Reviewerly/exHarmony huggingface dataset:
Hierarchical neural network: Integrate divide-and-conquer and unified approach for argument unit recognition and classification

Argument unit recognition and classification (AURC) is a promising and critical research topic in argument mining, which aims to extract the argument units that express support or opposing stance in a given argumentative text under controversial topics. Existing studies treated the AURC as a sequence labeling problem and designed a unified approach to predict argument unit boundary and argument unit stance simultaneously. In this paper, we propose a general framework hierarchical neural network (HNN) for AURC, by fusing two different approach: divide-and-conquer approach and unified approach. The divide-and-conquer approach considers the correlation of the two tasks inherent in AURC (task 1: argument unit recognition, AUR and task 2: argument unit classification, AUC), and jointly optimize them for prediction by a novel probability transition matrix. Finally, we used a token-level attention mechanism to efficiently fuse probability distributions obtained by our proposed divide-and-conquer approach and existing unified approach. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed framework.
Implementation of a graph-embedded topic model for analysis of population-level electronic health records

To address the need for systematic investigation of the phenome enabled by ever-growing genotype and phenotype data, we describe our step-by-step software implementation of a graph-embedded topic model, including data preprocessing, graph learning, topic inference, and phenotype prediction. As a demonstration, we use simulated data that mimic the UK Biobank data as in our original study. We will demonstrate topic analysis to discover disease comorbidities and computational phenotyping via the inferred topic mixture for each subject. For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).1.
Moving object detection based on enhanced frame difference

Aiming at the problem that frame difference method will deal with the moving target incorrectly when the target moves slowly or stops for some times, a novel method based on frame difference for moving object detection is proposed. Firstly, the algorithm confirms the number of frames used to generate background by comparing the binary frame difference of the current frame with the initial binary frame difference and establishes the background model using the determined frame; Then the background subtraction method is utilized to detect the moving object through the established background. Relevant experiments show that the algorithm can better deal with the detection of slow-moving targets.
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation

Human intent prediction (HIP) and human activity recognition (HAR) are important for human–robot interactions. However, human–robot interface signals are user-dependent. A classifier trained on labeled source subjects performs poorly on unlabeled target subjects. Besides, previous methods used a single learner, which may only learn a subset of features and degrade their performance on target subjects. Last, HIP and HAR require real-time computing on edge devices whose computational capabilities limit the model size. To address these issues, this paper designs an ensemble diverse hypotheses (EDH) and knowledge distillation (EDHKD) method. EDH mitigates the cross-subject divergence by training feature generators to minimize the upper bound of the classification discrepancy among multiple classifiers. EDH also maximizes the discrepancy among multiple feature generators to learn diverse and complete features. After training EDH, a lightweight student network (EDHKD) distills the knowledge from EDH to a single feature generator and classifier to significantly decrease the model size but remain accurate. The performance of EDHKD is theoretically demonstrated and experimentally validated. Results show that EDH can learn diverse features and adapt well to unknown target subjects. With only soft labels provided by EDH, the student network (EDHKD) can inherit the knowledge learned by EDH and classify unlabeled target data of a 2D moon dataset and two human locomotion datasets with the accuracy at 96.9%, 94.4%, and 97.4%, respectively, in no longer than 1 millisecond. Compared to the benchmark method, EDHKD lifts the target-domain classification accuracy by 1.3% and 7.1% in the two human locomotion datasets. EDHKD also stabilizes learning curves. Therefore, EDHKD significantly increases the generalization ability and efficiency of the HIP and HAR.
Recent results from KamLAND-Zen

A search for the neutrinoless double beta decay of 136 Xe was conducted with the xenon-loaded liquid scintillator detector KamLAND-Zen 800 using 970 kg•year of 136 Xe exposure.No significant signal excess from the expected background was observed from the energy spectrum fitting, and we set a lower limit on the half-life of 2.0 × 10 26 years at a 90% confidence level.A combined fit of the KamLAND-Zen 400 and 800 data sets gives a lower limit of the half-life of 2.3 × 10 26 years at a 90% confidence level.It corresponds to the upper limits on the effective Majorana neutrino mass of 36 -156 meV with various nuclear matrix element calculations.It is the first test for inverted mass ordering.
Fit Online: A Mobile Application for Fitness and Recreation Services

This paper presents the fitness and recreation mobile app development on a smartphone or tablet that has a function to support the users to buy e-tickets and vendors to promote their commercial business through the app. This study aims to develop a mobile application for fitness and recreation services through object-oriented research, design, and development process that could meet the necessity of many users who like to purchase fitness and recreation e-tickets. The name of the mobile app is Fit Online. In the local market, no apps provide facilities to buy many vendors' tickets in a single platform. Beginners to advance level of the tutorial is not available in the current system. This app will provide social networking services to users who can share their daily fitness activities with other users. Customer care will communicate clearly and adapt to every user. This app will inspire a user to exercise, and the vendor will find a platform to explore their expertise. Both Fit Online and the vendor system will be online. Available vendor tickets will be synchronized automatically with Fit Online. A copy of the e-ticket will send to the customer, vendor, and Fit Online system. Vendors will get extra customers via this company commerce policy. It will save users time and cost. Vendor, user, and Fit Online will be beneficial.
An Imperceptible and Robust image watermarking using RDWT and SVD in YCbCr color space

This paper proposes Redundant Discrete Wavelet Transform (RDWT) and Singular Value Decomposition (SVD) based an imperceptible and robust image watermarking in YCbCr color space. Luminance and chrominance components are extracted from colored cover image and watermark is embedded into its luminance component. Cover image is break down into four sub-bands by applying 1-level RDWT. LL sub-band singular values are modified using singular values of colored watermark. Peak Signal to Noise Ratio (PSNR), Structural SIMilarity (SSIM) and Normalized Correlation coefficient (NC) parameters are used to evaluate the proposed method's performance. Utilizing numerous test images, the suggested scheme's effectiveness is assessed. Comparison of results with existing techniques is also performed and it is clear from results that performance of proposed scheme is better in terms of PSNR, SSIM and NC.
Meta-learning with Hopfield Neural Network

In this paper, we propose a novel meta-learning method that leverages the advantages of both meta-learning and storage. In meta-learning, the neural network tries to learn parameters distributed across multiple tasks. Meta-learning provides quick learning with unseen meta-testing tasks. In model-based meta-learning methods, an external memory module is used to retain a memory of important parameters from one task to the other, enabling meta-learning. The model proposed in this work consists of a long short-term memory(LSTM) neural network with an external memory network known as Hopfield neural network. Hopfield neural network is a single-layer, non-linear, auto-associative model that uses an external memory network. Unlike previous methods, our proposed model <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$LSTM_{HAM}$</tex> , i.e., long short term memory with Hopfield associative memory focuses on storing knowledge that uses an additional memory network to store and retrieve patterns using different location-based access mechanisms. Our model extends the capabilities of the LSTM and performs meta-learning best on 5-way 10-shot task setting with an average accuracy of approximately 60 percent.
Risk Assessment of Insider Threats Based on IHFACS-BN

Insider threats, as one of the pressing challenges that threaten an organization’s information assets, usually result in considerable losses to the business. It is necessary to explore the key human factors that enterprise information security management should focus on preventing to reduce the probability of insider threats effectively. This paper first puts forward the improved Human Factors Analysis and Classification System (IHFACS) based on actual enterprise management. Then, the enterprise internal threat risk assessment model is constructed using the Bayesian network, expert evaluation, and fuzzy set theory. Forty-three classic insider threat cases from China, the United States, and Israel during 2009–2021 are selected as samples. Then, reasoning and sensitivity analysis recognizes the top 10 most critical human factors of the accident and the most likely causal chain of unsafe acts. The result shows that the most unsafe behavior was not assessing employees’ familiarity with the company’s internal security policies. In addition, improving the organizational impact of information security can effectively reduce internal threats and promote the sustainable development of enterprises.
Fuzzy Controller Design for Rotary Inverted Pendulum System Using Genetic Algorithms

This paper presents the design, optimization and implementation of a fuzzy controller applied to a rotatory inverted pendulum system, where a genetic algorithm is used to tune the parameters of the controller membership functions from experimental data. The proposed fuzzy control is based on a cascade control structure, which is useful for this nonlinear pendulum system. Furthermore, the LQR controller, which is commonly used to control an inverted pendulum system, was implemented for comparison purposes. It is empirically demonstrated that the proposed fuzzy controller covers a larger range of operating points than a conventional LQR controller. Finally, typical disturbances are added to the system states to test the robustness of the proposed fuzzy controller.
Robust Hierarchical Federated Learning with Anomaly Detection in Cloud-Edge-End Cooperation Networks

Federated learning (FL) enables devices to collaborate on machine learning (ML) model training with distributed data while preserving privacy. However, the traditional FL is inefficient and costly in cloud–edge–end cooperation networks since the adopted classical client-server communication framework fails to consider the real network structure. Moreover, malicious attackers and malfunctioning clients may be implied in all participators to exert adverse impacts as abnormal behaviours on the FL process. To address the above challenges, we leverage cloud–edge–end cooperation to propose a robust hierarchical federated learning (R-HFL) framework to enhance inherent system resistance to abnormal behaviours while improving communication efficiency in practical networks and keeping the advantages of the traditional FL. Specifically, we introduce a hierarchical cloud–edge–end collaboration-based FL framework to reduce communication costs. For the framework, we design a detection mechanism as partial cosine similarity (PCS) to filter adverse clients to improve performance, where the proposed lightweight technique has high computation parallelization. Besides, we theoretically discuss the influence of the proposed PCS on the convergence and stabilization of FL. Finally, the experimental results show that the proposed R-HFL always outperforms baselines in general cases under malicious attacks, which further shows the effectiveness of our scheme.
Taiwanese Across Taiwan Corpus And Its Applications

Taiwanese across Taiwan (TAT) corpus is the first large-scale and publicly released Taiwanese speech corpus which represents the modern Taiwanese around Taiwan. This paper briefly reviews the TAT corpus and a corresponding parallel Chinese, Hàn-Lô-Tâi-bûn, Tai-Luo and Péh-ōe-jī lexicon and demonstrate some of their potential applications including ASR, TTS and voice conversion. The corresponding pretrained ASR and TTS models, sample model usage codes and training scripts will also be released. More information could be found on the Formosa Speech in the Wild website: https: //sites. google. com/nycu.edu.tw/fsw.
Korean Dialect Identification Based on an Ensemble of Prosodic and Segmental Feature Learning for Forensic Speaker Profiling

Telephonic fraud, or voice phishing, is becoming a major issue in South Korea, and there has been a high demand on AI-assisted solutions in the forensic areas to effectively narrow down the boundary of possible suspects. One such demand is use of automated Korean dialect identification in speaker profiling, which aims to classify dialect candidates of a suspect by analyzing dialectal patterns in speech, which are found in segmental and prosodic parts of the language. In this paper, an ensemble of dialect classifiers is proposed that considers both the segmental and the prosodic speech features. The classifier network is an ensemble of two sub-networks, which are an attention-based bidirectional LSTM network and a vanilla DNN for prosodic and segmental feature learning, respectively. A public dataset of Korean conversational speech is used, and the proposed model shows 61.28% in F-measure, which outperforms the baseline by 25.87%p.
Auto-Scoring Feature Based on Sentence Transformer Similarity Check with Korean Sentences Spoken by Foreigners

This paper contains the development of a training service for foreigners to help them increase their ability to speak Korean. The service developed in this paper is implemented in the form of a mobile application that shows specific Korean sentences to the user for them to record themselves speaking the sentence. The objective is to generate the score automatically based on how similar the recorded voice with the actual sentence using Speech-To-Text (STT) engines and Sentence Transformers. The application is developed by selecting the four most commonly known STT engines with similar features, which are Google API, Microsoft Azure, Naver Clova, and IBM Watson, which are put into a Rest API along with the Sentence Transformer. The mobile application will record the user’s voice and send it to the Rest API. The STT engines will transcribe the file into a text and then feed it into a Sentence Transformer to generate the score based on their similarity. After measuring the response time and consistency as the performance evaluation by simulating a scenario using an Android emulator, Microsoft Azure with 1.13 s is found to be the fastest STT engine and Naver Clova is found to be the least consistent engine with nine different transcribe results.
an automated speech recognition system for phonological awareness of kindergarten students in filipino

Phonological awareness is the ability to hear and attend to different units (e.g. syllables, onset and rime, and phonemes) of language. It is an essential aspect of a child’s learning development as it is a predictor of a child’s success in reading and spelling. The implemented phonological awareness system includes a Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) based automated speech recognizer with pitch prosody-based data augmentation and vocal tract length normalization for children’s phonological awareness skill training and assessment. This study found that the use of pitch prosody-based augmentation is efficient in addressing the pitch and formant mismatch between the trained adult utterances and tested children’s utterances which entailed a WER of 15.08% or an increase in accuracy by 5.16% in comparison to the baseline system. The vocal tract length normalization (VTLN) adaptation did not show much significant improvement to the augmented system as the accuracy only increased by 0.12% or a WER of 14.96%.
Improving Vietnamese Accent Recognition Using ASR Transfer Learning

Accent Recognition (AR) is a critical task in voice-controlled systems. If accent information is known in advance, voice-controlled systems can switch to a suitable accent-specific mode to improve their performance and user experience. However, available accent datasets, especially Vietnamese, are relatively small, making AR very challenging. To deal with these drawbacks, this paper proposes a transfer learning method using pretrained ASR models for Vietnamese accent recognition. This helps the system utilize available speech recognition systems while capturing implicit linguistic and phonetic information learned in ASR to improve its performance. Several experiments were conducted on a Vietnamese 8kHz telephone call dataset, which showed a significant improvement of the proposed system over existing Vietnamese AR models.
Analysis of Medical Images using Image Registration Feature-based Segmentation Techniques

Image Segmentation is one of the very important optimistic and emerging fields in all image processing applications. It has a wide range of applications like machine vision, fingerprint recognition, digital forensics, medical imaging, and face recognition and so on. Based on specific application, various image segmentation techniques like thresholding, region growing, watershed, clustering algorithms, fuzzy algorithms etc., are used to segment or partition the input images, labels each pixel in the images, locate the points, edges, boundaries and objects to identify various problems in the medical images. Also the identification of important parameters, detection of fractures and diseases, to decrease the death rate of patients suffering from various health problems is challenging research work in medical images. In this paper, author carryout the analysis for the automatic detection of bone fracture in early stage by taking two input x-ray medical images that are captured at different timings. This process is carried out and registered in 4 stages: In first stage-acquire input images and perform pre-processing by using geometrical transformation and register the input images, in second stage- the registered image is segmented using adaptive k-means clustering method, in third stage- automatic detection of the important features in x-ray image is extracted using image registration feature-based method. Automatic feature extraction is carried out for the observation of bone fracture in initial phase to increase the complexity of geometrical alignments of input images. Finally in the fourth stage, the performance of the results is analyzed with respect to accuracy and error rate.
Text Mining from Party Manifestos to Support the Design of Online Voting Advice Applications

Voting advice applications (VAA) allow potential voters to compare their own policy positions to political parties running for an election. One of the key design elements of a VAA are the policy statements representing the political space covered by political parties. VAA designers face the challenge of coming up with policy statements in a short time frame. Even with medium-sized corpora of texts such as party manifestos, the formulation and selection of policy statements serving as a stimulus in the VAA is a tedious and time-consuming task. In addition, there is the risk of human selection bias. This study proposes a system to aid VAA designers in policy statement selection and formulation. The system uses the BERT language model with semantic similarity calculation to mine party manifesto sentences that are relevant to already existing VAA statements. For the experiments, VAA statements stemming from the 2021 elections and party manifestos issued for the previous two Japanese elections were used. To expand the policy space, VAA statements from the 2019 European Parliament elections were added. Results show that the proposed system is able to analyze large amounts of text in a short time, and mines text that provides practical support for designing and improving VAAs.
A Survey on Applications and Security Issues of Blockchain Technology in Business Sectors

One of the most talked-about topics of the past few years, block chain technology has already influenced numerous industries and businesses, altering the lives of countless people in the process. While the features of block chain technologies have the potential to provide us with more trustworthy and convenient services, there are still significant security concerns that must be addressed. The primary objective of this work is to explain and convey the idea of block chain, its modern-day uses in the business sector, and the numerous dangers and security challenges associated with block chain technology. The widespread adoption of block chain technology has the potential to solve the intractable trust problems in a variety of industries.
Noise reduction in Clinical MRI Scans employing Filter Combining Techniques

The implementation of Magnetic Resonance Imaging (MRI) pictures in the initial identification and treatment of a variety of disorders has become integral. These images are a set of data intended for visual inspections that are susceptible to specific noises and artefacts. Noise free MRI images are necessary for increasing the overall accuracy and clarity of assessment and therapy analytical evaluation. While gathering, processing and distribution many clinical images are influenced by various forms of sounds, resulting in the degradation of details pertaining with the image, which can impact the performance of illness treatment. To decrease the noise in medical scans for subsequent assessment, numerous filtering techniques are applied. The variety of digital filters, namely the Anisotropic filter, Median filter, Wiener filter and Non-Local Mean filter are discussed in this paper and their combinations are implemented with respect to all static parameters such as the Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Universal Quality Index (UQI). A noise removal strategy based on the Wiener filter is developed in this study analysis for enhancing the image quality of diverse diagnostic imaging. The optimum outcome is obtained by combining the Wiener filter with all the static parameters. Existing noise reduction filtering approaches are outperformed by the suggested method.