Reviewerly/exHarmony huggingface dataset:
Separate-and-conquer survival action rule learning

Action mining is a data mining method that aims to identify recommendations for changing attribute values that can lead to the classification of data instances as examples of another class. Action mining algorithms extract rules containing recommendations in the premises and class changes in the conclusion. To the best of the authors' knowledge, no method has been proposed yet for generating action rules based on censored data. This study introduces the first method for survival action rule generation. The method stems from the covering rule induction algorithm but generates rules defining the actions required to change not the class but the survival curve of the covered examples. Thus, this study poses a new research problem: generating action rules for censored data and survival analysis. This study evaluated the proposed method using 22 data sets in which two application domains of survival analysis were distinguished: medicine and predictive maintenance. In addition, more detailed analyses of the generated survival action rules were presented in the form of case studies for the two selected data sets. The results show that the proposed method generates good-quality survival action rules and changes in the survival curves, resulting from the identified actions are significant.
University Competitiveness on the Educational Market: Strategic Approach

The system of higher education in Russia today is characterized by a change in a paradigm. It is conditioned by a shift to the innovative activity caused by the introduction of a new economic model. One more factor to start this change includes some changes in the management structure of the university environment, shift to the entrepreneurial university, development of the competitive level of the university teachers. A university society comes across a number of challenges, among which there is a low motivation and low competitive level of teachers to work under new conditions. The article is aimed at studying the ways to form a competitive university system to function at the educational market. The novelty of the paper can be proved by the fact that in the process of overcoming some new challenges on the way to the new innovative policy appear. There is offered a new paradigm to build a university competitive status connected with a combination of strategic decisions in the sphere of educational programs, students’ activity and development of teachers’ competitive level. The problematic fields and the strategies of their further development are introduced to solve the matter. The most productive ways are changes in educational programs, ways to increase the motivation of students to take part in an innovation activity, development in trends to boost teachers’ activity and competitiveness. Such decisions contribute to realize the innovational strategies of the university development, to the work of teachers and students, to an efficient competitive university environment. The article can be of interest to managers in education, strategic managers, and teaching personnel.
The ore-forming process of the Qingshan gold deposit, Qaidam Basin: Evidence from LA-ICP-MS trace element analysis and S-Pb isotopic compositions

The Qingshan gold deposit is located in the Tanjianshan ore collection area, which is in the northern margin of Qaidam Basin and is one of the most important gold and polymetallic metallogenic belts in China. Based on the field investigation and optical microscopic observations, mineralization at the Qingshan deposit can be divided into three stages: (1) quartz-pyrite stage, (2) quartz-sulfide-gold stage, (3) quartz-calcite stage. Pyrite from the stage 1 (Py1), stage 2 (Py2) and stage 3 (Py3) are selected for LA-ICP-MS trace element analyses. Pyrite from the stage 1 (Py1), stage 2 (Py2) are selected for in situ sulfur isotopes analysis. Pyrite from the stage 1 (Py1), stage 2 (Py2) and stage 3 (Py3), galena from the stage 1 (Gn1) and stage 2 (Gn2) are selected for in situ lead isotopes analysis. The result shows that Py1 is relatively rich in Co, Ni, Zn and Ag contents and lacking in Sb contents. Py2 is relatively rich in Cu, As, Sb, Au, Pb and Ag contents and lacking in Co, Ni and Bi contents. Py3 is relatively rich in Co, Ni and Bi contents and lacking in Zn, As, Au and Pb contents. The Au/As ratio shows that invisible gold occurs in the form of solid solution (Au+). The values of Au/Ag and Co/Ni ratio show that the pyrites in the Qingshan deposit are hydrothermal origin. The S isotope values of pyrite range from -4.33 to 4.97‰ (mean: 3.71‰, n=31). The Pb isotope compositions with 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb values ranging from 18.002 to 18.791, 15.567 to 15.584, and 38.410 to 38.485, respectively. The results of in situ S-Pb isotopic indicate that sulfur and metals of the Qingshan deposit were mainly derived from the upper crust and orogenic belts. The Qingshan gold deposit is classified as an orogenic gold deposit, and was closely related to a large-scale ductile shear zone, which formed during the process of subduction between the Qaidam block and the Qilian block in the Variscan period. The mixing of fluids destroyed the stability of Au(HS)2- and is most likely the reason for gold precipitation.
Transformer based Answer-Aware Bengali Question Generation

Question generation (QG), the task of generating questions from text or other forms of data, a significant and challenging subject, has recently attracted more attention in natural language processing (NLP) due to its vast range of business, healthcare, and education applications through creating quizzes, Frequently Asked Questions (FAQs) and documentation. Most QG research has been conducted in languages with abundant resources, such as English. However, due to the dearth of training data in low-resource languages, such as Bengali, thorough research on Bengali question generation has yet to be conducted. In this article, we propose a system for producing varied and pertinent Bengali questions from context passages in natural language in an answer-aware input format using a series of fine-tuned text-to-text transformer (T5) based models. During our studies with various transformer-based encoder-decoder models and various decoding processes, along with delivering 98% grammatically accurate questions, our fine-tuned BanglaT5 model had the highest 35.77 F-score in RougeL and 38.57 BLEU-1 score with beam search. Our automated and human evaluation results show that our answer-aware QG models can create realistic, human-like questions relevant to the context passage and answer. We also release our code, generated questions, dataset, and models to enable broader question generation research for the Bengali-speaking community.
Weighted Group Sparse Regularized Tensor Decomposition for Hyperspectral Image Denoising

Hyperspectral imaging (HSI) has been used in a wide range of applications in recent years. But in the process of image acquisition, hyperspectral images are subject to various types of noise interference. Noise reduction algorithms can be used to enhance the quality of images and make it easier to detect and analyze features of interest. To realize better image recovery, we propose a weighted group sparsity-regularized low-rank tensor ring decomposition (LRTRDGS) method for hyperspectral image recovery. Tensor ring decomposition can be utilized by this approach to investigate self-similarity and global spectral correlation. Furthermore, weighted group sparsity regularization can be employed to depict the sparsity structure of the group along the spectral dimension of the spatial difference image. Moreover, we solve the proposed model using a symmetric alternating direction method multiplier with the addition of a proximity term. The experimental data verify the effectiveness of our proposed method.
Virtual Craft: Experiences and Aesthetics of Immersive Making Culture

As immersive media, including VR, AR, MR, and XR, continues to expand rapidly during the pandemic era, there remains limited research on its comprehensive characteristics and its potential to create new forms of experience and aesthetics. This may be attributed to a lack of an understanding that immersive technologies exist encompassing both realms of reality and virtuality, leading to misconceptions that they are radically disconnected from traditional notions of materiality. In contrast, this study identifies an emerging trend characterized by the material implementation of immersive technologies in the domain of making culture in VR, which is referred to as “virtual craft” in this research. By reviewing studies on immersive media, materiality, making culture, and triadic semiotics, an integrated conceptual framework was developed to assess the experiences, aesthetics, and potential of immersive making culture. This framework is then applied to a specific case study involving virtual craft, with a particular focus on the 3D painting application, Tilt Brush, and related applications that the author has observed and tested. In conclusion, this paper presents a vision of the future of virtual craft and discusses the sustainability of immersive making culture, highlighting the potential for continued innovation and integration in various fields.
Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms

Governments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagnosis of breast abnormalities on mammographic images remains a challenge due to qualitative variations in different tissue types and densities. Highly accurate computer-aided diagnosis (CADx) systems could assist in the differentiation between normal and abnormal tissue and the classification of abnormal tissue as benign or malignant. In this paper, classical, advanced fuzzy sets and fusion techniques for image enhancement were combined with three different thresholding methods (Global, Otsu and type-2 fuzzy sets threshold) and three different classifying techniques (K-means, FCM and ANFIS) for the classification of breast masses on FFDM. The aim of this paper is to identify the performance of the advanced fuzzy sets, fuzzy sets type-2 segmentation, decisions based on K-means and FCM, and the ANFIS classifier. Sixty-three combinations were evaluated on ninety-seven digital mammographic masses (sixty-five benign and thirty-two malignant). The performance of the sixty-three combinations was evaluated by estimating the accuracy, the F1 score, and the area under the curve (AUC). LH-XWW enhancement method with Otsu thresholding and FCM classifier outperformed all other combinations with an accuracy of 95.17%, F1 score of 89.42% and AUC of 0.91. This algorithm seems to offer a promising CADx system for breast cancer diagnosis on FFDM.
Gabor's “complex signal” revisited: Complexifying frames and bases

Abstract In 1946, Dennis Gabor introduced the analytic signal for real‐valued signals f . Here, H is the Hilbert transform. This complexification of functions allows for an analysis of their amplitude and phase information and has ever since given well‐interpretable insight into the properties of the signals over time. The idea of complexification has been reconsidered with regard to many aspects: examples are the dual tree complex wavelet transform, or via the Riesz transform and the monogenic signal, that is, a multi‐dimensional version of the Hilbert transform, which in combination with multi‐resolution approaches leads to Riesz wavelets, and others. In this context, we ask two questions: Which pairs of real orthonormal bases (ONBs), Riesz bases, frames and Parseval frames and can be “rebricked” to complex‐valued ones ? And which real operators A allow for rebricking via the ansatz ? In this short note, we give answers to these questions with regard to a characterization which linear operators A are suitable for rebricking while maintaining the structure of the original real valued family. Surprisingly, the Hilbert transform is not among them.
NTT and Inverse NTT Quantum Circuits in CRYSTALS-Kyber for Post-Quantum Security Evaluation

The emergence of quantum computers threatens current cryptographic systems, and NIST is preparing for the post-quantum era through the post-quantum cryptography (PQC) contest. CRYSTALS-Kyber is a lattice-based cipher suite that is used as a PQC standard. Lattice-based cryptography is considered quantum-safe for quantum computing because a quantum algorithm that can more efficiently solve the lattice problem of lattice-based cryptography compared to a classic algorithm has not been reported as yet. In this paper, we present quantum circuits tailored to NTT and inverse NTT, employed for optimized polynomial multiplication within CRYSTALS-Kyber. The proposed quantum circuits operate at Z3329[X]/(X256+1), which are the parameters of CRYSTALS-Kyber. We provide an in-depth description of the NTT/InvNTT quantum circuit’s operation and subsequently assess and analyze the quantum resources necessary for these functions. The NTT/InvNTT quantum circuits comprise four unique sub-functions, with the InvNTT additionally incorporating Barrett reduction. To the best of our knowledge, this represents the inaugural implementation of the CRYSTALS-Kyber NTT/InvNTT quantum circuits. We anticipate that our findings will aid in analyzing the security strengths of quantum computers for lattice-based cryptography.
Immersive Phobia Therapy through Adaptive Virtual Reality and Biofeedback

Phobias are a type of anxiety disorder characterized by severe fear of objects and situations. In recent years, virtual reality exposure therapy has emerged as a safer and more convenient method of treating phobias, with the same rate of success as classical therapy, the in vivo exposure to stimuli. Following extensive research concerning the available technologies and after studying the state of the art in the field, we developed the PhoVR (Phobia therapy through Virtual Reality) system during a 2-year long project that involved the participation of a technical university, a biology faculty, and an industry partner specialized in designing 3D applications. Our final prototype incorporates scenarios for acrophobia, claustrophobia, and fear of public speaking therapy, in which the user is required to perform gamified tasks and navigate virtual reality environments with biophysical data acquisition (electrodermal activity and heart rate), automatic anxiety level classification, biofeedback integrated into the scenes’ elements, the dynamic adaptation of the virtual environments, and relaxation techniques made available at any moment. The control panel is an application dedicated to psychotherapists for managing patients’ profiles and therapy sessions. The feedback obtained in a qualitative survey with subjects and psychotherapists who evaluated the prototype validated the system and provided suggestions for refinement.
China’s Future Countryside Model Construction and Development Level Evaluation

Communities and villages are holistic organisms, representing a complete system formed by natural space and human activities. Since the concept of “future community” was put forward by the Drucker Foundation in the United States at the end of the 20th century, “future community” has expanded from the city to the countryside. Governments and scholars of various countries have started the practical and theoretical research into “future community”. Based on the theory of “village organism”, this paper constructs the structural model of the future countryside, and then constructs the health evaluation index system of the future countryside. This effectively makes up for the shortcomings of previous studies, and provides a new perspective and systematic analysis method for the study of community and village issues. Using entropy weight method and TOPSIS method, Jindong District was used as a case to conduct the quantitative evaluation of the development level of 46 administrative villages in the region. The results showed that: (1) the development level of the villages in the region was not high, and far from the requirements of future countryside, indicating that countryside revitalization has a long way to go; (2) within the region, competitive convergence occurs in the development process of all villages; (3) of the two major countryside systems in the future, the health level of the physical space system is higher than that of the social system, indicating that the local government pays more attention to the construction of the “external” image of the countryside and ignores the improvement of the “internal” function of the countryside society; (4) in the prospective construction of nine scenes of future countryside, an observable trend towards “grouping” differentiation emerges. This phenomenon underscores existing deficiencies in countryside construction, indicating that countryside areas still fail to realize the function of being “self-hematopoietic”. The determination and selection of sample indicators exhibit regional cultural disparities, permitting various regions to customize indicators with their specific contextual circumstances. Nevertheless, the universal approach of treating countryside areas as holistic entities remains essential in scholarly inquiry.
Improving the Performance of Autonomous Driving through Deep Reinforcement Learning

Reinforcement learning (RL) is revolutionizing the artificial intelligence (AI) domain and significantly aiding in building autonomous systems with a higher level comprehension of the world as we observe it. Deep learning (DL) facilitates RL to scale and resolve previously intractable problems, for instance, allowing supervision principles designed for robots to be acquired directly from visual data, developing video game proficiency from pixel-level information, etc. Recent research shows that RL algorithms help represent problems dealing with high-dimensional, unprocessed data input and can have successful applications in computer vision, pattern identification, natural language analysis, and speech parsing. This research paper focuses on training a simulation model of a car to navigate autonomously on a racetrack using RL. The study explores several fundamental algorithms in Deep RL, namely Proximal Policy Optimization (PPO), Deep Q-network (DQN), and Deep Deterministic Policy Gradient (DDPG). The paper documents a comparative analysis of these three prominent algorithms—based on their speed, accuracy, and overall performance. After a thorough evaluation, the research indicates that the DQN surpassed the other existing algorithms. This study further examined the performance of the DQN with and without ε-decay and observed that the DQN with ε-decay is better suited for our objective and is significantly more stable than its non ε-decay counterpart. The findings from this research could assist in improving the performance and stability of autonomous vehicles using the DQN with ε -decay. It concludes by discussing the fine-tuning of the model for future real-world applications and the potential research areas within the field of autonomous driving.
Study on the Measurement Method of Wheat Volume Based on Binocular Structured Light

In this paper, we propose a grain volume measurement method based on binocular structured light to address the need for fast and high-precision grain volume measurement in grain stocks. Firstly, we utilize speckle structured light imaging to tackle the image matching problem caused by non-uniform illumination in the grain depot environment and the similar texture of the grain pile surface. Secondly, we employ a semi-global stereo matching algorithm with census transformation to obtain disparity maps in grain bins, which are then converted into depth maps using the triangulation principle. Subsequently, each pixel in the depth map is transformed from camera coordinates to world coordinates using the internal and external parameter information of the camera. This allows us to construct 3D cloud data of the grain pile, including the grain warehouse scene. Thirdly, the improved European clustering method is used to achieve the segmentation of the three-dimensional point cloud data of the grain pile and the scene of the grain depot, and the pass-through filtering method is used to eliminate some outliers and poor segmentation points generated by segmentation to obtain more accurate three-dimensional point cloud data of the grain pile. Finally, the improved Delaunay triangulation method was used to construct the optimal topology of the grain surface continuous triangular mesh, and the nodes of the grain surface triangular mesh were projected vertically to the bottom of the grain warehouse to form several irregular triangular prisms; then, the cut and complement method was used to convert these non-plane triangular prisms into regular triangular prisms that could directly calculate the volume. The measured volume of the pile is then obtained by calculating the volume of the triangular prism. The experimental results indicate that the measured volume has a relative error of less than 1.5% and an average relative error of less than 0.5%. By selecting an appropriate threshold, the relative standard deviation can be maintained within 0.6%. The test results obtained from the laboratory test platform meet the requirements for field inspection of the granary.
Novel Paintings from the Latent Diffusion Model through Transfer Learning

With the development of deep learning, image synthesis has achieved unprecedented achievements in the past few years. Image synthesis models, represented by diffusion models, demonstrated stable and high-fidelity image generation. However, the traditional diffusion model computes in pixel space, which is memory-heavy and computing-heavy. Therefore, to ease the expensive computing and improve the accessibility of diffusion models, we train the diffusion model in latent space. In this paper, we are devoted to creating novel paintings from existing paintings based on powerful diffusion models. Because the cross-attention layer is adopted in the latent diffusion model, we can create novel paintings with conditional text prompts. However, direct training of the diffusion model on the limited dataset is non-trivial. Therefore, inspired by the transfer learning, we train the diffusion model with the pre-trained weights, which eases the training process and enhances the image synthesis results. Additionally, we introduce the GPT-2 model to expand text prompts for detailed image generation. To validate the performance of our model, we train the model on paintings of the specific artist from the dataset WikiArt. To make up for missing image context descriptions of the WikiArt dataset, we adopt a pre-trained language model to generate corresponding context descriptions automatically and clean wrong descriptions manually, and we will make it available to the public. Experimental results demonstrate the capacity and effectiveness of the model.