Reviewerly/exHarmony huggingface dataset:
A comparison of deep transfer learning backbone architecture techniques for printed text detection of different font styles from unstructured documents

Authors:
raw_author_name: Supriya Mahadevkar
raw_affiliation_string: Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
raw_author_name: Shruti Patil
raw_affiliation_string: Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology Symbiosis International (Deemed University), Pune, Maharashtra, India
raw_author_name: Ketan Kotecha
raw_affiliation_string: Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology Symbiosis International (Deemed University), Pune, Maharashtra, India
raw_author_name: Ajith Abraham
raw_affiliation_string: Innopolis University, Innopolis, Republic of Tatarstan, Russia; School of Computer Science Engineering & Technology, Bennett University, Greater Noida, Uttar Pradesh, India

Abstract: Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article’s novelty lies in the detailed discussion and implementation of the various transfer learning-based different backbone architectures for printed text recognition. In this research article, the authors compared the ResNet50, ResNet50V2, ResNet152V2, Inception, Xception, and VGG19 backbone architectures with preprocessing techniques as data resizing, normalization, and noise removal on a standard OCR Kaggle dataset. Further, the top three backbone architectures selected based on the accuracy achieved and then hyper parameter tunning has been performed to achieve more accurate results. Xception performed well compared with the ResNet, Inception, VGG19, MobileNet architectures by achieving high evaluation scores with accuracy (98.90%) and min loss (0.19). As per existing research in this domain, until now, transfer learning-based backbone architectures that have been used on printed or handwritten data recognition are not well represented in literature. We split the total dataset into 80 percent for training and 20 percent for testing purpose and then into different backbone architecture models with the same number of epochs, and found that the Xception architecture achieved higher accuracy than the others. In addition, the ResNet50V2 model gave us higher accuracy (96.92%) than the ResNet152V2 model (96.34%).
Research on Event Target Recognition Based on DRUNet and Multi-scale Attention

Authors:
raw_author_name: Zi-Long Liu
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Abstract: Abstract Aiming at the problem of noise and insufficient feature extraction in event camera-based target recognition task, we proposes an event target recognition method based on DRUNet and multi-scale attention. Firstly, DRUNet is added as a filter to reduce the event noise during the conversion of the event stream into event tensor; secondly, a multi-scale convolutional layer is used instead of a single convolutional layer to extract feature information at different scales, and a depth-separable convolution is utilized to replace part of the standard convolution in the network structure to reduce the number of network parameters without losing the performance of the network; thirdly, multi-scale features are performed on different channel fusion and connecting the channel attention module to enhance the network’s representation of effective features; then the classifier is redesigned to reduce feature loss and improve recognition accuracy by compressing the semantic information layer-by-layer and step-by-step; finally, the Adam optimizer based on the gradient centered algorithm is used for training to improve the network’s generalization ability and training speed. On the N-Caltech101 and N-Cars datasets, the recognition accuracy of the model is 87.2 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> and 96.3 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , respectively, which is significantly higher than other algorithms.
TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences

Authors:
raw_author_name: Feng Qian
raw_author_name: Yongsheng Tu
raw_author_name: Chenyu Hou
raw_author_name: Bin Cao

Abstract: Purpose Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise. Design/methodology/approach This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise. Findings Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36. Originality/value At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.
A facies‐constrained geostatistical seismic inversion method based on multi‐scale sparse representation

Authors:
raw_author_name: Qin Su
raw_affiliation_string: Research Institute of Petroleum Exploration and Development-Northwest PetroChina Lanzhou China; School of Resources and Environment University of Electronic Science and Technology of China Chengdu China
raw_author_name: Xingrong Xu
raw_affiliation_string: Research Institute of Petroleum Exploration and Development-Northwest PetroChina Lanzhou China
raw_author_name: Jingjing Zong
raw_affiliation_string: School of Resources and Environment University of Electronic Science and Technology of China Chengdu China
raw_author_name: Hua Wang
raw_affiliation_string: School of Resources and Environment University of Electronic Science and Technology of China Chengdu China

Abstract: Abstract Geostatistical seismic inversion is an important method for establishing high‐resolution reservoir parameter models. There is no accurate representation method for reservoir structural features, and prior information about structural features cannot be incorporated into geostatistical inversion. Based on the assumption of the sparsity of stratigraphic sedimentary features, the same type of structural feature is used to represent the sedimentary pattern of reservoirs within the same facies. Different sparse representation patterns are used to represent the differences in sedimentary patterns between facies. Although changes in depositional environment might result in the multi‐scale characteristics of geological structures for varying sedimentary rhythms, this paper proposes a facies‐constrained geostatistical inversion method based on multi‐scale sparse representation to better accommodate such situation. Using the method of sparse representation combined with wavelet transform, the multi‐scale sedimentary structural features of reservoirs are learned from well‐logging data. Seismic facies and multi‐scale features are used as prior information for geostatistical inversion. Further, the likelihood function is constructed using seismic data to obtain the posterior probability distribution of reservoir parameters. Finally, the accurate inversion result is obtained by using multi‐scale sparse representation as a constraint in the posterior probability distribution of reservoir parameters. Compared with conventional geostatistical methods, this algorithm can better match the structural features of reservoir parameters with varying geological conditions. Field data tests have shown the effectiveness of this method in improving the accuracy and resolution of reservoir parameter structural features.
Heterogeneous text graph for comprehensive multilingual sentiment analysis: capturing short- and long-distance semantics

Authors:
raw_author_name: El Mahdi Mercha
raw_affiliation_string: ENSIAS, Mohammed V University in Rabat, Rabat, Morocco; HENCEFORTH, Rabat, Morocco
raw_author_name: Houda Benbrahim
raw_affiliation_string: ENSIAS, Mohammed V University in Rabat, Rabat, Morocco
raw_author_name: Mohammed Erradi
raw_affiliation_string: ENSIAS, Mohammed V University in Rabat, Rabat, Morocco

Abstract: Multilingual sentiment analysis (MSA) involves the task of comprehending people's opinions, sentiments, and emotions in multilingual written texts. This task has garnered considerable attention due to its importance in extracting insights for decision-making across diverse fields such as marketing, finance, and politics. Several studies have explored MSA using deep learning methods. Nonetheless, a majority of these studies depend on sequential-based approaches, which focus on capturing short-distance semantics within adjacent word sequences, but they overlook long-distance semantics, which can provide more profound insights for analysis. In this work, we propose an approach for multilingual sentiment analysis, namely MSA-GCN, leveraging a graph convolutional network to effectively capture both short- and long-distance semantics. MSA-GCN involves the comprehensive modeling of the multilingual sentiment analysis corpus through a unified heterogeneous text graph. Subsequently, a slightly deep graph convolutional network is employed to acquire predictive representations for all nodes by encouraging the transfer learning across languages. Extensive experiments are carried out on various language combinations using different benchmark datasets to assess the efficiency of the proposed approach. These datasets include Multilingual Amazon Reviews Corpus (MARC), Internet Movie Database (IMDB), Allociné, and Muchocine. The achieved results reveal that MSA-GCN significantly outperformed all baseline models in almost all datasets with a p-value < 0.05 based on student t-test. In addition, such approach shows prominent results in a variety of language combinations, revealing the robustness of the approach against language variation.
Data privacy protection model based on blockchain in mobile edge computing

Authors:
raw_author_name: Junhua Wu
raw_affiliation_string: School of Computer Science Qufu Normal University Rizhao Shandong China
raw_author_name: Xiangmei Bu
raw_affiliation_string: School of Computer Science Qufu Normal University Rizhao Shandong China
raw_author_name: Guangshun Li
raw_affiliation_string: School of Computer Science Qufu Normal University Rizhao Shandong China

Abstract: Abstract Mobile edge computing (MEC) technology is widely used for real‐time and bandwidth‐intensive services, but its underlying heterogeneous architecture may lead to a variety of security and privacy issues. Blockchain provides novel solutions for data security and privacy protection in MEC. However, the scalability of traditional blockchain is difficult to meet the requirements of real‐time data processing, and the consensus mechanism is not suitable for resource‐constrained devices. Moreover, the access control of MEC data needs to be further improved. Given the above problems, a data privacy protection model based on sharding blockchain and access control is designed in this paper. First, a privacy‐preserving platform based on a sharding blockchain is designed. Reputation calculation and improved Proof‐of‐Work (PoW) consensus mechanism are proposed to accommodate resource‐constrained edge devices. The incentive mechanism with rewards and punishments is designed to constrain node behavior. A reward allocation algorithm is proposed to encourage nodes to actively contribute to obtaining more rewards. Second, an access control strategy using ciphertext policy attribute‐based encryption (CP‐ABE) and RSA is designed. A smart contract is deployed to implement the automatic access control function. The InterPlanetary File System is introduced to alleviate the blockchain storage burden. Finally, we analyze the security of the proposed privacy protection model and statistics of the GAS consumed by the access control policy. The experimental results show that the proposed data privacy protection model achieves fine‐grained control of access rights, and has higher throughput and security than traditional blockchain.
Celebratory expressions and linguistic diversity: investigating congratulation patterns among Pakistani Facebook users commenting in English and Urdu

Authors:
raw_author_name: Aisha Saleem
raw_affiliation_string: Department of English, University of Gujrat, Gujrat, Pakistan

Abstract: This study explores verbal and nonverbal congratulatory responses in the context of joyful Facebook status updates in Pakistani English and Urdu. Employing Terkourafi’s frame-based approach, quantitative and qualitative analyses were conducted on 16,165 comments and 16,230 emoji reactions from 140 Pakistani users. Elwood’s taxonomy and Allami and Pishghadam’s refined framework were used for verbal coding, while nonverbal data followed Facebook’s algorithm guidelines and Dresner and Herring’s framework. The findings reveal users’ adept expression of happiness and convivial emotions, showcasing significant language adaptation to cultural norms, especially with the prevalence of English. Pakistani users employ various congratulatory techniques, emphasizing the role of illocutionary force-indicating devices (IFID) in reinforcing positive self-image and commending success. Nonverbal elements, primarily emojis, play a crucial role in Pakistani conversations, signaling speech genres, defining social relationships and negotiating social purposes. The study suggests that future research should explore the longitudinal evolution of emoticons, investigating their potential conventionalization and transformation into established textual markers and providing insights into their linguistic and cultural significance in digital interactions.
A deep learning approach for robust traffic accident information extraction from online chinese news

Authors:
raw_author_name: Yancheng Ling
raw_affiliation_string: School of Civil Engineering and Transportation South China University of Technology Guangzhou China
raw_author_name: Zhenliang Ma
raw_affiliation_string: Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm Sweden
raw_author_name: Xiaoxian Dong
raw_affiliation_string: School of Civil Engineering and Transportation South China University of Technology Guangzhou China
raw_author_name: Xiaoxiong Weng
raw_affiliation_string: School of Civil Engineering and Transportation South China University of Technology Guangzhou China

Abstract: Abstract Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence‐wise and context‐wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon‐BiLSTM‐CRF method. The proposed approach is validated by comparing it with state‐of‐the‐art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1‐score. It improves the performance of the best benchmark model (BiLSTM‐CRF) by 18.8% in precision and 12.08% in F1‐score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice.
Navigating legal challenges of deepfakes in the American context: a call to action

Authors:
raw_author_name: Mohamed Chawki
raw_affiliation_string: Naif Arab University for Security Sciences (NAUSS), Kingdom of Saudi Arabia

Abstract: Deepfakes are a rapidly growing technological trend that is revolutionizing many digital processes, from media production to digital access. They offer various advantages for both users and creators alike. For example, they can provide entertainment value by allowing users to create humorous or satirical versions of existing media content without having to film new scenes. They can also automate production workflows by generating background characters or extras for movies. Additionally, deepfakes could be used in healthcare applications, such as virtual avatars for doctors who require assistance during consultations with patients located far from hospitals or clinics. However, deepfakes also pose a threat to society as they can be used for malicious purposes, including spreading false information or exploiting the likenesses of others for financial gain. To address these concerns, various types of regulations have been introduced across jurisdictions worldwide. These laws prohibit doctored audio recordings and visual materials aimed at influencing election outcomes. Some countries have even gone as far as requiring companies producing deepfake materials to identify themselves as such, to make audiences aware that what they are seeing may not always be authentic representations of events. In this paper, the author will discuss what deepfakes are, their potential benefits and risks, the various types of deepfakes that exist, and how the USA has attempted to regulate them. The author will also suggest some policy recommendations aimed at mitigating the risks associated with deepfakes while still preserving their creative potential.
Blockchain Integrated with Internet of Things-benefits, Challenges

Authors:
raw_author_name: Geeta Amol Patil
raw_affiliation_string: BMS Institute of Technology and Management, Bengaluru, India

Abstract: All sectors are now using digital ways to facilitate humans. Be it health, finance, supply chain, communication, transport, IT, or education, all the sectors are now relying on technologies and the internet for providing facilities and also using them as sources of information. These sectors, when using traditional ways, faced a lot of challenges. For example, people earlier going to railway stations to book train tickets had to wait for long durations in queues, and if all the seats are filled by the time they reach or their turn comes to reserve seats, it goes all in vain to spend time traveling to the station and time in queues. Coming to the finance sector, people had to go to banks to create a bank account and for all the formalities. They had to spend time going to banks and then wait in queues to get their work done. Also, it took around 1-2 weeks for every task to complete in banks. So, the process was quite time-consuming, monotonous and unreliable in practice. Thus many sectors started looking for alternative methods to perform their daily tasks. Slowly, the sectors started digitizing, and started using computers to perform tasks, to store and update their data. They also started using the internet in their daily applications. Each organization of industry is now available on the internet. All of their information is present and one can apply for their services using their websites. Thus, IoT comes into the picture here. All the sectors using the internet to access and provide information, using the cloud to store their data are using IoT services. Internet of Things (IoT) technology will soon become an integral part of our daily lives to facilitate the control and monitoring of processes and objects and to change the way man interacts with the physical world. For all aspects of IoT to be fully functional, there are a few obstacles to overcome and important challenges to overcome. These include, but are not limited to, cyber security, data privacy, power consumption, and metrics. The dedicated Blockchain environment and its various processes provide a useful way to address these few IoT challenges.