Reviewerly/exHarmony huggingface dataset:
PENINGKATAN HASIL BELAJAR FISIKA PESERTA DIDIK MELALUI MELALUI PEMBELAJARAN TEAMS GAMES TOURNAMENT

This study aims to determine the application of cooperative learning models of the Teams Games Tournament (TGT) type on learning to improve student learning outcomes. The subject of this research was the tenth grade students of SMAN 1 Batang Onang. This type of research is Classroom Action Research. The research design used was the Kemmis and Mc model. Taggart which includes the stages of planning, action and observation, and reflection. The data collection instruments used were observation sheets, student activity scale, and LKS. The data analysis technique used is descriptive qualitative and quantitative data analysis. Based on the results of data analysis in the first cycle of meeting I showed an increase in student mathematics learning outcomes. Research Results, namely an increase in student learning outcomes The average grade value when the pre test is only 38.67. The class average value increased by 63 in cycle I. The average grade value in the first cycle still did not meet the KKM set by the school. In the second cycle the average value of the class experienced an increase of 85.33. Classical learning completeness in the first cycle was only 53%. In cycle II classical learning completeness becomes 96%. This shows an increase in students' classical learning completeness by 43%. Keywords: Teams Games Tournament (TGT) Learning Model, Learning Outcomes
LibGuides: Easy as ABC: Apps and Games

This guide focuses on alphabet picture books, and resources that can be used in conjunction with them.
BU Library: BU Canvas Support & Resources: The Template

We've moved to Canvas! This space offers essential updates and important information to help you learn to use it effectively. Check back for more updates. Informtaion on Using the Canvas Course Template
Revisiting the persuade-constructions in Korean with empirical evidence

Using the data-based methods of language research, this article addresses whether the seltukha-constructions (persuade-constructions) in Korean indeed undergo a control phenomenon as generally assumed and argued in the literature. As is well known, there are two essential properties of controls across languages; (i) the controllee should be co-indexed with another element in the sentence and (ii) the controllee should be silent. Focusing on these two properties, the present study provides multi-pronged data taken from corpus exploration, context-sensitive survey, and language experiment. The empirical investigation casts a doubt on the control analysis of seltukha-constructions; in particular, it is borne out that the embedded subject (controllee) of the seltukha-constructions can appear explicitly, and the embedded subject - whether it be silent or not - is not necessarily required to be co-indexed with another element in the sentence. These lead us to the conclusion that the seltukha-constructions can be accounted for by means of a pro-drop rather than a control. One remaining issue in the analysis is such that the seltukha-constructions may sound awkward if the two NPs (matrix object and embedded subject) refer to the same individual and they appear simultaneously. To account for the awkwardness, we propose the Anti-redundancy Hypothesis; i.e., two NPs referring to the same entity or having the same form tend not to appear right next to each other. The current experiment further demonstrates that the hypothesis works as a general tendency rather than a stipulation about seltukha-constructions. Building upon the empirical data, we propose that the theory of controls in Korean (and hopefully other languages) should be reexamined from the bottom up. (Jeonju University · Korea University)
Research Guides: Basic Math: PrepSTEP - GED

This Research Guide provides links to practice problems as well as information about how to find books, e-books, videos and websites that teach basic math concepts.
Pengaruh Pemanfaatan Perpustakaan Sekolah Dan Kemandirian Belajar Terhadap Prestasi Belajar Akuntansi Dasar Siswa Kelas X Akuntansi SMK Negeri 6 Medan Tahun Ajaran 2019/2020

This study refers to the learning of Utilization of School Libraries and Learning Independence on Basic Accounting Learning Achievement of Class X Accounting Students of SMK Negeri 6 Medan in Academic Year 2019/2020. This type of research is a survey research using Quantitative. The population in this study were students of class X of Utilization of School Libraries and Learning Independence at SMK Negeri 6 Medan Year 2019/2020. The analytical methods used in this study are the Normality Test, Multicollinearity Test, Multiple Linear Regression Analysis, Partial Test (t-Test) and Simultaneous Test (F Test). The results showed that: the use of school libraries has a positive and significant effect on students' basic accounting learning achievement, as indicated by the acquisition value which indicates that the value (3.690> 1.664) and a significance value (0.000 1,644) and a significance value (0.000 <0.05). There is a positive influence on the use of school libraries and learning independence together on students basic accounting learning achieverment with the results of the regression equation Y=12,972+ 0,362X1+ 0,485X2 and coefficient of determination 0,767
Introduction to the Symposium: How Will Artificial Intelligence Affect International Law?

“This symposium explores ways in which developments in artificial intelligence (AI) will affect the substance of international law, and, conversely, how international law may guide states' decisions to deploy AI. Even though AI increasingly is penetrating commercial, military, and scientific arenas, states have been slow to create new international agreements or to amend existing ones to catch up to these technological developments. Nevertheless, AI is certain to produce changes in the areas of human rights, the use of force, transnational law enforcement, global health, intellectual property regimes, and international labor law, among others. This symposium gets down to the hard work of assessing the gaps, ambiguities, and guidance in existing treaties and customary rules and begins to sketch a road map forward. In so doing, the essays offer an important lesson to international lawyers: that it is critically important — both for those who work for governments and those who wish to influence state decisions from the outside — to understand the basics of these technologies.”
"Sport Direct" - Mobile Application for Sports Events

In today’s world, the society are so busy chasing their dream jobs it became so stressful and packed life where they usually do not have time to spend on their favourite exercises and sport events. MALAYSIA has the dubious honour of having the highest obesity prevalence in Southeast Asia in a newspaper New Straits Times reported by Farezza Hanum Rashied (June 7, 2017) “The report showed that only a third of Malaysian adults had ever exercised, while only 14 per cent exercised adequately.Thus, an android application has been designed to ease the steps for fellow athletes (Amateur or professional) to discover various sport events happening within few taps. The prime objective of Sport Direct is to create a fully, fledged android application which would discover the sports events happening in and around the Malaysia so that no one will miss out being notified on interested sports events. The user can check all the sports events happening around Malaysia. Meanwhile, participant will get notification when their desired events are ready for registration. Moreover, this platform to easily reach thousands of participant directly without having to spend a penny on unnecessary advertisements by organisers.
Subject Guides: English 1121: DISCOURSE COMMUNITIES

Research basics for students writing about literature, current social issues, or discourse communities.
Library Guides: MSU Library Services for Faculty: Resources for Online Teaching

Discover the services the library provides in research & publication, teaching & learning, and technology services.
Main factors influencing communication in the health care facility

For those working in private health care facilities the percentage of those that the solidarity in the collective in which they work is good is higher (36.68%) than for those employed in public health establishments. In the case of the medics working in a team of less than twenty, the percentage of those thinkung that the collective in which they work is good (51.26%) is higher compared to the same (15.76%) for the medics working in bigger teams. Just over a third of the survey respondents believe the microclimate in their workforce is at a good level enough. In the group of employees in state healthcare establishments, the percentage of those believing that the microclimate within the group is good is comparatively lower than that of those working in private health establishments. The most common subject of conversations between medics at their workplace is the work itself. The majority of participants in our survey believe that their supervisor does not sufficiently set the methods of communication in their work. Secondary education respondents have the highest percentage of those who say that their supervisor is well-communicating with their colleagues.
LibGuides: Library Course Reserves: For Faculty / Instructors

Library Course Reserves use Leganto. Created by Ex Libris, Leganto is an online application that supports the creation and management of course resource reading lists.
LibGuides: Digital and Social Media: Study Help

A collection of blogs, databases and videos to get you started. This research guide provides information on locating current and historical information on Digital & Social Media and related disciplines for your studies and interests.
LibGuides: eBooks: The platforms we have and their features: General Information

This guide shows the various eBook platforms that Campbell subscribes to and the features that each have.
Generalised Convex Clustering: A Parametric Technique to Perform Market Segmentation

Market segmentation is a valuable analysis for businesses, especially when they have a diverse clientele. Clustering is the unsupervised learning method that can deal with market segmentation. While many clustering techniques exist, they generally suffer from instabilities and thus do not necessarily generate the best solutions to a problem. A novel method that does not suffer from these instabilities is convex clustering. Although convex clustering is already established for continuous and binary data, this paper extends the literature by generalising convex clustering for different data types. Two practical cases show that the generalised model has a practical use and can provide businesses with vital information. A simulation study furthermore reveals promising results: for categorical data this model outperforms the benchmark of an established method, especially when the relative difference between clusters is small. Finally, it is the first clustering method that is able to analyse Poisson distributed data and its performance in the simulation study is decent. Thus, this paper presents the generalisation for convex clustering for different data types and supports that this extension to the literature is logical and beneficial.
Research Guides: Using HathiTrust: Collections in HathiTrust

Description of how to find and use Hathi Trust resources at the University of New Hampshire
Cyber Security: New Standards, New Expectations for the 21st-Century Legal Practitioner

The somewhat organic nature of technology or digitalization and the evils thereof, coupled with the need for survival of the 21st-century legal practice on the same make cyber security a burning issue in the field of law. The legal practitioners/clients' privilege and legal practitioners' duty of confidentiality owed to clients make the subject more topical herein. It is now indubitable that cyber attacks are real. Different attacks, such as legal email compromise, denial-of-service, phishing, data infiltration and monitoring for insider trading opportunities, among others, have grown steadily in recent years and law firms are not left out as major victims. This paper considers the meaning of cyber security, its particular relevance in law, the compliance level of law firms with cyber security measures in the 21st-century and the consequences, and the way forward.
Deep Semantic Segmentation across environments for Autonomous Driving

In the autonomous driving context it has always been crucial to be able to teach a vehicle to precisely identify each entity in an image, in order to take appropriate decisions in various scenarios. Semantic segmentation aims at doing so by classifying each individual pixel, but, as well as all approaches based on Deep Learning, it requires lots of data to effectively train a network. Collecting big amounts of labeled data is far from trivial, so synthetic and real datasets were created to overcome such scarcity. This is not enough though, as real datasets are still too small and not various enough to satisfy this need, whereas readily available synthetic datasets do not offer a wide variety of scenarios to choose from. Moreover, semantic segmentation approaches have troubles dealing with different domains, being unable to generalize well to a given unseen domain. For example, training with synthetic samples and testing with real ones leads to a drop in performance. In this thesis work, a new and very large synthetic dataset is proposed in order to provide the research community with a novel challenge where a wide choice of domains is available, such as different towns, atmospheric conditions and vehicle viewpoints. The dataset is tested with a special focus to the domain shift issue, proving how the current state-of-the-art networks struggle with closing the domain gap, and demonstrating that further research in this area is needed.
Secret Image Sharing Scheme for Gray-Level Images Using Toeplitz Matrix Based Stream Cipher

The need of providing secret image sharing allows us protecting the information in terms of data integrity, confidentiality and authenticity, which has become obvious in the past few years. However, in the construction of secret image sharing schemes the disadvantages; image size extension and loss of contrast are necessary to be addressed. In this paper, an efficient gray-level image sharing scheme using Toeplitz Matrix-based Stream Cipher is proposed to addresses these problems. The Toeplitz one-way function based key is engendered using the sequence generated by the register with nonlinear update function (NLUF) and the public-key. Further, it is utilized in generating the secured dynamic keystream. The proposed scheme helps to achieve single-fold image sharing, unlike the existing Chen’s scheme. Also, the secret image is restored without any size extension and loss in contrast as well. The experiments such as entropy, correlation, differential, and the histogram of plaintext and ciphertext images are performed for analyzing the security performance of the proposed scheme. Further, the generated dynamic binary keystream sequence is analyzed for randomness using NIST statistical tests.
Short-Term Action Recognition by 3D Convolutional Neural Network with Pixel-Wise Evidences

Action recognition in videos is becoming popular these years. The difficulty is how to extract the temporal information, which is important in the target actions. In this paper, we propose a conceptually, simple network for short-term action recognition. The proposed network architecture is extended from standard neural network to Autoencoder, which estimates pixel-wise evidence in frames, and they are integrated to classify the actions in the simple classifier. In the proposed architecture, the standard 2D convolutional layers for image classification are extended to 3D convolutional layers in the Autoencoder to extract the temporal information in the target actions. In the training phase, classifiers are introduced in the middle of layer to let the features of the middle layers are well discriminated. Also, classifiers are introduced at the end of layer to improve performance of the standard classifier. We have performed experiments using UCF101 dataset to evaluate the effectiveness of the proposed architecture. The results show that our methods can get efficient performance in short-term action recognition.
Enabling Identity-Based Integrity Auditing and Data Sharing with Sensitive Information Hiding for Secure Cloud Storage

with cloud storage services, users can remotely store their data to the cloud and realize the data sharing with others. Remote data integrity auditing is proposed to guarantee the integrity of the data stored in the cloud. In some common cloud storage systems such as the Electronic Health Records (EHRs) system, the cloud file might contain some sensitive information. The sensitive information should not be exposed to others when the cloud file is shared. Encrypting the whole shared file can realize the sensitive information hiding, but will make this shared file unable to be used by others. How to realize data sharing with sensitive information hiding in remote data integrity auditing still has not been explored up to now. In order to address this problem, we propose a remote data integrity auditing scheme that realizes data sharing with sensitive information hiding in this paper. In this scheme, a sanitizer is used to sanitize the data blocks corresponding to the sensitive information of the file and transforms these data blocks’ signatures into valid ones for the sanitized file. These signatures are used to verify the integrity of the sanitized file in the phase of integrity auditing. As a result, our scheme makes the file stored in the cloud able to be shared and used by others on the condition that the sensitive information is hidden, while the remote data integrity auditing is still able to be efficiently executed. Meanwhile, the proposed scheme is based on identity- based cryptography, which simplifies the complicated certificate management. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient.
Evaluation framework for input layer preprocessing in a radial basis function neural network

The need for immediate situational awareness updates in a military environment can be partially mitigated by employing machine learning (ML) at the edge of the network, where the warfighter operates. Technical challenges for edge computing, like limited power and data, require unique hardware and software implementations for viable solutions. Low power neuromorphic processors running radial basis function artificial neural networks (RBFNN) makes ML at the edge more practical but can introduce limitations in the data throughput. This power and data limitation can be moderated using preprocessing of the input space to magnify the most pertinent data features. This paper presents a framework for evaluating different input space paradigms in a systematic manner. Using a representative small dataset for a pyroshock event, common in the military environment, several input preprocessing paradigms are evaluated. The correlation coefficient across the dataset, between the number of neurons and inference accuracy for the RBFNN has a <i>p</i>-value of 1 x 10<sup>-7</sup>.
SISTEM INFORMASI AKUNTANSI HARGA POKOK PRODUKSI DENGAN METODE ACTIVITY BASED COSTING PADA UKM UKIR JEPARA

The aim of this study is to design a production accounting information system for the determination of the cost of production that can help the SMEs Jepara carving in the determination of the cost of a product exactly. Information on the cost of a product is very important as a basis in determining the selling price of a product. so far the SMEs Jepara carving in determining the cost of a product is done by estimating the costs of production and often do not include a production cost that nominal little cost. So the cost of production does not reflect actual cost consumption. This can certainly cause the production cost to be undercost or uppercost. The approach in determining the cost of production using the method of ativity-based costing, activity-based costing method is the determination of the cost of a product based on activity. Research methods that Research method using Reseach and Development (R&D) method with prototype system development model. This study was conducted on SMEs carving Jepara. The result of this research is in the form of accounting information system product for determination of cost of production at SME Jepara carving which can assist in determination of cost of product. Implementation of the activity-based costing method as an approach in determining the cost of products is able to provide benefits that the charging over the cost can be more precise. Efficiency in costs can be obtained. The use of activity based costing method can also decrease the selling price of the product, so that Jepara carving SMEs will be better able to compete both for domestic market and international market.
Guides: National Library Week: Contact Us

Here is the Cal Poly Pomona University Library LibGuide on American Library Association's National Library Week.
Library Guides: MSU Library Services for Faculty: Connecting to Brightspace

Discover the services the library provides in research & publication, teaching & learning, and technology services.
A Robust Density Clustering Algorithm Based on Gravity Peak

Density Peak clustering algorithm has become a hotspot of clustering research in recent years because of its own characteristics of finding high-density centers. However, it and its derivatives are generally not good at distinguishing the cluster centers of uniform and heterogeneous data sets. Moreover, the common problem of the density-based clustering algorithm is that it cannot effectively distinguish clusters with overlaps. To address those problems, a new robust density clustering algorithm called RDC-GP is proposed in this paper. Introducing the law of gravity into density-based clustering algorithm can solve the first problem. In addition, we propose a gravity tilt detection method to identify and extract the edge points of clusters. It not only improve the accuracy of density clustering algorithm, but also well solve the defect that density peak algorithm cannot find cluster centers in unbalanced data effectively. We selected various types of data sets for experiments. Compared with other methods, our proposed has obvious robustness and significantly accuracy.
Analisis Bentuk Kesalahan dalam Mengarang Bahasa Jepang yang Dipengaruhi oleh Bahasa Pertama

Second language acquisition (SLA) for Indonesian students can be in the form of regional languages, Indonesian language, English as an international language or a foreign language. Foreign languages such as Mandarin, Korean and Japanese. The learning process of writing especially at the elementary level, students have difficulty in composing Japanese sentences. Based on observations, this happens because in the process of writing students are still influenced by the first language. Therefore, this study aims to determine the effect of first language in writing Japanese at the elementary level. The research method used in this study is a qualitative research method. The data of this research are in the form of students' essays in the Shokyuu Kohan sakubun course. And the data analysis technique used is the analysis of language errors. Based on the results and discussions, it can be concluded that writing Japanese essays is influenced by the first language, in this case the first language is Indonesian. Of the errors affected by the first language by 67% influenced by the sentence patterns of the first language and 33% influenced by the vocabulary of the first language.