Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated deep learning for trend prediction in time series data

Recently, Deep Neural Network (DNN) algorithms have been explored for predicting trends in time series data. In many real world applications time series data are captured from dynamic systems, which change over time. DNN models must provide stable performance when they are updated and retrained as new observations becomes available. In this work we explore the use of automated machine learning techniques to automate the algorithm selection and hyperparameter optimisation process for trend prediction with DNNs. We demonstrate how a recent AutoML tool, specifically the HpBandSter framework, can be effectively used to automate DNN model development. Our AutoML experiments found optimal configurations that produced models that compared well against the average performance and stability levels of configurations found from manual experiments across four datasets.
Practical Garbled RAM: GRAM with O(log2 n) Overhead

Garbled RAM (GRAM) is a powerful technique introduced by Lu and Ostrovsky that equips Garbled Circuit (GC) with a sublinear cost RAM without adding rounds of interaction. While multiple GRAM constructions are known, none are suitable for practice, due to costs that have high constants and poor scaling. We present the ﬁrst GRAM suitable for practice. For computational security parameter κ and for a size-n RAM that stores blocks of size w = Ω (log 2 n ) bits, our GRAM incurs amortized O ( w · log 2 n · κ ) communication and computation per access. We evaluate the concrete cost of our GRAM; our approach outperforms trivial linear-scan-based RAM for as few as 512 128-bit elements.
Experimental study of the voltage coefficient of precise compressed-gas capacitors

A study of the voltage coefficient of precise compressed-gas capacitors is described. A series of measuring methods based on the formula for calculating the voltage coefficient is presented: measuring the variations of capacitance when the capacitor is tilted in various directions to a specific angle; measuring the variations in capacitance when the capacitor is tilted in a specific direction to various angles; and measuring the variations in the coordinates of the electrodes when these electrodes are subjected to various mechanical forces, etc. These methods were used to test a 350 kV, 50 pF capacitor, and its voltage coefficient was found to be less than one part in a million from 0 kV up to 350 kV. >
A Two-Tone Wake-Up Receiver with an Envelope-Detector-First Architecture Using Envelope Biasing and Active Inductor Load Achieving 41/33dB In-Band Rejection to CW/AM Interference

The use of an ultra-low-power wake-up receiver (WuRx) can dramatically reduce the overall power consumption of a wireless link and is especially attractive for the increase of the battery lifetime of a wireless sensor node (WSN). Several recent works [1–3] demonstrated the feasibility of achieving sub-100nW WuRxs. The key is to remove the power-hungry RF LNA by utilizing the high passive gain from the external matching network (MN). Unfortunately, the resulting envelope-detector-first (ED-first) architecture suffers two problems and limits practical applications. First, their moderate sensitivity with sub-100nW power is only achieved at a very low data rate $(\lt200$ bps) with high latency $(\gt10$ mS). Second, the ED-first architecture is vulnerable to in-band interference and thus limits their use in the crowded ISM band. To cope with the in-band jammers, techniques such as the time-domain integrator [1], PID controller [2], and auto-offset-control-loop [3] are used to adapt the comparator threshold to suppress the DC offsets caused by the CW interferers. Yet, these loops are too slow such that AM interferers can deteriorate the carrier-to-interference-ratio (CIR). On the other hand, frequency-translation WuRx [4] or envelope-detector (ED) based WuRx with two-tone modulation (TTM) [5–7] can improve interference resilience. However, they either consume too much power (e.g., $\gt 100 \mu \mathrm{W})$ [4–5, 7] or require an off-chip MEMS filter [6].
Particle Filter based Predictive Beamforming for Integrated Vehicle Sensing and Communication

The dual-function radar communication system develops rapidly with the integration of sensing function and communication function, the combination of vehicle tracking and positioning and vehicle communication leads to a more efficient vehicle networking system in the future. This paper proposes a beam tracking prediction scheme for intergraded sensing and communications (ISAC) aided vehicle to infrastructure communications. In detail, we focus on the beam misalignment problem between roadside units (RSU) and high dynamic passing vehicles. To solve this problem, we propose a particle filter-based predictive beamforming method that can predict the motion parameters of vehicles by using transmitted ISAC signals and received the vehicle echoes. The simulation results show that the proposed particle filter algorithm can reduce the overhead and predict the vehicle motion parameters and the vehicle's angle relative to the RSU when the vehicle is moving.
CONTROL AND ROBUSTNESS ANALYSIS OF HOMOGENEOUS CHARGE COMPRESSION IGNITION USING EXHAUST

There has been an enormous global research effort to alleviate the current and projected environmental consequences incurred by internal combustion (IC) engines, the dominant propulsion systems in ground vehicles. Two technologies have the potential to improve the efficiency and emissions of IC engines in the near future: variable valve actuation (VVA) and homogeneous charge compression ignition (HCCI). IC engines equipped with VVA systems are proven to show better performance by adjusting the valve lift and timing appropriately. An electro-hydraulic valve system (EHVS) is a type of VVA system that possesses full flexibility, i.e., the ability to change the valve lift and timing independently and continuously, making it an ideal rapid prototyping tool in a research environment. Unfortunately, an EHVS typically shows a significant response time delay that limits the achievable closed-loop bandwidth and, as a result, shows poor tracking performance. In this thesis, a control framework that includes system identification, feedback control design, and repetitive control design is presented. The combined control law shows excellent performance with a root-mean-square tracking error below 40 μm over a maximum valve lift of 4 mm. A stability analysis is also provided to show that the mean tracking error converges to zero asymptotically with the combined control law. HCCI, the other technology presented in this thesis, is a combustion strategy initiated by compressing a homogeneous air-fuel mixture to auto-ignition, therefore, ignition occurs at multiple points inside the cylinder without noticeable flame propagation. The result is rapid combustion with low peak in-cylinder temperature, which gives HCCI improved efficiency and reduces NOx formation. To initiate HCCI with a typical compression ratio, the sensible energy of the mixture needs to be high
Emotion detection using EEG signals based on Multivariate Synchrosqueezing Transform and Deep Learning

Emotion recognition from EEG signals has gained a great research interest in brain-computer interface (BCI) studies. As the result of the outstanding success of deep neural networks in the image classification area, deep learning methods have become popular in the subject of emotion classification from EEG signals. In this study, we have used the Alexnet structure for the classification of emotions in Arousal and Valence domains separately. We generate TF images of 32-channel EEG data we collected by using Multivariate Synchrosqueezing Transform (MSST) and then these TF images are used to feed to the AlexNet model. A 3-fold cross-validation strategy was adopted to evaluate the robustness of the models. By training the AlexNet architecture an average accuracy of 71.60% is yielded on Arousal and an average accuracy of 67.93% is yielded on Valence. The results demonstrated that the proposed method achieved promising performance to classify emotions.
A synthesis-oriented approach to design microwave multidevice amplifiers with a prefixed stability margin

A design methodology that allows forcing a prefixed stability margin on microwave and millimeter-wave multidevice amplifiers during the synthesis procedure performed by CAD tools is proposed. To the best of our knowledge, for the first time expressions equivalent to stability margins have been determined to guide CAD optimizers to design circuits stable under parameter variations. Stability margins discussed in this paper allow inclusion of stability requirements among yield specifications in a rigorous way. A case study of a 4-FET distributed amplifier design is presented where stability under parameter variations has been achieved by using the proposed methodology.
Techno-Economic Analysis of Fiber Reinforced Polymer Substation

This paper studies on the technical and economic analysis of fiber reinforced polymer (FRP) substation framework. Firstly, based on the comparison of the material performance and price, we select the suitable fiber and resin as the main building material for substation framework. Then on the basis of numerical simulation, the bearing capacity of FRP substation truss-column under axial compression and bending is researched, and the initial geometric imperfection is considered in the models. Finally, the behavior of the typical two-span FRP gantry of substation is analyzed, and the economy of FRP substation framework is compared with the steel gantry which owns the same capability.
Self-Similarity of Twitter Users

Earlier studies have established that the (perceived) similarity of users is highly subjective and reflects more on how people respect/admire others rather than their characteristics or behavioral similarities. We study this phenomenon among Twitter users, and while confirm that it is indeed the case, we further explore the components of similarity by investigating it using data from three categories (interactions between egos and alters, profile-based activity history, and linguistic content in the messages). We use interactions as estimation for admiration and observe that it has more impact and a higher correlation to the perceived similarity than other objective measures, including similarity based on user profiles and their use of hashtags.
The Interaction Mechanism and Parameters Optimization of Multiple DC Filters for Second-order Harmonics in DC Microgrids

DC microgrid is one of the development direction of power distribution system. Affected by factors such as non-linear equipment and unbalanced load, the system is prone to second harmonic generation, which seriously threatens the stability of system operation and the safety of electrical equipment. The coordination of multiple active filters has become one of the methods for controlling second harmonics in DC microgrids. However, due to the coupling interference between the filters, the filtering performance and effect are quite different. This paper establishes the Norton equivalent grid-connected model of multiple filters. Based on the relative gain matrix theory, an analysis method of the interaction mechanism of multiple filters is proposed. The matrix relationship between the change of the filter output current and the change of the harmonic source current is constructed and the influence of DC microgrids line parameters and filter control parameters on the filtering effect is analyzed. An optimization model based on the influence factors of coupling interference is established, and NSGA-II is used to optimize the configuration of electrical and control parameters. Finally, a DC microgrid model with multiple DC filters is established in PSCAD/EMTDC to verify the rationality of the parameter analysis that affects the filtering effect in the scenario of multiple harmonic sources. The results show that the interaction between filters can be effectively suppressed and the effect of multi-filter cooperative filtering can be improved by optimizing the electrical distance and the parameters of filter controllers.
Non-Intrusive Load Monitoring based Demand Prediction for Smart Meter Attack Detection

The global implementation of smart meters that measure and communicate residential electricity consumption has resulted in the creation of new energy efficiency services such as automated energy management systems and billing systems. In view of the vulnerability of smart meters to cyber and physical attacks, this research presents a short-term load prediction method that uses energy disaggregation, to detect the False Data Injection (FDI) attack on smart meters. This method is constructed of an edge detection based Non-Intrusive Load Monitoring (NILM) module for energy disaggregation and a load forecaster. In the first step, we attempt to determine when the appliances are switching on/off. Second, the acquired switching events would be utilized as an input for machine learning algorithms including Support Vector Regression (SVR) and Elman Neural Network (ENN) to improve performance of the load forecaster for detecting FDI attacks. Validation of the results based on the data collected from twenty actual UK houses has indicated that the recommended method is a great solution for detecting cyberattacks on residential smart meters.
Development of Digital Image-Processing Algorithm to Compute Unified Crack Index for Salt Lake City

This paper presents the recent efforts in developing an image processing algorithm for computing a unified pavement crack index for Salt Lake City. The pavement surface images were collected using a digital camera mounted on a van. Each image covers a pavement area of 2.13 m (7 ft) × 1.52 m (5 ft), taken at every 30-m (100-ft) station. The digital images were then transferred onto a 1-gigabyte hard disk from a set of memory cards each of which can store 21 digital images. Approximately 1,500 images are then transferred from the hard disk to a compact disc. The image-processing algorithm, based on a variable thresholding technique, was developed on a personal computer to automatically process pavement images. The image is divided into 140 smaller tiles, each tile consisting of 40 × 40 pixels. To measure the amount of cracking, a variable threshold value is computed based on the average gray value of each tile. The program then automatically counts the number of cracked tiles and computes a unified crack index for each pavement image. The crack indexes computed from the image-processing algorithms are compared against the manual rating procedure in this paper. The image-processing algorithms were applied to process more than 450 surveyed miles of Salt Lake City street network.
Analysis and Related Preventive Measures of Explosion Risk Factors of LPG Rail Transport

Railway transportation safety management must adhere to the approach of safety and prevention first. The transport of dangerous goods should be paid more attention as a particularly important part. This paper made use of fault tree analysis to research and analyze the risk factors of dangerous goods LPG which may explode in the rail transport. The analysis was made from two aspects of chemistry and physics, then the minimal cut sets and structure importance of the specific factors which may cause explosion accidents are solved, and the importance of each factor is analyzed and sorted on the calculated results. Based on the above method, measures of LPG rail transport explosion prevention were proposed.
Trajectory Design and Bandwidth Assignment for UAVs-enabled Communication Network with Multi - Agent Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) is considered as a promising technique to enhance future wireless mobile communication. In this paper, the UAVs serve as aerial base stations (UBSs) to provide reliable service for terminal users (TUs). We construct the physical model and air-ground link model to formulate an optimization problem, aiming to maximize the total channel capacity of the network. To address this non-convex problem, we proposed a multi-agent deep reinforcement learning algorithm (MADRL) based on deep deterministic policy gradient (DDPG) to optimize the trajectory design and bandwidth assignment dynamically. Proper reward function is well designed to guide the agents to exploit the potential of the aerial network. Simulations demonstrate that our scheme can adapt the rapidly changing environment and obtains 29.49% performance improvement comparing to the baseline scheme.
Australian Education Journals: Quantitative and Qualitative Indicators

This paper reports on a study that applied citationbased measurements to Australian education journals. Citations data were drawn from two sources, Web of Science and Scopus, and these data were used to calculate each journal’s impact factor, h-index, and diffusion factor. The rankings resulting from these analyses were compared with draft rankings assigned to the journals by Excellence for Research in Australia (ERA). Scopus emerged as the citation source most favourable to these journals and some consistency of data across the citation-based measures was found.
Online Scheduling of Machine Learning Jobs in Edge-Cloud Networks

Compared with traditional cloud computing, edge-cloud computing brings many benefits, such as low latency, low bandwidth cost, and high security. Thanks to these advantages, a large number of distributed machine learning (ML) jobs are trained on the edge-cloud network to support smart applications, adopting the parameter server (PS) architecture. The scheduling of such ML jobs needs to consider different data transmission delay and frequent communication between workers and PSs, which brings a fundamental challenge: how to deploy workers and PSs on edge-cloud networks for ML jobs to minimize the average job completion time. To solve this problem, we propose an online scheduling framework to determine the location and execution time window for each job upon its arrival. Our algorithm includes: (i) an online scheduling framework that groups unprocessed ML jobs iteratively into multiple batches; (ii) a batch scheduling algorithm that maximizes the number of scheduled jobs in the current batch; (iii) two greedy algorithms that deploy workers and PSs to minimize the deployment cost. Large-scale and trace-driven simulations show that our algorithm is superior to the most common and advanced schedulers in today’s cloud systems.
Performance of a CDRT based Underlay NOMA With Combining at the Near User

In this paper, a coordinated direct and relay transmission (CDRT) based cognitive underlay downlink nonorthogonal multiple access network is considered, in which the source serves the near user (NU) directly while a dedicated relay assists the communication to the far user (FU). To ensure good performance in a power-constrained underlay scenario, NU combines (along with successive interference cancellation) the signals from the source and the relay in this unique CDRT framework. The secondary transmitter transmits with controlled power to ensure that the interference is below the interference temperature limit of the primary user. NU and FU performance is analyzed in terms of their outage probability and throughput in closed-form. Furthermore, we investigate how the choice of target rates and power allocation parameters is crucial to attaining the best performance. Extensive computer simulations are provided to validate the accuracy of the derived results.
Wind-Solar-Load Power Scenario Reduction Based on Residual Multi-Channel Convolutional Auto-Encoders

The traditional methods of generating typical scenarios are prone to information loss and feature extraction inaccuracy. Considering the characteristics of wind-solar-load data with physical individual independence, complex complementary coupling, and the quality of data dimensionality reduction, a method of generating typical wind-solar-load scenarios based on the residual multi-channel convolutional auto-encoder (RESMCAE) is proposed. With respect to data dimensionality reduction, each independent feature extraction encoder is improved by the residual module to solve the problem of gradient disappearance, reduce information loss and ensure the information integrity. The performance of the network is enhanced through integrating multi-resolution features by the decoder of multi-layer fusion structure. The KL divergence is used for optimizing the network structure, and the Adam algorithm for parameter tuning. The proposed method is verified by comparing with traditional clustering methods on Davies-Bouldin Index (DBI) and Calinski-Harabasz Index (CHI) scores. The results show that the RESMCAE is feasible.
Dynamic patterns within the default mode network in schizophrenia subgroups

In this study, resting-state functional magnetic resonance imaging (rs-fMRI) data of 125 schizophrenia (SZ) subjects were analyzed. Based on SZ demographic information and cognitive scores and using an unsupervised clustering method, we identified subgroups of patients and compared DMN dynamic functional connectivity (dFC) between the groups. We captured seven independent subnodes, including anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), in the DMN by applying group independent component analysis (group-ICA) and estimated dFC between component time courses using a sliding window approach. By using k-means clustering, we separated the dFCs into three reoccurring brain states. Using the statistical method, we compared the state-specific DMN connectivity pattern between two SZ subgroups. In addition, we used a transition probability matrix of a hidden Markov model (HMM) and occupancy rate (OCR) of each state between two SZ subgroups. We found SZ subjects with higher positive and negative syndrome scale (PNASS) showed lower within ACC and lower ACC and PCC connectivity (or ACC/PCC). In addition, we found the transition from state1 to same state is significantly different between two groups, while this result was not significant after multiple comparison tests.
Weatherscapes

Due to the complex interplay of various meteorological phenomena, simulating weather is a challenging and open research problem. In this contribution, we propose a novel physics-based model that enables simulating weather at interactive rates. By considering atmosphere and pedosphere we can define the hydrologic cycle - and consequently weather - in unprecedented detail. Specifically, our model captures different warm and cold clouds, such as mammatus, hole-punch, multi-layer, and cumulonimbus clouds as well as their dynamic transitions. We also model different precipitation types, such as rain, snow, and graupel by introducing a comprehensive microphysics scheme. The Wegener-Bergeron-Findeisen process is incorporated into our Kessler-type microphysics formulation covering ice crystal growth occurring in mixed-phase clouds. Moreover, we model the water run-off from the ground surface, the infiltration into the soil, and its subsequent evaporation back to the atmosphere. We account for daily temperature changes, as well as heat transfer between pedosphere and atmosphere leading to a complex feedback loop. Our framework enables us to interactively explore various complex weather phenomena. Our results are assessed visually and validated by simulating weatherscapes for various setups covering different precipitation events and environments, by showcasing the hydrologic cycle, and by reproducing common effects such as Foehn winds. We also provide quantitative evaluations creating high-precipitation cumulonimbus clouds by prescribing atmospheric conditions based on infrared satellite observations. With our model we can generate dynamic 3D scenes of weatherscapes with high visual fidelity and even nowcast real weather conditions as simulations by streaming weather data into our framework.