Reviewerly/exHarmony huggingface dataset:
Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa

This paper describes a method for retrieving evidence and predicting the veracity of factual claims, on the FEVEROUS dataset. The evidence consists of both sentences and table cells. The proposed method is part of the FEVER shared task. It uses similarity scores between TF-IDF vectors to retrieve the textual evidence and similarity scores between dense vectors created by fine-tuned TaPaS models for tabular evidence retrieval. The evidence is passed through a dense neural network to produce a veracity label. The FEVEROUS score for the proposed system is 0.126.
ANALISIS FISHBONE FAKTOR PENGHAMBAT BELUM TERLAKSANANYA PEMUSNAHAN DOKUMEN REKAM MEDISDI RSI IBNU SINA PADANG

Medical records that have expired and their usefulness can be destroyed, that is, where the medical records are physically destroyed. RSI Ibnu Sina Padang has not carried out the extermination because of the inhibiting factors of the elements of Man, Money, and Material. This study aims to determine the fishbone analysis of the inhibiting factors that have not been carried out in the destruction of medical record documents at RSI Ibnu Sina Padang. The research was conducted in June 2021 qualitatively with a phenomenological approach. The research was conducted by in-depth interviews with 5 informants, namely the head of the supporting field, the head of the medical records room, and 3 storage officers. Data processing using collaizzi method. The results of the study found that the factors that hindered the implementation of extermination were the lack of human resources to carry out retention accompanied by a team that had not been formed, training on extermination that they had never participated in, and inadequate facilities and infrastructure. Based on the conclusion, it was found that the aspects that hindered the extermination at RSI Ibnu Sina Padang were due to man and material factors. It is hoped that the management will immediately form an extermination team so that SOPs can be applied and training for the team will be carried out which will increase knowledge about the implementation of extermination.
Supply chain cybersecurity risk management, secure product development, secure manufacture, vulnerability management, and cybersecurity training

The purpose of this chapter is to define five cybersecurity capabilities (see Chapter 4 for the full list): Supply Chain Risk Management, Secure Product Development, Secure Design Transfer, Vulnerability Management, and Cybersecurity Training. This chapter is targeted primarily toward cybersecurity engineers, software and system engineers, or anyone responsible for securing medical devices during product development. This chapter presumes an intermediate to advanced knowledge of cybersecurity design principles.
Perancangan dan Pengembangan Aplikasi E-commerce Pada Catering Anugrah

The increasing use of the Internet by a society marked by soaring costumer and businessmen, to encourage the emergence of an internet service demand in excess of what can be obtained in the real world. This includes the opportunity to sell goods online commodity. Business people in general are still conducting business transactions manually through direct face to face or over the phone. But sometimes this method is not very effective, because not everyone knows the phone number of the company. Should be developed based E-commerce system that focuses on individual-based business transactions using the Internet (technology-based digital networks) with application of E-commerce transactions can be done more quickly and more intensively.&#x0D; Therefore the grace of catering company wants to advertise his wares by using websites that have a sales system based on e-commerce with the aim of selling efficiency of the way the previous sales system.&#x0D; The method used in the design and development of e-commerce in the catering grace Semarang is a Research and Development (R &amp; D) in this study only up to the stage six (6) to produce the end product is a prototype, so it was not until the stage of implementation of the product. The sixth step is Research and information collecting, Planning, Develop prelminaryformof product, Preliminary field testing, revision and Main Main product field testing.&#x0D; The results of the research prototype applications products that have been tested in the field and meet the objectives stated in the design and application development e-commerce in the catering grace Semarang
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models

Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge. However, 1) they are not effective as sentence encoders when used off-the-shelf, and 2) thus typically lag behind conversationally pretrained (e.g., via response selection) encoders on conversational tasks such as intent detection (ID). In this work, we propose ConvFiT, a simple and efficient two-stage procedure which turns any pretrained LM into a universal conversational encoder (after Stage 1 ConvFiT-ing) and task-specialised sentence encoder (after Stage 2). We demonstrate that 1) full-blown conversational pretraining is not required, and that LMs can be quickly transformed into effective conversational encoders with much smaller amounts of unannotated data; 2) pretrained LMs can be fine-tuned into task-specialised sentence encoders, optimised for the fine-grained semantics of a particular task. Consequently, such specialised sentence encoders allow for treating ID as a simple semantic similarity task based on interpretable nearest neighbours retrieval. We validate the robustness and versatility of the ConvFiT framework with such similarity-based inference on the standard ID evaluation sets: ConvFiT-ed LMs achieve state-of-the-art ID performance across the board, with particular gains in the most challenging, few-shot setups.
Distance Learning in Higher Education in France during the COVID-19 Pandemic Chapter 4

Since the beginning of the COVID-19 pandemic, students around the world have seen their schooling completely disrupted. Their teachers have had to reorganize in a hurry and even develop digital skills to be able to teach their classes synchronously but at a distance. Thanks to strong political incentives, both at national and university level, many tools have been massively deployed: digital learning management systems (e.g. Moodle), collaborative digital platforms (e.g. Google Meets, Microsoft Teams and Zoom) and even social networks (e.g. Facebook and Twitter). While this unprecedented health crisis has demonstrated the essential role of digital technologies in higher education, major questions arise regarding the quality of distance education, the learning process itself and the evaluation of knowledge and skills acquired at a distance. In this policy paper, about a hundred students from engineering schools and master's degrees in France were followed during several periods of confinement in order to provide some answers to these questions. The performance of the students in this very particular context is analyzed. The feelings of students, but also those of teachers, are also discussed, both on the use of digital technologies and on the digital transformation of higher education
Bellman-consistent Pessimism for Offline Reinforcement Learning

The use of pessimism, when reasoning about datasets lacking exhaustive exploration has recently gained prominence in offline reinforcement learning. Despite the robustness it adds to the algorithm, overly pessimistic reasoning can be equally damaging in precluding the discovery of good policies, which is an issue for the popular bonus-based pessimism. In this paper, we introduce the notion of Bellman-consistent pessimism for general function approximation: instead of calculating a point-wise lower bound for the value function, we implement pessimism at the initial state over the set of functions consistent with the Bellman equations. Our theoretical guarantees only require Bellman closedness as standard in the exploratory setting, in which case bonus-based pessimism fails to provide guarantees. Even in the special case of linear function approximation where stronger expressivity assumptions hold, our result improves upon a recent bonus-based approach by $\mathcal{O}(d)$ in its sample complexity when the action space is finite. Remarkably, our algorithms automatically adapt to the best bias-variance tradeoff in the hindsight, whereas most prior approaches require tuning extra hyperparameters a priori.
The Use of Small Group Discussion in Teaching Reading Comprehension at Junior High School

The background of this research is that the use of the small group discussion method is more effective than the preaching method in teaching reading comprehension. understanding because they can read more comprehensively with their friends in small groups and then discuss the information they have read. The purpose of this study was to determine the effect of student learning outcomes by using small group discussions in learning reading comprehension. This study uses a quantitative method by giving tests, namely pre-test and post-test to twenty eighth grade students at SMP Muhammadiyah Camba. This research applied a pre-experimental design. The test given to students is in the form of multiple-choice in the form of narrative text reading. The results of this study indicate that the experiment conducted by the researcher on students affects learning outcomes by Using Small Group Discussions in teaching reading comprehension. The average value in the pre-tests is 49.25 while the post-tests have an average value of 82.75. It was provided in the value of the t-test (8.152) which was greater than the value of the t-table (2.093). It indicated that there were significant differences, this is also indicated H1 was accepted and H0 was rejected.
LibGuides: Poetry: Close Reading - Where to Start

This is a general guide designed to help students get started reading poetry, aid them in beginning their research, and assist them in conducting a Close Reading.
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning

Data labeling is often the most challenging task when developing computational pathology models. Pathologist participation is necessary to generate accurate labels, and the limitations on pathologist time and demand for large, labeled datasets has led to research in areas including weakly supervised learning using patient-level labels, machine assisted annotation and active learning. In this paper we explore self-supervised learning to reduce labeling burdens in computational pathology. We explore this in the context of classification of breast cancer tissue using the Barlow Twins approach, and we compare self-supervision with alternatives like pre-trained networks in low-data scenarios. For the task explored in this paper, we find that ImageNet pre-trained networks largely outperform the self-supervised representations obtained using Barlow Twins.
$AIR^2$ for Interaction Prediction

The 2021 Waymo Interaction Prediction Challenge introduced a problem of predicting the future trajectories and confidences of two interacting agents jointly. We developed a solution that takes an anchored marginal motion prediction model with rasterization and augments it to model agent interaction. We do this by predicting the joint confidences using a rasterized image that highlights the ego agent and the interacting agent. Our solution operates on the cartesian product space of the anchors; hence the $"^2"$ in $AIR^2$. Our model achieved the highest mAP (the primary metric) on the leaderboard.
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents.

Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set. This paper develops the DeepXML framework that addresses these challenges by decomposing the deep extreme multi-label task into four simpler sub-tasks each of which can be trained accurately and efficiently. Choosing different components for the four sub-tasks allows DeepXML to generate a family of algorithms with varying trade-offs between accuracy and scalability. In particular, DeepXML yields the Astec algorithm that could be 2-12% more accurate and 5-30x faster to train than leading deep extreme classifiers on publically available short text datasets. Astec could also efficiently train on Bing short text datasets containing up to 62 million labels while making predictions for billions of users and data points per day on commodity hardware. This allowed Astec to be deployed on the Bing search engine for a number of short text applications ranging from matching user queries to advertiser bid phrases to showing personalized ads where it yielded significant gains in click-through-rates, coverage, revenue and other online metrics over state-of-the-art techniques currently in production. DeepXML's code is available at https://github.com/Extreme-classification/deepxml
Next QUANTUM COMPUTERS and their SPEEDS

As known, the Quantum Computer represents the most coveted and promising tool for transmitting information in the near future. Thus, with the next use of Quantum Computers (QCs), instead of current computers, the means of transmitting information will be replaced: in fact we will pass from electrons and bit, to photons and quantum bit. So the main element, that is the essential and indispensable tool to make the next QCs, will be represented by the photon, that is by the electromagnetic signal. These signals, as we know, are not all the same, but they differ in a wide range, based on their wavelength, on their energy, or even on their momentum (p). In short, the main task of our work is to ascertain whether, in this context, the photons with different frequencies transmit the information at the same speed, or not!
Inspecting Generalization of Reinforced Learners: The HALMA Benchmark

Author(s): Ma, Xiaojian | Advisor(s): Zhu, Song-Chun | Abstract: Humans learn compositional and causal abstraction, i.e., knowledge, in response to the structure of naturalistic tasks. When presented with a problem-solving task involving some objects, toddlers would first interact with these objects to reckon what they are and what can be done with them. Leveraging these concepts, they could understand the internal structure of this task, without seeing all of the problem instances. Remarkably, they further build cognitively executable strategies to rapidly solve novel problems. To empower a learning agent with similar capability, we argue there shall be three levels of generalization in how an agent represents its knowledge: perceptual, conceptual, and algorithmic. In this work, we devise the very first systematic benchmark that offers joint evaluation covering all three levels. This benchmark is centered around a novel task domain, HALMA, for visual concept development and rapid problem-solving. We conduct extensive experiments on reinforcement learning agents with various inductive biases and carefully report their proficiency and weakness.
Using Persistent Identifiers to Enhance Thesis and Dissertation Services

Join us for ETD2021 (Electronic Theses and Dissertations) Conference in November 2021 in the United Arab Emirates. Our theme, ‘Open scholarship in a post-pandemic world’ addresses the enhanced need for discoverability of research findings in a virtual world. Since the global impact of the COVID-19 pandemic has constrained international travel and mobility, we have all come to rely on working in the virtual world. These developments have merely accelerated the existing trends towards more electronic and digital repositories. Since 1991, the Networked Digital Libraries of Theses and Dissertations (NDLTD) have contributed to research collaboration by identifying and aggregating digital repositories of theses and dissertations as a previously fugitive class of research resources.
Pengembangan Media Balik Kotak Angka Brawas untuk Pembelajaran Pengenalan Angka Tunanetra

Starting from blind children who experience problems in recognizing numbers, and that is due to the absence of media that supports the learning process, causing something that should be known and understood, not at all known by children in learning, namely for learning basic mathematics such as recognizing numbers. This study aims to find out how to develop the media for the visually impaired square number box and to determine the feasibility of the square block media that has been developed by researchers in development research. This study uses a research and development approach to Research and Development (R&D). The result of this research is that the braille number box media is a suitable medium for blind children to use.
A Modern Model of the Educational Environment of the University with Regard to the Context of its Historic Developmen

For several centuries, the mission of the university, original created for the intellectual and cultural development of man, has changed little. The main task of the university is undergoing changes today. The growing antropogical contradiction: the uncertainty of the future, enormous technological variability and existential unpreparedness for this person, contributes to the revision of the university model. The purpose of the article is to develop a pedagogical model of the university that can respond to modern challenges. Methodology. The university always focuses on the future, accepts young people who will develop this future, so the emphasis of modern methodology in the design of educational systems should be “from the future”. This means that it should be based on the reflection of existing phenomena in culture today (see V.S. Stepin). In this work, university models from the late 17th century to the beginning of the 21st century were selected as materials of research in this work and their missions were analyzed. It is concluded that the era influences the formation of the university’s mission, its purpose, goals and methods of education. In particular, classical university education has always been built on three vectors: the vector of the search for the ideal self, the vector of social identification and the vector of cultural identification. In the modern period, the advantage of the vector of socialization over others is increasing. The results of the study. The pedagogical model of education at a modern university is presented. The basis is the thought of M.K. Mamardashvili about a self-fulfilling person and the principles of liberal education: the principle of active independent activity efforts, the principle of a varied educational environment, the principle of volumetric communication, communicativeness and creativity, the principle of reliance on independent learning. Thus, the modern educational environment of the university should be a varied, multi-level, practice-oriented, reflective environment in which 3 educational vectors are concentrated: the search for one’s purpose (idealization), cultural identification and social self-realization. Their combination allows us to overcome the one-sided emphasis of the educational process on socialization. The results of the research may be applied in design and implementation performance of blended learning models or their components in practice of universities.
Analyzing Gravity and Magnetic Data for the Detection of Deep-Seated Faults and Faults within the Sedimentary Cover Near Habbanieyah and Razzaza Lakes in the Middle of Iraq

This research deals with the processing and analyzing of magnetic and gravitational data for an area covering the region of Habbanieyah - Razzaza Lakes and its adjacent areas. The study includes data processing and mapping of the total gravity and magnetic anomalies for only the concerned region, then separating the residual anomalies by adopting the polynomial regression graphical method. The residual gravity anomaly reflects the variations of rock densities within the sedimentary cover. The horizontal gradient filter has been applied to the residual gravity anomaly in order to conduct the locations of fault planes within the sedimentary cover where sudden variations of gravity field take place. The quantitative interpretation for both gravity and magnetic anomalies yielded a preliminary determination for the depth to the center of major faults within the sedimentary cover. By constructing a gravity model along a profile which directed NE-SW and passing through the middle part of the study region, depth to the center of the effective faults found. This depth variation is due to the effect of tectonic activity which produced a set of faults, such faults caused the upward and downward structural motions and were responsible for positioning the deep high density causative slabs of bedrock. The residual magnetic field quantitative interpretation along two profiles crosses over anomalies at the NE and SW parts of the region yielded the depth to the top of magnetized basement rocks. The difference in depth of the basement rocks and the shifted anomaly locations reflects the effect of tectonic activity which may relate to a strike slip faulting in the higher depths.
Model for Semantic Network Generation from Low Resource Languages as Applied to Question Answering – Case of Swahili

Though it is well recognized that Question Answering systems play an important role in natural language processing applications, such as web search and information extraction, their adoption for low resource languages has been low. This is mainly due to lack of language resources such as machine learning tools and training datasets. This research contributes to language processing initiatives by providing a model for semantic network generation for such languages. The model can guide on how to process natural language text and give it structure. The structured language can then be used for downstream tasks such as Question Answering. Proof of concept experiment is done using the Swahili part of TyDi QA, a publicly available QA dataset. The results show that the model can be used to generate semantic networks that can be applicable to Question Answering, with accuracy of 64.8% based on the sampled data. Lack of diverse and task specific curated corpora however remain a challenge in developing tools for low resource languages.
The influence of el nino and la nina on rainfall data of east lampung regency, lampung province

This research was conducted to determine the impact of the areas where El Nino and La Nina were the most dominant. The data used in this study are daily rainfall data in 5 rain stations located in East Lampung. Data length from 1989 to 2018 at each station, this data was taken from the Mesuji River Basin - Sekampung (BBWSMS). This modeling is carried out using a data length of 30 years for a data term of 10957.By using observational data and applying it in 2 programs, namely the Fast Fourier Transform (FFT) and the Lomb Periodogram, it produces values that can be graphed for the spectrum of each of the 5 rain station areas, namely Jepara Lake, Labuhan Ratu, Taman Negeri, Kota Raman and Sri Gading. From the results, it can be seen which areas are predominantly affected by El Nino and La Nina. Based on the results of the two programs, both show that the rain station in the Lake Jepara area experiences more dominant El Nino and La Nina influences. Keywords: El Nino, La Nina, rain station, rainfall, Fast Fourier Transform, Lomb Periodogram