dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, it is used as a relay node between the smart thing and the data consumer. It also conducts the management of remote access control to smart things. 4) Smart things are resource-constrained devices that constitute the control system network. These devices are deployed in an area of interest and remotely controlled by data consumers. 5) Data consumers are the users who aim to communicate with IoT devices and execute remote actions on them. To decrypt a message, data consumers have not only to possess the set of attributes that satisfy the access structure but also to meet the contextual requirements related to time, location, situation sensitivity (normal/emergency), and data type (sensitive, nonsensitive).
B. Security Model To ensure the security of a CP-ABE scheme, the system should be indistinguishable against chosen plaintext attacks (IND-CPA). The details of the security model of CP-ABE can be found in  [22]. The proposed CAABAC can guarantee context-aware finegrained access control only when a user who satisfies the attribute set can access the data under a specific context. The proposed scheme is considered as compromised if data consumers satisfying the following conditions can decrypt the ciphertext: 1) A user whose attribute set does not match the access policy and 2) A user who does not meet the context requirements can gain access privileges, even if he/she has the proper attribute set. In CAABAC, we assume that AA and KGC are semi-trusted entities. Namely, they are honest-but-curious; meaning they can honestly execute the assigned tasks, but they will try to disclose as much sensitive content as possible.
The CM is considered fully trusted, and CM is managed by local administrators who aim to secure the communication between the communicated parties. The owner data is encrypted by both the IoT gateway and smart things. On the one hand, the IoT gateway is assumed to be fully trusted, and it cooperates with IoT devices to encrypt data and define access policies according to an attribute set and the contextual information. On the other hand, smart things are resource-constrained devices that may be easily compromised. In this article, we assume that smart things are available and not compromised. In fact, they can effectively perform symmetric encryption of the data. Data consumers are dishonest. We assume they try to decrypt data beyond the data for which they have access rights, so a malicious or unauthorized user may collude with other users to access data beyond his/her privileges.
C. Security Proof In this section, we perform formal security analysis. At first, we present the security model through a game between a challenger B and an adversary A. Then, we prove the data confidentiality of the proposed CAABAC scheme. 1) Security Model: 1) Setup: The challenger runs the initialization algorithm of CAABAC to generate public parameters PK, the master key MK and the context-related keys {δ c j , PK c j , F c j }. Then it gives public parameters to the adversary A. 2) Phase 1: The adversary can issue queries for a private key of a set of attributes S u , a set of contextual parameters c j , j ∈ [1, N], and a challenge access policy T, where S u does not satisfy T for the given context. The challenger generates the private key associated with S u and a series of access tokens that represent the context, and then gives the secret key to the adversary.
Furthermore, compared  to existing schemes  [17] ,  [20]  implementing CP-ABE, our scheme solves the key escrow problem. 2) Storage Overhead: The storage overhead is related to the size of users' secret keys. In the CAABAC scheme, the data consumer should store {SK u ={ D = g α h r , L = g r , D i = H 1 (att i ) r , ∀ att i ∈ S}}, w h o s es i z ei s(|att|+2)|G 1 | , where |att| is the cardinality of the attribute set. As shown in Table  IV , the data consumer in the CAABAC scheme has less storage overhead compared to existing schemes  [17] ,  [20]  using the CP-ABE algorithm. 3) Communication Overhead: The communication costs of the proposed scheme and the different benchmark schemes are compared in Table  V. The ciphertext is stored in the IoT gateway and transmitted to data consumers when requested.
Therefore, the communication overhead is mainly associated with the size of the encrypted data. In this analysis, we consider the transmitted messages between the data consumer, the IoT gateway, and the smart thing. In fact, the gateway has to forward to the data consumer the message ID i , TK, T e , AES(K s , M)> whose size is |T|+ | Č|+|C|+|C ′ x |+|T c j x |+|C x |+|σ |+|r|+|ID i |+|TK|+ |T e |+|AES(K s , M)|. We assume as  [17]  that |ID i |, |T e |, and |T| have 1-byte, 1-byte, and 4-bytes, respectively. In addition, the IoT gateway has to send to the smart thing the message <ID i , TK, T e > whose size is |ID i |+| TK|+| T e |. T h e smart thing has only to send to the IoT gateway the encrypted challenge M with a symmetric key, so, the message size is |AES(K s , M)|. As illustrated in Fig.
A. E-CAABAC Scheme Description In this construction, we assume that the contextual tokens are appended to arbitrary nodes. As shown in Fig. 6 , we denote T loc , T t the contextual tokens related to location and time, respectively. For instance, at location loc 1 , a user whose attribute set satisfies a subpolicy P 1 can access data at time t 1. A subpolicy can be either a single attribute or a set of multiple nodes. In addition, a user whose attributes satisfy P 1 and P 2 at loc 1 can get access privilege at time t 2 , while a data consumer whose attribute set only verifies P 3 cannot satisfy the policy until he/she reaches time t 3. The modification of the basic CAABAC construction occurs during the encryption and decryption phases. We give a detailed description of modified in Algorithms 5 and 6.
28. F x att = z∈S x F i,S ′ x (0) z = z∈S x (e(g, g) rβq z( 0) ) i,S ′ x (0) = z∈S x e(g, g) rβq x( i) i,S ′ x (0) = e(g, g) rβq x 1 where i = index(z)a n dS ′ x = {index(z):z ∈ S x } 29. Return root). The smart thing encrypts the challenge M using a symmetric key K s , then the IoT gateway performs the following steps to encrypt K s on the basis of CP-ABE scheme. 2) Decryption Algorithm: In this phase, we present the decryption algorithm for the case where the contextual information may be appended to arbitrary nodes. To decrypt a ciphertext, a data consumer has to verify not only the set of attributes but also the contextual information.
The modeling of the Micro-CHP unit operating in dual-fuel mode is performed based on experimental results carried out at the laboratory scale. The engine tests were performed on an AVL engine, with a maximum power of 3. 5 kW, using conventional diesel as pilot fuel and synthetic biogas as primary fuel. The biogas flow rate is evaluated using the experimental results from the literature, based on the anaerobic digestion in batch reactor of a mixture of 26 % of Oat Straw and 74 % of Cow Manure, diluted to contain only 4 % of volatile solid. The engine operation was modeled using the Artificial Neuron Network (ANN) method. Experimental engine tests were used as a database for training and validation phases of ANN models. Three different ANN models are developed to model respectively the pilot fuel flow rate, the airflow rate and the exhaust gas temperature.
Dual-fuel engine setup The engine tests were carried out on a single cylinder research engine (AVL 5402), instrumented to control and measure the operating parameters. The main design specifications as well as the technical operating data are given in Table  1. Under the same operating conditions, including air-fuel ratio, engine speed and engine loads, the performance parameters of this engine, despite being an water-cooled single-cylinder diesel engine, can be projected on a multi-cylinder engine that can be used in a CHP unit. During the tests, the engine speed was kept constant at 1500 rpm, similar on to that of electric power generator, producing electricity at a frequency of 50 Hz. All tests were carried out to develop an engine power ranging between 1. 75 and 3. 5 kW. The pilot fuel is a conventional diesel, while the primary fuel is a synthetic biogas, consisting of CH4 and CO2.
The experimental procedures consists in fixing the biogas methane content and the pilot fuel flow rate and maintaining the desired engine load acting on the biogas flow rate. It consists in varying the CH4 content of biogas between 20 and 60 % and the engine load between 50 and 100 % (i. e. engine power outlet between 1. 75 and 3. 5 kW). The recorded data from each test are the engine power, biogas methane content, biogas flow rate, pilot fuel flow rate, airflow rate, and exhaust gas temperature.
Engine parameters modeling The engine parameters have been modeled using methodology based on Artificial Neural Networks. An ANN is an architecture containing a huge quantity of neurons systematized in different layers and the neurons of one layer are linked to those of another layer of by dint of weights, and it can be prepared or trained to accomplish a specific duty via creating accurate alteration of its linking weights, bias and architecture  [43, 44]. In this study, ANN based models have been developed in the MATLAB environment using the Neural Network toolbox. The proposed ANN model consist in three discrete ANNs, developed to estimate engine parameters namely pilot fuel flow rate, intake airflow rate and the exhaust gas temperature. Where, ANN1 is used to delineate pilot fuel flow rate, ANN2 is used to delineate intake airflow rate, and ANN3 is used to delineate exhaust gas temperature.
Sequence data, curation and alignment Sequence data were obtained from the National Center for Biotechnology Information (NCBI) nucleotide database on the 5 July 2019, using the search term 'coronavir*'. This resulted in the identification of 30 249 sequences. A preliminary set of representative partial RdRp sequences was compiled with reference to recent publications describing coronavirus diversity across the subfamily Orthocoronavirinae  [27] , in order to include starting reference sequences with the largest possible diversity of coronaviruses. This preliminary list was then used to identify partial RdRp sequences from retrieved NCBI records by annotating regions that had at least 70 % identity to any reference sequence in the Geneious software package (version 9. 4. 1). Annotated regions and 200 bp of flanking sequence data were then extracted.
Data containing incomplete sequences in the form of strings of Ns or significant numbers of ambiguities (>5) were removed. Open reading frames with a minimum length of 300 bp were identified and extracted from the remaining sequences. In the case where the correct reading frame was ambiguous, pairwise alignment to reference sequence data was used to determine reading frame. The remaining sequences were then aligned in-frame using the multiple alignment program for amino acid or nucleotide sequences (MAFFT)  [28] , and the resulting alignment was further curated by visual inspection. Retained sequences were then trimmed to include only the most frequently sequenced partial region of RdRp and so that each sequence contained a minimum of 300 gap-free bases. The final alignment was 387 bp in length with 7544 individual sequences, of which 3155 were unique.
Genetic analyses Phylogenies were inferred from all unique sequences using the beast 2 software  [29]. Parameters were estimated for a GTR substitution model with four gamma categories and an estimated proportion of invariant sites. The Yule population model was used, and a log-normal distribution of priors was specified for birth rate and proportion of invariant site priors. Convergence of estimated parameters was assessed in Tracer v1. 7. 1  [30]. Three independent MCMC chains were run until effective sample sizes were above 200 for all estimated parameters after removing MCMC chains from the start of the analysis prior to convergence (burnin). Analyses were run until convergence criteria could be fulfilled whilst providing equal chain lengths after burn-in for all three repeats, meaning that the number of trees in the posterior distributions was the same for each independent repeat.
Recombination analysis To explore the extent to which ancestral recombination events confound our ability to classify coronaviruses at the subgenus level from a single gene locus, sequence accession numbers were identified from the pool of 7544 sequences analysed above that possessed data for both RdRp and structural spike gene regions. This search resulted in the identification of 2649 sequences. Due to sequence heterogeneity in the spike region, sequences were grouped and compared by genus based on the predicted classification obtained using the RdRp locus. The full-length spike gene was extracted, aligned in-frame using MAFFT, and maximum-likelihood phylogenies were generated for each genus using mega X removing all gapcontaining sites. Tanglegram representations were used to compare tree topologies obtained using data from the two different loci.
R package for assignment of user-generated sequences The purpose of the R package MyCoV is to allow users to classify coronavirus sequence data that include the relevant portion of the RdRp gene to the taxonomic level of subgenus, and to assess to what extent the classification is optimally based on the criteria presented herein. In order to achieve this, the 3155 unique partial sequences from the phylogenetic analyses were used to establish a reference blast database. Metadata pertaining to host organism, country of origin and date of collection were mined from NCBI and standardized by taxonomic grouping of the host and geographical region of origin to generate corresponding metadata for all 7544 NCBI reference sequences from which the unique sequence list was established. MyCoV was written as a basic wrapper script for blastn, which queries sequences of interest against the established database, and summarizes subgenus classification, subgenus posterior support of the most similar sequence in the phylogenetic analysis, pairwise distances to the most similar sequence in the database and their metadata using R packages 'ggplot2' , 'formattable' (available at https:// github.
com/ renkun-ken/ formattable) and 'ggtree'  [33]. As the MyCoV database was established prior to the recent emergence of the SARS-CoV-2, we used genomic sequence data from this virus as a test case for the utility of the MyCoV package. Outputs from this analysis are shown in Fig. 3. MyCoV is available at https:// github. com/ dw974/ MyCoV.
9 % identity, resulting in 98. 8 % precision and 99. 2 % accuracy of classification for subgenera of the deltacoronaviruses; and Fig. 3. MyCoV output plots for the analysis of the 2019nCoV. (a) Tabular output of best blastn hit of the query sequence to the reference database. The predicted subgenus and genus of the best-matching hit are displayed, as well as the posterior support for the assignment to the predicted subgenus (see the Methods section). Pairwise identity between the two sequences is shown and is calculated relative to the maximum possible alignment length against the reference sequences (387 bp). (b) For each queried sequence, pairwise identity values are mapped to all observations from pairwise comparisons between sequences in the database. The vertical dashed line represents the pairwise dissimilarity of the queried sequence.
An alternative strategy for coronavirus classification from partial sequence data may be using the spike proteinencoding S-gene, which our analyses demonstrate would provide identical subgenus classifications in the vast majority of cases. However, the use of this region is more common in epidemic outbreak scenarios and thus there are many S gene sequences in public databases that are either identical or extremely closely related. Performing comparative sequence searches by querying the NCBI nucleotide database with the two search terms '((coronavir* spike) OR (coronavir* S gene)) AND 'viruses'[porgn:__ txid10239]' and '((coronavir* RdRp) OR (coronavir* polymerase)) AND 'viruses'[porgn:__txid10239]' shows that there are approximately three times more sequences from the S gene, but that these sequences originate from approximately three times fewer viral taxa.
Instrument Development During the 2-week instrument development period, we recommended and facilitated that students create the DMIs using the Bela board hardware and the Pure Data software; other resources could be added, if needed. We also strongly encouraged the students to use Bela with batteries and an integrated loudspeaker in order to be able to create small and autonomous instruments. The primary expectation, however, was for each group to manufacture its child's described instrument as accurately as possible. The instrument interface building took place in the campus prototyping lab that provided materials and technologies such as a laser cutter for wood, a soldering station, a sewing machine, and a 3D printer. Three interfaces were assembled from the ground up, while two were reengineered from existing toys (see, e.
2 Problem Definition Numerical dataset. A numerical dataset (G, M, T ) is given by a set of objects G, a set of numerical attributes M and a numerical target label T. In a given dataset, the domain of any attribute m ∈ M (resp. label T ) is a finite ordered set denoted D m (resp. D T ). Fig. 1  (left) provides a numerical dataset made of two attributes M = {m 1 , m 2 } and a target label T. A subgroup p is defined by a pattern, i. e. , its intent or description, and the set of objects from the dataset where it appears, i. e. , its extent, denoted ext(p). For instance, in Fig. 1 , the domain of m 1 is {1, 2, 3, 4} and the intent  [2, 4] ,  [1, 3]  (see the definition of interval patterns later) denotes a subgroup whose extent is {g 3 , g 4 , g 5 , g 6 }. Quality measure, optimal subgroup. The interestingness of a subgroup in a numerical dataset is measured by a numerical value.
We consider here the quality measure based on the mean introduced in  [14]. Let p be a subgroup. The quality of p is given by: q a mean (p ) = |ext(p)| a × (µ ext(p) -µ ext(∅) ), a ∈ [0, 1]. |ext(p)| denotes the cardinality of ext(p), µ ext(p) is the mean of the target label in the extent of p, µ ext(∅) is the mean of the target label in the overall dataset, and a is a parameter that controls the number of objects of the subgroups. Let (G, M, T ) be a numerical dataset, q a quality measure and P the set of all subgroups of (G, M, T ). A subgroup p ∈ P is said to be optimal iff ∀p ∈ P : q(p ) ≤ q(p). Plant growth recipe and optimization measure. A plant growth recipe (M, P, T) is given by a set of numerical parameters M specifying the growing conditions thanks to intervals on numerical values, a numerical value P representing the number of stages of the growth cycle, and a numerical target label T to quantify the recipe quality.
An Efficient Algorithm for Optimal Subgroup Discovery Let us first introduce MinIntChange4SD, our branch and bound algorithm for the optimal subgroup discovery in purely numerical data. It exploits smart concepts about interval patterns from  [11]. Interval patterns, extent and closure. In a numerical dataset (G, M, T ), an interval pattern p is a vector of intervals p = [a i , b i ] i∈{1,. ,|M |} with a i , b i ∈ D mi , where each interval is a restriction on an attribute of M , and |M | is the number of attributes. Let g ∈ G be an object. g is in the extent of an interval pattern p = [a i , b i ] i∈{1,. ,|M |} iff ∀i ∈ {1,. , |M |}, m i (g) ∈ [a i , b i ]. Let p 1 and p 2 be two interval patterns. p 1 ⊆ p 2 means that p 2 encloses p 1 , i. e. , the hyperrectangle of p 1 is included in that of p 2.
It is said that p 1 is a specialization of p 2. Let p be an interval pattern and ext(p) its extent. p is defined as closed if and only if it is the most restrictive pattern (i. e. , the smallest hyper-rectangle) that contains ext(p). Traversing the search space with minimal changes. To guarantee the optimal subgroup discovery, we proceed to the so-called minimal changes introduced in MinIntChange. It enables an exhaustive enumeration within the interval pattern search space. A left minimal change consists in replacing the left bound of an interval by the current value closest higher value in the domain of the corresponding attribute. Similarly, a right minimal change consists in replacing the right bound by the current value closest lower value. The search starts with the computation of the minimal interval pattern that covers all the objects of the dataset.
The premise is to apply consecutive right or left minimal changes until obtaining an interval whose left and right bounds have the same value for each interval of the minimal interval pattern. In that case, the algorithm backtracks until it finds a pattern on which a minimal change can be applied. Fig. 1  (right) depicts the depth-first traversal of attribute m 2 from the dataset of Fig. 1  (left) using minimal changes. Compressing and pruning the search space We leverage the concept of closure to significantly reduce the number of candidate interval patterns. After a minimal change and instead of evaluating the resulting interval pattern, we compute its corresponding closed interval pattern. We exploit advanced pruning techniques to reduce the size of the search space thanks to the use of a tight optimistic estimate.
Experiments We work on urban farm recipe optimization while we do not have access to real farming data yet. One of our partners in the FUI DUF 4. 0 project (2018-2021) is designing new types of urban farms. We found a way to support the empirical study of our recipe optimizing framework thanks to inexpensive experiments enabled by a simulator. In an urban farm, plants grow in a controlled environment. In the absence of failure, recipe instructions are followed and we can investigate the optimization of the plant yield at the end of the growth cycle. We simulate recipe experiments by using the PCSE 2  simulation environment by setting the characteristics (e. g. , the climate) of the different growth stages. We focus on 3 variables that set the amount of solar irradiation (range [0,25000]), wind (range [0,30]) and rain (range [0,40]).
MinIntChange4SD vs SD-Map * We study the description of the best subgroup returned by MinIntChange4SD and SD-Map * , the state-of-the art algorithm for subgroup discovery in numerical data. Table  2  depicts the descriptions for a dataset comprised of 30 recipes generated randomly with the simulator. Besides the higher quality of the subgroup returned by MinIntChange4SD, the optimal subgroup description also enables to extract information that is missing from the description obtained with SD-Map *. In fact, where SD-Map * only offers a strong restriction on 3 attributes, MinIntChange4SD provides actionable information on all the considered attributes, i. e. , the 9 attributes. This confirms its qualitative superiority over SD-Map * which has to proceed to attribute discretizations.
Conclusion We investigated the optimization of plant growth recipes in controlled environments, a key process in connected urban farms. We motivated the reasons why existing methods fall short of real life constraints, including the necessity to minimize the number of experiments needed to provide good results. We detailed a new optimization framework that leverages subgroup discovery to iteratively find better growth recipes through the use of a virtuous circle. We also introduced an efficient algorithm for the optimal subgroup discovery in purely numerical datasets. It has been recently improved much further in  [17]. We avoid discretization and it provides a qualitative added-value (i. e. , more interesting optimal subgroups). Future work includes extending our framework to deal with multiple target labels at the same time (e.
Comparison between descriptions of the overall dataset (DS), the optimal subgroup returned by MinIntChange4SD (MIC4SD), the optimal subgroup returned by SD-Map *. "-" means no restriction on the attribute compared to DS, Q and S respectively the quality and size of the subgroup. Subgroup Rain P 1 Irrad P 1 Wind P 1 Rain P 2 Irrad P 2 Wind P 2 Rain P 3.