dataesr/scientific-paragraphs-categorization huggingface dataset:
The Hand Gesture Database Data is collected from a TOF sensor at a resolution of 320 × 160 pixels. Depth thresholding removes most of the irrelevant background information, leaving only hand and arm voxels. Principal-Component Analysis (PCA) is utilized to crop most of the negligible arm parts. The remaining part of the point cloud carries the relevant information, i. e. , the shape of the hand. Figure  1  shows the color-coded snapshot of a hand posture. We recorded four different hand gestures from a single person at one location for our database: close hand, open hand, pinch-in and pinch-out. The latter gestures are performed by closing/opening two fingers. For a single dynamic gesture recording, 40 consecutive snapshots (no segmentation or sub-sampling) are taken from the sensor and cropped by the aforementioned procedure.
Conclusion We presented a system for real-time hand gesture recognition capable of running in real time on a mobile device, using a 3D sensor optimized for mobile use. Based on a small database recorded using this setup, we prove that high speed and an excellent generalization capacity are achieved by our combined pre-processing+deep RNN-LSTM approach. As LSTM is a recurrent neural network model, it can be trained on gesture data in a straightforward fashion, requiring no segmentation of the gesture, just the assumption of a maximal duration corresponding to 40 frames. The preprocessed signals are fed into the network frame by frame, which has the additional advantage that correct classification is often achieved before the gesture is completed. This might make it possible to have an "educated guess" about the gesture being performed very early on, leading to more natural interaction, in the same way that humans can anticipate the reactions or statements of conversation partners.
In this classification problem, it is easy to see why "ahead of time" recognition might be possible as the gestures differ sufficiently from each other from a certain point in time onwards. A weak point of our investigation is the small size of the gesture database which is currently being constructed. While this makes the achieved accuracies a little less convincing, it is nevertheless clear that the proposed approach is basically feasible, since multiple cross-validation steps using different train/test subdivisions always gave similar results. Future work will include performance tests on several mobile devices and corresponding optimization of the used algorithms (i. e. , tune deep LSTM for speed rather than for accuracy), so that 3D hand gesture recognition will become a mode of interaction accessible to the greatest possible number of mobile devices.
UAVs Simulator The UAVs Simulator is the module responsible for performing the 3D drones' simulation in an urban environment. It consists of three different modules namely Autopilot, Physics Simulation and Ground Control Station (GCS). The Autopilot module contains the flight software allowing drones to fly stable during the flight. More specifically, the Software-In-The-Loop (SITL) 1  simulator was exploited to run the UAV flight code without any specific hardware. Within this simulation tool, the un-compiled autopilot code, which normally runs on the drone's onboard computer, is compiled, simulated and run by the SITL simulation software itself. In the specific case, the SITL software was used to run the PX4 Autopilot Flightcode 2  , an open source UAV firmware of a wide range of vehicle types.
The Physics Simulation module is responsible for replicating the real world physics of drones' flight. In this work, Gazebo 3  was exploited as a real-time physics engine in order to emulate the 3D models of UAVs, their physic properties and constraints and their sensors (e. g. laser, camera) in a 3D urban environment. Gazebo runs on Robot Operating System (ROS)  4  , which is a software framework developed for performing robotics tasks. The Ground Control Station (GCS) module contains the software needed to setup drones' starting GPS locations, get real-time flight information, plan and execute drones' missions. The communication between the PX4 Autopilot Flightcode and the GCS module is provided by the Micro Air Vehicle ROS (MAVROS) node with the MAVLink communication protocol. As illustrated in Fig.
Each row consists of the number of UAVs in the three "Alert" states, the operator's mental workload level, the outcome of the mission and his/her preferred LOA in that situation. An example of a test result is shown in Table  3. Then the corresponding line for building both training and validation sets is shown in Table  4. The Bayesian Network training phase was performed by exploiting the Netica Software 8 then the validation methodology was performed by obtaining a classification LOA accuracy equal to 83. 44%. Table  5  shows the confusion matrix for each level of autonomy considered in this study.
Dataset Description We used the publicly available dataset IVa from BCI competition III 1 to validate the proposed approach. The dataset consists of EEG recorded data from five healthy subjects (aa, al, av, aw, ay) who performed right-hand and right-foot MI tasks during each trial. According to the international 10-20 system, MI signals were recorded from 118 channels. For each subject, there were 140 trials for each task, and therefore 280 trials totally. The measured EEG signal was filtered using a bandpass filter between 0. 05-200 Hz. Then the signal was digitized at 1000 Hz with 16 bit accuracy and it is downsampled to 100 Hz for further processing.
Channel Selection and Preprocessing The dataset consists of EEG recordings from 118 channels which is very large to process. As we are using the EEG signal of two class MI tasks (right-hand and right-foot), we extract the needed information from premotor cortex, supplementary motor cortex and primary motor cortex  [11]. Therefore, from the 118 channels of EEG recording, 30 channels present over the motor cortex are considered for further processing. Moreover, removal of irrelevant channels helps to increase the robustness of classification system  [19]. The selected channels are FC2, FC4, FC6, CFC2, CFC4, CFC6, C2, C4, C6, CCP2, CCP4, CCP6, CP2, CP4, CP6, FC5, FC3, FC1, CFC5, CFC3, CFC1, C5, C3, C1, CCP5, CCP3, CCP1, CP5, CP3 and CP1. The motor cortex and the areas of motor functions, the standard 10 ± 20 system of electrode placement of 128 channel EEG system and the electrodes selected for processing is shown in Fig.
Proposed Spatial Filtering Based Sparsity Approach The proposed spatial filtering based sparsity (SFS) approach follows three steps such as CSP filtering, design of dictionary matrix and sparsity based classification. A detailed explanation of each of these steps are given below. CSP Filtering: Generally, for binary classification problems, CSP has been applied widely as it increases the variance of one class while it reduces the variance for the other class  [1]. In this paper, how CSP filtering is applied for the given two-class MI-based EEG dataset is explained briefly. Let X 1 and X 2 be the two epochs of a multivariate signal related to right-hand and right-foot MI classes, respectively. They are both of size (c × n) where c is the number of channels  (30)  and n is the number of samples (100 × 2).
Experimental Results The performance of the model in our experiment depends on the prediction performance of the classifier. A k-fold cross validation was performed on the dataset to split the entire data into k folds, from which k -1 folds were used to build the dictionary and one fold for testing the model. Each fold was used for testing iteratively and the accuracies were calculated. Two different dictionaries were built: one with bandpower features D BP and the other with energies of a wavelet transform D WE. Accuracy of a model based on training and testing test, is a good metric by itself to calculate the performance of the classifier.
Comparison with SVM and LR To evaluate the proposed SFS method, we compared our method using D WE as dictionary with the conventional SVM  [17, 18]  and LR  [13]  methods. As classif ier 1 gives better accuracy than classif ier 2 , it is used for comparison with the conventional methods. For real-time BCI applications, speed of the classifier is an important issue. Hence, CPU execution time is estimated for all the methods. All the classifier algorithms were performed using the same computer and same software Python 2. 7, making use of Scikit Learn 2 machine learning package. The accuracies and the CPU execution time obtained for different folds for the proposed SFS method using classif ier 1 and D WE as dictionary, and the conventional SVM and LR are listed in Table  2. The average values obtained indicates that the proposed SFS method delivers high average classification accuracy and lesser execution time than the SVM and LR methods.
Simulation Framework The basic idea inspiring the design of the present framework is to test different UAV flying scenarios in an urban environment. Such scenarios simulate potentially critical situations in which drones could be involved in. The logical components that were assembled to implement the proposed framework are illustrated in Fig. 2. By digging more in details, the UAVs Simulator is the module responsible for simulating swarm of autonomous drones flying in the 3D virtual environment. It consists of three different modules, namely: Autopilot, Physics Simulation and Ground Control Station (GCS). The Autopilot module is responsible for running drones flight stability software without any specific hardware. More specifically, it exploits the Software-In-The-Loop (SITL) 2 simulator to run the PX4 Autopilot Flightcode 3 -an open source UAV firmware of a wide range of vehicle types.
The Physics Simulation module is the block devoted to load the 3D urban environment and execute the drone flight simulation in it. Gazebo 4 physics engine was exploited in this block for modeling and rendering the 3D models of drones with their physic properties, constraints and sensors (e. g. laser, camera). In particular, Gazebo runs on Robot Operating System (ROS)  5  , which is a software framework developed for performing robotics tasks. Then, the Ground Control Station (GCS) module contains the software used for setting drones' starting locations, planning missions and getting real-time flight information. The communication between the Autopilot Flightcode and the GCS module is provided by the Micro Air Vehicle ROS (MAVROS) node with the MAVLink communication protocol (Fig. 2 ). Since drones communicate or transmit information through the network, low bandwidth coverage areas could lead to loss of communication and thus to potentially critical conditions.
At first, the EEG signal is first divided into several sub bands using a band pass filter, then the channel and frequency bands are modeled as preconditions before classifying and a heuristic of stochastic gradient boost is used to train the base learners under these preconditions. The effectiveness and robustness of the designed algorithm along with feature combination is evaluated on widely used benchmark dataset BCI competition IV (IIa). The remaining part of the paper is organized as follows; a detailed design of proposed Boosting Algorithm is given in Sect. 2, performance comparison results shown in Sect. 3. Finally, conclusion is given in Sect. 4.
Proposed Algorithm Under this section, a combination model of CSSBP (common spatial spectral boosting pattern) with feature combination is given in detail; it includes modeling the problem, and learning algorithm for the model. The model consists of five stages, data preprocessing which includes multiple spectral filtering by decomposing the signal into several sub bands using a band pass filter and spatial filtering, feature extraction using common spatial pattern (CSP), feature combination, training the weak classifiers, and pattern recognition with the help of a combinational model. The architecture of the designed Afterwards, the CSP algorithm is applied to extract features of the EEG training dataset and combine these feature vectors, then the weak classifiers {f m } M m=1 , are trained and combined to a weighted combination model.
Problem Design During BCI studies, the two main concerns are the channel configuration and frequency band, which are predefined as default for implementing EEG analysis. But, predefining these conditions without deliberations leads to poor performance while executing it in a real scenario due to subject variability in EEG patterns. Hence, an efficient and robust configuration is desirable in case of practical applications. To model this problem, let us denote the training dataset as E train = (x i , y i ) N i=1 , where E i is the i th sample and y i is its corresponding label. The main aim is to find a subset ω c ν, by using a set of all probable preconditions ν, which generates a combination model F by incorporating all sub models trained under condition W M (W M ϵ ω) and reducing the misclassification rate on the train dataset E train , given by, ω = arg min ω 1 N | | | Ei : F ( x i , ω ) ≠ y i N i=1 | | | (1) In the following part of this section, 2 homogeneous problems are modeled in detail and then an adaptive boosting algorithm is designed to solve them.
Spatial Channel and Frequency Band Selection. For channel selection, the aim is to select an optimal channel set S(S ⊂ U), where U is the universal set including all possible channel subsets for set of channels C so that each subset Um in U satisfies |Um| ≤ |C| (here |. | is used to represent the size of the corresponding set), which produces an optimal combination classifier F on the training data by combining base classifiers learned under different channel set preconditions. Therefore, we get, F ( E train ;S ) = ∑ S m ∈S 𝛼 m f m ( E train ;S m ) ( 2 ) Where F is the optimal combination model, f m is m th sub model learned with channel set precondition S m , E train is the training dataset, and 𝛼 m is combination parameter. The original EEG E i is multiplied with the obtained spatial filter, to obtain a projection of E i on channel set S m , which is the alleged channel selection.
Model Learning Algorithm Here, the models of channel selection and frequency selection are combined to form a two-tuple, ϑ m = (S m , B m ), it is used to denote a spatial-spectral precondition, and ν is represented as a universal set including all these spatial-spectral preconditions. Lastly, the combination function can be computed as F ( E train ;ϑ ) = ∑ ϑ m ∈ϑ 𝛼 m f m ( E train ;ϑ m ) (4) Hence, for each spatial-spectral precondition ϑ m ∈ ϑ, the training dataset E train is filtered under ϑ m. The CSP features are obtained by the filtered training dataset E train and these features of individual physiological nature were combined using PROB method  [1]. Let us denote the N features by random variables X i , i = 1, … , N having class labels as Y ∈ {±1}. An optimal classifier f i is defined for each feature i on the single feature space D i hence reducing the misclassification rate.
Algorithm 1: Architecture of Proposed Boosting Algorithm Input: The EEG training dataset given by {x i , y i } N i=1 , L(y, x) is the squared error loss function, number of weak learners denoted by M, and ν is the set of all preconditions. (1) Initialize the training data pool Po = E train = {x i , y i } N i=1 , (2) for m = 1 to M. (3) Generate a random permutation {π(i)} |P m-1 | i=1 = randperm(i) |P m-1 | i=1 (4) Select the first N elements {π(i)} N i=1 as (x i , y i ) N i=1 , from Po. (5) Use this {π(i)} N i=1 elements to optimize new learner f m and its related parameters is obtained in output as, Output: F is the optimal combination classifier, weak learners obtained as {f m } M m=1 , where {α m } M m=1 is the weights of weak learners and {ϑ m } M m=1 is the preconditions under which these weak learners are learned.
Result The robustness of the designed algorithm was assessed on dataset obtained from BCI competition IV (IIa) dataset  [2]. In order to remove artifacts obtained from eye and muscle movements, FastICA was employed  [15]. For comparing the performance and efficiency of the designed algorithm, Regularized CSP (RCSP)  [13]  was used for feature extraction. In this, model parameter λ for RCSP, were chosen on the training set using a Hold Out validation procedure. In case of the four-class motor imagery classification task for dataset II, one-versus-rest (OVR)  [21]  strategy was employed for CSP. PROB method  [1]  was utilized for feature combination which incorporates independence between ERD and LRP features. Feature selection was done to select relevant features, since as more features cannot improve the training accuracy.
The spectral weights were computed as given in  [12]  and then projected onto the frequency bands. In addition, the temporal information were also obtained and visualized. The training dataset are preprocessed under the spatial-spectral pre-condition ϑ m ∈ ϑ, which results in a new dataset on which spatial filtering is done using CSP to obtain the spatial patterns. Then the first two components obtained by CSP are projected onto the space yielding the CSP filtered signal E m. The peak amplitude P mCi for E m and each channel C i ∈ C. Then the P mCi is averaged over all set of preconditions ϑ m ∈ ϑ, computed as P C i = ( 1 |𝜗| ) ∑ ϑ m ∈ϑ α m P mC i where α m is the corresponding weight for the mth condition, which is then visualized using a 2-D topoplot map. From the topoplot, it can be observed that the left hand and right hand movement resulted in activation over the right and left hemisphere of the brain, the foot movement activated the central cortical area and tongue showed activation in the motor cortex region.
The classification results of the test dataset for the proposed method and the other competing method i. e. , Regularized CSP (RCSP) is detailed as follows. In all the subjects the maximum number of iterations, M of the boosting algorithm was set to 180, which was computed using early stopping strategy so as to avoid overfitting, and ϵ was set to 0. 05. The cohen's kappa values for all 9 subjects in the BCI IV(IIa) dataset is shown in Fig. 3. In case of dataset 2, the CSSBP outperformed the RCSP algorithm and showed highest average cohen's kappa value  [3]. From the kappa values, it can be seen that when feature vectors are combined in RCSP algorithm, there was a significant improvement in kappa values in all subjects (except for subjects S4 and S6). Whereas the proposed method improved the kappa values compared to the above algorithm and moreover when feature vectors were combined, it outperformed CSSBP with single feature when compared with combined feature vectors.
List Navigation Menus used in mobile phones are influenced by Linear Menus which were originally created for desktop graphical user interfaces (GUI). Such menus suit desktop environments, where large screen size can accommodate displaying more items. However, Linear Menus are not a good option for a mobile phone interface, as the screen is much smaller. Smartphone users are forced to do excessive scrolling to find an item in a Linear Menu since the screen can only display a handful of items at a time. Almost all menus are formatted in a linear manner, listing entries that are arranged from the top to the bottom of the screen. When presenting a list of items to the user, the available hardware and software have limited the computer system architecture to a linear format. Pull-Down Menus and Pop-Up Menus are a typical example of the linear arrangement.
Conclusion We developed the Edge Menu which is a U shaped menu fitted to the left, right and bottom edges of a mobile screen. An Edge Menu is superior to a Linear Menu by 23% to 40%. However, further research is required to enable the Edge Menu to support greater set of items -for example, languages with longer alphabet. While our findings suggest that the two variations of the Edge Menu will yield better performance in a larger list, this still needs to be verified using a formal study. The work explored the practicality and feasibility of Edge Menu design. Based on our user studies and experiments, it is proven that the Edge Menu yields better performance than the regular Linear Menu. By these results, encouraging software developers and application designers to start integrating Edge Menu with their designs instead of Linear Menu, and explore the capabilities offered by this relatively new design.
These checkpoints, although important to the MOOC taker at that instant, seem to fade away when the next video starts. The MOOC taker is reluctant on obligating to memory rather tend only to remember those parts which are needed to pass the quizzes. To address this issue, we propose a novel model whereby the MOOC taker can take notes on the fly when they are taking the course through watching videos. For the MOOC taker, the parts of the course which they intend to take note, it happens to be certain points in the video. It is assumed that the video is accompanied by an interactive transcript that scrolls and highlights what the instructor is saying at that moment of the video. During the video, there may happen to be equations, diagrams, graphs and example scenarios that explains the topic from various perspectives.
Design of COURSESEEKA Module As a starting point in achieving the goals set forth by, an online interface has been developed where the learners first objective i. e. the need to identify suitable courses that may address his current learning objective, from an array of courses enlisted in various course providers, namely coursera, udacity and udemy. edx had also been approached for their API on two occasions but both the requests got rejected. These course providers are fairly popular and have gained trust among the learning masses as the MOOC movement took place. Also, these have well defined APIs which enlist course related information that can be obtained easily. The COURSEEKA interface is based on the architecture as described by Fig. 5. The interface aims to find courses available from three course providers, namely courser.
Prototype Specifications The prototype is a web application that hosts a video with interactive transcript and has control buttons to preview and append notes. The interface aims to capture portions of the text of the video content i. e. the transcript along with screen captures, preview them and append to the notebook inside the webpage itself. Finally, the user has the option to download the notebook thus formed. All of this happens using client side scripting, which is relevant since time is of the essence when the user is taking the note as the video plays. This eliminates the load off the servers hosting massive amounts of data in the MOOC servers.
Evaluation Criterion The current MOOC interfaces available on the Internet featured on MOOC platforms like coursera, udacity etc. are designed to deliver content over multiple media formats. The primary format, namely videos are designed to be accompanied by in-video quizzes that assess the learners comprehension with the help of in-video quizzes as well as separate assessments module. But certain parts of the video are overlooked by the learner because he may be impulsively following the video to complete the quizzes and the assessments. For this purpose, it happens that the learner may have peeked into the questions beforehand and accordingly is inclined to get the answers from the video. So, he is skimming portions of the video in order to find the answers and thus is not open to effective learning.