Reviewerly/exHarmony huggingface dataset:
Internet of Things (IoT) Enabled Cloud Computing Drone for Smart Agriculture: Superior Growth and Life

In recent days, smart farming will reach every corner of the globe to expand the quality and quantity of production.Subsequently, the standard deployment of unmanned aerial vehicles (UAV) for smart farming is enormous and move towards fourth industrial revolution.In addition, drones outfitted with proper cameras, sensors and integrating elements that aid to attain transparent, efficient and precision agriculture.The agriculture sector is using the Internet of Things (IoT) and cloud computing more frequently, which has boosted crop output through cost control, performance monitoring, and maintenance.Because of the efficient use of resources and increased crop yield, this has immensely helped farmers.In order to further sustainable smart agriculture, the proposed research aims to create a smart drone for crop management that makes use of real-time data along with IoT and cloud computing technologies.Integrating these drone solutions with other cloud and IoT technologies can improve their potential for future development.The importance of IoT in agriculture and the realworld uses that can be made are also emphasized in this piece.
U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning

The APL notation would appear to be a clear match for convolutional neural networks, but traditional implementations of APL have lagged behind the performance of highly tuned, specialized frameworks designed to execute CNNs on the GPU. Moreover, most demonstrations of APL for neural networking have involved relatively small examples. We explore a more complex example in the U-net architecture and utilize a modern APL compiler with GPU support, Co-dfns, to compare the state of the art of APL against the current crop of specialized neural network frameworks in the form of PyTorch. We compare performance as well as the language design of APL for neural network programming and the clarity and transparency of the resulting code.
Network Media Content Model in the Era of Smart Devices

Recent developments in information technology, as seen by the democratization of the Internet and the widespread availability of laptops and smart gadgets, have resulted in the emergence of new business and communication models. The capability of today’s smartphones enables bidirectional and dynamic content processing. Users form virtual communities and transform into producers and consumers: prosumers. These contents are stored in the cloud, distributed via media (platforms), and swiftly propagate over the network. This essay investigates the evolution of these communication flows considering user-generated content and media convergence ideas. The data sequence’s cyclical structure, the evolution of the users, and the technological dependence on developing and distributing new discursive products are analyzed. Finally, a modelling approach based on user-defined algorithms is proposed.
PureCake: A Verified Compiler for a Lazy Functional Language

We present PureCake, a mechanically-verified compiler for PureLang, a lazy, purely functional programming language with monadic effects. PureLang syntax is Haskell-like and indentation-sensitive, and its constraint-based Hindley-Milner type system guarantees safe execution. We derive sound equational reasoning principles over its operational semantics, dramatically simplifying some proofs. We prove end-to-end correctness for the compilation of PureLang down to machine code---the first such result for any lazy language---by targeting CakeML and composing with its verified compiler. Multiple optimisation passes are necessary to handle realistic lazy idioms effectively. We develop PureCake entirely within the HOL4 interactive theorem prover.
Parameter-Free Matrix Decomposition for Specular Reflections Removal in Endoscopic Images

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Endoscopy is a medical diagnostic procedure used to see inside the human body with the help of a camera-attached system called the endoscope. Endoscopic images and videos suffer from specular reflections (or highlight) and can have an adverse impact on the diagnostic quality of images. These scattered white regions severely affect the visual appearance of images for both endoscopists and the computer-aided diagnosis of diseases. Methods & Results: We introduce a new parameter-free matrix decomposition technique to remove the specular reflections. The proposed method decomposes the original image into a highlight-free pseudo-low-rank component and a highlight component. Along with the highlight removal, the approach also removes the boundary artifacts present around the highlight regions, unlike the previous works based on family of Robust Principal Component Analysis (RPCA). The approach is evaluated on three publicly available endoscopy datasets: Kvasir Polyp, Kvasir Normal-Pylorus and Kvasir Capsule datasets. Our evaluation is benchmarked against 4 different state-of-the-art approaches using three different well-used metrics such as Structural Similarity Index Measure (SSIM), Percentage of highlights remaining and Coefficient of Variation (CoV). Conclusions: The results show significant improvements over the compared methods on all three metrics. The approach is further validated for statistical significance where it emerges better than other state-of-the-art approaches. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Clinical and Translational Impact Statement—</i> The mathematical concepts of low rank and rank decomposition in matrix algebra are translated to remove specularities in the endoscopic images The result shows the impact of the proposed method in removing specular reflections from endoscopic images indicating improved diagnosis efficiency for both endoscopists and computer-aided diagnosis systems
Monitoring Performance Computing Environments and Autoscaling Using AI

Modern organizations face unpredictable demands and dynamic workloads, necessitating effective management of computing resources.Autoscaling in cloud computing offers a solution by enabling applications to autonomously adjust their capacity in response to changing demand.This paper explores the utilization of AI-powered algorithms to address the challenges encountered in monitoring and autoscaling, considering factors such as memory requirements, network traffic, CPU utilization, and custom metrics.AI-driven models offer numerous advantages, including enhanced resource utilization, scalability, reliability, reduced maintenance overheads, continuous availability, cost-effectiveness, and simplified management of the computing environment.However, complexities in configuration, potential performance degradation, inconsistent performance, security concerns, and increased costs are notable drawbacks.By comparing AIpowered techniques with other traditional methods, this research evaluates the role of AI in overcoming challenges faced by alternative approaches.Through experimental evaluation and comprehensive analysis, this study demonstrates the superiority of AI-driven techniques in diverse aspects such as CPU utilization, memory utilization, throughput, and response time.Moreover, the paper identifies multiple areas for further improvement, aiming to enhance efficiency and reduce computing costs.
How certain are tansformers in image classification: uncertainty analysis with Monte Carlo dropout

Researchers have been inspired to implement transformer models in solving machine vision problems after their tremendous success with natural language tasks. Using the straightforward architecture and swift performance of transformers, a variety of computer vision problems can be solved with more ease and effectiveness. However, a comparative evaluation of their uncertainty in prediction has not been done yet. As we know, real world applications require a measure of uncertainty to produce accurate predictions, which allows researchers to handle uncertain inputs and special cases, in order to successfully prevent overfitting. Our study approaches the unexplored issue of uncertainty estimation among three popular and effective transformer models employed in computer vision, such as Vision Transformers (ViT), Swin Transformers (SWT), and Compact Convolutional Transformers (CCT). We conduct a comparative experiment to determine which particular architecture is the most reliable in image classification. We use dropouts at the inference phase in order to measure the uncertainty of these transformer models. This approach, commonly known as Monte Carlo Dropout (MCD), works well as a low-complexity estimation to compute uncertainty. The MCD-based CCT model is the least uncertain architecture in this classification task. Our proposed MCD-infused CCT model also yields the best results with 78.4% accuracy, while the SWT model with embedded MCD exhibits Pmaximum performance gain where the accuracy increased by almost 3% with the final result being 71.4%.
고급 수준 한국 EFL 대학생들의 디지털 도구를 활용한 글쓰기에서의 동료제안 및 수정에 관한 연구

Objectives This study investigates the use of digital tools, the types of peer reviews and revisions, and students’ perception in advanced Korean EFL college students’ essay writing. Methods Twelve advanced EFL freshmen college students participated in this triad peer review and revision process of their essay writing with eight digital tools while conducting surveys and reflective questionnaires. Results The findings show that the participants used the Google Docs and Microsoft Words the most as they make it possible to share and suggest ideas and opinions with ease, followed by Google Drawings and Grammarly. With Google Drawings, participants can take time to think before writing, plan for what they want to say, and they can organize their thoughts. In the case of Grammarly, they can discover out grammatical errors immediately and they can see the sections where they were wrong. In the first writing process, the results reveal that the participants used five types of peer reviews and revisions: content, grammar, mechanics, vocabulary, and organization. Among these types, peer reviews and revisions focused on grammar were the most frequently used, followed by mechanics and content. In the second writing process, the overall number of peer reviews and revisions were reduced and peer reviews and revisions on content were the most frequently used. Conclusions During the peer review and revision with the digital tools, the participants exchanged the constructive peer reviews in a collaborative way and revised the suggestions with digital tools. The results showed that the participants felt more confident in English essay writing with digital tools after the peer review process in their writings.
Optimizing Electromagnetic Cigarette Heaters Using PSO-NSGA II Algorithm: An Effective Strategy to Improve Temperature Control and Production Rate

The global tobacco industry has made significant strides in reducing the harmful emissions of tobacco by focusing on the development of non-burning cigarette products. Among these innovations, electrically heated noncombustible cigarette smoking sets have garnered attention. However, one of the challenges faced by these products is the reliance on empirical values for temperature control, resulting in subpar taste and low production rates. To address these issues, this research introduces an optimization strategy that utilizes advanced algorithms such as Particle Swarm Optimization (PSO) and traditional non-dominated sorting genetic algorithm-II (NSGA-II). By leveraging these algorithms, this study aims to optimize the performance of electrically heated noncombustible cigarette smoking sets. The research methodology encompasses a comprehensive review of relevant literature and the systematic introduction of a simulation method. Through the proposed approach, a finite element model verification is conducted, which demonstrates a minimal maximum relative error between the model values of the objective function and the simulation values of the optimized parameters. As a result, this multi-objective optimization approach proves to be highly effective in enhancing the performance of electromagnetic cigarette heaters. It not only addresses the taste and production rate issues associated with existing electrically heated noncombustible cigarette smoking sets but also provides a scientifically grounded method for optimizing these devices. This research paves the way for further advancements in the tobacco industry, contributing to the development of safer and more satisfying alternatives to traditional cigarettes.
Military Spying Robot

This system is designed to reduce casualties in terrorist attacks like the one that took place on November 26th.This problem can then be overcome by developing a spy robot based on it that uses wireless technology, making it easier to investigate competitors if necessary.This robot can silently enter enemy territory and send us information through sensors.Robotics has been the backbone of advanced manufacturing for over half a century.Mobile robotics are playing an increasingly important role in military affairs, from patrolling to dealing with potential explosives.The currently most popular gadget is the Android smartphone.There are many applications on the internet that use the built-in hardware such as Bluetooth in these mobile phones to control other devices.With the development of modern technology and Android smartphones, Bluetooth technology aims to exchange data wirelessly over short distances via radio transmission, including the functions of convenience, perception and control.Here we have designed a robot that can be controlled by an app running on an Android phone.Send control commands via Bluetooth connected to the controller.The controller can be connected to the Bluetooth module via the UART protocol.The movement of the robot can be controlled according to the commands received from the android.Therefore, the necessary measures can be taken.This project introduces a useful application with a real-time object detection system that can automatically detect user-defined important objects.By identifying these custom objects, we will immediately separate them with a robot.The problem discussed in this article is solved by detecting and using objects with defined sensors.We would also like to introduce an integrated technique for detection and detection of objects in real time.
Why the Educational Metaverse Is Not All About Virtual Reality Apps

This paper explores how the Metaverse can be used in the context of learning and collaboration. In it we seek to dispel the story that the Metaverse is just another synonym for Virtual Reality and future technology. Instead we will argue that the Metaverse is another interface scaffolding story akin to an interaction metaphor and can be used in an analogous manner to that of the Desktop Metaphor. How it is rendered in reality is a very flexible implementation detail. Instead, it should be thought of as a conceptual space and as an information sharing channel. As long as you are engaging in the Metaverse conceptually you can be there practically and in reality. As such it can be a low bandwidth and/or a high bandwidth information superhighway, depending on the specific mode of interaction. The use of metaphor and sharing has been with us a long time in learning technology and shared/collaborative interaction. So it is wrong to put down the aims and objectives of the Metaverse for Education as just an exercise in expensive high bandwidth Virtual Reality. It is not something that is based on technology for technology’s sake. It clearly has a role for those with access to advanced technologies, bandwidth, and hardware. In this paper we will however argue that this limited view fails to understand the possibilities it opens up for those who do not have such facilities available. Instead the Metaverse provides learning and collaboration possibilities for the many, not the privileged few.
Anomaly detection method for building energy consumption in multivariate time series based on graph attention mechanism

A critical issue in intelligent building control is detecting energy consumption anomalies based on intelligent device status data. The building field is plagued by energy consumption anomalies caused by a number of factors, many of which are associated with one another in apparent temporal relationships. For the detection of abnormalities, most traditional detection methods rely solely on a single variable of energy consumption data and its time series changes. Therefore, they are unable to examine the correlation between the multiple characteristic factors that affect energy consumption anomalies and their relationship in time. The outcomes of anomaly detection are one-sided. To address the above problems, this paper proposes an anomaly detection method based on multivariate time series. Firstly, in order to extract the correlation between different feature variables affecting energy consumption, this paper introduces a graph convolutional network to build an anomaly detection framework. Secondly, as different feature variables have different influences on each other, the framework is enhanced by a graph attention mechanism so that time series features with higher influence on energy consumption are given more attention weights, resulting in better anomaly detection of building energy consumption. Finally, the effectiveness of this paper's method and existing methods for detecting energy consumption anomalies in smart buildings are compared using standard data sets. The experimental results show that the model has better detection accuracy.
Analisis Risiko Logam Berat Cr dan Cu Pada DAS Cilamaya

The existence of human activities around the watershed can cause polluted water, one of the pollutant substances is heavy metals. The purpose of this study was to measure the concentration and analyze the risk of exposure to metals contained in river water. This research was conducted in the Cilamaya River, West Java Province. Sampling was carried out 3 times in March, June and October 2021 at 4 different sampling points. The data used is secondary data from the Department of Environment and Forestry of West Java Province and then calculated and analyzed using the equation formula. The results of this study indicate that the risk of Cr heavy metal contamination at points 1-4 is 0.010958904. Meanwhile, the risk of Cu heavy metal contamination at point 1-4 is 0.0109589. Then the risk value for both Cu and Cr metals is ≤ 1, which means that the Cilamaya watershed is not at risk.
ON THE FORMATION OF A UNIFIED EDUCATIONAL SPACE OF RUSSIA

One of the priorities of state policy in the sphere of education is forming unified educational space on the territory of the Russian Federation. In the article a survey is given concerning the measures taken by the state on establishing uniformity of the approaches to implementation of educational activity, beginning from the tsarist era till our days. Modern approaches to achieving uniformity in the field of education are analized as well.
Earth Is Noisy. Why Should Its Data Be Silent?

Combining visual and sonic representations of data can make science more accessible and help reveal subtle details. The recent decade-long eruption of Hawaii’s Kīlauea Volcano offers a prime example.
Logic and the scientific method: Virus and tobacco mosaic disease: What we know about infectious agents

In this paper, using the example of the discovery of viruses from the history of science in the experiments of D. J. Ivanovsky, the authors investigate how scientific research functions and how the scientific conception that could be determined as the ruling paradigm is gradually transformed. Respecting logical and scientific-methodological principles, Ivanovsky's discoveries, in the long run, led to the discovery of viruses as completely new agents and thus to the reconstruction of the paradigm of bacteria as the smallest living disease-causing agents. Thus, he contributed to what is defined as scientific growth in the philosophy of science. Through a comparative analysis, using the example of protective masks, we will show that today there is a tendency that the basic postulates of scientific work, which imply critical examination and the possibility of refutation, are no longer sufficiently respected.
Partisan US News Media Representations of Syrian Refugees

We investigate how representations of Syrian refugees (2011-2021) differ across US partisan news outlets. We analyze 47,388 articles from the online US media about Syrian refugees to detail differences in reporting between left- and right-leaning media. We use various NLP techniques to understand these differences. Our polarization and question answering results indicated that left-leaning media tended to represent refugees as child victims, welcome in the US, and right-leaning media cast refugees as Islamic terrorists. We noted similar results with our sentiment and offensive speech scores over time, which detail possibly unfavorable representations of refugees in right-leaning media. A strength of our work is how the different techniques we have applied validate each other. Based on our results, we provide several recommendations. Stakeholders may utilize our findings to intervene around refugee representations, and design communications campaigns that improve the way society sees refugees and possibly aid refugee outcomes.
Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community

In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict and ask the community to judge who is at fault (i.e., who is "the asshole"). These first person narratives are, in general, a unique storytelling domain where the author is not only the narrator (the person telling the story) but is also a character (the person living the story) and, thus, the author has two distinct voices presented in the story. In this study, we identify linguistic and narrative features associated with the author as the character or as a narrator. We use these features to answer the following questions: (1) what makes an asshole character and (2) what makes an asshole narrator? We extract both Author-as-Character features (e.g., demographics, narrative event chain, and emotional arc) and Author-as-Narrator features (i.e., the style and emotion of the story as a whole) in order to identify which aspects of the narrative are correlated with the final moral judgment. Our work shows that "assholes" as Characters frame themselves as lacking agency with a more positive personal arc, while "assholes" as Narrators will tell emotional and opinionated stories.