Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of SVJD Models with Bayesian Methods and Power-Variation Estimators

Methodology is proposed of how to utilize high-frequency power-variation estimators in the Bayesian estimation of Stochastic-Volatility Jump-Diffusion (SVJD) models. Realized variance is used as an additional source of information for the estimation of stochastic variances, while the Z-Estimator is used as an additional source of information for the estimation of asset price jumps. The models are estimated by a combination of a MCMC algorithm and a SIR Particle Filter. The performance of the models is evaluated on simulated times series as well as real world financial time series of the 4 major foreign exchange rates.
Heterogeneous Information Network-Based Content Caching in the Internet of Vehicles

With the rapid development of the Internet of Vehicles (IoV), the data in the network have become more complicated, and user demand for popular content has been growing. The focus of this paper is how to address the ever-changing mobile network environment of the IoV with the popular content caching strategy. To achieve the automatic selection, storage and delivery of popular content in the IoV, this paper modeled the network content as a heterogeneous information network (HIN). In this way, we can greatly reduce the load of the limited network with a small computational cost and give the user in the car a better experience with the automatic caching mode, in which the popular content can be cached in real time. For high-risk driving behavior, this popular content caching method is more secure and effective, which can reduce the network load, improve user satisfaction and bring a higher quality of experience (QoE) and a better quality of service (QoS) to users, as confirmed by the experimental results.
End-to-End Slicing as a Service with Computing and Communication Resource Allocation for Multi-Tenant 5G Systems

5G services, such as Ultra-reliable and Low Latency Communications, Massive Machine Type Communications and Enhanced Mobile Broadband, support low (or ultra-low) latency, a huge number of connections, and high bandwidth. Typically, 5G platforms are shared by multiple tenants, where each tenant has different service deployment requirements and thus requires different amounts of resources from the lower tier (i.e., radio access network and mobile edge) and upper tier (i.e., transport network and central office) of the 5G architecture. Consequently, slicing is a key technology in 5G networks for providing scalability and flexibility in allocating the 5G resources with appropriate isolation. This study proposes an Endto- End Slicing as a Service framework for slicing both computing and communication resources across the full 2-tier multi-access edge computing architecture. The framework is implemented using open source tools. It is shown that the framework successfully isolates the 5G resources, which are computing and communication, between slices (slicing isolation effect ratio equals 1) and ensures that resources of the deployed slices are merely sufficient to meet the latency requirements of the tenants. Moreover, the experiments show that URLLC and mMTC services require more than 70 percent of the needed computing and communication resources be provided by the edge and RAN in order to satisfy their tighter latency requirements. Overall, the results suggest a central to edge resource allocation ratio of 9:1, 3:7, and 1:9 for the eMBB, URLLC, mMTC applications, respectively. Therefore,
This is a wakeup call: Alarm clock headache

Hypnic headache, also known as alarm clock headache is one of the rare subtypes of primary headaches described in literature. It is a typical nocturnal headache which only occurs in sleep and generally affects elderly population. It needs to be differentiated from the other common primary headaches as the therapeutic options for it are very different from the other common headaches. The description is only through case reports and small case series. A review of this less known entity is discussed here.
Effects of model configuration for superparametrised long-term simulations – Implementation of a cloud resolving model in EMAC (v2.50)

Abstract. A new module has been implemented in the ECHAM5/MESSy Atmospheric Chemistry (EMAC) Model that simulates cloud related processes on a much smaller grid. This so called superparametrisation acts as a replacement for the convection parametrisation and large-scale cloud scheme. The concept of embedding an ensemble of cloud resolving models (CRMs) inside of each grid box of a general circulation model leads to an explicit representation of cloud dynamics. The new model component is evaluated against observations and the conventional usage of EMAC using a convection parametrisation. In particular, effects of applying different configurations of the superparametrisation are analyzed in a systematical way. Consequences of changing the CRMs orientation, cell size and number of cells range from regional differences in cloud amount up to global impacts on precipitation distribution and its variability. For some edge case setups the analysed climate state of superparametrised simulations even deteriorates from the mean observed energy budget. In the current model configuration different climate regimes can be formed that are mainly driven by some of the parameters of the CRM. Presently, the simulated cloud cover is at the lower edge of the CMIP5 model ensemble indicating that the hydrological overturning is too efficient. However, certain "tuning" of the current model configuration could improve the currently underestimated cloud cover, which will result in a shift of the climate. The simulation results show that especially tropical precipitation is better represented with the superparamerisation in the EMAC model configuration. Furthermore, the diurnal cycle of precipitation is heavily affected by the choice of the CRM parameters. However, despite an improvement of the representation of the continental diurnal cycle in some configurations, other parameter choices result in a deterioration compared to the reference simulation using a conventional convection parameterisation. The ability of the superparametrisation to represent latent and sensible heat flux climatology is dependent on the chosen CRM setup. Further interactions of the planetary boundary layer and the free troposphere can significantly influence cloud development on the large-scale. Therefore a careful selection of the CRM setup is recommended to compensate for computational expenses.
Nonlinear control of an exoskeleton seven degrees of freedom robot to realize an active and passive rehabilitation tasks

This doctoral thesis proposes the developments of an exoskeleton robot used to rehabilitate patients with upper-limb impairment, named ETS-MARSE robot. The developments included in this work are the design, and validation of a kinematic inverse solution and nonlinear control strategy for an upper limb exoskeleton robot. These approaches are used in passive and active rehabilitation motion in presence of dynamics and kinematics uncertainties and unexpected disturbances. Considering the growing population of post-stroke victims, there is a need to improve accessibility to physiotherapy by using the modern robotic rehabilitation technology. Recently, rehabilitation robotics attracted a lot of attention from the scientific community since it is able to overcome the limitations of conventional physical therapy. The importance of the rehabilitation robot lies in its ability to provide intensive physiotherapy for a long period time. The measured data of the robot allows the physiotherapist to accurately evaluate the patient’s performance. However, these devices are still part of an emerging area and present many challenges compared to the conventional robotic manipulators, such as the high nonlinearity, dimensional (high number of DOFs) and unknown dynamics (uncertainties). These limitations are provoked due to their complex mechanical structure designed for human use, the types of assistive motion, and the sensitivity of the interaction with a large diversity of human wearers. As a result, these conditions make the robot system vulnerable to dynamic uncertainties and external disturbances such as saturation, friction forces, backlash, and payload. Likewise, the interaction between human and the exoskeleton make the system subjected to external disturbances due to different physiological conditions of the subjects like the different weight of the upper limb for each subject. During a rehabilitation movement, the nonlinear uncertain dynamic model and external forces can turn into unknown function that can affect the performance of the exoskeleton robot. The main challenges addressed in this thesis are firstly to design a human inverse kinematics solution to perform a smooth movement similar to natural human movement (human-like motion). Secondly, to develop controllers characterized by a high-level of robustness and accuracy without any sensitivity to uncertain nonlinear dynamics and unexpected disturbances. This will give the control system more flexibility to handle the uncertainties and parameters’ variation in different modes of rehabilitation motion (passive and active).
Advanced robotics analysis toolbox for kinematic and dynamic design and analysis of high‐DOF redundant serial manipulators

This study aimed to develop a toolbox for kinematic and dynamic modeling and analysis of the high degree‐of‐freedom (DOF), redundant serial robots used widely in cooperative and space applications. With the Advanced Robotics Analysis Toolbox (ARAT), kinematic and dynamic modeling and analysis of the complex, high DOF, serial robots which may have universal, spherical, prismatic, or rotational joints with more than one DOF and a mobile or stationary base can be easily accomplished. The ARAT was used to design and analyze different complex structures of serial robots for robotic education students and robotics researchers who design and build exceptional new robots. The robot's kinematic and dynamic parameters including link lengths, directions, masses and inertias can be entered into the ARAT using a user‐friendly interface. Solid models of the robots can be created in a three dimensional (3D) environment for virtual reality simulations. In the ARAT, forward and inverse kinematic models, Jacobian matrix, trajectory planning, forward and inverse dynamic models can be produced in a computationally efficient way. All of the physical robot parameters such as angular and linear velocities, accelerations, forces, and torques at the joints, link origins and the center of mass of the links can be computed in 3D. The ARAT includes support for the most commonly used trajectory algorithms in serial robots, including polynomial, trigonometric (harmonic, cycloidal, elliptic), exponential, Gaussian, and Fourier‐based (Gutman, Freudenstein). These trajectories can be applied easily to each joint of the robots. To compare the kinematic and dynamic results obtained from ARAT and MATLAB‐Simscape Multibody simulation program, different structures of the serial robots were simulated in both programs. The results that produced from ARAT and MATLAB‐Simscape were similar. The ARAT is much better from the serial robot simulation toolboxes existing in literature in terms of computation capability, visual ability, and easy usage.
Gene expression data classification: some distance-based methods

Micro-array dataset is a classical example of high throughput data characterized with more features(genes) than sample points(gene expression levels). A number of classification techniques have been proposed in literature. Many of these methods are either computationally expensive or perform sub-optimally. In this paper, some distance functions are considered and classification rules based on the distance functions are formulated. The distance functions include average distance measure, distance to component-wise median, distance to mean. These methods are computationally simple and are expected to perform well for gene expression data. We also define a probabilistic approach to classification rules based on two of the distance measures. Gene selection technique based on shrunken centroids regularized discriminant analysis was employed on small round blue cell tissue, colon cancer, lymphoma, prostate cancer and leukaemia data before applying the classification rules. Three simulation studies were performed to mimic gene expression data. The performance of the classification methods mentioned above was compared with performance of some known classification methods in literature. The distance-based methods were also performed on gene expression data. The performance of the distance-based classification methods is competitive with some existing classification methods. Distance based methods implemented in this study are computationally simple and very cheap in terms of computational cost.
Predicting Semantic Labels of Text Regions in Heterogeneous Document Images

This paper describes the use of sequence labeling methods in predicting the semantic labels of extracted text regions of heterogeneous electronic documents, by utilizing features related to each semantic label. In this study, we construct a novel dataset consisting of real world documents from multiple domains. We test the performance of the methods on the dataset and offer a novel investigation into the influence of textual features on performance across multiple domains. The results of the experiments show that the Conditional Random Field method is robust, outperforming the neural network when limited training data is available. Regarding generalizability, our experiments show that the inclusion of textual features does not guarantee performance improvements.
On the Viable Area of Wireless Practical Byzantine Fault Tolerance (PBFT) Blockchain Networks

Distributed systems are crucial to the full realization of the Internet of Thing (IoT) ecosystem as it mitigates the challenges of trust, security, and scalability associated with the traditional centralized approach. In this paper, we present an analytical modeling framework for Practical Byzantine Fault Tolerance (PBFT)-a consensus method for blockchain in IoT networks. We define the viable area for the wireless PBFT networks which guarantees the minimum number of replica nodes required for achieving the protocol's safety and liveliness. We also present an analytical framework for obtaining the viable area which we later utilize for power optimization. Results show that significant energy saving can be achieved with the utilization of the viable area concept in wireless PBFT networks. The proposed framework can serve as a theoretical guidance for practical PBFT based wireless blockchain network deployment.
A Comparison of Feature Selection Methodology for Solving Classification Problems in Finance

Classification is one of the most important tasks in real world with the intention of finding the underlying patterns of the data and making use of the found patterns. Other than considering lots of parameters that may influence the classification model accuracy, the influence of the feature selections has been rarely discussed. SHAP (SHapley Additive exPlanation) value is a novel ensemble learning measurement that is the unique consistent and locally accurate attribution value. Its applications to feature selection are not mentioned in the literature. In this paper, we compare the prediction accuracy of several feature selection techniques on a set of Polish companies. We show that new feature selection method based on the SHAP values is superior (not worse) to other widely used prediction methods.
A Framework to Identify Critical Design Parameters for Enhancing User’s Satisfaction in Human-AI Interactions

The advancements in artificial intelligence technology have made changes in how people interact with systems. Unique features and user requirements of Human-AI Interactions (HAII) need to be identified with respect to those of Human-Computer interactions (HCI). This study proposes a way to find critical parameters of interaction design for enhancing user’s satisfaction when people interact with intelligent systems through voice user interfaces. We summarised distinguished user requirements for intelligent products identified from previous researches. Then match them with design parameters in terms of performance indexes that will make differences in the user’s satisfaction. The interaction scenario was set as users ask simple questions with their own voices to the system and the system answer to the questions with synthesized voices after it got to the answer by AI function. The critical performance indexes derived are the number of trials to get the right answer for a question, response time to get to the next interaction, sentence structures of the answer, and pace of the answer. An experimental setup is ready to evaluate user’s satisfaction among different levels of the above performance indexes by Wizard of Oz design method applied on a voice user interface we implement. We are going to validate the effects of performance indexes in HAII on the user’s satisfaction, which will be measured in terms of verbal and non-verbal measures.
Automatic Classification of CAD ECG Signals With SDAE and Bidirectional Long Short-Term Network

Coronary artery disease (CAD) has been one of main causes of heart diseases globally. The electrocardiogram (ECG) is a widely used diagnostic tool to monitor patients’ heart activities, and medical personnel need to judge whether there are abnormal heartbeats according to captured results. Therefore, it is significant to identify ECG signals accurately and fast. In this paper, a fast and accurate electrocardiogram (ECG) classification system based on deep learning is proposed. In our model, stacked denoising autoencoders (SDAE), as encoder, automatically learns semantic encoding of heartbeats without any complex feature extraction in unsupervised way. Then bidirectional LSTM (Bi-LSTM) classifier achieves classification of heartbeats with semantic encoding. SDAE implements noise-reduction while Bi-LSTM takes full advantage of temporal information in data. At the same time, this method relieves impacts from unbalanced data by employing cost-sensitive loss function. We validate our model on MIT-BIH Arrhythmias Database, SVDB and NSTDB respectively. Compared with state-of-art methods, the final result verify that this newly proposed method not only has high accuracy but also boosts classifying efficiency.
ANALISIS SENTIMEN OPINI PUBLIK BERITA KEBAKARAN HUTAN MELALUI KOMPARASI ALGORITMA SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR BERBASIS PARTICLE SWARM OPTIMIZATION

Sentiment analysis is a process to determine the content of text-based datasets which are positive or negative. At present, public opinion be an important resource in the decision of a person in finding a solution. Classification algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) is proposed by many researchers to be used in sentiment analysis for review opinion. The problem in this research is the selection of feature selection to improve accuracy values Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) and compare the highest accuracy for sentiment analysis review public opinion about the news of forest fires. The comparison algorithms, SVM produces an accuracy of 80.83% and AUC 0.947, then compared with SVM based on PSO with an accuracy of 87.11% and AUC 0.922. The test result data for K-NN algorithm accuracy was 85.00% and the AUC 0.918, then compared for accuracy by k-NN-based PSO amounted to 73.06% and the AUC 0.500. The results of the testing of the PSO algorithm can improve the accuracy of SVM, but are not able to improve the accuracy of the algorithm K-NN. SVM algorithm based on PSO proven to provide solutions to the problems of classification review news opinion forest fires in order to more accurately and optimally.
Improving Urban Crowd Flow Prediction on Flexible Region Partition

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, due to the diverse transportation modes in the mobility data. DeepFlowFlex explicitly groups crowd flows with respect to speed and time, and combines graph convolutional long short-term memory networks and graph convolutional neural networks to extract complex spatiotemporal dependencies, especially long-term and long-distance inter-region dependencies. Evaluations on two big cellular datasets and public GPS trace datasets show that DeepFlowFlex outperforms the state-of-the-art deep learning and big-data-based methods on both grid and non-grid city map partition.
Qualitative Research on Use of Voice of Customer in Czech Organisations

The article presents qualitative research conducted on a sample of Czech organisations operating in the B2C relationships within the Internet environment. The research aims to determine the approach of the organisations to the collecting and analysing Voice of Customer in measuring share-of-mind metrics as constituent elements of Customer Experience. Research improves the current knowledge and setting of Voice of Customer in Customer Experience and its measurement and management in a real business setting. From the interviews is observed the low maturity in Customer Experience in Czech companies and often a misunderstanding of the concept. The companies are not fully entrenched in analysing Voice of Customer with text analytics. The barriers of getting the full potential of analysing Voice of Customer within Customer Experience are identified. The article is a part of the initial research to ‘Voice of Customer integration in Customer Experience measurement’ in the author’s dissertation and will also be published as a chapter in the author’s dissertation.
Integration of Data-Driven Process Re-Engineering and Process Interdependency for Manufacturing Optimization Supported by Smart Structured Data

Process re-engineering and optimization in manufacturing industries is a big challenge because of process interdependencies characterized by a high failure rate. Research has shown that over 70% of approaches fail because of complexity as a result of process interdependencies during the implementation phase. This paper investigates data from a manufacturing operation and designs a filtration algorithm to analyze process interdependencies as a new approach for process optimization. The algorithm examines the data from a manufacturing process to identify limitations through cause and effect relationships and implements changes to achieve an optimized result. The proposed cause and effect approach of re-engineering is termed the Khan-Hassan-Butt (KHB) methodology, and it can filter the process interdependencies and use those as key decision-making tools. It provides an improved process optimization framework that incorporates data analysis along with a cause and effect algorithm to filter out the process interdependencies as an approach to increase output and reduce failure factors simultaneously. It also provides a framework for filtering the manufacturing data into smart structured data. Based on the proposed KHB methodology, the study investigated a production line process using the WITNESS Horizon 22 simulation package and analyzed the efficiency of the proposed approach for production optimization. A case study is provided that integrated the KHB methodology with data-driven process re-engineering to analyze the process interdependencies to use them as decision-making tools for production optimization.