Reviewerly/exHarmony huggingface dataset:
Automatic Identification of Textual Topic Structure-Topic Segmentation

With the continuous development of science and technology, extracting text information has been widely used in lots of aspects in our lives. Text segmentation is an extremely important part of text processing. Usually, after obtaining a text which contains a huge amount of information needs to be split into several parts, and then to be analyzed, studied and used. The TextTiling algorithm is one of the text segmentation methods, and this algorithm is a method proposed based on the structure of the article, which can realize the text extraction of several subtopics. Compared to storing the whole text, this method can greatly reduce the storage requirement, and make the efficiency of searching higher. Moreover, it can be applied in many fields, such as keyword search, information finding, automatic summary, etc.
Development and Application of Data Processing Software for Geo-resistivity

Observation and analysis of geo-resistivity is one of the important methods of earthquake prediction. Based on the geo-resistivity data observed by a seismic station in 2020, a data processing software is designed and developed. Based on the object-oriented programming thought and double-buffer drawing technology, the daily average, 5-day average and monthly average of geo-resistivity data can be easily displayed and drawn. The software is designed and developed in the Visual C++ integrated development environment, and provides a friendly interactive interface. The software provides practical tools for the analysis and application of geo-resistivity observation data, and plays an important role in daily work of seismic stations.
Automatic Modulation Recognition Based on Spatio-temporal Features Fusion

Automatic Modulation Recognition (AMR) has substantial implications in civilian and military communications. Lately, the neural network model has been increasingly used for AMR. However, the previous models have ignored the spatio-temporal characteristics of the signals, which leads to insufficient classification accuracy under low signal-to-noise ratio, especially for certain specific modulation types, like MQAM. Combining the advantages of the complex-valued convolutional neural networks and the long short-term memory networks (LSTM), this paper proposes a novel AMR method based on spatio-temporal features fusion with complex convolution modules and LSTMs, denoted as bi-channel complex-convolutional neural networks with LSTM (BCLNN). BCLNN efficiently explores the spatial-temporal properties of raw complex signals and achieves higher classification accuracy for AMR, especially under a low signal-to-noise ratio and for MQAM modulation. Experimental results dedicate that when the SNR is between −20dB and −10dB, the average accuracy of BCLNN is at least 7.9% higher than that of other networks. BCLNN has a more than 12% improvement of average recognition rate for 16QAM and 64QAM when the SNR is between −20dB and 18dB.
Joint Learning and Self-training for Poetry Sentiment Recognition

Poetry is a literary genre full of diverse sentiments, and its sentiment recognition task helps people to better understand the content of poetry, so it is a challenging and interesting NLP task. The existing work has achieved good results in the controllable generation of sentiment, while the recognition accuracy needs to be further enhanced. In this paper, we propose a joint loss method which calculate the line loss and the character loss, adopt self-training to help poetry line level sentiment recognition, and achieved good results in various indicators.
Twin-Field Quantum Key Distribution Protocol with Heralded Single-Photon Source

Twin-field quantum key distribution (TF-QKD) can overcome the fundamental rate-loss limit in the point-to-point QKD without a quantum repeater and achieve high key rates over long distances. However, TF-QKD protocol and its variants commonly use weak coherent pulses (WCPs) as light sources. The vacuum part of WCP accounts for a large proportion, resulting in the lower photon detection rate of the communication receiver, which leads to a shorter transmission distance of the secret key. In this paper, we propose an active-decoy TF-QKD protocol with heralded single-photon source (HSPS), which can achieve higher key rate and longer transmission distance than the active-decoy TF-QKD protocol with WCPs. Also, in order to resist the side channel attacks caused by actively modulating intensity of the pulses, we propose a passive-decoy TF-QKD protocol with HSPS to enhance the security, which can perform almost as good as the active-decoy TF-QKD protocol with WCPs.
Locally Linear Embedding Based on Neiderreit Sequence Initialized Pelican Optimization Algorithm

Locally linear embedding (LLE) algorithm is an important nonlinear dimensionality reduction method in the field of manifold learning of artificial intelligence (AI). The core idea of LLE is to find a homeomorphic mapping that can ensure that the linear algebraic structure of low-dimensional spatial data projected from high-dimensional spatial data remains unchanged. LLE contains two optimization sub-problems, and gradient descent (GD) or stochastic gradient descent (SGD) method is often used for parameter optimization. However, gradient dependent method is easy to fall into the trap of local minimum, resulting in failure to find the global optimal solution. In order to overcome the above problems, in this paper, a new Neiderreit sequence initialized pelican optimization algorithm (NSPOA) has been proposed, to help LLE find the global optimal parameter properly. Numerical experiment has shown that NSPOA has higher operation efficiency than traditional gradient dependent optimization methods.
Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach

Federated learning (FL) with its data privacy protection and communication efficiency has been considered as a promising learning framework for beyond-5G/6G systems. We consider a scenario where a group of downlink non-FL users are jointly served with a group of FL users using massive multiple-input multiple-output technology. The main challenge is how to utilise the resource to optimally serve both FL and non-FL users. We propose a communication scheme that serves the downlink of the non-FL users (UEs) and the uplink of FL UEs in each half of the frequency band. We formulate an optimization problem for optimizing transmit power to maximize the minimum effective data rates for non-FL users, while guaranteeing a quality-of-service time of each FL communication round for FL users. Then, a successive convex approximation-based algorithm is proposed to solve the formulated problem. Numerical results confirm that our proposed scheme significantly outperforms the baseline scheme.
Convergence Analysis of Cloud-Aided Federated Edge Learning on Non-IID Data

Federated edge learning has attracted great attention for edge intelligent networks. Due to the limited computation and energy, mobile devices usually need to offload data to nearby edge servers. Facing this scenario, we design a cloud-aided federated edge learning (CA-FEEL) framework where the edges cooperate with the cloud to train a federated learning model. Specifically, the edges adopt the gradient descent (GD) method in parallel to update the edge parameters and the cloud averages them to update the global parameter. By theoretical analysis, we find that the covariance of non-independent and identically distributed (non-IID) data sets hinders the convergence of the GD based FL. Thus, we propose a CA-FEEL algorithm by adding a simple judgment condition. It is proved to have a theoretical guarantee of convergence for convex and smooth problems. Experiment results indicate that the proposed algorithm outperforms the standard federated learning in terms of the convergence rate and accuracy.
Over-the-Air Federated Learning Exploiting Channel Perturbation

Federated learning (FL) is a promising technology which trains a machine learning model on edge devices in a distributed manner orchestrated by a parameter server (PS). To realize fast model aggregation, the uplink phase of FL could be carried out by over-the-air computation (OAC). On the one hand, engaging more devices in FL yields a model with higher prediction accuracy. On the other hand, the edge devices in OAC need to perform appropriate magnitude alignment to compensate for underlying channel coefficients. However, due to the limited power budget, this is not possible for devices experiencing deep fade. Consequently, these devices are excluded from the FL algorithm. In this paper, we propose a channel perturbation method so that no edge device is excluded due to experiencing deep fade. To this end, OAC is performed in multiple phases. In each phase, the radio frequency (RF) vicinity of PS’s antenna is intentionally perturbed by means of RF mirror structure coined in [1]. This yields independent realizations of channels between PS and devices in each phase. By using proper transmit scalars, all devices concurrently transmit their local model updates in each phase subject to a total power constraint. Then, the PS estimates the arithmetic sum of the local updates by properly combining the aggregated models obtained across all phases. The devices’ transmit scalars and PS’s de-noising factors can be efficiently found by solving a tractable optimization problem.
Neighborhood Graph Neural Networks under Random Perturbations and Quantization Errors

Graph convolutional neural networks (GCNNs) have emerged as a promising tool in the deep learning community to learn complex hidden relationships of data generated from non-Euclidean domains and represented as graphs. GCNNs are formed by a cascade of layers of graph filters, which replace the classical convolution operation in convolutional neural networks. These graph filters, when operated over real networks, can be subject to random perturbations due to link losses that can be caused by noise, interference and adversarial attacks. In addition, these graph filters are executed by finite-precision processors, which generate numerical quantization errors that may affect their performance. Despite the research works studying the effect of either graph perturbations or quantization in GCNNs, their robustness against both of these problems jointly is still not well investigated and understood. In this paper, we propose a quantized GCNN architecture based on neighborhood graph filters under random graph perturbations. We investigate the stability of such architecture to both random graph perturbations and quantization errors. We prove that the expected error due to quantization and random graph perturbations at the GCNN output is upper-bounded and we show how this bound can be controlled. Numerical experiments are conducted to corroborate our theoretical findings.
Detection of Impaired OFDM Waveforms Using Deep Learning Receiver

With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper, we describe deep learning based physical-layer receiver solution, with neural network layers in both time- and frequency-domain, to efficiently demodulate OFDM signals under coexisting IQ, PN and PA impairments. 5G NR standard-compliant numerical results are provided at 28 GHz band to assess the receiver performance, demonstrating excellent robustness against varying impairment levels when properly trained.
Event Detection and Analysis in Thai News Using Bi-LSTM

Event extraction is one of the most challenging tasks in information extraction. Extracted knowledge not only supports semantic search and natural language understanding but also serves as foundational representations in many applications. However, there are few event extraction researches on Thai datasets in Thailand and these prior works are based on classical machine learning techniques. Therefore, this paper presents a research of event detection and event analysis in Thai news using a bidirectional long short-term memory (Bi-LSTM) model. Within the model, event types are classified and event components, i.e., trigger types and argument roles, are identified. The results show that the model could achieve the accuracy of 73.41% in event type classification and 81.71% in event component identification, respectively. It outperforms many classical machine learning techniques on Thai datasets and comparable to other works in other languages.
Thai Variable-Length Question Classification for E-Commerce Platform Using Machine Learning with Topic Modeling Feature

At present, online shopping is a part of our life. Either a new joiner or an expertise sometimes has questions regarding applications. The most convenient and effective way is to contact the customer service via live chat. However, a huge number of customers causes a long waiting time affecting customers' experience. Thus, this article proposes Thai variable-length question classification for e-commerce platform to deal with this problem. A fusion of two model architectures, Latent Dirichlet Allocation (LDA) and Long Short-Term Memory (LSTM) has been proposed and used as a feature extraction before applying the softmax function to classify the questions. The experimental results have been shown that the proposed model is able to achieve an accuracy of 84.43% which is better than the other models.
A new Hybrid PSO-SCA using Horse Optimization Algorithm's group behavior update

This paper proposes a new Hybrid Algorithm between Particle Swarm Optimization (PSO) and Sine Cosine Algorithm (SCA) with Horse Optimization Algorithm (HOA) group assigning and updating methodology. The proposed algorithm is called Hybrid PSO-SCA with HOA group behavior update (HPSH) aims to solve the disadvantage of both PSO and SCA. HPSH start by assigning each particle a group with the same methodology as HOA and then classifies them based on the fitness of their current position. Each group of particles will share the same movement equation which is different between groups. Particles are periodically assigned to the new group as the iteration increase, assigning criteria is based on fitness value. Movement equations of HPSH are the combination of PSO and SCA, which depend on assigned group. HPSH is experimented in 24 benchmark functions, assigning majority of test functions with 100 dimensions. The experimental results indicate that HPSH has retained both PSO and SCA on almost every function, while some outperform PSO and SCA.
Multi-Label Classification for Articles in Thai Journal Database from Article's Abstract

The increasing number of Thai research articles makes it challenging to classify them into sub-categories. This task requires specialists and a lot of time to classify the different types of articles. Therefore, this research presents methods and techniques for multi-label classification of computer science articles in Thai journals. We present a comparison of different methods for multi-label classification, including Binary Relevance (BR), Classifier Chains (CC), and Label Power-set (LP) with a word segmentation method that uses a Support Vector Machine (SVM) classifier. We found that the CC-SVM method combined with Deepcut word segmentation and TF-IDF produces the best results for both example-based and label-based metrics, with ML-accuracy is 0.572, Subset accuracy is 0.286, F-Measure is 0.666, Micro-average precision is 0.57, and Micro-average F-Measure is 0.70. In Future work, Subset accuracy should be improved for the multi-label classification model in the Thai language.
Improving the Movie Showtime Scheduling Problem by Integrated Artificial Intelligence Techniques

This paper describes a development of an artificial intelligence system for efficiently scheduling movie showtimes. The strategy was to get the maximum amount of visitors by applying any appropriate artificial intelligence techniques to the problem of showtime schedule. The system consists of three key parts which are the movie showtime scheduling system, the predictive model of the total amount of visitors of each movie on selected days, and the web application. In this paper, five different branches of movie theaters were selected for examining the system. The total movie slots were calculated by the models and utilized to be used in the scheduling process following the criteria defined from exploratory data analysis (EDA). According to experiments, the final integrated system was evaluated with many appropriate test scenarios. The average number of visitors by the artificial intelligence system was greater than the average visitors normally reported by the movie theater company. Consequently, the developed system was showing ability to help the company increase the income and also decrease the staff's burden tasks. In addition, any mistakes caused by human errors were expected to alleviate as well.
Face Recognition Algorithms for Online and On-Site Classes

Face recognition is used in a wide variety of applications such as surveillance systems, human-computer interaction, automatic door access control systems, and network security. One of the policies of the smart university is to adopt technology to help with teaching and learning, especially during the Covid-19 pandemic. In this paper, a smart attendance system using face recognition algorithms with deep learning is proposed and used in the university's classroom. Instead of calling names to confirm the identity of students, our system does it automatically. The system was tested in 3 scenarios, namely, in online classes, in on-site classes, and in problematic cases using a standard dataset. The performances of the 3 scenarios were compared in the experiment in terms of precision, recall, F1 score, and percentage accuracy. Our result revealed that in online classes the recognition accuracy is as high as 100%. The implemented system is inexpensive and practical. The application can be used on any portable device such as tablets or smartphones. History viewing, multiple subjects handling, and file exporting features are also incorporated into the system.
Improved Particle Swarm Optimization using Evolutionary Algorithm

In this paper, the researchers applied the Particle Swarm Optimization (PSO) algorithm combined with the Evolutionary Algorithm (EA) and called this hybrid approach PSOEA. This approach combines the benefits of PSO with EA. Integrating the PSO with the EA's mutation, recombination, and selection processes, allows a more efficient global search and faster convergence rate to obtain the optimal solution. PSO can also escape from local optima using EA process. PSOEA is experiment with 24 benchmark functions comparing with the conventional PSO and other similar approaches. The experiment result showed that PSOEA can find solutions faster and better than compared algorithms.
The Distance - Based Selection Technique for Crossover in Genetic Algorithm

The aim of this research is to study and develop the natural inspired parent selections for the crossover operator in genetic algorithms. There are three distance-based methods of mating selection: the hamming distance-based selection (HS), the cosine coefficient distance-based selection (CS), and the Pearson coefficient distance-based selection (PS). The experiment conducts the comparison of the distance-based selection methods with two traditional selections: the roulette wheel selection (RWS) and the tournament selection (TS). In the experiment, all selection methods are evaluated based on four binary testing problems: one-max, zero-max, random-max, and two trap problems. [1] The measurement criterion is the number of generations when the answer is found and the fitness values when the correct answer is not found. From the experimental results, the suitable approaches are divided into two groups according to the characteristics of the benchmark problems. For the trap problem with many local optima [2], the distance-based selection methods outperformed the traditional selection. However, for the other benchmark problems, the tournament selection is the better method than others.
Regularization Strategy for Multi-organ Nucleus Segmentation with Localizable Features

Applying color normalization on H&E images is a famous protocol in digital pathology. Recently, the CutMix technique has a strong ability to generalize the classification models. In this paper, we propose the modified CutMix for segmentation tasks. We apply it to the MoNuSeg dataset. The U-Net with a MobileNet backbone is used for training and inferencing. Moreover, we compare it with the traditional color normalization. The results show that our modified CutMix outperformed color normalization with the +0.179 AJI score. It achieved the IoU score faster and got a higher AP for every IoU threshold.
Improved Generative Adversarial Networks for Intersection of Two Domains

The goal of generative models is to capture domain distribution based on training samples. Generative Adversarial Networks (or GANs) are a successful framework for training a generative model. In this paper, we consider a process for training generative models using GAN when the target domain is an intersection of two target domains. When two target domains only share a small intersection domain, we have identified an issue referred to as canceling gradients, caused by unintended optimization of learning loss. We propose a simple method based on gradient scaling and perform experiments to verify our remedy.
A Methodology for Writer Identification, With Application to An Important Historical Greek Document

In the present work, a general methodology is introduced for the determination of the writer of a given document, provided that texts of the same hand are available. The approach is successfully applied to an unsigned document that has been spotted in the warehouses of Greek Army History Directorate and led, for the first time, to the conclusion that it has been written by the great Greek Politician Eleftherios Venizelos. The content of the paper is important from the historical point of view.
Application and Research of Artificial Intelligence Embedded Service

With the rapid development of artificial intelligence, the library relies on the massive knowledge data to integrate the user behavior data, and continuously upgrade the library industry chain, the new form, the new service will bring the new experience for the user. The artificial intelligence technology based on machine learning and deep learning will promote the change of embedded service, upgrade the intelligence of embedded service physical scene in library, and advance the intelligence of information resources and virtual space. In the embedded service dimension, the artificial intelligence technology will promote the intelligent service supply of the library, the intelligent library will provide the “A I cloud processing” resource service for the user, the whole domain, the interactive scene service, the human-machine high-efficiency Coordinated Joint Service; In the dimension of user experience, artificial intelligence technology will bring a variety of user experience to the library. Taking the Library of Agricultural University as an example, this paper probes into the characteristics and modes of embedded service, puts forward the application of artificial intelligence in embedded service, and provides beneficial exploration for exerting the value of artificial intelligence in library service.