Masked Generative Light Field Prompting for Pixel-Level Structure Segmentations

Abstract: Pixel-level structure segmentations have attracted considerable attention, playing a crucial role in autonomous driving within the metaverse and enhancing comprehension in light field-based machine vision. However, current light field modeling methods fail to integrate appearance and geometric structural information into a coherent semantic space, thereby limiting the capability of light field transmission for visual knowledge. In this paper, we propose a general light field modeling method for pixel-level structure segmentation, comprising a generative light field prompting encoder (LF-GPE) and a prompt-based masked light field pretraining (LF-PMP) network. Our LF-GPE, serving as a light field backbone, can extract both appearance and geometric structural cues simultaneously. It aligns these features into a unified visual space, facilitating semantic interaction. Meanwhile, our LF-PMP, during the pretraining phase, integrates a mixed light field and a multi-view light field reconstruction. It prioritizes considering the geometric structural properties of the light field, enabling the light field backbone to accumulate a wealth of prior knowledge. We evaluate our pretrained LF-GPE on two downstream tasks: light field salient object detection and semantic segmentation. Experimental results demonstrate that LF-GPE can effectively learn high-quality light field features and achieve highly competitive performance in pixel-level segmentation tasks.


An Overview of Building a Global Data Area on the Web for Farming

Abstract: A collection of best practices for publishing as well as connecting structured data on the Web is referred to as “Linked Data.” In the past ten years, the practice of Linked Open Data has significantly increased the structured data available on the Web. This structured data provides machine-readable descriptions of real-world items, opening hitherto unheard-of opportunities for research around natural language processing. With the aid of technology, the agriculture sector advanced significantly, becoming more data-driven and intelligent. Studies on how these data can be used, for what kinds of tasks, and to what degree they can be helpful for agricultural tasks, however, are lacking. Our paper puts an effort into providing an overview of concepts and technologies in the Semantic Web and their applicability to farming.


Understanding Menstruation Material in Fiqh Learning for VII Grade Female Students at SMP Syubbanul Wathon 2 Bandongan Magelang

Abstract:
This research was conducted to determine the factors that influence female students' understanding of menstruation material, how they learn menstrual material, and the extent to which class VII female students understand menstruation material at SMP Syubbanul Wathon 2 Bandongan. This research was conducted at Syubbanul Wathon 2 Bandongan Middle School with a focus on female students' understanding of menstruation material. This research was conducted using qualitative research methods with a descriptive approach and various data analysis, collection and validity techniques to support optimal results. In this research, learning about menstruation material for class VII female students uses several learning stages: learning planning, menstrual learning material, learning methods, learning media, learning evaluation and assessment. The level of understanding of class VII students' material on menstruation at Syubbanul Wathon 2 Middle School is quite good. This can be seen from their interviews and written test results, although some students don't understand it and some of the material is difficult for them.
 Keywords: Understanding Menstruation Material, Fiqh, Syubbanul Wathon


Comparative study of variant shifting-phase coding method based on noise error model

Abstract: Abstract Phase shifting algorithm, which is well-known for its high-accuracy and high-resolution, plays a crucial role in fringe projection profilometry. A class of variant shifting-phase coding method (VSPCM) has been proposed for high-speed 3D measurement. The three kinds of VSPCM include the inner shifting-phase method (ISPM), the intensity-coded method (ICM) and the modified Carré algorithm (MCA). These methods project only four patterns avoiding the need for extra images, yet achieve accuracy comparable to the classical three-frequency heterodyne method. In VSPCM, the shifting-phase, as a key code-word for phase unwrapping, is encoded as a variable value. In practice, the noise and the encoding of shifting-phase have varying degrees of impact on the performance of these methods. But the coded ranges of the shifting-phase were previouly empirical values. Besides, there are no studies related to these factors, and also a lack of comparative researches on such methods. This paper compares three kinds of VSPCM for the first time. We derive the noise error models for shifting-phase and wrapped phase in three methods and examine the impact of variant shifting-phase on fringe orders and wrapped phase in these methods. We find that the abnormal shifting-phase errors predominantly cause fringe orders errors. Fortunately, median filtering can effectively handle shifting-phase errors to ensure successful phase unwrapping. To improve their accuracy, we also deduce that the optimal shifting-phase encoding range is [−77°, 77°] for ISPM, and is [−61°, 61°] for ICM, and is [34°, 74°] for MCA. For wrapped phase errors, the distributions of ICM and MCA exhibit a U-shaped distribution, while ISPM demonstrates a uniform distribution. Based on theoretical analysis and experiment results, this paper gives recommendations for better use of these methods to boost their accuracy and offers valuable guidance for selecting an appropriate VSPCM under the specific measurement requirements.


Evaluation Method of Higher Education Teaching Reform Based on Deep Learning Analysis Technology

Abstract: Considering the shortcomings of large evaluation errors, long time, human, and material resources in the evaluation process of the current college teaching mode to improve the accuracy of the evaluation of college teaching mode and reduce the cost of the evaluation, this study proposes an evaluation method for college teaching methods based on deep learning algorithms. Firstly, the research status of the evaluation of college teaching mode is analyzed, and the reasons for the poor evaluation results of the current college teaching mode are found; then the existing deep learning algorithm is improved, and the effectiveness and speed of the method are verified by comparing with other models. Then, the evaluation model of college teaching mode is established, and machine learning is performed on the evaluation data of college teaching mode; finally, the evaluation data of college teaching mode is collected, and the application example test of college teaching mode evaluation is performed.


Children’s Accent-Based Preferences and Stereotypes in Media Contexts

Abstract: Children spend a substantial amount of time consuming screen media—particularly television and mobile apps. Research has shown that non-native and non-standard accents tend to be underrepresented in adult-oriented and children’s media, and that representations of accents tend to be stereotypical in nature. Relatedly, research has shown that children demonstrate social preferences and ascribe traits on the basis of spoken accents. In a series of four experiments, the present research investigated children’s accent-based preferences and stereotypes in two media contexts. Children aged 5-6 and 9-10 years selected characters from a variety of exemplars that spoke in different accents to play different archetypical characters in a television program (Study 1) or to serve as teachers in an educational mobile app (Study 2). Study 1 (Experiments 1 and 2) showed that children generally preferred for television characters to speak with a native, Canadian accent (compared to high-status British and non-native accents). However, 9-10-year-old children held stereotypes about the Russian accent specifically, and preferred for negative television characters (e.g., mad scientist, villain) to speak with a Russian accent. The same pattern was not observed for Chinese or Indian accents. Study 2 (Experiments 3 and 4) revealed that children generally preferred for teaching characters in educational apps to speak with a native, Canadian accent (compared to high-status British and non-native accents), particularly when the subject matter being taught was culturally neutral (e.g., oceans, space). Children aged 9-10 preferred for teaching characters to speak with non-native accents (Russian, Chinese, and Indian) when the subject matter was culturally relevant (e.g., Russian dancing, Chinese pottery, Indian cuisine). Findings of this research contribute to our knowledge about children’s accent-based biases in social perception and accent-based assumptions about cultural expertise, and may help guide the development of more inclusive and less stereotypical media offerings for children.


Chapter 10: Audiovisual sample 10.1 - 10.3

Abstract:
 Audiovisual sample 10.1: In this video, by the Folkpick channel, Lakhan Das Baul performs at the Saturday market near the cultural tourism center of Santiniketan.

Audiovisual sample 10.2: In this clip posted by Purohitmoshay Shibshankar on YouTube, Sumanta Das Baul performs at Jaydev Mela, a large religious festival attended by pilgrims, music lovers, and entrepreneurs.

Audiovisual sample 10.3: In this clip posted by Soumik Dee on YouTube, the esteemed song specialist Ranojit Gosain sings at a sādhusaṅga at his home in Nadia district.


Chapter 15: Audiovisual sample 15.1 - 15.6

Abstract:
Audio sample 15.1:

Amidst loud drumming a kemantat at Libo Jaya, in 1996, shakes his rattler as he calls on the ethereal Ajo Mudo Bumain Sedan/Young Raja-driving-a-Sedan, to give medicine.

Audio sample 15.2:

The late kemantat Bah Sehari opening his dikei by calling on the ethereal de’o Anak Ajo/Child of the Raja to aid in healing. Pinggir, Riau, 1996.

Audio sample 15.3:

The voice of Mak Chiri, is heard piercing through ordering kemantat Bah Sehari to bring the semanget back to the patient, Pinggir 1997.

Audio sample 15.4:
 A gong accompanying the drumming is being hit on the first night of a kelonkap event in Pinggir, 1997.

Audio sample 15.5:
 The jubilatory sounds of a kelonkap Penaso, 1997.

Audio Sample 15.6:

Male kemantat, Bah Teba, performing the flirtatious female ethereal Nuk Ducy, swaying his hips from side to side in exaggeration and being teased by the women in the audience who suggest to him how he should walk and sometimes try to pinch his bottom to which he responds in exaggerated shock.


Chapter 12: Audiovisual sample 12.1 - 12.2

Abstract:
Audiovisual sample 12.1:

Bertarung Untuk Dewa (Fight for God), a short documentary (6:28) on ritual life in Tenganan. Directed by Citra Aryandari. 2009.

Audio sample 12.2:

Prasi recitation of Ida Bagus Buda, 2019.


Chapter 4: Audiovisual sample 4.1 - 4.4

Abstract:
Audio sample 4.1: Zakira Hashmat Fatima. Haji Lane Imambara, 2013.

Audio sample 4.2: Zakira Rana Meerza.

Mehfil e-Haidari, 2019.

Audio sample 4.3: Zakira Nuzhat Zahra, in 2014. 11 Pemantle Street.

Audio sample 4.4: Nuzhat Zahra, in 2019. 11 Pemantle Street.


Chapter 1: Audiovisual sample 1.1 - 1.7

Abstract:
Audiovisual sample 1.1:

From the website of City Harvest Church (URL https://the.chc.app/word/63ZVTFJTjBb6kLRYZSSeek). A bilingual sermon delivered on October 13, 2019, Kong Hee, “Seek God’s Glory, Our Delight / 上帝的荣耀、我们的喜乐,” City Harvest Church, 1:04:40, October 13, 2019.

Audiovisual sample 1.2:

From the website of City Harvest Church (URL https://the.chc.app/word/aOI5RcswqceQGxVzqwhMr). View the first example of speaking in tongues during the prayer segment at the end of the sermon, at 1:09:35. Kong Hee, “Secrets to Powerful Prayer (Part 1),” City Harvest Church, August 23, 2019.

Audiovisual sample 1.3:

From the website of City Harvest Church (https://the.chc.app/word/oh8o9yLnTqwUj1PwFGf7J) . View the second example of speaking in tongues at 0:55:12. Zhao Ren, “Fruits of the Holy Spirit: Peace,” City Harvest Church, November 10, 2019.

Audiovisual sample 1.4:

View the video clip of Lim possessed by Guan Di Ye. Facebook video, posted by Lim Tiong Kang, October 8, 2019.

Audiovisual sample 1.5:

View the video clip that archives the act of interpretation of the Emperor God’s utterances at 0:09:00. Facebook video, posted by “YewKew Jump Jump Jump,” September 25, 2019.

Audiovisual sample 1.6:
 View an example of a training video that demonstrates how to speak in tongues. “How to Speak in Tongues,” YouTube video, posted by EncounterTV, May 24, 2016.

Audiovisual sample 1.7:

On praying in tongues, one finds sermons delivered by megachurch pastors such as Joseph Prince, founder of New Creation Church, which is another independent megachurch in Singapore. “Why Do We Pray In Tongues? | Joseph Prince,” YouTube video, posted by Joseph Prince, June 11, 2020.


Chapter 4: Audiovisual sample 4.1 - 4.4


Rising to Meet the Challenge of Generative AI

Abstract: ChatGPT launched in November 2022 and quickly became the fastest-growing user application in history, marking one million users in two months—a milestone that took TikTok nine months to achieve and Instagram two and a half years.1 That explosive growth has come with an explosion of concern for the ability of scientists and regulators to understand what it is, how it works,2 and its potential to change life as we know it. Politicians and technology executives alike are calling for more and better regulation to ensure apocalyptic scenarios of artificial intelligence (AI)-aided disasters (everything from AI-created weapons to sentient AI systems) do not come to pass.3 Meanwhile, the practice of law is dealing with the implications of a tool that can pass the Bar exam4 and colleges and universities are grappling with the reality that students can use generative AI to complete just about any assignment they can give.5 This article is not intended to “solve” the problem of generative AI. Rather, recognizing the astonishing pace of development of generative AI tools and their impact on business law and other higher education classes, it seeks to provide specific, concrete steps that faculty can take to evolve alongside these tools. There is no way to “AI-proof” your classes. However, taking the steps outlined here can help you decide what you want to teach and how you should teach it. It offers a structure for identifying the content you want to keep and what you can let go of and tips for redesigning assignments and syllabi to clarify your approach to students and reduce academic misconduct. To understand the profound impact of generative AI and tools like ChatGPT, it is helpful to begin by unpacking some of the language that is used in this field.6 AI, short for “artificial intelligence,” generally refers to the use of machines, particularly computers, to perform tasks that simulate the use of human thinking, reasoning, and learning.7 We encounter AI throughout our day, when using computer applications like a Google search, interacting with a chatbot on a consumer website, or using a virtual assistant like Siri or Alexa. The ubiquity of AI in our daily lives is predicted to increase.8 In the near term, AI's use will likely become more ubiquitous as it becomes embedded in the way we interact with everyday items like our cars, office computers, and coffee machines.9 The term artificial “intelligence” is controversial because machines do not actually have the capacity to think or learn like humans. Their programming can simulate some aspects of human intelligence, but they do not reason like a human.10 For this reason, scientists distinguish between “strong AI” or “artificial general intelligence” and “weak AI.”11 Weak AI is what we generally have in use today—computers simulating human intelligence while they complete a specific type of task they have been programmed to perform. Strong AI is a theoretical system that thinks, reasons, and has self-awareness like a human (and could evolve to exceed human intelligence—thus prompting doomsday “AI takes over the world” scenarios). Machine learning is a type of AI that programs systems to “learn” and improve their ability to perform assigned tasks without explicit programming.12 Machine learning relies on algorithms that allow machines to sort vast quantities of data and use that data to make predictions and find patterns, ultimately teaching themselves through repetition and human intervention, while human programmers tweak the algorithms that guide the learning process.13 The most powerful and complex AI applications today use neural networks—a type of machine learning that simulates the way neurons in the human brain communicate information.14 Generative AI refers to the use of AI applications to create something new, including images, text, and other forms of media. Large language models (LLMs) use machine learning and highly sophisticated neural networks to process and generate text.15 LLMs are trained on enormous datasets with billions of inputs—imagine starting by dumping all of the Internet into a giant bucket and you get the idea. Somewhat disturbingly, the contents of those databases are not transparent,16 and litigation has alleged that the databases likely contain a significant amount of copyrighted material.17 The LLM uses this dataset to process inputs (i.e., human prompts to the LLM interface) and predict the most helpful, common, and relevant responses.18 In effect, the LLM crowdsources answers to the questions it is asked, by using its skills in pattern identification and prediction to generate the most likely and useful textual responses given the information in its database. The tools we know as ChatGPT, Bard, or Bing are simply interfaces using LLMs to produce responses to prompts from external users. Generative AI's flaws are predictable, given how the technology works. Though as humans, we are likely to anthropomorphize LLMs and attribute them with human characteristics,19 they are not thinking beings—they are simply computer models with predictable flaws arising out of an astonishingly complex but ultimately nonhuman system. One of the most glaring flaws of LLMs is their potential to produce inaccurate, false, or flawed responses, in which the model puts together strings of text that do not point to accurate information but mimic accurate information in a compelling way. For example, when prompted for a peer-reviewed journal article, an LLM might string together a series of notations that look like a journal article, with a title, volume, issue, and page numbers but which does not point to an existing source. LLMs can create a variety of types of false information, such as false journal articles, false meetings of historical figures, and false medical information.20 Early in the use of LLMs, these responses were dubbed “hallucinations.”21 Notably, even that term is anthropomorphically loaded—it suggests the LLM is operating in a semi-conscious human way, “imagining” events that did not occur, rather than simply acting as a flawed computer model producing inaccurate results. Another essential limitation of LLMs is also rooted in their technological structure. LLMs are, by their nature, predicting and generating text that aligns with information already in their database. The information in their database was generally created by humans—flawed and biased humans, producing information that is also flawed and biased, operating in societies with existing structural racism and bias. If faced with a question like “please list the top twenty jazz pianists,” the LLM does not independently assess the work of all living and dead jazz pianists and reach a conclusion about their relative merit. Rather, it crowdsources information, identifying patterns in commonly reported responses to this question and developing a prediction based on that information. LLMs cannot account for historical inequities and biases in reporting on artists, let alone the biases that go into the development of artists and their access to recording contracts or critical acclaim.22 In an area like jazz music, with deep historical roots of inequities in the treatment of men and women,23 one cannot expect an LLM to do anything other than repeat existing biases, which are reflected in popular source materials. As a result, when queried, ChatGPT, crowdsourcing from existing sources, reported no women in the top 20 jazz pianists.24 ChatGPT produces this result, despite the historical significance of figures like Mary Lou Williams, Alice Coltrane, and Shirley Scott, iconic female artists well-known to jazz scholars and aficionados alike.25 DALL-E, a generative AI tool that creates images based on textual prompts, produces similarly biased responses. When prompted to create a “realistic photograph quality picture of a college professor talking to a large group of students,” DALL-E created the following image, of a white male professor surrounded by white students Figure 1: Is this image “incorrect”? Survey data from 2019 suggests over seventy-five percent of professors are white, while fifty-five percent of undergraduates are white.26 Data from 2021 show white men continue to represent the largest percentage of tenure-track faculty.27 Given this landscape, it is entirely consistent with AI's programming to generate images that center white men as professors and to leave women out of a list of top jazz pianists. Of course, the answer begets a closer look at the question. If we are asking which jazz musicians have been most widely recognized or what the “average” college professor looks like, the tools are performing as programmed. But there are many other questions embedded in this call and response, and they get at the heart of what it means to use generative AI in education. If the average user of an LLM assumes that this list of jazz pianists accurately represents a list of the most talented jazz pianists ever to have lived, the technology is reinforcing inaccurate information based on existing societal biases. To be clear, the LLM cannot and is not answering that question. The LLM, in this scenario, is also not considering the question of what cultural or legal barriers might prevent or have prevented women from becoming known in the field of jazz music (though it could, if directly asked).28 It is simply reproducing a list of commonly mentioned “top” jazz pianists. Similarly, DALL-E is not “incorrect” when it creates a likely image of a professor as white and male. The problem here is that research suggests the images we view shape our ideas of what is possible.29 Thus, if popular media images are dominated by the “most likely” outcomes generated by AI models, stereotypical roles will be harder to reject and move past. Generative AI can emphasize and blind us to the way our cultural biases show up in everything from the medical literature to our notions of beauty because they are, by their programming, designed to reflect a majoritarian view of the world. To be clear, DALL-E and ChatGPT are not different in this action than existing search engines or media companies, which are also built on crowdsourcing and majoritarian views. The challenge is that, as these tools become ubiquitous and embedded in our daily lives, we may come to view them as definitive sources of truth, even more so than we currently do with search engines. When a student asks ChatGPT a medical question and receives a conversational response that mimics a medical professional, they may assume the answer they are receiving is accurate for all populations rather than understanding that the information is likely based on a medical study of young, white men and may not be representative of the population as a whole.30 These are two of the most prominent concerns about generative AI, but there are other flaws worth noting. Generative AI cannot replace the need for professionals in real life to “think on their feet” and react quickly in scenarios that require human empathy and communication skills. It cannot respond in real time to complex scenarios with constantly changing fact patterns and complicated human actors. It bases its responses on existing scenarios and patterns, so it is unlikely to make truly innovative, creative leaps. Because it lacks human intuition and feeling, it also cannot look below the surface of a question to respond to a deeper, more essential question, or even draw out the question that is not asked. As a consultant might say, “AI can only give the client what they ask for—so my job is secure.” Implied: We often do not know what we want, in business or in life, and we rely on the insight from the humans around us to help identify our underlying needs. One approach or response to the wave of generative AI would be to plant a flag in the ground and refuse to move or evolve. Rather than changing the content of their courses, in this scenario, faculty might argue that universities and colleges should instead institute more stringent academic integrity policies and punishments and develop better ways to “catch the cheaters.” This position is fundamentally flawed, for two primary reasons: (1) faculty will never be able to “AI-proof” their courses, and (2) learning the responsible use of AI is essential to students’ future success. First, while creating an environment that supports academic integrity is important, some assignments and assessments will always be able to evade restrictions on the use of AI. While many faculty have reasonably decided to turn toward more in-class assignments and assessments to reduce cheating, this option is not available to those teaching online. Moreover, not all assignments can be completed during class hours. What about developing more and better ways to catch the cheaters? That is, can we not just proctor our way out of this situation? Even keeping in mind that online proctoring has limited application and can only be used for time-limited assessments, the reality is that many universities had begun moving away from AI-enabled proctoring before ChatGPT came on the scene due to concerns about privacy, discrimination, and unreliability.31 What about AI-text detectors like ZeroGPT and GPTzero?32 Can we not use AI to detect AI? Unfortunately, these tools have been shown to be unreliable33 and biased against non-native English writers.34 In addition, work-arounds are notoriously easy to find, most commonly (and ironically) by using AI paraphrasing tools.35 Faculty can create assignments that are easier to complete with generative AI and those that are harder. In most cases, however, particularly in large undergraduate survey courses, generative AI will be able to complete most assignments, particularly those completed outside of class. If ChatGPT can pass the Bar exam, it can probably draft a better legal essay than most undergraduate students. It certainly can undertake any multiple-choice questions a faculty member might write. This means students will have to decide to complete their assignments knowing that they have the answer key (via ChatGPT) and knowing faculty probably cannot catch them cheating. Students inclined to cheat (for any number of reasons, which might include the struggle of balancing school with full-time work,36 caring for family members,37 or dealing with other challenging life circumstances like food insecurity38 or physical or mental illness39) now have a straightforward way to successfully complete their assignments. Students who do try to complete their assignments without assistance may as a result be at a disadvantage, particularly in classes using curved grading, and faculty would not be able to tell one group from the other. This could lead to grading inequities, not to mention even greater incentives for students who would not normally bend the rules, if only to be able to stand on a level playing field. Second, and I believe even more importantly, what faculty teach should have relevance to the way their students will undertake their future work. For those teaching undergraduate business law courses at a business school, students are unlikely to become lawyers. Rather, they will most likely be applying legal content to business situations. They need to learn how to apply the information they are learning to these real-world scenarios. Because their real world will likely include generative AI, learning how to use it safely, responsibly, and effectively to identify legal risks and strategic legal opportunities and how to fill in the known gaps and defects of the technology, particularly as relates to bias and inaccuracies. In my experience, faculty want to evolve but are not sure how. Even knowing that students will be likely to use AI tools in their future work, many faculty remain convinced that there is some basic, fundamental information that students need to use the technology responsibly and to avoid the limitations described above. At the same time, faculty are unsure how to integrate this technology into their courses while retaining the essential information students need and focusing on the human-thinking skills that generative AI cannot provide. Finally, many are aware of the limitations of generative AI discussed above and want to avoid using the tool in a way that furthers biases or inhibits creative and critical thinking. In this part, I begin to address these challenges. I discuss how to start using generative AI and how to adapt the curriculum to meet the needs of students today. This process will include a look at both content and pedagogy—the what and the how. Before moving further, however, I must emphasize that faculty themselves must become conversant with the basics of using generative AI and LLMs. Your subdiscipline's tools may vary, but at a minimum, anyone can start with ChatGPT, Bard, or Bing (all of which have a free version) to get familiar with the technology. Most colleges and universities with a center for teaching and learning have training resources available, including opportunities for coaching and mentoring, and articles about the integration of AI within and beyond their organization,40 and a quick web search for articles about “using ChatGPT in higher education” will yield thousands of results. To adapt the content of a course to meet the challenge of generative AI, I propose two primary steps. First, faculty should review their learning outcomes. Then they should consider if the content they are covering (and assessing) falls into an essential content area. After doing so, faculty will likely find that some, or perhaps much, of the content currently included in the syllabus is not essential. They may also find content that will be difficult or even impossible to assess, given the availability of generative AI. Once faculty remove that content and revise learning outcomes, they can then focus on how to best teach essential legal content, while also integrating generative AI into class assignments in a meaningful and relevant manner, allowing students to use the technology responsibly and effectively in the future. Learning outcomes are statements of an intended outcome for a course or program, often constructed in measurable terms to guide the development of metrics of assessment.41 A common tool used to guide the development of learning outcomes is Bloom's Taxonomy, a classification tool for learning outcomes that divides them into six categories of learning (remember, understand, apply, analyze, evaluate, and create). Bloom's Taxonomy follows a hierarchical manner, with lower levels serving as a base for higher levels of thinking and learning.42 An extensive literature surrounds the development of learning outcomes, and many higher education institutions now mandate the use of learning outcomes in courses and programs, often for accreditation purposes.43 To address generative AI, faculty must keep in mind that students will have basic information and content related to legal subject areas readily available. Moreover, generative AI can easily substitute and complete assignments that test lower levels of learning and cognition. As a result, focusing a course on recalling and identifying information is likely to make the course less useful to students and more vulnerable to cheating. Rather than focusing student learning on skills that AI is likely to perform as well or better than a human, faculty should teach and assess uniquely human skills that AI cannot replace. Faculty should focus on how students will use, apply, and think critically about the information they can readily find rather than memorizing content. The Oregon State University (OSU) Ecampus division of online learning has reframed Bloom's Taxonomy in light of the pressures of generative AI.44 For each level, this revised Bloom's Taxonomy compares what AI can do with associated human skills AI cannot replicate. For example, for the learning outcome of “recall,” this chart notes that AI can readily recall faculty information, while the associated uniquely human skills would be recalling information when technology is not available. At the level of “analyze,” the chart notes that AI can compare and contrast data, and infer and predict, while the associated human skills are critically thinking and reasoning within cognitive and affective domains. In general, the chart suggests that “[a]ll course activities and assessments will benefit from review given the capabilities of AI tools; those at the Remember and Analyze levels may be more likely to need amendment.”45 What might this amendment or reframing look like in a business law course? Consider a learning outcome such as: “[s]tudents will be able to identify and describe the concepts of offer and acceptance in a contractual setting.” Identify and describe are learning outcomes at the lower end of Bloom's Taxonomy and can easily be replicated by generative AI. This learning outcome could be revised to call on higher levels of thinking: “[s]tudents will be able to evaluate whether a hypothetical scenario could be considered legally binding based on contractual concepts of offer and acceptance.” Many faculty reviewing this example will point out, appropriately, that students cannot apply the concepts of offer and acceptance without learning what those terms mean, or what it means to use legal reasoning in a hypothetical fact scenario. This brings us to the next question faculty face when revising learning outcomes: What content-based information must remain in a course in order for students to engage in higher-level reasoning? I suggest there are three primary areas of content that are necessary to support higher levels of thinking: structural basics, key content areas, and magic words. I provide examples below of each of these concepts from an introductory business law class Figure 2. Structural basics are organizing concepts that higher-order concepts build and rely upon. These are basic elements of your field or discipline that are essential to applying more specific concepts using higher-level thinking. For example, students must understand the concept of a legal theory and how legal claims are often organized into “elements” before they can determine what legal theories might apply to a given fact scenario. Students also need to understand how the legal system is structured, including the difference between federal and state courts, to understand how, for example, federal constitutional rights differ from state statutory rights. Magic words are specialized words and concepts that can shift the outcome of an AI prompt. Students must learn magic words to create successful prompts that use generative AI capabilities effectively. In law, the concept of “magic words” can be considered the use of specific terminology to invoke legal meaning, typically created by statute or case law. For example, the concept of a “reasonable person”46 has a specific meaning in the context of negligence and tort law. In the context of generative AI, magic words can be considered language that will change the outcome of a prompt. Considering these concepts together, students may need specific legal language to craft a prompt in an LLM that will generate a useful and legally appropriate response. Consider the notion of a “reasonable accommodation” under the Americans with Disabilities Act (ADA). A “reasonable accommodation” has a specific legal meaning. As a result, asking ChatGPT whether an employee's request is a reasonable accommodation produces a different response than asking what might be a reasonable request. For example, when I asked ChatGPT whether it was reasonable for an employee to be given dictation software, ChatGPT discussed efficiency, fairness, and a potential employee contribution, among other factors. When my prompt used the term reasonable accommodation, the response included information about the interactive process, essential functions of the job, and legal requirements, including the ADA.47 Key content or concepts refers to concepts or content that can be applied when learning related or analogous content areas. For example, the concept of the reasonable person can be found in tort, criminal, and contract law, though it has different meanings based on the specific context. Understanding the concept of “protected classes” is essential when applying Title VII of the Civil Rights Act, but the same concept, with different protected classes, is useful when understanding the ADA, Age Discrimination in Employment Act, or state anti-discrimination laws. Understanding that employment law requires an analysis of whether a plaintiff has been discriminated against due to their identity or perceived identity as a member of a protected class allows students to learn about one area of content (i.e., Title VII) and apply that to another area of content (i.e., state-specific discrimination in employment laws). By identifying these essential content areas, I do not mean to suggest these areas must appear in specific learning outcomes or that students should be expected to memorize the definitions of these terms. Rather, students should be able to use this information when applying these concepts in a complex scenario. This content should remain but that does not require a new learning outcome at the “memorize” level. Let us apply this concept in the context of our contracts learning outcome. As noted above, faculty may believe that the concepts of offer and acceptance are essential foundational knowledge to a learning outcome that requires applying these concepts. Certainly, any overview or discussion of contracts will include a discussion of contract formation, which requires an understanding of offer and acceptance. This, however, does not mean that students must be able to recall those concepts (i.e., memorize) and reproduce them, unaided, in an assessment. They simply must be able to use those concepts effectively when analyzing a hypothetical scenario or generating a query for ChatGPT. When struggling to determine how to include this information in your curriculum, consider how students are likely to encounter those terms in their lives in the future. For example, I have worked with student-athletes who have received offers for name, image, and likeness (NIL)48 deals via Instagram direct message. The athletes often believe they cannot “accept” an offer via social media or through a direct message. When working with these students, I discuss the concepts of offer and acceptance. After a short conversation about these concepts, I can then ask them to assess whether a particular scenario (deliberately created to be ambiguous) includes a legally binding offer and acceptance. For an in-person class, I can have them discuss this scenario without the use of AI and then test their conclusions against what ChatGPT suggests. In an online class, where I cannot control the use of AI, I can have the students engage with ChatGPT to assess the scenario and then send me the prompts they used and a record of the conversation. I can then have them discuss which side they find more convincing, or ask them to decide which of several cases is most applicable to the scenario. In short, rather than trying to keep AI out of the conversation, I bring the technology in, just as they might in their real lives, and then ask them how they would take the next step of testing the AI's conclusion before they apply it to their lives. In addition to these areas of substantive content, three other areas of curriculum stand out as deserving to remain when updating a course in light of generative AI. They are focused on critical thinking skills to address the limitations of AI as applied to a specific topic area and include: research tools, decision-making factors, and areas of bias Figure 3. Research tools: As described above, generative AI may provide false or misleading responses to prompts. It may invent citations, prior facts, and data. Thus, students need to be taught how to verify the information they get from a generative AI tool. They need the skills to find the primary source behind a citation, such as a legal statute or database. They may need to be able to navigate legal websites, read cases, and analyze statutory language to cross-reference an AI-generated response. Instead of teaching specific case law, this guideline stresses the importance of teaching students how to read cases. Instead of teaching rules based on an existing statute, this guideline also suggests that students need to learn how to find the statutes for their state and how to apply the statutory language. Problem-solving: Metacognition is an umbrella term that has been used to describe a variety of cognitive processes.49 Meta-strategic knowledge (MSK) is a subcomponent of metacognition in which individuals have “an awareness of the type of thinking strategies being used in specific instances.”50 MSK includes knowledge of specific thinking strategies tuned to particular content areas and why and how to use them.51 In law, there is a very specific way of analyzing problems we often call “thinking like a lawyer,” which might also be called the MSK for law. The characteristics of legal thinking include considering both sides of an argument, developing counter-arguments, and stress-testing conclusions against opposing views.52 It also involves thinking through analogy—applying existing legal rules to novel situations that may look nothing like the original, and justifying that application through the identification of similarities in fact, precedent, and/or policy.53 To become adept at analyzing legal scenarios and developing good prompts for a generative AI tool, students must learn this way of thinking. Similarly, students in a chemistry class will need to understand the scientific process, while students in a literature class will need to learn what tools to use when analyzing litera


Multi-thread two-dimensional digital image correlation for full-field displacement and rotation angle measurement

Abstract: The conventional two-dimensional (2D) digital image correlation (DIC) method often fails to work when there is a large rotation between the reference and deformed images. The objective of this study is to propose a 2D DIC method to tackle this issue and calculate the full-field rotation angle. In our method, the deformed subset is chosen along the direction of gradient orientation angle error (GOAE) between the pseudo point of interest and the searching point. In addition, the initial deformation parameters for the inverse compositional Gaussian–Newton (IC-GN) method are set based on the GOAE to guarantee its convergence. Furthermore, a best fit method is presented to calculate the rotation angle of each target deformed subset using the converged deformation parameters from IC-GN method. Moreover, due to the high computation intensity of the proposed method, a multi-thread parallel computation technique is used to speed up its computation. Simulation and bending experiment results show that the proposed method can measure both the full-field in-plane displacement and rotation angle with relatively high accuracy and efficiency in the case of a large rotation, in comparison with the conventional DIC method and a ring template-based method. The proposed method is also robust to the strained deformation and image noise to some degree.


Cluster segmentation algorithm for enhancing edge information

Abstract: To solve the blurred image edge segmentation problem and the influence of a complex background on the segmentation accuracy of the clustering algorithm, this paper proposes an edge gravity mean shift (EGMS) image segmentation algorithm. Aiming at the problem of partial edge information loss when the Canny algorithm processes images, the L0 gradient minimization method is introduced to iteratively optimize the gradient map to extract more edge information, and then the L0 Canny edge detection algorithm is proposed. Aiming at the problem that the Epanechnikov mean shift algorithm will converge to the local optimum due to the non-smooth Epanechnikov kernel function, the optimal bandwidth is obtained, and the smoothness of the kernel function shape is improved by cross-validation and spline function. Then, the sleek Epanechnikov kernel function is constructed by integrating the weight function, which can reflect the richness of clustering information at the peak so that the kernel function can more accurately reflect the data characteristics of the peak. Based on the sleek Epanechnikov kernel function, the sleek Epanechnikov mean shift (SEMS) clustering algorithm is proposed. The EGMS image segmentation algorithm is constructed by fusing the L0 Canny and SEMS. The experimental results indicate that EGMS can retain more edge contour information when segmenting images, and it can generate better clustering clusters for clustered datasets. In addition, compared to other clustering segmentation algorithms, the proposed EGMS algorithm demonstrates superior clustering accuracy and convergence on the majority of image datasets. Our code is available at: https://gitee.com/zhiweilu111/edge-gravity-mean-shift.


Disease mongering, overdiagnosis, and media practices: a critical discourse analysis of sluggish cognitive tempo (SCT) and the motivational deficiency disorder (MoDeD) spoof

Abstract: Abstract This paper explores ways in which the strategic use of discursive and generic conventions has the potential to create a non-existent pathology and mislead the public. This case study compares and examines datasets of different genres (newspaper issue reports, online videos, and Wikipedia pages) dealing with a condition considered as an actual illness (Sluggish Cognitive Tempo, SCT), and another (Motivational Deficiency Disorder, MoDeD), invented as a spoof to raise awareness about disease mongering, overdiagnosis, and medicalization. We evince common language strategies that, irrespective of the genre, can be employed in media discourse, both in the name of genuine medical information and in pursuit of more ethically questionable ends. The methodological tools provided by Critical Discourse Analysis are applied to both the authentic and the hoax texts in order to investigate the media representations of SCT and MoDeD, juxtaposing the ways in which both are framed conceptually, defined linguistically, and popularized to lay audiences. The findings indicate the existence of a common repertoire of lexical-phraseological, rhetorical and discursive patterns that typify the popularization of medicalized statuses and combine to increase the persuasiveness and authority of overdiagnosis, ultimately advancing the case for medicalization with the public at large.


Postgraduate Institute of India (<scp>PGI</scp>) textbook of laboratory and clinical hematology. By Koyamangalath Krishnan (Ed.), Mumbai, India: Bhalani Publishing House. 2024. pp. 3244. ISBN: 9789381496763

Abstract: American Journal of HematologyVolume 99, Issue 4 p. 800-800 BOOK REVIEW Postgraduate Institute of India (PGI) textbook of laboratory and clinical hematology. By Koyamangalath Krishnan (Ed.), Mumbai, India: Bhalani Publishing House. 2024. pp. 3244. ISBN: 9789381496763 Rajiv K. Pruthi, Corresponding Author Rajiv K. Pruthi [email protected] orcid.org/0000-0002-6969-929X Division of Hematology, Department of Internal Medicine, Mayo Clinic College of Medicine, Mayo Comprehensive Hemophilia Center, Special Coagulation and Molecular Hematopathology Laboratories, Mayo Clinic, Rochester, Minnesota, USA Rajiv K. Pruthi, Division of Hematology, Department of Internal Medicine, Mayo Clinic College of Medicine, Mayo Comprehensive Hemophilia Center, Special Coagulation and Molecular Hematopathology Laboratories, Mayo Clinic, Rochester, MN 55905, USA. Email: [email protected]Search for more papers by this author Rajiv K. Pruthi, Corresponding Author Rajiv K. Pruthi [email protected] orcid.org/0000-0002-6969-929X Division of Hematology, Department of Internal Medicine, Mayo Clinic College of Medicine, Mayo Comprehensive Hemophilia Center, Special Coagulation and Molecular Hematopathology Laboratories, Mayo Clinic, Rochester, Minnesota, USA Rajiv K. Pruthi, Division of Hematology, Department of Internal Medicine, Mayo Clinic College of Medicine, Mayo Comprehensive Hemophilia Center, Special Coagulation and Molecular Hematopathology Laboratories, Mayo Clinic, Rochester, MN 55905, USA. Email: [email protected]Search for more papers by this author First published: 13 February 2024 https://doi.org/10.1002/ajh.27235Read 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. CONFLICT OF INTEREST STATEMENT Author declares no conflict of interest. Volume99, Issue4April 2024Pages 800-800 RelatedInformation


Chapter 2: Audiovisual sample 2.1 - 2.2

Abstract:
Audiovisual samples 2.1 & 2.2:

I warmly invite the readers to start this section by watching and listening to these audiovisual samples, recorded during my fieldwork at the Poderaccio camp and at the Coltano camp.

First file: Zikr for Sultan Nevruz, Poderaccio camp, March 2019.

Second file: Coltano camp, June 2019.


Chapter 6: Audiovisual sample 6.1 - 6.2

Abstract:
Audiovisual Sample 6.1: Excerpt from Sur Mazūrī, performed at the shrine of Shah Abdul Latif Bhittai in Bhit Shah, Sindh, Pakistan. Recorded by the author on January 2, 2015.

Audiovisual Sample 6.2: Tand and jhaṛ in Sur Husainī. Recorded by the author on January 12, 2015.


Chapter 5: Audiovisual sample 5.1 - 5.7

Abstract:
Audiovisual sample 5.1: A short extract from Oramics by Daphne Oram, 2007.

Audiovisual sample 5.2: “Tuning Meditation” by Pauline Oliveros (in 3D binaural audio).

Audio sample 5.3: Eliane Radigue’s Songs of Milarepa, with the voice of the Tibetan Lama Kunga Rinpoche.

Audiovisual sample 5.4: “On Behalf of Nature,” by Meredith Monk.

Audiovisual sample 5.5: “Scrytch,” by Lyra Pramukh.

Audiovisual sample 5.6: “Eternal,” by Holly Herndon.

Audiovisual sample 5.7: Aïsha Devi, Of Matter and Spirit. Album trailer.


Methodologies of Construction of Circular Models

Abstract: This paper is focused on various methodologies of construction of circular models contributed by researchers in directional data analysis for modeling angular/periodic data. Several methods of construction are discussed in detailed here. These methods can be adopted for modeling angular/circular/periodic data which may be occurred in many practical situations in various fields.


Chapter 1: Audiovisual sample 1.1 - 1.7


SITI Training: Viewpoints and the Suzuki Method for Cross-Cultural Collaboration

Abstract: The training that the Saratoga International Theater Institute (SITI) perfected during its thirty years of existence is its most relevant heritage. SITI believed that training was a crucial part of a performer’s education and ethos. Its constitutive elements – Viewpoints and the Suzuki Method for actor training – are here put in a dialectical opposition that nurtures hybridization. This article investigates how, in fact, these two trainings intersect and also form new ‘languages’ (that is, systems of representation) whenever they are performed. A contextual analysis of SITI’s training as a foundation for making work and as a means for educating actors provides a clearer understanding of why and how SITI training is an instrument that facilitates and fosters cross-cultural collaboration.


Reimagining Teacher Preparation: Digital Literacy in the Post-Pandemic Era

Abstract: The advent of COVID-19 exposed significant flaws in the capacity of educational institutions to deliver quality instruction to their students in a crisis. The rapid instigation of advanced digital technologies to facilitate remote learning by universities and colleges provided a pedagogical bridge to cover the time and space restrictions forced on the normal face-to-face classroom experience. Unfortunately, as the impact of the pandemic has waned, educational institutions drew down their investments in the digital technology infrastructure, hardware, and software that were indispensable to sustaining student learning over the two years the pandemic interrupted normal educational schedules. However, it is argued that it would be extremely prudent to expand rather than contract these resources. Research indicates that many teachers struggled with technologies in remote teaching during COVID-19. This raises the question of how teacher education programs can adequately provide future teachers with essential digital literacy skills to teach in diverse technology-enriched learning environments. The proposed frameworks aim to reshape the curriculum in teacher education programs to cultivate and develop preservice teachers’ digital literacy skills.


Analysis of the Influence of Knowledge, Work Procedures and Technology on Employee Performance

Abstract: The purpose of this study is to determine the influence of Knowledge, Work Procedures and Technology on Employee Performance at the Aceh Transportation Office both partially and simultaneously. This research method is by testing hypotheses using multiple linear regression analysis, F test (simultaneous) and t test (partial) intended to determine the influence of the independent variable on the dependent variable at a confidence level of 95% (α = 0.05). The object of this study is Knowledge, Work Procedures and Technology on Employee Performance at the Aceh Transportation Office The sample size of this study is as much as 96 employees. Research data were collected through questionnaires and documentation studies. The results showed that simultaneous tests between the variables of Knowledge, Work Procedures and Technology affect the Performance of Employees at the Aceh Transportation Office. Then partially, Knowledge affects the Performance of Employees at the Aceh Transportation Office. Work Procedures affect the Performance of Employees at the Aceh Transportation Office. Then partially, namely Technology affects the Performance of Employees at the Aceh Transportation Office.


Hubungan Penggunaan ChatGPT Dengan Pemenuhan Kebutuhan Mahasiswa

Abstract: The rise in the phenomenon of using Artificial Intelligence such as ChatGPT and the very high trust of the Indonesian people in the development of Artificial Intelligence, especially the Gen Z generation group with Higher Education status (students), has given rise to debate in terms of the impact of its uses as a technology that helps the learning process. On the positive side, ChatGPT can provide personalized and interactive learning, while on the negative side, ChatGPT can provide incorrect and inaccurate information. Meanwhile, penetration of its uses can no longer be avoided because it is easy to access, so the good and bad sides will continue to be felt, especially for its users. Moreover, until now there is still very little research on how this technology can help fulfill the needs of students within the research campus, especially the most important need for them as someone who is pursuing higher education, namely the need for satisfying learning. Therefore, this research aims to explain the relationship between the uses of ChatGPT and the fulfillment of learning needs for students at the Faculty of Communication Sciences, Padjadjaran University with the help of the Uses and Gratification theory approach. The research method usesd is a quantitative method with a type of correlational study design using the Spearman Rank correlation test The results of this study show that there is a positive and significant relationship between the uses of ChatGPT and fulfilling the learning needs of students at the Faculty of Communication Sciences, Padjadjaran University with a correlation coefficient of 0.834. So, it can be concluded that the higher the uses of ChatGPT, the higher the fulfillment of students' learning needs, which means that frequent uses of ChatGPT can indicate that it is closely related to satisfying learning needs.


Self-Identifying Alcohol Scene Detection from Video and Showing the Message Alcohol Consumption is Injurious to Health

Abstract: Abstract: The proposed project aims to detect alcohol consumption from real time video footage using machine learning and show the message "Alcohol consumption is injurious to health".


Perceived Communication Competence and Learning Styles in the New Normal

Abstract: The student has appreciated and adapted to the new normal phase. In the two years, they spent at home doing their modules and online classes, they have adapted quickly, as these students are in the technological age. This study intended to assess the communication competence and learning styles of the Bachelor of Elementary Education at St. Michael's College, Higher Education Department. It specifically aimed to answer the effect of the new normal modality of communication competence and learning styles in terms of visual, auditory, visual/iconic, and kinesthetic. This study utilized a descriptive correlation study research design, a simple random sampling procedure, and a survey questionnaire for learning styles and communication competence. Results revealed that some students were under moderate and high. The researchers used the mean and standard deviation Kendall Tau Rank, and Shapiro-Wilk Test to determine the significant relationship between learning styles and communication competence variables. The researchers recommended a program considered “Upgrading Learning Styles and Fundamental Communication Capabilities” to enhance and improve the learners' skills in different aspects, boost their confidence, and enhance their abilities in using English as a communication tool and learning.


Unveiling Proficiency Levels in Essay Construction among Learners: An In-Depth Exploration

Abstract: Writing essays is one of the activities that teachers frequently delegate to their classes; yet, it is also one of the many challenges that many students experience because it appears to be a dreaded task among them. Whether it is for a class assignment, a scholarship, or even a competition, many students find that the work of writing an essay to be quite challenging. Students have a tendency to ignore the significance of essay construction because of this fact alone, which elucidates that the difficulty is constantly present throughout the process of writing. This is the reason why students underestimate the relevance of essay building. The purpose of this study is to investigate the degree to which students in Grade 11 ABM have mastered the creation of essays within the framework of the subject of writing a summary of the results of the test that was carried out with the objective of determining the level of writing ability exhibited by the outputs produced by the respondents. The method of research used in this study was quantitative. In this particular investigation, a quantitative research design was utilized as the appropriate method of inquiry. A strategy that centers primarily on the application of analytical content evaluation. The assessment of overall essay construction performance revealed a substantial challenge. The findings highlight a considerable proportion of students grappling with challenges in their essay construction skills. Consequently, the study advocates for the implementation of an intervention program, termed the Writing Excellence Enhancement Program (WEEP).


Decision Support System Legalization Of Distribution Substation With Priority Weighting Bayes Method

Abstract: PT PLN Batam is a national company operating in the field of electricity in the Batam region. As of December 2023, the number of customers is 367,709. Electricity consumption on Batam Island continues to increase with an average annual growth of 6.39% (2013-2023), and the average customer growth is 4.07% per year (2013-2023). This growth is accompanied by an increase in the assets of electricity distribution equipment, especially distribution substations. Distribution substations in PT PLN Batam consist of two types: concrete distribution substations and portal distribution substations. The total number of distribution substations as of December 2023 is 1,849. Distribution substations are vital distribution assets, requiring proper handling, especially regarding legal ownership of the land occupied by the substations. Land issues primarily occur in locations where distribution substations are situated in business centers or along main road medians. With the continuous increase in distribution substations, land legalization issues also escalate. To mitigate the risk of these land issues, PT PLN Batam implements the Decision Support System for the Legalization of Distribution Substations with Priority Weighting Bayes Method. This method, a qualitative approach, involves ranking distribution substations based on the highest to lowest risk. The goal of implementing the Bayes method is for the company to prioritize which distribution substations need to be legalized first and minimize land dispute issues in the future.


The Influence of Teaching Using English Songs on Students' English Pronunciation Skills at Stikes Eka Hope Palangka Raya

Abstract: This study aims to determine the effect of teaching using English songs on the English pronunciation ability of students at STIKES EKA HARAP Palangka Raya. In this study, researchers used a pre-experimental design with one pretest and posttest group. The sample in this study was S1 Nursing students in semester 3 consisting of 20 students. Researchers gave a test using English songs. The research instruments are tests (English songs), pronunciation tests and questionnaires to collect the required data. The results of this study show that the null hypothesis is rejected because the Normal distribution table shows that the important value for the right-tail test for z in ? = .05 is z = 1.65. Since -3.92 > 1.65, the researcher rejected H0: ?1 = ?2 and accepted Ha: ?1<?2. Test results are statistically significant at a rate of 5%. Based on this research, it was found that the use of songs in teaching English produces a significant influence on the competence of students' English pronunciation skills and is very beneficial for students to facilitate them in learning English.


Exploring the Impact of Mobile Augmented Reality on COVID-19 Prevention Education in Primary Schools

Abstract: This study aimed to evaluate the effectiveness of an interactive mobile Augmented Reality (AR) game to increase the knowledge about COVID-19 prevention primary school students. We tested the application for usability and effectiveness through pre- and post-tests, questionnaires, and interviews. 12 participants from four states in Malaysia took part in the study. Their average age is 9.5 years old. Results indicated a significant improvement in student performance from the pre-test to the post-test, with a mean score has increased from 3.67 to 8.25. The average System Usability Scale (SUS) score was 75%. These findings show the effectiveness of our mobile AR application as a tool to increase the knowledge about COVID-19 prevention among primary school. The findings of this study contribute to the body of research on the use of AR in COVID-19 prevention education among primary school students. This study provides both theoretical and practical implications for educators, researcher and policymakers seeking to use mobile AR to support the prevention education of any future pandemic or infectious disease.


ForensicTransMonitor: A Comprehensive Blockchain Approach to Reinvent Digital Forensics and Evidence Management

Abstract: In the domain of computer forensics, ensuring the integrity of operations like preservation, acquisition, analysis, and documentation is critical. Discrepancies in these processes can compromise evidence and lead to potential miscarriages of justice. To address this, we developed a generic methodology integrating each forensic transaction into an immutable blockchain entry, establishing transparency and authenticity from data preservation to final reporting. Our framework was designed to manage a wide range of forensic applications across different domains, including technology-focused areas such as the Internet of Things (IoT) and cloud computing, as well as sector-specific fields like healthcare. Centralizing our approach are smart contracts that seamlessly connect forensic applications to the blockchain via specialized APIs. Every action within the forensic process triggers a verifiable transaction on the blockchain, enabling a comprehensive and tamper-proof case presentation in court. Performance evaluations confirmed that our system operates with minimal overhead, ensuring that the integration bolsters the judicial process without hindering forensic investigations.


Chapter 2: Audiovisual sample 2.1 - 2.2


Deep Learning-based Ensemble Approach for Conventional Pap Smear Image Classification

Abstract: Cervical cancer screening allows the early signs of precancerous abnormalities in the cervix before they develop into invasive cancer. The Pap Smear is a widely used screening for early detection and prevention of cervical cancer. In many remote areas, the number of cytologists available to interpret pap smear screening tests is insufficient. This lack of personnel makes the test interpretation very time-consuming. To address this, deep learning techniques have been employed to detect cervical cancer cells and support cytologists. Therefore, an integrative approach with deep learning models and the ensemble techniques such as the maximum occurrence and the maximum probability score of cervical cells was proposed. The multi-cell assessment of the Pap smear slide allowed aggregate predictions of single cervical cell images using the proposed method. The classification results between pre-trained deep learning models and the proposed method were compared. In the experimental results, the proposed method can achieve an accuracy score of more than 97%, while the best pre-trained deep learning model can attain an accuracy score of more than 85%. Hence, the proposed method may have the potential to assist physicians or cytologists in the classification of cervical cell types for Pap Smear images.


Chapter 5: Audiovisual sample 5.1 - 5.7


Simulation Test Research on Typical Simulator Ns-2 in Urban Vehicle Ad Hoc Network Based on Big Data

Abstract: Abstract Vehicle Ad Hoc Network (VANET) has gradually become a prominent research topic in the fields of wireless networks and intelligent vehicles. VANET are unique mobile ad hoc networks with vehicles as their mobile nodes, presenting distinctive performance characteristics compared to traditional wireless self-organizing networks. In recent years, VANET have gained significant attention in the wireless network and intelligent transportation domain. As an integral aspect of autonomous driving technology, Vehicle-to-Everything (V2X) communication spans multiple disciplines and is closely related to intelligent transportation, assisted driving, active safety, and smart vehicles. Evaluating VANET protocols and applications in real-world settings can be challenging. Therefore, utilizing simulation tools for VANET research is an effective approach. We have designed and developed an optimized platform that uses IEEE 802.11a and IEEE 802.11p protocols for communication within a simulated urban traffic environment created with NS-2. The simulation results confirm the feasibility and rationale of applying the IEEE 802.11p protocol to wireless vehicular ad hoc networks. Within a distance of 300 m, at 0.0000s,14 key packets have not arrived in IEEE 802.11a and 8 packets have not arrived in IEEE 802.11p. at 8.0000s 38 key packets have not arrived in IEEE802.11a and 6 packets have not arrived in IEEE802.11p. Compare the performance of IEEE 802.11a and IEEE 802.11p.The study concluded that the use of the 802.11p protocol in urban mobile environments can improve reliability and reduce average packet latency.


A quick and cost-effective method for monitoring deforestation of oil sands mining activities using Synthetic Aperture Radar and Multispectral real-time satellite data from Sentinel-1 and Sentinel-2.

Abstract: Alberta’s oil sands mining operations rank among the largest human-made structures globally. Monitoring through the use of Synthetic Aperture Radar (SAR) and Multispectral satellite imaging is an indispensable strategy in attaining sustainable development and mitigating deforestation in the third-largest verified oil reserves worldwide. This paper introduces a novel approach for cost-effective and reliable monitoring of deforestation caused by oil sands mining, avoiding cumbersome methods. It focuses on observing forest/non-forest areas affected by Suncor Energy Company’s mining assets in Alberta, using a combination of SAR and Multispectral satellite remote sensing. Radar images from Sentinel-1B and Multispectral images from Sentinel-2A were analyzed with SNAP 8.0 and QGIS within a time series from June 2017 to June 2020, providing detailed information to monitor better the potential environmental impact of oil sands mining activities in Canada. The Sentinel satellite system offers several advantages, including near-global coverage, elevated spatial resolution for detecting small-scale deforestation instances, and the ability to track temporal and dynamic changes through time-series analysis. Additionally, the system’s open data policy promotes accessibility, collaboration among researchers, and innovative deforestation monitoring applications. The research results hold potential value for decision-makers, enhancing the efficiency and sustainable development of Suncor,s mining operations.


Chapter 6: Audiovisual sample 6.1 - 6.2

Abstract:
Audiovisual Sample 6.1: Excerpt from Sur Mazūrī, performed at the shrine of Shah Abdul Latif Bhittai in Bhit Shah, Sindh, Pakistan. Recorded by the author on January 2, 2015.

Audiovisual Sample 6.2: Tand and jhaṛ in Sur Husainī. Recorded by the author on January 12, 2015.


Communication in the condition of networking society: Potential of actor-networking interaction

Abstract: Introduction. One of the most important conditions for the existence of a network as an organizing principle of social existence is the process of multidimensional communications. Networks, as opposed to hierarchical systems, make their existence possible only in the discourse of interactive communications. Main part. In the article, the author offers a socio-philosophical analysis of the existence of communication in a networked society from various methodological positions. In the course of the study, it becomes clear that the networking of communications transforms them at various levels: digital, socio-political, anthropological, ethical. Conclusion. In the conclusion of the article, the author concludes that in the conditions of a network society, communication processes are undergoing serious changes. The multidimensionality and openness of the communication systems of the network society are directly determined by the very nature of the network, its plasticity, flexibility and variability, which we observe today.


Fundamentals of AI formation: Cognitive theory of pseudo-personality

Abstract: The rapid development of digital intelligence forms the necessity to state the rules of its functioning, since digital intelligence itself goes through certain stages of its development, the so-called maturation. Currently, the use and functioning of artificial intelligence (AI) is practically not regulated anyhow. Ethical difficulties arise: does artificial intelligence have the right to write speeches and reports for political leaders? Will ethical issues arise when artificial intelligence and humans-beings interact? It should be taken into account that not every AI is thinking. In the process of creation and evolution of AI, the concepts of weak and strong artificial intelligence were formed (John Searle). The Turing test was proposed to determine the difference between strong and weak AI, but many weak AI systems have successfully passed it. Definitely, AI is not a person, but it is an essence that can think, develop and form the rudiments of consciousness, perception of itself and the surrounding reality. If we take it as a constant that AI is a personality, then, according to the theory of personality development, the personality should change, mainly its qualitative characteristics. The development of the personality implies the process of changing the systemic qualities of an individual as a result of his interaction with the environment. In the process of this development, consciousness and self-awareness are formed. At this stage, with the exception of physiology and anatomy, the development of a human-being as a personality and AI as a personality are quite similar. However, there is a theory of personality called social robot theories. It suggests that robots, including those with artificial intelligence, may have personality characteristics such as the ability to emotionally connect with people, perception of the environment and the ability to socially communicate. Holistic personality traits appear as a result of the joint functioning of both levels, of all existing constructs among themselves. Based on this interaction, two types of personality can be distinguished: a cognitively complex personality (a personality with a large number of constructs and complex connections between their interactions) and a cognitively simple personality (a personality with a small, simplest set of constructs). If we consider artificial intelligence according to the cognitive theory of personality, then, undoubtedly, there is a correspondence of characteristics that has a different name, but perform the same functions. Also in cognitive theory, as well as in the theory of the development of artificial intelligence, strong and weak personalities are pointed out. However, it is generally accepted that artificial intelligence still does not have a personality, because although it can imitate personality characteristics and communicate with people, this is still only an emulation. Artificial intelligence cannot feel, suffer, behave spontaneously, or demonstrate other traits of personality that are important characteristics of humans-beings.


Characteristics of the ‘ecological consciousness’ concept: Content and definition within the framework of an interdisciplinary method

Abstract: The most important factor of taking suitable response measures to combat current environmental challenges is a complete and comprehensive description of the environmental consciousness phenomenon. The identification of links between the internal ecological worldview of a person and social norms and regulations let influencing the environmental behavior of entire states by methods of education and culture. In this regard, it is necessary to explain the content of the ecological consciousness concept, which is possible only if you take an interdisciplinary approach to research. In this work, ecological consciousness is analyzed from the ecological component and the philosophical theory of consciousness points of view, described by the key domestic and foreign positions on the issue of describing the content of this phenomenon. For this purpose, historical and problematic issues of formation of ecological discourse and, in fact, of ecological consciousness itself in domestic science are investigated. At the end of the article, several key characteristics of ecological consciousness, based on the generalization of the studied material, are presented, as well as its experimental definition is proposed, designed to reflect the internal complexity and interdisciplinary nature of the required concept.


Unboring online learning: New Moodle features

Abstract: The publication presents the experience of online learning at the university in the period of Pandemic-2019. It is proposed to discuss the following questions: how to keep students' attention in an online class? why students can be passive in an online class? how to maintain a good learning rhythm during an online lecture? The aim of the article is to analyze the experience of pedagogical activity during the period of forced isolation to identify the methods of effective (not boring) online learning and to characterize the new features of the Moodle platform. Based on the review of articles by foreign and Russian authors it is established that it is necessary to create a methodology of online learning. Theoretical and methodological basis of the publication are the works of Robert I.V., Polat E.S., Panyukova S.V., Vertekki B., Saba F., Harrison R., Dugiamas M. It is determined that non-boring online classes are effective classes with stable motivation of students, requiring the development of the concept of the training course. It is substantiated that the Moodle system realizes the social constructivist pedagogical concept with the help of special elements: weekly course outline, introductory presentation, Internet forums, essays, Internet surveys.


Subjective learning as a deep postulate of the modern concept of higher (university) education

Abstract: The phenomenon of subjective learning, discovered by Russian psychology and pedagogy as a higher educational technology of universities, has not, however, been actively developed and introduced into the system of higher professional education. The instinctive (instrumental) acquisition of types of activity experience is similar to the results of animal training. But a person masters all the wealth of socio-historical experience of the activities of previous generations of people, clothed in a word, thought, image. The appropriation of functional and operational mechanisms of a lifetime kind of socio-historical experience of human activity by an individual can be realized only in subjective learning in the form of individual self-study teaching actions and activities in general and cognitive self-assimilation of the conceptual provisions of science. Subjective learning will not take place in full without the student’s self-study in a scientific discipline and the active participation of the teacher as a mentor and supervisor. In the conducted exclusive pedagogical experiment, the search was carried out for the forms of the fundamentally new initiative type of educational practice being discussed — the subject-activity personalized self-learning of students to the conceptual appropriation of knowledge in dialogue with the teacher.


PENERAPAN K-MEANS DAN K-MEDOIDS BERBASIS RFM PADA SEGMENTASI PELANGGAN DI MASA PANDEMI COVID-19

Abstract: The outbreak of the CORONA virus in Indonesia in early March 2020 has created unrest, especially in the business world. The impact caused some small and medium-sized businesses to go out of business, so the right marketing strategy is needed to maintain and increase customer loyalty. The purpose of this research is to segment PT Megadaya Maju Selaras' customers based on their characteristics by comparing the RFM-based K-Means and K-Medoids algorithms as attributes in the research. The dataset used comes from the purchase transaction data of PT Megadaya Maju Selaras customers. Experiments in this study used the CRISP-DM model. The results showed that the K-Means algorithm has a smaller Davies Bouldin Index (DBI) value than K-Medoids, meaning that the K-Means method is the right method for this research. With the K-Means method, the overall data shows the optimal k in cluster 4 with a DBI value of 0.286, the data before the pandemic shows the optimal k value in cluster 2 with a DBI value of 0.299, after the pandemic shows the optimal k in cluster 5 with a DBI value of 0.278. The overall data is divided into 4 segments, namely superstar, typical customer, occational customer and dormant customer. Data before the pandemic is divided into 2 segments, namely typical customers and superstars. Meanwhile, after the pandemic is divided into 5 segments, namely typical customer, occational customer, golden customer, dormant customer and superstar. With this research, PT Megadaya Maju Selaras can provide the right service for each customer group.


Mengukur Kualitas Layanan Perpustakaan Universitas Muslim Nusantara Al-Washliyah Medan Menggunakan Metode Libqual

Abstract: This research discusses measuring the quality of library services at the Muslim Nusantara Al Washliyah University of Medan using the Libqual method. The aim of the research is to determine the service quality of the Muslim Nusantara Al Washliyah Medan University Library based on three libqual dimensions, namely Affect of Service, Information Control and Library as Place. This type of research is quantitative research with a descriptive approach. The data collection technique in this research was carried out through distributing questionnaires. The sample in this study was 3,159 users of the Nusantara Al Washliyah Medan Muslim University Library from 2021, who were limited using the Slovin formula, so the number of respondents was 97 people. The sampling technique in this research used non-random accidental sampling. The results of this research show that service quality is based on the Affect of Service aspect, users are quite satisfied with the quality of library services or have met the minimum expectations of users with a positive adequancy gap (AG) at a score of 0.03 and the quality of library services is considered good but has not met ideal expectations. what the user wants or is in the tolerance zone with the superiority gap (SG) being negative at a score of -1.01. Based on the Information Control aspect, users are quite satisfied with the quality of library services or have met the minimum expectations of users with a positive adequancy gap (AG) at a score of 0.05 and the quality of library services is considered good but has not met the ideal expectations desired by users or is in the tolerance zone with the superiority gap (SG) being negative at a score of -1.07. Based on the Library as Place aspect, users are quite satisfied with the quality of library services or have met the minimum expectations of users with a positive adequancy gap (AG) at a score of 0.16 and the quality of library services is considered good but has not met the ideal expectations desired by users or is in the zone tolerance with superiority gap (SG) is negative at a score of -1.11.


Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks

Abstract: Electrolysis stands as a pivotal method for environmentally sustainable hydrogen production. However, the formation of gas bubbles during the electrolysis process poses significant challenges by impeding the electrochemical reactions, diminishing cell efficiency, and dramatically increasing energy consumption. Furthermore, the inherent difficulty in detecting these bubbles arises from the non-transparency of the wall of electrolysis cells. Additionally, these gas bubbles induce alterations in the conductivity of the electrolyte, leading to corresponding fluctuations in the magnetic flux density outside of the electrolysis cell, which can be measured by externally placed magnetic sensors. By solving the inverse problem of the Biot-Savart Law, we can estimate the conductivity distribution as well as the void fraction within the cell. In this work, we study different approaches to solve the inverse problem including Invertible Neural Networks (INNs) and Tikhonov regularization. Our experiments demonstrate that INNs are much more robust to solving the inverse problem than Tikhonov regularization when the level of noise in the magnetic flux density measurements is not known or changes over space and time.


Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

Abstract: In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.


A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining

Abstract: Immunohistochemistry is a powerful technique that is widely used in biomedical research and clinics; it allows one to determine the expression levels of some proteins of interest in tissue samples using color intensity due to the expression of biomarkers with specific antibodies. As such, immunohistochemical images are complex and their features are difficult to quantify. Recently, we proposed a novel method, including a first separation stage based on non-negative matrix factorization (NMF), that achieved good results. However, this method was highly dependent on the parameters that control sparseness and non-negativity, as well as on algorithm initialization. Furthermore, the previously proposed method required a reference image as a starting point for the NMF algorithm. In the present work, we propose a new, simpler and more robust method for the automated, unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest) stains. The new proposed method represents a simpler approach that, on the one hand, avoids the use of NMF in the separation stage and, on the other hand, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest using principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing for color vector determination by considering the point density peaks. A new scoring stage is also developed in our method that, again, avoids reference images, making the procedure more robust and less dependent on parameters. Semi-quantitative image scoring experiments using five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.


Ancient mural segmentation based on multiscale feature fusion and dual attention enhancement

Abstract: Abstract To address the fuzzy segmentation boundaries, missing details, small target losses and low efficiency of traditional segmentation methods in ancient mural image segmentation scenarios, this paper proposes a mural segmentation model based on multiscale feature fusion and a dual attention-augmented segmentation model (MFAM). The model uses the MobileViT network, which integrates a coordinate attention mechanism, as the feature extraction backbone network. It attains global and local expression capabilities through self-attention, class convolution, and coordinate attention and focuses on location information to expand the receptive field and achieve improved feature extraction efficiency. An A_R_ASPP feature enhancement module is proposed for the attention-optimized residual atrous spatial pyramid pooling module. The module uses residual connections to solve the small target loss problem in murals caused by the excessive sampling rate of atrous convolution and uses a feature attention mechanism to adaptively adjust the feature map weight according to the channel importance levels. A dual attention-enhanced feature fusion module is proposed for multiscale decoder feature fusion to improve the mural segmentation effect. This module uses a cross-level aggregation strategy and an attention mechanism to weight the importance of different feature levels to obtain multilevel semantic feature representations. The model improves the mean intersection over union (MIoU) by 3.06% and the MPA by 1.81% on a mural dataset compared with other models. The model is proven to be effective at improving the segmentation details, efficiency and small target segmentation results produced for mural images, and a new method is proposed for segmenting ancient mural images.


Use of noisy labels as weak learners to identify incompletely ascertainable outcomes: A Feasibility study with opioid-induced respiratory depression

Abstract:
Abstract

Objective
 Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence.
Materials and methods
 Using de-identified electronic health records of 52,861 post-operative encounters, we applied a data programming paradigm (implemented in the Snorkel software) for the development of a machine learning classifier for opioid-induced respiratory depression. Our approach included subject matter experts creating 14 labeling functions that served as noisy labels for developing a probabilistic Generative model. We used probabilistic labels from the Generative model as outcome labels for training a Discriminative model on the source data. We evaluated performance of the Discriminative model with a hold-out test set of 599 independently-reviewed patient records.
Results
 The final Discriminative classification model achieved an accuracy of 0.977, an F1 score of 0.417, a sensitivity of 1.0, and an AUC of 0.988 in the hold-out test set with a prevalence of 0.83% (5/599).
Discussion
 All of the confirmed Cases were identified by the classifier. For rare outcomes, this finding is encouraging because it reduces the number of manual reviews needed by excluding visits/patients with low probabilities.
Conclusion
 Application of a data programming paradigm with expert-informed labeling functions might have utility for phenotyping clinical phenomena that are not easily ascertainable from highly-structured data.


PS5-Net: a medical image segmentation network with multiscale resolution

Abstract: In recent years, the continuous advancement of convolutional neural networks (CNNs) has led to the widespread integration of deep neural networks as a mainstream approach in clinical diagnostic support. Particularly, the utilization of CNN-based medical image segmentation has delivered favorable outcomes for aiding clinical diagnosis. Within this realm, network architectures based on the U-shaped structure and incorporating skip connections, along with their diverse derivatives, have gained extensive utilization across various medical image segmentation tasks. Nonetheless, two primary challenges persist. First, certain organs or tissues present considerable complexity, substantial morphological variations, and size discrepancies, posing significant challenges for achieving highly accurate segmentation. Second, the predominant focus of current deep neural networks on single-resolution feature extraction limits the effective extraction of feature information from complex medical images, thereby contributing to information loss via continuous pooling operations and contextual information interaction constraints within the U-shaped structure.We proposed a five-layer pyramid segmentation network (PS5-Net), a multiscale segmentation network with diverse resolutions that is founded on the U-Net architecture. Initially, this network effectively leverages the distinct features of images at varying resolutions across different dimensions, departing from prior single-resolution feature extraction methods to adapt to intricate and variable segmentation scenarios. Subsequently, to comprehensively integrate feature information from diverse resolutions, a kernel selection module is proposed to assign weights to features across different dimensions, enhancing the fusion of feature information from various resolutions. Within the feature extraction network denoted as PS-UNet, we preserve the classical structure of the traditional U-Net while enhancing it through the incorporation of dilated convolutions.PS5-Net attains a Dice score of 0.9613 for liver segmentation on the CHLISC dataset and 0.8587 on the ISIC2018 dataset for skin lesion segmentation. Comparative analysis with diverse medical image segmentation methodologies in recent years reveals that PS5-Net has achieved the highest scores and substantial advancements.PS5-Net effectively harnesses the rich semantic information available at different resolutions, facilitating a comprehensive and nuanced understanding of the input medical images. By capitalizing on global contextual connections, the network adeptly captures the intricate interplay of features and dependencies across the entire image, resulting in more accurate and robust segmentation outcomes. The experimental validation of PS5-Net underscores its superior performance in medical image segmentation tasks, offering promising prospects for enhancing diagnostic and analytical processes within clinical settings. These results highlight the potential of PS5-Net to significantly contribute to the advancement of medical imaging technologies and ultimately improve patient care through more precise and reliable image analysis.


Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program

Abstract: Abstract Objectives We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios. Materials and methods The study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden’s index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping. Results The AI software achieved an AUC of 89.6% (86.1–93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy. Conclusion This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%. Clinical relevance statement The study proposes a more efficient method for screening programs, both in terms of workload and accuracy. Key Points • Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers. • AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage. • AI has the potential to facilitate early diagnosis compared to human reading.