dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset contains 6 classes including shake-hands, point, hug, push, kick and punch. There is a total of 20 video sequences whose lengths are around 1 minute. For IXMAS datasets we choose Material class that represent sequences for human-human interaction. For multiple human action recognition, we built our dataset named Multiple human action dataset (MHAD 1 ). MHAD, as the naming reflects, to provide s a new dataset that contains many actions made by many actors in the same video. In one hand and related to video surveillance needs, each actor can act many actions during his presence in the scene. This can represent a rich data for many tasks in computer vision including video summarization methods based on human action, motion detection and tracking methods, people detection and recognition and people counting.
Dataset characteristics: The proposed dataset includes a set of human actions representing usual human activities. MHAD composed of 10 actions, including: boxing, walking, running, hand waving, hand clapping, jogging, carrying, standing, backpack carrying, and two persons fighting. Generated videos contain from 3 to 5 persons acting in the scene. The duration of each video is 2-3 minutes and the duration of each action is from 2 to 3 minutes. In addition, three of the videos are outdoor and one is an indoor. The background is generated for each video and annotations of each moving actor is provided. In the current work, we only used datasets that are captured by a fixed camera because our approach consists of a modification of the background and the detection of moving human bodies before tracking each one of them.
For the same purpose, we have built our dataset containing three videos. For each video we can find persons acting different actions. Also, consecutive actions are performed by each person during his presence in the scene. The accuracy of summarization is related to the recognition accuracy. So, if actions are well recognized, the summarization is just a representation of these actions by one image for each person 's action. The ground truth of the action in our dataset depends on the succession of actions in each video. Unlike the other summarization methods, wherein the scene changes every time, our dataset is for multiple action recognition and the summarization is rather based on detection. Figure  7  represents the succession of actions for each person in two videos from our datasets.
Action recognition and summarization results In order to evaluate the proposed method for background modeling, SBI dataset is used. Figure  8  represents the generated background using the proposed approach. The obtained results are convincing and the using our method the background is built without artificial ghost for all videos. For Foliage and People&Foliage sequences the background the proposed method success to estimate the background with good results even the sequences a full of moving object during all time of the videos. In order to consolidate the visualized results, we used different metrics, including Gray-level Error (AGE), Total number of Error Pixels (EPs), Percentage of Error Pixels (pEPs), Total number of Clustered Error Pixels (CEPs), Peak-Signal-to-Noise-Ratio (PSNR), MultiScale Structural Similarity Index (MS-SSIM), Color image Quality Measure (CQM).
These metrics are presented in Table  2  that illustrate the obtained results comparing with two background modeling methods IMBS-MT  [54]  and  [43]  respectively. As shown, the proposed method succeed to modelized the background with good results compared to the other method in the most of dataset videos including HighwayI, Hall&Monitor, sallen, and Foliage. To evaluate the proposed method, obtained results are compared with state-of-the-art methods. Most action recognition methods perform the recognition on the original data that contains one person in action. The presence of more than one person in a scene can reduce the performance of the recognition process. In addition, most methods use the silhouette of the moving object to recognize the action. Hence, the accuracy of the recognition can be influenced by a bad binarizaion or segmentation.
The minimum value of all distance values represents the action. To evaluate the effectiveness of the proposed technique, KTH, Weizmann, and UCF-ARG datasets are tested against each other. In addition, we perform a test between the video of within each dataset. The extracted silhouette for each human body during a video sequence might contain more than one action. The proposed technique for detecting shots, which are similar images that contain the same action, is presented in Fig. 9. The histogram of similarity between each two consecutive images is computed. Then, a filter is applied to extract the shots using the difference between each two consecutive values. The histogram presented in Fig. 9b  shows the transaction between actions. From that, we can observe the transaction caused by the change from one action to another.
Tables  3, 4 , 5, 6 and 7 illustrate the similarity distances between HOGs of the actions of the three datasets: within the KTH dataset, within the Weizmann dataset, within the UCF-ARG dataset, and between the KHT and UCF-ARG datasets. The distance is computed using cosine similarity measure of equation (  4 ). The results tabulated in Table  2 , which represent the similarity distances within the actions of the KTH dataset, reveal that the distance between similar actions such as walking, and jogging is smaller than that between very different actions such as hand-waving and running. It can be clearly seen in Table  3  that the distance between running and walking is smaller than that between Fig. 8  Background results on the SBI dataset using theproposed approach   4  and 5. From tabulated results, it can be clearly seen that similar or close   6 , we can observe that the proposed method failed to recognize the jogging actions.
In addition, the boxing action of the KTH dataset is recognized as a waving action in the Ground dataset; this is due to the similarity in the appearance of boxing and waving in many situations. The errors rate in Table  7  represents 2% of the tested data, and between the closest actions like jigging and running. Also, in some cases, actions can be similar, and so the summarization using images can be useful to spot the differences. For the sake of comparison of the proposed method's results with some of the state-of-the-art methods, the accuracy of each approach is presented in Table  8. The two proposed method for action recognition are named respectively by: (1) the classification using cosine similarity measure between HOGs of the training set and the current HOG of the current action (CS-HOG-TDMap).
(2) the recognition using CNN model (CNN-TDMap). The accuracy for the state-of-the-art methods are the same values reported in the papers. While the accuracy rate obtained using the proposed method is the ratio between the number of recognized action s and the total number of actions. The KTH, Weizmann and UCF-ARG datasets have been used by many methods in the literature for the last three years. The obtained results using the proposed approach for all the datasets are convincing and robust. However, the proposed method succeeds in recognizing over 98% of actions in the KTH and Weizmann, and UCF-ARG datasets using CS-HOG-TDMap and 99% using CNN-TDMap, because of the simplicity of the data, which contains a simple background; in addition, the actions are clear and in normal situations. For instance, in the UT-interaction dataset, the proposed method reach 87% and 98% recognition rates.
For the IXMAS dataset, a large dataset with many situations of each action, the proposed method had a successful recognition rate of 99%. Compared to state-of-the-art-methods related to multiple human action recognition, that use the same category of  datasets represented, Table  9  represents the accuracy rate for each method, it can be observed that the proposed method results are improved and more effective. Obtained results are related to the use of the new representation of the data. Detection and recognition of multiple human action using the proposed method can be implemented in real-time via an extended version. The proposed approach is tested on our dataset by extracting the sequence of each person in the scene and apply the proposed algorithm. The obtained results are showed in the Fig.
10  illustrating the detected person and their actions on MHAD and PET datasets. The visualized results represent one example from PET09 dataset and three videos from our dataset. The proposed approach is validated in three major steps including human detection, subsequence extraction, recognition and summarization of each person actions. UIUCI dataset is use also for testing the trained actions. Figure  11a  shows some obtained results. These videos are not included in the training phase because they contain the same category of action in the used datasets. For UIUC dataset video the recognition rate reach 98%. For the human-human action we use two datasets such as IXMAS and UY-interaction Fig. 11b  and c  represents some obtained results of detection and recognition of human interactions.
For example, in IXMAS dataset we use some video where two persons fighting in the training phase. The obtained results shown in Fig. 11b  shows the recognition in four videos captured from different field of view (FOV). Also, results in Fig. 11c  illustrates some results from UT-interaction dataset representing some recognized actions. The accuracy of the proposed algorithm is related to the detection tracking and the segmentation of the human body in the scene. Additionally, the detected person may be in an invariant position that requires a large number of videos in the training phase to represent all of the actions in several positions. In this paper, we defined a video summary as a concatenation of action labels and keyframes of the video where the actors perform that action. The summarization using human actions presented in this paper consists of splitting the actions of each person present in the scene.
Based on the results of the recognition and the extraction of each action, the summarization using the entire image is generated. Figure  12  presents the results of the proposed method's application on our dataset, which includes two persons and many actions for each of them. The body silhouette of a person is extracted before detecting the shots that represent one action. For each shot, our algorithm selects frames that contain the corresponding body silhouette. The same process takes place for each person in the scene. If a person enters the surveilled zone and performs one action, walking into the scene for example, the summarization of this is one image. The proposed method can help video surveillance system operators show just the most important times in the video, noting that most areas are empty at most times.
Conclusions In this work, a novel approach for multiple human detection, recognition and summarization was developed, where actions of a person presenting in a scene are summarized. Herein, for a specific scene, motion/actions of each person are detected, tracked, and a sequence of each human body silhouette is generated. Upon recognizing and summarizing each action within a shot (i. e. each shot represents a subsequence that represent one action), shots detection operation was developed in order to determine the set of actions in the generated sequence. Shots that represent the homogeneous part of the sequence were selected using the cosine similarity measure of consecutive frames. The recognition of each action was based on two steps. First, using a training dataset of HOGs of TDMaps that was generated in each sub-sequence.
HOG-TDMap 98% CNN-TDMap 99. 85% UCF-ARG dataset Qazi et al. 2017 [42] 90% CS-HOG-TDMap 97% CNN-TDMap 99. 73% UT-interaction Saho et al. 2019 [50] 76% Jalal et al. 2018 [51] 83% CS-HOG-TDMap 87% CNN-TDMap 98. 9% IXMAS Liu et al. 2018 [52] 99. 9% CS-HOG-TDMap 98% CNN-TDMap 99. 6%.
TOPIC OVERVIEW Although true independent verification of computational results should be the goal when possible, resources and tools that aim to promote the replication of results using the original code are extremely valuable to the community. Platforms such as open source code sharing sites and model databases  (Birgiolas et al. , 2015; McDougal et al. , 2017; Gleeson et al. , 2019)  provide the means for increasing the impact of models and other computational approaches through re-use and allow for further development and improvement. Simulatorindependent model descriptions provide a further step toward reproducibility and transparency  (Gleeson et al. , 2010; Cope et al. , 2017; NineML Committee, 2020). Despite this progress, best practices for verification of computational neuroscience research are not well-established.
For data driven models, better reporting is needed regarding which data were used to constrain model development, the details of the data fitting process, and whether results are robust to small changes in conditions. In both cases, better approaches for parameter optimization and the exploration of the sensitivity of parameters are needed. Here we see several approaches toward more rigorous model validation against experimental data across scales, as well as multiple resources for better parameter optimization and sensitivity analysis. Viswan et al. describe FindSim, a novel framework for integrating experimental datasets with large multiscale models to systematically constrain and validate models. At the network level, considerable challenges remain over what metrics should be used to quantify network behavior.
Jedrzejewski-Szmek et al. provide a versatile method for the optimization of model parameters that is robust in the presence of local fluctuations in the fitness function and in high-dimensional, discontinuous fitness landscapes. This approach is also applied to an investigation of the differences in channel properties between neuron subtypes. Uncertainty quantification and sensitivity analysis can provide rigorous procedures to quantify how model outputs depend on parameter uncertainty. Tennøe et al. provide the community with Uncertainpy, which is a Python toolbox for uncertainty quantification and sensitivity analysis, and also provide examples of its use with models simulated with both NEURON  (Hines et al. , 2020)  and NEST  (Gewaltig and Diesmann, 2007). Approaches and resources for reproducibility advocated by Topic authors cross many spatial and temporal scales, from sub-cellular signaling networks (Viswan et al.
) to wholebrain imaging techniques  (Zhao et al. ). We discover that the NEURON simulation platform has been extended to include reaction-diffusion modeling of extracellular dynamics, providing a pathway to export this class of models for future crosssimulator standardization  (Newton et al. ). We also see how reproducibility challenges extend to other cell types such as glia as well as subcortical structures  (Manninen et al. ). At the network level, Pauli et al. demonstrate the sensitivity of spiking neuron network models to implementation choices, the integration timestep, and parameters, providing guidelines to reduce these issues and increase scientific quality. For spiking neuron networks specifically geared toward machine learning and reinforcement learning tasks, Hazan et al. provide BindsNET, a Python package for rapidly building and simulating such networks for implementation on multiple CPU and GPU platforms, promoting reproducibility across platforms.
• Automotive: Sensors in vehicles provide even more data on things like environmental conditions, tyre pressure, engine performance and environmental conditions. These integrated, safe, and robust embedded systems will in a near future lead to self-driving cars. • Railway (improvement of the management of rail networks): Widespread use of automation and sensor technologies, on tracks or trains, provides real-time data on resources in the network. This information can be used to predict when resources need repairing or replacing, reducing so the amount of time that rail resources are unavailable. • Healthcare: Connected medical devices and applications are already creating an Internet of Medical Things which is contributing to better health monitoring and preventive care. From improving patients' experience through to connecting medical devices to physicians' smartphones, the IoT is transforming healthcare.
Each node in the IoT has to be implemented with a certain degree of autonomy. Such nodes can therefore collect information about their respective environments and make decisions for their own behaviors. In this case, the data (including instructions and information) transmitted amongst nodes in a fully distributed form, is restricted into a limited area, which decreases heterogeneity of the whole network. Such distributed architecture also enables the self-assembling scheme to be scalable. Our approach is organized with respect to several autonomic capabilities  [11]. We recall the definitions for our approach in Table  1. Services have to be aware of their environment and therefore of their surrounding services. They need intelligent service discovery process. Services have to broadcast their features (function, offered/nominal QoS, threshold value from which the business component stops responding) to the others (self-defining).
This point is out of the scope of this paper. Several approaches have been proposed in literature like "Blockchain" which brings security. A blockchain  [12, 13] , or chain of blocks, is the implementation of a data storage and transmission technology without a control organ. Technically, it is a distributed database whose information, sent by users, is checked and grouped in blocks, linked and secured using cryptography. Our algorithm requires that each node maintains and disseminates global state information consisting of the whole assembly of services and computed link QoS. The amount of data to be shared is small. This can be done with use of blockchain technology but other simplest solutions may be sufficient. The following section presents our service composition entity on which we base our approach.
MOSDEN  [25]  supports a sensing as a service model  [26]. It is an IoT middleware for resource constrained computational mobile devices. MOSDEN can collect data from multiple different sensors and process them together. It is fully compatible with Global Sensor Network Middleware that runs on the cloud. Sensor discovery and service composition are not automated. UbiROAD  [27]  is a specialized platform for smart traffic environments. Semantic technologies are the basis for the discovery of heterogeneous resources and data integration across multiple domains. They are used both for descriptive specification of services delivered by the resources and for prescriptive specification of the expected behavior of resources as well as of the integrated system. UbiROAD guarantees a high level of safety while offering personalization, dynamic behaviour, customization, and autonomy of services and ensures context-awareness, and adaptive/reconfigurable composition.
Calvin  [28, 29]  is an open-source IoT middleware from Ericsson. IoT applications building is based on actors which are reusable software components that may represent a device, a computation, or a service. It comprises both a development framework for IoT application developers, and a runtime environment that handles the running application. Compositions are made by writing scripts called CalvinScript. Basically, an application consists of actor instances and connections between the ports of the actors, forming a data flow graph. To allow the reuse of scripts, it is also possible to define components. An application script can also contain deployment rules. Paraimpu  [30]  is a social aware IoT middleware that allows consumers to add, use, share, and interconnect RESTful IoT services whether physical or virtual.
Android's NSD APIs simplify the effort required to implement these features. Figure  2  shows the interface. It is divided into two parts. The top part contains the server logs and two buttons: the first starts the image capture service and the second stops it. The bottom part is for the client who searches for an image capture service. It can start and stop a discovering. When the discovering phase is on, it finds the service and collect his features (nominal/offered QoS, required memory, etc. ) in a key/value form and in an eXtensible Markup Language (XML) format form. This is the concretization of the self-defining property defined in Section 2. With NSD, services are aware of their surrounding environment. Services they found are in communication range. In the following section, we show that, thanks to our SCC component, we are able to compute an estimation of the link QoS that will be used by our algorithm.
Analysis of the approximate resource overhead (computation and memory) has been written in  [18]. We have analyzed the extra code needed by the sub-components located in the SCC membrane: InMonitor, OutMonitor and QoSControl. We have used profiling tools to measure memory and CPU consumption of our approach at runtime. The memory and CPU consumption of our control mechanism is very low for common usage. However, it must be compared to the size of the functional part. The control mechanism should have no or a little influence on the monitored service, so it must represent a low percentage of resource usage compared to it. Indeed, if the micro-service is too small, the processing done by the control mechanism can be in the same order of magnitude. In conclusion, as expected, increasing the number of layers (the number of service types) increases the number of combinations.
This system takes advantage of local gateway processing to present notification and feedback with the minimum latency. The assembly is dynamic whenever the patient is moving. We compute an estimation of the processing time of the services assembly on a Raspberry Pi. Each sensor is an SCC, each IoT gateway includes one or several SCCs and external services (hospital, rescue team) one or several SCCs too. To simplify, we consider only one SCC in an IoT gateway and in the external services. The layout is here a pyramidal decreasing layout (Figure  15 ). It's often the case in IoT because data are processed and analyzed at each level. The result comes thus from aggregated data from several sources. The number of services at each level so decreases. These assembling layouts lead to well-known or- ganizations like Edge  [43, 44] , Dew  [45]  or Fog computing  [46, 47]  [48] with three layers.
In modern computer systems, user processes are isolated from each other by the operating system and the hardware. Additionally, in a cloud scenario it is crucial that the hypervisor isolates tenants from other tenants that are co-located on the same physical machine. However, the hypervisor does not protect tenants against the cloud provider and thus, the supplied operating system and hardware. Intel SGX provides a mechanism that addresses this scenario. It aims at protecting user-level software from attacks from other processes, the operating system, and even physical attackers. In this paper, we demonstrate fine-grained software-based side-channel attacks from a malicious SGX enclave targeting co-located enclaves. Our attack is the first malware running on real SGX hardware, abusing SGX protection features to conceal itself.