OpenRaiser/Intern-Atlas huggingface dataset:
Developing Educational Computer Animation Based on Human Personality Types
Venue: arXiv cs.HC 2015
Year: 2015

Computer animation in the past decade has become one of the most noticeable features of technology-based learning environments. With today's high educational demands as well as the lack of time provided for certain courses, classical educational methods have shown deficiencies in keeping up with the drastic changes observed in the digital era. Without taking into account various significant factors such as gender, age, level of interest and memory level, educational animation may turn out to be insufficient for learners or fail to meet their needs. However, we have noticed that the applications of animation for education have been given only inadequate attention, and students' personality types have never been taken into account. We suggest there is an interesting relationship here, and propose essential factors in creating educational animations based on students' personality types. Particularly, we investigate how information in computer animation may be presented in a preferable way based on the fundamental elements of computer animation. The present study believes that it is likely to have wide benefits in the field of education. Considering the personality types in designing educational computer animations with the aid of gathered empirical results might be a promising avenue to enhance the learning process.
Thermal Covert Channels on Multi-core Platforms
Venue: arXiv cs.CR 2015
Year: 2015

Side channels remain a challenge to information flow control and security in modern computing platforms. Resource partitioning techniques that minimise the number of shared resources among processes are often used to address this challenge. In this work, we focus on multi-core platforms and we demonstrate that even seemingly strong isolation techniques based on dedicated cores and memory can be circumvented through the use of thermal side channels. Specifically, we show that the processor core temperature can be used both as a side channel as well as a covert communication channel even when the system implements strong spatial and temporal partitioning. Our experiments on an x86-based platform demonstrate covert thermal channels that achieve up to 12.5 bps and a weak side channel that can detect processes executed on neighbouring cores. This work therefore shows a limitation in the isolation that can be achieved on existing multi-core systems.
A parametric study of window-to-floor ratio of three window types using dynamic simulation
Venue: arXiv cs.OH 2015
Year: 2015

The windows can be responsible for unnecessary energy consumption in a building, if incorrectly designed, shadowed or oriented. Considering an annual thermal comfort assessment of a space, if windows are over-dimensioned, they can contribute to the increase of the heating needs due to heat losses, and also to the increase of cooling needs due to over-exposure to solar radiation. When under-dimensioned, the same space may benefit from reduced heat losses through the glazing surface but does not benefit from solar radiation gains. Therefore, it is important to find the optimum design that minimizes both the heating and cooling needs. This paper presents a parametric study of window type (single, double and triple glazing), orientation and opening size, located in the city of Coimbra, Portugal. An annual and a seasonal assessment were done, in order to obtain the set of optimum values around 360 degree orientation.
On the Complexity of Nondeterministically Testable Hypergraph Parameters
Venue: arXiv cs.DS 2015
Year: 2015

The paper proves the equivalence of the notions of nondeterministic and deterministic parameter testing for uniform dense hypergraphs of arbitrary order. It generalizes the result previously known only for the case of simple graphs. By a similar method we establish also the equivalence between nondeterministic and deterministic hypergraph property testing, answering the open problem in the area. We introduce a new notion of a cut norm for hypergraphs of higher order, and employ regularity techniques combined with the ultralimit method.
Modeling context and situations in pervasive computing environments
Venue: arXiv cs.CY 2015
Year: 2015

In pervasive computing environments, various entities often have to cooperate and integrate seamlessly in a \emph{situation} which can, thus, be considered as an amalgamation of the context of several entities interacting and coordinating with each other, and often performing one or more activities. However, none of the existing context models and ontologies address situation modeling. In this paper, we describe the design, structure and implementation of a generic, flexible and extensible context ontology called Rover Context Model Ontology (RoCoMO) for context and situation modeling in pervasive computing systems and environments. We highlight several limitations of the existing context models and ontologies, such as lack of provision for provenance, traceability, quality of context, multiple representation of contextual information, as well as support for security, privacy and interoperability, and explain how we are addressing these limitations in our approach. We also illustrate the applicability and utility of RoCoMO using a practical and extensive case study.
Optimal control in Markov decision processes via distributed optimization
Venue: arXiv cs.SY 2015
Year: 2015

Optimal control synthesis in stochastic systems with respect to quantitative temporal logic constraints can be formulated as linear programming problems. However, centralized synthesis algorithms do not scale to many practical systems. To tackle this issue, we propose a decomposition-based distributed synthesis algorithm. By decomposing a large-scale stochastic system modeled as a Markov decision process into a collection of interacting sub-systems, the original control problem is formulated as a linear programming problem with a sparse constraint matrix, which can be solved through distributed optimization methods. Additionally, we propose a decomposition algorithm which automatically exploits, if exists, the modular structure in a given large-scale system. We illustrate the proposed methods through robotic motion planning examples.
Universal Approximation of Markov Kernels by Shallow Stochastic Feedforward Networks
Venue: arXiv cs.LG 2015
Year: 2015

We establish upper bounds for the minimal number of hidden units for which a binary stochastic feedforward network with sigmoid activation probabilities and a single hidden layer is a universal approximator of Markov kernels. We show that each possible probabilistic assignment of the states of $n$ output units, given the states of $k\geq1$ input units, can be approximated arbitrarily well by a network with $2^{k-1}(2^{n-1}-1)$ hidden units.
Automated Test Input Generation for Android: Are We There Yet?
Venue: arXiv cs.SE 2015
Year: 2015

Mobile applications, often simply called "apps", are increasingly widespread, and we use them daily to perform a number of activities. Like all software, apps must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: code coverage, ability to detect faults, ability to work on multiple platforms, and ease of use. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android.
Initialization Strategies of Spatio-Temporal Convolutional Neural Networks
Venue: arXiv cs.CV 2015
Year: 2015

We propose a new way of incorporating temporal information present in videos into Spatial Convolutional Neural Networks (ConvNets) trained on images, that avoids training Spatio-Temporal ConvNets from scratch. We describe several initializations of weights in 3D Convolutional Layers of Spatio-Temporal ConvNet using 2D Convolutional Weights learned from ImageNet. We show that it is important to initialize 3D Convolutional Weights judiciously in order to learn temporal representations of videos. We evaluate our methods on the UCF-101 dataset and demonstrate improvement over Spatial ConvNets.
Morphological Analyzer and Generator for Russian and Ukrainian Languages
Venue: arXiv cs.CL 2015
Year: 2015

pymorphy2 is a morphological analyzer and generator for Russian and Ukrainian languages. It uses large efficiently encoded lexi- cons built from OpenCorpora and LanguageTool data. A set of linguistically motivated rules is developed to enable morphological analysis and generation of out-of-vocabulary words observed in real-world documents. For Russian pymorphy2 provides state-of-the-arts morphological analysis quality. The analyzer is implemented in Python programming language with optional C++ extensions. Emphasis is put on ease of use, documentation and extensibility. The package is distributed under a permissive open-source license, encouraging its use in both academic and commercial setting.
Wireless Power Charging Control in Multiuser Broadband Networks
Venue: arXiv cs.NI 2015
Year: 2015

Recent advances in wireless power transfer (WPT) technology provide a cost-effective solution to charge wireless devices remotely without disruption to the use. In this paper, we propose an efficient wireless charging control method for exploiting the frequency diversity in multiuser broadband wireless networks, to reduce energy outage and keep the system operating in an efficient and sustainable state. In particular, we first analyze the impact of charging control method to the operating lifetime of a WPT-enabled broadband system. Based on the analysis, we then propose a multi-criteria charging control policy that optimizes the transmit power allocation over frequency by jointly considering the channel state information (CSI) and the battery state information (BSI) of wireless devices. For practical implementation, the proposed scheme is realized by a novel limited CSI estimation mechanism embedded with partial BSI, which significantly reduces the energy cost of CSI and BSI feedback. Simulation results show that the proposed method could significantly increase the network lifetime under stringent transmit power constraint. Reciprocally, it also consumes lower transmit power to achieve near-perpetual network operation than other single-criterion based charging control methods.
One-Step Stochastic Processes Simulation Software Package
Venue: arXiv cs.SC 2015
Year: 2015

Background. It is assumed that the introduction of stochastic in mathematical model makes it more adequate. But there is virtually no methods of coordinated (depended on structure of the system) stochastic introduction into deterministic models. Authors have improved the method of stochastic models construction for the class of one-step processes and illustrated by models of population dynamics. Population dynamics was chosen for study because its deterministic models were sufficiently well explored that allows to compare the results with already known ones. Purpose. To optimize the models creation as much as possible some routine operations should be automated. In this case, the process of drawing up the model equations can be algorithmized and implemented in the computer algebra system. Furthermore, on the basis of these results a set of programs for numerical experiment can be obtained. Method. The computer algebra system Axiom is used for analytical calculations implementation. To perform the numerical experiment FORTRAN and Julia languages are used. The method Runge--Kutta method for stochastic differential equations is used as numerical method. Results. The program compex for creating stochastic one-step processes models is constructed. Its application is illustrated by the predator-prey population dynamic system. Conclusions. Computer algebra systems are very convenient for the purposes of rapid prototyping in mathematical models design and analysis.
Global Tracking Passivity--based PI Control of Bilinear Systems and its Application to the Boost and Modular Multilevel Converters
Venue: arXiv cs.SY 2015
Year: 2015

This paper deals with the problem of trajectory tracking of a class of bilinear systems with time--varying measurable disturbance. A set of matrices {A,B_i} has been identified, via a linear matrix inequality, for which it is possible to ensure global tracking of (admissible, differentiable) trajectories with a simple linear time--varying PI controller. Instrumental to establish the result is the construction of an output signal with respect to which the incremental model is passive. The result is applied to the boost and the modular multilevel converter for which experimental results are given.
Compressed sensing MRI using masked DCT and DFT measurements
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents modification of the TwIST algorithm for Compressive Sensing MRI images reconstruction. Compressive Sensing is new approach in signal processing whose basic idea is recovering signal form small set of available samples. The application of the Compressive Sensing in biomedical imaging has found great importance. It allows significant lowering of the acquisition time, and therefore, save the patient from the negative impact of the MR apparatus. TwIST is commonly used algorithm for 2D signals reconstruction using Compressive Sensing principle. It is based on the Total Variation minimization. Standard version of the TwIST uses masked 2D Discrete Fourier Transform coefficients as Compressive Sensing measurements. In this paper, different masks and different transformation domains for coefficients selection are tested. Certain percent of the measurements is used from the mask, as well as small number of coefficients outside the mask. Comparative analysis using 2D DFT and 2D DCT coefficients, with different mask shapes is performed. The theory is proved with experimental results.
Manual Character Transmission by Presenting Trajectories of 7mm-high Letters in One Second
Venue: arXiv cs.HC 2015
Year: 2015

In this paper, we report a method of intuitively transmitting symbolic information to untrained users via only their hands without using any visual or auditory cues. Our simple concept is presenting three-dimensional letter trajectories to the user's hand via a stylus which is mechanically manipulated. By this simple method, in our experiments, participants were able to read 14 mm-high lower-case letters displayed at a rate of one letter per second with an accuracy rate of 71.9% in their first trials, which was improved to 91.3% after a five-minute training period. These results showed small individual differences among participants (standard deviation of 12.7% in the first trials and 6.7% after training). We also found that this accuracy was still retained to a high level (85.1% with SD of 8.2%) even when the letters were reduced to a height of 7 mm. Thus, we revealed that sighted adults potentially possess the ability to read small letters accurately at normal writing speed using their hands.
Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter
Venue: arXiv cs.IR 2015
Year: 2015

Social spam produces a great amount of noise on social media services such as Twitter, which reduces the signal-to-noise ratio that both end users and data mining applications observe. Existing techniques on social spam detection have focused primarily on the identification of spam accounts by using extensive historical and network-based data. In this paper we focus on the detection of spam tweets, which optimises the amount of data that needs to be gathered by relying only on tweet-inherent features. This enables the application of the spam detection system to a large set of tweets in a timely fashion, potentially applicable in a real-time or near real-time setting. Using two large hand-labelled datasets of tweets containing spam, we study the suitability of five classification algorithms and four different feature sets to the social spam detection task. Our results show that, by using the limited set of features readily available in a tweet, we can achieve encouraging results which are competitive when compared against existing spammer detection systems that make use of additional, costly user features. Our study is the first that attempts at generalising conclusions on the optimal classifiers and sets of features for social spam detection over different datasets.
Compressed sensing with combinatorial designs: theory and simulations
Venue: arXiv cs.IT 2015
Year: 2015

In 'An asymptotic result on compressed sensing matrices', a new construction for compressed sensing matrices using combinatorial design theory was introduced. In this paper, we use deterministic and probabilistic methods to analyse the performance of matrices obtained from this construction. We provide new theoretical results and detailed simulations. These simulations indicate that the construction is competitive with Gaussian random matrices, and that recovery is tolerant to noise. A new recovery algorithm tailored to the construction is also given.
RANSAC based three points algorithm for ellipse fitting of spherical object's projection
Venue: arXiv cs.CV 2015
Year: 2015

As the spherical object can be seen everywhere, we should extract the ellipse image accurately and fit it by implicit algebraic curve in order to finish the 3D reconstruction. In this paper, we propose a new ellipse fitting algorithm which only needs three points to fit the projection of spherical object and is different from the traditional algorithms that need at least five point. The fitting procedure is just similar as the estimation of Fundamental Matrix estimation by seven points, and the RANSAC algorithm has also been used to exclude the interference of noise and scattered points.
A Survey of Classification Techniques in the Area of Big Data
Venue: arXiv cs.LG 2015
Year: 2015

Big Data concern large-volume, growing data sets that are complex and have multiple autonomous sources. Earlier technologies were not able to handle storage and processing of huge data thus Big Data concept comes into existence. This is a tedious job for users unstructured data. So, there should be some mechanism which classify unstructured data into organized form which helps user to easily access required data. Classification techniques over big transactional database provide required data to the users from large datasets more simple way. There are two main classification techniques, supervised and unsupervised. In this paper we focused on to study of different supervised classification techniques. Further this paper shows a advantages and limitations.
An Investigation into the Correlation between a Presidents Approval Rating and the Performance of His Party in the Midterm Elections
Venue: arXiv cs.SI 2015
Year: 2015

Over the years, American politics have become increasingly polarized. In the current political landscape, a president cannot easily collaborate with the opposite party and pass legislature. Ideologies between parties have drifted apart to the point that one party generally stonewalls any legislature proposed by the other party. Because of this political landscape, it is paramount for a president to have a majority of his party in Congress. Political parties invest a great deal of time and effort into making sure that first their Presidential candidate wins and is popular, and then their congressional candidates win seats in Congress. In this study, the effect of the former on the latter was investigated - how the approval rating of the president influences the number of seats won or lost in Congress during the midterm elections. The data used was collected from Gallup. An analysis of the data yielded the statistically significant linear model y = -107.423+1.594x, where x is the approval rating of the president and y is the number of Congressional seats won or lost by the party of the president. Further analysis yielded a 20 percent more statistically useful model for approval ratings greater than 50 percent: y = -275.461+4.37551x. As of the eve of the 2014 Midterms, President Barrack Obama had an approval rating of 44 percent. Using the originally derived linear model, it can be said with 95 percent confidence that the Democratic Party will lose between 27 and 48 seats in Congress, rounded to the nearest whole seat. This prediction has since been proven correct.
Universal Psychometrics Tasks: difficulty, composition and decomposition
Venue: arXiv cs.AI 2015
Year: 2015

This note revisits the concepts of task and difficulty. The notion of cognitive task and its use for the evaluation of intelligent systems is still replete with issues. The view of tasks as MDP in the context of reinforcement learning has been especially useful for the formalisation of learning tasks. However, this alternate interaction does not accommodate well for some other tasks that are usual in artificial intelligence and, most especially, in animal and human evaluation. In particular, we want to have a more general account of episodes, rewards and responses, and, most especially, the computational complexity of the algorithm behind an agent solving a task. This is crucial for the determination of the difficulty of a task as the (logarithm of the) number of computational steps required to acquire an acceptable policy for the task, which includes the exploration of policies and their verification. We introduce a notion of asynchronous-time stochastic tasks. Based on this interpretation, we can see what task difficulty is, what instance difficulty is (relative to a task) and also what task compositions and decompositions are.
An Evolutionary Algorithm for Error-Driven Learning via Reinforcement
Venue: arXiv cs.AI 2015
Year: 2015

Although different learning systems are coordinated to afford complex behavior, little is known about how this occurs. This article describes a theoretical framework that specifies how complex behaviors that might be thought to require error-driven learning might instead be acquired through simple reinforcement. This framework includes specific assumptions about the mechanisms that contribute to the evolution of (artificial) neural networks to generate topologies that allow the networks to learn large-scale complex problems using only information about the quality of their performance. The practical and theoretical implications of the framework are discussed, as are possible biological analogs of the approach.
Observer design for position and velocity bias estimation from a single direction output
Venue: arXiv cs.SY 2015
Year: 2015

This paper addresses the problem of estimating the position of an object moving in $R^n$ from direction and velocity measurements. After addressing observability issues associated with this problem, a nonlinear observer is designed so as to encompass the case where the measured velocity is corrupted by a constant bias. Global exponential convergence of the estimation error is proved under a condition of persistent excitation upon the direction measurements. Simulation results illustrate the performance of the observer.