Reviewerly/exHarmony huggingface dataset:
Image Aesthetic Assessment Based on Image Classification and Region Segmentation

The main goal of this paper is to study Image Aesthetic Assessment (IAA) indicating images as high or low aesthetic. The main contributions concern three points. Firstly, following the idea that photos in different categories (human, flower, animal, landscape, …) are taken with different photographic rules, image aesthetic should be evaluated in a different way for each image category. Large field images and close-up images are two typical categories of images with opposite photographic rules so we want to investigate the intuition that prior Large field/Close-up Image Classification (LCIC) might improve the performance of IAA. Secondly, when a viewer looks at a photo, some regions receive more attention than other regions. Those regions are defined as Regions Of Interest (ROI) and it might be worthy to identify those regions before IAA. The question “Is it worthy to extract some ROIs before IAA?” is considered by studying Region Of Interest Extraction (ROIE) before investigating IAA based on each feature set (global image features, ROI features and background features). Based on the answers, a new IAA model is proposed. The last point is about a comparison between the efficiency of handcrafted and learned features for the purpose of IAA.
Implementasi Model Pembelajaran Discovery Learning Untuk Meningkatkan Aktivitas dan Hasil Belajar Matematika Siswa SD

This research aims to improve the activities and results of learning mathematics using the discovery learning model. This type of research is a classroom action research conducted in two cycles. Subjects of this study was grade III B SDIT Generasi Rabbani Bengukulu. The data collection instrument used student activity observation sheets and learning outcomes tests. The data analysis technique uses arithmetic mean and the percentage of learning completeness. The results showed: 1) Teacher activity in the first cycle obtained an average of 3.38 and in the second cycle an average of 3.81, 2) Student activity in the first cycle obtained an average of 3.13 and in the second cycle an average of 3.67, 3) Learning result in the first cycle were obtained an average of 71.43 with learning completeness 64.29 %, an increase in the second cycle obtained an average value of 76.79 with 82.14% completeness learning. The conclusion of this study that the use of the discovery learning model can increase the activity and learning result of class III B students of SDIT Generasi Rabbani, Bengkulu City
Smash at SemEval-2020 Task 7: Optimizing the Hyperparameters of ERNIE 2.0 for Humor Ranking and Rating

The use of pre-trained language models such as BERT and ULMFiT has become increasingly popular in shared tasks, due to their powerful language modelling capabilities.Our entry to SemEval uses ERNIE 2.0, a language model which is pre-trained on a large number of tasks to enrich the semantic and syntactic information learned.ERNIE's knowledge masking pretraining task is a unique method for learning about named entities, and we hypothesise that it may be of use in a dataset which is built on news headlines and which contains many named entities.We optimize the hyperparameters in a regression and classification model and find that the hyperparameters we selected helped to make bigger gains in the classification model than the regression model.
Approximating the Median under the Ulam Metric

We study approximation algorithms for variants of the median string problem, which asks for a string that minimizes the sum of edit distances from a given set of m strings of length n. Only the straightforward 2-approximation is known for this NP-hard problem. This problem is motivated e.g. by computational biology, and belongs to the class of median problems (over different metric spaces), which are fundamental tasks in data analysis.Our main result is for the Ulam metric, where all strings are permutations over [n] and each edit operation moves a symbol (deletion plus insertion). We devise for this problem an algorithms that breaks the 2-approximation barrier, i.e., computes a (2 – δ)-approximate median permutation for some constant δ > 0 in time Õ(nm2 + n3). We further use these techniques to achieve a (2 – δ) approximation for the median string problem in the special case where the median is restricted to length n and the optimal objective is large Ω(mn).We also design an approximation algorithm for the following probabilistic model of the Ulam median: the input consists of m perturbations of an (unknown) permutation x, each generated by moving every symbol to a random position with probability (a parameter) ∊ > 0. Our algorithm computes with high probability a (1 + o(1/∊))-approximate median permutation in time O(mn2 + n3).
Color Content Descriptors of Images by Vector Quantization

In the paper, we propose color content descriptors of images by vector quantization in RGB color space compared to scalar ones.In order to obtain a much more accurate discrete representation of this space we use our algorithm for optimization of vector quantizers.We introduce several modifications of these descriptors such as global, structural as well as of dominant color.We consider a different number of bins and evaluated the similarity of the color content of images using mean square error of color histograms of a reference image and a searched image.Then, the color content of the image with the minimum error was the most similar to the reference image.We also used a parameter of variance, if the color content of several searched images was very similar based on the mean quadratic error for the structural descriptor.By vector quantization of RGB space we can achieve 2-3 times decreasing the number of bins at the same accuracy.
Developing Edu-Game “Ulun Smart-Kid” Learning Media of Banjar Language and Game Agent with Finite State Machine Model

The research entitled edu-game "Ulun Smart-Kid" is a research development of puzzle game type designed to hone memory in the form of language.In this game player must arrange letters in random and create a word in Banjar language.AI technology (artificial intelligence) will also be applied to this research.Using the finite state machine model method game that is built will have game agent character that will accompany a child to play like a teacher.The results of this study make this entitled educational become more interesting and interactive to children.Game agent in the form of teachers can give a sad and happy expression accordingly from the game environment.It accompanies child like a teacher.
Counting Maximal Satisfiable Subsets

Given an unsatisfiable set of constraints F, a maximal satisfiable subset (MSS) is a maximal subset of constraints C ⊆ F such that C is satisfiable. Over the past two decades, the steady improvement in runtime performance of algorithms for finding MSS has led to an increased adoption of MSS-based techniques in wide variety of domains. Motivated by the progress in finding an MSS, the past decade has witnessed a surge of interest in design of algorithmic techniques to enumerate all the MSSes, which has subsequently led to discovery of new applications utilizing enumeration of MSSes. The development of techniques for finding and enumeration of MSSes mirrors a similar phenomenon of finding and enumeration of SAT solutions in the early 2000s, which subsequently motivated design of algorithmic techniques for model counting. In a similar spirit, we undertake study to investigate the feasibility of MSS counting techniques. In particular, the focus point of our investigation is to answer whether one can design efficient MSS counting techniques that do not rely on explicit MSS enumeration. The primary contribution of this work is an affirmative answer to the above question. Our tool, CountMSS, uses a novel architecture of a wrapper W and a remainder R such that the desired MSS count can be expressed as |W| − |R|. CountMSS relies on the advances in projected model counting to efficiently compute |W| and |R|. Our empirical evaluation demonstrates that CountMSS is able to scale to instances clearly beyond the reach of enumeration-based techniques.
SSN_NLP_MLRG at SemEval-2020 Task 12: Offensive Language Identification in English, Danish, Greek Using BERT and Machine Learning Approach

Offensive language identification is to detect the hurtful tweets, derogatory comments, swear words on social media. As an emerging growth of social media communication, offensive language detection has received more attention in the last years; we focus to perform the task on English, Danish and Greek. We have investigated which can be effect more on pre-trained models BERT (Bidirectional Encoder Representation from Transformer) and Machine Learning Approaches. Our investigation shows the difference performance between the three languages and to identify the best performance is evaluated by the classification algorithms. In the shared task SemEval-2020, our team SSN_NLP_MLRG submitted for three languages that are Subtasks A, B, C in English, Subtask A in Danish and Subtask A in Greek. Our team SSN_NLP_MLRG obtained the F1 Scores as 0.90, 0.61, 0.52 for the Subtasks A, B, C in English, 0.56 for the Subtask A in Danish and 0.67 for the Subtask A in Greek respectively.
Revisiting SincNet: An Evaluation of Feature and Network Hyperparameters for Speaker Recognition

The SincNet architecture was recently introduced as an approach to the speaker recognition task. Its main innovation was the sinc layer-an elegant and lightweight way of extracting features from speech. Despite good performance on multiple datasets, little information was provided on the architectural choices. In this work, we aim to shed some light on the importance of the network topology and various hyperparameters. We replace the original network trunk with a lightweight trunk inspired from residual networks (ResNets) and optimize its hyperparameters. Furthermore, we carry an extensive study on the sinc layer's hyperparameters. Our main finding is that the stride and window size of the feature extractor plays a crucial role in obtaining good performance. Further experiments on conventional features, such as MFCCs and FBANKs, yield similar conclusions; in fact, by using optimal values for these two hyperparameters, traditional features are able to match the performance of sinc features. Surprisingly, the best results obtained go against conventional wisdom: an analysis window of only a couple of milliseconds and a stride of only a couple of samples are found to give the best results. Our code is available at https://bitbucket.org/doneata/sincnet.
Assessing the Trend of Climate Change in the Western Nepal

This study assesses the change in the climatic variables in the western region of Nepal.Monthly data on minimum temperature, maximum temperature and rainfall for five stations, namely, Chame, Lumle, Khudibazar, Khairenitar and Dumkauli of Manang, Kaski, Lumjung, Tanahu and Nawalparasi districts respectively, obtained from the Department of Hydrology and Meteorology (DHM) of Nepal are considered.Data availability for these three variables are not uniform and ranges earlier from 1970 to the latest 2018.To measure the change in the climatic variables, the first data were deseasonalized using classical multiplicative technique taking a moving average of twelve periods and central moving average and finally, the ordinary least squared method was applied to estimate the trend.Further, the student's t-test confirmed whether there is any significant change or time trend in the variable.The study also performed both Mann-Kendall and Sen's slope tests for robustness check to the trend.The study result shows that maximum temperature has increased in all stations; the minimum temperature has increased in Khudibazar, Khairenitar and Dumkauli stations; and rainfall has increased in Chame and, decreased in Lumle and Khairenitar stations.Further, both maximum temperature and minimum temperature have increased faster in higher altitudes; and rainfall has decreased slower in lower altitudes.Both Mann-Kendall and Sen's slope tests are consistent with the result.The outcome of the study which is based on local stations could be useful information for making climate change adaption strategy at the local level.
Augmented Reality and Journalism: 10 use-case analysis from television, printing and web media platforms

Augmented reality has inaugurated new developments that enable the creation of content of various kinds. Based on the case study methodology, the work analyzes 10 successful experiences of using augmented reality linked to television, the press and digital media. The investigation concludes that the use of augmented reality without a journalistic purpose can harm the media.
Psychological Analysis of Occupational Conditions in Extreme Environments and the Structure of Non-Standard Situations

Professional coaching career is associated with a wide range of issues and problems which need to be solved and are directly or indirectly determined by creative activity. Therefore, an appropriate training process of students, in particular prospective coaches, is an essential scientific and practical problem. The article presents a thorough psychological analysis of the structure of decision-making in extreme situations and identifies different types of non-standard situations in a coaching job. Also, it clarifies such concepts as “situation”, “non-standard situation” and “extreme situation” in the activity of the individual. It describes different types of non-standard situations which can be manifested in professional coaching and their impact on the human psyche. It analyzes extreme conditions in the activities of athletes and coaches and the role of various mental functions and personality traits in problem-solving. Importantly, the article emphasizes the role of the coach’s reflection and self-regulation in solving non-standard tasks under non-standard conditions. It indicates that two theoretical views on the nature of the human psyche and thinking correspond to two approaches to studying this particular problem. According to the first one, that is an adaptive approach, creative thinking as a form of behaviour is the result of learning, and only some individuals have creative ability. To teach creativity, however, one needs to include the elements of creative behaviour (problem-solving) and heuristics in the educational material. Another approach assumes that thinking is a productive process emerging as a result of a certain problematic situation. Prerequisites for the creative process are cognitive abilities and research activities of the individual.
A Straightforward Approach to Narratologically Grounded Character Identification

One of the most fundamental elements of narrative is character: if we are to understand a narrative, we must be able to identify the characters of that narrative.Therefore, character identification is a critical task in narrative natural language understanding.Most prior work has lacked a narratologically grounded definition of character, instead relying on simplified or implicit definitions that do not capture essential distinctions between characters and other referents in narratives.In prior work we proposed a preliminary definition of character that was based in clear narratological principles: a character is an animate entity that is important to the plot.Here we flesh out this concept, demonstrate that it can be reliably annotated (0.78 Cohen's κ), and provide annotations of 170 narrative texts, drawn from 3 different corpora, containing 1,347 character co-reference chains and 21,999 non-character chains that include 3,937 animate chains.Furthermore, we have shown that a supervised classifier using a simple set of easily computable features can effectively identify these characters (overall F 1 of 0.90).A detailed error analysis shows that character identification is first and foremost affected by co-reference quality, and further, that the shorter a chain is the harder it is to effectively identify as a character.We release our code and data for the benefit of other researchers 1 .
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification

The classic deep learning paradigm learns a model from the training data of a single task and the learned model is also tested on the same task.This paper studies the problem of learning a sequence of tasks (sentiment classification tasks in our case).After each sentiment classification task is learned, its knowledge is retained to help future task learning.Following this setting, we explore attention neural networks and propose a Bayes-enhanced Lifelong Attention Network (BLAN).The key idea is to exploit the generative parameters of naïve Bayes to learn attention knowledge.The learned knowledge from each task is stored in a knowledge base and later used to build lifelong attentions.The constructed lifelong attentions are then used to enhance the attention of the network to help new task learning.Experimental results on product reviews from Amazon.com show the effectiveness of the proposed model.
EXSEQREG: Explaining sequence-based NLP tasks with regions with a case study using morphological features for named entity recognition

The state-of-the-art systems for most natural language engineering tasks employ machine learning methods. Despite the improved performances of these systems, there is a lack of established methods for assessing the quality of their predictions. This work introduces a method for explaining the predictions of any sequence-based natural language processing (NLP) task implemented with any model, neural or non-neural. Our method named EXSEQREG introduces the concept of region that links the prediction and features that are potentially important for the model. A region is a list of positions in the input sentence associated with a single prediction. Many NLP tasks are compatible with the proposed explanation method as regions can be formed according to the nature of the task. The method models the prediction probability differences that are induced by careful removal of features used by the model. The output of the method is a list of importance values. Each value signifies the impact of the corresponding feature on the prediction. The proposed method is demonstrated with a neural network based named entity recognition (NER) tagger using Turkish and Finnish datasets. A qualitative analysis of the explanations is presented. The results are validated with a procedure based on the mutual information score of each feature. We show that this method produces reasonable explanations and may be used for i) assessing the degree of the contribution of features regarding a specific prediction of the model, ii) exploring the features that played a significant role for a trained model when analyzed across the corpus.
A complete classification of de mersenne´s primes and its iomplications for computing

A study of Mersenne’s primes is carried out using the multiplicative group modulo 360 and a complete classification is obtained by its residual classes. This allows the search for Mersenne’s primes to be classified into four subgroups mutually exclusive (disjoint) and contributes to the ordered selection of exponents to be computationally tested. According to this idea, Mersenne’s trapeze is presented with the purpose of giving a geometric representation for this classification. Finally, from one of the theorems presented and proven for primes in modulo 360, a conjecture is established that could be solved by computing.
GMM-Based Evaluation of Synthetic Speech Quality Using 2D Classification in Pleasure-Arousal Scale

The paper focuses on the description of a system for the automatic evaluation of synthetic speech quality based on the Gaussian mixture model (GMM) classifier. The speech material originating from a real speaker is compared with synthesized material to determine similarities or differences between them. The final evaluation order is determined by distances in the Pleasure-Arousal (P-A) space between the original and synthetic speech using different synthesis and/or prosody manipulation methods implemented in the Czech text-to-speech system. The GMM models for continual 2D detection of P-A classes are trained using the sound/speech material from the databases without any relation to the original speech or the synthesized sentences. Preliminary and auxiliary analyses show a substantial influence of the number of mixtures, the number and type of the speech features used the size of the processed speech material, as well as the type of the database used for the creation of the GMMs on the P-A classification process and on the final evaluation result. The main evaluation experiments confirm the functionality of the system developed. The objective evaluation results obtained are principally correlated with the subjective ratings of human evaluators; however, partial differences were indicated, so a subsequent detailed investigation must be performed.
Metacognitive Schemes as a Tool for Teaching English to Young Learners: Psychological Discourse

The article presents the results of a theoretical and empirical analysis of the problem of optimizing teaching English to students of primary school age, taking into account the requirements of the modern information society. It was stated that training should format an active approach to modeling the knowledge and competencies of students. This position of the authors allows us to consider thinking in a foreign language as the goal of learning and an indicator of the degree of language proficiency. As a research problem, the authors considered an attempt to evaluate the heuristic and developing resource of metacognitive schemes in the logic of projective-recursive technology for teaching English to primary school children. The authors present the results of a study of the dominant type of thinking of primary school students as an indicative construct of metacognitive development. It is proved that younger students are mostly characterized by sign and symbolic thinking; this confirms the possibility of using metacognitive schemes in teaching English to children of this age group. Particular attention is paid to the analysis of the practical experience of using metacognitive schemes in the practice of teaching English to primary school children at the Educational Centre «Interclass» (Kryvyi Rih). This technique involves the construction of a mental denotative graph that performs the function of sense formation and mental interpretations modeling in the students' minds. The obtained results complement the materials presented by scientists regarding the trends in teaching English to primary school children using a resource of metacognitive schemes.