Reviewerly/exHarmony huggingface dataset:
Virtual Human Talking-Head Generation

Abstract: Virtual humans created by computers using deep learning technology are being used widely in a variety of fields, including personal assistance, intelligent customer service, and online education. Human-computer interaction systems integrate multi-modal technologies like speech recognition, dialogue systems, speech synthesis, and virtual digital human video synthesis as one of the applications of virtual humans. In this paper, we first design the framework for a human-computer interaction system based on a virtual human; next, we classify the talking head video synthesis model according to the generation of a virtual human's depth; finally, we conduct a systematic review of the technical developments in talking head video generation over the last five years, highlighting seminal work.
Hierarchical Monte Carlo Tree Search for Latent Skill Planning

Monte Carlo Tree Search (MCTS) continues to confront the issue of exponential complexity growth in certain tasks when the planning horizon is excessively long, causing the trajectory’s past to grow exponentially. Our study presents Hierarchical MCTS Latent Skill Planner, an algorithm based on skill discovery that automatically identifies skills based on intrinsic rewards and integrates them with MCTS, enabling efficient decision-making at a higher level. In the grid world maze domain, we found that latent skill search outperformed the standard MCTS approach that do not contain skills in terms of efficiency and performance.
A Modified Fuzzy K-nearest Neighbor Using the Improved Sparrow Search Algorithm for Two-classes and Multi-classes Datasets

The Sparrow search algorithm is a new and effective swarm intelligence method proposed in recent years and studied in many publications. Based on the basic principle of sparrow search algorithm, this paper combines the inverse learning algorithm with the refined inverse solution to form an improved sparrow search (SSA) algorithm. Combining the fuzzy k-nearest neighbor method and the improved SSA, the numerical simulation of two-classes datasets and multi-classes datasets is carried out, and many numerical results are obtained, and the results are analyzed. At the same time, this paper lists the data comparison results and tables with other models. The hybrid SSA-FKNN proposed in this paper has a clear advantage in terms of accuracy (ACC).
Global-Local Framework for Medical Image Segmentation with Intra-class Imbalance Problem

Deep learning methods have been demonstrated effective in medical image segmentation tasks. The results are affected by data imbalance problems. The inter-class imbalance is often considered, while the intra-class imbalance is not. The intra-class imbalance usually occurs in medical images due to external influences such as noise interference and changes in camera angle, resulting in insufficient discriminative representations within classes. Deep learning methods are easy to segment regions without complex textures and varied appearances. They are susceptible to the intra-class imbalance problem in medical images. In this paper, we propose a two-stage global-local framework to solve the intra-class imbalance problem and increase segmentation accuracy. The framework consists of (1) an auxiliary task network(ATN), (2) a local patch network(LPN), and (3) a fusion module. The ATN has a shared encoder and two separate decoders that perform global segmentation and key points localization. The key points guide to generating the fuzzy patches for the LPN. The LPN focuses on challenging patches to get a more accurate result. The fusion module generates the final output according to the global and local segmentation results. Furthermore, we have performed experiments on a private iris dataset with 290 images and a public CAMUS dataset with 1800 images. Our method achieves an IoU of 0.9280 on the iris dataset and an IoU of 0.8511 on the CAMUS dataset. The results on both datasets show that our method achieves superior performance over U-Net, CE-Net, and U-Net++.
Heuristic Search for DNN Graph Substitutions

The research and development of deep learning cannot be separated from deep neural networks (DNNs). DNNs become deeper and more complex in pursuit of accuracy and precision, leading to significantly increasing inference time and training cost. Existing deep learning frameworks optimize a DNN to improve its runtime performance by transforming computational graphs based on hand-written rules. It is hard to scale when adding some new operators into DNNs. TASO can automatically generate graph substitutions that solve maintainability problems. An optimized graph will be explored by applying a sequence of graph substitutions. However, TASO only considers the runtime performance of the model during the search, which may lose potential optimization. We propose HeuSO, a fine-grained computational graph optimizer with heuristics to handle this problem. HeuSO extracts the type and number of operators of the computational graph and classifies them into four abstract types as high-level features, which facilitate subsequent heuristic search and pruning algorithms. HeuSO generates a better sequence of graph substitutions and finds a better-optimized graph by the heuristic function, which integrates the cost and high-level features of the model. To further reduce the time of searching, HeuSO implements a pruning algorithm. Through high-level specifications, HeuSO can quickly determine whether subgraphs of the original graph match the substitution rules. Evaluations on seven DNNs demonstrate that HeuSO outperforms state-of-the-art frameworks with 2.35 × speedup while accelerating search time by up to 1.58 ×.
Customer Service Hot event Discovery Based on Dynamic Dialogue Embedding

Frequent customer service conversations focus on hot topics of communication users, and automatic hot topic discovery is critical to improving user experience. Traditionally, Customer service relies on operator to write traffic summaries. It leads to the source of the conversation difficult to analyze, which makes difficult to spot aggregated hotspot events. In this paper, we propose a Customer Service hot event Discovery based on dynamic dialogue embedding (CShe-D). This model includes dynamic semantic representation of customer service dialogue, clustering-based customer service hot event discovery and new hot event prediction. In the dialogue semantic embedding module, we obtain the dynamic embedding of each dialogue with combining word importance and word length based on the pre-trained language model to capture richer semantic information in different contexts. We further apply a clustering iterative algorithm with dynamic dialogue embedding to discover customer service hotspots. It can monitor the change trend of events in real time, optimize the accuracy of hot event discovery in operator customer service. Finally, the effectiveness of our CShe-D model is verified by experiments on real dialogue data in the field of customer service.
Graph representation learning and software homology matching based A study of JAVA code vulnerability detection techniques

In nowadays using different tools and apps is a basic need of people's behavior in life, but the security issues arising from the existence of source code plagiarism among tools and apps are likely to bring huge losses to companies and even countries, so detecting the existence of vulnerabilities or malicious code in software becomes an important part of protecting information and detecting software security. This project is based on the aspect of JAVA code vulnerability detection based on graph representation learning and software homology comparison to carry out research. This project will be based on the content of deep learning, using a large number of vulnerable source code, extracting its features, and transforming it into a graph so that it can be tested source code for comparison and report the vulnerability content.
Gaussian-guided character erasure for data augment of industrial characters

The application of scene text erasure technology in privacy protection, camera-based virtual reality translation and image editing has attracted more and more research interests. Recent efforts on scene text erasing have shown promising results. We utilize text removal methods as a component of industrial characters generation procedure to generate large-scale synthetic character images so as to mitigate the issue of insufficient samples in the recognition task of industrial characters. Existing character erasure models has achieved good performance in natural scenes. However, in industrial scenes, these erasure networks are easily affected by salient no-character regions leading to the attention shift. To overcome this limitation, we proposed a character erasure network based on attention mechanism which embed an additional region awareness layer to guide attention to the correct character regions. Meanwhile, we devise a gaussian heat map supervision method for learning additional region awareness layer. The experiments show that the proposed method performs favourably on four industrial character datasets.
A Histo-Puzzle Network for Weakly Supervised Semantic Segmentation of Histological Tissue Type

Digital pathological images with a large range of Histological Tissue Types (HTTs) contain more sophisticated contours than natural images. In recent years, deep learning algorithms have been widely applied to assist HTT analysis in a weakly-supervised manner by exploiting the class activation maps (CAM). However, the previous methods tend to confusedly activate the most discriminative regions of feature maps, resulting in incomplete segmented contour. This paper proposes a Histo-Puzzle network to improve the HTTs classification and segmentation based on patch-level self-supervised learning. Specifically, our model separates the HTT images into tiled patches by a puzzle module. Then we train a classifier on the supervision of reconstructed CAMs and image-level labels simultaneously. Experiments are conducted on the digital pathology database with 51 hierarchical HTTs. The experimental results show that our proposed method outperforms previous state-of-the-art methods on segmentation tasks of morphological and functional types.
Agile Development Methodology for Software Re-engineering

Agile software development is often seen as a product development strategy to design and develop new software products. One of the biggest mistakes that most software development teams make is to consider software re-engineering as a part of their maintenance activities rather than an integral part of the product development lifecycle, and therefore, they don’t consider a systematic development approach for re-engineering activities. Even the most popular agile development frameworks such as Scrum, Kanban, and SAFe don’t have any specific guidelines for re-engineering projects. It is also commonly observed that most of the teams that used agile development methodologies faced several challenges and accumulated a huge technical debt due to their short-focused goals and lack of systematic re-engineering plans (Raza and Majeed in Int J Sci Eng Res 3(8):1–5 (2012) [1]). This paper describes the importance of continuous software re-engineering and the usage of agile methodologies to effectively achieve the re-engineering goals.
Targeted Attacks on Time Series Forecasting

Time Series Forecasting (TSF) is well established in domains dealing with temporal data to predict future events yielding the basis for strategic decision-making. Previous research indicated that forecasting models are vulnerable to adversarial attacks, that is, maliciously crafted perturbations of the original data with the goal of altering the model's predictions. However, attackers targeting specific outcomes pose a substantially more severe threat as they could manipulate the model and bend it to their needs. Regardless, there is no systematic approach for targeted adversarial learning in the TSF domain yet. In this paper, we introduce targeted attacks on TSF in a systematic manner. We establish a new experimental design standard regarding attack goals and perturbation control for targeted adversarial learning on TSF. For this purpose, we present a novel indirect sparse black-box evasion attack on TSF, n Vita. Additionally, we adapt the popular white-box attacks Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). Our experiments confirm not only that all three methods are effective but also that current state-of-the-art TSF models are indeed susceptible to attacks. These results motivate future research in this area to achieve higher reliability of forecasting models.
Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge

This paper describes our contribution to the MIDOG 2022 challenge for detecting mitotic cells. One of the major problems to be addressed in the MIDOG 2022 challenge is the robustness under the natural variance that appears for real-life data in the histopathology field. To address the problem, we use an adapted YOLOv5s model for object detection in conjunction with a new Domain Adaption Classifier (DAC) variant, the Radial-Prediction-DAC, to achieve robustness under domain shifts. In addition, we increase the variability of the available training data using stain augmentation in HED color space. Using the suggested method, we obtain a test set F1-score of 0.6658.
Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge

Robust mitosis detection on images from different tumor types, pathology labs, and species is a challenging task that was addressed in the MICCAI Mitosis Domain Generalization (MIDOG) 2022 challenge. In this work, we describe three reference algorithms that were provided as a baseline for the challenge: A Mask-RCNN-based instance segmentation model trained on the MIDOG 2022 dataset, and two different versions of the domain-adversarial RetinaNet which already served as the baseline for MIDOG 2021 challenge, one trained on the MIDOG 2022 dataset and the other trained only on human breast carcinoma from MIDOG 2021. The domain-adversarial RetinaNet trained on the MIDOG 2022 dataset had the highest F $$_1$$ score of 0.7135 on the final test set. When trained on breast carcinoma only, the same network had a much lower F $$_1$$ score of 0.4719, indicating a significant domain shift between mitotic figure and tissue representation in different tumor types.
Transformer Entity Automatic Extraction Models in Multi-layer Soft Location Matching Format

In this chapter, a multi-layer soft position matching format Transformer method is proposed to extract clinical medical entities. The new model is shown in Fig. 4.1. The proposed model consists of five parts: WordPiece preprocessing module, BERT module, multi-layer soft position matching module, word format Transformer, and fuzzy CRF module.
Medical Named Entity Recognition Models with the Attention Distraction Mechanism

In this chapter, a medical NER model, which is simply called as BERT-Attention-SCLSTM-CRF, is proposed by adding extended input units based on BiLSTM with attention distraction mechanism.
The Relationship Between Objectives and Stages of Agile Implementation in Organizations

The paper analyzes application of Agile in software development, revealing that Agile initially referred to software development, then in the context of agile began to be applied to the management of any processes in different fields and areas of knowledge. In this way, there was a shift. Therefore, “agile development methodology” and “agile management” may have different meanings. The survey results serve as the material for the study. These materials are interesting as they have the relevance of respondents to Agile. In addition, respondents answered the questions without additional incentives and could interrupt the survey at any time. 1,501 people have participated in the present survey. Purpose of the study: an exploration of the relationship between objectives and stages of agile implementation in organizations based on the opinions of developers. Within the present study, Pearson's rank correlation coefficient was used to calculate the interdependence between the variable “How many years has your company been applying Agile practices?” and the variable “Number of goals.” To figure out the interdependence between respondents' answers to the question “what stage of Agile implementation is your company at?” and the variable “Number of goals” that the respondent associates with Agile, the Spearman rank correlation coefficient was used. The results showed that the number of Agile implementation goals depends on the level of Agile competencies in the organization: then higher the level of Agile competencies in the organization, then more goals the organization has. The results of the study enrich the knowledge of goal-setting in Agile. In addition, it can stimulate to consider the problem from a new point of view - the study of the evolution of goals from implementing Agile.
Tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization

In this paper, we propose a method for mitosis detection in histopathology images in an unsupervised domain adaptation setting. Our method is a two-step approach. The first step is color normalization, which is an unsupervised domain adaptation at the input level. In the second step, we use an object detection method for mitosis detection. Using a final test set consisting of patches from whole slide images and containing 100 independent tumor cases across 10 tumor types, we evaluate our method and obtain an F1 score of 0.671.
Open Engineering Education Tools in the Context of Education 4.0

The article examines the impact of Open Engineering Education on the digital transformation of the social sphere. We focus on the methods of Open Engineering Education as a tool to eliminate interregional inequality and provide universal access to educational resources through its digitalization. The paper analyzes how the trends in the digitalization of education affect the development of its infrastructure within Education 4.0. We propose the concept of Open Engineering Education, which implements individual learning paths, and formulate recommendations on adapting teaching practice to changing requirements in the context of Education 4.0.
Related Existed Models

There are some related existed models of NLP such as Word Embedding, CRF, Deep Neural Networks et al. The simple and necessary introductions are listed as following.
Exploring tourists and local consumers’ attitudes on service automation in restaurant industry: the Spanish fast-food experience

In this chapter we present a general overview of the initiatives undertaken in the food industry with respect to the introduction of artificial intelligence (AI) for sustainability. We pay special attention to the restaurant industry, with the aim of offering an updated state-of-the-art for both the business and the consumer side. Evidence supports the hypothesis that many AI elements already implemented by restaurants in the prepandemic period have increased during the pandemic and are expected to further expand their presence in food tourism as a potential tool to address sustainable objectives. Evidence on consumers’ perceptions of AI in restaurants and its impact on the gastronomic experience doesn’t offer clear-cut results, as both optimistic and skeptical expectations are reflected. Spanish data confirm the trend, also showing that the generational effect, among others, plays a role, with younger people more likely to embrace and value the benefits of AI. Overall, both for restaurants and consumers, the positive and negative effects of AI are still to be seen, inviting further research.
Blockchain-based electronic health record system in the age of COVID-19

The COVID-19 pandemic has exposed the limitations of current healthcare systems in responding to public health emergencies in a timely and efficient manner. Despite the advantages of digitalized health records, a large portion of health systems are centralized and fall short in guaranteeing basic security and privacy requirements. As a decentralized and distributed technology, blockchain has been utilized for secure data access and information exchange in the healthcare domain. Moreover, COVID-19 health services such as antibody testing, vaccination proofing, contact tracing, health surveillance, and medical supply chains raise higher requirements in transparency, traceability, and immutability. Considering that the COVID-19 pandemic will have a long-term impact on all aspects of society, it is necessary to examine present developments for health information exchange. In this chapter, we discuss emerging platforms that adopt a blockchain architecture, with the focus on responses to the COVID-19 pandemic. Through a literature review, we demonstrate the critical role of blockchain technology in providing a more efficient, trustworthy, secure, and transparent health ecosystem. Finally, we identify future research directions along with recommendations.
Research and Education Skills as a core part of Digital Transformation in Healthcare in Saudi Arabia

The adoption of digitalization technologies in healthcare systems is crucial for improving the delivery of medical services, facilitating patient access, and enhancing the overall patient experience. However, the experience and challenges of the Saudi healthcare system in adopting digital transformation have received little attention in the literature. Thus, this chapter aims to provide an overview of the digital transformation process in the Saudi healthcare system. The authors also shed light on the major challenges and suggested technology-based solutions. This chapter was written in light of the available global and local literature discussing the digitization of the health sector. Moreover, the Saudi governmental documents and official reports were utilized to include the latest updates and future plans for the local digital transformation strategy. In paving the way toward digital transformation, many challenges have been faced in areas such as medical records unification, technical infrastructure, and workforce capability. Solutions such as telemedicine applications, artificial intelligence, cybersecurity, and blockchain have been gradually applied to achieve the goal of transformation by 2030. Saudi Arabia's digital transformation began in 2018, with the implementation of the Vision 2030’s rapid digital change. With the emergence of the COVID-19 pandemic, a leap in utilizing digital health solutions has been noticed locally. Nevertheless, more action plans are needed to address the challenges and implement suitable solutions accordingly.