Reviewerly/exHarmony huggingface dataset:
Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain

Managed pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relies heavily on manual experience, hindering the automation and intelligence process of well control. In response to this issue, an MPD knowledge graph is constructed in this paper that extracts knowledge from published papers and drilling reports to guide well control. In order to improve the performance of entity extraction in the knowledge graph, a few-shot Chinese entity recognition model CEntLM-KL is extended from the EntLM model, in which the KL entropy is built to improve the accuracy of entity recognition. Through experiments on benchmark datasets, it has been shown that the proposed model has a significant improvement compared to the state-of-the-art methods. On the few-shot drilling datasets, the F-1 score of entity recognition reaches 33%. Finally, the knowledge graph is stored in Neo4J and applied for knowledge inference.
Locality Preserving Property Constrained Contrastive Learning for Object Classification in SAR Imagery

Robust unsupervised feature learning is a critical yet tough task for synthetic aperture radar (SAR) automatic target recognition (ATR) with limited labeled data. The developing contrastive self-supervised learning (CSL) method, which learns informative representations by solving an instance discrimination task, provides a novel method for learning discriminative features from unlabeled SAR images. However, the instance-level contrastive loss can magnify the differences between samples belonging to the same class in the latent feature space. Therefore, CSL can dispel these targets from the same class and affect the downstream classification tasks. In order to address this problem, this paper proposes a novel framework called locality preserving property constrained contrastive learning (LPPCL), which not only learns informative representations of data but also preserves the local similarity property in the latent feature space. In LPPCL, the traditional InfoNCE loss of the CSL models is reformulated in a cross-entropy form where the local similarity of the original data is embedded as pseudo labels. Furthermore, the traditional two-branch CSL architecture is extended to a multi-branch structure, improving the robustness of models trained with limited batch sizes and samples. Finally, the self-attentive pooling module is used to replace the global average pooling layer that is commonly used in most of the standard encoders, which provides an adaptive method for retaining information that benefits downstream tasks during the pooling procedure and significantly improves the performance of the model. Validation and ablation experiments using MSTAR datasets found that the proposed framework outperformed the classic CSL method and achieved state-of-the-art (SOTA) results.
Automatic horizon tracking method based on knowledge self-distillation

In recent years, deep learning has achieved a large number of successful cases in the seismic horizon tracking; however, most of the existing studies apply more complex network structures to improve the horizon tracking accuracy, which significantly increases the model complexity and training time. In order to improve the tracking accuracy without increasing the model complexity, this paper proposes a knowledge-based self-distillation method for horizon tracking. In the first step, the teacher model is obtained based on UNet training to obtain the prior weights; in the second step, the untrained UNet is used as the student network, and the teacher model is used to guide the student network for knowledge distillation training to improve the knowledge transfer performance. After synthesis and actual data testing, using the knowledge distillation method for horizon tracking improves the horizon tracking accuracy without increasing the model complexity, and provides a new method for automatic horizon tracking. [Received: January 17, 2023; Accepted: March 21, 2023]
The Role of the KWL (Know-Want-Learned) Strategy in Fostering Reading Comprehension: A Case Study at MAN 1 Parigi, Indonesia

This research aims to prove how (KWL) Know-Want-Learn strategy can enhance the reading comprehension of MAN 1 Parigi students in the tenth grade. This research used a quantitative approach using a single pretest and posttest research design. The researcher conducted her research in two classes, one experimental and one control. In the experimental class, there were 28 students, while there were 27 in the control class. The researcher employed pretest and posttest to gather data. Paired sample t-test aided SPSS application version 25 for Windows was used to analyze the data. Based on the findings of this research, it is possible to conclude that (KWL) Know-Want-Learn strategy succeeded in improving the reading comprehension of tenth-grade students at MAN 1 Parigi. This is proven from the paired sample t-test analysis that the significance value (2-tailed) was 0.000 &lt; 0.05, meaning there is a significant distinction within the student learning outcomes for the pretest and post-test.
Vehicle Re-identification in Aerial Images and Videos: Dataset and Approach

In this work, we propose a large-scale dataset, VRAI, and an effective Orientation Adaptive and Salience Attentive (OASA) Network for vehicle re-identification (ReID) in aerial imagery. The VRAI dataset includes two subsets: VRAI-Image, which contains over 137,000 images of 13,000 vehicle instances, and VRAI-Video, which comprises more than 14,000 video trajectories of 7,000 identities. To our best knowledge, this is the largest dataset for UAV-based vehicle ReID, and the first dataset proposed for video-based ReID under UAV views. Based on the VRAI dataset, we design an OASA network to address two crucial challenges of vehicle ReID in aerial imagery. Firstly, the significant vehicle orientation variations in aerial images could cause great vehicle pattern deformations, making it difficult to identify vehicles across UAV views. To overcome this challenge, in our OASA, an orientation adaptive dynamic convolution module is designed, which constructs customized kernels for each vehicle instance to extract orientation-invariant features. Besides, the unique vertical view and long focal length of the UAV platform often render many salient vehicle attributes, such as logos and license plates, invisible, which brings a great challenge to ReID models to extract distinguishable vehicle features. To address this issue, in the OASA, we design a transformer-based salience attentive module (Trans-Attn) that guides the model to focus on subtle yet discriminative clues of vehicle instances in aerial imagery. Through extensive experiments, both of our designed modules are verified effective. Besides, our OASA model outperforms state-of-the-art algorithms both on our VRAI dataset and other surveillance-based datasets. Our VRAI dataset is available in https://github.com/JiaoBL1234/VRAI-Dataset.
Dynamic Output State Classification for Quantum Computers

Quantum computers promise a potentially disruptive approach to improving computation in fields such as physics, chemistry, cryptography, optimisation, and machine learning. However, testing quantum computations for faults is currently impractical because of the existence of noise and errors associated with the output. Executing in a quantum system a circuit with only a few valid output states can generate a significant number of implausible states that have zero probability in an ideal computation. Among other sources of noise, readout errors come from the difficulty of discriminating a measurement between 0 and 1 for the different qubits. These issues are affected by readout drift, requiring regular recalibration of the process. In this paper, we provide a novel technique for post-computation analysis of the output probability distributions that permits better discrimination of kerneled data, delaying the need for recalibration. We achieve this by altering the linear discrimination of the final output states by way of a dynamic state selection process that combines Gaussian mixture models with a probability threshold. As an initial assessment of the technique we examine its effect on three to five qubits GHZ states. Our results on almost every one of nine IBM quantum computers show that the number of implausible states is reduced significantly and that the resulting probability distribution is closer to the expected one.
Deep Feature-Based Text Clustering and Its Explanation

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. Our code is available at https://github.com/KEAML-JLU/DeepTextClustering.
Towards Material Digitization with a Dual-scale Optical System

Existing devices for measuring material appearance in spatially-varying samples are limited to a single scale, either micro or mesoscopic. This is a practical limitation when the material has a complex multi-scale structure. In this paper, we present a system and methods to digitize materials at two scales, designed to include high-resolution data in spatially-varying representations at larger scales. We design and build a hemispherical light dome able to digitize flat material samples up to 11x11cm. We estimate geometric properties, anisotropic reflectance and transmittance at the microscopic level using polarized directional lighting with a single orthogonal camera. Then, we propagate this structured information to the mesoscale, using a neural network trained with the data acquired by the device and image-to-image translation methods. To maximize the compatibility of our digitization, we leverage standard BSDF models commonly adopted in the industry. Through extensive experiments, we demonstrate the precision of our device and the quality of our digitization process using a set of challenging real-world material samples and validation scenes. Further, we demonstrate the optical resolution and potential of our device for acquiring more complex material representations by capturing microscopic attributes which affect the global appearance: we characterize the properties of textile materials such as the yarn twist or the shape of individual fly-out fibers. We also release the SEDDIDOME dataset of materials, including raw data captured by the machine and optimized parameteres.
Value and risks of morphing technology into strategy and business model

Digital technologies became the primary source of innovation in the private and public sectors. The Internet profoundly changed the way businesses are run catapulting “most digital” industries and companies to the top of the S&amp;P500. Two innovations that drive digital transformation changing the nature of competition are cloud computing and artificial intelligence (AI) technologies. Cloud-native business models and strategies proved successful in various industries, while AI is being tested in vivo by management mainstream. The publication provides an analysis of a multidimensional impact cloud computing makes on strategies and business models of companies. We show that what made cloud computing special in the management context was the way it morphed into strategies and business models best suited for the uncertain future. We also noted that as the focus of digital transformation shifts towards cloud-based AI powered decision-making solutions, managing the human aspect of “more digital” business models and related risks, recently referred to as an existential threat, becomes a priority of management research.
Secret image scrambling and DWT-based image steganography using smoothing operation and convolution neural networks

Steganography is known as a secret data sharing technique. It is a method of data protection which allows the user to send the information in enveloped form. That means the hidden data cannot be seen by the observer because one can only see the enveloped media. This paper presents a steganography technique using Pixel swapping-Quantum Hilbert image scrambling and discrete wavelet transformation, followed by stego image smoothing operation. Initial steps of proposed image steganography include best suitable cover image selection for secret image. This process compares the secret image with the different cover image presents in senders cover image database. Before embedding (encoding), the secret image is scrambled, and then performs the DWT transformation on this scrambled image and cover image. Next steps embed both resultant images and generate a new image named as stego image. The picture quality of stego image is increased using pixel swapping based operation. The Decoding process of proposed steganography scheme is just reverse of the former encoding process followed by application of Convolutional neural networks (CNN) to improve the extracted secret image quality. The results show the strength of the proposed technique.
Safe Motion Planning for Autonomous Vehicles by Quantifying Uncertainties of Deep Learning-Enabled Environment Perception

Conventional perception-planning pipelines of autonomous vehicles (AV) utilize deep learning (DL) techniques that typically generate deterministic outputs without explicitly evaluating their uncertainties and trustworthiness. Therefore, the downstream decision-making components may generate unsafe outputs leading to system failure or accidents, if the preceding perception component provides highly uncertain information. To mitigate this issue, this paper proposes a coherent safe perception-planning framework that quantifies and transfers DL-based perception uncertainties. Following the Bayesian Deep Learning paradigm, we design a probabilistic 3D object detector that extracts objects from LiDAR point clouds while quantifying the corresponding aleatoric and epistemic uncertainty. A chance-constrained motion planner is designed to formulate an explicit link between DL-based perception uncertainties and operation risk and generate safe and risk-bounding trajectories. The proposed framework is validated through various challenging scenarios in the CARLA simulator. Experiment results demonstrate that our framework can effectively capture the uncertainties in DL, and generate trajectories that bound the risk under DL perception uncertainties. It also outperforms counterpart approaches without explicitly evaluating the uncertainties of DL-based perception.
Machine Learning Techniques Applied to the Development of a Fall Risk Index for Older Adults

Falls are a leading cause of unintentional trauma-related deaths worldwide, and a significant contributor to elderly dependence. To address this, the goal of this project was to predict recurrent falls in the older population using machine learning techniques, with the aim of reducing the number of falls and their consequences.To achieve this, a dataset obtained from Getafe University Hospital’s Geriatric Falls Unit was used (obtained from the Hospital’s Electronic Health Records). This extensive dataset was one the key strengths of our work. Feature extraction was performed through natural language processing, which recognized pre-defined patterns and helped build the profiles of the 304 older adults who composed the dataset. The proposed data system was comprised of four main blocks: the senior’s profile and environment, clinical information and tests carried out in the hospital, medications, and different diseases they presented. Using the extracted attributes and data from those 304 older adults, this project compared the performance of various machine learning techniques in their ability to classify older adults between future fallers and non fallers. Training different models and ensembles and comparing the results, we obtained that Bagging with Random Forest as base model is the best classifier, predicting accurately 75.8% of the data with 70.0% sensitivity and 80.5% specificity. Ultimately, this research project aimed at setting the first stone to a larger study that could help monitoring older adults and obtaining dynamic and automatic predictions of falls.
GraphIoT: Accurate IoT Identification based on Heterogeneous Graph

IoT devices deployed on campus and enterprise networks facilitate people's lives and work. However, these devices also bring serious network asset management and security management problems. IoT device identification is the premise to solve these problems. Although current IoT identification methods can identify devices with relatively high accuracy in ideal environments, it is difficult to accurately identify devices in real-world complex environments (e.g., campus networks, enterprise networks). Therefore, we propose to use exact features. To solve the problem of different dimensions of exact features, we creatively model the IoT identification problem as a heterogeneous graph representation learning problem and design a new representation learning algorithm. We are the first to propose an approach to accurately identify IoT devices in real-world complex environments and solve this problem through heterogeneous graphs. The evaluation shows that GraphIoT's macro F1 is on average 13.58% and 12.77% higher than the other methods on two public datasets.
Information security awareness maturity: conceptual and practical aspects in Hungarian organizations

Purpose This paper aims to provide a maturity model for information security awareness (MMISA), based on the literature, expert interviews and feedback. In addition to developing the MMISA, the authors investigate the role of the three decisive factors that affect ISA maturity level: risk management mechanism, organizational structure and ISA. Design/methodology/approach The research methodology is a combined one; qualitative and quantitative methods were applied, including surveying the literature, interviews and developing a survey to collect quantitative data about decisive factors that affect ISA maturity level. The authors perform a variance-based partial least squares-structural equation modeling (PLS-SEM) investigation of the relationships between these factors. Findings The investigation of decisive factors of ISA maturity levels revealed that if the authors identify a strong risk assessment mechanism (through a documented methodology and reliable results), the authors can expect a high level of ISA. If there is a well-defined organizational structure with clear responsibilities, this supports the linking of a risk management mechanism with the level of ISA. The connection between organizational structure and ISA maturity level is supported by ISA activities: an increased level of awareness actions strengthens an organizational structure via the best practices learned by the staff. Originality/value The main contribution of the proposed MMISA model is that the model offers controls and audit evidence for maturity levels. Beyond that, the authors distinguish in the MMISA model controls supporting knowledge and controls supporting attitude, emphasizing that this is not enough to know what to do, but the proper attitude is required too. The authors didn't find any other ISA maturity model which has a similar feature. The contribution of the authors' work is that the authors provide a method for solving this complex measurement problem via the MMISA, which also offers direct guidance for the daily practices of organizations.
Development Directions of Means of Inclusive Education in Electronic Educational Systems

Technology has significant but largely untapped potential to support inclusive education for people with disabilities and other minority groups. In particular, it can provide a variety of means of presentation, representation and expression of learning, and enables students with disabilities to overcome barriers they would otherwise face in participating in the curriculum. It also has the potential to increase satisfaction and motivation. Distance education has the potential to increase access to education, but it should not come at the expense of ensuring the availability of institutional education. However, not all initiatives take into account the needs of people with disabilities. The need to control the educational process, namely the knowledge gained in the learning process for inclusive people, is shown, and the differences in evaluating the results of the classroom and distance mode of learning are highlighted. In this article, we will consider the technologies of artificial intelligence, inclusive education in electronic educational systems (ELS) that can improve the education process. The general concept of inclusive education is described both in ENS and classrooms, the role of teachers and parents is considered. The considered types of inclusions, their features and requirements The main principles of building inclusion systems, their functions and features are considered. Advantages and disadvantages of inclusive education methods are considered and analyzed. The integration of inclusive education in electronic educational systems is analyzed on the example of other countries. Considered the classification and types of inclusive disorder in people with ASD and their requirements for electronic devices. The software of ENS IO, which should be based on the application of adaptive technologies and elements of artificial intelligence in inclusive education for both teachers and students in the electronic space, is considered and given. The relevance of the development trend of inclusive education in electronic educational systems and problems that can be solved with the help of programs, parents and teachers are shown. Keywords: inclusive education, special educational needs, electronic learning systems.