Reviewerly/exHarmony huggingface dataset:
Long Context Question Answering via Supervised Contrastive Learning

Long-context question answering (QA) tasks require reasoning over a long document or multiple documents. Addressing these tasks often benefits from identifying a set of evidence spans (e.g., sentences), which provide supporting evidence for answering the question. In this work, we propose a novel method for equipping long-context QA models with an additional sequence-level objective for better identification of the supporting evidence. We achieve this via an additional contrastive supervision signal in finetuning, where the model is encouraged to explicitly discriminate supporting evidence sentences from negative ones by maximizing question-evidence similarity. The proposed additional loss exhibits consistent improvements on three different strong long-context transformer models, across two challenging question answering benchmarks -- HotpotQA and QAsper.
A new ranking scheme for modern data and its application to two-sample hypothesis testing

Rank-based approaches are among the most popular nonparametric methods for univariate data in tackling statistical problems such as hypothesis testing due to their robustness and effectiveness. However, they are unsatisfactory for more complex data. In the era of big data, high-dimensional and non-Euclidean data, such as networks and images, are ubiquitous and pose challenges for statistical analysis. Existing multivariate ranks such as component-wise, spatial, and depth-based ranks do not apply to non-Euclidean data and have limited performance for high-dimensional data. Instead of dealing with the ranks of observations, we propose two types of ranks applicable to complex data based on a similarity graph constructed on observations: a graph-induced rank defined by the inductive nature of the graph and an overall rank defined by the weight of edges in the graph. To illustrate their utilization, both the new ranks are used to construct test statistics for the two-sample hypothesis testing, which converge to the $\chi_2^2$ distribution under the permutation null distribution and some mild conditions of the ranks, enabling an easy type-I error control. Simulation studies show that the new method exhibits good power under a wide range of alternatives compared to existing methods. The new test is illustrated on the New York City taxi data for comparing travel patterns in consecutive months and a brain network dataset comparing male and female subjects.
Quantifying Synthesis and Fusion and their Impact on Machine Translation

Theoretical work in morphological typology offers the possibility of measuring morphological diversity on a continuous scale. However, literature in Natural Language Processing (NLP) typically labels a whole language with a strict type of morphology, e.g. fusional or agglutinative. In this work, we propose to reduce the rigidity of such claims, by quantifying morphological typology at the word and segment level. We consider Payne (2017)'s approach to classify morphology using two indices: synthesis (e.g. analytic to polysynthetic) and fusion (agglutinative to fusional). For computing synthesis, we test unsupervised and supervised morphological segmentation methods for English, German and Turkish, whereas for fusion, we propose a semi-automatic method using Spanish as a case study. Then, we analyse the relationship between machine translation quality and the degree of synthesis and fusion at word (nouns and verbs for English-Turkish, and verbs in English-Spanish) and segment level (previous language pairs plus English-German in both directions). We complement the word-level analysis with human evaluation, and overall, we observe a consistent impact of both indexes on machine translation quality.
EFEKTIVITAS PROGRAM PENGAJARAN TERHADAP MOTIVASI BELAJAR SISWA MELALUI PROGRAM KKN DI SDN 03 WAGIR

The purpose of writing on the study was to know and analyze the carried out of the teaching activities performed by the student keat in the wagir village ward. The method used is a qualitative descriptive study method using a two-form teaching technique - the teaching at school and outside the school guidance of the home-study system. As for research obtained on school teaching activities during 3 meetings, student motivation in learning increases. While in home study programs, the evaluations made at each meeting are obtained that an increased level of student motivation is generated from a change of behavior in the realm of affective senses. As for the increase in attendance attendance at any meeting of the home learning program also indicates a student's enthusiasm in following the learning process.
Pengaruh Perhatian Orang Tua Dan Motivasi Belajar Terhadap Prestasi Belajar Pada Mata Pelajaran Pendidikan Kewarganegaraan Di Kelas XI PMIA SMA Negeri 1 Pematang Siantar Tahun Ajaran 2022/2023

This research aims to find out whether or not the Influence of Parental Attention and Motivation on Student Learning Achievement. This research has been carried out in SMA Negeri 1 Pematang Siantar. Where the class that becomes the population and research sample is XI PMIA as many as 63 people. The data obtained through the Microsoft Excel application. Based on the discussion of research data analysis, the results of hypothesis testing are obtained by calculating the linear regression equation, namely Y over X1 is =79,49 + 0,09X1 , Y over X2 is = 77,59 + 0,10X2 ,Y over X1 and X2 is = 2,65 + 1,93X1 + 2,77X2. The coefficient of influence obtained is the effect of X1 on Y 1.63, the effect of X2 on Y 0.27, the effect of X1 on X2 0.96, the effect of X1 and X2 on Y 5.50. While the coefficient of Y on X1 is 14.62%, the coefficient of Y on X2 is 40.09%, the coefficient of X1 on X2 is 50.68%, the coefficient of Y on X1 and X2 is 3.02%
PENGARUH MEDIA FILM DOKUMENTER TERHADAP KETERAMPILAN MENULIS SISWA PADA MATERI TEKS EKSPLANASI KELAS V SD

The context of this research is that writing skills are very important for learning, because they can train students to think, channel their desires, emotions, and express their ideas.. The use of documentary film media tells true stories about natural events in accordance with explanatory text material that tells about natural or social phenomena that can occur, such as about water, plastic which is the source of human life. The objective of this research, the researcher wants to know is there any effect of documentary film media on the writing skill of the fifth grade students of Sedatigede II Elementary School. While the research methodology used is quantitative by using a Quasi-Experimental design in the form of Nonequivalent Control Group Design. The research subjects were class V A as the experimental class and class V C as the control class at SDN Sedatigede II Sidoarjo with a total of 35 students. The data collection method used is an assessment in the form of a performance test with the task of writing an explanatory text. Using statistical analysis methods, the formulas used are normality test, homogeneity test and T-test. The test before and after using the media with the SPSS version 22 application were used. The results of this study were based on the results of the independent sample test data analysis, which showed the value of sig &lt; 0.05, namely sig. (2-tailed) of 0.000. From these results, it was concluded that there was an effect of documentary film media on students' writing skills in the explanatory text material of fifth grade students of Sedatigede II Elementary School. This is reinforced by the significant difference in skill test scores in the experimental and control groups before and after treatment.
Introdução à Internet das Coisas Musicais: Prática utilizando Pure Data e Processing

The Internet of Musical Things is an interdisciplinary area that draws on principles from computer science and music. It proposes new interactions in art shows, opening new paths for collaborative activities, besides being useful in the teaching these concepts. Despite all these advantages, this area is still unexplored, mainly because it is a novelty and presents several open issues. This work, therefore, aims to exploring the potential of this technology, presenting tools that can be useful in the development of your systems, such as Pure Data and Processing.
Leveraging Linked Open Data: A Link Maintenance Framework

Connections among RDF (Resource Description Framework) data elements represent the core of LOD (Linked Open Data). These connections are built with semi-automatic linking algorithms using a variety of similarity methods. Interconnected data demand automatic methods to maintain their consistency. Constant update of RDF connections is relevant for the evolution of RDF datasets. However, changing operations can influence well-formed links, which turns difficult the consistency of the connections over time. This study investigated new methods responsible for fixing and updating links among structured data following ontologies rules and properties. We contribute with the design and development of an automatic method that updates RDF links based on changing operations in RDF datasets. The framework that implements our method - named LODMF - was evaluated in terms of discovering broken links in big and well-known Linked Open datasets.
Uso de padrões ordinais na caracterização e análise de ataques de botnets em Internet das Coisas (IoT)

The main objective of this tutorial is to present the fundamentals of temporal data analysis using ordinal patterns and descriptors from Information Theory, covering the tools and steps necessary for developing applications and services to detect botnets in the Internet of Things (IoT) scenarios. Thus, we investigated and presented the solutions proposed in the literature for the following questions: (i) What are the main advantages of the Bandt-Pompe methodology in the temporal data analysis process? (ii) How can we use Information Theory descriptors and ordinal patterns in data characterization activities? (iii) What are the main research problems? (iv) What are the main characteristics of the methodology that enable the development of applications in the context of IoT and botnet detection?
Spatio-temporal Localization of Actors in Video/360-Video and its Applications

The popularity of platforms for storing and transmitting video content has created a substantial volume of video data. Given a set of actors present in a video, generating metadata with the temporal determination of the interval in which each actor is present and their spatial 2D localization in each frame in these intervals can facilitate video retrieval and recommendation. In this work, we investigate Video Face Clustering for this spatio-temporal localization of actors in videos. We first describe our method for Video Face Clustering in which we take advantage of face detection, embeddings, and clustering methods to group similar faces of actors in different frames and provide the spatio-temporal localization of them. Then, we explore, propose, and investigate innovative applications of this spatio-temporal localization in three different tasks: (i) Video Face Recognition, (ii) Educational Video Recommendation and (iii) Subtitles Positioning in 360-video.
Dealing with Item Cold-Start in News Recommender at Globo

Globo is the largest Latin American mass media group, where its vertical information portals play an important role in content distribution. Among such portals, G1 is Globo’s journalism portal, being the most popular news portal in Brazil and responsible for delivering informative content to more than 100 million unique users per day. In this context, recommender systems play an important role in achieving a good user experience, offering personalized content. In this paper, we discuss how G1’s recommender system identifies and deals with the item cold-start problem, describing the recommendation scenarios and how the applied improvements in the currently deployed algorithms led to a decreased processing time and an increased CTR in the context of news recommendations.
Research on Identification System of Aero Engine Blade

This paper mainly focuses on the recognition of engraved code information, including three steps: character image acquisition, character image preprocessing and character recognition. To carry out the research of blade identity information integration technology, and realize the end-to-end identification of blade identity information.
Impact of Teaching a Proposed Unit on Successful Intelligence and Augmented Reality in Biology on Lateral Thinking and Science Fiction among High School Students in Al-Saih City, Saudi Arabia

The study examined the impact of teaching a unit based on the Theory of Successful Intelligence and Augmented Reality in Biology on developing lateral thinking and science fiction among high school students in Al-Saih City, Saudi Arabia. To verify the research experience's effect, a quasi-experimental design, the "Lateral Thinking Test," and the "Science Fiction Scale" were used. The research sample included 34 experimental and 37 control students (all high school students). The research tool used to examine both groups' lateral thinking contains 24 questions on concepts, alternatives, linkages, and ideas. Science fiction skills include alertness, flexibility, imagery, daydreaming, retreating from reality, and sustaining direction. The results demonstrated a statistically significant difference (0.05) between the average scores of the experimental and control groups for each lateral thinking skill and the lateral thinking test as a whole, in favor of the experimental group. Also, teaching a unit based on the Theory of Successful Intelligence and Applications of Augmented Reality in biology helps develop lateral thinking and science fiction. The research advocated applying the notion of Successful Intelligence and Augmented reality in high school, based on the study's results, to improve educational outcomes such as "lateral thinking" and "science fiction".
New approaches on education technologies: A global perspective for digital transformation

For education worldwide, the global pandemic was a major shock. The Internet did not stop, but for a while, going to school did. It caused a worldwide leap to online education. For educational technology, it might have been a step forward, with some hesitancy: some will soon be forgotten, but much will be retained. This writing outlines a number of ideas and directions, posed in the form of questions, seeking to continue to move educational efforts forward, for in a post-pandemic world, education is still challenged by technology.
An Empirical Investigation of the Impact of Monetary Policy on Economic Growth in Zambia

Purpose: The aim of this study was to investigate the impact of monetary policy on the real sector and to assess the effectiveness of various monetary policy transmission channels in Zambia Methodology: The Johansen cointegration approach, Error Correction Model (ECM), and Granger causality test were undertaken to achieve the study objectives Findings: The study results reveal that economic growth proxied by Gross Domestic Product (GDP) in Zambia is negatively affected by lending rates, inflation, and an increase in private sector credit, while exchange rate and deposit rates were found to have a positive impact on the other hand. These results confirm the presence of exchange rate and credit channels of monetary policy transmission in Zambia. Originality/Value: The study contributes to the theoretical and empirical literature on the impact of monetary policy on the real sector. Further, the study provides, through the results of the study, the effectiveness of various monetary policy transmission channels in Zambia. Therefore, the study is part of the studies the Central Bank and academic institutions and research think tanks can make reference to during further research and practice.
Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python

People frequently use the social media site Twitter to share their thoughts and show their emotions on various occasions. Data analysis using sentiment analysis can retrieve the sentiment that the data contains. Twitter sentiment analysis applies sentiment analysis to data from tweets on the social media platform in order to identify user sentiments. The amount of study in this area has steadily increased over the last few decades. The complex format of tweets, which makes processing harder, is the cause of this. The very small size of the tweet format creates a whole new set of issues, such as the use of slang and acronyms. In this essay, we evaluate a few publications on sentiment analysis research on Twitter, detailing the methodology used and.
Fine-Grained Vehicle Classification in Urban Traffic Scenes using Deep Learning

The increasingly dense traffic is becoming a challenge in our local settings, urging the need for a better traffic monitoring and management system. Fine-grained vehicle classification appears to be a challenging task as compared to vehicle coarse classification. Exploring a robust approach for vehicle detection and classification into fine-grained categories is therefore essentially required. Existing Vehicle Make and Model Recognition (VMMR) systems have been developed on synchronized and controlled traffic conditions. Need for robust VMMR in complex, urban, heterogeneous, and unsynchronized traffic conditions still remain an open research area. In this paper, vehicle detection and fine-grained classification are addressed using deep learning. To perform fine-grained classification with related complexities, local dataset THS-10 having high intra-class and low interclass variation is exclusively prepared. The dataset consists of 4250 vehicle images of 10 vehicle models, i.e., Honda City, Honda Civic, Suzuki Alto, Suzuki Bolan, Suzuki Cultus, Suzuki Mehran, Suzuki Ravi, Suzuki Swift, Suzuki Wagon R and Toyota Corolla. This dataset is available online. Two approaches have been explored and analyzed for classification of vehicles i.e, fine-tuning, and feature extraction from deep neural networks. A comparative study is performed, and it is demonstrated that simpler approaches can produce good results in local environment to deal with complex issues such as dense occlusion and lane departures. Hence reducing computational load and time, e.g. fine-tuning Inception-v3 produced highest accuracy of 97.4% with lowest misclassification rate of 2.08%. Fine-tuning MobileNet-v2 and ResNet-18 produced 96.8% and 95.7% accuracies, respectively. Extracting features from fc6 layer of AlexNet produces an accuracy of 93.5% with a misclassification rate of 6.5%.
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization

In the summarization domain, a key requirement for summaries is to be factually consistent with the input document. Previous work has found that natural language inference (NLI) models do not perform competitively when applied to inconsistency detection. In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level). We provide a highly effective and light-weight method called SummaCConv that enables NLI models to be successfully used for this task by segmenting documents into sentence units and aggregating scores between pairs of sentences. On our newly introduced benchmark called SummaC (Summary Consistency) consisting of six large inconsistency detection datasets, SummaCConv obtains state-of-the-art results with a balanced accuracy of 74.4%, a 5% point improvement compared to prior work. We make the models and datasets available: https://github.com/tingofurro/summac
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering

Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes. However, many popular knowledge bases share similarities in their underlying schemas that can be leveraged to facilitate generalization across knowledge bases. To achieve this generalization, we introduce a KBQA framework based on a 2-stage architecture that explicitly separates semantic parsing from the knowledge base interaction, facilitating transfer learning across datasets and knowledge graphs. We show that pretraining on datasets with a different underlying knowledge base can nevertheless provide significant performance gains and reduce sample complexity. Our approach achieves comparable or state-of-the-art performance for LC-QuAD (DBpedia), WebQSP (Freebase), SimpleQuestions (Wikidata) and MetaQA (Wikimovies-KG).
Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings

Uncertainty quantification in neural network promises to increase safety of AI systems, but it is not clear how performance might vary with the training set size. In this paper we evaluate seven uncertainty methods on Fashion MNIST and CIFAR10, as we sub-sample and produce varied training set sizes. We find that calibration error and out of distribution detection performance strongly depend on the training set size, with most methods being miscalibrated on the test set with small training sets. Gradient-based methods seem to poorly estimate epistemic uncertainty and are the most affected by training set size. We expect our results can guide future research into uncertainty quantification and help practitioners select methods based on their particular available data.
Self-Attending Task Generative Adversarial Network for Realistic Satellite Image Creation

We introduce a self-attending task generative adversarial network (SATGAN) and apply it to the problem of augmenting synthetic high contrast scientific imagery of resident space objects with realistic noise patterns and sensor characteristics learned from collected data. Augmenting these synthetic data is challenging due to the highly localized nature of semantic content in the data that must be preserved. Real collected images are used to train a network what a given class of sensor's images should look like. The trained network then acts as a filter on noiseless context images and outputs realistic-looking fakes with semantic content unaltered. The architecture is inspired by conditional GANs but is modified to include a task network that preserves semantic information through augmentation. Additionally, the architecture is shown to reduce instances of hallucinatory objects or obfuscation of semantic content in context images representing space observation scenes.