Reviewerly/exHarmony huggingface dataset:
Mechanistic models and machine learning: friends or foes?

Deep learning, as many other disciplines in artificial intelligence, makes use of great quantities of data to identify the important features to learn of an event or behavior. Nevertheless, when we introduce some knowledge, such as the principles of the thermodynamics, we optimize the process of learning and, what is more, we are capable of developing interpretable models. The present work proposes the implementation of the so-called Structure Preserving Neural Network (SPNN) [1] to build a model to emulate sloshing dynamics. The cited neural network learns the GENERIC structure [2] of the dynamics by means of data. Its constraints ensure the fulfillment of energy conservation and entropy generation to provide accurate, interpretable, and trustable results. Following the spirit of deep learning, and given the high dimensionality of the problem, the dataset has been projected to a lower dimensional manifold with a sparse-autoencoder. There, we perform the learning process optimally over the main features of the dynamics embedded in the latent space. The net has been tested with a proof-of-concept fluid, which will be the basis to learn new behaviors. In addition, we address the inclusion of data acquisition for learning corrections, as well as new behaviors, from limited real-world data. When working with synthetic data for data-driven learning, i.e. data obtained from computer simulations, we can work with state variables that are not easily measurable, or even internal variables. We propose an approach based on Recurrent Neural Networks to relate measurements of the free surface of a liquid from video frames with the embedded space previously found to learn, predict and correct new behaviors.
Faster Implementation of The Dynamic Window Approach Based on Non-Discrete Path Representation

The dynamic window approach (DWA) serves as a pivotal collision avoidance strategy for mobile robots, meticulously guiding a robot to its target while ensuring a safe distance from any perceivable obstacles in the vicinity. While the DWA has seen various enhancements and applications, its foundational computational process has predominantly remained constant, consequently resulting in a heightened level of time complexity. Inspired by the velocity invariance assumption inherent in the DWA and the utilization of polar coordinate transformations in the model, we introduce a high-speed version of the DWA.
IFCM: An improved Fuzzy C-means clustering method to handle Class Overlap on Aging-related Software Bug Prediction

Software aging refers to a problem of performance decay in long-running software systems. This phenomenon is primarily attributed to the accumulation of run-time errors, commonly known as aging-related bugs (ARBs). Detecting ARBs through Aging-related Bug Prediction (ARBP) is crucial in ensuring system reliability. The effectiveness of ARBP heavily relies on the quality of datasets. However, ARB datasets often suffer from class overlap, where instances from different classes exhibit similar feature values. Class overlap poses a significant challenge as it compromises the quality of training data and subsequently impacts ARBP accuracy. To address this issue, we propose an improved Fuzzy C-means clustering method named IFCM, designed to mitigate class overlap in ARBP tasks. IFCM can identify whether an instance occurs overlap, and identify the overlap degree of this instance through the predefined parameters. We evaluate our proposed method on two public datasets Linux and MySQL and one self-collected dataset NetBSD using five different classifiers with five performance metrics (AUC, F1, Balance, PD, PF). Comparison with four existing methods (No clean, NCL, IKMCCA, ROCT) demonstrates that IFCM is effective in alleviating class overlap in ARBP. For Instance, IFCM achieves promising results in terms of AUC blue (which are 0.762, 0.757, and 0.642) and Balance (which are 0.709, 0.736, and 0.595) at the dataset level.
Anomaly Detection from TLE Data

This paper presents a framework for the identification of anomalies in the trajectory of Low Earth Orbit (LEO) satellites. The algorithm can identify a broad class of anomalies from two-line element (TLE) data. The method is based on a combination of time series pre-processing techniques and Long Short Term Memory (LSTM) Autoencoder. The main contributions of this study are utilization of Gaussian derivative filter and demonstrating the ability to detect anomalies on rocket bodies, which have not been previously demonstrated.
The Effectiveness of Outline Technique on Students' Writing Skills in Coherent Paragraphs

Writing is the activity of expressing and communicating ideas in words. People write letters, text his messages, e-mails to communicate and may also express their feelings by writing diaries. By writing, people create and express their ideas in an organized process. The more they write, the better their thinking skill will be. This research is quantitative research using quasi-experimental design (post- test only). Re- searchers conducted research on students at MAN 02 Jember. There are two research variables, the independent variable is an outline technique, the dependent variable is writing skills. The researcher took two classes as samples, X Agama 1 consist of 25 students (experimental group) and X Agama 2 consist of 25 students (control group). The research instrument used in this method is essay questions. Based on the results, the average value of the experimental class for the post- test was 82.80. The average value of the experimental group is higher than the average value of the control group. The sig- nificance value based on the analysis on the test is 0.000 &lt;0.05. The alternative hy- pothesis (Ha) is accepted because there is a significant difference in writing skill between students who are taught with an outline technique and students who are taught with teacher conventional method. From the results, can be concluded that there is a significant effect of the Outline Technique on Students' Writing Skills in Coherent Paragraphs at MAN 02 Jember in the 2022/2023 academic year.
Are Cryptocurrencies Exposed to Factor Risk?

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Generative AI and IP Infringement

The rise of generative AI, as demonstrated by tools such as ChatGPT, has transformed internet exploration, allowing for deep dives without exhaustive searches. While it makes users' lives easier, it endangers creators' intellectual property. Generative AI responds to user prompts by simulating styles, as seen in the controversial "Heart on my Sleeve." Despite their popularity, these creations raise ethical concerns and call into question Intellectual Property Law. Artists face economic losses and decreased creative motivation due to a lack of regulations. The open-source nature of certain platforms blurs boundaries even further, calling into question the value of original artistic expression. In the digital age, this dynamic landscape calls into question the essence of creativity and ownership, sparking profound debates.
Deep-Sea Biological Detection Method Based on Improved YOLOv5n

Due to the complex imaging environment of deep sea, insufficient samples of biological images cause unsatisfied detection performance. To solve this problem, a deep-sea biological detection method, based on improved YOLOv5n (You Only Look Once Version 5n), is proposed. Firstly, an improved YOLOv5n is designed as the detection model, where the Ghost module and simAM attention mechanism are introduced. The Ghost module can generate redundant feature maps through cheap operations, so as to reduce the complexity of the model. The simAM attention mechanism is added to enable the model to assign more weight to the target biological features. Secondly, a training strategy, with the aid of transfer learning and knowledge distillation, is designed. An open-source marine biological dataset is used to pre train the model, so the marine biological features can be transferred into the model to reduce the model's dependence on data volume. Knowledge distillation is introduced during the fine-tuning to enhance the generalization ability of the model. Finally, deep-sea biological dataset is used to validate the effectiveness of the proposed method. Experimental results show that the proposed method can improve mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</inf> and mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5:0.95</inf> by 1% and 4.3%, respectively.
Combat situation suppression of multiple UAVs based on spatiotemporal cooperative path planning

Aiming at the suppression of enemy air defense (SEAD) task under the complex and complicated combat scenario, the spatiotemporal cooperative path planning methods are studied in this paper. The major research contents include optimal path points generation, path smoothing and cooperative rendezvous. In the path points generation part, the path points availability testing algorithm and the path segments availability testing algorithm are designed, on this foundation, the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path. In the path smoothing part, taking terminal attack angle constraint and maneuverability constraint into consideration, the Dubins curve is introduced to smooth the path segments. In cooperative rendezvous part, we take estimated time of arrival requirement constraint and flight speed range constraint into consideration, the speed control strategy and flight path control strategy are introduced, further, the decoupling scheme of the circling maneuver and detouring maneuver is designed, in this case, the maneuver ways, maneuver point, maneuver times, maneuver path and flight speed are determined. Finally, the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles (UAVs) is effectively realized, in this way, the combat situation suppression against the enemy can be realized in SEAD scenarios.
Scene image recognition with knowledge transfer for drone navigation

In this paper, we study scene image recognition with knowledge transfer for drone navigation. We divide navigation scenes into three macro-classes, namely outdoor special scenes (OSSs), the space from indoors to outdoors or from outdoors to indoors transitional scenes (TSs), and others. However, there are difficulties in how to recognize the TSs, to this end, we employ deep convolutional neural network (CNN) based on knowledge transfer, techniques for image augmentation, and fine tuning to solve the issue. Moreover, there is still a novelty detection problem in the classifier, and we use global navigation satellite systems (GNSS) to solve it in the prediction stage. Experiment results show our method, with a pre-trained model and fine tuning, can achieve 91.3196% top-1 accuracy on Scenes21 dataset, paving the way for drones to learn to understand the scenes around them autonomously.
Soft Dependencies Between Projects In Program Management

We analyzed program management tasks in the presence of soft dependencies between projects. Implementing soft dependencies reduces the time or cost of completing the immediate next project. The article formulates a problem and provides a solution, in which it is necessary to determine a set of executable soft dependencies under various conditions.
An Image Classification Method Based on Sequential Multi-source Domain Adaption

There are quantity differences among multi-source domains and data distribution differences between source domain and target domain in multi-source domain adaptation, which makes it difficult to extract transferability features among all domains and makes the classification performance of the target domain poor. To address this problem, this paper proposes an image classification method based on sequential multi-source domain adaption method (SMSDA). A source domain arrangement mechanism is proposed, which takes the distribution differences and quantity differences among domains as description metrics. The target domain can perform domain adaptation with the source domain sequentially, which solves the problem that the classification performance varies greatly under different domain adaption orders of the source domain and target domain. Meanwhile, SMSDA proposes a multi domains feature local alignment rule through the introduction of fine-grained idea. It perfects the low transfer performance in the global distribution alignment. Furthermore, an adaptive adjustment strategy of sample weight is designed to improve the low classification accuracy under class-imbalance. Finally, the experimental results on three benchmark datasets show that the proposed method performs better in classification accuracy compared with the conventional methods.
Bidirectional Mapping RTE for Fault Knowledge Graph Construction

In this paper, Bidirectional Mapping Relation Triple Extraction (BMRTE) is proposed to address the challenges of numerous overlapping triples and centralized knowledge in fault maintenance texts, enabling the construction of a fault knowledge graph. The pre-trained language model BERT is employed in BMRTE for the generation of an initial vector representation for each token in the texts. A binary tagging framework is used to identify base entities that may be related in the text on the basis of encoding tokens. To extract entity pairs from the text effectively, the bidirectional mapping framework is designed to map the base entities to their associated entities. Finally, multiple relation classification matrices are used to identify entity pairs and determine the relation triple. Our proposed model outperforms the compared baselines, as evidenced by the experimental results obtained from both a widely used public dataset and our labeled fault maintenance dataset.
R-SCN based Model Predictive Control for NOx Emissions of MSWI Process

Model predictive control (MPC) provides supports for controlling the NOx emission in municipal solid waste incineration (MSWI) process. The MSWI process suffers from dynamic characteristic, it is a challenge to precisely construct a prediction model online. In terms of this problems, an R-SCN based MPC method is proposed in this work. First, the nonlinear prediction model of NOx emissions is established offline based on stochastic configuration network (SCN). Then, the output weight of SCN hidden layer is updated online by recursive least square method. Second, the particle swarm optimization (PSO) method is designed for calculating the optimal control law of each control sequence in the constrained receding-horizon optimization. Finally, the historical data of a waste treatment plant is used to validate the proposed method. And the experimental results show that the dynamic nonlinear prediction model of NOx emissions can accurately predict the variation trend of NOx value and the proposed control method can realize the stable control of NOx concentration in MSWI process.
Research on Named Entity Recognition of Laboratory Safety Knowledge based on Deep Learning

Laboratory safety is an important subject of education safety. In order to strengthen its application and provide the groundwork for knowledge search, the effect of laboratory safety entity recognition is studied using deep learning. This paper proposes to integrate ALBERT into the conventional BiLSTM-CRF model to form the ALBERT-BiLSTM-CRF model. The model uses ALBERT as the embedding layer, obtains the character position information through BiLSTM and the optimal sequence is obtained by CRF. The final model increases the recognition effect of complex entities of laboratory safety. Because this field lacks an annotated corpus, this paper uses manual annotation to construct a corpus. Experiments demonstrate that the ALBERT-BiLSTM-CRF model’s laboratory safety entity recognition effect is superior to other conventional models with F1 value of 93.24%.
Model-based flight-test data labeling and rapid analysis technology

under the background of flight test data analysis’s digital transformation demand, this paper puts forward the Model-based flight-test data labeling and rapid analysis technology in the flight test data analysis. On the basis of the flight test standard test point library, build test data analysis model. With key technologies such as time sequence database, data feature extraction and human-computer interaction analysis, established the test data visual analysis platform that has been verified and applied in the flight test data analysis. This technology meet the user demand of test data analysis, improved the efficiency of flight test task analysis in telemetry monitoring.
Recurrent Transformer for 3D Human Pose Estimation

Currently, common three-dimensional (3D) human pose estimation algorithms achieve good results in representation learning, but still suffer from poor estimation accuracy and depth ambiguity at the joint points of the human skeleton, and extracting the image context is highly promising for mitigating the depth ambiguity. Therefore, an effective way to estimate human pose from monocular video images using redundant two-dimensional (2D) pose sequence spatio-temporal information is a research challenge. Many existing studies have mostly attempted to capture the spatial as well as temporal relationships of human poses in videos to solve these two problems. However, these works tend to overlook the fact that the lack of remote dependency modeling capabilities and parallelism makes the estimated 3D human poses often less accurate. We have proposed a Recurrent Transformer that can achieve a good balance between its efficiency and model size while still maintaining its effectiveness. This method will decompose the task into three phases: (i) serial cyclic spatial relationship modeling of the pose; (ii) parallel cyclic temporal relationship modeling of the pose; and (iii) summarization of multiple features and synthesis of the final 3D pose after linear regression. With the three main processes mentioned above, the final predicted 3D poses are not only improved compared to traditional methods but also more accurate in terms of precision. After experimental validation, Recurrent Transformer achieves leading results on Human3.6M.
T2Remoter: a Remote Table Tennis Coaching System Combining VR and Robotics

The spread of the new corona virus has imposed limitations on social physical interactions, increasing the importance of remote collaboration and work applications. This has especially effected tasks that require direct physical contact, such as skill-transfer. Accordingly, we have developed T2Remoter, a table tennis instruction system that combines robotics and VR. Our system consists of a VR table-tennis environment that is shared between the coach and the trainee, and a robot arm that is teleoperated by the coach and used to guide the hand-movements of the trainee. To the best of our knowledge, T2Remoter is the first teleoperated VR based table tennis training system. We conducted a series of focus groups to explore critical training routines and contexts for T2Remoter, involving 12 table tennis experts. We extract three main categories of training routines and contexts, and discuss their importance for implementation in T2Remoter.
Measuring the quantum state of the electromagnetic field at finite temperature

Abstract We show that it is possible to reconstruct the quantum state of light in a cavity subject to dissipation at finite temperature. By passing atoms through the cavity it is shown that the decay does not prevent the measurement of s-parametrized quasiprobability distributions. Because such distributions contain whole information about the initial quantized field, it is possible to recover its complete information.
Sentiment Text Spatio-Temporal Feature Analysis based on CharCNN and BiLSTM

This study employs social media data associated with Typhoon 'Lekima' as a case study to conduct text sentiment analysis by integrating text classification and sentiment analysis models. The goal is to investigate the patterns of user emotion distribution during the typhoon period. To initiate the process, a character-level Convolutional Neural Network (CharCNN) is utilized to effectively filter out irrelevant text data from Weibo content. Following this, the text content is represented using the word2vec model. Finally, a Bidirectional Long Short-Term Memory (Bi-LSTM) model is employed for sentiment analysis, enabling the extraction of emotional features from text sentences.
Gas Stations Performance Modeling in Terms of Geoanalytics

A conceptual approach has been developed to solve the location problem, allowing commercial organizations that own gas stations to assess the degree of influence of geographical characteristics of locations on profitability indicators. Data collection, processing, and spatial analysis have been conducted to build a model for forecasting financial results.
A Model to Support Intuitive Decision-Making

A mathematical model of a decision support system aimed at stimulating the subject's intellectual activity for making intuitive-experimental decisions is considered. The model is based on semiotic mental space model built by an expert. Methods for solving the inverse problem for finding formal solutions to achieve some goal are proposed. To interpret of formal solutions, it is proposed to apply vector static (Word2Vec) and dynamic (ruBERT, ruGPT) language models obtained by training neural networks with a text corpus. Examples of formal solutions interpretation of the inverse problem with the help of the language vector models are considered, and their possibilities of stimulating the intellectual activity of the subject for making intuitive-experimental decisions are analyzed.
Mobile Robot Navigation Algorithm Using Laser Rangefinder And Odometry In Static Environment

This method is proposed for position correction and the mobile robot movement course in an environment with a known position of obstacles and equipped with a set of laser rangefinders and an odometer. The advantages of the considered algorithm are the low requirements for the computing power of the onboard computer and the possibility of using the laser rangefinders instead of an expensive lidar.
Statistic-CAM: A Gradient-Free Visual Explanations for Deep Convolutional Network

Algorithms of class activation maps are used to provide visual explanations for CNN models to reduce the black-box nature and are widely used for model evaluation and optimization. However, it is not common to apply such algorithms to model deployment, although real-time explanations for the CNN model is much in need. The reason for that is state-of-the-art CAM algorithms are either gradient-based or complex. It is really difficult to deploy them on edge devices which are power-limited and may not support back-propagation. In response to the criticism of those techniques, we propose a new methodology to generate class activation maps for CNN models in a gradient-free and computation-saving way. Our approach, Statistic-CAM are built upon an existing CAM methodology (called backbone method) and consists of three stages: training stage, saliences fixing stage and inferring stage. By collecting and sorting channel saliences of the last feature maps by classes, the visual explanations can be generated via a simple forward propagation and a look-up in the dictionary. Compared to the backbones, our approach can achieve the performance of less than 2.1% mismatch and save up to 99.95% extra computational cost while maintain the feature of gradient-free. It is no doubt the easiest CAM algorithm to be deployed.