Reviewerly/exHarmony huggingface dataset:
VPN Remote Access OSPF-based VPN Security Vulnerabilities and Counter Measurements

Through the COVID-19 pandemic, the number of clients using Virtual Private Network (VPN) has dramatically increased. Consequently, VPN vulnerabilities have become target points to be exploited by attackers. However, studies have been released to defend against such attacks with the purpose of securing VPN. Nevertheless, attacks with high sophistication still target VPNs to comprise the critical data being communicated. VPN servers use protocols to secure connections with clients. However, these protocols are still targeted specifically with Denial-of-Service (DoS) attacks. This paper analyzes and treats the vulnerability of key negotiation process in the main mode as well as aggressive mode of Internet Key Exchange (IKE) protocol in IP Security (IPsec) VPN. We demonstrate experiments of a DoS attack based on Open Shortest Path First (OSPF) protocol adjacent route spoofing. Thereafter, we propose a method to tackle those attacks through exploiting the Suricata as an Intrusion Detection System (IDS) in defending the VPN against DoS attacks.
Human Detection and Action Classification Based on Millimeter Wave Radar Point Cloud Imaging Technology

Millimeter wave (mmw) radar has higher performance and stronger environment adaptability than optic sensors. With the improvement of millimeter wave radar integration technologies, mmw radar has being widely used in the field of ADAS. In this paper, we studied the output point cloud characteristics based on the 77GHZ millimeter wave MIMO radar AWR1443 verification system, and proposed a point cloud filtering method aiming at millimeter wave radar point clouds ADAS application. A point cloud classification network MMPointGNN was used in this paper, and gestures of traffic police were to be used as the training data. The mmw radar point cloud classification using MMPointGNN was proved through experiment of four gestures recognition, including stopping, turning right, turning left and holding. The code is available at https://github.com/dodowujiayu/Human-Detection-and-Action-Classification.
Accurate Visual Tracking with Attention Feature Fusion

The development of various video applications requires the tracker to have efficient classification and accurate object state estimation. Recently, the feature fusion plays a vital role in term of accuracy and robustness for a visual tracking system. The traditional feature fusion methods are generally performed via direct summation or concatenation operation, which are entirely unaware of importance of assigning appropriate weights to different levels of features for a robust model. To tackle this issue, a novel deep-leaning based tracker with attention fusion is proposed in this paper. We propose an improved network structure based on ResNet, which is more conducive for the fusion of hierarchical features. The proposed tracker adopts a nonlinear method for feature fusion in backbone network, and introduces an iterative multi-scale attention module to learn different weights of the hierarchical features. In the classification network which is learned online, the third and fourth layer features extracted from the backbone network are used to obtain the coarse location. The extracted features are assigned different weights by an attention mechanism and fed into the estimation network to perform an iterative refinement for the accurate bounding box estimation. The experiments show the proposed tracker’s efficiency and effectiveness.
APPROACHES TO ASSESSMENT OF TECHNOLOGICAL COMPETENCE OF TEACHERS OF PROFESSIONAL TRAINING

ScopusID:
Velocity-to-velocity human motion forecasting

Forecasting human motion from a sequence of human poses is an important problem in the fields of computer vision and robotics. Most previous approaches merely consider learning the temporal dynamics of body joints or joint angles, while neglect derivatives of body joints (i.e., pose velocities) which could reasonably reduce noise impact and improve stability. To exploit the benefits of pose velocities, we propose the velocity-to-velocity learning paradigm for human motion prediction which attempts to directly build the sequence-to-sequence model in the velocity space. Two variant architectures based on recurrent encoder-decoder networks are introduced under this paradigm. Considering human motion as kinematics of rigid bodies, joint angles which denote transformation are the computations of inverse kinematics. Accordingly, a novel loss function in terms of rotation matrices is designed during training for human motion prediction through a rotation matrix transformation (RMT) layer. Finally, we present an effective training algorithm which exploits sequence transformation to improve model generalization. Our approaches substantially outperform state-of-the-art approaches on two large-scale datasets, Human3.6M and CMU Motion Capture, for both short-term prediction and long-term prediction. In particular, our model can competently forecast human-like and meaningful poses up to 1000 milliseconds. The code is available on GitHub: https://github.com/hongsong-wang/RNN_based_human_motion_prediction.
LCCStyle: Arbitrary Style Transfer With Low Computational Complexity

Surprising performance has been achieved in style transfer since deep learning was introduced to it. However, the existing state-of-the-art (SOTA) algorithms either suffer from quality issues or high computational complexity. The quality issues include shape retention and the adequacy of style migration, and the computational complexity is reflected in the network complexity and additional updates when the style changes. To deal with the above problems, we propose a novel low computational complexity arbitrary style transfer algorithm (LCCStyle) that mainly consists of a transformation feature module (TFM) and learning transformation module (LTM). The TFM is responsible for transforming the content feature map into the stylized feature map without impact on the integrity of content information, which contributes to good shape retention and full style migration. In addition, to avoid additional updates when the style changes, we propose a new training mechanism for arbitrary style transfer to directly generate the parameters of the TFM by a hyper-network. However, the widely used hyper-networks are composed of fully connected layers, which cause a large number of parameters. Hence, we designed a hyper-network (LTM) consisting of one-dimensional convolution to adapt to the characteristics of the Gram matrix of the style feature map, contributing to a small model size and having no impact on quality. Quantitative comparison and user study show that LCCStyle achieves high performance both on the adequacy of style migration and shape retention. Furthermore, compared with the SOTAs, the size of the proposed model is reduced by a large margin of nearly 51.4% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 99.6%. When the input is 512×512 pixels, the processing speeds in the cases of unchanged style and constantly changing style are increased by at least 135% and 227%, respectively. On an Nvidia TITAN RTX GPU, LCCStyle reaches 60fps for 720p video and takes only 1 s to process 8 K images. <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HuangYujie94/LCCStyle</uri> .
VRLearner: A Virtual Reality Based Assessment Tool in Higher Education

We developed a Virtual Reality based assessment tool, VRLearner, to assess and help reinforce student learning outcomes in higher education. The tool was evaluated and compared with a regular assessment tool with the same contents. The evaluation results show that students, particularly underrepresented minority students from our focus group, prefer to use the VR based tool and find it is much more engaging and increased participants' sense of involvement. Our results suggest that virtual reality technology provides an opportunity to leverage learning relevant interaction, which can enhance the design of learning technology and serious games.
Deep Learning for Blind Detection of Interleaver and Scrambler

This paper considers the blind detection of interleaver and scrambler from the Gaussian noisy channel. Specifically, several architectures of the detectors are proposed based on the convolutional neural network (CNN) and recurrent neural network (RNN). By fully exploiting the neural network (NN) structures, the detectors can recognize the interleaver and scrambler with little prior information and only need several codewords. Finally, extensive experiments demonstrate that the constructed detectors can generalize new samples which are not in the training set.
Application of Visual-Cognitive Approach to Decision Support for Concept Development in Systems Engineering

Engineering of complex technical, organizational and other types of systems is characterized by heterogeneity and variability of processed information, a high level of information uncertainty and the increasing role in decision-making of man as a carrier of holistic perception of the problem being solved. The paper discusses methods based on a visual-cognitive approach for data analysis and decision support under information uncertainty. The urgency of using this approach for decision making at the initial stage of a complex system life cycle is substantiated. This stage of concept development is characterized by a high degree of uncertainty and the predominance of creative search tasks. Based on the analysis of the concept development process using a system engineering approach, the structure of the decision-making pipeline and a set of tools for the implementation of its components are proposed.
Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing

Within the field of instance segmentation, most of the state-of-the-art deep learning networks rely nowadays on cascade architectures [], where multiple object detectors are trained sequentially, re-sampling the ground truth at each step. This offers a solution to the problem of exponentially vanishing positive samples. However, it also translates into an increase in network complexity in terms of the number of parameters. To address this issue, we propose Recursively Refined R-CNN (\(R^3\)-CNN) which avoids duplicates by introducing a loop mechanism instead. At the same time, it achieves a quality boost using a recursive re-sampling technique, where a specific IoU quality is utilized in each recursion to eventually equally cover the positive spectrum. Our experiments highlight the specific encoding of the loop mechanism in the weights, requiring its usage at inference time. The \(R^3\)-CNN architecture is able to surpass the recently proposed HTC [] model, while reducing the number of parameters significantly. Experiments on COCO minival 2017 dataset show performance boost independently from the utilized baseline model. The code is available online at https://github.com/IMPLabUniPr/mmdetection/tree/r3_cnn.
Stepper, Swipe, Tilt, Force

Picking numbers is arguably the most frequently performed input task on smartwatches. This paper presents three new methods for picking numbers on smartwatches by performing directional swipes, twisting the wrist, and varying contact force on the screen. Unlike the default number picker, the proposed methods enable users to actively switch between slow-and-steady and fast-and-continuous increments and decrements during the input process. We evaluated these methods in two user studies. The first compared the new methods with the default input stepper method in both stationary and mobile settings. The second compared them for individual numeric values and values embedded in text. In both studies, swipe yielded a significantly faster input rate. Participants also found the method faster, more accurate, and the least mentally and physically demanding compared to the other methods. Accuracy rates were comparable between the methods.
Considering Airport Planners’ Preferences and Imbalanced Datasets when Predicting Flight Delays and Cancellations

A key part of efficient airport operational planning is to have insight into potential flight delays and cancellations.For airport planners, it is important to obtain flight delay or cancellation predictions with a high degree of certainty, i.e. a high precision.This allows planners to make sound decisions based on these predictions.To obtain such predictions, machine learning classification techniques are often applied.An important issue for classification problems is that of imbalanced class distributions: the number of actually cancelled/delayed flights is low.In general, the imbalance is addressed by resampling the data using one or more sampling techniques.However, resampling does not necessarily correspond to an imbalance ratio that leads to the best classification results.In this paper a systematic approach is presented to deal with imbalanced data for classification problems, while taking into account the preferences of airport planners.A range of feasible imbalance ratios, together with several classification algorithms and sampling techniques, are considered.An optimal imbalance ratio is identified with respect to relevant performance metrics.The approach is illustrated by performing binary classification of flight cancellations and delays at a large European airport.The results show that the highest prediction precision is obtained using a base imbalance ratio, whereas a higher imbalance ratio is needed to obtain the highest F1-score.Specifically, the cancellation prediction performance is increased by up to 243%, while its optimal imbalance ratio does not correspond to resampling.In general, the results underline the need to investigate the influence of varying data imbalance ratios on the performance of classification algorithms.
Multi-Objective UAV Trajectory Planning in Uncertain Environment

UAV trajectory planning is one of the research focuses in artificial intelligence and UAV technology. The asymmetric information, however, will lead to the uncertainty of the UAV trajectory planning; the probability theory as the most commonly used method to solve the trajectory planning problem in uncertain environment will lead to unrealistic conclusions under the condition of lacking samples, while the uncertainty theory based on uncertain measures is an efficient method to solve such problems. Firstly, the uncertainties in trajectory planning are sufficiently considered in this paper; the fuel consumption, concealment and threat degree with uncertain variables are taken as the objective functions; the constraints are analyzed according to the maneuverability; and the uncertain multi-objective trajectory planning (UMOTP) model is established. After that, this paper takes both the long-term benefits and its stability into account, and then, the expected-value and standard-deviation efficient trajectory model is established. What is more, this paper solves the Pareto front of the trajectory planning, satisfying various preferences, which avoids the defects of the trajectory obtained by traditional model only applicable to a certain specific situation. In order to obtain a better solution set, this paper proposes an improved backbones particle swarm optimization algorithm based on PSO and NSGA-II, which overcomes the shortcomings of the traditional algorithm such as premature convergence and poor robustness, and the efficiency of the algorithm is tested. Finally, the algorithm is applied to the UMOTP problem; then, the optimal trajectory set is obtained, and the effectiveness and reliability of the model is verified.
OCmst: One-class novelty detection using convolutional neural network and minimum spanning trees

We present a novel model called One Class Minimum Spanning Tree (OCmst) for novelty detection problem that uses a Convolutional Neural Network (CNN) as deep feature extractor and graph-based model based on Minimum Spanning Tree (MST). In a novelty detection scenario, the training data is no polluted by outliers (abnormal class) and the goal is to recognize if a test instance belongs to the normal class or the abnormal class. Our approach uses the deep features from CNN to feed a pair of MSTs built starting from each test instance. To cut down the computational time we use a parameter γ to specify the size of the MST's starting to the neighbours from the test instance. To prove the effectiveness of the proposed approach we conducted experiments on two publicly available datasets, well-known in literature and we achieved the state-of-the-art results on the CIFAR10 dataset.
Reachable-set characterization of an open quantum system by the quantum speed limit

In recent years, Arenz et al. proposed the idea of reachable set characterization based on the quantum speed limit (QSL); that is, the reachable set of the target unitary gate in a closed qubit system can be characterized by considering the QSL as the necessary condition that the control setup must satisfy in order to achieve the goal. Inspired by this idea, in this paper we characterize a general Markovian open quantum system based on the QSL derived in \cite{Kohei2}. Note that this bound is not only explicitly computable with respect to system parameters, but also tighter than the other bounds. Some examples for demonstrating this analysis will be given.
ARTSeg: Employing Attention for Thermal Images Semantic Segmentation

The research advancements have made the neural network algorithms deployed in the autonomous vehicle to perceive the surrounding. The standard exteroceptive sensors that are utilized for the perception of the environment are cameras and Lidar. Therefore, the neural network algorithms developed using these exteroceptive sensors have provided the necessary solution for the autonomous vehicle’s perception. One major drawback of these exteroceptive sensors is their operability in adverse weather conditions, for instance, low illumination and night conditions. The useability and affordability of thermal cameras in the sensor suite of the autonomous vehicle provide the necessary improvement in the autonomous vehicle’s perception in adverse weather conditions. The semantics of the environment benefits the robust perception, which can be achieved by segmenting different objects in the scene. In this work, we have employed the thermal camera for semantic segmentation. We have designed an attention-based Recurrent Convolution Network (RCNN) encoder-decoder architecture named ARTSeg for thermal semantic segmentation. The main contribution of this work is the design of encoder-decoder architecture, which employ units of RCNN for each encoder and decoder block. Furthermore, additive attention is employed in the decoder module to retain high-resolution features and improve the localization of features. The efficacy of the proposed method is evaluated on the available public dataset, showing better performance with other state-of-the-art methods in mean intersection over union (IoU).
Prediction model for cost data of a power transmission and transformation project based on Pearson correlation coefficient–IPSO–ELM

Abstract In view of the difficulty in predicting the cost data of power transmission and transformation projects at present, a method based on Pearson correlation coefficient–improved particle swarm optimization (IPSO)–extreme learning machine (ELM) is proposed. In this paper, the Pearson correlation coefficient is used to screen out the main influencing factors as the input-independent variables of the ELM algorithm and IPSO based on a ladder-structure coding method is used to optimize the number of hidden-layer nodes, input weights and bias values of the ELM. Therefore, the prediction model for the cost data of power transmission and transformation projects based on the Pearson correlation coefficient–IPSO–ELM algorithm is constructed. Through the analysis of calculation examples, it is proved that the prediction accuracy of the proposed method is higher than that of other algorithms, which verifies the effectiveness of the model.
Measuring the frequency of the academic formulas list across corpora: A case study based in TED talks and Yale lectures

Measuring lists of lexis across corpora is a well-established method in corpus linguistics. This article takes a novel approach and measures the frequency of occurrence of the Academic Formulas List (AFL; Simpson-Vlach and Ellis, 2010) across academic lectures (OYCLC) and an academic-adjacent corpus of TED talks (TTC). Frequency of occurrence is measured at three levels: overall inter- and intra-corpus variation; the composition of representation, to see which formulas are represented; and an investigation of the behaviour of formulas within texts. The corpora were found to be significantly different from each other in terms of overall representation with a medium effect size. The greatest difference concerned referential expressions and the smallest difference concerned stance expressions. In terms of intra-corpus variation the AFL was found to occur less often in the humanities and most often in the natural sciences for both corpora. The composition of coverage revealed Zipfian distributions for the AFL, with both corpora presenting a similar set of high frequency formulas within each group category. A combined ratio and minimum frequency measure identified salient formulas to each corpus. Concerning formula behaviour, differences were found between the corpora concerning the use of the same formulas. Pedagogic and methodological implications are discussed in the conclusion.
Cloud-Agnostic and Lightweight Big Data Processing Platform in Multiple Clouds Using Docker Swarm and Terraform

Big data necessitates the processing of data at scale and efficiency, which is beyond the capabilities of traditional IT infrastructure. Cloud-based big data processing platforms are the most efficient and established infrastructure to accomplish the big data processing requirements. Now organisations are recognising the requirement of a multi-cloud big data processing platform, as it has several benefits such as lower risk, resilience, flexibility, cost-effectiveness, and performance efficiency. However, the orchestration process of a multi-cloud platform is a very complex process due to the interoperability issue between clouds. Therefore, this paper proposes a distinctive application of Docker Swarm in building a cloud-agnostic and lightweight big data processing platform with the assistance of Infrastructure as Code (IaC) tool Terraform. It presents the architectural design and simulated implementation of the proposed big data processing platform in multiple clouds using Docker Swarm. Then, it illustrates the process of making this Docker-based big data platform, a cloud-agnostic and lightweight platform by using Terraform. Subsequently, it demonstrates the reason for using Terraform by comparing it with popular IaC tools. Finally, it performs a comparative evaluation of the proposed containerization based platform with the traditional virtualization based platform to expound the relative efficacy of the Docker Swarm-based big data processing platform.