Reviewerly/exHarmony huggingface dataset:
Parameter-Efficient Transfer Learning for Medical Visual Question Answering

The Contrastive Language-Image Pre-Training (CLIP) model, pretrained on large visual text corpora, has demonstrated significant improvements in visual and linguistic tasks and has been applied to various downstream tasks. At least two issues, however, hinder the transfer learning with such powerful pretrained models in the field of medical visual question answering (Med-VQA). That current methods tend to full fine-tune these large-scale models are suffering from increasingly expensive computational cost as the model size grows in development. Additionally, published Med-VQA datasets are small that may lead to overfitting when directly fine-tuning on them. In this article, we integrate two designs and propose an efficient transfer learning method for Med-VQA named VQA-Adapter. To alleviate training costs, we introduce a novel and parameter-efficient adapter component into Med-VQA. During training, only the proposed light-weight adapter needs to be tuned, while all parameters in the large-scale visual model of CLIP could be kept frozen. We further design a multi-stage label smoothing paradigm for Med-VQA to deal with the overfitting issue in small Med-VQA datasets. Experimental results on two popular Med-VQA datasets, i.e., VQA-RAD and SLAKE, demonstrate that our method can significantly outperform existing state-of-the-art methods on both open-ended and closed-ended question answering tasks. Furthermore, compared to directly fine-tuning the entire CLIP model, our approach only requires to update 2.38% of the parameters. Extensive ablation studies, analysis and visualizations convincingly demonstrate the great potential of designing light-weight frameworks to transfer large-scale pretrained models from natural vision-language tasks to domain-specific medical applications.
Distribution Fitting for Combating Mode Collapse in Generative Adversarial Networks

Mode collapse is a significant unsolved issue of generative adversarial networks (GANs). In this work, we examine the causes of mode collapse from a novel perspective. Due to the nonuniform sampling in the training process, some subdistributions may be missed when sampling data. As a result, even when the generated distribution differs from the real one, the GAN objective can still achieve the minimum. To address the issue, we propose a global distribution fitting (GDF) method with a penalty term to confine the generated data distribution. When the generated distribution differs from the real one, GDF will make the objective harder to reach the minimal value, while the original global minimum is not changed. To deal with the circumstance when the overall real data is unreachable, we also propose a local distribution fitting (LDF) method. Experiments on several benchmarks demonstrate the effectiveness and competitive performance of GDF and LDF.
Beyond Pattern Variance: Unsupervised 3-D Action Representation Learning With Point Cloud Sequence

This work pays the first research effort to address unsupervised 3-D action representation learning with point cloud sequence, which is different from existing unsupervised methods that rely on 3-D skeleton information. Our proposition is built on the state-of-the-art 3-D action descriptor 3-D dynamic voxel (3DV) with contrastive learning (CL). The 3DV can compress the point cloud sequence into a compact point cloud of 3-D motion information. Spatiotemporal data augmentations are conducted on it to drive CL. However, we find that existing CL methods (e.g., SimCLR or MoCo v2) often suffer from high pattern variance toward the augmented 3DV samples from the same action instance, that is, the augmented 3DV samples are still of high feature complementarity after CL, while the complementary discriminative clues within them have not been well exploited yet. To address this, a feature augmentation adapted CL (FACL) approach is proposed, which facilitates 3-D action representation via concerning the features from all augmented 3DV samples jointly, in spirit of feature augmentation. FACL runs in a global–local way: one branch learns global feature that involves the discriminative clues from the raw and augmented 3DV samples, and the other focuses on enhancing the discriminative power of local feature learned from each augmented 3DV sample. The global and local features are fused to characterize 3-D action jointly via concatenation. To fit FACL, a series of spatiotemporal data augmentation approaches is also studied on 3DV. Wide-range experiments verify the superiority of our unsupervised learning method for 3-D action feature learning. It outperforms the state-of-the-art skeleton-based counterparts by 6.4% and 3.6% with the cross-setup and cross-subject test settings on NTU RGB <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> D 120, respectively. The source code is available at https://github.com/tangent-T/FACL.
A Sub-pixel Location Method of Circular PCB Markers Based on Partial Area Effect

With the rapid development of electronic manufacturing industry, automatic optical inspection (AOI) systems are widely applied to mark positioning and defect inspection of printed circuit board (PCB). As the most common mark, the accurate positioning of circle is of great importance in the wiring and assembly of the PCB. Various sub-pixel algorithms have been presented in the past decade. In this paper, we propose an improved version of sub-pixel edge location method based on partial area effect to acquire better sub-pixel points for fitting the circular marks. In stead of the simple one direction offset model, a composite offset model is adopted to diminish the positioning error as much as possible in the case of small gradient differences between horizontal and vertical directions. Besides, the constraints of both gradient magnitude and angle are also introduced for filtering the sub-pixel points at each edge pixel in order to effectively improve the quality of the fitted circle. Experiments conducted on synthetic and real PCB mark images have verified the better performance of the proposed method in terms of anti-interference and location accuracy.
Ferroelectric-programmed photonic computing in monolayer WS2

Photonic computing has the potential to significantly improve energy efficiency and data processing speed beyond that of von Neumann architecture. Although various optical processing techniques have been developed during recent two decades, the photonic manipulation is still a big challenging due to the bosonic nature of photons. Herein, we propose a ferroelectric field-controlled photonic computing based on the heterostructure of ferroelectric/van der Waals semiconductor. The strong and tunable electrostatic coupling of ferroelectric (PMN-PT) with monolayer WS2 results in a multi-level (24 bits) photoluminescence (PL) output. Furthermore, combining device modeling with experiments, we find that the multi-level PL output is because of the regulation of ferroelectric polarization on the net recombination rate of WS2. The ferroelectric field-controlled multi-level PL output enables us to design an optical arithmetic operation in the PMN-PT/WS2 heterostructure, which provides an attractive solution for photonic information computing.
A New Generative Model for Textual Descriptions of Medical Images Using Transformers Enhanced with Convolutional Neural Networks

The automatic generation of descriptions for medical images has sparked increasing interest in the healthcare field due to its potential to assist professionals in the interpretation and analysis of clinical exams. This study explores the development and evaluation of a generalist generative model for medical images. Gaps were identified in the literature, such as the lack of studies that explore the performance of specific models for medical description generation and the need for objective evaluation of the quality of generated descriptions. Additionally, there is a lack of model generalization to different image modalities and medical conditions. To address these issues, a methodological strategy was adopted, combining natural language processing and features extraction from medical images and feeding them into a generative model based on neural networks. The goal was to achieve model generalization across various image modalities and medical conditions. The results showed promising outcomes in the generation of descriptions, with an accuracy of 0.7628 and a BLEU-1 score of 0.5387. However, the quality of the generated descriptions may still be limited, exhibiting semantic errors or lacking relevant details. These limitations could be attributed to the availability and representativeness of the data, as well as the techniques used.
PENGARUH MODEL PEMBELAJARAN THINK TALK WRITE TERHADAP KEMAMPUAN MENULIS TEKS EDITORIAL

This research was conducted to find out how the influence of the think talk write learning model on students' ability to write editorial texts properly and correctly. This research was located at the PAB 1 Helvetia Vocational School, Jl. Veterans Psr. IV Pulo Brayan Medan. The method in this research uses the one-group pretest-posttest experimental model and random sampling technique with a total population of 126 students. The data collection tool used was a written test in the form of an essay. The ability to write editorial texts using the think talk write learning model obtained perfect scores for 6 students with a percentage of 17.6%. The excellent category is 23 students, with a percentage of 67.6%. Enough category as many as 5 students with a percentage of 14.7%. However, for the fewer and significantly fewer categories, none were found. This means that the ability to write editorial texts is at a good level due to the influence of the think-talk write learning model. Thus the hypothesis is accepted, namely that there is an influence of the think talk write learning model on the ability to write editorial texts in XII students of SMK PAB 1 Helvetia in the 2022-2023 academic year.
Towards an Unsupervised GrowCut Algorithm for Mammography Segmentation

Breast cancer is the most frequent type of malignancy in women, with 2.3 million diagnostics only in 2020. However, as a consequence of early diagnosis and appropriate treatment, more and more women are being cured. Among screening methods, mammography is one of the most used, and segmentation is a crucial step of its analysis. Starting from an enhanced version of the GrowCut segmentation technique, the goal of this paper is to automatically generate the foreground seeds so as to minimize the human expert intervention to the identification of only one pixel. We propose a method that starts with the center of the anomaly and composes the foreground seeds set as a circle inside the anomaly. We test the proposed method on the mini-MIAS dataset for various radius dimensions and two background seeds variants, concluding that the best combination is a circle with a radius of 25 pixels as foreground seeds and the black pixels from the original image as background seeds.
The Creative Potential of the Modern Museum as a New Model of Communicative Culture

The object of the research in the article is virtualization of the space of a modern museum as a new model of communicative culture. The interpretation of the museum as a center for storing historically significant relics — forms of cultural memory in order to present and study them acquires a more voluminous content within the new paradigm, ensuring the influence of museum communications on the real cultural process. Based on the research of domestic museologists (Z. A. Bonami, T. M. Troshina, A. V. Smirnov, etc.), the author considers the processes of modifying the museum as a social institution in the era of globalization and digitalization and proves that thanks to network technologies, its functionality: the museum has turned from a repository and review of artifacts of the past into an open interactive space focused on dialogue with the visitors, awakening their creative activity and intellectual potential.
ANALYSIS OF WEB APPLICATION TESTING TOOLS

The article analyzes modern methods and tools used for security testing of web applications. The prevalence of security violations of web applications and the importance of their prevention made security testing an integral part of the software development life cycle (SDLC), which should detect vulnerabilities associated with providing a holistic approach to protecting the program from hacker attacks, viruses, unauthorized access to confidential data. To identify security vulnerabilities, there are various security testing tools, among which the popular ones are: static and dynamic application security testing (SAST and DAST), interactive application security testing (IAST), software composition analysis (SCA), runtime application self-protection (RASP), web application firewalls (WAF), cloud security posture management (CSPM). Analysis of modern security testing tools showed that they all have their advantages and disadvantages due to the specifics of their organization. Combining and using the advantages of each of them can ensure a high level of security for a web software product. Possible issues related to the web testing aspect of security are cracked or untrusted passwords, buffer overflows, manipulation of hidden fields, insecure use of cryptography, interception of cookies, incorrect server configurations, weak session management, disclosure of sensitive data, manipulation of parameters, social hacking, inadequate verification of input data, etc. Focusing on various questions and issues related to web application security testing pays significant dividends in identifying and remediating various risks, vulnerabilities, attacks, threats, viruses, and more. To adapt to the dynamic and heterogeneous nature of the Internet and to ensure the best protection of web applications, a comprehensive and balanced approach to testing their security and selecting appropriate tools is effective.
Community Call - Maintaining an R Package

For this Community Call, we’re trying something different. We’ll start with a short talk by &lt;strong&gt;Julia Silge&lt;/strong&gt;, then spend most of the time on Q &amp;amp; A with four panelists - &lt;strong&gt;Elin Waring&lt;/strong&gt;, &lt;strong&gt;Erin Grand&lt;/strong&gt;, &lt;strong&gt;Leonardo Collado-Torres&lt;/strong&gt;, and &lt;strong&gt;Scott Chamberlain&lt;/strong&gt; - moderated by Julia. Our panelists bring a wide range of perspectives so there’s something for everyone.
Adaptive federated few-shot feature learning with prototype rectification

Targeting to produce new features from limited data, few-shot feature generation approaches have attracted extensive attention and successfully mitigated the high cost of acquiring sufficient data. However, two main challenges remain underexplored among existing few-shot feature generation methods, namely the distribution gaps between base and novel classes, and the gradual tightening of data privacy. In order to ameliorate the performance drop induced by the distribution gap and alleviate the laborious cost of collecting massive data, in this paper, we propose a novel few-shot feature generation model that integrates domain alignment, prototype rectification, and federated learning into a unified framework. Concretely, the distance between across different classes is explicitly shrunk via domain alignment, facilitating more precise and reliable feature generation. Additionally, we develop prototype correction to reduce the intra-class discrepancy and make samples from the same class more clustered. Such that, the negative effects of the boundary samples are eliminated and thus boost the model performance. Finally, we combine our few-shot feature generation with the federated framework to protect data privacy and propose an adaptive federated scheme to provide customized services for individual clients. Extensive experiments are performed on three standard benchmark datasets to evaluate the effectiveness and superiority of our proposed method. The results consistently demonstrate that our proposed model gains substantial performance boosts and achieves state-of-the-art performance on the few-shot tasks.
How Good Is ChatGPT For Detecting Hate Speech In Portuguese?

This study evaluates OpenAI’s ChatGPT, a large language model, for its efficacy in detecting hate speech in Portuguese tweets, comparing it with purpose-trained models. Despite incurring considerable computational costs, ChatGPT as a zero-shot classifier demonstrated commendable performance, even superior to or on par with state-of-the-art methods, with an F1-score of 73.0% on the ToLD-BR. In a cross-dataset evaluation on the HLPHSP dataset, it secured a superior F1-score of 73%. The choice of prompt significantly impacts the outcome, with a wider scope prompt balancing precision and recall metrics. ChatGPT, due to its interpretability and resilience against data distribution shifts, could be a preferred choice for tasks prioritizing these factors.
Illuminating Muscle Memory’s Sinister Side: A Social Media Case Study

When a task is repeated, it becomes part of procedural memory. This type of memory dedicated to movement is called “muscle memory”, which allows one to perform actions unconsciously. Within the context of social media, muscle memory builds up if one uses SM applications frequently. In this paper, we investigate the effects of muscle memory within Instagram, and report the following findings: We designed a user study examining speed and accuracy of using a newly changed interface which showed slower reaction time and more errors. Combining these results with users’ perceived feelings lead us to conclude that in specific UX interface changes muscle memory can be applied as a dark pattern.
IMPROVING STUDENTS’ ACHIEVEMENT IN WRITING PROCEDURE TEXT BY USING MIND MAPPING TECHNIQUE AT THE SEVENTH GRADE OF UPTD SMP NEGERI 2 GUNUNGSITOLI UTARA IN 2022/2023

Improving Students’ Achievement in Writing Procedure Text By Using Mind Mapping Technique at the Seventh Grade of UPTD SMP NEGERI 2 Gunungsitoli Utara in 2022/2023, Adielya Harazaki Harefa, Jl. Yos Sudarso No. 118/E-S Kota Gunungsitoli, Writing is a way of finding out what people know and what people need to learn. Therefore, it is important for students to master writing in their daily life. In learning writing, most of students could not achieve MCC. There was problem faced by the students in studying writing namely: the students were lack of ideas to write procedure text, the students were lack of vocabulary, the students were unable to implement steps in procedure text. Thus, to overcome these problems, the researchers conducted research by using Mind Mapping Technique. The purpose of the research is to improve the students’ achievement in writing by using Mind Mapping Technique. So, to achieve the purpose, the researchers used Classroom Action Research (CAR). This research has been conducted in two cycles, which each cycle consisted of two meetings, and each meeting consisted of four steps, they were planning, action, observation, and reflection. The result of the data in Cycle I showed there were 6 students (24%) got score among 66-75 classified as “Adequate level”; 5 students (20%) got score among 56-65 classified as “Less level”; 9 students (36%) got score among 46-55 classified as “Less level”; 5 students (20%) got score among 36-45 classified as “Less level”. While in Cycle II there were 22 students (88%) got score among 80-90 and 3 students (12%) got score among 66-75. Based on the result, it is concluded that Mind Mapping Technique cani mprove the students’ writing at the seventh grade of SMP Negeri 2 Gunungsitoli Utara.
MPVF: 4D Medical Image Inpainting by Multi-Pyramid Voxel Flows

Generatinga detailed 4D medical image usually accompanies with prolonged examination time and increased radiation exposure risk. Modern deep learning solutions have exploited interpolation mechanisms to generate a complete 4D image with fewer 3D volumes. However, existing solutions focus more on 2D-slice information, thus missing the changes on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$z$</tex-math></inline-formula> -axis. This article tackles the 4D cardiac and lung image interpolation problem by synthesizing 3D volumes directly. Although heart and lung only account for a fraction of chest, they constantly undergo periodical motions of varying magnitudes in contrast to the rest of the chest volume, which is more stationary. This poses big challenges to existing models. In order to handle various magnitudes of motions, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-Pyramid Voxel Flows (MPVF)</i> model that takes multiple multi-scale voxel flows into account. This renders our generation network rich information during interpolation, both globally and regionally. Focusing on periodic medical imaging, MPVF takes the maximal and the minimal phases of an organ motion cycle as inputs and can restore a 3D volume at any time point in between. MPVF is featured by a Bilateral Voxel Flow (BVF) module for generating multi-pyramid voxel flows in an unsupervised manner and a Pyramid Fusion (PyFu) module for fusing multiple pyramids of 3D volumes. The model is validated to outperform the state-of-the-art model in several indices with significantly less synthesis time.
Lexical noun phrase chunking with Universal Dependencies for Portuguese

Partial parsing retrieves a limited amount of syntactic information from a sentence. This project describes the identification of a specific type of noun phrase, through partial syntactic analysis, defined as a lexical noun phrase (NPL), in texts written in Brazilian Portuguese, and annotated according to the Universal Dependency (UD) formalism. The Transformation Based Learning algorithm, TBL–Brill, applied as baseline, obtained an accuracy of 87.42% considering the UD dependency relations and 91.44% considering the UD morphosyntactic tags. Two other classifiers, one based on binary trees and the other based on a decision forest, had inferior performance.
Building a Frame-Semantic Model of the Healthcare Domain: Towards the identification of gender-based violence in public health data

Public data systems gather a series of different information about Brazilian citizens. Such information is inserted in the system both via the selection of parameterized options and via open text fields. In this paper we describe the effort of modeling semantic frames for the lexicon of the healthcare domain as a means of tagging the open text fields in public health data so as to make them more easily interpretable by machine learning systems. This effort is one of the steps in a larger project aiming at using data science and machine learning techniques for the identification of territories prone to suffer from gender based-violence. The modeling effort currently covers 1,787 lexical units in the healthcare domain in Brazilian Portuguese, distributed in 29 semantic frames.