Academic assistance chatbot-a comprehensive NLP and deep learning-based approaches

Abstract:
The rapid growth of digital technologies and natural language processing (NLP) have revolutionized the field of education, creating new demand for automated academic assistance systems. In this paper, we present an NLP-based academic assistance chatbot designed to provide comprehensive support to students and researchers using deep learning techniques.
 The chatbot incorporates a range of intelligent features to assist with university recommendations, article writing, automatic question answering (QA), and job search. By leveraging sentiment analysis and sarcasm detection models. The proposed chatbot could offer accurate and insightful university recommendations. Additionally, the chatbot incorporates spell and grammar checking, summarization, paraphrasing, and topic modeling capabilities to aid users in enhancing their writing skills. The QA module enables users to obtain quick and precise answers to factoid-based questions. Moreover, the chatbot helps with internships and job search. According to literature, this work presents the first assistance chatbot that encapsulates all features that may be needed by a university student to facilitate and improve his/her learning process. The results demonstrated clearly in the body of the paper showed the success achieved by the academic assistant proposed and built in this work in all its features or modules to offer help to university students and graduates.


The Nalima: A Multistable Membrane Instrument with Integrated Excitation

Abstract: The Nalima is a self-resonating vibrotactile instrument, a hybrid digital-electro-acoustic system based around interaction with a membrane. It uses a novel method of Integrated Excitation to offer new musical affordances, and makes use of complexity analysis to shape the sonic and interactive behaviour of the instrument, pushing it into musical zones on the edge of chaos. The design and construction of the Nalima is described, along with the theoretical background in cybernetics, complexity theory and multistability.


Color image encryption algorithm based on quantum random walk and multiple reset scrambling*

Abstract: Abstract In order to address the issues of privacy breaches, data tampering, and security threats in the realm of image encryption and information security, it is essential to ensure the confidentiality, integrity, and reliability of image data. In this paper, we propose a novel hybrid color image encryption scheme that increases the complexity of the key space and provides better data integrity protection by combining the quantum random walk and SHA-256 algorithm. At the same time, three chaotic systems were combined, and multiple XOR operations were introduced to effectively scramble the spatial and pixel levels of the color image. Through numerous simulation experiments and security analyses, the results demonstrate that the solution exhibits efficient encryption performance, excellent resistance to attacks, and outstanding privacy protection capabilities.


<i>Demos and Apparatus from the Aichi Physics Circle (Stray Cats) Japanese Physics Teacher’s Website</i>

Abstract: Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dan MacIsaac; Demos and Apparatus from the Aichi Physics Circle (Stray Cats) Japanese Physics Teacher’s Website. Phys. Teach. 1 February 2024; 62 (2): 158. https://doi.org/10.1119/10.0024602 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search


Coding Hotline

Abstract: This month’s Coding Hotline addresses questions about coding for social determinants of health, repair of laceration of the lip, and 2 distinct E/M services.


Quantum covariance scalar products and efficient estimation of maximum-entropy projections

Abstract: The maximum-entropy principle (max-ent) is a valuable and extensively used tool in statistical mechanics and quantum information theory. It provides a method for inferring the state of a system by utilizing a reduced set of parameters associated with measurable quantities. However, the computational cost of employing max-ent projections in simulations of quantum many-body systems is a significant drawback, primarily due to the computational cost of evaluating these projections. In this work, a different approach for estimating max-ent projections is proposed. The approach involves replacing the expensive max-ent induced local geometry, represented by the Kubo-Mori-Bogoliubov scalar product, with a less computationally demanding geometry. Specifically, a new local geometry is defined in terms of the quantum analog of the covariance scalar product for classical random variables. Relations between induced distances and projections for both products are explored. Connections with standard variational and dynamical mean-field approaches are discussed. The effectiveness of the approach is calibrated and illustrated by its application to the dynamic of excitations in a $XX$ Heisenberg spin-$\frac{1}{2}$ chain model.


Online Fan Practices and Informal Language Learning

Abstract: Extensive and repeated language exposure is essential for second language learning. While not all learners can get such exposure routinely in face-to-face contexts, English language learners today create pathways to opportunities for learning English through participation in social media communities. The informal exposure to English they obtain can be driven by their passionate interest in popular culture and affiliation with communities of fans. The potential of interaction in online fandoms for language learning, however, depends in part on the language that learners are exposed to. In this study, a lexical bundle analysis was conducted to identify frequently used word combinations in YouTube comments posted under South Korean music band BTS fan-edited videos and performance videos. Lexical bundles retrieved from a corpus of 8,000 comments were investigated structurally and functionally. The bundles were found to display structural similarities with spoken registers, even though comments have unique textual characteristics. Functional analysis results showed that many of the bundles express interpersonal meanings, such as positive appraisal, with stance expressions. Findings from the structural and functional analyses of lexical bundles increase knowledge of the type of language that English learners are exposed to and can potentially benefit from by engaging in online fan communities.


Penggunaan Metode Suku Kata Untuk Meningkatkan Hasil Belajar Membaca Pada Mata Pelajaran Bahasa Indonesia Siswa Kelas 1 SDN Tlogomas 2 Kota Malang

Abstract: The purpose of study was to describe the level of student learning in reading learning outcomes in Indonesian subjects using the syllable method for first-grade students at SDN Tlogomas 2 Malang City. This study used the Classroom Action Research (PTK) method, which consisted of two cycles, each cycle held 2 class meetings. The approach in this research is descriptive qualitative. The subjects of this study were all first-grade students at SDN Tlogomas 2 Malang City totaly of 20 of them 11 male students and 9 female students. Data collection techniques in this study used observation, interviews, and documentation. The results showed that: (1) the application of the Syllable learning method in cycle 1 meeting 1 presentation 65.00% with sufficient category, the second meeting 75.00% with good category, and in the implementation of cycle II meeting one presentation 85.00% with good category, second meeting 92.05% with very good category. (2) the improvement of Indonesian learning outcomes by using the syllable learning method in Indonesian subjects, cycle 1 meeting one presentation was 50% completeness, with an average score of 67.5%, while in the second meeting, the presentation was 60% completeness, with an average score 69% than in the implementation of cycle II experienced an increase at the first meeting the percentage of completeness was 70% with an average value of 79.5%, and at the second meeting the percentage of completeness was 80% with an average value of 85%. Therefore it can be concluded that using the syllable method in the learning process it can increase the activity and learning outcomes of Indonesian students in class I SDN Tlogomas 2 Malang.


Using Appsheet and Apps Script to Develop Management and Implementation Applications for University Education Programs

Abstract: A smart university management system is developed in a synchronized manner based on various interconnected modules. Managing and implementing the training program is a rigorous and highly synchronized process of school management. In this article, we introduce the use of Appsheet and Apps Script to build an application for managing the implementation of a university's training program. The result of this research, which effectively supports the university's operations and is expandable, integrates with other applications to form a smart educational ecosystem.


Abstract WP73: Temporal Effects on Mobile Stroke “Hot” Zones Using a Geo-Spatial Software Analysis

Abstract: Background: We sought to identify variations of mobile stroke unit (MSU) hotspot activity based on time of day by using a geospatial analysis software. Methods: The Mobile Stroke Treatment Unit of metropolitan Columbus, Ohio, provides emergency stroke care coverage for 325 square miles which spans 47 zip codes. Our MSU is operational from 0700-1900 and is dispatched for all 911 calls designated as probable or possible strokes. We retrospectively reviewed all MSU calls from May 2019 to June 2022. The calls were organized into zip codes and 3 time epochs (0700-1100, 1101-1500, 1501-1900). Our endpoint was to determine hotspots of MSU activity during these epochs using the ESRI/ArcGIS Online geospatial analysis software. Statistically significant hotspots are qualitatively defined as clustered regions with high activity and quantitatively defined as having a Gi Bin score > 2, which correlates to statistical confidence > 95%. Results: We received 5198 calls during our study period; 1535 from 0700-1100; 2002 from 1101-1500; 1661 from 1501-1900. All hotspots had an average separation of 6.2 miles. From 0700-1100, 5 zip codes (12.9% of calls) were considered hotpots (Figure-Top). From 1101-1500, 15 zip codes (44.7% of calls) were considered hotspots (Figure-Middle). From 1501-1900, 9 zip codes (34.1% of calls) were considered hotspots (Figure-Bottom). Five zip codes were statistical hotspots during the entire coverage period. Four additional zip codes also showed hotspot activity and overlapped 1101-1900 time epochs. Conclusions: Our study showed that hotspot activity can vary throughout the day and must be considered for strategic MSU deployment. Prospective validation is needed to determine if such strategic deployment is feasible and can improve clinical outcomes.


Non-Invasive Real-Time Multimodal Deception Detection Using Machine Learning and Parallel Computing Techniques

Abstract: Abstract Detecting deception reliably has been a sought-after goal for researchers since the early 20th century. This is due to its high stakes nature, especially when considering the setting under which a deception detection system would be utilized, such as law enforcement sectors, judicial bodies, and criminal investigation bodies. Therefore, recent literature has greatly focused on developing systems with ever increasing accuracies to minimize false positives. Attempts to diversify the sources of data being analyzed and classified as well as the reliance on artificial intelligence technologies have shown great success. But little attention was paid to the applicability of these systems in real-life scenarios. an idle deception detection system needs to exhibit accuracy but also perform in real-time, a feature that is lacking in the current state-of-the-art. In this work, a non-invasive real-time multimodal deception detection system is developed using advanced machine learning techniques. It combines data sources from video and audio streams to extract visual, acoustic, and linguistic features. It also utilizes parallel computing techniques to ensure high performance adequate for real-time usage. Furthermore, a user-friendly graphical user interface was built to facilitate the use of the system. Multiple experiments were conducted to determine its accuracy under various circumstances and combinations of features, the system had a detection accuracy of 99.83% under real-life, high-stakes scenarios using visual and acoustic features, and 91.03% accuracy under controlled environments, while an 89.54% accuracy was achieved using mixed environments and using visual, acoustic, and linguistic features.


Balancing Green and Gold: The Link Between Sustainability Reporting and Corporate Reputation in Malaysia

Abstract:
Corporate reputation has gained significant attention within academic circles and emerged as a critical concern in practical business contexts. However, prior studies on this topic have been somewhat fragmented, creating fertile ground for exploring new research avenues, particularly in assessing reputation. Sustainability reporting, corporate governance, and company attributes have been associated with enhancing corporate reputation. Nevertheless, there remains a dearth of research exploring these variables' impact on the novel reputation measurement proxy, especially in emerging economies like Malaysia. Therefore, this study investigates the intricate relationship between sustainability reporting and corporate reputation using the new proxy of reputation in the context of Malaysia, a dynamic and rapidly growing economy. Balancing the 'Green' aspects of sustainability with the 'Gold' aspects of company performance and reputation is a pivotal concern for organizations in the region. The results showed sustainability reporting significantly impacts corporate reputation to sustain a harmonious equilibrium characterized by "Green" and "Gold" elements. The findings encourage Malaysian PLCs to navigate this duality successfully and can reap long-term benefits regarding reputation, stakeholder trust, and sustainable profitability.
 Keywords: sustainability reporting, reputation, transparency, greenwashing, Malaysian public listed companies


PENERAPAN PSO UNTUK SENTIMEN ANALISIS PADA REVIEW MATA UANG KRIPTO MENGGUNAKAN METODE NAÏVE BAYES

Abstract: In the digital age emerging currencies using digital technology called currency crypto money. Many people use cryptocurrencies to invest. This triggered the sentiment in society on social media twitter, there are positive opinions and there are negative opinions. The purpose of this study is to determine the public sentiment regarding the review of crypto currency and then classify it into two sentiments, namely positive and negative sentiments. The classifier method used is Naïve Bayes, Naïve Bayes is a good classifier method but has shortcomings in the selection of features therefore Particle Swarm Optimization (PSO) is applied as a feature selection in order to improve the accuracy value. After conducted experiments using Naïve Bayes method, obtain accuracy value of 66% with AUC 0.482 and after Applied Particle Swarm Optimization (PSO) as feature selection in Naïve Bayes obtain accuracy value of 85% with AUC 0.716 has increased accuracy .


PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR

Abstract: Component products used for manufacturing a machine in manufacturing companies have two types of products, type A and B. The problem that often occurs in the industry is product sorting errors due to the traditional sorting process, using human labor. The disadvantages are limited human labor so that fatigue can occur, causing errors in sorting products and losses for the company. Many studies discuss object detection, Industrial problems in the checking process can be approached with the help of this technology. Object detection works to analyze frames with the method of finding objects. There are methods in digital image processing, CNN algorithms which include methods in computer vision. The growing framework makes the CNN algorithm more powerful. YOLO includes a framework based on the CNN algorithm. YOLOv5 detects objects by taking into account the object's confidence value, the output of the detected object is a bounding box on the object. The problem in the industry in the checking process can be approached with the help of this technology. For this reason, this research aims to create a model for product object detection in manufacturing companies. The process carried out is data collection, image annotation, training, testing, evaluation. The images collected were 137 for training data and 34 for validation data totaling 171 image data. The results of the model using YOLOv5 with epoch 1000 get a precision value of 100%, recall 100% and mAP 99%, the product detection results get an average value of 100%.


CLUSTERING DATA METEOROLOGI WILAYAH INDONESIA TIMUR DENGAN METODE K-MEANS DAN FUZZY C-MEANS

Abstract: Climate change is a global issue that affect human life and the environment. Signs of climate change can be observed from long-term meteorological data. This research uses clustering techniques with the K-Means and Fuzzy C-Means methods to group cities in the Eastern Indonesia region based on numerical daily time series meteorological data from 1 January 2010 to 31 August 2023. The variables are minimum temperature, maximum temperature, temperature average, humidity, rainfall, duration of sunlight, maximum wind speed, and average wind speed. The dataset was collected from 28 meteorological stations. The K-Means and Fuzzy C-Means methods obtained the same results, namely the highest silhouette value of 0.218 with the number of clusters k = 2. In general, the annual trend shows an increase in temperature and a decrease in wind speed which are signs of climate change. This research is an early study of climate change in East Indonesia. The results of this research are expected to contribute to the study of climate change in Indonesia.


Increasing Vocabulary of Grade VII Students at SMPS Miftaahul Khairaat Palu through Crossword Puzzle Game

Abstract: The main obstacles encountered by students to communicate in English is the lack of their vocabulary, Therefore, there should be strategic efforts made by teachers to increase students’ vocabulary mastery, like using Crossword Puzzle Game. The objective of this research was to examine whether the application of crossword puzzle game can increase vocabulary of grade VII students at SMPS Miftaahul Khairaat Palu or not. This study utilized the Class Action Research design, implemented in a cyclical process encompassing planning, action, observation, and reflection. The research involved 17 seventh-grade students during the academic year 2023-2024.This research was conducted in two cycles; the first cycle consisted of four meetings and the second one consisted of two meetings. Thus, there were totally six meetings in two cycles (cycle 1 and cycle 2). In collecting the data, there were three types of instruments used in this research: tests, observation sheets, and field notes. The results of quantities data analysis indicate that the mean score of posttests in cycle 2, 81.05 is higher than that of posttest in cycle 1, 69.05, and that of pretest in cycle 1, 60.58, Thus, based on the comparison of the students’ vocabulary achievement in pretest and in posttest in cycle 1 and in cycle 2, it can be concluded that the use crossword puzzle game can increase vocabulary of the grade VII students. While the results of qualitative data indicate that the students’ vocabulary increases from cycle 1 to cycle 2.


Precision camera calibration using known target motions along three perpendicular axes

Abstract: We present a method for precision intrinsic calibration of pinhole-model cameras. We refer to this method as “3-Axis.” The algorithm employs a target moving with known displacements along three perpendicular axes to decrease the number of unknown terms that must be determined. This method along with new evaluation strategies for comparing the accuracy of pinhole camera calibration algorithms is described in detail. The 3-Axis approach is then compared with the current state-of-the art algorithms in simulated and physical settings and is shown to exceed them in accuracy and computational performance assuming an equal number of samples acquired.


Implementasi Program Literasi terhadap Eskstrakkulikuler di MTsN 2 Medan

Abstract:
 This study aims to explain the condition of the extracurricular literacy program at MTSN 2 Medan. In addition, it also explains the effectiveness and constraints that occur in the development of this literacy program at MTSN 2 Medan. This research uses descriptive qualitative research. This study provides an overall picture of the conditions, effectiveness, and obstacles that occur in literacy programs. The results of the research show that the literacy program has not run optimally. Problems with the literacy program in learning Indonesian that occurred at MTSN 2 Medan included irrelevant learning strategies, the availability of inadequate and incomplete facilities and infrastructure, and students' low interest in reading. Sources of data in this study are documents and school implementation processes literacy program. The data in this study were collected using documentation, interviews and observation methods. The results of this study indicate that the governance of extracurricular literacy at MTSN 2 Medan is as follows (1) First, the school planning for the literacy program has been carried out by setting literacy goals for the school movement program. However, this planning has not been based on a needs analysis (2) extracurricular activities about Indonesian that have not been implemented at this school (4) the control that has been carried out is still limited to ensuring that the Thursday reading program is carried out or not. Controls to improve program quality have not been carried out. The management of the school literacy movement program needs to be managed properly, starting from the strategic planning process, organizing by giving job descriptions, implementing programs in accordance with strategic planning, and continuous evaluation
 Keywords Indonesian, Ekstrakulikuler, Program Literacy


Effectiveness of social robots as a tutoring and learning companion: a bibliometric analysis

Abstract: A long-term perspective on how technology will mature is needed whereby robotics and artificial intelligence (AI) have accomplished a consequential and remarkable impact by finding their way into mainstream higher education. Robots have already become an indispensable factor in society and possess high potency as a part of educational technology. Social robot education is limited to complementing the digital aptitude of students in the world of information, and the role of social robots is crucial in polishing students ‘cognitive and social abilities. This study reviews the effectiveness of social robots in education, where we highlight the application of educational robots, surrounded by a blend of social robots and enactive didactics, which could lead to promising ideas for tutoring activities in education. It is empirically proven that social robots can assist with literature, science, or technology education. We synthesize the role of social robots in education and weigh their pros and cons by examining the impact of their appearance on robots’ performance as tutors, tools, or peers in learning exercises. The current study is the first bibliometric analysis that reflects robots’ impact in the education field as tutors and learning companions. A total of 288 articles were reviewed, and the data were extracted to construct an overview through bibliometrics. The outcome of this study paves the way for educational institutes to make informed and fruitful decisions on the applicability of robots, which can help them comprehend the learning styles of students and create knowledgeable and well-adjusted learners.


Analysis of Student Perceptions of Teaching Campus Participants

Abstract: The Independent Campus Learning (MBKM) policy provides students with the opportunity to take part directly in society to solve the country's problems. The Teaching Campus is one of the MBKM programs with the aim of solving educational literacy and numeracy problems in Indonesia. The research in this article aims to describe the impact of implementing the MBKM program, namely the 6th Teaching Campus organized by the Ministry of Education and Culture, which comes from students from HKBP Nommensen Pematangsiantar University. The research used mixed methods. Quantitative data in this research includes questionnaires from teaching campus school partners and students participating in teaching campuses. The research data is in the form of perception interviews from students participating in the teaching campus class 6. Based on the research results, it is known that students' perceptions after attending the teaching campus are very good, namely on average 90%. This is in line with the partner's high perception of the quality and abilities of students, namely 93%. Based on student perceptions, campus teaching programs not only provide classroom teaching experience and literacy and numeracy skills but can also help improve students' collaboration abilities and soft skills.


CPT: Competence-progressive Training Strategy for Few-shot Node  Classification

Abstract: Graph Neural Networks (GNNs) have made significant advancements in node classification, but their success relies on sufficient labeled nodes per class in the training data. Real-world graph data often exhibits a long-tail distribution with sparse labels, emphasizing the importance of GNNs' ability in few-shot node classification, which entails categorizing nodes with limited data. Traditional episodic meta-learning approaches have shown promise in this domain, but they face an inherent limitation: it might lead the model to converge to suboptimal solutions because of random and uniform task assignment, ignoring task difficulty levels. This could lead the meta-learner to face complex tasks too soon, hindering proper learning. Ideally, the meta-learner should start with simple concepts and advance to more complex ones, like human learning. So, we introduce CPT, a novel two-stage curriculum learning method that aligns task difficulty with the meta-learner's progressive competence, enhancing overall performance. Specifically, in CPT's initial stage, the focus is on simpler tasks, fostering foundational skills for engaging with complex tasks later. Importantly, the second stage dynamically adjusts task difficulty based on the meta-learner's growing competence, aiming for optimal knowledge acquisition. Extensive experiments on popular node classification datasets demonstrate significant improvements of our strategy over existing methods.


Overview of the Student Learning Environment of the Universitas Padjadjaran Nursing Profession Study Program

Abstract: The learning environment is an important component in determining the quality and success of the nursing education curriculum. A learning environment that is not supportive can result in decreased student learning performance, including decreased interest and motivation, anxiety and stress, and reduced self-confidence. Evaluation of the learning environment helps improve the quality of the learning environment which consists of the learning process, teaching staff, academic achievement, learning atmosphere, and social environment. This research aims to identify a description of the learning environment for students in the Padjadjaran University nursing professional study program. This research design will use a quantitative descriptive research design. The sampling technique that will be used is proportionate stratified random sampling with 142 nursing professional students as samples. The instrument used is the Dundee Ready Education Environment Measure (DREEM) with 50 statement items. The data analysis that will be carried out is univariate analysis. The results of research on the learning environment dimension show an average score of 148.96/200 in the good category. The average is obtained for each sub-variable; learning process (35.44/48), teaching staff (33.74/44), academic achievement (25.44/32), learning atmosphere (34.49/48), and social environment (19.82/28). Academic achievement with the highest score and social environment with the lowest score. The assessment of the learning environment for nursing professional students shows the Good category, which means that the learning environment for the nursing profession is running well, although there are things that need to be improved and corrected to improve the quality of learning.


Utilization of Chat GPT Artificial Intelligence (AI) in Student’s Learning Experience Gen-Z Class

Abstract: Technology presents data very quickly, if students do not get enough assistance from lecturers in analytical thinking, the students will present data without studying further or it will convince them that what is presented is justifiable. Lecturers need to understand the students' learning habit nowadays. The focus in this study is the usage of Chat GPT AI in Student Learning Experience in Gen-Z classroom. This study provides in-depth attention to educators, especially lecturers, so that they can adapt to Gen Z's learning experience tendencies and the latest developments in learning media to create the material. The method used in this study is descriptive quantitative with questionnaires distributed to students. The respondent in this study is 392 Gen Z students chosen with purposive random sampling. The use of Chat GPT AI is familiar to Gen-Z students. Therefore, they should double-check the results of Chat GPT so as not to make misinformation and they provide a complete understanding. Lecturers as facilitators in the classroom must be more careful in reviewing the assignments collected by students, so that lecturers can measure precisely that the assignments are the same as the level of student understanding. In addition, the lecturers should notice that the information presented is correct.


The Effect of Emergency Response Training on Flood Disaster Preparedness on Student Council Administrators at SMPN 1 Tibawa

Abstract: The effect of emergency response training on flood disaster preparedness on student council administrators at SMPN 1 TIBAWA. Guided by PIPIN YUNUS as chairman and HASLINDA DAMANSYAH as member Disaster emergency response is a series of activities immediately at the time of a disaster to deal with the adverse effects caused. These activities include pre-disaster, during-disaster, and post-disaster. This study aims to determine the effect of emergency response training on flood disaster preparedness in student council administrators at SMPN 1 TIBAWA. This research method uses a quantitative approach with a type of quasi-experimental research with one group. Samples were taken using total sampling techniques, the number of samples was 36 respondents. Data collection using a questionnaire sheet with data analysis using a Paired T-test with meaning (p-value = 0.05). The results showed that there was an average value of preparedness before 46.61 and preparedness after 73.72 there was an increase of 27.11 preparedness values after being given treatment, then the statistical value was obtained p-value = 0.00 smaller than 0.05 (p<0.05). In conclusion, there is an effect of emergency response training on flood disaster preparedness.


The Effect of Paddressing and Scanning Training on the Skills of PMR Students in MAN 1 Pohuwato

Abstract: Emergency conditions can occur anywhere and anytime. However, it does not rule out the possibility of emergency conditions occurring in areas that are not covered by health workers. The purpose of this study was to determine the effect of dressing and scanning training on the skills of PMR MAN 1 Pohuwato students. This research method is quantitative research that uses the Two Group Pretest Posttest design. The population in this study was 22 students and the sample was 22 students. From the results of statistical tests on the application of dressing and scanning in the Experimental group of students, a p-value value of 0.003 was obtained, and the control group had a p-value of 1,000. Dressing and scanning training has an influence on students' skills which can be seen from the improvement of students' skills before and after being given dressing and scanning training in the experimental group, while for the control group, there is no change in skills, so it can be concluded that there is an influence of dressing and scanning training on students' skills. Suggestions for schools to conduct splint wrap training to improve students' skills.


The Relationship of Online Game Addiction with Aggressive Behavior for Teenagers at SMP Negeri 3 Banyumas

Abstract: There is a lot of verbal and physical aggression in online games today. Playing games with a high level of violence can lead to an increase in hostile attitudes and actions. The purpose of this study was to determine whether there is a relationship between online game addiction and the aggressive behavior of grade VIII students at SMP Negeri 3 Banyumas. This study is a correlational study using a cross-sectional approach. The population in this study consisted of 92 grade VIII students of SMP Negeri 3 Banyumas who played online games. A sample of 75 respondents was taken using a simple random sampling technique. The questionnaire was used as a data collector and analyzed with the Spearman rank correlation test. The results showed that most online game addicts were in the mild category (80%), most showed moderate aggressive behavior (62.7%), and there was a strong relationship between online game addiction and aggressive behavior class VIII SMP Negeri 3 Banyumas with a p-value of 0.000 (p-value <0.05; r = 0.579). This indicates that online game addiction can affect a person's aggressive behavior, the higher the level of online game addiction, the more aggressive behavior it leads. This research is expected to be a reference for pediatric nursing.


Persona-Based Segmentation: Customer Data Mapping as the Foundation of Corporate Marketing Strategy (Case Study on Scarlett Whitening)

Abstract: This research aims to analyze customer segmentation in energy resource companies using the K-Means Clustering method. In the era of increasingly fierce business competition, a deep understanding of customer preferences and behavior is crucial for companies to design effective marketing strategies. Customer data, including purchase frequency, customer service field, company type and geographic location, were collected and analyzed using the K-Means Clustering algorithm. The analysis results showed the formation of homogeneous customer groups, allowing the company to identify the distinctive characteristics of each group. Five main groups were formed from the processed customer data using SPSS. The first group consists of scarlett's facial wash lovers who are dominated by women with an average age of 31 years, earning an average income of Rp15 million per month who live outside Jakarta and work as private employees. This group recognizes the majority of scarlett products from Tiktok. The second group consists of scarlett serum lovers who are dominated by women with an average age of 32 years, earning an average income of IDR 23 million per month who live in South Jakarta and work as private employees. This group also recognizes the majority of scarlett products from Tiktok. The third group consists of scarlett's shower scrub lovers who are dominated by single women with an average age of 31 years, earning an average income of IDR 10 million per month who live in South Jakarta and work other than private employees. This group recognizes scarlett products mostly from Instagram. The fourth group consists of scarlett's body lotion lovers who are dominated by women with an average age of 32 years, earning an average income of IDR 19 million per month who live outside Jakarta and work as private employees. This group recognizes the majority of scarlett products from Instagram. The fifth group consists of scarlett's body scrub lovers who are dominated by women with an average age of 28 years, with an average income of IDR 6.5 million per month who live in South Jakarta and work as entrepreneurs. This group recognizes the majority of scarlett products from Tiktok. The implementation of marketing strategies based on the results of this analysis shows that Scarlett needs to use cluster 2 as its target segment in order to continue to increase its revenue. Because by choosing this cluster, Scarlett will find it easier to design its marketing strategy, namely by increasing social media strategies by involving creative content and collaboration on the Tiktok platform, offering promotions and special offers for customers who buy Serum. Scarlett could present a special bundle offer for the product or provide points that can be redeemed for further discounts or free products after reaching a certain amount and since cluster 2 is predominantly in the South Jakarta area, focus the marketing campaign in that area. Scarlett's value proposition lies in the use of brand ambassadors, digital marketing, product quality and brand trust. Thus, this study concludes that customer segmentation analysis using K-Means Clustering brings strategic benefits to scarlett sellers. A deeper understanding of customers enables sellers to improve the relevance of marketing strategies, achieve better customer satisfaction, and remain responsive to changes in the business environment.


Indonesian University Students’ Perceptions on the Use of Social Media for English Language Learning

Abstract: The use of social media is not only for communicating and enjoying leisure time but also for learning many things. Since that, English-related content has been one of the things that often be on their search. Various educative content can be found on social media. This study aims to find out the students’ perceptions on the use of social media for English language learning. The participants of this study are 98 university students in Jakarta. A descriptive qualitative method was used. The data was obtained by using a questionnaire and interview. The results showed that Instagram, YouTube, and TikTok are the most preferred platform for learning. The students have positive perceptions on the use of social media for English learning because social media is easy to access, fun, stress-free, and reinforces their independence. In addition, the most skill that can be learned from social media are reading.


Implikasi Etika pada Penggunaan Artificial Intelligence (AI) dalam Akuntansi Manajemen

Abstract: Artificial Intelligence (AI) has changed the way humans work, interact and manage data. One part of accounting work that is influenced by AI is management accounting. There is a gap between the demands for developing the capabilities of accountants in the AI era and the development of management accounting itself. The application of AI in the context of management accounting also raises several ethical issues that need to be considered. This research method uses a qualitative descriptive approach with an analysis of previous research literature being explored and explained. The research results suggest that it is important for future policymakers to ensure standardization of AI systems within the accounting paradigm to ensure high-quality systems that adhere to accounting principles and ethics. To address emerging ethical issues, it is important for organizations to involve ethicists and management accounting experts in the AI development and deployment process. This will help ensure that decisions made by AI systems adhere to widely accepted values and principles. This research provides novelty by filling a gap in the management accounting literature, exploring how AI can influence ethical practices in management accounting


Precursory Signals to Injection Induced Fault Reactivation in the Laboratory Using Active Ultrasonic Monitoring Methods

Abstract: Abstract Induced earthquakes are still highly unpredictable, and often caused by variations in pore fluid pressure. Monitoring and understanding the mechanisms of fluid‐induced fault slip is essential for seismic risk mitigation and seismicity forecasting. Fluid‐induced slip experiments were performed on critically stressed faulted sandstone samples, and the evolution of the actively sent ultrasonic waves throughout the experiment was measured. Two different fault types were used: smooth saw‐cut fault samples at a 35° angle, and a rough fault created by in situ faulting of the samples. Variations in the seismic slip velocity and friction along the fault plane were identified by the coda of the ultrasonic waves. Additionally, ultrasonic amplitudes show precursory signals to laboratory fault reactivation. Our results show that small and local variations in stress before fault failure can be inferred using coda wave interferometry for time‐lapse monitoring, as coda waves are more sensitive to small perturbations in a medium than direct waves. Hence, these signals can be used as precursors to laboratory fault slip and to give insight into reactivation mechanisms. Our results show that time‐lapse monitoring of coda waves can be used to monitor local stress changes associated with fault reactivation in this laboratory setting of fluid‐induced fault reactivation. This is a critical first step toward a method for continuous monitoring of natural fault zones, contributing to seismic risk mitigation of induced and natural earthquakes.


Sistem Informasi Manajemen dalam Data Pegawai Pendidikan

Abstract: This study aims to find out how the benefits of management information systems in the knowledge of employee data. This research is library research. The results of the study conveyed that for knowledge, the data processing process functions as an executor of arithmetic, logical processes for data, storing and processing data programs, taking data input programs so that they can be used at any time, minimizing human labor, and getting more accurate results and minimal errors. The function of the database is to group data to facilitate data identification, the database prepares data in accordance with user requests for information quickly and accurately. Employee data has important value for organizations because it can be used to make informed decisions and improve operational efficiency. Good data can be organized in a database. The benefit of using a management information system is that it makes it easier for management to plan, direct, and supervise the course of business.


Photovoltaic output power estimation model based on multi-meteorological variable dimension reduction and improved variational mode decomposition algorithm

Abstract: The instability and intermittence of photovoltaic (PV) power generation challenge the large-scale integration of PV power generation systems into the power grid and the development of energy storage systems. To accurately predict the output power of PV power generation, a power prediction model based on the optimized variational modal decomposition (OVMD)-adaptive t-distribution sparrow search algorithm (tSSA)-least squares support vector machine (LSSVM) algorithm was built. The performance of the model was then verified by three-year meteorological data and real-time PV output power data of two different climatic regions in China. The mean absolute percentage error (MAPE) and root mean square error (RMSE) of this model were less than 3.1% and 0.40, respectively, and the determination coefficient (R2) of this model was greater than 95%. Moreover, the prediction accuracy was greatly improved compared to models based on the support vector machine (SVM), LSSVM, variational modal decomposition (VMD)-LSSVM, and VMD-SSA-LSSVM algorithms. Finally, by performing dimension reduction of meteorological variables related to PV output power, the performance of the proposed OVMD-tSSA-LSSVM model was further improved.


Hybrid blockchain-based many-to-many cross-domain authentication scheme for smart agriculture IoT networks

Abstract: With the development of smart agricultural Internet of Things (IoT) projects, the need for extensive collaboration among agricultural devices from different domains has surged, necessitating the authentication of device identities for secure communication. Existing centralized architecture-dependent authentication mechanisms encounter issues like a sole point of failure and inefficiencies. Most authentication schemes are unable to support plentiful devices synchronously connecting to numerous data servers in other domains. To address these problems, we propose a many-to-many cross-domain authentication scheme based on the hybrid blockchain architecture for smart agriculture IoT networks. The scheme enables multiple devices simultaneously executing mutual authentication with several data service providers from other agricultural systems. This paper designs a groupable batch verification (GBV) algorithm that dynamically adjusts the batch size by performing group verification for a list of requests, enhancing the flexibility of cross-domain batch authentication. Furthermore, the proposed scheme provides a pseudonym update mechanism to protect the privacy of devices and guards services of different domains from illegal access by tracking malicious devices. The security analysis and performance evaluation demonstrate that the proposed scheme has superior security features and performance.


Disparities in Electronic Health Record Portal Activation Among Non-primary English-speaking Patients with Food Allergy

Abstract: Health disparities in food allergy are prevalent among minority and socioeconomically disadvantaged populations. Understanding of healthcare utilization among non-primary English-speaking patients with food allergy is limited. A retrospective review was conducted of patients 0-18 years old with food allergy seen in our institution’s allergy clinic between 1/1/2018-12/31/2022. Only patients with prior history of symptoms consistent with IgE-mediated reaction to food(s) and positive skin testing (wheal 3mm or greater) or food-specific IgE > 0.35 were included. Chi-squared tests and logistic regression (adjusted for age, sex, insurance, race and ethnicity, and median household income of zip code) examined the association between patient/guardian-reported primary language and healthcare utilization measures. Among the 1,418 patients included, 1,267 (89.4%) were primary English speakers and 151 (10.6%) were non-primary English speakers. Other primary languages included Spanish (8.4%), Portuguese (0.5%), Arabic (0.4%), Chinese (0.2%). Non-primary English speakers had significantly higher rates of Medicaid coverage (85% vs 34%, p=0.000) and higher odds of asthma (aOR=0.53, 95% CI 0.32-0.88, p=0.014) as compared to English speakers. Non-primary English-speaking patients had significantly lower odds of electronic health record (EHR) portal activation (aOR=0.48, 95% CI 0.32 – 0.74, p=0.001) than primary English speakers. No significant differences were found in the age at initial visit, total number of clinic visits, odds of having a follow up appointment or an oral food challenge between the two groups. Disparities in rates of EHR portal activation were identified among non-primary English-speaking patients. This study highlights a potential area for improvement in increasing healthcare accessibility for non-primary English-speaking patients.


Analysis Sentiment to Religious Based Issues Netizen Comments on President Jokowi 's Instagram using the Naive Bayes Classifier Method

Abstract: The presence of Instagram is one of the most popular social media platforms. Make people make a habit of posting photo and video content. In this way, Instagram can become a source of public opinion and sentiment data, which data can be used for social studies. Social studies is still a crucial issue for countries today, including Indonesia, is a matter of religion. Apart from problems regarding religion, public sentiment can be researched from Instagram. The sentiment dataset related to religion is taken from public comments from President Joko Widodo's content regarding Merry Christmas Greetings and Visits to Churches. The analysis system will use sentiment analysis using a method from machine learning, namely the naive Bayes classifier to determine positive & negative sentiments. The comment dataset is 2,600 in which the positive and negative sentiment class datasets are determined manually by experts in the field of linguistics or communication totaling 1,309 to be used as training data. the results of testing the Naïve Bayes classifier training data with a total of 1309 obtained an accuracy prediction of 97.63% and test data where positive & negative sentiment classes had not been determined before hand amounted to 1291 in sentiment analysis obtained positive sentiment 70.56% & negative sentiment 29.44%.


PROTOTYPE METHOD PADA APLIKASI SCHEDULER BERBASIS MOBILE

Abstract: PT United Tractors is currently implementing digitalization in its various activities. In its implementation, delivering information and archiving documents in the PSD4 Service Division still have obstacles due to the lack of effective and efficient access to information needed to do work. To overcome these problems, the solution that can be provided is to develop a mobile-based document filing system using the Flutter framework to maximize the development of mobile-based systems. For developers to interact directly with teams related to application capabilities, this system development method uses the Prototype method, which has 5 phases: Communication, Quick Plan, Modelling Quick Design, Construction of Prototype, and Development, Delivery, and feedback phases. The result of this research is an Android-based Scheduler Mobile document archiving application that has been made based on the steps in the prototype method. Testing using Black Box and Flutter DevTools is done to find out the results of the application that has been made. Black Box and Flutter DevTools testing results show that the application can run as expected.


ANALISIS SISTEM INFORMASI AKUNTANSI PENGGAJIAN PADA PT ALFA KREASI (WEEKEND INC)

Abstract: This writing aims to determine the application of the Payroll Accounting Information System carried out at PT Alfa Kreasi (WEEKEND INC), as well as to analyze the implementation of the Internal Control System and provide proposals for the Payroll Accounting System to support the effectiveness and efficiency of the Internal Control System at PT Alfa Kreasi (WEEKEND INC ). The type of data used is qualitative data, data that explains a phenomenon based on things that generally contain words where the data obtained and which will be processed later is in the form of primary data, which is obtained directly through face-to-face interviews with the parties concerned. The analytical tools that the author uses in this research are data flow diagrams (DFD) and document flow charts (flowcharts). From the research that has been carried out, the author concludes that PT Alfa Kreasi (WEEKEND INC) implements a computerized payroll system seen in the recording process carried out on Google Sheets and for all data regarding payroll in the process of paying employee salaries via bank transfer. And the analysis of the company's Internal Control System has also been implemented in accordance with the policies set by the leadership.


Effect of Employee Training on Organizational Performance in Public Sector in Rwanda. A Case Study of Rwanda Social Security Board (RSSB)

Abstract: This paper examined the effect of training on the performance of public-sector organizations using the Rwanda Social Security Board as a case study. The research approach design used in this study was descriptive, and a total of 130 questionnaires were distributed to respondents, of which 108 were fully completed and returned, and 10 interviews were conducted. The overall response rate was 90.7%. The sampling techniques used were simple random and purposive samplings. The analysis used both descriptive and inferential statistics of Pearson Correlation. Qualitative data was examined and categorized into themes, while quantitative data was coded and entered into the SPSS package.  The study found that there is a significant relationship between training and organizational performance. The first statistic, R, is .908a, indicating that there is a great deal of variance shared by the independent variables and dependent variables. The R Square value of 0.824 indicates that approximately 82.4% of the variability in the outcome variable can be explained by the predictors in the model. The unstandardized coefficients for the predictor variables show their impacts on organizational performance at the Rwanda Social Security Board. Specifically, Training regulations have a coefficient of (β= 0.285, t=5.050, p value=0.000), and types of Training have a coefficient of (β= 0.123, t=2.028, p value=0.045). The coefficients are statistically significant on Organizational Performance at Rwanda Social Security Board, as indicated by their associated Sig. Values below 0.05. Based on the findings, the study recommends that the Rwanda Social Security Board conduct training needs assessments before recommending training for its employees and establish a transparent training policy that provides equal opportunities in training Keywords: Employee training, types of training, training regulations and organizational performance


Can we Constrain Concept Bottleneck Models to Learn Semantically  Meaningful Input Features?

Abstract: Concept Bottleneck Models (CBMs) are considered inherently interpretable because they first predict a set of human-defined concepts before using these concepts to predict the output of a downstream task. For inherent interpretability to be fully realised, and ensure trust in a model's output, we need to guarantee concepts are predicted based on semantically mapped input features. For example, one might expect the pixels representing a broken bone in an image to be used for the prediction of a fracture. However, current literature indicates this is not the case, as concept predictions are often mapped to irrelevant input features. We hypothesise that this occurs when concept annotations are inaccurate or how input features should relate to concepts is unclear. In general, the effect of dataset labelling on concept representations in CBMs remains an understudied area. Therefore, in this paper, we examine how CBMs learn concepts from datasets with fine-grained concept annotations. We demonstrate that CBMs can learn concept representations with semantic mapping to input features by removing problematic concept correlations, such as two concepts always appearing together. To support our evaluation, we introduce a new synthetic image dataset based on a playing cards domain, which we hope will serve as a benchmark for future CBM research. For validation, we provide empirical evidence on a real-world dataset of chest X-rays, to demonstrate semantically meaningful concepts can be learned in real-world applications.


Institutional Platform for Secure Self-Service Large Language Model  Exploration

Abstract: This paper introduces a user-friendly platform developed by the University of Kentucky Center for Applied AI, designed to make large, customized language models (LLMs) more accessible. By capitalizing on recent advancements in multi-LoRA inference, the system efficiently accommodates custom adapters for a diverse range of users and projects. The paper outlines the system's architecture and key features, encompassing dataset curation, model training, secure inference, and text-based feature extraction. We illustrate the establishment of a tenant-aware computational network using agent-based methods, securely utilizing islands of isolated resources as a unified system. The platform strives to deliver secure LLM services, emphasizing process and data isolation, end-to-end encryption, and role-based resource authentication. This contribution aligns with the overarching goal of enabling simplified access to cutting-edge AI models and technology in support of scientific discovery.


Useful entanglement can be extracted from noisy graph states

Abstract: Cluster states and graph states in general offer a useful model of the stabilizer formalism and a path toward the development of measurement-based quantum computation. Their defining structure -- the stabilizer group -- encodes all possible correlations which can be observed during measurement. Those outcomes which are compatible with the stabilizer structure make error correction possible. Here, we leverage both properties to design feasible families of states that can be used as robust building blocks of quantum computation. This procedure reduces the effect of experimentally relevant noise models on the extraction of smaller entangled states from the larger noisy graph state. In particular, we study the extraction of Bell pairs from linearly extended graph states -- this has the immediate consequence for state teleportation across the graph. We show that robust entanglement can be extracted by proper design of the linear graph with only a minimal overhead of the physical qubits. This scenario is relevant to systems in which the entanglement can be created between neighboring sites. The results shown in this work may provide a mathematical framework for noise reduction in measurement-based quantum computation. With proper connectivity structures, the effect of noise can be minimized for a large class of realistic noise processes.


FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with  Contrastive Learning in Multimodal Electronic Health Records

Abstract: In the high-stakes realm of healthcare, ensuring fairness in predictive models is crucial. Electronic Health Records (EHRs) have become integral to medical decision-making, yet existing methods for enhancing model fairness restrict themselves to unimodal data and fail to address the multifaceted social biases intertwined with demographic factors in EHRs. To mitigate these biases, we present FairEHR-CLP: a general framework for Fairness-aware Clinical Predictions with Contrastive Learning in EHRs. FairEHR-CLP operates through a two-stage process, utilizing patient demographics, longitudinal data, and clinical notes. First, synthetic counterparts are generated for each patient, allowing for diverse demographic identities while preserving essential health information. Second, fairness-aware predictions employ contrastive learning to align patient representations across sensitive attributes, jointly optimized with an MLP classifier with a softmax layer for clinical classification tasks. Acknowledging the unique challenges in EHRs, such as varying group sizes and class imbalance, we introduce a novel fairness metric to effectively measure error rate disparities across subgroups. Extensive experiments on three diverse EHR datasets on three tasks demonstrate the effectiveness of FairEHR-CLP in terms of fairness and utility compared with competitive baselines. FairEHR-CLP represents an advancement towards ensuring both accuracy and equity in predictive healthcare models.


Recurrent Transformers with Dynamic Halt

Abstract: In this paper, we study the inductive biases of two major approaches to augmenting Transformers with a recurrent mechanism - (1) the approach of incorporating a depth-wise recurrence similar to Universal Transformers; and (2) the approach of incorporating a chunk-wise temporal recurrence like Temporal Latent Bottleneck. Furthermore, we propose and investigate novel ways to extend and combine the above methods - for example, we propose a global mean-based dynamic halting mechanism for Universal Transformer and an augmentation of Temporal Latent Bottleneck with elements from Universal Transformer. We compare the models and probe their inductive biases in several diagnostic tasks such as Long Range Arena (LRA), flip-flop language modeling, ListOps, and Logical Inference.


An Information-Theoretic Approach to Analyze NLP Classification Tasks

Abstract: Understanding the importance of the inputs on the output is useful across many tasks. This work provides an information-theoretic framework to analyse the influence of inputs for text classification tasks. Natural language processing (NLP) tasks take either a single element input or multiple element inputs to predict an output variable, where an element is a block of text. Each text element has two components: an associated semantic meaning and a linguistic realization. Multiple-choice reading comprehension (MCRC) and sentiment classification (SC) are selected to showcase the framework. For MCRC, it is found that the context influence on the output compared to the question influence reduces on more challenging datasets. In particular, more challenging contexts allow a greater variation in complexity of questions. Hence, test creators need to carefully consider the choice of the context when designing multiple-choice questions for assessment. For SC, it is found the semantic meaning of the input text dominates (above 80\% for all datasets considered) compared to its linguistic realisation when determining the sentiment. The framework is made available at: https://github.com/WangLuran/nlp-element-influence


Compressed image quality assessment using stacking

Abstract: It is well-known that there is no universal metric for image quality evaluation. In this case, distortion-specific metrics can be more reliable. The artifact imposed by image compression can be considered as a combination of various distortions. Depending on the image context, this combination can be different. As a result, Generalization can be regarded as the major challenge in compressed image quality assessment. In this approach, stacking is employed to provide a reliable method. Both semantic and low-level information are employed in the presented IQA to predict the human visual system. Moreover, the results of the Full-Reference (FR) and No-Reference (NR) models are aggregated to improve the proposed Full-Reference method for compressed image quality evaluation. The accuracy of the quality benchmark of the clic2024 perceptual image challenge was achieved 79.6\%, which illustrates the effectiveness of the proposed fusion-based approach.


Compositional Expected Cost Analysis of Functional Probabilistic  Programs

Abstract: Reasoning about resources used during the execution of programs, such as time, is one of the fundamental questions in computer science. When programming with probabilistic primitives, however, different samples may result in different resource usage, making the cost of a program not a single number but a distribution instead. The expected cost is an important metric used to quantify the efficiency of probabilistic programs. In this work we introduce $\mathbf{cert}$, a call-by-push-value (CBPV) metalanguage extended with primitives for probability, cost and unbounded recursion, and give it denotational semantics for reasoning about the average cost of programs. We justify the validity of the semantics by presenting case-studies ranging from randomized algorithms to stochastic processes and showing how the semantics captures their intended cost.


Quantum Multiple Eigenvalue Gaussian filtered Search: an efficient and  versatile quantum phase estimation method

Abstract: Quantum phase estimation is one of the most powerful quantum primitives. This work proposes a new approach for the problem of multiple eigenvalue estimation: Quantum Multiple Eigenvalue Gaussian filtered Search (QMEGS). QMEGS leverages the Hadamard test circuit structure and only requires simple classical postprocessing. QMEGS is the first algorithm to simultaneously satisfy the following two properties: (1) It can achieve the Heisenberg-limited scaling without relying on any spectral gap assumption. (2) With a positive energy gap and additional assumptions on the initial state, QMEGS can estimate all dominant eigenvalues to $\epsilon$ accuracy utilizing a significantly reduced circuit depth compared to the standard quantum phase estimation algorithm. In the most favorable scenario, the maximal runtime can be reduced to as low as $\log(1/\epsilon)$. This implies that QMEGS serves as an efficient and versatile approach, achieving the best-known results for both gapped and gapless systems. Numerical results validate the efficiency of our proposed algorithm in various regimes.


RDNF Oriented Analytics to Random Boolean Functions

Abstract: Dominant areas of computer science and computation systems are intensively linked to the hypercube-related studies and interpretations. This article presents some transformations and analytics for some example algorithms and Boolean domain problems. Our focus is on the methodology of complexity evaluation and integration of several types of postulations concerning special hypercube structures. Our primary goal is to demonstrate the usual formulas and analytics in this area, giving the necessary set of common formulas often used for complexity estimations and approximations. The basic example under considered is the Boolean minimization problem, in terms of the average complexity of the so-called reduced disjunctive normal form (also referred to as complete, prime irredundant, or Blake canonical form). In fact, combinatorial counterparts of the disjunctive normal form complexities are investigated in terms of sets of their maximal intervals. The results obtained compose the basis of logical separation classification algorithmic technology of pattern recognition. In fact, these considerations are not only general tools of minimization investigations of Boolean functions, but they also prove useful structures, models, and analytics for constraint logic programming, machine learning, decision policy optimization and other domains of computer science.


LatticeGraphNet: A two-scale graph neural operator for simulating  lattice structures

Abstract: This study introduces a two-scale Graph Neural Operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures. LGN has two networks: LGN-i, learning the reduced dynamics of lattices, and LGN-ii, learning the mapping from the reduced representation onto the tetrahedral mesh. LGN can predict deformation for arbitrary lattices, therefore the name operator. Our approach significantly reduces inference time while maintaining high accuracy for unseen simulations, establishing the use of GNOs as efficient surrogate models for evaluating mechanical responses of lattices and structures.


Distributed MCMC inference for Bayesian Non-Parametric Latent Block  Model

Abstract: In this paper, we introduce a novel Distributed Markov Chain Monte Carlo (MCMC) inference method for the Bayesian Non-Parametric Latent Block Model (DisNPLBM), employing the Master/Worker architecture. Our non-parametric co-clustering algorithm divides observations and features into partitions using latent multivariate Gaussian block distributions. The workload on rows is evenly distributed among workers, who exclusively communicate with the master and not among themselves. DisNPLBM demonstrates its impact on cluster labeling accuracy and execution times through experimental results. Moreover, we present a real-use case applying our approach to co-cluster gene expression data. The code source is publicly available at https://github.com/redakhoufache/Distributed-NPLBM.


Getting the most out of your tokenizer for pre-training and domain  adaptation

Abstract: Tokenization is an understudied and often neglected component of modern LLMs. Most published works use a single tokenizer for all experiments, often borrowed from another model, without performing ablations or analysis to optimize tokenization. Moreover, the tokenizer is generally kept unchanged when fine-tuning a base model. In this paper, we show that the size, pre-tokenization regular expression, and training data of a tokenizer can significantly impact the model's generation speed, effective context size, memory usage, and downstream performance. We train specialized Byte-Pair Encoding code tokenizers, and conduct extensive ablations on the impact of tokenizer design on the performance of LLMs for code generation tasks such as HumanEval and MBPP, and provide recommendations for tokenizer hyper-parameters selection and switching the tokenizer in a pre-trained LLM. We perform our experiments on models trained from scratch and from pre-trained models, verifying their applicability to a wide range of use-cases. We find that when fine-tuning on more than 50 billion tokens, we can specialize the tokenizer of a pre-trained LLM to obtain large gains in generation speed and effective context size.


Towards an Algebraic Framework For Approximating Functions Using Neural  Network Polynomials

Abstract: We make the case for neural network objects and extend an already existing neural network calculus explained in detail in Chapter 2 on \cite{bigbook}. Our aim will be to show that, yes, indeed, it makes sense to talk about neural network polynomials, neural network exponentials, sine, and cosines in the sense that they do indeed approximate their real number counterparts subject to limitations on certain of their parameters, $q$, and $\varepsilon$. While doing this, we show that the parameter and depth growth are only polynomial on their desired accuracy (defined as a 1-norm difference over $\mathbb{R}$), thereby showing that this approach to approximating, where a neural network in some sense has the structural properties of the function it is approximating is not entire intractable.


Quantum censorship -- and how to get around it

Abstract: A protocol for regulating the distribution of quantum information between multiple parties is put forward. In order to prohibit the unrestricted distribution of quantum-resource states in a public quantum network, agents can apply a resource-destroying map to each sender's channel. Since resource-destroying maps only exist for affine quantum resource theories, censorship of a nonaffine resource theory is established on an operationally motivated subspace of free states. This is achieved by using what we name a resource-censoring map. The protocol is applied to censoring coherence, reference frames, and entanglement. Because of the local nature of the censorship protocol, it is, in principle, possible for collaborating parties to bypass censorship. Thus, we additionally derive necessary and sufficient conditions under which the censorship protocol is unbreakable.


Semantic-Aware and Goal-Oriented Communications for Object Detection in  Wireless End-to-End Image Transmission

Abstract: Semantic communication is focused on optimizing the exchange of information by transmitting only the most relevant data required to convey the intended message to the receiver and achieve the desired communication goal. For example, if we consider images as the information and the goal of the communication is object detection at the receiver side, the semantic of information would be the objects in each image. Therefore, by only transferring the semantics of images we can achieve the communication goal. In this paper, we propose a design framework for implementing semantic-aware and goal-oriented communication of images. To achieve this, we first define the baseline problem as a set of mathematical problems that can be optimized to improve the efficiency and effectiveness of the communication system. We consider two scenarios in which either the data rate or the error at the receiver is the limiting constraint. Our proposed system model and solution is inspired by the concept of auto-encoders, where the encoder and the decoder are respectively implemented at the transmitter and receiver to extract semantic information for specific object detection goals. Our numerical results validate the proposed design framework to achieve low error or near-optimal in a goal-oriented communication system while reducing the amount of data transfers.


Unconditional Latent Diffusion Models Memorize Patient Imaging Data

Abstract: Generative latent diffusion models hold a wide range of applications in the medical imaging domain. A noteworthy application is privacy-preserved open-data sharing by proposing synthetic data as surrogates of real patient data. Despite the promise, these models are susceptible to patient data memorization, where models generate patient data copies instead of novel synthetic samples. This undermines the whole purpose of preserving patient data and may even result in patient re-identification. Considering the importance of the problem, surprisingly it has received relatively little attention in the medical imaging community. To this end, we assess memorization in latent diffusion models for medical image synthesis. We train 2D and 3D latent diffusion models on CT, MR, and X-ray datasets for synthetic data generation. Afterwards, we examine the amount of training data memorized utilizing self-supervised models and further investigate various factors that can possibly lead to memorization by training models in different settings. We observe a surprisingly large amount of data memorization among all datasets, with up to 41.7%, 19.6%, and 32.6% of the training data memorized in CT, MRI, and X-ray datasets respectively. Further analyses reveal that increasing training data size and using data augmentation reduce memorization, while over-training enhances it. Overall, our results suggest a call for memorization-informed evaluation of synthetic data prior to open-data sharing.


Expert Proximity as Surrogate Rewards for Single Demonstration Imitation  Learning

Abstract: In this paper, we focus on single-demonstration imitation learning (IL), a practical approach for real-world applications where obtaining numerous expert demonstrations is costly or infeasible. In contrast to typical IL settings with multiple demonstrations, single-demonstration IL involves an agent having access to only one expert trajectory. We highlight the issue of sparse reward signals in this setting and propose to mitigate this issue through our proposed Transition Discriminator-based IL (TDIL) method. TDIL is an IRL method designed to address reward sparsity by introducing a denser surrogate reward function that considers environmental dynamics. This surrogate reward function encourages the agent to navigate towards states that are proximal to expert states. In practice, TDIL trains a transition discriminator to differentiate between valid and non-valid transitions in a given environment to compute the surrogate rewards. The experiments demonstrate that TDIL outperforms existing IL approaches and achieves expert-level performance in the single-demonstration IL setting across five widely adopted MuJoCo benchmarks as well as the "Adroit Door" environment.


Quantifying analogy of concepts via ologs and wiring diagrams

Abstract: We build on the theory of ontology logs (ologs) created by Spivak and Kent, and define a notion of wiring diagrams. In this article, a wiring diagram is a finite directed labelled graph. The labels correspond to types in an olog; they can also be interpreted as readings of sensors in an autonomous system. As such, wiring diagrams can be used as a framework for an autonomous system to form abstract concepts. We show that the graphs underlying skeleton wiring diagrams form a category. This allows skeleton wiring diagrams to be compared and manipulated using techniques from both graph theory and category theory. We also extend the usual definition of graph edit distance to the case of wiring diagrams by using operations only available to wiring diagrams, leading to a metric on the set of all skeleton wiring diagrams. In the end, we give an extended example on calculating the distance between two concepts represented by wiring diagrams, and explain how to apply our framework to any application domain.


Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on  Learning With Errors

Abstract: Learning with Errors (LWE) is a hard math problem underlying recently standardized post-quantum cryptography (PQC) systems for key exchange and digital signatures. Prior work proposed new machine learning (ML)-based attacks on LWE problems with small, sparse secrets, but these attacks require millions of LWE samples to train on and take days to recover secrets. We propose three key methods -- better preprocessing, angular embeddings and model pre-training -- to improve these attacks, speeding up preprocessing by $25\times$ and improving model sample efficiency by $10\times$. We demonstrate for the first time that pre-training improves and reduces the cost of ML attacks on LWE. Our architecture improvements enable scaling to larger-dimension LWE problems: this work is the first instance of ML attacks recovering sparse binary secrets in dimension $n=1024$, the smallest dimension used in practice for homomorphic encryption applications of LWE where sparse binary secrets are proposed.


Specialized Language Models with Cheap Inference from Limited Domain  Data

Abstract: Large language models have emerged as a versatile tool but are challenging to apply to tasks lacking large inference budgets and large in-domain training sets. This work formalizes these constraints and distinguishes four important variables: the pretraining budget (for training before the target domain is known), the specialization budget (for training after the target domain is known), the inference budget, and the in-domain training set size. Across these settings, we compare different approaches from the machine learning literature. Limited by inference cost, we find better alternatives to the standard practice of training very large vanilla transformer models. In particular, we show that hyper-networks and mixture of experts have better perplexity for large pretraining budgets, while small models trained on importance sampled datasets are attractive for large specialization budgets.


Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance

Abstract: Emerging Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact. However, recent studies have identified new attack surfaces and risks caused by security, privacy, and fairness issues in AI systems. In this paper, we review representative techniques, algorithms, and theoretical foundations for trustworthy distributed AI through robustness guarantee, privacy protection, and fairness awareness in distributed learning. We first provide a brief overview of alternative architectures for distributed learning, discuss inherent vulnerabilities for security, privacy, and fairness of AI algorithms in distributed learning, and analyze why these problems are present in distributed learning regardless of specific architectures. Then we provide a unique taxonomy of countermeasures for trustworthy distributed AI, covering (1) robustness to evasion attacks and irregular queries at inference, and robustness to poisoning attacks, Byzantine attacks, and irregular data distribution during training; (2) privacy protection during distributed learning and model inference at deployment; and (3) AI fairness and governance with respect to both data and models. We conclude with a discussion on open challenges and future research directions toward trustworthy distributed AI, such as the need for trustworthy AI policy guidelines, the AI responsibility-utility co-design, and incentives and compliance.


Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and  Human-Centered Solutions

Abstract: Remarkable performance of large language models (LLMs) in a variety of tasks brings forth many opportunities as well as challenges of utilizing them in production settings. Towards practical adoption of LLMs, multi-agent systems hold great promise to augment, integrate, and orchestrate LLMs in the larger context of enterprise platforms that use existing proprietary data and models to tackle complex real-world tasks. Despite the tremendous success of these systems, current approaches rely on narrow, single-focus objectives for optimization and evaluation, often overlooking potential constraints in real-world scenarios, including restricted budgets, resources and time. Furthermore, interpreting, analyzing, and debugging these systems requires different components to be evaluated in relation to one another. This demand is currently not feasible with existing methodologies. In this postion paper, we introduce the concept of reasoning capacity as a unifying criterion to enable integration of constraints during optimization and establish connections among different components within the system, which also enable a more holistic and comprehensive approach to evaluation. We present a formal definition of reasoning capacity and illustrate its utility in identifying limitations within each component of the system. We then argue how these limitations can be addressed with a self-reflective process wherein human-feedback is used to alleviate shortcomings in reasoning and enhance overall consistency of the system.


Simulation of Graph Algorithms with Looped Transformers

Abstract: The execution of graph algorithms using neural networks has recently attracted significant interest due to promising empirical progress. This motivates further understanding of how neural networks can replicate reasoning steps with relational data. In this work, we study the ability of transformer networks to simulate algorithms on graphs from a theoretical perspective. The architecture that we utilize is a looped transformer with extra attention heads that interact with the graph. We prove by construction that this architecture can simulate algorithms such as Dijkstra's shortest path algorithm, Breadth- and Depth-First Search, and Kosaraju's strongly connected components algorithm. The width of the network does not increase with the size of the input graph, which implies that the network can simulate the above algorithms for any graph. Despite this property, we show that there is a limit to simulation in our solution due to finite precision. Finally, we show a Turing Completeness result with constant width when the extra attention heads are utilized.


Near-Optimal Reinforcement Learning with Self-Play under Adaptivity  Constraints

Abstract: We study the problem of multi-agent reinforcement learning (MARL) with adaptivity constraints -- a new problem motivated by real-world applications where deployments of new policies are costly and the number of policy updates must be minimized. For two-player zero-sum Markov Games, we design a (policy) elimination based algorithm that achieves a regret of $\widetilde{O}(\sqrt{H^3 S^2 ABK})$, while the batch complexity is only $O(H+\log\log K)$. In the above, $S$ denotes the number of states, $A,B$ are the number of actions for the two players respectively, $H$ is the horizon and $K$ is the number of episodes. Furthermore, we prove a batch complexity lower bound $\Omega(\frac{H}{\log_{A}K}+\log\log K)$ for all algorithms with $\widetilde{O}(\sqrt{K})$ regret bound, which matches our upper bound up to logarithmic factors. As a byproduct, our techniques naturally extend to learning bandit games and reward-free MARL within near optimal batch complexity. To the best of our knowledge, these are the first line of results towards understanding MARL with low adaptivity.


Double-Dip: Thwarting Label-Only Membership Inference Attacks with  Transfer Learning and Randomization

Abstract: Transfer learning (TL) has been demonstrated to improve DNN model performance when faced with a scarcity of training samples. However, the suitability of TL as a solution to reduce vulnerability of overfitted DNNs to privacy attacks is unexplored. A class of privacy attacks called membership inference attacks (MIAs) aim to determine whether a given sample belongs to the training dataset (member) or not (nonmember). We introduce Double-Dip, a systematic empirical study investigating the use of TL (Stage-1) combined with randomization (Stage-2) to thwart MIAs on overfitted DNNs without degrading classification accuracy. Our study examines the roles of shared feature space and parameter values between source and target models, number of frozen layers, and complexity of pretrained models. We evaluate Double-Dip on three (Target, Source) dataset paris: (i) (CIFAR-10, ImageNet), (ii) (GTSRB, ImageNet), (iii) (CelebA, VGGFace2). We consider four publicly available pretrained DNNs: (a) VGG-19, (b) ResNet-18, (c) Swin-T, and (d) FaceNet. Our experiments demonstrate that Stage-1 reduces adversary success while also significantly increasing classification accuracy of nonmembers against an adversary with either white-box or black-box DNN model access, attempting to carry out SOTA label-only MIAs. After Stage-2, success of an adversary carrying out a label-only MIA is further reduced to near 50%, bringing it closer to a random guess and showing the effectiveness of Double-Dip. Stage-2 of Double-Dip also achieves lower ASR and higher classification accuracy than regularization and differential privacy-based methods.


Enhanced Urban Region Profiling with Adversarial Self-Supervised  Learning

Abstract: Urban region profiling is pivotal for smart cities, but mining fine-grained semantics from noisy and incomplete urban data remains challenging. In response, we propose a novel self-supervised graph collaborative filtering model for urban region embedding called EUPAS. Specifically, region heterogeneous graphs containing human mobility data, point of interests (POIs) information, and geographic neighborhood details for each region are fed into the model, which generates region embeddings that preserve intra-region and inter-region dependencies through GCNs and multi-head attention. Meanwhile, we introduce spatial perturbation augmentation to generate positive samples that are semantically similar and spatially close to the anchor, preparing for subsequent contrastive learning. Furthermore, adversarial training is employed to construct an effective pretext task by generating strong positive pairs and mining hard negative pairs for the region embeddings. Finally, we jointly optimize supervised and self-supervised learning to encourage the model to capture the high-level semantics of region embeddings while ignoring the noisy and unimportant details. Extensive experiments on real-world datasets demonstrate the superiority of our model over state-of-the-art methods.


Scale Equalization for Multi-Level Feature Fusion

Abstract: Deep neural networks have exhibited remarkable performance in a variety of computer vision fields, especially in semantic segmentation tasks. Their success is often attributed to multi-level feature fusion, which enables them to understand both global and local information from an image. However, we found that multi-level features from parallel branches are on different scales. The scale disequilibrium is a universal and unwanted flaw that leads to detrimental gradient descent, thereby degrading performance in semantic segmentation. We discover that scale disequilibrium is caused by bilinear upsampling, which is supported by both theoretical and empirical evidence. Based on this observation, we propose injecting scale equalizers to achieve scale equilibrium across multi-level features after bilinear upsampling. Our proposed scale equalizers are easy to implement, applicable to any architecture, hyperparameter-free, implementable without requiring extra computational cost, and guarantee scale equilibrium for any dataset. Experiments showed that adopting scale equalizers consistently improved the mIoU index across various target datasets, including ADE20K, PASCAL VOC 2012, and Cityscapes, as well as various decoder choices, including UPerHead, PSPHead, ASPPHead, SepASPPHead, and FCNHead.


ReEvo: Large Language Models as Hyper-Heuristics with Reflective  Evolution

Abstract: The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design process. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs). This paper introduces Language Hyper-Heuristics (LHHs), an emerging variant of Hyper-Heuristics that leverages LLMs for heuristic generation, featuring minimal manual intervention and open-ended heuristic spaces. To empower LHHs, we present Reflective Evolution (ReEvo), a generic searching framework that emulates the reflective design approach of human experts while far surpassing human capabilities with its scalable LLM inference, Internet-scale domain knowledge, and powerful evolutionary search. Evaluations across 12 COP settings show that 1) verbal reflections for evolution lead to smoother fitness landscapes, explicit inference of black-box COP settings, and better search results; 2) heuristics generated by ReEvo in minutes can outperform state-of-the-art human designs and neural solvers; 3) LHHs enable efficient algorithm design automation even when challenged with black-box COPs, demonstrating its potential for complex and novel real-world applications. Our code is available: https://github.com/ai4co/LLM-as-HH.


A Construction of Evolving $k$-threshold Secret Sharing Scheme over A  Polynomial Ring

Abstract: The threshold secret sharing scheme allows the dealer to distribute the share to every participant such that the secret is correctly recovered from a certain amount of shares. The traditional $(k, n)$-threshold secret sharing scheme requests that the number of participants $n$ is known in advance. In contrast, the evolving secret sharing scheme allows that $n$ can be uncertain and even ever-growing. In this paper, we consider the evolving secret sharing scenario. Using the prefix codes and the properties of the polynomial ring, we propose a brand-new construction of evolving $k$-threshold secret sharing scheme for an $\ell$-bit secret over a polynomial ring, with correctness and perfect security. The proposed schemes establish the connection between prefix codes and the evolving schemes for $k\geq2$, and are also first evolving $k$-threshold secret sharing schemes by generalizing Shamir's scheme onto a polynomial ring. Specifically, the proposal also provides an unified mathematical decryption for prior evolving $2$-threshold secret sharing schemes. Besides, the analysis of the proposed schemes show that the size of the $t$-th share is $(k-1)(\ell_t-1)+\ell$ bits, where $\ell_t$ denotes the length of a binary prefix code of encoding integer $t$. In particular, when $\delta$ code is chosen as the prefix code, the share size achieves $(k-1)\lfloor\lg t\rfloor+2(k-1)\lfloor\lg ({\lfloor\lg t\rfloor+1}) \rfloor+\ell$, which improves the prior best result $(k-1)\lg t+6k^4\ell\lg{\lg t}\cdot\lg{\lg {\lg t}}+ 7k^4\ell\lg k$, where $\lg$ denotes the binary logarithm. When $k=2$, the proposed scheme also achieves the minimal share size for single-bit secret, which is the same as the best known scheme.


Learning Network Representations with Disentangled Graph Auto-Encoder

Abstract: The (variational) graph auto-encoder is extensively employed for learning representations of graph-structured data. However, the formation of real-world graphs is a complex and heterogeneous process influenced by latent factors. Existing encoders are fundamentally holistic, neglecting the entanglement of latent factors. This not only makes graph analysis tasks less effective but also makes it harder to understand and explain the representations. Learning disentangled graph representations with (variational) graph auto-encoder poses significant challenges, and remains largely unexplored in the existing literature. In this article, we introduce the Disentangled Graph Auto-Encoder (DGA) and Disentangled Variational Graph Auto-Encoder (DVGA), approaches that leverage generative models to learn disentangled representations. Specifically, we first design a disentangled graph convolutional network with multi-channel message-passing layers, as the encoder aggregating information related to each disentangled latent factor. Subsequently, a component-wise flow is applied to each channel to enhance the expressive capabilities of disentangled variational graph auto-encoder. Additionally, we design a factor-wise decoder, considering the characteristics of disentangled representations. In order to further enhance the independence among representations, we introduce independence constraints on mapping channels for different latent factors. Empirical experiments on both synthetic and real-world datasets show the superiority of our proposed method compared to several state-of-the-art baselines.


convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data

Abstract: Spontaneous neural activity, crucial in memory, learning, and spatial navigation, often manifests itself as repetitive spatiotemporal patterns. Despite their importance, analyzing these patterns in large neural recordings remains challenging due to a lack of efficient and scalable detection methods. Addressing this gap, we introduce convSeq, an unsupervised method that employs backpropagation for optimizing spatiotemporal filters that effectively identify these neural patterns. Our method's performance is validated on various synthetic data and real neural recordings, revealing spike sequences with unprecedented scalability and efficiency. Significantly surpassing existing methods in speed, convSeq sets a new standard for analyzing spontaneous neural activity, potentially advancing our understanding of information processing in neural circuits.


TransFR: Transferable Federated Recommendation with Pre-trained Language  Models

Abstract: Federated recommendations (FRs), facilitating multiple local clients to collectively learn a global model without disclosing user private data, have emerged as a prevalent architecture for privacy-preserving recommendations. In conventional FRs, a dominant paradigm is to utilize discrete identities to represent users/clients and items, which are subsequently mapped to domain-specific embeddings to participate in model training. Despite considerable performance, we reveal three inherent limitations that can not be ignored in federated settings, i.e., non-transferability across domains, unavailability in cold-start settings, and potential privacy violations during federated training. To this end, we propose a transferable federated recommendation model with universal textual representations, TransFR, which delicately incorporates the general capabilities empowered by pre-trained language models and the personalized abilities by fine-tuning local private data. Specifically, it first learns domain-agnostic representations of items by exploiting pre-trained models with public textual corpora. To tailor for federated recommendation, we further introduce an efficient federated fine-tuning and a local training mechanism. This facilitates personalized local heads for each client by utilizing their private behavior data. By incorporating pre-training and fine-tuning within FRs, it greatly improves the adaptation efficiency transferring to a new domain and the generalization capacity to address cold-start issues. Through extensive experiments on several datasets, we demonstrate that our TransFR model surpasses several state-of-the-art FRs in terms of accuracy, transferability, and privacy.


LLM-Detector: Improving AI-Generated Chinese Text Detection with  Open-Source LLM Instruction Tuning

Abstract: ChatGPT and other general large language models (LLMs) have achieved remarkable success, but they have also raised concerns about the misuse of AI-generated texts. Existing AI-generated text detection models, such as based on BERT and RoBERTa, are prone to in-domain over-fitting, leading to poor out-of-domain (OOD) detection performance. In this paper, we first collected Chinese text responses generated by human experts and 9 types of LLMs, for which to multiple domains questions, and further created a dataset that mixed human-written sentences and sentences polished by LLMs. We then proposed LLM-Detector, a novel method for both document-level and sentence-level text detection through Instruction Tuning of LLMs. Our method leverages the wealth of knowledge LLMs acquire during pre-training, enabling them to detect the text they generate. Instruction tuning aligns the model's responses with the user's expected text detection tasks. Experimental results show that previous methods struggle with sentence-level AI-generated text detection and OOD detection. In contrast, our proposed method not only significantly outperforms baseline methods in both sentence-level and document-level text detection but also demonstrates strong generalization capabilities. Furthermore, since LLM-Detector is trained based on open-source LLMs, it is easy to customize for deployment.


Truncated Non-Uniform Quantization for Distributed SGD

Abstract: To address the communication bottleneck challenge in distributed learning, our work introduces a novel two-stage quantization strategy designed to enhance the communication efficiency of distributed Stochastic Gradient Descent (SGD). The proposed method initially employs truncation to mitigate the impact of long-tail noise, followed by a non-uniform quantization of the post-truncation gradients based on their statistical characteristics. We provide a comprehensive convergence analysis of the quantized distributed SGD, establishing theoretical guarantees for its performance. Furthermore, by minimizing the convergence error, we derive optimal closed-form solutions for the truncation threshold and non-uniform quantization levels under given communication constraints. Both theoretical insights and extensive experimental evaluations demonstrate that our proposed algorithm outperforms existing quantization schemes, striking a superior balance between communication efficiency and convergence performance.