Miracle-dz/UniFAR-FTUs huggingface dataset:
Fear of Missing Out, Social Media Engagement and Social Anxiety Among Filipino First Year College Students: A Mediation Analysis

Social media use is rampant in today's generation. In the Philippines, which is one of the countries with the greatest number of people using social networking sites (SNS), it was found that 50% of Filipinos aged 14 to 23 use the internet. People's upsurge reliance on digital technology and social media engagement (SME) seems to be affecting certain aspects of their lives, especially psychologically. This study identified the significant positive relationship between (1) SME and social anxiety (SA); (2) SME and fear of missing out (FoMO); (3) FoMO and SA; and (4) FoMO as mediator to the relationship between SME and SA. The data gathering was conducted through online forms and standardized questionnaires (SMEQ, SIAS, and FoMOS), served as main instruments. The 263 respondents were first-year college students from private and public universities and colleges in Calamba City, Laguna and were selected using convenience sampling. The data were analyzed through the use of PLS-SEM. This study revealed that excessive exposure to social media increases the risk of having the FoMO (β = 0.133; p = 0.014) and demonstrated that young adults are more likely to develop SA if they have FoMO (β = 0.367; p<0.001). However, this study did not support the significant positive relationship between SME and SA (β = 0.133; p = 0.014) and the mediating effect of FoMO on the relationship between SME and SA (β = 0.049; p=0.130). The study's findings evidently contribute to the knowledge about the relation of social media to mental health, but also help conclude that other possible factors may affect the said variables and recommend a much in-depth investigation by future researchers.
Trust the Process: Analyzing Prospective Provenance for Data Cleaning

In the field of data-driven research and analysis, the quality of results largely depends on the quality of the data used. Data cleaning is a crucial step in improving the quality of data. Still, it is equally important to document the steps made during the data cleaning process to ensure transparency and enable others to assess the quality of the resulting data. While provenance models such as W3C PROV have been introduced to track changes and events related to any entity, their use in documenting the provenance of data-cleaning workflows can be challenging, particularly when mixing different types of documents or entities in the model. To address this, we propose a conceptual model and analysis that breaks down data-cleaning workflows into process abstraction and workflow recipes, refining operations to the column level. This approach provides users with detailed provenance information, enabling transparency, auditing, and support for data cleaning workflow improvements. Our model has several features that allow static analysis, e.g., to determine the minimal input schema and expected output schema for running a recipe, to identify which steps violate the column schema requirement constraint, and to assess the reusability of a recipe on a new dataset. We hope that our model and analysis will contribute to making data processing more transparent, accessible, and reusable.
CNSVRE: A Query Reformulated Search System with Explainable Summarization for Virtual Research Environment

Computational notebook environments have drawn broad attention in data-centric research applications, e.g., virtual research environment, for exploratory data analysis and algorithm prototyping. Vanilla computational notebook search solutions have been proposed but they do not pay much attention to the information needs of scientific researchers. Previous studies either treat computational notebook search as a code search problem or focus on content-based computational notebook search. The queries being considered are neither research-concerning nor diversified whereas researchers’ information needs are highly specialized and complex. Moreover, relevance evaluation for computational notebooks is tricky and unreliable since computational notebooks contain fragments of text and code and are usually poorly organized. To solve the above challenges, we propose a computational notebook search system for virtual research environment (VRE), i.e., CNSVRE, with scientific query reformulation and computational notebook summarization. We conduct a user study to demonstrate the effectiveness, efficiency, and satisfaction with the system.
Learning Geolocation by Accurately Matching Customer Addresses via Graph based Active Learning

We propose a novel adaptation of graph-based active learning for customer address resolution or de-duplication, with the aim to determine if two addresses represent the same physical building or not. For delivery systems, improving address resolution positively impacts multiple downstream systems such as geocoding, route planning and delivery time estimations, leading to an efficient and reliable delivery experience, both for customers as well as delivery agents. Our proposed approach jointly leverages address text, past delivery information and concepts from graph theory to retrieve informative and diverse record pairs to label. We empirically show the effectiveness of our approach on manually curated dataset across addresses from India (IN) and United Arab Emirates (UAE). We achieved absolute improvement in recall on average across IN and UAE while preserving precision over the existing production system. We also introduce delivery point (DP) geocode learning for cold-start addresses as a downstream application of address resolution. In addition to offline evaluation, we also performed online A/B experiments which show that when the production model is augmented with active learnt record pairs, the delivery precision improved by and delivery defects reduced by on an average across shipments from IN and UAE.
ProSA: A provenance system for reproducing query results

Good scientific work requires comprehensible, transparent and reproducible research. One way to ensure this is to include all data relevant to a study or evaluation when publishing an article. This data should be at least aggregated or anonymized, at best compact and complete, but always resilient. In this paper we present ProSA, a system for calculating the minimal necessary data set, called sub-database. For this, we combine the Chase — a set of algorithms for transforming databases — with additional provenance information. We display the implementation of provenance guided by the ProSA pipeline and show its use to generate an optimized sub-database. Furhter, we demonstrate how the ProSA GUI looks like and present some applications and extensions.
Katti: An Extensive and Scalable Tool for Website Analyses

Research on web security and privacy frequently relies on tools that analyze a set of websites. One major obstacle to the judicious analysis is the employment of a rock-solid and feature-rich web crawler. For example, the automated analysis of ad-malware campaigns on websites requests crawling a vast set of domains on multiple real web browsers, while simultaneously mitigating bot detections and applying user interactions on websites. Further, the ability to attach various threat analysis frameworks lacks current tooling efforts in web crawling and analyses. In this paper we introduce Katti, which overcomes several of today’s technical hurdles in web crawling. Our tool employs a distributed task queue that efficiently and reliably handles both large crawling and threat analyses requests. Katti extensively collects all available web data through an integrated person-in-the-middle proxy. Moreover, Katti is not limited to a specific use case, allowing users to easily customize our tool to their individual research intends.
Analysis and judgment matrix for an online training course for teachers from Brazilian public schools on preventing the use of alcohol and other drugs in the school context.

The issue involving use of alcohol and other drugs (UAOD) requires the articulation of intersectoral policies aligned towards the prevention and reduction of harm related to use. With this in mind, the Brazilian Ministry of Justice has proposed training for elementary and high school teachers in public schools, in order to prepare the entire school community to work on preventing and containing damage to the UAOD, especially in the school context. In order to verify if the objectives of the course were achieved and, considering the complexity and different actors involved in the theme, a matrix previously produced to evaluate online courses offered by the Ministry of Health was adapted for this course on UAOD, called online course on health and education. school security. We present in this article the adaptation of this matrix, which included the elaboration of a logical model and a logical theoretical model, in addition to Rationales on the main terms and subjects related to the evaluation items. Final AJM is composed 4 dimensions, 13 sub-dimensions, 49 indicators and 104 measures. The matrix underwent evaluation by experts. After validation, the matrix items were used to build a questionnaire to be applied to the graduates of the course. After validation, the matrix items were used to build a questionnaire to be applied to course graduates, in offers 6 and 7. Three experts were invited to assess this questionnaire. The agreement index used was the Content Validity Coefficient (CVC), which allowed the analysis of Clarity, Pertinence and Relevance of each item. A total of 1110 graduates responded to the questionnaire. Matrix allows to know if the learning objectives were achieved, about the engagement of the course participants, the involvement of the course participants and ther key actors of the school (such as parents of students, the management community, organizations, etc). In addition, it is possible to know the school context of the course participants and whether the course is suitable for the realities experienced in their territories. Possibly, this matrix can be used in other contexts if it is translated, validated and receives some adaptations in relation to the context items, according to the specificities of the territories of the students of the evaluated courses.
CAAW’23: The 2nd International Cryptoasset Analytics Workshop

This half-day workshop on Cryptoasset Analytics allows researchers from different disciplines to present their newest findings related to cryptoassets. This workshop is relevant for the Web research community for two reasons. First, on a technical level, fundamental concepts of cryptoassets are increasingly integrated with Web technologies. Second, we witness the formation of socio-technical cryptoasset ecosystems, which are tightly connected to the Web. The program will feature a mix of invited talks and a selection of peer-reviewed submissions. Topics range from empirical studies, over analytics methods and tools, to case studies, datasets, and cross-cutting issues like legal or ethical aspects.
Graph2Feat: Inductive Link Prediction via Knowledge Distillation

Link prediction between two nodes is a critical task in graph machine learning. Most approaches are based on variants of graph neural networks (GNNs) that focus on transductive link prediction and have high inference latency. However, many real-world applications require fast inference over new nodes in inductive settings where no information on connectivity is available for these nodes. Thereby, node features provide an inevitable alternative in the latter scenario. To that end, we propose Graph2Feat, which enables inductive link prediction by exploiting knowledge distillation (KD) through the Student-Teacher learning framework. In particular, Graph2Feat learns to match the representations of a lightweight student multi-layer perceptron (MLP) with a more expressive teacher GNN while learning to predict missing links based on the node features, thus attaining both GNN’s expressiveness and MLP’s fast inference. Furthermore, our approach is general; it is suitable for transductive and inductive link predictions on different types of graphs regardless of them being homogeneous or heterogeneous, directed or undirected. We carry out extensive experiments on seven real-world datasets including homogeneous and heterogeneous graphs. Our experiments demonstrate that Graph2Feat significantly outperforms SOTA methods in terms of AUC and average precision in homogeneous and heterogeneous graphs. Finally, Graph2Feat has the minimum inference time compared to the SOTA methods, and 100x acceleration compared to GNNs. The code and datasets are available on GitHub1.
Improve retrieval of regional titles in streaming services with dense retrieval

Customers search for movie and series titles released across the world on streaming services like primevideo.com (PV), netflix.com (Netflix). In non-English speaking countries like India, Nepal and many others, the regional titles are transliterated from native language to English and are being searched in English. Given that there can be multiple transliterations possible for almost all the titles, searching for a regional title can be a very frustrating customer experience if these nuances are not handled correctly by the search system. Typing errors make the problem even more challenging. Streaming services uses spell correction and auto-suggestions/auto-complete features to address this issue up to certain extent. Auto-suggest fails when user searches keywords not in scope of the auto-suggest. Spell correction is effective at correcting common typing errors but as these titles doesn’t follow strict grammar rules and new titles constantly added to the catalog, spell correction have limited success. With recent progress in deep learning (DL), embedding vectors based dense retrieval is being used extensively to retrieve semantically relevant documents for a given query. In this work, we have used dense retrieval to address the noise introduced by transliteration variations and typing errors to improve retrieval of regional media titles. In the absent of any relevant dataset to test our hypothesis, we created a new dataset of 40K query title pairs from PV search logs. We also created a baseline by bench-marking PV’s performance on test data. We present an extensive study on the impact of 1. pre-training, 2. data augmentation, 3. positive to negative sample ratio, and 4. choice of loss function on retrieval performance. Our best model has shown 51.24% improvement in Recall@16 over PV baseline.
BeyondFacts’23: 3rd International Workshop on Knowledge Graphs for Online Discourse Analysis

WORKSHOP SCOPE Expressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts (events, entities). This data constitutes a valuable source of insights for studies into misand dis-information spread, bias reinforcement, echo chambers, or political agenda setting. While knowledge graphs (KGs) promise to provide the key to a Web of structured information, they are mainly focused on facts without keeping track of the diversity, connection or temporal evolution of online discourse. As opposed to facts, claims are inherently more complex. Their interpretation strongly depends on the context and a variety of intentional or unintended meanings, where terminology and conceptual understanding strongly diverge across communities from computational social science and journalism, to argumentation mining, stance detection, or computational fact-checking. This workshop aims at strengthening the relations between these communities, providing a forum for shared works on the modeling, extraction and analysis of discourse on the Web. It will address the need for a shared understanding and structured knowledge about discourse data in order to enable machine-interpretation, discoverability and reuse, in support of scientifc or journalistic studies into the analysis of societal debates on the Web. Beyond research into information and knowledge extraction, data consolidation and modeling for KG building, the workshop
The One Hundred Year Web

The year 2023 marks the thirty-second anniversary of the World Wide Web being announced. In the intervening years, the web has become an essential part of the fabric of society. Part of that is that huge amounts of information that used to be available (only) on paper is now available (only) electronically. One of the dangers of this is that owners of information often treat the data as ephemeral, and delete old information once it becomes out of date. As a result society is at risk of losing large parts of its history. So it is time to assess how we use the web, how it has been designed, and what we should do to ensure that in one hundred years time (and beyond) we will still be able to access, and read, what we are now producing. We can still read 100 year-old books; that should not be any different for the web. This paper takes a historical view of the web, and discusses the web from its early days: why it was successful compared with other similar systems emerging at the time, the things it did right, the mistakes that were made, and how it has developed to the web we know today, to what extent it meets the requirements needed for such an essential part of society's infrastructure, and what still needs to be done.
A Study on the Influencing Factors of College Students’ Continual Use Intention of New Social Learning Platform: —Take Bilibili Barrage Network as an Example

With the development of network information technology, more and more UGC websites have appeared. Bilibili Barrage Network, a new social learning website, provides a brand new learning platform for people. This study used questionnaire survey and other research methods, proposed research hypotheses and built a theoretical model based on the TAM model to investigate the continuous use intentions of college students on the Bilibili Barrage Network, and put forward relevant improvement suggestions. Using SPSS to analyze the collected 224 answer sheets, the study has found that 9 of the 10 research hypotheses proposed in this paper are true, that is there are four positive effect: users' continual use intention of the Bilibili Barrage Network knowledge area is positively affected by subjective norms, attitude and perceived usefulness. User attitude is positively affected by perceived usefulness and perceived ease of use. Perceived usefulness is positively affected by perceived ease of use and social pleasure. Perceived ease of use is positively affected by social interaction and social pleasure.
Soil biodiversity in natural forests potentially exhibits higher resistance than planted forests under global warming

COPYRIGHT © 2023 Wang and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Opinion PUBLISHED 28 April 2023 DOI 10.3389/fpls.2023. 1135549
Text Mining-based Social-Psychological Vulnerability Analysis of Potential Victims To Cybergrooming: Insights and Lessons Learned

Cybergrooming is a serious cybercrime that primarily targets youths through online platforms. Although reactive predator detection methods have been studied, proactive victim protection and crime prevention can also be achieved through vulnerability analysis of potential youth victims. Despite its significance, vulnerability analysis has not been thoroughly studied in the data science literature, while several social science studies used survey-based methods. To address this gap, we investigate humans’ social-psychological traits and quantify key vulnerability factors to cybergrooming by analyzing text features in the Linguistic Inquiry and Word Count (LIWC). Through pairwise correlation studies, we demonstrate the degrees of key vulnerability dimensions to cybergrooming from youths’ conversational features. Our findings reveal that victims have negative correlations with family and community traits, contrasting with previous social survey studies that indicated family relationships or social support as key vulnerability factors. We discuss the current limitations of text mining analysis and suggest cross-validation methods to increase the validity of research findings. Overall, this study provides valuable insights into understanding the vulnerability factors to cybergrooming and highlights the importance of adopting multidisciplinary approaches.
Evidence for SST-Forced Anomalous Winds Revealed from Simultaneous Radiosonde Launches from Three Ships across the Kuroshio Extension Front

AbstractSimultaneous launches of radiosondes were conducted from three research vessels aligned meridionally across a sea surface temperature (SST) front on the flank of the Kuroshio Extension. The soundings carried out every 2 h over 5 days in early July 2012 provided a unique opportunity in capturing unambiguous data on anomalous easterly winds derived from a pronounced meridional SST gradient. The data indicate that a meridional contrast in surface heat fluxes from the underlying ocean enhanced the air temperature anomaly across the SST front, which was observed from the surface up to 300-m altitude. Correspondingly, high and low pressure anomalies that reached 800-m altitude formed on the north and south sides of the SST front, respectively. These temperature and pressure anomalies were maintained even during the passage of synoptic-scale disturbances. Although the free-tropospheric winds are overall westerly, winds below the 1000-m level were easterly due to geostrophic anomalies driven by the northw.
GATE Teamware 2: An open-source tool for collaborative document classification annotation

We present GATE Teamware 2: an open-source web-based platform for managing teams of annotators working on document classification tasks. GATE Teamware 2 is an entirely re-engineered successor to GATE Teamware, using contemporary web frameworks. The software allows the management of teams of multiple annotators, project managers and administrators - including the management of annotators - across multiple projects. Projects can be configured to control and monitor the annotation statistics and have a highly flexible JSON-configurable annotation display which can include arbitrary HTML. Optionally, documents can be uploaded with pre-existing annotations and documents are served to annotators in a random order by default to reduce bias. Crucially, annotators can be trained on applying the annotation guidelines correctly and then screened for quality assurance purposes, prior to being cleared for independent annotation. GATE Teamware 2 can be self-deployed, including in container orchestration environments, or provided as private, hosted cloud instances.GATE Teamware 2 is an open-source software and can be downloaded from https://github.com/GATENLP/gate-teamware.A demonstration video of the system has also been made available at https://youtu.be/KoXkuhc4fmM.
How do Words Contribute to Sentence Semantics? Revisiting Sentence Embeddings with a Perturbation Method

Understanding sentence semantics requires an interpretation of the main information from a concrete context. To investigate how individual word contributes to sentence semantics, we propose a perturbation method for unsupervised semantic analysis. We next re-examine SOTA sentence embedding models’ ability to capture the main semantics of a sentence by developing a new evaluation metric to adapt sentence compression datasets for automatic evaluation. Results on three datasets show that unsupervised discourse relation recognition can serve as a general inference task that can more effectively aggregate information to essential contents than several SOTA unsupervised sentence embedding models.