Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Algorithms for Finding Differences between Process Models

Information systems from various domains record their behavior in a form of event logs. These event logs can be further analyzed and formal process models describing hidden processes can be discovered. In order to relate real and expected process behavior, discovered (constructed from event logs) and reference (manually created by analysts) process models can be compared. The result of comparison should clearly present commonalities and differences between these models. Since most process models are represented by graph-based languages, a graph comparison technique can be applied. It is worth known that graph comparison techniques are computationally expensive. In this paper, we adapt different heuristic graph comparison algorithms to compare BPMN (Business Process Model and Notation) models. These algorithms are implemented and tested on large BPMN models discovered from event logs. We show that some of the heuristic algorithms allow to find nearly optimal solutions in a reasonable amount of time.
Android Malware Detection and Categorization Based on Conversation-level Network Traffic Features

The number of malware in Android environment is increasing. As a result, the conventional detection algorithms that employ signature detection methods are facing challenges to cope with the huge number of attacks. In this respect, a supervised-based model that can enhance the accuracy and the depth of the malware detection and categorization process using a conversation-level feature is presented. The ensemble learning technique was employed in order to select the most useful features. A comparison between the methods provided in this research and the results of other studies that used the same dataset is given. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75% for malware detection and 79.97% for malware categorization. Finally, this study has achieved significant enhancement in malware categorization rate by 30.2% for precision and 31.14% recall in comparison with other studies that used the same dataset.
A Healthcare Cost Calculator for Older Patients Over the First Year After Renal Transplantation

Forecasting tools that accurately predict post-transplantation healthcare use of older end-stage renal disease (ESRD) patients are needed at the time of transplantation in order to ensure smooth care delivery in the post-transplant period. We addressed this need by developing a machine-learning-based calculator that predicts the cost of healthcare for older recipients of a deceased-donor kidney over the first year following transplantation. Regression tree and regularized linear regression methods, including ridge regression, lasso regression and elastic net regression were explored on all cases of deceased-donor renal transplants performed for patients aged over 60 in Ontario, Canada between March 31, 2002 and April 31, 2013 (N=1328), The optimal model (lasso) identified age, membership of one of 14 regionalized Local Health Integration Networks, blood type, sensitization, having diabetes as the primary case of ESRD, total healthcare costs in the 12-month pre-workup period and the 6-month workup period to be inputs to the cost calculator. This cost calculator, in conjunction with clinical outcome information, will aid health system planning and performance to ensure better management of recipients of scarce kidneys.
Analysis of activity detection data pre-processing

Nowadays collecting sensor data is not just a trend, is a necessity. The increasing occurrence of smart home devices, which are now a permanent part of our homes and life, makes this process of gathering, storing and using this data crucial. One of the most important aspects is data pre-processing, as the process and methods involved must be carefully chosen to obtain the best results. We aim to improve data pre-processing for PIR based activity sensors. Our chosen activity detection system is CASAS, a complete system for detecting and storing activity, where we have access to raw data. The pre-processing is done in MatLab and we tackle the option to divide the main activity into multiple sub-activities or even segment the activity data into suitable windows. After that, we look over the data-labeling and feature selection process. Also, we analyze the possibility of making possible predictions based on existing data
Comparison of RD Algorithm and BP Algorithm in Video-SAR

Video synthetic aperture radar (Video-SAR) is a new imaging mode where a sequence of images are continuously formed and reproduced in video, which has unique advantages in moving target detection. In this paper, Range-Doppler (RD) and BackProjection (BP) algorithm are introduced for video-SAR image formation. The above two algorithms are applied to the moving target experiment. By comparing the quality parameters of the experimental results, the advantages and disadvantages of the two algorithms in video SAR imaging are analyzed.
The field emission properties of a new design: multi-pixel carbon nanotube field emitters for imaging application

In this study, a patterned cathode, on an n-type doped silicon resistor with a doping concentration of 1014 cm-3 and a cross section area of 1µm2, is designed for multi-pixel X-ray source with one anode. In this regard, individual carbon nanotube (CNT) field emitter arrays (FEAs) is fabricated by plasma enhanced chemical vapor deposition (PECVD) method on the Si cathode. The cathode has a density of 100×100 CNT arrays, and it is tested for x-ray generation at high voltage, the current and radiation dose are measured during the experiment. At the end of this work, we investigate a 3×3 multi-pixel X-ray source and see if the power of each X-ray pixel can be easily controlled and have more potential to achieve high resolution. For this purpose, COMSOL Multiphysics is used to simulate the electric field.
Research on the Characteristics of Community Network Information Transmission in Blockchain Environment

Based on information dissemination and block chain theory, combined with the characteristics of blockchain social network, this paper studies the information dissemination characteristics of the whole and subgroup networks based on the tags. The steemit data is obtained by the way of crawling and the social network analysis method is adopted. The Gephi which is visualization tool is used to process and visualize the data. The blockchain subgroups characteristics were compared and analyzed according to degree centrality, betweenness centrality and eccentricity. This paper concludes that the blockchain community information dissemination network is a scale-free network. Subgroup aggregation is not affected by subgroup size. Subgroup size affects the closeness between subgroups and the number of influence nodes.
DENA: An Intelligent Dynamic Flow Scheduling for Rate Adjustment in Green DCNs

We propose DENA as a deep reinforcement learning (DRL) based energy-efficient algorithm in the data center. To achieve the goal, DENA adjusts the transmission rate for each flow with deadlines and reduce the flow completion time (FCT) when the paths of flows are pre-given. Moreover, DENA includes a neural network classifier and a DRL system (DS). Accordingly, classifier separates the mice and elephant flows, then directly transfers the mice flows to avoid latency, while DS detects the interval of the most significant energy consumption density as a critical interval to schedule elephant flows. Besides, we apply deep deterministic policy gradient to DS with the advantage of an optimal base solution to enhance the accuracy of exploration. To the best of our knowledge, we are the first to use DRL for the rate adjustment. Our results show that besides saving energy, DENA reduces FCT for elephant flows workload 4.12%, data mining 1.04% and Hadoop 3.8%.
Ultra-sensitive SQUID systems for applications in biomagnetism and ultra-low field MRI

We present the use of our ultra-sensitive SQUID system in the field of biomagnetism and ultra-low field (ULF) MRI. A current sensor configuration is used where a pickup coil is inductively coupled to the SQUID. A 1st-order axial gradiometer system, operated in a liquid He dewar with negligible noise, achieves a measured coupled energy sensitivity $\epsilon_{c}$ of 40 $h$ and a white noise below 200 aT $\mathrm{Hz}^{-1/2}$. As an example of its use in biomagnetism, we discuss single trial magnetoencephalography measurements of high frequency bursts at 600 Hz from the somatosensory cortex which are related to synchronized spiking activity of individual neurons. We also deploy this system for ultra-low field MRI where it is operated inside an MRI coil system with several fast-switchable field and gradient coils. This necessitates the use of a current limiter in the input circuit and a 2nd-order axial gradiometer leading to an increased noise of 380 aT $\mathrm{Hz}^{-1/2}$. Here, we demonstrated full tensor current density imaging of impressed currents in phantoms. For further improvement of the noise, the fabrication process for the nanometer-sized Josephson junctions based on the HfTi self-shunted junction technology has been extended to a SIS process with AlOx as the insulating layer. We achieved noise levels of 330 $\mathrm{n}\Phi_{0}\mathrm{Hz}^{-1/2}$ and 550 $\mathrm{n}\Phi_{0}\mathrm{Hz}^{-1/2}$, corresponding to energy sensitivities of 5 $h$ and 20 $h$ for uncoupled and coupled SQUIDs, respectively.
Wide - beam Planar End-fire Circularly Polarized Antenna Based on SIW

A novel planar circularly polarized antenna with end-fire radiation beam in parallel with the antenna substrate's plane is proposed. The working principle of the antenna with combination of SIW and printed dipole is analyzed and design guidelines are laid down. Then, the antenna is designed and demonstrated to exhibit an end-fire beam in parallel with its plane within 6.93-7.14 GHz for S11 lower than −10dB and axial ratio smaller than 3dB, which achieves a gain of 4.18 dBi. Its beam can reach 5.38°-133.90° on $xoy$ plane while 20.56°-180° on $yoz$ plane.
Side Excited Spoof Localized Surface Plasmonic Resonator with ultra-high Q factor

Here a highly integrated spoof plasmonic resonator with ultra-high quality (Q) factor has been demonstrated. The spoof plasmonic resonator is composed of the grooved metal ring and the excited microstrip line structure on the same side of the dielectric substrate with ground. It has been shown that the Q factor of the Octupole mode on the resonator reaches up to 1115.5, which is much larger than the previous work. Simulation and experiments at the microwave frequencies agree well and verify the function. Hence, the integrated spoof plasmonic resonator may have potential application as plasmonic sensors.
Conservative Policy Gradient in Multi-critic Setting

Twin Delayed Deep Deterministic policy gradient algorithm (TD3) addressed the overestimation bias problem by adopting a clipped Double Q-Learning method. As the two Q networks are different, the updation of a policy to maximize one Q function might minimize another, which is called inconsistency in this paper. Therefore, we propose an algorithm based on TD3, conservative policy gradient (CPG), that optimizes the policy with respect to the lower bound of the two Q functions to deal with the inconsistancy. In Q function learning, one-step estimate is usually used in target value estimation. However, due to the constantly changing target networks, there will be fluctuations in the estimation. As the target Q function is changing slowly, we combine the one-step estimate with zero-step estimate to avoid sharp changes. The experimental results illustrate that CPG outperforms TD3 and some other reinforcement learning methods on multiple MuJoCo benchmarks.
Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal Databases

Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Weighted Frequent Neighborhood Itemsets (WFNI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an WFNI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say $PM2.5$ . The generated WFNIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all WFNIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.
IoT and WSN Based Air Quality Monitoring and Energy Saving System in SmartCity Project

SmartCity projects growing with the concept of IoT (Internet of Things) and WSN (Wireless Sensor Network) helps to manage assets and resources efficiently. Nowadays air is getting polluted due to industrialization, use of fertilizers and pesticides, urban development, etc. Also seven of the world's ten most polluted cities are in India. A reliable air quality monitoring system followed by required actions helps to limit these adverse effects to a great extent. New studies of energy consumption shows that 18 to 38% of the total energy bill is by street lighting, so this domain needs major attention to increase energy efficiency, by implementing a street light energy saving system. Also the increasing demand for power has led to burning of fossil fuels which in turn adversely effects the environment. The proposed system based on IoT and WSN, implements an efficient street light energy saving system and also gives real time data of air quality so that the authority can manage and take necessary actions to conserve the environment and valuable resources more effectively. With IoT & WSN, intelligent monitoring and management can be achieved. It helps create more efficient and cost effective municipal services and keeping citizens informed and engaged. The existing systems are short range and much power consuming, not suited for sensor nodes in WSN. The proposed system presents a low cost, low power, long range and scalable approach for SmartCity monitoring using LoRa Module based on LoRaWAN protocol which is a Low Power Wide Area Network (LPWAN) technology, which is much more efficient than many existing wireless communication systems. The design of the proposed system includes integrating sensors to the microcontroller, wireless LoRa module for transmitting and receiving sensor values, and ThingSpeak IoT platform for examining and visualizing air quality sensor values which have been uploaded.
Power Optimization of Electric Motor using PID-Fuzzy Logic Controller

This paper presents an optimization of 3-phase AC motor power consumption by means of intelligent control signal. The control signal is generated by the controller consists of Proportional, Integral and Derivative (PID) controller combined with fuzzy logic controller to produce the optimal control signal. The proposed controller is able to minimize the Root Mean Square Error (RMSE) of the response of motor. The PID controller parameters are obtained from the H∞ synthesis of the full state feedback form of the motor, and fuzzy logic is designed to reject the noise and to flatten the motor response implemented using Raspberry Pi. By generating control signal with the controller, the electric power consumption of the 3-phase AC motor is reduced to 0.58% - 4.32%, and the RSME of the motor response is decreased to 4.90% - 12.26 %.
CQDW: A Cyclic-Queue-based Dynamic Watermarking Mechanism for WSNs

Wireless sensor networks (WSNs) are data-centric and self-organized, so data security is a major concern in WSNs which relies heavily on key-based cryptography. However, data integrity and authenticity cannot be guaranteed. In this paper, we propose a lightweight watermarking security mechanism, cyclic-queue-based dynamic watermarking mechanism(CQDW) to ensure data transmission integrity in WSNs. The proposed watermarking mechanism analyzes the original collective data and dynamically generates watermarks based on the cyclic queues. Then the order and location of watermark embedding are determined by the wake-up time of the sensor nodes and the secret data queue. Therefore, the watermarking mechanism provides greater security performance at a lower resource cost to protect data integrity and authenticity. Compared with the previous watermarking mechanisms, the simulation results show that it has obvious superiority in detection accuracy and operation efficiency.
An End-to-End Abnormal Fastener Detection Method Based on Data Synthesis

Fasteners that hold rails in a fixed position are essentially important infrastructure components, and abnormal fasteners may cause a train to derail. The periodical inspection of fasteners is a significant guarantee for the safety of railway operation, and machine vision systems are popularly applied for fastener inspection. In this paper, we present an end-to-end abnormal fastener detection method, which identifies abnormal fasteners from a track image that contains a rail, fasteners, sleepers, etc. The proposed method is inspired from the well-established Faster R-CNN, but customizes with two aspects according to the application of fastener inspection. On the one hand, a light-weight backbone network is employed instead of complicated network to quicken detecting speed, and a threshold pruning algorithm is designed to reduce false positive rate. On the other hand, a hybrid loss function, combining weighted softmax loss function with center loss function, is devised to handle both the problems of class imbalance and small inter-class differences. Furthermore, two data synthesis methods are brought forward to solve the small sample problem. The proposed method is verified on a real-world image set of railway inspection, and the experimental results demonstrate that our method outperforms four established baselines.
Research on extraction and classification of EEG features for multi-class motor imagery

Aiming at solving the problems of low recognition accuracy and poor practicability caused by the small amount of data and insufficient training of the network weights when the deep neural network algorithm is directly used to classify the 4-class motor-imagery electroencephalogram signals(MI-EEG), We combine the one-versus-the-rest common spatial pattern (OVR-CSP) algorithm and a novel convolution neural networks (CNN) algorithm to extract features and classify 4-class MI-EEG signals. Firstly, the original EEG signal data is truncated and expanded by using a fixed-size overlapping window, the features of intercepted sub-signals are extracted by CSP algorithm and the obtained feature vectors are merged as the input sample matrix of CNN. Secondly, CNN algorithm performs second feature extraction and final classification on the input sample matrix. Finally, the validity of the proposed algorithm was verified by the datasets IIIa of the BCI2005 competition. The average recognition accuracy of the three subjects has reached 91.9%, this is an improvement over other algorithms in recent years.
Classification of White blood cell using Deep Convolutional Neural Network

White blood cells are the one of immune system that are involved in protecting the body against infection disease and foreign invaders. There are difference category of white blood cell and each category can indicate about the irregularity of body. Nowadays, White blood cell diagnosis is usually examined manually by doctor. This process consumes a lot time, cost and susceptible to error compare with automatic computerize process. An automatic classification technique for microscopic white blood cell images focusing on images from fresh blood smears[1] is proposed in this paper. The classification is conducted using a proposed method that consist of deep convolutional neural network (DCNN). 10,000 Microscopic blood images were tested and the classification method obtain 93%
Nondestructive Detection of Apple Mouldy Core Disease Based on Unbalanced Dielectric Data

In order to establish a nondestructive detection model for mouldy core disease in apples, 265 samples of healthy and suspected disease fruits were tested for 11 dielectric parameters in the 100hz - 3.98Mhz frequency. After dissection, the 17 diseased apples were observed. The Extra-Tree algorithm was used to extract the characteristics of samples. It found that the parameters of loss factor, conductance, negative impedance phase, parallel resistance, series resistance, and admittance in the low-frequency band of 100hz-1580hz had strong discriminability. As the number of healthy fruits and diseased fruits are unbalanced, using the SMOTE and other sampling techniques. Then, six classifiers were used to build the discriminant models. Experiments show that SMOTE-Tomek Link technique maintains the data distribution better, and GBDT classifier has the highest accuracy of detection, up to 95.97%. Thus, verifying the effectiveness of apple mouldy core disease nondestructive detection model based on unbalanced dielectric data.
Co-integrated Subthermionic 2D/2D WSe2/SnSe2 Vertical Tunnel FET and WSe2 MOSFET on same flake: towards a 2D/2D vdW Dual-Transport Steep Slope FET

In this work we report the fabrication, co-integration and resulting performance of 2D/2D van der Waals (vdW) Vertical p-type Tunnel FETs and p-MOSFETs in a WSe<inf>2</inf>/SnSe<inf>2</inf> material system. We demonstrate the best ever reported combined performance in terms of subthermionic subthreshold swing (point swing less than 35 mV/dec and average swing smaller than 50 mV/dec over 1.5 decade at V<inf>DS</inf> from 300 mV to 700mV), I<inf>OFF</inf> < 0.1 pA/um<sup>2</sup>, I<inf>ON</inf> ~10 nA/um<sup>2</sup> and I<inf>ON</inf> /I<inf>OFF</inf> > 10<sup>5</sup> at V<inf>DS</inf>= 500 mV, for a 2D/2D vertical Tunnel FET. Moreover, for the first time, the fabricated Tunnel FET shows clear regions of higher performance, in same 2D material system, than the 2D MOSFET below 500 mV. NDR at room temperature in the output characteristic, with I<inf>peak</inf>/I<inf>valley</inf>>10, demonstrates the dominant BTBT conduction. The low hysteresis of the devices show high quality HfO<inf>2</inf> gating with defect-free vdW gaps between the flakes.Finally, for the first time, we co-integrate on a single WSe<inf>2</inf> flake, using 4-terminal design and a common gate, a pTunnel FET and a pMOSFET with different threshold voltages. These can operate in parallel as Dual-Transport Switch: this device shows subthermionic swing (~55mV/dec point swing) of the TFET and a thermionic high on-current (~100nA/um<sup>2</sup> at V<inf>DS</inf> = 500mV) summing-up MOSFET and Tunnel FET currents, outperforming the individual components.