Reviewerly/exHarmony huggingface dataset:
Application Data Mining Technology in Construction of College English Blended Teaching Mode

Abstract With the opening up of China, the communication with the international community is increasing, and the technical talents who can adapt to the international trade are more and more eager. But at the same time, there are still some problems in English Teaching in higher vocational colleges, such as the lack of teaching time, the uneven English foundation of students due to different sources, and the low enthusiasm and fear of difficulties in English learning. Based on the practice design of mixed teaching mode, this paper analyzes the statistical results. According to the results, SIG. Value is 0.238, greater than 0.05, and sig. (bilateral) value is 0.047, less than 0.05. According to this significance level, we can know that there is a significant difference between the two classes. Therefore, compared with the traditional English teaching, SPOC teaching mode based on blended learning theory can improve students’ overall English level and learning efficiency.
Improving Neural Network Architecture Compression by Multi-Grain Pruning

Pruning techniques for neural networks are applied to achieve superior model compression while maintaining accuracy. Common pruning approaches rely on single granularity (e.g., weights, channels, or layers) compression techniques and miss valuable optimization potential. This major limitation results in a sequence of obsolete layers with a small number of channels or highly sparse weights. In this paper, we present a novel pruning approach to address this issue. More precisely, in this work, a Multi-Grain Pruning (MGP) framework is proposed to optimize neural network architectures from coarse to fine in up to four different granularities. Besides the traditional pruning granularities, a new granularity is introduced on so-called blocks, which consist of multiple layers. By combining multiple pruning granularities, models can be optimized even further. We evaluated the proposed framework with VGG-19 on CIFAR-10 and CIFAR-100 as well as ResNet-56 on CIFAR-10 and ResNet-50 on ImageNet. The results show that our technique achieves from 31.9x up to 185.3x model compression rates with an accuracy drop from 0.08% up to 1.73% with VGG-19 on CIFAR-10.
Collaborative Filtering Recommendation Algorithm of Weaponry Based on Knowledge Graph

Nowadays, modern warfare has changed from weapon-centric operations to network-centric system operations, which has led to the phenomenon of sea-level quantification of weaponry. It is an important research topic for combat commanders to select suitable weaponry from a large number of available resources when facing combat tasks. Recommendation technology is a solution to this problem, which can effectively solve the problem of information overload, so that combat commanders do not produce the phenomenon of “information loss”, in the shortest possible time to make the right decision. Therefore, this paper proposes a collaborative filtering recommendation algorithm for weaponry based on the knowledge graph and the idea of collaborative filtering. In this algorithm, the knowledge graph provides a clear data model with various association relationships, and the collaborative filtering algorithm can realize it without relying on professional experience, which can realize the recommendation process more actively, faster and more intelligently.
An intelligent agent to mine for frequent patterns in uncertain graphs

Nowadays, significant amounts of data are generated across almost all areas such as social networks and sensor networks, and useful information is retrieved from these data in the form of graphs. Using graphs, more frequent data are extracted by applying a frequent submining (FSM) algorithm. The FSM algorithm can get all subgraphs from a single large graph with a more significant edge connection. Graphs consist of vertices and edges, and sometimes these graphs are uncertain due to the high number of vertices and the low number of edges. Some vertices may have a considerable number of edges in uncertain graphs, and one aims to find frequent subgraphs from the uncertain graph. This work utilized two algorithms, i.e., an evaluation probabilistic algorithm and an evaluation estimate algorithm. The existing methods can perceive subgraphs but are unable to estimate whether they are frequent or infrequent. In this chapter, we use a min support threshold value to prune out all sparse subgraphs and take only those subgraphs whose frequent count is greater than the user-given threshold value.
Augmented Convolutional Neural Networks with Transformer for Wireless Interference Identification

As electromagnetic environments are more and more complex, wireless interference identification (WII) is becoming vital for non-cooperative communication systems in both civilian and military scenarios. With the enormous success of deep learning (DL), methods that optimize convolutional neural networks (CNNs) for WII have been proposed. However, due to the intrinsic characteristics of CNNs, the existing networks are difficult to capture long-range feature dependencies, causing the low recognition accuracy and the high computational complexity. Motivated by the success of transformers in natural language processing (NLP) domain, we propose an augmented convolutional neural network with transformer (ACNNT), which combines both the advantages of CNNs and transformers to simultaneously strengthen locality and establish long-range dependencies. Specifically, the ACNNT has multiple stages, and every stage consists of convolutional layers and transformer module to model local and long-range dependencies of context, respectively. At the end of the network, a classification token is used for classification. A channel attention (CA) module is proposed to further improve the expressive ability of the transformers. Extensive experiments demonstrate that the proposed method leads to performance improvement as compared to conventional DL based methods.
Human-Centered Design Pedagogies to Teach Values in Technology Education

Technology is value laden; hence technology education should create opportunities for students to learn about and practically apply value judgements to enable them to become future agents of change. Over the past three decades the rationale to include values, especially moral values, in technology education has gained increased momentum. Incorporating values in technology education would prevent the discipline from becoming mere technical education. The exploration of the context for designing and making is one stage in the technological process to support students’ exploration of value judgements. The current orthodox pedagogy should be replaced by one in which values relating to technology and technology education are co-constructed rather than imposed. Hence, a new pedagogy known as co-design is proposed. Co-design is an approach to human-centered design (HCD). Co-design is acknowledged as a novel design field which sees the user as a valuable contributor to counterbalance the values of the ‘hero-designer.’ Co-design can be applied as a pedagogy in design and technology education. However, design education is critiqued for the lack of opportunity for collaboration because of disciplinary silos even though the process begins with understanding core values of inclusion and questioning the notion of who designs in the age of collaboration. For co-design, the core values of inclusion and collaboration imply partnerships with users. Hence, co-design pedagogy aligns with technology education in socially constructed values which are inter-subjective and co-constructed. The first part of the chapter deliberates on a co-design pedagogy in fashion design education and findings revolving around three design principles emanating from HCD interventions, namely: (1) users as core and inspirational source, (2) design with users, and (3) identify user needs for integration with design. These three design principles act as input for design action, planning and making. Discussion then shifts to the second part where linkages are drawn to propose strategies for including the teaching of moral values in technology education.
Fuzzy Clustering Tree Algorithm for Medical Data Classification

Class imbalance is a common problem in medical data classification where uneven prevalence of certain features or diseases causes the learners to be biased toward the majority class. Another common issue is ambiguous samples where multiple identical feature vectors are labelled with different classes. To mitigate these issues, previous works have employed clustering or data resampling techniques at the cost of possible information loss. In this paper, we propose a classification method called fuzzy clustering tree classifier (FCTC) which is a semi-supervised, tree-based extension of fuzzy C-means (FCM) to address class imbalance and ambiguous samples without requiring data resampling. Tested on 8 well-known medical datasets, the proposed method achieves high overall classification performance with the presence of class imbalance and ambiguous samples. In addition, the proposed method provides good interpretability as it enables generation of logical rules from the learned tree.
Summary Narrative and the Diegetic Present

The use of the present tense to refer to the past in summary narrative, that is, narrative passages where small stretches of discourse time cover large stretches of story time, depends upon the idea that the designated events are currently accessible through the medium of the discourse. The present tense serves to highlight the importance of the designated events for the development of the discourse. This diegetic use of the present has a predilection for certain attention-management strategies, such as cataphoric reference and complex clause structure, which puts the main clause event in focus within the wider discourse context. More specifically, the diegetic present has two main functions: marking changes in the narrative dynamic, and marking changes in the status of referents.
Face Verification System with Liveness Detection

Many developments have taken place in the field of face-recognition and liveness analysis to improvise various device securities and attendance verification systems. Many approaches have incorporated 3D analysis of the face to predict the liveness of the person in front of it. Our method tries to account for this problem without using advanced 3D imaging techniques or hardware. This results in a solution that is both, more economical and also much easier to deploy. It consists of two parts; the former helps in face verification and the latter to check the liveness of the face. In the first part, we have used a model based on Google’s FaceNet Model which learns a mapping from face images to compact Euclidean space distances, which directly correspond to the measure of similarity of the images. Once the space has been produced, face verification can be easily implemented using standard techniques with embeddings as feature vectors. For the second part, we have employed a cascaded multi-task framework that extracts certain features from the facial image which are then used to check for liveness by tracking their relative displacements. These extracted features were used to check the liveness of the person’s face by asking them to perform some tasks in a random order like head and facial movements etc.
A Study On Personal Information Protection Mechanism Of Web Platform In Big Data Age

With the rapid development of network technology, people communicate more and more frequently on the Web platform. Especially in the trend of economic globalization, the world has gradually entered the era of big data. When people search for information on the Web platform, they will also leak their privacy. At this time, criminals will take the opportunity to use false information to infringe others' rights and interests. Therefore, on the basis of understanding the content of personal information protection in the era of big data, this paper analyzes how to build the corresponding protection mechanism for personal information according to various algorithms of data mining on the current Web platform.
Research on Intelligent Monitoring and Maintenance of Civil Air Defense Alarm System Based on Internet of Things

The fourth industrial revolution represented by the new generation of digital technology has fully penetrated into all areas of the economy and society, leading the rapid changes in production methods and business management models. Aiming at the problems encountered during the digital transformation of civil air defense alarm systems, such as inadequate state monitoring of alarm equipment, irregular manual inspections, etc., this paper proposes a system upgrade and transformation program based on the Internet of Things technology, and discusses the key technical points involved. The upgrade plan includes the development of an intelligent terminal monitoring the working condition of electronic siren of civil air defense and a set of integrated management platform software of civil air defense alarm system. Through the introduction of intelligent and digital management methods, it provides a scientific basis for the construction and decision-making of the alarm system, and provides technical support for future use in wartime. This work has important practical significance and strategic value.
Scaling Community, Conditions, Culture and Carryovers Through Apprenticing and Ecological Leadership: The SCAEL Model

The Singapore education system provides all schools with opportunities to innovate through educational interventions. Based on a review of research work in innovation diffusion in Singapore schools from 2013 to 2017, we have developed the SCAEL model- a context-sensitive translational and scaling framework that can translate theories to practices for sustained educational changes. SCAEL stands for ‘scaling community, conditions, culture and carryovers through apprenticing and ecological leadership’. Apprenticing leadership and ecological leadership refer to leadership that facilitates professional learning and support at the peer-to-peer (apprenticing leadership) and vertical levels (ecological leadership). We use the SCAEL model to analyse recent Singapore-based education innovations that have attained substantive traction as well as recent reforms in the German education system. We also propose an iterative practical framework for school leaders for operationalising SCAEL.
Attention-based Frequency Adaptation Graph Convolutional Network

Graph neural networks (GNNs) are widely used in deep learning of graph structured data. These architectures based on assortative network aggregate smooth information from neighbor nodes to achieve feature collection and update. However, these models cannot deal with the disassortative networks. That is, the connection nodes may have different class labels and different characteristics of the networks. Existing studies show that different information between nodes collected from the neighborhood can more effectively represent the characteristics of nodes in different situations. This strategy also shows good effect in disassortative network structure. Based on this insight, we propose an novel attention-based frequency adaptation graph convolutional network (AFAGCN) to learn dissimilarity information between nodes more effectively. This proposed network suggests a new attention mechanism through introducing the dot product attention into the traditional attention mechanism. So that it can aggregate the dissimilarity or similarity features from nodes adaptively in the process of feature transfer. Based on the dot product attention, we remove the weight matrices to reduce the memory cost and improve the robustness of the model. A number of experiments on six real networks demonstrate that the proposed approach not only outperforms than the latest technologies on assortative datasets, but also has more advantages on disassortative datasets.
Digital Development Strategy of Agricultural Planting and Breeding Enterprises Based on Intelligent Sensors

The digitalization of agricultural planting and breeding enterprises is the only way for agricultural development. Now with the development of various technologies, the digitalization of agricultural enterprises is becoming faster and faster. Today, the development of intelligent sensors provides platform support for the digitalization of agricultural enterprises. This article is aimed at introducing the application of intelligent sensors in agriculture to provide strategic research for the digital development of agricultural planting and breeding enterprises. This paper proposes the establishment of a system platform for network intelligent sensors and proposes the establishment of an agricultural short message management publishing platform. And the existing public information transmission methods are used to provide a cheap, simple, and fast way for agricultural producers to quickly obtain agricultural information, so as to provide a feasible plan for solving the agricultural “last mile of agriculture” problem. After inspection and analysis, the information management release platform can meet the design requirements, and the processing rate of short-term interest is above 98%, which can pave the way for the digital industrialization of agricultural enterprises.
The Open Context Model of Learning

Various digital technologies, the internet, the web, information appliances, smart phones, and particularly, Web 2.0 enable us to review and interrogate how technologies, business, social, personal, and learning technologies can help reconfigure the organisational infrastructure of learning to better align with how human beings learn about the world around us and ourselves. Hazel Henderson said, “Technology is the essence of politics,” but perhaps “Technology is the essence of education,” which for 1000 years has been based on a content-scarcity model of resources and focused on a content-delivery model of learning to an elite who will benefit from access to these scarce resources, themselves based on a subject-based taxonomy that took root in the 19th century and has dominated the design of 20th and 21st century educational institutions. The Open Context Model of Learning argues that we need new models of teaching and learning (obuchenie) built around the PAH continuum of pedagogy, andragogy, and heutagogy and an underpinning belief in the co-creation of learning and education between “teachers” and “learners.”
Deep Learning Based Classification of CP-OFDM RAT-Dependent Signals

Spectrum sharing is one of the key concepts in current Radio Access Technologies (RAT) and specially for 5G. The ability to detect and classify signals present in a specific band is relevant to optimize the use of these shared channels and to avoid interference. One of the most promising tools for this is Deep Learning. This paper explores several Deep Learning architectures to classify different CP-OFDM RAT signals, based on the difference in symbol length and cyclic prefix length parameters of these technologies.
Zero-shot reductive paraphrasing for digitally semi-literate

People in developing countries with restricted schooling, face hurdles with their digital enablement. Constrained education creates issues with comprehensibility of information on internet and other digital platforms. Most content on digital platforms use enriched vocabulary for more accomplished users and hence does not seem to be very useful for digitally semi-literate users. Artificial Intelligence (AI), Information Retrieval (IR) and Natural Language Processing (NLP) can be applied for text simplification to bridge the digital divide and empower these users. In this paper we propose to achieve reductive paraphrasing using Neural Machine Translation (NMT) framework along with encoder-decoder model and Gated Recurring Unit (GRU). Our approach combines Zero-shot Learning (ZSL) using multi-pivot method to execute our experiment. We have considered English as the base language and three pivot languages (French, German and Spanish) to verify our claims. We have designed the simplified vocabulary from movies for younger audience. It has been observed that using the approach as described in our paper, an improvement of an average of 25% in ease of comprehension can be achieved.
The State and Analysis of Tao Yuanming’s Poem Collections Shown in Ancient Poem Anthologies in the Qing Dynasty : Focusing on『Gushiuan(古詩源)』&『Gushishangxi(古詩賞析)』

Tao Yuanming (365 or 352-427) is a poet who was active in the East Qin Dynasty (317-420) and Liu Song Dynasty (420-479), the beginning of the Southern Dynasties, and as is well known, he is referred to as the ‘pastoral poet’. It is well known that Tao Yuanming sang an aloof life in nature by enjoying rural life without serving as a government official. What was the later evaluation of the poems of Tao Yuanming, the pastoral poet who is well-known for such an image of a hermit? There was no evaluation of Tao Yuanming in 《Wentsintiaolung(文心雕龍)》by Liu Xie and 《Shihpin(詩品)》by Chung Jung placed Tao Yuanming as a
Triplet-Attention Residual Network for Breast Cancer Histopathology Image Classification

Recently, breast cancer histopathology image classification using convolutional neural networks has achieved more and more attentions with the great progress. To capture more discriminant deep features for the classification, this paper proposes a novel triplet-attention residual network, i.e., TAResNet, to distinguish the breast cancer histopathology image. TAResNet employs the representative ResNet18 model to extract deep features of histopathology images, followed by a triplet-attention module to further boost the discriminability of deep features through expanding feature diversity and enhancing inter-dimensional dependency. Extensive experiments carried out on the public BreakHis dataset well evaluate the effectiveness the given TAResNet model. More specifically, TAResNet achieves its optimal classification accuracy of 98.34% and 98.77% at the image level and patient level, respectively.
Design and Analysis of a Ring Topology Network of Quantum LEO Mobile Satellite Group

In this paper, the loop topology network of LEO (low earth orbits) mobile satellite is designed. Quantum communication is used to form a three-dimensional cross mo-bile network on ground stations and satellites. Based on the quantum bipartite entanglement structure, this paper gives the communication process between the quantum transmitter and receiver, through the establishment of the three body GHZ entangled state with the graph state structure. Via Bell measurements, the topological structure transformation of the GHZ state in the network is obtained. Next, a simple derivation of the teleportation protocol is given. Then, the purification of quantum resources, the selection of routing and the influence of atmosphere on channel transmission are discussed.
Program Synthesis with Algorithm Pseudocode Guidance

The limitation of the current program synthesis method is that the synthesized program is small in scale and simple in logic. In this work, we introduce an effective program synthesis approach based on algorithm pseudocode. By parsing the pseudocode, critical information like control structure framework and variable names can be obtained which are used to guide the process of synthesis. Experiments show that information extracted from pseudocode helps to reduce the space of programs and enhance the ability of the synthesizer. It can synthesize some complex programs with control structures.