dataesr/scientific-paragraphs-categorization huggingface dataset:
Ontological representations provide the means to reuse a commonly agreed definition of activity concepts. The expressiveness limitations of OWL 1 have been recognized in various fields; therefore, this language was extended while keeping the decidability of its OWL 1 DL fragment. The result of this extension is the language OWL 2. OWL 2 introduces new functionality in comparison to OWL 1; some of the additional features are syntactic sugar that makes things easier to read or to express, e. g. , disjoint union of classes, while others offer new expressivity, including keys, property chains, richer data types, richer data ranges, qualified cardinality restrictions, asymmetric property, reflexive property, and disjoint property, and enhanced annotation capabilities  [58]. In  [54] , the use of OWL 2 is investigated for recognizing high-level activities.
Abnormal Human Behavior Recognition In the literature, abnormal human behavior is defined as unexpected behaviors of a user as they are different from his/her usual behaviors  [5]. The authors of  [89]  propose a probabilistic spatiotemporal model for recognizing human daily behaviors. Anomalies, which are defined as significant changes from the learned behaviors, are detected using a cross-entropy measure. In  [90] , the authors suggest using a DBN for identifying specific human behavior patterns and then employing the Likelihood Ratio Test (LRT) to detect abnormal behaviors by measuring cumulative abnormality. The data-driven approaches intended to detect abnormal human behaviors often use machinelearning models, such as Support Vector Data Description (SVDD)  [91] , SVM  [5] , and Recurrent Neural Network (RNN)  [92]  models.
DATASETS DESCRIPTION In this section, the datasets used in the study are described. [25]  This dataset was built using 236 sensors collecting data about the state of doors (open/close), use of electrical equipment, water consumption, etc. ; these data are of different types: binary, integer, real number, or categorical. An instrumented home with two floors was equipped with these sensors in different locations, see Fig. 4. To build this dataset, seventeen daily living activities were performed by one occupant during four consecutive weeks during working days. Three main context attributes of human activity are considered in this dataset, namely time, location, and activity. Table  1  shows activities grouped by their locations considered in this dataset. The Orange4Home dataset was chosen for the evaluation of the proposed framework due to its data sequential structure and long-term    [26]  In this dataset, participants in this study were fitted with a waist-mounted smartphone (Samsung Galaxy S II) equipped with inertial sensors (accelerometers and gyroscopes) to collect data related to their movements.
The third module conceptualizes human behavior context attributes using the HAT ontology. Modules fourth and fifth focus on PASP, which is a probabilistic version of ASP. PASP is exploited to represent human behavior context attributes and infer human behaviors. Abnormal human behaviors can be detected using PASP. In the latter, besides assigning a probability value to each rule to handle the uncertainty of defined PASP rules, a probability value is assigned to each predicate or fluent used in rules to handle the uncertainty of extracted attributes of human behavior context. An overview of the proposed framework can be seen in Fig. 1.
Context Attributes Extraction In this paper, an algorithm is proposed to extract the five context attributes of them; the context attribute related to frequent activities in particular dayparts are captured using PASP rules exploited in the human behavior recognition module. We define a novel function g that formalizes this algorithm: R loc , R obj , R dur , R f req , R seq = g( y A , y L , y O ) (6) where the list of frequent activities in specific locations is represented by R loc , with specific objects R obj , within specific ranges of duration R dur , with specific frequencies R f req , the list of the frequent sequences of activities is represented by R seq. Seven hash tables, namely locations, objects, minimum duration, maximum duration, minimum frequency, maximum frequency, and previous activity, are used in the developed algorithm to extract the mentioned five lists.
In this study, n, used in this axiom, sets to 4. It is noticeable that the activity sequence inferred using PASP is more comprehensive in comparison with those captured using the module of context attributes extraction since the latter is the most straightforward case of the former one, i. e. , n is equal to two. Axiom 6. daypart(Act, T, P 1 ) : -act(Act,T s , D 1 , P 1 ), T s < T H e , T s + D 1 ≥ T H s. daypart(Act, T, P 1 ) : -act(Act, T s , D 1 , P 1 ), T s > T s + D 1 , T s + D 1 ≥ T H s. The daypart of activities is an important context to recognize human behaviors, e. g. , eating breakfast in the early morning. In this study, ten different dayparts are considered; therefore, ten axioms are defined to represent these dayparts. The main structure of these axioms is shown in Axiom 6, where daypart represents the temporal parts of a day.
Abnormal Human Behavior Recognition We divide abnormal human behavior into six types: (1) Unexpected activities that occur repeatedly in specific locations, (2) Unexpected activities that occur repeatedly with specific objects, (3) Unexpected activities that occur repeatedly in particular dayparts, (4)Unexpected activities that occur repeatedly within particular ranges of duration, (5) Unexpected activities that occur repeatedly with particular frequencies per day, and (6) activities that occur repeatedly in unexpected sequences. In the abnormal human behavior recognition module, PASP is exploited to recognize abnormal human behaviors while handling the uncertainty of human contexts, e. g. , human activity, location, object, etc; i. e. , using PASP, probabilistic inferences can be made about abnormal human behaviors.
EXPERIMENTS, RESULTS, AND DISCUSSION An evaluation of the proposed framework is presented in this section in terms of F-measure, accuracy, precision, and recall on the Orange4Home  [25]  and the UCI HAR,  [26]  datasets. Since the objective of the proposed framework is human behavior and abnormal human behavior recognition, the evaluations are performed using different experiments to show the effectiveness of the proposed framework to infer new facts about human behavior context and to recognize abnormal human behaviors. The framework is implemented and evaluated on a computer equipped with an Intel i7-8650U 2. 11GHz CPU and 32GB RAM. The modules HAR and capturing human behavior contexts are implemented in Python 3. 8 using the Keras deep learning library. The mapping to ontology module is implemented in Protégé 5.
HAR performance Precision, recall, F-measure, and accuracy are the metrics used to evaluate LSTM models in HAR. With the Orange4Home dataset, two independent LSTM models are trained independently for activity and activity location recognition since this dataset contains activity and location labels. The obtained results given in Table  5  show that the LSTM models allow recognition activities and activity locations with high accuracy. The LSTM model dedicated to activity recognition achieves more than 95% in terms of all performance metrics while the LSTM model dedicated to location recognition gives more than 97%. The reason for this is that there is more differentiation between the classes of location in comparison to the activity classes due to the environmental sensors used in Orange4Home dataset.
The normalized confusion matrices for activity and location recognition are respectively shown in Fig. 5 and Fig. 6  to analyze recognition based on each class. Fig. 5  shows that the rate of activity recognition of most classes is higher than 90%. Two activities Going-up and Going-down are recognized with high accuracy using the LSTM model. Moreover, Fig. 6  shows that the location Staircase, which is the location of these two activities, is correctly classified. The LSTM model dedicated to HAR is compared with MultiLayer Perceptron (MLP) and SVM models used in  [118]  using the Orange4Home dataset. In  [118] , these models are exploited in two configurations, Home and Place-based. In the first configuration, a single classifier is used for activity classification, while for the second configuration, eight classifiers assigned to eight places of the instrumented home are used for activity classification, i.
e. , in the Place-based configuration, each classifier is assigned to a place of the home. Table  6  shows the results obtained using the LSTM, MLP, and SVM models in both configurations. The LSTM model shows superiority in comparison to other models due to the fact that MLP and SVM models do not take into account  temporal relationships between activities, while the LSTM model is more suitable for time series. In the case of the UCI HAR dataset, there is only one label, the activity label; therefore, an LSTM model is used for activity recognition. The obtained results, reported in Table  7 , show that this model achieves a performance greater than 94% in terms of F-measure, accuracy, precision, and recall. Fig. 7  shows the normalized confusion matrix for activity recognition based on the LSTM model on the UCI HAR dataset.
One can observe that the model obtained the best performance results in recognizing the Laying activity. This is explained by the fact that this activity consists of the different orientations of the accelerometer compared with other activities, which makes the Laying activity the most distinguishable activity. Hence, this activity is well-classified. Fig. 7  also shows high confusion between the activities Sitting and Standing. This can be explained by the fact that the positioning of the sensors cannot clearly differentiate these two activities  [119]. In the case of the UCI HAR dataset, the LSTM model is compared with approaches used in  [33] , namely KNN and the SVM model. Table  8  demonstrates the superiority of the LSTM model due to the fact that LSTM models can model activity sequences and temporal relationships while other models cannot.
Baseline models To show the ability of the LSTM model to recognize human activity, different baseline models, namely DT, RF, SVM, KNN, and CNN, commonly used in HAR, are applied to both datasets, the Orange4Home dataset and the UCI HAR dataset. The parameters of baseline models are estimated using a grid search technique, see Table  9  and Table  10. The results obtained in the case of the Orange4Home dataset and the UCI HAR dataset are respectively shown in Table  11.
Human behavior recognition performance PASP has been implemented using CLINGO  [121] , an ASP system for grounding and solving logic programs; CLINGO takes such a logic program and computes answer sets representing solutions to the given problem. Human behavior recognition is evaluated using two different metrics. The first one is the number of inferred facts related to the context attributes of human behavior from facts related to other attributes, while the second one is the number of inferred facts related to the location context attribute when the facts related to this context attribute are incomplete. Moreover, the accuracy of inferred facts related to the location context attribute is evaluated to show the effectiveness of the proposed framework in dealing with incomplete information.
This can be explained by the fact that the user's activities are similarly repeated; therefore, the number of inferred facts related to the temporal relations between activities is limited. The number of inferred facts related to the daypart context attribute depends on the number of initial facts related to the activity context attribute. When the number of initial facts varies from 50 to 100, the number of inferred facts related to the daypart context attribute varies from 15 to 26; when the number of initial facts varies from 100 to 600, the number of inferred facts increases slightly, up to 34 facts. This is due to the repeated activities in the specific dayparts. In the Orange4Home dataset, there are 17 activities and ten different dayparts; some of the activities cannot take place in specific dayparts; therefore, the possible inferred facts related to the daypart context attribute are few; consequently, when the number of initial facts related to the activity context attribute is greater than 100, the number of the inferred facts related to the daypart context attribute is few.
Handling incomplete information To show the ability of the human behavior recognition module to deal with incomplete information, another evaluation is performed. In this evaluation, the facts related to the location context attribute are removed from the initial facts, and then the facts related to the location context attribute are inferred using PASP. The latter allows inferring them accurately due to the fact that the activities and their related locations are represented with commonsense knowledge. An accuracy rate of 83. 43% is obtained in the case of 350 initial facts related to the activity context attribute. Table  14  shows the performance in terms of accuracy obtained with the human behavior recognition module for inferring facts related to the location context attribute. Since each activity is performed in one location, the number of inferred facts related to the location context attribute is the same as that of initial facts related to the activity context attribute.
Abnormal human behavior recognition performance Since the Orange4Home and UCI HAR datasets do not include the involved objects label in each sample data, abnormalities related to the "recurrent unexpected activities with specific objects" abnormal human behavior is not taken into account in the evaluation. In the case of the Orange4Home dataset, five abnormal human behaviors, namely AbnormalActLoc, AbnormalActTime, AbnormalActDur, AbnormalAct-Freq, and AbnormalSeqAct, are considered, see Table  4. In the case of the UCI HAR dataset, two abnormal human behaviors, namely AbnormalActDur and AbnormalSeqAct are considered. This can be explained by the fact the UCI HAR dataset does not include activity location label and time information. Due to the fact that neither of the datasets used in this study includes abnormal behaviors, these latter are randomly injected into the datasets to simulate their presence, allowing the framework to be evaluated    15  and Table  16.
The results show that PASP achieves greater than 93% on average in terms of F-measure, accuracy, precision, and recall for both datasets. One can observe that 100% precision is yielded using PASP, which means that abnormal human behaviors are correctly recognized. In other words, there is no false positive in the PASP-based abnormal human behavior recognition. The latter yields better results in the case of the AbnormalActTime abnormal human behavior due to its dependency on one activity prediction while it depends on multiple activity predictions in the case of the AbnormalActDur, and the AbnormalActFreq abnormal human behaviors. Table  16  shows that a better performance is obtained using PASP in the case of the AbnormalSeqAct abnormal human behavior in comparison to the case of the AbnormalActDur abnormal human behavior.
In terms of precision, the performance obtained with PASP is also 100% in the cases of the AbnormalSeqAct and Abnor-malActDur abnormal human behaviors. These results show clearly the effectiveness of the PASP-based framework to recognize accurately abnormal human behaviors. Since SVM and MLN are two of the most common models used for abnormality recognition  [5] ,  [122] ,  [73] , these models are selected as baselines for the evaluation of the abnormal human behavior recognition module. In the SVM model, the kernel function is Radial Basis Function (RBF). This kernel is defined  on two samples v and v ′ as follows: K(v, v ′ ) = exp(- ∥v -v ′ ∥ 2 2σ ) (7) The two samples v and v ′ represent the feature vectors in input spaces. ∥vv ′ ∥ 2 is seen as Euclidean distance between the two feature vectors  [123].
In this experiment, the total number of defined FOL rules to recognize abnormal human behaviors is 433. Table  17  and Table  18  show the results obtained using MLN and SVM in the case of Orange4Home and UCI HAR datasets. In the case of the Orange4Home dataset, the SVM model achieves 83. 68% in terms of precision while the MLN and PASP yield 85. 08 and 100%, respectively, see Table  17  and Table  15. In the case of the UCI HAR dataset, the SVM model gives 71. 51% in terms of average precision while MLN achieves 93. 29%. These results show the superiority of the MLN-based framework in comparison to the SVM-based one due to the fact that the latter is limited in considering the context attributes of human behavior, whereas MLN allows handling the uncertainty of rules by exploiting probabilistic rules.
and Table12. According to these results, the LSTM model outperforms the baseline models on every performance metric. In the case of the Orange4Home dataset, the LSTM model achieves 95. 63% in terms of F-measure; however, RF model yields 67. 78%. The performance obtained with the DT model and KNN model is similar in terms of all performance metrics. The RF model provides better results than the DT, KNN, and SVM models in terms of F-measure, accuracy, precision, and recall. However, the CNN model outperforms the RF model in terms of all performance metrics. As can be seen, the LSTM model yields the best results in comparison to the other ones. In the case of the UCI HAR dataset, the LSTM model achieves 95. 63% in terms of F-measure while 90. 93%, 87. 87%, 83. 90%, 74. 50%, and 72. 50% are respectively obtained with CNN, SVM, RF, KNN, DT models.
EEG Preprocessing All preprocessing of the EEG data was accomplished using mne-python (v1. 0. 2, 24). All code used for the analyses described henceforth is publicly available at https://github. com/LukeTheHecker/ambiguity_sources/. Raw data was referenced to common average and filtered using a FIR bandpass from 0. 01 -45 Hz. In some cases, line noise remained even after filtering. Therefore, we removed line noise at 50 Hz and all harmonics up until 500 Hz using a notch filter using a finite impulse response (FIR) filter and a 1 Hz width of the transition band. Eye artifacts were removed using independent component analysis, specifically the PI-CARD method  [25, 26]. Next, bad channels were detected and interpolated using random sampling consensus algorithm as implements by the autoreject package for python  [27, 28].
Forward Model Standard channel positions were assumed for all q = 32 electrodes according to 10-20 System since no true channel positions were available from the data set. We used the "fsaverage"  [29]  template T1 image as provided by the Freesurfer image analysis suite (https://surfer. nmr. mgh. harvard. edu/). The forward model was computed using the boundary element method (BEM, 30) as provided by mne-python. Each shell (brain, skull & scalp tissue) was composed of 5120 vertices. Conductivity was set to 0. 3S/m for brain and scalp tissue, and 0. 06S/m for the skull. The source space was created using p = 1284 dipoles with icosahedral spacing. A fixed orientation of dipoles orthogonal to the surface of the cortical sheet was assumed due to the reasonable physiological assumption and in order to reduce computational complexity.
Artificial Neural Network (ANN) Neural sources underlying the ERPs were calculated using a fully-connected ANN, which has shown to yield accurate inverse solutions with respect to source location and estimation of source extent  [31, 13]. We used simulated neural sources and scalp EEG data to train the fully-connected (FC) ANN model. The esinet package (v. 0. 2. 4, https://github. com/LukeTheHecker/esinet) for python was used to simulate neural sources and corresponding EEG data and to build and train the FC model. We simulated 20,000 samples of source and EEG activity. Each sample contained brain activity of 1 -15 randomly located and sized sources spanning 50 time points. The activity of each source was determined using noise that followed the frequency spectrum f -β , where β is the slope of the 1 f spectrum.
This slope was calculated for the ERP of each participant's EEG data and found in the range of 1. 3 to 1. 8. A value within this range was randomly selected within the range for each simulated sample. Signal-to-noise ratio (SNR) was also estimated from the ERPs of each participant and condition. This was achieved by first calculating the global field power (GFP) for each participant and condition. Then we calculated the ratio between the maximum of the GFP and the averaged GFP during a baseline interval from 200 ms pre-stimulus until stimulus onset. We found that the SNR of our participants ranged from 4. 2 -19. 9, which was again used as the parameter range for the simulation. For further details on the simulation procedure please refer to  [13]. The ANN was built and trained using tensorflow 2.
A widespread introduction of electric vehicles would require an advanced enriched fastcharging infrastructure and battery technology. Currently used silicon (Si) based power electronic devices limit their efficiencies, power density, and switching frequency. Designing fast-charging stations using these materials is not suitable due to low breakdown potential, less thermal stability, and less power handling abilities. The research will propose an off-board DC high-power density fast charging infrastructure with grid tie application. The EV station is designed by using ultra-wideband gap (UWBG) material-based power electronic devices to charge the EV vehicles in a few minutes up to an acceptable state of charge. The study will analyze the characteristics of Gallium III oxide (Ga2O3) material power devices by modeling them using SPICE and TCAD software tools.
However, the material Ga2O3 has advantages for device development from a perspective of economic cost due to its easy availability  [31] ,  [32]. These materials have a better figure of merits (FOM) and performance as compared to WBG materials  [33] ,  [34]. The study focuses on designing aspects of high-density DC fast chargers using efficient power devices. Most of the previous work is done on WBG devices-based DC EV fast chargers. In this research high power charger is discussed and simulated using a physical modeling tool (Simscape, MATLAB) designed by UWBG power devices is presented. UWBG (Ga2O3) device MATLAB (SPICE model) performance is further compared with Silvaco TCAD results to validate the model behavior. The simulation analysis is performed on a Li-Ion 100kWh capacity battery (nearly the same capacity is used in the latest 2022 EV e.
These parameters are used to model Ga2O3 power MOSFET in MATLAB using Simscape physical modeling SPICE NMOS model. N-Channel MOSFET (NMOS) used in Simscape modeling uses either Shichman-Hodges equations or surface-potential-based mode for MOSFET modeling  [37]. The Shichman-Hodges equations for MOSFET modeling in linear and saturation mode is given below. The device I-V curve is observed and compared using the TCAD and MATLAB model as shown in figure  2. Linear mode MOSFET Drain current, I D = ꞵ {( V GS -V th ) V DS - V DS 2 2 ⁄ )} (1 + ℓV DS ) (1) Saturation mode MOSFET Drain current, I D = ꞵ 2 ⁄ ( V GS -V th ) 2 (1 + ℓV DS ) (2) Where, V GS , V DS and V th are gate to source, drain to source and threshold voltages of MOSFET respectively, 'ꞵ' is the gain factor beta of the transistor and 'ℓ′ is channel modulation effect.