Reviewerly/exHarmony huggingface dataset:
Ανάλυση και σύγκριση μηχανισμών συναίνεσης σε Blockchain περιβάλλον

According to the general opinion of the majority of scientists in this field, Blockchain technology is perhaps the greatest invention after the Internet itself. Its most popular application that made it known to the world in 2008 was the Bitcoin cryptocurrency, followed by the creation of more and more applications and cryptocurrencies based on this pioneering technology. The reasons Blockchain is considered so important is that it is a distributed system of peer nodes, with encrypted data, using digital signatures, but mainly its decentralized database, which offers anonymity and immutability, as well as optimization in the transaction process among its users. However, its applications are not limited only to the financial sector but due to the constant experimentation of many people on this technology, new uses are discovered every day which can benefit companies, customers and ordinary users respectively. The most important role, however, is played by the consent mechanisms, which put the nodes of the system into proper operation using a set of algorithmic procedures and rules, such as complex mathematical functions and voting procedures between users. In this bachelor's thesis will be a brief analysis of the operation of Blockchain technology, the structure of Bitcoin and other known cryptocurrencies, decentralization and Blockchain systems. However, we will focus more on the study of the prevailing consensus mechanisms in terms of how their procedures are performed and will compare them with each other in terms of their efficiency. Our aim, after a bibliographic study, is for the reader to properly understand how Blockchain and its mechanisms work.
Off-Policy Training for Truncated TD($$\lambda $$) Boosted Soft Actor-Critic

TD(\(\lambda \)) has become a crucial algorithm of modern reinforcement learning (RL). By introducing the trace decay parameter \(\lambda \), TD(\(\lambda \)) elegantly unifies Monte Carlo methods (\(\lambda =1\)) and one-step temporal difference prediction (\(\lambda =0\)), which can learn the optimal value significantly faster than extreme cases with an intermediate value of \(\lambda \). However, it is mainly used in tabular or linear function approximation cases, which limits its practicality in large-scale learning and prevents it from adapting to modern deep RL methods. The main challenge of combining TD(\(\lambda \)) with deep RL methods is the “deadly triad” problem between function approximation, bootstrapping and off-policy learning. To address this issue, we explore a new deep multi-step RL method, called SAC(\(\lambda \)), to relieve this dilemma. Firstly, our method uses a new version of Soft Actor-Critic algorithm which stabilizes the learning of non-linear function approximation. Secondly, we introduce truncated TD(\(\lambda \)) to reduce the impact of bootstrapping. Thirdly, we further use importance sampling as the off-policy correction. And the time complexity of the training process can be reduced via parallel updates and parameter sharing. Our experimental results show that SAC(\(\lambda \)) can improve the training efficiency and the stability of off-policy learning. Our ablation study also shows the impact of changes in trace decay parameter \(\lambda \) and emerges some insights on how to choose an appropriate \(\lambda \).
Creating Arrays from Strings

Often, you will work with data that has been formatted or analyzed before you use it in your program. One of the common ways to send and transmit information is using a delimited list.
Martha Buskirk. Is It Ours? Art, Copyright, and Public Interest. Oakland, CA: University of California Press, 2021. 304p. Hardcover, $50.00 (ISBN 9780520344594).

In the past, intellectual property (IP) law wasn’t something the average person considered in their day-to-day lives, but the current pervasiveness of multimedia technology has brought it front and center. We depend on screens for work, education, and entertainment, and the internet and social media make it easy to consume, create, copy, and distribute content. Copyright issues that were previously the purview of creators and lawyers now impact everyone. Librarians are fielding questions from faculty and students, especially those in the arts: can I use this image/music/film in my project or class? And the answer is usually a very unsatisfying “it depends…” followed by a thorny discussion where, with disclaimers, we attempt to give advice that straddles the line between legalities and support for creative expression.
Data Safe Havens in the Cloud: Overview Poster from 2nd Research Software London and South East Workshop on 06 February 2020

Overview of the policy, process and technical implementation of The Alan Turing Institute's cloud-based Data Safe Haven for secure and productive research using sensitive data.Presented at the poster session at the second annual workshop of the Research Software London and South East community at the Royal Society on 06 February 2020.
The Construction of Educational Content System of Economics and Management Based on the Background of Public Management

Public management refers to the guidance of various laws, regulations, policies and plans by the government to solve problems that arise in the process of social and economic development, and to provide support and services when solving problems. Economy and management are important parts of my country's education system, and there is a close relationship between the two. In the process of social development, the two influence each other. The purpose of constructing the educational content system of economics and management is to promote the maturity and development of educational economics and management. This article mainly uses analysis method and questionnaire survey method to study the related theories, feasibility and development direction of the system construction. The survey data shows that 38.9% of seniors attach importance to the method system, 33.3% of the seniors attach importance to the application system, and 36.8% of the juniors attach importance to the theoretical foundation. Therefore, different grades have different requirements for this, but the construction of the overall education content system is supported by the majority of people.
Analysis of Frameworks in Cloud Environment

Cloud is one of the advances, whose utilization is being expanded step by step for various purposes according to client's prerequisite. For the most part this innovation is utilized for capacity reason as a result of this, the clients need not to have any equipment stockpiling gadgets rather than that their information will be put away on the system. These frameworks must be increasingly secure with the goal that they give secrecy, protection and assurance for their consumer loyalty. Different calculations were created to make sure about cloud systems yet these calculations can't meet the prerequisites of the clients. In this paper, there is a comparison, of a hybrid approach and the individual algorithm to get the best results.
Rainfall Forecasting in Medan City Using Arima Seasonal Method and Decomposition Method

Forecasting atmospheric parameters, especially rainfall, has become a national requirement. The main objective of this research is to find a model and obtain the results of rainfall forecasting for the next two periods, especially in the city of Medan. The method used in this research is the Box Jenkins method with the ARIMA Seasonal model approach and the Decomposition method. Both of these methods are suitable for data situations which are seasonal in nature. The results of forecasting these two methods are compared based on the Mean Absolute Percentage Error (MAPE) value. From the results obtained, it is known that the Seasonal method ARIMA (2,0,2) (1,0,1) 12 is better at predicting rainfall in Medan City when compared to the Decomposition method seen from the MAPE of each method, namely 18.051% for the method. Seasonal ARIMA and 26.559% for the Decomposition method.
Citizenship and the mechanisms for achieving it in Iraqi society

The issue of citizenship is a fundamental pillar in building the Iraqi citizen, which in turn leads to state building. Therefore, any defect that affects it leaves an impact on the building of society as a whole. Building the state. Therefore, the Iraqi state seeks, through the mechanisms of citizenship, to create and develop a sense of it in a way that makes it catch up with the progress and development witnessed by countries in the world.
WEATHER FORECASTING USING ARTIFICIAL NEURAL NETWORK

There has been a huge growth of research within the past few years with regards to weather forecasting. An attempt was made to establish a direct relation between the input weather data and the targeted data. Due to the lack of regularity in the nature of the weather data, there has been increase in the focus over the irregular nature prediction of the weather data. Since the weather data has an irregular trend, Artificial Neural Network (ANN) has advanced and considered to be a favorable method to bring out the structural relation between the different entities. This paper looks over the applicability of Artificial Neural Networks approach by creating a effective and robust predictive model for the analysis of weather and also to compare and assess the performance of the model using different hidden layer, transfer functions and neurons to forecast the weather for a whole year (365 days).Back-Propagation Algorithm and its use would be explained within the paper, based upon which the model has been created. The Back-Propagation Algorithm has been used to analysis the weather forecast. The paper also speaks about the output achieved in graphical format in the process while analyzing the weather. MATLAB R2021a has been used for processing the simulation.
Enhance Decision Support Systems Method Of Measure Student Levels And Attitude Cigarette Danger For Health Based On Analytical Hierarchy Process (Ahp)

This paper described about study determining the criteria in determining criteria and alternatives for student level knowledge of the dangers of smoking to health by the use of decision support systems so that it is easier to determine criteria by using selection methods that make it easy to obtain information on a system built to manage data- data so as to make it easy for users and can produce clear and accurate information and can produce reports that are expected to help teachers, and principals in making and making the right decisions later.
예비 한국어 교사의 TPACK에 대한 교육요구분석 -사이버대학교 한국어교육 전공생을 중심으로-

This study aimed to analyze the educational needs of pre-service Korean teachers for TPACK. The survey on TPACK was conducted for 62 students majoring in Korean language education at the Cyber University. The survey consisted of 39 items in 7 domains of TPACK. After that, the current level and importance of TPACK were analyzed using the Borich need assessment model and The Locus for Focus model, and then priorities were derived. The results show that PK, TPK, TCK and TPACK domains should be prioritized in education for pre-service teachers. As a result of analyzing the requirements for each detailed item, 2 items in the PK domain were selected as the highest priority, and 11 items were selected as the second priority. This result can give practical guidance to curriculum design for the improvement of TPACK of pre-service Korean teachers. (Korea University)
Preventing Accidental Overdoses by Innovating a Restrictive Prescription Pill Dispenser Vial (RPPDV)

Abstract: Rather than attempting to prevent all overdoses, the Prescription Pill Dispenser Vial (RPPDV) aims to prevent accidental overdoses by controlling how many pills a user can access within a twenty-four hour period. The RPPDV is much like a traditional pill vial. The body is similar to a conventional pill vial; however, it is modified to prevent the cap's opening without a special screw. The cap is heavily modified and uses a CR2032 or CR2025 coin cell battery to power a gate and the unlocking of the pill cap. The RPPDV works first when the user clicks the red button on the cap of the RPPDV, which opens the pill gate and opens space into a pill container section. This section is large enough for only one pill (or two if multiple pills are required simultaneously). Once a tablet has entered the pill container section, the button must be pressed again to close the pill gate. The user cannot open the lid to the pill container section until the pill gate is closed (see Fig 1.4.1). The RPPDV is similar to an escrow service, with the pill holder section acting as a middleman. The size of the pill holder container prevents access to multiple pills at the same time. The number of times the user presses the button is recorded and timed with a 555 timer set for twenty-four hours. For example, if a drug has a maximum dose of three pills per day, the button could be capped at four cycles or eight total pushes (to allow a grace cycle if a tablet is broken or stuck). Similarly, the gate cannot be opened unless the lid to the pill container section is closed. The production price of the RPPDV is significantly higher than a traditional pill vial and can get higher depending on the additional features added to the RPPDV. The necessary components to function include CR2032 or CR2025 lithium batteries, a 555 timer, and a coin battery holder, along with plastic and metal molds. The base model alone produces an estimated average cost per unit of $1.33 USD (see Fig 4.2.1). Additional features include adding a Bluetooth Low Energy (Bluetooth LE) component to customize the number of unlocks per day and RFIDs for storage and tracking. Prices vary depending on the specifications of the RPPDV.
DESAIN DAN SIMULASI SMART OFFICE BERBASIS INTERNET OF THINGS (IOT)

In this research, an IoT-based Smart Office network simulation has been built which consists of Smart Devices such as automatic doors, CCTV cameras, Fire Sprinkles, Solar Panels, automatic lights and Air Conditioners (AC). Each Smart Device has Conditions that function as automation which aims to minimize the interaction between the user and the device directly. Smart Devices can be accessed and monitored using Laptops in the office and Smartphones in different locations. The testing method in this study uses the Ping Test method and data analysis refers to the TIPHON standard for the Delay and Packet Loss parameters. The simulation was carried out using Cisco Packet Tracer software version 7.3.1. The results obtained indicate that the Smart Device can be simulated according to the predetermined conditions. The results of the connection test on the Smart Office network show that the delay with the highest value is found on the RFID Reader device at 06:00 – 08:00 WIB and the Motion Detector device at 14:00-16:00 WIB. Both devices scored 47.2ms. The delay with the lowest value is found on the AC Office device at 06:00-08:00 WIB with a value of 24.2ms. This value is in the Very Good category with an Index value of 4. The results of the connection test on the Smart Office network for the Packet Loss parameter show that all Smart Devices get a value of 0% in each test time. This value is included in the Very Good category with an Index value of 4.
Tabular Functional Block Detection with Embedding-based Agglomerative Cell Clustering

Tables are a widely-used format for data curation. The diversity of domains, layouts, and content of tables makes knowledge extraction challenging. Understanding table layouts is an important step for automatically harvesting knowledge from tabular data. Since table cells are spatially organized into regions, correctly identifying such regions and inferring their functional roles, referred to as functional block detection, is a critical part of understanding table layouts. Earlier functional block detection approaches fail to leverage spatial relationships and higher-level structure, either depending on cell-level predictions or relying on data types as signals for identifying blocks. In this paper, we introduce a flexible functional block detection method by applying agglomerative clustering techniques which merge smaller blocks into larger blocks using two merging strategies. Our proposed method uses cell embeddings with a customized dissimilarity function which utilizes local and margin distances, as well as block coherence metrics to capture cell, block, and table scoped features. Given the diversity of tables in real-world corpora, we also introduce a sampling-based approach for automatically tuning distance thresholds for each table. Experimental results show that our method improves over the earlier state-of-the-art method in terms of several evaluation metrics.
Two-photon multi-partite d-level cluster states and witness operators for their practical entanglement detection

We generate a multi-partite d-level cluster state via two photons simultaneously entangled in time and frequency, and validate its entanglement by developing witness operators that enable the practical detection of any complex photon state.
RSA-Based CP-ABE Scheme with Scalable User Revocation for IoT and Smartcard Devices

Sethia, Divyashikha Aggarwal, Ritik Bhayana, Saksham Mehta, SanchitPortable IoT devices influence everyday usage and communication, such as a programmable thermostat and smart lights in a smart home. Advanced technology can assist in remote management and selective authorized access by different users. The devices must also be secure from malicious users and have minimal overheads for revocation while providing uninterrupted access to valid users. Ciphertext-policy attribute-based encryption (CP-ABE) scheme can provide fine-grained, selective access to these portable IoT-based devices. This paper proposes a novel RSA-based CP-ABE scheme with scalable revocation (RCSR) scheme for IoT and smartcard devices, which extends an existing RSA-based CP-ABE scheme for scalable user revocation and a fix for a security breach for the collusion of keys. The proposed RCSR scheme is lightweight, with constant computation time and storage space for both keys and ciphertext. It provides efficient user revocation with uninterrupted access to valid users. Detailed quantitative performance comparison indicates that the proposed RCSR scheme performs better than the previous schemes with acceptable overheads.
AES Encryption and Description based: A Security Analysis of Cloud Computing with HSDRT and Rollback function

Cloud computing is an innovative mechanism that is changing the way enterprise hardware and software design. Because the cloud is simple, everyone moves data and application software to the cloud datacenter. The proposed algorithm serves two purposes. One is to allow users to collect information from any remote location without a network connection, the other is to recover files when they are deleted or the cloud is destroyed. The proposed HSDRT and rollback operation is that the algorithm proposed in this paper can solve various problems such as data security and data recovery. The proposed HSDRT algorithm using AES encryption technology focuses on the security of backup files stored on remote servers. Cloud storage provides online storage where data is stored in the form of virtual pools, typically hosted by third parties. This operation provides confidentiality and security of data based on cloud computing. They ensure user and device authentication, data and resource access control, and data privacy. Data users use evidence to validate documents and, if verified, decrypt encrypted files. If yes, the rollback operation is executed by the data owner to move the operation to the previous state, such as deletion of the currently uploaded file. This process increases the efficiency of the proposed system. The implementation of this proposed system runs on Java software.
Feedback on answers to questions involving fractions with fifth grade elementary school students through the use of an app

This study was carried out with four fifth grade students from Yahualica, Jalisco, in the context of the pandemic caused by Covid-19. Feedback is assumed in this paper to be one of the most powerful tools for learning and educational achievement. The research is based on the concept of feedback as a perspective of Visible Learning and Teaching proposed by Hattie (Hattie, Gan, 2011), and Hattie & Timperley’s Feedback model (2007). The methodological horizon used for this study corresponds to a qualitative perspective (Creswell, 2012) using a case study (Stake, 1999). The research method used participant observation (Creswell, 2012), as well as a semi-structured interview (Kvale, 2011) to collect qualitative data from the students, who interacted with an application for solving fractional problems. For the data analysis, the double coding cycle proposed by Saldana (2009) was used. As a result of our research, we concluded that students seek feedback to find improvement procedures, and for it to be used there must be an adequate learning environment where error is seen as an opportunity to learn.
MAgEC: Using Non-Homogeneous Ensemble Consensus for Predicting Drivers in Unexpected Mechanical Ventilation.

We conduct exploratory analysis of a novel algorithm called Model Agnostic Effect Coefficients (MAgEC) for extracting clinical features of importance when assessing an individual patient's healthcare risks, alongside predicting the risk itself. Our approach uses a non-homogeneous consensus-based algorithm to assign importance to features, which differs from similar approaches, which are homogeneous (typically purely based on random forests). Using the MIMIC-III dataset, we apply our method on predicting drivers/causers of unexpected mechanical ventilation in a large cohort patient population. We validate the MAgEC method using two primary metrics: its accuracy in predicting mechanical ventilation and the similarity of the proposed feature importances to a competing algorithm (SHAP). We also more closely discuss MAgEC itself by examining the stability of our proposed feature importances under different perturbations and whether the non-homogeneity of the approach actually leads to feature importance diversity. The code to implement MAgEC is open-sourced on GitHub (https://github.com/gstef80/MAgEC).
Research on preparation process of ethanol and C4 olefin based on linear and nonlinear regression fitting algorithm

First of all, aiming at studying the relationship between ethanol conversion, C4 olefin selectivity and temperature for each catalyst using the knowledge of fitting arbitrary data and adopting the basic idea of control variable method, linear regression equation and nonlinear regression equation model are established. Aiming at the solution of linear regression and non-linear regression model, this paper uses the fitting algorithm to calculate the functional relationship between the two variables, and uses Matlab to solve the specific function expression and draw the function relation diagram to further solve the relationship between catalyst combination, temperature and ethanol conversion and C4 olefin selectivity.
Learning Object-conditioned Exploration using Distributed Soft Actor Critic

Object navigation is defined as navigating to an object of a given label in a complex, unexplored environment. In its general form, this problem poses several challenges for Robotics: semantic exploration of unknown environments in search of an object and low-level control. In this work we study object-guided exploration and low-level control, and present an end-to-end trained navigation policy achieving a success rate of 0.68 and SPL of 0.58 on unseen, visually complex scans of real homes. We propose a highly scalable implementation of an off-policy Reinforcement Learning algorithm, distributed Soft Actor Critic, which allows the system to utilize 98M experience steps in 24 hours on 8 GPUs. Our system learns to control a differential drive mobile base in simulation from a stack of high dimensional observations commonly used on robotic platforms. The learned policy is capable of object-guided exploratory behaviors and low-level control learned from pure experiences in realistic environments.