Reviewerly/exHarmony huggingface dataset:
PENERAPAN MODEL PEMBELAJARAN TEAMS GAMES TOURNAMENT UNTUK MENINGKATKAN KEAKTIFAN BELAJAR SISWA PADA PEMBELAJARAN TEMATIK KELAS IV SEKOLAH DASAR NEGERI SADE BARABALI LOMBOK TENGAH

Background of this research problem is the low activity of student learning in Thematic learning in class IV SDN Sade Barabali Lombok Tengah that caused by the low quality of learning that used by teacher, where the lack of methods, models and strategies that used by the teacher during the process of thematic learning led to boredom on students and become passive during the learning process. This research is a Classroom Action Research (PTK) with qualitative descriptive method that using a qualitative approach reinforced by quantitative approach, by talking the background in SDN Sade Barabali Lombok Tengah, The subject of this research is the fourth grade students in SDN Sade Barabali Lombok Tengah, while the object of this research is the Application of The Teams Games Tournament Learning Models to Increase The Learning Activity in Cita-Citaku theme. This research was conducted in two cycles and through 4 stages including: (1) Planning, (2) Implementation, (3) Observation and (4) Reflection. The data obtained through observation, interviews, and documentation. Data analysis is done by using qualitative analysis that consist of data collection, data reduction, data presentation, conclusion of verification. The result of this research indicates that the application of models Teams Games Tournament can increase students learning activity in learning process. Activity can be measured from the evaluation cycle I, and cycle II of 63% and 77,5% cycle II. While the improvement of student activity can be measured from each cycle, student activity in cycle I is 3,1 with the active approaching category and student activity in cycle 4,1 with the approaching very active category. Thus the result of this research in Dasar Negeri School Sade Barabali Lombok Tengah has been achieved with good.
IMPACT OF EXTENSIVE READING ON ENGLISH LANGUAGE COMPETENCE: A CASE STUDY OF JUKWAA SENIOR HIGH SCHOOL

The study sought to assess the impact of extensive reading on English language competence. The study focused on Jukwaa Senior High School. The primary objective of this study is to assess the impact of extensive reading on English language competence in Jukwaa Senior High. A descriptive design was employed for this study and a quantitative methodology was used. Data collection tools employed in this study was questionnaire. Data was sorted and analyzed using IBM SPSS version 23.0. Descriptive statistics like percentages and frequency were used to develop tables and graphs to explain the findings. Correlation and Regression Analysis were used to study the strength of a relationship between the extensive reading and then the English language competence. The study revealed a strong significant and positive relationship between extensive reading and English language competence. The study also shows that extensive reading predicts 79.3% variance in explaining English language competence. Finally, the study also revealed a linear relationship between age, extensive reading and English language competence. The study recommends that extensive reading should be a compulsory reading activity at all levels of Senior High schools in Ghana in order to improve their vocabulary knowledge. This should be done by providing students with texts on African and non-African fictions and making it compulsory for student to read at least one text in a week. Secondly, Students’ reading progress should be monitored by keeping reading diaries indicating varieties of registers or vocabularies encountered and summaries of books read. They should also be reinforced by awarding them stars for books read.
Abstract 1160: Multi-scale deep learning feature distributions distinguish invasive ductal and lobular breast cancer slides

Abstract Advances in digital pathology have streamlined use of deep learning methodologies to connect morphology and phenotype in hematoxylin and eosin(H&amp;E) stained images. Whole slide images (WSIs) are gigantic, necessitating a tile-based analysis: a WSI is broken into smaller images called tiles, each tile is analyzed separately, and tile level information are then combined to get a slide level prediction. Context aware models integrating tiles of different sizes have been used to combine morphological features present at different scales, and attention-based pipelines have successfully been used to combine tile level information. Pipelines that combine attention-based and context aware learning are less studied. While both methodologies have shown utility when used individually, models that combine both tend to suffer from a large number of parameters to estimate, which frustrates reliable training. Here we show percentiles of deep learning features extracted at different scales serve as a crude multiscale attention mechanism. The proposed model separates TCGA-BRCA invasive ductal and lobular carcinoma FFPE WSIs (AUC=0.85±0.02), which improves upon the performance of the fully tile-based model of [1] (AUC=0.80 ±0.04). The proposed approach enjoys higher average AUC and lower standard deviation across multiple random train/test splits of data. Our results suggest percentile-based feature construction is an interesting alternative when reliable training of fully deep learning models is challenging. References: [1] Noorbakhsh, J., Farahmand, S., Foroughi pour, A. et al. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat Commun 11, 6367 (2020). https://doi.org/10.1038/s41467-020-20030-5 Citation Format: Patience Mukashyaka, Ali Foroughi Pour, Jeffrey H. Chuang. Multi-scale deep learning feature distributions distinguish invasive ductal and lobular breast cancer slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1160.
Methods to Visualize Structural and Functional Changes at Synapses

ARTICLES DISCUSSED: Pekarek, B. T., Hunt, P. J., Belfort, B. D. W., Liu, G., Arenkiel, B. R. Imaging and quantification of intact neuronal dendrites via CLARITY tissue clearing. Journal of Visualized Experiments: JoVE. (170), e62532 (2021). Frankfurt, M., Bowman, R. Rapid Golgi stain for dendritic spine visualization in hippocampus and prefrontal cortex. Journal of Visualized Experiments: JoVE. (178), e63404 (2021). Śliwińska, M. A., Cały, A., Szymański, J., Radwańska, K. Serial Block-Face Scanning Electron Microscopy (SBEM) for the study of dendritic spines. Journal of Visualized Experiments: JoVE. (176), e62712 (2021). Chan, U., Gautam, D., West, A. E. Utilizing in vivo postnatal electroporation to study cerebellar granule neuron morphology and synapse development. Journal of Visualized Experiments: JoVE. (172), e62568 (2021). Cuentas-Condori, A., Miller III, D. M. Imaging dendritic spines in Caenorhabditis elegans. Journal of Visualized Experiments: JoVE. (175), e62676 (2021).
Creating an Interactive Dashboard to Support Middle School Teachers’ Implementation of a Technology-Supported Problem-Based Learning Program

In this design case, we describe our experience of designing and creating a teacher’s dashboard as an accompanying tool to support sixth-grade teachers who implement a technology-supported problem-based learning program on sixth-grade space science in their classrooms. We describe in detail the background of this design case, our motivation to create this teacher’s dashboard, and our iterative design process over a period of five years. Our design process consists of three phases from the idea generation phase to both low- and high-fidelity versions of the prototyping phase to three iterations of the implementation phase. We present our rationales for making various design decisions over the process and our reflections on this design experience including the challenges we have encountered.
“I Survived”: Navigating a Fully Virtual Hiring Process

The hiring process in academic libraries is often complex.The COVID-19 pandemic only complicated things further, causing the customary full-day interview and job talk to shift towards fully virtual or hybrid models.After graduating in 2020, I secured a tenure-track academic librarian role.This poster features lessons learned from my experiences in pandemic-era interviews that may help job seekers and established professionals looking to refresh their toolkit.
VOICE AND TEXT BASED VIRTUAL PERSONAL ASSISTANT FOR DESKTOP

A virtual personal assistant (VPA) is a computer application that simulates human-to-machine communication through the Internet. Virtual personal assistants mimic and process human interactions, allowing users to converse as if they were speaking with real people. The goal of the project is to use facial recognition to offer security for a virtual personal assistant. Users have the option of communicating via voice or text. The virtual personal assistant will be able to tell the time, tell jokes, take screenshots, take photos using the webcam, search Wikipedia, open calendar, open applications, Google search, play music, perform mathematical calculations, volume control, send emails, and much more, and it will be very user-friendly. Virtual assistants will play a vital role in reducing human effort and also time spent on any given task.
Fast and Simple Collection of Adherent Fibers from Tape Lifts by Transferring Solvent using Capillary Action

Fibers could be usefully employed to prove the interrelationship between perpetrators, victims, or crime scenes in death or sexual violence cases that occur through mutual contact. The most common method of collecting fibers at crime scenes is tape lifting, and for various analyses, fibers must be separated from the tapes. Various methods of separating fibers are being used, but most of them require a lot of time and effort. Thus simpler and faster collecting method is required. By transferring the solvent to a fiber attached site by using the capillary action of tweezers to the area to which the fiber is attached, it is possible to immediately collect the fiber with tweezers after dissolving the adhesive component. In addition, it can significantly reduce the harmfulness caused by solvent inhalation due to a trace amount of solvent transfer by capillary action. This solvent transfer method by capillary action can be applied to the V-shaped cutting method for individual fiber preparation and the fiber attached surface collection one for a large number of fibers to enable fast collection of fibers.
Cooperative auto-classifier networks for boosting discriminant capacity

Although the training accuracy of deep learning using a deep structure is high, the depth of the deep-learning structure is directly proportional to the generalization error. To address this issue, we propose the auto-classifier, a novel classifier that automatically exploits dimensionality reduction. The proposed classifier contains both classifier and generator networks. It is dedicated to generating separable outcomes based on the label and implicitly capturing the latent variable; simultaneously, the generator network must be able to reconstruct the original data based on the given latent variable. We introduce a cooperative learning mechanism with a new loss function that enables the classifier and generator networks to cooperate to achieve the aforementioned objectives. Extensive experiments were conducted using benchmark datasets. The results revealed that the accuracy of the proposed classifier, without any data augmentation, distortion, or pretraining mechanism, was very competitive with the existing state-of-the-art benchmark datasets.
TECHNOLOGY IDENTIFICATION IN INDUSTRY 4.0 WITH THE HELP OF TEXT MINING

The point of this paper is to recognizable proof of highlighted innovation in Industry 4.0. approach: The most common way of distinguishing the dynamic/logical text with the assistance of text mining to investigate in this field. Suppositions of own iterative strategies for examine the logical texts we propose, with the utilization of R language, tokenization, lemmatization, n-grams and correspondence examination. The suspicions of the proposed strategy were utilized to dissect the 40 most frequently cited articles filed in the Web of Science. research: Based on the got results, 4 driving innovations were recognized. These are Cloud Computing, Internet of Things, Cyber-actual System and Big Data. Creativity: This paper tells a unique technique for distinguishing the highlighted advancements that are utilized in Industry 4.0. The strategies depends on text mining. Keywords: Text mining, industry 4.0, data and correspondence innovation, classifying and design.
Ensuring Privacy in Natural Language Processing: A PRISMA Oriented Literature Review

Using Natural Language Processing (NLP) as a discipline of machine learning, organizations can better organize their data in order to better represent their internal knowledge. To make NLP models easier to re-use in other contexts, they should be protected accordingly. This raises the question of the appropriate privacy-ensuring technology (PET). To be able to address this question this paper conducts a literature review regarding the ensuring of privacy in NLP following the PRISMA framework. After following the identification process of significant sources, 22 valuable studies were selected. Some of these studies have shown promising results, however the field of privacy ensuring in NLP is still uncategorized with different approaches difficult to compare.
MTL-DAS: Automatic Text Summarization for Domain Adaptation

Domain adaptation on text summarization task is always challenging, which is caused by the lack of annotated data in the target domain. Previous methodologies focused more on introducing knowledge in the target domain and shifted the model to the target domain. However, they mostly studied the adaptation to a single low-resource domain, which restricted practicality. In this paper, we propose MTL-DAS, a unified model for multidomain adaptive text summarization, which stands for Multitask Learning for Multidomain Adaptation Summarization model. Combined with BART, we investigate multitask learning method to enhance the generalization ability in multidomain. We adapt the ability of detect summary-worthy content from source domain and obtain the knowledge and generation style in target domains by text reconstruction task and text classification task. We carry out the domain adaptation ability experiment on AdaptSum dataset, which includes six domains in low-resource scenarios. The experiment shows the unified model not only outperforms separately trained models, but also is time-consuming and requires less computational resources.
Indonesia-Finland Renewable Energy Development Cooperation

As the largest archipelagic country globally, Indonesia requires much energy to develop and preserve development and its natural wealth. As a biologically rich country, Indonesia has eight (8) renewable energy sources that must be processed and then utilised for maximum energy fulfilment throughout the archipelago and must be adjusted to its geographical specifications. The declaration of cooperation between Indonesia and Finland was signed by diversifying renewable energy sources from solar energy, bioenergy, and ocean wave energy. The implementation based on the MoU is the first to develop practical and environmentally sound technology using wind energy and ocean wave energy. The application of ocean wave energy as an alternative source of electrical energy by using technology. Second, the development of natural resources and technology in the electricity sector. This condition is done by building a micro hydropower plant. This research shows that cooperation between the two countries is quite effective. Both countries depend on each other to fulfil their respective interests. Because of that, there has been an effect between countries to re-enhance cooperation in the energy sector for a relatively long period, starting again from 2011–to 2014.
How smart clothing can mediate the space between users and their environment, a case study using face masks

Augmenting clothes with sensors and actuators and turning them into smart clothes, offers new possibilities to mediate the space between users and their environment.The current COVID pandemic provides an ideal research opportunity, given that it challenges conventional norms of personal distance.We developed hairs with LEDs showing six types of dynamic behaviour as a research vehicle and mounted them onto face masks, commonly used during the pandemic.A qualitative phenomenological study was conducted with six subjects.The interview data from their experiences is analysed using the 'framework of context' from environmental psychology.We uncover three modes by which smart clothes can influence the userenvironment context: to increase the perception of the body; to alter the perception of space; or as a medium to communicate with our environment.These findings are interpreted to discuss new opportunities for the design of smart clothing that play an active role in mediating the user-environment context.
Integrated Metasystem for Fourier Optics and Machine Learning

We demonstrate the integrated metasystem on the silicon photonics platform. The integrated metasurface can tailor the phase and the amplitude of the transmitted light. Fourier transform can be achieved by an integrated metalens, and more complex applications such as analog computing and image classification can be achieved by metasystem with multiple layers of metasurfaces.
Manifestations of Madness in Literature, The Novel “In the Dark” As A Model

Literature is a reflection of the state of the society in which it lives, adopts its causes, and seeks to achieve its aspirations and hopes, so the literati were interested in the details of the actors in society, and perhaps the crazy person is one of the most important of these characters. These characters are in literary works, in the theater we find truly crazy characters and others fabricated to reveal some secrets, as for the novels, they are not without them, and our sample is a novel (In the Dark) full of an integral crazy character who delicately performs the author’s message in abuse of the ruling authority at the time and the prevailing customs and traditions as well.
Knowledge, attitudes and practices of caregivers and laboratory agents on the pre-analytical phase of haematological analyses in Burkina Faso

The pre-analytical phase of biological analyses is critical as it registers the most errors encountered within the testing process. This study aimed at assessing the knowledge, attitudes and practices of healthcare workers on the pre-analytical phase in haematological laboratory. A cross-sectional study using an auto-administered questionnaire and direct observations of haematological analyses’ prescribers, blood samples and laboratory staff were conducted from June to August 2020. The questionnaires were designed to gather the socio-professional characteristics, knowledge and attitudes of participants. The observation grids were focused on their practices in haematological analyses requesting, blood samples drawing, transportation and reception at the laboratory. A total of 388 respondents were included. Their average seniority in the profession were 11.4 ± 3.8 years and 13.6% received refreshing training in the last three years. All the laboratory agents, 94.1% of analyses’ prescribers and 76% of blood samplers had good knowledge and attitudes. Moreover, 83% of the request forms, 29.2% of blood samples collection, transportation and 74.8% of blood specimens’ reception at the laboratory were rated good. This study reported some inadequate knowledge, attitudes and practices in the pre-analytical phase that call for reinforcement of basic and continuing trainings and implementation of rigorous technical procedures.
[Long short-term memory and Logistic regression for mortality risk prediction of intensive care unit patients with stroke].

To select variables related to mortality risk of stroke patients in intensive care unit (ICU) through long short-term memory (LSTM) with attention mechanisms and Logistic regression with L1 norm, and to construct mortality risk prediction model based on conventional Logistic regression with important variables selected from the two models and to evaluate the model performance.Medical Information Mart for Intensive Care (MIMIC)-Ⅳ database was retrospectively analyzed and the patients who were primarily diagnosed with stroke were selected as study population. The outcome was defined as whether the patient died in hospital after admission. Candidate predictors included demogra-phic information, complications, laboratory tests and vital signs in the initial 48 h after ICU admission. The data were randomly divided into a training set and a test set for ten times at a ratio of 8 ∶2. In training sets, LSTM with attention mechanisms and Logistic regression with L1 norm were constructed to select important variables. In the test sets, the mean importance of variables of ten times was used as a reference to pick out the top 10 variables in each of the two models, and then these variables were included in conventional Logistic regression to build the final prediction model. Model evaluation was based on the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. And the model performance was compared with the forward Logistic regression model which hadn't conducted variable selection previously.A total of 2 755 patients with 2 979 ICU admission records were included in the analysis, of which 526 recorded deaths. The AUC of Logistic regression model with L1 norm was statistically better than that of LSTM with attention mechanisms (0.819±0.031 vs. 0.760±0.018, P < 0.001). Age, blood glucose, and blood urea nitrogen were at the top ten important variables in both of the two models. AUC, sensitivity, specificity, and accuracy of Logistic regression models were 0.85, 85.98%, 71.74% and 74.26%, respectively. And the final prediction model was superior to forward Logistic regression model.The variables selected by Logistic regression with L1 norm and LSTM with attention mechanisms had good prediction performance, which showed important implications on the mortality prediction of stroke patients in ICU.
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models

Cross-lingual transfer (CLT) is of various applications.However, labeled cross-lingual corpus is expensive or even inaccessible, especially in the fields where labels are private, such as diagnostic results of symptoms in medicine and user profiles in business.Although being lack of labels, there are off-theshelf models in these sensitive fields.Instead of pursuing the original labels, a workaround for CLT is to transfer knowledge from the off-the-shelf models without labels.To this end, we define a novel CLT problem named FreeTransfer-X that aims to achieve knowledge transfer from the off-the-shelf models in rich-resource languages.To address the problem, we propose a 2-step knowledge distillation (KD, Hinton et al., 2015) framework based on multilingual pre-trained language models (mPLM) 1 .The significant improvement over strong neural machine translation (NMT) baselines demonstrates the effectiveness of the proposed method.In addition to reducing annotation cost and protecting private labels, the proposed method is compatible with different networks and easy to be deployed.Finally, a range of analyses indicate the great potential of the proposed method.