Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust, Synchronous Optical Buffer, and Logic Operation in Dual-Pump Kerr Micro-Resonator

Temporal cavity solitons (CSs) have been proposed to store the information in the form of optical bits in a single-pump Kerr micro-resonator. However, such optical buffers (OBs) realized using a single-pump are essentially asynchronous in nature. In this paper, we show that dual-pump scheme can lead to both synchronous and robust OB. Numerical simulations reveal the resilience in the performance of the OB against the writing jitters and third-order dispersion induced temporal drift. The impact of nonideal operating conditions, such as unequal pump amplitudes and pump detuning mismatch on the performance of the synchronous OB, are evaluated and the robustness of the OB with respect to these aspects are explored both analytically and numerically. As an interesting application of the proposed dual-pump scheme, we have demonstrated the capability to perform simultaneous logic operations (AND, OR) of the proposed device. All the conclusions are supported by analytical and semi-analytical approaches.
Characterization of Wide Bandgap Semiconductor Devices for Cryogenically-Cooled Power Electronics in Aircraft Applications

Wide bandgap (WBG) semiconductor devices and cryogenic cooling are key enablers for highly-efficient ultra-dense power electronics converters, which are critical for future more electric aircraft applications. For the development and optimization of a cryogenically-cooled converter, an understanding of power semiconductor characteristics, especially for emerging WBG devices, is critical. This paper focuses on WBG device characterization at cryogenic temperatures. First, the testing setup for cryogenic temperature characterization is introduced. Then several WBG device candidates (e.g., 1200-V SiC MOSFETs and 650-V GaN HEMTs) are characterized from room to cryogenic temperatures. The test results are presented with trends summarized and analyzed, including on-state resistance, breakdown voltage, and switching performance.
H∞ Model Order Reduction of Uncertain Linear Systems Using generalized KYP Lemma

This article presents a new LMI-based approach for the $\mathcal{H_{\infty}}$ model order reduction problem of continuous-time uncertain linear systems over frequency ranges. Differently from the previous methods in the literature, the approach is specially developed to treat uncertain systems, being able not only to reduce the order of the system, but to remove uncertain parameters. By means ofthe generalized Kalman-Yakubovich-Popov (gKYP) Lemma design conditions are provided in terms of LMIs relaxations associated to scalar searches and iterative procedures to assess low-frequency specifications. Numerical examples from the literature are used to illustrate the potentialities of the proposed approach when compared to existing methods.
Action Recognition from 3D Skeleton Sequences using Deep Networks on Lie Group Features

This paper addresses the problem of human action recognition from sequences of 3D skeleton data. For this purpose, we combine a deep learning network with geometric features extracted from data lie on a non-Euclidean space, which have been recently shown to be very effective to capture the geometric structure of the human pose. In particular, our approach claims to incorporate the intrinsic nature of the data characterized by Lie Group into deep neural networks and to learn more adequate geometric features for 3D action recognition problem. First, geometric features are extracted from 3D joints of skeleton sequences using the Lie group representation. Then, the network model is built from stacked units of 1-dimensional CNN across the temporal domain. Finally, CNN-features are then used to train an LSTM layer to model dependencies in the temporal domain, and to perform the action recognition. The experimental evaluation is performed on three public datasets containing various challenges: UT-Kinect, Florence 3D-Action and MSR-Action 3D. Results reveal that our approach achieves most of the state-of-the-art performance.
Delay Minimized Task Scheduling in Fog-Enabled IoT Networks

Through offloading the computing tasks of the terminal nodes (TNs) to the fog nodes (FNs) located at the network edge, fog computing is expected to address the unacceptable processing delay and heavy link burden existed in current cloud-based Internet of Things (IoT) networks. How to take full use of the computing resources deployed on the FNs is vital to provide high quality offloading services for delay-sensitive TN tasks in fog-enabled IoT networks. In this paper, we construct a general analytical model for the task offloading delay in a fog-enabled IoT network. The optimal solution including the subtask sizes and the TN transmission power are obtained by the proposed Delay-Minimized Task Offloading (DMTO) algorithm that minimizes the task offloading delay. Extensive simulations are carried out in a fog-enabled IoT network, and the numerical results indicate that the proposed algorithm can effectively provide the optimal offloading solutions for the delay-sensitive tasks.
Enabling Strategic Technological Innovations in IS Outsourcing Relationships: Towards an Innovation-molding Framework

Outsourcing service providers are progressively being seen not just as efficient partners to handle peripheral tasks, but also as potential sources of impactful innovations. However, as clients reset the bar from cost efficiencies to mutual strategic value creation, the outcomes far too often fail to live up to such ambitious goals. This increasingly prevalent issue takes center stage in our conceptual paper, wherein we investigate the dynamics and effects of (mis)aligned strategic goals at the client- and service provider-side during the joint innovation formation process. By integrating goal-framing theory as the underlying theoretical perspective and drawing on relevant IS outsourcing and distributed innovation literature, we introduce the innovation-molding framework to shed light on the influence of strategic goals on collaborative activities at each phase of the development process in innovation-focused outsourcing engagements. We contribute to IS outsourcing literature and offer some directions for future research related to the emerging innovation through outsourcing stream.
A flow supervisory control scheme in generic modular form

A Discrete Event Systems (DESs) model is developed for the synchronization and time modulation of flow systems. For the case of a four-way intersection, the model is developed into the form of two subsystems. The first is the generic model of the allowability signals and the second is the time modulation system. The separate study of the two subsystems provides the development of a generic supervisor control scheme providing safe and functional operation of the system for two different operation cases.
Process optimization of extrusion variables and its effect on properties of extruded cocoyam (Xanthosoma sagittifolium) noodles

Abstract The current industrial demand for starchy foods has been dominated by other roots and tubers, while cocoyam, despite being rich in fiber, minerals, and vitamins has remained under exploited. In this study, the effect of feed moisture content (FMC), screw speed (SS) and barrel temperature (BT) on the quality characteristics of cocoyam noodles (proximate, thermo‐physical, physicochemical, texture, color, extrudate properties, and sensory characteristics) were investigated using central composite design (CCD) of response surface methodology (RSM). Flour was produced from fresh tubers of cocoyam (Xanthosoma sagittifolium) and subsequently processed into noodles using a twin screw extruder. Results showed that the proximate compositions, thermo‐physical, physicochemical properties, and color of the cocoyam noodles were significantly (p < 0.05) influenced by the extrusion process variables. The texture and extrudate properties of cocoyam noodles were equally significantly (p < 0.05) different. The experimental data obtained and predicted values of the response models were comparable, with statistical indices [absolute average deviation (AAD, 0–0.23), bias factor (B f, 1–1.08), and accuracy factor (A f, 1–1.23)] indicating the validity of the derived models. The optimal extrusion processing conditions for quality cocoyam noodles were FMC, SS, and BT of 47.5%, 700 rpm and 50°C, respectively, as cocoyam noodles obtained at these conditions had comparable properties and were most preferred and accepted by the sensory panelists.
Predicting the Resonance Frequencies in Geometric Nonlinear Actuated MEMS

The operation range of today’s micro-electromechanical systems (MEMS) continues to enter the geometric nonlinear regime due to miniaturization and performance requirements. One example is the MEMS Coriolis Vibratory Gyroscope (CVG), where the drive mode operates beyond the structural width, causing a change in resonance frequency upon displacement amplitude. Getting good estimates for this change in frequency has been a major issue in the development of CVGs over the past 20 years. In this paper, we present simulation methods and strategies to extract the nonlinear frequencies of such actuated MEMS from the finite element model of the device. The methods are explained in detail and benchmarked with two MEMS structures. As result, we find that the new methods fulfill the accuracy and performance requirements to aid MEMS designers in developing and optimizing the mechanical structure of their devices. [2018-0136]
Deep Domain Adaptation for Face Recognition using images captured from surveillance cameras

Learning based on convolutional neural networks (CNNs) or deep learning has been a major research area with applications in face recognition (FR). However, performances of algorithms designed for FR are unsatisfactory when surveillance conditions severely degrade the test probes. The work presented in this paper has three contributions. First, it proposes a novel adaptive-CNN architecture of deep learning refurbished for domain adaptation (DA), to overcome the difference in feature distributions between the gallery and probe samples. The proposed architecture consists of three components: feature (FM), adaptive (AM) and classification (CM) modules. Secondly, a novel 2-stage algorithm for Mutually Exclusive Training (2-MET) based on stochastic gradient descent, has been proposed. The final stage of training in 2-MET freezes the layers of the FM and CM, while updating (tuning) only the parameters of the AM using a few probe (as target) samples. This helps the proposed deep-DA CNN to bridge the disparities in the distributions of the gallery and probe samples, resulting in enhanced domain-invariant representation for efficient deep-DA learning and classification. The third contribution comes from rigorous experimentations performed on three benchmark real-world surveillance face datasets with various kinds of degradations. This reveals the superior performance of the proposed adaptive-CNN architecture with 2-MET training, using Rank-1 recognition rates and ROC and CMC metrics, over many recent state-of-the-art techniques of CNN and DA.
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains. We present a deep RL method that is practical for real-world robotics tasks, such as robotic manipulation, and generalizes effectively to never-before-seen tasks and objects. In these settings, ground truth reward signals are typically unavailable, and we therefore propose a self-supervised model-based approach, where a predictive model learns to directly predict the future from raw sensory readings, such as camera images. At test time, we explore three distinct goal specification methods: designated pixels, where a user specifies desired object manipulation tasks by selecting particular pixels in an image and corresponding goal positions, goal images, where the desired goal state is specified with an image, and image classifiers, which define spaces of goal states. Our deep predictive models are trained using data collected autonomously and continuously by a robot interacting with hundreds of objects, without human supervision. We demonstrate that visual MPC can generalize to never-before-seen objects---both rigid and deformable---and solve a range of user-defined object manipulation tasks using the same model.
Design of Artificial-Material-Based Antennas Using Inverse Scattering Techniques

A new approach to the design of graded artificial materials (GAMs)-based devices is proposed by exploiting the inverse scattering framework as a synthesis tool. The introduced general methodology can be applied to arbitrary far-field specifications, thus allowing the design of noncanonical devices. In particular, two different strategies are developed for GAMs based on either graded refractive index (GAM<inline-formula> <tex-math notation="LaTeX">$_{R}$ </tex-math></inline-formula>) or graded filling factor (GAM<inline-formula> <tex-math notation="LaTeX">$_{F}$ </tex-math></inline-formula>). In both strategies, the inverse scattering problem is solved by a proper reformulation of the contrast source inversion method, wherein a proper rescaling of the amplitude of the primary sources is also used. In particular, in the first strategy, the GAM<inline-formula> <tex-math notation="LaTeX">$_{F}$ </tex-math></inline-formula> is obtained by exploiting homogenization theories. In the second strategy, the GAM<inline-formula> <tex-math notation="LaTeX">$_{R}$ </tex-math></inline-formula> is synthesized by exploiting a suitable representation basis for the unknown contrast function, and then simple analytical formulas are used to determine the corresponding GAM<inline-formula> <tex-math notation="LaTeX">$_{F}$ </tex-math></inline-formula>. The proposed approach is assessed through the synthesis of an antenna generating a <inline-formula> <tex-math notation="LaTeX">$\Sigma /\Delta $ </tex-math></inline-formula> reconfigurable pattern.
Oil Price Forecasting Using Supervised GANs with Continuous Wavelet Transform Features

This paper proposes a novel approach based on a supervised Generative Adversarial Networks (GANs) model that forecasts the crude oil prices with Adaptive Scales Continuous Wavelet Transform (AS-CWT). In our study, we first confirmed that the possibility of using Continuous Wavelet Transform (CWT) to decompose an oil price series into various components, such as the sequence of days, weeks, months and years, so that the decomposed new time series can be used as inputs for a deep-learning (DL) training model. Second, we find that applying the proposed adaptive scales in the CWT method can strengthen the dependence of inputs and provide more useful information, which can improve the forecasting performance. Finally, we use the supervised GANs model as a training model, which can provide more accurate forecasts than those of the naive forecast (NF) model and other nonlinear models, such as Neural Networks (NNs), and Deep Belief Networks (DBNs) when dealing with a limited amount of oil prices data.
Fake News: A Survey of Research, Detection Methods, and Opportunities

The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news analysis, detection and intervention. This survey comprehensively and systematically reviews fake news research. The survey identifies and specifies fundamental theories across various disciplines, e.g., psychology and social science, to facilitate and enhance the interdisciplinary research of fake news. Current fake news research is reviewed, summarized and evaluated. These studies focus on fake news from four perspective: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) the credibility of its creators and spreaders. We characterize each perspective with various analyzable and utilizable information provided by news and its spreaders, various strategies and frameworks that are adaptable, and techniques that are applicable. By reviewing the characteristics of fake news and open issues in fake news studies, we highlight some potential research tasks at the end of this survey.
From 'Privacy Calculus' to 'Social Calculus': Understanding Self-Disclosure on Social Networking Sites

This study extends the privacy calculus by a social perspective building on interpersonal communication theory along with the act of perspective-taking in the Social Networking Sites context. Based on a two-step study, we provide evidence for the presence of perspective-taking in self-disclosure decisions using a qualitative approach and empirically test the influence of anticipated perceptions for others on subsequent self-disclosure decisions among 231 Facebook users. Our results show that SNS users are less egoistic as typically assumed by the privacy calculus model guided by an intrapersonal tradeoff between own benefits and concerns. Users are tensed between their pleasure of self-enhancement and others’ anticipated perception caused by their own behavior. However, although users think about the relevance of their message for others, their concern about others’ negative affect is self-focused as it does not directly relate to self-disclosure intent. It is mediated by a fear of being negatively evaluated by others.
Exploring the Recombination Mechanism Induced by Carrier Transport Layers in Perovskite Solar Cells

We present the exploration of characteristic impacts on perovskite solar cells (PSCs) induced by the organic or inorganic HTLs. In this work, the perovskite devices have been fabricated by employing the organic (PEDOT: PSS, PTAA) or inorganic (NiOx, CuI) HTLs and investigated the limitation factors by optoelectronic characterizations. The optoelectronics impacts on the devices were investigated by analyzing the current-voltage characteristics and capacitance spectra. A lower interface activation energy (EA) for PEDOT: PSS device indicates defect mediated recombination at the interface. The inorganic HTL induces rather deep defects than an organic one. This is presumed to be due to the diffusion of metal ions from inorganic HTL to the perovskite film. The recombination mechanism in the device is influenced by the nature of HTL on which perovskite film is grown.
Design and Verification of Autonomous Steering Control Based on Driver Modeling

The paper proposes the design and verification of an autonomous steering control system. The method is based on the control-oriented modeling of the human driver, which results in the comfort of the passengers and provides predictability for the drivers of the conventional vehicles and the pedestrians. The optimization procedure is based on test measurements, which is able to guarantee the steering characteristics of the driver in the autonomous control system. The designed controller is based on the robust $\mathcal{H}_{\infty}$ method, which is extended with a prediction rule. The proposed autonomous steering functionality is demonstrated through real vehicle tests, which show that the control system approximates the steering behavior of the driver accurately.
SMSBotHunter: A Novel Anomaly Detection Technique to Detect SMS Botnets

Over the past few years, botnets have emerged as one of the most serious cybersecurity threats faced by individuals and organizations. After infecting millions of servers and workstations worldwide, botmasters have started to develop botnets for mobile devices. Mobile botnets use different mediums to communicate with their botmasters. Although significant research has been done to detect mobile botnets that use the Internet as their command and control (C&C) channel, little research has investigated SMS botnets per se. In order to fill this gap, in this paper, we first divide SMS botnets based on their characteristics into three families, namely, info stealer, SMS stealer, and SMS spammer. Then, we propose SMSBotHunter, a novel anomaly detection technique that detects SMS botnets using textual and behavioral features and one-class classification. We experimentally evaluate the detection performance of SMSBotHunter by simulating the behavior of human users and SMS botnets. The experimental results demonstrate that most of the SMS messages sent or received by info stealer and SMS spammer botnets can be detected using textual features exclusively. It is also revealed that behavioral features are crucial for the detection of SMS stealer botnets and will improve the overall detection performance.
Exploring Institutionalization of Workarounds: The Case of ERP Implementation in Saudi Arabian SMEs

‘Workarounds’ have been defined in Information Systems research as improvisations that are devised locally to overcome the misfits between new Information technology (IT) and the organizational context. The literature mostly treats workarounds as static and temporary phenomena with short terms effects. This research has examined if workarounds can become ‘institutionalized’ and so contribute to achieving value from IT for the longer-term. The paper describes two case studies conducted to explore the evolution of workarounds that developed during and post an ERP implementation. The research was undertaken in two different SME’s in Saudi Arabia and follows an abductive approach in which the theoretical framework, empirical fieldwork, and case analysis evolve simultaneously. Initial findings suggest that advantageous workarounds can be institutionalized based on local judgment from multiple organizational actors. Further, constituent workaround practices can operate as a generative mechanism for further sociomaterial actions and improvisations which reveal the dynamic nature of workarounds.
Effect of Acoustic Wave Reflections on Space Charge Measurements with PEA Method

The Pulsed Electro-Acoustic (PEA) method is the most used technique for space charge detection in solid dielectrics. The methodology is largely employed in the field of High Voltage Direct Current (HVDC) transmission and is based on the detection of acoustic waves generated by charges vibration. One of the most common problems arising during the detection is the presence of multiple reflections taking place due to the presence of means discontinuity in the different PEA cell components. This reflection phenomenon, if not well taken into account, could cause incorrect interpretation of the PEA output signal. It is easy to understand that a simulation model is a basic tool to understand the waves behavior within each PEA cell component and finally to correctly interpret the output charge profile. Furthermore, a simulation model turns out to be useful for assessing what samples can be tested with an available PEA cell without reflection in the main signal. In this work, the PEA cell has been modeled in Matlab environment and the waves behavior has been simulated by using a numerical method. The model has been validated theoretically by making a comparison with the results of another work found in literature. In addition, the good performances of the model have been confirmed by experimental validations carried out using two different PEA cells.