Reviewerly/exHarmony huggingface dataset:
Extraction of an Explicit Level of Algorithms Implementation for Use in the Algo500 Project

The study and description of the algorithm properties is extremely important for their effective implementation on various types of target software and hardware platforms. This topical task is the subject of a project to create an AlgoWiki Open encyclopedia of parallel algorithmic features, launched at Lomonosov Moscow State University in 2014. Within the framework of the project, a unified universal scheme for describing the algorithm properties was proposed, in which special attention to the properties associated with parallelism was paid. The set of algorithms described according to this scheme served as the basis for describing the structure of the subject area within the framework of the “Problem–Method–Algorithm–Implementation” hierarchical scheme. However, for the further development of the AlgoWiki project, it was necessary to separate the implementations of the algorithms that were previously included in the descriptions of the properties of the algorithms. This paper presents a scheme for describing the properties of algorithm implementations, as well as a modification of the original scheme for describing the properties of the algorithms themselves. The transformation of descriptions in the AlgoWiki encyclopedia according to this scheme was performed for all pages of the project, and it allows not only to better describe the properties of algorithm implementations, but also to integrate the AlgoWiki encyclopedia project with the Algo500 project, which implements a scalable digital platform for joint analysis of the properties of algorithms and computer architectures.
Molecular Observation Network (MONet) v2

EMSL is leading the effort to develop a national network of environmental sampling and sensing sites to produce comprehensive molecular-level information on the composition and structure of soil, water, and resident microbial communities required to advance the span and accuracy of multiscale models of Earth systems. Through the Molecular Observation Network (MONet), EMSL will collaborate with a broad range of partners managing an expanding network of selected natural, urban, and managed watershed, coastal, continental, and atmospheric sites, both experimental and observational. To establish MONet for the Biological and Environmental Research user community, research will focus on seven areas. Research focused under current development include: Establishing the supporting field and experimental networks, Advancing methods for model–data exchange and multiscale modeling, Automating organic matter and soil analysis, Developing the next generation of rhizosphere sensors and other field sensors for plants, microbes, nutrients, biomarkers, and aerosols. The data and models generated through MONet will improve prediction of ecosystem function and response to disturbances, supporting the long-term U.S. Department of Energy goal of scientifically informed decision-making regarding the nation’s energy and environmental security and sustainability. For more information please visit:https://www.emsl.pnnl.gov/monet
Development of an Omnidirectional AGV by Applying ORB-SLAM for Navigation Under ROS Framework

The development of an automated guided vehicle with omni-wheels for autonomous navigation under a robot operating system framework is presented in this paper. Specifically, a laser rangefinder-constructed two-dimensional environment map is integrated with a three-dimensional point cloud map to achieve real-time robot positioning using the oriented features from accelerated segment test and rotated binary robust independent elementary feature detector-simultaneous localisation and mapping algorithm. In the path planning for autonomous navigation of the omnidirectional mobile robot, we applied the A* global path search algorithm, which uses a heuristic function to estimate the robot position difference and searches for the best direction. Moreover, we employed the time-elastic-band method for local path planning, which merges the time interval of two locations to realise time optimisation for dynamic obstacle avoidance. The experimental results verified the effectiveness of the applied algorithms for the omni-wheeled mobile robot. Further, the results showed a superior performance over the adaptive Monte Carlo localisation for robot localisation and dynamic window approach for local path planning.
Impact of Ethical Leadership on Creative Work Performance: The Empirical Mediating Roles of Work Engagement and Attitude Toward Performing Wells in Construction Organizations in Pakistan

According to grounded social exchange theory, this study examines ethical leadership's direct and indirect impact on worker’s creative performance. More specifically, this study will also investigate the intervening mediate roles of work engagement and employee’s attitudes towards performing well between Ethical leadership and Creative work performance. The study data was obtained through self-reporting survey questionnaires distributed to employees of construction organizations in Lahore, Pakistan. The findings supported that Ethical leadership directly relates to creative work performance. Furthermore, work engagement and attitude toward performing well positively relate to ethical leadership and creative work performance. The findings broaden our understanding of how factors such as a positive attitude, psychological states, and engagement at work may produce the worker’s performance and increase their potential for creative intellectual in implementing novel ideas at the workplace.
An experimental study of animating-based facial image manipulation in online class environments

Abstract Recent advances in artificial intelligence technology have significantly improved facial image manipulation, which is known as Deepfake. Facial image manipulation synthesizes or replaces a region of the face in an image with that of another face. The techniques for facial image manipulation are classified into four categories: (1) entire face synthesis, (2) identity swap, (3) attribute manipulation, and (4) expression swap. Out of them, we focus on expression swap because it effectively manipulates only the expression of the face in the images or videos without creating or replacing the entire face, having advantages for the real-time application. In this study, we propose an evaluation framework of the expression swap models targeting the real-time online class environments. For this, we define three kinds of scenarios according to the portion of the face in the entire image considering actual online class situations: (1) attendance check (Scenario 1), (2) presentation (Scenario 2), and (3) examination (Scenario 3). Considering the manipulation on the online class environments, the framework receives a single source image and a target video and generates the video that manipulates a face of the target video to that in the source image. To this end, we select two models that satisfy the conditions required by the framework: (1) first order model and (2) GANimation. We implement these models in the framework and evaluate their performance for the defined scenarios. Through the quantitative and qualitative evaluation, we observe distinguishing properties of the used two models. Specifically, both models show acceptable results in Scenario 1, where the face occupies a large portion of the image. However, their performances are significantly degraded in Scenarios 2 and 3, where the face occupies less portion of the image; the first order model causes relatively less loss of image quality than GANimation in the result of the quantitative evaluation. In contrast, GANimation has the advantages of representing facial expression changes compared to the first order model. Finally, we devise an architecture for applying the expression swap model to the online video conferencing application in real-time. In particular, by applying the expression swap model to widely used online meeting platforms such as Zoom, Google Meet, and Microsoft Teams, we demonstrate its feasibility for real-time online classes.
Analysis on Developing English Speaking Through Reading in Junior Middle Schools

In terms of English education, much research has been done on the combination of reading and writing, but there are relatively few studies on the combination of reading and speaking. In this study, the combination of reading and speaking in English through activities was expounded. The effect of this teaching mode was studied based on students’ understanding of the subject, practical application, and transfer of knowledge. The goal of this teaching mode is to develop students’ core literacy and to enable them to interpret and utilize text resources. With these as objectives, teachers can design lessons and evaluations that require students to verbally explain what they understand after reading lessons and guide students in a stepwise manner, from superficial text understanding to in-depth understanding and eventually to thinking beyond text learning, so as to promote the integration and development of students’ language skills, cultural awareness, thinking, and learning ability.
Effect of Financial Innovation on Money Demand in the East African Community

Tremendous financial changes have been witnessed in the East African Community’s (EAC’s) macroeconomic landscape over the past few years. These changes can shift various parameters of the money demand model. Many previous empirical studies examined the effect of scale and opportunity cost of holding money variables on money demand. However, most of them left out financial innovation which is one of the key factors influencing money demand. Additionally, they are just country specific studies and used time series data analysis technique. It is in this backdrop that a cross-country case study that investigates how financial innovation affects money demand function was carried out using the recent data and a different analysis technique which is panel data analysis. The objective of the study was to examine the effect of mobile financial innovation on money demand in the EAC. The study used secondary data for the period 2007 to 2020 and this data was obtained from the World Bank and International Monetary Fund. Both descriptive and inferential analyses were carried out. Levin-Lin-Chu test for panel unit root was done and all the study variables were found to be stationary at level. The results of balanced panel fixed effects regression analysis indicated that mobile money, ATMs, and real GDP were affecting money demand positively and their effects were also statistically significant. However, interest rate affected money demand negatively. Mobile money and ATMs were proxies for financial innovation whereas real GDP and interest rates were control variables. Therefore, it was observed that financial innovation has had a positive effect on money demand in the EAC. The findings of this study might be of great importance to monetary authorities and policy makers in the EAC. Future research studies can expand the period of study similar to this one and also increase the number of countries involved.
Pattern Recognition in Breast Cancer Using Machine Learning

Breast Cancer is the most often identified cancer among women and major reason for increasing mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. Data mining techniques contribute a lot in the development of such system. For the classification of benign and malignant tumor we have used classification techniques of machine learning in which the machine is learned from the past data and can predict the category of new input. This paper is a relative study on the implementation of models using Logistic Regression, Support Vector Machine (SVM) and Latent Dirichlet Allocation (LDA) is done on the dataset taken from the UCI repository. With respect to the results of accuracy, precision, sensitivity, specificity and False Positive Rate the efficiency of each algorithm is measured and compared. Breast Cancer is the most leading malignancy affecting 2.1 million women each year which leads to greatest number of deaths among women. Early treatment not only helps to cure cancer but also helps in prevention of its recurrence. And hence this system mainly focuses on prediction of breast cancer where it uses different machine learning algorithms for creating models like decision tree, logistic regression, random forest which are applied on pre-processed data which suspects greater accuracy for prediction. Amongst all the models, Random Forest Classification leads to best accuracy with 98.6% techniques
A SURVEY OF DATA LEAKAGE DETECTION IN CLOUD COMPUTING PLATFORM

Cloud computing is the foundation of the contemporary IT era, where both small and large businesses use its services directly or indirectly. Cloud computing is currently a necessary talent that any IT professional should possess. It is no longer a specialized expertise.. Cloud computing technology is now a standard for IT deployment in the business, educational, and governmental sectors as a result of its extensive adoption. Yet, there are security flaws in cloud technologies like Web-based dashboards and hypervisors that could allow data to leak. The consequences of data leakage are severe; for a corporation like Exactis, one such event resulted in the exposure of 340 million client details. This paper provides an overview of different cloud computing techniques for identifying data leakage. We'll start by discussing the possibility of data leaking while using the proposed method. The Audit Trail/Transaction, Watermarking Method, Data Allocation Techniques, and VM Migration Process will be covered in more detail later. Keywords: Data Leakage, Cloud Computing, Cloud Security, Information Security
Virtual Mouse Using Artificial Intelligence

The mouse is a small, movable device that allows you to control a variety of things on a computer. Currently, a wireless mouse or a Bluetooth mouse uses devices and is not completely free of devices because it relies on a battery for power and a dongle to connect to the PC. This limitation can be overcome in the proposed AI virtual mouse system by using a webcam or a built- in camera to capture hand gestures and detect hand tips using computer vision. Keywords: Hand tracking, fingertip recognition, cursor movement, computer vision, and human-computer interaction.
Digital Evidence and Pakistani Criminal Justice System: A Review Article

The value of digital evidence has expanded quickly in recent years due to increases in its dependability and complexity. Evident from national and international regulations. There is a presumption in the law that digital evidence is admissible, and authorities have been directed not to rule it out of court because it is not presented in a substantive and perceptible form. Domestic and international laws have educated domestic law, and today everyone agrees. This section examines the background of the issue, the laws at play, and how it presented the matter to the Supreme Court nationwide. The technique adopted in this review study is a literature search. "Digital evidence" refers to any evidence made, kept, or transferred via digital means. It is impossible to overstate the role that digital evidence plays in the administration of criminal justice. In Pakistan, there were no laws surrounding the acceptance of digital evidence in court until 2002. We review roughly twenty evaluations given by different scholars in their articles. Part one of this review article's subsections is split in two. These abstracts expand upon the newest parts of the supplied legislation. The last section often supplies a quick summary of the important points and supporting evidence presented throughout the essay.
A review: Impact of motivation and toxic work around job culture

Motivation (inducement) plays a crucial role in shaping the job culture in the corporate world. A motivated workforce is essential to achieving organizational objectives and maintaining a positive work environment. When employees are motivated, they are more engaged in their work, more productive, and tend to stay longer with their employer. One of the key factors that impact motivation in the corporate world is the job culture. A positive and supportive job culture can be a significant motivator for employees. This culture fosters a sense of community, promotes transparency and open communication, and provides opportunities for growth and development. Employees who feel valued, supported, and encouraged are more likely to be motivated to perform at their best. In contrast, a negative or toxic job culture can be a demotivating factor for employees. In such an environment, employees may feel unsupported, undervalued, and unappreciated. This can lead to reduced motivation, low productivity, and high turnover rates. In such a scenario, employees may lose their passion for work, and this can negatively impact the entire organization. Therefore, it is essential for organizations to create a positive job culture that promotes motivation and employee engagement. This can be achieved through several initiatives, such as providing regular feedback, recognizing and rewarding employees, promoting a work-life balance, and offering opportunities for skill development and career advancement. By prioritizing employee motivation and well-being, organizations can create a work environment that is conducive to success and growth, both for the employees and the company as a whole.
Effective method for low-light image enhancement based on the JND and OCTM models

Low-light images always suffer from dim overall brightness, low contrast, and low dynamic ranges, thus result in image degradation. In this paper, we propose an effective method for low-light image enhancement based on the just-noticeable-difference (JND) and the optimal contrast-tone mapping (OCTM) models. First, the guided filter decomposes the original images into base and detail images. After this filtering, detail images are processed based on the visual masking model to enhance details effectively. At the same time, the brightness of base images is adjusted based on the JND and OCTM models. Finally, we propose a new method to generate a sequence of artificial images to adjust the brightness of the output, which has a better performance in image detail preservation compared with other single-input algorithms. Experiments have demonstrated that the proposed method not only achieves low-light image enhancement, but also outperforms state-of-the-art methods qualitatively and quantitatively.
What is "where": Physical reasoning informs object location

A central puzzle the visual system tries to solve is: “what is where?” While a great deal of research attempts to model object recognition (“what”), a comparatively smaller body of work seeks to model object location (“where”), especially in perceiving everyday objects. How do people locate an object, right now, in front of them? In three experiments collecting over 35,000 judgements on stimuli spanning different levels of realism (line drawings, real images, and crude forms), participants clicked “where” an object is, as if pointing to it. We modeled their responses with eight different methods, including both human response-based models (judgements of physical reasoning, spatial memory, free-response “click anywhere” judgements, and judgements of where people would grab the object), and image-based models (uniform distributions over the image, convex hull, saliency map, and medial axis). Physical reasoning was the best predictor of “where,” performing significantly better than even spatial memory and free-response judgements. Our results offer insight into the perception of object locations while also raising interesting questions about the relationship between physical reasoning and visual perception.
A REVIEW ON REAL WORLD IMAGE INTO CARTOON STYLE IMAGE USING GENERATIVE ADVERSARIAL NETWORKS

Cartoonization is an art of transforming real-world images into cartoon-style images.Image style transfer especially this artistic style transfer is a challenging task in the field of computer vision because of the special characteristics of cartoon-style images like smooth edges, bright colors, smooth surfaces, and texture.A new state of an art method in canonization is being more popular with rising of Generative adversarial networks (GANs).With help of two neurons that use convolutional operations and different loss functions like color loss, content loss, edge loss, grayscale loss, and adversarial loss, the model itself generates a synthetic cartoon-style image of a given real-world image.Though GANs are difficult to train, some techniques make training smooth, like researchers use patchGAN discriminator, LeakyRelu in the discriminator, tanh activation function, residual blocks in the generator, and pre-training of the generator.This review paper gives a brief introduction to GAN, a summary of different GAN algorithms like CycleGAN, CartoonGAN, AnimeGAN, Whitebox Cartoonization and CartoonlossGAN used in cartoonization as a form of literature survey, and a comparison and discussion of these algorithms.
SummScreen: A Dataset for Abstractive Screenplay Summarization

We introduce SummScreen, a summarization dataset comprised of pairs of TV series transcripts and human written recaps. The dataset provides a challenging testbed for abstractive summarization for several reasons. Plot details are often expressed indirectly in character dialogues and may be scattered across the entirety of the transcript. These details must be found and integrated to form the succinct plot descriptions in the recaps. Also, TV scripts contain content that does not directly pertain to the central plot but rather serves to develop characters or provide comic relief. This information is rarely contained in recaps. Since characters are fundamental to TV series, we also propose two entity-centric evaluation metrics. Empirically, we characterize the dataset by evaluating several methods, including neural models and those based on nearest neighbors. An oracle extractive approach outperforms all benchmarked models according to automatic metrics, showing that the neural models are unable to fully exploit the input transcripts. Human evaluation and qualitative analysis reveal that our non-oracle models are competitive with their oracle counterparts in terms of generating faithful plot events and can benefit from better content selectors. Both oracle and non-oracle models generate unfaithful facts, suggesting future research directions.