Reviewerly/exHarmony huggingface dataset:
Security in the internet of Things: a systematic Mapping Study

&#x0D; &#x0D; &#x0D; &#x0D; The extend of clever gadgets has accelerated touchy statistics trade on the Internet the usage of most of the time unsecured channels. Since a large use of RFID (Radio-frequency Identification) tags in the transportation and development industries from 1980 to 1990, with the multiplied use of the Internet with 2G/3G or 4G when you consider that 2000, we are witnessing a new generation of related objects. A massive wide variety of heterogeneous sensors may also accumulate and dispatch touchy facts from an endpoint to a global community on the Internet. Privacy worries in Iot stay essential problems in the research. This paper aims to understand and additionally grant continuing doe’s research topic, challenge, and Future Direction related to Iot security. A systematic mapping finds out about (SMS) is thus utilized on the way to organize the chosen Articles into the following classification: contribution type, Type of Research, Iot Security, and their approach. We take out an overall of twenty-four Articles in support of this systematic discover out about also they categorize the following described criterion. The findings of this SMS are mentioned and the researcher was once given hints on the possible route for future research.&#x0D; &#x0D; &#x0D; &#x0D;
Revised web impact factor analysis of Timor Leste University website during COVID-19 pandemic

The global coronavirus disease 2019 (COVID-19) pandemic requires the Timor Leste government to make a school from home policy. This policy has encouraged several universities to improve the quality of their distance learning services. However, it seems that the government has not gone deep enough to conduct a preliminary study on each university website's readiness to face this. Based on previous research, the university website impact factor is one of the criteria for increasing webometrics rankings. Still, it is not explained in detail the types of referring pages and domains that can affect webometrics rankings. This research investigates the extent to which R-WIF affects webometrics and examines the factors that can improve the university website's quality. We surveyed the revised web impact factor (R-WIF) at seven universities in Timor Leste to determine their academic sites' relative standing. The results show, although some universities have increased the number of backlinks, the webometrics university ranking prefers backlinks that come from high-authorization sites such as .edu and .gov. The correlation value indicates this phenomenon between R-WIF and Webometrics of 0.464, which means moderate. Universities in Timor Leste need to implement several strategies to improve Webometrics rankings, especially visibility.
A Bibliometric Analysis of Articles on Text Simplification: Sample of Scopus Database

Literacy is a term generally used for adults and young people. Basically, it is an acquisition that includes the process of reading, writing and understanding symbols in any language. While this concept, whose definition and scope has expanded over time, refers to people who can only say their names in the past, today it refers to individuals who can perform more functional skills. What is expected from today’s literacy, which is also referred to as functional literacy, is not just saying the name, but also understanding what you read and harmonizing these information with the environment. It is critical to create texts that are easier to interpret, especially for poor readers and individuals learning foreign languages, in today’s world where reading comprehension and correct use of information have become extremely important. In this context, the text simplification method, which is one of the text modification methods, comes to the fore. In accordance with these information, this current study aims to presents the bibliometric analysis of articles on text simplification, which are published in journals indexed in Scopus database in the field of social sciences. The data set of the study consists of 194 articles on text simplification published in journals scanned in the field of social sciences and scopus database. These 194 articles were examined in terms of different variables. The research is generally a descriptive study and document analysis method was used as a method. In the data analysis stage of the research, VOSviewer visualization software version 1.6.16 was used. According to the results obtained from the study, the most articles on text simplification were written in 2020 (f: 21), most cited article is “Interpretation as Abduction” (f: 363) written by J.R Hobbs, M.E Stickel, D.E. Appelt and P. Martin. Findings obtained from the research were shared in the form of tables, graphs and figures. The most common keywords that preffered by authors is “Simplification” (f: 18). The most cited institution is “Artifical Intelligence Center” (f: 363). The most cited journal is “Artifical Intelligence” (f: 363). The most published country is United States of America (f: 30). The most cited country is United States of America (f: 863). All findings obtained from the research were shared as tables and figures in the findings section.
PPANet: Point-Wise Pyramid Attention Network for Semantic Segmentation

In recent years, convolutional neural networks (CNNs) have been at the centre of the advances and progress of advanced driver assistance systems and autonomous driving. This paper presents a point-wise pyramid attention network, namely, PPANet, which employs an encoder-decoder approach for semantic segmentation. Specifically, the encoder adopts a novel squeeze nonbottleneck module as a base module to extract feature representations, where squeeze and expansion are utilized to obtain high segmentation accuracy. An upsampling module is designed to work as a decoder; its purpose is to recover the lost pixel-wise representations from the encoding part. The middle part consists of two parts point-wise pyramid attention (PPA) module and an attention-like module connected in parallel. The PPA module is proposed to utilize contextual information effectively. Furthermore, we developed a combined loss function from dice loss and binary cross-entropy to improve accuracy and get faster training convergence in KITTI road segmentation. The paper conducted the training and testing experiments on KITTI road segmentation and Camvid datasets, and the evaluation results show that the proposed method proved its effectiveness in road semantic segmentation.
GANs with Conditional Independence Graphs: On Subadditivity of  Probability Divergences

Generative Adversarial Networks (GANs) are modern methods to learn the underlying distribution of a data set. GANs have been widely used in sample synthesis, de-noising, domain transfer, etc. GANs, however, are designed in a model-free fashion where no additional information about the underlying distribution is available. In many applications, however, practitioners have access to the underlying independence graph of the variables, either as a Bayesian network or a Markov Random Field (MRF). We ask: how can one use this additional information in designing model-based GANs? In this paper, we provide theoretical foundations to answer this question by studying subadditivity properties of probability divergences, which establish upper bounds on the distance between two high-dimensional distributions by the sum of distances between their marginals over (local) neighborhoods of the graphical structure of the Bayes-net or the MRF. We prove that several popular probability divergences satisfy some notion of subadditivity under mild conditions. These results lead to a principled design of a model-based GAN that uses a set of simple discriminators on the neighborhoods of the Bayes-net/MRF, rather than a giant discriminator on the entire network, providing significant statistical and computational benefits. Our experiments on synthetic and real-world datasets demonstrate the benefits of our principled design of model-based GANs.
CRITICISM OF CERTAIN PROVISIONS OF THE PERFORMANCE THEORY BY ERIKA FISCHER-LICHTE (on the example of participatory performances)

The article attempts to critically analyze the performance theory created by the German scholar Erika Fischer-Lichte. The theory is applicable to some performative art practices, but nevertheless its key provisions do not fully meet the objectives, capabilities and the very specifics of participatory performance. Instead of such concepts as “strong presence”, “liveness”, “authenticity” and the idea of “energy exchange”, the author suggests to analyze participatory practices using the method of “choragraphic communication”, which is understood as individual and collective generation-test of the possible, constant reinvention of the action figurativeness, re-shaping of the participants’ physicality, co-joint transformation of meaning-making. Comparison of the performances by J. Ono and M. Abramovich allows us to distinguish the key differences between the two approaches to the analysis of participatory performance. It is anticipated that the effect of a performance is to provoke in every viewer an affective experience of the diversity of the undone, of the unmanifested, which, however, might become possible through the realization of one of its incarnations in a specific action. The peculiarity of performative involvement is that the processes, triggered by the actions of the performers and the responses of the participantsspectators, make accessible the affective test of the possible as being in potency. Using the works of the modern performance artist Tino Sehgal as an example, the author shows that spectator’s participation as a special communication instrument appeals to a different culture of knowledge transfer, which is based not on presentation, documentation and archiving, but on the situational self-configuration of autopoietic systems, on the living muscle “memory” of the participants, actualizing such phenomena as “bodily knowledge”, “morethan- human” perception and procedural assemblage. Participatory performances create conditions under which “random” and unexpected actions of participants turn out to be a condition for (self) recreation of the form of the assemblage machine through constant reincarnation of the invariants of the whole. Sehgal’s works reveal each participant-spectator as a kind of an “actant machine” capable of reconfiguring the entire system by their actions, and offer an interface for realizing the possibilities of “electracy communication”. At the same time, the processes of joint reconfiguration themselves become available to the affective experience of each of the participants. They make the assemblage machine generate its own “proto-subjectivity”; and, probably, it is in the individual perception of each participant and in the experience of the stages of its formation where the aesthetics of participatory action may lie.
Unsupervised Wavelet-Feature Markov Clustering Algorithm for Remotely Sensed Images

Wavelet-feature Markov clustering algorithm for the remotely sensed data is based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying wavelet transform on the expanded multispectral data. The correlation ratio between the two samples is a statistical calculation of the matched peak point positions on the wavelet-feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. By applying a simulated annealing method and gradually shrunk clustering size, Markov clustering leads to the best class centers quickly at each clustering temperature. The experimental results about TM data have verified its acceptable clustering accuracy and high convergence velocity.
Choice of Environmental and Economic Path for Building a Supply Chain Financial Cloud Ecosystem under the Background of “Internet +”

With the development of the Internet age, the gradual deepening of reforms, and the continuous improvement of marketization, the financial ecological environment is directly related to the risk of financial institutions’ loans and the enthusiasm to support local economic development. Capital is accompanied by the physical development and evolution of the production and circulation of goods. After thousands of years of historical development and evolution, it has formulated its own development law and internal logic. In view of the unclear environmental and social evaluation indicators in supply chain research and the model’s failure to accurately describe the real supply chain network, an adaptive dynamic relaxation approximation algorithm model is proposed. This model introduces upstream and downstream transit stations as distributors and as a hub between customers, the transportation mode is improved to horizontal and vertical coordinated transportation, and multiple transportation methods are used for transportation. In this paper, the experimental group adopts a new model based on the approximate algorithm of dynamic relaxation factors, and the control group adopts the original old model. The two groups conduct comprehensive data analysis on social indicators, self-indicators, initial indicators, economic indicators, and environmental indicators in each year. Starting from the dimension, comprehensively consider the selection of indicators to measure the level of enterprise performance. The experiment proves that the transportation loss in the green supply chain is between the new model and the old model. When the number of partners is small, the degree of the optimal solution between the new and old models is not obvious, and the degree of optimization of the new model relative to the old model is increasing. It has a significant statistical difference ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). This shows that efforts to build a good financial ecological environment and an honest and reliable and healthy economic ecological environment are of great importance for promoting the continuous and rapid growth of the social economy.
Ultrasound image restoration through orthogonal and oblique subspace projection based on Lanczos decomposition

We introduced an efficient technique to suppress speckle noise in medical ultrasound images while maintaining low computational complexity. The medical ultrasound image is divided into overlapping subimages, and Lanczos decomposition is then applied to the average Hermitian covariance matrix of all subimages. The resultant orthonormal vectors are used for filtering speckle noise through orthogonal and oblique projections, i.e., by projecting noisy signal onto the signal subspace. After sorting the orthonormal vectors, an orthogonal projection matrix is formed by selecting the first K vectors contributing to the signal, whereas an oblique projection matrix is formed by selecting the first K vectors contributing to the signal and the last K vectors contributing to the noise. The procedure of Lanczos is also followed with singular value decomposition (SVD). Schemes are applied to real ultrasound images and two types of speckle noise simulations: fine and rough speckle noise. Numerical and visual results depict that proposed technique has outperformed various popular benchmark schemes, i.e., Frost, Lee, probabilistic nonlocal means, geometric nonlinear diffusion filter, guided speckle reducing bilateral filter, and SVD, while maintaining a competitive computational complexity. Lanczos-based scheme showed a relatively lagging performance in terms of resolution, which was always outshone by a leading performance in terms of key measures such as signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), feature similarity, and mean structural similarity. Orthogonal and oblique projections were found to perform the same except when speckle noise is fine, where insignificant differences were found in terms of SNR, PSNR, and resolution. Lanczos-based scheme tends to offer a better estimation of orthonormal vectors than SVD, consequently a better speckle noise suppression. Furthermore, it offers an efficient tuning parameter per block/subimage size to treat various speckle noise patterns.
Advanced Car Driving Assistant System: A Deep Non-local Pipeline Combined with 1D Dilated CNN for Safety Driving

Visual saliency refers to the part of the visual scene in which the subject's gaze is focused, allowing significant applications in various fields including automotive.Indeed, the car driver decides to focus on specific objects rather than others by deterministic brain-driven saliency mechanisms inherent perceptual activity.In the automotive industry, vision saliency estimation is one of the most common technologies in Advanced Driver Assistant Systems (ADAS).In this work, we proposed an intelligent system consisting of: (1) an ad-hoc Non-Local Semantic Segmentation Deep Network to process the frames captured by automotive-grade camera device placed outside the car, (2) an innovative bio-sensor to perform car driver PhotoPlethysmoGraphy (PPG) signal sampling for monitoring related drowsiness and, (3) ad-hoc designed 1D Temporal Deep Convolutional Network designed to classify the so collected PPG time-series providing an assessment of the driver attention level.A downstream check-block verifies if the car driver attention level is adequate for the saliencybased scene classification.Our approach is extensively evaluated on DH1FK dataset, and experimental results show the effectiveness of the proposed pipeline.
Introduction to Machine Learning

Big Data provides answers to all the questions generated by data and one can get these answers through Machine Leaning and Artificial Intelligence. This chapter will tell us all about machine learning, the different algorithms used to generate a model, and how Machine Learning can be used to get highly valuable outcomes from big data. Machine Learning algorithms can work on structured, unstructured, and semi-structured big data and are classified based on signals and feedbacks that are used to train the machine, namely, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. The type of output required differentiates machine learning models into regression, classification, and clustering. This chapter will also show how big data affects Natural Language Processing and will also deal with the concept of Deep Learning used in big data. At last the chapter will discuss how big data can be visualized using machine learning using a few graphical outputs and will also show how all this knowledge can be useful in the healthcare sector.
Latest trends in the Cybersecurity after the solar wind hacking attack

That dominance, in any case, has gotten to be a risk. On Sunday, Solar Winds alarmed thousands of its clients that an “outside country state” had found a back entryway into its most well-known item, an instrument called Orion that makes a difference organizations screen blackouts on their computer systems and servers. The company uncovered that programmers snuck a malevolent code that gave them inaccessible get to customers’ systems into an upgrade of Orion. The hack started as early as Walk, Solar Winds conceded, giving the programmers bounty of time to get to the customers’ inside workings. The breach was not found until the unmistakable cybersecurity company FireEye, which itself employments Solar Winds, decided it had experienced a breach through the program. FireEye has not freely faulted that breach on the Solar Winds hack, but it allegedly affirmed that was the case to the tech location Krebs On Security on Tuesday. FireEye depicted the malware’s bewildering capabilities, from at first lying torpid up to two weeks, to stowed away. That was December 13, 2020. FireEye gauges programmers to begin with picked up get to in Walk 2020. For about eight months, malevolent on-screen characters carted absent untold sums of touchy information from contaminated organizations — and the total scope of the breach is still unfolding. Despite Microsoft seizing the code’s command and control server (a common component in botnet assaults as well), a few security specialists think the assailants may still have get to the Solar Winds Orion program system. Others are conjecturing that these programmers cleared out behind extra, yet-to-be-seen malevolent code.
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances.

Several works have shown that perturbation instances of the MAP inference problem in Potts models can be solved exactly using a natural linear programming (LP) relaxation. However, most of these works give few (or no) guarantees for the LP solutions on instances that do not satisfy the relatively strict perturbation stability definitions. In this work, we go beyond these stability results by showing that the LP approximately recovers the MAP solution of a instance even after the instance is corrupted by noise. This noisy stable model realistically fits with practical MAP inference problems: we design an algorithm for finding close instances, and show that several real-world instances from computer vision have nearby instances that are perturbation stable. These results suggest a new theoretical explanation for the excellent performance of this LP relaxation in practice.
Implementasi Recurrent Neural Network dalam Memprediksi Kepadatan Restoran Berbasis LSTM

In this modern era, restaurants are becoming very popular, especially in big cities. However, this can lead to density or queues of visitors at a restaurant, which should be avoided during the current Covid-19 pandemic. So that accurate information that can predict the density of restaurant will be very useful. In predicting the density of restaurants, data processing on the number of visitors obtained from one of the restaurants is carried out using artificial intelligence in the form of LSTM (Long Short Term Memory) RNN (Recurrent Neural Network). The results of the research on Recurrent Neural Network based on LSTM (Long Short Term Memory) at the best learning rate parameter of 0.001 and a maximum epoch of 2000 resulted in an MSE value of 0.00000278 on the training data and 0.0069 on the test data
Solving Max-Min Separable Problem Using Hybrid Particle Swarm Optimization

The max-min separable optimization problem is depended on max-min algebra.This optimization problem focuses on the worst case for minimization of objective function.Additionally, this problem has separable max-min constraints.It was solved in another article by using difficult and complicated algebraic method for finding one solution only.In this paper, the Max-Min Separable problem under Max-Min Separable Linear Constraints (MMSMMLC) is solved by hybrid PSO.The main idea of the presented algorithms is combining GA processes with PSO algorithm to be suitable for solving MMSMMLC optimization problem under all types of constraints (equality and/or inequality).Finally, the new algorithms applied through several benchmark functions show that the new algorithms present all optimal solutions in best result with 100% and improve the optimal solution in some cases rather than algebraic method.