Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Automation by Integrating Generative Adversarial Networks and Topology Optimization

Recent advances in deep learning enable machines to learn existing designs by themselves and to create new designs. Generative adversarial networks (GANs) are widely used to generate new images and data by unsupervised learning. Certain limitations exist in applying GANs directly to product designs. It requires a large amount of data, produces uneven output quality, and does not guarantee engineering performance. To solve these problems, this paper proposes a design automation process by combining GANs and topology optimization. The suggested process has been applied to the wheel design of automobiles and has shown that an aesthetically superior and technically meaningful design can be automatically generated without human interventions.
On Optimizing Max Min Rate in Rechargeable Wireless Sensor Networks with Energy Sharing

We consider Rechargeable Wireless Sensor Networks (R-WSNs) where nodes harvest energy from both solar and the Radio Frequency (RF) transmissions of their neighbors. Our aim is to maximize the minimum source or sensing rate of nodes. This rate is determined by the available energy at sensor nodes as well as link capacity, which is determined by the set of transmitting nodes. In this paper, we first study and show the benefits of energy sharing. Intuitively, a sensor node should share its energy if doing so increases source rates. We present a novel Linear Program (LP) to determine the routing, link schedule, energy transmission, and reception time that maximize the minimum source rate of a given R-WSN. Our numerical results indicate that, on average, the minimum transmission rate of sensor nodes increased by 16.03 percent when nodes share energy. This motivates the development of a practical protocol called E-RSVP that iteratively increases the time slots of each source node. It also considers using time slots for transmission or reception of energy. Our simulation results show E-RSVP yields minimum source rates that are 14.80 percent higher as compared to the case without energy sharing.
Hyperspectral data applied in monitoring and evaluating the water trophic state of Lake Chagan

An approach for monitoring and evaluating the trophic state with field reflectance spectra in Lake Chagan,China is presented. Based on in situ water sampling and measurements with ASD FieldSpec spectrometer, algorithms of Chlorophyll-a, Secchi Disk Depth (SDD) and TP have been developed. Furthermore, with the TSI_M trophic state index model, the trophic state of Lake Chagan was monitored and evaluated. The results showed that: (1)TSI_M model with hyperspectral datamay obtain accurate results; (2)Single parameter TSI_M compared with multi-parameter TSI_M is limited, which may induce false result; and (3)Lake Chagan was in eutrophic state, which needs to be prevented from further worsen.
SOCIOECONOMIC STATUS, PARENTAL INVOLVEMENT IN LEARNING AND STUDENT’ MATHEMATICS ACHIEVEMENT IN INDONESIAN SENIOR HIGH SCHOOL

Abstract: The objectives of the research are to examine: (1) the relationship between parents’ education level as an indicator of socioeconomic status (SES) with students’ mathematics achievement, and (2) the interaction between parents’ education level and the intensity of parental involvement in predicting students’ mathematics achievement. The sample (N=2,867) was selected through stratified random sampling of Indonesian high school National Examination (NE) test takers in 2016. The sampling was based on careful consideration of the representativeness and the distribution of provinces, gender, type of school (general-vocational), and school status (public-private). Data was analyzed using parallel multiple mediator analyses. The findings highlight that: (1) compared to fathers, mothers’ education level had a stronger contribution to students’ achievement in mathematics, and (2) mothers’ involvement mediated the relationship between mothers’ level of education and students’ mathematics achievement. However, more intensive parental involvement was associated with lower mathematics achievement. Keywords: SES, mathematics, parents’ education levels, involvement STATUS SOSIAL EKONOMI, KETERLIBATAN ORANGTUA DALAM BELAJAR DAN HASIL BELAJAR MATEMATIKA SISWA SMA DI INDONESIA Abstrak: Penelitian ini bertujuan untuk menguji: (1) kaitan antara tingkat pendidikan orangtua sebagai indikator status sosial ekonomi (SSE) dengan hasil belajar matematika siswa, dan (2) interaksi antara tingkat pendidikan orangtua dengan intensitas keterlibatan orangtua dalam memprediksi hasil belajar matematika anak-anaknya. Sampel penelitian ini (N=2,867) dipilih secara stratified random sampling dari peserta Ujian Nasional di Indonesia tahun 2016 jenjang SMA. Pengambilan sampel didasarkan pada keterwakilan dan sebaran provinsi, jenis kelamin, jenis sekolah (SMA/MA atau SMK) dan status sekolah (negeri-swasta). Data dianalisis dengan menggunakan analisis parallel multiple mediator. Hasil penelitian menunjukkan: (1) pendidikan ibu memiliki peran sangat penting karena sangat berkonstribusi dalam menunjang hasil belajar matematika anaknya dibandingkan pendidikan ayah, dan (2) keterlibatan ibu dalam belajar anak yang dimediasi tingkat pendidikannya berpengaruh terhadap hasil belajar matematika anaknya. Namun demikian, keterlibatan orangtua yang terlalu intens dalam kegiatan belajar anak justru berdampak kurang baik terhadap hasil belajar matematika anaknya. Kata Kunci: SSE, matematika, tingkat pendidikan orangtua, keterlibatan
Trust-Based Service Management for Mobile Cloud IoT Systems

We propose and analyze a 3-tier cloud-cloudlet-device hierarchical trust-based service management protocol called IoT-HiTrust for large-scale mobile cloud Internet of Things (IoT) systems. Our mobile cloud hierarchical service management protocol allows an IoT customer to report its service experiences and query its subjective service trust score toward an IoT service provider following a scalable report-and-query design. We conduct a formal scalability analysis along with an ns-3 simulation performance analysis demonstrating that IoT-HiTrust not only achieves scalability without compromising accuracy, convergence, and resiliency properties against malicious attacks but also outperforms contemporary distributed and centralized IoT trust management protocols. We test the feasibility by applying IoT-HiTrust to two case studies: 1) a smart city travel service composition and binding application and 2) an air pollution detection and response application. The results demonstrate that IoT-HiTrust outperforms contemporary distributed and centralized trust-based IoT service management protocols in selecting trustworthy nodes to maximize application performance, while achieving scalability.
Reused Path of Heavy Metal Pollution in Soils and its Research Advance

Heavy metal contamination of agricultural soils as a result of industrial discharge is a serious threat to human and animals′ health because of bioaccumulation in the food chain. Current status of heavy metal pollution, remedial mechanisms, advantages and disadvantages of different methods, and feasibility of application are reviewed in this article. In addition, principle, characteristics and new biotechnological methods involving genetic engineering used in phytoremediation of soils contaminated by heavy metal and future prospect are discussed in details. The alternative methods provide novel approaches to restore the polluted ecological systems using highly efficient techniques.
Many Hands Make Work Light or Not? A Novel Tool for Group Decision Making with ANP

Although every problem has multiple solutions, many restrictions and various ways to be addressed, there are also many techniques, methods and approaches proposed by scholars and practitioners that can be implemented to help increase our decision making effectiveness. The Forum will address this issue by engaging participants to reach a group decision using a new tool in the form of a Microsoft Excel Add-In that supports the Group Analytic Network Process (GANP) and it is integrated with the Super Decisions software. An illustrative case concerning the decision of a city council on how to use a community lot based on given alternatives (among a new parking lot, a hospital and sustainable landfill) and preselected criteria will be used during the Forum. The results of this game will provide significant insights for the group decision making process, explore the proposed techniques and bring out the advantages and disadvantages reported in literature.
EVALUATING SHORT-WAVE INFRARED IMAGES ON A CONVOLUTIONAL NEURAL NETWORK

Machine learning algorithms, both neural networks and non neural networks, have been tested against RGB facial images for years. Their success is dependent on clearly distinguishable cropped faces. Poor lighting and distance are not beneficial. Short-wave infra-red (SWIR) imaging is higher in intensity and deeper penetrating than visible light. SWIR imaging overcomes the shortcomings of visible light. In adverse conditions SWIR can provide the images that visible light cannot. If we had lots of SWIR images we could train the neural networks as we do with the visual spectrum (RGB), but the data is expensive to produce. In this paper we examine the proficiency of a convolutional neural network facial recognition tool’s ability to process SWIR images. We utilized a dataset containing both RGB and SWIR images. Our hypothesis is that VGGFace, trained on RGB images, will perform as well on SWIR 1300nm facial images as it does on RGB images. We expect VGGFace’s feature extraction will work as well on SWIR images as RGB images, if not better. VGGFace performed better on the RGB images than the images captured in the SWIR 1300nm wavelength. The data failed to support our hypothesis while also revealing unexpected performance on the other SWIR wavelengths (935nm, 1060nm, and 1550nm).
Regional evapotranspiration retrieval in arid areas

Xinjiang is a typical arid area in China.The objective of this research is to present a description of a method based on the energy balance and evaporative fraction,which is obtained by broadband albedo and land surface temperature,to estimate evapotranspiration(ET) in arid areas based on MODIS images.In the validation,the simulated ET is as same as the measured ET by eddy covariance tower site and the mean difference is 0.40mm. In arid areas,the correlation between evapotranspiration and precipitation is of 82%,and the correlation with the temperature is of 46%. The ET always fluctuated from 0 to 2mm in the most part of region in 2005,especially the Tarim,Junggar and Turpan basins.In the mountain area,especially Tian-shan mountain and crop land of oasis,the ET values is upto 6mm.
DYNAMIC DATA DRIVEN WILDFIRE MODELING

A proposed system for real-time modeling of wildfires is described. The system involves numerical weather and fire prediction, automated data acquisition from Internet sources, and input from aerial photographs and sensors. The system will be controlled by a non-Gaussian ensemble filter capable of assimilating out-of-order data. The computational model will run on remote supercomputers, with visualization on PDAs in the field connected to the Internet via a satellite.
Real-Time All-in-Focus Video-Based Rendering Using a Network Camera Array

We present a real-time video-based rendering system using a network camera array. Our system consists of 64 commodity network cameras that are connected to a single PC through a Gigabit Ethernet. To render a high-quality novel view, we estimate a view-dependent per-pixel depth map in real-time by using a layered representation. The rendering algorithm is fully implemented on a GPU, which allows our system to efficiently use CPU and GPU independently and in parallel. Using QVGA input video resolution, our system renders a free-viewpoint video at up to 30 fps depending on rendering parameters. Experimental results show high-quality images synthesized from various scenes.
MODEL TEST ON DEFORMATION MECHANISM OF TUNNEL AT LANDSLIDE SITE

Based on the laboratory geomechanical model test, the interaction mechanism between landslide deformation and tunnels, the variation regularities of the pressure in surrounding rocks and lining ,the deformation features of landslides and tunnels are studied. The interaction relationship between landslide deformation and tunnels during sliding plane softening with water and creep occur is emphatically studied. The study result provides a scientific basis for prediction and treatment of the deformed tunnel.
Using an advanced discrete-event simulation framework to productive capacity management of a car-parts factory

This work presents the development and application of an advanced modelling, simulation and optimization framework focused on the production process of carparts factory in the Latin-American market. Lying on the concepts of the process-interaction approach, the principal components available in the discrete event simulation environment “SIMUL8” were used to achieve the best representation of the large and complex manufacturing system. Furthermore, advanced SIMUL8’s Visual Logic tools were utilized for modelling specific design and operation features arising in the process under study. The developed tool provides a valuable support system for making and testing operative, tactical and strategic decisions, allowing a quick evaluation of possible sceneries ranging from different operation schemes to potential alternatives of investment. The principal aim of this work is to provide a systematic methodology to validate, evaluate and improve the productive capacity management, enhancing both the process profitability and the degree of customer satisfaction.
FLORISTICS AND VEGETATION OF THE GENUS CARAGANA IN ORDOS PLATEAU

There are 9 species of the genus Caragana in the Ordos Plateau, which is a rich biodiversity region of the genus. Specially, Caragana korshinskii var. intermedia was treated as one of the most suitable species cultured to preserve soil and fix sand in the Maowusu sandland which located at eastern Ordos Plateau. There are two phytogeographical distribution elements, the Central Asia element and East Asia element of the genus in Ordos Plateau. The Central Asia element consists of Mongolia Plateau distribution, Alashan distribution, Gobi Mongolia distribution, and Inner Mongolia Plateau Loess Plateau distribution. The East Asia consists of North China distribution, North China Qinghai-Xizang (Tibet) Plateau distribution and southern Mongolia Plateau Qinghai-Xizang (Tibet) Plateau distribution. These elements showed the conjunction and transition between Ordos Plateau and the adjacent regions. The vicarism is illustrated by two typical species C. korshinskii and C. korshinskii var. intermedia. The former occurs in western Ordos Plateau and the latter in the east. The shrub communities are the dominant life form in the vegetation of Ordos Plateau. There are fourteen life forms, of four belong to Caragana community, i.e., Form. Caragana korshinskii var. intermedia , Form. Caragana stenophylla , Form. Caragana korshinskii , and Form. Caragana tibetica . Form. Caragana tibetica distributing in western Ordos Plateau and usually forming the small sand hill about 40～80 cm high, is one of the most typical natural landscapes in Ordos Plateau. These Caragana communities represent the characteristic of the vegetation zone, namely from eastern typical steppe and desert steppe to western steppe desert zones in Ordos Plateau.
Wideband In-Situ Measurement of Soil Electrical Parameters Using Planar Dipole Antennas

This paper presents the design of a wideband insitu probe for measurement of the dielectric properties of soil. A three-dimensional FDTD simulation is used to predict the performance of the probe. The probe uses wideband printed dipole antenna for the transmitting and receiving antennas. The transmitting antenna radiates a differentiated Gaussian pulse that propagates in the soil under investigation. The soil parameters are extracted by mapping the measured complex S21 parameter to the values obtained from the FDTD simulation.
Airflow from nasal pulse oximetry in the screening of obstructive sleep apnea

Obstructive Sleep Apnea (OSA) is recognized as an increasing health risk, leading to daytime sleepiness and various medical conditions, such as hypertension and heart failure. Polysomnography (PSG), the gold standard to diagnose OSA, is a resource-intensive and expensive investigation confined to the hospital.Portable home monitoring, i.e. pulse oximetry, may become an acceptable OSA screening method. The novel nasal pulse oximeter sensor (Xhale Alar) adds the possibility of combining pulse oximetry (SpO2) with airflow analysis by an integrated thermistor, which might increase the diagnostic accuracy.In the Alar pilot study, 39 adults were measured during an overnight PSG recording together with the Alar sensor. This study aims to investigate the additional value of an airflow signal compared to SpO2 analysis in OSA screening. Both time and spectral features were extracted from SpO2 and airflow signals recorded with the Alar sensor. Leave one out cross-validation was used to develop Random Forest models in screening for apnea-hypopnea index (AHI) thresholds 5 and 10. Using both AHI ≥ 5 and AHI ≥ 10 as the diagnostic cutoff, the airflow signal shows respectively an AUC of 89% and 80% compared to 78% and 77% with SpO2 analysis, showing a higher performance using an airflow signal in screening adults for OSA.
Performance Evaluation of Fingerprint Recognition Modules on Mobile Device Based on Rule of Three

Fingerprint recognition technology has been widely applied to various smart mobile devices, and its performance is closely related to privacy security and user experience. Therefore, performance evaluation for fingerprint recognition technology is of great significance. In this paper, a construction scheme for fingerprint databases based on the statistical theory Rule of Three is first proposed, which illustrates the minimum database size to evaluate the performance. A corresponding test method is obtained and its feasibility is verified by experiments.
Joint Iterative Training-based Hybrid Precoding and Combining for Millimeter Wave Systems

Hybrid precoding and combining is a key solution to reduce the hardware cost and power consumption of millimeter wave MIMO systems. However, estimating the entire channel matrix required for designing such a solution is very challenging. In this paper, we propose a joint iterative training based algorithm to design the digital precoder and combiner in the hybrid structure. We show that the proposed method can eliminate channel estimation and SVD computation needed for obtaining the precoders and combiners. Simulation results show that the proposed approach performs comparable to existing SVD-based methods in terms of spectral efficiency but with much less computational complexity.
Assessment of Photovoltaic Hosting Capacity of Existing Distribution Circuits

Government initiatives have been mandating the increased integration of distributed energy resources (DERs), such as residential and commercial photovoltaic (PV) systems. However, high levels of PV penetration can negatively affect the operations of the distribution system, particularly during mid-day low demand periods. It is of vital importance to gain more understanding of the system and to prepare mitigation plans before the amount of PV installations reaches a critical level. Therefore, properly assessing the PV hosting capacity is necessary.In this paper, the hosting capacities of three real circuits in Alberta, Canada are evaluated using Monte Carlo simulation-based probabilistic power flow (MCS-based PPF) method. The examined circuits cover the cities of Fort McMurray, Lloydminster, and Drumheller. These areas represent circuits of different sizes and complexities. The hosting capacities of the three regions were determined to be 10%, 60%, and 70%, respectively. Buses impacted by PV penetration were found in all three distribution networks. Factors influencing the PV hosting capacity are also identified and analyzed.
Learning from Mistakes: Weakly Supervised Learning of Rocks

A standard method for teaching an object detector a new class is to fine-tune it with a fully-supervised image set. The issue with fully-supervised image sets are that they are tedious to create and rely on human annotators. In this work we demonstrate that a new class can be learned by leveraging the ‘mistakes’ of a pre-trained object detector under a weakly supervised learning (WSL) paradigm and removing the need for a fully-supervised image set. Our method iteratively cycles over four stages: observation, filtering, generation, and fine-tuning. The observation stage uses an object detector to gather inferences on an image set containing many instances of the new class. These observed inferences are passed to the filtering stage that keeps only the most frequently observed class inferences. These filtered inferences are used as object-level annotations and are passed to the generation stage. In the generation stage a training set is created by superimposing the image content of the annotations onto a set of background images. The generated training set is then used in the fine-tuning stage to create an updated object detector. We trained an object detector to recognize the novel object class, rock using this method. We compared the average precision obtained by an object detector trained using our method to an object detector trained using the fully-supervised method. We were able to achieve $\sim 78$% of the average precision obtained by the fully-supervised version.
PPG-Based Blood Pressure Monitoring by Pulse Wave Analysis: Calibration Parameters are Stable for Three Months

The current non-invasive gold standard for the measurement of blood pressure (BP) is the oscillometric cuff at the upper arm, despite its known limitations. In particular, its poor adequacy with continuous monitoring and its measurement incommodity call for the development of simpler and more convenient solutions. Among these, solutions based on pulse wave analysis (PWA) and photoplethysmography (PPG) are of particular interest, due to their low-cost, strong patient compliance, and applicability in and out of clinical settings. In that context, we have recently disclosed a PPG-based PWA algorithm (oBPM™) dedicated to the continuous monitoring of BP in patients undergoing induction of general anesthesia. As is standard with PPG-based BP monitoring techniques, an initial calibration procedure with a reference device is required to allow the estimation of absolute values of BP (in mmHg). However, due to their sensitivity to peripheral effects such as vasomotion, the applicability of PPG-based techniques is often limited by the constant need of re-calibration procedures, sometimes in matters of minutes. In the present study, we evaluated the long-term stability of the calibration for our algorithm by performing PPG measurements at irregular time intervals over a period of 3 months in 13 healthy volunteers. For each measurement, diastolic BP (DBP) was assessed by an oscillometric device and estimated by the oBPM™ algorithm. We found the calibration to remain stable over the entire 3-month period, with estimation errors remaining stable over time and complying with the ISO 81060-2:2018 standard. In addition, we verified – in 11 of our 13 subjects – the sensitivity of the oBPM™ algorithm to changes in DBP. This was done in a protocol involving static leg extension exercises. Excellent trending ability (average per-subject concordance rate of 97.7 ± 5.2 %, and correlation coefficient of 0.98 ± 0.02, p < 0.001) was found between cuff-derived DBP changes and our estimates. These findings provide a strong added value to the practical usability of the proposed PPG-based PWA approach to BP monitoring, particularly for the clinical management of hypertensive patients in and out of clinics, for whom a simple and comfortable continuous alternative to the oscillometric cuff would be strongly preferred.