Search results

Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis.

Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers.

Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails.

It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

This strategy significantly reduces the computational overhead and storage overhead required when using the kernel density estimation method to calculate the abnormal evaluation value of the test sample.

To effectively deal with the security threats of botnets to the home and personal Internet of Things (IoT), especially for the objective problem of insufficient resources for anomaly detection in the home environment, a novel kernel density estimation-based federated learning-based lightweight Internet of Things anomaly traffic detection based on nuclear density estimation (KDE-LIATD) method. First, the KDE-LIATD method uses Gaussian kernel density estimation method to estimate every normal sample in the training set. The eigenvalue probability density function of the dimensional feature and the corresponding probability density; then, a feature selection algorithm based on kernel density estimation, obtained features that make outstanding contributions to anomaly detection, thereby reducing the feature dimension while improving the accuracy of anomaly detection; finally, the anomaly evaluation value of the test sample is calculated by the cubic spine interpolation method and anomaly detection is performed.

The simulation experiment results show that the proposed KDE-LIATD method is relatively strong in the detection of abnormal traffic for heterogeneous IoT devices.

With its robustness and compatibility, it can effectively detect abnormal traffic of household and personal IoT botnets.

The purpose of this paper was to examine consumer perceptions of aesthetic attributes of textile-based assistive devices (ADs) and the language used by consumers to express those perceptions and concerns. Previous investigations of user feedback for ADs have largely focused on functional attributes rather than aesthetics.

An interpretivist research philosophy was selected to investigate the meaning behind consumer perceptions and to understand their viewpoints on the aesthetic dimensions of ADs. Using product reviews for two ADs sold on Amazon.com as data, the researchers conducted qualitative data analysis through coding and interpretation of meanings behind reviews to determine consumers’ perceptions related to their ADs.

The authors identified consumer concerns linking to aesthetics evidenced as a multisensory integration of visual, tactile and olfactory cues. Consumer-preferred language used to address aesthetic preferences was found to supplement the literature. Aesthetic considerations were found to be impactful on avoiding stigma and encouraging or discouraging continued use of the devices.

Findings may contribute to the development of textile-based ADs with improved aesthetics to enhance user experiences. New ways of using consumer language to interpret user needs may assist in future research and design practice for consumer products.

The use of consumer product reviews as a rich source of user data is discussed in this paper. As previous research on assistive technology has largely focused on functionality, results of this analysis offer insight into consumers’ aesthetic judgments related to ADs and bring a sensory perspective to the research area.

The purpose of this study was to examine consumer data acquired by branded prescription drug websites and the ethics of privacy related to the interconnected web of personal information accessed, packaged and resold by tracker technologies.

The research used the DMI Tracker Tool to collect data on the top 17 branded prescription drug websites, with a specific interest in the tracker technologies embedded in those websites. That data was analyzed using Gephi, an open-source data visualization tool, to map the network of trackers embedded in those branded prescription drug websites.

Findings visualize the interconnections between tracker technologies and prescription drug websites that undergird a system of personal data acquisition and programmatic advertising vehicles that serve the interests of prescription drug marketers and Big Tech. Based on the theory of platform ethics, the study demonstrated the presence of a technostructural ecosystem dominated by Big Tech, a system that goes unseen by consumers and serves the interests of advertisers and resellers of consumer data.

The 17 websites used in this study were limited to the top-selling prescription drugs or those with the highest ad expenditures. As such this study is not based on a random sampling of branded prescription drug websites. The popularity of these prescription drugs or the expanse of advertising associated with the drugs makes them appropriate to study the presence of tracking devices that collect data from consumers and serve advertising to them. It is also noted that websites are dynamic spaces, and some trackers within their infrastructures are apt to change over time.

Branded prescription drug information has over the past three decades become part of consumers’ routine search for information regarding what ails them. As drug promotion moved from print to TV and the Web, searching for drug information has become a part of everyday life. The implications of embedded trackers on branded prescription drug websites are the subject of this research.

This study has significant social implications as consumers who are searching for information regarding prescription medications may not want drug companies tracking them in a way that many perceive to be an invasion of privacy. Yet, as the Web is dominated by Big Tech, web developers have little choice but to remain a part of this technostructural ecosystem.

This study sheds light on branded prescription drug websites, exploring the imbalance between the websites under study, Big Tech and consumers who lack awareness of the system that operates backstage.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

This paper aims to present a low-cost, edge-fed, windmill-shaped, notch-band eliminator, circular monopole antenna which is practically loaded with a complementary split ring resonator (CSRR) in the middle of the radiating conductor and also uses a partial ground to obtain wide-band performance.

To compensate for the reduced value of gain and reflection coefficient because of the full (complete) ground plane at the bottom of the substrate, the antenna is further loaded with a partial ground and a CSRR. The reduction in the length of ground near the feed line improves the impedance bandwidth, and introduced CSRR results in improved gain with an additional resonance spike. This results in a peak gain 3.895dBi at the designed frequency 2.45 GHz. The extending of three arms in the circular patch not only led to an increase of peak gain by 4.044dBi but also eliminated the notch band and improved the fractional bandwidth 1.65–2.92 GHz.

The work reports a –10dB bandwidth from 1.63 GHz to 2.91 GHz, which covers traditional coverage applications and new specific uses applications such as narrow LTE bands for future internet of things (NB-IoT) machine-to-machine communications 1.8/1.9/2.1/2.3/2.5/2.6 GHz, industry, automation and business-critical cases (2.1/2.3/2.6 GHz), industrial, society and medical applications such as Wi-MAX (3.5 GHz), Wi-Fi3 (2.45 GHz), GSM (1.9 GHz), public safety band, Bluetooth (2.40–2.485 GHz), Zigbee (2.40–2.48Ghz), industrial scientific medical (ISM) band (2.4–2.5 GHz), WCDMA (1.9, 2.1 GHz), 3 G (2.1 GHz), 4 G LTE (2.1–2.5 GHz) and other personal communication services applications. The estimated RLC electrical equivalent circuit is also presented at the end.

Because of full coverage of Bluetooth, Zigbee, WiFi3 and ISM band, the proposed fabricated antenna is suitable for low power, low data rate and wireless/wired short-range IoT-enabled medical applications.

The antenna is fabricated on a piece (66.4 mm × 66.4 mm × 1.6 mm) of low-cost low profile FR-4 epoxy substrate (0.54 λ_g × 0.54 λ_g) with a dielectric constant of 4.4, a loss tangent of 0.02 and a thickness of 1.6 mm. The antenna reflection coefficient, impedance and VSWR are tested on the Keysight technology (N9917A) vector network analyzer, and the radiation pattern is measured in an anechoic chamber.

This paper aims to highlight the expanding link between facility management (FM) and building automation and control systems (BACS) through a review of literature. It examines the opportunities and challenges of BACS for facility managers and proposes solutions for mitigating the risks associated with BACS implementation.

This paper reviews various research papers to explore the positive influences of BACS on FM, such as support with strategic decision-making, predictive maintenance, energy efficiency and comfort improvement. It also discusses the challenges of BACS, including obsolescence, interoperability, vendor lock-in, reliability and security risks and suggests potential solutions based on existing literature.

BACS offers numerous opportunities for facility managers, such as improved decision-making, energy efficiency and comfort levels in office buildings. However, there are also risks associated with BACS implementation, including obsolescence, interoperability, vendor lock-in, reliability and security risks. These risks can be mitigated through measures such as hardware and software obsolescence management plans, functional requirement lists, wireless communication protocols, advanced feedback systems and increased awareness about BACS security.

To the best of the authors’ knowledge, no prior academic research has been conducted on the expanding link between FM and BACS. Although some papers have touched upon the opportunities and challenges of BACS for FM, this paper aims to provide a comprehensive overview of these findings by consolidating existing literature.

The purpose of this study is to use empirical data to examine the hierarchical impact of the Internet of things capability on supply chain integration (SCI), supply chain capability (SCC) and firm performance (FP) in the UK retail industry.

A deductive approach was employed to carry out this research. Structural equation modelling (SEM) was performed using the partial least square method (SmartPLS 3.3.3) to test theoretical predictions which underlie the relationships among Internet of things capability (IoTC), SCI, SCC and FP. Data are collected using an online survey completed by senior executives of 66 large, medium and small firms within the UK retail industry.

The empirical results of this research reveal that IoTC has a significant positive effect on the UK retail industry FP through the mediating role of SCI and SCC.

The research results from this study provide useful management insights for firms within the retail industry into the development of effective strategies for integrating their supply chain alongside the adoption of IoTC into SCI, consequently leading to improvements in FP.

Although previous studies have explored the impact of IoT on FP through the sequential mediating role of SCI and SCC, few have explored the impact of the IoT capability (IoTC) on FP through sequential mediators, i.e. SCI and SCC. This study examines the relationship between IoTC, SCI, SCC and FP in the UK retail industry supply chain to address this knowledge gap. Moreover, this study examines the effects of IoTC on FP by applying partial least square (PLS)-SEM techniques. Testing the sequential mediating role of SCI and SCI is undertaken, and the relationships among IoT-enabled SCI and SCC is analysed to improve FP. The robustness check's result through PLSpredict analysis also confirms the power of the model proposed in this study.

High throughput and power efficient computing devices are highly essential in many autonomous system-based applications. Since the computational power keeps on increasing in recent years, it is necessary to develop energy efficient static RAM (SRAM) memories with high speed. Nowadays, Static Random-Access Memory cells are predominantly liable to soft errors due to the serious charge which is crucial to trouble a cell because of fewer noise margins, short supply voltages and lesser node capacitances.

Power efficient SRAM design is a major task for improving computing abilities of autonomous systems. In this research, instability is considered as a major issue present in the design of SRAM. Therefore, to eliminate soft errors and balance leakage instability problems, a signal noise margin (SNM) through the level shifter circuit is proposed.

Bias Temperature Instabilities (BTI) are considered as the primary technology for recently combined devices to reduce degradation. The proposed level shifter-based 6T SRAM achieves better results in terms of delay, power and SNM when compared with existing 6T devices and this 6T SRAM-BTI with 7 nm technology is also applicable for low power portable healthcare applications. In biomedical applications, Body Area Networks (BANs) require the power-efficient SRAM design to extend the battery life of BAN sensor nodes.

The proposed method focuses on high speed and power efficient SRAM design for smart ubiquitous sensors. The effect of BTI is almost eliminated in the proposed design.