Reviewerly/exHarmony huggingface dataset:
The Trajectories of Arts and Culture Students Transitioning from Rural Secondary Contexts to Higher Education: A Self-Study of Practice

This is a self-study of practice investigating the trajectories of Arts and Culture students transitioning from rural secondary contexts to Higher Education Institutions (HEIs). The main objective of this paper was to propose support strategies for first-year university students majoring in Arts and Culture at a specific HEI. First-time entering students (FTENs) struggle to transition from secondary to postsecondary education for a variety of reasons. The paper was based on Bronfenbrenner’s bioecological systems theory, which states that individuals are still influenced by their environment but become important through interactions with other influencers in each system. This research inquiry utilized an interpretive paradigm and a qualitative approach through a self-study of practice, and data was gathered using a qualitative questionnaire, observations, and document analysis. The data was thematically analyzed concurrently with data collection. Participants were selected from several ethnic groups, such as Xhosas, Zulus, Sothos, and others. They were selected purposefully, but Xhosas and females predominated. The findings revealed insufficient exposure to advanced and modern cultural activities; a lack of enthusiasm and passion for arts subjects; and the negative impact of transitioning on students’ learning. These hiccups have a detrimental effect on students’ transitions to HEIs. The paper recommended these support strategies: capacity building of students with proficient skills to transition effectively; use of teaching and learning strategies that cater for students’ different learning styles; and consideration of students’ backgrounds and indigenous knowledge. These variables may help first-year Arts and Culture students transition smoothly into higher education. Keywords: Transition, Arts and Culture, Rural students, teaching and learning, support strategies
FORMATION OF ELEMENTARY MATHEMATICAL NOTIONS AMONG CHILDREN WITH DEVELOPMENTAL DISABILITIES THROUGH PRACTICUM

The article presents and analyzes features of preschooler’s basic mathematical notion formation and organization of possible forms of activity. This work covers numerous research points of views which would allow to efficiently organize and implement educational process; from where start education, how to form and develop mathematical notions, concepts, logical thinking. In order to ensure the effective implementation of this process it is necessary to maintain a sequence, choose appropriate sequence, select suitable tasks, exercises and games. There are a number of issues regarding the formation of mathematical notions among preschool age children with developmental problems, since their development differs from the development of normal children of the same age. The efficiency of formation of basic mathematical notions and its development depends on the content, forms, means and the period of implementation of the classes held. The article also presents certain number of sections of program material concerning the formation of mathematical notions among preschoolers such as: “Quantity and tab,” “Magnitude,” “Shape,” “Orientation in time” "Element computing operation". The stages of the teaching material and their sequence are also covered in the paper. The article thoroughly substantiated the expediency and necessity of the formation of mathematical notions among preschoolers during the process of multifaceted development as well as during the effective school preparation process. The ways and means of conducting mathematics as a class subject are presented in detail.
Analyzing Bank Account Information of Nominees and Scammers in Thailand: Insights from ChaladOhn Website Data

The Internet has become an essential part of people’s lives, including e-commerce and online banking. While online transactions are convenient, it also opens up a new opportunity for scammers to easily attack victims. In this paper, data obtained from ChaladOhn, a website that helps prevent Thais from online e-commerce scams, from February 2022 to January 2023 were studied and investigated. According to the data, it has been found that the total loss value from the cases with a loss value of less than 1 million THB each was 3,126.5 million THB. Furthermore, it has been found that the top two banks involved in the scam were KBank and BBL, respectively, accounting for 22,400 or 43% of scam accounts, and 1,485 million THB or 47% of the loss. Since it’s expected that this problem will take a turn for the worst, the investigated results should be informed to the regulators and the related organization, so that an effective solution can be designed, proposed, and implemented to cope with the rapidly growing number of online scamming cases.
Research on MNIST Handwritten Number Recognition Based on Convolutional Neural Networks

The application of handwritten numeral recognition has great practical significance in daily life, so confusing numbers, difficult-to-recognize labels, and low recognition accuracy are issues that need to be optimized today. Problems with overly complex and inaccurate algorithm schemes for handwritten numeral recognition exist. Based on traditional convolutional neural networks, this paper proposes using a two-layer convolutional layer for feature extraction and then using a two-layer fully connected layer for feature smoothing to optimize handwritten numeral recognition. In the first layer, keep is used_prob performs an overfitting operation, utilizing the softmax cross entropy function in the second layer to maintain the accuracy of the model output while reducing function loss. At the same time, it realizes the visualization of handwritten digits in the TensorFlow environment. Finally, based on Adam optimizer, the results of three different optimization algorithms are compared under different learning rates. A perturbation model is added to the system to verify the experimental results. The optimization algorithm and learning rate conditions with the highest recognition rate are obtained. Their relatively good detection accuracy proves the effectiveness of this structure, which improves the accuracy while simplifying the model.
Research on path planning of mobile robot based on genetic algorithm

From a certain point of view, the level of the mobile robotics field also represents the degree of development of advanced science and technology in the country. Traditional algorithms gradually show some defects with the development of the times and cannot adapt well to the complex working environment of mobile robots. The advantage of genetic algorithm is its high level of overall retrieval and good robustness, so this study chooses genetic algorithm when arranging paths for mobile robots.In this study, the scene conditions of movement are created for the mobile robot by raster map method in MATLAB software, and the adaptation level function is created with reference to the need of its movement smoothing level and route distance, and the appropriate weighting factors of smoothing level and route distance as well as the relevant parameters of genetic algorithm are found, and it is concluded that the changes of different parameters will have a large impact on the movement path of the mobile robot, and by the The adjustment of the parameters can enable the mobile robot to find the optimal path in different environments smoothly.
Emerging Trends in Multi-Accelerator and Distributed System for ML: Devices, Architectures, Tools and Applications

As the complexity and diversity of machine/deep learning models is increasing at a rapid pace, multi-accelerator and distributed systems are becoming a critical component of the machine learning (ML) stack. Besides efficient compute engines and communication mechanisms, these systems also require intelligent strategies for mapping workloads to accelerators and memory management to achieve high performance and energy efficiency, while meeting the demands for high-performance ML/AI systems. This article presents an overview of the emerging trends in multi-accelerator and distributed systems designed for handling complex AI-powered application workloads.
Image recognition software geometry with Python and OpenCV

The project aims to develop a software application using Python and various libraries for image processing and manipulation. The software used consists of Windows 10 Pro Education, Python 3.8.2, OpenCV 4.5.3, NumPy 1.22, Imutils 0.5.4, Pillow 9.4.0, Tkinter, and Visual Studio Code 1.75.1. The methodology is divided into four sprints. In the first sprint, the necessary dependencies, including OpenCV, NumPy, Imutils, Pillow, and Tkinter, were installed. The second sprint involved developing a command-line interface and integrating a live webcam video feature. In the third sprint, color detection was implemented by converting the image to black and white and applying filters to identify specific colors. Finally, the fourth sprint focused on developing the selection of geometric figures and colors. When a figure and color were selected, the software would identify and outline the corresponding object. This project utilized Python and various libraries to create an application for image processing and manipulation. The methodology followed a sprint-based approach to gradually develop different features and functionalities of the software.
EDUCACIÓN NO PRESENCIAL. EL SENTIDO OCULTO DE LA ESCUELA EN LA PANDEMIA.

This article is part of the analysis of the experiences of the teachers of the Jorge Gaitán Cortés IED School in the face of the transformation of education caused by the global pandemic, Covid -19. To do this, an exercise of discursive analysis and collection of experiences focused on the teacher's work was used. The study is a content analysis based on the Laurence Bardin method that allows to show ruptures, relationships and heuristics when assuming non-face-to-face education in a district school.
Online Incentive Mechanism Designs for Asynchronous Federated Learning In Edge Computing

In this paper, we consider incentive mechanism designs in asynchronous federated learning (FL) systems. With the consideration of unique characteristics inherent in asynchronous FL, such as dynamic participating and multi-minded IoT nodes such as mobile users, requirements of model training (i.e., training accuracy and convergence time), and limited uplink bandwidth, we formulate considered system as an online incentive mechanism design problem, where each mobile user is not only a buyer for communication resource, but also a seller for computation service. To address the challenges involved in the design, we first derive the relationship between the number of participants and the global training accuracy in asynchronous FL. Then, based on that, we propose a novel mechanism, called online incentive mechanism for asynchronous FL (OIMAF). To the best of our knowledge, this is the first work to design incentive mechanisms for asynchronous FL. Furthermore, in order to obtain a more robust mechanism, an improved online mechanism, called two-shot based online incentive mechanism (TOIM), is proposed by using OIMAF as a building block. Theoretical analyses show that our proposed online incentive mechanisms can guarantee individual rationality, truthfulness, a sound performance, and solution feasibilities. We further conduct comprehensive simulations to validate the effectiveness of our proposed mechanisms.
MPEG: A Multi-Perspective Enhanced Graph Attention Network for Causal Emotion Entailment in Conversations

Emotion causes constitute a pivotal component in the comprehension of emotional conversations. Recently, a new task named Causal Emotion Entailment (CEE) has been proposed to identify the causal utterances for the target emotional utterance in a conversation. Although researchers have achieved some progress in solving this problem, they failed to adequately incorporate speaker characteristics and overlooked the effects of temporal relations in conversation structures. To fill such a research gap to some extent, we propose a novel causal emotion entailment framework, namely MPEG (Multi-Perspective Enhanced Graph attention network). The training of MPEG consists of three stages. Firstly, we utilize a speaker-aware pre-trained model and two attention mechanisms to obtain the utterance representations that incorporate local contexts as well as the speaker and emotional information. Then, these representations are fed into a graph attention network to model the conversation structures and emotional dynamics from both local and global perspectives. Finally, a fully-connected network is implemented to predict the relationships between emotional utterances and causal utterances. Experimental results show that MPEG achieves state-of-the-art performance. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/slptongji/MPEG</uri> .
Archival users and AI tools for reference and access: a study within the InterPARES Trust AI project

Evaluating the effectiveness of search tools, the ease of use of the selection and display interfaces of archival descriptions and documents, and the satisfaction of end users are not everyday activities among archive service managers. A culture of quality should instead consider actual effectiveness, adopting a user-centred design and a systematic comparison with final users. Within the international project InterPARES Trust AI, focused on the impact of Artificial Intelligence on archival functions, specific studies have been activated on user access to archives and documents from the AI perspective. This contribution presents this project, its assumptions, objectives, and results and the first results of the study in Italy on users.
A Meta-Analysis of Ontological Guidance and Users' Understanding of Conceptual Models

&lt;p&gt;Information systems are intended to be faithful accounts of real-world applications. As an integral part of the development process, analysts create conceptual models in order to understand the application and communicate requirements. Failure to do so has been a prominent reason for IT projects' failure. Hence, improving the quality of models could have a major impact on the information systems' success. To guide the modeling process, researchers use ontology to create more expressive representations of reality. However, improving expressiveness can make the models complicated and cause cognitive hurdles for users. Therefore, the question is whether ontological guidance is worth the trade-off between expressiveness and complexity. This paper describes a meta-analysis of empirical research examining the impact of ontological guidance on users' understandability. The results show that ontological guidance can improve users' understanding of conceptual models, especially those requiring deeper understanding, thus providing support for ontological guidance in conceptual modeling.&lt;/p&gt;
Web Connector: A Unified API Wrapper to Simplify Web Data Collection

Collecting structured data from Web APIs, such as the Twitter API, Yelp Fusion API, Spotify API, and DBLP API, is a common task in the data science lifecycle, but it requires advanced programming skills for data scientists. To simplify web data collection and lower the barrier to entry, API wrappers have been developed to wrap API calls into easy-to-use functions. However, existing API wrappers are not standardized, which means that users must download and maintain multiple API wrappers and learn how to use each of them, while developers must spend considerable time creating an API wrapper for any new website. In this demo, we present the Web Connector, which unifies API wrappers to overcome these limitations. First, the Web Connector has an easy-to-use program-ming interface, designed to provide a user experience similar to that of reading data from relational databases. Second, the Web Connector's novel system architecture requires minimal effort to fetch data for end-users with an existing API description file. Third, the Web Connector includes a semi-automatic API description file generator that leverages the concept of generation by example to create new API wrappers without writing code.
Sniffer: A Novel Model Type Detection System against Machine-Learning-as-a-Service Platforms

Recent works explore several attacks against Machine-Learning-as-a-Service (MLaaS) platforms (e.g., the model stealing attack), allegedly posing potential real-world threats beyond viability in laboratories. However, hampered by model-type-sensitive , most of the attacks can hardly break mainstream real-world MLaaS platforms. That is, many MLaaS attacks are designed against only one certain type of model, such as tree models or neural networks. As the black-box MLaaS interface hides model type info, the attacker cannot choose a proper attack method with confidence, limiting the attack performance. In this paper, we demonstrate a system, named Sniffer, that is capable of making model-type-sensitive attacks "great again" in real-world applications. Specifically, Sniffer consists of four components: Generator, Querier, Probe, and Arsenal. The first two components work for preparing attack samples. Probe, as the most characteristic component in Sniffer, implements a series of self-designed algorithms to determine the type of models hidden behind the black-box MLaaS interfaces. With model type info unraveled, an optimum method can be selected from Arsenal (containing multiple attack methods) to accomplish its attack. Our demonstration shows how the audience can interact with Sniffer in a web-based interface against five mainstream MLaaS platforms.
Databases on Modern Networks: A Decade of Research That Now Comes into Practice

Modern cloud networks are a fundamental pillar of data-intensive applications. They provide high-speed transaction (packet) rates and low overhead, enabling, for instance, truly scalable database designs. These networks, however, are fundamentally different from conventional ones. Arguably, the two key discerning technologies are RDMA and programmable network devices. Today, these technologies are not niche technologies anymore and are widely deployed across all major cloud vendors. The question is thus not if but how a new breed of data-intensive applications can benefit from modern networks, given the perceived difficulty in using and programming them. This tutorial addresses these challenges by exposing how the underlying principles changed as the network evolved and by presenting the new system design opportunities they opened. In the process, we also discuss several hard-earned lessons accumulated by making the transition first-hand.
CM-Explorer: Dissecting Data Ingestion Problems

Data ingestion validation, the task of certifying the quality of continuously collected data, is crucial to ensure trustworthiness of analytics insights. A widely used approach for validating data quality is to specify, either manually or automatically, so-called data unit tests that check whether data quality metrics lie within expected bounds. We employ conditional unit tests based on conditional metrics (CMs) that compute data quality signals over specific parts of the ingestion data and therefore allow for a fine-grained detection of errors. A violated conditional unit test specifies a set of erroneous tuples in a natural way: the subrelation that its CM refers to. Unfortunately, the downside of their fine-grained nature is that violating unit tests are often correlated: a single error in an ingestion batch may cause multiple tests (each referring to different parts of the batch) to fail. The key challenge is therefore to untangle this correlation and filter out the most relevant violated conditional unit tests, i.e., tests that identify a core set of erroneous tuples and act as an explanation for the errors. We present CM-Explorer, a system that supports data stewards in quickly finding the most relevant violated conditional unit tests. The system consists of three components: (1) a graph explorer for visualizing the correlation structure of the violated unit tests; (2) a relation explorer for browsing the tuples selected by conditional unit tests; and, (3) a history explorer to get insight why conditional unit tests are violated. In this paper, we discuss these components and present the different scenarios that we make available for the demonstration.
DeepVQL: Deep Video Queries on PostgreSQL

The recent development of mobile and camera devices has led to the generation, sharing, and usage of massive amounts of video data. As a result, deep learning technology has gained attention as an alternative for video recognition and situation judgment. Recently, new systems supporting SQL-like declarative query languages have emerged, focusing on developing their own systems to support new queries combined with deep learning that are not supported by existing systems. The proposed DeepVQL system in this paper is implemented by expanding the PostgreSQL system. DeepVQL supports video database functions and provides various user-defined functions for object detection, object tracking, and video analytics queries. The advantage of this system is its ability to utilize queries with specific spatial regions or temporal durations as conditions for analyzing moving objects in traffic videos.
Identity-based proxy blind signature scheme on lattice in high-speed network environment

The methods of network attacks are becoming more and more diverse In high-speed networks, and protecting private data is a key issue for users’ security. Proxy blind signature technology combines proxy and blind signature technology to achieve signature transitivity and message confidentiality. However, current signature schemes will not be secure in quantum environments. Meanwhile, the certificate storage of the existing scheme is relatively complicated. Therefore, to avoid the need for certificates and resist quantum attacks, this paper proposes an anti-quantum identity-based proxy blind signature (ID-Proxy-BS) scheme on lattice. This ID-Proxy-BS scheme improves the efficiency of signature through Gaussian distribution and sampling techniques. Security analysis shows that the scheme satisfies correctness, efficiency, blindness, and unforgeability.
Kora: A Cloud-Native Event Streaming Platform for Kafka

Event streaming is an increasingly critical infrastructure service used in many industries and there is growing demand for cloud-native solutions. Confluent Cloud provides a massive scale event streaming platform built on top of Apache Kafka with tens of thousands of clusters running in 70+ regions across AWS, Google Cloud, and Azure. This paper introduces Kora , the cloud-native platform for Apache Kafka at the core of Confluent Cloud. We describe Kora's design that enables it to meet its cloud-native goals, such as reliability, elasticity, and cost efficiency. We discuss Kora's abstractions which allow users to think in terms of their workload requirements and not the underlying infrastructure, and we discuss how Kora is designed to provide consistent, predictable performance across cloud environments with diverse capabilities.
Design and Implement a Nonrelational Data Model

Data modeling is an extremely important aspect of NoSQL design. Throw 3NF (third normal form) out the window and adopt the perspective of the developer, instead of the DBA. What does the developer of a critical, globally distributed application care about? Low-latency transactions and multi-write replication. This isn’t to say relational databases which have long ago adopted 3NF can be tossed out with the water, but applications seeking low-latency global availability cannot be tied to these restrictions. There is of course the rise of a hybrid approach in NewSQL, but that is a topic outside of the scope of this book.