753 real negative results, null findings, and replication failures in Computer Science. 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.
Lu Yu, Jason Schwier, Ryan Craven et al. · 2013 · IEEE Transactions on Knowledge and Data Engineering
Hidden Markov models (HMMs) are used to analyze real-world problems. We consider an approach that constructs minimum entropy HMMs directly from a sequence of observations. If an insufficient amount of observation data is used to generate…
View details →David Graber, Peter Stockinger, Fabian Meyer et al. · 2025 · Nature Machine Intelligence
The field of computational drug design requires accurate scoring functions to predict binding affinities for protein-ligand interactions. However, train-test data leakage between the PDBbind database and the Comparative Assessment of Scoring Function benchmark datasets has severely inflated the performance metrics of currently available deep-learning-based binding affinity prediction models, leading to overestimation of their generalization capabilities. Here we address this issue by proposing PDBbind CleanSplit, a training dataset curated by a new structure-based filtering algorithm that elim
View details →Preesha Premsagar, Colleen Aldous, Tonya M. Esterhuizen et al. · 2022 · Informatics in Medicine Unlocked
Machine learning is used to process big data volumes with complex non-linear relationships between predictive variables and predictions. Research into the usefulness of machine learning in small data volumes remains limited. To compare…
View details →Christian Bock, Thomas Gumbsch, Michael Moor et al. · 2018 · Bioinformatics
Motivation: Most modern intensive care units record the physiological and vital signs of patients. These data can be used to extract signatures, commonly known as biomarkers, that help physicians understand the biological complexity of…
View details →Qingyu Shi, Fang Wang, Dan Feng · 2020 · IEEE Transactions on Network and Service Management
Datacenter network load balancing schemes handle network traffic generated by massive different applications. Some packet-based or flowlet-based schemes capture traffic bursts for load balancing. But frequent rerouting within a flow can…
View details →Andreas Ketterer, Satoya Imai, Nikolai Wyderka et al. · 2022 · Physical review. A/Physical review, A
We consider statistical methods based on finite samples of locally randomized measurements in order to certify different degrees of multiparticle entanglement in intermediate-scale quantum systems. We first introduce hierarchies of…
View details →Bismark Nyaaba Akanzire, Matthew Nyaaba, Macharious Nabang · 2025 · Journal of Digital Educational Technology
This rapid study explores teacher educators’ perceptions of generative artificial intelligence (GenAI) in teacher education, conducted through a descriptive survey involving 55 teacher educators from two colleges of education in Ghana. A convenience sampling technique was adopted for data collection, and a data analysis using <i>exploratory factor analysis</i> was used to identify primary factors shaping perceptions and preparedness of GenAI integration. Key findings reveal a generally positive perception among the teacher educators, who recognize GenAI’s potential to support acade
View details →Anton Kotelyanskii, Gregory M. Kapfhammer · 2014
Although search-based test-data generators, like EvoSuite, efficiently and automatically create effective JUnit test suites for Java classes, these tools are often difficult to configure. Prior work by Arcuri and Fraser revealed that the…
View details →Emma Croxford, Yanjun Gao, Elliot First et al. · 2025 · npj Digital Medicine
Electronic Health Records (EHRs) contain vast clinical data that are difficult for providers to synthesize. Generative AI with Large Language Models (LLMs) can summarize records to reduce cognitive burden, but ensuring accuracy requires…
View details →Julio Cesar Munguía Mondragón, Paula Branco, Guy-Vincent Jourdan et al. · 2025 · Applied Intelligence
Globally, cyberattacks are growing and mutating each month. Intelligent Intrusion Network Detection Systems are developed to analyze and detect anomalous traffic to face these threats. A way to address this is by using network flows, an…
View details →Tianjiao Cao, Jiahao Lyu, Weichao Zeng et al. · 2024
Scene text detection has seen the emergence of high-performing methods that excel on academic benchmarks. However, these detectors often fail to replicate such success in real-world scenarios. We uncover two key factors contributing to…
View details →Shervin Malmasi · 2016 · Figshare
The prediction of an author's native language using only their second language writing -- a task called Native Language Identification (NLI) -- is usually tackled using supervised classification. This is underpinned by the presupposition…
View details →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 →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 →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 →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 →Catriona L. Scrivener, Tijl Grootswagers, Alexandra Woolgar · 2026 · European Journal of Neuroscience
Multivariate pattern analysis (MVPA) is a popular technique that can distinguish between condition-specific patterns of activation. Applied to neuroimaging data, MVPA decoding for inference uses above chance decoding to identify statistically robust condition-specific information in neuroimaging data, which may be missed by univariate methods. However, several analysis choices influence decoding results, and the combined effects of these choices have not been fully evaluated. In particular, an increasingly popular approach is to average data from several trials together before training an MVPA
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 →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 →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 →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 →Sarah Kettley · 2026 · Edinburgh Napier Research Repository (Edinburgh Napier University)
The purpose of the research described in this thesis was to develop a design methodology for Wearable Computing concepts that could potentially embody authenticity. The Wearables community, still firmly rooted in the disciplines of engineering and ergonomics, had made clear its aspirations to the mainstream market (DeVaul et al 2001). However, at this point, there was a distinct lack of qualitative studies on user perceptions of Wearable products. A review of the market research literature revealed significant consumer demand for authenticity in goods and services, and it was this need that dr
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 →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
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