Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction Model of User Purchase Behavior Based on Machine Learning

In recent years, with the continuous popularity of the Internet, the number of online shopping users in my country has reached 639 million, which contains huge commercial value. In order to maintain the prosperity, diversity and order of merchants, and fully meet consumers' one-stop shopping needs, it is necessary to analyze and predict user purchase behaviors more accurately. This article uses the interaction between users and products within a certain period of time provided by JD.com to predict whether users will place orders in designated categories and shops in the next week. First of all, we aim at the problem that the original features are not strongly related, and use feature fusion to construct strong features. For high-dimensional systems that are slow to calculate and prone to overfitting, we use random forests to filter features, reducing the complexity of the model. Finally, a fusion algorithm based on XGBoost and LightGBM is proposed to predict the purchase behavior of users, and the prediction effects of random forest and GBDT algorithm are compared at the same time. The results show that the fusion model has the best prediction effect, and can help merchants more accurately conduct marketing activities to potential customers based on the prediction results.
Ensembling tree-based classifiers for improving the accuracy of cyber attack detection

Nowadays, the new generation of technology has completely relied on the network-based services. So the wide use of the Internet gives an opportunity to cyber attackers to target the systems which process and save vital information and disrupt their functionality. According to this, the need for finding a way to prevent these attacks and make computer systems more secured is essential, and cyber security turns to a fundamental concern for researchers. A well-known technology in detecting unusual access to the network is Intrusion Detection Systems (IDS). High accuracy and low False Alarm Rate could be pivotal challenges in developing IDS. To address this issue, this paper introduced an intrusion detection system by ensembling tree-based classifiers including decision tree, random forest and Gradient Boosted tree. The model is tested by different feature selection methods, and for evaluating its performance, the NSL-KDD dataset is applied. The results obtained show an improvement in accuracy in comparison with some existing methods.
Fully-Integrated Reconfigurable Charge Pump With Two-Dimensional Frequency Modulation for Self-Powered Internet-of-Things Applications

In this paper, we propose a fully-integrated reconfigurable charge pump in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process; this converter is applicable for self-powered Internet-of-Things applications. The proposed charge pump uses a two-dimensional frequency modulation technique, which combines both the pulse-frequency modulation (PFM) and pulse-skip modulation (PSM) techniques. The PFM technique adjusts the operating frequency of the converter according to the variations in the load current, and the PSM technique regulates the output voltage. The proposed two-dimensional frequency modulation technique can improve the overall power conversion efficiency and the response time of the converter under light load conditions. A photovoltaic cell was chosen as the input source of the proposed converter. To adapt to the variations in the output voltage of a photovoltaic cell under different light illumination intensities, we built a reconfigurable converter core with multiple power conversion ratios of 2, 2.5, and 3 for the regulated output voltage of 1.2 V when the input voltage ranged from 0.53 V to 0.7 V. Our measurement results prove that the proposed capacitive power converter could achieve a peak power conversion efficiency of 80.8%, and the efficiency was more than 70% for the load current that ranged from <inline-formula> <tex-math notation="LaTeX">$10~\mu \text{A}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$620~\mu \text{A}$ </tex-math></inline-formula>.
Effect of physico-chemical conditions and operating parameters on flux and retention of different components in ultrafiltration and nanofiltration fractionation of sweet whey

Butylina Svetlana Effect of physico-chemical conditions and operating parameters on flux and retention of different components in ultrafiltration and nanofiltration fractionation of sweet whey Lappeenranta 2007 126 p. Acta Universitatis Lappeenrantaensis 268 Diss. Lappeenranta University of Technology ISBN 978-952-214-378-5 ISSN 1456-4491 ISBN 978-952-214-379-2 (PDF) In this thesis the effect of physico-chemical conditions and operating parameters on the fractionation of sweet whey was studied. In the beginning of the thesis the environmental impact of whey and the routes of its utilisation including the treatment with membranes were reviewed. The experimental part is divided into two main sections considering ultrafiltration and nanofiltration steps of sweet whey fractionation. The selection of ultrafiltration membrane was made using the molar mass cut-off value determined with single polyethylene glycol solutions under conditions providing low concentration polarisation. The critical flux concept was used for protein concentration by ultrafiltration of sweet whey because whey solution contains proteins, which are well-known as fouling agents. Sieving characteristics of different whey components have been studied. Peptide fractions of whey ultrafiltration permeates were characterised by using size exclusion chromatography and MALDI-TOF mass spectrometry. The average pore sizes of nanofiltration membranes were characterised using solutions of neutral solutes. The zeta potentials of the studied nanofiltration membranes were evaluated from streaming potential measurements. Model solutions of amino acids were applied to study the role of pore size and charge of the membranes in separation. The retention of amino acids was affected by pH and by the ionic strength of the solution, as well as by intermolecular interactions. Whey ultrafiltration permeates containing low molar mass peptides, lactose and salts were tested in nanofiltration at acidic and alkaline pH. The nanofiltration of whey ultrafiltration permeates was found to be more feasible at alkaline pH, where the irreversible fouling decreased and the permeate flux was higher. At this pH the selectivity of lactose separation from peptides was increased, compared to that at acidic pH.
Rickettsial Endosymbionts of Ticks

Rickettsiae are widely known to be human bacterial pathogens transmitted by blood-sucking ectoparasites, such as ticks, fleas, and lice. However, most rickettsial species are nonpathogenic endosymbionts with various groups of organisms, such as arthropods, protists, and other eukaryotes. While attention has been given to rickettsial endosymbionts of insects, rickettsial endosymbionts of ticks have been less well studied. Tick hosts are found across the phylogeny of Rickettsiae; hence, the tick was the most probable ancestral host of Rickettsiae associated with arthropods. Here, we focus on rickettsial endosymbionts of ticks, describing their role in association with ticks and comparing them to tick-borne vertebrate pathogens.
Improved Number Detection For Low Resolution Image Using the Canny Algorithm

Improved number detection at low resolutions using the Canny algorithm in this paper. Data from low resolution numbers are randomly selected for this algorithm’s approval. There are a number of problems, image detection rates at low resolutions are needed Methods that can improve edge detection images to get better. In finding the right way to change numbers at low resolution using an algorithm that compares like the Sobel operator, Laplacian operator, and Canny operator. From the experimental results 2 numbers in low resolution are read and clear detection using the Canny operator algorithm as the following images are MSE 3,371 and PSNR 42,852, image 2 is worth MSE 30,060 and PSNR 33,350 with these other algorithms namely Sobel operator and Laplacian operator.
A Systematic Literature Review and Quality Analysis of Javascript Malware Detection

Context: JavaScript (JS) is an often-used programming language by millions of web pages and is also affected by thousands of malicious attacks. Objective: In this investigation, we provided a general view and a quick understanding of JavaScript Malware Detection (JSMD) research reported in the scientific literature from several perspectives. Method: We performed a Systematic Literature Review (SLR) and quality analysis of published research articles on the topic. We investigated 32 articles published between the year 2009 to the year 2019. Results: Selected 32 papers explained in this article reflect the outline of what was published so far. One of our key findings is the performance of Machine Learning (ML) based detection models were relatively higher than others. We also found that only a few papers were able to achieve high scores according to the quality assessment criteria. Conclusion: In this SLR, we summarized and synthesized the existing JSMD studies to identify the previous research practices and also to shed light on future guidelines in the malware detection space. This study will guide and help future researchers to investigate the previous literature efficiently and effectively.
Coding Analog of Superadditivity Using Entanglement-Assisted Quantum Tensor Product Codes Over $\mathbb {F}_{p^k}$

We provide a procedure to construct entanglement-assisted Calderbank–Shor–Steane (CSS) codes over qudits from the parity check matrices of two classical codes over <inline-formula><tex-math notation="LaTeX">$\mathbb {F}_q$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$q=p^k$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> is prime, and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is a positive integer. The construction procedure involves the proposed Euclidean Gram–Schmidt orthogonalization algorithm, followed by a procedure to extend the quantum operators to obtain stabilizers of the code. Using this construction, we provide a construction of entanglement-assisted tensor product codes from classical tensor product codes over <inline-formula><tex-math notation="LaTeX">$\mathbb {F}_q$</tex-math></inline-formula>. We further show that a nonzero rate entanglement-assisted tensor product code can be obtained from a classical tensor product code whose component codes yield zero rate entanglement-assisted CSS codes. We view this result as the coding analog of superadditivity.
Classification of Pulmonary Images By Using Generative Adversarial Networks

Using deep learning networks has made developments in the computer vision field. Due to the growth in interstitial lung diseases (ILDs), the necessity of using modern computer-aided diagnosis (CAD) is increased. Regarding the importance of this issue, many kinds of research have been done on this subject. However, the extreme similarity between lung nodules and the complexity of detecting nodules characteristics is a severe challenge to design a system that can classify lung diseases with high accuracy. This study is done in 3 steps. First, the Convolutional Neural Network (CNN) is used due to the effectiveness and higher accuracy it can provide in comparison to the previous methods. This proposed network consists of four convolutional layers with 2×2 kernels and LeakyReLU, four average pooling layers, and three fully-connected layers. The last layer has five outputs equivalent to the considered classes: healthy, ground-glass opacity (GGO), micromodels, consolidation, reticulation. The main challenge in the field of medical images is the lack of labeled samples. So in the second step, Generative Adversarial Network (GAN) is used to generate data and increase the accuracy of the convolutional network structure by creating non-realistic but useful data in network learning. GAN structure is based on two neural networks. The generator, generates new data instances, while the discriminator, evaluates them for authenticity. The CNN which is used in the structure of discriminator can carefully detect the similarity of produced data and lung nodule classes. To train the CNN, in the third step, Interstitial Lung Diseases (ILDs) dataset (containing 3527 images) and 3200 GAN produced images is used. In the test phase for making an evaluation, real images that are extracted from the dataset are used. The accuracy of categorizing five types of lung nodules in the designed system is 88 %, which is 5 percent more than previous studies.
A Planar Ultrawideband Wide-Angle Scanning Array Loaded With Polarization-Sensitive Frequency-Selective Surface Structure

This article presents a fully planar ultrawideband wide-angle scanning tightly coupled array. A tightly coupled element, which has a low input resistance, is used as the basic array element. The element can be fed by a simple microstrip-to-coaxial transition, obviating the need for an additional impedance transform structure. To improve the scan performance of the array in both the E- and H-planes, a polarization-sensitive frequency-selective surface (FSS) structure, whose effective dielectric constant for the TM mode incidence wave and TE mode incidence wave can be changed, respectively, by different structural parameters, is designed to serve as the wide-angle impedance matching (WAIM) layer. The FSS structure is printed on the superstrate dielectric directly without an additional dielectric layer, resulting in a low-cost array. The proposed array can work in 3.7:1 bandwidth with voltage standing wave ratio (VSWR) < 3 for E-plane scanning up to 75° and H-plane scanning up to 60°. Based on the designed unit cell, an $11\times11$ prototype is fabricated and measured. The measured results of the fabricated array show good agreement with simulations, proving the feasibility of the design.
Deep learning approach to creating bone models

The paper discusses the usefulness of deep neural networks in tasks of automatic segmentation of bone structures from CT data. Three different neural network models (PSPNet, U-net and Segnet) were trained to perform the segmentation task. Pixels in the CT image are assigned to 3 following classes: background, patient outline and bones. The mean and classwise Intersection over Union (IoU) measure was evaluated for each network outcome. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%.
Series Stacked Modular DC–DC Converter Using Simple Voltage Balancing Method

This letter proposes a high output voltage dc–dc converter topology with series stacked converter modules. To implement the proposed modular dc–dc converter structure, output voltage balancing of the modules and simplification of the control circuitry were considered the major design objectives. To achieve voltage balancing between the modules, we proposed a split-secondary transformer winding structure and distributed connections to the rectifier circuit blocks. Each transformer includes two secondary windings, with each wire pair connected to a different rectifier circuit. Consequently, one rectifier receives ac power simultaneously by sharing the secondary windings fed from two different transformers. The proposed method was verified using a modular LCC resonant converter, which is widely employed in high-voltage applications. As a result, the control circuit was simplified by using a single controller to regulate the total output voltage and by applying a compact sensing circuit referenced to the output voltage of the module placed at the lowest grounding point.
An EKF Based Approach to Radar Inertial Odometry

Accurate localization is key for autonomous robotics. Navigation in GNSS-denied and degraded visual environment is still very challenging. Approaches based on visual sensors usually fail in conditions like darkness, direct sunlight, fog or smoke.Our approach is based on a millimeter wave FMCW radar sensor and an Inertial Measurement Unit (IMU) as both sensors can operate in these conditions. Specifically, we propose an Extended Kalman Filter (EKF) based solution to 3D Radar Inertial Odometry (RIO). A standard automotive FMCW radar which measures the 3D position and Doppler velocity of each detected target is used. Based on the radar measurements, a RANSAC 3D ego velocity estimation is carried out. Fusion with inertial data further improves the accuracy, robustness and provides a high rate motion estimate. An extension with barometric height fusion is presented.The radar based ego velocity estimation is tested in simulation and the accuracy evaluated with real world datasets in a motion capture system. Tests in indoor and outdoor environments with trajectories longer than 200m achieved a final position error below 0.6% of the distance traveled. The proposed odometry approach runs faster than realtime even on an embedded computer.
An Integrated GNSS/UWB/DR/VMM Positioning Strategy for Intelligent Vehicles

Intelligent vehicle positioning is an enabling technology for decision-making, trajectory planning, and motion control, and is a key technology in intelligent transportation systems (ITSs). In order to obtain a low-cost and high-precision positioning strategy, an integrated positioning strategy for intelligent vehicles based on the global navigation satellite system (GNSS), dead reckoning (DR), ultra-wide band (UWB), and visual map matching (VMM) was proposed in this paper. First, the error sources of the three independent positioning methods of GNSS, DR, and UWB were analyzed, and independent positioning algorithms with improved accuracy were proposed. Then, the fusion positioning algorithm of GNSS/DR/UWB with an adaptive information distribution coefficient was established using a federated Kalman filter to improve the positioning accuracy and continuity. Afterward, to avoid reliance on the previous moment, the positioning result was further corrected by VMM. Finally, offline simulation and real vehicle tests were conducted under typical working conditions with the impact of real-world noise and the real-motion states of vehicles. The results showed that the GNSS/UWB/DR/VMM positioning algorithm could effectively improve the positioning accuracy and reliability of intelligent vehicles at a low cost.
Performance Analysis of a Delay Constrained Data Offloading Scheme in an Integrated Cloud-Fog-Edge Computing System

The recent growth in intensive services and applications demand has triggered the functional integration of cloud computing with edge computing capabilities. One of the main goals is to allow a fast processing to tasks with strict real time constraints in order to lower the task dropping probability due to expiration of the associated deadlines. This paper deals with the performance evaluation and optimization of a three layers cloud-fog-edge computing infrastructure by resorting to the use of queueing theory results. In particular, a Markov queueing system model with reneging is proposed for the cloud subsystem, in order to consider the premature computation requests departure due to their deadline expiration. Furthermore, a computational resources allocation method is proposed with the aim at maximizing the social welfare metric, constrained to specific quality of service requirements. Finally, the proposed queueing theory analysis as well as of the computational resources allocation approach is validated by comparing the obtained analytical predictions with simulation results.
Identification of an Entire Workload's CPU-Vmin from the n-First Seconds of its Execution Based on Performance Counters

CPU pessimistic voltage margins set by hardware designers to address voltage-noise, aging and static variations, limit the power efficiency of computing systems. These margins are necessary to avoid voltage emergencies that lead to silent data corruption and application/system crashes which are not acceptable in most situations. However, the voltage margins required by different applications may vary and, therefore, an opportunity may exist to improve the power-efficiency if we can adapt the CPU voltage margins per workload. This paper presents a comprehensive correlation analysis of an application's minimum operating voltage (CPU-Vmin) with hardware's performance counters on a real multicore system. The analysis reveals that a subset of the performance counters-the same ones across different workloads-have a strong correlation with a workload's CPU-Vmin. Moreover, the results show that the CPU-Vmin is accurately identifiable by monitoring a workload's performance-counters during the n-first seconds of its execution. Our findings serve as the basis of a software-based CPU-Vmin identification method that monitors an application for the first n-seconds and then sets the CPU supply voltage to a specific value for the rest of the execution. Our evaluation shows that when n-first equals to 20 seconds, the CPU-Vmin workload identification method provides a safe CPU-Vmin, 99.4% of the time and reduces power on average by 3.8% and 7.1% as compared to when operating at a safe and a nominal supply voltage, respectively.
Graphene-Based AMC Polarization Converter for Antenna Applications at Microwave Frequency Band

In this paper, reconfigurable polarization converter from linear polarization to circular polarization is designed based on graphene artificial magnetic conductor surface. The reconfigurable conductivity of the graphene material allows the change of electromagnetic wave polarization left-hand and right-hand circular polarization (CP) through electrical DC biasing. The gain enhancement of V-shaped dipole antenna is achieved by backing it with AMC array. The antenna gain is increased up to 4.7 dBi with AMC ground plane. Different AMC ground plane sizes and their effect on polarization conversion are investigated. The polarization conversion bandwidth is 19.44% for 7x7 AMC array. Full-wave analysis is used to model the array structure.
3D Residual Dense Convolutional Network for Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment

Alzheimer's disease (AD) is a common and incurable dementia, so it is particularly important to correctly distinguish AD from normal people. In recent years, the development of deep learning has pointed out a new direction for the study of AD classification. This paper proposes a 3D residual densely connected convolutional network for AD and mild cognitive impairment (MCI) diagnosis. First, the Residual Dense Block (RDB) is used to expand the information flow to avoid the disappearance of gradients. Then we use the global feature fusion method to comprehensively consider the shallow and deep features to improve the accuracy of classification. This paper mainly uses the MRI data of the ADNI dataset for experiments to prove the superior performance of the proposed model.