Reviewerly/exHarmony huggingface dataset:
SEPT: Towards Scalable and Efficient Visual Pre-training

Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing the scale of unlabeled pre-training data in real-world scenarios requires prohibitive computational costs and faces the challenge of uncurated samples. To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains. Buttressed by the hypothesis, we propose the first yet novel framework for Scalable and Efficient visual Pre-Training (SEPT) by introducing a retrieval pipeline for data selection. SEPT first leverage a self-supervised pre-trained model to extract the features of the entire unlabeled dataset for retrieval pipeline initialization. Then, for a specific target task, SEPT retrievals the most similar samples from the unlabeled dataset based on feature similarity for each target instance for pre-training. Finally, SEPT pre-trains the target model with the selected unlabeled samples in a self-supervised manner for target data finetuning. By decoupling the scale of pre-training and available upstream data for a target task, SEPT achieves high scalability of the upstream dataset and high efficiency of pre-training, resulting in high model architecture flexibility. Results on various downstream tasks demonstrate that SEPT can achieve competitive or even better performance compared with ImageNet pre-training while reducing the size of training samples by one magnitude without resorting to any extra annotations.
DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding

In this paper, we study the problem of visual grounding by considering both phrase extraction and grounding (PEG). In contrast to the previous phrase-known-at-test setting, PEG requires a model to extract phrases from text and locate objects from image simultaneously, which is a more practical setting in real applications. As phrase extraction can be regarded as a 1D text segmentation problem, we formulate PEG as a dual detection problem and propose a novel DQ-DETR model, which introduces dual queries to probe different features from image and text for object prediction and phrase mask prediction. Each pair of dual queries are designed to have shared positional parts but different content parts. Such a design effectively alleviates the difficulty of modality alignment between image and text (in contrast to a single query design) and empowers Transformer decoder to leverage phrase mask-guided attention to improve the performance. To evaluate the performance of PEG, we also propose a new metric CMAP (cross-modal average precision), analogous to the AP metric in object detection. The new metric overcomes the ambiguity of Recall@1 in many-box-to-one-phrase cases in phrase grounding. As a result, our PEG pre-trained DQ-DETR establishes new state-of-the-art results on all visual grounding benchmarks with a ResNet-101 backbone. For example, it achieves 91.04% and 83.51% in terms of recall rate on RefCOCO testA and testB with a ResNet-101 backbone.
Amodal Instance Segmentation via Prior-Guided Expansion

Amodal instance segmentation aims to infer the amodal mask, including both the visible part and occluded part of each object instance. Predicting the occluded parts is challenging. Existing methods often produce incomplete amodal boxes and amodal masks, probably due to lacking visual evidences to expand the boxes and masks. To this end, we propose a prior-guided expansion framework, which builds on a two-stage segmentation model (i.e., Mask R-CNN) and performs box-level (resp., pixel-level) expansion for amodal box (resp., mask) prediction, by retrieving regression (resp., flow) transformations from a memory bank of expansion prior. We conduct extensive experiments on KINS, D2SA, and COCOA cls datasets, which show the effectiveness of our method.
Improving Long-Horizon Imitation through Instruction Prediction

Complex, long-horizon planning and its combinatorial nature pose steep challenges for learning-based agents. Difficulties in such settings are exacerbated in low data regimes where over-fitting stifles generalization and compounding errors hurt accuracy. In this work, we explore the use of an often unused source of auxiliary supervision: language. Inspired by recent advances in transformer-based models, we train agents with an instruction prediction loss that encourages learning temporally extended representations that operate at a high level of abstraction. Concretely, we demonstrate that instruction modeling significantly improves performance in planning environments when training with a limited number of demonstrations on the BabyAI and Crafter benchmarks. In further analysis we find that instruction modeling is most important for tasks that require complex reasoning, while understandably offering smaller gains in environments that require simple plans. More details and code can be found at \url{https://github.com/jhejna/instruction-prediction}.
Parameter-Efficient Model Adaptation for Vision Transformers

In computer vision, it has achieved great transfer learning performance via adapting large-scale pretrained vision models (e.g., vision transformers) to downstream tasks. Common approaches for model adaptation either update all model parameters or leverage linear probes. In this paper, we aim to study parameter-efficient model adaptation strategies for vision transformers on the image classification task. We formulate efficient model adaptation as a subspace training problem and perform a comprehensive benchmarking over different efficient adaptation methods. We conduct an empirical study on each efficient model adaptation method focusing on its performance alongside parameter cost. Furthermore, we propose a parameter-efficient model adaptation framework, which first selects submodules by measuring local intrinsic dimensions and then projects them into subspace for further decomposition via a novel Kronecker Adaptation method. We analyze and compare our method with a diverse set of baseline model adaptation methods (including state-of-the-art methods for pretrained language models). Our method performs the best in terms of the tradeoff between accuracy and parameter efficiency across 20 datasets under the few-shot setting and 7 image classification datasets under the full-shot setting.
Neural Architecture Search for Wide Spectrum Adversarial Robustness

One major limitation of CNNs is that they are vulnerable to adversarial attacks. Currently, adversarial robustness in neural networks is commonly optimized with respect to a small pre-selected adversarial noise strength, causing them to have potentially limited performance when under attack by larger adversarial noises in real-world scenarios. In this research, we aim to find Neural Architectures that have improved robustness on a wide range of adversarial noise strengths through Neural Architecture Search. In detail, we propose a lightweight Adversarial Noise Estimator to reduce the high cost of generating adversarial noise with respect to different strengths. Besides, we construct an Efficient Wide Spectrum Searcher to reduce the cost of adjusting network architecture with the large adversarial validation set during the search. With the two components proposed, the number of adversarial noise strengths searched can be increased significantly while having a limited increase in search time. Extensive experiments on benchmark datasets such as CIFAR and ImageNet demonstrate that with a significantly richer search signal in robustness, our method can find architectures with improved overall robustness while having a limited impact on natural accuracy and around 40% reduction in search time compared with the naive approach of searching. Codes available at: https://github.com/zhicheng2T0/Wsr-NAS.git
Breast ultrasound diagnostic performance and outcomes for mass lesions in Dharma Yadnya general hospital in 2018-2023

Background: The latest report by the world health organization and international agency for research on cancer told that the incidence and mortality of breast cancer is the most among female cancer patients. Methods: This is descriptive study which uses medical record of 50 samples patients in Dharma Yadnya general hospital. This study include patients at least 25 years old who presented to the Dharma Yadnya general hospital between January 2019 to March 2023 which get breast ultrasound examination and pathology examination, benign or malignant mass. Results: In ultrasound findings, there are 43 (86%) people with benign cancer, 7 (14%) people with malignant cancer, 43 (86%) people with hipoechoic density and solid lession, 7 (14%) people with isoechoic density and cystic lession, 43 (86%) people with regular margin, 7 (14%) people with irregular margin, 43 (86%) people without lymph nodes axillary, 7 (14%) people with lymph nodes axillary, and by Doppler there are 43 (86%) people without neovascularitation inside lession and 7 (14%) people with neovascularitation inside lession. There are statistically significant relationship (p&lt;0.05) between diagnosis (benign and malignant) with age of patients, density, margin, existence of lymph nodes axillary, and neovascularitation inside the lession by doppler. Conclusions: Breast sonography is the modality of choice for further investigation of palpable breast findings that are not clearly benign and mammographic screen-detected abnormalities.
Evolutionary Approach for Building, Exploring and Recommending Complex Items With Application in Nutritional Interventions

Over the last few years, the ability of recommender systems to help us in different environments has been increasing. Several systems try to offer solutions in highly complex environments such as nutrition, housing, or traveling. In this paper, we present a recommendation system capable of using different input sources (data and knowledge-based) and producing a complex structured output. We have used an evolutionary approach to combine several unitary items within a flexible structure and have built an initial set of complex configurable items. Then, a content-based approach refines (in terms of preferences) these candidates to offer a final recommendation. We conclude with the application of this approach to the healthy diet recommendation problem, addressing its strengths in this domain.
The Role of Universitas Terbuka-Indonesia in Improving Higher Education Services in Indonesia

This study examines UT Indonesia's experience and learning in an effort to expand the quantity and improve the quality of higher education in Indonesia. The aim is to comprehensively analyze the role of UT Indonesia in providing higher education services, increasing the outreach of higher education, and quality assurance. This study uses literature studies to assess, critique, and synthesize literature on research topics that allow for the emergence of new theoretical frameworks and perspectives. The review process includes four phases: design, implementation, analysis, and finally writing a review. Studies show that UT Indonesia has a very important role in optimizing the field of higher education for the community. UT's excellence as a distance university is largely determined by the quality of service in the form of student satisfaction. In addition, with the characteristics of the distance education system, UT can be considered as a solution to various educational problems, especially those related to equity and democratization of education, as well as expanding access to quality education for all levels of society across time and space. Lastly, high-tech learning models and quality assurance systems can be implemented based on ICT. It is important to use a quality circle approach in developing DE courses and learning materials for students. UT has an important role in improving the accessibility and quality of ICT-based education in Indonesia because of its cost effectiveness and flexibility in the implementation of learning.
Probability Guided Loss for Long-Tailed Multi-Label Image Classification

Long-tailed learning has attracted increasing attention in very recent years. Long-tailed multi-label image classification is one subtask and remains challenging and poorly researched. In this paper, we provide a fresh perspective from probability to tackle this problem. More specifically, we find that existing cost-sensitive learning methods for long-tailed multi-label classification will affect the predicted probability of positive and negative labels in varying degrees during training, and different processes of probability will affect the final performance in turn. We thus propose a probability guided loss which contains two components to control this process. One is the probability re-balancing which can flexibly adjust the process of training probability. And the other is the adaptive probability-aware focal which can further reduce the probability gap between positive and negative labels. We conduct extensive experiments on two long-tailed multi-label image classification datasets: VOC-LT and COCO-LT. The results demonstrate the rationality and superiority of our strategy.
On Error and Compression Rates for Prototype Rules

We study the close interplay between error and compression in the non-parametric multiclass classification setting in terms of prototype learning rules. We focus in particular on a recently proposed compression-based learning rule termed OptiNet. Beyond its computational merits, this rule has been recently shown to be universally consistent in any metric instance space that admits a universally consistent rule---the first learning algorithm known to enjoy this property. However, its error and compression rates have been left open. Here we derive such rates in the case where instances reside in Euclidean space under commonly posed smoothness and tail conditions on the data distribution. We first show that OptiNet achieves non-trivial compression rates while enjoying near minimax-optimal error rates. We then proceed to study a novel general compression scheme for further compressing prototype rules that locally adapts to the noise level without sacrificing accuracy. Applying it to OptiNet, we show that under a geometric margin condition further gain in the compression rate is achieved. Experimental results comparing the performance of the various methods are presented.
Good Helper Is around You: Attention-Driven Masked Image Modeling

It has been witnessed that masked image modeling (MIM) has shown a huge potential in self-supervised learning in the past year. Benefiting from the universal backbone vision transformer, MIM learns self-supervised visual representations through masking a part of patches of the image while attempting to recover the missing pixels. Most previous works mask patches of the image randomly, which underutilizes the semantic information that is beneficial to visual representation learning. On the other hand, due to the large size of the backbone, most previous works have to spend much time on pre-training. In this paper, we propose Attention-driven Masking and Throwing Strategy (AMT), which could solve both problems above. We first leverage the self-attention mechanism to obtain the semantic information of the image during the training process automatically without using any supervised methods. Masking strategy can be guided by that information to mask areas selectively, which is helpful for representation learning. Moreover, a redundant patch throwing strategy is proposed, which makes learning more efficient. As a plug-and-play module for masked image modeling, AMT improves the linear probing accuracy of MAE by 2.9% ~ 5.9% on CIFAR-10/100, STL-10, Tiny ImageNet, and ImageNet-1K, and obtains an improved performance with respect to fine-tuning accuracy of MAE and SimMIM. Moreover, this design also achieves superior performance on downstream detection and segmentation tasks.
PatchNAS: Repairing DNNs in Deployment with Patched Network Architecture Search

Despite being widely deployed in safety-critical applications such as autonomous driving and health care, deep neural networks (DNNs) still suffer from non-negligible reliability issues. Numerous works had reported that DNNs were vulnerable to either natural environmental noises or man-made adversarial noises. How to repair DNNs in deployment with noisy samples is a crucial topic for the robustness of neural networks. While many network repairing methods based on data argumentation and weight adjustment have been proposed, they require retraining and redeploying the whole model, which causes high overhead and is infeasible for varying faulty cases on different deployment environments. In this paper, we propose a novel network repairing framework called PatchNAS from the architecture perspective, where we freeze the pretrained DNNs and introduce a small patch network to deal with failure samples at runtime. PatchNAS introduces a novel network instrumentation method to determine the faulty stage of the network structure given the collected failure samples. A small patch network structure is searched unsupervisedly using neural architecture search (NAS) technique with data samples from deployment environment. The patch network repairs the DNNs by correcting the output feature maps of the faulty stage, which helps to maintain network performance on normal samples and enhance robustness in noisy environments. Extensive experiments based on several DNNs across 15 types of natural noises show that the proposed PatchNAS outperforms the state-of-the-arts with significant performance improvement as well as much lower deployment overhead.
Ensemble-in-One: Ensemble Learning within Random Gated Networks for Enhanced Adversarial Robustness

Adversarial attacks have threatened modern deep learning systems by crafting adversarial examples with small perturbations to fool the convolutional neural networks (CNNs). To alleviate that, ensemble training methods are proposed to facilitate better adversarial robustness by diversifying the vulnerabilities among the sub-models, simultaneously maintaining comparable natural accuracy as standard training. Previous practices also demonstrate that enlarging the ensemble can improve the robustness. However, conventional ensemble methods are with poor scalability, owing to the rapidly increasing complexity when containing more sub-models in the ensemble. Moreover, it is usually infeasible to train or deploy an ensemble with substantial sub-models, owing to the tight hardware resource budget and latency requirement. In this work, we propose Ensemble-in-One (EIO), a simple but effective method to efficiently enlarge the ensemble with a random gated network (RGN). EIO augments a candidate model by replacing the parametrized layers with multi-path random gated blocks (RGBs) to construct an RGN. The scalability is significantly boosted because the number of paths exponentially increases with the RGN depth. Then by learning from the vulnerabilities of numerous other paths within the RGN, every path obtains better adversarial robustness. Our experiments demonstrate that EIO consistently outperforms previous ensemble training methods with smaller computational overheads, simultaneously achieving better accuracy-robustness trade-offs than adversarial training methods under black-box transfer attacks. Code is available at https://github.com/cai-y13/Ensemble-in-One.git
Sistem Informasi E-Commerce Rumah Batik Desa Lambur 1 Tanjung Jabung Timur

Rumah Batik is a sales shop that is engaged in making batik such as various types of batik typical of Tanjung Jabung Timur, batik basic fabrics and community handicrafts, at Rumah Batik has obstacles in marketing their wares, because currently Rumah Batik customers only come directly to visit. stores to purchase products, as well as product promotions only by word of mouth and brochures, at a distance from the house far from the store location. So an E-commerce information system was designed at the Batik House of Lambur 1 Tanjung Jabung Timur Village with the aim of making it easier to order products, increase sales, and reduce promotional costs. In this study using prototyping modeling, it is hoped that the results of this study make it easier for customers to make purchases, order and confirm payments. Make it easy for administrators to manage goods data, order data and product purchases. For the future, the analysis and design of the designed E-commerce system can be used at the Batik House of Lambur 1 Tanjung Jabung Timur Village.
The Use of Code-Switching and Code-Mixing in English Learning for Children:

The research aimed to determine the types of communication applied in an intercultural family. They had diverse nationalities, customs, values, and beliefs. The researchers viewed communication as the main issue that could occur in an intercultural family. One of the examples of an intercultural family was Aprillia, a Makassarese woman married to Shawn Mullins, a U.S Military. They caused code-switching and code-mixing, types of code-switching and code-mixing applied in the family, children’s communication process, and strategies to solve communication problems in the intercultural family. The research’s finding was gathered from April’s family using an in-depth interview. By applying a qualitative approach, the researchers found the type of code-switching, code-mixing, and communication processes applied in April’s family. Furthermore, the researchers discover that techniques for resolving communication problems and linguistic barriers include avoiding ethnocentrism, challenging prejudices, recognizing differences, adjusting communication, equality, and a balanced split. Consequently, the most effective technique for resolving communication issues within an intercultural family is to remember that every individual has a distinct perspective. To avoid miscommunication during a conversation, individuals must learn to adapt to the capacity of the person with whom they are communicating.
The Automatic Classification System for Academic Performance Evaluation at the Faculty of Information Technology Atma Jaya University of Makassar

Abstract - The Faculty of Information Technology currently carries out performance evaluations at the end of each semester and involves students as sources of data evaluation. The evaluation activity took place online on the website ss.fti.uajm.ac.id. With the number of active students, the number of evaluations that need to be read and the number read by faculty stakeholders also increases. This is inversely proportional to the time that stakeholders need time to read, evaluate, and categorize comments entered by students as part of the performance evaluation. In this study, a multi-classification of student comments related to evaluations at the Faculty of Information Technology UAJM will be carried out. Text pre-processing will use the Sastrawi library which includes stopword removal, stemming, and transformation of text into TFIDF form. The results of the pre-processing text will be used as input on Naive Bayes and using three scenarios to evaluate the classifier model. The average accuracy values of the Naive Bayes algorithm for category and sentiment labels are 79% and 81%, respectively. Furthermore, the expected result of this research is to reduce the time for FTI UAJM stakeholders to read and comment/suggest faster because the evaluation results are obtained in real-time.