Reviewerly/exHarmony huggingface dataset:
An Efficient Data Analysis Method for Big Data Using Multiple-Model Linear Regression

This paper introduces a new data analysis method for big data using a newly defined regression model named multiple model linear regression(MMLR), which separates input datasets into subsets and construct local linear regression models of them. The proposed data analysis method is shown to be more efficient and flexible than other regression based methods. This paper also proposes an approximate algorithm to construct MMLR models based on $$(\epsilon ,\delta )$$ -estimator, and gives mathematical proofs of the correctness and efficiency of MMLR algorithm, of which the time complexity is linear with respect to the size of input datasets. This paper also empirically implements the method on both synthetic and real-world datasets, the algorithm shows to have comparable performance to existing regression methods in many cases, while it takes almost the shortest time to provide a high prediction accuracy.
MobileSAM-Track: Lightweight One-Shot Tracking and Segmentation of Small Objects on Edge Devices

Tracking and segmenting small targets in remote sensing videos on edge devices carries significant engineering implications. However, many semi-supervised video object segmentation (S-VOS) methods heavily rely on extensive video random-access memory (VRAM) resources, making deployment on edge devices challenging. Our goal is to develop an edge-deployable S-VOS method that can achieve high-precision tracking and segmentation by selecting a bounding box for the target object. First, a tracker is introduced to pinpoint the position of the tracked object in different frames, thereby eliminating the need to save the results of the split as other S-VOS methods do, thus avoiding an increase in VRAM usage. Second, we use two key lightweight components, correlation filters (CFs) and the Mobile Segment Anything Model (MobileSAM), to ensure the inference speed of our model. Third, a mask diffusion module is proposed that improves the accuracy and robustness of segmentation without increasing VRAM usage. We use our self-built dataset containing airplanes and vehicles to evaluate our method. The results show that on the GTX 1080 Ti, our model achieves a J&amp;F score of 66.4% under the condition that the VRAM usage is less than 500 MB, while maintaining a processing speed of 12 frames per second (FPS). The model we propose exhibits good performance in tracking and segmenting small targets on edge devices, providing a solution for fields such as aircraft monitoring and vehicle tracking that require executing S-VOS tasks on edge devices.
An Improved YOLOv5-Based Lightweight Submarine Target Detection Algorithm

Submarine recognition plays a critical role in maritime security and military defense. However, traditional submarine recognition algorithms face limitations in feature representation capability and robustness. Additionally, deploying deep learning methods on embedded and mobile platforms presents a bottleneck. To address these challenges, we propose an innovative and practical approach-an improved YOLOv5-based lightweight submarine automatic recognition detection algorithm. Our method leverages the Feature Pyramid based on MobileNetV3 and the C3_DS module to reduce computation and parameter complexity while ensuring high precision in submarine recognition. The integration of the adaptive neck from the SA-net strategy further mitigates missed detections, significantly enhancing the accuracy of submarine target detection and recognition. We evaluated our improved model on a submarine dataset, and the results demonstrate remarkable advancements in Precision, Recall, and mAP0.5, with respective increases of 8.54%, 6.02%, and 3.36%. Moreover, we achieved a notable reduction of 34.1% in parameter quantity and 67.9% in computational complexity, showcasing its lightweight effects. Overall, our proposed method introduces novel improvements to submarine recognition, addressing existing limitations and offering practical benefits for real-world deployment on embedded and mobile platforms. The enhanced performance in precision and recall metrics, coupled with reduced computational requirements, emphasizes the significance of our approach in enhancing maritime security and military applications.
Enhancing downstream tasks in Knowledge Graphs Embeddings: A Complement Graph-based Approach Applied to Bilateral Trade

Forecasting international trade flows is crucial for understanding global economic activity and serving as critical economic indicators used by economists and policymakers with significant implications for respective countries’ economic policies. However, this is a challenging task due to the complexity of the relationships between entities involved in international trade. While several approaches have been proposed, knowledge graph-based models have emerged as promising solutions for predicting international trade flows. In this paper, we introduce a synthetic triple-generation algorithm for enhancing downstream tasks in knowledge graph embeddings based on the graph complement. The algorithm identifies missing relationships between entities by exploiting the complement graph and generates high-quality synthetic triples that improve the accuracy of predictions. We validate the generated triples using several knowledge graphs embedding methods and computing metrics. To perform an initial result screening, an international trade scenario is explored, demonstrating the effectiveness of the proposed approach in enhancing the performance of knowledge graph-based models for predicting international trade flows.
A Comparative Study of Machine Learning Algorithm for Predicting Project Management Methodology

Selecting the appropriate project management methodology is one of the challenges faced by project managers. Predicting the adoption of agile or traditional methodology during the early stage of the project could contribute to its success. The purpose of this paper is to predict project management methodology based on machine learning algorithms. Our study aims to help project managers make the right decisions and select the most suitable project management approach. The performance of Machine Learning (ML) models was evaluated using metrics such as accuracy, precision, recall, and F1-score. Gradient Boosting outperformed other ML models, achieving an accuracy of 94%.
UMAP for Geospatial Data Visualization

In this paper, we examine the efficacy of unsupervised learning approaches, particularly clustering and dimensionality reduction techniques, in practical applications such as image compression and geospatial data visualization. Initially, we scrutinize the applicability of the elbow rule in determining the optimal number of clusters within synthetic datasets of varying structures using the k-means algorithm. Subsequently, we evaluate the potency of density-based (DBSCAN) and hierarchical clustering algorithms in unearthing intrinsic patterns within these datasets. Shifting our focus towards practical applications, we leverage the k-means clustering for image compression, demonstrating a notable reduction in storage requirements without a significant compromise in visual quality. Our findings articulate that a strategic selection of cluster numbers can yield substantial compression rates. In the final segment of our investigation, we delve into the utility of dimensionality reduction techniques, specifically t-SNE and UMAP, in the realm of geospatial data visualization. Utilizing a dataset comprising distances between Spanish provinces, we gauge the proficiency of these techniques in preserving relative distances upon projection onto a two-dimensional plane. Our observations denote that both t-SNE and UMAP can generate precise visual representations of the original geographic layouţ with UMAP exhibiting exceptional performance in comparison with alternative methodologies. On a broader scale, our study underscores the versatility and practical utility of unsupervised learning techniques across an array of applications, ranging from image compression to geospatial data visualization. It emphasizes the significance of comprehending their foundational mechanisms and fine-tuning their hyperparameters to achieve optimal performance.
An Ensemble-based Fraud Detection Model for Financial Transaction Cyber Threat Classification and Countermeasures

Fraud remains a pervasive challenge within the banking industry, where financial institutions and their clients grapple with substantial annual losses. The proliferation of digital transactions and online banking has created new avenues for fraudsters to exploit vulnerabilities, leading to financial harm to unsuspecting victims. Consequently, the imperative to promptly and accurately detect fraudulent transactions has grown significantly, both as a safeguard against financial crimes and as a pillar of trust between customers and the banking sector. This paper introduces an innovative fraud detection model designed for bank payment transactions using advanced ensembling techniques. This study presents a comprehensive evaluation of an ensembling model conducted on the Bank Account Fraud (BAF) dataset. Through meticulous analysis, the performance of various base models and ensembling methods was assessed and compared, employing a variety of critical metrics including accuracy, precision, recall, and F1-score. The proposed ensemble model, referred to as "Stacking," exhibited remarkable performance, attaining a commendable accuracy score of 0.98. This result reaffirmed its prowess as a comprehensive and balanced solution to the multifaceted challenges of fraud detection. This study has paramount implications for the banking industry, offering a robust and adaptable solution to deal with the increasing threats posed by financial fraud. Furthermore, it emphasizes the significance of precision-recall trade-offs in fraud detection and underscores the potential of ensemble methods, particularly the "Stacking" model, to fortify the resilience and efficacy of existing security systems.
Dual‐granularity feature fusion in visible‐infrared person re‐identification

Abstract Visible‐infrared person re‐identification (VI‐ReID) aims to recognize images of the same person captured in different modalities. Existing methods mainly focus on learning single‐granularity representations, which have limited discriminability and weak robustness. This paper proposes a novel dual‐granularity feature fusion network for VI‐ReID. Specifically, a dual‐branch module that extracts global and local features and then fuses them to enhance the representative ability is adopted. Furthermore, an identity‐aware modal discrepancy loss that promotes modality alignment by reducing the gap between features from visible and infrared modalities is proposed. Finally, considering the influence of non‐discriminative information in the modal‐shared features of RGB‐IR, a greyscale conversion is introduced to extract modality‐irrelevant discriminative features better. Extensive experiments on the SYSU‐MM01 and RegDB datasets demonstrate the effectiveness of the framework and superiority over state‐of‐the‐art methods.
Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption

To clarify the copyrights of 3D models of oblique photography (3DMOP) and guarantee their security, a novel security protection scheme of 3DMOP was proposed in this study by synergistically applying digital watermarking and data encryption. In the proposed scheme, point clouds were clustered first, and then the centroid and feature points of each cluster were calculated and extracted, respectively. Afterward, the watermarks were embedded into the point clouds cluster-by-cluster, taking distances between feature points and centroids as the embedding positions. In addition, the watermarks were also embedded using texture coordinates of 3DMOP to further enhance the robustness of the watermarking algorithm. Furthermore, Arnold transformation was performed on texture images of 3DMOP for security protection of classified or sensitive information. Experimental results have verified the strong imperceptibility and robustness of the proposed watermarking algorithm, as well as the high security of the designed data encryption algorithm. The outcomes of this work can refine the current security protection methods of 3DMOP and thus further expand their application scope.
Lightweight DB-YOLO Facemask Intelligent Detection and Android Application Based on Bidirectional Weighted Feature Fusion

Conventional facemask detection algorithms face challenges of insufficient accuracy, large model size, and slow computation speed, limiting their deployment in real-world scenarios, especially on edge devices. Aiming at addressing these issues, we proposed a DB-YOLO facemask intelligent detection algorithm, which is a lightweight solution that leverages bidirectional weighted feature fusion. Our method is built on the YOLOv5 algorithm model, replacing the original YOLOv5 backbone network with the lightweight ShuffleNetv2 to reduce parameters and computational requirements. Additionally, we integrated BiFPN as the feature fusion layer, enhancing the model’s detection capability for objects of various scales. Furthermore, we employed a CARAFE lightweight upsampling factor to improve the model’s perception of details and small-sized objects and the EIOU loss function to expedite model convergence. We validated the effectiveness of our proposed method through experiments conducted on the Pascal VOC2007+2012 and Face_Mask datasets. Our experimental results demonstrate that the DB-YOLO model boasts a compact size of approximately 1.92 M. It achieves average precision values of 70.1% and 93.5% on the Pascal VOC2007+2012 and Face_Mask datasets, respectively, showcasing a 2.3% improvement in average precision compared to the original YOLOv5s. Furthermore, the model’s size is reduced by 85.8%. We also successfully deployed the model on Android devices using the NCNN framework, achieving a detection speed of up to 33 frames per second. Compared to lightweight algorithm models like YOLOv5n, YOLOv4-Tiny, and YOLOv3-Tiny, DB-YOLO not only reduces the model’s size but also effectively improves detection accuracy, exhibiting excellent practicality and promotional value on edge devices.
Umwelt as the foundation of an ethics of smart environments

Abstract Due to the increasing embeddedness of pervasive and immersive technologies in the built environment, a new type of spaces known as smart environments emerges. With them arise many ethical issues related to freedom, agency, consciousness, and the governance of human-machine interactions. In this paper, I use Jakob von Uexküll’s Umwelt theory to devise principles for an applied ethics of umwelt, as part of a broader ethics of smart environments. Umwelt ethics is one of human beings’ surrounding world in the context of environmental capitalism. As umwelt becomes a resource to be exploited by economic agents with de facto or de jure rights to control space, defining ethical principles pertaining to the digitalization of space in the built environment is more important than ever.
Accurate harmonic vibrational frequencies for diatomic molecules via quantum computing

During the noisy intermediate-scale quantum (NISQ) era, quantum computational approaches refined to overcome the challenge of limited quantum resources are highly valuable. However, the accuracy of the molecular properties predicted by most of the quantum computations nowadays is still far off (not within chemical accuracy) compared to their corresponding experimental data. Here, we propose a promising qubit-efficient quantum computational approach to calculate the harmonic vibrational frequencies of a large set of neutral closed-shell diatomic molecules with results in great agreement with their experimental data. To this end, we construct the accurate Hamiltonian using molecular orbitals, derived from density functional theory to account for the electron correlation and expanded in the Daubechies wavelet basis set to allow an accurate representation in real space grid points, where an optimized compact active space is further selected so that only a reduced small number of qubits is sufficient to yield an accurate result. To justify the approach, we benchmark the performance of the Hamiltonians spanned by the selected molecular orbitals by first transforming the molecular Hamiltonians into qubit Hamiltonians and then using the exact diagonalization method to calculate the results, regarded as the best results achievable by quantum computation. Furthermore, we show that the variational quantum circuit with the chemistry-inspired UCCSD ansatz can achieve the same accuracy as the exact diagonalization method except for systems whose Mayer bond order indices are larger than 2. For those systems, we demonstrate that the heuristic hardware-efficient RealAmplitudes ansatz, even with a shorter circuit depth, can provide a significant improvement over the UCCSD ansatz, verifying that the harmonic vibrational frequencies could be calculated accurately by quantum computation in the NISQ era.
Efficient Hamiltonian simulation for solving option price dynamics

Pricing financial derivatives, in particular European-style options at different time-maturities and strikes, means a relevant problem in finance. The dynamics describing the price of vanilla options when constant volatilities and interest rates are assumed is governed by the Black-Scholes model, a linear parabolic partial differential equation with terminal value given by the payoff of the option contract and no additional boundary conditions. Here, we present a digital quantum algorithm to solve the Black-Scholes equation on a quantum computer by mapping it to the Schr\"odinger equation. The non-Hermitian nature of the resulting Hamiltonian is solved by embedding its propagator into an enlarged Hilbert space by using only one additional ancillary qubit. Moreover, due to the choice of periodic boundary conditions, given by the definition of the discretized momentum operator, we duplicate the initial condition, which substantially improves the stability and performance of the protocol. The algorithm shows a feasible approach for using efficient Hamiltonian simulation techniques as quantum signal processing to solve the price dynamics of financial derivatives on a digital quantum computer. Our approach differs from those based on Monte Carlo integration, exclusively focused on sampling the solution assuming the dynamics is known. We report expected accuracy levels comparable to classical numerical algorithms by using nine qubits to simulate its dynamics on a fault-tolerant quantum computer, and an expected success probability of the post-selection procedure due to the embedding protocol above 60%.
Memento: Architectural Support for Ephemeral Memory Management in Serverless Environments

Serverless computing is an increasingly attractive paradigm in the cloud due to its ease of use and fine-grained pay-for-what-you-use billing. However, serverless computing poses new challenges to system design due to its short-lived function execution model. Our detailed analysis reveals that memory management is responsible for a major amount of function execution cycles. This is because functions pay the full critical-path costs of memory management in both userspace and the operating system without the opportunity to amortize these costs over their short lifetimes.
Pix2Pix Hyperparameter Optimisation Prediction

Hyperparameter tuning is an important aspect in machine-learning especially for deep generative models. Tuning models to stabilize training and to get the best accuracy can be a time consuming and protracted process. Generative models have a large search space requiring resources and knowledge to find the best parameters. Therefore, in most cases the search space is reduced and parameters are limited to a selected few to save time and computation time. This paper explores three different strategies to predict high impact hyperparameters for Pix2Pix. The achieved results show, that binary classification and regression achieve good results and reliably predict good hyperparameter combinations.
Socratic Video Understanding on Unmanned Aerial Vehicles

In this work, we propose a system for video understanding through zero-shot reading comprehension using Socratic Models. Specifically, we create a language-based world-state history of events and objects present in a scene captured by an Unmanned Aerial Vehicle (UAV). To achieve this, video footage from RYZE Tello microdrones is transmitted to a ground computer for further processing. The semantically rich information offered by Large Language Models (LLMs) enables open-ended reasoning, such as event forecasting with minimal human intervention, in a cost-effective robotic system. BLIP-2 is employed to answer a given set of instructional prompts, creating a log-state of objects, humans, and hazards that can be searched. Simultaneously, it suggests probable actions in the scene and can assist the human controller with an estimated best command. The BLIP-2 instructional prompts are then combined with OpenAI's da-vinci-003/gpt-3.5-turbo to generate comprehensive video descriptions and summarize likely actions. The LLM-enhanced generated texts achieve a GUNNING Fog median grade level in the range of 7-12.
On the use of Pretrained Language Models for Legal Italian Document Classification

Document classification is helpful for law professionals to improve content browsing and retrieval. Pretrained Language Models, such as BERT, have become established for legal document classification. However, legal content is quite diversified. For example, documents vary in length from very short maxims to relatively long judgements; certain document types are rich of domain-specific expressions and can be annotated with multiple labels from domain-specific taxonomies. This paper studies to what extent existing pretrained models are suited to the legal domain. Specifically, we examine a real business case focused on Italian legal document classification. On a proprietary dataset with thousands of diversified categories (e.g., legal judgements, maxims, and legal news) we explore the use of Pretrained Language Models adapted to handle various content types. We collect both quantitative and qualitative results, highlighting best and worst cases, anomalous categories, and limitations of currently available models.
Fuzzy cluster-scaled principal component analysis for mixed data and its application of educational effect based on device selection

This paper proposes a fuzzy cluster-scaled principal component analysis (fuzzy cluster-scaled PCA) for mixed data, which consists of both numerical and categorical data with respect to quantitative and qualitative variables. The fuzzy cluster-scaled PCA has been proposed for high-dimension, low-sample size (HDLSS) data in which the number of variables (or dimensions) is much larger than the number of objects (or samples). In this case, the target data is only for the numerical data. The proposed fuzzy cluster-scaled PCA in this paper can be applied to the mixed type of HDLSS data by utilizing the feature of the fuzzy cluster-scaled correlation, which is decomposed into two parts: the first part is the correlation of classification structures between variables and the second part is the correlation between variables. Then, the first part can be adapted to the categorical data, and the second part can be used for the numerical data through the same objects. Several numerical examples used data from a survey concerned with the relationship between students' choice of devices and scores of examination/class marks to measure the educational effect show a better performance of this proposed method, and insightful, important information on educational effectiveness is clarified.