693 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.
Fabien Gaud, Baptiste Lepers, Jérémie Decouchant et al. · 2014 · Open Repository and Bibliography (University of Luxembourg)
Application virtual address space is divided into pages, each requiring a virtual-to-physical translation in the page table and the TLB. Large working sets, common among modern applications, necessitate a lot of translations, which…
View details →Mohammed Saqib, Ying Sha, May D. Wang · 2018
Sepsis is a life-threatening condition caused by infection and subsequent overreaction by the immune system. Physicians effectively treat sepsis with early administration of antibiotics. However, excessive use of antibiotics on false…
View details →David Rozado · 2024 · PLoS ONE
I report here a comprehensive analysis about the political preferences embedded in Large Language Models (LLMs). Namely, I administer 11 political orientation tests, designed to identify the political preferences of the test taker, to 24 state-of-the-art conversational LLMs, both closed and open source. When probed with questions/statements with political connotations, most conversational LLMs tend to generate responses that are diagnosed by most political test instruments as manifesting preferences for left-of-center viewpoints. This does not appear to be the case for five additional base (i.
View details →Xi Yang, Xing He, Hansi Zhang et al. · 2020 · JMIR Medical Informatics
BACKGROUND: Semantic textual similarity (STS) is one of the fundamental tasks in natural language processing (NLP). Many shared tasks and corpora for STS have been organized and curated in the general English domain; however, such resources are limited in the biomedical domain. In 2019, the National NLP Clinical Challenges (n2c2) challenge developed a comprehensive clinical STS dataset and organized a community effort to solicit state-of-the-art solutions for clinical STS. OBJECTIVE: This study presents our transformer-based clinical STS models developed during this challenge as well as new mo
View details →Matias Lindgren · 2020 · Aaltodoc (Aalto University)
This thesis applies deep learning based classification techniques to identify natural languages from speech. The primary motivation behind this thesis is to implement accurate techniques for segmenting multimedia materials by the languages…
View details →Marco Roccetti, Giovanni Delnevo, Luca Casini et al. · 2021 · Journal Of Big Data
Abstract Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many important questions, their utility is still harshly criticized, being extremely challenging to identify which data descriptors are the most adequate to represent a given specific phenomenon of interest. With a recent experience in the development of a deep learning model designed to detect failures in mechanical water meter devices, we have learnt that a sensible deterioration of the
View details →Xinsong Du, John Novoa-Laurentiev, Joseph M. Plasek et al. · 2024 · EBioMedicine
BACKGROUND: Large language models (LLMs) have shown promising performance in various healthcare domains, but their effectiveness in identifying specific clinical conditions in real medical records is less explored. This study evaluates LLMs for detecting signs of cognitive decline in real electronic health record (EHR) clinical notes, comparing their error profiles with traditional models. The insights gained will inform strategies for performance enhancement. METHODS: This study, conducted at Mass General Brigham in Boston, MA, analysed clinical notes from the four years prior to a 2019 diagn
View details →Zongyao Li, Kazuhiro Kitajima, Kenji Hirata et al. · 2021 · EJNMMI Research
Abstract Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[ 18 F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[ 18 F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a
View details →Νικόλαος Πέλλας · 2025 · Education Sciences
Artificial Intelligence (AI) has gained significant prominence in science education, yet its practical applications, particularly in teacher training, remain underexplored. Specifically, there is a lack of research on AI’s potential to support personalized professional development through automated analysis of classroom interactions and tailored feedback. As science teacher education requires skill development in complex scientific concepts within problem-based learning (PBL) contexts, there is a growing need for innovative, technology-driven instructional tools. AI-generated instructional vid
View details →Sung-Min Kim, Yosoon Choi · 2017 · International Journal of Environmental Research and Public Health
To develop appropriate measures to prevent soil contamination in abandoned mining areas, an understanding of the spatial variation of the potentially toxic trace elements (PTEs) in the soil is necessary. For the purpose of effective soil sampling, this study uses hot spot analysis, which calculates a z-score based on the Getis-Ord Gi* statistic to identify a statistically significant hot spot sample. To constitute a statistically significant hot spot, a feature with a high value should also be surrounded by other features with high values. Using relatively cost- and time-effective portable X-r
View details →Erkan Er, Eduardo Gómez‐Sánchez, Yannis Dimitriadis et al. · 2019 · Interactive Learning Environments
This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning learning design (LD) and learning analytics (LA) during the design of a predictive analytics solution and from involving the…
View details →Anne Lohr, Michael Sailer, Matthias Stadler et al. · 2024 · Teaching and Teacher Education
We investigated factors that are potentially associated with teaching and learning with digital technology, by replicating and extending Sailer, Murböck, and Fischer's (2021) study with a representative sample of 407 German secondary school teachers. In line with the replicated study, teachers' technology-related teaching skills were crucial for different forms of students' active learning, whereas the digital technology equipment available in a school was less important. School support was positively related to successful digital teaching and learning at schools. The success of Bring-Your-Own
View details →Jutta R. de Jong, Anouk Keizer, Manja M. Engel et al. · 2017 · Experimental Brain Research
The sense of how we experience our physical body as our own represents a fundamental component of human self-awareness. Body ownership can be studied with bodily illusions which are generated by inducing a visuo-tactile conflict where individuals experience illusionary ownership over a fake body or body part, such as a rubber hand. Previous studies showed that different types of touch modulate the strength of experienced ownership over a rubber hand. Specifically, participants experienced more ownership after the rubber hand illusion was induced through affective touch vs non-affective touch.
View details →Dipesh Niraula, Kyle C. Cuneo, Ivo D. Dinov et al. · 2025 · Nature Communications
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma. We investigated two levels of collaborative behavior: model-agnostic and model-specific; and found that Human-AI
View details →The First Waters · 2026 · Zenodo (CERN European Organization for Nuclear Research)
This paper develops government structural reference infrastructure for AI and AGI environments. It addresses the need for public institutions to preserve reference continuity across public records, citizen submissions, administrative documents, authority-condition references, state histories, seal-reference contexts, role accountability records, economic references, and propagation-sensitive records without becoming dependent on a single AI provider, platform, model, or cloud environment. The framework does not perform public decision-making, grant authority, issue permits, approve benefits, c
View details →J. M. Górriz, R. Martin-Clemente, F. Segovia et al. · 2026 · Information Fusion
As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference in multi-source and heterogeneous information fusion scenarios. K-fold cross-validation (CV) is the most common approach for ascertaining the likelihood that a machine learning outcome is generated by chance and frequently outperforms conventional hypothesis testing. This improvement arises from measures directly obtained from machine learning classifications, such as accuracy, that do not have a parametric description. To approach a frequentist analysis within fusi
View details →Kirsty Kitto, Ben Hicks, Simon Buckingham Shum · 2023 · British Journal of Educational Technology
Abstract An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and…
View details →Shervin Malmasi, Joel Tetreault, Mark Dras · 2015
We examine different ensemble methods, including an oracle, to estimate the upper-limit of classification accuracy for Native Language Identification (NLI). The oracle outperforms state-of-the-art systems by over 10% and results indicate that for many misclassified texts the correct class label receives a significant portion of the ensemble votes, often being the runner-up. We also present a pilot study of human performance for NLI, the first such experiment. While some participants achieve modest results on our simplified setup with 5 L1s, they did not outperform our NLI system, and this perf
View details →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 →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 →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 →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 →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 →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 →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 →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…
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