Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed Far-Field and Near-Field Source Localization Using a Linear Electromagnetic-Vector-Sensor Array With Gain/Phase Uncertainties

This paper investigates the problem of mixed far-field (FF) and near-field (NF) source localization using a linear electromagnetic-vector-sensor array with gain/phase uncertainties. Firstly, several special fourth-order cumulant matrices are constructed, such that the shift invariance structure in the cumulant domain can be derived to estimate the DOA and polarization angles of each source at two electromagnetic vector sensors (EMVSs). Then, by computing the determinant of the coefficient matrix, the sources types can be classified with the prior knowledge of the number of both the FF and NF sources. On this basis, the range of NF sources and the DOAs of mixed sources at the phase reference point are captured subsequently. Finally, these estimates can be employed to generate the unknown gain/phase errors. Compared to the existing methods, the proposed one exploits both the spatial and polarization information of sources and provides a satisfactory parameters estimation performance under unknown phase/gain responses. Moreover, it does not need to perform any spectral search and not impose restriction on EMVSs placement, as well as realizes a more reasonable classification of the signal types. Simulations are carried out to verify the effectiveness of the proposed method.
Faster Convergence of Modified Policy Iteration for Quantitative Verification of Markov Decision Processes

Probabilistic model checking is a formal approach for verifying qualitative and quantitative properties of probabilistic and stochastic systems. Discrete-time Markov chains and Markov decision processes (MDPs) are used to modeling this class of systems. Reachability probabilities and expected costs are two classes of properties that are used to specify the requirements of probabilistic systems. Value iteration and policy iteration are well-known approaches for computing these classes of properties. In this paper, we consider MDPs with different levels of non-determinism. We show that modified policy iteration performs better than the other standard iterative methods when the degree of non-determinism increases. We propose several methods to improve the performance of the modified policy iteration method. Our approach is to define several criteria to avoid useless iterations and updates of the modified policy iteration method. As a result, the total numbers of iterations and updates are reduced, which results in an improvement in the performance of the method.
Sodium selenate inhibits adipogenesis in vitro

Adipogenesis refers to the differentiation of predadipocytes into mature adipocytes. This process is a potential target to treat or prevent obesity. Adipogenesis not only increases fat accumulation, but also elevates inflammation and oxidative stress. Selenium, an essential micromineral, has been shown to have potent anti‐oxidative and anti‐inflammatory properties. We therefore hypothesized that selenium can serve as a potential anti‐adipogenic agent. Sodium selenate was chosen over sodium selenite due to its relatively low toxicity in 3T3‐L1 cells. We found that selenate significantly inhibits adipogenesis in vitro in a dose dependent manner. Complete inhibition was observed at 50 microM, at which there were no negative effects on cell viability. We revealed that selenate's inhibitory action is limited to the early stage (day 0 to 2) of adipogenesis. Furthermore, we found that selenate reduced mRNA expression levels of adipogenic transcription factors, such as, peroxisome proliferator‐activated receptor‐gamma (PPAR‐gamma). More importantly, selenate was likely to maintain preadipocyte factor‐1 (Pref‐1), a preadipocyte marker gene expression, during adipogenesis. Taken together, our results support selenate's anti‐adipogenic properties in vitro. These effects are likely due to selenate‐mediated alteration of the adipogenic transcriptional program in the early stage of differentiation. This project is partially funded by the USDA‐AFRI grant and The Showalter Trust Fund Award.
Applications of Deep Learning: A Review

The utilization of deep learning and its related techniques was growing in a faster manner by day to day research. As the technology was developing, the utilization of these technologies also growing in a faster manner. This technology is the latest and advanced technology available in the market today. The utilization of these techniques is providing some good and encouraging results when compared to the previous existing methods and solutions for the current day problems. The implementation of AI, ML and Deep learning methods are providing the researchers some set of new ways and new solutions. These results are encouraging the researchers further such that to go for next level of research by incorporating these techniques in the more and more related areas. Hence, in the current paper, brief details about the deep learning and its applications are presented. The various existing applications and the technologies available in the market can be processed and further can be studied with the other set of applications.
A NEW STRATEGY FOR PHASE SWAPPING LOAD BALANCING RELYING ON A META-HEURISTIC MOGWO ALGORITHM

The significant spread of the single-phase loads in the consumer homes make the distribution network suffering from many dangerous problems like the load unbalancing. This problem comes because the single-phase devices continuously plugged in and out to different phases each time. This paper cared about this problem and solved it efficiently by the new meta-heuristic algorithm called GWO that applied for the first time to solve the load balancing issue. The algorithm has the ability based on the smart meter included swapping mechanism to disconnects the appropriate home phases from their initial connection to specific feeders and reconnected again to other feeders for satisfying the balancing in the secondary distribution network. The algorithm adapted to reaching the balancing with a minimal number of swaps and take care of the online PVs if the consumer likes to buy energy to the national utility. It distributed all the PVs in a manner that not cause a balancing problem or lead to a stability issue. The proposed method has been applied to some unbalanced areas with random data generated in MATLAB to confirm the efficacy of the proposed algorithm.
A Panoramic Segmentation Network for Point Cloud

Scene segmentation mainly consists of semantic segmentation and instance segmentation. The latest research points out that combining the two segmentation methods to achieve panoramic segmentation can understand the current scene better. The point cloud contains rich spatial information, but panoramic segmentation research in this field is rarely discussed. How to use the unified model framework to obtain the results of instance segmentation and semantic segmentation is the key to realize the task of point cloud panoramic segmentation. In this paper, we propose a panoramic segmentation network for point cloud. In feature encoding stage, we introduce the potential correlation information among points to improve the performance of feature extraction. Then, an output module is presented to combine the results of the two decoders which uses objective distance to enhance the semantic and instance segmentation. Experiments show that our model has good performance on the panoramic segmentation task of point cloud.
Indobufen Versus Warfarin in the Secondary Prevention of Major Vascular Events in Nonrheumatic Atrial Fibrillation

Background and Purpose The results of a large prospective randomized trial have shown the efficacy of oral anticoagulation in the secondary prevention of major vascular events in patients with nonrheumatic atrial fibrillation (NRAF); less well established is the role of antiplatelet agents. The present study compared the effects of indobufen, a reversible inhibitor of platelet cyclooxygenase, with those of warfarin in this setting. Methods A total of 916 patients with NRAF and a recent (≤15 days) cerebral ischemic episode were admitted to this multicenter, randomized study, during which they were treated with either indobufen (100 or 200 mg BID) or warfarin (to obtain an international normalized ratio of 2.0 to 3.5) for 12 months. The two groups (462 on indobufen and 454 on warfarin) were well balanced in terms of their main baseline characteristics. The primary outcome of the study was the combined incidence of nonfatal stroke (including intracerebral bleeding), pulmonary or systemic embolism, nonfatal m.
Design research on calculus: Students’ journey in learning definition of definite integral

Some researchers report that students find difficulties in learning definition of definite integration. Therefore, classroom activities designed with design research method to help university students gain better understanding on this topic. In order to support students, realistic mathematics approach was chosen. Series of instructional activities had been designed to reach mathematical goal. Thirty of first year university students in Universitas Negeri Padang were chosen to implement the classroom activities that have been designed. This paper will report students’ journey in learning definition of definite integral in our local instructional theory (LIT). The research shows that students understanding about area, function, inscribed/circumscribed polygon, limit play important role to understand the definition of definite integral. Students need to see those concepts as a unit which cannot be separated to understand the definition of definite integral in a designed classroom activity.
Ux Design of Artificial Intelligence News Robot

Background: Artificial Intelligence and 5G are coming with an unstoppable tide. As a representative of traditional industries, the news industry is transitioning from labor-intensive to intellectual-intensive by using the advanced AI technologies. Product Design: In order to help news industry to catch up with the rest of high-tech world, a UX designer’s job is to make the interaction between users and AI useable and useful. By understanding journalists’ pain points and what AI can do to help, UX designers found out the best landing scenarios and designed 25 intelligent media robots. With the robots’ help, reporters can collect and process news resources faster and better. Conclusions: This study proves that developing intelligent news robots among journalists by using a theory-based, evidence-driven, and user-centered approach has the potential benefits of low cost, high accessibility, and good adherence. The productivity of journalists has been greatly improved with intelligent media robots. Intelligent news robots have advantages such as fast producing speed, high efficiency, low cost, high accuracy and strong news digging ability. However, it also has limitations such as limited reporting fields, poor readability and lack of depth.
Robust Motorbike License Plate Detection and Recognition using Image Warping based on YOLOv2

Automatic License Plate Recognition (ALPR) is a technology required for many applications such as Intelligent Transportation Systems and Video Surveillance Systems. Most of the studies have studied were about the detection and recognition of license plates on cars, and there is very little about detecting and recognizing license plates on motorbikes. In the case of a car, the license plate is located at the front or rear center of the vehicle and is a straight or slightly sloped license plate. Also, the background of the license plate is mainly monochromatic, and license plate detection and recognition process is less complicated. However since the motorbike is parked by using a kickstand, it is inclined at various angles when parked, so the process of recognizing characters on the motorbike license plate is more complicated. In this paper, we have developed a 2-stage YOLOv2 algorithm to detect the area of a license plate after detection of a motorbike area in order to improve the recognition accuracy of license plate for motorbike data set parked at various angles. In order to increase the detection rate, the size and number of the anchor boxes were adjusted according to the characteristics of the motorbike and license plate. Image warping algorithms were applied after detecting tilted license plates. As a result of simulating the license plate character recognition process, the proposed method had the recognition rate of license plate of 80.23% compared to the recognition rate of the conventional method(YOLOv2 without image warping) of 47.74%. Therefore, the proposed method can increase the recognition of tilted motorbike license plate character by using the adjustment of anchor boxes and the image warping which fit the motorbike license plate.
Research on parameter optimization of Double-D coils for electric vehicle wireless charging based on magnetic circuit analysis

Compare with circular coils, Double-D coils are widely used in the wireless charging system of electric vehicles for its high coupling coefficient and excellent offset tolerance. In order to obtain higher coupling coefficient and reduce the leakage magnetic flux in the central part of the coils, this paper proposes a method that optimizing stray parameters, which can achieve more than 95% of transmitting efficiency. The investigation is performed by joint simulation for the choice of experimental devices concerning current-withstanding characteristic. According to simulations and experiments, more than 11 kW output power can be achieved for a wide range of frequency.
Heart Sound Data Acquisition and Preprocessing Techniques

Heart sound or phonocardiogram (PCG) signal quantifies the information about the mechanical activity of the heart, and the medical practitioners use the stethoscope to listen to this sound. The PCG signal can be used for clinical applications such as detection of various valvular diseases and non-clinical applications such as biometric system, stress and emotion detection, etc. The PCG signal acquisition and preprocessing are important tasks for the diagnosis of heart valve-related disorders and other applications. The heart sound preprocessing techniques include denoising of PCG signal, segmentation of first and second heart sound (S1, S2) and other heart sound components from the PCG signal, feature extraction from the segmented heart sound components, followed by classification. This chapter reviews the state-of-the-art approaches for heart sound acquisition and pre-processing techniques and also provides the information that is commonly used by the researchers for the validation of their PCG signal processing algorithms. Heart Sound Data Acquisition and Preprocessing Techniques: A Review
G-LOMARC-TS: Lookahead group matchmaking for time/space sharing on multi-core parallel machines

Parallel machines with multi-core nodes are becoming increasingly popular. The performances of applications running on these machines are improved gradually due to the resource competition in each node. Researches have found that coscheduling different applications with complementary resource characteristics on the same set of nodes (semi time sharing) may improve the performance. We propose a scheduling algorithm G-LOMARC-TS which incorporates both space and semi time sharing scheduling methods and matches groups of jobs if possible for coscheduling. Since matchmaking may select jobs further down the waiting queue and the jobs in front of the queue may be delayed subsequently, fairness for each individual job will be watched and the delay will be kept within a limited bound. Several heuristics are used to solve the NP-complete problem of forming groups. Our experiment results show both utilization gain and average relative response time improvements of G-LOMARC-TS over other several scheduling policies.
Comparing the Effect of 6 Weeks of Resistance Training with and without Vascular Occlusion on Growth Hormone Levels in Female Physical Education Students

Accepted: 29 Aug, 2016 Abstract Background and Objectives: Resistance training with vascular occlusion is a type of resistance training, which causes various changes through blood flow restriction in muscular function and hormonal responses. The purpose of the current study was to compare between the effects of low-intensity resistance training with vascular occlusion and high-intensity traditional resistance training on resting growth hormone (GH) levels in active females.
An Effective Sharpsat Solution Method Based on Learnt Clause Minimization Approach

The problem of satisfiability of the propositional logic formula (SAT problem) in computer science is an important and difficult problem. Learning clauses often contain redundant literal in CDCL SAT solving, which may have a negative impact on the performance of the solver. To overcome this shortcoming, we show a new sharpSAT solver based on learning clause minimization is proposed. By recoding the CNF formula to reduce the storage space, and applying Boolean constraint propagation to eliminate redundant literals in the learning clause, the algorithm reduces the time cost. The complexity is compared with the existing solver. The experimental results show that the solver has significant application value, reduces the time of instance solution, and increases the number of maximum solvable instances.
Personalized Ranking with Importance Sampling

As the task of predicting a personalized ranking on a set of items, item recommendation has become an important way to address information overload. Optimizing ranking loss aligns better with the ultimate goal of item recommendation, so many ranking-based methods were proposed for item recommendation, such as collaborative filtering with Bayesian Personalized Ranking (BPR) loss, and Weighted Approximate-Rank Pairwise (WARP) loss. However, the ranking-based methods can not consistently beat regression-based models with the gravity regularizer. The key challenge in ranking-based optimization is difficult to fully use the limited number of negative samples, particularly when they are not so informative. To this end, we propose a new ranking loss based on importance sampling so that more informative negative samples can be better used. We then design a series of negative samplers from simple to complex, whose informativeness of negative samples is from less to more. With these samplers, the loss function is easy to use and can be optimized by popular solvers. The proposed algorithms are evaluated with five real-world datasets of varying size and difficulty. The results show that they consistently outperform the state-of-the-art item recommendation algorithms, and the relative improvements with respect to NDCG@50 are more than 19.2% on average. Moreover, the loss function is verified to make better use of negative samples and to require fewer negative samples when they are more informative.
Mutual authentication protocols for RFID special schemes

Radio Frequency IDenti cation (RFID) is a wireless identi cation technology that uses radio waves to identify tagged objects. RFID systems provide low-cost tagging capabilities for many applications such as access control systems, transportation ticketing, and supply chain management. Providing security and preserving privacy for these systems is challenging. The tags utilised in such applications are low-cost tags with limited resources that cannot a ord the use of conventional cryptographic primitives. Thus, low-cost RFID tags might be vulnerable to passive attacks, such as eavesdropping, and active attacks, such as tag cloning, impersonation, replay, data de-synchronization attacks, tag data leakage, forward secrecy invasion and location tracking. There has been considerable research into the mutual authentication of passive RFID tags to combat passive and active attacks, and in this thesis we present analysis of the prior art, which led us to make ve academic research contributions. Security is increasingly important, especially for tagging of important objects, and there are growing concerns from users about their privacy. To this end, in this thesis, we studied RFID security and privacy in several schemes, such as in RFID-enabled supply chains, RFID cloud-based scheme, and multi-tag group reading schemes. We focused on how to improve and propose RFID mutual authentication protocols in such schemes that are practical and cost e ective, and satisfy the security and privacy requirements. Lastly, we provide a formal analysis of the proposed protocols using CasperFDR and Scyther tools, along with the implementation of the proposed protocols with their performance measures.
Uma Revisão Sistemática acerca das Técnicas de Identificação e Análise de Tendências

This paper consists of developing a systematic review about methods and techniques used for identifying trends in different application types. The first goal is to understand what are the main techniques being used and the way that these techniques are organized by the researchers. The second goal consists of understanding which of these techniques could be applied to social networks.
An Improved Memetic Algorithm for Traction Characteristic Curve Fitting of Urban Rail Vehicle

An Improved Memetic Algorithm (IMA) for the traction characteristic curve fitting of urban rail vehicle is studied in this paper. Combining strong global search ability of Genetic Algorithm (GA), quick convergence ability of Fruit Fly Algorithm (FFA) and strong local search ability of Hill Climbing Algorithm (HCA), this paper constructs a hybrid algorithm to improve the search efficiency for MA. A univariate equation with n orders of the instantaneous speed is adopted for segmented curve fitting because of its simple structure. Besides, this paper proposes a new learning mechanism that enables them to learn from each other between elite individuals and non-elite individuals for MA. Finally, In the Matlab2016a GUI platform, by using several different curve fitting optimization algorithms to carry out simulation experiments, simulation results show that the IMA can find more accurate traction characteristics fitting curve at the same condition.
Quantum Internet: Realization and Security

- The Quantum Internet [5] is the biggest and another surprising advancement of Quantum Mechanics [6], and would deal not only with crucial things of today’s world, but provides a complete solution to the theory of Cryptography. It is not of novel science, rather an old one [1]. In Information theory communication is defined as message transfer between two parties and in basic definitions of artificial intelligence, if a system is misunderstood by a human to distinguish from a machine or a human, then the system is intelligent and can break on secure communication. In this paper, I shed light on how a perfectly fit system can be realized using Quantum Computer on Quantum Internet and providing a secure communication between two parties.