Reviewerly/exHarmony huggingface dataset:
An hourly solar radiation prediction model using eXtreme gradient boosting algorithm with the effect of fog-haze

Hourly global solar radiation data is an important factor for solar energy utilization. Due to the lack of solar radiation observation stations in many areas, some hourly solar radiation models are proposed to predict hourly solar radiation. However, the existing models perform poorly in heavy fog-haze areas because the weakening effect of fog-haze on solar radiation is not considered. Thus, in this paper, hourly global solar radiation prediction models are developed considering air quality index (AQI) using XGBoost algorithm. The results show a general improvement in the accuracy of models with AQI as an additional input (Model B1-B6) compared to models that do not consider AQI (Model A1-A6). Compared to Model A, Model B have an increase in R value from 0.927 to 0.948, a decrease in RMSE value from 0.300 to 0.282 and a decrease in MAPE value from 0.159 to 0.145. In addition, for hourly solar radiation prediction, the six most important inputs are the day of the year, air temperature difference, surface temperature difference, hour, AQI, and total cloud cover.
A unique database synthesis technique for coverless data hiding

Coverless data hiding is a powerful technique for data security, but synthesizing text or images, often leads to semantically incorrect results. Prior state-of-the-art works have typically used media files as covers or as references for cover synthesis. This scope of improvement has inspired us to propose a novel data hiding technique by synthesizing self-sufficient, independent object without using any cover, even as metadata for data hiding. The approach focuses on synthesis of a database, by generating a dataset for the domain values of the attributes of it. Application of comparison-based sorting technique to the dataset, reveals the hidden message. The skill of the technique lies in unlimited hiding capacity, while preserving the semantic integrity of the database. Multiple security measures are taken into account along with thorough analysis of time complexity, to evaluate the efficacy of our method, that surpasses recently proposed approaches, providing solution for highly resistant covert communication.
Object-level complete coverage path planning for excavators in earthwork construction

Autonomous excavators are gradually gaining attention because they can reduce the waste of human resources and improve the efficiency. This study proposed a complete coverage path planning algorithm for autonomous excavators based on the Rotating Calipers Path Planning (RCPP) algorithm, which called the Excavator-Rotating Calipers Path Planning (E-RCPP) algorithm. This study uses boustrophedon cellular decomposition (BCD) to decompose the construction area to obtain the convex and non-convex sub-areas without obstacles, and describes a non-decomposition principle to determine whether to decompose non-convex areas that are difficult to plan. To obtain the optimal path, an adaptive spacing adjustment model which is used to adjust the spacing between parallel paths is designed. To improve the coverage rate at the corner, this study proposed a novel boundary corner turning method. The algorithm's cost function considers the path length, the number of turns, the coverage rate and the overlap rate. The Digital Orthophoto Map (DOM) of the construction area is created by Unmanned Aerial Vehicle (UAV) and cropped into three polygonal areas, the 2D top-views of the them are used for simulation experiments to verify the performance of E-RCPP algorithm, the results show that the E-RCPP algorithm has better performance when applied to the complete coverage path planning for excavator compared with the traditional RCPP algorithm.
FFCSA - Finite Field Constructions, Search, and Algorithms

In this work we present the new GAP package FFCSA - Finite Field Constructions, Search, and Algorithms. It was designed to enable Design Space Exploration for hardware implementations of cryptographic algorithms defined over finite fields. FFCSA constructions and searches are used to produce the design space, and FFCSA algorithms, parameterized for the current candidate field, are used to generate expressions needed for implementation in hardware.
Dynamic Evolutionary Game Approach for Blockchain-Driven Incentive and Restraint Mechanism in Supply Chain Financing

The sustainability of financing is an important measure in the development of supply chains. However, the difficulty and high cost of financing have always been critical factors hindering the sustainable development of small and medium-sized enterprises (SMEs). Blockchain technology (BT) is an effective tool to relieve the current problem. Based on it, this study aims to establish Blockchain-driven incentives and restraint mechanisms for SME financing using a dynamic game model in which financial institutions (FIs) and new agricultural business entities (NABEs) are regarded as game subjects. By analyzing the impact of key parameters on the equilibrium state of the game for all stakeholders, several findings are drawn as follows. (1) The usage of BT affects FI loan strategies by influencing their cost in supply chain financing. (2) The usage of BT affects NABE’s strategy choice by influencing their loan interest rate. (3) The usage of BT affects NABE’s strategy choice by influencing their default losses. (4) The usage of BT affects NABE’s and FI’s strategy choices by influencing their additional benefits. This study provides decision-making support for optimal strategy decisions under different conditions and serves as a theoretical reference for the government in formulating financing incentive and restraint mechanisms.
Vehicle-to-Blockchain (V2B) Communication: Integrating Blockchain into V2X and IoT for Next-Generation Transportation Systems

As smart transportation systems evolve, secure and efficient V2X communication between vehicles and infrastructure becomes crucial. This paper introduces a Vehicle-to-Blockchain (V2B) communication architecture, leveraging blockchain technology for transparent and decentralized interactions. Our work contributes to the integration of blockchain into V2X and IoT for next-generation transportation systems. We propose several novel blockchain use cases, including a blockchain-based vehicle ownership system based on the multi-token standard, a vehicle scoring system, blockchain–IoT integration, and a decentralized ticket management system for transportation services. The architecture addresses key aspects, such as data integration, validity, and secure messaging, and introduces a decentralized payment system and marketplace for transportation in smart cities. We specifically emphasize the technical implementation of smart contracts for these use cases, underscoring their role in ensuring robust and reliable interactions. Through our decentralized approach, we pave the way for a transformative transportation ecosystem that is adaptable, resilient, and capable of meeting the evolving needs of smart cities.
Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring

In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one’s knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel.
On Galois groups of linearized polynomials related to the general linear group of prime degree

Let L(x) be any q-linearized polynomial with coefficients in Fq, of degree qn. We consider the Galois group of L(x)+tx over Fq(t), where t is transcendental over Fq. We prove that when n is a prime, the Galois group is always GL(n,q), except when L(x)=xqn. Equivalently, we prove that the arithmetic monodromy group of L(x)/x is GL(n,q), except when L(x)=xqn, and also equivalently, we prove that the image of the mod-(t) Galois representation of the Drinfeld module arising from L(x) is all of GL(n,q).
MS-IRTNet: Multistage information interaction network for RGB-T semantic segmentation

The complementary information from RGB and thermal images can remarkably boost semantic segmentation performance. Existing RGB-T segmentation methods usually use simple interaction strategies to extract complementary information from RGB and thermal images, which ignores recognizability features from different imaging mechanisms. To address these problems, we propose a multistage information interaction network for RGB-T semantic segmentation called MS-IRTNet. MS-IRTNet has a dual-stream encoder structure that can extract multistage feature information. To better interact with multimodal information, we design a gate-weighted interaction module (GWIM) and a feature information interaction module (FIIM). GWIM can learn multimodal information weights in different channels, while FIIM integrates and fuses weighted RGB and thermal information into a single feature map. Finally, multistage interactive information is fed into the decoder for semantic prediction. Our method achieves 60.5 mIoU on the MFNet dataset, outperforming state-of-the-art methods. Notably, MS-IRTNet also achieved state-of-the-art results in tests of daytime images (51.7 mIoU) and nighttime images (62.5 mIoU). The code and pre-trained models are available at https://github.com/poisonzzw/MS-IRTNet.
Mutual structure learning for multiple kernel clustering

Multiple kernel clustering (MKC) has garnered considerable attention in recent years, aiming to obtain an optimal partition from multiple base kernels. Existing MKC methods typically focus on either learning the pairwise structure by constructing an optimal kernel from the base kernels or learning the cluster structure by integrating multiple base partitions into a consensus one for clustering. However, these previous approaches overlook the potential for mutual structure learning between the two aspects, i.e., pairwise and cluster structure, of clustering. To address this limitation, we propose a novel multiple kernel clustering method, referred to as MSL-MKC, which simultaneously learns pairwise and cluster structure to achieve an improved consensus partition matrix for clustering. Specifically, MSL-MKC extends the adaptive neighbor graph learning approach into kernel space to construct a discriminative similarity graph from multiple base kernels for pairwise structure learning. The consensus partition is formulated by aligning it with multiple base partitions for cluster structure learning. We utilize a Laplacian regularization term to preserve the pairwise structure in the consensus partition and inject the cluster structure into the discriminative similarity graph. The proposed method integrates similarity graph learning, partition fusion, and Laplacian regularization into a unified framework, optimized using an iterative algorithm. Experimental results on various benchmark datasets demonstrate the efficacy of MSL-MKC. The demo code of this work is publicly available at https://github.com/guanyuezhen/MSL-MKC.
An efficient and privacy-preserving query scheme in intelligent transportation systems

Privacy preservation is a crucial aspect of intelligent transportation systems, particularly in queries involving sensitive data stored in the Cloud. However, resource-constrained vehicles require security, privacy, and communication efficiency. To address these challenges, we propose an efficient and Privacy-Preserving Query (PPQ) scheme for intelligent transportation systems. The proposed scheme leverages matrix decomposition techniques to reorganize routes in a flexible manner. Additionally, all query operations are performed in the ciphertext state to protect the vehicle's route. Lightweight authentication is employed to ensure legitimate user access between vehicles and roadside units. Furthermore, security analysis confirms that the proposed scheme meets the security goals of privacy protection. Performance analysis demonstrates that the computational and communication overhead of the scheme are greatly improved compared to similar schemes. Therefore, the proposed solution is well-suited for resource-constrained vehicles.
Hybrid AI models allow label-free identification and classification of pancreatic tumor repopulating cell population

Human pancreatic cancer cell lines harbor a small population of tumor repopulating cells (TRCs). Soft 3D fibrin gel allows efficient selection and growth of these tumorigenic TRCs. However, rapid and high-throughput identification and classification of pancreatic TRCs remain technically challenging. Here, we developed deep learning (DL) models paired with machine learning (ML) models to readily identify and classify 3D fibrin gel-selected TRCs into sub-types. Using four different human pancreatic cell lines, namely, MIA PaCa-2, PANC-1, CFPAC-1, and HPAF-II, we classified 3 main sub-types to be present within the TRC population. Our best model was an Inception-v3 convolutional neural network (CNN) used as a feature extractor paired with a Support Vector Machine (SVM) classifier with radial basis function (rbf) kernel which obtained a test accuracy of 90%. In addition, we compared this hybrid method of supervised classification with other methods of supervised classifications and showed that our working model outperforms others. With the help of unsupervised machine learning algorithms, we also validated that the pancreatic TRC subpopulation can be clustered into 3 sub-types. Collectively, our robust model can detect and readily classify tumorigenic TRC subpopulation label-free in a high-throughput fashion which can be very beneficial in clinical settings.
Morpho-logic from a topos perspective – application to symbolic AI

Modal logics have proved useful for many reasoning tasks in symbolic artificial intelligence (AI), such as belief revision, spatial reasoning, among others. On the other hand, mathematical morphology (MM) is a theory for non-linear analysis of structures, that was widely developed and applied in image analysis. Its mathematical bases rely on algebra, complete lattices, topology. Strong links have been established between MM and mathematical logics, mostly modal logics. In this paper, we propose to further develop and generalize this link between mathematical morphology and modal logic from a topos perspective, i.e. categorial structures generalizing space, and connecting logics, sets and topology. Furthermore, we rely on the internal language and logic of a topos. We define structuring elements, dilations and erosions as morphisms. Then we introduce the notion of structuring neighborhoods, and show that the dilations and erosions based on them lead to a constructive modal logic, for which a sound and complete proof system is proposed. We then show that the modal logic thus defined (called morpho-logic here), is well adapted to define concrete and efficient operators for revision, merging, and abduction of new knowledge, or even spatial reasoning.
Graph embedding-based Anomaly localization for HVAC system

As a major energy consumption system in buildings, anomaly detection on multivariate time series monitored by sensors in HVAC systems has been a significant challenge. However, existing data-driven anomaly detection methods are dedicated to improving the performance of abnormal time interval detection that fails to localize abnormal time series within abnormal time intervals. In this study, we design a novel anomaly localization method based on graph embedding and apply it to the HVAC system. The proposed method aims to learn feature representations of time intervals and time series by graph embedding and to detect abnormal time intervals and time series by identifying outlier degrees of embedding vectors. It constructs dynamic graphs representing time intervals by extracting correlations between multivariate time series and proposes an entropy-based graph embedding model to embed the dynamic graph. In the embedding space, the local outlier factor is used to measure abnormal embedding vectors to detect abnormal time intervals. After detecting abnormal time intervals, large-scale information network embedding is applied to embed each time series within the abnormal time interval for detecting abnormal time series. Our method is evaluated on three different abnormal patterns of the HVAC system. The experimental results reveal that the F1 scores of our method beat the existing methods in terms of abnormal time interval detection and abnormal time series detection of the HVAC system.
Risk-aware shielding of Partially Observable Monte Carlo Planning policies

Partially Observable Monte Carlo Planning (POMCP) is a powerful online algorithm that can generate approximate policies for large Partially Observable Markov Decision Processes. The online nature of this method supports scalability by avoiding complete policy representation. However, the lack of an explicit policy representation hinders interpretability and a proper evaluation of the risks an agent may incur. In this work, we propose a methodology based on Maximum Satisfiability Modulo Theory (MAX-SMT) for analyzing POMCP policies by inspecting their traces, namely, sequences of belief-action pairs generated by the algorithm. The proposed method explores local properties of the policy to build a compact and informative summary of the policy behaviour. Moreover, we introduce a rich and formal language that a domain expert can use to describe the expected behaviour of a policy. In more detail, we present a formulation that directly computes the risk involved in taking actions by considering the high-level elements specified by the expert. The final formula can identify risky decisions taken by POMCP that violate the expert indications. We show that this identification process can be used offline (to improve the policy's explainability and identify anomalous behaviours) or online (to shield the risky decisions of the POMCP algorithm). We present an extended evaluation of our approach on four domains: the well-known tiger and rocksample benchmarks, a problem of velocity regulation in mobile robots, and a problem of battery management in mobile robots. We test the methodology against a state-of-the-art anomaly detection algorithm to show that our approach can be used to identify anomalous behaviours in faulty POMCP. We also show, comparing the performance of shielded and unshielded POMCP, that the shielding mechanism can improve the system's performance. We provide an open-source implementation of the proposed methodologies at https://github.com/GiuMaz/XPOMCP.
The Comparative Pathology Workbench: Interactive visual analytics for biomedical data

Pathologists need to compare histopathological images of normal and diseased tissues between different samples, cases, and species. We have designed an interactive system, termed Comparative Pathology Workbench (CPW), which allows direct and dynamic comparison of images at a variety of magnifications, selected regions of interest, as well as the results of image analysis or other data analyses such as scRNA-seq. This allows pathologists to indicate key diagnostic features, with a mechanism to allow discussion threads amongst expert groups of pathologists and other disciplines. The data and associated discussions can be accessed online from anywhere in the world. The Comparative Pathology Workbench (CPW) is a web-browser-based visual analytics platform providing shared access to an interactive "spreadsheet" style presentation of image and associated analysis data. The CPW provides a grid layout of rows and columns so that images that correspond to matching data can be organised in the form of an image-enabled "spreadsheet". An individual workbench can be shared with other users with read-only or full edit access as required. In addition, each workbench element or the whole bench itself has an associated discussion thread to allow collaborative analysis and consensual interpretation of the data. The CPW is a Django-based web-application that hosts the workbench data, manages users, and user-preferences. All image data are hosted by other resource applications such as OMERO or the Digital Slide Archive. Further resources can be added as required. The discussion threads are managed using WordPress and include additional graphical and image data. The CPW has been developed to allow integration of image analysis outputs from systems such as QuPath or ImageJ. All software is open-source and available from a GitHub repository.
Olive: Oblivious Federated Learning on Trusted Execution Environment against the Risk of Sparsification

Combining Federated Learning (FL) with a Trusted Execution Environment (TEE) is a promising approach for realizing privacy-preserving FL, which has garnered significant academic attention in recent years. Implementing the TEE on the server side enables each round of FL to proceed without exposing the client's gradient information to untrusted servers. This addresses usability gaps in existing secure aggregation schemes as well as utility gaps in differentially private FL. However, to address the issue using a TEE, the vulnerabilities of server-side TEEs need to be considered---this has not been sufficiently investigated in the context of FL. The main technical contribution of this study is the analysis of the vulnerabilities of TEE in FL and the defense. First, we theoretically analyze the leakage of memory access patterns, revealing the risk of sparsified gradients, which are commonly used in FL to enhance communication efficiency and model accuracy. Second, we devise an inference attack to link memory access patterns to sensitive information in the training dataset. Finally, we propose an oblivious yet efficient aggregation algorithm to prevent memory access pattern leakage. Our experiments on real-world data demonstrate that the proposed method functions efficiently in practical scales.