Miracle-dz/UniFAR-FTUs huggingface dataset:
Light Graph Convolutional Collaborative Filtering With Multi-Aspect Information

The personalized recommendation has become increasingly prevalent in real-world applications, to help users in discovering items of interest. Graph Convolutional Network (GCN) has achieved great success and become a new state-of-the-art for collaborative filtering. However, most of the existing GCN based methods can only capture information about the user’s purchase (or click) history, reflecting only one aspect of the user preferences and item characteristics. To provide more accurate recommendations, we need to go beyond modeling user-item interactions and take auxiliary information into consideration. In this paper, we propose a Light GCN based Aspect-level Collaborative Filtering model (LGC-ACF) to exploit multi-aspect user-item interaction information. First, we construct aspect-level user-item interaction graphs according to the interaction history and the knowledge information of items, then feed them to a delicately designed Light GCN based model to learn aspect-level representations of users and items. Finally, the representations of all aspects and all propagation layers are fused for recommendation. We apply LGC-ACF to three datasets: Movielens, Amazon, and Taobao. The experiment results show that LGC-ACF achieves average NDCG improvements of 5.31%, 4.06%, 14.9% in Movielens, Amazon, and Taobao datasets, respectively, compared with state-of-the-art baselines for recommendation.
Vertical Federated Learning Based Privacy-Preserving Cooperative Sensing in Cognitive Radio Networks

Machine learning-based cooperative sensing scheme, despite its effectiveness in significantly improving the sensing performance, suffers from privacy threats because the sensing reports shared by secondary users (SUs) are highly correlated to their locations, which can be maliciously exploited to infer private information. In this paper, we propose a novel vertical federated learning-based cooperative sensing (VFL-CS) scheme where sensing results are kept locally at each smart SU (SSU) and the model is trained in a decentralised collaborative learning setting. A multi-user deep learning-based FL architecture is constructed with detailed training and evaluation processes explained and security analysed. Simulation results show that our proposed VFL-CS scheme outperforms conventional soft-fusion based cooperative sensing (SF-CS) scheme in terms of much higher area under curve (AUC) score with high data privacy-preserving capability.
Comparison of the Deep Learning Methods Applied on Human Eye Detection

For the fatigue driving detection of a driver wearing a mask, the traditional fatigue driving detection method cannot effectively detect the face. The characteristics of the mouth area are disappeared due to the mask’s occlusion. Therefore, the extraction of fatigue features in the eye area becomes very important. The accuracy of the eye area detection will directly affect the performance of the fatigue driving detection algorithm. At present, YOLOv3 and Faster-RCNN are both excellent models in the field of target detection. Therefore, this article uses the same data set and sets the same training parameters during training. Under a unified evaluation standard, the YOLOv3 model and the Faster-RCNN model are evaluated. Experimental results show that YOLOv3 has a better effect on human eye detection under the same conditions.
Learning Analytics based on Bayesian Optimization of Support Vector Machines with Application to Student Success Prediction in Mathematics Course

Learning analytics is receiving a growing attention from both machine learning and education communities, where support vector machines (SVM) are gaining popularity over existing data mining techniques. In the scope of this work, we employ SVM to predict student success in mathematics course in Portugal under two common nonlinear kernel functions: polynomial and radial basis function kernel. In addition, we employ the k-nearest-neighbor (kNN) algorithm as a reference model since it is known to be fast and effective in various classification problems. Furthermore, we adopt the Bayesian optimization (BO) technique in a cross-validation framework to optimize SVM key parameters; namely, the slack parameter and penalty coefficient. The obtained experimental results show that the SVM outperform k-nearest-neighbor algorithm under both nonlinear kernel functions. Additionally, processing time associated with SVM optimization process increases with polynomial order. Furthermore, the SVM trained with third-order polynomial kernel performs the best. Finally, k-nearest-neighbor algorithm is found to be faster compared to all SVM classifiers.
On the determination of partial structure functions in small-angle scattering exemplified by Al89Ni6La5 alloy

A comparison between the resonant scattering curve obtained by anomalous small-angle X-ray scattering at the X-ray absorption edge of Ni and the complementary small-angle neutron scattering curve from an Al89Ni6La5 alloy sample is reported. The sample does not comply with the two-phase approximation. The two resulting scattering curves are approximately proportional to each other in this particular case. The anomalous small-angle X-ray scattering resonant curve at the Ni absorption edge equals the Ni–Ni partial structure factor and, owing to the favourable neutron scattering lengths of Ni, La and Al, the neutron scattering curve is also proportional to that partial structure factor.
Forecasting Daily Close Prices of Stock Indices using LSTM

The stock market remains one of the most un-predictable and volatile activity, numerous arithmetical and computational methods have been designed to get the maximum reap from it. A lot of predictive methods and models have been designed in the recent years such as Linear Regression, Multiple Regression and RNN’s to help traders, analysts and investors make the most probabilistic decision. Here we use RNN’s Long Short-Term Memory architecture, a purpose oriented RNN to predict the bear market tables. Statistical techniques such as linear regression, polynomial regression are popular techniques for traders to see the association between variables and hence predict stocks. Using RNN’s and LSTM gives a better understanding of the changing prices seen during trading of equity to day traders and financial advisers since LSTM is a beautiful and efficient way of dealing with time stepped data.A lot of money has been made and lost by capitalizing the stock market, prediction models are not accurate but specify a more calculated approach to make money. By no means do any of the financial models or predictive models guarantee the making of money.This project aims to forecast the prices of the Apple stock from the year 2014-2019 and analyze the day to day change in the value, using LSTM in keras and the backend tensor flow, from the data extracted from yahoo.com. The data is then split into training and testing datasets, where the test data is our predicted value of the stock. This model, once accomplished shows us that for day to day analysis, this can be an appropriate alternative to other models of forecasting.Keeping in mind the potential of the project, the future scope including applications using the project has been discussed in this paper.
Identification of Chemical Species of Silica in Natural Hot Water using Fast Atom‐Bombartment Mass Spectrometry

Abstract The identification of silica species dissolved in natural hot water was performed. Three typical hot spring waters, which were collected from Hakone Hot Spring (Kowakudani), Nakafusa Hot Spring, and Tateyama Hot Spring Shin‐yu, were examined. Generally, it is known that, in Nakafusa Hot Spring and Tateyama Hot Spring shin‐yu, oolite opal (siliceous oolite) and massive opal are sintered. These hot spring waters were analyzed with ICP‐AES, ICP‐MS, colorimetry, ion chromatography, and FAB‐MS (fast atom‐bombardment mass spectrometry). Nakafusa Hot Spring water is characterized by the high pH indicates that the sinter of the Nakafusa Hot Spring water contains a high concentration of arsenic while, in Tateyama Hot Spring Shin‐yu, arsenic ions were contained at high concentrations from the water. The concentration of silica in Nakafusa Hot Spring and Tateyama Hot Spring Shin‐yu hot spring waters was measured by colorimetry and ICP‐AES. The concentration of silica in those waters, measured by ICP‐AES, is higher than that measured by colorimetry but, in the Hakone hot spring water, the concentrations of silica, as measured by the ICP‐AES and colorimetry were the same. This indicates that large species of silica, which could not be detected by colorimetry, were present in Nakafusa Hot Spring and Tateyama Hot Spring Shin‐yu. The silica species was identified to have several chemical forms and all of the species are replaced with sodium ions. It was notable that the peak intensity ratios of 329/311 and 195/173 in Nakafusa Hot Spring and Tateyama Hot Spring Shin‐yu waters were higher than those obtained by experiments employing the simple system of sodium chloride (NaCl) solution with silica and the Hakone Hot Spring water. Therefore, the silica species in both spring waters hydrated after the precipitation of silica as particles.
Analysis and Design of a CMOS Bidirectional Passive Vector-Modulated Phase Shifter

This paper presents a passive vector-modulated phase shifter (VMPS). The passive X-type attenuator consisting of digitally controlled transistor-array units is employed to perform the phase-invertible gain tuning, and thus enables phase shift in all four quadrants. The Wilkinson-like power combiner is utilized to sum up the quadrature signals and avoid impedance mismatches. Analysis proves that the proposed passive VMPS can provide consistent phase-shift performance for bidirectional operation. The proof-of-concept VMPS is implemented in 40-nm CMOS technology and occupies a core chip area of 0.15 mm2. Measured results prove that it can provide consistent bidirectional 6-bit phase-shift operation, with accurate phase tuning (i.e., RMS phase error <24°) and low gain error (i.e., ±0.6 dB) over the whole 70–90 GHz band.
Block Copolymer−Ceramic Hybrid Materials from Organically Modified Ceramic Precursors

The study of amphiphilic polymer based functional organic−inorganic hybrid materials is an emerging research area offering enormous scientific and technological promise. Here, we show that employing poly(isoprene-block-ethylene oxide) block copolymers (PI-b-PEO) and a silicon precursor, which contains a polymerizable organic moiety, unprecedented morphology control on the nanoscale is obtained. This control is based on a unique polymer−ceramic interface, which is characterized using a multi-nuclei solid-state NMR approach. The results show that the hydrophilic parts of the polymer are completely integrated into the ceramic phase, thereby leading to a quasi “two-phase system”, allowing for a more rational hybrid morphology design based on the current understanding of the phase behavior of block copolymers and copolymer−homopolymer mixtures. Examination of the full phase space of the hybrid materials reveals the existence of a new bicontinuous cubic structure that was not known to exist in polymer systems.
Corner Multipath in Through-the-Wall Radar Imaging based on Compressive Sensing

Due to the need of fast data acquisition and measurement reduction, compressive sensing (CS) has been widely used in through-the-wall radar imaging (TWRI). Particularly, based on the method of CS, most existing research works of multipath in TWRI concentrate on indoor targets, which are relatively far from building corners. In this paper, multipath in radar imaging through the corner of a building is considered, and this kind of multipath is named as ‘corner multipath’. With the propagation characteristic of corner multipath, the sparse model of through-corner target scene is built, and then the corresponding CS method is proposed. With the direct comparison to the existing CS-based imaging method, reconstruction results are reported to indicate the better performance of the proposed method on ghost suppression.
Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing

Collaborative edge computing (CEC) is a recently popular paradigm enabling sharing of data and computation resources among different edge devices. Task offloading is an important problem to address in CEC as we need to decide when and where each task is executed. However, it is challenging to solve task offloading in CEC as tasks can be offloaded to a multihop neighboring device leading to bandwidth contention among network flows. Most existing works do not jointly consider network flow scheduling that can lead to network congestion and inefficient performance in terms of completion time. Another challenge is to formulate and solve the problem considering the dependencies among dependent tasks and conflicting network flows. Few recent works have considered multihop computation offloading; however, these works focus on independent tasks and do not jointly consider the dependencies with network flows. In this work, we mathematically formulate the problem of jointly offloading multiple tasks consisting of dependent subtasks and network flow scheduling in CEC to minimize the average completion time of tasks. We have proposed a joint dependent task offloading and flow scheduling heuristic (JDOFH) that considers both dependencies in task directed acyclic graph and start time of network flows. Performance comparison done using simulation for both real application task graph and simulated task graphs shows that JDOFH leads to up to 85% improvement in average completion time compared to benchmark solutions which do not make a joint decision.
14.6 A 76-to-81GHz 2×8 FMCW MIMO Radar Transceiver with Fast Chirp Generation and Multi-Feed Antenna-in-Package Array

Millimeter-wave (mm-wave) radar is an essential sensor of advanced driver assistance systems and autonomous driving. Its detection requirement extends from traditional long-to-medium range to emerging short and ultra-short range for surround sensing, which is from sub-1 meter to 40 meters and requires a compact and low-cost solution with fast chirp generation to improve the range resolution [1] –[5]. In [2], 3 transmitters (TX) and 4 receivers (RX) are utilized to create a multi-mode radar transceiver, however its on-board antenna integration solution limits its size and cost for short-distance applications. In [3], 12TX and 16RX phase-domain multiple-input multiple-output (MIMO) radar is presented. However an external PLL is required, and the digitalprocessing power consumption is high. Furthermore its size and cost are not suitable for short and ultra-short applications. In [4], a compact 1TX and 1RX frequency-modulated continuous-wave (FMCW) radar is applied with an antenna-in-package (AiP) for short-distance applications, however its detection range is limited to only 20meters. This work presents a compact 76-to-81GHz FMCW MIMO radar with fast chirp generation and integrated with an AiP array in embedded glass fan-out (eGFO) technology for short and ultra-short range application. A fast-modulated chirp signal is especially crucial in MIMO radar for improving range resolution with update rate, and it also moves IF frequency away from the 1/f noise corner. Thanks to the dynamic bias technique, the chirp rate is improved to 312.5MHz/μs and the maximum modulation bandwidth is 7.2GHz. In addition, the 2TX and 6RX array antennas are integrated in package, and effective isotropic radiated power (EIRP) of the TX antenna is improved by 7.5dB by a coaxial feeding structure in eGFO technology with multi-feed antenna technique.
Analytical Model of Dielectric Modulated Trench Double Gate Junctionless FET for Biosensing Applications

A 2-D analytical model of a dielectric modulated trench double gate junctionless FET (DM-TDGJLFET) is developed for label-free detection of biomolecules. The channel potential is obtained by solving the 2-D Poisson’s equation using the parabolic approximation with appropriate boundary conditions. The drain current and threshold voltage are obtained from the minimum channel potential. The proposed DM-TDGJLFET structure has two gates which are vertically placed in separate trenches. The two cavities for biomolecules immobilization are carved in the gate oxide region. The DM-TDGJLFET has been studied for threshold voltage sensitivity of neutral as well as charged biomolecules. For neutral biomolecules, the shift in threshold voltage is obtained as <inline-formula> <tex-math notation="LaTeX">$745 {mV}$ </tex-math></inline-formula> for change in dielectric constant (<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>) from 1 to 12. The DM-TDGJLFET performance is also evaluated using commercially available <inline-formula> <tex-math notation="LaTeX">$ {ATLAS}^{ {TM}}$ </tex-math></inline-formula> device simulator. The model results show good agreement with simulated data.
RnR: Retrieval and Reprojection Learning Model for Camera Localization

Camera localization is an essential technique in many applications, such as robot navigation, mixed reality, and unmanned vehicle. We are committed to solving the problem of predicting the 6-DoF pose of cameras from a single color image in a given three-Dimensional (3D) environment. In this paper, we proposed a robust learning model for it. Basically, our proposed methodology consists of two steps: image retrieval and space reprojection. The former is in charge of simultaneous localization and mapping based on pre-captured reference images that rely on the correspondence between pixel points and scene coordinates; whereas the latter carries out camera calibration between the 2D image plane and the 3D scene. Given a two-Dimensional (2D) image, the initial localization is accomplished rapidly by matching a reference image using Siamese networks. More precise localization is achieved by camera calibration between the 2D image and the 3D scene using a fully convolutional network. The experimental results on the public dataset show that our model is more robust and expandable than the previous methods. At the end of this paper, we also apply the system to Unmanned Aerial Vehicle (UAV) localization and achieve good results.
Recursive Multi-Signal Temporal Fusions With Attention Mechanism Improves EMG Feature Extraction

The design of pattern recognition-based myoelectric interfaces has been heavily explored and contested in the research literature. A considerable proportion of the performance of these interfaces has been linked to the quality of the feature extraction (FE) stage used to describe the underlying myoelectric signal. In this paper, we address two important factors of FE that have not been fully exploited; 1) Spatial focus - traditional FE methods focus mainly on the concatenation of features extracted from individual channels and 2) Temporal focus - available FE methods are cross-sectional in nature, largely ignoring the temporal information that may exist between feature windows. To overcome these limitations, several spatiotemporal FE methods have been proposed including hand-crafted and deep learning (DL) models, with the latter showing significant performance enhancements at the cost of increased computational burden. This paper tackles the aforementioned limitations by 1) proposing novel extensions to simple time-domain FE methods, including the waveform length, zero crossings and root mean square, that can capture the relation between any number of channels, and 2) leveraging the long short-term memory concepts from deep neural networks to build a recursive framework that overcomes the cross-sectional nature of traditional methods. The advantages offered by the proposed Recursive Multi-Signal Temporal Fusion (RMTF) features include improved performance, competing with state-of-the-art FE methods without the computational costs associated with leading DL models, and the simplicity of the concepts making them suitable for real-time implementations. Experiments on 65 intact-limbed and amputee subjects reveal an approximate average of 15% reduction in classification errors as compared to models built with other feature sets.
A 28μW 134dB DR 2nd-Order Noise-Shaping Slope Light-to-Digital Converter for Chest PPG Monitoring

Photoplethysmography (PPG) is a key non-invasive technique to extract vital signs like heart rate (HR), heart rate variability (HRV), and blood oxygen saturation (SpO2). Integrated in smartwatches, health patches and finger clips, PPG sensors are widely used in fitness, glucose, blood pressure, and medical monitoring with good user comfort. Integrating PPG in medical chest patches is interesting for COVID-19 patients since it can yield, next to HR and respiration, also SpO2, which traditional ECG-based patches cannot provide. However, measuring PPG/SpO 2 on the chest is challenging due to large motion artifacts, small AC/DC perfusion index (PI $\lt 0.1$%), and ambient light. Therefore, it poses a more stringent requirement on dynamic range (DR $\gt 120$ dB) for the PPG readout circuit, while still requiring low power consumption for long battery life. On-chip pinned-photodiodes (PPDs) have been used as passive integrators, resulting in very low power consumption $(2.63 \mu \mathrm{W})$ for the PPG readout [1]. However, the DR and linearity can be limited by the PPDs’ parasitic capacitors. Direct light-todigital converter (LDC) topologies are proposed in [2 - 4], but their DR is still below 120dB. In [5], a digital-controlled TIA is exploited to control the transimpedance gain, which extends the DR to 130dB while consuming $72 \mu \mathrm{W}$ in the analog front-end (AFE).
Application of 3D Laser Scanner in Digitization of Movable Cultural Relics

With the rapid development of three-dimensional scanning technology, graphics and image processing, virtual reality and other technologies, the protection and inheritance of cultural relics has entered the information age, and the digital protection of cultural relics has become an important research direction. High precision 3D model plays an important role in the ontology representation, spatial feature information and history tracing of cultural relics. This paper summarizes the current research status through the application of three-dimensional model of cultural relics. Three dimensional laser scanning is an important method of cultural three-dimensional reconstruction. According to the characteristics of cultural relic point cloud and the need of point cloud data compression, this paper analyzes the common data compression methods of point cloud, and carries out compression test. According to the curvature characteristics of cultural relic point cloud, two compression methods are proposed. Based on MATLAB platform, the compression of point cloud data is realized by programming, and the function of Geo magic is realized.
Exploration on structural morphology and properties of novel poly(urethane-imide)/TiO2 bionanocomposites derived from L-tyrosine based diol

In this study, an optically active biodegradable poly(urethane-imide) (PUI) derived from L-tyrosine was synthesized. Since biopolyurethanes cannot be fully competitive with conventional thermoplastics and are sometimes too weak for practical use, these biopolymers have been formulated and associated with nano-sized fillers. For the preparation of PUI/titanium dioxide (TiO2) bionanocomposites (BNC)s, the high-intensity ultrasound was applied. The PUI/TiO2 BNCs were characterized by Fourier transform infrared spectroscopy, powder X-ray diffraction, transmission electron microscopy (TEM), scanning electron microscopy (SEM), atomic force microscopy, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and UV–vis spectroscopy. The TEM and SEM results specified that the nanoparticles were dispersed homogeneously in PUI matrix on nanoscale. TGA/DSC confirmed that the heat stability of the novel BNCs was improved. UV–vis studies indicated that the resulting BNCs had high absorbance value than those values of pure PUI.