Reviewerly/exHarmony huggingface dataset:
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization

We study generating abstractive summaries that are faithful and factually consistent with the given articles. A novel contrastive learning formulation is presented, which leverages both reference summaries, as positive training data, and automatically generated erroneous summaries, as negative training data, to train summarization systems that are better at distinguishing between them. We further design four types of strategies for creating negative samples, to resemble errors made commonly by two state-of-the-art models, BART and PEGASUS, found in our new human annotations of summary errors. Experiments on XSum and CNN/Daily Mail show that our contrastive learning framework is robust across datasets and models. It consistently produces more factual summaries than strong comparisons with post error correction, entailment-based reranking, and unlikelihood training, according to QA-based factuality evaluation. Human judges echo the observation and find that our model summaries correct more errors.
Sequence-to-Sequence Learning with Latent Neural Grammars

Sequence-to-sequence learning with neural networks has become the de facto standard for sequence prediction tasks. This approach typically models the local distribution over the next word with a powerful neural network that can condition on arbitrary context. While flexible and performant, these models often require large datasets for training and can fail spectacularly on benchmarks designed to test for compositional generalization. This work explores an alternative, hierarchical approach to sequence-to-sequence learning with quasi-synchronous grammars, where each node in the target tree is transduced by a node in the source tree. Both the source and target trees are treated as latent and induced during training. We develop a neural parameterization of the grammar which enables parameter sharing over the combinatorial space of derivation rules without the need for manual feature engineering. We apply this latent neural grammar to various domains -- a diagnostic language navigation task designed to test for compositional generalization (SCAN), style transfer, and small-scale machine translation -- and find that it performs respectably compared to standard baselines.
Gomoku: analysis of the game and of the player Wine.

Gomoku, also known as five in a row, is a classical board game, ideally suited for quickly testing novel Artificial Intelligence (AI) techniques. With the aim of facilitating a developer willing to write a new Gomoku player, in this report we present an analysis of the main game concepts and strategies, which is wider and deeper than existing ones. Moreover, after discussing the general structure of an artificial player, we present and analyse a strong Gomoku player, named Wine, the code of which is freely available on the Internet and which is an excelent example of how a modern player is organised.
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models.

The task of learning from only a few examples (called a few-shot setting) is of key importance and relevance to a real-world setting. For question answering (QA), the current state-of-the-art pre-trained models typically need fine-tuning on tens of thousands of examples to obtain good results. Their performance degrades significantly in a few-shot setting (< 100 examples). To address this, we propose a simple fine-tuning framework that leverages pre-trained text-to-text models and is directly aligned with their pre-training framework. Specifically, we construct the input as a concatenation of the question, a mask token representing the answer span and a context. Given this input, the model is fine-tuned using the same objective as that of its pre-training objective. Through experimental studies on various few-shot configurations, we show that this formulation leads to significant gains on multiple QA benchmarks (an absolute gain of 34.2 F1 points on average when there are only 16 training examples). The gains extend further when used with larger models (Eg:- 72.3 F1 on SQuAD using BART-large with only 32 examples) and translate well to a multilingual setting . On the multilingual TydiQA benchmark, our model outperforms the XLM-Roberta-large by an absolute margin of upto 40 F1 points and an average of 33 F1 points in a few-shot setting (<= 64 training examples). We conduct detailed ablation studies to analyze factors contributing to these gains.
Do Long-Range Language Models Actually Use Long-Range Context?

Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve their predictions. Recent efforts to improve the efficiency of self-attention have led to a proliferation of long-range Transformer language models, which can process much longer sequences than models of the past. However, the ways in which such models take advantage of the long-range context remain unclear. In this paper, we perform a fine-grained analysis of two long-range Transformer language models (including the \emph{Routing Transformer}, which achieves state-of-the-art perplexity on the PG-19 long-sequence LM benchmark dataset) that accept input sequences of up to 8K tokens. Our results reveal that providing long-range context (i.e., beyond the previous 2K tokens) to these models only improves their predictions on a small set of tokens (e.g., those that can be copied from the distant context) and does not help at all for sentence-level prediction tasks. Finally, we discover that PG-19 contains a variety of different document types and domains, and that long-range context helps most for literary novels (as opposed to textbooks or magazines).
Deep learning method for karate motion identification using inertial sensor data

Small inertial sensors having low cost and simple structure have recently been developed owing to improvements in microelectromechanical systems. Therefore, inertial sensors are being widely used in various sports for quantitative measurements and athletes’ evaluation. However, no wearable device has been specifically designed to capture karate movements such as punching, blocking, and kicking, and these movements are being recorded manually in the field of karate training. Automated recording and identification of karate movements using wearable sensors can lead to improved training and performance, therefore we developed an identification method for various karate movements using inertial sensor data. First, we created a dataset of karate movements by acquiring measurements from 22 karate players at the karate club of Waseda University. Their competitive levels were wide-ranging, from Japanese local competition level to international competition level. Inertial sensors were attached to five parts of each participant’s body (right hand, left hand, right foot, right leg, and waist), and basic karate movements (reverse punch, upper block, and front kick) were performed by each participant for measurement. Then, we established the dataset containing inertial sensor data paired with the correct karate movement label. Next, we designed a deep learning network based on long short-term memory and trained the network using part of the dataset. We evaluated the network using leave-one-out cross-validation, obtaining the F1-scores of 0.90 for right reverse punch, 0.89 for left reverse punch, 0.88 for right upper block, 0.86 for left upper block, 0.90 for right front kick, and 0.90 for left front kick. In addition, the micro-average of the F1-score was 0.89. Therefore, high identification accuracies were achieved for all types of karate movements.
해양 (미세)플라스틱이 기후인자에 미치는 영향에 관한 고찰

Although the effects of marine microplastics on living organisms and their roles as carriers of organic pollutants to living organisms and the marine environment have already been studied, this paper focuses on the effects of marine microplastics on climate that haven’t been studied yet. Microplastics in the oceans absorb more solar radiation and alter the biophysical properties of the surface microlayer, resulting in an increase in the sea surface temperature (SST). An increase in the SST and the concentration of microplastics that cover the surface layer, interrupts the physical interaction between seawater and carbon dioxide (CO2) in the atmosphere. Consequently, the solubility of carbon dioxide in seawater is lowered, and thereby increases the retention time of CO2 in the atmosphere. Furthermore, plastics that are in the ocean can be decomposed by solar radiation, such as UV-B rays and directly release greenhouse gases such as methane, ethylene, ethane, and propylene, and thereby accelerates the greenhouse effect. Therefore, considering the marine microplastics as a new factor in the ocean- atmosphere coupled model, the extent of the impact of microplastics on weather should be quantitatively reviewed.
Evaluating State-of-the-Art Classification Models Against Bayes Optimality

Evaluating the inherent difficulty of a given data-driven classification problem is important for establishing absolute benchmarks and evaluating progress in the field. To this end, a natural quantity to consider is the \emph{Bayes error}, which measures the optimal classification error theoretically achievable for a given data distribution. While generally an intractable quantity, we show that we can compute the exact Bayes error of generative models learned using normalizing flows. Our technique relies on a fundamental result, which states that the Bayes error is invariant under invertible transformation. Therefore, we can compute the exact Bayes error of the learned flow models by computing it for Gaussian base distributions, which can be done efficiently using Holmes-Diaconis-Ross integration. Moreover, we show that by varying the temperature of the learned flow models, we can generate synthetic datasets that closely resemble standard benchmark datasets, but with almost any desired Bayes error. We use our approach to conduct a thorough investigation of state-of-the-art classification models, and find that in some -- but not all -- cases, these models are capable of obtaining accuracy very near optimal. Finally, we use our method to evaluate the intrinsic hardness of standard benchmark datasets, and classes within those datasets.
Foreign Students in Polyethnic Space of Russian Universities: Problems of Adaptation and Integration in Research Practices

Objective of the study is to consider the features of disciplinary practices in the field of studying foreign students in Russia in the context of their adaptation and integration to the multi-ethnic conditions of the host regional community and the university environment. The methodological basis of the research is based on the principles and provisions of an interdisciplinary approach, which assumes the possibility and prospects for the synthesis of accumulated scientific knowledge in a particular scientific field in order to form an integrated approach to the study of an urgent scientific problem, in this case related to the socio-cultural adaptation of migrant students. Research results allowed us to substantiate the point of view about the greatest methodological potential in studying the adaptation of foreign students in the multi-ethnic space of Russian regions and universities of a multidisciplinary approach, which, being at the junction of disciplinary practices, allows us to optimally integrate the accumulated knowledge in this subject field, formed in various fields and spheres of social and humanitarian science. &nbsp; Prospects of the study are determined by the lack of independent sociological research carried out in the subject field, which actualizes the factor of polyethnicity in the adaptation of foreign students studying at Russian universities. The presence of separate storylines included in the general adaptation context of the problems related to foreign students in Russia does not remove from the scientific agenda the issue of conducting independent sociological research aimed at studying the barriers to adaptation and integration of foreign citizens studying at universities in multi-ethnic regions of Russia. This position is justified by the empirically confirmed facts of the influence of the ethnic/racial affiliation of foreign students on the difficulties of adaptation in the multi-ethnic environment of Russian universities and integration into the local regional community. &nbsp
El bioarte a la luz de la propiedad intelectual (Bioart in the Scope of Intellectual Property)

Spanish Abstract: Este articulo de revision tiene como objetivo plantear los retos del bioarte frente a la propiedad intelectual, y, en consecuencia, las barreras que estos artistas pueden encontrar tanto en la proteccion de sus obras, como en el uso de tecnologias protegidas a la hora de desarrollar sus procesos creativos. Para esto, se analizan los conceptos y tecnicas usadas en varias obras de bioarte creadas desde finales del siglo xx frente a los postulados tradicionales de la propiedad intelectual, de acuerdo con la normatividad de la CAN, asi como jurisprudencia y doctrina nacional e internacional. En tanto el bioarte es producto de una relacion compleja entre arte y ciencia, el articulo se divide en dos bloques: el primero estudia el bioarte desde el Derecho de autor, abarcando la no proteccion de las ideas, irrelevancia del merito y criterios de originalidad, creacion intelectual, y fijacion, y, el segundo lo estudia desde el Derecho de patentes, abarcando elegibilidad, criterios de patentabilidad y libertad de operacion. Tambien se incluyen reflexiones en torno a la discusion bioetica y su relacion con el objeto de estudio.English Abstract: This review article seeks to show the different challenges bioart generates in the scope of intellectual property, and therefore, the barriers bioart artists can find both in the search of protection for their works, and in the use of patented technologies while developing their creative processes. For this, the concepts and technics used in various bioart works created from the end of the twentieth century are analyzed in the scope of traditional intellectual property postulates, according to acn regulations, and national and international jurisprudence and doctrine. Considering that bioart is the product of a complex relationship between art and science, this article is divided into two sections: the first section covers bioart in the scope of copyright, including non-protection of ideas, irrelevance of merit, and originality, intellectual creation, and fixation criteria; and the second section covers bioart in the scope of patent law, including eligibility, patentability requirements, and freedom to operate. It also covers bioethical discussions and their relationship with bioart.
Meeting Summarization with Pre-training and Clustering Methods

Automatic meeting summarization is becoming increasingly popular these days. The ability to automatically summarize meetings and to extract key information could greatly increase the efficiency of our work and life. In this paper, we experiment with different approaches to improve the performance of query-based meeting summarization. We started with HMNet\cite{hmnet}, a hierarchical network that employs both a word-level transformer and a turn-level transformer, as the baseline. We explore the effectiveness of pre-training the model with a large news-summarization dataset. We investigate adding the embeddings of queries as a part of the input vectors for query-based summarization. Furthermore, we experiment with extending the locate-then-summarize approach of QMSum\cite{qmsum} with an intermediate clustering step. Lastly, we compare the performance of our baseline models with BART, a state-of-the-art language model that is effective for summarization. We achieved improved performance by adding query embeddings to the input of the model, by using BART as an alternative language model, and by using clustering methods to extract key information at utterance level before feeding the text into summarization models.
Моделі фахово орієнтованого навчання хімічних дисциплін

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Android-Based Area Mapping in the Village of Kampunganyar - Banyuwangi

Kampunganyar is one of the villages located in Glagah District, Banyuwangi Regency. In this village the internet is connected smoothly that can be accessed by anyone. So it has called "Smart Kampung". This village has a lot of potential including tourism, plantations, agriculture, Micro Small and Medium Enterprises or known as UMKM. Many people do not know about the potential of this village. Therefore, in community service activities we built a mapping area system in Kampunganyar village that covers all potential areas with Android-based. In this system there is all information about the potential of the village in each region and its location. So that the general public can access the information through mobile devices freely. The public can find out what the tourism potential is and the location and photos of the tour. In addition, there are data on plantations and agriculture in several locations that can be known from this system. This system is very useful for marketing the potential of villages in the village of Kampunganyar so that it can be known by the general public.
Analisis Kedalaman Gerusan Lokal (Local Scouring) di Hilir Bangunan pelimpah (Stepped Modified Spillway) dengan Model Fisik 2D

Spillway is a hydraulic structure whose existence is very important because it is built to release excess water or flood discharge that cannot be accommodated in the dam. The hydraulics analysis of this study used a spillway design (Stepped modified Spillway) with a 2-D physical model and the sediment used was sand with a uniform diameter of 1.8 mm or passed sieve no. 16. This research was carried out 5 times for every 25 minutes of upstream water level observations. In the Stepped spillway spillway the maximum scour (dsmax) occurs at an upstream height (H0) of 3 cm of 0.125 m with an Lds of 0.09 m from the downstream of the spillway. Meanwhile, for the Stepped Modified Spillway spillway, the maximum scour (dsmax) occurs at an upstream height ( H0) 3 cm is 0.076 m with Lds of 0.221 m. This shows that the greater the flow rate, the greater the depth of scour that occurs. So it can be concluded that the Lacey method is the closest to the laboratory results. This is because a balance has been achieved. The deeper the local scour that occurs, the more volume of sediment carried downstream of the weir. Keywords: Stepped and stepped modified spillway, local scour, sediment.
Developing games while learning programming

The article proposes the development of games in the learning process as a possible approach to increase the motivation of students. The well-known approach of connecting learning to play has been chosen. Linguistic and algorithmic elements are considered through interesting tasks - games. It turns out that games can be programmed, even with basic programming knowledge. Students take on the role of game developers, which gives them the opportunity to practice a professional activity very similar to real one. Based on several selected game tasks, it is discussed how games can be developed in parallel with learning basic programming structures. The development of computer games during the training can spark interest in new topics and stimulate the memorization of what has already been learned.
No Oil, Just Auto-Tune !

Cher, T-Pain, Saez, Châton, Booba, Benjamin Biolay, Bon Iver, Yung Beef, Lady Gaga, Neil Young, and PNL: these artists share a common practice — the use of Auto-Tune, a software that permits the adjustment or correction of the voice after its recording or performance. If initially Auto-Tune was used to correct the pitch, it now is also used to produce sound modulations that give a robotic, artificial, non-linear character to the human voice. The first recording to use Auto-Tune was Cher’s “Believe” in 1998: a direct hit. Since then, if some artists — like Jay-Z and its record D.O.A. for “Death of the Auto-Tune” (2009) — criticize the practice, Auto-Tune has conquered the music industry and is said to be used in most of the musical productions of today (Reynolds 2019) regardless of their aesthetic leanings. In spite of this broad circulation, there are few academic papers tackling its implications. What is at stake in Auto-Tune?
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions. However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are treated equally in previous works. In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. Our method achieves the state-of-the-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets
Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging

There have been efforts in cross-lingual transfer learning for various tasks.We present an approach utilizing an interpolative data augmentation method, Mixup, to improve the generalizability of models for part-of-speech tagging trained on a source language, improving its performance on unseen target languages.Through experiments on ten languages with diverse structures and language roots, we put forward its applicability for downstream zeroshot cross-lingual tasks.
ARTE/EDUCAÇÃO, ENSINO NÃO FORMAL E A PANDEMIA DA COVID-19: OS DESAFIOS DAS AULAS ONLINE

In this text, I propose to discuss my experiences as an art/educator in the Pró Criança Movement, an NGO in Recife -PE, during the current Covid-19 pandemic.The visual arts classes at the NGO took a remote format, from March 2020 to the present day and it has been a challenge for me who worked in person in the service proposed by non-formal education.I start with the introduction talking about my place of speech and some characteristics of the non-formal space of education, with references such as Carvalho (2008) and Gohn (2016;2020).Then, I bring my perceptions, challenges, adaptations, mistakes and successes about the current moment and art/education remotely in visual arts classes with children and teenagers.How can you attract them?What matters most to them in the teaching of visual arts that makes them keep the focus of teaching/learning in this area?