Reviewerly/exHarmony huggingface dataset:
A Phone-Level Speaker Embedding Extraction Framework with Multi-Gate Mixture-of-Experts Based Multi-Task Learning

Previous research has demonstrated that phonetic information can be incorporated into speaker embedding models to improve the performance of the speaker recognition system. However, the current speaker recognition process base on phonetic information is not in the line with the forensic voice identification process, where the identity of a speaker is determined by comparing confident phones one by one. This mainly manifests in two aspects. Firstly, real-world speech signals containing unreliable noisy data are directly used as model inputs. Secondly, the traditional utterance-level speaker embedding consisting of a single vector is a coarse-grained representation of speaker identity. To address these issues, this paper proposes a phone-level speaker embedding extraction framework with Multi-gate Mixture-of-Experts (MMoE) based multi-task learning. In the proposed framework, confident mono-phone segments are obtained by a well-trained ASR model and used as the inputs of the speaker embedding extractor with residual structure and self-attention mechanism. MMoE in the framework is supposed to increase the modeling sensitivity of different accents. To improve the fineness of the speaker representation, a vector set generated from different mono-phone segments is used as the phone-level speaker embedding. On a large-scale Mandarin speaker identification dataset, the proposed system using phone-level speaker embeddings and the MMoE technique significantly outperformed the utterance-level baseline system by the Top-l average error rate reduction of 47.0% relatively.
BERT-based Chinese Medicine Named Entity Recognition Model Applied to Medication Reminder Dialogue System

The general public in Taiwan generally believes that traditional Chinese medicine (TCM) is mild and has no side effects, but they ignore the safety of traditional Chinese medicine. If the Chinese medicine name and disease name can be correctly identified in the human-machine dialogue, it can help the dialogue system to give correct medication reminders. In this study, a named entity recognition was constructed and applied to the identification of Chinese medicine names and disease names, and the results could be further used in the human-computer dialogue system to provide people with correct Chinese medicine medication reminders. First, this study uses a web crawler to organize network resources to become a TCM named entity corpus, collecting 1097 articles, 1412 disease names and 38714 TCM names. Then we use the Chinese medicine name and BIO labeling method to label each article. Finally, this study trains and evaluates BERT, ALBERT, RoBERTa and GPT2 with biLSTM and CRF. The experimental results show that the NER system of RoBERTa combined with biLSTM and CRF achieves the best system performance, where the Precision is 0.96, the Recall is 0.96 and the F1-score is 0.96.
Deep Transfer Tensor Factorization for Multi-View Learning

This paper studies the data sparsity problem in multi-view learning. To solve data sparsity problem in multi-view ratings, we propose a generic architecture of deep transfer tensor factorization (DTTF) by integrating deep learning and cross-domain tensor factorization, where the side information is embedded to provide effective compensation for the tensor sparsity. Then we exhibit instantiation of our architecture by combining stacked denoising autoencoder (SDAE) and CANDE-COMPIPARAFAC (CP) tensor factorization in both source and target domains, where the side information of both users and items is tightly coupled with the sparse multi-view ratings and the latent factors are learned based on the joint optimization. We tightly couple the multi-view ratings and the side information to improve cross-domain tensor factorization based recommendations. Experimental results on real-world datasets demonstrate that our DTTF schemes outperform state-of-the-art methods on multi-view rating predictions.
Cost-reference Particle Filter Bank for Fast Instantaneous Estimation of Nonlinear FM Signal

Instantaneous frequency (IF) estimation of unknown nonlinear frequency modulated (FM) signal in complex background is an essential issue in radar. In this paper, a cost-reference particle filter (CRPF) based filter bank, i.e. CRPF bank is proposed for fast and accurate IF estimation. The proposed CRPF bank has entirely parallel structure, which consists of a large number of parallel CRPFs using different prior information. After filtering, the output of the CRPF with minimum cumulate cost is selected as the IF estimation of the CRPF bank. Experiments on IF estimation illustrates that, by comparing with the similar algorithms, the execution time of the proposed method is greatly minimized and also its estimate is improved.
The Exploration of Emotions from Print Design Course using Augmented Reality

This paper is a study based on the reflexivity approach toward teaching and learning using the action research process of a print design course. The initial motivation is to explore and guide students to integrate Augmented Reality (AR) with print book design because the current digital trajectory allows prospective designers to showcase their artwork's uniqueness. The action research method was applied to explore factors affecting planning and the processes from print content to AR; namely, Taboo Augmented Reality book (TARbook). This study uses Zappar, the AR technology to project motion graphics, as a tool to bridge the gap between the physical and digital realm and it is believed that this study is helpful for the professional growth of graphic design lecturers as well as students' learning; 6 students were chosen for an Focus Group Discussion (FGD) session after they experienced the TARbook. SentiStrength's results positively affect the overall trinary score, which shows that the TARbook gives positive emotions towards the students. Future directions of research are to create more such motion graphics pictures book.
Vehicles Detection Using Deep Learning With Improved Single Shot Detector

Training datasets manually with a variety of car models’ images would be time consuming due to the huge datasets. Hence, mobilenet V2 network architecture is implemented on existing single shot detector (SSD) network to improve the detection accuracy (mean average precision, mAP), inference time and sensitivity towards small objects in complex backgrounds without increasing the computation complexity. The transfer learning mechanism for the custom dataset is applied to increase detection accuracy against small objects and reduce training time. Custom datasets are used for training and testing, where the datasets are annotated using labelImg. Google Colab and some open-source libraries, Tensorflow and Keras are used in model training. The performance of improved-SSD in object detection is evaluated based on inference time and mean average precision (mAP). All models are pretrained using Common Objects in Context dataset (COCO). Based on the result obtained, 1.76 seconds needed for Faster R-CNN model to perform inference per image whereas 1.24 seconds needed for proposed model to perform the same tasks. The inference time of proposed model is approximate 30% faster than the Faster R-CNN model. The mean average precision of the proposed model is 73.4% whereas the average recall rate of the proposed model is 80%. Besides, the proposed model obtains approximately 10% improvement in terms of mAP detecting small object if compared with Faster R-CNN model. The proposed model able to detect vehicles with shorter inference time and good accuracy.
Process Noise Adaptive Kalman Filtering with Stein Variational Gradient Descent

Adaptive Kalman filtering has become a widely used state estimation method when the system noise statistics are not available a priori. In this article, we consider the application of Stein variational gradient descent (SVGD) in adaptive Kalman filtering for the linear Gaussian state-space model with inaccurate process noise statistics. The proposed algorithm separately estimates the system state and noise parameters with the Kalman filter and SVGD online inference. Compared to the existing state-of-the-art adaptive Kalman filters, the proposed method can approximate the noise statistics better and thus provides higher state estimation accuracy, which is confirmed by the simulation results on a target tracking example.
Event-Triggered Acceleration Algorithms for Distributed Stochastic Optimization

In this chapter, we focus on introducing and exploring how to improve the computational and computational efficiency in distributed optimization problems, and the problem under study remains the problem of distributed optimization to minimize a finite sum of convex cost functions over the nodes of a network where each cost function is further considered as the average of several constituent functions. Reviewing the existing work, no method can improve communication efficiency and computational efficiency simultaneously. To achieve the above goal, we will introduce an effective event-triggered distributed accelerated stochastic gradient algorithm, namely ET-DASG. ET-DASG can improve communication efficiency through an event-triggered strategy, improve computational efficiency by using SAGA’s variance-reduction technique, and accelerate convergence by using Nesterov’s acceleration mechanism, thus achieving the target of improving communication efficiency and computational efficiency simultaneously. Furthermore, we will provide in this chapter a convergence analysis that demonstrates that ET-DASG can converge to the exact optimal solution within the average value with a well-selected constant step-size. Also, thanks to the gradient tracking scheme, the algorithm can achieve linear convergence rates when each constituent function is strongly convex and smooth. Moreover, under certain conditions, we prove that the time interval between two successive trigger moments is larger than the iteration interval for each node. Finally, we also confirm the attractive performance of ET-DASG through simulation results.
Privacy Preserving Algorithms for Distributed Online Learning

In this chapter, we focus on introducing a distributed online optimization problem for a set of nodes communicating on a time-varying unbalanced directed network, while considering the problem of how to preserve the privacy of their local cost functions. The main goal of this set of nodes is to cooperatively minimize the sum of all locally known convex cost functions (global cost function). We propose a differentially private distributed stochastic subgradient-push algorithm, named DP-DSSP, to solve such an optimization problem in a collaborative and distributed manner. The algorithm ensures that nodes interact with their in-neighbors and collectively optimize the global cost function. Unlike most existing distributed algorithms that do not consider privacy issues, DP-DSSP successfully protects the privacy of participating nodes through a differential privacy strategy, which is more practical in applications involving sensitive information, such as military affairs or medical treatment. An important feature of DP-DSSP is that it handles distributed online optimization problems in the context of time-varying unbalanced directed networks. Theoretical analysis shows that DP-DSSP can effectively protect differential privacy and can achieve sublinear regret. The tradeoff between the level of privacy and accuracy of DP-DSSP is also revealed. Moreover, DP-DSSP is able to handle arbitrarily large but uniformly bounded delays in the communication link. Finally, simulation experiments confirm the usefulness of DP-DSSP and the findings of this chapter.
Pairwise Sequence Alignment

In a nutshell. A lot of bioinformatics consists of evaluating the similarity and differences between molecules, be they protein, RNA, or DNA molecules. Our understanding of the similarity of two sequences, be they amino acids or nucleotides, is based on how we line up the amino acids or nucleotides in one molecule with the amino acids or nucleotides in the other molecule. We begin by looking at how we align two sequences.
Point pattern analysis of thermal anomalies in geothermal fields and its use for inferring shallow hydrological processes

Geothermal areas are studied with a diversity of techniques, including remote sensing (e.g. hyperspectral and thermal imagery) which can provide a time and cost efficient method to assess the surface of geothermal areas and infer subsurface processes. This study presents the analysis of thermal anomalies extracted from a single thermal infrared image covering Waiotapu Geothermal Field, New Zealand. Point pattern analyses were undertaken to assess any dominant orientations, and departures from randomness based on anomaly locations. The thermal anomalies present orientations that approximately align with nearby active faults, suggesting fault control in the Waiotapu Geothermal Field. Two distinct regions were observed, northern areas and southern areas. Northern areas show higher temperatures (i.e. >47 C°) and lower densities of thermal anomalies, with an orientation close to that of the whole field (∼ N22E°) and separation within cluster separations of ∼250 m, signalling to a high content of clays and proximity to the heat source. Southern areas exhibit lower temperatures (i.e. <47 C°) and a higher density of clustered thermal anomalies in a variety of orientations with clustering at all distances, indicating higher permeability at shallow depths. This is likely caused by a thinner litho-cap layer and a history of hydrothermal eruptions and collapse craters. Thermal imagery can therefore be used to infer the subsurface processes taking place at geothermal areas, elucidating information about the shallow permeability and heat movement. Our method can be applied to any other geothermal field for exploration to understand the shallow substrate and its control on porosity and permeability structure.
A data-driven approach to the “Everesting” cycling challenge

The "Everesting" challenge is a cycling activity in which a cyclist repeats a hill until accumulating an elevation gain equal to the elevation of Mount Everest in a single ride. The challenge experienced a surge in interest during the COVID-19 pandemic and the cancelation of cycling races around the world that prompted cyclists to pursue alternative, individual activities. The time to complete the Everesting challenge depends on the fitness and talent of the cyclist, but also on the length and gradient of the hill, among other parameters. Hence, preparing an Everesting attempt requires understanding the relationship between the Everesting parameters and the time to complete the challenge. We use web-scraping to compile a database of publicly available Everesting attempts, and we quantify and rank the parameters that determine the time to complete the challenge. We also use unsupervised machine learning algorithms to segment cyclists into distinct groups according to their characteristics and performance. We conclude that the power per unit body mass of the cyclist and the tradeoff between the gradient of the hill and the distance are the most important considerations when attempting the Everesting challenge. As such, elite cyclists best select a hill with gradient > 12%, whereas amateur and recreational cyclists best select a hill with gradient < 10% to minimize the time to complete the Everesting challenge.
Augmented Reality Assisted Maintenance and Monitoring at Onpremise Data Center

Augmented Reality is a combination of a real-world and a computer-generated environment that can enhance the operation and maintenance experience. This paper aims to describe the implementation process of Augmented Reality in monitoring the health and condition of selected assets in Tenaga Nasional Berhad Research (TNBR) Data Center. This research focuses on asset maintenance and monitoring that requires frequent maintenance and close monitoring that rigorously requires human interventions. Integration with the off-the-shelf solution (Zabbix) and custom-made web-based applications, mobile app and AR viewer is established via Application Programming Interface (API) and respective Software Development Kit (SDK). Artificial Intelligence (AI) modules are embedded with the existing knowledge base to rank the best possible solution for each alert. As a result, the solution shortensdecision-making time and the troubleshooting process, especially with limited expertise.
Micam: Visualizing Feature Extraction of Nonnatural Data

Convolutional Neural Networks (CNN) continue to revolutionize image recognition technology and are being used in non-image related fields such as cybersecurity. They are known to work as feature extractors, identifying patterns within large data sets, but when dealing with nonnatural data, what these features represent is not understood. Several class activation map (CAM) visualization tools are available that assist with understanding the CNN decisions when used with images, but they are not intuitively comprehended when dealing with nonnatural security data. Understanding what the extracted features represent should enable the data analyst and model architect tailor a model to maximize the extracted features while minimizing the computational parameters. In this paper we offer a new tool Model integrated Class Activation Maps, (MiCAM) which allows the analyst the ability to visually compare extracted feature intensities at the individual layer detail. We explore using this new tool to analyse several datasets. First the MNIST handwriting data set to gain a baseline understanding. We then analyse two security data sets: computers process metrics from cloud based application servers that are infected with malware and the CIC-IDS-2017 IP data traffic set and identify how re-ordering nonnatural security related data affects feature extraction performance and identify how reordering the data affect feature extraction performance.
A Study on Technology Innovation at Incheon International Airport: Focusing on RAISA

Airports are implementing innovative technologies represented as Information and Communication Technologies (ICT) to prepare for increase of customer numbers and provide better experience in post-corona era. This study aims to explore the role of technologies recently introduced in airports and the benefits of such technology-driven transition. To achieve the purpose, this study focuses on technologies of robot, AI and service automation (RAISA) along service process from the perspective of airport customers. For the analysis, the best practice of airport in South Korea is selected and provide basic data for RAISA utilization. Through the case analysis, this study categorizes the core technologies utilized in airports and explores the advantages, for instance attaining process efficiency and providing customer convenience from RAISA. In conclusion, despite suffering from COVID-19 pandemic, it can be a possible opportunity to restructure and reorganize the airport service process through implementation of RAISA.
The Way Forward for Security Vulnerability Disclosure Policy: Comparative Analysis of US, EU, and Netherlands

If someone who finds vulnerability does not disclose it, the vulnerability may never be revealed or may be exploited by other malicious users. Therefore, it is necessary to disclose vulnerabilities to enhance security. Some countries already operating vulnerability disclosure policy (VDP). As a case study, this paper analyzed the cases of the US, EU, and Netherlands to derive key elements of the VDP and propose tasks to be considered when operating and improving the VDP in the future. The main implications of this study are that: (1) It is important to protect security researchers from criminal or civil liability, (2) the VDP should be spread across the public and private sector, (3) Manage the known vulnerabilities and ensure transparency and accountability of the management entity.
Pricing Policies and Their Impact on Customer Satisfaction

This research aims to focus on pricing, policies and their impact on customer satisfaction in the five-star hotels in Aqaba. The technique that was used in the present research is described in this section. There are many components of this document: the research model, operational definitions of the study’s independent and dependent variables, research hypotheses, as well as a data collection instrument, research population, and research sample. The components of this study are developed on the basis of prior literature, which may be either theoretical or empirical in nature. Indeed, the factors utilized in this research are often found in the literature on tourist development. The data analysis results have shown that services in restaurants in Aqaba hotels are applied at a very high level, which the mean score is 4.38. Also, customer satisfaction and pricing cognition (pricing) existed on a high base with mean of 3.86 and 3.74 respectively. Specifically, the research seeks to develop price policies for all categories and to work on the design of all pricing criteria to fit the quality of service and the degree of customer satisfaction, which shows that a high level of customer satisfaction may be achieved.
Does Strategic Change Mediate the Relationship Between Total Quality Management and Organizational Culture

The study conducts an empirical analysis of the relationship between total quality management (TQM) practices, with the organizational culture (OC) Cameron and Quinn (Diagnosing and changing organizational culture: Based on the competing values framework, John Wiley & Sons, 2011) model which was divided into four dimensions: leadership supportive, customer focus, strategic planning, and workforce focus. In addition, the study evaluates the mediating role of strategic change (SCH), between organizational culture (OC) and total quality management (TQM). The study uses data obtained from a survey of 341 respondents by using google form online from the employees of the Emirates Post Group (EPG), which is considered as one of the government-owned agencies that controls the logistics business in the UAE, Dubai. The result of the study revealed that strategic change has a direct impact on the TQM practices. In addition to that, the strategic change can be explained as a partial mediator in the relationship between organizational culture variables and TQM practices. It also found that there is a positive and significant effect of organizational culture on strategic change. This study makes a novel contribution by offering a detailed and deeper understanding of the relationship between Organizational culture, strategic change, and total quality management. According to the results, the study recommended that future studies might investigate if other possible mediating variables may also mediate whatever totally or partially the effect of the relationship between TQM and organizational culture.
Development of Data Mining Expert System Using Naïve Bayes

The consumer spectrum consists of a wide range, including the affluent, middle-income, and low-income. This consumer shows different behaviors or motivations towards choosing clothes. We want to develop a framework for a Sale Recommendation System. These expert System can be helpful for sale persons, fashion designer, promoter, brand manager as well as sponsor of Recommendation System. The study implemented the Data Science approach and techniques to see how reliable Recommendation Systems are and in our selected dataset we have applied different modelling techniques such as KNN, SVM, Bayes Naïve and Decision Tree and fond the NB as the most suitable and practical method of modelling in regard to the accuracy, recall and runtime.