Reviewerly/exHarmony huggingface dataset:
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend \textit{Pythia} to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics. Trained models, analysis code, training code, and training data can be found at \url{https://github.com/EleutherAI/pythia}.
PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models

Large Language Models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple natural language prompt. Part of the appeal for LLMs is their approachability to the general public, including individuals with no prior technical experience in NLP techniques. However, natural language prompts can vary significantly in terms of their linguistic structure, context, and other semantics. Modifying one or more of these aspects can result in significant differences in task performance. Non-expert users may find it challenging to identify the changes needed to improve a prompt, especially when they lack domain-specific knowledge and lack appropriate feedback. To address this challenge, we present PromptAid, a visual analytics system designed to interactively create, refine, and test prompts through exploration, perturbation, testing, and iteration. PromptAid uses multiple, coordinated visualizations which allow users to improve prompts by using the three strategies: keyword perturbations, paraphrasing perturbations, and obtaining the best set of in-context few-shot examples. PromptAid was designed through an iterative prototyping process involving NLP experts and was evaluated through quantitative and qualitative assessments for LLMs. Our findings indicate that PromptAid helps users to iterate over prompt template alterations with less cognitive overhead, generate diverse prompts with help of recommendations, and analyze the performance of the generated prompts while surpassing existing state-of-the-art prompting interfaces in performance.
How capable are they of Becoming a Digital Teacher? Correlation Analysis of Individual Characteristics, Digital Self-efficacy, and Digital Citizenship among Pre-service Teachers in Northeast Thailand

Understanding pros and cons of digital technology is an essential competency for pre-service teachers so that they are able to conduct online instruction appropriately. They need to prepare themselves comprehensively to be able to use the new educational technology that is now widely accessible particularly since the Covid-19 pandemic incidence. This paper investigates the relationship between individual characteristics, digital self-efficacy, and digital citizenship among pre-service teachers in higher education. Cluster sampling was used to select 384 pre-service teachers from three higher education institutes located in the northeast of Thailand. A research tool used in the research was an online questionnaire. The data analysis utilized descriptive statistics, the Chi-square test, and the Pearson correlation. The research discovered that pre-service teachers had a relatively high level of digital self-efficacy, while having relatively low level of digital citizenship. Correlation analysis observed a positive relationship among individual characteristics, digital self-efficacy and digital citizenship. The implications of this research highlighted the significance of implementing civic education in higher education level in order to foster adequate knowledge, skill, and awareness of citizenship in pre-service teachers.
5G Network-Aided Online Music Learning and Teaching System

The environment for training and recognition in Chinese speech recognition under computer-aided design may vary due to the difference of channel and background noise.When the trained model cannot well represent the test data, the recognition rate of the system will drop sharply.The computer-aided design method focuses on using a small amount of Chinese voice data to improve the performance of the system in the test environment.In this paper, we choose the BiLSTM CRF word separation model under deep learning as the improved benchmark model, and combine the Bert language pre-training module to enhance the performance of Chinese word separation.Combining the deep learning sample transfer learning theory and the improved sampling strategy, an adaptive translation model for intelligent Chinese domain is constructed.The experimental results show that Bert Chinese word segmentation model is superior to other word segmentation models in different data sets and has the best word segmentation performance, which can provide reliable support for the application experiment of this model.The test results show that this method can achieve high speech recognition accuracy and good application results.
Towards a framework for developing visual analytics in supply chain environments

Visual Analytics (VA) has shown to be of significant importance for Supply Chain (SC) analytics. However, SC partners still have challenges incorporating it into their data-driven decision-making activities. A conceptual framework for the development and deployment of a VA system provides an abstract, platform-independent model for the whole process of VA, covering requirement specification, data collection and pre-processing, visualization recommendation, visualization specification and implementation, and evaluations. In this paper, we propose such a framework based on three main aspects: 1) Business view, 2) Asset view, and 3) Technology view. Each of these views covers a set of steps to facilitate the development and maintenance of the system in its context. The framework follows a consistent process structure that comprises activities, tasks, and people. The final output of the whole process is the VA as a deliverable. This facilitates the alignment of VA activities with business processes and decision-making activities. We presented the framework's applicability using an actual usage scenario and left the implementation of the system for future work.
Bootstrap approach for clustering method with applications

Discovering patterns of big data is an important step to actionable insights data. The clustering method is used to identify the data pattern by splitting the data set into clusters with associated variables. Various research works proposed a bootstrap method for clustering the array data but there is a weak view of statistical or theoretical results and measures of the model consistency or stability. The purpose of this paper is to assess model stability and cluster consistency of the K-number of clusters by using bootstrap sampling patterns with replacement. In addition, we present a reasonable number of clusters via bootstrap methods and study the significance of the K-number of clusters for the original data set by looking at the value of the K-number that provides the most stable clusters. Practically, bootstrap is used to measure the accuracy of estimation and analyze the stability of the outcomes of cluster methods. We discuss the performance of suggestion clusters through running examples. We measure the stability of clusters through bootstrap. A simulation study is presented in order to illustrate the methods of inference discussed and examine the satisfactory performance of the proposed distributions.
Training of higher pedagogical staff in Soviet Buryatia

The article deals with the issues of training the teaching staff of Buryatia in the Soviet era. Special attention in the organization of this process is paid to the experience of the Buryat State Pedagogical Institute. The study of the activities of the BSPI is an important component in the issue of training teachers with higher education. The purpose of the paper is to study the contribution of the BSPI to the training of teachers from 1932 to 1991. The following tasks of the study are presented: determination of the prerequisites for the creation of a pedagogical institute; study of the main stages of the formation of the pedagogical institute; identifying the most significant graduates of the institute employed in the regional education system. During the period under study, the Pedagogical Institute graduated a pleiad of talented and distinguished teachers who did a lot for the development of the educational system of Buryatia.
The Most Common Characteristics of Fragile Video Watermarking

The progress of network and multimedia technologies has been phenomenal during the previous two decades. Unauthorized users will be able to copy, retransmit, modify reproduction, and upload the contents more easily as a result of this innovation. Malicious attackers are quite concerned about the development and widespread use of digital video. Digital watermarking technology gives solutions to the aforementioned problems. Watermarking methods can alleviate these issues by embedding a secret watermark in the original host data, allowing the genuine user or file owner to identify any manipulation. In this study, lots of papers have been analyzed and studied carefully, in the period 2011–2022. The historical basis of the subject should not be forgotten so studying old research will give a clear idea of the topic. To aid future researchers in this subject, we give a review of fragile watermarking approaches and some related papers presented in recent years. This paper presents a comparison of many relevant works in this field based on some of the outcomes and improvements gained in these studies, which focuses on the common characteristics that increase the effect of watermarking techniques such as invisibility, tamper detection, recovery, and security&#x0D;
Deep Forest Based Internet of Medical Things System for Diagnosis of Heart Disease

Due to advancement in internet of medical things, the conventional health-care systems are transformed into smart health-care systems. The medical emergence services can be significantly enhanced by integration of IoMT and data analytic techniques. These technologies also examine the unexplored area of medical services that are still unseen and provide opportunity for investigation. Moreover, the concept of smart cities is not achievable without providing a smart connected healthcare scheme. Hence, the main purpose of this research is to come up with a smart healthcare system based on IoMT, Cloud and Fog computing and intelligent data analytic technique. The major objective of the proposed healthcare system is to develop a diagnostic model capable for earlier treatment of heart disease. The suggested scheme consists of distinct phases such as data acquisition, feature extraction, FogBus based edge/fog computing environment, classification, and evaluation. In data acquisition, different IoMT such as wearables and sensors devices are considered to acquire the data related to heart disease and the various features related to signal and data are extracted. Further, the deep forest technique is integrated into the proposed system for classification task and effective diagnosis capabilities of heart issues. The performance of the suggested scheme is evaluated through set of well-defined parameters. Comparison with other healthcare model was conducted for the purpose of performance evaluation. It is concluded that the proposed model has a superiority over other all other models in different aspects namely, the sensitivity measure, accuracy measure, and specificity.
Comparing Knowledge Acquisition and Retention Between Mobile Learning and Traditional Learning in Teaching Respiratory Therapy Students: A Randomized Control Trial

Mobile learning (m-learning) is one of the trends in health professions' education. It has a promising future in education, but it also presents various challenges and risks. This research seeks to highlight some of the greatest accomplishments, opportunities, and issues related to m-Learning in teaching and learning. We believe the findings help us maximize the positive effects of m-Learning while minimizing any potential drawbacks associated with the technological changes taking place in education. The study aimed to compare respiratory therapy students' knowledge acquisition and retention between using m-learning and traditional learning.Randomized pre-test, post-test, control group design was used. All 3rd year (N = 46) respiratory therapy students in one governmental university in Saudi Arabia were randomly assigned to either intervention or control group. Both groups took the same content of arterial blood gas lecture for 2 hours. The intervention group took the lecture using m-learning; two mobile apps: (1) ABG Book, (2) Arterial Blood Gas (Lite). The traditional learning (lecture-based learning) was used for the control group. The same test was conducted before, immediately after, and two weeks after the lecture. The duration of the test was 30 minutes. The data were analyzed using independent t-test and repeated measured ANOVA using p < 0.05. IRB Approval was obtained.Forty-five students participated. Although no statistically significant difference was found on knowledge acquisition and retention between the two types of learning (p = 0.305, p = 0.904, respectively), it was found among the three time-points within each group (p < 0.001).Both m-learning and traditional learning are effective in increasing knowledge acquisition and retention. However, no one is better than the other. Further researches were needed with larger sample size through multi-institutional studies to validate the results of this study.
Clay minerals study of the Kolosh Formation at selected sections from northern Iraq: Implications for provenance history

The mineralogical study using X-ray diffraction (XRD) supported by scanning electron microscopic (SEM) examination and energy-dispersive spectroscopy (EDS) on the claystone of the Kolosh Formation from northern Iraq was conducted to Shows the provenance history of rocks. Chlorite, montmorillonite, illite, palygorskite, and kaolinite were recorded in different amounts in the study area. The association of montmorillonite and chlorite in the claystone of the Kolosh Formation (Paleocene) refers to the marine environment. Chlorite and montmorillonite are the common minerals in the Kolosh Formation with less common of illite, kaolinite and palygorskite. These clay minerals are of authigenic, detrital and diagenetically origin, which are controlled mainly by the source rocks, paleoclimatic conditions and the burial diagenesis. The clay minerals assemblages refer to be derived mainly from Fe-Mg rich with minor Si-Al rich silicate minerals, which are very common in the ophiolites associated with the basic igneous rocks. These rocks composed the major lithological units in the Zagros Thrust Belt of NE Iraq.
Enhancing the Protection of Medical Data Using Integer Wavelet Discrete Cosine Transform

Telemedicine has gained a lot of popularity and recognition in the field of healthcare, as it provides medical assistance remotely by experts. As medical data is transferred through the public network, the alteration of data can be done intentionally or mistakenly by attackers or intruders which may result in the wrong diagnosis. In order to provide security to sensitive medical data, various advanced techniques are adopted. Watermarking approach is one among them which is used to protect the integrity of sensitive medical data. Thus, this paper proposed Integer Wavelet Discrete Cosine Transform (IWDCT) watermarking technique to preserve the integrity, confidentiality, and authentication of sensitive information of medical data. IWDCT has better coding performance and provides low computational time for embedding as well as extraction, and it ensures the visual quality of an image. The morphological operations were used to extract the Region of Interest (RoI) from the medical image. The workaround aim of IWDCT is to safeguard the RoI by embedding it into its Region of Non-Interest (RoNI). The integrated RoI is extracted by the reverse application of IWDCT. As a result, the intruder can only see the original image, while the RoI is interred. The accuracy of the watermarked image can be evaluated using the metrics like Peak Signal Noise to Ratio (PSNR). Because of its higher performance, the proposed mechanism can be employed in the secure transmission of medical images.
The advance of Adversarial attack and defense

Neural network technology has made remarkable achievements in many fields, such as the field of computer vision, the field of speech recognition, the field of natural language processing and other. However, the interpretability of the neural network model makes its application in natural scenes have potential security risks. Many studies have pointed out that using anti-sample technology to disturb the input sample extremely weakly can mislead most mainstream neural network models to make wrong judgment results. This phenomenon reveals that the existing neural network technology still has defects in security and robustness. To study the adversarial sample technology to improve the safety and robustness of neural network model and promote researchers' in-depth understanding and understanding of the learning mechanism of the model is of great significance. Firstly, in this paper, we briefly summarize the development process of counter-sample technology. Then, according to the classification system of technical principles and application fields, the related research progress is introduced in detail. Then, the problems and challenges faced by neural networks and countermeasure sample technology are analysed and discussed. Finally, the future research direction and development trend prospect and the paper are summarized.
Paired Reading and Paired Summarizing Strategy: Exploring Motivation and Effects Of EFL Students’ Reading Comprehension

Paired Reading and Paired summarizing is one teaching strategy that can use in teaching reading. The main purpose of this study is to find out the effects of Paired Reading and Paired Summarizing Strategy on students’ reading comprehension and motivation at SMK Abdurrab Pekanbaru. The design of this research is a quasi-experiment design with pre-test and post-test procedures. The total population is 97 students of the tenth grade. This research uses a cluster sampling technique to get samples. The total sample is 64 students. The sample is divided into two classes; the experimental class and the control class. The data are analyzed using the t-test method to find out whether there is a significant difference between the experimental class taught by using Paired Reading and Paired Summarizing as a treatment, and the control class is taught conventionally. it found that significant probabilities are higher than 0.05meaning that there is a significant difference in students' reading comprehension and learning motivation in the pre-test of both classes and post-test of both classes, and there is a significant difference in students' reading comprehension and learning motivation pre-test and post-test of experimental class. There is an effect of using Paired Reading and Paired Summarizing on students’ reading comprehension and learning motivation. At last, it suggested that the English teachers Paired Reading and Paired Summarizing in teaching are reading.
Rethinking causality-driven robot tool segmentation with temporal constraints

Vision-based robot tool segmentation plays a fundamental role in surgical robots perception and downstream tasks. CaRTS, based on a complementary causal model, has shown promising performance in unseen counterfactual surgical environments in the presence of smoke, blood, etc. However, CaRTS requires over 30 iterations of optimization to converge for a single image due to limited observability.To address the above limitations, we take temporal relation into consideration and propose a temporal causal model for robot tool segmentation on video sequences. We design an architecture named Temporally Constrained CaRTS (TC-CaRTS). TC-CaRTS has three novel modules to complement CaRTS-temporal optimization pipeline, kinematics correction network, and spatial-temporal regularization.Experiment results show that TC-CaRTS requires fewer iterations to achieve the same or better performance as CaRTS on different domains. All three modules are proven to be effective.We propose TC-CaRTS, which takes advantage of temporal constraints as additional observability. We show that TC-CaRTS outperforms prior work in the robot tool segmentation task with improved convergence speed on test datasets from different domains.
Improving the Recognition Percentage of the Identity Check System by Applying the SVM Method on the Face Image Using Special Faces

Face recognition has attracted tremendous attention during the last three decades because it is considered a simple pattern recognition and image analysis method. Also, many facial recognition patterns have been introduced and used over the years. The SVM algorithm has been one of the successful models in this field. In this article, we have introduced the special faces first. In the following, we have fully explained the SVM method and its subsets, including linear and non-linear support vector machines. Suggestions for improving the recognition percentage of a person's identity check system by applying the SVM method on the face image using special faces are presented. For this test, 10 face images of 40 people (400 face images in total) have been selected from the ORL database. In this way, by choosing the optimal parameter C, determining the most suitable training samples, comparing more accurately with training images and using the distance with the closest training sample instead of the average distance, the proposed method has been implemented and tested on the famous ORL database. The obtained results are FAR=0.23% and FRR=0.48%, which shows the very high accuracy of the operation following the application of the above suggestions.
From Tech Hub to Banking Failure: Exploring the Implications of CBDCs on the Destiny of Silicon Valley Bank

The potential implications of central bank digital currencies (CBDCs) on Silicon Valley Bank (SVB) are vast, particularly when it comes to oversight of the bank. This paper aims to explore how the introduction of CBDCs could have impacted SVB and its eventual collapse. The introduction of CBDCs has the potential to disrupt traditional banking systems, which could impact the stability of the financial industry. However, CBDCs can also provide real-time monitoring and oversight, which could help to prevent bank failures. This paper examines the potential impact of CBDCs on SVB and how it could have been impacted by the real-time monitoring provided by the Federal Reserve. The findings suggest that the introduction of CBDCs could have helped to prevent the collapse of SVB by allowing for real-time monitoring and oversight. The implications of this research are significant, as it highlights the potential benefits of CBDCs in preventing future banking failures and strengthening financial stability.