Miracle-dz/UniFAR-FTUs huggingface dataset:
Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm’s achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.
The use of a new diagram for the analysis of the daily cycles in the air-pollution data

From a temporal viewpoint, air pollution has significant daily patterns/cycles of behaviour. These cycles are conditioned by anthropogenic and natural phenomena. In both cases, a detailed observation and an understanding of the daily cycles rules or daily patterns of air pollution can be significant and at the same time can contribute to more effective measures to reduce the harmful impact of air pollution on human health. In this paper the new sunflower diagram is presented. The key advantage of the sunflower diagram is the ease of understanding the result and the ability to present information in the form of a graphic pattern, allowing the user to quickly understand the content. Using the sunflower diagram, we will present an analysis of the meteorological parameters that are important for understanding air pollution and air-pollution data for different locations in Slovenia.
Collaborating around Digital Tabletops: Children’s Physical Strategies from the UK, India and Finland

IZDIHAR JAMIL, University of Bristol, UK CALKIN SUERO MONTERO, University of Eastern Finland, Finland MARK PERRY, Brunel University, UK KENTON O’HARA, Microsoft Research, Cambridge, UK ABHIJIT KARNIK, Lancaster University, UK KAISA PIHLAINEN, University of Eastern Finland, Finland MARK T MARSHALL, Sheffield Hallam University, UK SWATHI JHA, NIIT, India SANJAY GUPTA, NIIT, India SRIRAM SUBRAMANIAN, University of Sussex, UK
Étude en soufflerie atmosphérique des interactions entre canopée urbaine et basse atmosphère par PIV stéréoscopique

La couche de surface urbaine en conditions neutres est etudiee experimentalement a l'aide d'une simulation en soufflerie atmospherique et en utilisant des mesures de vitesse issues de l'anemometrie a fils chauds et de la PIV stereoscopique. D'abord, l'ecoulement genere est caracterise par des quantites statistiques en un point, telles que la vitesse moyenne, les tensions de Reynolds et le spectre d'energie de la composante longitudinale, en fonction de l'altitude. Les parametres de rugosite et la structure de la couche de surface sont determines. Puis, l'analyse de cartes de coefficients de correlation spatiale en deux points et des quadrants des tensions croisees de Reynolds confirme la presence de mouvements coherents apparaissant dans les modeles d'organisation de la turbulence de couche limite sur paroi plane. Ceci va dans le sens d'une universalite de cette organisation pour la couche limite turbulente sur paroi, a partir d'une certaine altitude. Un interet particulier est porte sur la presence et l'action des mouvements coherents transversaux qui, couples aux mouvements coherents verticaux, participent aux transferts de flux a proximite de la canopee. Enfin, l'analyse de la dynamique de la couche de cisaillement au sommet d'un obstacle met en exergue un mouvement de battement correle avec des lâchers de grappes de tourbillons originaires de l'obstacle, des mouvements coherents verticaux de grande echelle originaires de l'ecoulement de la couche limite et penetrant la canopee, ainsi que des mouvements coherents d'ejection de petite echelle. Ceci met en exergue une influence mutuelle de la couche limite atmospherique et de l'ecoulement issue de la canopee urbaine.
Incremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering

Traditional cluster ensemble approaches have three limitations: (1) They do not make use of prior knowledge of the datasets given by experts. (2) Most of the conventional cluster ensemble methods cannot obtain satisfactory results when handling high dimensional data. (3) All the ensemble members are considered, even the ones without positive contributions. In order to address the limitations of conventional cluster ensemble approaches, we first propose an incremental semi-supervised clustering ensemble framework (ISSCE) which makes use of the advantage of the random subspace technique, the constraint propagation approach, the proposed incremental ensemble member selection process, and the normalized cut algorithm to perform high dimensional data clustering. The random subspace technique is effective for handling high dimensional data, while the constraint propagation approach is useful for incorporating prior knowledge. The incremental ensemble member selection process is newly designed to judiciously remove redundant ensemble members based on a newly proposed local cost function and a global cost function, and the normalized cut algorithm is adopted to serve as the consensus function for providing more stable, robust, and accurate results. Then, a measure is proposed to quantify the similarity between two sets of attributes, and is used for computing the local cost function in ISSCE. Next, we analyze the time complexity of ISSCE theoretically. Finally, a set of nonparametric tests are adopted to compare multiple semisupervised clustering ensemble approaches over different datasets. The experiments on 18 real-world datasets, which include six UCI datasets and 12 cancer gene expression profiles, confirm that ISSCE works well on datasets with very high dimensionality, and outperforms the state-of-the-art semi-supervised clustering ensemble approaches.
Identification of Genes Associated with Resistance to Persulcatusin, a Tick Defensin from Ixodes persulcatus

Antimicrobial peptides (AMPs) are present in a wide range of plants, animals, and microorganisms. Since AMPs are characterized by their effectiveness against emergent antibiotic-resistant bacteria, they are attracting attention as next-generation antimicrobial compounds that could solve the problem of drug-resistant bacteria. Persulcatusin (IP), an antibacterial peptide derived from the hard tick Ixodes persulcatus, shows high antibacterial activity against various Gram- positive bacteria as well as multidrug-resistant bacteria. However, reports on the antibacterial action and resistance mechanisms of IP are scarce. In this study, we spontaneously generated mutants showing increased a minimum inhibitory concentration (MIC) of IP and analyzed their cross-resistance to other AMPs and antibiotics. We also used fluorescent probes to investigate the target of IP activity by evaluating IP-induced damage to the bacterial cytoplasmic membrane. Our findings suggest that the antimicrobial activity of IP on bacterial cytoplasmic membranes occurs via a mechanism of action different from that of known AMPs. Furthermore, we screened for mutants with high susceptibility to IP using a transposon mutant library and identified 16 genes involved in IP resistance. Our results indicate that IP, like other AMPs, depolarizes the bacterial cytoplasmic membrane, but it may also alter membrane structure and inhibit cell-wall synthesis.
Smart Diary: A Smartphone-Based Framework for Sensing, Inferring, and Logging Users’ Daily Life

In this paper, we present Smart Diary, a novel smartphone based framework that analyzes mobile sensing data to infer, predict, and summarize people's daily activities, such as their behavioral patterns and life styles. Such activities are then used as the basis for knowledge representation, which generates personal digital diaries in an automatic manner. As users do not need to intentionally participate into this process, Smart Diary is able to make inferences and predictions based on a wide range of information sources, such as the phones' sensor readings, locations, and interaction history with the users, by integrating such information into a sustainable mining model. This model is specifically developed to handle heterogeneous and noisy sensing data, and is made to be extensible in that users can define their own logic rules to express short-term, mid-term, and long-term event patterns and predictions. Our evaluation results are based on the Android platform, and they demonstrate that the Smart Diary framework can provide accurate and easy-to-read diaries for end users without their interventions.
Automatic image segmentation by wave propagation

We develop a level set based region growing method for automatic partitioning of color images into segments. Previous attempts at image segmentation either suffer from requiring a priori information to initialize regions, being computationally complex, or fail to establish the color consistency and spatial connectivity at the same time. Here, we represent the segmentation problem as monotonic wave propagation in an absorbing medium with varying front speeds. We iteratively emit waves from the selected base points. At a base point, the local variance of the data reaches a minimum, which indicates the base point is a suitable representative of its local neighborhood. We determine local variance by applying a hierarchical gradient operator. The speed of the wave is determined by the color similarity of the point on the front to the current coverage of the wave, and by edge information. Thus, the wave advances in an anisotropic spatial-color space. The absorbing function acts as a stopping criterion of the wave front. We take advantage of fast marching methods to solve the Eikonal equation for finding the travel times of the waves. Besides, each region boundary is represented as a mixture of Gaussian models. This formulation enables segmentation of multi-modal color objects. Our method is superior to the linkage-based and snake-based region growing techniques since it prevents leakage and imposes compactness on the region without over-smoothing its boundary. Furthermore, we can deal with sharp corners and changes in topology. The automatic segmentation method is Eulerian, thus it is computationally efficient. Our experiments illustrate the robustness, accuracy, and effectiveness of the proposed method.
Neuro-Fuzzy Methods for Environmental Modelling

The Department of Information Science is one of six departments that make up the Division of Commerce at the University of Otago. The department offers courses of study leading to a major in Information Science within the BCom, BA and BSc degrees. In addition to undergraduate teaching, the department is also strongly involved in postgraduate research programmes leading to MCom, MA, MSc and PhD degrees. Research projects in spatial information processing, connectionist-based information systems, software engineering and software development, information engineering and database, software metrics, distributed information systems, multimedia information systems and information systems security are particularly well supported. The views expressed in this paper are not necessarily those of the department as a whole. The accuracy of the information presented in this paper is the sole responsibility of the authors. the condition that the authors and the Series are given due acknowledgment. Reproduction in any form for purposes other than research or teaching is forbidden unless prior written permission has been obtained from the authors. Correspondence This paper represents work to date and may not necessarily form the basis for the authorsÕ final conclusions relating to this topic. It is likely, however, that the paper will appear in some form in a journal or in conference proceedings in the near future. The authors would be pleased to receive correspondence in connection with any of the issues raised in this paper, or for subsequent publication details. Please write directly to the authors at the address provided below. (Details of final journal/conference publication venues for these papers are also provided on the DepartmentÕs publications web pages: This paper describes combined approaches of data preparation, neural network analysis, and fuzzy inferencing techniques (which we collectively call neuro-fuzzy engineering) to the problem of environmental modelling. The overall neuro-fuzzy architecture is presented, and specific issues associated with environmental modelling are discussed. A case study that shows how these techniques can be combined is presented for illustration. We also describe our current software implementation that incorporates neuro-fuzzy analytical tools into commercially available geographical information system software.
Random Survival Forests

Random survival forests (RSF) is a flexible nonparametric tree-ensemble method for the analysis of rightcensored survival data. In this article we provide a short overview of RSF. We review survival splitting rules for growing random survival trees, in-bag and out-of-bag (OOB) ensemble estimators, prediction performance, variable importance, and partial plots. We also briefly describe the extension of RSF to competing risks. Copyright © 2018 John Wiley & Sons, Ltd.
A general framework for minimizing translation effort: towards a principled combination of translation technologies in computer-aided translation

This paper motivates the need for an homogeneous way of measuring and estimating translation effort (quality) in computeraided translation. It then defines a general framework for the measurement and estimation of translation effort so that translation technologies can be both optimized and combined in a principled manner. In this way, professional translators will benefit from the seamless integration of all the technologies at their disposal when working on a translation job.
Leadership in MMOGS: Emergent and Transformational Leadership Candidates

Player-founded organizations, or guilds, within massively multiplayer online games (MMOG) are complex social entities with organizational forms that mirror real-world companies. These guilds require leaders who possess a diverse array of skills. Examples of the skills required read like the introductory course of a business management degree – mediating conflict, planning, controlling, motivating. These skills are important just as with real-world companies, failures on the part of leaders may explain the high degree of guild failures witnessed. Within popular MMOGs, the game mechanics allow the promotion of regular guild members to officer status. This begs the question, how might potential leaders be identified? Drawing from the emergent leadership literature, we discuss a mixed-methods study which attempts to identify potential leaders and their attributes by examining a multi-year database which represents one guild’s activities within the MMOG Everquest. Follow-up interviews with guild leadership provides a rich understanding of skills and attributes potential leaders require in coordinating a heterogeneous workforce within synchronous, non-collocated, entirely computer-mediated environments where the workforce serves on a voluntary basis. These interviews suggest these characteristics related to transformational leadership are highly sought after.
Assessment of riverine dragonflies (Odonata: Gomphidae) and the emergence behaviour of their larvae based on exuviae data on the reach of the river Tisza in Szeged

Abundance, phenology, sex ratio, emergence pattern, mortality and larval emergence behaviour of riverine dragonflies (Odonata: Gomphidae) were studied at the Lower-Tisza reach at Szeged (168–173 rkm) during the emergence period in 2011. Three 20 meter long sampling sites were chosen and searched systematically for exuviae, dead specimens and dragonfly wings, which were left behind by bird predators. At the studied reach of the river two species form stable populations: G. flavipes and G. vulgatissimus. G. flavipes was much more abundant than G. vulgatissimus. Exuviae indicated the excess of females in the G. vulgatissimus population (altough there were no significant difference between sexes), while in the case of G. flavipes the number of individuals in both sexes were almost the same. G. vulgatissimus started to emerge first as a ’spring species’, while G. flavipes started to emerge about a month later showing the characteristics of a ’summer species’. The rate of mortality in the G. flavipes population during emergence was slight and quite normal compared to the abundance of the species. Selection of emergence support of G. flavipes showed that the significant majority of the larvae chose soil, but this could have been caused by the notable minority of other types of substrates at the sampling sites. The distance crawled by the larvae from the water-front to the emergence site differed significantly between the two species, G. vulgatissumus crawled further, and in the case of G. flavipes the effect of the measured background variables to the distance had not been proven.
Dynamic psychological games

The motivation of decision makers who care for various emotions, intentions-based reciprocity, or the opinions of others may depend directly on beliefs (about choices, beliefs, or information). Geanakoplos, Pearce and Stacchetti [J. Geanakoplos, D. Pearce, E. Stacchetti, Psychological games and sequential rationality, Games Econ. Behav. 1 (1989) 60–79] point out that traditional game theory is ill-equipped to address such matters, and they pioneer a new framework which does. However, their toolbox – psychological game theory – incorporates several restrictions that rule out plausible forms of belief-dependent motivation. Building on recent work on dynamic interactive epistemology, we propose a more general framework. Updated higher-order beliefs, beliefs of others, and plans of action may influence motivation, and we can capture dynamic psychological effects (such as sequential reciprocity, psychological forward induction, and regret) that were previously ruled out. We develop solution concepts, provide examples, explore properties, and suggest avenues for future research. © 2008 Elsevier Inc. All rights reserved. JEL classification: C72; C73
Performance Evaluation of Tree Object Matching

Multi-Scale Singularity Trees (MSSTs) represents the deep structure of images in scale-space and provide both the connections between image features at different scales and their strengths. In this report we present and evaluate an algorithm that exploits the MSSTs for image matching. Two versions of the algorithm is presented: an exact and an approximation. Several experiments are conducted to empirically evaluate the MSST matching algorithm under image distortions. Further, the performance of the MSST matching algorithm is measured on three databases: the ORL face database, magazine covers, and the COIL database. Finally the performance is compared with algorithms based on the Scale Invariant Feature Transform (SIFT), and the Position of Catastrophes (CAT).
A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System with Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4× and 4.3× in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39× with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6× and 2.8× savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1× in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.
Model Based Detection of Tubular Structures in 3 D Images

Detection of tubular structures in 3D images is an important issue for vascular medical imaging. We present in this paper a new approach for centerline detection and reconstruction of 3D tubular structures. Several models of vessels are introduced for estimating the sensitivity of the image second order derivatives according to elliptical cross-section, to curvature of the axis, or to partial volume effects. Our approach uses a multiscale analysis for extracting vessels of different sizes according to the scale. For a given model of vessel, we derive an analytic expression of the relationship between the radius of the structure and the scale at which it is detected. The algorithm gives both centerline extraction and radius estimation of the vessels allowing their reconstruction. The method has been tested on synthetic images, an image of a phantom, and real images, with encouraging results.