Reviewerly/exHarmony huggingface dataset:
TEACHER’S PERCEPTION TOWARD THE USE OF DIALOGUE IN IMPROVING ENGLISH COMMUNICATION AT PGRI SENIOR HIGH SCHOOL OF PAGARALAM

This research is intended to know and analyze the teacher’s perception toward the use of dialogue in improving English communication at PGRI senior high school of Pagaralam. This research is descriptive qualitative research. Observations, interviews, questionnaires, and documents were used to collect data in this research. Sources of data used by researchers are interviews and questionaires used talks with teachers. because of that researchers get accurate information about what was done during the activity and find out the results of these activities using the data types and data sources described. The results of this research indicate that conversational learning using dialogue could improve communication, and students' motivation to learn English. Keywords: Perception, Dialogue, English Communication
Blok Zinciri, Kripto Para Muhasebesi ve Piyasa Dinamikleri: Kripto Para Değerlemeleri ve Piyasa Faiz Oranları Arasındaki Korelasyon

In this study, the five most well-known cryptocurrencies in the blockchain-based decentralized financing structure were compared with the centralized market interest rates, and it was examined whether there is a significant relationship between the changes in market interest rates and the prices of cryptocurrencies. Key findings indicate a significant relationship between most cryptocurrencies, such as Dash, Litecoin, Ethereum, and Bitcoin, with market interest rates. However, XRP emerges as an exception. In addition to the comparative analysis between cryptocurrencies and market interest rates, this study delves into the underlying mechanisms that govern these relationships. It explores the role of blockchain technology in shaping the decentralized financing structure and highlights the intricacies of various cryptographic algorithms. The research also emphasizes the need for specialized accounting practices that cater to the unique challenges posed by cryptocurrencies. This study bridges the understanding between conventional economic mechanisms and the innovative world of cryptocurrencies, offering inferences that are important for investors, financial analysts, and accountants in the digital age.
Analysis of Histogram and Grayscale on Chest X-Ray in Lung Cancer Using Image-J

Posteranterior (PA) chest radiographic examination is a support in screening for the diagnosis of lung cancer. Computed radiography (CR) modality can produce chest images quickly, optimally and can be processed as needed. However, so far radiologists interpret images only by visual assessment, so the results are very subjective. Therefore digital medical image processing can be done by looking at the histogram and gray scale values to increase the accuracy of enforcing patient diagnoses. This study aims to analyze the comparison of histograms and gray degree values on CR chest images between normal patients and lung cancer patients. The study was conducted using 30 chest images consisting of normal and lung cancer patient groups with 15 images each. All images are calculated grayscale and display histogram graphics with the Image-J application and statistically analyzed using the Independent T-Test. The results show that there is a difference in grayscale values between normal chest images and lung cancer (p&lt;0.001). The grayscale and histogram values on lung cancer chest images ( 103.2908 + 6.119 ) are higher and tend to the right compared to the grayscale and histogram values on normal chest images ( 64.5848 + 3.28) . Histogram and grayscale values add objective image interpretation in diagnosing lung cancer.
STUDY OF THE PARAMETERS OF THE DIGITAL CONTENT MATRIX BLOCKS IN DIFFERENT STORAGE FORMATS AS A THEORETICAL BASIS FOR THE METHODS OF DETECTING VIOLATIONS OF ITS INTEGRITY

Unauthorized changes of digital information contents, in particular images, videos, which are considered in the work, the detection of which is a difficult and urgent task, require the development of new approaches and methods. In case of unauthorized changes in digital contents, there is often a change in the format (lossy/lossless) of its preservation (in whole or in part), in particular when organizing a steganographic communication channel, photomontage, etc. Thus, the identification of the fact of re-preservation of digital content in a format different from the original one is a pointer to the violation of its integrity, making the task of separating content in different formats urgent. The aim of the work is to study the properties of the formal parameters of blocks of original digital content to create a theoretical basis for the methods of separating content in various storage formats. In the course of the study: the formal parameters – the smallest singular values of the blocks of the corresponding matrices, based on the properties of which the proposal to introduce a formal research object – the matrix of the smallest singular values of the blocks, corresponding to the digital content and having properties that differ depending on from the digital content storage format – were determined; for a sequence of digital images of the same format, for digital video, a formal mathematical object is defined – a histogram of modes of histograms of matrices of the smallest singular values of blocks of images/frames of video, the properties of which differ significantly for different storage formats, which can be used to develop an appropriate expert method. Establishing quantitative characteristics for qualitative separators obtained in the work will provide an opportunity to form effective methods of separating digital contents in various storage formats, which can be applied as a component of the steganalysis process, in the process of detecting the results of photomontage, where contents in various formats were involved, etc.
Research on Evidence Issues in Telecommunications Network Fraud Cases

With the development of communication tools such as mobile phones and computers, telecommunications network fraud crimes in China have continued to be rampant, causing huge financial losses to the public. Due to the particularity of telecommunications network fraud, its evidence presents characteristics such as difficulty in the collection, differences in electronic certification, and a high degree of electronification. There are many problems in evidence collection, examination, and determination. Given the above issues, this paper proposes suggestions such as improving the custody chain regulations and using experience rules to assist the proof model based on the Anti-Telecommunications Network Fraud Law of the Peoples Republic of China, to more effectively combat telecommunications network crimes.
Detection of Phishing Website Using Support Vector Machine and Light Gradient Boosting Machine Learning Algorithms

Phishing is one of the most popular and hazardous cybercrime attacks. These attacks are designed to steal information used by people and companies to complete transactions. Phishing websites use a variety of indicators in their text and web browser-based data. This research presents a novel approach to classifying phishing websites by making use of the extreme learning machine (ELM). In this study, SVM, light GBM algorithm was used to detect phishing websites according to characteristics such as the length of their URLs, the number of capital letters they include and the presence of HTML elements. The findings indicate that ELM has a classification accuracy of 94.2% when it comes to phishing websites. This demonstrates the potential of ELM to classify websites that are used for phishing and to improve the safety of users who do their activities online.
Transforming traffic surveillance: a YOLO-based approach to detecting helmetless riders through CCTV

CCTV systems, while ubiquitous for traffic surveillance in Indonesian roadways, remain underutilized in their potential. The integration of AI and Computer Vision technologies can transform CCTV into a valuable tool for law enforcement, specifically in monitoring and addressing helmet non-compliance among motorcycle riders. This study aims to develop an intelligent system for the accurate detection of helmetless motorcyclists using image analysis. The approach relies on deep learning, involving the creation of a dataset with 764 training images and 102 testing images. A deep convolutional neural network with 23 layers is configured, trained with a batch size of 10 over ten epochs, and employs the YOLO method to identify objects in images and subsequently detect helmetless riders. Accuracy assessment is carried out using the mean Average Precision (mAP) method, resulting in a notable 82.81% detection accuracy for riders without helmets and 75.78% for helmeted riders. The overall mAP score is 79.29%, emphasizing the system's potential to substantially improve road safety and law enforcement efforts
The function/content word distinction and eye movements in reading.

A substantial quantity of research has explored whether readers' eye movements are sensitive to the distinction between function and content words. No clear answer has emerged, in part due to the difficulty of accounting for differences in length, frequency, and predictability between the words in the two classes. Based on evidence that readers differentially overlook function word errors, we hypothesized that function words may be more frequently skipped or may receive shorter fixations. We present two very large-scale eyetracking experiments using selected sentences from a corpus of natural text, with each sentence containing a target function or content word. The target words in the two classes were carefully matched on length, frequency, and predictability, with the latter variable operationalized in terms of next-word probability obtained from the large language model GPT-2. While the experiments replicated a range of expected effects, word class did not have any clear influence on target word skipping probability, and there was some evidence for a content word advantage in fixation duration measures. These results indicate that readers' tendency to overlook function word errors is not due to reduced time spent encoding these words. The results also broadly support the implicit assumption in prominent models of eye movement control in reading that a word's syntactic category does not play an important role in decisions about when and where to move the eyes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Joint Intra &amp; Inter-Grained Reasoning: A New Look Into Semantic Consistency of Image-Text Retrieval

Multimodal understanding aims at constructing semantic correlations among modalities of data while performing various downstream tasks. As one of the primary multimodal downstream tasks, image-text retrieval imposes a high demand on semantic alignment because of the independent expression paradigms of images and text. Existing methods mainly construct a joint embedding space at a single granularity level (either global or local). However, such single reasoning paradigms lack granularity interaction, resulting in semantic inconsistency and cross-domain catastrophes. To address these issues, we design a novel Joint Intra and Inter-grained Network (JIIGNet), focusing on not only intra- but also inter-grained interaction between modalities by combining scene information (global) with region-level (local) instances. Specifically, we simultaneously initiate three specific alignment modules, i.e., global-grained, local-grained, and cross-grained alignment modules, followed by Triplet Attention Refinement to better refine the fused embedding at the alignment-level with proper self and cross attention. For different scenarios, a Style Adaptation Head is further designed to smartly accommodate different samples. We validate JIIGNet through extensive experiments conducted on two widely used datasets: Flickr-30 K and MS-COCO, demonstrating the effectiveness of our proposed method.
Deformable Dynamic Sampling and Dynamic Predictable Mask Mining for Image Inpainting

Existing image inpainting methods often produce artifacts that are caused by using vanilla convolution layers as building blocks that treat all image regions equally and generate holes at random locations with equal probability. This design does not differentiate the missing regions and valid regions in inference and does not consider the predictability of missing regions in training. To address these issues, we propose a deformable dynamic sampling (DDS) mechanism which is built on deformable convolutions (DCs), and a constraint is proposed to avoid the deformably sampled elements falling into the corrupted regions. Furthermore, to select both valid sample locations and suitable kernels dynamically, we equip DCs with content-aware dynamic kernel selection (DKS). In addition, to further encourage the DDS mechanism to find meaningful sampling locations, we propose to train the inpainting model with mined predictable regions as holes. During training, we jointly train a mask generator with the inpainting network to generate hole masks dynamically for each training sample. Thus, the mask generator can find large yet predictable missing regions as a better alternative to random masks. Extensive experiments demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively.
SOCIOLOGICAL ASPECTS OF ANALYSIS OF POINT-RATING SYSTEM FOR ASSESSING THE QUALITY OF STUDENTS’ KNOWLEDGE

The aim of this article is empirical analysis of university model of a point-rating system in comparison with other forms of assessment of students’ achievements used by some teachers for learning purposes (on the example of Udmurt State University). The results of university teachers’ survey demonstrated two main practices of using the point-rating system and other forms of evaluation: rigid and flexible. The first group of teachers supports centralized and rigid mechanisms for regulating the system; the second one insists on providing the opportunities for teachers to establish standards. Further development of pedagogical technologies based on individual learning trajectories will require searching new methods and approaches for assessment of competences.
3D-Label-Free Human Mesh Recovery Using Multi-view Consistency

For human action analysis, 3D mesh reconstruction of the human body is very important. Most methods require a large number of training data with 3D human-annotated labels. Such training cost is high and the scales of the existing 3D human-annotated label datasets generally cannot match complex actions in complex environments. Therefore, some recent researchers have begun to study the 3D pseudo-label methods. To have sufficient constraints, most 3D pseudo-label human mesh recovery algorithms rely heavily on 3D pseudo-labels provided by some existing unsupervised 3D human pose estimation algorithms. Unfortunately, it is hard to guarantee an accurate 3D pose estimation with unsupervised learning approaches. The inaccurate 3D pseudo-labels bring negative effects on model training. To solve this problem, we propose an end-to-end 3D-label-free training framework by using multi-view consistency to provide sufficient constraints instead of any 3D human-annotated labels or 3D pseudo-labels. The multi-view consistency exploits the human body consistency attributes in multi-view images to provide self-supervised constraints. Our method is evaluated on two benchmark datasets (Human3.6M and MPI-INF-3DHP) and exhibits competitive experimental results.
The Relationship Between Readability and Text Design in Educational Websites1

Purpose: Purpose of this study is to investigate the readability levels of the websites that have educational contents.&#x0D; Design/Methodology/Approach: Web-site readability is generally examined through the structural properties of the texts, or their physical properties. This research performs readability measurements are performed by employing both of the above- mentioned methods.&#x0D; Findings: The findings illustrate that half of the websites are at the medium difficulty level, 20% of the remaining websites are easy, and 30% of them are difficult in terms of readability. It is concluded that readability and text design levels of the investigated websites are consistent with each other.&#x0D; Highlights: The most important features in terms of text design elements in educational websites are the tonal value of the text that makes it easier to read, the balanced use of the spaces between the letters, attention to the text integrity in the text, the listing levels not exceeding two levels, and the clickability of the links. It has been seen that the least considered features in terms of text design elements in educational websites are glossary and bibliography arrangement, not including scroll bars, explaining links with texts, distinguishing visited links, inverted pyramid technique, using in-page links. It has been seen that the texts on the educational websites are at a medium difficulty level according to the text design elements scale. According to the result obtained from the readability formula of the texts on the educational websites, it was seen that the readability levels were at the medium difficulty level.
List of Reviewers for this Special Issue

Journal of Management StudiesVolume 60, Issue 8 p. 2125-2127 List of Reviewers List of Reviewers for this Special Issue First published: 25 October 2023 https://doi.org/10.1111/joms.13017Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume60, Issue8Special Issue: Rethinking Control and Trust Dynamics in Organizations: Expanding our Understanding of Formal and Informal Mechanisms of Collaboration across Levels of AnalysisDecember 2023Pages 2125-2127 RelatedInformation
Improving serious illness conversations in oncology: A machine learning approach that integrates natural language processing for mortality prediction.

590 Background: ML-based mortality prediction tools in oncology can optimize clinical decisions and prompt end-of-life care discussions. Patients with advanced cancer who have engaged in Goals of Care (GoC) conversations report improved quality of life and better care alignment. However, oncologists often have overly optimistic prognoses and miss timely GoC discussions. Clinical notes are a valuable source of information, but processing and extracting data from them is time-consuming and labor-intensive. To address this issue, we have developed a machine learning application that ingests clinical notes and structured data from electronic health records (EHRs) to generate a 180-day mortality risk, prompting oncologists for GoC conversations. Methods: A predictive machine learning model was developed using data from cancer patients aged 21 and above, diagnosed between January 2016 and December 2021. Data was collected from various sources, including cancer and death registry and the EHR. By analyzing structured and unstructured data from ambulatory progress notes, a clinical profile was created for each patient. The model utilized Spark-NLP for preprocessing, applying word2vec embedding and pre-trained NER models to extract information on diseases, symptoms, procedures, treatments, and medications. Feature engineering techniques were used to select the best NLP features, combined with structured data. The model was trained using 894 patients, employing Random Forest Classifier with 10-fold cross-validation, and tested on a separate set of 43,274 patients. Performance evaluation included ROC AUC, PR AUC, and F1 Score metrics. Results: After the fine tuning, the best model showed an AUC-ROC of 0.88 on the train set and 0.75 on the test set. At a threshold of 0.44, the model achieved a balanced performance with a sensitivity of 0.70 and specificity of 0.71 on the testing set. Conclusions: Our team pioneered the development of an automated multi-modality pipeline that combines unstructured real-world data with structured data, allowing for training and testing of a fusion model. This automation opens doors for scaling and dissemination, to enhance mortality prediction. Future works will involve qualitative analysis of implementation and acceptance in clinical practice.
VISGRADER: Automatic Grading of D3 Visualizations

Manually grading D3 data visualizations is a challenging endeavor, and is especially difficult for large classes with hundreds of students. Grading an interactive visualization requires a combination of interactive, quantitative, and qualitative evaluation that are conventionally done manually and are difficult to scale up as the visualization complexity, data size, and number of students increase. We present VISGRADER, a first-of-its kind automatic grading method for D3 visualizations that scalably and precisely evaluates the data bindings, visual encodings, interactions, and design specifications used in a visualization. Our method enhances students' learning experience, enabling them to submit their code frequently and receive rapid feedback to better inform iteration and improvement to their code and visualization design. We have successfully deployed our method and auto-graded D3 submissions from more than 4000 students in a visualization course at Georgia Tech, and received positive feedback for expanding its adoption.
Multimodal AI &amp; LLMs for Peacekeeping and Emergency Response

When an emergency event, or an incident relevant for peacekeeping first occurs, getting the right information as quickly as possible is critical in saving lives. When an event is ongoing, information on what is happening can be critical in making decisions to keep people safe and take control of the particular situation unfolding. In both cases, first responders and peacekeepers have to quickly make decisions that include what resources to deploy and where. Fortunately, in most emergencies, people use social media to publicly share information. At the same time, sensor data is increasingly becoming available. But a platform to detect emergency situations and deliver the right information has to deal with ingesting thousands of noisy data points per second: sifting through and identifying relevant information, from different sources, in different formats, with varying levels of detail, in real time, so that relevant individuals and teams can be alerted at the right level and at the right time. In this talk I will describe the technical challenges in processing vast amounts of heterogeneous, noisy data in real time, highlighting the importance of interdisciplinary research and a human-centered approach to address problems in peacekeeping and emergency response. I will give specific examples specifically discussing how LLMs can be deployed at scale, including relevant future research directions in Multimedia.