dataesr/scientific-paragraphs-categorization huggingface dataset:
Gesture Recognition Methods Based on Deep Learning Aiming at the problem of abnormal gesture recognition in RGB-D video, a fine fusion model combining the Res-C3D network and long short-term memory (LSTM) was proposed  [32]. The key to this design was to learn discriminative representations of abnormal gesture sequences by fusing multiple features from different models. Then, a fusion scheme was proposed to fuse the classification results through the weight fusion layer, which adaptively obtained the dominant weight of a class through training. Their experimental results showed that the proposed method can effectively distinguish abnormal gesture samples and achieve the best performance on the IsoGD dataset. However, the fusion strategy is not deep enough in extracting and representing abnormal gesture features.
Hand Pose Recognition from Video Frames For deep learning to cope with the task, this paper opts for vertically positioned cameras to make data acquisition much easier; pays more attention to the improvement of the state-of-the-art model to enhance the common features; reduces the cost of trained parameters and computational force; and makes the combined effect of the processing of all parts optimal, which is more suitable for this problem, as illustrated in Figure  1. Currently, because there is no dataset on the hands of students in experimental classrooms, it is important to guarantee the performance of the model in this paper with the aid of mainstream datasets and homemade datasets-that is, standard datasets and part of homemade datasets were used when training the model. The collected data were used to verify a novel heteromorphic ensemble algorithm of this work.
We collected 230 videos as a sample of the homemade dataset, of which 180 videos with a duration of about six seconds recorded content of a single behavior and the remaining 50 videos with a duration of about two minutes recorded all behaviors. The participants came from graduate students, doctors, and teachers of the subject, all of whom were aware of the data collection and agreed to it. The behaviors are mainly divided into using a computer, writing, experimental debugging, and playing with a mobile phone. Using a computer includes typing and using a mouse, which accounts for 30% and 25% of the size of the datasets, respectively. Experimental debugging includes using an instrument and using an experimental board (boarding), which account for 10% and 5% of the size of the video set, respectively.
Feature Extraction: HandPose-PSA In this paper, a method based on deep learning is applied to extract information from joint-based gestures. Namely, input data from the regression of YOLOv4-STD are reflected in feature maps and formalized as an objective function to obtain the signals. All this should be entirely due to the improved efficient pyramid squeezed attention network (EPSAnet)  [22]. The authors of  [22]  proposed a lightweight and effective attention method called the pyramid extrusion attention (PSA) module; by replacing the 3 × 3 convolution in the ResNet bottleneck block with the PSA module, a new representation block was obtained, called EPSA. Generally, the size of the training image is usually fixed to 224 × 224, and some important features may be lost in the process of image conversion.
Therefore, the spatial pyramid pooling (SPP)  [14]  module is introduced at the end of the feature extraction network in EPSAnet, which could allow the model to input pictures of any scale, thus retaining more effective feature information and avoiding information loss. Moreover, SPP generates a fixed-length representation of any region and avoids the repeated calculation of convolution features. Importantly, the batch size becomes smaller due to a large image size, which could lead to a decrease in model accuracy. In order to solve this problem and improve the model detection effects, the EvoNorms-S0  [40]  normalized activation layer is applied to replace the batch normalization (BN) and rectified linear unit (ReLU) in the original ESPAnet. The improved EPSAnet is illustrated in Figure  3.
Model Evaluation 4. 1. YOLOv4-STD There are two main sources of the hand target detection dataset. The first is the 32,417 pictures selected from the TV hand and coco hand dataset  [46]. The second is the pictures intercepted from this paper's method of the video taken from a specific angle in the electronic design automation (EDA) experimental class, with a total of 12,600 pictures made into a VOC format dataset with Labelimg. Each labeled image in the dataset corresponds to an XML file, which contains the category and border position of the object to be detected in the labeled image. For the student hand detection dataset, 1/3 of the self-labeled data are used as the test set named Stu-hand-test and the rest are used as the training set named hand-train. The enhanced YOLOv4-STD algorithm, which is built on a CSPDarknet-Tiny backbone feature network, improves the feature extraction of the output of the residual layers and incorporates a channel attention mechanism to obtain much more effective information.
HandPose-PSA Based on the enhanced HandPose-PSA network, we compare performance by replacing our feature extraction network with other famous backbones. The datasets meeting the experiment were mainly selected from the Large-scale Multiview 3D Hand Pose Dataset  [47] , totaling 49,560 pictures, and used to generate the handpose-test by labeling the outputs from our YOLOv4-STD detector, totaling 2096 pictures. During the HandPose-PSA network model training, we set experimental conditions with 256 batch sizes and 100 epochs. Additionally, the origin learning rate is 1 × 10 -3 , and the loss function is the cross-entropy loss function. Two indicators are emphasized, floating-point operations per second (FLOPs) and mean average precision (mAP), to evaluate the model performance as a result of several backbones.
Experimental Results The experiment of this paper is to complete the training and testing on the desktop operating system with Ubuntu Linux, distribution version 18. 04, as the system kernel. The parallel computing engine adopts Nvidia's CUDA version 11. 2. 2. The fusion classification of the two classifiers was realized based on the fusion decision rules mentioned above, which was conducted during training and testing of the dataset illustrated in part II. Then, the three classifiers were applied for classification. Furthermore, to classify the behaviors directly in the video captured by the camera, we tested the influence of different frame windows on the classification results. Firstly, we conducted preliminary experiments to find some suitable values for the hyperparameters to set the Pearson correlation coefficient.
The classifier model's classification abilities significantly increase if it learns all of the behavior features. When the video frame window size was 1, 5, 10, 15, 20, 25, and 30, respectively, hand behavior features were extracted to identify the ideal action window size. Finally, training and testing were carried out using data from the experimental classroom behavior dataset to contrast the SVM classifier's and the RF classifier's classification accuracies with various time windows. It can be seen from Table  6  that each action has different optimal time window sizes, and different time window sizes also have an impact on the classification results. This is because the actions are continuous in time. Obtaining the hand key points of continuous characteristic changes with appropriate window sizes can effectively reduce behavior misjudgments.
In 2009, Turkestan province had 3,415,000 sheep and goats, 716,000 cows and 144,000 horses (no data on camels)  [41]. Kazakhstan's subsoil is rich in various mineral resources including oil, gas, uranium, coal, copper, zinc, gold, chromium, manganese, iron and lead  [42, 43]. In 2016, the extractive sector contributed almost 30% of the country's GDP  [44]. The Shu-Syrdarya mining region, in which our study area is located, was discovered and explored between 1971 and 1991, and is now the largest uranium mining region in Kazakhstan  [45]. The Kazakh-French joint venture KATCO (Kazatomprom-Orano) owns the mines at the Muyunkum and Tortkuduk deposits in the Muyunkum sandy desert of the Sozak district. Since the 1990s, the insitu recovery (ISR) technique became widespread for uranium extraction, used for ore located between impermeable soil layers  [37].
Data Analysis Interview data were processed using NVivo  [57] , a qualitative data analysis software. Interviews were described according to interviewees' attributes: gender, stakeholder category and village. Then, interviews were coded by ES and landscape units. For ES codes, we used the hierarchical structure of the CICES. From the CICES class level, details on the ES and the benefits from the ES were added. Where necessary, ES categories were added, following CICES recommendations  [16]. The landscape unit codes were added according to the landscape described for the provision of ES (maps from Google Earth and Google maps or oral location). However, some ES had no landscape boundaries, such as services related to existence value  [22]  ('Characteristics or features of living systems that have an existence value' in CICES  [16] ).
In these cases, we coded certain ES in a special category that we called no landscape frontier. From the organisation and coding of data, we could create matrix coding queries with the NVivo software. We used matrix coding queries to analyse in detail the relationships between the following sets of data, and to produce tables of data that included the following information for each cell (x,y): (i) Ecosystem services (each level of CICES) and landscape units: number of interviewees who cited the ES (x) in the landscape unit (y). Subsequent analysis based on this matrix will verify the hypothesis that different landscapes provide different ES. (ii) Stakeholder categories and ecosystem services (each level of CICES): number of interviewees from a category (y) who cited the ES (x). (iii) Gender and ecosystem services (each level of CICES): number of male and female interviewees (y) who cited the ES (x).
Acknowledgments: We would like to thank all the participants who agreed to take part in the interview, the employees of the akimats in the eight villages for organising the interviews and welcoming us to their offices, KATCO's  CSR department  for organising the interviews prior to the fieldwork, our  Kazakh-French interpreter Akbope Saparaliyeva, KATCO  for the logistics, support and assistance provided in Kazakhstan,  Alexia Stokes  (native English speaker) who proofread the article, and Orano for granting permission to publish our results. We also thank the anonymous reviewers and editors for their valuable comments and suggestions.
Design of the TEMPO device and software A 96-well culture plate holder was designed using the online software OnShape and 3D-printed. Ultrabright blue LEDs (470 nm, #C503B-BCS-CV0Z0461, Farnell) were arranged on an electronic plate and connected by blocks of 4. Each block of 4 LEDs was soldered with an analog switch (16 pins, #DG201BDJ PDIP, RS Component), connected to one of the outputs of an Arduino board. Four blocks of LEDs were not connected for negative (non-photoactivated) controls in the 96-well plate. A tropicalization coating was applied to protect the components from the humidity. A protective box was built using laser cutting to protect all electronic components. To program photoactivation sequences, a dedicated software with a friendly graphical user interface was developed in Python using the tkinter library.
The software allows to assign to each individual LED block a photoactivation sequence defined by the pulse number, the pulse duration and the interval between pulses. The TEMPO software can establish a direct connection with the TEMPO device in order to upload photoactivation sequences onto the Arduino board. In vitro live imaging of calcium signals ZART T cells were cultured in complete RPMI without phenol red supplemented with 2% heat-inactivated fetal bovine serum, 50 U mL À1 penicillin, 50 mg mL À1 streptomycin, 1 mM sodium pyruvate, 10 mM HEPES and 50 mM 2-mercaptoethanol and maintained at 37 C. Cells were placed in a petri dish above the TEMPO device. In vitro two-photon imaging of calcium signals was performed with an upright microscope (FVMPE-RS, Olympus), a 253/1. 05 numerical aperture, water-dipping objective and using FV31S-SW software (Olympus).
(D) The mean calcium signal was quantified over time, 5 min before and up to 60 min after photoactivation. Data are representative of 2 independent experiments. (E) Monitoring the calcium response after repeated photoactivation. ZART T cells were subjected to a single photoactivation (5s pulse) or two rounds of photoactivation (5s pulses) 1 h apart using TEMPO. Cell aliquots were collected at various time points after each photoactivation and analyzed by time-resolved flow cytometry to monitor calcium levels using the Twitch2B fluorescent reporter. Each cell aliquot was acquired for 15 min and immediately replaced by a new aliquot to generate an almost continuous signal curve by concatenation. Aliquot switching can results in minor changes in calcium index. Note that the responses after the first or the second photoactivation are similar in intensity and duration.
III. COVR TOOLKIT AS A KNOWLEDGE BASE The COVR Toolkit (www. safearoundrobots. com) is a webaccessible knowledge base (see Fig. 2 ) for cobot stakeholders interested in safety of HRC applications  [33]. The first version consisted of a simple database to identify applicable standards and regulations based on the robot type and application domain of an HRC application. It also featured a database of system level validation methods, which could be filtered according to their domain, safety skill involved, and type of robotic device. From 2019-2021, the Toolkit was updated in yearly cycles to systematically address the needs of several stakeholders along the different life cycle phases. In the following, the various aspects of the Toolkit and their relation to the lifecycle phases from the previous section will be described.
E. General Background Information-Test Equipment, Publications, Legal Tool and FAQs In addition to the Toolkit sub-sections that correspond directly to the life cycle model, there are four further features that provide useful background information for stakeholders. The section "test equipment" contains a database of measurement equipment that is suitable for the execution of SLVM and is closely related to the protocols. Whereas protocols are by design measurement equipment agnostic, users have indicated that they would also appreciate support in finding suitable measuring equipment. The database can be filtered according to the specific physical dimension to be measured, such as pressure, distance, etc. The library is the result of market research, but should not be considered as an endorsement for any specific manufacturer.
The section also solicits Toolkit stakeholders to suggest new devices to be included in the database. The "publications" section of the Toolkit lists over 90 different publications, that are relevant to cobot safety. The publications were initially curated by the COVR consortium based on their research and experience. External users can also submit suggestions for additions. This section is targeted towards the academic community. The most recent addition to the COVR Toolkit is the legal tool. It is designed to provide guidance to users on the legal and legislative aspects of cobots. It is strongly based on the output of the COVR Award "LEGARA," which is white paper published by the law firm CO:Play  [34]. The legal tool covers topics such as the European legislative landscape, CE marking, liability for accidents, and legal risk assessments.
The Universal Robot control software was used to program the UR3e robot arm, which also controls the connected gripper. The PCL is programmed with U-Create Studio, while Proconweb is used for the visualization of the touch display, which are software tools developed by Weidmüller. For quality control, the Cognex Insight Explorer camera software was used. In general, the application has a high degree of variability. One workstation can be physically composed with different workspaces to produce a set of different products. The geometrical properties of the products combined with the properties of the tool (while being grasped and handled by the cobot), may result in potentially different risks. 2) Project Outcome: CoR-Lab and Weidmüller did a comprehensive literature survey on normative safety requirements for modularized workstations to review the current state of the art and regulations for such installations.
After the therapeutic motion exercises that are to be assessed were executed by the robot system connected to the dummy test rig, the data analysis sheet will present the protocol user with an overview of the movement durations in the riskless zone, precautionary zone and prohibited zone of all relevant joint angles. The safety validation is passed if the duration of prohibited zones in all angles is equal to 0%. 3) Toolkit Usage and Feedback: The award beneficiaries used the toolkit to collect information on cobot safety assessment and identified a missing link between device type and standards, which was then corrected. An analysis of industrial cobot systems in medical systems was performed. A short summary of the analysis and a link to the complete analysis can be found in a case story on the COVR toolkit.
ACKNOWLEDGMENT The authors are grateful to  J. Nacsa ,  D. Szur , and  K. Abai  from  SZTAKI ,  Jozsef Toth  from  Hepenix Ltd  and  Greta Rácz  from  Green Drops Farm Ltd  for their professional and successful development of the  Hydrocobotics  project. Authors are thankful to  D. Wigand  and  S. Krüger  from  Bielefeld University  and  M. Edler  from  Weidmüller Group  for their professional consultancy and support of the  CoSMo  project. The authors also would like to thank  S. David  and  A. -A. G. n M. A. Kaid  from  BME ,  M. O. Bauer  and  T. Patai  from  DARPAMOTION Ltd  for their professional contribution to the  DOROTHY  project. I. P. Author thanks for the  ED_18-2-2018-0006  grant on "Research on prime exploitation of the potential provided by the industrial digitalization. ".
This work describes a new version of a previously published Python packagegym-saturation: a collection of OpenAI Gym environments for guiding saturation-style provers based on the given clause algorithm with reinforcement learning. We contribute usage examples with two different provers: Vampire and iProver. We also have decoupled the proof state representation from reinforcement learning per se and provided examples of using a known ast2vec Python code embedding model as a first-order logic representation. In addition, we demonstrate how environment wrappers can transform a prover into a problem similar to a multi-armed bandit. We applied two reinforcement learning algorithms (Thompson sampling and Proximal policy optimisation) implemented in Ray RLlib to show the ease of experimentation with the new release of our package.
Introduction This work describes a new version (0. 10. 0, released 2023. 04. 25) of a previously published  [28]  Python packagegym-saturation 1 : a collection of OpenAI Gym  [6]  environments for guiding saturation-style provers (using the given clause algorithm) with reinforcement learning (RL) algorithms. The new version partly implements the ideas of our project proposal  [29]. The main changes from the previous release (0. 2. 9, on 2022. 02. 26) are: -guiding two popular provers instead of a single experimental one (Sect. 3) -pluggable first-order logic formulae embeddings support (Sect. 4)  -examples of experiments with different RL algorithms (Sect. 5) -following the updated Gymnasium  [35]  API instead of the outdated OpenAI Gym gym-saturation works with Python 3. 8+. One can install it by pip install gym-saturation or conda install -c conda-forge gym-saturation.
The version described in this work also provides a couple of default options. 3 Architecture and Implementation Details 3. 1 Architecture gym-saturation is compatible with Gymnasium  [35] , a maintained fork of nowoutdated OpenAI Gym standard of RL-environments, and passes all required environment checks. As a result of our migration to Gymnasium, its maintainers featured gym-saturation in a curated list of third-party environments  2. Previously, gym-saturation guided an experimental pure Python prover  [28]  which happened to be too slow and abandoned in favour of existing highly efficient provers: Vampire and iProver. Although the gym-saturation user communicates with both iProver and Vampire in the same manner, under the hood, they use different protocols. For Vampire, we relied on the so-called manual (interactive) clause selection mode implemented several years ago for an unrelated task  [11].
In this mode, Vampire interrupts the saturation loop and listens to standard input for a number of a given clause instead of applying heuristics. Independent of this mode, Vampire writes (or not, depending on the option show_all) newly inferred clauses to its standard output. Using Python package pexpect, we attach to Vampire's standard input and output, pass the action chosen by the agent to the former and read observations from the latter. In manual clause selection mode, Vampire works like a server awaiting a request with an action to which it replies (exactly what an environment typically does). iProver recently added support of being guided by external agents. An agent has to be a TCP server satisfying a particular API specification. So, iProver behaves as a client which sends a request with observations to some server and awaits a reply containing an action.
ast2vec and Our Contributions to It In  [20] , the authors proposed a particular neural network architecture they called Recursive Tree Grammar Autoencoders (RTG-AE), which encodes abstract syntax trees produced by a programming language parser into real vectors. Being interested in education applications, they also published the pre-trained model for Python  [21]. To make use of it for our purpose, we furnished several technical improvements to their code (our contribution is freely available 4  ): -a TorchServe  [24]  handler for HTTP POST requests for embeddings -request caching with the Memcached server [9] -Docker container to start the whole subsystem easily on any operating system Fig. 2.
Usage Examples We provide examples of experiments easily possible with gym-saturation as a supplementary code to this paper 5. We don't consider these experiments as being of any scientific significance per se, serving merely as illustrations and basic usage examples. Tweaking the RL algorithms' meta-parameters and deep neural network architectures is out of the scope of the present system description. We coded these experiments in the Ray framework, which includes an RLlib -a library of popular RL algorithms. The Ray is compatible with Tensorflow  [1]  and PyTorch  [22]  deep learning frameworks, so it doesn't limit a potential gym-saturation user by one. In the experiments, we try to solve SET001-1 from the TPTP with max_clauses=20 (having no more than twenty clauses in the proof state) for guiding Vampire and max_clauses=15 for iProver.