Reviewerly/exHarmony huggingface dataset:
Taming Complexity with Self-managed Systems.

Modern computer information systems are highly complex, networked, have numerous configuration knobs, and operate in environments that are highly dynamic and evolving. Therefore, one cannot expect that configurations established at design-time will meet QoS and other non-functional goals at run-time. For that reason, the design of complex systems needs to incorporate controllers for adapting the system at run time. In this talk I will describe the four properties of self-managed systems: self-configuring, self-optimizing, self-healing, and self-protecting. I will also describe how these properties are enforced by controllers I designed for a variety of domains including cloud computing, fog/cloud computing, internet datacenters, distributed software systems, and database systems.
Meta-Analysis on the Effectiveness Research of Teaching-Learning Strategy using Goal-Based Scenarios

The purpose of this study is to carry out a meta - analysis on the effectiveness of GBS on the domestic papers which have been studied and reported empirically on the educational program applying the GBS as the teaching - learning design model. The effect size according to the control variable as well as the total effect are examined. Variables for the control effect are categorized into cognitive domain and affective domain, and the online and offline domains are separated.BR The selected data are the education program applying the GBS teaching - learning design strategy and the seven research papers corresponding to experimental design and quasi - experimental design designed by dividing experiment group and control group. There, we derived the effect sizes of 34 sub-variables. he effectiveness of this study was verified. In addition, the effect size heterogeneity of the selected data and the bias of publication bias were analyzed. This study was analyzed using R 3.5.2 program.
High dimensional document classification using novel similarity function

Document dimensionality is a major concern and worrying factor when high dimensionality documents are used for classification. Reducing the dimensionality can have both positive and negative effects. If dimensionality reduction is not appropriate then the classification performed using the reduced dimensionality documents may not give good classification results. Our previous research was focused on addressing dimensionality reduction using novel similarity function, but it did not address text classification. This paper addresses the classification task performed by applying the proposed similarity function. Experiment results prove the classifier performance with dimensionality reduction is better to the performance without dimensionality reduction.
Neural Network Based Denoising in the Wireless Channel Characterization

Channel denoising is of much importance for further channel characterization and fading analysis. In this paper, we proposed a novel method for the wireless channel impulse response (CIR) denoising based on neural network. Discriminating effective signals from the noise is considered as a binary classification problem, which can be resolved by some machine learning methods. The back propagation neural network (BPNN) model with the amplitude and angle information of CIR as input data is implemented to settle the classification problem. What's more, the Fβ-score, which combines both Precision and Recall together, is selected as the evaluation index to assess the classification performance. Compared with the performance of wavelet transform, the proposed method shows a better denoising result at both high and low signal-to-noise ratio (SNR) due to its full utilization of amplitude and angle information, while the BPNN with only amplitude as input data shows the worst result comparing with the other two methods.
Pattern Recognition in Big Data Analysis

In this chapter problems and methods of pattern recognition are considered and analyzed. Fuzzy neural network NEF Class is presented and algorithms of its training are considered. Unlike conventional neural networks (NN) FNN NEFClass may work with uncertain and fuzzy input data and additionally it may use expert knowledge in the form of fuzzy rule base. The training algorithms of FNN NEFClass have accelerated convergence in comparison to conventional NN which make it efficient tools for some classification problems with BD. The applications of NEFClass for pattern recognition of optical images and classification of medical images of human organ tissue in medical diagnostics problems are presented and their experimental results are analyzed. The hybrid fuzzy CNN network for medical images of breast cancer classification is suggested in which CNN VGG-16 is used for informative features extraction while FNN NEF Class is used as classifier of breast tumors. The experimental investigations of hybrid CNN network are presented and comparison with conventional CNN was performed. The problem of features dimensionality reduction in classification problem is considered and for its solution PCM method was applied and its efficiency explored.
A 0.72pJ/bit 400μm&lt;sup&gt;2&lt;/sup&gt; Physical Random Number Generator Utilizing SAR Technique for Secure Implementation on Sensor Nodes

This paper presents a low-power small-area-overhead physical random number generator utilizing SAR ADC embedded in sensor SoCs.An unpredictable random bit sequence is produced by an existing comparator in typical SAR ADCs, which results in little area overhead.Unlike the other comparator-based physical random number generator, this proposed technique does not require an offset calibration scheme since SAR binary search algorithm automatically converges the two input voltages of the comparator to balance the differential circuit pair.Although the randomness slightly depends on an quantization error due to sharing AD conversion scheme, the input signal distribution enhances the quality of random number bit sequence which can use for various security countermeasures such as masking techniques.Fabricated in 180 nm CMOS, 1 Mb/s random bit generator achieves high efficiency of 0.72 pJ/bit with only 400 µm 2 area overhead, which occupies less than 0.5% of SAR ADC, while remaining 10-bit AD conversion function.
Pyramid Text Recognition Based on A New Text Representation Model

Pyramid selling over the Internet is a new form of pyramid scheme with the characteristics of high concealment, strong deception, wide involvement and fast dissemination. Internet pyramid can disrupted the eunomie of the Internet and undermine the healthy development of the market economy as well as social harmony and stability. This study aims at detecting Internet pyramid selling organizations by analyzing the contents of articles about pyramid selling. In the paper, we characterize the text of pyramid selling which reveals that the introduction of investment returns (IIR) can represent the main characteristics of documents on pyramid selling. We then propose a new text presentation method for text classification to detect pyramid selling documents. Experiment conducted shows that the proposed method can achieve a good recognition rate in the analysis of documents on pyramid selling.
Impact of Virtual Environment Design on the Assessment of Virtual Agents

Virtual agents usually come in a virtual environment that can be designed in various ways which might affect users. This paper presents a study that examines whether the design of the virtual environment has an impact on the assessment of the virtual agent and the interaction. In a virtual job interview training, participants interacted with a virtual interviewer that behaved exactly the same, but the background and lighting conditions were manipulated. Our results indicate that the environmental design affects the assessment of the interviewer as well as the interview process.
Study of Performance of Bio- Inspired Strategies Applied to Pursuit Evasion Game Under Feedback Laws

Pursuit Evasion Game (PEG) is an abstract model of various significant problems that appear in both civil and military applications.Bio-Inspired strategies are found to be very useful in studying the PEG.While optimal response to the pursuit strategies are available using geometric control theory, it is shown in this paper that application of linear feedback control laws can further improve the time and tracking response of these strategies in capturing the evader by the pursuer.Empirical results based on computer simulation are used to illustrate the findings.Further, considering the case of sudden turn of the evader, moving at a lower speed, it is shown that both in theory and simulation that the evader can delay the capture by pursuer and in some cases even escape from being captured.These findings are in line with what is found in nature.
Pyramid Hologram in Projecting Medical Images

To read 2D slices from medical scanners on a flat screen and analyze them into full 3D structure in one’s own mind requires long-time training and experiences. Therefore, aside from doctors and experts, it is very difficult for patients to follow their own state of illness only by mere explanation from their physicians and a series of 2D slices displayed on flat screens. Up until now, pyramid holograms have only been used for advertising and entertainment, but not in medical field since it was not able to interact. Hence, the research of our team is to enhance this system with the interactive ability. The first step for this is to compute a hologram from multi-layer images captured by CT or MRI scanners in the form of DICOM via pyramid hologram, with the use of MATLAB programming language and a lot of it’s available functions for image processing. The whole procedure of the team research contains six main steps. The first four steps include: reconstructing 3D images from a folder of CT or MRI multi-layer images, capturing four orthogonal projections of the 3D image, arranging these four side views into one appropriate png image, saving the final image. Step five is to reduce a layer from the original folder of CT or MRI multi-layer images and repeating the first four steps until running out of layers inside the folder, gradually. All the saved images for each layer reduction will be computed into a video which can show the outside through the inside of the images from CT or MRI scanner via a pyramid hologram. Users can pause the video at any time to show the structure of the images they desire. This paper traces the design concept but can only confirms the first four steps for coding function of creating a hologram image from the original medical devices by a prototype demonstration. Future work for this research will include full six steps and designing for a hardware system that can be combined with the pyramid hologram for interaction between users and the model.
An End-User Semantic Web Augmentation Tool

Web Augmentation is usually applied to add, remove and change Web sites’ functionalities, content, and presentation. Content-based Web Augmentation is commonly performed by integrating content from an external Web site into the current one. In this article, we explore the use of the Semantic Web as a source of information to be incorporated to any Web site, aiming to simplify the development of Web Augmentation based on Semantic Web data. Our approach allows end-users without any programming skills to build Web Augmentation scripts that takes some information from the current Web page, and produce new related information gathered from the Semantic Web. This article introduces a pipeline process for building Semantic Web Augmentations and an end-user development tool called SWAX to create augmentation layers without the need for any programming or Semantic Web skills.
Ensemble Model for Segmentation of Lateral Ventricles from 3D Magnetic Resonance Imaging

The paper proposes an ensemble model to segment lateral ventricles from 3D Magnetic Resonance Imaging (MRI) brain scan. Thresholding and Active Contour techniques combining with a noise removal method were applied to segment lateral ventricles from the brain images. The experiments were conducted by segmenting 73 MRI brain scans using our proposed model and then comparing their volumes to those using manual model conducted by an expert. The experimental results indicated that the proposed model segmented lateral ventricles as excellent as the manual model in terms of accuracy but outperformed the manual model in terms of time performance. The contribution of the paper is that the segmented lateral ventricles can be used for further analysis such as medical condition classification.
Preliminary study for developing instantaneous quantum computing algorithms (IQCA)

Abstract Since the mid-1990s theoretical quadratic exponential and polynomial Quantum Computing (QC) speedup algorithms have been discussed. Recently the advent of relativistic information processing (RIP) introducing a relativistic qubit (r-qubit) with additional degrees of freedom beyond the current Hilbert space Bloch 2-sphere qubit formalism extended theory has appeared. In this work a penultimate form of QC speedup – Instantaneous Quantum Computing Algorithms (IQCA) is proposed. Discussion exists on passing beyond the quantum limits of locality and unitarity heretofore restricting the evolution of quantum systems to the standard Copenhagen Interpretation. In that respect as introduced in prior work an ontological-phase topological QC avails itself of extended modeling. As well-known by EPR experiments instantaneous connectivity exists inherently in the nonlocal arena. As our starting point we utilize Bohm’s super-implicate order where inside a wave packet a super-quantum potential introduces nonlocal connectivity. Additionally EPR experiments entangle simultaneously emitted photon pairs by parametric down-conversion. Operating an IQCA requires a parametric up-conversion cycle an M-Theoretic Unified Field Mechanical (MUFM) set of topological transformations beyond the current Galilean Lorentz-Poincairé transforms of the standard model (SM). Yang-Mills Kaluza-Klein (YM-KK) correspondence is shown to provide a path beyond the semi-quantum limit to realize the local-nonlocal duality required to implement IQCA.
Scope and Challenges of Language Modelling - An Interrogative Survey on Context and Embeddings

In this work we explore the domain of Language Modelling. We focus here on different context selection strategies, data augmentation techniques, and word embedding models. Many of the existing approaches are difficult to understand without specific expertise in this domain. Therefore, we concentrate on appropriate explanations and representations that enable us to compare several approaches.
Records with rank polymorphism

In a rank-polymorphic programming language, all functions automatically lift to operate on arbitrarily high-dimensional aggregate data. By adding records to such a language, we can support computation on data frames, a tabular data structure containing heterogeneous data but in which individual columns are homogeneous. In such a setting, a data frame is a vector of records, subject to both ordinary array operations (, filtering, reducing, sorting) and lifted record operations—projecting a field lifts to projecting a column. Data frames have become a popular tool for exploratory data analysis, but fluidity of interacting with data frames via lifted record operations depends on how the language’s records are designed.
A Fuzzy Clustering Algorithm with Multi-medoids for Multi-view Relational Data

There is an increasing interest for multi-view clustering due to its ability to manage data from several sources. The majority of multi-view clustering algorithms are suitable to analyse vector data, but much less attention has been given for the analysis of relational data. This paper provides a fuzzy clustering algorithm with multi-medoids for multi-view relational data (MFMMdd). Experiments with real multi-view data sets show the good performance of the MFMMdd in comparison with previous multi-view clustering algorithms for relational data, concerning the quality of the partitions provided by these algorithms.
An Improvement of Correlation Analysis for Vectorial Boolean Functions

This paper investigates the correlation of n-bit to m-bit vectorial Boolean functions denoted by F. At Crypto 2000, Zhang and Chan showed that the maximum of linear approximations for F with Boolean functions g have a higher bias than those based on the usual correlation attack. The correlation for this linear approximation has been named the maximum correlation and has been shown to be a useful tool for correlation attack resistance. In this work, we deal with two issues. Firstly, we show that combining F with any g does not always increase the bias as stated by several works. To justify such results, we demonstrate the exact correlation link between F, g and the combination of F by g. Secondly, we provide the exact condition in which the correlation coefficients for this approximation are maximum.
Security of Low Level IoT Protocols

Application of formal methods in security is demonstrated. Formalism for description of security properties of low level IoT protocols is proposed. It is based on security property called infinite step opacity. We prove some of its basic properties as well as we show its relation to other security notions. Finally, complexity issues of verification and security enforcement are discussed. As a working formalism timed process algebra is used.
A Matrix-based component decomposition algorithm of Tibetan characters

A component is the basic unit of Tibetan characters, and component decomposition of Tibetan characters is a fundamental step for Tibetan informatization. According to the character formation rules and writing features in the vertical and horizontal dimensions of Tibetan characters, a matrix-based algorithm for the component decomposition of Tibetan characters is proposed. Firstly, the algorithm decomposes Tibetan characters in vertical and horizontal dimensions, based on their structural features and writing order. Secondly, it goes to decomposition each dimension respectively. Finally, 48 different structures of modern Tibetan characters were tested. The test results show that the accuracy of the algorithm reaches 100%.
Introduction to the Special Issue on Image-Based Information Retrieval from the Web

In recent years, following the tremendous growth of the Web, extremely large amounts of digital multimedia content are being produced every day and are shared online mainly through several newly emerged channels, such as social networks [...]
Automatic Generation of Dictionaries: The Journalistic Lexicon Case

Text normalisation is an important task in the context of Natural Language Processing. By normalisation, free text is mapped into dictionaries, i.e. indexed collections of locutions recognised as typical of a particular jaergon. In general, technical dictionaries are difficult to build and validate. They are typically constructed by hand on the basis of everyday human work and they are agreement-based. This is indubitably time consuming and the approach requires a strong human supervision and does not provide a general methodology. In this paper, we perform the first steps towards the to automatic building of a dictionary for Italian journalistic lexicon, called NewsDict, based on sub dictionaries able to characterise main topics occurring in newspaper articles. We exploit a dataset of annotated documents from some Italian newspapers and a statistical techniques based on the Mutual Information Principle. Documents contains information such as the release date and the topic of the article and has been directly annotated by the author. To check the accuracy of the dictionary we built, we develop an initial test. We normalise a control set of journal article into NewsDict. Crossing results presented in this paper against the human annotation, we provide a fist measure of performances of the described methodology.
Survival Analysis of Un-identical Recurrence Using Conditional I, Conditional II, and Marginal Method

Abstract Survival analysis can be used to examine events that may occur more than once in one individual or is called a recurring event. This event can be seen in cases of stroke recurrence. The occurred recurrence shows a more severe recurrence stage than before (recurring events are not identical). The Stratified Cox model with Conditional I, Conditional II, and Marginal methods can be used to analyse recurring events, especially in recurrent events that are not identical. The used data are secondary data from the medical record archives of stroke patients at Chasbullah Abdulmadjid Hospital Bekasi City from March 1, 2016 to April 30, 2016. Based on the Wald test and the backward elimination steps, we found two explanatory variables that influence the recurrence rate of stroke which were hypertension and hypercholesterolemia. The model from the three methods is a model with a combination of variables of hypertension and hypercholesterolemia. Patients suffering from hypertension or hypercholesterolemia have the potential to experience a recurrence of stroke compared to patients who do not have hypertension or hypercholesterolemia. Based on the AIC value of each method, the best method for recurring data on stroke patients is the Conditional I method.
Hyperspectral phase imaging with denoising in SVD image subspace

We propose a modified denoising algorithm for hyperspectral data. The algorithm is based on a complex domain block-matching 3D filter, on estimation of the noise correlation matrix and on dimension reduction of the Singular Value Decomposition (SVD) eigenspace.
Simulation Results of the Type-2 Fuzzy Sugeno Integral

In this chapter we present the simulation results obtained using the proposed method in two study cases to demonstrate its efficiency
Introducing the Theory of Probabilistic Hierarchical Learning for Classification

This is the 5th paper in our series of papers on hierarchical learning for classification. Hierarchical learning for classification is an automated method of creating hierarchy list of learnt models that are on the one hand capable of partitioning the training set into equal number of subsets and on the other hand are also capable of classifying elements of each corresponding subset into classes of the problem. In this paper, the probabilistic hierarchical learning for classification has been formalized and presented as a theory. The theory asserts that the accurate models of complex datasets can be produced through hierarchical application of low complexity models. The theory is validated through experiments on five popular real-world datasets. Generalizing ability of the theory is also tested. Comparison with the contemporary literature points towards promising future for this theory. The theory is covered by four postulates, which are carved out elegantly through mathematical formalisms.