Reviewerly/exHarmony huggingface dataset:
BEAT-Traffic: a Blockchain-Enabled infrastructure for Anonymous-yet-Traceable Traffic reporting

Intelligent Transportation Systems (ITS) have become increasingly popular in recent years, and are viewed as the future of transportation. These systems rely heavily on open communication networks to ensure road safety and efficiency. However, the rapid and secure sharing of information over large-scale cyber-physical systems such as ITS poses significant challenges, including data lineage, data consistency, access rights management, and privacy preservation. In this paper, we propose a solution to improve the sharing of sensitive data over ITS using blockchains and distributed cryptography. We demonstrate how these technologies can be applied to the reporting of Road Hazard Warnings, creating a blockchain-based data collection system that ensures the dissemination and security of reported messages. Our approach combines blockchains and group signatures to achieve the necessary security properties, including privacy preservation and censorship resistance, while maintaining performance levels comparable to existing literature. We provide a theoretical analysis of the solution’s security properties and expected performance characteristics. Our results demonstrate the potential of blockchain-based solutions for addressing the challenges of secure and efficient information sharing in ITS. We believe that our work will contribute to the development of secure and privacy-preserving ITS systems, and encourage further exploration of blockchain-based solutions in the field of intelligent transportation.
Enhancing IoT Security and Privacy with Trusted Execution Environments and Machine Learning

With the increasing popularity of Internet of Things (IoT) devices, security concerns have become a major challenge: confidential information is constantly being transmitted (sometimes inadvertently) from user devices to untrusted cloud services. This work proposes a design to enhance security and privacy in IoT based systems by isolating hardware peripheral drivers in a trusted execution environment (TEE), and leveraging secure machine learning classification techniques to filter out sensitive data, e.g., speech, images, etc. from the associated peripheral devices before it makes its way to an untrusted party in the cloud.
Enhancing the Reliability of Perception Systems using N-version Programming and Rejuvenation

Machine Learning (ML) has become indispensable for real-world complex systems, such as perception systems of autonomous systems and vehicles. However, ML-based systems are sensitive to input data, faults, and malicious threats that can degrade output quality and compromise the complete system’s correctness. Ensuring a reliable output of ML-based components is crucial, especially for safety-critical systems. In this paper, we investigate architectures of perception systems using N-version programming for ML to mitigate the dependence on a singular ML component and combine it with a time-based rejuvenation mechanism to maintain a healthy system over extended periods. We propose models and functions to evaluate the reliability of N-version perception systems subject to faults, malicious threats, and rejuvenation. Our numerical experiments show that a rejuvenation mechanism could benefit a multiple-version system, with a reliability improvement superior to 13%. Also, the results indicate that rejuvenation could improve output reliability when ML modules’ accuracy is high.
Tailoring and Verification of the Trust Boundaries in a Heterogeneous TEE Landscape

Confidential computing services enable users to run or use applications in Trusted Execution Environments (TEEs) leveraging secure hardware, like Intel SGX or AMD SEV, and verify them by performing remote attestation. Typically this process is very rigid and not always aligned with the trust assumptions of the users regarding the hardware identities, stakeholders and software that are considered trusted. In our work, we enable the users to tailor their trust boundaries according to their security concerns and remotely attest the different TEEs specifically based on those.
Assured Point Cloud Perception

Existing work on verification of neural networks has largely focused on the image domain, where issues of adversarial robustness are the main concern. In this paper, we exploit the geometric nature of point cloud data that makes it a natural domain in which neural network verification technology can provide even stronger guarantees. We illustrate this in the context of estimation of surface normals by showing how neural network verification can be used to analyze correctness properties related to this task, thereby allowing proofs of correctness that provide universally quantified guarantees over positive measure sets of patches. Whereas previous applications of neural network verification to point clouds have focused on the task of classification, here we apply neural network verification to point cloud regression. Our contribution includes a novel representation of local point cloud patches invariant to point cloud density, as well as small network architectures that can be more readily analyzed by existing neural network verification tools and may be more suitable for deployment on size, weight and power constrained platforms than state-of-the-art architectures. Our approach allows for a model trained only in simulation to successfully transfer to diverse real-world systems (including on a US Army autonomous vehicle platform) and sensors without any additional training or fine-tuning. Applying our input representation to existing approaches achieves improved performance on unoriented surface normals in low-noise environments.
Exploring the Environmental Benefits of In-Process Isolation for Software Resilience

Memory-related errors remain an important cause of software vulnerabilities. While mitigation techniques such as using memory-safe languages are promising solutions, these do not address software resilience and availability. In this paper, we propose a solution to build resilience against memory attacks into software, which contributes to environmental sustainability and security.
Rich and Expressive Specification of Continuous-Learning Cyber-Physical Systems

Neural networks are being applied to a growing variety of applications, including safety-critical domains like autonomous vehicles and aircraft, due to their ability to approximate complex functions from limited datasets and adapt by continuing to learn from real-world data after deployment.Ensuring dependability in cyber-physical systems with neural network controllers requires verification beyond linear input-output constraints and local robustness of the network in isolation and must address uncertainties introduced by learning and network behaviour on unseen data, especially when continuous learning after deployment is allowed. Verification of these continuous-learning cyber-physical systems requires assessing probabilistic, temporal, and behavioural specifications that express concepts like resilience and other key properties.Our research seeks to develop strategies and associated tooling for rigorously specifying continuous-learning cyber-physical systems and verifying them against rich and expressive specifications.
Security Risk Growth Models for Software Vulnerability Assessment

Information system administrators must pay attention to system vulnerability information and take appropriate measures against security attacks on the systems they manage. However, as the number of security vulnerability reports increases, the time required to implement vulnerability remediation also increases, therefore vulnerability risks must be assessed and prioritized. Especially in the early stages of vulnerability discovery, such as zero-day attacks, the risk assessment must consider changes over time, since it takes time to spread the information among adversaries and defenders.The Common Vulnerability Scoring System (CVSS) is used widely for vulnerability risk assessment, but it cannot be said that it can sufficiently cope with temporal changes of risk of attacks. In this paper, we proposed software vulnerability growth models to assist system administrators in decision making. Experimental results show that these models can provide a visual representation of the risk over time.
Real-Time Detection of Small Targets for Video Surveillance Based on MS-YOLOv5

In our work, an improved YOLOv5 model is proposed to detect small targets such as pedestrians in video surveillance in real time. We propose an improved model: MS-YOLOv5, by introducing the Transformer multi-head self-attention mechanism on the basis of YOLOv5, the deep convolutional neural network is combined with the Transformer to explore the potential of feature representation. At the same time, the Neck is added to feature up-sampling to enlarge the features of small targets, so as to improve the feature fusion structure of FPN(Feature Pyramid Networks)+PAN(Path-Aggregation Network) and further improve the detection precision of small targets. After the improvement of the above method, YOLOv5 was finally named MS-YOLOv5. By training the MS-YOLOv5 model using small targets in video surveillance, detection experiments are carried out for the main types of targets appearing in video surveillance. The MS-YOLOv5 detection results were compared with the original YOLOv5 detection results, and the precision was increased by 13.7% and the recall was increased by 11.3%, and the MS-YOLOv5 model significantly improved the detection precision of small targets and reduced the missed detection rate of small targets.
A Simple and Effective Design for Intelligent Sub-Nyquist Modulation Recognition

Automatic modulation recognition of sub-Nyquist spectrum sensing is essential to demodulate and process the signals in a spectrum- and energy-efficient Internet of Things system. Motivated by the recent advances of deep learning, a promising direction is to automatically predict the modulation based on data-driven representation instead of hand-crafted features. Specifically, our solution SHELL (Simple solution witH ELegant detaiLs) provides a simple yet effective approach, which is capable of achieving high modulation recognition accuracy without complex network structure. Our solution obtained the 1st place in the ‘GBSense challenge 2022’ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> http://www.gbsense.net/challenge/
A Fast Path Planning with Strong Guidance for Synchronous Growth of Expansion Trees

This paper presents a fast path planning algorithm with strong guidance based on RRT*, which mainly includes the path search strategy for partition synchronous growth of expansion trees and the path optimization method for fast reconnection of partition trees. First, the straight initial path is used to lock the useful obstacles along the way and the space partition operation is performed according to the key guidance information provided by the obstacles. Then RRT* is used to realize the synchronous growth of multi-partition expansion trees to form a priori path candidate set. Second, all the candidate nodes of each partition in the candidate set are selected by two rounds of procedure, and a fast reconnection path optimization method is established. In particular, a local escape mechanism is proposed to guide the planner to choose an optimal path escape direction for the case where the initial or goal state is in a concave trap. Compared with other algorithms, SG-RRT* can quickly search for a high-quality initial solution with the lowest or close to the lowest path cost, and it also has the ability to be good at escaping from concave obstacles.
New Opportunities for AI Innovation with Big Data: Indirect Docking between GLPS and LLM

The combination of MSPS and LLM aims to optimize the process of HCMA and turn the black box into a white box for the development of AI mathematics. The methods are: first, connect the HCII in parallel to the two systems; Then, observe the whole process of HMC; Finally, every step of GLPS reusing human and machine speech behavior is recorded transparently. MSPS is the foundation of GLPS with IKC and the function of AI mathematical characteristic. The result is that the two sides of HCI, not only have digital records, but also have abstract records of the relational database between language and speech used by users. Its significance lies in that the whole process between brain and computer has not only changed from a black box to a white box, but also the formal system are clearly recorded and can be reproduced at any time.
W&amp;G-BERT: A Pretrained Language Model for Automotive Warranty and Goodwill Text

The demand for accurate information extraction architectures for industrial-based automotive warranty and goodwill (W&G) text data is steadily increasing. The analytical competence of current information extraction architectures is based on the Transformer architecture. Labeled datasets are necessary to finetune transformer-based language models like BERT on a domain-specific information extraction downstream task. We use customer related human-annotated W&G datasets to train BERT for Warranty and Goodwill Automotive Entity Recognition (W&G-BERT). As far as we know, we are the first authors publishing an automotive W&G-BERT model. Hence, we define the state-of-the-art in identifying the respective failure location and failure type in W&G feedback texts. Furthermore, the experimental results show that the pretraining of BERT on 2 million unlabeled W&G text data improves the performance by 1.5% (2.0%) f1-score (ensemble). We make the weights of the pretrained and finetuned models publicly available via the following link: lukasweber/WG_BERT · Hugging Face.
Multi-target Detection of Metal Parts Based on Improved Deep Learning Algorithms

In the traditional industrial production process, the sorting of product parts mainly depends on human eye detection, which has high production cost and low production efficiency. At present, automatic identification of parts has become a major trend of the reform and development of the manufacturing industry. However, current researches of metal parts identification mainly focuses on the identification of common parts, such as bolts, nuts, gaskets, etc.. The number of parts is small, and the identification accuracy still needs to be improved in these researches. Based on the improved two-stage object detection algorithm Faster RCNN and the one-stage object detection algorithms YOLOv3 and YOLOv5s, this paper realized the recognition of 40 types of metal parts. The recognition accuracy of all improved models was more than 97%, of which the lowest was YOLOv3 (97.3%), followed by Faster RCNN, with the recognition accuracy of 98.1%. The recognition accuracy of YOLOv5s model was the highest, reaching 99.4%, and the detection speed reached 179 frames per second. The results showed that the improved YOLOv5s algorithm achieved the highest recognition accuracy and recognition speed in the identification of metal parts, and could meet the needs of industrial scenes. The research in this paper could play an important role in promoting the application of these algorithms in the industrial field.
The Lightweight Domain Ontology Construction of Sichuan Cuisine Service

The domain ontology has been widely applied in various fields, but the Sichuan cuisine service (SCS). In this paper, a novel lightweight domain ontology of the SCS (DO-SCS) is artificially constructed to promote the extraction and sharing of knowledge (e.g., the service recommendation). First, the proposed DO-SCS consists of three concept levels which are extended from the root one, namely the SCS. Second, the ontologies in each level are given appropriate features with the inheritance principle. Third, the relations among the level-1 ontologies are constructed by their features. Finally, the visualization of ontologies, features, and relations are present to improve the readability. Through the evaluations of industry experts, our DO-SCS can support the fusion of Sichuan cuisine in various industries.
Augmenting RetinaFace Model with Conditional Generative Adversarial Networks for Hair Segmentation

In this paper, an augmentation of the RetinaFace model for hair segmentation is proposed by incorporating a Conditional Generative Adversarial Network (cGAN). The proposed model is trained to generate high-quality hair segmentation masks by considering various hair textures, colors, and styles. Our approach is based on the idea that hair segmentation can benefit from the use of cGANs, because they can learn to generate realistic hair images and help improve the performance of RetinaFace. Experimental results show that our model outperforms the RetinaFace model on several benchmarks, achieving state-of-the-art performance.
Sequential Feature Selection and Instance Selection Using SpFSR and SpFixedIS

In this paper two-dimensional data reduction is examined in a sequential manner for the emerging class of optimisation-based wrapper methods, namely SpFixedIS for instance selection and SpFSR for feature selection. The study examines the performance of instance selection on a feature reduced dataset (IFS) and the performance of feature selection on an instance reduced dataset (FIS). The results show that on average FIS marginally outperforms IFS across all 6 of the different classifiers, however the majority of tests are statistically equivalent. IFS provides less variability in performance compared to FIS regardless of selection rate. However, the performance of the one-dimensional reductions generally outperformed their two-dimensional counterparts. Whilst utilising the Gaussian Naive Bayes wrapper, FIS and IFS provide an average improvement over the full training set of 9.281% and 8.170% respectively. This study is motivated by future work to propose novel optimisation-based wrapper hybrid selection algorithms, that is data reduction approaches which can perform simultaneous instance and feature selection.
Gesture Estimation Method Based on Efficient MultiScale Fusion Network

Gesture estimation is a vital but challenge task for human-computer interaction. A desired gesture estimation method is supposed to be efficient for real-time requirement, yet still accurate. In this paper, we propose a novel network named Efficient Multi-Scale Fusion Net(EMF-Net), which effectively trades off between latency and accuracy. The cores of EMF-Net are two subnetworks, a efficient feature extraction network and a multi-scale fusion network. By presenting an improved feature extraction network to reduce latency of the model, and creating a multi-scale fusion scheme to alleviate the weak spatial generalization ability, the two subnetworks greatly improve accuracy while maintaining efficiency. Experimental results demonstrate our method attains superior accuracy with comparable computational burden.
Improved Mask R-CNN for Disturbed Area Extraction in Construction Projects from High-Resolution Satellite Imagery

This study introduces an enhanced Mask R-CNN model for automatically extracting disturbed areas in construction projects from satellite imagery, aiding soil and water conservation authorities in monitoring tasks. The model optimizes the feature extraction network structure and loss function to bolster object recognition and segmentation accuracy. By integrating a channel attention mechanism, SENet, the model’s capacity to detect target objects is augmented. The loss function optimization results in more precise edge detection. Utilizing the high-resolution satellite data from GF-1, the effectiveness of the proposed method has been successfully demonstrated in the study area of Liuyang City, Hunan Province, China. The proposed model exhibits substantial improvement over the traditional Mask R-CNN, delivering accurate segmentation and precise identification of disturbed areas in construction projects.
Acute lymphoblastic leukemia Classification Based on Convolutional Neural Network

Acute lymphoblastic leukemia (ALL) is a type of blood cancer that affects white blood cells, primarily affecting children. Early diagnosis is crucial for successful treatment and recovery. In recent years, convolutional neural networks (CNNs) have been increasingly used in medical image analysis, including the detection and classification of ALL from medical images. In this paper, we propose a model called QCResNet for the classification of ALL from peripheral blood smear images. Our proposed model achieved a high accuracy of 98.9% on a dataset of 15,135 images, outperforming several state-of-the-art methods. Our results demonstrate the potential of QCResNet for accurate and rapid effective diagnosis of acute lymphocytes.
CALRec: Counterfactual Active Learning for Sequential Recommendation

Sequential recommendation prevails among real-world applications like e-commerce, marketing, and entertainment. On the other hand, the problem of data sparsity in sequential recommendation has attracted much attention. To this issue, a popular solution is data augmentation. However, previous trivial augmentation methods, such as cropping and reordering, are introducing too much randomness due to a lack of guidance. In this paper, we leverage Active Learning strategies to help with model-based guided data augmentation. We propose a novel framework called CALRec(Counterfactual Active Learning for Sequential Recommendation). Experiments on several real-world datasets demonstrate our framework’s effectiveness and generalizability.
Look AI – An Intelligent System for Socialization of Visually Impaired

It has found that 30% of the people among the visually impaired people are having a significant number of depressive symptoms whereas, the prevalence of depression in blind people was reported to be 33%, or nearly double the rate of the general population. Therefore, social activities among visually impaired people should increase. This research paper provides a unique system for assisting visually impaired individuals in navigating indoor environments heavily relies on the use of artificial intelligence, particularly deep learning and computer vision techniques. By leveraging these advanced technologies, the system can analyze and process large amounts of visual data in real-time, accurately identifying and locating various objects in the environment, including faces, doors, stairs, cross walks, traffic lights, potholes and other obstacles. The system then provides real-time feedback to the user via an audio interface, enabling them to navigate indoor and outdoor environments safely and independently. The use of artificial intelligence in this system is particularly significant, as it enables the system to adapt and improve over time based on user feedback and additional data. That means, the system can continuously improve its performance and accuracy, ultimately resulting in a more effective and reliable tool for visually impaired individuals. The authors have also evaluated the system in a real-world setting, demonstrating its effectiveness in assisting visually impaired individuals with indoor navigation. Overall, the proposed system has the potential to significantly enhance the quality of life for visually impaired individuals, showcasing the transformative power of artificial intelligence in addressing real-world challenges.