Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distance-Based Method for Building an Encrypted Malware Traffic Identification Framework

The popularity of encryption method brings a great challenge to malware traffic identification. Traditional classes defined by expert experience are usually classified based on the host behaviors of malware, such as banking malware or ransomware, which are often irrelevant to its communication traffic behaviors. It leads to the fact that the boundaries of traffic feature dataset of different malware classes are fuzzy and make these traditional classes unhelpful for classification based on traffic features. Meanwhile, traditional machine learning-based encrypted malware traffic identification methods, such as using the multi-classification supervised learning model, are inefficient both in model training and detection, and its detection accuracy cannot meet the demand. In this paper, we propose a distance-based method, which utilizes unsupervised learning algorithm Gaussian mixture model (GMM) and ordering points to identify the clustering structure (OPTICS) to calculate the <inline-formula> <tex-math notation="LaTeX">$Distance$ </tex-math></inline-formula> between malwares and make use of the <inline-formula> <tex-math notation="LaTeX">$Distance$ </tex-math></inline-formula> to define the new malware class called <inline-formula> <tex-math notation="LaTeX">$FClass$ </tex-math></inline-formula>. Then, a set of models are trained by XGBoost algorithm to build an identification framework based on the <inline-formula> <tex-math notation="LaTeX">$FClass$ </tex-math></inline-formula>. The performance of the proposed method has been evaluated by comparing it with the other four methods. The results show that the proposed distance-based method is more efficient and accurate.
Microarray Data Classification Based on Computational Verb

Computational verb (CV) theory is a relatively new research field in mathematics and has been applied to many different fields. In the field of pattern recognition, the CV-based rule induction algorithm can generate some simple rules with CVs and adverbs by linguistically interpretable forms. In this paper, we present an interpretable rule extraction framework based on CV rule theory for the classification of microarray data. In contrast to the existing rule-based methods, the CV method enables to explicitly express the relationships of the genes based on some mathematical templates and hence enhance the understanding on the data results. Stay is a typical verb used in the CV to describe the trend of changes. In our algorithm, Stay is applied to generate CVR by a gene pair, named SCVR. The corresponding evolving and similarity functions for calculating the difference between SCVR rules are also presented to illustrate this process. Similar to other rule-based methods, the SCVR can achieve significant gene selection and cancer classification task concurrently. To evaluate the performance of our proposed approach, we conduct the experiments on several binary class and multiclass microarray datasets. Experiments confirm that the proposed method can outperform many rule-based classiers with the fusion of five rules.
Active Rectifiers in Wireless Power Transmission Systems

In wireless power transmission systems, the continuous time comparator based active rectifier suffers from the multiple pulsing problem (MPP) and the large power consumption of the comparator, which limits the reliability and the power conversion efficiency (PCE) of the active rectifier. In this paper, the limitations of active rectifier in traditional structure are pointed out by analyzing the conditions and reasons of the multi-pulse problem. An active rectifier structure based on adaptive delay time controller is described, which achieves a conversion efficiency of up to 94.1% with a static power consumption of no more than 230μW. Based on the structure of the adaptive delay time controller, a single-stage AC-DC converter with dual-regulated outputs is proposed to realize the combination of rectification, boosting and adjusting output functions in a highly efficient circuit. The proposed adaptive delay time controller shows its low power consumption and easy to expand characteristics, so it can be widely used in various active switching circuits to improve the performance of the circuit.
Capacitive and Optical Sensing for Automatic Detection and Characterization of Cleaning Sponges in Fiber Optic Microduct Installations

Optical fiber duct installation requires blowing of cleaning sponges for dirt and moisture removal before blowing the fiber cables. The traditional method requires one operator that blows the sponge and one operator in the receiving end that manually evaluate the sponges until a dry sponge is received. The proposed system eliminates the need of a second operator by introducing a solution for automatic sponge detection and characterization of moisture in sponges at the receiving end. An optical sensor is used for detection and a capacitive sensor is developed to measure the sponge’s wetness. Sensor data is automatically transmitted back to the operator at the feeding end via a mobile phone. The system is characterized to work with sponges ranging from saturated with wetness to what is deemed as dry.
Design and Deployment of UAV-Aided Post-Disaster Emergency Network

Designing a reliable, resilient, and quickly deployable emergency communication network is a key challenge for post-disaster management. In this paper, a UAV-assisted emergency Wi-Fi network is proposed to expedite the rescue operations by guiding the survivors to the nearest rescue camp location. Here, the Raspberry PI (RPI) development board, mounted on UAV is considered to form a Wi-Fi chain network over the disaster region. During network set-up, the proposed solutions for the design challenges like UAV synchronization, avoid communication disruption and surveillance data management are the key contributions of this paper. The designed UAV network is capable of doing on-site surveillance and transmitting the data to the relief center for better rescue planning. One major challenge is to alert a survivor about the emergency network, which is addressed by designing a captive portal. Furthermore, to extend the Wi-Fi network, an Android-based application is developed by which each smartphone acts as a relay for its neighbor. Three types of field experiment are carried out to evaluate the performance of the designed prototype. It is found from the field results; the Wi-Fi access point mode and user datagram protocol are more suitable for network design as compared to Ad-Hoc mode and transmission control protocol, respectively. It is also observed from the experiment that the maximum hop distance for the prototype is 280 meters and 290 meters for a Wi-Fi configuration following IEEE 802.11n and IEEE 802.11ac protocol, respectively.
CLEAN: Frequent Pattern-Based Trajectory Spatial-Temporal Compression on Road Networks

The volume of trajectory data has become tremendously large in recent years. How to efficiently maintain and compute such trajectory data becomes a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road networks. By treating the mined patterns as dictionary items, we have the chance to encode a long trajectory by shorter paths, thus leading to smaller space cost. Meanwhile, we design an error-bounded temporal compression on top of the identified spatial patterns for much low space cost. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of both space saving and runtime of trajectory compression.
Stagnant Zone Segmentation with U-Net

Silo discharging and monitoring process for industrial or research application depend on computerized segmentation of different parts of images such as stagnant and flowing zones which is the toughest task. X-ray Computed Tomography (CT) is one of a powerful non-destructive technique for cross-sectional images of a 3D object based on X-ray absorption. CT is the most proficient for investigating different granular flow phenomena and segmentation of stagnant zone as compared to other imaging techniques. In any case, manual segmentation is tiresome and erroneous for further investigations. Hence, automatic and precise strategies are required. In the present work, a U-net architecture is used for segmenting the stagnant zone during silo discharging process. This proposed image segmentation method provides fast and effective outcomes by exploiting a convolutional neural networks technique with an accuracy of 97 percent.
METSK-HD-Angeleno: How to predict fruit quality using Multiobjective Evolutionary learning of TSK systems

The United Nations Food and Agriculture Organization (FAO) ranks Spain 13th in the world in plum and sloes production and 4th in the member states of the European Union. Cultivation of this fruit is clearly important in our country, and is even more so in Extremadura, a Mediterranean region in south-western Spain, which focus their economic activity on the primary sector. A plum production must be differentiated by its quality, but the quality of the fruit is traditionally perceived by the experience of farmers and technicians, based solely on their visual perception. This traditional decision-making process sometimes leads to errors in determining the optimum date for harvesting.Among the quality parameters used by the food industry are the soluble solids content and/or the firmness. These parameters will measure the fruit quality, allowing to obtain the best quality of the fruit when the optimal values are reached. The parameters must be calculated using destructive techniques and sophisticated laboratory equipment. In this work we present a new method to predict the soluble solids content or the firmness of a fruit, by means of software techniques that do not require a destruction fruit process and expensive laboratory equipment. The results presented in this work allow us to affirm that it is possible to provide farmers and agricultural technicians with software tools that help them make the right decision regarding the ripening of their fruit, in order to obtain the highest quality products and be more competitive in the sector.
Generative Temporal Modelling of Neuroimaging - Decomposition and Nonparametric Testing

Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task inferred supervised learning with restrictive assumptions in the regression set-up, restrict the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals, by incorporating a temporal source prior. fMRI source signals, biological stimuli and non-stimuli related artifacts, are all smooth over a time-scale compatible with the sampling time (TR), and we therefore propose Gaussian process ICA (GPICA), which facilitates temporal dependency of the extracted sources, by use of Gaussian Process priors. On two fMRI data sets with different sampling frequency, we show that the GPICA inferred temporal components, and associated spatial maps, that allow for a more definite interpretation than standard ICA methods. The temporal structure of the sources are controlled by the covariance of the Gaussian Process, specified by a kernel function with a interpretable and controllable temporal length scale parameter. We propose a hierarchical model specification, considering both instantaneous and convolutive mixing, and infer Preprint submitted to NeuroImage June 8, 2016 56 Gaussian Process Based Independent Analysis for Temporal Source Separation in fMRI
3D Printed Interconnects on Bendable Substrates for 3D Circuits

3D printing systems are expanding to realising fully embedded, multi-purpose, out-of-plane circuits. It is possible to utilise the characteristics of 3D printing to produce customisable, complex and bendable 3D structures and sensors that go beyond the use of standard polymer materials used with the current technology. With multi-material 3D printing, the additive manufacturing could be advanced to produce fully embedded sensors and electronic systems that cannot be otherwise produced in a one-step automated process. Our goals are concentrated towards embedding sensing circuits into next generation prosthetics and robotic arms for more advanced and smoother operation. These devices, along with other similar interests such as healthcare wearable devices, will inevitably include moving parts. Therefore, the embedded printed connections and readout circuits should withstand the repeatable bending of the robotic phalanges or sensing devices without degrading in performance or showing any cracks.
Page Weight-Based Buffer Replacement Algorithm for Flash-Based Databases

With the development of flash memory technology and the increase of flash memory capacity, the flash memory storage is widely used, which brings opportunities and challenges to flash-based database management. The traditional buffer replacement algorithm for disk-oriented database fails to make full use of flash memory as a result of different read-write ways and read-write properties for flash memory and disk. This paper proposes the buffer replacement algorithm for flash-based database based on page weight called DPW-LRU. Based on the data page access frequency, the algorithm divides the buffer into hot list and cold list, and wherein, the former is used to store data pages accessed over two times, while the latter is used to store those accessed for once; at the same time, the dirty page is assigned weight to evict the low weight page preferentially. The experimental results show that the DPW-LRU algorithm is effective and feasible because it ensures hit ratio while reducing I/O access overhead.
Line Feature based Multiple Video Synchronization

— In this paper, we present a novel method for synchronizing multiple (more than 2) un-calibrated video sequences recording the same event by free-moving full-perspective cameras. Unlike previous point feature based synchronization methods, our method takes advantage of line features for their better performance in measuring geometric alignment when video frames are synchronized. In particular, the tri-ocular geometric constraint of line features, which is evaluated by tri-focal transfer, is enforced when building the timeline maps for sequences to be synchronized. A hierarchical approach is used to reduce the computational complexity. To achieve sub-frame synchronization accuracy, a Levenberg-Marquardt method based optimization is performed to refine the synchronization. The experimental results on several synthetic and real video datasets demonstrate the effectiveness and robustness of our method over previous methods.
K3 TAM Optimization for Testing 3D-SoCs using Non-Regular Time-Division-Multiplexing

Two-dimensional time-division-multiplexing (2DTDM) was recently proposed to minimize both the time for testing 3D Systems-on-Chips (SoCs) and the number of through-siliconvias (TSVs). Even though 2D-TDM exploits the short vertical interconnections to transfer very fast the test-data to the various layers of the stack, the bus-based test-access-mechanism (TAM) at each layer imposes excessive routing overhead and long intra-die delays that compromise the test-time benefits, especially when the stack layers are unbalanced in terms of test-times. In this paper, we propose a new TDM-based TAM architecture for 3D SoCs, which supports non-regular division of the frequency among the various layers of the stack using very short daisy-chain intra-die connections. The proposed 3D TAM architecture is optimized by the means of the K3 design-automation process that combines the Kruskal algorithm, the Complete Karmarkar-Karp heuristic and a new optimization heuristic proposed in this paper. Experiments performed on a 3D benchmark integrated circuit (IC) show that significant test-time and routing savings are achieved.
Video Smoke Detection Based on Convolution Neural Network

In this paper, a new method of video smoke detection based on convolution neural network and Mixture Gaussian background model is proposed for the low smoke detection rate in complex scene. The CNN model is trained by GoogLeNet, and the recognition rate is limited in the process of model recognition. The experimental results show that GoogLeNet can obtain good results with limited training data and limiting recognition rate of the model can reduce false positives effectively. The method proposed in this paper can meet the requirements of real-time smoke detection.
Preparation of Composite Insulating Paper With Decreased Permittivity, Good Mechanical and Thermal Properties by Kevlar/Nano Cellulose Fibrils/Softwood Pulp Hybrid

Kevlar pulp treated with low-temperature plasma and nano cellulose fibrils (NCFs) have been introduced as reinforcements in the preparation for composite insulating paper with low relative permittivity, dielectric loss, good mechanical properties, and thermal stability. More polar groups of Kevlar fibers could be exposed after plasma treatment. Results showed that the relative permittivity of the composite paper containing 20% Kevlar pulp, 10% NCF, and 70% softwood pulp is decreased by 29.6% and the dielectric loss is decreased by 43.2% at 50 Hz, 25°, compared with the conventional paper formed by pure softwood pulp. The change was due to the introduction of Kevlar pulp with intrinsic low dielectric constant and loss, which decreases the whole polarizability of the paper. The mechanical properties of the composite paper became worse after introducing 20% Kevlar pulp. About 10% NCF can work as a mechanical reinforcement to compensate for the tensile and Yang’s modulus loss. Scanning electron microscopy results showed that NCF could fill the void defects and improve the interface bonding of the fibers to enhance the paper mechanical properties. The composite paper also had better insulating and thermal properties than the conventional paper. It has been concluded that the Kevlar pulp/NCF/softwood pulp composite paper has the potential to replace the pure softwood paper for the application of high-voltage ac transformer insulation.
Low Power Karnaugh Map Approximate Adder for Error Compensation in Loop Accumulations

Approximate computing has recently emerged as a promising paradigm to achieve considerable energy savings at the expense of degraded computing accuracy. In this paper, an approximate adder is proposed to reduce the power consumption while providing minimal computation errors for loop accumulation, which is a crucial operation in various signal processing algorithms. The proposed adder is based on smart modification of Karnaugh map to generate compensation effect with loop accumulation. With the proposed approximate adder, the power consumption is reduced by up to 42.8% and 24.9% compared to fully-accurate adder and the previously published approximate adders, respectively, in an industrial 65-nm CMOS technology. Furthermore, the computation accuracy is enhanced by up to 31x with the proposed approximate adder compared with the previously published approximate adders. Using the product of normalized mean error distance (NMED) and power consumption as the Figure-of-Merit (FoM), the proposed approximate adder improves the FoM by up to 37.7x compared to the previously published approximate adders.
Design and Evaluation of AlGaN/GaN High Electron Mobility Transistor Comparator

As one key building block for mixed-signal IC applications, AlGaN/GaN high electron mobility transistor (HEMT) voltage comparator shows obvious advantages. This paper aims to study on the dc and dynamic characterization of AlGaN/GaN HEMT voltage comparator by simulation under different conditions. In this paper three characteristics of the AlGaN/GaN HEMT comparator are simulated by Advanced Design System (ADS) software: the DC characteristics of discrete E and D mode AlGaN/GaN HEMTs, the voltage transfer characteristics of the HEMTs voltage comparator and the propagation delay time. These results show that the AlGaN/GaN HEMT comparator has the features of a good voltage transfer characteristics and a small propagation delay time.
Investigating the Relationship between Earthquakes and Online News

News agencies have the leading role of deciding what coverage is given on earthquakes. This study exploits the use of text mining tools to automatically map in near real-time earthquake events with newspaper articles. An application was developed to automatically download multilingual news articles published by 23 leading news agencies in real-time from six continents around the world. Articles discussing earthquakes are then identified; clustered using TF-IDF and cosine similarity; and processed for information extraction. Earthquake-related features that are mined include the magnitude, location and timestamp from the corresponding news reports, as well as the number of casualties, injured and structural damage caused. Cluster information extraction is also carried out for crossreferencing purposes. Each cluster is then mapped against earthquake events, aggregated from seismic readings stored by United States Geological Survey (USGS). The results are visualised on two dashboards and complemented with a series of tests evaluating the accuracy of the extraction of information and clustering of data, followed by conclusions and recommendations for future work in this domain area. This tool paves the way for a more in-depth analysis on the possible temporal correlation (how long the disaster and its effects remain discussed in the news) and spatial correlation (geographical distance between news sources reporting the event and the location of the disaster itself) that may exist between newspaper articles and earthquakes.
A Machine Learning Technique to Classify LSST Observed Astronomical Objects Based on Photometric Data

The light curve analysis of the heavenly bodies is an indispensable tool for understanding the physical phenomena that govern them. Large telescopes like the LSST will produce an excess of data produced that will necessitate the need for automated methods to sift through it quickly and efficiently as doing so manually can be truly laborious. Furthermore, such a method should be able classify the observed astronomical objects accurately. Keeping this in view, we have proposed an automated classification method using the simulated, photometric light curves in to 14 different classes. We have built our classification model by extracting several features and employing Random Forest classifier. Our proposed methodology performs reasonably well for most of the classes while others still offer a little room for improvement. As our proposed methodology relies on features extracted from photometric light curves, therefore it can be adapted and extended for use in other fields that rely on similar light curves.
Load Pattern Recognition and Prediction Based on DTW K-Mediods Clustering and Markov Model

With the development of smart grid, the electricity consumption data of consumers are recorded through smart meters. Analyzing and modeling the power user data, exploring the behavior habits and characteristics of electricity use, is of great significance of demand side management, dynamic pricing, and improving energy efficiency. This study proposed DTW K-mediods clustering and Markov model for Load pattern recognition and load prediction. Firstly, K-mediods clustering and DTW-based similarity measure are used to get shape-based typical load pattern. Then, the load data of time series are coded as discrete state series based on clustering. Finally, Markov model is used to explore the rule of load mode transform, and make prediction of lode mode, which achieves good results in the test of open data sets.