Miracle-dz/UniFAR-FTUs huggingface dataset:
Contour Extraction and Quality Inspection for Inner Structure of Deep Hole Components

This paper focuses on the contour extraction for the inner wires of a kind of deep hole component to achieve a high-accuracy inspection. The vision system consisting of a camera and an endoscope is developed to acquire high-quality images of the internal structure. For the acquired images, a contour extraction method is proposed, which could be divided into the following steps. First, the start points of wires referred as the prior information on the component are obtained with a predefined filter, and on the basis, several regions of interest (ROIs) are defined. Second, the multiscale probability of boundary operator is utilized to detect edges in the ROIs. Third, a Brownian motion model is established to calculate the connectivity between edges. The prior information obtained previously is used again to determine the probabilities of the edges belonging to the contours. Finally, the symmetric ratio contour method is used to form the wires’ contours with the edges. In the proposed method, the edges belonging to the wires’ contours are enhanced by making full use of the prior information, resulting in the improvement in accuracy and real-time performance. As evidenced by the experiments, the proposed method can efficiently extract the inner wires’ contours from the component’s image with low-contrast conditions, noises, and shadows.
Age and Gender Differences in Performance for Operating a Robotic Manipulator

This paper examines the performance differences across gender and age when operating a robotic manipulator arm and also seeks to determine which human factors are considered important predictors of performance for each group. To examine these differences, 93 participants were recruited and divided up into both male (46) and female (47) as well as young (46) and old (47). While men and women had nearly identical human factors, except for women exhibiting better dexterity, different navigation strategies were utilized by the genders leading men to perform the tasks quicker and with less overall moves than women. While task completion speed was affected most by working memory (WM) and spatial abilities for men, it was seen to be mostly dependent on physical abilities for women. Substantial differences were seen between the age cohorts in WM and dexterity which resulted in the younger cohort completing tasks quicker and with a higher rate of commands than the older cohort; no difference was observed in the total number of moves which provided evidence of a similar navigation strategy across the age groups. To improve task speed performance, older adults used all facets of their information processing and spatial abilities as compared to the younger group who used a narrower subset. To compensate for the aforementioned variations in important human factors, human–computer interface design considerations are suggested.
Estimating urban mobility with mobile network geolocation data mining. (Estimation de la mobilité urbaine par l'exploitation des données de géolocalisation de téléphonie mobile)

In the upcoming decades, traffic and travel times are expected to skyrocket, following tremendous population growth in urban territories. The increasing congestion on transport networks threatens cities efficiency at several levels such as citizens well-being, health, economy, tourism and pollution. Thus, local and national authorities are urged to promote urban planning innovation by adopting supportive policies leading to effective and radical measures. Prior to decision making processes, it is crucial to estimate, analyze and understand daily urban mobility. Traditionally, the information on population movements has been gathered through national and local reports such as census and surveys. Still, such materials are constrained by their important cost, inducing extremely low-update frequency and lack of temporal variability. On the meantime, information and communications technologies are providing an unprecedented quantity of up-to-date mobility data, across all categories of population. In particular, most individuals carry their mobile phone everywhere through their daily trips and activities. In this thesis, we estimate urban mobility by mining mobile network data, which are collected in real-time by mobile phone providers at no extra-cost. Processing the raw data is non-trivial as one must deal with temporal sparsity, coarse spatial precision and complex spatial noise. The thesis addresses two problematics through a weakly supervised learning scheme (i.e., using few labeled data) combining several mobility data sources. First, we estimate population densities and number of visitors over time, at fine spatio-temporal resolutions. Second, we derive Origin-Destination matrices representing total travel flows over time, per transport modes. All estimates are exhaustively validated against external mobility data, with high correlations and small errors. Overall, the proposed models are robust to noise and sparse data yet the performance highly depends on the choice of the spatial resolution. In addition, reaching optimal model performance requires extra-calibration specific to the case study region and to the transportation mode. This step is necessary to account for the bias induced by the joined effect of heterogeneous urban density and user behavior. Our work is the first successful attempt to characterize total road and rail passenger flows over time, at the intra-region level. Although additional in-depth validation is required to strengthen this statement, our findings highlight the huge potential of mobile network data mining for urban planning applications.
The Building Area Recognition in Image Based on Faster-RCNN

Building area recognition technology is a rising up research topic in the field of pattern recognition and it has a wide application prospect in the fields of military, city planning and other fields. This paper combines building area recognition algorithm based on Faster-RCNN and some of the problems in it are discussed respectively and the corresponding solutions are given. Through grabbing a streets cape at Baidu API and real shot in street with SLR camera, building my own image dataset and doing some corresponding training and tests based on Faster-RCNN after images are annotated. The experimental results show that the algorithm above-mentioned can achieve better recognition results. The main work of this article is collecting a large number of image samples and building own image dataset; annotating the images; building and training building recognition model based on Faster-RCNN and combining ZF convolutional neural network to do building area recognition.
Robot-Assisted Rehabilitation System Based on SSVEP Brain-Computer Interface for Upper Extremity

Traditional rehabilitation therapies have limited effect on the motor recovery for a tetraplegia patient. Brain-computer interface (BCI) systems allow patients to send commands or intents to control external devices without depending on the normal way of peripheral nerves and muscles. And hence it can provide an alternative control and communication method with a potential to replace, restore, even reinforce lost movement ability for individuals with neurological damages. In the study, we proposed a robot-assisted rehabilitation system for upper extremity based on non-invasive electroencephalogram (EEG) BCI, which enables the injured upper extremity to achieve motor function. Six participants conducted three speed modes of movement with a BCI-controlled robot. The steady-state visual evoked potentials (SSVEPs) was adopted to establish the BCI system. Experimental results of the healthy participants were analyzed to indicate the feasibility of a BCI-driven robot-assisted rehabilitation system, with an average accuracy of about 80 %. This study gives a preliminary evidence that the integrated robot-assisted rehabilitation system combined with SSVEP-based BCI will make future rehabilitation therapy more effective.
Chroma Interval Content as a Key-Independent Harmonic Progression Feature

This paper introduces a novel chroma-based harmonic feature called Chroma Interval Content (CIC), which extends Directional Interval Content (DIC) vectors to audio data. This feature represents key-independent harmonic progressions, but unlike the Dynamic Chroma feature vector it represents pitch-class energy motions based on a symbolic voice-leading approach, and can be computed more efficiently (in time $\mathcal{O}(N\log N)$ as opposed to $\mathcal{O}(N^{2}))$. We present theoretical properties of Chroma Interval Content vectors and explore the expressive power of CIC both in representing isolated chord progressions, establishing links to its symbolic counterpart DIC, as well as in specific harmony-related MIR tasks, such as key-independent search for chord progressions and classification of music datasets according to harmonic diversity.
Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities

In modern wireless communication systems, wireless channel modeling has always been a fundamental task in system design and performance optimization. Traditional channel modeling methods, such as ray-tracing and geometry- based stochastic channel models, require in-depth domain-specific knowledge and technical expertise in radio signal propagations across electromagnetic fields. To avoid these difficulties and complexities, a novel generative adversarial network (GAN) framework is proposed for the first time to address the problem of autonomous wireless channel modeling without complex theoretical analysis or data processing. Specifically, the GAN is trained by raw measurement data to reach the Nash equilibrium of a MinMax game between a channel data generator and a channel data discriminator. Once this process converges, the resulting channel data generator is extracted as the target channel model for a specific application scenario. To demonstrate, the distribution of a typical additive white Gaussian noise channel is successfully approximated by using the proposed GAN-based channel modeling framework, thus verifying its good performance and effectiveness.
Searching Behavior of a Simple Manipulator Only with Sense of Touch Generated by Probabilistic Flow Control

Various behaviors of creatures emerge from the lack of information. We generate search behaviors of a simple manipulator by the probabilistic flow control (PFC) method. Though PFC uses a motion planning result without consideration of the uncertainty for decision making, it generates some kinds of search behavior by probabilistic calculation. In the simulation of this paper, we assume a two-link manipulator which can perceive the existence of an object with its tactile sense. This manipulator is given a task to search a rod fixed in an environment. In the task, the manipulator has shown some kinds of search behavior.
A Comprehensive Review on Electric Vehicles Operation, Development and Grid Stability

With the objective of zero carbon emission, energy security and efficiency, electric vehicles are inclined to overtake the future transportation. Due to the capability of offering reduced fuel consumption and reduced CO2 emissions, electric vehicles are proved to be promising devices in smart grid. This paper broadcast an assessment on the studies, state-of-art, research and work done on the subject matter of electric vehicles. Based on the different proposed approaches, a detailed literature survey has been presented in the paper which will be highly helpful and benefiting the research bodies and industry professionals as an arranged ready reference on the trends of electric vehicles.
Horses For Courses: Making The Case For Persuasive Engagement In Smart Systems

Current thrusts in explainable AI (XAI) have focused on using interpretability or explanatory debugging as frameworks for developing explanations. We argue that for some systems a different paradigm – persuasive engagement – needs to be adopted, in order to affect trust and user satisfaction. In this paper, we will briefly provide an overview of the current approaches to explain smart systems and their scope of application. We then introduce the theoretical basis for persuasive engagement, and show through a use case how explanations might be generated. We then discuss future work that might shed more light on how to best explain different kinds of smart systems.
The Double-Plane Algorithm: A simple algorithm for the closest vector problem

Abstract—We present a new algorithm for solving the closest vector problem. The algorithm is called the double-plane algorithm and it is an extension of Babai’s nearest plane algorithm. The algorithm is an approximative algorithm, and the performance of the algorithm depends on the quality of the lattice basis. However, given a high quality basis, the algorithm achieves correctness for lattices of sufficiently low rank, and very low error-rates when correctness can no longer be achieved. The computational complexity of the double-plane algorithm is upper bounded by that of sphere decoding using the Schnorr-Euchner enumeration strategy, and the higher the rank of the lattice, the larger the gap between the complexities of these decoding algorithms.
A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal

As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and related hazards. The use of remote sensing and geographic information systems (GIS) for landslide susceptibility mapping can help planning authorities to prepare for and mitigate the consequences of future hazards. In this study, we developed landslide susceptibility maps using GIS-based statistical models at the regional level in central Nepal. Our study area included the districts affected by landslides after the Gorkha earthquake and its aftershocks. We used the 23,439 landslide locations obtained from high-resolution satellite imagery to evaluate the differences in landslide susceptibility using analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) models. The nine landslide conditioning factors of lithology, land cover, precipitation, slope, aspect, elevation, distance to roads, distance to drainage and distance to faults were used as the input data for the applied landslide susceptibility mapping (LSM) models. The spatial correlation of landslides and these factors were identified using GIS-based statistical models. We divided the inventory into data used for training the statistical models (70%) and data used for validation (30%). Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the results. The area under the curve (AUC) values obtained from the ROC approach for AHP, FR and hybrid SMCE were 0.902, 0.905 and 0.91, respectively. The index of relative landslide density, R-index, values in sample datasets of AHP, FR and hybrid SMCE maps were 53%, 58% and 59% for the very high hazard classes. The final susceptibility results will be beneficial for regional planning and sustainable hazard mitigation.
Improved Verifiable Delegated Private Set Intersection

Verifiable delegated private set intersection, VD-PSI, is a kind of secure function evaluation protocol to compute the set intersection between clients datasets which are stored in the cloud in encrypted form without leaking any information. In VD-PSI, clients can verify validity of the computation result returned from the cloud. At FC 2016, Abadi et al. proposed a VD-PSI scheme based on a homomorphic encryption. However, their scheme has several problems in a practical sense; First, their scheme requires that clients must establish a secure channel between them in advance. Next, costs for clients are not fair because computational and communication costs for a client are much heavier than the peer. In this paper, we introduce an improved VD-PSI scheme. Our scheme has two main advantages: First, no secure channel is required even between clients; and thus, clients do not need to negotiate with each other in advance. The other advantage is efficiency and complexity fairness of clients because communication costs for clients are balanced, and total costs for clients are about 62% of the previous scheme.
Improving Misalignment Tolerance for IPT System Using a Third-Coil

To improve misalignment tolerance for an inductive power transfer (IPT) system, a third-coil is employed to reversely connect with the primary coil of the loosely coupled transformer (LCT) in series. The LCT with a third-coil (LCT-TC) not only can improve the misalignment performance in x- and y-directions, but also does not affect the inherent output characteristics (such as constant current or voltage output) of original compensation topologies. In addition, the proposed approach can keep the IPT system operating with relatively high efficiency against misalignment. Then, a detailed design method of LCT-TC is presented. Finally, a double-side LCC-compensated IPT system is chosen as an example to verify the feasibility of proposed LCT-TC. A 3.4-kW prototype is constructed, and experimental results show that the proposed method has a good misalignment performance. The LCC-LCC IPT system with LCT-TC can retain 96% of the well-aligned power at 40% misalignment, and the end-to-end (dc–dc) overall system efficiency is at least 92%. Besides, the fluctuation of the output current is [–2%, 5%] while the load varies from 15 to 34 Ω within 40% misalignment.
Optimized Tank Detector Based on Modern Convolutional Neural Networks

This paper suggests a method to assist selecting the appropriate architecture that achieves right speed and accuracy balance for detecting military tanks. Nowadays, convolutional neural networks(CNN) have emerged as the powerful tool in the object detection area. In this paper, we have considered the faster region based convolutional neural networks(R-CNN) and single shot detectors(SSD) architectures. We show that, using different feature extractors and varying the critical parameters such as learning rate, momentum optimizer value, intersection over union(IOU) threshold, stride size and maximum region proposals we can obtain optimized tank detector.
Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses

The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information visualization, these representations are inefficient for visualizing data with complex temporal and spatial extents and the variation of data at multiple temporal and spatial scales. This article presents alternative representations that incorporate the scale dimension into time and space. The article first reviews a series of work about the triangular model (TM), which is a multi-scale temporal model. Then, it introduces the pyramid model (PM), which is the extension of the TM for spatial data, and demonstrates the utility of the PM in visualizing multi-scale spatial patterns of land cover data. Finally, it discusses the potential of integrating the TM and the PM into a unified framework for multi-scale spatio-temporal modeling. This article systematically documents the models with alternative arrangements of space and time and their applications in analyzing different types of data. Additionally, this article aims to inspire the re-thinking of organizations of space, time, and scales in the future development of GIS and analytical tools to handle the increasing quantity and complexity of spatio-temporal data.
A Method for Estimating Task Granularity for Automating GPU Cycle Sharing

In this paper, we propose a method for estimating task granularity to reduce guest's effort in graphics processing unit (GPU) cycle sharing systems. A cycle sharing system should maximize both the frame rate for resource donators (i.e., hosts) and the acceleration effect for scientific job submitters (i.e., guests). This can be realized by selecting the appropriate granularity for guest tasks. However, a previous cooperative multitasking method requires manual interactions to find the appropriate task granularity. To avoid such interactions, the proposed method automatically estimates the appropriate granularity by measuring the length of idle periods on the host. We show how this measurement can be realized on donated resources, where the host program cannot be instrumented due to the lack of code availability. In experiments, the proposed method automatically estimated the appropriate task granularity, which not only maintained the frame rate for an image filter (i.e., host task) but also maximized the performance of matrix multiplication (i.e., guest task).
A Web-Based System For Suggesting New Practice Material To Music Learners Based On Chord Content

In this demo paper, a system that suggests new practice material to music learners is presented. It is aimed at music practitioners of any skill set, playing any instrument, as long as they know how to play along with a chord sheet. Users need to select a number of chords in a web app, and are then presented with a list of music pieces containing those chords. Each of those pieces can then be played back while its chord transcription is displayed in sync to the music. This enables a variety of practice scenarios, ranging from following the chords in a piece to using the suggested music as a backing track to practice soloing over. We set out the various interface elements that make up this web application and the thoughts that went behind them. Furthermore, we touch upon the algorithms that are used in the app. Notably, the automatic generation of chord transcriptions – such that large amounts of music can be processed without human intervention – and the query resolution mechanism – finding appropriate music based on the user input and transcription quality – are discussed.
Delimitating Urban Commercial Central Districts by Combining Kernel Density Estimation and Road Intersections: A Case Study in Nanjing City, China

An urban, commercial central district is often regarded as the heart of a city. Therefore, quantitative research on commercial central districts plays an important role when studying the development and evaluation of urban spatial layouts. However, conventional planar kernel density estimation (KDE) and network kernel density estimation (network KDE) do not reflect the fact that the road network density is high in urban, commercial central districts. To solve this problem, this paper proposes a new method (commercial-intersection KDE), which combines road intersections with KDE to identify commercial central districts based on point of interest (POI) data. First, we extracted commercial POIs from Amap (a Chinese commercial, navigation electronic map) based on existing classification standards for urban development land. Second, we calculated the commercial kernel density in the road intersection neighborhoods and used those values as parameters to build a commercial intersection density surface. Finally, we used the three standard deviations method and the commercial center area indicator to differentiate commercial central districts from areas with only commercial intersection density. Testing the method using Nanjing City as a case study, we show that our new method can identify seven municipal, commercial central districts and 26 nonmunicipal, commercial central districts. Furthermore, we compare the results of the traditional planar KDE with those of our commercial-intersection KDE to demonstrate our method’s higher accuracy and practicability for identifying urban commercial central districts and evaluating urban planning.