Single User Research Articles

"I Upload... All Types of Different Things to Say the World of Blindness Is More Than What They Think It Is": A Study of Blind TikTokers' Identity Work from a Flourishing Perspective

Identity work in Human-Computer Interaction (HCI) has examined the asset-based design of marginalized groups who use technology to improve their quality of life. Our study illuminates the identity work of people with disabilities, specifically, visual impairments. We interviewed 45 BlindTokers (blind users on TikTok) from various backgrounds to understand their identity work from a positive design perspective. We found that BlindTokers leverage the affordance of the platform to create positive content, express their identities, and build communities with the desire to flourish. We proposed flourishing labor to present the work conducted by BlindTokers for their community's flourishing with implications to support the flourishing labor. This work contributes to understanding blind users' experience in short video platforms and highlights that flourishing is not just an activity for any single blind user but also a collective effort that necessitates serious and committed contributions from platforms and the communities they serve.

2SpamH: A Two-Stage Pre-Processing Algorithm for Passively Sensed mHealth Data.

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated.

B-025 Revolutionizing NGS-driven Laboratory Developed Tests: The impact of automated liquid handling on plate-based bead clean-up

Abstract Background Automated liquid handling systems are proving to be a transformative technology in the landscape of Laboratory Developed Tests (LDTs), especially those utilizing Next-Generation Sequencing (NGS). By enhancing accuracy, boosting efficiency, and offering scalable solutions, these systems enable labs to meet current and future challenges more effectively. The right system not only improves the quality of diagnostics but also positions labs to be agile and adaptive in a rapidly advancing healthcare environment. firefly® is a well-suited liquid handling solution for diagnostics labs developing LDTs due to its compact footprint, user-friendly software and exceptional versatility. Validated protocol templates that support NGS library preparation are readily available from the firefly cloud and can be adapted to meet specific lab needs. For example, automated bead-based clean-ups designed for 96- and 384- NGS applications are available on firefly. These remove DNA fragments of undesirable sizes, enzymes or primers rom the amplified DNA after a PCR reaction. Automating this process facilitates higher throughput and scaling. firefly has a unique combination of 6 positive displacement dispense heads and a 96/384-well air-displacement pipetting head, making it ideal to distribute the reagents required for bead clean-ups. Methods firefly protocols were developed and optimized to be compatible with a range of elution volumes and bead ratios. Protocol performance was tested using library DNA (∼300bp). The same input DNA was plated out and quantified using the QuantiFluor dsDNA system (Promega) before and after running the protocol. This data was used to assess any reduction in DNA concentration and uniformity across the plate. The protocol's ability to successfully modulate the fragment size distribution was demonstrated using a 50bp DNA ladder and different bead ratios. Data was obtained using a Fragment Analyzer (Agilent, DNF-474 HS NGS Fragment Kit). Well-to-well contamination was assessed by running no-template-controls (NTCs) in wells adjacent to library DNA, then analyzed by qPCR using a LightCycler 480 System (Roche, KAPA Library Quantification kit). Results 96 format has a setup time of 10 minutes, run time of 43 and %CV library concentration of <6%. 384 format has a setup time of 10 minutes, run time of 47 and %CV library concentration of <8%. Uniformity of concentration and fragment sizes was seen across the plates. Comparison of the library concentration before and after the clean-up showed a 5% reduction in DNA concentration after the 96-well plate clean-up and an 11% reduction in library concentration when using the 384-well plate clean-up. Fragment size analysis showed that fragment sizes can be successfully modulated on firefly by changing the bead ratio of the firefly run and no detectable well-to-well contamination was detected by qPCR. Conclusions We have demonstrated that automated bead clean-ups on firefly are uniform show uniformity across both 96- and 384-well plates, with no detectable well-to-well contamination, to deliver the consistent and robust results required for an LDT. Bead-based cleanups on firefly offer a fast, 100% walkaway solution. By running 96-samples at once on firefly, we see approximately a 6-fold increase in throughput than when performed manually by a single user.

A blockchain-enabled private parking space allocation with improved parking space utilization

With the rapid development of the world’s infrastructure, we are moving towards making cities smart, known as smart cities. To make smart cities sustainable, designing efficient solutions is needed to reduce traffic congestion due to the lack of parking spaces within the constrained land areas of many metropolitan city centres. Parking problems in urban areas can be reduced by sharing vacant private parking spaces with others. In this work, we propose a decentralized and shared private parking management system using blockchain, which helps private parking owners and users fulfil their demands without the involvement of central authority. The proposed parking allocation method assigns parking to a single user from multiple parking providers, but the requester does not need to relocate between different parking spaces. The proposed method significantly improves private parking space utilization compared to a state-of-the-art parking allocation system. Hence, the system satisfies more user requests and generates more profit for parking owners than the state-of-the-art parking allocation system. The proposed system is a winning strategy because it maximizes the utilization of private parking spaces for the entire community’s benefit and helps to reduce traffic congestion. Experiments conducted in the simulated environment validate the benefits of the proposed model and show that it outperforms the state-of-the-art parking allocation system. A prototype of the proposed system is also developed in the Ethereum blockchain to validate the work, and the gas cost used to deploy the proposed model is analyzed.

Dynamic and efficient device collaborations in 5G‐advanced and 6G networks

AbstractCollaborative transmission, comprising multiple devices owned by a single user, is progressively evolving into an essential strategy to meet the stringent demands of burgeoning collaborative scenarios in 5G‐advanced and 6G networks. This paper proposes three novel use cases for device collaboration, namely data duplication, data splitting and wireless backup, to address these requirements. To provide dynamic and efficient collaboration, both non‐transparent mode via the medium access control layer collaboration and transparent mode via the physical layer collaboration are proposed. The paper further introduces a comprehensive design framework including protocol stack design, user equipment capability reporting, user equipment pairing, scheduling mechanism and transmission mechanism for different collaborative use cases with different collaborative modes. Evaluation outcomes reveal that the recommended methods could decrease the resources consumed for data duplication while increasing the user perceived throughput for data duplication and data splitting. The proposed methods also augment transmission reliability for both data duplication and wireless backup.

Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm

The research delves into the intricate domain of security offloading within the context of non-orthogonal multiple access (NOMA) heterogeneous mobile edge computing (het-MEC) networks operating over Rayleigh fading channels. The investigation centers on a system model comprising a single antenna-equipped edge user, denoted as U, which strategically offloads computational tasks to two distinct heterogeneous wireless access points (APs): the far AP (AP1) and the near one (AP2), employing NOMA techniques. Notably, the research accounts for a passive eavesdropper (E) intending to intercept the U−AP2 transmission. A four-phase protocol is proposed to ensure the security offloading process, namely SAPS, which leverages wireless access point selection (APS) and physical layer security (PLS) techniques. The focus extends to derive a closed-form expression for a novel critical system performance metric: the secrecy successful computation probability (SSCP). Furthermore, an algorithm based on Ant Colony Optimization (ACO) within the continuous domain is introduced, which aims to enhance the SSCP by intelligently determining system parameters. The impact of critical factors such as transmit power, power allocation coefficient, bandwidth, CPU frequency, and task division ratio under the SAPS scheme is explored and compared to the conventional approach using pure NOMA. Remarkably, the algorithm in the proposed scheme demonstrates up to a 3% performance improvement. The validity and accuracy of the study findings are verified through Monte-Carlo simulations. The work contributes significantly to advancing secure offloading strategies in NOMA-based MEC networks, offering valuable insights for practical deployment and optimization.

FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning

Modern recommender systems may output considerably different recommendations due to small perturbations in the training data. Changes in the data from a single user will alter the recommendations as well as the recommendations of other users. In applications like healthcare, housing, and finance, this sensitivity can have adverse effects on user experience. We propose a method to stabilize a given recommender system against such perturbations. This is a challenging task due to (1) the lack of a “reference” rank list that can be used to anchor the outputs; and (2) the computational challenges in ensuring the stability of rank lists with respect to all possible perturbations of training data. Our method, FINEST, overcomes these challenges by obtaining reference rank lists from a given recommendation model and then fine-tuning the model under simulated perturbation scenarios with rank-preserving regularization on sampled items. Our experiments on real-world datasets demonstrate that FINEST can ensure that recommender models output stable recommendations under a wide range of different perturbations without compromising next-item prediction accuracy.

Flexible bandwidth-efficient simplified coherent IFoF using twin-OSSB with a dually modulated EML.

A bandwidth-efficient coherent analog IFoF approach is proposed, employing a twin optical sideband (twin-OSSB) modulation of intermediate frequency-over-fiber (IFoF) signals in a dually modulated electroabsorption modulated laser (EML), demonstrating stable modulations without complex bias drifts and polarization control. The receiver (Rx) utilizes heterodyne detection with phase noise canceling (PNC) techniques for channel recovery. Successful demonstrations include an 8 Gbps aggregated twin-OSSB of two QPSK channels, with measurements covering optical back-to-back (btb) and 25 km of a standard single-mode fiber (SSMF), revealing sensitivities of -30 dBm and -25 dBm for QPSK-twin-OSSB and 16-QAM single user, respectively.

Keep your friends close, and your enemies closer: structural properties of negative relationships on Twitter

The Ego Network Model (ENM) is a model for the structural organisation of relationships, rooted in evolutionary anthropology, that is found ubiquitously in social contexts. It takes the perspective of a single user (Ego) and organises their contacts (Alters) into a series of (typically 5) concentric circles of decreasing intimacy and increasing size. Alters are sorted based on their tie strength to the Ego, however, this is difficult to measure directly. Traditionally, the interaction frequency has been used as a proxy but this misses the qualitative aspects of connections, such as signs (i.e. polarity), which have been shown to provide extremely useful information. However, the sign of an online social relationship is usually an implicit piece of information, which needs to be estimated by interaction data from Online Social Networks (OSNs), making sign prediction in OSNs a research challenge in and of itself. This work aims to bring the ENM into the signed networks domain by investigating the interplay of signed connections with the ENM. This paper delivers 2 main contributions. Firstly, a new and data-efficient method of signing relationships between individuals using sentiment analysis and, secondly, we provide an in-depth look at the properties of Signed Ego Networks (SENs), using 9 Twitter datasets of various categories of users. We find that negative connections are generally over-represented in the active part of the Ego Networks, suggesting that Twitter greatly over-emphasises negative relationships with respect to “offline” social networks. Further, users who use social networks for professional reasons have an even greater share of negative connections. Despite this, we also found weak signs that less negative users tend to allocate more cognitive effort to individual relationships and thus have smaller ego networks on average. All in all, even though structurally ENMs are known to be similar in both offline and online social networks, our results indicate that relationships on Twitter tend to nurture more negativity than offline contexts.

Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI

Device-to-device (D2D) communication supports direct communications between nearby devices, which has potential to improve network capacity, spectrum efficiency and energy efficiency. Considering the high overhead to obtain the complete channel state information (CSI), we investigate resource allocation problems of D2D communication underlaying cellular networks with incomplete CSI to minimize the total power consumption. To deal with the challenge brought by incomplete CSI in estimating the instantaneous rates of D2D pairs (DPs), we consider two QoS metrics in terms of the expected rate and outage probability using statistical CSI. Based on that, we first investigate the energy-efficient power control problem for single cellular user (CU) and single DP sharing the same spectrum. With rigorous theoretical analysis of the intrinsic properties of CU rate and two QoS metrics, we design an optimal energy-efficient power control (EPO) algorithm for single CU and single DP. Using EPO as a building block, we then propose an energy-efficient resource allocation algorithm for multiple CUs and multiple DPs with incomplete CSI. Simulation results show that our algorithms consume the lowest powers compared with two baseline algorithms.

Anonymous Complaint Aggregation for Secure Messaging

Private messaging platforms provide strong protection against platform eavesdropping, but malicious users can use privacy as cover for spreading abuse and misinformation. In an attempt to identify the sources of misinformation on private platforms, researchers have proposed mechanisms to trace back the source of a user-reported message (CCS '19,'21). Unfortunately, the threat model considered by initial proposals allowed a single user to compromise the privacy of another user whose legitimate content the reporting user did not like. More recent work has attempted to mitigate this side effect by requiring a threshold number of users to report a message before its origins can be identified (NDSS '22). However, the state of the art scheme requires the introduction of new probabilistic data structures and only achieves a "fuzzy" threshold guarantee. Moreover, false positives, where the source of an unreported message is identified, are possible. This paper introduces a new threshold source tracking technique that allows a private messaging platform, with the cooperation of a third-party moderator, to operate a threshold reporting scheme with exact thresholds and no false positives. Unlike prior work, our techniques require no modification of the message delivery process for a standard source tracking scheme, affecting only the abuse reporting procedure, and do not require tuning of probabilistic data structures.

Multiple graphical views for automatically generating SQL for the MycoDiversity DB; making fungal biodiversity studies accessible.

Fungi is a highly diverse group of eukaryotic organisms that live under an extremely wide range of environmental conditions. Nowadays, there is a fundamental focus on observing how biodiversity varies on different spatial scales, in addition to understanding the environmental factors which drive fungal biodiversity. Metabarcoding is a high-throughput DNA sequencing technology that has positively contributed to observing fungal communities in environments. While the DNA sequencing data generated from metabarcoding studies are available in public archives, this valuable data resource is not directly usable for fungal biodiversity investigation. Additionally, due to its fragmented storage and distributed nature, it is not immediately accessible through a single user interface. We developed the MycoDiversity DataBase User Interface (https://mycodiversity.liacs.nl) to provide direct access and retrieval of fungal data that was previously inaccessible in the public domain. The user interface provides multiple graphical views of the data components used to reveal fungal biodiversity. These components include reliable geo-location terms, the reference taxonomic scientific names associated with fungal species and the standard features describing the environment where they occur. Direct observation of the public DNA sequencing data in association with fungi is accessible through SQL search queries created by interactively manipulating topological maps and dynamic hierarchical tree views. The search results are presented in configurable data table views that can be downloaded for further use. With the MycoDiversity DataBase User Interface, we make fungal biodiversity data accessible, assisting researchers and other stakeholders in using metabarcoding studies for assessing fungal biodiversity.

86‐1: An Eye Tracking Method to Extend the Viewing Zone in Multiview 3D Displays

We describe the design and performance of a multi‐view 3D display system displaying an adjustable number of views. This uses an 8K panel and a simple eye tracking method to extend the viewing zones over a very large range and provides a multi‐view 3D display with high resolution and smooth motion parallax that gives the viewer good freedom of movement. The results are obtained from a system built around a 32" 8k monitor with a 45 lpi lenticular screen; it gives good image depth with motion parallax to a single user, with a viewing angle more than 60.

A Comparative study of Operating System for Internet of Things

Internet Of Things is nowadays one of the most advanced and in trend technology. Internet Of Things basically means the network which internally connects all hardware and software devices. All these devices connected through Internet Of Things can be controlled by the user. One of the core components of Internet Of Things is Operating System which is a kind of software written through which the use of punch cards and magnetic tapes were replaced and programs can be run by a single user with less efforts and less time consumption. Review paper is basically about how the Internet Of Things and Operating System operates, there pros and cons, features, core components, their cause of origin and how the Operating System is crucial component of Internet Of Things. The most renowned companies serving Internet Of Things as well as Operating System services are also listed, when and how they started, their products which were made specifically for the Internet Of Things purpose or were afterwards made to be used for Internet Of Things purpose. Not only the information about Internet Of Things and Operating System technologies are listed but also how these two are related to each other is also mentioned review paper. What was the past and present of the Internet Of Things and Operating System technology is listed here but what can be the future of the Internet Of Things and Operating System is also tried to be predicted on the basis of facts and figures. As a conclusion Operating System itself is a powerful technology but Internet Of Things needs Operating System to be implemented otherwise without Operating System Internet Of Things is just some hardware machines which needs to be operated in same old way though knobs and switches or Operating System can be said as a core of Internet Of Things world to some extent. Keywords- Internet Of Things, Operating System, Kernel, UI, GUI, NLI, CLI, API, Blockchain.

Visualising mixed reality simulation for multiple users

Blended reality seeks to encourage co-presence in the classroom, blending student experience across virtual and physical worlds. In a similar way, Mixed Reality, a continuum between virtual and real environments, is now allowing learners to work in both the physical and the digital world simultaneously, especially when combined with an immersive headset experience. This experience provides innovative new experiences for learning, but faces the challenge that most of these experiences are single user, leaving others outside the new environment. The question therefore becomes, how can a mixed reality simulation be experienced by multiple users, and how can we present that simulation effectively to users to create a true blended reality environment? This paper proposes a study that uses existing screen production research into the user and spectator to produce a mixed reality simulation suitable for multiple users. A research method using Design Based Research is also presented to assess the usability of the approach.

An Efficient Lightweight Authentication Scheme for Smart Meter

With the rapid development of the information age, smart meters play an important role in the smart grid. However, there are more and more attacks on smart meters, which mainly focus on the identity authentication of smart meters and the security protection of electricity consumption data. In this paper, an efficient lightweight smart meter authentication scheme is proposed based on the Chinese Remainder Theorem (CRT), which can realize the revocation of a single smart meter user by publishing a secret random value bound to the smart meter identity. The proposed scheme not only protects the security of smart meter electricity consumption data by using encryption, but also resists identity attacks from both internal and external adversaries by using hash functions and timestamps. Experiment shows that the proposed scheme has lower computation overhead and communication overhead than other authentication schemes and is more suitable for smart meter authentication.

Role of Synchronous, Moderated, and Anonymous Peer Support Chats on Reducing Momentary Loneliness in Older Adults: Retrospective Observational Study.

Older adults have a high rate of loneliness, which contributes to increased psychosocial risk, medical morbidity, and mortality. Digital emotional support interventions provide a convenient and rapid avenue for additional support. Digital peer support interventions for emotional struggles contrast the usual provider-based clinical care models because they offer more accessible, direct support for empowerment, highlighting the users' autonomy, competence, and relatedness. This study aims to examine a novel anonymous and synchronous peer-to-peer digital chat service facilitated by trained human moderators. The experience of a cohort of 699 adults aged ≥65 years was analyzed to determine (1) if participation, alone, led to measurable aggregate change in momentary loneliness and optimism and (2) the impact of peers on momentary loneliness and optimism. Participants were each prompted with a single question: "What's your struggle?" Using a proprietary artificial intelligence model, the free-text response automatched the respondent based on their self-expressed emotional struggle to peers and a chat moderator. Exchanged messages were analyzed to quantitatively measure the change in momentary loneliness and optimism using a third-party, public, natural language processing model (GPT-4 [OpenAI]). The sentiment change analysis was initially performed at the individual level and then averaged across all users with similar emotion types to produce a statistically significant (P<.05) collective trend per emotion. To evaluate the peer impact on momentary loneliness and optimism, we performed propensity matching to align the moderator+single user and moderator+small group chat cohorts and then compare the emotion trends between the matched cohorts. Loneliness and optimism trends significantly improved after 8 (P=.02) to 9 minutes (P=.03) into the chat. We observed a significant improvement in the momentary loneliness and optimism trends between the moderator+small group compared to the moderator+single user chat cohort after 19 (P=.049) and 21 minutes (P=.04) for optimism and loneliness, respectively. Chat-based peer support may be a viable intervention to help address momentary loneliness in older adults and present an alternative to traditional care. The promising results support the need for further study to expand the evidence for such cost-effective options.

An efficient polynomial-based verifiable computation scheme on multi-source outsourced data

With the development of cloud computing, users are more inclined to outsource complex computing tasks to cloud servers with strong computing capacity, and the cloud returns the final calculation results. However, the cloud is not completely trustworthy, which may leak the data of user and even return incorrect calculations on purpose. Therefore, it is important to verify the results of computing tasks without revealing the privacy of the users. Among all the computing tasks, the polynomial calculation is widely used in information security, linear algebra, signal processing and other fields. Most existing polynomial-based verifiable computation schemes require that the input of the polynomial function must come from a single data source, which means that the data must be signed by a single user. However, the input of the polynomial may come from multiple users in the practical application. In order to solve this problem, the researchers have proposed some schemes for multi-source outsourced data, but these schemes have the common problem of low efficiency. To improve the efficiency, this paper proposes an efficient polynomial-based verifiable computation scheme on multi-source outsourced data. We optimize the polynomials using Horner’s method to increase the speed of verification, in which the addition gate and the multiplication gate can be interleaved to represent the polynomial function. In order to adapt to this structure, we design the corresponding homomorphic verification tag, so that the input of the polynomial can come from multiple data sources. We prove the correctness and rationality of the scheme, and carry out numerical analysis and evaluation research to verify the efficiency of the scheme. The experimental indicate that data contributors can sign 1000 new data in merely 2 s, while the verification of a delegated polynomial function with a power of 100 requires only 18 ms. These results confirm that the proposed scheme is better than the existing scheme.

Digital marketing automation

Increased competition in an uncertain digital environment requires new approaches to improving marketing and business efficiency. Automation of business processes and marketing ensures management decisions aimed at improving customer experience by collecting, processing, and analyzing a huge amount of objective customer data from various sources and software services. For the company, it saves time and resources by automating routine processes, and, given the improved customer experience and corresponding loyalty of the target audience, it helps to increase the efficiency of marketing investments and improve the effectiveness of digital marketing (DM) and business. The article is aimed at substantiating the structure of a single user profile based on the integration of ESP and other DM software services with a CRM system. The hypothesis is formulated: creating a single user profile based on the integration of ESP and other DM software services with a CRM system by speeding up communication and improving customer experi­ence will increase the efficiency of DM and business in general. The research was conducted using general scientific and special methods: analysis and synthesis, system-structural analy­sis, comparison, systematic, logical generali­zation of results. Considerable attention is paid to email marketing, which remains one of the most popular DM automation tools, as it allows you to smoothly guide the consumer through the sales funnel with a high return on marketing investment. The effectiveness of integrating a CRM system and an ESP is proved, due to the specialization and functional features of the latter in interacting with consumers via email. The technologies, stages and tools of DM automation are systematized. It is proposed to collect all information about a particular consumer in a single user profile. This requires finding a technical solution for each business process and connecting software services into a single digital ecosystem. Collecting information about the company’s customers and their journey at all points of interaction in one place by integrating the services used into a single user profile allows you to make more effective decisions in marketing and business and achieve business goals

Methods of preference aggregation in group recommender systems

The rapid growth of data volumes has led to information overload, which impedes informed decision-making. To solve this problem, recommender systems have emerged that analyze user preferences and offer relevant products on their own. One type of recommender system is group recommender systems, which are designed to facilitate collaborative decision-making, increase user engagement, and promote diversity and inclusion. However, these systems face challenges such as accommodating diverse group preferences and maintaining transparency in recommendation processes. In this study, we propose a method for aggregating preferences in group recommendation systems to retain as much information as possible from group members and improve the accuracy of recommendations. The proposed method provides recommendations to groups of users by avoiding the aggregation process in the first steps of recommendation, which preserves information throughout the group recommendation process and delays the aggregation step to provide accurate and diverse recommendations. When the object of a collaborative filtering-based recommender system is not a single user but a group of users, the strategy for calculating similarity between individual users to find similarity should be adapted to avoid aggregating the preferences of group members in the first step. In the proposed model, the nearest neighbors of a group of users are searched, so the method of finding neighbors is adapted to compare individual users with the group profile. An experimental study has shown that the proposed method achieves a satisfactory balance between accuracy and diversity. This makes it well suited for providing recommendations to large groups in situations where accuracy is more or less important compared to diversity. These results support the assumption that retaining all information from group members without using aggregation techniques can improve the performance of group recommender systems, taking into account various features.