Reviewerly/exHarmony huggingface dataset:
A Study on Teachers’ Perceptions of Safety after Nuri Curriculum Revision in 2019

This study attempted to comprehensively search how teachers perceive 'safety' when operating the curriculum and what difficulties and demands are in the operation process as the Nuri curriculum was revised in 2019 to emphasize infant-centered and play-oriented. To this end, in order to select early childhood teachers with expertise suitable for the purpose of this study, six early childhood teachers with more than 5 years of maturation experience were targeted based on the teacher development stage theory (Katz, 1985). The data collection method was selected as a focus group interview (FGI) to collect and analyze data suitable for the purpose of the study to emphasize infant autonomy and examine teachers' perceived safety awareness and difficulties in the revised Nuri curriculum. As a result of analyzing the data collected in this study, three topics and six subtopics were derived: (1) the importance of safety unrelated to the curriculum (2) finding the boundary between play and safety (3) finding out safe play together. Regardless of the curriculum change, teachers recognized that risk exists, safety should be protected, considered the scope of looking at play and safety, experienced a dilemma between safety and play, and safety was with risk, and experienced changes in teachers and infants for safe play. When discussing the main implications for safety in the revised Nuri curriculum situation based on the results according to the derived topic, first, a positive reconceptualization of teachers' perception of safety is required. Second, there is a need for dilemma-oriented teacher education that teachers can face in the field. Third, a culture that guarantees teachers' expertise and autonomy is required. Fourth, education is required to allow infants and children to internalize safe attitudes and behaviors based on play and experience. Finally, it is required to continuously develop and distribute data and evaluation tools to provide a safe environment.
A Personalized Movie Recommendation System Based on Knowledge Graph

In order to alleviate the problems of low accuracy, poor interpretability and data sparsity of movie recommendation system, this paper simultaneously considers movie attribute information and user-movie interaction information, and proposes a personalized movie recommendation algorithm based on knowledge graph, referred to as KGCFR (Knowledge Graph Collaborative Filtering Recommendation) algorithm. On the one hand, the movie knowledge graph is constructed by using the relationship between movies and movie attributes, and the relationship between movies is extracted. The user preference is calculated by the KGCFR model. On the other hand, the user preference is calculated by the collaborative filtering algorithm using the interaction information between movies and users. Finally, the recommendation list is obtained by combining the above two aspects, and the Top-K recommendation is performed according to the recommendation list. The experimental results show that the proposed method has a significant improvement in the accuracy of the recommendation effect, and has better interpretability and alleviates the data sparsity problem.
DS-KGAT: A Deep Session GAT with Knowledge Enhancement for CTR Prediction

Click-through rate (CTR) prediction accuracy is an important metric for recommender system strategies, and it always involves dynamic and continuous features. Previous methods based on user historical behavior usually capture the short-term interests of users, while ignoring the complex transitions between various historical behaviors of users and lack of interpretability. In order to model the user behavior sequence and capture the user fine-grained interests, we propose a novel model named Deep Session GAT with Knowledge Enhancement (DS-KGAT). Specifically, the user behavior sequence is divided into sessions of multiple graph data structures. On the basis of the session graph, Graph Attention Network (GAT) can represent each node as an aggregation of neighbor node embeddings. At the same time, the user’s interest in each session is propagated on the knowledge set to form the user’s preference distribution for candidate items. Then combining the long-term and short-term interest attention mechanism, each session is represented as a combination of item node embeddings. Finally, we utilize Bi-LSTM to model the interactions between sessions and the long-term evolution of user interests. We have done extensive experiments on the genuine dataset. The experimental consequences demonstrate that DS-KGAT improves the accuracy of click-through rate prediction, and has made substantial progress.
Robot Control Policy Transfer based on Progressive Neural Network

In the field of robotic control,it is appealing to solve complicated control tasks through deep learning techniques. However, collecting enough robot operating data to train deep learning models is difficult. Thus, in this paper a transfer approach based on progressive neural network (PNN) and deep deterministic policy gradient (DDPG) is proposed. By linking the current task model and pretrained task models in the model pool with a novel structure, the control strategy in the pretrained task models is transferred to the current task model. Simulation experiments validate that, the proposed approach can successfully transfer control policies learned from the source task to the current task. And compared with other baselines, the proposed approach takes remarkably less time to achieve the same performance in all the experiments.
An image encryption algorithm based on cascaded chaotic map and staggered combination diffusion

In this paper, a cascaded chaotic map (SL-L map) is improved to obtained wider chaotic ranges and more complex chaotic behaviors. Using the characteristics of bit plane decomposition, an image encryption algorithm based on improved chaotic map and staggered combination diffusion is designed. Firstly, the plain image is separated into 8 bit-planes by bit plane decomposition, and 4 bit-planes are formed by staggered combination. Then the improved Zigzag transform and bit-level diffusion are used to complete the permutation and diffusion respectively. Finally, the encryption is completed by using chaotic sequence to control plane synthesis. Simulation results and security analysis show that the algorithm can effectively resist various typical attacks.
Path Planning of Mobile Robot Based on Optimized D*Lite Algorithm

In view of the problems of the existing D*Lite algorithm in the path planning of mobile robots, such as incomplete consideration of robot safety and excessive turning times, an optimized D*Lite path planning algorithm is proposed. The optimized D*Lite algorithm changes the safe path to the safe path in path search, and adopts the method of reverse search connection optimization after path planning.Smoothing the path planned by D*Lite algorithm is the same as marking the grid map with temporary obstacles when necessary, so as to avoid repeated searching of unnecessary grid areas by path planning algorithm. To verify the effectiveness of this method, the simulation is carried out by MATLAB software, The results show that the improved algorithm can obviously improve the safety of robot movement and the efficiency of reaching the target in practical application.
A Joint Entity and Relation Extraction Model Based on Encoder-Decoder

Relation extraction is a task of automatically detecting and identifying predefined semantic relationships between identified entities in text. As a core and basic technology of knowledge acquisition in knowledge engineering, relation extraction endows artificial intelligence with strong ability of knowledge understanding. Massive text data, as the carrier of human knowledge, is rapidly submerged in the tide of information with the explosive growth of information. Mining knowledge hidden in these texts, is not only the theoretical demand of natural language processing but also the practical demand of human civilization inheritance. Natural language processing based on deep learning methods has made great progress in relation extraction field, effectively promoting knowledge discovering in texts of various granularity. However, some problems in relation extraction still need solving in the practical research process. In view of the existing work, most of the relations extraction task is divided into two independent sub-tasks, named entity recognition and relation classification, which lack the interaction between named entity recognition and relation classification in the sentence, and cannot handle the overlapping entity and relationship triples well. To solve these problems, a joint entity and relation extraction model based on Encoder-Decoder structure is proposed.
Hybrid path planning algorithm for mobile robot based on A* algorithm fused with DWA

Mobile robots operating in unknown environments and require collision-free real-time path planning. In this paper, a hybrid path planning algorithm is proposed to solve the navigation problem of mobile robots in the presence of unpredictable obstacles. First, to improve the global algorithm’s planning efficiency, the heuristic function of the A* algorithm is improved. In addition, this paper forms a hybrid path-planning algorithm by fusing the DWA algorithm. The local path planning is carried out with the intersection of the scope of the local path planning and the global path as the intermediate target point. The trajectory similarity evaluation coefficient is added to the heuristic function. When the original global path needs to be re-opened due to unknown obstacles, the local planning function is then fed back to the improved A* algorithm for dynamic path re-planning. The simulation results show that the hybrid path algorithm can avoid dynamic environmental obstacles. At the same time, when encountering unknown obstacles, it adopts local planning and adjusts the global path to meet the navigation requirements of mobile robots in complex environments.
Image style transfer based on local matching and global alignment

In recent years, image style transfer and deep learning technology have developed rapidly. It has been widely used in many fields, such as film and TV filters and art creation. However, the quality of the generated stylized images still needs improvement, often with content structure distortion, content detail loss, lack of semantic perception, and other problems. In this paper, we propose an image style transfer method based on local matching and global alignment. The local matching module is used to enhance the semantic similarity perception and improve the local style. The global alignment module improves the content structure distortion problem by aligning the statistical information of features. The integration of local matching information and global alignment information can better balance the style and content structure. The comparison experiments of stylized effects demonstrate that our approach can generate high-quality stylized images.
Design of Intelligent Monitoring and Identification System for Abnormal Behavior

Traditional camera monitoring systems had been unable to meet the growing demand, in this case, intelligent monitoring technology had been rapidly developed. With the development of intelligent monitoring systems, great progress had been made in both the ability to respond to unexpected situations and the efficiency of hindsight viewing. By using artificial intelligence algorithms to detect people and their behavior, a lot of human and financial costs and time had been saved, and many valuable case investigation activities had been won over.
Exploring the Integration of National Cultural Resources through Content Retrieval and Visual Semantic Segmentation Display

In the era of ubiquitous information and abundant data, the ability to extract relevantinformation from scattered, obscure, and heterogeneous data sources is of paramount importance. Despitethe considerable richness of ethnic cultural resources, they are often characterized by significant heterogeneity and fragmentation. Semantic segmentation technology can effectively leverage the power of computers to extract image features and compile feature databases, enabling the fusion of heterogeneous knowledge. To this end, this paper proposes an image classification method that combines object semantics and deep learning algorithms to identify object areas within an image and retrieve matching images from a database based on implicit semantic information. Through the integration of national cultural resources, this approach enables content retrieval and visual semantic segmentation. The use of multi-layer perceptrons for supervised learning facilitates the generation of object semantic templates to address the challenge of object semantic similarity. The resulting templates can then be applied to classify images, leading to a high classification accuracy rate for binary semantic images. Experimental results demonstrate that the proposed method enables automatic feature comparison with target images in the feature library during the retrieval of ethnic cultural resources, allowing for the mining of previously undiscovered and valuable knowledge. Finally, the best matching results and relevant information are output to users, enabling the effective use of these resources
Access Control Policy Management Based on Data Classification and Hierarchy

The integrated management information system gathers all kinds of data on a unified storage, management and application platform. Establish strict data classification and hierarchy mechanism and corresponding access control methods to ensure the security of data access. This paper proposes a policy-based access control mechanism for data classification and hierarchy. It uses the attributes of the subject and object, environment variables, and related information to control the access of resources. It can describe users’ various access control requirements more flexibly and accurately, and implement access control quickly according to the changes in the operating environment to ensure the security of important data and function modules.
Adaptive E-Learning System Based on Learner Portraits and Knowledge Graph

Adaptive E-learning has been a focused topic in the field of education technology and computer science for decades. For adult learners of open education with huge background differences, it is far more challenging. In this paper, we propose an adaptive E-learning solution. In order to acquire learning requirements of different types of learners, we construct portraits for open education learners. The learning resources are organized based on the technology of knowledge graph, which is different from the traditional linear structural E-learning platforms. Each learning material is labeled with its difficulty level, form, type and learning duration. The adaptation engine makes learning planning and resource recommendation for the learners according to their individual requirements. Evaluation shows that the proposed system performs well. It is helpful for adult learners to promote their learning and can be used as an effective supplement to regular learning in open education.
Documents Compression based on Content Similarities

The great explosion of data processing and the reliance on information technology are results from the widespread of computers. This results in the need for huge memory for data storing and fast transmission rate. Thus, two challenges nowadays facing information technology namely reducing the required memory size to save data on storage media and reducing the required time for data exchange between computing nodes. Many techniques were developed in both directions and others are still in progress and development. In this paper a method is introduced to reduce the size of text and image documents by utilizing the similarities between adjacent bytes in the document content. The method is analyzed and assessed experimentally using MATLAB to show its capability to reduce the size of such documents. The results are then extracted and discussed.
An Enhanced HMAX Model to Improve Object Recognition

A new approach is proposed within this paper to accelerate the process of a biologically inspired Hieratical Model and X (HMAX), which is a biologically stimulated method, this model has been an efficient procedure for object recognition. Object recognition is beneficial in numerous applications such as driverless cars, and security systems. However, patches in HMAX are extracted randomly, causing in production redundant and uninformed extracted patches. Also, there is not any mechanism to evaluate them, these play a big challenge step for achieving high recognition accuracy. This paper presents a technique for selecting specific kinds of features of an image established on a Shi-Tomasi Corner Detector. A proposed approach is applied to the Caltech5 dataset. In the training stage, 70% of images are used. The results appear significant achievements in object recognition accuracies.
A Peer to Peer Federated Graph Neural Network for Threat Intelligence

Threat intelligence is the process of collecting and analyzing information about potential cyber threats. Several approaches have been conducted for cyber threat detection based on the federated learning method. These approaches aim to establish a collaborative threat intelligence sharing between the participants, in order to reinforce their security defense systems. However, these approaches face scalability limitations and raise security and privacy issues: availability, inference attacks, poisoning attacks. To address these issues, we propose a peer-to-peer federated graph neural network (FGNN) approach for threat intelligence. The approach incorporates techniques to ensure data security and privacy. It includes secure aggregation methods and a decentralized sampling technique to reduce the number of exchanged messages. This approach includes also a reputation scoring technique to detect and prevent poisoning attacks, which makes it resilient in the presence of malicious participants.
Design of Table Tennis Collection System Based on STM32 and μC/OS-III

Aiming at the problem of frequent manual picking up ping-pong balls during training, an automatic system for picking up ball is designed. The system combines single-chip computer technology, sensor technology and embedded technology to achieve the recognition, tracking, and picking up of ping-pong balls. The OpenMV visual module is used to identify and track table tennis balls, and the roller structure is used to pick up balls. The μC/OS-III embedded real-time operating system is transplanted to STM32 to achieve multitask management and task scheduling of the system, which improves the performance and stability of the system at a certain extent. The practice of the automatic ping-pong ball collection system shows that the rate of success picking up ball is high and its stability is good, which can reduce the burden of manual ball picking at a certain extent.
Chapter 2: Spectrum of Models

As discussed in Section 1.2, different models of computation vary in data representation, operators, and how operations are composed. This chapter describes briefly many of these differences by separating them into categories.
Chapter 9: Parallel von Neumann

The von Neumann model (Chapter 8) is based on using a small amount of local state information to choreograph some explicit step-by-step series of computations. Each step involves some relatively simple operation and/or an interaction with some location in memory. The model assumes that each step is completed in its entirety before the next step is begun.
Chapter 19: Neural Morphic Models

The nervous system and brain of most biological organisms are made up of neurons. Each such neuron cell has a cell body or soma where processing is performed. Axons carry signals from the soma to other neurons. At the end of an axon are axon terminals where signals are transferred across synapses to other cells. The receptor for such signals at each neuron is called a dendrite. Processing and transmission are a combination of chemical and electrical activity.
Chapter 16: Logic Computing

The von Neumann model defines computing in a rigorous step-by-step manner. This is not the way humans solve many problems—we “reason” about things and look at relationships and the “truthfulness” of various statements. We use terms like inductive reasoning or deductive reasoning to describe such processes. Logic computing addresses computing models that solve problems in a similar fashion. They answer questions such as “Is there any way that the following statement can be true?” without specifying the process to determine the answer. Looking back at Section 7.6 we see the Boolean satisfiability (SAT) problem as an example that asks the question “Is there a truth assignment to a set of variables that makes some clause true?” That this is Turing complete implies that logic computing ought to be considered at the same level of computational power as von Neumann. This chapter explores this model in detail.
Chapter 13: MapReduce

MapReduce was introduced by Google in 2004 [55] as a simplified parallel model of computation for many forms of big data analytics. Notional uses inside Google included distributed grep, distributed sort, web link-graph reversal, term-vector per host. web access log stats, inverted index construction, document clustering, machine learning, and statistical machine translation.
An efficient approach for faster matching of approximate patterns in graphs

Graphs have proven to be an efficient problem representation scheme in many real-world applications and can serve to address mining of patterns in large volumes of data. This work addresses the data management issue in graph databases for shortest path queries, verification of reachability, and pattern matching queries. The key issue is to match all possible patterns in a large data graph that matches a user-provided pattern, known as the query graph. The large size of the graph makes the problem complex when searching for all patterns that are similar to the user’s query. For a given query, a match occurs when a graph with n vertices has the same label as the corresponding vertices in the query graph. This work addresses such pattern matching query problems on large graphs and presents an efficient algorithm to mine all patterns that satisfies the given query. The current work presents a novel Fast Graph Pattern (FGP) mining algorithm that performs a label-specific walk on a graph and stores all vertices that are visited during the walk and hashes them on a labeled root. Experiments are performed using five benchmark datasets, and the proposed approach is compared with three state-of-the-art methods. The obtained results suggest better performance of the current proposal by achieving five times quicker results.