Reviewerly/exHarmony huggingface dataset:
The Reachable Set of a Drone: Exploring the Position Isochrones for a Quadcopter

Quadcopters are increasingly popular for robotics applications. Being able to efficiently calculate the set of positions reachable by a quadcopter within a time budget enables collision avoidance and pursuit-evasion strategies.This paper examines the set of positions reachable by a quadcopter within a specified time limit using a simplified 2D model for quadcopter dynamics. This popular model is used to determine the set of candidate optimal control sequences to build the full 3D reachable set. We calculate the analytic equations that exactly bound the set of positions reachable in a given time horizon for all initial conditions. To further increase calculation speed, we use these equations to derive tight upper and lower spherical bounds on the reachable set.
Countermeasure against Deepfake Using Steganography and Facial Detection

As deepfake technology continues to advance at a rapid pace, there is a constant need to develop new methods to counteract its use.Physical copies of authentic items like signed baseball memorabilia use a certification to identify the authenticity.This paper proposes using steganography to embed a signed watermark inside a digital image.By using RSA to generate, sign, and verify the watermark, individuals will be able to authenticate personal images or try to verify signed images for authenticity.We evaluate the proposed approach with images manipulated by deepfake algorithms.The experiment results show 100% detection rate on deepfake images.The signing time and the verification time are around 300 ms according to our experiments.So the overhead of the countermeasure are negligible.
Event-Driven Collision-Free Path Planning for Cooperative Robots in Dynamic Environment

This paper presents an event-driven safe collision-free path planning method for robotic manipulator in human-robot cooperation. To meet the rapidity requirement of real-time robotic systems, the event-driven is introduced, and the collision prediction based on kinematics is used to trigger the rapid-exploring random tree (RRT) planner, while the quintic polynomial path planner is used when the event is not triggered. The fast planning and dynamical obstacle avoidance can be then achieved by the combination of quintic polynomial and RRT planner. By introducing the event-driven method, the safe collision-free path planning can be abstracted and standardized in human-robot cooperation. Finally, the simulation results show that the effectiveness of the proposed event-driven quintic-RRT path planning method.
Event Argument Extraction via a Distance-Sensitive Graph Convolutional Network

Event argument extraction (EAE) is a challenging task due to that some event arguments are far away from event triggers. Recently there have been many works using dependency trees to capture long-range dependencies between event triggers and event arguments. However, not all information in the dependency trees are useful for the EAE task. In this paper, we propose a Distance-Sensitive Graph Convolutional Network (DSGCN) for the EAE task. Our model can not only capture long-range dependencies via the graph convolution over the dependency trees, but also keep the information relevant to EAE via our designed distance-sensitive attention. Furthermore, dependency relation type information is utilized to enrich the representation of each word to further distinguish the roles of event arguments playing in an event. Experiment results on the ACE 2005 English dataset show that the proposed model achieves superior performance than the peer state-of-the-art methods.
Design and Implementation of Edge Computing for Detection on Embedded Electromobility

The proposed algorithm architecture of deep learning after darknet dropout are porting on embedded evaluation board with artificial intelligence board of Nvidia Jetson TX2. This article uses the post-training results to implement the actual road testing of edge computing. This design presents accurate identification of vehicles, front signal of traffic light status, road speed limit signs, and vehicle location for safe driving behavior modification system.
Jangseung: A Guardian for Machine Learning Algorithms to Protect Against Poisoning Attacks

Many smart city applications rely on machine learning; however, adversarial perturbations can be injected into training data to cause models to return skewed results. Jangseung is a preprocessor limits the effects of poisoning attacks without impeding on accuracy. Jangseung was created to guard support vector machines from poisoned data by utilizing anomaly detection algorithms. The preprocessor was tested through experiments that utilized two different datasets, the MNIST dataset and the UCI breast cancer Wisconsin (diagnostic) dataset. With both datasets, two identical models were trained and then attacked using the same adversarial points, one with Jangseung protecting it and the other unguarded from attack. In all cases, the protected model out-performed the unprotected model and in the best case scenario, the Jangseung-protected model outperformed the unguarded model by 96.2%. The under-trained, undefended MNIST models had an average accuracy of 53.2%. When Jangseung was present, their identical counterparts had a drastically higher average accuracy at 91.1%. Likewise, in the UCI-Cancer dataset, attack sequences lowered the accuracy of the model to as low as 75.51%, but Jangseung-defended models performed with 88.18% accuracy or better. Jangseung was an effective defense against adversarial perturbations for SVMs using different datasets and anomaly detection algorithms.
A Hybrid Chaotic Activation Function for Artificial Neural Networks

In recent years, chaos theory has been applied to neuroscience to help understand the human brain. Researchers have suggested that the brain is a dynamical system which behaves chaotically. This paper proposes the introduction of chaos into an artificial neural network (ANN) by using chaotic neurons in the network’s inner layer. A chaotic neuron uses two activation functions (AF): the sigmoid function and the logistic map function. The chaotic neuron generates a unique activation value to send to each neuron in the following layer. The model was tested by solving the XOR problem and showed significant improvements over a typical ANN.
Online Shopping with Artificial Intelligence: What Role to play for Trade Marks?

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Efficient TBox Reasoning with Value Restrictions using the wer Reasoner

Abstract The inexpressive Description Logic (DL) ${\cal F}{{\cal L}_0}$ , which has conjunction and value restriction as its only concept constructors, had fallen into disrepute when it turned out that reasoning in ${\cal F}{{\cal L}_0}$ w.r.t. general TBoxes is E xp T ime -complete, that is, as hard as in the considerably more expressive logic ${\cal A}{\cal L}{\cal C}$ . In this paper, we rehabilitate ${\cal F}{{\cal L}_0}$ by presenting a dedicated subsumption algorithm for ${\cal F}{{\cal L}_0}$ , which is much simpler than the tableau-based algorithms employed by highly optimized DL reasoners. Our experiments show that the performance of our novel algorithm, as prototypically implemented in our ${\cal F}{{\cal L}_0}$ wer reasoner, compares very well with that of the highly optimized reasoners. ${\cal F}{{\cal L}_0}$ wer can also deal with ontologies written in the extension ${\cal F}{{\cal L}_ \bot }$ of ${\cal F}{{\cal L}_0}$ with the top and the bottom concept by employing a polynomial-time reduction, shown in this paper, which eliminates top and bottom. We also investigate the complexity of reasoning in DLs related to the Horn-fragments of ${\cal F}{{\cal L}_0}$ and ${\cal F}{{\cal L}_ \bot }$ .
CultReal—A Rapid Development Platform for AR Cultural Spaces, with Fused Localization

Virtual and augmented reality technologies have known an impressive market evolution due to their potential to provide immersive experiences. However, they still have significant difficulties to enable fully fledged, consumer-ready applications that can handle complex tasks such as multi-user collaboration or time-persistent experiences. In this context, CultReal is a rapid creation and deployment platform for augmented reality (AR), aiming to revitalize cultural spaces. The platform's content management system stores a representation of the environment, together with a database of multimedia objects that can be associated with a location. The localization component fuses data from beacons and from video cameras, providing an accurate estimation of the position and orientation of the visitor's smartphone. A mobile application running the localization component displays the augmented content, which is seamlessly integrated with the real world. The paper focuses on the series of steps required to compute the position and orientation of the user's mobile device, providing a comprehensive evaluation with both virtual and real data. Pilot implementations of the system are also described in the paper, revealing the potential of the platform to enable rapid deployment in new cultural spaces. Offering these functionalities, CultReal will allow for the fast development of AR solutions in any location.
Suggestion of Maintenance Criteria for Electric Railroad Facilities Based on Fuzzy TOPSIS

This paper is on the suggestion of maintenance items for electric railway facility systems. With the recent increase in the use of electric locomotives, the utilization and importance of railroad electrical facility systems are also increasing, but the railroad electrical facility system in Korea is rapidly aging. To solve this problem, various methodologies are applied to ensure operational reliability and stability for railroad electrical facility systems, but there is a lack of detailed evaluation criteria for railroad electrical facility system maintenance. Also, maintenance items must be selected in a scientific and systematic method. Therefore, railroad electrical facility systems are selected for study. Design Structure Matrix (DSM) is utilized to establish considerations tailored to the maintenance characteristics, and the Fuzzy-TOPSIS methodology is utilized for determining the maintenance detail evaluation item baseline weights, a multi-criteria decision-making problem. Studies show that degradation, insulation items have the highest weight of 14.63%, and capacity items have the lowest weight of 5.34%. The results of this may be contributed to the underlying research in carrying out maintenance activities to ensure the reliability and safety of railroad electrical facility systems.
SMILE: Semantically-guided Multi-attribute Image and Layout Editing

Attribute image manipulation has been a very active topic since the introduction of Generative Adversarial Networks (GANs). Exploring the disentangled attribute space within a transformation is a very challenging task due to the multiple and mutually-inclusive nature of the facial images, where different labels (eyeglasses, hats, hair, identity, etc.) can co-exist at the same time. Several works address this issue either by exploiting the modality of each domain/attribute using a conditional random vector noise, or extracting the modality from an exemplary image. However, existing methods cannot handle both random and reference transformations for multiple attributes, which limits the generality of the solutions. In this paper, we successfully exploit a multimodal representation that handles all attributes, be it guided by random noise or exemplar images, while only using the underlying domain information of the target domain. We present extensive qualitative and quantitative results for facial datasets and several different attributes that show the superiority of our method. Additionally, our method is capable of adding, removing or changing either fine-grained or coarse attributes by using an image as a reference or by exploring the style distribution space, and it can be easily extended to head-swapping and face-reenactment applications without being trained on videos.
A Macrocause Classification Model for Violent Crime Analysis in the Field of Public Safety Based on Machine Learning Techniques

In developing effective public security policies, two aspects are essential: intelligence and information. In this context, the classification of Macrocauses can help governments better understand some aspects of public security related to the analysis of violent crimes in a state. In the Brazilian state of Rio Grande do Norte (RN), these analyses are currently done by analyzing raw data done by an expert without any specific auxiliary system, known as traditional approaches. It is a time-consuming task, and different experts may have different views on the same case. With this in mind, in this work, we intend to assist these criminal experts by providing a predictive model that classifies the Macrocauses of crimes into a set of pre-defined types to guide the work of the experts and, as a consequence, to speed up the analysis and management processes. Thus, a feature preprocessing was performed in which three different classification techniques are analyzed. The results show that when using XGBoost, the model accuracy reached 0.932378, which is considered excellent for this application.
Real-time Obstacles Avoidance for Crawler Crane based on DQN

Safety always is a crucial consideration for crawler cranes running with dozens of tons of load in its work site. However, around its work site, besides static obstacles, there are many dynamic obstacles, such as workers and engineering vehicles. These obstacles are potential risk for crane in congested work site. Therefore, risk probability and loss can be reduced in project of lifting engineering by dynamic lift-path planning system. However, presently, most proposed methods pay attention to search path in totally known environment. In this paper, we present a dynamic obstacles avoidance planner for crawler cranes in no-walk scenarios in partially known environment. This planner considers the lifted module as a 3DOF convex robot with discrete rotational and translational motions. First, we improved the structure of neural network of Deep Q-network by applying Resnet block for achieving real-time path planning for crawler crane. Thereafter, we also improved Artificial Potential Field Method (APFM) and apply it to search path for lifted module. And for shortening training time, we also apply the data generated by APFM to pre-train our neural network. Finally, we verified and compared the performance of APFM and DQN by simulation. The result of simulation can demonstrate the effectiveness of the presented planner.
Based on the Application of Improved Delphi Method and Analytic Hierarchy Process in Epidemic Prevention and Control

In order to establish a reasonable epidemic prevention and control system, and improve the ability to respond to sudden epidemics, this article adopts the literature review method and the improved Delphi method to launch expert consultations involving 12 experts in disease control, medical care, and municipal management. Using expert authority as the weight to evaluate expert opinions, 4 first-level indicators and 15 second-level indicators were obtained, and an index system for evaluating the effectiveness of epidemic prevention and control was constructed. The questionnaire method and the analytic hierarchy process are used to determine the weights of indicators at all levels, establish an evaluation model, and provide solutions for resource allocation, thereby improving the ability of epidemic prevention and control.
Utilization of Online Collaborative Tools in Software Engineering: An Empirical Study on Review Meetings

Software development involves a significant amount of team effort where collaboration and communication of the team members are crucial. The team meetings are core activities in all stages of the software development process. Even though these meetings often are conducted face-to-face (F2F) with a lack of technology utilization, changing global conditions such as the COVID-19 pandemic require other solutions urgently without interrupting the software development schedule. For this purpose, online collaborative tools provide new opportunities for software teams to work together avoiding waste in time and resources and the relevant literature is immature. This study aims to assess the factors affecting the integration of online collaborative tools to SE practices with a special reference to review meetings. For this purpose, a sample of 73 SE sophomore and junior students who are future software professionals participated in experimental review meetings based on predefined scenarios. The findings indicate that the utilization of OCT’s has positive effects on the participants’ actual performance and improves the interaction between team members compared to F2F meetings.
Self-Guided Instance-Aware Network for Depth Completion and Enhancement

Depth completion aims at inferring a dense depth image from sparse depth measurement since glossy, transparent or distant surface cannot be scanned properly by the sensor. Most of existing methods directly interpolate the missing depth measurements based on pixel-wise image content and the corresponding neighboring depth values. Consequently, this leads to blurred boundaries or inaccurate structure of object. To address these problems, we propose a novel self-guided instance-aware network (SG-IANet) that: (1) utilize self-guided mechanism to extract instance-level features that is needed for depth restoration, (2) exploit the geometric and context information into network learning to conform to the underlying constraints for edge clarity and structure consistency, (3) regularize the depth estimation and mitigate the impact of noise by instance-aware learning, and (4) train with synthetic data only by domain randomization to bridge the reality gap. Extensive experiments on synthetic and real world dataset demonstrate that our proposed method outperforms previous works. Further ablation studies give more insights into the proposed method and demonstrate the generalization capability of our model.
Alleviating the Issues of Recommendation System Through Deep Learning Techniques

Social media and other online sources generate abundance of data which leads to the problem of information overload. Recommender systems have emerged as a solution to deal with this problem which suggest items to users based on their information need. Despite the success of these systems, they also suffer from few issues such as cold start problem, sparsity, scalability, and overspecialization, among others. Although several attempts were made in the past to deal with these issues, several current recommender systems continue to face these issues. Deep learning algorithms can also be used to deal with recommendation system issues. In this paper, we discuss the issues of recommendation systems and how their impact can be alleviated through deep learning algorithms.
An Emulation of Quantum Error-Correction on an FPGA device

Practical fault-tolerant quantum computation faces enormous engineering challenges and emulation is a key support for quantum algorithm development in the meantime. Field-Programmable Gate Array are promising hosts for these emulators but have seen almost no application to this problem so far. This paper presents emulation of an error-correct qubit via a 9-qubit Shor code on Xilinx FPGA. It shows how by exploiting sparsity linked to the errors, operational complexity can be reduced by more than 99% and high-fidelity operation enabled by 11-bit floating point computation.
Path Planning of Seeding Robot Based on Improved Ant Colony Algorithm

Aiming at solving the problems that using ant colony algorithm in the route planning results in slow convergence speed and too long search time of seeding robot, an improved ant colony algorithm based on grid model is proposed. In order to make the seeding robot travel the shortest path when it arrives at the work place from the starting point, the proposed algorithm is used. In this algorithm, the transfer operator and adjustment operator of butterfly optimization algorithm are introduced into ant colony algorithm to optimize the distribution of original pheromone, solve the blindness of original search path, expand the search space of solution and improve the global of solution; Cauchy mutation operator is used to optimize the probability transfer rule to increase various solutions and improve the convergence speed. In order to verify the superiority of the improved algorithm in the 20 m × 20 m grid map, the simulation experiments of the two algorithms are implemented. Through simulation, it is shown that convergence speed of the improved ant colony algorithm is greatly improved compared with the basic ant colony algorithm. It provides a certain research value for the future research of seeding robot route planning.
Unsupervised Time Series Anomaly Detection using Moving Memorial Dynamic Filter

Anomaly detection on time series data is something of great importance nowadays. From the unsupervised machine learning method like K-Means to newly developed artificial neural networks, people try very hard to give a precise detection of where the anomalies locate. In this paper, a new method, the moving memorial dynamic filter, which combines Fourier seasonality decomposition, distance transformation and anomaly determination is introduced. First, the seasonal component is split off based on Fourier transformation. Then the time series data is mapped to a center-to-center distance subsequence using a dynamic circumscribed circle filter, which could use a low dimensional series to represent high dimensional information. At last, moving memorial value at risk is introduced and serves as a threshold to determine whether a certain data point should be labeled as an anomaly. Our method is tested and evaluated using the Numenta Anomaly Benchmark, an open-sourced dataset for time series anomaly detection. The result shows that our method outperforms the competing algorithms in terms of accuracy on most profiles of the NAB with a shorter latency in time consumption.
Relevant Epistemic Logic with Public Announcements and Common Knowledge

Building on our previous work in non-classical dynamic epistemic logic, we add common knowledge operators to a version of public announcement logic based on the relevant logic \(\mathsf {R}\). We prove a completeness result with respect to a relational semantics, and we show that an alternative semantics based on information states is dual to the relational one. We add a question-forming inquisitive disjunction operator to the language and prove a completeness result with respect to the information semantics. It is argued that relevant public announcements are particularly suitable for modelling public argumentation.
Research on Chinese classification based on TF-IDF

Chinese text classification has been in the research stage, there are many machine learning algorithms that can be used, such as logical regression, SVM, KNN, naive Bayes, random forest, neural network and so on. In this paper, taking Chinese modern novels as an example, we use various algorithms for classification and comparison, and choose the best algorithm for naive Bayes and neural network. After adjusting the TF-IDF algorithm and processing the participle according to the TF-IDF value, the accuracy of classification is improved obviously. The logistic regression with the lowest accuracy can increase about 6.7%,while the simple Bias and neural network can reach 100%.