Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified Optimization Toolbox for Solving Popularity Bias, Fairness, and Diversity in Recommender Systems

Historically, the main criterion for a successful recommender system was how accurate the recommendations were according to the user’s taste. This emphasis on accuracy was later challenged by researchers asking for other types of metrics such as novelty, diversity, fairness of the recommendations. Researchers have proposed different algorithms to improve these metrics of recommendation, but the problem is that each proposed algorithm improves a certain metric (diversity, novelty, etc.) and, usually, it is difficult to improve two or more aspects simultaneously. In this paper, we unify different considerations into a constrained optimization framework where different sets of metrics can be improved by simply using different sets of constraints. Therefore, our framework improves the non-accuracy metrics of the recommendations by combining different constraints designed for separate metrics. Our biggest contribution is offering models that are simple, easy to combine, and data independent. We create models considering popularity, fairness, and diversity metrics since they are the metrics widely investigated in the literature; however, our framework can include other metrics following the ideas proposed in this paper. Experimental results confirm that our general framework has comparable performance with the state-of-the-art methods designed for improving each individual metric, and offers the benefit of being able to accommodate a wide range of considerations.
Question Answering over Knowledge Graphs

With the increasing maturity of large-scale knowledge graphs, question answering over knowledge graphs has become a crucial topic and attracted massive attention. A knowledge graph-based question answering system targets to leverage facts in knowledge graphs to answer natural language questions and assist users to access the meaningful and pertinent knowledge, without knowing data structures. This research intends to propose an approach to answer natural language questions over knowledge graphs in three main steps including identification of optimal subgraphs, creation of candidates and answer selection. The proposed approach leverages the state-of-the-art techniques including graph alignment, neural networks and natural language process to generate more accurate answers for questions, either simple questions or multi-hop questions. Experiments are to be conducted over different knowledge graphs to demonstrate the effectiveness of the approach, which can outperform novel existing approaches.
Multilevel Modeling with ConceptBase

Multilevel modeling aims at improving the expressiveness and conciseness of conceptual modeling languages by allowing to express domain knowledge at higher abstractions levels. In this demonstration, we go thru two variants of multilevel extensions for the ConceptBase system, which had originally been used more for the design of domain-speciﬁc conceptual modeling languages. The demonstration highlights the partial evaluation feature of the deductive rule engine of Concept-Base. It also shows how multilevel modeling is essentially about a better understanding how instantiation, specialization, and attribution relate to each other in conceptual modeling.
Unboxing the Algorithm: Designing an Understandable Algorithmic Experience in Music Recommender Systems

1 After decades of the existence of algorithms in everyday use technologies, users have developed an algorithmic awareness, but they still lack the confidence to grasp them. This study explores how understandability as a principle drawn from sociology, design, and computing can enhance the algorithmic experience in music recommendation systems. The preliminary results of this Research-Through-Design showed that users had limited mental models so far but had a curiosity to learn. Further, it confirmed that explanations as a dialogue could improve the algorithmic experience in music recommendation systems. Users could comprehend recommendations the best when they were easy to access and understand, directly related to user behavior, and when they allowed the user to correct the algorithm. To conclude, our study reconfirms that designing experiences that help users to understand the algorithmic workings will make authentic recommendations from intelligent systems more applicable in the long run.
Production of fermentable sugars and lignin from the biomass using supercritical fluids

1. A method for increasing glucose produced from lignocellulosic biomass, comprising: manufacturing a fractionated biomass, which contains: the first solid fraction, which consists of: cellulose inerastvorimy lignin; and a first liquid fraction; mixing the above solids of water for producing a slurry; optionally preheating the aforementioned slurry to a temperature of less than the critical temperature of water, contacting the above-mentioned slurry to the second reaction fluid in which are formed: a second solid fraction containing insoluble lignin; and a second liquid fraction comprising a saccharide selected from the group consisting of cellooligosaccharides, glucose and mixtures thereof; wherein the above-mentioned second reaction fluid comprises water and optionally carbon dioxide, wherein the above-mentioned second reaction fluid has a temperature and pressure above the critical point of water and carbon dioxide ; iumenshenie temperature above the reaction mixture to a temperature below the critical temperature of water; ineobyazatelny hydrolysis of said second liquid fraction to obtain glyukozy.2. A method according to Claim. 1, in which the above method is nepreryvnym.3. A method according to Claim. 1, wherein the reduction temperature above the above reaction mixture to a temperature below the critical temperature of water comprises contacting the reaction mixture with the above composition containing vodu.4. A method according to Claim. 1, in which the above reduction reaction mixture above the temperature to below for
Luminescence Properties of ZnO Twin Nanorod–Ag Heteronanocrystals and Interfacial Exciton–Surface Plasmon Coupling

The epitaxial semiconductor–metal interface has unique optical properties. However, people know little about the coupling modes at the semiconductor–metal interface of heteronanocrystals both experimentally and theoretically. The coupling between the semiconductor excitons and the metal surface plasmons in the ZnO twin nanorod–Ag heteronanocrystals is studied. These heteronanocrystals are synthesized through photoreduction of silver ions on the ZnO twin‐nanorods. The interfacial epitaxial relation of the perfect heteronanocrystals is found to be (0001)ZnO//(111)Ag. The hybrid exciton energy splitting of 97 meV is observed from the absorption spectrum. The photoluminescence of the hybrid excitons shifts to red significantly relative to that of the uncoupled excitons. The theoretical model concerning the exciton–surface plasmon coupling is proposed to account for the experimental phenomena.
Improving EFL Majors' Translation Competence Using Self-Regulated Learning -Based Program

The main concern of the present study was to examine the effectiveness of developing a translation course based on the strategic self-regulated learning in improving EFL majors’ translation competence. Adopting quasi-experimental design, the participants, consisting of 60 EFL majors at the Faculty of Foreign Languages and Translation, Misr University for Science and Technology, were randomly selected and distributed equally to two groups, the experimental group and the control group. To fulfill the purpose of the study, a translation competence test was constructed and validated. Results revealed the effectiveness of the developed course in improving translation competence of the experimental group students. The results also showed that the experimental group students outperformed the control group on the posttest translation competence test. It is recommended that self-regulation training should be accorded adequate attention in EFL curriculum in order to cater for independence and life-long learning that meet the demands of the 21 st century. The study also called for adopting competence-based orientation in designing translation courses through making available training workshops for EFL majors and giving prominence to selecting appropriate strategies particularly those emphasizing autonomy in the learning process.
Glycosyltransferase inhibitors: A promising strategy to pave a path from laboratory to therapy

Abnormal glycosylation is a common feature in disease that typically results from misregulation in expression and/or activity of glycosyltransferases (GTs) and glycosidases. Interfering with these enzymes, by developing a prospect of targeted inhibitors, has opened newer avenues to meet the challenge of abnormal glycosylation. Progress in GT inhibitors has been relatively slower in comparison to glycosidases. In case of GTs, their polyspecific nature, structural homology, overlapping specificities, multi-substrate catalytic mechanism and relatively less available 3D-structural data stance a big challenge to explore the whole potential of GT target inhibitors in comparison to glycosidases for therapeutic purposes. In this review, we focus on GT specific inhibitors, which are organised as conventional donor analogues (viz. donor, acceptor and transition state mimetic), glycomimetic, alternative inhibitor chemotype and metabolic chain terminator. Conventional donor analogues are however limited by the poor membrane permeability and chemical instability. Thus, in the last two decades, alternative inhibitor chemotypes caught attention as a promising lead, which are not structurally derived from GT substrates and possess drug like properties offering an alternate non-substrate like inhibitor based strategy for drug development. Although successful cellular GT-targeted molecules are yet to be achieved, recent designing of metabolic inhibitors i.e., dead end substrates are emerging as an impetus to explore the potential of GT families as therapeutic targets.
Alterations in ambipolar characteristic of graphene due to adsorption of Escherichia coli bacteria

In order to evaluate the interaction between biomaterials and graphene from the perspective of its ambipolar characteristic, we have investigated the alteration in ambipolarity of graphene-based field effect transistors (G-FET) after the adsorption of Escherichia coli (E. coli) bacteria onto its graphene layer. We confirmed a positive shift in the ambipolar curve of the G-FETs after the adsorption of E. coli, presumably due to the negative charge of the adsorbed E. coli. However, we did not observe any decrease in the electron mobility or conductivity of the G-FETs, which implied that E. coli did not chemically react with the carbon atoms of graphene, nor introduce any damage on the graphene lattice, but were only physically adsorbed onto the graphene surface. These findings may extend the prominence of graphene as a stable yet sensitive material to be fully utilized in future biosensing applications. These results were then compared to those of ferritin adsorption, which is a protein shell and biomaterial like E. coli, and radical oxygen doping onto the graphene surface.
Semi-autonomous, site-wide A11Y testing using an intelligent agent

Most countries across the globe have now ratified and/or signed the United Nations Convention on the Rights of Persons with Disabilities, the culmination of an effort started in the 1980's to protect the rights and dignity of people with disabilities. W3C's Web Content and Accessibility Guidelines documents, WCAG 2.0 (2008), ISO/IEC 40500 (2012), and WCAG 2.1 (2018), establish conformance requirements, which most nations have adopted as their de jure standard for determining website compliance with the UN convention. Although several tools support static WCAG web page analysis, present tools lack the automation required to automatically evaluate an entire website. In addition, current techniques are capable of discovering less than 30% of WCAG Level A and Level AA conformance issues. This paper presents an intelligent testing agent, Agent A11Y, capable of semi-autonomously exploring a website and more thoroughly evaluating its compliance with WCAG guidelines. To expand the system's automated WCAG guideline coverage, Agent A11Y employs novel dynamic testing strategies in combination with existing static web page analysis solutions. Additional tooling focuses manual testing activities on WCAG requirements that are difficult to fully automate. Supporting the generation of effective and actionable reports, Agent A11Y utilizes site exploration information to improve the aggregation and categorization of site-wide testing results. In conjunction with a discussion of the aforementioned Agent A11Y exploration, testing, and reporting methods, the paper presents experimental results and analysis to illustrate the comparative usefulness of the described techniques for conducting site-wide accessibility assessments.
Human Genetics of Face Recognition: Discovery of MCTP2 Mutations in Humans with Face Blindness (Congenital Prosopagnosia)

Face recognition is important for both visual and social cognition. While congenital prosopagnosia (CP) or face blindness has been known for seven decades and electrophysiological studies have characterized face specific neurons for half a century, no molecular analyses have been undertaken. Here we report results after 14 years of research combining classic genetics and modern genomics involving over 7000 subjects. From a large family with 18 CP members, we uncovered a fully co-segregating private mutation in the MCTP2 gene which encodes a calcium binding transmembrane protein expressed in the central nervous system. More rare mutations in MCTP2 were detected in CP families screened from a cohort of 2904 college students and were also associated with CPs in a second cohort of 1928. In a third cohort of 1757, face recognition was different between 14 carriers with a frameshift mutation S80fs in MCTP2 and 19 non-carrying volunteers. 6 families including one with 10 members showed the S80fs-CP correlation. Functional magnetic resonance imaging (fMRI) indicates that impaired recognition of individual faces by CPs with the MCTP2 mutations is associated with inability to recognize the same faces in the right fusiform face area (rFFA). Our results have revealed the genetic predisposition of MCTP2 mutations in CP, 74 years after the initial report of CP. This is the first time a gene required for a higher form of visual social cognition was found in humans.
Mental Health and Time Management Behavior among Students During COVID-19 Pandemic: Towards Persuasive Technology Design

The study explored the impact of the COVID-19 pandemic on students' mental health in higher education while capturing their perceptions and attitudes towards time management. The aim was to examine relationships between stress, anxiety, and specific time management related factors. Considering possible differences between genders and degree levels, we developed five structural equation models (SEMs) to delineate these relationships. Results of a large-scale study of 502 participants show that students suffered from stress and two types of COVID-19-related anxiety: disease and consequences. Students' preference for organization was the only factor that significantly promoted their perceived control over time, which contributes to reducing stress, hence, anxiety. However, female students reported higher stress and anxiety levels than male students. Graduate students reported higher anxiety levels related to the consequences of the pandemic compared to undergrads. To promote students' preference for organization, we map the three categories of organization to corresponding persuasive strategies which could be used in the design of persuasive interventions. This creates an opportunity for developing technological interventions to improve students' perceived control over time, thus, reduce stress and anxiety.
Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications

Introduction The infection fatality rate (IFR) of COVID-19 has been carefully measured and analysed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries. Methods We systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using representative samples collected by February 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analysed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible. Results In most locations in developing countries, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups. Age-specific IFRs were roughly 2 times higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure. Conclusion The burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to ensure medical equity to populations in developing countries through provision of vaccine doses and effective medications.
The Ambiguous Competitive Effects of Passive Partial Forward Integration

In a two-tier industry with an upstream monopolist supplier and downstream competition with differentiated goods, we show that passive partial forward integration (PPFI) has ambiguous effects on competition and welfare. When vertical trading is conducted via linear tariffs, PPFI is pro-competitive and welfare-increasing. While under two-part tariffs, it is anti-competitive and welfare-decreasing. These hold irrespectively of the degree of product differentiation, the observability or secrecy of contract terms, the mode of downstream competition, and the distribution of bargaining power between firms.
Employee Satisfaction and Long-Run Stock Returns, 1984–2020

Abstract Economic theory predicts that (in the absence of mispricing) the excess return to socially responsible businesses is negative in equilibrium. In contrast, using the state-of-art empirical models and a sample spanning four decades (1984–2020), an equal-weighted portfolio of companies that treat their employees the best earns an excess return of 2% to 2.7% per year. The estimated alphas are positive in most periods within the sample (with no upward or downward trend) and are particularly large during crisis periods. Overall, the results suggest that the stock market (still) undervalues employee satisfaction.
Successful, but not Too Much: Managing Platform Complementors in the Presence of Network Effects

Firms that own platforms often actively manage firms that offer products through the platform (i.e., platform complementors). To manage complementors, platform owners tend to have fewer tools available at their disposal relative to traditional organizations. Prior work has started examining how platform owners achieve both value creation and appropriation using novel tools such as product endorsements. We extend the literature by developing a theoretical framework on the role of direct network effects of the products introduced through the platform. We test our predictions using a detailed dataset of game apps introduced on the Apple’s app store and examine which apps received Apple’s prestigious Editors’ Choice Awards given at the time of introduction. We find that apps with network effects are more likely to receive the award. This likelihood increases when the app is introduced by a developer with a larger market share but is lower if the app was introduced in a concentrated segment. The likelihood decreases further if the app is introduced in a concentrated segment by a developer that holds a larger market share. We also observe that in concentrated segments, the “challenger” developers have a higher likelihood of receiving the award relative to
State-dependent asset allocation using neural networks

Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods.
Non-Random Survival and Long-Run Firm Performance

Firm survival is not random: it depends on firm performance. This fact has been neglected in prior studies that assess firms’ long-run abnormal performance. I show that, because of non-random survival, a legitimate long-run abnormal performance measure (1) should assign equal weights to each period rather than equal weights to each firm; and (2) should not use firms with different likelihoods of survival as a benchmark. A measure that infringes these principles will misstate long-run abnormal performance. Applying this rationale to IPO firms, I show that the average IPO firm’s abnormal return relative to matched seasoned firms understates IPO firm performance because this measure infringes the two principles: it assigns equal weights to IPO firms, and it uses seasoned firms, which have survived much longer than IPO firms, as a benchmark. The rationale predicts that IPO abnormal performance will concentrate in IPO firms delisted shortly after their IPO. Indeed, I find that only early-delisted IPO firms earn low-er returns than matched seasoned firms; IPO firms that survive long enough to turn into sea-soned firms earn the same returns as matched seasoned firms. This study sheds light on the debate over how to evaluate long-run firm performance and how to conduct long-run event studies.
An information theory of efficient differential treatment

When are differential treatment policies—such as preferential treatment, affirmative action, and gender equity policies—justified by efficiency concerns? I propose a nonparametric assignment model where a policymaker assigns agents to different treatments or positions to maximize total surplus, based on the agents’ characteristics and noisy information about their types. I provide necessary and sufficient conditions on the agents’ signal structures, which characterize whether surplus maximization requires differential treatment or not, and study how the bias and informativeness of signal structures determine the efficiency implications of differential treatment. I examine implications of this model for inequality, decentralization, and empirical work. (JEL D63, D82, D83, I23, I24, J71)