Miracle-dz/UniFAR-FTUs huggingface dataset:
Business Ecosystem Management : A Model for the Governance, Auditing and Design of Business Ecosystems

Current trends such as the increasing commoditization of hardware and the growing generation of customer benefits through software-based services are forcing companies in the mechanical and plant engineering industry to develop innovative Smart Product-Service Systems (PSS) based on new business models. The associated increasing complexity requires the integration of new competencies and resources. This leads to a transformation of individually acting companies to inter-organizational business ecosystems. However, since traditional product-oriented companies in the mechanical and plant engineering industry do not have enough awareness or knowledge about the management of business ecosystems, a management model is required. The goal of this paper is to design a model for the management of business ecosystems within the development of Smart PSS. For this purpose, a comprehensive literature review was conducted in which existing approaches were identified. Subsequently, these were evaluated according to defined criteria and the necessary components were derived, as the basis for the created model. The model should enable orchestrating companies to design, audit and govern the business ecosystem with the help of three modules. The model contains methods for practical application and considers the different characteristics of a business ecosystem over its life cycle.
Dissipative Two-Mode Tavis-Cummings Model with Time-Delayed Feedback Control

We investigate the dynamics of a two-mode laser system by extending the two-mode Tavis-Cummings model with dissipative channels and incoherent pumping and by applying the mean-field approximation in the thermodynamic limit. To this end we analytically calculate up to four possible non-equilibrium steady states (fixed points) and determine the corresponding complex phase diagram. Various possible phases are distinguished by the actual number of fixed points and their stability. In addition, we apply three time-delayed Pyragas feedback control schemes. Depending on the time delay and the strength of the control term this can lead to the stabilization of unstable fixed points or to the selection of a particular cavity mode that is macroscopically occupied.
Threshold Voltage Modeling of Negative Capacitance Double Gate TFET

An analytical gate threshold voltage model for Negative Capacitance Double Gate Tunnel Field Effect Transistor (NC-DG TFET) based on the physical definition of threshold voltage in TFETs is proposed in this paper. Analytical expressions for surface potential are derived using Gauss’s law. The model is validated by numerical simulation involving two dimensional TCAD simulation of DG TFET with one dimensional Landau-Khalatnikov (LK) equation. The model matches very well with the device simulation results. The impact of oxide dielectric variations, Ferro-Electric (FE) thickness variations are also studied in this paper.
Analysis of Call Admission Control Mechanisms in Nodes of Elastic Optical Networks

Today, when the demand for bandwidth increases in networks, it becomes necessary to ensure an appropriate and reliable service level for certain traffic classes. Optimal allocation and use of resources can be achieved through the use of call admission control (CAC) mechanisms in networks. One of the most popular mechanisms is the resource reservation mechanism and the threshold mechanism. In the reservation mechanism, some resources are available only to selected (predefined) services. However, when the threshold mechanism is used, the amount of resources allocated to individual classes depends on the network load. This article discusses simulation studies of the traffic characteristics of elastic optical network (EON) nodes with a Clos structure and implemented CAC mechanisms. In the article, the results obtained with the use of the simulator developed by the author were presented.
DeepBlue: Bi-Layered LSTM for Tweet popUlarity Estimation

In social networks, one of the most significant challenges is how to estimate the tweet popularity. Prior studies focus on leveraging different aspects of just a single tweet, while ignoring the impact of historical tweets. In this article, we propose to leverage such historical information and rethink the problem of tweet popularity estimation. From historical information, there are two important factors that can be extracted: (1) user reputation feature, which can represent coarse-grained level of tweet popularity and (2) tweet related features, which can represent fine-grained level of tweet popularity. To incorporate these two factors from historical information, we design a novel deep neural architecture, a Bi-layered LSTM for tweet popUlarity Estimation, called DeepBlue. Specifically, we first propose a user-reputation aware mechanism to combine coarse-grained and fine-grained level estimation into a united LSTM model. We also design a content attention mechanism to consider different impacts of historical tweets in terms of content similarity. We then propose a time aware mechanism to address the time interval irregularity issue. Finally, we apply the Poisson regression model to obtain the overall loss for tweet popularity estimation. Extensive experiments demonstrate the superiority of our proposed approach to other state-of-the-arts in terms of MAE and SRC.
Cloth Defect Detection Network Based on Feature Subtraction and Attention Mechanism

Recently, with the rapid development of deep learning, object detection has made significant progress. Though there are many excellent object detection algorithms, they still cannot achieve satisfactory results when applied to the detection of cloth defects with complex images directly. Due to the influence of complex images in the background, there exists a large number of missed detections and false detections, which also affects the location of defects. In this work, we make both normal cloth and defective cloth input into the feature extraction network. Then the respective features are obtained and subtracted, so as to remove the influence of background. The defects on the cloth are often too small and have large difference in shape. However, the propagation paths of previous feature pyramid networks are usually lateral and top-down, which means the information can only flow downwards and low-level information is not well utilized, resulting in low accuracy when detecting these defects. This paper improves the feature pyramid structure by fusing each layer in the feature pyramid with its adjacent layers and introducing an attention mechanism to allow the network to learn information more effectively, thus improving the performance. We also modified the loss function of the final classification-regression network to improve the influence of easily separable targets on model parameters. After extensive experiments, our Feature Subtraction and Attention Cascade RCNN (FSA-Cascade RCNN) network achieves 57.18% mAP and 92.12% Acc, which is superior than existing algorithms.
Emotion Classification from Electroencephalogram Signals Using a Cascade of Convolutional and Block-Based Residual Recurrent Neural Networks

To determine the quality of experience for users of technological devices, we must consider the human influential factors, which encompass the emotional state. Hence, we propose a model to estimate user emotions from Electroencephalogram (EEG) signals. The model is a cascade of deep learning networks consisting of a pre-trained convolutional neural network which extracts spatial relations and residual block(s) of recurrent neural network which learn the temporal relations of multi-channel EEG signals and uses shortcuts across the neural layers for a more effective training of the deep network. We adopted the DEAP dataset to train and evaluate our model. To confirm that the proposed work is user-independent, we ensure that the data in the test set corresponds to subjects that are not included in the training set. We explored several input sets to determine the one that performs best on the DEAP dataset. We implemented existing popular state-of-the-art methods and compared with the proposed model. The results indicate that the proposed model consistently outperforms the previous models achieving 0.61 and 0.63 accuracy on the validation and 0.65 and 0.68 accuracy on the test dataset for valence and arousal respectively.
SEDCN: An improved Deep & Cross Network Recommendation Algorithm based on SENET

Advertising ranking is essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising ranking systems, click-through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, factorization machine based models,and deep learning based CTR models. However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features. In this paper, we propose a new feature importance network model SEDCN based on deep crossover network. SEDCN can learn the importance of features dynamically through the SENET mechanism. We compare the performance of SEDCN with other depth models, such as DeepFM and DCN.
Vital Signal Detection for Continuous-Wave Radar Sensor using Compensation Technique

A beam-controllable radar sensor and its signal processing are proposed for improving sensitivity in vital signal detection caused by static clutters. The main beam direction of the continuous-wave radar sensor is adjusted by controlling the single-pole single-thru (SPST) RF switches configured in the series patch antenna array. The periodic change of the radar beam direction generated by continuously controlling the switches causes modulated signals at the baseband. The vital signals can be extracted from the separated baseband signals by the envelope detection method. The signal-to-noise ratio (SNR) of the vital signals is increased from the processing gain of the modulated signals by using the cross-correlation technique including the detrending method. Experimental results showed that the SNRs of respiration and heartbeat detection were increased by 5.2 dB and 6.6 dB, respectively, by beam switching and signal processing of the proposed radar sensor.
Continuous Trajectory Similarity Search for Online Outlier Detection

In this paper, we study a new variant of trajectory similarity search from the context of continuous query processing. Given a moving object from <inline-formula><tex-math notation="LaTeX">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq1-3046670.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$d$</tex-math><alternatives><mml:math><mml:mi>d</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq2-3046670.gif"/></alternatives></inline-formula>, following a reference route <inline-formula><tex-math notation="LaTeX">$T_r$</tex-math><alternatives><mml:math><mml:msub><mml:mi>T</mml:mi><mml:mi>r</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="zhang-ieq3-3046670.gif"/></alternatives></inline-formula>, we monitor the trajectory similarity between the reference route and the current partial route at each timestamp for online detour detection. Since existing trajectory distance measures fail to adequately capture the deviation between a partial route and a complete route, we propose a partial trajectory similarity measure to bridge the gap. In particular, we enumerate all the possible routes extended from the partial route to reach the destination <inline-formula><tex-math notation="LaTeX">$d$</tex-math><alternatives><mml:math><mml:mi>d</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq4-3046670.gif"/></alternatives></inline-formula> and calculate their minimum distance to <inline-formula><tex-math notation="LaTeX">$T_r$</tex-math><alternatives><mml:math><mml:msub><mml:mi>T</mml:mi><mml:mi>r</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="zhang-ieq5-3046670.gif"/></alternatives></inline-formula>. We consider deviation calculation in both euclidean space and road networks. In euclidean space, we can directly infer the optimal future path with the minimum trajectory distance. In road networks, we propose an efficient expansion algorithm with a suite of pruning rules. Furthermore, we propose efficient incremental processing strategies to facilitate continuous query processing for moving objects. Our experiments are conducted on multiple real datasets and the experimental results verify the efficiency of our query processing algorithms.
Nonlinear Landau damping in an inhomogeneous plasma

A theory of nonlinear damping (growth) of Langmuir waves in a collisionless inhomogeneous plasma is developed for the case when the degree of plasma inhomogeneity exceeds the critical value at which particle capture by the potential wells of the waves becomes impossible. An expression for the increment of the waves is derived and investigated as a function of the field amplitude and the inhomogeneity parameter in the entire regions of these parameters where there are no trapped particles.
Technical and Non-Technical Enablers Required for the Design of a Process- and Product-Innovation Framework to Enable a Client-Centric Culture

The journey to client centricity through structured innovation has a beginning but no end. It starts with the conceptualisation of an idea, develops through organisational alignment and iterations of implementation, learning and improvement. Client centricity has proven to be an elusive goal for many organisations. Agile disruptive innovation is required to remain relevant in the areas in which they operate. A paradigm shift is required and organisations need to change the strategies from being product centric to focusing on client centricity instead. The biggest issue and major challenge typically faced by organisations attempting make this shift, is the organisational culture. Qualitative research, combined with action-based research, was conducted to assess the technical and non-technical enablers required to implement a generic product and process innovation framework to establish a client-centric culture in a diverse and decentralised international logistics solutions organisation.
Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

Large-scale multi-objective optimization problems (LSMOPs) pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces. While evolutionary algorithms are good at solving small-scale multi-objective optimization problems, they are criticized for low efficiency in converging to the optimums of LSMOPs. By contrast, mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems, but they have difficulties in finding diverse solutions for LSMOPs. Currently, how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored. In this paper, a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method. On the one hand, conjugate gradients and differential evolution are used to update different decision variables of a set of solutions, where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front. On the other hand, objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions, and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent. In comparison with state-of-the-art evolutionary algorithms, mathematical programming methods, and hybrid algorithms, the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.
ChaseMe: A Heuristic Scheme for Electric Vehicles Mobility Management on Charging Stations in a Smart City Scenario

Towards achieving the goal of green transportation, the usage of battery powered electric vehicles (BEVs) has been continuously growing across the globe. However, considering the limited number of Charging Stations (CSs) in the cities, electric vehicle charging problem has become a challenging task, especially, due to the constraints of longer waiting time and dynamic pricing at the CHs. This issue has led to the degradation in Quality of Experience (QoE) for BEV drivers. Moreover, Charging Point (CP) service providers in the cities also suffer from lack of space which causes higher congestion at the CSs. In this context, we propose ChaseMe, a heuristic scheme for optimizing CS management by scheduling BEVs based on availability and type (fast/ultra-fast) of CPs by considering delay and charging time for CPs reservation. The proposed heuristic scheme consists of two soft computing techniques i) Harris Hawk Optimization (HHO) and ii) Fuzzy Inference System (FIS). Former technique is used to map the CP reservation requests to the best-suited CS by considering Quality of Service (QoS) parameters and acting as a global optimizer. FIS locally manages CPs at a particular CS in coordination with proposed meta-heuristic technique. The experimental results prove the benefits of the proposed ChaseMe framework as compared to the state-of-the-art techniques considering various charging metrics for BEVs.
Smart-Hop: Low-Latency Multi-hop Networking for LoRa

Low Power Wide Area Networks (LPWANs) provide the Internet of Things (IoT) with energy-efficient, low cost, and long-range networking. LoRaWAN is one of the most widely deployed and studied LPWAN technologies due to its license-free operation and open standard, which has enabled 3rd parties to deploy millions of networks worldwide. LoRa allows transmission range and speed to be traded off by configuring the Spreading Factor (SF) setting. By tuning this parameter, the performance envelope of the network can be configured to favor a higher speed or longer range. While the SF of LoRA provides a simple and elegant means to tailor performance, at the highest spreading factor, maximum throughput drops to a few bits per second, dramatically increasing data extraction times. In this paper, we address this problem by proposing Smart-Hop, a novel multi-hop MAC layer protocol for LoRa which implements low-overhead routing. In contrast to prior work, Smart-Hop exploits the variable range afforded by different SFs to eliminate much of the complexity and overhead of traditional multi-hop mesh networking. Smart-Hop reduces the number of end-devices operating at high SFs while implementing low overhead routing in order to reduce data extraction times. These improvements are achieved while preserving the maximum range offered by LoRa in its highest SF configuration.
Solar Radiation Pressure Estimation and Analysis of a GEO Class of High Area-to-Mass Ratio Debris Objects

A population of deep space objects is thought to be high area-to-mass ratio (AMR) debris having origins from sources in the geosynchronous orbit (GEO) belt. The typical AMR values have been observed to range anywhere from 1's to 10's of m(sup 2)/kg, and hence, higher than average solar radiation pressure effects result in long-term migration of eccentricity (0.1-0.6) and inclination over time. However, the nature of the debris orientation-dependent dynamics also results time-varying solar radiation forces about the average which complicate the short-term orbit determination processing. The orbit determination results are presented for several of these debris objects, and highlight their unique and varied dynamic attributes. Estimation or the solar pressure dynamics over time scales suitable for resolving the shorter term dynamics improves the orbit estimation, and hence, the orbit predictions needed to conduct follow-up observations.
Data-Driven and Neuro-Volatility Fuzzy Forecasts for Cryptocurrencies

The forecasting problems in Computational Finance involve modelling the vagueness and imprecision inherent to the financial markets. Fuzzy set theory has a unique ability to quantitatively and qualitatively model and analyze such problems. Volatility forecasting plays an important role in financial risk management and in option pricing. Recently, there has been a growing interest in data-driven volatility models and neurovolatility models for risk forecasting of stocks and index funds. However, even these state-of-the-art models do not take into account the fuzzy volatility in their risk forecasts.Cryptocurrencies are a novel financial asset class based on the Blockchain technology. Cryptocurrencies have gained popularity among retail investors as a financial asset with high risks and high returns. The extremely volatile nature of cryptocurrencies (compared to traditional assets) makes forecasting their volatility more challenging. A simple algorithmic trading approach, Simple Moving Average (SMA) crossover strategy, is used to calculate the Algo returns. This paper provides fuzzy forecasts of the volatility of Algo returns using the data-driven Exponentially Weighted Moving Average (DD-EWMA) and neuro models for six major cryptocurrencies. We also compute and compare fuzzy volatility forecasts of four major tech stocks and Chicago Board Options Exchange’s (CBOE) volatility index (VIX) using DD-EWMA and neuro models. Our experimental results show that the data-driven models produce better forecasts for cryptocurrencies as compared to the neuro models, while for the regular stocks and indexes, no such definitive conclusion could be drawn.
Effects of Lighting and Window Length on Heart Rate Assessment through Video Magnification

Screening people for signs of illness through contactless measurement of vital signs could be beneficial in public transportation settings or long-term care facilities. To achieve this goal, one solution could utilize Red/Green/Blue (RGB) video cameras to measure heart rate. In this work, we present results for the assessment of heart rate through Video Magnification (VM) techniques applied to RGB face video recordings from 19 subjects. The work specifically explores (1) the effect of two lighting illumination levels and (2) the effect of window length on the accuracy of heart rate extraction via Video Magnification. The results show that higher illumination, as a result of combining halogen light with LED, yielded lower average errors in heart rate measured through Video Magnification. Additionally, the results show that increasing the window length from 10 seconds up to 30 seconds improves VM heart rate accuracy when there are small frequent head movements in the video but decreases heart rate accuracy in the absence of head motion.