Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Stage Approach for Knowledge-Guided Predictions With Application to Additive Manufacturing

Inspired by sequential additive manufacturing operations, we consider prediction tasks arising in processes that comprise of sequential sub-operations and propose a multi-stage inference procedure that exploits prior knowledge of the operational sequence. Our approach decomposes a data-driven model into several easier problems each corresponding to a sub-operation and then introduces a Bayesian inference procedure to quantify and propagate uncertainty across operational stages. We also complement our model with an approach to incorporate physical knowledge of the output of a sub-operation which is often more practical in reality relative to understanding the physics of the entire process. Comprehensive simulations and two case studies on additive manufacturing show that the proposed framework provides well-quantified uncertainties and superior predictive accuracy compared to a single-stage predictive approach. Note to Practitioners—This paper is motivated by sequential operations that often occur in manufacturing processes. For example, several additive manufacturing processes consist of multiple sequential steps, e.g., printing, washing, and curing in stereolithography, or printing, debinding, and sintering in binder jetting. In such settings, a complex data-driven model that blindly throws all given data into a single predictive model might not be optimal. To this end, we propose a multi-stage inference procedure that decomposes the problem into easier sub-problem using the prior knowledge of the operational sequence, and propagates uncertainty across stages using Bayesian neural networks. Here we note that even if sequential operations are not existent in reality, one may conceptually decompose a complex system into simpler pieces and exploit our procedure. Also, our approach is able to incorporate physical knowledge of the output of a sub-operation.
Modelling Sentiment Analysis scores and acoustic features of emotional speech with neural networks: A pilot study

Abundant literature has shown that emotional speech is characterized by various acoustic cues. However, most studies focused on sentences produced by actors, disregarding more naturally produced speech due to the difficulty in finding suitable emotional data. In our previous work we had performed an analysis of audiobook data in order to see if sentiment analysis could be of help in selecting emotional sentences from read speech. A regression analysis with Linear Mixed Models had revealed small effects, and the power of the models was low. We propose here an analysis with a neural network classifier predicting sentiment on the basis of acoustic cues, given the success of such models in the speech literature. However, the accuracy of the output was merely +0.13 above chance levels, suggesting that the different components used to express emotions (acoustic and lexical) tend to be complementary rather than additive, at least in audiobooks.
ChainSensing: A Novel Mobile Crowdsensing Framework With Blockchain

Mobile crowdsensing (MCS) is a promising paradigm of large-scale sensing. A group of mobile users is recruited with their smart devices to accomplish various sensing tasks in specific areas. The mobility and intelligence of mobile users enable MCS to achieve a sufficient coverage ratio of sensing tasks or areas. Currently, MCS is generally proposed and implemented in a centralized way under a platform’s control. However, this centralized structure is vulnerable to a single point of failure. The platform’s failure leads to a shutdown of the entire system. In addition, there is a trust issue between the platform and mobile users because of computational transparency and financial security. It is possible that the platform manipulates the working process of MCS to obtain an improper gain. To overcome these problems, we propose a decentralized MCS framework, named ChainSensing, by leveraging blockchain. In ChainSensing, mobile users interact with blockchain via smart contracts to complete their operations, e.g., publishing sensing tasks and submitting collected data. Since there are computationally intensive problems in ChainSensing, e.g., path planning, path selection, and reward determination, it is significantly expensive to solve such problems in blockchain. Therefore, we propose to leverage smart devices and computing oracles to solve these problems. Specifically, we propose a heuristic algorithm to solve the path planning problem in smart devices of mobile users; we employ computing oracles to solve the path selection and reward determination problems. Finally, we conduct numerical simulations based on Ethereum to evaluate the performance of ChainSensing.
Exploring Authentication for Security-Sensitive Tasks on Smart Home Voice Assistants

Smart home assistants such as Amazon Alexa and Google Home are primarily used for day-to-day tasks like checking the weather or controlling other IoT devices. Security-sensitive use cases such as online banking and voice-controlled door locks are already available and are expected to become more popular in the future. However, the current state-of-the-art authentication for smart home assistants consists of users saying low-security PINs aloud, which does not meet the security requirements of security-sensitive tasks. Therefore, we explore the design space for future authentication mechanisms. We conducted semi-structured interviews with N = 16 Alexa-users incorporating four high-risk scenarios. Using these scenarios, we explored perceived risks, mitigation strategies, and design-aspects to create secure experiences. Among other things, we found that participants are primarily concerned about eavesdropping bystanders, do not trust voice-based PINs, and would prefer trustworthy voice recognition. Our results also suggest that they have context-dependent (location and bystanders) requirements for smart home assistant authentication. Based on our ﬁndings, we construct design recommendations to inform the design of future authentication mechanisms.
SIMULATION OF THE CARDIOVASCULAR AND AUTONOMIC NERVOUS SYSTEM FOR OPTIMIZATION OF AURICULAR VAGUS NERVE STIMULATION

Percutaneous electrical stimulation of the auricular vagus nerve (pVNS) is a promising approach in management of postoperative and chronic low back pain. pVNS has shown beneficial effects on systemic physiological parameters, such as heart rate variability. Although there are some efforts on modelling pVNS and its effects on the cardiovascular system, in order to control and optimise pVNS, the contribution of the autonomic nervous system is not modelled sufficiently yet. In this study, we model jointly the cardiovascular and autonomic nervous system, to provide a basis for the development of a feedback-based control of pVNS stimulation parameters.
Value‐based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models

When dealing with complex models (i.e., models with many variables, a high degree of dependency between variables, or many states per variable), the efficient representation of quantitative information in probabilistic graphical models (PGMs) is a challenging task. To address this problem, this study introduces several new structures, aptly named value‐based potentials (VBPs), which are based exclusively on the values. VBPs leverage repeated values to reduce memory requirements. In the present paper, they are compared with some common structures, like standard tables or unidimensional arrays, and probability trees (PT). Like VBPs, PTs are designed to reduce the memory space, but this is achieved only if value repetitions correspond to context‐specific independence patterns (i.e., repeated values are related to consecutive indices or configurations). VBPs are devised to overcome this limitation. The goal of this study is to analyze the properties of VBPs. We provide a theoretical analysis of VBPs and use them to encode the quantitative information of a set of well‐known Bayesian networks, measuring the access time to their content and the computational time required to perform some inference tasks.
Heat Flux Predictions for a 3-D Compost Model

A 3-D compost model was constructed based on a truncated cone geometry (4 m radius and 3 m height); in this, an energy balance is applied for a two phase system (solidair). Compost energy processes are modeled with a modified heat transfer equation which includes: volumetric heat capacity, chemical oxidation and biological growing and inhibition. A sensitivity parameter analysis was run in order to get temperature predictions closer to the ones observed in organic residues composting processes; in which, according to reports, it is required 40-90 days in order to get temperatures in the range of 313 -343 K. Obtained predictions have shown that the composting process exhibits higher sensitivity to parameters as: compost material oxidation activation energy, biomass growth activation energy. Otherwise, middle sensitivity is exhibited respect to parameters as: biomass growth oxidation factor, porosity, compost thermal conductivity, biological heat generation and biomass density.
Genetic analysis of growth curve of Afshari lambs by legendre polynomials-based random regression models

The aim of this study was to apply Simple Repeatability Model (SRM) and Random Regression Models (RRMs) to describe growth curve of Afshari lambs. Results revealed the inadequacy of SRM to model variation in growth curve of Afshari lambs. A RRM with orders 3, 2, 3, and 2 for direct genetic, direct permanent environment, maternal genetic and maternal permanent environmental effects was selected as the parsimonious model. Direct heritability (h) and maternal permanent environmental effect (c) were maximal at 98 days of age, and decreased with age until the end of growth trajectory. Direct permanent environmental effect (p) and maternal heritability (m) were minimal at 98 days of age, but increased thereafter to a peak at 525 days of age. In conclusion, results revealed substantial genetic potential for selection responses in early growth of Afshari lambs and that this genetic potential can be exploited by breeders to improve growth performance of Afshari lambs.
Safranin-O removal from aqueous solutions using lignin nanoparticle-g-polyacrylic acid adsorbent: Synthesis, properties, and application

In this study, alkali lignin modified by ethylene glycol and lignin nanoparticles was prepared through acid precipitation technology. Lignin nanoparticle-g-polyacrylic acid adsorbent was prepared using copolymerization reactions between lignin nanoparticle and polyacrylic acid in the presence of potassium persulfate as the radical initiator. Then, lignin nanoparticle-g-polyacrylic acid adsorbent was used to remove Safranin-O from an aqueous environment. The adsorbent structures and morphologies of lignin nanoparticle and lignin nanoparticle-g-polyacrylic acid adsorbent were investigated using scanning electron microscopy, Fourier transform infrared spectroscopy, and thermogravimetric analysis. Nanoparticle sizes were assessed using dynamic light scattering. Equilibrium isotherms were compared using the Langmuir, Freundlich, and Temkin adsorption models. Both adsorbent lignin nanoparticle and lignin nanoparticle-g-polyacrylic acid adsorbent showed good agreement with the Langmuir isotherm. The maximum adsorption capacities of lignin nanoparticle and lignin nanoparticle-g-polyacrylic acid adsorbent reached 99 and 138.88 mg g−1 using model-like, pseudo-second-order, and intraparticle diffusion, respectively. Experimental results showed that adsorbent lignin nanoparticle and lignin nanoparticle-g-polyacrylic acid adsorbent followed the pseudo-second order kinetic model.
Leaf Image Classification Using Deep Learning Network

This paper focuses on leaf cultivar classification, which is a long-established challenge in agricultural artificial intelligence. The difficulties in this task come from the fact that there is large amount of intra-class variability, arising from form changes during the growth of leaves and different physical development, and subtle inter-class differences, originating by belonging to the same species. To cope with this challenging task, we study the possibility of using deep learning techniques for distinguishing leaf cultivars. We employed a soybean leaf cultivar dataset and conducted extensive experiments on it for a comparison study of handcrafted methods and deep learning methods on leaves cultivar recognition tasks. The experimental results indicate the supervisor performance of the deep learning methods over the traditional methods.
Hydrothermal Extraction of Antioxidant Compounds from Green Coffee Beans and Decomposition Kinetics of 3-o-Caffeoylquinic Acid

Separation of antioxidant compounds (caffeoylquinic acids (CQAs), phenolics, melanoidin, and caffeine) from green coffee beans with hydrothermal extraction and decomposition kinetics of 3-o-caffeoylquinic acid (3-CQA) are reported. Antioxidant capacity (AOC) of the extracts increased as extraction temperature was increased up to 410 K and then it decreased up to extraction temperatures of 500 K. As extraction temperature was further increased above 500 K, AOC remarkably increased. The decomposition rate of 3-CQA in water was determined from 433 to 513 K. The increase and decrease in AOC with extraction temperature can be attributed to the hydrolysis of oligomeric structures (glycosides) in the coffee beans that yield CQAs, the decomposition of the CQAs, and to the formation of melanoidins that had a characteristic brown color. Hydrothermal extraction provides an effective method for the separation of antioxidant compounds from green coffee beans, and the effluent extracts may be suitable for food products.
Nanofabrication with the thermal AFM metallic tip irradiated by continuous laser

ABSTRACT Based on the near-field optical lithography with the metallic AFM tip irradiated by pulse laser, the nanofabrication is proposed with the thermal AFM tip irradiated by continuous laser. The corresponding electric and thermal fields are calculated and simulated to reveal the mechanism. With the thermal AFM tip, the nanostructures can be fabricated with the thermal melting and mechanical extrusion effects based on the thermal expansion effect of the metallic AFM probe.
MACHINE LEARNING METHODS FOR PREDICTING SEISMIC

Aging building clusters worldwide, especially in high seismic regions, will require a retrofit approach to improve the resilience of the built environment. One of the main challenges of retrofitting existing buildings is the associated cost. Fung et al. [1] develop a predictive modeling approach to estimating seismic retrofit costs. The predictive modeling approach uses historical data to predict retrofit costs for existing buildings based on observable building characteristics (“the features”). The advantages of this approach are that it is (1) cheap, (2) fast, and (3) can be applied to a single building or a large inventory of buildings. However, the approach relies on a linear model for prediction, which assumes a restrictive relationship between retrofit cost and the features. In this paper, we consider machine learning methods that can capture more complex, potentially nonlinear relationships between retrofit cost and the features. The paper considers ensemble methods (bagging and boosting) as well as neural networks and compares their performance to that of the linear model. The results show that a neural network provides the best performance in terms of prediction error. In applications, a user faces a tradeoff between accuracy and interpretability: while ensemble methods and deep neural networks may improve accuracy, they are not as easily interpretable as the linear model.
GRAPHENE/POLYPROPYLENE NANOCOMPOSITES

Graphene has been regarded as a multifunctional nanofiller for manufacturing polymer nanocomposites. A small amount of graphene nanosheets is expected to significantly improve the properties of base polymers. The property enhancement, however, is a function of the degree of exfoliation and dispersion of graphene as well as its compatibility with the polymer. The non-polar nature of polyolefins such as polyethylene (PE) and polypropylene (PP) restricts the homogeneous dispersion of graphene, leading to significant agglomeration of the nanosheets and thus, limiting the expected property improvements in polyolefins. Currently, more efforts are focused on finding strategies to improve graphene dispersion in polyolefins. In this thesis, two strategies were followed to improve the dispersion efficacy of graphene in PP: (1) using compatibilizers, and (2) covalent functionalization of graphene. First part of thesis discusses a new compatibilizer (ethylene butyl acrylate (EBA)) for graphene/PP nanocomposites, and compares it with a conventional compatibilizer (polypropylene-grafted-maleic anhydride (PP-g-MA)). The nanocomposites were prepared by varying graphene concentrations and compatibilizer/graphene (C/G) ratio using the conventional melt blending approach. Increasing C/G reduced the %crystallinity of EBA-compatibilized nanocomposites whereas a slight decrease in %crystallinity was observed for MA-compatibilized nanocomposites. The compatibilized nanocomposites showed marked increment in the tensile modulus where EBA-compatibilized nanocomposites exhibited 44% increment and MA-compatibilized nanocomposites showed 32% increment at 5 wt% graphene. However, elongation at break increased significantly with increasing the compatibilizers compared to neat PP/graphene nanocomposites. Higher values of viii elongation at break for EBA-compatibilized nanocomposites is attributed to lower %crystallinity in these nanocomposites. In the second approach, various types of amine-functionalized graphenes have been used to prepare PP/Functionalized-graphene nanocomposites. The nanocomposites were prepared by melt and solution blending methods. The x-ray diffraction and transmission electron microscopy confirmed exfoliated and sheet-like morphology of functionalized graphene. Interestingly, both solution and melt blended nanocomposites exhibited equivalent tensile moduli. The tensile strength of solution-blended nanocomposites was lower than that of melt-blended nanocomposites. However, the elongation at break increased to 3 times in solutionprocessed nanocomposites.
System for

The increase in domain specific text processing applications are demanding tools and techniques for domain specific Text Classification (TC) which may be helpful in many downstream applications like Machine Translation, Summarization, Question Answering etc. Further, many TC algorithms are applied on globally recognized languages like English giving less importance for local languages particularly Indian languages. To boost the research for technical domains and text processing activities in Indian languages, a shared task named ”TechDOfication2020” is organized by ICON’20. The objective of this shared task is to automatically identify the technical domain of a given text which provides information about coarse grained technical domains and fine grained subdomains in eight languages. To tackle this challenge we, team MUCS have proposed three models, namely, DL-FineTuned model applied for all subtasks, and VC-FineTuned and VC-ngrams models applied only for some subtasks. ngrams and word embedding with a step of finetuning are used as features and machine learning and deep learning algorithms are used as classifiers in the proposed models. The proposed models outperformed in most of subtasks and also obtained first rank in subTask1b (Bangla) and subTask1e (Malayalam) with f1 score of 0.8353 and 0.3851 respectively using DL-FineTuned model for both the subtasks.
Internalizing and externalizing behaviors in school-aged children are related to state anxiety during magnetic resonance imaging

Magnetic resonance imaging (MRI) procedures often evoke anxiety in children. Further, anxious children may be less likely to participate in MRI research, leading to a possible selection bias, and may be more likely to move during image acquisition, resulting in lower image quality and possible information bias. Therefore, state anxiety is problematic for functional and structural MRI studies. Children with behavioral problems, such as internalizing and externalizing behaviors, may be more likely to experience state anxiety prior to and during MRI scanning. Therefore, our first aim was to investigate the relationship between internalizing/externalizing behavior and children's MRI-related state anxiety. Our second aim was to investigate the relationship between internalizing and externalizing behavior and MRI research participation. Our final aim was to investigate the effect of internalizing and externalizing behavior as well as MRI-related anxiety on image quality in children. We included 1,241 six- to ten-year-old children who underwent a mock MRI. Afterwards, if not too anxious, these children were scanned using a 3-Tesla GE Discovery MRI system (n = 1,070). Internalizing and externalizing behaviors were assessed with the Child Behavior Checklist. State anxiety was assessed with a visual analogue scale. Internalizing behaviors were positively associated with child state anxiety, as reported by child, parent, and researcher. For state anxiety reported by the parent and researcher, this relationship was independent of externalizing behaviors. Externalizing behaviors were related to state anxiety reported by the researcher, but this difference was not independent of internalizing behaviors, pointing towards a relationship via the shared variance with internalizing behaviors. Further, children with more internalizing and externalizing behaviors were less likely to participate in the actual MRI scanning procedure. Lastly, internalizing and externalizing behaviors, as well as MRI-related state anxiety were associated with worse image quality. These results underscore the potential for biases and methodological issues caused by MRI-related state anxiety in children.
Mental health impacts of the COVID-19 pandemic on children and adolescents with chronic health conditions

Objective: To investigate the changes in mental health and activities of children with chronic health conditions (CHC) during the pandemic. Additionally, to gather information from parents about healthcare experiences, family stressors and mental health during the COVID-19 pandemic. Design: A prospective longitudinal single site cohort study Setting: Royal Childrens Hospital, Melbourne, Australia Participants: 151 parents of children aged 1.5-17 years (M = 9.8 years, 58.3% male) with a CHC (colorectal disorder, cleft palate and neurofibromatosis type 1) completed the survey. Main outcome measures: An adapted version of the COVID-19 Wellbeing and Mental Health Survey for Children and Adolescents (Parent/Caregiver version) (CRISIS) was utilised. The CRISIS tool collects data on child activities and mental health and parent mental health prior to, and during, the COVID-19 pandemic. Healthcare experiences families was also examined. Parents completed the Kessler 10. Results: Compared to pre-COVID lockdown, parents reported their children were experiencing higher rates of loneliness, irritability, worry and anxiety. Parents reported that the restrictions in face-to-face contact with family and friends had been stressful for 80.0% of children. The activities of the children changed considerably during the COVID-19 pandemic with screen time increasing by 40%. Thirty percent of parents reported significant distress of their own. Parents felt telehealth were of poorer quality than face-to-face appointments. Conclusions: Children with CHC experienced a significant increase in mental health symptoms during lockdown for the COVID-19 pandemic. Our findings highlight the increased need for clinical monitoring for children with CHC during periods of community stress and restrictions.