Reviewerly/exHarmony huggingface dataset:
Distributed point-to-point routing method for tasks in cloud control systems

With the rapid development of cloud computing and control theory, a new paradigm of networked control systems called cloud control systems is proposed to meet the requirements of large-scale and complex applications.Currently, cloud control systems are mainly built by using a centralized architecture.The centralized system is overly dependent on the central control plane and has huge challenges in large-scale heterogeneous node systems.In this paper, we propose a decentralized approach to establish cloud control systems by proposing a distributed point-to-point task routing method.A considerable number of tasks in the system will not rely on the central plane and will be directly routed to the target devices through the pointto-point routing method, which improves the horizontal scalability of the cloud control system.The point-to-point routing method directly gives a unique address to every task, making inter-task communication more efficient in a complex heterogeneous and busy cloud control systems.Finally, we experimentally demonstrate that the distributed point-to-point task routing approach is compatible against the state-of-the-art central systems in large-scale task situations.
Transformer-based Video Summarization With Spatial-Temporal Representation

Video summarization is an important topic studied by researchers. With the application of deep learning, CNN and RNN have also been used to generate video summarization. However, because a video contains many frames and the video timing span is large, the spatial-temporal architecture of the video is complex, but it is necessary to abstract the spatial-temporal structure information to generate a summarization, and it is also the focus of the researchers recently. Based on previous researchers' research, we put forward a new way for video summary generation, which consists of three deep neural network models. First, a 2D convolutional CNN is used to process video frames, convert a short video into a vector form that can be flexibly calculated, and then use 1D convolution to perform sequence analysis on the timing information of data, and then use the Transformer encorder model that is currently used in natural language processing to further extract timing information, and finally use up-sampling to obtain output to make its dimension the same as video frames number in input short video. Through training learning allows the model to get importance scores which indicate the importance of video frames, and then select key shots to obtain video summaries. Experimental data illustrates that our model get better performance than existing methods on two generic datasets.
A Robust Approach to Adversarial Attack on Tabular Data for Classification Algorithm Testing

Adversarial machine learning (AML) is a significant measure to detect the performance and robustness of the machine learning models, and it has obtained intense interest. While research on the topic has mainly been focused on a specific model or classifier, the generality of adversarial perturbations has been constrained a lot. To tackle this issue, a robust attack approach based on data distribution features has been proposed is this paper. Firstly, the objective function is selected with the distribution features of the data accordingly. Then, the perturbation vector is so obtained from the objective function. Finally, the rationality and feasibility of the robust attack approach are verified by testing on specific cases. Experiments on comparing with other commonly used attack algorithms show that the new method outperforms the state-of-the-art.
Applications of Artificial Intelligence and IoT in the Development of Sports Training Education Management

With the persistent improvement of information technology, regular physical education showing techniques are presently not pertinent. To guarantee the objectivity of school physical education assessment, this paper plans a bunch of school physical education the board information framework in view of artificial intelligence technology. The weighting calculation in the understudy execution assessment module and educator execution assessment module in the framework takes on the canny calculation in light of FNN brain organization. In this review, the artificial intelligence and current technology of physical education have explored and talked about which to give the best hypothetical premise to the advanced technology of physical development and improvement. Obviously, artificial intelligence has a place with serious areas of strength for a disciplines which is growing yet in addition one of the principal heading of the PC and its connected advancement interdisciplinary examination, impacting the whole educating progress. Artificial intelligence enjoys many benefits in the use of educating technology.
Tropical Cryptography Based on Multiple Exponentiation Problem of Matrices

Because there is no multiplication of numbers in tropical algebra and the problem of solving the systems of polynomial equations in tropical algebra is NP-hard, in recent years some public key cryptography based on tropical semiring has been proposed. But most of them have some defects. This paper proposes new public key cryptosystems based on tropical matrices. The security of the cryptosystem relies on the difficulty of the problem of finding multiple exponentiations of tropical matrices given the product of the matrices powers when the subsemiring is hidden. This problem is a generalization of the discrete logarithm problem. But the problem generally cannot be reduced to discrete logarithm problem or hidden subgroup problem in polynomial time. Since the generating matrix of the used commutative subsemirings is hidden and the public key matrices are the product of more than two unknown matrices, the cryptosystems can resist KU attack and other known attacks. The cryptosystems based on multiple exponentiation problem can be considered as a potential postquantum cryptosystem.
Study on Military Equipment Knowledge Construction based on Knowledge Graph

With the continuous innovation and application of science and technology in the military, the huge amount of knowledge and information of military equipment is overloaded. This paper starts from the military equipment knowledge, considers the attributes of various indicators of military equipment itself and the specific knowledge of the application of military equipment in combat, and constructs military equipment knowledge based on the knowledge graph and the operational graph. In addition, the graph construction techniques such as knowledge system construction, knowledge extraction, knowledge fusion and knowledge differences resolving are described. Finally, taking the military equipment knowledge involved in patrol and anti-submarine as example, the demonstration of military equipment knowledge based on graph is given.
Calibration-Free Cross-Camera Target Association Using Interaction Spatiotemporal Consistency

In this paper, we propose a novel calibration-free cross-camera target association algorithm that aims to relate local visual data of the same object across cameras with overlapping FOVs. Unlike other methods using object's own characteristics, our approach makes full use of the interactions between objects and explores their spatiotemporal consistency in projection transformation to associate cameras. It has wider applicability in deployed overlapping multi-camera systems with unknown or rarely available calibration data, especially if there is a large perspective gap between cameras. Specifically, we first extract trajectory intersection which is one of the typical object-object interactive behaviors from each camera for feature vector construction. Then, based on the consistency of object-object interactions, we propose a multi-camera spatiotemporal alignment method via wide-domain cross-correlation analysis. It realizes time synchronization and spatial calibration of the multi-camera system simultaneously. After that, we introduce a cross-camera target association approach using aligned object-object interactions. The local data of the same target are successfully associated across cameras without any additional calibration. Extensive experimental evaluations on different databases verify the effectiveness and robustness of our proposed method.
ScreenID: Enhancing QRCode Security by Utilizing Screen Dimming Feature

Quick response (QR) codes have been widely used in mobile applications, especially mobile payments, such as Alipay, WeChat, PayPal, etc due to their convenience and the pervasive built-in cameras on smartphones. Recently, however, attacks against QR codes have been reported and attackers can capture a QR code of the victim and replay it to achieve a fraudulent transaction or intercept private information, just before the original QR code is scanned. In this study, we enhance the security of a QR code by identifying its authenticity. We propose ScreenID, which embeds a QR code with information of the screen which displays it, thereby the QR code can reveal whether it is reproduced by an adversary or not. In ScreenID, PWM frequency of screens is exploited as the unique screen fingerprint. To improve the estimation accuracy of PWM frequency, ScreenID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that ScreenID can differentiate screens of different models, types, and manufacturers and thus improve the security of QR codes.
Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From ShanghaiTech University

In SC20, (Srivastava et al. 2020) proposed a Parallel F <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ram</b> ework for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ayesian <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Le</b> arning, or ramBLe, for short, which is a highly parallel and efficient framework for learning the structure of Bayesian Networks (BNs) from samples, <!--?query id="Q1"--> There was a discrepancy in Bibliography in the PDF and the source file. We have followed the source file. ?> particularly large genome-scale networks. As part of our participation in the SC21 Student Cluster Competition, our task was to verify conclusions from the original work (Srivastava et al. 2020). Here we present the outcome of our experiments, which were performed on a four-node cluster from the Oracle Cloud HPC platform. We reproduce the numerical results from (Srivastava et al. 2020), namely the algorithm's performance and scaling behavior using MPI and different Python and Boost libraries on the Oracle cloud.
Contour-Aware Equipotential Learning for Semantic Segmentation

With increasing demands for high-quality semantic segmentation in the industry, hard-distinguishing semantic boundaries have posed a significant threat to existing solutions. Inspired by real-life experience, i.e., combining varied observations contributes to higher visual recognition confidence, we present the equipotential learning (EPL) method. This novel module transfers the predicted/ground-truth semantic labels to a self-defined potential domain to learn and infer decision boundaries along customized directions. The conversion to the potential domain is implemented via a lightweight differentiable anisotropic convolution without incurring any parameter overhead. Besides, the designed two loss functions, the point loss and the equipotential line loss implement anisotropic field regression and category-level contour learning, respectively, enhancing prediction consistencies in the inter/intra-class boundary areas. More importantly, EPL is agnostic to network architectures, and thus it can be plugged into most existing segmentation models. This paper is the first attempt to address the boundary segmentation problem with field regression and contour learning. Meaningful performance improvements on Pascal Voc 2012 and Cityscapes demonstrate that the proposed EPL module can benefit the off-the-shelf fully convolutional network models when recognizing semantic boundary areas. Besides, intensive comparisons and analysis show the favorable merits of EPL for distinguishing semantically-similar and irregular-shaped categories.
Research on Path Planning of Mobile Robot with a Novel Improved Artificial Potential Field Algorithm

For the path planning and obstacle avoidance problem of mobile robots in unknown surroundings, a novel improved artificial potential field (IAPF) model was proposed in this study. In order to overcome the shortages of low efficiency, local optimization trap, and unreachable target in the classical artificial potential field (APF) method, the new adaptive step length adjustment strategy was proposed in IAPF, which improved the path planning and obstacle avoidance efficiency. A new triangular navigation method was designed to solve the local optimization trap in joint force zero condition for a variety of path planning. In order to solve the target unreachable problem, a new target attraction model was established based on the distance of obstacle to improve convergence rate, and the new method was designed such as adding the aim factor to optimize the rejection force function and so on. The two methods of IAPF and APF are compared using MATLAB simulation, the average path planning efficiency of IAPF is increased by 42.8% compared with APF, the average path length is reduced by 8.6%, and the average target convergence rate is increased by 26.1%. Finally, the physical test of the mobile robot verified the effectiveness and accuracy of IAPF.
Comparative Study on Transfer Learning for Object Classification and Detection

The recent development of deep neural learning achieved remarkable breakthroughs in object classification and detection. Deep learning has the capability of learning features automatically from data using general-purpose learning procedures. However, because deep neural networks require large amounts of data to train the parameters in the network, it is challenging to develop any object classification or detection system with a relatively small dataset. Transfer learning is an important machine learning technique that transfers the learned features in a pre-trained Convolution Neural Network (CNN) model into a new system. In this study, current state-of-the-art CNN models are reviewed in their architectures and characteristics. For the comparative study of transfer learning, the object classification and the detection systems are implemented using transfer learning with six state-of-the-art CNN models. The object classification model has achieved an accuracy of 97.01% for the three-class classification task using transfer learning. Furthermore, six different Faster R-CNN architectures are implemented for object detection. The performances of the different transferred models are compared in terms of the accuracy and the deploying speed of the new model. Experiments show that transfer learning saves training time and achieves accurate performance by fine-tuning the pre-existing deep learning model.
3D Object Detection for Autonomous Driving

As an important application of Artificial Intelligence (AI), autonomous driving has developed rapidly in recent years. 3D object detection in autonomous driving has attracted more and more attention because it provides precise range and size information of an object. Some of the detection algorithms rely on point cloud data acquired from LiDAR. Compared with LiDAR, the optical image solution for autonomous driving is cheaper, so that image-based detection approaches are also popular. The chapter overview the recent advance on 3D object detection using 3D point cloud or image directly. For the computing simplicity, the approaches which detect 3D object from the input images without 3D point reconstruction benefit the vehicle computational platform. However, the object location accuracy of such approaches is limited. Therefore, a proposed method named as Descriptor Enhanced Stereo R-CNN (DESR-CNN) is specified in detail. Several 3D object detection algorithms are tested on KITTI dataset. The experimental results demonstrate that DESR-CNN outperforms most of the existing 3D object detection methods based on binocular images.
Prevalence norms for 40,777 Catalan words: An online megastudy of vocabulary size

In this study, we present word prevalence data (i.e., the number of people who know a given word) for 40,777 Catalan words. An online massive visual lexical decision task involving more than 200,000 native speakers of this language was carried out. The characteristics of the participants as well as those of the words which mostly influence word knowledge were examined. Regarding the participants, the analysis of the data revealed that their age was the main factor influencing vocabulary size, followed by their educational level and other variables such as the number of languages spoken and their level of proficiency in Catalan. Concerning the words, by far the most determining factor was lexical frequency, with a minor influence of both length and the size of the orthographic neighborhood. These data mainly agree with those reported in other languages in which the same variables have been analyzed (Dutch, English, and Spanish, thus far). Therefore, the list is increased with Catalan, a language which, due to its use in an essentially bilingual context, is of special interest to researchers interested in the field of bilingualism and second language acquisition.
Privacy-preserving Model Training for Disease Prediction Using Federated Learning with Differential Privacy

Machine learning is playing an increasingly critical role in health science with its capability of inferring valuable information from high-dimensional data. More training data provides greater statistical power to generate better models that can help decision-making in healthcare. However, this often requires combining research and patient data across institutions and hospitals, which is not always possible due to privacy considerations. In this paper, we outline a simple federated learning algorithm implementing differential privacy to ensure privacy when training a machine learning model on data spread across different institutions. We tested our model by predicting breast cancer status from gene expression data. Our model achieves a similar level of accuracy and precision as a single-site non-private neural network model when we enforce privacy. This result suggests that our algorithm is an effective method of implementing differential privacy with federated learning, and clinical data scientists can use our general framework to produce differentially private models on federated datasets. Our framework is available at https://github.com/gersteinlab/idash20FL.
Eating detection with a head-mounted video camera

In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9% and F1 score 78.7% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or mobile platforms, in terms of computation, memory, and power. We believe this paper is the first work to experiment with video-based (rather than image-based) eating detection in free-living scenarios.
Graph Knowledge Transfer for Offensive Language Identification with Graph Neural Networks

Identifying offensive language (OL) has become ever more important with the rise of online social media (OSM). Most works on OL identification have applied sequential models to learn offensive semantics. In a different light, recent popular graph neural networks (GNNs) model text in a word graph and learn local word-level usages for each document. This work aims to explore text GNNs on learning OL usages in a non-sequential view yet there are two barriers: (1) Similar to sequential models, GNNs also suffer from the out-of-vocabulary (OOV) issue due to the informal usages of OSM texts. (2) Lacking of appropriate edge weights derived from short content to the word graph. Motivated by the rich resources of OSM, this work leverages existing OL corpus to build a global reference graph and proposes a graph knowledge transfer mechanism (GKtran) to tackle the limited extent of learning from short texts. To embed OL knowledge, GKtran infers edge weights from a reference graph and transfers weights to each document's word graph. Edges that connect OOV words are assigned weights through weight propagation. Thus, each document graph is fully weighted for graph classification. Experimental results show that the additional information from training or external data transferred by GKtran effectively improves OL identification with graphs.
Hybrid VTOL UAV Autonomous Operations from Mobile Landing Pad

Flight missions carried out from mobile landing pads are a challenge for hybrid VTOL UAVs. An approach to precise landing on a moving pad requires implementing an appropriate control system supported by accurate relative positioning. The paper presents the results of real conditions test on such a UAV system, which is a combination of hybrid VTOL UAV and dedicated landing pad navigation station that was developed to allow UAVS to take off and land on a mobile platform. Tests were carried out by employing a mobile landing platform mounted on a trailer pulled by a car. During the tests, the hybrid VTOL UAV was able to take off and land on the landing platform moving at a speed up to 25 km/h, and all flight trials ended with success. These tests improved the UAV system before the final test on maritime conditions.
A Method of Calibration for the Distortion of LiDAR Integrating IMU and Odometer

To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the odometer is fused, and the pose of the LiDAR is obtained using the linear interpolation method. The ICP method is used to scan and match the LiDAR data. The data fused by the IMU and the odometer provide the optimal initial value for the ICP. The estimated speed of the LiDAR is introduced as the termination condition of the ICP method iteration to realize the compensation of the LiDAR data. The experimental comparative analysis shows that the algorithm is better than the ICP algorithm and the VICP algorithm in matching accuracy.