Miracle-dz/UniFAR-FTUs huggingface dataset:
Haptic Stimulation with High Fidelity Vibro-Kinetic Technology Psychophysiologically Enhances Seated Active Music Listening Experience

In the last decade, entertainment industries have been creating multi-sensory experiences using haptic stimulation delivered via vibro-kinetic (VK) seats that produce vibration and motion coincidental with audiovisual content. However, the effects of VK stimulation during purely auditory experiences remains unclarified. We addressed this scientific shortcoming by investigating the effects of high fidelity VK (HFVK) stimulation, on psychophysiological indices of emotional valence and arousal. The HFVK stimulation was delivered to 24 healthy participants through an HFVK enhanced seat during active music listening. Participants listened to six popular songs randomly chosen from a pool of 15 songs, with the songs presented randomly with or without HFVK stimulation. Psychological emotional valence and arousal were indexed based on two items of the Self-Assessment Manikin (SAM) scale. Physiological emotional valence and arousal were respectively indexed based on micro facial expressions and electrodermal activity. Our results revealed that psychological and physiological valence, and psychological arousal were higher with HFVK stimulation compared to a control condition where the chair was static. A post-experiment questionnaire further revealed greater subjective appreciation for the HFVK stimulation condition compared to the control condition. Overall, our results highlight the potential for HFVK technologies to enhance auditory listening experiences.
Identification of Humanoid Biped Robot During Walking with Linear Regression Models

The purpose of the paper is to investigate walking dynamics of a biped humanoid robot with a multivariable linear time invariant structure. The assumption is that during low speed walking the angular velocity in a joint from the construction will vary proportionally to small deviations in the walking parameters. First a basic walking algorithm is implemented on a developed laboratory model of a humanoid robot with 17 degrees of freedom (DOF). Then a set of experimental data is recorded which is used for the identification procedure. After initial identification with a multivariable ARX a final state-space model from 24th order is estimated which is with 10 inputs and 6 outputs. The model is validated with an independent set of data and can be used for the purpose of closed-loop control.
Transfer Learning for Speech Emotion Recognition

Speech emotion recognition is still challenging due to the complexity of emotion. Most of the current research is based on the enough data, and the training data and test data are from the same corpus. Obviously, this is not in line with the actual application scene. In addition, the emotion annotation of large amounts speech data is also a challenging task. This paper investigates and summarizes the concepts and categories, methods and applications of transfer learning briefly, and studies the combination of transfer learning and deep learning, and the application of speech emotion recognition, and then points out the key issues that need to be further solved in the research.
Leveraging Multilayer Telemetry to Realize AI-assisted Service Provisioning in IP over Elastic Optical Networks: (Invited Paper)

We discuss how to design automatic and effective network control and management (NC&M) for IP over elastic optical networks (IPoEONs). We first develop a flexible multilayer inband network telemetry (ML-INT) system and then propose the artificial intelligence (AI) assisted NC&M framework to leverage the ML-INT data for application-aware service provisioning.
A Comparison of Methods of Data Mining Algorithms Directed Predictive Pharmacosafety Networks for Adverse Drug Event Detection

Adverse drug events (ADEs) detection is the critical concern in the field of pharmacovigilance, and it is also necessary to optimize the ADEs prediction to reduce the drug related morbidity and mortality. Here we propose a novel methods of data mining algorithms directed predictive pharmacosafety networks (PPNs) to compare their predictive performance and investigate the differences between data mining algorithms. The combinations of 152 cancer drugs and 633 ADEs in the 2010 FDA Adverse Event Reporting System(FAERS) data is the training data, and 2011-2015 FAERS data is the validation data. We find that performance of empirical Bayes geometric mean (EBGM) is closer to proportional reporting ratio (PRR), and greater than reporting odds ratio (ROR) in ADE detection. Further, only information component (IC) directed the PPNs have better predictive performance comparing to other data mining algorithms, the predictive performance of which reaches to AUROC =0.908 comparing to the existing AUROC =0.823, and the performance of IC is greater than EBGM in ADE detection.
Mimicking Predictive Control with Neural Networks in Domestic Heating Systems

Optimal control methods are a promising technology that may help to reduce the energy demand of domestic buildings. Paradoxically, the technical simplicity of some of the system components results in difficult optimal control problems. Heat generators and heat pumps, for example, often only accept on-off or discrete input values, which result in mixed integer programs. We tackle the issue by use of deep learning methods. Specifically, we train a neural network on optimal control policies acquired by hybrid model predictive control offline, in order to develop an implementable controller for lean embedded hardware. A case study reveals that the neural net is able to mimic the control performance of the predictive controller. The trained neural net was implemented on lean embedded hardware, where the evaluation could be performed in less than 7 ms.
Vacant Parking Lot Detection System Using Random Forest Classification

Intelligent parking system is an essential attribute nowadays as the time is being wasted in finding the vacant slot for parking. The proposed method focuses on detecting any vacant space that is out of human vision. A camera is used for capturing the images of parking space. Then a combination of both canny edge detection and LUV based colour variation detection methods are used to precisely extract the edges of each parking slot. The extracted features are then trained using Random Forest classifier to obtain the model which is used to classify the parking space as empty and occupied. For experimentation, parking lot images from the PKlot dataset captured under different weather conditions has been used. When trained with a subset of the dataset with 37,680 parking spaces, after extracting the features and applying the Random Forest classification algorithm the proposed method can able to achieve a better accuracy of 98.31% compared to the existing methods.
3D Semantic Modelling With Label Correction For Extensive Outdoor Scene

Semantic point cloud labeling is an important step of 3D urban scene mapping. One doable method is projecting labels from 2D semantic image to 3D point cloud. But the projection accuracy depends on the performance of calibration which cannot be absolutely accurate. In addition, the difference of information dimension between image and point cloud, with the addition of different sensor viewpoints can lead to label error in final 3D semantic results. This paper describes a projection based 3D semantic point cloud building method for extensive outdoor scene, and proposed a segmentation based voting strategy to handle the mislabeling problem in raw projection result. Firstly, based on pre-calibration among sensors, we get semantic point cloud by label projection from semantic image to point cloud. Secondly, we employ a segmentation method to cluster point cloud into different parts which belongs to different objects, this process increases the spatial consistency of semantic label at the object-level. Finally, in each part, we use a voting strategy to filter the label of each point. This strategy was tested in real world urban scene, experiments demonstrate the outstanding performance in decreasing the rate of semantic label error.
Distributed Average Tracking over Weight-Unbalanced Directed Graphs

The distributed average tracking (DAT) problem in an unbalanced directed network is studied in this paper, in which each agent aims at tracking the average of a group of time- varying reference signals using only local information and local communication. While the existing literature primarily focuses on the case of undirected or weight-balanced directed networks, we deal with the much more challenging case of unbalanced directed networks. We propose a distributed continuous algorithm with a chain of two integrators coupled with a distributed estimator. We show that if the deviations among the reference accelerations tend to zero (respectively, are bounded), the algorithm can achieve DAT with zero (respectively, bounded) tracking error under an unbalanced directed network. The algorithm is robust to initialization errors in terms of agent states and achieve DAT for a wide class of reference signals. Numerical examples are presented to illustrate the derived theoretical results.
Design and Stray Field Evaluation of Inductive Power Transfer in Electric Vehicle Charging

The influence of magnetic stray fields in the vicinity of a vehicle is a concern when inductive power transfer (IPT) is used for wireless charging for electric vehicles. To accurately evaluate the stray fields, the design of the coils and the chassis configuration should be considered. In this paper the analysis and design of a series-series compensated 3.3 kW IPT charger is presented. A prototype of the charger is constructed and mounted on a plug-in hybrid electric vehicle. The stray fields are measured at rated power and compared with finite element analysis simulations. The simulated and measured magnetic flux density are in good agreement. The highest measured magnetic flux density is 1.67 $\mu$T at the left tire with the ground assembly misaligned by 100 mm. It is only 6.2% of the flux density allowed for the general public in the ICNIRP standard. It is concluded that for low power IPT chargers, the requirement on leakage flux density is easily fulfilled.
Numerical shape optimization in Fluid Mechanics at low Reynolds number

In this paper, a simple and robust numerical shape optimization approach is presented. This approach is based on the Hadamard geometric optimization method and tested on 3 two-dimensional case studies representing standard flows in fluid dynamics, namely the flow around an obstacle, in a 90° elbow pipe and in a dyadic tree. This viscous flows are driven by the stationary Navier-Stokes equations without turbulence model. Low velocities are imposed at the inlet of each case study in order to operate in laminar flow regime. The objective is to determine the shape of the 3 aforementioned case studies that minimizes the energy dissipation in the fluid due to the work of viscous forces under a volume constraint.The required gradients of the performance index and constraint with respect to the shape are computed by means of the adjoint system method. The Navier-Stokes equations and the adjoint system are implemented and solved by using the finite volume method within OpenFOAM CFD software. The solver "adjointShapeOptimizationFoam" is modified in order to implement the optimization algorithm and determine the best shape in each of the three considered case studies. The optimal shapes obtained in the three case studies are in very good agreement with the available literature works. Moreover, they allow a significant reduction of the dissipated energy ranging from 10.8 to 53.3 %. Therefore, a decrease of the pressure losses in each case is also achieved in the same proportion.
Midair Click of Dual-Layer Haptic Button

In this study, we propose a method to create a quasi-click sensation in midair based on two types of tactile stimulation methods that create different sensations both in the intensity and quality. We consider two-layers of regions in the space that forms a virtual button. A user’s hand is tracked by a sensor and stimulated by the two methods according to the hand position. When the user’s hand is in the upper or lower layer, the hand skin is stimulated weakly or strongly by the two methods, respectively. These two states indicate a neutral position and action completion. This midair click was enabled by a recent finding where an ultrasound focus motion on the skin produced a stronger perception than amplitude modulation given at a constant position. We conducted experiments to confirm whether two haptic layers can be perceived. In addition, we investigated whether a blind operation of button selection can be performed.
A Mobility-Aware Cross-Edge Computation Offloading Framework for Partitionable Applications

Mobile Edge Computing has already become a new paradigm to reduce the latency in data transmission for resource-limited mobile devices by offloading computation tasks onto edge servers. However, for mobility-aware computation-intensive services, existing offloading strategies cannot handle the offloading procedure properly because of the lack of collaboration among edge servers. A data stream application is partitionable if it can be presented by a directed acyclic dataflow graph, which makes cross-edge collaboration possible. In this paper, we propose a cross-edge computation offloading (CCO) framework for partitionable applications. The transmission, execution and coordination cost, as well as the penalty for task failure, are considered. An online algorithm based on Lyapunov optimization is proposed to jointly determine edge site-selection and energy harvesting without priori knowledge. By stabilizing the battery energy level of each mobile device around a positive constant, the proposed algorithm can obtain asymptotic optimality. The-oretical analysis about the complexity and the effectiveness of the proposed framework is provided. Experimental results based on a real-life dataset corroborate that CCO can achieve superior performance compared with benchmarks where crossedge collaboration is not allowed.
Extension of the 3DCoAutoSim to Simulate Vehicle and Pedestrian Interaction based on SUMO and Unity 3D

The protection of vulnerable road users (VRUs) has been an active research topic in recent years. In this context, P2V (Pedestrian-to-Vehicle) and V2P (Vehicle-to-Pedestrian) communication systems have become crucial technologies to minimize potential dangers, due to the high detection rates and the high user-satisfaction levels they achieve. The recent release of the latest versions of SUMO (Simulation of Urban Mobility) and TraaS (TraCI as a Service) reveals the possibility of developing both P2V and V2P communications in a simulated 3D environment with microscopic modeling of vehicles and pedestrians, as well as the possibility of testing the efficiency of the proposed system. To this end, we implemented a 3D driving simulator with pedestrian integration, using a powerful game engine (Unity 3D) that accessed data from the SUMO open source traffic simulator and was therefore capable of simulating vehicle-pedestrian interaction through Transmission Control Protocol (TCP) connection. Results from a defined use case validated the presented approach.
Smoothed Bootstrap for Survival Function Inference

A new generalized smoothed bootstrap technique is presented for data including right-censored observations. The method is based on Banks’ bootstrap [2] and the right-censoring A(n) assumption introduced by [7], which is a generalization of Hill’s A(n) assumption [12]. We use two examples to show how the smoothed bootstrap is applied to make inferences about a survival function, namely an estimate of the survival function and corresponding bootstrap based point-wise confidence intervals. We compare the inferences to the Kaplan-Meier estimates and we apply two further methods, presented in the literature, for creating pointwise confidence intervals for a survival function at a fixed time t.
A Chebyshev pseudospectral method for solving fractional-order optimal control problems

This paper presents a new pseudospectral method for solving optimal control problems with fractional orders including state and control input constraints. The proposed method employs an operational matrix of fractional-order differentiation discretizing the feasible optimal solution of the optimal control problem at Chebyshev-Gauss-Lobatto points. Besides, the Clenshaw–Curtis quadrature formula is used to discretize the performance integral. As a result, the optimization problem associated with fractional-order differential equations transforms into a nonlinear programming problem, which can be solved by means of well-developed techniques. The feasibility and effectiveness of the proposed method are illustrated by comparing it with other methods in a numerical example.
Certificateless Aggregate Message Authentication for Hierarchical Trusted Authority based VANET

In VANET, vehicles periodically transmit beacon messages to the neighboring vehicles and the RSU. To establish the authenticity of these messages, a number of digital signature schemes have been proposed in literature. Many of these schemes enable an RSU to perform aggregate verification of the signatures to deal with high vehicle density scenarios. These schemes are either based on traditional PKC concept involving certificate management overhead or identity based cryptography having key escrow problem. Further, these schemes require the existence of OBU device which is resistant to side channel attacks. In this paper, we propose a hierarchical trusted authority privacy preserving certificateless aggregate signature scheme for VANET. In addition to providing message authentication, integrity and non-repudiation, our scheme is resistant to message forgeability attack. The proposed scheme assumes hierarchical organization of network such that vehicles operate under multiple trusted authorities (TA) which in turn are controlled by single root TA. Using our scheme, the entity could verify messages received from vehicles which operate under multiple TAs. The proposed scheme is free from key escrow problem and resistant to side channel attacks on OBU. It also possesses conditional linkability such that originator of a message could be revealed whenever required. Simulations confirm the efficient nature in terms of verification delay as compared to other well known schemes proposed in literature.
Kinetic Modeling of Dry Anaerobic Co-Digestion of Lignocellulosic Biomass

Anaerobic digestion is a widely used conversion process for recovering energy from bio degradable waste materials. Dry anaerobic co-digestion of lignocellulosic biomass such as rice straw (RS) with cow dung (CD) offers various benefits other than mono digestion or liquid state anaerobic digestion. However dry anaerobic co-digestion of lignocellulosic biomass has restrictions due to low moisture content and retarded mass transfer within the mixture of substrates which contains in the reactor. Three consecutive batch experiments (E1, E-2, and E-3) were conducted by reusing solid digestate of E1 as an inoculum to E-2 and solid digestate of E-2 as an inoculum for E-3 in lab scale reactors using RS and CD as substrates. In E-1, CD was the only inoculum and it acted as a substrate as well. Total solids (TS) content of the substrate mixtures of E-1, E-2 and E-3 were 15%, 16%, and 20% respectively. Then mathematical modeling was applied to estimate kinetic parameters related to dry anaerobic co-digestion process using the modified Gompertz model for the three experiments. Modified Gompertz model very closely predicted the ultimate methane yield (M$_{\mathbf{max}}$) with R$^{\mathbf{2}}$ almost 0.99 in each scenario. Degradation kinetics improved drastically with the strategy of reusing of digestate, as for the E-2 the lag phase period ($\lambda$) reduced from 14 days to almost zero. Ultimate methane yield increased by 104% through this approach. Degradation kinetics were negatively affected with the increase of TS% within the substrate mixture even though digestate was reused as an inoculum. In E-3 ultimate methane yield was 138 ml/g volatile solids (VS) which was a 38% reduction compared to E-2, even though digestate was used as the main inoculum source for the both experiments. But it was a 27% increase compared to E-1 which CD was used as the only inoculum.
MS-Rank: Multi-Metric and Self-Adaptive Root Cause Diagnosis for Microservice Applications

This paper presents a self-adaptive root cause diagnosis framework, named MS-Rank, to analyze multiple metrics collected from micro-service architecture. MS-Rank decomposes the task into four phases: impact graph construction, random walk diagnosis, result precision calculation and metrics weight update. First, we introduce a series of basic and implied metrics into MS-Rank, and design an impact graph construction algorithm to discover causal relationship between services during anomalies. Second, we propose a random walk algorithm with forward, selfward and backward transitions to heuristically identify the root cause service. Third, we establish a self-optimizing mechanism to dynamically update the confidence weight of different metrics according to their diagnosis precision. We develop a prototype system and integrate MS-Rank into IBM Cloud, to validate and compare it with selected benchmarks. Experimental results show that MS-Rank offers fast identification and precise diagnosis result. In multiple rounds of diagnosis, MS-Rank optimizes itself effectively.
Automated Insulin Delivery for Type 1 Diabetes Mellitus Patients using Gaussian Process-based Model Predictive Control

The human insulin-glucose metabolism is a time-varying process, which is partly caused by the changing insulin sensitivity of the body. This insulin sensitivity follows a circadian rhythm and its effects should be anticipated by any automated insulin delivery system. This paper presents an extension of our previous work on automated insulin delivery by developing a controller suitable for humans with Type 1 Diabetes Mellitus. Furthermore, we enhance the controller with a new kernel function for the Gaussian Process and deal with noisy measurements, as well as, the noisy training data for the Gaussian Process, arising therefrom. This enables us to move the proposed control algorithm, a combination of Model Predictive Controller and a Gaussian Process, closer towards clinical application. Simulation results on the University of Virginia/Padova FDA-accepted metabolic simulator are presented for a meal schedule with random carbohydrate sizes and random times of carbohydrate uptake to show the performance of the proposed control scheme.
PCA-CIPSO-GRNN-based Cyclic Cooling Water Corrosion Prediction

In order to improve the efficiency and accuracy of corrosion measurement of circulating cooling water in petrochemical enterprises, Reduce measurement errors caused by human measurement and save measurement costs, this article analyzes the three-year measurement data of a petrochemical company. Since the Generalized Regression Neural Network (GRNN) still has a good fitting effect on prediction of noisy data, therefore, it was chosen as the main body of the forecasting model. Principal component analysis (PCA) is used to reduce the dimension of GRNN input sample parameter information, extract principal components, initially build the PCA-GRNN algorithm model, and using chaotic immune particle swarm optimization (CIPSO) algorithm to optimize the smoothing factor in GRNN, Corrosion prediction of actual production data of circulating cooling water. Experiments show that compared with the PSO-GRNN algorithm, this optimization algorithm has higher prediction accuracy and generalization ability.