Reviewerly/exHarmony huggingface dataset:
An Adaptive Modeling-Based Aeromagnetic Maneuver Noise Suppression Method and Its Application in Mine Detection

Aeromagnetic measurement plays an important role in mineral exploration, but unmanned aerial vehicles generate maneuvering noise during aerial flight, which negatively impacts the accuracy of aeromagnetic measurement data. Therefore, aeromagnetic compensation is an indispensable step in aeromagnetic data processing. The multicollinearity of variables in the aeromagnetic compensation model based on linear regression affects its accuracy, resulting in a large difference in the compensation effect of the same group of compensation coefficients in different directions. In order to obtain high-quality aeromagnetic data, this study proposes an adaptive model-based method for suppressing aeromagnetic maneuvering noise. First, due to the fact that the variables that cause multiple collinearity in the compensation model are related to the flight heading, the model variables are adaptively assigned to each heading based on the characteristics of the variable data for different headings. The compensation model is optimized and improved, and the impact of multiple collinearity is thus suppressed. In adaptive modeling, variables with greater significance and smaller multicollinearity are automatically allocated to build the optimal heading model, and then high-precision compensation coefficients are obtained. This algorithm was applied to the data collected by a certain unmanned aerial vehicle aeromagnetic measurement platform in Ma’anshan and compared with traditional methods. The experimental results show that the adaptive modeling-based aeromagnetic compensation algorithm is superior to traditional algorithms, with fewer errors and a higher improvement ratio. Hence, the method can effectively solve the ill-conditioned problem of a model affected by multicollinearity and further improve its compensation accuracy and robustness. Moreover, the feasibility and value of this algorithm were verified in actual mineral resource detection.
DASANet: A 3D Object Detector with Density-and-Sparsity Feature Aggregation

In the field of autonomous driving and robotics, 3D object detection is a difficult, but important task. To improve the accuracy of detection, LiDAR, which collects the 3D point cloud of a scene, is updated constantly. But the density of the collected 3D points is low, and its distribution is unbalanced in the scene, which influences the accuracy of 3D object detectors in regards to object location and identification. Although corresponding high-resolution scene images from cameras can be used as supplemental information, poor fusion strategies can result in decreased accuracy compared with that of LiDAR-point-only detectors. Thus, to improve the detection performance for the classification, localization, and even boundary location of 3D objects, a two-stage detector with density-and-sparsity feature aggregation, called DASANet, is proposed in this paper. In the first stage, dense pseudo point clouds are generated with images from cameras and are used to obtain the initial proposals. In the second stage, two novel feature aggregation modules are designed to fuse LiDAR point information and pseudo point information, which refines the semantic and detailed representation of the feature maps. To supplement the semantic information of the highest-scale LiDAR features for object localization and classification, a triple differential information supplement (TDIS) module is presented to extract the LiDAR-pseudo differential features and enhance them in spatial, channel, and global dimensions. To increase the detailed information of the LiDAR features for object boundary location, a Siamese three-dimension coordinate attention (STCA) module is presented to extract stable LiDAR and pseudo point cloud features with a Siamese encoder and fuse these features using a three-dimension coordinate attention. Experiments using the KITTI Vision Benchmark Suite demonstrate the improved performance of our DASANet in regards to the localization and boundary location of objects. The ablation studies demonstrate the effectiveness of the TDIS and the STCA modules.
SSANet: spatial stain attention network for pathological images classification

Histopathological images classification plays a significant role in cancer diagnosis, but current deep learning methods fail to account for the unique characteristics of histopathological images. To address this limitation, we present SSANet, a Spatial and Stain Attention Network focusing on staining information in the cell nucleus and cytoplasm of histopathological images. Our approach first separates the stain channels and generates a stain attention map using a specialized stain attention module, which then activates staining information in the feature map. The experiments on two computational pathology problems, CRC-HE and BreakHis datasets, demonstrate that our method outperforms state-of-the-art methods with test F1 values up to 97.63% and 94.91% for cancer subtypes classification. Our contributions include the novel SSANet architecture, a stain attention module that enhances the focus on crucial cytosolic and cytoplasmic information, and improved classification results for histopathological images.
Convolutional neural networks applied to data organized as <scp>OLAP</scp> cubes

Abstract This paper presents a Convolutional Neural Network (CNN) architecture named OlapNet , which incorporates implicit operations of OLAP cubes (or data cubes). OLAP cubes are produced from database tables or spreadsheets and they allow particular operations that support performing complex queries efficiently. OlapNet permits evaluating various combinations of these OLAP operations in its search space and thus, it enables, in part, to automate the data transformation step in the knowledge discovery process. A sample of data from an actual database containing anonymized data on the debt history of customers of a financial institution has been used to evaluate our proposal. A predictive classification problem to estimate the probability of any given customer contracting new credits in the next three months has been modelled from these data. Then, traditional methods of Machine Learning and CNN were applied. The results showed that CNN, using the OlapNet architecture, outperforms traditional methods in almost all cases, indicating that the proposed architecture is quite promising.
Control of Correlation Using Confinement in Case of Quantum System

Abstract This article investigates the behavior of a Moshinsky atom in a 1D harmonic trap. Focus is given on the theoretical foundations of confinement and its impact on the correlation between particles in the Moshinsky atom. The investigation begins by illustrating the (de)localization of the probability density function using Shannon entropy. The basics of correlation and interpretation of correlation using tools such as mutual information and statistical correlation coefficients and how these can be quantified are discussed. Then the concept of confinement is explored. The impact of interaction strength and confinement on Shannon entropy, statistical correlation coefficients, and mutual information is investigated. How interaction strength and confinement can be used to induce correlations between previously uncorrelated particles, as well as how they can be used to suppress correlations between previously correlated particles is discussed. Their implications for quantum information processing and quantum simulation are discussed. In conclusion, confinement is a powerful tool for controlling correlations in quantum systems, and its impact on correlation can be understood through theoretical models. The importance of experimental studies in this field, which provide insights into the behavior of quantum systems under confinement and pave the way for future applications in quantum technology is also emphasized.
IoVT-based efficient solution for optimal active smart camera selection in a tracking mission

Nowadays, to ensure people’s and property’s safety, cities have dozens of cameras installed on almost every corner. However, the haphazard deployment of these cameras often results in overlapping fields of view, leading to redundant processing of the same events by multiple cameras. This situation requires more power, huge bandwidth, and more storage capacity for collecting, processing and distributing multimedia data over the network. To overcome these challenges, we propose a novel Internet of Video Things-based system that efficiently reduces the number of cameras involved in processing redundant events. The proposed system operates through a two-step process. Firstly, a leader is elected from a group of cameras based on the degree of overlap in their fields of view, serving as the coordinator. Secondly, the system intelligently selects suitable assistants from neighboring cameras to complement the leader’s vision when it becomes inadequate for effective object tracking. Consequently, only the leader and its assistants remain active, while other neighboring cameras remain in an inactive state. The evaluation demonstrates that our IoVT-based solution offers an efficient approach by minimizing redundancy, optimizing resource utilization and overcoming the challenges posed by the random installation of cameras in urban environments.
Generalizing probabilistic material implication and Bayesian conditionals

Conditional statements in natural language of the form “if A then B” have multiple interpretations that require different logical treatment. In this paper, we focus on probabilistic “if A then B” rules that are given either a Bayesian interpretation via conditional probabilities P(B|A), or couched as probabilistic material implication. While some have argued that Bayesian conditionals are the correct way to think about such rules, there are challenges with standard inferences such as modus ponens and modus tollens that might make probabilistic material implication a better candidate at times for rule-based systems employing forward-chaining; and arguably material implication is still suitable when information about prior or conditional probabilities is not available at all. We investigate a generalization of probabilistic material implication and Bayesian conditionals that combines the advantages of both formalisms in a systematic way and prove basic properties of the generalized implication, in particular, various bounds as well as properties of inference chains in graphs. And most importantly we provide a novel and natural interpretation of the generalized implication's main parameter.
A locally weighted multi-domain collaborative adaptation for failure prediction in SSDs

Recently, domain adaptation methods are widely applied to intelligent failure prediction to solve the problem of lack of labeled data for newly designed equipment. Generally, domain adaptation assumes that the target to be predicted is consistent with the label space of the source domain. However, a more practical scenario may be that the distribution of the source domain is a subset of the target’s since the new types of equipment may have different failure characteristics. Learning from a single source domain is insufficient to support the prediction task on the target domain. Multiple-source domain adaptation becomes a desperately needed solution. To this end, this paper proposes a locally weighted multi-domain collaborative adaptation method (LWMDCA). A locally weighted technique is introduced to construct the multi-source collaborative joint domain based on the similarity weighted regular coefficient to provide complete diagnostic knowledge. Moreover, to further utilize the knowledge, a new feature extractor based on multi-instance learning with attention mechanism is designed to focus on the important failure characteristics. Finally, comparative analysis with many state-of-the-art failure prediction methods using Alibaba’s SSDs datasets is presented to demonstrate the effectiveness of the proposed method.
Depth-defying OOF-GNN: Sailing smoothly amidst GNN waves

When it comes to machine learning on graphs, Graph Neural Networks(GNNs) is a potent tool. By iteratively propagating neural messages along the edges of the input graph, GNNs integrate node feature information with graph structure. Nonetheless, not all neighborhood data is valuable. Some of the neighbor data may not be necessary for the downstream task. For a controlled and relevant information aggregation, we propose a flexible method OOF-GNN (Over-smoothing and Over-squashing Free Graph Neural Network). The main notion of this technique is to limit the impact of irrelevant and excessive information propagation during message passing. An extensive discussion of the theoretical and experimental justifications, associated with this method is presented. This technique works on all baseline models (e.g. GCN, GraphSAGE, and GAT). It allows them to gather information from the K-hop neighborhood without node information being over-smoothed and over-squashed. The efficacy of OFF-GNN in addressing the challenges of under-reaching, over-smoothing, and over-squashing, as well as improving the performance of GNN models in node classification tasks, has been demonstrated through comprehensive experimentation on both academic benchmarks and real-world datasets.
AIMED: An automatic and incremental approach for business process model repair under concept drift

Real-life business processes may change over time in response to new business requirements, market changes, new policies or regulations, etc., which is called concept drift in the data mining area. How to identify and deal with the concept drift problem is a significant challenge in business process mining. Currently, the research mainly focuses on the problems of concept drift detection. However, the proposed approaches do not intuitively explain how the process changes. In this paper, we provide an integrated framework whose core technique is an Automatic and Incremental approach for business process Model rEpair under concept Drift (AIMED). AIMED streamlines the functions of concept drift detection, localization and model repair. More specifically, when concept drift is detected, it updates the process model automatically by precisely localizing the sub-structure of the process model that concept drift affects and updating this sub-structure accordingly. Concept drift is explained intuitively by presenting the repaired model. In particular, AIMED can resist the noise that greatly affects the performance of current concept drift detection and process model repair techniques. The properties of AIMED are theoretically proven. We also conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that AIMED outperforms the state-of-the-art methods in both concept drift detection and process model repair and works well even when there is noise in logs.
Artificial neural network‐driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

Summary In recent years, smart healthcare, artificial intelligence (AI)‐aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL‐based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre‐established datasets that fail to capture real‐life applicability. Therefore, this study proposes an AI‐enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare‐based wearable devices (WD) for real‐time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL‐based architecture aggregates the optimizer weights of many clients using a fifth‐generation (5G) communication channel. Then, the performance of the proposed FL‐based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.
What happened to the modal expressions? Modality in translating Chinese texts into English

This article investigates how modality is handled in Chinese-to-English translation, in order to identify a range of choices available for translators and the relative frequencies of the choices. The study uses a self-built parallel corpus of Chinese government documents on Tibet and Xinjiang and their English translations. Modal expressions in the source and translated texts are analyzed according to the type and value of modality. Results show that the strategies of modality omission, modality retaining, and modality addition are adopted in the process of translation. High-valued modal expressions in the Chinese source texts are likely to be left out or translated into expressions of lower values. Median-valued modal expressions tend to be retained, and most added English modal expressions are of median value. Low-valued modal expressions enjoy similar distributions in the source and translated texts. These translation strategies result in a decrease in high-valued modal expressions and an increase in median-valued modal expressions. The type of modality tends to remain unchanged in the process of translation. Possible factors affecting the translation choices are discussed in terms of the communicative conventions and the tenor of the source and target texts.
PBL Initiatives for Regional Collaboration:

Naraha-machi, Fukushima Prefecture, is undergoing reconstruction from the Great East Japan Earthquake. After the lifting of the evacuation order, many of the townspeople who have returned to Naraha-machi from other cities are elderly, and there is a need to improve the working population ratio. As part of this effort, the town is promoting activities to collaborate with university students and other younger generations. Therefore, students in the author’s laboratory engaged in PBL aimed at solving problems in Naraha-machi. As a result, they were able to learn about the actual problems facing Naraha-machi and their solutions. In addition, the students were able to improve their motivation for future graduation research and to demonstrate the potential of the project as an initiative to prevent the disaster from fading away.
オンラインものづくりPBLの利点と課題

In this study, we surveyed four students who participated in face-to-face training (2018), online training (2020 and 2021), all PBL training conducted at the University of Malaya, Malaysia. A rubric assessment, semi-structured interviews, and verbal data collection were conducted in order to conduct qualitative data analysis. The advantages of working online were that participants could spend their time freely and concentrate on their work. Also, due to not having to travel overseas, the participants did not feel tired. Future PBL will also proceed with a hybrid of online and face-to-face. Both methods have advantages and problems, but we would like to make use of the advantages of both and connect them to better PBL in the future.
機体の形状を考慮した制御バリア関数の積分設計法

In recent years, control barrier functions have attracted much attention for ensuring safety in collision avoidance and human assist control. However, control laws cannot guarantee the solution of differential equations without properly considering the shape of the target system. In this study, we designed a control law that considers the shape of the control target and designs a control barrier function for non-convex safe sets. Based on this, we proposed an integral control barrier function and confirmed its effectiveness by testing it for collision avoidance between a three-link robot arm and obstacles through simulation and experiment.
Development of Teaching Material on Torsional Rigidity

We devised a torsion tester that can be manufactured relatively easily, and developed teaching materials that can understand the relationship between cross-sectional shape and torsional rigidity by torsional test bars with various cross-sectional shapes with this tester. After the experiment, a questionnaire and a test were conducted, and the results of the questionnaire were favorable, and most of the students answered all the questions correctly, indicating the effectiveness of this teaching material.
New classes of affine-invariant codes sandwiched between Reed–Muller codes

Reed–Muller codes have been extensively studied due to their favorable theoretical and mathematical properties. We propose some new classes of extended cyclic codes sandwiched between generalized Reed–Muller codes Rq(r−1,n) and Rq(r,n) with n=3m, m⩾1. We show that these new codes are affine-invariant, and compute their dimensions and dual codes. We also give lower bounds for their minimum distances. In addition, we explicitly determine the minimum vectors of these codes for some special cases. Our new method can be used to construct codes of length qn with odd n, which makes up for the shortcomings of the previous construction method which works only for even n. Moreover, our new extended cyclic codes have higher rate than those in Xu et al. (2023) [17].
Social Informatics in the Global Perspective

Social Informatics (SI), in its broadest sense, is an approach for understanding interactions between various societal systems and information-communication technologies.
Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition

Acquiring annotated training data for large-scale supermarket product recognition applications is challenging and often infeasible due to the vast and dynamic product assortments containing tens of thousands of products. To address this problem, we propose a highly scalable data synthesis pipeline that can automatically produce realistic, domain-aligned training data for on-shelf product detectors and classifiers. Additionally, we present three new publicly available synthetic datasets generated by our pipeline. Among them is the SPS8k dataset, featuring 16,224 shelf images with 1,981,967 instance-level bounding boxes and GTIN class labels for 8,112 grocery products. Finally, in a comprehensive ablation study, we evaluate the effects of synthetic-to-real domain translation on model performance, demonstrating its effectiveness.
Performance Characterization of 2D CNN Features for Partial Video Copy Detection

2D CNN are main components for Partial Video Copy Detection (PVCD). 2D CNN features serve for the retrieval and matching of videos. Robustness is a key property of these features. It is a well-known problem in the computer vision field but little investigated for PVCD. The contributions of this paper are twofold: (i) based on a public video dataset, we provide large-scale experiments with 700 B of comparisons of 4.4 M feature vectors. We report conclusions for PVCD consistent with the state-of-the-art. (ii) the regular protocol for performance characterization is misleading for PVCD as it is bounded to the video level. A method for the characterization of key-frames with 2D CNN features is proposed. It is based on a goodness criterion and a time series modelling. It provides a fine categorization of key-frames and allows a deeper characterization of a PVCD problem with 2D CNN features.