Reviewerly/exHarmony huggingface dataset:
Cloning Object Detectors with Limited Access to In-Distribution Samples

An object detector identifies and locates objects in images. Object detectors are widely deployed in practice, for example in driver assistance systems. Recently it has been shown that state-of-the-art object detectors based on neural networks can be cloned successfully if the adversary has oracle access to the detector, and if the adversary has access to either: (i) sufficiently many images drawn from the same distribution as the images of the detector's train set, also referred to as in-distribution samples, or (ii) a publicly available generative AI network capable of generating images that are close to in-distribution samples. This paper presents a new cloning attack that uses images from a publicly and freely available dataset, referred to as out-of-distribution samples, and a limited number of in-distribution samples. The new attack includes a strategy for combining in-and out-of-distribution samples during training and a calibration step to better mimic the functionality of the oracle detector. Our experiments show that CenterNet and RetinaNet object detectors trained with the Oxford-IIIT Pet, the WIDER FACE, or the Tsinghua-Tencent 100K dataset can be cloned successfully using images from the ImageNet-1K dataset supplemented with a limited number of in-distribution samples.
Networked Test System Attack Detection Based on Deep Generative Models

With the widespread application of networked test systems in aviation and other fields, ensuring the safety and reliability of these systems is of great importance. This paper proposes an innovative attack detection model, which combines the improved density peak clustering algorithm (mdpca) and deep confidence network (DBN), and is named mdpca-dbn. The model aims to address various security challenges that networked test systems may face, including data theft, tampering, and interference during the test process. By integrating density peak clustering and deep confidence network, the model can better identify potential security threats, and has shown satisfactory performance in experiments. The introduction of this model is expected to enhance the security of networked test systems and ensure the accuracy and reliability of test results. In the future, we will continue to improve and optimize this model to adapt to evolving network security threats.
Analysis of the Performance of Four Filter Types for Drone Tracking

In this work, extensive simulations are done to compare the performance of the 4 filter types; Linear Kalman filter (LKF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). A simple nearly constant velocity (NCV) motion model is used with a Gaussian noise measurement model. Simulations were done with different ground truths, different measurements covariance matrices, and different speeds of the drone. Stone soup software was used in the simulations. The analyses revealed informative results that gave us more understanding of the behavior of the four filters when a common type of motion model such as the NCV model is used.
Using a Gamification Literature Circles Strategy to Develop 6th Primary Stage Pupils&amp;#039; some Reading Comprehension Skills and Vocabulary Enrichment

The aim of the present study was to investigate the effectiveness of using a Gamification Literature Circles strategy in developing some reading comprehension skills and vocabulary enrichment for primary-six pupils. The research adopted the quasi-experimental approach. The study participants were 54 pupils. The subject of the research was the sixth primary pupils of Manfalout Model Azhari Primary Institute. They were distributed into two groups: the experimental group (27 pupils) taught reading comprehension and vocabulary by a Gamification Literature Circles strategy, and the control group (27 pupils) taught reading comprehension and vocabulary by using the traditional method. The instrument of the current research was two pre-post reading comprehension and vocabulary tests. The results demonstrated that there was a statistically significant difference at 0.05 level between the mean scores of the two groups in the post implementation in the results of the reading comprehension test and the vocabulary test in favor of the treatment group. Results also revealed that the strategy has a significant effect on developing some reading comprehension skills and vocabulary enrichment of primary six school pupils. Based on the obtained results, many recommendation, and suggestions were made. Keywords: Gamification – Literature Circles – reading comprehension - vocabulary enrichment
Investigating the Relation between Corporate Entrepreneurship and Employee Turnover Intention, Mediated by Job Satisfaction and Organizational Commitment: An Applied Study on Egyptian Petrochemicals Sector

This research is designed to investigate the effect of Corporate Entrepreneurship (CE) on Turnover Intention (TI) with mediation role of Job satisfaction (JS) and Organizational Commitment (OC): evidence from Egyptian Petrochemicals Sector. The objectives of This research are to investigate the relation among CE and TI, to test the relation among CE and JS, to test the relation among CE and OC, to explore the relation among JS and TI, to test the relation among OC and TI, to test the mediation role of JS among CE and TI. and to investigate the mediation role of OC among CE and TI. Using a questionnaire to collect data and AMOS software to conduct structural equation model (SEM) analyses, the methodology will be based on quantitative analysis. The primary findings of this paper are the direct effect among CE and TI is statistically significant, the direct effect among CE and JS is statistically significant, the direct effect among CE and OC is statistically significant, the direct effect among JS and TI is statistically significant, the direct effect among OC and TI is statistically significant, the study found that there is partial mediation effect of the OC among the relation of CE and TI in the Egyptian Petrochemicals Sector. Finally, the study found that there is partial mediation effect of the JS among the relation of CE and TI in the Egyptian Petrochemicals Sector.
An Assessment on the Students’ Level of Earthquake Awareness and Preparedness on “The Big One”

Since the Philippines has been subjected to numerous seismic events throughout the past several decades, it is only prudent to be aware and prepared for what will unavoidably happen when the Marikina West Valley Fault System moves. The study aims to assess the College of Science students' earthquake awareness and preparedness level, an essential part of their understanding of proper response and management in natural disasters. It will also allow the community and the environment to establish necessary precautions. A descriptive-survey study approach was applied to assess the level of awareness and preparedness of 160 respondents from all year levels from the College of Science at Bulacan State University. Results show that most respondents are highly aware and prepared for an earthquake. However, low levels were shown in some components of earthquake public awareness and disaster risk reduction preparedness. This includes the respondents' contingency plan awareness, security of household materials, and coordination with local government units regarding potentially high-risk objects. A significant difference was also seen in the respondents' awareness of the existing contingency plan and participation in first-aid training for risk reduction. In addition, first-year students mainly acquired their awareness from School/Office, while second- to fourth-year students acquired it mainly from the Internet. With the obtained results, the authors created a proposed intervention consisting of programs regarding the university's Incident Command System, Contingency Plan, and Disaster Risk Reduction Management.
Calculation Option Price on Shares PT. Nippon Indosari Corpindo Tbk. with Fuzzy Binomial Tree Model

The Cox-Ross-Rubinstein binomial tree model is widely used in stock and derivative securities calculations, such as options calculations. The binomial CRR model assumes that the parameter increases in option prices and decreases in option prices so this model produces stock price movements up and down stock price movement. However, stock price movements show price fluctuations and cause the volatility of the value to be unsuitable. In this study, modeling stock and option price movements using a fuzzy binomial tree model. The data used was data on the movement of the stock price of Nippon Indosari Corpindo Ltd Plc from February 2021 to January 2022. The results showed that for February 2022, with a risk size of 90%, the selling price options with the greatest volatility of 51.6484081, medium volatility of 33.33154354, and the smallest volatility of 28.17155892.
Estimation and Investigation of Photosynthetically Active Radiation Using Meteorological Parameters over Ikeja, Nigeria

Photosynthetically active radiation (PAR; 0.4 - 0.7μm) is a key driver in ecosystem biochemical processes, and thus a critical factor in agriculture productivity, ecosystem-atmosphere energy, and CO2 fluxes. In this study, the measured monthly averaged daily global solar radiation, relative humidity, wind speed, minimum and maximum temperature dataset was utilized to estimate and investigate the Photosynthetically Active Radiation (PAR) for Ikeja (latitude 6.58 N, longitude 3.33 E and 39.4 m above sea level) located in the coastal region ofNigeria. The meteorological parameters used in this work were obtained from the archives of National Aeronautics and space Administration (NASA) for a period of thirty-eight years (1984-2021).The newly developed temperature PAR based models were statistically tested using the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test and index of Agreement (IA) to ascertain the accuracy and applicability of the models. The results show that, the highest and lowest values of PAR were found during the rainy season with 8.3426 MJ and 6.4936 MJ in the months of April and July respectively. Based on the developed models, the model equation (16g) is the most suitable for estimating PAR for Ikeja with R2, MBE, RMSE, MPE, t-test and IA values of 80.9 , 0.0062 MJ , 0.3415 MJ -0.1974 0.0601 and 92.8712 respectively.
Bayes Estimator for Dagum Distribution Parameters Using Non-Informative Prior Rules with K-Loss Function and Entropy Loss Function

The parameter estimator discussed is the p parameter estimator of the Dagum distribution with the K-loss function and the entropy loss function using the Bayes method. To get the Bayes estimator from the scale parameter of the Dagum distribution, the Jeffrey non-informative prior distribution is used based on the maximum likelihood function and the loss function for the K-loss function and the entropy loss function to obtain an efficient estimator. Determination of the best estimator is done by comparing the variance values generated from each estimator. An estimator that uses the entropy loss function is the best method for estimating the parameters of the Dagum distribution of the data population with efficient conditions met.
Adapting Stage Play to Virtual Reality Comics

The digital revolution has given rise to hybrid mediums and a digital culture constructed through new communication technologies. This has created opportunities for sequential storytelling to expand from its conventional form towards immersive mediums. Comics, conventionally developed in codex book form, are currently experimenting with new media tools, where screens often replace pages. This phenomenon is considered the meta-panel, which has the flexibility to take any form, shape, or size. Virtual reality comics can immerse viewers in unique ways such as immersion in a 360-degree space, ambisonic sound, motion, and interaction with the content. Studies suggest that cinematic virtual reality and stage plays have several similarities, such as long takes, exaggeration in performance, and the viewers being located at a considerable distance from the performance. Therefore, designing for virtual reality can benefit from stage methods. This study experiments with the adaptation of theatrical storytelling into comics. This paper adopts a practice-based research method to develop a virtual reality comic, The Great Golden Gang. This paper includes a discussion on the design process for developing such a virtual reality comic. To test the engagement of the virtual reality comic, Chapter One was previewed by selected participants using head-mounted displays. A participant survey demonstrates the viewer’s readiness for virtual reality comics and suggests the potential of hybrid mediums of a stage play, comics, and virtual reality. The Great Golden Gang is an example of the meta-panel and highlights the potential of virtual reality to contribute to new ways of experiencing the stage and comics.&#x0D; Keywords: stay-play adaptation; virtual reality; immersive comics; visual narrative
Cloud Quantum Coin-Tossing Gambling

Quantum computers are an alternative way to create multipartite probabilities for a game as a function of participant’s inputs. In some situations, quantum gambling could be an improvement over the predictability of certain types of random number generators. However, NISQ computers require a protocol whose expected statistical gains (losses) can be confirmed empirically given the participants’ inputs. A zero-sum coin-tossing protocol with Nash equilibrium [1] is tested with a quantum computer where hypothetical players enter parameters, in their respective qubits, and are compensated 1 or R coin(s) after each outcome. In theory, independently of R, the protocol implies that there is no gain improvement for a player when the other maintains the equilibrium parameter; gain is zero or better for the player maintaining it. However, outcomes obtained with several setting combinations imply Nash equilibrium only when R is a small fraction. For R≫1, given thousands of outcomes, there is Nash-like equilibrium such that a player may not improve gain significantly by changing the parameter if the other maintains it, that is, losses (gains) are considerably minimized with the parameter. The data suggests that gains (losses) would be expected statistical functions of the participants’ choices if two played in this manner.
Heart Disease Classification Using Deep Neural Network with SMOTE Technique for Balancing Data

Heart disease is the leading cause of premature death worldwide. According to the WHO, heart disease causes about 30% of the total 58 million deaths and mostly occurs in individuals who are in their productive age. This condition can occur to anyone, including individuals who do not show symptoms of heart disease. However, heart disease can be prevented with early detection. By understanding the various risk factors that can increase the potential for heart disease. Therefore, this study aims to classify heart disease using Deep Neural Network algorithm and SMOTE technique to overcome data imbalance. This research resulted in a validation accuracy of 90% with precision evaluation of 0.85, recall 0.92, and f1-score 0.88. Based on the results obtained, the Deep Neural Network algorithm after SMOTE is superior to the model without SMOTE.
Development of a Common Information System to Create a Digital Career Center Together with Partner Higher Schools

The purpose of the report presented by the authors is to present a methodical development of a software product and the sequence of its implementation to create a common information system of career centers in Technical University of Sofia and the partner organizations. In fulfillment of the contract BG05M2OP001-2.016-0008-С02 “Innovations, Science and Education for high quality and compliance with the labor market” at the Technical University of Sofia and Partnership (INNOTECH PRO), the goal is to create a single Digital Career Center (DCC), to create a single network by upgrading and modernizing the electronic platforms used so far by the partner higher schools. Through career counseling and guidance and the creation of a Digital Career Exchange (DCE) to give students, graduates, alumni and doctoral students more detailed information and opportunities to find the right job position for each.
ITALIAN NEWSPAPERS AND COVID-19: THE PANDEMIC’S IMPACT ON THE LANGUAGE

This text aims to highlight the changes that have occurred within the Italian language during the COVID-19 pandemic. To do so, we will mention some headlines from articles published in major Italian newspapers such as Corriere della Sera, la Repubblica, and Il Messaggero. The article is essentially composed of three parts: the first part elaborates on one of the major linguistic debates that arose during the pandemic period, namely the one related to the gender of the word COVID-19; the second part shows how the meaning of the term positive has changed; while the third part focuses on the technicalities and foreign words that have become part of our new daily reality following the emergence of the coronavirus. Keywords: COVID-19; Italian language; Corriere della Sera; la Repubblica; Il Messaggero.
Enhancing Financial Literacy in Malaysian Preschoolers Through A Financial Management Game

Play-based learning approaches are deemed crucial in early childhood education, particularly in preschool settings where the integration of literacy and cognitive skills significantly impacts learning outcomes. This study explores the impact of a financial management game called “Smart Money Kid” on the financial literacy of preschool students. Both descriptive and inferential analyses were employed to assess preschoolers' financial literacy levels. Results indicate that both experimental and control groups demonstrated increased literacy levels, with the experimental group exhibiting significantly higher proficiency. The findings emphasize the potential of play-based learning, specifically through a designed financial management game, in enhancing preschoolers' financial literacy.
Comparison of Naive Bayes, K-Nearest Neighbor, and Support Vector Machine Classification Methods in Semi-Supervised Learning for Sentiment Analysis of Kereta Cepat Jakarta Bandung (KCJB)

Transportation technology has developed very rapidly in the 21st century; one of them is high-speed trains. Currently, the Indonesian government is implementing the construction of the Kereta Cepat Jakarta-Bandung (KCJB) project in collaboration with China. The construction of this fast train project has attracted various comments and opinions from the public on Twitter and social media. This research aims to compare the classification methods of Naïve Bayes, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) in classifying sentiment in tweets about high-speed trains obtained by scraping Twitter. The comparison process was carried out using semi-supervised learning, and the results showed that the semi-supervised SVM model had the best performance with an average accuracy of 86%, followed by the semi-supervised Naïve Bayes model and semi-supervised K-NN with an average accuracy of 81% and 58% respectively. Overall, the prediction results from the three models conclude that there are more tweets with negative sentiment than tweets with positive and neutral sentiment.
Development of Student’s Uniform Compliance Detection System Using Real Time Image Recognition at Politeknik Statistika STIS

Regulations in the Politeknik Statistika STIS (hereinafter called Polstat STIS) aims to produce graduates who are qualified, with integrity and trusted. In enforcing regulations in Polstat STIS, there are a student squad of regulations enforcement, which is called Satuan Penegak Disiplin or SPD in Indonesian, which aims to maintain the order, discipline and student ethics during their activities on and off campus. In upholding the regulations, SPD carries out surprise inspection and during the weekly morning assembly to check completeness and tidiness of student’s uniform as well as his/her look. However, previous research related to the student’s commitment to the campus regulations shows that half of the students have low commitment. This is partly due to the lack of supervision of students. Therefore, it is necessary to monitor the discipline and neatness of students on an ongoing basis. In order to conduct the monitoring and inspection on a more regular basis, the method of image recognition can be used to assist in overseeing student discipline and neatness. In this study, we developed a system which can detect in a real-time manner the completeness of attributes the student wears. The system we developed uses object detection to detect the completeness of student attributes. The system shows and records student(s) whose attributes are incomplete. The system expected to improve the discipline and neatness of the students.
Tracking and Tracing in Israel during COVID-19

Abstract In March 2020, the Israeli government decided that its internal security agency may collect, process, and use technological information measures to tackle the spread of COVID-19. This was done by tracking the cellphones of those who may have contracted the virus, along with obtaining details on those who were in proximity for more than fifteen minutes and fourteen days prior to the positive outcome of those traced. The article discusses the controversial track and trace measure and proposes an alternative model of using tracing technology, considering the obligation to preserve human life and the right to individual privacy among other rights and liberties. It is argued that measures infringing on the right to privacy must be effectively restricted in time and meet standards of necessity, proportionality, and scientific validity, as required by constitutional standards. The government needs to balance the right to health against the right to privacy.
An innovative method for topic modeling employing speed reading

As the pace of product reviews and similar textual content accelerates, the power of natural language becomes evident in its ability to effectively convey vast and intricate information through succinct communication. This proficiency is applied in Latent Dirichlet Allocation (LDA), a machine-learning algorithm that explores documents to uncover hidden topics. This principle, LDA, proves crucial in generating summaries from a provided collection of documents, significantly expediting and enhancing the comprehension of textual data. Yet, LDA faces difficulties in accurately identifying topics within concise documents or within data that is noisy. This article introduces an innovative strategy for topic modeling using LDA, centered around speed reading’s influence on sentences. From the experimental results, it was found that our suggested approach is more powerful than the baseline method in terms of coherence rate (75.64%), and execution time (1.395 seconds). The overall approach, evaluated via evaluating metrics, demonstrates that speed reading using LDA is efficacy in identifying text data attributes.