Let gambling hide nowhere: Detecting illegal mobile gambling apps via heterogeneous graph-based encrypted traffic analysis

Abstract: Mobile gambling apps, as a new type of online gambling service, have not only enriched people’s online entertainment activities but also brought about negative impacts for both individuals and society. Existing gambling app detection methods mainly extract static content-based characteristics, such as vision and textual features, to mine the key information from the user interface to detect gambling apps. In this paper, we are the first to introduce a dynamic communication characteristic-based approach by utilizing encrypted traffic analysis. Firstly, we conduct an analysis of the communication characteristics of 175 popular gambling apps in China, and unveil their important server domain name randomization and inter-app level familial characteristics, which cannot be well-leveraged by previous encrypted traffic analysis methods. Based on the analysis results, we design HeCGamb, a Heterogeneous Communication Graph-based method to enhance the Gambling app detection performance. HeCGamb models the inter-flow relations from various traffic flows to mine the flow-level communication patterns of gambling apps. Based on the inter-flow relations, HeCGamb further constructs inter-app relations to utilize the app-level familial characteristics. Finally, the multi-view semantic information from both servers and apps with inter-app relations are fused to generate the app node representations to comprehensively leverage the characteristics from server domains and familial apps. Extensive experiments not only demonstrate the superior performance(94.1% F1-score and 96.8% AUC score in open world) of HeCGamb, but also highlight its potential in gambling industry chain tracking.


Audio-visual saliency prediction with multisensory perception and integration

Abstract: Audio-visual saliency prediction (AVSP) is a task that aims to model human attention patterns in the perception of auditory and visual scenes. Given the challenges associated with perceiving and combining multi-modal saliency features from videos, this paper presents a multi-sensory framework for AVSP. This framework is designed to extract audio, motion and image saliency features and integrate them effectively, which can then serve as a general architecture for the AVSP task. To obtain multi-sensory information, we develop a three-stream encoder that extracts audio, motion and image saliency features. In particular, we utilize a pre-trained encoder with knowledge related to image saliency to extract saliency features for each frame. The image saliency features are then incorporated with motion features using a spatial attention module. For motion features, 3D convolutional neural networks (CNNs) like S3D are commonly used in AVSP models. However, these networks are unable to effectively capture the global motion relationship in videos. To tackle this problem, we incorporate Transformer- and MLP-based motion encoders into the AVSP models. To learn joint audio-visual representations, an audio-visual fusion block is exploited to enhance the correlation between audio and visual motion features under the supervision of a cosine similarity loss in a self-supervised manner. Finally, a multi-stage decoder integrates audio, motion and image saliency features to generate the final saliency map. We evaluate our methods on six audio-visual eye-tracking datasets. Experimental results demonstrate that our method achieves compelling performance compared to the state-of-the-art methods. The source code will be available at https://github.com/oraclefina/MSPI.


A comparison of deep transfer learning backbone architecture techniques for printed text detection of different font styles from unstructured documents

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

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

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

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

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.


Small Portfolio Construction with Cryptocurrencies

Abstract: In this paper, we describe and apply different models of portfolio construction in the selection between a small number of big-cap cryptocurrencies. Our purpose is to select the minimum riskiness between cryptocurrencies, comparing different risk measures and maximum diversification. We build our models without the constraints of the expected returns. Without relying on expected returns, we have the same condition on the comparison between them. Cryptocurrencies are not common stock or other assets indexed in the market but it is interesting to study how diversification can significantly improve investment performance. We first give the methodology to use high-frequency observation data, in the numeral approximation especially in the novel application of the Risk parity models, used with different risk measures we can achieve a very good result, from the position of gaining and variation. Since Risk parity models divide the weights of the asset in equal risk contribution proportion, it is suggested to use a small number of cryptocurrencies, otherwise their performance will be close to the uniform portfolio. To the traditional Mean Variance model, and the alternative, Expected shortfall/Conditional Value at Risk, we use three versions of Risk Parity with two different risk measures and a naive risk parity. The uniform portfolio is used as a benchmark for selection comparison with the other portfolio models. We give the conditions for the Risk Parity with the Expected shortfall/Conditional Value at Risk (CVaR) to guarantee convergence with the numerical approximation. In the end, we study the tradeoff between each model and which is more suitable for a small cryptocurrency portfolio.


Data privacy protection model based on blockchain in mobile edge computing

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

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

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

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.


Evaluating and analyzing student labor literacy in China's higher vocational education: an assessment model approach

Abstract: Introduction This study addresses the gap in evaluating labor literacy amongst vocational students in China's higher vocational education system. It aims to develop a comprehensive framework for assessing essential labor competencies, thereby contributing to a nuanced understanding of vocational education's role in skill development. Methods Employing a multifaceted research methodology, this study integrates questionnaire surveys, econometric analyses, and the Delphi method to assess labor literacy among 749 students from three leading vocational institutions. A pioneering labor literacy assessment model is introduced: S = 0.5466 B 1 +0.1816 B 2 +0.1623 B 3 +0.1095 B 4 , where S denotes the overall labor literacy score. Here, B 1 represents labor concepts, B 2 denotes habits and qualities, B 3 signifies knowledge and skills, and B 4 encapsulates emotions and attitudes, illustrating a comprehensive approach to measuring labor literacy. Results Our findings reveal pronounced disparities in labor literacy across the identified dimensions, with particular deficiencies in labor concepts. The study also identifies six determinants—gender, political profile, academic performance, internship and training base utilization, inclination towards innovation and entrepreneurship, and labor education evaluation mechanisms—that significantly influence labor literacy outcomes. Discussion Highlighting the imperative for a contextually informed and holistic approach to labor literacy, this study's insights advocate for educational strategies that are both aligned with labor market demands and cognizant of socio-cultural nuances. The developed assessment model not only propels the theoretical discourse in vocational education forward but also provides a pragmatic guide for educators and policy makers, aiming to mitigate disparities and enhance labor competencies through refined educational practices.


Blockchain Integrated with Internet of Things-benefits, Challenges

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.


A Dynamic Adaptive and Resource-Allocated Selection Method Based on TOPSIS and VIKOR in Federated Learning

Abstract: Abstract Federated learning (FL) is a decentralized and privacy-preserving machine learning technique that protects data privacy by learning models locally and not sharing datasets. However, due to limited computing resources on devices and highly heterogeneous data in practical situations, the training efficiency and resource utilization of federated learning is low. In order to resolve these challenges, we introduce a blockchain-assisted dynamic adaptive and personalized federated learning framework (TV-FedAvg) in the presence of restricted computing power resources and data heterogeneity. After each round of local training, we utilize an improved scoring model based on VIKOR and TOPSIS to comprehensively score the devices. The scores are then utilized to choose devices for participation in global aggregation and to carry out model aggregation through blockchain consensus. Furthermore, resources are reallocated for the next round to enhance resource efficiency, model fairness, and performance. Finally, we demonstrate through experimentation that TV-FedAvg outperforms other models such as pFedMe, FedAvg, Per-FedAvg, and TOPSIS in terms of both efficiency and performance.


Intelligent visually lossless compression of dental images

Abstract: Background: Tendencies to increase the mean size of dental images and the number of images acquired daily makes necessary their compression for efficient storage and transferring via communication lines in telemedicine and other applications. To be a proper solution, lossy compression techniques have to provide a visually lossless option (mode) where a desired quality (invisibility of introduced distortions for preserving diagnostically valuable information) is ensured quickly and reliably simultaneously with a rather large compression ratio. Objective: Within such an approach, our goal is to give answers to several practical questions such as what encoder to use, how to set its parameter that controls compression, how to verify that we have reached our ultimate goal, what are additional advantages and drawbacks of a given coder, and so on. Methods: We analyze the performance characteristics of several encoders mainly based on discrete cosine transform for a set of 512 × 512 pixel fragments of larger size dental images produced by Morita and Dentsply Sirona imaging systems. To control the visual quality of compressed images and the invisibility of introduced distortions, we have used modern visual quality metrics and distortion invisibility thresholds established for them in previous experiments. Besides, we have also studied the so-called just noticeable distortions (JND) concept, namely, the approach based on the first JND point when the difference between an image subject to compression and its compressed version starts to appear. Results: The rate-distortion dependences and coder setting parameters obtained for the considered approaches are compared. The values of the parameters that control compression (PCC) have been determined. The ranges of the provided values of compression ratio have been estimated and compared. It is shown that the provided CR values vary from about 20 to almost 70 for modern coders and almost noise-free images that is significantly better than for JPEG. For images with visible noise, the minimal and maximal values of produced CR are smaller than for the almost noise-free images. We also present the results of the verification of compressed image quality by specialists (professional dentists). Conclusion: It is shown that it is possible and easy to carry out visually lossless compression of dental images using the proposed approaches with providing quite high compression ratios without loss of data diagnostic value.


Virtual reality usage intention in an intradialysis exercise program: Predictive analysis through a structural equation model

Abstract: In recent years several exercise programs have begun to use technology, the most used has been virtual reality (VR). Technology-based programs must first be accepted by the population, since, if they do not see the need, the probability of rejection is high. Therefore, it is necessary to first evaluate the intention to use technology to ensure the effectiveness of the intervention. The study aimed to determine the predictive capacity of variables associated with theories of human behavior, on the intention to use VR in an intradialysis exercise program. A sample of 324 hemodialysis patients (F = 140; M = 184) from Chile, filled out a questionnaire. Before this, information was provided regarding VR and the exercise program. Structural equation modeling was used to analyze results through SmartPLS. Results showed that the dependent variable had a good amount of variance explained by the model. The latent variables that strongly influenced the intention to use were perceived enjoyment, perceived utility, and social influence. It can be predicted that users will intend to use VR to perform intradialysis exercises if they consider that it´s entertaining, useful, and validated by their peers.


Disposable and Flexible Paper‐Based Optoelectronic Synaptic Devices for Physical Reservoir Computing

Abstract: Abstract Health monitoring using wearable artificial intelligence (AI) sensors with sensing and cognitive capabilities has garnered significant attention. The development of self‐contained AI sensors that can operate with low power consumption, akin to the human brain, is necessary. Physical reservoir computing (PRC), which mimics the human brain using physical phenomena, offers a low‐power consumption architecture. Nevertheless, creating a flexible and easily disposable sensors using PRC capable of processing optical signals with sub‐second response times suitable for biological signals presents a challenge. In this study, a disposable and flexible paper‐based optoelectronic synaptic devices are designed, which are composed of nanocellulose and ZnO nanoparticles, for PRC. This device exhibits synaptic photocurrent in response to optical input. To assess its performance, a classification and time‐series forecasting tasks are conducted. The memory capacity of short‐term memory task, indicating the device's ability to store past information, is 1.8. The device can recognize handwritten digits with an accuracy of 88%. These results highlight the potential of the device for PRC. In addition, subjecting the device to 1000 rounds of bending do not affect its accuracy. Furthermore, the device burn in a few seconds, much like regular office paper, demonstrating its disposability.


How Depth of Field in Screen Images Affects Egocentric Distance Perception

Abstract: In virtual environments (VEs) presented on flat screens, accurate egocentric distance perception is crucial for enhancing immersion. This study investigated depth of field-induced blur, typically perceived as an ordinal depth cue, to understand its role as a quantitative depth cue in VEs. We employed verbal judgment and position adjustment methods to quantify observers’ egocentric distance perception across various depth of field settings (n = 30 participants). Our analysis also examined the interplay between blur and other depth cues. Results indicated that increased blur in VEs leads to a greater underestimation of distances. Moreover, blur operates as a relatively independent depth cue, showing minimal interaction with other cues. These findings address a gap in research on the independent role of depth of field as a quantitative depth cue in VEs. This contributes to the visual design of VEs and supports the enhancement of user experience in human–computer interactions.


No‐reference image quality assessment via a dual‐branch residual network

Abstract: Abstract The No‐Reference Image Quality Assessment (NR‐IQA) method can predict quality scores of distorted images without the reference image. However, due to the variability in both color and structure of images, existing NR‐IQA methods struggle to accurately predict quality scores of distorted images. Therefore, an NR‐IQA method based on a Dual‐Branch Residual Network (DBRIQA) for evaluating the quality scores of color‐distorted images is proposed. First, guided filtering is applied to the hue images in the HSV space to extract distortion information from the image's color. Then, due to significant differences in distorted images, enhancements are made to the traditional residual blocks, forming a feature extraction module that captures multi‐scale features from the image. In order to capture the global relationships in the distorted image, a Global‐Level Attention Block (GLAB) is introduced, facilitating the interaction of information among the extracted features. Experiments were conducted across four publicly available IQA datasets, including LIVE, CSIQ, TID2008, and TID2013. The experimental results demonstrate that the proposed method exhibits strong performance and generalization capabilities in predicting image quality compared to peer methods.


Fraud victimization across the lifespan: evidence on repeat victimization using perpetrator data

Abstract: Older adults are thought to be more susceptible to scams, yet understanding the relationship between chronological age and victimization is limited by underreporting. This study avoids underreporting bias by merging four longitudinal databases of Americans (N = 1.33 million) who paid money in response to mail scams over 20 years. We investigate the risk of repeat victimization and victimization by multiple scam types over the life course. Victims in their 70s and 80s are 9% more likely to experience another victimization incident than those in their 50s. Those age 18 to 29 are 24% less likely to experience another victimization incident. Relative to adults in their 50s, the odds of victimization by multiple scams are greater for those in their 60s and 70s, but lower for those 80 + . This study demonstrates the research potential in using scammers' data to understand patterns of victimization. Fraud prevention efforts should target older individuals who are at higher risk of repeat victimization and suffer greater losses as a result.


The Education Leadership Challenges for Universities in a Postdigital Age

Abstract: Abstract The executive leadership of universities internationally faces significant uncertainty in how to respond to ongoing technological innovation in a postdigital age. The current rapid progress in generative artificial intelligence (GAI) is exacerbating their plight. This study uses emergent research on GAI in learning and teaching to problematise the challenges of university education leadership in the face of such disturbances to core academic work. Considering the benefits and risks associated with the design of GAI, its inclusion in learning and teaching, and how it is reconfiguring the design of degree programmes and the strategies of local leaders providing support services through university organisational structures, this paper offers a principled ecosystemic approach to leadership that considers key elements for effective strategy implementation. The interrelatedness of strategy, governance, policy, management, and investment are discussed and a way forward for effective education leadership in a context of uncertainty is offered.


Copyright Law and the Ruse of Culture: ‘Traditional Cultural Expressions and Expressions of Folklore’ as a Conception of Racial Difference

Abstract: ‘Traditional Cultural Expressions’ and ‘Expressions of folklore’ (TCEs/folklore) have received very little critical attention within intellectual property (IP) jurisprudence. Where TCEs/folklore have been treated, it has only been to the extent that international IP does not offer sufficient protection over them as an analogous class of IP or to the extent that TCEs/folklore themselves are philosophically incompatible with IP, and accordingly fail to adequately capture the cultural expressions of indigenous peoples. This article offers a different argument regarding TCEs/folklore which neither seeks their further protection nor their legislative and policy development. Instead, this article traces the genealogy of TCEs/folklore within the imperialist foundations of international law, arguing that TCEs/folklore are the function of an already existing discursive arrangement of power that is consistent with colonial valuations of knowledge and culture.


Practical high-dimensional quantum key distribution protocol over deployed multicore fiber

Abstract: Quantum key distribution (QKD) is a secure communication scheme for sharing symmetric cryptographic keys based on the laws of quantum physics, and is considered a key player in the realm of cyber-security. A critical challenge for QKD systems comes from the fact that the ever-increasing rates at which digital data are transmitted require more and more performing sources of quantum keys, primarily in terms of secret key generation rate. High-dimensional QKD based on path encoding has been proposed as a candidate approach to address this challenge. However, while proof-of-principle demonstrations based on lab experiments have been reported in the literature, demonstrations in realistic environments are still missing. Here we report the generation of secret keys in a 4-dimensional hybrid time-path-encoded QKD system over a 52-km deployed multicore fiber link forming by looping back two cores of a 26-km 4-core optical fiber. Our results indicate that robust high-dimensional QKD can be implemented in a realistic environment by combining standard telecom equipment with emerging multicore fiber technology.


Exploring language alternation and participation in an ‘in-between learning scenario’: a case study of a WhatsApp chat with secondary students of English

Abstract: The study of participation from a socio-interactional perspective relates to the exploration of the interactional practices displayed by interactants and the close examination of the multimodal resources they employ. Here we observe the language choices made by 2 teachers and 10 students who participate in a WhatsApp chat over the summer break. Since language choice is a social category-bound activity linked to social forms of participation, our analysis focuses on the negotiation participants engage in to determine their practiced language policy and to select their medium of interaction. Our analysis reveals that language choices reshape and construct what we would refer to as an ‘in-between learning scenario’. This scenario creates tensions between the teachers’ and the students’ agenda, observed through their alignment with other interactants and their interpretation of the task at hand. Language selection is not a strict, personal choice. Instead, it is socially situated and depends on how speakers co-construct the communicative event they participate in and who holds the control of topic selection.


Occupying Another’s Digital Space: Privacy of Smartphone Users as a Situated Practice

Abstract: Abstract A smartphone’s screen is commonly regarded as a private space, and the action of looking at it is usually considered a violation of one’s privacy both by researchers and designers. However, our study demonstrates how participants in the interaction themselves negotiate moment by moment and achieve an understanding of someone’s screen space as public or private. In this paper, we analyze the interactional sequences of uninvited looks at another participant’s phone. Drawing on visual ethnography and ethnomethodologically informed multimodal interaction analysis, we video-recorded and analyzed everyday interactions between friends and acquaintances. Our findings show that looking at someone’s smartphone display is often performed and oriented to as a resource in interaction rather than an invasion of privacy. We therefore characterize the interactional functions of gazes and glances at another’s screen. We also discuss the research and design implications of approaching privacy as a situated practice.


A Quick Fix for Creating Stimulating Lessons Using Images, Videos, and Audios from the Library of Congress Website

Abstract: Online learning has put pressure on instructors to create more stimulating lessons using images, videos, and the like. While the Internet offers resources, many sites require educators to pay in order to use them legally. Furthermore, instructors need images representing a diversity of cultures to be inclusive. One website that overcomes these challenges and offers a quick fix for creating invigorating lessons is the Library of Congress website.


Physics‐Aware Analytic‐Gradient Training of Photonic Neural Networks

Abstract: Abstract Photonic neural networks (PNNs) have emerged as promising alternatives to traditional electronic neural networks. However, the training of PNNs, especially the chip implementation of analytic gradient descent algorithms that are recognized as highly efficient in traditional practice, remains a major challenge because physical systems are not differentiable. Although training methods such as gradient‐free and numerical gradient methods are proposed, they suffer from excessive measurements and limited scalability. State‐of‐the‐art in situ training method is also cost‐challenged, requiring expensive in‐line monitors and frequent optical I/O switching. Here, a physics‐aware analytic‐gradient training (PAGT) method is proposed that calculates the analytic gradient in a divide‐and‐conquer strategy, overcoming the difficulty induced by chip non‐differentiability in the training of PNNs. Multiple training cases, especially a generative adversarial network, are implemented on‐chip, achieving a significant reduction in time consumption (from 31 h to 62 min) and a fourfold reduction in energy consumption, compared to the in situ method. The results provide low‐cost, practical, and accelerated solutions for training hybrid photonic‐digital electronic neural networks.


A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision

Abstract: Abstract Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z ‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1 mm when the measurement distance is less than 40 m. When the measurement distance is equal to 90 m, the measurement error is less than 5 mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities.


A Personalized Federated Learning Method Based on Clustering and Knowledge Distillation

Abstract: Federated learning (FL) is a distributed machine learning paradigm under privacy preservation. However, data heterogeneity among clients leads to the shared global model obtained after training, which cannot fit the distribution of each client’s dataset, and the performance of the model degrades. To address this problem, we proposed a personalized federated learning method based on clustering and knowledge distillation, called pFedCK. In this algorithm, each client has an interactive model that participates in global training and a personalized model that is only trained locally. Both of the models perform knowledge distillation with each other through the feature representation of the middle layer and the soft prediction of the model. In addition, in order to make an interaction model only obtaining the model information from the client, which has similar data distribution and avoids the interference of other heterogeneous information, the server will cluster the clients according to the similarity of the amount of parameter variation uploaded by different interaction models during every training round. By clustering clients, interaction models with similar data distributions can cooperate with each other to better fit the local dataset distribution. Thereby, the performance of personalized model can be improved by obtaining more valuable information indirectly. Finally, we conduct simulation experiments on three benchmark datasets under different data heterogeneity scenarios. Compared to the single model algorithms, the accuracy of pFedCK improved by an average of 23.4% and 23.8% over FedAvg and FedProx, respectively; compared to typical personalization algorithms, the accuracy of pFedCK improved by an average of 0.8% and 1.3%, and a maximum of 1.0% and 2.9% over FedDistill and FML.


A Practical Approach to Automatic Earthquake Catalog Compilation in Local OBS Networks Using Deep-Learning and Network-Based Algorithms

Abstract: Abstract In land-based seismology, modern automatic earthquake detection and phase picking algorithms have already proven to outperform classic approaches, resulting in more complete catalogs when only taking a fraction of the time needed for classic methods. For marine-based seismology, similar advances have not been made yet. For ocean-bottom seismometer (OBS) data, additional challenges arise, such as a lower signal-to-noise ratio and fewer labeled data sets available for training deep-learning models. However, the performance of available deep-learning models has not yet been extensively tested on marine-based data sets. Here, we apply three different modern event detection and phase picking approaches to an ∼12 month local OBS data set and compare the resulting earthquake catalogs and location results. In addition, we evaluate their performance by comparing different subcatalogs of manually detected events and visually revised picks to their automatic counterparts. The results show that seismicity patterns from automatically compiled catalogs are comparable to a manually revised catalog after applying strict location quality control criteria. However, the number of such well-constrained events varies between the approaches and catalog completeness cannot be reliably determined. We find that PhaseNet is more suitable for local OBS networks compared with EQTransformer and propose a pick-independent event detection approach, such as Lassie, as the preferred choice for an initial event catalog compilation. Depending on the aim of the study, different schemes of manual repicking should be applied because the automatic picks are not yet reliable enough for developing a velocity model or interpreting small-scale seismicity patterns.


Development and Practical Applications of Computational Intelligence Technology

Abstract: Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.


A Design of Digital Bag for Pilgrim's Safety

Abstract: The study aimed to design digital bags that comply with the functional requirements of Hajj and Umrah pilgrims in the light of ergonomics and then to identify the differences between specialists in achieving design patterns of bags made for pilgrims' safety. It also aimed to measure the extent to which Hajj and Umrah pilgrims are satisfied with the proposed designs. The research employed a mixed-methods approach, with a study sample of (301) individuals who had participated in Hajj and Umrah pilgrimages. The study's findings indicated statistically significant variations, at a significance level of (0.01) among the six suggested designs for smart bags, based on the feedback from Hajj and Umrah pilgrims. When testing the direction of significance, the two researchers found that design no. 1 was the best among all designs, followed by design no. 3, then design no. 6. Results also showed statistically significant differences at the level of (0.01) among the six suggested designs for smart bags based on the experts' assessments. When testing the direction of significance, the researchers found that design no. 1 stood out as the optimal choice, followed by design no. 6, then design no. 2. The study concluded that the ergonomic standards should be applied in the smart design, taking into consideration the safety factor, as the two researchers followed the ergonomic standards within the framework of designing smart bags through several elements, namely locating the bag, combating thefts, and enabling bags with RFID technology to identify the user's identity. The study provided an overview of recent trends in the ergonomic field to improve human performance and achieve product development. It resulted in a practical guide to understanding the ergonomic guidelines in smart design. The two researchers recommended considering the ergonomic aspects of fashion design and its supplements, especially the smart ones. They also recommended conducting several studies dealing with the impact of ergonomic considerations on fashion design and its supplements.


Intent Identification by Semantically Analyzing the Search Query

Abstract: Understanding and analyzing the search intent of a user semantically based on their input query has emerged as an intriguing challenge in recent years. It suffers from small-scale human-labeled training data that produce a very poor hypothesis of rare words. The majority of data portals employ keyword-driven search functionality to explore content within their repositories. However, the keyword-based search cannot identify the users’ search intent accurately. Integrating a query-understandable framework into keyword search engines has the potential to enhance their performance, bridging the gap in interpreting the user’s search intent more effectively. In this study, we have proposed a novel approach that focuses on spatial and temporal information, phrase detection, and semantic similarity recognition to detect the user’s intent from the search query. We have used the n-gram probabilistic language model for phrase detection. Furthermore, we propose a probability-aware gated mechanism for RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Approach) embeddings to semantically detect the user’s intent. We analyze and compare the performance of the proposed scheme with the existing state-of-the-art schemes. Furthermore, a detailed case study has been conducted to validate the model’s proficiency in semantic analysis, emphasizing its adaptability and potential for real-world applications where nuanced intent understanding is crucial. The experimental result demonstrates that our proposed system can significantly improve the accuracy for detecting the users’ search intent as well as the quality of classification during search.


Scene text recognition with context-aware autonomous bidirectional iterative models

Abstract: This research focuses on Scene Text Recognition (STR), a crucial component in various applications of artificial intelligence such as image retrieval, office automation, and intelligent traffic systems. Recent studies have shown that semantic-aware approaches significantly improve the performance of STR tasks, with context-aware STR methods becoming mainstream. Among these, the fusion of visual and language models has shown remarkable effectiveness. We propose a novel method (PABINet) that incorporates three key components: a Visual-Language Decoder, a Language Model, and a Fusion Model. First, during training, the Visual-Language Decoder masks the original labels in the Transformer decoder using permutation masks, with each mask being unique. This enhances word memorization and learning through contextual semantic information, resulting in robust semantic knowledge. During the inference stage, the Visual-Language Decoder employs autonomous Autoregressive model (AR) inference to generate results. Subsequently, the Language Model scrutinizes and corrects the output of the Visual-Language Encoder using a cloze mask approach, achieving context-aware, autonomous, bidirectional inference. Finally, the Fusion Model concatenates and refines the outputs of both models through iterative layers.Experimental results demonstrate that our PABINet performs exceptionally well when handling various quality images. When trained with synthetic data, PABINet achieves a new STR benchmark (average accuracy of 92.41%), and when trained with real data, it establishes new state-of-the-art results (average accuracy of 96.28%).


Digital transformation in higher education: A comprehensive review of e-learning adoption

Abstract: BACKGROUND: Data-driven practices are now crucial in the digital age for streamlining operations and boosting output across a range of industries. With e-Learning emerging as a cutting-edge and promising strategy to improve educational delivery and student engagement, higher education institutions are also adopting this trend. Despite the potential advantages, e-Learning adoption in higher education is still a developing topic with scant published research. OBJECTIVE: This study aims to conduct a thorough analysis of existing literature, seeking to determine the present status of e-Learning adoption in higher education while also identifying promising avenues for further research. METHODS: In this study, a systematic literature review methodology and a bibliometric approach are utilized to examine 63 papers sourced from the Scopus database, encompassing the years 2017 to 2021. These papers were specifically chosen due to their pertinence to e-Learning adoption in higher education institutions, following the exclusion and inclusion criteria outlined in the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework. The analysis techniques employed include descriptive overviews, citation analysis, and keyword co-occurrence. RESULTS: The systematic review of the literature revealed a significant increase in e-Learning adoption within higher education institutions (HEIs) in recent times. The findings highlighted several crucial factors influencing e-Learning adoption, including perceived ease of use, usefulness, learner attitude, self-efficacy, and facilitating conditions. However, further investigation is needed to better understand the impact of perceived learning and teaching styles on e-Learning acceptance in higher education settings. CONCLUSION: This study suggests conducting future investigations on e-Learning in developing countries, with a specific focus on intention, usage, adoption, and impact. Additionally, it emphasizes the importance of exploring the influence of learning and teaching styles on e-Learning acceptance within higher education institutions. These valuable insights will facilitate the development of effective strategies for the successful implementation of e-Learning in higher education settings.


Efficient bosonic nonlinear phase gates

Abstract: Abstract Continuous-variable (CV) quantum information processing harnesses versatile experimental tools that leverage the power of infinite-dimensional oscillators controlled by a single qubit. Increasingly available elementary Rabi gates have been proposed as a resource for implementing universal CV gates, but the requirement of many weak, non-commuting gates is a bottleneck in scaling up such an approach. In this study, we propose a resource-efficient technique using Fourier expansion to implement arbitrary non-linear phase gates in a single oscillator. This method reduces the number of sequentially required gates exponentially. These gates represented by cubic, quartic, and other arbitrary nonlinear potentials have applications in CV quantum information processing with infinite-dimensional oscillators controlled by a single qubit. Our method outperforms previous approaches and enables the experimental realization of a wide range of applications, including the development of bosonic quantum sensors, simulations, and computation using trapped ions and superconducting circuits.


Collaborative Modality Fusion for Mitigating Language Bias in Visual Question Answering

Abstract: Language bias stands as a noteworthy concern in visual question answering (VQA), wherein models tend to rely on spurious correlations between questions and answers for prediction. This prevents the models from effectively generalizing, leading to a decrease in performance. In order to address this bias, we propose a novel modality fusion collaborative de-biasing algorithm (CoD). In our approach, bias is considered as the model’s neglect of information from a particular modality during prediction. We employ a collaborative training approach to facilitate mutual modeling between different modalities, achieving efficient feature fusion and enabling the model to fully leverage multimodal knowledge for prediction. Our experiments on various datasets, including VQA-CP v2, VQA v2, and VQA-VS, using different validation strategies, demonstrate the effectiveness of our approach. Notably, employing a basic baseline model resulted in an accuracy of 60.14% on VQA-CP v2.


The ABC of academic writing: non-native speakers’ perspective

Abstract:
Abstract
 Academic writing is difficult, especially for non-native English speakers. We share a perspective on writing with a set of heuristics called the Writing Alphabet, consisting of Accurate, Brief, Clear, Dynamic, Engaging, Flowing, Goal, Habit, and Investment. These points can help struggling writers identify issues and, importantly, internalise good writing practices.


Using early LLMs for corpus linguistics: Examining ChatGPT's potential and limitations

Abstract: This study evaluates the extent to which information can be obtained from early Large Language Models (LLMs) for corpus linguistic research. Various tasks were conducted using ChatGPT 3.5, such as generating word frequency lists, collocations, words that fit certain grammatical patterns, and identifying genres. These were then compared with the search results from a large-scale general corpus (COCA). While favorable results were not achieved in identifying the genres of words or paragraphs, there was notable congruence in the frequency lists (75.0%), collocations (42.8%), and grammatical patterns (53.0%) for the top 20 items. Even when the generated items did not perfectly match those from COCA, it was evident that high-frequency items were produced. Although LLMs may not be sufficient for rigorous academic research, the results are adequate for discerning overall trends or assisting learners. In addition, the results of this study show that the ability to search at the phrase level is an advantage of using LLMs for corpus research.


Dataset of controversial news posts in Spanish from the reader's perspective

Abstract: This paper presents a corpus of Spanish news posts obtained from X with the annotation of controversy made via crowdsourcing. A total of 60 tweets were obtained from 8 different newspapers. For the annotation task, a survey was developed and sent to 31 different participants to answer it with the controversy level they perceived from the news post summary and headline presented on the post. The most frequent selected option was assigned as the initial controversy level of the post. The final annotation of the corpus was made via an analysis of the raw data by computing the Inter Annotator Agreement (IAA). The analysis showed that the binarization of the data was the most convenient way to annotate it. A potential use for this dataset is detailed in further sections.


Criminal expertise and hacking efficiency

Abstract: Criminal expertise plays a crucial role in the choices offenders make when committing a crime, including their modus operandi. However, our knowledge about criminal decision making online remains limited. Drawing on insights from cyber security, we conceptualize the cybercrime commission process as the sequence of phases of the cyber kill chain that offenders go through. We assume that offenders who follow the sequence consecutively use the most efficient hacking method. Building upon the expertise paradigm, we hypothesize that participants with greater hacking experience and IT skills undertake more efficient hacks. To test this hypothesis, we analyzed data from 69 computer security and software engineering students who were invited to hack a vulnerable website in a computer lab equipped with monitoring software, which allowed to collect objective behavioral measures. Additionally, we collected individual measures regarding hacking expertise through an online questionnaire. After quantitatively measuring efficiency using sequence analysis, a regression model showed that the expertise paradigm may also apply to hackers. We discuss the implications of our novel research for the study of offender decision-making processes more broadly.


Improved monte Carlo ray-tracing algorithm based on importance sampling

Abstract: Abstract This paper proposes an improved ray-tracing algorithm with importance sampling (IS) to reduce the computational complexity. For multiple receiver situation, we provide improved IS including adaptive and weighted optimization.


Graph-SeTES: A Graph based Search Task Extraction using Siamese Network

Abstract: Search task extraction is a sub-process for query suggestion/reformulation, personalized recommendation, and advertisement in search engines and e-commerce platforms. However, they face both internal and external challenges. Internal challenges include short and misspelled queries and incomplete keywords. External datasets include an unknown number of clusters and a limited number of labeled datasets. Deep-learning models require a large amount of data for training; however, search task datasets are rare and small. To overcome these limitations, we proposed Graph-SeTES, which integrates feature extraction with a decision network that utilizes both distance metrics and decision networks. The existing graph-clustering algorithms use the similarities between search query pairs. Because the Siamese network (SN) finds similarities between two objects, it fits search task extraction. Compared with existing models, SN requires fewer parameters for training due to the shared weights. However, it yields good results even with less labeled data, overcoming the external challenges. We benefit from both distance metrics and narrowing of the linear layers for decision networks. Graph-SeTES was compared with state-of-the-art models, and it outperformed its counterparts. The results were 6% better than those of the best baseline on the CSTE dataset, which maintained this performance difference on the WSMC12 dataset.


Mode combinability: Exploring convex combinations of permutation aligned models

Abstract: We explore element-wise convex combinations of two permutation-aligned neural network parameter vectors ΘA and ΘB of size d. We conduct extensive experiments by examining various distributions of such model combinations parametrized by elements of the hypercube [0,1]d and its vicinity. Our findings reveal that broad regions of the hypercube form surfaces of low loss values, indicating that the notion of linear mode connectivity extends to a more general phenomenon which we call mode combinability. We also make several novel observations regarding linear mode connectivity and model re-basin. We demonstrate a transitivity property: two models re-based to a common third model are also linear mode connected, and a robustness property: even with significant perturbations of the neuron matchings the resulting combinations continue to form a working model. Moreover, we analyze the functional and weight similarity of model combinations and show that such combinations are non-vacuous in the sense that there are significant functional differences between the resulting models.


Validation of artificial intelligence contrast mammography in diagnosis of breast cancer: Relationship to histopathological results

Abstract: Contrast-enhanced mammography (CEM) is used for characterization of breast lesions with increased diagnostic accuracy compared to digital mammography (DM). Artificial intelligence (AI) approaches are emerging with accuracies equal to an average radiologist. However, most studies trained deep learning (DL) models on DM images and there is a paucity in literature for discovering the application of AI using CEM.To develop and test a DL model that classifies CEM images and produces corresponding highlights of lesions detected.Fully annotated 2006 images of 326 females available from the previously published Categorized Digital Database for Contrast Enhanced Mammography images (CDD-CESM) were used for training. We developed a DL multiview contrast mammography model (MVCM) for classification of CEM low energy and recombined images. An external test set of 288 images of 37 females not included in the training was used for validation. Correlation with histopathological results and follow-up was considered the standard reference. The study protocol was approved by the Institutional Review Board and patient informed consent was obtained.Assessment was done on an external test set of 37 females (mean age, 51.31 years ± 11.07 [SD]) with AUC-ROC for AI performance 0.936; (95 % CI: 0.898, 0.973; p < 0.001) and the best cut off value for prediction of malignancy using AI score = 0.28. Findings were then correlated with histopathological results and follow up which revealed a sensitivity of 75 %, specificity 96.3 %, total accuracy of 90.1 %, positive predictive value (PPV) 87.1 %, and negative predictive value (NPV) 92 %, p-value (<0.001). Diagnostic indices of radiologists were sensitivity 88.9 %, specificity 92.6 %, total accuracy 91.7 %, PPV 80 %, and NPV 96.2 %, p-value (<0.001).A deep learning multiview CEM model was developed and evaluated in a cohort of female participants and showed promising results in detecting breast cancer. This warrants further studies, external training, and validation.


A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins

Abstract: The process of data-driven clustering to uncover geochemical anomalies linked to sample catchment basins (SCBs) includes a comprehensive framework to discern areas exhibiting unique geochemical attributes within a specified study area. The Ordering Points to Identify the Clustering Structure (OPTICS) method can serve as a robust methodology for detecting geochemical anomalies in SCBs. This is attributed to its capacity to effectively manage varying cluster densities, adaptively identify cluster numbers, exhibit resilience to noise, and display minimum sensitivity to parameters. A comparison was conducted in this research between the outcomes of the OPTICS clustering algorithm and two traditional clustering techniques, namely the Gaussian Mixture Model (GMM) and K-means clustering. In the following, the Expectation-Maximization (EM) technique is employed to train the GMM for clustering. Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as two validate statistical metrics implemented to ascertain the optimal number of components (clusters) belong to the GMM. It should be noted that the effectiveness of the clustering algorithms was further assessed using the Calinski-Harabasz (CH) index and the success-rate curves. OPTICS, a density-based clustering approach, was confirmed to be more effective than K-means and GMM for identifying MVT PbZn anomalies in Varcheh district, western Iran. Furthermore, the specified anomalies show a geo-spatial correspondence with the geological facts, and it has been observed that strong anomalies are more discoverable in close proximity to MVT PbZn occurrences. This work suggests a novel anomaly detection approach based on OPTICS, which exhibits superior performance and data-modeling efficiency. The main emphasis is on effectively distinguishing geochemical anomalies from sample data originating from populations with uncertain distributions.


Self-restrained contrastive enhanced network for graph structure learning

Abstract: Existing graph neural networks (GNNs) are mostly applied to representation scenes with complete graph structure. However, the graph structures of complex systems from the real world are generally missing or misconnected. In addition, due to the indistinguishability of graph node features, most of the existing GNNs are limited by the over-smoothing problem when performing classification tasks. To address these issues, we propose a model for learning and optimizing graph structure—Self-restrained Graph Contrastive Enhanced Network (GCEN). Firstly, the multiple branch generation structure is adopted to obtain various graph structures, which are updated by multi-order information fusion to obtain a new graph structure. Secondly, the performance of each generated structure branch is improved by self-restrained network, and then the structure information of the graph is continuously optimized to obtain a high-quality graph structure. Finally, the feature contrast enhancement module is utilized to adaptively adjust the features of nodes to obtain distinguishable features, thus alleviating the over-smoothing problem of GNNs in classification tasks. Extensive experiments on several benchmark datasets indicate that our GCEN is superior to several models for learning task-specific graph structure in optimizing the graph structure.


Unveiling ChatGPT in tourism education: exploring perceptions, advantages and recommendations from educators

Abstract: Purpose Following the remarkable debut of ChatGPT and its rapid adoption by a global audience since its launch in November 2022, this study delves into educators' perceptions of ChatGPT within the specialized domains of tourism and hospitality education. While acknowledging ChatGPT’s swift rise and advanced capabilities, this research aims to comprehensively explore educators' perspectives, advantages and concerns regarding its integration into academic settings. Design/methodology/approach A qualitative approach was utilized to reveal dominant themes from in-depth, semi-structured face-to-face interviews with twenty lecturers in tourism faculties in North Cyprus. Collected responses from respondents were subjected to analysis using Leximancer software. Findings Our analysis unearthed seven significant themes encapsulating educators' perceptions of ChatGPT: 1 – “reliability and accuracy concerns”; 2 – “dependency”; 3 – “student engagement”; 4 – “ethical considerations”; 5 – “time efficiency and productivity”; 6 – “teacher-student interaction” and 7 – “learning flexibility”. Noteworthy positive perceptions emerged regarding “student engagement,” “time efficiency and productivity,” and “learning flexibility.” Originality/value This study contributes to the originality of research by addressing the underexplored aspect of educators' perceptions of ChatGPT within the domains of tourism and hospitality education, shedding light on its potential implications, advantages and drawbacks in a specialized educational context. Furthermore, it aims to offer insights into educators' recommendations for the effective incorporation of ChatGPT technologies into this specific educational setting, filling a crucial gap in understanding the integration of artificial intelligence (AI) in specialized fields of study.


Partially reflecting jamming objects in correlation-enhanced target detection with entangled photons

Abstract: In the optical detection of distant objects, one can benefit from quantum light properties over those of classical light. Two-beam correlation-enhanced protocols can improve imaging or target detection even under strong background noise and in the low photon flux regime. We demonstrate that a partially reflecting jamming object introduces noise to these protocols, including quantum illumination. In contrast to background noise, the signal radiation reflected from the jammer is correlated with the corresponding idler beam. We analyze the means to mitigate this noise and introduce an indistinguishability parameter μ, showing how efficiently the jammer can be distinguished from the target. We use the quantum Chernoff bound and the density matrix orthogonalization procedure to separate the contributions from the target, the background, and the jammer. We illustrate our findings with an experiment using optical entangled photon pairs at 800 nm. Our results can be applied to the design of target detection protocols and advanced imaging techniques.


BapFL : You can Backdoor Personalized Federated Learning

Abstract: In federated learning (FL), malicious clients could manipulate the predictions of the trained model through backdoor attacks, posing a significant threat to the security of FL systems. Existing research primarily focuses on backdoor attacks and defenses within the generic federated learning scenario, where all clients collaborate to train a single global model. A recent study conducted by Qin et al. [24] marks the initial exploration of backdoor attacks within the personalized federated learning (pFL) scenario, where each client constructs a personalized model based on its local data. Notably, the study demonstrates that pFL methods with parameter decoupling can significantly enhance robustness against backdoor attacks. However, in this paper, we whistleblow that pFL methods with parameter decoupling are still vulnerable to backdoor attacks. The resistance of pFL methods with parameter decoupling is attributed to the heterogeneous classifiers between malicious clients and benign counterparts. We analyze two direct causes of the heterogeneous classifiers: (1) data heterogeneity inherently exists among clients and (2) poisoning by malicious clients further exacerbates the data heterogeneity. To address these issues, we propose a two-pronged attack method, BapFL , which comprises two simple yet effective strategies: (1) poisoning only the feature encoder while keeping the classifier fixed and (2) diversifying the classifier through noise introduction to simulate that of the benign clients. Extensive experiments on three benchmark datasets under varying conditions demonstrate the effectiveness of our proposed attack. Additionally, we evaluate the effectiveness of six widely used defense methods and find that BapFL still poses a significant threat even in the presence of the best defense, Multi-Krum. We hope to inspire further research on attack and defense strategies in pFL scenarios. The code is available at: https://github.com/BapFL/code.


VertexShuffle-Based Spherical Super-Resolution for 360-Degree Videos

Abstract: 360-degree video is an emerging form of media that encodes information about all directions surrounding a camera, offering an immersive experience to the users. Unlike traditional 2D videos, visual information in 360-degree videos can be naturally represented as pixels on a sphere. Inspired by state-of-the-art deep-learning-based 2D image super-resolution models and spherical CNNs, in this paper, we design a novel spherical super-resolution (SSR) approach for 360-degree videos. To support viewport-adaptive and bandwidth-efficient transmission/streaming of 360-degree video data and save computation, we propose the Focused Icosahedral Mesh to represent a small area on the sphere. We further construct matrices to rotate spherical content over the entire sphere to the focused mesh area, allowing us to use the focused mesh to represent any area on the sphere. Motivated by the PixelShuffle operation for 2D super-resolution, we also propose a novel VertexShuffle operation on the mesh and an improved version VertexShuffle_V2. We compare our SSR approach with state-of-the-art 2D super-resolution models and show that SSR has the potential to achieve significant benefits when applied to spherical signals.


Research on joint model relation extraction method based on entity mapping

Abstract: Relationship Extraction (RE) is a central task in information extraction. The use of entity mapping to address complex scenarios with overlapping triples, such as CasRel, is gaining traction, yet faces challenges such as inadequate consideration of sentence continuity, sample imbalance and data noise. This research introduces an entity mapping-based method CasRelBLCF building on CasRel. The main contributions include: A joint decoder for the head entity, utilizing Bi-LSTM and CRF, integration of the Focal Loss function to tackle sample imbalance and a reinforcement learning-based noise reduction method for handling dataset noise. Experiments on relation extraction datasets indicate the superiority of the CasRelBLCF model and the enhancement on model's performance of the noise reduction method.


A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents

Abstract: Abstract Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by human cognition have gained significance. This study contributes to existing research by introducing a cognitive-attentional system employing a constructive neural network-based learning approach for continuous acquisition of procedural knowledge. We replace an incremental tabular Reinforcement Learning algorithm with a constructive neural network deep reinforcement learning mechanism for continuous sensorimotor knowledge acquisition, thereby enhancing the overall learning capacity. The primary emphasis of this modification centers on optimizing memory utilization and reducing training time. Our study presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. The proposed learning mechanism allows the model to dynamically create and modify elements in its procedural memory, facilitating the reuse of previously acquired functions and procedures. Additionally, it equips the model with the capability to combine learned elements to effectively adapt to complex scenarios. A constructive neural network was employed, initiating with an initial hidden layer comprising one neuron. However, it possesses the capacity to adapt its internal architecture in response to its performance in procedural and sensorimotor learning tasks, inserting new hidden layers or neurons. Experimentation conducted through simulations involving a humanoid robot demonstrates the successful resolution of tasks that were previously unsolved through incremental knowledge acquisition. Throughout the training phase, the constructive agent achieved a minimum of 40% greater rewards and executed 8% more actions when compared to other agents. In the subsequent testing phase, the constructive agent exhibited a 15% increase in the number of actions performed in contrast to its counterparts.


On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition

Abstract: With the rise of deep neural networks, the performance of biometric systems has increased tremendously. Biometric systems for face recognition are now used in everyday life, e.g., border control, crime prevention, or personal device access control. Although the accuracy of face recognition systems is generally high, they are not without flaws. Many biometric systems have been found to exhibit demographic bias, resulting in different demographic groups being not recognized with the same accuracy. This is especially true for facial recognition due to demographic factors, e.g., gender and skin color. While many previous works already reported demographic bias, this work aims to reduce demographic bias for biometric face recognition applications. In this regard, 12 face recognition systems are benchmarked regarding biometric recognition performance as well as demographic differentials, i.e., fairness. Subsequently, multiple fusion techniques are applied with the goal to improve the fairness in contrast to single systems. The experimental results show that it is possible to improve the fairness regarding single demographics, e.g., skin color or gender, while improving fairness for demographic subgroups turns out to be more challenging.


Assessing the Rapid Expansion of Technology in Africa

Abstract: The chapter assesses the state of digital technology in Africa as a catalyst for economic growth and development. The chapter also appraises the state of higher education on the continent in order to determine its preparedness to produce professionals who can function in a digital economy. A number of technological diffusion models which include diffusion of innovation (DIO), theory of reasoned action, and technology adoption model (TAM) were used to explore the diffusion of innovation to Africa from developed countries. Although there is a rapid growth in technological penetration in Africa impacting sectors such as agriculture, education, and finance, more investment in technology is still needed to power strides towards optimum levels of economic development. Africa, with its rapidly growing population and ever expanding market, is perceived as the next economic frontier after Asia. Higher education through curriculum models such as Education 5.0 can lead the way to make Africa the next economic frontier.


A topic relevance-aware click model for web search

Abstract: To better understand users’ behavior patterns in web search, numerous click models are proposed to extract the implicit interaction feedback. Most existing click models are heavily based on the implicit information to model user behaviors, ignoring the impact of explicit information between queries and documents in search sessions. In this paper, we fully consider the topic relevance between queries and documents in search sessions and propose a novel topic relevance-aware click model (TRA-CM) for web search. TRA-CM consists of a relevance estimator and an examination predictor. The relevance estimator consists of a topic relevance predictor and a click context encoder. In the topic relevance predictor, we utilize the pre-trained BERT model to model the content information of queries and documents in search sessions. Meanwhile, we use transformer to encode users’ click behaviors in the click context encoder. We further apply a two-stage fusion strategy to obtain the final relevance scores. The examination predictor estimates the examination probability of each document. We further utilize learnable filters to attenuate log noise and obtain purer input features in both relevance estimator and examination predictor, and investigate different combination functions to integrate relevance scores and examination probabilities into click prediction. Extensive experiment results on two real-world session datasets prove that TRA-CM outperforms existing click models in both click prediction and relevance estimation tasks.


Diffusion models in text generation: a survey

Abstract: Diffusion models are a kind of math-based model that were first applied to image generation. Recently, they have drawn wide interest in natural language generation (NLG), a sub-field of natural language processing (NLP), due to their capability to generate varied and high-quality text outputs. In this article, we conduct a comprehensive survey on the application of diffusion models in text generation. We divide text generation into three parts (conditional, unconstrained, and multi-mode text generation, respectively) and provide a detailed introduction. In addition, considering that autoregressive-based pre-training models (PLMs) have recently dominated text generation, we conduct a detailed comparison between diffusion models and PLMs in multiple dimensions, highlighting their respective advantages and limitations. We believe that integrating PLMs into diffusion is a valuable research avenue. We also discuss current challenges faced by diffusion models in text generation and propose potential future research directions, such as improving sampling speed to address scalability issues and exploring multi-modal text generation. By providing a comprehensive analysis and outlook, this survey will serve as a valuable reference for researchers and practitioners interested in utilizing diffusion models for text generation tasks.


Study of real-time parameter measurement of ring rolling pieces based on machine vision

Abstract: Real time parameter measurement cannot be carried out to dynamic ring parts during automation ring rolling processes so that rolling process parameters cannot be adjusted in time. Considering effects of shaping of ring rolling parts, a visual measurement platform was set up and a machine vision-based non-contact real -time measurement method was put forward. This article improves the subpixel level edge extraction algorithm to extract edge data information of circular rolling pieces. Based on the characteristics of circular rolling pieces, an RG-Hough transform method is proposed to fit the detected edge data information. The conversion relationship between pixel and actual sizes were determined in combination with the camera calibration to gain parameters of ring rolling parts. Measurements of ring parts (OD: 462.12mm; and ID: 315.53mm) were applied to verify the effectiveness of our method. Our measurement error is ±0.25mm and our average speed can be up to 104ms/frame. Our study can provide powerful technical supports for intelligent control of ring rolling pieces.


Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Abstract: This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, the BOA is formulated and mathematically modeled. Evaluation on the CEC 2017 test suite showcases the BOA's ability to balance exploration and exploitation, delivering competitive solutions. Comparative analysis against twelve well-known metaheuristic algorithms demonstrates the BOA's superior performance across various benchmark functions, with statistically significant advantages. Moreover, application to constrained optimization problems from the CEC 2011 test suite highlights the BOA's effectiveness in real-world optimization tasks.


Quantitative assessment of H&E staining for pathology: development and clinical evaluation of a novel system

Abstract: Staining tissue samples to visualise cellular detail and tissue structure is at the core of pathology diagnosis, but variations in staining can result in significantly different appearances of the tissue sample. While the human visual system is adept at compensating for stain variation, with the growth of digital imaging in pathology, the impact of this variation can be more profound. Despite the ubiquity of haematoxylin and eosin staining in clinical practice worldwide, objective quantification is not yet available. We propose a method for quantitative haematoxylin and eosin stain assessment to facilitate quality assurance of histopathology staining, enabling truly quantitative quality control and improved standardisation.The stain quantification method comprises conventional microscope slides with a stain-responsive biopolymer film affixed to one side, called stain assessment slides. The stain assessment slides were characterised with haematoxylin and eosin, and implemented in one clinical laboratory to quantify variation levels.Stain assessment slide stain uptake increased linearly with duration of haematoxylin and eosin staining (r = 0.99), and demonstrated linearly comparable staining to samples of human liver tissue (r values 0.98-0.99). Laboratory implementation of this technique quantified intra- and inter-instrument variation of staining instruments at one point in time and across a five-day period.The proposed method has been shown to reliably quantify stain uptake, providing an effective laboratory quality control method for stain variation. This is especially important for whole slide imaging and the future development of artificial intelligence in digital pathology.


Multi-sensor fusion federated learning method of human posture recognition for dual-arm nursing robots

Abstract: Human posture estimation is a significant pre-imperative for intelligent nursing robot. However, most existing studies suffer from the driven data deficiencies, because of the concerns about poor information safety in the private healthcare environment. Federated Learning is able to solve the problem, as it can improve the learning ability of the models as well as protecting the data privacy. In this paper, we proposed a Federated Learning Human Posture Recognition (FL-HPR) framework according to image and point cloud fusion. FL-HPR benefits the information flow in the global model while ensuring the data privacy of local models. Furthermore, FL-HPR optimizes the local dynamic graph edge convolution network of robot, which improves the recognition accuracy of each body limb and enhance robustness. Experiments on Non-IID datasets illustrate that the presented FL-HPR remarkably outperforms non-federated learning methods. The developed frame improves the accuracy of human joint estimation, which indicates the proposed FL-HPR can be integrated into the intelligent nursing robot in practical. The open-source code and videos are available at https://github.com/Hebut-LEVO/.


Melanoma Prevention using an Augmented Reality-based Serious Game

Abstract: The purpose of this study was to field-test a recently developed AR-based serious game designed to promote SSE self-efficacy, called Spot. Thirty participants played the game and answered 3 questionnaires: a baseline questionnaire, a second questionnaire immediately after playing the game, and a third questionnaire 1 week later (follow-up). The majority of participants considered that the objective quality of the game was high, and considered that the game could have a real impact in SSE promotion. Participants showed statistically significant increases in SSE self-efficacy and intention at follow-up. Of the 24 participants that had never performed a SSE or had done one more than 3 months ago, 12 (50.0%) reported doing a SSE at follow-up. This study provides supporting evidence to the use of serious games in combination with AR to educate and motivate users to perform SSE. Spot seems to be an inconspicuous but effective strategy to promote SSE, a cancer prevention behavior, among healthy individuals. Patient education is essential to tackle skin cancer, particularly melanoma. Serious games, such as Spot, have the ability to effectively educate and motivate patients to perform a cancer prevention behavior.


High invisibility image steganography with wavelet transform and generative adversarial network

Abstract: Recently, extensive research is showing that deep learning has enormous potential in image steganography. However, most steganography methods based on deep learning are not sufficiently invisible. In this paper, a novel end-to-end deep neural network for image steganography based on generative adversarial network (GAN) and discrete wavelet transformation (DWT) is proposed, which can greatly improve the invisibility of the method. Firstly, the cover image is transformed from the RGB domain to the DWT domain to embed information in frequency. Furthermore, a multi-scale attention convolution to fuse different scales of feature information is proposed, which can help the model to find a better location to embed the information. Finally, the fusion module is used to embed the information into the cover image at different modification intensities. In addition, a new Discriminator that compares cover images and stego images in a patch-to-patch way to improve the ability of the Discriminator. We analyze our algorithm from multiple aspects (e.g., security and invisibility) to verify the superior performance of the proposed method. To demonstrate generalization ability, experiments are conducted on three widely used datasets and show the superior performance of our method compared to other steganography methods based on deep learning.