Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Small Samples SAR Image Recognition Detection Method Based on Transfer CNN

Synchronous Aperture Radar (SAR) images can be acquired by different microwave imaging modes, systems, etc. For the recognition detection of SAR images of heterogeneous small samples(ie a small number of samples),this paper proposes a transfer-based Convolutional Neural Network (CNN) small samples SAR images ground object recognition detection method.First of all,the source domain sufficient samples were used to pre-train CNN,and then CNN was fine-tuned by using the transfer of small samples of the target domain SAR images to obtain a new CNN.The network finally realizes ground object recognition detection of small samples SAR images.The effectiveness and accuracy of the proposed method are verified by experiments,and good results can be obtained.
Aspect Based Sentiment Analysis on Product Reviews

With the popularity and growing availability of opinion rich sources such as reviews from e-commerce sites, choosing the right product from huge product brands have difficult for the user. In order to enhance the sales and customer satisfaction, most of the sites provide opportunity for the user to write review aspects about the product. These reviews are in text format and increases day by day. It is difficult for the user and manufacturer to understand likes and dislikes of a customer about the product. In this situation sentiment analysis helps the people to analyze the reviews and come to conclusion whether it is good or bad. Sentiment Analysis which also known as opinion mining is one of the subsection in Natural Language processing in which it learns about Sentiment or subjectivity from reviews. The main purpose of the project is to develop a system to extract the reviews from e-commerce site, extract aspect from the reviews and categorize reviews into positive and negative. We have implemented Sentiment analysis with unsupervised machine learning technique like uni-gram Lexicon, bi-gram Lexicon and Supervised technique like Support vector machine (SVM). These techniques are experimented on real time e-commerce dataset. Out of three techniques, SVM outperformed with an accuracy of 84%.
Capacity/cost trade-off for 5G small cell networks in the UHF and SHF bands

To fulfill the demand for high data rates from a wide number of users, higher frequencies can be used to provide the required capacity but with a different cost structure. 5G allows for very high data rates, which needs large bandwidths and requires very high throughput. This paper studies the economic trade-off of small cell networks deployed in Ultra High Frequency (UHF) and Super High Frequency (SHF) bands. Aiming at evaluating the variation of the carrier-to-noise-plus-interference ratio with the cell radius one considers the two-slope urban micro Line-of-Sight (UMiLoS) path loss model (ITU-R 2135 Report). The system capacity is analysed by the implicit function formulation to compute the supported cell throughput through the mapping of the PHY throughput into the step distances that correspond to the minimum carrier-to-interference-plus-noise ratio for each consecutive modulation and coding scheme. The variation of the equivalent supported throughput with the cell size at the 2.6, 3.5 and 5.62 GHz frequency bands facilitates to study the influence of the carrier-to-noise-plus-interference ratio in the dimensioning process. It is possible to conclude that, for the shortest cell radii, the profit is very low, starting to increase at a distance equal to the ratio between the break-point distance and the co-channel reuse factor attaining maxima for values of the cell radius approximately equal to 310, 390 and 740 m, at 2.6, 3.5 and 5.62 GHz, respectively (by assuming that the 5.62 GHz frequency band is unlicensed).
Towards Adversarial Robustness via Feature Matching

Image classification systems are known to be vulnerable to adversarial attacks, which are imperceptibly perturbed but lead to spectacularly disgraceful classification. Adversarial training is one of the most effective defenses for improving the robustness of classifiers. We introduce an enhanced adversarial training approach in this work. Motivated by human’s consistently accurate perception of surroundings, we explore the artificial attention of deep neural networks in the context of adversarial classification. We begin with an empirical analysis of how the attention of artificial systems will change as the model undergoes adversarial attacks. Observation is that the class-specific attention gets diverted and subsequently induces wrong prediction. To that end, we propose a regularizer encouraging the consistency in the artificial attention on the clean image and its adversarial counterpart. Our method shows improved empirical robustness over the state-of-the-art, secures 55.74% adversarial accuracy on CIFAR-10 with perturbation budget of 8/255 under the challenging untargeted attack in white-box settings. Further evaluations on CIFAR-100 also show our potential for a desirable boost in adversarial robustness for deep neural networks. Code and trained models of our work are available at: https://github.com/lizhuorong/Towards-Adversarial-Robustness-via-Feature-matching
Standard Cell Tuning Enables Data-Independent Static Power Consumption

Physical attacks, namely invasive, observation and combined, represent a great challenge for today’s digital design. Successful class of strategies adopted by industry, allowing hiding data dependency of the side channel emissions in CMOS is based on balancing. Although attacks on CMOS dynamic power represent a class of state-of-the-art attacks, vulnerabilities exploiting data dependency in CMOS static power and light- modulated static power were recently presented. In this paper, we describe structures and techniques developed to enhance and balance traditional static CMOS bulk structures. To enable data dependency hiding, we propose low-level techniques based on complementary-value induced balancing currents, constant current source behavioral approximation, and light-sensing capability of traditional CMOS structures. The proposed techniques may be used to build a dual-rail circuit balanced from both perspectives: static and dynamic power. The publicly available TSMC180nm node standard cell simulation is used for evaluation.
A renaissance in studies of ancient life

In the Origin of Species, published a century‐and‐a‐half ago, Darwin was mystified by the lack of a ‘pre‐Cambrian’ fossil record, the existence of which he regarded as pivotal to his theory of evolution. For the next 100 years, this ‘missing’ fossil record—unknown and thought unknowable'stood out as arguably the single greatest blemish to Darwin's theory. Beginning in the 1950s, the answer to Darwin's problem began to be unearthed, a Precambrian record of flourishing communities of microscopic organisms now known to extend to 3500 million years ago. During recent years, studies of such ancient microbes have markedly increased, spurred by an influx of new workers and, especially, by the introduction of new analytical techniques, three of which are featured here: confocal laser scanning microscopy, and Raman‐spectral and fluorescence‐spectral imagery. Used together, these techniques provide evidence of the three‐dimensional form, cellular anatomy, and molecular structure of rock‐embedded microscopic fossils and of the minerals in which they are entombed that is unavailable by any other means.
Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users. This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users’ preferences and item properties dynamically change over time. Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance. We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with the associated topic evolution of review texts by using topic modeling based on the dynamic environment. We evaluate the accuracy on real datasets, and the experimental results show that the model leads to a significant improvement compared with the state-of-the-art dynamic CF models.
Fault Diagnosis of Rolling Bearing Based on the Permutation Entropy of VMD and Decision Tree

Rolling bearing is the most important component of rotating machinery. The research of the fault diagnosis of rolling bearing can effectively improve the running reliability of rotating machinery, avoid sudden failures and ensure the safety of workers. Because of the characteristics with non-stationary and nonlinear of the rolling bearing vibration signal, a fault diagnosis method for rolling bearing based on permutation entropy of VMD and decision tree is proposed. The original vibration signal is decomposed by VMD to obtain a series of BIMFs. And then, the permutation entropy value of each BIMF is extracted as the fault feature. Finally, the decision tree is used to predict the fault category of rolling bearing. The experimental results show that the propose method can accurately diagnose rolling bearing faults, and it is an effective method for fault diagnosis of rolling bearings.
Tracking Registration Algorithm for Augmented Reality based on Random Fern

For markerless tracking registration methods, there exist a lot of problems like tracking registration failure and slow speed in the complex environment. To solve the problem, a random fern classifier is applied to the process of the markerless augmented reality tracking registration. This method uses a target object image in a real scene as a template, and the target of each frame of image is detected by the random fern classifier. By estimating the three-dimensional pose, the registered virtual object can be rendered to implement the augmented reality tracking registration. This method can solves the problems of tracking registration failures due to the change of ambient lighting or the targets were occluded. It has better real-time performance than the traditional wide baseline matching algorithm.
Mobile Edge Computing Offloading Strategy Based on Improved BP Neural Network

Mobile edge computing is a key technology for future 5G communication. Mobile edge computing helps to achieve the requirements of ultra-low latency, high energy efficiency, ultra-reliability, and ultra-high connection density for the new services of the fifth generation of mobile communication (5G). To solve multi-user intensive task scheduling problem under 5G cellular networks, this paper proposes a computation offloading strategy based on back propagation neural network. The genetic algorithm is used to optimize the weights and biases of the back propagation neural network, and the back propagation neural network model trained in advance is used to adaptively and jointly optimize the system energy consumption and time delay to generate the computation offloading results. Compared with the benchmark algorithm, the algorithm proposed in this paper can effectively reduce system overhead, meet the requirements of 5G environment for low latency and low energy consumption, and improve quality of service.
β2-Microglobulin, Neutrophil Gelatinase-Associated Lipocalin, and Endocan Values in Evaluating Renal Functions in Patients with β-Thalassemia Major

Abstract Chronic anemia, transfusion-associated iron deposition, and chelating agents lead to renal impairment in β-thalassemia (β-thal) patients. The present study aimed to determine the most reliable and practical method in assessing and predicting renal injury in β-thal major (β-TM) patients. Therefore, we assessed the predictive values of urine β2-microglobulin (β2-MG) and neutrophil gelatinase-associated lipocalin (NGAL) levels, their ratios to urine creatinine, and serum endocan level. Sixty β-TM patients and 30 healthy controls were included. Renal functions of the patients and controls were evaluated by means of urine protein/creatinine ratio, urine β2-MG, urine NGAL, and serum endocan level. The β-TM and control groups were comparable in terms of the demographic characteristics. Of the β-TM patients, 26.7% had glomerular hyperfiltration and 41.7% had proteinuria. Compared with the control group, the β-TM group had significantly higher levels of urine protein/creatinine, urine β2-MG, urine β2-MG/creatinine, urine NGAL, urine NGAL/creatinine, and serum endocan. These parameters did not differ between the chelating agent subgroups in the patient group. Urine β2-MG/creatinine and NGAL/creatinine ratios were the parameters with high specificity in predicting proteinuria. There were significant correlations of urine β2-MG, urine NGAL, and serum endocan levels with serum ferritin concentration. Urine β2-MG/creatinine, NGAL/creatinine, and protein/creatinine ratios were correlated with each other in the patient group. Positive correlations of urine β2-MG, urine NGAL, and serum endocan levels with serum ferritin concentration indicated that iron deposition was associated with endothelial damage and renal injury.
Design Optimization of Magnetic Material Distribution by Using Encoder-Decoder with Additive Mixing for Design Conditions

Recently, the deep learning technology attracts much attention in various industrial fields. In our previous research, we developed an Encoder-Decoder precisely reproducing the optimization process of conventional optimization method, that is, the level-set method which is one of the gradient methods, by means of Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). The developed method enables us to implement high speed search of solutions, which means the possibility of better and effective optimization starting with various initial shapes. This method can deal with only the initial shape as design parameter for optimization. Thus, it is necessary to re-train the Encoder-Decoder when the design conditions change, e.g., the displacement of permanent magnet. To overcome this drawback, we have developed a novel network structure to incorporate the design conditions into the training data. Finally, to confirm the validity of the proposed method, we evaluate its calculation time and computational accuracy by using a magnetic circuit design model.
A Sensor Attack Detection Method in Intelligent Vehicle with Multiple Sensors

With the rapid development of intelligent vehicles, more and more researchers are paying attention to their security issues. This paper considers a vehicle system with multiple sensors measuring the same physical variable. Some of these sensors may be maliciously attacked, resulting in the system being unable to work properly. This paper mainly addresses the detection and identification of malicious attacks on sensors in the presence of transient faults. Although there are some solutions at present, the existing methods can hardly capture attacks when a professional attacker manipulates the sensor output very slightly or infrequently. To address this problem, we design a resilient sensor attack detection algorithm. This algorithm adds an additional virtual sensor to the system and builds a fault model for each sensor, and using the pairwise inconsistencies between the sensors to detect the attack. To improve the detection rate in the presence of transient faults, we consider the system dynamics model and incorporates historical measurements into the detection algorithm. In addition, we propose a method to select transient fault model parameters to obtain a more accurate sensor fault model in a dynamic environment. Finally, this paper obtains real experimental data from the EV3 platform to verify the algorithm. Experimental results show that the proposed method is more robust and can detect and identify more attacks. Especially for stealth attacks which are extremely difficult to detect, the detection rate and recognition rate are increased by about 90%.
Low-Rank Regularized Multimodal Representation for Micro-Video Event Detection

Currently, micro-videos are becoming one of the most representative products in the new media age. Although the length of micro-videos is limited to cater to the fast pace of life and are beneficial for rapid distribution, micro-videos are usually recorded in specific scenarios and tend to convey relatively complete events. To more accurately obtain the event types of micro-videos to facilitate potential applications, we propose a low-rank regularized multimodal representation method for micro-video event detection. To solve the less descriptive power of each modality, the latent common representation of micro-videos is obtained by exploiting complementarity among modalities. A considerable gain in accuracy on this basis can be achieved by further considering the low-rank constraint for the lowest-rank intrinsic representation and a flexible label-relaxation strategy for mappings between representations and their correspondences. A newly constructed micro-video dataset is used to verify the advantages of our proposed model. The experimental results demonstrated the superior performance of our proposed method compared with state-of-the-art methods.
Corner smoothing interpolation algorithm based on PH curve

In the process of machining, sharp corners will exist at the joints of continuous short line segments, and most spline curves cannot establish the accurate relationship between arc length and parameters. For the corner transition path of the short segment, the five-degree PH˄Pythagorean-Hodograph˅ curve is adopted to realize high-speed smooth transition by adjusting the control points of the curve and restricting the length of the transition segment. The corner error is small by experimentally comparing the NURBS algorithm. Effectively reduce the tool vibration.
Wind Power Forecasting Using a New and Robust Hybrid Metaheuristic Approach: A Case Study of Multiple Locations

Wind is a type of renewable energy which has stochastic and intermittent structure. Almost every 10% error made in the wind speed forecast, leads to about 30% error in wind energy generation prediction. Therefore, accurate estimation of the wind speed is vital for wind farms which are increased rapidly. For this aim, a new accurate, quick, and robust hybrid metaheuristic model is used for wind power forecasting. The model is developed based on Radial Movement Optimization (RMO) and Particle Swarm Optimization (PSO). To compare the performance of the designed hybrid metaheuristic model with existing hybrid algorithms, some hybrid metaheuristic models were developed based on the literature, using MATLAB software. The data were obtained from wind measurement stations in two different locations at Burdur and Osmaniye cities, Turkey. Wind power forecasting studies were performed using temperature, humidity and pressure data, except to the wind speed data. Forecasting studies of without using wind speed data, is the most significant factor that challenges the success of all hybrid models. The results show that the error values of ANNs trained with PSO+RMO model were the lowest compared with the other hybrid models.
Morpho‐physiological Approaches to Investigate Lime‐Induced Chlorosis in Deciduous Fruit Tree Species

Abstract Several deciduous fruit tree species are affected by iron chlorosis when grown in calcareous soils and worldwide research on rootstock breeding and screening in relation to iron deficiency is carried out. For many years the goal of our department has been to investigate morphological and physiological aspects related to lime‐induced chlorosis, and to identify markers usable as tools for tolerance screening of clonal rootstocks. Experimental strategies using pot, hydroponic and in vitro culture have been used to understand if they are able to originate suitable indicators on developmental and biochemical changes resulting from iron deficiency. Results risen up from in vitro trials suggest that iron deficiency sensitive system should work in a divergent way from that of bicarbonate system. Physiological and morphological data highlight diverse syndromes and behaviors among the genotypes, probably reflecting properly perception, regulation, and activation of biological answer system to iron deficiency and bicarbonate. Recently, quince (Cydonia oblonga Mill.) minus‐ and plus‐variants have been obtained by in vitro somaclonal variation to clarify the agonistic relationship between bicarbonate and iron deficiency sensitive systems, that in turn can induce the activation of two specific gene expression pathways.
Spironolactone mitigates, but does not reverse, the progression of renal fibrosis in a transgenic hypertensive rat

Hypertension plays an important role in the development and progression of chronic kidney disease. Studies to date, with mineralocorticoid receptor antagonists (MRA), have demonstrated varying degrees of results in modifying the development of renal fibrosis. This study aimed to investigate whether treatment with a MRA commenced following the establishment of hypertension, a situation more accurately representing the clinical setting, modified the progression of renal fibrosis. Using male Cyp1a1Ren2 rats (n = 28), hypertension was established by addition of 0.167% indole‐3‐carbinol (w/w) to the rat chow, for 2 weeks prior to treatment. Rats were then divided into normotensive, hypertensive (H), or hypertensive with daily oral spironolactone treatment (H + SP) (human equivalent dose 50 mg/day). Physiological data and tissue were collected after 4 and 12 weeks for analysis. After 4 weeks, spironolactone had no demonstrable effect on systolic blood pressure (SBP), proteinuria, or macrophage infiltration in the renal cortex. However, glomerulosclerosis and renal cortical fibrosis were significantly decreased. Following 12 weeks of spironolactone treatment, SBP was lowered (not back to normotensive levels), proteinuria was reduced, and the progression of glomerulosclerosis and renal cortical fibrosis was significantly blunted. This was associated with a significant reduction in macrophage and myofibroblast infiltration, as well as CTGF and pSMAD2 expression. In summary, in a model of established hypertension, spironolactone significantly blunted the progression of renal fibrosis and glomerulosclerosis, and downregulated the renal inflammatory response, which was associated with reduced proteinuria, despite only a partial reduction in systolic blood pressure. This suggests a blood pressure independent effect of MRA on renal fibrosis.
Botnets Detection Using Back Tracking In Wired Networks

Modern computer threats are more complicated compared to the past. The noteworthy problem faced by many network enterprises mostly is from bots. A network of private computers infected with malicious software and controlled as a group without the owner’s knowledge is known as botnet. We propose a new algorithm which uses backtracking approach to detect the bots in a network. This paper mainly focuses on bot properties and its behavior in the network. The performance metrics which we considered are response time, delay, network traffic, packets dropped and flood packets.
Automated provisioning method for a modular PON-OLT device toward Plug & Provision

We propose a new automation concept for access network equipment called “Plug & Provision. ” This concept aims at reducing the workload in provisioning passive optical network (PON) service by utilizing a modular optical line terminal (OLT) and automating the setup sequence. To realize this concept, the automation platform must automatically detect the modular OLT plugged into a switch port and execute the configuration sequence that it triggered. We utilize open-source software in the control/management layer, especially focusing on OpenFlow as a control protocol. We present the overall architecture including a newly proposed PON abstraction and in-band control method for deeply disaggregated modular OLTs, and we demonstrate the feasibility of the architecture.