Reviewerly/exHarmony huggingface dataset:
Integrating Computer Vision and CAD for Precise Dimension Extraction and 3D Solid Model Regeneration for Enhanced Quality Assurance

This paper focuses on the development of an integrated system that can rapidly and accurately extract the geometrical dimensions of a physical object assisted by a robotic hand and generate a 3D model of an object in a popular commercial Computer-Aided Design (CAD) software using computer vision. Two sets of experiments were performed: one with a simple cubical object and the other with a more complex geometry that needed photogrammetry to redraw it in the CAD system. For the accurate positioning of the object, a robotic hand was used. An Internet of Things (IoT) based camera unit was used for capturing the image and wirelessly transmitting it over the network. Computer vision algorithms such as GrabCut, Canny edge detector, and morphological operations were used for extracting border points of the input. The coordinates of the vertices of the solids were then transferred to the Computer-Aided Design (CAD) software via a macro to clean and generate the border curve. Finally, a 3D solid model is generated by linear extrusion based on the curve generated in CATIA. The results showed excellent regeneration of an object. This research makes two significant contributions. Firstly, it introduces an integrated system designed to achieve precise dimension extraction from solid objects. Secondly, it presents a method for regenerating intricate 3D solids with consistent cross-sections. The proposed system holds promise for a wide range of applications, including automatic 3D object reconstruction and quality assurance of 3D-printed objects, addressing potential defects arising from factors such as shrinkage and calibration, all with minimal user intervention.
Applications of edge analytics: a systematic review

Abstract With the development and expansion of the Internet of Things, computing at the edge is becoming increasingly important, especially for applications where real-time response is important. In this paper, we made a systematic review of the literature on analytics at the edge. We extracted data from 40 selected primary relevant studies from the complete set of 419 papers retrieved from scientific databases. In our analysis of the full text of every primary study we investigated: temporal distribution of primary studies, publication types, domain and application areas of the primary papers, used machine learning and deep learning methods. We also elaborated on the main themes of the primary studies and recommended some possible interesting future research possibilities.
EndoNet: A Model for the Automatic Calculation of H-Score on Histological Slides

H-score is a semi-quantitative method used to assess the presence and distribution of proteins in tissue samples by combining the intensity of staining and the percentage of stained nuclei. It is widely used but time-consuming and can be limited in terms of accuracy and precision. Computer-aided methods may help overcome these limitations and improve the efficiency of pathologists’ workflows. In this work, we developed a model EndoNet for automatic H-score calculation on histological slides. Our proposed method uses neural networks and consists of two main parts. The first is a detection model which predicts the keypoints of centers of nuclei. The second is an H-score module that calculates the value of the H-score using mean pixel values of predicted keypoints. Our model was trained and validated on 1780 annotated tiles with a shape of 100 × 100 µm and we achieved 0.77 mAP on a test dataset. We obtained our best results in H-score calculation; these results proved superior to QuPath predictions. Moreover, the model can be adjusted to a specific specialist or whole laboratory to reproduce the manner of calculating the H-score. Thus, EndoNet is effective and robust in the analysis of histology slides, which can improve and significantly accelerate the work of pathologists.
Energy-Efficient and Timeliness-Aware Continual Learning Management System

Continual learning has recently become a primary paradigm for deep neural network models in modern artificial intelligence services, where streaming data patterns frequently and irregularly change over time in dynamic environments. Unfortunately, there is still a lack of studies on computing cluster management for the processing of continual learning tasks, particularly in terms of the timeliness of model updates and associated energy consumption. In this paper, we propose a novel timeliness-aware continual learning management (TA-CLM) system aimed at ensuring timely deep neural network model updates for continual learning tasks while minimizing the energy consumption of computing worker nodes in clusters. We introduce novel penalty cost functions to penalize quantitatively deep neural network model update latency and present the associated optimization formulation to ensure the best task allocation. Additionally, we design a simulated annealing-based optimizer, which is a meta-heuristic technique and easy to implement, to solve the non-convex and non-linear optimization problem. We demonstrate that the proposed TA-CLM system improves both latency and energy performance over its competitors by an average of 51.3% and 51.6%, respectively, based on experimental results using raw data from well-known deep neural network models on an NVIDIA GPU-based testbed and a large-scale simulation environment.
Vehicle Detection in UAV Images via Background Suppression Pyramid Network and Multi-Scale Task Adaptive Decoupled Head

Vehicle detection based on unmanned aerial vehicle (UAV) aerial images plays a significant role in areas such as traffic monitoring and management, disaster relief, and more, garnering extensive attention from researchers in recent years. However, datasets acquired from UAV platforms inevitably suffer from issues such as imbalanced class distribution, severe background interference, numerous small objects, and significant target scale variance, presenting substantial challenges to practical vehicle detection applications based on this platform. Addressing these challenges, this paper proposes an object detection model grounded in a background suppression pyramid network and multi-scale task adaptive decoupled head. Firstly, the model implements a long-tail feature resampling algorithm (LFRA) to solve the problem of imbalanced class distribution in the dataset. Next, a background suppression pyramid network (BSPN) is integrated into the Neck segment of the model. This network not only reduces the interference of redundant background information but also skillfully extracts features of small target vehicles, enhancing the ability of the model to detect small objects. Lastly, a multi-scale task adaptive decoupled head (MTAD) with varied receptive fields is introduced, enhancing detection accuracy by leveraging multi-scale features and adaptively generating relevant features for classification and detection. Experimental results indicate that the proposed model achieves state-of-the-art performance on lightweight object detection networks. Compared to the baseline model PP-YOLOE-s, our model improves the AP50:95 on the VisDrone-Vehicle dataset by 1.9%.
An Efficient Document Retrieval for Korean Open-Domain Question Answering Based on ColBERT

Open-domain question answering requires the task of retrieving documents with high relevance to the query from a large-scale corpus. Deep learning-based dense retrieval methods have become the primary approach for finding related documents. Although deep learning-based methods have improved search accuracy compared to traditional techniques, they simultaneously impose a considerable increase in computational burden. Consequently, research on efficient models and methods that optimize the trade-off between search accuracy and time to alleviate computational demands is required. In this paper, we propose a Korean document retrieval method utilizing ColBERT’s late interaction paradigm to efficiently calculate the relevance between questions and documents. For open-domain Korean question answering document retrieval, we construct a Korean dataset using various corpora from AI-Hub. We conduct experiments comparing the search accuracy and inference time among the traditional IR (information retrieval) model BM25, the dense retrieval approach utilizing BERT-based models for Korean, and our proposed method. The experimental results demonstrate that our approach achieves a higher accuracy than BM25 and requires less search time than the dense retrieval method employing KoBERT. Moreover, the most outstanding performance is observed when using KoSBERT, a pre-trained Korean language model that learned to position semantically similar sentences closely in vector space.
Hyperspectral Facies Analysis as a Lithological Interpretation Tool for Carbonate Rocks

In a pilot study, hyperspectral image analysis was applied to four boreholes from the North Midlands Block in Ireland. The selected holes are extremely well characterised lithologically and have a detailed micropalaeontology accurately constraining stratigraphic positions. Hyperspectral facies were defined using features extracted from the hyperspectral data and compared with existing litho- and biostratigraphic logs and samples. These were able to distinguish changes in the lithologies of the cores and were useful for defining unbiased lithological contacts and for regional correlations.
Blockchain distributed identity management model for cross-border data privacy protection

Cross-border data privacy protection often involves personal privacy data from different regions, where cross-border vehicle identity authentication requires a large amount of sensitive data. The cross-border movement of this sensitive data poses a significant threat to privacy. A distributed identity management blockchain model for cross-border data privacy protection is proposed to avoid the cross-border transmission of sensitive data through identity authentication. The model combines the SM2 and SM9 algorithms and blockchain technology to guarantee the security of stored data while providing a method to avoid sensitive data crossing borders and realizing cross-border identity authentication. The model was originally designed for the Northbound Travel for Macao scenario but can still be applied to other cross-border authentications. The generation speed of a Non-Fungible Token is verified through experiments, and the generation time and efficiency of Non-Fungible Tokens satisfy the actual needs of Internet of Vehicles authentication.
Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network

Abstract Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.
Enhancing the Understanding of E-commerce Reviews through Aspect Extraction Techniques: A BERT-Based Approach

The growth of online customer reviews on e-commerce platforms has led to an overwhelming volume and variety of data, making manual analysis impractical for both consumers and managers. Consequently, machine learning techniques, such as Aspect-Based Sentiment Analysis (ABSA), have gained prominence for their ability to determine sentiment at the aspect level. This study aims to fine-tune natural language processing models for aspect extraction in e-commerce customer reviews. We manually annotated 2781 online user review sentences in English and employed different extensions of the BERT model to identify implicit and explicit aspects. This approach diverges from prior studies, as our dataset comprises real user reviews from five prominent e-commerce platforms. The findings demonstrate the models’ effectiveness in extracting aspects from diverse e-commerce user reviews, yielding a deeper understanding of user-generated content and customer satisfaction trends, and providing valuable insights for managerial decision-making. This study contributes to the ABSA literature and offers practical implications for e-commerce platforms aiming to improve their products and services based on customer feedback.
Probing for Privacy: A Digital Design Method to Support Reflection of Situated Geoprivacy and Trust

Every day, and in myriad ways, users of digital media are required to navigate and negotiate the sharing of their personal location data. Devices, apps, and platforms require users to make privacy choices in order to perform mundane activities such as plotting a course to a destination in a mapping app, ordering food via a food delivery app, checking the local forecast in a weather app, catching public transport, or using a ride-hailing service. Recognising location as a uniquely sensitive datapoint and geoprivacy as an emergent and highly charged form of informational privacy, in this article, we present and reflect on our development and deployment of a digital cultural probe as a design method for exploring situated and contextual decision-making about everyday location-data sharing through digital media. We first examine how location-sharing and geoprivacy can be addressed through the lens of contextual integrity and how a cultural probes approach can aid in drawing out those contextual, situated, and affective aspects of geoprivacy. We then detail the probe study itself along with the insights it provided into the potential for materialising location awareness, eliciting and encouraging reflection on affective and ambivalent responses to situated privacy, as well as affects of trust in design and designing for privacy. The study's findings suggest that given the situated and contextual nature of privacy in general—and geoprivacy in particular—the situated and playful materiality of digital cultural probes can help to surface relational and affective elements of contextual decision-making about privacy and trust. In doing so, digital cultural probes offer themselves as useful tools for trustworthy design.
Randomized Kaczmarz algorithm with averaging and block projection

The randomized Kaczmarz algorithm is a simple iterative method for solving linear systems of equations. This study proposes a variant of the randomized Kaczmarz algorithm by combining block projection and weighted averaging techniques. Here, block projection quickly decreases iterative errors, and averaging reduces randomness and enables parallel computation simultaneously. Their combination can balance the convergence rate, convergence horizon, and computational complexity. In addition, three adaptive weights are designed to balance multiple block calculations and accelerate the proposed method. Exponential convergence is established for general linear systems (overdetermined or underdetermined, full-rank or deficient-rank, and consistent or inconsistent). Numerical simulations explain and verify the results.
Malicious Insider Threat Types – An Empirical Analysis

Malicious insider threats represent a particular challenge not only for defense, but also for research, as it is estimated there is a high number of unreported cases. Current taxonomies and typologies usually focus on specific aspects, such as goal or motivation, and tend to have tight boundaries. A number of malicious insider threat attack scenarios were identified in our research through qualitative interviews, enhanced with a game-based creative approach. The resulting data was used to develop a malicious insider threat typology in an empirical bottom-up approach. We developed an analysis scheme from existing taxonomies and typologies and used it in an empirical analysis of malicious insider roles and attack scenarios. We were able to identify eleven archetypes of malicious insider threats considering multiple facettes. This paper describes the analysis and the identified types.
Feature Reduction Networks: A Covolution Neural Network-Based Approach to Enhance Image Dehazing

Image dehazing represents a dynamic area of research in computer vision. With the exponential development of deep learning, particularly convolutional neural networks (CNNs), innovative and effective image dehazing techniques have surfaced. However, in stark contrast to the majority of computer vision tasks employing CNNs, the output from a dehazing model is often treated as uninformative noise, even though the model’s filters are engineered to extract pertinent features from the images. The standard approach of end-to-end models for dehazing involves noise removal from the hazy image to obtain a clear one. Consequently, the model’s dehazing capacity diminishes, as the noise is progressively filtered out throughout the propagation phase. This leads to the conception of the feature reduction network (FRNet), which is a distinctive CNN architecture that incrementally eliminates informative features, thereby resulting in the output of noise. Our experimental results indicate that the CNN-driven FRNet surpasses previous state-of-the-art (SOTA) methods in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) evaluation metrics. This highlights the effectiveness of the FRNet across various image dehazing datasets. With its reduced overhead, the CNN-based FRNet demonstrates superior performance over current SOTA methods, thereby affirming the efficacy of CNNs in image dehazing tasks.
A Formation Control and Obstacle Avoidance Method for Multiple Unmanned Surface Vehicles

This study introduces a method for formation control and obstacle avoidance for multiple unmanned surface vehicles (USVs) by combining an artificial potential field with the virtual structure method. The approach involves a leader–follower formation structure, where the leader autonomously avoids collisions using an artificial potential field based on the target’s position as a reference. It also determines the ideal position of each follower in the formation based on its own position, heading angle, and the formation structure. To effectively avoid obstacles and maintain formation, the follower selects the position of the target or its ideal position as a reference during movement, depending on whether it is being repelled by obstacles. Additionally, this paper modifies the attractive force model of the traditional artificial potential field method to restrict the maximum magnitude of the attractive force when encountering repulsive forces, thus expediting departure from obstacle areas. The dynamic characteristics of USVs are taken into account by constraining the maximum linear speed and angular speed. Formation stability is ensured by maintaining a constant speed for the leader, while the linear speed of the follower varies based on the distance to the reference object during movement. Simulation experiments demonstrated that this method can effectively avoid obstacles and maintain formation.
The Min-Entropy of Classical-Quantum Combs for Measurement-Based Applications

Learning a hidden property of a quantum system typically requires a series of interactions. In this work, we formalise such multi-round learning processes using a generalisation of classical-quantum states, called classical-quantum combs. Here, "classical" refers to a random variable encoding the hidden property to be learnt, and "quantum" refers to the quantum comb describing the behaviour of the system. The optimal strategy for learning the hidden property can be quantified by applying the comb min-entropy (Chiribella and Ebler, NJP, 2016) to classical-quantum combs. To demonstrate the power of this approach, we focus attention on an array of problems derived from measurement-based quantum computation (MBQC) and related applications. Specifically, we describe a known blind quantum computation (BQC) protocol using the combs formalism and thereby leverage the min-entropy to provide a proof of single-shot security for multiple rounds of the protocol, extending the existing result in the literature. Furthermore, we consider a range of operationally motivated examples related to the verification of a partially unknown MBQC device. These examples involve learning the features of the device necessary for its correct use, including learning its internal reference frame for measurement calibration. We also introduce a novel connection between MBQC and quantum causal models that arises in this context.
Object detection and tracking using TSM-EFFICIENTDET and JS-KM in adverse weather conditions

An efficient model to detect and track the objects in adverse weather is proposed using Tanh Softmax (TSM) EfficientDet and Jaccard Similarity based Kuhn-Munkres (JS-KM) with Pearson-Retinex in this paper. The noises were initially removed using Differential Log Energy Entropy adapted Wiener Filter (DLE-WF). The Log Energy Entropy value was calculated between the pixels instead of calculating the local mean of a pixel in the normal Wiener filter. Also, the segmentation technique was carried out using Fringe Binarization adapted K-Means Algorithm (FBKMA). The movement of segmented objects was detected using the optical flow technique, in which the optical flow was computed using the Horn-Schunck algorithm. After motion estimation, the final step in the proposed system is object tracking. The motion-estimated objects were treated as the target that is initially in the first frame. The target was tracked by JS-KM algorithm in the subsequent frame. At last, the experiential evaluation is conducted to confirm the proposed model’s efficacy. The outcomes of Detection in Adverse Weather Nature (DAWN) dataset proved that in comparison to the prevailing models, a better performance was achieved by the proposed methodology.
Water level prediction model based on blockchain and LSTM

Aiming at the shortcomings of traditional water level prediction methods such as insufficient information mining ability and unclear mechanism of heuristic algorithms, this paper proposes for the first time a water level prediction method based on blockchain technology fused with long short-term memory (LSTM) network. The method utilizes blockchain and LSTM neural network to build a combined model, and directly uploads monitoring data such as import and export water flow and water level to predict the water level, which avoids the secondary error brought by the indirect calculation of flow. In this paper, the flow compensation strategy is proposed for the first time, and the monitoring data with large deviations are compensated accordingly to reduce the prediction error from the source. The results show that the combined Blockchain-LSTM model has the smallest prediction error after adopting the compensation strategy, with the MAE of 0.290 and the RMSE of 0.490, which are smaller than those of other models, and has high prediction accuracy and practicability, which provides technical support for real-time scheduling of the South-to-North Water Diversion Reservoir.