Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification

Funnelling (Fun) is a recently proposed method for cross-lingual text classification (CLTC) based on a two-tier learning ensemble for heterogeneous transfer learning (HTL). In this ensemble method, 1st-tier classifiers, each working on a different and language-dependent feature space, return a vector of calibrated posterior probabilities (with one dimension for each class) for each document, and the final classification decision is taken by a meta-classifier that uses this vector as its input. The meta-classifier can thus exploit class-class correlations, and this (among other things) gives Fun an edge over CLTC systems in which these correlations cannot be brought to bear. In this article, we describe Generalized Funnelling (gFun), a generalization of Fun consisting of an HTL architecture in which 1st-tier components can be arbitrary view-generating functions, i.e., language-dependent functions that each produce a language-independent representation (“view”) of the (monolingual) document. We describe an instance of gFun in which the meta-classifier receives as input a vector of calibrated posterior probabilities (as in Fun) aggregated to other embedded representations that embody other types of correlations, such as word-class correlations (as encoded by Word-Class Embeddings), word-word correlations (as encoded by Multilingual Unsupervised or Supervised Embeddings), and word-context correlations (as encoded by multilingual BERT). We show that this instance of gFun substantially improves over Fun and over state-of-the-art baselines by reporting experimental results obtained on two large, standard datasets for multilingual multilabel text classification. Our code that implements gFun is publicly available.
Plan Early, Revise More

Receiving feedback on preliminary work allows content creators to gain insight and improve outcomes. However, a lack of commitment for gathering feedback and experiencing evaluation apprehension can delay feedback seeking. In this paper, we operationalize goal setting theory for planning feedback goals and test the effects on feedback seeking and revision. In an online experiment, participants (N=245) wrote an initial story after planning feedback goals (or not), submitted the story for feedback at a time of their choice, and revised the story based on feedback received. Participants anticipated feedback from a supervisor or peer to induce different levels of evaluation apprehension. We found that participants who planned proximal feedback goals sought feedback when their stories were less developed and revised the stories more after receiving feedback compared to when participants planned distant goals. Additionally, participants who anticipated feedback from a supervisor, regardless of goal planning, improved the quality of their stories the most. We did not find that goal setting or the provider's role affected evaluation apprehension. Our findings indicate that content creators should be guided to plan proximal feedback goals to encourage sharing of early drafts of creative work and receive feedback from someone in a position of higher perceived power to foster the most revision and improvement on those drafts.
Personalization Paradox in Behavior Change Apps

Social comparison-based features are widely used in social computing apps. However, most existing apps are not grounded in social comparison theories and do not consider individual differences in social comparison preferences and reactions. This paper is among the first to automatically personalize social comparison targets. In the context of an m-health app for physical activity, we use artificial intelligence (AI) techniques of multi-armed bandits. Results from our user study (n=53) indicate that there is some evidence that motivation can be increased using the AI-based personalization of social comparison. The detected effects achieved small-to-moderate effect sizes, illustrating the real-world implications of the intervention for enhancing motivation and physical activity. In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change. Additionally, we propose research directions to mitigate this Personalization Paradox.
A study of the effectiveness of transfer learning in individualized asthma risk prediction

Deep Learning classifiers require a vast amount of data to train models that generalize well and perform effectively on unseen data. However, small sizes of training data, especially in the medical domain, make this a challenging task. Transfer Learning (TL) can help overcome a scarcity of data by focusing on fine tuning a pre-trained model with a small amount of specialized training data. In the last few years, several studies have been performed on TL with medical images, and they point towards significant gains available with this method. However, to date no such studies have been performed in the area of individualized asthma prediction with limited training data for each patient. In this paper, we conduct a systematic study of transfer learning in this domain in the context of neural networks. Our TL approach trains the source model with population data of 25 asthma patients and then retrains the target model with a target patient's data. Our results show that transfer learning yields promising results in improving model performance on an individual basis. Further research directions that are worth investigating based on our results are pointed out as future work directions.
Land of the lost: privacy patterns' forgotten properties: enhancing selection-support for privacy patterns

Privacy patterns describe core aspects of privacy-enhancing solutions to recurring problems and can, therefore, be instrumental to the privacy-by-design paradigm. However, the privacy patterns domain is still evolving. While the main focus is currently put on compiling and structuring high-quality privacy patterns in catalogs, the support for developers to select suitable privacy patterns is still limited. Privacy patterns selection-support means, in essence, the quick and easy scoping of a collection of patterns to the most applicable ones based on a set of predefined criteria. To evaluate patterns against these criteria, a thorough understanding of the privacy patterns landscape is required. In this paper, (i) we show that there is currently a lack of extensive support for privacy patterns selection due to the insufficient understanding of pattern properties, (ii) we propose additional properties that need to be analyzed and can serve as a first step towards a robust selection criteria, (iii) we analyze and present the properties for 70 privacy patterns, and (iv) we discuss a potential approach of how such a selection-support method can be realized.
Influence of the Pore Geometry on the Micropore Size Distribution Function of Active Carbons

A simple procedure for the evaluation of the micropore size distribution function of active carbons from the experimental adsorption isotherm of a single gas is discussed. In this method, the adsorption potential distribution and the dependencies of this potential on the micropore size for three different models, i.e. slit-like, cylindrical and spherical, are utilized. It was shown that the geometrical shape of pores may have a substantial influence on the micropore size distribution. While the micropore size distributions calculated assuming slit-like and cylindrical pore geometries are similar, spherical pore geometry leads to a substantially different distribution. A qualitative agreement was found between the micropore size distribution obtained by the current procedure, which is equivalent to the Horvath–Kawazoe method, and that evaluated by the density functional theory (DFT) method.
Exploring the Effects of Incorporating Human Experts to Deliver Journaling Guidance through a Chatbot

Chatbots are regarded as a promising technology for delivering guidance. Prior studies show that chatbots have the potential of coaching users to learn different skills; however, several limitations of chatbot-based approaches remain. People may become disengaged from using chatbot-guided systems and fail to follow the guidance for complex tasks. In this paper, we design chatbots with (HC) and without (OC) human support to deliver guidance for people to practice journaling skills. We conducted a mixed-method study with 35 participants to investigate their actual interaction, perceived interaction, and the effects of interacting with the two chatbots. The participants were randomly assigned to use one of the chatbots for four weeks. Our results show that the HC participants followed the guidance more faithfully during journaling practices and perceived a significantly higher level of engagement and trust with the chatbot system than the OC participants. However, after finishing the journaling-skill training session, the OC participants were more willing to keep using the learned skills than the HC participants. Our work provides new insights into the design of integrating human support into chatbot-based interventions for delivering guidance.
The wisdom of the rankers: a cost-effective method for building pooled test collections without participant systems

Information Retrieval is an area where evaluation is crucial to validate newly proposed models. As the first step in the evaluation of models, researchers carry out offline experiments on specific datasets. While the field started around ad-hoc search, the number of new tasks is continuously growing. These tasks demand the development of new test collections (documents, information needs, and judgments). The construction of those datasets relies on expensive campaigns like TREC. Due to the size of modern collections, obtaining the relevance for each document-topic pair is infeasible. To reduce this cost, organizers usually apply a technique called pooling. When building pooled test collections, assessors only judge a portion of the documents selected among the participants' results. Although the judgments will not be exhaustive, they will be sufficiently complete and unbiased if pooling is done correctly. Therefore, researchers may safely use pooled collections to evaluate new models. However, the application of pooling depends on the existence of participant systems. This need is a handicap for tasks for which it is necessary to release training data before the celebration of the competition or for those with few participants. In this paper, we present a simple method for building pooled collections when such restrictions exist. Our proposal relies on two principles: the wisdom of the rankers and the application of pooling. By creating enough artificial participant systems, we can apply pooling on their results to select the documents that merit human assessment. Using an innovative approach to evaluate our method, we show that researchers may use it to produce high-quality collections on the absence of participant systems.
RE-BERT: automatic extraction of software requirements from app reviews using BERT language model

Traditionally, developers restricted themselves to collecting opinions from a small group of users by using techniques such as interviews, questionnaires, and meetings. With the popularization of social media and mobile applications, these professionals have to deal with crowd users' opinions, who want to voice the software's evolution. In this context, one of the main related tasks is the automatic identification of software requirements from app reviews. Recent studies show that existing methods fail at this task, since review texts usually contain informal language, contain grammatical and spelling errors, as well as the difficulty in filtering out irrelevant information that has no practical value for developers. In this paper, we present the RE-BERT (Requirements Engineering using Bidirectional Encoder Representations from Transformers). Our method innovates by using pre-trained neural language models to generate semantic textual representations with contextual word embeddings. Our RE-BERT performs fine-tuning of the BERT model with a focus on the local context of the software requirement tokens. A statistical analysis of the experimental results involving eight different apps showed that our RE-BERT outperforms existing state-of-the-art methods.
Using Game-Based Learning for Supply Chain Education

This research presents the implementation of a serious game in a supply chain course. Teaching in the supply chain aims to coordinate activities to organize materials, machinery and information in order to optimize the operation of a company. This knowledge area represents a challenge for students because it integrates concepts related to mathematics, statistics and management. The use of simulators allows students to experiment with different strategies and observe the consequences of their decision making in the management of the supply chain. However, most researchers focus on student learning and have not related knowledge acquisition and game scoring. The results of this study show that the students by using this simulator, the students acquired knowledge about supply chain management and that this learning is related to their score in the game. The results of this study suggest that the simulator offers learning opportunities in the logistics area to people with different levels of knowledge, contributing to the literature on industrial engineering education.
How Not to Measure Social Network Privacy

Privacy has been conceptualized as a multi-dimensional construct in prior research. However, most multi-dimensional conceptualizations were developed based on populations from western countries. It remains an open question whether the underlying dimensions of privacy stays consistent in non-western countries. Through a series of factor analyses on two survey datasets, we compare the dimensions of privacy concern, information disclosure, general disclosiveness, and privacy management strategies among social network users in the US, China and South Korea. We find significant cross-country differences in the dimensions of these privacy-related concepts, indicating that the fundamental understanding of these concepts varies substantially across these countries. We discuss possible explanations of these cross-country differences and make methodological suggestions for future work.
Someone Is Wrong on the Internet

Good faith disagreements and healthy conflict management are essential to deliberative democracy and building strong relationships. People increasingly use computer-mediated communication during disagreements, which raises the question of how technology and design impact users' disagreements and relationships. We conducted a mixed-methods study with 257 participants to understand how design impacts disagreements across both existing social media platforms and novel, user-generated designs. Through interviews, a survey, and storyboard evaluations, we found that users often want to discuss challenging topics online but avoid them due to fear of hurting their relationships. Further, we found that users are most excited about design interventions that empower collective group action, humanize others online, or support channel switching to more private or socially rich contexts. Our results suggest that although technology has the potential to support users during conflict, it is also rife with possibilities to do more harm than good by diluting users' intentions, intruding, or backfiring. We introduce "interpersonal design,'' which centers relationships in the design process, an essential step in supporting users in the challenging task of arguing well.
A comparative study of smartphone and smartwatch apps

Despite that our community has spent numerous efforts on analyzing mobile apps, there is no study proposed for characterizing the relationship between smartphone and smartwatch apps. To fill this gap, we present to the community a comparative study of smartphone and smartwatch apps, aiming at understanding the status quo of cross-phone/watch apps. Specifically, in this work, we first collect a set of cross-phone/watch app pairs and then experimentally look into them to explore their similarities or dissimilarities from different perspectives. Experimental results show that (1) Approximately, up to 40% of resource files, 30% of code methods are reused between smartphone/watch app pairs, (2) Smartphone apps may require more than twice as many as permissions and adopt more than five times as many as user interactions than their watch counterparts, and (3) Smartwatch apps can be released as either standalone (can be run independently) or companion versions (i.e., have to co-work with their smartphone counterparts), for which the former type of apps tends to require more permissions and reuse more code, involve more user interactions than the latter type. Our findings can help developers and researchers understand the ecosystem of smartwatch apps and further gain insight into migrating smartphone apps for smartwatches.
AI-Assisted Human Labeling

Human labeling of training data is often a time-consuming, expensive part of machine learning. In this paper, we study "batch labeling", an AI-assisted UX paradigm, that aids data labelers by allowing a single labeling action to apply to multiple records. We ran a large scale study on Mechanical Turk with 156 participants to investigate labeler-AI-batching system interaction. We investigate the efficacy of the system when compared to a single-item labeling interface (i.e., labeling one record at-a-time), and evaluate the impact of batch labeling on accuracy and time. We further investigate the impact of AI algorithm quality and its effects on the labelers' overreliance, as well as potential mechanisms for mitigating it. Our work offers implications for the design of batch labeling systems and for work practices focusing on labeler-AI-batching system interaction.
New Glomeromycotan Taxa, Dominikia glomerocarpica sp. nov. and Epigeocarpum crypticum gen. nov. et sp. nov. From Brazil, and Silvaspora gen. nov. From New Caledonia

Examination of fungal specimens collected in the Atlantic rain forest ecosystems of Northeast Brazil revealed many potentially new epigeous and semihypogeous glomerocarp-producing species of the phylum Glomeromycota. Among them were two fungi that formed unorganized epigeous glomerocarps with glomoid spores of almost identical morphology. The sole structure that distinguished the two fungi was the laminate layer 2 of their three-layered spore wall, which in spores of the second fungus crushed in PVLG-based mountants contracted and, consequently, transferred into a crown-like structure. Surprisingly, phylogenetic analyses of sequences of the 18S-ITS-28S nuc rDNA and the rpb1 gene indicated that these glomerocarps represent two strongly divergent undescribed species in the family Glomeraceae. The analyses placed the first in the genus Dominikia, and the second in a sister clade to the monospecific generic clade Kamienskia with Kamienskia bistrata. The first species was described here as Dominikia glomerocarpica sp. nov. Because D. glomerocarpica is the first glomerocarp-forming species in Dominikia, the generic description of this genus was emended. The very large phylogenetic distance and the fundamental morphological differences between the second species and K. bistrata suggested us to introduce a new genus, here named as Epigeocarpum gen. nov., and name the new species Epigeocarpum crypticum sp. nov. In addition, our analyses also focused on an arbuscular mycorrhizal fungus originally described as Rhizophagus neocaledonicus, later transferred to the genus Rhizoglomus. The analyses indicated that this species does not belong to any of these two genera but represents a new clade at the rank of genus in the Glomeraceae, here described as Silvaspora gen. nov.
Case Report: A Rare Presentation of NSAID-Induced Secondary Membranous Nephropathy in a Pediatric Patient

Background: Membranous nephropathy (MN) is a common cause of nephrotic syndrome in adults, but it is responsible for <5% of nephrotic syndrome cases in children. MN has primary and secondary forms. Secondary MN is caused by viral infections, autoimmune diseases like lupus, or drugs. Non-steroid anti-inflammatory drug (NSAID)-induced secondary MN is rarely described in the pediatric population. Thus, the clinical presentation and time to recovery are vastly unknown in the pediatric subgroup. Clinical Presentation: We report a case of a 15-year-old female who presented with acute onset of nephrotic range proteinuria, significant hypoalbuminemia, hyperlipidemia, and lower extremity edema related to the presence of nephrotic syndrome. She had a history of ibuprofen use periodically for 6 months before presentation because of menstrual cramps and intermittent lower abdominal pain. After the presentation, we performed a renal biopsy that reported stage 1–2 MN, likely secondary. The phospholipase A2 receptor (PLA2R) antibody on the blood test and PLA2R immune stain on the renal biopsy sample were negative. We performed a comprehensive evaluation of the viral and immune causes of secondary MN, which was non-revealing. She had stopped ibuprofen use subsequent to the initial presentation. She was prescribed ACE inhibitor therapy. After 6 months of ACE inhibitor treatment, the proteinuria had resolved. Conclusion: Proteinuria can last for several weeks when NSAID induces secondary MN and nephrotic syndrome. With the widespread use of NSAIDs prevalent in the pediatric community, further studies are needed to evaluate and study the role of NSAIDs in this condition.
Voriconazole Induced Hallucinations and Visual Disturbances in a Female Child: A Case Report and Literature Review

Voriconazole is a second-generation azole widely used for the prevention and treatment of fungal infection in leukemia patients. We report a case of 9-year-old girl with T-cell acute lymphoblastic leukemia who developed hallucinations and visual disturbance after using voriconazole twice. These symptoms began acutely after treatment with voriconazole and resolved rapidly when the voriconazole was stopped. No specific cause was identified, and thus the symptoms were considered to be the adverse drug reactions (ADRs) of voriconazole. Simultaneous development of hallucinations and visual disturbance caused by voriconazole in children rarely have been reported before and the causes of these ADRs are unknown. Several other cases of hallucinations and (or) visual disturbance caused by voriconazole among 15–81 years old patients have been reported in the literature, and are reviewed. Those patients reminded us of the importance of being aware of hallucinations and visual disturbance associated with voriconazole treatment. In addition, we speculate that the hallucinations and visual disturbance are not related to the dosage form of voriconazole. We emphasize that it is also important to monitor the concentration of voriconazole regularly to avoid potential toxicity.