OpenRaiser/Intern-Atlas huggingface dataset:
Cognitive Internet of Things: A New Paradigm beyond Connection
Venue: arXiv cs.AI 2014
Year: 2014

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named Cognitive Internet of Things (CIoT), to empower the current IoT with a `brain' for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.
Baselining Wireless Internet Service Development: An Experience Report
Venue: arXiv cs.SE 2014
Year: 2014

New, emerging domains such as the engineering of wireless Internet services are characterized by a lack of experience based on quantitative data. Systematic tracking and observation of representative pilot projects can be seen as one means to capture experience, get valuable insight into a new domain, and build initial baselines. This helps to improve the planning of real development projects in business units. This article describes an approach to capture software development experience for the wireless Internet services domain by conducting and observing a series of case studies in the field. Initial baselines concerning effort distribution from the development of two wireless Internet pilot services are presented. Furthermore, major domain-specific risk factors are discussed based on the results of project retrospectives conducted with the developers of the services.
A SageTeX Hypermatrix Algebra Package
Venue: arXiv cs.MS 2014
Year: 2014

We describe here a rudimentary sage implementation of the Bhattacharya-Mesner hypermatrix algebra package.
On the efficiency of transmission strategies for broadcast channels using finite size constellations
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, achievable rates regions are derived for power constrained Gaussian broadcast channel of two users using finite dimension constellations. Various transmission strategies are studied, namely superposition coding (SC) and superposition modulation (SM) and compared to standard schemes such as time sharing (TS). The maximal achievable rates regions for SM and SC strategies are obtained by optimizing over both the joint probability distribution and over the positions of constellation symbols. The improvement in achievable rates for each scheme of increasing complexity is evaluated in terms of SNR savings for a given target achievable rate or/and percentage of gain in achievable rates for one user with reference to a classical scenario.
Observation-based Development of Software Process Baselines: An Experience Report
Venue: arXiv cs.SE 2014
Year: 2014

The creation and deployment of software development processes for new domains (such as wireless Internet services) is a challenging task due to the lack of knowledge about adequate engineering techniques and their effects. In addition, time-to-market pressure prevents applying long-lasting maturation of processes. Nevertheless, developing software of a predetermined quality in a predictable fashion can only be achieved with systematic development processes and the use of engineering principles. A descriptive approach promises to quickly create initial valuable process models and quantitative baselines that can be seen as starting points for continuous improvement activities. This paper describes the creation of software development processes for the development of wireless Internet services based on the observation of pilot projects that were performed at distributed development sites. Different techniques and tools such as descriptive process modeling, process documentation generation, goal-oriented measurement, and capturing qualitative experience were combined to gain process baselines. Results show that the observation-based approach helped to quickly come up with stable context-oriented development processes and get a better understanding of their effects with respect to quantitative and qualitative means.
Privacy for Personal Neuroinformatics
Venue: arXiv cs.CY 2014
Year: 2014

Human brain activity collected in the form of Electroencephalography (EEG), even with low number of sensors, is an extremely rich signal. Traces collected from multiple channels and with high sampling rates capture many important aspects of participants' brain activity and can be used as a unique personal identifier. The motivation for sharing EEG signals is significant, as a mean to understand the relation between brain activity and well-being, or for communication with medical services. As the equipment for such data collection becomes more available and widely used, the opportunities for using the data are growing; at the same time however inherent privacy risks are mounting. The same raw EEG signal can be used for example to diagnose mental diseases, find traces of epilepsy, and decode personality traits. The current practice of the informed consent of the participants for the use of the data either prevents reuse of the raw signal or does not truly respect participants' right to privacy by reusing the same raw data for purposes much different than originally consented to. Here we propose an integration of a personal neuroinformatics system, Smartphone Brain Scanner, with a general privacy framework openPDS. We show how raw high-dimensionality data can be collected on a mobile device, uploaded to a server, and subsequently operated on and accessed by applications or researchers, without disclosing the raw signal. Those extracted features of the raw signal, called answers, are of significantly lower-dimensionality, and provide the full utility of the data in given context, without the risk of disclosing sensitive raw signal. Such architecture significantly mitigates a very serious privacy risk related to raw EEG recordings floating around and being used and reused for various purposes.
Aggregate Estimation Over Dynamic Hidden Web Databases
Venue: arXiv cs.DB 2014
Year: 2014

Many databases on the web are "hidden" behind (i.e., accessible only through) their restrictive, form-like, search interfaces. Recent studies have shown that it is possible to estimate aggregate query answers over such hidden web databases by issuing a small number of carefully designed search queries through the restrictive web interface. A problem with these existing work, however, is that they all assume the underlying database to be static, while most real-world web databases (e.g., Amazon, eBay) are frequently updated. In this paper, we study the novel problem of estimating/tracking aggregates over dynamic hidden web databases while adhering to the stringent query-cost limitation they enforce (e.g., at most 1,000 search queries per day). Theoretical analysis and extensive real-world experiments demonstrate the effectiveness of our proposed algorithms and their superiority over baseline solutions (e.g., the repeated execution of algorithms designed for static web databases).
Generalizers: New Metaobjects for Generalized Dispatch
Venue: arXiv cs.PL 2014
Year: 2014

This paper introduces a new metaobject, the generalizer, which complements the existing specializer metaobject. With the help of examples, we show that this metaobject allows for the efficient implementation of complex non-class-based dispatch within the framework of existing metaobject protocols. We present our modifications to the generic function invocation protocol from the Art of the Metaobject Protocol; in combination with previous work, this produces a fully-functional extension of the existing mechanism for method selection and combination, including support for method combination completely independent from method selection. We discuss our implementation, within the SBCL implementation of Common Lisp, and in that context compare the performance of the new protocol with the standard one, demonstrating that the new protocol can be tolerably efficient.
State Dependent Statistical Timing Model for Voltage Scaled Circuits
Venue: arXiv cs.AR 2014
Year: 2014

This paper presents a novel statistical state-dependent timing model for voltage over scaled (VoS) logic circuits that accurately and rapidly finds the timing distribution of output bits. Using this model erroneous VoS circuits can be represented as error-free circuits combined with an error-injector. A case study of a two point DFT unit employing the proposed model is presented and compared to HSPICE circuit simulation. Results show an accurate match, with significant speedup gains.
Learning Deep Face Representation
Venue: arXiv cs.CV 2014
Year: 2014

Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easy-to-implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. In this paper, we present a very easy-to-implement deep learning framework for face representation. Our method bases on a new structure of deep network (called Pyramid CNN). The proposed Pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computation-efficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation. Our basic network is capable of achieving high recognition accuracy ($85.8\%$ on LFW benchmark) with only 8 dimension representation. When extended to feature-sharing Pyramid CNN, our system achieves the state-of-the-art performance ($97.3\%$) on LFW benchmark. We also introduce a new benchmark of realistic face images on social network and validate our proposed representation has a good ability of generalization.
People Like Us: Mining Scholarly Data for Comparable Researchers
Venue: arXiv cs.DL 2014
Year: 2014

We present the problem of finding comparable researchers for any given researcher. This problem has many motivations. Firstly, know thyself. The answers of where we stand among research community and who we are most alike may not be easily found by existing evaluations of ones' research mainly based on citation counts. Secondly, there are many situations where one needs to find comparable researchers e.g., for reviewing peers, constructing programming committees or compiling teams for grants. It is often done through an ad hoc and informal basis. Utilizing the large scale scholarly data accessible on the web, we address the problem of automatically finding comparable researchers. We propose a standard to quantify the quality of research output, via the quality of publishing venues. We represent a researcher as a sequence of her publication records, and develop a framework of comparison of researchers by sequence matching. Several variations of comparisons are considered including matching by quality of publication venue and research topics, and performing prefix matching. We evaluate our methods on a large corpus and demonstrate the effectiveness of our methods through examples. In the end, we identify several promising directions for further work.
An Approach for Normalizing Fuzzy Relational Databases Based on Join Dependency
Venue: arXiv cs.DB 2014
Year: 2014

Fuzziness in databases is used to denote uncertain or incomplete data. Relational Databases stress on the nature of the data to be certain. This certainty based data is used as the basis of the normalization approach designed for traditional relational databases. But real world data may not always be certain, thereby making it necessary to design an approach for normalization that deals with fuzzy data. This paper focuses on the approach for designing the fifth normal form (5NF) based on join dependencies for fuzzy data. The basis of join dependency for fuzzy relational databases is derived from the basic relational database concepts. As join dependency implies an multivalued dependency by symmetry the proof of join dependency based normalization is stated from the perspective of multivalued dependency based normalization on fuzzy relational databases.
Sparse Recovery with Linear and Nonlinear Observations: Dependent and Noisy Data
Venue: arXiv cs.IT 2014
Year: 2014

We formulate sparse support recovery as a salient set identification problem and use information-theoretic analyses to characterize the recovery performance and sample complexity. We consider a very general model where we are not restricted to linear models or specific distributions. We state non-asymptotic bounds on recovery probability and a tight mutual information formula for sample complexity. We evaluate our bounds for applications such as sparse linear regression and explicitly characterize effects of correlation or noisy features on recovery performance. We show improvements upon previous work and identify gaps between the performance of recovery algorithms and fundamental information.
Secure Beamforming For MIMO Broadcasting With Wireless Information And Power Transfer
Venue: arXiv cs.IT 2014
Year: 2014

This paper considers a basic MIMO information-energy (I-E) broadcast system, where a multi-antenna transmitter transmits information and energy simultaneously to a multi-antenna information receiver and a dual-functional multi-antenna energy receiver which is also capable of decoding information. Due to the open nature of wireless medium and the dual purpose of information and energy transmission, secure information transmission while ensuring efficient energy harvesting is a critical issue for such a broadcast system. Assuming that physical layer security techniques are applied to the system to ensure secure transmission from the transmitter to the information receiver, we study beamforming design to maximize the achievable secrecy rate subject to a total power constraint and an energy harvesting constraint. First, based on semidefinite relaxation, we propose global optimal solutions to the secrecy rate maximization (SRM) problem in the single-stream case and a specific full-stream case where the difference of Gram matrices of the channel matrices is positive semidefinite. Then, we propose a simple iterative algorithm named inexact block coordinate descent (IBCD) algorithm to tackle the SRM problem of general case with arbitrary number of streams. We proves that the IBCD algorithm can monotonically converge to a Karush-Kuhn-Tucker (KKT) solution to the SRM problem. Furthermore, we extend the IBCD algorithm to the joint beamforming and artificial noise design problem. Finally, simulations are performed to validate the performance of the proposed beamforming algorithms.
Improved Bully Election Algorithm for Distributed Systems
Venue: arXiv cs.DC 2014
Year: 2014

Electing a leader is a classical problem in distributed computing system. Synchronization between processes often requires one process acting as a coordinator. If an elected leader node fails, the other nodes of the system need to elect another leader without much wasting of time. The bully algorithm is a classical approach for electing a leader in a synchronous distributed computing system, which is used to determine the process with highest priority number as the coordinator. In this paper, we have discussed the limitations of Bully algorithm and proposed a simple and efficient method for the Bully algorithm which reduces the number of messages during the election. Our analytical simulation shows that, our proposed algorithm is more efficient than the Bully algorithm with fewer messages passing and fewer stages.
FAUST$^2$: Formal Abstractions of Uncountable-STate STochastic processes
Venue: arXiv cs.SY 2014
Year: 2014

FAUST$^2$ is a software tool that generates formal abstractions of (possibly non-deterministic) discrete-time Markov processes (dtMP) defined over uncountable (continuous) state spaces. A dtMP model is specified in MATLAB and abstracted as a finite-state Markov chain or Markov decision processes. The abstraction procedure runs in MATLAB and employs parallel computations and fast manipulations based on vector calculus. The abstract model is formally put in relationship with the concrete dtMP via a user-defined maximum threshold on the approximation error introduced by the abstraction procedure. FAUST$^2$ allows exporting the abstract model to well-known probabilistic model checkers, such as PRISM or MRMC. Alternatively, it can handle internally the computation of PCTL properties (e.g. safety or reach-avoid) over the abstract model, and refine the outcomes over the concrete dtMP via a quantified error that depends on the abstraction procedure and the given formula. The toolbox is available at http://sourceforge.net/projects/faust2/
Mitigating Denial Of Services Using Secure Overlay Service Model
Venue: arXiv cs.NI 2014
Year: 2014

Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks continue to threaten the reliability of networking systems. Previous approaches for protecting networks from DoS attacks are reactive in that they wait for an attack to be launched before taking appropriate measures to protect the network. This leaves the door open for other attacks that use more sophisticated methods to mask their traffic. A secure overlay services (SOS) architecture has been proposed to provide reliable communication between clients and a target under DoS attacks. The SOS architecture employs a set of overlay nodes arranged in three hierarchical layers that controls access to the target. We propose an architecture called secure overlay services (SOS) that proactively prevents denial of service (DoS) attacks, which works toward supporting emergency services, or similar types of communication. The architecture uses a combination of secure overlay tunneling, routing via consistent hashing, and filtering. We reduce the probability of successful attacks by: 1) performing intensive filtering near protected network edges, pushing the attack point into the core of the network, where high-speed routers can handle the volume of attack traffic and 2) introducing randomness and anonymity into the forwarding architecture, making it difficult for an attacker to target nodes along the path to a specific SOSprotected destination. Using simple analytical models, we evaluate the likelihood that an attacker can successfully launch a DoS attack against an SOS protected network. Our analysis demonstrates that such an architecture reduces the likelihood of a successful attack to minuscule levels.
Capacity of a Nonlinear Optical Channel with Finite Memory
Venue: arXiv cs.IT 2014
Year: 2014

The channel capacity of a nonlinear, dispersive fiber-optic link is revisited. To this end, the popular Gaussian noise (GN) model is extended with a parameter to account for the finite memory of realistic fiber channels. This finite-memory model is harder to analyze mathematically but, in contrast to previous models, it is valid also for nonstationary or heavy-tailed input signals. For uncoded transmission and standard modulation formats, the new model gives the same results as the regular GN model when the memory of the channel is about 10 symbols or more. These results confirm previous results that the GN model is accurate for uncoded transmission. However, when coding is considered, the results obtained using the finite-memory model are very different from those obtained by previous models, even when the channel memory is large. In particular, the peaky behavior of the channel capacity, which has been reported for numerous nonlinear channel models, appears to be an artifact of applying models derived for independent input in a coded (i.e., dependent) scenario.
Collective attention in the age of (mis)information
Venue: arXiv cs.SI 2014
Year: 2014

In this work we study, on a sample of 2.3 million individuals, how Facebook users consumed different information at the edge of political discussion and news during the last Italian electoral competition. Pages are categorized, according to their topics and the communities of interests they pertain to, in a) alternative information sources (diffusing topics that are neglected by science and main stream media); b) online political activism; and c) main stream media. We show that attention patterns are similar despite the different qualitative nature of the information, meaning that unsubstantiated claims (mainly conspiracy theories) reverberate for as long as other information. Finally, we categorize users according to their interaction patterns among the different topics and measure how a sample of this social ecosystem (1279 users) responded to the injection of 2788 false information posts. Our analysis reveals that users which are prominently interacting with alternative information sources (i.e. more exposed to unsubstantiated claims) are more prone to interact with false claims.
Performance Comparison of Two Clipping Based Filtering Methods for PAPR Reduction in OFDM Signal
Venue: arXiv cs.NI 2014
Year: 2014

The growth of wireless communication technologies has been producing the intense demand for high-speed, efficient, reliable voice & data communication. As a result, third generation partnership project (3GPP) has implemented next generation wireless communication technology long term evolution (LTE) which is designed to increase the capacity and speed of existing mobile telephone & data networks. LTE has adopted a multicarrier transmission technique known as orthogonal frequency division multiplexing (OFDM). OFDM meets the LTE requirement for spectrum flexibility and enables cost-efficient solutions for very wide carriers. One major generic problem of OFDM technique is high peak to average power ratio (PAPR) which is defined as the ratio of the peak power to the average power of the OFDM signal. A trade-off is necessary for reducing PAPR with increasing bit error rate (BER), computational complexity or data rate loss etc. In this paper, two clipping based filtering methods have been implemented & also analyzed their modulation effects on reducing PAPR.