Miracle-dz/UniFAR-FTUs huggingface dataset:
A Graph-Voxel Joint Convolution Neural Network for ALS Point Cloud Segmentation

A deep convolution neural network is frequently used in airborne laser scanning (ALS) point cloud segmentation. In this study, we propose a joint graph-voxel convolution network to recognize on-ground objects accurately. In our network, the spatial context features of an input point cloud are first extracted by a designed U-Net via sparse convolution neural networks (SU-Net). Next, the extracted features are used as input features of a designed graph convolution network. We design a graph convolution module called the G-Net to extract the local spatial structure of each point. To enhance the representation of spatial context information, we initialize a graph based on a horizontal direction to enhance the difference between objects and the ground. The output probabilities of SU-Net and the graph convolution model are weighted as the input of the conditional random field optimizing model. The framework proposed in this study exhibits high processing efficiency. Experiments on the semantic 3D labeling dataset of the International Society for Photogrammetry and Remote Sensing (ISPRS) and 2019 IEEE Data Fusion Contest Dataset demonstrate that the proposed model significantly improve the highest F1 score and outperforms various previous networks.
An Optimal Charging of Plug-In Electric Vehicles in Unbalanced Three-Phase Distribution Network

Electric vehicles (EVs) are developing due to concerns over global warming and the major role of the transportation sector in emissions. EVs can also flatten the power curve and increase the reliability of power grids when renewable energy sources are used. Despite of these benefits, EVs impose new loads on distribution networks. Simultaneous charging of EVs, especially at high penetration levels, can create new load peaks in the power curves as well as overloading transformers, shortening their service life. In actual applications, most electric cars are single-phase loads that need to be charged from household or commercial outlets. In this paper, an optimization method is presented to coordinate the dynamic charge operation of single-phase EVs in an unbalanced three-phase distribution network. In the proposed method, the main goal of charging management is to minimize the total cost, which considers both network security constraints and electric vehicle constraints. The proposed method is tested on a sample distribution network and the numerical results prove the effectiveness of the method.
A Machine Learning Approach to Classify Security Patches into Vulnerability Types

With the increasing usage of open source software (OSS) in both free and proprietary applications, vulnerabilities embedded in OSS are also propagated to the underlying applications. It is critical to find security patches to fix these vulnerabilities, especially those essential to reduce security risk. Unfortunately, given a security patch, currently there does not exist a way to automatically recognize the vulnerability that is fixed. In this paper, we first conduct an empirical study on security patches by type (i.e., corresponding vulnerability type), using a large-scale dataset collected from the National Vulnerability Database (NVD). Based on analysis results, we develop a machine learning-based system to help identify the vulnerability type of a given security patch. The evaluation results show that our system achieves good performance.
Personalized tourism route recommendation based on user’s active interests*

Tourism is both an important industry and popular leisure activity undertaken by millions around the world. How to effectively mine the user’s travel mode and visit preferences based on the user’s historical travel data is a challenge. The tourism resources of different visiting areas, such as the popularity of POI (Point of Interest), influence the user’s interests dynamically. Therefore, a user’s interest preferences during traveling may differ between geographical region. In this paper, we introduce a personalized travel route recommendation framework, named PTDR, based on region dependent personal interest. PTDR consists of two parts, which are POI recommendation and itinerary generation. We analyzed the user’s history interest from check-in behavior in detail and constructed a convolutional neural network to extract the potential features of the target visiting area. Then the user’s active interest is learned from the user’s history interest and the potential features of the target area. Finally, we optimize the itinerary for a user based on the orienteering problem, which takes into account the user’s travel restrictions, such as time limits, starting attraction restrictions, and destination attraction restrictions. We evaluated the proposed algorithm on four cities of Flickr datasets and compared them to existing travel recommendations, including accuracy, recall, and F1. Experiments verify the effectiveness of the proposed method.
IFLoc: Indoor Height Estimation by Telco Data

Understanding the fine-grained distribution of telecommunication (Telco) signals in terms of a three-dimensional (3D) space is important for Telco operators to manage, operate and optimize Telco networks. It is particularly true in nowadays urban cities with a large number of high buildings. One of the key tasks is to infer the location height of mobile devices, e.g., the floor within a high building where mobile devices are located. However, precise height estimation is challenging due to complex Telco signal propagation within an indoor 3D space, sparse cell tower deployment and scarce training samples. To tackle these issues, in this paper, we propose an indoor MR height estimation framework, namely IFLoc, via a machine learning model. IFLoc first builds a training MR database via a pre-processing step to comfortably tag raw MR samples by precisely inferred height from auxiliary data such as GPS and barometer readings. Next, IFLoc trains a regression model for height estimation by a set of developed techniques including 3D space division, post-processing techniques, feature augmentation and an improved SVR (Supported Vector Regression) model. Our evaluation on eight real datasets collected within five representative high buildings in Shanghai validates that IFLoc outperforms state-of-the-art counterparts in particularly with scarce training data.
Autonomic correlates of attention tests delivered by commercial tools

Attention tests are among the most widely used for characterizing one of the most important human cognitive domains. However, their link with emotional processes and physiological reactions is not fully clear and often debated. To this extent, in this pilot we used commercially available tools for administering a validated attentional test and, at the same time, to unobtrusively record physiological parameters related to the autonomic nervous system activity in a small cohort of young, healthy individuals. Preliminary results demonstrated the feasibility of this approach and suggested that the methodology employed was somewhat sensitive to detect changes related to the task administered in the cohort studied. Such results would probably pave the way for a future exploitation of integrated hardware/software systems tailored at detecting psychophysiological changes eventually occurring during cognitive testing administration.
Unmanned Aerial Vehicle platform based on low-power components and environmental sensors: technical description and data analysis on real-time monitoring of air pollutants

Nowadays, environmental pollution levels are a worldwide concern due to the risks for people’s health. The demand for cutting-edge technologies to determine the sensible areas where pollutants are present in dangerous concentrations is ever increasing, along with the stringent requirements of avoiding the exposure of human operators to toxic substance. For this reason, the proposed work presents a novel method for the surveillance and monitoring of large areas by means of unmanned aerial vehicles. The work encompasses both hardware and software integration of a low-power multi-sensor platform hosted by the aerial vehicle. The platform comprises a multi-spectral camera, digital optics sensors and toxic/pollutant gas concentration detectors coupled with low-power computing and wireless communication capabilities. Furthermore, a base station platform is implemented to collect data and to provide the analytical tools that facilitate the responsible authorities in the decision of intervention.
Gonopodial Structures Revisited: Variation in Genital Morphology Within and Across Four Populations of the Western Mosquitofish (Gambusia affinis) in Texas

Abstract We report on gonopodial structures in the western mosquitofish (Gambusia affinis) including forked distal hooks and presence-absence of the elbow and gap. In addition, we present information on gonopodial variation across the Trinity, San Jacinto, and Brazos river drainages in Texas. Resumen Reportamos la estructuras gonopodiales en el pez-mosquito (Gambusia affinis) incluyendo las horquillas de los ganchos distales y presencia-ausencia del codo y brechas del gonopodio. Además, se presenta información sobre la variabilidad en el gonopodio a través de las cuencas de los ríos Trinity, San Jacinto, y Brazos en Texas.
SiC-based Four-Port DAB Converter for High Power Density Fast Charging Station

Number of electric vehicles (EVs) continues to expand rapidly. This leads to significantly increase the amount of charging stations with reduced charge time. This paper focuses on design and realization of the isolated Four-Port bidirectional Dual Active Bridge DC-DC Converter (4P-DAB) to be used in fast charging applications. Each port of the 4P-DAB can manage different sources and loads thanks to the full bridge converter. A magnetic node is created connecting all the ports through a high-frequency multi-winding transformer. The efficiency conversion as a function of the output power has been carried out using Silicon-Carbide (SiC) power semiconductors. Different control strategies have been applied to improve the performance of the converter. Simulation and optimization results are verified by realizing the switching model of 100 kVA 4P-DAB in MATLAB/Simulink.
Towards Data-Driven Characterization of Brute-Force Attackers

Brute-force login attempts are common for every host on the public Internet. While most of them can be discarded as low-threat attacks, targeted attack campaigns often use a dictionary-based brute-force attack to establish a foothold in the network. Therefore, it is important to characterize the attackers’ behavior to prioritize defensive measures and react to new threats quickly. In this paper we present a set of metrics that can support threat hunters in characterizing brute-force login attempts. Based on connection metadata, timing information, and the attacker’s dictionary these metrics can help to differentiate scans and to find common behavior across distinct IP addresses. We evaluated our novel metrics on a real-world data set of malicious login attempts collected by our honeypot Honeygrove. We highlight interesting metrics, show how clustering can be leveraged to reveal common behavior across IP addresses, and describe how selected metrics help to assess the threat level of attackers. Amongst others, we for example found strong indicators for collusion between ten otherwise unrelated IP addresses confirming that a clustering of the right metrics can help to reveal coordinated attacks.
Non intrusive electrical load identification through an online SFRA based approach

The paper describes some initial evaluations concerning the possible use of SFRA based method of analysis to the pattern of energy use in the domestic residential sector. The paper illustrates the SFRA techniques of investigation that are well-consolidated tools in various research sectors as for the identification of faults in transformers and induction motors. The SFRA techniques are then applied to the very current interesting problem of domestic appliance signature identification in non-intrusive approaches of load research. Some first results of the applications of the techniques as obtained from laboratory tests conducted on a domestic electric installation simulator
Study on fault ride‐through control of islanded wind farm connected to VSC‐HVDC grid based on the VSC converter AC‐side bus forced short circuit

Wind farms integrated to an AC power system through a voltage source converter-based high-voltage direct current system (VSC-HVDC) are viewed as islanded wind farms. The wind farm side converter of VSC-HVDC maintains a constant frequency of the wind farm collection network and also controls its voltage. However, the transient responding time of VSC-HVDC is very short, and its fault responding feature is at the millisecond (ms) level. As a result, under grid fault conditions, the VSC station could be overloaded and even operation could be quitted because of blocking if islanded wind farms fail to limit their power generation in time. This study proposes a fault ride through control method for VSC-HVDC connected with islanded wind farms based on the forced short circuit of the AC-side bus of the VSC station. When there is a fault in the VSC-HVDC and the protection equipment is not able to cut off the excessive power at the ms level, the short-circuit resistance installed on the AC side of the VSC station would be switched to make a forced short circuit. That would activate the low-voltage ride through mode of islanded wind farms, and therefore reduce the power flowing into the VSC quickly. The HVDC fault and short circuit on the AC side would be cleared at the same time. The VSC-HVDC system accomplishes fault ride through.
Modeling human visual responses with a U-shaped deep neural network for motion flow-field estimation

: Deep Neural Networks (DNNs) for visual classification have recently been shown to exhibit representations homologous to those observed in the human visual system. For further exploring the relationship between artificial and natural neural networks, we focus on a U-shaped contraction-expansion architecture for DNNs, which recursively refine the network output for solving pixel-wise 2D-prediction tasks. By using FlowNet (Dosovitskiy et al., 2015), a U-shaped DNN for motion flow-field estimation, we show that the DNN-features extracted from FlowNet accurately predict human visual responses to natural movie stimuli. We further present that the features from the expansion compared to contraction layers yield higher encoding performance for the mid-level regions in the dorsal visual pathway. Our results may support the notion of the information integration between the early processing stages preserving fine spatial information and the downstream processing stages providing global contextual cues for motion estimation in the human visual system.
SMART-Env

In this work, we present SMART-Env (Spatial Multi-Agent Resource search Training Environment), a spatio-temporal multi-agent environment for evaluating and training different kinds of agents on resource search tasks. We explain how to simulate arbitrary spawning distributions on real-world street graphs, compare agents’ behavior and evaluate their performance over time. Finally, we demonstrate SMART-Env in a taxi dispatching scenario with three different kinds of agents.
Case Study: First-Time Success ASIC Design Methodology Applied to a Multi-Processor System-on-Chip

Achieving first-time success is crucial in the ASIC design league considering the soaring cost, tight time-to-market window, and competitive business environment. One key factor in ensuring first-time success is a well-defined ASIC design methodology. Here we pro- pose a novel ASIC design methodology that has been proven for the RUMPS401 (Rahman University Multi-Processor System 401) Multiprocessor System-on-Chip (MPSoC) proj- ect. The MPSoC project is initiated by Universiti Tunku Abdul Rahman (UTAR) VLSI design center. The proposed methodology includes the use of Universal Verification Methodology (UVM). The use of electronic design automation (EDA) software during each step of the design methodology is also presented. The first-time success RUMPS401 demonstrates the use of the proposed ASIC design methodology and the good of using one. Especially this project is carried on in educational environment that is even more limited in budget, resources and know-how, compared to the business and industrial counterparts. Here a novel ASIC design methodology that is tailored to first-time success MPSoC is presented. simulation After the physical design of the RUMPS401 MPSoC is completed, Resistance and Capacitance (RC) characteristics can be extracted from the metal routes. These RC numbers provide more accurate timing delays across the signal paths. Another round of Static Timing Analysis is performed to consider these RC delays information. Adjustment is done to the physical design to eliminate any reported timing violations. In the first STA run, many hold time violations are reported, especially in the scan chain test mode. These are due to the direct path between the output pin of the flip-flops to the scan input of the next flip-flops. The parasitic RC delays affect the clock skew and the path delays. However, the violations are very minor and can be in the fractions of nanoseconds. Delay cells insertions are done to fix these violations. After all violations are fixed, the final timing reports are then used for design sign-off.
Model-Based System-Level EMI/EMC Simulation for BCI Pass-Fail Prediction

Electromagnetic Interference contributes to a significant percentage of failures in the verification stage of automotive electronics design cycle. Today these issues are caught late in the design cycle leading to delay in time-to-market. An effective simulation methodology is key to catching the problems at an early design stage. However, accurate system-level analysis is time intensive. This letter presents a model-based simulation methodology for system-level immunity characterization at an early design stage. The simulation framework is comprised of a hybrid 2D-3D electromagnetic solver and a circuit solver. IC immunity model (ICIM) is inducted into the simulation environment to accurately predict the immunity behavior in a Bulk Current Injection (BCI) test. The proposed method is validated by comparing the simulation prediction with the actual BCI measurement. Further, layout changes are made based on simulation to mitigate the measurement failure. This emphasizes the use of model-based simulation to predict immunity performance at early product design cycle.
Secure Strong Coordination

We consider a network of two nodes separated by a noisy channel, in which the source and its reconstruction have to be strongly coordinated, while simultaneously satisfying the strong secrecy condition with respect to an outside observer of the noisy channel. In the case of non-causal encoding and decoding, we propose a joint source-channel coding scheme for the secure strong coordination region. Furthermore, we provide a complete characterization of the secure strong coordination region when the decoder has to reliably reconstruct the source sequence and the legitimate channel is more capable than the channel of the eavesdropper.
Chirality Nets: Exploiting Structure in Human Pose Regression

We propose Chirality Nets, a family of deep nets that is equivariant to the “chirality transform,” i.e . , the transformation to create a chiral pair. Using parameter sharing, odd and even symmetry, we develop variants of standard building blocks of deep nets that satisfy the equivariance property, including fully connected layers, convolutional layers, batch-normalization, and LSTM/GRU cells. The proposed layers lead to a more data efﬁcient representation and a reduction in computation due to their symmetry. To assess efﬁcacy, we study three pose regression tasks: 3D pose estimation from video, 2D pose forecasting, and skeleton based activity recognition. Our approach achieves/matches state-of-the-art results, with more signiﬁcant gains on small datasets and limited-data settings.
Intake of a Japanese-Style Healthy Lunch Has Possibilities of Contributing to the Normalization of Serum Lipids and Adipokines: A Non-Randomized Controlled Trial Pilot Study

To date, the incidence of metabolic syndrome has been increasing continuously worldwide; and recently, low vegetable consumption has become a major issue of concern in Japan. Furthermore, Japan is facing a shortfall in places offering food that can help prevent metabolic syndrome. Our study was designed to determine the influence of an on-going healthy lunch on metabolic syndrome outcomes (blood pressure, serum TNF-alpha, IL-6, serum lipids) in adult middle-aged office male workers, in Japan. We conducted a non-randomized controlled trial among 38 middle-aged office male workers (control group: 7 males, intervention group: 31 males) with mostly low levels of physical activity, by providing a Japanese-style healthy lunch for 3 months (intervention group) at a workplace cafeteria. The control group consumed their habitual lunches without restriction and only the nutrient contents were assessed. Furthermore, the intervention group was divided into two (non-metabolic [non-MS] and metabolic syndrome [MS] groups) for analysis. Overall 38 males with a mean age of 47.2 ± 7.9 years were included. Abdominal circumference level (p<0.05), systolic and diastolic (p<0.05) blood pressure (p<0.01), as well as serum low-density lipoprotein (LDL-Chol), serum tumor necrosis factor (TNF-α) (p<0.001) and interleukin (IL-6) (p<0.01) decreased significantly after intervention compared with the baseline in the intervention/non-MS group and intervention/MS groups. Furthermore, ghrelin (p<0.001) and desacyl ghrelin (p<0.05) increased significantly after intervention compared with the baseline in the intervention/non-MS group and intervention/MS groups. These showed an effective outcome, demonstrated by the ongoing intake of a Japanese-style healthy lunch, of decreased blood pressure, serum TNF-α, IL-6, serum LDL-Chol and total cholesterol. This study presents new empirical data based on an original intervention program showing that the consumption of a Japanese-style healthy lunch containing many vegetables can help prevent and/or improve metabolic syndrome. The findings of this study could also lead to the opportunity for participants to practice following healthy menus at home. We therefore consider that this is valuable in promoting improvement in diet in the food environment at the workplace.