In recent years, integrated circuits (ICs) have become significant for various industries and their security has been given greater priority, specifically in the supply chain. Budgetary constraints have compelled IC designers to offshore manufacturing to third-party companies. When the designer gets the manufactured ICs back, it is imperative to test for potential threats like hardware trojans (HT). In this paper, a novel multilevel game-theoretic framework is introduced to analyze the interactions between a malicious IC manufacturer and the tester. In particular, the game is formulated as a non-cooperative, zerosum, repeated game using prospect theory (PT) that captures different players’ rationalities under uncertainty. The repeated game is separated into a learning stage, in which the defender learns about the attacker’s tendencies, and an actual game stage, where this learning is used. Experiments show great incentive for the attacker to deceive the defender about their actual rationality by “playing dumb” in the learning stage (deception). This scenario is captured using hypergame theory to model the attacker’s view of the game. The optimal deception rationality of the attacker is analytically derived to maximize utility gain. For the defender, a first-step deception mitigation process is proposed to thwart the effects of deception. Simulation results show that the attacker can profit from the deception as it can successfully insert HTs in the manufactured ICs without being detected. This paper has been accepted for publication in IEEE Cyber Science Conference 2020

Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates simulation-based machine learning approaches from a device physics point of view. Strategies are suggested for achieving more realistic results.

Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system’s runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows’ size and achieves fast anomaly detection. The experimental results confirm that ADR is effective for both offline and online anomaly detection.

Pandemic brought new forms of remote education into the discussion. However, facilitating students' access to good computing infrastructure is not a widespread task. This paper presents a report on the use of computing resources in the AWS cloud by students in the Operating Systems class during the pandemic, from March to July 2020. The use of these computational resources was essential to consolidate some of the concepts covered in the course and, at the same time, to complement the students' knowledge about cloud computing. The results of this survey were very positive. Students said they learned more about the resources available in the cloud, the potential of cloud computing, and how to use it. Besides, they were able to create their remote infrastructure to carry out the work proposed in the Operating System class.

Nowadays, the h-index is an index that attempts to measure both the productivity and impact of the published work of a scientist or scholar. The index is based upon the set of the scientist's most cited papers and the number of citations that they have received in other publications. Besides, the most commonly used measure of journal quality is Impact Factor. This is a number which attempts to measure the impact of a journal in terms of the average number of citations to recent articles published in the journal. So, receiving more citation is very important for authors and journals to get high h-index and impact factor. In this paper, we tried to analyses the effect of the number of available version from the web on receive more citations. We analyzed 10162 papers which are published in Scopus database in year 2010. Then we developed a software to collect the number of citations and versions of each paper from Google Scholar automatically.

We are concerned with the challenge of coronavirus disease (COVID-19) detection in chest X-ray and Computed Tomography (CT) scans, and the classification and segmentation of related infection manifestations. Even though it is arguably not an established diagnostic tool, using machine learning-based analysis of COVID-19 medical scans has shown the potential to provide a preliminary digital second opinion. This can help in managing the current pandemic, and thus has been attracting significant research attention. In this research, we propose a multi-task pipeline that takes advantage of the growing advances in deep neural network models. In the first stage, we fine-tuned an Inception-v3 deep model for COVID-19 recognition using multi-modal learning, i.e., using X-ray and CT scans. In addition to outperforming other deep models on the same task in the recent literature, with an attained accuracy of 99.4%, we also present comparative analysis for multi-modal learning against learning from X-ray scans alone. The second and the third stages of the proposed pipeline complement one another in dealing with different types of infection manifestations. The former features a convolutional neural network architecture for recognizing three types of manifestations, while the latter transfers learning from another knowledge domain, namely, pulmonary nodule segmentation in CT scans, to produce binary masks for segmenting the regions corresponding to these manifestations. Our proposed pipeline also features specialized streams in which multiple deep models are trained separately to segment specific types of infection manifestations, and we show the significant impact that this framework has on various performance metrics. We evaluate the proposed models on widely adopted datasets, and we demonstrate an increase of approximately 4% and 7% for dice coefficient and mean intersection-over-union (mIoU), respectively, while achieving 60% reduction in computational time, compared to the recent literature.

This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues (VAT and SAT) measured by magnetic resonance imaging (MRI); to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors (BSDs); and to develop a classifier to predict the fat distribution clusters using the BSDs. 66 male and 54 female participants were scanned by magnetic resonance imaging (MRI) and a stereovision body imaging (SBI) to measure participants’ abdominal VAT and SAT volumes and the BSDs. A fuzzy c-means algorithm was used to form the inherent grouping clusters of abdominal fat distributions. A support-vector-machine (SVM) classifier, with an embedded feature selection scheme, was employed to determine an optimal subset of the BSDs for predicting internal fat distributions. A five-fold cross-validation procedure was used to prevent over-fitting in the classification. The classification results of the BSDs were compared with those of the traditional anthropometric measurements and the Dual Energy X-Ray Absorptiometry (DXA) measurements. Four clusters were identified for abdominal fat distributions: low VAT and SAT, elevated VAT and SAT, higher SAT, and higher VAT. The cross-validation accuracies of the traditional anthropometric, DXA and BSD measurements are 85.0%, 87.5% and 90%, respectively.

Using thermal inertia in district heating systems (DHSs) to improve the dispatch flexibility and economy of integrated heat and electricity systems (IHESs) is a research hotspot and difficulty. In most existing studies, the partial differential equations (PDEs) of thermal inertia are approximated by discrete-time models, making it difficult to accurately describe the continuous dynamic processes. In this paper, we propose a novel generalized phasor method (GPM) for thermal inertia in DHSs with constant mass flow. Based on the analytical solution of the PDEs and the Fourier transform, the intractable PDEs are transformed into a series of complex algebraic equations represented by phasors. The GPM has higher accuracy compared to traditional discrete models because it is essentially a continuous model in the time domain. Then, we present a different representation of an integrated heat and electricity dispatch (IHED) model combining a DHS model in phasor form and a traditional electrical power system model. The IHED model is a convex programming problem and can be easily solved. The effectiveness of the proposed GPM and dispatch model is verified in three test systems. Compared with traditional methods for modeling the thermal inertia, the proposed GPM is more accurate.

[Note] Method in this article found as wrong and does not work. Entanglement is important on quantum gate based computer. Simple implementation of simulated entanglement on conventional digital circuits requires spatial complexity O(2^N) where N is number of qubits. In this article, array representation of entanglement which requires spatial complexity O(N^2) is proposed for simulated quantum gate on conventional digital circuits. Simulation shows proposed method can solve the problem which Deutsch-Jozsa algorithm solves.

How to represent spatiotemporal information in an artificial neuron model has been a problem of longstanding interest in artificial intelligence. After a brief review of recent advances, Caianiello's neuronic convolutional model is extended in this paper for spatiotemporal information representation. The kernel functions that correspond to the convolutional neuron's receptive field profile can be described by neural wavelets. The convolutional neuron-based multilayer network and its back propagation algorithm are developed to perform spatiotemporal pattern processing. The results provide a natural framework for the discussion of spatiotemporal information representation in an artificial neural network

With the increase of the number and geographic dispersion of distributed energy resources (DERs), the microgrid (MG) has become scattered along with the higher complexity of control and communication. This paper proposes a two-level regulation strategy to achieve the local objective of frequency regulation and power sharing as well as the global objective of economic operation in the real time for the scattered MG. Firstly, the MG integrated with multiple DERs is partitioned into several nanogrids (NGs) such that DERs connecting on the same feeder are grouped in the same NG for the efficient and cost-effective communication and control. Then in the NG level, the total power mismatch of the entire MG can be learned and an optimal incremental cost can be agreed by each NG through the fastest distributed linear averaging (FDLA) and discrete-consensus algorithm, respectively. In the DER level, a cost-driven droop gain is developed to indicate the willingness of each DER in the NG to participate into the frequency regulation service. The pinning-based protocol is formulated to regulate the frequency and meanwhile enable the proportional power sharing among DERs based on the economic droop function. Case studies satisfactorily demonstrate the effectiveness of the proposed regulation strategy for the economic frequency regulation in the tested MG.

Poynting theorem plays a very important role in analyzing electromagnetic phenomena. The electromagnetic power flux density is usually expressed with the Poynting vector. However, since Poynting theorem basically focuses on the power balance in a system, it is not so convenient in some situations to use it for evaluating the electromagnetic energies. The energy balance issue for time varying fields is revisited in this paper, and a set of energy balance equations are introduced, and a modified method for evaluating power flux is proposed.

Objective: Although spirometry is the most common pulmonary function test, there is no method to quantitatively infer about airway resistance or other properties from the flow-volume curves. Recently, an identifiable inverse model for forced expiration was proposed, as well as the idea to deduce changes in airway resistances and compliances from spirometric curve evolution. The aim of this work was to combine the above advances in a method for assessing the airway response to bronchial tests from a spirometric curve shift. Methods: The approach is based on the differential measurement of the degree, site of maximal effect and width of changes, further recalculated into relative changes in the distribution of airway resistances (δRg) and compliances (δCg) along the bronchial tree. To this end, appropriate models were identified using the pre- and post-test spirometry data. The accuracy was validated using sets of data simulated by the anatomy and physiology based models. Finally, the method was used to analyze the bronchodilation tests of three asthmatic subjects. Results: The expected errors in assessing the degree, site and width of changes in the zone of conducting airways were 6.3%, 2.4 generations and 22%, respectively, and for δRg and δCg were 5-10% and 13-16%, respectively. The analyses of clinical data indicated a significant reduction in resistances and an increase in compliances of airway generations 8-12, consistent with clinical knowledge. Conclusion: An unprecedented method to plausibly transforming the spirometry data into the site and degree of changes in airway properties has been proposed. Significance: The method can be used to deduce about the effects of bronchial tests, as well as to monitor changes in the airways between visits or to investigate how inhaled pharmaceuticals affect the bronchi.

This paper considers the deployment of intelligent reflecting surfaces (IRSs) technology for wireless multi-hop backhauling of multiple basestations (BSs) connected in a mesh topology. The performance of the proposed architecture is evaluated in terms of outage and symbol error probability in Rician fading channels, where closed-form expressions are derived and demonstrated to be accurate for several cases of interest. The analytical results corroborated by simulation, show that the IRS-mesh backhauling architecture has several desired features that can be exploited to overcome some of the backhauling challenges, particularly the severe attenuation at high frequencies.

This paper introduces the measure of approximate-degree and the concept of approximate-degree function between numerical values, thus developing a new interpolation method —— approximation-degree-based interpolation, i.e., AD interpolation. One-dimensional AD interpolation is done directly by using correlative interpolation formulas; n(n>1)-dimensional AD interpolation is firstly separated into n parallel one-dimensional AD interpolation computations to do respectively, and then got results are synthesized by Sum-Times-Difference formula into a value as the result value of the n-dimensional interpolation. If the parallel processing is used, the efficiency of n-dimensional AD interpolation is almost the same as that of the one-dimensional AD interpolation. Thus it starts a feasible and convenient approach and provides an effective method for high-dimensional interpolations. Furthermore, if AD interpolation is introduced into machine learning, a new instance-based learning method is expected to berealized.

Highlights of the article are: • Presented a systematic study of Deep Learning (DL), Deep Transfer Learning (DTL) and Edge Computing(EC) to mitigate COVID-19. • Surveyed on existing DL, DTL, EC, and Dataset to mitigate pandemics with potentialities and challenges. • Drawn a precedent pipeline model of DTL over EC for a future scope to mitigate any outbreaks. • Given brief analyses and challenges wherever relevant in perspective of COVID-19.

Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

Many studies were dedicated to the analysis of the propagation of High Energy Laser beams in free space. Laser beams propagation performance in the presence of atmospheric constituents may degrade by some interrelated linear and nonlinear phenomena. In this study, it is proposed a self-contained model including non-linear air temperature effect producing thermal distortion in laser propagation. In order to better understand the underlying phenomena, a set of quantifiers are described leading to the characterization of the laser thermal distortion. A parametric study is finally proposed in order to highlight the influence of the different parameters of the simulations.