Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting gene-disease associations from the heterogeneous network using graph embedding

The discovery of gene-disease associations is important for the prevention, diagnosis and treatment of diseases. The studies on gene-disease associations have produced diverse data, which can facilitate the gene-disease association prediction. Integrating diverse information is critical for developing high-accuracy prediction models. In this paper, we propose a heterogeneous network-based method that enhances gene-disease association prediction by using graph embedding and ensemble learning, abbreviated as “HNEEM”. A heterogeneous network is constructed based on gene-disease associations, gene-chemical associations and disease-chemical associations, to combine diverse information. The network uses genes, diseases and chemicals as nodes, and uses their associations as edges. The graph embedding methods are utilized to extract representation vectors of nodes in the heterogeneous network, and the feature vectors of genes and diseases are merged to represent gene-disease pairs, and the random forest is employed to build the prediction model based on gene-disease pairs. We consider six types of graph embedding methods, and take the individual graph embedding method-generated features to build prediction models and use them as base predictors, and then combine base predictors to develop the ensemble learning model HNEEM. We comprehensively compare different graph embedding methods, and results demonstrate that the graph embedding methods produce satisfying results in the gene-disease association prediction, and integrating different graph embedding methods can make further improvements. In computational experiments, HNEEM produces better results compared to the state-of-the-art gene-disease perdition methods, and HNEEM is robust to the data richness as well. Moreover, the usefulness of the proposed method HNEEM is validated by the case studies. In conclusion, HNEEM is a promising method for predicting gene-disease associations.
Privacy Control In Social Networks By Trust Aware Link Prediction

Social networks are exceedingly common in today's society. A social network site is an online platform where people build social relations with others and share information. For the last two decades, rapid growth in the number of users and applications with these social networking sites, make the security as the most challenging issue. In this virtual environment, some greedy people intentionally perform illegal activities by accessing others” private information. This paper proposes a novel approach to detect the illegal access of a particular”s information by using trust-aware link prediction. The facebook dataset is used for experiments and the results justify the robustness and trustworthiness of the proposed model.
Identification of protein hot regions by integrated machine learning algorithm

Discovering hot regions in protein-protein interaction is important for understanding the interactions between proteins, while because of the complexity and time-consuming of experimental methods, the computational prediction method can be very helpful to improve the efficiency to predict hot regions. In previous researches, some models are based on a single aspect, such as structure, energy, and sequence, each aspect has its advantage and limitations. In this paper, a new method that combing structure-based classification, energy-based clustering and sequence-based conservation in evolution is proposed. This method makes full use of three aspects of protein information and compensates for the limitations of using one single aspect information. Experimental results show that the proposed method significantly improves the prediction performance of hot regions.
ZytleBot: FPGA Integrated Development Platform for ROS Based Autonomous Mobile Robot

The FPT2019 FPGA Design Competition is a competition aimed at recommending innovations in utilizing FPGAs to realize level 5 autonomous driving vehicles. We have developed "ZytleBot", an ROS based robot utilizing FPGA for the contest. ZytleBot calculates all processing necessary for autonomous driving in realtime within a programmable SoC. Therefore, it is possible to go around the course imitating an actual road, detect signals and obstacles and take appropriate behavior without any external operation. Robot development requires a wide range of knowledge and technology, but we have proceeded robot development efficiently by using ROS, a robot development middleware, and TurtleBot3, a robot development platform. As an application of FPGA, road surface image preprocessing and traffic signal detector using machine learning is implemented in the FPGA. The traffic signal detector uses HOG features and SVM classifiers, which runs over 270 times faster than running on the processor. We also provide ZytleBot as a platform for efficient development of FPGA integrated ROS robots.
Customizing CMOS/ReRAM Hybrid Hardware Architecture for Spiking CNN

Spiking CNN can greatly reduce the computation and accordingly reduces the energy consumption with a small penalty to the accuracy, but the network latency is significantly increased because of the long spike trains. In this paper, we first introduce layer factors in the normalization of the synaptic weights and propose a greedy method to search proper layer factors for improving the network latency. Second, we present a customizable CMOS/ReRAM hybrid hardware architecture for Spiking CNN. The architecture consists of an array of configurable basic units (CBU), which involves a CMOS adder for neuron operations and a 1T1R memristive crossbar for storing synaptic weights. A column of CBUs can be configured as spiking neurons corresponding to convolutional kernels with various sizes while the whole array can be configured to provide row parallelism for a kernel convolution and inter-kernel parallelism. The input spike trains are transmitted between CBUs and are reused by different spike neurons when performing parallel computing of rows in spike feature maps. The results show that the network latency can be reduced 42.3% for an accuracy of 98.04% in LeNet-5 by introducing layer factors. To our best knowledge, this is the first work to design customizable hardware architecture for Spiking CNNs. The simulation results show that the Spiking CNN consumes only 15.4% of the energy consumption of PipeLayer when processing the MNIST dataset.
Building Block Sorting Method Based on Binocular Vision

Aiming at the problems of low manual sorting efficiency and tedious work in the production and sorting process of building blocks, a building blocks sorting method based on binocular vision is proposed. First of all, this paper introduces how to transform 2D image coordinates into 3D world coordinate so as to achieve building block positioning through images of building blocks. Secondly, it introduces how to pretreat images of building blocks, apply median filtering algorithm to process original images and achieve classification of building block colors through transformation of RGB space into HSV space. Finally, the positioning experiment and sorting experiment of building blocks indicate this method improves building block sorting efficiency. Key words- binocular vision; median filtering; HSV space
The role of Increasing the Number of Iterations for Numerical Methods upon Digital Image

Due to its numerical procedure, an expected error occurred in applying the numerical algorithms. A challenging attempt toward using such approximation methods on digital image has been utilized here. Bisection, Newton Raphson, and secant methods are some numerical techniques which have been used here for certain considerations to test their evaluation upon a digital image through investigating the role of increasing the number of iterations each time. Results show that Bisection method is the best numerical technique than other used techniques in its approximated use upon an image with least and stable absolute and relative errors. Increasing the number of iterations didn't affect its numerical procedure while an alternative errors with a bell shape resulted in the case of Newton- Raphson and secant methods. Absolute error seems to be more sensitive estimator than relative one toward detecting the suitable number of iterations that caused a least significant error
Broadband and Low-Profile Penta-Polarization Reconfigurable Metamaterial Antenna

In this paper, a penta-polarization reconfigurable antenna with mushroom-type metamaterial loading is proposed, which can operate in <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-direction linear polarization (<inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-LP), <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-LP, 45°-LP, left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP). In order to realize polarization reconfigurability, a dual-port dual LP mushroom antenna excited by crossed H-shape slot is designed by characteristic mode analysis (CMA). Through shifting the states of PIN diodes on the reconfigurable feeding network, the amplitude and phase distributions of two ports of the dual LP antenna can be dynamically controlled and the five polarization modes can be achieved. A prototype with a profile of <inline-formula> <tex-math notation="LaTeX">$0.06\lambda _{0}$ </tex-math></inline-formula> at 5.2 GHz (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the operated wavelength in free space) is simulated and measured. The measurement results exhibit a wide impedance bandwidth over 33.2% for LPs and over 38.7% for CPs. In addition, the measured axial ratio bandwidths are 28.0% for LHCP and 30.3% for RHCP, and the maximum measured boresight gains of all modes are higher than 8.2 dBi. The simulated and measured results verify that the proposed antenna can achieve a balance between low-profile and broadband on the basis of penta-polarization reconfigurability. The proposed antenna can be applied to polarization diversity and C band satellite communication.
A New Generation of Static Timing Analysis Technology Based on N7+ Process—POCV

With the continuous development of semiconductor technology, timing analysis is becoming more and more complicated. Static timing analysis is constantly improving the constraint mode. The traditional on-chip variation (OCV) has become increasingly unsatisfactory under advanced technology. The design requirements of the tens of millions of high-speed chips; the advanced on-chip methodology (advanced OCV, AOCV), while solving the pessimism of the traditional method, will increase the analysis time. This article will introduce the new generation of static timing analysis technology applied in the 7Plus process - Parametric on-chip variation (POCV); POCV simulates the delay distribution probability obtained by the variation in a certain case, which is more accurate than AOCV. And reduce the time of timing analysis, increasing the number of iterations of timing analysis.
Study on Performance of Capacitor-less LDO with Different Types of Resistor

This paper studies the impact of utilizing different resistor types in capacitor-less low drop-out (LDO) voltage regulator on its key performance characteristics. In order to achieve this, a 1.8 V LDO voltage regulator is designed and characterized using 180 nm CMOS technology with a supply voltage of 3.3 V. Simulations are done in schematic level using Cadence on five different types of resistor for the same LDO, and the performance in terms of output voltage accuracy, stability and power supply rejection ratio (PSRR) are compared. From the simulation results, significant differences are observed in the LDO's performance with different types of resistor. The LDO with hpoly resistor gives the best results in terms of stability while pdiffb resistor LDO produces the highest PSRR.
Frequency Stability Support of a DFIG to Improve the Settling Frequency

To ensure the frequency stability of a power grid, the settling frequency following a frequency event should be stabilized within the maximum steady-state frequency deviation (SSFD) band. If the frequency is stabilized at a value beyond the maximum SSFD band for high penetrations of variable renewable energy, the frequency cannot be restored to the nominal frequency because automatic generation cannot be activated. This paper proposes a primary frequency control support scheme of a doubly-fed induction generator (DFIG) to improve the settling frequency following an event. In the proposed scheme, if the frequency following an event is stabilized at a value beyond the maximum SSFD band, to reduce the output power of synchronous generators, the output power of a DFIG is instantly increased by a constant and maintained until the frequency increases to within the maximum SSFD band. At the same time, the mechanical input power of the synchronous generators is maintained so that the difference between the mechanical input power and electrical output power of the synchronous generators increases the frequency to a value within the maximum SSFD band. The simulation results demonstrate that the proposed scheme can improve the settling frequency while avoiding additional system defense plans when the settling frequency is stabilized at a value beyond the maximum SSFD band.
Experimental Demonstration of superior SiC MOSFET and GaN HEMT Performance within Single Phase Microinverter for Low-Power Photovoltaic Applications

This paper presents an experimental demonstration of superior Wide Bandgap (WBG) power semiconductors performance within a single phase 1 kW DC to AC converter for photovoltaic (PV) applications. Three different power semiconductors Super Junction (SJ) MOSFET, Silicon Carbide (SiC) MOSFET and Gallium Nitride (GaN) HEMT were selected to built three identical converter prototypes in order to evaluate and compare their performances in terms of power losses, efficiency and number of needed power semiconductors. The proposed power converter generates a sinusoidal output voltage waveform at the network frequency from a photovoltaic DC power source using a DC to DC Buck converter followed by an H-Bridge converter operating at network frequency. The completed design procedure, simulation and experimental results are given. The results show the superior performance of the SiC MOSFET and the GaN HEMT for this application and the possibility of reducing the number of power semiconductor needed compared to the solution with the SJ MOSFET.
A Novel MIM sandwich structure of the SCM probe for tool inspection

In this article we combine FIB-CVD technology on the probe and create a novel MIM structure for tool inspection, it does not require surface preparation process and easy to measure the p-n junction effectively in order to observe the electronic hole impurity distribution. We’ve replaced traditional SCM probe tip by new probe which are consisted of the tungsten layer and oxide layer as the capacitive dielectric. We solved the problem of the sample preparation process bring about SCM image quality instability problem. In addition, the most advantage is the new MIM probe can achieve the goal of direct measurement on sample without any sample pretreatment procedure.
Unified Extracted Pole Filter Synthesis: Bridging the Gap Between EM and Circuit Simulations

This article reviews the three key concepts of modern microwave-filter synthesis using coupled resonators: coupling matrix (CM), extracted pole (EP), and nonresonating node (NRN). CM synthesis is no doubt the most popular and powerful of the three. EP has been around longer as a circuit-synthesis tool, but it seems to have only limited applications. Both CM and EP were developed before the proliferation of electromagnetic (EM) simulators and fast modern computers. CM is widely used because the simple mapping relationship between the physical dimension of coupling structures and synthesized coupling values is well developed. NRN is the newest concept and has drawn a lot of attention. It is a result of circuit synthesis but can also be expressed using CM/EP.
Outdoor Localization System with Augmented State Extended Kalman Filter and Radio-Frequency Received Signal Strength

In this paper, we propose a mobile robot localization system based on the Received Signal Strength Indicator (RSSI) of three radio module beacons, to enhance or replace GPS positioning in scenarios with little or no GPS availability, such as urban environments with blocked sky view (near buildings or tall trees). Our approach uses an Extended Kalman Filter with augmented states to estimate a robot's trajectory by fusing sensor data from odometry, gyroscope, GPS (when available), and three RSSI from the beacons, which have its biases corrected in the Kalman filter itself. Our contribution is a low-cost localization system to be used in outdoor environments where other sensors suffer from a lack of availability and uncertainty. To test our approach, we experiment by collecting several datasets in an outdoor environment with a wheeled ground robot, where it travels in a predefined trajectory with a total length of 48.78 meters. We use these datasets to estimate the robot's trajectory offline, where our approach achieves a best mean trajectory error of 0.308 meters, an 82.58% improvement over the baseline GPS error.
A Method of Hierarchical Image Retrieval for Real-Time Photogrammetry Based on Multiple Features

In near real-time photogrammetry, the first step in processing each new added image is determining the most relevant image in pre-sequence unordered images quickly and exactly, which is pivotal for accurate image matching and 3D reconstruction. This paper presents a hierarchical image retrieval algorithm based on multiple features and details the choice for representation of multiple features which is critical to the improvement of accuracy of this algorithm. First, we represent global features using AlexNet-FC7(fully connected layers) or ResNet101-Pool5(pooling layers) and local features using SIFT (scale-invariant feature transform) in two parallel threads with support of GPU (Graphics Processing Unit). Next, we obtain candidates based on cosine similarities between global features of each pre-sequence image and new added image. Finally, we determine the most relevant image from those candidates according to feature matching results for each candidate and new added image. The experimental results confirm that the second step is rather fast and the third step is necessary to tackle the problem that global features cannot distinguish objects from the same class. The total time our algorithm takes is about 83.6ms for determining the most relevant image in 5063 pre-sequence unordered images of size $1024\times 768$ , which outperforms exhaustive pairwise matching, Bag of Words and multi-vocabulary trees. Accuracy of our algorithm also perform better than the state-of-the-art methods on three benchmark datasets. SIFT matching results obtained in the third step after eliminating mismatches with RANSAC (Random Sample Consensus) can also be used for high-precision incremental SFM (Structure from Motion) reconstruction.
Experimental comparison of the effect of the structure on MZI fiber gas sensor performance

A compact refractive index (RI) sensor, based on Mach Zehnder Interferometer (MZI), was developed for high sensitive detection and measurement of gases using Single Mode Fiber (SMF), Photonic Crystal Fiber (PCF), and Hollow-Core Photonic Crystal Fibers (HC-PCF). The device was constructed by placing a short stub of sensing element fiber between a lead-in and a lead-out single mode fiber (SMF) with an air-gap at each interface. Three MZIs were fabricated using 4 mm stub of PCF, 10 µm HC-PCF and 20 µm HC-PCF as the sensing element, and performance of these sensors were compared. The gas sample was able to effectively fill the air gaps between fibers. Using PCF and HC-PCF as the sensing element provides a great opportunity for gas sensing because of the possibility of a stronger interaction between the light and gas molecules. Additionally, gas molecules penetrate the cavities within the air holes of photonic crystal fibers. The resulting fiber sensors were able to measure the RI of the gas filled cavity and showed excellent sensitivity to helium, methane, and carbon dioxide. The highest RI sensitivity of 3210 nm/RIU was demonstrated in the RI range of 1.0000347 – 1.000449 for a sensor with 4 mm length of 10 µm HC-PCF. The cyclic tests show great measurement repeatability of different gases. Response and recovery times were found for these MZIs. It was concluded that 20 µm HC-PCF sensor has the lowest and the PCF sensor has the highest response/recovery time. This study shows that sensors fabricated by the proposed method have good potential for detecting and quantifying pure gases. Additionally, they are highly sensitive to low percentages of methane and CO2 which is suitable for greenhouse gas measurement.
Psychological Effects and Their Role in Online Privacy Interactions: A Review

Because of the increasing dependency on online technologies in even the most ordinary activities, people have to make privacy decisions during everyday online interactions. Visual design often influences their choices. Hence, it is in the hands of choice architects and designers to guide users towards specific decision outcomes. This “nudging” has gained much interest among scholars in interdisciplinary research, resulting in experimental studies with visual cues that may have the potential to alter attitudes and behaviors. Attitude and behavior changes are often attributed to several psychological effects manifesting in cognitive processing and decision-making. This article presents the results of a systematic literature review carried out to identify which psychological effects have been previously studied in the context of online privacy interactions. Subsequently, fifteen articles were selected and thoroughly reviewed, resulting in the identification of twenty psychological effects. The visual cues triggering these effects were recognized and classified against their capabilities to alter privacy attitudes and behaviors. Specifically, the visual cues were divided into two categories: privacy-enhancing and privacy-deteriorating. This review discusses the applicability of such cues in research and UI design. Further, the findings are discussed against the existing research on digital nudges. The authors conclude with a discussion on issues of research quality in the privacy-related field and outline the road to improvement.
Effects of Different Tunnel Layers on the Performance and Light Stability of Electron-Selective TiO2 Contacts

We have investigated effects of different tunnel oxide layers on the performance and light stability of tunnel oxide/TiO2 stack electron-selective contacts on n-type Si. From XPS analysis, we found that chemical properties of a SiOx interlayer at an interface between a tunnel oxide/TiO2 stack electron-selective contact and an n-type Si significantly depend on stack materials and fabrication processes of tunnel oxide layers. We also found that although chemical SiO2 and thermal atomic layer deposited AlOx tunnel layers show higher passivation performance, plasma-enhanced atomic layer deposited AlOx tunnel layers demonstrate light-enhanced τeff with an ITO capping layer as well as the highest light stability without an ITO capping layer.
SPRINTER: A Discrete Locomotion Robot for Precision Swarm Printing

This paper presents SPRINTER, a system and method for multi-robot printing. In this paper, we discuss the design of a quasi-holonomic mobile robot and present a method which uses a group of such robots to distributively print a large graphical image. In the distributive printing method, we introduce the concept of image cellularization for segmenting the graphic into a group of smaller printing tasks. We then discuss a centralized method to allocate these tasks to each robot and execute the printing process. In summary, we present a multi-robot printing system which enhances the printing speed and maximizes the printing area of traditional industrial printers.