dataesr/scientific-paragraphs-categorization huggingface dataset:
Overview of Blockchain technology The term Blockchain was first introduced by Satoshi Nakamoto in  [4]. Blockchain technology is informally defined as a distributed database (peer-to-peer network) that records all transactions that occur in the network in which smart contracts operate in a decentralized, secure, and reliable manner. Smart contracts  [31]  are one of the best applications of Blockchain technologies and are also crucial in facilitating the negotiation of a transaction (traceable and irreversible) without third parties in a Blockchain. The history of all transactions is stored in the Blockchain, which makes it immutable and very difficult to tamper with. Nowadays, the use of Blockchain in cloud computing is one of the most common innovations that can solve the challenges of security, anonymity, and data integrity without any third party in a cloud environment because of its underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation.
The nodes can be divided into two types in this network: one is a server that participates in the consensus activity and the other is the client which only transfers the funds. Each server will have a unique node list (UNL). The database will ask the nodes present in the UNL to determine whether to post transactions to the ledger and if it gets agreement from more than 80% of all servers, those transactions would be aggregated into the distributed ledger and successfully verified by enough servers. -Proof of Authority (PoA): This consensus algorithm aims to give a small, designated number of Blockchain actors the power to validate transactions or interactions with the network and update its more or less distributed ledger  [44]. There are many similarities between PoA and PoS, for example, they do not require mining to generate a new block and hence the rights to generate new blocks are granted to nodes that have proven their authority to do so.
Discussion and comparative analysis In this section, we explain the main comparative results of the solutions described in the previous section. Table  6  shows us the characteristics used to evaluate the level of trust and security of the different solutions in the literature, namely confidentiality, integrity, authentication, access control, the process of sharing the key encryption, and auditing to trust the cloud service provider. Based on previous investigations, we have found that the integration of Blockchain and cloud has the following main advantages: Since Blockchain is a reliable distributed database, it can be used to store important data generated by different applications with transactions to ensure data integrity and it can also be used to store file metadata. However, using the characteristics of the smart contract, the cloud computing management mechanism can be run automatically, and the Blockchain can be used for resource planning, resource distribution, transactions, tracking, auditing, identity management, access control, and authentication.
The detection range of Leap Motion is about 25-600mm above the controller, and the recognition range is basically an inverted pyramid. First, the jumping motion creates a right-handed coordinate system, with the control center as the starting point, in millimeters, the X and Z axes are horizontal, and the Y axis is vertical  [24]. When a target object, such as a finger, enters the detection range, Leap Motion software assigns a unique identifier within the unchanged field of view (objects entering the detection area will be redirected to the identifier). In addition, the related functions provided by the jumping exercise will be added. The controller sends periodic motion data (called frames) of the target object. The frame in the frame contains functions such as fingers, hands, pointing, and tools, and is responsible for providing monitoring data, gestures and data related to the overall motion factors within the visual range  [25].
Based on the data identified in each frame, the controller translates the motion into factors such as translation, rotation, and upgrade. It uses complex algorithms to create motion information, identify and judge the trajectory of the target object, and finally interact with the display screen. Leap Motion uses infrared imaging principles to monitor hand activities. Fingers and portable devices use a unique combination of hardware and software technology to realize human-computer interaction with both hands and interact with digital content in the real world. It has extremely high accuracy and extremely low delay. The error is within 0. 01 mm, and the delay is 5-10 ms. It can track both hands at a speed of not less than 200 carats per second. This accuracy to a large extent guarantees that the user successfully completes the functions required for human-computer interaction  [26].
Along similar lines, Grujić et al. (2009) report evidence of disassortative mixing in the bipartite network affiliating users and movies in a popular internet-based movie database: users who recommend many movies recommend movies that are not recommended by many other users. In a recent study of Wikipedia-the open, online encyclopedia-  Lerner and Lomi (2020b)  find that particularly active contributors (i. e. , individuals responsible for a high volume of editing activity) are less likely to modify articles that receive many edits from other contributors. In consequence, more active users seem to dedicate a larger share of their attention to less popular articles than less active users. These empirical studies report patterns of disassortative mixing in large bipartite systems composed of individuals and items competing for their attention, such as music tracks, movies, and web pages.
Empirical Setting We study the dynamics of attention allocation in the Apache HTTP server, a large and successful free/opensource software (F/OSS) project. The term F/OSS typically refers to software products released under a license that allows inspection, use, modification, and redistribution of the original software source code  (Crowston et al. 2012). Developing teams  (Lee and Cole 2003, Crowston and Scozzi 2008)  contribute private effort to the production of what is effectively a public good  (Spaeth et al. 2008 , von Krogh et al. 2012 ). Developers can be unpaid volunteers  (Hars and Ou 2002, Lakhani and Wolf 2005)  or paid by third parties  (Henkel 2006 , Stam 2009 , Rolandsson et al. 2011). Their motivations vary widely and change over time  (von Krogh et al. 2012) , ranging from ideological belief  (Haruvy et al.
Bug repositories provide an occasion for coordinating the highly decentralized activities of developers and channeling collective attention. Apache HTTP server is one of the most popular web server softwares, serving approximately 33% of all existing websites. 8 The development and maintenance of Apache HTTP server is overseen by a project management committee chaired by a vice president. Members of the committee are appointed among the developers who have acquired significant merit in the project with When accumulating activity on the platform, participants diversify their scope, diverting their attention away from popular ideas. Participants extend their collaboration with other participants by allocating attention to proximal ideas in their attention network. Bossless organizations (e. g.
Scientific research production Researchers accumulate specialized knowledge by focusing their attention repeatedly on specific topics. Researchers are attracted by topics that are well attended in their scientific community. When accumulating activity, researchers broaden their expertise by engaging with topics that are less popular in the scientific community. Researchers extend collaboration with other researchers to proximate topics in their attention network. Newcomers looking for ways to contribute to a project are explicitly encouraged by the Apache Software Foundation (2022a) to tackle a problem reported in the bug repository that stimulates their own interest. Given the freedom that developers experience when contributing to solve tasks in a very modular structure, the extensive reliance on volunteer participants, and the complete transparency of their attentional processes, we think Apache represents a particularly apt environment for testing our hypotheses.
Data We extracted the complete sequence of attention allocation events connecting project participants to software bugs stored in the official bug repository of the Apache HTTP server. Our sample includes all the bugs ever reported on releases 2. X of the software from the first bug report  (March 2001)  until the end of the observation period  (March 2013). During the observation period, a total of 13,526 actions by 4,338 unique developers on 6,000 distinct bug reports were recorded. Information on each individual event is exact to the second. Thus, the data set we constructed contains information on real-time attention allocation events observed throughout the life history of the project. 9 We adopt the definition of "developer" provided by ASF as individuals who "contribute to a project in the form of code or documentation.
They take extra steps to participate in a project, are active on the developer mailing list, participate in discussions, provide patches, documentation, suggestions, and criticism" (Apache Software Foundation 2022b). In the rest of the paper, we use the generic term organizational "participants" to refer to developers and the term organizational "problems" to refer to software bugs. We collected the raw data by parsing the individual, publicly available web pages of all bug reports included in the repository with the web-crawler software Bicho  (Robles et al. 2009). In detail, a participant encountering a problem when running or developing the software usually starts the bug reporting process and Attention reinforcing (Hypothesis 2) Preferential tendency of a participant (i1) to allocate attention at time t2 to problems (m1) that attracted a higher level of collective attention at time t1.
8)) F/OSS projects vary greatly in the extent to which they contain formal elements of organizational structure related to modularity. The necessity to adopt a modular structure increases as a function of the size and complexity of a project. Apache HTTP server, for example, is sufficiently large to sustain a formalized modular organization of its code. Examples of such modules include the "core" code, software extensions, and the corresponding software documentation. The software code is devoted to the basic processing of HTTP requests and responses. Each of the modules extends this basic functionality with a specific feature. For example, the module "mod ssl" provides Apache HTTP server with cryptographic capabilities now almost universally used by web servers. Alongside the software code composing core and modules, the code repository of the project contains software documentation that developers write and maintain as the project evolves.
A software bug can affect the software as a whole, that is, in multiple areas of the code base, across core and multiple modules. A software bug can affect only the core, one of the modules, or the documentation. Furthermore, a software bug may be reported as affecting the "build"-that is, the compilation of the source needed before execution-as well as the installation procedure of the software. To assess the extent to which the modular structure of the project affects our results, we assigned each software problem to one of five categories representing the different modules. This is the variable Module. The first category (omitted) includes all software bugs identified as affecting the software globally and serves as a baseline category. The second category includes all software bugs affecting the core module of Apache HTTP server.
The third category includes all software bugs affecting build and installation of the software. The fourth category includes all software bugs affecting software documentation. The fifth category includes all software bugs affecting one of the other noncore modules of Apache HTTP server. We incorporate information on the modular structure of the project by including Preferential modularity as a covariate controlling for the tendency of participants to work preferentially on problems within the same module. The interpretation of organizational structure provided by  Cohen et al. (1972)  is perfectly consistent with our quasi-experimental representation of Preferential modularity as an exogenous constraint on the access that solutions carried by participants have to problems that the project generates.
In more general terms, modularity exemplifies a case of "specialized access-or an attempt to force solutions to specialize in the kinds of problems that can be associated to them"  (Cohen et al. 1972, p. 6). In open-source software projects, the primary function of modularity is to help participants search efficiently for opportunities to contribute aligned with their interests and skills  (MacCormack et al. 2006). For this reason, modularization is a common organizing principle in open-source software projects  (von Krogh et al. 2003 ), but it is particularly salient in Apache HTTP server, which is one of the largest opensource softwares in existence. Because of its size and complexity, the overall project is parsed into modules that reflect major functional areas of the software. Model M3 in Table  5  incorporates the effects of modularity.
A. 3. Hypotheses Attention focusing is the cumulative number of attention allocation events connecting participant i to problem j before time t-downweighted by the elapsed time-and is defined as where T e gj is the time stamp of attention allocation event e from participant g to problem j and the function w(t, T e gj ) defined in Equation (A. 2) assigns a temporal weight for the event e. To examine mixing patterns (or assortativity), Attention mixing is simply the interaction of cumulative attention and attention reinforcing: Attention mixing(t, i, j) � X m∈M X Nim(t � ) e�1 w(t, T e im ) • X g∈I X Ngj(t � ) e�1 w(t, T e gj ): (A. 5) In a bipartite data structure, such as the one we analyze, closure cannot involve an odd number of links. Therefore, Attention clustering takes the form of a bipartite four-cycle and can be formally defined as follows: Attention clustering(t, i, j) � X m≠j I[N im(t � ) > 0] • X g≠i I[N gj(t � ) > 0] • I[N gm(t � ) > 0], (A.
C. 4. Extreme Outliers. In our empirical setting, the number of recorded attention allocation acts varies significantly across participants and is not normally distributed. Indeed, we expect the mechanisms underlying our hypotheses to produce an uneven concentration in attention allocation, and our modeling approach is suited to handle this skew. Nonetheless, could it be that the results we find are only driven by the actions of the most active participants? To answer this question, we reestimated our models, excluding the more severe outliers among the participants. We computed the 90% quantile of cumulative attention over all events and removed all observations above that threshold from the data set. The results in Table  C. 4 show that the new estimates are stable and consistent with our previous results.
com/technologies/overview/web_server. 9 A dynamic visualization of the data we analyze in the empirical part of the study may be accessed by following the link https:// zenodo. org/record/7564503. The actual data we collected and used in the analysis are publicly available and may be found at the following address: https://github. com/juergenlerner/eventnet/tree/ master/data/apache. 10 All the statistics are time-weighted according to a time decay parameter defined in Equation (A. 2). In this way, more recent events have a heavier weight on the prediction of the next event-a weight that progressively decreases for events in the more distant past. 11 As with clustering defined for one-mode (social) networks, clustering for bipartite networks involves path shortening behavior (or closure): the difference is that, in bipartite networks, path shortening closes an open three-path connecting a participant indirectly to a problem through another problem and another participant (see Equation A.
Acknowledgments We are grateful to the associate editor,  Professor T. Knudsen , for his experienced guidance and advice throughout the editorial process. Our gratitude extends to  Professor W. Ocasio  for his insightful comments and interest in our work. We acknowledge the contribution of colleagues at  LMU Munich ,  Erasmus University Rotterdam , and conference participants at the  Academy of Management Annual Meetings, Organization Science Winter Conference , and the Organizing in the Digital Age (DigiERA) conference hosted by the  ETH Zurich at Monte Veritá (Ascona). Finally, we are pleased to acknowledge our debt of gratitude to the late  Professor James March  for providing the encouragement, support, and intellectual guidance that made this work possible and worth doing.
Wissenschaftlichen Forschung  [Grant  100018_150126 ] (" Relational event modes for bipartite networks with application to collaborative problem solving ,"  P. I. Alessandro Lomi ) and by the  Deutsche Forschungsgemeinschaft  [Grant  321869138 ] ("Statistical analysis of time-stamped multi- actor events in social networks ," P. I. J üergen  Lerner ). video containing the dynamic visualization of the data is available at https://zenodo. org/.
Data anonymization techniques can help to protect sensitive information by removing or masking identifying information from datasets. Encryption technologies can help to ensure that sensitive data is protected during storage, transmission, and processing. For sensitive health data to be protected and kept private, PET must be used in HIoT-based systems. By implementing state-of-the-art technologies and frameworks, organizations can help to mitigate the risks of cyber-attacks and data breaches, while also ensuring compliance with relevant regulations and standards. 1. 1. The key contributions of the proposed system 1. Enhanced Privacy and Security: The use of block-chain technology ensures the confidentiality and integrity of sensitive patient data. The decentralized and immutable nature of the block-chain ledger reduces the risk of fraud and unauthorized access, providing a secure environment for data exchange.
Mandatory Access Control (MAC): In high-security settings like military or governmental organizations, this paradigm is frequently utilized. Access to resources is determined by the security level of the user and the sensitivity of the data or system being accessed. Access is granted based on the user's demonstrated ability to perform certain tasks. In addition to these access control models, several access management standards are commonly used in IAM systems. While Security Assertion Mark-up Language (SAML) and Extensible Access Control Mark-up Language (XACML) are used for sharing authorization and attribute information between different systems, OpenID-Connect and OAuth are frequently used for authentication and authorization  [7, 8]. IAM is organized using multiple digital identity standards and data protection rules, and there are four IAM models: isolated, centralized, federated, and user-centric.
Literature survey Despite there aren't many proposals for applications using blockchain to manage identity and access, decentralized personal data management is one application area that has received a lot of attention. Zyskind et al. [23]  presented a method to protect personal data, allow users choice over what data to publish, and give users the ability to withdraw access to that data by combining a public block-chain network with off-block-chain storage. A similar idea is applied in the system suggested in this study, where user data is stored off-block-chain. Instead of a dedicated store, the identity and access management system in the block-chain network is connected to an existing database, which is simple to do. Since permissions can be accessed and verified by other members of the consortium block-chain network, this proposed system stores permissions in the block-chain rather than off-block-chain storage, which eliminates the need to replicate the off-block-chain storage on other nodes compared to conventional centralized systems, this strategy offers a more effective and secure method of managing identification and access.
Additionally, because any type of data stored on the block-chain could potentially be accessed by anyone with network access, it's crucial to think about the security implications. In writing an example of a decentralized system that uses block-chain to maintain patient electronic health records (EHRs) is the MedRec system, which was developed on the Ethereum platform. The network's stakeholders are incentivized to participate using two incentives: anonymous medical data and the crypto currency Ether. Li et al. 's architecture is built on the Hyper-ledger Fabric permission idea, which calls for all potential block-chain participants to register and create identities before connecting to the network and submitting transactions. Overall, this idea is intriguing and may also be utilized to scale other block-chain systems.
The decentralized nature and immutability of the scalable computation result are ensured by the reliability of block-chain technology. Liu Y  [28]  et al. recommended a new Delegated Proof of Stake (DPoS) consensus mechanism, which can automatically actuate the number of agent nodes for selection, depending on the degree of decentralization and the total number of nodes in the block-chain. Zhang Q et al. [29]  suggested a data security sharing model based on privacy protection (DSS-PP) for block-chain-enabled IIoT for secure sharing of resources in IIoT. This approach uses authentication technologies with concealed features to secure user data and only the cipher-text index data is kept in the block while the encrypted shared resources are kept in the block-chain's off-chain database. It reduces the block-chain's storage load.
Workflow of proposed system In the modern era of interconnected systems and digital services, the protection of sensitive resources and user data is of paramount importance. To achieve secure access to these resources, a robust authentication and authorization mechanism is required. Our suggested system is one of such mechanism. In this section Fig. 2  explains the step-by-step process involved in accessing a sensitive resource using OAuth 2. 0 authentication server and OAuth 2. 0 authorization server for verification and validation of user identity. To ensure transparency and tamper-proof transactions, the entire access and authorization transactions are recorded on the Hyper-ledger Fabric block-chain. This decentralized ledger technology provides an immutable record of all interactions, enhancing security and maintaining the integrity of the system.
System design In the proposed system, the authentication and authorization server is designed to use block-chain technology to validate transactions. Fig. 3  depicts the conceptual design of the suggested identity and access management system. It is made up of a database (DB), an application server (App-server), an authentication and authorization server (Auth-server), and a client application (App). To authenticate the user's identity and authorize their actions through the block-chain network, the App-server must connect with the Auth-server. The block-chain network provides an immutable and transparent ledger for recording user transactions, making it difficult for unauthorized users to access the system. Here this distributed system architecture involves several components such as an App, App-server, Auth-server, DB, and block-chain network.