Reviewerly/exHarmony huggingface dataset:
UPAYA MENINGKATKAN HASIL BELAJAR BAHASA INDONESIA MATERI PUISI MELALUI STRATEGI TTW (THINK TALK WRITE) SISWA KELAS IV-B DI UPT. SD NEGERI 01 LIMO KAUM

One of the teacher's efforts to improve student learning outcomes is to choose an appropriate learning model. One of them is the Think Talk Write (TTW) Strategy learning model, which is a learning model in which students work in groups and take turns explaining the material being studied. The objectives of this study were (1) To determine teacher activity using the Think Talk Write (TTW) Strategy learning model, (2) To determine student activities using the Think Talk Write Strategy (TTW), (3) To determine student learning outcomes with the Strategy. Think Talk Write (TTW) on poetry material for grade IV even semester. The subjects in this research were students of class IV-B UPT, SD Negeri 01 Limo Kaum for the 2019/2020 school year, which consisted of 26 students. This research uses classroom action research (PTK), data collected through (1) teacher observation sheets, (2) student observation sheets (3) test questions on student learning outcomes. The data analysis in this study used a percentage formula. Based on the results of the research data analysis, it was found that (1) Teacher activity in cycle I was 70.83% (good category), and increased in cycle II by 90.83% (very good category), (2) Student activity in cycle I was 66.67% are in (quite good category), and increased in cycle II by 87.50% (very good category), (3) Student learning outcomes in the first cycle are 73.08% in the good category, and increased in the second cycle amounted to 88.46% (very good category). Thus it can be concluded that the application of the Think Talk Write (TTW) Strategy in Indonesian language lessons in poetry material can increase teacher activity, student activity and improve learning outcomes in Indonesian language material in the theme of the Cita-Citaku theme and the sub-theme of Aku and Cita-Citaku for grade IV students. B UPT, SD Negeri 01 Limo Kaum.
Prediction of environmental indicators in landleveling using the artificial intelligence Techniques

Land leveling is one of the most significant strides in soil planning and development. Despite the fact that land leveling with machines requires significant measure of vitality, it conveys a reasonable surface incline with negligible decay of the dirt and harm to plants and different living beings in the dirt. In any case, specialists during late years have attempted to decrease petroleum derivative utilization and its pernicious symptoms utilizing new procedures, for example, Artificial Neural Network (ANN),Imperialist Competitive Algorithm – ANN (ICA- ANN), and relapse and Adaptive Neuro-Fuzzy Inference System (ANFIS) and Sensitivity Analysis that will prompt a perceptible improvement in the earth. In this examination impacts of different soil properties, for example, Embankment Volume, Soil Compressibility Factor, Specific Gravity, Moisture Content, Slope, Sand Percent, and Soil Swelling Index in vitality utilization were researched. The examination was comprised of 90 examples were gathered from 3 unique areas. The lattice size was set 20 m in 20 m (20*20) from a farmland in Karaj territory of Iran. The point of this work was to decide best direct model Adaptive Neuro- Fuzzy Inference System (ANFIS) and Sensitivity Analysis so as to anticipate the vitality utilization for land leveling. As indicated by the aftereffects of Sensitivity Analysis, just three boundaries; Density, Soil Compressibility Factor and, Embankment Volume Index had critical impact on fuel utilization. As per the aftereffects of relapse, just three boundaries; Slope, Cut-Fill Volume (V) and, Soil Swelling Index (SSI) had huge impact on vitality utilization. utilizing versatile neuro-fluffy derivation framework for forecast of work vitality, fuel vitality, complete apparatus cost, and all out hardware vitality can be effectively illustrated. In correlation with ANN, all ICA-ANN models had higher exactness in forecast by their higher R2 worth and lower RMSE esteem.
LibGuides: Access to library resources – issues and tips: Tools and Apps

Having trouble getting access to an article or database? Find out about known issues and learn some tips and tricks.
Parameterized Hamiltonian simulation using quantum optimal control

Analog quantum simulation offers a hardware-specific approach to studying quantum dynamics, but mapping a model Hamiltonian onto the available device parameters requires matching the hardware dynamics. We introduce a paradigm for quantum Hamiltonian simulation that leverages digital decomposition techniques and optimal control to perform analog simulation. We validate this approach by constructing the optimal analog controls for a superconducting transmon device to emulate the dynamics of an extended Bose-Hubbard model. We demonstrate the role of control time, digital error, and pulse complexity, and we explore the accuracy and robustness of these controls. We conclude by discussing the opportunity for implementing this paradigm in near-term quantum devices.
A compositional semantics for free choice constituent unconditionals

The realization of unconditionals is subject to variation across different languages (Haspelmath and Konig, 1998). In this paper, we zoom in on European French, and propose a unified analysis of two types of constituent unconditionals (CUs) in the language. On the syntactic side, we propose that in both types of CUs, the adjunct clause always contains a free choice item (FCI), but this FCI can be partially elided. On the semantic side, we propose a fully compositional analysis of these two types of CUs. We further derive the characteristic properties of CUs (Haspelmath and Konig, 1998; Rawlins, 2013) from exhaustification (Chierchia, 2013: among others) and the principle of viability (Dayal, 2013), which have been argued to play a crucial role in the semantics of FCIs. Our approach is thus closely related to recent work on alternative unconditionals in Hungarian (Szabolcsi, 2019) and free choice in Romance (Caponigro and Falaus, 2018), and contrasts with previous work on English, where the CU adjunct clause is analyzed as a wh-question (Rawlins, 2013).
{이} 중출 구문의 주제어와 복합서술어

Language and Information Society 41. This paper aims to examine syntactic and semantic differences between the syntactic topic construction and the complex predicate construction in multiple nominative sentences with two NPs with the nominative ending -i-. In a sentence with a syntactic topic, the second NP with the ending -i-, i.e., NP2, can be omitted in the subsequent sentence and substituted with an anaphoric expression or replaced with another noun which has a similar semantic feature. However, in a sentence with a complex predicate, these phenomena are disallowed. The reason for these differences is that NP2 in a syntactic topic sentence qualifies as an independent syntactic unit whereas NP2 in a complex predicate sentence closely combines with the adjacent V. Therefore, these complex predicates have the distinct argument structures.
Fintech and Blockchain

The recent development for crypto-assets make the interest on blockchain technology growing. To understand the interest of this innovation for the economy and the finance, it is important to study the basements of the concept around the technology blockchain. First, we will introduce the notion of peer-topeer lending which is one of the component of the question, and then we will explain the concept of Bitcoin which is the first crypto-asset which has been developed based on a PoW (Proof-of-work) protocol. We will analyze at the same time the risks associated to this crypto-asset and the regulation which exists. In a second step, we will present and analyze different classes of blockchains and the mechanisms associated to them. We will introduce the concept of close and open blockchain, public and private. This will permit to introduce the different kinds of protocols which govern these blockchains. Finally, we will introduce the concept of ICO, new way to funds using cryptocurrencies. The analysis of the governance of this new kind of start-up will be provided. The risks associated to these ICO will be analysed and the recent regulation on them provided.
Laboratorium Virtual sebagai Penunjang Praktikum di Masa Belajar dari Rumah

Learning from home during this pandemic makes educators strive to keep learning activities running well. One of them is by using information technology, so that the subject matter can be accepted by students well. Information technology and computers have developed so rapidly, and have had a tremendous impact. One of the implications can be felt in the development of learning media which are now assisted by computers and the internet. The aim of this study was to deepen the development of virtual laboratory support media during a pandemic. This study included a literature review with relevant and accurate data. This learning media is often called multimedia learning. Multimedia is a media that uses more than 2 elements, namely graphics (images, photos, animation), sound (audio) and video in an integrated manner. Continuous innovation in multimedia finally gave birth to a virtual laboratory, or what is often called a virtual laboratory
Sifting out the features by pruning: Are convolutional networks the winning lottery ticket of fully connected ones?

Pruning methods can considerably reduce the size of artificial neural networks without harming their performance. In some cases, they can even uncover sub-networks that, when trained in isolation, match or surpass the test accuracy of their dense counterparts. Here we study the inductive bias that pruning imprints in such lottery tickets. Focusing on visual tasks, we analyze the architecture resulting from iterative magnitude pruning of a simple fully connected network (FCN). We show that the surviving node connectivity is local in input space, and organized in patterns reminiscent of the ones found in convolutional networks (CNN). We investigate the role played by data and tasks in shaping the architecture of pruned sub-networks. Our results show that the winning lottery tickets of FCNs display the key features of CNNs. The ability of such automatic network-simplifying procedure to recover the key features hand-crafted in the design of CNNs suggests interesting applications to other datasets and tasks, in order to discover new and efficient architectural inductive biases.
중국어권 한국어 학습자의 부사 사용에 대한 연구

In this paper, I analyzed the usage patterns of Chinese Korean learners in the Korean learners’ corpus. To this purpose, I compared NIKL learners’ corpus 674,553 words with the native speakers’ corpus 1,055,790 words. According to the analysis, Chinese Korean learners used about 28 more adverbs per 1,000 words than native Korean in their writing. And Chinese Korean learners have either overused or underused the high frequency adverbs, the degree of overuse was stronger than underuse. And compared to native speakers, they lacked the diversity of the use of adverbs.From this corpus analysis, we were able to identify the characteristics of Chinese Korean learners’ use of adverbs. Korean learners overused adverbs such as ‘너무, 아주’and modal adverbs ‘정말, 진짜’to reinforce their own discourse, and they also used a lot of mimetic adverbs due to the influence of teaching. In addition, through the analysis of the learners’ corpus, we were able to identify problems with the use of adverbs by Chinese Korean learners. Chinese Korean learners should try to expand available adverbs and diversify their choice of adverbs in their composition. And they should also develop the recognition of written and spoken registers when selecting adverbs.
FAST IMAGE ENCRYPTION BASED ON RANDOM IMAGE KEY

Image encryption can be defined in such a way that it is the process of encoding secret image with the help of some encryption algorithm in such a way that unauthorized users can't access it. To send an image over the network secretly, the sender tries to find encryption algorithm to hide image information. In this paper t aims at designing an efficient encryption algorithm for color image using random image key generated with minimum time execution for encryption and decryption operations. XOR operation is used here to make more diffusion of the encrypted image to maintain a higher level of security upon transference than it is with the original image. In this paper introduces a new concept for image encryption using a binary “key-image”. The key-image is either a bit plane or an edge map generated from another image, which has the same size as the original image to be encrypted. In addition, we introduce two new lossless image encryption algorithms using this key image technique. The performance of these algorithms is discussed against common attacks such as the brute force attack, ciphertext attacks and plaintext attacks. The analysis and experimental results show that the proposed algorithms can fully encrypt all types of images. This makes them suitable for securing multimedia applications and shows they have the potential to be used to secure communications in a variety of wired/wireless scenarios and real-time application such as mobile phone services.
Photovoltaic Power Generation Forecasting Utilizing Long Short Term Memory

The modernization of the world has considerably reduced the prime sources of energy, such as coal, diesel and gas. Thus, alternative energysources based on renewable energy have been the main concentration nowadays to address the world’s energy demand and at the same time torestrict global warming. Among these renewable energies, solar energy is the main source used to generate electricity through photovoltaic(PV) systems. However, the output of PV power is highly intermittent. Thus accurately forecasting the output of PV systems is an importantrequirement to ensure the stability and reliability of the grid. This study develops and validates a short-term PV power forecasting modelby using the combination of a genetic algorithm (GA) and Long Short Term Memory (LSTM). The performance of the proposed model iscompared with LSTM baseline model by three errors (Root mean square error(RMSE), Mean absolute error (MAE), and Mean absolutepercentage error (MAPE)) in two case studies.
The Study of Plant Utilization in Aquatic Waste Management

Household, industrial, office and various other human activities produce waste. Based on the type, waste consists of liquid, solid, gas and B3. Each waste management needs to be carried out sustainable so that it will not damage the environment and human health. This paper aims to find out the effectiveness of plants in aquatic waste management. The method used in this research is literature study in the form of research articles that have been published in related fields. The data found will be analyzed descriptively qualitatively and determine the type of plant that is effective in aquatic waste management and the way plants in degrade hazardous substances that are in the waste. The results of the literature study show that some plants have a special mechanism in reducing hazardous substances contained in waste both actively and passively.
2D Pose Estimation of Subject Body via Deep Neural Networks

A large part of a computer vision project’s task is to identify an object in a camera and also to identify its position and orientation of a specific system of coordinates. The pose of an object is the combination of its position and orientation. Efficient pose estimation is a much desired trait of any application in the computer vision sector. We propose an industrially efficient model which will effectively estimate the pose of an object in any given image. Our team proposes a new methodology toward the field of pose estimation which employs an object detection algorithm You Look Only Once (YOLOv3) trained to detect human body parts and limbs. These detected regions are then used to obtain a 2D pose of the human in focus. Our approach is to adapt the existing YOLO network supplied with new training images with annotated body parts of humans and changes to the network configuration file. A post-processing step would be to convert the bounding boxes to a few pixels or to obtain the centroid of the bounding boxes to draw a point indicating the position of the limbs and draw lines to connect the limbs together.
Multilingual Toxic Comment Classification

With the growth of the Internet and data collection in the last twenty years, we have seen a rise with Internet Relay Chats (IRC) as well, especially in 2020 due to COVID. Now more than ever, it is important that we stay connected despite being physically separated and to make sure communications systems are as inclusive as ever. Our goal in this project is to categorize toxic comments used in IRC chat rooms and forum pages via a machine learning model in order to keep communications safe and welcoming to the public. We define toxicity as any message that is rude, disrespectful, or otherwise likely to make someone leave a discussion. If these toxic contributions can be identified, we could have a safer, more collaborative internet. As a result of our models, we will be able to identify a dataset of toxic comments with at least a 70% success rate. Additionally, we want to be able to successfully identify both new and previously existing comments as toxic or non-toxic in multiple languages that others have previously label incorrectly. Using a dataset provided by Kaggle, we demonstrate how we are able to detect if a Wikipedia talk page comment is toxic (scored as a 1) or non-toxic (scored as a 0) by using statistical methods, data analysis, and various data science approaches.
MACHINE LEARNING IN A TWITTER SENTIMENT ANALYSIS OF HEALTH CARE TWEET & USING BIG DATA FRAME WORK

Content mining has moved toward becoming pole mainstream inquire about zone. It can manage AI utilizing content investigation. It contains unstructured content which enormous measure of data can't just strategy by preparing PC and information from unstructed content finished by content mining. Content mining manages numerous strategies just as from data recovery, data extraction and furthermore common language handling and associate with calculation and techniques for KDD, web index and data reterival have most explicit pursuit Query language. This exploration fields use information mining calculation, for example, grouping, bunching, associatioie rule. This paper contains, though commentator of content mining strategy devices, and different applications
Development of the Forensic Storage Framework using the Composite Logic method

Along with the development of information technology users, it is possible that crimes that take advantage of information technology continue to develop both directly and indirectly. Criminals often use computer devices to commit crimes. This is a major concern, so the need for handling digital evidence is very important. Therefore, a storage forensic framework is needed for handling digital evidence. This framework is designed by applying the Composite Logic method. The Composie Logic method is applied to determine the role model of each variable or initial pattern of the stages that you want to collaborate with. Composite Logic produces a role model who has a role to produce patterns so that they achieve the same goal. The logic composite method collaborates with existing frameworks for handling hdd, ssd, vmware and cloud which are then combined into a foreign storage framework. Based on the results of the tests carried out, this research has produced a new framework called the storage forensic framework. The advantage of this storage forensic framework compared to several other frameworks is that it is far required that it can be used to generate digital evidence in four storage, namely, SSD, HDD, VmWare, and cloud. This framework produces four main stages, namely preparation, collection, analysis and reports.
An Analysis of Speculative Type Confusion Vulnerabilities in the Wild

Spectre v1 attacks, which exploit conditional branch misprediction, are often identified with attacks that bypass array bounds checking to leak data from a victim's memory. Generally, however, Spectre v1 attacks can exploit any conditional branch misprediction that makes the victim execute code incorrectly. In this paper, we investigate speculative type confusion, a Spectre v1 attack vector in which branch mispredictions make the victim execute with variables holding values of the wrong type and thereby leak memory content. We observe that speculative type confusion can be inadvertently introduced by a compiler, making it extremely hard for programmers to reason about security and manually apply Spectre mitigations. We thus set out to determine the extent to which speculative type confusion affects the Linux kernel. Our analysis finds exploitable and potentially-exploitable arbitrary memory disclosure vulnerabilities. We also find many latent vulnerabilities, which could become exploitable due to innocuous system changes, such as coding style changes. Our results suggest that Spectre mitigations which rely on statically/manually identifying bad code patterns need to be rethought, and more comprehensive mitigations are needed.
Can't Say A Word With You: Glossophobia

Glossophobia or commonly called speech anxiety where mostly suffer from strong fear in social performances and even in academic performance. The researchers used in-depth interview. Where the researchers found out that culture have a big impact for being glossophobic. Also being not open-minded and discrimination hinders someone to develop the communication skills. And in order to overcome glossophobia, you must be motivated and inspired. The participant also stated that his goals drove him to fight this kind of fear that made him speak in public thoroughly and express his thoughts through speaking. Therefore, glossophobia is possible to overcome.
First Order Locally Orderless Registration

First Order Locally Orderless Registration (FLOR) is a scale-space framework for image density estimation used for defining image similarity, mainly for Image Registration. The Locally Orderless Registration framework was designed in principle to use zeroth-order information, providing image density estimates over three scales: image scale, intensity scale, and integration scale. We extend it to take first-order information into account and hint at higher-order information. We show how standard similarity measures extend into the framework. We study especially Sum of Squared Differences (SSD) and Normalized Cross-Correlation (NCC) but present the theory of how Normalised Mutual Information (NMI) can be included.
BasicBlocker: ISA Redesign to Make Spectre-Immune CPUs Faster

Recent research has revealed an ever-growing class of microarchitectural attacks that exploit speculative execution, a standard feature in modern processors. Proposed and deployed countermeasures involve a variety of compiler updates, firmware updates, and hardware updates. None of the deployed countermeasures have convincing security arguments, and many of them have already been broken. The obvious way to simplify the analysis of speculative-execution attacks is to eliminate speculative execution. This is normally dismissed as being unacceptably expensive, but the underlying cost analyses consider only software written for current instruction-set architectures, so they do not rule out the possibility of a new instruction-set architecture providing acceptable performance without speculative execution. A new ISA requires compiler and hardware updates, but these are happening in any case. This paper introduces BasicBlocker, a generic ISA modification that works for all common ISAs and that allows non-speculative CPUs to obtain most of the performance benefit that would have been provided by speculative execution. To demonstrate the feasibility of BasicBlocker, this paper defines a variant of the RISC-V ISA called BBRISC-V and provides a thorough evaluation on both a 5-stage in-order soft core and a superscalar out-of-order processor using an associated compiler and a variety of benchmark programs.