dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , attention maps), so that we can compare the exploration from digit-tracking and eye tracking. Also, we built computational models to predict the participants' attention and compared the performance of different models. Linear and generalized linear mixed-effects model. This procedure was implemented by the lmerTest package  48  in R (version 4. 0. 3). We used linear mixed models (LMM) to analyze the continuous variables (i. e. , log-transformed reaction time and search latency) and generalized mixed model (GLMM) to analyze the binary variable (i. e. , the probability of the first attended object). The fixed effect was semantic relatedness. The random effects were, both as intercepts and slopes, were Participant and Target. The models were fitted using restricted maximum likelihood approach and p-values were computed using Satterthwaite's method (LMM) and asymptotic Wald tests (GLMM).
Attention maps. Attention heat maps for Experiment 3 data were computed as described in Lio et al. 4. Briefly, coordinates of tracking points were transformed from the screen space to the picture space. Attention map for each subject and each picture, i. e. the probability density of exploration, was calculated from the tracking data using kernel density estimation with a Gaussian kernel weighted by duration and a bandwidth of 1° of visual angle. Each subject-level density was then normalized using the Min-Max normalization. Finally, grouplevel maps were generated by averaging scaled subject-level densities and normalized using the same normalization procedure. Attention maps for eye-tracking data from the Ehinger et al. 24  database were computed from segmented eye fixations following the same procedure.
Acknowledgments This work is part of  Quantum Flagship  project  QMICS  that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  820505. We acknowledge  IARPA  and  Lincoln Labs  for providing a Josephson Traveling-Wave Parametric Amplifier. The devices were fabricated in the cleanrooms of  ENS de Lyon ,  Collège de France ,  ENS Paris ,  CEA Saclay , and  Observatoire de Paris. We thank  Mikel Sanz ,  Mateo Casariego ,  Joonas Govenius ,  Jeff Shapiro ,  Pierre Rouchon  and  Daniel Estève  for fruitful discussions.
Note that each run of the SMT solver took less than one day to solve, except for some instances of KATAN where it took two days. Experiments were performed on a computer with two AMD EPYC 7282 processors, for a total of 64 parallel threads at 3. 2GHz. Our implementations of this model is available at https://github. com/xbonnetain/ quadratic-feistel-boomerangs.
Acknowledgments The authors would like to thank  Schloss Dagstuhl  and the organizers of the Dagstuhl Seminar 22141 "Symmetric Cryptography" where this work was initiated, and  Yu Sasaki  for suggesting the study of this topic. This work has been partially supported by the  French Agence Nationale de la Recherche  through the  DeCrypt  project under Contract  ANR-18-CE39-0007  and the  OREO  project under Contract  ANR-22-CE39-0015.
Leukemia Data Set This data set comes from the study by  Golub et al. (1999) , showing how new cases of cancer can be classified by gene expression, providing a general approach for identifying new cancer classes and assigning tumors to known classes. This data was used to classify patients with acute myeloid leukemia (label 1 ) and acute lymphoblastic leukemia (label 0). It is composed of two sub-datasets: the initial (training, 38 samples) and independent (test, 34 samples) data sets. We aggregate these two data sets into one, comprising 7 129 features for 72 samples. We filter the data set such that we only keep the 25 % of the features with highest variance, resulting in 1 783 remaining transcripts.
Bladder Cancer Data Set This data set comes from the study by  Urquidi et al. (2012) , aiming to profile and differentiate tumoral urothelia samples from normal ones. This data set has been retrieved from CuMiDa  (Feltes et al. , 2019)  and refers to GSE31189. It comprises 37 healthy samples (label 0) and 48 tumor samples (label 1). After only keeping the 25 % of the features with highest variances, we are left with 2 734 transcripts remaining.
Varying k of Algorithm 1 Figure  4  shows the percentage of classifiers for which Algorithm 1 does not compute the LOOCVE. This reduction is massive in all cases, reducing at least 80 % of the classifiers in the process. Since the running time of Algorithm 1 is mainly impacted by the LOOCVE calculations, this leads to heavily reduced running times compared to the naive approach of evaluating the LOOCV of all classifiers. Not surprising, Figure  4  shows that, as k increases, fewer classifiers get eliminated, as the output size of Algorithm 1 is larger, hence requiring more classifiers to be fully evaluated via LOOCV. More interestingly is that the percentage reduces in steps. For example, for the dengue data set, except for the case k = 5, multiple values of k share the same amount of eliminated classifiers.
Discussion In this paper, we present a preprocessing method for the fast computation of optimal binary monotonic pair classifiers (Section 2). We prove that the regression error (RE) of a classifier is a lower bound for its leave-one-out cross-validation (LOOCV) error (Theorem 1). We provide an algorithm that uses the RE in a straightforward way as a lower bound for the identification of good pair classifiers. On biological data sets, we find that this approach typically avoids the calculation of LOOCV for 90 % or more of the possible pair classifiers that are evaluated in the naïve approach, corresponding to speedups of at least 10-20. The open-source implementation we provide with this paper (https://gitlab. pasteur. fr/ofourque/mem_python) implements this speedup. We see impacts of the resulting drastic speedup in at least three areas.
Acknowledgments 366  Océane Fourquet  and  Benno Schwikowski  have received funding 367 from the  European Union 's  Horizon 2020 research and innova-368 tion programme  under grant agreement No. 965193  for  DECIDER. 369   Martin S. Krejca  has received funding from the  European Union 's  370 Horizon 2020 research and innovation program  under the  Marie 371 Sk   lodowska-Curie  grant agreement No. 945298-ParisRegionFP. 372  Our work was also supported by the  Paris Ile-de-France region , 373 via  DIM RFSI  project  Opt4SysBio.
A task model looks like a tree diagram with nodes being either a task or a temporal ordering operator (e. g. , >> stands for sequence, [> stands for deactivation). HAMSTERS-XL provides support to represent refined types of user tasks  [14] : motor, perceptive, and cognitive (depicted in Fig. 1 ). Cognitive tasks can also be refined into cognitive analysis tasks or cognitive decision tasks (on the right in Fig. 1 ), to indicate the information that must be managed by the user; they are not necessary detected with sensors. HAMSTERS-XL provides support to represent data such as devices, information, knowledge, and objects manipulated by users (which can be physical objects or software objects), and are depicted by using labels preceded by the abbreviation of the type of data  [15]. Fig. 2  shows examples of such representation, where arcs between data and tasks represent how the data is used.
Introduction It is largely accepted that organizations rely on business processes (BPs) to achieve goals, sustain growth, improve services, and many other things that could keep them competitive. In the Information and Communication Technology (ICT) community, 2 major trends related to BPs design exist: activity-centric and data-centric. The activity-centric trend produces process models that define the execution chronology of BPs' activities along with who does what, where, when, and why (5W's)  [24]. And, the data-centric trend produces Data Artifacts (DAs) that define BPs' data and events along with the changes that these data are subject to because of these events  [17]. Independently of how these 2 trends examine BPs design since the activity-centric trend is about what should be done and the data-centric trend is about what can be done  [11] , a BP remains "the coding of a lesson learnt in the past, transformed into a standard by a group of experts, and established as a mandatory flow for those who must effectively carry out the work"  [18].
In addition to BPs, organizations rely on other ICTs like lately Blockchain, data analytics, cloud/edge computing, and Internet of Things (IoT) that is the focus of this paper. IoT is about anything and everything (e. g. , smartphones, kitchen appliances, and embedded systems) that connect with other things to enable control of users' cyber-physical surroundings, for example. According to Techjury, 1 25 billion connected things were in use in 2021 and will reach 64 billion by 2025. All these things are feeding computers with data to address real-world problems and are also sensing and reacting to the real world of humans  [21]. It is predicted that the total economic impact of IoT will reach between $3. 9 trillion and $11. 1 trillion per year by 2025  [8]. According to Nigam and Caswell  [17] , a DA is a concrete, identifiable, self-describing chunk of information that can be used by a business person to actually run a business, and has a set of states that constitute the DA's life cycle.
This definition checks existing works on DAs use for BPs design. 2. Identification of social relations between TAs residing in an IoT ecosystem. This identification checks existing works on having things participate in social relations. 3. Analysis of data quality impact on TAs collaboration. This analysis specializes data quality into uncertainty, redundancy, and inconsistency and defines the impact of each on future social relations between TAs. 4. And, development of a system demonstrating and evaluating TAs put into action as part of a complete IoT ecosystem. The rest of this paper is organized as follows. Section 2 defines the key concepts of IoT and DA. Sect. 3 suggests a case study motivating both IoT and TAs adoption. Sect. 4 is related to TA-based design of IoT ecosystems. The impact of data quality on TAs collaboration is discussed in Sect.
Internet of Things. A good amount of works on IoT exist in the literature, which in fact does not help come up with a unique definition of IoT (e. g. ,  [1] ,  [5] ,  [23] , and  [26] ). For instance, Abdmeziem et al. discuss IoT characteristics and enabling technologies  [1]. Characteristics include distribution, interoperability, scalability, resource scarcity, and security. And, enabling technologies include sensing, communication, and actuating and are mapped onto a three-layer IoT architecture referred to as perception, network, and application. In  [5] , Barnaghi and Sheth identify IoT's requirements and challenges. Requirements include quality, latency, trust, availability, reliability, and continuity that should impact efficient access and use of IoT data and services. And, challenges result from today's IoT ecosystems featuring billions of dynamic things that make existing search, discovery, and access techniques inappropriate for IoT data and services.
Finally, Qin et al. define IoT from a data perspective as "In the context of the Internet, addressable and interconnected things, instead of humans, act as the main data producers, as well as the main data consumers. Computers will be able to learn and gain information and knowledge to solve real world problems directly with the data fed from things. As an ultimate goal, computers enabled by the Internet of Things technologies will be able to sense and react to the real world for humans"  [21]. Data artifacts. Many benefits could be achieved through the use of DAs for instance, reducing process model complexity through the identification of DAs whose behaviors are modeled as life cycles and tracking process model execution as a set of communicating DAs' life cycles. A relevant illustration of a DA-centric approach is given in  [7]  with IBM Global Financing (IGF).
Case study To illustrate the blend of IoT with BPs from a data-centric perspective and hence, identify TAs that would contribute to IoT ecosystems design, our case study targets elderly people in a healthcare facility. Many studies confirm that population aging is a dominant global demographic trend of the twentyfirst century. 2  In addition, many studies confirm the benefits of IoT for elderly people for instance IoT for seniors: Solutions and Use Cases by Cogniteq  3  and How can IoT help with elderly care? by Telefònica Tech. 4 Let us start with an ecosystem that would revolve around the living room in the healthcare facility. An immediate situation requiring design would expose things like remote control, smart TV, and light switches. For a successful design so that these things would allow watching movies in an enjoyable atmosphere by automatically dimming the lights and selecting the best sound effects, a first step would consist of identifying what each thing does, how and why a thing acts upon the data it manipulates, how and when a thing interacts with peers, and how a thing responds to peers' messages.
While Table  1  provides a sample of operations that some things would perform, a TA-based IoT ecosystem design would privilege data over operations like shown in this table's data column. Besides the data to associate with a TA's functionality, a TA will have a life cycle for its behavior and a set of interaction flows for the messages that it sends and receives. In the same living room, another situation could arise involving this time a smart watch that synchronizes with a medical dispenser the automatic release of medicine doses to the concerned person. The synchronization will be set according to the treatment's duration but, then, extended, should the treatment be renewed. To accommodate this synchronization and extension during an IoT ecosystem design, TAs are associated with interactions flows and life cycles.
A thing actuates data including those that are sensed. Finally, a thing communicates with the cyberphysical surrounding the sensed and/or actuated data. With reference to the case study, we abstract the light switch with an actuating turning the lights on and off. A composite functionality puts primitive functionalities together according to specific cases since it is unlikely that one thing alone would cater for the needs of today's IoT applications. These cases are sensing. actuating. communicating  Medical dispenser change (modify the intake frequency per type of pill), configure (select the container per type of pill), display (notify availability of pills on the TV screen), etc. Medical dispenser name of medicine, dosage of medicine, frequency of medicine, side effects of medicine, etc.
Definition of thing artifact In line with Qin et al. who state that processing data in IoT is different from traditional Internet environments  [21] , we argue that a data-centric perspective would make data a "first-class citizen" during IoT ecosystems design following the blend of IoT with BPs. First of all, BPs' activities act upon data in compliance with create-read-updatedelete (CRUD) operations. Contrarily, things act upon data in compliance with their sensing, actuating, and communicating functionalities. By having both BPs' activities and things' functionalities act upon data, we define a TA as a chunk of information that refers to its functionality, captures its life cycle, and tracks its interaction flows. Functionality is what a TA does; life cycle is how a TA behaves; and interaction flow is with whom a TA exchanges messages.
Data quality cases In  [21] , Qin et al. present an IoT-related data taxonomy categorized into data generation (specialized into velocity, scalability, dynamics, and heterogeneity), data quality (specialized into uncertainty, redundancy, ambiguity, and inconsistency), and data interoperability (specialized into semantics and incompleteness). Adopting Qin et al. 's second category as it falls into IoT ecosystems' data concerns, we focus on uncertainty that could be triggered because of missing readings by TAs and poor accuracy of reading outcomes by TAs, for example, redundancy that could be triggered because of duplicate reading outcomes from the same TA and similar reading outcomes from similar TAs nearby, for example, and, finally, inconsistency that could be triggered because of inappropriate reading outcomes by separate TAs, for example.
For instance, avoid-me relation would refer to opponent TAs since they offer the same functionality. Once all the details, excluding operation time intervals, about the TAs and relations are stored in the TAs-Relations repository, the connector notifies the deployer about the readiness of TAs for deployment upon the request of end-users. To respond to these requests, the deployer parses the TAs-Relations repository looking for relevant TAs along with analyzing how they are related to each other as per Algorithm 1 whose outcome Fig. 9  System architecture for implementing TAs is TAs orchestration obtained thanks to the scheduling techniques presented in Sect. 4. 3. Although the selection of a particular scheduling technique could be at the IoT engineer's discretion, we recommend using data dependencies between TAs like presented in Algorithm 1.
Once a scheduling technique is selected, the IoT engineer sets the values of the different TAs' operation time intervals. Based on these values, the necessary time interval relations between the TAs are identified as per Algorithm 2. These time interval relations are used to form interaction flows in compliance with Table  4. After deploying TAs over run-time platforms, the deployer informs the monitor of the ongoing execution of these TAs. This one tracks the events that could impact this execution like TA failure and data inconsistency along with using relations to come up with solutions to these events. To this end, the monitor updates details about the interaction flows stored in the dedicated repository. In conjunction with the monitor's operations, the deployer consults the interaction flows repository to analyze the execution progress of TAs.
System in operation The system's simulator programmed in Python runs over configuration, execution, and monitoring stages. Each stage involves specific modules along with their interactions depicted in Fig. 9. During the configuration stage (involving the transformer and connector modules), the IoT engineer carries out the following steps: 1. Run-time and Virtual Machines (VMs) configuration: the engineer deploys run-time platforms as virtual machines, each labeled as a device in the simulator. These VMs have their own processing, storage, and communication capabilities. VMs also operate in their own isolated environment, including their own guest operating sys- for the future generated TAs based on their respective creation times. In addition to the steps above, we associate a TA with a Processing Time (PT ) defining how long it will lock a device, a deadline for getting a work done using this device once deployed, and an amount of data that it will process over this device.
The deadline is relative to a TA's expected starting time and the amount of data binds a TA to a device according to its capacity. In the execution stage (involving the deployer module), the IoT engineer initiates Algorithm 1, so that the simulator proceeds with every newly established TAs-relation connection obtained in the configuration stage's step 6 as follows: 1. Initiate a particular scheduling technique based on the participating TAs' data as per Algorithm 1's lines 2-7. 2. Identify a time relation as per Algorithm 3 that corresponds to the selected scheduling technique in order to determine the TA that will start first. 3. Run the TAs participating in the identified time relation based on their respective start times over devices. It could happen that some devices do not have enough resources to host TAs.
We would also like to expand further the impact discussion of data quality on the "longevity" of IoT ecosystems. Uncertain, redundant, and inconsistent data could shorten things artifacts' "lifetimes. " Finally, we would like to examine the role of relations between TAs in the dynamic management of IoT ecosystems. How to orchestrate TAs that could become involved in satisfying many users' demands? Could relations help prioritize their involvement? And, could relations help avoid conflicts on resources?.
This work was supported in part by the  Royal Academy of Engineering  under the  Leverhulme Trust Research Fellowship scheme  ( Derakhshani-LTRF1920\16\67 ), by the  Leverhulme Trust   Research Project Grant  under grant number  RPG-2017-129 , by the  UK the Engineering and Physical Sciences Research Council (EPSRC)  under grant numbers  EP/N007840/1  and  EP/R006385/1. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY).
This encompasses the need for access to extensive, high-quality datasets and the essential technological infrastructure for processing data, prerequisites critical for realizing AI's full potential  (Keegan et al. , 2022). Unlike standalone AI applications, AI within CRM necessitates seamless integration with existing CRM platforms and databases, and it often involves complex data environments and minimal disruptions. Moreover, CRM demands a keen focus on scalability and customization requirements  (Perna and Baraldi, 2014)  which can introduce complexity in terms of data mapping, synchronization, real-time updates, and configuration and thus can lead to specific challenges. As AI systems operate autonomously, the crucial task of defining precise objectives for AI algorithms comes to the forefront  (Keegan et al.
Data collection and analysis Data collection and analysis proceeded in tandem  (Foley et al. , 2021) , for a total of 22 interviews (see Table  1 ). For data analysis, we transcribed the interviews verbatim and promptly organized these transcriptions, along with written memos, into a database. Subsequently, we conducted thematic and content analysis on the interviews, employing codes extracted from ATLAS. ti software, a specialized tool for qualitative data analysis (see  Ghani et al. , 2022  and Helmy et al. , 2020 for similar approaches). We systematically categorized the challenges and allocated them to the specific step of the AI implementation process where they emerged. The data analysis consisted of two main steps: the within-analysis, where each interview was analyzed independently, and the crossanalysis, which aimed to compare and examine these cases collectively, following the approach outlined by  Eisenhardt (1989).
, 2020) , we adopted a data triangulation approach to support our theory-building process  (Lincoln and Guba, 1985; Thorpe and Holt, 2011) : during the interviews, the respondents referred to or provided us with documents relevant to our research. In parallel, we conducted an autonomous search on public databases to further verify the completeness and accuracy of primary data and to incorporate any neglected elements, as appropriate. These secondary sources included company websites, official documents, press articles, public videos, interviews, and reports. To ensure data accuracy and reliability, we asked five participants to review and confirm the accuracy of the data  (Lincoln and Guba, 1985). Transferability has been achieved by explicitly describing the boundaries of the research  (Lincoln and Guba, 1985).