Reviewerly/exHarmony huggingface dataset:
An Effective Sentiment Analysis Model for Tobacco Consumption

Analysis over product reviews has drawn much attention due to its wide application. Most of the sentiment analysis research focuses on entertainment and catering due to the limitation of existing public datasets. In order to promote the comprehensiveness of data in the field of sentiment analysis, we present a new large-scale multi-sentiment tobacco dataset by distilling effective consumer experience information from massive online reviews of tobacco consumption. The release of this dataset would push forward the research in tobacco field. With the goal of advancing and facilitating the research of the overall sentiment of sentences with multiple aspects, we propose simple yet effective EHCRNN model, which combines the strengths of recent NLP advances. Experiments on our new dataset and the public nlpcc2014 task dataset show that the proposed model significantly outperforms the state-of-the-art baseline methods.
Vehicle Re-identification Based on Multi-Scale Attention Feature Fusion

It is important to extract vehicle appearance features for vehicle re-identification. The appearance variation of the same vehicle from different viewpoints and the appearance similarity between vehicles from different classes bring challenges for capturing the descriptive features. Considering these, we propose a multi-scale attention feature fusion network (MSAF) for vehicle re-identification. It uses ResNet50 as the backbone, and introduces a scalable channel attention module for each feature channel. Then a multi-scale fusion module is designed to output the final extracted vehicle features. Experimental results on the VERI-Wild dataset indicate that the proposed MSAF achieves high Rank-1 index of 91.20% with mAP of 80.20%.
Federated Learning: Solutions, Challenges, and Promises

In Federated Learning (FL), a model is trained by monitoring and managing a server by a group of clients so that the data trained in this way is stored in a decentralized manner. Therefore, each client uses its private data to train its local model. Then each client uploads their updated model to the server to aggregate and build the final global model. Finally, the server sends the finalized global model to the local clients after reaching the expected threshold/accuracy to use this model for their own purposes. In fact, FL aims to preserve the privacy of training data generated by local clients and processed locally and only periodically provides local model updates to the server. This paper provides a comprehensive overview of FL with an emphasis on combining FL with other technologies and techniques. Since FL is vulnerable to several attacks, researchers combined the FL concept with approaches such as homomorphic encryption and blockchain. Here, we divided the existing solutions into the categories of FL, the fusion of blockchain and FL, the fusion of FL and homomorphic encryption, and the fusion of FL, homomorphic encryption, and blockchain. In addition, for all the compared approaches, their advantages and disadvantages were explained, and based on these, open problems were highlighted.
A Recommendation Algorithm Incorporating Self-Attention Mechanism and Knowledge Graph

To address the problems of sparse data, low recommendation accuracy and poor recommendation effect in recommendation systems. In this paper, we propose a recommendation algorithm that fuses the self-attention mechanism and knowledge graph. The algorithm mainly includes recommendation module, knowledge graph feature learning, and self-attention. In this algorithm recommendation system module, a user and an item are input, and the input item vector and entity vector are embedded in the self-attention module, so that the feature representation of these two vectors is enhanced. The knowledge graph feature representation module maps the head entities and relations in the triad into a continuous vector space, and calculates the corresponding values through the score function. The recommendation module and the knowledge graph representation model are connected through the cross-compression unit embedded in the self-attentive mechanism. Finally, the loss of each module is calculated by a loss function. Experiments on three different publicly available datasets show that: the embedded attention mechanism module introduced can well solve the accuracy problem of the recommendation system; Secondly, the embedded attention mechanism cross-compression unit module enhances the recommendation system in which vectors are compressed in horizontal and vertical directions. Finally, through experiments comparing other algorithms, the proposed method improves the recommendation accuracy and effectiveness in the recommendation system.
A Symmetric Dual-Attention Generative Adversarial Network with Channel and Spatial Features Fusion

Many existing generative adversarial networks (GANs) lack effective semantic modeling, leading to unnatural local details and blurring in generated images. In this work, based on DivCo, we propose a Symmetric Dual-Attention Generative Adversarial Network (DivCo-SDAGAN) with channel and spatial feature fusion in which the Dual-Attention Module (DAM) is introduced to strengthen the feature representation ability of the network to synthesize photo-realistic images with more natural local details. The Channel Weighted Aggregation Module (CWAM) and the Spatial Attention Module (SAM) of the DAM are designed to capture the semantic information of channel dimension and spatial dimension, respectively, and they can be easily integrated into other GANs-based models. Extensive experiments show that the proposed DivCo-SDAGAN can produce more diverse images under the same input, achieving more satisfactory results than other existing methods.
VA-TransUNet: A U-shaped Medical Image Segmentation Network with Visual Attention

Abstract: Medical image segmentation is clinically important in medical diagnosis as it permits superior lesion detection in medical diagnosis to help physicians assist in treatment. Vision Transformer (ViT) has achieved remarkable results in computer vision and has been used for image segmentation tasks, but the potential in medical image segmentation remains largely unexplored with the special characteristics of medical images. Moreover, ViT based on multi-head self-attention (MSA) converts the image into a one-dimensional sequence, which destroys the two-dimensional structure of the image. Therefore, we propose VA-TransUNet, which combines the advantages of Transformer and Convolutional Neural Networks (CNN) to capture global and local contextual information and consider the features of channel dimensionality. Transformer based on visual attention is adopted, it is taken as the encoder, CNN is used as the decoder, and the image is directly fed into the Transformer. The key of visual attention is the large kernel attention (LKA), which is a depth-wise separable convolution that decomposes a large convolution into various convolutions. Experiment on Synapse of abdominal multi-organ (Synapse) and Automated Cardiac Diagnosis Challenge (ACDC) datasets demonstrate that we proposed VA-TransUNet outperforms the current the-state-of-art networks. The codes and trained models will be publicly and available at https://github.com/BeautySilly/VA-TransUNet.
MMOT: Motion-Aware Multi-Object Tracking with Optical Flow

Modern multi-object tracking (MOT) benefited from recent advances in deep neural network and large video datasets. However, there are still some challenges impeding further improvement of the tracking performance, including complex background, fast motion and occlusion scenes. In this paper, we propose a new framework which employs motion information with optical flow, enable directly distinguishing the foreground and background regions. The proposed end-to-end network consists of two branches to separately model the spatial feature representations and optical flow motion patterns. We propose different fusion mechanism by combining the motion clues and appearance information. The results on MOT17 dataset show that our method is an effective mechanism in modeling temporal-spatial information.
Multi-Scale Features Integration for Handwritten Mathematical Expression Recognition

Handwritten mathematical expression recognition (HMER) is a challenging task due to the complex two-dimensional structure of mathematical expressions and the similarity of handwritten texts. Most existing methods for HMER only consider single-scale features while ignoring multi-scale features that are very important to HMER. Few works have explored the fusion of multi-scale features in HMER, but exhibited an extra branch that brings more parameters and computation. In this paper, we propose an end-to-end method to integrate multi-scale features using a unified model. Specifically, we customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture the multi-scale features of the input image meanwhile expanding the receptive fields. Moreover, we added a symbol classifier using focal loss to better discriminate and recognize similar symbols, to further improve the performance of HMER. Experiments on the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016 and 2019 shows that the proposed method achieves superior performance to most state-of-the-art methods, demonstrating the effectiveness of the proposed method.
Gas flow meter anomaly data detection based on fused LOF-DBSCAN algorithm

Anomaly detection for gas flowmeter data is one of the important means to improve the reliability of fair trade of natural gas transmission and distribution. However, the field environment of natural gas in the industrial scene has the characteristics of complex anomaly categories and difficult to distinguish some anomalies. At the same time, the traditional anomaly detection methods are difficult to accurately analyze the abnormal state for a period of time, and are easy to be disturbed by many factors. For example, although DBSCAN (density based spatial clustering of applications with noise) can cluster dense data sets of arbitrary shape, it will greatly affect the classification effect of data sets with uneven density, and the noise points will also interfere to a certain extent, resulting in the weakening of the ability of the algorithm to distinguish anomalies. LOF(local outliers factor) algorithm realizes outlier detection by calculating the local density deviation of a given data point relative to its neighborhood. In view of the above problems. A more accurate anomaly detection strategy is proposed. Firstly, the local anomaly factor algorithm is used to eliminate outliers with too large LOF value, so as to reduce the clustering effect of DBSCAN due to uneven density as much as possible. Experiments show that the clustering effect of this strategy is significantly improved compared with the traditional detection methods.
Video Forgery Detection Using Spatio-Temporal Dual Transformer

The fake videos generated by deep generation technology pose a potential threat to social stability, which makes it critical to detect fake videos. Although the previous detection methods have achieved high accuracy, the generalization to different datasets and in realistic scenes is not effective. We find several novel temporal and spatial clues. In the frequency domain, the inter-frame differences between the real and fake videos are significantly more obvious than the intra-frame differences. In the shallow texture on the CbCr color channels, the forged areas of the fake videos appear more distinct blurring compared to the real videos. And the optical flow of the real video changes gradually, while the optical flow of the fake video changes drastically. This paper proposes a spatio-temporal dual Transformer network for video forgery detection that integrates spatio-temporal clues with the temporal consistency of consecutive frames to improve generalization. Specifically, an EfficientNet is first used to extract spatial artifacts of shallow textures and high-frequency information. We add a new loss function to EfficientNet to extract more robust face features, as well as introduce an attention mechanism to enhance the extracted features. Next, a Swin Transformer is used to capture the subtle temporal artifacts in inter-frame spectrum difference and the optical flow. A feature interaction module is added to fuse local features and global representations. Finally, another Swin Transformer is used to classify the videos according to the extracted spatio-temporal features. We evaluate our method on datasets such as FaceForensics++, Celeb-DF (v2) and DFDC. Extensive experiments show that the proposed framework has high accuracy and generalization, outperforming the current state-of-the-art methods.
A Novel Chinese Resume Named Entity Recognition Model Based on Lexical Enhancement

The resume's popularity on the Internet has greatly increased with the development of the communication form. It is a concern of researchers to analyze the resumes of job applicants using the Named Entity Recognition (NER) method. The difficulty of Chinese Resume NER rests with word segmentation ambiguity and domain knowledge complexity. To tackle the issue, a novel lexical enhancement Long Short-Term Memory (LSTM) model with the average encoding strategy (LEAE-LSTM) is proposed. First, through the pre-trained models, the representations of characters and words are encoded separately. The lexical features with complementary information are introduced for the character sequence by matching the lexicon. Furthermore, to improve contextual awareness, the multi-metadata embeddings are combined as the input of the LSTM layer. The sentence's implicit correlations are picked up by the self-attention mechanism. Experiments on the benchmark resume dataset demonstrate that LEAE-LSTM surpasses other state-of-the-art methods. For the Chinese resume dataset, LEAE-LSTM gains a 1.8% improvement in F1 score over the baseline model Lattice LSTM.
An Efficient Lightweight Spatio-temporal Attention Module for Action Recognition

Effective feature learning is one of the prime components for human action recognition algorithm. Three-dimensional convolutional neural network (3D CNN) can directly extract spatio-temporal features, however it is insufficient to capture the most discriminative part of the action video. The redundant spatial regions within and between temporal frames would weak the descriptive ability of the 3D CNN model. To address this problem, we propose a lightweight spatio-temporal attention module (ST-AM), composed of spatial attention module (SAM) and temporal attention module (TAM). SAM and TAM can effectively encode the semantic spatial areas and suppress the redundant temporal frames to reduce misclassification. The proposed SAM and TAM have complementary effects and can be easily embedded into the existing 3D CNN action recognition model. Experiment on UCF-101 and HMDB-51 datasets shows that the ST-AM embedded model achieves impressive performance on action recognition task.
Fraud detection in Online Payment Transaction using Machine Learning Algorithms

Online payment transaction is a transaction in which payment is made using digitalized currency. Customers all over the world prefer online payments to purchase almost everything from furniture to clothing, from food to medicines, from gadgets to appliances, and whatnot. the online transaction has now evolved into many platforms. It is one of the most efficient methods provided by many companies to its customer. It not only helps build the company's revenue but also impacts the growth of the company. Let us not forget that with pros come the cons with the greatest advantage of having tedious transactions done at the fingertips comes the fear of fraudulent activity in which our hard-earned money can get theft within seconds. These activities can be determined by machine learning algorithms by feeding adequate data about these transactions. We have used machine learning algorithms such as SVM (Support Vector Machine), LR (Logistic regression), Naive Bayes, Decision tree, and Random Forrest for the same. It is observed that Random Forest classifier has outperformed comparing to other classifiers with the accuracy of 99.94%
AI Based Approach For Path Traversal

The manuscript intends to provide holistic overview of AI based forward traversal path and backward traversal path. These path are obtained from a collection of web accesses, once the user session are identified. The proposed work contains an alternative approach to generate path traversal and reduced the forward and backward traversal Path.
A Novel Artificial Intelligence Inspired Approach for Analysing Sentiments using Neural Network

Sentiment Analysis is a text classification system used to analyse the incoming text and determine if the sentiment evolved from the argument text is positive, negative or neutral. It holds specific significance in different areas of Technology and it has varied applications. It combines the power of two most significant fields of Artificial Intelligence, viz. Deep Learning and Natural Language Processing. Deep Learning has evolved as an advanced and efficient technology to target and solve real-world problems with greater accuracy and efficiency. Neural Networks are used to deal with enormous amounts of data without any considerable domain engineering and adapt from its own method of computing. Sentiments within a text can become misleading if the whole sentence is taken as a collection of independent words, since each and every word is connected to other in some sense. This research is used to propose and develop an efficient model that can analyse the sequential sentiments with greater prediction and accuracy. This research also compares the prediction accuracy of the proposed model with other existing novel methods.
Semantic Web based Recommendation System in E-Commerce Websites

World Wide Web, an online platform via which we can access and share information. It has become need of the hour because of the recent trend of e-commerce in the market. In order to survive in this competitive world and to make business out of it, these e-commerce websites are required to use such algorithms and tools, by which the user’s behavior pattern can be traced, mined, analyzed, and predicted for the requests he will make in future. This requires some techniques by which the process of decision making can be improved and the users are provided with the desired results. Because of the huge data available, the analysis of user’s behavior becomes very tricky. So, there is a need of some innovation in the web analysis process. In this paper, a novel way for recommendation system based on ontology has been proposed, which shows a great improvement over the traditional recommender system in terms of recall and precision.
AI Driven Voice Command Henchman

In every day-to-day life, we come across a lot hectic tasks while working on our computers and in today’s world it is crucial for everyone to be multitasking but sometimes it can become a challenge. So to ease the multitasking, this system comes into play to help you out and follow your voice commands and perform the tasks on your computer. The overall digital AI assistant who can provide you with the assistance you need while working on your computer. The proposed method is for desktop so to perform quite a few tasks on desktop but not that efficient and smooth in comparison of mobile ones; So this situation provides motivation to build an AI driven voice based smart Voice Assistant that will cover all the aspects of assistant that will ease the work of the user.
A Distributed Traceable Anonymous Transaction Scheme

The traditional network transaction needs a trusted center to verify the user’s transaction, and it is easy to fall into a single point of failure, and the existing anonymous transaction schemes only focus on the anonymity of the transaction, there is little research on the tracing of illegal trade, especially the distributed tracing scheme. In order to solve these problems, this paper designs a Consortium blockchain composed of multiple trading centers (TCs) to replace the traditional CA, and verifies the user’s transaction content through PBFT consensus mechanism. In our scheme, Multiple trace authorities (TRAs) are designed to jointly tracking of the real identity of the user, which effectively prevents the abuse of supervision by the only supervisor.
Entity-enhanced Multi-granularity Pooling BERT for Event Extraction

In the field of natural language processing, event extraction is a challenging and important task of information extraction, aiming at identifying specific types of events and arguments in texts. Each event usually appears in a single sentence, in which the event trigger indicates the occurrence of the event, and the event argument is usually the time, place and other information related to the event. The common pooling method can only extract the most important information in a sentence, and some valuable information may be missed in some cases. We proposed an entity-enhanced multi-granularity pooling BERT (EMPBERT), which obtains the pooling range of different granularity according to the location of the triggers and arguments of the event and the entity, so as to retain a variety of information of different granularity. The experimental results show that our method is obviously superior to the baseline method, and our proposed pooling method can extract more information than the max-pooling method.
Joint Extraction Model of Entity Relations Based on BERT-CRF

Entity and relation extraction (ERE) is a major task in information extraction and knowledge mapping. Existing methods usually consider two tasks, named entity recognition (NER) and relation extraction (RE), separately using a pipeline approach, which loses a lot of interaction information between tasks and contextual information of text sequences. In order to settle this problem, this paper proposes an end-to-end entity relation joint extraction method based on the head-entity attention mechanism and fusing contextual semantic features. The overall structure of this method adopts BERT-CRF to decode the header entity and its type, and then uses the header entity information as the Query in the attention mechanism, while fusing entity type label embedding and entity relative position to achieve feature enhancement, which enhances the information interaction between entity model and relational model. In the experiments of the commonly used English dataset NYT and Chinese dataset DuIE, this method has achieved high extraction accuracy and F1 score. It is shown that the model is applicable in both English and Chinese contexts.
A Chinese short text classification method based on Bert sentence embedding integrating external statistical features

The classification of Chinese short text suffers from the problem of feature sparsity. In this paper, we propose a Chinese short text classification method based on Bert sentence embedding integrating external statistical features (CSTCBERTSE-ESF). [Methods] The CSTC-BERTSE-ESF method uses BERT sentence embedding as the feature base, then concatenate domain features obtained from the domain corpus by statistical methods as the complete features of the classifier, and finally uses Random Forest and Softmax for the classification. [Results] To verify the effectiveness of the method, a series of experiments were conducted in this paper using THUNews with the Book dataset. The accuracy of the method reached 93.8% and 93% on the two datasets, respectively, and the F1 value of CSTC-BERTSE-ESF can be improved by 8.8% compared to CSTC-BERTSE on the book dataset. [Limitation] Due to the limitation of computing power, the domain corpora used in this paper are small, and the domain features will be more accurate if the capacity of the corpus is increased. [Conclusion] The fusion of external domain features can effectively improve the classification of Chinese short texts, especially the data with strong domain knowledge.