Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based Customer Churn Analysis

Traditional customer churn is predicted by machine learning and data mining methods. The advantages of big data are not fully utilized. In this paper, we use deep learning based customer churn analysis to establish a predictive model of customer churn, to achieve a warning real time. We use stacked autoencoder network to extract features from the data and then use logistic regression (LR) to classify customers. We first pretrain stacked autoencoder network, which is a deep learning model that uses the greedy layerwise unsupervised learning algorithm to train. After pretraining each layer separately, we will stack the each layer to form stacked autoencoder network, using backpropagation (BP) algorithm to reverse tuning parameters, and then train the logistic regression layer. From the perspective of overall accuracy, we use accuracy to evaluate the model, if the company pays more attention to all the churner to be predicted, we use the recall to evaluate the model, if the company pays more attention to the predicted accuracy of the churner, we can use the precision to evaluate the model. As to the customers who predict the loss, change the shortages in the operation timely, and reduce the loss of customers.
FEM-based Resonant Ultrasound Spectroscopy Method for Measurement of the Elastic Properties of Irregular Solid Specimens

Resonant ultrasound spectroscopy (RUS) is a well-established method of identifying the elastic coefficients of solid materials. The Rayleigh-Ritz method (RRZ) is usually used to calculate the natural frequencies of a specimen, where the specimen needs to be processed into a certain regular body. This requirement is hard to meet for some materials with size limitations or special physical/chemical properties. Our objective was to address this limitation with a new approach adapted to specimens of arbitrary geometry by combining RUS with micro computed tomography (μ-CT) and finite element modeling (FEM). The accuracy of the proposed approach was assessed using titanium irregular specimens. The elastic coefficient results showed good agreements (below 2%) with values of a rectangular parallelepiped specimen measured by the RRZ-based RUS. This study overcomes the limitation of RUS to specimen geometry and allows identification of the elastic properties of irregular specimens with good accuracy.
A prototype of a cooperative conveyance system by wireless-network control of multiple robots

Growing shortage of the labor force calls for automation of conveyance operations through the use of autonomous robots. These robots are difficult to implement in complicated facilities such as distribution centers and warehouses because of frequent changes in placements of conveyed goods and dollies. In this paper, we propose a conveyance system using multiple robots and external sensors. Our conveyance system offers adjustability to environmental changes and low cost by linking the robots and the sensors via a wireless network. Moreover, we introduce a prototype system of cooperative conveyance that controls multiple robots based on location information of the robots detected by a stereo camera.
Further Enhanced Multimedia Broadcast/Multicast Service in LTE-Advanced Pro

Consumer demand for high definition video is growing, but current Long Term Evolution (LTE) networks lack sufficient capacity to broadcast the typical 10 to 20 TV channels concurrently broadcast in a geographic region. In response to growing demand, the Third Generation Partnership Program initiated a study into TV enhancements for multimedia broadcast/multicast service (MBMS) for LTE. 3GPP's further enhanced MBMS (FeMBMS) enables higher per cell bandwidth for MBMS services, simultaneous reception of both unicast and multicast services from different mobile network operators (MNOs), MBMS-dedicated cells, standalone MBMS, non-subscriber reception of TV services, and larger cell sizes. In addition, shared enhanced MBMS (eMBMS) networks and MBMS transport services enable MNOs to minimize costs from the content providers, while at the same time incentivizing the content providers to exploit LTE for linear TV services. Finally, by utilizing MBMS on demand (MooD) and dynamic MBMS, the resource allocations to the MBMS flows can be dynamically adapted; for example, the users' quality of experience can be further improved beyond that of legacy static MBMS resource allocation. This article describes the enhancements made to LTE eMBMS related to TV services, MooD, and use cases for each of the enhancements. Additionally, MBMS in 5G next-generation radio is also discussed. The scope of this article comprises the terminal, radio access, core architecture, and related services.
NFC-Enabled Far-Field Antenna on PET Flexible Substrate for 3G/4G/LTE Mobile Devices

This research presents a far-field (FF) antenna with near-field communication (NFC) capability for 3G/4G/LTE mobile devices. The integrated far- and near-field communication (FNFC) antenna was fabricated using conductive silver ink on polyethylene terephthalate (PET) flexible substrate. The FF segment of the antenna is operable in the frequency range of 1.8–2.1 GHz, and the NFC antenna is of dual loop and operable at 13.56 MHz. In the antenna realization, simulations were performed and, to validate, an antenna prototype was fabricated. The experimental results revealed that the FF antenna achieved an impedance bandwidth of 37% (1.52–2.21 GHz), given <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert $ </tex-math></inline-formula> of less than −6 dB, with the minimum and maximum efficiency of 92.1% and 98%. The experimental gains are 1.76–2.08 dBi across the target operating frequency band, with near-omnidirectional radiation pattern. The simulation and experimental results are in good agreement. Moreover, the FNFC antenna achieves strong magnetic field distributions in <inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {x}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {y}}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$H_{\mathrm {z}}$ </tex-math></inline-formula> orientations and holds promising potential for 3G/4G/LTE applications.
A Study on Active Wheel Level Control of Robots with Steering Moving Omnidirectionally

This paper a study of a vehicle that goes over small objects by indoor. Recently, the robot industry market has spread. Among them, many companies advancing the introduction and utilization of service robots purpose to the substitution of human work. Consequently, only 60% answered that they were achieved results. The reason for the low results is that conventional robots do not have enough size, traveling performance and positioning accuracy. In this study, after examining the robot implementation and the conventional robot, we aim at the one that functions sufficiently the size, traveling performance and omnidirectional movement within the indoor environment. To this end, we propose a vehicle that can move in omnidirectional and can move each foot independently in the vertical direction. In the experiment of this study, a distance sensor was attached to the foot to detect the front floor height. When the floor height fluctuated, it was considered that there were small objects, and the foot was raised according to the height of them, and they were gone over. As a result, the sensor could detect small objects and realize the going over action. In the future, we aim to realize autonomous driving by combining it with the technology of omnidirectional movement.
1 Genetic-Based Multiresolution Noisy Color Image Segmentation

Segmentation of a color image composed of different kinds of regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum number of segmentation areas in an image when it contains similar and/or unstationary texture fields. A novel neighborhood-based segmentation approach is proposed. Genetic algorithm is used in the proposed segmentpass optimization process. In this pass, an energy function, which is defined based on Markov Random Fields, is minimized. In this paper uses an adaptive threshold estimation method for image thresholding in the wavelet domain based on the generalized Gaussian distribution (GGD) modeling of sub band coefficients. This method called Normal Shrink is computationally more efficient and adaptive because the parameters required for estimating the threshold depend on sub band data energy that used in the pre-stage of segmentation. A quadtree is employed to implement the multiresolution framework, which enables the use of different strategies at different resolution levels, and hence, the computation can be accelerated. The experimental results using the proposed segmentation approach are very encouraging.
Smile Intensity Detection in Multiparty Interaction using Deep Learning

Emotion expression recognition is an important aspect for enabling decision making in autonomous agents and systems designed to interact with humans. In this paper, we present our experience in developing a software component for smile intensity detection for multiparty interaction. First, the deep learning architecture and training process is described in detail. This is followed by analysis of the results obtained from testing the trained network. Finally, we outline the steps taken to implement and visualize this network in a real-time software component.
Séminaire Delange-Pisot-Poitou. Théorie des nombres

© Séminaire Delange-Pisot-Poitou. Théorie des nombres (Secrétariat mathématique, Paris), 1974-1975, tous droits réservés. L’accès aux archives de la collection « Séminaire Delange-Pisot-Poitou. Théorie des nombres » implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.
Mining Related Environmental Database for West Coast and Southland: Data Structure and Preliminary Geochemical Results

Geochemical data related to AMD for the West Coast and Southland regions have been collated into a relational database and GIS system. The types of information include; rock geochemical data, water chemistry data and aquatic macro-invertebrate abundance data. This information will be combined with eco-toxicology experiments to determine impact thresholds on aquatic ecosystems. Ultimately these datasets will be combined with remediation investigations to produce a Framework to assist decision making related to mine planning, consent and compliance. Current geochemical research examines the controls on As concentrations downstream of gold mining areas and the variability of AMD from Brunner Coal Measure rocks. Assessment of factors that control the chemistry of Brunner Coal Measure AMD such as mine type, hydrogeology, geology, geochemistry and climate is in progress. Minerals that control the concentration of aluminium downstream of AMD point sources include alunite, jurbanite and various forms of Al(OH)3.
Fowl adenovirus infections in Belgian broilers: a ten-year survey Fowl adenovirus-infecties bij vleeskuikens in België: een overzicht van tien jaar

Between May 2002 and May 2012, fowl adenovirus (FAdV) infections were diagnosed in 38 of 310 diseased Belgian broiler fl ocks. FAdV isolates were usually derived from pools of multiple organs, predominantly incorporating liver, pancreas and bursa. The FAdV isolates belonged to the serotypes FAdV 1 (fi ve strains), FAdV 2/11 (thirteen strains), FAdV 3 (one strain), FAdV 5 (eight strains) and FAdV 8a (four strains); seven isolates could not be typed with certainty. The clinical complaints in infected fl ocks consisted of poor growth, wet litter, respiratory disease and/or lameness in 63%, 37%, 26% and 24% of the cases, respectively. Increased mortality occurred in 39% of the fl ocks. In 53% of the infected farms, the clinical signs had been showing up in multiple successive production cycles. The most consistent lesions were hepatitis, nephritis, myocarditis, pancreatitis, tracheitis and proventriculitis. Concurrent infections with reovirus, infectious bronchitis virus, avian metapneumovirus, infectious bursal disease virus, chicken anemia virus, Escherichia coli, Enterococcus cecorum and/or Eimeria were observed in 53% of the fl ocks, predominantly in those that were experiencing increased mortality. It was concluded that fowl adenoviruses are frequently involved in disease of Belgian broilers, alone or in combination with other infectious agents.
Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points

This paper proposes a novel method for dynamically extracting and monitoring the entrances of areas of interest (AOIs). Most AOIs in China, such as buildings and communities, are enclosed by walls and are only accessible via one or more entrances. The entrances are not marked on most maps for route planning and navigation in an accurate way. In this work, the extraction scheme of the entrances is based on taxi trajectory data with a 30 s sampling time interval. After fine-grained data cleaning, the position accuracy of the drop-off points extracted from taxi trajectory data is guaranteed. Next, the location of the entrances is extracted, combining the density-based spatial clustering of applications with noise (DBSCAN) with the boundary of the AOI under the constraint of the road network. Based on the above processing, the dynamic update scheme of the entrance is designed. First, a time series analysis is conducted using the clusters of drop-off points within the adjacent AOI, and then, a relative heat index ( R H I ) is applied to detect the recent access status (closed or open) of the entrances. The results show the average accuracy of the current extraction algorithm is improved by 24.3% over the K-means algorithm, and the R H I can reduce the limitation of map symbols in describing the access status. The proposed scheme can, therefore, help optimize the dynamic visualization of the entry symbols in mobile navigation maps, and facilitate human travel behavior and way-finding, which is of great help to sustainable urban development.
Model-based Speed Control of a DC Motor Using a Combined Control Scheme

The separately-excited DC motor is a high-performance variable speed drive vital for industrial applications such as robotics, actuation, control and guided manipulation because of its precision, simplicity, continuous control feature and wide speed range. Hence there is need to accurately regulate and drive the motor at desired speed. The individual armature voltage and field current methods are flawed in their inability to control the motor speed over a wider range. Hence this paper is proposing a combined armature voltage and field current control method using Internal Model Control (IMC) feedback control scheme that will ensure reference speed tracking, a fast and non-oscillatory response for the DC motor speed. The armature voltage and field current control techniques are both modelled, fully capturing the dynamic effects of the armature and field excitation of the DC motor. The derived model is then used to obtain optimal settings for a PID controller that will control the motor speed. This model-based controller enhances the performance of the motor. Computer simulations are presented to show the effectiveness of the proposed control scheme.
An IoT-Based Framework of Webvr Visualization for Medical Big Data in Connected Health

Recently, telemedicine has been widely applied in remote diagnosis, treatment and counseling, where the Internet of Things (IoT) technology plays an important role. In the process of telemedicine, data are collected from remote medical equipment, such as CT machine and MRI machine, and then transmitted and reconstructed locally in three-dimensions. Due to the large amount of data to be transmitted in the reconstructed model and the small storage capacity, data need to be compressed progressively before transmission. On this basis, we proposed a lightweight progressive transmission algorithm based on large data visualization in telemedicine to improve transmission efficiency and achieve lossless transmission of original data. Moreover, a novel four-layer system architecture based on IoT has been introduced, including the sensing layer, analysis layer, network layer and application layer. In this way, the three-dimensional reconstructed data at the local end is compressed and transmitted to the remote end, and then visualized at the remote end to show reconstructed 3D models. Thus, it is conducive to doctors in remote real-time diagnosis and treatment, and then realize the data processing and transmission between doctors, patients and medical equipment.
Attention Learning with Retrievable Acoustic Embedding of Personality for Emotion Recognition

Modeling multimodal behavior streams to automatically identify emotion states of an individual has progressed extensively especially with the advancement of deep learning algorithms. Emotion, being an abstract internal state, creates substantial differences in an individual's behavior expressivity, the development of personalized recognition framework is a critical next step to improve algorithm's modeling capacity. In this work, we propose to integrate the target speaker's personality embedding into the learning of multimodal (speech and language) attention based network architecture to improve recognition performances. Specifically, we propose a Personal Attribute-Aware Attention Network (PAaAN) that learns its multimodal attention weights jointly with the target speaker's retrievable acoustic embedding of personality. Our acoustic domain adapted personality retrieval strategy mitigates the common issue on the lack of personality scores in the current available emotion databases, and our proposed PAaAN then learns its attention weight by jointly considering an individual target speaker's personality profile with his or her multimodal acoustic and lexical modalties. In this work, we achieve a 70% unweighted accuracy in the IEMOCAP 4-class multimodal emotion recognition task. Further analysis shows the effect of integrating personality on the variation of our attention weights of each acoustic and lexical behavior modality for each speaker in the IEMOCAP database.
Twitter-based Polarised Embeddings for Abusive Language Detection

We present a method to generate polarised word embeddings using controversial topics as search terms in Twitter as proxies for interactions among social media communities that may be liable to use abusive language. We investigate to what extent models trained with these embeddings perform with respect to generic embeddings across four data sets of abusive language, both in the same domain and out of domain, using simple linear classifiers. Our results show that the polarised embeddings are competitive in the same domain data sets, and perform better in out of domain one.
Integrated Position and Force Sensing for Optical Artificial Skin using Machine Learning Methods

In this paper a flexible and transparent gel material is employed as an optical artificial skin. The setup involves providing white light from one side of the rectangular gel slab and measuring the amount of light received on the opposite side with phototransistors. It is shown that using two (opposite) sides only is enough to get the x, y position of the pressure points to within 0.57 cm of accuracy and also the force level to within 1.37 N of accuracy. Using only two sides is beneficial for applications such as robotic arms and prostheses as once the gel is wrapped around them, the light source and the sensors would end up placed next to each other, in a line. This maximizes the functional area and facilitates installation. The reflective, dispersive and refractive properties of the layout was captured with adaptive neuro-fuzzy inference system techniques. The resulting model consists of four fuzzy inference systems which, once trained, could be easily embedded in a low-cost microcontroller.
Multi-Modal Depression Detection and Estimation

Depression and anxiety disorders are critical problems in modern society. The WHO studies suggest that roughly 12.8 percent of the world's population are suffering from a depressive disorder. In this work, we propose several novel approaches towards multi-modal depression detection and estimation. Our previous studies mainly explored the multi-modal features and multi-modal fusion strategies, experimental results showed that the proposed hybrid depression classification and estimation multi-modal fusion framework obtains promising performance. The current work contains two parts: 1) In order to mitigate the impact of lack of data on training depression deep models, we utilize Generative Adversarial Network (GAN) to augment depression audio features, so as to improve depression severity estimation performance. 2) We propose a novel FACS3D-Net to integrate $3D$ and $2D$ convolution network for facial Action Unit (AU) detection. As far as we know, this is the first work to apply $3D$ CNN to the problem of AU detection. Our future work will 1) focus on combining depression estimation with dimensional affective analysis through the proposed FACS3D-Net, and 2) collect Chinese depression database. When completed, these studies will compose the author's dissertation.
Towards Optimal Area Synthesis of Small Combinational Circuits

This paper presents two main contributions regarding optimum area-oriented synthesis of small combinational circuits. The first contribution is a MUX-XORAND-Inverter Graph (MXAIG) data structure with choice nodes, which can be used as a subject description for technology mapping. The proposed MXAIG structure can represent distinct decompositions, allowing a more accurate area cost representation. The second contribution is an exact algorithm to cover a subject description, such as the proposed MXAIG, with minimum cost in terms of circuit area. The proposed covering algorithm can avoid local choices by efficient express coverings through integer representations. This representation allows postponing decisions in order to achieve a global optimum solution, even considering logic sharing. Preliminary results demonstrate that the proposed method is feasible for small combinational circuits. Keywords— Combinational synthesis; covering; choice nodes; technology mapping; digital circuit; EDA.
Underdetermined Direction-of-Arrival Estimation for Wideband Signals Based on SsF Scheme

Aiming at estimating the direction-of-arrival (DOA) of wideband signals under the underdetermined scenarios, a new method, named as synthesizing and self-focussing (SsF) scheme, is proposed. The contributions are tow-fold. Base on the theory of difference co-array, a new linear array geometry is designed to strengthen the degree-of-freedom (DOF); and based on the subspace theory, a new self-focusing strategy without requiring the information of initial DOA is proposed for fusing the array data of different frequency bins. Compared with other traditional methods by numerical simulations, the effectiveness and superiority of the proposed one can distinguish more wideband signals, and simultaneously can attain higher accuracy of DOA estimation.