OpenRaiser/Intern-Atlas huggingface dataset:
Strategy-proof Pricing Approach for Cloud Market
Venue: arXiv cs.DC 2015
Year: 2015

In this paper, we design and develop a pricing model applicable to strategy proof pricing. To provide an economic stability towards its consumers. The economic model we use is Vickrey-Clarke-Groves (VCG). By this each service provider has to provide a true cost of its services in the cloud market. For the selection of suitable service for the consumer we adopt a dynamic programing based algorithm and VCG is used to calculate the payment. Strategy proof pricing offers a unique cloud pricing service that takes the complexity out of traditional pricing and enables cloud providers to price accurately, consistently and competitively
Applicative Bisimulation and Quantum $λ$-Calculi (Long Version)
Venue: arXiv cs.LO 2015
Year: 2015

Applicative bisimulation is a coinductive technique to check program equivalence in higher-order functional languages. It is known to be sound, and sometimes complete, with respect to context equivalence. In this paper we show that applicative bisimulation also works when the underlying language of programs takes the form of a linear $λ$-calculus extended with features such as probabilistic binary choice, but also quantum data, the latter being a setting in which linearity plays a role. The main results are proofs of soundness for the obtained notions of bisimilarity.
Skip-Thought Vectors
Venue: arXiv cs.CL 2015
Year: 2015

We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice. We will make our encoder publicly available.
DeepStereo: Learning to Predict New Views from the World's Imagery
Venue: arXiv cs.CV 2015
Year: 2015

Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision, but their use in graphics problems has been limited. In this work, we present a novel deep architecture that performs new view synthesis directly from pixels, trained from a large number of posed image sets. In contrast to traditional approaches which consist of multiple complex stages of processing, each of which require careful tuning and can fail in unexpected ways, our system is trained end-to-end. The pixels from neighboring views of a scene are presented to the network which then directly produces the pixels of the unseen view. The benefits of our approach include generality (we only require posed image sets and can easily apply our method to different domains), and high quality results on traditionally difficult scenes. We believe this is due to the end-to-end nature of our system which is able to plausibly generate pixels according to color, depth, and texture priors learnt automatically from the training data. To verify our method we show that it can convincingly reproduce known test views from nearby imagery. Additionally we show images rendered from novel viewpoints. To our knowledge, our work is the first to apply deep learning to the problem of new view synthesis from sets of real-world, natural imagery.
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Venue: arXiv cs.CV 2015
Year: 2015

Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Venue: arXiv cs.CV 2015
Year: 2015

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.
Improving search order for reachability testing in timed automata
Venue: arXiv cs.LO 2015
Year: 2015

Standard algorithms for reachability analysis of timed automata are sensitive to the order in which the transitions of the automata are taken. To tackle this problem, we propose a ranking system and a waiting strategy. This paper discusses the reason why the search order matters and shows how a ranking system and a waiting strategy can be integrated into the standard reachability algorithm to alleviate and prevent the problem respectively. Experiments show that the combination of the two approaches gives optimal search order on standard benchmarks except for one example. This suggests that it should be used instead of the standard BFS algorithm for reachability analysis of timed automata.
Two-Body Assignment Problem in the Context of the Israeli Medical Internship Match
Venue: arXiv cs.GT 2015
Year: 2015

This thesis was submitted as partial fulfillment of the requirements for the Masters Degree in the Department of Computer Science, Bar-Ilan University. The final step in getting an Israeli M.D. is performing a year-long internship in one of the hospitals in Israel. Internships are decided upon by a lottery, which is known as "The Internship Lottery". In 2014 we redesigned the lottery, replacing it with a more efficient one. The new method is based on calculating a tentative lottery, in which each student has some probability of getting to each hospital. Then a computer program "trades" between the students, where trade is performed only if it is beneficial to both sides. This trade creates surplus, which translates to more students getting one of their top choices. The average student improved his place by $0.91$ seats. The new method can improve the welfare of medical graduates, by giving them more probability to get to one of their top choices. It can be applied in internship markets in other countries as well. This thesis presents the market, the redesign process and the new mechanism which is now in use. There are two main lessons that we have learned from this market. The first is the "Do No Harm" principle, which states that (almost) all participants should prefer the new mechanism to the old one. The second is that new approaches need to be used when dealing with two-body problems in object assignment. We focus on the second lesson, and study two-body problems in the context of the assignment problem. We show that decomposing stochastic assignment matrices to deterministic allocations is NP-hard in the presence of couples, and present a polynomial time algorithm with the optimal worst case guarantee. We also study the performance of our algorithm on real-world and on simulated data.
Cross-matching Engine for Incremental Photometric Sky Survey
Venue: arXiv cs.DB 2015
Year: 2015

For light curve generation, a pre-planned photometry survey is needed nowadays, where all of the exposure coordinates have to be given and don't change during the survey. This thesis shows it is not required and we can data-mine these light curves from astronomical data that was never meant for this purpose. With this approach, we can recycle all of the photometric surveys in the world and generate light curves of observed objects for them. This thesis is addressing mostly the catalog generation process, which is needed for creating the light curves. In practice, it focuses on one of the most important problems in astroinformatics which is clustering data volumes on Big Data scale where most of the traditional techniques stagger. We consider a wide variety of possible solutions from the view of performance, scalability, distributability, etc. We defined criteria for time and memory complexity which we evaluated for all of the tested solutions. Furthermore, we created quality standards which we also take into account when evaluating the results. We are using relational databases as a starting point of our implementation and compare them with the newest technologies potentially usable for solving our problem. These are noSQL Array databases or transferring the heavy computations of clustering towards supercomputers by using parallelism.
Unconfused ultraconservative multiclass algorithms
Venue: arXiv cs.LG 2015
Year: 2015

We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. The two-class version of this problem was studied a few years ago where the proposed approaches to combat the noise revolve around a Per-ceptron learning scheme fed with peculiar examples computed through a weighted average of points from the noisy training set. We propose to build upon these approaches and we introduce a new algorithm called UMA (for Unconfused Multiclass additive Algorithm) which may be seen as a generalization to the multiclass setting of the previous approaches. In order to characterize the noise we use the confusion matrix as a multiclass extension of the classification noise studied in the aforemen-tioned literature. Theoretically well-founded, UMA furthermore displays very good empirical noise robustness, as evidenced by numerical simulations conducted on both synthetic and real data.
A Novel Feature Extraction Method for Scene Recognition Based on Centered Convolutional Restricted Boltzmann Machines
Venue: arXiv cs.CV 2015
Year: 2015

Scene recognition is an important research topic in computer vision, while feature extraction is a key step of object recognition. Although classical Restricted Boltzmann machines (RBM) can efficiently represent complicated data, it is hard to handle large images due to its complexity in computation. In this paper, a novel feature extraction method, named Centered Convolutional Restricted Boltzmann Machines (CCRBM), is proposed for scene recognition. The proposed model is an improved Convolutional Restricted Boltzmann Machines (CRBM) by introducing centered factors in its learning strategy to reduce the source of instabilities. First, the visible units of the network are redefined using centered factors. Then, the hidden units are learned with a modified energy function by utilizing a distribution function, and the visible units are reconstructed using the learned hidden units. In order to achieve better generative ability, the Centered Convolutional Deep Belief Networks (CCDBN) is trained in a greedy layer-wise way. Finally, a softmax regression is incorporated for scene recognition. Extensive experimental evaluations using natural scenes, MIT-indoor scenes, and Caltech 101 datasets show that the proposed approach performs better than other counterparts in terms of stability, generalization, and discrimination. The CCDBN model is more suitable for natural scene image recognition by virtue of convolutional property.
$(1-2u^2)$-constacyclic codes over $\mathbb{F}_p+u\mathbb{F}_p+u^2\mathbb{F}_p$
Venue: arXiv cs.IT 2015
Year: 2015

Let $\mathbb{F}_p$ be a finite field and $u$ be an indeterminate. This article studies $(1-2u^2)$-constacyclic codes over the ring $\mathbb{F}_p+u\mathbb{F}_p+u^2\mathbb{F}_p$, where $u^3=u$. We describe generator polynomials of this kind of codes and investigate the structural properties of these codes by a decomposition theorem.
CommuniSense: Crowdsourcing Road Hazards in Nairobi
Venue: arXiv cs.CY 2015
Year: 2015

Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obtain reliable data on traffic and road infrastructure conditions. In this paper, we investigate the use of mobile crowdsourcing as means to gather and document Nairobi's road quality information. We first present the key findings of a city-wide road quality survey about the perception of existing road quality conditions in Nairobi. Based on the survey's findings, we then developed a mobile crowdsourcing application, called CommuniSense, to collect road quality data. The application serves as a tool for users to locate, describe, and photograph road hazards. We tested our application through a two-week field study amongst 30 participants to document various forms of road hazards from different areas in Nairobi. To verify the authenticity of user-contributed reports from our field study, we proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to verify whether submitted reports indeed depict road hazards. We found 92% of user-submitted reports to match the MTurkers judgements. While our prototype was designed and tested on a specific city, our methodology is applicable to other developing cities.
Key Pre-Distributions From Graph-Based Block Designs
Venue: arXiv cs.NI 2015
Year: 2015

With the development of wireless communication technologies which considerably contributed to the development of wireless sensor networks (WSN), we have witnessed an ever-increasing WSN based applications which induced a host of research activities in both academia and industry. Since most of the target WSN applications are very sensitive, security issue is one of the major challenges in the deployment of WSN. One of the important building blocks in securing WSN is key management. Traditional key management solutions developed for other networks are not suitable for WSN since WSN networks are resource (e.g. memory, computation, energy) limited. Key pre-distribution algorithms have recently evolved as efficient alternatives of key management in these networks. In the key pre-distribution systems, secure communication is achieved between a pair of nodes either by the existence of a key allowing for direct communication or by a chain of keys forming a key-path between the pair. In this paper, we propose methods which bring prior knowledge of network characteristics and application constraints into the design of key pre-distribution schemes, in order to provide better security and connectivity while requiring less resources. Our methods are based on casting the prior information as a graph. Motivated by this idea, we also propose a class of quasi-symmetric designs referred here to as g-designs. These produce key pre-distribution schemes that significantly improve upon the existing constructions based on unital designs. We give some examples, and point out open problems for future research.
Conservativeness of untied auto-encoders
Venue: arXiv cs.LG 2015
Year: 2015

We discuss necessary and sufficient conditions for an auto-encoder to define a conservative vector field, in which case it is associated with an energy function akin to the unnormalized log-probability of the data. We show that the conditions for conservativeness are more general than for encoder and decoder weights to be the same ("tied weights"), and that they also depend on the form of the hidden unit activation function, but that contractive training criteria, such as denoising, will enforce these conditions locally. Based on these observations, we show how we can use auto-encoders to extract the conservative component of a vector field.
When Pilots Should Not Be Reused Across Interfering Cells in Massive MIMO
Venue: arXiv cs.IT 2015
Year: 2015

The pilot reuse issue in massive multi-input multi-output (MIMO) antenna systems with interfering cells is closely examined. This paper considers scenarios where the ratio of the channel coherence time to the number of users in a cell may be sufficiently large. One such practical scenario arises when the number of users per unit coverage area cannot grow freely while user mobility is low, as in indoor networks. Another important scenario is when the service provider is interested in maximizing the sum rate over a fixed, selected number of users rather than the sum rate over all users in the cell. A sum-rate comparison analysis shows that in such scenarios less aggressive reuse of pilots involving allocation of additional pilots for interfering users yields significant performance advantage relative to the case where all cells reuse the same pilot set. For a given ratio of the normalized coherence time interval to the number of users per cell, the optimal pilot assignment strategy is revealed via a closed-form solution and the resulting net sum-rate is compared with that of the full pilot reuse.
DeepMatching: Hierarchical Deformable Dense Matching
Venue: arXiv cs.CV 2015
Year: 2015

We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a hierarchical, multi-layer, correlational architecture designed for matching images and was inspired by deep convolutional approaches. The proposed matching algorithm can handle non-rigid deformations and repetitive textures and efficiently determines dense correspondences in the presence of significant changes between images. We evaluate the performance of DeepMatching, in comparison with state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al 2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013) datasets. DeepMatching outperforms the state-of-the-art algorithms and shows excellent results in particular for repetitive textures.We also propose a method for estimating optical flow, called DeepFlow, by integrating DeepMatching in the large displacement optical flow (LDOF) approach of Brox and Malik (2011). Compared to existing matching algorithms, additional robustness to large displacements and complex motion is obtained thanks to our matching approach. DeepFlow obtains competitive performance on public benchmarks for optical flow estimation.
Evaluation of Synchronous and Asynchronous Reactive Distributed Congestion Control Algorithms for the ITS G5 Vehicular Systems
Venue: arXiv cs.NI 2015
Year: 2015

The IEEE 802.11p is the technology dedicated to vehicular communications to support road safety, efficiency, and comfort applications. A large number of research activities have been carried out to study the characteristics of the IEEE 802.11p. The key weakness of the IEEE 802.11p is the channel congestion issue, where the wireless channel gets saturated when the road density increases. The European Telecommunications Standardization Institute (ETSI) is in the progress of studying the channel congestion problem and proposed so-called Reactive Distributed Congestion Control (DCC) algorithm as a solution to the congestion issue. In this report we investigate the impacts of the Reactive DCC mechanism in comparison to the conventional IEEE 802.11p with no congestion control. Our study shows that the Reactive DCC scheme creates oscillation on channel load that consequently degrades communication performance. The results reveal that the channel load oscillation is due to the fact that in the Reactive DCC, the individual CAM (Cooperative Awareness Message) controllers react to the channel congestion in a synchronized manner. To reduce the oscillation, in this report we propose a simple extension to Reactive DCC, Asynchronous Reactive DCC, in which the individual CAM controllers adopt randomized rate setting, which can significantly reduce the oscillation and improve the network performance.
A multidimensional maximum bisection problem
Venue: arXiv cs.DM 2015
Year: 2015

This work introduces a multidimensional generalization of the maximum bisection problem. A mixed integer linear programming formulation is proposed with the proof of its correctness. The numerical tests, made on the randomly generated graphs, indicates that the multidimensional generalization is more difficult to solve than the original problem.
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Venue: arXiv cs.GT 2015
Year: 2015

Potential games form a class of non-cooperative games where unilateral improvement dynamics are guaranteed to converge in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.
Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database
Venue: arXiv cs.DB 2015
Year: 2015

This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.