Since its publication in 1887,Arthur Conan Doyle’s A Study in Scarlet has become one of the most influential works in detective fiction worldwide,renowned for its innovative narrative techniques,compelling plot,and d...Since its publication in 1887,Arthur Conan Doyle’s A Study in Scarlet has become one of the most influential works in detective fiction worldwide,renowned for its innovative narrative techniques,compelling plot,and deep engagement with themes of justice and morality.The novel has seen 311 Chinese publications,among which two translations stand out:Xieluoke Qian Kaichan translated by Lin Shu and Wei Yi in late Qing dynasty in 1914 and Xuezi Yanjiu,transalted by Ding Zhonghu and Yuan Dihua in the Reform and Opening-up era in 1981.This study examines these two significant Chinese translations from a narrative theory perspective.Lin’s version employs classical allusions and imaginative language,frequently uses internal focalization to enhance reader involvement,incorporates rhetorical embellishments,and reinterprets speeches through adaptation to intensify emotional and plot dynamics.In contrast,Din’s translation adopts vernacular language complemented by explanatory notes to provide cultural context,maintains the original focalization patterns,favors direct translation of dialogues to preserve stylistic authenticity,and adheres closely to the linear narrative structure of the source text.This study not only describes the different translation strategies across two defining historical periods but also contributes to a deeper understanding of how narrative voice,cultural positioning,and reader engagement are negotiated in the translation of classic detective fiction.展开更多
This paper aims to examine the characterizational shift of C.Auguste Dupin in Edgar Allan Poe’s detective stories.First,Poe’s detective stories were written when the Enlightenment,which emphasizes Reason,was being e...This paper aims to examine the characterizational shift of C.Auguste Dupin in Edgar Allan Poe’s detective stories.First,Poe’s detective stories were written when the Enlightenment,which emphasizes Reason,was being embedded in the fabric of American culture.Meanwhile,beneath the Enlightenment was also an undercurrent of irrationality.In Poe’s“The Murders in the Rue Morgue”and“The Mystery of Marie Rogêt,”Dupin typifies a flat character standing for Reason/Good.However,in Poe’s“The Purloined Letter,”Dupin has been depicted as a round character;not only is he characterized a lot more vividly but also he bears striking resemblance to his opponent,Minister D.Namely,the dichotomous relationship between them has been erased,and Dupin has been portrayed more like a real person walking on the thin line between Good and Evil.Speaking of dissecting this characterizational shift of Dupin,I believe the key lies in the fact that Poe actually has taken an attitude of openness about Reason and Unreason,and that he has a way with opposing elements.In“The Murders in the Rue Morgue”and“The Mystery of Marie Rogêt,”Poe intends for Reason,represented by Dupin,to keep under control Unreason,represented by the criminals.In such a case,Dupin only needs to be a flat character representing Good/Reason.But in“The Purloined Letter,”Poe intends for Reason/Good and Unreason/Evil to be merged.Under such circumstances,Dupin will conveniently evolve into a round character.展开更多
Sherlock Holmes is a fictional detective created by Sir Arthur Conan Doyle, the Scottish author and physician. As a London-based "consulting detective" whose abilities border on the fantastic, Holmes is famo...Sherlock Holmes is a fictional detective created by Sir Arthur Conan Doyle, the Scottish author and physician. As a London-based "consulting detective" whose abilities border on the fantastic, Holmes is famous for his astute logical reasoning, his ability to adopt almost any disguise, and his use of forensic science skills to solve difficult cases. The paper tries to analyze the characteristics of the Holmes and how did Holmes observe evidences and analyze clues.展开更多
Compared with other kinds of fiction,detective story is a kind of fiction with different characteristics,it involves a process of thinking,analysis,inference,interaction.This passage mainly discusses detective story&#...Compared with other kinds of fiction,detective story is a kind of fiction with different characteristics,it involves a process of thinking,analysis,inference,interaction.This passage mainly discusses detective story's characteristics as a kind of intellectual game and reader's psychology during reading.展开更多
This paper,in the frame of Barthes and Foucault's ideas about the"Author",explores the complicated relationship between the author and reader by comparing the classical detective story,Edgar Allen Poe...This paper,in the frame of Barthes and Foucault's ideas about the"Author",explores the complicated relationship between the author and reader by comparing the classical detective story,Edgar Allen Poe's The Murder in the Rue Morgue,and the metaphysical detective story,Paul Auster'City of Glass and Umberto Eco's The Name of the Rose.These two stories investigate the perspectives;the story of crime and the story of investigation.展开更多
In Conan Doyle’s detective stories mainly including“The Resident Patient,”“The Gloria Scott,”“The Adventure of Blanched Soldier,”and“The Crooked Man,”featuring the master sleuth character Sherlock Holmes,he d...In Conan Doyle’s detective stories mainly including“The Resident Patient,”“The Gloria Scott,”“The Adventure of Blanched Soldier,”and“The Crooked Man,”featuring the master sleuth character Sherlock Holmes,he depicts the return of the colonials from British colonies,mostly India,with physically deformed or ravaged body and traumatic past that haunt and trouble his characters’present life.Doyle allegorically uses returned colonials or poor whites who turn into figures of retributive ghosts that function as pathetic memories and inner fears from British colonies.The seeing of ghostly figures and haunting past events delineated in these stories cause characters’sense of uncanny horror and remind them of their past trauma.These monstrous returned colonials or poor whites often create a fear and a social menace that must be appropriately dealt with when the master sleuth is commissioned to pin down the truth of client’s cases.Why are these bodies of ghostly figures so“irregular”and ravaged?What do these deformities signify?How can returned colonial’s or poor white’s traumatic past be related to retributive ghost?This paper attempts to probe into these issues in order to find out possible answers.展开更多
Sir Arthur Conan Doyle wrote many mystery and detective stories from 1890s to 1910s, years saw the advancement of powerful modem science and technology, especially inventions of transportation means or machines that a...Sir Arthur Conan Doyle wrote many mystery and detective stories from 1890s to 1910s, years saw the advancement of powerful modem science and technology, especially inventions of transportation means or machines that accelerate mobility power in late-Victorian and Edwardian society. In some of these mystery or detective stories especially featuring the well-known sleuth Sherlock Holmes, Doyle tended to integrate an early subject＇s experience of shrunken space and reduced time into an unknown fear by delineating his characters who perceive horror and nervousness while facing or riding on a railway transportation, including mainly the steam railway in mysterious tales like ＂The Lost Special＂ and ＂The Man with the Watches＂ as well as in detective stories like ＂The Adventure of the Engineer＇s Thumb＂, ＂The Adventure of Bruce-Partington Plan＂, ＂Valley of Fear＂ and several others. How can this spatiotemporal mobility be connected to mysterious affairs which lead Doyle＇s quasi-detective characters and police power to spring into investigative action？ Railway, mobility, and horror are woven together into a driving force that facilitates our geographical and forensic exploration of Doyle＇s stories.展开更多
Allan Poe has been deemed as the founder of modern detective story. This paper mainly talks about his contributions tomake this new genre a formal sub-genre of literature. Techniques he used in his short stories, lock...Allan Poe has been deemed as the founder of modern detective story. This paper mainly talks about his contributions tomake this new genre a formal sub-genre of literature. Techniques he used in his short stories, locked-room murder and the arm-chair detective, have become the classical conventions of detective story. The eccentric but brilliant protagonist, Auguste Dupin inhis story, has become a model of the later detectives. Poe has also contributed to define the detective story as some kind of intellec-tual game, the plot of which concentrates on the process of investigation.展开更多
Since 2000 A.D.,lots of translated detective novels have being published in Taiwan,China,which demonstrates that detective novel is popular in Taiwan,China,but there are seldom local detective novels to be published.T...Since 2000 A.D.,lots of translated detective novels have being published in Taiwan,China,which demonstrates that detective novel is popular in Taiwan,China,but there are seldom local detective novels to be published.Through the theory of field of cultural production by Pierre Bourdieu,the paper analyzed how the creators and cultural intermediaries’form of capitals and aesthetics construct the mechanism of the publishing industry,and how the market of detective novels in Taiwan,China are dominated by foreign products.The study adopted second documentary analysis and in-depth interview.The former is to calculate the published detective novels from 2001 to September 2015 sold in the dominant on-line bookstore,Books.com.tw,in Taiwan,China,while the latter is to interview 15 related agencies included writers,editors,translators,and a manager of bookstore.The results contain three following issues.Firstly,local production has re-started since 1980’s after a long-time decline.Considering the large cost to cultivate local writers,Taiwan region of China publishers prefer to produce well-known foreign works.Secondly,literary awards are the vital way in the production of local works.The writers receive symbolic capital through awards,and even obtain more opportunities to publish their works or cooperate with other related organization,which means the acquirement of social capital.Finally,the market of local detective novels is forced to be the field of restricted production as a result of supplanted by translated novels.As a consequence,the production of local detective novels becomes popular literature of niche market.展开更多
Despite of only producing five ratiocinative tales in the whole life,Edgar Allan Poe is acknowledged as the "father of the detective story".In those tales,Poe portrays the hero Dupin who is the first detecti...Despite of only producing five ratiocinative tales in the whole life,Edgar Allan Poe is acknowledged as the "father of the detective story".In those tales,Poe portrays the hero Dupin who is the first detective image in the history of the western literature vividly.Based on the stories in which Dupin appeared,concerns on the creation of Dupin,the analysis of his features and the function of the setting fellows,like friend and police,summarizing the traditional image pattern of detective stories created by Poe,revealing the great influence Poe had on the development of detective literature,even on the literature of the whole world.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods...Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods are ineffective against novel attacks,and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments.To address these challenges,this study proposes a deep feature-driven hybrid framework that integrates Transformer,BiLSTM,and KNN to achieve accurate and robust DDoS detection.The Transformer component extracts global temporal dependencies from network traffic flows,while BiLSTM captures fine-grained sequential dynamics.The learned embeddings are then classified using an instance-based KNN layer,enhancing decision boundary precision.This cascaded architecture balances feature abstraction and locality preservation,improving both generalization and robustness.The proposed approach was evaluated on a newly collected real-time ICN traffic dataset and further validated using the public CIC-IDS2017 and Edge-IIoT datasets to demonstrate generalization.Comprehensive metrics including accuracy,precision,recall,F1-score,ROC-AUC,PR-AUC,false positive rate(FPR),and detection latency were employed.Results show that the hybrid framework achieves 98.42%accuracy with an ROC-AUC of 0.992 and FPR below 1%,outperforming baseline machine learning and deep learning models.Robustness experiments under Gaussian noise perturbations confirmed stable performance with less than 2%accuracy degradation.Moreover,detection latency remained below 2.1 ms per sample,indicating suitability for real-time ICS deployment.In summary,the proposed hybrid temporal learning and instance-based classification model offers a scalable and effective solution for DDoS detection in industrial control environments.By combining global contextual modeling,sequential learning,and instance-based refinement,the framework demonstrates strong adaptability across datasets and resilience against noise,providing practical utility for safeguarding critical infrastructure.展开更多
Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of...Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of object detection,there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes.In particular,the YOLO(You Only Look Once)series of detection models,renowned for their real-time performance,have undergone numerous adaptations aimed at improving the detection of small targets.In this survey,we summarize the state-of-the-art YOLO-based small object detection methods.This review presents a systematic categorization of YOLO-based approaches for small-object detection,organized into four methodological avenues,namely attention-based feature enhancement,detection-head optimization,loss function,and multi-scale feature fusion strategies.We then examine the principal challenges addressed by each category.Finally,we analyze the performance of thesemethods on public benchmarks and,by comparing current approaches,identify limitations and outline directions for future research.展开更多
The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.Thi...The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.展开更多
Breast cancer screening programs rely heavily on mammography for early detection;however,diagnostic performance is strongly affected by inter-reader variability,breast density,and the limitations of conven-tional comp...Breast cancer screening programs rely heavily on mammography for early detection;however,diagnostic performance is strongly affected by inter-reader variability,breast density,and the limitations of conven-tional computer-aided detection systems.Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening,yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited.This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset,developed within the Turkish National Breast Cancer Screening Program.The dataset comprises 12,740 patient cases collected between 2016 and 2022,annotated with BI-RADS categories,breast density levels,and lesion localization labels.A total of 31 models were evaluated,including One-Stage,Two-Stage,and Transformer-based architectures,under a unified experimental framework at both patient and breast levels.The results demonstrate that Two-Stage architectures consistently outperform One-Stage models,achieving approximately 2%–4%higher Macro F1-Scores and more balanced precision–recall trade-offs,with Double-Head R-CNN and Dynamic R-CNN yielding the highest overall performance(Macro F1≈0.84–0.86).This advantage is primarily attributed to the region proposal mechanism and improved class balance inherent to Two-Stage designs.One-Stage detectors exhibited higher sensitivity and faster inference,reaching Recall values above 0.88,but experienced minor reductions in Precision and overall accuracy(≈1%–2%)compared with Two-Stage models.Among Transformer-based architectures,Deformable DEtection TRansformer demonstrated strong robustness and consistency across datasets,achieving Macro F1-Scores comparable to CNN-based detectors(≈0.83–0.85)while exhibiting minimal performance degradation under distributional shifts.Breast density–based analysis revealed increased misclassification rates in medium-density categories(types B and C),whereas Transformer-based architectures maintained more stable performance in high-density type D tissue.These findings quantitatively confirm that both architectural design and tissue characteristics play a decisive role in diagnostic accuracy.Overall,the study provides a reproducible benchmark and highlights the potential of hybrid approaches that combine the accuracy of Two-Stage detectors with the contextual modeling capability of Transformer architectures for clinically reliable breast cancer screening systems.展开更多
Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Tr...Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Training(AT)enables NIDS agents to discover and prevent newattack paths by exposing them to competing examples,thereby increasing detection accuracy,reducing False Positives(FPs),and enhancing network security.To develop robust decision-making capabilities for real-world network disruptions and hostile activity,NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity.The accuracy and timeliness of the IDS were crucial to the network’s availability and reliability at this time.This paper analyzes ARL applications in NIDS,revealing State-of-The-Art(SoTA)methodology,issues,and future research prospects.This includes Reinforcement Machine Learning(RML)-based NIDS,which enables an agent to interact with the environment to achieve a goal,andDeep Reinforcement Learning(DRL)-based NIDS,which can solve complex decision-making problems.Additionally,this survey study addresses cybersecurity adversarial circumstances and their importance for ARL and NIDS.Architectural design,RL algorithms,feature representation,and training methodologies are examined in the ARL-NIDS study.This comprehensive study evaluates ARL for intelligent NIDS research,benefiting cybersecurity researchers,practitioners,and policymakers.The report promotes cybersecurity defense research and innovation.展开更多
基金funded by 2025 Guangdong Provincial Project on Foreign Language Teaching Reform Research and Practice for Undergraduate Universities:Research on the AI-Assisted Teaching Model for Translation(grant number 25GWYB04).
文摘Since its publication in 1887,Arthur Conan Doyle’s A Study in Scarlet has become one of the most influential works in detective fiction worldwide,renowned for its innovative narrative techniques,compelling plot,and deep engagement with themes of justice and morality.The novel has seen 311 Chinese publications,among which two translations stand out:Xieluoke Qian Kaichan translated by Lin Shu and Wei Yi in late Qing dynasty in 1914 and Xuezi Yanjiu,transalted by Ding Zhonghu and Yuan Dihua in the Reform and Opening-up era in 1981.This study examines these two significant Chinese translations from a narrative theory perspective.Lin’s version employs classical allusions and imaginative language,frequently uses internal focalization to enhance reader involvement,incorporates rhetorical embellishments,and reinterprets speeches through adaptation to intensify emotional and plot dynamics.In contrast,Din’s translation adopts vernacular language complemented by explanatory notes to provide cultural context,maintains the original focalization patterns,favors direct translation of dialogues to preserve stylistic authenticity,and adheres closely to the linear narrative structure of the source text.This study not only describes the different translation strategies across two defining historical periods but also contributes to a deeper understanding of how narrative voice,cultural positioning,and reader engagement are negotiated in the translation of classic detective fiction.
文摘This paper aims to examine the characterizational shift of C.Auguste Dupin in Edgar Allan Poe’s detective stories.First,Poe’s detective stories were written when the Enlightenment,which emphasizes Reason,was being embedded in the fabric of American culture.Meanwhile,beneath the Enlightenment was also an undercurrent of irrationality.In Poe’s“The Murders in the Rue Morgue”and“The Mystery of Marie Rogêt,”Dupin typifies a flat character standing for Reason/Good.However,in Poe’s“The Purloined Letter,”Dupin has been depicted as a round character;not only is he characterized a lot more vividly but also he bears striking resemblance to his opponent,Minister D.Namely,the dichotomous relationship between them has been erased,and Dupin has been portrayed more like a real person walking on the thin line between Good and Evil.Speaking of dissecting this characterizational shift of Dupin,I believe the key lies in the fact that Poe actually has taken an attitude of openness about Reason and Unreason,and that he has a way with opposing elements.In“The Murders in the Rue Morgue”and“The Mystery of Marie Rogêt,”Poe intends for Reason,represented by Dupin,to keep under control Unreason,represented by the criminals.In such a case,Dupin only needs to be a flat character representing Good/Reason.But in“The Purloined Letter,”Poe intends for Reason/Good and Unreason/Evil to be merged.Under such circumstances,Dupin will conveniently evolve into a round character.
文摘Sherlock Holmes is a fictional detective created by Sir Arthur Conan Doyle, the Scottish author and physician. As a London-based "consulting detective" whose abilities border on the fantastic, Holmes is famous for his astute logical reasoning, his ability to adopt almost any disguise, and his use of forensic science skills to solve difficult cases. The paper tries to analyze the characteristics of the Holmes and how did Holmes observe evidences and analyze clues.
文摘Compared with other kinds of fiction,detective story is a kind of fiction with different characteristics,it involves a process of thinking,analysis,inference,interaction.This passage mainly discusses detective story's characteristics as a kind of intellectual game and reader's psychology during reading.
文摘This paper,in the frame of Barthes and Foucault's ideas about the"Author",explores the complicated relationship between the author and reader by comparing the classical detective story,Edgar Allen Poe's The Murder in the Rue Morgue,and the metaphysical detective story,Paul Auster'City of Glass and Umberto Eco's The Name of the Rose.These two stories investigate the perspectives;the story of crime and the story of investigation.
文摘In Conan Doyle’s detective stories mainly including“The Resident Patient,”“The Gloria Scott,”“The Adventure of Blanched Soldier,”and“The Crooked Man,”featuring the master sleuth character Sherlock Holmes,he depicts the return of the colonials from British colonies,mostly India,with physically deformed or ravaged body and traumatic past that haunt and trouble his characters’present life.Doyle allegorically uses returned colonials or poor whites who turn into figures of retributive ghosts that function as pathetic memories and inner fears from British colonies.The seeing of ghostly figures and haunting past events delineated in these stories cause characters’sense of uncanny horror and remind them of their past trauma.These monstrous returned colonials or poor whites often create a fear and a social menace that must be appropriately dealt with when the master sleuth is commissioned to pin down the truth of client’s cases.Why are these bodies of ghostly figures so“irregular”and ravaged?What do these deformities signify?How can returned colonial’s or poor white’s traumatic past be related to retributive ghost?This paper attempts to probe into these issues in order to find out possible answers.
文摘Sir Arthur Conan Doyle wrote many mystery and detective stories from 1890s to 1910s, years saw the advancement of powerful modem science and technology, especially inventions of transportation means or machines that accelerate mobility power in late-Victorian and Edwardian society. In some of these mystery or detective stories especially featuring the well-known sleuth Sherlock Holmes, Doyle tended to integrate an early subject＇s experience of shrunken space and reduced time into an unknown fear by delineating his characters who perceive horror and nervousness while facing or riding on a railway transportation, including mainly the steam railway in mysterious tales like ＂The Lost Special＂ and ＂The Man with the Watches＂ as well as in detective stories like ＂The Adventure of the Engineer＇s Thumb＂, ＂The Adventure of Bruce-Partington Plan＂, ＂Valley of Fear＂ and several others. How can this spatiotemporal mobility be connected to mysterious affairs which lead Doyle＇s quasi-detective characters and police power to spring into investigative action？ Railway, mobility, and horror are woven together into a driving force that facilitates our geographical and forensic exploration of Doyle＇s stories.
文摘Allan Poe has been deemed as the founder of modern detective story. This paper mainly talks about his contributions tomake this new genre a formal sub-genre of literature. Techniques he used in his short stories, locked-room murder and the arm-chair detective, have become the classical conventions of detective story. The eccentric but brilliant protagonist, Auguste Dupin inhis story, has become a model of the later detectives. Poe has also contributed to define the detective story as some kind of intellec-tual game, the plot of which concentrates on the process of investigation.
文摘Since 2000 A.D.,lots of translated detective novels have being published in Taiwan,China,which demonstrates that detective novel is popular in Taiwan,China,but there are seldom local detective novels to be published.Through the theory of field of cultural production by Pierre Bourdieu,the paper analyzed how the creators and cultural intermediaries’form of capitals and aesthetics construct the mechanism of the publishing industry,and how the market of detective novels in Taiwan,China are dominated by foreign products.The study adopted second documentary analysis and in-depth interview.The former is to calculate the published detective novels from 2001 to September 2015 sold in the dominant on-line bookstore,Books.com.tw,in Taiwan,China,while the latter is to interview 15 related agencies included writers,editors,translators,and a manager of bookstore.The results contain three following issues.Firstly,local production has re-started since 1980’s after a long-time decline.Considering the large cost to cultivate local writers,Taiwan region of China publishers prefer to produce well-known foreign works.Secondly,literary awards are the vital way in the production of local works.The writers receive symbolic capital through awards,and even obtain more opportunities to publish their works or cooperate with other related organization,which means the acquirement of social capital.Finally,the market of local detective novels is forced to be the field of restricted production as a result of supplanted by translated novels.As a consequence,the production of local detective novels becomes popular literature of niche market.
文摘Despite of only producing five ratiocinative tales in the whole life,Edgar Allan Poe is acknowledged as the "father of the detective story".In those tales,Poe portrays the hero Dupin who is the first detective image in the history of the western literature vividly.Based on the stories in which Dupin appeared,concerns on the creation of Dupin,the analysis of his features and the function of the setting fellows,like friend and police,summarizing the traditional image pattern of detective stories created by Poe,revealing the great influence Poe had on the development of detective literature,even on the literature of the whole world.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
基金supported by the Extral High Voltage Power Transmission Company,China Southern Power Grid Co.,Ltd.
文摘Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods are ineffective against novel attacks,and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments.To address these challenges,this study proposes a deep feature-driven hybrid framework that integrates Transformer,BiLSTM,and KNN to achieve accurate and robust DDoS detection.The Transformer component extracts global temporal dependencies from network traffic flows,while BiLSTM captures fine-grained sequential dynamics.The learned embeddings are then classified using an instance-based KNN layer,enhancing decision boundary precision.This cascaded architecture balances feature abstraction and locality preservation,improving both generalization and robustness.The proposed approach was evaluated on a newly collected real-time ICN traffic dataset and further validated using the public CIC-IDS2017 and Edge-IIoT datasets to demonstrate generalization.Comprehensive metrics including accuracy,precision,recall,F1-score,ROC-AUC,PR-AUC,false positive rate(FPR),and detection latency were employed.Results show that the hybrid framework achieves 98.42%accuracy with an ROC-AUC of 0.992 and FPR below 1%,outperforming baseline machine learning and deep learning models.Robustness experiments under Gaussian noise perturbations confirmed stable performance with less than 2%accuracy degradation.Moreover,detection latency remained below 2.1 ms per sample,indicating suitability for real-time ICS deployment.In summary,the proposed hybrid temporal learning and instance-based classification model offers a scalable and effective solution for DDoS detection in industrial control environments.By combining global contextual modeling,sequential learning,and instance-based refinement,the framework demonstrates strong adaptability across datasets and resilience against noise,providing practical utility for safeguarding critical infrastructure.
基金supported in part by the by Chongqing Research Program of Basic Research and Frontier Technology under Grant CSTB2025NSCQ-GPX1309.
文摘Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of object detection,there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes.In particular,the YOLO(You Only Look Once)series of detection models,renowned for their real-time performance,have undergone numerous adaptations aimed at improving the detection of small targets.In this survey,we summarize the state-of-the-art YOLO-based small object detection methods.This review presents a systematic categorization of YOLO-based approaches for small-object detection,organized into four methodological avenues,namely attention-based feature enhancement,detection-head optimization,loss function,and multi-scale feature fusion strategies.We then examine the principal challenges addressed by each category.Finally,we analyze the performance of thesemethods on public benchmarks and,by comparing current approaches,identify limitations and outline directions for future research.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01276).
文摘The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.
文摘Breast cancer screening programs rely heavily on mammography for early detection;however,diagnostic performance is strongly affected by inter-reader variability,breast density,and the limitations of conven-tional computer-aided detection systems.Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening,yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited.This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset,developed within the Turkish National Breast Cancer Screening Program.The dataset comprises 12,740 patient cases collected between 2016 and 2022,annotated with BI-RADS categories,breast density levels,and lesion localization labels.A total of 31 models were evaluated,including One-Stage,Two-Stage,and Transformer-based architectures,under a unified experimental framework at both patient and breast levels.The results demonstrate that Two-Stage architectures consistently outperform One-Stage models,achieving approximately 2%–4%higher Macro F1-Scores and more balanced precision–recall trade-offs,with Double-Head R-CNN and Dynamic R-CNN yielding the highest overall performance(Macro F1≈0.84–0.86).This advantage is primarily attributed to the region proposal mechanism and improved class balance inherent to Two-Stage designs.One-Stage detectors exhibited higher sensitivity and faster inference,reaching Recall values above 0.88,but experienced minor reductions in Precision and overall accuracy(≈1%–2%)compared with Two-Stage models.Among Transformer-based architectures,Deformable DEtection TRansformer demonstrated strong robustness and consistency across datasets,achieving Macro F1-Scores comparable to CNN-based detectors(≈0.83–0.85)while exhibiting minimal performance degradation under distributional shifts.Breast density–based analysis revealed increased misclassification rates in medium-density categories(types B and C),whereas Transformer-based architectures maintained more stable performance in high-density type D tissue.These findings quantitatively confirm that both architectural design and tissue characteristics play a decisive role in diagnostic accuracy.Overall,the study provides a reproducible benchmark and highlights the potential of hybrid approaches that combine the accuracy of Two-Stage detectors with the contextual modeling capability of Transformer architectures for clinically reliable breast cancer screening systems.
文摘Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Training(AT)enables NIDS agents to discover and prevent newattack paths by exposing them to competing examples,thereby increasing detection accuracy,reducing False Positives(FPs),and enhancing network security.To develop robust decision-making capabilities for real-world network disruptions and hostile activity,NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity.The accuracy and timeliness of the IDS were crucial to the network’s availability and reliability at this time.This paper analyzes ARL applications in NIDS,revealing State-of-The-Art(SoTA)methodology,issues,and future research prospects.This includes Reinforcement Machine Learning(RML)-based NIDS,which enables an agent to interact with the environment to achieve a goal,andDeep Reinforcement Learning(DRL)-based NIDS,which can solve complex decision-making problems.Additionally,this survey study addresses cybersecurity adversarial circumstances and their importance for ARL and NIDS.Architectural design,RL algorithms,feature representation,and training methodologies are examined in the ARL-NIDS study.This comprehensive study evaluates ARL for intelligent NIDS research,benefiting cybersecurity researchers,practitioners,and policymakers.The report promotes cybersecurity defense research and innovation.
