OpenRaiser/Intern-Atlas huggingface dataset:
AltecOnDB: A Large-Vocabulary Arabic Online Handwriting Recognition Database
Venue: arXiv cs.CV 2014
Year: 2014

Arabic is a semitic language characterized by a complex and rich morphology. The exceptional degree of ambiguity in the writing system, the rich morphology, and the highly complex word formation process of roots and patterns all contribute to making computational approaches to Arabic very challenging. As a result, a practical handwriting recognition system should support large vocabulary to provide a high coverage and use the context information for disambiguation. Several research efforts have been devoted for building online Arabic handwriting recognition systems. Most of these methods are either using their small private test data sets or a standard database with limited lexicon and coverage. A large scale handwriting database is an essential resource that can advance the research of online handwriting recognition. Currently, there is no online Arabic handwriting database with large lexicon, high coverage, large number of writers and training/testing data. In this paper, we introduce AltecOnDB, a large scale online Arabic handwriting database. AltecOnDB has 98% coverage of all the possible PAWS of the Arabic language. The collected samples are complete sentences that include digits and punctuation marks. The collected data is available on sentence, word and character levels, hence, high-level linguistic models can be used for performance improvements. Data is collected from more than 1000 writers with different backgrounds, genders and ages. Annotation and verification tools are developed to facilitate the annotation and verification phases. We built an elementary recognition system to test our database and show the existing difficulties when handling a large vocabulary and dealing with large amounts of styles variations in the collected data.
A Feasibility Study on Programmer Specific Instruction Set Processors (PSISPs)
Venue: arXiv cs.AR 2014
Year: 2014

ASIPs are designed in order to execute instructions of a particular domain of applications. The designing of ASIPs addresses the major challenges faced by a system on chip such as size, cost, performance and energy consumption. The higher the number of similar instructions within the domain to be mapped the lesser the energy consumption, the smaller the size and the higher the performance of the ASIP. Thus, designing processors for domains with more similar programs would overcome these issues. This paper describes the investigation of whether the domains of programmer specific programs have any significance like application specific program domains and thus, whether the approach of designing processors known as Programmer Specific Instruction Set Processors is worthwhile. We performed the evaluation at the instruction level by using four different measures to obtain the similarity of programs: (1) by the existence of each instruction, (2) by the frequency of each instruction, (3) by two consecutive instruction patterns and (4) by three consecutive instruction patterns of application specific and programmer specific programs. We found that although programmer specific instructions show some impact on the similarity measures, they are much smaller and therefore insignificant compared to the impact from application specific programs.
Fractal descriptors based on the probability dimension: a texture analysis and classification approach
Venue: arXiv cs.CV 2014
Year: 2014

In this work, we propose a novel technique for obtaining descriptors of gray-level texture images. The descriptors are provided by applying a multiscale transform to the fractal dimension of the image estimated through the probability (Voss) method. The effectiveness of the descriptors is verified in a classification task using benchmark over texture datasets. The results obtained demonstrate the efficiency of the proposed method as a tool for the description and discrimination of texture images.
Gabor wavelets combined with volumetric fractal dimension applied to texture analysis
Venue: arXiv cs.CV 2014
Year: 2014

Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced using a canonical analysis function and concatenated to form the final feature vector. Experiments were conducted on several texture image databases to prove the power and effectiveness of the proposed method. Results obtained shown that this method outperforms other early proposed method, creating a more reliable technique for texture feature extraction.
Improving controllability of complex networks by rewiring links regularly
Venue: arXiv cs.SY 2014
Year: 2014

Network science have constantly been in the focus of research for the last decade, with considerable advances in the controllability of their structural. However, much less effort has been devoted to study that how to improve the controllability of complex networks. In this paper, a new algorithm is proposed to improve the controllability of complex networks by rewiring links regularly which transforms the network structure. Then it is demonstrated that our algorithm is very effective after numerical simulation experiment on typical network models (Erdös-Rényi and scale-free network). We find that our algorithm is mainly determined by the average degree and positive correlation of in-degree and out-degree of network and it has nothing to do with the network size. Furthermore, we analyze and discuss the correlation between controllability of complex networks and degree distribution index: power-law exponent and heterogeneity
Dynamic Algorithms for Interval Scheduling on a Single Machine
Venue: arXiv cs.DS 2014
Year: 2014

We investigate dynamic algorithms for the interval scheduling problem. Our algorithm runs in amortised time $O(\log n)$ for query operation and $O(d\log^2 n)$ for insertion and removal operations, where $n$ and $d$ are the maximal numbers of intervals and pairwise overlapping intervals respectively. We also show that for a monotonic set, that is when no interval properly contains another interval, the amortised complexity is $O(\log n)$ for both query and update operations. We compare the two algorithms for the monotonic interval sets using experiments.
An NBDMMM Algorithm Based Framework for Allocation of Resources in Cloud
Venue: arXiv cs.DC 2014
Year: 2014

Cloud computing is a technological advancement in the arena of computing and has taken the utility vision of computing a step further by providing computing resources such as network, storage, compute capacity and servers, as a service via an internet connection. These services are provided to the users in a pay per use manner subjected to the amount of usage of these resources by the cloud users. Since the usage of these resources is done in an elastic manner thus an on demand provisioning of these resources is the driving force behind the entire cloud computing infrastructure therefore the maintenance of these resources is a decisive task that must be taken into account. Eventually, infrastructure level performance monitoring and enhancement is also important. This paper proposes a framework for allocation of resources in a cloud based environment thereby leading to an infrastructure level enhancement of performance in a cloud environment. The framework is divided into four stages Stage 1: Cloud service provider monitors the infrastructure level pattern of usage of resources and behavior of the cloud users. Stage 2: Report the monitoring activities about the usage to cloud service providers. Stage 3: Apply proposed Network Bandwidth Dependent DMMM algorithm .Stage 4: Allocate resources or provide services to cloud users, thereby leading to infrastructure level performance enhancement and efficient management of resources. Analysis of resource usage pattern is considered as an important factor for proper allocation of resources by the service providers, in this paper Google cluster trace has been used for accessing the resource usage pattern in cloud. Experiments have been conducted on cloudsim simulation framework and the results reveal that NBDMMM algorithm improvises allocation of resources in a virtualized cloud.
Hierarchical Bayesian Models with Factorization for Content-Based Recommendation
Venue: arXiv cs.IR 2014
Year: 2014

Most existing content-based filtering approaches learn user profiles independently without capturing the similarity among users. Bayesian hierarchical models \cite{Zhang:Efficient} learn user profiles jointly and have the advantage of being able to borrow discriminative information from other users through a Bayesian prior. However, the standard Bayesian hierarchical models assume all user profiles are generated from the same prior. Considering the diversity of user interests, this assumption could be improved by introducing more flexibility. Besides, most existing content-based filtering approaches implicitly assume that each user profile corresponds to exactly one user interest and fail to capture a user's multiple interests (information needs). In this paper, we present a flexible Bayesian hierarchical modeling approach to model both commonality and diversity among users as well as individual users' multiple interests. We propose two models each with different assumptions, and the proposed models are called Discriminative Factored Prior Models (DFPM). In our models, each user profile is modeled as a discriminative classifier with a factored model as its prior, and different factors contribute in different levels to each user profile. Compared with existing content-based filtering models, DFPM are interesting because they can 1) borrow discriminative criteria of other users while learning a particular user profile through the factored prior; 2) trade off well between diversity and commonality among users; and 3) handle the challenging classification situation where each class contains multiple concepts. The experimental results on a dataset collected from real users on digg.com show that our models significantly outperform the baseline models of L-2 regularized logistic regression and traditional Bayesian hierarchical model with logistic regression.
How to select the largest k elements from evolving data?
Venue: arXiv cs.DS 2014
Year: 2014

In this paper we investigate the top-$k$-selection problem, i.e. determine the largest, second largest, ..., and the $k$-th largest elements, in the dynamic data model. In this model the order of elements evolves dynamically over time. In each time step the algorithm can only probe the changes of data by comparing a pair of elements. Previously only two special cases were studied[2]: finding the largest element and the median; and sorting all elements. This paper systematically deals with $k\in [n]$ and solves the problem almost completely. Specifically, we identify a critical point $k^*$ such that the top-$k$-selection problem can be solved error-free with probability $1-o(1)$ if and only if $k=o(k^*)$. A lower bound of the error when $k=Ω(k^*)$ is also determined, which actually is tight under some condition. On the other hand, it is shown that the top-$k$-set problem, which means finding the largest $k$ elements without sorting them, can be solved error-free for all $k\in [n]$. Additionally, we extend the dynamic data model and show that most of these results still hold.
Improving approximate RPCA with a k-sparsity prior
Venue: arXiv cs.NE 2014
Year: 2014

A process centric view of robust PCA (RPCA) allows its fast approximate implementation based on a special form o a deep neural network with weights shared across all layers. However, empirically this fast approximation to RPCA fails to find representations that are parsemonious. We resolve these bad local minima by relaxing the elementwise L1 and L2 priors and instead utilize a structure inducing k-sparsity prior. In a discriminative classification task the newly learned representations outperform these from the original approximate RPCA formulation significantly.
A $2k$-Vertex Kernel for Maximum Internal Spanning Tree
Venue: arXiv cs.DS 2014
Year: 2014

We consider the parameterized version of the maximum internal spanning tree problem, which, given an $n$-vertex graph and a parameter $k$, asks for a spanning tree with at least $k$ internal vertices. Fomin et al. [J. Comput. System Sci., 79:1-6] crafted a very ingenious reduction rule, and showed that a simple application of this rule is sufficient to yield a $3k$-vertex kernel. Here we propose a novel way to use the same reduction rule, resulting in an improved $2k$-vertex kernel. Our algorithm applies first a greedy procedure consisting of a sequence of local exchange operations, which ends with a local-optimal spanning tree, and then uses this special tree to find a reducible structure. As a corollary of our kernel, we obtain a deterministic algorithm for the problem running in time $4^k \cdot n^{O(1)}$.
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
Venue: arXiv cs.NI 2014
Year: 2014

Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider a MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources-the transmit precoding matrices of the MUs-and the computational resources-the CPU cycles/second assigned by the cloud to each MU-in order to minimize the overall users' energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to express the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. Then, we reformulate the algorithm in a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.
Accurate Localization in Dense Urban Area Using Google Street View Image
Venue: arXiv cs.CV 2014
Year: 2014

Accurate information about the location and orientation of a camera in mobile devices is central to the utilization of location-based services (LBS). Most of such mobile devices rely on GPS data but this data is subject to inaccuracy due to imperfections in the quality of the signal provided by satellites. This shortcoming has spurred the research into improving the accuracy of localization. Since mobile devices have camera, a major thrust of this research has been seeks to acquire the local scene and apply image retrieval techniques by querying a GPS-tagged image database to find the best match for the acquired scene.. The techniques are however computationally demanding and unsuitable for real-time applications such as assistive technology for navigation by the blind and visually impaired which motivated out work. To overcome the high complexity of those techniques, we investigated the use of inertial sensors as an aid in image-retrieval-based approach. Armed with information of media other than images, such as data from the GPS module along with orientation sensors such as accelerometer and gyro, we sought to limit the size of the image set to c search for the best match. Specifically, data from the orientation sensors along with Dilution of precision (DOP) from GPS are used to find the angle of view and estimation of position. We present analysis of the reduction in the image set size for the search as well as simulations to demonstrate the effectiveness in a fast implementation with 98% Estimated Position Error.
Semantics for a Quantum Programming Language by Operator Algebras
Venue: arXiv cs.LO 2014
Year: 2014

This paper presents a novel semantics for a quantum programming language by operator algebras, which are known to give a formulation for quantum theory that is alternative to the one by Hilbert spaces. We show that the opposite category of the category of W*-algebras and normal completely positive subunital maps is an elementary quantum flow chart category in the sense of Selinger. As a consequence, it gives a denotational semantics for Selinger's first-order functional quantum programming language QPL. The use of operator algebras allows us to accommodate infinite structures and to handle classical and quantum computations in a unified way.
Modified Design of Microstrip Patch Antenna for WiMAX Communication System
Venue: arXiv cs.OH 2014
Year: 2014

In this paper, a new design for U-shaped microstrip patch antenna is proposed, which can be used in WiMAX communication systems. The aim of this paper is to optimize the performance of microstrip patch antenna. Nowadays, WiMAX communication applications are widely using U-shaped microstrip patch antenna and it has become very popular. Our proposed antenna design uses 4-4.5 GHZ frequency band and it is working at narrowband within this band. RT/DUROID 5880 material is used for creating the substrate of the microstrip antenna. This modified design of the microstrip patch antenna gives high performance in terms of gain and return loss.
Liquidity on Web Dynamic Network
Venue: arXiv cs.SI 2014
Year: 2014

Nowadays, the exponentially growing of the Web renders the problem of correlation among different topics of paramount importance. The proposed model can be used to study the evolution of network depicted by different topics on the web correlated by a dynamic "fluid" of tags among them. The fluid-dynamic model depicted is completely evolutive, thus it is able to describe the dynamic situation of a network at every instant of time. This overcomes the difficulties encountered by many static models. The theory permits the development of efficient numerical schemes also for very large networks. This is possible since dynamic flow at junctions is modeled in a simple and computationally convenient way (resorting to a linear programming problem). The obtained model consists of a single conservation law and is on one side simple enough to permit a complete understanding, on the other side reach enough to detect the evolution of the dynamic network.
iReclass - An automatic system for recording classes
Venue: arXiv cs.CY 2014
Year: 2014

This paper presents the details of a system capable of recording on video a traditional class. By traditional class it is meant a teacher, a blackboard and a white canvas where course notes are projected. The system is able to track the movements of the lecturer, while recording it on video at the required frame rate (e.g., 25 fps). The system is also capable of understanding five arm gestures made by the lecturer with the intent of controlling which scenario is recorded: himself, the blackboard or the white canvas. The remaining two gestures are for start/stop the recorder. The system is composed by a Kinect sensor, a video camera, a microphone, one pan-tilt system and one pan system, using a total of three step motors.
Assigning UPDRS Scores in the Leg Agility Task of Parkinsonians: Can It Be Done through BSN-based Kinematic Variables?
Venue: arXiv cs.CY 2014
Year: 2014

In this paper, by characterizing the Leg Agility (LA) task, which contributes to the evaluation of the degree of severity of the Parkinson's Disease (PD), through kinematic variables (including the angular amplitude and speed of thighs' motion), we investigate the link between these variables and Unified Parkinson's Disease Rating Scale (UPDRS) scores. Our investigation relies on the use of a few body-worn wireless inertial nodes and represents a first step in the design of a portable system, amenable to be integrated in Internet of Things (IoT) scenarios, for automatic detection of the degree of severity (in terms of UPDRS score) of PD. The experimental investigation is carried out considering 24 PD patients.
Precision Arithmetic: A New Floating-Point Arithmetic
Venue: arXiv cs.DM 2014
Year: 2014

A new deterministic floating-point arithmetic called precision arithmetic is developed to track precision for arithmetic calculations. It uses a novel rounding scheme to avoid excessive rounding error propagation of conventional floating-point arithmetic. Unlike interval arithmetic, its uncertainty tracking is based on statistics and the central limit theorem, with a much tighter bounding range. Its stable rounding error distribution is approximated by a truncated normal distribution. Generic standards and systematic methods for validating uncertainty-bearing arithmetics are discussed. The precision arithmetic is found to be better than interval arithmetic in both uncertainty-tracking and uncertainty-bounding for normal usages. The precision arithmetic is available publicly at http://precisionarithm.sourceforge.net.
The traveling salesman problem: A Linear programming formulation
Venue: arXiv cs.CC 2014
Year: 2014

In this paper, we present a polynomial-sized linear programming formulation of the Traveling Salesman Problem (TSP). The proposed linear program is a network flow-based model. Numerical implementation issues and results are discussed. (The exposition and proofs are much more detailed in an edition which I wrote in collaboration with Dr. M.H. Karwan in 2012-2014 . That edition is available at http://users.business.uconn.edu/mdiaby/P=NPProofPapers/tspPaper.pdf)
Design and Analysis of the REESSE1+ Public Key Cryptosystem v2.21
Venue: arXiv cs.CR 2014
Year: 2014

In this paper, the authors give the definitions of a coprime sequence and a lever function, and describe the five algorithms and six characteristics of a prototypal public key cryptosystem which is used for encryption and signature, and based on three new problems and one existent problem: the multivariate permutation problem (MPP), the anomalous subset product problem (ASPP), the transcendental logarithm problem (TLP), and the polynomial root finding problem (PRFP). Prove by reduction that MPP, ASPP, and TLP are computationally at least equivalent to the discrete logarithm problem (DLP) in the same prime field, and meanwhile find some evidence which inclines people to believe that the new problems are harder than DLP each, namely unsolvable in DLP subexponential time. Demonstrate the correctness of the decryption and the verification, deduce the probability of a plaintext solution being nonunique is nearly zero, and analyze the exact securities of the cryptosystem against recovering a plaintext from a ciphertext, extracting a private key from a public key or a signature, and forging a signature through known signatures, public keys, and messages on the assumption that IFP, DLP, and LSSP can be solved. Studies manifest that the running times of effectual attack tasks are greater than or equal to O(2^n) so far when n = 80, 96, 112, or 128 with lgM = 696, 864, 1030, or 1216. As viewed from utility, it should be researched further how to decrease the length of a modulus and to increase the speed of the decryption.