Miracle-dz/UniFAR-FTUs huggingface dataset:
Complete Odometry Estimation of a Vehicle Using Single Automotive Radar and a Gyroscope

In this paper, we propose an algorithm for complete odometry of a vehicle on a horizontal plane. , that is. , estimation of linear velocity vector (forward and sideslip speeds) and angular speed of a vehicle. The vehicle is equipped with an automotive Radar sensor and a vertical gyro. The Radar sensor provides radial speed and azimuth angle of number of objects in the environment. We first derive the kinematic constraints imposed on the vehicle motion and stationary points in the environment. Using the constraints we classify the points detected by the Radar to stationary and non-stationary points. It is known that using data from a single Radar. , the abovementioned constraints are singular. Previous works have thus proposed the use of more than one Radar sensor. , or they have neglected the sideslip speed. In our work, we then use the Radar data of the stationary objects and a gyro data to solve an optimization algorithm to calculate vehicle odometry. Experimentation has been performed with a non-road vehicle driven on a straight path and on a circular path. We report our findings and show efficacy of the algorithm in comparison to the state of art [8] as well as wheel odometry and a complete navigation solution (including GNSS) as the reference path.
Towards Efficient Software Protection Obeying Kerckhoffs's Principle using Tamper-proof Hardware

We propose the first software protection scheme obeying Kerckhoffs’s principle that is suited for practical implementation. Previous schemes have either been closed source or too inefficient to be considered practically viable. A key technique of our scheme is to partition the software in such a way that a hacker who knows a set of parts cannot learn additional ones. To achieve a partition with this property, our scheme exploits the domain knowledge that is necessary to create the software as well as the inherent complexity of the software’s code. If a software is sufficiently complex to admit such a partition then we can prove that there are no successful attack strategies on our scheme other than storing every line of code that has been executed.
Establishing cut-points for physical activity classification using triaxial accelerometer in middle-aged recreational marathoners

The purpose of this study was to establish GENEA (Gravity Estimator of Normal Everyday Activity) cut-points for discriminating between six relative-intensity activity levels in middle-aged recreational marathoners. Nighty-eight (83 males and 15 females) recreational marathoners, aged 30–45 years, completed a cardiopulmonary exercise test running on a treadmill while wearing a GENEA accelerometer on their non-dominant wrist. The breath-by-breath V̇O2 data was also collected for criterion measure of physical activity categories (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous). GENEA cut-points for physical activity classification was performed via Receiver Operating Characteristic (ROC) analysis. Spearman’s correlation test was applied to determine the relationship between estimated and measured intensity classifications. Statistical analysis were done for all individuals, and separating samples by sex. The GENEA cut-points established were able to distinguish between all six-relative intensity levels with an excellent classification accuracy (area under the ROC curve (AUC) values between 0.886 and 0.973) for all samples. When samples were separated by sex, AUC values were 0.881–0.973 and 0.924–0.968 for males and females, respectively. The total variance in energy expenditure explained by GENEA accelerometer data was 78.50% for all samples, 78.14% for males, and 83.17% for females. In conclusion, the wrist-worn GENEA accelerometer presents a high capacity of classifying the intensity of physical activity in middle-aged recreational marathoners when examining all samples together, as well as when sample set was separated by sex. This study suggests that the triaxial GENEA accelerometers (worn on the non-dominant wrist) can be used to predict energy expenditure for running activities.
A Multistage Robust Transmission Expansion Planning Model Based on Mixed Binary Linear Decision Rules—Part I

This two-paper series introduces a new multistage robust approach for solving the transmission expansion planning (TEP) problem under the uncertainty of loads and wind power productions. The proposed multistage robust TEP model is formulated using mixed binary and linear decision rules, which aims at finding a solution withstanding any realization of the uncertain parameters belonging to a set-based uncertainty model. The robustness of the optimal expansion plan can be adjusted by the budget of uncertainty. The first part of this two-paper series presents the extended and compact formulations for a deterministic TEP problem. Then, the compact formulation of the TEP problem under the uncertainty of loads and wind power productions is introduced as a multistage robust optimization problem, which is reformulated using mixed binary and linear decision rules. The extended formulation of these decision rules, as functions of uncertain parameters, is presented in this part as well.
PU-ABE: Lightweight Attribute-Based Encryption Supporting Access Policy Update for Cloud Assisted IoT

Cloud-assisted IoT applications are gaining an expanding interest, such that IoT devices are deployed in different distributed environments to collect and outsource sensed data to remote servers for further processing and sharing among users. On the one hand, in several applications, collected data are extremely sensitive and need to be protected before outsourcing. Generally, encryption techniques are applied at the data producer side to protect data from adversaries as well as curious cloud provider. On the other hand, sharing data among users requires fine grained access control mechanisms. To ensure both requirements, Attribute Based Encryption (ABE) has been widely applied to ensure encrypted access control to outsourced data. Although, ABE ensures fine grained access control and data confidentiality, updates of used access policies after encryption and outsourcing of data remains an open challenge. In this paper, we design PU-ABE, a new variant of key policy attribute based encryption supporting efficient access policy update that captures attributes addition to access policies. PU-ABE contributions are multifold. First, access policies involved in the encryption can be updated without requiring sharing secret keys between the cloud server and the data owners neither re-encrypting data. Second, PU-ABE ensures privacy preserving and fine grained access control to outsourced data. Third, ciphertexts received by the end-user are constant sized and independent from the number of attributes used in the access policy which affords low communication and storage costs.
The response of bone cells to titanium surfaces modified by simvastatin-loaded multilayered films

Abstract The aim of this study was to enhance cytocompatibility of titanium substrates by loading a multilayer film of chitosan (Chi), gelatin (Gel) and simvastatin (SV). This was fabricated using a spin-assisted layer-by-layer (LBL) technique. The surface properties of the different substrates were characterized by field emission scanning electron microscopy (FE-SEM), atomic force microscope (AFM), X-ray photoelectron spectroscopy (XPS) and contact angle measurement, respectively. Simvastatin release in vitro was measured by ultraviolet-visible spectrophotometer. A well morphology with filopodia extensions was observed in mesenchymal stem cells (MSCs) grown on simvastatin loaded multilayered films-modified titanium substrates. After 7, 14 and 21 days of culture, the simvastatin loaded multilayered films increased cell proliferation, improved osteoblastic differentiation of alkaline phosphatase (ALP) and mineralization. Additionally, osteoclast diffentiation marker tartrate-resistant acid phosphatase (TRAP) was decreased in simvastatin loaded multilayered films. This study provides a new insight for the fabrication of titanium-based implants to enhance osseointegration especially for osteoporosis patients in orthopedic application.
Nonlinear Power System Analysis Using Koopman Mode Decomposition and Perturbation Theory

A new model-based framework for studying nonlinear dynamic behavior of stressed power systems that combines Koopman mode analysis and perturbation theory is proposed. First, a systematic approach to deriving high-dimensional representations of nonlinear dynamical systems based on perturbation theory is presented. Nonlinear behavior is interpreted as a projection of the Koopman operator eigenfunctions of an extended coordinate system onto the physical variables of the system. Analytical formulations are then obtained for the construction of the extended coordinate system based on perturbation theory. The analysis methodology is demonstrated on three test systems: A single-machine, infinite-bus system, a three-machine test system, and the IEEE 50-machine test system.
Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods

Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression profiles of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular organism growth played important roles in OA. Our results shed light on OA diagnosis through liquid biopsy.
Load Balancing in Cloud Environment using Stackelberg's Approach

Cloud Computing, one of the latest standard of large scale and parallel computing, attracts more users requiring utility computing with better and fast service. Load balancing is one of the key parameters that determine a cloud data center's performance. One of the main problems with respect to load balancing is that all the physical hosts in a data center are not efficiently used resulting in an imbalance and suboptimal performance. This paper has focused on how the physical hosts for deploying requested tasks is selected based on the requirement of the request by using Stackelberg's approach. Most of the works that has been done previously in this area utilize a series of algorithms that selects the optimal host in the data center based on the intelligence that is confined to the algorithm alone and doesn't have a holistic approach that considers the unutilized resources amongst all hosts within a data center. The proposed model in this work makes a decision of which physical host must be allocated to a requestbased on the requirements of the incoming task, current load on the data center hosts, available or unused resources in the data center. The model uses First-in-First-out allocation strategy for task assignments. Simulation results compared with the existing works show that the proposed approach has decreased the failure number of task deployment events obviously, and reduced the makespan.
Design and Modeling of MPPT for Solar Based Power Source

Solar energy is freely available, abundant and sustainable in nature throughout the year and other resources are depleting. Solar energy is being converted to various forms such as heat, electricity etc. Solar panel has Photovoltaic (PV) arrays which directly converts light into electricity. The best PV cell has an efficiency of up to 30%. Hence output power is small for input that it takes. Therefore, panel is required to operate at its maximum power point (MPP) to achieve high efficiency. The output characteristics of PV module depends on the solar irradiance, temperature and output voltage of PV module. As the PV cell has nonlinear Current - Voltage (IV) and Power - Voltage (PV) characteristics, it is necessary to design a control circuit or algorithm so that the cell is tuned to operate at MPP irrespective of the load connected and external parameters variations such as illumination and temperature. The panel output characteristics are nonlinear in nature; therefore, it is necessary to model and simulate for use in PV system applications. In order to improve the output efficiency of PV system, a novel variable step size perturbation and observation P&O method is proposed to track the maximum power point of PV system.
Location of Things (LoT): A Review and Taxonomy of Sensors Localization in IoT Infrastructure

Internet of Things (IoT) is a novel design paradigm, intended as a network of billions to trillions of tiny sensors communicating with each other to offer innovative solutions to real time problems. These sensors form a network named as wireless sensor networks (WSNs) to monitor physical environment and disseminate collected data back to the base station through multiple hops. WSN has the capability to collect and report data for a specific application. The location information plays an important role for various wireless sensor network applications. A majority of the applications are related to location-based services. The development of sensor technology, processing techniques, and communication systems give rise to a development of the smart sensor for the adaptive and innovative application. So a single localization technique is not adequate for all application. In this paper, a recent extensive analysis of localization techniques and hierarchical taxonomy and their applications in the different context is presented. This taxonomy of the localization technique is classified based on presence of offline training in localization, namely self-determining and training dependent approaches. Finally, various open research issues related to localization schemes for IoT are compared and various directions for future research are proposed.
Modeling Stock Prices with Text Contents in 10-Q Reports

Stock market prediction was once considered to be infeasible. Recent studies on using text contents of information reporting platforms has opened up new ways of analyzing the stock market with machine learning. we propose using the Securities and Exchange Committee (SEC) mandated 10-Q form as a possible source of data for stock predictions. Using the 10-Q reports of S&P 500 companies, we create our corpus by extracting bag-of-words (BOW) of any additions made to the 10-Q documents. Then, we create feed-forward multilayer neural network on stock price ratios of different target prediction periods and achieve positive prediction rates. We demonstrate that text contents of 10-Q form may have information value to stock price prediction models.
Multi-Objective Approach for Support Vector Machine Parameter Optimization and Variable Selection in Cardiovascular Predictive Modeling

We present a heuristic-based approach for Support Vector Machine (SVM) parameter optimization and variable selection using a real-valued cooperative Multi-Objective Evolutionary Algorithm (MOEA). Due to the possibility to optimize several criteria simultaneously, we aim to maximize the SVM performance as well as minimize the number of input variables. The second criterion is important especially if obtaining new observations for the training data is expensive. In the field of epidemiology, additional model inputs mean more clinical tests and higher costs. Moreover, variable selection should lead to performance improvement of the model used. Therefore, to train an accurate model predicting cardiovascular diseases, we decided to take a SVM model, optimize its meta and kernel function parameters on a true population cohort variable set. The proposed approach was tested on the Kuopio Ischemic Heart Disease database, which is one of the most extensively characterized epidemiological databases. In our experiment, we made predictions on incidents of cardiovascular diseases with the prediction horizon of 7–9 years and found that use of MOEA improved model performance from 66.8% to 70.5% and reduced the number of inputs from 81 to about 58, as compared to the SVM model with default parameter values on the full set of variables.
Fixed Similes: Measuring aspects of the relation between MWE idiomatic semantics and syntactic flexibility

We shed light on aspects of the relation between the semantics and the syntactic flexibility of multiword expressions by investigating fixed adjective similes (FS), a predicative multiword expression class not studied in this respect before. We find that only a subset of the syntactic structures observed in the data are related with idiomaticity. We identify and measure two aspects of idiomaticity, one of which seems to allow for predictions about FS syntactic flexibility. Our research draws on a resource developed with the semantic and detailed syntactic annotation of web-retrieved Modern Greek material, indicating frequency of use of the individual similes.
Methods and Technologies of Computer Simulators Synthesis for Control by Electrotechnological Installations in SCADA HMI

The synthesis method and technologies of computer simulators in the medium SCADA-systems are offered. Simulators are used for personnel training and increase of the control efficiency by electrotechnological installations in manufactures of aluminum, calcium carbide and phosphorus. The functional structure of simulators, information and mathematical software, software realization examples are developed.
Novel Framework of Risk-Aware Virtual Network Embedding in Optical Data Center Networks

The traffic between geographically distributed data centers (DCs) becomes bandwidth hungry. Since the optical interconnection has a high capacity, the optical data center network (ODCN)—where DCs are located at the edge of the optical backbone—emerges. By virtualization, the virtual networks—representing service requirements—are embedded onto the same part of the substrate ODCN. Each virtual network has virtual machine (VM) nodes interconnected by virtual links (VLs). Therefore, a virtual network embedding (VNE) operation includes two components: 1) the VM mapping for putting a VM into the server of an appropriate DC and 2) the VL mapping for establishing one substrate path to support inter-VM communications. In this paper, we focus on a risk-aware VNE framework because a blind VNE operation would result in severe information leakage among coresident VMs in the server. By evaluating VM threat and vulnerability, risky VMs are identified according to experimental results. To perform physical isolation between risky and security VMs, a risk-aware VNE heuristic algorithm is proposed. The simulation results show that our heuristic algorithm performs better than the benchmark in terms of maintaining ODCN security and earning rental revenue. There is also a good match between our algorithm solution and the problem bound.
Symmetrical Load of Supply Capacitors with Reduced Switch Count Space Vector PWM for 3L-NPC Inverter

This paper presents modifications to the space vector modulation for 3L-NPC inverter which are oriented towards symmetric load of the supply voltage dividing capacitors. Modification are made upon the space vector modulation, that uses prediction algorithm and additional transistors' states corresponding to output voltages, to reduce the number of individual state changes in power transistors (switch count). Reduction of switch count can be used to lessen the commutation losses. Two modification of previous method of modulation, that equalize the load of supply capacitors, are shown. Both methods were implemented, measured and compared with previous method, which was fully oriented towards switch count reduction. Presented modifications do not weaken switch count reduction ratio.
Pedestrian Dead Reckoning With Smartphone Mode Recognition

Smartphone mode recognition is becoming a key aspect of many applications, such as daily life monitoring, health care, and indoor positioning. In the later, two approaches exist to perform positioning using smartphone inertial sensors: traditional inertial navigation algorithms and pedestrian dead reckoning (PDR). Usually, PDR is preferred since it requires less integrations on the noisy sensory data. Step length estimation is a critical stage in PDR. It requires a calibration phase to determine appropriate gains, prior to PDR application. Using an incorrect gain will result in a position error. Such gains are very sensitive to different smartphone modes, such as talking, texting, swing, or pocket. Therefore, each smartphone mode requires a different gain. In this paper, the effect of the smartphone mode on PDR positioning accuracy is highlighted. To circumvent this error source, we employ machine learning classification algorithms to recognize the smartphone modes and thereby enabling the choice of a proper gain value to improve PDR positioning accuracy. To that end, a methodology of training on a single user and testing on multiple users, as well as unique features for the classification process, is implemented. Experimental results obtained using 13 participates walking in different indoor conditions and smartphone modes, show successes of more than 95% in classifying the smartphone modes and as a consequence may improve PDR positioning performance.
An Effective Method of Predicting the Polarity of Airline Tweets using sentimental Analysis

Sentiment Analysis is an approach of analyzing the sentiments using text analysis and Natural Language processing Methods. In Sentiment Analysis, the conceptive information is identified and extracted from the various sources. It aims to identify the mindset of a user across various aspects. Globally, it is used for Opinion extraction and recognition of sentiments, which helps Business establishments in understanding the needs of the end users. In this Paper, an effective yet simple approach of sentiment analysis is presented, which involves calculation of scores based on positive and Negative words. Tweets are classified positive, Negative and Neutral based on the scores.
Distributed Estimation in Networks of Linear Time-invariant Systems

This paper is concerned with the problem of distributed Kalman filtering in a network of several interconnected subsystems. We consider networks, which can be either homogeneous or heterogeneous, of linear time-invariant subsystems, given in state-space form. We propose a distributed Kalman filtering scheme for this setup. The proposed scheme provides estimates based only on locally available measurements. We compare its outcomes with those of a centralized Kalman filter, which offers the best minimum error variance estimate, using all measurements available all over the network. We show that the estimate produced by the proposed method asymptotically approaches to that of the centralized Kalman filter, i.e., the optimal one with global knowledge of all network parameters, and we are able to bound the convergence rate. Moreover, if the initial states of all subsystems are mutually uncorrelated, the estimates of these two schemes are identical at each time step.