Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF COOPERATIVE LEARNING MODELSPICTURE TYPE AND PICTUREHELPED MEDIA POSTERS TO THE RESULTS LEARNING CLASS VIII STUDENTS IN CHRISTIAN JUNIOR HIGH SCHOOL WAIBAKUL

Causes some students to be often tired in class, while others become docile, lack confidence, pay less attention, and be distracted by friends. The results of students' learning are impacted by this At Waibakul Christian Middle School, grade VIII students participated in this study to find out how the Cooperative Learning Model, which uses pictures and pictures typed out by media posters, affected their learning outcomes. Research of this kind is experimental research. Class VIII MIPA 1 served as the control class in this study whereas VIII MIPA 3 served as the experimental class. Using a quasi-experimental research design. 49 students made up the sample for this study, which employed non-probability sampling methods, namely the purposive sampling methodology. From pre- and post-testing, learning outcomes in the cognitive domain are determined. The statistical method used for the analysis is a test paired sample t-test. The study's findings showed that the experimental class's average value was 71 and the control class's was 55. It can also be deduced that the cooperative learning model, image and picture kinds, and poster medium supported by Waibakul Christian Middle School class VIII students' learning outcomes since test paired sample t-test earned value sig (2-tailed) 0.000 0.05. Keywords: Cooperative, Picture and Picture, Poster Media, Learning Outcomes, Learning ModelAbstrak Menyebabkan sebagian siswa sering lelah di dalam kelas, sedangkan sebagian lainnya menjadi penurut, kurang percaya diri, kurang perhatian, dan teralihkan perhatiannya oleh teman. Hasil belajar siswa dipengaruhi oleh hal tersebut Di SMP Kristen Waibakul, siswa kelas VIII berpartisipasi dalam penelitian ini untuk mengetahui bagaimana Model Pembelajaran Cooperative yang menggunakan gambar dan gambar yang diketik dengan media poster mempengaruhi hasil belajar mereka. Jenis penelitian ini adalah penelitian eksperimen. Kelas VIII MIPA 1 sebagai kelas kontrol dalam penelitian ini sedangkan VIII MIPA 3 sebagai kelas eksperimen. Menggunakan desain penelitian eksperimen semu. Sampel penelitian ini berjumlah 49 siswa yang menggunakan metode non-probability sampling yaitu metodologi purposive sampling. Dari pra-dan pasca-tes, hasil belajar dalam domain kognitif ditentukan. Metode statistik yang digunakan untuk analisis adalah uji paired sample t-test. Hasil penelitian menunjukkan bahwa nilai rata-rata kelas eksperimen adalah 71 dan kelas kontrol adalah 55. Dapat juga disimpulkan bahwa model pembelajaran kooperatif, jenis gambar dan gambar, dan media poster didukung oleh hasil belajar siswa kelas VIII SMP Kristen Waibakul. karena uji paired sample t-test diperoleh nilai sig (2-tailed) 0,000 0,05.
How It All Came Together: Building a Local App to Keep Track of the Digitization Workflow

Thanks to an Andrew W. Mellon Foundation grant, the General Archives of Puerto Rico started a mass digitization project in 2020. The goal was to establish a digitization center and implement FADGI guidelines. As the project developed and the volume of work grew, a fast and simple way to track the items through their different stages was needed. Although several software options were available, they required more resources than we had on hand at the time. Understanding our needs and goals, our team&#x2019;s IT technician built an app tailored to the project&#x2019;s requirements. In the past year, we have not only successfully kept track of the objects through the digitization workflow, but the app also proved effective for maintaining team communication, collecting technical metadata, and recording relationships between objects and their collections.
Improving Color Accuracy When Imaging Cultural Heritage Using a Bi-color LED Source

Cultural-heritage imaging is a critical aspect of the efforts to preserve world treasures. This field is so demanding of color accuracy that the inherent limitations of RGB imaging can often be an issue. Various imaging systems of increasing complexity have been proposed, up to and including those that report full spectral reflectance for each pixel. These systems improve color accuracy, but their complexity and slow operational speed hamper their widespread use in this field. A simpler and faster bi-color lighting and dual-RGB processing system is proposed that improves the color accuracy of profiling and verification targets. The system can be used with any off-the-shelf RGB camera, including prosumer models.
An Improved and Efficient Content-Based Medical Image Retrieval Technique vs State-of-Art Texture Feature Extraction Techniques

Areas in the health sector such as X-rays, Dermatology, High-resolution Computed Tomography – HRCT, Endoscopy, Radiology, Cardiology and Magnetic Resonance Imaging – MRI heavily depend on medical images for their activities hence it has become complex for managing and accessing these images from their repositories. As a matter of concern content-based medical image retrieval-CBMIR has been the system proposed by many researchers for handling access to similar medical image(s) as the input image, yet there is a caveat that has to be addressed with respect to which technique best suits CBMIR system with respect to a given performance metric. Medical images are mostly of grayscale and therefore color feature extraction techniques may not work effectively on them. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric, this work seeks to comparatively evaluate the performance of the following state-of-the-art texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and a Proposed Technique using the metrics; Precision, Recall, F1-score, Mean Squared Error (MSE), Accuracy and Time. The results showed that the proposed technique is best suited for systems focusing on precision with an average precision score of 100% over 10.5k of raw medical images (dataset) using an appreciable minimum time with time complexity of 0(n).
AI Powered Tools for Improving Usability in Digital Archiving

A software package and PoC to help usability in various cases like quality issues and metadata collecting after digitization is introduced. Here usability means the improvement of the process using AI based automation as much as possible and providing easy-to-use interfaces for the end user. This is done with the help of existing open source tools.
Ontology

The term ontology may be transliterated as ‘account of what there is.’ The ontology of a field of science comprises the basic elements and relations assumed to allow a complete analysis of its phenomena. For example, the Presocratics tried to explain nature based on an ontology of fire, water, air, and earth (Fairbanks 1898). Today, the ontology of physics is based on a space-time continuum, protons, electrons, neutrons, quarks, neutrinos, etc. (Dürr 2015).
Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising

Algorithm audits are powerful tools for studying black-box systems without direct knowledge of their inner workings. While very effective in examining technical components, the method stops short of a sociotechnical frame, which would also consider users themselves as an integral and dynamic part of the system. Addressing this limitation, we propose the concept of sociotechnical auditing: auditing methods that evaluate algorithmic systems at the sociotechnical level, focusing on the interplay between algorithms and users as each impacts the other. Just as algorithm audits probe an algorithm with varied inputs and observe outputs, a sociotechnical audit (STA) additionally probes users, exposing them to different algorithmic behavior and measuring their resulting attitudes and behaviors. As an example of this method, we develop Intervenr, a platform for conducting browser-based, longitudinal sociotechnical audits with consenting, compensated participants. Intervenr investigates the algorithmic content users encounter online, and also coordinates systematic client-side interventions to understand how users change in response. As a case study, we deploy Intervenr in a two-week sociotechnical audit of online advertising (N = 244) to investigate the central premise that personalized ad targeting is more effective on users. In the first week, we observe and collect all browser ads delivered to users, and in the second, we deploy an ablation-style intervention that disrupts normal targeting by randomly pairing participants and swapping all their ads. We collect user-oriented metrics (self-reported ad interest and feeling of representation) and advertiser-oriented metrics (ad views, clicks, and recognition) throughout, along with a total of over 500,000 ads. Our STA finds that targeted ads indeed perform better with users, but also that users begin to acclimate to different ads in only a week, casting doubt on the primacy of personalized ad targeting given the impact of repeated exposure. In comparison with other evaluation methods that only study technical components, or only experiment on users, sociotechnical audits evaluate sociotechnical systems through the interplay of their technical and human components.
Pengaruh Metode Probing-Prompting terhadap Hasil Belajar PKN Murid Kelas IV SDN 30 Sumpang Bita

Problems with this study were the students' lack of enthusiasm for the learning process and poor learning outcomes in citizenship. The main objective of this study was to determine the impact of probing methods on learning outcomes in Class IV SDN 30 Sumpang Bita. This research method uses a mixed method of collecting data using quantitative and qualitative data results. The study informant used his 15 subject sample and the study participant used her 14 simple random sample. Google Forms surveys are used as a quantitative research tool. Qualitative measures are observations, interviews and documentation. The qualitative data analysis used consisted of data reduction, data display and quantitative data analysis, i.e. validity test. The results showed that the probing prompt was effective, as it was already in the high category with an offer rate of 79%. Therefore, this model makes students more active during the learning process. Think lively, discuss lively, and create better communication between teachers and students. The conclusion of this study is that the probing-prompt method has a significant impact on improving student learning outcomes.
Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments

Cybersecurity increasingly relies on machine learning (ML) models to respond to and detect attacks. However, the rapidly changing data environment makes model life-cycle management after deployment essential. Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models. However, detecting drift in unsupervised environments can be challenging. This study introduces a novel approach leveraging Shapley additive explanations (SHAP), a widely recognized explainability technique in ML, to address drift detection in unsupervised settings. The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches. To validate the effectiveness of the proposed approach in a real-world scenario, we applied it to an environment designed to detect domain generation algorithms (DGAs). The dataset was obtained from various types of DGAs provided by NetLab. Based on this dataset composition, we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs. The results revealed that more than 90% of the drift data exceeded the threshold, demonstrating the high reliability of the approach to detect drift in an unsupervised environment. The proposed method distinguishes itself from existing approaches by employing explainable artificial intelligence (XAI)-based detection, which is not limited by model or system environment constraints. In conclusion, this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity. The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability. It is versatile and suitable for various real-time data analysis contexts beyond DGA detection environments. This study significantly contributes to the ML community by addressing the critical issue of managing ML models in real-world cybersecurity settings. Our approach is distinguishable from existing techniques by employing XAI-based detection, which is not limited by model or system environment constraints. As a result, our method can be applied in critical domains that require adaptation to continuous changes, such as cybersecurity. Through extensive validation across diverse settings beyond DGA detection environments, the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts. It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks.
Is Autoencoder Truly Applicable for 3d CT Super-Resolution?

Featured by a bottleneck structure, autoencoder (AE) and its variants have been largely applied in various medical image analysis tasks, such as segmentation, reconstruction and de-noising. Despite of their promising performances in aforementioned tasks, in this paper, we claim that AE models are not applicable to single image super-resolution (SISR) for 3D CT data. Our hypothesis is that the bottleneck architecture that resizes feature maps in AE models degrades the details of input images, thus can sabotage the performance of super-resolution. Although U-Net proposed skip connections that merge information from different levels, we claim that the degrading impact of feature resizing operations could hardly be removed by skip connections. By conducting large-scale ablation experiments and comparing the performance between models with and without the bottleneck design on a public CT lung dataset, we have discovered that AE models, including U-Net, have failed to achieve a compatible SISR result (p < 0.05 by Student’s t-test) compared to the baseline model. Our work is the first comparative study investigating the suitability of AE architecture for 3D CT SISR tasks and brings a rationale for researchers to re-think the choice of model architectures especially for 3D CT SISR tasks. The full implementation and trained models can be found at: https://github.com/Roldbach/Autoencoder-3D-CT-SISR
Deep Clustering Survival Machines with Interpretable Expert Distributions

We develop deep clustering survival machines to simultaneously predict survival information and characterize data heterogeneity that is not typically modeled by conventional survival analysis methods. By modeling timing information of survival data generatively with a mixture of parametric distributions, referred to as expert distributions, our method learns weights of the expert distributions for individual instances based on their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that our method is capable of obtaining promising clustering results and competitive time-to-event predicting performance.
Inter-Modal Conditional-Guided Fusion Network with Transformer for Grading Hepatocellular Carcinoma

Multimodal medical imaging plays an important role in the diagnosis and characterization of lesions. Transformer pays more attention to global relationship modeling in data, which has obtained promising performance in lesion characterization. However, there are still challenges in transformer-based multimodal feature fusion. First, simple concatenation of information from other modalities, global and local information within the modality cannot balance the importance of them, so it is necessary to consider how to adaptively fuse to optimize the feature extraction of the modality. Second, inter- and intra- modality information are complementary, which has been ignored by reported feature fusion methods. It is necessary to consider how to use the complementary inter-modal information to restrict the conditional learning of intra-modal information. In this work, we propose an inter-modal conditional-guided fusion network with Transformer (ICF-Former) to realize adaptive fusion of intra- and inter-modal information and intra-modal feature learning constrained by other modality joint condition information. The experimental results of the clinical hepatocellular carcinoma (HCC) dataset show that the proposed method is superior to the previously reported multimodal fusion methods for HCC grading.
Robust Federated Learning For Image Semantic Transmission Under Byzantine Attacks

Semantic communication provides an efficient solution for image transmission, leveraging the advancements in joint source and channel coding facilitated by deep neural networks (DNNs). To address the limited communication and computing resources in edge devices, federated learning (FL) has emerged as a promising approach for collaboratively training semantic encoder/decoder models of semantic communication systems. However, FL-enabled semantic communication systems are still susceptible to malicious attacks, such as raw data tampering and incorrect transmission of semantic models, which pose significant security and integrity risks. In this paper, we propose a robust FL framework for image semantic transmission (IST) to defend against Byzantine attacks, i.e., data and model poisoning attacks, enabling more robust model training with fewer user data fitting. In the proposed framework, we design an end-to-end IST model based on convolutional neural networks (CNNs) to process semantic information. Then, we investigate the impact of Byzantine attacks on image recognition tasks in semantic communication systems and propose an improved Weiszfeld algorithm for model aggregation at the edge server, effectively mitigating the effects of Byzantine attacks on the IST model training process. Simulation results show that the proposed robust FL framework can adaptively select reliable users for aggregation while rejecting adversarial and low-quality users, thereby ensuring the robustness of semantic communication systems for image transmission.
Immersive Hand Instructions in AR – Insights for Asynchronous Remote Collaboration on Spatio-Temporal Manual Tasks

Collaborative Virtual Environments (CVEs) have recently become more prominent in academic research and industrial applications. Physical tasks often rely on sharing spatial information between the collaborators, which are difficult to communicate verbally or by 2D media. Immersive technologies, like Mixed Reality (MR), can sup-port more natural and intuitive interaction between the collaborators, share important non-verbal cues, like gaze and deictic gestures, and improve the performance time of remote guidance tasks. In this work we present HoloHands - a concept for immersive hand instructions which can be used for asynchronous remote collaboration on spatial tasks. A Hololens device is utilized to capture hand motions and interactions with virtual representations of physical objects to create a guidance by simply demonstrating the instruction steps. The recordings can be consumed by other collaborators at a later time to perform the physical task which we validated in a preliminary user experiment. Since it allows the creation and retention of digital information for a later consumption, it provides the possibility to revisit previous work sessions at any time and thus opens new possi-bilities to collaborate asynchronously on spatio-temporal tasks. In this work we share valuable insights from our experience with the HoloHands concept and the utilization of the Hololens headset.
High-Throughput Microscopy Image Deblurring with Graph Reasoning Attention Network

High-quality (HQ) microscopy images afford more detailed information for modern life science research and quantitative image analyses. However, in practice, HQ microscopy images are not commonly available or suffer from blurring artifacts. Compared with natural images, such low-quality (LQ) microscopy ones often share some visual characteristics: more complex structures, less informative background, and repeating patterns. For natural image deblurring, deep convolutional neural networks (CNNs) achieve promising performance. But they usually suffer from large model sizes, heavy computation costs, or small throughput, which are critical for high-throughput microscopy image deblurring. To address those problems, we collect HQ electron microscopy and histology datasets and propose a graph reasoning attention network (GRAN). Specifically, we treat deep feature points as embedded visual components, build a graph describing the relationship between all pairs of visual components, and perform reasoning in the graph with a graph convolutional network. The reasoning results are then transferred as attention and residual learning is introduced to achieve graph reasoning attention block (GRAB). We conduct extensive experiments to demonstrate the effectiveness of our GRAN.
Block-Wise Partner Learning for Model Compression

Despite the great potential of convolutional neural networks (CNNs) in various tasks, the resource-hungry nature greatly hinders their wide deployment in cost-sensitive and low-powered scenarios, especially applications in remote sensing. Existing model pruning approaches, implemented by a "subtraction" operation, impose a performance ceiling on the slimmed model. Self-knowledge distillation (Self-KD) resorts to auxiliary networks that are only active in the training phase for performance improvement. However, the knowledge is holistic and crude, and the learning-based knowledge transfer is mediate and lossy. Here, we propose a novel model-compression method, termed block-wise partner learning (BPL), which comprises "extension" and "fusion" operations and liberates the compressed model from the bondage of baseline. Different from the Self-KD, the proposed BPL creates a partner for each block for performance enhancement in training. For the model to absorb more diverse information, a diversity loss (DL) is designed to evaluate the difference between the original block and the partner. Besides, the partner is fused equivalently instead of being discarded directly. After training, we can simply adopt the fused compressed model that contains the enhancement information of partners but with fewer parameters and less inference cost. As validated using the UC Merced land-use, NWPU-RESISC45, and RSD46-WHU datasets, the BPL demonstrates superiority over other compared model-compression approaches. For example, it attains a substantial floating-point operations (FLOPs) reduction of 73.97% with only 0.24 accuracy (ACC.) loss for ResNet-50 on the UC Merced land-use dataset. The code is available at https://github.com/zhangxin-xd/BPL.