Reviewerly/exHarmony huggingface dataset:
An Improved Neural Network Model for Early Detection of Joint Injuries in Tai Chi Sports

Medical experts and academics are progressively becoming aware of knee injuries faced by tai chi practitioners in China. This occurs when the knee is bent with weight on the foot turned during Tai Chi. We propose an enhanced convolutional neural network (CNN) technique for early warning of joint injury risk during Tai Chi exercise in this research. This improved neural network approach can detect the risk of knee joint injury in practitioners early on to aid in early precaution and treatment. A multiscale feature extraction module is developed by performing several scales of convolutional layer extraction on the input data features and then combining the results to maximize the amount of feature information included in the extracted joint data. The results revealed that in experiments on a Taiwanese Tai Chi community dataset, the proposed method had an average diagnostic accuracy greater than 90 percent, significantly higher than the average diagnostic accuracy of the comparison methods on the dataset.
Mathematical Modeling of Cyber Attack and Defense

In this paper, a new game formulation is proposed that combines simulation and game-theoretical approaches to the application of security games in cyberspace. The model presented here builds upon a security economic approach that models the adversary and defender motives and goals in the context of empirically derived countermeasure efficacy metrics. The approach is based on a two-player strategic game to determine optimal strategy selection for both adversary and defender. Besides, not only the solution to the game but also a mathematical and graphical representation of “what if” scenarios in the context of the game.&#x0D; In this study, it has been shown that game-theoretic calculations can serve as a useful tool for identifying effective strategies in cyberwar games. For scenarios that need to penetrate multiple layers in a defense-in-depth security configuration, the calculation of the attacker's and defensive costs and the probability of infiltration requires the presence of cost-benefit matrices and probability matrices. Inspection of the matrices allows players to deduce preferred strategies based on game-theoretical equilibrium solutions. The matrices also help in analyzing the anticipated effects of potential human-based choices of wargame strategies and counter-strategies. Also, a mathematical game-theoretical form has been defined. This paper shows how game-theoretical calculations can indeed provide a useful tool for effective decision-making during cyber wars.
Optimization of Physical Education Model Based on Cluster Analysis in the Context of Big Data

In recent years, many foreign advanced teaching concepts have been introduced into China, and teaching reforms have been deepened, increasing the demand for physical education in China. Due to the various influences on the physical education curriculum and changes in the way it operates, curriculum standards provide only theoretical guidance and are often too general and abstract to play a concrete role in physical education. The diversity and inclusiveness of educational goals have led to the emergence of new areas of physical education. The repetitive nature of all physical education content settings is generally high and is detrimental to physical education reform. Based on the method of optimization of physical education models by cluster analysis, this paper proposes to classify and analyze the characteristics of the sample of students. We propose teaching principles and various teaching methods based on education and students’ abilities. The experimental results show that among the 200 data samples, the highest percentage of student performance sample data by grade level was 90–50, indicating that the optimization of the movement model under cluster analysis was more successful. Therefore, the research results of this project provide a theoretical basis for the establishment and research of physical education in Chinese schools and further promote the reform of physical education teaching methods in China.
Evaluation Method of Creative Dance Teaching Quality Based on Fuzzy Comprehensive Evaluation

In order to improve the teaching quality of creative dance, this paper puts forward an evaluation method of creative dance teaching quality based on fuzzy comprehensive evaluation. On the basis of clarifying the evaluation criteria, the overall framework of the creative dance teaching quality evaluation system is given; the hardware part optimizes the design of the database and storage module of creative dance teaching-related materials, and the software part completes the quality evaluation of creative dance teaching through the fuzzy comprehensive evaluation method. Experiments show that the proposed method can effectively evaluate the quality of creative dance teaching; when collecting data, the resource discovery rate is high, and the data collection effect is better; the method has high data storage capacity, low rejection times, and good data storage performance.
A Protocol-Independent Botnet Detection Method Using Flow Similarity

The detection of botnets has always been a hot spot in the field of network security. However, there are still many challenges in botnet detection. Most of the current botnet detection approaches, such as machine learning and blacklists, cannot discover evolving botnet variants. These methods are usually only valid for specific botnet protocols which are not general. Even they may be difficult to deal with encrypted botnet traffic. In this paper, we design a protocol-independent botnet detection method for these challenges. Our detection method takes advantage of the group characteristic of the botnet, which is the inherent characteristics of the botnet. We use the sequence of packet length as the characteristic of a flow. Then, we calculate the similarity between these sequences to detect botnets. Our method has an excellent generality, which is not affected by encrypted traffic and the protocols of the botnet. Experiments on a challenging dataset ISCX show that the proposed method can effectively detect botnets with a high average detection rate and low false alarm, which significantly outperforms the state-of-the-art methods. Therefore, the proposed detection method is robust and has a wide range of adaptability in detecting botnets.
Neural Networks and Their Accelerated Evolution From an Economic Analysis Perspective

In 1943, the neuropshychologist Warren McCulloch and the mathematician Walter Pitts published the paper “A Logical Calculus of the Ideas Immanent in Nervous Activity,” establishing the foundation of the neural networks. The transition from the biological neuron to the artificial one, from the perceptron to the multi-layer perceptron, from Hopfield networks to Kohonen networks, from bi-directional associative memories to Boltzmann machines, from basic radial functions to Hamming networks, represent a strong proof of the long journey in the study of the neural networks. Using the characteristics of a neural network, an overview of the works in the neural computing are included in the paperwork, namely a taxonomy of the neural networks based on a number of criteria. The paperwork includes the conclusion of the research. At the end, possible further research directions are highlighted.
Metallogenic Mechanism of the Galinge Polymetallic Iron Skarn Deposit, Qiman Tagh Mountains, Qinghai Province

In the Qiman Tagh metallogenic belt, Fe, Zn, Pb, Cu and Au skarns and epithermal Cu and Mo deposits are spatially and temporally associated with Triassic granitoid rocks including granodiorite, monzogranite and syenogranite which are only occurred in the South Qiman Tagh Terrane as a result of completely different geotectonic setting between the SQT and NQT. The plutonic associations related to mineralization have various ages between middle-Triassic (237 Ma–226 Ma) and late-Triassic (226 Ma–204 Ma). The abundant volatile components evolved from magma are responsible for the significant transportation of metals. The initial oxygen fugacity of magma will affect the different mineralization types.
Properties of Uniform Fuzzy Modules and Semiuniform Fuzzy Modules

In this research paper, the concepts of uniform fuzzy modules and semiuniform fuzzy modules were studied. We discussed the necessary and sufficient conditions between uniform fuzzy modules (and semiuniform fuzzy modules) in fuzzy set theory and uniform modules (and semiuniform modules) in module theory.
Surrogate Classification based on Accuracy and Complexity

The prevalence of various machine-learning modeling techniques and numerous possible model configurations generates a long list of unique surrogate forms. Exhaustive enumeration and search for the best surrogate form from a large pool of candidate forms is a non-trivial task. In this work, we aim to assess similarities in modeling capabilities among many different surrogate forms. We examine modeling capabilities for noisy and non-noisy data based on two different surrogate performance metrics. We use a similarity metric to identify similar pairs of surrogate forms, and then group mutually similar forms into distinct families. The similarities among various forms vary depending on the quality of data set and choice of performance metric. This work enables us to exploit families of similar forms to create a reduced search set of contrasting surrogate forms, and facilitate surrogate form selection.
The Development of Reading Comprehension and Social Media Literacy According to Akita Action Guidelines for Elementary School

The research objectives were 1) to study the effectiveness of the skill enhancement form according to the guideline of the Akita action lesson plan on reading comprehension ability and social media literacy; 2) to compare the learning achievement on reading comprehension and social media literacy with the guideline of the Akita action lesson plan before and after learning; 3) to study the satisfaction of students using the guideline of the Akita action lesson plan on reading comprehension and social media literacy; and 4) to study guidelines for solving problems on reading comprehension and social media literacy by implementing the guideline of the Akita action lesson plan. The sampling group consisted of 142 Grade 6 students in Loei Province and interviewed 4 teachers from 4 schools in Loei Province in the second semester of the academic year 2021. The instruments used in this study were the guideline of the lesson plan for developing reading comprehension abilities and social media literacy according to Akita action guidelines, the achievement test, the satisfaction questionnaire, and the semi-structured interview. Learning achievement was compared before and after the study using Paired Sample t-test statistics.
Teaching and Learning in the Digital Age

Technological pedagogical content knowledge (TPACK) is a dynamic theoretical description of teachers' knowledge for designing, implementing, and evaluating curriculum and instruction with digital technologies. TPACK portrays the complex interaction among content knowledge, pedagogical knowledge, and technological knowledge for guiding all teachers (K-12 and higher education faculty) in the strategic thinking of when, where, and how to direct students' learning with technologies. Teacher educators' and educational researchers' acceptance of the TPACK construct mirrors the acceptance of its parent construct of pedagogical content knowledge (PCK). The importance of teachers' continued practice in integrating technologies is essential for extending and enhancing their TPACK. Connections with the knowledge-of-practice construct suggest calling TPACK TPACK-of-practice to more accurately describe the process of the knowledge development efforts for guiding inservice and preservice teachers in gaining, developing, and transforming their knowledge for teaching as new and more powerful technologies emerge for integration in education. Ultimately, the very nature of the TPACK construct describes a transformation of teachers' knowledge for teaching in the 21st century – a century reframed by robust and advanced technologies that have been integrated into the fabric of a more complex social, cultural, and educational environment.
Cyber-physical UAV Navigation and Operation

The purpose of this paper is to present a work in progress pertaining to drone pose estimation and flight calibration.This paper intends to underline the increasing importance of determining alternative path planning instruments through accurate localization for Unmanned Aerial Vehicles (UAVs) with the purpose of achieving complex flight operations for the emerging applications of autonomous robotics in surveying, design, fabrication, and on-site operations.This research is based on the implementation of novel technologies such as Augmented Reality (AR), Robot Operating System (ROS), and computational approaches to define a drone calibration methodology, leveraging existing methods for drone path planning.Drones are equipped with measurement systems to provide geo-location and time information such as onboard Global Positioning System (GPS) sensors, and Inertial Measurement Units (IMU).As stated in previous research, to increase navigation capabilities, measurements and data processing algorithms have a critical role (Daponte et al. 2015).The outcome of this work in progress showcases valuable results in calculating and assessing accurate positioning for UAVs, and developing data exchanges in transmission, reception, and tracking.
Object Motion Detection

This project aims to provide a robust and informative algorithm to detect object motion in the presence of camera motion. Our algorithm detects moving objects in a frame, generates bounding boxes around them and interprets them accurately and logically. Consequently, motion detection can be more readily applied in fields like autonomous vehicles and robots where computational resources are scarce. To detect motion from a moving camera, we identify the deviation of a region’s optical flow from that of the surrounding objects. Next, we determine moving objects by analyzing pixel motions using two-dimensional histogram and image morphology. We use histogram of orientated gradient feature descriptor and intersection over union to identify moving objects, increase the robustness of our identification approach and establish connections of identical objects between frames. Even in instances where the detection fails, temporal consistency is maintained through the identification functionality. Nevertheless, false positive results do occur sometimes as our assumption—the pixel intensity of the same object is consistent between consecutive frames—may not always be satisfied in real-life situations, such as when shadows appear.
Personalized Guided Perceptual Learning: A Proof of Concept Study

Conventional training often uses fixed-intensity programs which can lead to plateaus in learning trajectories and sub-optimal outcomes. Hence, individualized programs are adopted to achieve a highly personalized outcome, which can be applicable to cognitive training, specifically perceptual training in our case. Artificial intelligence (AI), in this case, can identify single subject (N-of-1) learning profiles and enhance learning trajectories by designing personalized training regimens. The objective of this study is a proof of concept that aims to test whether personalized guided learning programs are warranted and to assess if CURATE.AI can be used in perceptual learning training. Participants were required to train under both adaptive training programs and CURATE.AI training programs. Their perceptual learning ability was assessed based on their performance in terms of accuracy and difficulty level across the sessions. Perceptual learning ability of each participant has improved in varying degrees and even worsened in some cases under the adaptive training. CURATE.AI was harnessed for personalized training. Results from traditional adaptive approach highlight high variance across participants and indicate that individualized approaches are warranted. In our proof of concept, we were able to generate a CURATE.AI personalized perceptual learning profile. Future work is warranted to test the efficacy of CURATE.AI-guided perceptual learning training compared to traditional adaptive learning and fixed-intensity training methods.
An Eldercare Control System in IoT Cloud Environment

Being one of the earlier countries to experience an increasingly aging population in Asia, Singapore will have to accommodate to the age structure in terms of policies planning and allowing the golden age to be self-sustainable alone. In this paper, we developed an eldercare control system based on Internet of things (IoT), named Merdeka-Pioneer monitoring system (MPMS), which provides care for silver and golden-aged citizens. The MPMS targets to monitor the air quality of the environment and detect motions and falling. It also incorporates the concept of IoT over the cloud environment, which allows caretakers or next of kin (NOK) to monitor data collected from the system remotely. The data collected can support preventive measures which can be adopted before any danger and misfortunes, especially in absence of the caretakers or NOK. In this paper, both hardware and software design and implementation are demonstrated in details. The prototype MPMS system has the ability to accurately detect a fall and changes in the environment and are able to send alerts and trigger alarms to the caretakers and NOK.
AI-Driven Data Analytics Assessing Methods to Localize Preexisting Natural Language-Processing Data

BERT, which stands for bidirectional encoder representations from transformers, is a language representation model that has gained popularity lately for its novelty and uniqueness. This algorithm has already been used in many different fields which requires natural language processing (NLP), and because its bidirectional, unlike many other NLP models, the BERT model is able to learn the context of the word based on the sentence it is in instead of just the word itself. Because Singlish is normally spoken in an informal context and has deeper layers of meaning, hidden under sarcasm, we believed that the BERT model would be appropriate in analyzing Singlish text. Our group aims to exploit and adapt BERT for sentiment analysis of Singlish (Wikipedia Contributors in Singlish (2021), [1]) text. In this project, by applying a random forest classifier to sentence embeddings extracted from Singlish BERT, we obtained a 17% improvement in accuracy as compared to the original BERT, at 67% and 50% accuracies, respectively, on Singlish (Wikipedia Contributors in Singlish (2021), [1]) text.
Applying James–Stein Estimation to b-Bit Minwise Hashing

b-bit minwise hashing (bBMWH) is an efficient hashing algorithm used in machine learning. The James–Stein (JS) estimator paradoxically produces a lower mean square error (MSE) than the traditional maximum likelihood estimator. Using 1000 documents from the Bag of Words Datasets (KOS) in the UCI Machine Learning Repository, we computed the pairwise resemblance for all documents in the dataset. We compared the performance of bBMWH with b from 1 to 4 bits with and without JS estimation, by calculating the precision, recall, F1-score, and MSE in classifying pairs with resemblance ≥ R0, with R0 from 0.30 to 0.60. Our results for R0 = 0.30 demonstrated that for b = 4 with JS estimation, the precision was high at 0.9 for a small sample size k < 100 and was maximized at 1.0 for higher k, while recall was decreased to 0.8. For b = 3, JS estimation improved precision without a significant drop in recall. JS estimation decreased the MSE of bBMWH for all b values investigated and especially for small k where MSE is higher. Our findings may be useful when precision is optimized over recall, e.g., spam detection. In cases where we want to estimate the pairwise resemblances for machine learning, bBMWH with JS estimation requires a smaller k to achieve the same MSE as bBMWH alone, thus saving computational time and storage space.
Detection and Removal of Obstructions in Still Images Utilising Machine Learning

Photos play a vital role in any individual life by capturing memorable moments. However, non-optimal imaging conditions often present themselves as obstructions in otherwise perfect photos. Such visual obstructions—such as fences—are often impossible to avoid and difficult to edit out.
Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method

Email is one of the most widely used ways to communicate, with millions of people and businesses relying on it to communicate and share knowledge and information on a daily basis. Nevertheless, the rise in email users has occurred a dramatic increase in spam emails in recent years. Processing and managing emails properly for individuals and companies are getting increasingly difficult. This article proposes a novel technique for email spam detection that is based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms. During system training, the network is selectively focused on necessary parts of the email text. The usage of convolution layers to extract more meaningful, abstract, and generalizable features by hierarchical representation is the major contribution of this study. Additionally, this contribution incorporates cross-dataset evaluation, which enables the generation of more independent performance results from the model's training dataset. According to cross-dataset evaluation results, the proposed technique advances the results of the present attention-based techniques by utilizing temporal convolutions, which give us more flexible receptive field sizes are utilized. The suggested technique's findings are compared to those of state-of-the-art models and show that our approach outperforms them.
Graphevo Dataset: Network Portrait Divergence Based Defect Dataset and its Assessment

Context: Predicting software defects early in the release cycles can enhance resource efficiency and improve overall software quality. Researchers have been probing through popular datasets such as PROMISE, MDP, and datasets built on top of popular datasets to perform Software Defect Prediction (SDP). Only a few datasets have been the foundation for further research from too many datasets, while other datasets are not publicly available, lack reproducibility, or are incomplete. Information theory-based metrics such as Network Portrait divergence (NPD) have proven helpful in SDP.\\Objective: Our goal is to calculate class-level software metrics utilizing GraphEvoProcessor utility, including NPD, and build a dataset for open-source Java applications. As research on SDP in software engineering expands, it is critical to comprehend the usability of these solutions.\\\textbf{Method}: We hypothesize that NPD based GraphEvo dataset has distinct characteristics from existing datasets and thus could be a potential candidate for SDP research. To investigate our hypothesis, we mined 19 Java projects with varying sizes, domains, and defects from GitHub, appended defect information from other sources, and ran a qualitative study for SDP significance using machine learning algorithms. We also performed a comparative study with existing datasets such as BugHunter and Github bug datasets.\\Results: The most important take-away results can be summarized as follows: (i) The GraphEvo dataset showed predictive performance with an average F-measure of 0.81 and AUC of 0.69 when assessed with 12 machine learning classification algorithms.; (ii) Random forest might be an excellent fit for SDP.; (iii) GraphEvo dataset outperforms BugHunter and GitHub Bug Datasets for SDP.\\Conclusion: We conclude that the NPD-based GraphEvo dataset is relevant for SDP when utilizing machine learning approaches to create prediction models for open-source Java projects. Our dataset, GraphEvoProcessor utility to calculate metrics, and all Java binary files are publicly available, repeatable, and extensible.