Reviewerly/exHarmony huggingface dataset:
Maskomaly:Zero-Shot Mask Anomaly Segmentation

We present a simple and practical framework for anomaly segmentation called Maskomaly. It builds upon mask-based standard semantic segmentation networks by adding a simple inference-time post-processing step which leverages the raw mask outputs of such networks. Maskomaly does not require additional training and only adds a small computational overhead to inference. Most importantly, it does not require anomalous data at training. We show top results for our method on SMIYC, RoadAnomaly, and StreetHazards. On the most central benchmark, SMIYC, Maskomaly outperforms all directly comparable approaches. Further, we introduce a novel metric that benefits the development of robust anomaly segmentation methods and demonstrate its informativeness on RoadAnomaly.
An Empirical Comparison of LM-based Question and Answer Generation Methods

Question and answer generation (QAG) consists of generating a set of question-answer pairs given a context (e.g. a paragraph). This task has a variety of applications, such as data augmentation for question answering (QA) models, information retrieval and education. In this paper, we establish baselines with three different QAG methodologies that leverage sequence-to-sequence language model (LM) fine-tuning. Experiments show that an end-to-end QAG model, which is computationally light at both training and inference times, is generally robust and outperforms other more convoluted approaches. However, there are differences depending on the underlying generative LM. Finally, our analysis shows that QA models fine-tuned solely on generated question-answer pairs can be competitive when compared to supervised QA models trained on human-labeled data.
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks

We study the phenomenon of \textit{in-context learning} (ICL) exhibited by large language models, where they can adapt to a new learning task, given a handful of labeled examples, without any explicit parameter optimization. Our goal is to explain how a pre-trained transformer model is able to perform ICL under reasonable assumptions on the pre-training process and the downstream tasks. We posit a mechanism whereby a transformer can achieve the following: (a) receive an i.i.d. sequence of examples which have been converted into a prompt using potentially-ambiguous delimiters, (b) correctly segment the prompt into examples and labels, (c) infer from the data a \textit{sparse linear regressor} hypothesis, and finally (d) apply this hypothesis on the given test example and return a predicted label. We establish that this entire procedure is implementable using the transformer mechanism, and we give sample complexity guarantees for this learning framework. Our empirical findings validate the challenge of segmentation, and we show a correspondence between our posited mechanisms and observed attention maps for step (c).
Extremely weakly-supervised blood vessel segmentation with physiologically based synthesis and domain adaptation

Accurate analysis and modeling of renal functions require a precise segmentation of the renal blood vessels. Micro-CT scans provide image data at higher resolutions, making more small vessels near the renal cortex visible. Although deep-learning-based methods have shown state-of-the-art performance in automatic blood vessel segmentations, they require a large amount of labeled training data. However, voxel-wise labeling in micro-CT scans is extremely time-consuming given the huge volume sizes. To mitigate the problem, we simulate synthetic renal vascular trees physiologically while generating corresponding scans of the simulated trees by training a generative model on unlabeled scans. This enables the generative model to learn the mapping implicitly without the need for explicit functions to emulate the image acquisition process. We further propose an additional segmentation branch over the generative model trained on the generated scans. We demonstrate that the model can directly segment blood vessels on real scans and validate our method on both 3D micro-CT scans of rat kidneys and a proof-of-concept experiment on 2D retinal images. Code and 3D results are available at https://github.com/miccai2023anony/RenalVesselSeg
Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model

Large language models (LLMs) have made significant advancements in natural language processing (NLP). Broad corpora capture diverse patterns but can introduce irrelevance, while focused corpora enhance reliability by reducing misleading information. Training LLMs on focused corpora poses computational challenges. An alternative approach is to use a retrieval-augmentation (RetA) method tested in a specific domain. To evaluate LLM performance, OpenAI's GPT-3, GPT-4, Bing's Prometheus, and a custom RetA model were compared using 19 questions on diffuse large B-cell lymphoma (DLBCL) disease. Eight independent reviewers assessed responses based on accuracy, relevance, and readability (rated 1-3). The RetA model performed best in accuracy (12/19 3-point scores, total=47) and relevance (13/19, 50), followed by GPT-4 (8/19, 43; 11/19, 49). GPT-4 received the highest readability scores (17/19, 55), followed by GPT-3 (15/19, 53) and the RetA model (11/19, 47). Prometheus underperformed in accuracy (34), relevance (32), and readability (38). Both GPT-3.5 and GPT-4 had more hallucinations in all 19 responses compared to the RetA model and Prometheus. Hallucinations were mostly associated with non-existent references or fabricated efficacy data. These findings suggest that RetA models, supplemented with domain-specific corpora, may outperform general-purpose LLMs in accuracy and relevance within specific domains. However, this evaluation was limited to specific questions and metrics and may not capture challenges in semantic search and other NLP tasks. Further research will explore different LLM architectures, RetA methodologies, and evaluation methods to assess strengths and limitations more comprehensively.
Characterizing and Measuring Linguistic Dataset Drift

NLP models often degrade in performance when real world data distributions differ markedly from training data. However, existing dataset drift metrics in NLP have generally not considered specific dimensions of linguistic drift that affect model performance, and they have not been validated in their ability to predict model performance at the individual example level, where such metrics are often used in practice. In this paper, we propose three dimensions of linguistic dataset drift: vocabulary, structural, and semantic drift. These dimensions correspond to content word frequency divergences, syntactic divergences, and meaning changes not captured by word frequencies (e.g. lexical semantic change). We propose interpretable metrics for all three drift dimensions, and we modify past performance prediction methods to predict model performance at both the example and dataset level for English sentiment classification and natural language inference. We find that our drift metrics are more effective than previous metrics at predicting out-of-domain model accuracies (mean 16.8% root mean square error decrease), particularly when compared to popular fine-tuned embedding distances (mean 47.7% error decrease). Fine-tuned embedding distances are much more effective at ranking individual examples by expected performance, but decomposing into vocabulary, structural, and semantic drift produces the best example rankings of all considered model-agnostic drift metrics (mean 6.7% ROC AUC increase).
Manifold Regularization for Memory-Efficient Training of Deep Neural Networks

One of the prevailing trends in the machine- and deep-learning community is to gravitate towards the use of increasingly larger models in order to keep pushing the state-of-the-art performance envelope. This tendency makes access to the associated technologies more difficult for the average practitioner and runs contrary to the desire to democratize knowledge production in the field. In this paper, we propose a framework for achieving improved memory efficiency in the process of learning traditional neural networks by leveraging inductive-bias-driven network design principles and layer-wise manifold-oriented regularization objectives. Use of the framework results in improved absolute performance and empirical generalization error relative to traditional learning techniques. We provide empirical validation of the framework, including qualitative and quantitative evidence of its effectiveness on two standard image datasets, namely CIFAR-10 and CIFAR-100. The proposed framework can be seamlessly combined with existing network compression methods for further memory savings.
VALIDITAS E-MODUL INTERAKTIF BERBASIS CANVA MATERI HIMPUNAN DI PRODI PGSD UNIVERSITAS SULAWESI BARAT

The study aimed to determine the validity of a Canva-based interactive e-module. The background of this research was because there were no e-modules available in study programs that can lead to interaction between students and e-modules. This research was conducted to produce a valid e-module. This research was development research using a 4-D model consisting of defining, designing, developing, and disseminating. The e-module was validated by three validators from different campuses, namely the University of Muhammadiyah Makassar, the University of Papua, and the University of West Sulawesi. Validators from the Makassar Muhammadiyah University and West Sulawesi University were chosen because they have an educational background, namely mathematics education so that it fitted with the e-module content, while the validator from the Papua University was chosen because the validator has experience in making modules. Based on the results of the assessment of the three validators, the average result for all aspects of the e-module was in the very valid category with a score of 3.5. For each of these aspects, the e-module format aspect obtained an average of 3.6 with a very valid category, the content aspect obtained an average of 3.7 with a very valid category, the language aspect obtained an average of 3.2 with a valid category, and the benefit aspect obtained an average of 3.4 with a valid category. So it could be concluded that the e-module was valid from all aspects of the assessment.
PENINGKATAN HASIL BELAJAR IPAS MELALUI MEDIA CHROMEBOOK

The progress of teaching and science is growing faster and faster affecting all aspects of human life, including in the domain of teaching. In the current 4.0 era, the realm of education must adjust to the development of technological advances that make learning more innovative and fun. In this case, teachers have an important role in terms of creating development, thinking for the use of innovation in learning. Progress and part of innovation in teaching has been so visible, that the use of tools, educational equipment, and teaching in schools began to be adapted to advances and technological developments. With the development of interactive learning patterns enabled by technology, learning can be enriched, made more enjoyable, and enhanced for students. Students discover new environments and experiences with media such as Chromebooks software activities. In order to develop and improve classroom learning practices with higher quality for students, this study uses a type of classroom action research using quantitative methods Based on the discussion and research results, it can be concluded that through chromebook media as a tool for finding information, additional knowledge, innovation, games, deepening learning materials and evaluation for grade IV students of SDN Tanjungrejo 01 can improve learning outcomes Natural and Social Sciences. In the implementation of learning, teachers should use the latest media as an effort to utilize adequate facilities and infrastructure and teachers should always reflect on learning so that learning is not monotonous and continues to develop following the times.
PENERAPAN MODEL PROBLEM BASED LEARNING BERBANTUAN MEDIA PAPAN KERAGAMAN BUDAYA INDONESIA TERHADAP HASIL BELAJAR IPAS MATERI KERAGAMAN BUDAYA KELAS IV SD NEGERI GABUSBANARAN JOMBANG

The conclusion obtained by the researcher is that in learning that only relies on explanations from the teacher it will make the learning atmosphere less attractive to students. So it is hoped that teaching and learning activities will provide creative and innovative learning media and facilities so that students' interest in learning increases and children's curiosity becomes strong. One of them is by introducing the cultural board concrete learning media. In this study the advice that can be given is that schools should provide facilities to help students and teachers in channeling media innovations in learning, by providing space to develop learning media independently or with media that already has advanced technology because it is in accordance with the independent curriculum where learning is centered for students, teachers are given facilities to broaden their knowledge with knowledge to make the learning atmosphere in the classroom more active and improve learning outcomes, for researchers it is hoped that they can continue this research even better with the same goal in order to get a deeper understanding. From this study the low ability of students' understanding in understanding cultural diversity material was due to the lack of teachers practicing interesting learning models. Grade IV students at SDN Gabusbanaran are the subject of research, observation and documentation are used as research evidence. The success of obtaining the criterion indicator was 85% with a minimum completeness score of 70. A total of 20 children took two tests, namely the pretest and posttest. This test is used as a reference for the success of learning outcomes that have been carried out before introducing learning media and after introducing learning media. The results obtained by children's learning outcomes increased with an average of 87.5 without anyone getting a score below the minimum value.
PENINGKATAN KETRAMPILAN BERPIKIR KREATIF DALAM MEMBUAT PANTUN MENGGUNAKAN MODEL PROJECT BASED LEARNING

Creative thinking skills for elementary school students still cannot be realized optimally. Creative thinking skills must be mastered because they can be useful for students in solving problems in life. Thus, a learning model is needed that can explore the creative thinking potential of elementary school students. This research was conducted to know the level of effectiveness of the Project Based Learning (PjBL) model on poetry writing skills. This research was conducted in two cycles with 2 meetings in each cycle. The subjects of this study were students of Class VA at SDN 2 Junrejo, Batu City, which totaled 20 students. Data was collected through the teacher's observation sheet. The results of this study state that the application of the Project Based Learning (PjBL) model can significantly improve students' creative thinking skills and can encourage students to be more independent, especially in writing pantun literature.
PENINGKATAN KETERAMPILAN MENYIMAK SISWA KELAS IV SD MUHAMMADIYAH 4 KOTA BATU MELALUI MODEL PBL PADA MATERI CERITA RAKYAT

&#x0D; Listening skills are one of the most important things that must be considered because listening skills are one of the guidelines for students' language skills. In fact, listening skills, especially in listening to students on folklore material at Muhammadiyah 4 Elementary School, Batu City, are still lacking. As a result, students have low listening skills so efforts need to be made to improve this ability. This classroom action research aims to improve students' listening skills through the application of the Problem-Based Learning (PBL) model. This research was conducted through four stages, namely observation, planning, action, and reflection. This research was carried out in two cycles, each cycle consisting of two meetings. This research was conducted in the Muhammadiyah 4 Elementary School class, Batu City, which consisted of 30 students who were selected by purposive sampling. Meanwhile, this study uses qualitative data analysis techniques. As for the qualitative data analysis technique, namely data reduction in the form of exposure to data obtained from data from observations, interviews, and tests. Then, through the data the researcher draws conclusions based on individual and classical completeness. The results showed that the implementation of the Problem-Based Learning (PBL) model at SD Muhammadiyah 4 Kota Batu has increased. This is evidenced in the completeness of students in cycle I by 70% and student completeness in cycle II increased to 92%. This shows that there is an increase in listening skills from cycle I to cycle II. Based on this, it can be concluded that the Problem-Based Learning (PBL) model can be used to increase knowledge and insight in choosing an effective learning model to improve language skills, especially listening skills.
Development of an ethnobiology supplement book based on the study of medicinal plants in Wonawa District, Kepulauan Yapen Serui Regency

This study aims to produce a supplement book for ethnobiology courses based on research on traditional medicinal plants in Wonawa District, Kepulauan Yapen Serui Regency. The type of research is research and development. The ADD (analysis, design, and development) stage of the ADDIE model is applied to the development of supplement books. Interviews are used for data collection and identification of medicinal plants. Experts validate books made while students fill out response questionnaires. The validity criteria for supplement books with a value of 88.63% based on the findings of the research are Very Valid. In the Excellent category are responses to supplement books from students with an average rate of 87.5%. The developed supplement book can be used for student learning because it fulfills the valid criteria and the response has been overwhelmingly positive.
Community Participation In Fire Disaster Mitigation In The City Of Banjarmasin

Very heterogeneous community conditions where urban environmental conditions cause areas to become congested and result in environmental conditions that tend to be slums. City environmental conditions, housing, factories and so on must have good access, in order to avoid hazards that will occur such as the danger of residential fires. The development paradigm that exists in Banjarmasin City, the government's role as a provider of public facilities has changed slightly with the community's efforts to participate in assisting the city government in the community-based fire disaster mitigation process. The purpose of this study was to find out how the form of community participation in mitigating fires in the Malay Village, Central Banjarmasin District, Banjarmasin City. This type of research is a qualitative descriptive research. Data collection techniques in this study, researchers used documentation study techniques, guided interviews and direct observation from the field. The data analysis technique that will be used is descriptive qualitative method. The results of the study show that the presence of the self-help Fire Brigade (BPK) has become the identity of the city both at the national and international levels, as a city of a thousand rivers and a city of a thousand firefighters. Community participation is quite diverse and very innovative in reducing the risk of residential fire disasters in Malay sub-districts. Interestingly, the presence of the self-help Fire Brigade (BPK) is unpaid, the members on duty do not get a salary in carrying out this humanitarian task, their participation is sincere and voluntary
A review of UAV Visual Detection and Tracking Methods

This paper presents a review of techniques used for the detection and tracking of UAVs or drones. There are different techniques that depend on collecting measurements of the position, velocity, and image of the UAV and then using them in detection and tracking. Hybrid detection techniques are also presented. The paper is a quick reference for a wide spectrum of methods that are used in the drone detection process.
Do GPTs Produce Less Literal Translations?

Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models. In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics. We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions.
Analisis Sistem Manajemen Keamanan Di Perusahaan Tokopedia Dalam Menigkatkan Proteksi Data Dan Privasi Pengguna

With the increasing use of information technology in various fields, including e-commerce, ensuring user data and privacy protection is crucial for companies like Tokopedia, one of the largest e-commerce companies in Indonesia. This research aims to identify Tokopedia's security management system and its challenges in protecting user data and privacy. The study utilizes a literature review methodology, analyzing various documents, books, and journals related to Tokopedia's security management system. The research objectives are to understand the system's effectiveness, challenges, and Tokopedia's efforts to improve data and privacy protection. Tokopedia has prioritized information security since its establishment, implementing tight security measures, and continuously improving them. Tokopedia Shield, a security feature introduced in 2015, has significantly reduced fraud risk in the platform. Additionally, the company has taken several steps to secure user data, such as data encryption, access restriction, and password quality enhancement. Tokopedia continuously develops its security system, incorporating the latest technologies such as Artificial Intelligence (AI) and Big Data Analytics to detect potential threats early and improve the security system's responsiveness.