Abstract The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one's identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter's anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment.

An AC-AC converter with high-frequency link employing LLC resonant converter operating in the vicinity of the resonance frequency is studied, in which the output stage is unique and formed by a high-frequency AC-AC converter employing four quadrant switches. The topology, its operation and the modulation strategy are presented. The high-frequency stage switches located on the primary side of the transformer operate with soft switching of the ZVS type, while the four quadrant switches that form the output stage operate with soft switching of the ZCS type. Experimental data on a 1.5 kW experimental prototype that was designed, built and tested in the laboratory, with 220 VRMS input, 220 VRMS output and 40 kHz switching frequency are given in the paper. The studied converter can be considered a candidate for the building block of medium voltage solid-state transformers (SST) for power distribution systems.

The lift-off spacing distance between the eddy current sensor and test piece will influence the detected signals and accuracy of the measurement. Various techniques including novel sensor designs, features (lift-off point of intersection, lift-off invariance phenomenon), and algorithms have been proposed for the compensation of error caused by the lift-off effect using the eddy current sensor. However, few of these have directly measured the lift-off spacing distance, particularly for the distance up to 15 mm.In this paper, a lift-off tolerant pancake sensor has been designed. By analysing the sensitive region of the magnetic vector potential change (due to the test piece), the receiver of the sensor is designed as a circular spiral pancake coil with a large mean radius and span length (the difference between inner and outer radius). Experiments on the inductance measurement of three different non-magnetic samples have been carried out using both the designed pancake sensor and the previous triple-helix sensor. From the experiment result, the detected signal of the designed sensor has been proved much larger than that of the triple-helix sensor. Besides, simplified algorithms have been proposed for the measurement of the lift-off spacing and thickness of non-magnetic samples when using the proposed pancake sensor. Results show that the lift-off spacing and thickness can be measured with a small error of 0.14 mm (absolute error under 209.66 kHz), and 1.35 % (relative error, under low working frequencies of 142, 238, and 338 Hz) for the lift-off spacing from 1 to 15 mm.

Physical layer secret key generation is a promising candidate to achieve one-time-pad encryption approach for wireless communication system. The main issue of this method is the insufficient secret key rate. Especially in a static environment, the entropy of the channel complex coefficient is limited due to the lack of channel time-variation. To solve the problem, this paper proposes a novel secret key generation scheme assisted by intelligent reflecting surface (IRS) with discrete phase. We construct a dynamic time-varying channel by switching the phase of the reflection elements rapidly and randomly, and use channel estimation samples to update the secret key. Based on the common IRS channel model, we derive the expression of secret key rate. Monte Carlo simulation and numerical results show that the scheme can update secret key even if the channel coefficient is constant, and the secret key rate is higher than schemes based on artificial random signal. In addition, we comprehensively analyze the influence of various factors, such as the number of reflection elements and the IRS switching frequency on the secret key rate.

The AI, deep learning and machine learning algorithms are gaining the ground in every application domain of information technology including information security. In formation security domain knows for traditional password management systems, auto-provisioning systems and user information management systems. There is another raising concern on the application and system level security with ransomware. On the existing systems cyber-attacks of Ransomware asking for ransom increasing every day. Ransomware is the class of malware where the goal is to gain the data through encryption mechanism and render back with the ransom. The ransomware attacks are mainly on the vulnerable systems which are exposed to the network with weak security measures. With the help of machine learning algorithms, the pattern of the attacks can be analyzed. Create or discuss a workaround solution of a machine learning model with combination of cryptographic algorithm which will enhance the effectiveness of the system response to the possible attacks. The other part of the problem, which is hard part to create an intelligence for the organizations for preventing the ransomware attacks with the help of intelligent system password management and intelligent account provisioning. In this paper I elaborate on the machine learning algorithms analysis for the intelligent ransomware detection problem, later part of this paper would be design of the algorithm.

In the software version release management process, there is a need, on the part of human specialists, to classify the criticality of each software version However, the subjectivity of this classification may be present according to the experience acquired by specialists over the years. In order to reduce subjectivity in the process, an Artificial Intelligence technique called the Expert System (ES) can be applied to represent the knowledge of human specialists and use it in problem solving. Thus, the aim of this paper was to reduce the subjectivity in the criticality classification of the software version with the support of the Expert System.To this end, a questionnaire was developed with the objective of obtaining the criticality opinions classified as High, Medium and Low in each specialist’s software version to assist in the preparation of the ES production rules. ES generated 17 production rules with a 100% confidence level applied to a production database. The results of the classification carried out by the ES corresponded to the classification carried out by the specialists in the production base, that is, the ES was able to represent their knowledge. Then, another questionnaire was applied to the specialists in order to verify the perception of satisfaction regarding the use of the ES with a result obtained of 4.8, considered satisfactory. It was concluded, then, that the ES supported the reduction of subjectivity in the classification of the criticality of software version.

A document by Nail Tosun . Click on the document to view its contents.

Volumetric video (or hologram video), the medium for representing natural content in VR/AR/MR, is presumably the next generation of video technology and a typical use case for 5G and beyond wireless communications. To realize volumetric video applications, efficient volumetric video streaming is in critical demand. This article responds to the challenges of and propose solutions to wireless transmission systems of point cloud video, which is the most popular and favored way to represent volumetric media and significantly differs from the other types of videos. In particular, we first introduce point cloud video technology and its applications, and then discuss the challenges of and solutions to point cloud video streaming, including encoding, tiling, viewing angle prediction, decoding, quality assessment and transmission optimization. Furthermore, we explain a prototype of MPEG DASH-based point cloud video streaming system as a preliminary study, along with more simulation results to verify its performance. Finally, we identify future research directions for providing high-quality point cloud video streaming.

In this paper, a novel search algorithm that based on the Contraction-Expansion algorithm and integrated three operators Exchange, Move, and Flip (EMF-CE) is proposed for the traveling salesman problem (TSP). EMF-CE uses a negative exponent function to generate critical value as the feedback regulation of algorithm implementation. Also, combined Exchange Step, Move step with Flip step and constitute of more than twenty combinatorial optimizations of program elements. It has been shown that the integration of local search operators can significantly improve the performance of EMF-CE for TSPs. We test small and medium scale (51-1000 cities) TSPs were taken from the TSPLIB online library. The experimental results show the efficiency of the proposed EMF-CE for addressing TSPs compared to other state-of-the-art algorithms.

To curb redundant power consumption in portable embedded and real-time applications, processors are equipped with various Dynamic Voltage and Frequency Scaling (DVFS) techniques. The accuracy of the prediction of the operating frequency of any such technique determines how power-efficient it makes a processor for a variety of programs and users. But, in the recent techniques, the focus has been too much on saving power, thus, ignoring the user-satisfaction metric, i.e. performance. The DVFS technique used to save power, in turn, introduces unwanted latency due to the high complexity of the algorithm. Also, many of the modern DVFS techniques provide feedback manually triggered by the user to change the frequency to conserve energy efficiently, thus, further increasing the reaction time. In this paper, we imple- ment a novel Artificial Neural Networks-driven frequency scaling methodology, which makes it possible to save power and boost performance at the same time, implicitly i.e. without any feedback from the user. Also, to make the system more inclusive concerning the kinds of processes run on it, we trained the ANN not only for CPU-intensive programs but also on the ones that are more memory-bound, i.e. which have frequent memory accesses during its average CPU cycle. The proposed technique has been evaluated on Intel i7-4720HQ Haswell processor and has shown performance boost by up to 20%, SoC power savings up to 16%, and Performance per Watt improvement by up to 30%, as compared to the existing DVFS technique. An open-source memory-intensive benchmark kit called Mibench was used to verify the utility of the suggested technique.

Background: This article quantitatively presents the relationship between volume of BME publications produced from 1990 to 2019 in the member states of the ASEAN and 12 indicators of overall and physical health. The objective was to show that ASEAN states that recognize BME as an academic and professional discipline have been successful in producing research in the field, and thus, have advanced the provision of high-quality healthcare for their people. Results: Acceptable relationships [Pearson correlation coefficients (PCC) > 0.6000] were found between BME publication volume and all indicators. Stronger relationships (PCC > 0.7000) were found between BME publication volume and the natural logarithm of the indicators. Brunei data behaved anomalously and was removed to check for any improvements in PCC. Indeed, PCCs for all indicators improved significantly upon exclusion of Brunei data [PCC > 0.8000 for six indicators (linear scale), PCC > 0.9000 for five indicators (log scale)]. These PCCs signify especially strong relationships between BME research yield and healthcare quality in a country. To visualize the relationships, BME publication volume was plotted against GDP per capita, while the remaining 11 indicators were each plotted against BME publication volume. Linear, logarithmic, and exponential regression curves were overlaid on the datapoints. Coefficients of determination (R2) were calculated to measure the aptness of the fits. R2 > 0.9000 for two indicators, R2 > 0.8000 for five indicators, R2 > 0.7000 for four indicators, and R2 < 0.7000 for only one indicator were calculated from the curve fits overlaid on the datapoints excluding Brunei data. Conclusion: We believe that it is time for the Philippines to adopt BME as an academic and professional discipline, so that it may one day enjoy the benefits brought about by advancements in the provision of healthcare, which are experienced by its neighbors that have gone ahead with movements to cultivate the highly essential discipline.

Today Time-Of-Flight in PET scanners assumes a unique, well-defined timing resolution for all coincidence events. However, recent BGO-Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation signal, are capable of sorting events into multiple mixture timing kernels and therefore increase the available information for the image reconstruction. The number of Cherenkov photons detected per event impacts directly the signal rise time, which can therefore be used to improve the timing resolution. In this work, we present a simulation toolkit that outputs data with multiple timing resolutions and image reconstruction that incorporates this information. A full cylindrical BGO-Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against non-TOF and an LYSO model with time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested; mixture Gaussian and decomposed simple Gaussian models. The decomposed model used the exact mixture component applied in the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise as compared to the decomposed simple Gaussian kernels. Although, the later converged faster. Related to the standard LYSO model, the BGO-Cherenkov provided similar contrast, although, in most cases, with more iterations. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.

Convolutional Neural Networks (CNNs) and Deep Learning (DL) revolutionized numerous research fields including robotics, natural language processing, self-driving cars, healthcare, and others. However, DL is still relatively under-researched in fields such as physics and engineering. Recent works on DL-assisted analysis showed emerging interest and enormous potential of CNN applications. This paper explores the possibility of developing an end-to-end DL pipeline for the analysis of electrical machines. The CNNs are trained on conventional finite element method (FEA) data to predict the output torque curves of electric machines. FEA is only used for dataset collections and CNN training, whereas the analysis is done solely using CNNs. The required depth in CNN architecture is studied by comparing a simplistic CNN with three ResNet architectures. The effects of dataset balancing and data normalization are studied and torque clipping inspired by offset normalization is proposed to ease CNN training and improve the prediction accuracy. The relation between architecture depth and accuracy is identified showing that deeper CNNs improve the curve shape prediction accuracy even after torque magnitude prediction accuracy saturates. Over 90% accuracy for analysis conducted under a minute is reported for CNNs, whereas FEA of comparable accuracy required 200 hours. Predicting multidimensional outputs can improve CNN performance, which is essential for multiparameter optimization of electrical machines.

Background Knee osteoarthritis (OA) remains a leading aetiology of disability worldwide. Clinical assessment of such knee-related conditions has improved with recent advances in gait analysis. Despite being a gold standard method, gait data acquired by motion capture (mocap) technology are highly non-linear and dimensional, which make traditional gait analysis challenging. Thus, extrinsic algorithms need to be used to make sense of gait data. Supervised Machine Learning (ML)-based classifiers outperform conventional statistical methods in revealing intrinsic patterns that can discern gait abnormalities when using mocap data, making them a suitable tool for aiding diagnosis of knee OA. Research question Studies have demonstrated the accuracy of supervised ML-based classifiers in gait analysis. However, these techniques have not gained wide acceptance amongst biomechanists for two reasons: the reliability of such methods has not been assessed and there is no consensus on which classifier or group of classifiers to select. Specifically, it is not clear whether classifiers that leverage optimal separating hyperplanes (OSH) or artificial neural networks (ANN) are more accurate and reliable. Methods A systematic review and meta-analysis were conducted to assess the capability of such algorithms to predict pathological kinematic and kinetic gait patterns as indicators of knee OA. With 153 eligible studies, 6 studies met the inclusion criteria for a subsequent meta-analysis, accounting for 273 healthy subjects and 313 patientswith symptomatic knee OA. The classification performance of supervised ML classifiers (OSH- or ANN-based) used in these studies was quantitatively assessed and compared across four following performance metrics: classification accuracy on the test set (ACC), sensitivity (SN), specificity (SP), and area under the receiver operating characteristic curve (AUC). Results There was no statistically significant discrepancy in the ACC between OSH- and ANN-based classifiers when dealing with kinetic and kinematic data concurrently, as well as when considering only kinematic data. However, there was a statistically significant difference in their SN and SP, with the ANN-based classifiers having higher SN and SP than OSH-based algorithms. As only one of the eligible studies reported AUC, this metric could not be assessed statistically across studies. Significance This study supports the use of ANN-based algorithms for classifying knee OA-related gait patterns as having a higher sensitivity and specificity than OSH-based classifiers. Considering their higher reliability, leveraging supervised ANN-based methods can aid biomechanists to diagnose knee OA objectively.

This Paper presents improvement and extension of previous methodology about timing steganography based on network steganography. The previous article uses time interval between two successive transmissions mixed with cryptography prior to hiding. However this improvement tend to extend and provide new methods based on time format such as hours, minutes, second, millisecond and Nanosecond etc. It further examine how to handle effect of different time zone and high precision timing for ultrafast timing such as millisecond, Nanoseconds, picosecond, femtosecond which human action is too slow for perfect timing. In addition, the extension based on TCP-IP status codes where each elements of set of status code are index and the index represents certain numeric of combination for hiding. Finally, the cryptography method is improved and extended to series based cryptography with any defined number of different cryptographic methods combined altogether with multiple keys generated dynamically. The methods for both cryptography and steganography was integrated and each module carefully tested for their feasibility and appropriate analysis, comparisons is presented too.

This work exploits Riemannian manifolds to introduce a geometric framework for online state and community classification in dynamic multilayer networks where nodes are annotated with time series. A bottom-up approach is followed, starting from the extraction of Riemannian features from nodal time series, and reaching up to online/sequential classification of features via geodesic distances and angular information in the tangent spaces of a Riemannian manifold. As a case study, features in the Grassmann manifold are generated by fitting a kernel autoregressive-moving-average model to the nodal time series of the multilayer network. The paper highlights also numerical tests on synthetic and real brain-network data, where it is shown that the proposed geometric framework outperforms state-of-the-art deep-learning models in classification accuracy, especially in cases where the number of training data is small with respect to the number of the testing ones. ——- © 20XX 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.

e review recent work on narrowband orthogonally polarized optical RF single sideband generators as well as dual-channel equalization, both based on high-Q integrated ring resonators. The devices operate in the optical telecommunications C-band and enable RF operation over a range of either fixed or thermally tuneable frequencies. They operate via TE/TM mode birefringence in the resonator. We achieve a very large dynamic tuning range of over 55 dB for both the optical carrier-to-sideband ratio and the dual-channel RF equalization for both the fixed and tunable devices.

We develop datasets and methods to perform visual question answering on pathology images.