Reviewerly/exHarmony huggingface dataset:
Graph factorization machine based recommendation algorithm with graph construction and attention mechanism

Graphs, as a type of data structure that exists in numerous scenarios, are applied in various fields of the internet, such as recommendation algorithms, community detection, path search, and so on. In recent years, with the rapid development of the internet, the amount of graph data has greatly increased, and analyzing and utilizing these graph data to establish graph network models and apply them to various real-life production and living scenarios is of significant importance, such as using user-item data to establish a recommendation algorithm model for recommendations. Currently, many graph neural network models have achieved good results, but there is still room for improvement. Better algorithms can more accurately understand the user's needs and bring a better recommendation experience to the user. The paper designs a new way of constructing graphs and improves the graph network message-passing algorithm. A new graph neural network algorithm, GAFM (Graph Attention Factorization Machines), is proposed. Compared with traditional algorithms, this algorithm will construct graph structures of users and items based on original data, and can also construct item graph data by linking to a knowledge base to introduce new items. When this algorithm performs message passing between nodes, it will consider the first-order neighbor set of the central node and the second-order cross-item of the first-order neighbor set, and use an attention mechanism to regulate the weight in the message passing process to capture high-dimensional neighbor information and improve accuracy. The experimental results on real-world datasets show that the algorithm has better performance compared to existing graph recommendation algorithms.
The implications of ‘May 4, 1970 and its aftermath’ for citizenship education in the United States and Korea: “The constitutional rights against national security ideology”

This study aims to analyze the influence of the ‘May 4, 1970 Kent State Shootings and its aftermath’(Ma4a) on citizenship education in American high schools and to identify implications for the institutionalization of movement-oriented citizenship education in Korea. The data were analyzed using the methodology of ‘qualitative content analysis.' The data on Ma4a was collected by visiting the ‘May 4 Site,’ ‘May 4 Visitors Center,’ and ‘May 4 Digital Archive’ at KSU in 2021 and 2022. It was also useful to refer to the undergraduate course on Ma4a. The data on high school citizenship education in the United States were collected from Ohio Curriculum Standards of social studies, history textbooks, and teachers’ lesson plans. ‘May 4, 1970’ was described as “civil disobedience” rather than “riot” in social studies curricula, history textbooks, and lesson plans after its institutionalization from the late 1990s to the early 2000s. The constitutional right of ‘freedom of speech and assembly’ was not undermined by the ‘national security ideology.’ However, after the events of September 11, 2001, and the 2008 economic crisis, the Ohio Department of Education described the people's right to protect themselves from only “undue” governmental interference. The paradigm of citizenship education in U.S. schools changed due to the contest between state-centric citizenship and participatory citizenship. Social movements influenced citizenship education through the institutionalization of movement knowledges by movement intellectuals, which took a lot of time. This study has implications for the institutionalization of movement-oriented citizenship education, such as ‘Jeju 4·3 education for peace and human rights’ in Korean schools.
Exploration of the structure for diagnosis and support of basic academic abilities in the lower elementary grades of unit schools

After COVID-19, as the overall problem of poor academic performance intensified, concerns about the poor basic academic ability of students in the lower grades of elementary school spread. In order to respond to these concerns, this study explored the school structure related to guaranteeing basic academic ability in order to find a plan to support basic academic ability in the lower elementary grades of unit school. We selected 4 elementary schools in the Gyeonggi area. Interviews were conducted with school administrators, external instructors, parents, superintendents at the Office of Education, and counselors at the Learning Comprehensive Clinic Center. The study have shown that the structural problems related to support for basic academic ability include project-oriented support rather than student-centered support, individual teacher's individual battle-style support rather than teacher community-centered support, low utilization of online platforms, and basic academic ability dependent on external support instructors. Also ,the support was focused on a specific area (literacy). Solutions to these structural problems were discussed, and plans for establishing a systematic basic education support system centered on unit schools were presented.
References

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
Working Remotely, a Practical Guide for Librarians.

"Working Remotely, a Practical Guide for Librarians.." Journal of Electronic Resources Librarianship, 35(2), pp. 168–169
Academic Libraries and Public Engagement with Science and Technology.

"Academic Libraries and Public Engagement with Science and Technology. ." Journal of Electronic Resources Librarianship, 35(2), pp. 162–163
Towards Systematic Evaluation of User- Generated Vs AI-Generated Contents in Online Knowledge Exchange Communities

The increasing importance and popularity of online knowledge exchanging platforms have resulted in an extensive body of literature aimed at quality evaluation of knowledge. However, the existing evaluation tools are overly relying on popular vote to decide the quality. As the capabilities of AI (Artificial Intelligence) continue to improve, AI may become an effective tool for manipulating and misleading humans, making popular-vote-based evaluations less reliable. Henceforth, in this study, we deem it is important to design a systematic quality-assessment framework to understand peopleâ€™s knowledge-evaluation processes. In doing so, we can examine if the processes are significantly difference between AI-generated knowledge contents and human-contributed contents. We draw on IAM (Information Adoption Model) to build the framework. To increase the validity of the study, we also examine the impact of knowledge type and characteristics. With this study, we expect to provide a new valid use case that extends the IAM by considering the AI factor together with the potential contingency effects caused by knowledge types and characteristics. Finally, we believe this study will open a route to reliable, accurate, readily extensible, and adjustable framework.
An Exploratory Investigation of Cognitive Mapping for Analyzing Needs in UX Design

Needs analysis is a major concern in innovation projects both for organizations pursuing business objectives and for the users whose needs should be satisfied. Different needs analysis methods can be used, depending on the scope and complexity of the project. However, not all the existing methods provide efficient decision-making support for designers whose task is to categorize and prioritize the needs. The aim of this article is to explore the application of cognitive mapping as a needs analysis method that will more efficiently analyze the nature of the needs and their interrelations. This analysis will provide a different perspective for understanding needs and, thus, contribute to decision making and prioritization. To this end, the proposed method was tested with the UX team of a large established North American transportation company. The feedback from the multiple groups involved in needs analysis indicated that the need mapping technique was perceived as useful and could be applied as a decision support.
How Blockchain Is Revolutionizing the Waste Management Industry towards Circular Economy

Blockchain technology has the potential to revolutionize the waste management industry by enhancing accuracy, transparency, and accountability in waste disposal processes. This article explores how blockchain enables the tracking and tracing of waste from its source to the disposal site, creating a tamper-proof record of its journey. A case study of Plastic Bank demonstrates how blockchain is utilized to incentivize and trace plastic waste collection, benefiting both the environment and impoverished communities. The implications of blockchain in waste management include improved efficiency, reduced environmental impact, and opportunities for businesses to demonstrate their commitment to sustainability. However, challenges such as regulatory compliance and technological integration need to be addressed to fully leverage blockchain's potential. Complementary initiatives such as waste reduction, recycling, and transitioning to a circular economy are also crucial for a sustainable future.
An improved feature point-based LK optical flow algorithm for robotic arm control

Robot vision technology has been widely used in many fields since its emergence, and real-time control of robotic arm control systems has long been a challenging problem. When using optical flow algorithms for tracking in conventional vision control, its accuracy and large movements are affected by the size of the integration window to track fast-moving objects, making it difficult to achieve real-time control in robotic arm vision control. By using a smaller integration window, the Pyramid Lucas-Kanade (LK) method can solve the problem that large motions cannot be tracked, but the basic Pyramid LK method is not very accurate. Therefore, an improved LK optical flow method is proposed for the practical application of the traditional LK method for robotic arm control with poor real-time performance and accuracy, applying a combination of the improved FAST (Features from Accelerated Segment Test) corner point detection and the pyramid LK optical flow algorithm. With the improved FAST algorithm, the corner points with the strongest grey-scale variations can be extracted quickly. This approach allows better estimation of optical flows with strong corner points to track moving objects. The corner points calculated by applying the improved FAST feature point detection are first used as candidate feature points; then the candidate feature points are re-extracted by setting filtering conditions with the information obtained from the robotic arm; and finally, the target feature points are tracked using the LK optical flow method.
GymSense: Exploration in Measuring Bench Press Form Using Sensor Technology

Proper weightlifting form is essential to prevent injuries, particularly for beginners. Our GymSense project is a computer system that monitors and provides feedback on critical aspects of form during the bench press exercise, including grip width, shoulder distance, shoulder abduction angle, tilt, and back arch. The system employs force and camera sensors, ArUco markers, and the BlazePose algorithm to measure the key parameters of form and provides feedback to the user during and after each set. The feedback loop allows users to identify areas for improvement and reduce the risk of injury. We conducted a user study to assess the accuracy of the first iteration of the system. We found through the user study that our system is not as accurate as a personal trainer, but our project presents the initial phase of an alternative approach for obtaining feedback on a user’s bench press form.
Speech Acts and Political Commitment: An Analysis of APC Presidential Nomination Acceptance speech of Asiwaju Ahmed Tinubu

An acceptance speech is one of the political speeches being delivered by political aspirants across the world. This paper investigates and identifies the speech act features of the nomination acceptance speech of a notable Nigeria politician, Asiwaju Bola Ahmed Tinubu. The theoretical framework of Speech Act Theory of Austin and modified by Searle was adopted for its analysis. The analysis specifically focuses on the illocutionary acts that were used in the speech. These illocutionary acts are: assertives, directives, commissives, expressives and declaratives. The findings reveal that Asiwaju Bola Ahmed Tinubu uses illocutionary acts: expressives (33.3%), assertives (13.3%), commissives (36.7%), directives (16.7%) and declaratives (0%). He uses the nomination acceptance speech to inform, thank, promise, request, state, assure, persuade and reflect on the present political challenges ahead his party, the All Progressive Congress in the 2023 Presidential election. It was further revealed from the illocutionary acts in the speech that Senator Asiwaju Bola Ahmed Tinubu’s commitment to the development of the nation cannot be overemphasised. Those identified speech acts further foster his political interest and commitment to the affairs and welfares of Nigerians. The work adds to the knowledge of speech act analysis and provides insights into the relevance of political commitment by politicians in Nigeria.
Adaptive Multi-Hop Deep Learning based Drug Recommendation System with Selective Coverage Mechanism

The goal of drug recommendation is to generate drug prescriptions based on patient’s electronic medical records and provide clinical decision support for doctors. Extracting temporal patterns and contextual information in electronic medical records is the key to successful drug recommendation. However, previous studies have ignored the relationship between patients. As a result, there are differences in the data volume of medical records among different patients, and it is impossible to adjust the focus of attention and the number of data reading iterations in the data reading process according to the individual conditions of different patients. To solve the above problems, this paper proposes a selective coverage mechanism and adaptive memory neural network, The drug recommendation model combined with reading. The model uses the neural memory network to store the temporal pattern encoding results corresponding to the patient’s health status and uses the coverage mechanism to perform data filtering and attention weight adjustment in the iterative reading process. At the same time, the model is based on the patient’s situation, Adaptively determining the number of readings of the neural memory network. Experimental results based on accurate clinical data show that this model can adaptively extract essential data from electronic medical records, construct an effective representation vector of the patient’s health status, and then complete drug recommendations.
Design of Lower-Error and Area-Efficient Sine Function Generators for Image Encryptions Using Logarithmic Number Systems (LNS)

In order to protect the privacy of images, sine functions can be used for the encryption of images. However, the operations for generating sine functions are becoming the area-consuming bottleneck for image encrypt/decrypt applications. In this work, lower-error and area-efficient sine function generators are proposed using logarithmic number system (LNS). Expressing the sine functions using LNS-based representations, we can avoid time-consuming operations. Synthesis results show that our proposed generators are area efficient than conventional with lower errors.
Building a Quantum Computer

Nature allows the storage and manipulation of data in new and powerful ways using quantum mechanics. I will explain the basic concepts behind the exponential power of this technology and how to build a quantum computer using superconductors. I will show recent experimental data on a “quantum supremacy” experiment with 53 qubits: the Sycamore processor takes about 200 seconds to run a quantum circuit a million times - the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. Finally, I will explain some of the future uses of quantum algorithms and what may be in store for the field in the next 10 years.
Designing the Most Energy-Efficient Recommendation Inference Chip

NEUCHIPS' purpose-built recommender system accelerator, RecAccel™ N3000, includes optimized hardware and software stack and delivers unparalleled performance, energy efficiency – and the lowest TCO. Recommendation systems predict the “rating” or “preference” a user would give to an item. Most internet service providers employ recommendation systems to deliver their products or services. Recommendations usually account for most of AI inference workload in data centers. A typical neural personalized recommendation model consists of multiple embedding table lookup, feature interaction, and multiple multilayer perceptron (MLP) [1]. Different models employ different architecture for various applications and service level agreements (SLAs) target [2]. Some models are compute-intensive while others are memory-intensive. Therefore, in addition to efficient high performance GEMM engines, an accelerating solution must provide high-capacity, high-bandwidth, and low-latency storage as well as efficient allocation and access strategies.
Generative AI: How It Changes Our Lives? Take Vision &amp; Language as an Example

In this talk, we will explore the cutting-edge field of generative AI, with a focus on its applications in vision and language models. Generative AI involves creating intelligent systems that can generate new and diverse data or content, such as images, videos, music, and text. We will start by discussing the history of generative AI and how recent breakthroughs in deep learning and neural networks have revolutionized the field. We will then delve into generative AI for dealing with multiple data modalities. More particularly, we will talk about recent advances in vision and language, and we will cover two of our recent works on image captioning and text-to-image synthesis, which are recently published at NeurIPS 2022 and AAAI 2023.
An Exploration into Hybrid Agile Development Approach

The aim of this paper is to provide a review of the different hybrid agile models. This study raised the question “what are the types of hybrid agile models and their features”? The systematic review was done using Preferred Reporting Items for Systematic Reviews and Meta-Analyses Model for comprehensive searching. Scopus is used as the database for searching articles. A total of 131 papers related to agile and hybrid agile models were retrieved and finally after screening and filtering, only 26 papers were included in the study. This paper probes into the features of agile and hybrid agile models and thus recommends hybrid agile models as one of the best-suited models in software development due to several reasons. Some of the reasons are due to its comprehensiveness in managing a large-scale project with good documentation and developing better methods for business analysis. This paper concludes by providing insight into the different types of hybrid agile models in software development. This paper starts with an Introduction section, followed by a Materials and Method section, continued with a Results and Discussion section, and finally concludes the research in the Conclusion section.&#x0D;
Promoting the Smart Tourism by Implementing Virtual Reality and Augmented Reality

In the new era, the latest technologies are emerging in Industry 4.0, currently out of these technologies two are at peak known as Augmented reality which basically converts reality in virtual scenario and Virtual reality which converts virtuality into reality. Tourism the name itself has a peace and for most of the countries and peoples it is the major source of income or we say functioning. What happen if we sync these two with each other and we can refer it as Smart Tourism by implementing AR-VR. With that note we proposed a model regarding the smart view of tourist spot which will generate more curiosity to know that place by visiting them in personally. Reason behind it is that humans have psychology that regard of just viewing images or listening if we experience any incident by our own we get more fascinated towards it and also keep it remembered for longer period of time. So, here when tourists view locations using AR-VR they feel like they are present at that location, it also helps them to decide whether to visit in real or not. It is cost effective also as user saves a lot by not visiting if place is not of their interest. It also helps to cover all the spots that are even not so famous of specific locations which is not covered in Itinerary provided by most of the tour operators. As tourist will enjoy this and also their expectations and curiosity will increases to visit there, this will boost tourism industry and will become very helpful for locals and others depending on this sector.
An Efficient and Cost Effective Performance of Autonomous Attendance System Using Convolution Neural Networks

This research work is aimed to developing a less disruption, cost effective and robust implementation of autonomous attendance management system. To keep track of students' daily presence, marking attendance is a common practice for all educational institutions at all levels. There was once a time when attendance was tracked manually. Although they are time-consuming and labor-intensive for a large number of pupils, these approaches are exact and exclude the possibility of fraudulent enrollment. To address the drawbacks of manual systems, autonomous systems based on radio frequency recognition scanning, fingerprint scanning, face recognition, and iris scanning are being developed. Each technique has benefits and drawbacks. However, the majority of these systems are limited by the requirement for individual human engagement during the recording of attendance. To close the gaps in the current human and autonomous attendance management systems, we developed a robust and efficient attendance recording system using a single group shot to detect face identification and recognition algorithms. A high-definition camera mounted in a stable location records a series of images for each student seated in a classroom. The faces are then extracted from the group shot using a standard procedure, and they are identified using a convolutional neural network that is well-known to students from a face database. We evaluated our technique using several datasets and group pictures. Our research shows that the suggested framework performs better in terms of effectiveness, usability, and implementation than the current attendance tracking methods. The suggested solution is a self-contained attendance system that necessitates little contact between humans and machines, making integration into a smart classroom simple.