Reviewerly/exHarmony huggingface dataset:
Application Research of Manuscript Writing Robot Based upon Laser Sensor in News Dissemination Field

Under the background of the information and data age, intelligent technology has had a large and profound impact on the production field of each industry, and the field of news dissemination has inevitably become one of them. With the rapid development of new media technologies, the traditional news production model has been impacted by the development of the times. Out of the needs of the transformation and upgrading of its own industry and the further development of laser sensor smart technology, a manuscript writing robot based on laser sensors came into being. Manuscript writing robot is the first time in the history of the development of news dissemination that the main body of news manuscripts has been changed from a natural person to a machine, realizing the transformation of the news production process from semifixed to completely relying on machine automation. This paper first briefly introduces the manuscript writing robot’s working principle and work characteristics, analyzes its influence on other aspects, then models the machine hardware system, and finally conducts a quality comparison test between the manuscript writing robot and traditional artificial news production. Through the analysis of experimental data, the language organization ability of the manuscript writing robot is generally inferior to the traditional artificial language organization ability. However, the quality of manuscript writing robot data is 1.1 times that of traditional humans, and the speed of producing news is also 1.3 times faster than traditional humans. In terms of the depth and breadth of the scope of news, the overall average value of writing robots is 0.475 higher than that of traditional humans. Generally speaking, manuscript writing robots have clear advantages compared with traditional manual labor in news production and have a large room for development.
Pattern Recognition of Holographic Image Library Based on Deep Learning

The final loss function in the deep learning neural network is composed of other functions in the network. Due to the existence of a large number of non-linear functions such as activation functions in the network, the entire deep learning model presents the nature of a nonconvex function. As optimizing the nonconvex model is more difficult, the solution of the nonconvex function can only represent the local but not the global. The BP algorithm is an algorithm for updating parameters and is mainly applied to deep neural networks. In this article, we will study the volume holographic image library technology, design the basic optical storage path, realize single-point multistorage in the medium, and multiplex technology with simple structure to increase the information storage capacity of volume holography. We have studied a method to read out the holographic image library with the same diffraction efficiency. The test part of the system is to test the entire facial image pattern recognition system. The reliability and stability of the system have been tested for performance and function. Successful testing is the key to the quality and availability of the system. Therefore, this article first analyzes the rules of deep learning, combines the characteristics of image segmentation algorithms and pattern recognition models, designs the overall flow chart of the pattern recognition system, and then conducts a comprehensive inspection of the test mode to ensure that all important connections in the system pass through high-quality testing is guaranteed. Then in the systematic research of this paper, based on the composite threshold segmentation method of histogram polynomial fitting and the deep learning method of the U-NET model, the actual terahertz image is cut, and the two methods are organically combined to form terahertz. The coaxial hologram reconstructs the image for segmentation and finally completes the test of the system. After evaluation, the performance of the system can meet the needs of practical applications.
Internet.org and the rhetoric of connectivity

This article analyzes the discursive construction of Facebook’s goal to build Internet connectivity in India through its “internet.org” project. I argue that the rhetoric of connectivity emergent in this project uses the language of empowerment and innovation to operationalize a mode of entrepreneurial philanthropy amenable to millennial development that in reality reflects notions of progress reminiscent of colonial modernization practices. I also contend that this case demonstrates the need to understand the particular technopolitics of everyday encounters with infrastructures that cannot be reduced to any single vision of connectivity or singular understanding of digital contexts.
An Effective Framework for Enhancement of Hazed and Low-Illuminated Images

Abstract: Haze removal is important for computer photography and computer vision applications. However, most of the existing methods for removing theha- ziness are designed for daytime images and may not always work well at hazy night images. Unlike image conditions during the sunny day, images captured in winter night conditions can suffer from irregular lighting due to artificial light sources with varying colors and non-uniform illumination, which show low brightness, contrast and color distortion. In this paper, we propose a new frame- work for presenting night-time hazy imaging, which works on haze removal and low-illumination correction algorithm taking into consideration both the non-uni- form illumination of artificial light sources and the effects of dispersion and at- tenuation of fog. Therefore, firstly, we will give a hazy low-illuminated image having low light as input and then apply a technique to clarify the visibility of the input image. Then, apply the contrast enhancement and after that apply the LIME technique and finally, apply the white balance technique and we will get our improved output image. The experimental results show that the proposed algorithm can achieve an illumination balance, results without haziness and good color cor- rection capacity. Keywords: Image Enhancement, Low Illumination, Reflectance, Low Contrast, Low Light Images, Nighttime Images, Low Visibility Images, Nighttime Haze Removal.
The Relationship Between Leadership Functions and Performance Continuity in UAE Public Organizations: Moderating Role of Technology Business Analytics

Performance continuity is a major challenge for several organizations. In this regard the leadership plays a significant role. Thus, the purpose of the current research is to identify the role of leadership behaviors including challenging the process, inspiring a shared vision, and enabling others to act over performance continuity in UAE public sector organizations. Furthermore, the study also examined the moderating role of technology business analytics over the relationship between challenging the process, inspiring a shared vision, enabling others to act, and performance continuity in UAE public sector organizations. For the said purpose primary research has been conducted and the data has been collected from adopted instruments. The findings of the study revealed that challenging the process, inspiring a shared vision, and enabling others to act has a significant impact over performance continuity. Likewise the moderating role of technology business analytics over the relationship between challenging the process, inspiring a shared vision, enabling others to act, and performance continuity has also been found significant. The study enriched social exchange theory with the help of system theory to support the moderating role of technology business analytics. The study is significant for the academicians as well as practitioners to understand the leadership behavior required to enhance performance continuity.
Modelling and forecasting based on recursive incomplete pseudoinverse matrices

Time series modelling has a wide spectrum of applications in several fields including engineering and finance. Most traditional modelling techniques rely on assumptions related to the input data and manual pre-processing, based on user observations, rendering them unsuitable for analysing time-series with varying characteristics automatically, while more general modelling techniques usually require increased computational work for application and tuning. Recently, a general modelling framework based on a recursive Schur complement technique, that utilizes an adaptively determined set of basis functions, has been proposed. Herewith, a novel modified approach based on recursive incomplete pseudoinverse matrices in conjunction with preconditioned iterative methods for large datasets, is proposed. This sparse approach greatly reduces storage requirements and the recursive nature of the procedure avoids recomputation of the preconditioner after addition of a new basis function. Moreover, update of the coefficients for a different window of data and predefined basis functions can be performed utilizing the incomplete pseudoinverse matrix as preconditioner. The case of sinusoidal basis functions is presented along with a novel adaptive frequency estimation technique. The stability of the resulting model is discussed with respect to the choice of basis functions. The case of basis derived from machine learning techniques is also discussed. Numerical results are given depicting the applicability, generality and effectiveness of the proposed technique. Comparative results with other methods show forecasting RMSE improvement between 7% to 80%, for the majority of the chosen time series.
Detecting Text Anomalies in Social Networks using Different Machine Learning Algorithms

Today information is very powerful tool and this information is highly unreliable and wrong information can be very manipulative. It can cause harm to life as well. This in today’s world is called fake news or yellow journalism. Media sometime tries to manipulate and take advantage of innocent viewers by making them believe something that is not real. This has come to publicity recently due to the US presidential election and how media was used to operate the results of the election. Sometime news channel want to garner attention hence they intentionally put this news and as it is our human tendency we always go for the bad apple and this spreads faster than a normal true news would have. The news source got what they needed but they did so by making a fool out of millions of people. So how do we make this problem go away? We train different machine learning algorithms to recognize between fake and real and we know that a machine cannot be subjective and its decision will always be fair and mathematical. Hence in this project we create an API which can tell whether given news is real or fake based of our training data.
Automating Data Science

Abstract It has been observed that, in data science, a great part of the effort usually goes into various preparatory steps that precede model-building. The aim of this chapter is to focus on some of these steps. A comprehensive description of a given task to be resolved is usually supplied by the domain expert. Techniques exist that can process natural language description to obtain task descriptors (e.g., keywords), determine the task type, the domain, and the goals. This in turn can be used to search for the required domain-specific knowledge appropriate for the given task. In some situations, the data required may not be available and a plan needs to be elaborated regarding how to get it. Although not much research has been done in this area so far, we expect that progress will be made in the future. In contrast to this, the area of preprocessing and transformation has been explored by various researchers. Methods exist for selection of instances and/or elimination of outliers, discretization and other kinds of transformations. This area is sometimes referred to as data wrangling . These transformations can be learned by exploiting existing machine learning techniques (e.g., learning by demonstration). The final part of this chapter discusses decisions regarding the appropriate level of detail (granularity) to be used in a given task. Although it is foreseeable that further progress could be made in this area, more work is needed to determine how to do this effectively.
Verb third as Cluster movement in German

In this paper, I analyze specific syntactic properties of verb third sentences in German. It is argued that verb third is derived from the process of XP- cluster formation, i.e. a derivation in which elements representing a complex pre-field such as [CP α β [C’ V … ]] are merged by an adjunction operation [α[α β]] in which a maximal projection β is adjoined to another Xmax α similar to heads in clitic clusters or morphemes in verb movement operations in the middle field (TP) before the multi-segmental element moves higher up into the C-system. My analysis implies that verb third (V3) sentences in German can be regarded as a variant of verb second sentences because only one constituent precedes the finite verb in C0. The possibility of XP-cluster-movement in V3-sentences is linked to the scrambling (free constituent order) nature of German. Other scrambling languages such as, for example, Persian, Japanese and Korean, also show XP- cluster formation with subsequent cluster movement but without the combination of the V2- constraint. Further potential analyses of the V3-phenomenon in German, such as the multiple specifier analysis and the headless VP-fronting analysis are compared to the cluster analysis from an empirical and theoretical point of view.
Textual Filter for Detecting &amp;amp; Preventing Offensive Content in Social Networks for English Words

Person to person communication locales have been massively well known nowadays. Over 70% of the total populace is utilizing person to person communication locales and Specifically in India, Communication in English Language is predominant. More than 80% of India’s population uses English as a medium of communication. Destinations like Facebook, Twitter, LinkedIn, Instagram and WhatsApp have vigorously been used by individuals and particularly by teens nowadays. Interpersonal organizations like Twitter, Facebook and so forth, a ton of content is shared. Ultimately, these stages have advanced as a troublesome stage to fill the needs for the clients to communicate their every day exercises, sentiments and assessment by posting straightforward (messages) inside their companions circle. Day by day huge loads of data are being shared on various interpersonal organization stages. In this paper we are going to present and explicitly discuss the implementation of Textual filters for English Languages for detecting and preventing offensive contents in Social networks.
14. Multilevel thresholding using crow search optimization for medical images

Segmentation is the process of delineation of the desired region of interest and in the case of medical images; the region of interest are anatomical organs, tumor or cyst. The multilevel thresholding gains much importance for the images with complex objects and the role of the optimization algorithm is vital in the selection of threshold values. Thresholding is a classical segmentation algorithm and for the estimation of threshold values, Otsu’s or Kapur’s technique is used. This research work employs various optimization techniques like electromagnetic optimization, harmony search optimization and crow search optimization for the optimum selection of threshold values. The electromagnetism like optimization algorithm (EMO) is based on the electromagnetism laws of physics. The harmony search algorithm key concept is that, when musicians compose the harmony, they usually try various possible combinations of the music pitches stored in the memory, based on this concept, this algorithm was evolved. The bio-inspired optimization techniques are gaining prominence in many applications and the crow search optimization is based on the biological traits of crows. The multilevel thresholding, when coupled with crow search optimization, was found to yield efficient results. The less number of parameter tuning and less complexity makes crow search optimization, an efficient one for solving the real-world problems. The algorithms are developed in Matlab 2010a tested on real-time CT abdomen DICOM data sets. The performance metrics evaluation reveals the efficiency of crow search optimization in the multilevel thresholding segmentation approach.
A modification proposal for the reconciliation mechanism of the key exchange algorithm NewHope

Abstract The latest advances in quantum computing forced the National Institute of Standards and Technology (NIST) to launch an initiative for selecting quantum-resistant cryptographic algorithms. One of the best-known proposals is NewHope, an algorithm that was initially designed as a key exchange algorithm. In its original design, NewHope presented a reconciliation mechanism that is complex and represents an entry barrier for potential implementers. This contribution presents equivalent schemes in one, two and three dimensions, which allow anyone to make the transition to the 4D NewHope algorithm easier to undertake. In addition to that, the effect of modifying some of the parameters associated to NewHope’s reconciliation mechanism is studied, which has allowed us to propose different sets of parameters that could increase the security of NewHope implementations.
Robust Watermarking Technique for Sharing Family Photos on Social Media using Aadhar Number and DCT

The mind setup of persons has been changed in today’s environment due to the easily available of internet and smart phone on very low-price cost. Smart phone and internet are two main resources which are being used by persons most of the time in his/her daily routine specially in lockdown due to COVID-19. In this lockdown, persons are doing some creative activity, making fun, etc and recording all his/her this personal information in the form of multimedia contents like text, images, audio and video. This created multimedia content is shared by persons frequently on globe through internet in the daily routine life and some other persons are watching this daily routine activity and making huge business with these data by sometimes with original content or sometimes with modified content without concerns/information/permission of the originator. In this process if everything is going in right way then no issues but if something going wrong then require legal issues and for this, we need to protect our data legally through some methodology. So this paper proposed secure watermarking technique for protecting multimedia content like images using Aadhar number and Discrete Cosine Transform (DCT) technique. In this proposed methodology individual can share the information’s with watermarked information which is hidden in shared images and on demand at the time of legal issue originator will show the actuality and its ownership. This paper explained details concepts of the embedding and reverse of embedding ( i.e. extracting) process for authentication of the images and its protection from the misuse or fraud. The experimental result of the proposed methodology is shown on different family photos shared on globe and found robust results.
Dataset Characteristics (Metafeatures)

Summary This chapter discusses dataset characteristics that play a crucial role in many metalearning systems. Typically, they help to restrict the search in a given configuration space. The basic characteristic of the target variable, for instance, determines the choice of the right approach. If it is numeric, it suggests that a suitable regression algorithm should be used, while if it is categorical, a classification algorithm should be used instead. This chapter provides an overview of different types of dataset characteristics, which are sometimes also referred to as metafeatures. These are of different types, and include so-called simple, statistical, information-theoretic, model-based, complexitybased, and performance-based metafeatures. The last group of characteristics has the advantage that it can be easily defined in any domain. These characteristics include, for instance, sampling landmarkers representing the performance of particular algorithms on samples of data, relative landmarkers capturing differences or ratios of performance values and providing estimates of performance gains . The final part of this chapter discusses the specific dataset characteristics used in different machine learning tasks, including classification, regression, time series, and clustering.
Engineering Application of Laplace Transform

Basically Laplace Transform will convert function in some domain into a function in another domain, without changing value of the function. In This paper will discuss the various applications of Laplace Transform in different engineering fields. As well as it discuss about, how to solve differential equations by using Laplace Transform, How to find transfer function (T.F.) of mechanical system, How to use Laplace Transform in Nuclear Physics as well as Automation Engineering, Control Engineering and Signal processing.
A Divide-and-Conquer Bat Algorithm with Direction of Mean Best Position for Optimization of Cutting Parameters in CNC Turnings

Optimization of machining parameters is an important problem in the modern manufacturing world due to production efficiency and economics. This problem is well known to be complex and is regarded as a strongly nondeterministic polynomial (NP)-hard problem. To reduce the production cost of work-pieces in computer numerical control (CNC) machining, a novel optimization algorithm based on a combination of the bat algorithm and a divide-and-conquer strategy is proposed. First, the basic bat algorithm (BA) is modified with the aim to avoid finding the local optimal solution. In addition, a Gaussian quantum bat algorithm with direction of mean best position is developed. Second, in order to reduce the complexity of the optimization problem, the whole optimization problem is divided into several subproblems by using a divide-and-conquer strategy according to the characteristic of multipass turning operations. Finally, under a large number of machining constraints, the cutting parameters of the two stages of roughing and finishing are simultaneously optimized. Simulation results show that the proposed algorithm can find better combinations of the machining parameters than other algorithms proposed previously to further reduce the production cost. In addition, the outcome of our work presents a novel way to solve the complex optimization problem of machining parameters with a combination of traditional mathematical methods and swarm intelligence algorithms.
Textbook Evaluation of English Learning Material at SMKN 1 Lembah Melintang

One source of English learning material is the textbook.Textbooks aren't merely for teachers to utilize when they're teaching or learning.Textbook aids learners in acquiring material linked to their English studies.This study attempted to evaluate the English learning material in textbooks, particularly textbooks for vocational school students, based on the preceding rationale.Source of data in this study is students' textbooks that used at Grade XI of SMKN 1 Lembah Melintang.This research comes under the heading of evaluative research, which evaluates phenomena in order to describe the state of things in the field.The documentation will be used to assess the textbooks used by students as English learning materials.As a result of the study, textbook evaluation related to the English learning material is identified and classified based on BNSP's (2011) classification.It consists of content, language, and presentation aspect.