Reviewerly/exHarmony huggingface dataset:
Joint Entity and Relation Extraction Method Based on Knowledge Representation Attention

Relation extraction is a fundamental task in natural language processing and is a key step in information extraction tasks and construction of large-scale knowledge graphs, etc. Knowledge graph ontology information is useful for guiding triplet construction, but existing methods do not make full use of relevant information such as relation. Therefore, this paper proposes a joint extraction method of subject-aware entity relation combined with knowledge relation representation. The relation information of knowledge graph is extracted using TransE. Firstly, the subject entity information is extracted based on the text representation, the subject entity information and the relation representation are computed with attention to enhance the connection between the subject and the relation, and the subject information combined with the relation information is used as input to extract the triplets corresponding to the current subject. Compared with the baseline model, the method achieves an increase in F1 value on the Baidu DuIE test set. The experimental results show some improvement in the accuracy and recall of the triplets, and the attention based on relation representation enhances the dependencies between entity pairs and relations. The effectiveness of relation representation modeling is verified.
Arabic-Chinese Language Mobile App for Children

The Chinese language is becoming dominant in the world due to the economic and political rise of China recently. The purpose of this paper is to help Arabic children at an early age, from five to ten years, to learn Chinese language, via a mobile application design developed to teach the basics of Chinese vocabulary, including numbers, colors, foods, clothes, events, and also common phrases, with quizzes. To improve the learning experience, this application link for learning the Chinese language and culture is configured so that every component (vocabulary or phrases) reflects the information about Chinese culture. A questionnaire was distributed to explore the expected benefits of using such applications. In addition, the presented prototype helped to identify some significant requirements to consider when designing similar mobile applications. In conclusion, this study highlights the importance of introducing cultural components when designing mobile applications for teaching a foreign language, which has vast opportunities to develop the children’s personality and introduce them to universal values.
Research on Online Diagnosis Model of Metering Point Anomaly Based on High Precision and High Resolution High Frequency Acquisition Station Area Master Meter

The power metering devices in residential areas often have errors and omissions, so it is necessary to set the abnormal diagnosis model of metering points. It is difficult to accurately read the abnormal data in the conventional metering point abnormal diagnosis model, which results in the low accuracy of the model in the process of abnormal diagnosis. Therefore, based on the high-precision high-resolution high-frequency acquisition station area master table, the metering point abnormal online diagnosis model is studied. Calculate the power consumption of the meter, set the judgment index of the abnormal state of the metering point, extract the abnormal data of the metering point of the master meter with high precision and high resolution through the classification of the abnormal characteristics of the metering point, set the automatic question answering model, and build the online diagnosis model of the abnormal metering point. The experimental results show that the average F-measure value of the three data sets in 1-10 cluster nodes is about 93.7% - 82.7%. Compared with the conventional three models, the maximum value of F-measure is about 2.7%, 8%, 2% higher, and the minimum value of F-measure is about 3.4%, 2.7%, 10.7% higher. Thus, compared with the conventional diagnosis model, this model can enhance the ability of abnormal diagnosis of metering points to a certain extent, and can judge the state of metering points more accurately.
Smart Banknotes and Cryptobanknotes: Hybrid Banknotes for Central Bank Digital Currencies and Cryptocurrency Payments

We are heading for a cashless world. At some point, we will say goodbye to all those pieces of paper and polymer and switch to an electronic alternative. The only problem with these statements is that people have been saying them since the late 1960s. Banknotes have a robust technology and will be around for quite some years to come.What is needed is a transitional device that will ease the transition from nineteenth-century cash to twenty-first-century digital currency. The answer is a hybrid banknote.Basically, a hybrid banknote is a physical banknote on a paper or polymer substrate that can transfer value over an electronic network. It is denominated and has all the physical properties of a traditional banknote, allowing it to pass hand to hand. However, when the need arises, the user can access an electronic network and transfer the denominated value off the hybrid banknote.In this paper, we look at the past and present of hybrid banknotes, identifying their two basic forms—smart banknotes and cryptobanknotes—and how they differ. We also offer three hybrid banknote models that can be used to address pressing needs in payments technology.
Listening Learning System Based on Android Platform

With the continuous development of distance education technology and digital learning on campus, mobile learning, as a new learning mode on campus, is constantly promoted and applied based on mobile computing and digital technology. According to the high usage rate of Android system, this paper develops a college English mobile learning platform based on Android system, puts forward the principles of platform development and system requirements, and gives the test results of the system.
Weigh-in-Motion: Lightweight Real-Time Identification of Gbps Wireless Traffic

Distinguishing between wireless and wired traffic in a network middlebox is an essential ingredient for numerous applications including security monitoring and quality-of-service (QoS) provisioning. The majority of existing approaches have exploited the greater delay statistics, such as round-trip-time and inter-packet arrival time, observed in wireless traffic to infer whether the traffic is originated from Ethernet (i.e., wired) or Wi-Fi (i.e., wireless) based on the assumption that the capacity of the wireless link is much slower than that of the wired link. However, this underlying assumption is no longer valid due to increases in wireless data rates over Gbps enabled by recent Wi-Fi technologies such as 802.11ac/ax. In this paper, we revisit the problem of identifying Wi-Fi traffic in network middleboxes as the wireless link capacity approaches the capacity of the wired. We present Weigh-in-Motion, a lightweight online detection scheme, that analyzes the traffic patterns observed at the middleboxes and infers whether the traffic is originated from high-speed Wi-Fi devices. To this end, we introduce the concept of ACKBunch that captures the unique characteristics of high-speed Wi-Fi, which is further utilized to distinguish whether the observed traffic is originated from a wired or wireless device. The effectiveness of the proposed scheme is evaluated via extensive real experiments, demonstrating its capability of accurately identifying wireless traffic from/to Gigabit 802.11 devices.
Communication-Efficient Federated Learning with Multi-layered Compressed Model Update and Dynamic Weighting Aggregation

Federated learning (FL) aims to build a deep learning model based on distributed datasets. Different from traditional deep learning, federated learning does not need to centralize data from multi-party. Clients store datasets locally and train a central model through interaction with the central server. And the data privacy of clients could be preserved very well. However, sending a huge number of information to the central server will lead to huge communication overhead. This article presented two methods to solve the problem. The first is the enhanced federated learning technique with multi-layered compressed model. The second is the dynamic weighting aggregation algorithm considering the size of the dataset, local learning accuracy and the frequency of local model update for each client. The result of experiments demonstrates that the proposed framework with multi-layered compressed model and dynamic weighting aggregation performs better than the baseline algorithm in both accuracy and communication efficiency.
Attention graph: Learning effective visual features for large-scale image classification

In recent years, the research of deep learning has received extensive attention, and many breakthroughs have been made in various fields. On this basis, a neural network with the attention mechanism has become a research hotspot. In this paper, we try to solve the image classification task by implementing channel and spatial attention mechanism which improve the expression ability of neural network model. Different from previous studies, we propose an attention module consisting of channel attention module (CAM) and spatial attention module (SAM). The proposed module derives attention graphs from channel dimension and spatial dimension respectively, then the input features are selectively learned according to the importance of the features. Besides, this module is lightweight and can be easily integrated into image classification algorithms. In the experiment, we combine the deep residual network model with the attention module and the experimental results show that the proposed method brings higher image classification accuracy. The channel attention module adds weight to the signals on different convolution channels to represent the correlation. For different channels, the higher the weight, the higher the correlation which required more attention. The main function of spatial attention is to capture the most informative part in the local feature graph, which is a supplement to channel attention. We evaluate our proposed module based on the ImageNet-1K and Cifar-100 respectively. Through a large number of comparative experiments, our proposed model achieved outstanding performance.
Communication-Efficient Edge Learning

Edge learning has enabled the training of large-scale machine learning models on a big dataset by implementing data parallelism in multiple nodes. However, the iterative interaction generated by multiple learning nodes together with the considerable quantity of communication data on each interaction yields huge communication overhead, which greatly hinders the scalability of Edge Learning. In this chapter, we introduce the mainstream approaches to achieve communication efficiency of edge training, including compressing communication data, reducing the synchronous frequency, overlapping computation and communication, and optimizing the transmission network. Specifically, we propose two hybrid mechanisms for communication-efficient Edge Learning. The first one is QOSP that integrates gradient quantization for communication compression and overlap synchronization parallel for simultaneous computation and communication. The second mechanism improves communication efficiency during the aggregation of client-side updates by quantizing the gradients and exploiting the inherent superposition of radio frequency signals. Finally, we discuss the future directions of communication-efficient edge learning.
By Using Number State Filtered Coherent States to Improve Phase Sensitivity with Multiple Passes

In this paper, the phase estimation is carried out on an ultra-stable interferometer with multiple passes by using number state filtered coherent state, such as even, odd and three coherent states. By calculating and comparing, it is shown that the scheme enables us to estimate a phase for a considerable accuracy and to surpass the standard quantum limit.
The Infinity Mirror Test for Graph Models

Graph models, like other machine learning models, have implicit and explicit biases built-in, which often impact performance in nontrivial ways. The model's faithfulness is often measured by comparing the newly generated graph against the source graph using any number or combination of graph properties. Differences in the size or topology of the generated graph, therefore, indicate a loss in the model. Yet, in many systems, errors encoded in loss functions are subtle and not well understood. In the present work, we introduce the Infinity Mirror test for analyzing the robustness of graph models. This straightforward stress test works by repeatedly fitting a model to its own outputs. A hypothetically perfect graph model would have no deviation from the source graph; however, the model's implicit biases and assumptions are exaggerated by the Infinity Mirror test, exposing potential issues that were previously obscured. Through an analysis of thousands of experiments on synthetic and real-world graphs, we show that several conventional graph models degenerate in exciting and informative ways. We believe that the observed degenerative patterns are clues to the future development of better graph models.
Image Hashing Using Low Rank Decomposition and Local Binary Pattern

Image hashing is a good method for finding issues related to several image data protection applications such as image authentication, watermarking and tamper detection, etc. Hashing converts an image into short binary codes. It contains image features. As a result, one of the most difficult aspects of image hashing techniques is feature extraction. Using low rank decomposition and Local Binary Pattern (LBP) feature extraction, this paper provides an effective method for creating image hashes. The proposed hashing scheme can be applied in image hash generation and tamper detection. Non negative matrix factorization (NMF) is employed in the low rank decomposition step. For local texture feature extraction, LBP feature extraction is used. In the stage of extracting features, using LBP, the difference between center pixel and their neighboring pixels are calculated. After that, the LBP feature vector is transformed into the final hash sequence. The difference between hash sequences is used to determine whether the image is tampered or not.The experimental results gives, The suggested image hashing approach is resilient against various popular content preservation methods and effective for tamper detection.
MMG-HCI: A Non-contact Non-intrusive Real-Time Intelligent Human-Computer Interaction System

With the continuous development of science and technology, human-computer interaction (HCI) technology has been paid more and more attention by researchers. Traditional HCI methods, such as torch-screen, speech recognition and so on, have the problem of inconvenient application in some scenarios. The research of HCI technology based on millimeter-wave radar makes up for the deficiency of current HCI, and plays a very important role in its development. The main tasks of this paper are as follows: 1, Design of non-contact and non-invasive real-time human activity recognition system MMG-HCI. By analyzing the working features of millimeter-wave radar and combining them with the principle of computer multi-process synchronization, we designed a real-time system which integrates millimeter-wave radar data acquisition, point cloud data visualization, deep learning model inference and HCI. 2, Construction of millimeter-wave radar gesture dataset. Since there is no suitable open-source dataset at present, we collect and build our own dataset through the real-time system. 3, Training graph neural network (GNN) for HAR based on millimeter-wave radar. In order to classify the mmWave radar point cloud more accurately, we use the best performance MMPointGNN as the deep learning inference model of a real-time system. Experiments and demonstration show that we have designed an efficient, accurate, non-contact and non-invasive real-time HCI system.
YouTube Content Analysis For Hair Extension’s Business

From2018 to 2024, the global hair extension market is projected to grow by 8%, reaching more than $5 billion. Hair care was the second-largest segment in the global beauty industry in 2017, accounting for 18% of total sales, after skincare. A big driver of this market is the need to enhance one's physical appearance which most of the times are affected by any hair loss diseases like cancer, alopecia, pregnancy, thyroid, and so on. Endorsing wigs and hair extensions is becoming increasingly common on social media. Specialty shops, hair salons and beauty stores, and hair clinics account for a large portion of the sale of hair wigs and extensions.Our System provides a unique analytical view for solving problems of customers by helping them choose amongst various brands of hair extensions. It does this by using the video’s content to perform sentiment analysis rather than just focusing on public comments on the videos. It also performs statistical analysis and provides an overview about the best channels and influencers. To give a better decision base to customers, the paper focuses on providing content extraction of various YouTube videos covering fashion influencers' reviews as well as common people’s reviews and for the investors, insights of market trends to judge its potential.
Enhanced Attribute Alignment Based on Semantic Co-Attention for Text-Based Person Search

Person search by natural language description aims to retrieve the most related person in the image gallery according to the given textual descriptions. This task is challenging due to the gap of cross-domain and cross-modality. Previous methods align the local visual-textual features based on the global matching score while ignoring capturing the fine-grained cross-modal correspondence between image and text. In this paper, we propose a novel framework named Enhanced Attributes Alignment based on Semantic Co-Attention (EAA-SCA) for text-based person search. The proposed SCA consists of Self-Attention (SA) modules and Relationships Attention (RA) modules cascaded in depth. SA module takes visual attribute features and textual features as input respectively to learn the internal dependencies of single modality. Then self-attended visual attribute features and self-attended textual features are feed into RA module to learn more fine-grained visual attribute features rich in semantic relationships between visual attributes and textual description, which contributes to more precise attributes alignment. Experimental results on CUHK-PEDES dataset demonstrate the effectiveness of the proposed method. With assistance from SCA, the performance improves 2.43% on Rank-1 in text-based person search.
Object Matching in Autonomous Vehicle Perception System Using ORB Feature Matching with SVM Classifier

Autonomous vehicles require a well-developed perception system to ensure the accuracy of action decision making. This work is focused on the object tracking using object matching as part of this perception system. The application of ORB object matching algorithm is proposed to extract eight different features from numerous image pairs, which is then processed by a Support Vector Machine (SVM) technique as machine learning model to increase matching accuracy. The object matching algorithm is optimized using a simple genetic algorithm to minimize a failed feature extraction on low resolution images. The SVM model was tested using four different kernels with different parameters and used a grid-based optimizing method. It is found that the SVM linear kernel performed the best, and that the Generalized Intersection-Over-Union feature was very dominant in determining matching image pairs. The SVM model used in this investigation resulted in an accuracy of 96%, although numerous bad anomalies were also found.
A Framework for Knowledge-Derived Query Suggestions

Search engines for domain-specific media collections often rely on rich metadata being available for the content items. The annotations may not be complete or rich enough to support an adequate retrieval effectiveness. As a result, some search queries receive only a small result set (low recall) and others might suffer from reduced relevance (low precision). To alleviate this, we present a framework that exploits external knowledge to provide entity-oriented reformulation suggestions for queries that contain entities. We propose that queries be added as surrogate nodes to an external Knowledge Graph (KG) via the use of state-of-the-art entity linking algorithms. Embedding methods are invoked on the augmented graph, which contains additional edges between surrogate nodes and KG entities. We introduce a new evaluation setting to evaluate the quality of these embeddings. Experimental results on seven datasets confirm the effectiveness of the approach.
The Usefulness of ICT in Education During the Lockdown for COVID-19

Digital literacy through integration of information and communication technology (ICT) into the education systems is globally gaining popularity. Nowadays, schools use diverse ICT tools to create, update, store, communicate, as well as manage information. The purpose of this chapter is to identify the usefulness of ICT in the education system during the lockdown for COVID-19 globally. The study covers innovative use of ICT in teaching and learning, accessibility, and quality of education, as well as learners' academic performance during the COVID-19 lockdown. Making use of the technology improves accessibility as well as quality of education during the COVID-19 lockdown. ICT eliminates the need for physical interaction between educators and learners by creating virtual learning environment. Learners can take courses online with the cost far cheaper than the physical learning environment. ICT promotes learners' motivation and engagement by facilitating the shift to learner-centred methods.
Adaptive Inclined Text Detection in Natural Scenes Fusing Convolutional Recurrent Neural Network

Image text detection and recognition has always been one of the most important topics in the field of intelligent information processing. Given the diversity of text size, orientation and complex background problems while detecting text in natural scenes, this paper proposes a robust and adaptive text detection algorithm. We present a text box grouping strategy and an inclined text box fitting approach to make inclined text detection robust and accurate. We also fuse the Convolutional Recurrent Neural Network (CRNN) behind to eliminate the error detection results. Our algorithm achieves 0.84 accuracy, 0.76 recall rate and 0.8 F-Measure on the MSRA-TD500 dataset.
SynthTriplet GAN: Synthetic Query Expansion for Multimodal Retrieval

This paper addresses the problem of object retrieval using a multimodal query (a query that combines visual input with additional semantic information in natural language feedback). We propose a SynthTriplet GAN framework that resolves this task by expanding the multimodal query with a synthetically generated image that captures semantic information from both image and text input. We introduce a novel triplet mining method that uses a synthetic image as an anchor to directly optimize for embedding distances of generated and target images. We demonstrate that apart from the added value of retrieval illustration with a synthetic image with the focus on customization and user feedback, the proposed method greatly surpasses other multimodal generation methods and achieves state of the art results in the multimodal retrieval task. We also show that in contrast to other retrieval methods, our method provides explainable embeddings.