Miracle-dz/UniFAR-FTUs huggingface dataset:
ST3DNetCrime: Improved ST-3DNet Model for Crime Prediction at Fine Spatial Temporal Scales

Crime prediction is crucial for sustainable urban development and protecting citizens’ quality of life. However, there exist some challenges in this regard. First, the spatio-temporal correlations in crime data are relatively complex and are heterogenous in time and space, hence it is difficult to model the spatio-temporal correlation in crime data adequately. Second, crime prediction at fine spatial temporal scales can be applied to micro patrol command; however, crime data are sparse in both time and space, making crime prediction very challenging. To overcome these challenges, based on the deep spatio-temporal 3D convolutional neural networks (ST-3DNet), we devise an improved ST-3DNet framework for crime prediction at fine spatial temporal scales (ST3DNetCrime). The framework utilizes diurnal periodic integral mapping to solve the problem of sparse and irregular crime data at fine spatial temporal scales. ST3DNetCrime can, respectively, capture the spatio-temporal correlations of recent crime data, near historical crime data and distant historical crime data as well as describe the difference in the correlations’ contributions in space. Extensive experiments on real-world datasets from Los Angeles demonstrated that the proposed ST3DNetCrime framework has better prediction performance and enhanced robustness compared with baseline methods. In additon, we verify that each component of ST3DNetCrime is helpful in improving prediction performance.
Thermodynamics of flowing systems : with internal microstructure

PART 1: THEORY Introduction 1. Symplectic geometry in optics 2. Hamiltonian mechanics of discrete particle systems 3. Equilibrium thermodynamics 4. Poisson brackets in continuous media 5. Non-equilibrium thermodynamics 6. The dissipation bracket PART 2: APPLICATIONS 7. Incompressible viscoelastic flows 8. Transport phenomena in viscoelastic fluids 9. Non-standard transport phenomena 10. The dynamical theory of liquid crystals 11. Multi-fluid transport/reactions models with application in the modelling of weakly ionized plasma dynamics Epilogue Bibliography Appendices
Presenting Suspicious Details in User-Facing E-mail Headers Does Not Improve Phishing Detection

Phishing requires humans to fall for impersonated sources. Sender authenticity can often be inferred from e-mail header information commonly displayed by e-mail clients, such as sender and recipient details. People may be biased by convincing e-mail content and overlook these details, and subsequently fall for phishing. This study tests whether people are better at detecting phishing e-mails when they are only presented with user-facing e-mail headers, instead of full e-mails. Results from a representative sample show that most phishing e-mails were detected by less than 30% of the participants, regardless of which e-mail part was displayed. In fact, phishing detection was worst when only e-mail headers were provided. Thus, people still fall for phishing, because they do not recognize online impersonation tactics. No personal traits, e-mail characteristics, nor URL interactions reliably predicted phishing detection abilities. These findings highlight the need for novel approaches to help users with evaluating e-mail authenticity.
Research of Static and Dynamic Properties of Power Semiconductor Diodes at Low and Cryogenic Temperatures

Systems with high-temperature superconductors (HTSC) impose new requirements on power conversions, since the main part of the losses in such systems is induced in the semiconductors of the converters. Within the framework of this study, the possibility of improving the static and dynamic characteristics of power semiconductor diodes using cryogenic cooling was confirmed; in some cases, a loss reduction of up to 30% was achieved.
Detection of Tomato Ringspot Virus Based on Microfluidic Impedance Sensor

A microfluidic impedance sensor embedded with gold interdigitated array microelectrodes was utilized to rapidly detect Tomato Ringspot Virus (ToRSV) and achieve efficient and precise detection. The electrochemical impedance spectrum was obtained by immobilizing ToRSV antibody on the surface of a gold interdigital array microelectrode and mixing it with ToRSV to generate an impedance change. The electrochemical impedance spectrum was obtained. The equivalent circuit was established to analyze the mechanism of impedance change, and the quantitative linear relationship between ToRSV concentration and impedance was established. According to an equivalent circuit analysis, ToRSV increases the solution resistance Rs, the electron transfer resistance Ret on the electrode surface, and the double layer capacitance Cdl, resulting in an increase in impedance. The results reveal that the ToRSV concentration detected in the range of 0.001 to 10 μg/mL ranges from 248.8 to 687.2 kΩ at the ideal detection frequency of 10.7 Hz, with a good linear connection, R2 = 0.98. When this method’s detection limit is tested, the impedance value is 367.68 kΩ. 0.0032 μg/mL was the detection limit. The sensor is quick and easy to use, has high detection sensitivity, and can be used to detect other plant viruses.
The Polygonal 3D Layout Reconstruction of an Indoor Environment via Voxel-Based Room Segmentation and Space Partition

An increasing number of applications require the accurate 3D layout reconstruction of indoor environments. Various devices including laser scanners and color and depth (RGB-D) cameras can be used for this purpose and provide abundant and highly precise data sources. However, due to indoor environment complexity, existing noise and occlusions caused by clutter in acquired data, current studies often require the idealization of the architecture space or add an implication hypothesis to input data as priors, which limits the use of these methods for general purposes. In this study, we propose a general 3D layout reconstruction method for indoor environments. The method combines voxel-based room segmentation and space partition to build optimum polygonal models. It releases idealization of the architectural space into a non-Manhattan world and can accommodate various types of input data sources, including both point clouds and meshes. A total of four point cloud datasets, four mesh datasets and two cross-floor datasets were used in experiments. The results exhibit more than 80% completeness and correctness as well as high accuracy.
Spatio-Temporal Modelling Informing Wolbachia Replacement Releases in a Low Rainfall Climate

Simple Summary Aedes aegypti is a mosquito that is responsible for spreading viral diseases including dengue fever, Zika, and chikungunya. Disease spread can be reduced by releasing mosquitoes containing bacteria known as Wolbachia, which inhibit transmission. Before such releases, it is important to collect data about where Ae. aegypti occur in urban landscapes and how populations vary over space and time. In this study, we present a pre-release analysis of mosquito populations using ovitraps (traps that provide a substrate for female mosquitoes to lay eggs), in Jeddah, Saudi Arabia. Our study contains a number of important findings. Firstly, we showed that there was no difference in the numbers of eggs laid between basement and non-basement locations. Secondly, we showed that, for some study sites, there was significant spatial structure to populations, meaning that where numbers of eggs were high (or low) they tended to remain high (or low) on average for many hundreds of meters. We also found that when mosquitoes are present in an area during the dry season, they tend to remain into the wet season; however, regions of high egg production could change between seasons. This suggests that in Jeddah’s arid environment, the quality of breeding environments is inconsistent over time. Abstract Releases of Aedes aegypti carrying Wolbachia bacteria are known to suppress arbovirus transmission and reduce the incidence of vector-borne diseases. In planning for Wolbachia releases in the arid environment of Jeddah, Saudi Arabia, we collected entomological data with ovitraps across a 7-month period in four locations. Herein, we show that mosquito presence in basements does not differ from that of non-basement areas of buildings. In modelling mosquito presence across the study sites, we found the spatial structure to be statistically significant in one of the four sites, while a significant spatial structure was found for egg production data across three of the four sites. The length scales of the spatial covariance functions fitted to the egg production data ranged from 143 m to 574 m, indicating that high productivity regions can be extensive in size. Rank-correlation analyses indicated that mosquito presence tended to persist from the dry to wet season, but that egg production ranks at locations could reverse. The data suggest that, in Jeddah, the quality of the local environment for breeding can vary over time. The data support the feasibility of dry season releases but with release numbers needing to be flexible depending on local rates of invasion.
Pre- and post-crisis dynamics of ﬁnancial globalisation for ﬁnancial development in Africa

This study unites two streams of research by simultaneously focusing on the impact of financial globalisation on financial development and pre- and post-crisis dynamics of the investigated relationship. The empirical evidence is based on 53 African countries for the period 2004-2011 and Generalised Method of Moments. The following findings are established. First, whereas marginal effects from financial globalisation are positive on financial dynamics of activity and size, corresponding net effects (positive thresholds) are negative (within range). Second, while decreasing financial globalisation returns are apparent to financial dynamics of depth and efficiency, corresponding net effects (negative thresholds) are positive (not within range). Third, financial development dynamics are more weakly stationary and strongly convergent in the pre-crisis period. Fourth, the net effect from the: pre-crisis period is lower on money supply and banking system efficiency; post-crisis period is positive on financial system efficiency and pre-crisis period is positive on financial size. Policy implications are discussed. JEL
Some Problems with Properties A Study on Property-Based Testing in Industry

Property-based testing (PBT) is a popular testing methodology from the functional programming community that is beginning to see broader use. For developers who already think in terms of formal specifications of the behavior of their code, PBT provides an easy way to check those specifications; for those who do not yet, PBT’s successful track record offers a good reason to start. However, like any up-and-coming technology, PBT has plenty of room to grow and improve. We present results from a preliminary study that begins to explore the challenges and opportunities for PBT in industrial settings. Drawing on interviews with seven professional users of a contemporary PBT tool, we identify several areas in need of attention if PBT tools are to better meet developers where they are. Namely, developers encountered obstacles imagining properties to test, defining generators, integrating PBT into their development workflow, and educating themselves about PBT’s foundations and best practices. We detail participants’ challenges, highlighting directions where further research and tool-building efforts could mitigate the challenges. We conclude with an agenda for further deepening understanding of the pitfalls and potentials of PBT in industry. We offer a brief overview of research activities we plan to undertake in the coming years, including an expanded interview study to be carried out in partnership with Jane Street, LLC, a large software company that leads in its use of functional programming tools. We hope that disseminating this research program within the HATRA community will help galvanize further research into how to make PBT more widely useful.
Low-precision logarithmic arithmetic for neural network accelerators

Resource requirements for hardware acceleration of neural networks inference is notoriously high, both in terms of computation and storage. One way to mitigate this issue is to quantize parameters and activations. This is usually done by scaling and centering the distributions of weights and activations, on a kernel per kernel basis, so that a low-precision binary integer representation can be used. This work studies low-precision logarithmic number system (LNS) as an efficient alternative. Firstly, LNS has more dynamic than fixed-point for the same number of bits. Thus, when quantizing MNIST and CIFAR reference networks without retraining, the smallest format size achieving top-1 accuracy comparable to floating-point is 1 to 3 bits smaller with LNS than with fixed-point. In addition, it is shown that the zero bit of classical LNS is not needed in this context, and that the sign bit can be saved for activations. The proposed LNS neuron is detailed and its implementation on FPGA is shown to be smaller and faster than a fixed-point one for comparable accuracy. Secondly, low-precision LNS enables efficient inference architectures where 1 / multiplications reduce to additions; 2/ the weighted inputs are converted to classical linear domain, but the tables needed for this conversion remain very small thanks to the low precision; and 3/ the conversion of the output activation back to LNS can be merged with an arbitrary activation function.
Bi-objective Optimization for Collaborative UAV Secure Communication under Forest Channel

Unmanned aerial vehicles (UAVs) have the advantages of low-cost and flexible deployment. It can be used as the aerial transmitter in forests scenarios. However, forest terrain has severe multi-path reflections and may have several eavesdroppers, which degrade the performance of the communications. In this paper, we consider a collaborative UAV secure communication model under a forest channel, where multiple UAVs form a virtual antenna array (VAA) to communicate with a ground base station (BS) and circumvent the influence of the eavesdropper. Moreover, we formulate a multi-objective optimization problem (MOP) to simultaneously optimize the secure performance and energy efficiency of the considered system. Then, we propose an enhanced multi-objective particle swarm optimization algorithm (EMOPSO) which is based on chaos theory, linear weight reduction method and optimal set-based mutation approach to solve the formulated MOP. Simulation results show that the proposed method is effective and the proposed EMOPSO outperforms other benchmark algorithms.
Recursive MCC Algorithm with Proportionate Update Mechanism for Robust Sparse System Identification

This paper proposed an effective robust adaptive filtering algorithm (RAFA) for sparse system identification (SSI) considering impulsive noise interference. Because of the robustness of the correntropy, it has been utilized as a cost, called maximum correntropy criterion (MCC), for designing robust sparse AFA via proportionate update method. However, the original proportionate MCC algorithm has some defects in the initial convergence speed. To address this problem, the proportionate update matrix is inserted into the gain matrix of the recursive MCC algorithm, which takes advantage of both proportionate update method and recursive RAF =A. Therefore, the proposed proportionate recursive MCC (PRMCC) algorithm not only can obtian low steady-state misalignment, but also can show a fast convergence speed for SSI in presence of impulsive noise. Some numerical simulations are performed to test the robustness of the proposed PRMCC method under different conditions, and the simulation results show that it can outperform other outstanding algorithms for SSI in non-Gaussian noise with outliers environments.
Medium Voltage Diode Rectifier Design for High Step-Up DC-DC Converter

A medium voltage rectifier, relying on series connection of SiC Schottky diodes, is designed and tested within a unidirectional isolated DC-DC converter. The design is realized considering efficiency, static and dynamic balancing and failure mode considerations. The model of the rectifier circuit is improved compared to the previous literature enabling to precisely represent the voltage oscillations due to switching, facilitating the design of the snubbers. Experimental results are presented to validate the design. The developed model results show good fit with the measurements.
A Proposed Work for Reduction Of Power In Level Shifter

The growing urge of VLSI technology to exhibit low power and high speed operating devices has always led to the need of devices that operate at multiple voltage domains. In order to incorporate this multiple voltage devices on a single VLSI chip, the level shifter forms an essential circuit. This power domain issue is more prominent in the devices exhibiting sub-threshold operations. Thus designing a level shifter plays a crucial role especially in the design of complex circuits that have multi-voltage domains. The major challenges in the design of level shifters are low power dissipation, transistor count, high performance and robustness. This paper focuses on an attempt to change the circuit in order to get low power dissipation. Multiple Threshold Level Shifter (MTLS) that incorporates transistors with different threshold voltages within the circuit. Compared to the existing Differential cascode voltage switch (DCVS) logic, the power delay product (PDP) of MTLS is found to be efficient. Thus MTLS could be used to cascade complex circuits.
UAV-enabled Wireless Powered Communication Networks: A Joint Scheduling and Trajectory Optimization Approach

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCN) are promising technologies in Internet of Things (IoTs). However, energy-constrained devices and connectivity in complex environments are two major challenges for IoTs. We consider a UAV-enabled WPCN scenario that a UAV can connect with the ground IoT devices (IoTDs). To connect and fly faster, UAV needs to be scheduled reasonably and the corresponding trajectory should be optimized. Thus, we formulate a UAV scheduling and trajectory optimization problem (USTOP) to minimize the total time so that improving the charging and transmission efficiency. Since conventional methods are difficult to solve USTOP, we propose an improved simulated annealing (ISA) with the variable size changing mechanism, the conflict resolution mechanism and the hybrid evolution method to solve it. Simulation results verify the effectiveness and performance of ISA under different scales of the network, and the stability of the proposed algorithm is verified.
Room Acoustic Properties Estimation from a Single 360° Photo

Estimating room impulse responses (RIRs) in real spaces is a time-consuming and expensive process requiring multiple pieces of equipment, recordings, and processing. A simple computer-vision-based method from a single 360° photo is proposed to estimate the acoustic material properties of the space by reconstructing an approximated 3D geometry. A 3D semantic geometry model is reconstructed from a 360° image by monocular depth estimation and semantic scene completion. The material properties of semantic objects in the scene are estimated using the transformer-based dense material segmentation method. This model is used to simulate a 3D acoustic room model on the Unity platform with Steam spatial audio plug-in. Acoustic properties of the space are estimated from this virtual reproduction and evaluated against the actual ones in the real environment.
Energetic Response of Human Walking With Loads Using Suspended Backpacks

The suspended backpack is a wearable robot applied in load carriage to reduce the energetic cost of human walking or harvest energy and generate electricity. An essential problem in the control objective of the suspended backpack is the unknown energetic response of humans, which would affect the human walking cost and energy harvesting efficiency. In this article, we aim to figure out the human response to different patterns of relative load movement and provide high-level instructions for controlling the actively suspended backpack. The additional force induced by the relative load movement is characterized by two independent features: The impulse during the double-support phase, and the mechanical work over a step. We predict the human response to different combinations of these two features, with the results validated by experimental energetic cost versus phase of relative load movement, and compared to the empirical data from previous reports. The analyses suggest that the upward impulse during the double-support phase and inputting energy could reduce the energetic cost of humans, and the upward impulse and outputting energy could generate electricity with high-energy harvesting efficiency.
Building instance segmentation in high-resolution remote sensing images based on multi-task learning

In the high-resolution remote sensing image building extraction algorithm based on deep neural network, the building extraction is regarded as a binary classification problem (that is, the pixels in the remote sensing image are divided into two categories: buildings and non-buildings), and individual buildings cannot be distinguished The limitations of the U-Net deep neural network method based on the Xception module are combined with the multi-task learning method to segment the building instances. While obtaining the results of the second classification of buildings, different building individuals are distinguished, and Inria Aviation is selected. The image dataset is used to validate the method. The results show that the improved U-Net method based on the Xception module is significantly better than the U-Net method in the extraction of building binary classification from high-resolution remote sensing images, and the extraction accuracy is increased by 1.4%; the deep neural network method combined with multi-task learning Not only can the instance segmentation of buildings be realized, but also the extraction accuracy of two-class buildings can be improved by about 0.5%.
Dropout Prediction System to Enhance Massive Open Online Courses

Statistics have shown a growth rate of 120% from 2019 to the end of 2020 in MOOCs Courses around the world. This research proposes a new methodology in predicting student result in MOOCs modules. Since dropouts and failure rates of MOOCs’ students is a well noticed problem, the proposed methodology contributed a new model that uses various feature selection algorithms and Probabilistic Neural Network (PNN) classification algorithm. Results showed that using certain feature selection algorithms in combination with PNN resulted in enhancing trend exploration and prediction.
Motion Planning Combines Human Motion Prediction for Human-Robot Cooperation

Human safety is the most important re-quirement for human robot cooperation (HRC). Al-though all kinds of robots have come to our side, human safety is still an ongoing research problem for human-robot coexisting scenarios. Different from other motion planning algorithms that only consider dynamic obstacle avoidance in ordinary environment, this paper combines the user's head pose in a human-robot coexisting environment. First of all, based on the correlation between head rotation and wrist (dynamic obstacle) motion, this paper takes the user's head pose, wrist position and wrist speed as the input of prediction model based on LSTM(Long Short-Term Memory), and then predicts the wrist position. Secondly, we propose a motion planning method combined with predicted wrist position, which can ensure that the distance between the robot and the user's wrist is in a safe range and the path length of manipulator end-effector is shorter. Finally, we demonstrate our idea on a 7-DOF TIAGo robot for human-robot cooperation task. Results show that the robot becomes sense motive, and responds to human motion quickly and efficiently.