Reviewerly/exHarmony huggingface dataset:
PENGGUNAAN APLIKASI WHATSAPP DAN GOOGLE CLASSROOM PADA PEMBELAJARAN MATEMATIKA DI MASA PANDEMI COVID-19

The purpose of this study was to describe (1) the process of mathematics learning using Whatsapp and Google classroom and (2) the constraints experienced by teachers and students regarding the use of these two applications in Mathematics learning. This research is a descriptive study with a qualitative approach. The data collection technique used is interviews, observation, and documentation. The research subjects were a mathematics teacher and two students. The results showed that (1) Mathematics learning process was running smoothly where Whatsapp was used for core learning activities and Google classroom was used to access material and practice questions; (2) The obstacles faced by teachers and students are more directed at the less stable internet network. In general, the use of Whatsapp and Google classroom in learning Mathematics runs quite well. Keywords: Learning; Mathematics; Application; Covid-19
Iterative enhancement scheme of synthesized color and depth images for immersive video system

Immersive video allows viewers to freely switch the viewpoints. The intensity of realistic experience greatly relies on the quality of synthesized depth maps. However, there exist distorted regions due to inaccurate depth estimation or compression.
Chapter 11: Preliminary explanations of serendipity based on non-linear information process

The book explores the nature, underlying causes, and the information processing mechanism of serendipity. It proposes that natural or social survival demands drive serendipity, and serendipity is conditional on the environment and the mindset, on both individual and collective levels. From Darwin’s evolution theory to Sun Tzu’s war tactics, major innovations throughout human history are unified by this key concept. In the rapidly changing world, information is abundant but rather chaotic. The adaptive power of serendipity allows people to notice treasures within this wild sea, but only for those who understand how it works. To increase the probability of encountering and attaining serendipity, one should employ the mindsponge mechanism and the 3D process of creativity, for without these frameworks, serendipity is truly an elusive target. The book also discusses methods to build environments and cultures rich in navigational and useful information to maximize the chance of finding and capitalizing on serendipity. As a skill, serendipity has a resemblance to how kingfishers observe and hunt their prey Description of the peer review process for the book titled A New Theory of Serendipity ”A New Theory of Serendipity: Nature, Emergence and Mechanism” is covered by the following services:• Baidu Scholar•
Ethics in digital finance: towards a new paradigm of self-regulation1

The chapter deals with the impact of digitisation on the understanding of ethical standards in finance and the forms of their institutionalisation. This impact was recognized as an extension of the responsibility of financial institutions due to the increase in their ability to influence customer values within the digital infrastructure and through the use of tools provided by new technologies. These technologies were therefore treated not only as a new and more effective medium of communication but also as a phenomenon that significantly increases the cognitive and operational abilities of financial institutions. The chapter proposes an interpretation according to which these technologies exert pressure on a more depersonalized and consumer model of relations with customers and at the same time on increasing the degree of innovation of financial products. The postulated ethical response to this should be a stronger moral co-responsibility of financial institutions and commitment to values manifested in proposing institutional innovations designed through the use of the same tools. Thanks to digitisation, however, these innovations can not only be designed more effectively but also implemented in the recommended paradigm of ethical self-regulation defined as a bottom-up collective. It is possible, among others due to the &quot;self-execution&quot; of ethical standards in digital reality.
Investigating the Impact of Manipulating Electronic Reader Programs for Developing EFL Senior Learners' Reading Speed and Loud Reading Skills

This chapter investigated the impact of manipulating electronic reader programs for developing EFL senior learners' reading speed and loud reading skills. The study's sample consisted of 20 EFL senior participants from King Marriott Academy in Alexandria, Egypt. Reading speed and loud reading skills were improved as a result of the treatment. Students who took the post-reading test increased their regular reading speed by 45 words per minute (WPM) and their loud reading skills by 20%. There is no statistically significant link between rapid reading and reading comprehension, according to the study.
Aggregate Service Using One Stop Shop and Web Crawler in Cloud Computing

Cloud computing services has become an important paradigm as it is reliable and provides a cost effective way of storing and hosting applications. Cloud storage is growing exponentially and monitor the data in a secure manner. Cloud computing is the delivery of different services through the Internet. These resources include tools and applications like data storage, servers, databases, networking, and software. As long as an electronic device has access to the web, it has access to the data and the software programs to run it. The one-stop shop expanded over time to include business services. The shifted from a wide product offering to capture more of the customer's grocery purchase to one of offering all the complementary products and services to a client in a particular area. Web crawlers are mainly used to create a copy of all the visited pages for later processing by a search engine,that will index the downloaded pages to provide fast searches.
Competitive Analysis for the Auditing Cloud Consistency

Cloud storage is one of the service of cloud comput- ing. Cloud storage services are commercially popular because to their advantages. It allows data owners to move data from their local computing systems to the cloud. It offers high quality and on-demand data storage services to users. A cloud is essentially a large-scale distributed system; each piece of data is replicated on multiple geographically distributed servers to achieve high availability and high performance. A cloud service provider (CSP) keeps multiple replicas for user’s data on geographically distributed servers. A main problem of using the replication technique in clouds is that it is very expensive to achieve strong consistency on a worldwide scale. In this paper, we are reviewing consistency as a service (CaaS) model, a two-level auditing architecture and a heuristic auditing strategy (HAS) that reveals as many violations as possible.
FasterAI: A Lightweight Library for Creating Sparse Neural Networks

FasterAI is a PyTorch-based library, aiming to facilitate the utilization of deep neural networks compression techniques such as sparsification, pruning, knowledge distillation, or regularization. The library is built with the purpose of enabling quick implementation and experimentation. More particularly, compression techniques are leveraging Callback systems of libraries such as fastai and Pytorch Lightning to bring a user-friendly and high-level API. The main asset of FasterAI is its lightweight, yet powerful, simplicity of use. Indeed, because it was developed in a very granular way, users can create thousands of unique experiments by using different combinations of parameters. In this paper, we focus on the sparsifying capabilities of FasterAI, which represents the core of the library. Performing sparsification of a neural network in FasterAI only requires a single additional line of code in the traditional training loop, yet allows to perform state-of-the-art techniques such as Lottery Ticket Hypothesis experiments
Developing information security metrics and measures for risk assessment of an organization

The absence of public safety normalization bodies can antagonistically affect the reception of worldwide security guidelines and best practices. This research is done for the benefit of people, process and technology for any organization. This paper presents a novel pragmatic network protection evaluation structure that is custom-made to the ISO 2700x standard necessities for the improvement of Information Security Management System (ISMS). This model can be utilized for both self-appraisal and examining/scoring instruments by public network protection specialists. Utilizing this model, associations can consider their current data security the broad frameworks in contrast to nearby and global guidelines by using worked in pre-review instruments. All things considered, the model will assist associations with assessing and improving their status for developing dangers and dangers. In this system, an original numerical model was likewise planned and carried out for the scoring/rating instrument, specifically, the public digital protection list (aeNCI). The aeNCI utilizes various boundaries to decide the development of existing network safety programs at public associations and produce a classiﬁcation and correlation reports. The outcomes empowered the partner to confirm the security conﬁguration of their frameworks and recognize possible attacks/hazard vectors.
Blockchains and NFTs: Tragedy of the Digital Commons or Anticommons?

The literature on property rights and associated commons has expanded in recent decades to incorporate the rise of the internet and the prevalence of digital goods. The more recent developments in technologies like blockchain and non-fungible tokens (NFTs) have further complicated the landscape of digital property rights. In this paper I argue that the rise of Non-Fungible Tokens (NFTs) as the latest technical innovation of blockchains is not merely a frivolous application, but a serious development that merits scholarly attention. I theorize that NFTs represent a very different approach to property rights compared to not only prior digital technologies, but also prior innovations such as blockchains which provide their infrastructural underpinnings. By introducing the attributes of subtractability and exclusion to digital goods – hitherto missing – blockchains and NFTs represent a new era to the rise of the digital commons and risk converting it to an anticommons. I end by explicating implications of my theorization for future research and practice.
CRFormer: A Cross-Region Transformer for Shadow Removal

Aiming to restore the original intensity of shadow regions in an image and make them compatible with the remaining non-shadow regions without a trace, shadow removal is a very challenging problem that benefits many downstream image/video-related tasks. Recently, transformers have shown their strong capability in various applications by capturing global pixel interactions and this capability is highly desirable in shadow removal. However, applying transformers to promote shadow removal is non-trivial for the following two reasons: 1) The patchify operation is not suitable for shadow removal due to irregular shadow shapes; 2) shadow removal only needs one-way interaction from the non-shadow region to the shadow region instead of the common two-way interactions among all pixels in the image. In this paper, we propose a novel cross-region transformer, namely CRFormer, for shadow removal which differs from existing transformers by only considering the pixel interactions from the non-shadow region to the shadow region without splitting images into patches. This is achieved by a carefully designed region-aware cross-attention operation that can aggregate the recovered shadow region features conditioned on the non-shadow region features. Extensive experiments on ISTD, AISTD, SRD, and Video Shadow Removal datasets demonstrate the superiority of our method compared to other state-of-the-art methods.
Arduino Based Covid Disinfection Box

The work titled “Arduino Based Covid Disinfection box” aims to build a disinfection box which will sterilize various day-to-day objects like masks and keys. Outbreaks of the virus are highly contagious and life-threatening. One of the necessary steps in the fight against the outbreak of the epidemic and victory over the global pandemic is the disinfection of infected surfaces. Keeping public places safe and clean can be quite challenging, especially if other people are in frequent contact with each other. Common areas and high-contact surfaces are manually disinfected using the hand-held disinfectant. Manual disinfection exposes a cleaner to a contaminated surface by increasing the risk of infection. The use of disinfection box reduces the risk of infection, and overcomes the costs associated with manual cleaning agents. Excessive use of the disinfectant can cause serious health problems. This pandemic forced us to develop and build secure disinfection equipment for UVC disinfection for public spaces. UVC Rays can easily be skipped during manual cleaning and are not harmful to human skin and eyes. The disinfection box provides an effective and efficient solution for disinfecting high-risk and contact surfaces without human intervention.
Capping Mobile Data Access Creates Value for Bottom-of-the-Pyramid Consumers -- Experimental Evidence

Smartphones have enabled the delivery of life-improving information services to bottom-of-the-pyramid (BOP) consumers. However, little is known about how the poor interact with the digital world. Through a novel app we developed to investigate real-time smartphone usage, we identify a heretofore unnoticed barrier to digital information access by the poor -- data shortages. We offer a practical operational solution to this problem, in the form of a commitment device that curbs data binge usage. Based on analyzing over 9.4 million minutes of smartphone usage data from 929 residents of a Mumbai settlement, we find that entertainment consumes 61% of their phone time. We randomly assign participants to a `capped plan' -- with daily data usage caps -- or a standard plan. Our data reveal that absent usage caps, participants binge on YouTube and social media, resulting in subsequent data shortages. Assignment to the capped plan increases late-plan access of invites to health camps sent via WhatsApp, increases attendance at these in-person camps by 27%, and reduces social media checking. Our results are consistent with users prioritizing entertainment even when data is scarce, or facing data stockouts. Most participants (particularly those with low self-control and high fear of missing out) prefer the capped plan, even at a higher price – a clear signal of demand. Because capped plans are also inherently cheaper to provide, offering such plans could enable providers to increase BOP customer value and expand access. Subsidizing capped plans could amplify the impact of life-improving services targeted at the poor.
LINDEX, an End-to-End Landsat-8 Timeseries Index Processing Framework

As Earth's ecological landscape continues to change, it will become increasingly important to understand how it has changed, and how it may change in the future. Freely available multispectral remote sensing datasets, such as the Landsat-8 dataset accessed through the USGS EarthExplorer tool, provide large scale, high resolution satellite imagery that can be leveraged by researchers across scientific disciplines for timeseries index analysis. LINDEX provides an extensible framework that automates Landsat-8 timeseries index analysis, resulting in an average ninety-four percent reduction in overall processing time compared to hands-on methods. Traditionally, in order to make use of this historical data, researchers must acquire the data, uncompress each scene, crop each scene to their region of interest, sort each cropped scene by date and cloud cover, and then either use GIS tools to run index analyses or code the index analyses themselves. This process is time consuming, requires extensive computational knowledge, and is prone to human error. In order to address these challenges, we developed LINDEX, a fully containerized end-to-end extensible processing framework for Landsat-8 timeseries index analysis. LINDEX is open source and leverages open source python packages to automate decompression, cropping, cloud cover detection, sorting by date, and index analysis for Landsat-8 data while also providing a customisable and growing library of eleven ecologically useful indices including NDWI, NDMI, NDSI and NDGI. LINDEX is designed to work synergistically with the EarthExplorer Bulk Download Application as well as QGIS, providing a bridge from download through analysis. The LINDEX framework uses a well defined methodology for incorporating custom indices into your workflow, making LINDEX a useful tool for researchers interested in exploring any Landsat-8 multispectral index while saving time, and reducing error.
A Targeted Assessment of SRC vs ORC Processing in Neural Language Models and Humans

This paper is to investigate the distinct aspects of processing subject and object relative clauses (SRC and ORC) in neural language models (LMs) and humans using the materials, which are constructed by manipulating two RC types (SRC vs ORC) and two intervening PP types (locative vs temporal). Surprise values are collected from the transformer GPT-2 and BERT language models. Reading time data for humans are taken from Lowder and Gordon’s (2021) eye-tracking experiment. According to Lowder and Gordon (ibid.) that take as a critical region the matrix verb after the RC in the subject position, for humans the locative PP contained in the RC register longer reading times ORCs than SRCs, while the temporal PP does so for SRCs than ORCs. By contrast, for the two neural LMs, surprisals are higher for ORCs than SRCs regardless of whether the PP in question is locative or temporal. There was no statistically significant linear fit between human and the LMs’ responses. The result shows that to the extent that the distinction between locative and temporal PPs in RCs is syntactico-semantic, neither of the two neural language models is able to acquire human-like sensitivity to such a distinction.
[RETRACTED] Ree Drummond CBD Gummies Shark Tank: Does It Scam Or Legit? v1

[RETRACTED] Ree Drummond CBD Gummies Shark Tank: Does It Scam Or Legit?
BiTe-REx

To satiate the comprehensive information need of users, retrieval systems surpassing the boundaries of language are inevitable in the present digital space in the wake of an ever-rising multilingualism. This work presents the first-of-its-kind Bilingual Text Retrieval Explanations (BiTe-REx) aimed at users performing competitor or wage analysis in the automotive domain. BiTe-REx supports users to gather a more comprehensive picture of their query by retrieving results regardless of the query language and enables them to make a more informed decision by exposing how the underlying model judges the relevance of documents. With a user study, we demonstrate statistically significant results on the understandability and helpfulness of the explanations provided by the system.
KECERDASAN EMOSIONAL DITINJAU DARI PERILAKU ALTRUISME PADA MAHASISWA JURUSAN KEPERAWATAN DI UNIVERSITAS RATU SAMBAN

Students majoring in nursing are prospective nurses whose job is to serve and assist in healing patients. So that prospective nurses must have high altruism behavior and be smart in understanding what other people feel which is called emotional intelligence. This study aims to examine the relationship between emotional intelligence and altruism behavior in nursing students. The variables of emotional intelligence and altruism behavior were measured using a scale. The subjects of this study amounted to 32 students majoring in nursing at Ratu Samban University. The sampling technique in this study used a probability sampling technique. The analytical technique used in this research is quantitative analysis using Karl Pearson's product moment with SPSS Version 20 for Windows. Based on the results of the study, it is known that emotional intelligence has a positive relationship with altruism behavior in nursing students at Ratu Samban University. There is a significant correlation (r0.893 with a significance &lt;0.05) between the variable emotional intelligence and altruism behavior in nursing students, namely 0.000 and the significance value is below/smaller than 0.05, this means that the higher emotional intelligence, the higher the altruism behavior in students. students majoring in nursing on the contrary, the lower the emotional intelligence of students majoring in nursing, the lower the altruism behavior in students majoring in nursing. In nursing students, emotional intelligence has the most moderate level category with a contribution of 44% and altruism behavior has the most moderate level category with 53% contribution.
Implementation of Tweet Stream Summarization for Malicious Tweet Detection

In contrast to traditional media, social media is populated by anonymous individuals who have the freedom to broadcast whatever they choose. This online social media culture is dynamic in nature, and the move from traditional media to digital media is growing increasingly popular among people. While conventional media will continue to be used less frequently in the future, the increasing use of online social networks (OSNs) will obfuscate the real information provided by traditional media. Genuine users provide material that is beneficial to the broader public; on the other hand, spammers transmit irrelevant or misleading content that turns social media into a front for spreading false information. Consequently, undesirable text or susceptible links might be delivered to targeted users. These fabricated texts are anonymous, and they are occasionally linked to possible URLs. A precise statistical classification for a piece of news is not possible with the current systems because of data limitations and communication types. We will look at a variety of research publications that employ a variety of strategies for master training in the prediction and detection of harmful material on social media websites and networks. In this study, we attempted to identify spam tweets from a large collection of tweets by utilizing TVC algorithm to identify it. When a content paper is incorporated in this manner, a brief summary produced by utilizing the most important keywords from the first document is known as summarization. It is necessary to have a dynamic approach of dealing with the condensed information that is supplied through Twitter feeds. This research presents a novel way for producing a major substance-based summary in a shorter period of time than previously available. We also propose to detect harmful tweets both offline and online and to do so in real time. Most notably, when compared to the other current frameworks, the proposed framework performs multi-subject summarization on an online dataset, which results in a reduction in the amount of time required.