694 real negative results, null findings, and replication failures in Computer Science · Negative / Null Result Report. Search the index →
WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record compiled from open scholarly databases; the abstract is shown in full only where the paper is openly licensed, otherwise a short excerpt under fair use. Classifications are automated and approximate.
Akhil Arora, Mayank Sachan, Arnab Bhattacharya · 2014
The steady growth of graph data in various applications has resulted in wide-spread research in finding significant sub-structures in a graph. In this paper, we address the problem of finding statistically significant connected subgraphs…
View details →Michael S. Vitevitch, Gavin J. D. Mullin · 2021 · Brain Sciences
Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retr
View details →Joshua K. Hartshorne, Lauren Skorb, Sven L. Dietz et al. · 2019 · Collabra Psychology
We report the first set of results in a multi-year project to assess the robustness – and the factors promoting robustness – of the adult statistical word segmentation literature. This includes eight total experiments replicating six different experiments. The purpose of these replications is to assess the reproducibility of reported experiments, examine the replicability of their results, and provide more accurate effect size estimates. Reproducibility was mixed, with several papers either lacking crucial details or containing errors in the description of method, making it difficult to ascert
View details →Wilhelmiina Hämäläinen · 2010 · Työväentutkimus Vuosikirja
Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is…
View details →Robert J. Smith · 2016 · Methods in Ecology and Evolution
Summary Dissimilarity measures, which gauge compositional resemblance between sample units, tend to lose information with increasing distance along ecological gradients. This undesirable property is especially common in high‐beta‐diversity…
View details →Jundong Li, Osmar R. Zaı̈ane · 2017 · Intelligent Data Analysis
Established associative classification algorithms have shown to be very effective in handling categorical data such as text data. The learned model is a set of rules that are easy to understand and can be edited. However, they still suffer…
View details →Nima Jamalian, Marco Gillies, Frédéric Fol Leymarie et al. · 2022
Until recently, Virtual Reality (VR) applications relied on controllers to enable user interaction in virtual environments. With advances in tracking technology, HMDs are now able to track the movements of users’ hands in real-time with…
View details →Arvinder Kaur, Kamaldeep Kaur, Shilpi Jain · 2016
The objective of this paper is to study the relationship between different types of object-oriented software metrics, code smells and actual changes in software code that occur during maintenance period. It is hypothesized that code smells…
View details →Scott Grimm, Louise McNally · 2026 · Proceedings of the Amsterdam Colloquium
Syntacticians have widely assumed since [11] that there is a fundamental difference between so-called argument structure nominals (AS-nominals, also called Complex Event Nominals), e.g. destruction, and non-AS-nominals, e.g. book ([1, 5], i.a.). Grimshaw provided a list of properties characterizing AS-nominals, most notably that they have obligatory arguments (e.g. the destruction *(of Carthage) by the Romans). She and others have associated having argument structure with having event structure, but it has never been clear what having or lacking such structures amounts to semantically. In this
View details →Sun Kyong Lee, Hyunjin Park, Seoyoung C. Kim · 2024 · Behaviour and Information Technology
This study investigated the impact of task types (functional vs. social) and the gendered voices (female vs. male) of Siri, an intelligent virtual assistant (IVA), on social presence and trust perceptions toward the IVA.In an online…
View details →Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Osama Halabi et al. · 2024 · Journal of Innovation & Knowledge
Recommendation agents (RAs) have proven to be effective decision-making tools for customers, as they can boost trust and loyalty when customers shop online. They can analyse large amounts of data using machine learning algorithms and…
View details →Robert Ranisch, Joschka Haltaufderheide · 2025 · Digital Society
Abstract Conversational agents are increasingly used in healthcare, with Large Language Models (LLMs) significantly enhancing their capabilities. When integrated into social robots, LLMs offer the potential for more natural interactions. However, while LLMs promise numerous benefits, they also raise critical ethical concerns, particularly regarding hallucinations and deceptive patterns. In this case study, we observed a critical pattern of deceptive behavior in commercially available LLM-based care software integrated into robots. The LLM-equipped robot falsely claimed to have medication remin
View details →Remita Austin, R. Parvathi · 2025 · Scientific Reports
The absence of reliable early treatment serves as one of the main causes of cervical cancer. Hence, it is crucial to detect cervical cancer early. The biggest challenge in diagnosing cervical cancer early is that it is asymptomatic until it develops into invasive carcinoma. In medical applications, the use of machine learning and deep learning is successful as a classifier in the preliminary identification of cancerous cells in the cervical region. In our study, we present a CNN-based method for the classification of cervical cancer cells. We present a method for accurately classifying Pap sme
View details →Nikunj Arora, Markku Suomalainen, Matti Pouke et al. · 2022 · IEEE Transactions on Visualization and Computer Graphics
We propose augmenting immersive telepresence by adding a virtual body, representing the user's own arm motions, as realized through a head-mounted display and a 360-degree camera. Previous research has shown the effectiveness of having a virtual body in simulated environments; however, research on whether seeing one's own virtual arms increases presence or preference for the user in an immersive telepresence setup is limited. We conducted a study where a host introduced a research lab while participants wore a head-mounted display which allowed them to be telepresent at the host's physical loc
View details →Vincent Adegoke, Daqing Chen, Ebad Banissi et al. · 2017
In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms…
View details →Subrata Datta, Kalyani Mali · 2021
Traditional support-confidence framework based association rule mining approaches often generate huge number of rules including the insignificant ones. These insignificant association rules are useless in knowledge discovery. One of the…
View details →Haoze Wu · 2017
In this project, we aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem. We first used a logistic regression model to predict the…
View details →Suhe Ji, Ke Li, Linfeng Zou · 2019 · 2019 International Joint Conference on Information, Media and Engineering (IJCIME)
Although the increased use of authentic materials in listening comprehension teaching has become general practice, it is crucial to find out whether or not the new technology-supported authentic material could be beneficial to English as a…
View details →Conor Hanrahan · 2019 · ARROW@Dublin Institute of Technology (Dublin Institute of Technology)
The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural…
View details →Fabrice Djatsa · 2019 · Journal of Information Security
As the economy increases its dependence on the internet to increase efficiency and productivity in all aspects of society, close attention has been directed to solve the challenges related to internet security. Despite the large amount of…
View details →Ramteja Sajja, Yusuf Sermet, Brian Fodale et al. · 2026 · Scientific Reports
As generative AI becomes increasingly integrated into higher education, understanding how students engage with these technologies is essential for responsible adoption. This study evaluates the Educational AI Hub, an AI-powered learning…
View details →Pertti Vakkari · 2011 · Online Information Review
Purpose This paper seeks to evaluate to what extent Google retrieved correct answers to queries inferred from factual and topical requests in a digital Ask‐a‐Librarian service. Design/methodology/approach In total, 100 factual and 100…
View details →Mohammadreza Farrokhnia, Saeed Latifi, Pantelis M. Papadopoulos et al. · 2026 · International Journal of Educational Technology in Higher Education
Abstract Providing high-quality feedback on student writing is essential yet increasingly difficult due to rising class sizes and limited instructional capacity. Generative AI (GenAI) offers a promising and scalable alternative, but its effectiveness compared to traditional teacher feedback, particularly across different prompting techniques, remains uncertain. This study employed a quantitative, randomized three-group experimental design with 70 graduate students to compare the effects of teacher feedback and GenAI feedback generated using two prompting techniques: Zero-shot and chain-of-thou
View details →Eisha Hassan, Fazila‐Tun‐Nesa Malik, Qazi Waqas Khan et al. · 2025 · IEEE Access
Document Clustering has attracted the interest of many researchers who have created several solutions to this problem by combining different techniques, models, and algorithms. While famous for its simplicity, the most commonly used algorithm, K-Means, suffers from issues such as finding the optimal value for k and random initialization of the centroids. In this paper, we propose a hybrid methodology combining K-Means++ with the metaheuristic algorithm PSO to overcome the challenges of both these algorithms. K-Means++ is a smart initialization technique that selects clusters based on probabili
View details →Sterling Williams-Ceci, Maurice Jakesch, Advait Bhat et al. · 2026 · Science Advances
Artificial intelligence (AI) writing assistants powered by large language models (LLMs) are increasingly used to make autocomplete suggestions to people as they write text. Can these AI writing assistants affect people’s attitudes in this…
View details →Andrea Espis, Chiara Marzi, Stefano Diciotti · 2025 · Scientific Reports
Self-supervised learning (SSL) in computer vision has shown its potential to reduce reliance on labeled data. However, most studies focused on balanced, large, broad-domain datasets like ImageNet, whereas, in real-world medical applications, dataset size is typically limited. This study compares the performance of SSL versus supervised learning (SL) on small, imbalanced medical imaging datasets. We experimented with four binary classification tasks: age prediction and diagnosis of Alzheimer's disease from brain magnetic resonance imaging scans, pneumonia from chest radiograms, and retinal dise
View details →M. Said Doğru, Emily Faulconer · 2025 · Research in Science Education
Abstract The emergence of ChatGPT brings an opportunity to lighten the workload of in-person teaching assistance in science laboratory courses. We investigated the use of the language model developed by OpenAI as a virtual teaching assistant for an introductory undergraduate biology laboratory course. Student-generated questions related to a laboratory exercise on enzyme activity were separately provided to ChatGPT and a cohort of graduate teaching assistants (TAs). Responses were evaluated for content accuracy and teaching effectiveness. Results revealed that human TAs were more accurate in t
View details →Simon Holmbacka, Robert Müller · 2017
Current benchmark suites for evaluating energy efficiency of modern computer systems fail to replicate real-world streaming applications accurately enough because of indeterministic load pattern which depends heavily on the input data of…
View details →Jiarui Xie, Mutahar Safdar, Andrei Mircea et al. · 2024 · arXiv
Machine learning (ML)-based cyber-physical systems (CPSs) have been extensively developed to improve the print quality of additive manufacturing (AM). However, the reproducibility of these systems, as presented in published research, has not been thoroughly investigated due to a lack of formal evaluation methods. Reproducibility, a critical component of trustworthy artificial intelligence, is achieved when an independent team can replicate the findings or artifacts of a study using a different experimental setup and achieve comparable performance. In many publications, critical information nec
View details →Xun Liang, Huayi Lai, Hanyu Wang et al. · 2025 · arXiv
Large language models (LLMs) have gained significant traction in medical decision support systems, particularly in the context of medical question answering and role-playing simulations. A common practice, Prompt-Based Role Playing (PBRP), instructs models to adopt different clinical roles (e.g., medical students, residents, attending physicians) to simulate varied professional behaviors. However, the impact of such role prompts on model reasoning capabilities remains unclear. This study introduces the RP-Neuron-Activated Evaluation Framework(RPNA) to evaluate whether role prompts induce disti
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