Miracle-dz/UniFAR-FTUs huggingface dataset:
A Network Slicing Strategy for Telemedicine based on Classification

Telemedicine is the inevitable trend of medical development. Diverse categories of telemedicine put forward different demands to network. As an important enabler of 5G, network slicing can meet different service requirements in telemedicine scenario by slicing multiple isolated virtual networks on the same physical infrastructure. How to ensure telemedicine services through network slicing with limited resources has always been a problem to be solved. In this paper, we consider hierarchical telemedicine slicing to serve customized services. What’s more, a telemedicine services classification model based on Radial Basis Function (RBF) neural network is proposed to match 5G remote health services with appropriate slice. Then we propose a hierarchical resource allocation strategy based on genetic algorithm to adjust inter-slice and intra-slice resources. Finally, simulation results show the effectiveness of the proposed scheme.
Detection of Material Defects Using Graph-Based Manifold Ranking and Heterogeneous Image Features

The development of a method for detecting material defects using graph-based manifold ranking and heterogeneous image features is considered in the article. The aim of the research is to develop a method that allows high-precision detection of defect in images with low color contrast of detecting and background areas. The image is pre-segmented into regions to calculate the saliency map. The graph is based on regions. The defect is determined based on background area queries. The areas adjacent to the edges of the image belong to the background areas. Color features of the image are used in the existing approach based on the manifold ranking. Texture features are used in the proposed method to improve accuracy. Gabor's energy function is used to calculate texture features. The proposed method has shown good results for detection of material defect in images in which the background color and object color are in similar ranges. The experimental results are presented on test images. Precision-recall curves showing the advantage of the developed method are constructed.
Classifying Oscillatory Signatures of Expert vs NonExpert Meditators

EEG oscillatory correlates of expert meditators have been studied in the time-frequency domain. Machine Learning techniques are required to expand the understanding of oscillatory signatures. In this work, we propose a methodological pipeline to develop machine learning models for the classification between expert and nonexpert meditative state. We carried out this study utilizing the online repository consisting of EEG dataset of 24 meditators that categorized as 12 experts and 12 nonexperts meditators. The pipeline consists of four stages that include feature engineering, machine learning classifiers, feature selection, and visualization. We decomposed signals using five wavelet families consisting of Haar, Biorthogonal(1.3-6.8), Daubechies( orders 2-10), Coiflet(orders 1-5), and Symlet(2-8), followed by feature extraction using relative entropy and power. We classified the meditative state between expert and non-expert meditators employing twelve classifiers to build machine learning models. Wavelet coefficients d8 shows the maximum classification accuracy in all the wavelet families. Wavelet orders Bior3.5 and Coif3 produce the maximum classification performance with the detail coefficient d8 using relative power. We have successfully classified the meditative state between expert and non-expert with 100% accuracy using d5,d6,d7,d8,a8 coefficients. Multi-Layer Perceptron and Quadratic Discriminant Analysis attain the highest accuracy. We have figured out the most discriminating channels during classification and reported 20 channels involving frontal, central and parietal regions. We plot the high dimensional structure of data by utilizing two feature reduction techniques PCA and t-SNE.
Message Relay Between Short-Range Radio Devices in Traffic Flow

The article is devoted to process of message relay algorithms between radio devices based on technology Dedicated Short Range Communications (DSRC). The devices are installed on vehicles, gets access to its telemetry data and allows to exchange information messages. The main distinguishing feature of such intelligent transport systems is ability to provide subscribers with help and assistance information services without direct access to global information networks. We consider the problem of message delivery from stationary vehicle located in area without mobile connection to global network. There are two possible cases: devices do not have any information about coverage area of mobile operator, and information about some points of coverage area is accumulated by each vehicle device during their movement. In the first case we use to select relative recipient positions of vehicles, but in second one only accumulated information about coverage area. Corresponding algorithms and results of simulation computer experiments are proposed.
Integrated High- and Low-Frequency Current Ripple Suppressions in a Single-Phase Onboard Charger for EVs

Current ripples produced in single-phase onboard charging systems of electric vehicles (EVs) impact the lifetime of their batteries. In this article, an isolated multifunctional charger topology is proposed. The charging system can be used to charge the auxiliary batteries or serve as an active filter (AF) for the power battery charger. By time-sharing multiplexing it, the high-frequency (HF) and low-frequency (LF/second harmonic) current ripple can be suppressed in driving and parking charging modes, respectively. The proposed topology can also achieve zero voltage switching for all power switches under a full-load range in the driving charging mode. In addition, the integration and the power density of the charging system can be improved due to the reconstruction of the capacitive energy storage AF in the parking charging mode. Finally, a 400-W experimental prototype is developed and the experimental results are presented to validate the performance and feasibility of the proposed topology.
On DoA Estimation for Rotating Arrays Using Stochastic Maximum Likelihood Approach

The flexibility needed to construct DoA estimators that can be used with rotating arrays subject to rapid variations of the signal frequency is offered by the stochastic maximum likelihood approach. Using a combination of analytic methods and Monte Carlo simulations, we show that for low and moderate source correlations the stochastic maximum likelihood estimator that assumes noncorrelated sources has accuracy comparable to the estimator that includes the correlation coefficient as one of the parameters. We propose several fast approximations of the stochastic maximum likelihood estimator and compare their accuracy with the Crámer-Rao lower bound. We also discuss the model order selection problem for the binary- and multiple-hypotheses cases.
A Semantic-aware Representation Framework for Online Log Analysis

Logs are one of the most valuable data sources for large-scale service management. Log representation, which converts unstructured texts to structured vectors or matrices, serves as the the first step towards automated log analysis. However, the current log representation methods neither represent domain-specific semantic information of logs, nor handle the out-of-vocabulary (OOV) words of new types of logs at runtime. We propose Log2Vec, a semantic-aware representation framework for log analysis. Log2Vec combines a log-specific word embedding method to accurately extract the semantic information of logs, with an OOV word processor to embed OOV words into vectors at runtime. We present an analysis on the impact of OOV words and evaluate the performance of the OOV word processor. The evaluation experiments on four public production log datasets demonstrate that Log2Vec not only fixes the issue presented by OOV words, but also significantly improves the performance of two popular log-based service management tasks, including log classification and anomaly detection. We have packaged Log2Vec into an open-source toolkit and hope that it can be used for future research.
Set-Theoretic Control for Active Detection of Replay Attacks with Applications to Smart Grid

In this paper, we propose a novel physical watermarking technique for the active detection of replay attacks in cyber-physical systems. The proposed strategy exploits the set-theoretic model predictive control paradigm to design control input that, whenever needed, can be safely and continuously applied to the system for an apriori known number of steps. Such a control scheme enables the design of a physical watermarked control signal that is obtained by properly randomly dropping the last computed command input. As an example application, we apply the proposed control scheme to the IEEE new England 39-bus system. We prove that, in the attack-free case, the generators' transient stability is achieved for all admissible watermarking signals and that the closed-loop system enjoys uniformly ultimately bounded stability. Our simulation results confirm that the proposed solution is effective in detecting replay attacks and is also capable of mitigating the control performance loss drawback typical of watermarking solutions.
Solver-Based Mixed Integer Linear Programming (MILP) Based Novel Approach for Hydroelectric Power Generation Optimization

In Pakistan, hydroelectric power is one of the reliable sources of electricity with a capacity of 8,713 MW, which is 29% of the total energy mix. Hence, with such a vast resource capacity of hydroelectric power, its optimization and dispatch planning will have a great significance. This research work discusses the importance of hydroelectric power generation planning for both storage and run-of-river (ROR) hydropower plants, as well a; a solver-based optimization technique is proposed for the first time to resolve the intricate job of generation planning for hydroelectric power plants in MATLAB. A mathematical-optimization model is also developed, which uses a Mixed-Integer Linear-Programming (MILP) algorithm, based on the objective function of profit maximization, which considers a random varying revenue plan as model input. Three hydropower generators of different capacities and efficiencies are considered for the optimization problem. MILP based solution is proposed for both storage and ROR hydropower plants with two dispatch schedules, i.e., Normal dispatch schedule and optimum dispatch schedule. The objective functions are solved, and the profit (in dollars) from each dispatch schedule is calculated and compared. The preliminary optimization results show an increase of $22,000 and $29,130 in the profit for storage and ROR hydropower plants, respectively, which is 19% more than average income. Hence, ensuring the credibility of the proposed algorithm for maximizing the revenue ($), is aimed to facilitate and assist better planning for electric power producers.
Modeling and Analysis of Thermal Characteristics of Magnetic Coupler for Wireless Electric Vehicle Charging System

The operating temperature of the magnetic coupler in wireless power transfer (WPT) systems for electric vehicles (EVs) determines the system reliability and service life. This paper studies the modeling and analysis of temperature characteristics of a general magnetic coupler. Firstly, a thermal circuit model is established to show the heat transfer of the magnetic coupler in the air. Secondly, the heating mechanism of the coil, core and aluminum (Al) shield plate is studied. The thermal power of each component is calculated, and the temperature distribution is qualitatively given based on the proposed thermal circuit model. Then, the temperature distribution of each component is simulated, and the results are consistent with the theoretical analysis. Finally, a 6.6 kW WPT prototype is set up, and the temperature of each component is measured after 30 minutes of operation. The temperature of the coil, 34.6 °C, is the highest among that of the coil, core, Al plate and coil base. The agreement between experimental results and the theoretical simulation results shows that the thermal field simulation can accurately predict the temperature of the magnetic coupler by reasonably setting the thermal power addition method.
Prediction of Software Fault Detection and Correction Processes With Time Series Analysis

Most software reliability models have focused on merely fault detection process, and ignored the time delay for correcting the detected faults. Some recent works considering both fault detection process and correction process are proposed, mainly based on the Non-homogeneous Poisson Process. However, the analytical models are usually developed based on some specific assumptions on the fault occurrence time distribution, time delay distribution, etc. Differently, this paper proposes the time series analysis model for predicting both the detected number of faults and corrected number of faults during software testing process. Real dataset is used to illustrate the approach.
Models of Rental Payments Formation for Agricultural Land Plots Taking into Account the Ecological Level of Economy

The article provides options for constructing a model for establishing rents for agricultural land. The process of optimizing the calculation of rent using economic and mathematical modeling is proposed. A general model of interaction between the tenant and the landowner is constructed. The econometric analysis of the rent in the Ternopil region is carried out. It is shown that the tenant and the owner adhere to myopic strategies in consequence of the insufficient level of economic institution development. Basis of short and long-term fiscal characteristics is calculated.
Residual Neural Networks for Heterogeneous Smart Device Localization in IoT Networks

Location-based services assume significant importance in the Internet of Things (IoT) based systems. In the scenarios where the satellite signals are not available or weak, the Global Positioning System (GPS) accuracy degrades sharply. Therefore, opportunistic signals can be utilized for smart device localization. In this paper, we propose a smart device localization method using residual neural networks. The proposed network is generic and performs smart device localization using opportunistic signals such as Wireless Fidelity (Wi-Fi), geomagnetic, temperature, pressure, humidity, and light signals in the IoT network. Additionally, the proposed method addresses the two significant challenges in IoT based smart device localization, which are noise and device heterogeneity. The experiments are performed on three real datasets of different opportunistic signals. Results show that the proposed method is robust to noise, and a significant improvement in the localization accuracy is obtained as compared to the state-of-the-art localization methods.
Conflicts in Human-Machine Systems as an Intersection of Bio- and Technosphere: Cooperation and Interaction Patterns for Human and Machine Interference and Conflict Resolution

Human-Machine Systems are right at the intersection of biosphere and technosphere and therefore profit from both nature/life sciences and technical sciences. When machines become more intelligent, e.g. with automation or AI, different intents and conflicts between humans and machines will occur and have to be resolved. Examples for conflicts in the biosphere can help to structure human-machine cooperation and conflict resolutions. Arbitration, mediation, dissonance, consonance and interference are key concepts to understand and build successful human-machine conflict resolutions. Another example from the biosphere, AiKiDo, an art of humanhuman conflict resolution, can be used as a design and process metaphor for the fostering of human-machine conflict resolution.
Real-Time Experimental Optimization of Closed-Loop Crosswind Flight of Airborne Wind Energy Systems via Recursive Gaussian Process-based Adaptive Control

Power generated by airborne wind energy (AWE) systems can be dramatically increased by moving the system in controlled crosswind flight patterns. In this paper, the crosswind flight pattern of an AWE system is optimized in real time using recursive Gaussian process (RGP)-based adaptive control. RGP-based adaptive control fuses machine learning tools with real-time adaptive control principles. Traditionally, Gaussian process (GP) modeling requires a complete database of all the previously tested data points and their associated performance values. By utilizing a recursive update law, the RGP-based modeling used here avoids the need to maintain a complete database. The RGP-based modeling estimates a predictive mean and variance model over the control parameter design space based on instantaneous performance feedback. The candidate design space is explored by selecting points at locations of maximum uncertainty. Design points that are determined to be statistically inferior to the perceived optimum are rejected from the candidate design space. In this work, the RGP-based adaptation is applied to a lab-scale platform for experimental crosswind flight of AWE systems. Experimental crosswind flight results presented here demonstrate a 60% increase in power augmentation over traditional stationary flight.
A Robust Audio Fingerprinting Using a New Hashing Method

To enhance the tracking performance of illegal audio copies, we introduce a robust audio fingerprinting method against various attacks in this paper. Most audio fingerprints consist of the information in the frequency band of audio. These fingerprinting methods may lose the uniqueness of the audio fingerprint by irregular movement such as an attack with pitch value changes. The proposed fingerprint method in a fundamental frequency band makes up for the weakness of existing methods generated from frequency domain. Using the geometrical property of the proposed method, a new hashing method is employed in the similarity calculation process to compare the audio contents. In order to prove the validity of proposed algorithm, we experiment for six environments such as tempo, pitch, speed modification, noise addition, low pass filter and high pass filter. The proposed method shows the highest level of performance in most experimental environments. Especially, with respect to the tempo, pitch, and speed manipulation experiments, the proposed method archives the precision rate of the range from 95 to 100% according to the degree of manipulation, which compares favorably with the precision rate obtained by traditional approaches, and yields a precision rate between 85 and 100% in noise addition and filtering experiments.
On the relief of Phantom Limp Pain using Augmented Reality and Edge Computing

Melding Augmented Reality and traditional eHealth applications has the potential to deliver disruptive services while in the same time improve the lives of millions of people. Facilitated by the technological improvements and progressive features of the upcoming 5G telecommunication era, the new exciting amalgam of services will most likely become an everyday reality in the next few years. This paper presents a conceptual AR application architecture based on Multi-access Edge Computing, aiming to relief Phantom Limp Pain and help amputees resume a certain percentage of their daily activity while avoiding medication. In addition, we provide a detailed analysis of the overall prototype implementation process, emphasizing on the scope of our work and how certain issues were efficiently addressed.
Graph Convolution Over Multiple Latent Context-Aware Graph Structures for Event Detection

Event detection is a particularly challenging problem in information extraction. The current neural network models have proved that dependency tree can better capture the correlation between candidate trigger words and related context in the sentence. However, syntactic information conveyed by the original dependency tree is insufficient for detecting trigger since the dependency tree obtained from natural language processing toolkits ignores semantic context information. Existing approaches employ a static graph structure based on original dependency tree which is incompetent in terms of distinguishing interrelations among trigger words and contextual words. So how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenging research question. To address this problem, we investigate a graph convolutional network over multiple latent context-aware graph structures to perform event detection. We exploit a multi-head attention mechanism on BERT representation and original adjacency matrix to generate multiple latent context-aware graph structures (a “dynamic cutting” strategy), which can automatically learn how to select the useful dependency information. Furthermore, we investigate graph convolutional networks with residual connections to combine the local and non-local contextual information. Experimental results on ACE2005 dataset show that our model achieves competitive performances compared with the methods based on dependency tree for event detection.
Mathematical Model for Traffic Optimization in Flexible Manufacturing Systems

The paper presents a suggested complex of mathematical models for searching for the traffic structure in flexible manufacturing systems. It shows that the usage of the elaborated models allows increasing significantly the efficiency of the manufacturing systems. This paper offers the methodology of searching for a rational AMS traffic structure. The basis of the developed methodology is the combined method, which combines imitating and analytical modelling. The effectiveness of the offered approach is shown by the example of traffic optimization in the automatized flexible assembly system. The methodology offered in the paper allows considering the influence of incidental factors on the AMS functioning process.
The origin and diversification of an evolutionary novelty: lessons from Drosophila oogenesis

In any area of study, it is necessary to define the subject of investigation. In the case of evolutionary novelty this is a particularly di cult task, as a clear definition of the concept that is suitable across di↵erent levels of biological organization has yet to emerge. We proceed with a definition for morphological novelty proposed by Müller and Wagner (1991), and introduce the dorsal appendages on eggs of Drosophilidae as such a novelty. These structures are part of the eggshell, and help supply oxygen to the embryo. A wide variety of phenotypes can be found, which is thought to have a single origin in the common ancestor of Drosophilidae. To investigate both the evolutionary origin and subsequent diversification of dorsal egg appendages from a genetic and developmental viewpoint, we first review the current knowledge on the developmental background of the structure: oogenesis in Drosophila melanogaster. The Drosophila egg chamber consists of 16 germline cells, one of which is the oocyte, surrounded by a layer of somatic follicle cells. This follicle cell layer is responsible for depositing the eggshell. Demarcation of specific domains on this epithelium occurs via patterning governed by the activity of several signalling pathways, of which EGF and Dpp are the most important. Interestingly, these signalling pathways are pleiotropic during oogenesis, and also function for example in the definition of the main embryonic body axes (EGF), or are required for essential cell migration (Dpp). With this delineation of the playing field we introduce the research presented in this thesis, which will address both the origin and the diversification of this novel trait.