Reviewerly/exHarmony huggingface dataset:
A study on a recommendation algorithm based on spectral clustering and GRU

Authors:
raw_author_name: Qingyuan Liu
raw_affiliation_string: College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China.
raw_author_name: Miaoyuan Bai
raw_affiliation_string: College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China.
raw_author_name: Ming Yu
raw_affiliation_string: College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China.

Abstract: With the development of e-commerce, the importance of recommendation algorithms has significantly increased. However, traditional recommendation systems struggle to address issues such as data sparsity and cold start. This article proposes an optimization method for a recommendation system based on spectral clustering (SC) and gated recurrent unit (GRU), named the GRU-KSC algorithm. Firstly, this paper improves the original spectral clustering algorithm by introducing Kmc2, proposing a novel spectral clustering recommendation algorithm (K-means++ SC, KSC) based on the existing SC algorithm. Secondly, building upon the original GRU model, the paper presents a hybrid recommendation algorithm (Hybrid GRU, HGRU) capable of capturing long-term user interests for a more personalized recommendation. Experiments conducted on real datasets demonstrate that our method outperforms existing benchmark methods in terms of accuracy and robustness.
Contrastive image adaptation for acquisition shift reduction in medical imaging

Authors:
raw_author_name: Pierre-Henri Conze
raw_affiliation_string: Département lmage et Traitement Information; Laboratoire de Traitement de l'Information Medicale
raw_author_name: Vincent Bourbonne
raw_affiliation_string: Centre Hospitalier Régional Universitaire de Brest; Laboratoire de Traitement de l'Information Medicale
raw_author_name: Vincent Jaouen
raw_affiliation_string: Département lmage et Traitement Information; Laboratoire de Traitement de l'Information Medicale
raw_author_name: Dimitris Visvikis
raw_affiliation_string: Laboratoire de Traitement de l'Information Medicale

Abstract: The domain shift, or acquisition shift in medical imaging, is responsible for potentially harmful differences between development and deployment conditions of medical image analysis techniques. There is a growing need in the community for advanced methods that could mitigate this issue better than conventional approaches. In this paper, we consider configurations in which we can expose a learning-based pixel level adaptor to a large variability of unlabeled images during its training, i.e. sufficient to span the acquisition shift expected during the training or testing of a downstream task model. We leverage the ability of convolutional architectures to efficiently learn domain-agnostic features and train a many-to-one unsupervised mapping between a source collection of heterogeneous images from multiple unknown domains subjected to the acquisition shift and a homogeneous subset of this source set of lower cardinality, potentially constituted of a single image. To this end, we propose a new cycle-free image-to-image architecture based on a combination of three loss functions : a contrastive PatchNCE loss, an adversarial loss and an edge preserving loss allowing for rich domain adaptation to the target image even under strong domain imbalance and low data regimes. Experiments support the interest of the proposed contrastive image adaptation approach for the regularization of downstream deep supervised segmentation and cross-modality synthesis models.
An Assessment of Rules of Thumb for Software Phase Management, and the Relationship Between Phase Effort and Schedule Success

Authors:
raw_author_name: Clay Koschnick
raw_affiliation_string: AFIT, Wright Patterson Air Force Base, OH, USA

Abstract: In the planning of a software development project, managers must estimate the amount of effort needed for distinct phases of activity. A number of rules of thumb exist in the literature to help the program manager in this task. However, very little work has been done to validate these rules of thumb. Applying least square models and Hotelling’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> test, we evaluate these rules of thumb against a large database of Department of Defense projects. We determine that variability limits the simple application of any such rule. However, there are some worthy of closer attention, and we recommend adjustments for improved application. We also determine that projects which give extra attention to early phases experience less schedule growth. These findings were robust across developmental process type, military service, and project size.
Designing Electronic Data Capture Systems for Sustainability in Low-Resource Settings: Viewpoint With Lessons Learned From Ethiopia and Myanmar

Authors:
raw_author_name: Natalie Benda
raw_affiliation_string: School of Nursing, Columbia University, New York, NY, United States
raw_author_name: Ruth M Masterson Creber
raw_affiliation_string: School of Nursing, Columbia University, New York, NY, United States

Abstract: Electronic data capture (EDC) is a crucial component in the design, evaluation, and sustainment of population health interventions. Low-resource settings, however, present unique challenges for developing a robust EDC system due to limited financial capital, differences in technological infrastructure, and insufficient involvement of those who understand the local context. Current literature focuses on the evaluation of health interventions using EDC but does not provide an in-depth description of the systems used or how they are developed. In this viewpoint, we present case descriptions from 2 low- and middle-income countries: Ethiopia and Myanmar. We address a gap in evidence by describing each EDC system in detail and discussing the pros and cons of different approaches. We then present common lessons learned from the 2 case descriptions as recommendations for considerations in developing and implementing EDC in low-resource settings, using a sociotechnical framework for studying health information technology in complex adaptive health care systems. Our recommendations highlight the importance of selecting hardware compatible with local infrastructure, using flexible software systems that facilitate communication across different languages and levels of literacy, and conducting iterative, participatory design with individuals with deep knowledge of local clinical and cultural norms.
Examining the role of self-reported somatosensory sensations in body (dis)ownership: A scoping review and empirical study of patients with a disturbed sense of limb ownership

Authors:
raw_author_name: Valentina Moro
raw_affiliation_string: Npsy-Lab.Vr, Department of Human Sciences, University of Verona, Italy
raw_author_name: Michele Scandola
raw_affiliation_string: Npsy-Lab.Vr, Department of Human Sciences, University of Verona, Italy
raw_author_name: Aikaterini Fotopoulou
raw_affiliation_string: Research Department of Clinical, Educational and Health Psychology, University College London, UK
raw_author_name: Paul M. Jenkinson
raw_affiliation_string: ISN Psychology, Institute for Social Neuroscience, Melbourne, Australia; Research Department of Clinical, Educational and Health Psychology, University College London, UK

Abstract: Patients with a disturbed sense of limb ownership (DSO) offer a unique window of insight into the multisensory processes contributing to the sense of body ownership. A limited amount of past research has examined the role of sensory deficits in DSO, and even less is known regarding the role of patient self-reported somatosensory sensations in the pathogenesis of DSO. To address this lack of knowledge we first conducted a systematic scoping review following PRISMA-SR guidelines, examining current research into somatosensory deficits and patient self-reported somatosensory sensations in patients with DSO. Eighty studies, including 277 DSO patients, were identified. The assessment of sensory deficits was generally limited in scope and quality, and deficits in tactile sensitivity and proprioception were most frequently found. The reporting of somatosensory sensations was even less frequent, with instances of paraesthesia (pins-and-needles), stiffness/rigidity, numbness and warmth, coldness and heaviness amongst the deficits recorded. In a second part of the study, we sought to directly address the lack of evidence concerning the impact of patient self-reported somatosensory sensations in DSO by measuring DSO and self-reported somatosensory sensations in a large (n = 121) sample of right-hemisphere stroke patients including N = 65 with DSO and N = 56 hemiplegic controls. Results show that feelings of coldness and stiffness modulate DSO symptoms. Sense of heaviness and numbness are more frequent in patients with DSO but do not have a clear impact on disownership symptomology. Although preliminary, these results suggest a role of subjective sensations about the felt body in the sense of limb ownership.
Enhancing patient education in cancer care: Intelligent cancer patient education model for effective communication

Authors:
raw_author_name: Qihuan Fang
raw_affiliation_string: Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
raw_author_name: Linlin Wang
raw_affiliation_string: Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China

Abstract: The incidence of cancer is on the rise annually, whereas there exists a significant deficit of healthcare personnel. Inadequate communication between healthcare providers and patients may result in adverse emotional outcomes for the latter and interfere with their treatment progress. A viable solution to alleviate patient distress involves utilizing text generation models as an efficacious tool for delivering patient education.In this study, we proposed an intelligent cancer patient education model (ICPEM) based on the pre-trained T5 model. Meanwhile, we presented a new method for optimizing the model's comprehension of the patient's intent through simulating the inquiries that the patient may ask. The datasets used include a doctor and patient dialogue dataset and a cancer patient education scenario dataset. After prompt-tuning, the model is capable of educating patients through four major aspects including medical examination, health care, radiotherapy, chemotherapy.We conducted a comprehensive evaluation of the model by employing both automated and manual metrics. Our findings indicate that the responses generated by the model effectively catered to the requirements of patient education. Furthermore, our visualization analysis demonstrated the model's proficiency in processing sentences that are prone to confusion while maintaining a robust comprehension of human intent. Finally, several shortcomings of the model are presented, such as the inadequate amount of knowledge and the limited range of responses.We proposed a method for training and deploying medical language models in a low-resource environment. The proposed model facilitated the comprehension of cancer patients' intentions, resulting in suitable responses, which promotes effective patient-provider communication.
Low-power laser images enhancement via deep feature recovery for HDR 3D measurement

Authors:
raw_author_name: Xingman Niu
raw_author_name: Fuqian Li
raw_author_name: Chenglin Zuo
raw_affiliation_string: Low Speed Aerodynamics Institute
raw_author_name: Qican Zhang
raw_author_name: Yajun Wang

Abstract: Laser 3D measurement has gained widespread applications in industrial metrology . Still, it is usually limited by surfaces with high dynamic range (HDR) or the colorful surface texture of measured surfaces, such as metal and black industrial parts. Currently, conventional methods generally work with relatively strong-power laser intensities, which could potentially damage the sample or induce eye-safety concerns. For deep-learning-based methods, due to the different reflectivity of the measured surfaces, the HDR problem may require cumbersome adjustment of laser intensity in order to acquire enough training data. Even so, the problem of inaccurate ground truth may occur. To address these issues, this paper proposes the deep feature recovery (DFR) strategy to enhance low-light laser stripe images for achieving HDR 3D reconstruction with low cost, high robustness, and eye safety. To the best of our knowledge, this is the first attempt to tackle the challenge of high measurement costs associated with measuring HDR surfaces in laser 3D measurement. In learning the features of low-power laser images, the proposed strategy has a superior generalization ability and is insensitive to different low laser powers and variant surface reflectivity. To verify this point, we specially design the experiments by training the network merely using the diffusely reflective masks (DRM951) and testing the performance using diffusely reflective masks, metal surfaces, black industrial parts (contained in the constructed datasets DRO690, MO191, and BO107) and their hybrid scenes. Experimental results demonstrate that the proposed DFR strategy has good performances on robustness by testing different measurement scenes. For variously reflective surfaces, such as diffusely reflective surfaces, metal surfaces, and black parts surfaces, the reconstructed 3D shapes all have a similar quality to the reference method.
Weakly supervised image segmentation beyond tight bounding box annotations

Authors:
raw_author_name: Bin Xia
raw_affiliation_string: Shenzhen SiBright Co. Ltd., Tinwe Industrial Park, No. 6 Liufang Rd., Shenzhen, Guangdong 518052, China

Abstract: Weakly supervised image segmentation approaches in the literature usually achieve high segmentation performance using tight bounding box supervision and decrease the performance greatly when supervised by loose bounding boxes. However, compared with loose bounding box, it is much more difficult to acquire tight bounding box due to its strict requirements on the precise locations of the four sides of the box. To resolve this issue, this study investigates whether it is possible to maintain good segmentation performance when loose bounding boxes are used as supervision. For this purpose, this work extends our previous parallel transformation based multiple instance learning (MIL) for tight bounding box supervision by integrating an MIL strategy based on polar transformation to assist image segmentation. The proposed polar transformation based MIL formulation works for both tight and loose bounding boxes, in which a positive bag is defined as pixels in a polar line of a bounding box with one endpoint located inside the object enclosed by the box and the other endpoint located at one of the four sides of the box. Moreover, a weighted smooth maximum approximation is introduced to incorporate the observation that pixels closer to the origin of the polar transformation are more likely to belong to the object in the box. The proposed approach was evaluated on two public datasets using dice coefficient when bounding boxes at different precision levels were considered in the experiments. The results demonstrate that the proposed approach achieves state-of-the-art performance for bounding boxes at all precision levels and is robust to mild and moderate errors in the loose bounding box annotations. The codes are available at https://github.com/wangjuan313/wsis-beyond-tightBB.
A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution

Authors:
raw_author_name: Ruhaila Maskat
raw_affiliation_string: College of Computing, Informatics and Mathematics of Universiti Teknologi MARA Shah Alam, 40450, Selangor, Malaysia
raw_author_name: Norazmiera Ayunie Azman
raw_affiliation_string: College of Computing, Informatics and Mathematics of Universiti Teknologi MARA Shah Alam, 40450, Selangor, Malaysia
raw_author_name: Mohd Mukhlis Mohd Sharif
raw_affiliation_string: College of Computing, Informatics and Mathematics of Universiti Teknologi MARA Shah Alam, 40450, Selangor, Malaysia
raw_author_name: Hairulnizam Mahdin
raw_affiliation_string: Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

Abstract: Low-resource languages, like Malay, face the threat of extinction when linguistic resources become scarce. This paper addresses the scarcity issue by contributing to the inventory of low-resource languages, specifically focusing on Malay-English, known as Manglish. Manglish speakers are primarily located in Malaysia, Indonesia, Brunei, and Singapore. As global adoption of second languages and social media usage increases, language code-switching, such as Spanglish and Chinglish, becomes more prevalent. In the case of Malay-English, this phenomenon is termed Manglish. To enhance the status of the Malay language and its transition out of the low-resource category, this unique text corpus, with binary annotations for biological gender and anonymized author identities is presented. This bi-annotated dataset offers valuable applications for various fields, including the investigation of cyberbullying, combating gender bias, and providing targeted recommendations for gender-specific products. This corpus can be used with either of the annotations or their composite. The dataset comprises of posts from 50 Malaysian public figures, equally split between biological males and females. The dataset contains a total of 709,012 raw X posts (formerly Twitter), with a relatively balanced distribution of 53.72% from biological female authors and 46.28% from biological male authors. Twitter API was used to scrape the posts. After pre-processing, the total posts reduced to 650,409 posts, widening the gap between the genders with the 56.88% for biological female and 43.12% for biological male. This dataset is a valuable resource for researchers in the field of Malay-English code-switching Natural Language Processing (NLP) and can be used to train or enhance existing and future Manglish language transformers.
Unsupervised topological analysis of polarized light microscopy: application to quantitative birefringence imaging

Authors:
raw_author_name: Julie Bouhy
raw_affiliation_string: Department of Physics, Namur Institute of Structured Matter, University of Namurhttps://ror.org/03d1maw17, 61 Rue de Bruxelles, 5000 Namur, Belgium
raw_author_name: nicolas roy
raw_affiliation_string: Department of Physics, Namur Institute of Structured Matter, University of Namurhttps://ror.org/03d1maw17, 61 Rue de Bruxelles, 5000 Namur, Belgium
raw_author_name: Olivier Deparis
raw_affiliation_string: Department of Physics, Namur Institute of Structured Matter, University of Namurhttps://ror.org/03d1maw17, 61 Rue de Bruxelles, 5000 Namur, Belgium

Abstract: The determination of birefringence (magnitude and axis orientation) of optical materials is of significant interest in various fields. In the case of composite samples, this task becomes complicated and time-consuming; therefore, a partially automated procedure for reconstructing birefringence spatial distribution becomes valuable. Herein, we propose a procedure to reconstruct the spatial distributions of the retardance and optical axis orientation in a geological thin section from sparse quantitative birefringence measurements, using automatic boundary detection on cross-polarized light microscopy images. We examine two particular areas on the selected geological thin section: one that presents a uniaxial crystal with a circular cross-section of its refractive index ellipsoid and the other with grains of varying orientations. The measurement gives the orientation of the grain’s optical axis both in and out of the plane of the thin section, which explains the qualitative observations with the cross-polarized light microscope. Future work will connect the measured orientation of the rock thin section with its 3D geological orientation in the field.
Focal plane coincidence method for a multi-view telecentric 3D imaging system

Authors:
raw_author_name: Dezhao Wang
raw_affiliation_string: Changchun University of Science and Technology; Institute of Microelectronics of the Chinese Academy of Sciences
raw_author_name: WEIHU ZHOU
raw_affiliation_string: Changchun University of Science and Technology; Institute of Microelectronics of the Chinese Academy of Sciences; University of Chinese Academy of Sciences
raw_author_name: zili Zhang
raw_affiliation_string: Institute of Microelectronics of the Chinese Academy of Sciences; University of Chinese Academy of Sciences
raw_author_name: Fanchang Meng
raw_affiliation_string: Institute of Microelectronics of the Chinese Academy of Sciences; University of Chinese Academy of Sciences
raw_author_name: Chao Gao
raw_affiliation_string: Institute of Microelectronics of the Chinese Academy of Sciences; University of Chinese Academy of Sciences

Abstract: Multi-view microscopic fringe projection systems, which use high-resolution telecentric lenses and the Scheimpflug condition, face challenges in coinciding focal planes accurately, resulting in inconsistent measurements between views. In this Letter, we developed a sharpness evaluation function based on the total power of the line-spread function, which was subsequently used to generate a full-field sharpness distribution map. Then we employed the correlation between the sharpness map and orientation of the focal plane to precisely coincide the focal planes. Experimental results validate the proposed method and demonstrate its improved consistency in 3D reconstruction.
Generative adversarial networks with attentional multimodal for human face synthesis

Authors:
raw_author_name: Sowmya BJ
raw_affiliation_string: Ramaiah Institute of Technology
raw_author_name: Meeradevi Meeradevi
raw_affiliation_string: Ramaiah Institute of Technology
raw_author_name: Seems Shedole
raw_affiliation_string: Ramaiah Institute of Technology

Abstract: &lt;p&gt;&lt;span&gt;Face synthesis and editing has increased cumulative consideration by the improvement of generative adversarial networks (GANs). The proposed attentional GAN-deep attentional multimodal similarity modal (AttnGAN-DAMSM) model focus on generating high-resolution images by removing discriminator components and generating realistic images from textual description. The attention model creates the attention map on the image and automatically retrieves the features to produce various sub-areas of the image. The DAMSM delivers fine-grained image-text identical loss to generative networks. This study, first describe text phrases and the model will generate a photorealistic high-resolution image composed of features with high accuracy. Next, model will fine-tune the selected features of face images and it will be left to the control of the user. The result shows that the proposed AttnGAN-DAMSM model delivers the performance metrics like structural similarity index measure (SSIM), feature similarity index measure (FSIM) and frechet inception distance (FID) using CelebA and CUHK face sketch (CUFS) dataset. For CelebFaces attribute (CelebA) dataset, the SSIM achieves 78.82% and for CUFS dataset, the SSIM achieves 81.45% which ensures accurate face synthesis and editing compared with existing methods such as GAN, SuperstarGAN and identity-sensitive GAN (IsGAN) models.&lt;/span&gt;&lt;/p&gt;