Reviewerly/exHarmony huggingface dataset:
The Use of ICT in Thirukkural Teaching and Facilitation

This study aims to analyze the use of ICT in the teaching and facilitating of Thirukkural. In this era of globalization, 21st-century learning and teaching are very important for students. The current trend has led the Education Ministry of Malaysia to produce various ICT platforms for utilisation by the teachers, students, parents, school administration, District Education Office, State Education Department, and Ministry Of Education. The use of ICT in the teaching and learning of Thirukkural can enhance the understanding and applicable in a student's daily life. ICT platforms produce students with high-level thinking skills. The TPACK theory was used as the basis of this research. This study was conducted qualitatively. The researcher used questionnaires and conducted interviews with the Tamil language teachers from selected schools to obtain good research findings. Findings of this study showed that from 12 teachers, 30% of them actively used ICT in teaching and facilitation of Thirukkural. Another 30% used the ICT facilities in teaching and learning of Thirukkural on a moderate level. The study also revealed that 50% of the respondents were not very competent and did not effectively use ICT in the teaching and facilitating of Thirukkural. Descriptive analysis showed that the need for sufficient and well-equipped ICT infrastructure and facilities must also be addressed by the relevant parties, mainly on technical support, administrative roles, and encouragement from the MOE. According to TPACK theory, the aspects of pedagogical skills and ICT skills coherently influence students' progress; however, despite being in the 21st century, the usage of these skills has been shallow among Tamil language teachers. The basic use of ICT, such as PowerPoint, should be applied as an in-house course to teachers to be included in the teaching of Thirukural. These aspects are very important to objectify this information transformation process. Keywords: Thirukkural, Information and Communication Technology, Frog Virtual Learning Environment, Teaching and Learning, Facilitation, Google Classroom.
LibGuides: Library Instruction at Indiana University Northwest: First Year Seminar

This is an internal LibGuide for all librarians who deliver library instruction session.Includes scheduling logistics, forms, links, help, and resources.
Privacy-preserving distributed deep learning

Deep neural networks are becoming popular in a variety of fields due to their ability to learn from large-scale data sets. Recently, researchers have proposed distributed learning architectures that allow multiple users to share their data to train deep learning models. Unfortunately, privacy and confidentiality concerns limit the application of this approach, preventing certain organizations such as medical institutions to fully benefit from distributed deep learning. To overcome this challenge, researches have proposed algorithms that only share neural network parameters. This approach allows users to keep their private datasets secret while still having access to the improved deep neural networks trained with the data from all participants. However, existing distributed learning approaches are vulnerable to attacks where a malicious user can use the the shared neural network parameters to recreate the private data from other users. We propose a distributed deep learning algorithm that allows a user to improve its deep-learning model while preserving its privacy from such attacks. Specifically, our approach focuses on protecting the privacy of a single user by limiting the number of times other users can download and upload parameters from the main deep neural network. By doing so, our approach limits ability of the attackers to recreate private data samples from the reference user while maintaining a highly accurate deep neural network. Our approach is flexible and can be adapted to work with any deep neural network architectures. We conduct extensive experiments to verify the proposed approach. We observe that the trained neural network can achieve an accuracy of 95.18%, while protecting the privacy of the reference user by preventing it from sharing both its private data and deep neural network parameters with the server or other users.
Convolutional Neural Network and OpenCV Based Mobile Application to Detect Wear out in Car Tyres

This work proposes a technique for detecting wear out of car  tyres. Tyre is the only part of the vehicle which is in contact with road. Hence tyre condition should be monitored timely in order to have a safe drive. Tyre wear out occurs because of the parameters such as when the tread limit of tyre is less than 1.6 cm, rubber degradation, when there are around 4 to 5 punctures, bulged tyre. We consider some of the above parameters to assess the wear of tyre using the computer vision techniques such as opencv and convolutional neural networks. Opencv and convolutional neural networks are most used in object detection and image classification. We used these techniques and obtained an accuracy of 90.95%, with which we can predict the wear of tyre to avoid dangerous accidents.
PERANGKAT LUNAK MOBILE PEMILIHAN KETUA RT MENGGUNAKAN METODE BORDA BERBASIS ANDROID (STUDI KASUS PADA RT 46 KELURAHAN 3-4 ULU)

Borda is a voting method used in group decision making for single or multiple winner elctions. Borda determiner the winner by assigning a certain number of points to each candidate. The election of the head of the RT in the RT. 46 area is still carried out conventionally. The election process like this has many shortcomings, among others, the inadequate counting of votes and election equipmrny, there are still errors of citizens to vote, as well as the lengthy counting votes. Therefore i want to make people in the RT area left work to come to elect the head of the RT. The solution that can be applied in overcoming the above problems is to conduct online elections via mobile software.  Online election through mobile software is a method of voting and counting votes in general elections using mobile software. An application that can be uses for general elections, where by using this application the general election process is made easier by ensuring the privacy or confidentiality of voters and the accuracy of vote counting.
Guides: Criminology: Referencing

Study smarter. Research effectively. Your guide to library tools and research skills. Chicago B (Harvard) and APA referencing styles tools and tips. Learn to reference your sources with confidence.
Neighborhood aggregation based graph attention networks for open-world knowledge graph reasoning

Knowledge graph reasoning or completion aims at inferring missing facts based on existing ones in a knowledge graph. In this work, we focus on the problem of open-world knowledge graph reasoning—a task that reasons about entities which are absent from KG at training time (unseen entities). Unfortunately, the performance of most existing reasoning methods on this problem turns out to be unsatisfactory. Recently, some works use graph convolutional networks to obtain the embeddings of unseen entities for prediction tasks. Graph convolutional networks gather information from the entity’s neighborhood, however, they neglect the unequal natures of neighboring nodes. To resolve this issue, we present an attention-based method named as NAKGR, which leverages neighborhood information to generate entities and relations representations. The proposed model is an encoder-decoder architecture. Specifically, the encoder devises an graph attention mechanism to aggregate neighboring nodes’ information with a weighted combination. The decoder employs an energy function to predict the plausibility for each triplets. Benchmark experiments show that NAKGR achieves significant improvements on the open-world reasoning tasks. In addition, our model also performs well on the closed-world reasoning tasks.
Visual Content Creation by Generative Adversarial Networks

Author(s): Azadi, Samaneh | Advisor(s): Darrell, Trevor | Abstract: We live in a world made up of different objects, people, and environments interacting with each other: people who work, write, eat, and drink; vehicles that move on land, water, or in the air; rooms that are furnished with chairs, tables, and carpets. This vast amount of information can be easily collected from the recorded videos and photographs shared online. However, it still remains a challenge to teach an intelligent machinery agent to reliably analyze and understand this extensive collection of data. Generative models are one of the most compelling methods towards modeling visual realism from the large corpus of available images, which operate by teaching a machine to create new contents. These models are not only beneficial in understanding the visual world, but more deeply in visual synthesis and content creation. They can assist human users in manipulating and editing an existing visual content. In the last few years, Generative Adversarial Networks (GANs) as an important type of generative models have made remarkable enhancements in learning complex data manifolds by generating data points from scratch. The GAN training procedure pits two neural networks against each other, a generator and a discriminator. The discriminator is trained to distinguish between the real samples and the generated ones. The generator is trained to fool the discriminator into thinking its outputs are real. The network learns the real-world distribution while generating high-quality images, translating a text phrase into an image, or transforming images from one domain to another. This dissertation investigates algorithms to improve the performance of such models in creating new visual content specifically in structural and compositional domains in a wide range from hand-designed fonts to natural complex scenes. In Chapter 2, we consider text as a visual element and propose tools to synthesize new glyphs in a font domain and transfer the style of the seen characters to the generated ones. From Chapter 3, we focus on the domain of natural images and propose GAN models capable of synthesizing complex scene images with lots of variations in the number of objects, their locations, shapes, etc. In Chapter 4, we explore the role of compositionality in the GAN frameworks and propose a new method to learn a function that maps images of different objects sampled from their marginal distributions into a combined sample that captures the joint distribution of object pairs. Despite all the improvements in training GANs, it still remains a challenge to fully optimize the GAN generator in a two-player adversarial game, resulting in samples that do not always follow the target distribution. In Chapter 5, instead of trying to improve the training procedure, we propose an approach to improve the quality of the trained generator by post-processing its generated samples using information from the optimized discriminator.
The Perils of Complexity: A Multi-Stage Study to Determine Necessary Images for Digitized Scrapbook Representation

Article on a multi-stage project to examine current practices and gain user perspectives on the images needed for optimal scrapbook representation in a digital library. The results from their work have enabled the implementation of clear guidelines on scrapbook digitization at their institution that can be applied throughout the field.
Stability Technology Of Wind Speed Indication In Underground Coal Mine

The sensor based on pitot tube principle in coal mine is affected by the installation position, temperature and humidity, dust, surrounding electrical equipment and other electromagnetic interference, resulting in large fluctuation of sensor indication and inaccurate measurement. The structure of S-type pitot tube is improved to enhance the EMC performance of sensor power supply and communication circuit interface, and soft filtering algorithm is used to suppress the stability of sensor measurement value. Compared with the traditional wind speed sensor test, the problem of data fluctuation has been improved obviously.
Prototype Completion for Few-Shot Learning

Few-shot learning aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes marginal improvements. In this paper, 1) we figure out the reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning feature extractor is less meaningful; 2) instead of fine-tuning feature extractor, we focus on estimating more representative prototypes. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative features for seen attributes as priors. Second, a part/attribute transfer network is designed to learn to infer the representative features for unseen attributes as supplementary priors. Finally, a prototype completion network is devised to learn to complete prototypes with these priors. Moreover, to avoid the prototype completion error, we further develop a Gaussian based prototype fusion strategy that fuses the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments show that our method: (i) obtains more accurate prototypes; (ii) achieves superior performance on both inductive and transductive FSL settings.
Single-Pixel Machine Vision Using Spectral Encoding Through Diffractive Optical Networks

We present and experimentally demonstrate a deep learning-driven machine-vision framework that trains diffractive surfaces to encode the spatial information objects into the output power spectrum for all-optical image classification using a single-pixel spectroscopic detector.
Real Time Tunnel Structure Monitoring System Based on Edge Computing

In this paper, a real-time tunnel structure monitoring system is designed by using cloud-side-end collaboration and Variational Mode Decomposition (VMD) methods. The system can monitor multiple structural indicators. When the structure is abnormal, the system can inform the engineer in time to effectively ensure the safety of the tunnel. At the same time, through the monitoring of the tunnel, the original data of structural strain is obtained, which provides relevant data and analysis services for related scientific research. The system has excellent performance, high monitoring accuracy, and gives full play to the advantages and characteristics of edge computing. It has important social significance, economic value and wide application prospects.
The universal unambiguous discriminator between two unknown qudit states

Abstract In this work, the unambiguous discrimination between two unknown states in d -dimensional <?CDATA $(d\geqslant2)$ ?> Hilbert space is investigated. To achieve this goal, the Hilbert space of the states is divided into a series of subspaces, where the components of the two states projected in some of these subspaces can be unambiguously distinguished, and then the unambiguous discriminator between two unknown qudit states can be constructed. Moreover, the optimal success probability of the discrimination between two unknown states is obtained and the form of detection operators is given.
국방 안전관리시스템 개선을 위한 주요 외국군 사례분석 연구

Department of Defense, Republic of Korea has put in significant efforts towards safety over the years by developing defense safety management systems, such as establishing safety management organizations and enacting laws and regulations. Therefore, the number of casualties due to military activities has been reduced dramatically. However, many errors and problems are present in the safety management system (SMS) owing to a lack of proper SMS. That brings inefficiency and discontinuity in SMS. Hence, typical phenomena such as lack of safety management professionals and reactive safety management activities have not been properly corrected. This study reviews various academic papers on the SMS and research reports of major foreign institutions to redefine the concept and components of the defense safety management system to ultimately suggest improvement in the R.O.K defense safety management system. Additionally, the safety management system of the U.S. and British forces, considered to be the leading safety management system, were analyzed and compared to R.O.K defense SMS to derive their implications.
Exploration and Safety in Deep Reinforcement Learning

Author(s): Achiam, Joshua | Advisor(s): Sastry, Shankar; Abbeel, Pieter | Abstract: Reinforcement learning (RL) agents need to explore their environments in order to learn optimal policies by trial and error. However, exploration is challenging when reward signals are sparse, or when safety is a critical concern and certain errors are unacceptable. In this thesis, we address these challenges in the deep reinforcement learning setting by modifying the underlying optimization problem that agents solve, incentivizing them to explore in safer or more-efficient ways.In the first part of this thesis, we develop methods for intrinsic motivation to make progress on problems where rewards are sparse or absent. Our first approach uses an intrinsic reward to incentivize agents to visit states considered surprising under a learned dynamics model, and we show that this technique performs favorably compared to naive exploration. Our second approach uses an objective based on variational inference to endow agents with multiple skills that are distinct from each other, without the use of task-specific rewards. We show that this approach, which we call variational option discovery, can be used to learn locomotion behaviors in simulated robot environments.In the second part of this thesis, we focus on problems in safe exploration. Building on a wide range of prior work on safe reinforcement learning, we propose to standardize constrained RL as the main formalism for safe exploration; we then proceed to develop algorithms and benchmarks for constrained RL. Our presentation of material tells a story in chronological order: we begin by presenting Constrained Policy Optimization (CPO), the first algorithm for constrained deep RL with guarantees of near-constraint satisfaction at each iteration. Next, we develop the Safety Gym benchmark, which allows us to find the limits of CPO and inspires us to press in a different direction. Finally, we develop PID Lagrangian methods, where we find that a small modification to the Lagrangian primal-dual gradient baseline approach results in significantly improved stability and robustness in solving constrained RL tasks in Safety Gym.
Research Guides: Using Muntz Library Online: SwoopSearch

Have questions about using the library resources or services online? This guide is here to help!
Classification Accuracy of Spanish CBMs for Students in a Dual Immersion Program

Author(s): Suchilt, Luisana | Advisor(s): Geraghty, Cathleen A | Abstract: A vast amount of research has supported the use of English CBMs to identify students at-risk of future reading difficulties. However, less research has focused on using Spanish CBMs with students receiving bilingual instruction. The purpose of this study was to assess the classification accuracy of a Spanish oral reading fluency CBM (FLO) with a group of third grade students participating in a dual immersion (DI) program and receiving 30 percent of instruction in English and 70 percent in Spanish. Overall, findings suggest that Spanish CBMs are effective tools for predicting reading performance for students who are receiving instruction in English and Spanish. Analyses revealed that the FLO measure can also be used with students with varying levels of English proficiency, including students who are English Language Learners (ELLs), English Only (EO), and Reclassified. Despite these findings, additional research is needed in order to further determine best practices when working with this designated population of students. Limitations of the study and future directions are discussed.
Robust Group-Based Secret Sharing for Secure Cloud Key Management

Cloud services are now ubiquitous in each organization. However, they are facing different challenges for using Key Management System (KMS) with those cloud solutions. Besides, they must deal with concerns specific to multi-cloud key management. In This paper, we present a schema based on robust secret share based on group of shares that fulfills the requirements of multi-cloud deployment. The system split the encryption keys into a blinded version of n shares that will be stored at cloud side and deals with a group of t+1 shares to compute the initial secret. To demonstrate the practicality of the proposed design, we implement a fully featured prototype and evaluate its performance. Results analysis shows that the proposed design is highly efficient and can serve as groundwork for using secret share to protect keys in a multicloud environment.
ANALYSIS OF TRANSLATION SHIFT IN A NOVEL (ENGLISH-INDONESIAN TRANSLATION)

This research aims to find out the class shift and structure shift in the simple sentences of Turtles All The Way Down novel by John Green (2018) and the translation into Bahasa Indonesia by Prisca Primasari (Penerbit Qanita, 2018). This research uses the theory of translation shift by Catford and equivalence by Nida. Catford divided translation shift into two, and they are level shift and category shift. Category shift is divided into four: structure shift, class shift, unit shift, and intra- system shift. From those kinds of translation shifts, the writer chose two types of translation shifts: class shift and structure shift. According to Nida, there are two types of equivalence; they are formal equivalence and dynamic equivalence. After analyzing all data that has been collected by using those two theories which have been mentioned above, the writer finds out nine kinds of class shift which occur. They are pronoun to the noun, adverb to the adjective, the preposition to the verb, verb to an adverb, verb to pronoun, noun to adjective, adjective to the verb, the verb to the adjective, and adverb to verb. Other than that, for structure shift, the writer finds out that word order changes, extra word added, and removal of some sentence elements in the simple sentences. The structure shift and class shift done by the translator show that each simple sentence with structure shift and class shift in the novel has one of two types of equivalence based on Nida’s theory.Keyword: Translation, class shift, structure shift, translation shift, equivalence