Reviewerly/exHarmony huggingface dataset:
Development and Improvement of Web Services Selections using Immigrants Scheme of Multi-Objective Genetic Algorithm

Quality of service is a significant part of formulating a composing of web services to satisfy the user's request, especially when several services exist and have been implemented in the same field and functionality.The selection process of web services among many options can be taken based on the quality of service considerations, fitness parameters, multiple objectives, and constraints.Moreover, some non-functional factors such as quality of service indicators such as integrity, cost, availability, reliability, security, and response time.Several composite services are required to achieve the user's requirements.This paper presents an integrated approach based on immigrant schemes and multi-objective genetic algorithm (ISMOGA) to specify and maintain the diversity of composite services more efficiently.Research experiments evaluate the performance of elitism-based ISMOGA based on different probabilities of immigrant mutation to produce an adaptive mechanism and to improve the performance in the dynamic optimization problem.The proposed method is compared to the standard technique of genetic algorithms and multi-objective algorithms.The experimental results show that ISMOGA outperforms other algorithms and improves the searching process.Moreover, the proposed algorithm can quickly adapt to the different resources and environmental changes as well as increases the high-quality solutions to satisfy QoS requirements in different configuration networks and communications channels.
Recommendation for Effective Standardized Exam Preparation

Finding an optimal learning trajectory is an important question in educational systems. Existing Artificial Intelligence in Education (AiEd) technologies mostly used indirect methods to make the learning process efficient such as recommending contents based on difficulty adjustment, weakness analysis, learning theory, psychometric analysis, or domain specific rules.
Integration of Basic Descriptors for Image Retrieval

Several methods have been proposed to retrieve images from the database based on descriptors such as color, texture, and shapes. Most of the retrieval methods considered only individual descriptor to retrieve image from database which does not provide more accuracy. The large volume of images is generating day by day from the Internet of things (IoT) devices. To provide better retrieval accuracy of image from large database integration of the multiple descriptors are required. In this work, two approaches, additive and sequential are used to integrate multiple descriptors and the performance has been evaluated to enhance the accuracy of image retrieval. To analyse and establish the result, both approaches individual and integration of multiple descritpors have been evaluated with real-world databases and using various image retrieval techniques as a means for evaluation. This has been done by extracting features of the image; techniques used are color histogram, discrete wavelet transform, and canny edge detector for color, texture, and shape, respectively. The detailed analysis of individual and multiple approaches shows that several combinations of the color, texture, and shape features enhance the retrieval accuracy. The experimental results demonstrate the effectiveness of the method used for analysis.
Predicting Social Events using Entity Interaction Graph Sequences

Focusing on the task of social event prediction, dynamic graph neural network (DGCN) gains good performance on real datasets. It proposes a context-based dynamic neural network for predicting events and generating a context graph for decision-making. Inspired by DGCN, this paper presents a model based on GDELT (Global Data on Event, Location and Tone) event database, using graph neural networks to model the entity interaction in the process before a target event. Our model overcomes deficiencies of weak relevance between keywords and events caused by context-based modeling. Based on realizing event prediction, the entity interaction graph is generated, which accurately depicts the interaction changes of a series of related entities before social events, making the generated entity interaction graph more conducive to narrative. The results show that the proposed model is effective on real datasets.
A Comprehensive Enterprise System Metamodel for Quality Assurance

One of the biggest challenges in code quality assurance is the amount of code that needs to be reviewed at an instance before the code is deployed on production. Reviewers need to check not only coding practices and formatting but also the meaning of the code and its compliance with requirements. Enterprise systems are notoriously known for the large codebase, challenging business logic, and advanced code constructs, which require significant resources for code review. However, enterprise systems use coding constructs that reveal aspects and constraints about the business logic, such as validation, database connection, and API. We extract these aspects and their relationships into a comprehensive metamodel. Next, we persist the metamodel into a graph database and conduct quality assurance checks via database queries. This method significantly reduces the amount of information that needs to be processed while maintaining key enterprise aspects. The method enables system administrators or project managers to discover defects and inconsistencies without reading the code.
Lesson Learnt by Using DevOps and Scrum for Development a Traceability Software

Agile has significantly impacted the software development lifecycle by introducing methodologies such as Scrum, Extreme Programming, and Lean Software Development. Recently, DevOps approach has attracted and gained a wide interest of the software development society. DevOps provides a set of principles that enables Continuous Development and Continuous Integration of a system. This paper presents a case study where it is designed and developed a food traceability software for a Greek meat company; it discusses the lesson learnt by applying the DevOps principles for the software development and by using the Scrum methodology for management purposes.
Conclusion and Future Works

This chapter summarizes the whole book. After a brief review of the topics covered, several conclusions will be drawn. Guided with these conclusions, it is worth exploring several possible topics in the future.
Research Information

This chapter describes the framework of an analytical study around the computational intelligence algorithms, which are prompted by natural mechanisms and complex biological phenomena. These algorithms are numerous and can be classified in two great families: firstly the family of evolutionary algorithms (EA) such as genetic algorithms (GAs), genetic programming (GP), evolutionary strategy (ES), differential evolutionary (DE), paddy field algorithm (PFA); secondly, the swarm intelligence algorithms (SIA) such as particle swarm optimisation (PSO), ant colony optimization (ACO), bacteria foraging optimisation (BFO), wolf colony algorithm (WCA), fireworks algorithm (FA), bat algorithm (BA), cockroaches algorithm (CA), social spiders algorithm (SSA), cuckoo search algorithm (CSA), wasp swarm optimisation (WSO), mosquito optimisation algorithm (MOA). The authors have detailed the functioning of each algorithm following a structured organization (the descent of the algorithm, the inspiration source, the summary, and the general process) that offers for readers a thorough understanding. This study is the fruit of many years of research in the form of synthesis, which groups the contributions offered by several researchers in the meta-heuristic field. It can be the beginning point for planning and modelling new algorithms or improving existing algorithms.
Creating a Digital Mirror of Creative Practice

This paper describes an ongoing project to create a "digital mirror" to my practice as a composer of contemporary classical music; that is, a system that takes descriptions (in code) of aspects of that practice, and reflects them back as computer-generated realisations. The paper describes the design process of this system, explains how it is implemented, and gives some examples of the material that it generates. The paper further discusses some broader issues about the technological approach to building creative systems, in particular the advantages and disadvantages of building bespoke algorithms for generating creative content vs. the use of optimisation or learning from examples.
Reinforcement Learning for Improving Coherence of Multi-turn Responses in Deep Learning-Based Chatbots

Chatbots are still far behind in their ability to hold meaningful conversations. The objective of the work is to implement and improve the multi-turn responses of deep learning-based chatbots. Multi-turn response is the ability of a chatbot to give coherent and sensible responses in successive turns. Firstly, sequence to sequence (Seq2Seq) model was built, and its responses were analyzed by varying training parameters. Secondly, the reinforcement learning (RL) method using the Seq2Seq model was implemented, and it is demonstrated that this improves coherence in multi-turn conversations. The RL model performed better than the Seq2Seq model in terms of BiLingual Evaluation Understudy (BLEU) score with a score of 0.3334 compared to 0.2336 of the Seq2Seq model. Average conversation length was found to increase with RL with 3.75 turns compared to 3.05 turns with Seq2Seq.
A Task Scheduling Algorithm Based on Big.LITTLE Architecture in Cloud Computing

In recent years, cloud computing has been extensively used in various fields, where task scheduling plays a vital role for task and resource management. Traditionally, all cores in one system-on-chip (SoC) are treated equally for cloud task scheduling. However, the advent of big.LITTLE architecture enables heterogeneous computing in a SoC, thus providing the opportunity for task scheduling at SoC level. In this paper, we propose a task scheduling algorithm that leverages performance features of cores on big.LITTLE architecture. We first characterize the performance of different cores in detail. Then We adopt a decision tree model to predict the execution times of tasks on a single core. Finally we design a task scheduling strategy to speed up the execution speed of big data tasks by comprehensively considering the program characteristics, resource requirements and computing performance on big and LITTLE cores. Experimental results show that our task scheduling algorithm makes more effective use of the computing capabilities of heterogeneous cores, and reduces the running time of big data tasks by 17% on average.
Improving Small-Scale Dataset Classification Performance Through Weak-Label Samples Generated by InfoGAN

Sufficient training data typically are required to train learning models. However, due to the expensive manual process for labeling a large number of samples, the amount of available training data is always limited (real data). Generative Adversarial Network (GAN) has good performance in generating artificial samples (generated data), the generated samples can be used as supplementary data to make up for the problem of small dataset with small sample size and insufficient diversity. Unfortunately, the generated data usually do not have annotation label. To make better use of the generated data, a learning framework WGSForest is proposed, which realizes the use of real data and generated data to train the classifier jointly. In the WGSForest model, the supplementary data is generated through an improved InfoGAN to increase the amount and diversity of training data. Moreover, the generated supplementary data will be weakly labeled through InfoGAN. We utilize the advantage of deep forest on small dataset and take the generated data with a weak label as the supplement of real training data to optimize deep forest. In detail, The cascade forest in the improved deep forest (SForest) dynamically updates each generated data label to proper confidence, then the real data and generated data are combined to train the following layers of the improved cascade forest jointly. Experiment results showed that adding the weak label generated data effectively improves the classification performance of deep forest. On mnist (1000) subset, 100% generation rate can obtain 1.17% improvement, and 100% generation rate can obtain 6.2% improvement on 1000 cifar10 subset. Furthermore, each dataset can determine performance at a specific generation rate.
PHILOSOPHICAL ASPECTS OF URBAN STUDIES AND THE FORMATION OF COMPETENCIES OF STUDENTS OF THE UNIVERSITY OF CIVIL ENGINEERING

The competence of a professional in the construction industry should include understanding the development trends of a modern city, expressing a new type of changing sociality, which is becoming more and more relevant in the post-pandemic world. One of the most important tasks of the construction of engineering science and urban development is to create a comfortable environment for human life.The author considers the "smart city" project as an object of complex implementation by means of building technical sciences and social and humanitarian knowledge. The most relevant parameters of this project, causing the need for humanitarian reflection, are the concepts of a smart environment and a landscape of knowledge, the problem of creating new types of public spaces and the formation of socio-cultural identity in the context of standardization of the urban environment. The article explains the question of the development of monitoring mechanisms of innovative knowledge and ways of its application.The concept of a knowledge landscape is revealed as non-economic cultural contexts associated with a certain type of organization of the urban environment, and having a formative influence on the interaction between the processes of development of knowledge, space and the city. Professional competencies are transformed into personal qualities, forming a unity of knowledge and understanding. The author emphasizes that at all levels of construction education it is necessary to form the ability of critical reflection on technological solutions.
Recommender Systems in Healthcare: A Socio-Technical Systems Approach

The design, development, implementation, and evaluation of recommender systems (RSs) in healthcare requires a multifaceted and multidisciplinary approach. Healthcare systems by their very nature are complex and dynamic, and their structure and functioning are determined by social values, conventional methods, and economical aspects. This chapter describes healthcare as a socio-technical system comprising of interacting human and technical agents. Thus, any change to the system, such as an introduction of RS tools or methods, requires careful consideration of the technical and social aspects. The chapter presents the main issues which should be considered in the deployment of RS-based tools and techniques in healthcare: (1) adherence to the medical regulations for medical software (e.g., Software as Medical Device), (2) compliance with the medical data privacy, (3) provisioning for transparency and trustworthiness of recommendations, (4) analysis of short-term and long-term ethical issues, such as belief formation and behavior modification, and (5) consideration for the patient empowerment (patient-centered care), doctor-patient relationship (relationship-centered care), and human aspects (human-centered care).
Big Data Knowledge Map Based on Entity Recognition

In order to directly and succinctly reflect the composition, dosage, processing and other information between traditional Chinese medicine, as well as the common relationship between traditional Chinese medicine and traditional Chinese medicine, this project collected and processed a large number of traditional Chinese medicine data, through the construction of knowledge map of traditional Chinese medicine, Chinese medicine workers intuitively display the composition of traditional Chinese medicine information, Chinese medicine between direct or indirect related information, so as to facilitate a deep understanding of the hidden knowledge of traditional Chinese medicine information; The project takes the prescription in the information of traditional Chinese medicine as the main research body, studies the composition of the prescription of traditional Chinese medicine and other relevant information, and shows the correlation between the prescription itself, the composition of traditional Chinese medicine, the prescription and the prescription, the prescription and the medicine in this intuitive and concise way, so that more people can understand the prescription and understand the traditional Chinese medicine. The public can easily obtain relevant knowledge of traditional Chinese medicine according to their own situation. The above two aspects have played an important role in human health and promoted the inheritance and promotion of traditional Chinese medicine.
Development of learning media patisserie based on hybrid learning

Abstract Utilization of the internet in patisseries learning is not optimal yet, the use of media in learning is not entirely interesting and varied, task collection and evaluation of learning outcomes are still print-based, one of the difficulties associated with technical issues is understanding the material and collecting assignments. These three factors are the background of the learning management system in the implementation of hybrid learning based patisseries. Learning Management System is one way to develop online learning or e-learning, which is a web-based learning management application that facilitates learners to learn as they should in a virtual classroom. The Learning Management System used in this study is a Web-based Learning Management System that is available at https://spot.upi.edu/. The design of the Learning Management System model in Patisserie learning is useful to assist in the teaching and learning process. Learning programs based on the use of internet technology (online) that can be done without the limitation of space and time ‘any time anywhere’. This Learning Management System application model, made based on the web, with the aim to enable lecturers and students who become the user model of this application, to access learning plans and learning material through the internet wherever they are. Supporting facilities in the form of network and network server at UPI have reached 800 Mbps, then from the SPOT process aspects using licensed software namely Microsoft Office and hardware +-970 with the core 2 processor, P4 flat slim monitor. In addition to accessing learning material, this learning management system helps users to be able to discuss topics by on-line using the forum provided. This study aims to develop a hybrid learning based learning management system for the readiness of students’ competency test.
A Systematic Approach as one of the Main Approaches in Shaping the Professional Competence of Future Social Educators

This article is devoted to identifying the systematic nature of pedagogical phenomena and the degree of preparation for the professional activities of future social teachers. The purpose of this study is to analyze various approaches and reveal the main components in the formation of professional competence of future teachers: unity of theory and practice, transition of educational to educational and professional activity (quasi-professional activity), use of reproductive and active methods (problem lectures, discussions, analysis of pedagogical situations, design of content and forms of activity of students, psychological training), presence of motivational and value attitude (entertaining, motivational and problem situations), readiness to perform professional.
A Secure Encapsulation Schemes Based on Key Recovery System

Network users apply encryption to send and receive data securely. Since ciphertext can be encrypted and decrypted only by lawful users, third parties do not have the ability to know the content of an encrypted message. However, a secret key is uesed for encryption, and if the secret key is lost or corrupted, there is a problem that the encrypted text cannot be decrypted. Additionally, malicious use of this encryption will cause problems. If encryption is used maliciously, the government cannot prevent criminal activity. Because of this law enforcement agencies need support for lawful interception to decrypt criminals or suspect’s ciphertexts. We need a key recovery system that can safely recover these secret keys or decrypt messages for lawful interception. There are two types of key recovery systems, a key escrow method and a key encapsulation method. This paper proposes secure schemes using key encapsulation. The key encapsulation method requires the key information used in the ciphertext, and the key information can be obtained from the KRF (Key Recovery Field). The obtained key can be used to decrypt the ciphertext. however, various security threats exist in key recovery system. Such as forgery and alteration of KRF, single point of failure, inability to recover keys, and collusion attacks. To solve these problems, we propose secure encapsulation schemes based on key recovery system.
Bayesian sparse convex clustering via global-local shrinkage priors

Abstract Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted $$L_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm is usually employed for the regularization term in sparse convex clustering, its use increases the dependence on the data and reduces the estimation accuracy if the sample size is not sufficient. To tackle these problems, this paper proposes a Bayesian sparse convex clustering method based on the ideas of Bayesian lasso and global-local shrinkage priors. We introduce Gibbs sampling algorithms for our method using scale mixtures of normal distributions. The effectiveness of the proposed methods is shown in simulation studies and a real data analysis.
Stop the Spread of COVID-19 with Digital Health: A Real-World Case Study

As the novel coronavirus disease 2019 (COVID-19) continues to ravage the world, innovative measures to stop its spread and to provide high-quality patient care have become imperative. We present here a real-world case study on Taiwan, which has successfully prevented the spread of COVID-19 without ever needing to lockdown, thanks to innovative digital health technologies and the lessons learned from combatting SARS in 2003. We will discuss how Taiwan recognized the crisis on Dec. 30, 2019, and how Taiwanese government and people have worked together to have kept the COVID-19 death rate to be less than 0.3 per million, ranked 192th lowest among 215 countries in the world.