Miracle-dz/UniFAR-FTUs huggingface dataset:
SU8 Out-of-Plane Stress Reduction Via Design of Experiment and Machine Learning

Stress-induced bending in a stand-alone SU8 shaft structure lowers the yield and prolongs the development of brain-machine interface (BMI) that is packaged with SU8. The stress and bending can be reduced by properly adjusting several experiment parameters. In this work, design of experiment(DoE) used to systematically plan factors and levels to get conditions with minimal bending and high fidelity. The data bending results of the released SU8 shaft structure are processed with analysis of variance (ANOVA) and the parameter space is mapped out for the first time by Machine Learning algorithm (Support Vector Machine with RBF kernel). The algorithm predicts the optimal range of conditions for low shaft deflection and is experimentally validated.
Inorganic capping layers in advanced photosensitive polymer based RDL processes: processing and reliability

In this work, thin inorganic ALD-deposited capping layers are integrated into polymer-based redistribution layer (RDL) processes with 2–4 $\mu \mathrm{m}$ line/space. The primary objective of enhanced reliability is shown, with secondary process improvements detailed. Impact and respective mitigation on Lithography process reviewed and the introduction of the capping etch are further detailed.
Precise large deviations of aggregate claims in a risk model with size dependence and non stationary arrivals

ABSTRACT Consider a risk model with claims of heavy tails for non stationary arrival processes that satisfy a large-deviation principle. Assume that the claim sizes and interarrival times form a sequence of random pairs, with each pair obeying a dependence structure via the conditional distribution of the interarrival time given the subsequent claim size being large, and then a precise large-deviation formula of the aggregate amount of claims is obtained.
A Risk-Based Approach to Prognostics and Health Management Combining Bayesian Networks and Continuous-Time Bayesian Networks

Performing general prognostics and health management (PHM), especially in electronic systems, continues to present significant challenges. The low availability of failure data makes learning generalized models difficult and constructing generalized models during the design phase often requires a level of understanding of the failure mechanisms that elude the designers. In this paper, we present a generalized approach to PHM based on two types of probabilistic models, Bayesian Networks (BNs) and Continuous-Time Bayesian Networks (CTBNs), and we pose the PHM problem from the perspective of risk mitigation rather than failure prediction. This paper also constitutes an extension of previous work where we proposed this framework initially [1]. In this extended version, we also provide a comparison of exact and approximate sample-based inference for CTBNs to provide practical guidance on conducting inference using the proposed framework.
Proposal of an Energy Efficient LED LMM Class AB Driver with Bias-T Capable of VLC IEEE 802.15.7

This article provides a brief introduction to Visible Light Communication (VLC) and Li-Fi, describing some of the key aspects of the technology and its expansion. The article presents a case study characterizing a data rate range from 1 Mb/s to 20 Mb/s, aiming to define the best power converter architecture for VLC, referenced to IEEE 802.15. 7-2018 standard - PHY II. After analyzing the converter architectures, the linear mode modulator (LMM) was selected, followed by an investigation between class A and class AB, resulting in a better efficiency of the LMM Class AB converter, which was coupled to the DC level and information data through a Bias-T.
Synthesis and characterization of a new biotinylated gramicidin

A new linear gramicidin analog bearing a biotinyl group grafted on C‐terminal part was designed to study ligand–receptor interactions. The C‐terminal alcohol in the native peptide was first replaced by an amino group. Then the peptide was synthesized on a polystyrene resin functionalized by the 2‐chlorotrityl chloride following a biotinylation performed in solution. This new N′‐biotinyl‐(EDA)15‐Gramicidin A was reconstituted in planar lipid bilayers and exhibited channel activities similar to those of natural gramicidin, with unitary conductance value about 30 ps in 1 m KCl. Furthermore this ionophore activity was quenched by addition of streptavidin in the surrounding medium. Our system is an outstanding tool for monitoring ligand–receptor interactions and could be used for designing a new biosensor. © 1998 European Peptide Society and John Wiley & Sons, Ltd.
g 2 o: A general Framework for (Hyper) Graph Optimization

In this document we describe a C++ framework for performing the optimization of nonlinear least squares problems that can be embedded as a graph or in an hyper-graph. An hyper-graph is an extension of a graph where an edge can connect multiple nodes and not only two. Several problems in robotics and in computer vision require to find the optimum of an error function with respect of a set of parameters. Examples include, popular applications like SLAM and Bundle adjustment. In the literature, many approaches have been proposed to address this class of problems. The naive implementation of standard methods, like Levenberg-Marquardt or Gauss-newton can lead to acceptable results for most applications, when the correct parameterization is chosen. However, to achieve the maximum performances substantial efforts might be required. go stands for General (Hyper) Graph Optimization. The purposes of this framework are the following:
A Test Suite Minimization Technique for Testing Numerical Programs

Metamorphic testing is a technique that uses metamorphic relations (i.e., necessary properties of the software under test), to construct new test cases (i.e., follow-up test cases), from existing test cases (i.e., source test cases). Metamorphic testing allows for the verification of testing results without the need of test oracles (a mechanism to detect the correctness of the outcomes of a program), and it has been widely used in many application domains to detect real-world faults. Numerous investigations have been conducted to further improve the effectiveness of metamorphic testing. Recent studies have emerged suggesting a new research direction on the generation and selection of source test cases that are effective in fault detection. Herein, we present two important findings: i) a mutant reduction strategy that is applied to increase the testing efficiency of source test cases, and ii) a test suite minimization technique to help reduce the testing costs without trading off fault-finding effectiveness. To validate our results, an empirical study was conducted to demonstrate the increase in efficiency and fault-finding effectiveness of source test cases. The results from the experiment provide evidence to support our claims.
Consideration of Semantics between Q&A Statements to Obtain Factor Score

In order to solve the issues of mismatches between the intentions of questioners and respondents at Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements were obtained through factor analysis applied to the results of impression evaluation experiments. Then through multiple regression analysis, factor scores were estimated by using the feature values of statements. The factor scores estimated and obtained were subsequently utilized for detecting respondents who are expected to appropriately answer a posted question. Nevertheless, up to now the meanings and contents of Q&A statements have not been taken into consideration. Therefore, this paper aims to consider the semantics between Q&A statements. The feature values are reviewed and narrowed down to syntactic information, closing sentence expressions, 2-gram, and word2vec. The analysis result conveys that all the trials show good estimation with the consideration of cross-validation. It has also been suggested that applying word2vec could play a vital role in estimating improved factor scores.
Effects of Partnerships between Adolescents with Developmental Disabilities and Service Dogs

Background: With increasing frequency, service dogs are being placed with children with developmental disabilities (DDs). Occupational therapists and other professionals have advocated for the therapeutic use of service dog partnerships to facilitate greater independence and quality of life. There are no studies that examine service dog intervention with adolescents. Method: This study focused on the effects of partnerships between service dogs and three participant dyads, each including an adolescent with DDs and a parent. A single-subject, alternating treatment design was used to compare the effects of two conditions (service dog present or not present). The effects were examined for adolescents’ anxiety behaviors during transitions and during grocery store shopping, for social interactions during grocery store shopping, and for parents’ reported levels of stress. Results: Findings were that service dog partnerships reduced the presence of anxiety behaviors during transitions for one of the three adolescents; reduced the presence of anxiety behaviors during grocery store visits for two of the three adolescents; increased social interactions for all three of the participant dyads; and had no meaningful impact on self-reported parental stress level. Conclusion: For adolescents with DDs, professionals may want to consider service dog partnerships to decrease anxiety behaviors and increase social interactions in the community. Comments The authors report they have no conflicts of interest to disclose.
Challenging the status quo: Predicting violence with sparse decision-making data

Abstract This article addresses the discrepancy between the explanation and the prediction of political violence through the development of different models that approximate the decision-making on war and peace. Borrowing from the crisis bargaining literature, the prediction models particularly consider the situational attributes through which players can challenge the status quo. We distinguish between direct and indirect proxies of a weakening of the status quo and show that adding decision-making data can improve the accuracy of cross-sectional forecasting models. The study, which demonstrates the increased conflict risk due to the COVID-19 pandemic and thus another development upsetting the status quo, discusses the usefulness of decision-making forecasts through various case study illustrations. Este artículo aborda la discrepancia entre la explicación y la predicción de la violencia política mediante la elaboración de diversos modelos que se acercan a la toma de decisiones sobre la guerra y la paz. Inspirados en las publicaciones sobre negociaciones de crisis, los modelos de predicción consideran, en particular, las características situacionales a través de las cuales las piezas claves pueden desafiar el statu quo. Distinguimos entre indicadores directos e indirectos de un debilitamiento del statu quo y demostramos que la incorporación de datos sobre la toma de decisiones puede mejorar la precisión de los modelos de previsión transversal. El estudio, que demuestra el aumento del riesgo de conflicto durante la pandemia de la COVID-19 y, por lo tanto, otro acontecimiento que altera el statu quo, analiza la utilidad de las previsiones para la toma de decisiones mediante diferentes ejemplos de casos prácticos. Cet article aborde la divergence entre l’explication et la prédiction de la violence politique par le développement de différents modèles qui permettent une estimation des prises de décisions sur la guerre et la paix. S’inspirant de la littérature sur les négociations de crises les modèles de prédiction prennent en particulier en compte les attributs situationnels par lesquels les acteurs peuvent remettre en question le statu quo. Nous distinguons les variables directes des variables indirectes de l’affaiblissement du statu quo et montrons que l’ajout de données sur les prises de décisions peut améliorer la précision des modèles de prévision transversaux. L’étude, qui démontre l’augmentation du risque de conflit par la pandémie de COVID et donc une autre évolution bouleversant le statu quo, discute de l’utilité des prévisions de prises de décisions à travers diverses illustrations par des études de cas.
Fake News Detection Using Machine Learning Algorithms

: In our modern era where the internet is ubiquitous, everyone relies on various online resources for news. Along with the increase in the use of social media platforms like Facebook, Twitter, etc. news spread rapidly among millions of users within a very short span of time. The spread of fake news has far-reaching consequences like the creation of biased opinions to swaying election outcomes for the benefit of certain candidates. Moreover, spammers use appealing news headlines to generate revenue using advertisements via click-baits. In this paper, we aim to perform binary classification of various news articles available online with the help of concepts pertaining to Artificial Intelligence, Natural Language Processing and Machine Learning. We aim to provide the user with the ability to classify the news as fake or real and also check the authenticity of the website publishing the news.
Single electro-optic curve for RGB colours in blue-phase liquid crystal display

ABSTRACT The double-layer penetration electrode structure is proposed to obtain the low-operating voltage blue-phase liquid crystal display (BPLCD) with single electro-optic curve for red-green-blue (RGB) colours in every pixel, which is more suitable for the field-sequential-colour display. The different influences of electrode’s parameters and driving methods on the electro-optical properties of the proposed BPLCD are investigated. The results demonstrate that the operating voltage can be reduced from 53.80 V to 23.00 V for red colour. Besides, the coincident voltage-dependent transmittance curves for RGB colours are obtained by adjusting the applied voltage of sub-electrode. Thus, RGB lights can pass through every pixel with single electro-optic curve, which is good for resolution enhancement and single gamma driving. Besides, the maximum gamma shift of the proposed BPLCD is less than 0.1099 at 60° polar angle for the red colour, and the gamma shift difference between red and blue colours is only 0.0542. If the high dielectric constant material is used as the protrusion, the operating voltage can be further decreased, which is close to that of the BPLCD with wall-shaped electrode structure. GRAPHICAL ABSTRACT
Accuracy Improvement of Flooded Area Detection from Satellite Images using Novel K-Nearest Neighbors in Comparison with Support Vector Machine

In this study, we examine the outcomes of employing Novel K-nearest Neighbors and Support Vector Machines to assess the accuracy of recognizing flooded areas. The data set that was used in this inquiry had a total of 118 samples. These samples were divided into two sets: a training set that contained 70 samples, and a testing set that contained 48 samples. An alpha value of 0.05 and a G power of 80% were used in the calculation to determine the total number of samples that were necessary for the investigation. The accuracy of the new K-nearest neighbor algorithm is 80.26 percent, whereas the accuracy of the support vector machine is 75.48%, with a significance of 0.000078 (p<0.05). The newly created K-nearest neighbor algorithm performs much better than the Support vector machine when it comes to the task of finding areas that have been flooded.
AI Augmented Edge and Fog Computing: Trends and Challenges

In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems.
A wearable sensor system for lower-limb rehabilitation evaluation using the GRF and CoP distributions

Wearable sensors are attractive for gait analysis because these systems can measure and obtain real-time human gait and motion information outside of the laboratory for a longer duration. In this paper, we present a new wearable ground reaction force (GRF) sensing system for ambulatory gait measurement. In addition, the GRF sensor system is also used to quantify the patients’ lower-limb gait rehabilitation. We conduct a validation experiment for the sensor system on seven volunteer subjects (weight 62.39 ± ? > 9.69 kg and height 169.13 ± ? > 5.64 cm). The experiments include the use of the GRF sensing system for the subjects in the following conditions: (1) normal walking; (2) walking with the rehabilitation training device; and (3) walking with a knee brace and the rehabilitation training device. The experiment results support the hypothesis that the wearable GRF sensor system is capable of quantifying patients’ lower-limb rehabilitation. The proposed GRF sensing system can also be used for assessing the effectiveness of a gait rehabilitation system and for providing bio-feedback information to the subjects.
Searching and Ranking in Entity-relationship Graphs Kurzfassung

The Web bears the potential to become the world’s most comprehensive knowledge base. Organizing information from the Web into entity-relationship graph structures could be a first step towards unleashing this potential. In a second step, the inherent semantics of such structures would have to be exploited by expressive search techniques that go beyond today’s keyword search paradigm. In this realm, as a first contribution of this thesis, we present NAGA (Not Another Google Answer), a new semantic search engine. NAGA provides an expressive, graph-based query language that enables queries with entities and relationships. The results are retrieved based on subgraph matching techniques and ranked by means of a statistical ranking model. As a second contribution, we present STAR (Steiner Tree Approximation in Relationship Graphs), an efficient technique for finding “close” relations (i.e., compact connections) between k(≥ 2) entities of interest in large entity-relationship graphs. Our third contribution is MING (Mining Informative Graphs). MING is an efficient method for retrieving “informative” subgraphs for k(≥ 2) entities of interest from an entity-relationship graph. Intuitively, these would be subgraphs that can explain the relations between the k entities of interest. The knowledge discovery tasks supported by MING have a stronger semantic flavor than the ones supported by STAR. STAR and MING are integrated into the query answering component of the NAGA engine. NAGA itself is a fully implemented prototype system and is part of the YAGONAGA project.
Investigating surface structures by EUV scattering

As the industry continues to progress along the ITRS roadmap, not only the device dimensions shrink, but the architectures also increase in 3D complexity. Therefore, new metrology approaches for small structures are required. Small angle X-ray scattering has the potential for fast in line metrology but suffers from the large spot size needed in grazing incidence reflection and the low signal for the transmission geometry. Turning the incidence angle closer to normal and tuning the wavelengths into the EUV spectral range allows to decrease the spot size while keeping the signal high. We present an exploration of soft X-ray and EUV-scatterometry from grazing to near normal incidence, including a new approach in the design of metrology targets to avoid the footprint problem in grazing incidence geometry. Measurements were performed on e-beam written silicon gratings. The reconstructed geometrical line shape models are statistically validated by applying a Markov-Chain Monte Carlo sampling technique. Experimental data and simulation results provide a first insight into the potential of EUV Scatterometry.
PAL-XFEL Dipole Magnet Power Supplies

Seong Hun Jeong#, Ki-Hyeon Park, Hyung Suck Suh, Sang-Bong Lee, Bongi Oh, Young-Gyu Jung, Hong-Gi Lee, Dong Eon Kim, Heung-Sik Kang and In Soo Ko PAL, Pohang, Republic of Korea Abstract Total 632 magnet power supplies (MPSs) are under operating in PAL-XFEL. These magnet power supplies can be categorized as three types corrector, quadrupole and dipole. The dipole MPSs are ranging from 110A/80V bipolar PS to 310A/200V unipolar PS. The long term stability of bipolar power supply is 10 ppm with 250 A 40V output for gun solenoid. The three types of dipole MPSs are developed for PAL-XFEL. Precise measurement results show that all power supplies meet the required specifications. The long term operation stability of the MPSs are appeared to be sufficient for a stable operation of the PAL-XFEL. INTRODUCTION
Digital Twins of Smart Campus: Performance Evaluation Using Machine Learning Analysis

The Internet of Things (IoT) paradigm is gradually becoming more prevalent through numerous devices and technologies, including sensors, actuators, microcontrollers, cloud-enabled services, and analytics. IoT objects gain intelligence by integrating with wireless sensor networks (WSNs), mobile computing and communication, and others. With sensors, smart things can be enabled by monitoring and identifying environmental changes related to motion, temperature, humidity, pressure, light, vibration, etc. To timely keep track of state changes, researchers are considering developing a cyber replicator, denoted as Digital Twin (DT), of real physical systems as a way to visualize, model, and work with complex cyber-physical systems (CPS). In this paper, we first refine the dataset to a format that can be easily used for deep learning (DL) experiments, IoT data pipeline development, data modeling and simulation, data aggregation, etc. We then demonstrate that DT data can be used to determine space occupancy based on the ambient light sensor, which tends to indicate occupancy in particular spaces because the building has smart lighting that will switch off when rooms are unoccupied after a certain time. Given the apparent developments in machine learning technology, it is clear that machine learning-based prediction has the ability to enhance resource utilization and further forecast future events. Particularly, we use a DT-based dataset and Long-Short-Term Memory (LSTM) neural network architecture to forecast the campus building’s internal temperature.