Reviewerly/exHarmony huggingface dataset:
SegLoc: Visual Self-supervised Learning Scheme for Dense Prediction Tasks of Security Inspection X-ray Images

Lately, remarkable advancements of artificial intelligence have been attributed to the integration of self-supervised learning (SSL) scheme. Despite impressive achievements within natural language processing (NLP), SSL in computer vision has not been able to stay on track comparatively. Recently, integration of contrastive learning on top of existing visual SSL models has established considerable progress, thereby being able to outperform supervised counterparts. Nevertheless, the improvements were mostly limited to classification tasks; moreover, few studies have evaluated visual SSL models in real-world scenarios, while the majority considered datasets containing class-wise portrait images, notably ImageNet. Thus, here, we have considered dense prediction tasks on security inspection x-ray images to evaluate our proposed model Segmentation Localization (SegLoc). Based upon the model Instance Localization (InsLoc), our model has managed to address one of the most challenging downsides of contrastive learning, i.e., false negative pairs of query embeddings. To do so, our pre-training dataset is synthesized by cutting, transforming, then pasting labeled segments, as foregrounds, from an already existing labeled dataset (PIDray) onto instances, as backgrounds, of an unlabeled dataset (SIXray;) further, we fully harness the labels through integration of the notion, one queue per class, into MoCo-v2 memory bank, avoiding false negative pairs. Regarding the task in question, our approach has outperformed random initialization method by 3% to 6%, while having underperformed supervised initialization, in AR and AP metrics at different IoU values for 20 to 30 pre-training epochs.
Visual Attention-Prompted Prediction and Learning

Explanation(attention)-guided learning is a method that enhances a model's predictive power by incorporating human understanding during the training phase. While attention-guided learning has shown promising results, it often involves time-consuming and computationally expensive model retraining. To address this issue, we introduce the attention-prompted prediction technique, which enables direct prediction guided by the attention prompt without the need for model retraining. However, this approach presents several challenges, including: 1) How to incorporate the visual attention prompt into the model's decision-making process and leverage it for future predictions even in the absence of a prompt? and 2) How to handle the incomplete information from the visual attention prompt? To tackle these challenges, we propose a novel framework called Visual Attention-Prompted Prediction and Learning, which seamlessly integrates visual attention prompts into the model's decision-making process and adapts to images both with and without attention prompts for prediction. To address the incomplete information of the visual attention prompt, we introduce a perturbation-based attention map modification method. Additionally, we propose an optimization-based mask aggregation method with a new weight learning function for adaptive perturbed annotation aggregation in the attention map modification process. Our overall framework is designed to learn in an attention-prompt guided multi-task manner to enhance future predictions even for samples without attention prompts and trained in an alternating manner for better convergence. Extensive experiments conducted on two datasets demonstrate the effectiveness of our proposed framework in enhancing predictions for samples, both with and without provided prompts.
Jailbreaking Black Box Large Language Models in Twenty Queries

There is growing interest in ensuring that large language models (LLMs) align with human values. However, the alignment of such models is vulnerable to adversarial jailbreaks, which coax LLMs into overriding their safety guardrails. The identification of these vulnerabilities is therefore instrumental in understanding inherent weaknesses and preventing future misuse. To this end, we propose Prompt Automatic Iterative Refinement (PAIR), an algorithm that generates semantic jailbreaks with only black-box access to an LLM. PAIR -- which is inspired by social engineering attacks -- uses an attacker LLM to automatically generate jailbreaks for a separate targeted LLM without human intervention. In this way, the attacker LLM iteratively queries the target LLM to update and refine a candidate jailbreak. Empirically, PAIR often requires fewer than twenty queries to produce a jailbreak, which is orders of magnitude more efficient than existing algorithms. PAIR also achieves competitive jailbreaking success rates and transferability on open and closed-source LLMs, including GPT-3.5/4, Vicuna, and PaLM-2.
Identifying reducible k-tuples of vectors with subspace-proximity sensitive hashing/filtering

We introduce and analyse a family of hash and predicate functions that are more likely to produce collisions for small reducible configurations of vectors. These may offer practical improvements to lattice sieving for short vectors. In particular, in one asymptotic regime the family exhibits significantly different convergent behaviour than existing hash functions and predicates.
Machine Learning Assisted Cognitive Construction of a Shallow Depth Dynamic Ansatz for Noisy Quantum Hardware

The development of various dynamic ansatz-constructing techniques has ushered in a new era, rendering the practical exploitation of Noisy Intermediate-Scale Quantum (NISQ) hardware for molecular simulations increasingly viable. However, they exhibit substantial measurement costs during their execution. This work involves the development of a novel protocol that capitalizes on regenerative machine learning methodologies and many-body perturbation theoretic measures to construct a highly expressive and shallow ansatz within the variational quantum eigensolver (VQE) framework. The machine learning methodology is trained with the basis vectors of a low-rank expansion of the N-electron Hilbert space to identify the dominant high-rank excited determinants without requiring a large number of quantum measurements. These selected excited determinants are iteratively incorporated within the ansatz through their low-rank decomposition. The reduction in the number of quantum measurements and ansatz depth manifests in the robustness of our method towards hardware noise, as demonstrated through numerical applications. Furthermore, the proposed method is highly compatible with state-of-the-art neural error mitigation techniques. This approach significantly enhances the feasibility of quantum simulations in molecular systems, paving the way for impactful advancements in quantum computational chemistry.
A Survey of Heterogeneous Transfer Learning

The application of transfer learning, an approach utilizing knowledge from a source domain to enhance model performance in a target domain, has seen a tremendous rise in recent years, underpinning many real-world scenarios. The key to its success lies in the shared common knowledge between the domains, a prerequisite in most transfer learning methodologies. These methods typically presuppose identical feature spaces and label spaces in both domains, known as homogeneous transfer learning, which, however, is not always a practical assumption. Oftentimes, the source and target domains vary in feature spaces, data distributions, and label spaces, making it challenging or costly to secure source domain data with identical feature and label spaces as the target domain. Arbitrary elimination of these differences is not always feasible or optimal. Thus, heterogeneous transfer learning, acknowledging and dealing with such disparities, has emerged as a promising approach for a variety of tasks. Despite the existence of a survey in 2017 on this topic, the fast-paced advances post-2017 necessitate an updated, in-depth review. We therefore present a comprehensive survey of recent developments in heterogeneous transfer learning methods, offering a systematic guide for future research. Our paper reviews methodologies for diverse learning scenarios, discusses the limitations of current studies, and covers various application contexts, including Natural Language Processing, Computer Vision, Multimodality, and Biomedicine, to foster a deeper understanding and spur future research.
Cross-Episodic Curriculum for Transformer Agents

We present a new algorithm, Cross-Episodic Curriculum (CEC), to boost the learning efficiency and generalization of Transformer agents. Central to CEC is the placement of cross-episodic experiences into a Transformer's context, which forms the basis of a curriculum. By sequentially structuring online learning trials and mixed-quality demonstrations, CEC constructs curricula that encapsulate learning progression and proficiency increase across episodes. Such synergy combined with the potent pattern recognition capabilities of Transformer models delivers a powerful cross-episodic attention mechanism. The effectiveness of CEC is demonstrated under two representative scenarios: one involving multi-task reinforcement learning with discrete control, such as in DeepMind Lab, where the curriculum captures the learning progression in both individual and progressively complex settings; and the other involving imitation learning with mixed-quality data for continuous control, as seen in RoboMimic, where the curriculum captures the improvement in demonstrators' expertise. In all instances, policies resulting from CEC exhibit superior performance and strong generalization. Code is open-sourced at https://cec-agent.github.io/ to facilitate research on Transformer agent learning.
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias

It is desirable for policies to optimistically explore new states and behaviors during online reinforcement learning (RL) or fine-tuning, especially when prior offline data does not provide enough state coverage. However, exploration bonuses can bias the learned policy, and our experiments find that naive, yet standard use of such bonuses can fail to recover a performant policy. Concurrently, pessimistic training in offline RL has enabled recovery of performant policies from static datasets. Can we leverage offline RL to recover better policies from online interaction? We make a simple observation that a policy can be trained from scratch on all interaction data with pessimistic objectives, thereby decoupling the policies used for data collection and for evaluation. Specifically, we propose offline retraining, a policy extraction step at the end of online fine-tuning in our Offline-to-Online-to-Offline (OOO) framework for reinforcement learning (RL). An optimistic (exploration) policy is used to interact with the environment, and a separate pessimistic (exploitation) policy is trained on all the observed data for evaluation. Such decoupling can reduce any bias from online interaction (intrinsic rewards, primacy bias) in the evaluation policy, and can allow more exploratory behaviors during online interaction which in turn can generate better data for exploitation. OOO is complementary to several offline-to-online RL and online RL methods, and improves their average performance by 14% to 26% in our fine-tuning experiments, achieves state-of-the-art performance on several environments in the D4RL benchmarks, and improves online RL performance by 165% on two OpenAI gym environments. Further, OOO can enable fine-tuning from incomplete offline datasets where prior methods can fail to recover a performant policy. Implementation: https://github.com/MaxSobolMark/OOO
Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

Breast cancer is the most common cancer in Indonesia.One way to detect it early is by screening using mammography.Previous trials showed that mammography screening in women aged 40-49 years could reduce breast cancer mortality by 25%.However, misdiagnosis may occur abaout breast density and the patient's physical size due to machines.In addition, human reader errors can occur concerning the reader's experience and perception.Therefore, diagnostic aids are needed to distinguish benign and malignant cases and receive appropriate treatment.The methodology in this research consists of three stages: preprocessing, texture feature extraction, and data classification.Preprocessing consists of filtering, contrast, cropping, and resizing, while texture feature extraction consist of Histogram and (Gray Level Co-occurrence Matrix) GLCM.Data classification using Support Vector Machines (SVM), Naive Bayes, Multi-Layer Perceptron (MLP), Multiclass classifier, and Random Forest methods with Weka Machine Learning software.It produces an accuracy of 62.00%, 62.00%, 88.00%, 82.00%, and 100.00%, respectively.The results of data classification using the Random Forest method show that the accuracy, specifications, and specificity reach 100%.Random forest can be used as the most optimal classification method to distinguish benign and malignant cases based on texture features in mammography images using Weka Machine Learning software.This can help radiologists and medical professionals to diagnose cases and take further steps, such as therapy.
Detection of abnormal human behavior using deep learning

The complete human body or the various limb postures are involved in human action. These days,Abnormal Human Activity Recognition (Abnormal HAR) is highly well noticed and surveyed in manystudies. However, because of complicated difficulties such as sensor movement, positioning, and so on,as well as how individuals carry out their activities, it continues to be a difficult process. Identifyingparticular activities benefits human-centric applications such as postoperative trauma recovery, gesturedetection, exercise, fitness, and home care help. The HAR system has the ability to automate orsimplify most of the people’s everyday chores. HAR systems often use supervised or unsupervisedlearning as their foundation. Unsupervised systems operate according to a set of rules, whereassupervised systems need to be trained beforehand using specific datasets. This study conducts detailedliterature reviews on the development of various activity identification techniques currently being used.The three methods—wearable device-based, pose-based, and smartphone sensor—are examined in thisinquiry for identifying abnormal acts (AAD). The sensors in wearable devices collect data, whereas thegyroscopes and accelerometers in smartphones provide input to the sensors in wearable devices. Tocategorize activities, pose estimation uses a neural network. The Anomalous Action Detection Dataset(Ano-AAD) is created and improved using several methods. The study examines fresh datasets andinnovative models, including UCF-Crime. A new pattern in anomalous HAR systems has emerged,linking anomalous HAR tasks to computer vision applications including security, video surveillance,and home monitoring. In terms of issues and potential solutions, the survey looks at vision-based HAR.
A Scaled Denoising Attention-Based Transformer for Breast Cancer Detection and Classification

Breast cancer significantly threatens women's health, and early, accurate diagnosis via mammogram screening has considerably reduced overall disease burden and mortality. Computer-Aided Diagnosis (CAD) systems have been used to assist radiologists by automatically detecting, segmenting, and classifying medical images. However, precise breast lesion diagnosis has remained challenging. In this paper, we propose a novel approach for breast cancer detection and classification in screening mammograms. Our model is a hybrid of CNN and Transformers, specifically designed to detect and classify breast cancer. The model first utilizes a depthwise convolution-based hierarchical backbone for deep feature extraction, coupled with an Enhancement Feature Block (EFB) to capture and aggregate multi-level features to the same scale. Subsequently, it introduces a transformer with Scale-Denoising Attention (SDA) to simultaneously capture global features. Finally, the model employs regression and classification heads for detecting and localizing lesions and classifying mammogram images. We evaluate the proposed model using the CBIS-DDSM dataset and compare its performance with those of state-of-the-art models. Our experimental results and extensive ablation studies demonstrate that our method outperforms others in both detection and classification tasks.
&lt;span&gt;The reality of students&amp;rsquo; communication skills in Faculty of Social Sciences Teacher Education, Dong Thap University&lt;/span&gt;

Communication skills play an important role in students’ learning and professional ability. This sociological research was to survey the opinions of teachers and students of Faculty of Social Sciences Teacher Education, Đong Thap University on learning activities aimed at cultivating students’ communication skills, contributing to develop the students’ professional competence to meet the requirement of currently educational innovation.
Emisi Dinitrogen Oksida (N2O) pada Beberapa Metode Pengelolaan Limbah Sawah di Kecamatan Anjir

Wetlands have unique biodiversity and natural phenomena. The climate has recently changed rapidly due to greenhouse gas emissions, especially nitrous oxide from human activities such as paddy rice farming. The purpose of this study is to determine the effect of rice field waste management on the population of nitrogen-fixing bacteria and N2O emissions, and as to determine the shape and closeness of the relationship between the population of nitrogen-fixing bacteria and N2O emissions with different waste management methods. The research method used is a one-factor RGD. Close chamber technique is used for N2O emmisions analysis. The factor tested was the method of managing paddy waste before rice planting with five treatments, namely: A = Rice field waste was slashed and then lifted into the mound after a few days of being returned to the field; B = Rice field waste is carried out management slashed, rolled up, reversed and stretched; C = Rice field waste is slashed, planted with traces and then slashed again after the trace is transferred to land outside the research plot; D = Rice field waste sprayed with herbicide 2 times; E = Rice field waste sprayed with herbicide 1 time and then soil in the tractor. Each treatment was repeated four times so that 20 units of the experiment were obtained. The results showed that the method of rice field waste management carried out had no real effect on N2O emissions and nitrogen-fixing bacteria. N2O emissions not correlate with the activity of nitrogen-fixing bacteria from several methods of rice field waste management carried out
Federated Learning Meets HPC and Cloud

HPC and AI are fated to meet for several reasons. This article will discuss some of them and argue why this will happen through the methods and technologies underpinning cloud computing. As a paradigmatic example, we present a new Federated Learning (FL) system that collaboratively trains a deep learning model in different supercomputing centers. The system is based on the StreamFlow workflow manager designed for hybrid cloud-HPC infrastructures.
PENGARUH PENGGUNAAN MEDIA PEMBELAJARAN E-LEARNING TERHADAP PENINGKATAN PRESTASI BELAJAR SISWA PADA MATA PELAJARAN PAI DI SMK RIYADLUL QUR’AN NGAJUM

This research is motivated by the Covid-19 pandemic so schools are recommended to carry out online schools (on the network), therefore online learning media e-learning is related to student learning achievement. The aims of this study were 1) to determine the use of e-learning based learning media (website) at Riyadlul Qur'an Ngajum Vocational School, 2) to determine the effect of e-learning learning media (website) on student achievement at Riyadlul Qur'an Ngajum Vocational School. This research uses quantitative research design with survey correlation method. Data collection uses a questionnaire/questionnaire and documentation on the PTS (Middle Semester Assessment) and PAT (End of Year Assessment) scores, and uses parametric inferential descriptive statistical data analysis, while the hypothesis calculation data analysis technique uses the Person deviation formula to calculate Product Moment correlation. Based on the results of the study with the aim of knowing how to use E-learning-based Learning Media (websites) in the online learning process, namely by accessing the links that have been shared, logging in with their respective passwords, then just selecting the subjects to be studied and the student achievement of SMK Riyadlul Qur'an Ngajum there is an increase after using learning media based on E-Learning and research data with NORMAL distribution after testing the data received through the normality test and product moment correlation test with the results of the normality test results with a significance result of 0.23 &gt; 0.05 and the results of the correlation test with the value of r count &gt; 0.165, namely 0.172 &gt; 0.165 on the PTS value and 0.181 &gt; 0.165 on the PAT value.
CHILDREN'S FOLKLOR LITERATURE IN ELEMENTARY SCHOOL CHILDREN

Children's literature studies need to be developed in schools. This is reasonable because literature has an important role in building children's character. Literary literacy has the scope of empowering elementary school children to love literature, one of which is folklore. Folklore is a vehicle for achieving goals in understanding various aspects of life, acting as a step in preserving existing local culture. This is felt to be necessary at this time because many of the younger generation have forgotten the culture which is the heritage of their ancestors and the pride of their identity. Children prefer to watch television or play games on cell phones. Teachers' knowledge of literature is very low, literature is taught by unprofessional teachers, teachers do not understand how to teach literature well, teachers have not taught with appropriate strategies in literary literacy. Elementary school is the main means for developing literary literacy. Elementary school is an important means of balancing the development of character education while continuing to teach everything related to good values. One of the processes that includes learning is literary literacy. Teacher creativity in literary literacy learning needs to be increased because with creative literature teachers it is hoped that the literary learning that occurs will really be liked by children. Likewise, the local government's political attention must be to appreciate and accommodate literary literacy in schools, study groups, libraries and reading houses.
The Effect of Grit, Mindset and Intention to Continue Training of Yoga Participants

The purpose of this study is to investigate the effect of yoga practitioners' grit on their mindset and intention to continue training. For the data processing of this study, SPSS 28.0 was used for proceeding statistically, and for analyzing the data, this study conducted the reliance analysis, the correlation analysis, the multiple regression analysis. The results drawn from such analyses were as follows. First, in the results of examining the effects of grit on mindset, the factors of grit such as significantly positive effects on the growth mindset. Second, in the results of examining the effects of yoga participants grit on continuous Training Intention, the factors of grit such as significantly positive effects on intention to continue training. Third, in the results of examining the effects of yoga participants mindset on continuous training Intention, the factors of mindset such as significantly positive effects on the intention to continue training.