Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Incremental Semi-Supervised Graph Based Clustering

Incremental clustering or one-pass clustering is very useful when we work with data stream or dynamic data. In each incremental clustering algorithm, two process including insertion and deletion for new data points are used for updating the current clusters. In fact, for traditional clustering such as K-Means, Fuzzy C-Means, DBSCAN, etc., many versions of incremental clustering have been developed. However, to the best of our knowledge, there are no incremental semi-supervised clustering in literature. This paper introduces a new incremental semi-supervised clustering which was based on a graph of k-nearest neighbor using seeds, namely IncrementalSSGC. Experiments conducted on some data sets from UCI and the 802.11 network data set (AWID) show the effectiveness of our new IncrementalSSGC.
C-V2X Supported Automated Driving

Automated driving is expected to improve road safety and traffic efficiency. Host vehicle onboard sensing systems typically sense the environment up to 250 m ahead of the vehicle. Today's LiDARs can see approximately 120 m, and recognition of small objects, such as animals or dropped cargo, however, today reliably drop when range is more than 50m. Connected driving adds an electronic horizon to the onboard sensing system which could extend the sensing range and greatly improves the efficiency. Therefore, collaborative sensing in which the vehicle exchanges not only status messages but also real data has recently been intensively discussed. Current cellular 3G/4G networks have enhanced the downlink capacity for sharing large data blocks. However, uplink is limited and therefore vehicles are unable to share point clouds of what they see in front. This article investigates the opportunities of 5G-based cellular vehicle-to-everything (C-V2X) collaborative sensing based on the results of trials conducted at test sites in China and Finland. The results indicate that the round-trip is stable (< 60 ms) even when exchanging 1 MB/s between vehicles. Finally, the automotive industry perspective is taken into account in identifying and prioritizing potential use case scenarios for utilizing 5G based connected driving applications.
Impacts of Linnik Flight Usage Patterns on Cuckoo Search for Real-Parameter Global Optimization Problems

Several contemporary algorithms, including cuckoo search (CS), were applied to the CEC 2017 problem set, which includes a wide variety of 120 very difficult subproblems. We found that the algorithms were ineffective, especially when the number of dimensions was high. We configured several usage patterns of Linnik flight with the inverse of the golden ratio (1/ $\Phi $ ) to replace Lévy flight in CS, resulting in a new search mechanism that increased the efficiency of the CS algorithm. The impacts of each Linnik flight usage pattern were evaluated using the CEC 2017. The experimental results showed that: 1) CS variants that used Linnik flight were more capable than CS variants that used Lévy flight and 2) CS variants that used a mixture of Linnik flight and quantum-behaved mechanisms were even more capable. The primary effect of Linnik flight is the strengthening of the ranking, while that of the quantum-behaved mechanisms is a decreased error. A chaotic-initialized quantum-Linnik flight CS (CQLCS) algorithm is proposed. Among the 66 competitive methods applied to the CEC 2017, CQLCS ranked first and won the contemporary competitive algorithms section, which also included several advanced variants of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The CQLCS could potentially be improved further by adjusting the probability of the occurrence of Linnik flight. The processes for building improved variants were analyzed to discover how, or if, this improvement could be achieved. Finally, the CQLCS algorithm required fewer lines of code to run than did the DE and PSO variants.
Correspondence Analysis Applied to Large Scale Evo-Devo Data

Correspondence analysis (CA) is an exploratory method used for visualizing and understanding high dimensional data. It is mostly applied to contingency tables. Although the method has been used in various fields for decades, including bioinformatics, to our knowledge it has not yet been applied to the evolutionary analysis of gene expression data during developmental process. In this paper we present the application of the CA method to linking evolutionary age of genes and their developmental expression profiles. As a test dataset, we used evolutionary age of genes obtained by phylostratigraphic approach and expression data collected during development of a model microorganism. The obtained results show that CA could complement current approaches for coupling evolutionary and developmental information at the molecular level.
Crowd-Centric Counting via Unsupervised Learning

Counting targets (people or things) within a monitored area is an important task in emerging wireless applications, including those for smart environments, safety, and security. Conventional device-free radio-based systems for counting targets rely on localization and data association (i.e., individual-centric information) to infer the number of targets present in an area (i.e., crowd-centric information). However, many applications (e.g., affluence analytics) require only crowd-centric rather than individual-centric information. Moreover, individual-centric approaches may be inadequate due to the complexity of data association. This paper proposes a new technique for crowd-centric counting of device-free targets based on unsupervised learning, where the number of targets is inferred directly from a low-dimensional representation of the received waveforms. The proposed technique is validated via experimentation using an ultra-wideband sensor radar in an indoor environment.
Negative Sequence-Based Schemes for Power System Protection - Review and Challenges

This paper presents a review of the negative sequence-based protection relays development and their applications on electrical power networks and discusses the related challenges. Recent power system requires selective, reliable, rapid fault detection and clearance mechanisms especially for the transmission line networks, which are highly exposed to environmental incidents. Most of the negative sequence protection techniques and recent challenges are discussed in this paper.
Integrating the industrial consumer into smart grid by load curve forecasting using machine learning

Integration of industrial consumers into the smart grid concept can be facilitated by optimizing short and very short-term forecasts of load curves for industrial consumers. Minimizing forecast errors can improve the supplier-consumer relationship by reducing supplier balancing costs, which can lower the energy bill, and anticipate possible network faults or network congestion which means improved operation across the entire power system. The present paper aims at demonstrating the usefulness of energy forecasting by using machine learning methods. In the context of installing monitoring systems with load curve recording data at short intervals of time, huge amounts of data are obtained that need to be capitalized in real time (automated), otherwise the investments necessary to modernize the way in which the energy is used are not justified, consequently disfavouring the development and digitization of electrical networks. Integration of the industrial consumer into the smart grid concept can be applied in detail at large industrial consumers where forecasts can be made on certain areas inside factories or electric equipment with high installed electric capacity. The use of smart grid technologies removes the current limits for monitoring, processing and communication between consumption and production. To be able to capitalize on the forecasts, a very important aspect is the automation of their realization and quick access to the decision makers who may be human or other smart grid integrated systems. That is why the present paper proposes the use of machine learning methods in the short and very short forecast, and the results will be compared with the classical forecasting methods. Hourly load curves from the industrial consumers will be used for forecasts.
Experimental Characterization of Latency in Distributed IoT Systems with Cloud Fog Offloading

The Internet of Things (IoT) enables users to gather and analyze data from a large number of devices. Knowledge obtained by these systems is valuable in order to understand, control, and enhance the monitored process. The mass of information to process leads however to new challenges related to required resources for both data processing and data transportation. Two critical metrics are latency and consumed energy to complete a given task. Both metrics might be exceed if all processing is done locally at the sensor device level. Cloud and Fog computing concepts can help to mitigate this effect. However, using such offloading concepts add complexity and overhead to the system. In this paper, we study the latency for processing and communication tasks in a distributed IoT systems with respect to cloud or fog offloading and derive characteristic cost functions for the studied tasks. Our results give valuable insights into the tradeoffs and constraint within our example scenario. The developed characterization methodology can however be applied to any kind of IoT system and thus allowing more general analysis.
An Approach for The Electricity Consumption Prediction based on Artificial Neural Network

This paper studies the day-ahead prediction of electricity consumption for power supply-demand balance in electric power networks. To handle the uncertainties in weather forecast and the nonlinearity relation between the electricity consumption and the weather conditions, this paper proposes a Radial Basis Function like Artificial Neural Network (RBF-like ANN) model with temperature, humidity, and sampling times as inputs. Then the Least Absolute Deviation, i.e., the $L_{1}$ norm condition, is employed as the optimization cost which is minimized in the model training process. To solve the $L_{1}$ optimization problem, two approaches, namely least square $(L_{2})$ based and alternating direction method of multipliers (ADMM), are utilized and compared. The simulations on real data collected in California shows that the latter approach performs better, and the number of neurons does not affect much to the prediction performance of the latter approach while it does influence on that of the former approach. Further, the proposed RBF-like ANN model equipped with ADMM solving approach provides reasonably good prediction of the electricity consumption in spite of the imprecise weather forecast.
Impact of STI mechanical stress in highly scaled MOSFETs

Intensive experiment on highly scaled MOSFETs with mask gate lengths down to 90 nm shows significant sensitivities (up to 10%) of drive current per unit width to the shrinking of active area size down to 0.6 /spl mu/m as well as to the gate placement distance from STI (shallow trench isolation) edge. This suggests the impact of STI induced mechanical stress along the direction of the channel current flow. Even n-and p-channel FETs are observed to behave in opposite trends with respect to the lateral active area size. Mechanical stress simulation of underlying entire front-end process line is conducted also intensively. Systematic analysis turns out strikingly that the experimental drive current sensitivity tracks well the compressive-type strain along the channel, leading to a correlation established between the two. This work promises exploration of mechanical stress issues in future nanoscale devices and circuits.
Optimization of Relaying Wireless Sensor Network With RF Energy Harvesting

In this paper, we optimize relay power allocation ratio to improve the transmission rates of the simultaneous wireless information and power transfer (SWIPT) networks. The decode-and-forward (DF) relay strategy is first adopted in the relay interference channels (IFC) when the single source communicates with destination nodes through dedicated EH relays. Then a successive convex approximation (SCA) algorithm is introduced to transform the original NP-hard problem into a concave optimization problem in order to guarantee the normal operation of all EH relays, maximize the data transmission rates and improve the communication quality of the whole network. Finally, the Lagrangian multiplier method based on the Karush-Kuhn-Tucker(KKT) condition is applied to achieve the optimal power allocation for each relay. Numerical results demonstrate that the proposed algorithm can improve the transmission rates, has a fast convergence and high accuracy.
Proactive Eavesdropping Via Jamming for Power-Limited UAV Communications

Unmanned aerial vehicles (UAVs) have been used in legitimate surveillance systems to eavesdrop suspicious communications. In this paper, we consider an actual scenario that a UAV has limited power to eavesdrop suspicious communications of two UAVs. Based on this scenario, we formulate an optimization problem to maximize the effective eavesdropping rate throughout flight time. The formulated problem can be solved by an integer linear programming algorithm. Although the optimal scheme can be obtained by integer linear programming algorithm, algorithm complexity of it is very high. Therefore, we develop a power sorting algorithm as a suboptimal solution with lower algorithm complexity. Numerical results show that the effective eavesdropping rate over flight time obtained by power sorting algorithm is close to integer linear programming algorithm. Our jamming power allocation schemes outperform passive eavesdropping and equally distributed jamming power allocation schemes.
Diagnosis Status Guided Brain Imaging Genetics Via Integrated Regression And Sparse Canonical Correlation Analysis

Brain imaging genetics use the imaging quantitative traits (QTs) as intermediate endophenotypes to identify the genetic basis of the brain structure, function and abnormality. The regression and canonical correlation analysis (CCA) coupled with sparsity regularization are widely used in imaging genetics. The regression only selects relevant features for pre-chctors. SCCA overcomes this but is unsupervised and thus could not make use of the diagnosis information. We propose a novel method integrating regression and SCCA together to construct a supervised sparse bi-multivariate learning model. The regression part plays a role of providing guidance for imaging QTs selection, and the SCCA part is focused on selecting relevant genetic markets and imaging QTs. We propose an efficient algorithm based on the alternative search method. Our method obtains better feature selection results than both regression and SCCA on both synthetic and real neuroimaging data. This demonstrates that our method is a promising bi-multivariate tool for brain imaging genetics.
Women on the net: An exploratory study of gender dynamics on the soc.women computer network

Disclosed is a machine including a plurality of containers for a variety of paint colorants and a plurality of measuring receptacles, one connected for fluid communication with each of the containers. A cyclic pumping system simultaneously pumps metered volumes of colorant into predetermined measuring receptacles from their associated containers during a withdrawal cycle and then dispenses the metered volumes during a discharge cycle. Adjustment of selector mechanisms associated with each measuring receptacle provides the exact quantities of colorant required to formulate a specific volume of a desired paint color. A cycle timer establishes a uniform time period for each of the sequential withdrawal and dispensing cycles regardless of the colorant quantities being dispensed and a sequence timer can be preset to provide the number of sequential withdrawal and dispensing cycles required to formulate paint volumes that are a given multiple of the specific volume.
The Integrated Graphic Modeling Method of Fault Perception and Control Based on the Stateflow

Fault perception model and control strategy model are typically two different sets of algorithm in one device. They are separated. It cannot adjust the failure control strategy in time when performance degradation or failure is detected. Therefore, this paper proposes a method of integrated fault modeling and control based on Stateflow. Based on the dependent model and combined with the operating principle of the product, the method can dynamically display the simulation process in real time and influence the control process through the proposed Fault_Flag. The effectiveness and dynamics of the proposed method are verified in the integrated modeling of the integrated transmission.
The Study of a Textile Punching Robot Based on Combined Deep Reinforcement Learning

Punching of textile uppers is an important process in the shoe production line, which determines the accuracy of positioning and affects all subsequent shoemaking steps. At present, there are few researches on the 3-DOF punching robot in this field. The critical value of the punching force of the end mechanism is not clear, and the punching path planning is not adopted with a suitable algorithm, resulting in large power consumption and low work efficiency. Therefore, this paper describes the design of a punching robot to meet the action requirements of punching the textile uppers and complete the path planning to improve efficiency. The first step is to model a 3-DOF robot for textile upper punching including rail type and articulated type to meet the needs of punching movements. The second step is to gain the lowest critical value of the pressure with ANSYS by simulating deformation process when the punch punches in the vamp until breaks it. And this outcome is the theory basis for motor selection and robot design. The third step is multi-point punching path planning, where a combined deep reinforcement learning (DRL) method is proposed. The DRL pre-training is performed through asynchronous advantage actor-critic (A3C). Training data is adopted to optimize the recurrent neural networks (RNN) that parameterizes the stochastic policy. Then the inspire planning (IP) algorithm is conducted to get the optimal path. The experimental results show that this method can jump out of the local optimal solution and outperform other state-of-the-art methods.
Construction of 4D Neonatal Cortical Surface Atlases Using Wasserstein Distance

Spatiotemporal (4D) neonatal cortical surface atlases with densely sampled ages are important tools for understanding the dynamic early brain development. Conventionally, after non-linear co-registration, surface atlases are constructed by simple Euclidean average of cortical attributes across different subjects, which leads to blurred folding patterns and therefore hampers the reliability and accuracy when used to register new subjects. To better preserve the sharpness and clarity of cortical folding patterns on surface atlases, we propose to compute the Wasserstein barycenter, which represents a geometrically faithful population mean under the Wasserstein distance metric, for the construction of 4D neonatal surface atlases. Comparing to the direct vertex-wise Euclidean average, the Wasserstein distance takes into account the alignment of spatial distribution of cortical attributes, thus is robust to potential registration errors during atlas building. Using this method, we constructed 4D neonatal cortical surface atlases at each week, from 39 to 44 postmenstrual weeks, based on a large-scale dataset with 764 subjects. Our 4D atlases show sharper and more geometrically faithful cortical folding patterns compared to the state-of-the-art methods, thus leading to boosted accuracy when used to align new subjects and facilitating early brain development studies.
Energy Efficient Green Solution for Hierarchical Resource Management for Mobile Cloud Computing

Infrastructure-as-a-Service model In the cloud computing offers the provision of acquiring virtualized resources under pay-as-you-go policy. With increasing penetration of cloud in various domains such as real-time systems, the need for efficient resource allocation has become of paramount interest. In real-time systems, the resource allocation problem is NP-hard and becomes even more challenging when real-time systems being deploying in cloud computing environment where tasks present timings constraints. As a solution, hybrid approaches provide the opportunities to investigate efficient resource allocation for resource sharing problems. Therefore, this paper presents a hierarchical system architecture for Mobile Cloud Computing based on a novel 5G system architecture. Initially, a hierarchical system architecture is divided into three phases, i.e., foglet layer, service layer, and communication layer. Foglet layers provide the functionality of efficient resource sharing, the service layers assists the network by providing cloud services, and finally, the communication layers help in providing energy efficient communication using 5G system architecture. Simulation results show that the proposed scheme outperforms the existing cloud computing example scenario regarding cost and time.
Sensorless Control of Multi-Motors BLDC using Back-EMF Observer

This article presents a BLDC multi-motor sensorless control system with rigid coupling. The control systems of each motor are achieved in cascade, where the external speed loop includes a PI controller, and the inner loop includes an on-off hysteresis controller. The rotor position is estimated by using a back-EMF observer. The back-EMF observer provides both the rotor speed and the commutation signals used in the inverter commands. The main task of the BLDC motor master and slave control systems is to maintain the same speed of the master and slave motors with the reference, and to generate the electromagnetic torque for each motor according to the torque required by the load when the load is distributed both as balanced and unbalanced for the two motors. The main functional blocks in the Simulink implementation are described, along with the control parameters. Real-time implementation of the BLDC multi-motor sensorless control system with rigid coupling is recommended by the good results obtained from numerical simulations.
License plate location based on a dynamic PCNN scheme

This paper describes a new dynamic ATR, automatic target recognition, scheme based on a pulse coupled neural network, PCNN. The PCNN is used to generate candidate regions that may contain a license car plate. Candidate regions are generated from pulsed images, output of the PCNN network. Statistics of the Fourier transform are used to determine if a candidate region contains a license plate. If the license plate is not located in the set of candidate regions, the PCNN network parameters are adjusted to generate a new pulsed image and new candidate regions are extracted and analyzed. The proposed system is robust in the sense that it does not restrict the location of the plate, neither the illumination conditions when the image is acquired. The purpose of no restrictions is to evaluate the proposed system under conditions where a person can succeed. The system performance was 85% on a set of images. One advantage of the proposed scheme is that it may be adapted to solve different ATR problems.