Reviewerly/exHarmony huggingface dataset:
Pixel by Pixel: The Art of <i>CHANCE</i>

AbstractThis article introduces a generative art challenge. "A person paints with their brains and not with their hands." This quote, attributed to Michelangelo (and modified by us), captures the spirit of the challenge-Pixel by Pixel: The Art of CHANCE. For this challenge, readers will use their brains and their computational skills to create generative art. The submission categories are middle and high school students, undergraduate students, graduate students, and professionals. Awards will be given in each category. Each submission should include a short descriptive essay of the creative process, including which artist served as the inspiration and motivation for the tools used. Additional informationNotes on contributorsWendy MartinezWendy Martinez is the senior mathematical statistician for data science in the Research and Methodology Directorate of the U.S. Census Bureau. Previously, she served as director of the Mathematical Statistics Research Center in the Bureau of Labor Statistics (BLS) and worked in several research positions in the Department of Defense. Her research interests include computational statistics, exploratory data analysis, text analysis, and data visualization. She received her PhD in computational sciences and informatics with an emphasis on computational statistics at George Mason University in 1995. She completed a master of science degree in engineering at George Washington University in 1991 and a bachelor of science degree in mathematics and physics at Cameron University in 1989.Donna LaLondeDonna LaLonde is the associate executive director at the ASA, where she works with colleagues to advance the vision and mission of the association. Before joining the ASA, she was a faculty member at Washburn University and served in various administrative positions, including interim chair of the Education Department and associate vice president for academic affairs. At the ASA, LaLonde has supported activities associated with presidential initiatives, professional development, and accreditation.
The Construction of Subject Knowledge Mapping in the Background of Smart Education

This study addresses the issue of mixed teaching resources in online courses, which makes resource use inefficient and information gained unsystematic. Starting with the goal of constructing a knowledge map of the subject area to aid teaching and learning, it logically processes the complicated and trivial subject knowledge, and attempts to investigate the construction of a standardized and systematic knowledge map to reveal the relationship between the trivial knowledge points in the subject area, promote the effective use of educational resources, and assist learners in building a flexible knowledge base. With the example of Computer Composition and Architecture, the process and method of creating knowledge maps are explained. It also illustrates the role of knowledge mapping in assisting the continuous development of the field of education and teaching, which cannot be underestimated in promoting the wisdom of education.
Teaching Reform Practice for Introduction to Artificial Intelligence under the Background of New Finance and Economics

with the arrival of the information age, the universities of finance and economics are facing a new situation in talent training. Curriculum construction also needs timely adjustment. Taking the course Introduction to Artificial Intelligence in Hebei University of Economics and Business as an example, this paper expounds the course characteristics, orientation, objectives, content and teaching methods. Finally, we reflect on the teaching effect. This paper has actively explored the implementation of discipline integration and the promotion of new finance and economics.
Automating Students’ Decision Processes in a Smart Campus

Given the technology evolution and the introduction of the smart campus concept, this paper considers their impact on educational processes in the specialization of automation and industrial informatics. The goal was to study the characteristics implemented in existing systems and to create an application based on use cases defined in our faculty, for guidance and automation of students’ decisions in regard with their educational path. In order to apply smart campus concepts, we focused on administrative processes, like subjects’ exchange, with support from a virtual assistant designed to guide students through their decision activities. This also includes the generation of a personalized calendar for students and professors, based on their own timetable.
A Study of Interactive Behavioral Characteristics of High School IT Classroom Teaching in a Smart Classroom Environment

In this regard, we conducted a study on teacher-student interaction in the IT smart classroom of the experimental high school, and came up with the characteristics of improved student subjectivity, "lecture-question-answer-feedback" as the main behavior in the high school IT smart classroom, lack of wisdom in the use of technology and lack of effectiveness in the application of classroom data, and proposed strategies to optimize teachers' teaching reflection and teaching questions in the context of the existing environment.
The Construction of "Management Accounting" MOOC and the Construction of Entropy Weight of Teaching Quality-TOPSIS Evaluation Model

Management accounting is a comprehensive and marginal subject formed by management and accounting. Management accounting mainly serves the internal management needs of the unit and is a management activity that plays an important role in the planning, decision-making, control and evaluation of the unit through the use of relevant information and the organic integration of financial and operational activities. With the development of internet application technology, MOOCs as a form of online course development have been widely promoted in countries around the world. As an educational model with great potential and social influence, MOOCs have attracted great attention from people at home and abroad. MOOCs are an innovative product of the "Internet + education" model and a challenge to the traditional teaching model. Facing the rapid development of large-scale online open courses, how to carry out the teaching reform of management accounting courses in universities is one of the concerns of higher education workers. The evaluation index system of Management Accounting online course teaching quality construction includes teaching team and MOOC teaching content MOOC teaching resources, MOOC teaching effect. Among them, the weights of goal realization, learner performance and MOOC recording quality were 0.571, 0.619 and 0.333, respectively, and the weight of teaching content organization is 0.830, the weight of resource richness is 0.628 and so on. Based on this, this paper firstly introduces the characteristics of MOOCs management accounting courses, analyzes the course orientation and course objectives of Management Accounting course, the specific contents of MOOC construction of Management Accounting course and the MOOC construction process of Management Accounting course, and proposes the MOOC construction stage of Management Accounting course, the micro-course design of Management Accounting and the platform of MOOCs framework design. The construction module design of the MOOC online course of Management Accounting, the production requirements and course construction characteristics of the MOOC video of Management Accounting, and finally the construction of the MOOC of the online course of Management Accounting and its teaching quality entropy-weight-TOPSIS evaluation model are analyzed. For the reference of relevant personnel.
Random number generation in hardware reconfigurable wireless sensor nodes

In order to achieve a greater reliability, sensor nodes working with limited resources has some drawbacks when it comes to ensure secure communication between sensor nodes. Because in theory there is no need to hide the algorithm as long as the cryptographic keys are safe, the more random a key is the harder for an attacker is to find it. Although hardware reprogrammable platforms consume more energy than microcontrollers, they prove to have considerable advantages when implementing RNG's regarding the speed and the possibility of using bitwise operations, thus being more versatile. In this paper, a Filed Programmable Gate Array (FPGA) design who use data collected from different on board sensors to generate cryptographic keys. Randomness analysis of the generated data is then performed using National Institute of Standards and Technology (NIST) methodology in order to validate the proposed design.
Optimized method for polarization-based image dehazing

Image dehazing is desired under the foggy, rainy weather, or the underwater condition. Since the polarization-based image dehazing utilizes additional polarization information of light to de-scatter, image detail can be recovered well, but how to segment the polarization information of the background radiance and the object radiance becomes the key problem. For solving this problem, a method which combing polarization and contrast enhancement is demonstrated. This method contains two main steps, (a) by seeking the region of large mean intensity, low contrast and large mean degree of polarization, the no-object region can be found, and (b) through defining a weight function and judging whether the dehazed image can achieve high contrast and low information loss, the degree of polarization for object radiance can be estimated. Based on the estimated parameters, the scatter of light by the mediums can be diminished considerably. The theoretical derivation shows that this method can achieve advantages complementation, such as being able to obtain more details like the polarization-based method and high image contrast like the contrast enhancement based method. Besides, it is physically sound and can achieve good dehazing performance under different conditions, which has been verified by different hazing polarization images.
Violentometer: measuring violence on the Web in real time

This paper describes a system for monitoring in real time the level of violence on Web platforms through the use of an artificial intelligence model to classify textual data according to their content. The system was successfully implemented and tested during the electoral campaign period of the Brazilian 2022 elections by using it to monitor the attacks directed to thousands of candidates on Twitter. We show that, despite an accurate and absolute quantification of violence is not feasible, the system yields differential measures of violence levels that can be useful for understanding human behavior online.
A hybrid learning frame work for recognition abnormal events intended from surveillance videos

The need for a monitoring system has grown as a result of rising crime and anomalous activity. To avoid unusual incidents, the common man initiated video surveillance of important areas, which was then passed on to the government. In typical surveillance operations, surveillance devices create a vast volume of data that must be manually analysed. Manually handling huge data sets in real time results in information loss. To prevent abnormal incidents, the actions in sensitive areas can be properly monitored, evaluated, and alerted to the appropriate authorities. Previous deep learning-based activity identification methods have appeared, but the findings are inaccurate, and the proposed Hybrid Machine Learning Algorithms (HMLA) incorporate two detection methods for surveillance videos like as Transfer Learning (TL) and Continual Learning (CL). As a result, the suspicious activity in the video may be missed. Consequently, numerous image processing and computer vision technologies were used in activity detection to decrease human effort and mistakes in surveillance operations. Activities in sensitive areas can be properly monitored and evaluated to avoid unusual incidents, and the appropriate authorities may be alerted. Hence, in order to decrease human error and effort in surveillance operations, activity recognition embraced a variety of image processing and computer vision technologies. In this present work, the capacity has constraints that impact recognition accuracy. Consequently, this research paper presents a HMLA based technique that uses feature extraction using multilayer (Long Short Term Memory) LSTM, Convolutional Neural Networks (CNN), and Temporal feature extraction using multilayer LSTM to improve identification accuracy by 96% while requiring minimal execution time. To show the superior performance of the proposed hybrid machine learning technique, a standard UCF crime dataset was utilised for experimental analysis and compared to existing deep learning algorithms.
Decoding Prompt Syntax: Analysing its Impact on Knowledge Retrieval in Large Language Models

Large Language Models (LLMs), with their advanced architectures and training on massive language datasets, contain unexplored knowledge. One method to infer this knowledge is through the use of cloze-style prompts. Typically, these prompts are manually designed because the phrasing of these prompts impacts the knowledge retrieval performance, even if the LLM encodes the desired information. In this paper, we study the impact of prompt syntax on the knowledge retrieval capacity of LLMs. We use a template-based approach to paraphrase simple prompts into prompts with a more complex grammatical structure. We then analyse the LLM performance for these structurally different but semantically equivalent prompts. Our study reveals that simple prompts work better than complex forms of sentences. The performance across the syntactical variations for simple relations (1:1) remains best, with a marginal decrease across different typologies. These results reinforce that simple prompt structures are more effective for knowledge retrieval in LLMs and motivate future research into the impact of prompt syntax on various tasks.
A standalone software for real-time facial analysis in online conferences and e-lessons

Nowadays, many meetings, lessons, conferences, and presentations are organized online, where it is complicated to communicate with an audience and control their engagement and emotions. In this article, we present a novel C++ application that is led to help estimate facial identities and expressions. It captures a screen with a window of an arbitrary online conference tool. Next, facial regions are detected, and two efficient neural networks are used to identify a participant of an online event, track his or her face, predict emotional state, and provide a log with timestamps and facial expressions of each person.
A stochastic configuration networks based on Harris hawks optimizer

Stochastic configuration networks (SCNs), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCNs is affected by the assignation and selection of some network parameters significantly. Harris hawk optimizer (HHO) algorithm is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of Harris hawk. In this paper, a SCNs based on HHO algorithm is first introduced, termed as HHO-SCNs. As the performance of SCNs is related to regularization parameter r and scale factor lambda of weights and biases, then HHO is employed to give better parameters for SCNs automatically. A numerical function and six benchmark datasets are used to verify the regression performance of the proposed model. Three benchmark datasets are introduced to illustrate the effectiveness of the proposed model for classification performance. Experimental results demonstrate the feasibility and validity of HHO-SCNs compared with incremental random vector functional link, SCNs, fast SCNs, and SCNs based on whale optimization algorithm. The proposed HHO-SCNs improves the generalization performance of standard SCNs, and provides a new idea for expanding the development and application of SCNs.
Integrated Ranking for News Feed with Reinforcement Learning

With the development of recommender systems, it becomes an increasingly common need to mix multiple item sequences from different sources. Therefore, the integrated ranking stage is proposed to be responsible for this task with re-ranking models. However, existing methods ignore the relation between the sequences, thus resulting in local optimum over the interaction session. To resolve this challenge, in this paper, we propose a new model named NFIRank (News Feed Integrated Ranking with reinforcement learning) and formulate the whole interaction session as a MDP (Markov Decision Process). Sufficient offline experiments are provided to verify the effectiveness of our model. In addition, we deployed our model on Huawei Browser and gained 1.58% improvements in CTR compared with the baseline in online A/B test. Code will be available at https://gitee.com/mindspore/models/tree/master/research/recommend/NFIRank.
LLMs to the Moon? Reddit Market Sentiment Analysis with Large Language Models

Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters. The resulting lack of high-quality labeled data stands in the way of conventional supervised learning methods. In this work, we conduct a case study approaching this problem with semi-supervised learning using a large language model (LLM). We select Reddit as the target social media platform due to its broad coverage of topics and content types. Our pipeline first generates weak financial sentiment labels for Reddit posts with an LLM and then uses that data to train a small model that can be served in production. We find that prompting the LLM to produce Chain-of-Thought summaries and forcing it through several reasoning paths helps generate more stable and accurate labels, while training the student model using a regression loss further improves distillation quality. With only a handful of prompts, the final model performs on par with existing supervised models. Though production applications of our model are limited by ethical considerations, the model’s competitive performance points to the great potential of using LLMs for tasks that otherwise require skill-intensive annotation.
Privacy–Preserving Online Content Moderation: A Federated Learning Use Case

Users are exposed to a large volume of harmful content that appears daily on various social network platforms. One solution to users’ protection is developing online moderation tools using Machine Learning (ML) techniques for automatic detection or content filtering. On the other hand, the processing of user data requires compliance with privacy policies. In this paper, we propose a framework for developing content moderation tools in a privacy-preserving manner where sensitive information stays on the users’ device. For this purpose, we apply Differentially Private Federated Learning (DP–FL), where the training of ML models is performed locally on the users’ devices, and only the model updates are shared with a central entity. To demonstrate the utility of our approach, we simulate harmful text classification on Twitter data in a distributed FL fashion– but the overall concept can be generalized to other types of misbehavior, data, and platforms. We show that the performance of the proposed FL framework can be close to the centralized approach – for both the DP–FL and non–DP FL. Moreover, it has a high performance even if a small number of clients (each with a small number of tweets) are available for the FL training. When reducing the number of clients (from fifty to ten) or the tweets per client (from 1K to 100), the classifier can still achieve AUC. Furthermore, we extend the evaluation to four other Twitter datasets that capture different types of user misbehavior and still obtain a promising performance (61% – 80% AUC). Finally, we explore the overhead on the users’ devices during the FL training phase and show that the local training does not introduce excessive CPU utilization and memory consumption overhead.
Islamic Influence on the Local Majapahit Hindu Dwelling of Indonesia in the 15th Century

Majapahit was one of Indonesia’s most prominent and last Hindu kingdoms in the 12–15th centuries. During this time, there was a process of changing religious beliefs among the Javanese population, who then embraced Hinduism to eventually become Muslim. In the 14th and 15th centuries, according to estimates, Islam began to spread throughout the Majapahit Empire. There was also a cultural shift in the Majapahit Kingdom’s society, from how they dressed to the shape of their houses. This study aims to determine how structures from that time were interpreted using a 3D model based on field surveys and previous studies. This documentation’s findings are divided into typology, spatial distribution, and architectural elements. Each of these three groups was further defined through Islamic law to determine the extent of Islam’s Influence on dwellings throughout the Majapahit Era. The impact of Islam on Majapahit architecture during that time was characterised by openness in terms of building typology, compactness in the internal layout of buildings, and the employment of ornamental architectural features based on plant and abstract patterns.
System Level Benchmarking of Public Clouds

It is important for cloud users to be able to evaluate and compare different cloud services to achieve high performance and maximize cost savings. To that end, this research benchmarked Amazon web services elastic compute cloud and Rackspace cloud infrastructure and compared the results for the two public cloud providers. The intent of the study was to determine how these selected providers perform with regards to system parameter usage and hence three system-level benchmarks: STREAM, IOR, and NPB-EP were run on different configurations to provide an insight to the cloud users in selection of provider on VM clusters of 1,2,4,6, and 8 nodes. The clusters were created with similar virtual machines from both providers. The benchmarks examined bandwidth, I/O and CPU performance. A comparison of results for the two providers is presented graphically and T-test applied to determine if differences are significant. Observations were taken at multiple times at different time periods on weekdays and weekends to examine variance of cloud performance.
Towards a Decentralized Data Hub and Query System for Federated Dynamic Data Spaces

This position paper proposes a hybrid architecture for secure and efficient data sharing and processing across dynamic data spaces. On the one hand, current centralized approaches are plagued by issues such as lack of privacy and control for users, high costs, and bad performance, making these approaches unsuitable for the decentralized data spaces prevalent in Europe and various industries (decentralized on the conceptual and physical levels while centralized in the underlying implementation). On the other hand, decentralized systems face challenges with limited knowledge of/control over the global system, fair resource utilization, and data provenance. Our proposed Semantic Data Ledger (SDL) approach combines the advantages of both architectures to overcome their limitations. SDL allows users to choose the best combination of centralized and decentralized features, providing a decentralized infrastructure for the publication of structured data with machine-readable semantics. It supports expressive structured queries, secure data sharing, and payment mechanisms based on an underlying autonomous ledger, enabling the implementation of economic models and fair-use strategies.