Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Acceleration Data for Detecting Temporary Cognitive Overload in Health Care Exemplified Shown in a Pill Sorting Task

In this paper we propose a new approach for detecting temporary cognitive overload. Due to the raising propagation of wearable devices with various integrated sensors, the idea is to detect such overload situations based on acceleration data out of these sensors at task relevant body parts. We executed an experiment in order to investigate the performance differences of people in a relaxed state and under cognitive load. The loaded state was simulated in a dual-task test. Additionally, we analyzed changes in the participants' motion behaviors at their hips and both of their wrists. We could show, that dual-task measuring is a suitable way for generating ground truth data for cognitive load. For this reason we used the study's data also as ground truth for the subsequent developed classification system. After investigating different features from the data we could discriminate the two states ("relaxed" and "loaded") with an accuracy of 90% and an MCC of 0.7986, which indicates a high correlation between ground truth and classified data. That outperforms other ACC based systems and approaches the performance of vital parameter based ones. Moreover, it could be shown that the dominant hand's data have greater influence to the results than the recessive one's. However, using data from both hands leads to further improvements.
MSO+∇ is undecidable

This paper is about an extension of monadic second-order logic over the full binary tree, which has a quantifier saying “almost surely a branch <tex>$\pi \in\{0,1\}^{\omega}$</tex> satisfies a formula <tex>$\varphi(\pi)$</tex>”, This logic was introduced by Michalewski and Mio; we call it <tex>$\mathrm{MSO}+\nabla$</tex> following notation of Shelah and Lehmann. The logic <tex>$\mathrm{Mso}+\nabla$</tex> subsumes many qualitative probabilistic formalisms, including qualitative probabilistic <tex>$check$</tex>, probabilistic LTL, or parity tree automata with probabilistic acceptance conditions. We show that it is undecidable to check if a given sentence of <tex>$\mathrm{MSO}+\nabla$</tex> is true in the full binary tree<sup>1</sup><sup>1</sup>Independently and in parallel another proof of this result was given employing different techniques in [3].
Intelligent topology-oriented load shedding scheme in power systems

Load shedding is a prevalent emergency control designed to prevent system blackouts. The higher complexity and lower predictability of modern power systems makes it difficult to rely on conventional load shedding schemes which shed loads in pre-defined steps without sufficient consideration of the actual system characteristics and the disturbance situations. In case of large disturbances, the power system stability can be improved by optimal load shedding in more effective load buses. Proposed scheme utilizes improved Power Flow Tracing method to determine the amount and location of load drops considering power system constraints. Numerical simulations conducted on IEEE 39 bus standard test system in DigSILENT PowerFactory illustrates the accuracy of the proposed method.
Improving Persian Dependency-Based Semantic Role Labeling using Semantic and structural Relations

In the automatic processing of the natural language, understanding the meaning of the sentence is accomplished by the semantic role labeler. The semantic role labeler does this task by examining the semantic connections between words (often verbs and their dependents) and imposing semantic roles on each of them (dependents) according to the occurred event in the sentence. This paper provides a dependency-based semantic role labeler with the help of clustering algorithms for Persian. We have examined the semantic role labeling problem as a classification problem. In our proposed method, for each verbal predicate, the candidate arguments are identified with the help of dependency relationships, and then the feature vector for them is extracted using the information in the dependency trees. In the next step, using the classification algorithms, the appropriate label for each of the arguments is determined. Finally, to improve the results, the sentences are projected to semantic and structural vector spaces and clustering is performed on them. The resulted information from clustering is used to correct semantic role labels. Experiments have been done on the first semantic role corpus in Persian language and the corpus provided by the authors. The achieved Macro-average F1-measure is 74.87 for the first corpus and 73.62 for the second one.
Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models

This paper introduces the real-time Healthcare 4.0 system, the VILIAlert system and a new approach that we propose for the robust assessment of it's performance. The VILIAlert system alerts clinicians when a patient's tidal volume value rises above the clinically accepted level of 8 ml/kg as beyond this point (> 8 ml/kg), a patient is considered high risk of permanent damage to their lungs. In order to ensure success with the VILIAlert system, the ideal scenario is to ensure that as soon as patients in the Intensive Care Unit experience tidal volume values beyond the 8 ml/kg level, a clinical intervention can be carried out so to minimise the risk of patients ever having permanent damage. The approach has been implemented in the Intensive Care Unit at the Royal Victoria Hospital Belfast, Northern Ireland demonstrating the potential for such an approach to be used across all hospitals in the region.
How should a robot approach two people?

Experiments were conducted to quantify the direction of robot approach that a pair of seated people find most comfortable. Three maximally-different seating configurations and eight directions of robot approach were considered. The data were analysed using Rayleigh's test of uniformity, a directional statistics method. Results from 140 participants showed robot approach directions that minimise participant discomfort align spatially with regions that allow good sight of the robot by both people and are centred on the pair's largest unoccupied area of p-space.
Active Semantic Labeling of Street View Point Clouds

Semantic 3D models have shown their importance in many fields such as autonomous driving. However, it remains a tough task to assign semantic labels to various scenes. In this paper, we propose an Active Learning based method for semantic labeling of street view point clouds with a small amount of annotated data samples. The proposed method takes a point cloud and registrated images as the input, and yields a point cloud with semantic labels. We iteratively fine-tunes a network with the ever-enlarging training set to exploit the semantic information of the scene, and fuse the semantic labels in 3D space. To deal with the imbalanced data in street view scenes, a label biased criterion for query selection is proposed to help select images to efficiently improve the performance of the network and the quality of the semantic model. Experimental result shows that the proposed method demands limited human labor and works well in assigning semantic labels to the imbalanced scenes like street view scenes.
Monitoring and Classification System of River Water Pollution Conditions with Fuzzy Logic

The development of the current era, and the rapid development of technology and the need for a significant increase in demand, as well as pollution, the water sector, especially the river has experienced a decline in water quality even to the occurrence of pollution, resulting in water can no longer be consumed either by human body also for other needs. Some of the systems that were developed began to be able to process existing data, be it conditions from water, chemical observations or physically. This is done because water is a necessity that cannot be tolerated, so this research is done to help fulfill or even provide a calm warning of water quality. With the development of Intemet of Things (IoT) the monitoring system will develop, because with the existence of technology such as low-power wide-area network (LPWAN) as specific as possible, short data can be sent using lower power. In this research, it was proven that the author could make a monitoring system and classification of river water pollution. By using an artificial intelligence, using the fuzzy logic method. The results of system testing show that the average accuracy of the monitoring system results is 99.7% and the results of the appropriate classification values are based on the results of system testing.
Training Data Enhancements for Robust Polyp Segmentation in Colonoscopy Images

Automatic polyp detection systems are an important tools to aid in the diagnosis and prevention of colorectal cancer. Currently, methods based on deep learning approaches have presented promising results. However, the performance of these techniques is highly associated with the use of large and varied data samples for training. This is one of the main limitations of applying Deep Learning techniques in the medical field since the amount of data for training is generally limited compared to nonmedical disciplines. This work proposes a novel method to increase the quantity and variability of training images from a publicly available colonoscopy dataset. The developed approach enrich the training data adding polyps to regions of nonpolypoid samples, creating automatically new data with their appropriate labels. Performance results show that convolutional neural networks trained in these syntactically-enhanced datasets improved the accuracy on polyps segmentation task while reducing the false positive rate. These results open new possibilities for advancing the study and implementation of new methods to automatically increase the number of samples in datasets for computer-assisted medical image analysis.
Hierarchical Model Predictive Speed and Current Control of an Induction Machine Drive with Moving-Horizon Load Torque Estimator

Speed-controlled induction machine (IM)drives are the working horses in many industrial applications. When applying model predictive control (MPC)to such tasks, two major challenges have to be faced: First, an accurate and dynamic load torque estimation is required for sufficient steady-state and transient control performance. Second, the speed and the underlying current control loop show widely differing time-constants and, therefore, designing a control structure with sufficient prediction horizons for both control variables while ensuring real-time capability on typical hardware platforms is crucial. To overcome the latter problem, a hierarchical MPC approach with different sampling intervals for speed and current control is proposed. Furthermore, a moving-horizon estimator is seamlessly introduced within the speed MPC structure for suitable load torque computation. The proposed control framework is implemented on a rapid-control prototyping test bench utilizing the ACADO toolkit to solve the underlying optimization problems. The method's feasibility is demonstrated by experimental investigations on a laboratory test bench.
A Sleeve-Free Interior Permanent Magnet High Speed Motor with Non-Uniform Airgap

The extra electromagnetic airgap thickness, high core losses and complicated rotor assembly are three crucial problems for the surface-mounted permanent magnet (SPM) motors with sleeve-protected rotor in high speed (HS) fields. In this paper, a novel sleeve-free interior permanent magnet (IPM) HS motor with non-uniform airgap is proposed on account of high torque density, low losses and easy of manufacture. Firstly, the features and advantages of the proposed rotor structure are detailed illustrated in term of mechanical strength, dynamic stability, core losses and material properties. Thereafter, in order to take fully advantage of the proposed structure, the optimization of the rotor non-uniform outer periphery and the shaft diameter is carried out by using finite element analysis (FEA) method. Finally, the comprehensive electromagnetic and mechanical performance of the proposed motor are evaluated and compared with a uniform airgap IPM motor and a sleeve-protected SPM motor in HS operation. And it is concluded that the proposed sleeve free non-uniform airgap IPM motor presents prominent advantages in motor losses, mechanical strength, dynamic stability and manufacture.
Effect of oil–paper–pressboard mass ratio on furfural content in transformer oil

Furfural content in oil is widely accepted as an effective parameter to mirror aging of insulation paper. However, pressboards are also abundant in transformers, which are always neglected in furfural analysis. Different masses of insulation paper and pressboard produce different amounts of furfural during the aging process. Thus, the furfural content in oil may differ in systems with different oil-paper-pressboard mass ratios. To address this problem, accelerated thermal aging experiments and furfural equilibrium experiments with different oil-paper-pressboard mass ratio systems were conducted under laboratory conditions. Furfural contents in oil, paper, and pressboard were measured. In a simplified oil-paper system, the furfural content in oil differs in systems with different oil-paper mass ratios. Further analysis shows the furfural amount produced by 1 g insulation paper is the same with good repeatability in groups with different mass ratios. Different oil-paper mass ratios change the distribution coefficient of furfural between oil and paper, and an increase in the mass of insulation paper leads to more furfural remaining in the insulation paper. The pressboards in field transformers produce little furfural during the aging process, but absorb part of the furfural from oil, which decreases the furfural content in oil. Therefore, more pressboard in an oil-paper-pressboard system indicates lower furfural content in oil. An increase in insulation paper in the oil-paper-pressboard system causes a decrease in the effect of pressboard on furfural content in oil.
The Patient Perspectives on Future Therapeutic Options in NASH and Patient Needs

Background: Non-alcoholic steatohepatitis (NASH) is a chronic liver disease with severe complications and without approved therapies. Currently, there is limited data on the overall burden of the disease for patients or on patient needs and preferences. This study investigates patient preferences in relation to potential future therapies for NASH. In addition, the factors that are relevant to patients and their importance in relation to future treatment options are explored. Method: Telephone in-depth interviews (TDIs) preceded an online 30-min quantitative survey. The online survey included (1) multiple choice questions (MCQs) on NASH diagnosis and disease background. (2) An exercise to determine patients' satisfaction levels with information provided at diagnosis, and to explore symptomatology in detail. (3) Exercises to evaluate potential new products and product attributes, including a “drag and drop” ranking exercise, and an adaptive choice-based conjoint exercise (ACBC). (4) The EQ-5D-5L questionnaire and the Visual Analog Scale (VAS), which measures patients' health status. (5) Collection of socio-demographic data, and (6) Questions to measure patient satisfaction with the survey. Results: There were 166 patients included in this study from Canada [n = 36], Germany [n = 50], the UK [n = 30], and USA [n = 50]. Fifty seven percent of patients [n = 94] had had a liver biopsy for confirmation of NASH. Patients were often unable to link their symptoms to NASH or other conditions. ACBC results showed that efficacy, defined as “impact on liver status” was the single most important attribute of a potential future NASH therapy. Other attributes considered to have secondary importance included impact on weight, symptom control and the presence of side effects. The EQ-5D utility score was 0.81 and VAS = 67.2. Conclusion: “Impact on liver status” is the primary outcome sought. Patients demonstrate a general lack of understanding of their disease and appeared to be unfamiliar with longer-term consequences of NASH. It is necessary to improve patient understanding of NASH and its progressive nature, and there is a need for improving confirmatory diagnosis and monitoring.
Random Graphs and Complex Networks: Volume 1

This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unified framework to enable systematic study. Designed for a master's-level course, where students may only have a basic background in probability, the text covers such important preliminaries as convergence of random variables, probabilistic bounds, coupling, martingales, and branching processes. Building on this base - and motivated by many examples of real-world networks, including the Internet, collaboration networks, and the World Wide Web - it focuses on several important models for complex networks and investigates key properties, such as the connectivity of nodes. Numerous exercises allow students to develop intuition and experience in working with the models.
Meta-Surface Antenna Array Decoupling Designs for Two Linear Polarized Antennas Coupled in H-Plane and E-Plane

In this paper, meta-surface superstrates are used to decouple two linear polarized antennas coupled in H-plane and E-plane, respectively. By properly designing the geometry of the double layer short wires as the unit cell of the meta-surface, as well as the height of the meta-superstrate, two linearly polarized antennas can be decoupled in H-plane and E-plane, respectively. Both the antenna pairs coupled in E- and H-planes with and without meta-surfaces are fabricated and measured. The results demonstrate that the antennas with the meta-surface superstrate are able to operate in the band of 3.3–3.7 GHz with reflection better than −15 dB and the isolation can be improved from 10 to 25 dB in the H-plane case and can be improved from 15 to 30 dB in the E-plane case. Such decoupling method can be applied extensively in 5G base stations where size constraints are becoming stringent.
Cloud Computing Adoption: An Effective Tailoring Approach

Many organisations are currently moving their legacy systems to the cloud as it offers reduced cost, improved operational efficiency, on-demand, and pay-as-you-go service models. While any cloud migration scenario to make legacies cloud-enabled may have their own characteristics, there is no universally superior or applicable method for all scenarios. In situations like this, designing customisable methods that fit characteristics of migration scenarios would be pivotal for successful adoption of cloud computing. The literature review reveals that issues surrounding method tailoring for the cloud migration have so far been missing and this is despite the call made by previous researches to adopt a tailoring perspective to the cloud enablement. To effectively address this shortcoming, this study applied the idea of situational method engineering to develop a framework which facilitates the design and maintenance of bespoke cloud migration methods. The paper demonstrates the applicability of the proposed framework via presenting two scenarios.
Acoustic echo and noise cancellation schemes using time and frequency domain adaptive techniques

Acoustic echo occurs in real life environment when speech signal coming out from a loudspeaker is delayed, attenuated and reflected back to the source microphone. Most communication systems are prone to acoustic echo which can severely degrade the quality and intelligibility of the signals transferred through the communication channels. In conventional acoustic echo cancellation (AEC) methods gradient based adaptive filter algorithms, such as least mean squares (LMS) and normalized LMS are employed where an error function is minimized to obtain the optimal filter coefficients corresponding to the acoustic echo path. The main problem of these methods is the necessity of the dual channels, one for the reference signal and the other for the echo corrupted signal. However in many practical applications only one channel is available, such as a conference hall environment with single microphone and a loudspeaker. Due to the unavailability of separate reference signal in single channel scenario, the task of echo cancellation becomes extremely difficult and is attempted by a few researchers. In this thesis, first a single channel echo cancellation scheme is developed based on the gradient based LMS adaptive filter algorithm, where, unlike conventional dual channel schemes, a delayed version of an estimated echo cancelled signal is utilized as a reference signal. In the proposed formulation, the effect of flat delay, i.e. the time required to produce an echo, is incorporated with a view to reduce the number of unknown parameters of the acoustic echo path, which offers a faster convergence. Moreover, based on energy and cross-correlation coefficients of the reference and current frames, a multi-step stopping criteria is developed, which can efficiently control the updating procedure of the proposed LMS adaptive filter. Extensive experimentation is carried out on real life speech signals corrupted by echoes using the proposed single channel LMS algorithm with and without the multi-step update constraints. It is found that the performance of former one, the controlled LMS algorithm, is far better than that of the later one in
UvA-DARE ( Digital Academic Repository ) Birds of a feather can butt heads : When Machiavellian employees work with Machiavellian leaders

Machiavellians are manipulative and deceitful individuals willing to utilize any strategy or behavior needed to attain their goals. This study explores what occurs when Machiavellian employees have a Machiavellian leader with the same negative, manipulative disposition. We argue that Machiavellian employees have a negative worldview and are likely to trust their leaders less. This reduced trust likely results in these employees experiencing higher stress and engaging in more unethical behavior. In addition, we expect these negative relationships to be exacerbated when such followers experience Machiavellian leadership. Thus, we test a moderated mediation model assessing whether Machiavellianism affects employees and whether combining Machiavellian leaders and Machiavellian employees is toxic in the sense of exacerbating the negative impact of Machiavellianism on employee trust. Results do not support the proposed conditional indirect effect of trust for either stress or unethical behavior. Instead, we find a conditional direct effect of employee Machiavellianism on both trust and stress: When Machiavellian employees have Machiavellian leaders, their trust in their leader significantly decreases, and their level of stress significantly increases. We also find support for an unconditional indirect effect of trust for employee stress (but not for unethical work behaviors), Machiavellianism in employees relates to stress via lowered trust in the leader. For unethical behavior, we only find a main effect of employee Machiavellianism.