Reviewerly/exHarmony huggingface dataset:
Extracting Basic-and-Advanced Relationships Based on the Structure of Teaching Material Documents

With an increase in the necessity for “learning,” such as lifelong learning, we examine ways to provide broadcast content in conjunction with various educational services and content on the Internet to make it useful for learning. We propose a method for extracting basic-and-advanced relationships between words—focusing on the structure of teaching material documents—with the aim of automatically estimating relationships in the learning sequence of various content. Using the proposed method, we extracted basic-and-advanced relationships between words from teaching material documents of broadcast programs and evaluated the extraction accuracy.
FedDD: Federated Double Distillation in IoV

In 6G Internet of Vehicles (IoV) system, Federated Learning (FL) is usually used to structure the joint training model between vehicles and RSU. However, due to the mobility of the vehicles, the link between vehicles is unstable and the parameters trained by FL are exchanged frequently, which may increase the communication overheads. Therefore, we propose a communication-efficient Federated Double Distillation (FedDD) framework in this paper. In particular, the cluster-heads are dynamically selected as the distributed learning clients combined with three-dimensional attributes to improve the collaborative transmission efficiency. Then, the knowledge distillation is further integrated into the federated learning framework to reduce communication overheads caused by the frequent parameters exchange in the instable link. The experimental results show that, compared with the benchmark FedAvg algorithm, the FedDD reduces the communication overheads by three orders of magnitude. Moreover, the FedDD improves the communication efficiency of FL while sacrificing only a small amount of accuracy.
The Impact of Distributed Data Preprocessing on Automotive Data Streams

Vehicles have transformed into sophisticated computing machines that not only serve the objective of transportation from point A to point B but serve other objectives including improved experience, safer journey, automated and more efficient and sustainable transportation. With such sophistication comes complex applications and enormous volumes of data generated from diverse types of vehicle sensors and components. Automotive data is not sedentary but moves from the edge (the vehicle) to the cloud (e.g., infrastructure of the vehicle manufacturers, national highway agencies, insurance companies, etc.). The exponential increase in data volume and variety generated in modern vehicles far exceeds the rate of infrastructure scaling and expansion. To mitigate this challenge, the computational and storage capacities of vehicle components can be leveraged to perform in-vehicle operations on the data to either prepare and transform (preprocess) the data or extract information from (process) the data. This paper focuses on distributing data preprocessing to the vehicle and highlights the benefits and impact of the distribution including on the consumption of resources (e.g., energy).
Smart Contract-based Distributed Spectrum Sensing for Blockchain-enabled Spectrum Sharing

As massive radio devices being employed in future 6G wireless networks, dynamic spectrum sharing based on cognitive radio and blockchain technologies presents an effective way to maximize spectrum utilization. However, ubiquitous, accurate and timely spectrum sensing is an important prerequisite for dynamic spectrum sharing. How to improve the accuracy and the participation enthusiasm of spectrum sensing are the key research issues. Furthermore, in the practical application of spectrum blockchain, the secure and credible guarantee mechanism of spectrum ledger and the incentive mechanism in spectrum sharing are prominent problems. In this paper, a smart contract-based distributed spectrum sensing scheme is proposed for blockchain-enabled spectrum sharing, which leverages the interaction between spectrum sensing and incentive mechanism for secondary users. In this way, secondary users under favorable channel condition are more likely being selected for spectrum sensing. The performance of mobile sensing is analyzed when performing spectrum sensing based on cycle detection algorithm. On the basis of spectrum sensing, a reputation-based incentive algorithm is employed in the smart contract, recording the spectrum sensing results and rewards so as to enhance the enthusiasm of secondary users and the security of sensing. Simulation shows that more sensing nodes can be employed with the same task budget, while the primary user detection probability can be increased by 19.6% when adopting the proposed scheme with sufficient budget. In addition, when the number of sensing nodes is the same, the proposed incentive method can ensure that the sensing nodes obtain a higher average rewards, which helps to enhance the enthusiasm of secondary users to participate in the spectrum sensing task.
Application of Generic Programming to Unsolved Mathematical Problems

This study proposes a method to solve unsolved mathematical games using generic programming. Although AI can replace humans in some jobs, it also can help humans by enhancing their abilities. In this study, generic programming allows humans to find mathematical formulas that are beyond the imagination of humans, and this is a new method to enhance the mathematics ability of humans. For this purpose, the Python generic programming package “gplearn” was customized by implementing new features, such as conditional branching, a new common error metric, and a few discrete functions selected for the study. In this paper, they had four experiments on using gplearn under different conditions. First, they demonstrated how the formula with the least error size would be selected. After that, the authors compared the effectiveness of the default fitness function and the customized percentage fitness function and discovered that the percentage fitness function is better for a specific type of data. They also tried to use integers instead of real numbers in generic programming. They implemented conditional branching and found out that conditional branching is very powerful for a specific type of data. In the last section, the authors got a new theorem that is a counter-example to a well-known conjecture. The research result shows the possibility of generic programming in combinatorial games and widens the scope of the research on symbolic regression.
Blind Signal Recognition Method of STBC Based on Multi-channel Convolutional Neural Network

Blind signal recognition (BSR) is a significant research topic in the field of intelligent signal processing. However, existing BSR of space-time block codes (STBC) mainly depends on conventional algorithms, which require priori information and can only identify a relatively limited amount of STBC. Although deep learning (DL) has been widely used in signal recognition, so far there are few studies on BSR of STBC in multiple-input multiple-output (MIMO) systems using DL. In this paper, a blind recognition approach for STBC based on multichannel convolutional neural network (MCNN) is proposed. By leveraging the structure of multiple input channel, the in-phase and quadrature (IQ) channel information of STBC signals can be comprehensively extracted. Simulation results demonstrate that the proposed algorithm extends the recognizable STBC codes to 6, and can also improve the recognition accuracy in comparison to traditional convolutional neural network (CNN). The model proposed in this paper has been validated with two datasets and experimentally proved to be well generalized.
FPGA Implementation of a Interval Type-2 Fuzzy Inference Based Wildfire Monitoring System

In this paper, we propose an FPGA implementation of the Interval Type-2 Fuzzy Inference (IT2FI) system for sensor network nodes in wildfire monitoring. The proposed system utilizes carbon dioxide concentration, humidity and temperature as input values from sensing device to obtain numerical values for the degree of danger which considers the signs of fire and its occurrence. These values provide an indication of whether sensor data should be transmitted or not, thus avoiding redundant communication for efficient power and bandwidth consumption.
Throat microphone speech recognition using wav2vec 2.0 and feature mapping

Throat microphones can record the voice which and simultaneously suppress the impact of external noise. This work aims to improve speech recognition performance using throat microphones within a high-noise environment. However, as there is no large database on throat microphone speech, training data are insufficient. This study proposes a method to realize the improved throat microphone speech recognition by utilizing self-supervised learning models such as wav2vec 2.0. However, because the volume of throat microphone speech data available for training of the model is rather limited, linguistic information is not particularly well trained. Therefore, we apply feature mapping to a large Japanese speech corpus to generate a quantity of pseudo-throat microphone speech features. It was confirmed that a significant improvement in the recognition rate could be achieved by utilizing the generated data for fine tuning of the wav2vec 2.0 model.
Privacy Preservation Authentication: Group Secret Handshake with Multiple Groups

The technique of group secret handshake (GSH) has been used to help the members affiliated with the same group in achieving private authentication. After executing GSH protocols, the participants affiliated with the group can compute a shared secret key, or generate a public encryption key while the true participants can self-compute their decryption keys. This paper presents a concrete GSH protocol with Multiple Groups. Only a legitimate member can prove that it belongs to a set of legitimate affiliations, but which affiliation it belongs to will not be leaked. The Group Authority can reveal the real identities of the fellows in the proposed scheme after analyzing the flow of communication. The proposed scheme can provide affiliation-hiding and detectability. In addition, it achieves Perfect Forward Secrecy.
A Combination of Fuzzy Techniques and Chow Test to Detect Structural Breaks in Time Series

In a series of papers, we suggested a non-statistical method for the detection of structural breaks in a time series. It is based on the applications of special fuzzy modeling methods, namely Fuzzy transform (F-transform) and selected methods of Fuzzy Natural Logic (FNL). In this paper, we combine our method with the principles of the classical Chow test, which is a well-known statistical method for testing the presence of a structural break. The idea is to construct testing statistics similar to that of the Chow test which is formed from components of the first-degree F-transform. These components contain an estimation of the average values of the tangents (slopes) of the time series over an imprecisely specified time interval. In this paper, we illustrate our method and its statistical test on a real-time series and compare it with three classical statistical methods.
Coherent optical neuron control based on reinforcement learning

Optical neural networks take optical neurons as the cornerstone to achieve complex functions. The coherent optical neuron has become one of the mainstream implementations because it can effectively perform natural and even complex number calculations. However, its state variability and requirement for reliability and effectiveness render traditional control methods no longer applicable. In this Letter, deep reinforcement coherent optical neuron control (DRCON) is proposed, and its effectiveness is experimentally demonstrated. Compared with the standard stochastic gradient descent, the average convergence rate of DRCON is 33% faster, while the effective number of bits increases from less than 2 bits to 5.5 bits. DRCON is a promising first step for large-scale optical neural network control.
Optimal Feature Selection through Search-Based Optimizer in Cross Project

Cross project defect prediction (CPDP) is a key method for estimating defect-prone modules of software products. CPDP is a tempting approach since it provides information about predicted defects for those projects in which data are insufficient. Recent studies specifically include instructions on how to pick training data from large datasets using feature selection (FS) process which contributes the most in the end results. The classifier helps classify the picked-up dataset in specified classes in order to predict the defective and non-defective classes. The aim of our research is to select the optimal set of features from multi-class data through a search-based optimizer for CPDP. We used the explanatory research type and quantitative approach for our experimentation. We have F1 measure as our dependent variable while as independent variables we have KNN filter, ANN filter, random forest ensemble (RFE) model, genetic algorithm (GA), and classifiers as manipulative independent variables. Our experiment follows 1 factor 1 treatment (1F1T) for RQ1 whereas for RQ2, RQ3, and RQ4, there are 1 factor 2 treatments (1F2T) design. We first carried out the explanatory data analysis (EDA) to know the nature of our dataset. Then we pre-processed our data by removing and solving the issues identified. During data preprocessing, we analyze that we have multi-class data; therefore, we first rank features and select multiple feature sets using the info gain algorithm to get maximum variation in features for multi-class dataset. To remove noise, we use ANN-filter and get significant results more than 40% to 60% compared to NN filter with base paper (all, ckloc, IG). Then we applied search-based optimizer i.e., random forest ensemble (RFE) to get the best features set for a software prediction model and we get 30% to 50% significant results compared with genetic instance selection (GIS). Then we used a classifier to predict defects for CPDP. We compare the results of the classifier with base paper classifier using F1-measure and we get almost 35% more than base paper. We validate the experiment using Wilcoxon and Cohen’s d test.
The Use of Mobile Instant Messaging in English Language Teaching: The Case of South Korea

While learning management systems and video conferencing platforms have become a pivotal means for implementing emergency remote learning during the COVID-19 pandemic, the integration of mobile technologies into online teaching has been largely recommended in order to create an interactive educational environment. Focusing on the case of an English Language Program in South Korea, this study investigates the actual use of mobile instant messaging (MIM) applications as a learning aid in online language classrooms and how this was implemented in creating interactive language learning environments during the current outbreak. Based on a questionnaire survey collected from 979 students, it then compares students’ level of perceived satisfaction with their language learning and communication in online language classrooms between the group of students that used MIM during instruction and the group that did not. The discussion presented in this paper provides language educators, school administrators and policymakers at all levels with practical suggestions and ideas to consider, given that our daily communication has become highly dependent on and is increasingly mediated by mobile digital communication technologies and new media.
Simultaneous Seismic Phase Picking and Polarity Determination with an Attention-Based Neural Network

Abstract Earthquake focal mechanisms present information on fault plane and stress direction, which is crucial for understanding tectonics and seismicity. Source focal mechanisms of small earthquakes are often difficult to determine from waveform modeling but feasible to infer from initial polarities. Here, we employ a state-of-the-art neural network infused with an attention mechanism to simultaneously pick arrivals and determine the first-motion polarity. The model is trained and tested with data from southern California. Compared with polarity inference with manual picks in the catalog, predicted polarity inference can help obtain more focal mechanism solutions in southern California. We test this model with data from different regions and observe high generalizability. The predicted arrival and polarity data are consistent with the labeled arrival and polarity data in Japan. The average-picking error is 0.04 s, and the accuracy of polarity classification is 99%. We infer the focal mechanisms from the predicted polarity in Oklahoma. The derived focal mechanisms are consistent with referencing focal mechanisms. This method allows routinely obtaining arrival and polarity data, and deriving focal mechanism solutions for events.
A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism

Face recognition technology is a powerful means to capture biological facial features and match facial data in existing databases. With the advantages of noncontact and long-distance implementation, it is being used in more and more scenarios. Affected by factors such as light, posture, and background environment, the face images captured by the device are still insufficient in the recognition rate of existing face recognition models. We propose an AB-FR model, a convolutional neural network face recognition method based on BiLSTM and attention mechanism. By adding an attention mechanism to the CNN model structure, the information from different channels is integrated to enhance the robustness of the network, thereby enhancing the extraction of facial features. Then, the BiLSTM method is used to extract the timing characteristics of different angles or different time photos of the same person so that convolutional blocks can obtain more face detail information. Finally, we used the cross-entropy loss function to optimize the model and realize the correct face recognition. The experimental results show that the improved network model indicates better identification performance and stronger robustness on some public datasets (such as CASIA-FaceV5, LFW, MTFL, CNBC, and ORL). Besides, the accuracy rate is 99.35%, 96.46%, 97.04%, 97.19%, and 96.79%, respectively.
Explore Long-Range Context Features for Speaker Verification

Multi-scale context information, especially long-range dependency, has shown to be beneficial for speaker verification (SV) tasks. In this paper, we propose three methods to systematically explore long-range context SV feature extraction based on ResNet and analyze their complementarity. Firstly, the Hierarchical-split block (HS-block) is introduced to enlarge the receptive fields (RFs) and extract long-range context information over the feature maps of a single layer, where the multi-channel feature maps are split into multiple groups and then stacked together. Then, by analyzing the contribution of each location of the convolution kernel to SV, we find the traditional convolution with a square kernel is not effective for long-range feature extraction. Therefore, we propose cross convolution kernel (cross-conv), which replaces the original 3 × 3 convolution kernel with a 1 × 5 and 5 × 1 convolution kernel. Cross-conv further enlarges the RFs with the same FLOPs and parameters. Finally, the Depthwise Separable Self-Attention (DSSA) module uses an explicit sparse attention strategy to capture effective long-range dependencies globally in each channel. Experiments are conducted on the VoxCeleb and CnCeleb to verify the effectiveness and robustness of the proposed system. Experimental results show that the combination of HS-block, cross-conv, and DSSA module achieves better performance than any single method, which demonstrates the complementarity of these three methods.
Detection of Inconsistencies in SysML/OCL Models Using OWL Reasoning

Abstract Requirement change management is a challenging issue in software development. One of the main objectives of the Intent-Defined Adaptive Software program is to verify the satisfaction of requirement changes during software development. In this paper, we develop an ontology-based method to detect inconsistencies in Systems Modeling Language (SysML) models with Object Constraint Language (OCL) constraints as a first step of requirement change management. Specifically, we map the SysML/OCL models to Web Ontology Language (OWL), so that the consistency of the corresponding ontology can be checked by OWL reasoners automatically. We propose a set of mapping rules to interpret the components of SysML state machine diagrams, along with OCL constraints, to OWL. Toward this objective, we demonstrate three consistency reasoning tasks over a state machine diagram using OWL reasoners. In each case, the result of reasoning is accompanied by an explanation of the logic behind the decision.
AI Virtual Hardware

The computer is one of the wonderful and fascinating inventions of technology and has come to significant use to humans in every sector. The existing computer technology is already advanced and modern. Even so, this proposed system will provide better ease of technology to humans. The proposed system is an Artificial Intelligence (AI) application with three combined features that are AI Virtual Mouse, Keyboard and Painter These three features (AI Virtual Mouse, Keyboard and Painter) use a common hand tracking module. Hand tracking module is a python file which has a class name Hand Detector and it contains 4 member functions that are findDistance, findHands, findPosition and fingerUp. Using this module the three features work successfully. The proposed system opens with a main window which is a GUI screen made with the help of module Tkinter. This main GUI page contains all the three features (AI Virtual Mouse, Keyboard and Painter) combined. The common libraries used for system execution are OpenCV, CVZone, numpy, autopy, mediapipe, etc.
Federated Lung Cancer Prediction Using Histopathological Medical Images

Machine learning is increasingly significant in health science because it can infer valuable information from high-dimensional data. However, combining research and patient data from various organizations and hospitals is frequently not practical due to privacy concerns. In this research, we conduct a study of federated learning for lung cancer prediction to demonstrate the effectiveness of collaborative and decentralized learning in a context where data is privacy preserved. We also conducted visual interpretation using GradCAM to validate the robust performance of the decentralized method in predicting lung cancer.
M3E-Yolo: A New Lightweight Network for Traffic Sign Recognition

Traffic sign recognition is committed to ensuring the safety of automatic driving. Inspired by YOLOv5, this paper proposes a new model to solve the problem of poor balance between the accuracy and efficiency of existing algorithms in traffic sign recognition. Firstly, the lightweight network MobileNetV3 is introduced for feature extraction to reduce the number of parameters. Secondly, attention mechanism module is introduced to enhance channel features, which makes up for the reduced accuracy caused by the simplified model. Experiments show that the mAP value trained by our model on the Chinese traffic sign dataset reaches 93.6%, which is similar to the level of YOLOv5, and the number of parameters is less than a quarter of YOLOv5.