318 research outputs found
The achievement gap between Roma and non-Roma students in East Central Europe and its potential causes
This study quantifies the achievement gap between Roma and non-Roma students in East Central
Europe and assesses the potential causes of the gap. Using the UNDP survey of 2011, the only
comparable data on the Roma spanning many countries, we show that the gap in the chances to
get secondary education is substantial in all countries. When comparing young adults living with
parents of comparable income and educational attainment, the gap drops by more than a half in
most countries. Using unique data from Hungary, we assess the gap in standardized test scores and
show that it is comparable to the size of the Black-White test score gap in the U.S.A. in the 1980âs.
The test score gap in Hungary is almost entirely explained by social differences in income, wealth
and parental education, and ethnic factors do not play a significant role. We identify two major
mechanisms by which the social disadvantages of Roma students lead to lower skills. Their home
environment is less favorable for their cognitive development, and their schools are characterized
by a lower quality educational environment. Ethnic differences in the home environment are,
again, explained by social differences, and ethnicity seems to play no additional role. On the other
hand, while access to higher quality schools is strongly related to social differences, Roma students
seem to face additional disadvantages. The results suggest that besides policies that aim at
alleviating poverty, well-designed interventions influencing the mechanisms can also improve the
skill development of Roma and other disadvantaged children
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions
This work was supported in part by the Ministry of Higher Education under the Fundamental Research Grant Scheme under Grant FRGS/1/2018/ICT04/UTM/01/1; and in part by the Faculty of Informatics and Management, University of Hradec Kralove, through SPEV project under Grant 2102/2022.Phishing has become an increasing concern and captured the attention of end-users as well
as security experts. Existing phishing detection techniques still suffer from the de ciency in performance
accuracy and inability to detect unknown attacks despite decades of development and improvement.
Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention
to phishing detection that capitalizes on machine learning techniques. Deep learning has emerged as a branch
of machine learning that becomes a promising solution for phishing detection in recent years. As a result,
this study proposes a taxonomy of deep learning algorithm for phishing detection by examining 81 selected
papers using a systematic literature review approach. The paper rst introduces the concept of phishing and
deep learning in the context of cybersecurity. Then, taxonomies of phishing detection and deep learning
algorithm are provided to classify the existing literature into various categories. Next, taking the proposed
taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques
and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues
that deep learning faces in phishing detection and proposes future research directions to overcome these
challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning
techniques in a practical context, and to highlight the related issues that motivate researchers in their future
works. The results obtained from the empirical experiment showed that the common issues among most of
the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and de cient
detection accuracy.Ministry of Higher Education under the Fundamental Research Grant Scheme FRGS/1/2018/ICT04/UTM/01/1Faculty of Informatics and Management, University of Hradec Kralove, through SPEV project 2102/202
Tidying Up the Conversational Recommender Systems' Biases
The growing popularity of language models has sparked interest in
conversational recommender systems (CRS) within both industry and research
circles. However, concerns regarding biases in these systems have emerged.
While individual components of CRS have been subject to bias studies, a
literature gap remains in understanding specific biases unique to CRS and how
these biases may be amplified or reduced when integrated into complex CRS
models. In this paper, we provide a concise review of biases in CRS by
surveying recent literature. We examine the presence of biases throughout the
system's pipeline and consider the challenges that arise from combining
multiple models. Our study investigates biases in classic recommender systems
and their relevance to CRS. Moreover, we address specific biases in CRS,
considering variations with and without natural language understanding
capabilities, along with biases related to dialogue systems and language
models. Through our findings, we highlight the necessity of adopting a holistic
perspective when dealing with biases in complex CRS models
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