25 research outputs found
What Explains the Canada-US ICT Investment Intensity Gap?
No full textIt is widely recognized that machinery and equipment investment intensity is less in Canada than in the United States. What is less well know is that it is information and communications technology (ICT) investment that largely accounts for this gap. The author documents trends in ICT investment in both Canada and the United States and attempts to explain why ICT investment per worker in the Canadian business sector in 2004 was only 45 per cent of that in the US business sector. While no definitive explanation emerges, among the factors he identifies as playing a role are industrial structure, firm size distribution of employment, the price of labour compared to ICT investment goods, and the underestimation of ICT investment in official statistics.Machinery and equipment investment, information and communications technology, ICT, Investment gap, Business sector, Industrial structure, Firm size
Estimating ambient visibility in the presence of fog: a deep convolutional neural network approach
No full textNext-generation intelligent transportation systems are based on the acquisition of ambient data that influence traffic flow and safety. Among these, is the ambient visibility range whose estimation, in the presence of fog, is extremely useful for next-generation intelligent transportation systems. However, existing camera-based approaches are based on "engineered features" extraction methods that use computer algorithms and procedures from the image processing field. In this contribution, a novel approach to estimate visibility range under foggy weather conditions is proposed which is based on "learned features" instead. More precisely, we use AlexNet deep convolutional neural network (DCNN), trained with raw image data, for feature extraction and a support vector machine (SVM) for visibility range estimation. Our quantitative analysis showed that the proposed approach is very promising in estimating the visibility range with very good accuracy. The proposed solution can pave the way towards intelligent driveway assistance systems to enhance awareness of driving weather conditions and hence mitigate the safety risks emanating from fog-induced low visibility conditions.This research was financially supported by Zayed University Cluster Research Grant No. R17075.Kamoun, F (reprint author), ESPRIT Sch Engn, Tunis, Tunisia.
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Multi-label classification of a real-world image dataset
No full textDeep neural networks have shown increasing performance in image classification recent years. However, most of the methods developed in research are created, trained and compared on standard, general image collections. While some of such datasets contain realistic pictures with multiple objects and interaction between them, the selection of pictures and categories is broad and designed for the training of automatic classification systems. Therefore, questions remain on how modern image classification approaches can be applied to real-world datasets. What challenges can arise in the implementation of systems based on such datasets and how can they be solved? What level of classification performance can be expected? This study looks into these questions and gives insights on building such classification systems for real-world image collections.
The Norwegian News Agency provided the author with a unique labeled dataset of one million images for the purpose of this research. The study consists of a trial and main experiment. The goal of the trial experiments was to learn necessary tools, test classification system implementation, training and testing pipelines as well as different architectural design choices. These set of experiments were performed on a subset of the original image collection with a limited number of categories. Insights from these experiments were further used to perform model training on a much broader set of manually selected categories. Two network architectures were employed and compared in the research: CaffeNet and GoogleNet.
Results from the study suggest a big potential of using pre-trained convolutional neural networks in solving the task of multi-label image classification on a real-world dataset. However, the study also highlights a number of challenges connected with developing such system in a realistic environment, including:
A unique set of categories which requires either to train a new model or to reuse a pre-trained neural network with adjustments in the architecture to solve classification problems.
Difficulties in category tree transformation connected with the decision of which categories include, exclude and merge.
Categories which are unsuitable for automatic training purposes including contextual, abstract and ambiguous labels.
Contextual images which can not be classified only by visual features and require additional information.
Issues connected with the classification of objects and people located in the background of the pictures.
Duplicates of images in the dataset which can cause issues for both training and testing processes.
The research gives further insights on the challenges mentioned, discusses their impact and possible solutions to them. The main limitation of the study is that only
one dataset was employed in the research. Therefore, results are considered likely to be more generalizable to datasets similar to the one used in the study
Mobile NFC Services: Adoption Factors and a Typology of Business Models
No full textThe integration of Near Field Communication (NFC) into mobile devices has recently emerged as a disruptive innovation and a strong enabler of a wide range of new mobile applications and services. Yet, despite this great potential, the widespread adoption of mobile NFC services remains restrained. This chapter investigates the success factors that are contributing towards the proliferation of mobile NFC services. It also presents an in-depth analysis of the key hurdles standing in the way of full NFC commercialization, with the caveat that disagreement about the proper business model among the key ecosystem players is currently the major adoption restraint. The chapter articulates the vision of a cooperative model that can enable the sharing of services, infrastructure, cost, and revenues among various NFC ecosystem players. The author also adopts a four-tier classification approach to categorize NFC business models into a number of typologies. Some recommendations for future research are also provided
Multi-label classification of a real-world image dataset
No full textDeep neural networks have shown increasing performance in image classification recent years. However, most of the methods developed in research are created, trained and compared on standard, general image collections. While some of such datasets contain realistic pictures with multiple objects and interaction between them, the selection of pictures and categories is broad and designed for the training of automatic classification systems. Therefore, questions remain on how modern image classification approaches can be applied to real-world datasets. What challenges can arise in the implementation of systems based on such datasets and how can they be solved? What level of classification performance can be expected? This study looks into these questions and gives insights on building such classification systems for real-world image collections.
The Norwegian News Agency provided the author with a unique labeled dataset of one million images for the purpose of this research. The study consists of a trial and main experiment. The goal of the trial experiments was to learn necessary tools, test classification system implementation, training and testing pipelines as well as different architectural design choices. These set of experiments were performed on a subset of the original image collection with a limited number of categories. Insights from these experiments were further used to perform model training on a much broader set of manually selected categories. Two network architectures were employed and compared in the research: CaffeNet and GoogleNet.
Results from the study suggest a big potential of using pre-trained convolutional neural networks in solving the task of multi-label image classification on a real-world dataset. However, the study also highlights a number of challenges connected with developing such system in a realistic environment, including:
A unique set of categories which requires either to train a new model or to reuse a pre-trained neural network with adjustments in the architecture to solve classification problems.
Difficulties in category tree transformation connected with the decision of which categories include, exclude and merge.
Categories which are unsuitable for automatic training purposes including contextual, abstract and ambiguous labels.
Contextual images which can not be classified only by visual features and require additional information.
Issues connected with the classification of objects and people located in the background of the pictures.
Duplicates of images in the dataset which can cause issues for both training and testing processes.
The research gives further insights on the challenges mentioned, discusses their impact and possible solutions to them. The main limitation of the study is that only
one dataset was employed in the research. Therefore, results are considered likely to be more generalizable to datasets similar to the one used in the study
Human and Organizational Factors of Healthcare Data Breaches
No full textCopyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Over the past few years, concerns related to healthcare data privacy have been mounting since healthcare information has become more digitized, distributed and mobile. However, very little is known about the root cause of data breach incidents; making it difficult for healthcare organizations to establish proper security controls and defenses. Through a systematic review and synthesis of data breaches literature, and using databases of earlier reported healthcare data breaches, the authors re-examine and analyze the causal factors behind healthcare data breaches. The authors then use the Swiss Cheese Model (SCM) to shed light on the technical, organizational and human factors of these breaches. The author\u27s research suggests that incorporating the SCM concepts into the healthcare security policies and procedures can assist healthcare providers in assessing the vulnerabilities and risks associated with the maintenance and transmission of protected health information
Data Fusion for ITS: Techniques and Research Needs
No full textAbstractIntelligent transportation system (ITS) infrastructures contain sensors, data processing, and communication technologies that assist in improving passenger safety, reducing travel time and fuel consumption, and decreasing incident detection time. Multisource data from Bluetooth® and IP-based (cellular and Wi-Fi) communications, global positioning system (GPS) devices, cell phones, probe vehicles, license plate readers, infrastructure-based traffic-flow sensors, and in the future, connected vehicles enable multisource data fusion to be exploited to produce an enhanced interpretation of the monitored or observed situation. This occurs by decreasing the uncertainty present in individual source data. Although demonstrated for more than two decades, data fusion (DF) is still an emergent field as related to day-to-day traffic management operations. Data fusion techniques applied to date include Bayesian inference, Dempster-Shafer evidential reasoning, artificial neural networks, fuzzy logic, and Kalman filtering. This paper provides a survey of ITS DF applications, including ramp metering, pedestrian crossing, automatic incident detection, travel time prediction, adaptive signal control, and crash analysis and prevention, and indicates directions for future research. The encouraging results so far should not conceal the challenges that remain before widespread operational deployment of DF in transportation management occurs
Dopamine Pathway and Parkinson's Risk Variants Are Associated with Levodopa‐Induced Dyskinesia
No full textAbstract Background Levodopa‐induced dyskinesia (LID) is a common adverse effect of levodopa, one of the main therapeutics used to treat the motor symptoms of Parkinson's disease (PD). Previous evidence suggests a connection between LID and a disruption of the dopaminergic system as well as genes implicated in PD, including GBA1 and LRRK2 . Objectives Our goal was to investigate the effects of genetic variants on risk and time to LID. Methods We performed a genome‐wide association study (GWAS) and analyses focused on GBA1 and LRRK2 variants. We also calculated polygenic risk scores (PRS) including risk variants for PD and variants in genes involved in the dopaminergic transmission pathway. To test the influence of genetics on LID risk we used logistic regression, and to examine its impact on time to LID we performed Cox regression including 1612 PD patients with and 3175 without LID. Results We found that GBA1 variants were associated with LID risk (odds ratio [OR] = 1.65; 95% confidence interval [CI], 1.21–2.26; P = 0.0017) and LRRK2 variants with reduced time to LID onset (hazard ratio [HR] = 1.42; 95% CI, 1.09–1.84; P = 0.0098). The fourth quartile of the PD PRS was associated with increased LID risk (OR fourth_quartile = 1.27; 95% CI, 1.03–1.56; P = 0.0210). The third and fourth dopamine pathway PRS quartiles were associated with a reduced time to development of LID (HR third_quartile = 1.38; 95% CI, 1.07–1.79; P = 0.0128; HR fourth_quartile = 1.38; 95% CI = 1.06–1.78; P = 0.0147). Conclusions This study suggests that variants implicated in PD and in the dopaminergic transmission pathway play a role in the risk/time to develop LID. Further studies will be necessary to examine how these findings can inform clinical care. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.National Institute of Neurological Disorders and Stroke https://doi.org/10.13039/10000006
60th anniversary of the Malta Historical Society : a commemoration
No full textTwo books published in Tunisia in 2006 have direct relevance to the history of Malta during Arab rule. The first one, published by the Institut National du Patrimoine, consists of a two-volume work by Bahri Fathi entitled Les Hommes du pouvoir et les hommes du savoir en Ifnqiyya aglabide (184-296/800-909), wherein the author discusses the main personages that held power and knowledge during the time when Malta fell into the hands of the Arabs. The second publication, a festschrift entitled Les Communautis Miditerraniennes de Tunisie, Hommage au Doyen Mohamed Hidi Cherij, contains a number of articles that are of direct and indirect interest to Malta. There is one particular article on which I will be focusing my attention due to its relevance to Maltese medieval history: a paper by Faouzi Mahfoudh entitled 'Itiniraire d'un affranchi aghlabide: Kahalef(sic.}, un constructeur hors pair (203-254 de l'hegire/818-867)'. In my opinion, these two books bring to the fore some of the hidden dynamics behind the conquest of Malta by the Arabs and furnish an Arabic view of the rather complex and unclear history of how the islands of Malta and Gozo fell into Aghlabid hands at the end of a bloody period dominated by harsh wars and perpetual fighting between the Arabs and the Byzantines in the Central Mediterranean.peer-reviewe
