Reviewerly/exHarmony huggingface dataset:
Variants of Recursive Consequent Parameters Learning in Evolving Neuro-Fuzzy Systems

A wide variety of evolving (neuro-)fuzzy systems (E(N)FS) approaches have been proposed during the last 10 to 15 years in order to handle (fast and real-time) data stream mining and modeling processes by dynamically updating the rule structure and antecedents. The current denominator in the update of the consequent parameters is the usage of the recursive (fuzzily weighted) least squares estimator (R(FW)LS), as being applied in almost all E(N)FS approaches. In this paper, we propose and examine alternative variants for consequent parameter updates, namely multi-innovation RFWLS, recursive corr-entropy and especially recursive weighted total least squares. Multi-innovation RLS guarantees more stability in the update, whenever structural changes (i.e. changes in the antecedents) in the E(N)FS are performed, as the rule membership degrees on (a portion of) past samples are actualized before and properly integrated in each update step. Recursive corr-entropy addresses the problematic of outliers by down-weighing the influence of (atypically) higher errors in the parameter updates. Recursive weighted total least squares takes into account also a possible noise level in the input variables (and not solely in the target variable as in RFWLS). The approaches are compared with standard RFWLS i.) on three data stream regression problems from practical applications, affected by (more or less significant) noise levels and one embedding a known drift, and ii.) on a realworld time-series based forecasting problem, also affected by noise. The results based on accumulated prediction error trends over time indicate that RFWLS can be largely outperformed by the proposed alternative variants.
Forecasting solar energy in a complex urban environment Case study: Casablanca, Morocco

This study presents an assessment of photovoltaic potential in urban areas using "solar analyst tool" and a three-dimensional urban model derived from the photogrammetric restitution. The methodology was applied on Elhank, Racine and Bourgogne districts in Casablanca city. These selected urban areas consist of various urban zones characterized by different morphology and density factors. The analysis has shown a high photovoltaic potential and a height rate of suitable rooftop surface. However, this potential shows a large spatial and temporal variations caused by urban environments and seasons.
A Novel Algorithm for Salient Region Detection

Salient region is the most prominent object in the scene which attracts to the human vision system. This paper presents a novel algorithm that is based on the separated Red, Green and Blue colour channels. Most prominent regions of all the three channels of RGB colour model are extracted using mean value of the respective channels. Pixels of extracted salient region of RGB channels are counted and then some specified rules are applied over these channels to generate final saliency map. To evaluate the performance of the proposed novel algorithm, a standard dataset MSRA-B has been used. The proposed algorithm presents better result and outperformed to the existing approaches.
Cuckoo Search Algorithm with Various Walks

Bio-inspired algorithms are emerging soft computational techniques. They have grabbed attention of researchers from various disciplines. Cuckoo Search (CS) Algorithm, which is inspired by the breeding mechanism of fascinating cuckoo birds, a well-known bio-inspired algorithm. CS is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight, which is known to be generating unexpected sharp movements during search. Thus, it can be put forward that analysing the effects of some other types of movements are still a hot topic. Accordingly, the present study introduces some new movement procedures including quantum, Brownian and random walks for CS, which adopts Lévy flights in the standard form. The proposed modifications are tested by using both unconstrained and constrained optimization problems including some well-known mechanical design problems. Statistically verified results demonstrate that the proposed movements induce significant improvements over the standard CS.
Text Separation From Document Images

Text separation in document image analysis is an important preprocessing step before executing an optical character recognition (OCR) task. It is necessary to improve the accuracy of an OCR system. Traditionally, for separating text from a document, different feature extraction processes have been used that require handcrafting of the features. However, deep learning-based methods are excellent feature extractors that learn features from the training data automatically. Deep learning gives state-of-the-art results on various computer vision, image classification, segmentation, image captioning, object detection, and recognition tasks. This chapter compares various traditional as well as deep-learning techniques and uses a semantic segmentation method for separating text from Devanagari document images using U-Net and ResU-Net models. These models are further fine-tuned for transfer learning to get more precise results. The final results show that deep learning methods give more accurate results compared with conventional methods of image processing for Devanagari text extraction.
Negotiating digital marginalization: Immigrants, computers, and the adult learning classroom

The discourse of the digital divide continues to shift from questions of physical access to questions of empowerment and equality. Implicit in this shift is the need to make sense of technology use within marginalized communities, prompting the central question of this study: What happens to marginalized individuals after they gain digital access, and importantly, training? To explore this question, I observed and assisted a group of 18 mostly transnational, Latina individuals during a five-month long computer class in Newark, New Jersey. The study positions computers as a kind of "boundary object" existing as a material thing embedded within associated systems, local meanings, and practices as interpreted by the researcher. The voices and experiences of the classroom participants are examined through cross-disciplinary theoretical frameworks drawn primarily from Science and Technology Studies (STS) and a specific transnational study. Thus, the article makes a contribution to the field of Communication by bringing these theoretical concepts into conversation with pedagogical ethnography and Internet Communication Technologies (ICT).
Combinatorial Properties of Degree Sequences of 3-Uniform Hypergraphs Arising from Saind Arrays

The characterization of k-uniform hypergraphs by their degree sequences, say k-sequences, has been a longstanding open problem for $$k\ge 3$$ . Very recently its decision version was proved to be NP-complete in [3]. In this paper, we consider Saind arrays $$S_n$$ of length $$3n-1$$ , i.e. arrays $$(n,n-1,n-2, \dots , 2-2n)$$ , and we compute the related 3-uniform hypergraphs incidence matrices $$M_n$$ as in [3], where, for any $$M_n$$ , the array of column sums, $$\pi (n)$$ turns out to be the degree sequence of the corresponding 3-uniform hypergraph. We show that, for a generic $$n \ge 2$$ , $$\pi (n)$$ and $$\pi ({n+1})$$ share the same entries starting from an index on. Furthermore, increasing n, these common entries give rise to the integer sequence A002620 in [15]. We prove this statement introducing the notion of queue-triad of size n and pointer k. Sequence A002620 is known to enumerate several combinatorial structures, including symmetric Dyck paths with three peaks, some families of integers partitions in two parts, bracelets with beads in three colours satisfying certain constraints, and special kind of genotype frequency vectors. We define bijections between queue triads and the above mentioned combinatorial families, thus showing an innovative approach to the study of 3-hypergraphic sequences which should provide subclasses of 3-uniform hypergraphs polynomially reconstructable from their degree sequences.
Optimization of Type-2 and Intuitionistic Fuzzy Systems in Intelligent Control

In this paper a framework for finding the optimal design of intuitionistic fuzzy systems in control applications is presented. Traditional models deal with type-0 values, which mean using precise numbers in the models, but since the seminal work of Prof. Zadeh in 1965, type-1 fuzzy models emerged as a powerful way to represent human knowledge and natural phenomena. Later type-2 fuzzy models were also proposed by Prof. Zadeh in 1975 and more recently have been studied and applied in real world problems by many researchers. In addition, as another extension of type-1 fuzzy logic, Prof. Atanassov proposed Intuitionistic Fuzzy Logic, which is a very powerful theory in its own right. Previous works of the author and other researchers have shown that certain problems can be appropriately solved by using type-1, and others by interval type-2, while others by using intuitionistic fuzzy logic. Bio-inspired and meta-heuristic optimization algorithms have been commonly used to find optimal designs of type-1, type-2 or intuitionistic fuzzy models for applications in control, robotics, pattern recognition, time series prediction, just to mention a few. However, the question still remains about if even more complex problems (meaning non-linearity, noisy, dynamic environments, etc.) may require even higher types, orders or extensions of type-1 fuzzy models to obtain better solutions to real world problems. In this paper a framework for solving this problem of finding the optimal fuzzy model for a particular problem is presented. To the knowledge of the author, this is the first work to propose a systematic approach to solve this problem, and we envision that in the future this approach will serve as a basis for developing more efficient algorithms for the same task of finding the optimal fuzzy system.
Compression of Next-Generation Sequencing Data and of DNA Digital Files

The increase in memory and in network traffic used and caused by new sequenced biological data has recently deeply grown. Genomic projects such as HapMap and 1000 Genomes have contributed to the very large rise of databases and network traffic related to genomic data and to the development of new efficient technologies. The large-scale sequencing of samples of DNA has brought new attention and produced new research, and thus the interest in the scientific community for genomic data has greatly increased. In a very short time, researchers have developed hardware tools, analysis software, algorithms, private databases, and infrastructures to support the research in genomics. In this paper, we analyze different approaches for compressing digital files generated by Next-Generation Sequencing tools containing nucleotide sequences, and we discuss and evaluate the compression performance of generic compression algorithms by confronting them with a specific system designed by Jones et al. specifically for genomic file compression: Quip. Moreover, we present a simple but effective technique for the compression of DNA sequences in which we only consider the relevant DNA data and experimentally evaluate its performances.
Feature Aggregation Networks for Image Steganalysis

Since convolutional neural networks have shown remarkable performance on various computer vision tasks, many network architectures for image steganalysis have been introduced. Many of them use fixed preprocessing filters for stable learning, which have a disadvantage of limited use of the information of the input image. The recently introduced end-to-end learning method uses a structure that limits the number of channels of feature maps close to the input and stacks residual blocks. This method has limitations in generating feature maps of various levels and resolutions that can be effective for steganalysis. We therefore propose the feature aggregation-based steganalysis networks: expand the number of channels of convolutional blocks close to the input data, aggregate feature maps of various levels and resolutions, and utilize rich information to improve steganalysis performance. In addition, the capped activation function is applied to obtain better generalization performance. The proposed method outperforms the state-of-the-art steganalysis on detection of the advanced steganography algorithms J-UNIWARD and UED, for JPEG quality factor 75 and 95.
The Alloga quarry, Southwestern Sinai, Egypt: geological studies, radioactivity and mineralogical investigations

Gabal Alloga area is located in the southwestern part of the Sinai Peninsula. It represents one of the more significant economic and uranium mineralized areas. It is located within a low basin and most of the high radioactive anomalies recorded in the sediments of Um Bogma Formation within this basin. The Lower Carboniferous Um Bogma Formation is the main rock units constituting the face wall of the Allouga quarry. The wall shows very high radioactivity with eU-contents reaches 3000 ppm. Sklodowskite and carnotite represent the main uranium minerals detected in the face wall of the quarry with anomalous contents of gold. The gold content reaches 1.78, 1.74, 2.02, 1.6, 1.78 and 1.8 ppm in the ferruginous sandstone, dolostone, black shale, marl, claystone and gibbsite, respectively. The structure (faulting), lithology (carbonaceous material and clay minerals in addition to iron oxides), topography (low basin) and biogenic effects (organic matter) are the main factors that controlled the localization and concentration of the uranium and gold minerals within the Um Bogma Formation in the G. Alloga area, especially in Alloga Quarry. In addition to the secondary ascending hydrothermal solutions carry out the radioactive and gold minerals to deposit mainly along with fractures and faults.
Who wrote <i>Wuthering Heights</i>?

Abstract Emily Brontë published Wuthering Heights in 1847 under the pseudonym Ellis Bell. It was not until the later second edition, published after Emily’s death, that she was credited as the novel’s author. Those Victorian attitudes towards women which compelled Brontë to publish as Bell have not been wholly eradicated, with her legitimacy as the sole author being called into question by male commentators at several junctures since. Their claim is that Emily’s brother Branwell is the real author of Wuthering Heights. Using stylometry, a computer-assisted technique which measures the likely author of a text, this brief experiment demonstrates that it is highly unlikely that Branwell Brontë contributed to the writing of Wuthering Heights, and that Emily, as generally considered, is the novel’s sole author. Furthermore, considering a number of limitations with the corpus being tested, this study provides a good and necessary example of stylometry in practice, and how such an experiment should be conducted in less-than-ideal circumstances.
Archimedean Choice Functions

If uncertainty is modelled by a probability measure, decisions are typically made by choosing the option with the highest expected utility. If an imprecise probability model is used instead, this decision rule can be generalised in several ways. We here focus on two such generalisations that apply to sets of probability measures: E-admissibility and maximality. Both of them can be regarded as special instances of so-called choice functions, a very general mathematical framework for decision making. For each of these two decision rules, we provide a set of necessary and sufficient conditions on choice functions that uniquely characterises this rule, thereby providing an axiomatic foundation for imprecise decision making with sets of probabilities. A representation theorem for Archimedean choice functions in terms of coherent lower previsions lies at the basis of both results.
Efficient Extreme Learning Machine (ELM) Based Algorithm for Electrocardiogram (ECG) Heartbeat Classification

Electrocardiogram (ECG) estimates the electric signals activity of the human heart and is extensively used for sensing heart aberrations due to ease of use and non-invasive application on human body. Human heart is a one of the vital organs of human body. In an industrial environment, heart impairments and abnormalities are attributed to the different causes including work overload, occupational and workplace stress. Cardiovascular Disease (CD) of heart refers the conditions involving different heart’s frequency deviations and are mostly ascribed to the workplace stress, fatigue and strain. Early detection of deviated heartbeats may prevent premature morbidity and unhealthy rhythms under occupational stress. The Electrocardiography (ECG) is one of the widely used diagnostic test tools that cardiologists use to diagnose heart anomalies, impairments and diseases. Various approaches have been proposed to correctly classify the ECG signals. In this study, a fast ECG classification method based on Extreme Learning Machines (ELM) algorithm is proposed to classify the frequency rhythms in heartbeat. The MIT-BIH Arrhythmia Database having recordings of 47 subjects is used in this study. Proposed ELM method is evaluated and analyzed by dividing diagnostics datasets in 60:40 train-test split ratio and findings are compared with similar studies. Results confirm the feasibility of newly proposed ELM method both in terms of classification accuracy 97.55%, speed and computational power.
sl-LSTM

The volume of data in diverse data formats from various data sources has led the way for a new drift in the digital world, Big Data. This article proposes sl-LSTM (sequence labelling LSTM), a neural network architecture that combines the effectiveness of typical LSTM models to perform sequence labeling tasks. This is a bi-directional LSTM which uses stochastic gradient descent optimization and combines two features of the existing LSTM variants: coupled input-forget gates for reducing the computational complexity and peephole connections that allow all gates to inspect the current cell state. The model is tested on different datasets and the results show that the integration of various neural network models can further improve the efficiency of approach for identifying sensitive information in Big data.
FPGA Performance Evaluation of Present Cipher Using LCC Key Generation for IoT Sensor Nodes

IoT which enables data transmission through different kind's interrelated networks, mostly the data is exchanged between wireless networks, and chances of hacking. Security is the most important aspect, and the data should be confidential to avoiding hacking. The cryptography solutions are utilized as an answer for improving security and the customary calculations because their limitation setting is not perfect for IoT gadgets. It is therefore possible to use the lightweight cryptographic algorithm as a solution to IoT security problems. However, there are a number of algorithms to choose from the distinctive execution criteria and conditions; the PRESENT cipher template in this paper is the encryption method using the 64-bit key for 64-bit data for hardware-level data protection input. To improve the security, Lorenz Chaotic Circuit with Dual-port Read Only Memory-based Present Algorithm (LCC-DROM-PA) architecture is proposed in this work, and for generating the key value, LCC is an essential design, and DROM is used for S-box design and P-layer design. After designing this architecture, FPGA performances are evaluated by the count of LUTs, flip-flops, slices, and frequency.
Hoax news validation using similarity algorithms

Abstract News that is presented every day on social media dramatically affects the feelings, feelings, thoughts, or even actions of a person or group. Hoax News is one of them which is disturbing the public and raising noise in various fields, ranging from politics, culture, security, and order, to the economy. Inseparable from social media users. How every day, there is information on social media, which is not necessarily true so that people are provoked by hoax on social media. The news detection system in this study was designed using Unsupported Learning so that it does not require data training. The system was built using the Equation algorithm to calculate the validity of document similarity. Extraction results used to search for content related to user input using a detection engine, then the similarity value and the time needed to utilize hoax news are calculated. System validation testing by using a four text similarity algorithm called the Equation algorithm, the Levenshtein algorithm, the Smith-Waterman algorithm, the Damerau Levenshtein algorithm; this algorithm is used to find the best analytical solution of news hoaxes and submissions needed to find the news hoax password. The final results of the deception detection research using a script that has been done for Validation using an algorithm, get the value of accuracy in detection using the Smith-Waterman algorithm, which produces an accuracy value of text similarity of 99.29% and can be used a process of 6, 57 seconds, followed by the second sequence that is the similarity algorithm produces an accuracy of 75% and requires a processing time of 4.94 seconds, then the third sequence is the Levenshtein algorithm with an accuracy of 55.02% and requires a processing time of 5.49 seconds, and is used today is Damerau Levenshtein algorithm is 55.02% and requires a processing time of 7.54%. The results of research tests on this text can conclude the more text on the detection engine, the higher the verification value and the higher the time needed to process hoax news.
Lexical Feature Based Feature Selection and Phishing URL Classification Using Machine Learning Techniques

Phishing is an illegitimate method to collect secret information of any person or organization. Information like debit card, credit card details, PIN no, OTP, passwords, etc. are stolen by the attackers through phishing sites. Researchers have used different techniques to detect those phishing sites. But it is difficult to stay on a particular technique as attackers come with new tactics. In this paper, phishing and legitimate URL classifications are performed based on the lexical features of URLs. Feature selection technique is used to select the relevant features only. Accuracy for all combination of features with different numbers of features each time was evaluated to find the best possible combination of features. Performances are analyzed for different datasets with various parameters using four different machine learning techniques.
A perturbation algorithm for the pointers of Franke–Gorini–Kossakowski–Lindblad–Sudarshan equation

This paper is devoted to the study of behavior of open quantum systems consistently based on the Franke–Gorini–Kossakowski–Lindblad–Sudarshan (FGKLS) equation which covers evolution in situations when decoherence can be distinguished. We focus on the quantum measurement operation which is determined by final stationary states of an open system—so called pointers. We find pointers by applying the FGKLS equation to asymptotically constant density matrix. In seeking pointers, we have been able to propose a perturbative scheme of calculation, if we take the interaction components with an environment to be weak. Thus, the Lindblad operators can be used in some way as expansion parameters for perturbation theory. The scheme we propose is different for the cases of non-degenerate and degenerate Hamiltonian. We illustrate our scheme by particular examples of quantum harmonic oscillator with spin in external magnetic field. The efficiency of the perturbation algorithm is demonstrated by its comparison with the exact solution.