Reviewerly/exHarmony huggingface dataset:
Does the origami plot really improve the radar plot? Comment on the article by Duan et al.

I read the paper by Duan et al. [ [1] Duan R. Tong J. Sutton A.J. Asch D.A. Chu H. Schmid C.H. et al. Origami plot: a novel multivariate data visualization tool that improves radar chart. J Clin Epidemiol. 2023; 156: 85-94 Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar ] on the ‘origami plot’ with interest but also with some concerns.
University cultural activity: evaluation necessity

The article analyzes the problems of evaluation of cultural activity of modern classical universities. It is emphasized that in the postmodern world this activity requires a triune symbiosis of science, education and culture, which should mutually enrich each other, i.e. it is proved that a new paradigm of university life is needed. The example of the activity of V. N. Karazin Kharkiv National University demonstrates how the desire of a modern university to be not only a center of knowledge and professional education, but also a center of active creative cultural practices is manifested. It is noted that such practices are very diverse, that they differ in their focus, objectives and forms of work. It turns out that due to such diversity there is a need to subordinate these practices to the main goal and determine an integrated assessment of their effectiveness, development and improvement. It is demonstrated that the latest model of evaluation as an interactive, communicative, self-corrected process of evaluating the classical university cultural activities is best suited for the development of the university as a cultural hub. This model is described as contributing to a deeper and more systematic understanding and development of such activities. In this sense, evaluation is seen not so much as a tool for evaluating activities, but as a tool for its development and improvement in changing conditions, i.e. in emergent social contexts. It is argued that in the evaluationary process, in addition to communication, research methods to study and analyze the opinions, positions, proposals, critiques of all actors and beneficiaries of the cultural project should be actively used. As such, sociological surveys, expert assessments, focused group interviews are provided, which provide valuable information about the real state of the project, its strengths and weaknesses, current issues. Conclusions about the status of evaluation in modern educational practices are formulated. Potential directions of further researches of evaluative practices in educational contexts are described.
Fuzzy Technology Design for Early Detection of Diseases in Tobacco Plants

Tobacco is an agricultural product that uses leaves to be processed into pesticides, medicines and cigarettes. Tobacco quality is determined by plant maintenance and reduced pest and disease attacks. To avoid these disturbances, control is needed quickly, precisely and accurately so that the tobacco plant disease cannot spread throughout agricultural land. In making fuzzy, diseases and symptoms in tobacco plants are used as a rule base in making a fuzzy expert system. The expert system created in this research is an expert system using the concept of fuzzy logic to diagnose tobacco plant diseases, using the Mamdani inference method and the defuzzification process using the centroid method (firmness value) to get the right conclusions in diagnosing tobacco plant diseases. From the results of Mamdani's design and manual fuzzy calculations, it can be concluded that the design is ready to be further implemented into the required programming language. From the sample calculation results, it was found that damping off disease has a moderate degree of risk with a value of 41.54. With the construction of this system, it will provide easy information for farmers to carry out and find out what symptoms are contracting diseases in tobacco plants.
GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields

Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes. Our goal is to mitigate these imperfections from various sources with a joint solution: we take advantage of the ability of generative adversarial networks (GANs) to produce realistic images and use them to enhance realism in 3D scene reconstruction with NeRFs. To this end, we learn the patch distribution of a scene using an adversarial discriminator, which provides feedback to the radiance field reconstruction, thus improving realism in a 3D-consistent fashion. Thereby, rendering artifacts are repaired directly in the underlying 3D representation by imposing multi-view path rendering constraints. In addition, we condition a generator with multi-resolution NeRF renderings which is adversarially trained to further improve rendering quality. We demonstrate that our approach significantly improves rendering quality, e.g., nearly halving LPIPS scores compared to Nerfacto while at the same time improving PSNR by 1.4dB on the advanced indoor scenes of Tanks and Temples.
Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition

Information extraction(IE) is a crucial subfield within natural language processing. In this study, we introduce a Sentence Classification and Named Entity Recognition Multi-task (SCNM) approach that combines Sentence Classification (SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label Generation (SLG) framework for SCNM and construct a Wikipedia dataset containing both SC and NER. Using a format converter, we unify input formats and employ a generative model to generate SC-labels, NER-labels, and associated text segments. We propose a Constraint Mechanism (CM) to improve generated format accuracy. Our results show SC accuracy increased by 1.13 points and NER by 1.06 points in SCNM compared to standalone tasks, with CM raising format accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects between SC and NER, and integration enhances both tasks' performance.
Chromosome Detection in Metaphase Cell Images Using Morphological Priors

Reliable chromosome detection in metaphase cell (MC) images can greatly alleviate the workload of cytogeneticists for karyotype analysis and the diagnosis of chromosomal disorders. However, it is still an extremely challenging task due to the complicated characteristics of chromosomes, e.g., dense distributions, arbitrary orientations, and various morphologies. In this article, we propose a novel rotated-anchor-based detection framework, named DeepCHM, for fast and accurate chromosome detection in MC images. Our framework has three main innovations: 1) A deep saliency map representing chromosomal morphological features is learned end-to-end with semantic features. This <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not only</i> enhances the feature representations for anchor classification and regression <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">but also</i> guides the anchor setting to significantly reduce redundant anchors. This accelerates the detection and improves the performance; 2) A hardness-aware loss weights the contribution of positive anchors, which effectively reinforces the model to identify hard chromosomes; 3) A model-driven sampling strategy addresses the anchor imbalance issue by adaptively selecting hard negative anchors for model training. In addition, a large-scale benchmark dataset with a total of 624 images and 27,763 chromosome instances was built for chromosome detection and segmentation. Extensive experimental results demonstrate that our method outperforms most state-of-the-art (SOTA) approaches and successfully handles chromosome detection, with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AP</i> score of 93.53%.
Rapid one-shot dual-shearing digital shearography using a spatial light modulator

Digital shearography is a non-contact, whole-field, and high-accuracy laser-based optical interferometric method. It is widely used in the field of non-destructive testing and material evaluation. Dual-shearing DS, as the state-of-the-art method, can detect defects or measure the derivative of deformation in two sensitive directions. Most existing dual-shearing DS is realized with a bulky Michelson or Mach-Zehnder interferometer; recently, the usage of a spatial light modulator (SLM) in DS offers a new approach to designing a simple and light shearographic system. However, prior proposed SLM-based DS requires multiple shots for the phase map acquisition, with the classic temporal phase-shift (TPS) technique severely limiting its applications. This paper proposes a novel, to our best knowledge, one-shot dual-shearing DS by creating a dual-stripe pattern in the SLM. Two separate phase maps, with different sensitive directions, were acquired simultaneously via the spatial phase-shift technique. The measurement can be easily done within a single shot in its compact and light body. Moreover, the shearing amount of the two sensitive directions can be set independently and precisely. These advantages promote that the proposed system can be applied in various applications, especially for dynamic and complicated composite material testing. A detailed theory and experimental validation are described.
Augmenting Character Designers’ Creativity Using Generative Adversarial Networks

Recent advances in Generative Adversarial Networks (GANs) continue to attract the attention of researchers in different fields due to the wide range of applications devised to take advantage of their key features. Most recent GANs are focused on realism; however, generating hyper-realistic output is not a priority for some domains, as in the case of this work. The generated outcomes are used here as cognitive components to augment character designers’ creativity while conceptualizing new characters for different multimedia projects. To select the best-suited GANs for such a creative context, we first present a comparison between different GAN architectures and their performance when trained from scratch on a new visual character’s dataset using a single Graphics Processing Unit (GPU). We also explore alternative techniques, such as transfer learning and data augmentation, to overcome computational resource limitations, a challenge faced by many researchers in the domain. Additionally, mixed methods are used to evaluate the cognitive value of the generated visuals on character designers’ agency conceptualizing new characters. The results discussed proved highly effective for this context, as demonstrated by early adaptations to the characters’ design process. As an extension for this work, the presented approach will be further evaluated as a novel co-design process between humans and machines to investigate where and how the generated concepts are interacting with and influencing the design process outcome.
The Role of Additional Information in the Control System

As a result of rapidly evolving technology, the energy hunger of the world is also increasing.Depletion of fossil resources is also a problem in addition to growing energy hunger.Climate change also presents us with ongoing challenges that also affect energy supplies.These problems and challenges must be answered and solutions must be found.Mankind needs to switch to the use of climate-neutral resources and to the operation of energy-efficient models.Increasing efficiency also requires the development of an effective control system.The basic element of system control is the cybernetic loop.The present study examines the efficiency of the first phase of the cybernetic loop, the efficiency of sampling.
Competitive peer influence on knowledge contribution behaviors in online Q&amp;A communities: a social comparison perspective

Purpose Users' knowledge contribution behaviors are critical for online Q&amp;A communities to thrive. Well-organized question threads in online Q&amp;A communities enable users to clearly read existing answers and their evaluations before contributing. Based on the social comparison and peer influence literature, the authors examine peer influence on the informativeness of knowledge contributions in competitive settings. The authors also consider three levels of moderating factors concerning individuals' perception of competitiveness: question level, thread level and contributor level. Design/methodology/approach The authors collected data from one of the largest online Q&amp;A communities in China. The hypotheses were validated using hierarchical linear models with cross-classified random effects. The generalized propensity score weighting method was employed for the robustness check. Findings The authors demonstrate the peer influence due to social comparison concerns among knowledge contribution behaviors in the same question thread. If more prior knowledge contributors choose to contribute long answers in the question thread, the subsequent contributions are more informative. This peer influence is stronger for factual questions and questions with higher popularity of answering but weaker in recommendation-type and well-answered questions and for contributors with higher social status. Originality/value This research provides a new cue of peer influence on online UGC contributions in competitive settings initiated by social comparison concerns. Additionally, the authors identify three levels of moderating factors (question level, thread level and contributor level) that are specific to online Q&amp;A settings and are related to a contributor's perception of competitiveness, which affect the direct effect of peer influence on knowledge contributions. Rather than focus on motivation and quality evaluation, the authors concentrate on the specific content of online knowledge contributions. Peer influence here is not based on an actual acquaintance or a following relationship but on answering the same question. The authors also illustrate the competitive peer influence in subjective and personalized behaviors in online UGC communities.
Multiclass Text Emotion Recognition in Social Media Data

Sentiment analysis is closely connected to Emotion Detection and Recognition from Text, which is a relatively recent area of research. Sentiment Analysis is a subset of Emotion Recognition that focuses on emotion extraction and analysis. Numerous research has been undertaken in the subject of Emotion Analysis since it is a growing trend to detect people’s feelings and sentiments in their day-to-day lives on a variety of personal and social concerns. Emotion Analysis attempts to recognise and perceive numerous emotions represented in texts, such as anger, contempt, fear, pleasure, sorrow, and surprise. People’s ability to accurately discern the emotions of others differs tremendously. Some of the feeling classifications include neutral, joy, sadness, love, hate, disgust, surprised, anger, fear, and so on. The data source was Twitter, a well-known social networking site. Text Mining or Natural Language Processing Algorithms are used to extract textual information for categorisation. Multiclass Emotions will be recognised in this proposed study utilising deep learning algorithms such as CNN, LSTM, and GRU using social media, specifically Twitter, as the data source, employing natural language processing methods, data augmentation, and feature extraction approaches.
Single-copy entanglement purification for Greenberger–Horne–Zeilinger states

A multiparticle entangled state is an indispensable resource in quantum information processing. However, the inherent noise in quantum channels may degrade the maximally entangled state to a mixed entangled state. An entanglement purification protocol (EPP) is a powerful tool to distill high-quality entanglement from low-quality entanglement. Conventional multiparticle EPPs (MEPPs) mainly focus on two noisy copies. In this paper, we investigate a single-copy MEPP (SCMEPP) in linear optics that requires only a pair of hyperentanglements. Specifically, we first purify the bit-flip errors for three-particle polarization Greenberger–Horne–Zeilinger (GHZ) states at the cost of time-bin entanglement, and then an analysis of correcting the phase-flip errors is carried out. Additionally, we extend this SCMEPP into purification for arbitrary multiparticle GHZ states. Moreover, we discuss the discarding components that have been regarded as failure in conventional two-copy MEPPs, and they still have entanglement if the fidelities of the initial states satisfy specific conditions. This can be reused to distill high-quality entanglement, therefore increasing the efficiency of the SCMEPP, which may have potential application to realize high-efficiency long-distance quantum communication.
Explaining machine-learning models for gamma-ray detection and identification

As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations.
Integration of augmented reality smart glasses as assembly support: a framework implementation in a quick evaluation tool

Augmented reality smart glasses (ARSG) have been successfully used as operator support in production. However, their use is not yet widespread, likely in part due to a lack of knowledge about how to integrate ARSG into production. This lack of knowledge can also make it hard to estimate whether this is a worthwhile investment. Our solution is to provide an online evaluation tool to help production planners estimate the likelihood that ARSG will be worth the investment cost in specific production cases. Based on a strawman design, multiple design iterations were followed by a pilot test performed by participants from different manufacturing companies involved in planning production for operators. A Likert scale survey was used to evaluate the tool. The results show a slightly positive evaluation of the tool with suggestions for improvement, including widening the scope and granularity of the tool. Future works include further iterations and case studies. [Submitted 9 April 2021; Accepted 31 July 2021]
Gradient Directional Predictor for Reconstructing High-Fidelity Images

A good pixel prediction strategy is essential to have a good prediction error expansion based reversible data hiding technique. Several pixel prediction strategies exist in literature. Gradients are used to predict the current pixel. Several Researchers have been proposed based on the gradient estimation to predict the current pixel for embedding more data in the current pixel. In this paper a novel approach based on the gradients of the image using $$3 \times 4 $$ neighbourhood has been proposed for better predicting the current pixel. Adaptive histogram bin shifting has been used to insert more data with less distortion, depending on the local complexity of the pixel. The experimental study shows that the proposed technique performs better than some existing methods.
Analysis of Integration Technologies for High-Speed Analog Neuromorphic Photonics

While the use of graphic processing units fueled the success of artificial intelligence models, their future evolution will likely require overcoming the speed and energy efficiency limitations of current implementations with the use of specialized neuromorphic hardware. In this scenario, neuromorphic photonic processors have recently proved to be a feasible solution. In this paper, we first discuss basic analog photonic processing elements based on Mach-Zehnder modulators and assess their effective bit resolution. Then we evaluate how different photonic integration technologies affect the performance and the scalability of analog optical processors, in order to provide a clearer path toward real-world implementations of such engines. To this aim, we focus our analysis on the silicon on insulator (SOI), lithium niobate on insulator (LNOI), and indium phosphide (InP) platforms. In particular, we have numerically evaluated the performance of the Photonic Electronic Multiply- Accumulate Neuron (PEMAN) and its tensorial version, both based on Mach-Zehnder modulators, with the three technologies in terms of resolution, energy efficiency, and footprint efficiency. LNOI modulators achieve the best resolution at high speed, with 4.3 bits at 56 GMAC/s for the single PEMAN and 3.6 bits at 224 GMAC/S for the tensorial version. The energy consumption in InP and LNOI platforms is the lowest, accounting for just 13.2 pJ/MAC and 4.6 pJ/MAC for the single and tensorial PEMAN, respectively. Nonetheless, SOI devices outperform the others in terms of footprint efficiency, reaching 18.6 GMAC/s/mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> in the single-neuron version and 29.6 GMAC/s/mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> in the tensorial version.
Segmentation of the Abnormal Regions in Breast Cancer X-Ray Images Using U-Net

We present a segmentation of the abnormal regions in breast cancer X-Ray images using U-Net.The breast cancer X-Ray images dataset was collected by the Vietnam National Cancer Hospital radiologists and consists of three image classes: Normal, Benign, and Malignant. The hospital radiologists labeled the dataset's images and performed the localization of lesions of the Benign and Malignant images. We use the breast cancer X-Ray image dataset to train the U-Net model for segmenting Benign and Malignant masses to visualize Benign and Malignant areas in the images. The images of Normal, Benign, and Malignant classes are converted into a PNG 16-bit format. Before model training, the data pre-processing is realized. We flip the images of R-CC and R-MLO views horizontally so that all breast images are leftward-oriented. Then, we crop the image by probing along the edge of the breast. After that, this image is resized into 512 × 512 pixels. Our network achieves an average Dice of 0.74357. We have compared segmentation masks of model prediction with doctors’ annotation masks. The result shows that the segmentation model has performed well. Finally, our system combines the segmentation result with the heatmap result to have more supporting information on abnormal region localization.