Negative / Null Result ReportComputer Science
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…
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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…
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Dunigan Parker Folk, Elizabeth Dunn · 2026 · Psychological Science
Advances in AI have enabled chatbots to provide warm, personalized support. Yet little is known about the long-term consequences of AI companionship. Across a 12-month longitudinal study with more than 2,000 adults from four Western…
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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
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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…
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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…
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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…
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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…
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Mohammadreza Kamsari, Soroush Sadeghi, Gabriel Gomes de Oliveira et al. · 2025 · Scientific Reports
This study conducts a comprehensive analysis of various configurations of the UNet + ConvNeXt Tiny architecture for breast tumor segmentation. We assess the influence of data augmentation, skip connections, attention mechanisms, and…
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Muhammad Dawood, Kim Branson, Sabine Tejpar et al. · 2026 · Nature Biomedical Engineering
Deep learning models that infer clinically relevant biomarker status from tissue images are being explored as rapid and low-cost alternatives to molecular testing. Here we show, through statistical analysis across multiple cancer types, datasets and modelling approaches, that the datasets used to train these models contain strong dependencies between biomarkers and clinicopathological features, which prevent models from isolating the effect of a single biomarker and lead them to learn confounded signals. Consequently, their prediction accuracy varies substantially with the status of codependen
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Michael Vaccaro, Mikayla Friday, Arash E. Zaghi · 2025 · Applied Sciences
Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and a Rewrite agent dynamically adapts science passages via an explicit message-passing protocol. We implement structured system and user prompts as inter-agent communication schemas to enable real-time content adaptation. The results of an ordinal logistic regression analysis hinted that
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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…
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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…
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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
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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
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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
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Changhun Oh, Youngrong Lim, Bill Fefferman et al. · 2021 · arXiv
Sampling from probability distributions of quantum circuits is a fundamentally and practically important task which can be used to demonstrate quantum supremacy using noisy intermediate-scale quantum devices. In the present work, we examine classical simulability of sampling from the output photon-number distribution of linear-optical circuits composed of random beam splitters with equally distributed squeezed vacuum states and single-photon states input. We provide efficient classical algorithms to simulate linear-optical random circuits and show that the algorithms' error is exponentially sm
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Conrad Borchers, Dalia Sara Gala, Benjamin Gilburt et al. · 2022 · arXiv
The growing capability and availability of generative language models has enabled a wide range of new downstream tasks. Academic research has identified, quantified and mitigated biases present in language models but is rarely tailored to downstream tasks where wider impact on individuals and society can be felt. In this work, we leverage one popular generative language model, GPT-3, with the goal of writing unbiased and realistic job advertisements. We first assess the bias and realism of zero-shot generated advertisements and compare them to real-world advertisements. We then evaluate prompt
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Carmine Abate, Arthur Azevedo de Amorim, Roberto Blanco et al. · 2018 · arXiv
We propose a new formal criterion for evaluating secure compilation schemes for unsafe languages, expressing end-to-end security guarantees for software components that may become compromised after encountering undefined behavior---for example, by accessing an array out of bounds. Our criterion is the first to model dynamic compromise in a system of mutually distrustful components with clearly specified privileges. It articulates how each component should be protected from all the others---in particular, from components that have encountered undefined behavior and become compromised. Each comp
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Daniel Rajchwald, Natasha Markuzon · 2016 · arXiv
The importance of peer influence on consumer actions plays a vital role in marketing efforts. However, peer influence effects are often confounded with latent homophily, which are unobserved commonalities that drive friendship. Understanding causality has become one of the pressing issues of current research. We present an approach to explicitly account for various causal influences. We implement a simulation framework to show the effectiveness of two latent homophily proxies, latent coordinates and community membership, in improving peer influence effect estimates on game downloads in a Japan
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V. Bertin, E. Nezri, J. Orloff · 2002 · arXiv
We study potential signals of neutralino dark matter indirect detection by neutrino telescopes in a wide range of CMSSM parameters. We also compare with direct detection potential signals taking into account in both cases present and future experiment sensitivities. Only models with neutralino annihilation into gauge bosons can satisfy cosmological constraints and current neutrino indirect detection sensitivities. For both direct and indirect detection, only next generation experiments will be able to really test this kind of models.
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Maxim Krasnov, Ufuk Aydemir, Maxim Khlopov · 2025 · arXiv
We investigate the particle production by the Nambu-Goldstone boson in the spontaneous baryogenesis scenario for large misalignment angles. Studying numerically the case of an arbitrary initial phase, we reproduce the cubic dependence on the initial value of the phase of baryon asymmetry in the case of small oscillations and present our results for an initial phase close to π. Our calculations indicate that there is a saturation in particle production as the initial phase approaches π in Minkowski spacetime. Furthermore, we present numerical solutions in the Friedmann-Lemaître-Robertson-Walker
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S. Haouat, K. Nouicer · 2013 · arXiv
In this paper we have studied the problem of scalar particles pair creation by an electric field in the presence of a minimal length. Two sets of exact solutions for the Klein Gordon equation are given in momentum space. Then the canonical method based on Bogoliubov transformation connecting the "in" with the "out" states is applied to calculate the probability to create a pair of particles and the mean number of created particles. The number of created particles per unit of time per unit of length, which is related directly to the experimental measurements, is calculated. It is shown that, wi
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Yoel Zimmermann, Adib Bazgir, Zartashia Afzal et al. · 2024 · arXiv
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and
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Xinzhe Chen, Jianjiang Li · 2023 · arXiv
The escalating surge in data generation presents formidable challenges to information technology, necessitating advancements in storage, retrieval, and utilization. With the proliferation of artificial intelligence and big data, the "Data Age 2025" report forecasts an exponential increase in global data production. The escalating data volumes raise concerns about efficient data processing. The paper addresses the predicament of achieving a lower compression ratio while maintaining or surpassing the compression performance of state-of-the-art techniques. This paper introduces a lossy compressio
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Liyun Zhang, Jiayi Guo · 2026 · arXiv
We document an empirical phenomenon in chain-of-thought and ReAct agents driven by ten large language models from seven architecture families: meaning-bearing perturbations (e.g., paraphrase, synonym) alter final answers more often than presentation perturbations (e.g., formatting, reordering) of comparable severity. Across 68 cells spanning GSM8K, MATH, and HotpotQA (1,530 originals and $\sim$11,150 variants), the inconsistency gap averages +19.69 pp after severity matching (paired $t=9.58$, $p<0.0001$), with 64/68 cells positive. The gap survives four severity-proxy audits and remains signif
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Rohit Agarwala, Kalyan Dey · 2025 · arXiv
We employ PYTHIA8 simulations to study forward-backward (FB) correlations in pp collisions at LHC energies, probing non-perturbative QCD dynamics via color reconnection (CR) and QCD radiation (ISR/FSR). Using \texttt{PYTHIA8} (v8.311) under ALICE/ATLAS kinematics, we analyze: extensive: FB multiplicity ($b_{\rm corr}^{\rm mult}$) and summed $p_{\rm T}$ ($b_{\rm corr}^{\sum p_{\rm T}}$) correlations; intensive: FB mean $p_{\rm T}$ ($b_{\rm corr}^{\overline p_{\rm T}}$) correlations; and \textit{strongly intensive:} $Σ_{\rm N_F N_B}$ quantity in symmetric pseudorapidity intervals, validated agai
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Minghao Wu, Alham Fikri Aji · 2023 · arXiv
As large language models (LLMs) continue to advance, accurately and comprehensively evaluating their performance becomes increasingly challenging. Ranking the relative performance of LLMs based on Elo ratings, according to human judgment, is gaining more popularity. However, the extent to which humans and LLMs are capable evaluators remains uncertain. This study investigates the behavior of crowd-sourced and expert annotators, as well as LLMs, when comparing outputs from different models. To achieve this, we curate a dataset of intentionally flawed machine-generated answers. Our findings revea
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Jayoo Hwang, Xiaowen Zhang, Vedant Padwal · 2026 · arXiv
Autonomous web navigation remains challenging for LLM agents, and the strongest generalist systems rely on proprietary reasoning models whose inference cost is prohibitive for the repetitive tasks where such agents would be most useful. We argue this gap stems not from insufficient model capability but from agent architectures that fail to replicate three human cognitive advantages: selective attention to relevant page regions, persistent memory of website structure, and procedural fluency with common interaction patterns. We introduce WebChallenger, a web agent framework that addresses each g
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Shaoxuan Zhou, Yafei Sun, Jing Zhang et al. · 2026 · arXiv
Short-video platforms like Douyin and Kwai have become central to adolescent digital life, but they also risk exposing teens to algorithmically amplified harmful content. Despite its societal importance, the scale, mechanisms, and real-world impact of this exposure remain poorly understood. Measuring it is challenging: recommendation feeds are personalized black boxes, harmful content employs sophisticated evasion tactics, and naive crawlers fail to replicate authentic teen behavior. To bridge this gap, we propose PHTV-Scout, the first large-scale, behaviorally grounded measurement framework f
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