Reviewerly/exHarmony huggingface dataset:
Mutual-inclusive learning-based multi-swarm PSO algorithm for image segmentation using an innovative objective function

This paper presents a novel image segmentation algorithm formed by the normalised index value (Niv) and probability (Pr) of pixel intensities. To reduce the computational complexity, a mutual-inclusive learning-based optimisation strategy, named mutual-inclusive multi-swarm particle swarm optimisation (MIMPSO) is also proposed. In mutual learning, a high dimensional problem of particle swarm optimisation (PSO) is divided into several one-dimensional problems to get rid of the 'high dimensionality' problem whereas premature convergence is removed by the inclusive-learning approach. The proposed Niv and Pr-based technique with the MIMPSO algorithm is applied on the Berkley Dataset (BSDS300) images which produce better optimal thresholds at a faster convergence rate with high functional values as compared to the considered optimisation techniques like PSO, genetic algorithm (GA) and artificial bee colony (ABC). The overall performance in terms of the fidelity parameters of the proposed algorithm is carried out over the other stated global optimisers.
Reprint of: Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts

The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure through the introduction of the notion of output concepts and minimizing the number of concepts and connections between them. The proposed approach allows for: (1) the selection of key concepts based on graph theory metrics and determining the connections between them; (2) the determination of the criterion of learning based on output concepts and fitting the learning process to the analyzed problem. A simulation analysis was done with the use of synthetic and real-life data. Experiments confirm that the proposed approach improves the learning process compared to the standard approaches.
Exhaustive Search for Various Types of MDS Matrices

MDS matrices are used in the design of diffusion layers in many block ciphers and hash functions due to their optimal branch number. But MDS matrices, in general, have costly implementations. So in search for efficiently implementable MDS matrices, there have been many proposals. In particular, circulant, Hadamard, and recursive MDS matrices from companion matrices have been widely studied. In a recent work, recursive MDS matrices from sparse DSI matrices are studied, which are of interest due to their low fixed cost in hardware implementation. In this paper, we present results on the exhaustive search for (recursive) MDS matrices over GL(4, F2). Specifically, circulant MDS matrices of order 4, 5, 6, 7, 8; Hadamard MDS matrices of order 4, 8; recursive MDS matrices from companion matrices of order 4; recursive MDS matrices from sparse DSI matrices of order 4, 5, 6, 7, 8 are considered. It is to be noted that the exhaustive search is impractical with a naive approach. We first use some linear algebra tools to restrict the search to a smaller domain and then apply some space-time trade-off techniques to get the solutions. From the set of solutions in the restricted domain, one can easily generate all the solutions in the full domain. From the experimental results, we can see the (non) existence of (involutory) MDS matrices for the choices mentioned above. In particular, over GL(4, F2), we provide companion matrices of order 4 that yield involutory MDS matrices, circulant MDS matrices of order 8, and establish the nonexistence of involutory circulant MDS matrices of order 6, 8, circulant MDS matrices of order 7, sparse DSI matrices of order 4 that yield involutory MDS matrices, and sparse DSI matrices of order 5, 6, 7, 8 that yield MDS matrices. To the best of our knowledge, these results were not known before. For the choices mentioned above, if such MDS matrices exist, we provide base sets of MDS matrices, from which all the MDS matrices with the least cost (with respect to d-XOR and s-XOR counts) can be obtained. We also take this opportunity to present some results on the search for sparse DSI matrices over finite fields that yield MDS matrices. We establish that there is no sparse DSI matrix S of order 8 over F28 such that S8 is MDS.
AN APPROACH FOR MULTISPECTRAL IMAGE COMPRESSION

Compression plays a key role in reducing transmission and storage requirements.Compression of Multispectral data is important as it demands lots of memory space for storage.In the present work, the paper proposes the use of two transform coding techniques such as Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for compressing the multispectral data.The two transform coding techniques are applied for multispectral input data and are compared with respect to Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR).Both the techniques are implemented individually on various bands of multispectral image.
The Generalized Haar Spaces and Their Adaptive Decomposition

This paper is devoted to the numericalinformation flows and adaptive decompositions of thegeneral Haar functions connected with them. The aim ofthis paper is to propose an adaptive wavelet decompositionusing an adaptive compression algorithm for a flow ofnumerical information of length M with complexity𝑶(𝑴) and with a given precision of 𝜀&gt; 0. The numericalflows are associated with irregular spline grids. This paperdiscusses the calibration relations, the embedding of thegeneral Haar spaces and their wavelet decompositions.The structure of the decomposition/ reconstructionalgorithms are done. The cases of the finite and the infiniteflows are considered. The paper discusses various methodsof adaptive Haar approximations for the flow of functionvalues. Assuming that the values of the first derivative ofthe approximated function is known (exactly orapproximately), the complexity of using an adaptive grid isestimated for a priori specified approximation accuracy.The number of K knots in the adaptive grid determine therequired amount of memory for storage of thecompression results. The number of M knots of the initialgrid characterizes the number of operations required toobtain the adaptive compression. In the case of access tothe derivative values (or their approximations) thenumber of digital operations is proportional to the numberM. If it does not have access to the last ones then thenumber of required operations has the order of M2 (in thegeneral case). If additionally, the approximated flow isconvex, then the number of required operations has theorder of M log2M. In all cases the result requires thecomputer memory amount to be of the order of K
A Novel Non-Invasive Algorithm Using the concept of Nail Based Anemia Disease Detection

Anemia is a danger disease for the human life. If anemia diagnosis is not found in time than its very difficult to recover the patient specially in COVID-19 time it’s a deadly disease. As we know in this era 2020 COVID is create a huge change in human life that’s why after 2019 is called New life. As we know there is lots of approaches are there to identify the anemia, but there is very few approaches are there which are non-invasive, and those approaches are not good in terms of the quality of the result and most important they are not a good real time analysis system. So, in this paper we proposed a novel non-invasive algorithm which is able to detect the anemia using the human nails. In this approach we use computer vision, machine and deep learning concept and based on that only we decide the anemia level on any particular patient. Our propose approach is complete real time and this system is able to provide result in very less time. Key Words:Invasive, Non-Invasive, SPO2, Hardware, Device.
Autonomous Virtual Machine Sizing and Resource Usage Prediction for Efficient Resource Utilization in Multi-Tenant Public Cloud

In recent years, the use of cloud computing has increased exponentially to satisfy computing needs in both big and small organizations.However, the high amounts of power consumed by cloud data centres have raised concern.A major cause of power wastage in cloud computing is inefficient utilization of computing resources.In Infrastructure as a Service, the inefficiency is caused when users request for more resources for virtual machines than is required.In this paper, we propose a technique for automatic virtual machine sizing and resource usage prediction using neural networks, for multi-tenant Infrastructure as a Service cloud service model.The proposed technique aims at reducing energy wastage in data centres by efficient resource utilization.An evaluation of our technique on CloudSim Plus cloud simulator and WEKA shows that effective VM sizing not only achieves energy savings but also reduces the cost of using cloud services from a customer perspective.
Digital Pathology

Digital pathology is the practice of pathology using digital imaging. The application of this disruptive technology is on the verge of becoming standard of care in the field of pathology. Digital pathology usage has employed static images, live streaming of images, and most recently whole slide imaging (WSI). WSI permits digitization of entire glass slides, thereby eliminating the need for pathology practice to be locally tethered to a conventional microscope. Digital images lend themselves to many clinical and nonclinical applications. Clinical uses include making primary pathology diagnoses directly from digital images, telepathology, archiving, and image analysis. Nonclinical uses include proficiency testing, research, and education. There are several barriers to the widespread adoption of digital pathology including technology limitations, regulatory obstacles, cost of equipment, difficulties with implementation, and pathologist technophobia. Digital pathology is nevertheless deemed to become increasingly vital for the future practice of pathology.
Student Smart Attendance Through Face Recognition using Machine Learning Algorithm

In today’s competitive world, with very less classroom time and increasing working hours, lecturers may need tools that can help them to manage precious class hours efficiently. Instead of focusing on teaching, lecturers are stuck with completing some formal duties, like taking attendance, maintaining the attendance record of each student, etc. Manual attendance marking unnecessarily consumes classroom time, whereas smart attendance through face recognition techniques helps in saving the classroom time of the lecturer. Attendance marking through face recognition can be implied in the classroom by capturing the image of the students in the classroom via the camera installed. Later through the HAAR Cascade algorithm and MTCNN model, face region needs to be taken as interest and the face of each student is bounded through a bounding box, and finally, attendance can be marked into the database based on their presence by using Decision Tree Algorithm.
Nonlinear time series forecasting using a novel self-adaptive TLBO-MFLANN model

In this paper, we have proposed a multiplicative FLANN (MFLANN) model for time series forecasting. In addition, an improved version of teaching learning based optimisation (TLBO), called self-adaptive TLBO (SATLBO) has been proposed to train the MFLANN model. The proposed SATLBO uses the gradient descent learning algorithm in the teacher phase while uses the past experience to adapt the learners parameters. This unique integration of SATLBO with gradient descent learning algorithm is used to determine the near optimal weight set of MFLANN. The proposed method is implemented in MATLAB environment and the obtained results are compared with other methods (DE-based MFLANN, TLBO-based MFLANN, CRO-based MFLANN, Jaya-based MFLANN and ETLBO-JPSNN) considering 11 benchmark univariate time series datasets. Extensive statistical analysis on obtained results indicated that the proposed SATLBO-MFLANN method is better and statistically significant in comparison to its counterparts.
Extended Reality in Global Software Delivery - Towards a Common Fabric of Understanding and Insights

Large IT organizations depend on a global software delivery model which involves large teams with various roles and stakeholders. As software delivery is inherently collaborative, it requires the different roles to share artifacts, knowledge and insights with each other throughout the delivery life-cycle. Typically, each role has different insight and understanding needs, based on her/his context and activities. An architect may need to understand the system from a modularity perspective, a developer from code quality purposes, whereas a tester would need to understand it from a user-triggered flow perspective. However, the way we represent, understand, and collaborate on software artefacts, is still limited by the confines of traditional 2D computer screens. In this paper, we present our early work of an Extended Reality (XR) based approach that leverages affordances of natural human perception to represent and visualize software applications in three dimensions. This immersive approach is aimed at becoming a common fabric across global delivery roles, making activities like application comprehension, architecture analysis, knowledge communication, and analysis of a software's dynamic aspects, more contextual, richer, and intuitive. We posit that the use of XR, specifically augmented/mixed reality-based multidimensional views of different software artefacts, can be adapted to be an intuitive bridge across different roles, and foster a novel way of collaboration (both locally and globally). Here, we present our immersive approach and its prototype implementation, along with examples of its usage by different project roles. We also discuss some early feedback and the way forward.
Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach

With Bitcoin, Ether and more than 2000 cryptocurrencies already forming a multi-billion dollar market, a proper understanding of their statistical and financial properties still remains elusive. Traditional economic theories do not explain their characteristics and standard financial models fail to capture their statistic and econometric attributes such as their extreme variability and heteroskedasticity. Motivated by these findings, we study Bitcoin and Ether prices via a Non-Homogeneous Pólya Gamma Hidden Markov (NHPG) model that has been shown to outperform its counterparts in conventional financial data. The NHPG algorithm has good in-sample performance and identifies both linear and non-linear effects of the predictors. Our results indicate that all price series are heteroskedastic with frequent changes between the two states of the underlying Markov process. In a somewhat unexpected result, the Bitcoin and Ether prices, although correlated, are significantly affected by different variables. We compare long term to short term Bitcoin data and find that significant covariates may change over time. Limitations of the current approach—as expressed by the large number of significant predictors and the poor out-of-sample predictions—back earlier findings that cryptocurrencies are unlike any other financial asset and hence, that their understanding requires novel tools and ideas.
An Analysis of Automated Essay Grading Systems

Essays are one of the most important method for assessing learning and intelligence of a student. Manual essay grading is a time consuming process for the evaluator, a solution to such problem is to make evaluation through computers. Many systems were proposed over past few decades. Each system works on different approach having focus on different attributes. Aim of this paper is to understand and analyze current essay grading systems and compare them primarily focusing on technique used, performance and focused attributes.
Cyberpsychology: The Modern Stage of Development

In connection with the intensified development of digital technologies and the active (universal and daily) use of smart phones, computers and/or various gadgets by people of all ages, there emerge two problems. On the one hand, there is anxiety on the subject of types of behavior connected with the assimilation and use of new technologies that might carry along novel and still not fully understood social and psychological risks. On the other hand, one can notice the enthusiasm and “fan” attitude towards gadgets, which are increasingly replacing traditional ways of implementing communicative, labor, cognitive, game and other kinds of activity, mediate dyadic and group relations, as well as act as a&nbsp;new type of media. Thus, there is an increasing interest within the global society in conducting thorough psychological research aimed at identifying the real (not mythological) pros and cons of everyday use of digital technology products and in recommending the boundaries of this application. It is as a&nbsp;psychological discipline that is summoned to elaborate these kinds of answers to the society’s requests that cyberpsychology positions itself. The main goal of the article is to determine whether cyberpsychology is a&nbsp;separate branch of psychological science, or whether cyberpsychological problems are still being solved within the framework of traditional psychological disciplines. The status of cyberpsychology is revealed at the organizational level: the presence of interest in the results, the personnel and personnel training, professional associations, channels for informing the specialists about the results of the work, as well as prospects for the advance of competence. Although the approaches to the solution of organizational problems are largely different in Russia and in Western countries, the article emphasizes the approximation in the development processes of cyberpsychology as an independent branch of psychological science in the near future. The most noticeable organizational steps in this direction have been taken, as the article ascertains, in the very recent years.
Machine Learning Decision Tree Classifier and Logistics Regression Model

Machine learning is being used in several aspects and revolutionized the method of believing.It is branch of Artificial Intelligence that takes input data for training the models.In present paper we focus on widely used model of machine learning i.e.Decision tree classifier and Logistics Regression, additionally tracking their performance along with their accuracy.Machine learning is emerging notion in IoT environment as security of IoT devices are crucial due to their resource constrained properties.
A recommendation algorithm for point of interest using time-based collaborative filtering

Location-based social networks (LBSNs) make it possible for people to share their visited places by uploading the check-in information. To improve the efficiency of recommendation algorithm, researchers introduce check-in data into point of interest (POI) recommendation to help users find new and interesting place. However, some researches ignore the signification of time factor for POI recommendation in LBSNs. In this paper, we propose a time-based collaborative filtering algorithm according to the similarity between users which combines the global similarity during a long period and local similarity within a short time interval. The experimental results show that the method we proposed can get more accurate recommendation.
Importance Gauss-Hermite Gaussian Filter for Models with Non-Additive Non-Gaussian Noises

The paper deals with the state estimation of nonlinear stochastic systems with non-additive non-Gaussian noises. A new algorithm is proposed based on the computationally efficient Gaussian filter. The non-additivity and non-Gaussianity of the noises prevents the usage of standard quadratures to evaluate the moment integrals present in the Gaussian filter as these are not Gauss-weighted. The proposed algorithm leverages the importance Gauss-Hermite method to evaluate the integrals by means of the Gaussian proposal PDF. In order to improve the evaluation quality, an iterative improvement of the proposal PDF is employed. The paper also discusses the algorithm for special cases of the model with either process or measurement noise being additive yet non-Gaussian. The performance of the proposed algorithm is illustrated using a numerical example.
CURC: a CUDA-based reference-free read compressor

Abstract Motivation The data deluge of high-throughput sequencing (HTS) has posed great challenges to data storage and transfer. Many specific compression tools have been developed to solve this problem. However, most of the existing compressors are based on central processing unit (CPU) platform, which might be inefficient and expensive to handle large-scale HTS data. With the popularization of graphics processing units (GPUs), GPU-compatible sequencing data compressors become desirable to exploit the computing power of GPUs. Results We present a GPU-accelerated reference-free read compressor, namely CURC, for FASTQ files. Under a GPU–CPU heterogeneous parallel scheme, CURC implements highly efficient lossless compression of DNA stream based on the pseudogenome approach and CUDA library. CURC achieves 2–6-fold speedup of the compression with competitive compression rate, compared with other state-of-the-art reference-free read compressors. Availability and implementation CURC can be downloaded from https://github.com/BioinfoSZU/CURC. Supplementary information Supplementary data are available at Bioinformatics online.
A New Wavelet-Based Privatization Mechanism for Probability Distributions

In this paper, we propose a new privatization mechanism based on a naive theory of a perturbation on a probability using wavelets, such as a noise perturbs the signal of a digital image sensor. Wavelets are employed to extract information from a wide range of types of data, including audio signals and images often related to sensors, as unstructured data. Specifically, the cumulative wavelet integral function is defined to build the perturbation on a probability with the help of this function. We show that an arbitrary distribution function additively perturbed is still a distribution function, which can be seen as a privatized distribution, with the privatization mechanism being a wavelet function. Thus, we offer a mathematical method for choosing a suitable probability distribution for data by starting from some guessed initial distribution. Examples of the proposed method are discussed. Computational experiments were carried out using a database-sensor and two related algorithms. Several knowledge areas can benefit from the new approach proposed in this investigation. The areas of artificial intelligence, machine learning, and deep learning constantly need techniques for data fitting, whose areas are closely related to sensors. Therefore, we believe that the proposed privatization mechanism is an important contribution to increasing the spectrum of existing techniques.