Reviewerly/exHarmony huggingface dataset:
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers

We propose a new framework for reasoning about generalization in deep learning. The core idea is to couple the Real World, where optimizers take stochastic gradient steps on the empirical loss, to an Ideal World, where optimizers take steps on the population loss. This leads to an alternate decomposition of test error into: (1) the Ideal World test error plus (2) the gap between the two worlds. If the gap (2) is universally small, this reduces the problem of generalization in offline learning to the problem of optimization in online learning. We then give empirical evidence that this gap between worlds can be small in realistic deep learning settings, in particular supervised image classification. For example, CNNs generalize better than MLPs on image distributions in the Real World, but this is because they optimize faster on the population loss in the Ideal World. This suggests our framework is a useful tool for understanding generalization in deep learning, and lays a foundation for future research in the area.
Extracting Search Tasks from Query Logs Using a Recurrent Deep Clustering Architecture

Users fulfill their information needs by expressing them using search queries and running the queries in available search engines. The mining of query logs from search engines enables the automatic extraction of search tasks by clustering related queries into groups representing search tasks. The extraction of search tasks is crucial for multiple user supporting applications like query recommendation, query term prediction, and results ranking depending on search tasks. Most existing search task extraction methods use graph-based or nonparametric models, which grow as the query log size increases. Deep clustering methods offer a parametric alternative, but most deep clustering architectures fail to exploit recurrent neural networks for learning text data representations. We propose a recurrent deep clustering model for extracting search tasks from query logs. The proposed architecture leverages self-training and dual recurrent encoders for learning suitable latent representations of user queries, outperforming previous deep clustering methods. It is also a parametric approach that offers the possibility of having a fixed-sized architecture for analyzing increasingly large search query logs.
Recognition of Strong Earthquake–Prone Areas with a Single Learning Class. Caucasus, М ≥ 6.0

Earthquake-prone areas recognition is an essential part of seismic zoning – the most important problem of seismology. In the classical EPA approach for recognition of strong earthquake-prone areas (М ≥ М0), as a rule, the training dichotomy algorithm “Cora-3” is used. Its starting stage is the formation of training samples of high- and low-seismic classes on the basis of which the algorithms are trained. In fact, the task itself, which is the ultimate recognition problem, the low-seismic training class contains potential errors. These are the objects that will transit into the high-seismic class as a result of solving the problem.In the “Barrier-3” algorithm, developed at the GC RAS, training is carried out exceptively in the high-seismic class. Algorithm “Barrier-3” is not a dichotomy algorithm. However, it can be effectively used as the part of the EPA approach, instead of the aforementioned “Cora-3” dichotomy algorithm. The development of the “Barrier” algorithm can be considered as a new step in solving the problem of strong earthquake-prone areas recognition. Thus, in the classical EPA method, the pattern recognition block can be varied.One of the possible interpretations of the joint recognition results by the “Barrier-3” and “Cora-3” algorithms can be its definition as a fuzzy set of recognition objects, in the neighborhood of which strong earthquakes (М ≥ М0) can occur.
The Impact of Entrepreneurial Culture On Economy Competitiveness in the Arab Region

The main emphasis of this study was assessing to what scope entrepreneurial culture among Arab counties affects the overall regional competitiveness of the MENA region. Data from each country were first solely analyzed then grouped by its subsequent trading blocks. An average assertion of the combined data was granted in the bid of assessing any entrepreneurial cultures. The entrepreneurial culture analysis of the countries and regions within the study were based on the time span through which the referenced entrepreneurial activity has been dominant and the education level of stakeholder. Most Arab countries have the same entrepreneurial structure and economic competitiveness despite various external factors affecting them. The results also revealed that the relationship between each country's internal economic competitiveness and the overall posture of the Arab region is positive. Besides, the relationship between dominant entrepreneurial cultures and the overall economic posture of the Arab region is also statistically confirmed. Much of the past studies failed to include and relate the external factors like the growing entrepreneurial cultures among states and regions. Basing conclusions on one region while others could convey disparities was regraded rather impractical. As such, to affirm the validity of conclusions; The relationship between the Economic Community of West Africa (ECOWAS) and North African countries from one part, and the Association of Southern Asian Nations (ASEAN) and the other Arab countries from another hand, had to be taken into consideration.
Conversational System, Intelligent Virtual Assistant (IVA) Named DIVA Using Raspberry Pi

Humans are living in an era where they are interacting with machines day in and day out. In this new era of the 21st century, a virtual assistant (IVA) is a boon for everyone. It has opened the way for a new world where devices can interact their own. The human voice is integrated with every device making it intelligent. These IVAs can also be used to integrate it with business intelligence software such as Tableau and PowerBI to give dashboards the power of voice and text insights using NLG (natural language generation). This new technology attracted almost the entire world like smart phones, laptops, computers, smart meeting rooms, car InfoTech system, TV, etc. in many ways. Some of the popular voice assistants are like Mibot, Siri, Google Assistant, Cortana, Bixby, and Amazon Alexa. Voice recognition, contextual understanding, and human interaction are some of the issues that are continuously improving in these IVAs and shifting this paradigm towards AI research. This research aims at processing human natural voice and gives a meaningful response to the user. The questions that it is not able to answer are stored in a database for further investigation.
MUCS@ - Machine Translation for Dravidian Languages using Stacked Long Short Term Memory

Dravidian language family is one of the largest language families in the world. In spite of its uniqueness, Dravidian languages have gained very less attention due to scarcity of resources to conduct language technology tasks such as translation, Parts-of-Speech tagging, Word Sense Disambiguation etc,. In this paper, we, team MUCS, describe sequence-to-sequence stacked Long Short Term Memory (LSTM) based Neural Machine Translation (NMT) model submitted to “Machine Translation in Dravidian languages”, a shared task organized by EACL-2021. The NMT model was applied on translation using English-Tamil, EnglishTelugu, English-Malayalam and Tamil-Telugu corpora provided by the organizers. Standard evaluation metrics namely Bilingual Evaluation Understudy (BLEU) and human evaluations are used to evaluate the model. Our models exhibited good accuracy for all the language pairs and obtained 2nd rank for TamilTelugu language pair.
Foreign Funded Credit: Funding the Credit Cycle?

This study investigates what drives the credit cycle, focusing on the role of foreign funded bank credit (FFC). Considering credit cycles in 41 countries over the period 1985-2015, this study finds that credit booms are associated with an increase in the share of FFC in an economy, both in emerging and developed economies and for business as well as for household credit cycles. The impact of FFC on credit booms is however significantly higher in emerging countries. While FFC increases rapidly during the boom, the period preceding the boom is characterized by an in increase in domestically funded credit relative to FFC. FFC thus accelerates credit during the boom. The increased credit needs during a boom may cause the subsitution of domestically funded credit by FFC, as the growth in FFC is less restricted than domestically funded credit, for example by the domestic deposit base.
Research on the Application of Artificial Intelligence Technology in Animal Embryo Transfer

Abstract Animal embryo transfer technology can show strong practical value, and its use in the animal husbandry industry is particularly prominent. It should be because this technology can significantly increase the birth rate and survival rate of animal pups within a period of time. This technology can also the animal’s own reproductive ability has been further improved. With the popularization of this animal embryo transfer technology, the animal husbandry industry can achieve rapid development in a short period of time, enabling farmers to obtain higher and better economic benefits. Since animal embryo transfer technology entered the field of animal husbandry in the 1970s, the role of animal embryo transfer technology has become increasingly apparent. This technology has penetrated into biological reproduction technologies such as animal breeding, gender control and animal cloning. And animal reproduction technology has become leading the cutting-edge scientific reproduction technology of the livestock industry in the 21st century. Nowadays, artificial intelligence technology combines the research results of many disciplines such as theoretical science, technical science, and applied science. As a practical technology leading a new generation of technological revolution and industrial reform, artificial intelligence technology has a significant impact on human social progress, economic development, and biotechnology. However, the transformation of artificial intelligence technology has also brought some huge challenges to social stability. How to treat artificial intelligence and animal embryo transfer technology, how to integrate and coordinate these two technologies, and how to solve these technical design problems have become the urgent problems that need to be solved.
Pelatihan Pelaksanaan Pembelajaran Jarak Jauh dengan Menggunakan Aplikasi Google Classroom untuk Guru-Guru Ekonomi Akuntansi di SMK Yadika Natar Lampung Selatan

Online learning functions as a liaison between educators and students with an internet network that can be accessed anytime and anywhere. The media used in this community service will be more specific to Google Classroom. The purpose of this Community Service is to analyze the effectiveness of online learning, provide additional knowledge about distance learning, still maximize learning even though face-to-face learning has not been allowed due to the Covid 19 outbreak, and to know the role of the internet as a means of education in an effort to educate the nation's life . The activity that was attended by Accounting Economics (IPS) teachers at SMK Yadika Natar was held on Monday, September 28, 2020 at SMK Yadika which lasted for 8 hours starting at 08.00 to 16.00 which was attended by 6 participants. This Community Service activity has relevance to the needs of teachers regarding online system distance learning activities.
SentText: A Tool for Lexicon-based Sentiment Analysis in Digital Humanities

We present SentText, a web-based tool to perform and explore lexicon-based sentiment analysis on texts, specifically developed for the Digital Humanities (DH) community. The tool was developed integrating ideas of the user-entereddesign process and we gathered requirements via semi-structured interviews. The tool offers the functionality to perform sentiment analysis with predefined sentiment lexicons or self-adjusted lexicons. Users can explore results of sentiment analysis via various visualizations like bar or pie charts and word clouds. It is also possible to analyze and compare collections of documents. Furthermore, we have added a close reading function enabling researchers to examine the applicability of sentiment lexicons for specific text sorts. We report upon the first usability tests with positive results. We argue that the tool is beneficial to explore lexicon-based sentiment analysis in the DH but can also be integrated in DH-teaching.
Formal Verification and Development of Living Assistance System

Formal verification and validation of computer software has always been beneficial for the end user. It helps to tackle the future errors in the system which may cause the system to perform inadequately. Currently, the world is moving towards systems which use machine learning and deep learning concepts. A lot of work regarding formal verification is still required in this aspect. Formal verification of systems involving deep learning networks is relatively new and requires a hand load of efforts. In this paper, we verify and validate a living assistance system which detect human activities (i.e Falling and Choking) through machine learning techniques and informs the end user. We used IAR VisualState and UPPAAL to perform verification and validation of this system. By formally verifying the system, we were able to ensure the correctness of our system; making it trustworthy and reliable to be deployed in a real world environment.
Inferring Exoplanet Disequilibria with Multivariate Information in Atmospheric Reaction Networks

Inferring the properties of exoplanets from their atmospheres, while confronting low resolution and low signal-to-noise in the context of the quantities we want to derive, poses rigorous demands upon the data collected from observation. Further compounding this challenge is that inferences of exoplanet properties are built from forward models, which can include errors due to incomplete or inaccurate assumptions in atmospheric physics and chemistry. The confluence of observational noise and model error makes developing techniques to identify predictive features that are robust to both low s/n and model error increasingly important for exoplanet science. We demonstrate how both issues can be addressed simultaneously by taking advantage of underutilized multivariate information already present in current atmospheric models, including thermodynamic statistics and reaction network structure. To do so, we provide a case study of the prediction of vertical mixing (parameterized as eddy diffusion) in hot Jupiter atmospheres and show how prediction efficacy depends on what model information is used - e.g. chemical species abundances, network statistics, and/or thermodynamic statistics. We also show how the variables with the most predictive power vary with planetary properties such as temperature and metallicity. Our results demonstrate how inferences built on single metrics do not have utility across all possible use cases. We also show how statistical measures derived from network analyses tend to be better predictors when accounting for the possibility of missing data or observational uncertainty. We discuss future directions applying multivariate and network model information as a framework for increasing confidence in inferences aimed at extracting features relevant to exoplanet atmospheres and future applications to the detection of life on terrestrial worlds.
Related Named Entities Classification in the Economic-Financial Context

The present work uses the Bidirectional Encoder Representations from Transformers (BERT) to process a sentence and its entities and indicate whether two named entities present in a sentence are related or not, constituting a binary classification problem. It was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations to identify a relation between entities without using other lexical-semantic resources. The results of the experiments show an accuracy of 86% of the predictions.
Highly accurate Gaussian process tomography with geometrical sets of coherent states

Abstract We propose a practical strategy for choosing sets of input coherent states that are near-optimal for reconstructing single-mode Gaussian quantum processes with output-state heterodyne measurements. We first derive analytical expressions for the mean squared-error that quantifies the reconstruction accuracy for general process tomography and large data. Using such expressions, upon relaxing the trace-preserving (TP) constraint, we introduce an error-reducing set of input coherent states that is independent of the measurement data or the unknown true process—the geometrical set. We numerically show that process reconstruction from such input coherent states is nearly as accurate as that from the best possible set of coherent states chosen with the complete knowledge about the process. This allows us to efficiently characterize Gaussian processes even with reasonably low-energy coherent states. We numerically observe that the geometrical strategy without trace preservation beats all nonadaptive strategies for arbitrary TP Gaussian processes of typical parameter ranges so long as the displacement components are not too large.
Batch Monte Carlo Tree Search

Making inferences with a deep neural network on a batch of states is much faster with a GPU than making inferences on one state after another. We build on this property to propose Monte Carlo Tree Search algorithms using batched inferences. Instead of using either a search tree or a transposition table we propose to use both in the same algorithm. The transposition table contains the results of the inferences while the search tree contains the statistics of Monte Carlo Tree Search. We also propose to analyze multiple heuristics that improve the search: the $$\mu $$ FPU, the Virtual Mean, the Last Iteration and the Second Move heuristics. They are evaluated for the game of Go using a MobileNet neural network.
Optimisation of Student Information System for Mobile Devices

This research study optimises student information system for mobile devices using Student Academic Registration Information System(SARIS) as a case study. The study specifically optimises SARIS to improve user experience, page loading speed, portability, and search for mobile devices. Optimisation of SARIS for mobile users' convenience, together with an increasing trend for offering specific content for mobile devices were the main reasons for optimisation of SARIS. The optimised SARIS was evaluated to determine the extent it was efficient for mobile devices. The study was conducted at Tumaini University Makumira (TUMA), in Arusha, Tanzania. The study used qualitative and quantitative methods for data collection and analysis. The results show that optimisation of information system for mobile devices improves page loading speed, user experience, portability, and searching capabilities on these small devices. It is recommended that a future research could be conducted in other information systems.
Phenomenon of Machine Mother Tongue Culture Transfer in English under the Internet with Pattern Recognition Models

In our daily lives, people encounter different problems in pattern recognition applications, such as fingerprint unlocking, face recognition, DNA detection, mother tongue culture transfer. The essenoe of pattern recognition is to match the known sample with the given pattern by comparing the known sample with the given pattern, so as to achieve the purpose of classification. In fuzzy sets, membership degree is often used to describe samples and patterns, and use fuzzy measure to calculate the similarity between samples and patterns, so that the overall characteristics of patterns can be well reflected, and samples are allowed to have a certain degree of interference. Sometimes, it is required to deduce the corresponding conclusions from the existing rule base through fuzzy reasoning algorithm, and sort them to get the classification results. Mother tongue transfer is an inevitable problem in English translation, which has a great influence on foreign language learning and translation. Language transfer usually includes four forms: overuse, avoidance, convenience and misuse, which can be divided into positive transfer and negative transfer. Due to the obvious differences between Chinese culture and English culture, negative transfer of mother tongue cannot be avoided, which increases the difficulty of English translation. This paper studies the pattern recognition based culture transfer of machine native language in the network environment.
Cultural Construction of University Library in Mobile New Media Era

With the rise of new media, readers can choose more diversified ways of reading. Digital resources are gradually accepted and relied on by readers due to their advantages of large quantity, fast updating and convenient carrying. Therefore, major university libraries actively promote the construction of digital resources platform, to provide convenience for readers’ mobile reading. Information dissemination media especially mobile new media the all-round development and utilization of the reading activities will add strength to the tiger. On the basis of keeping the traditional propaganda methods, university libraries should also keep pace with the times. Mobile new media provides a new development space for the cultural construction of university library, By leveraging new mobile media platforms, University libraries can not only adjust the way of literature resources construction, Carrying out various forms of reading promotion activities and strengthening cooperation with other departments in the school can also narrow the distance with readers, tightly surround the cultural spirit and connotation of the library around college students’ readers, and enable college students to improve their cultural accomplishment and humanistic spirit imperceptibly.
Don’t Change Me! User-Controllable Selective Paraphrase Generation

In the paraphrase generation task, source sentences often contain phrases that should not be altered.Which phrases, however, can be context dependent and can vary by application.Our solution to this challenge is to provide the user with explicit tags that can be placed around any arbitrary segment of text to mean "don't change me!" when generating a paraphrase; the model learns to explicitly copy these phrases to the output.The contribution of this work is a novel data generation technique using distant supervision that allows us to start with a pretrained sequenceto-sequence model and fine-tune a paraphrase generator that exhibits this behavior, allowing user-controllable paraphrase generation.Additionally, we modify the loss during fine-tuning to explicitly encourage diversity in model output.Our technique is language agnostic, and we report experiments in English and Chinese.
SOME CRITERIA OF PROFESSIONAL TEACHING

Lesson is the main form of organization of the educational process. At the same time, it is a mirror of the general pedagogical culture of a teacher, a measure of his intellectual wealth, an indicator of his worldview and erudition. In our article the key terms and definitions concerning questions of "lesson", "modern lesson", "professional teaching" as important pedagogical phenomena and key forms of the organization of educational process are presented. A description of possible criteria for professional teaching and recommendations for teachers on the organization, construction and conduct of effective training were analyzed. According to the evidence of empirical teaching research on the central characteristics of professional teaching, it can be argued that practical training remains a key form of organization and is the educational place where the very processes of learning, education and personal development take place.