Reviewerly/exHarmony huggingface dataset:
Multi-UAV Path Planning with Collision Avoidance in 3D Environment Based on Improved APF

Aiming at the problem of path planning for multiple unmanned aerial vehicles (UAVs) in complex obstacle environ-ment, a new algorithm for obstacle avoidance and collision avoidance path planning is proposed. Based on the Artificial Potential Field (APF) method, the algorithm optimizes the attractive potential field function and the repulsive potential field function. Besides, this paper adds both a priority collision avoidance strategy between UAVs and temporary virtual target strategy. This algorithm not only improves the success rate of path planning, but also considers the collision problem when multiple UAVs cooperate to complete the task. The experimental simulation results show that, compared with the traditional APF method, the algorithm can accurately and effectively complete the path planning task in the complex obstacle environment, and realize the collaborative path planning of multi- UAV formations at the same time.
Optimal Bandwidth Selection for DENCLUE Algorithm

In modern day industry, clustering algorithms are daily routines of algorithm engineers. Although clustering algorithms experienced rapid growth before 2010, innovation related to the research topic has stagnated after deep learning became the de facto industrial standard for machine learning applications. In 2007, a density-based clustering algorithm named DENCLUE was invented to solve clustering problem for nonlinear data structures. However, its parameter selection problem was largely neglected until 2011. In this paper, we propose a new approach to compute the optimal parameters for the DENCLUE algorithm, and discuss its performance in the experiment section.
Obstacle Avoidance Based on Deep Reinforcement Learning and Artificial Potential Field

Obstacle avoidance is an essential part of mobile robot path planning, since it ensures the safety of automatic control. This paper proposes an obstacle avoidance algorithm that combines artificial potential field with deep reinforcement learning (DRL). State regulation is presented so that the pre-defined velocity constraint could be satisfied. To guarantee the isotropy of the robot controller as well as reduce training complexity, coordinate transformation into normal direction and tangent direction is introduced, making it possible to use one-dimension controllers to work in a two-dimension task. Artificial potential field (APF) is modified such that the obstacle directly affects the intermediate target positions instead of the control commands, which can well be used to guide the previously trained one-dimension DRL controller. Experiment results show that the proposed algorithm successfully achieved obstacle avoidance tasks in single-agent and multi-agent scenarios.
An Empirical Study of Impact of Solidity Compiler Updates on Vulnerabilities

Vulnerabilities are an important issue in Ethereum smart contracts. Although various vulnerability analysis tools have been developed, the effectiveness of compilers themselves in eliminating vulnerable code has not been investigated to the best of the author's knowledge. In this paper, we investigate how many vulnerable contracts are created in Ethereum smart contracts for each compiler version. In particular, we collected 503,572 contracts whose Solidity source codes are publicly available in the Ethereum blockchain and then analyzed the vulnerabilities of these contracts. Focused on three vulnerabilities with high severity, i.e., Locked Money, Using tx.origin, and Unchecked Call, we confirm that the appearance rates of these vulnerabilities are decreased by major updates of the Solidity compiler. We then found the following insights. First, the appearance rate for Locked Money is decreased after version v0.6, and second, that of Using tx.origin is quite limited independently of the compiler update. Third, the appearance rate for Unchecked Call is decreased by v0.8, but it is still high due to several factors, such as code cloning. Thus, countermeasures are needed for Unchecked Call.
Learning Using Privileged Information for Wearable-based Human Activity Recognition

Most sensor-based Human Activity Recognition (HAR) approaches assume that the same set of sensors is available for both training and testing. Existing public HAR datasets collected in controlled environments often contain multiple and various types of sensors, and HAR models are often trained using all or most of them. However, expecting people to wear multiple sensors in everyday life may not be realistic, and only a subset of those sensors may only be available in practice. It would be beneficial to use the extra sensors, only available at training time, to train better-performing HAR models. Such a learning paradigm is often referred to as Learning Using Privileged Information (LUPI). In this research project, we will explore different LUPI approaches and identify in which situations LUPI can be beneficial for wearable-based HAR.
Soulbound Token for Covid-19 Vaccination Certification

Soulbound Tokens were introduced last May by Vitalik Buterin as a new kind of non-fungible, non-transferable token in the Ethereum ecosystem. The main idea behind such tokens is that they are born and die at the same address without ever changing ownership. Because there is typically no one-to-one correspondence between blockchain addresses and people, asserting that such new tokens are associated with a person for life is a strong assertion. Nevertheless, these new tokens promise to envision practical applications related to personal reputation or ownership of specific, non-transferable assets, and their real-world potential applications remain largely unexplored. In this paper, we present a practical application of such tokens, investigating a use case where a real-world problem largely impacting modern life has been exploited: COVID vaccine certification. The approach can easily be extended to other vaccination cases and relies on the use of a DApp where Soulbound Tokens have been implemented. In our scheme, two main actors are involved. The system administrator, represented by a local medical organization, is able to insert into a database the list of vaccines for which certification will be sent, and the citizen, after receiving the vaccination, will be able to receive and visualize the certifications provided by the first actor.
Fast and Lightweight UAV-based Road Image Enhancement Under Multiple Low-Visibility Conditions

The amalgamation of Unmanned Aerial Vehicle (UAV) based systems with models built on Artificial Intelligence (AI) and Computer Vision approaches have enabled several applications in urban planning and smart cities, such as remote health monitoring of roads and infrastructure. However, most of such existing models are trained and evaluated for clear lighting conditions, and they do not perform well under low visibility. This work proposes a fast and lightweight approach for deployment on UAV-based systems that can (i) detect the low-visibility condition in a road image captured by a UAV, and (ii) alleviate it and enhance the quality of the road image. The proposed approach achieves state-of-the-art results and thus establishes itself as an essential precursor to downstream Computer Vision tasks related to remote monitoring of roads, such as identification of different distress conditions.
Balancing Federated Learning Trade-Offs for Heterogeneous Environments

Federated Learning (FL) is an enabling technology for supporting distributed machine learning across several de-vices on decentralized data. A critical challenge when FL in practice is the system resource heterogeneity of worker devices that train the ML model locally. FL workflows can be run across diverse computing devices, from sensors to High Performance Computing (HPC) clusters; however, these resource disparities may result in some devices being too burdened by the task of training and thus struggle to perform robust training when compared to more high-power devices (or clusters). Techniques can be applied to reduce the cost of training on low-power devices, such as reducing the number of epochs to perform during training. However, such techniques may also negatively harm the performance of the locally-trained model, introducing a resource-model performance trade-off. In this work, we perform robust experimentation with the aim of balancing this resource-model performance trade-off in FL. Our results provide intuition for how training hyper-parameters can be tuned to improve this trade-off in FL.
A Tool for Automated Generation of Domain Specific Symbolic Models From Texts

Recent studies in the area of pervasive computing have shown that it is possible to create systems which combine symbolic models incorporating domain knowledge and sensor observations in order to perform tasks such as activity recog-nition in health monitoring use-cases. Creating symbolic models manually can, however, be a very time consuming endeavour and in some cases it also requires experts in the domain of interest. In this work, we showcase a tool for automatic generation of symbolic models from texts written in natural language. The output of our tool is a model description in Planning Domain Definition Language (PDDL). We concentrate on instructional texts in the cooking domain (cooking recipes), but our tool can be also applied in other areas of interest.
An Anchor Free Car Damage Detection Method

Automatic car damage assessment is an intriguing problem in the practice of artificial intelligence. With the help of car damage assessment algorithms, automobile insurance companies, car rental, and car-sharing businesses could attain automatic auxiliary loss assessment or identify the insurance fraud problem. It would save amounts of time and money to replace the manual examination process in traditional car damage assessment with computer-aided damage examination. In this paper, we introduce an anchor-free object detection method for auxiliary car damage assessment adopting a car damage dataset. We use the coordinate attention mechanism and focal loss design to get higher accuracy with fewer parameters and GFLOPs compared to the baseline model. On the test set, our model gets 59.2% AP50 and 39.9% AP, outperforming the baseline model by 5.5%, and 8.7%, respectively. And the method reduces parameters by about 1.42M and GFLOPs by about 1.18.
Metaverse and Investing

The listing of Roblox on the New York Stock Exchange in 2021, with its emphasis on the metaverse concept, has sparked investment in the metaverse among the financial community. The metaverse presents a diverse range of investment opportunities, including virtual reality and augmented reality technology, gaming, virtual real estate, social media, NFTs, cloud platforms, and infrastructure. However, the metaverse is a relatively new and rapidly evolving field, and the long-term potential for this technology is not yet fully understood. This chapter presents an analysis of the capital market performance of companies working on the metaverse and lists potential risks associated with investing in it. Investing in this field can be risky, and investors should be prepared for market volatility. It is important to conduct thorough research and consult with a professional financial advisor before making any investments.
BugFinder: Automatic Data Extraction Approach for Bug Reports from JIRA-Repositories

Issue tracking systems (ITS) are used by organizations to organize and document the work of their projects. GitHub, GitLab, and Bugzilla are among the ITSs that have been explored thus far. Jira is one of the most extensively used ITSs in practice. Jira includes a lot of information on many projects. Each project confronts a variety of difficulties, including bug reports, improvements to already-existing features, features for a newer version, and completed jobs. Since each sort of problem has a unique set of characteristics, gathering such a large amount of data manually would be laborious, potentially fault-prone, and time-consuming. To eliminate errors brought on by human error and enhance accuracy, our main goal is to automate compiling bug reports. This paper proposed an automated bug report extraction approach called BugFinder. Our technique is implemented in Python and pulls data from the Jira repository. We used an automated technique to retrieve issues or bug reports from 15 projects and extracted 176,953 issues from the Apache JIRA repository and out of 103,018 are bug reports. The produced reports include issue characteristics such as key, priority, resolution, summary, and numerous more attributes. These reports can be used to identify various sorts of defects, such as security, memory, aging defects, and so on. The result also demonstrates that the number of bugs in Brooklyn is 72%, which is the highest among all the given projects, and the Lucene project is 41%.
Certificateless Provable Data Possession Scheme Suitable for Smart Grid Management Systems

A large amount of data are generated at each section of the smart grid. Safely and efficiently storing and managing this data is a crucial challenge faced by the smart grid. Cloud-based smart grid management system (CB-SGMS) is a great choice for addressing this problem. Various departments in the smart grid upload the collected data to the cloud for storage. Due to the lack of physical control over the data stored in the cloud, it is a significant issue to judge that the data are intact. Provable data possession (PDP) scheme can solve this problem. Each department sends data to the cloud and entrusts it to a third-party verifier (TPV) to conduct integrity checks on data. In 2018, He et al. put forward a certificateless provable data possession (CL-PDP) scheme for CB-SGMS and professed that their scheme is secure. Unfortunately, we pointed out that there are security flaws in the scheme by showing two concrete attacks. We then came up with an improved scheme to make up for the security flaws. We presented the security requirements and gave the security proofs based on the discrete logarithm problem. In the end, we execute a comparison on performance for the improved scheme and the other five schemes. The results show that the improved scheme is more secure and efficient than other ones, which makes it suitable for CB-SGMS.
Physical-Layer Adversarial Robustness for Deep Learning-Based Semantic Communications

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare. However, robustifying semantic interpretation is challenging as ESC is extremely vulnerable to physical-layer adversarial attacks due to the openness of wireless channels and the fragileness of neural models. Toward ESC robustness in practice, we ask the following two questions: Q1: For attacks, is it possible to generate semantic-oriented physical-layer adversarial attacks that are imperceptible, input-agnostic and controllable? Q2: Can we develop a defense strategy against such semantic distortions and previously proposed adversaries? To this end, we first present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MobileSC</monospace> , a novel semantic communication framework that considers the computation and memory efficiency in wireless environments. Equipped with this framework, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SemAdv</monospace> , a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria, thus answering the Q1. To better characterize the real-world effects for robust training and evaluation, we further introduce a novel adversarial training method <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\texttt {SemMixed}$ </tex-math></inline-formula> to harden the ESC against <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SemAdv</monospace> attacks and existing strong threats, thus answering the Q2. Extensive experiments on three public benchmarks verify the effectiveness of our proposed methods against various physical adversarial attacks. We also show some interesting findings, e.g., our <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MobileSC</monospace> can even be more robust than classical block-wise communication systems in the low SNR regime.
Near-Optimal Multi-Robot Motion Planning with Finite Sampling

An underlying structure in several sampling-based methods for continuous multirobot motion planning (MRMP) is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tensor roadmap</i> , which emerges from combining multiple probabilistic roadmap (PRM) graphs constructed for the individual robots via a tensor product. We study the conditions under which the tensor roadmap encodes a near-optimal solution for MRMP—satisfying these conditions implies near optimality for a variety of popular planners, including dRRT*, and the discrete methods M* and conflict-based search, when applied to the continuous domain. We develop the first finite-sample analysis of this kind, which specifies the number of samples, their deterministic distribution, and magnitude of the connection radii that should be used by each individual PRM graph, to guarantee near-optimality using the tensor roadmap. This significantly improves upon a previous asymptotic analysis, wherein the number of samples tends to infinity. Our new finite sample-size analysis supports guaranteed high-quality solutions in practice within finite time. To achieve our new result, we first develop a sampling scheme, which we call the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">staggered grid</i> , for finite-sample motion planning for individual robots, which requires significantly fewer samples than previous work. We then extend it to the much more involved MRMP setting, which requires to account for interactions among multiple robots. Finally, we report on a few experiments that serve as a verification of our theoretical findings and raise interesting questions for further investigation.
Specific Emitter Identification via Sparse Bayesian Learning Versus Model-Agnostic Meta-Learning

Specific emitter identification (SEI) is a technique to identify the unknown emitters by using the hardware impairment of the transmitter. In this paper, we consider the effect of the wireless channel on the SEI, which deteriorates the identification performance severely. Two identifiers are proposed to address the wireless channel effect from the model-based and the data-based perspectives, respectively. From the model-based perspective, the fingerprint extractor using the sparse Bayesian learning (SBL) is first proposed to jointly estimate the fingerprint parameters, the wireless channel, and the noise power in the multipath fading channels. Then, the classifier using the weighted Euclidean distance is designed to identify the unknown emitter. From the data-based perspective, the model-agnostic meta-learning (MAML) algorithm is adopted to meta-train the convolutional neural network (CNN) on the task collection, which is generated based on the transmitter distortion mechanism and the channel distribution. The trained CNN is fine-tuned on the unseen SEI task and then is used to identify the unknown emitter. Moreover, the Cramer-Rao lower bounds of the estimation of the fingerprint parameters are derived to evaluate the performance of the proposed fingerprint extractor. Numerical results show that the SBL identifier outperforms the MAML one in a small number of samples, while the MAML identifier outperforms the SBL one in a large number of samples. Both identifiers are robust to the wireless channel, obtain better identification performance, and require a small number of samples compared to the existing methods. Furthermore, the simulation results indicate that the mean squared error performance of the SBL fingerprint extractor is close to the performance lower bound.
Artifact Detection and Restoration in Histology Images With Stain-Style and Structural Preservation

The artifacts in histology images may encumber the accurate interpretation of medical information and cause misdiagnosis. Accordingly, prepending manual quality control of artifacts considerably decreases the degree of automation. To close this gap, we propose a methodical pre-processing framework to detect and restore artifacts, which minimizes their impact on downstream AI diagnostic tasks. First, the artifact recognition network AR-Classifier first differentiates common artifacts from normal tissues, e.g., tissue folds, marking dye, tattoo pigment, spot, and out-of-focus, and also catalogs artifact patches by their restorability. Then, the succeeding artifact restoration network AR-CycleGAN performs de-artifact processing where stain styles and tissue structures can be maximally retained. We construct a benchmark for performance evaluation, curated from both clinically collected WSIs and public datasets of colorectal and breast cancer. The functional structures are compared with state-of-the-art methods, and also comprehensively evaluated by multiple metrics across multiple tasks, including artifact classification, artifact restoration, downstream diagnostic tasks of tumor classification and nuclei segmentation. The proposed system allows full automation of deep learning based histology image analysis without human intervention. Moreover, the structure-independent characteristic enables its processing with various artifact subtypes. The source code and data in this research are available at https://github.com/yunboer/AR-classifier-and-AR-CycleGAN.
Collaborative Sampling for Partial Multi-Dimensional Value Collection Under Local Differential Privacy

In big data era, companies and organizations are keen to collect data from users and analyse their behaviour patterns to make decisions or predictions for profits. However, it undermines users’ privacy because the collected data can be quite sensitive and easy to leak. To address privacy problems, local differential privacy (LDP) has been proposed for untrusted data collectors to obtain statistical information without compromising user privacy. Most studies on LDP assume that all users fully cooperate and contribute to the data collection process and thus the collected dataset is complete. However, in practice, especially when user population is large, such assumption seldom holds due to communication loss, user unresponsiveness or unwillingness, and incomplete user-side data. Unfortunately, state-of-the-art LDP-based data collection schemes, such as GRR, OUE and OLH, cannot handle partial data collection effectively. In this paper, we propose collaborative sampling to address partial data collection in a multi-dimensional setting. Thanks to a two-phase mechanism, we can derive the optimal sampling rate for each dimension. The optimality is shown and proved with respect to the variance of estimated frequency. Besides that, collaborative sampling is general and can be used in GRR, OUE and OLH with minimal adaption. Through experimental results, we show collaborative sampling outperforms existing mainstream data collection schemes in partial multi-dimensional data collection.