Reviewerly/exHarmony huggingface dataset:
A Survey on Autonomous Offline Path Generation for Robot-Assisted Spraying Applications

Robot-assisted spraying is a widespread manufacturing process for coating a multitude of mechanical components in an efficient and cost-effective way. However, process preparation is very time-consuming and relies heavily on the expertise of the robot programmer for generating the appropriate robot trajectory. For this reason, industry and academia investigate the possibility of supporting the end-user in the process by the use of appropriate algorithms. Mostly partial concepts can be found in the literature instead of a solution that solves this task end-to-end. This survey paper provides a summary of previous research in this field, listing the frameworks developed with the intention of fully automating the coating processes. First, the main inputs required for the trajectory calculation are described. The path-generating algorithm and its subprocesses are then classified and compared with alternative approaches. Finally, the required information for the executable output program is described, as well as the validation tools to keep track of program performance. The paper comes to the conclusion that there is a demand for an autonomous robot-assisted spraying system, and with a call-for-action for the implementation of the holistic framework.
A Comparative Study on Embedding Models for Keyword Extraction Using KeyBERT Method

KeyBERT is a method for keywords/keyphrases extraction, which has three steps. The first step is selecting candidate keywords from a text using sklearn library, the second step is the embedding operation of the text and its candidate keywords; this operation is done by BERT to get a numerical representation that represents the meanings. The third step is calculating the cosine similarity between individual keywords vectors and document vector. In this paper, we focus on the second step of KeyBERT (embedding step). Although KeyBERT has a lot of supported models for the embedding operation, there are no extensive previous comparative studies to analyze and study the effect of using different supported models in KeyBERT. We introduce a comparative study of two commonly used groups of models; the first group is sentence-transformers pretrained models, supported via the sentence-transformers library, and the second group includes the Longformer model, supported via the Hugginface Transformers library. We conduct the comparative study of models on benchmark datasets, which contain English text documents of multi-domains with different text lengths. Based on the study, we found that the Paraphrase-mpnet-base-v2 model provides the best results among all other models in keyword extraction in terms of effectiveness (f1-score, recall, precision, MAP) on all datasets, with higher efficiency (time) on short text compared with using it on long text; accordingly, we recommend using it in that context. On the other hand, the Longformer model is the most efficient/fastest in keyword extraction among all other models on all datasets and this superiority has been evident, especially in long text; accordingly, we recommend using it in that context.
Out-of-Distribution Detection with Confidence Deep Reinforcement Learning

Effective out-of-distribution (OOD) detection is vital for deep neural networks. The failure to recognize OOD samples from in-distribution (ID) samples can damage the performances of deep neural networks. Confidence-based OOD detection is an essential series of approaches with less computational complexity and easy deployment. However, the confidence estimations are dynamic and uncertain during the training process. Small modifications in inputs can cause neglectable changes in the confidence predictions. These introduced errors in the OOD detector damage the confidence estimation via rejecting OOD samples wrongly. Besides, current confidence-based approaches require domain knowledge to define OOD samples and penalties. This paper proposes a confidence-based deep reinforcement learning OOD detector to bridge these gaps. Without domain knowledge, our proposed algorithm can detect OOD samples with modified confidence values in the uncertain and dynamic training process. Our approach is compared with two OOD datasets, two neural network models, and three baselines to prove its effectiveness. Furthermore, our approach addresses the challenge of input attacks during the training process. Our algorithm is designed to mitigate the impact of such attacks by learning robust policies. According to experiments, our proposed deep reinforcement learning algorithm can surpass the performances of these commonly used baselines.
Transforming ground disaster response: Recent technological advances, challenges, and future trends for rapid and accurate real-world applications of survivor detection

Recent technological advancements, encompassing cutting-edge sensors, drones, and AI systems, present novel prospects for enhancing survivor detection in disaster scenarios. This article systematically reviews sixty-seven studies spanning 2009 to 2023, concentrating on technologies for locating survivors within land-based disaster contexts. Adhering to PRISMA guidelines, the review delineates the literature landscape, unveiling unmanned vehicles, radars, wireless sensors, smartphones, and Wi-Fi as prominent modalities. Significant challenges include limited actual-physical world validation, on-site deployment logistics, reliance on communication infrastructure, and power consumption. Nevertheless, innovative strategies such as multi-sensory integration and on-site processing using TinyML, exhibit the potential to reduce these limitations, signifying promising opportunities for future research. The conclusions take a practical perspective, excluding less mature technologies unsuited for early physical world implementation, given the prevalence of studies based on unrealistic assumptions—a major criticism of the literature. In terms of contributions, a conceptual framework, the Ground Disaster Information Management System, is introduced. This framework accentuates the importance of emerging technologies and communication protocols for efficient deployment and standardized data exchange. Ultimately, by synthesizing merits, constraints, and knowledge gaps, this review discerns challenges in survivor detection while underscoring technology-driven resolutions to enhance rescue operations. These encompass pragmatic paradigms for sensing, computation, communication, and coordination, targeting device deployment, in-situ sensors, communication systems, multi-sensory modalities, and augmented autonomy.
Information extraction and knowledge linkage of geological profiles and related contextual texts from mineral exploration reports for geological knowledge graphs construction

Geological profiles serve as invaluable repositories of data for knowledge extraction and the identification of geological entities, constituting a fundamental resource in scientific investigation. By encapsulating the stratigraphic arrangement and rock characteristics, these profiles offer a visually compelling and efficacious representation of stratigraphy along the vertical axis. Within geological profiles, intricate semantic associations frequently emerge, connecting various objects such as strata, rocks, and minerals across two or more profiles. These relationships often unveil inherent correlations or evolutionary attributes among geological entities. Understanding the general semantic relations between geological objects by manually scrutinizing vast quantities of profiles proves arduous, if not impracticable. In response, this study proposes an innovative framework capable of automated comprehension of extensive geological profiles and their associated contextual textual information. Built upon a natural language processing model and employing image vectorization techniques, this framework enables extraction of semantic information encompassing geological profiles, semantic relationships, and properties. In this article, we propose the comprehensive architecture of the framework, delineating its individual modules and subsequently applying it within a case study. Moreover, we conduct exploratory visualizations utilizing a knowledge graph to depict the extracted semantic information concerning diverse geological objects. The proposed framework presents a promising approach for supporting large-scale content analysis in the realm of stratigraphic research, facilitating advancements in our understanding of the evolution of stratigraphy.
Information Security Evaluation of Industrial Control Systems Using Probabilistic Linguistic MCDM Method

Industrial control systems (ICSs) are widely used in various fields, and the information security problems of ICSs are increasingly serious. The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts. Thus, this paper introduces the probabilistic linguistic term sets (PLTSs) to model the evaluation information of experts. Meanwhile, we propose a probabilistic linguistic multi-criteria decision-making (PL-MCDM) method to solve the information security assessment problem of ICSs. Firstly, we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods. Secondly, we use the Best Worst Method (BWM) method and Criteria Importance Through Inter-criteria Correlation (CRITIC) method to obtain the subjective weights and objective weights, which are used to derive the combined weights. Thirdly, we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje (PL-VIKOR) method. Finally, we apply the proposed method to solve the information security assessment problem of ICSs. When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério (PL-TODIM) method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution (PL-TOPSIS) method, the case example shows that the proposed method can provide more reasonable ranking results. By evaluating and ranking the information security level of different ICSs, managers can identify problems in time and guide their work better.
THE MEDIATING ROLE OF INNOVATION ON THE RELATIONSHIP BETWEEN SUPPLY CHAIN MANAGEMENT AND COMPANY PERFORMANCE IN THE KINGDOM OF BAHRAIN

This research focuses on critical independent variables, including information quantity and quality sharing, and the adoption of lean practices within companies. It examines these variables to understand their effects on corporate performance using innovation as a mediator. Data from registered non-financial businesses in Bahrain are utilized, and Smart PLS is recommended for analysis. The study aims to unravel the intricate relationships among these variables in Bahrain's nonfinancial sector. Conceptually, it contributes to a deeper understanding of the interplay between information sharing, lean methods, innovation, and firm performance, with a focus on the mediating role of innovation. Practically, it offers insights for business leaders, policymakers, and professionals, enhancing awareness of information sharing, quality, and lean methods, thereby enabling informed decisions to improve corporate performance and foster innovation-driven growth. The uniqueness of this research lies in its holistic approach, examining the interconnectedness of information sharing, lean practices, innovation, and firm performance. Unlike previous studies that explored these aspects in isolation, this research integrates them into a comprehensive framework, enhancing its value to the field and potential impact on business operations. It serves as a valuable resource for academics, researchers, corporate executives, employees, and regulators. Academics and researchers benefit from its conceptual and empirical contributions, while corporate stakeholders gain practical insights for enhancing performance and promoting innovation. Regulators can leverage the findings to develop legislative structures that facilitate information sharing, quality, and lean practices in Bahrain's nonfinancial sector.
Transforming Airport Security: Enhancing Efficiency through Blockchain Smart Contracts

In the aviation industry, the issuance of airside passes often encounters significant delays, posing logistical challenges and hindering crucial operations. This study delves into the potential of implementing blockchain technology, particularly smart contracts, to streamline and expedite airport security processes. Our analysis of data from leading UK airports reveals notable inefficiencies in the current airside pass issuance procedures, necessitating a transformative solution. We advocate for the integration of blockchain smart contracts as a pioneering approach to substantially reduce processing times. By automating execution based on predefined conditions, smart contracts have the potential to revolutionize airport security operations. This research signifies a groundbreaking advancement in the use of smart contracts within the airline industry, underscoring the substantial efficiency improvements that can be achieved. As we conclude this study, we foresee further research and practical implementations to unlock the full transformative impact of blockchain technology on aviation security.
Self-Attention Based Generative Adversarial Networks For Unsupervised Video Summarization

In this paper, we study the problem of producing a comprehensive video summary following an unsupervised approach that relies on adversarial learning. We build on a popular method where a Generative Adversarial Network (GAN) is trained to create representative summaries, indistinguishable from the originals. The introduction of the attention mechanism into the architecture for the selection, encoding and decoding of video frames, shows the efficacy of self-attention and transformer in modeling temporal relationships for video summarization. We propose the SUM-GAN-AED model that uses a self-attention mechanism for frame selection, combined with LSTMs for encoding and decoding. We evaluate the performance of the SUM-GAN-AED model on the SumMe, TVSum and COGNIMUSE datasets. Experimental results indicate that using a self-attention mechanism as the frame selection mechanism outperforms the state-of-the-art on SumMe and leads to comparable to state-of-the-art performance on TVSum and COGNIMUSE.
HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous Systems

Gathering knowledge about surroundings and generating situation awareness for autonomous systems is of utmost importance for systems developed for smart urban and uncontested environments. For example, a large area surveillance system is typically equipped with multi-modal sensors such as cameras and LIDARs and is required to execute deep learning algorithms for action, face, behavior, and object recognition. However, these systems are subjected to power and memory limitations due to their ubiquitous nature. As a result, optimizing how the sensed data is processed, fed to the deep learning algorithms, and the model inferences are communicated is critical. In this paper, we consider a testbed comprising two Unmanned Ground Vehicles (UGVs) and two NVIDIA Jetson devices and posit a self-adaptive optimization framework that is capable of navigating the workload of multiple tasks (storage, processing, computation, transmission, inference) collaboratively on multiple heterogenous nodes for multiple tasks simultaneously. The self-adaptive optimization framework involves compressing and masking the input image frames, identifying similar frames, and profiling the devices for various tasks to obtain the boundary conditions for the optimization framework. Finally, we propose and optimize a novel parameter split-ratio, which indicates the proportion of the data required to be offloaded to another device while considering the networking bandwidth, busy factor, memory (CPU, GPU, RAM), and power constraints of the devices in the testbed. Our evaluations captured while executing multiple tasks (e.g., PoseNet, SegNet, ImageNet, DetectNet, DepthNet) simultaneously, reveal that executing 70% (split-ratio=70%) of the data on the auxiliary node minimizes the offloading latency by ≈ 33% (18.7 ms/image to 12.5 ms/image) and the total operation time by ≈ 47% (69.32s to 36.43s) compared to the baseline configuration (executing on the primary node).
AMME-YOLOv7: Improved YOLOv7 Based on Attention Mechanism and Multiscale Expansion for Electric Vehicle Driver and Passenger Helmet Wearing Detection

To address the current problems of low accuracy of small target detection in electric vehicle helmets and imperfect detection schemes for drivers and rear passengers, in this paper, we propose an improved YOLOv7 based on hybrid attention mechanism and multi-scale extension, named AMME-YOLOv7. The model introduces Convolutional Attention Module (CBAM) and Coordinated Attention Module (CA) to improve the accuracy of YOLOv7 by YOLOv7's Backbone and Head to improve accuracy by inserting these two attention modules to better focus on key information. A helmet detection system for electric vehicle drivers was also built by adding multi-scale feature fusion detection and combining it with a dense connectivity network to improve feature extraction. Validation on a self-built EV helmet fitting dataset showed that the AMME-YOLOv7 algorithm improved the average accuracy by 8.9% and the recall by 2.7% over the original YOLOv7 algorithm. The experimental results show that the proposed improved YOLOv7 algorithm better meets the requirements for the detection accuracy of non-motorised vehicles and their helmets in practical situations, and reduces the incidence of motorised vehicle accidents to a certain extent.
An Overview of Cybersecurity Knowledge Graphs Mapped to the MITRE ATT&amp;CK Framework Domains

A large volume of cybersecurity-related data sets are generated daily from systems following disparate protocols and standards. It is humanly impossible for cybersecurity experts to manually sieve through these large data sets, with different schema and metadata, to determine potential attacks or issues. A myriad of applications and tool sets are offered to automate the analysis of large cyber data sets. Semantic Web's community has been studying the field of cybersecurity for over a decade and produced numerous knowledge graphs and frameworks. The Unified Cybersecurity Ontology (UCO) connected many of the leading knowledge representation frameworks, providing a holistic mapping of cyber data, beginning in 2016. MITRE ATT&CK is used by a wide variety of practitioners to understand how their current data and tooling prepare them to defend against both Advanced Persistent Threats (APTs) and less formal threat actors. The UCO and MITRE ATT&CK have provided researchers and practitioners, respectively, with tools to standardize data collection, correlation, and analysis. However, it is not apparent how current knowledge graphs and their applications in the cybersecurity domain utilize ATT&CK. In this paper, we present the results of our study on whether current cybersecurity knowledge graphs have mapped the main MITRE ATT&CK matrices.
Exploiting Temporal Information in Real-time Portrait Video Segmentation

Portrait video segmentation has been widely used in applications such as online conferencing and content creation. However, it is challenging for mobile devices with limited computation resources to achieve accurate and temporal consistent real-time portrait segmentation. In this work, we propose a segmentation method based on the classic encoder-decoder architecture with a lightweight model design. To facilitate the efficient use of temporal guidance, our method takes an RGB-M input where M is a guidance portrait mask concatenated to the RGB input. Furthermore, we leverage the temporal guidance to enable model inference on the adaptive portrait region of interest (ROI). We introduce a two-stage training strategy to compensate for the limited data variety of portrait video datasets. Our method is evaluated on portrait videos including different types of daily activities, and outperforms existing portrait segmentation methods in terms of segmentation accuracy. Without introducing significant delay, our method is suitable for applications requiring real-time processing.
Text-based Person Search in Full Images via Semantic-Driven Proposal Generation

Finding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance. However, different from the real-world scenarios where the bounding boxes are not available, existing text-based person re- trieval methods mainly focus on the cross modal matching between the query text descriptions and the gallery of cropped pedestrian images. To close the gap, we study the problem of text-based person search in full images by proposing a new end-to-end learning framework which jointly optimize the pedestrian detection, identification and visual-semantic feature embedding tasks. To take full advantage of the query text, the semantic features are leveraged to instruct the Region Proposal Network to pay more attention to the text-described proposals. Besides, a cross-scale visual-semantic embedding mechanism is utilized to improve the performance. To validate the proposed method, we collect and annotate two large-scale benchmark datasets based on the widely adopted image-based person search datasets CUHK-SYSU and PRW. Comprehensive experiments are conducted on the two datasets and compared with the baseline methods, our method achieves the state-of-the-art performance.
Efficient GPU Resource Management under Latency and Power Constraints for Deep Learning Inference

Recent rapid development in deep learning (DL) applications generates harsh requirements for DL inference services provided by GPU servers. On one hand, a high volume of different DL workloads always demands better processing throughput. On the other hand, GPU servers need to meet both the constraints of latency and power: each inference request must be responded in real-time with strict latency requirements; GPU servers need to be operated within a fixed power cap to prevent system failures from power overloading or overheating. Therefore, how to efficiently manage GPU resources to achieve better throughput under both latency and power constraints has become a key challenge.To address this issue, we first perform comprehensive measurements of inference tasks and have studied the impact of several critical knobs, including batch size, frequency, and GPU spatial sharing, on system performance in throughput, latency, and power. Then, we propose Morak, a multi-knob resource management framework for DL inference under the constraints of latency and power. A key mechanism of Morak is GPU resource partitioning with efficient space multiplexing for DL models. To further improve throughput, Morak efficiently explores the search space of GPU frequency and batch size under the constraints. Experiment results on a hardware testbed show that Morak can achieve as much as 67.7% throughput improvement compared with several state-of-the-art baselines under tight constraints of latency and power.