Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a Time-of-Flight Sensor With Standard Pinned-Photodiode Devices Toward 100-MHz Modulation Frequency

We present an indirect Time-of-Flight (ToF) sensor based on standard pinned-photodiode (PPD) devices and design guides to pave the way for the development of a ToF pixel operating at 100 MHz modulation frequency. The standard PPDs are established well as predominant devices for 2-D color imagers in these days because of their low noise characteristic, but slow transfer speed of photo-generated electrons still prevents them from being employed to 3-D depth imagers. Optimized PPD structure with no process modifications is introduced to create a lateral electric field for enhancing charge transfer speed inside the PPD, and essential design parameters for achieving high operating frequency such as the epitaxial layer thickness, the pinning voltage, and the threshold voltage of the transfer gates are discussed with TCAD simulation results in this paper. Prototype indirect ToF sensors with various structures and parameters were fabricated using a 0.11- $\mu \text{m}$ standard CIS process and characterized fully. We successfully evaluated the demodulation contrast of each pixel at 10 to 75 MHz frequencies, figuring out the suitable conditions of the PPD-based pixel. The best pixel operating at 50 MHz frequency demonstrated a depth resolution of less than 13 mm and a linearity error of about 3.7% between 1 and 3 m distance with a zero-order calibration. We believe further optimization of the ToF pixel incorporated with the PPD devices is possible to improve the performance, operating it towards 100 MHz modulation frequency.
Analysis of Progression of Scratch Users based on their Use of Elementary Patterns

Block based programming languages are a popular way to introduce computer programming to young students. However, concerns have been raised that students may not be learning important programming skills. Previous analyses of Scratch projects online have revealed that the use of important computational elements such as variables, if statements and controlled loops is extremely low, and introducing elementary patterns to the teaching sequence emerged as a potential solution. Although students might have success in the Scratch environment with a limited vocabulary of constructs, it has been pointed out in the literature that the transition process to a different programming language might be difficult. A concern with prior studies of a static analysis of uploaded Scratch projects is that they might be biased towards one-off or brief encounters with programming, where students have a short experience, upload a simple program, and share no further work. We address this concern in this study by measuring the progress of Scratch users over the years, particularly through their use of elementary patterns. Our study has identified all projects submitted by 34,832 users in the Scratch community, and for each user we have compared the skills demonstrated in the first half with those in the second half of their projects. There were no clear signs of any progression, and even some indications of negative progression, and even after more than a year of programming, for most users we found that the use of key elements of programming was still low.
A Cost-Sensitive Feature Selection Method for High-Dimensional Data

With the increase of data dimension in many application fields, feature selection, as an essential step to avoid the curse of dimensionality and enhanced the generalization of the model, is attracting more and more research attention. However, most existing feature selection methods always assume the features have the same cost. These research efforts mainly focus on features’ relevance to learning performance while neglecting the cost to obtain them. Feature cost is a crucial factor need to be considered in feature selection problem especially for the real world applications. For example, in the process of medical diagnosis, each feature may have a very different testing cost. To select low-cost subsets of informative features, in this paper, we propose a stratified random forest-based cost-sensitive feature selection method. Unlike commonly used two-step cost-sensitive feature selection approaches, in our model, the cost of features is incorporated into the construction process of the base decision tree, that is, the cost and the performance of each feature are optimized simultaneously. Moreover, we adopt a stratified sample method to enhance the performance of the feature subset for high-dimensional data. A series of experimental results show that compared with the state-of-the-art methods, the proposed approach can lower the cost of the selected feature subset while maintaining comparable learning performance.
Orthogonality Deficiency of Massive MIMO Channels: Distribution and Relationship With Performance

In this letter, we examine orthogonality deficiency (<inline-formula><tex-math notation="LaTeX">$od$</tex-math></inline-formula>) of massive multi-input multi-output (mMIMO) channels. The Weibull distribution is adopted for modeling the fading envelopes, and various propagation channels are simulated by varying the Weibull parameters. By studying the statistics of <inline-formula><tex-math notation="LaTeX">$od$</tex-math></inline-formula>, we offer insights on the mMIMO size for linear detectors (LDs) to achieve desirable performance. By proposing an approximate distribution of <inline-formula><tex-math notation="LaTeX">$od$</tex-math></inline-formula>, we show that for various propagation channels, if the number of receive antennas exceeds a certain number while the number of transmit antennas is fixed, LDs achieve the same diversity as that of the maximum likelihood detector with high probability in practice. Theoretical analysis is validated by simulations.
Stay Time Prediction for Individual Stay Behavior

Stay time is important for understanding people’s travel behavior and mobility motivation. In this paper, by leveraging private car trajectory data, we propose a novel systematic approach for implementing stay behavior detection and stay time prediction. Specifically, we first propose a fuzzy logic-based stay detection method for detecting stay behavior in a large-scale private car trajectory dataset. Then, we design a spatiotemporal feature extraction method called clustering and kernel (CaK) by considering the spatial similarity, temporal periodicity and spatiotemporal correlation of stay behavior data. Furthermore, we propose a stay time predictor (STP) based on gradient-boosting regression trees and a long short-term memory network that can estimate the future durations of private car users’ stays in various scenarios. We perform extensive experiments based on two real-life trajectory datasets. The experimental results demonstrate that the STP achieves a predictive accuracy (specifically, the root-mean-square error) of 123.94 and R2 of 0.893 for stay time prediction of individual stays. This study provides a new perspective for understanding people’s stay behavior.
Integration of Flipped Classroom and Problem Based Learning Model and its Implementation in University Programming Course

In this paper we report an experiment of implementing a flipped classroom combined with problem based learning approach in teaching a university programming course. We give the method of how to make the pre-recorded lecture videos and how to design a problem-based in-class activities. The results of the experiment show that our teaching approach, compared with traditional teaching approach, is more effective. Students in experiment class get higher scores in the final exam. And our teaching approach also have a positive impact on students’ learning, engagement, performance, collaboration and retention.
A Multivariate Learning Evaluation Model for Programming Course in Online Learning Environment

The items used for learning evaluation in online learning are not only scores, but also students’ learning behavior, including engagement in learning contents, activities in online forum. This paper proposes a multivariate learning evaluation model to assess students learning in online learning environment for programming course. The learning behavior is accessed by data flow. The data flow is divided into four categories, which includes learning guidance, understanding innovation, interactive sharing and learning support. The correlation analysis of various structures and unstructured data flow generated in learning activities will be embodied in the multiple learning evaluation model as parameters. And the results are visualized to learners. The findings show that multivariate learning evaluation is helpful to improve students’ achievement and reflection towards their learning.
Design of Intelligent Personal Schedule Management System

Based on the current status of the schedule management software, this paper analyzes the basic functions of the schedule management software, and proposes the design and improvement of the intelligent management personal schedule system for its single and inefficient schedule management method. At present, the schedule management method of the schedule management software, through the intelligent statistics and analysis of the user schedule, pushes scientific and reasonable scheduling schemes and suggestions to the user, and achieves the purpose of helping the user to effectively improve the life and work efficiency.
A New Model Microstrip Antenna like Microphone Structure for 5G Application

In this paper the parametric study of new antenna structure containing parasitic structure rounded on the circular like microphone structure for 5G application is presented. The propose antenna was feed by microstrip feeding technique where the radiating element was designed in the form of a microphone technical drawing structure. The antenna has been printed with double layer substrate with radiating patch at the top layer and full ground on the bottom same substrate. The antenna was simulated and optimized at 18 GHz using Computer Simulation Technology (CST) with permittivity, εr = 2.2 and thickness, h = 1.57mm on Roger RT-Duroid 5880 substrate. The antennas are reasonable well matched at their corresponding frequency of operations. The simulation results have shown that the antenna works well.
MVPN: Multi-View Prototype Network for 3D Shape Recognition

Convolutional Neural Networks (CNNs) have been widely used for 3D shape recognition. Despite its significant performance, we point out that there is still some rooms for improvement, including the lack of sufficient training samples and the extraction of discriminative features. In this work, we address the above limitations by suggesting a novel end-to-end learning framework named Multi-Veiw Prototype Network (MVPN). MVPN can jointly learn the features from multiple views of 3D shapes and the prototype representations of each class, recognition can then be performed by finding the nearest class prototype in the embedding space for a query sample. Furthermore, to obtain more discriminative features, we propose a discriminative loss, which encourages intra-class compactness and inter-class separability between learned representations, making the representation more discriminative and robust. Extensive experiments are conducted on two benchmarks: ModelNet dataset and ShapeNet Core55 dataset, and superior results have been achieved compared with state-of-the-art approaches.
Precautionary Effort Investment under Cross Risk Aversion

Abstract This paper deals with the effort problem under multiple risks in bivariate utility setting. We identify preference conditions to insure positive or negative effect of a background variable uncertainty on effort in the presence of other risks. We allow for the simultaneous presence of wealth and background variable uncertainties. We investigate the joint effect of two-source uncertainties on effort when two risks are either small or positive quadrant dependent. Our work extends the previous model of effort to bivariate utility framework and presents new insights into the issue of optimal effort under uncertainty.
Foreign-Versus Domestic-Owned firms in the Predicament ‘Cui bono?’

Abstract Background: This article examines the productivity of domestic firms in the case of the foreign ownership. Objectives: Foreign direct investments affect the competitive competences of domestic firms; thus, the objective is to see the way foreign ownership drives the growth of domestic firms. Methods/Approach: The study uses standard models to analyse productivity; they are applied to data sets of Macedonia, a Southeast European economy, and it is concluded that foreign ownership has a major role in domestic firms’ restructuring processes increasing their productivity. Results: Surely, the results support the principal notion that the augmented presence of foreign firms is to influence the restructuring and business activity of domestic companies positively. Conclusions: Our analysis verifies that foreign ownership has influenced the overall economy and particularly domestic-owned firms with the constant increase in employment and especially direct export.
Surface EMG-based Surgical Instrument Classification for Dynamic Activity Recognition in Surgical Workflows

Abstract We introduce a wearable-based recognition system for the classification of natural hand gestures during dynamic activities with surgical instruments. An armbandbased circular setup of eight EMG-sensors was used to superficially measure the muscle activation signals over the broadest cross-section of the lower arm. Instrument-specific surface EMG (sEMG) data acquisition was performed for 5 distinct instruments. In a first proof-of-concept study, EMG data were analyzed for unique signal courses and features, and in a subsequent classification, both decision tree (DTR) and shallow artificial neural network (ANN) classifiers were trained. For DTR, an ensemble bagging approach reached precision and recall rates of 0.847 and 0.854, respectively. The ANN network architecture was configured to mimic the ensemble-like structure of the DTR and achieved 0.952 and 0.953 precision and recall rates, respectively. In a subsequent multi-user study, classification achieved 70 % precision. Main errors potentially arise for instruments with similar gripping style and performed actions, interindividual variations in the acquisition procedure as well as muscle tone and activation magnitude. Compared to hand-mounted sensor systems, the lower arm setup does not alter the haptic experience or the instrument gripping, which is critical, especially in an intraoperative environment. Currently, drawbacks of the fixed consumer product setup are the limited data sampling rate and the denial of frequency features into the processing pipeline.
Mobilitätsbedingte Klimawirkung einer alpinen Tourismusdestination

Zusammenfassung Anhand von Befragungen in der Tiroler Gemeinde Alpbach wird untersucht, in wie weit sich das Bewusstsein für die Zusammenhänge zwischen Tourismus und Klimawandel bei Bewohnern, Beschäftigten und Gästen unterscheidet. Zudem wird eine Gesamtjahresbilanz der Mobilität von Gästen, Bewohnern und Beschäftigten und des damit verbundenen CO2-Ausstoßes dieser Gemeinde abgeschätzt. Zwar ist bei allen Gruppen ein grundsätzliches Problembewusstsein festzustellen und eine gewisse Bereitschaft für Maßnahmen zur Reduktion von Energieverbrauch und CO2-Emissionen auf Reisen, soweit diese keine wesentlichen Einschränkungen im Urlaubsverhalten betreffen. Im Detail zeigen sich jedoch signifikante Unterschiede. Da die Gesamtbilanz der Mobilität die hohe Bedeutung der An- und Abreise für die CO2-Jahresbilanz untermauert, sind mit dem Wissen über das vorhandene Bewusstsein Maßnahmen abzuleiten.
Financial Products in Portugal

Abstract The offerings of financial products to investors have evolved considerably over the last decades. This study was undertaken to understand the importance of financial products in Portugal for banks and private clients. Different financial products can provide to banks an important role on their revenues and liquidity. For clients allows to invest their savings according the risk, complexity and potential return they pretend be exposed. Financial products can be divided into four groups: deposits, investment funds, securities, and insurance banking. The six largest banks with Portuguese headquarters were identified based on an analysis of net asset value, and the products offered were analyzed. It was found that on an overall basis, the offerings of financial products available in banks are rather diverse and, at the same time, uniform. It was also noted that investment decisions are dependent on investors and that there are financial innovations that attract differentiated savings.
Multi-criteria decision method for choosing ERP cloud systems in Industry 4.0 era

Abstract The article presents the issues covering the modern ERP cloud systems for manufacturing companies within the currently developed concept Industry 4.0. In this approach, research problems arose as to which parameters and characteristics of ERP cloud systems are the most important from the point of view of manufacturing companies as well as how the process of choosing an ERP system should be conducted. In addition, mutual relationships between these parameters were established. In the article, a multi-criteria decision analysis (MCDA) was used to assess these characteristics. The Decision making trial and evaluation laboratory (DEMATEL) technique was chosen for this assessment. The article can be useful to people interested in modern IT solutions in the production industries.
Experimental Investigation of Shear Behavior of Two-Span Fiber Reinforced Concrete Beams

Abstract Experimental tests were carried out to assess the failure model of steel and basalt fiber reinforced concrete two-span beams. Experimental research was focused on observing the changes in behavior of tested elements in dependence on the ratio of shear reinforcement and type of fiber. The beams had varied stirrup spacing. The steel fiber content was 78.5 kg/m3 (1.0% by vol.) and basalt fiber content was 5.0 kg/m3 (0.19% by vol.). Concrete beams without fibers were also examined. Two-span beams with a cross-section of 120×300 mm and a length of 4150 mm were loaded in a five-point bending test. Shear or flexural capacity of tested members was recorded. The effectiveness of both sorts of fibers as shear reinforcement was assessed and the differences were discussed. It was shown that fibers control the cracking process and the values of deflections and strains. Fibers clearly enhance the shear capacity of reinforced concrete beams.
Quantifying the uncertainty of deep learning-based computer-aided diagnosis for patient safety

Abstract In this work, we discuss epistemic uncertainty estimation obtained by Bayesian inference in diagnostic classifiers and show that the prediction uncertainty highly correlates with goodness of prediction. We train the ResNet-18 image classifier on a dataset of 84,484 optical coherence tomography scans showing four different retinal conditions. Dropout is added before every building block of ResNet, creating an approximation to a Bayesian classifier. Monte Carlo sampling is applied with dropout at test time for uncertainty estimation. In Monte Carlo experiments, multiple forward passes are performed to get a distribution of the class labels. The variance and the entropy of the distribution is used as metrics for uncertainty. Our results show strong correlation with ρ = 0.99 between prediction uncertainty and prediction error. Mean uncertainty of incorrectly diagnosed cases was significantly higher than mean uncertainty of correctly diagnosed cases. Modeling of the prediction uncertainty in computer-aided diagnosis with deep learning yields more reliable results and is therefore expected to increase patient safety. This will help to transfer such systems into clinical routine and to increase the acceptance of machine learning in diagnosis from the standpoint of physicians and patients.
Modeling Human Pupil Dilation to Decouple the Pupillary Light Reflex

Abstract The aim of this paper is to present methods to calculate pupil size based on various parameters, such as: luminance, age, corneal flux density or monocular/binocular effect. These models allow to distinguish pupil dilation caused by the influence of light and other factors such as psychological state of participants.The developed methods were presented based on empirical data. Various researchers estimate their equations based on oculographic data obtained in the course of experiments. The presented plots are based on those equations. Different approaches can be compared to show the difference between particular models.The methods presented in this paper enable a more detailed investigation of the influence of various parameters on the pupil. It can be used to better estimate the influence of light on pupil size. The main changes occurring in pupil size, i.e. contractions and dilation, are caused by light. Other criteria such emotional arousal, cognitive processes or even memory operations can also alter the pupil, among which the decoupling of light is important. The presented approach is distinct from other similar studies because it decouples the pupillary light reflex.
Modelling Delays in Bridge Construction Projects Based on the Logit and Probit Regression

Abstract Construction projects, even exemplarily planned and organized, bear a risk of unforeseen events and problems which can result in completion of the works after the deadline, that is delays. The construction of bridges is an inseparable part of road and rail projects and construction and expansion of the transport network. The paper aims at finding a relationship between the independent variables characterizing bridge projects and the delays during their implementation. Two alternative models were proposed to solve the problem: logit and probit. The data set comprising road and rail bridges built in Poland in the last 12 years (2005 – 2017) was used to build the models. The evaluation, quality and accuracy parameters of proposed models were determined in the final part of the paper.
Establishment of Renal Failure Models by Laparoscopy in Bama Pigs Which Underwent Partial Nephrectomy and Radical Contralateral Nephrectomy

Abstract Introduction The miniature pig possesses unmatched advantages as an animal model because of its high homology with humans. Our experiment aimed to build a chronic renal failure (CRF) model in pigs via laparoscopy. Material and Methods Laparoscopic surgery was performed twice to build a CRF model. The first surgery was a left partial nephrectomy and the second was a right radical nephrectomy. Pigs were grouped by the total renal tissue to be resected: ⅔, ¾ or ⅚. Physiological parameters (rectal temperature and heart rate), haematological parameters (WBC and RBC) and renal function (serum creatinine – CR and blood urea nitrogen – BUN) were measured preoperatively and every week postoperatively. Results After renal resection the pigs manifested chronic renal failure. Heart rate and body temperature declined to varying degrees over 12 postoperative weeks. No significant difference was observed between the different groups. The result of renal function tests found that postoperative serum CR and BUN in all groups were continuously elevated, and the level of serum CR at two weeks post procedure differed very significantly from its preoperative value (P < 0.05). BUN was significantly elevated at one week (P < 0.05). The renal function decreased significantly faster in the ⅚ group than in the other two groups. The trend of renal function change was similar among groups, but progress was slower in the ⅔ and ¾ groups. Conclusion ⅚ kidney resection was the optimal miniature pig model of CRF.