Reviewerly/exHarmony huggingface dataset:
A Distance Measure for Perspective Observability and Observability of Riccati Systems

Systems governed by Riccati differential equations arise in several areas of control system theory. In combination with a linear fractional output, observability of such systems is relevant in the context of robotics and computer vision, for example, when studying the reconstruction of point locations from their perspective projections. The so-called perspective observability criteria exist to verify this observability property algebraically, but they provide only a binary answer. The present contribution studies the assessment of perspective and Riccati observability in a quantitative way, in terms of the distance to the closest nonobservable system. For this purpose, a distance measure is proposed. An optimization problem for determining it is derived, which features a quadratic cost function and an orthogonality constraint. The solution of this optimization problem by means of a descent algorithm is discussed and demonstrated in the course of a practically motivated numerical example.
Getting Started with Scala

Scala is not one of those popular languages that you learn at university, so most people don't even know that it exists. However, there are many benefits or reasons for using it. Scala was born as a language with a short number of features but with the idea of doing things simply and clearly. For that reason, most developers use it for different projects, and some of them collaborated to create the recent version of Scala.
Implementing 3D Model LNG Tanker Ship Cargo Handling System Equipment for Training Using Augmented Reality

Abstract The learning system of the vessel is very important for a marine engineer to understand the characteristic of machinery system. This can be conducted by looking at the block diagram and seeing the 3D picture or directly observing the system on a vessel in order to get a complete understanding. It is difficult to do direct observation due to safety regulations and company confidentiality, especially for LNG tanker ship. To overcome this restriction, this study will propose a 3D drawing for training that can represent the real operational condition of cargo handling system equipment of an LNG tanker vessel. Augmented Reality 3D is used to recreate the system and put necessary information about the object along with the 3D drawing. The augmented reality 3D object and information showing with the android application by scanning the object augmented reality marker. In this research, the user is satisfied with the experience and interface based on user data collected from the questionnaire method with aspect of attractiveness, uses ability, effectiveness, user interface experience, and measure the understanding about the application with questionnaire value measurement.
Preparing Trainees

This is the story of the development of a system to prepare participants for training. We prepared our trainees to be ready to learn as well as they could to increase the advantages that our training might afford them. We describe and illustrate how we stated goals and designed and delivered appropriate instructional methods to develop trainees who would be oriented, motivated, capable, and confident. Finally, we summarized general operating rules for preparing training participants.
How Will VR and AR Impact Occupations?

We evaluate the prospective impact of virtual and augmented reality (VR/AR, collectively termed "XR") technologies on occupations. First, we define four areas in which VR and AR technologies are leveraged. Then, we examine several case studies in which these technologies are changing the ways in which people work or interact with one other. Based on these case studies, we develop five indices that represent the levels of potential XR-enabled enhancement of human abilities and actions. Finally, we apply our results to calculate an XR impact index for 873 occupations from the O*NET database of the US Department of Labor, and we identify the 15 occupations most impacted by VR/AR technologies, which include pilots, firefighters, and healthcare workers. Implications for skill accumulation and productivity are discussed.
Modification of Pre-service Teacher Education

Our country attaches great importance to the training of teachers' professional talents. Teacher education curriculum is the core curriculum of normal majors, which should promote students to complete the transformation from theory to practice and the transition from pre-service and in-service to post-service. However, at present, there are some problems in the teaching of teacher education curriculum, such as teacher allocation, curriculum content organization and teaching implementation. In this paper, the teaching mode of “duality, three-stages and three transformations” is constructed, which highlights the characteristics of teacher education curriculum and the goal of personnel training, and focuses on improving students' comprehensive quality to improve the quality of teaching.
Copy-Move Forgery Detection Using Local Binary Pattern Histogram Fourier Features

In this chapter, a new system for image copy-move forgery detection using Local Binary Pattern Histogram Fourier features (LBP-HF) [1] is proposed and implemented. In this system, initially, the input forged image is partitioned into constant-sized overlapping blocks, and all overlapping blocks are lexicographically sorted. Thereafter, LBP-HF features are extracted from each block. The extracted features are compared to find the forged regions of the image.
Environmental Quality Assessment in the Kuwait Bay (Kuwait): An Integrated Approach

The increased urbanization and developmental activities in Kuwait and the consequent effluent discharges into marine environments have resulted in changes in the water and sediment quality of coastal and marine ecosystems. These areas, like the Kuwait Bay, have been negatively affected by the discharges of hydrocarbon, heavy metals and other organic compounds impacting marine ecosystems and the biota living therein. The Kuwait Bay is among the most critical marine and productive ecosystems along the Kuwaiti coast. In light of it, this baseline investigation aims to document by the integration of physicochemical data on water, sediment geochemistry both organic and inorganic and preliminary data of benthic foraminiferal assemblages, the environmental conditions within the Kuwait Bay that will be used for the implementation of future biomonitoring programs.
Design and Implementation of Live Stream System Based on Kurento

The traditional live stream technology has a large latency. In order to obtain more effective interaction between the presenter and the audience, this paper designs and implements a low latency live stream platform based on Kurento. The core architecture is based on Kurento, which is also a combination of WebRTC Media Server and a set of APIs. We use Spring Boot as the framework of application server, and WebSocket protocol and JSON format for signaling channel. We choose the Derby database to store the data. The platform breaks through geographical restrictions, and uses UDP protocol to push the stream to reduce the delay of live stream. This not only improves the interaction efficiency between the presenter and the audience, but also solves the shortcomings of the traditional live stream system to a certain extent. In addition, the cross-platform characteristic of WebRTC makes video live stream more flexible and simpler, which can be used as a reference for the rapid development of similar live stream businesses.
A Digital Forensic View of Windows 10 Notifications

Windows Push Notifications (WPN) is a relevant part of Windows 10 interaction with the user. It is comprised of badges, tiles and toasts. Important and meaningful data can be conveyed by notifications, namely by so-called toasts that can popup with information regarding a new incoming email or a recent message from a social network. In this paper, we analyze the Windows 10 Notification systems from a digital forensic perspective, focusing on the main forensic artifacts conveyed by WPN. We also briefly analyze Windows 11 first release’s WPN system, observing that internal data structures are practically identical to Windows 10. We provide an open source Python 3 command line application to parse and extract data from the Windows Push Notification SQLite3 database, and a Jython module that allows the well-known Autopsy digital forensic software to interact with the application and thus to also parse and process Windows Push Notifications forensic artifacts. From our study, we observe that forensic data provided by WPN are scarce, although they still need to be considered, namely if traditional Windows forensic artifacts are not available. Furthermore, toasts are clearly WPN’s most relevant source of forensic data.
Maximum Entropy based Local Multiple Patterns for Texture Classification

This paper proposes a maximum entropy based local multiple patterns (LMP) to describe image textures. In traditional LMP, the thresholds have to be set empirically and the feature dimension is high. We adopt a maximum entropy based thresholding scheme, which computes the thresholds by dividing the intensity difference histogram of an image equally; We also use the split-concatenate encoding to form shorter and more effective feature vectors. Experimental results on four test suits with an SVM classifier show that our proposed method achieves overall better performances than both LBP and LMP in illumination and rotation invariant grayscale texture classification.
Prediction of Stock Price Data of PT. Ramayana Lestari Sentosa Tbk. using Long Short Term Memory Model

The investment that Indonesian people are interested in during the pandemic is investing in stocks. However, stock investment will not only generate profits but also risk losses so that to minimize the risk of loss, forecasting is required. One of the prediction methods that can be used is Long Short Term Memory (LSTM) for stock data modeling, this is because LSTM is suitable for predicting and processing important events with relatively long intervals and delays in the time series data. The data used in this research is time series data of PT. Ramayana Lestari Sentosa Tbk. in the period 2 March 2020 to 15 September 2021 with LSTM model using the same number of epochs but different number of nodes, activation functions and optimizers which show varying results and accuracy levels. The selection of the number of epochs 5 is the optimal number of epochs selected. In the experiment, the researchers use 2 types of nodes (4 and 16), 4 types of activation functions (Linear, Sigmoid, Relu, and Tanh), and 3 types of optimizers (Adam, SGD, and Adagrad). The results of the experiment show that the best LSTM model based on the smallest RMSE test value is 15.37 on the use of LSTM model with 4 nodes, Linear activation function, and Adam optimizer. Meanwhile, the stock price prediction for the next day (16 September 2021) obtained based on LSTM model is IDR 639.05. Based on this, it can be concluded that in predicting RALS stock price data using LSTM model with the number of nodes 4 is better than the number of nodes 16. Linear activation function is also better than the activation function of Sigmoid, Relu and Tanh. Then, Adam optimizer is better than SGD and Adagrad optimizer.
An Improved DCT interpolation using Bilateral filter

The image interpolation method is used to improve the resolution of low resolution (LR) images. Several methods, such as polynomial and edge-directed methods, are proposed, but interpolated images are associated with blurring and ringing artifacts. As a result, one pre-processing method based on a bilateral filter is proposed. The bilateral filter is applied to the LR image, which blurs it using spatial and range filtering. The mask is determined by subtracting the LR image from the filtered image. The sharpened LR image is obtained by combining the LR image with a weighted mask image using the unsharp masking concept. Finally, using the discrete cosine transform (DCT), the restored high resolution (HR) image is obtained by interpolating the sharpened LR image. The high frequency as well as texture details are preserved as a result of this pre-processing. In terms of PSNR and SSIM, the proposed method outperforms both subjective and objective measures.
Exploiting Temporal Information of Videos for Facial Landmark Detection: A Modified SDM

Facial landmarks are used for localization and representation of silent features of the face that are located on tips, corners and mid-points of facial segments. Detecting facial fiducial points in the video, play a fundamental role in facial images analysis during the past couple of decades due to its different facial image applications for example facial expression analysis, face recognition and gaze identification. Previous methods of face fiducial points detection gives good result of frontal face, but have problem generalizing to tilting and nodded faces. to solve this problem, we proposed a robust method for facial fiducial point detection for tilting and nodding faces in the video sequence using the second order regression based method. First, we detect the face from the first frame of the video sequence using Voila and Jones method. The proposed system extracts face from the videos and images and then detect the facial fiducial point using the Modified Supervised Descent Method (MSDM). Five public base databases YouTube celebrity, IMM, BU head tracking dataset, LFPW and VidTIMIT used to evaluate the performance. The comparison of proposed algorithm, i.e The proposed MSDM and existing algorithm SDM (Supervised Descent Method) has been made in terms of percentage accuracies. These accuracies are obtained by taking a distance measure of calculating facial landmarks with the ground truth. Results showed significant improvement and effectiveness against the standard datasets.
Individual Movement Monitor Using Data Mining

Data mining is becoming an essential part of our daily lives, with many of our systems relying on it for anything from movie suggestions to health monitoring. Furthermore, its ease of use and capacity to manage enormous amounts of data without fail are remarkable, which is why it is utilised in the majority of applications and by all organisations these days. The data mining process particularly represents a simple extraction and pattern discovery of bigger datasets relating to various techniques at the junction of machine learning, databases and statistics. Henceforth, this paper presents a complete process about how data mining is used for monitoring every movement of an individual with the objective of providing them with great solutions, which will aid in their health and also track them in emergency situations. This research work is also developed for delivering Post COVID-19 care, which is becoming very essential for high risk individuals with prior conditions or new conditions emerging afterwards. This research work intends to explore the classification accuracy report for different learning algorithms and tally of records for each stated individual task by accelerometer and gyroscope. Further, this research work exploits the results for logistic regression and support vector classifier. The dataset utilized in this project is obtained from the UCI machine learning repository and also it has obtained the samples from 30 individuals aged 19–48 years. Since no changes are made to the dataset; it does not require any cleaning and transformation. The Python version 3.7 with Libraries like NumPy, pandas and sklearn alongside Jupyter Notebooks are used for implementation. The proposed system is expected to yield the following total percentage of time spent on every movement, algorithm accuracy percentage and number of activities, which are measured by the movement measures. Based on the findings, stakeholders or decision makers can take necessary steps regarding the individual’s health structure, food regiment, or care structure.
Single Dendritic Neural Classification with Functional Weight-enhanced Differential Evolution

As current mainstream deep learning models based on neural networks have been long criticized because of their complex structures, attempts in formulating a single neural model have raised much attention. Owing to the nonlinear information processing ability, dendritic neuron model (DNM) has shown its great potential in classification problems over the past decades. However, designing an effective learning algorithm for training DNM is still an open question due to the issues of local optima trapping and overfiting caused by traditional back-propagation (BP) algorithm. In this study, a novel functional weight-enhanced differential evolutionary algorithm (termed FWDE) is proposed to solve the aforementioned problems. By introducing Gaussian distribution function into weight generation of fitness-distance balance selection strategy, FWDE obtains significantly better classification accuracy with faster convergence speed compared with other representative non-BP and BP algorithms. The experimental results verify the great performance of FWDE, indicating that DNM with an powerful learning algorithm is considerably more effective.
European Digital Competence Frameworks and Engineering Pedagogy

This contribution deals with the digitalisation of life and work and the accompanying, changing competence profiles of employees, which increasingly include digital competences. The European Commission has been working for several years to define and promote digital competency frameworks for all citizens, as well as for workers and educators. However, it demands validation and implementation considering regional, local, sectoral and education-specific circumstances and should respond to needs at the level of practice. Thus, it is necessary to adapt the existing digital competency frameworks to the specific circumstances of engineering education and to validate them for different engineering degree programmes. The contribution summarises the existing European Digital Competency Frameworks and discusses the options for integrating them into engineering pedagogy curricula and programmes. At the end, proposal to establish a joint working group of the International Society for Engineering Pedagogy and the Scientific Society for Engineering Education will be discussed.
Reversible Data Hiding in Encrypted Images Based on Chinese Remainder Theorem and Secret Sharing Mechanism

Secret sharing refers to a method for dividing a secret into several shares and distributing them among a series of participants. When enough shares are received, the secret can be reconstructed without information loss. This paper proposes a new method for reversible data hiding in encrypted images based on the Chinese remainder theorem (CRT) and secret sharing strategy. The most significant bit (MSB) and 7 least significant bit (LSB) layers are separately encrypted during the encryption procedure, in which 7 LSB layers are scrambled with a CRT secret sharing scheme. During the embedding phase, the additional bits are embedded into each share based on the strategies of MSB flipping and MSB substitution. The receiver side can recover the original image and extract the additional bits according to the characteristics of secret sharing and strong correlation between adjacent pixels. Experimental results have demonstrated the effectiveness of the proposed method.
Basics of Scala

Scala offers the chance to combine different paradigms with a lot of incredible features. Some of them appear in the recent version, which removes the complexity of common operations. But before going deeply into these features, you need to know the basics of this language.
An FPGA Prototyping of the GIFT Cipher for Image Security Applications

Nowadays, images are frequently used for vital information sharing; therefore, its data security is of prime concern. In this work, the GIFT is implemented on an FPGA device with 256×256 and 512×512 sized gray and RGB images. Hardware prototyping of the architecture is done on the Xilinx ZCU102 platform featuring the Zynq UltraScale+ xczu9eg-2ffvb1156 FPGA. The resource usage, efficiency, throughput, and power studies are highlighted. At a maximum clock frequency of 770/530 MHz, encryption/decryption consumes 643/660 mW of power. Furthermore, statistical analyses for histogram, correlation, entropy, and key sensitivity are carried out. In this study, the average entropy of the original-to-encrypted image is enhanced to 11.38% and improved compared to previous work. The average correlation coefficient is reduced to 99.90/99.80/99.90% in the vertical/horizontal/diagonal direction.
Improving Entanglement of Even Entangled Coherent States Via Superposition of Number-Conserving Operations

A scheme for improving entanglement is proposed by repeatedly performing number-conserving generalized superposition of products (GSP) operation, i.e., (saa†+ta†a)m with s²+t²=1, on each modes of even entangled coherent state (EECS). Both Einstein-Podolsky-Rosen (EPR) correlation and concurrence are used as the measures for describing entanglement. It is found that both single-mode and two-mode-symmetric GSP operations can be used for the entanglement improvement. In particular, for the optimal EPR correlation, the GSP-EECS with two-mode-symmetric first-order operations presents a better performance for the entanglement improvement than the cases of high-order GSP operations. Compared to several special non-Gaussian operations, including photon subtraction-then-addition (PSTA) (s=0) and photon-addition-then-subtraction (PATS) (s=1), it is shown that the entanglement of GSP-EECS is always superior to those of both PSTA-EECS and PATS-EECS at a small amplitude level. When the GSP-EECS is used as the entangled resource for teleportation, our optimal teleportation fidelity presents the best performance.
Does Entrepreneurship Financing Create Employment in Nigeria? The Role of Small and Medium Scale Enterprises (SMEs)

The current level of unemployment in Nigeria in the recent times has been worrisome, and various advocacies by scholars and policy analysts have suggested that entrepreneurship, especially small and medium scale enterprises could generate employment in the large measures. Meanwhile, inadequate financing has been identified as a strong factor causing a setback to entrepreneurship development in Nigeria. Against this backdrop, this paper provided empirical answers to whether entrepreneurship financing, from the SMEs perspective could generate employment in Nigeria. In answering the research question, data were extracted from 1992 to 2019 from secondary sources. Consequently, the data passed through various pre-tests such as unit roots and Cointegration tests before it was estimated via the application of Canonical Cointegrating Regression (CCR). The findings of this study were summarized as follows; the Commercial Banks Loans to SMEs contributed both positive and significant impact to employment generation in Nigeria. Likewise, that Commercial Banks total credits to private sector contributed both positive and significant impact to employment generation in the country. Hence, this study submits that that entrepreneurship (SMEs) financing contributes to employment generation in Nigeria. In the light of the above submission, this study recommends that any time the policymakers in Nigeria desire to address the current devastating level of unemployment in Nigeria, financing of entrepreneurship, especially SMEs should be the priority of the government at levels in the country. And as such, employment generation will be stimulated in the long run.