Reviewerly/exHarmony huggingface dataset:
Expanding Elementary School Computer Science Education with an Introduction to Machine Learning Through Rhythmic Studies

Introducing elementary students to computer science and computational thinking (CS/CT) can enhance their problem solving skills and enhance their confidence and sense of belonging in computing. Project moveSMART aims to introduce learning activities into elementary classrooms that address computer science concepts in a way that integrates with core curriculum requirements and promotes physical activity. In this paper, we explore an extension to an initial set of Project moveSMART computer science learning activities to introduce elementary students to machine learning concepts in a way that is integrated with required learning objectives covered in a Physical Education course. Specifically, students use the BBC micro:bit and its on board sensors to capture rhythmic movement data, explore and analyze patterns in the data, and use a learned "dance move recognition" application that uses their data in order to learn about machine learning in an age appropriate way. To demonstrate feasibility of supporting dance move recognition on the resource-constrained device, we developed a prototype, which is able to detect 5 different dance moves with a 96.6% accuracy.
Waste Not, Want Not: Service Migration-Assisted Federated Intelligence for Multi-Modality Mobile Edge Computing

Future mobile edge computing (MEC) is envisioned to provide federated intelligence to delay-sensitive learning tasks with multimodal data. Conventional horizontal federated learning (FL) suffers from high resource demand in response to complicated multi-modal models. Multi-modal FL (MFL), on the other hand, offers a more efficient approach for learning from multi-modal data. In MFL, the entire multi-modal model is split into several sub-models with each tailored to a specific data modality and trained on a designated edge. As sub-models are considerably smaller than the multi-modal model, MFL requires fewer computation resources and reduces communication time. Nevertheless, deploying MFL over MEC faces the challenges of device mobility and edge heterogeneity, which, if not addressed, could negatively impact MFL performance. In this paper, we investigate an Service Migration-assisted Mobile Multi-modal Federated Learning (SM3FL) framework, where the service migration for sub-models between edges is enabled. To effectively utilize both communication and computation resources without extravagance in SM3FL, we develop the optimal strategies of service migration and data sample collection to minimize the wall-clock time, defined as the required training time to reach the learning target. Our experiment results show that the proposed SM3FL framework demonstrates remarkable performance, surpassing other state-of-art FL frameworks via substantially reducing the computing demand by 17.5% and dramatically decreasing the wall-clock time by 25.3%.
Phone-based CSI Hand Gesture Recognition with Lightweight Image-Classification Model

As years pass, smartphones are becoming a larger part of daily lives, causing users to interact with them more than ever. There are moments, however, when it becomes difficult for the user to operate their device directly. Currently, a user can either touch their devices for direct interaction, or use voice commands for simpler tasks. Although these two methods are very capable means of interacting with the devices, they have their limitations. Touching a physical device is not always practical, while voice commands become ineffective in loud environments. A good example would be if the user is washing dishes in a noisy environment, where neither physical control nor voice commands are convenient. Existing systems of smartphone CSI gesture recognition rely on manual feature extraction which could be hard to implement as gestures grow in number and complexity. We study the feasibility of using lightweight image classification models with minimal preprocessing by implementing and testing the performance of such an architecture. We collect data for five gestures from three setups and two phones, on which our system is able to obtain 90.0% accuracy. Additionally, we investigate the impact of different people, distances, and phones on the system's performance.
Poster: Timestamp Verifiability in Proof-of-Work

Various blockchain systems have been designed for dynamic networked systems. Due to the nature of the systems, the notion of "time" in such systems is somewhat subjective; hence, it is important to understand how the notion of time may impact these systems. This work focuses on an adversary who attacks a Proof-of-Work (POW) blockchain by selfishly constructing an alternative longest chain. We characterize optimal strategies employed by the adversary when a difficulty adjustment rule alà Bitcoin applies.
Cache-Enabled Federated Learning Systems

Federated learning (FL) is a distributed paradigm for collaboratively learning models without having clients disclose their private data. One natural and practically relevant metric to measure the efficiency of FL algorithms is the total wall-clock training time, which can be quantified by the product of the average time needed for a single iteration and the number of iterations for convergence. In this work, we focus on improving FL efficiency with respect to this metric through caching. Specifically, instead of having all clients download the latest global model from a parameter server, we select a subset of clients to access, with a smaller delay, a somewhat stale global model stored in caches. We propose CacheFL - a cache-enabled variant of FedAvg, and provide theoretical convergence guarantees in the general setting where the local data is imbalanced and heterogeneous. Armed with this result, we determine the caching strategies that minimize total wall-clock training time at a given convergence threshold for both stochastic and deterministic communication/computation delays. Through numerical experiments on real data traces, we show the advantage of our proposed scheme against several baselines, over both synthetic and real-world datasets.
Emotional intelligence, success and psychological adaptability to the environment as factors in ensuring the effectiveness of social management

Abstract: The problems of emotional intelligence (emotional quotient, EQ), success and psychological adaptability to the environment in the complex and systemic integrity are important factors in ensuring the effectiveness of social administration. The modern form of social administration involves the factors of emotional leadership and emotion management, including the ability to connect with people, inspire and encourage them, keep them highly motivated and achieve the development and prosperity of the social organisation as a whole. The concept of «emotional intelligence» is closely related to the categories of «social success» and «psychological adaptability to the environment». The latter two factors should be interpreted as a consequence of sufficient development of emotional intelligence of a fully developed person. People due to objective reasons have different levels of EQ. Following this, they are differentiated as either socially successful or not socially successful, adaptive or not susceptible to the mechanisms of psychological adaptation to the environment. This differentiation is partly a genetic predisposition of a person to manifest organisational abilities and leadership qualities, but no less important are the purposeful and organised efforts of people to learn, develop and self-improve their emotional and intellectual qualities. In social management it is of particular importance, because it significantly affects the effectiveness of interaction between objects and subjects of administration, thus creating a favourable emotional climate in the team or, on the contrary, contributing to the rise of negative, pessimistic and emotionally unstable moods in the managed community.&#x0D; Research methods. When studying the problems of emotional intelligence, success and psychological adaptability to the environment, the author used a wide range of methods of cognition, which are developed and tested by modern socio-psychological science. Among them there are analysis and synthesis, induction and deduction, observation, survey, method of analysing documents and results of social activity.&#x0D; Results. As a result, the author proposes a number of theoretical and methodological constructs and practical guidelines for social managers, which enable them to take into account the emotional content of administratively significant information, to maintain a proper sensual atmosphere of communication, to minimise socio-psychological conflictogens and to suppress unconstructive interaction between people.
Investigating AI languages’ ability to solve undergraduate finance problems

AbstractThe rapid advancement of artificial intelligence (AI) has given rise to sophisticated language models that excel in understanding and generating human-like text. With the capacity to process vast amounts of information, these models effectively tackle problems across diverse domains. In this paper, we present a comparative analysis of prominent AI language models—ChatGPT and Google Bard—focusing on their ability to solve undergraduate finance problems. We find that GPT-4 significantly outperforms Bard-1.0, excelling in easy problems but struggling with complex ones. The results suggest that it is crucial to handle AI with care in order to uphold academic integrity.Keywords: Artificial intelligenceChatGPTfinancial educationhigher educationundergraduate finance AcknowledgmentsThe authors would like to thank Shishir Paudel, Shiang Liu, and Taggert Brooks for their help.Disclosure statementThe authors report there are no competing interests to declare.Additional informationFundingThis work was supported by grants from the University of Wisconsin-La Crosse College of Business Administration and Menard Family Midwest Initiative for Economic Engagement and Research.
LEAF: Navigating Concept Drift in Cellular Networks

Operational networks commonly rely on machine learning models for many tasks, including detecting anomalies, inferring application performance, and forecasting demand. Yet, model accuracy can degrade due to concept drift, whereby the relationship between the features and the target to be predicted changes. Mitigating concept drift is an essential part of operationalizing machine learning models in general, but is of particular importance in networking's highly dynamic deployment environments. In this paper, we first characterize concept drift in a large cellular network for a major metropolitan area in the United States. We find that concept drift occurs across many important key performance indicators (KPIs), independently of the model, training set size, and time interval---thus necessitating practical approaches to detect, explain, and mitigate it. We then show that frequent model retraining with newly available data is not sufficient to mitigate concept drift, and can even degrade model accuracy further. Finally, we develop a new methodology for concept drift mitigation, Local Error Approximation of Features (LEAF). LEAF works by detecting drift; explaining the features and time intervals that contribute the most to drift; and mitigates it using forgetting and over-sampling. We evaluate LEAF against industry-standard mitigation approaches (notably, periodic retraining) with more than four years of cellular KPI data. Our initial tests with a major cellular provider in the US show that LEAF consistently outperforms periodic and triggered retraining on complex, real-world data while reducing costly retraining operations.
Adoption of blockchain as an open accounting information system in Vietnam: A triple-entry accounting approach

Blockchain technology has emerged as an indispensable force driving economic development across various sectors in Vietnam. However, prior research in the realm of blockchain technology has predominantly focused on areas such as logistics, journalism, and banking, neglecting the pivotal domains of accounting and auditing. Recognizing this theoretical gap and the significant role played by accounting and auditing in economic systems, this paper endeavors to augment our understanding of blockchain technology through the lens of a novel "triple-entry accounting method." Additionally, it seeks to assess the level of acceptance of this potentially transformative innovation within the Vietnamese business landscape. Blockchain, renowned for its decentralized architecture and distributed data system, represents a fundamental departure from conventional accounting practices. It functions as an open accounting information system, wherein data is distributed among multiple computers with identical copies, devoid of central administration but validated by a network of participants. Drawing upon responses from a sample of 225 participants and employing statistical analysis with SPSS, this study investigates attitudes and intentions regarding the adoption of blockchain technology in the field of accounting in Vietnam. The results of this analysis reveal a positive correlation between four variables-perceived usefulness, perceived ease of use, comparability, and complexity-and the attitude toward blockchain application, further positively influencing the intention to use it. However, the variable of Perceived Risk does not exhibit statistical significance. These findings underscore the necessity of establishing intelligent accounting procedures through blockchain adoption and underscore the importance of understanding factors influencing attitudes and intentions to enhance the widespread acceptance of this innovation in the Vietnamese business context.
Data‐Driven Fuzzy Weights‐Of‐Evidence Model for Identification of Potential Zeolite‐Bearing Environments on Mars

Abstract The evolution of the climate and hydrochemistry of Mars is still a mystery but it must have been at least occasionally warm and wet to have formed the ancient fluvial and lacustrine landforms observed today. Terrestrial examples and geochemical modeling under proposed early Mars conditions show that zeolite minerals are likely to have formed under alkaline (pH &gt; 8) conditions with low water/rock ratio and surface temperatures below 150°C. The identification and spatial association of zeolites on the surface of Mars could thus be used to reconstruct the paleoclimate, paleohydrochemistry, and geological evolution of some locations on Mars. Previous studies identified the zeolite analcime and discuss the difficulties of identifying other zeolite species on the surface of Mars using orbital spectroscopy. We used published global mineralogical, geological, geomorphological, hydrological, physical, and elemental abundance maps and the locations of hydrous minerals detected and mapped using orbital data to create a map that delineates favorable areas to look for zeolites on Mars. We used the data‐driven fuzzy‐based Weights‐of‐Evidence method to identify and map favorable areas for zeolites on the surface of Mars up to ±40° latitude toward the poles. The final map shows that the eastern and western Arabia deposits, some sites in the Medusae Fossae formation, and some areas within and near Valles Marineris, Mawrth Vallis, highlands north of Hellas, and the Terra Cimmeria and Terra Sirenum regions would be favorable areas to look for zeolites using targeted orbital spectral analysis or future in situ observations.
Automated Test Equipment Simulation in CoppeliaSim Using C# Over WebSocket

Purpose: Nowadays, we are surrounded by electronic gadgets. Most gadgets have more or less printed circuit boards (PCB). It needs to test before it is launched into the market. Otherwise, it can fail to sustain itself in the market. If we test manually in all aspects, a motherboard will take so many times that the production will go very slowly. So automated tests are becoming popular. There is various vendor that produces automated test equipment. ATE or automated test equipment is too costly. Purchasing the ATE system for training or learning the process flow is sometimes not feasible. This research work demonstrates a simulation framework to view the ATE working procedure. We use CoppeliaSim for animation and C# to control it over Websocket. All source codes are available. The researcher can customize and extend the functionality to their need.Design/Methodology/Approach: We developed a framework to demonstrate the ATE activity. It consists of CoppeliaSim Simulation software. We use C# language to control the simulator. At first, we created a model of ATE inside the CoppeliaSim. Add LUA code to receive commands from the remote host. When CoppeliaSim starts, it starts a WebSocket server. It listens to port number 8088. Then we created an application using C#. We designed a graphical user interface (GUI). We added some text boxes, buttons, and a WebSocket client. When connect button is pressed, the apps try to connect with CoppeliaSim. Then send the command by pressing the button.Findings/Result: To understand the ATE working principle, we developed a simulation in CoppeliaSim, which controls using a C# websocket client. The simulation works like a natural system. Observing the process flow, the researcher gets an understanding better. So using this example and customizing it according to the need is the best way to understand it.Originality/Value: Several procedures are available to understand the ATE system. Here we developed a simplistic approach to it. The CoppeliaSim is suitable for visualizing process automation. The C# language is better for controlling the simulation. The communication between two software is websocket which is easy to integrate, and send/received command string is also comparatively easy. The researcher trying to understand or work with the ATE system can get some reference information for their research work. So the complete framework process is the best value to the researcher.
Rethinking Tort Liability Regimes for AI-Related Claims in Canada and Europe: A Case Study of AI Applications in Disease Diagnosis

Artificial intelligence (AI) systems are increasingly being integrated into various activities along the continuum of patient care. In oncology, which relies heavily on radiology, AI applications are used for oncologic imaging, ranging from imaging acquisition to cancer screening to treatment planning and response monitoring. This has resulted in AI-assisted radiology playing an increasingly important role in cancer screening, diagnosis staging, response assessment, and prognosis. In clinical settings where the permissible margin of error is slim and where AI-assisted decision-making can significantly affect human lives, the reliability of AI is of utmost importance. However, studies have shown that there is a risk to patient safety when the poorly or incorrectly labeled data of these systems lead to faulty predictions, or its trained algorithms are biased. This has left policymakers with the problem of providing clear liability rules on who is liable for the output produced by an AI system or its failure to produce an output, which could harm the end-users of these technologies, without stifling AI innovation. The scholarship on how to frame the optimal liability framework for AI-related tort claims for Canada is animated by the inability of traditional liability schemes to address the nuances of how to prove harm or loss that results from the use of AI technology or whom to hold responsible for the harm or loss. There is currently a need for more research and analysis in Canada on how to assign legal responsibility for AI-related claims. Lessons from how AI regulation is developing in Europe suggest that various factors will shape this framework, including law, market forces, norms, and technology. This paper examines the challenges of establishing tort liability for AI-related claims in healthcare, with a comparative focus on Canada and Europe. The paper begins by providing an overview of the current legal frameworks on AI in both regions, including an analysis of the challenges of applying traditional tort liability principles to AI-related claims. The paper argues that traditional tort liability principles are inadequate for addressing the unique nuances of AI-related claims and that targeted statutory tort liability rules are necessary to provide adequate remedies to the victims, on the one hand, and promote innovation in the field, on the other hand. Through a case study analysis of sensitive use cases of AI in healthcare, such as medical diagnosis, the paper concludes by recommending clearer tort liability rules implemented through risk-based regulation, similar to the European Union’s regulatory framework, to prioritize safety and reliability while preserving the social benefits of AI technology.
Does Formula-Driven Supervised Learning Work on Small Datasets?

Does formula-driven supervised learning (FDSL) work effectively with fine-tuning on small datasets? Additionally, how many natural images do a network pre-trained with FDSL require to acquire sufficient image features? FDSL is a pre-training method that employs mathematical formulas to automatically generate images and their corresponding labels. These questions are crucial to address, as the acquisition of features valuable for natural image recognition tasks necessitates the opportunity to learn a certain number of natural images through pre-training and fine-tuning to achieve optimal results. Furthermore, because FDSL is progressively gaining attention as a promising method to mitigate concerns about privacy violations, fairness protection, and labor-intensive efforts associated with annotating natural images, clarifying its effectiveness and limitations is essential for widespread adoption. In this study, we compare FDSL with ImageNet-1k pre-training and training from scratch through fine-tuning on datasets of the order of 100 to 10,000 images. Through our experiments, we discovered that (i) there is a significant difference from ImageNet-1k pre-training when using datasets containing approximately 100 to 1,000 images, and (ii) approximately 50,000 images are required for FDSL to be equivalent to ImageNet-1k pre-training. Moreover, we verified the validity of the hyper-parameters during fine-tuning. We firmly believe that this study elucidates the current limitations of FDSL and offers valuable guidance for future research, ultimately contributing to the field of computer vision.
ASEP: An Autonomous Semantic Exploration Planner With Object Labeling

In this paper, we present a novel 3D autonomous exploration planner called the Autonomous Semantic Exploration Planner (ASEP), designed for GPS-denied indoor environments. ASEP combines real-time mapping, exploration, navigation, object detection, and object labeling onboard an Unmanned Aerial Vehicle (UAV) with limited resources. The planner is based on a frontier exploration strategy that utilizes semantic information about the environment in the exploration policy. The policy is extended to incorporate both geometric and semantic information provided by a deep convolutional neural network (DCNN) for semantic segmentation. This semantically-enhanced exploration algorithm directs the exploration toward the quick labeling of all objects of interest in the environment. An extended path planning algorithm continuously checks for path validity, enabling safe navigation in challenging environments. The overall system is designed to be modular and easily extended or replaced with custom modules. The proposed planner is evaluated and analyzed in both simulation and real-world environments using a UAV. Experimental studies demonstrate the effectiveness of the ASEP strategy compared to state-of-the-art methods. Results show that the objects in the environment are explored faster and total exploration time is reduced while the computational time remains consistent regardless of the semantic segmentation processing involved.