Reviewerly/exHarmony huggingface dataset:
Optimal distributed multiparameter estimation in noisy environments

We consider the task of multiple parameter estimation in the presence of strong correlated noise with a network of distributed sensors. We study how to find and improve noise-insensitive strategies. We show that sequentially probing GHZ states is optimal up to a factor of at most 4. This allows us to connect the problem to single parameter estimation, and to use techniques such as protection against correlated noise in a decoherence-free subspace, or read-out by local measurements.
PENINGKATAN LITERASI SAINS MELALUI PENGEMBANGAN MEDIA BIG BOOK

This research is a level 3 research and development (RD) which aims to develop existing products. The product developed is the big book learning media which is then used as a means to improve the literacy science of students at MIN Bone. This research adopts Borg and Gall development research steps which include research and information gathering, planning, initial product development, conducting initial testing by media/design experts, material/content experts and learning practitioners, product revisions, conducting field trials, revisions the final product. There are three data collection techniques used in this study, namely validation carried out by the validator, effectiveness obtained through increasing literacy science and student responses to learning media. The results of the big book media validity test conducted by media/design experts are 85% with good qualifications, material/content experts are 91% with very good qualifications and learning practitioners are 91% with very good qualifications. The effectiveness of the big book media in improving literacy science. through the N-Gain 32 test which got a score of (g) 0.7 as many as 2 students, a value of 0.7 (g) 0.3 as many as 31 students, a value of (g) 0 ,3 as many as 2 students. The student response to the attractiveness of the big book media is 91% with very good qualifications.
Reimagining Retrieval Augmented Language Models for Answering Queries

We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where models integrate model parameters and knowledge from external data sources to make their predictions, as opposed to the parametric nature of vanilla large language models. We give initial experimental findings that semi-parametric architectures can be enhanced with views, a query analyzer/planner, and provenance to make a significantly more powerful system for question answering in terms of accuracy and efficiency, and potentially for other NLP tasks
Study of Student Personality Trait on Spear-Phishing Susceptibility Behavior

Spear-phishing emails are an effective cyber-attack method due to the fact that the emails sent are highly personalized to look like a regular legitimate email.Recently, it was discovered that personality traits of the victim have an impact on a person's susceptibility to spear-phishing.This study aims to identify which personality traits affect spear-phishing susceptibility besides other traits such as Information Technology background, gender, and age.In addition, measure of the effectiveness of embedded training systems and see whether message framing can further help increase its effectiveness.A personality trait survey was sent to 100 participants, followed by a real-life spear-phishing simulation to measure a certain personality trait's influence on phishing susceptibility.After a two-week period, the second round of spear-phishing emails was sent again to measure message framing effectiveness.The personality traits analysis results show that users with higher levels of Internet anxiety are less susceptible to spear-phishing emails.While the message framing did not show any significant results, the embedded training program reduced the click rate.Findings revealed that certain people are more susceptible to spear-phishing emails than others.Thus, this work can guide an institution or organizations to identify which group of people are more vulnerable to spearphishing.
Making Speaker Diarization System Noise Tolerant

The goal of speaker diarization is to identify and separate different speakers in a multi-speaker audio recording. However, noise in the recording can interfere with the accuracy of these systems. In this paper, we explore methods such as multi-condition training, consistency regularization, and teacher-student techniques to improve the resilience of speaker embedding extractors to noise. We test the effectiveness of these methods on speaker verification and speaker diarization tasks and demonstrate that they lead to improved performance in the presence of noise and reverberation. To test the speaker verification and diarization system under noisy and reverberant conditions, we created augmented versions of the VoxCeleb1 cleaned test and Voxconverse dev datasets by adding noise and echo with different SNR values. Our results show that, on average, we can achieve a 19.1% relative improvement in speaker recognition using the teacher-student method and a 17% relative improvement in speaker diarization using consistency regularization compared to a multi-condition trained baseline.
Density Based Spatial Clustering of Applications and Spatial Pattern Analysis In Mapping the Distribution of ISPA Disease in Bireuen Regency

ISPA is an infectious disease transmitted through the air. ISPA disease can be detected through the regional distribution maps of disease. ISPA disease can be prevented in the early detection before the disease gets worse. This can prevent the spread of the disease further. Data on ISPA cases in 2019 to 2021 in Bireuen Regency from the medical records of dr. Fauziah Bireuen from an average of 13.18 to 59.24 per year. This research aims to determine the clusters of ISPA spread areas across the district of Bireuen and to analyze the distribution of disease patterns with Spatial Pattern Analysis and to map the respiratory diseases in each area with Flexibly Shaped Spatial Scan Statistics. The methodology in this research are collecting the data at the hospital for each area of ISPA patients and the data processed by using DBSCAN to obtain the cluster points on the map. The result of the DBSCAN model are clusters. The cluster results are next processed with the distribution of hotspot point on the map using the Spatial Pattern Analysis model, and the map of the prone of the spread of ISPA in Bireuen Regency is processed by using the Flexibly Shaped Spatial Scan Statistic method. Information from the map obtained that the first 4 clusters of the area are formed, namely 3 clusters and 1 outlier. Cluster 1 and outliers consist of 6 sub-districts, cluster 2 consist of 4 sub-districts and cluster 3 only has 1 sub-district. There are 6 hotspot areas in 2019, 5 hotspot areas in 2020, and 6 hotspot areas in 2021. The results of this research are each ISPA disease clustering map shows the results of the ISPA distribution on the map and areas that are prone to ISPA disease in Bireuen Regency are Jeunieb and Peusangan
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents

Recent work has shown that infusing layout features into language models (LMs) improves processing of visually-rich documents such as scientific papers. Layout-infused LMs are often evaluated on documents with familiar layout features (e.g., papers from the same publisher), but in practice models encounter documents with unfamiliar distributions of layout features, such as new combinations of text sizes and styles, or new spatial configurations of textual elements. In this work we test whether layout-infused LMs are robust to layout distribution shifts. As a case study we use the task of scientific document structure recovery, segmenting a scientific paper into its structural categories (e.g., "title", "caption", "reference"). To emulate distribution shifts that occur in practice we re-partition the GROTOAP2 dataset. We find that under layout distribution shifts model performance degrades by up to 20 F1. Simple training strategies, such as increasing training diversity, can reduce this degradation by over 35% relative F1; however, models fail to reach in-distribution performance in any tested out-of-distribution conditions. This work highlights the need to consider layout distribution shifts during model evaluation, and presents a methodology for conducting such evaluations.
ANALISIS PENERAPAN MODEL PEMBELAJARAN SCRAMBLE TERHADAP HASIL BELAJAR SISWA X TKR3 SMK DARUSSALAM BLOKAGUNG TEGALSARI BANYUWANGI PADA POKOK BAHASAN MATRIKS TAHUN PELAJARAN 2021/2022

This study aims to find out and analyze the application of the scramble learning model to student learning outcomes on the subject of the matrix. The sample used in this study was students of class X TKR3 SMK Darussalam, totaling 40 students. Collecting data in this study using observation and test methods. The test instrument was tested for validity using Pearson's correlation, reliability test using Crobach Alpha. In addition, a test of the difficulty level of the questions was also carried out. Data analysis in this study used data analysis of the Paired Sample T-Test. The results of testing the research hypothesis showed that there was a difference in the mean before and after the scramble learning model was applied to the learning outcomes of the subject matter matrix of class X TKR3 SMK Darussalam. The results of the study also show that the level of the mathematics learning outcomes category of students who study using the Scramble Method on the matrix subject matter of class X TKR3 SMK Darussalam Blokagung Tegalsari Banyuwangi is in the high category.
From Phishing Behavior Analysis and Feature Selection to Enhance Prediction Rate in Phishing Detection

Phishing incidents have captured the attention of security experts and end users in recent years as they have become more frequent, widespread, and sophisticated.The researchers offered a variety of strategies for detecting phishing attacks.Over time, these approaches suffer from insufficient performance and the inability to identify zero attacks.One of the limitations with these methods is that phishing techniques are constantly evolving, and the proposed methods are not keeping up, making it a hard nut to crack.The objective of this research is to develop a URL phishing detection model that can demonstrate its robustness against constantly changing attacks.One of the most significant contributions of this paper is the selection of a novel combination of features based on literal and recent phishing behavior analysis.This makes the model competent sufficient to recognize zero attacks and able to adjust to changes in phishing attacks.Furthermore, eleven machine learning classification techniques are utilized for classification tasks and comparative objectives.Moreover, three datasets with different instance distributions were constructed at different times for the model's initial construction and evaluation.Several experiments were carried out to investigate and evaluate the proposed model's performance, effectiveness, and robustness.The experiments' findings demonstrated that the GaussianNB method is the most durable, capable of maintaining performance even in the absence of retraining.Additionally, the LightGBM, Random Forest, and GradientBoost algorithms had the highest levels of performance, which they were able to maintain by routinely retraining the model with newer types of attacks.Models that employed these three suggested algorithms outperformed other current detection models with an average accuracy of about 99.7%, making them promising.
Improving Students' Activities and Achievement Using the Themed Worksheets Through Discovery Learning

Preliminary studies showed the low activity of students in science learning with classical learning completeness of 22%. This study aimed to improve students’ learning activities and learning achievements through discovery learning assisted by themed worksheets. This study improved previous research related to the implementation of discovery learning to improve the learning quality. The research was designed using the classroom action research method and was carried out in three cycles, involving 32 second grade students of secondary school. Experts validated interview, observation, and questionnaire sheets, and the test items were validated statistically. This study showed that discovery learning assisted by themed worksheets improved the average student activity to 93% and the average science learning achievements to 71.61% with a classical completeness rate of 78%. Students also accepted the themed worksheets with very high satisfaction. This study did not examine the students’ character, the correlation between activity data and student learning discovery, neither did it accommodate students with special needs. These results can be a reference for teachers to improve the quality of learning, especially for schools in rural areas that have limited technological infrastructure.&#x0D; Keywords: class action research, discovery learning, learning achievements, students’ activities, themed worksheets
Time-Domain Video Watermarking Algorithm for Data Security and Content Authentication

Abstract: Video watermarking is an essential method employed to invisibly and reliably incorporate information into digital videos. Its primary purposes include copyright protection, content authentication, and ownership verification. With the increasing prevalence of digital media and the ease of video sharing online, safeguarding intellectual property rights and ensuring video content integrity have become crucial. Video watermarking is an essential method employed to invisibly and reliably incorporate information into digital videos. Its primary purposes include copyright protection, content authentication, and ownership verification. With the increasing prevalence of digital media and the ease of video sharing online, safeguarding intellectual property rights and ensuring video content integrity have become crucial. Robustness is a crucial aspect of video watermarking, as watermarked videos may be subjected to various attacks or modifications. Robust video watermarking techniques employ error correction codes, spread spectrum modulation, or cryptographic algorithms to ensure the resilience of watermarks against attacks such as compression, filtering, and geometric transformations. Various aspects and performance characteristics must be taken into account while developing an algorithm. It is necessary to evaluate performance indicators like NCC, PSNR, BER, WER, SSIM, VIFP, VMAF, and many more.
C-E Translation of Micro Film Spreading Mode and Local Opera Development Path under the Guidance of Skopos Theory

With the continuous acceleration of globalization in today’s world, international cultural exchanges are becoming more and more frequent. However, the development of Chinese opera culture is facing challenges in contemporary era. As a new communication channel, unique advantages of micro-film can provide a new path for the development of local opera. The source text, Micro Film Spreading Mode and Local Opera Development Path, aims at passing information, with practical, informative and communicative functionality, so its translation must meet the actual needs of the target language readers to understand the content. Under the guidance of Skopos Theory on the translation of academic text, translation strategies such as literal translation, addition, and conversion have been adopted to ensure the translated text to be accurate in meaning as much as possible to the source text, and the target language is similar to that of the original author in style.
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD algorithms either rely on restricted assumptions or fail to exploit environment information in training data. In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs. We further develop an adversarial training strategy to jointly optimize these two properties for causal subgraph discovery with theoretical guarantees. Extensive experiments and analysis show that LECI significantly outperforms prior methods on both synthetic and real-world datasets, establishing LECI as a practical and effective solution for graph OOD generalization. Our code is available at https://github.com/divelab/LECI.
An Analysis of Bias in Facial Image Processing: A Review of Datasets

Facial image processing is a major research area in digital signal processing.According to recent studies, most commercial facial image processing systems are prejudiced by bias towards specific races, ethnicities, cultures, ages, and genders.In some circumstances, bias may be traced back to the algorithms employed, while in others, bias can be elicited from the insufficient representations in datasets.This study tackles bias based on insufficient representations in datasets.To tackle this, the research undertakes an exploratory review in which the context of facial image dataset is analyzed to thoroughly examine the rate of bias.Facial image processing systems are developed using widely publicly available datasets since generating datasets are costly.However, these datasets are strongly biased towards Whites and Asians, and other geo-diversity such as indigenous Africans are underrepresented.In this study, 40 large publicly accessible facial image data sets were examined.The races of the datasets used for this study were visualized using the tdistributed Stochastic Neighbor Embedding (t-SNE) visualization method.Then, to measure the geo-diversity and rate of bias of the dataset, k-means clustering, principal component analysis (PCA) and the Oriented FAST and Rotated BRIEF (ORB) feature extraction techniques were used.The findings from this study indicate that these datasets seem to exhibit an obvious ethnicity representation bias, particularly for native African facial images; as a result, additional African indigenous datasets are required to reduce the bias currently present in the most publicly available facial image datasets.
A Multi- Pragmatic Study of Sarcasm in Political Texts

Sarcasm is one of the strategies that people use to attack the listener indirectly or in a way that seems to be kind on the surface. There is a shady relation between sarcasm and irony. Accordingly, this study sets the following aims which are identifying the most frequent type of speech acts that is used to convey the sarcastic meaning; knowing the conversational maxim that is breached by the sarcast to express sarcasm; shading light on the type of politeness maxim that is violated in the sarcastic messages; specifying the social functions of sarcasm in political texts; and revealing the linguistic mechanisms that are employed to reflect sarcasm. The researcher hypothesizes the following: expressives are the most frequent type of speech acts which are used to reflect sarcasm; sarcasm is the result of breaching quality maxim only; the most common violated politeness maxim in sarcasm is tact maxim; sarcasm mainly serves as a social control tool in the political contexts; and metaphor and explicitation are the most frequent mechanisms of sarcasm in political texts. The researcher adopts an eclectic model which consists of Speech Acts Theory of Searle and Vanderveken (1985), Grice's Conversational Implicature (1989), Leech's Politeness Principle (1983-2014), Ducharme's Functions of Sarcasm (1994), and Tabacaru's Linguistic Mechanisms of Sarcasm (2019). By using this model, the researcher finds that the most frequent type of speech acts is assertives, breaching quality maxim is a basic requirement to reflect sarcasm, the approbation maxim is the most violated maxim in the sarcastic texts, the dominant function of sarcasm in political texts is social control, and metaphor is used more frequent in the political context than other mechanisms.
Data-Driven Diagnostics: Leveraging Machine Learning for Precision Medicine

The field of precision medicine holds great promise in revolutionizing healthcare by providing personalized and targeted treatments. However, the successful implementation of precision medicine heavily relies on accurate and efficient diagnostic tools. This paper explores the potential of leveraging machine learning techniques for data-driven diagnostics in precision medicine
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data

The impressive advances and applications of large language and joint language-and-visual understanding models has led to an increased need for methods of probing their potential reasoning capabilities. However, the difficulty of gather naturally-occurring data for complex multi-modal reasoning tasks bottlenecks the evaluation of AI methods on tasks which are not already covered by an academic dataset. In this work, we leverage recent advances in high resolution text-to-image generation to develop a framework for generating evaluation data for multi-modal reasoning tasks. We apply this framework to generate context-dependent anomaly data, creating a synthetic dataset on a challenging task which is not well covered by existing datasets. We benchmark the performance of a state-of-the-art visual question answering (VQA) model against data generated with this method, and demonstrate that while the task is tractable, the model performs significantly worse on the context-dependent anomaly detection task than on standard VQA tasks.
Integration of character education in android-based m-learning media on problem-solving ability

This study aims to produce M-learning media that is effective, practical, effective by including character education and the ability to solve problems in the media in cube material and building blocks. This study adopted the ADDIE development design model which included five main stages, namely analysis, design, development, implementation and evaluation. The subject of this study was a grade VIII student of SMP Negeri 10 Sungai Kakap, totaling 15 students. The instruments used in this study were validation sheets, questionnaires, and pretest and posttest questions. The results showed that the average level of verification of mobile learning media by the three experts was 85.56%, meets the very valid criteria. The cumulative score of teacher and student questionnaires reaches 88.66%, very practical. The results of inferential statistical tests from pretest and posttest using the t test, it was found that T_Hitung -t_table with significant α = 5% which means there is a significant difference in the ability to solve problems between before and after using mobile learning media so that it can be said that the media is classified as effective. Therefore, it can be concluded that the use of mobile learning media combined with character education on the ability to solve the geometry problem of junior high school students is valid, practical and effective.