Learning by ignoring, which identifies less important things and excludes them from the learning process, is broadly practiced in human learning and has shown ubiquitous effectiveness. There has been psychological studies showing that learning to ignore certain things is a powerful tool for helping people focus. In this paper, we explore whether this useful human learning methodology can be borrowed to improve machine learning. We propose a novel machine learning framework referred to as learning by ignoring (LBI). Our framework automatically identifies pretraining data examples that have large domain shift from the target distribution by learning an ignoring variable for each example and excludes them from the pretraining process. We formulate LBI as a three-level optimization framework where three learning stages are involved: pretraining by minimizing the losses weighed by ignoring variables; finetuning; updating the ignoring variables by minimizing the validation loss. A gradient-based algorithm is developed to efficiently solve the three-level optimization problem in LBI. Experiments on various datasets demonstrate the effectiveness of our framework.

The analysis of power system dynamics is usually conducted using traditional models based on the standard nonlinear differential algebraic equations (DAEs). In general, solutions to these equations can be obtained using numerical methods such as the Monte Carlo simulations. The use of methods based on the Stochastic Hybrid System (SHS) framework for power systems subject to stochastic behavior is relatively new. These methods have been successfully applied to power systems subjected to stochastic inputs. This study discusses a class of SHSs referred to as Markov Jump Linear Systems (MJLSs), in which the entire dynamic system is jumping between distinct operating points, with different local small-signal dynamics. The numerical application is based on the analysis of the IEEE 37-bus power system switching between grid-tied and standalone operating modes. The Ordinary Differential Equations (ODEs) representing the evolution of the conditional moments are derived and a matrix representation of the system is developed. Results are compared to the averaged Monte Carlo simulation. The MJLS approach was found to have a key advantage of being far less computational expensive.

A document by Zeeshan Mumtaz . Click on the document to view its contents.

The paper belongs to the field of ultrafast optics. Acousto-optic pulse shaping has been studied experimentally for binary intensity modulation in a chirped pulse amplification laser system. Direct time-domain measurement of pulse front duration with a picosecond streak camera was performed for a Ti:sapphire regenerative amplifier. It was discovered that the sign of the second order dispersion produced by the acousto-optic dispersion delay line affects the modulation rise/fall time. Two algorithms for synthesis of ultrasonic waveforms for feeding the delay line, dispersive Fourier synthesis and the Gerchberg-Saxton algorithm, were compared. Minimum pulse front duration of 3.6 ps for 3 mJ pulses with the linear chirp of 6.2 ps/nm at the wavelength of 795 nm was obtained.

We derive an analytical formulation of the Raman-induced frequency shift experienced by a fundamental soliton. By including propagation losses, self-steepening, and dispersion slope, the resulting formulation is a high-order (HO) extension of the well-known Gordon’s formula for soliton self-frequency shift (SSFS). The HO-SSFS formula closely agrees with numerical results of the generalized nonlinear Schrödinger equation, but without the computational complexity and required computation time. The HO-SSFS formula is a useful tool for the design and validation of wavelength conversion systems and supercontinuum generation systems.

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.

Inferring CT perfusion parameters and their uncertainties using a Bayesian approach

This paper discusses the interest of explicit software processes of design and architecture for (1) architectural view understanding and communication, (2) design reuse and principles of Jxta-based game software development. The considered architecture includes hierarchical and logical views which are modeled in the Java-based Maiar API and achieved by Jxta message exchanges. The topology of the world map of the game software can be viewed as undirected graph in which vertices (nodes) represent the rooms and the edges represent the rooms and the edges represent the playable movements between nodes.

Object removal is a technique for removing the undesired object(s) and then fill-in the empty region(s) in an image such that the modified image is visually plausible. The existing algorithms are unable to provide promising results when the region to be removed - has varying textured-neighborhood, is small in size and the depth of the image and, is of specific geometric shapes such as triangle and rectangle. In this paper, we proposed a new algorithm by incorporating the merits of partial differential equations (PDEs) and exemplar-based schemes to address these challenges. The data term, which measures the continuity of isophotes in exemplar-based methods, is modified by incorporating a regularizer term and partial derivatives up to second order of the input image. This regularizer enhances the strength of isophotes striking the boundary and boosts the information propagation in an unbiased manner, in terms of pixel intensity values. Additionally, the low-cost, agility, and accessing flexibility benefits of cloud services have attracted user’s attention today. Besides, users are concerned about utilizing them for their data, as they are supported by untrusted third parties. Addressing these privacy concerns for object-removal in an image over the cloud server, we extended and modified our algorithm to make it compatible for (T; N)-threshold Shamir secret sharing scheme (SSS). This privacy-preserving system is an end-to-end system for object-removal in the ED over the cloud server namely Crypt-OR. Crypt-OR is evaluated by removing synthetically imposed objects in real-images. Further, Crypt-OR has proved to be secure under various pixel-based cryptographic attacks such as frequency-known attack and pixel-correlation attack.

The most important features of the proposed method include: 1) introducing an innovation in Leach algorithm and applying it in a different field from its common application, 2) using standard centrality indicators to calculate the correlation degree in order to improve the identification of opinion leaders in social networks, 3)this algorithm is completely automatic and works without operator intervention.

Analysis of Machine Learning for Ransomware Detection - A Systematic Literature Review

Chronic gastrointestinal disorders impose a heavy economic burden and stress on society and the health care system. Among these chronic disorders is irritable bowel syndrome. This disease can be diagnosed by careful evaluation of clinical signs, medical records and simple tests that only require a lot of time and high knowledge. On the other hand, data mining is one of the multidisciplinary fields derived from scientific fields such as statistics, mathematics, computers and artificial intelligence, whose applications are expanding in various fields such as health and treatment. Applying data mining on medical data brings vital, valuable and effective achievements and can enhance the physician’s knowledge to take the necessary measures and also speed up the diagnosis process. Therefore, in this study, the best data mining algorithm for the diagnosis of irritable bowel syndrome was determined using WEKA software. For this purpose, the data mining process using Decision Stump, Random Tree and j48 algorithms on 59 samples collected from the files of people referring to Erfan Hospital in Tehran (to collect information about patients) and questionnaires distributed among employees of a company. Software was used to collect information from healthy individuals. Finally, the total data included 55.93% male sample and 44.06% female sample. The results were compared in terms of speed, accuracy and computational cost. Finally, j48 algorithm with 96.6% accuracy was introduced as the optimal algorithm for IBS detection.

A document by Zeeshan Mumtaz . Click on the document to view its contents.

Abstract We illustrate the connection between image-texture and defined regions in a polar spatial frequency coordinate system. This is provided within the context of describing a non-parametric method for characterizing two-dimensional spectra of gray scaled images, described in part previously. The spectrum is divided into concentric radial bands of fixed bandwidth. Each band is summarized, producing a set of measures characterizing the image as a function of radial spatial frequency. This set of measures has a parallel multiscale description as texture in the image domain. This approach was modified to capture texture with directional attributes. This is a data reduction technique that gives a full accounting of the image variance as function of image texture and direction indexed to regions in the polar frequency plane. Application examples include analyzing the summarized spectrum graphically, making inter/intra image spectral comparisons, and evaluating spectral models. Simulated noise images, an image of peppers, and a gray scale image with well-defined linear structure were used to illustrate the principles.

In this paper an investigation into magnetic control (MC) of hard-switching (HS) DC-DC converters is carried out. The proposed control method is based on the modulation of the effective filter inductance of the converter when operating in discontinuous conduction mode (DCM). It is well known that the output characteristic of a HS DC-DC converter operating in DCM is dependent on the effective inductance of the output filter. This way, by using a variable inductance the output of the converter can be controlled. The proposed control method can be applied to any converter topology, namely buck, boost, buck-boost, flyback, forward, and so on. In this paper, the operation of the buck converter with MC is investigated in detail as a case study. This work proves that the proposed control method can be effectively used to control DC-DC converters on its own or by combination with other control parameters as duty cycle and/or frequency. An experimental prototype has been built to test the proposed control method and modeling process and to demonstrate its feasibility and possibilities.

This supplementary document is part of the original 2-Part manuscript titled “A SensitivityBased Three-Phase Weather-Dependent Power Flow Algorithm for Networks with Local Controllers.” This research focuses on proposing a novel sensitivity-based three-phase weather-dependent power flow algorithm for distribution networks with local voltage controllers (LVCs). The proposed algorithm has four distinct characteristics: a) it considers the three-phase unbalanced nature of distribution systems, b) the operating state of LVCs is calculated using sensitivity parameters, which accelerates the convergence speed of the algorithm, c) it considers the precise switching sequence of LVCs based on their reaction time delays, and d) the nonlinear influence of weather variations in the power flow is also taken into consideration. In this supplementary document, the relevant derivations of the sensitivity parameters are presented to complement the original 2-Part manuscript.

This work develops a low-power and low-voltage differential Gm-C filter structure that effectively achieves selfcommon-mode control (SCC), including DC stabilization and common-mode (CM) rejection, without employing extra control circuitry. The structure relies upon an incorporation of voltageinverting amplifiers to make it inherently contain no CM positive feedback loops for DC stabilization, and to enable splitting of the core transconductors into pairs for CM signal rejection. A DC CM stability analysis reveals that stabilization of the SCC structure can be reached without any dedicated CM control circuitry. An analytical comparison on power consumption of a high-order lowpass Gm-C filter implemented using an inverter-based transconductor for the SCC structure and the same transconductor with a CM control network (the Nauta’s technique) for the conventional structure indicates theoretical overhead power saving by over 50%. Furthermore, an even higher overhead power saving at over 70Ên be achieved in the complex SCC Gm-C filter because no additional inverting amplifiers are required to eliminate CM positive feedback loops in the crossing transconductors for complexification. The impact of the inverting amplifiers on the noise and frequency characteristics as well as the compensation technique are outlined to enable design optimization. The SCC filter was verified via extensive simulations of a 5th-order 1.1-MHz elliptic complex filter in a 0.18-m CMOS process. As compared to the conventional filter counterpart with similar SNR (~63dB) and inband/out-of-band SFDRs (~52dB/56dB), the proposed structure yields an overhead power saving by 70% with an improved figure-of-merit over 40% under a 1-V supply.

In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach. Postprint accepted for publication in IEEE Transactions on Control Systems Technology How to cite: P. Scarabaggio, S. Grammatico, R. Carli and M. Dotoli, (2021) “Distributed Demand Side Management With Stochastic Wind Power Forecasting, ”IEEE Transactions on Control Systems Technology, 2022. DOI: http://doi.org/10.1109/TCST.2021.3056751 © 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.