the label sets of the individual classifiers. We present a heuristic approach and a principled graphical model to aggregate the label predictions by grounding them into the available taxonomy. Our model aggregates the labels using the taxonomy structure as constraints to find the most likely hierarchically consistent class. We experimentally validate our proposed method on image and text classification tasks.
Linear Large-Scale MIMO Data Detection for 5G Multi-Carrier Waveform Candidates
Venue: arXiv cs.IT 2015
Year: 2015

Fifth generation (5G) wireless systems are expected to combine emerging transmission technologies, such as large-scale multiple-input multiple-output (MIMO) and non-orthogonal multi-carrier waveforms, to improve the spectral efficiency and to reduce out-of-band (OOB) emissions. This paper investigates the efficacy of two promising multi-carrier waveforms that reduce OOB emissions in combination with large-scale MIMO, namely filter bank multi-carrier (FBMC) and generalized frequency division multiplexing (GFDM). We develop novel, low-complexity data detection algorithms for both of these waveforms. We investigate the associated performance/complexity trade-offs in the context of large-scale MIMO, and we study the peak-to-average power ratio (PAPR). Our results show that reducing the OOB emissions with FBMC and GFDM leads to higher computational complexity and PAPR compared to that of orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency division multiple access (SC-FDMA).