Reviewerly/exHarmony huggingface dataset:
Enedis IoT Smart Grid Solutions for More Efficiency

Enedis has established a smart grid roadmap in France (also called Road 22) with two main objectives to achieve by 2022: This paper presents the impact of the IoT on Enedis Smart Grid Strategy, describes several IoT solutions developed by Enedis and gives some use cases based on IoT solutions. Due to smart metering (more than 23 million of Linky smart meters have been already deployed in mid-December 2019) and other connected objects, Enedis is now able to know what happens on its network in real-time.
Survey on Sentiment Analysis for Mix Code Text

Sentiment analysis is difficult nowadays when we collect data from social media sites as people from every walk of life come there and write their opinion about various things. Social media is very powerful, but it has a lot of liberty too. People while posting text online do not give any importance about how grammatically correct they are. Moreover, they do not think even once before mixing languages in their text. This sounds cool to them, but this transliterated code-mixed text makes the task of sentiment analysis a bit more difficult due to the complexity. In this paper, we will be discussing the issues related to sentiment analysis in mixed code text, some important works done in this field as well as the pros and cons of their approaches. We will also focus on how a perfect solution to the problem can benefit society.
Using Evolutionary Algorithm in On-line Deployment

There is a problem with arranging many elements in the window in many applications. It is more difficult when the elements are dynamically created or selected from a particular set. There is then a risk of obstruction by objects, which reduces the legibility of the window. Such a problem arises when generating dynamic help for items in a window in the workflow management system designed by Syndatis laboratories. Artificial intelligence algorithms can be used for this purpose, but the obstacle is their long calculations, which in the described case should be completed in less than a second. We proposed to use the evolutionary algorithm with a limited number of generations, which gives satisfactory results.
Image Compression Using Histogram Equalization

The camera, scanner, or other digital devices create huge images, which have excess and redundant information not visualized by the human eye. The compression method eliminates extra and redundant data that does not hamper the image’s visual perception. Image compression is beneficial for large-size image transmission and huge amounts of image storage in databases. The histogram equalization-based image compression method is proposed for performance analysis of the different image compression methods. The paper attempts to present a compression model by selecting different stages of compression. The first standard model’s steps are the Discrete Wavelet Transform, Quantization, and Huffman coding. The proposed new model’s steps are Histogram Equalization, Discrete Wavelet Transform, Quantization, and Huffman coding. The objective of the proposed model is to improve redundancy. These two models have been experimented with the four standard grayscale images using the Matlab platform. The image quality and compression efficiency were evaluated using Mean Square Error, Peak Signal to Noise Ratio, and Compression Ratio. The experimental outcomes indicate that our model achieved a higher Compression Ratio value, 17.3819, than the standard image compression model and the other five traditional image compression models. The obtained results show that our model has provided the required level of Peak Signal to Noise Ratio and Mean Square Error values.
Getting Started with QDK and Q#

This chapter is dedicated to familiarizing the reader with Quantum Development Kit and the Q# programming language. The major objectives include ensuring successful development environment setup and providing familiarity with the main Q# language concepts. The chapter offers a tour through the language features along with relevant examples that can be used as reference point later. Overall, this chapter is a pre-requisite for the formalism employed later in the book, where every theoretical discussion about quantum computing concept is accompanied by a runnable Q# code example.
A Novel Clustering Algorithm with Dynamic Boundary Extraction Strategy Based on Local Gravitation

Clustering has been widely used in visual analysis, pattern recognition, privacy protection and other fields. In recent years, numerous clustering methods have received increasing attention. However, discovering arbitrarily shaped clusters, determining the location and number of clustering cores and dealing with fuzzy boundaries is tough for most algorithms. We propose a novel clustering algorithm with dynamic boundary extraction strategy based on local gravitation (DBELG) which extracts boundary in a natural way, rather than mechanically defining a few core points. In order to identify fuzzy boundaries, a novel gravity model that makes use of three significant information about the data objects is proposed. The structure of the reserved core groups is clear and easy to cluster. On this basis, the core group clustering (CGC) is further proposed to cluster the core points. The experimental results show that DBELG achieves better performance than existing methods in handling datasets with fuzzy boundaries and complex structures.
Codify: 'Natural Language to Code, for Data Science Applications'

According to recent studies, a large number of data scientists spend most of their time on tasks like data cleaning and organizing the data which is very time-consuming. Data scientists have to memorize long complex syntaxes for major tasks in the data science life cycle. Often these tasks are deemed to be redundant and time-consuming across all the data science projects like data cleaning and modeling. So, we propose to build a smart system that enables data scientists to perform all the tedious and time-consuming tasks such as EDA, data cleaning, data pre- processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English) and the system will automatically give out all the relevant python code snippets. Existing applications involving text-to-code generation and code-search are very limited and most of them do not work in non-ideal conditions. The main reason for this is the dataset that the existing models are based on. These datasets do not account for real-world factors like slang language, acronyms, paraphrased sentences, etc. Therefore, a new dataset was created consisting of real-world user queries which represent the scenarios users are most likely to face during daily usage of this system. Furthermore, using the same dataset we train various intent classification models which include GloVe based word embedding, Facebook InferSent, Semantic Subword Hashing, and TF-IDF model to find the best technique to classify the intent of user query from our dataset and NER (Named Entity Recognition) model, to identify all the entities present in the userâ€™s sentence. Hence, we plan to build a logic-oriented system that only requires the user to simply convey the logic correctly in natural language via text. This will most certainly save time exponentially and data scientists can devote most of their time to building logic rather than focusing on code.
DRN: Detection and Removal of Noisy Instances with Self Organizing Map

Identification of noisy instances provides an effective solution to improve the predictive performance of machine learning algorithms. The presence of noise in a data set poses two major negative consequences: (i) a decrease in the classification accuracy (ii) an increase in the complexity of the induced model. Therefore, the removal of noisy instances can improve the performance of the induced models. However, noise identification can be especially challenging when learning complex functions which often contain outliers. To detect such noise, we present a novel approach: DRN for detecting instances with noise. In our approach, we ensemble a self-organizing map (SOM) with a classifier. DRN can effectively distinguish between outlier and noisy instances. We evaluate the performance of our proposed algorithm using five different classifiers (viz. J48, Naive Bayes, Support Vector Machine, $$k $$ -Nearest Neighbor, Random Forest) and 10 benchmark data sets from the UCI machine learning repository. Experimental results show that DRN removes noisy instances effectively and achieves better accuracy than the existing state-of-the-art algorithm on various datasets.
Towards an Efficient Facial Image Compression with Neural Networks

Digital images are more and more part of everyday life. Efficient compression methods are needed to reduce the disk-space usage for their storage and the bandwidth for their transmission while keeping the resolution and the visual quality of the reconstructed images as close to the original images as possible. Not all images have the same importance. The facial images are being extensively used in many applications (e.g., law enforcement, social networks) and require high efficient facial image compression schemes in order to not compromise face recognition and identification (e.g., for surveillance and security scenarios). For this reason, we propose a promising approach that consists of a custom loss that combines the two tasks of image compression and face recognition. The results show that our method compresses efficiently face images guaranteeing high perceptive quality and face verification accuracy.
Real-Time Multi-Object Detection Using Enhanced Yolov5-7s on Multi-Gpu for High-Resolution Video

Multiple objects tracking in a video sequence can be performed by detecting and distinguishing the objects that appear in the sequence. In the context of computer vision, the robust multi-object tracking problem is a difficult problem to solve. Wide-area video surveillance is increasingly using advanced imaging devices with increased megapixel resolution and increased frame rates. As a result, there is a huge increase in demand for highperformance computation system of video surveillance for real-time processing of high-resolution videos. In this paper we used a single stage framework to solve the MOT problem. We proposed a novel architecture that allows for the efficient use of one and multiple GPUs are used to process Full High Definition video in real time. For high-resolution video and images, the suggested approach is real-time multi-object detection based on
English Translation Software Accuracy Correction Based on Machine Learning Technology

With the rapid development of the mobile Internet and the expansion of the audience of smart mobile electronic devices, business opportunities have been brought to the further development of electronic translation software. English translation software can provide high-quality and accurate English translation services, which provides great convenience for students and even working groups to learn and use English. Therefore, this paper studies English translation software based on machine learning technology, uses correction algorithms to optimize software translation accuracy, compares the translation results before and after correction from the perspectives of translation completeness error rate and coherence error rate, and finds the corrected software translation error rate is significantly reduced, which shows that the accuracy of translation has improved.
A Case for COVID-19: Considering the Hidden States and Simulation Results

In this chapter, you’ll carry out a set of sequential methods to discern patterns in confirmed COVID-19 cases in the US. To begin, you’ll use the Gaussian Hidden Markov Model to inherit the series, model it, and consider the hidden states, including the means and covariance in those states. Subsequently, you’ll use the Monte Carlo simulation method to replicate confirmed US COVID-19 cases across multiple trials, therefore providing a rich comprehension of patterns in the data.
Examples of Applications

In this chapter we present some applications of the regularized RKHS-based learning algorithms, which have been described and analyzed in this book. We mainly restrict ourselves to learning in the ranking setting, because examples of the application of RKHS-regression learning algorithms can easily be found in the literature (see, for instance, Chapters 4 and 5 of [8] and the references therein). We begin with a demonstration of the performance of regularized ranking algorithms on some publicly available benchmark datasets. Then we discuss possible applications in Diabetes Technology. Finally, we consider one more biomedical application which is automatic stenosis detection from magnetic resonance imaging data, where we employ results on regularization of unsupervised domain adaptation in RKHS.
Segmentation of Image Using Hybrid K-means Algorithm

Image segmentation is a crucial step to recognizing an object. During the segmentation process, pixels in an image are categorized based on their gray color. In pixel classifications, the K-means clustering algorithm is commonly used. In this approach, the centroid of the segment was measured using arithmetic mean and Euclidean distance. In the proposed paper, the centroid was updated using the hybridization of harmonic and arithmetic means. The proposed algorithm makes use of the harmonic and arithmetic mean features. The experimental results are compared to conventional K-means and harmonic K-means algorithms, demonstrating that the proposed algorithm performs better when checking segmentation consistency.
QNX6

Abstract The QNX6 filesystem is present in Smartphones delivered by Blackberry (e.g. Devices that are using Blackberry 10) and modern vehicle infotainment systems that use QNX as their operating system. In 2015 QNX as an OS was used in over 50 million vehicles [6] and can hence be considered as one of the most important operating systems in the automotive world. Today’s digital forensics tools don’t recover a lot from this filesystem, have difficulties with different block sizes, or even don’t support the filesystem at all. So it’s crucial for the forensic examiner to understand the principles of this filesystem used. This chapter gives an overview of how the filesystem generally stores the files and metadata to give the examiner the chance to get the most information out of the evidence.
Image Analysis

Analysis of discrete images is introduced. Separate sections cover segmentation, feature extraction, measurement, classification and description.
Robot Path Planning Method Based on Improved Grey Wolf Optimizer

Aiming at the shortcomings of the classical grey wolf optimizer in solving the path planning problem of mobile robots, such as low search efficiency and broken line turning, this paper proposes an improved Grey Wolf Optimizer based on dynamic programming to apply to robot path planning. In the process of initial population generation, ant colony optimization is introduced to generate feasible paths, and the idea of random walk, sine function and logarithmic function is used to optimize the wolf group algorithm, balance the global and local exploration capabilities, and improve the search efficiency. At the same time, dynamic programming and B-spline curve are introduced to trim redundant path points and smooth turning angles. Simulation results show that the algorithm can efficiently avoid obstacles in various path planning environments. It has the characteristics of short time-consuming, good optimization effect, smooth corner and not easy to fall into local optimization. It is an efficient algorithm for solving robot path planning problems.
Primer, Part 2

In this chapter, I continue to describe the basic features of TypeScript and JavaScript that are required for Angular development and briefly touch on the RxJS package, which is used extensively by Angular and is required for some advanced features.
Choosing Wordlists for Password Guessing: An Adaptive Multi-armed Bandit Approach

Abstract A password guesser often uses wordlists (e.g. lists of previously leaked passwords, dictionaries of words in different languages, and lists of the most common passwords) to guess unknown passwords. The attacker needs to make a decision about what guesses to make and in what order. In an online guessing environment this is particularly important as they may be locked out after a certain number of wrong guesses. In this paper, we employ a multi-armed bandit model to show that an adaptive strategy can actively learn characteristics of the passwords it is guessing, and can leverage this information to dynamically weight the most appropriate wordlist. We also show that this can be used to identify the nationality of the users in a password set, and that guessing can be improved by guessing using passwords chosen by other users of the same nationality.
Analysis System of City Price Based on Big Data

This paper endeavours to develop an investigation model of city cost, by joining the large information system with the data of city cost, in order to give the reference to the government to execute the strategy of the precise cost control. This paper thinks about that the investigation arrangement of city cost ought to incorporate basic leadership layer, supporting layer furthermore, demonstrating layer on the reasonable level, and its transmission way ought to incorporate the information assortment, information the executives, information mining, basic leadership and security insurance. At the point when it goes to the development of the subsystem, mechanical instruments for example, information mining, distributed computing and representation ought to be utilized, fundamentally so as to manufacture information procurement subsystem, information the board subsystem, information investigation subsystem, information transmission subsystem, etc., and give the realistic depiction for the comparing innovative way at the same time.
Using Service Providers

In the previous chapter, I introduced services and explained how they are distributed using dependency injection. When using dependency injection, the objects that are used to resolve dependencies are created by service providers, known more commonly as providers. In this chapter, I explain how providers work, describe the different types of providers available, and demonstrate how providers can be created in different parts of the application to change the way that services behave. Table 18-1 puts providers in context.
MENTORING AS A TECHNOLOGY FOR DEVELOPING THE LABOR POTENTIAL OF EMPLOYEES

The article reveals the essence of mentoring (mentoring, mentoring) as a way of transferring professional experience to the younger generation, gives the concept and different points of view on the problem of mentoring in Russia, shows its positive aspects and the need to reflect this method at the legislative level. The material introduces the areas of the most effective use of the mentoring method, the problems of introducing mentoring (mentoring) in organizations and ways to attract experienced professionals to work in the mentoring format. The article provides an opinion about the method being studied by representatives of the manufacturing sector, government authorities, and business. Various forms of existence of mentoring in modern Russian realities are shown. The article uses material published from 2017 to 2022, mainly in electronic periodicals, or on the websites of companies engaged in mentoring activities.
Creating Structural Directives

Structural directives change the layout of the HTML document by adding and removing elements. They build on the core features available for attribute directives, described in Chapter 13 , with additional support for micro-templates, which are small fragments of contents defined within the templates used by components. You can recognize when a structural directive is being used because its name will be prefixed with an asterisk, such as *ngIf or *ngFor. In this chapter, I explain how structural directives are defined and applied, how they work, and how they respond to changes in the data model. Table 14-1 puts structural directives in context.
Environmental Geological Studies

Careful environmental studies, including pollution, environmental hazards, flooding and other factors, are an integral part of any careful study of alluvial fan sediments (Pourmorad et al. 2021). In this chapter, the aim is to study the various factors affecting the environment of these areas in terms of various environmental pollutants (soil pollution and groundwater quality assessment) and various hazards (including seismicity and flooding). What makes these studies important along with the studies of alluvial fan sediments in these areas is the very close and direct relationship between these sediments and the increase and intensification of hazards and pollution in these areas. In fact, the type of sediment, the slope of alluvial fans and the intensity of debris currents can cause human catastrophes in these areas along with high-risk earth activities such as earthquakes, surface runoff and landslides. On the other hand, the type of lithology of the constituent formations can also have a direct impact on groundwater quality. In fact, the main purpose of writing this chapter was to go one step beyond the science of pure sedimentology and by applying it, to provide this information to the safety of human society in the study areas, to achieve this goal, the missing link It will be between pure studies and applied studies and the strength of these studies. In this chapter, first, environmental pollution in terms of the presence of heavy elements and rare earth elements and groundwater quality assessment of these areas are studied, and in the next stage, geological hazards in terms of seismicity and flooding in the studied areas are investigated.
Generation of Nepalese Handwritten Characters Using Generative Adversarial Network

Recently, there has been interest in generating natural looking handwritten characters that the observers could not distinguish whether they are generated or written by humans. The generation of handwritten characters is a popular research topic having several applications. In the context of Nepalese handwritten character, very few of the steps were put forward for the generation of Nepalese character. In this research, a novel step to build a model that could generate the natural, realistic and visually appealing Nepalese handwritten characters using generative adversarial network has been proposed. This research has been focused primarily on generating Nepalese handwritten characters with appropriate modification of generative adversarial network architecture and finely tuned parameters and hyperparameters. Parameters such as accuracy, training time, activation function, optimization function, objective function and training accelerator are tested on different GAN architectures. The efficiency of each GAN architecture is evaluated using the performance metrics such as mean opinion score (MOS), visual turing test, number of epochs required and average training time. It was observed that generated images resemble more likely to real images with MOS score of 2.91 and true recognition rate (TRR) of about 63%.
A Comparative Analysis on Web Personalization Techniques

The Internet has a large amount of data and is expanding continuously; therefore, it is very important to organize this information in order to provide the users with the best recommendation based on their interests. This is where personalization comes into play. In terms of web pages, personalization refers to the customization of web pages with information that is relevant to a specific user which is determined by tracking the user’s surfing history. This paper aims at discussing the various personalization approaches used in web page personalization and concludes the paper with our findings in this domain.