Miracle-dz/UniFAR-FTUs huggingface dataset:
GPU-Accelerated Stream Clustering of Geotagged Text Data for Crisis Management

Social media services are endlessly producing large amounts of streaming data, and one of the most important ways of discovering and analyzing interesting trends in the data is through stream clustering. When clustering streaming data, it is crucial to access incoming data only once, and the clustering model should evolve over time, while not losing important feature statistics of the streaming data. We previously developed a stream clustering system for crisis management that clusters social media data based on their textual content and displays the clusters and their locations on an interactive map. Our system takes advantage of a text stream clustering algorithm called TextClust [6], which uses two-phase clustering, composed of micro-clustering and macro-clustering. The online micro-clustering phase incrementally creates microclusters that represent enough key information about topics occurring in the text stream. The off-line macro-clustering phase clusters micro-clusters for a user-specified time interval and can change macro-clustering algorithms dynamically. However, due to the sheer volume of an infinite data stream, stream clustering techniques on a single processor have inevitably hit their bottleneck. In this paper, we assess the benefits of utilizing the enormous computational power of the Graphics Processing Unit (GPU) to improve the performance of our micro-clustering process. We transformed our previous sequential clustering algorithm into a parallel version that can be executed efficiently on the GPU. Our experiments demonstrated that our GPU-based implementation has an average speedup of 2.9 when clustering multiple temporary micro-clusters.
Determining the Type of Interference in a Receiving Antenna Using Machine Learning in Fog Computing Environments

This article describes a small view of the interference type in the receiving antenna. The new opportunities of computing vision for the conception of oscillograms for a specialist in digital signal processing are described. A new approach is proposed for the interaction of a specialist in digital signal processing and interference with pattern recognition using a smartphone. According to three standards, at the design stage, a description of the architecture of the proposed system is given. An approach for a smartphone has been created and viewed. Efficiency of using fog and cloud computing to determine types of interference using pattern recognition has been proven, and specific examples are provided.
Exploring Three-Dimensional Locomotion Techniques in Virtual Reality

Most existing Virtual Reality (VR) locomotion interfaces are designed for ground-based locomotion, limiting the degrees of freedom (DoF) during movement as they would in the real world. However, an optimized three-dimensional travel technique is required for certain virtual scenes such as flying and diving. This study evaluated three different interface types: Slider, Teleport, and Point-Tug, that were created based on existing ground-based locomotion methods in order to investigate the features required for three-dimensional (3D) movement. We conducted a within-subjects study with 3D navigation tasks and evaluated the performance when navigating a virtual environment using three different 3D locomotion interfaces. The results show that Slider, which allows users to move continuously, has an advantage over the others, and interfaces such as teleportation, which are widely used in ground-based locomotion, are likely to be perceived differently in 3D locomotion.
Should We Forget the PID Control?

Proportional-integral-derivative (PID) control represents the most used automatic control technology and the PI controller is the prevailing controller in practice. One will easily come across such statements in automatic control textbooks, but they also touch on other areas, such as artificial intelligence or signal processing. Even if hidden in the most diverse applications, PI and PID controllers can be discovered at every step of our lives. Despite this apparent dominance of PI and PID controllers, it can be shown that in reality everything can be significantly more complicated, even regulators that do not have any integrator are called integral and thus the current development of automatic control deserves more attention, precision and often substantial reassessments. This presentation is dedicated to a brief reminder of the contribution of the first pioneers in the field of automatic control, along with pointing out the roots of these misunderstandings and problems. It shows that their essence is the long-accepted equivalence of two different types of solutions, and just from the point of view of the latest research, the need for substantial corrections of several experienced approaches has been shown. And this is important not only with regard to a more correct interpretation of the past, but mainly with regard to future progress in the field of generalization and improvement of existing controllers. The delicacy of the situation is increased by the fact that a small inconsistency created a century ago can today affect a huge spectrum of solutions that have been created in the meantime, not to mention the inertia of the human factor itself.
The first known Mira-type variable star in IC 1613

King et al. (1999) discovered Nova 1999 in IC 1613 at the Lick Observatory. Both Fugazza et al. ([CITE]) and Borissova et al. ([CITE]) questioned this classification, because they were able to detect the star on images obtained in previous years. In infrared frames taken on Oct. 15, 1998, Nova 1999 has $(J-K) = 1.14$ and $K = 14.69$ mag. Our light curve study, based primarily on 92 unfiltered Lick images, suggested that the object could be a Mira-type variable with a period of 640.7 days. This period is very close to that obtained by Fugazza et al. ([CITE]) - 631 days. The star is overluminous with respect to the period-luminosity (PL) relation derived by Feast et al. ([CITE]) for Mira variables in the LMC. At longer periods $(P > 400 \rm days)$, many LMC Miras show such behavior and the PL relation appears to break down. It is possible that the situation in IC 1613 is similar. An optical spectrum obtained with the Keck-II telescope shows features typical of M3Ie or M3IIIe stars. We conclude that the star is a normal long-period M-type Mira variable, the first such star confirmed in IC 1613.
Enabling Scalable SFCs in Kubernetes with eBPF-based Cross-Connections

Service Function Chains (SFCs) are composed of an ordered set of Network Functions (NFs) that provide network services to the handled traffic. However, traffic is highly variable over time, thus telco operators need to deploy scalable chains that can quickly and easily adapt to the load fluctuations. Although Kubernetes has already brought benefits in terms of increased scalability and flexibility to general-purpose applications, it is not natively suitable for network workloads since it lacks some functionalities required by network services. This paper presents a simple, cloud-native architecture that integrates SFCs in Kubernetes, with the aim of seamlessly leveraging cloud-native features such as horizontal autoscaling. The solution is based on flexible cross-connections, namely logical links that connect adjacent network functions, which can promptly adapt the distribution of the network traffic to the existing network functions in case of scale in/out events affecting the number of NF instances. The architecture has been validated with an open-source proof-of-concept implementation based on dedicated Kubernetes operators and an eBPF load balancer, demonstrating the feasibility and the efficiency of the proposed approach.
Population-coded Spiking Neural Network with Reinforcement Learning for Mapless Navigation

Most of the navigation methods currently applied to mobile robots cost too much in building and maintaining maps. Therefore, it is crucial to implement mapless navigation for mobile robots. Although the recent deep reinforcement learning (DRL) methods have been able to make full use of the on-board resources to explore unknown space, their high energy cost limits their application. The low energy consumption of the spiking neural network (SNN) can help the DRL to overcome this difficulty. In this paper, we combine the SNN with the deep deterministic policy gradient (DDPG) method. To address the problem of long intervals between two adjacent pulses in the integrate-and-fire (LIF) neuron model, we change the way the membrane voltage resets and the dynamics of the neuron during the refractory period. On this basis, the environmental information was encoded using neuron population coding method and the two networks were trained jointly using an extended spatial-temporal backpropagation (STBP) method. The simulation results show that the proposed method achieves a higher success rate in navigation compared to traditional deep learning algorithms.
A General Intelligent Portfolio Theory with Strength Investing and Sector Rotation in Stock Markets

This paper walks through mathematical evolution of modern portfolio theory and multi-factor models and advances with a General Intelligent Portfolio Theory and underlying applications in stock markets. Following up the earlier form of the Intelligent Portfolio Theory, the new generalization extends in 3 dimensions: 1) three forms of intelligent portfolios – multi-asset multi-strategy, multi-strategy multi-asset and multi-trader; 2) strength investing with momentum rotation as an engine driving dynamic re-selection of assets or strategies or traders; 3) sector rotation in stock markets as a main form of strength investing and as a paradigm shift from diversification in portfolio theory. Applications in Chinese stock markets and international index futures are demonstrated with nontrivial performance achieved through testing on historical data.
Implementation and Improvement of Autonomous Robot Car using SoC FPGA with DPU

Autonomous driving cars currently under development use many sensors, among which CCD/CMOS cameras are the most important. To improve real-time image processing technology and FPGA utilization for autonomous driving, FPGA design contests have been held on the theme of creating an automated small robotic car using FPGA. This paper describes our FPGA robot car under development for participation in the FPGA design contest to be held at FPT2022. Our design incorporates a Xilinx SoC FPGA and uses both hardware and software for control. We use Xilinx DPU IP for object detection with Yolo v3 tiny. Furthermore, we are working on improving the stability and reliability of the FPGA robot car by modifying our previous FPGA robot car.
Personalized e-Coaching as a Support in the Social Inclusion Process

Risk groups of people are exposed to a higher risk of social exclusion. Reasons for this situation may include poor information about gathering help, alternatively problematic availability of information or incorrect mental setting. This work focuses on personalized e-coaching, which aims for positive change of personal habits and thus achieving a healthy, long-term sustainable lifestyle. In case of e-coaching, the whole process is controlled by a software. To achieve the goals, persuasive techniques tend to be used. As a part of the e-coaching process, it is appropriate to provide the respondent with guidelines in the form of personalized recommendations, which may include activities or specific resources depending on preferences, interests and responsibilities of the respondent. We proposed a solution with a client-server architecture, based on the PSD model. The primary task of the server application is obtaining, storing, and providing context-specific personalized data and recommendations. Data is obtained through client applications and external services, combining implicit and explicit methods. Recommendations are based on available data about the respondent, as well as on available contextual data. The application will gradually dynamically learn the preferences and interests of the respondent based on the interaction with individual resources and services of the application.
Data-Driven City Traffic Planning Simulation

Big cities are well-known for their traffic congestion and high density of vehicles such as cars, buses, trucks, and even a swarm of motorbikes that overwhelm city streets. Large-scale development projects have exacerbated urban conditions, making traffic congestion more severe. In this paper, we proposed a data-driven city traffic planning simulator. In particular, we make use of the city camera system for traffic analysis. It seeks to recognize the traffic vehicles and traffic flows, with reduced intervention from monitoring staff. Then, we develop a city traffic planning simulator upon the analyzed traffic data. The simulator is used to support metropolitan transportation planning. Our experimental findings address traffic planning challenges and the innovative technical solutions needed to solve them in big cities.
A Robust Adaptive Predictive Super-Twisting Sliding Mode controller for grid-tied converters

This paper presents a Robust Adaptive Predictive Super-Twisting Sliding Mode controller for grid-injected current control of a grid-tied power converter with LCL filter. The proposed controller is robust enough to consider the LCL filter model as a first order transfer function in its design. Also, the only prior information about a plant is its relative degree and gain’ sign. To implement it, the controller only requires system output measurements. Experimental results are presented to corroborate the controller’s performance.
Comparison of UPF acceleration technologies and their tail-latency for URLLC

The industry is striving to deploy private cellular networks in their factories. The envisioned use cases, such as industrial automation, require Ultra-Reliable and Low-Latency Communications (URLLC). In 5G, the User Plane Function (UPF) handles user traffic and hence significantly influences the performance in terms of latency and throughput. However, present UPFs are often user-space applications running on commercial off-the-shelf (COTS) servers introducing computational delays that follow long-tailed distributions. In this work, we compare modern packet processing acceleration technologies such as XDP, DPDK, and P4 and their respective tail-latency performance. Our results demonstrate the clear advantage of hardware-based P4 implementations in terms of reduced tail-latency of about 6μ8 at a percentile of 99.999% for small packets. Furthermore, we also demonstrate that software-based frameworks can compete with hardware-based approaches. For example, DPDK achieves tail-latencies of approximately 23μ8 at a percentile of 99.999 %. Based on our findings, we illustrate the trade-offs between low-cost software- and high-performant hardware-based packet processing systems.
An Agile Tile-based Platform for Adaptive Heterogeneous Many-Core Systems

Computing heterogeneity is a crucial demand for today's systems-on-chip requirements. Current many-core computing architectures feature a scalable number of heterogeneous compute units supporting a wide range of application domains. However, supporting both heterogeneity and computing scalability brings significant design challenges related to on-chip communication between heterogeneous components and run-time management. This leads to growing design time, development cost, and lack of hardware modularity and re-usability. This PhD work aims to develop and design a modular and adaptive hardware platform for realizing different types and taxonomies of heterogeneous many-core systems targeting FPGAs reusing the same hardware components. The proposed platform is based on a modular and scalable tile-based architecture supporting heterogeneous instruction set architectures (ISAs), seamless integration of custom hardware accelerators and several memory hierarchies. In this paper, the proposed tile-based platform, preliminary results, and evaluation are presented targeting FPGAs. Finally, planned and future works are highlighted.
CAPI-Precis: Towards a Compute-Centric Interface for Coherent Shared Memory Accelerators

Emerging shared memory accelerator interfaces promote a tighter coupling between traditional general-purpose processing cores and accelerator units through cache-coherence and shared virtual address space capabilities. However, different interface standards solving similar problems often require custom designs and optimizations depending on the adopted interface. This work introduces CAPI-Precis, an abstract layer between CAPI, a cache-coherent interface standard proposed by IBM, and the Accelerator Functional Unit (AFU). CAPI-Precis provides a Compute-Centric FIFO-based paradigm with the shared memory accelerator interface, hiding CAPI complexities and latency requirements in an abstract layer focusing on optimized, efficient, and scalable AFUs. Such a layer adapts to other shared memory interfaces, such as CCIX or CXL, with minimal overhead in area and performance while preserving the algorithm logic design.
Information System for Asset Management in Buildings

Since the University of Žilina is constantly expanding, we decided to develop information system, which will provide a unified environment for property, rooms and buildings management. Within my bachelor thesis I focused on the development of a web application for asset management in the buildings of the University of Zilina. The created system enables various options for working with property, its evidence, management, decommission, creation of statistics and reports.
Predictive Modeling of Slurry PH Based on Gated Recirculating Unit Neural Network

The PH value of slurry in the absorber is an important parameter affecting the efficiency of wet flue gas desulfurization system in coal-fired power plants. The wet flue gas desulfurization system of coal-fired power plant has the characteristics of large lag, nonlinear and strong coupling. It is difficult to accurately control the PH value of the slurry in the absorption tower. In this paper, the advantages of gated recurrent unit (GRU) neural network in processing time series data are used to predict the PH value of slurry in the absorber. Firstly, the data collected in coal-fired power plants were analyzed to screen out the variables with strong correlation with the PH value of grout. Then, the time series data of these variables are used as the input of the model to train the model, and the prediction model of the slurry PH value in the absorption tower in the wet desulfurization system is obtained. Finally, the real data are collected to test the model. The results show that compared with BP neural network model, radial basis function neural network (RBF), recurrent neural network (RNN) and long short-term memory (LSTM) neural network, the proposed model is more accurate and practical.
Digital Precompensation for Luminance Nonuniformities in Augmented Reality Head Mounted Displays

The luminance nonuniformity on augmented reality head mounted displays (AR HMDs) presents a significant challenge for usage in medical applications, such as surgery planning and interoperative procedures. Digital precompensation for the luminance nonuniformity, also known as flat-field corrections, presents a potential solution by sacrificing maximum display luminance for increased luminance and color uniformity. Here, we present results for an absolute luminance precompensation method that increase the luminance uniformity on the Microsoft HoloLens 2. The flat-field correction method was tested at different locations in the eyebox of the HMD and at different angular locations in the field of view. The results demonstrate that the luminance nonuniformity depends on the interpupillary distance (IPD) of the user and a unique flat-field correction should be applied based on the IPD of the user for optimal luminance uniformity.
Edge-Enabled: A Scalable and Decentralized Data Aggregation Scheme for IoT

The data aggregation technique has been widely adopted in the Internet of Things (IoT) to protect data privacy while ensuring data availability. Homomorphic encryption is a typical technique that guarantees accurate computing results. However, it brings heavy computation overhead for edge nodes and exposes the aggregated results to the central server, which significantly threatens the confidentiality of results. This article gets rid of the server-centric style existing in most data aggregation schemes and proposes a scalable and decentralized data aggregation scheme for edge-enabled IoT. In the proposed scheme, edge nodes can freely form, join, and exit from the data aggregation group to aggregate data correctly, securely, and efficiently. Besides, two structure-based data aggregation methods are proposed to reduce the aggregation overhead to <inline-formula><tex-math notation="LaTeX">$O(n\sqrt{n})$</tex-math></inline-formula> with constant rounds, as opposed to <inline-formula><tex-math notation="LaTeX">$O(n\log n)$</tex-math></inline-formula> with <inline-formula><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> round. Symmetric encryption and online/offline signature computation are adopted to mitigate the online computation burden. Moreover, the proposed scheme can rigorously defend against forgery attack, eavesdropping attack, and collusion attack. The performance evaluation and experiment results show that the proposed scheme improves the efficiency of communication with affordable computation costs for edge nodes.
A Multi-objective (MO) Optimal Management Model of Enterprise Economy Based on Memetic Algorithm

In the real world, optimization problems exist widely, and the improvement of optimization technology can generate huge economic benefits, reflect excellent social value and promote the rapid development of ecology. However, while the optimization problem shows a trend of diversification and scale, it is also accompanied by nonlinear, high-dimensional and other characteristics. The optimization objectives involved in these practical problems may be one or even conflicting with each other, and often with harsh constraints. The continuous deepening of its complexity brings continuous challenges to the selection of optimization schemes and the performance of optimization algorithms. The main purpose of this paper is to study the MO optimization management model of enterprise economy based on Memetic algorithm. This paper mainly conducts simulation experiments through the establishment of the fitness function and the analysis of its calculation method. Experiments show that the optimal value obtained by the Memetic algorithm is significantly higher than the other two schemes, and the algorithm requires relatively less computing time, and the success rate is greatly improved.
Dynamic coverage infrastructure of vehicles in VANET with scalable environments on demand

Wireless communication in highways is an important issue for a correct growth of developing cities, generating studies of location and coverage on vehicles communication infrastructure when designing wireless networks on dynamics environment with changing topology. This paper presents the deployment of a VANET infrastructure in several urban scenarios and therefore seeks through an optimization algorithm to efficiently locate the RSUs in the planning area and estimate a changing V2I coverage radius, after that, main objective to be achieved is to provide dynamic coverage of the vehicular network with a previous statistical analysis, that simultaneously with the K-Means clustering method, will differentiate areas with higher vehicular density and to be able to finish with the analysis on the infrastructure, besides estimating the necessary radius change for the RSUs.