Reviewerly/exHarmony huggingface dataset:
Kernel Clustering With Sigmoid Regularization for Efficient Segmentation of Sequential Data

The segmentation of sequential data can be formulated as a clustering problem, where the data samples are grouped into non-overlapping clusters with the constraint that all members of each cluster are in a successive order. A popular algorithm for optimally solving this problem is dynamic programming (DP), which has quadratic computation and memory requirements. Given that sequences in practice are too long, this algorithm is not a practical approach. Although many heuristic algorithms have been proposed to approximate the optimal segmentation, they have no guarantee on the quality of their solutions. In this paper, we take a differentiable approach to alleviate the aforementioned issues. First, we introduce a novel sigmoid-based regularization to smoothly approximate the constraints. Combining it with objective of the balanced kernel clustering, we formulate a differentiable model termed <i>Kernel clustering with sigmoid-based regularization</i> (KCSR), where the gradient-based algorithm can be exploited to obtain the optimal segmentation. Second, we develop a stochastic variant of the proposed model. By using the stochastic gradient descent algorithm, which has much lower time and space complexities, for optimization, the second model can perform segmentation on overlong data sequences. Finally, for simultaneously segmenting multiple data sequences, we slightly modify the sigmoid-based regularization to further introduce an extended variant of the proposed model. Through extensive experiments on various types of data sequences performances of our models are evaluated and compared with those of the existing methods. The experimental results validate advantages of the proposed models. Our Matlab source code is available on github.
Extreme Learning Machine versus Multilayer perceptron for rainfall estimation from MSG Data

The application of artificial neural networks (ANN) in several fields has shown considerable success for classification or regression. Learning algorithms such as artificial neural networks must constantly readjust during the learning phase. This requires a relatively long learning time compared to the size and dimension of the data used. Contrary to these considerations, a new neural network, such as Extreme Learning Machine (ELM) has recently been implemented. The ELM does not care much about the size of the neural network, the hidden layer parameters are randomly generated and remain constant instead of being adjusted during training. In this paper, we will present a comparison between two neural networks, namely ELM and MLP (Multilayer perceptron) implemented for the precipitation estimation from meteorological satellite data. The architecture chosen for the two neural networks consists of an input layer (7 neurons), a hidden layer (8 neurons) and an output layer (7 neurons). The MLP has undergone standard training as soon as the ELM is trained according to the characteristics mentioned above. The results show that MLP prevails over ELM. However, the time cost during learning is too high for MLP compared to ELM.
Human-Robot Interaction System Using Vietnamese

Human-robot interaction is an important field that affects a robot’s friendliness and ability to communicate. This paper proposes a human-robot interaction system capable of communicating with humans in Vietnamese. The system is built and developed based on the human communication process. First, the identity and expression of the interlocutor are recognized by the system. This is to combine with speech of the interlocutor to form input data. They are categorized into search text and conversation text. The response of the search text is initialized from the results returned from the internet. Conversation text has responses generated from the trained model and data about the expressions and information of the interlocutor. These data are combined to form the final system response. Simultaneously, important data during the current conversation is also stored to use in the next conversation. The designed system is based on the interaction model of real people. That creates friendliness and empathy for the system. As the experimental results, the level of satisfaction in the process of interacting with the system reaches 78%.
Getting Ready

Angular taps into some of the best aspects of server-side development and uses them to enhance HTML in the browser, creating a foundation that makes building rich applications simpler and easier. Angular applications are built around a clear design pattern that emphasizes creating applications that are
Practical Space-Efficient Index for Structural Pattern Matching

In structural pattern matching, two n-length strings X and Y over $$\varSigma $$ are said to match, if there exists a one-to-one function $$f:\varSigma \rightarrow \varSigma $$ such that (i) for $$0\le i<n$$ , $$f(X[i])=Y[i]$$ and (ii) for any $$x,y\in \varSigma $$ whose complements are $$x'$$ and $$y'$$ , respectively, if $$f(x)=y$$ then $$f(x')=y'$$ . In this paper, we present a $$2n\lg \sigma +2n+o(n)$$ -bit index for this problem. Although it does not theoretically achieve the succinctness for a general alphabet, the proposed method is more practical and the space requirement can be smaller than the previous succinct solution especially when $$\sigma $$ is small. A source code is available at: https://github.com/sunghwank/spmindex .
Classification of Homogeneous and Non Homogeneous Single Image Dehazing Techniques

Due to the suspended particles in the atmosphere formed by hazy or foggy weather conditions produces a degraded image which causes change of color, reduced contrast and poor visibility. Practically haze cannot always be uniform in nature sometime non uniform haze can be formed. On the basis of haze formation single image dehazing can be categorized into two type’s i.e. homogeneous and non homogeneous haze. In this paper we have discuss different methods of single image dehazing used for homogeneous and non homogeneous haze. As qualitative parameter such as Peak Signal to Noise ratio (PSNR) and Structure Similarity Index Measures (SSIM) is used to check the quality of output dehazed image. On the basis of result obtained we have compared different techniques of single image dehazing. Average PSNR and SSIM value also calculated for 30 set of samples for both homogeneous and non homogeneous hazy images. Result is also analysis with plotted graph of average PSNR and SSIM value.
Using Data Bindings

The example application in the previous chapter contains a simple template that was displayed to the user and that contained a data binding that showed how many objects were in the data model. In this chapter, I describe the basic data bindings that Angular provides and demonstrate how they can be used to produce dynamic content. In later chapters, I describe more advanced data bindings and explain how to extend the Angular binding system with custom features. Table 10-1 puts data bindings in context.
Measures of Outlierness in High-Dimensional Data Under Correlation of Features – With Application for Open-Set Classification

This work deals with the problem of open-set classification in high-dimensional data. This task is realized using outlier detection methods which rely on outlierness measures appropriate for high-dimensional data. We numerically evaluate which of the outlierness measures (outlierness factors) proposed in literature, such as the local density-based LOF, angle-based ABOF, etc., is most suitable in terms of sensitivity and specificity of detection of outstanding observations in the specific task of open-set recognition. The analysis is done for varying dimensionality of data, varying distance between the outliers and the typical data, as well as varying correlation between features.
Sectoral Loan Distribution: Evidence from Aggregate Data of Nepalese Banks and FIs

This paper is prepared to highlight the key issues under loan distribution in a national discussion environment with special attention towards sectoral loan distribution of Nepalese Banks and FIs including the movement of agricultural finance and also deprived sector loan. The data for the study is extracted from the data set of NRB monthly and annual reports, world bank data, and Statista digital data set and figure. This paper aims to contribute the areas of research specific to national economy that has strong impact on so called claimed rational distribution of funds as per the established policies and guidelines specific to national interest and international references. The overall sectoral loan distribution trend shows the remarkable growth of Nepalese banks and FIs. This means that there is evidenced the sign of improvement in access of finance. Adequate information in the field of finance and specific to banking sectors is the primary concern for analyst. This paper highlights that putting big figure and figure as a whole might not get proper analytical space for rational researches in the areas of loan distribution. Sectoral loan distribution has not yet clearly presented in the national reporting data specifically, for instance, national reporting data is not yet evident showing product-wise loan distribution in the area of manufacturing. This shows data the reporting improvement is utmost in the Nepalese context.
Deep Reinforcement Learning for Autonomous Navigation in Robotic Wheelchairs

We propose a novel efficient method for autonomous navigation of nonholonomic mobile robots within complex indoor environments, with application to Electric Powered Wheelchairs (EPW). It is designed and developed using the Deep Reinforcement Learning (RL) framework. Specifically, an end-to-end navigation model is devised utilizing an “off-policy” RL algorithm, which fully exploits information from a realistic setup to perform map-less, collision-free navigation. The model takes as input noisy sensor readings and produces moving commands for the mobile robot, thus, mapping a flow of environmental observations to a continuous space of driving actions, to reach a desired target. The effectiveness and efficiency of the proposed approach is tested through experiments in simulation, in both seen and unseen environments, and is compared to human performance, as well as state-of-the-art motion planners. The results show that our trained planner is not only able to navigate the nonholonomic mobile robot (EPW) through the challenging scenarios with significantly high success rates, but also, it outperforms the baseline methods in a range of performance aspects.
Performance Analysis and Comparison of Acceleration Methods in JavaScript Environments Based on Simplified Standard Hough Transform Algorithm

In this paper, we present an analysis of popular acceleration methods in JavaScript execution environments including Chrome, Firefox, Node, and Deno. We focus evenly on adopting the same codebase to take advantage of every method, benchmarking our solutions and caveats of building libraries compatible with multiple environments. To compare performance, we use a simplified standard Hough transform algorithm. As reference points of our benchmarks, we use a sequential version of the algorithm written in both JavaScript and C++. Our study shows that Chrome is the fastest JS environment in every benchmark and Firefox is the slowest in which we identified optimization problems. WebGL appears as the fastest acceleration method. Without parallel execution native C++ addon in Node is the most performant. This analysis will help to find the most efficient way to speed up execution making JavaScript a more robust environment for CPU-intensive computations.
Deep Learning Algorithms

Conventionally, many techniques of machine learning and signal processing have been used widely with structured architectures containing a single layer and thus a lack of multiple layers of features make it less efficient technique. Overcoming these challenges, deep learning has become a significant field in the areas like image analysis, object and speech recognition, big data, etc. Emerging technologies require a flexible and efficient tool to process large amount of data, thus making deep learning a powerful tool to work in the field of machine learning and artificial intelligence. Deep learning being considered as equivalent to the human brain in terms of processing, outperforms the conventional algorithms. These algorithms are linked directly to the neural networks algorithms and process data or information about the images by passing them through layersof neural networks. This paper addresses important algorithms of deep learning widely used like CNN, RNN, deep belief networks and deep neural networks.
Android Forensic Tool

With the increase in use of mobiles, the smartphone has become a storehouse of data and can be a hub for criminal evidences. Nowadays data is very important and precious also it may be confidential and based upon it we can do many things. Data plays a crucial role in day-to-day life of every person. So, to deal with this, mobile forensic tools are used. In this paper we are presenting an application for extracting mobile contents like contacts, call logs and SMS using android app. So, if we lose data for any reason, we will be able to quickly access it. Our desktop application will show the data after backup in a user-friendly format (e.g., doc, pdf) so it is very convenient and easy for user. We have used .Net for desktop software and android studio for mobile application.
SmartTeV—Leveraging eVoting with Smart Voting Ballots

Research and commercial approaches on electronic voting (e-Voting) exist and the recently emerged blockchain technology has given this research area yet another boost. However, one may claim that the actual deployment of e-Voting is at its infancy, mainly due to election integrity issues such as vote non-tampering, voter anonymity, and vote verifiability. The integration of cryptography and blockchain technology in a single framework could provide a solid foundation for secure, anonymous and still verifiable electronic voting. The TrustedEVoting (TeV) framework is a blockchain-based eVoting approach, which wraps not only the required key elements for secure and verifiable e-Voting but also additional features such as support for re-voting and post-election vote check, among others. This paper presents a novel feature to be integrated in TeV, the smart voting ballot, that implements a ranked-choice voting by allowing the voter to cast two votes: a primary vote and an alternative vote. The latter one is utilized in the case the primary vote went to an election party that didn’t reach the threshold. In this way, a more flexible voting scheme is available to a voter, mitigating the voter reluctancy to vote for smaller parties. In order to protect the voter’s privacy by not revealing trends/preferences/statistics regarding the two votes, an intelligent mechanism is in place that provides a privacy shield for the alternative votes not being used in the voting tallying.
Using the Built-in Directives

In this chapter, I describe the built-in directives that are responsible for some of the most commonly required functionality for creating web applications: selectively including content, choosing between different fragments of content, and repeating content. I also describe some limitations that Angular puts on the expressions that are used for one-way data bindings and the directives that provide them. Table 11-1 puts the built-in template directives in context.
Part-of-Speech (POS) Tagging for the Nyishi Language

Part of speech is the building block of any language, and to operate efficiently in any language, it is beneficial to know about the part of speech of that particular language. The prime purpose of this paper is to create resources to carry out part-of-speech tagging of the Nyishi language, which in turn, will create a proper structured data for the Nyishi language. Nyishi part-of-speech (POS) tagging is more difficult than its English equivalent because it needs to be solved together with the problem of word identification. For the Nyishi part of speech tagging, we have built a 36 item tag sets, and from Nyishi-to-English dictionary, we have collected more than 25,000 entries both manually and automatically. In this paper, we will explain how the dictionary creation and part of speech of Nyishi language is done. Therefore, we have designed a tag set first based on training the data which will then be used in the construction of an automatic POS tagger for Nyishi language. And there are many challenges like ambiguity, foreign words, orthography, etc., to overcome.
Effective Crowdsourcing in the EDT Project with Probabilistic Indexes

Many massive handwritten document images collections are available in archives and libraries worldwide with their textual contents being practically inaccessible. Automatic transcription results generally lack the level of accuracy needed for reliable text indexing and search purposes if the recognition systems are not trained with enough training data. Creating training data is expensive and time-consuming. The European Digital Treasures project intended to explore crowdsourcing techniques for producing accurate training data. This paper explores crowdsourcing techniques based on Probabilistic Indexes. A crowdsourcing tool was developed in which volunteers could amend incorrectly transcribed words. Confidence measures were used to guide and help the users in the correction process. In further steps, this new corrected data will be used to re-train the Probabilistic Indexing system.
Work on the Background Service

Back to the college days, iPhone was a luxury device and only few could afford, Android was not even born yet, and Nokia dominated the world market of mobile phones. At that time, I used to enjoy Nokia’s Symbian OS because it supported background task which a lot of other phones did not support. I could make phone calls or listen to music keeping QQ live on the background, how cool was it! I also used to naively think that smart phones are those that support background tasks.
Enhanced Security with Crystography Using AES and LSB

In the digital modern age, where all data is processed with respect to automated transfers and protection provision are a major concern the remaining reason for the intruders/hackers’ popularity is that much of the intelligence they receive from a device is they can interpret and can easily understand. Even if the data is encrypted, the attacker may quickly access the encrypted data and can use techniques such as the method of brute force and attempt to decrypt it and may even succeed in decrypting it also reveal the information to other incognito users or alter the data to deceive a person or organization or the knowledge gathered could be used to launch an attack. Appropriately, a technique for hiding sensitive messages during transmission is required. Communication of data between the parties while preserving its secrecy is a big problem everywhere, so a non-conventional solution to Crystography is recommended to enhance the protection of shared data. Crystography the advanced method of encryption approach in which steganography, cryptography is fused into two major methods. In this, cryptography is used for data scrambling, while steganography is to mask scrambled data in a file of image which provides two layers of encryption. The purpose of this is to concentrate on AES symmetric cryptography, its applications and techniques, and steganography of images and to providing the most convenient move of stegno image through a zip file format. In this paper the advanced approach of images using histogram is used which has conceals the hidden image recovery without failure of data.
Applications of Artificial Intelligence in Software Testing

Software testing is a critical aspect of the software development process. It involves test cases or test suites to ensure the software product conforms to the user’s requirements. However, some issues currently affect the software testing process, such as human error, an unstable environment, and incorrect and incomplete requirements documentation. This paper explains how artificial intelligence (AI) concepts, including machine learning, deep learning, neural networks, and expert systems, can be applied to software testing to ease its rigorous and tedious process. This paper would also overview the different types of software testing, the advantages of using artificial intelligence pillars to software testing, and AI challenges in software testing.
Routing and Navigation: Part 2

In the previous chapter, I introduced the Angular URL routing system and explained how it can be used to control the components that are displayed to the user. The routing system has a lot of features, which I continue to describe in this chapter and Chapter 26 . This emphasis in this chapter is about creating more complex routes, including routes that will match any URL, routes that redirect the browser to other URLs, routes that navigate within a component, and routes that select multiple components. Table 25-1 summarizes the chapter.
Conceptual Model of Controlling Risks of Smart University Core Activities

Currently, smart university is a rapidly developing center of educational and scientific activities through the use of modern methods and tools of smart pedagogy, virtual learning environments. The changing conditions of the external and internal environment provoke the emergence of various types of risks to which the activities of modern smart universities are exposed. In this regard, it is not always possible to achieve pre-planned results of the success of personnel training processes that meet the requirements of stakeholders. This situation requires the implementation of operational and preventive measures to reduce the likelihood of risks or eliminate the negative consequences of their occurrence. In order to solve such problems, it is necessary to introduce a controlling system that takes into account the specifics of all the processes of the main activity of a smart university. The article highlights the main processes of the smart university. A classification of risks is presented and important directions of their manifestation are identified. The theoretical foundations for identifying the risks of the educational activities of a smart university are considered. The result of the study is the proposed conceptual model for controlling the risks of the core business of a smart university and the technology for its phased implementation. The results obtained were tested during risk controlling in the university's Smart division using the example of the department.
Blockchain Technology for Assisting the Analysis of Viruses and Other Pathogens

In this paper, we are trying to explore how blockchain technology can assist in the field of medical research. Here, we propose the use of this technology to store the records and observations after the research on various pathogens conducted by doctors and scientists. By this method we can create a data pool that is trustworthy, on which the researchers on this field can depend upon for the future studies of these pathogens and the diseases that they cause. This can also accelerate the process of developing a vaccine due to the abundance of accurate information from all over the world. The Coronavirus scenario showed us that our healthcare system could be made much better and we need to keep improving our technologies and methods to effectively identify, analyze and take necessary actions with life threatening pathogens in an early stage. By integrating the technique of blockchain to the framework of our healthcare system we can make the process of transferring information much more secure and efficient.