dataesr/scientific-paragraphs-categorization huggingface dataset:
Searchable Symmetric Encryption (SSE) schemes are a special case of STE that specializes for keyword search. In this setting, a data owner creates a data structure with efficient search support, such as an inverted index. Each document in the dataset is then encrypted, forming the Encrypted DataBase (EDB) and outsourced to an external search service. This enables performing queries on the dataset without revealing information about the dataset or the queries to the search service. The encrypted dataset consists of a list of document identifier and keyword-set pairs. Queries are performed using a search token, generated by the data owner, that allows the server to search through the index. A search query returns the document identifiers that satisfy the query expression. In general, an SSE scheme includes the following main algorithms  [35] ,  [42] : (Message Owner) pk = g sk G, g, q sk ∈ Z q u, s, d ∈ R Z q z = (info) a = g u , b = g s z d a, b ← --------------- t 1 , t 2 , t 3 , t 4 ∈ R Z q z = (info) α = ag t1 pk t2 , β = bg t3 z t4 ε = (α, β, z, m) e = ε -t 2 -t 4 mod q e --------------→ S = e -d mod q R = u -S • sk mod q R, S, s, d ← ---------------- ρ = R + t 1 mod q ω = S + t 2 mod q σ = s + t 3 mod q δ = d + t 4 mod q • (sk, ∆) ← Setup ← (1 κ , DB): Using a security parameter κ as input and a database DB, consisting of a list of document identifiers and keywords, it outputs the secret key sk and an encrypted data structure ∆ that will be outsourced to the data server.
Register (Λ, sk U , RD) ← Register ← (ID, sk P SP , spar): The purpose of this protocol is to add a new user to the system. The Register algorithm is presented in full notation in Figure  7. On the input of the PSP's private key sk P SP and the user's identifier ID, the algorithm outputs the user's private key sk U , user's access credential Λ and updates the PSP's revocation database RD. The algorithm is run within the Registration phase as an interactive protocol between the PSP and the user device. The system user is then able to require parking permits and access parking lots. The PSP inputs its private key sk P SP and the user inputs the identity ID. If the ID is valid, the protocol generates user's private key sk U ∈ R Z q and outputs the wBB signature Λ = g.
Issue (sk Cred , CRED) ← Issue ← (Λ, sk P LT , pk P LT , P D, spar): The parking permit is issued after the payment is done. The algorithm is run within the Issue parking permit phase between the user device and the PLT through the PSP. The Issue algorithm is presented in full notation in Figure  8. The algorithm inputs the user's access credential Λ, user's parking data P D, the PLT's secret key sk P LT , the PLT's public key pk P LT and system parameters spar. It outputs the parking permit secret key sk Cred and the parking permit CRED that consists of the following elements (ρ||ω||σ||δ|| Λ||ĝ||P D): • Λ: The user's access credential raised to a randomly chosen parking permit secret key sk Cred ∈ R Z q , i. e. Λ = Λ sk Cred. • ĝ1 : The generator raised to a randomly chosen parking permit secret key sk Cred ∈ R Z q , i.
1. • P D: The public parking data P D. The P D includes the PLT's identifier PLTid, parking time period time duration and information about extended parking time EPT. To sign data, the PLT uses its secret key sk P LT. • (ρ||ω||σ||δ): The PLT's signature on the user's partially blinded message, i. e. , blinded values Λ and ĝ, and the public parking data P D. First, the PLT commits to the public data P D by computing commitments a = g u 1 , b = g s 1 z d , where z = F(P D). Then, the user partially blinds the message. In particular, the user blinds the values Λ and ĝ and computes commitments α = ag t1 1 pk t2 P LT , β = bg t3 1 z t4 using the PLT's public key pk P LT , the PLT's commitments (a, b) and the public data z = F(P D). The user generates the hash ϵ on all these data, derives value e from ϵ, and sends it to the PLT.
Update (sk Cred , CRED) ← Update ← (sk U , sk Cred , Λ, CRED, pk P LT , pk P SP , sk P LT , EPT, spar): The purpose of this protocol is to extend parking time of a parking permit. The algorithm inputs the user's secret key sk U , the parking permit secret key sk Cred , the user's access credential Λ, the parking permit CRED, the PSP's public key pk P SP , the PLT's public key pk P LT , the PLT's secret key sk P LT , the extension parking time EPT, and system parameters spar. The algorithm is run in two steps. 1. (0/1) ← Verify ← (sk U , sk Cred , Λ, CRED, pk P LT , pk P SP , spar): The Verify algorithm is run first. The user specifies the parking permit CRED for extension parking time by sending PLTid and PPID information. The PLT finds corresponding parking permit CRED in its database and stars the Verify algorithm.
2. Non-traceable and unlinkable reservations: User's actions cannot be bound together by third parties. Proof. The PLT is receiving anonymous blinded user's access credential within the Issue parking permit phase and anonymous randomized user's access credential within the Parking vehicle phase. No personal or other linkable information is provided during these processes. Due to this fact, the PLT cannot bind any subsequent parking reservation requests of the user. In the same vein, as user's revocable data ( Λ|| Λ) are stored in the parking permit CRED and only provided to the PSP in case of a fraud attempt, neither the PSP can link user's reservations. The parking permit is always anonymous and unlinkable due to the zero-knowledge property of the proof of knowledge protocol. Distribution of Λ, Λ, ĝ1 is random and uniform in Z q as sk Cred is selected randomly and uniformly from Z q : Λ = Λ sk Cred Λ = Λ-sk U ĝ1 = g sk Cred 1 Lemma 5.
3. Fraud avoidance: A user cannot be falsely inquired about not completing a payment process. Proof. The Issue parking permit phase is run after the payment for parking is made. The parking permit CRED includes the paid parking time, so, any false accusation from the PLT can be denied. Lemma 5. 4. Non-repudiation and integrity: Evidences generated from entities interaction can be neither denied nor counterfeited. Proof. The PLT proofs its identity by signing the parking permit CRED, the PSP proofs its identity by signing the user's access credential Λ, and the user proofs the possession of a valid secret keys sk U and sk Cred within the Parking vehicle phase. As a result of the Issue parking permit phase, only the user obtains complete parking permit CRED, containing the parking reservation details P D and revocable data ( Λ|| Λ).
Experimental results In this section, we provide our experimental results. In particular, we show the efficiency of our proposal on Android devices. We use the Android phones: Honor 8X (chip: Kirin 810, OS: Android 10, RAM: 4 GB) and OnePlus Nord 5G (chip: Snapdragon 765G, OS: Android 11, RAM: 8 GB). In order to perform cryptographic operations, we use the MCL  [58]  ++ library (using C++17 version of the ISO/IEC 14882 standard) and Android Native Development Kit (NDK). The Android NDK allows us to execute a program in C/C++ on Android devices instead of using Java libraries, and therefore, to achieve better performance results. The source code of the Android application is available online on the GitLab repository 7. Our benchmark test on both phones for different arithmetic operations and fields is presented in Table  2.
• Parking end timestamp: Time when parking has ended and the user must leave the parking lot. • Parking transaction cost: The price that the user must pay for the parking time in the selected parking lot. Using these data items as search keywords, statistics can be extracted on parking spot demand, peak hours and availability. These statistics can facilitate decisions on pricing strategies and parking spots allocation and management, as well as possible custom offers and packages for specific companies/venues. The objective of our scheme is to support the following functional and efficiency requirements, additionally to the security and privacy requirements identified in Section 2. 4, as they are desired for the SSE scheme and appropriate for the parking scenario: • Multi-user functionality: Searching the dataset is possible for authorized third parties other than the data owner.
• Query expressiveness (boolean query support): Complex queries need to be supported to enable extracting useful statistics from the dataset. • Efficiency: The search functionality needs to be efficient and scalable, in order for the solution to be applicable in practice. We propose our MC-SSE scheme (for Multi-Client SSE scheme), which extends the efficient and expressive BIEX SSE scheme  [42]  with the multi-user functionality, not supported by the original BIEX scheme. An open source library of the BIEX SSE scheme, known as Clusion 8  , is publicly available, which is particularly attractive in the idea of proposing an extension with experimental results.
Our MC-SSE System Model and Overview The following entities are interacting in the data processing system for the parking scenario, as illustrated in Figure  12:  • The data owner (D): The data owner creates an encrypted dataset and outsources it. In our scenario the PLT acts as the data owner. • The storage and query server (S): They handle the encrypted dataset storage and performs queries on it. • Search clients (C): They are allowed to search on the encrypted dataset. The PSP, other PLTs in the parking system, or any other interested stakeholder can act as a search client. To achieve the multi-client extension of BIEX, the data owner provides Search clients an authorization token that enables them to create search tokens and submit queries to the Server limited by the keywords contained in the authorization token.
Refining the Security and Privacy Threat Model As considered in classical SSE schemes, the Server S is honest-but-curious, thus fulfilling the search tasks over the database correctly, but attempting to collect as much data as possible. The Clients C are malicious and the data owner D is honest. We only consider internal attackers as all the channels between any interacting entities -D-S, D-C and S-C -are authenticated. In the light of the system model of Section 6. 1, additionally to the security and privacy requirements identified in Section 2. document privacy, query privacy, access pattern privacy, the query authorization requirement is revisited the multi-client SSE scheme as follows: • Query authorization: Only authorized clients on authorized keywords are able to extract statistics from the server.
Our MC-SSE Algorithms The Multi-Client SSE (MC-SSE) scheme consists of the following algorithms: • (sk, EDX, EMM) ← Setup ← (κ, DB): Taking as input a security parameter κ and an index database DB, it outputs the secret key sk, an encrypted dictionary EDX, and an encrypted multi-map EMM. The algorithm is run by the data owner (D). • (Γ) ← Authorization token generation ← (sk, W auth ): Using as input the secret key sk and a vector of keywords W auth = (w 1 , • • • , w q ), for each keyword w i in the vector, it creates a sub-token Γ i = (gtk i , dtk i , ltk j ) containing a global token gtk i , a dictionary token dtk i and for all keywords w j , with 1 ≤ j ≤ q and j ̸ = i, a local token ltk j. The algorithm is run by the data owner (D) and outputs authorization token Γ = (Γ 1 ,. , Γ q ). • (γ) ← Search token generation ← (Γ, ∆): Using as input an authorization token Γ and a boolean query ∆, where ∆ is a query written in conjunctive normal form (CNF) as ∆ 1 ∧ • • • ∧ ∆ l , and where each ∆ i = w i,1 ∨ • • • ∨ w i,q is a disjunction, for the first disjunction ∆ 1 it calculates the disjunction IEX sub-token γ 1 as follows: for each keyword w 1,j in ∆ 1 except the last, it creates the sub-token γ 1,j = ( ḡtk 1,j , dtk 1,j , ltk k,j ) containing the global token ḡtk 1,j , the dictionary token dtk 1,j and for all keywords w 1,k , with j + 1 ≤ k ≤ q ′ , the local token ltk k,j.
Experimental results The MC-SSE evaluation consisted of experiments with up to 1 M documents, containing synthetic parking transactions, resulting in 21 M document-keyword pairs. Experiments were conducted on both the original BIEX scheme and the MC-SSE scheme for the same dataset. The source code of the Clusion BIEX library is available online on the GitHub repository 9  and the source code for the multi-client extension of the Clusion BIEX library is available on the on the GitHub repository  10. A docker container with the multi-client library extension bundled with a web application for testing its functionality is also available online on the Docker Hub repository In particular, as illustrated in Figure  13 , the efficiency of the search functionality for MC-SSE is consistent with the original BIEX performance, taking approximately 3-30 ms, to perform a boolean search over 1 M documents (21 M document-id pairs).
Note that the query expression includes 2 disjunctions (sub-queries) with 2 keywords each, of the form ((w∨x)∧(y∨z)), with the search time depending on the selectivity of the first disjunction ((w ∨ x)) of the query, i. e. , the number of documents returned by the first sub-query. The slightly smaller times in the MC-SSE search duration presented, mainly in higher values of the search times, is due to slight improvements in the Java code for the implementation of the search functions in the Clusion library. In the multi-client version of the scheme, the main difference is the creation of the authorization token Γ, which includes a superset of all the sub-tokens for the included keywords, hence being larger in size compared to the equivalent BIEX search token. The experimental evaluation for the overhead introduced by the authorization token Γ consisted of creating authorization tokens and BIEX tokens for the same keywords and measuring the creation time and serialized size of the resulting tokens.
For both types of tokens, the creation of tokens for keyword set sizes from N ∈ [1, • • • , 100] was evaluated, taking each time the N most frequent keywords in the dataset. As illustrated in Figure  14 , the creation of the authorization token Γ for the multi-client extension of BIEX, shows that the performance of the creation of the authorization token Γ displays the same general trend as the BIEX token creation, being exponential with the number of keywords in the token. Despite the creation time of the authorization token Γ being higher than the one of the 1,000 2,000 10,000 20,000 100,000 200,000 1,000,000 Selectivity of second disjunction (#) BIEX token, it is just a constant factor higher and remains reasonable in absolute terms. The increased creation time is expected, as the authorization token Γ includes approximately double the elements compared to the corresponding BIEX token for the same keyword set, as illustrated in Figure  15.
e, during parking reservations and vehicle parking actions). On the other hand, the system allows revocation and de-anonymization of malicious users committing fraud. To do so, more system entities, namely PSP, PLT, and IDP, must collaborate. The cryptographic core of our system is built on provable secure PETs technologies such as group and blind signatures. We provide both security analysis of our system and experimental results. Finally, we address the research question RQ3: How to allow third parties to perform statistical analyses on the parking transaction data, in a privacy preserving way? Which PET can be used to support this task? To do so, we deploy mechanisms for privacy-preserving data processing to our parking system. Completed parking transactions are stored in a dataset, containing only a subset of the data items concerning the transaction information, following the data minimization principle.
Using a Searchable Symmetric Encryption (SSE) scheme, this dataset is outsourced to an external search service and stored as a searchable encrypted dataset. The existing efficient and secure BIEX SSE scheme  [42] , with high query expressiveness support is extended to the multi-client setting, to allow for authorized parties to perform searches on the encrypted dataset. In this manner, queries can be submitted to the search server to produce statistics on the parking system usage. A security analysis is provided for the proposed solution and experimental results show the applicability and efficiency of the system. To our knowledge, our work in this paper is the first to consider both compliance to regulations (e. g. , GDPR) and privacy protection for parking solutions. Most existing solutions mainly focus on some particular technological aspects such as route planning or autonomous parking.
Acknowledgments This paper is partly supported in part by  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  830892 , project  SPARTA , and in part by the  Ministry of the Interior of the Czech Republic  under grant  VJ01030002. Author Florian Jacques has been partly supported by the project  VIADUCT  under the reference 7982 funded by  Service Public de Wallonie (SPW) ,  Belgium. The publication only reflects opinion of the authors. Experiments presented in Section 6 of this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including  CNRS ,  RENATER  and several Universities as well as other organizations (see https://www. grid5000. fr).
If a mutation was near a binding site and significantly shifted the energetic stability of the protein, it is likely that the mutation alters compound binding. Selection was determined using the toolkit made from the GISAID database 3  , for all SARS-CoV-2 genomes up to 2 January 2022. In Figure  2C , site selection in terms of fixedeffects likelihood (FEL) (Kosakovsky  Pond and Frost, 2005)  is displayed (blue and red stem plots), FEL is a measure of selection pressure in phylogenetic trees and is calculated by comparing the expected number of non-synonymous mutations with the actual observed rate. In short, observing a higher than expected frequency of non-synonymous mutations suggests positive selection, i. e. , evolutionary pressure for the protein to change. Observing fewer than expected non-synonymous mutations is evidence of negative purifying selection, whereby mutants are not likely to survive and reproduce.
Individual studies often use different assays and thus report different values. Additionally, the toxicity prediction software was not always successful, and therefore several different tools were used: the Quantitative Structure-Activity Relationship (QSAR) toolbox, developed by the Organization for Economic Cooperation and Development (OECD)  (Dimitrov et al. , 2016) ; the Toxicity Estimation Software Tool (TEST) software developed by the US Environmental Protection Agency (US-EPA)  (Martin et al. , 2008) ; and the lazar toxicity prediction web server (Maunz   , 2013). For some compounds, particularly pharmaceutical drugs, toxicity data was accessible from public documents for their approval by either the FDA or the European Medicines Agency. Many of the natural products included have long histories of use in food as well as herbal medicines  (Wang and Yang, 2020, 2021; Yang and Wang, 2021; Wang et al.
1 ). The first two steps comprise the Temporal Abstraction (TA) task, which involves the transformation of series of values indexed by time (i. e. ⟨0. 2, 8:00⟩, ⟨0. 2, 12:00⟩, ⟨0. 1, 13:00⟩, ⟨0. 2, 14:00⟩, ⟨0. 8, 15:00⟩, ⟨0. 7, 16:00⟩ into time intervals and the corresponding symbols that abstract the variable values along them (i. e. ⟨A,  [8:00, 13 :00]⟩, ⟨A, [13:00, number of samples in SAX, while in TA4L, intervals are constructed based on a certain duration. This fact plays an important role when the dataset has missing values and could eventually lead to losing some TIRPs. Conversely, TA4L split input data into segments of equal time duration that later are concatenated. Such pre-processing achieves a more reliable time representation of the variables inside intervals and provides a solid basis for forthcoming TIRP mining.
• Use of special data structure that allows performing intervals insertions into LSTISs efficiently. • Different approaches to apply parallelism to the process. To sum up, TA4L converts multivariate time series into LSTISs, and it is intended to be used as a preprocessing algorithm in the TIRPs mining field. The code of the TA4L algorithm will be publicly available from the Bitbucket repository (see Appendix A). This paper is organised as follows. In Section 2, we provide some background and review related work on pre-processing methods for TIRP mining algorithms. In Section 3, we formulate the pre-processing problem. In Section 4, the novel TA4L algorithm is introduced. In Section 5, we give a detailed description of the datasets and the setup used in our experiments, and in Section 6, the results and a discussion about them are presented.
The pre-processing in the TIRPs mining field If we analyse the literature on TIRP mining, we will find that pre-processing is tightly related to the kind of data used to test the algorithms. When using synthetic datasets (as in  [2, 6, 7] ), data are usually generated with a synthetic data generator like  [8] , which have been modified to generate sorted symbolic time-intervals, and the pre-processing, in this case, is nonexistent. On the other hand, when using real datasets as in  [3, 4, 9, 10] , or on both synthetic and real datasets (as in the present work)  [11] [12] [13] , we can find different approaches to pre-processing to convert multivariate time series into LSTISs: internal  [14] [15] [16]  or external  [11] [12] [13]  to the TIRP mining algorithm. In general, the different steps followed in the literature are summarised in Fig.
TA4L_se_paral_var Simple list Sorts at the end and parallelism applied to variables (third image of the Fig. 7 ) • Z-normalisation • Framing using a given amount of samples w • Discretisation using the SAX approach • Sequence generation with concatenation, without any gap constraint (i. e. max_gap = ∞) The state of the art approach is labelled as the Baseline in the experiments. Is it possible to observe that these configurations are close to the SAX approach; however, in a sensitivity analysis, we also test the discretisation approaches of EWD and KBTA, therefore covering a broad representation of the currently available best approaches. All of the experiments were carried out on a virtual machine, Lubuntu 19. 10, 64 bits, eight CPUs, and 12 GB of RAM. The experiments were conducted with both synthetic and real datasets under different scenarios, as described below.
Synthetic datasets To better explore the efficiency of our algorithm, we generated numerous synthetic datasets in which the following factors were considered: (i) Percentage of missing values (PM); (ii) Number of variables (n); (iii) Number of sequences (q); (iv) Number of samples per sequence (n sid ). The Vocabulary size (|Σ|) and Interval size (δ) are the parameters of our algorithm. The synthetic datasets have been created as follows. Given n, n sid , and q, a dataset with this shape is created. A random part of columns in the dataset are decimal, and the rest are numeric. Then, each column from the dataset is populated with random samples from a uniform distribution over [0, 1), in the case of decimal columns; and from the ''discrete uniform'' distribution in the ''half-open'' interval [low, high) for integer columns.
In the case of integer columns, the low value was set to 0, while a high value was chosen randomly from a range (0,1000000). Finally, in the same way, a filter to delete the desired percentage of missing values for each column was applied. In addition, synthetic datasets with different autocorrelations have been generated, and additional experiments to test the behaviour of the algorithm in the function of the autocorrelation of time series was performed. In this regard, no significant changes have been detected regarding the algorithm performance. For more details, address Appendix C. We experimented with values for PM of 1%.