Reviewerly/exHarmony huggingface dataset:
Gazebo – Simulink Framework for Trajectory Tracking in a Multi-Quadcopter Environment

Physical testing of quadcopter controllers is challenging as the quadcopters are vulnerable to damages, and the cost needed for setting up a flying arena is enormous. This work aims to establish a simulation framework that can be used to analyze and verify the performance of the control algorithms developed for quadcopters before actually deploying them in the actual quadcopters. The framework seamlessly integrates the controller models developed in MATLAB/Simulink and the physics-based Gazebo simulator through Robot Operating System. The framework is extended to simulate multiple quadrotors and to test the performance of each of their control algorithms. In this work, two independent Proportional, Integral, Derivative (PID) controllers developed in Simulink have been used to control two different Crazyflie quadcopters to make them follow the desired trajectories. The framework can be used for pedagogy and by the researchers for developing and testing the performance of control algorithms developed for any of the physics-based quadcopter models in the Gazebo simulator.
An empirical evaluation of strategies based on the triangle inequality for accelerating the k-means algorithm

The k-means clustering algorithm has a long history of success in a wide range of applications in many different research areas. Part of its success is due to both, the simplicity of the algorithm, which helps its quick implementation and the good results it produces. Despite success, however, the original k-means has some shortcomings. One of them relates to the processing time required for the algorithm to finish the iterative process that, given a set of data instances, and an integer value k, induces a clustering having k clusters of the given data instances. This article presents an empirical evaluation of three strategies found in the literature that employ the triangle inequality, with the purpose of accelerating the k-means processing time. Experiments were conducted using two groups of datasets, seven real datasets and ten artificially created datasets. Besides empirically evaluating the impact of variables involved in clustering processes that can interfere with accelerating processes, the article also discusses the different ways the triangle inequality concept is employed by the strategies.
Gapping in Modern Standard Arabic: An Agree-Based Analysis

This paper investigates the phenomenon of Gapping in coordination structures in Modern Standard Arabic (MSA). It proposes an analysis based on Chomsky's (2005, 2008) Feature-Inheritance model of Agree (FI, henceforth). The analysis diverges from other works on coordination such as Deletion (e.g., Hankamer,1979; Wilder, 1994), Low VP Coordination and VP-Ellipsis (e.g., Toosarvandani, 2013), and Low vP Coordination followed by an Across-the-Board (ATB) movement (Johnson, 2009); instead, it analyzes coordination in MSA as a binary relation where every coordinated conjunct is a full CP, and the conjunction is the head of a ConjP. The analysis uses semantic as well as syntactic explanation for Gapping in MSA, thus rendering the need for a distinct structure for coordination unnecessary.
ANALISIS IMPLEMENTASI SISTEM PENGENDALIAN INTERN PEMERINTAH DAN PENGARUHNYA TERHADAP PENGELOLAAN KEUANGAN DAERAH

This research is quantitative research to exam of the implementation of Government Internal Control System (GICS) which include the control environment, risk assessment, control activities, information and communication, and monitoring of financial management. The research was conducted on the entire regional work units (40 SKPD) Bengkulu city government. Data obtained from questionnaires to 40 respondents, the toplevel officials at all on education. The results showed that the Government Internal Control System Implementation (GICS) includes control environment, risk assessment, control activities, information and communication, and monitoring simultaneously affect the financial management area. The better implementation of the GICS, the better financial management. GICS components partially proven positive effect on financial management is risk assessment, information and communication, and monitoring. The better the risk assessments conducted, information and communication systems are applied, and evaluation/ monitoring of implementation GICS good, the better the financial management area in Bengkulu city government on education.
Face Recognition Method Based on Adaptive Feature Fusion

The rapid development of computer software and hardware technology has laid a very solid foundation for computer vision tasks such as target recognition that use images as input carriers. The purpose of this paper is to study face recognition methods based on adaptive feature fusion. The principle of Adaboost algorithm and the training process of strong classifier and the cascade process of strong classifier of Adaboost algorithm are studied. The algorithm of face detection is improved through Haar-like feature extraction and weight update during algorithm iteration. At the same time, in order to detect the face more accurately and quickly, the method of skin color detection is introduced to extract the candidate face region, and an initialization model that can adapt to the change of the target face is proposed based on the previous invariant average initialization model. Then, the Adaboost algorithm is experimentally tested through the face database, network pictures, and video images combined with the previous image preprocessing method. The detection rate of the face database can reach 97.56%. The test results show that the algorithm can meet the design requirements.
Purpose of Encryption and Available Tools

In this chapter we discuss the purpose of encryption and why you might be thinking about encrypting data in the first place, as well as what you hope to achieve by it. Then we take a brief overview of the tools available for encryption within SQL Server and recommend some overall strategies for using the tools in concert to provide the best level of security.
A Wireless Sensor Based Multi-layer Hybrid Deep Learning Model for Highly Correlated Human Activity Recognition

Sensor based human activity recognition has obtained more attentions due to its low-cost, low-data throughput, and immunity to environmental effects. However, traditional work in this field mainly focuses on the recognition of simple and small volume human activities. This work targets complicated, correlated and larger size of human activity recognition. In this paper, a multi-layer hybrid deep learning model is built with convolutional neural networks (CNN) and long short-term memory (LSTM). The multi-layer architecture improves the learning and exploration capacity of local features and temporal dependencies, and the hybrid architecture enriches the diversity for data fusion. In addition, Bayesian optimization is applied to the hybrid model to get the optimal parameters and best performance. The experimental results demonstrate the effectiveness of the proposed model with a recognition rate of 89% for 27 correlated activities. Its performance is better than traditional machine learning and other hybrid deep learning models.
14. Introduction to Part II

Abstract This chapter is basically an advertisement for the material in part II of the book. That part begins with Shannon's communication model of information, Dretske's application of that to knowledge, and Barwise and Seligman's extension of Dretske's notion to generalized distributed systems. Then this apparatus is used to construct a complete theory of experimental knowledge.
Smart resource scheduling by correlating discriminative quality factors to optimise resource utilisation in IAAS of cloud networks

The significant requirement in cloud computing is detecting the deterministic model for performing resource scheduling. Resource scheduling is crucial because of the presence of several anomalies and high dimensionality values shown during resource scheduling. The number of tasks envisioned in contention at resource broker of IAAS and higher dimensionality of proposed anomalies values for quality resource aspects was out of scope concerning the existing resource scheduling techniques. Therefore, the contemporary resource scheduling techniques were not prominent for optimal resource scheduling. For optimising resource scheduling in terms of earlier mentioned properties like the maximum amount of tasks and maximum dimensionality anomalies projection, an ensemble resource scheduling technique has been derived in this article under batch scheduling classification. Further, the simulation study envisioned that the projected model of this contribution is more significant and robust for delivering optimum resource scheduling when compared to other contemporary models in terms of dimensionality and volume of anomalies.
Towards Practical and Formal Security Risk Analysis of IoT (Internet of Things) Applications

We present the initial results of developing a security risk analyzer for Internet of Things (IoT) applications that analyses both evitable and inevitable yet known and unknown cyber-attacks and as a result produces the adversarial strategies (multi-stages of attack) that can compromise the application. Our risk analyzer is rigorous and qualitative, performing technical analysis, as well as quantitative yet useful, identifying sub-attacks and their quantitative risks. In contrast, conventional security risk analyzers either provide too specific risk assessment or provide a too generic risk assessment of a given application. Such analyzers are typically not practical against constantly changing attacks of the variable extent and complex modern IoT applications. We demonstrate the usability of our methodology through the detection of an example attack model from a real-world incident in real-time.
Automatic Localization of Potential Faults for Java Runtime Exceptions

Exceptions are unintended or undesired events that occur during program execution and have a negative effect on the robustness of the program. In the Java language, exceptions are divided into two types: checked exceptions and unchecked exceptions. A checked exception is required to be handled at compile time whereas an unchecked exception occurs at run time, so it is also called a runtime exception. Runtime exceptions can cause serious consequences like system crashes. Today's programs are still likely to contain a large number of runtime exceptions, such as null pointer exceptions and arithmetic exceptions in real-world bugs, indicating that the error-checking strategy is insufficient to clear the occurrence of runtime exceptions. In this paper, we propose a potential fault localization approach for Java runtime exceptions. It mainly uses regular expressions to support the detection of code fragments containing such exceptions and checklists to help the developer examine whether the exception may occur to cause errors.
Image Authentication Using Watermarking Approach

In healthcare, maintaining the confidentiality of patient records is critical to preserving trust between healthcare providers and patients. Now a days, consultation sessions are provided online which requires the healthcare professional to be aware of the patient’s medical history for better understanding of their purpose of visit. This creates a need for an online data transmission system that helps healthcare professionals to send the patient records to and forth. While this said system does exist, it is observed that often, sensitive data transmitted via this system over public networks has led to data losses or modifications by an unauthorized third party which may lead to the misdiagnosis of a patient’s issue. Therefore, the need for a system that ensures the data transmission takes place in a protected channel and is authenticated at the receiving healthcare professional’s end, making sure that the sensitive data received by them has not been tampered with, is born. The proposed system’s functions are to watermark the image with the logo of the healthcare institution that is sending the data. The watermarked image further undergoes double encryption processes for additional security before being sent to the receiver where it would be authenticated and decrypted using a randomly generated key sent by the sender. Systems performance is evaluated by calculating different evaluation parameters like SSIM and PSNR. The BER and NCC values for the extracted watermark are calculated once the image sent is authenticated and decrypted using the key. Thus, the proposed system is dynamic, robust and has achieved the objective of creating a secure environment for transmission of sensitive data abiding confidentiality.
Cancer Detection and Diagnosis Using Artificial Intelligence

Healthcare sector in today’s era is swapping drastically with the latest technologies like artificial intelligence, machine learning, and deep learning. These technologies have notable determined very aspects of the hospital. Many precise tasks such as image recognition for example classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors can be done with the help of engineering science and present day technology called artificial intelligence. With such an amazing technology, there has been a great engrossment in developing AI diagnostics solutions for the detection and diagnosis of cancer. With the immediate requirement of AI systems, there will be great change in the healthcare sector. This advanced and extraordinary technology will play a great role in stopping the rapid growth of cancers, low awareness among the extended population, and lack of sufficient services and clinical expertise so that AI systems can assist the clinicians in this domain. Analysis of big datasets can be done very easily and with less cost and time by using AI technologies. Many deep learning algorithms, AI solutions are developed by researchers to detect and diagnose cancer precisely and to handle the skin datasets smartly. The advancement and endorsement of AI algorithms need large volumes of well-structured data, and these algorithms must work with changing levels of data quality. It is very important to understand how AI algorithms function to stop the severe conditions of cancers like breast cancer, gastric cancer, prostate cancer, and many more. The clinicians must understand this technology deeply and how it perfectly fits into everyday clinical practice and how patients are calmly addressed. This paper surveys how different types of cancers are detected and diagnosed using the latest AI technology as AI provides good support to doctors for the detection and diagnosis. It also plays a clear and supreme role in aiding patients in cancer detection in the future and will be thrilling to check the benefits that arise for patients and doctors from its use in everyday practices. .
From drawings to puppet shows: practical ways in teaching vocabulary to primary school EFL learners

Mastering vocabulary is one of fundamental conditions that determine learners’ success to be skilled in learning English. The present study aims to explore how young EFL learners engage in learning vocabulary through puppet shows. Grounded in a qualitative approach, this study employed classroom action research (CAR) consisting of two cycles. 11-12 years-old sixth graders of an Islamic primary school in Pekalongan, Central Java, Indonesia volunteered to take part in this study. Data were collected through classroom observation, written documents, and interviews. The data were analyzed by reducing data, displaying data, and verifying conclusion. Findings of the study show that the young EFL learners were more enthusiastic about learning English and participating in vocabulary activities. The learners were encouraged to actively respond to the question and provide meaningful feedback. By supplying the EFL learners with engaging puppet shows, the researchers set fascinating activities for them to guarantee that they were not bored in class. Many vocabularies had already been introduced to the students when the study was overtaken. This study contributes to empirical insights into how puppet shows are adopted in teaching vocabularies to primary school EFL learners that has shifted from drawings as educational media.
«THE TRUTH OF HISTORY» AND DOCUMENTARY MEMORY

The purpose of the article is to consider and correlate the concepts of "the truth of history" and documentary memory. The author examines the framework concept of historical memory and proposes a new typology of forms of historical memory (ordinary, constructivist, scientific and cognitive). In addition, the concept of documentary memory is introduced separately – the result of the transformation of a document as a regulator of vital activity. As a result of the research, the author analyzes various properties of documentary historical memory (continuity, inactivation, inviolability, etc.), as well as its functions which are important for cognition of the past.
An Exploration On Mammographic Image Abnormality Using Computer Aided Detection (Cad) System

Many sectors of research and development use digital image processing to development digital images and extract usable features since the data, whichever may subsequently be castoff to make key judgments by high accuracy. Such approaches were also used in medical solicitations, notably now the identification of breast cancer. Due to the limits of human observation, computers obligate frolicked a vital part in identifying initial signs to cancer over the years, resulting in the creation of a high-accuracy Computer Aided Detection System (CAD). This paper provides a brief overview of the Computer Aided Detection System, whichever could remain used as a choice sustenance system for the automatic detection for breast cancer, with the predictions after CAD schemes being used toward help radiologists make better diagnoses.
Fully informed ABC algorithm for large scale job shop scheduling problem

The large-scale job-shop scheduling problem (LSJSSP) is a complex scheduling problem. Previously, although the nature-inspired algorithm, specially the swarm intelligence (SIA) based algorithms have been efficiently applied to solve the LSJSSP, finding the best solution for LSJSSP instances remains a challenging task. Therefore, in this paper, a novel SIA is applied to solve the 105 LSJSSP instances. The selected SIA is a fully informed artificial bee colony (FABC) algorithm. The FABC algorithm is a variant of the ABC algorithm in which the position update process is inspired from the GABC. In the FABC, the onlooker bee process of the ABC strategy is modified and designed such that the new position of the solution search agent is obtained while learning from all the nearby agents. The results obtained by the FABC are compared with the strategies available in the literature. The results analysis shows that the proposed approach to solving LSJSSP is competitive in the field of SIA.
A Survey of Ciphertext Processing Techniques

The demand for data privacy is rapidly increasing with the growing volume of personal and sensitive information shared to compute mathematical functions. Some of the traditional data encryption techniques provide data privacy but fail in hassle-free sharing. Therefore, the third-party users can not perform mathematical computations over the data to generate meaningful outcomes. In this situation, ciphertext processing techniques are used. Ciphertext processing techniques provide computations over encrypted data. The survey discusses the ciphertext processing techniques that enable mathematical computations over the encrypted data to third-party users while maintaining data privacy. The ciphertext processing techniques also enable privacy-preserving data analysis using machine learning or statistical tools. In addition, we discuss the applications of ciphertext processing techniques. We present the research gaps in the domain of ciphertext processing and provide future research directions.
Dynamic grasping parameter estimation based on feedforward radial basis function network

In this paper, we propose a motion estimation method based on a feed-forward radial basis network for grasping arbitrary moving objects. We first employ a kernel correlation filtering (KCF) algorithm to track the target position in real-time and establish the motion model of the target. Using the feed-forward radial base network, we then adjust the sampling time of the Kalman filter (KF) to predict the motion parameters of the target. Since that, we can reduce the computing time and improve the accuracy of the estimation of the motion parameters. Compared with the feed-forward perceptron network, the proposed method shortens the required time for grasping by 20%, which can avoid a failure grasp due to the arbitrary movement of the object in grasping.
A Multisource Multi-Bernoulli Filter for Multistatic Radar

Compared with conventional monostatic or bistatic radar, multistatic radar has wider coverage and better performance localization and tracking accuracy. However, the multistatic radar architecture poses challenges to the implementation for multitarget tracking in coping with highly uncertainty of data association for the fusion of multisource information. In this paper, the theoretically rigorous formulas for the multisource multi-Bernoulli (MeMBer) filter are derived by using the Finite set statistics (FISST) calculus built on the standard MeMBer filter. The multisource MeMBer filter propagates a set of MeMBer parameters approximately characterizing the multisource corrected posterior multitarget random finite set (RFS). Since the equations for the proposed filter multisource corrector are computationally intractable, we go further to develop an analytic Sequential Monte Carlo (SMC) implementation of multisource MeMBer recursion. The theoretical analysis and simulations show that the proposed filter perform well and accommodate nonlinear multistatic radar tracking scenario under the approximate conditions.
Revocable key aggregate searchable encryption with user privacy and anonymity

The KASE schemes allow fine-grained delegation of search rights over a selected dataset using an aggregate key. However, when the existing KASE schemes are deployed in real-time applications, the support of revocation of delegated rights is highly required to manage users' access control. Therefore, in this paper we propose two solutions for the fine-grained revocation of delegated rights considering two different scenarios. First, we present a basic scheme that supports fine-grained revocation of the delegated rights on document level, instead of coarse-grained all-or-nothing access. Then, the user is not allowed to search the document by the old trapdoor if his search privileges are revoked on that document. Under a multi-user setting, we propose an advance scheme that can make the fine-grained revocation of the delegated rights on the user level. The proposed approaches also preserve users' privacy and anonymity. We present the schemes' correctness proof, formal security analysis, and performance analysis, which confirm that they are provably secure and practically efficient.