Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing HMD-Based and Paper-Based Training

Collaborative Systems are in daily use by millions of people promising to improve everyone's life. Smartphones, smartwatches and tablets are everyday objects and life without these unimaginable. New assistive systems such as head-mounted displays (HMDs) are becoming increasingly important for various domains, especially for the industrial domain, because they claim to improve the efficiency and quality of procedural tasks. A range of scientific laboratory studies already demonstrated the potential of augmented reality (AR) technologies especially for training tasks. However, most researches are limited in terms of inadequate task complexity, measured variables and lacking comparisons. In this paper, we want to close this gap by introducing a novel multimodal HMD-based training application and compare it to paper-based learning for manual assembly tasks. We perform a user study with 30 participants measuring the training transfer of an engine assembly training task, the user satisfaction and perceived workload during the experiment. Established questionnaires such as the system usability scale (SUS), the user experience questionnaire (UEQ) and the Nasa Task Load Index (NASA-TLX) are used for the assessment. Results indicate significant differences between both learning approaches. Participants perform significantly faster and significantly worse using paper-based instructions. Furthermore, all trainees preferred HMD-based learning for future assembly trainings which was scientifically proven by the UEQ.
Real-Time Automatic License Plate Recognition through Deep Multi-Task Networks

With the increasing number of cameras available in the cities, video traffic analysis can provide useful insights for the transportation segment. One of such analysis is the Automatic License Plate Recognition (ALPR). Previous approaches divided this task into several cascaded subtasks, i.e., vehicle location, license plate detection, character segmentation and optical character recognition. However, since each task has its own accuracy, the error propagation between each subtask is detrimental to the final accuracy. Therefore, focusing on the reduction of error propagation, we propose a technique that is able to perform ALPR using only two deep networks, the first performs license plate detection (LPD) and the second performs license plate recognition (LPR). The latter does not execute explicit character segmentation, which reduces significantly the error propagation. As these deep networks need a large number of samples to converge, we develop new data augmentation techniques that allow them to reach their full potential as well as a new dataset to train and evaluate ALPR approaches. According to experimental results, our approach is able to achieve state-of-the-art results in the SSIG-SegPlate dataset, reaching improvements up to 1.4 percentage point when compared to the best baseline. Furthermore, the approach is also able to perform in real time even in scenarios where many plates are present at the same frame, reaching significantly higher frame rates when compared with previously proposed approaches.
Musical Composition by Interactive Evolutionary Computation and Latent Space Modeling

In recent years, the possibility of using recurrent neural networks (RNNs) to model long-term dependencies in music and to generate novel pieces of music has been actively investigated. However, previous work on music generation by RNNs has simply generated music autonomously without user control. Thereby, previous studies have not been effective in assisting human composers. To overcome this, we incorporated the method of interactive evolutionary computation (IEC) in order to search through the latent space of music modeled by recurrent variational auto-encoders. By using IEC, we were able to integrate human perception into the system and enable the user to search for their desired type of music by interacting with the system. The proposed method effectively combines the impressive sequential data modeling capabilities of RNNs with the interactivity and user-friendliness of IEC-based systems. We conducted a user study to evaluate the usefulness of our method in comparison to conventional composition methods. The results revealed that our proposed method was effective in aiding human composition.
The Effect of Solder Joint Microstructure on the Drop Test Failure—A Peridynamic Analysis

We investigate the drop reliability of different solder joint microstructures using a coupled board-level finite-element (FE) analysis and the microscale peridynamic (PD) simulations that can capture fracture in the heterogeneous solder joint. A new PD model for elastic behavior across a material interface is introduced and used in computing the damage and failure in the two-phase microstructure of a solder joint. This new model eliminates oscillations in strains at an interface. The microstructural geometry is digitized from the scanning electron microscopy images of experimentally tested samples. To simulate the drop test conditions, we employ the input acceleration method in the board-level FE model and calculate nodal velocities for the boundary conditions imposed on the microscale PD model. We test two different microstructures of intermetallic component (IMC) (AuSn4) and SAC305: flash and thick Au content. The results show significantly more fracture in the samples with larger Au content compared to the flash samples, which correlate well with experimental observations. The computed results show that the failure mechanism for solder joints with high Au content is a fracture through the AuSn4 IMC as well as along the interface between the inclusions and the matrix. A newly introduced measure, the quasi-damage index, estimates the locations in the microstructure of the solder joint with the highest risk for initiation of cracks and damage.
Machine Learning Approaches to Automatic Stress Detection: A Review

People experience mental stress on a daily basis from a variety of different reasons, including environmental reasons (traffic, noise, or bad weather), social reasons (family issues, friends, and financial problems), or from events such as wedding planning or giving a presentation in front of large audience. A manageable amount of stress is healthy and can motivate a person; however, a large amount of continuous stress or a strong response to stress can be harmful. For this reason, the detection of mental stress, as well as its prediction, has become a significant area of research. In this paper, we review and summarize various approaches found in the literature for stress detection using machine learning and suggest directions for future research and interventions.
Techniques for System Information Broadcast in Cell-Free Massive MIMO

We consider transmission of system information in a cell-free massive MIMO system, when the transmitting access points do not have any channel state information and the receiving terminal has to estimate the channel based on downlink pilots. We analyze the system performance in terms of outage rate and coverage probability and use space-time block codes to increase performance. We propose a heuristic method for pilot/data power optimization that can be applied without any channel state information at the access points. We also analyze the problem of grouping the access points, which is needed when the single-antenna access points jointly transmit a space-time block code.
Predicting Computer Performance Based on Hardware Configuration Using Multiple Neural Networks

How can we accurately predict the performance of a Personal Computer (PC) configuration without time consuming simulation? In this work, we predict the performance of a computer hardware configuration using Multiple Neural Networks (MNN). We use Principal Component Analysis (PCA) during data preprocessing as guidance for model creation. The input data includes the internal component characteristics of a computer. A deep learning model is used to infer a benchmark score given a hardware configuration. The finished model takes input data such as Central Processing Unit (CPU) type, frequency, number of cores, memory size and speed, flash or disk architecture, network configuration and correlates it against the corresponding performance benchmark value and system response to a benchmark workload. We demonstrate the accuracy and effectiveness of the MNN and PCA machine learning models using the Standard Performance Evaluation Corporation (SPEC) benchmarks (SPEC CPU2006 and SPEC CPU2017), and a set of approximately 50,000 commercial machines configurations. Our MNN model is able to achieve an average accuracy rate of 97.5% for all benchmarks. Our results provide both personal and enterprise users a tool that can accurately estimate system configuration performance without lengthy and resource intensive benchmarking sessions.
MAllEC: Fast and Optimal Scheduling of Energy Consumption for Energy Harvesting Devices

Energy consumption scheduling algorithms allow energy harvesting Internet of Things (IoT) devices to maximize the amount of harvested energy that they consume, while maintaining uninterrupted, indefinite, operation. The existing works in the area show a tradeoff between solution quality and computational complexity. At one end fast but suboptimal algorithms can lead to energy waste and power outages. At the other, optimal algorithms are computationally prohibitive for the constrained hardware of the IoT. This paper argues that the tradeoff can be avoided, and presents the MAllEC energy consumption scheduler that maximizes the allowed energy consumption while minimizing energy waste and power outages, with linear time complexity. MAllEC is compared against the state of the art through simulations using long term (14 years) traces of solar irradiance, and shown to consistently achieve the minimum energy waste and power outage. The linear time complexity of MAllEC is measured on constrained IoT hardware (8-bit Tmote Sky) to be low enough so that MAllEC can be executed unintrusively. This paper provides proof of MAllEC’s optimality and shows that, in an application with dynamic, adjustable packet rate, MAllEC can maintain indefinite, uninterruptible, operation at an average rate of almost 100 packets per minute, where a 3-Ah battery powered device, at the same rate, would deplete after less than 200 days.
Leveraging Ensemble Pruning for Imbalanced Data Classification

The effectiveness of machine learning algorithms depends on the quality of the supplied training data. Any problems embedded in the nature of data will result in obtaining incorrect classification models, especially imbalanced data distribution is among the most significant learning difficulties that can affect classifiers. As one of the classes has much more instances than the other, the learning process becomes biased towards it. Therefore, methods for alleviating the impact of skewed distributions are highly sought after. Ensemble learning has emerged as one of the leading paradigms for imbalanced data. Creation of an efficient pool of classifiers is not a trivial task and one needs to carefully select which classifiers should be combined to obtain the best predictive power. In this paper, we propose a compound ensemble pruning algorithm for imbalanced data. It aims to retain classifiers that offer the best performance on both minority and majority classes, and display a high level of diversity. Remaining learners are discarded from the pool. This is achieved by the means of a multi-criteria evolutionary algorithm. Extensive experimental study show that our proposal is able to create smaller ensembles than the state-of-the-art methods, while offering an improved robustness to imbalanced class distributions.
Dual-Band, Linearly Polarized, Electrically Small Huygens Dipole Antennas

Two electrically small, dual-band Huygens dipole antennas are reported. In both designs, two pairs of magnetic and electric near-field resonant parasitic (NFRP) elements are combined organically within an electrically small, low profile package. The NFRP elements are excited effectively using only one coaxial-fed-driven element. One dual-band Huygens system produces parallel, linearly polarized (LP) fields at its two operating frequencies. The other dual-band system produces two orthogonal LP fields. Additional parasitic elements are introduced to mitigate the mutual coupling effects between the pairs of NFRP elements. The measured values for prototypes of both antennas in the L-band demonstrate their electrically small size (<inline-formula> <tex-math notation="LaTeX">$ka< 1$ </tex-math></inline-formula>) and low profile (<inline-formula> <tex-math notation="LaTeX">$\sim 0.03\lambda _{0}$ </tex-math></inline-formula>). They also confirm their broadside radiation and polarization performance characteristics, as well as the isolation between each operating frequency. Their fractional bandwidths, peak realized gains, front-to-back ratios, and radiation efficiencies are, respectively, ~0.6%, > 2 dBi, > 10 dB, and > 60% at both frequencies. These dual-band systems would provide multifunctional performance in a variety of portable, compact wireless devices.
Performance Guaranteed Control for Multi-Agent Systems with Unknown Control Directions

In this paper, a performance guaranteed control algorithm is presented for the consensus control of a class of unknown nonlinear multi-agent systems (MASs) with unknown control directions. It is shown that the states of agents stay within prescribed time-varying restrictions for all time. To attain these new results, an equivalent unconstrained MAS is generated from the original constrained one via a state transformation technique. Stabilization and consensus of the transformed agent states ensure both the consensus of the original agent states as well as the satisfaction of the prescribed restrictions. Based on the Nussbaum gain technique, the unknown control direction problem is solved. Consensus task is achieved theoretically via using Lyapunov synthesis, along with all the closed-loop signals being bounded. Additionally, in the proposed control design, each agent only exchanges the information with its neighbours. Hence, the proposed consensus protocol is distributed. Finally, simulation results demonstrate the effectiveness of the developed controller.
Autonomic Author Identification in Internet Relay Chat (IRC)

With the advances in Internet technologies and services, the social media has been gaining excessive popularity, especially because these technologies provide anonymity where they use nicknames to post their messages. Unfortunately, the anonymity feature has been exploited by the cyber-criminals to hide their identities and their operations. Hence, there is a growing interest in cybersecurity research domain to identify the authors of malicious messages and activities. Internet Relay Chat (IRC) channels are widely used to exchange messages and information among malicious users involved in cybercrimes. In this paper, we present an autonomic author identification technique based on personality profile and analysis of IRC messages. We first monitor the IRC channels using our autonomic bots and then create a personality profile for each targeted author. We demonstrate that personality analysis for author detection/identification is an efficient approach and has high detection rates.
A New Protocol for On-line User Authentication Based on 1 Out of n Types of Personal Data

In this paper, we propose a new protocol for on-line user authentication based on multiple personal data. Needless to say, some kind of personal data must be used for authentication. In most cases, the type (e.g. finger print and vein) of the personal data is fixed, and authentication is done by comparing the input data with those registered in the system. In this paper, we propose a protocol that allows authentication based on 1 out of n types of personal data. For registration, the user may register 1 out of n types of personal data, and use it for authentication. If n is large, the difficulty for a third party to spoof the original user can be higher because he/she must try a lot of times before getting the correct data type to attack. The basic idea of the proposed protocol is to "normalize" all types of personal data into the same format, and conduct authentication using the same machine learning model. This idea is verified via experiments on several public databases.
Higher-Order Sliding-Mode Control for Linear Time-Varying Systems

In this paper, assuming uniform controllability, a formula to select an output for desired relative degree and zero dynamics in single-input linear time-varying (LTV) systems is developed. The proposed formula allows us to construct a Lyapunov transformation, such that the original LTV system is Lyapunov equivalent to a linear time-invariant one with prescribed zero dynamics. This formula is then suggested to be used as a part of a two-step higher-order sliding-mode control strategy for LTV systems, ensuring finite-time stability in the presence of matched perturbations. It is also shown that if the formula is used to select an output of relative degree equal to the system order, then the formula to select a flat output is recovered. The proposed method is tested on an extended version of a perturbed mass-spring system.
Use of Decoupling Cells for Mitigation of SET Effects in CMOS Combinational Gates

This paper investigates the applicability of CMOS decoupling cells for mitigating the Single Event Transient (SET) effects in standard combinational gates. The concept is based on the insertion of two decoupling cells between the gate's output and the power/ground terminals. To verify the proposed hardening approach, extensive SPICE simulations have been performed with standard combinational cells designed in IHP's 130 nm bulk CMOS technology. Obtained simulation results have shown that the insertion of decoupling cells results in the increase of the gate's critical charge, thus reducing the gate's soft error rate (SER). Moreover, the decoupling cells facilitate the suppression of SET pulses propagating through the gate. It has been shown that the decoupling cells may be a competitive alternative to gate upsizing and gate duplication for hardening the gates with lower critical charge and multiple (3 or 4) inputs, as well as for filtering the short SET pulses induced by low-LET particles.
Text Categorization by Weighted Features

Text representation, which represent texts of variable lengths into feature vectors, is a fundamental task in machine learning. Early approaches extract lexical and syntactic features, which are discrete and sparse and can hardly capture the semantic relatedness between words. Recent advances in deep learning, which represents text segments into dense and continuous vectors, has shed light on this problem. However, the main limitation is they are usually based on complex neural network structure, which are resource-consuming to train and make inference. To remedy this, we propose a novel text representation approach which considers word local and global importance via the BM25 weighing schema. We use word vectors pretrained from large text corpus to capture the latent semantic relatedness between words. Experimental results show that our approach is effective and efficient compared with existing feature-based, unsupervised and supervised baselines.
Generalized Globally-Coupled Low-Density Parity-Check Codes

In this paper, two globally-coupled low-density parity check (GC-LDPC) code constructing methods are presented. The first is a Reed-Solomon-like GC-LDPC code that eases the design parameter searching in algebraic code constructions. The parameters such as the finite field, the associated prime factors, or the number of local codes can be more easily determined. The directly masking approach, the second proposed scheme, facilitates flexible structure designs of the parity check matrices. The two approaches can be regarded as generalizations of the original GC-LDPC codes. The design examples and the simulation results show the feasibility of the proposed techniques. In addition to code constructions, some applications and usage scenarios of GC-LDPC codes are addressed as well.
xHiPP: eXtended Hierarchical Point Placement Strategy

The complexity and size of data have created challenges to data analysis. Although point placement strategies have gained popularity in the last decade to yield a global view of multidimensional datasets, few attempts have been made to improve visual scalability and offer multilevel exploration in the context of multidimensional projections and point placement strategies. Such approaches can be helpful in improving the analysis capability both by organizing visual spaces and allowing meaningful partitions of larger datasets. In this paper, we extend the Hierarchy Point Placement (HiPP), a strategy for multi-level point placement, in order to enhance its analytical capabilities and flexibility to handle current trends in visual data science. We have provided several combinations of clustering methods and projection approaches to represent and visualize datasets; added a choice to invert the original processing order from cluster-projection to projection-cluster; proposed a better way to initialize the partitions, and added ways to summarize image, audio, text and general data groups. The tool's code is made available online. In this article, we present the new tool (named xHiPP) and provide validation through case studies with simpler and more complex datasets (text and audio) to illustrate that the capabilities afforded by the extensions have managed to provide analysts with the ability to quickly gain insight and adjust the processing pipeline to their needs.
High Performance 2D Perovskite/Graphene Optical Synapses as Artificial Eyes

Conventional von Neumann architectures feature large power consumptions due to memory wall. Partial distributed architecture using synapses and neurons can reduce the power. However, there is still data bus between image sensor and synapses/neurons, which indicates plenty room to further lower the power consumptions. Here, a novel concept of all distributed architecture using optical synapse has been proposed. An ultrasensitive artificial optical synapse based on a graphene/2D perovskite heterostructure shows very high photo-responsivity up to 730 A/W and high stability up to 74 days. Moreover, our optical synapses has unique reconfigurable light-evoked excitatory/inhibitory functions, which is the key to enable image recognition. The demonstration of an optical synapse array for direct pattern recognition shows an accuracy as high as 80%. Our results shed light on new types of neuromorphic vision applications, such as artificial eyes.
Transductive Transfer Learning Based on Broad Learning System

The latest proposed Broad Learning System (BLS) demonstrates an efficient and effective learning capability in many machine learning problems. In this paper, we apply the BLS to address transductive transfer learning problems, where the training (source) and test (target) data are drawn from the different but related distributions, which is a.k. a domain adaptation. We aim at learning from source data a well performing classifier on a different (but related) target data. A unified domain adaptation framework based on the BLS is developed for improving its transfer learning capability without loss of the computational efficiency. Two algorithms including BLS based source domain adaptation (BLS-SDA) and BLS based target domain adaptation (BLS-TDA) are proposed under this framework. Experiments on benchmark datasets show that our approach outperforms several existing domain adaptation methods while maintains high efficiency.