Reviewerly/exHarmony huggingface dataset:
Motivational Goal Bracketing with Non-Rational Goals

We provide a tractable model of motivational goal bracketing by a present-biased individual, extending previous work to show that the main insights from models with rational goals carry over to a setting with non-rational goals. Goals motivate because they serve as reference points that make substandard performance psychologically painful. A broad goal allows high performance in one task to compensate for low performance in the other. This partially insures against the risk of falling short of ones' goal(s), but creates incentives to shirk in one of the tasks. Narrow goals have a stronger motivational force and thus can be optimal, providing an explanation for observed instances of narrow bracketing. In particular, if one task outcome becomes known before working on the second task, narrow bracketing is always optimal.
Classification of product review polarity using LSTM

In this rapid increasing technological world, the data can be taken from various social networking platforms such as Facebook, Twitter, LinkedIn etc. The data is mainly in the form of text. The Customer reviews play a very important role, while buying the products . In various machine learning techniques were used to extract the polarity from the reviews but those machine learning techniques failed to understand the semantics and context of the review. The sequential model capture the semantics of the product reviews. In this paper we propose LSTM (Long Short Term Memory) technique, which classifies the polarity of the product reviews automatically. The results indicate that the LSTM Technique outperform over the baseline Machine Learning Techniques.
Single Image Dehazing using a Weighted Fusion of Dark and Bright Channel Prior with Gamma Correction

Nowadays, haze or fog has become a major hurdle for several computer vision applications. The images captured under such scenarios usually have poor visibility. This is because haze primarily affects the air light and hides the scene details. Such images, when directly used for computer vision algorithms, affect the performance of these algorithms and cause unsatisfactory results. This paper proposes an improved haze removal technique that uses a prior-based approach for dehazing. The method makes use of a weighted fusion of Dark and Bright Channel Prior for estimating the transmission medium. Further, an improved version of Color Constancy Prior is used to calculate the ambient illumination and Gamma Correction was used for post-processing. The comparative analysis was done using the PSNR, SSIM, and Entropy scores have shown the efficiency of the proposed method.
IMPROVING THE STUDENTS’ WRITING SKILL IN DESCRIPTIVE TEXT USING MIND MAPPING

The students had difficulties writing such as; organizing and exploring the idea, combining the sentences, and applying grammatical structure. The purpose of this study is to improve the students’ writing skill using mind mapping. The research method used was Classroom Action Research in which procedurally required the collaboration between the researcher and the English teacher. This subject of the study was the seventh grade which consist 30 students at SMP 6 Pasuruan in 2017/2018 academic year. To collect the data, the researcher used the instruments such interview, observation, documentation, and test. The findings of this research indicated that the implementation of mind mapping was successful in improving the students writing skill. The improvement of the students’ quality of writing could be seen from the number of the students’ who achieved the criteria of success. The criteria of success of this study was if equal to or more 75% of the students who passed the KKM. In cycle one the researcher found that there were 70% and 96% in cycle two who achieve the criteria of success. In conclusion, mind mapping very helpful for the students to improve their writing skill. Keywords : improving the students’ writing, mind mapping, writing skill
KEMAMPUAN MENGANALISIS TEKS CERITA PENDEK PADA SISWA KELAS XI SEMESTER GENAP SMA TAMANSISWA BANDAR LAMPUNG TAHUN PELAJARAN 2018/2019

The problems in this paper are related to students' ability to analyze short story texts. The short story analyzed is a short story titled 'Rasa' by Putu Wijaya. The purpose of the study was to find out and describe the ability to analyze short story texts by looking at aspects of the study including the accuracy of answers, the logic of argumentation, and the accuracy of words and sentences. The research method uses qualitative descriptive. The population and study sample were only 25 students. The technique of collecting data is by assigning students to read and examine the short story text and analyze it. Analysis was carried out by qualitative analysis. Based on the analysis, the results of the study showed that the ability to analyze short story texts in class XI even semester Tamansiswa Bandar Lampung High School 2018/2019 Year showed 76 in the good category. The logic of the argumentation or disclosure of short story arguments in class XI students in analyzing short story texts on the plot there are some that are not and are not logical. In addition, the accuracy of words and sentences or the breakdown of short stories in class XI students still use inappropriate words and sentences. The difficulties of students in analyzing short stories are in the groove because the grooves are not only flowing forward, backward or mixed. But it must explain problems, conflicts, climax and so on. Students are still mistaken in understanding the flow in the short stories that have been provided. Some students do not write an explanation or proof in determining the intrinsic element.
Text mining in career studies: generating insights from unstructured textual data

The abundance of unstructured data, primarily consisting of text, coupled with the development of modern techniques to analyze them, offer new opportunities for theory generation, theory testing, and contextualization within the field of career studies. This chapter explains the process of text mining as it may be applied to the study of careers, starting with text preprocessing and ending with the application of analytical techniques. Both conventional techniques such as latent semantic and principal components analysis, and more novel text analytical techniques such as word embeddings are discussed with the hope of inspiring careers researchers to further embrace text analysis as a means to answer novel research questions. Furthermore, this chapter elaborates on how to evaluate the validity of information extracted from text. In order to illustrate the text analytical process, we provide an example that leverages an abundant and growing source of data that may be of interest to career researchers, namely, vacancy data. In the example, vacancy data are used to identify those knowledge, skills and abilities that (co)determine the salary associated with particular occupations. We end the chapter by providing a guide on how text mining can be applied to study existing and new career concepts.
Web Server Based StackGAN-v2 Implementation

Implementation of StackGAN on a website with the help of a web server to generate unique pictures based on a description given by a user. Computer vision generating good quality images from the user's text description is a troublesome problem and has many useful applications. Samples from current Text to Image methods can essentially demonstrate comprehension of the explanations given, but they fail to provide the information needed and the vivid sections of the objects. With this paper, we use Stacked Generative Adversarial Networks (StackGAN) to get realistic image using the text descriptions given by the user. Via a method of sketch-refinement we break the hard issue down into more manageable subproblems. We have used StackGAN because StackGAN-v2 shows more robust training behavior than StackGAN-v1 by approximating several distributions together. In StackGAN images are generated from different branches of the tree at multiple scales corresponding to the same scene. Comprehensive tests and comparisons with benchmark data sets indicate that significant changes are made by the suggested approach in generating photorealistic images based on text descriptions. Keywords: Text to Image; StackGAN-v2; GANs
A movie recommendation model combining time information and probability matrix factorisation

A deep analysis and discussion of matrix factorisation technologies are given in this paper taking into account the defects of traditional collaborative filtering recommendation algorithms. In addition, we provide an analysis of the effects of feature vector dimensions on the recommendation quality and efficiency of a probability matrix factorisation (PMF) algorithm. A PMF algorithm will lead to inaccurate recommendations if it does not consider possible dynamic changes in a user's interest over time. Accordingly, a TPMF model, a PMF algorithm integrated with time information, is proposed in this article. Its feasibility and effectiveness are empirically verified using movie recommendation datasets, and higher prediction accuracy is confirmed compared to existing recommendation algorithms.
Insertion-based Tree Decoding

Sequences are typically decoded in a leftto-right fashion, requiring as many decoding steps as there are tokens in the sequence.Recently, several works have proposed nonautoregressive decoders that are sub-linear, allowing to decode a sequence using fewer decoding steps than the length of the sequence, and thus substantially speed up inference.In contrast, non-autoregressive decoding of trees is less well-analysed, even though trees are used in important applications like semantic parsing and code generation.In this work, we present a novel general-purpose partially autoregressive tree decoder that uses treebased insertion operations to generate trees in sub-linear time.We evaluate our approach on semantic parsing and compare it against strong baselines, including an insertion-based sequence decoder.The results demonstrate that the partially autoregressive tree decoder reaches competitive accuracies while clearly reducing the number of decoding steps.
THE DEVELOPMENT OF INSTRUCTIONAL VIDEO ON SCIENCE SUBJECTS TO IMPROVE STUDENT ACHIEVEMENT AT SDN 3 BOLOREJO KAUMAN TULUNGAGUNG

Abstrack The limitations of supporting media resulted in the student learning process not being optimal and not attracting students' attention. The strategy for using computers as teaching media is to develop a multimedia-based media and teaching materials. Microsoft Power Point is a software that will help compiling an effective, professional, and easy presentation. Microsoft Power Point will help an idea more interesting and clear when presented. So that researchers use the RnD method, according to Sugiyono (2011: 407) research and development is a research method used to produce certain products and test the effectiveness of these products. The development of interactive multimedia-based teaching materials produces products in the form of learning media using the macromedia flash professional 8 program which is accompanied by a Borg and Gall development design, namely: 1) researchers and data collection, 2) planning, 3) product draft development, 4) trial and improvement of the initial product, 5) revising the product, 6) field testing, 7) refinement of the product from the trial, 8) field implementation test, 9) final product refinement  10) dissimilation and implementation. The assessment carried out by design experts is 83%, it is suitable for use, while the percentage of content experts is 88%,  it is suitable for use by students. The percentage of learning experts is 90%, it means that it is feasible to be used in the learning process. From some of these validation experts, it means that this learning media does not need to be revised. This attractiveness was obtained from the results of the questionnaire assessment conducted by the experimental class group of fifth grade students at SDN 3 Bolorejo Tulungagung. Based on the percentage obtained by 92%, interactive multimedia-based learning media on understanding the concept of a simple plane is stated to be  interesting for research respondents because it has interesting animations.
IMPROVED TEXT NORMALIZATION AND LANGUAGE MODELS FOR SPEED'S AUTOMATIC SPEECH RECOGNITION SYSTEM

Automatic speech recognition (ASR) systems that use word-based language models require periodical updates to include new named entities (e.g. coronavirus, COVID-19) or collocations. Moreover, in particular for the Romanian language, the new hyphenated words pose additional problems. In this context, our study presents SpeeD's efforts in collecting new text corpora and using them for language modelling in the context of ASR. We also present the improvements made in the text normalization module to address the problems posed by hyphenated words. We evaluate the resulting language models both in terms of their ability to predict future words (perplexity and out-of-vocabulary rate) and in terms of their usefulness in ASR (word error rate). We report ASR relative improvements of around 10% for spontaneous speech, with small degradations for read speech.
Learning sparse mixtures of permutations from noisy information

We study the problem of learning an unknown mixture of k permutations over n elements, given access to noisy samples drawn from the unknown mixture. We consider a range of different noise models, including natural variants of the “heat kernel” noise framework and the Mallows model. We give an algorithm which, for each of these noise models, learns the unknown mixture to high accuracy under mild assumptions and runs in n^{O(log k)} time. Our approach is based on a new procedure that recovers an unknown mixture of permutations from noisy higher-order marginals.
Closing the Gender Gap in Secondary School Enrolment in sub-Saharan Africa: Does women's political empowerment matter?

This study investigates the effect of female political empowerment on gender equality in secondary school enrolment in a sample of 20 sub-Saharan Africa (SSA) countries over the period 2000-2017. Results from Ordinary Least Square (OLS) and Instrumental variable (IV)-Two-Stage Least Squares (2SLS) reveal that the gender gap in secondary education decreases with female political empowerment. Therefore, increasing the representation of women in politics is an important lever of economic policy to improve the secondary education of women, allowing them to be more productive and more fulfilled.
The Evolution of the Violin

Pyotr Ilyich Tchaikovsky is one of the world’s most beloved composers. Yet, his greatest work remains his most enigmatic. Tchaikovsky’s Symphony No. 6 was the last major work he completed before his untimely death at the age of 53. Its title is the Pathétique, which in French means passionate or emotional. The symphony certainly lives up to that title, particularly its fourth and final movement. Unlike the traditional boisterous or triumphant climax of a major symphony, Tchaikovsky experimented with an extended lament. Not only that, but the first few lines of the movement introduce the melody in an unprecedented way: distributed across the first and second violins so that no individual instrument actually plays the melody. In other words, this “emergent melody” is heard but not explicitly played! Why would Tchaikovsky introduce such a peculiar innovation? Efforts to answer this question have yet to succeed.
KEMAMPUAN MENULIS TEKS NEGOSIASI PADA SISWA KELAS X SEMESTER GENAP SMA TAMANSISWA TELUK BETUNG BANDAR LAMPUNG TAHUN PELAJARAN 2018/2019

The purpose of this study was to find out and describe the ability to write negotiating texts on students in class X Even Semester of SMA Tamansiswa Teluk Betung Bandar Lampung Academic Year 2018/2019. The ability to write student negotiation texts that are analyzed includes the selection and formulation of negotiating text titles relevance, content, organization of ideas, language, and mechanics. The method of this study will use quantitative descriptive. The study population was 50 with a sample of 25 students taken by random sampling technique. The main technique uses written test techniques. The data analysis technique uses quantitative descriptive analysis. The results of the study showed that the ability of the X semester students in Tamansiswa Teluk Betung Bandar Lampung High School 2018/2019 Academic Year was included in the less category. This is based on the achievement of a score of 52. Of the 25 students who wrote negotiating texts, there were 19 students who were said to be in the poor category or in the cumulative frequency of 76%, while students who achieved sufficient ability were only 4 students or 16%. However, there are still students who reach good categories, namely 2 students or 8%, so it can be concluded that students are still considered less able to write negotiating texts because the success rate is only 8%. The factors that make it difficult for students to formulate titles are that students have not been able to scan the outline of the negotiating text and have not been able to determine what topics to take.
Multiscale Spatial and Temporal Analysis of the b-value in volcanic areas

&amp;lt;p&amp;gt;Determining the b-value of the Gutenberg-Richter law is of great importance in Seismology. However, its estimate is strongly dependent upon selecting a proper temporal and spatial scale due to the multiscale nature of the seismicity. This characteristic is especially relevant in volcanoes where dense clusters of earthquakes often overlap the background seismicity and where this parameter displays a higher spatial and temporal variability.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;For this reason, we devised a novel approach called MUST-B (MUltiscale Spatial and Temporal estimation of the B-value) which allows a consistent estimate of the b-value, avoiding subjective &amp;amp;#8220;a priori &amp;amp;#8221; choices, by considering simultaneously different temporal or spatial scales. This approach also includes a consistent estimation of the completeness magnitude (Mc) and the uncertainties over both b and Mc. We applied this method to datasets in volcanic areas proving its effectiveness to analyze complex seismicity patterns and its utility in volcanic monitoring and geothermal exploration. Besides, it may provide a way to distinguish seismicity caused by tectonic faults and volcanic sources in zones where there is a mix of both of them.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present MUST-B applications to three volcanic areas: Long Valley caldera (USA), Tenerife and El Hierro (Canary Islands). The spatial analysis of the b-value in Long Valley shows an impressive chimney-like volume characterized by high b-values which coincide with the main pathway of geothermal fluids inferred by independent studies. For Tenerife, we applied MUST-B to analyze both spatial and temporal variations. The spatial pattern shows an interesting variation between 2004-2005 and the period 2016-2020. In both cases, high b-values appear in an area that hosted increased seismicity because of seismo-volcanic crises. These high b-values are also evidenced by the temporal analysis, which shows an increase in correspondence between these two periods. For El Hierro, we analyzed the seismicity preceding the 2011 submarine eruption of Tagoro volcano using a joint spatio-temporal analysis. Results show high b-values in the area where the vent opened and a drop of this parameter just before the beginning of the eruption.&amp;lt;/p&amp;gt;
On the Compilability and Expressive Power of State-Dependent Action Costs

While state-dependent action costs are practically relevant and have been studied before, it is still unclear if they are an essential feature of planning tasks. In this paper, we study to what extent state-dependent action costs are an essential feature by analyzing under which circumstances they can be compiled away. We give a complete classification for all combinations of action cost functions and possible cost measures for the compilations. Our theoretical results show that if both task sizes and plan lengths are to be preserved polynomially, then the boundary between compilability and non-compilability lies between FP and FPSPACE computable action cost functions (under a mild assumption on the polynomial hierarchy). Preserving task sizes polynomially and plan lengths linearly at the same time is impossible.
Learning Vocabularies to Embed Graphs in Multimodal Rank Aggregation Tasks

This paper introduces Supervised Bag of Graphs (SBoG), a supervised vocabulary learning approach for multi-modal graph-based rank aggregation tasks. In our formulation, collection objects are represented based on complementary views provided by different ranks, defined in terms of multiple modalities. Ranks are encoded into a graph (fusion graph), which is later embedded into a vector representation (fusion vector), based on a vocabulary of graph words. SBoG explores different strategies for exploring collection labels to define suitable vocabularies that lead to effective representations. Experiments considered the use of SBoG-based representations in multimedia classification tasks. Obtained results demonstrate that SBoG leads to gains up to 28% when compared with state-of-the-art and traditional approaches.
HTGAN: An architecture for Hindi Text based Image Synthesis

Synthesis of high-quality images that are semantically consistent with their text descriptions has emerged as an essential and onerous problem in the areas of CV and NLP. Various significant steps have been taken in the task of generation of images using English textual descriptions, with Multimodal GANs being at the forefront of all these efforts. In this work we attempt to extend the existing English text to image generation (T2I) techniques to the novel task of Hindi T2I using language translation models. To achieve this, the input Hindi sentences were translated to English using a transformer based Neural Machine Translation module the output of which was then fed to the Generative Adversarial Networks based Image Generation Module. The outcomes of this approach have been evaluated using well established metrics such as Inception Score and BLEU Score which yield scores that indicate the generation of lifelike images which semantically align with the input text descriptions.
Entornos virtuales de aprendizaje como instrumento de creación colectiva: estudio de caso Universidad Autónoma Indígena Intercultural UAIIN-CRIC

Objective: This research article aims to emphasize the process of designing and creating a tool as a creative design input for virtual learning environments. Methodology: Through the qualitative research method, through collaborative tools, an intercultural, participatory, open and inclusive academic product was achieved. Finding: The implemented LMS shows an Interrelationship structure in the construction of meanings, as an interactive educational innovation for the transmission of ancestral knowledge in one's own education. Conclusion: It is in collective participation that multiple worldviews are built on inclusion and teamwork as the fundamental pillars in collective intelligence.