User Choice Research Articles

Digitally nudging users to explore off-profile recommendations: here be dragons

In many application domains of recommender systems, e.g., on media streaming sites, one main goal of the provider of the recommendation service is to increase the engagement of users by helping them discover new types of content they like. Standard collaborative filtering algorithms by design often lead to a certain level of discovery. Nonetheless, in certain domains, it may be helpful to more actively promote content to users beyond their past preference profile (“off-profile”) and thereby help users explore new content. However, when showing such off-profile content to users in combination with more familiar content, the new content items may be overlooked. In this research, we explore to what extent digital nudging, i.e., subtly directing user choices in a specific direction, can help to raise the attention and interest of users for off-profile content. We conducted a user study (N=1064\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=1064$$\\end{document}) on a real-world social book recommendation app. We find that users who are nudged towards recommended books of their non-preferred genres significantly more often put these off-profile books on their reading lists, thus confirming the effectiveness of digital nudging in this application. However, we also found that digital nudges may negatively impact the users’ beliefs and attitudes towards the system and a more limited intention to use the system in the future. As a result, we find that digital nudging in recommendations, while effective in the short run, must be done with due care, keeping an eye on the overall quality perceptions by users and potentially harmful long-term effects.

LANGUAGE CHOICE IN PUBLIC INSTITUTIONS: THE CASE OF THE UNIVERSITY OF MAROUA, CAMEROON

This study sets out to investigate language choice in a public setting. The study intends to scrutinize the factors accounting for the choice of some language users at the University of Maroua, i.e., to choose any other languages different from the two official languages. To do this, a random sampling of 250 participants was used. The instrument used is a questionnaire with a purely sociolinguistic undertone. Spolsky’s (2009) Language Management backed up the study as the frame. At the end of the analyses, it has been shown that students, the teaching staff (involving some university administrators), and the support staff have some positive and negative attitudes following the language choices made in relation to where the communication takes place as well as the language situations such as enabling mutual intelligibility, social inclusion/exclusion, intimacy and showing identity, solidarity, and work coordination.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0021/a.php" alt="Hit counter" /></p>

Toward Interactive Self-Supervised Denoising

Self-supervised denoising frameworks have recently been proposed to learn denoising models without noisy-clean image pairs, showing great potential in various applications. The denoising model is expected to produce visually pleasant images without noise patterns. However, it is non-trivial to achieve this goal using self-supervised methods because 1) the self-supervised model is difficult to restore the perceptual information due to the lack of clean supervision, and 2) perceptual quality is relatively subjective to users’ preferences. In this paper, we make the first attempt to build an interactive self-supervised denoising model to tackle the aforementioned problems. Specifically, we propose an interactive two-branch network to effectively restore perceptual information. The network consists of a denoising branch and an interactive branch, where the former focuses on efficient denoising, and the latter modulates the denoising branch. Based on the delicate architecture design, our network can produce various denoising outputs, allowing the user to easily select the most appealing outcome for satisfying the perceptual requirement. Moreover, to optimize the network with only noisy images, we propose a novel two-stage training strategy in a self-supervised way. Once the network is optimized, it can be interactively changed between noise reduction and texture restoration, providing more denoising choices for users. Existing self-supervised denoising methods can be integrated into our method to be user-friendly with interaction. Extensive experiments and comprehensive analyses are conducted to validate the effectiveness of the proposed method.

Understanding evolving user choices: a neural network analysis of TAXI and ride-hailing services in Barcelona

Urban mobility stands as a fundamental element worthy of consideration by both society and its leaders. Often, decisions in this realm are made by governing figures without duly factoring in the preferences and needs of citizens. In our study, we delve into the changes that have unfolded within Barcelona from the standpoint of its users. The primary aim of this article is to observe the preferences that these users hold regarding the array of mobility options available to them in the urban environment. To this end, we’ve incorporated various aspects of significant relevance and contemporary presence in today’s society, such as mobility and sustainability, focusing specifically on the two most frequently used types of commercial passenger vehicles (CPV) within urban contexts: taxis and ride-hailing services. To gather the necessary sample data, a survey was conducted with a significance level of 95%. Following an exhaustive examination of the existing literature surrounding these concepts, we proceeded with the analysis of the sample using neural networks. The outcomes garnered encompass user receptiveness to the technological evolution shaping the sector, the utilization of mobile applications, a predisposition to opt for fixed tariffs, and the pivotal role of new blockchain-based technologies (NFTs and fan tokens) in influencing user decision-making.

Optimal matching for coexisting ride-hailing and ridesharing services considering pricing fairness and user choices

Mobility-on-demand (MoD) has the potential to revolutionise the patterns of urban mobility. Typically, an MoD platform provides both ride-hailing and ridesharing services, exacerbating the challenges of operating a city-scale real-time MoD system. Existing studies assume that travellers are fully compliant with the platform’s decisions regarding pricing and vehicle assignments, whereas, in reality, travellers can choose different modes based on monetary costs and travel experience, which may conflict with the results derived from the system perspective. In this study, we relax this assumption by accounting for pricing fairness and the travellers’ modal choices within a framework designed to optimise vehicle–traveller matching when both ride-hailing and ridesharing services are provided by an MoD platform. Six fairness principles are defined to characterise fair pricing for shared rides. Computationally efficient optimisation problems are formulated accounting for co-existing ride-hailing and ridesharing services. In numerical experiments, we assess the effectiveness of our method and compare it with state-of-the-art ones using a dataset of taxi requests for New York City. The results show that our optimisation strategy can significantly increase the service ratio and profit without sacrificing the service quality.

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm.

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix's elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

Context based diversification on keyword search by conceptualization of typed terms of the query

The human era is evolved and dominated through the ultimate intention to know about the Universe and its assets more and more. This in turn persuaded him to gather the immense information of need in the form of theory, tools, intuitions, visuals, and ultimately as the form of abstract objects. In modern Information practice, the art of Conceptualization and the induction of the data driven paradigm identifies the user query intention over a Conceptual network to mold a confident search segment of user choice. The proposed Conceptualization with Typed Terms of Query (CTTQ) approach derives a confident segment of the user query which is contextually measured by means of novel randomized Machine learning algorithms and its surrogates. The Normalized Discounted Cumulative Gain (nDCG) measures of the CTTQ show the usefulness of the query suggestions. It's efficiency improvements are represented with varying impact lexical feature indicator (θ) values.

Host Profit Maximization: Leveraging Performance Incentives and User Flexibility

The social network host has knowledge of the network structure and user characteristics and can earn a profit by providing merchants with viral marketing campaigns. We investigate the problem ofhost profit maximizationby leveraging performance incentives and user flexibility. To incentivize the host's performance, we propose setting a desired influence threshold that would allow the host to receive full payment, with the possibility of a small bonus for exceeding the threshold. Unlike existing works that assume a user's choice is frozen once they are activated, we introduce the Dynamic State Switching model to capture"comparative shopping"behavior from an economic perspective, in which users have the flexibilities to change their minds about which product to adopt based on the accumulated influence and propaganda strength of each product. In addition, the incentivized cost of a user serving as an influence source is treated as a negative part of the host's profit.Thehost profit maximizationproblem is NP-hard, submodular, and non-monotone. To address this challenge, we propose an efficient greedy algorithm and devise a scalable version with an approximation guarantee to select the seed sets. As a side contribution, we develop two seed allocation algorithms to balance the distribution of adoptions among merchants with small profit sacrifice. Through extensive experiments on four real-world social networks, we demonstrate that our methods are effective and scalable.

Investment Portfolio Management System

Abstract: The idea of this project is to automate the process of investing or trading based on users choice , by providing references to financial markets trajectory. This project helps a retail investor to meet his financial goals by automating the process and thereby remains manageable for them. The current projects usually reside on the principle of predicting the trend of the market or particular financial instrument .This can be done by using LSTM or prophet seasonality prediction model to predict non-linear trend in the market. Designing a bot to invest in instruments based on the users requirements will further help in automating the entire process. This ideated system is thought by integrating the smart predicting feature of the machine.

Designing Jakarta Halal Tourism Application with Collaborative and Location-Based Filtering Method as Jakarta Smart City Optimization

Jakarta is one of the Muslim-friendly tourist destinations in Indonesia. The DKI Jakarta government has various limitations in managing existing facilities, so community participation is needed. It encourages people to utilize application-based new media to answer the challenges and problems faced by Jakarta. A platform is required in the form of a digital application that can load destinations ranging from places, lodging, food, and facilities owned by tourist attractions labeled halal tourism to make it easier to find halal tourist destinations in Jakarta as an optimization of intelligent cities and contribute to SDGs. Halal tourism applications make it easier for people to get information related to Halal Tourism in Jakarta with the most popular development methods, namely collaborative filtering and location-based filtering methods. This method uses knowledge gathered from monitoring the behavior and personal choices of system users, who are generally known as personal profile users. The research method used is Research and Development (R&D) by designing an Android-based system. Data collection is an interview with sources with the DKI Jakarta Tourism and Creative Economy Office accompanied by expert information. The result of this research is the design of the Jakarta City Halal Tourism Application (SI PAHAJI) aimed at local and foreign tourists. The application contains information about tourist attractions, accommodations, and other facilities that are friendly to halal visitors in DKI Jakarta to help recommend tourist destinations in DKI Jakarta that follow user preferences and locations.

Customizable optical tissue phantom platform for characterization of fluorescence imaging device sensitivity.

Optical tissue phantoms serve as inanimate and stable reference materials used to calibrate, characterize, standardize, and test biomedical imaging instruments. Although various types of solid tissue phantoms have been described in the literature, current phantom models are limited in that they do not have a depth feature that can be adjusted in real-time, they cannot be adapted to other applications, and their fabrication can be laborious and costly. Our goal was to develop an optical phantom that could assess the imaging performance of fluorescence imaging devices and be customizable for different applications. We developed a phantom with three distinct components, each of which can be customized. We present a method for fabricating a solid optical tissue that contains (1)an adjustable depth capability using thin film phantoms, (2)a refillable chip loaded with fluorophores of the user's choice in various desired quantities, and (3)phantom materials representative of different tissue types. This article describes the development of phantom models that are customizable, adaptable, and easy to design and fabricate.

Assessment of users' adoption behaviour for stock market investment through online applications

Investing in stocks has become increasingly accessible, with beginners able to start an account via a brokerage's website or mobile app with very little money. Online trading through applications allows you to trade independently without a broker's interference. This not only lowers the overall cost of trading but also makes it hassle-free, increasing the value of the business significantly. Using an online trading account gives an investor more control. This paper examined the user's perspective on stock market investment through online applications. The impact of users' awareness, benefits & choice of investment, reliability, safety, risk-related factors, financial literacy, technical aspect, and dependency was analyzed on users' adoption behaviour. An empirical survey with a valid sample of 424 respondents from India was collected from the respondent and analyzed using descriptive analysis, correlation analysis, reliability analysis, analysis of variance, and regression analysis. Results indicated that users' awareness, benefits & choice of investment, reliability, safety, risk-related factors, financial literacy, technical aspect, and dependency significantly influence the users' adoption behaviour in using stock trading applications. The findings will assist potential investors in comprehending the factors influencing the adoption of such apps and aiding the organizations engaged in operating or creating such applications in understanding user behaviour, which will significantly add value to the society at large.

Substrate Versatile Roller Ball Pen Writing of Nanoporous MoS2 for Energy Storage Devices.

Low-cost fabrication of customizable supercapacitors and batteries to power up portable electronic devices is a much-needed step in advancing energy storage devices. The processing methods and techniques involved in developing small-sized entities in complex patterns are expensive, tedious, and time-consuming. Here, we demonstrate the fabrication of customizable electrochemical supercapacitors and batteries by simply employing the universal and conventional paradigm of direct pen writing with hands and evaluating their energy storage performance. The fabrication technique involves the refilling of MoS2 ink into the pen and then scripting of MoS2 nanostructures onto various substrates. The electrode material employed here consists of nanoporous microspheres of MoS2 synthesized by a simple one-step hydrothermal method. Direct pen writing with porous MoS2 in complex patterns enables easy, affordable, and simple fabrication of energy storage devices as and when required based on user choice toward distributed manufacturing and sustainability.

Modeling of OECD/NEA P2M Benchmark Cases by Means of TRANSURANUS Code

This paper presents the fuel performance simulation of two past experiments that are concerned with partial fuel melting. The activity is conducted in the framework of the Organisation for Economic Co-operation and Development/Nuclear Energy Agency Power to Melt and Maneuverability (P2M) – Second Framework for Irradiation Experiments (FIDES-II) Joint ExpErimental Programme (JEEP). The xM3 transient experiment involved uranium oxide (UO2) fuel with a ZIRconium Low Oxidation alloy (ZIRLO) cladding base irradiated up to 27 MWd/kg U, followed a ramp test up to 70 kW/m leading to inception of melting. The High Burnup Chemistry Experiment 4 (HBC4) transient was performed according to a rapid power ramp, on a UO2 fuel with a Zircaloy-4 cladding base irradiated up to 47 MWd/kg U. The linear heat generation rate reached 66.3 kW/m at the end of the transient leading to inception of melting and rodlet failure. Three main cases per each of the two transients were modeled with the TRANSURANUS code version 2015 in agreement with the benchmark specifications to consider uncertainty on the power during the ramping phases. The paper presents and discusses the analysis of these tests and provides an in-depth sensitivity analysis to assess the capabilities of the code as well as the effect of user choices in simulating the base irradiation and the inception of melting in light water reactor fuel rods.

Utilizing the Use of Google Trends to Discover The Architectural Attractiveness of a Place in Indonesia

A person's memory of the image of a place is powerful, which is influenced by a uniqueness supported by continuity of information. Every visitor's image will be very subjectively evaluated through the internet information media. The challenge lies in subjective perception through internet media information which only sometimes leads to real quality due to perceptions formed from the continuity of information received. A shift in meaning allegedly led to a phenomenon of similar motivation between video game users and traveling for healing purposes. From the perspective of a Google search, the word "traveling" refers to buildings and places with ecological and natural concepts. Meanwhile, video games also offer a natural concept but are more competitive. Using Google Trends, this study attempts to formulate city popularity data using the keyword "traveling." Natural Ecology is the highest choice for social media users, and "healing" is the primary motivation. The value of natural architectural instruments can be a focus to support a sustainable development process for a place based on the popularity of today's needs.

Strengthening the Understanding of the Context for Airport City Planning: A Case Study on Airport City Parafield

Airport Cities have gained momentum all over the world in the last decades. However, Airport City planning often follows a one-size-fits-all blueprint approach, with many elements incompatible with the regional, economic, and cultural context. Using a general aviation Airport City in Adelaide in South Australia as the case context, we survey the retail choices of airport users and compare them with existing facilities and those retail facilities at Airport Cities of large commercial airports from the literature and industry examples. We found that desired retail facilities at Airport City Parafield are widely different from those at large-scale commercial hub airports and commonly promoted in the Airport City literature. The results indicate that preferences for retail facilities in the Airport City differ depending on the airport user groups, which are determined by the role of the airport. These findings suggest that the often practised one-size-fits-all approach in Airport City planning should be reconsidered. In summary, this study helps to augment both knowledge and practice by reinforcing the importance of planning retail facilities in Airport Cities in alignment with the airport role, the airport user groups, and the regional, economic, and cultural context.

When Do Humans Heed AI Agents' Advice? When Should They?

We manipulate the presence, skill, and display of artificial intelligence (AI) recommendations in a strategy game to measure their effect on users' performance. Many applications of AI require humans and AI agents to make decisions collaboratively. Success depends on how appropriately humans rely on the AI agent. We demonstrate an evaluation method for a platform that uses neural network agents of varying skill levels for the simple strategic game of Connect Four. We report results from a 2 × 3 between-subjects factorial experiment that varies the format of AI recommendations (categorical or probabilistic) and the AI agent's amount of training (low, medium, or high). On each round of 10 games, participants proposed a move, saw the AI agent's recommendations, and then moved. Participants' performance improved with a highly skilled agent, but quickly plateaued, as they relied uncritically on the agent. Participants relied too little on lower skilled agents. The display format had no effect on users' skill or choices. The value of these AI agents depended on their skill level and users' ability to extract lessons from their advice. Organizations employing AI decision support systems must consider behavioral aspects of the human-agent team. We demonstrate an approach to evaluating competing designs and assessing their performance.

Community Readiness for the Use of the Cash on Delivery (COD) Application Without a Marketplace Using an SPSS-based Likert Scale

This study aims to analyze the community's readiness for the Cash on Delivery (COD) without marketplace application as a payment method in online shopping transactions. Questionnaires were distributed to 30 respondents aged 18 to 25 years with various levels of education. The results of the questionnaire were analyzed using the SPSS statistical software, and the data obtained included age, gender, education level, and responses to several questions that were relevant to community readiness for COD services. The results of the analysis show that the majority of the community shows a high level of readiness for the COD application, with around 50.0% to 60.0% of respondents belonging to the ready and very ready categories. The diverse age range of respondents indicates that the COD application can reach young age groups, and the varying levels of education indicate that this application attracts interest from various walks of life. The gender of the respondents did not show a significant difference in the level of interest in COD services. Even though the majority showed high readiness, there were some respondents who showed a lower level of readiness. Therefore, a special approach is needed to provide further information and education to this group to increase their readiness to use COD services. In conclusion, the COD application has great potential to become a popular and reliable choice for users to make purchases online. However, effective marketing, education and product development efforts need to be continued to reach the full potential of COD services and provide better benefits to potential users. By focusing on good user experience, data security, and the right marketing strategy, COD applications have a chance of success in a competitive market.

View recommendation for multi-camera demonstration-based training

While humans can effortlessly pick a view from multiple streams, automatically choosing the best view is a challenge. Choosing the best view from multi-camera streams poses a problem regarding which objective metrics should be considered. Existing works on view selection lack consensus about which metrics should be considered to select the best view. The literature on view selection describes diverse possible metrics. And strategies such as information-theoretic, instructional design, or aesthetics-motivated fail to incorporate all approaches. In this work, we postulate a strategy incorporating information-theoretic and instructional design-based objective metrics to select the best view from a set of views. Traditionally, information-theoretic measures have been used to find the goodness of a view, such as in 3D rendering. We adapted a similar measure known as the viewpoint entropy for real-world 2D images. Additionally, we incorporated similarity penalization to get a more accurate measure of the entropy of a view, which is one of the metrics for the best view selection. Since the choice of the best view is domain-dependent, we chose demonstration-based training scenarios as our use case. The limitation of our chosen scenarios is that they do not include collaborative training and solely feature a single trainer. To incorporate instructional design considerations, we included the trainer’s body pose, face, face when instructing, and hands visibility as metrics. To incorporate domain knowledge we included predetermined regions’ visibility as another metric. All of those metrics are taken into account to produce a parameterized view recommendation approach for demonstration-based training. An online study using recorded multi-camera video streams from a simulation environment was used to validate those metrics. Furthermore, the responses from the online study were used to optimize the view recommendation performance with a normalized discounted cumulative gain (NDCG) value of 0.912, which shows good performance with respect to matching user choices.

Database Creator for Mass Analysis of Peptides and Proteins, DC-MAPP: A Standalone Tool for Simplifying Manual Analysis of Mass Spectral Data to Identify Peptide/Protein Sequences.

Proteomic studies typically involve the use of different types of software for annotating experimental tandem mass spectrometric data (MS/MS) and thereby simplifying the process of peptide and protein identification. For such annotations, these softwares calculate the m/z values of the peptide/protein precursor and fragment ions, for which a database of protein sequences must be provided as an input file. The calculated m/z values are stored as another database, which the user usually cannot view. Database Creator for Mass Analysis of Peptides and Proteins (DC-MAPP) is a novel standalone software that can create custom databases for "viewing" the calculated m/z values of precursor and fragment ions, prior to the database search. It contains three modules. Peptide/Protein sequences as per user's choice can be entered as input to the first module for creating a custom database. In the second module, m/z values must be queried-in, which are searched within the custom database to identify protein/peptide sequences. The third module is suited for peptide mass fingerprinting, which can be used to analyze both ESI and MALDI mass spectral data. The feature of "viewing" the custom database can be helpful not only for better understanding the search engine processes, but also for designing multiple reaction monitoring (MRM) methods. Post-translational modifications and protein isoforms can also be analyzed. Since, DC-MAPP relies on the protein/peptide "sequences" for creating custom databases, it may not be applicable for the searches involving spectral libraries. Python language was used for implementation, and the graphical user interface was built with Page/Tcl, making this tool more user-friendly. It is freely available at https://vit.ac.in/DC-MAPP/.