LTL Verification of Automaton Programs by K. A. Vasileva, E. V. Kuzmin

“…The automata programming technology is effective enough in design and verification (the analysis of correctness) software for reactive and controlling systems. This technology, besides other methods of software construction "without errors", is much more constructive, as it allows to begin "struggling against errors" at the algorithmization stage. …”
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AMaze: an intuitive benchmark generator for fast prototyping of generalizable agents by Kevin Godin-Dubois, Karine Miras, Anna V. Kononova

“…Thus, recent benchmarks frequently rely on multiple environments, for instance, by providing stochastic noise, simple permutations, or altogether different settings. In practice, such collections result mainly from costly human-designed processes or the liberal use of random number generators. …”
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Project quality, regulation quality by Elena Mussinelli

“…The norm thus becomes a «tool for guiding and controlling design choices», which as such «must be assumed in the organisation of the starting data» (Del Nord, 1992). …”
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IDENTIFICATION OF TRAJECTORY INFORMATION BY APPLYING FUZZY AUTOMATIC CLASSIFICATION IN THE INFORMATION ASSOCIATION PROBLEM by A. V. Khizhniak, A. A. Belous, A. S. Bely

“…It is described an approach to solving the problem of trajectory data identification in algorithms for tracking aerial objects based on the application of the theory of fuzzy sets.…”
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A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology by Rohit ANAND, Roaf Ahmad PARRAY, Indra MANI, Tapan Kumar KHURA, Harilal KUSHWAHA, Brij Bihari SHARMA, Susheel SARKAR, Samarth GODARA, Shideh MOJERLOU, Hasan MIRZAKHANINAFCHI

“…The spectral data sets were analyzed using decision tree and support vector machine (SVM) algorithms to identify the most accurate model for distinguishing diseased and healthy plants. …”
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The Need for High-Fidelity Robotics Sensor Models by Phillip J. Durst, Christopher Goodin, Burhman Q. Gates, Christopher L. Cummins, Burney McKinley, Jody D. Priddy, Peter Rander, Brett Browning

“…Simulations provide a safe, controlled setting for testing and are therefore ideal for rapidly developing and testing autonomous mobile robot behaviors. …”
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Data-Driven Clustering of Plantar Thermal Patterns in Healthy Individuals: An Insole-Based Approach to Foot Health Monitoring by Mark Borg, Stephen Mizzi, Robert Farrugia, Tiziana Mifsud, Anabelle Mizzi, Josef Bajada, Owen Falzon

“…Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. …”
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Remote clinical decision support tool for Parkinson’s disease assessment using a novel approach that combines AI and clinical knowledge by Harel Rom, Ori Peleg, Yovel Rom, Anat Mirelman, Gaddi Blumrosen, Inbal Maidan

“…Methods Facial image of 67 PD patients and 52 healthy-controls (HCs) were collected via standard video recording. …”
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A Versatile Panel of Reference Gene Assays for the Measurement of Chicken mRNA by Quantitative PCR. by Karen Staines, Ambalika Batra, William Mwangi, Helena J Maier, Steven Van Borm, John R Young, Mark Fife, Colin Butter

“…GeNorm and NormFinder algorithms were able to select appropriate reference gene sets in each case. …”
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“Voice is the New Blood”: a discourse analysis of voice AI health-tech start-up websites by Alden Blatter, Hortense Gallois, Emily Evangelista, Yael Bensoussan, Bridge2AI-Voice Consortium, Jean-Christophe Bélisle-Pipon, Yael E. Bensoussan, Olivier Elemento, Jean-Christophe Bélisle-Pipon, David Dorr, Satrajit Ghosh, Alistair Johnson, Phillip Payne, Maria E. Powell, Anaïs Rameau, Vardit Ravitsky, Alexandros Sigaras

“…We conclude that the lack of incentive to share key information about datasets is due to contextual factors that start-ups cannot control, mainly the absence of clear standards and regulatory guidelines for voice data collection. …”
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Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review by Rongrong Zhong, XiaoHui Wu, Jun Chen, Yiru Fang

“…For each included study, the following information was extracted: demographic characteristics, diagnostic criteria or psychiatric assessments, details of the technological tools and data types, duration of data collection, data preprocessing methods, selected variables or features, machine learning algorithms or statistical tests, validation, and main findings. …”
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