{FairRec}: {T}wo-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One item (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality.Comment: In Proceedings of The Web Conference (WWW) 202

Scheduling Virtual Conferences Fairly: {A}chieving Equitable Participant and Speaker Satisfaction

Recently, almost all conferences have moved to virtual mode due to the pandemic-induced restrictions on travel and social gathering. Contrary to in-person conferences, virtual conferences face the challenge of efficiently scheduling talks, accounting for the availability of participants from different timezones and their interests in attending different talks. A natural objective for conference organizers is to maximize efficiency, e.g., total expected audience participation across all talks. However, we show that optimizing for efficiency alone can result in an unfair virtual conference schedule, where individual utilities for participants and speakers can be highly unequal. To address this, we formally define fairness notions for participants and speakers, and derive suitable objectives to account for them. As the efficiency and fairness objectives can be in conflict with each other, we propose a joint optimization framework that allows conference organizers to design schedules that balance (i.e., allow trade-offs) among efficiency, participant fairness and speaker fairness objectives. While the optimization problem can be solved using integer programming to schedule smaller conferences, we provide two scalable techniques to cater to bigger conferences. Extensive evaluations over multiple real-world datasets show the efficacy and flexibility of our proposed approaches.Comment: In proceedings of the Thirty-first Web Conference (WWW-2022). arXiv admin note: text overlap with arXiv:2010.1462

Presenting a Labelled Dataset for Real-Time Detection of Abusive User Posts

Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, we present a dataset of user comments, using crowdsourcing for labelling. Since abusive content can be ambiguous and subjective to the individual reader, we propose an aggregated mechanism for assessing different opinions from different labellers. In addition, instead of the typical binary categories of abusive or not, we introduce a third class of ‘undecided’ to capture the real life scenario of instances that are neither blatantly abusive nor clearly harmless. We have performed preliminary experiments on this dataset using best practice techniques in text classification. Finally, we have evaluated the detection performance of various feature groups, namely syntactic, semantic and context-based features. Results show these features can increase our classifier performance by 18% in detection of abusive content

Patch antenna in isotropic plasma : Resonant frequency

A method has been developed to compute the resonant frequency of a rectangular microstrip antenna immersed in a linear isotropic plasma medium using Wolf's dynamic dielectric constant model. The results obtained are in agreement with those obtained using spectral domain technique. It has been observed that the antenna resonates at a higher frequency inside the plasma than in free space

Reconstruction of Network Evolutionary History from Extant Network Topology and Duplication History

Genome-wide protein-protein interaction (PPI) data are readily available thanks to recent breakthroughs in biotechnology. However, PPI networks of extant organisms are only snapshots of the network evolution. How to infer the whole evolution history becomes a challenging problem in computational biology. In this paper, we present a likelihood-based approach to inferring network evolution history from the topology of PPI networks and the duplication relationship among the paralogs. Simulations show that our approach outperforms the existing ones in terms of the accuracy of reconstruction. Moreover, the growth parameters of several real PPI networks estimated by our method are more consistent with the ones predicted in literature.Comment: 15 pages, 5 figures, submitted to ISBRA 201

Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier

Structural alterations have been thoroughly investigated in the brain during the early onset of schizophrenia (SCZ) with the development of neuroimaging methods. The objective of the paper is an efficient classification of SCZ in 2 different classes: Cognitive Normal (CN), and SCZ using magnetic resonance imaging (MRI) images. This paper proposed a lightweight 3D convolutional neural network (CNN) based framework for SCZ diagnosis using MRI images. In the proposed model, lightweight 3D CNN is used to extract both spatial and spectral features simultaneously from 3D volume MRI scans, and classification is done using an ensemble bagging classifier. Ensemble bagging classifier contributes to preventing overfitting, reduces variance, and improves the model's accuracy. The proposed algorithm is tested on datasets taken from three benchmark databases available as open-source: MCICShare, COBRE, and fBRINPhase-II. These datasets have undergone preprocessing steps to register all the MRI images to the standard template and reduce the artifacts. The model achieves the highest accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall 94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current state-of-the-art techniques. The performance metrics evidenced the use of this model to assist the clinicians for automatic accurate diagnosis of SCZ

Nationwide trends of modern endodontic practices related to working length, instrumentation, magnification, and obturation: a comparative cross-sectional survey comparing endodontic and non-endodontic specialties practicing root canal treatment in India

Aim: The present study was designed to assess trends in contemporary endodontic practice regarding the techniques and materials used in endodontic therapy among dental practitioners from various regions of India. Methods: A cross-sectional questionnaire-based study was conducted amongst dentists who were pursuing postgraduates in endodontics (PG Endo) and other branches (PG-OB), specialists from other branches (MDS-OB) and specialists in endodontics (MDS-Endo) in various dental colleges representing East, West, North, South, and Central zones through an e-survey using Google forms. State-wise postgraduate dental college lists were obtained from the Dental Council of India (DCI) website. Using a multistage cluster random sampling method and considering the unanticipated response rate, emails were sent to 2100. A 29-item close-ended questionnaire, framed according to different aspects of endodontic treatment, was used to record the responses. Results: When the distribution of the groups of dentists was compared, the central zone had the highest number of PG-OB (44.2%) and the lowest number of MDS-Endo (8.4%). The electronic apex locator (EAL) method of working length determination has been reported less among MDS-Endo than MDS-OB. The difference between the usage of various methods for working length determination was significant among the different groups in all the zones. (p < 0.0001) Most MDS-Endo preferred the rotary method of instrumentation over the combination method for different zones. The majority of dental practitioners preferred a combination method of instrumentation. Conclusion: Zone-wise comparisons among dentists showed the majority of general dental practitioners preferred the combination method (radiographs and electronic apex locator) for working length determination. Most MDS-Endo preferred the rotary method of instrumentation over the combination method for different zones. All dental practitioners did not so commonly use magnification in all the zones. The single cone technique was the most opted by dental practitioners of all the zones

A REVIEW ON PHARMACO KINETIC DRUG INTERACTIONS OF STATINS

The 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) are generally well tolerated as monotherapy. Statins are associated with two important adverse effects, asymptomatic elevation in liver enzymes and myopathy. Myopathy is most likely to occur when statins are administered with other drugs. Statins are substrates of multiple drug transporters (including OAT- -P1B1, BCRP and MDR1) and several cytochrome P450 (CYP) enzymes (including CYP3A4, CYP2C8, CYP2C19, and CYP2C9). Possible adverse effects of statins can occur due to interactions in concomitant use of drugs that substantially inhibit or induce their methabolic pathway. This review aim is to summarize the most important interactions of statins