Miracle-dz/UniFAR-FTUs huggingface dataset:
An Incremental Technique for Mining Coverage Patterns in Large Databases

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. Typically, pattern mining is carried out from huge databases, which tend to get updated several times. Consequently, as a given database is updated, some of the patterns discovered may become invalid, while some new patterns may emerge. This has motivated significant research efforts in the area of Incremental Mining. The goal of incremental mining is to efficiently and incrementally mine patterns when a database is updated as opposed to mining all of the patterns from scratch from the complete database. Incidentally, research efforts are being made to develop incremental pattern mining algorithms for extracting different kinds of patterns such as frequent patterns, sequential patterns and utility patterns. However, none of the existing works addresses incremental mining in the context of coverage patterns, which has important applications in areas such as banner advertising, search engine advertising and graph mining. In this regard, the main contributions of this work are three-fold. First, we introduce the problem of incremental mining in the context of coverage patterns. Second, we propose the IncCMine algorithm for efficiently extracting the knowledge of coverage patterns when incremental database is added to the existing database. Third, we performed extensive experiments using two real-world click stream datasets and one synthetic dataset. The results of our performance evaluation demonstrate that our proposed IncCMine algorithm indeed improves the performance significantly w.r. t. the existing CMine algorithm.
A Data-based Approach to Predict the Response Time of Cloud-based Vehicle Functions

Future vehicular applications require sufficient computing and storage resources. Actual on-board resources are limited and inadequate to deal with steadily increasing requirements. In order to overcome the computational limitation in vehicles, computing intensive functions can be offloaded to a cloud. The so-called cloud-based vehicle functions run in the cloud and utilize cloud capability instead of vehicle's on-board resources. The suitability of the vehicle functions for outsourcing to the cloud should be ensured in an early stage of development. Otherwise, shifting improper functions can lead to serious consequences for the vehicle control system. By the suitability analysis several criteria like functional safety, data dependency and response time should be considered. In this paper, we focus on the criterion response time and present a framework to predict the response time along a sample route.
A New 24 GHz Triple-Push Voltage Controlled Oscillator Architecture in $0.25\ \mu \mathrm{m}$ BiCMOS SiGe:C Technology

This paper presents the design and the implementation of a new 24 GHz Triple-Push Voltage Controlled Oscillator (VCO) architecture, integrated and implemented in the QUBiC4X $0.25\ \mu\mathrm{m}$ SiGe:C BiCMOS process of NXP semiconductor. A differential LC VCO is first designed at 8 GHz, and a new approach is used to couple the three VCOs together through a resistor via the varactor diodes in order to obtain the 120° phase shift necessary to cancel the fundamental and second harmonic and add only the third harmonic at 24 GHz. The post layout simulation results of the proposed triple-push VCO provides an output power of −5.76 dBm at 24 GHz with a variation of 0.55 dB when frequency varies from 21.94 GHz to 25.64 GHz. The rejection of the fundamental and second harmonic is more than 27.67 dB and 19.65 dB respectively at 24 GHz. The power consumption ($50\ \Omega$ outputs) is 113.64 mW with a supply voltage of 3 V. The total surface area of the chip is 1.227x 1.176mm2 including the pads.
Diffusion Kurtosis Imaging and Voxel-Based Morphometry Analysis of the Brain Development in Children with Sensorineural Hearing Loss

The aim of this study is to assess the value of diffusion kurtosis imaging (DKI) and voxel-based morphometry (VBM) in detecting the changes of microstructure and volume changes in children with congenital sensorineural hearing loss (SNHL). Seventy-two SNHL children and 38 healthy individuals received conventional MRI, DKI examination of the brain. Compared with healthy individuals, 72 SNHL patients displayed significantly reduced FA or MK values in the auditory pathway, auditory cortex and language and emotion related cortex (all P< 0.05). Analysis of individual ROIs revealed children (>3 years old) have significant differences in MK in more brain's major areas than FA, and the grey matter volume decreased in the left middle temporal cortex, bilateral middle frontal cortex and the frontal lobe(P <0. 01). There is no statistical difference of volume changes in children (≤3 years old). Structural changes of the brain in SNHL patients could be detected by using DKI and VBM. DKI is more beneficial than VBM to reveal the delayed development in brain of child with SNHL.
Automatic Detection of Smishing Attacks by Machine Learning Methods

Phishing is a type of cyber-attack aiming to steal people’s confidential information using a bait. Smishing is a form of phishing that is carried out via SMS messages sent to mobile phones. Attackers intend to steal the private information of their victims through the content they send in their SMS messages. Therefore, the detection of smishing messages has a vital role in information security. In this work, a machine learning-based model is proposed to detect smishing messages. Experiments conducted on a relatively large dataset, which contains legitimate and smishing messages, have shown that the proposed model offers a promising detection performance.
Improving Performance of Relation Extraction Algorithm via Leveled Adversarial PCNN and Database Expansion

This study introduces database expansion using the Minimum Description Length (MDL) algorithm to expand the database for better relation extraction. Different from other previous relation extraction researches, our method improves system performance by expanding data. The goal of database expansion, together with a robust deep learning classifier, is to diminish wrong labels due to the incomplete or not found nature of relation instances in the relation database (e.g., Freebase). The study uses a deep learning method (Piecewise Convolutional Neural Network or PCNN) as the base classifier of our proposed approach: the leveled adversarial attention neural networks (LATTADV-ATT). In the database expansion process, the semantic entity identification is used to enlarge new instances using the most similar itemsets of the most common patterns of the data to get its pairs of entities. About the deep learning method, the use of attention of selective sentences in PCNN can reduce noisy sentences. Also, the use of adversarial perturbation training is useful to improve the robustness of system performance. The performance even further is improved using a combination of leveled strategy and database expansion. There are two issues: 1) database expansion method: rule generation by allowing step sizes on selected strong semantic of most similar itemsets with aims to find entity pair for generating instances, 2) a better classifier model for relation extraction. Experimental result has shown that the use of the database expansion is beneficial. The MDL database expansion helps improvements in all methods compared to the unexpanded method. The LATTADV-ATT performs as a good classifier with high precision P©100=0.842 (at no expansion). It is even better while implemented on the expansion data with P©100=0.891 (at expansion factor k=7).
Network Load Adaptation for Collective Perception in V2X Communications

Collective perception uses V2X communications to increase the perception capabilities of vehicles. Relying on the perceived data from their local sensors, nodes exchange information about the objects they detect in their surroundings. An object can be anything significant for the nodes' safety, e.g., obstacles on the road, other vehicles or pedestrians. The amount of data generated by each node is determined by the number of perceived objects and the generation frequency of the messages carrying the detected objects. Considering the limited bandwidth of the wireless channel, the data load generated by collective perception can easily exceed the channel capacity. In this paper, we investigate three schemes that filter the number of objects in the messages and thereby adjust the network load in order to optimize the transmission of perceived objects. Our simulation-based performance evaluation indicates that the use of filtering is an effective approach to improve network-related performance metrics, whereas the expected impairment of the perception quality is rather small. The comparison of the filtering algorithms provide insights into the tradeoff between network-related metrics and perception quality.
Antenna Radiation Pattern Measurement in a Reverberating Enclosure Using the Time-Gating Technique

This letter describes an original approach to measure the radiation pattern of an antenna within reverberating enclosures. In such kinds of enclosures creating a multipath environment, the key point is to distinguish the line-of-sight (LOS) path between the antenna under test and the measuring antenna from all the other indirect paths. This is made possible by taking advantage of the time-gating technique, considering that the LOS path arrives the first on the measuring antenna. With respect to the existing methods proposed recently, the method leads to significantly simplify the measurement setup and shorten the measurement time (similar to the one obtained in anechoic chamber) as no stirring process or any movement of the antenna under test (apart of course from the rotation on itself) is required. Moreover, the accuracy of this method is presumably better in reason of the ability of the time-gating technique to discard the influence of the indirect paths. The proposed technique tested for the first time within reverberating enclosures is experimentally validated on a broadband horn antenna from 1.5 to 12 GHz. The main parameters influencing the result (the measurement bandwidth, the frequency resolution, the time-gating window width, and the effect of loading the enclosure) are discussed.
Data Redundancy Mitigation in V2X Based Collective Perceptions

Collective perception is a new paradigm to extend the limited horizon of individual vehicles. Incorporating with the recent vehicle-2-x (V2X) technology, connected and autonomous vehicles (CAVs) can periodically share their sensory information, given that traffic management authorities and other road participants can benefit from these information enormously. Apart from the benefits, employing collective perception could result in a certain level of transmission redundancy, because the same object might fall in the visible region of multiple CAVs, hence wasting the already scarce network resources. In this paper, we analytically study the data redundancy issue in highway scenarios, showing that the redundant transmissions could result in heavy loads on the network under medium to dense traffic. We then propose a probabilistic data selection scheme to suppress redundant transmissions. The scheme allows CAVs adaptively adjust the transmission probability of each tracked objects based on the position, vehicular density and road geometry information. Simulation results confirm that our approach can reduce at most 60% communication overhead in the meanwhile maintain the system reliability at desired levels.
Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

As the silicon material is severely influenced by the ambient temperature, the silicon piezo-resistive pressure sensor remarkably suffers from a strong nonlinearity in the response characteristic as the ambient temperature varies. To address this crucial issue, an adaptive mutation particle swarm optimization optimized support vector regression (AMPSO-SVR) combined with improved AdaBoost.RT algorithm is presented. The opposition-based learning initialization and Levy mutation is applied in the adaptive mutation particle swarm optimization (AMPSO) to achieve the appropriate model selection task which directly determines the performance of SVR. The performance of original AdaBoost.RT is improved by a dynamical modification approach for threshold and quoted error criterion. In order to verify the effectiveness of the proposed temperature compensation approach, several additional optimization methods such as Cuckoo search (CS), dragonfly algorithm (DA), multi-verse optimizer (MVO), conventional particle swarm optimization (PSO), Levy flight improved particle swarm optimization (Levy-PSO) and the AMPSO combined with SVR are investigated. The minimum quoted error, maximum quoted error, the mean quoted error and the variance of the quoted error over testing data obtained by the proposed method are <inline-formula> <tex-math notation="LaTeX">$6.8764\times 10^{-5}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$6.4463\times 10^{-4}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$3.2619\times 10^{-{4}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2.5714\times 10^{-8}$ </tex-math></inline-formula> respectively, which are superior to the corresponding indices obtained by other methods. The analysis of simulation results indicates the method proposed in this research is applicable, effective and efficient for industrial application.
End to End Background Subtraction by Deep Convolutional Neural Network

In this work, we use different methods from predecessors to perform background deductions, and our most important part still uses the network framework of CNN (deep convolution neural network). The advantage of this method is obvious. We can save the process of adjusting parameters and feature engineering. Because in the process of training a single video, it can learn structural network parameters from data. At the same time, we use the SUBSENSE structure to train data. It is a background structure that can monitor the moving objects in the video. It can better detect the foreground objects camouflaged and minimize the influence of unrelated factors such as illumination. Second, we use the long and short memory network (LSTM) to predict the significance of video, which can effectively avoid a limited number of training video over fitting, using our data through training successfully to learn the spatial and temporal significance of the estimation. Through our method, the final saliency prediction has achieved good results.
Improving Software-based Techniques for Soft Error Mitigation in OoO Superscalar Processors

High-performance superscalar processors have been developed to satisfy the growing demand for processing modern applications. However, as the transistor size shrinks, the increase in vulnerability to soft errors poses a great challenge, since these effects have been proven to also affect ground-level safety-critical applications. Considering that vulnerability strategies may span across different layers of the system stack, we propose a mixed SW/HW-based design-time methodology aiming to achieve the best tradeoff between vulnerability and area/energy for superscalar processors. By injecting over 10 million faults in 12 significant micro-architectural structures to different versions of the complex BOOM OoO superscalar processor modeled with RTL accuracy, we show that SW-based protection techniques always reach an upper bound that may not be satisfactory. Then, we propose to insert extra low-level HW-based DMR protection at strategic micro-architectural points by reducing this task to the Knapsack Problem. In one of the evaluated cases, we optimally find the best structures to be protected with 55% area overhead to achieve a 95% vulnerability improvement for a quad-issue processor.
Automatic Diagnosis of Rectal Cancer Based on CT Images by Deep Learning Method

Rectal cancer is one of the most common malignant tumors in the digestive tract. Rectal cancer often invades the extra intestinal space and metastases to lymph nodes and other organs. Computed tomography (CT) scans are performed to determine the location and status of the tumor. In this paper, the CT image of rectal cancer was analyzed using deep neural network (DNN) and back propagation (BP) neural network. The mask image was extracted from the CT image of rectal with DNN. A Dice coefficient was used to measure the similarity between the actual cancer region and the region segmented by DNN. Based on the segmented mask image, the features of tumors were extracted and a metastasis evaluation model established to determine lymph node metastasis of the rectal cancer. The experimental results have shown that the mean value of Dice coefficient was 96.7%, the diagnosis rate of rectal cancer was 100%, and the identification rate of lymph node metastasis was 91.6%.
UWB Indoor Positioning Algorithm Based on TDOA Technology

The traditional positioning system based on Global Positioning System (GPS) has provided a lot of convenience for people in the outdoor environment. With the continuous improvement of people's living standards in recent years, the indoor location is more and more popular in intelligent shopping malls and intelligent campus. The traditional GPS location accuracy is limited, so it cannot be used indoor. At present, indoor location technology mainly includes infrared, Bluetooth, Wi-Fi and wireless location which relates to indoor mobile network. Among them, wireless location technology is a research hotspot. Least Squares (LS) method, Chan algorithm and Taylor algorithm based on TDOA in UWB indoor positioning technology are analyzed and tested with dynamic and static data in indoor line-of-sight environment. Results show that the three positioning algorithms can achieve decimeter-level positioning accuracy, and the Taylor algorithm can achieve a 1-decimeter positioning accuracy.
Strategy for Promoting Programming in KPK

Programming demonstrated to be a venturing stone toward the changing position of nation in comity of a countries. The improvement of programming includes labor with specialized help, where the chief spotlight in on PC programming. In this manner, creating an enormous number of adroit PC developers in Pakistan would positively help the reason for building up programming houses, which thusly, will draw in the western world to re-appropriate their product undertakings Pakistan. It can surely become like a distinct advantage for the Pakistan's economy by gaining a tremendous income. In exploration we displayed a philosophy to expand the enthusiasm of the Pakistani individuals in PC programming giving a bilingual PC improvement condition in Pashto and the English dialects. This demonstration may absolutely open the new components of instructing PC programming. In conclusion, this paper had displayed visual plan of such bilingual condition alongside engineering adjustment in preprocessor a C++ language to help Pashto and English dialects for composing PC programs.
Performance Analysis of Uplink Sparse Code Multiple Access in Rician Fading

This paper investigates the error performance of uplink sparse code multiple access (SCMA) over Rician fading channels, with the assumption of joint maximum likelihood multiuser detection. We provide an expression for the pairwise-error probability of SCMA codewords, for multiuser signaling in the presence of additive white Gaussian noise and independent Rician fadings. Based on the derived codeword pairwise-error probability, a tight upper bound on the average codeword error probability is then developed by using multiuser union bound estimation, which gives an accurate evaluation of the error performance of uplink SCMA. Simulations are carried out for different Rician channel scenarios, to verify the effectiveness of the analysis.
Least squares and maximum likelihood estimation of sufficient reductions in regressions with matrix-valued predictors

We propose methods to estimate sufficient reductions in matrix-valued predictors for regression or classification. We assume that the first moment of the predictor matrix given the response can be decomposed into a row and column component via a Kronecker product structure. We obtain least squares and maximum likelihood estimates of the sufficient reductions in the matrix predictors, derive statistical properties of the resulting estimates and present fast computational algorithms with assured convergence. The performance of the proposed approaches in regression and classification is compared in simulations.We illustrate the methods on two examples, using longitudinally measured serum biomarker and neuroimaging data.
Classification of Tropical Cyclone Intensity on Satellite Infrared Imagery Using SVM Method

Today, weather changes can't be predicted due to extraordinary events due to global warming. One of the effects of climate change has led to the proliferation of tropical cyclone events on Earth. In facilitating the process of classification of tropical cyclone intensity, a machine learning based system was created. The algorithm used in the feature extraction process is GLCM while in the classification process is SVM. First of all, the infrared image recognition process is done by extracting 14 GLCM features in the RGB, Ycbcr and Grayscale color spaces. Next, a combination of 3, 4 and 5 features is carried out before entering the classification stage. At each stage of SVM classification testing with OAO and OAA coding design will also be tested with the use of Gaussian, Linear and Polynomial kernels including the influence of 3, 4 and 5 GLCM combination features to see the effect on the results of accuracy. From this testing process, the system can be used to classify tropical cyclone intensity in the form of infrared images with an accuracy rate of 88% which corresponds to the saffir-simpson hurricane wind scale.
Dynamic Placement Optimization for Bio-Inspired Self-Repairing Hardware

Bio-inspired self-repairing hardware is a distributed self-adaptive system, characterized by powerful fault-tolerant ability and environment adaptivity. However, it suffers from some difficulties such as large resource consumption and degraded circuit performances. From the viewpoint of cybernetics and computer science, the cellular differentiation and substitution process of bio-inspired self-repairing hardware can be converted into dynamic placement problems on a reconfigurable system. Current systems can only generate some predefined fault-free placements from a finite number of initial placements. The aim of this paper was to achieve high-quality placements from arbitrary initial placements. Based on P systems, an analysis has been made on the limitations of current systems and an improved computing system has been developed to achieve the ergodic property. Its computational power has been verified by a constructive proof. Moreover, centering on the problem how to improve the placement quality on a distributed platform, the optimization model, task allocation, optimization strategy, and membrane optimization algorithm have been designed and developed. The optimization performances were verified and the calculation amount was exhibited by experiments. Finally, it indicated by comparison that the proposed approach would reduce the resource consumption and maintain good circuit performances.