Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicular Grouping Protocol: Towards Cyber Physical Network Intelligence

Vehicular network structures present a range of challenges and opportunities for efficiently managing awareness of road dynamics and network connectivity. An enhanced manageable organization can offer a better reaction to safety-related road events, facilitate dynamic topological flexibility, relate to road layout, and interact with unpredictable distribution of the vehicles. Vehicular grouping is one of the suggested structural techniques that offers a great benefit in grouping vehicles and modelling data routing, giving importance to road structure and the occurrence of a dynamic event within the associated group of vehicles. The approach discussed in this paper is based on a dynamic grouping through phases of self-formation, self-joining, self-leaving and self-healing as key components of the protocol operational cycle. Both vehicular physical connected resources and the remote computational cloud could be used for data processing and monitoring of road dynamics. This, in effect, encourages an Internet of Things (IoT) environment that enhances the dynamic performance through direct interaction between the virtualized network of vehicles and the physical network on the road leading to Internet of Vehicles (IoV). The objective of this paper is to develop a concept of network self-formation algorithm based on vehicle grouping strategy wherein the node can flexibly switch its function, be it an IoT gateway or a router node, based on the proposed fitness election model to be elected as group head. Testing using Contiki-Cooja simulator has been implemented on various road condition scenarios reflects the operational ability of the algorithm taking into consideration the network performance based on the ultimate capacity of the road.
Estimation of Lithium Primary Battery Capacity Based on Pulse Load Test

Lithium primary batteries are widely used in devices that require long-life power supplies, and their capacity determines the reliability of the system. According to the voltage response characteristics of lithium primary batteries under pulse load test, a new method using the back propagation neural network to accurately estimate the ampere-hour capacity of lithium primary batteries is proposed. Since the voltage response measured under the same conditions changes with the decrease of battery capacity, the capacity features are extracted from the voltage response under pulse load test, and the combination of features and the corresponding capacity are used to build a capacity estimator. The advantages of this method are fast and simple measurement, and small capacity loss. The new approach is fully validated by experiments under various load conditions of lithium thionyl chloride batteries with different storage times.
SVM-based evaluation of Thai tone imitations by Thai-naïve Mandarin and Vietnamese speakers

Native listener judgements and acoustic comparisons are sensitive to deviations between non-native speech and native productions, but both have drawbacks and are inefficient for evaluating large databases. To probe whether Support Vector Machines (SVM) might offer an efficient alternative, we used three SVM models trained with native Thai lexical tones to eval-uate new native stimuli and non-native imitations by Mandarin and Vietnamese speakers. The optimal SVM model categorized native tones accurately but showed lower accuracy with non-native imitations, like native judges do, thus confirming its sensitivity to deviations from native productions. Thai falling tone imitations yielded the lowest classification accuracy, indicating that both groups' imitations were constrained by their native falling tones. Thai rising tones were better recognized for Viet-namese than Mandarin imitators, reflecting differences between their native rising tones. Thus, SVM modeling may provide an effective alternative to traditional perceptual- or acoustic-based evaluations of non-native speech.
Dynamic Target Simulation System for Automotive Millimeter Wave Radar

Automotive millimeter wave radars are currently widely used in Advanced Driving Assistance Systems (ADAS), such as in ACC, AEB, BSD and other systems. Millimeter wave radars mainly complete target detection and environmental perception tasks. The output data includes relative distance, relative angle, relative speed, radar reflection cross-sectional and other parameters, which then will be handed over to the controller to complete the target calculation. Finally, it will formulate the corresponding control strategy to issue the actuator. As the main source of target information, millimeter wave radar has become particularly important in its detection performance. Because of the high cost, poor repeatability and long test period, at present, the target detection performance test of millimeter wave radar mainly adopts the form of target simulator. The target simulator can simulate the position, distance and speed of the target by cooperating with a high-precision turntable or manipulator to complete the calibration test of basic parameters. The traditional radar target simulator uses a single-channel transceiver to simulate a body target as a single reflection point. In conventional road scenes, the proportion of large-scale vehicle targets is very high. Simplifying it into a single point can easily cause the lack of target characteristics, and cannot effectively verify the radar’s ability to recognize large-scale targets. This article provides a way to simulate a target based on an antenna array, which uses a multichannel transceiver system, and a high-speed RF channel switch to complete the continuous variations of the target orientation. Based on the reflection characteristics database of the target at different azimuth angles, there are multiple strong reflection points for a physical target, which can be achieved by combining multiple point targets. After the consideration of economy and final effect, we chose three point targets to synthesize a body target. The synthesized body target can be modified freely in orientation, horizontal posture, distance, and speed. For typical targets such as pedestrians, cars, vans, and bicycles, the target simulation system we proposed can achieve more realistic target reflection characteristics. At the same time, the target simulation system can produce multiple targets which represent multiple traffic participants. The relative motion between them can be applied to reproduce the real traffic scene Therefore, the logic function test of the hardware-in-loop simulation can be carried out for the ADAS system based on millimeter wave radar.
Improving Training Efficiency of LSTMs While Forecasting Precipitable Water Vapours

Long-short-term-memory (LSTM) cells have been widely used in the literature for time series forecasting applications. However, when it comes to the highly varying weather variables like precipitable water vapours (PWV), it becomes very hard to perform effective training of these networks. On the other hand, the naïve or persistence method has been noted to perform exceedingly well in these cases. Appreciating the closeness of naïve methods to the optimal forecasts, this paper proposes a novel regularizer based on the naïve forecasting approach to improve the efficiency of the training procedure of LSTMs while forecasting PWV values.
Lightning Insurance: A Fast Claim, High Accuracy Insurance Platform Based on Blockchain Technology and NASNET Algorithm

Insurance is a financial instrument with a long histo-ry and conducive to social development. With the modernization process, centralized insurance companies gradually replaced mu-tual insurance, completing insurance design, insurance sales, and claims review. A large number of cases of fraudulent insurance and insurance companies delaying the application of reasonable claims, resulting in the buyer's distrust of the insurance company and the insurance company's distrust of the purchaser. Through blockchain and deep learning, this paper constructs an insurance business alliance, which links the mature and stable intelligent, discriminating scenarios into the entire insurance system, thus reducing the long redundant nuclear insurance process to the scope of machine identification and minute-level insurance. At the same time, a number of underwriting companies combined through blockchains can achieve very high accuracy rates with limited algorithm accuracy. It enhances the user experience of claimants who are in urgent need of financial assistance, thereby significantly enhancing user trust, in order to increase people's trust in insurance products from a macro perspective, and thus promote the popularization of insurance in China.
Wideband Hexagon Antenna Array for Millimeter-wave 5G Applications in 38GHz

In this paper, a novel millimeter wave antenna element, working in 38GHz, is proposed. The simulated results show that the maximum gain of the antenna element at 38GHz is 5.5dBi, and the −10 dB bandwidth is 36.74GHz–41.87GHz. Based on the above millimeter wave antenna element, two compact $2 \times 2$ array antennas are designed. The operating frequency bands of the two array antennas are 36.82GHz–41.60GHz and 36.91GHz–41.45GHz. The maximum gain at 38GHz is 10.3dBi and 5.4dBi, respectively. Without taking any decoupling measures, the isolation between the antenna elements of the two array antennas has reached below −20 dB. Both array antennas have the advantages of wide bandwidth, compact structure, and miniaturization, and can be applied to 5G millimeter wave communications in the 38GHz frequency band.
A Smart City for Public Value: Digital Transformation Through Agile Governance - The Case of 'Smart Dubai'

What makes a city “smart” in the digital age? How can a complex urban fabric develop intelligence that drives planning, decision-making and positively affects the lives of its population? What ingredients — be they organizational, technological, cultural or regulatory — enable a metropolis to re-invent its modus operandi and achieve sustained developmental strides? In short, how can a local government build a “smart city” and generate public value? In exploring these questions, this study provides an in-depth analysis of the critical first phase of Dubai’s mega-transformation into a smart city. Dubai’s stated objective in building the smart city is to improve the quality of life and raise the levels of public “happiness”. Its vision is not just to be the “smartest” city in the world by 2017, but also to be one of the “happiest” places on earth to live and work. Achieving these two ambitious goals requires addressing numerous organizational, technological, cultural and policy challenges. This study analyzes these challenges as well as the milestones reached, through tracing the paths taken by different stakeholders and documenting the multifaceted lessons learned. The findings provide ample evidence suggesting that the technological infrastructure, regulatory frameworks and organizational structures are now in place as critical foundations for building the smart city. More importantly, indicators suggest that public value is being generated throughout the evolution of the project, which is potentially improving the quality of government. The first phase of the Smart Dubai’s journey has already triggered important cross-government cultural transformations. The government’s agility and its entrepreneurial approach enabled it to bridge entrenched silos, infuse a culture of openness and transparency, in addition to fostering a collaborative governance style. This mode of governance also created a shift in collective thinking across the government; in particular from its traditional sectoral view into a city-wide view, with people’s wellbeing and “happiness” at the center. Reflecting on the lessons learned from Smart Dubai’s digital transformation will be critical for the next phase of its development. Moreover, studying the policy challenges, the barriers and the enabling factors in the city’s transformational journey provides timely insight to support wider regional urban development initiatives. Beyond the region, the experiences of “Smart Dubai” in digital-era urban development, and the way it manages — and measures — the accompanying socio-technical impact at a city level, also provide valuable policy learning opportunities at a global level. At this stage, the question then becomes: Had Dubai achieved its vision of becoming the smartest city in the world by 2017; going forward, what does the city need to do to maintain that status and ensure it remains one of the “happiest” cities to live and work? This question is explored here based on the rich findings of the study.
An Entropy-based DDoS attack Detection and Classification with Hierarchical Temporal Memory

Detecting real-time DDoS attacks is a big challenge for network security. This paper proposes a hybrid machine learning model for the detection and classification of $D$ DoS attacks. The system consists of a real-time detecting module capable of processing Entropy-based features. In addition, the classification module, based on the Hierarchical Temporal Memory and KNN classifier, is capable of mining changes in Entropy features for the classification of different types of DDoS attacks. Furthermore, it has the incremental learning capability to learn new traffic behavior and recognize new types of attacks. Finally, the simulation is conducted based on the CICDDoS 2019 dataset. As a result, the proposed system can successfully identify different types of attacks with high accuracy and precision.
Study on Simultaneous Estimation of Glottal Source and Vocal Tract Parameters by ARMAX-LF Model for Speech Analysis/Synthesis

Correct estimation of glottal source as well as vocal tract parameters is crucial for speech analysis and synthesis. Nearly all methods for estimating these parameters are based on the source-filter assumption. However, the separation and estimation of the source and filter parts are still challenging due to the unreasonable modeling related to physiological processes of speech production or inappropriate estimation procedures. We propose a model that combines the autoregressive moving average exogenous (ARMAX) and Liljencrants- Fant (LF) models, called the ARMAX-LF model, to accurately represent the physiological processes of speech production. The ARMAX model represents the vocal tract as a pole-zero filter with an additional exogenous residual signal, and the LF model represents glottal source wave-form as a parametrized time-domain model. Furthermore, we propose a two-stage iterative estimation procedure to separately and simultaneously estimate the parameters of the ARMAX-LF model. The estimated parameters were evaluated objectively and subjectively with synthesized vowels, synthesized consonants, and natural speech. The results indicate that the ARMAX-LF model with the estimated parameters can separately represent the glottal source and vocal tract characteristics and can be widely used in speech analysis and synthesis.
Graph Kernel Recursive Least-Squares Algorithms

This paper presents graph kernel adaptive filters that model nonlinear input-output relationships of streaming graph signals. To this end, we propose centralized and distributed graph kernel recursive least-squares (GKRLS) algorithms utilizing the random Fourier features (RFF) map. Compared with solutions based on the traditional kernel trick, the proposed RFF approach presents two significant advantages. First, it sidesteps the need to maintain a high-dimensional dictionary, whose dimension increases with the number of graph nodes and time, which renders prohibitive computational and storage costs, especially when considering least-squares algorithms involving matrix inverses. Second, the distributed algorithm developed in this paper, referred to here as the graph diffusion kernel recursive least-squares (GDKRLS) algorithm, does not require centralized dictionary training, making it ideal for distributed learning in dynamic environments. To examine the performance of the proposed algorithms, we analyze the mean convergence of the GDKRLS algorithm and conduct numerical experiments. Results confirm the superiority of the proposed RFF-based GKRLS and GDKRLS over their LMS counterparts.
Effects of Dielectric Values and Substrate Materials on Electromagnetic (EM) Absorption in Human Head

Abstract The aim of this study was to considered possible discrepancy in electromagnetic (EM) fascination in the human head. Commercially available software CST Microwave Studio based on the finite-difference time-domain (FDTD) method was adopted in this study. In this calculation, the conductivity and permittivity of all tissues were increased from 10 to 20% except using if not the same revelation conditions. Familiar cellular phone frequencies of 835, 900, and 1900 MHz were investigated in this research. The rise of up to 20% in conductivity, permittivity and varied substrate material always caused a SAR variation of 30.42% for SAR 1 g and 23.75% for SAR 10 g at 835 MHz, variation of 22.41% for SAR 1 g and 21.96% for SAR 10 g at 900 MHz and variation of 11.96% for SAR 1 g and 14.29% for SAR 10 g at 1900 MHz respectively.
A GuessWhat?! Game for Goal-Oriented Visual Dialog: A Survey

Visual Dialog requires an AI-agent to hold a meaningful natural conversation about visual content. For a given image, dialog history, and a current question about the image, the AI-agent is required to understand the question, infer the context from the history and ground this information from the given image to be able to answer the current question correctly. One of the Visual Dialog tasks is a guessing game between two AI-agents for visual object discovery. This work represents a survey over different approaches done for the guessing game task. In this survey, we are showing the methodologies and architectures used by the relevant work as well as the results achieved by them. We present the most recent five approaches for this game from 2017 to 2020. GuessWhat? is the dataset, introduced mainly for addressing the guessing game for visual dialog task, used in all works we are mentioning here.
Domain Specific Description in Halide for Randomized Image Convolution

We propose a domain-specific language for finite impulse response (FIR) filters using a randomized algorithm. FIR filters such as Gaussian and bilateral filters are fundamental tools for signal processing and image processing. The computational time of these convolutions increases as the convolution kernel size becomes more extensive because the computational time depends on the kernel size. Approximating the kernel by random sub-sampling is one of the methods to reduce the computational time. The advantage of this approximation is easily controllable for the balance between its approximation accuracy and computational time. However, it is tricky to optimize this approximation to suit the multi-core and SIMD instructions provided in recent CPUs. Halide is a domain-specific language for image processing and can powerfully accelerate image processing with a concise description. One of the remarkable points of Halide is that it can be described as a separate description of the algorithm, which is the essence of image processing, and scheduling, which optimizes the processing such as parallelization and vectorization. Using Halide, we optimize the approximation of the FIR filter by the randomized algorithm. Also, we design a new domain-specific language that outputs the optimized Halide code in a concise description.
Convolutional Autoencoder based Deep Learning Model for Identification of Red Palm Weevil Signals

This paper presents a Convolutional Autoencoder based Deep Learning model for identification of Red Palm Weevil acoustic emissions from other background noise. Mel spectrogram of acoustic samples was chosen as the extracted feature for the proposed model. The designed Convolutional Autoencoder was trained using Mel spectrogram images of Red Palm Weevil acoustic activities which are regarded as the normal instances. Unbiased evaluation of the model was done with a test dataset composed of normal RPW acoustic emissions as well as anomalous acoustic samples. The model could achieve a very high classification accuracy of 95.85%. The results confirmed that the proposed method is highly efficient for the identification of Red Palm Weevil signals.
Performance Characterization of Rasterization Algorithms for Reconfigurable Graphics Processor

For different rasterized scenes, even different areas of the same scene, the performance bottleneck of rasterization may be different, and current graphics processors cannot choose the appropriate rasterization algorithm according to the specific rendering scene. Therefore, a reconfigurable graphics processor that supports switching of algorithms to achieve the best performance is a promising choice. The existing graphics processor suffers the constraints of calculation, memory and power consumption in different rasterization application scenarios. Therefore, it is very important to determine how to schedule different rasterization algorithms with different performance according to the actual requirements in the reconfigurable graphics hardware. This paper evaluates and analyzes the performance characteristics of three main-stream rasterization algorithms (scan-line filling algorithm, edge filling algorithm, and flood filling algorithm) in different application scenarios. Pearson correlation coefficient (PCC) analysis is leveraged to analyze the relationship between performance/energy and evaluation metrics. Based on these performance characterization data, this paper puts forward some reconstruction suggestions for the reconfigurable graphics processor. We hope to contribute to reconfigurable graphics processing.
HierVAE++: An Update of Hierarchical Generation of Molecular Graphs Using Structural Motifs

Molecular generation techniques are increasingly being used for drug discovery. Previous graph generation methods utilized relatively large motifs extracted from thousands of existing molecules, limiting their effectiveness for molecular diversity. In this paper, we propose an updated version of hierarchical and graphical encoder-decoder-based method, called HierVAE++, that employs relatively smaller and more diverse graphical motif patterns as the basic blocks. The activation function is changed to a Leaky ReLU in the encode, and the hyperparameters of the neural network are adjusted accordingly. We evaluate our model in terms of the chemical properties, physical structures, the similarities of source molecules, etc. The results show that our HierVAE++ is more advantageous in generating structural diverse molecules which remain high average chemical property scores. This provides a new thinking of the drug generation industry.
Contact Bounce Model Considering Arc Ignition for Relay Contact Reliability Evaluation

Effective and comprehensive information of the bounce behavior for the relay will greatly affect the contact reliability evaluation. Due to the coexistence of the vibration problem and the correlations evaporation in the physical behaviors of the components, the contact process is difficult to simulate especially for relay with arc ignition. In view of the importance of the contact bounce model for obtaining accurate contact information, a new contact bounce model considering arc phenomena is established in this study, which can provide a quantitative explanation for the experimental behavior. The advantages of proposed bounce model are: 1) it involves the impact, bounce, electrical fields, magnetic force, repulsion force, and electrical arc during the closing operation for a real relay; and 2) it is computationally efficient and capable in simulating dynamic process and collecting bounce parameters. The application on a typical commercial relay demonstrates the performance of the proposed model. It is found that the bouncing tendency of the contact reed is determined by the plasma pressure and the impact effect in the contact area.