Reviewerly/exHarmony huggingface dataset:
Multiscale Modeling using Signal Adapted Filter Bank

In this work, we present a multi-scale modelling technique using a signal adapted multirate filter bank. Unlike the existing multi-resolution modelling strategies, the proposed algorithm can be implemented using a signal adaptive, fast, recursive algorithm which is computationally efficient. The analysis filter bank, used in this work is a multivariate whitening filter whose outputs are orthogonal in time as well as across channels. The analysis filter bank whitens the input signal in different spectral bands. The synthesis filter bank, proposed here, has been designed to reconstruct only the given signal and not every energy signal, as is done in the perfect reconstruction filter bank case. Outputs of the M-synthesis filters, thus, provide the required M-orthogonal sub-band constituents of the given signal. Since the proposed modelling scheme is signal dependent it can be efficient used for applications like energy compaction, dimension reduction etc. The performance of the proposed algorithm does not deteriorate for long data and, also, the number of sub-bands is not arbitrary. The efficacy of the algorithm has been verified via simulation studies.
Real-Time Implementation of Artificial Neural Network in FPGA Platform

Atibi, Mohamed Boussaa, Mohamed Bennis, Abdellatif Atouf, IssamIn this paper, we present the implementation of artificial neural networks in the FPGA embedded platform. The implementation is done by two different methods: a hardware implementation and a softcore implementation, in order to compare their performances and to choose the one that best approaches real-time systems and processes. For this, we have exploited the tools of this platform such as blocks Megafunctions and softcore NIOS II processor. The results obtained in terms of execution time have shown that the hardware implementation is much more efficient than that based on the NIOS II softcore.
A Fuzzy Rule-Based Krill Herd Algorithm

Standard Krill Herd (SKH) optimization algorithm is a novel heuristic optimization algorithm, and its control parameters play an important role for its performance. In this paper, an improved Krill Herd algorithm is proposed, in which the fuzzy system is utilized as the parameter tuner to adjust control parameters by observing the progress of solving the problem in each step. The innovation is that both scaling factor and inertia weight are considered, and these parameters can be adjusted automatically according to the particle situation. In order to evaluate the proposed FKH algorithm, the efficiency of FKH algorithm is verified by using 16 benchmark functions, the results indicate the superiority of proposed FKH optimization algorithm in comparison with the standard KH.
Robust Semantic Segmentation for Street Fashion Photos

In this paper, we aim to produce the state-of-the-art semantic segmentation for street fashion photos with three contributions. Firstly, we propose a high-performance semantic segmentation network that follows the encoder-decoder structure. Secondly, we propose a guided training process using multiple auxiliary losses. And thirdly, the 2D max-pooling-based scaling operation to produce segmentation feature maps for the aforementioned guided training process. We also propose mIoU+ metric taking noise into account for better evaluation. Evaluations with the ModaNet data set show that the proposed network achieves high benchmark results with less computational cost compared to ever-proposed methods.
Influence of Depth Cues on Eye Tracking Depth Measurement in Augmented Reality Using the MagicLeap Device

The eye-tracking technology is currently implemented in many mixed reality devices. However, eye-tracking measurements must be precise enough to permit correct localization in the environment; to allow, for example, linking such spatial positions to virtual objects. According to the human vision, major measurement issues would more likely occur in the depth axis rather than in the horizontal and vertical axes. In the literature, depth cues are known for improving human depth perception. In this study, our hypothesis is that, in an augmented reality environment, the more realistic virtual objects are displayed, thanks to depth cues, the more precise the eye-tracking device depth measures would be. Thus, using the MagicLeap device, we studied the effects of lighting and textures on eye-tracking depth measurement precision, by comparing the measures obtained under varying conditions of lights and textures, on both real and virtual objects. The results confirmed our general hypothesis, and we noticed a more significant influence of lights rather than textures on the precision of the measures. Moreover, we found that these depth cues reduce the measurement imprecision among observers, making the eye-tracking system more accurate when measuring depth.
Dynamic path planning for percutaneous procedures in the abdomen during free breathing

Percutaneous procedures are increasingly used for the treatment of tumors in abdominal structures. Most of the time, these procedures are planned based on static preoperative images and do not take into account any motions, while breathing control is not always applicable. In this paper, we present a method to automatically adjust the planned path in real time according to the breathing. First, an estimation of the organs motions during breathing is performed during an observation phase. Then we propose an approach named Real Time Intelligent Trajectory (RTIT) that consists in finding the most appropriate moments to push the needle along the initially planned path, based on the motions and the distance to surrounding organs. We also propose a second approach called Real Time Straight Trajectory (RTST) that examines sixteen scenarios of needle insertion at constant speed, starting at eight different moments of the breathing cycle with two different speeds. We evaluated our methods on six 3D models of abdominal structures built using image datasets and a real-time simulation of breathing movements. We measured the deviation from the initial path, the target positioning error, and the distance of the actual path to risky structures. The path proposed by RTIT approach is compared to the best path proposed by RTST. We show that the RTIT approach is relevant and adapted to breathing movements. The modification of the path remains minimal while collisions with obstacles are avoided. This study on simulations constitutes a first step towards intelligent robotic insertion under real-time image guidance.
Some optimal cyclic $ \mathbb{F}_q $-linear $ \mathbb{F}_{q^t} $-codes

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{F}_{q^t} $\end{document}</tex-math></inline-formula> be a finite field of cardinality <inline-formula><tex-math id="M4">\begin{document}$ q^t $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M5">\begin{document}$ q $\end{document}</tex-math></inline-formula> is a power of a prime number <inline-formula><tex-math id="M6">\begin{document}$ p $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M7">\begin{document}$ t\geq 1 $\end{document}</tex-math></inline-formula> is a positive integer. Firstly, a family of cyclic <inline-formula><tex-math id="M8">\begin{document}$ \mathbb{F}_q $\end{document}</tex-math></inline-formula>-linear <inline-formula><tex-math id="M9">\begin{document}$ \mathbb{F}_{q^t} $\end{document}</tex-math></inline-formula>-codes of length <inline-formula><tex-math id="M10">\begin{document}$ n $\end{document}</tex-math></inline-formula> is given, where <inline-formula><tex-math id="M11">\begin{document}$ n $\end{document}</tex-math></inline-formula> is a positive integer coprime to <inline-formula><tex-math id="M12">\begin{document}$ q $\end{document}</tex-math></inline-formula>. Then according to the structure of this kind of codes, we construct <inline-formula><tex-math id="M13">\begin{document}$ 60 $\end{document}</tex-math></inline-formula> optimal cyclic <inline-formula><tex-math id="M14">\begin{document}$ \mathbb{F}_q $\end{document}</tex-math></inline-formula>-linear <inline-formula><tex-math id="M15">\begin{document}$ \mathbb{F}_{q^2} $\end{document}</tex-math></inline-formula>-codes which have the same parameters as the MDS codes over <inline-formula><tex-math id="M16">\begin{document}$ \mathbb{F}_{q^2} $\end{document}</tex-math></inline-formula>.
Comparison of Interactivity Performance of Linux CFS and Windows 10 CPU Schedulers

One of the main goals of a user-oriented operating system design is to ensure low response time of interactive tasks. Otherwise, high scheduling latencies will become obvious to the user and will in turn affect the user experience. One of the most popular benchmarks that can measure interactive tasks response time is Interbench. However, Interbench was only available in Linux, thus making interactivity performance benchmarking for other operating systems impossible. This research ported the Interbench to Windows operating system so that the interactive performance of Windows and Linux can be evaluated and compared. We studied the effect of various simulated workloads on interactive performance of both operating systems and concluded that the Linux CPU scheduler tends to have lower latencies than Windows 10 in most scenarios, except when a heavy background load is executed concurrently with heavy load interactive task.
Design of eLearning Platform for Adult Learner

Many organizations and companies are beginning to realize that their employees need increased knowledge and skills so they can able to adjust to their environment. Adults often find it challenging to learn, because of limited space and time. eLearning is a medium that is suitable for adults to learn because it can eliminate these restrictions. To meet the learning needs of adults, eLearning should be adjusted to adult learning theory. In this paper, the analysis of activities on existing eLearning platforms was carried out to determine the services and technologies used. Next, the eLearning platform is proposed for use in adult learning that focuses on sharing experiences and knowledge through multimedia content and social interaction. On this platform, users can share multimedia content and perform various types of social interactions in the learning process.
Microwave Oven Controller Using a Fuzzy Logic System

Artificial Intelligence is promoting various control methods, and these are being utilized in different fields of the industry. Fuzzy logic-based control framework is one of the most conventional control systems. Fuzzy logic utilizes statements rather than numerical model for solving a given issue. In this paper, a typical household appliance microwave oven is controlled and operated with the assistance of Fuzzy logic. Both the simulation and the control of the microwave oven have been done using MATLAB's fuzzy logic tool box.
Stochastic Admm For Byzantine-Robust Distributed Learning

In this paper, we aim at solving a distributed machine learning problem under Byzantine attacks. In the distributed system, a number of workers (termed as Byzantine workers) could send arbitrary messages to the master and bias the learning process, due to data corruptions, computation errors or malicious attacks. Prior work has considered a total variation (TV) norm-penalized approximation formulation to handle Byzantine attacks, where the TV norm penalty forces the regular workers' local variables to be close, and meanwhile, tolerates the outliers sent by the Byzantine workers. The stochastic subgradient method, which does not consider the problem structure, is shown to be able to solve the TV norm-penalized approximation formulation. In this paper, we propose a stochastic alternating direction method of multipliers (ADMM) that utilizes the special structure of the TV norm penalty. The stochastic ADMM iterates are further simplified, such that the iteration-wise communication and computation costs are the same as those of the stochastic subgradient method. Numerical experiments on the COVERTYPE and MNIST dataset demonstrate the resilience of the proposed stochastic ADMM to various Byzantine attacks.
Two-Stream Network with 3D Common-Specific Framework for RGB-D Action Recognition

This paper presents a novel end-to-end network called TSN-3DCSF, which completes the task of RGB-D action recognition in video. Considering depth information can well express the relationship between different body parts, which is very helpful in action recognition, we employ it together with RGB information as inputs in our framework. Despite the characteristics of these two modalities are quite different, they have consistent semantic information and extracting the common and specific features is very meaningful for action recognition. Unlike most works which obtain temporal information by optical flow, our approach utilizes 3D convolution to build a layer to extract temporal information and common-specific feature simultaneously, which enhances the accuracy and reduces the amount of computation. Extensive experiments on three widely used RGB-D action datasets show that our method achieves comparable performance to the state-of-the-art methods.
Unveiling and Dedication of IEEE Radar Historic Milestone

An article in the September 2019 issue of the AES Magazine described the background of the IEEE Historic Milestone to mark the invention and first demonstration of the radar by the German inventor Christian H€ulsmeyer [1]. On 17 May 1904, H€ulsmeyer arranged a demonstration of his invention in K€oln (Cologne), Germany, which was reported in the K€olner Tageblatt and K€olnischer Zeitung newspapers the following day. This device was conceived as a means of detecting ship targets, even under conditions of poor visibility, and hence preventing collisions. By the standards of modern radar, it was very crude, using a spark-gap transmitter and a coherer-type receiver. He patented his invention both in Germany and in the U.K. On 19 October 2019, the invention was formally marked as an IEEE Historic Milestone. This article describes the event and the various contributions that came together to make a very special day for IEEE AES and for the worldwide radar community.
The Cost Perspective of Password Security

This study technically analyses the maximum number of combinations for common passwords up to 12 characters long. A maximum storage size necessary for the creation of a data base that holds all possible passwords up to 12 characters is also presented along with a comparison against the publicized cost of storage from popular cloud storage providers and the national budget for intelligence and defense activities of a nation. Authors prove that it is technically possible that any password could be computed within seconds with nothing more than currently commercially available components. The study concludes that it is possible that nation states or even combined nation states working in collaboration could or already have bought private citizens' and businesses' passwords revealing that it may already be an age where the password may not be a legitimate defense for privacy anymore.
Deep Image Deblurring Using Local Correlation Block

Dynamic scene deblurring is a challenging problem due to the various blurry source. Many deep learning based approaches try to train end-to-end deblurring networks, and achieve successful performance. However, the architectures and parameters of these methods are unchanged after training, so they need deeper network architectures and more parameters to adapt different blurry images, which increase the computational complexity. In this paper, we propose a local correlation block (LCBlock), which can adjust the weights of features adaptively according to the blurry inputs. And we use it to construct a dynamic scene deblurring network named LCNet. Experimental results show that the proposed LC-Net produces compariable performance with shorter running time and smaller network size, compared to state-of-the-art learning-based methods.
An Optimal Design of a Short-Channel RF Low Noise Amplifier Using a Swarm Intelligence Technique

In this paper, an optimal sizing of a cascode short-channel CMOS low noise amplifier (LNA) with inductive source degeneration by using the ant colony optimization (ACO) and the artificial bee colony (ABC) techniques is presented. The reason for employing this proposed topology is to provide a high conversion gain and improved noise figure (NF) at the operating frequency of 2.3 GHz. In order to validate the obtained results by ACO and ABC techniques, the advanced design system (ADS) simulator is used for this purpose using 90 nm CMOS technology.
Research on Teaching System for Painting Robot Based on Binocular Stereo Vision

For spraying robots, the major challenge of applied research is to organize the spray track which used for spray recurrence operation. This paper proposed a new manual teaching system based on binocular stereo vision. First, the real time position data and orientation data are calculated by motive tracking system. Then, a pose data optimization method combing pauta criterion and moving average method is used, to remove abnormal value and measurement noise of the raw data. At last, optimized pose data is converting into robot joint execution code, and the spray recurrence operation of spray robot is realized. Two experiment are analyzed, which test the accuracy of color ball center position and the accuracy of yaw angle and pitch angle, respectively. The result shown that the position data error is within 2mm, and the orientation data error is within ±3 degree. Those result also display that teaching system is accuracy and visible for spraying robot track planning operation.
Image Caption Generation with Local Semantic Information and Global Information

Different regions in the image would play different roles in the image description domain, while some key information exists in a small region or some importance features need to be extracted from the whole image. Generally, we only use CNN to extract the features of an image and then utilize those features to generate the description of the image. However, this method is easy to ignore some importance information in the image. In this paper, we propose an image description method which combines the local information and global features of an image. The local information is extracted by a target detection model (SSD) and the global feature is extracted by the multiinstance learning (MIL) method. Our model which works with the above two methods has a good performance on the public dataset MSCOCO.
A stable matching method for cloud scheduling

Cloud computing has been one of the revolutionary breakthroughs of this decade in the ICT world and its popularity is soaring more than ever. More and more data centers are being deployed in order to accommodate the physical resources needed by cloud systems. As an important side effect the global energy demand of data centers are also on the rise. In the meantime the advancement in virtualization technologies has made migrating virtual machines from one host to another without shutting them down possible. Therefore the optimization of data center operations through the dynamic placement of virtual machines became a reality. This paper formalizes the well-studied cloud scheduling problem in a matching theoretical model in which the virtual machine to physical server mapping is translated into a stable matching problem. We build on an advanced algorithm from the matching theory domain in order to find the most accommodating scheduling arrangement. Hindered by the complexity of the algorithm, we evaluate various heuristics in numerical simulations of cloud environments. After the verification of the selected heuristic algorithm, we present the implementation of the proposed method as a custom compute scheduler for OpenStack.
Rare Lazy Learning Associative Classification Using Cogency Measure for Heart Disease Prediction

Discovery of class association rules from the enormous amount of database is considered as a vital task in data mining. Nowadays, the development of computer technologies, for example, database management system and data storage has given the stage for gathering and dealing with a lot of information. Finding frequent class association rules combines two significant data mining techniques such as association rule mining and classification for maximum accuracy. Since this hybrid mining yields a large number of class rules during database scan, the processing time will increases significantly. Moreover, many of these rules may be irrelevant and sometimes it misses the most important rules during classifier construction. Rare lazy learning associative classification is the process of recognizing rare class rules without generating classifier construction by focusing on the useful features from patient data of the test instance which is important for rule generation. This method is profoundly appropriate for biomedical field like heart disease prediction system which requires higher accuracy. The proposed algorithm uses simple local caching mechanism on heart disease data and constructing classifier using cogency measure that will make the lazy associative classification fast and achieve higher accuracy than traditional algorithms. Experimental results show that the proposed cogency-based algorithm is more efficient than traditional algorithms for heart disease with high prediction accuracy and the proposed algorithm may also be useful in anomaly detection, fraud detection, detection of network failures and many more bio medical areas.
Performance bound for Target Localization under Model Misspecification

In positioning problems, the assumed data distribution could differ from the true one, leading to inaccurate target position estimation. Cramér-Rao bound (CRB), a lower bound on the variance of estimators that requires the parametric model to be correctly specified, malfunctions in such a misspecified model problem. To illustrate the limits on achievable positioning accuracy under model misspecification, this paper derives the performance bound of a typical positioning scenario using time-difference-of-arrival (TDOA) measurements under misspecification of the target altitude. Specifically, the misspecified Cramér-Rao bound (MCRB) on the error covariance matrix of a misspecified-unbiased estimator is developed in detail, and the positioning performance loss subject to the model misspecification is given by comparing it and CRB under true model. The simulation results of the performance bound and comparisons with the performance of the misspecified maximum likelihood (MML) estimator verify the correctness of theoretical derivation.
A Closer Look At The Network Middleboxes

to improve security and performance in the networks most of the enterprises deploy middlebox processing services. But these infrastructures are expensive, prone to failures and complex to manage. Hence it can be argued that middlebox can have a better performance in a cloud platform. It is challenging to have an implementation which is functionally equivalent to the traditional middlebox deployments. This paper will discuss, what a middlebox is, introduction to frequently used middleboxes, recent trends in middlebox, and challenges faced by these middleboxes.