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Article
Publication date: 1 March 1992

M. Pavšek, D. Belavič, U. Kunaver and M. Hrovat

The design of temperature‐compensated quartz crystal oscillators (TCXOs) in thick film hybrid technology is described. TCXOs controlled by varicap diodes are usually…

Abstract

The design of temperature‐compensated quartz crystal oscillators (TCXOs) in thick film hybrid technology is described. TCXOs controlled by varicap diodes are usually realised with discrete NTC thermistors and resistors. Data obtained by precision measurements of voltages on varicap diodes for the same oscillator frequencies over the operating temperature range are used for calculating values of the NTC thermistors and resistors. In most cases these values cannot be found in the Renard scale, with the result that manipulation or ‘juggling’ of values is necessary. The realisation of temperature‐compensating circuits in thick film technology has certain advantages, such as miniaturisation, better characteristics at high frequencies and in particular the possibility to trim thick film resistors and NTC thermistors to values calculated for each oscillator. The method of realisation of TCXOs in thick film hybrid technology was developed and verified on prototypes. The compensation curves were obtained by measuring compensation voltages for each oscillator over the operating temperature range from — 20°C to 70°C. From these data the values of resistors and NTC thermistors were calculated. A computer program was used to minimise frequency instability error as a function of six parameters (resistance). The frequency stability (Δf/f) of TCXOs obtained was better than ±2 ppm.

Details

Microelectronics International, vol. 9 no. 3
Type: Research Article
ISSN: 1356-5362

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Article
Publication date: 9 January 2020

Duen-Ren Liu, Yun-Cheng Chou and Ciao-Ting Jian

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits…

Abstract

Purpose

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Design/methodology/approach

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

Findings

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Originality/value

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

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Article
Publication date: 1 June 2003

A. Krysztafkiewicz, S. Binkowski, A. Kaczmarek and T. Jesionowski

Two types of amorphous silica namely, the precipitated silica and the pyrogenic silica, were studied. The surfaces of such silica were modified with silane coupling agents…

Abstract

Two types of amorphous silica namely, the precipitated silica and the pyrogenic silica, were studied. The surfaces of such silica were modified with silane coupling agents such as 3‐aminopropyltriethoxysilane, N‐2‐(aminoethyl)‐3‐aminopropyltrimethoxysilane and 3‐ureidopropyltrimethoxysilane. Pigments were obtained by the adsorption of organic dyes, C.I. Reactive Blue 19 and C.I. Acid Green 16, onto the modified silica surface. Structural properties of the modified silica and the pigments obtained were evaluated using scanning electron microscopy, zeta potential analysis and particle size measurement techniques. Moreover, colour of the pigments obtained was evaluated using the CIE L *a*b* colour space system. The specific surface area of the pigment obtained was estimated using the BET method.

Details

Pigment & Resin Technology, vol. 32 no. 3
Type: Research Article
ISSN: 0369-9420

Keywords

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Article
Publication date: 21 October 2019

Adekunle Oluseyi Afolabi and Pekka Toivanen

The roles recommendation systems play in health care have become crucial in achieving effective care and in meeting the needs of modern care giving. As a result, efforts…

Abstract

Purpose

The roles recommendation systems play in health care have become crucial in achieving effective care and in meeting the needs of modern care giving. As a result, efforts have been geared toward using recommendation systems in the management of chronic diseases. Effectiveness of these systems is determined by evaluation following implementation and before deployment, using certain metrics and criteria. The purpose of this study is to ascertain whether consideration of criteria during the design of a recommendation system can increase acceptance and usefulness of the recommendation system.

Design/methodology/approach

Using survey-style requirements gathering method, the specific health and technology needs of people living with chronic diseases were gathered. The result was analyzed using quantitative method. Sets of harmonized criteria and metrics were used along with requirements gathered from stakeholders to establish relationship among the criteria and the requirements. A matching matrix was used to isolate requirements for prioritization. These requirements were used in the design of a mobile app.

Findings

Matching criteria against requirements highlights three possible matches, namely, exact, inferential and zero matches. In any of these matches, no requirement was discarded. This allows priority features of the system to be isolated and accorded high priority during the design. This study highlights the possibility of increasing the acceptance rate and usefulness of a recommendation system by using metrics and criteria as a guide during the design process of recommendation systems in health care. This approach was applied in the design of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People. The result has shown that with this method, it is possible to increase acceptance rate, robustness and usefulness of the product.

Research limitations/implications

Inability to know the evaluation criteria beforehand, inability to do functional analysis of requirements, lack of well-defined requirements and often poor cooperation from people living with chronic diseases during requirements gathering for fear of stigmatization, confidentiality and privacy breaches are possible limitations to this study.

Practical implications

The result has shown that with this method, it is possible to isolate more important features of the system and use them during the design process, thereby speeding up the design and increasing acceptance rate, robustness and usefulness of the system. It also helps to see in advance the likely features of the system that will enhance its usefulness and acceptance, thereby increasing the confidence of the developers in their ability to deliver a system that will meet users’ needs. As a result, developers know beforehand where to concentrate their efforts during system development to ascertain the possibility of increasing usefulness and acceptance rate of a recommendation system. In addition, it will also save time and cost.

Originality/value

This paper demonstrates originality by highlighting and testing the possibility of using evaluation criteria and metrics during the design of a recommender system with a view to increasing acceptance and enhancing usefulness. It also shows the possibility of using the metrics and criteria in system’s development process for an exercise other than evaluation.

Details

Journal of Systems and Information Technology, vol. 21 no. 3
Type: Research Article
ISSN: 1328-7265

Keywords

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Article
Publication date: 17 June 2021

Ambica Ghai, Pradeep Kumar and Samrat Gupta

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be…

Abstract

Purpose

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

Design/methodology/approach

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

Findings

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

Research limitations/implications

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

Practical implications

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

Social implications

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

Originality/value

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

Details

Information Technology & People, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-3845

Keywords

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Article
Publication date: 10 November 2020

Samira Khodabandehlou, S. Alireza Hashemi Golpayegani and Mahmoud Zivari Rahman

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start…

Abstract

Purpose

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

Design/methodology/approach

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

Findings

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

Research limitations/implications

The research data were limited to only one e-clothing store.

Practical implications

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

Originality/value

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

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Article
Publication date: 1 May 2019

Bo Wang, Yanhua Zhang, Haiyan Tan and Jiyou Gu

The purpose of the study was to prepare melamine-urea-formaldehyde (MUF) resin that would be resistant to boiling water and high temperature and exhibit low formaldehyde emission.

Abstract

Purpose

The purpose of the study was to prepare melamine-urea-formaldehyde (MUF) resin that would be resistant to boiling water and high temperature and exhibit low formaldehyde emission.

Design/methodology/approach

The authors prepared MUF resin with different F/(M + U) and changed the amount of melamine added, through the analysis of MUF resin properties to get the best reaction parameters, and used different amino acid cure systems including NH4Cl cured the resin.

Findings

Resin’s heat resistance and water resistance are mainly determined by the amount of melamine added, and formaldehyde emission of the plywood can be changed by adjusting F/(M + U). The peak temperature of the curing agent-cured resin increases as compared with the self-curing resin. Stronger the acidity of curing agent, faster the viscosity increased in probation period and lower the bonding strength and heat resistance of the resin.

Research limitations/implications

Melamine improves the heat resistance and water resistance of the resin. When the amount of melamine is more than a certain value, water resistance of the resin decreased.

Practical implications

MUF resin that is resistant to boiling water and exhibits low formaldehyde emission can be used in high temperature, high humidity and strict formaldehyde emission environment and can also be combined with other materials.

Social implications

It was helpful to reduce the effect of formaldehyde emission on people’s health and environmental pollution and is also beneficial for the expansion of the application range of aldehyde resin.

Originality/value

The originality is twofold: the influence of the acid strength of curing agent on the bonding strength of the resin adhesive and the method for preparing high performance MUF resin by following the traditional process.

Details

Pigment & Resin Technology, vol. 48 no. 3
Type: Research Article
ISSN: 0369-9420

Keywords

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Article
Publication date: 13 March 2017

Nikolaos Polatidis, Christos K. Georgiadis, Elias Pimenidis and Emmanouil Stiakakis

This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile…

Abstract

Purpose

This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable.

Design/methodology/approach

This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests.

Findings

The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected.

Originality/value

This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.

Details

Information & Computer Security, vol. 25 no. 1
Type: Research Article
ISSN: 2056-4961

Keywords

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