bluuebunny/crossref_metadata_2025_split huggingface dataset:
EFL Teachers’ Sources of Remote Teaching Anxiety: Insights and Implications for EFL Teacher Education
Authors: Akbana Yunus Emre , Dikilitaş Kenan 
Published: March 2022

Abstract

Introduction: This study aims to explore an under-researched issue; namely, remote teaching anxiety.

Methods: This study employed a sequential mixed-methods exploratory design where participants initially reported their remote teaching anxiety sources and then rated each. For the analysis, inductive content analysis and statistical tests were employed.

Results: The content analysis revealed two major themes: digitalisation-related concerns and online pedagogy-related concerns. Statistically significant difference was only found between anxiety sources and online teaching experience but not between gender, age, teaching experience, work setting and anxiety sources.

Discussion: Several studies (Çoklar, Efilti, Şahin, & Akçay, 2016; Hassan et al., 2019) found digitalisation-related concerns causing stress among teachers; however, remote teaching anxiety remains an underexplored construct (Russell, 2020). Although online pedagogy-related concerns were found to cause teaching anxiety in our study, a recent study (Lazarevic & Bentz, 2020) found using technology helpful to decrease anxiety.

Limitations: The data were limited to the views of 96 EFL teachers in the Turkish context.

Conclusion: We can conclude that limited experience with remote teaching can contribute to higher anxiety among EFL teachers. Additionally, this research can contribute to the relevant literature with several implications on the future of language education.
Toxic metals in soil depths from selected abandoned sites: Occurrence, sources, ecological and human health risk
Authors: Emoyan Onoriode Onos , Tesi Godswill Okeoghene , Ohwo Efe , Olisah Chijioke 
Published: March 2022

Abstract
This study provides a comparative assessment of cadmium (Cd), lead (Pb), chromium (Cr), nickel (Ni), copper (Cu), manganese (Mn), zinc (Zn), and iron (Fe) pollution occurrence, sources, and exposure risk in soils from selected abandoned sites. The concentrations of metals were determined using atomic absorption spectrophotometry. The metals occurrence ranged from 0.02 (Zn) to 16600 mg kg−1 (Fe) in the order of subsoil > topsoil with petroleum tank farm and fuel/gas service station exhibiting high metal loading. The sources of metals are anthropogenic and geologic. The hazard index values for infants’ were higher than that of adults, and the inhalation risk for adults’ was considerably higher than for infants’ exposure. The ecological risk of Cd, Pb, Cr, Ni, and Zn falls in the contamination to pollution index. This study revealed the need for clean-up and restoration of abandoned site soils.
An extensive note on various fractional-order type operators and some of their effects to certain holomorphic functions
Authors: Irmak Hüseyin 
Published: March 2022

Abstract
The aim of this paper is to present background information in relation with some fractional-order type operators in the complex plane, which is designed by the fractional-order derivative operator(s). Next we state various implications of that operator and then we show some interesting-special results of those applications.
The Actions Carried Out by the Cyberparc Incubator of Sidi Abdellah During the Period of 2010–2020: State of Play and Perspectives
Authors: Arabeche Zina 
Published: March 2022

Abstract
This paper aims to describe the actions carried out by the Cyber-park incubator of Sidi Abdellah during the period of 2010–2020 in order to support young project holders. The Cyber-park of Sidi Abdellah is the first Algerian experience of creating a technology park. The project is very ambitious; it reflects the Algerian political will to build an information society and accelerate the transition of Algeria to a knowledge economy. However, through our reading of the statistics provided, we can say that the number of project holders affiliated to the incubator of Sidi Abdullah, especially the number of projects accepted, is still far from the objectives presented by the incubator. On the other hand, we have found that the Cyber-park incubator focuses much more on the training of entrepreneurs, while the financial side is totally absent.
The present participle and gender assignment in Swedish
Authors: Skrzypek Dominika 
Published: March 2022

AbstractThe paper considers gender assignment of deverbal nouns, originally present participles, in Swedish. The perspective is diachronic. The corpus consists of a choice of Swedish texts from 1225-1732. The results show that nouns denoting entities ranking higher in the Animacy hierarchy show tendencies to be placed in the utrum gender (originally masculine and feminine genders) and nouns denoting mass, collective or abstract referents to be assigned neuter gender. This tendency is visible throughout the history of the Swedish language.
Mutualism Within a Simulated Microgravity Environment - Piriformospora indica Promotes the Growth of Medicago truncatula
Authors: Hayes Martin W. , Stutte Gary W. , McKeon-Bennett Michelle , Murray Patrick G. 
Published: March 2022

Abstract
The endophytic fungus, Piriformospora indica, developed a subepidermal infection within Medicago truncatula at 1 g and at simulated microgravity over a period of 15 days, resulting in intracellular colonization of mature host tissue. At 1 g, P. indica inoculation affected the growth and morphology of M. truncatula, predominantly roots. Inoculated M. truncatula had a significantly greater number of roots (102%), total root length (88%), and dry root weight (25%) than non-inoculated plants. Effects on shoot morphology of P. indica inoculated M. truncatula included longer (31%) and heavier (30%) shoots, along with increased leaf surface area (98%). P. indica retained the ability to promote the growth of M. truncatula under simulated microgravity conditions upon two dimensional clinostatic rotation, significantly increasing root number by 51% and root length by 48%. These physiological and morphological changes may mitigate biotic and abiotic stresses that would otherwise limit crop productivity.
Relation of Natural Frequencies to Traffic Pedestrians’ Comfort of Some Steel Footbridges
Authors: Cătălin Moga , Delia Drăgan , Raluca Nerișanu 
Published: March 2022

Abstract
In this paper several aspects regarding the dynamic behaviour of the steel footbridge structures under the action of the loads generated by the pedestrians’ displacement, correlated with the traffic comfort of the pedestrians crossing the structure are presented. The crossing pedestrians’ comfort criterion requires the values of vibration frequencies and accelerations of the structures in certain limits so that the vibration amplification or resonance is avoided. The paper takes under investigation eight footbridges on steel, composite steel-concrete girders and a cable stayed structure from the viewpoint of frequencies in vibration Mode 1 in a vertical plane, generated by their own weight - natural frequency, or from a partial pedestrian loading, as well as the resulting resonance risk and the traffic comfort.
Factors Concerning the Dynamics of Footbridges and Methods for Improving Pedestrians’ Comfort
Authors: Mircea Suciu , Delia Drăgan , Raluca Nerișanu 
Published: March 2022

Abstract
Pedestrian bridges are constructions that are part of the category Bridges, having the particularity that due to much lower loading as compared to that of road or railway bridges, their decks are much slenderer, and can be included in the category of light structures. In this paper several aspects regarding the dynamic behavior of the footbridge structures under the traffic actions correlated with the pedestrians’ comfort are presented. The comfort criteria depend of the frequencies and accelerations of the structure and have to be situated between certain limits. If the frequencies and accelerations of the structure are in the critical domains, measures to modify them have to be taken, some of them being presented in this paper as they are the most usually used.
The Green and Digital Transformation - the New Challenges of the Construction Market
Authors: Marichova Aneta 
Published: March 2022

Abstract
The new EU industrial strategy proposes new measures to strengthen the sustainability of the European single market (especially in times of crisis) and to accelerate the green and digital transformation. It focuses on 14 industrial ecosystems (one of which is construction), which face the most important challenges of achieving the climate, sustainability and digital transformation goals that are key to market competitiveness. The purpose of the study is: 1) Analysis of the problems related to the need to accelerate the green and digital transformation of the construction market, 2) Development of models that support and ensure the double transition in the construction market.
Statistical Analysis and Classification of Pollutants in the Nokoué Lake (Benin)
Authors: Judicael Deguenon , Jean Deguenon , Ștefan Dumitriu Cristian 
Published: March 2022

Abstract
The water quality of a lake is an important factor for human health. Since the pollution level of the Nokoué Lake (Benin) became a concern, the present study was carried out to better understand and manage the quality of the surface water of this lake, to reduce the risks to damage the population health and the ecosystem equilibrium. The objective of the present work is the statistical study of the pollutants concentration data using Principal Component Analysis (PCA) and clustering. The concentration of five pollutants, namely NO3
−, NH4
+, NT (total nitrogen), PO4
3−, and total phosphates (PT) were sampled on thirty-three sites of the Nokoué Lake and analyzed. Three principal components have been selected and the sites have been grouped in three clusters, one of them containing only the sites on Cotonou.
How to Ensure the Quality of Construction Works for Marine Platforms with Reference to Midia Gas Development Project - ANA Platform & Doina Well & Gas Treatment Plant -
Authors: Schiau Roxana Doina 
Published: March 2022

Abstract
The Midia Gas Development Project is a new offshore gas project which has been developed and built in the last 30 year in the maritime territory of Romania. Once completed, it will have the ability to cover around 10% of gas needs of Romanian population.
Currently there is about 10 Bcm of resources to be produced at a rate of about 1 Bcm per annum.
The infrastructure was thought to be used for the production of gas from the reservoirs of Ana and Doina, in the first place. But the long term vision of the project consists in converting the already existent infrastructure to develop green energy projects, such an offshore wind plant, solar panels fields or production of hydrogen, projects that may start time before decommissioning of the gas production platform.
Are iPhones Really Better for Privacy? A Comparative Study of iOS and Android Apps
Authors: Kollnig Konrad , Shuba Anastasia , Binns Reuben , Van Kleek Max , Shadbolt Nigel 
Published: March 2022

AbstractWhile many studies have looked at privacy properties of the Android and Google Play app ecosystem, comparatively much less is known about iOS and the Apple App Store, the most widely used ecosystem in the US. At the same time, there is increasing competition around privacy between these smartphone operating system providers. In this paper, we present a study of 24k Android and iOS apps from 2020 along several dimensions relating to user privacy. We find that third-party tracking and the sharing of unique user identifiers was widespread in apps from both ecosystems, even in apps aimed at children. In the children’s category, iOS apps tended to use fewer advertising-related tracking than their Android counterparts, but could more often access children’s location. Across all studied apps, our study highlights widespread potential violations of US, EU and UK privacy law, including 1) the use of third-party tracking without user consent, 2) the lack of parental consent before sharing personally identifiable information (PII) with third-parties in children’s apps, 3) the non-data-minimising configuration of tracking libraries, 4) the sending of personal data to countries without an adequate level of data protection, and 5) the continued absence of transparency around tracking, partly due to design decisions by Apple and Google. Overall, we find that neither platform is clearly better than the other for privacy across the dimensions we studied.
Understanding Privacy-Related Advice on Stack Overflow
Authors: Tahaei Mohammad , Li Tianshi , Vaniea Kami 
Published: March 2022

Abstract
Privacy tasks can be challenging for developers, resulting in privacy frameworks and guidelines from the research community which are designed to assist developers in considering privacy features and applying privacy enhancing technologies in early stages of software development. However, how developers engage with privacy design strategies is not yet well understood. In this work, we look at the types of privacy-related advice developers give each other and how that advice maps to Hoepman’s privacy design strategies.
We qualitatively analyzed 119 privacy-related accepted answers on Stack Overflow from the past five years and extracted 148 pieces of advice from these answers. We find that the advice is mostly around compliance with regulations and ensuring confidentiality with a focus on the inform, hide, control, and minimize of the Hoepman’s privacy design strategies. Other strategies, abstract, separate, enforce, and demonstrate, are rarely advised. Answers often include links to official documentation and online articles, highlighting the value of both official documentation and other informal materials such as blog posts. We make recommendations for promoting the under-stated strategies through tools, and detail the importance of providing better developer support to handle third-party data practices.
SoK: Plausibly Deniable Storage
Authors: Chen Chen , Liang Xiao , Carbunar Bogdan , Sion Radu 
Published: March 2022

AbstractData privacy is critical in instilling trust and empowering the societal pacts of modern technology-driven democracies. Unfortunately it is under continuous attack by overreaching or outright oppressive governments, including some of the world’s oldest democracies. Increasingly-intrusive anti-encryption laws severely limit the ability of standard encryption to protect privacy. New defense mechanisms are needed.Plausible deniability (PD) is a powerful property, enabling users to hide the existence of sensitive information in a system under direct inspection by adversaries. Popular encrypted storage systems such as TrueCrypt and other research efforts have attempted to also provide plausible deniability. Unfortunately, these efforts have often operated under less well-defined assumptions and adversarial models. Careful analyses often uncover not only high overheads but also outright security compromise. Further, our understanding of adversaries, the underlying storage technologies, as well as the available plausible deniable solutions have evolved dramatically in the past two decades. The main goal of this work is to systematize this knowledge. It aims to: (1) identify key PD properties, requirements and approaches; (2) present a direly-needed unified framework for evaluating security and performance; (3) explore the challenges arising from the critical interplay between PD and modern system layered stacks; (4) propose a new “trace-oriented” PD paradigm, able to decouple security guarantees from the underlying systems and thus ensure a higher level of flexibility and security independent of the technology stack.This work is meant also as a trusted guide for system and security practitioners around the majorchallengesin understanding, designing and implementing plausible deniability into new or existing systems.
User-Level Label Leakage from Gradients in Federated Learning
Authors: Wainakh Aidmar , Ventola Fabrizio , Müßig Till , Keim Jens , Cordero Carlos Garcia , Zimmer Ephraim , Grube Tim , Kersting Kristian , Mühlhäuser Max 
Published: March 2022

Abstract
Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the common belief that this provides privacy benefits, we here add to the very recent results on privacy risks when sharing gradients. Specifically, we investigate Label Leakage from Gradients (LLG), a novel attack to extract the labels of the users’ training data from their shared gradients. The attack exploits the direction and magnitude of gradients to determine the presence or absence of any label. LLG is simple yet effective, capable of leaking potential sensitive information represented by labels, and scales well to arbitrary batch sizes and multiple classes. We mathematically and empirically demonstrate the validity of the attack under different settings. Moreover, empirical results show that LLG successfully extracts labels with high accuracy at the early stages of model training. We also discuss different defense mechanisms against such leakage. Our findings suggest that gradient compression is a practical technique to mitigate the attack.
Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring
Authors: Hasan Rakibul , Fritz Mario 
Published: March 2022

AbstractEducation technologies (EdTech) are becoming pervasive due to their cost-effectiveness, accessibility, and scalability. They also experienced accelerated market growth during the recent pandemic. EdTech collects massive amounts of students’ behavioral and (sensitive) demographic data, often justified by the potential to help students by personalizing education. Researchers voiced concerns regarding privacy and data abuses (e.g., targeted advertising) in the absence of clearly defined data collection and sharing policies. However, technical contributions to alleviating students’ privacy risks have been scarce. In this paper, we argue against collecting demographic data by showing that gender—a widely used demographic feature—does notcausallyaffect students’ course performance: arguably the most popular target of predictive models. Then, we show that gender can be inferred from behavioral data; thus, simply leaving them out does not protect students’ privacy. Combining a feature selection mechanism with an adversarial censoring technique, we propose a novel approach to create a ‘private’ version of a dataset comprising of fewer features that predict the target without revealing the gender, and are interpretive. We conduct comprehensive experiments on a public dataset to demonstrate the robustness and generalizability of our mechanism.
Who Knows I Like Jelly Beans? An Investigation Into Search Privacy
Authors: Kats Daniel , Silva David Luz , Roturier Johann 
Published: March 2022

AbstractInternal site search is an integral part of how users navigate modern sites, from restaurant reservations to house hunting to searching for medical solutions. Search terms on these sites may contain sensitive information such as location, medical information, or sexual preferences; when further coupled with a user’s IP address or a browser’s user agent string, this information can become very specific, and in some cases possibly identifying.In this paper, we measure the various ways by which search terms are sent to third parties when a user submits a search query. We developed a methodology for identifying and interacting with search components, which we implemented on top of an instrumented headless browser. We used this crawler to visit the Tranco top one million websites and analyzed search term leakage across three vectors: URL query parameters, payloads, and theRefererHTTP header. Our crawler found that 512,701 of the top 1 million sites had internal site search. We found that 81.3% of websites containing internal site search sent (orleakedfrom a user’s perspective) our search terms to third parties in some form. We then compared our results to the expected results based on a natural language analysis of the privacy policies of those leaking websites (where available) and found that about 87% of those privacy policies do not mention search terms explicitly. However, about 75% of these privacy policies seem to mention the sharing of some information with third-parties in a generic manner. We then present a few countermeasures, including a browser extension to warn users about imminent search term leakage to third parties. We conclude this paper by making recommendations on clarifying the privacy implications of internal site search to end users.
FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting
Authors: Bahrami Pouneh Nikkhah , Iqbal Umar , Shafiq Zubair 
Published: March 2022

Abstract
Browser fingerprinting is a stateless tracking technique that aims to combine information exposed by multiple different web APIs to create a unique identifier for tracking users across the web. Over the last decade, trackers have abused several existing and newly proposed web APIs to further enhance the browser fingerprint. Existing approaches are limited to detecting a specific fingerprinting technique(s) at a particular point in time. Thus, they are unable to systematically detect novel fingerprinting techniques that abuse different web APIs. In this paper, we propose FP-Radar, a machine learning approach that leverages longitudinal measurements of web API usage on top-100K websites over the last decade for early detection of new and evolving browser fingerprinting techniques. The results show that FP-Radar is able to early detect the abuse of newly introduced properties of already known (e.g., WebGL, Sensor) and as well as previously unknown (e.g., Gamepad, Clipboard) APIs for browser fingerprinting. To the best of our knowledge, FP-Radar is the first to detect the abuse of the Visibility API for ephemeral fingerprinting in the wild.
Analyzing the Feasibility and Generalizability of Fingerprinting Internet of Things Devices
Authors: Ahmed Dilawer , Das Anupam , Zaffar Fareed 
Published: March 2022

AbstractIn recent years, we have seen rapid growth in the use and adoption of Internet of Things (IoT) devices. However, some loT devices are sensitive in nature, and simply knowing what devices a user owns can have security and privacy implications. Researchers have, therefore, looked at fingerprinting loT devices and their activities from encrypted network traffic. In this paper, we analyze the feasibility of fingerprinting IoT devices and evaluate the robustness of such fingerprinting approach across multiple independent datasets — collected under different settings. We show that not only is it possible to effectively fingerprint 188 loT devices (with over 97% accuracy), but also to do so even with multiple instances of the same make-and-model device. We also analyze the extent to which temporal, spatial and data-collection-methodology differences impact fingerprinting accuracy. Our analysis sheds light on features that are more robust against varying conditions. Lastly, we comprehensively analyze the performance of our approach under an open-world setting and propose ways in which an adversary can enhance their odds of inferring additional information about unseen devices (e.g., similar devices manufactured by the same company).