Reviewerly/exHarmony huggingface dataset:
Examining and Combating Spurious Features under Distribution Shift

A central goal of machine learning is to learn robust representations that capture the causal relationship between inputs features and output labels. However, minimizing empirical risk over finite or biased datasets often results in models latching on to spurious correlations between the training input/output pairs that are not fundamental to the problem at hand. In this paper, we define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics. We prove that even when there is only bias of the input distribution (i.e. covariate shift), models can still pick up spurious features from their training data. Group distributionally robust optimization (DRO) provides an effective tool to alleviate covariate shift by minimizing the worst-case training loss over a set of pre-defined groups. Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations that occur in the data. To address this, we further propose to minimize the worst-case losses over a more flexible set of distributions that are defined on the joint distribution of groups and instances, instead of treating each group as a whole at optimization time. Through extensive experiments on one image and two language tasks, we show that our model is significantly more robust than comparable baselines under various partitions. Our code is available at https://github.com/violet-zct/group-conditional-DRO.
IMPLEMENTATION OF CONTINUITY IN TEACHING THE COURSE OF MATHEMATICAL ANALYSIS IN SCHOOLS AND PEDAGOGICAL UNIVERSITIES

The rebuilding of the school on the principles of humanization and democratization in accordance with the law of RK "On education", the emergence of new educational standards, an ongoing process of updating the content of General secondary education and the transition to a digital economy are placing new demands on the quality of teacher training in mathematics. At the same time, an important problem remains the preservation of continuity in teaching mathematical disciplines at school and pedagogical University. The course of mathematical analysis plays an important role in shaping the professional training of a mathematics teacher, including the justification of the theoretical and practical provisions of the fundamental questions of the school course of algebra and the principles of mathematical analysis. This article discusses some ways to solve the problem of implementing the continuity of teaching the course of mathematical analysis in schools and pedagogical universities.
Learning compositional structures for semantic graph parsing

AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of the compositional tree structures for training. In the past, these were obtained using complex graphbank-specific heuristics written by experts. Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics. We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.
Scrambled Translation Problem: A Problem of Denoising UNMT

In this paper, we identify an interesting kind of error in the output of Unsupervised Neural Machine Translation (UNMT) systems like \textit{Undreamt}(footnote). We refer to this error type as \textit{Scrambled Translation problem}. We observe that UNMT models which use \textit{word shuffle} noise (as in case of Undreamt) can generate correct words, but fail to stitch them together to form phrases. As a result, words of the translated sentence look \textit{scrambled}, resulting in decreased BLEU. We hypothesise that the reason behind \textit{scrambled translation problem} is 'shuffling noise' which is introduced in every input sentence as a denoising strategy. To test our hypothesis, we experiment by retraining UNMT models with a simple \textit{retraining} strategy. We stop the training of the Denoising UNMT model after a pre-decided number of iterations and resume the training for the remaining iterations -- which number is also pre-decided -- using original sentence as input without adding any noise. Our proposed solution achieves significant performance improvement UNMT models that train conventionally. We demonstrate these performance gains on four language pairs, \textit{viz.}, English-French, English-German, English-Spanish, Hindi-Punjabi. Our qualitative and quantitative analysis shows that the retraining strategy helps achieve better alignment as observed by attention heatmap and better phrasal translation, leading to statistically significant improvement in BLEU scores.
Innovative Approaches to Ensuring the Quality of Education, Scientific Research and Technological Processes. 3.8.4 Problems of formation of information ict-competence of the teacher by means of modern innovative technologies

During the transition to the information society, it is necessary to prepare the teacher for the rapidperception and processing of large amounts of information, mastering modern tools, methods andtechnology of working with information resources. Information competence deserves specialattention because it allows the teacher to be modern, to act actively in the information environment,to use the latest advances in technology in their professional activities.
Research Guides: HIST 72/172: The Civil War Era (Gudgeirsson): Searching for Primary Sources on the Open Web

An online research guide designed to help students in Hist 172 find resources for their research paper
UCBFed: Using Reinforcement Learning Method to Tackle the Federated Optimization Problem

Federated learning is a novel research area of AI technology that focus on distributed training and privacy preservation. Current federated optimization algorithms face serious challenge in the aspects of speed and accuracy, especially in non-i.i.d scenario. In this work, we propose UCBFed, a federated optimization algorithm that uses the Upper Confidence Bound (UCB) method to heuristically select participating clients in each round’s optimization process. We evaluate our algorithm in multiple federated distributed datasets. Comparing to most widely-used FedAvg and FedOpt, the UCBFed we proposed is superior in both the final accuracy and communication efficiency.
VIRTUALITY IN TEACHING OF RESEARCH IN UNIANDES LAW CAREER: CON-COVID VERSUS POST-COVID

The challenges of university processes in times of emergency in history do not refer to substantial changes until March 2020 with the appearance of the new SARS COV-19 coronavirus. The substantive processes in higher education institutions have undergone transformations in their conceptions and methodologies, in the case of the academy, in the development of classes with the virtual modality, these changes have been extremely renovating in some cases and in others of assumption of some semi-presential and distance modalities. The main purpose is to describe the process of virtuality in classes and their interactions between teachers and students, at first with the COVID 19 state of emergency, and to induce prospective elements of these experiences in a POST-COVID 19 scenario. This research explores and describes the main ways in which teachers interact with students and vice versa, in the development of classes, under the study and analysis of the sample of research subjects taught by the authors in the Law program at the Puyo branch of Uniandes, based on a virtual survey conducted after the first month of the new modality. The main results point to an understanding of graduating the interactions and the depth of these in terms of content and permanent evaluations of all the subjects of the curriculum, and the decisions to opt for the appropriate virtual resources.
The Nexus between Work Engagement, Job Stress, and Employee Turnover Intention in Riyadh Private Sector

This study aims at investigating the nexus between work engagement, job stress, and turnover intention. A descriptive-analytical approach is used to collect and analyze the research data. Survey data included 176 participants and IBM SPSS version 22 was conducted to test the research hypotheses. Survey questionnaire is applied as a primary data collection tool. The findings indicated that job stress (JS) has a significant positive impact on turnover intention (TI), whereas work engagement (WE) has a significant negative impact on turnover intention (TI). Also, it is evidenced that work engagement (WE) has a significant negative impact on job stress (JS). In addition, there are significant differences between turnover intention (TI) based on demographic variables (gender and age). This study offers private sector leaders a better understanding of reasons behind turnover intention and actual turnover to prevent employees from making such decisions that affect overall organizational effectiveness.
Growth of Indian Research in Science and Technology: A Scientometric Analysis

This article carried out a Scientometric analysis of research publications published in various science and technology journals from 2011 to 2020 from India. Required data was retrieved from WoS database during the study period it provides information about the science and technology related publications from India. Especially, scientific data on the top research areas and distribution of publication collaborations in different dimensions of geography, authors, funding agencies, subjects and others. It examines the year wise publications, annual distribution of publications, most prolific authors, authors and organisations productivity, etc. Analysis found that, highest research publications are published in the year 2019 with 13.16%, followed by 2020 with 12.98% and in the year 2018 with 11.51%. Research articles are published 81.51% compared to any other type of publications from all over India. 88% of the research fund is provided by the Government of India, and remaining are from the western world among the top ten funding agencies. Out of top ten journals, seven (76%) of them are originated from India, two are from United Kingdom and one journal is from United States of America. During the study period, it is observed that, a mean relative growth rate is 0.26. Notably, the doubling time for the publications has decreased from 0.35 in the first five years to 0.17 in the last five years. Publications are raised on an average of 4.72% per year. On the whole, 208 countries collaborators contributed to these publications.
PSRU Construction Management Mobile Application Development

Abstract PibulsongkramRajabhat University has a lot of construction project. The construction project management according to government regulations. The work status of the employees and the staff on a daily basis. The former process uses a lot of paper to make notes that are difficult to store and verify the information. In addition, paper documents are not durable. May be damaged after a long period of time. The researcher has the idea to use technology to facilitate work. We have the idea to use technology to facilitate the work by developing a mobile application for the tracking of construction work. We develop applications using Ionic cross-platform. Can display location, time recorder, weather condition, and also can send notification emails to the parcel receiving committee and the parcel receiving committee. By developing mobile applications for construction tracking specifically to replace paper documents to reduce paper usage and able to track work more conveniently, quickly, and more efficiently. Keywords: Software Development, Application, IONIC cross platform, User Interface, Construction Management
IMPLICATION OF STORYTELLING THROUGH THE UTILIZATION OF PRIMA LIBRARY SERVICES AS ASPECT OF STRENGTH OF LITERATURE LEARNING FOR CHILDREN'S DEVELOPMENT IN EDUCATIONAL SOCIOLOGY PERSPECTIVE

The purpose of the study is to describe; (1). Storytelling implications for child development. The object of research-based discussion is fundamental about content storytelling to foster student interest in reading through the use of library collections. This research focuses on the manifestation of excellent service manifestations because storytelling is also an existing activity and is one form of library service. This research can be called a characteristic historical approach, which is critical research using primary data which is the main information. Collecting data is library research, through the reading activities of various literary literature subjected to, the use of library collections and children's interest in reading, the role of teachers and librarians in children's education. Analysis of research is descriptive qualitative and comes from a variety of reading materials. Content analysis is used to analyze the meaning of storytelling conceptualization, reading interest and the use of library collections. Careful observations are made in the library and classroom, to support constructing the theory. The results of the research implications of storytelling are very influential for children's development.
How Cloud-Based Document Management Supports Remote Government

Swapping papers is difficult when you're not in the office, so state governments depend on digital systems for file access.
HELP: Hardware-Adaptive Efficient Latency Predictor for NAS via Meta-Learning

For deployment, neural architecture search should be hardware-aware, in order to satisfy the device-specific constraints (e.g., memory usage, latency and energy consumption) and enhance the model efficiency. Existing methods on hardware-aware NAS collect a large number of samples (e.g., accuracy and latency) from a target device, either builds a lookup table or a latency estimator. However, such approach is impractical in real-world scenarios as there exist numerous devices with different hardware specifications, and collecting samples from such a large number of devices will require prohibitive computational and monetary cost. To overcome such limitations, we propose Hardware-adaptive Efficient Latency Predictor (HELP), which formulates the device-specific latency estimation problem as a meta-learning problem, such that we can estimate the latency of a model's performance for a given task on an unseen device with a few samples. To this end, we introduce novel hardware embeddings to embed any devices considering them as black-box functions that output latencies, and meta-learn the hardware-adaptive latency predictor in a device-dependent manner, using the hardware embeddings. We validate the proposed HELP for its latency estimation performance on unseen platforms, on which it achieves high estimation performance with as few as 10 measurement samples, outperforming all relevant baselines. We also validate end-to-end NAS frameworks using HELP against ones without it, and show that it largely reduces the total time cost of the base NAS method, in latency-constrained settings.
Marketing of educational services for continuous professional development of modern educator

The reform of the continuing teachers’ training system in Ukraine is meant to provide teachers with the opportunities for professional development and lifelong learning according to the requirements set by the State education policy, employers and key stakeholders. This implies creation of flexible competitive educational environment using mobile advanced training programs and modernization of content, forms, methods techniques of teaching. The authors present a set of andragogic and psychological assessment instruments to use for continuing teachers’ training, including feedback questionnaires, complex questionnaires, blitz polls, tests, selection of monitoring methods on teachers’ professional and lifelong development.
Smart Electronic Voting Machine for Detection of Fraud Voters by Using Raspberry Pi

The main aim of this project is to provide secured identification and authentication processes for the voters and candidates by using fingerprint and face detection. The basic idea of this project is to make an electronic voting machine that will help to reduce the manual voting systems and prior versions of electronic voting. Due to some illegal activities the polling places are misused and people's vote to right has been dennied. This system includes multiple layers of verifications to ensure the reliability of the device. Each voter is entered into the system after being recognized and checked with the given database of enlisted voters with including fingerprint sensor and Pi camera. The final vote is then displayed onto a monitoring system for the satisfaction of voters. The voting problem is still critical in terms of safety and security. This project is about the design and development of a voting system using fingerprint and face detection to provide a high performance with high security to the voting system. Fingerprint is widely used for identification. The captured finger prints and faces recognition, which is compared with existing Images. If the image is matched, the computer sends the command the person is valid to voting and displayed. If anyone is try to do fraud vote then it displayed the message of dennied of vote. The proposed project displays transparency and also carries the feature of being autonomous during the course of operation. Keywords: Electronic voting machine, raspberry Pi model 3 B+, Pi camera, fingerprint sensor, monitoring system
PELATIHAN PENGGUNAAN SISTEM OJS BAGI PENDIDIK DI SMP N 1 KEFAMENANU

Based on interviews with head master of SMP N 1 Kefamenanu, data was obtained only 2% out of 20 teachers understood the OJS system who received promotions with the condition that they did scientific publications in national journals. Based on these problems, the authors conducted training them to use OJS system to increase the capacity of scientific publications at SMP N 1 Kefamenanu. This activity use participatory action research (PAR) as a method which use three stages, namely preparation, implementation and evaluation. The responses from the participating teachers indicated that this training activity was very useful. Teachers of SMP N 1 Kefamenanu now understand and understand how to submit articles using the OJS system. In addition, 1 out of 16 assisted subjects have succeeded in getting a response that their articles were received with corrections by the journal editor as a form of success in training activities.
The Effect of Bugis Language Interference on The Ability of Students to Speak Indonesian Both and Correctly at SDN 15 Bonto-Bonto, Pangkep Regency

Abstract. Indonesian is official language of Republic of Indonesia and as a unifying language. As Juanda (2017: 22) says that the Indonesian language is recognized as a National language and as a unifying tool by all existing ethnic groups, so divisions will be avoided because ethnic groups feel one. however, only a small proportion of Indonesian population actually use it as a mother tongue. For most part, Indonesian is a second language. In South Sulawesi, especially in Pangkep Regency, most of population uses Bugis language as their first language. So that use of Indonesian is influenced by first language (B1), this is due to language interference, namely influence of regional languages. The interference of first language (B1) to second language (B2) resulted in several errors in use of Indonesian. The research objective was to determine effect of Bugis language interference on ability of students to speak Indonesian properly and correctly at SDN 15 Bonto-bonto, Ma'rang District, Pangkep Regency. The results of analysis of research data calculated using simple linear regression showed that rcount <of significance value (0.033 <0.05), so there is a significant influence between Bugis language interference on students' ability to use good and correct Indonesian in SD Negeri 15 Bonto-bonto, District Ranger of Pangkep Regency and significance value is 0.334 Keywords: Quantitative research, bugis language interference, to indonesian
A Convergent and Dimension-Independent First-Order Algorithm for Min-Max Optimization

Motivated by the recent work of Mangoubi and Vishnoi (STOC 2021), we propose a variant of the min-max optimization framework where the max-player is constrained to update the maximization variable in a greedy manner until it reaches a *first-order* stationary point. We present an algorithm that provably converges to an approximate local equilibrium for our framework from any initialization and for nonconvex-nonconcave loss functions. Compared to the second-order algorithm of Mangoubi and Vishnoi, whose iteration bound is polynomial in the dimension, our algorithm is first-order and its iteration bound is independent of dimension. We empirically evaluate our algorithm on challenging nonconvex-nonconcave test-functions and loss functions that arise in GAN training. Our algorithm converges on these test functions and, when used to train GANs on synthetic and real-world datasets, trains stably and avoids mode collapse.
Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots based on Artificial Intelligence

Obstacle-avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies fuzzy logic to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on multi-layer perceptron and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive obstacle-avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their &amp;ldquo; experience &amp;rdquo; by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.