OpenRaiser/Intern-Atlas huggingface dataset:
Breaking Barriers: Assistive Technology Tool as Educational Software to support Writing
Venue: arXiv cs.HC 2014
Year: 2014

The preliminary report by Siriraj Hospital suggested that 6% of population who are students in Thailand could be estimated to have learning disabilities. It is therefore necessary for our institute to develop suitable ICT technologies to assist the education of these learning disabilities children. We therefore developed a program called Thai Word Prediction Program. Thai Word Prediction program aims to assist students with learning disabilities in their writing. After the usability engineering, we conducted the experiment with students with learning disabilities at the School in Bangkok. Hence, the results indicated that all three students with learning disabilities in this study improved their ability of writing by 50%, 81.89% and 100% respectively.
Line Maps in Cluttered Environments
Venue: arXiv cs.RO 2014
Year: 2014

This paper uses the smoothing and mapping framework to solve the SLAM problem in indoor environments; focusing on how some key issues such as feature extraction and data association can be handled by applying probabilistic techniques. For feature extraction, an odds ratio approach to find multiple lines from laser scans is proposed, this criterion allows to decide which model must be merged and to output the best number of models. In addition, to solve the data association problem a method based on the segments of each line is proposed. Experimental results show that high quality indoor maps can be obtained from noisy data
Erasure-Coded Byzantine Storage with Separate Metadata
Venue: arXiv cs.DC 2014
Year: 2014

Although many distributed storage protocols have been introduced, a solution that combines the strongest properties in terms of availability, consistency, fault-tolerance, storage complexity and the supported level of concurrency, has been elusive for a long time. Combining these properties is difficult, especially if the resulting solution is required to be efficient and incur low cost. We present AWE, the first erasure-coded distributed implementation of a multi-writer multi-reader read/write storage object that is, at the same time: (1) asynchronous, (2) wait-free, (3) atomic, (4) amnesic, (i.e., with data nodes storing a bounded number of values) and (5) Byzantine fault-tolerant (BFT) using the optimal number of nodes. Furthermore, AWE is efficient since it does not use public-key cryptography and requires data nodes that support only reads and writes, further reducing the cost of deployment and ownership of a distributed storage solution. Notably, AWE stores metadata separately from $k$-out-of-$n$ erasure-coded fragments. This enables AWE to be the first BFT protocol that uses as few as $2t+k$ data nodes to tolerate $t$ Byzantine nodes, for any $k \ge 1$.
Arbitrary Transmission Power in the SINR Model: Local Broadcasting, Coloring and MIS
Venue: arXiv cs.DS 2014
Year: 2014

In the light of energy conservation and the expansion of existing networks, wireless networks face the challenge of nodes with heterogeneous transmission power. However, for more realistic models of wireless communication only few algorithmic results are known. In this paper we consider nodes with arbitrary, possibly variable, transmission power in the so-called physical or SINR model. Our first result is a bound on the probabilistic interference from all simultaneously transmitting nodes on receivers. This result implies that current local broadcasting algorithms can be generalized to the case of non-uniform transmission power with minor changes. The algorithms run in $Ø(Γ^{2} Δ\log n)$ time slots if the maximal degree $Δ$ is known, and $Ø((Δ+ \log n)Γ^{2} \log n)$ otherwise, where $Γ$ is the ratio between the maximal and the minimal transmission range. The broad applicability of our result on bounding the interference is further highlighted, by generalizing a distributed coloring algorithm to this setting.
Detecting Opinions in Tweets
Venue: arXiv cs.CL 2014
Year: 2014

Given the incessant growth of documents describing the opinions of different people circulating on the web, including Web 2.0 has made it possible to give an opinion on any product in the net. In this paper, we examine the various opinions expressed in the tweets and classify them positive, negative or neutral by using the emoticons for the Bayesian method and adjectives and adverbs for the Turney's method
Alternation in Quantum Programming: From Superposition of Data to Superposition of Programs
Venue: arXiv cs.PL 2014
Year: 2014

We extract a novel quantum programming paradigm - superposition of programs - from the design idea of a popular class of quantum algorithms, namely quantum walk-based algorithms. The generality of this paradigm is guaranteed by the universality of quantum walks as a computational model. A new quantum programming language QGCL is then proposed to support the paradigm of superposition of programs. This language can be seen as a quantum extension of Dijkstra's GCL (Guarded Command Language). Surprisingly, alternation in GCL splits into two different notions in the quantum setting: classical alternation (of quantum programs) and quantum alternation, with the latter being introduced in QGCL for the first time. Quantum alternation is the key program construct for realizing the paradigm of superposition of programs. The denotational semantics of QGCL are defined by introducing a new mathematical tool called the guarded composition of operator-valued functions. Then the weakest precondition semantics of QGCL can straightforwardly derived. Another very useful program construct in realizing the quantum programming paradigm of superposition of programs, called quantum choice, can be easily defined in terms of quantum alternation. The relation between quantum choices and probabilistic choices is clarified through defining the notion of local variables. We derive a family of algebraic laws for QGCL programs that can be used in program verification, transformations and compilation. The expressive power of QGCL is illustrated by several examples where various variants and generalizations of quantum walks are conveniently expressed using quantum alternation and quantum choice. We believe that quantum programming with quantum alternation and choice will play an important role in further exploiting the power of quantum computing.
Power and Bit Allocation for Wireless OFDM Channels with Finite-Rate Feedback and Subcarrier Clustering
Venue: arXiv cs.IT 2014
Year: 2014

The study investigated the allocation of transmission power and bits for a point-to-point orthogonal frequency-division multiplexing channel assuming perfect channel information at the receiver, but imperfect channel information at the transmitter. Channel information was quantized at the receiver and was sent back to the transmitter via a finite-rate feedback channel. Based on limited feedback from the receiver, the corresponding transmitter adapted the power level and/or modulation across subcarriers. To reduce the amount of feedback, subcarriers were partitioned into different clusters and an on/off threshold-based power allocation was applied to subcarrier clusters. In addition, two options were proposed to interpolate a channel frequency response from a set of quantized channel gains and apply the optimal water-filling allocation or a greedy bit allocation based on channel interpolation. Proposed schemes with finite feedback rates were shown to perform close to the optimal allocation without a feedback-rate constraint. In the numerical example, channel capacity decreased about 6% from the optimum when one bit of feedback per subcarrier was used.
On Big Data Benchmarking
Venue: arXiv cs.PF 2014
Year: 2014

Big data systems address the challenges of capturing, storing, managing, analyzing, and visualizing big data. Within this context, developing benchmarks to evaluate and compare big data systems has become an active topic for both research and industry communities. To date, most of the state-of-the-art big data benchmarks are designed for specific types of systems. Based on our experience, however, we argue that considering the complexity, diversity, and rapid evolution of big data systems, for the sake of fairness, big data benchmarks must include diversity of data and workloads. Given this motivation, in this paper, we first propose the key requirements and challenges in developing big data benchmarks from the perspectives of generating data with 4V properties (i.e. volume, velocity, variety and veracity) of big data, as well as generating tests with comprehensive workloads for big data systems. We then present the methodology on big data benchmarking designed to address these challenges. Next, the state-of-the-art are summarized and compared, following by our vision for future research directions.
Extended Breadth-First Search Algorithm
Venue: arXiv cs.AI 2014
Year: 2014

The task of artificial intelligence is to provide representation techniques for describing problems, as well as search algorithms that can be used to answer our questions. A widespread and elaborated model is state-space representation, which, however, has some shortcomings. Classical search algorithms are not applicable in practice when the state space contains even only a few tens of thousands of states. We can give remedy to this problem by defining some kind of heuristic knowledge. In case of classical state-space representation, heuristic must be defined so that it qualifies an arbitrary state based on its "goodness," which is obviously not trivial. In our paper, we introduce an algorithm that gives us the ability to handle huge state spaces and to use a heuristic concept which is easier to embed into search algorithms.
Twofold Video Hashing with Automatic Synchronization
Venue: arXiv cs.MM 2014
Year: 2014

Video hashing finds a wide array of applications in content authentication, robust retrieval and anti-piracy search. While much of the existing research has focused on extracting robust and secure content descriptors, a significant open challenge still remains: Most existing video hashing methods are fallible to temporal desynchronization. That is, when the query video results by deleting or inserting some frames from the reference video, most existing methods assume the positions of the deleted (or inserted) frames are either perfectly known or reliably estimated. This assumption may be okay under typical transcoding and frame-rate changes but is highly inappropriate in adversarial scenarios such as anti-piracy video search. For example, an illegal uploader will try to bypass the 'piracy check' mechanism of YouTube/Dailymotion etc by performing a cleverly designed non-uniform resampling of the video. We present a new solution based on dynamic time warping (DTW), which can implement automatic synchronization and can be used together with existing video hashing methods. The second contribution of this paper is to propose a new robust feature extraction method called flow hashing (FH), based on frame averaging and optical flow descriptors. Finally, a fusion mechanism called distance boosting is proposed to combine the information extracted by DTW and FH. Experiments on real video collections show that such a hash extraction and comparison enables unprecedented robustness under both spatial and temporal attacks.
Topicality and Social Impact: Diverse Messages but Focused Messengers
Venue: arXiv cs.SI 2014
Year: 2014

Are users who comment on a variety of matters more likely to achieve high influence than those who delve into one focused field? Do general Twitter hashtags, such as #lol, tend to be more popular than novel ones, such as #instantlyinlove? Questions like these demand a way to detect topics hidden behind messages associated with an individual or a hashtag, and a gauge of similarity among these topics. Here we develop such an approach to identify clusters of similar hashtags by detecting communities in the hashtag co-occurrence network. Then the topical diversity of a user's interests is quantified by the entropy of her hashtags across different topic clusters. A similar measure is applied to hashtags, based on co-occurring tags. We find that high topical diversity of early adopters or co-occurring tags implies high future popularity of hashtags. In contrast, low diversity helps an individual accumulate social influence. In short, diverse messages and focused messengers are more likely to gain impact.
Energy Management for a User Interactive Smart Community: A Stackelberg Game Approach
Venue: arXiv cs.SY 2014
Year: 2014

This paper studies a three party energy management problem in a user interactive smart community that consists of a large number of residential units (RUs) with distributed energy resources (DERs), a shared facility controller (SFC) and the main grid. A Stackelberg game is formulated to benefit both the SFC and RUs, in terms of incurred cost and achieved utility respectively, from their energy trading with each other and the grid. The properties of the game are studied and it is shown that there exists a unique Stackelberg equilibrium (SE). A novel algorithm is proposed that can be implemented in a distributed fashion by both RUs and the SFC to reach the SE. The convergence of the algorithm is also proven, and shown to always reach the SE. Numerical examples are used to assess the properties and effectiveness of the proposed scheme.
On the $k$-error linear complexity for $p^n$-periodic binary sequences via hypercube theory
Venue: arXiv cs.CR 2014
Year: 2014

The linear complexity and the $k$-error linear complexity of a binary sequence are important security measures for key stream strength. By studying binary sequences with the minimum Hamming weight, a new tool named as hypercube theory is developed for $p^n$-periodic binary sequences. In fact, hypercube theory is based on a typical sequence decomposition and it is a very important tool in investigating the critical error linear complexity spectrum proposed by Etzion et al. To demonstrate the importance of hypercube theory, we first give a standard hypercube decomposition based on a well-known algorithm for computing linear complexity and show that the linear complexity of the first hypercube in the decomposition is equal to the linear complexity of the original sequence. Second, based on such decomposition, we give a complete characterization for the first decrease of the linear complexity for a $p^n$-periodic binary sequence $s$. This significantly improves the current existing results in literature. As to the importance of the hypercube, we finally derive a counting formula for the $m$-hypercubes with the same linear complexity.
Soft Consistency Reconstruction: A Robust 1-bit Compressive Sensing Algorithm
Venue: arXiv cs.IT 2014
Year: 2014

A class of recovering algorithms for 1-bit compressive sensing (CS) named Soft Consistency Reconstructions (SCRs) are proposed. Recognizing that CS recovery is essentially an optimization problem, we endeavor to improve the characteristics of the objective function under noisy environments. With a family of re-designed consistency criteria, SCRs achieve remarkable counter-noise performance gain over the existing counterparts, thus acquiring the desired robustness in many real-world applications. The benefits of soft decisions are exemplified through structural analysis of the objective function, with intuition described for better understanding. As expected, through comparisons with existing methods in simulations, SCRs demonstrate preferable robustness against noise in low signal-to-noise ratio (SNR) regime, while maintaining comparable performance in high SNR regime.
Distributed Spectrum-Aware Clustering in Cognitive Radio Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

A novel Distributed Spectrum-Aware Clustering (DSAC) scheme is proposed in the context of Cognitive Radio Sensor Networks (CRSN). DSAC aims at forming energy efficient clusters in a self-organized fashion while restricting interference to Primary User (PU) systems. The spectrum-aware clustered structure is presented where the communications consist of intra-cluster aggregation and inter-cluster relaying. In order to save communication power, the optimal number of clusters is derived and the idea of groupwise constrained clustering is introduced to minimize intra-cluster distance under spectrum-aware constraint. In terms of practical implementation, DSAC demonstrates preferable scalability and stability because of its low complexity and quick convergence under dynamic PU activity. Finally, simulation results are given to validate the proposed scheme.
Structure Tensor Based Image Interpolation Method
Venue: arXiv cs.CV 2014
Year: 2014

Feature preserving image interpolation is an active area in image processing field. In this paper a new direct edge directed image super-resolution algorithm based on structure tensors is proposed. Using an isotropic Gaussian filter, the structure tensor at each pixel of the input image is computed and the pixels are classified to three distinct classes; uniform region, corners and edges, according to the eigenvalues of the structure tensor. Due to application of the isotropic Gaussian filter, the classification is robust to noise presented in image. Based on the tangent eigenvector of the structure tensor, the edge direction is determined and used for interpolation along the edges. In comparison to some previous edge directed image interpolation methods, the proposed method achieves higher quality in both subjective and objective aspects. Also the proposed method outperforms previous methods in case of noisy and JPEG compressed images. Furthermore, without the need for optimization in the process, the algorithm can achieve higher speed.
Collective oscillation period of inter-coupled biological negative cyclic feedback oscillators
Venue: arXiv cs.SY 2014
Year: 2014

A number of biological rhythms originate from networks comprised of multiple cellular oscillators. But analytical results are still lacking on the collective oscillation period of inter-coupled gene regulatory oscillators, which, as has been reported, may be different from that of an autonomous oscillator. Based on cyclic feedback oscillators, we analyze the collective oscillation pattern of coupled cellular oscillators. First we give a condition under which the oscillator network exhibits oscillatory and synchronized behavior. Then we estimate the collective oscillation period based on a novel multivariable harmonic balance technique. Analytical results are derived in terms of biochemical parameters, thus giving insight into the basic mechanism of biological oscillation and providing guidance in synthetic biology design.
Formal Specification and Quantitative Analysis of a Constellation of Navigation Satellites
Venue: arXiv cs.SY 2014
Year: 2014

Navigation satellites are a core component of navigation satellite based systems such as GPS, GLONASS and Galileo which provide location and timing information for a variety of uses. Such satellites are designed for operating on orbit to perform tasks and have lifetimes of 10 years or more. Reliability, availability and maintainability (RAM) analysis of systems has been indispensable in the design phase of satellites in order to achieve minimum failures or to increase mean time between failures (MTBF) and thus to plan maintenance strategies, optimise reliability and maximise availability. In this paper, we present formal models of both a single satellite and a navigation satellite constellation and logical specification of their reliability, availability and maintainability properties respectively. The probabilistic model checker PRISM has been used to perform automated analysis of these quantitative properties.
A Novel Histogram Based Robust Image Registration Technique
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, a method for Automatic Image Registration (AIR) through histogram is proposed. Automatic image registration is one of the crucial steps in the analysis of remotely sensed data. A new acquired image must be transformed, using image registration techniques, to match the orientation and scale of previous related images. This new approach combines several segmentations of the pair of images to be registered. A relaxation parameter on the histogram modes delineation is introduced. It is followed by characterization of the extracted objects through the objects area, axis ratio, and perimeter and fractal dimension. The matched objects are used for rotation and translation estimation. It allows for the registration of pairs of images with differences in rotation and translation. This method contributes to subpixel accuracy.
Information-Theoretic Bounds for Adaptive Sparse Recovery
Venue: arXiv cs.IT 2014
Year: 2014

We derive an information-theoretic lower bound for sample complexity in sparse recovery problems where inputs can be chosen sequentially and adaptively. This lower bound is in terms of a simple mutual information expression and unifies many different linear and nonlinear observation models. Using this formula we derive bounds for adaptive compressive sensing (CS), group testing and 1-bit CS problems. We show that adaptivity cannot decrease sample complexity in group testing, 1-bit CS and CS with linear sparsity. In contrast, we show there might be mild performance gains for CS in the sublinear regime. Our unified analysis also allows characterization of gains due to adaptivity from a wider perspective on sparse problems.
Distributed Data and Programs Slicing
Venue: arXiv cs.PL 2014
Year: 2014

This paper presents a new technique for data slicing of distributed programs running on a hierarchy of machines. Data slicing can be realized as a program transformation that partitions heaps of machines in a hierarchy into independent regions. Inside each region of each machine, pointers preserve the original pointer structures in the original heap hierarchy. Each heap component of the base type (e.g., the integer type) goes only to a region of one of the heaps. The proposed technique has the shape of a system of inference rules. In addition, this paper presents a simply structure type system to decide type soundness of distributed programs. Using this type system, a mathematical proof that the proposed slicing technique preserves typing properties is outlined in this paper as well.
Providing Traceability for Neuroimaging Analyses
Venue: arXiv cs.SE 2014
Year: 2014

With the increasingly digital nature of biomedical data and as the complexity of analyses in medical research increases, the need for accurate information capture, traceability and accessibility has become crucial to medical researchers in the pursuance of their research goals. Grid- or Cloud-based technologies, often based on so-called Service Oriented Architectures (SOA), are increasingly being seen as viable solutions for managing distributed data and algorithms in the bio-medical domain. For neuroscientific analyses, especially those centred on complex image analysis, traceability of processes and datasets is essential but up to now this has not been captured in a manner that facilitates collaborative study. Over the past decade, we have been working with mammographers, paediatricians and neuroscientists in three generations of projects to provide the data management and provenance services now required for 21st century medical research. This paper outlines the finding of a requirements study and a resulting system architecture for the production of services to support neuroscientific studies of biomarkers for Alzheimers Disease. The paper proposes a software infrastructure and services that provide the foundation for such support. It introduces the use of the CRISTAL software to provide provenance management as one of a number of services delivered on a SOA, deployed to manage neuroimaging projects that have been studying biomarkers for Alzheimers disease.