Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach of Traffic Accident Scene Reconstruction Using Unmanned Aerial Vehicle Photogrammetry

Accurate and detailed information of traffic accident scene is important for accident investigation, and the current investigation methods (tape measuring and total station survey) always need highway closure, and their working process is time-consuming. From different angles or at different altitudes, unmanned aerial vehicle (UAV) can monitor accident site without interrupting the traffic flow, therefore, UAV is introduced for accident scene reconstruction. Firstly, the method framework of accident scene reconstruction was proposed, in which UAV was used to take pictures of accident site, and imaging system was adopted to reconstruct the 2D and 3D accident scene. Then, 3D reconstruction, point cloud generation, and model optimization were presented. Next, a UAV flight experiment was conducted for traffic accident scene reconstruction, and two evaluation indexes, signal-to-noise ratio and structural similarity, were introduced to assess the image quality of accident scene reconstruction. The case study demonstrates that compared with current methods, the proposed method is efficient; moreover, the effect of accident scene reconstruction is satisfactory.
Random Graph Modeling

Random graph (RG) models play a central role in complex networks analysis. They help us to understand, control, and predict phenomena occurring, for instance, in social networks, biological networks, the Internet, and so on. Despite a large number of RG models presented in the literature, there are few concepts underlying them. Instead of trying to classify a wide variety of very dispersed models, we capture and describe concepts they exploit considering preferential attachment, copying principle, hyperbolic geometry, recursively defined structure, edge switching, Monte Carlo sampling, and so on. We analyze RG models, extract their basic principles, and build a taxonomy of concepts they are based on. We also discuss how these concepts are combined in RG models and how they work in typical applications like benchmarks, null models, and data anonymization.
Seamless Parametrization with Arbitrary Cones for Arbitrary Genus

Seamless global parametrization of surfaces is a key operation in geometry processing, e.g., for high-quality quad mesh generation. A common approach is to prescribe the parametric domain structure, in particular, the locations of parametrization singularities (cones), and solve a non-convex optimization problem minimizing a distortion measure, with local injectivity imposed through either constraints or barrier terms. In both cases, an initial valid parametrization is essential to serve as a feasible starting point for obtaining an optimized solution. While convexified versions of the constraints eliminate this initialization requirement, they narrow the range of solutions, causing some problem instances that actually do have a solution to become infeasible. We demonstrate that for arbitrary given sets of topologically admissible parametric cones with prescribed curvature, a global seamless parametrization always exists (with the exception of one well-known case). Importantly, our proof is constructive and directly leads to a general algorithm for computing such parametrizations. Most distinctively, this algorithm is bootstrapped with a convex optimization problem (solving for a conformal map), in tandem with a simple linear equation system (determining a seamless modification of this map). This initial map can then serve as a valid starting point and be optimized for low distortion using existing injectivity preserving methods.
Improving Flash Translation Layer Performance by Using Log Block Mapping Scheme and Two-level Buffer for Address Translation Information

In the era of big data, the requirement of mass storage and fast access of data makes solid state disk(SSD) based on NAND flash be widely used. However, increasing flash memory capacity imposes huge SRAM consumption for logical-physical translation table in a page-level flash translation layer(FTL). Existing FTL schemes selectively cache the on-demand address mappings to quicken the address translation, while keeping all address mappings in flash memory. But the page-level catching mechanism causes a certain degree of cache pollution. In this paper, we manage page-level address translation information at hybrid-level mapping scheme and use two-level buffer for map groups to decrease SRAM consumption while reducing the cache pollution. What's more, an efficient replacement policy is designed. We can increase the cache hit ratio and reduce the write backs of evicted dirty entries and decrease garbage collection operations by these means. The performance and lifetime of the flash memory is improved. Experimental results show that the proposed scheme increases cache hit ratio by up to 28% and decreases the average response time by up to 23% compared with the existing FTL schemes.
SYG-Net: A New High-Precision Vehicle Detection Network

To improve the accuracy of vehicle detection, a vehicle detection neural network SYG-Net with YOLOv3 network as the main body was proposed and combined with generalized Intersection over Union (GIoU) and spatial pyramid pooling (SPP) module. The backbone network of SYG-Net network is the basic network structure of YOLOv3. However, a layer of SPP was added before the main network structure of feature extraction, namely, darknet and YOLO layers. In this manner, the features before the input of YOLO layer can obtain spatial features. GIoU was used as the regression loss of BBox at the end of the network layer and tested on UA-DETRAC data set. Results showed that the map and recall values of SYG-Net network increased substantially. Meanwhile, loss and average GIoU converged quickly and had good effect. SYG-Net was 0.75% and 0.75% more accurate than YOLOv3 and 0.7 YOLOv3-SPP, respectively. Results showed that SYG-Net was effectively detects vehicle. This paper looks forward to the combination of SYG-Net and other modules.
Gas what?: I can see your GasPots. Studying the fingerprintability of ICS honeypots in the wild

Internet connectivity of electronic devices has brought us the ease of centralized management and these days more and more devices are connected to this globally accessible network. At the same time, this landscape has opened new doors for malicious actors. While internet connectivity is a built-in feature for desktop and mobile devices, Industrial Control Systems (ICS) lag behind. Traditionally, ICS networks have been air-gapped and as a result, many ICS devices are not well-equipped to be connected to the internet. Absence of proper authentication and other security mechanisms is commonly observed on these devices. In response to the new threats of connected ICS systems, various ICS honeypots have been developed during the past decade. These honeypots are used to collect information on the attack landscape of ICS systems. In this research, we show that ICS honeypots should be designed more carefully and existing honeypots can fairly easily be fingerprinted by the attackers. We systematically study the categories of often overlooked behaviors that make ICS honeypots fingerprintable. Moreover, to demonstrate the impact of these flaws, we perform a large scale analysis over the internet to detect GasPot honeypots that emulate automatic tank gauges (ATG). We were able to find 17 existing honeypot instances which is more than the number of discovered GasPots by Shodan. Finally, we released our ICS honeypot scanner and our ATG honeypot which provides full protocol support and fixes the existing flaws within GasPot that makes it detectable.
Lithography Hotspot Detection with FFT-based Feature Extraction and Imbalanced Learning Rate

With the increasing gap between transistor feature size and lithography manufacturing capability, the detection of lithography hotspots becomes a key stage of physical verification flow to enhance manufacturing yield. Although machine learning approaches are distinguished for their high detection efficiency, they still suffer from problems such as large-scale layout and class imbalance. In this article, we develop a hotspot detection model based on machine learning with high performance. In the proposed model, we first apply an Fast Fourier Transform--based feature extraction method that can compress large-scale layout to a multi-dimensional representation with much smaller size while preserving the discriminative layout pattern information to improve the detection efficiency. Second, addressing the class imbalance problem, we propose a new technique called imbalanced learning rate and embed it into the convolutional neural network model to further reduce false alarms without accuracy decay. Compared with the results of current state-of-the-art approaches on ICCAD 2012 Contest benchmarks, our proposed model can achieve better solutions in many evaluation metrics, including the official metrics.
Triple Data Encryption algorithm based Multiple Authority Access Control in cloud system using optimal threshold

Cloud storage is most important service provided by cloud system. In cloud storage service, data is stored on cloud server from different owners. Access control to the stored data is one of big challenge for data owners. Stored data can be accessed by cloud authority without knowledge of data owners. To overcome this problem the new technology is introduced known as Cipher text policy attribute dependent encryption. This encryption technique is one of the suitable technique to offer proper data access control in cloud storage. In existing Attribute dependant encryption techniques, single authority maintain the attribute key for encryption and decryption of stored data which carry single point blockage on security as well as performance. This paper suggested optimal threshold based multi Authentication attribute dependant data access control method which offer significant, proficient and revoked data access control scheme. 1 International Journal of Pure and Applied Mathematics Volume 118 No. 24 2018 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl. eu/hub/ Special Issue http://www.acadpubl. eu/hub/
Deep Neural Network Based Inertial Odometry Using Low-Cost Inertial Measurement Units

Inertial measurement units (IMUs) have emerged as an essential component in many of today's indoor navigation solutions due to their low cost and ease of use. However, despite many attempts for reducing the error growth of navigation systems based on commercial-grade inertial sensors, there is still no satisfactory solution that produces navigation estimates with long-time stability in widely differing conditions. This paper proposes to break the cycle of continuous integration used in traditional inertial algorithms, formulate it as an optimization problem, and explore the use of deep recurrent neural networks for estimating the displacement of a user over a specified time window. By training the deep neural network using inertial measurements and ground truth displacement data, it is possible to learn both motion characteristics and systematic error drift. As opposed to established context-aided inertial solutions, the proposed method is not dependent on either fixed sensor positions or periodic motion patterns. It can reconstruct accurate trajectories directly from raw inertial measurements, and predict the corresponding uncertainty to show model confidence. Extensive experimental evaluations demonstrate that the neural network produces position estimates with high accuracy for several different attachments, users, sensors, and motion types. As a particular demonstration of its flexibility, our deep inertial solutions can estimate trajectories for non-periodic motion, such as the shopping trolley tracking. Further more, it works in highly dynamic conditions, such as running, remaining extremely challenging for current techniques.
Anthropogenic Occurrence of Phthalate Esters in Beach Seawater in the Southeast Coast Region, South Korea

In modern times, humans have contributed to the occurrence of phthalate esters (PEs) in various environmental matrices by consuming plastics that generally contain PEs, which are essential synthetic organic chemicals for fabrication of various consumer products. Five beaches in Ulsan and Busan, South Korea, were investigated for the anthropogenic occurrence of four PEs (dimethyl phthalate (DMP), diethyl phthalate (DEP), di-n-butyl phthalate (DnBP), and bis(2-ethylhexyl) phthalate (DEHP)) in beach seawater. Using stir-bar sorptive extraction-thermal desorption-gas chromatography/mass spectrometry, DnBP and DEHP were determined as generally dominant. At Ilsan Beach, both the northern and southern ends showed higher DnBP and DEHP concentrations than the middle of the beach, while DMP and DEP showed similar concentrations at all points. The differences in the DnBP and DEHP concentrations may be related to the fishery industry and domestic sewage inflow to the beach. In addition, a comparison among Ilsan, Imnang, and Songjeong beaches suggested the effects of neighboring industrial areas on the DnBP and DEHP ratio. This study provides information on the effects of seasons, weather events, the proximity of rivers, industrial behavior, and domestic sewage to PE concentrations in beach seawater.
Assessment of the Effects of Overgrazing on the Soil Physical Characteristic and Vegetation Cover Changes in Rangelands of Hosainabad in Kurdistan Province, Iran

Soil physical properties have an important role on vegetation growth through affecting the development of root system. The aim of this study was to investigate the trampling effect of livestock grazing on soil physical properties and vegetation cover changes. The experiment was conducted on three range condition sites (Reference, Key and Critical area) with ten frequencies in Hosseinabade Kudistan in 2009. Soil physical characteristics consist of humidity, bulk density, porosity and aggregate stability index. In this study remarked soil cover changes in three mentioned regions. The results showed that soil moisture reduced from reference to critical area. Soil bulk density was the lowest in reference area and the highest in critical area. Reference site had the highest soil porosity while the soil porosity reduced in critical area. However, as the density of grazing increased, soil bulk density increased and soil moisture, soil porosity, aggregate stability index and vegetation cover percent decreased. Bare soil percentage was the highest in critical region. According to the results of this research, the grazing seems caused to major changes in the physical properties of the topsoil. Since the region has high ecological potential, if the intensity of grazing would be in a moderate level, the region soil will be able to compensate the negative aspects of livestock trampling.
A system perspective on the choice determinants of vegetable consumption and balanced diets in urban Nigeria

Vegetable consumption is a generally accepted element in a balanced and sufficient diet, yet the determinants of vegetable consumption in an urban African population are poorly understood from a system perspective. The omission in knowledge on the vegetables intake and choice determinants prevents the definition of possibly effective interventions for countervailing the so-called nutrition transition. We examine the determinants of vegetable consumption in urban Nigeria across several socioeconomic classes and types of marketing channels, in order to identify points of intervention in the food and nutrition system. Data is collected among 1200 households in several districts of Lagos and Ibadan. This summary reports on the research design and the anticipated contributions to the scientific discourse on effective food system interventions.
A new FOC technique based on predictive current control for PMSM drive

This paper presents a new predictive current control (PCC) strategy using field oriented controller (FOC) for permanent magnet synchronous motor (PMSM). The suggested technique estimates the desirable electrical torque to track mechanical torque at a fixed speed operation of PMSM. The estimated torque is used to calculate the required current of motor based on conventional FOC technique. Finally, the duty cycle of the inverter is determined to feed the PMSM at a fixed switching frequency. The performance of the control schemes is evaluated in terms of torque and current ripple, and transient response to step variations of the torque command. Numerical simulations tests have been carried out to validate the proposed method in MATLAB.
Antibiotic Resistance - The Global Perspective

Antibiotics have been used in pig production for over 60 years. They are very useful in managing bacterial infections of pigs, but the emergence of antibiotic resistance and calls for action to contain resistance are changing the availability of these drugs and our views of how they should be used. The purpose of this article is to provide some insights into antibiotic resistance and its impact on the pig industry.
Shelving spectroscopy of a UV transition in Dysprosium atoms

The subject of this thesis is to characterize a UV transition in Dysprosium at 359 nm, which is an essential preliminary step for the realization of a predicted magic wavelength for the clock transition at 1001 nm to the realize the goal of single-site resolution using quantum gas microscope for ultracold Dy atoms in an UV lattice. This is achieved by performing laser spectroscopy on atomic Dysprosium in a high vacuum chamber. The UV transition is a relatively weak transition and given that it is predicted to involve a doubly excited state, the detection is done using shelving spectroscopy. By obtaining a high resolution shelving spectrum, the isotopes shifts and the excited state hyperfine constants are obtained. A King plot analysis is performed to estimate the electronic nature of the excited state. An upper bound for the natural linewidth of the transition is estimated from the decay of the excited state atoms.
Effect of Duty Cycle on Corrosion Resistance and Mechanical Properties of Tertiary Al2O3/ Y2O3/Graphene Pulsed Electrodeposited Ni-based Nano-composite

In recent years exclusive properties of nanoparticle coatings on metal matrix has attracted researchers to study more on synthesis of Ni-based nano-composites by electrodeposition process. In this paper the effects of duty cycle on the microstructure, hardness, wear and corrosion resistance were investigated on an AISI 1005 low carbon steel specimen electroplated in Watt's type bath. The obtained coating was strong and well adherent to the steel substrate. The Ni matrix nano-composite coatings with / /Graphene uniformly dispersed particles were fabricated by using electroplating technique with the aid of ultrasound condition. Scanning electron microscope (SEM) was used to study cross section profile and thickness measurement of the coatings. Increasing duty cycle in an average current density and constant frequency of 1000 Hz resulted in improvement of the mechanical properties and corrosion resistance of coated samples. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee of UFGNSM15.
A simple compression method using movion vector of video encoder in the destributed system

With the technological advancements of PCs, it has been possible to edit and encode video using software. Parallel processing has been possible with low-price PCs. We have developed a parallel encoding system for a cluster or Grid with multi-core PCs. Because the transmission between PCs may become bottlenecked if an uncompressed video image is the first sent, we have proposed a simple compression that can be transmitted via a LAN. In this paper, we presented a simple compression method and we proposed the improved method by using motion vector from video encoder. Also, the results of our performance evaluation were presented. As a result from some experimentation, maximum and average of the data amount were reduced by using motion vector.
CDN Slicing over a Multi-Domain Edge Cloud

We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN service instance which is created upon a content provider's request, is autonomously managed, and spans multiple, potentially heterogeneous, edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video service, we derive empirical models of video QoE as a function of service workload, which, coupled with multi-level service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.
Organizational responses to performance feedback: A meta-analytic review

Performance feedback theory has been recognized as a generative theory in organization and management studies that explains why, when, and how organizations initiate or discontinue specific strategic actions. Over the past decades, an extensive body of empirical research has tested the theory, refined its key tenets, and broadened its applications. Yet, empirical results on the effects of performance feedback often vary and even produce conflicting insights that are difficult to interpret. Following recent developments, we suggest that empirical controversies can be largely reconciled once we consider different performance feedback conditions, organizational actions, and boundary conditions. We conducted a meta-analytic review of 113 empirical studies to statistically evaluate how and why the effects of performance feedback may vary according to various factors. By identifying factors shaping organizational responses to performance feedback, this study helps integrate existing empirical evidence and offers new directions for future theoretical development and empirical research.