Reviewerly/exHarmony huggingface dataset:
Performance analysis of localisation strategy for island model genetic algorithm in population diversity preservation

The genetic algorithm (GA) is one of the most common solutions to solve many optimisation problems. Its distributed version, Island Model GA (IMGA), was introduced to overcome more complex and scalable cases. However, there is a recurrent problem in IMGA called premature convergence as a consequence of selection in the migration. This process is a mechanism of migrating individuals from one into another island to keep population diversity. The primary cause is the structural similarity of a migrated individual because of the genetic operator configurations are identical. Localised IMGA (LIMGA) tries to implement different island characteristics to avoid premature convergence. The main motivation of this paper is to investigate the performance of LIMGA capability in maintaining population diversity. In detail, the contributions of this research are (1) to prove LIMGA concept in handling general optimisation problem, (2) to analyse the performance LIMGA in diversity preservation, and (3) compare LIMGA performance with the current solvers. By harmonising three different GA cores, LIMGA could overcome computationally expensive functions with a great result and acceptable execution time. Moreover, because of its success in maintaining the diversity, Localised Island Model Genetic Algorithm (LIMGA) could lead to the among other current solvers for this case.
Using Genetic Programming and Decision Trees for Team Evolution

This paper presents work done to evolve soccer strategies through Genetic Programming. Each agent is controlled by an algorithm in the form of a decision tree to act on the environment given its percepts. Several experiments were performed and an analysis of the performance of the algorithm was documented afterwards. Experimental results showed that it is possible to implement soccer learning in a multi-agent system through Genetic Programming, although the evolution of higher-level soccer strategies is a more difficult task.
Image Super-resolution Reconstruction Based on Bionic Intelligence

Traditional super-resolution reconstruction methods usually process a low resolution image as a whole, which ignores the differences among image patches, resulting in ineffective reconstruction result. In this paper, we adopt the concept of patch as the basic unit of super resolution reconstruction, and establish a novel image super-resolution reconstruction method based on bionic intelligence. The original low resolution image is initially reconstructed by bicubic interpolation. Then, a biological visual attention mechanism inspired approach is applied to the initial reconstruction result to detect the salient regions. Subsequently, a super-resolution convolutional neural network model is applied to the salient image patches so as to get the precise reconstruction results. Extensive experiments manifest that the proposed method outperforms many current state-of-the-art schemes in reconstruction quality, and achieves fast speed for practical application.
Construction of Foreign Language Curriculum Resources Under the Background of Big Data

With the advent of the Information Age, the informatization, digitization and networking of teaching resources for higher education have become an inevitable trend. Under the background of big data, this paper firstly analyzes the opportunities brought by the big data era for the construction of foreign language curriculum resources on the basis of defining the important concepts of “big data” and “curriculum resources”, and then probes into a new idea on curriculum resource construction that setting up an integrated curriculum resource platform by multi-party cooperation. In order to solve the problems existing in the current curriculum resource construction, supporting measures like system construction, financial and technical support and teacher training programs should be taken to guarantee the effective construction of foreign language curriculum resources. This research is expected to offer some insights for the language educators so as to achieve the purpose of keeping pace with the Information Age and improving the quality of foreign language education.
Simulation of a big number of microservices in a highly distributed vast network

The rapidly increasing number of instances and types of services, especially microservices, and the growing complexity of systems create uncertainty if the existing algorithms and solutions for such problems as service discovery or load balancing are still the case. Building a huge infrastructure for the verification purposes is often too costly and impractical in the production environment therefore it is necessary to find other ways to test architectural approaches and algorithms. One of them is simulators. For simulating very big systems like a telecommunications radio access network the simulator should have certain features: capability of simulating a huge number of services, possibility to model heterogenic and vast network, facility to add a new type of services and logic. This paper presents a concept of such a simulator of services.
Training Adaptive Hardware for Reconfigurability: A Simplified Case Study

Reconfigurability of hardware has drawn attention from researchers. In this paper, we try to employ training in hardware and find possible hardware architecture that can achieve the goal of automatic self-organization. As it is the first step, we only focus on a simplified case of this problem. A gridlike processing element (PE) array which employs training is proposed and our simulation results show that our proposed architecture is able to approximately emulate required input-output relations.
Realistic Dilemma and Breakthrough Path of “Innovation and Entrepreneurship” Education in Private Colleges and Universities Under the Background of Informatization

Since in 2015, Guiding Opinions on Developing Maker Space and Promoting the Public to Innovate and Start Business and Implementation Opinions of General Office of the State Council on Deepening Innovative and Entrepreneurial Education Reform in Universities and Colleges have been successively issued. The Party and the country try every effort to guide and support the further implementation of the policy of “innovation and entrepreneurship” and the number of college students is increasing day by day. According to Investigation Report of Innovation and Entrepreneurship of College Students in China in 2017 issued by Center for China and Globalization (CCG) on September 26, 2017, “more than 60% college students have interest in innovation and entrepreneurship. Among them, the top three students come from engineering majors, management science and economics” [1]. The actual proportion of starting the business can not reach 5%. This data has a huge gap compared with entrepreneurship ratio in developed countries, such as America ([2] Yunhua in Journal of Wuhan University of Technology [Social Science Edition]:141–147, 2013).
Research on Fuzzy Cognitive Map Model of the Cement Pre-grinding Production Process

The cement pre-grinding production process has the characteristics of nonlinearity, large lag and strong coupling. It is difficult to establish a traditional mathematical model. This paper briefly introduces the process of cement pre-grinding process, analyzes the mechanism between various parameters, uses the LS-FCM method to calculate the weight between each parameter, and finally applies the fuzzy cognitive map to the pre-grind cement production process. The expression was made to establish a mechanism model for the cement pre-grinding process The coupling relationship reflected by the fuzzy cognitive map is in accordance with the actual system. The simulation result confirms that the model can predict the parameter value of the cement pre-grinding process for a period of time, and the fitting degree is good.
Sleeping Eyes

The aim of this art-based research is to explore the duality of embodiment and presence in virtual reality (VR) through a narrative about the South Korean narcoleptic artist, Sungeun Lee. Narcolepsy is a neurological disorder where a person falls asleep unexpectedly. A narcoleptic struggles with a blurred and unsure sense of self, as well as confusion between reality and dreaming. We used VR to simulate his traumatized dream world and symptoms of narcolepsy. However, we also investigated the fundamental duality of embodiment/disembodiment in the virtual space and its parallels in the dream world. A non-linear narrative and generative poetic aesthetics driven by biometric feedback were applied to represent a surreal and uncertain sense of self. Gesture-based navigable interaction was used to connect people with their physical bodies, the virtual environment, and Sungeun's experiences.
A Deep Feature Identity-Preserved Method for Cross-Age Face Verification

In recent years, the academic communities have proposed a number of discriminative methods for solving the cross-age face verification task, and achieved considerable results. However, these methods are based on the supervising information provided by the category labels and do not consider the potential of the deep feature space. Therefore, we propose a novel network called identity-preserved multi-task convolutional neural network (IPMT-CNN), which combines an identity subnet with an auxiliary age subnet. In the meanwhile, the identity-preserved module is added to the deep feature space to constrain the deep features, making the deep features learned by the convolution unit have preliminary age-invariant property. The two subnets extract the identity-related features and the age-related features from the deep features shared by the convolution unit, respectively. Identity-related features are used for final cross-age face verification. Through multi-task supervised learning, the network can obtain strong discriminative age-invariant face features with identity-preserved capability. Experiments have been performed on two public domain benchmark aging datasets, including CACD-VS and Cross Age LFW. The results have demonstrated the effectiveness and superiority of our model.
Biclustering-sim: A Novel Method to Identify Abnormal Co-occurrence Medical Visit Behaviors

Abnormal co-occurrence medical visit behavior refers to the fraudulent behavior of fraudsters who hold multiple medical insurance cards and frequently purchase medicines within the same time period at the same location to defraud insurance medical insurance fund. Identifying abnormal co-occurrence medical visit behaviors plays a critical role in automated medical insurance fraud detection. However, the conventional methods mainly focus on the mining of frequent patterns, which may lead to misjudgement of normal patients who regularly seek medical treatment for a long time. In order to address this problem, we propose a novel biclustering algorithm, which finds suspicious patient groups who frequent purchased medicines within the same time period at the same location, and filters out normal patients from suspicious patients. Experimental results show that Biclutering-sim outperforms the competitor in detecting medical insurance fraud.
Contextualized Relevance Feedback for Precision Medicine

Precision Medicine (PM) is viewed as an information retrieval (IR) task, in which biomedical articles containing treatment information about specific diseases or genetic variants are retrieved in response to patient record, aiming at providing medical evidence to the point-of-care. Previous PM approaches are mostly based on unigram matching of individual query terms, or concepts, to the target articles to produce the ranking list, while ignoring the context of the matched query terms of concepts. To this end, this paper presents a preliminary investigation of utilizing contextualized representation of text for pseudo relevance feedback (PRF) to enhance PM search effectiveness. By considering the multi-aspect word relations, we propose a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$BERT_{NPRF}$</tex> model to integrate PRF with the fine-tuned BERT model for contextualized interaction of document-document pairs. Experimental results on the standard Text REtrieval Conference (TREC) PM track benchmark show that our proposed method with interpolation can improve the performance in PM.
The benefits of reading skills in the educational process

The aim of this article is to give all advantages of using reading skills in learning and their effect teaching process, to give useful and vital information about the advantages of reading. In this case, the benefits of reading skills in the educational process of the research have been pinpointed as the whole. In conclusion, research has been finalizing outcomes and drawbacks as the major points of the development.
Transfer Spectral Clustering Algorithm Based on Manifold Distance

Transfer learning is a method of helping target domain to cluster or classify using source domain knowledge. It can solve the problem of low amount of data available or having noise in the target domain when clustering or classifying. But the distance measure of the common transfer clustering usually uses the traditional European distance. In some cases, the method of similarity measurement based on traditional European distance cannot reflact the complex spatial structures between different data clusters. In order to solve this problem, the transfer spectral clustering based on manifold distance (TSC-MD) algorithm is proposed, which utilizes the manifold distance as a measure of similarity. TSC-MD algorithm can improve the transfer clustering performance of the original algorithm by taking into account the global consistency and complex spatial distribution characteristics of the sample. Compared with the clustering algorithm of similar transfer learning methods, the experiments of synthetic and real-life scenarios demonstrate that the algorithm can achieve better clustering results.
Medical Use of Deep Learning: Malaria Testing Using Pre-trained ResNet

Malaria is a contagious blood disease caused by Plasmodium parasites transmitted by the bite of an anopheles female mosquito, or in some case by humans. Malaria Test can be done ether using microscope or by Rapid Test. The first method is very accurate but time-consuming because it needs the intervention of human. On the other hand the second one is very fast but not accurate. Several works have contributed to this subject to combine the two criteria using Artificial Intelligence techniques. Our solution is based on the Deep Learning, it uses a Residual Neural Network Model applied to labeled images taken by a microscope and classified by the healthcare experts (Supervised Learning), the data set used is proposed by NIH (National Institutes of Health). In this study, we evaluate the performance of the ResNet model as a classifier. The validation of the results demonstrates the compromises: model size, training time and accuracy. We have used our model after, to build a desktop GUI application and a Web Service architecture that will automate the Malaria test process and help healthcare agents, doctors or normal person for a final goal of saving thousands of lives.
Logical Model and Verification of Emotion Triggers for BDI Agents

Triggering emotions in virtual multi-agents is widely believed to enhance medical user interactions. Researchers have investigated the use of logical methods to develop rigorous specifications for how emotions should be triggered in artificial agents. However, verifying whether an emotional model works as intended is challenging. This paper provides a logical formalization of event-based emotional triggering for multi-agent systems psychologically grounded on the cognitive theory of emotions proposed by Ortony, Clore, and Collins, and also addresses how symbolic model checking techniques can be applied to verify the properties of the emotional belief, desire, and intention (BDI) multi-agents model proposed herein by employing a modified version of the model checker for knowledge, belief, desire, and intention (MCKBDI). The results confirm that the modified MCKBDI can be applied to verify the emotion specifications of finite-state emotional multi-agent systems and that one can program agents capable of using and reasoning over emotions. This approach can be utilized to confirm that the computational model of emotion is formalized as intended.
Application of the Internet of Things Technology in the Urban Ecological Protection and Environmental Rehabilitation

With the continuous development of cities, the urban water pollution has become more and more serious, and the urban water pollution has become an important factor restricting the further development of cities. Nowadays, the water ecological environment of many cities in China has been affected and destroyed to varying degrees. Protecting the water ecological environment and reducing the damage to the water ecological environment has become an urgent problem. The relevant departments and the relevant personnel should continue to study and explore, in order to find ways to mitigate the damage to the water ecological environment and to repair the damaged water ecological environment while the urban economy continues to develop. This paper focuses on the analysis of the characteristics and connotation of the Internet of Things technology, and combines the background of the application of this technology in the urban ecological protection and environmental restoration, so as to explore the application strategy of the Internet of Things technology in the urban ecological protection and environmental restoration.
Research on Evaluation Technology of Police Robot Video and Image Application

Nowadays, intelligent technology is more and more widely used, especially in video and image area. The quality of the algorithm or model, as well as the adaptability to the application directly affects the output of the application software. Research institutions and development enterprises can find the flaws of their own technology through comprehensive evaluation. They can also find valuable research direction by observing the comprehensive performance evaluation results on the system platform and seeking technological innovation, thus promoting the overall progress of intelligent video application technology. The purpose of algorithm and system performance evaluation is to find the valuable direction by comparing the performance difference between algorithms and evaluating the level of detection or recognition technology. This paper proposes a series of evaluation methods and indexes for two kinds of intelligent video applications: face detection and recognition, object detection.
The Use of VR in Journalism

After games, education, health, and military training, virtual reality (VR) conquered journalism, too. During the last several years, more and more journalists decide to experiment with the new technology, while some media organizations, like BBC, have dedicated departments for its development. Still, like any innovation, VR brought many opportunities, but also as many challenges (or maybe even more). Besides putting the audience in the middle of the event and increasing credibility, the use of VR for journalistic production also means new skills, new responsibilities towards the audience, and issues related to distribution of the products. This chapter starts with necessary definitions and background research, and then presents three opportunities and three challenges of the use of VR in journalism, finishing with further research suggestions as a conclusion.
Verification of a Rule-Based Expert System by Using SAL Model Checker

Verification of a rule-based expert system ensures that the knowledge base of the expert system is logically correct and consistent. Application of verification into a rule-based expert system is one approach to integrate software engineering methodology and knowledge base system. The expert system, which we has built, is a rule-based system developed by using forward chaining method and Dempster-Shafer theory of belief functions or evidence. We use Z language as the modelling language for this expert system and SAL model checker as the verification tool. To be able to use SAL model checker, Z2SAL will translate the Z specification, which models the system. In this paper, we present some parts of our Z specification that represent some parts of our rule-based expert system. We also present some parts of our SAL specification and theorems that we added to this SAL specification. At the last, we present the usage of SAL model checker over these theorems. Based on these model-checking processes, we argue that the results are expected. This means that each of theorems can be model checked and the outputs of those model checking are the same as the outputs that we obtain from manual investigation; either it is VALID or INVALID. Other interpretation of the model check's results is some parts of our rule-based expert system have been verified.
A New Visual Cryptography Scheme with Perfect Contrast using Galois Fields

This article proposes a novel <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> out of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> visual cryptography procedure using secret sharing techniques for Galois Fields. This method can be applied to share a gray-scale or a colour image between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> participants such that any <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> out of them should be able to perfectly recover the shared undisclosed image unaccompanied by any distortion or loss of contrast.
Watermarking Models of Video Sequences

Video watermarking involves several cases due to a wide application of various compression techniques practically for all types of video content. In this chapter, we built the models for multilevel adaptive watermarking schemes of the uncompressed and compressed video sequences in the highlights of H.264/SVC standard. We analyzed the architecture of SVC encoder, as well as, three strategies for watermark embedding using H.264/SVC: watermark embedding before video encoding (Strategy 1), integrated watermark embedding and coding (Strategy 2), and compressed-domain embedding after encoding (Strategy 3). Also, basic requirements for the watermarking schemes for video sequences are discussed in detail.
A Chinese Character Generation Model for Cloud Information Security

This paper presents a Chinese character generation model for cloud information security. The model, which includes the structure and style of Chinese Characters, is defined by the effective Chinese character stroke output method and the Chinese character structure dynamic generation scheme can be used for the information security and protection. Compared with the Chinese character coding system, the description system makes it easier to store the Chinese characters into the web and output in the client in monitoring. It overcomes the shortcomings caused by lack of service information security in entire characters of modern Chinese characters. It provides an effective strategy and method for cloud storage and cloud data security services, and at same time, it also provides a deeper cloud Character information service basis for the system of information in the cloud.