Reviewerly/exHarmony huggingface dataset:
Automatic Annotation Method of Terror Image Based on Integrated Deep Migration Learning

With the continuous development of Internet technology, anti-terrorism images have become an important part of network anti-terrorism.In this paper, a method of automatic annotation of terror images based on integrated deep migration learning is proposed by combining parameter migration and ensemble learning, which can help filter terror information in web pages.Firstly, the deep convolution neural network model is used to train the terror image model in the source domain, and then the migration from the source domain to the target domain is realized through the migration learning technology.Then, the integrated learning framework is used to integrate the transfer learning model.Experimental results show that the accuracy and recall rate of the proposed algorithm are obviously improved.
Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks

As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce. First, the user’s micro-behavior is constructed and converted into directed graph structure data for model embedding. It can fully consider the embedding of first-order proximity nodes and second-order proximity nodes, which can effectively enhance the transformation ability of features. Secondly, this model adopts the multi-task learning method, and uses knowledge graph embedding to effectively deal with the one-to-many or many-to-many relationship between users and commodities. Finally, it is verified by experiments that MTL-DGCNR has a higher interpretability and accuracy in the field of e-commerce recommendation than other recommendation models. The ranking evaluation experiments, various training methods comparison experiments, and controlling parameter experiments are designed from multiple perspectives to verify the rationality of MTL-DGCNR.
QR Codes to Prevent Copyright Infringement: Case Study of Trusmi Batik in Cirebon, Indonesia

A QR code is a form of visualization that is encrypted by user data. Observations were made to examine the QR code added to Trusmi batik motifs, which are traded online through electronic transactions. This study aims to examine the prevention of copyright infringement through the use of a QR code that is added and traded online through electronic transactions. Applying the QR code, which is added to Trusmi batik motifs traded online through electronic commerce, requires legal protection. The protection is in the form of policy rules that specifically regulate QR codes as additional motifs.
A Comparison of Chinese and Russian sports Films—A Case study of Leap and 3 Seconds

This paper will compare Chinese and Russian sports movies from four aspects of theme, characters, narrative mode, and audio-visual language, and then show the similarities and differences between Chinese and Russian sports movies in theme and characters.By analyzing the different narrative techniques and audio-visual designs of the two directors, we can feel the charm of sports movies of the two countries.
A comparitative study of machine learning models for the detection of Phishing Websites

Global cybersecurity threats have grown as a result of the evolving digital transformation. Cybercriminals have more opportunities as a result of digitization. Initially, cyberthreats take the form of phishing in order to gain confidential user credentials.As cyber-attacks get more sophisticated and sophisticated, the cybersecurity industry is faced with the problem of utilising cutting-edge technology and techniques to combat the ever-present hostile threats. Hackers use phishing to persuade customers to grant them access to a company’s digital assets and networks. As technology progressed, phishing attempts became more sophisticated, necessitating the development of tools to detect phishing.Machine learning is unsupervised one of the most powerful weapons in the fight against terrorist threats. The features used for phishing detection, as well as the approaches employed with machine learning, are discussed in this study.In this light, the study’s major goal is to propose a unique, robust ensemble machine learning model architecture that gives the highest prediction accuracy with the lowest error rate, while also recommending a few alternative robust machine learning models.Finally, the Random forest algorithm attained a maximum accuracy of 96.454 percent. But by implementing a hybrid model including the 3 classifiers- Decision Trees,Random forest, Gradient boosting classifiers, the accuracy increases to 98.4 percent.
Research on the Overall Evaluation of Physical Education Teaching Environment

School physical education is an important part of education.With the continuous development of society, physical education in colleges and universities must also keep up with the rhythm of the development of the times, and the concept of educational evaluation must also undergo corresponding changes.In reality, the physical education environment in colleges and universities should be evaluated holistically.This manuscript systematically analyzes the significance of the overall evaluation of the physical education environment by using research methods of literature and logical analysis to measures such as teaching content, improving teaching methods, etc., reform the physical education teaching plan according to the current social development situation, so as to enrich and improve the content and form of physical education teaching, and promote the overall coordination between physical education and other disciplines.
Implementation of UI Methods and UX in VR in Case of 3D Printer Tutorial

In this work, the development of virtual reality software for “industrial applications” is considered. It is argued that, in this context, the vast experience from the development of computer games cannot be used directly. Especially, the specific nature of solutions dedicated to industrial applications requires taking into account their specificities, needs, and limitations of VR. This brings about the formulation of a domain-specific approach to creation of VR-related courses for the industry. As an example, the development of course concerning the operation of a professional 3D printer, which is used in industry, as well as in prototyping, is discussed. In particular, it is used to illustrate implementation of UI and UX aspects of the developed VR-environment.
Extending Compositional Attention Networks for Social Reasoning in Videos

We propose a novel deep architecture for the task of reasoning about social interactions in videos. We leverage the multi-step reasoning capabilities of Compositional Attention Networks (MAC), and propose a multimodal extension (MAC-X). MAC-X is based on a recurrent cell that performs iterative mid-level fusion of input modalities (visual, auditory, text) over multiple reasoning steps, by use of a temporal attention mechanism. We then combine MAC-X with LSTMs for temporal input processing in an end-to-end architecture. Our ablation studies show that the proposed MAC-X architecture can effectively leverage multimodal input cues using mid-level fusion mechanisms. We apply MAC-X to the task of Social Video Question Answering in the Social IQ dataset and obtain a 2.5% absolute improvement in terms of binary accuracy over the current state-of-the-art.
Comparison of Regularization and Data Augmentation as Means of Improving Malaria Classification using Convolutional Neural Network

One of the major limiting factors of the application of deep learning in automating malaria diagnosis is the insufficiency of labeled data for network training which leads to overfitting of the model on training data. Network regularization and data augmentation are two major techniques employed during network design and training to mitigate the effect of overfitting. To investigate and compare the effect of these two techniques, this study developed three deep learning models based on convolutional neural network which were trained on 27558 images of equal instances of malaria (P.Falciparum specie) infected and uninfected cells (NIH malaria dataset). The first model which consist of three convolution layers and one hidden layer achieved a validation accuracy of 95%. A second model was developed which employed network regularization, by training the model on fewer neurons to increase its robustness. Drop out regularization of 20%, 30% and 50% were embedded on successive convolution layers which resulted in a final validation accuracy of 95.1%. Data augmentation in the form of various image transformation was employed. This resulted in a validation accuracy of 93.7%.
Supervised Learning Techniques for Sentiment Analysis

Data mining implies the application of techniques of obtaining useful knowledge from a huge data. Another term for data mining is knowledge discovery from data. For the same, various data mining technologies are available such as statistics (lay the foundation of data mining), artificial intelligence (applying human thoughts like processing of data), and machine learning (union of statistics and artificial intelligence). In this research work, authors employ natural language processing in order to perform sentiment analysis using various feature extraction techniques of NLP. Sentiment analysis is especially important to gain users’ feedback and opinion about products. In this paper, authors perform sentiment analysis of twitter data. Each data point (tweets in considered case) will be classified as “positive tweet” or “negative tweet”. For this classification, six different techniques, i.e., information gain, Gini index (GI), Naive Bayes, K-nearest neighbor, random forest, and gradient boost are used. In the end, classification through all these techniques are analyzed and a comparative analysis is made based upon accuracy, precision, recall, and F1-score. Experimental results suggest that random forest aces the current analysis by yielding an accuracy of 97%.
MaCOnto: A robust maize crop ontology based on soils, fertilizers and irrigation knowledge

The demand for relevant information in a timely manner portrays the significance of knowledge management in all areas of lives; for instance, agriculture. To this end, soils, fertilizers and irrigation as agronomic concepts are essential knowledge inputs for any crops, such as maize. Conversely, there is always difficulty in timely retrieval of these relevant information owing to the unstructured nature of data in repositories, and complexity of concepts mismatch. Sequel to this development, ontology, a semantic data modeling technique is promising as it has been recently employed to deal with these challenges across different domains. However, the robustness of ontology, in terms of semantic expressivity of hidden knowledge, and autonomous growth of ontology leave some gaps to contend with. In view of this development, this research aims to design a robust OWL Rule based ontology for maize crop domain by considering primarily soils, fertilizers and irrigation agronomic concepts capable to evolve autonomously. The proposed ontology herein christened MaCOnto, is developed using the adapted six steps ontology-engineering principle. Over 1,430 entities are encoded in OWL; eighty Competency Questions (CQs) validated by domain experts are modeled in FOL, and implemented as rules via SWRL. Thus, the ontology is queried by SQWRL. Besides, the novel algorithmic design for the ontology to autonomously evolve is implemented in Java environment by employing WordNet. The results obtained from structural based evaluation show an outstanding performance across the eight metrics. Similarly, the results of the competency-based evaluation are also promising. Therefore, the proposed MaCOnto is a robust application based ontology capable to infer and responds to user's query based on its contextual information.
A Self-regularized Low-light Image Enhancement Based on the Brightness Information

Low-light images have low brightness, low contrast, narrow gray range, color distortion and other problems, resulting in poor visual perception of the image, but also lead to subsequent image recognition, classification and other tasks of the accuracy of the reduction. However, existing low-light image enhancement algorithm has the difficulty of obtaining the training pairing data, and the color recovery of the enhanced image has some problems. In view of the above problems, we propose a self-regularized lowlight image enhancement algorithm based on the brightness information of HSV color space. First it converts low-light images to HSV space, preserves color features (hue, saturation) in lowlight images. Second, builds a brightness enhancement network on the value channel to enhance the brightness characteristics of lowlight images. The brightness enhancement network does not need paired low-light and normal lighting images, but uses non-reference loss function to regularize the network, effectively avoids the problem of over-fitting the label data with the model. Only the single channel of low-light images is enhanced, the computational complexity of the network is low and the computing speed is fast. Experiments show that our method can effectively enhance the low-light image, and can well retain the original color characteristics of the image, reduce the color deviation of the enhanced image, in the objective index and the sense of the supervisor is better than the current multiple method.
Ensemble Long Short-Term Tracking with ConvNeXt and Transformer

Visual object tracking is an important research topic in Computer Vision. The widely used Siamese network architecture learns a similarity metric between target objects and search regions, and locates the targets in video sequences. In this paper, we present an ensemble long short-term tracking algorithm based on ConvNeXt and Transformer. Firstly, a Siamese network with the ConvNeXt backbone is applied to extract features for both target and search regions. Secondly, an encoder-decoder transformer is introduced to capture global feature dependencies. In addition, an IoU-confidence-based tracking ensemble algorithm is designed to capture both long-term stable appearances and short-term variable appearances of the target. The proposed tracker, called STARK-NeXt, achieves a success rate of 68.9% on LaSOT, outperforming STARK by 1.8%.
Natural Language Processing Challenges and Issues: A Literature Review

Natural Language Processing (NLP) is the computerized approach to analyzing text using both structured and unstructured data. NLP is a simple, empirically powerful, and reliable approach. It achieves state-of-the-art performance in language processing tasks like Semantic Search (SS), Machine Translation (MT), Text Summarization (TS), Sentiment Analyzer (SA), Named Entity Recognition (NER) and Emotion Detection (ED). NLP is expected to be the technology of the future, based on current technology deployment and adoption. The primary question is: What does NLP have to offer in terms of reality, and what are the prospects? There are several problems to be addressed with this developing method, as it must be compatible with future technology. In this paper, the benefits, challenges and limitations of this innovative paradigm along with the areas open to do research are shown.
Exemplars and Counterexemplars Explanations for Skin Lesion Classifiers

Explainable AI consists in developing models allowing interaction between decision systems and humans by making the decisions understandable. We propose a case study for skin lesion diagnosis showing how it is possible to provide explanations of the decisions of deep neural network trained to label skin lesions.
A Novel Similarity Measure for Context-Based Search Engine

Analyzing the multiple relevant documents returned in reply to an end-user request by an information retrieval system is challenging. It is very time-consuming and less efficient to find analogous web pages without applying the clustering. Clustering of web pages arranges a large number of web documents into relevant small clustered groups. In this paper, a novel similitude degree computation technique is proposed to provide the web documents related to the context in which multiple related web documents are the members of the same cluster. The clustering module results in web documents’ arrangement with their associated topic and corresponding computed similitude or similarity score. This provides the user clusters containing equivalent web documents related to the issue of desire. This context-based grouping of web documents reduces the time taken for searching relevant data and improves the results in response to a user request. Moreover, the comparison and analysis of the proposed technique are done with different existing similarity measures on the basis of performance metrics purity and entropy. It has shown the proposed scheme provides better results to the user.
Double Encryption Algorithm for Massive Personal Biometric Authentication Images Based on Chaotic Mapping for Future Smart Cities

To address the problems of low encryption security, slow encryption speed, and small key space of personal biometric authentication image, a double encryption algorithm of image based on chaotic mapping is proposed. The global block-matching strategy of sparse clustering is used to remove the image noise, and the encrypted key information in the biometric authentication image is extracted by mathematical morphology. The logistic index is used to disrupt the image information distribution, bit scrambling is used to double scramble the image, and the Secure Hash Algorithm (SHA)-256 hash function is used to generate the initial value of the chaotic system to obtain the chaotic map and the random variable sequence of hyperchaotic Chen system, and the chaotic sequence is transformed into the ciphertext image matrix after exclusive OR (XOR) operation in order to realize the double encryption of personal biometric authentication image. The results show that the proposed method has large key space, high encryption accuracy, fast efficiency, and better security performance.
Analysis of English Education Quality Evaluation and Internationalization Integration Based on Deep Learning

English education is one of the most active research directions in the field of natural language processing. With the gradual implementation of deep learning in various fields, more and more industries have begun to use deep learning to carry out more efficient work. In the field of education, it is also urgent to adopt a more intelligent set of algorithms to relieve the pressure of teachers to correct test papers, and also to increase the fairness of non-subjective evaluations in the process of scoring. Teachers conduct teaching evaluation when the concept of teaching evaluation is not clear; there are defects in learning evaluation goals; there are many problems in the relationship between ability evaluation and knowledge evaluation; in the process of English teaching evaluation, the phenomenon of using summative evaluation instead of procedural evaluation is very serious. Therefore, this subject uses deep learning to study the problem of text line positioning and recognition. At the same time, this subject also builds a text scoring network based on RNN and STLM as a quantitative evaluation index for text line detection and recognition algorithms. We will examine students later. Whether problem-based learning theory can be used to promote deep learning among students to determine whether students’ systematic use of PBL in teaching can promote the use of deep learning in college English courses. Finally, comparing the effects of deep learning and shallow learning, it is concluded that the evaluation of deep English teaching can provide students with more learning opportunities, access to more learning-related materials, and questions are more transparent and are free, which is easy. It is speculated that the purpose of this problem is to facilitate the use of deep learning methods to find meaning types.
PromptAttack: Prompt-Based Attack for Language Models via Gradient Search

As the pre-trained language models (PLMs) continue to grow, so do the hardware and data requirements for fine-tuning PLMs. Therefore, the researchers have come up with a lighter method called Prompt Learning. However, during the investigations, we observe that the prompt learning methods are vulnerable and can easily be attacked by some illegally constructed prompts, resulting in classification errors, and serious security problems for PLMs. Most of the current research ignores the security issue of prompt-based methods. Therefore, in this paper, we propose a malicious prompt template construction method (PromptAttack) to probe the security performance of PLMs. Several unfriendly template construction approaches are investigated to guide the model to misclassify the task. Extensive experiments on three datasets and three PLMs prove the effectiveness of our proposed approach PromptAttack. We also conduct experiments to verify that our method is applicable in few-shot scenarios.
Plug-and-Play Module for Commonsense Reasoning in Machine Reading Comprehension

Conventional Machine Reading Comprehension (MRC) has been well-addressed by pattern matching, but the ability of commonsense reasoning remains a gap between humans and machines. Previous methods tackle this problem by enriching word representations via pretrained Knowledge Graph Embeddings (KGE). However, they make limited use of a large number of connections between nodes in Knowledge Graphs (KG), which can be pivotal cues for building the commonsense reasoning chains. In this paper, we propose a Plug-and-play module to IncorporatE Connection information for commonsEnse Reasoning (PIECER). Beyond enriching word representations with knowledge embeddings, PIECER constructs a joint query-passage graph to explicitly guide commonsense reasoning by the knowledge-oriented connections between words. Further, PIECER has high generalizability since it can be plugged into any MRC model. Experimental results on ReCoRD, a large-scale public MRC dataset requiring commonsense reasoning, show that PIECER introduces stable performance improvements for four representative base MRC models, especially in low-resource settings (The code is available at https://github.com/Hunter-DDM/piecer .).
Overview of the NLPCC 2022 Shared Task: Multi-modal Dialogue Understanding and Generation

In this paper, we give an overview of multi-modal dialogue understanding and generation at NLPCC 2022 shared task, which includes three sub-tasks: dialogue scene identification, dialogue session identification, and dialogue response generation. A bilingual multi-modal dialogue dataset consisting of 100M utterances was made public for the shared task. The dataset contains 119K dialogue scene boundaries and 62K dialogue session boundaries which are both annotated manually. Details of the shared task, dataset, evaluation metric and evaluation results will be presented in order.