Reviewerly/exHarmony huggingface dataset:
Generation and entanglement study of generalized N-mode single-photon perfect W-states

We propose generation and entanglement detection schemes for generalized N-mode single-photon perfect W-states. These states are suitable for perfect teleportation and superdense coding. Based on the evolution of single-photon wavefunction in scalable integrated photonic lattices, we present schemes to generate these states using both planar and ring type waveguide structures. The integrated waveguide structures can be precisely fabricated, offer low photon propagation losses and can be integrated on a chip. In addition, we derive set of generalized entanglement conditions using the sum uncertainty relations of generalized su(2) algebra operators. We show that any given genuinely entangled N-mode single-photon state is a squeezed state of a specific su(2) algebra operator and can be expressed as superposition of a pair of orthonormal generalized N-mode single-photon perfect W-states which are eigenstates of that specific su(2) algebra operator. Within the single-photon subspace, the generalized entanglement condition reduces to a simplified single-photon separability condition. Detection of entanglement of single-photon states, using this single-photon separability condition, requires finding fidelity with two pairs of orthonormal generalized N-mode single-photon perfect W-states and hence more suitable for our purpose. Finally, we propose an experimental scheme to verify the entanglement using the proposed conditions. This scheme uses a photonic circuit consisting of directional couplers and phase shifters. The same photonic circuit can also be used to generate generalized N-mode single-photon perfect W-states.
The impact of cryptocurrencies on the gold, WTI, VIX index, G7 and BRICS index before and during COVID-19: a quantile regression and NARDL analysis

Purpose This study aims to explore the impact of cryptocurrencies (Bitcoin, Ethereum, Monero and Ripple) on the gold, WTI, VIX index, G7 and the BRICS index before and during COVID-19. Design/methodology/approach This research analyzes the impact of cryptocurrencies (Bitcoin, Ethereum, Monero and Ripple) on the gold, WTI, VIX index, G7 and the BRICS index before and during COVID-19, using the quantile regression approach for the 2016–2020 period. In addition, to catch long- and short-run asymmetries of cryptocurrencies on aforementioned dependent variables, an asymmetric nonlinear co-integration (nonlinear autoregressive distributed lag [NARDL]) approach is applied. Findings The result of the quantile regression shows that in a high market, which corresponds to the 90th quantile, the FTSE MIB, CAC40, SSE, BSE 30, and BVSP stock market showed a statistically insignificant negative coefficient, on the Bitcoin price. In a middle and low markets, which correspond to the 0.2, 0.3 and 0.5th quantiles, the BVSP, FTSE MIB, S&amp;P/TSX, SSE and Nikkei stock markets show statistically significant and positive on Bitcoin. Evidence from the NARDL shows a statistically significant positive impact of cryptocurrencies on the gold, WTI, VIX index, G7 and BRICS indices before and during COVID-19 pandemic. Originality/value These results can provide investors with valuable analysis and information and help them make the best decisions and adopt the best strategies. Therefore, future investigations may concentrate and examine the monetary and governmental policies to be adapted to face the COVID-19 pandemic’s dangerous effects on both the society and the economy. For this reason, investors should take this into account when making their asset allocation decisions. Moreover, the portfolio managers, such as index funds, may consider few eligible cryptocurrencies for their inclusion into the portfolio. However, the speculators present in both stock and crypto markets may opt for a spread strategy to improve their portfolio returns.
<scp>DL</scp>‐<scp>DC</scp>: Deep learning‐based deadline constrained load balancing technique

Summary Load balancing is a crucial feature of cloud computing that evenly distributes workload between servers, network interfaces, and hard drives. Because of dynamic computing over the internet, cloud computing suffers from request overloading. To address this challenge, this paper proposes a new deep learning‐based deadline constrained (DL‐DC) load balancing technique. The proposed DL‐DC technique will improve resource utilization, reduce cost, latency, and response time, as well as balance load between servers and improve reliability. The proposed DL‐DC technique will direct traffic to a load balancer, which will forward the load with a deadline to a deep Inception ResNet. This network considers some parameters, such as sticky session, content‐based and instance health check and efficiently predicts the schedule for the task. Finally, a predicted task schedule has been derived using the DL‐DC model which is used to distribute the task to the virtual machine. The proposed DL‐DC load balancing algorithm is compared with other existing algorithms such as QMPSO, DQTS, and ACSO in terms of cost, makespan, response time, transmission time and task migration. The proposed method achieves up to 21.08% low response time, 27.3% decrease in make span, 25.5% decrease in task migration, and 38.9% decrease in cost respectively.
Optimal Classification Model for Text Detection and Recognition in Video Frames

Currently, the identification of text from video frames and normal scene images has got amplified awareness amongst analysts owing to its diverse challenges and complexities. Owing to a lower resolution, composite backdrop, blurring effect, color, diverse fonts, alternate textual placement among panels of photos and videos, etc., text identification is becoming complicated. This paper suggests a novel method for identifying texts from video with five stages. Initially, “video-to-frame conversion”, is done during pre-processing. Further, text region verification is performed and keyframes are recognized using CNN. Then, improved candidate text block extraction is carried out using MSER. Subsequently, “DCT features, improved distance map features, and constant gradient-based features” are extracted. These characteristics subsequently provided “Long Short-Term Memory (LSTM)” for detection. Finally, OCR is done to recognize the texts in the image. Particularly, the Self-Improved Bald Eagle Search (SI-BESO) algorithm is used to adjust the LSTM weights. Finally, the superiority of the SI-BESO-based technique over many other techniques is demonstrated.
FINE-GRAINED SENTIMENT ANALYSIS IN SOCIAL MEDIA USING GATED RECURRENT UNIT WITH SUPPORT VECTOR MACHINE

Social media platforms are widely used to share opinions, leading to a large growth of text data on the internet. This data can be a key source of up-to-date and inclusive information by conducting sentiment analysis. Typically, sentiment analysis research classifies binary based on the polar values generated. However, this has its limitations, such as classifying sentences containing positive and negative expressions, leading to incorrect predictions. Fine-grained sentiment analysis provides more precise results by associating values with more than two classification targets. The objective of this study is to carry out sentiment analysis at a fine-grained level related to public policy in Indonesia using the GRU-SVM model with feature extraction and expansion techniques. However, sentiment analysis research still faces challenges in NLP. Deep learning have successfully overcome the challenges of traditional machine learning models in terms of efficiency and performance. This study proposes GRU-SVM model. GRU is used because it can adaptively control dependencies, making it more efficient in memory usage, while SVM is used as it is state-of-the-art in sentiment analysis. Result of the study show that the selection of word representation techniques, the addition of feature extraction techniques, datasets, data ratios, and feature expansion are crucial in the model testing process. The GRU-SVM model achieved the best performance with an accuracy of 96.02%. Overall, the results of this study demonstrate that the GRU-SVM method is effective in analyzing sentiments in Indonesian tweets.
A Comparison of Performance Simulation Between PID and Fuzzy Logic Controller for an Autonomous Trajectory of a Floater Glider

In industrial processes, PID controllers are commonly used for their simplicity and effectiveness on linear and non-linear systems, while fuzzy logic controllers excel in mimicking human decision-making and handling uncertainties in control systems. The performance of a control system for a floater glider at a specific location is crucial. In this comparative study, simulations have been conducted to evaluate the performance of a fuzzy logic controller and a PID controller implemented through a mission planner software in generating actual trajectories for the floater glider system. The desired trajectory has consisted of a rectangular path with 16 waypoints. Disturbances have been introduced, including scenarios without disturbances and waves with heights of Hs = 0.5 m and 1 m. For the PID method, the gain values of Kp = 0.2, Ki = 0.3, and Kd = 0.02 have been utilized based on previous studies. On the other hand, the fuzzy logic method has been employed to model the rectangular trajectory, employing selected member functions (NB, NM, NS, ZE, PS, PM, PB) for inputs and outputs, resulting in a rule base comprising 49 combinations. The results of the comparative study indicate that the fuzzy logic method produces significantly smaller errors compared to the PID method, particularly when the floater glider experiences higher disturbances. This highlights the superior performance of the fuzzy logic method in controlling the trajectory and mitigating disturbances.
Sistem Pengamanan Lukisan Digital Menggunakan Metode Rivest Shamir Adleman (RSA)

Digital painting is a result of the process of making digital images/paintings using digital brushes that can produce lines. This painting has become a modern art that can be disseminated through the internet. However, with the ease of disseminating information, digital artwork on the internet is very easy to fake or steal. To solve this problem, a system was created to secure digital painting files using the Rivest Shamir Adleman (RSA) encryption and decryption method. RSA has the basis for encryption and decryption, namely the concepts of prime numbers and modulo arithmetic. Both keys are integers. Based on the research conducted, digital painting files are converted into text and then will go through an encryption process by RSA into text files. Furthermore, to restore the encryption results, the decryption process is carried out with the private key. The weakness of the current system is its inability to process large image files.
LECTURERS ADMISSIONS SELECTIONS MODEL USING FUZZY K-NEAREST NEIGHBOR METHOD

Higher Education, or tertiary education, is the final stage which is optional in formal education. It is usually organized in the form of a university, academy, seminary, high school, or institute. Every tertiary institution needs qualified and professional educators because they have an important role in the process of implementing the Tri Dharma of Higher Education. Recruitment for teaching staff usually has several stages and standardization of assessment in selection proces. In order for the process of selecting educators to be carried out objectively, a support system is need to carry out the assessment process. This study applies the Fuzzy K-Nearest Neighbor (FK-NN) method for the classification process in determining prospective educators who pass or not. Data classification is a new data or object grouping into classes or labels based on certain attributes. The application of the FK-NN method has several stages, namely weighting the criteria, then calculating the closeness of the test data and training data, finding the value of k-nearest neighbors between the training data and testing data and determining the membership of each data. Tests were carried out using the Confusion matrix method on several variations of the k value where the highest percentage was obtained from the value of k = 5. The test results for all k values obtained an average accuracy rate of 89.22%, 89.22% precision and 82.45% recall with 114 training data and 50 test data. Based on the average value of the test results, it can be concluded that the FK-NN method is feasible and good to use for the selection of educators with the classification of pass or not.
INDIVIDUAL IDENTIFICATION BY IRIS USING HISTOGRAM OF ORIENTED GRADIENT (HOG) AND BACKPROPAGATION NEURAL NETWORK

The eye’s iris biometrics is a type of biometric for individual identification that is more stable than other types of biometrics because a person's iris eye’s has a delicate fiber pattern and unique characteristics. Especially with the rapid development of the times, the need for identity recognition systems is also increasing. Introducing individuals in traditional ways is still less effective than biometric systems because, compared to conventional methods, biometric systems are safer and are not easily stolen, imitated, or accessed by any unauthorized person. In this research has been carried out by designing a simulation system for individual identification through iris eyes images using the Histogram of Oriented Gradien (HOG) method for image extraction. They were continued with classification using Artificial Neural Network (ANN) Backpropagation. The dataset used is primary data taken directly through smartphone cameras from 30 individuals.Based on the test results and analysis of the Histogram of Oriented Gradien method using an image size of 128×128 pixels, parameters of Cell Size 16×16 cells, Bins Numbers 12, Size Block 2×2 cells, L2-Hys normalization scheme, and JST backpropagation classification with Random state value 1, Learning Rates 0.001, Epoch 200, Hidden Layer 100 with the system's sigmoid activation function can produce a performance system with the most significant performance accuracy of 91.93% , using 1500 training data and 1500 iris eyes image test data.
The State of Cryptocurrency Regulation: Nigeria

Nigeria gradually transitions from a anti-crypto state to one embracing blockchain technology & regulating digital assets, but regulators are still focused on curbing cryptocurrency usage within the county's traditional financial markets. The research provides an overview of cryptocurrency adoption in Nigeria and the country's state of blockchain and cryptocurrency regulation as the Web3 market expands within Africa.
Efektifitas Pengembangan E-Learning dengan LMS Moodle dan Laboratorium Virtual Pada Pendidikan Tinggi

This study aims to analyze how effective the development of e-learning and virtual laboratories in the universities. Because so far students have difficult in finding lecture references and besides that the learning process cannot be monitored by the institution and the unavailability of media used to support practicum courses in the laboratory. The method used in this research is research and development with five steps including: a. Conduct product analysis to be developed b. Develop initial product c. Revision d. Small-scale field trials and product revisions e. Large-scale field trials and final products. To measure the effectiveness of e-learning used the Technology Acceptanced Model (TAM) using 2 factors including a. usefullnes factors (users believe that their performance will improve by using this system) and b. ease of use (the user believes that the use of this system will free from the trouble). To analyze how effective the use of e-learning and virtual laboratories is, the researchers used a sample with 20 lecturers as respondents. From the results of the research conducted, it can be concluded that 92% of lecturer respondents agree and are effective towards the developed e-learning and virtual laboratory
Automatic Detection of Pedestrian Crossing Platform using Congestion Monitoring

Abstract: One of the primary problems faced globally is the amount of traffic on the roads and the number of pedestrian accidents. The risk when crossing or walking on roads in urban and rural regions with significant traffic is a major factor in these incidents. A novel idea is put forth to avert such situations. Using congestion monitoring, automatic detection of pedestrian crossing platforms. An IR (Infrared) sensor module is used to continuously monitor pedestrian and traffic congestion. When there are more pedestrians on the road, the traffic light for automobiles turns red, allowing pedestrians to cross at the zebra crossing. In the proposed system, we are counting the packed pedestrians at the signal point and performing congestion monitoring of the pedestrian and vehicle count, which will be sensed by IR Infrared Sensors. This technique guarantees pedestrian crossing safety and prevents drivers from flouting the law, which could cause accidents.
Quantum Lyapunov exponent in dissipative systems

The out-of-time order correlator (OTOC) has been widely studied in closed quantum systems. However, there are very few studies for open systems and they are mainly focused on isolating the effects of scrambling from those of decoherence. Adopting a different point of view, we study the interplay between these two processes. This proves crucial in order to explain the OTOC behavior when a phase space contracting dissipation is present, ubiquitous not only in real life quantum devices but in the dynamical systems area. The OTOC decay rate is closely related to the classical Lyapunov exponent---with some differences---and more sensitive in order to distinguish the chaotic from the regular behavior than other measures. On the other hand, it is revealed as a generally simple function of the longest lived eigenvalues of the quantum evolution operator. We find no simple connection with the Ruelle-Pollicott resonances, but by adding Gaussian noise of ${\ensuremath{\hbar}}_{\text{eff}}$ size to the classical system we recover the OTOC decay rate, which is a consequence of the correspondence principle put forward in Phys. Rev. Lett. 108, 210605 (2012) and Phys. Rev. E 99, 042214 (2019).
Information Architecture

In this chapter, I will dive into the engrossing world of Information Architecture (IA) as it relates to engineering documentation.
New data on the types of sulfide copper-nickel ores of the Kharaelakh trough and the main patterns of their distribution

The types of magmatic sulfide ores on the Western flank of the Oktyabrsky deposit have been studied, and the patterns of their distribution have been established. The features of ore-bearing rocks within the western branch of the Kharayelakh intrusive Talnakh ore field have been studied. The morphology of ore bodies is described and mineralogical characteristics of ore types in the western part of the Kharaelakh intrusion are given according to drilling data of recent years. The data of earlier studies on the types of ores and their spatial placement on the flanks of the Oktyabrsky deposit are summarized and confirmed. The main patterns of spatial distribution, composition and morphology of copper-nickel sulfide ore deposits on the western flank of the Oktyabrsky deposit are identified, which can be used as search criteria for further geological exploration.
Bayesian Functional Principal Components Analysis via Variational Message Passing with Multilevel Extensions

Standard approaches for functional principal components analysis rely on an eigendecomposition of a smoothed covariance surface in order to extract the orthonormal eigenfunctions representing the major modes of variation in a set of functional data. This approach can be a computationally intensive procedure, especially in the presence of large datasets with irregular observations. In this article, we develop a variational Bayesian approach, which aims to determine the Karhunen-Loève decomposition directly without smoothing and estimating a covariance surface. More specifically, we incorporate the notion of variational message passing over a factor graph because it removes the need for rederiving approximate posterior density functions if there is a change in the model. Instead, model changes are handled by changing specific computational units, known as fragments, within the factor graph – we demonstrate this with an extension to multilevel functional data. Indeed, this is the first article to address a functional data model via variational message passing. Our approach introduces three new fragments that are necessary for Bayesian functional principal components analysis. We present the computational details, a set of simulations for assessing the accuracy and speed of the variational message passing algorithm and an application to United States temperature data.
FAKE CURRENCY DETECTION USING SIMPLE IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES

The automation of technology results in a more extensively duplicated currency, which slows the expansion of the nation's economy.Note detection must be very constant and dependable in order to be required.Edge detection, extraction of features, segmentation techniques, sepia conversion, and image comparison are some of the procedures involved in the identification of paper money.This essay also includes a review of the literature on various detection strategies.Whenever we use some effective preparation and feature extraction approaches, it assists in refining the algorithms and also the detection method, according to the review to detect malpractice.Machine learning techniques assist in creating the tools needed and required for research work, and we can create computer learning designs, implementations, and strategies to distinguish between counterfeit and real money.We have suggested employing a deep neural network based on convolution to detect fake money.Through analysis of the currency photos, our technique detects counterfeit money.To learn the feature map of the currencies, the transfer learnt convolutional neural networks is trained using a total of two thousand currency note set data.The network is prepared to recognize fraudulent currencies in real time after the feature map has been learned.
A Designer's Journey of Writing — Reimagining Design: Unlocking Strategic Innovation

It was my chance to reflect on a multidisciplinary career and leave an indelible fingerprint of perspective that could hopefully serve others in their creative pursuits. Never imagining he'd write a book, along the way he discovered he had a unique perspective to communicate as a Black man at the intersection of design, business, and technology.
THE INFLUENCE OF JOB SATISFACTION ON EMPLOYEE ATTRITION IN INSURANCE INDUSRY

Insurance is a human intensive business and therefore the role of human resource is very crucial in the growth of an insurance industry.In present days cut throat market competition, a consistent increase in the rate of employee attrition is identified as a growing critical issue among the Indian life insurance service providers.One of the leading challenges in management has been implementing effective human development strategies to enhance organizational performance and accountability.As a result of the emphasis on performance, researchers in human resource management have stressed effective human resources strategies such as job satisfaction, team empowerment, participative management, and strategic planning.The fact that employees of organizations are becoming key to strategic decision making seems reasonably indisputable even in insurance sector The major focus of this study was to understand the factors that affect the job satisfaction of employees in the Insurance sector, in order of their importance.It was also necessary to find out the major factors that cause dissatisfaction among the employees so that the Human Resource department can undertake measures to rectify.