Reviewerly/exHarmony huggingface dataset:
Road Semantic Segmentation Oriented Dataset for Autonomous Driving

There are various datasets for segmentation such as KITTI, CamVid, Cityscapes, ApolloScape, Mapillary Vistas, etc. These datasets try to include as many classes as possible, and classify objects in pixels across all regions of an image. However, the greater the number of classes, the more difficult it is to learn through deep learning. In particular, from an autonomous driving point of view, it is more important to understand the road situation than to detect the sky or buildings. This paper proposes a Road Semantic Segmentation Oriented (RSSO) Dataset. This dataset divides the various markers into eight classes, including lanes on the road. ApolloScape's lane segmentation dataset is also provided by labeling only road markers. However, RSSO dataset has the advantage of finding drivable regions by including asphalt in the class. We expect the proposed dataset to be useful for a variety of autonomous driving applications.
A Real-time Hand Motion Detection System for Unsupervised Home Training

Hand motion tracking plays a vital role in health-care like Physical therapy (PT) rehabilitation that helps the patients restore their physical movement of the hand. These treatments are taken by patients suffering from stroke, accidents, and any other kind of neurological disorder. We have developed a low-cost system to track hand movements and detect the gestures of hand for unsupervised home training. The system integrated a convolutional neural network-based hand motion system and a gesture detection system to serve a training session sequential for hand movement rehabilitation. We combined part of open datasets and our novel dataset(total: 16605, 4 labels) for the final training, six directions, and four gestures were predicted in real-time based on our proposed model. An adaptive GUI application was developed to respond to individual performance patterns. This system can be deployed easily on a laptop/PC with a web camera, which makes it easier and low-costs for a doctor to track the hand movement of the patients and also support to quantify the improvements index after several training sessions. These tracked data can also be stored and sent to the remote clinic center and used for further studies. This kind of system would be beneficial to the patients and the training center intelligently. It could be deployed at home or the clinic with more optimization of functions such as safety measures.
On the classification of binary completely transitive codes with almost-simple top-group

A code C in the Hamming metric, that is, is a subset of the vertex set VΓ of the Hamming graph Γ=H(m,q), gives rise to a natural distance partition {C,C1,…,Cρ}, where ρ is the covering radius of C. Such a code C is called completely transitive if the automorphism group Aut(C) acts transitively on each of the sets C, C1, …, Cρ. A code C is called 2-neighbour-transitive if ρ⩾2 and Aut(C) acts transitively on each of C, C1 and C2. Let C be a completely transitive code in a binary (q=2) Hamming graph having full automorphism group Aut(C) and minimum distance δ⩾5. Then it is known that Aut(C) induces a 2-homogeneous action on the coordinates of the vertices of the Hamming graph. The main result of this paper classifies those C for which this induced 2-homogeneous action is not an affine, linear or symplectic group. We find that there are 13 such codes, 4 of which are non-linear codes. Though most of the codes are well-known, we obtain several new results. First, a non-linear completely transitive code that does not explicitly appear in the existing literature is constructed, as well as a related non-linear code that is 2-neighbour-transitive but not completely transitive. Moreover, new proofs of the complete transitivity of several codes are given. Additionally, we consider the question of the existence of distance-regular graphs related to the completely transitive codes appearing in our main result.
Experimentation of Human Activity Recognition by Using Accelerometer Data Based on LSTM

The majority of studies on Human Activity Recognition (HAR) were conducted utilizing video and/or image data which caused critical problems such as privacy exposure and storage capacity issues. To tackle these issues, we propose a Long Short-Term Memory (LSTM) architecture for HAR taking accelerometer data acquired from smartphones as its input values. The experimental results showed the outstanding performance of the proposed model against a convolutional neural network (CNN) based model which is one of the state-of-art methods for HAR. More precisely, it was observed that the accuracy of the LSTM based model is about 12% superior to that of the CNN-based model.
Definition and Verification of Security Configurations of Cyber-Physical Systems

The proliferation of Cyber-Physical Systems (CPSs) is raising serious security challenges. These are complex systems, integrating physical elements into automated networked systems, often containing a variety of devices, such as sensors and actuators, and requiring complex management and data storage. This makes the construction of secure CPSs a challenge, requiring not only an adequate specification of security requirements and needs related to the business domain but also an adaptation and concretion of these requirements to define a security configuration of the CPS where all its components are related. Derived from the complexity of the CPS, their configurations can be incorrect according to the requirements, and must be verified. In this paper, we propose a grammar for specifying business domain security requirements based on the CPS components. This will allow the definition of security requirements that, through a defined security feature model, will result in a configuration of services and security properties of the CPS, whose correctness can be verified. For this last stage, we have created a catalogue of feature models supported by a tool that allows the automatic verification of security configurations. To illustrate the results, the proposal has been applied to automated verification of requirements in a hydroponic system scenario.
Cyber Profiling in Criminal Investigation

The line between street crimes and cybercrimes has been increasingly blurry, given the prevalence of internet access. In all kinds of criminal investigations, digital information has become relevant and even crucial in most cases. Investigators are gradually relying on information retrieved from electronic platforms to find leads and evidence. When comprehensive information is not always attainable, the use of cyber profiling may provide further insight into crucial questions regarding identity, time, and location. Cyber profiling is essentially an analysis on the digital footprints associated with a person whose identity may or may not have been known. The purpose is to reveal information that is not readily visible in the digital footprints, such as personality, motives, and counter-forensic efforts. This chapter introduces what cyber profiling is and how it can help with criminal investigation.
Simulation to Real Domain Adaptation for Lane Segmentation

As the cost of labelling and collecting real world data remains an issue for companies, simulator training and transfer learning slowly evolved to be the foundation of many state-of the-art projects. In this paper these methods are applied in the Duckietown setup where self-driving agents can be developed and tested.Our aim was to train a selected artificial neural network for right lane segmentation on simulator generated stream of images as a comparison baseline, then use domain adaptation to be more precise and stable in the real environment. We have tested and compared four knowledge transfer methods that included domain transformation using CycleGAN and semi-supervised domain adaptation via Minimax Entropy. As the latter was previously untested in semantic segmentation according to our best knowledge, we have contributed to showing it is indeed possible and produces promising results. Finally we have shown that it could also create a model that fulfills our performance requirements of stability and accuracy.We show that the selected methods are equally eligible for the simulation to real transfer learning problem, and that the simplest method delivers the best performance.
Performance Comparison of Small Object Detection Algorithms of UAV based Aerial Images

Traffic controls in modern society are part of urban management. With the assistance of unmanned aerial vehicles (UAVs) equipped with mounted cameras, researchers could capture aerial (bird-view) images from appropriate altitude. The perspective in aerial images makes appearances of objects squat, although aerial images can supply more contextual information about the environment by a broader view angle, the object instances may be detected by mistake. This fact diminishes the aerial images that can be fed to a network with higher dimensions that increases the computational cost to prevent the diminishing of pixels belonging to small objects. To compare model performance on small objects with aerial images, this study trains and tests two object detectors, i.e. YOLOv4 and YOLOv3, on the AU-AIR dataset, and exploited the parameterization of YOLO based models for small object detection. Finally, the key numerical results and observations are presented.
Large Array DOA Estimation Based on Extreme Learning Machine and Random Matrix Theory

Estimation of the direction-of-arrival (DOA) in large array systems owns wide prospect in radar applications. However, the traditional subspace DOA estimation algorithm has deteriorated performance when the numbers of antenna elements and samples grow in the same rate. Also, they are poorly adapted to the actual low signal-to-noise ratio (SNR) environment. In this paper, we investigate the large array DOA estimation based on extreme learning machine (ELM) and random matrix theory (RMT). Conventionally, ELM requires the activation function to be infinitely differentiable, which may lead to slow training rate for large arrays. According to RMT, it is proved that ELM with ReLU function as its activation function has asymptotic convergence in large dimension regime. Thus, a ReLU-ELM method for large array DOA estimation is put forward. Numerical simulations show that under low SNR, it has better performance than the traditional algorithms. Compared with the least square support vector machine (LSSVM) method, the ReLU-ELM algorithm can greatly reduce the training and testing time and improve the learning efficiency under the premise of ensuring excellent estimation performance.
Bagging-ELM Model for Heating Furnace Thermal Efficiency Prediction

Thermal efficiency is a very important index for heating furnace in chemical industry. In order to predict the complicated variables, the Bagging-ELM network based on ensemble learning and Extreme Learning Machine is adopted. The proposed model is applied to predicting the net hourly electrical energy output in Combined Cycle Power Plant data set and the thermal efficiency of the heating furnace. There is a comparison between the results generated by Bagging-ELM model and those generated by BP model, RBF model, ELM model. Compared with other models, the Bagging-ELM can obtain higher accuracy and stability.
Experimental Evaluation of Solar Radiation and Solar Efficacy Models and Performance of Data-Driven Models

Weather data are major input for building energy usage predictions. However, solar-radiation-related historical and real-time weather data are unavailable or incomplete in many locations. Therefore, many models, which use more available weather parameters to predict solar radiation’s components, were developed in the last 30 years. An experimental evaluation of these models is needed since measurement devices and satellite techniques are improved, and weather files are consequently updated. In this study, we review, calibrate, and validate the accuracy of global and diffuse irradiation prediction models and efficacy models using experimental data over an 18-month data collection period in Taipei, Taiwan, and Kingsville, Texas. The evaluation also covers data-driven models such as neural networks. The results show that the SUNY model provides good solar irradiance estimations; Perez and Muneer efficacy models provide good daylight illuminance estimations; and Erbs, Muneer, Reindl, and Perez models have similar accuracy but different error trends when separating direct and diffuse irradiance. The results can be used as a guideline when filling in solar-radiation-related fields.
Urban green economic development indicators based on spatial clustering algorithm and blockchain

The unbalanced development strategy makes the regional development unbalanced. Therefore, in the development process, resources must be effectively utilized according to the level and characteristics of each region. Considering the resource and environmental constraints, this paper measures and analyzes China’s green economic efficiency and green total factor productivity. Moreover, by expounding the characteristics of high-dimensional data, this paper points out the problems of traditional clustering algorithms in high-dimensional data clustering. This paper proposes a density peak clustering algorithm based on sampling and residual squares, which is suitable for high-dimensional large data sets. The algorithm finds abnormal points and boundary points by identifying halo points, and finally determines clusters. In addition, from the experimental comparison on the data set, it can be seen that the improved algorithm is better than the DPC algorithm in both time complexity and clustering results. Finally, this article analyzes data based on actual cases. The research results show that the method proposed in this paper is effective.
A Nonlinear Proportional Integral Derivative-Incorporated Stochastic Gradient Descent-based Latent Factor Model

Recommender system (RS) commonly describes its user-item preferences with a high-dimensional and sparse (HiDS) matrix. A latent factor (LF) model relying on stochastic gradient descent (SGD) is frequently adopted to extract useful information from such an HiDS matrix. In spite of its efficiency, an SGD-based LF model commonly takes many iterations to converge. When processing a large-scale HiDS matrix, its computational efficiency should be further improved by further accelerating its convergence rate as well as maintaining its learning ability. To address this issue, this paper innovatively proposes novel SGD algorithm which incorporates a nonlinear proportional integral derivative (NPID) controller into its learning scheme for building an LF model. The main idea is to adopt an NPID controller to model the learning residual achieved in the past iterations, thereby adjusting the learning direction and step size of the current iteration, thereby making a resultant model converge fast. With the NPID-incorporated SGD algorithm, this study proposes an NPID-SGD-based LF (NSLF) model. Experimental results on two HiDS matrices demonstrate that compared with a standard SGD-based LF model, the proposed model achieves higher computational efficiency and prediction accuracy for missing data of an HiDS matrix.
MA2DF: A Multi-Agent Anomaly Detection Framework

Time-sensitive safety-critical systems store traces as a collection of time-stamped messages that are generated while a system is operating. Analysis of these traces becomes a key task as it allows one to find faults or errors within a system that is otherwise difficult to discern, especially in complex systems. Furthermore, finding any form of anomalous behaviour becomes critical in time-sensitive and safety-critical systems where a late detection will often lead to dire consequences. Most available approaches are generally used in networking or business process analysis. We focus on creating a lightweight and explainable approach for time-sensitive safety-critical systems. By using a set of system traces under both normal and anomalous conditions, our approach attempts to classify whether or not a trace is anomalous. In this work, we introduce MA2DF, Multi-Agent Anomaly Detection Framework, a novel multi-agent based graph design approach for online and offline anomaly detection in system traces. Our approach takes advantage of the timing information between a sequence of events and also the event sequences to learn and discern between normal and anomalous traces. We present two approaches, an offline approach to discern anomalous behaviour by utilizing the event occurrence workflow graph. The second approach is an online streaming algorithm that monitors the sequence of events as they arrive in real-time. This can be used to detect anomalies, find the cause, and improve system resilience. We show how our approach, MA2DF, is superior to other state-of-the-art models. The paper will explore the technical feasibility and viability of MA2DF by utilizing industry strength case study using traces from a field-tested hexacopter.
Link Prediction in Social Graphs using Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN)

In recent times, Social Network Analysis (SNA) has become a very important and interesting subject matter with regard to Artificial Intelligence (AI) in that a vast variety of processes, comprising animate and inanimate entities, can be examined by means of SNA. As a result, prediction tasks within social network structures have become significant research problems in SNA. Hidden facts and details about social network structures can be effectively and efficiently harnessed for training AI models with the goal of predicting missing links/ties within a given social network. Thus, important factors such as the individual attributes of spatial social actors, and the underlying patterns of relationship binding these social actors must be taken into consideration because these factors are relevant in understanding the nature and dynamics of a given social network structure. In this paper, we have proposed an interesting hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). Our proposition herein is designed for examining, extracting, and learning meaningful facts for resolving link prediction problems about social network structures. RLVECN utilizes an edge sampling approach for exploiting the representations of a social graph, via learning the context of each actor with respect to its neighboring actors, with the goal of generating vector-space embeddings per actor which are further harnessed for innate representations via a Convolutional Neural Network (ConvNet) sublayer. Successively, these relatively low-dimensional representations are fed as input features to a downstream classifier for solving link prediction problems in a given social network. Our proposition, RLVECN, has been trained and evaluated on six (6) real-world benchmark social graph datasets.
An Empirical Study of English Teaching Model in Higher Vocational Colleges Based on Data Analysis

With China’s accession to the WTO, the rapid development of market economy and the acceleration of global integration, China’s international status continues to improve. Professional English continues to infiltrate professional positions in various industries, and English teaching in higher vocational industries is particularly important. The goal of China’s higher vocational English course is to train students to use the English which they have learned in future professional positions. Based on a questionnaire survey of 324 students from Jilin Engineering Normal University, the article conducted a one-year empirical study on two tourism management classes from the perspective of the EOP Teaching Theory. Through comparative analysis of the data before and after the experiment, it is proved that the career-oriented teaching model is effective in vocational college English teaching.
JTang Dubhe: a Service Pattern Modeling and Analysis System

Business models now become essential parts of an enterprise's strategy. Many newly-rising crossover business models require multi-industry companies involved. The communication and cooperation process between the enterprises shows the characteristics of complexity and high uncertainty, which is detrimental to business value analysis. However, existing methods can not quantify the value demands of their stakeholders. Service pattern models business from the perspective of enterprise application. It is executable and suitable for quantitative analysis. In this paper, we cooperate with management experts and propose a new ontology-based modeling and multi-agent-based simulation method that supports the quantitative analysis of the stakeholder's value demands at the business process level. With the method proposed, a corresponding simulation system is developed, Dubhe. It is a user-friendly modeling, simulation, and visualization tool for corporate decision-makers. Finally, the paper uses a case study to prove that the method and system can evaluate the value of a business process and discover the bottlenecks.
An Efficient PHD Filter for Multi-target Tracking with Out-of-Sequence Measurement

In this paper, we address the update problem of the out-of-sequence measurement (OOSM) in multi-sensor multitarget tracking (MTT). Based on the probability hypothesis density (PHD) filter in the framework of random finite set (RFS), we propose an efficient algorithm for OOSM update in the MTT scenario, named as E-OOSM-PHD, which involves two stages: retrodiction and posterior update. The stage of retrodiction facilitates the incorporation of the OOSM at the appropriate time. The posterior update is to incorporate the OOSM and compensate the cardinality at the current time, so as to improve the multi-target tracking performance. The effectiveness of the proposed algorithm is demonstrated in a challenging tracking scenario via simulation results.
RETRACTED: Virtual simulation design and effect of high jump technology action optimization based on complex embedded system

It proposes a programmed human movement investigation and movement acknowledgement for serious video test programs. Four significant human focuses are recognized and used by powerful camera estimation and target localization methods to calculate human contour tracking. Statistical analysis of tracking point movements finds time divisions for upward runs and jump levels. This method uses a motion image test from camera movement, and the athlete's performance characteristics are robust and independent of pole vaulting, high jumps, triple jumps and long jumps. Experimental results are sufficient to show that the program is running with complex content and motion sequences. The proposed Extended Convolutional Neural Network (RCNN) architecture contains two main modules. First, four significant people are measured and tracked using pre-calculated silhouettes of people. It is calculated using standard algorithms to detect silhouettes and find moving objects in the video. The algorithm's necessary steps are camera motion estimation, change detection based on Bayesian statistics, and label propagation. The long human axis, upper running and jumping stages of the walk cycle and time division are estimated using a statistical analysis of the tracking point movement. In the second module, the above features are used for behavior recognition tasks.