Reviewerly/exHarmony huggingface dataset:
Node-Asynchronous Implementation of Filter Banks on Graphs

Filter banks on graphs are shown to be useful for analyzing data defined over networks, as they decompose a graph signal into components with low variation and high variation. Based on recent node-asynchronous implementation of graph filters, this study proposes an asynchronous implementation of filter banks on graphs. In the proposed algorithm nodes follow a randomized collect-compute-broadcast scheme: if a node is in the passive stage it collects the data sent by its incoming neighbors and stores only the most recent data. When a node gets into the active stage at a random time instance, it does the necessary filtering computations locally, and broadcasts a state vector to its outgoing neighbors. When the underlying filters (of the filter bank) are rational functions with the same denominator, the proposed filter bank implementation does not require additional communication between the neighboring nodes. However, computations done by a node increase linearly with the number of filters in the bank. It is also proven that the proposed asynchronous implementation converges to the desired output of the filter bank in the mean-squared sense under mild stability conditions. The convergence is verified also with numerical experiments.
Non-Mobile Software Modernization in Accordance With the Principles of Model-Driven Engineering

Smartphones are at the core of new paradigms such as cloud computing, pervasive computing, and internet of things. Frequently, the development of mobile software requires adaptation of valuable and tested non-mobile software. In this context, most challenges are related to the diversity of platforms on the smartphones market and to the need of systematic and reusable processes with a high degree of automation that reduce time, cost, and risks. To face these challenges, this chapter presents an analysis of non-mobile software modernization through an integration of MDE (model-driven engineering) with cross-platform development. Two approaches are analyzed. One of them is a lightweight process that combines MDA (model-driven architecture) with traditional static and dynamic analysis techniques of reverse engineering. The other approach is based on ADM (architecture-driven modernization) standards. A scenario for the migration of C/C++ or Java software through the multiplatform Haxe language is described.
Developing an Ontology for Emergence Using Formal Concept Analysis

The phenomenonSingh, Shweta of emergence has been studied in a variety of fields. Each of these fields has their own understanding and interpretation of what emergence means.Kokar, Mieczyslaw M. Thus far, there is no agreed-upon definition of emergence.Suresh, Durga In fact, the debate about what emergence is and its validity in scientific understanding is still continuing. Having a clear understanding concept of emergence is critical for analysis of such behaviors. In this work-in-progress paper, we discuss our approach for developing an ontology of emergence which can be viewed as a descriptive theory of emergence. The ontology will create a foundation which will support the understanding of the meaning and implications of emergence. A side benefit of this ontology will be the taxonomy of both the emergent behaviors and the emergence research. Thus researchers in this field will be able to identify which aspects of emergence are covered by the various research works and which aspects are not covered and thus open for investigation. Conceptually, the ontology will be a meta-theory of emergence. By raising the level of abstraction, the meta-theory of emergence will allow to express various theories of emergence within one framework, thus allowing multiple theories that may contradict each other to live in the same place. This will allow one to query the ontology about both the commonalities and differences between the theories. We discuss the development of such ontology and evaluation approach in this paper.
On Some Mathematically Interpreted Linguistic Issues

Two linguistic issues are interpreted in mathematical terms. In the first section different negation behaviour of nouns and verbs is explained using the mathematical theory of sets. In addition, a hypothesis is outlined, assuming the capability of nouns to define sets and thereby enabling a tentative definition of some lexical categories. In the following sections the mathematical notions of operation, operator and operand are explained and their analogy with the traditional syntactic notions like immediate constituent, modifier, argument, governor, dependent etc. is exhibited. The central thesis of the article asserts that most (perhaps all) syntactic composites consist of two unequal components to which the asymmetric roles of operator and operand can be assigned. The possibilities to assign a role to a lexical category (verb, noun, adjective, prepositionâ€¦) are predetermined and strongly depend on the category. The operator-operand based grammar differs from the established phrase structure grammar based on head-defined phrases. The incompatibilities are highlighted and adapted alternative approaches to sentence parsing are proposed, able to map the operator-operand relations.
Fully Bayesian Aggregation

Can a group be an orthodox rational agent? This requires the group's aggregate preferences to follow expected utility (static rationality) and to evolve by Bayesian updating (dynamic rationality). Group rationality is possible, but the only preference aggregation rules which achieve it (and are minimally Paretian and continuous) are the linear-geometric rules, which combine individual values linearly and combine individual beliefs geometrically. Linear-geometric preference aggregation contrasts with classic linear-linear preference aggregation, which combines both values and beliefs linearly, but achieves only static rationality. Our characterisation of linear-geometric preference aggregation has two corollaries: a characterisation of linear aggregation of values (Harsanyi's Theorem) and a characterisation of geometric aggregation of beliefs.
Personal identification system based on multi biometric depending on cuckoo search algorithm

Abstract In modern devices, many personal identification systems are used using various biometrics to confirm the identity of an individual and identify him for several purposes. Some of these essential biometrics are used in this paper to help identify a person while attaining social distance because of the widespread epidemics. The features of the face, eyes, nose, and finally, the features of the mouth are used in this article. The work begins by detecting the parts of multibiometric from the input images using Viola-Jones face detection algorithm with a modification to it then segment them. After that, various initial treatment processes begin which helps clarify them to facilitate subsequent operations. Also, a Histogram of oriented gradient method(HOG) is used to extract the significant features of those image segments. The extracted features from these segments are entered into the developed cuckoo search algorithm(DCSA), the best image segment within the used dataset is searched for similar in terms of characteristics to the entered image segment. The work has also been developed so that the system is executed on two cores using parallel processing technology to utilize the processor as much as possible and reduce the time it takes to implement the system and identify the person concerned. A high identification rate has been reached, reaching 99.25%, and overall speed up 1.40323 sec relative to serial execution.
Research on Text Multi-Feature Fusion Algorithm Based on AM-CNN

Abstract How to know the changes of students’ emotions in real time in online education has always been one of the important issues concerned by education departments and teachers. Making use of the special functions of global, part of speech and positional attention mechanisms in text processing, a deep learning network model based on the combination of multiple attention mechanisms and convolution neural networks is proposed. Firstly, the blending characteristics between the types of attention mechanism and CNN are analyzed by using the standard ChnSentiCorp_htl_all data set to clarify the effectiveness of the combination of the three attention mechanisms and CNN. Then the model is applied to the analysis of the evaluation text of the course “big data Technology principles and applications” on the MOOC of Chinese universities, and it is verified that the evaluation indexes of this model are better than the existing conventional models.
OnCall Operator Scheduling for Satellites with Grover’s Algorithm

The application of quantum algorithms on some problems in NP promises a significant reduction of time complexity. This work uses Grover’s Algorithm, designed to search an unstructured database with quadratic speedup, to find valid a solution for an instance of the on-call operator scheduling problem at the German Space Operation Center. We explore new approaches in encoding the problem and construct the Grover oracle automatically from the given constraints and independent of the problem size. Our solution is not designed for currently available quantum chips but aims to scale with their growth in the next years.
Differentially Private Anomaly Detection for Interconnected Systems

Detecting anomalies in large-scale distributed systems, such as cyber-attacks launched against intelligent transportation systems and other critical infrastructures, or epidemics spreading in human populations, requires the collection and processing of privacy-sensitive data from individuals, such as location traces or medical records. Differential privacy is a powerful theoretical tool that by applying so-called randomized mechanisms to individual data, allows to do meaningful computations at the population level whose results are insensitive, in a probabilistic sense, to the data of any given individual. So far, differential privacy has been applied to several control problems, such as distributed optimization and estimation, filtering and anomaly detection. Still, several issues are open, regarding the balance between the accuracy of the computation results and the guaranteed privacy level for the individuals, as well as the dependence of this balance on the type of randomized mechanism used and on where, in the data acquisition and processing pipeline, the noise is applied. In this chapter, we explore the possibility of using differentially private mechanisms to develop fault-detection algorithms with privacy guarantees and discuss the resulting trade-offs between detection performance and privacy level.
Detection of Heart Abnormalities Based On ECG Signal Characteristics using Multilayer Perceptron with Firefly Algorithm-Simulated Annealing

Heart disease is one of the causes of death worldwide. Therefore, detecting heart disease is very important to reduce the increased mortality rate. One of the methods used to detect the abnormalities or disorders of the heart is to use computer assistance to determine the characteristics of an electrocardiogram. Electrocardiogram (ECG) is a test that detects and records the activity of the heart through small metal electrodes attached to the skin of one's chest, arms and legs. This test shows how fast the heart beats and whether the rhythm is stable or not. The purpose of this thesis is to apply a multi-layer perceptron model with firefly algorithm and simulated annealing in detecting cardiac abnormalities based on the ECG signal characteristics. The initial step of this research is image processing. The stages of ECG image processing are grayscale, thresholding, edge detection, segmentation and normalization processes. The results of this image processing are used as input matrices in the perceptron multilayer network training using firefly algorithm and simulated annealing. In the training process, we will get optimal weights and biases for validation tests on test data. The training data in this thesis uses 20 ECG images and in the validation test process uses 10 ECG images. The validation results in the validation test show that the accuracy in detecting heart abnormalities based on the characteristics of ECG signals using multi- layer perceptron with firefly algorithm and simulated annealing is 100%.
A Deep Learning Based Method for Structuring the Chinese Pathological Reports of Lung Specimen

As a kind of electronic reports in text form, the Chinese pathology report of lung specimen contains a large amount of information that is important for clinicians to further analysis and mining. However, various expressions and no fixed format increases the difficulty of extracting and standardizing this information. In this paper, we focus on the extraction of lung lesion locations and the corresponding diagnosis from these reports. And to overcome the difficulties, a structured processing method based on deep learning and the idea of part-of-speech (POS) tagging was proposed. Firstly, the data of lung pathology specimen reports are preprocessed to normalize the medical terms. Secondly, the bidirectional Long Short-Term Memory (Bi-LSTM) neural network is adopted to extract the information of lesion locations and pathological diagnosis from each report. Finally, the obtained information is screened by an information filter method to generate the final structured results. Experimental results on the self-constructed datasets indicated that the proposed method can be beneficial for structuring pathology reports of lung specimen and obtained state-of-the-art results.
Design and Inheritance of Iron Painting Intangible Cultural Heritage Based on Modern Information Technology

Wuhu iron painting is an important part of my country's intangible cultural heritage. It is the highly condensed creation of creative artists over the past 300 years, revealing the continuous cultural heritage of our country. Under the multiple influence of modern information technology, iron painting design and inheritance presents a new development trend: iron painting information collection methods represented by three-dimensional laser scanning technology and panoramic technology can reconstruct iron painting works and store outstanding intangible cultural heritage for a long time; The iron painting information database with user data association function pushes non-heritage information that meets the interests and preferences of various types of users; designers use virtual reality technology as a platform to conceive iron painting shapes to reduce the creation errors of iron painting works. The combination of information technology and iron painting design has facilitated the large-scale dissemination of iron paintings and intangible cultural heritage, and has deeply consolidated the mass foundation of iron paintings.
Review on Hands Gestures Using American Sign Languages

The Disability of speech impairment affects the ability to speech and communicates with others, and such disability makes person to use other medium to communicate such as sign language. And it is a challenge to make communication between people who understand sign language and person who doesn’t understand sign language. Sign language is no yet so popular method among the hearing people. To overcome this sign language issue, a sign language detection technology is used by using image classification and machine learning. Sign language recognition points to covert information in the form of sign language to user who have a little knowledge regarding sign language in the form of text or voice and this will be a huge support for communication between deaf-mute and normal people.
An automated cognitive analysis model for online asynchronous discussion

Abstract Evaluation of students’ cognitive abilities needs to be done as part of evaluating student learning outcomes. This evaluation must not only be done in conventional learning, but this must also be done in online learning. Evaluation of students’ cognitive abilities can be done through cognitive evaluation of messages posted by students in online asynchronous discussion. There are several studies that have addressed the problem of evaluating students ‘cognitive abilities in on-line learning, but the process of identifying students’ cognitive abilities in these studies has not been fully automated. There are parts of the identification process still carried out by humans. This study proposes a model that can produce classification automation of cognitive levels of messages posted by students in online asynchronous discussion. The messages used in this study are messages written in Indonesian. The proposed model consists of four main processes, namely the process of creating corpus, extraction and selection of features, classifier and training development, and testing. This model can help lecturers to identify cognitive levels of messages. The proposed model is expected to improve the performance of evaluation systems of e-learning learning.
Particle Swarm Optimization Based on Random Walk

Particle swarm optimization (PSO) undergoes several modifications since it was first proposed by J. Kennedy and R. Eberhart in 1995. There are many variants of PSO till date. These variants often confuse researchers about the improvement, applicability and novelty of those variants. As a result, in 2007 Daniel Bratton and James Kennedy defined a standard for particle swarm optimization which is the extension of the original algorithm with latest improvement. This latest improvement of Particle Swarm Optimization often known as SPSO when tested against baseline benchmark functions it fails to achieve required performance in non-separable and asymmetrical functions. Here, a new algorithm has been proposed to modify the canonical PSO algorithm by introducing the concept of random walk and particle reinitialization. Inherent nature of random walk balances the “exploration” and “exploitation” properties in search area and successfully handles the problem of trapping in local and global optima, which most of the algorithm fails to handle. This newly proposed variant clearly outperforms other well-known algorithms which belong to the category where velocity term is eliminated. Also, it achieves better performance when tested with same benchmark function where SPSO fails to perform well.
Digital Modulation Classification Based On Chicken Swarm Optimization and J48 Algorithm

Abstract Automatic Modulation Recognition (AMR) has a significant impact in the military as well as civil applications. Recognizing the modulation of the received signal has been considered as an intermediate step between the detection and demodulation of the signal. Which is why, in many military and communication systems, the AMR is considered as part of the system. Presently, due to increasing digital modulations in military and civil applications. Digital modulation recognition is especially important. Usually for the AMR a small number of the received signal features are obtained and utilized. The choice of the suitable feature plays an important part in the increase of AMR efficiency. The presented paper indicates hybrid intelligent system for the recognitions of digital signal types, consisting of 3 major modules: classifier module, feature extraction module and J48 Classifier that was used for the first time in our research in the field of classification of modulated signals and optimization module by Chicken Swarm Optimization (CSO). To get better results of the system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong relevant features Chicken Swarm Optimization. The results of simulation confirm the high accuracy of recognition that is related to the suggested system even at low SNR.
Secure Voting System using Blockchain Technology

India is the world's biggest vote based system with a populace of more than 10 million; Casting a ballot is the extension between the represented and government. The most recent couple of years have welcomed a re-established center around to the innovation utilized in the democratic cycle. The current democratic framework has numerous security openings, and it is hard to demonstrate even basic security properties about them. A democratic framework that can be demonstrated right has numerous worries. There are a few explanations behind an administration to utilize electronic frameworks are to expand decisions exercises and to lessen the races costs. Still there is some extent of work in electronic democratic framework in light of the fact that it is extremely unlikely of distinguishing proof by the electronic democratic framework if the user is credible and getting electronic democratic machine from reprobates. The proposed framework is to build up a viable democratic machine with high security by utilizing Block-chain innovation to expand security and straight forwardness between the users.
Robust and sub-pixel accurate wavelet-based segmentation of laser lines formed by lenticular lenses

Triangulation based optical measuring systems are an important element of industrial quality assurance. Due to their robustness and cost-effectiveness Laser Light Section Sensors have become a widespread solution for Geometry measurements. In order to reconstruct the scene, it is necessary to identify the corresponding laser line, which is distorted due to the geometrical properties of the specimen, in the camera image. In Order to achieve the highest precision possible, the line segmentation has to be performed at sub-pixel accuracy. Furthermore, in an industrial environment, interfering light sources may be present. A distinction between these influences and the laser light ensures a robust measurement. The projected Laser Line of a triangulation sensor is usually formed by a Powell lens from a point source, which results in a uniformly distributed intensity. Another option to achieve highly uniform intensity distributions is by means of a lenticular lens. A side effect of these optics is that the fine-structure of the projected line is formed by a chain of equidistant dots. Across the laser line the intensity distribution can be considered as a Gaussian profile. Challenges to the segmentation are from the fine, dotted structure of the line. Although conventional methods, such as centroid based algorithms can be applied, with the drawback of imprecise peak detection. In order to insure both segmentation accuracy und robustness, this paper introduces a novel segmentation method based on wavelet-transformation. In a first step the periodic fine-structure of the line is utilized for a definite identification of the line with distinction from scattered light. In a second step a gaussian wavelet is used to achieve sub-pixel accuracy in peak detection. The developed method is compared to conventional peak detection methods.