dataesr/scientific-paragraphs-categorization huggingface dataset:
Participant Sampling and Recruitment Between May and July 2019, patients were recruited through convenience sampling during their routine visit to the nurse-led clinic. Researchers did not specifically identify patients with a specific combination of conditions, as the intent was an evaluation of feasibility. A sample of 15 to 25 participants based on specific inclusion criteria was deemed to provide sufficient data to explore the feasibility of TM in a nurse-led clinic, including reaching saturation for the patient interview data  [50, 51]. Patients were eligible to participate if they (1) were aged at least 18 years; (2) were able to take home measurements as intended; (3) were diagnosed with HF, HTN, or DM and one additional chronic condition; and (4) could understand English. Patients were excluded if they had visual or cognitive impairments that would prevent them from using the system or if they were already enrolled in a TM program.
Quantitative Data Collection and Analysis Patient adherence to taking readings was quantitatively assessed by analyzing the server logs of the TM system. Adherence was used as a measure to indicate TM's acceptability in this context and as an indicator of the perceived usefulness of TM. In this case, adherence was used as a pertinent implementation factor to determine feasibility  [41]  and was evaluated by measuring the frequency of TM readings for each condition (Table  1 ). For HF, as patients were instructed to take daily readings, adherence was measured as the percentage of days readings were taken over 6 months. For HTN, as per the algorithm, adherence was measured as the percentage of time patients took 2 consecutive BP readings at least once every 2 weeks. For DM, adherence was defined by taking readings at least once per week during the 6-month study.
Questionnaires were administered at the start and end of the study. Questionnaires included 3 sections: (1) the 36-Item Short Form Survey (SF-36) was used as a baseline indicator to evaluate the overall health status  [52]  of our participant group, (2) a series of technology questions were utilized to better understand the demographics of whom multi-condition TM is feasible within a nurse-led model, and (3) the participants' level of comfort with technology. Determining statistical significance in a small feasibility study was not the goal of including the SF-36 tool but rather to contextualize patient health status data in this care model. A chart review was conducted to collect basic demographic information (ie, age, sex, marital status, education level, etc). Sociodemographic data and clinical histories for all participants were summarized using a descriptive analysis.
Qualitative Data Collection and Analysis All participants were invited to participate in a poststudy interview, each lasting between 30 and 60 min. Interviews were conducted onsite or over the telephone upon study completion. A semistructured interview guide was developed, informed by the 4 constructs of NPT (Multimedia Appendix 1)  [43, 44, 53]. Participants were asked to describe their experiences and perspectives on using the TM system. Caregivers were not asked any specific questions during the interview but were able to participate in the discussion. All interviews were audio taped and transcribed verbatim for analysis. An interpretative descriptive approach was used to guide the qualitative analysis  [54, 55] , as this approach examines a clinical phenomenon with the goal of identifying themes and patterns among subjective perspectives while also accounting for variations between individuals  [56].
Despite more than half of the participants owning a cellphone or smartphone, the comfort level across the participants was mixed. In the end, the response rate for the prestudy questionnaire was fairly high (16/17, 94%) but lower for the poststudy (10/17, 59%) questionnaires. One participant did not complete a prestudy questionnaire. An evaluation of the SF-36 data did not find any meaningful results, likely because of the low poststudy response rate.
Concerns Moving Forward Without TM Participants expressed strong concerns about having to go back to managing their conditions at home without Medly, even to the point where they were willing to pay for the program out of pocket, or ask what store they could purchase the devices immediately. Participants described a sense of anxiety, knowing that no one would be monitoring their readings going forward: Yeah. I like knowing someone's keeping an eye on me. Certainly no one else will keep an eye on me now. [After Medly you mean?] Yes, there's no one now. I just liked it knowing, they were there looking out for me. [MCCP0024].
Not only REST is stateless  [4]  (meaning that the service consumer does not have to store information about the way he is using the service); it is design to reduce the coupling between software pieces. In the Iot, REST may allow reconfiguration of the object, it reuse in a different application for a multiplatform application, REST service structure is easy to describe. REST allows machine to machine communication and is simplier to do than SOAP. The communication is done through the HTTP protocol. Nevertheless the possibility of using the resource via the web given by the REST, it still lacks intelligent data processing. Our contribution aim to add a complex data processing of the object web. We then opt for the reuse of our search fruit in  [5]  which we call Jason-RS or Jason-Rest for more precision.
Jason-RS gives the REST Web Service ability to the BDI agent Service  [5]. Jason is a multi-agent platform based on the Agentspeak-L language  [6]   [7]. It is a platform for BDI agent, that is to say goal-oriented software agent, it is a smart agent. We have to look at the choice of the BDI agent because it is close to human behavior. [8]   [9]. So the connexion of the latter with the REST Web Service allows him to publish his capacity as a Web Service. The processing of a complex action can be done inside Jason (prediction, composition of services, constraint logic programming) and could be published as a Web Service. Jason-RS can serve as an intelligent treatment center. We focus in the collaboration between agent and Iot platform cause connected object had no intelligence, designed differently, so with this collaboration we can open in the intelligence of Iot with a sensor less, little human intervention.
Castellani and Al focused on the implementation of both CoAP and EXI technology for communication between objects by adopting the REST approach  [16]. They implemented the CoAP protocol to enable communication between distributed connected objects. Then they also implemented the EXI library for compressing XML data into binary. In the paper  [2] , they discuss how to create an event SOA. They found a new approach for coordinating IoT's services with the resource service. They named EDSOA the result of their research. It is an asynchronous SOA and responsive it can be considered as an event based service. We have inspired by this approach for the technical aspect of Jason-RS. The Web of Object is used in almost any IoT field even in the field of health. On what  [17]  proposed a new instance of cloud server and set up data collection services on the heterogeneous devices they use.
Each with its strong point in communication and interconnection in the world of IoT. They all almost used the REST Web Service. But for us, a simple service is not enough not only to bathe the connected objects in the artificial intelligence but also to treat in intelligent way the data captured at the level of the objects. This treatment helps automatic decision-making thanks to the intelligent BDI agent that has the same reasoning mode as humans. We focus our research on the intelligent Web Service, that is, a Web Service associated with artificial intelligence in order to reduce human intervention to the orchestration of services in connected objects and decision-making. We have exploited here our previous research work, Jason-RS. An intelligent Web Service with BDI agents that perform the resolution and automation of certain tasks thanks to their ability to cooperate and exchange using the AgenSpeak (L) agent language.
1. Generally the IoT server is a centralized platform of (Cloud, big data) whether it is an IoT server that administers the objects. Jason plays a complex problem solver role as well as the complex system such as optimization. The autonomous and goal-oriented capability through the BDI agent that it implements allows it to perform a task reasonably. This capability is now exploitable through a distributed component as a service  [5]  through Jason-RS. Then the objects can use it not only to send the sensor data but also to initiate a decision-making process at the agent level. Communication between the IoT and the web is done through the HTTP protocol. With Jason-RS we can use all the existing methods of the REST and most importantly there is an interaction with the agent. As a result we can send data to the central control servers via the POST method.
Application of the architecture with a python object and BeC3 Scenario. To illustrate and motivate our approach, we introduce a concrete application using the following scenario. We take the example of the disposal of household waste in our paper  [5]. Waste disposal agents have waste disposal costs that vary according to time constraints, people, distances traveled and evacuation loads. A decision-maker who gives these opinions to optimize the evacuation of waste thanks to the information that the evacuator agents gives him. Information of each of its agents could be changed thanks to the perception emitted by different connected objects. The Fig. 3  illustrates the application of this scenario. We used BeC 3 for the service Mashup. BeC 3 is a composer who allows a user to choreograph heterogeneous objects.
In our experimentation we have created sensor objects choreographed in BeC 3 with other objects. This sensor sends the data captured via Jason-RS in POST for the agents to change the perception of the Jason agent (receiver agent). A decision-maker analyzes the present data and the cost of performing the tasks for each of his agents in order to decide which one is durable, and which one can perform a task at lower cost. The sensor is controlled by the Python version of service compatible with BeC 3. When the service is launched, it can send a POST request to BeC 3 to identify itself and then another POST request to set up its feature handled by the device. Any mashup designed by a user can then be assured by a communication the service on each object. The decision made by the agent could be consumed via a GET request to the Jason-RS.
Result and discussion We tested this approach in two different objects: a python object, a mobile phone. We used Jason-RS to communicate the BeC 3 platform with choreographed objects and BDI agents who take the place of a decision maker instead of a human being (such as it is originally designed in BeC 3. There is a demo script (in Python) APIDemo. py in Pylite project (resources dir). We have used xmpp 2  server for login, its specificity is the presence. So as we describe in the section 3 we must log in BeC 3 in order to create a virtual programmable object: POST /login with content { "username": {xmpp login}, "password": {xmpp password}, "service": {xmpp service (for example im. bec3. com)} } After login, create a BeC 3 object with the following JSON format: POST /feature with content { "name": "tot2", "path" : "truc/bidul21", "type": "switch", "details": "ooo", "widget": "none", "mqtt": false } Available types are accelerometer, button, buzzer, gauge, gps, These object can be delete, update.
DELETE /feature/{id} PUT /feature/{id} { "data": {data (dependent of object type} } One object is associate with one BDI agent. To connect BeC 3 an URL is available from Jason-RS. To change the agent BDI perception a POST request is send by this url format: POST jason_rs_based_url/object_agent/ { "data":value_from_python_object } To receive a decision from BDI agent, Jason-RS provide an URL that we can consume: GET jason_rs_based_url/agent_decider/decision The following sequence diagram presented in Fig. 4  shows the exchange between these two platforms.
Conclusion This article describes our contribution on service improvement in the world of Web of Object. We have integrated our previous Jason-RS research work into the sensor device connected to the web. Object of the IoT do not support the execution of artificial intelligence within themselve because there resources are very limited. Our approach is towards the decentralization of this AI treatment with Jason-RS. Not only is Jason-RS a Web Service but it has an artificial intelligence BDI engine running. So the complex data processing in the connected web object is provided by the Jason. The communication is done through HTTP protocol as it is more flexible and commonly adopted in the world of web. We tested our approach with a Mashup service platform for heterogeneous connected objects called BeC 3.
Multi-platform data introduce new possibilities in the context of data fusion, as they allow to exploit several remotely sensed images acquired by different combinations of sensors. This scenario is particularly interesting for the sharpening of hyperspectral (HS) images, due to the limited availability of high-resolution (HR) sensors mounted onboard of the same platform as that of the HS device. However, the differences in the acquisition geometry and the nonsimultaneity of this kind of observations introduce further difficulties whose effects have to be taken into account in the design of data fusion algorithms. In this study, we present the most widespread HS image sharpening techniques and assess their performances by testing them over real acquisitions taken by the Earth Observing-1 (EO-1) and the WorldView-3 (WV3) satellites.
Data fusion techniques provide a possible solution to this issue that has been validated in several studies performed on both on real and simulated datasets  [6] [7] [8]. In principle, high spatial resolution improvement factors can be attained for hyperspectral data, but the scarcity of exploitable companion high-resolution (HR) data represents a major issue. In fact, it is just possible to find very few examples of hyperspectral sensors co-located onboard of the same platform with high spatial resolution devices, such as panchromatic (PAN) and/or multispectral (MS) sensors. Since the Earth Observing-1, which mounted both a panchromatic and a multispectral camera onboard, is currently dismissed, the only remaining satellites to assure the availability of companion panchromatic sensors are the new Prisma and HypXIM, which are characterized by a six and four times higher spatial resolution with respect to the HS instrument, respectively.
The presence of a high-resolution sensor mounted on the same platform represents the ideal setting for the data fusion problem since the two images to combine are almost simultaneously acquired from the same point of view. However, in addition to the cited difficulty in finding platforms with this feature, the resolution ratio between the HS images and the companion high-resolution image is constrained to be very small, ranging from a value of 3 (EO-1 case) to 6 (Prisma case). Further resolution enhancement would require an additional upsampling procedure at one point in the algorithmic stack, thus strongly compromising the quality of the final fused product. An alternative is constituted by the fusion of data acquired by multiple platforms, which, on the other hand, implies further difficulties related to the different observation geometry and the unavoidable lack of simultaneity between the acquisitions.
Indeed, a specific channel of the MS image has to be assigned to each hyperspectral band to complete the fusion process by means of classical techniques. The assignation algorithm (AA) significantly impacts the final results, and, for this reason, several algorithms have been proposed for completing this task  [14, 15]. The latter fusion algorithms have been properly developed for the fusion of the HS and MS data and thus can be straightforwardly applied to the problem at hand. They include proper modifications of classical algorithms (hypersharpening)  [16, 17]  and applications of more general statistical approaches, as, for instance, the Bayesian framework  [18] , which is employed with naive  [18, 19]  and sparse Gaussian priors  [20]  and with alternative regularization terms  [21, 22].
Three different datasets collected form the Earth Observing-1 and the World-View-3 (WV3) satellites were employed in this study to evaluate the performance of the fusion algorithms. The tests were conducted according to the reduced resolution (RR) assessment procedure, based on Wald's protocol  [23]. Specifically, the available HS image is employed as reference (or ground truth (GT)), and the images to fuse are constituted by properly degraded versions of the available data. This facilitates the use of accurate indexes for evaluating the quality of the final products thanks to the presence of a reference image. The availability of real data allowed to draw conclusions about the behavior of the different types of fusion algorithms and, in the case of classical pansharpening, about the assignation approaches.
The hyperspectral sharpening framework The data fusion procedure to sharp hyperspectral images consists in augmenting the spatial information contained in a low-resolution (LR) hyperspectral image, by injecting information from high-resolution data. In the following, we will denote a generic acquisition composed by N A channels as a set of bidimensional matrices, as follows: A ¼ A k fg k¼1, … ,N A. Moreindetail,theHS datacube will be denoted by H ¼ H k fg k¼1, … ,N H , an MS acquisition by M ¼ M k fg k¼1, … ,N M ,andaPANimagebyP ¼ P k fg k¼1. The enhancement ratio, namely, the ratio between the spatial resolution of the original HS image and the desired spatial resolution, is indicated by R. We restrict the analysis of the fusion problem to the combination of two images, i. e. , the details to be injected are extracted by a single image.
Classical pansharpening approaches Classical pansharpening algorithms are designed to operate with a monochromatic image, which acts as source to extract details to be injected into the LR image. Consequently, as long as the HR image is still monochromatic, the framework of classical pansharpening can be directly applied to this scenario, with the straightforward adjustment of using the HS image as the LR source image to fuse. Conversely, when the details are extracted from a multichannel image, the application of pansharpening approaches requires an assignment procedure between each HS band and a specific channel of the high-resolution MS image. Data fusion through classical pansharpening approaches can be formalized by the following equation  [24] : b H k ¼ e H k þ G k ∘ Y k À Y LP k ÀÁ , (1) which represents the sharpening procedure of a generic k-th channel of the HS image.
Assignation algorithms As seen in the previous section, in the case of HS/PAN fusion, the only possible choice for HR data Y in (1) is represented by the PAN image. Conversely, for the HS/MS fusion, any of the MS channels can act as HR data, demanding an assignation algorithm to couple a specific MS band with a given HS channel. This problem was addressed in previous papers by defining a series of criteria for selecting the most suitable MS channel  [15, 41]. The possible approaches can be either data-independent, exclusively utilizing the characteristics of the sensors, or data-dependent, for which the assignment depends on the particular datasets. The analysis reported in  [14]  highlights the superior performances of the second approach but at the cost of requiring an additional computational effort to evaluate the new assignation for each new dataset.
Bayesian approaches Most novel methods for sharpening HS images exploit the Bayesian statistical formalization of the fusion problem. In this approach, both the LR and HR available data are modeled as transformations, operating, respectively, in the spatial and in the spectral domains of an unknown ideal HR hyperspectral image denoted as Z  [9]. Accordingly, the equation relating the target HR and the available LR image is written as: h ¼ zBS þ n H , (9) where the lowercase letters denote the version of the matrices in lexicographic order (obtained by concatenating the columns of each channel), B is the blurring matrix, S is the downsampling matrix, and n H is the noise accounting for the unmodeled effects corrupting the relationship. (  9 ) is coupled either to: p ¼ R P z þ n P , (10) or to m ¼ R M z þ n M , (11) in the HS/PAN and HS/MS cases, respectively.
Quality assessment of fusion products In this section, we present the performance assessment setup procedure for sharpening the HS data. The objective is to test the viability of fusion algorithms to reach a resolution enhancement factor R that goes beyond the limitations of single-platform setups. In the specific testbed, the HS data are constituted by acquisitions taken by the Hyperion sensor, which is characterized by a GSI of 30 m. The satellite platform also features the a PAN sensor, called ALI, whose GSI is 10 m, which corresponds to a nominal enhancement factor R ¼ 3. For more ambitious factors, two extra scenarios are considered; in particular a very interesting comparison can be taken at R ¼ 6andR ¼ 12 by analyzing different behaviors for single-and multi-platform with a selection of 12 state-of-the-art fusion algorithms.
Datasets Three datasets are used for illustrating the capabilities of data fusion algorithms in producing very high-resolution hyperspectral images. The images have been acquired by the Earth Observing-1 and WorldView-3 sensors. The different settings allow to examine the features of the sharpening algorithms in the presence of the most common issues implied by multi-platform data fusion, namely, the different points of view and the temporal changes in the illuminated scenes between the two acquisitions. In this study we employ the visible near-infrared (VNIR) bands B09-B57, acquired by the sensor Hyperion, as HS data. The single-platform companion data are constituted by the PAN images collected by the ALI sensor, having a 10 m spatial resolution. All EO-1 data share a radiometric resolution of 15 bits.
Experimental results The performance of the fusion algorithms are evaluated both by calculating the numerical values of the chosen quality indexes and by assessing the final products by visual inspection. The first proposed dataset is about Harlem that has the purpose of illustrating the capabilities of producing a significant improvement of the spatial quality of the original HS images through the compared algorithms. To this aim, we report in Figure  1  the results related to all the tested enhancement factors (R = 3,6,12), using one exemplary algorithm of each class. The RGB images are built by averaging a group of channels in the red, green, and blue frequency ranges (B29-B33, B17-B22, and B11-B15, respectively) to construct the required channels. Naturally, all the reference images (or ground truth) coincide, since they are represented by the original HS image; see Figure  1(a), (m),  and (y ).
On the other hand, the images achievable by using the ALI PAN have a satisfactory aspect only for R=3, as it was arguable by the 10 m resolution of the employed HR sensor. The effect of the interpolation is clearly visible in Figure  1 (n) and (z), and, thus, this approach could be preferable only when spatial or temporal misalignments among the multi-platform data cannot be avoided. Those results are perfectly matched to the index values contained in Table  1. Actually, the numerical values point out that in most cases, the use of perfectly aligned images coming from a different satellite can produce images with superior quality also in the case of R=3. In this case, the closer correspondence between the details extracted in the MS channel and the missing spatial information of the HS image can justify the outcome.