Reviewerly/exHarmony huggingface dataset:
A COMPUTER-VISION BASED TOOL FOR THE AUTOMATIC IDENTIFICATION OF BLASTOCYSTS’ REGIONS. A STEP CLOSER TO DECODING TIME-LAPSE?

to assess the capabilities of a computer-vision method to automatically identify blastocysts´ regions, from static blastocysts’ micrographs Prospective analysis of retrospectively obtained blastocysts’ micrographs from two IVF centers a carefully curated dataset of 370 blastocyst images was created from the first 500 images generated at two IVF clinics over six consecutive months. All images were manually segmented identifying regions of interest. From this ground truth, 10% of the images were selected testing. All images were preprocessed, and then passed through 21 different filters (e.g. entropy, Gaussian blur, Laplacian, and Sobel) and convolved by 41 texture energy-kernels each, to identify a total of 861 textures. Data-augmentation was performed on the training set. 50 pixels were randomly select per blastocyst’s region of interest , and its texture-based feature-vectors extracted (database of 300,400 data-points), used to training a neural-network (three 400 nodes layers, Adam optimizer, and categorical cross-entropy as the loss-function), during 500 epochs ( validation split of 10%), a callback learning-rate reduction (factor of 0.5, patience of 25 epochs, and validation loss as monitor) and an early stopping callback (patience of 60 epochs, validation loss as monitor, and a minimal delta of 0.001). With this texture-based model we computed five masks for each image within the training-set, and then trained an autoencoder (AE) using the ground-truth masks. This AE consist of three convolutional and three max-pooling layer for the encoding, three convolutional, and three up-sampling layers for the decoding. This model was trained with mean squared error as the loss-function, RMS prop as optimizer, early stopping, and learning rate-reduction callbacks (100 epochs), with a validation-split of 10%. The testing dataset was assessed using the Sorensen-Dice coefficient (SDC). Following data-augmentation, a total 1,332 images became available for training the system. Overall, the model yield a mean SDC of 0.8744. The blastocoele, background, inner cell mass, trophectoderm, and zona pellucida respectively achieved 0.8399, 0.9690, 0.5684, 0.6586, and 0.7424 respectively. Fully automation of the decision making process when using time-lapse has been challenging, partly due to the difficulties that automated blastocyst segmentation present. In this study, we present with an innovative approach to the challenge of blastocyst segmentation with encouraging. The accurate identification of blastocyst regions presented in our results, suggest that blastocyst micrograph segmentation is feasible with a combination of artificial-vision techniques, and an autoencoder to improve its initial result. This approach could pave the way for building further computational tools that might automatically track the regions of interest through time of any given blastocyst.
InfoGuides: Primary & Secondary Sources: Primary or Secondary Resource?

This brief guide will help you understand the difference between primary and secondary sources AND show you ways to find either or both.
Workshop: Applying test equating methods using R

The goal of this workshop is to introduce equating and to provide the necessary tools to be able to perform different equating methods in practice by using available R packages. The aim of test equ ...
Bi-Real Net V2: Rethinking Non-linearity for 1-bit CNNs and Going Beyond

Binary neural networks (BNNs), where both weights and activations arebinarized into 1 bit, have been widely studied in recent years due to its greatbenefit of highly accelerated computation and substantially reduced memoryfootprint that appeal to the development of resource constrained devices. Incontrast to previous methods tending to reduce the quantization error fortraining BNN structures, we argue that the binarized convolution process ownsan increasing linearity towards the target of minimizing such error, which inturn hampers BNN's discriminative ability. In this paper, we re-investigate andtune proper non-linear modules to fix that contradiction, leading to a strongbaseline which achieves state-of-the-art performance on the large-scaleImageNet dataset in terms of accuracy and training efficiency. To go further,we find that the proposed BNN model still has much potential to be compressedby making a better use of the efficient binary operations, without losingaccuracy. In addition, the limited capacity of the BNN model can also beincreased with the help of group execution. Based on these insights, we areable to improve the baseline with an additional 4~5% top-1 accuracy gain evenwith less computational cost. Our code will be made public athttps://github.com/zhuogege1943/birealnetv2.
Subject and Course Guides: COVID-19 Details for UIC Libraries: Returning Materials

This guide is to keep people updated on requirements for entering the library during Covid-19 restrictions
Can Community Plans Really Talk? Integrating and Strengthening Communications Through Artificial Intelligence

This paper discusses the need and ability to enhance risk reduction through an integrated planning approach by applying Artificial Intelligence (AI) and Machine Learning (ML). By introducing this important application, we describe the process by which AI and ML can be applied to enhance disaster preparedness, and to ensure disaster risk reduction measures and policies are integrated throughout an entire jurisdictional planning framework. As there are many potential planning documents and initiatives existent within a jurisdictional framework, and potential challenges associated with performing the process manually, this paper describes a two-phase approach to automating the process. By ‘Building the Brain’ and ‘Processing the Plans’, the automation process will eliminate or significantly ease the challenges associated with performing the plan review and integration process manually. Using AI and ML to perform the plan review and integration process will help quickly identify specific gaps between different documents, and generate appropriate and applicable recommendations made to close those gaps. Automation of the plan review and integration process provides jurisdictions with a pro-active and high-tech approach to enhancing overall disaster resilience and mitigating the hazard impacts, through enhanced intergovernmental coordination.
Human-Centric Object Interactions - A Fine-Grained Perspective from Egocentric Videos

This talk aims to argue for a fine(r)-grained perspective onto human-object interactions. Motivation: Observe a person chopping some parsley. Can you detect the moment at which the parsley was first chopped? Whether the parsley was chopped coarsely or finely? The skill level of the person chopping? Finally, can the knowledge learnt from this task be useful to understand a video of another person slicing an apple? Problem statement: Finer-grained understanding of object interactions from video is the topic of this talk. This will be primarily studied from a first-person (or egocentric) video, that is video captured using a wearable (head-worn or glass-worn) audio-visual sensor (camera). Approach: Using multi-modal footage (appearance, motion, audio, language), I will present approaches for determining skill or expertise from video sequences [CVPR 2019], assessing action "completion" ? i.e. when an interaction is attempted but not completed [BMVC 2018], dual-domain and dual-time learning [CVPR 2020, CVPR 2019, ICCVW 2019] as well as multi-modal fusion using vision, audio and language [CVPR 2020, ICCV 2019, BMVC 2019]. All project and publication details at: https://dimadamen.github.io/index.html#Projects I will also introduce EPIC-KITCHENS-100, the largest egocentric dataset in people's homes. The dataset now includes 20M frames of 90K action segments and 100 hours of recording fully annotated, based on unique annotations from the participants narrating their own videos, thus reflecting true intention. Dataset available from: http://epic-kitchens.github.io
HSFE Network and Fusion Model based Dynamic Hand Gesture Recognition

Dynamic hand gesture recognition(d-HGR) plays an important role in human-computer interaction(HCI) system.With the growth of hand-pose estimation as well as 3D depth sensors, depth, and the hand-skeleton dataset is proposed to bring much research in depth and 3D hand skeleton approaches.However, it is still a challenging problem due to the low resolution, higher complexity, and self-occlusion.In this paper, we propose a hand-shape feature extraction(HSFE) network to produce robust hand-shapes.We build a hand-shape model, and hand-skeleton based on LSTM to exploit the temporal information from hand-shape and motion changes.Fusion between two models brings the best accuracy in dynamic hand gesture (DHG) dataset.
Bangladesh: Stifling expression using Digital Security Act must not be the norm to address COVID-19 pandemic

In times of crisis, people's health depends at minimum on access to information both off and online. Silencing journalists and activists and blocking websites, is not an effective public health strategy.
Research Guides: Finding Primary Sources for History: Citations

Research strategies and recommended library resources for finding primary and historical sources.
Language Design for Synthesizing Diagrams, Layouts, and Invariants

Author(s): Sarracino, John | Advisor(s): Polikarpova, Nadia | Abstract: Program synthesis is a promising area of research concerned with automatically producing program implementations from high-level specifications of their behavior. Using synthesizers, programmers can write declarative and natural specifications instead of low-level implementations, and the synthesizer ensures that the resulting program does not contain errors.The promise of synthesis is both elegant and compelling: wouldn't it be great if we didn't need to code and the compiler could magically transform a clearly, obviously correct specification into an executable implementation?While this promise is enticing, alas there is no free lunch. Synthesis is fundamentally a search problem over the unbounded space of possible implementations. As a consequence, applications of program synthesis must bound the search space in an intelligent way, typically through clever language design of the space of possible implementations. Broadly speaking research in this field involves a tradeoff between the generality of the implementation language (i.e. how domain-specific possible programs are), and the completeness of the synthesizer (i.e. what types of programs the synthesizer can find). In this thesis, I develop synthesizers in three different problem domains and explore the tradeoff space, from a very domain-specific and complete synthesizer to a general, domain-agnostic synthesizer that significantly restricts the space of output solutions.In particular, I present synthesis algorithms and languages for: 1) enabling non-programmers to add interactive behavior to static diagrams; 2) inferring dynamic visual layouts from input-output examples; 3) simpler and robust imperative programs through automatically maintained data invariants.Pleasingly in all cases, I demonstrate ways in which a synthesizer can deliver on the promise of easing and eliminating the burden of programming.
The Faculty of Computing building will be operational next October

Pekan, 9 September 2020 - The Faculty of Computing (FK) building, Universiti Malaysia Pahang (UMP) in Pekan will be fully operational soon when it is officially handed over by the Public Works Department of Malaysia (JKR) to the university in a ceremony held at the Lecture Hall, FK recently.
PENGARUH LKS DALAM MODEL PEMBELAJARAN CTL TERHADAP HASIL BELAJAR DAN KEMANDIRIAN BELAJAR SISWA PADA MATERI LARUTAN ELEKTROLIT DAN NON ELEKTROLIT

Abstract: INFLUENCE OF LKS IN CTL LEARNING MODEL TOWARDS LEARNING AND INDEPENDENCE LEARNING STUDENTS IN CHAPTER OF ELECTROLITE AND NON ELECTROLITE SOLUTION. This study aims to determine the effect of the LKS media in the CTL learning model on learning outcomes and student learning independence on electrolyte and non-electrolyte solution material. The sampling technique uses simple random sampling. This study uses a pretest-posttest control group design. Hypothesis testing uses independent samples t-test with the application of SPSS 21. The results of the study show: (1) test requirements analysis, the data are normally distributed and homogeneous; (2) hypothesis test I, significant (1-tailed) 0.1725&gt; 0.05 then Ho is accepted; and (3) hypothesis test II, significant (1-tailed) 0.005 &lt;0.05 then Ho is rejected. It can be concluded that the learning outcomes of students using the LKS media in the CTL learning model are not higher than the learning outcomes of students who are taught without using the LKS media in the CTL learning model on electrolyte and non electrolyte solution material, but the learning independence of students taught using the LKS media in the learning model CTL is higher than the independence of learning students who are taught without using the LKS media in the CTL learning model on the material electrolyte and non-electrolyte solutions.Keywords: LKS Media, CTL Learning Model, Student Learning Outcomes, Student Learning Independence, Electrolyte and Non-Electrolyte SolutionsAbstrak: PENGARUH LKS DALAM MODEL PEMBELAJARAN CTL TERHADAP HASIL BELAJAR DAN KEMANDIRIAN BELAJAR SISWA PADA MATERI LARUTAN ELEKTROLIT DAN NON ELEKTROLIT. Penelitian ini bertujuan untuk mengetahui pengaruh media LKS dalam model pembelajaran CTL terhadap hasil belajar dan kemandirian belajar siswa pada materi larutan elektrolit dan non elektrolit. Teknik pengambilan sampel menggunakan sampling random sederhana. Penelitian ini menggunakan rancangan pretes postes control group design. Uji hipotesis menggunakan independent samples t-test dengan aplikasi SPSS 21. Hasil penelitian menunjukkan : (1) uji persyaratan analisis, data berdistribusi normal dan homogen; (2) uji hipotesis I, signifikan (1-tailed) 0,1725 &gt; 0,05 maka Ho diterima; dan (3) uji hipotesis II, signifikan (1-tailed) 0,005 &lt; 0,05 maka Ho ditolak. Dapat disimpulkan bahwa hasil belajar siswa yang diajar menggunakan media LKS dalam model pembelajaran CTL tidak lebih tinggi daripada hasil belajar siswa yang diajar tanpa menggunakan media LKS dalam model pembelajaran CTL pada materi larutan elektrolit dan non elektrolit, tetapi kemandirian belajar siswa yang diajar menggunakan media LKS dalam model pembelajaran CTL lebih tinggi daripada kemandirian belajar siswa yang diajar tanpa menggunakan media LKS dalam model pembelajaran CTL pada materi larutan elektrolit dan non elektrolit. Kata kunci: Media LKS, Model Pembelajaran CTL, Hasil Belajar Siswa, Kemandirian Belajar Siswa, Larutan Elektrolit dan Non Elektrolit
A quantum autoencoder: using machine learning to compress qutrits

The compression of quantum data will allow increased control over difficult-to- manage quantum resources. We experimentally realize a quantum autoencoder, which learns to compress quantum data with a classical machine learning routine.
AI in the Media Spotlight

The use of AI technology offers many new opportunities for the media sector; in particular, it leads to an increase in productivity and efficiency to convey relevant information to appropriate viewers quickly and accurately.
LOS VIDEOJUEGOS Y SU RELACIÓN CON EL RENDIMIENTO ACADÉMICO EN JOVENES UNIVERSITARIOS. DAVID, CHIRIQUÍ:

Fecha de recepcion, febrero 2019Fecha de aprobacion, marzo 2019Paginas: 4To achieve this objective, the time they devoted to their studies, the time they devote to video games was determined, the effects of the continuous use of his entertainment and the type of academic performance they show in their clases were described.  A non-experimental, transversal methodological design with a descriptive and correlational qualitative approach was used.  The results showed that there is a close relationship between the use of videogames and the academic performance of these young people, finding that they occupy more than four hours a day with this entertainment, producing time and satisfaction consumption to a large and good extent.  Measure and deliver tasks on time, be punctual and get good grades with regular weights and little measure.
The Main Trends in the Formation of a Virtual Component of Personal Anthroponymicons of Russian, English and Spanish-Speaking Peoples

This article is about the main tendencies in e-mail addresses forming in English, Spanish and Russian linguistic cultures. The aim of this article is to find the special features in e-mail naming and to compare English, Spanish and Russian e-mail addresses. The paper addresses the impact of the digital era on personal anthroponymicon. The data have been collected through a questionnaire method among native speakers including three linguistic cultures (Spanish, English, Russian) and contain their email addresses. These have been modeled according to the anthroponymic information contained in them and examined from the perspective of personal anthroponymicon development. The findings reveal current trends in including anthroponymic information in e-mail addresses typical for  different social groups in this research.
Computer Vision System to Track Moving Objects with Unknown Periodic Moving Pattems-Based on DREM Algorithm

Objects tracking system is a research field of great importance. The two main reasons for this: 1 -Attempting to replace humans with robots or automatic systems in every field wherever possible. 2 -The accuracy, effectiveness and speed of those systems compared to human performance. In this research we propose a new approach for tracking objects and predicting the future trajectory of these objects. the main idea is to observe the object for about 15 seconds, this gives us part of the moving pattern, then “Dynamic Regressor Extension and Mixing (DREM)” algorithm is used to estimate the main frequencies involved in the detected movement pattern. The estimated frequencies are used to predict the future trajectory of the object. The accuracy of this method was tested using 5DOF arm robot, which try to grip the object at each moment of its future trajectory. This research performed on a virtual object designed in “vredit” in MATLAB. The object moves on an LCD screen. The results were represented as the difference between the predicted trajectory and the real future trajectory. Results show that tracking error of a ball moves on the screen for different moving pattern is less than 1cm, where the screen stands 500mm far from arm base.
LibGuides: Library & Technology Services: Borrowing: Check-out, Inter-Library Loan & More

A resource guide with information about accessing electronic resources and Law Library and Technology services.
ARTIFICIAL SOLAR OXYGEN TREE

The reduction in oxygen levels is being felt all over the world. Oxygen deficiency leads to mental and physical disorders not only in humans but also in sea creatures. Planting trees in urban areas is almost impossible with so many skyscrapers and industries already being there. The artificial solar oxygen tree would compensate for this loss to some extent at least. The artificial solar oxygen tree systems follow the sun throughout the day to maximize energy output. The artificial solar oxygen tree is a proven different-axis auto switching technology that has been custom designed to integrate with solar modules and reduce system costs. The multiple Solar panels generates up to 25% more energy than fixed single axis mounting systems and provides a bankable energy production profile preferred by utilities.Aim: Design & Implementation of Intelligent Artificial Solar Oxygen Tree Using Embedded Technology
THE EFFECTIVENESS OF E-LEARNING APPLICATION BASED ON FRAMEWORK CODEIGNITER IN THE COVID19 PANDEMIC PERIOD (CASE STUDY STAIN TEUNGKU DIRUNDENG MEULABOH)

The rapid development of the covid-19 virus requires learning and teaching to be carried out online or remotely, the online learning process is carried out to break the chain of spreading the covid-19 virus. In the application of online learning STAIN Tengku Dirundeng Meulaboh uses an e-learning application developed with a codeigniter framework. The use of the existing MVC method in the Codeigniter framework can facilitate the development of e-learning applications that must adapt to user needs, therefore a study was conducted to determine the effectiveness of STAIN Tengku Dirundeng Meulaboh e-learning users during the Covid-19 pandemic. Measuring the effectiveness of this is done through a questionnaire by asking several questions to measure user satisfaction based on the End User Computing Satisfaction (EUCS) method. Based on the research results, it was found that 94% of e-learning users were greatly helped in the learning process and the level of user satisfaction was 85%.
LibGuides: HACC User Support: Google Apps

Tech support provides effective and timely technical support for all HACC students, faculty, and staff.
Interoperability and crisis management in pandemic outbreak scenarios an overview on different case studies of the austrian approach to tackle the SARS-CoV-2 spread and adherent management challenges

The global spread of the novel Corona Virus, SARS-CoV-2, in the first half of 2020, resulted in a massive strain of multiple and diverse crisis management structures all over the world Interoperability of governmental and non-governmental responder organizations as well as of analysis and communication infrastructure were put under a real stress test and concepts of the response to this pandemic outbreak were rapidly set up, modified, changed, and applied according to multiple integrated and changing data sources Austria, as a country with a relatively mild development of the COVID effects, will be selected as a use case provider to exemplify lessons learned This paper will give a comprehensive overview of the dynamic development of the initial and follow up steps Examples from a public service provider AGES, the Austrian Agency for Health and Food Safety, the research organization and technology provider AIT, and the first responder and social services organization Johanniter Unfallhilfe, will show lessons learned, good practice, but also challenges and open questions or gaps in the wake of this complex crisis scenario © 2019 by the authors
Library: HathiTrust Digital Library Emergency Temporary Access Service (ETAS): HathiTrust Digital Library Emergency Temporary Access Service (ETAS)

HathiTrust provides access to 1.2 million digitized titles HathiTrust provides access to 1.2 million digitized titles.
Reproducibility Companion Paper: Instance of Interest Detection

To support the replication of "Instance of Interest Detection", which was presented at MM'19, this companion paper provides the details of the artifacts. Instance of Interest Detection (IOID) aims to provide instance-level user interest modeling for image semantic description. In this paper, we explain the file structure of the source code and publish the details of our IOID dataset, which can be used to retrain the model with custom parameters. We also provide a program for component analysis to help other researchers to do experiments with alternative models that are not included in our experiments. Moreover, we provide a demo program for using our model easily.
The Library: Research Skills: Analysing and Presenting Data

Leeds Beckett University Skills for Learning Topic Guide covering research skills Leeds Beckett University Skills for Learning guide covering research skills. This page is about analysing data, and presenting data.