Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards the Development of a Web Support System for Improving Accuracy in Coding Discharge Diagnosis

The practice of coding morbidity data using international standard diagnostic classifications has become increasingly important and recognized as a difficult and timeconsuming task. Clinical coders/physicians assign codes to each patient episode based on their interpretation of the available case notes or the electronic patient record systems. Therefore, accurate coding depends on the legibility of the case notes and on the coders‘ understanding of medical terminology. Several studies have indicated poor reproducibility of clinical coding. To support physicians, in the coding of diagnoses and procedures using ICD 9th revision, Clinical Modifications (ICD-9-CM), and in identifying the main condition to be filled in Hospital Discharge Records, the SISCO.web service is proposed. The service has the aim of improving the accuracy of coding by using the combination of NLP algorithms, controlled vocabularies mapped to ICD-9-CM, as well as coding-rules and a decision trees for the identification of the main condition.
Predictive Inverse Kinematics: optimizing Future Trajectory through Implicit Time Integration and Future Jacobian Estimation

This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.
Glucometry and Pulse Oximetry - Comparative Noninvasive Methods for Determining Blood Glucose

The paper aims to study non-invasive detection methods for blood glucose determination. Glucometry and pulse oximetry are two methods that are based on the absorption of light in tissues. Pulse oximetry measures blood oxygen saturation based on red (infrared) light, using a wavelength of 600-750 nm, and infrared light, using a wavelength of 850-1000 nm. For detecting blood glucose we will use the same method. Glucose can also be found in hemoglobin, according to the latest studies. Glycemia is a less well-known blood test than conventional blood glucose, but it is especially useful when there is a suspicion of diabetes or prediabetes or to monitor long-term, in the case of diabetics.
Synchronizing Virtual Constraints and Preview Controller: a Walking Pattern Generator for the Humanoid Robot COMAN+

In this paper we propose a novel hybrid walking pattern generator which combines results from the virtual constraints and the preview control theories for bipedal locomotion. This choice is motivated by findings in biomechanics that show how the dynamic motion of the human walk is mainly generated by the sagittal component of the stepping. Thus, we choose the conservative preview control to generate the lateral motion while we pick a more dynamical framework such as the virtual constraints for the sagittal motion. We investigate how the time-dependent preview control and the time-independent virtual constraints approach can be integrated together in a humanoid locomotion and finally we show promising results on COMAN+, the new humanoid robot from Istituto Italiano di Tecnologia.
Power Delay Profile in Coordinated Distributed Networks: User-Centric v/s Disjoint Clustering

Power delay profiles (PDPs) are an important factor in the design of wireless networks, e.g., in choosing the length of a cyclic prefix. While distributed networks are receiving increasing attention, the impact of cooperation on the PDP has not been addressed. We address this issue in this paper. Specifically, we analyze a network where each user is served by a cluster of Remote Radio Heads (RRHs) with RRH locations modeled as a Poisson point process. We compare and contrast user-centric (where the serving RRHs are in a circle centered at the user) and the disjoint clustering (where the network is partitioned into fixed regions and all users within a region are served by all RRHs in the same region). Our analysis provides effective tools for the design of distributed networks illustrating the benefits of user-centric clustering in both power and delay metrics.
Predictive and adaptive maps for long-term visual navigation in changing environments

In this paper, we compare different map management techniques for long-term visual navigation in changing environments. In this scenario, the navigation system needs to continuously update and refine its feature map in order to adapt to the environment appearance change. To achieve reliable long-term navigation, the map management techniques have to (i) select features useful for the current navigation task, (ii) remove features that are obsolete, (iii) and add new features from the current camera view to the map. We propose several map management strategies and evaluate their performance with regard to the robot localisation accuracy in long-term teach-and-repeat navigation. Our experiments, performed over three months, indicate that strategies which model cyclic changes of the environment appearance and predict which features are going to be visible at a particular time and location, outperform strategies which do not explicitly model the temporal evolution of the changes.
Metric Monocular Localization Using Signed Distance Fields

Metric localization plays a critical role in vision-based navigation. For overcoming the degradation of matching photometry under appearance changes, recent research resorted to introducing geometry constraints of the prior scene structure. In this paper, we present a metric localization method for the monocular camera, using the Signed Distance Field (SDF) as a global map representation. Leveraging the volumetric distance information from SDFs, we aim to relax the assumption of an accurate structure from the local Bundle Adjustment (BA) in previous methods. By tightly coupling the distance factor with temporal visual constraints, our system corrects the odometry drift and jointly optimizes global camera poses with the local structure. We validate the proposed approach on both indoor and outdoor public datasets. Compared to the state-of-the-art methods, it achieves a comparable performance with a minimal sensor configuration.
Analysis of Spreading Factor Variations on LoRa in Rural Areas

This study will analyze the use of spreading factor variations from Long Range (LoRa) as Wireless Sensor Network (WSN) in the rural area to support the Internet of Things (IoT) technology. IoT is a Machine-to-machine (M2M) technology that requires an internet connection to send data. Internet connections in each region (especially in rural areas) are certainly different, one of the problems is the coverage of the mobile network. Because coverage from the internet is limited, IoT must be supported by WSN networks to retrieve data from non-mobile and non-TCP networks. One of the most popular technologies in the WSN is LoRa, which is Low Power Wide Area Network (LPWAN) technology. The advantage is that it has a wide coverage area, is cheap and has low power. So it is necessary to do a LoRa coverage test to find out how far it can reach especially in rural areas. In this paper, the testing of LoRa coverage in rural areas is tested and has hilly characteristics which have quite many obstacles so that it can be the worst possibility in LoRa coverage. This research uses the entire configuration in the Spreading Factor to determine the effect on distance. The test results show that LoRa can send data up to 980 meters in SF12 with a PDR of 12% and PER 100%.
Inchworm-inspired soft climbing robot using microspine arrays

Animals in nature, such as geckos, inchworms, and felines can climb on various surfaces using different mechanisms and serve as references for the study of bio-inspired robots. This paper presents an inchworm-inspired climbing robot that consists of soft body and feet. The soft robot is actuated by shape memory alloy wires and utilizes microspine arrays to attach its feet to rough or soft surfaces. A series of experiments to test the functionality of the feet and torso of the designed robot have verified the theoretical feasibility of the robot. Results have shown that the designed bio-inspired robot can climb on inclined or vertical curved surfaces and flat surfaces. The robot can also adapt to the underwater environment. Thus, this robot has great potential for various applications such as pipeline inspection.
Explore, Approach, and Terminate: Evaluating Subtasks in Active Visual Object Search Based on Deep Reinforcement Learning

Searching for objects and distinguishing task-relevant objects from others is a key requirement for service robots. We propose a reinforcement learning solution to the active visual object search problem. Our method successfully learns to explore the environment, to approach the target object, and to decide when to terminate the search as the target object has been found. We demonstrate the efficiency of our solution on a dataset of real-world images collected by a robot. Our approach outperforms state-space planning or other baseline search strategies, reaching a higher success rate in a shorter time. We also study individual subtasks of active visual object search. Although strong baselines exist for the subtasks, our RL solution outperforms them in the overall search task.
A Stochastic Geometry Approach to Model and Analyze Future Vehicular Communication Networks

Safety message dissemination in a vehicular communication network requires vehicle-to-vehicle (V2V) communication with low latency and high reliability. To fulfill the stringent requirements in terms of reliability associated with safety applications, a control information assisted three-step (CIAT) resource selection scheme based on cellular vehicle-to-everything (C-V2X) technology for future vehicular communication network is investigated in this paper. To get a mathematical understanding of the process and performance of the CIAT scheme, in this paper, we develop an effective analytical approach to characterize the properties of the CIAT resource selection scheme in vehicular communication network by using the tool of stochastic geometry. In particular, the resource selection process and the key performances are analyzed mathematically; The closed-form expressions for the key performances are derived, which include the resource exclusion ratio, the average successful transmission probability and the packet inter-reception time; Furthermore, the performances of CIAT resource selection scheme are compared with those of random resource selection scheme mathematically. All the analytical results in this paper are validated through extensive Monte Carlo simulations, and the results provide insights to proper parameter settings for future vehicular communication network.
Wi-Fi Secure Access Control System Based on Geo-fence

With the rapid spread and development of the wireless network, secure access control has caught significant attention. The existing Wi-Fi network system access control methods, mainly based on the static password, the MAC address or the username/password, are prone to spoofing attacks, such as malicious users forge the authentication information to access the network and realize the penetration attack of Intranet. In this paper, we propose a new secure access control model based on Geo-fence to solve the above problems, further strengthening the security of the wireless network system. Our novel solution facilitates 1) providing convenient wireless network service in the trusted zone without authentication 2) isolating cyber-attacks outside the security zone 3) monitoring the access smart mobile device real time without affecting users’ using the wireless network. Furthermore, we present the consideration of the smart mobile device heterogeneity in our model. Through using three different smart mobile devices to experiment, the analytical and experimental results demonstrate that the difference among the devices have a great impact on the device localization and the final access control effect.
Modeling, Simulation and Experimental Validation of a Tendon-driven Soft-arm Robot Configuration - A Continuum Mechanics Method

This paper presents the mathematical derivation and experimental validation of a computational model, which accurately predicts static, large-strain deformations of tendon driven non-slender soft-arm manipulators subjected to gravity. The large strain behaviors are captured by employing the Green-Lagrange strain and by deriving analytical expressions for the variation of the equivalent Young modulus of the structure due to the large strains. No simplifying assumptions are made regarding the curvature of the structure, the stretching or the compression. Furthermore the paper proposes an iterative method for numerically solving the resultant non-linear system of coupled differential equations and demonstrates a number of application scenarios. The model is experimentally validated using a set-up comprising one segment of tendon driven soft-arm, which integrates stretchable and compressible hyperelastic (rubber-type) materials into its non-homogeneous back bone structure.
Two-Stage Optimal Scheduling Strategy for Large-Scale Electric Vehicles

This paper proposes a two-stage scheduling strategy for large-scale electric vehicles to reduce the adverse impact of the uncontrolled charging of the electric vehicles on the grid. Based on the statistical data of private car travel, the uncontrolled charging demand of individual electric vehicles and their aggregation are simulated. In the first stage, the electric vehicles and thermal power units are jointly scheduled. To minimize the total cost and standard deviation of the total load curve, the charging and discharging load guiding curve of the electric vehicles and the optimal output plans of the thermal power units in each period of the scheduled day are formulated. In the second stage, the electric vehicle load management and control centre formulates specific charging and discharging plans for the users through rolling optimization to follow the guiding load curve. The cost of vehicle discharge compensation is considered to improve the willingness of users to participate in scheduling and the user satisfaction. To avoid the “dimension disaster” caused by the centralized dispatching of large numbers of electric vehicles, the K-means clustering algorithm is used to divide the vehicles into different groups. Next, each group is scheduled as a unit, and the model is solved by using the particle swarm optimization algorithm. By comparing the optimization results of different scenarios, the feasibility and effectiveness of the proposed strategy are verified.
Audio-based Eating Analysis and Tracking Utilising Deep Spectrum Features

This This paper proposes a deep learning system for audio-based eating analysis on the ICMI 2018 Eating Analysis and Tracking (EAT) challenge corpus. We utilise Deep Spectrum features which are image classification convolutional neural network (CNN) descriptors. We extract the Deep Spectrum features by forwarding Mel-spectrograms from input audio through deep task-independent pre-trained CNNs, including AlexNet and VGG16. We then use the activations of first (fc6), second (fc7), and third (fc8) fully connected layers from these networks as feature vectors. We obtain the best classification result by using the first fully connected layer (fc6) of AlexNet for extracting the features from Mel-spectrograms with a window size of 160 ms and a hop size of 80 ms and a viridis colour map. Finally, we build Bag-of-Deep-Features (BoDF) which is the quantisation of the Deep Spectrum features. In comparison to the best baseline results on the test partitions of the Food Type and the Likability sub-challenges, unweighted average recall is increased from 67.2 percent to 79.9 percent and from 54.2 percent to 56.1 percent, respectively. For the test partition of the Difficulty sub-challenge the concordance correlation coefficient is increased from . 506 to . 509.
Monocular Depth Estimation in New Environments With Absolute Scale

In this work we propose an unsupervised training method that finetunes a single image depth estimation CNN towards a new environment. The network, which has been pretrained on stereo data, only requires monocular input for finetuning. Unlike other unsupervised methods, it produces depth estimations with absolute scale – a feature that is essential for most practical applications, yet has mostly been overlooked in the literature. First, we show how our method allows adapting a network trained on one dataset (Cityscapes) to another (KITTI). Next, by splitting KITTI in subsets, we show the sensitivity of pretrained models to a domain shift. We then demonstrate that, by finetuning the model using our method, it is possible to improve the performance on the target subset, without using stereo or any form of groundtruth depth and with preservation of the correct absolute scale.
ALTEE: Constructing Trustworthy Execution Environment for Mobile App Dynamically

TEE(Trusted Execution Environment) has became one of the most popular security features for mobile platforms. Current TEE solutions usually implement secure functions in Trusted applications (TA) running over a trusted OS in the secure world. Host App may access these secure functions through the TEE driver. Unfortunately, such architecture is not very secure. A trusted OS has to be loaded in secure world to support TA running. Thus, the code size in secure world became large. As more and more TA is installed, the secure code size will be further larger and larger. Lots of real attack case have been reported [1]. In this paper, we present a novel TEE constructing method named ALTEE. Different from existing TEE solutions, ALTEE includes secure code in host app, and constructs a trustworthy execution environment for it dynamically whenever the code needs to be run. separate TA is difficult to deploy
Development of Load Weight and Height Classifier in Lifting-Up Task Using Body Motion Metrics

This paper presents a new weight and height class classifier for the lift-up task, focusing on future use in the control of wearable power assist devices. The proposed classifier retrieves body motion feature metrics using small accelerometers and determines the weight class (0 ky40% of the subject body weight but not exceeding 25 kg) and the height of the handle (0cm/30cm/60 cm) of the load. Three different classifiers have been trained/configured and tested. The trained classifiers are shown to correctly classify the evaluation dataset with 80-90% accuracy. An additional experiment was conducted to collect feature quantities in which subjects were requested to lift up a weight placed inside a jute bag on the floor. A discrimination accuracy of approximately 80% for the jute bag experiment verifies the versatility of the classifier for more general on-the-job lifting tasks.
ARMOR: An Architecture for Diagnosis and Remediation of Network Misconfigurations

SDN and NFV propose the use of software-based solutions to perform functions that historically have been implemented by physical devices. The use of software-based solutions can introduce misconfiguration in critical network services, such as routing inconsistencies. This paper proposes and evaluates ARMOR, an architecture for handling network misconfigurations when different types of software-based components are inserted or removed from the network. ARMOR is capable of monitoring software-based solutions using network information gathering mechanisms, such as SNMP, SFlow and REST API. Additionally, it is capable of identifying and addressing configuration issues using historical remediation schemes and machine learning techniques. As a result, the ARMOR architecture is a flexible mechanism to monitor and remediate network misconfigurations.
Evolving Switch Architecture toward Accommodating In-Network Intelligence

Motivated by the breakthroughs of AI in both theory and applications, we are perceiving a great potential for network innovations from a new dimension of intelligence. However, as the engine of networks, switches are designed as "dumb" network elements with the sole purpose of forwarding network packets, and thus a barrier against the entrance of AI as an intrinsic part of the network is unconsciously erected. This article proposes an evolved switch architecture aimed at breaking this barrier and accommodating in-network intelligence. We enhance the current switch architecture by embedding an intelligence plane, which is externalized as an intelligent computation pluggable module of an evolved switch. The module employs an integrated solution of "X86 CPU+GPU+DPDK," which provides a high-performance and high-throughput open platform for hosting in-network intelligence. We further conceive a flexible processing framework for an intelligent traffic measurement, recognition, and traffic regime. We also carry out extensive experiments to demonstrate the capability of the evolved switch by deploying a prototype in a campus network, with two promising application scenarios: in-network application identification and in-network anomaly detection.
Underwater Acoustic Object Discrimination for Few-Shot Learning

Deep neural networks are the go to algorithm when it comes to image classification [1-5]. This is partly because they can have arbitrarily large number of trainable parameters. However, this comes at a cost of requiring a large amount of data, which is sometimes not available. In the field of underwater acoustic object discrimination, data are scarce, no public dataset can be used for training, and data can only be collected by sea trials. In order to mitigate such an issue, we choose a few-shot learning with Siamese networks method. Siamese networks are a special type of neural network architecture. Instead of a model learning to classify its inputs, the neural networks learn to differentiate between two inputs. We trained our model to use only eighty single beam trajectories, divided into eight types of objects, and calculated the differences between each type of object, and the result show that our Few-shot Learning with Siamese networks can achieve good performance, we can achieve the target stable tracking on the multi-beam graph by the result of the difference between each type. This provides an important reference method for the application of deep learning in the field of underwater acoustic object recognition.