Miracle-dz/UniFAR-FTUs huggingface dataset:
RGB-D People Detection and Tracking from Small-Footprint Ground Robots

Reliably detecting and tracking people from a low-lying viewpoint is a challenging problem, due to the uncommon viewpoint, from which only few features are visible. This paper presents a lower part-based approach, which enables to detect and track people in indoor environment from a small-footprint ground robot using a RGB-D sensor. The approach is based on the fact that the lower part of the human body is the most prominent feature from a low-lying viewpoint. By removing the ground plane firstly, the objects on it within a limited height are clustered. And their HOG features are computed and then fed to a pre-trained SVM binary soft classifier. The clusters with high HOG confidence are classified as people, and then they are performed as input of the tracking module. A two-term likelihood consisting of color coherence and distance coherence is exploited for the data association process for looking for the correct tracks. Tests have been performed in three different indoor environments. The experimental results show that the precision, recall and f1-score, which are the people detection metrics reaches 0.82, 0.78, and 0.80 respectively.
Satiety Factors Oleoylethanolamide, Stearoylethanolamide, and Palmitoylethanolamide in Mother’s Milk Are Strongly Associated with Infant Weight at Four Months of Age—Data from the Odense Child Cohort

Regulation of appetite and food intake is partly regulated by N-acylethanolamine lipids oleoylethanolamide (OEA), stearoylethanolamide (SEA), and palmitoylethanolamide (PEA), which induce satiety through endogenous formation in the small intestine upon feeding, but also when orally or systemic administered. OEA, SEA, and PEA are present in human milk, and we hypothesized that the content of OEA, SEA, and PEA in mother’s milk differed for infants being heavy (high weight-for-age Z-score (WAZ)) or light (low WAZ) at time of milk sample collection. Ultra-high performance liquid chromatography-mass spectrometry was used to determine the concentration of OEA, SEA, and PEA in milk samples collected four months postpartum from mothers to high (n = 50) or low (n = 50) WAZ infants. Associations between OEA, SEA, and PEA concentration and infant anthropometry at four months of age as well as growth from birth were investigated using linear and logistic regression analyses, adjusted for birth weight, early infant formula supplementation, and maternal pre-pregnancy body mass index. Mean OEA, SEA, and PEA concentrations were lower in the high compared to the low WAZ group (all p < 0.02), and a higher concentration of SEA was associated with lower anthropometric measures, e.g., triceps skinfold thickness (mm) (β = −2.235, 95% CI = −4.04, −0.43, p = 0.016), and weight gain per day since birth (g) (β = −8.169, 95% CI = −15.26, −1.08, p = 0.024). This raises the possibility, that the content of satiety factors OEA, SEA, and PEA in human milk may affect infant growth.
Characterizing Inhibitor Content of Transformer Oil in the UV-Visible Waveband

Over the years, transformer oil has been used in majority of the power transformers to provide a reliable insulating system that is effective against dielectric stresses. Oxidation often occurred in transformer oil in the presence of oxygen and moisture which will affect the stability and insulating properties of the oil. The oxidation process cannot be eliminated but it can be delayed with the presence of inhibitor content. Even though inhibitor content can reduce the oxidation rate, the amount of inhibitor content still depletes over time. Thus, a monitoring system to detect the inhibitor content concentration is very crucial as it will be able to prolong the life span of the transformer. This paper focuses on the optical characterization of inhibitor content in transformer oil by utilizing the ultravioletvisible (UV-Vis) spectroscopy technique. It was found that oil samples with inhibitor content produce multiple absorbance peaks in the range of 350 nm to 500 nm. A clear difference in peak absorbance near 450 nm indicates the difference in the inhibitor content concentrations. Based on the results of this work, a portable and low cost optical sensing device can potentially be developed for the detection of inhibitor content in transformer oil using UV-Vis spectroscopy.
Task-oriented Conversational Agent Self-learning Based on Sentiment Analysis

One of the biggest issues in creating a task-oriented conversational agent with natural language processing based on machine learning comes from size and correctness of the training dataset. It could take months or even years of data collection and the resulting static resource may get soon out of date thus requiring a significant amount of work to supervise it. To overcome these difficulties, we implemented an algorithm with the ability of improving learning efficiency based on the emotions and reactions arising from the conversation between a user and the bot, automatically and in real time. To this end, we have studied an error function that, as in any closed loop control system, corrects the input to improve the output. The proposed method is based on both calibrating the interpretation given to the initial dataset and expanding the dictionary with new terms. Thanks to this innovative approach, the satisfaction of the interlocutors is higher if compared to algorithms with a static dataset or with semi-automatic self-learning rules.
A service-oriented middleware framework for manufacturing industry 4.0

The advantages of the Internet of things (IoT) initiated the vision of Industry 4.0 in Europe and smart manufacturing in USA. Both visions aim to implement the smart factory to achieve similar objectives by utilizing new technologies. These technologies include cloud computing, fog computing, cyber-physical systems (CPS), and data analytics. Together they help automate and autonomize the manufacturing processes and controls to optimize the productivity, reliability, quality, cost-effeteness, and safety of these processes. While both visions are promising, developing and operating Industry 4.0 applications are extremely challenging. This is due to the complexity of the manufacturing processes as well as their management, controls, and integration dynamics. This paper introduces Man4Ware, a service-oriented middleware for Industry 4.0. Man4Ware can help facilitate the development and operations of cloud and fog-integrated smart manufacturing applications. Man4Ware offers many advantages through service level interfaces to enable easy utilization of new technologies and integration of different services to relax many of the challenges facing the development and operations of such applications1.
Mobile App Review Labeling Using LDA Similarity and Term Frequency-Inverse Cluster Frequency (TF-ICF)

User review mining has attracted many researchers to analyze and develop innovative models. The models provide technical recommendation for software developers to make decisions during software maintenance a software evolution. One of the recommendations is user review categorization. There are many categorizations have been popularly used, namely bug errors, feature requests, and noninformative. There are many methods that have been done to classify user reviews. One of the classification methods is Latent Dirichlet Allocation (LDA). LDA is a topic modelling method which ables to map hidden topics resided in a document. Thus, one of techniques to map hidden topics into categories is calculating term similarity value between hidden topic and the pre-defined signifier term list. However, the limited signifier term list of each category becomes a problem. Meanwhile Term Frequency-Inverse Corpus Frequency (TF-ICF) is able to take important terms on a cluster. Therefore, this paper introduces a method that combines TF-ICF with LDA clustering based on similarity (LDAS TF-ICF) to overcome it. The classification results were calculated by using precision, recall, and F1-score. The results show the method can outperform LDA. The best performance of LDAS TF-ICF occured when 75% expanded term list was used, given the precision, recall, dan f-measure values 0.564, 0.507, and 0.491, respectively.
Morphological, Rheological and Electromagnetic Properties of Nanocarbon/Poly(lactic) Acid for 3D Printing: Solution Blending vs. Melt Mixing

The limitation of poor mechanical stability and difficulties in printing electrically conductive components can be overcome owing to the recent introduction of nanotechnology into the field of additive manufacturing (AM) and the consequent development of nonconventional polymer nanocomposites suitable for 3D printing. In the present work, different weight percentages (up to 6 wt % in total) of carbon-based nanostructures—multiwalled carbon nanotubes (MWCNTs), graphene nanoplatelets (GNPs), and a combination of both fillers (MWCNTs/GNPs)—were incorporated into poly(lactic) acid (PLA, Ingeo™) in an attempt to overcome several limitations of conventional 3D manufacturing based on insulating materials. Solution blending and melt mixing were the two fabrication methods adopted for preparation of the samples under test. A comparison of the morphological, rheological, and electrical properties of the resulting nanocomposites was carried out. Moreover, for the same weight concentrations, the influence of physical and geometrical features (i.e., functionalization and aspect ratio) of the embedded fillers was also investigated. Rheological methods were applied to control the quality of fillers dispersion in PLA matrix. The rheological percolation threshold was considered as reference in order to evaluate the internal structure of nanodispersions. TEM visualization, combined with rheological characterizations, was used for efficient control of the nanofiller dispersion. DC characterization revealed that lower electrical percolation thresholds and higher values of electrical conductivity were achieved using fillers with a larger aspect ratio and melt mixing, respectively. Moreover, given the possibility of obtaining complex and appropriate shapes for electromagnetic compatibility (EC) applications, electromagnetic (EM) response of the nanocomposites at the highest filler concentration was investigated in GHz and THz regions. It was found that the electromagnetic shielding efficiency (EMI) of nanocomposites strongly depended on the aspect ratio of the nanofillers, whereas the type of processing technique did not have a significant effect. Therefore, a careful choice of methods and materials must be made to address the final application for which these materials and further 3D printed architectures are designed.
Guide to Crickets of Australia

Cricket song is a sound of the Australian bush. Even in cities, the rasping calls signify Australia’s remarkable cricket biodiversity. Crickets are notable for a variety of reasons. When their population booms, some of these species become agricultural pests and destroy crop pastures. Some introduced species are of biosecurity concern. Other crickets are important food sources for native birds, reptiles and mammals, as well as domestic pets. Soon you might even put them in your cake or stir-fry, as there is a rapidly growing industry for cricket products for human consumption. Featuring keys, distribution maps, illustrations and detailed colour photographs from CSIRO’s Australian National Insect Collection, A Guide to Crickets of Australia allows readers to reliably identify all 92 described genera and many species from the Grylloidea (true crickets) and Gryllotalpoidea (mole crickets and ant crickets) superfamilies. Not included are the Raspy Crickets (Gryllacrididae), King Crickets (Anostostomatidae) or the so-called ‘Pygmy Mole Crickets’ (Caelifera), which despite their common names are not related to true crickets. Natural history enthusiasts and professionals will find this an essential guide.
SNR TOF Gain in High Time Resolution PET systems

The key performance parameter of a Time-of-flight (TOF) positron emission tomography (PET) system is the time resolution, which has a direct effect on the TOF signal-to-noise ratio (SNR) gain. TOF gain has been modeled, simulated and measured in past literature, and advanced models have been proposed. In his work we are able to compare models and simulations with experimental data below 250ps time resolution, using a next generation Siemens SiPM PET/CT prototype scanner. TOF gain is assessed using uniform cylindrical phantoms of different diameters (20cm, 37cm). Filtered back projection (FBP) reconstruction was used for the reconstruction, in TOF and nonTOF version.A SNR TOF gain up to 3.9, equivalent to a TOF sensitivity gain of about 15.1, was measured in this preliminary experiment. In addition, results at variable random fraction are presented.
Efficient Privacy-Preserving Scheme for Real-Time Location Data in Vehicular Ad-Hoc Network

The development of vehicular ad-hoc network (VANET) allows vehicle users to enjoy location-based services through the network at any time and place. However, it also raises location privacy issues. Privacy-preservation of large amounts of real-time location data is a critical issue. Existing research work, such as the mix-zone scheme, has protected users’ location privacy and driving routes to a certain extent. However, existing schemes have some deficiencies in security and efficiency. This paper presents a novel location privacy protection scheme. Vehicles can generate virtual locations dynamically based on the situation of surrounding vehicles to provide misleading information regarding the driving route and thus achieve location privacy. We use entropy of anonymity set and tracking success ratio as metrics to measure the level of privacy-preserving. Simulation experiments have been carried out to compare with other existing schemes. The results show that entropy of anonymity set in our scheme is 100% larger than the compared scheme and the tracking success ratio has decreased from 10% to 1%. So, the results illustrate that the proposed scheme offers better security and location-based services.
Extracting Packet Dependence from NoC Simulation Traces Using Association Rule Mining

Networks on-chip (NoCs) interconnect complex parallel applications on multiprocessors systems-on-chip (MPSoCs). In order to rapidly evaluate NoCs, designers replace processing elements by communication traces on simulations. However, trace-based simulations have low accuracy due to the lack of information about packet dependence. The methods to obtain packet dependence require running multiple simulations or modifying application and simulator source code, both are very costly. In this paper, we model packet dependence extraction as an association rule mining problem. We use Apriori algorithm to extract communication patterns using traces from only one full-system simulation. The experiments with real and synthetic traffic show about 84% of packet dependence are captured.
Message analyses about vaccines in the print press, television and radio: characteristics and gaps in previous research

ABSTRACT Background: Vaccines have attracted extensive media attention in recent years, owing in large part to now-discredited claims about safety. This media content can impact public perception of disease preventive measures and influence decisions. Previous studies have analyzed how the media construct and frame messages about vaccination programs. Therefore, our objective was to review the studies analyzing media messages about vaccines and describe the characteristics and research gaps thereof. Method: A review was conducted searching content analyses of media messages about vaccines in 5 international databases for articles published from 2007 to 2018 in television, radio and print media. Results: The search yielded 27 eligible studies; media messages about vaccines have been largely studied during the last decade. The findings revealed that 63% (n = 17) of studies analyzed the HPV vaccine, 93% (n = 25) examined newspapers, and 56% (n = 15) examined North-American media. Negative messages were more frequent than positive ones in the studies (n = 8 versus n = 5). The media message analyses were related to the following categories: ‘positive messages’, ‘negative messages’, ‘information quality’, ‘sources of information’ and ‘costs’. All studies found inaccurate messages about vaccines or vaccination. Conclusions: We have identified common characteristics and research gaps in the literature as few studies analyzing vaccines messages in television and radio, different types of vaccines and geographical areas such as in low-income countries. Negative messages and inaccurate information are frequent in media messages about vaccines or vaccination. Practical implications are discussed.
Discovering Causal Relations in Semantically-Annotated Probabilistic Business Process Diagrams

Business Process Diagrams (BPDs) have been used for documenting, analyzing and optimizing business processes. Business Process Modeling and Notation (BPMN) provides a rich graphical notation which is supported by a formalization that permits users automating such tasks. Stochastic versions of BPMN allows designers to represent the probability every possible way a process can develop. Nevertheless, this support is not enough for representing conditional dependencies between events occurring during process development. We show how structural learning on a Bayesian Network obtained from a BPD is used for discovering causal relations between process events. Temporal precedence between events, captured in the BPD, is used for pruning and correcting the model discovered by an Inferred Causation (IC) algorithm. We illustrate our approach by detecting dishonest bidders in an on-line auction scenario.
Prognostic value role of radiofrequency lesion size by cardiac magnetic resonance imaging on outcomes of ablation in patients with ischemic scar-related ventricular tachycardia

Abstract Inadequate ablation lesion formation may be responsible for post-ablation ventricular tachycardia (VT) recurrences. We aimed to evaluate whether visualisation of radiofrequency (RF) lesion size by cardiac magnetic resonance imaging (CMR) has any role in predicting adequacy of lesion and in estimating outcome. Retrospective pilot study Nine consecutive patients (8 male, age 60 ± 13 years) underwent ablation for sustained VT because of ischemic scar were evaluated for pre- and post-procedure scar tissue by CMR to characterize ablation lesions. Microvascular obstruction (MVO) surrounded by late gadolinium enhancement was defined as irreversible RF lesion. All patients were followed for at least 6 months for recurrences. Five of the patients had previous inferior myocardial infarction (MI), whereas remaining 4 had anterior MI. Acute procedural success, as defined by termination of the arrhythmia without recurrence in 30 minutes, was attained in all patients. Contrast enhancement and wall motion abnormality in presumed infarction area were confirmed by pre-ablation CMR images. MVO was detected at the reported ablation site in 6/9 patients, all arrhythmia- and symptom-free at median 24 months (range 8–38 months) follow-up. In remaining 3 patients who had VT recurrence (clinical VT in 2, sustain VT with a new morphology in 1), MVO was not detected despite achievement of acute procedural success. There was no correlation with pre-ablation scar size and clinical arrhythmia recurrence. CMR is a useful imaging modality to guide ablation procedures by detecting scar tissue. Additionally MVO seen by post-procedural imaging may be related to adequacy of RF ablation lesions and may correlate with clinical outcome.
Graded CTL* over Finite Paths

In this paper we introduce Graded Computation Tree Logic over finite paths (GCTLf , for short), a variant of Computation Tree Logic CTL∗, in which path quantifiers are interpreted over finite paths and can count the number of such paths. State formulas of GCTLf are interpreted over Kripke structures with a designated set of states, which we call "check points". These special states serve to delineate the end points of the finite executions. The syntax of GCTLf has path quantifiers of the form E≥gψ which express that there are at least g many distinct finite paths that a) end in a check point, and b) satisfy ψ. After defining and justifying the logic GCTLf , we solve its model checking problem and establish that its computational complexity is PSPACE-complete.
Adjusting for Confounding in Unsupervised Latent Representations of Images

Biological imaging data are often partially confounded or contain unwanted variability. Examples of such phenomena include variable lighting across microscopy image captures, stain intensity variation in histological slides, and batch effects for high throughput drug screening assays. Therefore, to develop "fair" models which generalise well to unseen examples, it is crucial to learn data representations that are insensitive to nuisance factors of variation. In this paper, we present a strategy based on adversarial training, capable of learning unsupervised representations invariant to confounders. As an empirical validation of our method, we use deep convolutional autoencoders to learn unbiased cellular representations from microscopy imaging.
Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning

Due to the increasing complexity of Integrated Circuits (ICs) and System-on-Chip (SoC), developing high-quality synthesis flows within a short market time becomes more challenging. We propose a general approach that precisely estimates the Quality-of-Result (QoR), such as delay and area, of unseen synthesis flows for specific designs. The main idea is training a Recurrent Neural Network (RNN) regressor, where the flows are inputs and QoRs are ground truth. The RNN regressor is constructed with Long Short-Term Memory (LSTM) and fully-connected layers. This approach is demonstrated with 1.2 million data points collected using 14nm, 7nm regular-voltage (RVT), and 7nm low-voltage (LVT) FinFET technologies with twelve IC designs. The accuracy of predicting the QoRs (delay and area) within one technology is $\boldsymbol{\geq}$\textbf{98.0}\% over $\sim$240,000 test points. To enable accurate predictions cross different technologies and different IC designs, we propose a transfer-learning approach that utilizes the model pre-trained with 14nm datasets. Our transfer learning approach obtains estimation accuracy $\geq$96.3\% over $\sim$960,000 test points, using only 100 data points for training.
Solving the Capacitated Vehicle Routing Problem Using a Parallel Micro Genetic Algorithm

The Capacitated Vehicle Routing Problem (CVRP) is a very extensively studied combinatorial optimization problem that aimes to determine the order in which to visit a set of customers using a homogeneous fleet of vehicles based at a single depot so that the total travel cost is minimized. In this paper, a novel coarse-grained parallel micro genetic algorithm is used for solving CVRP while the conventional migration is replaced by a synchronously seeding the overall best solution to all search processes. The computational experiments examined the behaviour of the proposed approach.