Reviewerly/exHarmony huggingface dataset:
MENINGKATKAN AKTIVITAS DAN PRESTASI BELAJAR SISWA KELAS VII SMPN 1 DOMPU MATERI POKOK HIMPUNAN MELALUI PENERAPAN PENDEKATAN KONSTRUKTIVISME TAHUN PELAJARAN 2011/2012

Study properly is learning which is able to interaction and understand study material when it’s running one of the study purposed is learning through contructive approach.The purpose of this research is to improve students’ activity and achievement through contructive approach in class VII  SMPN 1 Dompu, Acdemic years 2011/2012 on collecting material. The kind of this research is dealetd with some approaches, there are kualitatif and kuantitatif thad consist of techinigue collecting data, observation and test. Analisis of technique data is observation result of study evaluation. The result found that to carry out teaching and learning on siklus 1 of fulfillment percentage is 31 and study percentage is 73,8%, while students activity is category fairly active on siklus II. The students who fulfil is about 37 and fulfillment percentage is 88,10%, and students activity on category active. It is cloncluded that using constructive approach able to improve study achiement on mathematics of collecting material for students in class VII  SMPN 1 Dompu Academic years 2011/2012.
Hierarchical summarization of financial reports with RUNNER

With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it’s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special “language”. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.
Developing a systems engineering concept for digitalizing higher education institutions

The systems engineering concept of digitalization of higher education institutions (HEI) was proposed. The concept integrates the systemic, process-based, and project-based approach to displaying the objects and processes of the HEI activity in a unified digital space. This will make it possible to use it for automated solutions of functional problems in the educational, scientific, economic, and management processes of the HEI. Within the framework of the concept, the approaches to implementation were proposed: processes of the HEI digital transformation; functions and structures of the HEI digitalization project management; tools of a digital university. The concept is based on a unified digital space, which includes: digital and functional environment, tools for its formation, tools for using the digital environment. It was shown that the success of the transformation of the HEI into a digital university directly depends on the effectiveness of the organization of processes of digitalization project management. And this requires the use of the problem-oriented methodology of project management; the creation of a special division of the HEI digitalization; the development of the structure and technology of filling and using the digital space of an institution of higher education.The result of the application of the HEI digitalization concept is the creation of the specified methodology for project management, which is focused on the digital transformation of an institution of higher education. New organizational principles of digitalization of higher education institutions were proposed, based on the project-oriented organizational structure – a digital transformation office. The structure and the functions of such an office were determined.The processes of higher education institutions from the positions of readiness for the implementation of the systems engineering concept of digitalization were explored. Examples of the HEI digitalization using the proposed concept were given
Classification and Prediction of Text Data by Using a Natural Language Processing Algorithm

Natural Language Generation (NLG) uses AI and tongue-processing methodologies to create computational structures that give upward thrust to reports, narratives, and other kinds of texts in human language. NLG makes use of textual content investigative and NLP to induce relevant information about non-linguistic information and to test textual summaries and motives of that information, which helps people understand and benefit from them. In this regard, NLG may be a studies' field that addresses the statistics-fee chain by the use of the tongue as a tool for bridging the distance between facts and valuable records communicated to users at some point in an understandable way, adapted to their records needs. The resultant system trains stop-to-give up neutral community models consisting of an encoder version to provide an unseen depiction of the textual source content, accompanied by the aid of a decoder version to urge the target. While such fashions have substantially fewer pieces than the earlier device, considerable tuning is still required to attain the right performance. For text engendering, the decoder acts potentially in undesired ways, thereby way of producing truncated outputs. Output band and regularly occurring responses or in some cases generating ungrammatical gibberish. This paper is supposed as a practical guide for resolving such undesired bring about text era models, intending to help enable real-world applications.
Improved Gaussian Process Acquisition for Targeted Bayesian Optimization

A black-box optimization problem is considered, in which the function to be optimized can only be expressed in terms of a complicated stochastic algorithm that takes a long time to evaluate. The value returned is required to be sufficiently near to a target value, and uses data that has a significant noise component. Bayesian Optimization with an underlying Gaussian Process is used as an optimization solution, and its effectiveness is measured in terms of the number of function evaluations required to attain the target. To improve results, a simple modification of the Gaussian Process ‘Lower Confidence Bound’ (LCB) acquisition function is proposed. The expression used for the confidence bound is squared in order to better comply with the target requirement. With this modification, much improved results compared to random selection methods and to other commonly used acquisition functions are obtained.
Enter the Cyborgs: Health and Human Rights in the Digital Age

In Donna Haraway's 1992 A Cyborg Manifesto, the medical anthropologist describes the cyborg as a hybrid of machine and organism living the boundary between fact and fiction: We are all chimeras, theorized and fabricated hybrids of machine and organism. In 2020, the world that Haraway imagined has arrived, accelerated by the isolation and surveillance enforced in the COVID-19 pandemic. In this special section, contributors explore the role of big data, technology, and artificial intelligence in the prevention, detection, tracing, and treatment of COVID-19 in a world being rapidly reshaped by this pandemic. In 1992, Haraway observed that medicine was already witnessing the growth of people's dependency on computers or other machinery. But as of late 2020, the computer and the mobile phone have moved fully into the center of our lives. In order to mitigate the risk of COVID-19 transmission, a significant portion of the world's population now works, socializes, shops, and seeks entertainment and love online.
Sentence Transformers and Bayesian Optimization for Adverse Drug Effect Detection from Twitter

This paper describes our approach for detecting adverse drug effect mentions on Twitter as part of the Social Media Mining for Health Applications (SMM4H) 2020, Shared Task 2. Our approach utilizes multilingual sentence embeddings (sentence-BERT) for representing tweets and Bayesian hyperparameter optimization of sample weighting parameter for counterbalancing high class imbalance.
On the Basics of Scenarios for the Development of Social Education

The article describes the experience of developing scenarios for the development of Russian social education. The concept of a forecast scenario is formulated; the difference between this forecasting method and the foresight method is shown; the technology of developing a predictive scenario for social phenomena is characterized, the foundations for scenarios of the development of social education are presented (background data, description of the object and subject of the stage, scenario parameters); examples of developed scenarios are presented; their assessments by experts; correction of scenarios taking into account the new social and educational situation in 2020. On the basis of the formulated scenario provisions, new research topics are proposed for the problems of social education, the relation of predictive scenarios to the really unfolding processes in the sphere of social education is indicated.
Perfect photon absorption based on the optical parametric process*

The perfect photon absorption is studied in a cavity quantum electrodynamics (CQED) system, in which an optical parameter amplifier (OPA) is coupled to the cavity mode. This makes it possible to control the optical phase to realize the perfect photon absorption. It is found that in the presence of one and two injected fields, the perfect photon absorption is present in these two cases and can be controlled by adjusting the parametric phase. Moreover, different from the previous predictions of perfect photon absorption in atomic CQED systems, the perfect photon absorption can be changed significantly by the relative phase. Our work provides a new platform to use the parametric processes to make an available way to control the behaviors of photons and to take advantage of the optical phase to achieve the perfect photon absorption.
Artificial Intelligence in Education

The article examines the impact of Information Technology (IT) and Artificial Intelligence (AI) in education, the joint design of working with high technology and the need for effective policy development. The use of AI in education is an important topic for teachers from both a conceptual and a practical point of view. Ethical challenges in the training and working with AI are on the agenda. It is necessary to adapt the educational programs for future pedagogues in regards to implementing AI in teaching and training.
DESIGN OF ENGLISH LEARNING BASED ON LIVE STREAMING OF DIGITAL ERA IN COVID-19

The important of this paper is live streaming using gadget as a learning English speaking in Covid-19 era relying on the internet and utilizing the social media platform facebook. This research is a descriptive qualitative study, used on 40 students. The result identified seven steps to create a Facebook group and learning tool to produce lecture units, student activity sheets, and student worksheets. The analysis of this study discussed the seven steps of English learning design of English learning devices based on live streaming Facebook; 1) starting from creating an online Facebook group, 2) entering the design phase or design begins with entering or clicking on the facebook application, and then create a class facebook group, 3) write the name of the group of courses taught in class, 4) click type closed groups, 5) click to find the link, 6) click "Share" to see the class group link, 7) click copy to the clipboard. Student responses to learning are good, thus this learning design can be applied in the classroom. so the result of this design steps, They can practice learning English, especially speaking in live streaming video, which is a new activity for students on social media, namely Facebook.
Extracting meaning by idiomaticity: Description of the HSemID system at CogALex VI (2020)

The HSemID system, submitted to the CogALex VI Shared Task is a hybrid system relying mainly on metric clusters measured in large web corpora, complemented by a vector space model using cosine similarity to detect semantic associations. Although the system reached ra-ther weak results for the subcategories of synonyms, antonyms and hypernyms, with some dif-ferences from one language to another, it is able to measure general semantic associations (as being random or not-random) with an F1 score close to 0.80. The results strongly suggest that idiomatic constructions play a fundamental role in semantic associations. Further experiments are necessary in order to fine-tune the model to the subcategories of synonyms, antonyms, hy-pernyms and to explain surprising differences across languages. 1 Introduction
Quantifying Solar PV Variability at Multiple Timescales for Power Systems Studies

This paper presents two metrics - Fourier coefficients of irradiation ramps and average irradiation ramps to quantify solar photovoltaic (PV) variability at multiple temporal scales to improve the accuracy of power systems studies under high solar PV penetrations. The key findings based on these metrics are: (i) a single sensor significantly exaggerates the variability of a solar PV power plant at seconds to 1-minute temporal resolution, but it can be fairly accurate at lower than 1-minute resolutions, (ii) at each 1-minute and lower temporal resolution, largest solar irradiation ramps appear to be independent of the geographical location, and (iii) significant reduction in solar PV plant variability over a region with multiple plants is possible at all temporal scales, but more high temporal resolution data is needed to conclusively quantify the reduction.
Risk assessment method for IoT software supply chain vulnerabilities

Abstract With the development of IoT technology, the number of attacks against IoT software chain vulnerabilities is greater than ever, and a reasonable vulnerability assessment system needs to be established for research. The Common Vulnerability Scoring System (CVSS) is a free, public risk assessment system used by information security vendors to assess the severity of vulnerability threats. However, CVSS is insufficient because of the strong subjectivity in the selection of measurement standards and the allocation of evaluation index weights. Based on this reason, this paper designs and proposes a more objective risk assessment method for IoT software chain vulnerabilities, and verifies the feasibility and effectiveness of the method through experiments.
AMEX-AI-LABS: Investigating Transfer Learning for Title Detection in Table of Contents Generation

We present a transfer learning approach for Title Detection in FinToC 2020 challenge. Our proposed approach relies on the premise that the geometric layout and character features of the titles and non-titles can be learnt separately from a large corpus, and their learning can then be transferred to a domain-specific dataset. On a domain-specific dataset, we train a Deep Neural Net on the text of the document along with a pre-trained model for geometric and character features. We achieved an F-Score of 83.25 on the test set and secured top rank in the title detection task in FinToC 2020.
Ixamed's submission description for WMT20 Biomedical shared task: benefits and limitations of using terminologies for domain adaptation.

In this paper we describe the systems developed at Ixa for our participation in WMT20 Biomedical shared task in three language pairs, en-eu, en-es and es-en. When defining our approach, we have put the focus on making an efficient use of corpora recently compiled for training Machine Translation (MT) systems to translate Covid-19 related text, as well as reusing previously compiled corpora and developed systems for biomedical or clinical domain. Regarding the techniques used, we base on the findings from our previous works for translating clinical texts into Basque, making use of clinical terminology for adapting the MT systems to the clinical domain. However, after manually inspecting some of the outputs generated by our systems, for most of the submissions we end up using the system trained only with the basic corpus, since the systems including the clinical terminologies generated outputs shorter in length than the corresponding references. Thus, we present simple baselines for translating abstracts between English and Spanish (en/es); while for translating abstracts and terms from English into Basque (en-eu), we concatenate the best en-es system for each kind of text with our es-eu system. We present automatic evaluation results in terms of BLEU scores, and analyse the effect of including clinical terminology on the average sentence length of the generated outputs. Following the recent recommendations for a responsible use of GPUs for NLP research, we include an estimation of the generated CO2 emissions, based on the power consumed for training the MT systems.
SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing

This paper presents our approach to multi-class text categorization of tweets mentioning prescription medications as being indicative of potential abuse/misuse (A), consumption/non-abuse (C), mention-only (M), or an unrelated reference (U) using natural language processing techniques. Data augmentation increased our training and validation corpora from 13,172 tweets to 28,094 tweets. We also created word-embeddings on domain-specific social media and medical corpora. Our hybrid pipeline of an attention-based CNN with post-processing was the best performing system in task 4 of SMM4H 2020, with an F1 score of 0.51 for class A.
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains

We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e.g., dogs and cars). The goal is to learn a generative model that learns an intermediate distribution, which borrows a subset of properties from each domain, enabling the generation of images that did not exist in any domain exclusively. This challenging problem requires an accurate disentanglement of object shape, appearance, and background from each domain, so that the appearance and shape factors from the two domains can be interchanged. We augment an existing approach that can disentangle factors within a single domain but struggles to do so across domains. Our key technical contribution is to represent object appearance with a differentiable histogram of visual features, and to optimize the generator so that two images with the same latent appearance factor but different latent shape factors produce similar histograms. On multiple multi-domain datasets, we demonstrate our method leads to accurate and consistent appearance and shape transfer across domains.
The AFRL WMT20 News Translation Systems.

This report summarizes the Air Force Research Laboratory (AFRL) machine translation (MT) systems submitted to the news-translation task as part of the 2020 Conference on Machine Translation (WMT20) evaluation campaign. This year we largely repurpose strategies from previous years’ efforts with larger datasets and also train models with precomputed word alignments under various settings in an effort to improve translation quality.
Sentence Classification with Imbalanced Data for Health Applications

Identifying and extracting reports of medications, their abuse or adverse effects from social media is a challenging task. In social media, relevant reports are very infrequent, causes imbalanced class distribution for machine learning algorithms. Learning algorithms typically designed to optimize the overall accuracy without considering the relative distribution of each class. Thus, imbalanced class distribution is problematic as learning algorithms have low predictive accuracy for the infrequent class. Moreover, social media represents natural linguistic variation in creative language expressions. In this paper, we have used a combination of data balancing and neural language representation techniques to address the challenges. Specifically, we participated the shared tasks 1, 2 (all languages), 4, and 3 (only the span detection, no normalization was attempted) in Social Media Mining for Health applications (SMM4H) 2020 (Klein et al., 2020). The results show that with the proposed methodology recall scores are better than the precision scores for the shared tasks. The recall score is also better compared to the mean score of the total submissions. However, the F1-score is worse than the mean score except for task 2 (French).
Sentiment Analysis Model and Its Role in Determining Social Media’s Influence on Decision Making

In recent times, social media platforms have been acting as a significant means for framing a successful business. Its impact on the organizations is helping them to build an appropriate and confining atmosphere to help in the decision-making process which promotes customers to have a better experience in their regular practice. Convinced models have been developed using various social media platforms to refine the decision-making process, which indeed failed in providing persistent accuracy. In this research, we propose a model that minimizes the complications using sentiment analysis and performs certain steps of meticulous analysis which include influence and opinion analysis, data mining preprocessing techniques, machine learning and deep learning. To implement this model, we consider the input as the reviews and requirements of the customer who is utilizing the feature, to produce a valid and acceptable response. These results and evaluations will enable in enhancing the decision-making process of the organization.
Dental X-ray Image Segmentation using Octave Convolution Neural Network

In this paper, we present a Unet architecture made of octave convolution for dental image segmentation problem. In this architecture, the requirements for memory and accuracy are significantly improved compared to previous works in the literature. Compare to state-of-art models on this topic the classification accuracy in dental image segmentation is increased by %2, and the memory usage is decreased by %70. Suggested architecture showed a performance of success on ISBI2015 dataset.
Comparison of the effects of attention mechanism on translation tasks of different lengths of ambiguous words

In recent years, attention mechanism has been widely used in various neural machine translation tasks based on encoder decoder. This paper focuses on the performance of encoder decoder attention mechanism in word sense disambiguation task with different text length, trying to find out the influence of context marker on attention mechanism in word sense disambiguation task. We hypothesize that attention mechanisms have similar performance when translating texts of different lengths. Our conclusion is that the alignment effect of attention mechanism is magnified in short text translation tasks with ambiguous nouns, while the effect of attention mechanism is far less than expected in long-text tasks, which means that attention mechanism is not the main mechanism for NMT model to feed WSD to integrate context information. This may mean that attention mechanism pays more attention to ambiguous nouns than context markers. The experimental results show that with the increase of text length, the performance of NMT model using attention mechanism will gradually decline.
Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees

This study describes our proposed model design for the SMM4H 2020 Task 1. We fine-tune ELECTRA transformers using our trained SVM filter for data augmentation, along with decision trees to detect medication mentions in tweets. Our best F1-score of 0.7578 exceeded the mean score 0.6646 of all 15 submitting teams.