Reviewerly/exHarmony huggingface dataset:
SocialIQA: Commonsense Reasoning about Social Interactions

We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations (e.g., Q: "Jordan wanted to tell Tracy a secret, so Jordan leaned towards Tracy. Why did Jordan do this?" A: "Make sure no one else could hear"). Through crowdsourcing, we collect commonsense questions along with correct and incorrect answers about social interactions, using a new framework that mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. Empirical results show that our benchmark is challenging for existing question-answering models based on pretrained language models, compared to human performance (>20% gap). Notably, we further establish Social IQa as a resource for transfer learning of commonsense knowledge, achieving state-of-the-art performance on multiple commonsense reasoning tasks (Winograd Schemas, COPA).
PCDM and PCDM4MP: New Pairwise Correlation-Based Data Mining Tools for Parallel Processing of Large Tabular Datasets

The paper describes PCDM and PCDM4MP as new tools and commands capable of exploring large datasets. They select variables based on identifying the absolute values of Pearson’s pairwise correlation coefficients between a chosen response variable and any other existing in the dataset. In addition, for each pair, they also report the corresponding significance and the number of non-null intersecting observations, and all this reporting is performed in a record-oriented manner (both source and output). Optionally, using threshold values for these three as parameters of PCDM, any user can select the most correlated variables based on high magnitude, significance, and support criteria. The syntax is simple, and the tools show the exploration progress in real-time. In addition, PCDM4MP can trigger different instances of Stata, each using a distinct class of variables belonging to the same dataset and resulting after simple name filtering (first letter). Moreover, this multi-processing (MP) version overcomes the parallelization limitations of the existing parallel module, and this is accomplished by using vertical instead of horizontal partitions of large flat datasets, dynamic generation of the task pattern, tasks, and logs, all within a single execution of this second command, and the existing qsub module to automatically and continuously allocate the tasks to logical processors and thereby emulating with fewer resources a cluster environment. In addition, any user can perform further selections based on the results printed in the console. The paper contains examples of using these tools for large datasets such as the one belonging to the World Values Survey and based on a simple variable naming practice. This article includes many recorded simulations and presents performance results. They depend on different resources and hardware configurations used, including cloud vs. on-premises, large vs. small amounts of RAM and processing cores, and in-memory vs. traditional storage.
Recognizing Our Collective Responsibility in the Prioritization of Open Data Metrics

With the rise in data-sharing policies, development of supportive infrastructure, and the amount of data published over the last decades, evaluation and assessment are increasingly necessary to understand the reach, impact, and return on investment of data-sharing practices. As biomedical research stakeholders prepare for the implementation of the updated National Institutes of Health (NIH) Data Management and Sharing Policy in 2023, it is essential that the development of responsible, evidence-based open data metrics are prioritized. If the community is not mindful of our responsibility in building for assessment upfront, there are prominent risks to the advancement of open data-sharing practices: failing to live up to the policyâs goals, losing community ownership of the open data landscape, and creating disparate incentive systems that do not allow for researcher reward. These risks can be mitigated if the community recognizes data as its own scholarly output, resources and leverages open infrastructure, and builds broad community agreement around approaches for open data metrics, including using existing standards and resources. In preparation for the NIH policy, the community has an opportune moment to build for researchersâ best interests and support the advancement of biomedical sciences, including assessment, reward, and mechanisms for improving policy resources and supportive infrastructure as the space evolves.
Geological and Mineralogical Mapping Based on Statistical Methods of Remote Sensing Data Processing of Landsat-8: A Case Study in the Southeastern Transbaikalia, Russia

The work considers the suitability of using multispectral satellite remote sensing data Landsat-8 for conducting regional geological and mineralogical mapping of the territory of south-eastern Transbaikalia (Russia) based on statistical methods for processing remote sensing data in conditions of medium–low-mountain relief and continental climate. The territory was chosen as the object of study due to its diverse metallogenic specialization (Au, U, Mo, Pb-Zn, Sn, W, Ta, Nb, Li, fluorite). Diversity in composition and age of ore-bearing massifs of intrusive, volcanogenic, and sedimentary rocks are also of interest. The work describes the initial data and considers the procedure for their pre-processing, including radiometric and atmospheric correction. Statistical processing algorithms to increase spectral information content of satellite data Landsat-8 were used. They include: principal component analysis, minimum noise fraction, and independent component analysis. Eigenvector matrices analyzed on the basis of statistical processing results and two-dimensional correlation graphs were built to compare thematic layers with geological material classes: oxide/hydroxide group minerals containing transition iron ions (Fe3+ and Fe3+/Fe2+); a group of clay minerals containing A1-OH and Fe, Mg-OH; and minerals containing Fe2+ and vegetation cover. Pseudo-colored RGB composites representing the distribution and multiplication of geological material classes are generated and interpreted according to the results of statistical methods. Integration of informative thematic layers using a fuzzy logic model was carried out to construct a prediction scheme for detecting hydrothermal mineralization. The received schema was compared with geological information, and positive conclusions about territory suitability for further remote mapping research of hydrothermally altered zones and hypergenesis products in order to localize areas promising for identifying hydrothermal metasomatic mineralization were made.
Frozen section telepathology service: Efficiency and benefits of an e-health policy in South Tyrol

Telepathology has been widely adopted to allow intraoperative pathology examinations to be performed remotely and for obtaining second opinion teleconsultation. In the Italian northern region of South Tyrol, the widespread geographical distances and consequent cost for the health system of having a travelling pathologist cover intraoperative consultations in peripheral hospitals was a key driver for the implementation of a telepathology system.In 2010, four Menarini D-Sight whole slide scanners to digitize entire pathology slides were placed in the peripheral hospitals of Merano, Bressanone, Brunico, and in the hub hospital of Bolzano. Digital workstations were also installed to allow pathologists to remotely perform intraoperative consultations with digital slides. This study reviews the outcome after 12 years of telepathology for this intended clinical use.After an initial validation phase with 100 cases which yielded a sensitivity of 65% (CI 43-84%) and specificity of 100% (CI 95-100%), there were 2058 intraoperative consultations handled by telepathology. The cases evaluated were mainly breast sentinel lymph nodes, followed by urological, gynecological and general surgical pathology frozen section specimens. There were no false-positive cases and 165 (8%) false-negative cases, yielding an overall sensitivity and specificity of 65% (CI 61-69%) and 100% (CI 99-100%), respectively.Telepathology is reliable for remote intraoperative diagnosis and, despite technical issues and initial acquaintance issues, proved beneficial for patient care in satellite hospitals, improved standardization, promoted innovation, and resulted in cost savings for the health system.
A Review and Comparative Analysis of Sentiment Analysis Techniques

Social networking platforms have become a major source of information, which covers a wide range of topics and has gained a large volume of usage by people around the world. Platforms such as Twitter, Facebook, Instagram, and LinkedIn have attracted huge numbers of users who create public profiles and communicate with other users in the network. They exchange videos, posts, and comments. Social networking requires appropriate techniques to analyze the huge amount of complex, and frequently updated data generated. Sentiment Analysis is one such method of handling this vast volume of data and extracting useful knowledge from it. Social networking contents are analyzed using different techniques to gain insight from this data and use it in decision-making processes. The aim of this work is to study the sentiment analysis concept and present state-of-the-art techniques as well as provide a comparative study of these techniques.
HIGH SCHOOL STUDENTS� READING HABIT AND PERCEPTION ON READING FOR PLEASURE

This paper aims to identify high school students reading habit and describe their perceptions on reading for pleasure. There were 41 high school students grade XI involved in this study. Data were collected from questionnaires, observation, and an interview. Results indicate that during holidays nearly 50% of the students read sometimes, 12% read once a week, 9% once in a month, and 29% never read although books are available. The students agree that reading for pleasure give many benefits, but the quantitative data indicate students low interest in reading. This may be due to the reading culture which has not been sufficiently developed since the early ages. In addition, the availability and accessibility of reading materials seems to play roles in determining the students reading interest. Reading materials should also vary in genres, in themes, and in forms of both printed and on-screen regarding students digital nativity. This study has, therefore, initiated the needs for libraries with good book collection, facilities, and interesting design to attract students.DOI: doi.org/10.24071/ijiet.2019.030108
English or Indonesian Language? Parents’ Perception Toward Children's Second Language Learning Context

The phenomenon of teaching English and Indonesian language, especially in North Sulawesi, as a second language overlaps, where some argue that teaching Indonesian language should come first so that children will not forget Indonesian language or, in other words, children will master their national language. In this study, the method used is a descriptive method with a qualitative approach. The sample used as respondents by the researcher is parents in North Sulawesi who have children who can speak English and Indonesian, in addition, to their mother tongue (Manado language/Regional language), and the respondents consisted of 10 parents. The researcher used questionnaires to gather parents' perceptions of whether the children should be taught English or Indonesian Language first. However, some people think teaching English is very important because mastering English as an international language is necessary. English is suitable for children as their second language, but teaching Indonesian language as the national language is also very important. Therefore teaching between these two languages must be balanced so that it does not only English that can be mastered by children or not only Indonesian language.
Arabic Sentiment Analysis Using Naïve Bayes and CNN-LSTM

Sentiment analysis (SA) is a useful NLP task.There are hundreds of Arabic sentiments analysis systems.However, because of the morphological nature of the Arabic languages, there are still many challenges that need more work.In this paper, two classifiers have been used: Naive Bayes and CNN-LSTM models.The experiments are conducted on Arabic tweets dataset that consists of 58k tweets written in several dialects, the same preprocessing steps have been done before fitting the models.The experimental results show that the deep Learning CNN-LSTM classifier fits better for this task which achieved an accuracy of 98% while Naive Bayes achieved 87.6%.
An Effective Approach of Vehicle Detection Using Deep Learning

With the rise of unmanned driving and intelligent transportation research, great progress has been made in vehicle detection technology. The purpose of this paper is employing the method of deep learning to study the vehicle detection algorithm, in which primary-stage target detection algorithms, namely, YOLOv3 algorithm and SSD algorithm, are adopted. Therefore, the method first processes the image data in the open-source road vehicle dataset for training. Then, the vehicle detection model is trained by using YOLOv3 and SSD algorithms to show the detection effect, respectively. The result is by comparing the detection effects of the two models on vehicles. The researchers accomplished the result analysis and summarized the characteristics of various models obtained by training, to apply to target tracking, semantic segmentation, and unmanned driving.
Lash 1.0 (System Description)

Abstract Lash is a higher-order automated theorem prover created as a fork of the theorem prover Satallax. The basic underlying calculus of Satallax is a ground tableau calculus whose rules only use shallow information about the terms and formulas taking part in the rule. Lash uses new, efficient C representations of vital structures and operations. Most importantly, Lash uses a C representation of (normal) terms with perfect sharing along with a C implementation of normalizing substitutions. We describe the ways in which Lash differs from Satallax and the performance improvement of Lash over Satallax when used with analogous flag settings. With a 10 s timeout Lash outperforms Satallax on a collection TH0 problems from the TPTP. We conclude with ideas for continuing the development of Lash.
High-efficiency and fast photon-number resolving parallel superconducting nanowire single-photon detector

Photon-number resolving (PNR) single-photon detectors are an enabling technology in many areas such as photonic quantum computing, non-classical light source characterisation and quantum imaging. Here, we demonstrate high-efficiency PNR detectors using a parallel superconducting nanowire single-photon detector (P-SNSPD) architecture that does not suffer from crosstalk between the pixels and that is free of latching. The behavior of the detector is modelled and used to predict the possible outcomes given a certain number of incoming photons. We apply our model to a 4-pixel P-SNSPD with a system detection efficiency of 92.5%. We also demonstrate how this detector allows reconstructing the photon-number statistics of a coherent source of light, which paves the way towards the characterisation of the photon statistics of other types of light source using a single detector.
Multiscale Structure and Texture Feature Fusion for Image Inpainting

In order to achieve interaction between structure and texture information in generative adversarial image inpainting networks and improve the semantic veracity of the restored images, unlike the original two-stage inpainting ideas where texture and structure are restored separately, this paper constructs a multi-scale fusion approach to image generation, which embeds images into two collaborative subtasks, that is, structure generation and texture synthesis under structural constraints.We also introduce a selfattention mechanism into the partial convolution of the encoder to enhance the long range contextual information acquisition of the model in image inpainting, and design a multi-scale fusion network to fuse the generated structure and texture feature, so that the structure and texture information can be reused for reconstruction, perception and style loss compensation, thus enabling the fused images to achieve global consistency.In the training phase, feature matching loss are introduced to enhance the image in terms of structural generation plausibility.Finally, through comparison experiments with other inpainting networks on the CelebA, Paris StreetView and Places2 datasets, it is demonstrated that our method constructed in this paper has better objective evaluation metrics, more effective inpainting of structural and texture information of corrupted images and better image inpainting performance.
Efficient high-utility occupancy itemset mining algorithm on massive data

Mining interesting itemsets on massive data is a necessary topic in data mining. Nowadays, most studies use frequency or utility as primary measure. However, using these two measures individually has its own limitations. For example, itemsets with high frequencies may have low profits while itemsets with high utilities perhaps appear occasionally, so they might be misleading. In addition, the existing algorithms can only deal with small-medium scale database, and their performances degrade significantly when data is expanded. To address these drawbacks, this paper proposes a novel high utility occupancy itemset mining algorithm SHO (Suffix-based High-utility Occupancy itemset mining), it considers both quantities and profits of itemsets. SHO designs the algorithm from suffix-based partitioning, generation pruning and itemsets linking, it can mine high utility occupancy itemsets on large-scale database effectively. At the beginning, the database are divided into some non-overlapping suffix-based partitions and stored in vertical format, then the support and utility occupancy of itemset can be calculated in a certain partition instead of traversing total database. Besides, two optimization strategies and four pruning strategies are proposed to make SHO faster. The extensive experiments show that SHO is much better than the current state-of-the-art algorithm, the efficiency can be improved up to 3 orders of magnitude.
Different normalization techniques as data preprocessing for one step ahead forecasting of solar global horizontal irradiance

Nowadays, solar energy use is not restricted to standalone mode—it can also be expanded to include the grid-interconnected mode. Due to intermittent weather conditions, higher uncertainties and randomness are present in the output power of photovoltaic panels. Therefore, accurate and precise prediction of solar global horizontal irradiance (GHI) is recommended for grid stability. This chapter presents various normalization techniques—z-score, MinMax, median, and decimal–generally used to make the input data stationary in a solar GHI forecast. Four different forecasting models—feedforward neural network, nonlinear autoregressive exogenous, support vector machine (SVM), and linear regression—are developed to observe the effect of normalization on forecasting accuracy. The clear sky index (CSI) technique is also discussed in the chapter to provide a best method to make the dataset stationary, even better than conventional normalizations. The performance of different normalization techniques over various models is evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) for one-hour ahead solar GHI forecast. A detailed analysis is also performed to observe the effect of training data size on forecasting model performance. This study proves that the CSI method outperforms other normalization techniques with an average yearly RMSE of 70.63 (w/m2), MAPE of 9.17%, and NSE of 0.90 for the SVM model.
The Metaverse: Exploring consumer's expectations, their attitudes, and it's meaning to the fashion industry

The following paper aims to find out consumers' expectations and attitudes towards the innovation "Metaverse". It will also be explored which role the Meta Group plays in mass adaption and how the company influences consumers' possible use and opinion on the project. These results are connected to the fashion industry, further exploring new types of products and a possible distribution channel. Therefore, this study is useful to developers of Metaverses and AR/VR products, the Meta Group, and fashion companies. The main results of this research are: Meta and the Metaverse are seen as critical, the required technology has not yet reached mainstream use, but interest is present. Digital fashion had participants divided, some not willing to spend any money and some already having spent over 100€, although the Metaverse's influence on future purchases is little. The Metaverse could serve as a new distribution channel for clothing products. To conduct this research Google Forms was used. The research is classified as survey-based. The biggest limitation is the nonexistence of the Metaverse as envisioned by Meta, making it hard for participants to answer some of the questions asked.
Investigation of Relationships between Discrete and Dimensional Emotion Models in Affective Picture Databases Using Unsupervised Machine Learning

Digital documents created to evoke emotional responses are intentionally stored in special affective multimedia databases, along with metadata describing their semantics and emotional content. These databases are routinely used in multidisciplinary research on emotion, attention, and related phenomena. Affective dimensions and emotion norms are the most common emotion data models in the field of affective computing, but they are considered separable and not interchangeable. The goal of this study was to determine whether it is possible to statistically infer values of emotionally annotated pictures using the discrete emotion model when the values of the dimensional model are available and vice versa. A positive answer would greatly facilitate stimuli retrieval from affective multimedia databases and the integration of heterogeneous and differently structured affective data sources. In the experiment, we built a statistical model to describe dependencies between discrete and dimensional ratings using the affective picture databases NAPS and NAPS BE with standardized annotations for 1356 and 510 pictures, respectively. Our results show the following: (1) there is a statistically significant correlation between certain pairs of discrete and dimensional emotions in picture stimuli, and (2) robust transformation of picture ratings from the discrete emotion space to well-defined clusters in the dimensional space is possible for some discrete-dimensional emotion pairs. Based on our findings, we conclude that a feasible recommender system for affective dataset retrieval can be developed. The software tool developed for the experiment and the results are freely available for scientific and non-commercial purposes.