Reviewerly/exHarmony huggingface dataset:
The Needs Analysis of Pre-secondary Teachers for Communication Competency as a Core Competency of Teachers

The purpose of this study is to analyze the needs of preliminary secondary school teachers to find out what communication competency are necessary as the core competencies of secondary school teachers. To this end, 159 students in the teaching course of Busan P University were surveyed on a total of 21 questions corresponding to three categories of social, instrumental, and creative communication and seven sub-competencies (understanding, expression, attitude, digital literacy, information data literacy, collaboration, and innovation). For demand analysis, the difference between importance and current retention was analyzed through the corresponding sample t-test, and the priority of communication subcapabilities was derived through Borich demand analysis and The Locus for Focus Model. As a result of the overall analysis, there was a significant difference between the required level and the current level in all questions, and the top priority competencies derived by comparing the Borich demand and the LF model were ‘understanding competency’ and ‘information and data literacy competency’. By department, the humanities and social science departments and arts and sports departments had the highest demands for information and data literacy competency, and science and engineering departments had the highest demands for innovation competency. These results can be used as basic data for the development of curriculum and educational programs in future teacher training institutes.
Graph Alignment Transformer for More Grounded Image Captioning

The Industrial Internet of Things (IIoT) generates massive amounts of data that are the cornerstone for companies to increase productivity and provide reliable services. Based on these data, predictive and in-depth analysis can be used to identify weaknesses and make improvements. And how to analyze these data efficiently, effectively and safely requires us to explore. We expect to exploit these data by using methods of deep learning, and image captioning is one of the meaningful tasks. Image captioning aims to describe a given image in natural language. It is well believed that mining relationships between objects is a proven method to improve the performance of reasoning. These methods often extract the general relational expressions on another visual relationship benchmark. And it usually brings redundant connections between region pairs. In this paper, we propose a novel Graph Alignment Transformer (GAT) that models visual relationships in an unsupervised way to perform multimodal representation. Without taking the pre-training approach to obtain the explicit relational expressions, our model still achieves comparable results. Furthermore, we design a Graph Alignment (GA) module to explore semantic and visual alignment at node-level and graph-level, lead to accurate captions. We measured our method on the benchmark MSCOCO image captioning dataset and conduct ablation studies to investigate its effectiveness both quantitatively and qualitatively. Compared to state-of-the-art manners, our propose approach yields an impressive result.
Adoptability of Open-Source Face Recognition (FR) on Automated Criminal Identification System for Law Enforcement in the Philippines: A Systematic Review

Crime prevention in society has persisted in being a significant issue that needs to be controlled through efficient investigation processes. Among these methods, biometric facial recognition is particularly effective because it is adaptable and non-intrusive. Face recognition has grown in popularity due to its numerous uses in entertainment, smart cards, information security, law enforcement, and surveillance. It is a vital topic in pattern recognition, computer vision, and image processing. For feature extraction, two basic methods are used: appearance-based and model-based. Methods based on appearance employ global Face identification using representations. Model-based face approaches seek to create a human face model that captures facial diversity. The distance between the vectors of two photographs is defined as image similarity. This research primarily analyzed open-source Face Recognition algorithms based on the Labeled Faces in the Wild (LFW) model. The study will be of significant consideration in integrating Face Recognition technology on the current Mobile Automated Fingerprint Identification System (MAFIS) using Minutiae Structure.
Feature Selection Method Based on FSA-Choquet Fuzzy Integral

With the instant development and wide application of sensor technology, human behavior recognition based on wearable sensors has attracted wide attention and become a hot research topic. The typical Choquet fuzzy integral feature selection method considers the interaction between classes, but the preferred features are not tested in the feature selection process. The selected feature subset is not suitable for different classifiers and different classification actions, and there are redundant features. Consequently, this paper proposes an adaptive feature selection method named Feature Selection based on the Adaptive Choquet fuzzy integral (FSA-Choquet). This proposed method can get the optimal subset by two times of feature selections. First, Choquet fuzzy integral is applied to optimize the current optimal feature subset. Then the feature selection is processed by combining the maximum redundancy calculation with the backward floating search strategy and classifier. At last, Experiments show that the feature based on FSA-Choquet selection has a higher classification recognition rate in behavior recognition. Under the comparison based on SVM classifier, the recognition accuracy of the features selected by FSA-Choquet is as high as 89.6 %, and the recognition rate of the features selected by Choquet is 77.36 %. Besides, Under the classifier based on Naive Bayes (NB), the classification recognition rate of FSA-Choquet is 91.67 %, while that of Choquet is only 82.3 %.
Reliable Synchronization Technology for Continuous Variable Quantum Key Distribution System

Data synchronization is a critical technique for subsequent signal processing and secret key extraction of continuous variable quantum key distribution (CVQKD). In this paper, we propose a four-segment synchronization frame structure for CVQKD data synchronization at the first attempt. In order to verify the effectiveness of the structure, we build a data synchronization model containing device noise and fiber nonlinear noise. Simulation results indicate that the synchronization structure can effectively resist the influences of device electronic and fiber nonlinear noise, which is characterized by strong noise immunity, high reliability and easy implementation. The probability of bit synchronization and frame synchronization is 0.44 and 0.76 respectively when link distance is 50km.
Reducing Workload of Manual Annotation for User Requests via a Novel Active Learning Framework

Classifying user requests is the first step of transforming them into structured requirements specifications. Manual classification is time-consuming, labor-intensive and error prone. Many supervised learning methods therefore have been proposed for automatic classification. These methods need to manually annotate large amount of data instances to train a model to automatically classify the others. To reduce workload of manual annotation, Active Learning (AL) have been proposed. Few works have been done to employ AL in user requests classification. However, these works could not avoid missed class effect and sampling bias, which are two common problems in AL and would be severer in user requests classification. In this paper, we propose a novel active learning framework, including seed selection strategy and query strategy, to solve above two problems respectively. Seed selection strategy extends Gaussian Mixture Model through text similarity and query strategy combines uncertainty sampling with diversity measured by domain knowledge of user requests. An empirical evaluation on there datasets containing user requests from different open source projects demonstrates that our method could reduce more workload of manual annotation than baseline approaches while performance of the model does not decrease.
Lightweight Searchable Encryption with Small Clients on Edge Cloud

In view of the limited storage and computing power of the client and the high delay of interaction with the cloud platform in public key searchable encryption, a new public key searchable encryption scheme SE-EPOMFC based on edge cloud network is proposed. The scheme adopts a multi cloud multi edge node architecture. By delegating the task of generating searchable ciphertext, trapdoor and general keyword set from the client to the edge node, the storage and computing overhead of the client is reduced. The edge network caches the frequently searched hot data, and the client can search on the edge network, so as to reduce the traffic load of the backbone network. At the same time, the response speed of the system is improved. A filtering algorithm based on partial homomorphic encryption is designed to filter completely mismatched tasks, which reduces the communication overhead between distributed systems and saves storage space for cloud services. The filtering algorithm can be calculated in the ciphertext state, which proves that it is safe under the collusion attack of semi trusted edge cloud nodes. In addition, the distributed two trapdoor public key cryptosystem is used to divide the keys for multiple nodes. Through the subset decisionmaking mechanism, the relationship between keywords is represented by binary strings to realize the search of multiple keywords. The simulation results show that the communication time of se-epomfc is saved by 25.46% in the case of task set matching degree II and 62.21% in the case of task set matching degree I.
A Novel Dimension Reduction Framework Based on UMAP for Improving the Timeliness of Small Samples

UMAP (Uniform Manifold Approximation and Projection) is a fantastic non-linear dimension reduction method, having the capability of quickly processing large datasets. However, it is challenging to balance the timeliness and accuracy when reducing the dimension of the datasets with small samples and noise. To further enhance its timeliness, we propose a novel dimension reduction framework based on UMAP by introducing information entropy and LRR (Low-Rank Representation). We firstly perform LRR on the small sample dataset to remove noise. Besides, we innovatively calculate the entropy threshold with the entropy weight of each data feature to select valuable features. Finally, the dimension of the dataset with valuable features is reduced by UMAP. The datasets generated by us and several UCI datasets are employed to verify that the proposed framework is feasible and effective.
Bot-pelganger: Predict and Preserve Game Bots' Behavior

In most multiplayer online games, players' repetitive tasks (i.e., spec-up) are required to grow their characters. However, some users use illegal programs, “game bots,” to achieve a high level fast or gain cyber-money. Various methods have been proposed to identify game bots. However, the methods have generalization issues. Because the methods use features only existed in the specific game. Thus, we carefully use common features that existed in multiple datasets broadly, such as ‘login’ or ‘exit’ events to detect bots. Choosing such general events gives merits from the applicability view; however, if we only use time or space-related features, we fail to detect bots from normal users because the bots' behavior patterns are omitted too much. We use a convolutional LSTM (ConvLSTM) model to overcome this problem, superimpose their behavioral histories over time, and record them as image sequences. By finding a user who shows high self-similar behavior, we regard it as an unidentified bot; then, we update their behavior patterns for future use. As a result, the proposed model showed a high accuracy of 98% in classifyina game bot users.
Application of Computer Simulation Technology in the Research of Metering Visual Intelligent Blood Transfusion Device

Computer technology, the core of information technology, has experienced more than 50 years of development since its birth. As an important part of computer technology, computer 3D visual simulation technology is widely used in national economy and social life. According to the latest statistical data, computer simulation technology has the characteristics of effectiveness, economy, safety and intuitiveness, and is one of the most widely used practical technologies. According to the nomenclature in the “Data Processing Vocabulary” part of the International Organization for Standardization (ISO) standard, the content contained in both “simulation” and “simulation” currently belongs to the category of “simulation”, which is represented by “Simulation”. Information technology plays a vital role in the research of visual intelligent blood transfusion set, which provides an automated information platform for the modern blood transfusion set system, and further improves the efficiency of intelligent blood transfusion monitoring. In this paper, computer simulation technology is used to connect the power supply wire into the control interface, and a drug feeding port is set on the side wall of the connecting head. In addition, the lower end of the liquid outlet is connected with a transparent visual tube, which can directly reflect the blood transfusion volume through the liquid crystal display. Finally, the current situation of inaccurate blood transfusion volume is improved through intelligent control of electronic components, which effectively improves intelligent blood transfusion monitoring and provides the basis for accurate blood transfusion for doctors and patients.
Sentiment Analysis on Medical Personal Protective Equipment (PPE) Shops Customer Reviews

Online shopping through e-commerce sites is becoming more prevalent with the expansion towards a more digital age in our society together with recent factors such that of the COVID-19 Pandemic. Through Machine Learning and the concept of Sentiment Analysis, algorithms would be able to identify the sentiment of reviewers by processing the words used in the sentence. The research aims to determine the reliability of star ratings compared to sentiment analysis and which classification algorithm suits best for text classification by Filipino customer reviews in Shopee for Medical Personal Protective Equipment or PPEs. It also aims to identify the best classifier model to use in terms of its performance. The study was divided into two models: star ratings and sentiment analysis. Both data sets performed different preprocessing techniques and tested for Naive Bayes and Support Vector Machine classification models, and their performance measures were obtained. The findings of the study show that star ratings and annotated reviews present high similarity in terms of the sentiment and polarity classified per review. In terms of the best performing model, Support Vector Machine achieved the best scores for the performance measures among the tests.
MaskGAN: A Facial Fusion Algorithm for Deepfake Image Detection

The rapid development of deepfakes has caused serious harm to social cognition. However, the current deepfake detection algorithms generally have the problem of poor generalization, and the accuracy rate drops sharply on datasets with unknown deepfake methods. In this paper, we propose a facial fusion algorithm called MaskGAN to enable more generalized deepfake detection. The generator of MaskGAN uses U-Net and SSE to extract the features of face images, and realizes mask generation and face fusion, The discriminator of MaskGAN uses the convolution layer to discriminate the face-swaping images generated by MaskGAN. Then, the obtained face-swaping images are used as training sets and input into an improved Deeplab V3+for training, so that the network can extract the fusion feature circle generated during the face-swaping process from the face-swaping images, so as to identify the authenticity of the face-swaping images. We achieve accurate face swapping with only fused features introduced, generating a face swapping dataset with fused labels. It solves the common problems of over-fitting and poor generalization of existing algorithms. Through a large number of experiments, it is proved that MaskGAN enabled Deeplab V3+detection model can perform well in the case of unknown tampering methods, which achieved 23.02% and 6.9% cross-domain AUC performance improvement.
Application Maturity Research of Intelligent Condition Monitoring Technology for Rail Transit Electrical Equipment

Under the background of the requirement of intelligent interconnection of rail transit electrical equipment, this paper organized the intelligent online monitoring technology, and established the application maturity evaluation system of intelligent electrical equipment condition monitoring technology, involving intelligent sensing, big data analytics, cloud computing and the Internet of things. Using analytic hierarchy process and fuzzy evaluation matrix, the monitoring technology of rail transit electrical equipment is analyzed based on the production capacity, application implementation, implementation benefits, technical reliability, technical economy, technical intelligence, technical automation, policy environment and asset operation. Aiming at the weak links, THE development plan and suggestions of rail transit electrical equipment monitoring technology are put forward, which lays the foundation for the establishment of rail transit electrical equipment intelligentization and its development framework in the field of condition monitoring.
Design and Implementation of a Hybrid Recommender System Based on Apache Spark

With the rapid development of Internet technology, people are surrounded by a variety of data information, but among this massive data information, only a very small amount of data information is of interest to users., so how to find the useful information in the massive data information and push it to the targeted user is a problem to be studied by the recommendation system. Through the analysis of the recommendation system, it is found that the traditional recommendation algorithm has some shortcomings, and the emergence of the hybrid recommendation algorithm effectively alleviates these problems.
Cryptanalysis and Discussion on Two Attribute-Based Encryption Schemes

Attribute-based encryption (ABE), which was first conceptualized by Sahai and Waters in 2005, has been developed into one of the most popular research topics in modern cryptography. Various variants of ABE has been designed and proposed in literature, e.g., ABE supporting user/attribute revocation, pairing-free ABE, etc. In this work, we study two ABE schemes proposed by Sethia et al. and Guo et al., respectively, in 2001. We found that, the scheme of Sethia et al. is insecure against the collusion attacks, and the scheme of Guo et al. fails to revoke a user. Therefore, in this manuscript, we will review on their schemes, and give the corresponding cryptanalysis. Besides, the discussion on the reasons to the attacks and possible improvement will be presented as well.
Construction of Credit Bank System Based on Blockchain Technology

With the continuous development of blockchain technology and the widening of application scenarios, its advantages in the credit banking system are reflected. This paper proposes to construct a block including data layer, network layer, consensus layer, contract layer, and application layer. Chain credit banking system, and discuss the process of key data on-chain processing from a business point of view, thus truly improving the credibility and efficiency of credit bank learning achievement certification and conversion.
Lattice-Based Accumulator with Efficient Updating

An accumulator is a function that hashes a set of inputs into a short, constant-size string while preserving the ability to efficiently prove the inclusion of a specific input element in the hashed set. A concrete accumulator is constructed by using strong RSA assumption. Thanks to their practical features, accumulators are used in various protocols such as zero-knowledge proofs, group signatures, and blockchain. However, lattice-based accumulators are not as well studied as the strong RSA assumption. In 2019, Ling et al. constructed a lattice-based accumulator that is enable to update the member in the list, called LLNW in this paper. However, the update algorithm of the LLNW scheme is not complete, since it requires recalculation to any member regardless of whether or not the member is updated. In this paper, we propose an efficient update algorithm, called EfficientAccWitUpdate, to LLNW, which enables us to update members more efficiently than that in LLNW. In our method, only a member who updates requires recalculation. Specifically, the number of multiplications required for updating in EfficientAccWitUpdate is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\frac{1}{2}$</tex> of LNWX, and the number of additions required for updating in EfficientAccWitUpdate is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\frac{nk+1}{2nk-1}$</tex> of LNWX. Consequently, by incorporating the EfficientAccWitUpdate accumulator scheme into the zero-knowledge protocol, group signature, and blockchain, it is possible to realize a more efficient application.
An ID-Based Robust Identification Approach Toward Multitype Noncooperative Drones

With the rapid growth of the global drone market, the security threats caused by drones have attracted widespread attention. Since legitimate and intrusive drones of multiple types may coexist in the sky, distinguishing them becomes critical for security. This article proposes an ID-based robust individual identification approach toward multitype noncooperative drones. Specifically, a novel ID feature and an extraction method are proposed to obtain features from waveforms without the waveform prior knowledge. We leverage deep learning (DL) and a data augmentation method to train a robust identification model to distinguish individual drones of the same type. In order to realize the individual identification of different types of drones without increasing the model size, we design a multihead DL model regarding the idea of multitask learning. The experiment involves 18 drones of three types and their controllers. Results confirm that our approach can achieve excellent performance for multitype individual drone identification and intrusive drone detection while being robust to noise and channel effects. It is also validated that our approach has good performance for identifying drone controllers that transmit frequency hopping signals.