Reviewerly/exHarmony huggingface dataset:
LibGuides: Newspapers: How to Use Newspapers for Research: Newspapers Through the Library

Learn what newspapers are available free and through the library. Discover the newspaper available through the library
Anti-Fraud in International Supply Chain Finance: Focusing on Moneual Case

Purpose – This study analyzes the scope of due diligence and risks of banks and K-Sure in trade finance covered by EFF focusing on Moneual...
LibGuides: Children's Popular Books: Fantasy

This is a guide with children's books sorted by genre and with read-likes to assist children in picking their next book.
EFFECT OF COGNITIVE SELF-INSTRUCTION ON BULLYING BEHAVIOUR OF BASIC SCHOOL PUPILS IN ILORIN-EAST LOCAL GOVERNMENT OF KWARA STATE :

Bullying is a common destructive form of behaviour among the children that has been given much concern to educational researchers and stakeholders. This study therefore examined the effect of cognitive self-instruction on bullying behaviour of middle basic school pupils in Ilorin-East, Local Government Area, Kwara State, Nigeria. This study adopted a non-randomized pretest, post-test experimental and control group design. The population for this study comprised of the basic school pupils in the study area and the target population comprised of middle basic school pupils in basic five. The researchers used NICHQ-Vanderbilt Assessment Scale-Teacher Informant (NICHQ-VAS-TI) and Bullying Identification Questionnaire (BIQC) to purposively select bullies. The research participants were subjected to pretest and posttest after training with Pupil’s Peer Relation Instrument (PPRI) which was adapted from Prada. Training Package with Cognitive Self-instructional Training (CSIT) developed by Rigby was adapted to train bullies. Frequency counts and percentages were used to describe the demographic characteristics of the participants; t-test was used to test hypotheses one and two while Multivariate Analysis of Variance (MANOVA) was used to test hypothesis three and all at 0.05 level of significance.The findings of this study showed that, there was significant effect of Cognitive Self-instruction on bullying behaviour of middle basic school pupils in Ilorin-East Local Government, Nigeria, this is evident in p-value .000 which is less than 0.05 (.000 < 0.05). This shows that there is significant result. It is therefore recommended among others that educational psychologists should be available in basic schools to modify cognitive thoughts of children by exposing them to cognitive self-instructional training.
Dialogue Types, Argumentation Schemes, and Mathematical Practice: Douglas Walton and Mathematics

Douglas Walton's multitudinous contributions to the study of argumentation seldom, if ever, directly engage with argumentation in mathematics. Nonetheless, several of the innovations with which he is most closely associated lend themselves to improving our understanding of mathematical arguments. I concentrate on two such innovations: dialogue types (§1) and argumentation schemes (§2). I argue that both devices are much more applicable to mathematical reasoning than may be commonly supposed.
Attention Models for Activity Detection

Author(s): Ulutan, Oytun | Advisor(s): Manjunath, Bangalore S | Abstract: Video action detection is an important part of video understanding and analysis. There are many possible applications such as smart home environments to recognizing user actions and acting, smart robotics such as autonomous cars, robot assistants, selfie drones following your gesture commands, automated security systems analyzing the environment and assessing events. This thesis focuses on introducing novel machine learning algorithms for video action detection. A central contribution of this research is in developing a context-aware attention model for atomic actions. An atomic action is a simple action which can be described with 1-3 words or atomic body movements such as walking, drinking, holding an object. While observing actions/activities, humans infer from the entire context and our perception depends on the surrounding objects, actors, and scene. Inspired by this, our Actor Conditioned Attention Maps(ACAM) model utilizes the surrounding scene for each actor and uses context for improving action/interaction detection. The modularity of the ACAM model allows us to detect, track and recognize actions over extended time periods. We further extend this framework to detect complex activities which are composed of sequences of atomic actions. We demonstrate the effectiveness of our proposed methods on aerial videos and videos from camera networks.
Streamlining EM into Auto-Encoder Networks

We present a new deep neural network architecture, named EDGaM, for deep clustering. This architecture can seamlessly learn deep auto-encoders and capture common group features of complex inputs in the encoded latent space. The key idea is to introduce a differentiable Gaussian mixture neural network between an encoder and a decoder. In particular, EDGaM streamlines the iterative Expectation-Maximum (EM) algorithm of the Gaussian mixture models into network design and replaces the alternative update with a forward-backward optimization. Being differentiable, both network weights and clustering centroids in EDGaM can be learned simultaneously in an end-to-end manner through standard stochastic gradient descent. To avoid preserving too many sample-specific details, we use both the clustering centroid and the original latent embedding for decoding. Meanwhile, we distill the soft clustering assignment for each sample via entropy minimization such that a clear cluster structure is exhibited. Our experiments show that our method outperforms state-of-the-art unsupervised clustering techniques in terms of both efficiency and clustering performance.
Βελτιστοποίηση ποιότητας επιστημονικών μετρήσεων με χρήση δικτύων αισθητήρων

The enormous importance of information, which is the result of data processing, has been realized for several years. This gives great value to the processes and actions that concern those data. One of those processes is data storage. Increasing data accumulation makes it imperative to find tactics and systems to reduce space consumption from junk data.Considering this, in this project, an IoT network has been developed for the collection of acceleration data, adapted to avoid the storage of unnecessary data (noise). The idea is to set one of the sensors as master and the others as slave. The data will be stored only for the master, while the slaves will remain inactive. Slave data will only be stored in the event of an event (earthquake or other vibrating activity).
Continual Reinforcement Learning using Feature Map Attention Loss

Despite the growing interest in continual learning (CL) in recent years, continual reinforcement learning (CRL) has still been a challenging task because deep neural networks must infer appropriate action from each never-seen observation from new tasks sustaining old tasks' performance. To solve this problem, some CRL algorithms used both regularization-based methods constraining the weights and replay-based method used in the conventional CL. However, it costs a lot of time to learn because it required a considerable amount of memory in terms of replay-based and have complex regularization terms. In this paper, we propose a simple framework for preserving knowledge FMAL among related sequential tasks, namely Feature Map Attention Loss. Our method leverages general CNNs of a model that can perform all of the sequential tasks well, and the attention mechanism is used to extract essential features to transfer. Also, FMAL uses both the regularization method and the replay-based method, like the existing CRL method. However, the amount of memory required for learning is much smaller, and the term of regularization is relatively simple. We evaluate FMAL with the state of the art algorithms. The experiment results show that our method outperforms these baselines with higher rewards.
Gambaran Kepuasan Mahasiswa Tentang Kinerja Dosen Program Studi Keperawatan Fakultas Ilmu Kesehatan Universitas Muhammadiyah Surakarta

Lecturer performance is a task done by lecturers in the institution where they work. Higher education has various components in it, one of which is a lecturer. A lecturer has an important function in improving the quality of higher education, as stated by Sumardjoko (2010) that lecturers have a role in teaching, research, and community service matters, so that lecturers have a very important role and component in determining the quality of higher education. this research is twofold. The first objective is to describe the characteristics of respondents covering semester and gender. The second objective is to describe student satisfaction regarding lecturer performance including lecturing methods, lecture strategies, time discipline, lecturer attitudes, lecture material.This research method uses quantitative descriptive research with cross sectional approach. The sampling technique used is consecutive sampling. The number of respondents in this study were 100 students of UMS Nursing Study Program covering semesters 1, 3, 5 and 7. Data analysis used univariate analysis. The most respondents in semester one semester one. The majority of respondents are female. The performance of lecturers that influence the satisfaction of UMS Nursing Study Program students with the highest average is lecture material, the two lecture methods, the three lecture strategies, the four lecturer attitudes and the lowest time discipline.
LibGuides: So You Want to Be a Writer?: Teen Writing Guide: Resource Citations

A writing guide for teens aimed at writers of any skill level, from those who are just getting started on their writing journey to those who have been writing for some time.
Accountable Attribute-based Encryption with Public Auditing and User Revocation in the Personal Health Record System

In the system of ciphertext policy attribute-based encryption (CP-ABE), only when the attributes of data user meets the access structure established by the encrypter, the data user can perform decryption operation.So CP-ABE has been widely used in personal health record system (PHR).However, the problem of key abuse consists in the CP-ABE system.The semi-trusted authority or the authorized user to access the system may disclose the key because of personal interests, resulting in illegal users accessing the system.Consequently, aiming at two kinds of existing key abuse problems: (1) semi-trusted authority redistributes keys to unauthorized users, (2) authorized users disclose keys to unauthorized users, we put forward a CP-ABE scheme that has authority accountability, user traceability and supports arbitrary monotonous access structures.Specifically, we employ an auditor to make a fair ruling on the malicious behavior of users.Besides, to solve the problem of user leaving from the system, we use an indirect revocation method based on trust tree to implement user revocation.Compared with other existing schemes, we found that our solution achieved user revocation at an acceptable time cost.In addition, our scheme is proved to be fully secure in the standard model.
Recycling sub-optimial Hyperparameter Optimization models to generate efficient Ensemble Deep Learning

Ensemble Deep Learning improves accuracy over a single model by combining predictions from multiple models. It has established itself to be the core strategy for tackling the most difficult problems, like winning Kaggle challenges. Due to the lack of consensus to design a successful deep learning ensemble, we introduce Hyperband-Dijkstra, a new workflow that automatically explores neural network designs with Hyperband and efficiently combines them with Dijkstra's algorithm. This workflow has the same training cost than standard Hyperband running except sub-optimal solutions are stored and are candidates to be selected in the ensemble selection step (recycling). Next, to predict on new data, the user gives to Dijkstra the maximum number of models wanted in the ensemble to control the tradeoff between accuracy and inference time. Hyperband is a very efficient algorithm allocating exponentially more resources to the most promising configurations. It is also capable to propose diverse models due to its pure-exploration nature, which allows Dijkstra algorithm with a smart combination of diverse models to achieve a strong variance and bias reduction. The exploding number of possible combinations generated by Hyperband increases the probability that Dijkstra finds an accurate combination which fits the dataset and generalizes on new data. The two experimentation on CIFAR100 and on our unbalanced microfossils dataset show that our new workflow generates an ensemble far more accurate than any other ensemble of any ResNet models from ResNet18 to ResNet152.
Info Guides: Popular services: Home

If you're looking for our most popular digital library services, you're in the right place. Get started here.
RETRACTED: Research on the optimization model of English intelligent learning based on the perspective of big data

It is a theoretical innovation of teaching concept that the distance English teaching integrates with intelligent learning based on the perspective of big data. The acquisition of distance English teaching data requires the realization of automatic collection, forming the collection of data systematization, standardization, sustainability and real-time. The design mode and storage mode of data resources in the early stage have strict requirements. The analysis of distance English teaching data is to provide guarantee for future intelligent learning, because of the complexity of teaching data, the continuous and changing dynamic data, difficulties in mining the law of change and large volatility affected by individuals. To explore the inherent law and value of distance English teaching data will effectively promote the improvement of learners’ learning efficiency, the improvement of the overall level of distance education, the further expansion of educational opportunities and the enhancement of employability, and provide reference value for other related fields. Therefore, it is very important and urgent to study scientific methods which are suitable for distance English intelligent learning from the perspective of big data.
Robust Ensembles of Neural Networks using Itô Processes

Residual neural networks (ResNets) can be modeled as dynamical systems where the evolution of dynamical systems represents the inference in ResNets. We exploit this connection and the theory of stochastic dynamical systems to construct a novel ensemble of Ito processes as a new deep learning representation that is more robust than classical residual networks. An Ito process obtained by solving a suitably-formulated stochastic differential equation derived from a residual network has a probability density function that is not readily perturbed by small changes in the neural network’s inputs. Our robust stochastic Ito ensemble of neural networks achieve an accuracy of 73.91% on the CIFAR-10 dataset against the PGD attack with e = 2.0 under the L2 norm, while the accuracy of Madry’s robustness toolbox on the same attack is 18.59%. Similarly, our stochastic Ito ensemble of neural networks achieves an accuracy of 79.66% on PGD attack with e = 16/255 under the L∞ norm, while the accuracy of Madry’s robustness toolbox on the same attack is 18.13%. The Ito ensemble trained on ImageNet achieves an accuracy of 28.53% against PGD attacks under the L∞ norm with e = 16/255 and accuracy of 65.74% under the L2 norm with e = 3.0, respectively. This significantly improves state-of-the-art accuracy of 5% and 35.16% for Madry’s robustness tool against the same PGD attacks under the L∞ and L2 norms, respectively. Further, our approach achieves these high robustness values without any explicit adversarial training or a significant loss of accuracy on benign inputs.
The Icelandic Wordweb

A single RDF file housing the Icelandic Wordweb in an LT-appropriate format. The Wordweb's features have been encoded with OntoLex and SKOS, with the new version designed in such a way as to replicate all core functionality of the original, and offer an extensive suite of new options for inspection and research.Ein stok RDF skra sem hýsir Islenskt orðanet a maltaeknivaenlegu sniði. Eiginleikar Orðanetsins hafa verið utfaerðir með OntoLex og SKOS, og nýja utgafan honnuð a þann hatt að hun baeði styður alla grunnvirkni upprunalega Orðanetsins, auk þess að bjoða itarlegar og fjolbreyttar nýjar leiðir til að kanna innihald þess og beita þvi til rannsokna.
A Self-Constructed CNN Classifier for Keyhole Detection and Location

With the development of the demand for robots to have the ability of opening and unlocking, a real-time detection algorithm for keyhole is proposed in this paper, which combines a fast circle detection based on circle edge feature and a keyhole classifier using self-constructed CNN. The algorithm needs three steps: CNN construction, circle detection and keyhole classification of local circle image. Firstly, collecting keyhole images as positive samples, collecting non-keyhole images and random images as negative samples. Then, constructing a CNN classifier to solve the classification problems and training it based on the samples collected before. Consequently, detecting the real-time collected image whether it has circles and where they are. Furthermore, extract images might have circle area and using the CNN keyhole classifier trained before to determine whether each of them is a keyhole image or not. The experimental results show that using the same training and testing samples, the accuracy of self-constructed CNN classifier(>98 %) is about 1 % lower than that the CNN classifier using AlexNet migration learning network (>99 %), but its time use is about 95% less than the AlexNet and its net size is about 99.5% less than the AlexNet.
Consortia, Features, Major Objectives, Models and their Benefits for Libraries in Digital Era

A Library Consortium is a group of Libraries who partner to coordinate activities, share resources, and combine expertise. The International Coalition of Library Consortia is an informal discussion group of such consortia. Library consortia offer significant advantages to increasingly strapped libraries. This paper is mainly highlights for Consortia definition, features, Major Objectives, Needs, functions, models, funding, Licensing, Advances, factors, goals in detailed about consortia etc.,
Exertion Of Community Associate In Veterinary Field

As a bustling vet, it tends to be amazingly testing to create and cultivate associations with our customers in the brief timeframe we have during an arrangement. Examination has appeared, nonetheless, that it is these connections that make faithful customers, and that those unwavering customers will need to impart their experience to their own loved ones through informal. Things being what they are, how might we defeat this obstacle of time imperatives and still associate with our customers? The appropriate response is straightforward: through veterinary online media promoting. The point of this paper is to feature only a couple of the numerous advantages Community Associate can have for your training.
NASA Researcher Finds Solutions Using Questions

NASA researcher Natalia Alexandrov works from home near the Langley Research Center in Virginia. She and Danette Allen are co-principal investigators on ATTRACTOR, an activity designed to determine how autonomous systems like those intended for drones can be certified to be trusted to safely fly.
Regression from Upper One-side Labeled Data

We address a regression problem from weakly labeled data that are correctly labeled only above a regression line, i.e., upper one-side labeled data. The label values of the data are the results of sensing the magnitude of some phenomenon. In this case, the labels often contain missing or incomplete observations whose values are lower than those of correct observations and are also usually lower than the regression line. It follows that data labeled with lower values than the estimations of a regression function (lower-side data) are mixed with data that should originally be labeled above the regression line (upper-side data). When such missing label observations are observed in a non-negligible amount, we thus should assume our lower-side data to be unlabeled data that are a mix of original upper- and lower-side data. We formulate a regression problem from these upper-side labeled and lower-side unlabeled data. We then derive a learning algorithm in an unbiased and consistent manner to ordinary regression that is learned from data labeled correctly in both upper- and lower-side cases. Our key idea is that we can derive a gradient that requires only upper-side data and unlabeled data as the equivalent expression of that for ordinary regression. We additionally found that a specific class of losses enables us to learn unbiased solutions practically. In numerical experiments on synthetic and real-world datasets, we demonstrate the advantages of our algorithm.
Workshop E-Learning Berbasis Web E-Class Untuk Meningkatkan Interaksi Pembelajaran Jarak Jauh Di Sdn 5 Tapanrejo Banyuwangi

Schools with a distance learning process using digital media are a school concept that takes advantage of the development of information technology in their interactions. Schools with distance learning cover various aspects of interaction such as providing material, discussion, evaluation, and others. To reach the school, effective interaction media such as E-Learning Web-based E-Class are used. The E-Class Web-Based E-Learning Workshop was attended by 20 people consisting of 6th-grade students and teachers as the main elements and most have the maturity level using digital devices at the elementary school level. During this workshop, participants were given material about IoT, distance learning, Dating Learning Activity Design, and E-Class Web-Based E-Learning. In the practical session, participants get the use of E-Class Web-Based E-Learning. E-Learning Web-Based E-Class which is used such as school activity schedules, teacher teaching, joint discussions, and online joint exams. The purpose of this workshop is to increase knowledge and expertise in the field of E-Learning Web-Based E-Class which can improve the interaction and quality of teaching and learn in schools. The method used is theoretical learning with several material sessions and practical with evaluation tests so that it can be concluded from these activities to increase student interest in developing and continuing mobile web-based applications at school and outside of school.
RETRACTED: Research on English Teaching Model Based on Computer Big Data

Abstract Today is the Internet era, and our country is currently developing at a high speed, for the training of talents is also very important, in the era of globalization, the country for talent language level requirements are constantly improving. The traditional language teaching mode is no longer suitable for the development of the needs of language teaching mode should also be combined with the development of The Times. University is higher education institutions, but also introduce advanced language teaching mode, the development of the Internet for language teaching is a very rare opportunity and challenge, the Internet to bring great convenience and shortcut English teaching mode, teaching efficiency can be greatly improved, the rise of big data offers language teaching model. In this paper, the deficiencies of English teaching, combined with the characteristics of the Internet, through the relationship between the two online English teaching theory.