dataesr/scientific-paragraphs-categorization huggingface dataset:
SwarmScreen relies on BitTorrent and was designed to provide user privacy through plausible deniability. Since a user behavior can be deduced by her interests, SwarmScreen connects the user to other users outside of her community of interest, which can disguise her interests and thus her behavior. Many systems such as ANts P2P  10  and MUTE  11  have been proposed as anonymous P2P file sharing softwares. They use anonymity in order to make the user untrackable, hide her identity and encrypt everything she is sending/receiving from others. Censorship-resistant systems such as Dagster and Tangler usually use anonymity techniques to hide users identities. Thus, it is not possible to enforce censorship on data belonging to a specific user.
Data protection against corruption and deletion. It is hard to prevent data from being corrupted or deleted. However, data integrity techniques and digital checksums can be used to help users to verify if data have suffered unauthorized changes. OceanStore and Freenet use digital checksums to verify that data content has not been tampered with. Past uses smarcards to sign data in order to authenticate them and verifies if they have been modified or corrupted. Thus, although private data are not protected against corruption and deletion, malicious changes can be detected. Countermeasures can be taken against malicious peers in order to demotivate any future corruption. In other systems, data checksums are not used, so data changes cannot be detected. Limited disclosure. In order to limit data disclosure, access control is used to prevent unauthorized requesters from accessing data.
In addition to access control, encryption is used to prevent unauthorized disclosure due to collusion between servers and requesters. Even if an unauthorized requester receives encrypted data, it cannot access data content without having the corresponding decryption keys. In addition, trust techniques can be used to make owners feel more comfortable about the use of their data as they can verify the trustworthiness of the requester. Past and OceanStore use access control to limit disclosure only for authorized data owners. 12 Freenet, Dagster, and Tangler do not use access control since the peers are anonymous. Piazza uses access control to limit disclosure for authorized users. Office SharePoint Workspace and OneSwarm use access control but also trust techniques, thus, data disclosure is limited not only to authorized peers but also to trustworthy ones.
User anonymity. Four types of anonymity guarantees are defined in  [10] : 1. Author (i. e. , owner) anonymity: which users created which documents? 2. Server anonymity: which peers store a given document? 3. Reader (i. e. , requester) anonymity: which users access which documents? 4. Document anonymity: which documents are stored at a given peer? Past guarantees author and server anonymity due to pseudonymity techniques. Each user holds an initially unlinkable pseudonym in the form of a public key. The pseudonym is not easily linked to the user's real identity. If desired, a user may have several pseudonyms to hide that certain operations were initiated by the same user. Past users do not need to reveal their identity, nor the data they are retrieving, inserting, or storing. Office SharePoint Workspace guarantees author, server, and reader anonymity, due to anonymous workspaces.
Office SharePoint Workspace guarantees denial of linkability outside workspaces due to anonymous workspaces. OneSwarm, Dagster, Tangler, Freenet, and SwarmScreen guarantee denial of linkability, due to anonymous communication. Content deniability. Content deniability refers to whether peers can deny the knowledge on the content stored or transmitted (document anonymity). Knowledge on content can be denied if the content is not readable by the peer that holds it. In all systems that use data encryption, knowledge on data content can be denied. This is the case of OceanStore, Mnemosyne, Office SharePoint Workspace, Piazza, OneSwarm, Dagster, Tangler, and Freenet. In Past and SwarmScreen, servers can access data, so they cannot deny the knowledge on the data content they store. Table  4  provides a summary of the techniques used to ensure privacy by the systems we evaluate.
Access control. Access control is essential to guarantee that data will not be read or shared with unauthorized peers. In Past, access control is based on the use of smartcards which generate and verify various certificates. Users may access data or not within the access rights related to their certificate. In OceanStore, access control is based on two types of restrictions: reader and writer restrictions. In the reader restriction, to prevent unauthorized reads, the data decryption keys are distributed by the data owner to users with read permissions. To revoke read permissions, the data owner requests users to delete replicas or re-encrypt them with new encryption keys. A recently-revoked reader is able to read old data from cached copies or from misbehaving servers that fail to delete or re-encrypt.
Hippocratic databases Inspired by the Hippocratic oath and its tenet of preserving privacy, Hippocratic databases (HDB) aim at incorporating privacy protection within relational database systems  [2]. The important concepts of HDBs are the following. -Privacy policies. A privacy policy defines for each column, row, or cell of a table (a) the usage purpose(s), (b) the external recipients, and (c) the retention period. -Privacy authorizations. A privacy authorization defines which purposes each user is authorized to use on which data. HDBs define ten founding principles to protect data privacy according to users preferences. 1. Purpose Specification. For personal information stored in the database, the purposes, for which the information has been collected, shall be associated with that information.
2. Consent. The purposes associated with personal information shall have the consent of the owner of the personal information. 3. Limited Collection. The personal information collected shall be limited to the minimum necessary for accomplishing the specified purposes. 4. Limited Use. The database shall run only those queries that are consistent with the purposes for which the information has been collected. 5. Limited Disclosure. The personal information stored in the database shall not be communicated outside the database for purposes other than those for which there is a consent of the owner of the information. 6. Limited Retention. Personal information shall be retained only as long as necessary for the fulfillment of the purposes for which it has been collected. 7. Accuracy. Personal information stored in the database shall be accurate and up-to-date.
8. Safety. Personal information shall be protected by security safeguards against theft and other missappropriations. 9. Openness. An owner shall be able to access all information about her stored in the database. 10. Compliance. An owner shall be able to verify compliance with the above principles. Similarly, the database shall be able to address a challenge concerning compliance. In an HDB, queries are submitted along with their intended purpose. Query execution preserves privacy by using query rewriting and restrictions by column, row, or cell. Purpose specification. Purpose specification is the cornerstone of an HDB. It states that purposes should be specified and attached to data items to control their usage. In order to do this, simple specification language such as Platform for Privacy Preferences (P3P)  [9]  can be used as a starting point.
A scalable architecture for enforcing limited disclosure rules and conditions at the database level is proposed in  [25]. For enforcing privacy policies in data disclosure, privacy policies can be stored and managed in the database. These policies are expressed in high-level privacy specification languages (e. g. , P3P). Enforcing privacy policies does not require any modification to existing database applications. Authors provide techniques for enforcing a broad class of privacy policies by automatically modifying all queries that access the database in a way that the desired disclosure semantics is ensured. They examine several implementation issues, including privacy metadata storage, query modification algorithms, and structures for storing conditions and individual choices. HDB implementation.
It creates data keys by hashing the triplet (data references, purposes, operations). This component offers two functions, the first one, used during the put() process, returns the created data key and the second, used during the get() process, returns the created data key and adds it the identifier of the requester. Id manager. Its role is to identify peers from their names (e. g. , URI). Peer identifiers are used in access control to authorize or prohibit access to data. Log manager. Its role is to manage logs. It stores logs in a dedicated database on each peer. These logs can be used for recovery and auditing process. Time Manager. Its role is to give the current time. It is used for synchronizing clocks of all connected peers. PriServ orchestrator. It is the central component of PriServ.
Conclusion This chapter gave an overview of current solutions for supporting data privacy in P2P systems. Our evaluation of existing solutions is made based on the techniques used to protect privacy of data and users (i. e. , access control, anonymity, trust, and cryptography) and the guaranteed privacy properties (i. e. , protection against unauthorized reads, corruption and deletion, limited disclosure, anonymity guarantees, denial of linkability, and content deniability). This analysis showed that while the notion of purpose of Hippocratic databases (HDB) is gaining more attention, in particular, because of the OECD guidelines, it has not been used in P2P systems. Then we developed in more details a complete solution (Primod and Priserv) for data privacy in P2P systems that supports the notion of purpose of HDB.
Another important goal of this study was to compare the effect of current type on the performance GAC-enhanced EC process. Also, three center points were added for each level of the categorical factor in order to estimate the experimental error and verify whether there is any curvature in the model to be fitted  [31]. Experimental data were analyzed statistically by means of Minitab software. Table  3  presents the experimental matrix of the FFD. Table  3. Experimental design matrix of the 2 7-3 fractional factorial design (FFD). Run No. A B C D E = ABC F = BCD G = ACD i, A/m 2 pH t, min GAC dose, g/L [NaCl] mM Ci, mg/L Current type 1 - - - - - - DC 2 + - - - + - APC 3 - + - - + + DC 4 + + - - - + APC 5 - - + - + + APC 6 + - + - - + DC 7 - + + - - - APC 8 + + + - + - DC 9 - - - + - + APC 10 + - - + + + DC 11 - + - + + - APC 12 + + - + - - DC 13 - - + + + - DC 14 + - + + - - APC 15 - + + + - + DC 16 + + + + + + APC 17 0 0 0 0 0 0 DC 18 0 0 0 0 0 0 APC 19 0 0 0 0 0 0 DC 20 0 0 0 0 0 0 APC 21 0 0 0 0 0 0 DC 22 0 0 0 0 0 0 APC Note: --‖ represents the low level; 0-center lever; +-high level.
Effects on the UED Response Unit Energy Demand (UED) response is defined by Equation (  2 ). Values obtained for UED response range between 0. 03 and 30. 04 kWh/kg, and the ratio of maximum to minimum is greater than 10 (898. 9), which indicates that a transformation is required  [34]. According to the Box-Cox plot for power transformation, Minitab software indicated that the logarithm of response values is the proper transformation. Figure  10  presents the normal plot and Pareto chart. It can be noted in the normal plot of UED response (Figure  10a ) that no straight line crosses the points representing the effects of the coefficients in the model. The Pareto plot in Figure  10b  pinpoints that all the main and interaction effects of 2 7-3 FFD have significant effects on UED response. Figure  11  presents the main effects and interaction plots for UED response.
e. , k = 3. The optimization of the overall desirability function, D, implies the maximization of the response of color removal efficiency and the minimization of UED and UEMD responses. This function is subject to the constraint. In this regard, all the factors take values in the limit of the experimental region explored. Bezerra et al. [38]  described in detail the methodology of desirability function. The algorithm of multi-objective optimization Minitab software allowed us to obtain the predicted optimal values. Three confirmation runs were carried out in order to check experimentally the optimal point. Optimal values of responses correspond to a current density of 2. 73 A/m 2 , pH value of 3, GAC dose of 0. 5 g/L, salt concentration of 50 mM, dye initial concentration of 50 mg/L, duration of 180 min and APC mode.
Electrical Operating Costs In order to determine these operational costs, economic data were gathered from the EU market in 2012. Thus the electrical energy price for industrial use is averaged at 0. 1 $/kWh, while mild steel plate sheets were estimated at 1. 5 $/kg  [27]. As described in Section 2. 3, Electrical Operational Costs (EOCs) are composed mainly of the costs of electrical energy and electrode material consumed. These costs were related to the amount of pollutant removed. The EOC can be determined by means of Equation (  4 ). According to the local optimum found by desirability function, treating an aqueous solution of 50 mg/L of dye at 2. 73 A/m 2 , and adding 0. 5 g/L of GAC dose and 26 mM of NaCl, a removal efficiency of 92. 24% after 180 min was experimentally obtained. This corresponds to an EOC of 7.
1. 27. 0) software package  [56]  for curation and analysis of sequences. Mothur's implementation of PyroNoise was used with a default of 450 flows in denoising, and sequences were trimmed to a minimum length of 200 bp with default parameters in mothur. Sequences were aligned against the silva database in mothur, filtered, trimmed, and pre-clustered if they were within 2 bases of a more abundant sequence. Chimeras were detected with mothur's implementation of uchime, using each library as the reference set. The Gold reference database as an option in chimera. uchime (mothur. org) often works best for microbial communities that are well-studied, and it identified only about a third of the chimeric sequences found by using the P. umbilicalis pyrosequencing data as an internal reference to detect chimeras with the chimera.
uchime command  [57]. Sequences identified as being chimeric in the 12 samples in each dataset (V5V7: 39. 6%+/28. 4% [mean, SD]; V8V9: 33. 2%+/25. 5% [mean, SD]) were removed using the mothur pipeline. Curation of the V5V7 sequences to reduce pyrosequencing errors resulted in a dataset in which sequences had a mean length of 266 bases (range: 249-281). Sequences began near E. coli position 800  [58]  and ended near position 1066; this dataset thus begins in the conserved region preceding V5 and extends through variable region V6 to end in the conserved region forward of the V7; this dataset is referred to in the Results as the V5V6 dataset. Curation of the V8V9 sequences produced a dataset in which sequence mean length was 222 bases (range 197-232 bases) beginning near E. coli position 1190 and ending near position 1407.
Taxonomic classification, estimation of community richness and statistical analysis Sequences were classified using the Ribosomal Database Project's rdp9 reference set in the mothur pipeline at an 80% confidence level, which is regarded as 89% accurate in classification at the genus level  [59]. Operational taxonomic units (OTUs) were formed at 0. 03, 0. 05 and 0. 10 distances, which are generally considered to correspond to species-, genus-and family-level classifications, using the average neighbor algorithm in mothur. We present alpha and beta diversity metrics for V8 samples before and after normalization (i. e. , normalized in mothur to the V8 sample with the lowest number of sequences); however, we confine our analyses of V5V6 to taxon discovery in support of the V8 data (see Results and Discussion).
Thetayc (based on membership and abundance of OTUs) and Jaccard (based on membership) distance matrices were used in Analysis of Molecular Variance (AMOVA) to test whether the variation among fall, winter and lab normalized V8 samples was greater than variation between samples within each group. The level of significance for each experiment's AMOVA tests was set at 0. 0125 (4 tests in each experiment). Biplots of OTUs significantly (p-value #0. 05) associated with the axes of the non-metric dimensional scaling (nmds) ordination used to plot relationships of the 12 samples were plotted using the statistical computing package R (v. 2. 5. 1  [60] ), including the vegan package (v. 2. 0-5,  [61] ). Axis 1-axis 3 plots from a 3-dimensional nmds plot (R = 0. 99, stress = 0. 0399) are shown; calibrations with 3 abundant OTUs that were found only in winter or fall or on lab blades showed that OTUs significantly associated with axis 3 show affinity for the winter group; OTU vectors that point away from the axis 1 label have lab group affinities; OTU vectors that point toward the axis 1 label in the biplot graph have fall affinities.
We obtained relevant environmental data for the Maine coast close to our study site from a coastal GoMOOS environmental station (http://www. neracoos. org/gomoos; Accessed 2012 April 16) from October 1-31, 2010 (fall) and December 1-31, 2010 (winter). Photoperiod was estimated from the Eldridge Tide and Pilot Book 2012  [62]. Although our analysis of the fall and winter blades likely shows seasonal trends, we emphasize that an additional year of sampling in each season would be required to confirm them as seasonal differences, as opposed to two different time points, because for logistical reasons, sampling was performed only once per season.
Classification of the reads A total of 106,366 (V5V6) and 41,514 (V8) sequences (Table  1 ) were obtained for analysis from raw reads following curation that included denoising, trimming, identification and removal of chimeras (see Methods), and removal of a small number of chloroplast sequences. Sequences assigned to the Cyanobacteria_Chloroplast category by the rdp9 database in mothur were all identified as Porphyra umbilicalis chloroplast sequences (best BLAST hit, followed by hits to four other bangialean plastids) and removed (1. 1% [V5V6] and 0. 3% [V8-V9] of sequences before this final curation) from trimmed and chimera-checked sequences before tabulation of curated sequences (Table  1 ). Most of the curated sequences (V5V6: 74. 8%; V8: 90. 7%) were classified to a particular bacterial phylum and class (or lower taxon).
Acknowledgments We are grateful to members of the  Porphyra Algal Genomics' Research Collaboration Network  ( NSF 0741907 ) for discussion, and to two anonymous reviewers for their constructive comments. SHB thanks her instructors and all participants in the 2012 '' Strategies and Techniques for Analyzing Microbial Population Structures (STAMPS) '' course at the  Marine Biological Laboratory (Woods Hole, MA ),  Qiong Wang  (The  Ribosomal Database Project at Michigan State University ) for discussion of efficiency of the primers used in this study for recovery of TM7 and  Firmicutes ,  Alexandra Chouldechova  and  Maxell Grazier G' sell ( Statistics Dept. , Stanford University ) for help understanding biplots, and especially  Sarah Westcott  (the  Mothur Project, University of Michigan ) for discussion and advice for use of the mothur pipeline.
Despite the valuable contributions on self-adaptation, most implemented approaches assume adaptation goals and monitoring infrastructures as non-mutable, thus constraining their applicability to systems whose context awareness is restricted to static monitors. Therefore, separation of concerns, dynamic monitoring, and runtime requirements variability are critical for satisfying system goals under highly changing environments. In this chapter we present DYNAMICO, a reference model for engineering adaptive software that helps guaranteeing the coherence of (i) adaptation mechanisms with respect to changes in adaptation goals; and (ii) monitoring mechanisms with respect to changes in both adaptation goals and adaptation mechanisms. DYNAMICO improves the engineering of self-adaptive systems by addressing (i) the management of adaptation properties and goals as control objectives; (ii) the separation of concerns among feedback loops required to address control objectives over time; and (iii) the management of dynamic context as an independent control function to preserve context-awareness in the adaptation mechanism.
Hence, as context information requirements evolve over time, due not only to changes in the execution environment, but also to the evolution of the adaptive system and its requirements, monitoring infrastructures are also required to be self-adaptive. Furthermore, in these cases the adaptation of the monitoring infrastructure implies to update the context analyzer of the target system's adaptation mechanism. Therefore, these changes must be coordinated by an independent feedback loop, that is, the one that manages changing control objectives and adaptation goals at runtime, thus preserving context-awareness in the system evolution. In this chapter we present DYNAMICO (Dynamic Adaptive, Monitoring and Control Objectives model), a reference model for engineering context-based self-adaptive software composed of three types of feedback loops.
In addition, our characterization of the latter interactions allows our reference model to be applied partially, that is omitting any of its feedback loops, targeting self-adaptive systems where supporting changes in any of the three levels of dynamics is a crucial requirement. In light of this, we argue that, in order to regulate the satisfaction of adaptation goals and managed application's requirements continuously, (i) each of the feedback loop elements and their interactions must be independently analyzable; and (ii) the monitoring elements must be able to process the different kinds of information that the varying context can produce appropriately. DYNAMICO was inspired by classical control theory and the autonomic element proposed by IBM researchers  [15]. With this reference model we aim to contribute to the design of self-adaptive software by making its instances consider these aspects explicitly: (i) the achievement of adaptation goals and their usage as the reference control objectives; (ii) the separation of control concerns by decoupling the different feedback loops required to satisfy the reference objectives as context changes; and (iii) the specification of context management as an independent control function to preserve the contextual relevance with respect to internal and external context changes.
The remainder of this chapter is organized as follows. Section 2 describes an industrial-based application example that we use to explain our reference model and its application. In Sect. 3 we re-visit fundamental ideas and concepts that have shaped the engineering of self-adaptive software in the last years, and from which we distill our reference model. Section 4 presents our proposed reference model including the feedback loop interactions and their governance, as well as some variations that DYNAMICO admits. Finally, Sect(s). 5 and 6 discuss related work and conclude the chapter, respectively.