Reviewerly/exHarmony huggingface dataset:
EASIT IO3 report

This report includes the results stemming from the activities carried out in Intellectual Output 3. This Intellectual Output was primarily aimed at creating the skills card(s) for the new professional profile(s) that can be linked to easy-to-understand (E2U) content creation. Content creation should be understood here as the creation from scratch, the adaptation and, if considered relevant, its validation by end users. The output is a skills card for each of the identified professional profiles.
Learning Structured Distributions: Power-Law and Low-Rank

Author(s): Falahatgar, Moein | Advisor(s): Orlitsky, Alon | Abstract: Utilizing the structure of a probabilistic model can significantlyincrease its compression efficiency and learning speed. We considerthese potential improvements under two naturally-omnipresentstructures.Power-Law: English words and many other natural phenomena arewell-known to follow a power-law distribution. Yet this ubiquitousstructure has never been shown to help compress or predict thesephenomena. It is known that the class of unrestricted distributionsover alphabet of size k and blocks of length n can never becompressed with diminishing per-symbol redundancy, when kgn. Weshow that under power-law structure, in expectation we can compresswith diminishing per-symbol redundancy for k growing as large assub-exponential in n.For learning a power-law distribution, we rigorously explain theefficacy of the absolute-discount estimator using less pessimisticnotions. We show that (1) it is adaptive to an effectivedimension and (2) it is stronglyrelated to the Good--Turing estimator and inherits itscompetitive properties.Low-Rank: We study learning low-rank conditional probabilitymatrices under expected KL-risk. This choice accentuates smoothing,the careful handling of low-probability elements. We define a lossfunction, determine sample-complexity bound for its global minimizer,and show that this bound is optimal up to logarithmic terms. Wepropose an iterative algorithm that extends classical non-negativematrix factorization to naturally incorporate additive smoothing andprove that it converges to the stationary points of our loss function.Power-Law and Low-Rank: We consider learning distributions inthe presence of both low-rank and power-law structures. We studyKneser-Ney smoothing, a successful estimator for the N-gram languagemodels through the lens of competitive distribution estimation. Wefirst establish some competitive properties for the contextualprobability estimation problem. This leads to Partial Low Rank,a powerful generalization of Kneser-Ney that we conjecture to haveeven stronger competitive properties. Empirically, it significantlyimproves the performance on language modeling, even matching thefeed-forward neural models, and gives similar gains on the task ofpredicting attack types for the Global Terrorism Database.
Il centenario che saltò dalla finestra e scomparve : Analisi della traduzione dei realia culturospecifici in un romanzo svedese tradotto in italiano

This thesis examines the translation of cultural-specific elements known as realia from Swedish to Italian based on the theoretical framework of translation studies. Realia, often without correspon ...
The Influence of Debate Method, Traditional Teaching Method and Learning Motivation Towards Speaking Ability of the First Semester Students of STIE Prabumulih 2017

Abstract This research is concerned with the influence of debate method, traditional teaching method and students’ learning motivation towards their speaking ability of the first semester of STIE Prabumulih 2017. In this study 60 students of the first semester of STIE Prabmulih 2017 in the academic year of 2017/2018 were chosen as the samples by means of cluster random sampling. This study applied factorial research design. The data were collected by using questionnaire and t-test. The data were analyzed by using Paired Sample t-test, Independent Sample t-test and Two Way Anova. The results showed that, first there was a significant difference in speaking ability between the students with high learning motivation and those with low learning motivation taught using debate method. Second there was a significant difference in speaking ability between the students with high learning motivation and those with low learning motivation taught using traditional teaching method. Third there was a significant difference in speaking ability between the students with high learning motivation and those with low learning motivation taught using debate method and traditional teaching method. Last, there were not any significant interaction effects of debate method and learning motivation towards the students’ speaking ability. The Finding suggests that there were significant influences of debate method, traditional method and students’ learning motivation towards their speaking ability of the first semester of STIE Prabumulih 2017. Keywords: Debate Method, Traditional Teaching Method, Learning Motivation, Speaking Ability
LibGuides: Library Virtual Tour: Services

Floor maps of the University of Lethbridge Library showing locations of collections, study areas and service points. The Library provides access to audiovisual equipment and services to support the academic programs of the University of Lethbridge
Biomedia ACM MM Grand Challenge 2019

The Biomedia ACM MM Grand Challenge focuses on medical applications with a task to detect and classify abnormalities within gastrointestinal (GI) tract. As a part of the submission for this challenge, several methods we applied are reported in this paper. The data we used is from the KVASIR dataset and the NEETHUS dataset. It contains training and test data in form of image or video. The main challenge of this task is the data's insufficiency and unbalance, which will significantly decrease the performance. To solve this problem and achieve better result, we conduct multiple data enhancement operations on the data. The method is proved to be efficient. Apart from the operations applied on the data, we also test several classification structures include SCNN (Shallow Convolutional Neural Network), SCNN-SVM (Support Vector Machine), ResNet32-SVM, SVM, ResNet16, ResNet32, ResNet50, ResNet101 and Residual Attention Network. We finally chose ResNet50 as the main structure considered with the balance of accuracy and efficiency. We obtain 91.51% precision, 87.45% sensitivity, 99.48% specificity, 87.93% F1-score (the harmonic mean of precision and sensitivity) and 91.40% MCC (Matthews correlation coefficient) on the test dataset.
Teachers’ Influence on Student Motivation in the Language Classroom

Motivation plays an important role in the studentsâ€™ learning process and it could be provoked by the teacher at the same time. It is accepted that intrinsic motivation and extrinsic motivation are distinguished. This paper attempts to argue that teachers could largely influence student motivation in the classroom considering the context of Chinese universities. Teachers could enhance the quality of the language classes and demonstrate teachersâ€™ art to provoke studentsâ€™ extrinsic motivation as well as facilitate the studentsâ€™ intrinsic motivation. At the same time, teachers are recommended to distinguish between internal sources and externals sources of student motivation and take measures according to the specific circumstance.
Aluminum Pole Cap for 3 Inch Diameter Round Poles

Product Description: This industrial grade aluminum pole cap fits perfectly over any round straight aluminum pole with a 3 inch diameter. Features:
Author Correction: First-hand, immersive full-body experiences with living cells through interactive museum exhibits

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
A Camera Placement Method Based On Genetic Algorithm For Shipborne Video Surveillance System

Camera placement is very important for any video surveillance system, which could influence the performance of the whole system and the design of surveillance algorithms. The optimization model of camera placement is constructed by this paper. In this model, we attempt to maximize cameras’ coverage area which meets the requirement of surveillance quality within the surveillance area, when the number of cameras is fixed. Meanwhile, we proposed the solving algorithm for the shipborne multi-camera placement optimization model based on genetic algorithm. At last two experiments about shipborne multi-camera placement were introduced to verify the optimization model and the solving algorithm. The result indicates that the method for shipborne multi-camera placement can give the precise camera placement scheme, which contains every camera’s installation site, yaw angle, pitch angel, and the coverage of these cameras, and improve the performance of the whole shipborne video surveillance system.
Ensembles based on conformal instance transfer

In this paper we propose a new ensemble method based on conformal instance transfer. The method combines feature selection and source-instance selection to avoid negative transfer in a model-independent way. It was tested experimentally for dierent types of classiers on several benchmark data sets. The experiment results demonstrate that the new method is capable of outperforming signicantly standard instance transfer methods. Keywords: Instance Transfer, Conformal Prediction, Ensembles
Pengembangan Media Ajar Jalur Evakuasi Gunung Api Berbasis SIG sebagai Dasar Pengetahuan Kebencanaan di SMP Negeri 1 Kemalang

Important disaster mitigation education is carried out at the junior high school levelin Kemalang Subdistrict given the condition of the area in the sub-district which isprone to disasters. This study aims to determine: (1) effective evacuation paths basedon GIS analysis, (2) the feasibility of the media developed, and (3) the effectiveness ofanimated video media on SSB extracurricular activities in Kemalang Junior HighSchool 1. This research is a research and development that uses the developmentmodel of Borg & Gall. The development design uses one group pre-test post-test designwith the number of respondents as many as 30 students participating in SSBextracurricular activities at Kemalang 1 Junior High School. Data used in makingevacuation route maps include road network data, initial and destination locations,and DEM. GIS analysis techniques in making evacuation route maps are least costpath analysis techniques. Making animated videos using CorelDraw Software, AdobeIllustrator, Adobe After Effect, Adobe Audition, and Adobe Premiere Pro. Dataanalysis techniques to test the feasibility of the media are quantitative descriptive,while the effectiveness of the media uses the T test (t-test). The results show that: (1)There are 20 effective evacuation routes based on the results of GIS analysis using theleast cost path method. (2) Based on the assessment of media feasibility by materialexperts, media experts, teacher educators, and students showed an average score of4.6. This shows that the media is worthy of being used and included in the Goodcategory. (3) Based on the assessment of the pre-test and post-test shows that theaverage value of students has increased, the pre-test is 64.5 while the post-test is 85.The results of the Paired Sample Test hypothesis show a significance value of 0,000which means that value <0.05. This shows that the animation video teaching media iseffectively used in learning because it can increase the understanding of students whotake part in SSB extracurricular activities at Kemalang 1 Junior High Schoolregarding volcanic disaster mitigation material.
Loras College Library: Foundational Writing Library Research Lab: For Instructors Only

In Engaging Differences you learned the basics, this guide takes you deeper into the skills you'll need to be an excellent researcher.
Applications with Graphical User Interfaces

A graphical user interface (GUI) can be created in three different ways. The first method is to create a GUI programmatically. In this instance, commands are usually written directly in the editor. The second way is to use the graphical user interface development environment (GUIDE). The third way is to create the GUI via the App Designer. In this chapter, we cover how to create GUIs in all of these cases, and illustrate how to program applications with them.
Workshop Math And Science Untuk Menstimulus Kognitif PAUD Melalui Kegiatan Fun Cooking

Goal of this Charity Program is giving experience and knowledge of teacher about Introducing Math and Science to stimulate The Cognitive Growing of students in Kindergarten by Fun Cooking activities making healthy, nutritious and interesting dish at Satu Atap (Satap) Wasur Kindergarten. This Charity Program will be conducted in 2 days at Satap Wasur Kindergarten of  Merauke that involved all of the teachers as participant. Discussion and practice is the method to explain the activities of introducing math and science to the Kindergarten is important for the teachers during sharing and remembering their former knowledge appropriate with the up to date changes and development. It means that knowledge and concept of interesting introduce math and science fit with the growing step of the students in Kindergarten.
Attention-Based Label Consistency for Semi-supervised Deep Learning

Semi-supervised deep learning, which aims to effectively use the available labeled and unlabeled data together to improve the accuracy of model, is a hot topic recently. In this paper, we propose a novel attention-based label consistency (ALC) model for semi-supervised deep learning. The relationships between different samples are well exploited by the proposed scheme of channel and sample attention, while the class estimations are required to be smooth for nearby unlabeled data. We have implemented the proposed ALC model in the framework of $$\varPi $$ model and MeanTeacher, and the experimental results on three benchmark datasets, (e.g., Fashion-MNIST, CIFAR-10 and SVHN) clearly show the advantages of our proposed method.
AN ANALYSIS ON ENGLISH LEARNING MOTIVATION AT THE ELEVENTH GRADE STUDENTS IN SMAN 10 KOTA JAMBI ACADEMIC YEAR 2018/2019

This study analyzed about extrinsic motivation student in SMA N 10 Kota Jambi. This study is aimed to know about the factor extrinsic motivation student in SMA N 10 Kota Jambi. After giving the questionnaire, the data are analyzed by using a descriptive qualitative research method. The usage of the qualitative is used to know about the extrinsic motivation student in SMA N 10 Kota Jambi. This study can be concluded from 92 students. From the results of questionnaire we know that the largest percentage is student who answered agree and the second largest is students who answered strongly agree, than disagree and strongly disagree. So we can know that extrinsic factors can greatly influence students’ motivation   in learning
Spatial-temporal Fusion Network with Residual Learning and Attention Mechanism: A Benchmark for Video-Based Group Re-ID

Video-based group re-identification (Re-ID) remains to be a meaningful task under rare study. Group Re-ID contains the information of the relationship between pedestrians, while the video sequences provide more frames to identify the person. In this paper, we propose a spatial-temporal fusion network for the group Re-ID. The network composes of the residual learning played between the CNN and the RNN in a unified network, and the attention mechanism which makes the system focus on the discriminative features. We also propose a new group Re-ID dataset DukeGroupVid to evaluate the performance of our spatial-temporal fusion network. Comprehensive experimental results on the proposed dataset and other video-based datasets, PRID-2011, i-LIDS-VID and MARS, demonstrate the effectiveness of our model.
Neural generative models and representation learning for information retrieval

Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensional information. Recently, advances in deep learning techniques provide new opportunities for representation learning and generative models for information retrieval. In contrast to statistical models, neural models have much more flexibility because they model information and data correlation in latent spaces without explicitly relying on any prior knowledge. Previous studies on pattern recognition and natural language processing have shown that semantically meaningful representations of text, images, and many types of information can be acquired with neural models through supervised or unsupervised training. Nonetheless, the effectiveness of neural models for information retrieval is mostly unexplored. In this thesis, we study how to develop new generative models and representation learning frameworks with neural models for information retrieval. Specifically, our contributions include three main components: (1) Theoretical Analysis : We present the first theoretical analysis and adaptation of existing neural embedding models for ad-hoc retrieval tasks; (2) Design Practice : Based on our experience and knowledge, we show how to design an embedding-based neural generative model for practical information retrieval tasks such as personalized product search; And (3) Generic Framework : We further generalize our proposed neural generative framework for complicated heterogeneous information retrieval scenarios that concern text, images, knowledge entities, and their relationships. Empirical results show that the proposed neural generative framework can effectively learn information representations and construct retrieval models that outperform the state-of-the-art systems in a variety of IR tasks.
Extended Magic for Negation: Efficient Demand-Driven Evaluation of Stratified Datalog with Precise Complexity Guarantees.

Given a set of Datalog rules, facts, and a query, answers to the query can be inferred bottom-up starting from the facts or top-down starting from the query. For efficiency, top-down evaluation is extended with memoization of inferred facts, and bottom-up evaluation is performed after transformations to make rules driven by the demand from the query. Prior work has shown their precise complexity analysis and relationships. However, when Datalog is extended with even stratified negation, which has a simple and universally accepted semantics, transformations to make rules demand-driven may result in non-stratified negation, which has had many complex semantics and evaluation methods. This paper presents (1) a simple extension to demand transformation, a transformation to make rules demand-driven for Datalog without negation, to support stratified negation, and (2) a simple extension to an optimal bottom-up evaluation method for Datalog with stratified negation, to handle non-stratified negation in the resulting rules. We show that the method provides precise complexity guarantees. It is also optimal in that only facts needed for top-down evaluation of the query are inferred and each firing of a rule to infer such a fact takes worst-case constant time. We extend the precise relationship between top-down evaluation and demand-driven bottom-up evaluation to Datalog with stratified negation. Finally, we show experimental results for performance, as well as applications to previously challenging examples.
Towards A Logical Account of Epistemic Causality

Reasoning about observed effects and their causes is important in multi-agent contexts.While there has been much work on causality from an objective standpoint, causality from the point of view of some particular agent has received much less attention.In this paper, we address this issue by incorporating an epistemic dimension to an existing formal model of causality.We define what it means for an agent to know the causes of an effect.Then using a counterexample, we prove that epistemic causality is a different notion from its objective counterpart.
On the quantum complexity of computing the median of continuous distributions

We study the approximation of the median of an absolutely continuous distribution with respect to the Lebesgue measure given by a probability density function f. We assume that f has r continuous derivatives, with derivative of order r being H\"older continuous with the exponent \rho. We study the quantum query complexity of this problem. We show that the \ve-complexity up to a logarithmic factor is of order \ve^{-1/(r+\rho+1)}. We also extend the results to the problem of computing the vector of quantiles of an absolutely continuous distribution.
Data Visualization and Animation

MATLAB provides very powerful techniques to visualize data. In this chapter, we look at how to visualize data and create animations. Visualizing data was already touched on in Chapter 1 . Therefore, the animation techniques are emphasized in this chapter. Animations can be created by using three basic techniques in MATLAB. These methods can be summarized as updating coordinates, applying transformation to objects, and creating movies.
“Never fry carrots without cutting.” Cooking Recipe Generation from Videos Using Deep Learning Considering Previous Process

Research on captioning that modifies the contents of images and moving images with natural language using deep training has had considerable results and attracted attention in recent years. In this research, we aim to generate recipe sentences from cooking videos acquired from YouTube. We treat this as image captioning and propose methods suitable for the task. We propose a method that adds a vector of a sentence already generated in the same recipe to the input of a captioning model. Then, we compare generated sentences and correct sentences to calculate scores. We also propose a data-processing method to improve accuracy. We employ several widely used metrics to evaluate image-captioning problems. We then train the same data with the simplest encoder-decoder model, compare it with correct recipe sentences, and calculate the metrics. The results indicate that my proposal methods help increase accuracy.
Design of Low Power Montgomery Multiplier Using Clock Technique

Looking at the current scenario, digital systems are more beneficial than the analog system. The power consumption and speed are the major performance criteria for any digital system. It is very important to have a high speed and less power consumption in all devices. The priority of having a high speed or less power consumption depends on the need of the system. This paper presents the implementation of high-performance Montgomery modular multiplier which is also low power consuming. It works upon a simple and efficient Montgomery multiplication algorithm. Hardware as well as software fields find applications of Montgomery modular multiplication. Hardware implementations use direct data path, making them faster in contrast to software implementations. Some real-time applications of software implementations are not fast enough; the reason being their flexibility. Such kind of implementations hasve an additive advantage that they are capable of being customized as per new algorithms. The power consumption of the proposed design is 14mW and has been improved by 59.1% from the existing design.