OpenRaiser/Intern-Atlas huggingface dataset:
Complete Weight Enumerators of Some Linear Codes
Venue: arXiv cs.IT 2015
Year: 2015

Linear codes have been an interesting topic in both theory and practice for many years. In this paper, for an odd prime $p$, we determine the explicit complete weight enumerators of two classes of linear codes over $\mathbb{F}_p$ and they may have applications in cryptography and secret sharing schemes. Moreover, some examples are included to illustrate our results.
Polynomially Low Error PCPs with polyloglog n Queries via Modular Composition
Venue: arXiv cs.CC 2015
Year: 2015

We show that every language in NP has a PCP verifier that tosses $O(\log n)$ random coins, has perfect completeness, and a soundness error of at most $1/\text{poly}(n)$, while making at most $O(\text{poly}\log\log n)$ queries into a proof over an alphabet of size at most $n^{1/\text{poly}\log\log n}$. Previous constructions that obtain $1/\text{poly}(n)$ soundness error used either $\text{poly}\log n $ queries or an exponential sized alphabet, i.e. of size $2^{n^c}$ for some $c>0$. Our result is an exponential improvement in both parameters simultaneously. Our result can be phrased as a polynomial-gap hardness for approximate CSPs with arity $\text{poly}\log\log n$ and alphabet size $n^{1/\text{poly}\log n}$. The ultimate goal, in this direction, would be to prove polynomial hardness for CSPs with constant arity and polynomial alphabet size (aka the sliding scale conjecture for inverse polynomial soundness error). Our construction is based on a modular generalization of previous PCP constructions in this parameter regime, which involves a composition theorem that uses an extra `consistency' query but maintains the inverse polynomial relation between the soundness error and the alphabet size. Our main technical/conceptual contribution is a new notion of soundness, which we refer to as {\em distributional soundness}, that replaces the previous notion of "list decoding soundness", and that allows us to prove a modular composition theorem with tighter parameters. This new notion of soundness allows us to invoke composition a super-constant number of times without incurring a blow-up in the soundness error.
A syntactic soundness proof for free-variable tableaux with on-the-fly Skolemization
Venue: arXiv cs.LO 2015
Year: 2015

We prove the syntactic soundness of classical tableaux with free variables and on-the-fly Skolemization. Soundness proofs are usually built from semantic arguments, and this is to our knowledge, the first proof that appeals to syntactic means. We actually prove the soundness property with respect to cut-free sequent calculus. This requires great care because of the additional liberty in freshness checking allowed by the use of Skolem terms. In contrast to semantic soundness, we gain the possibility to state a cut elimination theorem for sequent calculus, under the proviso that completeness of the method holds. We believe that such techniques can be applied to tableaux in other logics as well.
An algorithm for multipication of Kaluza numbers
Venue: arXiv cs.DS 2015
Year: 2015

This paper presents the derivation of a new algorithm for multiplying of two Kaluza numbers. Performing this operation directly requires 1024 real multiplications and 992 real additions. The proposed algorithm can compute the same result with only 512 real multiplications and 576 real additions. The derivation of our algorithm is based on utilizing the fact that multiplication of two Kaluza numbers can be expressed as a matrixvector product. The matrix multiplicand that participates in the product calculating has unique structural properties. Namely exploitation of these specific properties leads to significant reducing of the complexity of Kaluza numbers multiplication.
Towards Modeling Energy Consumption of Xeon Phi
Venue: arXiv cs.DC 2015
Year: 2015

In the push for exascale computing, energy efficiency is of utmost concern. System architectures often adopt accelerators to hasten application execution at the cost of power. The Intel Xeon Phi co-processor is unique accelerator that offers application designers high degrees of parallelism, energy-efficient cores, and various execution modes. To explore the vast number of available configurations, a model must be developed to predict execution time, power, and energy for the CPU and Xeon Phi. An experimentation method has been developed which measures power for the CPU and Xeon Phi separately, as well as total system power. Execution time and performance are also captured for two experiments conducted in this work. The experiments, frequency scaling and strong scaling, will help validate the adopted model and assist in the development of a model which defines the host and Xeon Phi. The proxy applications investigated, representative of large-scale real-world applications, are Co-Design Molecular Dynamics (CoMD) and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics (LULESH). The frequency experiment discussed in this work is used to determine the time on-chip and off-chip to measure the compute- or latencyboundedness of the application. Energy savings were not obtained in symmetric mode for either application.
Rendering-as-a-Service: Taxonomy and Comparison
Venue: arXiv cs.DC 2015
Year: 2015

The movies like the Avatar are a good example of the stunning visual effects that the animation could bring into a movie.The 3D wire frame models are converted to 3D photo realistic images using a process called the rendering. This rendering process is offered as a service in the cloud, where the animation files to be rendered are split into frames and rendered in the cloud resources and are popularly known as Rendering as a Service. As this is gaining high popularity among the animators community, this work intends to enable the animators to Gain basic knowledge about Rendering as a Service. Understand the variety in the service models through the taxonomy,Explore, compare and classify the services quickly using the tree-structured taxonomy of services. In this paper, the various characteristics of the services are organized in the form of a tree to enable quick classification and comparison of the services. To enhance the understandability, three popular services have been classified and verified according to the proposed tree-structured taxonomy.
Expresso : A user-friendly GUI for Designing, Training and Exploring Convolutional Neural Networks
Venue: arXiv cs.CV 2015
Year: 2015

With a view to provide a user-friendly interface for designing, training and developing deep learning frameworks, we have developed Expresso, a GUI tool written in Python. Expresso is built atop Caffe, the open-source, prize-winning framework popularly used to develop Convolutional Neural Networks. Expresso provides a convenient wizard-like graphical interface which guides the user through various common scenarios -- data import, construction and training of deep networks, performing various experiments, analyzing and visualizing the results of these experiments. The multi-threaded nature of Expresso enables concurrent execution and notification of events related to the aforementioned scenarios. The GUI sub-components and inter-component interfaces in Expresso have been designed with extensibility in mind. We believe Expresso's flexibility and ease of use will come in handy to researchers, newcomers and seasoned alike, in their explorations related to deep learning.
Novel Mining of Cancer via Mutation in Tumor Protein P53 using Quick Propagation Network
Venue: arXiv cs.CE 2015
Year: 2015

There is multiple databases contain datasets of TP53 gene and its tumor protein P53 which believed to be involved in over 50% of human cancers cases, these databases are rich as datasets covered all mutations caused diseases (cancers), but they haven't efficient mining method can classify and diagnosis mutations patient's then predict the cancer of that patient. This paper proposed a novel mining of cancer via mutations because there is no mining method before offers friendly, effective and flexible predict or diagnosis of cancers via using whole common database of TP53 gene (tumor protein P53) as dataset and selecting a minimum number of fields in training and testing quick propagation algorithm which supporting this miming method. Simulating quick propagation network for the train dataset shows results the Correlation (0.9999), R-squared (0.9998) and mean of Absolute Relative Error (0.0029), while the training for the ALL datasets (train, test and validation dataset) have results the Correlation (0.9993), R-squared (0.9987) and mean of Absolute Relative Error (0.0057).
A Time-Success Ratio Analysis of wPRF-based Leakage-Resilient Stream Ciphers
Venue: arXiv cs.CR 2015
Year: 2015

Weak pseudorandom functions (wPRFs) found an important application as main building blocks for leakage-resilient ciphers (EUROCRYPT'09). Several security bounds, based on different techniques, were given to these stream ciphers. The security loss in these reduction-based proofs is always polynomial, but has not been studied in detail. The aim of this paper is twofold. First, we present a clear comparison of quantitatively different security bounds in the literature. Second, we revisit the current proof techniques and answer the natural question of how far we are from meaningful and provable security guarantees, when instantiating weak PRFs with standard primitives (block ciphers or hash functions). In particular, we demonstrate a flaw in the recent (TCC'14) analysis of the EUROCRYPT'09 stream cipher, which means that we still don't know if it offers provable security when instantiated with a standard block cipher. Our approach is a \emph{time-to-success Ratio} analysis, a universal measure introduced by Luby, which allow us to compare different security bounds.
Tracking Employment Shocks Using Mobile Phone Data
Venue: arXiv cs.SI 2015
Year: 2015

Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them, and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural break model to correctly detect the date of a mass layoff and estimate its size. We then use a Bayesian classification model to identify affected individuals by observing changes in calling behavior following the plant's closure. For these affected individuals, we observe significant declines in social behavior and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviors, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure micro economic behavior and improve estimates of critical economic indicators.
Modeling the Interference Generated from Car Base Stations towards Indoor Femto-cells
Venue: arXiv cs.SY 2015
Year: 2015

In future wireless networks, a significant number of users will be vehicular. One promising solution to improve the capacity for these vehicular users is to employ moving relays or car base stations. The system forms cell inside the vehicle and then uses rooftop antenna for back-hauling to overcome the vehicular penetration loss. In this paper, we develop a model for aggregate interference distribution generated from moving/parked cars to indoor users in order to study whether indoor femto-cells can coexist on the same spectrum with vehicular communications. Since spectrum authorization for vehicular communications is open at moment, we consider two spectrum sharing scenarios (i) communication from mounted antennas on the roof of the vehicles to the infrastructure network utilizes same spectrum with indoor femto-cells (ii) in-vehicle communication utilizes same spectrum with indoor femto-cells while vehicular to infrastructure (V2I) communication is allocated at different spectrum. Based on our findings we suggest that V2I and indoor femto-cells should be allocated at different spectrum. The reason being that mounted roof-top antennas facing the indoor cells generate unacceptable interference levels. On the other hand, in-vehicle communication and indoor cells can share the spectrum thanks to the vehicle body isolation and the lower transmit power levels that can be used inside the vehicle.
WiFlix: Adaptive Video Streaming in Massive MU-MIMO Wireless Networks
Venue: arXiv cs.NI 2015
Year: 2015

We consider the problem of simultaneous on-demand streaming of stored video to multiple users in a multi-cell wireless network where multiple unicast streaming sessions are run in parallel and share the same frequency band. Each streaming session is formed by the sequential transmission of video "chunks," such that each chunk arrives into the corresponding user playback buffer within its playback deadline. We formulate the problem as a Network Utility Maximization (NUM) where the objective is to fairly maximize users' video streaming Quality of Experience (QoE) and then derive an iterative control policy using Lyapunov Optimization, which solves the NUM problem up to any level of accuracy and yields an online protocol with control actions at every iteration decomposing into two layers interconnected by the users' request queues : i) a video streaming adaptation layer reminiscent of DASH, implemented at each user node; ii) a transmission scheduling layer where a max-weight scheduler is implemented at each base station. The proposed chunk request scheme is a pull strategy where every user opportunistically requests video chunks from the neighboring base stations and dynamically adapts the quality of its requests based on the current size of the request queue. For the transmission scheduling component, we first describe the general max-weight scheduler and then particularize it to a wireless network where the base stations have multiuser MIMO (MU-MIMO) beamforming capabilities. We exploit the channel hardening effect of large-dimensional MIMO channels (massive MIMO) and devise a low complexity user selection scheme to solve the underlying combinatorial problem of selecting user subsets for downlink beamforming, which can be easily implemented and run independently at each base station.
Characterization of SINR Region for Multiple Interfering Multicast in Power-Controlled Systems
Venue: arXiv cs.IT 2015
Year: 2015

This paper considers a wireless communication network consisting of multiple interfering multicast sessions. Different from a unicast system where each transmitter has only one receiver, in a multicast system, each transmitter has multiple receivers. It is a well known result for wireless unicast systems that the feasibility of an signal-to-interference-plus-noise power ratio (SINR) without power constraint is decided by the Perron-Frobenius eigenvalue of a nonnegative matrix. We generalize this result and propose necessary and sufficient conditions for the feasibility of an SINR in a wireless multicast system with and without power constraint. The feasible SINR region as well as its geometric properties are studied. Besides, an iterative algorithm is proposed which can efficiently check the feasibility condition and compute the boundary points of the feasible SINR region.
A Novel Geographic Partitioning System for Anonymizing Health Care Data
Venue: arXiv cs.OH 2015
Year: 2015

With large volumes of detailed health care data being collected, there is a high demand for the release of this data for research purposes. Hospitals and organizations are faced with conflicting interests of releasing this data and protecting the confidentiality of the individuals to whom the data pertains. Similarly, there is a conflict in the need to release precise geographic information for certain research applications and the requirement to censor or generalize the same information for the sake of confidentiality. Ultimately the challenge is to anonymize data in order to comply with government privacy policies while reducing the loss in geographic information as much as possible. In this paper, we present a novel geographic-based system for the anonymization of health care data. This system is broken up into major components for which different approaches may be supplied. We compare such approaches in order to make recommendations on which of them to select to best match user requirements.
Transforming Telemedicine Through Big Data Analytics
Venue: arXiv cs.CY 2015
Year: 2015

A look at how big data is transforming telemedicine to provide better care by tapping into a larger source of patient information. Telemedicine will have a profound impact on patient care, increase access and quality, and represent an opportunity to keep health care costs down. Data generated by smart devices will enable the real-time monitoring of chronic diseases, allowing optimal dosage of drugs and improve patient outcomes.
Some Open Problems in Optimal AdaBoost and Decision Stumps
Venue: arXiv cs.LG 2015
Year: 2015

The significance of the study of the theoretical and practical properties of AdaBoost is unquestionable, given its simplicity, wide practical use, and effectiveness on real-world datasets. Here we present a few open problems regarding the behavior of "Optimal AdaBoost," a term coined by Rudin, Daubechies, and Schapire in 2004 to label the simple version of the standard AdaBoost algorithm in which the weak learner that AdaBoost uses always outputs the weak classifier with lowest weighted error among the respective hypothesis class of weak classifiers implicit in the weak learner. We concentrate on the standard, "vanilla" version of Optimal AdaBoost for binary classification that results from using an exponential-loss upper bound on the misclassification training error. We present two types of open problems. One deals with general weak hypotheses. The other deals with the particular case of decision stumps, as often and commonly used in practice. Answers to the open problems can have immediate significant impact to (1) cementing previously established results on asymptotic convergence properties of Optimal AdaBoost, for finite datasets, which in turn can be the start to any convergence-rate analysis; (2) understanding the weak-hypotheses class of effective decision stumps generated from data, which we have empirically observed to be significantly smaller than the typically obtained class, as well as the effect on the weak learner's running time and previously established improved bounds on the generalization performance of Optimal AdaBoost classifiers; and (3) shedding some light on the "self control" that AdaBoost tends to exhibit in practice.
An Implementation Model for Interaction Nets
Venue: arXiv cs.LO 2015
Year: 2015

To study implementations and optimisations of interaction net systems we propose a calculus to allow us to reason about nets, a concrete data-structure that is in close correspondence with the calculus, and a low-level language to create and manipulate this data structure. These work together so that we can describe the compilation process for interaction nets, reason about the behaviours of the implementation, and study the efficiency and properties.
Semi-autonomous, context-aware, agent using behaviour modelling and reputation systems to authorize data operation in the Internet of Things
Venue: arXiv cs.CY 2015
Year: 2015

In this paper we address the issue of gathering the "informed consent" of an end user in the Internet of Things. We start by evaluating the legal importance and some of the problems linked with this notion of informed consent in the specific context of the Internet of Things. From this assessment we propose an approach based on a semi-autonomous, rule based agent that centralize all authorization decisions on the personal data of a user and that is able to take decision on his behalf. We complete this initial agent by integrating context-awareness, behavior modeling and community based reputation system in the algorithm of the agent. The resulting system is a "smart" application, the "privacy butler" that can handle data operations on behalf of the end-user while keeping the user in control. We finally discuss some of the potential problems and improvements of the system.
Differentially Private Response Mechanisms on Categorical Data
Venue: arXiv cs.DM 2015
Year: 2015

We study mechanisms for differential privacy on finite datasets. By deriving \emph{sufficient sets} for differential privacy we obtain necessary and sufficient conditions for differential privacy, a tight lower bound on the maximal expected error of a discrete mechanism and a characterisation of the optimal mechanism which minimises the maximal expected error within the class of mechanisms considered.
Anonymous online purchases with exhaustive operational security
Venue: arXiv cs.CR 2015
Year: 2015

This paper describes the process of remaining anonymous online and its concurrent operational security that has to be performed. It focusses particularly on remaining anonymous while purchasing online goods, resulting in anonymously bought items. Different aspects of the operational security process as well as anonymously funding with cryptocurrencies are described. Eventually it is shown how to anonymously purchase items and services from the hidden web, as well as the delivery. It is shown that, while becoming increasingly difficult, it is still possible to make anonymous purchases. Our presented work combines existing best-practices and deliberately avoids untested novel approaches when possible.
Valuations in Nilpotent Minimum Logic
Venue: arXiv cs.LO 2015
Year: 2015

The Euler characteristic can be defined as a special kind of valuation on finite distributive lattices. This work begins with some brief consideration on the role of the Euler characteristic on NM algebras, the algebraic counterpart of Nilpotent Minimum logic. Then, we introduce a new valuation, a modified version of the Euler characteristic we call idempotent Euler characteristic. We show that the new valuation encodes information about the formulæ in NM propositional logic.
Privacy in the Internet of Things: Threats and Challenges
Venue: arXiv cs.CR 2015
Year: 2015

The Internet of Things paradigm envisions the pervasive interconnection and cooperation of smart things over the current and future Internet infrastructure. The Internet of Things is, thus, the evolution of the Internet to cover the real-world, enabling many new services that will improve people's everyday lives, spawn new businesses and make buildings, cities and transport smarter. Smart things allow indeed for ubiquitous data collection or tracking, but these useful features are also examples of privacy threats that are already now limiting the success of the Internet of Things vision when not implemented correctly. These threats involve new challenges such as the pervasive privacy-aware management of personal data or methods to control or avoid ubiquitous tracking and profiling. This paper analyzes the privacy issues in the Internet of Things in detail. To this end, we first discuss the evolving features and trends in the Internet of Things with the goal of scrutinizing their privacy implications. Second, we classify and examine privacy threats in this new setting, pointing out the challenges that need to be overcome to ensure that the Internet of Things becomes a reality.