dataesr/scientific-paragraphs-categorization huggingface dataset:
• Polynomial kernel: K(u,v) = tanh(gamma * u v + coeff ), • RBF kernel: K(u,v) = exp (-gamma * | | u-v| | 2 ), • Sigmoid kernel: K(u,v) = (gamma * u v + coeff ) d. We also tested several types of distance measurements (Euclidean, Euclidean squared, Manhattan, and Chebyshev) and several K values for kNNs. The random forest algorithm has been tested with different numbers of trees. The tuning of parameters for all these three methods were determined via grid search optimization on each of the training sets, as implemented in the MATLAB toolboxes.
Classification Accuracy Since there was a problem of acquisition with cardiac or ocular sensors for 5 participants, only the data of 17 participants were retained to achieve the supervised learning of physiological states. After raw data was cleansing and normalization, mean values of pupillary diameter and HRV were calculated every second. The supervised learning was carried out with 2 or 3 categories, at two different layers, one on all participants and one on each participant independently. For the first classification (all participants), the learning was achieved from the data of 13 participants, and the data of the 4 left participants were used as a test sample to check the accuracy of the process. For the second one (for each participant), 75% of the data was used as training   sample, and the resulting classification was then tested on the remaining 25% of the data.
It is impossible to reverse or change the hash. If a single change is made to the input, then the hash is generated completely differently. In order for a malicious node to corrupt the data in the network it must change the data stored in the ledger on every node. This is highly complex if the network consists of millions of nodes and each node has the same digital copy of the ledger. Each transaction in a blockchain network is stored and a hash of the block is recorded in the next block to trace the transaction and ensure transparency. The main existing privacy-preserving strategies and solutions for blockchain are identified in this paper, to provide insight into the different cryptographic primitives and privacy-preserving approaches, methods, and techniques used in blockchain. This paper proposes an efficient, secure, decentralized Conditional Privacy-Preserving and Authentication (CPPA) scheme for IoV networks.
The proposed scheme is based on Hyperledger Fabric for the selection of leaders in the consensus algorithm. It also provides traceability and anonymity ensure that authorities can trace the vehicle nodes in case of disputes. Hyperledger Fabric is an open source blockchain developed by Linux foundation. Hyperledger is a permissioned blockchain technology in which all participants are identified and authenticated. Hyperledger allows the execution of smart contracts which are called chaincodes. Most importantly, it ensures privacy by facilitating confidential transactions. The main contributions of this paper are as follows: • The proposed scheme handles multiple transactions at once and provides scalability by using blockchain technology.
These types of solutions are not feasible for IoV networks. The authors in  [24]  presented a hybrid method for anomaly detection using metaheuristic methods for high speed networks. The hybrid method uses large scale datasets and detector generation based on multi-start metaheuristic and genetic methods. The proposed method achieved accuracy of 96. 1% with machine learning algorithms. However, this method was designed for fixed networks and is not feasible for ad hoc networks such as IoV.
For the state database, we use the Level DB because we prioritize speed. All the peer nodes on the channels are required to utilize the same state database (CouchDB or Level DB). To maintain anonymity and isolation for such transactions, channels are deployed depending on the geographical area. After the CA has been configured, it can be utilized to register and enroll vehicles. The administrator of the CA assigns a username and password for the vehicle in the first stage. The vehicles are also granted roles and associations. It now builds a directory known as an MSP, which includes the public certificate of the CA granting the certificate in addition to the CA's root of trust. The vehicle is registered and enrolled in both an 'Enrollment CA' and a 'TLS CA', much like an admin identity. The CA assigns the function of orderer or peer, rather than admin, when registering the vehicle.
Registration and Enrollment In the registration and enrollment phase, when any vehicle wants to connect to the network for the first time, it requires registration from the CA. The CA generates a public/secret key-pair and sends credentials to the vehicle through TLS after verifying the information's identity. The CA stores public keys on its database. This database can be checked to determine if a car is registered in the network by looking up the public key of the vehicle in the database. The association between the public key (pk) and the vehicle's identification details is known only to the CA. Any vehicle in each area can register in the same regions in the intermediate CA. The Fabric CA automatically functions as an Idemix issuer. When the CA is started with the "init" command, two files are generated in the CA's home directory: "IssuerPublicKey" and "IssuerRevocationPublicKey".
Experiment Setup and Results Hyperledger Fabric was selected for the implementation of blockchain-based IoV. The designed network consists of three categories including peer nodes, ordering services, and law enforcement departments. First, we selected the database for Fabric, and selected the Level database (DB) for the state database. After selecting the database and organizations, the dual-headed certificate was adopted as the authority that involves two CAs. One TLS CA is responsible for secure communications and generating TLS certificates. The other CA is an organizational CA, which is responsible for generating the admin certificates of an organization. After deployment of CAs, the channels are deployed and configured for the privacy of transactions, so that members on the other channels cannot access the transactions.
The use of firewalls is also necessary for the deployment of IoV because nodes that belong to an organization can require access to other organizations; thus, there is a need for the configuration of advanced networking. The docker is deployed for peer nodes and other entities on the laptop. Then, the volumes are mounted for external entities where the entities are placed. Due to limited space, we used one channel. Nonetheless, the resources must be monitored to ensure that there is sufficient space for the blockchain and the database. In Hyperledger Fabric, CA is the first entity that must be deployed because the certificates of the nodes must be created before creation of the nodes themselves to identify who is the admin of this node. The dual-headed CA is used based on different geographical locations.
However, this consensus scheme faces limited scalability. The authors in [18] proposed a reputation-based data sharing scheme using a subjective logic model to improve data integrity and provide a secure data exchanging system in vehicular communication. In this paper, proof of work is utilized for exchanging infor-Acknowledgments: The authors extend their appreciation to the  Deanship of Scientific Research at King Khalid University  for funding this work through  Research Groups  under grant number ( R. G. P. 1/127/42 ).
Accurate evaluations of the available and technically exploitable wave energy resources are fundamental to optimise the design and implementation of energy converters in the marine environment. However, long-term resource assessments have been primarily conducted for largescale devices in offshore energetic locations, thus ignoring onshore sites such as harbours with easier access, installation and accessibility to devices. Here, we conducted a ten-year evaluation of the performance of wave energy converters (WECs) off Roscoff harbour (northern Brittany, France). As the site of application shows moderately energetic conditions, particular attention was dedicated to small-scale WECs by adapting ratings to the local wave climate. This investigation combined (i) a high-spatial resolution (~5 m) hindcast database established with SWAN with (ii) generic and specific assessments of WEC performance.
Thus, wave energy resource assessments near harbours have relied on hindcast simulations with phase-averaged models and spatial resolutions reaching values over one hundred metres, thus poorly adapted to the dimensions of maritime structures. Contestabile et al. [5]  characterised the potential of the OBREC in different harbours of southwestern Australia by relying on MIKE 21 SW simulations with spatial resolutions of 150 m. More recently, Lastiri et al. [15]  built a 44-year wave hindcast database with SWAN (Simulating Wave Nearshore)  [16]  to assess energy spatiotemporal variability in the surroundings of Bayonne Harbour (Bay of Biscay, France) with a minimum onshore spatial resolution of 150 m. Another investigation was conducted by Cascajo et al. [17]  in the surroundings of the Port of Valencia (Spain) by exploiting WAM large-scale embedded simulations of the wave climate over a seven-year period.
The present investigation complements these different characterisations of the available wave energy resource in harbour areas with an advanced evaluation based on (i) embedded high-resolution numerical simulations of the wave climate and (ii) refined assessments of WEC performance. Thus, meeting the IEC standards, we established a ten-year wave hindcast database in the vicinity of the harbour breakwater with the SWAN wave model set up at a spatial resolution of 5 m. As exhibited before, more accurate resource characterisations may be reached by extending the period of simulations to 20-30 years. However, given the refined spatial resolutions reached near the breakwater, this investigation is a step forward in conducting long-term simulations of the wave climate with higher computational resources in harbour areas.
This hindcast database was therefore exploited to characterise the annual and seasonal variability of the available resource in the vicinity of the harbour. As technological characteristics of WECs were mainly available for offshore systems designed for energetic locations, device performances were first investigated with an original approach combining a generic evaluation independent from the technology. This generic assessment was then complemented by a specific evaluation based on scaled power matrices adapted to the local wave climate and derived from the Oyster technology  [18, 19]. The site of application is the harbour of Roscoff-Bloscon in the northern part of western Brittany (France) (Figure  1 ). This harbour, which welcomes fishing, yachting and freight transport activities, is the most important in France for maritime traffic to Ireland, hosting a ferry terminal for Brittany and Irish Ferries.
After a comparison between model predictions and in situ measurements (Section 4. 1), Section 4. 2 discusses the spatiotemporal variability of the available resource off the harbour breakwater based on exploitation of the ten-year hindcast database derived from SWAN simulations. Finally, Section 4. 3 describes the generic and specific assessments of WEC performance. Beyond the comparison of indicators such as the capacity factor, particular attention is dedicated to the annual and seasonal variability in the performance of small-scale devices. The present paper is organised as follows. Section 2 describes the study site including its environmental and physical characteristics. Section 3 successively presents the wave model, the available in situ measurements exploited to assess predictions, performance indicators, and wave energy metrics retained to evaluate the model performance and the temporal variability of the available resource.
After a comparison between model predictions and in situ measurements (Section 4. 1), Section 4. 2 discusses the spatiotemporal variability of the available resource off the harbour breakwater based on exploitation of the ten-year hindcast database derived from SWAN simulations. Finally, Section 4. 3 describes the generic and specific assessments of WEC performance. Beyond the comparison of indicators such as the capacity factor, particular attention is dedicated to the annual and seasonal variability in the performance of small-scale devices.
In spite of the shelter provided by the isle of Batz, the breakwater is exposed to northwesterly and northeasterly waves. Thus, the harbour is subjected to a twin exposure to (i) swell from the Atlantic Ocean refracted around the isle and (ii) wind waves generated locally in the English Channel. The analysis of measurements at the wave buoy south of the harbour (wave buoy 02910 of the French CANDHIS database, "Centre d'Archivage National de Données de Houle In Situ", Cerema, France) exhibited two prominent sea states characterised by peak periods between (i) 3 and 6 s and (ii) 8 and 14 s (Figure  3 ). The variability in harbour wave exposure is also increased by tide-induced refraction. Thus, tidal currents induced refraction of the waves, leading to changes of the wave direction towards areas with lower propagation speed of the crest, and this results in modulations of wave exposure with semidiurnal variations of the significant wave height and wave direction  [21].
The spatial distribution of the bathymetry was derived from a compilation of (i) the large-scale Homonim database covering the English Channel with a spatial resolution of 0. 01 • (~100 m)  [30]  and (ii) the high-resolution coverage established during the Litto3D project with a spatial resolution up to several metres in nearshore areas  [31]. In domain R1, the model was driven by Pierson-Moskowitz spectra  [32]  recomposed from wave components (significant wave height, peak period, direction and spreading) extracted at an hourly time step from the hindcast database Homere  [33]. The Homere database consists in hindcast simulations conducted with Wavewatch III during the period 1994-2019 in French coastal shelf seas of the Atlantic Ocean and the English Channel. Wind velocity components at 10 m above the free surface were provided at a time step of one hour from spatial interpolation of Homere data.
Tidal free-surface elevation and depth-averaged currents were extracted from hourly predictions conducted with a spatial resolution of 250 m as part of the MARC project  [34]. In domain R2, input forcings were derived from predictions established in domain R1. TheS extraction of input boundary conditions from the Homere database was restricted to a ten-year period between 2006 and 2015 to match IEC standards for assessment of the wave climate and associated energy resource. Thus, simulations in domains R1 and R2 were conducted for this ten-year period.
In Situ Measurements Available data used to assess wave model performance consist of archived wave buoy measurements from the French CANDHIS database ("Centre d'Archivage National de Données de Houle In Situ", Cerema, France). These data include (i) a location in the southwestern part of the Isle of Batz at water depths of 30 m (02922, lon = -4. 072 • W, lat = 48. 728 • N) and (ii) a location in nearshore waters south of the breakwater of Roscoff-Bloscon harbour at water depths of 5 m (02910, lon = -3. 961 • W, lat = 48. 717 • N) (Figure  2 ). The two wave buoys were directional, thus giving access to the direction of incoming waves. In offshore waters, data were available from 27 March 2019 to 31 December 2021, covering a period exceeding 2. 5 years. In nearshore waters, measurements covered an extended period of 9.
Performance Indicators and Wave Energy Metrics Predictions were interpolated at the different time steps of available measurements. Performances of SWAN were evaluated by relying on a series of statistical and scoring metrics. For the significant wave height and the wave period, we considered (i) the mean absolute error MAE = N ∑ i=1 | S i -O i | N (1) (ii) the normalised bias NBI = N ∑ i=1 (S i -O i ) N ∑ i=1 O i (2) and (iii) the symmetrically normalised root mean square error introduced by Hanna and Heinold  [35] HH = N ∑ i=1 (S i -O i ) 2 N ∑ i=1 S i O i (3) with N the number of data in the discretised times series considered, and (S i ) and (O i ) the two sets of simulated and observed values, respectively. For the wave direction, following Kalourazi et al. [36] , we relied on the modified normalised bias NBI θ = N ∑ i=1 mod -π,π (θ Si -θ Oi )/(2πN) (4) and the normalised root mean square error NRMSE θ = 1/(2π) N ∑ i=1 [mod -π,π (θ Si -θ Oi )] 2 N (5) Module operator mod -π,π indicates that (i) 2π is subtracted from θ Si -θ Oi if θ Si -θ Oi > π and (ii) 2π is added to θ Si -θ Oi if θ Si -θ Oi < -π.
Conclusions A refined assessment of the available wave energy resource and technically exploitable energy output was conducted in the vicinity of the harbour of Roscoff breakwater (northern Brittany, France). This evaluation relied on (i) a hindcast database of CF for a scaled Oyster WEC with a nominal power of 90 kW (Figure  13 ). Thus, whereas the vicinity of the harbour showed restricted interest for an extended exploitation of the wave energy resource, it may be considered as an experimental site for testing small-scale prototypes of wave energy converters in western Brittany, having the advantages of (i) a wide panel of sea wave conditions (including swell and wind seas) and (ii) reduced effects of the tide on WEC operation (which may be significant in western Brittany, characterised by important wetting-drying areas along the coastline).
Conclusions A refined assessment of the available wave energy resource and technically exploitable energy output was conducted in the vicinity of the harbour of Roscoff breakwater (northern Brittany, France). This evaluation relied on (i) a hindcast database of the wave climate derived from numerical simulations conducted at high spatial resolution near the harbour infrastructure, (ii) a generic assessment of WEC performance and (iii) a refined evaluation based on a series of scaled power matrices associated with the Oyster technology. The main outcomes of the present investigation follow: 1. The phase-averaged spectral wave model SWAN reproduced the observed evolution of wave conditions in offshore and nearshore waters, including the vicinity of the harbour breakwater. Thus, besides approaching the observed variations between storm and calm weather conditions, the model reproduced the semidiurnal modulations of significant wave heights and incoming wave directions resulting from tide-induced refraction.
Acknowledgments: The present paper is a contribution to the research program of the team " Hydraulique et Aménagement " (Cerema, DTecREM, http://www. cerema. fr(accessed on  23 June 2022) ). The authors warmly thank the harbour authorities of  Roscoff-Bloscon  for the discussion and exchanges about the exploitation and design of the breakwater " Pierre Lemaire ". Numerical simulations and treatments of hindcast databases were conducted on computer facilities DATARMOR of " Pôle de Calcul et de Données pour la Mer" (PCDM)  (https://wwz. ifremer. fr/pcdm,accessed on  23 June 2022 ).
The first computer "art" work was Electronic Abstractions by Ben F. Laposky in 1952  [7] , produced with an oscilloscope. Ivan Sutherland introduced Computer Graphics in 1961, which has been studied in depth by many experts and slowly led to computer painting, digital media art and new media art  [8]. The involvement of AI technology in visual arts can be traced back to 1973, when Harold Cohen invented the AARON painting robot  [9]. In the 1990s, Leonel Moura created a painting robot that also utilized robotic devices to mimic traditional painting methods. Moura's "Robotic Action Painter" (RAP, Swarm of Robots) in 2006, which was permanently installed by the American Museum of Natural History, worked by mocking the group behavior of ants  [10]. In 2014, Google created an AI program named DeepDream  [11]  for blending and generating images.
Even so, the demand for large databases and high-quality hardware can make AI less practical in reality. Firstly, AI programs must learn from the sample data, no matter which algorithm they are based on. The better the sample data is, the more "intelligent" the AI program will be. But collecting samples is undoubtedly a huge effort. Take the work "Portrait of Edmond Belamy" as an example. It is based on a sample of 15,000 European portraits from the 14th to 20th centuries. In cases of "Snowflake" and "Waters of the Yellow River" mentioned above, it takes a considerable amount of manpower to collect and arrange samples, which seems hard for common artists to accomplish without teamwork. Secondly, the comprehensive performance of the hardware is also crucial. Computing power of the machine determines work efficiency to a certain extent, and its function of display is also highly required in visual art.
Background With the invention of the microscope, researchers gained a preliminary understanding of the chromosome's tertiary structure. However, it was difficult to gain a more global picture, that is, until the development of chromosome conformation capture (3C)  [1]  and its variations 4C [2], 5C [3], and HiC [4], which have made available spatial information on the whole genome. There are now many HiC pipelines available  [5, 6]. However, there is no suitable tool to access HiC map results except for visualization (Table  1 ), that is, no systematic way to extract HiC contact information for a specific query. For example, given the list of CTCF binding sites, a custom script is needed to compute all pairwise contacts between them. Therefore, we have developed HiCmapTools, which helps biologists efficiently query HiC maps and perform permutation tests.
Implementation HiCmapTools is implemented in C+ + , which facilitates using common programming data structures and functions from the Standard Template Library (STL). Users input HiC maps in either. hic format generated by Juicer  [8]  or bin-contact pair files following hicpipe  [11, 12]. The input contact map is stored as a hash structure using pair bins as keys. The size of the bin is specified by the user (-in_hic_resol for. hic) or depends on the input file (bin-contact pair files). A query is binned into a corresponding key based on its position to facilitate efficient extraction of contact frequency via STL hash operation (O(1) for lookup). Also, we measure the significance of the extracted frequencies using permutation tests which rank the frequency among random samples. The usage of the query mode and random test are explained below.
Rscript visualPermutationTest. R Ubxt-bait_random_1. txt UbxPermuTest. pdf Besides Drosophila, HiCmapTools could handle other species. We test whether there is local contact enrichment around the Tox gene in mouse T Cell. The HiC map, D0_mm10. hic, is available at https:// doi. org/ 10. 6084/ m9. figsh are. 18586 106 instead of GitHub due to the size limit. hicmaptools -in_hic D0_mm10. hic -bait Tox_mm10. bed -random 0 -output Tox_bait_D0. txt.
Results HiC data has shown chromosome structure to be formed by 3D folding of a higher-order unit, the topologically associating domain (TAD)  [14] [15] [16]. In Drosophila, TADs can be further classified into four epiTADs-active, null, PcG, and HP1-based on ChIP-seq binding profiles  [14]. Although a substantial proportion of TADs form loops, with loop bases frequently located at the TAD boundaries in mammals  [17, 18] , few examples of TAD loop structure have been reported in the fly genome  [19]. These loops generally involve Polycomb binding sites, but whether any preference exists regarding epiTADs has not been systematically addressed. We selected two related experiments to determine whether HiCmapTools can help biologists address these questions. We approached the question by applying HiCmapTools on high-resolution Drosophila HiC data (1 kb, merged embryo from GSE 34,453, 61,471, 94,115, and 99,105, "Availability of data and materials").