Finding Information Research Articles

Exact and Approximate Schemes for Robust Optimization Problems with Decision-Dependent Information Discovery

Exact and Approximate Schemes for Robust Optimization Problems with Decision-Dependent Information Discovery

Design and Implementation of Personalized Recommendation Algorithm in Alumni Using Natural Language Processing

Recently, new possibilities for its web portal application have been presented by the automatic discovery of information in educational data, which has been expanding its horizons. The recommendation of university courses for high school students is an unexplored area. Traditional recommendation systems rely on collaborative filtering, which, in this instance, does not apply since the number of items and users needed to achieve high-performance levels is too small. One of the most well-known recommendation approaches in the course recommendation career is Natural Language Processing (NLP). Association rules have been added using data from alumni and assessments of student recommendations. The rules have been created by first mining data about the course that students completed, then comparing the discovered relationships to the existing course tree to improve it. Hence, a Natural Language Processing-based Personalized Recommendation System (NLP-PRS) has been established to help students choose courses that would take them to their desired careers by analyzing data from alumni and job offers. This study can use the alumni web portal and content formation through NLP to extract relevant information from course programs. It then presents recommendations based on students’ performance, interests, and results in each course that makes up each program. In addition, for the best results in improving their learning skills, students’ decisions should inform the recommended compliance. Because students vary in preferences (based on prior knowledge, learning style, emotional state, etc.), it is impossible to recommend a set timeframe for the recommended sequence. Based on the experimental outcome data, it gives the impression that the system is superior for allowing alumni to engage, share updates, and interact with current students who are alumni.

On Development of a SIM-Based Prototype for Enhancing the Usability of Mobile Value-Added Services

The advent of Mobile Value-Added Services (VAS) has witnessed widespread adoption in developing regions, notably within East Africa. This surge is attributed to advancements in information and communication technology, 3G/4G proliferation, growth of mobile internet accessibility, and rising smartphone penetration. Despite this progress, a significant segment of the population continues to rely on basic phones. Studies have shown that the text-based applications used on these types of mobile phones still pose significant obstacles in delivering a user-friendly VAS experience. This paper explores the potential for improving the usability of mobile value-added services on basic phones. We employed an action research approach, utilizing quantitative and qualitative data collection methods. Questionnaires gathered feedback on end-user testing, while in-depth interviews with five industry experts provided insights into potential solutions. This multi-faceted approach ensures a comprehensive understanding of user feedback and expert insights, culminating in the design of an intuitive SIM-based user interface prototype. This highly customised prototype prioritises services and functionalities simplify the user experience through improved navigability and accessibility. The effectiveness of this prototype was quantitatively analysed using descriptive statistics, focusing on ease of use, efficiency, effectiveness, and user satisfaction. With an average ease of use score of 85.8%, a geometric mean time on task of 29.43 seconds, and a user satisfaction score of 79.5%, the prototype demonstrates impressive functionality and garners acclaim for improved navigation and information discovery from users and experts. Further, comparative analysis from expert interviews reveald a more nuanced understanding of technology preferences for basic phone usability, contrary to previous studies. Building upon these demonstrably positive results, the paper concludes by advocating for user interface standardisation and recommending user-generated content for a more inclusive VAS ecosystem.

Online Multi-View Learning With Knowledge Registration Units.

In this work, we investigate online multi-view learning according to the multi-view complementarity and consistency principles to memorably process online multi-view data when fused across views. Online diverse features through different deep feature extractors under different views are used as input to an online learning method to privately and memorably optimize in each view for the discovery and memorization of the view-specific information. More specifically, according to the multi-view complementarity principle, a softmax-weighted reducible (SWR) loss is proposed to selectively retain credible views and neglect incredible ones for the online model's cross-view complementarity fusion. According to the multi-view consistency principle, we design a cross-view embedding consistency (CVEC) loss and a cross-view Kullback-Leibler (CVKL) divergence loss to maintain the cross-view consistency of the online model. Since the online multi-view learning setup needs to avoid repeatedly accessing online data to handle the knowledge forgetting in each view, we propose a knowledge registration unit (KRU) based on dictionary learning to incrementally register newly view-specific knowledge of online unlabeled data to the learnable and adjustable dictionary. Finally, by using the above strategies, we propose an online multi-view KRU approach and evaluate it with comprehensive experiments, thereby showing its superiority in online multi-view learning.

Mildred Codding (1902–1991): Neurosurgery Medical Illustrator at Harvard Medical School, Peter Bent Brigham Hospital, and Boston's Children Hospital

This historical account reviews the life and times of Mildred Codding (1902-1991) in neurosurgery and medical illustration. The writing of this project was sparked by the discovery of original scientific and bibliographical information about Codding, testimony on personal relationships, and viewpoints after comprehensive compilation of information. Mildred Codding learned the carbon dust technique from Max Brödel at the Johns Hopkins Hospital (JHH) (Baltimore, Maryland USA), and worked at Peter Bent Brigham Hospital, now Brigham and Women's Hospital (Boston, Massachusetts, USA). Codding was Dr. Cushing's medical illustrator, colleague, and friend. She provided photographs, diagrams, and sketches for the majority of Cushing's collection of intracranial tumors from 1929 to 1932. A 1991 interview of Codding was hosted by Brigham and Women's Hospital and Boston Children's Hospital for the permanent collection of the Archives of the American Association of Neurological Surgeons and provides additional photographs and insignia, drawings with pen and ink method, multi-stepped layering, and illustration implements. Importantly, the Brigham Surgical Alumni insignia showing a surgeon's hand with scalpel was modeled after Codding's left hand. Outside of the operating room, Codding's interests included Pediatrics, Zoology and Genetics, atlantes of surgical operations, teatime, and paint with watercolor, particularly seascapes, lighthouses and dunes. This article provides glimpses into the personality and marked influence of Mildred Codding on neurosurgery and medical education and adds to the growing literature on her person.

“It answers questions that I didn’t know I had”: PhD students’ evaluation of an information-sharing knowledge graph

PurposeInterdisciplinary PhD programs can be challenging as the vital information needed by students may not be readily available; it is scattered across the university’s websites, while tacit knowledge can be obtained only by interacting with people. Hence, there is a need to develop a knowledge management model to create, query and maintain a knowledge repository for interdisciplinary students. This study aims to propose a knowledge graph containing information on critical categories (faculty, classes, dissertations, etc.) and their relationships, extracted from multiple sources, essential for interdisciplinary PhD students. This study evaluates the usability of a participatory-designed knowledge graph intended to facilitate information exchange and decision-making.MethodologyThe authors used data from multiple sources (such as university websites, faculty profiles, publication and dissertation metadata and crowdsourced data) to generate a knowledge graph in the Neo4J Bloom platform. The authors recruited 15 interdisciplinary PhD students using convenience sampling from the University of Hawaiʻi at Mānoa at various PhD stages to design and populate the knowledge graph. Next, the authors conducted a mixed methods study to perform its usability evaluation. First, the authors engaged the students in a participatory design workshop to identify relevant graph queries. Second, the authors conducted semi-structured interviews to determine the usability of the knowledge graph and rate the queries. Each interview was coded with structural and thematic codes and was further analyzed using sentiment analysis in R programming language.FindingsThe usability findings demonstrate that interaction with this knowledge graph benefits PhD students by notably reducing uncertainty and academic stress, particularly among newcomers. Knowledge graph supported them in decision-making, especially when choosing collaborators (e.g. supervisor or dissertation committee members) in an interdisciplinary setting. Key helpful features are related to exploring student–faculty networks, milestones tracking, rapid access to aggregated data and insights into crowdsourced fellow students’ activities. However, they showed concerns about crowdsourced data privacy and accessibility. Although participants expressed the need for more qualitative data in the graph, they noted it helped identify people to talk to about the topics of their interest.OriginalityThe knowledge graph provides a solution to meet the personalized needs of doctoral researchers and has the potential to improve the information discovery and decision-making process substantially. It also includes the tacit knowledge exchange support missing from most current approaches, which is critical for this population and establishing interdisciplinary collaborations. This approach can be applied to other interdisciplinary programs and domains globally.

Corporate debt value under transition scenario uncertainty

AbstractWe develop a structural model for pricing a defaultable bond issued by a company subject to climate transition risk. We assume that the magnitude of the transition risk impacts depends on a transition scenario, which is initially unknown but is progressively revealed through the observation of the carbon tax trajectory. The bond price, credit spread, and optimal default/restructuring thresholds are then expressed as function of the firm's revenue level and the carbon tax. Numerical implementation of the resulting formulas is discussed and illustrated using real data. Our results show that under transition scenario uncertainty, carbon tax adjustments are more likely to trigger a default than when the true scenario is known because after each adjustment, the more environmentally stringent scenario becomes more likely. We also find that faster discovery of scenario information leads to higher credit spreads since better information allows the shareholders to optimize the timing of default, increasing the value of default option and decreasing the bond price. As an extension, we consider the situation where the company may invest into abatement technology, increasing the value of both the share price and the bond price.

Quality and Quantity Control of Mitochondria Injection Into Single Cells With Robot-Aided Micro-Manipulation System

Mitochondrial dysfunction plays a significant role in the development of fatal diseases such as aging, cancer, and Alzheimer’s. Transferring mitochondria to cells is a new and potential treatment for mitochondrial DNA (mtDNA)-related illnesses. This article describes a novel technique to control the quality and quantity of mitochondria injected into single live cells using a robot-aided microneedle and optical tweezers (OTs)-based micromanipulation system. Isolated mitochondria and cells are patterned in a 1-dimensional (1-D) array in a microfluidic device, and a robot-aided microneedle collects the predefined number of functional mitochondria with the help of OTs. Then, the microneedle precisely and non-invasively injects these collected mitochondria into single live cells. Given that the two manipulation tools of OTs and microneedle were used, a switch controller strategy is developed to enable mitochondria trapping and injection with OTs and microneedle-based micromanipulator, respectively. The effectiveness of the developed robotic system is experimentally demonstrated with automated injections of isolated mitochondria into HeLa and mesenchymal stem cells (aMSCs). A precise and efficient quality and quantity control of mitochondria injection was possible by using Ots in conjunction with microneedle and microfluidics technologies. Quality-and quantity-controlled ability is analyzed and compared with the traditional mitochondria transfer method (co-culture). Biological tests are further conducted to assess the viability of mitochondria recipient cells. Experimental results demonstrate that the developed system can non-invasively transfer healthy mitochondria into single live cells while precisely controlling the quantity and quality of injected mitochondria. The proposed mitochondrial transfer method has the potential to advance precision medicine methods, particularly for cellular therapy of (mtDNA)-related diseases. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study was motivated by the problem of inefficient methods of mitochondria injection into single, small, living cells. Due to the issue of microneedle clogging, existing mitochondria injection methods can only inject mitochondria into cells with a 0.3 to 2% efficiency. To address this issue, we have developed an automated method of mitochondria injection into cells that can inject mitochondria into single cells non-invasively with an efficiency of up to 80 %. Existing methods of mitochondria injection also ignored the heterogeneity of mitochondria, such as functionality and the precise number of mitochondria prior to injection. Our methodology can efficiently and precisely control the quality and quantity of mitochondria injection into single cells, which can be useful for the discovery of new information concerning precision medicine, particularly in diseases associated with mtDNA.

The pure benefit of risk in production: real options in general equilibrium

An increase in the riskiness of a technology will raise economy-wide expected output: large productivity realizations lead to gains while small realizations are mitigated by using alternative technologies. Some usage of even the riskiest technologies can therefore bring Pareto improvements. The observed expected output of risky technologies can nevertheless be less than that of safer technologies: empirical estimates of expected output are therefore a poor measure of efficiency. Firms will adopt the efficient riskier technologies when markets are competitive, contrary to the view that innovation requires a monopoly reward. These results require a suitable definition of risk for general equilibrium models and a refinement of competitive equilibrium that allows for information discovery. The efficiency gain of increases in risk is related to real options theories of investment but applies even to single-period economies.

An Examination of Faceted Searching in Discovery Systems and the Impact on Information Discovery

An Examination of Faceted Searching in Discovery Systems and the Impact on Information Discovery

Enhancing knowledge sharing platforms with machine learning

In the contemporary digital landscape, knowledge sharing platforms serve as crucial tools for facilitating collaboration and information exchange among individuals and organizations. However, ensuring the relevance and effectiveness of shared knowledge remains a persistent challenge. This abstract explores the application of machine learning techniques to enhance knowledge sharing platforms. By leveraging machine learning algorithms and predictive analytics, these platforms can better anticipate users' information needs, personalize content recommendations, and improve overall user experience. Through a comprehensive analysis of existing knowledge sharing platforms and the implementation of machine learning models, this abstract highlights the potential benefits of integrating machine learning techniques into knowledge sharing platforms. The findings underscore the significance of personalized content delivery, improved information discovery, and enhanced collaboration facilitated by machine learning-driven knowledge sharing platforms. Conclusion: Ultimately, the integration of machine learning techniques represents a promising approach to address the evolving demands of knowledge management and foster more efficient and effective knowledge sharing processes in various domains.

Information Discovery by Analysts

We provide direct evidence that analysts engage in information discovery, and that investors value this discovery. The innovation in our article is to read through the summaries of over 3,500 analyst reports to identify whether analysts engage in discovery and/or interpretation. Analysts discover new information from management sources (example: personal meetings) and non-management sources (example: channel checks). Information discovery is prevalent in 17% of the reports. Investors react more strongly to analyst reports containing changes in price targets or Earnings per Share if those reports are accompanied by information discovery. Economic determinants predict whether a report contains discovery.

Leveraging P2P Architecture and Semantic Web for Enhanced Resource Discovery

Web 4.0, also known as the next generation, intelligent internet, possesses the potential to become a widely and universally used communication medium for various types of information. However, its decentralized architecture lacks strong semantic support, resulting in an internet that is disorganized. The current system lacks the capacity to facilitate users' effective information discovery, extraction, and integration from multiple sources. Additionally, it fails to give consumers efficient tools for manipulating and turning acquired data into knowledge that is useful. Peer-to-peer (P2P) overlay technologies have recently come to light as a way to improve resource discovery on the internet. In dynamic and large-scale situations, these technologies provide a scalable framework for allocating, sharing, and gaining access to resources. The purpose of this research is to discuss on semantically enabled web architecture that makes use of P2P overlay technology. This architecture aims to facilitate structured and precise access to internet resources and promote knowledge sharing among community members who share similar interests. The paper examines the core elements of the semantic web architecture, which encompass the services and protocols responsible for resource advertising, discovery, and management, methods and material. It then delve into the hybrid peer-to-peer (P2P) overlay structure, specifically focusing on indexing and resource location, and explores the mechanisms necessary to facilitate scalable routing within a distributed environment.

“Knowing these stories”

In this article, I explore the ways participants in underground dance music (UDM) scenes learn about the political, historical, and social context of their leisure pursuit, such as its queer-of-colour roots, gender dynamics, and economic structures. Semi-structured interviews and information horizon mapping with three Toronto UDM scene members revealed that the online and physical spaces of UDM served as information grounds for the participants’ initial serendipitous discovery of social context information, which led to further intentional seeking in books, libraries, and beyond. Established UDM information sources and normative expectations shaped the participants’ information behaviour, leading them to infer the political stances of DJs and other scene participants based on musical taste and identity. Finally, social context information shapes the participants' experiences of UDM, affecting their sense of community, safety, and belonging. The serious leisure perspective has thus far focused on information behaviours related to the pursuit of the hobby itself, such as, in the present context, finding events or learning to DJ. This study opens up a new direction by considering how social context information flows through and shapes the social world of the hobby. As rising mainstream awareness of structural oppressions leads to more people and communities considering how these oppressions manifest in every aspect of their lives, this perspective will be germane to information behaviour studies in several contexts.

Optimal adaptive decentralized under-frequency load shedding for islanded smart distribution network considering wind power uncertainty

The increasing share of renewable distributed generations has raised a challenge about the frequency stability. Under-frequency load shedding (UFLS) can deal with this challenge, but it usually considers a prorated load-shedding strategy and neglects the impact of uncertainty from renewable generation. This may result in unnecessary additional economic losses from the system blackout. In this paper, a risk-decision-based optimal adaptive decentralized UFLS method is proposed to coordinate the distribution of shedding load in the smart distribution network to avoid unnecessary additional economic losses, while considering the wind power uncertainty. Firstly, the uncertainty from wind power generation is modeled by confidence intervals and evaluated by the calculation of the conditional value at risk (CVaR). Then, to economically distribute the power deficit among load agents, a decentralized consensus on the incremental cost among decentralized agents is reached, where the interruption cost of the local agent varies with its load as a nonlinear relationship. Finally, a two-layer adaptive risk-decision decentralized UFLS control framework is proposed to enable the frequency stability and economic operation of the smart distribution network. With the proposed strategy, local information sharing and important global information discovery are achieved in a distributed way. Simulations on an improved IEEE 33-bus system show that the proposed strategy can effectively avoid rigid shedding and implement the optimal allocation of the system power deficit.

Accelerating research processes with Scopus AI: A place branding case study

Academic databases play a crucial role in advancing science by hosting a vast array of peer-reviewed literature. However, academic database search tools involve a relatively slow and rather unintuitive process of searching and evaluating content. To address these challenges, in January 2024, Elsevier introduced Scopus AI, a generative artificial intelligence that synthesises evidence indexed in Scopus based on prompts. This study assesses the utility of Scopus AI (in its beta version at the time of the research), within the context of a doctoral thesis through a specific case study. By employing a relational prompt and three follow-up questions, the study aims to pinpoint intersections between different topics within the realm of Social Sciences and, more specifically, Communication, with a case on place branding. The consolidated result provides an initial list of references, offers a comprehensive overview, and allows to generate a meta-synthesis based on the summaries provided by each prompt. Scopus AI (beta) presents features that enable researchers to identify influential authors and works, explore relevant keywords, review recent literature, and identify potential research gaps. Although Scopus AI has some limitations, such as the dependence on the abstracts of documents indexed in Scopus, the simplification of concepts, or the relative disconnection between arguments, the results demonstrate the value of this tool in accelerating research processes, as it synthesises research in a given area, maps its main characteristics and allows for information discovery.

Hybrid Recommender System for Tourism Based on Big Data and AI

With the advancement of the Internet, technology, and communication channels, the generation of tourist data has significantly increased across various sectors such as hotels, restaurants, transportation, heritage sites, tourist events, and activities. This surge is particularly notable with the rise of Online Travel Agencies (OTAs). However, the sheer volume of options provided to tourists by web search engines or specialized tourism websites often overwhelms them, burying relevant results in a sea of information "noise." This inundation hinders or slows down the decision-making process. To alleviate this issue and aid tourists in trip planning by facilitating the discovery of relevant information, numerous recommender systems have emerged. This article offers an overview of the diverse recommendation approaches employed in the tourism domain. Through this investigation, we propose an architecture and conceptual framework for a tourism recommender system founded on a hybrid recommendation approach. This system surpasses merely suggesting a list of tourist attractions tailored to individual preferences. Instead, it functions as a comprehensive trip planner, crafting detailed itineraries comprising a variety of tourism resources, customized for specific visit durations. KeyWords: Internet Development, Tourist Data Production, Trip Planning, Conceptual Framework.

The effect of institutional herding on stock prices: The differentiating role of credit ratings

This paper investigates the impact of institutional herding on stock price formation, conditional on firms’ credit ratings, using 13F data from 1986 to 2019. In line with the current literature, we find herding intensity is driven by past returns consistent with momentum trading; however, we also find that herding is more sensitive to past returns for non-investment grade (NIG) stocks than investment grade (IG) stocks, resulting in a market bifurcation. We then examine the price impact of these trades and find that herding in NIG equities enhances price discovery. One plausible explanation is that information gradually diffuses within non-investment grade stocks, and herding behavior strengthens information discovery. Finally, we show both momentum-triggered herding and non-momentum-triggered herding contribute to price discovery among non-investment grade stocks.

Spatial data infrastructure components to provide regional climate information services

The assessment of expected climate changes on a regional scale requires appropriate data and information products that must be easily accessible and usable for decision makers. The regional climate information platform ReKIS is a long-established project of three German federal states to address this purpose. However, with increasing content, this web-based data exchange platform lacked in suitable metadata descriptions, search functionalities and interoperability, thus hampering the discovery and access of suitable information. This paper describes the extending and enhancing of ReKIS using state-of-the-art components of Spatial Data Infrastructures (SDI). The components support metadata acquisition, maintenance and publishing using a metadata catalogue. The implementation of a WebGIS server increases interoperability by offering OGC-compliant services and a web framework for knowledge-transfer allows for non-expert access to climate services. The approaches were developed and implemented in a real-world scenario and are suitable for a transfer to other comparable platforms and use cases.

Adversarial Machine-Learning-Enabled Anonymization of OpenWiFi Data

Data privacy and protection through anonymization is a critical issue for network operators or data owners before it is forwarded for other possible use of data. With the adoption of Artificial Intelligence (AI), data anonymization augments the likelihood of covering up necessary sensitive information; preventing data leakage and information loss. OpenWiFi networks are vulnerable to any adversary who is trying to gain access or knowledge on traffic regardless of the knowledge possessed by data owners. The odds for discovery of actual traffic information is addressed by applied conditional tabular generative adversarial network (CTGAN). CTGAN yields synthetic data; which disguises as actual data but fostering hidden acute information of actual data. In this paper, the similarity assessment of synthetic with actual data is showcased in terms of clustering algorithms followed by a comparison of performance for unsupervised cluster validation metrics. A well-known algorithm, K-means outperforms other algorithms in terms of similarity assessment of synthetic data over real data while achieving nearest scores 0.634, 23714.57, and 0.598 as Silhouette, Calinski and Harabasz and Davies Bouldin metric respectively. On exploiting a comparative analysis in validation scores among several algorithms, K-means forms the epitome of unsupervised clustering algorithms ensuring explicit usage of synthetic data at the same time a replacement for real data. Hence, the experimental results aim to show the viability of using CTGAN-generated synthetic data in lieu of publishing anonymized data to be utilized in various applications.