Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and design of two link robotic manipulator for grading and sorting of rotationally symmetric products

This paper present a design and modeling for a two link robotic manipulator for grading and sorting system. The mechanical design calculation of the robotic manipulator is accomplished firstly to estimate the torques and positions of manipulator that are required to move a certain payloads from one to another position, which is resulted by choosing of the right electrical motors. The mechanical design drawings for this manipulator system are fully done using Autodesk Inventor Software which concerns the real joint of the robotic manipulator. The dynamic equation of the robotic manipulator system is derived using the Lagrange equation which is then represented in the state space method to make simple for utilization in Simulation and real-time systems.
Joint computation and bandwidth resources allocation for generalized computing model in mobile edge computing

: Mobile edge computing (MEC) has restricted computation resource and limited bandwidth resource when serving multiple users simultaneously. In general, a user’s task can not be always parallelized. In this paper, we study a joint computation and bandwidth resource allocation problem, considering a generalized task computing model. The computation capacity is modeled as a non-decreasing concave function about allocated computation resource, and the joint computation and bandwidth resources allocation problem is solved by cvx opt. framework. Simulation results show that our proposed scheme is greatly superior to the serial processing benchmark scheme for Non-linear model. With non-linear model, higher computational multiplexing gain can be achieved, however, for linear model, there is no gain that can be obtained from the proposed scheme.
GMM-UBM based Person Verification using footfall signatures for Smart Home Applications

In this paper, we propose a novel person verification system based on footfall signatures using Gaussian Mixture Model-Universal Background Model (GMM-UBM). Ground vibration generated by footfall of an individual is used as a biometric modality. We conduct extensive experiments to compare the proposed technique with various baselines of footfall based person verification. The system is evaluated on an indigenous dataset containing 7750 footfall events of twenty subjects. Different scenarios are created for analyzing the robustness of the system by varying the number of registered and non registered users. We obtained a Half Total Error Rate (HTER) of 7% with the proposed model and achieved an overall performance gain of ~46% and ~33% over Support Vector Machine (SVM) and Convolution Neural Network (CNN) based techniques respectively. Experimental results validate the efficacy of the proposed algorithms.
A case of hereditary novel mutation in SLC26A2 gene (c.1796 A.> C) identified in a couple with a fetus affected with atelosteogenesis type 2 phenotype in an antecedent pregnancy

Abstract Atelosteogenesis type 2 also known as atelosteogenesis de la Chapelle type, De la Chapelle dysplasia, McAlister dysplasia or neonatal osseous dysplasia 1 is an extremely lethal osteochondrodysplasia with unknown incidence. Very few cases have been reported in the literature. We present a case with findings of prenatal ultrasound, autopsy and cytogenetic work up with report of a novel familial mutation.
Generating Formality-Tuned Summaries Using Input-Dependent Rewards

Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content. Recent efforts based on sequence-to-sequence networks only allow the generation of a single summary. However, it is often desirable to accommodate the psycho-linguistic preferences of the intended audience while generating the summaries. In this work, we present a reinforcement learning based approach to generate formality-tailored summaries for an input article. Our novel input-dependent reward function aids in training the model with stylistic feedback on sampled and ground-truth summaries together. Once trained, the same model can generate formal and informal summary variants. Our automated and qualitative evaluations show the viability of the proposed framework.
Computing Surface PolyCube‐Maps by Constrained Voxelization

We present a novel method to compute bijective PolyCube‐maps with low isometric distortion. Given a surface and its pre‐axis‐aligned shape that is not an exact PolyCube shape, the algorithm contains two steps: (i) construct a PolyCube shape to approximate the pre‐axis‐aligned shape; and (ii) generate a bijective, low isometric distortion mapping between the constructed PolyCube shape and the input surface. The PolyCube construction is formulated as a constrained optimization problem, where the objective is the number of corners in the constructed PolyCube, and the constraint is to bound the approximation error between the constructed PolyCube and the input pre‐axis‐aligned shape while ensuring topological validity. A novel erasing‐and‐filling solver is proposed to solve this challenging problem. Centeral to the algorithm for computing bijective PolyCube‐maps is a quad mesh optimization process that projects the constructed PolyCube onto the input surface with high‐quality quads. We demonstrate the efficacy of our algorithm on a data set containing 300 closed meshes. Compared to state‐of‐the‐art methods, our method achieves higher practical robustness and lower mapping distortion.
Mobile Social Cyberbullying: Why are Keyboard Warriors Raging?

ABSTRACT Cyberbullying on social media has become a serious matter and is the most rampant form of bullying among students nowadays. Accordingly, this study examines mobile social cyberbullying perpetration, a new form of cyberbullying perpetration that is conducted over mobile social media, through the theoretical lenses of Barlett and Gentile Cyberbullying Model (BGCM) and Mobile Technology Acceptance Model (MTAM). The Partial Least Squares Structural Equation Modeling analysis was performed on the 489 samples gathered from university students in order to obtain inferential statistics. Perceived anonymity and belief in the irrelevance of muscularity from the BGCM are positively related to mobile usefulness and mobile ease of use from the MTAM, while all of them are reported to have a positive association with the positive cyberbullying attitudes, which ultimately explain mobile social cyberbullying perpetration. From the results, it is ascertained that the well-founded integrated model has made a novel contribution toward the current literature of cyberbullying perpetration, particularly the rarely studied mobile social cyberbullying perpetration. Several suggestions on reducing the mobile social cyberbullying perpetration are then made to the practitioners, especially the government policymakers and mobile social media service providers.
Unsupervised Texture Segmentation Using Gabor Filters1

We presenf a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain. We propose a systematic filter selection scheme which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of " energy " in a window around each pixel. An unsupervised square-emr clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial adjacency information in the clustering process is also proposed. We report experiments on images with natural textures as well as artificial textures with identical 2nd-and 3rd-order statistics.
Intellectual Classifier Development of Citizens' Messages on the "Our St. Petersburg" Portal: Experience in Using Machine Learning Methods

Functional features are investigated and shortcomings in the existing process of sending messages about city problems on the “Our St. Petersburg” portal are revealed. The approach to automatic classification development of citizens ' messages by existing on portal categories is described. Based on reports submitted by citizens in the amount of 1.5 million, training and test samples were formed in the ratio of 80% and 20% of texts main volume, respectively. Based on training data sample and 194 categories, the algorithm of automatic classification was trained using such classical methods of machine learning as naive Bayes classifier, decision trees and artificial neural networks. Using the method of determining effectiveness of the classification and test sample, trained algorithm was tested and checked. The analysis revealed that algorithm based on the use of artificial neural networks shows the best result among the other methods used. The average classification accuracy of the algorithm was approximately 82%. The trained algorithm was used in the development of an intellectual classifier, which is a web application and implements API mechanisms for interaction with main modules of the portal information system.
Dynamic Business Models: a comprehensive Classification of literature

Business models are vital to companies’ success; to stay competitive, companies continuously adapt and innovate their business model. The conceptualisation of business models has received much attention from prior research and the focus of research is shifting from a static perspective to a more dynamic perspective. This research is a comprehensive and up-to-date literature analysis of the concept of dynamic business models. To achieve a systematic and objective penetration of the research field, we used a classification framework consisting of 15 evaluation dimensions. We identified the main research streams on the topic and present the most relevant approaches, such as system dynamics modelling. A total of 42 relevant literature sources were found. Finally, we highlighted gaps for future research, such as a need for more detailed analyses of the interdependencies between the components a business models consists of.
Learning face similarities for face verification using hybrid convolutional neural networks

Face verification focuses on the task of determining whether two face images belong to the same identity or not. For unrestricted faces in the wild, this is a very challenging task. Besides significant degradation due to images that have large variations in pose, illumination, expression, aging, and occlusions, it also suffers from large-scale ever-expanding data needed to perform one-to-many recognition task. In this paper, we propose a face verification method by learning face similarities using a Convolutional Neural Networks (ConvNet). Instead of extracting features from each face image separately, our ConvNet model jointly extracts relational visual features from two face images in comparison. We train four hybrid ConvNet models to learn how to distinguish similarities between the face pair of four different face portions and join them at top-layer classifier level. We use binary-class classifier at top-layer level to identify the similarity of face pairs which includes a conventional Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), Native Bayes, and another ConvNet. There are 3 face pairing configurations discussed in this paper. Results from experiments using Labeled face in the Wild (LFW) and CelebA datasets indicate that our hybrid ConvNet increases the face verification accuracy by as much as 27% when compared to individual ConvNet approach. We also found that Lateral face pair configuration yields the best LFW test accuracy on a very strict test protocol without any face alignment using MLP as top-layer classifier at 87.89%, which on-par with the state-of-the-arts. We showed that our approach is more flexible in terms of inferencing the learned models on out-of-sample data by testing LFW and CelebA on either model.
Driving Impairment and Accident Risk in Sleep Apnea: We Need Better Assessment Tools

Driving Impairment and Accident Risk in Sleep Apnea: We Need Better Assessment Tools Obstructive sleep apnea (OSA) is strongly associated with neurobehavioral impairment and increased motor vehicle accident (MVA) risk resulting in an estimated 1400 road fatalities and a cost of $15.9 billion annually in the United States alone. Despite these alarming figures, the elevated MVA risk might only relate to a subset of the OSA patient population, while a large proportion of patients show minimal or no driving impairment and present no relative risk on the road. At present, assessing an OSA patient’s MVA risk, and their eligibility to hold an unrestricted driver’s licence, is challenging. Current clinical metrics of OSA severity and daytime sleepiness are limited in their ability to identify at-risk patients. There is a need for novel assessment tools that may better reflect the risk of neurobehavioral impairment and MVA in individuals, allowing for targeted prioritisation of clinical resources and ultimately, improving traffic safety.
A Method of Making Video Trailer Based on Artificial Intelligence

Based on the flownet2 optical flow algorithm, cascade CNN face detection and facenet face recognition algorithm, the method of automatically generating video trailers by using the sum of the optical flow vector sum per second and the absolute value of the audio loudness amplitude per second and the character duration information is proposed. It can effectively edit the visual essence clip, the video essence clip corresponding to the audio, the main character clip and the story outline to generate a trailer.
Intervention time series modeling with parametric and nonparametric approach: comparative study on Corporation tax in Togo

Time series are often subject to structural changes caused by external events such as strikes, new fiscal measures, or policy changes. In the current paper, we conduct a comparative study of a parametric and nonparametric approach to intervention time series modeling to model the impact, on corporate tax, of the important tax reform in December 2012 in Togo (the establishment of a Togolese Revenue Office). The comparison of the two models has led us to conclude that the non-parametric approach is superior in terms of predictive quality as well as the measurement of the effect of the reform.
Convex Optimization-based Controller Design for Stochastic Nonlinear Systems using Contraction Analysis

This paper presents an optimal feedback tracking controller for a class of Itô stochastic nonlinear systems, the design of which involves recasting a nonlinear system equation into a convex combination of multiple non-unique State-Dependent Coefficient (SDC) models. Its feedback gain and controller parameters are found by solving a convex optimization problem to minimize an upper bound of the steady-state tracking error. Multiple SDC parametrizations are utilized to provide a design flexibility to mitigate the effects of stochastic noise and to ensure that the system is controllable. Incremental stability of this controller is studied using stochastic contraction analysis and it is proven that the controlled trajectory exponentially converges to the desired trajectory with a non-vanishing error due to the linear matrix inequality state-dependent algebraic Riccati equation constraint. A discrete-time version of stochastic contraction analysis with respect to a state- and time-dependent metric is also presented in this paper. A simulation is performed to show the superiority of the proposed optimal feedback controller compared to a known exponentially-stabilizing nonlinear controller and a PID controller.
MR reconstruction of FLAIR weighted images with simulated lesions.: A comparison between Compressed Sensing and a Recurrent Inference Machine

Background: For both hospitals and patients it would be beneficial if the scan time of MR images could be reduced. At the moment, Compressed Sensing (CS) is introduced to reduce the scan time, however, new methods are developed such as a deep learning method, called the Recurrent Inference Machine (RIM). In this study the effect of reconstructing undersampled MRI images with lesions, using the RIM and CS, was evaluated. In data of a healthy control, lesions were simulated. Evaluation is done by checking if the lesion has the correct intensity and shape after reconstruction of undersampled data. Methods: In raw data of a healthy control lesions were simulated. To test the RIM and CS, the images with lesions where first undersampled 4x, 6x, 8x and 10x. After undersampling, the images were reconstructed with both RIM and CS. First, the peak intensity difference was measured between the reference image (with simulated lesions) and reconstructed images for both RIM and CS. Second, one lesion was undersampled ten times with different undersampling masks creating different noise, for 3 different acceleration factors (4x, 6x, 8x). These lesions were reconstructed with both RIM and CS. The maximum intensity difference between reference and reconstructed image was measured and averaged over the ten different undersampled images. Results: In total seven different lesions were simulated in a healthy control with different intensities varying between 10\% and 100\% of the GM-lesion intensity in a FLAIR weighted scan. The intensities of all lesions were more accurately reconstructed with the RIM compared to CS at higher acceleration factors: the average intensity per lesions after 10 times reconstruction with RIM was more equal to the correct intensity compared to the reconstruction with CS. Conclusion: The RIM shows robust and accurate results on data with simulated lesions. Moreover, the RIM outperformed CS on data that was more undersampled. Therefore, the RIM may be used for reconstruction of MRI data that is acquired with shorter acquisition time. And since the reconstruction time is better, it could replace CS in the future. However, before the RIM could be used, further evaluations on actual patient data are needed.
Online Scheduling via Learned Weights

Online algorithms are a hallmark of worst case optimization under uncertainty. On the other hand, in practice, the input is often far from worst case, and has some predictable characteristics. A recent line of work has shown how to use machine learned predictions to circumvent strong lower bounds on competitive ratios in classic online problems such as ski rental and caching. We study how predictive techniques can be used to break through worst case barriers in online scheduling. The makespan minimization problem with restricted assignments is a classic problem in online scheduling theory. Worst case analysis of this problem gives Ω(log m ) lower bounds on the competitive ratio in the online setting. We identify a robust quantity that can be predicted and then used to guide online algorithms to achieve better performance. Our predictions are compact in size, having dimension linear in the number of machines, and can be learned using standard oﬀ the shelf methods. The performance guarantees of our algorithms depend on the accuracy of the predictions, given predictions with error η , we show how to construct O (log η ) competitive fractional assignments. We then give an online algorithm that rounds any fractional assignment into an integral schedule. Our algorithm is O ((log log m ) 3 )-competitive and we give a nearly matching ˜Ω(log log m ) lower bound for online rounding algorithms. 1 Altogether, we give algorithms that, equipped with predictions with error η , achieve O (log η (log log m ) 3 ) competitive ratios, breaking the Ω(log m ) lower bound even for moderately accurate predictions.
NormalNet: Learning-based Normal Filtering for Mesh Denoising

Mesh denoising is a critical technology in geometry processing that aims to recover high-fidelity 3D mesh models of objects from their noise-corrupted versions. In this work, we propose a learning-based normal filtering scheme for mesh denoising called NormalNet, which maps the guided normal filtering (GNF) into a deep network. The scheme follows the iterative framework of filtering-based mesh denoising. During each iteration, first, the voxelization strategy is applied on each face in a mesh to transform the irregular local structure into the regular volumetric representation, therefore, both the structure and face normal information are preserved and the convolution operations in CNN(Convolutional Neural Network) can be easily performed. Second, instead of the guidance normal generation and the guided filtering in GNF, a deep CNN is designed, which takes the volumetric representation as input, and outputs the learned filtered normals. At last, the vertex positions are updated according to the filtered normals. Specifically, the iterative training framework is proposed, in which the generation of training data and the network training are alternately performed, whereas the ground truth normals are taken as the guidance normals in GNF to get the target normals. Compared to state-of-the-art works, NormalNet can effectively remove noise while preserving the original features and avoiding pseudo-features.
Towards a Unified Understanding of Data-Driven Support for Emergency Medical Service Logistics

Time-critical medical emergencies challenge emergency medical service (EMS) systems worldwide every day. In order to respond to these incidents as soon as possible, EMS logistics’ approaches can help locating and dispatching ambulances. Many of these approaches use estimates for the demand as well as the driving, service and turnaround times. In order to determine useful solutions and make informed decisions, reliable forecasts are necessary that take the characteristics and constraints of the planning problems at different levels into account. While many different approaches have been presented and tested in literature, a common understanding is still missing. This paper therefore proposes a taxonomy on EMS forecasting that distinguishes between medical emergencies and patient transports, demand and time intervals in the response process, as well as the three planning levels strategic, tactical and operational. In addition, an illustrative example and a research agenda are presented based on the findings for the taxonomy.