EXAMIND

examind

Authors:
(1) Seokil Ham, KAIST;
(2) Jungwuk Park, KAIST;
(3) Dong-Jun Han, Purdue University;
(4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms E Discussions on Performance Degradation at Later Exits As can be seen from the results for the anytime prediction in the main manuscript, the adversarial test accuracy of the later exits is sometimes lower than the performance of earlier exits. This phenomenon can be explained as follows: In general, we observed via experiments that adversarial examples targeting later exits has the higher sum of losses from all exits compared to adversarial examples targeting earlier exits. This makes max-average or average attack mainly focus on attacking the later exits, leading to low adversarial test accuracy at later exits. The performance of later exits can be improved by adopting the ensemble strategy as in the main manuscript for the budgeted prediction setup. This paper is available on arxiv under CC 4.0 license. Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Authors: Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Works 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References 5. Conclusion, Acknowledgement and References A. Experiment Details A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms E Discussions on Performance Degradation at Later Exits As can be seen from the results for the anytime prediction in the main manuscript, the adversarial test accuracy of the later exits is sometimes lower than the performance of earlier exits. This phenomenon can be explained as follows: In general, we observed via experiments that adversarial examples targeting later exits has the higher sum of losses from all exits compared to adversarial examples targeting earlier exits. This makes max-average or average attack mainly focus on attacking the later exits, leading to low adversarial test accuracy at later exits. The performance of later exits can be improved by adopting the ensemble strategy as in the main manuscript for the budgeted prediction setup. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Examining the Adversarial Test Accuracy of Later Exits in NEO-KD Networks

Table of Links Abstract and 1 Introduction 1.1 The twincode platform 1.2 Related Work 2 Research Questions 3 Variables 3.1 Independent Variables 3.2 Dependent Variables 3.3 Confounding Variables 4 Participants 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis Acknowledgments and References 1.2 Related Work Several literature reviews [10, 15, 21] have compiled the empirical research on using pair programming in higher education, with [6] being focused on distributed pair programming from a teaching perspective. By means of controlled experiments, remote and co–located pair programming are compared in [1, 22], showing comparable results. In most of the cases, the analyzed variables are related to performance in terms of time, quality, or code tests passed. Students perceptions have been also analyzed in terms of confidence, satisfaction, motivation, or personality [20]. Table 1 summarizes the empirical studies on the influence of gender in pair programming, including findings such as (i) same– gender pairs are more “democratic”; (ii) women working in pairs were more confident than those working solo; and (iii) in mixedgender, pairing women particularly do not benefit [15]. Although such studies reveal that gender seems to be a key factor in pair programming, none of them study gender bias in pair programming. Many factors other than gender may affect the outcomes of remote programming sessions [2, 23]. Previous research on productive pairing looked at factors such as skill levels, autonomy in choosing one’s partner [25], and different personalities [11]. Nevertheless, the work on gender composition of pairs found conflicting results about whether same-gender or mixed-gender pairings are more effective [3, 4, 12, 16]. One possible explanation is that gender correlates with other dimensions that may affect the pairs’ collaboration, but these correlations may vary between different environments. For example, women in a class may, on average, have higher skill level than men because they had to face more societal barriers to enter the class. On the other hand, they may, on average, have lower skill level if women with no background are more actively recruited. Authors:
(1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es);
(2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es);
(3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es);
(4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu);
(5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu);
(6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 The twincode platform 1.1 The twincode platform 1.2 Related Work 1.2 Related Work 2 Research Questions 2 Research Questions 3 Variables 3 Variables 3.1 Independent Variables 3.1 Independent Variables 3.2 Dependent Variables 3.2 Dependent Variables 3.3 Confounding Variables 3.3 Confounding Variables 4 Participants 4 Participants 5 Execution Plan and 5.1 Recruitment 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis 5.4 Data Analysis Acknowledgments and References Acknowledgments and References 1.2 Related Work Several literature reviews [10, 15, 21] have compiled the empirical research on using pair programming in higher education, with [6] being focused on distributed pair programming from a teaching perspective. By means of controlled experiments, remote and co–located pair programming are compared in [1, 22], showing comparable results. In most of the cases, the analyzed variables are related to performance in terms of time, quality, or code tests passed. Students perceptions have been also analyzed in terms of confidence, satisfaction, motivation, or personality [20]. Table 1 summarizes the empirical studies on the influence of gender in pair programming, including findings such as (i) same– gender pairs are more “democratic”; (ii) women working in pairs were more confident than those working solo; and (iii) in mixedgender, pairing women particularly do not benefit [15]. Although such studies reveal that gender seems to be a key factor in pair programming, none of them study gender bias in pair programming. Many factors other than gender may affect the outcomes of remote programming sessions [2, 23]. Previous research on productive pairing looked at factors such as skill levels, autonomy in choosing one’s partner [25], and different personalities [11]. Nevertheless, the work on gender composition of pairs found conflicting results about whether same-gender or mixed-gender pairings are more effective [3, 4, 12, 16]. One possible explanation is that gender correlates with other dimensions that may affect the pairs’ collaboration, but these correlations may vary between different environments. For example, women in a class may, on average, have higher skill level than men because they had to face more societal barriers to enter the class. On the other hand, they may, on average, have lower skill level if women with no background are more actively recruited. Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). Authors: Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

Examining Gender Bias in Pair Programming: Insights from Empirical Studies

COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 4 of 42. 3. The LATEX files: Main.tex and E-Cash-main.tex (i) Dr Wright’s main LaTeX file “main.tex”, “Candidate L” {H/340/1} 56. This file is a LaTeX source file, taken from a folder on Dr Wright’s Overleaf account. Dr Wright has stated that, as Satoshi Nakamoto, he wrote the Bitcoin White Paper using LaTeX. He has also stated that this document “uniquely codes for the Bitcoin White Paper”, and that when compiled in Overleaf, produce a copy of the Bitcoin White Paper “in the same form as” and “materially identical to” that published by Satoshi Nakamoto. [Wright 6 E/21/3; Field 1 [27] P3/13/10] 57. Among all his LaTeX files, Dr Wright has nominated this file in particular as the source file that he relies on to compile the Bitcoin White Paper itself (Shoosmiths’ fourth letter of 4 January 2024 M/2/802). This file is Candidate L in Mr Rosendahl’s report. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.

This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 4 of 42. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 4 of 42. HackerNoon’s Legal PDF Series HackerNoon’s Legal PDF Series here here 3. The LATEX files: Main.tex and E-Cash-main.tex (i) Dr Wright’s main LaTeX file “main.tex”, “Candidate L” {H/340/1} 56. This file is a LaTeX source file, taken from a folder on Dr Wright’s Overleaf account. Dr Wright has stated that, as Satoshi Nakamoto, he wrote the Bitcoin White Paper using LaTeX. He has also stated that this document “uniquely codes for the Bitcoin White Paper”, and that when compiled in Overleaf, produce a copy of the Bitcoin White Paper “in the same form as” and “materially identical to” that published by Satoshi Nakamoto. [Wright 6 E/21/3; Field 1 [27] P3/13/10] 57. Among all his LaTeX files, Dr Wright has nominated this file in particular as the source file that he relies on to compile the Bitcoin White Paper itself (Shoosmiths’ fourth letter of 4 January 2024 M/2/802). This file is Candidate L in Mr Rosendahl’s report. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. judiciary.uk judiciary.uk

Court Examines LaTeX Source Files in Craig Wright’s Claim to Bitcoin White Paper

Authors:
(1) Pietro Saggese, Complexity Science Hub Vienna (CSH);
(2) Esther Segalla, Oesterreichische Nationalbank (OeNB);
(3) Michael Sigmund, Oesterreichische Nationalbank (OeNB);
(4) Burkhard Raunig, Oesterreichische Nationalbank (OeNB);
(5) Felix Zangerl, Austrian Financial Market Authority (FMA);
(6) Bernhard Haslhofer, Complexity Science Hub Vienna (CSH). Table of Links Abstract and 1. Introduction Background and related literature
VASPs: A Closer Examination
Measuring VASPs Cryptoasset Holdings
Closing The Data Gap
Conclusions, Declaration of Competing Interest, and References Appendix A. Supplemental material 3. VASPs: A Closer Examination Using information from the Austrian Financial Market Authority (FMA), we first describe what financial services they offer and what cryptoassets they support. Next, we complement the FMA data with additional public information collected from the VASPs websites to group them based on similarity scores. Finally, we compare their economic functions highlighting similarities and differences to traditional financial intermediaries. 3.1. The Austrian VASP landscape VASPs in Austria are supervised by the Financial Market Authority (FMA) under the Anti-Money Laundering Act. In December 2022, 24 VASPs were registered in the FMA database[7]. Figure 2a shows the aggregate number of VASPs registered for each service described in Table 1. The vast majority of them (N = 20) offers V2F-Exchange, i.e., services to exchange virtual assets and fiat currencies; nine also facilitate the exchange from and to other virtual assets (V2V-Exchange). In most cases, customer funds are or can be kept in custody by the VASP (N = 15). Finally, only a few of them are legally authorized to transfer virtual assets and to issue and sell them (respectively services Payment, N = 4, and Issuance, N = 3). Additional details on the number of services offered per VASP are reported in Appendix A. Figure 2b shows what virtual assets are used by the Austrian VASPs. We follow the taxonomy described in Auer et al. (2023) to aggregate cryptoassets into five categories. We could retrieve reliable information for 20 VASPs out of the 24 in the FMA database. Notably, all VASPs offer services related to bitcoins (N = 20). More than 75% support Ethereum (N = 16), and the latter typically also support Ethereum tokens, i.e., ERC-20 and (or) ERC-721 compatible non-native tokens, and stablecoins (respectively N = 8 and N = 12). A limited number of VASPs also provide services related to privacy-focused cryptoassets (i.e., Monero, Dash, Zcash). Finally, several VASPs also support tokens native to other DLTs (e.g., Litecoin or Cardano). In addition to FMA data, we collect additional public information documented on their websites. Our aim is to categorize VASPs by their service offering. We construct categorical variables that indicate whether the VASP offers custody services, facilitates payments, allows users to exchange cryptoassets, implements a trading platform, or offers consulting or investment services. We consider 21 VASPs for which we could gather sufficient information. Data for each (anonymized) VASP are reported in Appendix A. Whilst the sample is small and the features are few, to ensure consistency and objectivity in categorizing VASPs we exploit an unsupervised learning method. We aggregate them using the hierarchical agglomerative clustering (HAC) method (Murtagh & Contreras, 2012). With this bottom-up approach, objects are iteratively clustered based on their similarity. The two main parameters of HAC are the distance among objects, and the linkage method, i.e., the distance used to merge groups. In our setting, we select the Euclidean distance and the Ward metric, and distances are iteratively computed using the Lance–Williams update formula. Results are similar when using other parameters. We report our classification in Figure 3. It categorizes VASPs into five clusters: the first one (red rectangle, N = 7) includes VASPs that do not keep customers’ funds in custody, and only facilitate the exchange of virtual assets for fiat currencies and (or) other virtual assets. Some of them automate the process through physical vending machines. Thus the first left branch mainly separates VASPs that offer custody and those that do not. We identify them as “Group 1”. The green rectangle identifies VASPs providing investment advice and/or portfolio management in addition to custody services (N = 6). They propose investing strategies, give advice on portfolio management and coin selection services, and in some cases, lend customer funds. These VASPs are referred to as “Group 2”. The purple rectangle (“Group 5”) aggregates VASPs that act as cryptoasset custodians. Typically, they also facilitate the exchange of cryptoassets and are similar to VASPs in the blue rectangle (“Group 3”). These VASPs in addition provide customers with an internal trading platform, manage and match orders in a private limit order book, and update their account balances in cryptoasset or fiat money when trades are executed. Trades executed in private ledgers do not affect the public distributed ledgers unless the customers withdraw cryptoassets from the service. Such VASPs play an essential role in the crypto-financial system. As a result of the matching mechanism for demand and supply, these are the platforms where price formation takes place. The other VASPs derive their offered prices from other platforms as an exogenous variable. In the following, we consider these two as a single group (i.e., Group 3). All the VASPs in the groups described above are cryptoasset centralized exchanges, or CEXs. The remaining VASP in the yellow rectangle is instead a payment processor service. It offers solutions to facilitate the purchase and sale of commodity goods with cryptoassets; such VASPs play a minor role in the crypto ecosystem. 3.2. A comparison with traditional financial intermediaries Having outlined the landscape of VASPs in Austria, we are now interested in understanding how they differ from traditional financial intermediaries. Figure 4 stylizes the traditional financial intermediaries on the right and the VASPs on the left. In the middle, rectangles represent the primary economic services, and links indicate what services each intermediary category offers. The comparison shows that an analogy with traditional intermediaries exists for three out of the four groups described in Figure 3. More specifically, VASPs in group 1 operate similarly to money exchanges. Indeed, the only service they offer is to buy and sell virtual assets for customers. VASPs in group 2 provide investment services to their users, akin to funds. Third, groups 3 (and 5) include VASPs allowing users to trade, keep their funds in custody, and thus act as brokers, connecting buyers and sellers to facilitate a transaction. The last group that provides payment services can be compared to payment processor systems. Interestingly, we find that the comparison of VASPs to banks can be misleading: while the two share overall several financial services, such as exchanging money, trading, or investing, banks also enable customers to open loan positions with the funds they hold and to open savings and deposit positions. On this note, we mention that some VASPs have recently acquired an e-money institution license. However, that does not qualify them to offer bank-type financial services automatically. First, e-money institutions do not have the same supervisory requirements as traditional banks. Second, they do not necessarily offer bank-type financial services — they can e.g. use the license only to process their fiat payments. Further information on the taking up, pursuit, and prudential supervision of the business of electronic money institutions can be found in the Directive 2009/110 of the European Commission (EC, 2009). This paper is available on arxiv under CC BY 4.0 DEED license. [7] https://bit.ly/3kMUkwg Authors: (1) Pietro Saggese, Complexity Science Hub Vienna (CSH); (2) Esther Segalla, Oesterreichische Nationalbank (OeNB); (3) Michael Sigmund, Oesterreichische Nationalbank (OeNB); (4) Burkhard Raunig, Oesterreichische Nationalbank (OeNB); (5) Felix Zangerl, Austrian Financial Market Authority (FMA); (6) Bernhard Haslhofer, Complexity Science Hub Vienna (CSH). Authors: Authors: (1) Pietro Saggese, Complexity Science Hub Vienna (CSH); (2) Esther Segalla, Oesterreichische Nationalbank (OeNB); (3) Michael Sigmund, Oesterreichische Nationalbank (OeNB); (4) Burkhard Raunig, Oesterreichische Nationalbank (OeNB); (5) Felix Zangerl, Austrian Financial Market Authority (FMA); (6) Bernhard Haslhofer, Complexity Science Hub Vienna (CSH). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and related literature VASPs: A Closer Examination Measuring VASPs Cryptoasset Holdings Closing The Data Gap Conclusions, Declaration of Competing Interest, and References Background and related literature Background and related literature VASPs: A Closer Examination VASPs: A Closer Examination Measuring VASPs Cryptoasset Holdings Measuring VASPs Cryptoasset Holdings Closing The Data Gap Closing The Data Gap Conclusions, Declaration of Competing Interest, and References Conclusions, Declaration of Competing Interest, and References Appendix A. Supplemental material Appendix A. Supplemental material 3. VASPs: A Closer Examination Using information from the Austrian Financial Market Authority (FMA), we first describe what financial services they offer and what cryptoassets they support. Next, we complement the FMA data with additional public information collected from the VASPs websites to group them based on similarity scores. Finally, we compare their economic functions highlighting similarities and differences to traditional financial intermediaries. 3.1. The Austrian VASP landscape 3.1. The Austrian VASP landscape VASPs in Austria are supervised by the Financial Market Authority (FMA) under the Anti-Money Laundering Act. In December 2022, 24 VASPs were registered in the FMA database[7]. Figure 2a shows the aggregate number of VASPs registered for each service described in Table 1. The vast majority of them (N = 20) offers V2F-Exchange, i.e., services to exchange virtual assets and fiat currencies; nine also facilitate the exchange from and to other virtual assets (V2V-Exchange). In most cases, customer funds are or can be kept in custody by the VASP (N = 15). Finally, only a few of them are legally authorized to transfer virtual assets and to issue and sell them (respectively services Payment, N = 4, and Issuance, N = 3). Additional details on the number of services offered per VASP are reported in Appendix A. Figure 2b shows what virtual assets are used by the Austrian VASPs. We follow the taxonomy described in Auer et al. (2023) to aggregate cryptoassets into five categories. We could retrieve reliable information for 20 VASPs out of the 24 in the FMA database. Notably, all VASPs offer services related to bitcoins (N = 20). More than 75% support Ethereum (N = 16), and the latter typically also support Ethereum tokens, i.e., ERC-20 and (or) ERC-721 compatible non-native tokens, and stablecoins (respectively N = 8 and N = 12). A limited number of VASPs also provide services related to privacy-focused cryptoassets (i.e., Monero, Dash, Zcash). Finally, several VASPs also support tokens native to other DLTs (e.g., Litecoin or Cardano). In addition to FMA data, we collect additional public information documented on their websites. Our aim is to categorize VASPs by their service offering. We construct categorical variables that indicate whether the VASP offers custody services, facilitates payments, allows users to exchange cryptoassets, implements a trading platform, or offers consulting or investment services. We consider 21 VASPs for which we could gather sufficient information. Data for each (anonymized) VASP are reported in Appendix A. Whilst the sample is small and the features are few, to ensure consistency and objectivity in categorizing VASPs we exploit an unsupervised learning method. We aggregate them using the hierarchical agglomerative clustering (HAC) method (Murtagh & Contreras, 2012). With this bottom-up approach, objects are iteratively clustered based on their similarity. The two main parameters of HAC are the distance among objects, and the linkage method, i.e., the distance used to merge groups. In our setting, we select the Euclidean distance and the Ward metric, and distances are iteratively computed using the Lance–Williams update formula. Results are similar when using other parameters. We report our classification in Figure 3. It categorizes VASPs into five clusters: the first one (red rectangle, N = 7) includes VASPs that do not keep customers’ funds in custody, and only facilitate the exchange of virtual assets for fiat currencies and (or) other virtual assets. Some of them automate the process through physical vending machines. Thus the first left branch mainly separates VASPs that offer custody and those that do not. We identify them as “Group 1”. The green rectangle identifies VASPs providing investment advice and/or portfolio management in addition to custody services (N = 6). They propose investing strategies, give advice on portfolio management and coin selection services, and in some cases, lend customer funds. These VASPs are referred to as “Group 2”. The purple rectangle (“Group 5”) aggregates VASPs that act as cryptoasset custodians. Typically, they also facilitate the exchange of cryptoassets and are similar to VASPs in the blue rectangle (“Group 3”). These VASPs in addition provide customers with an internal trading platform, manage and match orders in a private limit order book, and update their account balances in cryptoasset or fiat money when trades are executed. Trades executed in private ledgers do not affect the public distributed ledgers unless the customers withdraw cryptoassets from the service. Such VASPs play an essential role in the crypto-financial system. As a result of the matching mechanism for demand and supply, these are the platforms where price formation takes place. The other VASPs derive their offered prices from other platforms as an exogenous variable. In the following, we consider these two as a single group (i.e., Group 3). All the VASPs in the groups described above are cryptoasset centralized exchanges, or CEXs. The remaining VASP in the yellow rectangle is instead a payment processor service. It offers solutions to facilitate the purchase and sale of commodity goods with cryptoassets; such VASPs play a minor role in the crypto ecosystem. 3.2. A comparison with traditional financial intermediaries 3.2. A comparison with traditional financial intermediaries Having outlined the landscape of VASPs in Austria, we are now interested in understanding how they differ from traditional financial intermediaries. Figure 4 stylizes the traditional financial intermediaries on the right and the VASPs on the left. In the middle, rectangles represent the primary economic services, and links indicate what services each intermediary category offers. The comparison shows that an analogy with traditional intermediaries exists for three out of the four groups described in Figure 3. More specifically, VASPs in group 1 operate similarly to money exchanges. Indeed, the only service they offer is to buy and sell virtual assets for customers. VASPs in group 2 provide investment services to their users, akin to funds. Third, groups 3 (and 5) include VASPs allowing users to trade, keep their funds in custody, and thus act as brokers, connecting buyers and sellers to facilitate a transaction. The last group that provides payment services can be compared to payment processor systems. Interestingly, we find that the comparison of VASPs to banks can be misleading: while the two share overall several financial services, such as exchanging money, trading, or investing, banks also enable customers to open loan positions with the funds they hold and to open savings and deposit positions. On this note, we mention that some VASPs have recently acquired an e-money institution license. However, that does not qualify them to offer bank-type financial services automatically. First, e-money institutions do not have the same supervisory requirements as traditional banks. Second, they do not necessarily offer bank-type financial services — they can e.g. use the license only to process their fiat payments. Further information on the taking up, pursuit, and prudential supervision of the business of electronic money institutions can be found in the Directive 2009/110 of the European Commission (EC, 2009). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [7] https://bit.ly/3kMUkwg

Assessing VASP Solvency For Cryptoassets: A Closer Examination

In recent years, AI has significantly impacted various industries through innovative applications and automated solutions. One area seeing growth in AI utilization is writing and education, where new tools are helping users generate and refine written work more efficiently. Among the many AI-enabled writing assistants now available is EssayWriter, a leading web-based platform focusing on streamlining the essay creation process. With a range of customizable features, EssayWriter aims to aid students, researchers, and writers of any experience level in crafting high-quality essays and research papers. This review examines EssayWriter's offerings, analyzing its core functions, ease of use, and overall effectiveness as an AI-powered writing assistant. What Is EssayWriter EssayWriter is designed to be a one-stop essay writing partner, offering a full range of AI tools to assist with every step of essay creation. Whether you're brainstorming ideas, drafting initial content, researching relevant sources, citing materials properly, or putting finishing touches on your work, EssayWriter strives to simplify and speed up the writing process. EssayWriter also comes with a convenient web-based editor that acts as a workspace for all composition and revision tasks. Within this digital workspace, users are equipped with an array of AI-powered writing aids. Features like automatic content recommendations, plagiarism scanning, and a built-in database of academic sources simplify conducting research and citations. What Can EssayWriter Do? Let's examine the core essay writing and editing features of EssayWriter Intelligent Writing Support At the core of EssayWriter's value is its AI-driven writing assistance. Powered by advanced natural language models like GPT-3.5/4, the platform's smart essay crafting tools aim to help users brainstorm ideas, create outline, and draft content. EssayWriter as a virtual writing partner, offering advice and pointers based on a user's requirements as well as the overall essay topic and context. For those experiencing writer's block or looking to enhance flow, logic, and adherence to style guidelines, this feature delivers automated writing support "over the shoulder." Humanized Essays with High Human Scores EssayWriter offers the ability to generate content that reads as though entirely written by a human writer, bypassing all AI detection tools. This provides an option for students wanting results their institution will accept without flagging as AI-assisted. Whether crafting from scratch or refining existing drafts, users can be confident the platform's writing style will appear organic. Simplified Research and Citation Integral to EssayWriter is its expansive academic resource library, designed to streamline researching and citing from materials across multiple topics and fields. With a single click, you can access the database directly within the writing interface to find and effortlessly cite applicable sources using common styles like APA, MLA, and Chicago. Safeguarding from Accidental Plagiarism Preventing plagiarism stands out as another key EssayWriter capability. As the essay takes shape, the built-in plagiarism scanner monitors for potential instances of copying or not properly attributing outside information. This helps safeguard users from accidental plagiarism and provides suggestions to properly cite or rephrase any flagged content, ensuring academic honesty is maintained. Abundant Essay Examples EssayWriter also offers an extensive library of essay examples. This vast collection serves as an invaluable resource for users, enabling them to draw inspiration, learn from well-structured examples, and understand different writing styles. Whether you're working on a narrative, argumentative, descriptive, or expository essay, EssayWriter's comprehensive library has got you covered. Utilizing EssayWriter: A Simple and Natural Workflow EssayWriter is an easy-to-use platform. Once you've set up a free account, you can dive right into the online editor and kickstart your writing endeavor. Creating the Draft: You can begin by drafting your essay in the editor. Feel free to type away or use the AI writing assistant to spark ideas and provide text suggestions.
Research and Citation: While you're writing, you can effortlessly search for and cite relevant sources from EssayWriter's built-in academic database. The citation generator will ensure your references are correctly formatted.
Polishing Your Work: After your initial draft is ready, you can use EssayWriter's AI editing tools to enhance grammar, structure, and overall quality. The plagiarism detection feature will help spot and rectify any accidental plagiarism.
Sharing Your Work: Once you're pleased with your essay, you can export the final document in a variety of formats like PDF or DOC and share it as you wish. What Sets EssayWriter Apart? There are a handful of AI writing assistants out there, but here's how EssayWriter stands out: All-In-One Toolset: EssayWriter provides an extensive suite of tools that covers the entire essay writing process. From brainstorming to research, citation, editing, and plagiarism detection, this integrated approach simplifies the workflow and removes the need for several separate tools. Tailored for Academics: EssayWriter is crafted specifically for academic writing. Its features and resources are designed to cater to the needs of students, researchers, and scholars. Cutting-Edge AI Models: EssayWriter uses top-tier language models like GPT-3.5/4, making its AI writing assistance more advanced and capable of producing superior content compared to many other AI writing tools.\ User-Friendly Design: The platform's sleek and intuitive interface is user-friendly for all skill levels, reducing the learning curve and enabling a seamless writing experience. Privacy and Security: EssayWriter highly values user privacy and security. It uses advanced encryption to safeguard personal information and written content. EssayWriter's Possible Drawbacks Despite its advanced AI, integrated research tools, plagiarism detection, and a user-friendly interface, it's important to know EssayWriter has a few potential downsides: AI Capabilities: While the AI behind EssayWriter is advanced, it might struggle with understanding extremely complex or specific topics or creating highly specialized content.
Subscription Model: EssayWriter does offer a free plan, but you might need to consider a paid subscription to unlock all its features and fully utilize the platform.
Dependence on AI: Over reliance on the AI writing assistance could potentially hamper the growth of critical thinking and independent writing skills. Wrapping Up EssayWriter stands out with its extensive toolset, academic focus, and advanced AI models. However, it's crucial to be aware of its potential limitations, such as the inherent constraints of AI technology and the risk of becoming too dependent on the writing assistant. In the end, EssayWriter offers a promising solution for those looking to streamline their essay writing process and harness the power of AI to boost their writing skills and productivity. But it's vital to use the platform wisely, enhancing and supporting your own critical thinking and writing abilities instead of overusing AI assistance. In recent years, AI has significantly impacted various industries through innovative applications and automated solutions. One area seeing growth in AI utilization is writing and education, where new tools are helping users generate and refine written work more efficiently. Among the many AI-enabled writing assistants now available is EssayWriter, a leading web-based platform focusing on streamlining the essay creation process. With a range of customizable features, EssayWriter aims to aid students, researchers, and writers of any experience level in crafting high-quality essays and research papers. This review examines EssayWriter's offerings, analyzing its core functions, ease of use, and overall effectiveness as an AI-powered writing assistant. What Is EssayWriter EssayWriter is designed to be a one-stop essay writing partner, offering a full range of AI tools to assist with every step of essay creation. Whether you're brainstorming ideas, drafting initial content, researching relevant sources, citing materials properly, or putting finishing touches on your work, EssayWriter strives to simplify and speed up the writing process. EssayWriter EssayWriter EssayWriter also comes with a convenient web-based editor that acts as a workspace for all composition and revision tasks. Within this digital workspace, users are equipped with an array of AI-powered writing aids. Features like automatic content recommendations, plagiarism scanning, and a built-in database of academic sources simplify conducting research and citations. What Can EssayWriter Do? Let's examine the core essay writing and editing features of EssayWriter Intelligent Writing Support At the core of EssayWriter's value is its AI-driven writing assistance. Powered by advanced natural language models like GPT-3.5/4, the platform's smart essay crafting tools aim to help users brainstorm ideas, create outline, and draft content. EssayWriter as a virtual writing partner, offering advice and pointers based on a user's requirements as well as the overall essay topic and context. For those experiencing writer's block or looking to enhance flow, logic, and adherence to style guidelines, this feature delivers automated writing support "over the shoulder." Humanized Essays with High Human Scores EssayWriter offers the ability to generate content that reads as though entirely written by a human writer, bypassing all AI detection tools. This provides an option for students wanting results their institution will accept without flagging as AI-assisted. Whether crafting from scratch or refining existing drafts, users can be confident the platform's writing style will appear organic. Simplified Research and Citation Integral to EssayWriter is its expansive academic resource library, designed to streamline researching and citing from materials across multiple topics and fields. With a single click, you can access the database directly within the writing interface to find and effortlessly cite applicable sources using common styles like APA, MLA, and Chicago. Safeguarding from Accidental Plagiarism Preventing plagiarism stands out as another key EssayWriter capability. As the essay takes shape, the built-in plagiarism scanner monitors for potential instances of copying or not properly attributing outside information. This helps safeguard users from accidental plagiarism and provides suggestions to properly cite or rephrase any flagged content, ensuring academic honesty is maintained. Abundant Essay Examples EssayWriter also offers an extensive library of essay examples . This vast collection serves as an invaluable resource for users, enabling them to draw inspiration, learn from well-structured examples, and understand different writing styles. Whether you're working on a narrative, argumentative, descriptive, or expository essay, EssayWriter's comprehensive library has got you covered. extensive library of essay examples extensive library of essay examples Utilizing EssayWriter: A Simple and Natural Workflow EssayWriter is an easy-to-use platform. Once you've set up a free account, you can dive right into the online editor and kickstart your writing endeavor. Creating the Draft: You can begin by drafting your essay in the editor. Feel free to type away or use the AI writing assistant to spark ideas and provide text suggestions. Research and Citation: While you're writing, you can effortlessly search for and cite relevant sources from EssayWriter's built-in academic database. The citation generator will ensure your references are correctly formatted. Polishing Your Work: After your initial draft is ready, you can use EssayWriter's AI editing tools to enhance grammar, structure, and overall quality. The plagiarism detection feature will help spot and rectify any accidental plagiarism. Sharing Your Work: Once you're pleased with your essay, you can export the final document in a variety of formats like PDF or DOC and share it as you wish. Creating the Draft: You can begin by drafting your essay in the editor. Feel free to type away or use the AI writing assistant to spark ideas and provide text suggestions. Research and Citation: While you're writing, you can effortlessly search for and cite relevant sources from EssayWriter's built-in academic database. The citation generator will ensure your references are correctly formatted. Polishing Your Work: After your initial draft is ready, you can use EssayWriter's AI editing tools to enhance grammar, structure, and overall quality. The plagiarism detection feature will help spot and rectify any accidental plagiarism. Sharing Your Work: Once you're pleased with your essay, you can export the final document in a variety of formats like PDF or DOC and share it as you wish. What Sets EssayWriter Apart? There are a handful of AI writing assistants out there, but here's how EssayWriter stands out: All-In-One Toolset: EssayWriter provides an extensive suite of tools that covers the entire essay writing process. From brainstorming to research, citation, editing, and plagiarism detection, this integrated approach simplifies the workflow and removes the need for several separate tools. Tailored for Academics: EssayWriter is crafted specifically for academic writing. Its features and resources are designed to cater to the needs of students, researchers, and scholars. Cutting-Edge AI Models: EssayWriter uses top-tier language models like GPT-3.5/4, making its AI writing assistance more advanced and capable of producing superior content compared to many other AI writing tools.\ User-Friendly Design: The platform's sleek and intuitive interface is user-friendly for all skill levels, reducing the learning curve and enabling a seamless writing experience. Privacy and Security: EssayWriter highly values user privacy and security. It uses advanced encryption to safeguard personal information and written content. EssayWriter's Possible Drawbacks Despite its advanced AI, integrated research tools, plagiarism detection, and a user-friendly interface, it's important to know EssayWriter has a few potential downsides: AI Capabilities: While the AI behind EssayWriter is advanced, it might struggle with understanding extremely complex or specific topics or creating highly specialized content. Subscription Model: EssayWriter does offer a free plan, but you might need to consider a paid subscription to unlock all its features and fully utilize the platform. Dependence on AI: Over reliance on the AI writing assistance could potentially hamper the growth of critical thinking and independent writing skills. AI Capabilities: While the AI behind EssayWriter is advanced, it might struggle with understanding extremely complex or specific topics or creating highly specialized content. Subscription Model: EssayWriter does offer a free plan, but you might need to consider a paid subscription to unlock all its features and fully utilize the platform. Dependence on AI: Over reliance on the AI writing assistance could potentially hamper the growth of critical thinking and independent writing skills. Wrapping Up EssayWriter stands out with its extensive toolset, academic focus, and advanced AI models. However, it's crucial to be aware of its potential limitations, such as the inherent constraints of AI technology and the risk of becoming too dependent on the writing assistant. In the end, EssayWriter offers a promising solution for those looking to streamline their essay writing process and harness the power of AI to boost their writing skills and productivity. But it's vital to use the platform wisely, enhancing and supporting your own critical thinking and writing abilities instead of overusing AI assistance.

EssayWriter Review: A Careful Examination of The AI Essay Writer

প্রবন্ধ লেখক পর্যালোচনা: এআই প্রবন্ধ লেখকের একটি যত্নশীল পরীক্ষা

EssayWriter-Rezension: Eine sorgfältige Untersuchung des AI Essay Writer

Revisión de EssayWriter: un examen cuidadoso del escritor de ensayos con IA

Examen d'EssayWriter : un examen attentif du rédacteur d'essais sur l'IA

EssayWriter समीक्षा: AI निबंध लेखक की एक सावधानीपूर्वक जांच

EssayWriter レビュー: AI エッセイライターの徹底検証

EssayWriter Review: AI 에세이 작가에 대한 세심한 조사

Revisão do EssayWriter: um exame cuidadoso do redator de ensaios de IA

Обзор EssayWriter: тщательное изучение AI Essay Writer

EssayWriter İncelemesi: Yapay Zeka Deneme Yazarının Dikkatli Bir İncelemesi

Đánh giá người viết bài luận: Sự kiểm tra cẩn thận của người viết bài luận AI

EssayWriter 评论：对 AI 论文写作器的仔细检查

Examining Social Network Design In The Context Of Web3

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References LITERATURE REVIEW Search result overlap By the mid-1990s, researching the overlap of search results between different search engines sparked interest for the purpose of estimating the size of the Web (Bharat, 1998; Ding & Marchionini, 1996; Lawrence & Giles, 1999). The generally small degrees of overlap indicated diverse underlying databases, each of limited size. Therefore, metasearch engines that combined the results of several search engines were meant to provide an additional value (Chignell et al., 1999) and attracted further research (i.e., Meng et al., 2002). Consequently, Spink et al. (Spink et al., 2006) included the meta search engine Dogpile in their extensive study on the overlap of search results. For a collection of 12,570 queries, the results found on the first search engine result page (SERP) of Ask Jeeves, Google, MSN Search, and Yahoo were captured. 84.9% of the results were unique to one search engine, while only 1.1% were shared by all search engines. Additionally, the low overlap between search engines also manifests in the ranking since only 7% of the top results were similar. This is consistent with previous studies that reported low overlap in ranking (Bar-Ilan, 2005; Bar‐Ilan et al., 2006) Subsequent studies report an increase of overlap in search results, while ranking algorithms appear to be the leading cause for differences in the presentation of the results. Bilal and Ellis (2011) compared major web search engines (Google, Bing, Yahoo) with search engines specifically designed for children (Ask Kids, Yahoo Kids) for queries of various lengths. Bing and Yahoo shared the same overlap with Google for nearly all queries, ranging between 22% and 40%. In contrast, Yahoo Kids had mostly unique results. However, the disparate relevance ranking between Bing and Yahoo suggests different ranking algorithms. Cardoso and Magalhães (2011) measured the overlap of search result rankings based on URLs and website contents. Search results for 40,000 queries were retrieved from Google and Bing. The overlap in domains was about 29%, and Google had more exclusive domains. When looking at the result sets without considering the positions, the similarity between the result sets increased. This indicates that Google and Bing have different ranking preferences but index mostly the same sources. Similarly, Agrawal et al. (2016) based their overlap analysis on content but factored in the description of the result snippets. The top 10 results of 67 informational queries were collected from Google and Bing. The results indicate a high overlap between Google and Bing, with a slightly higher similarity among the top 5 results. Most recently, a study about information on Covid-19 was conducted by Makhortykh et al. (2020). The first 50 results for queries in different languages were collected from Baidu, Bing, DuckDuckGo, Google, Yandex, and Yahoo. DuckDuckGo and Yahoo shared nearly 50% of their results, whereas the other pairs remained under 25%. Google had an overlap of around 10% with Bing and a negligible one with DuckDuckGo, Yandex, Yahoo, and Baidu. Source diversity Various studies took a closer look at search results and evaluated the domains, the types of sources, as well as their diversity across result sets and search engines. Comparing Google, Live Search, and Yahoo, Thelwall (2008) reported that Yahoo returned the highest amount of different domains to a query and also returned around 10% more top-level domains than other search engines. Höchstötter & Lewandowski (2009) showed that while the sources in top results of various search engines differ, there is a concentration on some popular sources in top results. More recently, Lewandowski & Sünkler (2019) collected search results on queries related to insurance providers and identified the distribution of top-level domains. Only ten different domains were found in the first result across all queries. The five most popular domains in the top 5 results are price comparison websites, which make up 88.4% of all first results. The share of popular domains decreases according to the ranking but remains at 42.9% at position ten. Unkel and Haim (2019) observed Google search results prior to the German parliamentary elections in 2017. The study found that result lists for candidates and parties exhibit a high share of self-administered websites. In contrast, results about election facts, guidance, and issues are mainly from general interest news, government information, and privately run websites. Among the top ten domains across all search results are seven news websites which account for a quarter of all search results. By contrast, Wikipedia made up 5.4% of all search results. The high share of news and Wikipedia articles is confirmed by Steiner et al. (2022) who compared the first results for queries on debated topics in Germany across the search engines Ask, Bing, DuckDuckGo, Google, and Ixquick. All search engines positioned Wikipedia in the first rank for queries on climate change and the Transatlantic Trade and Investment Partnership (TTIP); Ask did so for almost every query. In the other search engines, news sites were placed in the first rank most of the time. However, for the topic Covid-19, the sources are more diverse. As Makhortykh et al. (2020) observed, only Yahoo incorporates recent information from legacy media, whereas Bing strongly relied on healthcare-related sources and Google highlighted government-related websites. Yandex was the only search engine that included alternative media in its top 20 search results. Especially for recent topics that lack an established knowledge basis, the choice of search engine may considerably impact what information a user gets to see. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References LITERATURE REVIEW Search result overlap By the mid-1990s, researching the overlap of search results between different search engines sparked interest for the purpose of estimating the size of the Web (Bharat, 1998; Ding & Marchionini, 1996; Lawrence & Giles, 1999). The generally small degrees of overlap indicated diverse underlying databases, each of limited size. Therefore, metasearch engines that combined the results of several search engines were meant to provide an additional value (Chignell et al., 1999) and attracted further research (i.e., Meng et al., 2002). Consequently, Spink et al. (Spink et al., 2006) included the meta search engine Dogpile in their extensive study on the overlap of search results. For a collection of 12,570 queries, the results found on the first search engine result page (SERP) of Ask Jeeves, Google, MSN Search, and Yahoo were captured. 84.9% of the results were unique to one search engine, while only 1.1% were shared by all search engines. Additionally, the low overlap between search engines also manifests in the ranking since only 7% of the top results were similar. This is consistent with previous studies that reported low overlap in ranking (Bar-Ilan, 2005; Bar‐Ilan et al., 2006) Subsequent studies report an increase of overlap in search results, while ranking algorithms appear to be the leading cause for differences in the presentation of the results. Bilal and Ellis (2011) compared major web search engines (Google, Bing, Yahoo) with search engines specifically designed for children (Ask Kids, Yahoo Kids) for queries of various lengths. Bing and Yahoo shared the same overlap with Google for nearly all queries, ranging between 22% and 40%. In contrast, Yahoo Kids had mostly unique results. However, the disparate relevance ranking between Bing and Yahoo suggests different ranking algorithms. Cardoso and Magalhães (2011) measured the overlap of search result rankings based on URLs and website contents. Search results for 40,000 queries were retrieved from Google and Bing. The overlap in domains was about 29%, and Google had more exclusive domains. When looking at the result sets without considering the positions, the similarity between the result sets increased. This indicates that Google and Bing have different ranking preferences but index mostly the same sources. Similarly, Agrawal et al. (2016) based their overlap analysis on content but factored in the description of the result snippets. The top 10 results of 67 informational queries were collected from Google and Bing. The results indicate a high overlap between Google and Bing, with a slightly higher similarity among the top 5 results. Most recently, a study about information on Covid-19 was conducted by Makhortykh et al. (2020). The first 50 results for queries in different languages were collected from Baidu, Bing, DuckDuckGo, Google, Yandex, and Yahoo. DuckDuckGo and Yahoo shared nearly 50% of their results, whereas the other pairs remained under 25%. Google had an overlap of around 10% with Bing and a negligible one with DuckDuckGo, Yandex, Yahoo, and Baidu. Source diversity Various studies took a closer look at search results and evaluated the domains, the types of sources, as well as their diversity across result sets and search engines. Comparing Google, Live Search, and Yahoo, Thelwall (2008) reported that Yahoo returned the highest amount of different domains to a query and also returned around 10% more top-level domains than other search engines. Höchstötter & Lewandowski (2009) showed that while the sources in top results of various search engines differ, there is a concentration on some popular sources in top results. More recently, Lewandowski & Sünkler (2019) collected search results on queries related to insurance providers and identified the distribution of top-level domains. Only ten different domains were found in the first result across all queries. The five most popular domains in the top 5 results are price comparison websites, which make up 88.4% of all first results. The share of popular domains decreases according to the ranking but remains at 42.9% at position ten. Unkel and Haim (2019) observed Google search results prior to the German parliamentary elections in 2017. The study found that result lists for candidates and parties exhibit a high share of self-administered websites. In contrast, results about election facts, guidance, and issues are mainly from general interest news, government information, and privately run websites. Among the top ten domains across all search results are seven news websites which account for a quarter of all search results. By contrast, Wikipedia made up 5.4% of all search results. The high share of news and Wikipedia articles is confirmed by Steiner et al. (2022) who compared the first results for queries on debated topics in Germany across the search engines Ask, Bing, DuckDuckGo, Google, and Ixquick. All search engines positioned Wikipedia in the first rank for queries on climate change and the Transatlantic Trade and Investment Partnership (TTIP); Ask did so for almost every query. In the other search engines, news sites were placed in the first rank most of the time. However, for the topic Covid-19, the sources are more diverse. As Makhortykh et al. (2020) observed, only Yahoo incorporates recent information from legacy media, whereas Bing strongly relied on healthcare-related sources and Google highlighted government-related websites. Yandex was the only search engine that included alternative media in its top 20 search results. Especially for recent topics that lack an established knowledge basis, the choice of search engine may considerably impact what information a user gets to see. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Examining Source Diversity in Web Search: A Comparative Study of Top Search Engines

Authors:
(1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento;
(2) Marco Robol, Department of Information Engineering and Computer Science University of Trento;
(3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento;
(4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 5 Selection algorithm and example of execution Based on the insights derived from our experimental results, we propose the selection algorithm represented in Algorithm 1. The algorithm is designed to select the appropriate search algorithm based on various priorities alongside the characterization of the environment used. The selection algorithm emphasizes the desired priority first, which could manifest in various aspects of pathfinding tasks, including the path cost, memory usage, or the time taken to find a solution. The rational reason for emphasizing ”Priority” at the beginning of the selection algorithm is that, based on this user choice, different search algorithms may be considered to suit best the addressed problem. Thus, by tackling the ”Priority” upfront, The selected algorithm will eventually, cater to the main requirements of the task at hand. The algorithm takes as input the Grid that is of size NxN with the start and goal positions, the distance threshold, and the priority criterion. If we aim to minimize memory usage, path cost, or both (Line 2), the algorithm suggests using D* Lite. However, If minimizing the solving time is our primary concern (line 4), we should first calculate the Euclidean distance between the start and the goal using the function in line 12. If the distance is higher or equal to the threshold (in our experiments it is equal to 140), the algorithms suggest using RTAA*. If the distance is less, then the selection algorithm suggests using ARA*. 5.1 Example of execution To showcase the efficiency of our selection algorithm, we have designed an execution example that consists of generating a grid with random parameters i.e., choose a random grid size, obstacle density, and start to goal distance. After that, we generate an identical grid, however, we change only one parameter each time while keeping the other parameters as they were in the initial grid. We do the same for generating personalized grid environments, where we generate a grid with a random choice of wall number, and then we change the wall lengths. We run RTAA*, ARA*, and D*Lite algorithms on the generated grids (all obtained results are shown in table 1) alongside our selection algorithm, so that we can compare the obtained results with what the selection algorithm is suggesting to use for the given grid. It is essential to highlight that the randomly chosen values for the grid parameters are all within the range of the values used in the experiments, which ensures that the thresholds chosen are relevant. Also, the reason behind using only RTAA*, ARA*, and D*Lite in this execution example is because of their standout performance in our experiments. For each grid, we run our selection algorithm each time with a different priority, and it suggests using an algorithm that will perform the best given the selected priority. The highlighted values in the table refer to the outcomes derived from the suggested search algorithm by our selection algorithm. These highlighted values are notably the best that we can obtain for each grid given a certain priority except for the red one; yellow represents the most efficient paths, green represents the best values that we can obtain if we want to reduce memory consumption, and the values highlighted in purple indicates the best solving time. However, our algorithm failed in selecting the right algorithm to use in the fourth grid when prioritizing the solving time, where the shortest solving time was found by RTAA*, instead our algorithm suggested using ARA*. This paper is available on arxiv under CC 4.0 license. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 5 Selection algorithm and example of execution Based on the insights derived from our experimental results, we propose the selection algorithm represented in Algorithm 1. The algorithm is designed to select the appropriate search algorithm based on various priorities alongside the characterization of the environment used. The selection algorithm emphasizes the desired priority first, which could manifest in various aspects of pathfinding tasks, including the path cost, memory usage, or the time taken to find a solution. The rational reason for emphasizing ”Priority” at the beginning of the selection algorithm is that, based on this user choice, different search algorithms may be considered to suit best the addressed problem. Thus, by tackling the ”Priority” upfront, The selected algorithm will eventually, cater to the main requirements of the task at hand. The algorithm takes as input the Grid that is of size NxN with the start and goal positions, the distance threshold, and the priority criterion. If we aim to minimize memory usage, path cost, or both (Line 2), the algorithm suggests using D* Lite. However, If minimizing the solving time is our primary concern (line 4), we should first calculate the Euclidean distance between the start and the goal using the function in line 12. If the distance is higher or equal to the threshold (in our experiments it is equal to 140), the algorithms suggest using RTAA*. If the distance is less, then the selection algorithm suggests using ARA*. 5.1 Example of execution To showcase the efficiency of our selection algorithm, we have designed an execution example that consists of generating a grid with random parameters i.e., choose a random grid size, obstacle density, and start to goal distance. After that, we generate an identical grid, however, we change only one parameter each time while keeping the other parameters as they were in the initial grid. We do the same for generating personalized grid environments, where we generate a grid with a random choice of wall number, and then we change the wall lengths. We run RTAA*, ARA*, and D*Lite algorithms on the generated grids (all obtained results are shown in table 1) alongside our selection algorithm, so that we can compare the obtained results with what the selection algorithm is suggesting to use for the given grid. It is essential to highlight that the randomly chosen values for the grid parameters are all within the range of the values used in the experiments, which ensures that the thresholds chosen are relevant. Also, the reason behind using only RTAA*, ARA*, and D*Lite in this execution example is because of their standout performance in our experiments. For each grid, we run our selection algorithm each time with a different priority, and it suggests using an algorithm that will perform the best given the selected priority. The highlighted values in the table refer to the outcomes derived from the suggested search algorithm by our selection algorithm. These highlighted values are notably the best that we can obtain for each grid given a certain priority except for the red one; yellow represents the most efficient paths, green represents the best values that we can obtain if we want to reduce memory consumption, and the values highlighted in purple indicates the best solving time. However, our algorithm failed in selecting the right algorithm to use in the fourth grid when prioritizing the solving time, where the shortest solving time was found by RTAA*, instead our algorithm suggested using ARA*. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

A Comprehensive Examination of Algorithmic Behaviors in Diverse Grid Settings

In a by and the team at the intricate relationship between Attention-Deficit/Hyperactivity Disorder (ADHD) and an array of psychiatric conditions has been brought to the forefront. Engaging over 1,500 participants across a broad spectrum of backgrounds, the research unveils that an astonishing 40.4% of individuals diagnosed with ADHD also grapple with symptoms of anxiety. recent study Dr. Adeel Sarwar ADHDtest.ai, This revelation not only highlights the multifaceted psychiatric landscape accompanying ADHD but also underlines the pressing need for comprehensive therapeutic approaches. The study explores beyond the realm of anxiety, casting light on ADHD's interconnection with conditions such as Obsessive-Compulsive Disorder (OCD), insomnia, and Post-Traumatic Stress Disorder (PTSD). This extensive inquiry underscores the importance of adopting a holistic lens in both the diagnosis and management of ADHD. "Our findings reiterate the intertwined nature of ADHD and psychiatric disorders, accentuating the imperative for all-encompassing therapeutic strategies," Dr. Sarwar notes, emphasizing the study's impact on ADHD management paradigms. In an innovative stride, ADHDtest.ai has opened its research data to the public, setting the stage for further scholarly exploration in this vital domain. The use of an online platform for analyzing data from a substantial participant base has not only propelled our understanding of ADHD and its psychiatric comorbidities but also ensured the ethical management and confidentiality of participant data. A key element of this research is its statistical scrutiny, which has uncovered significant trends, especially among gender-diverse groups. The analysis shows a pronounced incidence of anxiety among non-binary and transgender individuals, with a notable portion of women with ADHD indicating no prior conditions. These findings signal potential gender-specific differences in ADHD presentations, echoing the call for gender awareness in ADHD assessment and treatment, in line with personalized medicine principles. Moreover, the study sheds light on the prevalence of insomnia and PTSD within the ADHD demographic, suggesting a complex weave between ADHD, sleep disturbances, and trauma. The detection of Autism Spectrum Disorder (ASD), dyslexia, substance abuse disorders, Bipolar Disorder, and Borderline Personality Disorder among ADHD diagnoses hints at a layered relationship deserving of further exploration into their shared genetic and neurobiological underpinnings. This collaborative endeavor not only maps out the nuanced connections between ADHD, gender identity, and various comorbidities but also exemplifies the transformative potential of digital platforms in deepening our grasp of these intricate interplays. The research's insights underscore the necessity for a refined clinical approach that transcends conventional one-size-fits-all methodologies, advocating for customized ADHD interventions across gender spectra. In essence, this study represents a pivotal advancement in our comprehension of ADHD and its association with a wide spectrum of psychiatric conditions. By challenging traditional perceptions of ADHD in isolation and spotlighting critical gender-related insights, it beckons a reevaluation of diagnostic and therapeutic biases, paving the pathway towards more individualized and efficacious treatment paradigms.

New Research Examines Connection Between ADHD and Anxiety: What It Says

United States v. Apple INC Court Filing, retrieved on March 21, 2024 is part of . You can jump to any part in this filing . This part is 18 of 25. HackerNoon’s Legal PDF Series here VIII. Jurisdiction, Venue, and Commerce 191. The United States brings this action pursuant to Section 4 of the Sherman Act, 15 U.S.C § 4, to prevent and restrain Apple’s violations of Section 2 of the Sherman Act, 15 U.S.C. § 2. 192. The Attorneys General assert these claims based on their independent authority to bring this action pursuant to Section 16 of the Clayton Act, 15 U.S.C. § 26, and common law, to obtain injunctive and other equitable relief based upon Apple’s anticompetitive practices in violation of Section 2 of the Sherman Act, 15 U.S.C. § 2. 193. The Attorneys General are the chief legal officers of their respective states. They have authority to bring actions to protect the economic wellbeing of their states and residents, and to seek injunctive relief to remedy and protect against harm resulting from violations of the antitrust laws. 194. Apple’s actions and course of conduct are ongoing and are likely to continue or recur, including through other practices with the same purpose or effect. 195. Apple’s actions complained of herein have harmed, and continue to harm, competition, consumers, and the general welfare and economies of the Plaintiff States. This Court has subject matter jurisdiction over this action under Section 4 of the Sherman Act, 15 U.S.C. § 4, and 28 U.S.C. §§ 1331, 1337(a), and 1345. 196. The Court has personal jurisdiction over Apple, and venue is proper in this District under Section 12 of the Clayton Act, 15 U.S.C. § 22, and under 28 U.S.C. § 1391, because Apple transacts business and is found within this District. 197. Apple is a corporation headquartered in Cupertino, California. Apple is one of the largest publicly traded companies in the world, generating hundreds of billions of dollars from the sale of smartphones, computers, tablets, and related services and accessories. 198. Apple engages in, and its activities substantially affect, interstate trade and commerce. Apple provides a range of products and services that are marketed, distributed, and offered to consumers throughout the United States, in the plaintiff States, across state lines, and internationally. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 21, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. justice.gov

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