Reviewerly/exHarmony huggingface dataset:
Exploring emoji as a viable cultural tool in WhatsApp communications among Malaysian undergraduates

An emoji is a form of a symbol in communication available on almost all social media platforms. Its convenience brings potential implications for its usage in digital communication. This study aims to explore the effectiveness of emojis in WhatsApp communication and the influence of culture on emoji usage among undergraduates in Malaysia. The study employed a qualitative approach in which semi-structured interviews were conducted with eight undergraduates of Universiti Sultan Zainal Abidin (UniSZA). The data were analyzed using inductive thematic analysis. Overall, the findings revealed that emojis increase communication efficiency. The participants have positive attitudes towards emoji usage. They describe emojis as viable symbols that help them to maintain social relations and express feelings. Additionally, the findings showed that Malaysian culture influences emoji communication by promoting harmony and relationships as well as instilling high context values in emoji usage. This research concludes that emojis can influence WhatsApp Communication positively if utilized correctly.
Beyond Bitcoin: The Economics of Cryptocurrencies and Blockchain Technologies (Chapter 6: Smart Contracts and Blockchain)

In this chapter we discuss smart contracts and functionalities they afford, especially in combination with blockchain. 6.1. The rise of Ethereum. 6.1.1. History. 6.1.2. Ethereum is “different”. 6.2. Smart contracts. 6.2.1. Smart contracts on Ethereum. 6.2.2. What do smart contracts need a blockchain for? 6.3. Tokens. 6.3.1. What are tokens? 6.3.2. Use of tokens. 6.4. Initial coin offering. 6.5 Non-fungible tokens. 6.5.1 NFTs and Smart contracts do not solve digital art ownership problems. 6.5.2. New markets enabled by NFTs. 6.6. DAPPS. 6.7 Blockchain governance, DAPPS and smart contracts. For Chapter 1 (Introduction), see https://ssrn.com/abstract=3135021. For Chapter 2 (Means of Exchange: Ever-present Competition), see https://ssrn.com/abstract=3135028.For Chapter 3 (Platform-based Currencies), see https://ssrn.com/abstract=3135030. For Chapter 4 (Bitcoin and Emergence of Cryptocurrencies), see https://ssrn.com/abstract=3135043. For Chapter 5 (The Rich Land of Crypto), see https://ssrn.com/abstract=3135057. For Chapter 6 (Smart Contracts and Blockchain), see https://ssrn.com/abstract=3894110.For Chapter 7 (Enterprise Blockchains), see https://ssrn.com/abstract=3894117.For Chapter 8 (Conclusions: Future Full of Possibilities), see https://ssrn.com/abstract=3894118.
Rstac: An R Package to Access Spatiotemporal Asset Catalog Satellite Imagery

Every day, several Earth observation satellites produce images from around the world. Cloud computing is becoming the main location where all these satellite images are being stored and distributed. This document features an R-package that implements a SpatioTemporal Assets Catalog API client that allows querying and access to a growing set of global satellite imagery providers.
Ad-Blocking with AdGuard Network wide Ad-Blocking with Raspberry Pi

The internet is a scary place. Businesses track users through advertisements (Ads) that they push. Cyber criminals try to scam and steal data through Ads within minutes. Hence it is necessary to overcome the problem of Ads. Ads are annoying and pose a threat to security and also compromise performance and efficiency. It quickly becomes scary as the products that were recently searched for appear as Ads to the user. In the digital area, Privacy is considered as a Myth and Ads sum up to be a big part of that reason. Hence to preserve privacy, the solution is proposed in this paper as Adguard which filters the Ads and blocks them at a network level with DNS filtering. The Adblocker blacklists Ad agency domains, in turn, the request doesn't reach the Ad agency and thereby Ads are not fetched and displayed. Adguard is a collection of software packages that provide filtering and advertisement blocking for all internet services on the network. It alleviates what might traditionally be done via Ad blockers or per-device software because it blocks things at the DNS level. It uses DNS masq, Flask , FTL and some other packages to tie all this together into a nice, easy-to-use web interface. Adguard applications can be installed on any existing Raspberry Pi device or a Virtual Machine. Adguard utilizes several well-known and trusted internet blacklists and keeps them up-to-date. At the DNS level it can then thwart unwanted Ads, malware domains and other unsavory internet denizens from appearing on any of the internet devices or computers. The main motive of the system is to make the internet a safe place and preserve the user’s privacy using Ad-blocking application Ad-Guard.
Design Of Learning System Using E-Learning 4.0 Based On Website

Various studies illustrate that e-learning has a positive impact on the best service efforts in the world of education. However, the implementation of e-learning requires the belief of all parties that e-learning has the power to improve a learning environment that is beneficial for students, in addition to mastery and maturity of concepts and accuracy in choosing strategies and evaluations. Especially in the field of education and distance learning, various concepts have been developed to replace traditional learning methods, one of which is the concept of e-learning. E-learning can be used as an alternative to problems in education as well as a substitute for existing learning activities. For this reason, the author has the idea of a learning system using e-learning. The method used is a prototype with the stages of needs analysis, designing, implementing the system, and testing the system. The results obtained from this test are students and lecturers can carry out learning outside of school, by using computerization, the desired report will be quickly provided for those who need it, with a computerized system, each presentation of information that has been programmed in such a way will be faster when compared to manual systems
Problems of forming managerial competence in master's degree program in “socio-cultural management”

The aim of the study is to reveal the content of the structural components of managerial competence, determine the influence of certain pedagogical conditions on its formation among students during the training in the master's program in “socio-cultural management”, and experimental verification and quantitative analysis of these conditions. The object of the study is the process of personal-professional formation of master's students of the faculties of humanitarian management. The positive influence of high motivation on the level of managerial competence has been proved. It has been revealed that the value-motivational component has the least dependence on the duration of training. The article offers effective conditions of forming managerial competence, which have been tested experimentally and confirmed the possibility of their introduction into practice.
A type system for extracting functional specifications from memory-safe imperative programs

Verifying imperative programs is hard. A key difficulty is that the specification of what an imperative program does is often intertwined with details about pointers and imperative state. Although there are a number of powerful separation logics that allow the details of imperative state to be captured and managed, these details are complicated and reasoning about them requires significant time and expertise. In this paper, we take a different approach: a memory-safe type system that, as part of type-checking, extracts functional specifications from imperative programs. This disentangles imperative state, which is handled by the type system, from functional specifications, which can be verified without reference to pointers. A key difficulty is that sometimes memory safety depends crucially on the functional specification of a program; e.g., an array index is only memory-safe if the index is in bounds. To handle this case, our specification extraction inserts dynamic checks into the specification. Verification then requires the additional proof that none of these checks fail. However, these checks are in a purely functional language, and so this proof also requires no reasoning about pointers.
Argumentation Reasoning with Graph Neural Networks for Reddit Conversation Analysis

The automated analysis of different trends in online debating forums is an interesting tool for sampling the agreement between citizens in different topics. In these online debating forums, users post different comments and answers to previous comments of other users. In previous work, we have defined computational models to measure different values in these online debating forums. A main ingredient in these models has been the identification of the set of winning posts trough an argumentation problem that characterizes this winning set trough a particular argumentation acceptance semantics. In the argumentation problem we first associate the online debate to analyze as a debate tree. Then, comments are divided in two groups, the ones that agree with the root comment of the debate, and the ones that disagree with it, and we extract a bipartite graph where the unique edges are the disagree edges between comments of the two different groups. Once we compute the set of winning posts, we compute the different measures we are interested to get from the debate, as functions defined over the bipartite graph and the set of winning posts. In this work, we propose to explore the use of graph neural networks to solve the problem of computing these measures, using as input the debate tree, instead of our previous argumentation reasoning system that works with the bipartite graph. We focus on the particular online debate forum Reddit, and on the computation of a measure of the polarization in the debate. Our results over a set of Reddit debates, show that graph neural networks can be used with them to compute the polarization measure with an acceptable error, even if the number of layers of the network is bounded by a constant.
Strength Analysis of Real-Life Passwords Using Markov Models

Recent literature proposes the use of a proactive password checker as method for preventing users from creating easy-to-guess passwords. Markov models can help us create a more effective password checker that would be able to check the probability of a given password to be chosen by an attacker. We investigate the ability of different Markov models to calculate a variety of passwords from different topics, in order to find out whether one Markov model is sufficient for creating a more effective password checker. The results of our study show that multiple models are required in order to be able to do strength calculations for a wide range of passwords. To the best of our knowledge, this is the first password strength study where the effect of the training password datasets on the success of the model is investigated.
Detection of Trace Metal Contamination Around a Dumpsite in Iyara Area Warri Nigeria Using Geoelectrical and Geochemical Methods

Trace metal concentration in soils of Iyara area of Warri, Delta State was studied using geophysical and geochemical techniques. Eight vertical electrical sounding (VES) using SAS 1000 Terrameter with maximum current electrode spread of 150 m in the Schlumberger configuration was carried out. The geoelectric layers obtained are topsoil, clay, sand and fine-medium grained sand. The fine to medium grained sand of the third and fourth layers mainly constitute the aquifer with a depth ranging from 4.8 - 11.0 m. Soil samples were collected at three of the VES stations covering the dumpsite and stations away from the dumpsite. The trace metal content was higher than the maximum allowable levels in soils as specified by the World Health Organisation (WHO) and Department of Petroleum Resources of Nigeria (DPR) except for Pb with undetected concentration in VES 3, Mn with 135 mg/kg in VES 1 and undetected in VES 3 and VES 7. The Multiple pollution index, Enrichment factor, and Non-carcinogenic hazard decreases with increasing distance from the dumpsite. The soil around the dumpsite has been impacted and the results of this study will serve as background information for soil studies and groundwater development in the area.
An Investigation on the Availability of Contribution Information in Open-Source Projects

Open-source projects commonly receive new feature requests from different types of users from layperson end users to developers, who actively contribute code to the project. However, the submission of new feature requests and the processes adopted for handling them is not always clear. In this work, we aim at investigating the availability of the contribution information, and in particular on the new feature requests, on 66 out of the 100 most starred GitHub projects. We examined the contribution guidelines and other documentation from those 66 projects. We particularly searched for whether the projects openly welcomed new contributions, such as feature requests. Our finding shows that even the most starred GitHub projects are often not reporting information on how to contribute and, in particular, how new feature requests are managed.
Deep Reactive Planning in Dynamic Environments

The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution. While goal conditioning of policies has been studied in the RL literature, such approaches are not easily extended to cases where the robot's goal can change during execution. This is something that humans are naturally able to do. However, it is difficult for robots to learn such reflexes (i.e., to naturally respond to dynamic environments), especially when the goal location is not explicitly provided to the robot, and instead needs to be perceived through a vision sensor. In the current work, we present a method that can achieve such behavior by combining traditional kinematic planning, deep learning, and deep reinforcement learning in a synergistic fashion to generalize to arbitrary environments. We demonstrate the proposed approach for several reaching and pick-and-place tasks in simulation, as well as on a real system of a 6-DoF industrial manipulator. A video describing our work could be found \url{this https URL}.
Integrating Open Data Cube and Brazil Data Cube Platforms for Land Use and Cover Classifications

The potential to perform spatiotemporal analysis of the Earth's surface, fostered by a large amount of Earth Observation (EO) open data provided by space agencies, brings new perspectives to create innovative applications. Nevertheless, these big datasets pose some challenges regarding storage and analytical processing capabilities. The organization of these datasets as multidimensional data cubes represents the state-of-the-art in analysis-ready data regarding information extraction. EO data cubes can be defined as a set of time-series images associated with spatially aligned pixels along the temporal dimension. Some key technologies have been developed to take advantage of the data cube power. The Open Data Cube (ODC) framework and the Brazil Data Cube (BDC) platform provide capabilities to access and analyze EO data cubes. This paper introduces two new tools to facilitate the creation of land use and land over (LULC) maps using EO data cubes and Machine Learning techniques, and both built on top of ODC and BDC technologies. The first tool is a module that extends the ODC framework capabilities to lower the barriers to use Machine Learning (ML) algorithms with EO data. The second tool relies on integrating the R package named Satellite Image Time Series (sits) with ODC to enable the use of the data managed by the framework. Finally, water mask classification and LULC mapping applications are presented to demonstrate the processing capabilities of the tools.
Integrasi Sistem Informasi Akademik dan BOT Telegram Sebagai Media Pengakesan Informasi di Universitas Mataram

This research aims to build a chatbot based on Telegram application that integrated with academic information system in University of Mataram. The Telegram bot is expected as an alternative and more efficient way to access the academic information system. The development used the prototyping model as method of development. The Telegram bot is built using PHP that utilized the application programming interface provided by Telegram. The developed Telegram works properly displaying the results according to the commands entered. Performance of the integrated bot was measured by microtime function, to show the access time of any command. The results are then compared with the access time of the web based version that measured using the same function. The comparation shows that the access time of Telegram bot is more efficient than the web based version.
Combined total variation of first and fractional orders for Poisson noise removal in digital images

Introduction: Many methods have been proposed to handle the image restoration problem with Poisson noise. A popular approach to Poissonian image reconstruction is the one based on Total Variation. This method can provide significantly sharp edges and visually fine images, but it results in piecewise-constant regions in the resulting images. Purpose: Developing an adaptive total variation-based model for the reconstruction of images contaminated by Poisson noise, and an algorithm for solving the optimization problem. Results: We proposed an effective way to restore images degraded by Poisson noise. Using the Bayesian framework, we proposed an adaptive model based on a combination of first-order total variation and fractional order total variation. The first-order total variation model is efficient for suppressing the noise and preserving the keen edges simultaneously. However, the first-order total variation method usually causes artifact problems in the obtained results. To avoid this drawback, we can use high-order total variation models, one of which is the fractional-order total variation-based model for image restoration. In the fractional-order total variation model, the derivatives have an order greater than or equal to one. It leads to the convenience of computation with a compact discrete form. However, methods based on the fractional-order total variation may cause image blurring. Thus, the proposed model incorporates the advantages of two total variation regularization models, having a significant effect on the edge-preserving image restoration. In order to solve the considered optimization problem, the Split Bregman method is used. Experimental results are provided, demonstrating the effectiveness of the proposed method. Practical relevance: The proposed method allows you to restore Poissonian images preserving their edges. The presented numerical simulation demonstrates the competitive performance of the model proposed for image reconstruction. Discussion: From the experimental results, we can see that the proposed algorithm is effective in suppressing noise and preserving the image edges. However, the weighted parameters in the proposed model were not automatically selected at each iteration of the proposed algorithm. This requires additional research.
Few-shot Learning for Multi-Modality Tasks

Recent deep learning methods rely on a large amount of labeled data to achieve high performance. These methods may be impractical in some scenarios, where manual data annotation is costly or the samples of certain categories are scarce (e.g., tumor lesions, endangered animals and rare individual activities). When only limited annotated samples are available, these methods usually suffer from the overfitting problem severely, which degrades the performance significantly. In contrast, humans can recognize the objects in the images rapidly and correctly with their prior knowledge after exposed to only a few annotated samples. To simulate the learning schema of humans and relieve the reliance on the large-scale annotation benchmarks, researchers start shifting towards the few-shot learning problem: they try to learn a model to correctly recognize novel categories with only a few annotated samples.
DSDD

With the push for transparency and open data, many datasets and data repositories are becoming available on the Web. This opens new opportunities for data-driven exploration, from empowering analysts to answer new questions and obtain insights to improving predictive models through data augmentation. But as datasets are spread over a plethora of Web sites, finding data that are relevant for a given task is difficult. In this paper, we take a first step towards the construction of domain-specific data lakes. We propose an end-to-end dataset discovery system, targeted at domain experts, which given a small set of keywords, automatically finds potentially relevant datasets on the Web. The system makes use of search engines to hop across Web sites, uses online learning to incrementally build a model to recognize sites that contain datasets, utilizes a set of discovery actions to broaden the search, and applies a multi-armed bandit based algorithm to balance the trade-offs of different discovery actions. We report the results of an extensive experimental evaluation over multiple domains, and demonstrate that our strategy is effective and outperforms state-of-the-art content discovery methods.
Development of Task-Space Nonholonomic Motion Planning Algorithm Based on Lie-Algebraic Method

In this paper, a Lie-algebraic nonholonomic motion planning technique, originally designed to work in a configuration space, was extended to plan a motion within a task-space resulting from an output function considered. In both planning spaces, a generalized Campbell–Baker–Hausdorff–Dynkin formula was utilized to transform a motion planning into an inverse kinematic task known for serial manipulators. A complete, general-purpose Lie-algebraic algorithm is provided for a local motion planning of nonholonomic systems with or without output functions. Similarities and differences in motion planning within configuration and task spaces were highlighted. It appears that motion planning in a task-space can simplify a planning task and also gives an opportunity to optimize a motion of nonholonomic systems. Unfortunately, in this planning there is no way to avoid working in a configuration space. The auxiliary objective of the paper is to verify, through simulations, an impact of initial parameters on the efficiency of the planning algorithm, and to provide some hints on how to set the parameters correctly.
A Study into Load Balancing in Cloud Computing Based on Whale Optimization Algorithm

In view of the low efficiency and low utilization of load balancing under cloud computing, this paper proposes a cloud computing load balancing scheme based on whale optimization algorithm. Firstly, the balanced load under cloud computing is explained; secondly, based on the whale optimization algorithm, the population diversity is improved through population initialization, the sine and cosine algorithm is used for local optimization, and the leapfrog algorithm is used for individual screening; finally, the optimized Whale optimization algorithm is used in cloud computing load balancing. The simulation experiment shows that the algorithm in this paper has better effects in task execution time, fairness and energy consumption compared with ant colony algorithm, particle swarm algorithm, and whale optimization algorithm.