Reviewerly/exHarmony huggingface dataset:
DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification

Diffusion Probabilistic Models have recently shown remarkable performance in generative image modeling, attracting significant attention in the computer vision community. However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification. In this paper, we propose the first diffusion-based model (named DiffMIC) to address general medical image classification by eliminating unexpected noise and perturbations in medical images and robustly capturing semantic representation. To achieve this goal, we devise a dual conditional guidance strategy that conditions each diffusion step with multiple granularities to improve step-wise regional attention. Furthermore, we propose learning the mutual information in each granularity by enforcing Maximum-Mean Discrepancy regularization during the diffusion forward process. We evaluate the effectiveness of our DiffMIC on three medical classification tasks with different image modalities, including placental maturity grading on ultrasound images, skin lesion classification using dermatoscopic images, and diabetic retinopathy grading using fundus images. Our experimental results demonstrate that DiffMIC outperforms state-of-the-art methods by a significant margin, indicating the universality and effectiveness of the proposed model. Our code is publicly available at https://github.com/scott-yjyang/DiffMIC .
Screen Perturbation: Adversarial Attack and Defense on Under-Screen Camera

Smartphones are moving towards the fullscreen design for better user experience. This trend forces front cameras to be placed under screen, leading to Under-Screen Cameras (USC). Accordingly, a small area of the screen is made translucent to allow light to reach the USC. In this paper, we utilize the translucent screen's features to inconspicuously modify its pixels, imperceptible to human eyes but inducing perturbations on USC images. These screen perturbations affect deep learning models in image classification and face recognition. They can be employed to protect user privacy, or disrupt the front camera's functionality in the malicious case. We design two methods, one-pixel perturbation and multiple-pixel perturbation, that can add screen perturbations to images captured by USC and successfully fool various deep learning models. Our evaluations, with three commercial full-screen smartphones on testbed datasets and synthesized datasets, show that screen perturbations significantly decrease the average image classification accuracy, dropping from 85% to only 14% for one-pixel perturbation and 5.5% for multiple-pixel perturbation. For face recognition, the average accuracy drops from 91% to merely 1.8% and 0.25%, respectively.
Teachers’ Self-Efficacy and Attitudes towards the Use of Information Technology in Classrooms

This research aims to measure the level of integration of ICT in teaching, learning, and assessment practices during the COVID-19 pandemic. We propose measuring teachers’ attitude, self-efficacy, and skills in using technology because the quality of education during the pandemic was influenced by these factors. An online questionnaire was distributed amongst 189 teachers from all levels of education, from kindergarten to high school, and the sample was described using the means, distribution, and number of years of experience in education. The statistical analyses employed for the quantitative data included correlational study and mediation analysis. We found a correlation between gender, environment, barrier level and teachers’ attitude, self-efficacy, and skills. Also, the teachers’ attitude mediates the association between ICT skills and ICT integration in the educational process. The results reveal high positive scores in terms of ICT integration in teaching practices, teachers’ attitude, self-efficacy, and ICT skills. The novelty of this research is that this is the first time a questionnaire based on attitude, self-efficacy, ICT skills, and ICT integration in the classroom has been applied in the northern Moldavian region of Romania, and the results underline the educational implications for in-service teacher training.
Exploring the Cybercrime Prevention Campaign on Twitter: Evidence from the Indonesian Government

Cybercrime in Indonesia has recently become a significant problem in line with the increase in internet users, which is a serious issue among online community networks. Therefore, this makes government authorities strive to take preventive measures to prevent the spread of cybercrime. In this case, this article aims to explore the Indonesian government's efforts under the National Cyber and Crypto Agency to run a cybercrime prevention campaign through the @BSSN_RI Twitter account with the tagline Guard Cyberspace. Exploration studies have been initiated and visualized with the code of the NVivo tool. This research has produced several findings, including: Firstly, the Indonesian government has taken current preventive measures, and there has been a call to action to prevent cybercrime during the last two years. Secondly, the Indonesian government has maximized its efforts to prevent cybercrime through various tweets, posters, and the hashtag. Furthermore, Twitter users (outside of public sector accounts) are also contributing to spreading cybercrime prevention campaigns. In addition, there are several words and hashtag frequencies that echo the point of view of the campaign. Moreover, this research also contributes significantly to increasing the richness of literature on how government authorities’ use social media as a cybercrime prevention campaign tool, which past studies have limited.
Reading and Writing Files

Reading data from and writing data to a file are very common within many programs. Python provides a large amount of support for working with files of various types. This chapter introduces you to the core file IO functionality in Python.
A new accuracy metric under three classes when subclasses are involved and its confidence interval estimation

"Compound multi-class classification" refers to the setting where three or more main classes are involved and at least one of the main classes have multiple subclasses. A common practice in evaluating biomarker performance under "compound multi-class classification" is "subclasses pooling." In this article, we first explore the downsides of accuracy metrics based on pooled data. Then we propose a new accuracy measure proper for "compound multi-class classification" with three ordinal main classes, namely "volume under compound ROC$$ ROC $$ surface ( VUSC$$ VU{S}_C $$ )." The proposed VUSC$$ VU{S}_C $$ evaluates the accuracy of a biomarker appropriately by identifying main classes without requiring specification of an ordering for marker values of subclasses within each main class. For confidence interval estimation of VUSC$$ VU{S}_C $$ , both parametric and nonparametric methods are studied, and simulation studies are carried out to assess coverage probabilities. A subset of Alzheimer's Disease Neuroimaging Initiative study dataset is analyzed.
Communication consistency, completeness, and complexity of digital ideography in trustworthy mobile extended reality

Communication barriers long-associated with ideographs, including combinatorial grapholinguistic complexity, computational encoding-decoding complexity, and technological rendering and deployment, become trivialized through advancements in interoperable smart mobile digital devices. Such technologies impart unprecedented extended-reality user hazards only mitigated by unprecedented colloquial and bureaucratic societal norms. Digital age norms thus influence natural ideographic language origins and evolution in ways novel to human history.
Analisis Rekaman Suara pada Aplikasi Magic Call dengan Metode Forensik Audio untuk Mendapatkan Bukti Digital

Audio forensics is a method used to analyze sound or audio recordings. Voice or audio recording is one of the digital evidence that is easy to manipulate. One way to manipulate sound is to use magic call. Magic call has several levels of character voices that can be used such as cartoon, children, male and female voices. The analysis of the original voice recording with the magic voice recording is done by comparing the magic call sound and the voice with the original voice recording. The purpose of this study was to determine the voice recording produced by magic call from the magic call applications. As for the method used in this research is audio forensics, research on magic call sound using audio has never been done before. The results of this study indicate that the analysis of magic call sound recordings can be done using formant analysis and spectrograms, while pitch analysis on magic call voice recordings cannot be used. The formant and spectrogram values on magical voice recordings can still be searched because the original voice recordings have characteristics that are still attached to the magic recording calls.
Light Field Super-Resolution Using Decoupled Selective Matching

Non-local self-similarity has been well exploited in the single image super-resolution task as an effective prior. However, due to the difficulty of modeling the 4D correspondence globally, the potential of the non-local prior is less revealed for light field (LF) super-resolution. Meanwhile, existing non-local models only utilize the global spatial correspondence, but largely neglect the global geometric correspondence. To address the aforementioned problems, we propose a Decoupled Selective Matching Network (DSMNet) for LF super-resolution, by designing a novel selective matching mechanism to flexibly extract non-local information from specific 4D positions in an LF. Such a mechanism matches the reference patch with several auxiliary patches dynamically searched from predefined windows, which promotes efficiency while improving performance compared to the existing non-local models. Specifically, our DSMNet decouples the whole LF into Sub-Aperture Images (SAIs) and Epipolar Plane Images (EPIs). For each SAI patch, we separately perform the selective matching inside the current SAI and cross different SAIs to exploit the global spatial correspondence efficiently. For each EPI patch, we separately perform the selective matching in EPIs of different orientations to embed robust LF geometric information into features by enhancing EPI textures, which exploits the global geometric correspondence in an efficient manner. Comprehensive experiments validate that DSMNet outperforms state-of-the-art LF super-resolution methods both quantitatively and qualitatively.
Tweet sentiment analysis using logistic regression

Twitter is like a blogging platform where any users can send status and messages, called ``tweets,&apos;&apos; to other people. Provided a large database of so-called emotions, its separation can be done by supervised learning. In order to making supervised learning models, segmentation algorithms need a large set of data with proxy labels. However, categorized data is often hard and costly to get, promoting an unrevealed reading interest. This type of reading uses label less data to be compatible with details given with labeled data in the training program; therefore, it is especially meaningful in programs that include analyzing tweet sentiments, in which a large amount of unspecified data is accessible. While attractive, the slowpaced reading of the tweet emotional analysis is new. We present an exhaustive survey of slow-tracking methods used to tweet segregation. Such methods contain graph-based methods, conclusions and subject-based methods. Comparative study of supported algorithms in self-training, co-training, title modelling, and remote monitoring expose their bias and shed light the factors an expert should consider in real-world systems.
A Design Space Exploration of Power-efficient Gaussian Filter Architectures using Logical Optimization and Approximated Adders

Gaussian filtering is an important step in many image-processing applications because it reduces image noise.However, this step is also compute-intensive, so power-optimized hardware architectures are necessary to allow its adoption in embedded devices. This work presents a design space exploration of power-efficient Gaussian filter architectures.Differently from related work, this work shows the impacts of the design decisions on two target applications: the Canny Algorithm and an Automatic License Plate Recognition system.To explore the design space, the 3x3, 5x5, and 7x7kernels were logically refactored using Multiplierless Constant Multiplication and Common Sub-expression Exploration. The adders were approximated using the copy strategy to further reduce power consumption. Systematic experiments show the effects of the adopted strategies on power savings and quality of results compared to exact baselines using the two applications.The approximate strategy reached up to 51 dB of Peak Signal-to-Noise Ratio with power reductions of up to 48% in the best-case scenario of the standalone Gaussian filters. Also, the total power of the Gaussian filter in the Canny Algorithm can be reduced down to 34% while maintaining the precision of results between 57% and 90%. Finally, the proposed strategies reduce up to 64% of the Gaussian filter power consumption when adopted in the plate detection solution with a similar detection rate compared to the exact filter architecture.
A Gaze-Based Intelligent Textbook Manager

IT technologies and their rapid development can greatly support and significantly improve Human–Computer Interaction, defining new communication methods for a fast and direct user experience. One very promising technology nowadays is eye tracking. The main contribution of our research is to propose a gaze-based intelligent textbook manager that will support complete document analysis. Our solution includes tools providing translation support (single or entire sentences), keywords annotations, and the creation of document summaries, that contain all the areas that the user has read, with their time and words of major interest.
Development of Wetland-Based Pop-Up Book to Improve English Vocabulary and Numeracy Literacy Skill in Kindergarten

This development research was motivated by children's low numeracy literacy and not being taught English vocabulary from an early age. This research aims to develop a wetland-based pop-up book to improve English vocabulary skills and numeracy literacy in kindergarten that is valid, practical, and effective. The type of research used is development research. The model used is the ADDIE model. The data analysis techniques used are quantitative descriptive and qualitative descriptive analysis techniques. Quantitative descriptive analysis is used to analyze the results of questionnaire data obtained from the results of validation questionnaires, teacher and student response questionnaires, as well as student learning outcomes tests in the form of scores. Qualitative descriptive analysis is used to analyze comments or suggestions from expert validators, teachers and students in the form of descriptive data. The research results show (1) the pop-up book is valid based on the assessment of Indonesian language experts with an average score of 3.8, English with an average score of 4, mathematics with an average score of 4 and design with an average score of 4, (2) practical pop-ups based on teacher assessment with an average score of 3.8, and (3) pop-up books are effective for children to use. Thus, wetland-based pop-up books to improve English vocabulary skills and numeracy literacy in kindergarten can effectively improve English vocabulary and numeracy literacy skills in kindergarten.
Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning

Federated learning (FL) is a popular approach to facilitate privacy-aware machine learning since it allows multiple clients to collaboratively train a global model without granting others access to their private data. It is, however, known that FL can be vulnerable to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">membership inference attacks</i> (MIAs), where the training records of the global model can be distinguished from the testing records. Surprisingly, research focusing on the investigation of the source inference problem appears to be lacking. We also observe that identifying a training record's source client can result in privacy breaches extending beyond MIAs. For example, consider an FL application where multiple hospitals jointly train a COVID-19 diagnosis model, membership inference attackers can identify the medical records that have been used for training, and any additional identification of the source hospital can result the patient from the particular hospital more prone to discrimination. Seeking to contribute to the literature gap, we take the first step to investigate source privacy in FL. Specifically, we propose a new inference attack (hereafter referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source inference attack</i> – SIA), designed to facilitate an honest-but-curious server to identify the training record's source client. The proposed SIAs leverage the Bayesian theorem to allow the server to implement the attack in a non-intrusive manner without deviating from the defined FL protocol. We then evaluate SIAs in three different FL frameworks to show that in existing FL frameworks, the clients sharing gradients, model parameters, or predictions on a public dataset will leak such source information to the server. We also conduct extensive experiments on various datasets to investigate the key factors in an SIA. The experimental results validate the efficacy of the proposed SIAs, e.g., an attack success rate of 67.1% (baseline 10%) can be achieved when the clients share model parameters with the server. Comprehensive ablation studies demonstrate that the success of an SIA is directly related to the overfitting of the local models.
The Aplication of E-Test On English Subject Using The Model View Controller

The problem that occurs in one of the high schools is that there is no online-based English exam system, and they still use paper for their assessments. This makes teachers unable to give exams to students if they do not attend school. The purpose of this research is to create an online exam system to facilitate exam administration using the Model View Controller (MVC) to assess online English exams. The analytical method uses the System Usability Scale (SUS), to collect data using an electronic test (e-Test).Unifield Modeling Language (UML), a visual modeling technique used in designing and building object-oriented software for E-Test applications.The results showed that the use of the E-test application in a comparison class in English subjects could improve student learning outcomes to a higher level, from a score of 81.7 to 90.9. Compared to the control class, there was a slight increase, namely an increase in value from 71.5 to 75.8. This proves that the E-test application is used effectively by students for English subjects.
Computing Challenges in Unlocking the Secrets of the Universe

overdriving the Tier-0 infrastructure during data collection with recovery when the beam is off. In addition to the larger number of triggered events, there will be pressure in the first year to collect and analyze more complete data formats as the summarized formats mature. The large event formats impact the required storage, bandwidth, and processing capacity across all the computing centers. While the understanding of the detector and the event selections is being improved, there will likely be a larger number of reconstruction passes and skims performed by both central operations and individual users. We discuss how these additional stresses impact the allocation of resources and the changes from the baseline computing model.
SMONAC: Supervised Multiobjective Negative Actor–Critic for Sequential Recommendation

Recent research shows that the sole accuracy metric may lead to the homogeneous and repetitive recommendations for users and affect the long-term user engagement. Multiobjective reinforcement learning (RL) is a promising method to achieve a good balance in multiple objectives, including accuracy, diversity, and novelty. However, it has two deficiencies: neglecting the updating of negative action <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> values and limited regulation from the RL Q-networks to the (self-)supervised learning recommendation network. To address these disadvantages, we develop the supervised multiobjective negative actor–critic (SMONAC) algorithm, which includes a negative action update mechanism and multiobjective actor–critic mechanism. For the negative action update mechanism, several negative actions are randomly sampled during each time updating, and then, the offline RL approach is utilized to learn their <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> values. For the multiobjective actor–critic mechanism, accuracy, diversity, and novelty <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> values are integrated into the scalarized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> value, which is used to criticize the supervised learning recommendation network. The comparative experiments are conducted on two real-world datasets, and the results demonstrate that the developed SMONAC achieves tremendous performance promotion, especially for the metrics of diversity and novelty.
Cryptographic Primitives in Privacy-Preserving Machine Learning: A Survey

Advances in machine learning have enabled a broad range of complex applications, such as image recognition, recommendation system and machine translation. Data plays an important role in our increasingly complex and diverse environments, and this also reinforces the importance of data privacy in machine learning-enabled applications. Although there are a number of literature survey articles on machine learning, only a few studies have investigated the cryptographic primitives used in privacy-preserving machine learning (PPML). In other words, there is no, or limited, systematization of knowledge (SoK) that provides a comprehensive introduction to cryptography that have been deployed in PPML. In this paper, we firstly introduce some basic concepts such as machine learning tasks and processes. Then, we review and systematize the cryptographic primitives used in PPML. We analyze these existing privacy-preserving schemes in their learning process, especially training and inference. Finally, we conclude our survey and provide an outlook on future trends and research directions in the field.