Miracle-dz/UniFAR-FTUs huggingface dataset:
Metrics for Self-Adaptive Queuing in Middleware for Internet of Things

Internet of Things (IoT) is a cornerstone technology for automation in the physical world. In particular, IoT allows industrial automation, also known as Industry 4.0. With overwhelming amount of sensor types and communication protocols, management of IoT middleware becomes unfeasible. This problem might be addressed by implementing self-adaptive functionality in IoT middleware. The presented paper contributes to the studies of the self-adaptive message queuing in IoT middleware: an estimated waiting time (EWT) metric for automating the scaling of message queuing subsystems is proposed and evaluated on CPU-intensive and blocking I/O-intensive tasks. Mixed metrics (with conventional CPU utilization and processing capacity) were also evaluated. Evaluation of the proposed metrics based on Google Kubernetes Engine revealed cost reduction potential of EWT and the well-balanced quality of queuing IoT middleware deployments provided by processing capacity metric.
Exemplar-Based Image Inpainting With Multi-Resolution Information and the Graph Cut Technique

Filling holes in an image is achieved in a manner similar to peeling the onion. The order of filling affects the image inpainting results, especially concerning the content of complex images. When high-resolution images are used to extract edge information, they are susceptible to high-frequency information, such as complex textures and noise. Furthermore, edge information is extracted in different resolutions, while the main contour information of the image can be obtained more easily. In this paper, multi-resolution information is used to prioritize which target patches in an image to fill, which helps to elucidate the optimal sequence for image repair. Multi-resolution images provide more information than single-resolution images, and similar patches are computed on multi-resolution images to obtain multiple candidate patches. Similar patch calculations use a variety of information on colors, gradients, and boundaries to more accurately search for similar patches. We chose the most reasonable candidate patch by means of the structural similarity index measure (SSIM). When pasting the patch to fill the target region, we used graph cut technology to eliminate blockiness. Compared with the state-of-the-art repair algorithm, the experimental results prove that the proposed repair algorithm can repair the image very well.
Deep Multi-agent Reinforcement Learning in a Common-Pool Resource System

In complex social-ecological systems, multiple agents with diverse objectives take actions that affect the long-term dynamics of the system. Common pool resources are a subset of such systems, where property rights are typically poorly defined and dynamics are unknown a priori, creating a social dilemma reflected by the well-known ‘tragedy of the commons. ’ In this paper, we investigated the efficacy of deep reinforcement learning in a multi-agent setting of a common pool resource system. We used an abstract mathematical model of the system, represented as a partially-observable general-sum Markov game. In the first set of experiments, the independent agents used a deep Q-Network with discrete action spaces to guide decision-making. However, significant shortcomings were evident. Consequently, in a second set of experiments, a Deep Deterministic Policy Gradient learning model with continuous state and action spaces guided agent learning. Simulation results show that agents performed significantly better in terms of both sustainability and economic goals when using the second deep learning model. Despite the fact that agents do not have perfect foresight nor understanding of the implications of their ‘harvesting’ efforts, deep reinforcement learning can be used effectively to ‘learn in the commons. ’
Impact of Overlapped AoAs on the Achievable Uplink Rate of Hybrid Beamforming for Massive MIMO mm-Wave Systems for Industrial Environments

In this paper, we develop novel precoder and combiner schemes for multi-user hybrid digital-to-analog (D-A) beamforming for uplink massive multiple-input multiple-output millimeter wave (mm-Wave) systems, where the number of radio frequency chains is much smaller than the number of antennas each transceiver is equipped with. Industry 4.0 targets to accelerate the digitalization of manufacturing processes by allocating fixed and mobile robotics which use wireless communication. Such development requires high data throughput for which the utilization of short distance wireless communication such as mm-Wave system is crucially required. Based on our measurements, the probability of mm-Wave propagation waves to be reflected and refracted from metallic surfaces are shown to be significantly high. Consequently, uplink transmissions from different users such as robotics, machines, and sensors can go through paths sharing the same physical scatters, some transmission paths of different users may have overlapped angle of arrivals (AoAs) at the base station. Under such circumstance, the correlation between the channel vectors also increases considerably, which affects the achievable uplink rate severely. Therefore, the intrinsic focus is on substantially maximizing the desired signal while reducing the system interference. The proposed analog precoders and combiners are designed by using the power iteration and the Riemannian optimization method based on Stiefel manifold algorithms, respectively. The proposed digital combiner adapts the minimum mean-square error by judiciously exploiting the effective uplink analog channel gains. Furthermore, the channel estimation is investigated through a newly designed two-step procedure. In each scattering point, there is the strongest power point which is detected and then used to improve the accuracy by employing an angular domain scheme. The impact of this paper will be the boosting of the achievable uplink rate in industrial environments.
A Novel Online Scheduling Algorithm and Hierarchical Protocol for Large-Scale EV Charging Coordination

For electric vehicle (EV) charging coordination, relaxation and approximation methods are usually employed to improve scheduling efficiency in solving integer linear programming (ILP) problems. Nevertheless, they have a few inherent flaws, such as frequent charging interruption and large deviation in final SOC. Based on the recursive method, this paper proposes a search-swapping algorithm (SSA) for optimal approximation. It leverages the non-aftereffect property of charging scheduling to approximate the optimal solution as well as uses single recursion instead of multiple recursion to reduce computation cost from exponential time to polynomial time. Compared with the conventional algorithms, it has much higher efficiency, less charging interruption, and better final SOC uniformity. Furthermore, a single-round interactive protocol is designed for hierarchical coordinated framework to achieve power dispatch. Numerous simulations show that the SSA has outstanding efficiency and satisfactory optimality.
Enhancing LQR Controller Using Optimized Real-time System by GDE3 and NSGA-II Algorithms and Comparing with Conventional Method

Control algorithms are essential in the modern world to tackle with ever-changing environmental disturbances while meeting adequate safety standards. Control systems must be appropriately tuned for each application to ensure reliability and safety. This paper applies a multi-objective optimization method, Generalized Differential Evolution (GDE3), to tune a Linear Quadratic Regulator (LQR) while enabling posteriori decision-making. The utilized case study is the aircraft pitch control. We compare the results of GDE3 with Non-Dominated Sorting Genetic Algorithm (NSGA-II) and conventional tuning. The current findings show that the GDE3 performs better than the other methods. This paper sheds light on how trade-off and condition-specific based optimization can enhance real-time control systems.
IJTAG Compatible Timing Monitor with Robust Self-Calibration for Environmental and Aging Variation

The deployment of embedded instruments (EI) for online monitoring of a cyber-physical system-on-chip (CPSoC) for safety-critical applications has started getting more attention in recent years. Among these different types of EIs, the timing embedded instruments (to observe timing violations) are widely adopted to ensure a dependable operation during its operational lifetime. However, infield temperature variations together with self-aging of the timing EI affects its output, making it less reliable. This paper presents an IJTAG compatible, in-situ slack-delay timing embedded instrument to monitor critical paths in a CPSoC. The main contribution of the proposed design is the infield, online calibration mechanism. It enables the EI to self-calibrate for relatively fast-changing environmental variations like temperature, as well as slow changing variations like aging. The design has been implemented using a TSMC 40nm LP standard cell library. Simulation results show an average resolution of 13ps with a monitoring window of 13ps–416ps, which is sufficient to monitor the CPSoC for small timing margins. To further demonstrate the IJTAG compatibility, an FPGA implementation of the proposed design is also presented.
Toward Pose Invariant and Completely Contactless Finger Knuckle Recognition

In order to advance application of finger knuckle modality in new domains, especially using widely popular smartphones, completely contactless and pose invariant knuckle identification are highly desirable. Earlier work in the literature incorporates finger knuckle images under fixed poses, which is not realistic for completely contactless biometrics applications. This paper makes a first such attempt to investigate the possibility of recognizing completely contactless finger knuckle images acquired under varying poses. New approach to automatically normalize and align contactless finger knuckle images is introduced, and the performance of a specialized matcher is investigated to achieve superior performance. A new database from 221 different subjects’ contactless finger knuckle images is also developed and made available in public domain to advance much needed research in this area. Experimental results illustrated in this paper are promising and validate the usefulness of normalization and matching algorithms to recognize finger knuckle with different poses. Spatial and spectral domain features can provide complimentary cues and are simultaneously employed for the performance improvement. Such performance improvement only serves as the baseline for much needed further improvement in recognition accuracy for database presented in this paper.
Open Data Publishing by Public Libraries

Public libraries in the USA are part of a broad civic information ecosystem that is rapidly adopting transparency legislation aimed at publishing structured open data for public reuse. In this prelimi- nary results paper we look specifically at the open data publishing practices of 85 public libraries in the USA. We find that less than half of these libraries have published any open data, and that there is no relationship between revenue nor staff size and open data publishing practices. Categorizing public library open data by type we find overwhelmingly the most frequent type of open data pub- lished by libraries are geospatial (map) information. We plan use these findings to develop a proposal for public libraries to engage in publishing a core set of open data, and conclude by discussing the potential for reuse of open public library data.
Crude Oil Distillation Optimization Using Surrogate-aided Constrained Evolutionary Optimization

Evolutionary optimization of a crude oil distillation operation is a time-consuming task. Therefore, this paper proposes a surrogate-aided constrained evolutionary optimization algorithm (SACEO), in which surrogate models' establishment and management are combined to search for an optimal result. By establishing global and local surrogate models with the goal of maximizing profit, an adaptive constrained optimizer is developed for the global and local surrogate models' infilling operations and optimization searching. Thus, time-consuming strict mechanism models can gradually be approximated by continuously updating the surrogate models until the optimized results are obtained. The optimization results obtained for benchmark systems and the crude oil distillation system indicate that SACEO is similar to other constrained optimization algorithms in terms of its optimization accuracy and stability, while the number of evaluations of time-consuming models can be considerably reduced. Thus, the economic efficiency of crude oil distillation processes can be improved while satisfying the production conditions.
Rollout based Heuristics for the Quantum Circuit Compilation Problem

This study investigates the makespan minimization problem that arises when compiling a general class of quantum algorithms into near-term quantum hardware. The problem is referred to as quantum circuit compilation problem (QCCP). Two new heuristics are proposed for solving the problem. Firstly, a rollout based sequential decision making approach is investigated. This heuristic decides on which operation to schedule next based on the makespan projection given by a guiding priority rule. Secondly, a stochastic version of rollout heuristic is proposed which iteratively switches between rollout and a simple priority rule to explore the search space. The two proposed heuristics are able to show improvement in makespan across a range of instance sizes and characteristics.
Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm

Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a Hierarchical Genetic Algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.
Conductive textiles via electroless deposition for flexible electronics

In this work, we report on conductive cellulose fabrics obtained via electroless copper deposition, and demonstrate their use as (1) flexible electrical circuits for low energyconsumption devices, (2) temperature sensors, and (3) flexible capacitors. The results illustrate new concepts for the further development of electronic textiles (e-textiles). E-textiles target the use of fabrics as substrates to create flexible electronic devices with a diverse variety of applications, from home care, medical, wellness, security, sports, to communications and technical applications. However, current technologies available for the production of electrically conductive textiles exhibit limitations in combining the desired electrical conductivity with integration of complex architectures of electronics and sensors, while maintaining stretch and comfort properties of textiles.
Ensemble Programming for Multipotent Systems

The orchestration and controlling of groups of robots, i.e., programming ensembles, is a complicated task to do. Deciding and defining what the ensemble needs to do in which order often requires in-depth problem domain specific knowledge. Further, an ensemble programmer needs to put much effort into the design, implementation, and evaluation of distributed algorithms to coordinate multi-robot executions. Most commonly, expertise for the problem domain and programming knowledge is not united in the same person. To tackle this, we propose Maple, an approach for a Multi-Agent Programming Language for Ensembles. Using Maple simplifies an ensemble programmer's typical work-flow by providing generalized solutions for solving technical tasks in ensemble programming. Maple is a graphical ensemble programming language based on the formalism of hierarchical task networks that enables the online generation of ensemble programs. Maple also enables non-technical domain experts to generate ensemble programs for specific use-cases.
Contactless Vector Network Analysis of Embedded Two-Port Test Objects

In the course of the increasing electrification of aviation, it is becoming more and more important to investigate the high-frequency properties of components directly at the location of installation. One variant is the contactless vector network analysis with impedance probes. This is a special type of current probe that can be used very well for contactless network analysis due to the corresponding calibration procedure. Existing measurement methods with these sensors require the test object to be disconnected from its external circuitry. In this paper an approach is presented with which test objects that remain in their external wiring can also be analysed using the contactless vector network analysis and impedance probes as contactless couplers.
A Channel Selection Approach Based on Convolutional Neural Network for Multi-channel EEG Motor Imagery Decoding

For many disabled people, brain computer interface (BCI) may be the only way to communicate with others and to control things around them. Using motor imagery paradigm, one can decode an individual's intention by using their brainwaves to help them interact with their environment without having to make any physical movement. For decades, machine learning models, trained on features extracted from acquired electroencephalogram (EEG) signals have been used to decode motor imagery activities. This method has several limitations and constraints especially during feature extraction. Large number of channels on the current EEG devices make them hard to use in real-life as they are bulky, uncomfortable to wear, and takes lot of time in preparation. In this paper, we introduce a technique to perform channel selection using convolutional neural network (CNN) and to decode multiple classes of motor imagery intentions from four participants who are amputees. A CNN model trained on EEG data of 64 channels achieved a mean classification accuracy of 99.7% with five classes. Channel selection based on weights extracted from the trained model has been performed with subsequent models trained on eight selected channels achieved a reasonable accuracy of 91.5%. Training the model in time domain and frequency domain was also compared, different window sizes were experimented to test the possibilities of realtime application. Our method of channel selection was then evaluated on a publicly available motor imagery EEG dataset.
Distributed NSGA-II Sharing Extreme Non-dominated Solutions for Improving Accuracy and Achieving Speed-up

A recent trend of multi-objective evolutionary algorithms is to approximate the Pareto optimal front with high accuracy by increasing the size of the population. On the other hand, the cconventional method NSGA-II algorithm widely used in multi-objective optimization ranks solutions using non-dominant sorting that increase in computational complexity proportional to the population size squared. In engineering applications, the increased execution time can be a problem. Also, even if standard techniques for parallel processing of genetic manipulation are applied to multi-objective evolutionary computation, it is difficult to speed up while maintaining the accuracy of the solution search. This paper proposes the distributed NSGA-II which performs a hierarchical non-dominated sorting in a many-core environment, and migration that shares the extreme solutions of the latest Pareto optimal solutions among all cores. In the evaluation, using the test functions provided by the NSGA-II source code and the constrained knapsack problem with two-objective and three- objective, we describe that our proposed method contributes to the improvement of solution diversity search and for enhancing solution accuracy and speeding up.
A Machine Learning-based Approach to Optimize Repair and Increase Yield of Embedded Flash Memories in Automotive Systems-on-Chip

Nowadays, Embedded Flash Memory cores occupy a significant portion of Automotive Systems-on-Chip area, therefore strongly contributing to the final yield of the devices. Redundancy strategies play a key role in this context; in case of memory failures, a set of spare word- and bit-lines are allocated by a replacement algorithm that complements the memory testing procedure. In this work, we show that replacement algorithms, which are heavily constrained in terms of execution time, may be slightly inaccurate and lead to classify a repairable memory core as unrepairable. We denote this situation as Flash memory false fail. The proposed approach aims at identifying false fails by using a Machine Learning approach that exploits a feature extraction strategy based on shape recognition. Experimental results carried out on the manufacturing data show a high capability of predicting false fails.
Curves orthogonal to a vector field in Euclidean spaces

A curve is rectifying if it lies on a moving hyperplane orthogonal to its curvature vector. In this work, we extend the main result of [B. -Y. Chen, Tamkang J. Math. \textbf{48} (2017) 209--214] to any space dimension: we prove that rectifying curves are geodesics on the hypersurface of higher dimensional cones. We later use this association to characterize rectifying curves that are also slant helices in three-dimensional space as geodesics of circular cones. In addition, we consider curves that lie on a moving hyperplane normal to (i) one of the normal vector fields of the Frenet frame and to (ii) a rotation minimizing vector field along the curve. The former class is characterized in terms of the constancy of a certain vector field normal to the curve, while the latter contains spherical and plane curves. Finally, we establish a formal mapping between rectifying and spherical curves in any dimension.
On Antenna Array Out-of-Band Emissions

We substantiate and extend recent research on the behavior of the out-of-band emissions in antenna array transmitters. Specifically, with multi-user precoding, we show that the emissions are always beamformed in the directions of the intended receivers, contrary to some claims in the recent literature. Moreover, while also other spurious directions exist, we show that the emissions are always strongest in the directions of the intended receivers. We also show that power amplifiers with mutually different nonlinear phase characteristics will reduce the beamforming gain of the unwanted emissions, with the gain approaching the noncoherent combining limit as the phase deviations are increased.