Reviewerly/exHarmony huggingface dataset:
An Analysis of Digraphs and Period Properties of the Logistic Map on <i>Z</i>(<i>p</i><sup><i>n</i></sup>)

This paper corrects some mistakes about three theorems, two figures and some expressions on “finite field” in a literature, and then it analyzes the digraphs and period properties of the logistic map on residue class rings [Formula: see text] and [Formula: see text]. Some theorems and conjectures are proved or given. To validate these properties, we make some numeral experiments and draw the relative figures and tables. So the theoretical analysis and numeral experiments show that there exists periods in the logistic map on [Formula: see text] and on [Formula: see text] and it can be applied in the pseudo-random generators, cryptography, spread spectrum communications and so on.
Lifetime-aware information aggregation under uncertainty for advanced driver assistance systems

Advanced Driver Assistance Systems require a huge amount of sensor information. Information sensed by the vehicle’s on-board sensors is often not sufficient due to physical limitations, thus, vehicles exchange information with each other to enhance their knowledge. However, both local and remote measurements are only a noisy view on the environment. If a vehicle receives faulty information about its environment, its decision-making may perform wrong actions. In this paper, we focus on the accuracy of information w. r. t. the correctness of the provided information. To increase the accuracy, we use a data representation that depicts every measurement as a probability vector. This representation contains all available information and tracks accuracy while aggregating and fusing information. To incorporate measurements sensed at different times and locations, we model the environmental changes. As information expires after a certain time, we combine a Markov model with an aging model to keep the aggregated information stable even after message expiration. We achieve this by reducing the impact of information with increasing age. Our extensive evaluation shows that our novel approach outperforms state-of-the-art approaches significantly.
Review on Image Representation Compression and Retrieval Approaches

Image representation and compression is one of the important fields of computer vision that contribute to the reduction of size of an image and other types of application areas such as image restoration, retrieval, etc. Image representation is important with respect to storage of image information, and it further extends to the compression, which may be lossy or lossless. Image compression can be applied to various applications which mainly include medical imaging, traffic monitoring, military, multimedia transmission, smart cell devices, and almost in all the domains that require less transmission and storage cost, specifically image retrieval processing. This chapter presents the various image representation compression and retrieval approaches. The retrieval approaches on personal computer and smart cell devices are discussed. Finally, the key issues are identified for image representation compression and retrieval on the basis of performance evaluation parameters like encoding time, decoding time, compression ratio, precision, recall, and elapsed time.
Probabilistic collision estimation for tracked vehicles based on corner point self-activation approach

Detection and tracking of Manned-Unmanned Tracked Vehicles (MUTV) have a great demand in defense surveillance applications. This research paper mainly focuses on developing a Corner Point Self-activating Threshold Tuning (CSTT) feature extraction method to locate the position of MUTV. The Collision avoidance system has been recognized as a smart secured system for handling critical scenarios. The Exponential Probability based Collision Avoidance (EPCA) technique has been proposed for estimating the Time of Collision (TOC) between multiple vehicles. The EPCA is structured using a continuous probability distribution to compute the inter-arrival time between the two consecutive arrivals of MUTV. The proposed CSTT feature extraction method was able to detect the MUTV for YouTube dataset with the detection rate of 13.53 s when compared to the existing Harris feature extraction technique of 17.019 s. The system measures the relative speed and distance between the MUTV vehicles towards the critical zone. Many of the existing collision avoidance systems track vehicles in the unidirectional path. Our proposed EPCA system track vehicles from the tri-directional route. Experimental results show that the proposed system can avoid the interaction between the objects/vehicles before the collision occurs.
Hardness of RNA folding problem with four symbols

An RNA sequence is a string composed of four types of nucleotides, A,C,G, and U. The goal of the RNA folding problem is to find a maximum cardinality set of crossing-free pairs of the form {A,U} or {C,G} in a given RNA sequence. The problem is central in bioinformatics and has received much attention over the years. Abboud, Backurs, and Williams (FOCS 2015) demonstrated a conditional lower bound for a generalized version of the RNA folding problem based on a conjectured hardness of the k-clique problem. Their lower bound requires the RNA sequence to have at least 36 types of symbols, making the result not applicable to the RNA folding problem in real life (i.e., alphabet size 4). In this paper, we present an improved lower bound that works for the alphabet size 4 case. We also investigate the Dyck edit distance problem, which is a string problem closely related to RNA folding. We demonstrate a reduction from RNA folding to Dyck edit distance with alphabet size 10. This leads to a much simpler proof of the conditional lower bound for Dyck edit distance problem given by Abboud, Backurs, and Williams (FOCS 2015), and lowers the alphabet size requirement.
Technology Impact on K-12 Education

Technology can be a leveraging tool that allows learning experiences to be reimagined to allow the creation of meaningful connections between acquiring a base of knowledge and making meaningful connections to peers and mentors in our daily lives and global workforce. Therefore, it is important to consider and explore methods for integrating technological tools and resources into school environments so that technology in the classroom can continue to expand from information gathering to include product creation and collaboration for all students. Therefore, this chapter will focus on the following: 1) learning and technology integration frameworks, 2) technology-enabled learning in action, and 3) overcoming obstacles for technology integration.
Robust and real-time stroke order evaluation using incremental stroke context for learners to write Kanji characters correctly

Writing Kanji characters of Chinese origin in the correct stroke order and direction is still one of the important subjects in Japanese elementary education. So far, the stroke order evaluation was made by stroke-to-stroke matching without stroke context so that it was unrobust to Kanji characters having multiple similar strokes. Here, we employ shape context features around each feature point in not only conventional fan-shaped bins but also in square bins with applying a Gaussian function. We also propose simple incremental context and augmented context from future strokes. Our approach can judge whether the stroke order and direction are correct or not every time a new stroke is written on a tablet by matching a partially written Kanji pattern with the reference pattern written to the same number of strokes. Evaluation shows that the best-tuned method with square bins and the Gaussian function records the highest performance and correctly evaluates stroke order by 98.5% with the maximum time of 0.12 sec. /character for Kanji patterns after all strokes are written using an average desktop PC. The method is also shown to possess high reliability to detect wrong stroke order and direction incrementally every time after each stroke is written.
Situation awareness in the speech therapy domain: A systematic mapping study

Situation Awareness (SA) involves the correct interpretation of a situation, allowing a system to respond to the observed environment and providing support for decision making in many systems domains. Speech therapy is an example of domain where situation awareness can provide benefits, since practitioners should monitor the patient in order to perform therapeutic actions. However, there are a few studies in the area that address reasoning about a situation to improve these tasks. For this reason, this systematic mapping study aims to identify and compare different proposals in the speech therapy domain in order to verify which aspects related to obtaining and maintaining SA are supported. Our analyzes provide useful insights on whats aspects of SA are best integrated in the speech domain - such as knowledge bases and adaptation - and other aspects that remain to be improved - like the action support and projection abilities. Also, this paper includes statistics, methodologies used by different authors and other issues involving research processes. As main contributions, this work presents an overview of the SA integration in the speech therapy domain, discussing challenges in the area and providing directions for further research.
Multi-task learning for object keypoints detection and classification

Object keypoints detection and classification are both central research topics in computer vision. Due to their wide range potential applications in the real world, substantial efforts have been taken to advance their performance. However, these two related tasks are mainly treated separately in previous works. We argue that keypoints detection and classification can be complementary tasks and beneficial to each other. Knowing the category of a object is able to reduce the searching space of keypoints detection models and facilitate more precise localization. On the other hand, having the knowledge of object keypoints can make classification models pay more attention on areas that are more associated with the object, which will inevitably promote classification accuracy. Embracing this observation, we propose to model keypoints detection and classification in a multi-task learning framework. Specifically, a multi-task deep network is designed and trained to conduct both tasks, where we devise the model structure delicately to carry out sufficient training of both tasks. Extensive experiments are set up on the AIFASHION DATASET and Human3.6M DATASET to validate our proposal, we show that our algorithm outperforms separate models trained individually on each task.
Characterizing partitioned assemblies and realizability toposes

We give simple characterizations of the category PAsm(A) of partitioned assemblies, and of the realizability topos RT(A) over a partial combinatory algebra A. This answers the question for an ‘extensional characterization’ of realizability toposes.
Support structures to facilitate the dissemination and implementation of a national standard for research information – the German case of the Research Core Dataset

The German science and higher education system is characterized by federalism, multi-level governance and interwoven regulatory competences of different levels of government. Establishing binding standards and harmonized policies for German higher education institutions and non-university research institutions is a complex task that requires concerted action and co-operation between the federal and state governments. In 2013, the German Council of Science and Humanities recognized the need for research information (RI) standards in order to reduce the efforts in and to create an incentive for optimized RI management as well as to enhance the quality and informative value of RI. For this reason, the science council issued recommendations that called for the specification of the so-called research core dataset (RCD) – a set of standard definitions and concepts as well as data formats for RI – and recommended its (voluntary) implementation in the German science system in 2016. This paper is intended as a descriptive case report with the aim to provide insights from a national standardization project and to share experiences made during this project. Particular emphasis is on the support structures currently in place to facilitate the dissemination and implementation of the RCD in the federal and multi-level governed German science system.
An empirical evaluation of the approximation of subjective logic operators using Monte Carlo simulations

In this paper we analyze the use of subjective logic as a framework for performing approximate transformations over probability distribution functions. As for any approximation, we evaluate subjective logic in terms of computational efficiency and bias. However, while the computational cost may be easily estimated, the bias of subjective logic operators have not yet been investigated. In order to evaluate this bias, we propose an experimental protocol that exploits Monte Carlo simulations and their properties to assess the distance between the result produced by subjective logic operators and the true result of the corresponding transformation over probability distributions. This protocol allows a modeler to get an estimate of the degree of approximation she must be ready to accept as a trade-off for the computational efficiency and the interpretability of the subjective logic framework. Concretely, we apply our method to the relevant case study of the subjective logic operators for binomial multiplication and fusion, and we study empirically their degree of approximation.
New method of enhancement using wavelet transforms applied to SODISM telescope

PICARD is a space-based observatory hosting the Solar Diameter Imager and Surface Mapper (SODISM) telescope, which has continuously observed the Sun from July 2010 and up to March 2014. In order to study the fine structure of the solar surface, it is helpful to apply techniques that enhance the images so as to improve the visibility of solar features such as sunspots or faculae. The objective of this work is to develop an innovative technique to enhance the quality of the SODISM images in the five wavelengths monitored by the telescope at 215.0 nm, 393.37 nm, 535.7 nm, 607.1 nm and 782.2 nm. An enhancement technique using interpolation of the high-frequency sub-bands obtained by Discrete Wavelet Transforms (DWT) and the input image is applied to the SODISM images. The input images are decomposed by the DWT as well as Stationary Wavelet Transform (SWT) into four separate sub-bands in horizontal and vertical directions namely, low-low (LL), low–high (LH), high-low (HL) and high–high (HH) frequencies. The DWT high frequency sub-bands are interpolated by a factor 2. The estimated high frequency sub-bands (edges) are enhanced by introducing an intermediate stage using a Stationary Wavelet Transform (SWT), and then all these sub-bands and input image are combined and interpolated with half of the interpolation factor α/2, used to interpolate the high-frequency sub-bands, in order to reach the required size for IDWT processing. Quantitative and visual results show the superiority of the proposed technique over a bicubic image resolution enhancement technique. In addition, filling factors for sunspots are calculated from SODISM images and results are presented in this work.
Team-optimal online estimation of dynamic parameters over distributed tree networks

We study online parameter estimation over a distributed network, where the nodes in the network collaboratively estimate a dynamically evolving parameter using noisy observations. The nodes in the network are equipped with processing and communication capabilities and can share their observations or local estimates with their neighbors. The conventional distributed estimation algorithms cannot perform the team-optimal online estimation in the finite horizon global mean-square error sense (MSE). To this end, we present a team-optimal distributed estimation algorithm through the disclosure of local estimates for tracking an underlying dynamic parameter. We first show that the optimal estimation can be achieved through the diffusion of all the time stamped observations for any arbitrary network and prove that the team optimality through disclosure of local estimates is only possible for certain network topologies such as tree networks. We then derive an iterative algorithm to recursively calculate the combination weights of the disclosed information and construct the team-optimal estimate for each time step. Through series of simulations, we demonstrate the superior performance of the proposed algorithm with respect to the state-of-the-art diffusion distributed estimation algorithms regarding the convergence rate and the finite horizon MSE levels. We also show that while conventional distributed estimation schemes cannot track highly dynamic parameters, through optimal weight and estimate construction, the proposed algorithm presents a stable MSE performance.
A distance between populations for n-points crossover in genetic algorithms

The theoretical study of Genetic Algorithms and the dynamics induced by their genetic operators is a research field with a long history and many different approaches. In this paper we complete a recently presented approach to model one-point crossover using pretopologies (or Čechtopologies) in two ways. First, we extend it to the case of n-points crossover. We extend the definition of crossover distance between populations to work for n-points crossover, proving that computing it can be performed in polynomial time for any fixed number of crossover points. Secondly, we experimentally study how the distance distribution changes when the number of crossover points increases. In particular, the average distance between a population and the optimum decreases with the increase in the number of crossover points, showing that increasing the latter can reduce the number of crossover operations needed to evolve an optimal solution.
On the computational power of the light: A plan for breaking data encryption standard

The successful of the light-based solutions for some NP-complete problems, such as Hamiltonian path problem, have demonstrated the power of light-based computing. The capabilities of the light-based computing such as massive parallelism of light, allow it to solve hard computational problems in polynomial time, while the conventional computers require exponential time. In this study we show how the light-based solution can be applied to break the Data Encryption Standard (DES). Under the assumption of having one given (plain-text, cipher-text) pair, our method recovers the DES key in a efficient time. We describe how to implement XOR gates, circular shifts, P-boxes, and S-boxes of DES in a light-based approach. The proposed solution encrypts the given plain-text with all possible keys and afterwards pair (Key; cipher-text) is extracted from them. We demonstrate that under chosen plain-text attack, it is possible to recover the DES key by providing all DES components in a reasonable time.
Performance and usability tradeoff in a cluster display wall

Cluster-based display walls provide cost-effective and scalable display infrastructures with high resolution and large display area, making them suitable for a wide range of high-resolution applications. As a consequence, a wide offer of new cluster display-wall platforms together with their software frameworks have been proposed. Their performance and the satisfaction of their users have aroused the interest of some researchers. This work is focused on the Liquid Galaxy cluster display wall originally built to run Google Earth to create an immersive experience for the users. In this paper, the Liquid Galaxy is benchmarked running Google Earth, as a representative interactive application with high performance requirements, in different configurations and environments, to test the satisfaction, effectiveness and efficiency. Thus, we wish to know how users react to the system performance. In order to do so, we use a performance metric defined in previous research to relate the performance of the system with the user’s perception. Taking into account the trend of this metric in the experimentation, we model the behavior of the system in a way that the performance for any given visualization cluster running Google Earth could be predicted by using a reference system.
Accelerated partial decoding in wavelet trees

A Wavelet Tree is a compact data structure which is used in order to perform various well defined operations directly on the compressed form of a file. As random access is one of these operations, the underlying file is not needed anymore, and is often discarded because it can be restored, when necessary, by repeated accesses. This paper concentrates on cases in which partial decoding of a contiguous portion of the file, or even its full decoding, is still needed. We show how to accelerate the decoding relative to repeatedly performing random accesses on the consecutive indices. Experiments on partial and full decoding support the effectiveness of our approach, and present an improvement of about 50% of the run-time for full decoding, and about 30% or more for partial decoding of large enough ranges.
Fast Edge Preserving 2D Smoothing Filter Using Indicator Function

Edge-preserving smoothing filter smoothes the textures while it preserves the information of sharp edges. In image processing, this filter is used as a fundamental process of many applications. In this paper, we propose a new approach for edge-preserving smoothing filter. Our method uses 2D filter to smooth images and we apply indicator function to restrict the range of filtered pixels for edge-preserving. To define the indicator function, we recalculate the distance between each pixel by using edge information. The nearby pixels in the new domain are used for smoothing. Since our method constrains the pixels used for filtering, its implementation is quite fast. We demonstrate the usefulness of our new edge-preserving smoothing method for some applications.
Metrics Analysis in Object Oriented and Aspect Oriented Programming

Background: Object oriented programming (OOP) is a programming paradigm that has been used for several years by the software engineering community. The best practice of OOP was gathered and they are known as Design Patterns. They provide guidelines for developing flexible software applications. Recent studies claim that some patterns have limitations and their implementations could be improved. Researchers claim that Aspect Oriented Programming (AOP) is a paradigm that provides features to overcome the limitations of OOP and patterns. However, even with the good results achieved using AOP, it is possible to cause side effects in code. We revised all patents relating to aspect oriented programming of applicability. This paper tries to implement a subset of the patterns with AOP and identify merits and demerits in comparison with the traditional OOP implementations. In another term if a method which is called several time in different class so we use to write code on both classes (if we don’t want to overload it) and manually write code of function call after those methods which we want to execute. Methods: Aspect-Oriented Programming entails breaking down program logic into distinct parts called so-called concerns. The functions that span multiple points of an application are called cross-cutting concerns and these cross-cutting concerns are conceptually separate from the application's business logic. There are various common good examples of aspects like logging, auditing, declarative transactions, security, and caching, etc. Results: after the implement AOP concept with OOPs, the response time is reduce and throughput rate is increases. And the development of program is become more easy and reliable. Conclusion: so those method which is called several time in a program execution these type of method must be written in AOP so it is triggered automatically when the pointcode if occurred.