Search

ABSTRACT Street View Imagery (SVI) is crucial for urban environmental audits. Although Volunteered Street View Imagery (VSVI) has the potential to provide higher spatial and temporal resolution than traditional SVI, the use of such user‐generated data is currently limited by the inherent spatial heterogeneity in contributions and unclear quality control. To explore the underlying mechanisms, this study focused on the validity of Linus’ law in VSVI quality improvement in terms of spatial and temporal qualities related to environmental audits, using Mapillary as an example. It is assumed that as the number of street revisits for a road increases, so does the level of data quality. Results from regression and correlation analyses show that Linus’ law applies to VSVI quality improvement with different relationships depending on quality elements and road types. Furthermore, this study determines the number of revisits required to exceed the traditional SVI or achieve a higher quality level. [ABSTRACT FROM AUTHOR]

Determining the maliciousness of a cybersecurity incident is essential to establish effective measures against it. To process large volumes of data in an automated way, machine learning techniques are commonly applied to the problem. One of the main obstacles to apply machine learning effectively is that the data distribution is not stationary, so a model trained on old data tends to degrade as new data with a different distribution is processed. This change in the distribution of data over time is known as concept drift and affects the reports of new events, which may compromise model performance. To tackle this problem this paper evaluates the effectiveness of transfer learning techniques in reducing the impact of concept drift on the performance of models for assigning maliciousness to IPs. We compare this approach with the application of online-updated models, which are another common approach to adapt to concept drift in the data. We analyse the performance of both approaches to determine which may be more effective in this setting. [ABSTRACT FROM AUTHOR]

Dynamic agenda representation assumes a linkage between the policy emphases prescribed by various democratic inputs (electoral promises and public opinion polls) and policy agendas ranging from the media to executive orders. An extrapolation of this idea would propose that, in the U.S. context, policy emphasis in major programmatic messages such as State of the Union addresses would be followed by the president's day‐to‐day communication. We investigate this congruence with a new database of presidential speeches that, for the first time, offers a deep learning‐enhanced sentence‐level policy topic coding of various forms of the speeches U.S. presidents made from Truman to Trump (for a total count of 16,523 speeches divided into nearly 2 million individual sentences). Using this database, we demonstrate that presidents' occasional, day‐to‐day remarks strongly correlate with the annual policy messages—in this sense, presidents are staying on the democratic script. [ABSTRACT FROM AUTHOR]

Alpine landscapes are notable regarding their sensitivity to environmental change. Surface waters are especially sensitive as many are influenced by glacial meltwaters that are diminishing with the retreat of glaciers worldwide. The Macun Lakes region, Swiss National Park, is an alpine cirque landscape housing a number of lakes interconnected by streams. The area has been non-glaciated for decades, although rock glaciers are still present in the south basin. Surface waters, 10 stream sites in the present study, have been monitored in mid-summer since the year 2001 for physico-chemistry, periphyton and macroinvertebrates (including 74 species of chironomids). Water physico-chemistry revealed that the two main basins in the catchment differed, reflecting the inputs of rock glacier waters in the south basin. However, trends suggest that waters are becoming more similar as rock glacier inputs diminish over time along with a general decrease in nitrogen levels in all waters. Periphyton biomass showed some spatial differences among sites and a general decrease after 2010. Macroinvertebrate assemblages, including chironomids, clearly differed among basins and spatially along the stream network in each basin. Notably, no significant temporal trend was observed in the long-term data for macroinvertebrates, including chironomids, at the monitored sites. The results suggest that lotic macroinvertebrates may be buffered by the interconnectedness of streams and lakes in the landscape, which mitigates major response patterns of running waters to environmental change. [ABSTRACT FROM AUTHOR]

US Presidential speeches are considered among the most influential speeches not only in the US but also around the world. This article is aimed at identifying lexical and grammatical features in English Farewell Addresses by U.S. Presidents (EFAUPs) and revealing the messages embedded in these speeches. The framework of critical discourse analysis (CDA) proposed by Fairclough with a three-stage procedure combined with Halliday's Functional Grammar was applied. The research data were four English farewell addresses made by the Presidents of the United States in the first two decades of the 21st century. The results reveal that there are seven typical lexical features and eight grammatical features, which serve to express Presidents' power and ideologies. It is hoped that the research results can illustrate a way of conducting discourse analysis based on a combination of Fairclough's model and Halliday's theory, thereby bringing useful implications to the teaching and learning of CDA. [ABSTRACT FROM AUTHOR]

In vertical vehicle dynamics control, semi-active dampers are used to enhance ride comfort and road-holding with only minor additional energy expenses. However, a complex control problem arises from the combined effects of (1) the constrained semi-active damper characteristic, (2) the opposing control objectives of improving ride comfort and road-holding, and (3) the additionally coupled vertical dynamic system. This work presents the application of Reinforcement Learning to the vertical dynamics control problem of a real street vehicle to address these issues. We discuss the entire Reinforcement Learning-based controller design process, which started with deriving a sufficiently accurate training model representing the vehicle behavior. The obtained model was then used to train a Reinforcement Learning agent, which offered improved vehicle ride qualities. After that, we verified the trained agent in a full-vehicle simulation setup before the agent was deployed in the real vehicle. Quantitative and qualitative real-world tests highlight the increased performance of the trained agent in comparison to a benchmark controller. Tests on a real-world four-post test rig showed that the trained RL-based controller was able to outperform an offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average. [ABSTRACT FROM AUTHOR]

Historical review and epidemiology of disease contribute to effective planning against any disease. The present study aimed to evaluate case detection methods and the historic prevalence and seasonality of malaria and to assess the effectiveness of insecticide sprays to reduce the malaria rate in the Bannu zone during 1965–97. It is a cross-sectional study consisting of both laboratory and official data. Data on malaria/case detections and insecticide spray and the number of houses sprayed were collected from the official record. Active case detection remained the dominant method during 1966–89 and was replaced by passive case detection. Malaria rates remained low during 1966–74, increased to a peak in 1975, and suddenly declined in 1977. Malaria showed a steady increase from 1989 with a peak in 1995 followed by a reduced rate in 1996. Plasmodium vivax malaria was much more prevalent than Plasmodium falciparum malaria throughout the study period. Slide positivity rates of different surveillance methods were comparable up to 1977. Slide positivity rates of Plasmodium vivax and Plasmodium falciparum were comparable based on combined data. Slide positivity rates and annual parasite incidence were fairly well correlated up to 1985 and followed by good correspondences based on the combined data. Transmission of malaria occurred from August to December of the same year. The estimated number of the sprayed houses exceeded the recorded. Five insecticides were used annually in variable quantities over the study that demonstrated a correlation with reduced malarial rates during 1966–88, except in 1975. The malaria showed seasonality characterized by Plasmodium vivax malaria that remained dominant, and the prevalence rate revealed fluctuation over the period. Active case detection was dominant for the most of period. Insecticide spray effectively contributed to reducing the rate of malaria in the study area. [ABSTRACT FROM AUTHOR]

Aggravated assault and homicide both increased in 2020 and 2021 in the United States, generating considerable public concern. As such, the current study examines whether (1) the increase in violence during the 2020–2021 period uniquely affected some local street segments more so than others when compared to the previous years 2017–2019 and, in turn, (2) whether specific socio-demographic characteristics are associated with different changes in local violence during the 2020–2021 period. The current study focuses on the City of Little Rock, Arkansas, examining violence across streets nested within block groups. Descriptive analyses are followed by mixed-effect models to examine the potential change (increase, decrease) or stability in violence occurrence, including whether a first incident occurred in the years 2020–2021. The central finding was that the violence surge only impacted a small number of street segments – just over 14% of the total sample in Little Rock – and roughly the same proportion experienced a decrease in violence during this same period of time. The results are further discussed from a crime prevention and policing perspective. [ABSTRACT FROM AUTHOR]

To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale variations in power generation. Frequency regulation through demand response has the potential to coordinate temporally flexible loads, such as air conditioners, to counteract these variations. Existing approaches for discrete control with dynamic constraints struggle to provide satisfactory performance for fast timescale action selection with hundreds of agents. We propose a decentralized agent trained with multi-agent proximal policy optimization with localized communication. We explore two communication frameworks: hand-engineered, or learned through targeted multi-agent communication. The resulting policies perform well and robustly for frequency regulation, and scale seamlessly to arbitrary numbers of houses for constant processing times. [ABSTRACT FROM AUTHOR]

The present study aims to understand key factors influencing the House Sparrow population across different habitat scales in Bhavnagar, Gujarat, India. Correspondingly, different variables such as changes in habitat composition, sound levels, and density of mobile phone base stations were considered with reference to the occurrence of the House Sparrows across the study area. During the study period (December 2016 to November 2018), the number of House Sparrows was recorded through point count without distance estimate method. Non-parametric tests were employed to assess variations in different variables and their correlation with the presence of House Sparrows, revealing that changes in local habitat composition significantly influence their occurrence. Shrubby vegetation, cowsheds, and old/traditional structures emerged as crucial predictors positively impacting House Sparrow's presence, particularly in urban areas where suitable habitat patches are scarce due to urbanization and modern lifestyles. The decline in these habitats has significantly impacted House Sparrow populations. To counter this decline, implementing strategies like providing artificial nest sites is being considered. However, it's crucial to ensure that there are adequate shelter and food resources available to effectively conserve the species. [ABSTRACT FROM AUTHOR]

Drawing on approximately 4000 face‐to‐face interviews with lower house members from seventeen Latin American countries, this article demonstrates how partisan considerations shape legislators' preferences for power‐sharing institutions. Distributive models argue that reforming parties derive preferences based on potential advantages in power allocation, yet the role of individual legislators remains underexplored. The evidence reveals that legislators favor institutional arrangements that maximize their party's influence. Representatives from larger parties prefer electoral rules that reduce the number of parties in the House, while incumbent lawmakers support a stronger executive. Notably, party positions often shift following changes in electoral fortunes: Parties become more favorable of proportional representation after losing seats and advocate for reinforced congressional authority upon leaving office. [ABSTRACT FROM AUTHOR]

Street design and land subdivision are significant tasks in the development and redevelopment planning process. Optimizing street and land subdivision layouts within a unified framework to achieve solutions that meet a set of objectives and constraints (e.g., minimizing parcel area deviation from standard values, minimizing land consumption for street construction, etc.) is a critical concern for planners, particularly in complex contexts such as blocks with irregular shapes and parcels of varying sizes and requirements. To address this challenge, a mathematical formulation is presented for the bi-level street network and land subdivision optimization problem. Subsequently, the solution procedure is outlined, which utilizes a genetic-based algorithm for street design and a memetic–genetic-based algorithm for land subdivision. Finally, two cases are presented, solved, and discussed to analyze and verify the proposed mathematical model and solution procedures. The results suggest that the formulated problem is suitable for addressing the coupled street network and land subdivision design problem, and it can be adapted and extended to other case studies. Additionally, the introduced ideas and algorithms satisfactorily solved the stated problem. [ABSTRACT FROM AUTHOR]

From 1901 until 1920, Albert Kohn, director of the Berlin insurance organization Allgemeine Ortskrankenkasse, conducted a systematic housing survey of defective apartments. The work included statistics, reports, and photographs. I situate the project within the context of social surveys in the years around 1900. In the larger history of housing surveys, Kohn's project was one of the first that amalgamated diverse media and data visualizations. The original publication exhibits a crucial connection between statistics, reports, and apartment photographs. I will show that both reports and photographs epistemically hinged on numerical data gained from a questionnaire. The assemblage of shocking figures in statistics, reports, and photographs was intended to make visible an epistemic object: the misery of the lower classes. Hence, Kohn's depictions of urban misery did not depend on a specific form of representation, but rather on the consistency between descriptive registers. [ABSTRACT FROM AUTHOR]

This article examines the evolution of the EU's self-perception as a global actor over the past decade by analysing twelve State of the European Union addresses (SOTEU) from 2011-2023. By combining Niklas Luhmann's system theory with narrative theory as presented by Miskimmon, Roselle, Chaban, et al., the article conducts study of each speech. A series of 'matrixes of narrative logics' position the identified narratives of each speech on the two axes posed by system theory and narrative theory. The analysis reveals a significant shift in the EU's self-perception as a global actor over the past decade. Through the SOTEUs, the EU recognizes and communicates its self-vision of economic influence, regulatory prowess, and foreign policy values. It also narrates its transformation from enthusiastic involvement in global politics to a well-established civilian and regulatory global force. The article argues that the EU's self-narratives projected through SOTEUs mirror conceptualizations of the EU found in relevant academic literature. It finds some continuity in the EU self-perception but also a gradual change. From a vocal ambition to federalize in the early years of the SOTEUs, the EU grows more content with its structural composition although wishes to reform resurface in the latest speech. The EU also comes to see itself as gradually more established on the world stage. [ABSTRACT FROM AUTHOR]

There is growing recognition of the importance of a well-connected cycling infrastructure. Common connectivity measures apply aggregate area-based metrics, such as the number of streets per transportation analysis zone or census area. However, such measures capture cyclist movement insufficiently, as cyclist movement easily extends beyond areas, and variations in the sizes and shapes of areas lead to inconsistent outcomes. In contrast to area-based connectivity, network connectivity considers the connectivity of the entire cycling network, capturing the continuity or discontinuity of routes. Certain measures can account for other important aspects, such as topology and the relative importance of individual links in large networks. Although previous reviews have covered several aspects of cycling infrastructure, no review has been dedicated to the measures, methods and models applied to assess network connectivity or the impact of increased network connectivity on cycling behaviour. We conduct a scoping review of measures, methods, models, and data sources to assess cycling network connectivity and review associations between network connectivity and travel behaviour. The findings suggest an increase in the number of publications on network connectivity up to 2019, followed by a plateau in the number of studies but with more complex methods. However, that complexity may constitute a barrier to practical implementation. We conclude that empirical verifications regarding the effects of network connectivity on travel behaviour remain a research gap, even in high-cycling countries, with evidence further limited by limited link-level travel data. Future research should also focus on developing more consistent and validated metrics. [ABSTRACT FROM AUTHOR]

This editorial from the Journal of Management History discusses the challenges of writing editorials and the diverse perspectives within the field of management history. The author emphasizes the importance of understanding how the past informs the present and future, particularly in light of current business controversies and ethical lapses. The editorial introduces several articles that explore topics such as the emergence of revolutionary business techniques, the role of Quakerism in scientific management, the history of Barilla pasta, the impact of political movements on management theory, the use of history in branding, and the measurement of firm performance. These articles provide valuable insights into the field of management history and its relevance to contemporary issues. [Extracted from the article]

The emergence of the COVID-19 virus in late 2019 initiated a global crisis, leading to millions of infections and hundreds of thousands of deaths worldwide. Governments worldwide implemented various measures to curb the spread of the virus, including lockdowns, social distancing, and hygiene protocols. In response to these measures, leaders employed persuasive language techniques to encourage compliance and reassure citizens. This study explores the linguistic strategies used by four national leaders—Donald Trump, Vladimir Putin, Boris Johnson, and Uhuru Kenyatta—in their speeches addressing the COVID-19 pandemic. The analysis focuses on the rhetorical strategies employed, including ethos, logos, and pathos, as well as linguistic devices such as metaphors and enumeration. The findings of this study show that leaders strategically utilize persuasive language to convey information, evoke emotions, and inspire collective action. The study underscores the importance of understanding linguistic persuasion in effective communication during crises and highlights the relevance of classical rhetorical strategies in contemporary political discourse.

Nisu and Muji of the Yi nationality and Tuliao of the Zhuang nationality are ethnic groups from the Mengzi area of Honghe Hani and Yi Autonomous Prefecture. These three settlements may overlap or become closely connected, showing a distribution state that “the ethnic groups live among each other, while some live in concentrated communities of their own”. Despite the different languages of each ethnic group, people in the three ethnic groups can all use the local dialect of Mengzi for communication and have long had intersections in production and life. Thus, the daily dresses of the three ethnic groups shows a unified trend that is difficult to distinguish from the outside world. Therefore, the analysis of the evolution of the three costumes is conductive to interpreting the phenomenon of costume acculturation and revealing the deep reasons for their cultural blending and symbiosis. Based on field research, this paper provides substantial evidence such as image data, clothing objects and ancient literature, and analyzes the evolution and causes of the daily dresses of the three ethnic groups. As shown by the research, the daily dresses of the three ethnic groups have experienced an evolutionary process from dissimilarity to convergence, as specifically reflected in three aspects. First, the daily dresses of the three ethnic groups in the Qing Dynasty are quite different from the current gradually convergent dresses. Due to the policy of bureaucratization of native officers and the influence of Han culture, their tops gradually evolve into the “Da Jin Yi” style, with lapels leaning towards the right side. The “Zhuiji” (a cone-shaped hair bun atop the head) is replaced by the “Baotou” (a headdress that wraps the head with a headscarf), but the separate style of upper and lower garments persists. Second, due to the geographical proximity of their respective settlements as well as frequent interactions such as intermarriage and economic exchanges in various forms, the customs of the three ethnic groups gradually merge, inducing to a closer resemblance in clothing. However, distinctive elements of each ethnic group are still retained in their clothing. On the one hand, the typical wear for females in the daily dress of the three ethnic groups includes wearing tops, pants, and Baotou, with a focus on apron adorned with distinctive patterns around the chest and abdomen. Nonetheless, there are differences in apron decorations, embroidered shoe styles, and headwear designs among them. On the other hand, paper-cutting embroidery is a common technique for the costume of the three ethnic groups. This decorative craft is most exquisite among the Nisu women, while Muji and Tuliao females have largely lost the paper-cutting skill. Furthermore, the disappearance of the Zhuang language has gradually detached females’ clothing from its ethnic system. The blending and mutual influence of cultures inevitably assimilates their clothing, ultimately presenting a distinct “Yi” style. Third, as a new cultural construction phenomenon, the current clothing image results from the interaction, integration, acceptance, and adaptation among the three ethnic groups. It not only maintains the aesthetic characteristics and regional features of each ethnic group in terms of patterns and styles, but also absorbs and draws upon elements of Han clothing culture. Thus, the clothing finally shows the acculturation characteristics of the blending of multiple cultures. This paper takes the integration and evolution of females’ daily dresses of the three ethnic groups as the starting point. Based on the field research, clothing data are collected from these groups. Besides, historical literature is combined with oral accounts from cultural inheritors. The study is aimed to clarify the characteristics, similarities, and differences of typical daily dresses since the Qing Dynasty. Additionally, the paper seeks to gain an insight into the reasons behind the three ethnic groups’ mutual integration, so as to shed light on exploring cultural identity and cultural blending among different ethnic groups. [ABSTRACT FROM AUTHOR]

Georeferenced data are crucial for addressing societal spatial challenges, as most corporate and governmental information is location-compatible. However, many open-source solutions lack automation in geocoding while ensuring quality. This study evaluates the functionalities of various R packages and their integration with external APIs for converting postal addresses into geographic coordinates. Among the fifteen R methods/packages reviewed, tidygeocoder stands out for its versatility, though discrepancies in processing times and missing values vary by provider. The accuracy was assessed by proximity to original dataset coordinates (Madrid street map) using a sample of 15,000 addresses. The results indicate significant variability in performance: MapQuest was the fastest, ArcGIS the most accurate, and Nominatim had the highest number of missing values. To address these issues, an alternative web scraping methodology is proposed, substantially reducing the error rates and missing values, but raising potential legal concerns. This comparative analysis highlights the strengths and limitations of different geocoding tools, facilitating better integration of geographic information into datasets for researchers and social agents. [ABSTRACT FROM AUTHOR]

This article is concerned with the transition of a longitudinal survey from a single-mode design to a web-first mixed-mode design and the role that text messages to sample members can play in smoothing that transition. We present the results of an experiment that investigates the effects of augmenting the contact strategy of letters and emails with text messages, inviting the sample members to complete a web questionnaire and reminding them of the invite. The experiment was conducted in a subsample of Understanding Society , a household panel survey in the United Kingdom, in the wave that transitioned from a CAPI-only design to a sequential design combining web and CATI. In the experiment, a quarter of the sample received letters and emails, while the rest received between one and three text messages with a personalized link to the questionnaire. We examine the effect of the text messages on response rates, both at the web phase of a sequential design and at the end of the fieldwork after a CATI follow-up phase, and explore various mechanisms that might drive the increase in response rates. We also look at the effects on the device used to complete the survey and field efforts needed at the CATI stage. The findings indicate that text messages did not help to significantly increase response rates overall, although some subgroups benefited from them, such as panel members who had not provided an email or postal address before. Likewise, the text messages increased web completion among younger panel members and those with an irregular response pattern. We only found a slight and nonsignificant effect on smartphone use and no effect on the web household response rate, a proxy for fieldwork efforts. [ABSTRACT FROM AUTHOR]

COVID‐19 lockdowns have resulted in significant societal disruptions, intensifying family stress and escalating instances of intimate partner violence (IPV). Lockdowns necessitated prolonged proximity to abusers, magnifying home's significance as a space of victimization. The current study conducted in Korea (n = 813) examined the impact of housing density and room count on COVID‐19‐related IPV. Analyzing data from Seongnam City, Korea, the study examined a couple of pre‐/postpandemic, factors covariates like gender, age, employment, education, and income. Using STATA 13.0, the study conducted multiple logistic regression to examine the association between housing conditions and IPV during and prior to COVID‐19, with all covariates included. Housing density was associated with emotional IPV victimization before COVID‐19, while the number of rooms was associated with physical and sexual IPV victimization before COVID‐19 and sexual IPV victimization after COVID‐19. Time spent with partners was significantly associated with emotional IPV victimization, and the association between the number of rooms and sexual IPV victimization remained significant after adjusting for COVID‐19‐related variables. Practical implications and recommendations are discussed to address this issue and protect vulnerable populations from harm. Although poor housing conditions have been linked to increased stress levels and comprised mental health, scant attention has been paid to those relationships. [ABSTRACT FROM AUTHOR]

Microarchitectural attacks utilize the performance optimization constructs that have been studied over decades in computer architecture research and show the vulnerability of such optimizations in a realistic framework. One such highly performance-driven vulnerable construct is speculative execution. In this paper, we focus on the problem of breaking the kernel address-space layout randomization (KASLR) on modern mobile devices without using cache memory as a medium of observation. However, there are some challenges to breaking KASLR on ARM CPUs. The first challenge is that eviction strategies on ARM CPUs are slow, and the microarchitectural attacks exploiting the cache as a covert channel cannot be implemented on modern ARM CPUs. The second challenge is that non-canonical addresses are stored in the store buffer, although they are invalid. As a result, previous microarchitectural attacks distinguish such addresses as valid kernel addresses erroneously. In this paper, we focus on these challenges to close current gaps in the implementation of recent attacks against modern CPUs. We show how a translation look-aside buffer can be used to circumvent the cache memory as a covert channel in order to attack ASLR on both ARM and Intel CPUs. To the best of our knowledge, we are the first to break KASLR on ARM-based Android and iOS mobile devices. Furthermore, our attacks can be performed in JavaScript to break KASLR of the browser without the need for an Evict + Reload operation, which consumes a lot of time. The results of our attacks show that the attacker can distinguish whether or not the virtual address is valid in less than 0.0417 s and 0.0488 s on Android and iOS mobile devices, respectively. Also, in order to break ASLR on Intel CPUs, we demonstrated that the attacker can leverage unprivileged access to the Intel running average power limit interface, which discloses values directly related to power consumption. [ABSTRACT FROM AUTHOR]

This study explores house price fluctuations in the USA and shed light on which factor is the main contributor driving house prices. In order to decompose the changes in house prices, structural vector autoregression with sign restrictions for the US housing market is applied. In addition to including housing market-based fundamental variables such as the number of housing permits, housing rent prices, house prices, a measure of credit conditions, and the housing sentiment index are also included to distinguish four different shocks, namely housing supply shocks, shocks to the housing service demand, credit shocks, and speculative demand shocks. Empirical findings show that the main contributors to house price fluctuations are credit shocks and housing supply shocks in the long-run. In addition, while housing supply and credit conditions are the most important contributors during the boom, the contributions of credit conditions and speculative demand become larger after the boom. In fact, credit conditions are the largest contributor during the post-boom period. [ABSTRACT FROM AUTHOR]

Web application firewalls (WAFs) and other Intrusion Detection Systems (IDS) techniques are employed to defend the network against web attacks. Even so, attacks may succeed since most WAFs demand extensive configuration expertise that depends on filters. Despite notable successes, deep information has been utilized in varied applications. Still, it's crucial to have a reliable method for detecting the attack due to the attacker's various ways of concealment of the URLs. Several methods were introduced for detecting the attacks in web applications; still, the accuracy of detection and the computation burden are challenging aspects. Hence, a web attack detection mechanism is introduced in this research using the deep learning framework using the URL request. The proposed method utilizes a three-fold attack detection strategy to detect the attack with minimal computation complexity. Initially, the profile is checked to determine the genuinity of a user, and then, the bot scanners are identified using the generalized adversarial network (GAN). Finally, the attack detection is employed using the transformer neural network, wherein the adjustable parameters are modified using the weighted mean of vectors (INFO) optimization technique. The performance of a proposed method is evaluated based on various assessment measures like Accuracy, Precision, Recall, F-Measure, TPR, FPR, FNR and TNR and acquired the values of 99.97%, 99.96%, 99.97%, 99.97%, 99.97%, 0.03%, 0.03%, and 99.97% respectively. [ABSTRACT FROM AUTHOR]

Optical neural networks (ONNs) are promising hardware platforms for next-generation neuromorphic computing due to their high parallelism, low latency, and low energy consumption. However, previous integrated photonic tensor cores (PTCs) consume numerous single-operand optical modulators for signal and weight encoding, leading to large area costs and high propagation loss to implement large tensor operations. This work proposes a scalable and efficient optical dot-product engine based on customized multi-operand photonic devices, namely multi-operand optical neuron (MOON). We experimentally demonstrate the utility of a MOON using a multi-operand-Mach–Zehnder-interferometer (MOMZI) in image recognition tasks. Specifically, our MOMZI-based ONN achieves a measured accuracy of 85.89 % in the street view house number (SVHN) recognition dataset with 4-bit voltage control precision. Furthermore, our performance analysis reveals that a 128 × 128 MOMZI-based PTCs outperform their counterparts based on single-operand MZIs by one to two order-of-magnitudes in propagation loss, optical delay, and total device footprint, with comparable matrix expressivity. [ABSTRACT FROM AUTHOR]

Cybercrime is a major challenge facing the world, with estimated costs ranging from the hundreds of millions to the trillions. Despite the threat it poses, cybercrime is somewhat an invisible phenomenon. In carrying out their virtual attacks, offenders often mask their physical locations by hiding behind online nicknames and technical protections. This means technical data are not well suited to establishing the true location of offenders and scholarly knowledge of cybercrime geography is limited. This paper proposes a solution: an expert survey. From March to October 2021 we invited leading experts in cybercrime intelligence/investigations from across the world to participate in an anonymized online survey on the geographical location of cybercrime offenders. The survey asked participants to consider five major categories of cybercrime, nominate the countries that they consider to be the most significant sources of each of these types of cybercrimes, and then rank each nominated country according to the impact, professionalism, and technical skill of its offenders. The outcome of the survey is the World Cybercrime Index, a global metric of cybercriminality organised around five types of cybercrime. The results indicate that a relatively small number of countries house the greatest cybercriminal threats. These findings partially remove the veil of anonymity around cybercriminal offenders, may aid law enforcement and policymakers in fighting this threat, and contribute to the study of cybercrime as a local phenomenon. [ABSTRACT FROM AUTHOR]

Despite representing a very accurate method for assessing spatial distribution, Marcon and Puech's M has been insufficiently exploited so far, most likely because its computation relies on pairing every point of interest (i.e., firms, plants) with every other point within the area under analysis. Such a figure rapidly grows to unmanageable levels when said area is larger than a neighborhood or when every industry is taken into account. Consequently, practical applications of M have been exclusively experimental and circumscribed to very limited areas or to a handful of industries. This seems much regrettable since M provides many advantages compared to conventional measures of spatial distribution and also to alternative distance measures. In this article, we assess the reliability of using small administrative units instead of exact postal addresses for the localization of plants, in order to reduce M's computational burden. Working with a dataset that provides the location, the specific industry and the number of employees for every single plant/establishment in Italy for both manufacturing and services, we can also draw a preliminary but certainly interesting picture of Sardinia's economic geography and its development through the Great Recession toughest years between 2007 and 2012. [ABSTRACT FROM AUTHOR]

Economic damages from riverine flooding are expected to grow because of climate change. Yet, there are few studies analyzing flooding damages in the USA that clearly measure the roles of hazard, exposure, and vulnerability separately and locally. A lack of this knowledge prohibits spatially detailed predictions of future damages. By being able to separate into these three risk factors, we provide all necessary inputs for uncertainty analysis of the flooding damages forecasts that can incorporate new predictive scenarios for each component. To analyze the flooding risk factors of non-coastal counties within the contiguous USA between 1999 and 2018, we gathered information on (1) property and human damages from flooding, (2) maximum annual river discharge, (3) the number of housing units and the years in which they were built, and (4) the incidence of flooding events. We used the method of trimmed least absolute deviated and trimmed least square estimators to obtain the individual impact of hazard, exposure, and vulnerability in the context of censored flooding damages for panel data. The resulting estimates indicate that exposure has been the main driver of flooding risk for most counties in the USA. We use these estimates to describe the main source of flooding risk for non-coastal counties for the 1999–2018 period. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Art History / Sanat Tarihi Dergisi is the property of Ege University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents the development of a real-time application-specific Automatic Speech Recognition (ASR) system for voice-activated navigation services. The system is designed to recognize Urdu-English code-mixed street addresses, which is challenging due to their complex nature and structure, especially in under-resourced languages such as Urdu. Two separate corpora are collected for ASR system development: Unicode Urdu consisting of general Urdu recordings of around 61.82 hours by 144 speakers and Roman Urdu-English code-mixed Addresses of around 16.89 hours by 20 speakers. The Unicode Urdu data is developed to provide acoustic models with general language understanding and code-mixed street addresses to provide code-mixing or switching coverage. The hybrid ASR system employed in this study plays a crucial role in addressing the multifaceted challenges of low-resource settings (only 16.89 hours of task-specific data), especially in the context of Urdu-English code-switching. The study compares various acoustic models, with mixed Time Delay Neural Network and Long Short-Term Memory (TDNN-LSTM) performing best with a Word Error Rate (WER), Character Error Rate (CER), and Sentence Error Rate (SER) of 4.02%, 0.8%, and 15.14% respectively, on random street addresses. In addition to testing street addresses, we performed accent-based and manual decoding testing on the developed ASR system. Results indicate the need to develop and deploy custom ASR systems for better accent adaptation and application-specific coverage. The developed ASR system is integrated into the TPL Maps (https://tplmaps.com/) mobile application. It is Pakistan’s first Large Vocabulary Continuous Speech Recognition (LVCSR) real-time system to provide Urdu-based voice-activated navigation services.

This paper measures how much more households pay for less density in their immediate surroundings. Using transaction and administrative data and exploiting the introduction of a regulation that restricted the number of housing units for certain land lots, we find that households discount density: a 10% increase in within‐development density decreases the price per square meter by 5%. Further, the mean price per square meter of the average development increased by 1%–3% after the regulation was introduced, while the amount of built‐up space remained constant. The increase in total revenue suggests developers may underestimate the externality caused by density. [ABSTRACT FROM AUTHOR]

The craze for historical pageants staged in Britain by local communities at the beginning of the twentieth century stimulated a widespread public engagement with historical costume. As well as thousands of performers, and tens of thousands of spectators, pageants involved hundreds of local people in sewing parties who spent months making the costumes required for these outdoor re-enactments of episodes of local history. This article investigates how pageant costumes were designed, made or sourced, on the large scale required, and the cost implications this involved. Whilst costume designers were acknowledged, the employment of professional dressmakers and milliners often necessary to complete the work received less recognition. Florence Edwards, a professional theatrical dressmaker, is one of the few who can be identified. The role of the London theatrical costumier Willie Clarkson, a supplier to many pageants, is also examined. During pageant preparations, local people actively researched dress history, and in the case of Emily Ashdown her interest led to a lifelong career as a dress historian. [ABSTRACT FROM AUTHOR]

An extant farthingale sleeve support and a silk satin sleeve to wear over it form part of a collection of garments still owned by the Willoughby family to which it originally belonged in the 1590s or early 1600s. This paper provides an insight into its history as part of the Willoughby women's wardrobe and a technical description of its materials, manufacture and current form. Its construction matches contemporary documentary evidence for farthingale sleeves which were assumed to form part of elite dress. Evidence presented here suggests that the fashion moved rapidly through society and became a feature of ordinary women's dress by the early seventeenth century. [ABSTRACT FROM AUTHOR]

In this research work, a fifth-order weighted essentially non-oscillatory (WENO) scheme is created for traffic flow problems on networks. Street systems can be numerically demonstrated as a graph, whose edges are a limited number of streets that connect at intersections. A scalar hyperbolic conservation law can portray the advancement on each street, and traffic distribution matrices are considered to define coupling conditions at the network intersections. In this paper, numerical results for road networks with rich solution structures will be presented. These numerical results show that the new proposed scheme in this paper can generate essentially non-oscillatory and high resolution solutions. [ABSTRACT FROM AUTHOR]

The article focuses on Edgar Degas's exploration of laundresses as a motif in his artwork, specifically examining the technical aspects of ironing movements and their socio-cultural implications. Topics include Degas's interest in motion, the overlooked series of laundresses in his oeuvre, and the intersection of techniques, tools, and materials in portraying the labor milieu of late nineteenth-century France. The analysis delves into Degas's representations of ironers.

Copyright of Investigaciones Historicas is the property of Universidad de Valladolid, Facultad de Filosofia y Letras and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Today, most activities of the statistical offices need to be adapted to the modernization policies of the national statistical system. Therefore, the application of machine learning techniques is mandatory for the main activities of statistical centers. These include important issues such as coding business activities, address matching, prediction of response propensities, and many others. One of the common applications of machine learning methods in official statistics is to match a statistical address to a postal address, in order to establish a link between register-based census and traditional censuses with the aim of providing time series census information. Since there is no unique identifier to directly map the records from different databases, text-based approaches can be applied. In this paper, a novel application of machine learning will be investigated to integrate data sources of governmental records and census, employing text-based learning. Additionally, three new methods of machine learning classification algorithms are proposed. A simulation study has been performed to evaluate the robustness of methods in terms of the degree of duplication and purity of the texts. Due to the limitation of the R programming environment on big data sets, all programming has been successfully implemented on SAS (Statistical analysis system) software. [ABSTRACT FROM AUTHOR]