Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis and Uplink Scheduling for QoS-Aware NB-IoT Networks in Mobile Computing

Low-power wide-area (LPWA) communication has gained increasing attention in recent years with the rapid growth of fifth generation evolution (5G), the Internet of Things (IoT), and mobile computing. Narrowband Internet of Things (NB-IoT) is one kind of LPWA technology based on cellular IoT, which supports massive connections, wide area coverage, ultra-low power consumption, and ultra-low cost. Research on NB-IoT communication is increasingly attractive. Network calculus theory facilitates the performance analysis and network optimization of the NB-IoT system. We aim to analyze and optimize the NB-IoT networks. In this paper, we construct a random access traffic model including the NB-IoT user equipment (UE) arrival process and eNB service process. Then, we utilize the stochastic network calculus (SNC) to analyze the network delay in NB-IoT traffic model. Random latency bounds in different arrival processes are derived. Simulations show that SNC can evaluate the system delay under different distributions effectively. For the condition that numerous UEs access simultaneously following the Beta distribution, we first propose an improved K-means algorithm to cluster the NB-IoT terminals. Then, we raise the scheduling strategy on the basis of priority. It consists of the priority generation algorithm IPGNTQ and the NB-IoT task scheduling algorithm SANTQ. The extensive experiment results verify that our proposed optimized strategy can alleviate the network congestion effectually. Moreover, we compare our proposed optimized scheme with four existing uplink traffic scheduling schemes, showing that ours outperforms all of them.
Fine-Grained Event Structure Representations for Language: Aspect, Force Dynamics, Mental Spaces

This presentation reviews current work in a project to develop event structure representations for all of the verb classes in VerbNet (verbs.colorado.edu/verbnet) based on the “three-dimensional” representations introduced in Croft (2009a, 2012, 2015). The three dimensions include continuous dimensions of time (t) and (change in) qualitative state (q) for aspect, and a third “dimension” representing a causal chain of force dynamic interactions between participants. The event expressed by a verb is decomposed into a separate subevent for each participant; each participant has an aspectual contour representing what it does/what happens to it over time, and each participant’s subevent is in a force dynamic relationship with the other participants’ subevents, forming the causal chain. An example is given in Figure 1.
On the Path of Piracy Legal Regulation from the Degree of Pirate Legal Interest Infringement: The Statistical Research Method as an Approach to Intervention

The article encodes the data of piracy cases in recent ten years provided by the international maritime administration and splits the cases into nine variables: the occurrence sea area, whether the pirates boarded the vessel, the status of infringed ship, the type of vessel infringed, the registry of the infringed ship, carrying weapons by the pirates, the number of pirates, the treatment method, and the degree of infringement of legal interests, of which the first eight are independent variables and the degree of infringement of legal interests is dependent variables. It is concluded that the occurrence sea area, ship status, ship type, weapon carrying situation, and handling measures after piracy will generate significant impact on the dependent variables. In the dependent variables, there are five categories divided into potential damage offense, attempted infringement of property, accomplished infringement of property, complete violation of human health, complete violation of property, and human health, with the degree of legal interest violation increasing gradually. The Chi-square test shows that any of the independent variables will have an impact on the dependent variables. The evaluation of piracy by means of combined punishment for several crimes will fully take into account the development of piracy and the situation of the accomplished and attempted acts, so as to achieve the compatibility of crime and punishment.
Continuous Video Recording of Electronic Health Record User Sessions to Support Usability and Safety

Electronic health records (EHRs) have been shown to improve safety and quality. However, usability and safety issues with EHRs have been reported. The current state of the art in usability testing is to have clinicians conduct simulated activities in a usability lab. In this poster, we describe our experience with continuous recording of real-world EHR use to improve safety and usability.
Comparing Mediation Inferences and Explaining Away Inferences on Three Variable Causal Structures

People reliably make two errors when making inferences about three-variable causal structures: they violate what is known as the Markov assumption (mediation) on causal chains and common cause structures, and fail to sufficiently ‘explain away’ on common effect structures. Our goal for the present study was to quantitatively compare these two errors after subjects have learned the statistical relations between three variables using procedures designed to maximize the accuracy of their learning and inferences. Aligning with prior research, we found that subjects violated the Markov assumption, and did not sufficiently explain away. We also found judgments about mediation were worse than judgments about explaining away for one inference, but better for another, suggesting that people are not uniquely worse at reasoning about one structure than another. We discuss the results in terms of a theory of cue consistency.
Remind vitamin C measurement: about 4 cases… of scurvy

Hypovitaminose C, at the origin of the scurvy, did not disappear. We report 4 cases of desocialized patients and whose very unbalanced diet was at the origin of the vitamin deficit. In addition to the hemorrhagic demonstrations described among 4 patients, one finds a modification of the superficial body growths. Physiologically, the vitamin C takes part in the stability of the collagen of the vascular wall and the bone. The biological assessment of the 4 patients was normal apart from the low ascorbemy, a syndrome of denutrition and anaemia related to the haemorrhage. The quality of the vitamin C determination requires to respect the recommended pre-analytical conditions. The treatment quickly effective, consists of an oral administration of ascorbic acid. The evolution for 3 of the 4 described patients was favorable without after-effects. The proportioning of the vitamin C should be more often prescribed in geriatrics, psychiatry and cancerology. It should systematically form part of the biological assessment of the people living alone and/or homelessness.
Differential Private Multiple Classification Algorithm for SVM

Data mining algorithm has privacy leakage problem. To solve this problem, a privacy preserving support vector machine (SVM) algorithm under differential privacy for multiple classification is proposed in the paper. The algorithm disturbs the kernel function by three different ways including direct Laplace noise injection, Taylor formula replacement, and combination of previous two methods. The whole classification model is disturbed indirectly by the value of the normal vector obtained from disturbance. It is expected to protect the small sample data and not to interfere with the classification effect of the model to the whole data set. During experiment phase, the algorithm is verified by linear kernel, polynomial kernel and Gaussian kernel. The experimental results show that the algorithm can achieve data utility of classification result while satisfying privacy protection requirements.
Automatic Extraction of a Health Indicator from Vibrational Data by Sparse Autoencoders

We present a method for automatically extracting a health indicator of an industrial component from a set of signals measured during operation. Differently from traditional feature extraction and selection methods, which are labor-intensive and based on expert knowledge, the method proposed is automatic and completely unsupervised. Run-to-failure data collected during the component life are fed to a Sparse AutoEncoder (SAE), and the various features extracted from the hidden layer are evaluated to identify those providing the most accurate quantification of the component degradation. The method is applied to a synthetic and a bearing vibration dataset. The results show that the developed SAE-based method is able to automatically extract an efficient health indicator.
Análise da evolução das emissões de CO2 ao longo do tempo a partir do design da informação

Os registros meteorológicos mostram uma tendência do aumento da temperatura que é fortemente influenciada pelas mudanças nas áreas urbanas provocadas pelas intervenções da população. Contudo, a análise desses dados não é uma tarefa trivial, principalmente, para não especialistas. Uma das alternativas para democratizar esse conhecimento é usar técnicas de visualização de dados. Desta forma, o objetivo deste trabalho foi construir um artefato para visualizar um conjunto de dados públicos sobre a emissão de dióxido de carbono usando uma adaptação do framework de Design de Informação Computacional. O artefato elaborado foi avaliado a partir de duas perspectivas: eficiência e eficácia em auxiliar os usuários na análise dos dados e a satisfação do usuário. Dessa maneira, foi possível identificar que os usuários sentiram-se confortáveis ao usar o artefato, assim como aspectos importantes relacionados às principais dificuldades encontradas.
Fuzzy-Based Lion Pride optimization for Grayscale Image Segmentation

In this paper, we propose a novel image segmentation algorithm, Fuzzy-Based Differential Lion Pride optimization (FDLPO). We combine the concept of the Fuzzy C Means (FCM) and an improved Lion Pride optimization (DLPO) with a new crossover strategy which is inspired by Differential Evolution (DE). First, we search for optimum clusters by fuzzy membership function and LPO which has better performance than most of the other algorithms. Then, the crossover mechanism of the differential evolution is used to improve the diversity of offspring and speed up convergence in lion pride optimization. FDLPO becomes more efficient as it overcomes the drawbacks of FCM and LPO which does not depend on the choice of initial cluster centers and it performs better in terms of convergence, time complexity and robustness. The experiments with DLPO, LPO and PSO are executed over some benchmark test functions. Meanwhile, the efficiency of FDLPO is proven by both quantitative and qualitative measures.
Research and Implementation of Centralized Data Distribution Module for Super Base Station

Due to the rapid growth of mobile data and the requirement of wireless access equipment, it will bring about the problem of high-density base station deployment and resource waste and serious cell interference [1]. In view of this shortcoming, super base station [2] is proposed which is new access network architecture, the study of its protocol stack software centralized data distribution module function. The design of data structure of data distribution module and the realization of memory key technology are expounded.
An FPGA implementation and performance analysis between Radix-2 and Radix-4 of 4096 point FFT

The rapid grown in wireless 4G and 5G technology push to the edge to high input data processing. High input data processing required advance Orthogonal Frequency Division Multiplexing (OFDM). The main block in any OFDM transceiver is the Fast Fourier Transform (FFT). FFT consider the transformation bridge between the time and frequency domains. In this research an implementation and direct analysis between radix-2 and radix-4 FFT algorithms presented. Memory-based architecture adopted for the all algorithms. The entire algorithm designed by Altera Quartus II and synthesis for Altera DE2-70 field programmable gate arrays (FPGA) board, in order to investigate and determine the desired algorithm based on the application used for and the system requirement.
A Bayesian Analysis of Moral Norm Malleability during Clarification Dialogues

One of the principle tenets of modern behavioral ethics is that human morality is dynamic and malleable. Recent work in technology ethics has highlighted the role technologies can play in this process. As such, it is the responsibility of technology designers to actively identify and address possible negative consequences of such technological mediation. In this work, we examine dialogue systems employed by current robotic agents, arguing that they can have deleterious effects on both the human moral ecosystem and human perception of the robots, regardless of the robots’ actual ethical competence. We present a preliminary Bayesian analysis of empirical data suggesting that the architectural status quo of clarification request generation systems may (1) cause robots to unintentionally miscommunicate their ethical intentions (our two tests for this yielded Bayes factors of 1319 and 1099) and (2) weaken humans’ contextual application of moral norms (Bayes factor of 1069).
How is an ant navigation algorithm affected by visual parameters and ego-motion?

Ants typically use path integration and vision for navigation when the environment precludes the use of pheromones for trails. Recent simulations have been able to accurately mimic the retinotopic navigation behaviour of these ants using simple models of movement and memory of unprocessed visual images. Naturally it is interesting to test these navigation algorithms in more realistic circumstances, particularly with actual route data from the ant, in an accurate facsimile of the ant world and with visual input that draws on the characteristics of the animal. While increasing the complexity of the visual processing to include skyline extraction, inhomogeneous sampling and motion processing was conjectured to improve the performance of the simulations, the reverse appears to be the case. Examining closely the assumptions about motion, analysis of ants in the field shows that they experience considerable displacement of the head which when applied to the simulation leads to significant degradation in performance. The family of simulations rely upon continuous visual monitoring of the scene to determine heading and it was decided to test whether the animals were similarly dependent on this input. A field study demonstrated that ants with only visual navigation cues can return the nest when largely facing away from the direction of travel (moving backwards) and so it appears that ant visual navigation is not a process of continuous retinotopic image matching. We conclude ants may use vision to determine an initial heading by image matching and then continue to follow this direction using their celestial compass, or they may use a rotationally invariant form of the visual world for continuous course correction.
A High Performance Muscle-like Actuator using Multi-layer Electrostatic Film Motors

Artificial muscles have been researched for decades; however, most of them have difficulty in control with high accuracy due to non-linearity, such as hysteresis and creep phenomenon. On the other hand, electrostatic film motors have fine controllability, as well as high performance, flexibility and high accuracy, and are excepted to be used in robotics. The rigid prototype developed in a previous work had low force-to-weight ratio due to its bulky structure, while another flexible prototype only provided 4.5 N due to its small size and few layers. This paper reported a new muscle-like actuator with high force-to-weight ratio using multi-layer electrostatic film motors, in which 11 pairs of double-sided drive films were assembled together. Experiments were carried out under two conditions, static and dynamic, in which the force performance was evaluated using a load cell, or by lifting a weight, respectively. The prototype weighing 235 g recorded a force of 163 N under voltage of 1.8 kV and lifted around 4 kg weight under voltage of 1.3 kV. Compared with the existing structure with similar sizes, the prototype in this work has 4.7 times larger force-to-weight ratio.
Research on Ship Berthing Monitoring Technology Based on 3D Laser Point Cloud Data

With the continuous development of maritime transportation, ships tend to be larger, alongside increase risk, in order to ensure the ship berthing more safe, high effective, various monitoring techniques have been applied, such as sonar, infrared, radar, etc. Although the two-dimensional laser has been used to monitor the ship berthing, there are some limitations in use. The 3D laser scanning technology has great advantages in accuracy, speed and scope, so it is necessary to be apply it to the ship berthing monitoring. The technology is based on full use of laser ranging efficiency and accuracy advantages on solving a two-dimensional laser monitoring system for its poor target berthing ability, resulting in the overall accuracy of the monitoring system is not high, and the technical approach for dealing with different berth, ship, bring new ideas to the development of ship monitoring technology.
Multisite Schizophrenia Classification Based on Brainnetome Atlas by Deep Learning

The paper presents large-scale multisite schizophrenia classification using cutting-edge deep learning tools. In this study, we collected 1275 participants at 8 sites, including 662 schizophrenic patients. We combine the fine-grained Brainnetome Atlas to extract features. And we propose the generalized feature-invariant deep neural network framework to ensure the model generalization in automatic diagnosis schizophrenia. Our model evaluate with 10-fold cross-validation and leave-one-site validation prediction, Average accuracy brings 2.7\% gain in classification accuracy against standard leave-one-site-out validation. Besides, our model gives the promising result on all sites classification and great potentials for computer-aided diagnosis of psychiatric disorders with simple and meaningful biomarkers.
Least Squares DCGAN Based Semantic Image Inpainting

The generative adversarial network (GAN) provide a new way for semantic image inpainting problem. The missing semantic information can be predicted by generating an image with similar distribution of corrupted image based on GAN. In this paper, we propose a high vision quality semantic inpainting algorithm based on a LS-DCGAN. We discuss the optimization of GAN training and introduce the least squares loss function to solve the vanishing gradient problem of DCGAN. Based on a trained LS-DCGAN, we propose a new adversarial loss function for optimizing inpainting network input. Experiment on two datasets show that our algorithm is stable and effective, and have higher naturalness, validity and semantic similarity on visual experience than the state-of-the-art algorithms.
You can sweat the small stuff, too: Abstraction subordinates perceptual salience to the larger goal in a category learning paradigm

Three experiments investigated the role of conceptual abstraction in category learning. We found that people in a low-level mindset over-weighted global features in classifying novel exemplars whereas those in a high-level mindset did not (Experiments 1 and 3). The effect was on the learning process, independent of perceptual response preference (Experiment 3) and occurred despite evidence of perceptual global dominance for all groups during learning (Experiments 2 and 3). We conclude that abstraction can subordinate perceptual salience to the larger goal, integrating discrete encounters into a comprehensive representation of the underlying structure.
Theoretical system capacity of multi-user MIMO-OFDM THP in the presence of terminal mobility

: This letter investigates the theoretical system capacity of multi-user multiple-input multiple-output (MU-MIMO) orthogonal frequency division multiplexing (OFDM) Tomlinson-Harashima precoding (THP) in the presence of terminal mobility. Considering that MU-MIMO THP has been adopted for OFDM-based mobile broadband systems, analyzing the e ﬀ ects of time-selective fading and the modulo loss peculiar to THP on system capacity is essential. In this study, we theoretically derive both the multi-user interference (MUI) and inter-carrier interference (ICI) resulting from terminal mobility and incorporate these into the modulo loss analysis based on the mod-Λ channel. The theoretical results obtained by the proposed analysis are compared with those of linear precoding, which demonstrates the applicability of THP to OFDM-based mobile broadband systems.
Joint Parameters Estimation method based on Wide-Band Ambiguity Function

This paper proposes a novel method based on wide-band ambiguity function in Wideband bistatic multiple input multiple output (MIMO) Radar System. Firstly, Doppler stretch and time delay are jointly estimated by peak searching the wide-band ambiguity function (WBAF). Secondly, two sub-array models are constructed based on the WBAF. Furthermore, two modified algorithms are proposed for the estimation of DODs and DOAs, including the wide-band ambiguity function based on MUSIC algorithm (WBAF-MUSIC) and the wide-band ambiguity function based on ESPRIT algorithm (WBAF-ESPRIT). Simulation results are presented to verify the effectiveness of the proposed method.
CysLT2R Antagonist HAMI 3379 Ameliorates Post-Stroke Depression through NLRP3 Inflammasome/Pyroptosis Pathway in Gerbils

Post-stroke depression (PSD) is a kind of prevalent emotional disorder following stroke that usually results in slow functional recovery and even increased mortality. We had reported that the cysteinyl leukotriene receptor 2 (CysLT2R) antagonist HAMI3379 (HM3379) contributes to the improvement of neurological injury. The present study was designed to investigate the role of HM3379 in PSD-induced chronic neuroinflammation and related mechanisms in gerbils. The gerbils were subjected to transient global cerebral ischemia (tGCI) and spatial restraint stress to induce the PSD model. They were randomized to receive the vehicle or HM3379 (0.1 mg/kg, i.p.) for a consecutive 14 days. In the PSD-treated gerbils, HM3379 had noteworthy efficacy in improving the modified neurological severity score (mNSS) and depression-like behaviors, including the sucrose preference test and the forced swim test. HM3379 administration significantly mitigated neuron loss, lessened TUNEL-positive neurons, and reduced the activation of microglia in the cerebral cortex. Importantly, HM3379 downregulated protein expressions of the NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome and pyroptosis including NLRP3, cleaved caspase-1, interleukin-1β (IL-1β), IL-18, cleaved gasdermin-N domain (GSDMD-N), and apoptosis-associated speck-like protein containing a caspase activation and recruitment domain (ASC). Mechanistically, HM3379 could repress pyroptosis via inhibiting NLRP3 inflammasome activation under oxygen-glucose deprivation (OGD) stimulation. Knockdown of CysLT2R by short hairpin RNA (shRNA) or overexpression of CysLT2R by lentivirus (LV)-CysLT2R could abolish or restore the anti-depression effect of HM3379. Our results demonstrated that the selective CysLT2R antagonist HM3379 has beneficial effects on PSD, partially by suppressing the NLRP3 inflammasome/pyroptosis pathway.