9,414 research outputs found

    Reimagining the Journal Editorial Process: An AI-Augmented Versus an AI-Driven Future

    Get PDF
    The editorial process at our leading information systems journals has been pivotal in shaping and growing our field. But this process has grown long in the tooth and is increasingly frustrating and challenging its various stakeholders: editors, reviewers, and authors. The sudden and explosive spread of AI tools, including advances in language models, make them a tempting fit in our efforts to ease and advance the editorial process. But we must carefully consider how the goals and methods of AI tools fit with the core purpose of the editorial process. We present a thought experiment exploring the implications of two distinct futures for the information systems powering today’s journal editorial process: an AI-augmented and an AI-driven one. The AI-augmented scenario envisions systems providing algorithmic predictions and recommendations to enhance human decision-making, offering enhanced efficiency while maintaining human judgment and accountability. However, it also requires debate over algorithm transparency, appropriate machine learning methods, and data privacy and security. The AI-driven scenario, meanwhile, imagines a fully autonomous and iterative AI. While potentially even more efficient, this future risks failing to align with academic values and norms, perpetuating data biases, and neglecting the important social bonds and community practices embedded in and strengthened by the human-led editorial process. We consider and contrast the two scenarios in terms of their usefulness and dangers to authors, reviewers, editors, and publishers. We conclude by cautioning against the lure of an AI-driven, metric-focused approach, advocating instead for a future where AI serves as a tool to augment human capacity and strengthen the quality of academic discourse. But more broadly, this thought experiment allows us to distill what the editorial process is about: the building of a premier research community instead of chasing metrics and efficiency. It is up to us to guard these values

    Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent

    Full text link
    Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general, this has a cubic cost in dataset size and is sensitive to conditioning. We explore stochastic gradient algorithms as a computationally efficient method of approximately solving these linear systems: we develop low-variance optimization objectives for sampling from the posterior and extend these to inducing points. Counterintuitively, stochastic gradient descent often produces accurate predictions, even in cases where it does not converge quickly to the optimum. We explain this through a spectral characterization of the implicit bias from non-convergence. We show that stochastic gradient descent produces predictive distributions close to the true posterior both in regions with sufficient data coverage, and in regions sufficiently far away from the data. Experimentally, stochastic gradient descent achieves state-of-the-art performance on sufficiently large-scale or ill-conditioned regression tasks. Its uncertainty estimates match the performance of significantly more expensive baselines on a large-scale Bayesian optimization task

    On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past

    Full text link
    A big part of software developers’ time is spent finding answers to their coding-task-related questions. To answer their questions, developers usually perform web searches, ask questions on Q&A websites, or, more recently, in chat communities. Yet, many of these questions have frequently already been answered in previous chat conversations or other online communities. Automatically identifying and then suggesting these previous answers to the askers could, thus, save time and effort. In an empirical analysis, we first explored the frequency of repeating questions on the Discord chat platform and assessed our approach to identify them automatically. The approach was then evaluated with real-world developers in a field experiment, through which we received 142 ratings on the helpfulness of the suggestions we provided to help answer 277 questions that developers posted in four Discord communities. We further collected qualitative feedback through 53 surveys and 10 follow-up interviews. We found that the suggestions were considered helpful in 40% of the cases, that suggesting Stack Overflow posts is more often considered helpful than past Discord conversations, and that developers have difficulties describing their problems as search queries and, thus, prefer describing them as natural language questions in online communities

    Algorithmic personalization and brand loyalty: An experiential perspective

    Get PDF
    This article explores the relationship between algorithmic personalization and brand loyalty by examining how personalization experiences are articulated within the context of music streaming consumption. Despite previous acknowledgement of the link between personalization and brand loyalty, an experientially grounded understanding of how this works has yet to be articulated. Building upon the concept of ‘experiential brand loyalty’, the Algorithmic Personalization/Depersonalization Loop highlights the development of brand loyalty through consumers’ interactions with algorithm-backed brands. Being seen and understood by the algorithm sets off an iterative, two-way learning relationship that ultimately heightens the consumers’ experience, activates positive emotions, and deepens the relational bond with the brand, leading to brand loyalty. If, however, the algorithm is unsuccessful in personalizing the service experience, a ‘depersonalization’ process can occur that erodes brand loyalty and can lead to brand switching or even consumer activism

    The Use of Clustering Methods in Memory-Based Collaborative Filtering for Ranking-Based Recommendation Systems

    Get PDF
    This research explores the application of clustering techniques and frequency normalization in collaborative filtering to enhance the performance of ranking-based recommendation systems. Collaborative filtering is a popular approach in recommendation systems that relies on user-item interaction data. In ranking-based recommendation systems, the goal is to provide users with a personalized list of items, sorted by their predicted relevance. In this study, we propose a novel approach that combines clustering and frequency normalization techniques. Clustering, in the context of data analysis, is a technique used to organize and group together users or items that share similar characteristics or features. This method proves beneficial in enhancing recommendation accuracy by uncovering hidden patterns within the data. Additionally, frequency normalization is utilized to mitigate potential biases in user-item interaction data, ensuring fair and unbiased recommendations. The research methodology involves data preprocessing, clustering algorithm selection, frequency normalization techniques, and evaluation metrics. Experimental results demonstrate that the proposed method outperforms traditional collaborative filtering approaches in terms of ranking accuracy and recommendation quality. This approach has the potential to enhance recommendation systems across various domains, including e-commerce, content recommendation, and personalized advertising

    A survey on vulnerability of federated learning: A learning algorithm perspective

    Get PDF
    Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning

    A survey on vulnerability of federated learning: A learning algorithm perspective

    Get PDF
    Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning

    Systematic mapping of software engineering management with an agile approach

    Get PDF
    El enfoque ágil ha generado una amplia variedad de estrategias para administrar con éxito diversos proyectos de software en todo el mundo. Además, podemos asegurar que los proyectos de software se han beneficiado de los métodos ágiles ya conocidos. En este sentido, este artículo busca demostrar cómo se aplica el enfoque ágil en las áreas de la gestión en la ingeniería del Software. Para ello, este estudio realiza un mapeo sistemático para identificar las principales tendencias en la gestión de la ingeniería de software con un enfoque ágil. Se han identificado un total de 1137 artículos, de los cuales 165 son relevantes para los fines de este estudio, estos indican que la entrega temprana de valor, un principio clave de la agilidad, sigue siendo la principal tendencia para el uso de métodos ágiles. Sin embargo, también existen fuertes tendencias enfocadas en puntos clave de la gestión en ingeniería de software, como optimizar la gestión de calidad, optimizar la especificación de requisitos, optimizar la gestión de riesgos y mejorar la comunicación y coordinación del equipo, estos resultados permitirán generar nuevas líneas de investigación para cada punto clave de la gestión en la ingeniería del software impactado por el enfoque ágil.The agile approach has generated a wide variety of strategies to successfully manage various software projects worldwide. In addition, we can ensure that software projects have benefited from the already known agile methods. In this sense, this article seeks to demonstrate how the agile approach is applied in Software engineering management areas. To do this, this study performs a systematic mapping to identify the main trends in software engineering management with an agile approach. A total of 1137 articles have identified, of which 165 are relevant for the purposes of this study, these indicate that early value delivery, a key principle of agility, continues to be the main trend for the use of agile methods. However, there are also strong trends focused on key points of management in software engineering, such as optimize quality management, optimize requirements specification, optimize risk management, and improve team communication and coordination, these results will allow generating new lines of research for each key point of management in software engineering impacted by the agile approach

    A reinforcement learning recommender system using bi-clustering and Markov Decision Process

    Get PDF
    Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning
    • …
    corecore