Miracle-dz/UniFAR-FTUs huggingface dataset:
Entlohnung als Instrument zur Informationsaufdeckung bei der Personalauswahl

Innerhalb einer Arbeitnehmer-Arbeitgeber-Beziehung stellt die Entlohnung eines Arbeitnehmers ein bewährtes Instrument zur Anreizgestaltung dar. In dem vorliegenden Beitrag soll gezeigt werden, daß der Entlohung bereits vor dem Beginn eines Beschäftigungsverhältnisses begleitend zur Personalauswahl eine wichtige Zusatzfunktion als Instrument zur Informationsaufdeckung zugeschrieben werden kann. Hierbei werden Stellenbewerber - beispielsweise im Rahmen eines Einstellungsinterviews - mit derartigen Fragen und Problemstellungen konfrontiert, daß sie zu einer Aufdeckung relevanter Informationen über ihre Leistungsbereitschaft und ihr Leistungsvermögen veranlaßt werden. Die aufgedeckten Bewerberinformationen können anschließend für die eigentliche Personalauswahlentscheidung durch den Arbeitgeber genutzt werden.
Semiasynchronous BCI Using Wearable Two-Channel EEG

Motor imagery (MI)-based electroencephalogram can be modulated voluntarily by users through feedback provided by a brain–computer interface (BCI). In MI-based BCIs, feedback design plays an important role in MI training, especially in BCIs that use a limited number of electrodes. This paper presents a semiasynchronous MI-based BCI system that uses discrete and continuous feedback together, in order to improve practicability and training efficiency. Results show that the proposed method can reduce command generation time to 3.77 s with the accuracy of 77%, using our proposed continuous feedback with a two-channel MI-based BCI system. Our proposed system is noninvasive and wearable, featuring improvement in the practical utility, and operational convenience of a BCI system.
An Adaptive and Energy-Efficient Algorithm for Surface Gateway Deployment in Underwater Optical/Acoustic Hybrid Sensor Networks

Underwater optical/acoustic hybrid sensor networks (UOASNs) play a crucial role in the tradeoffs among transmission rate, communication range, and bandwidth. The reasonable deployment of surface gateways, such as proper number and well-distributed locations, significantly decreases the error probability and propagation delay during transmission. In this letter, an adaptive and energy-efficient scheme for dense surface gateway deployment (SGD) is presented for the UOASN. First, an affiliated-node discovery algorithm is proposed to calculate the distribution coefficient of the nodes, which directly influences the SGD. Then, the SGD applied to the UOASN is formulated as a mixed programming problem, and solved by fuzzy theory to optimize the quantity of the surface gateways and the energy consumption of the network. Extensive simulation results are demonstrated to validate the effectiveness of the proposed scheme.
Scene wireframes sketching for UAVs

This paper introduces novel insights to improve the state of the art line-based unsupervised observation and abstraction models of urban environments. The scene observation is performed by an UAV, using self-detected and matched straight segments from streamed video frames. The increasing use of autonomous UAV s inside buildings and human built structures demands new accurate and comprehensive representations for their environment. Most of the 3D scene abstraction methods published are using invariant feature point matching, nevertheless some sparse 3D point clouds do not concisely represent the structure of the environment. Likewise, line clouds constructed by short and redundant segments with unaccurate directions will limit the understanding of the objective scenes, that include environments with no texture, or whose texture resembles a repetitive pattern. The presented approach is based on observation and representation models using the straight line segments, whose resemble the limits of an urban indoor or outdoor environment. The goal of the work is to get a better 3D representation for future autonomous UAV.
A Privacy-Preserving Trust Model Based on Blockchain for VANETs

The public key infrastructure-based authentication protocol provides basic security services for the vehicular ad hoc networks (VANETs). However, trust and privacy are still open issues due to the unique characteristics of VANETs. It is crucial to prevent internal vehicles from broadcasting forged messages while simultaneously preserving the privacy of vehicles against the tracking attacks. In this paper, we propose a blockchain-based anonymous reputation system (BARS) to establish a privacy-preserving trust model for VANETs. The certificate and revocation transparency is implemented efficiently with the proofs of presence and absence based on the extended blockchain technology. The public keys are used as pseudonyms in communications without any information about real identities for conditional anonymity. In order to prevent the distribution of forged messages, a reputation evaluation algorithm is presented relying on both direct historical interactions and indirect opinions about vehicles. A set of experiments is conducted to evaluate BARS in terms of security, validity, and performance, and the results show that BARS is able to establish a trust model with transparency, conditional anonymity, efficiency, and robustness for VANETs.
Hyperspectral Image Classification in the Presence of Noisy Labels

Label information plays an important role in a supervised hyperspectral image classification problem. However, current classification methods all ignore an important and inevitable problem—labels may be corrupted and collecting clean labels for training samples is difficult and often impractical. Therefore, how to learn from the database with noisy labels is a problem of great practical importance. In this paper, we study the influence of label noise on hyperspectral image classification and develop a random label propagation algorithm (RLPA) to cleanse the label noise. The key idea of RLPA is to exploit knowledge (e.g., the superpixel-based spectral–spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation. Specifically, the RLPA first constructs a spectral–spatial probability transform matrix (SSPTM) that simultaneously considers the spectral similarity and superpixel-based spatial information. It then randomly chooses some training samples as “clean” samples and sets the rest as unlabeled samples, and propagates the label information from the “clean” samples to the rest unlabeled samples with the SSPTM. By repeating the random assignment (of “clean” labeled samples and unlabeled samples) and propagation, we can obtain multiple labels for each training sample. Therefore, the final propagated label can be calculated by a majority vote algorithm. Experimental studies show that the RLPA can reduce the level of noisy label and demonstrates the advantages of our proposed method over four major classifiers with a significant margin—the gains in terms of the average overall accuracy, average accuracy, and kappa are impressive, e.g., 9.18%, 9.58%, and 0.1043. The MATLAB source code is available at https://github.com/junjun-jiang/RLPA.
A Low-Power High-Speed Spintronics-Based Neuromorphic Computing System Using Real-Time Tracking Method

In spintronic-based neuromorphic computing systems (NCSs), the switching of magnetic moment in a magnetic tunnel junction (MTJ) is used to mimic neuron firing. However, the stochastic switching behavior of the MTJ and process variations effect lead to a significant increase in the stimulation time of such NCSs. Moreover, current NCSs need an extra phase to read the MTJ state after stimulation, which is in contrast with real neuron functionality in human body. In this paper, the read circuit is replaced with a proposed real-time sensing (RTS) circuit. The RTS circuit tracks the MTJ state during stimulation phase. As soon as switching happens, the RTS circuit terminates the MTJ current and stimulates the post neuron. Hence, the RTS circuit not only improves the energy consumption and speed, but also makes the operation of the NCS similar to real neuron functionality. The simulation results in 65-nm CMOS technology confirm that the energy consumption and speed of the proposed RTS-based NCS are improved at least by 40% and 2.22X compared with a typical NCS, respectively. Finally, utilizing the RTS-based NCS in image processing applications, such as character recognition and edge detection, can lead to 90.3% improvement in energy delay products compared with the typical NCS.
Leveraging JAK-STAT regulation in myelofibrosis to improve outcomes with allogeneic hematopoietic stem-cell transplant

Primary myelofibrosis (PMF) is a disease characterized by bone marrow fibrosis, extramedullary hematopoiesis, risk of transformation to acute myeloid leukemia, and a substantial symptom burden with diminished quality of life. Allogeneic hematopoietic cell transplantation (HCT) is the only curative option; however, disease relapse and graft versus host disease (GVHD) are significant barriers to long-term survival. The discovery of the JAK2 V617F mutation, and subsequent development of JAK inhibitors, resulted in improved survival and significant improvements in spleen volumes and symptom scores. Though the effect of JAK inhibition on transplant outcome is poorly understood, using JAK inhibition to achieve maximal response prior to HCT is standard practice at major centers. After allogeneic HCT, a significant proportion of patients with steroid-refractory GVHD have clinical responses to JAK inhibition. Targeting this pathway is a key component in the management of patients with PMF before and after allogeneic HCT.
Contention-Aware Reliability Efficient Scheduling on Heterogeneous Computing Systems

Energy efficiency and system reliability are the two main measurements in modern high-performance computing. The majority of previous recent studies have focused on realizing parallel task scheduling with low energy consumption or fast execution time. These approaches were developed with the classic scheduling model. However, the contention model is gaining increasing recognition as a more practical tool to create accurate and efficient schedules. This study proposes a contention-aware reliability management with deadline and energy budget constraints (CARMEB) algorithm for parallel task scheduling in heterogeneous computing systems. CARMEB involves three phases, namely, task priority calculation, communication edge allocation, and slack reclaiming. Results are validated by conducting extensive experiments, including randomly generated task graphs and three types of task graphs in real-world applications. This study demonstrates that our algorithm significantly improves system reliability.
Parental uncertainty about transferring their adolescent with congenital heart disease to adult care

Abstract Aims To study parent's levels of uncertainty related to the transfer from pediatric to adult care in adolescents with congenital heart disease (CHD) and to identify potentially correlating factors. Background Parents acknowledge that during transition they struggle with finding ways of feeling secure in handing over the responsibility and letting go of control. Well‐prepared and informed parents who feel secure are most likely better skilled to support their adolescent and to hand over the responsibility. Design A cross‐sectional study. Methods Overall, 351 parents were included (35% response rate). Parental uncertainty was assessed using a Linear Analogue Scale (0–100). Data were collected between January ‐ August 2016. Potential correlates were assessed using the readiness for transition questionnaire and sociodemographic data. Results The mean parental uncertainty score was 42.5. Twenty‐four percent of the parents had a very low level of uncertainty (score 0–10) and 7% had a very high level (score 91–100). Overall, 26% of the mothers and 36% of the fathers indicated that they had not started thinking of the transfer yet. The level of uncertainty was negatively associated with the level of perceived overall readiness. Adolescents' age, sex, CHD complexity, and parental age were not related to uncertainty. Conclusion A wide range in the levels of uncertainty was found. Parents who were less involved in the care, or perceived their adolescent as readier for the transition, felt less uncertain. Still, thirty percent of the parents had not started to think about the transfer to adult care.
Parallel Individuation Supports Numerical Comparisons in Preschoolers

While the approximate number system (ANS) has been shown to represent relations between numerosities starting in infancy, little is known about whether parallel individuation – a system dedicated to representing objects in small collections – can also be used to represent numerical relations between collections. To test this, we asked preschoolers between the ages of 2 ½ and 4 ½ to compare two arrays of figures that either included exclusively small numerosities (< 4) or exclusively large numerosities (> 4). The ratios of the comparisons were the same in both small and large numerosity conditions. Experiment 1 used a between-subject design, with different groups of preschoolers comparing small and large numerosities, and found that small numerosities are easier to compare than large ones. Experiment 2 replicated this finding with a wider range of set sizes. Experiment 3 further replicated the small-large difference in a within-subject design. We also report tentative evidence that non- and 1-knowers perform better on comparing small numerosities than large numerosities. These results suggest that preschoolers can use parallel individuation to compare numerosities, possibly prior to the onset of number word learning, and thus support previous proposals that there are numerical operations defined over parallel individuation (e.g., Feigenson & Carey, 2003; https://doi.org/10.1111/1467-7687.00313).
IaaS Reactive Autoscaling Performance Challenges

The main feature of a cloud application is its scalability. Major IaaS cloud services providers (CSP) employ autoscaling on the level of virtual machines (VM). Other virtualization solutions (e.g. containers, pods) can also scale. An application scales in response to change in observed metrics, e.g. in CPU utilization. Occasionally, cloud applications exhibit the inability to meet the Quality of Service (QoS) requirements during the scaling caused by the reactivity of autoscaling solutions. This paper provides the results of the autoscaling performance evaluation for two-layered virtualization (VMs and Kubernetes pods) conducted in the public clouds of AWS, Microsoft and Google using the approach and the Autoscaling Performance Measurement Tool developed by the authors.
Laypersons’ Beliefs and Intuitions About Free Will and Determinism

We linked between the social psychology and experimental philosophy paradigms for the study of folk intuitions and beliefs regarding the concept of free will to answer three questions: (1) What intuitions do people have about free will and determinism? (2) Do free will beliefs predict differences in free will and determinism intuitions? and (3) Is there more to free will and determinism than experiencing certainty or uncertainty about the nature of the universe? Overall, laypersons viewed the universe as allowing for human indeterminism, and they did so with certainty. Examining intuitions of prosociality, future orientation, learning, meaningfulness, human uniqueness, and well-being, ratings were highest in the indeterministic universe condition and lowest in the deterministic universe condition, both significantly different from the uncertain universe condition. Participants’ free will beliefs had only weak impact on realism, happiness, and learning intuitions but did not reverse the general intuition favoring indeterminism and showed no impact on other intuitions.
Moving the United States Medical Licensing Examination Step 1 After Core Clerkships: An Outcomes Analysis

Purpose Schools undergoing curricular reform are reconsidering the optimal timing of Step 1. This study provides a psychometric investigation of the impact on United States Medical Licensing Examination Step 1 scores of changing the timing of Step 1 from after completion of the basic science curricula to after core clerkships. Method Data from four schools that recently moved the examination were analyzed in a pre–post format using examinee scores from three years before and after the change. The sample included scores from 2008 through 2016. Several confounders were addressed, including rising national scores and potential differences in cohort abilities using deviation scores and analysis of covariance (ANCOVA) controlling for Medical College Admission Test (MCAT) scores. A resampling procedure compared study schools’ score changes versus similar schools’ in the same time period. Results The ANCOVA indicated postchange Step 1 scores were higher compared with prechange (adjusted difference = 2.67; 95% confidence interval: 1.50–3.83, P < .001; effect size = 0.14) after adjusting for MCAT scores and rising national averages. The average score increase in study schools was larger than changes seen in similar schools. Failure rates also decreased from 2.87% (n = 48) pre change to 0.39% (n = 6) post change (P < .001). Conclusions Results suggest moving Step 1 after core clerkships yielded a small increase in scores and a reduction in failure rates. Although these small increases are unlikely to represent meaningful knowledge gains, this demonstration of “noninferiority” may allow schools to implement significant curricular reforms.
Extend Capability of Low-End Android Devices by Scheduling Apps Between Local and Cloud

In this paper, we propose a multi-layer mobile application (app) scheduling method to extend the capability of low-end Android devices. With a quantitative analysis, we find that the increase of installed apps will negatively affect quality of experience (QoE) of the user, e.g., the action response time of a mobile device, by producing more periodically or irregularly background tasks. On the other hand, the user tends to install more apps than needed in case they could play a role someday, as indicated by the consumer app usage statistics. When the storage is running out, being forced to choose an app to uninstall due to space budget is a painful experience for the user. This contradiction is intensified for low-end devices due to limited resources. We try to reduce this dilemma by a multi-layer app scheduling (MAS) schema, along with a cloud service. For the first layer, we utilize the “freeze” feature of Android to prevent non-essential background activities. For the second layer, it is a network scheduler, which automatically schedules the available apps, together with their data, between local and cloud according to user’s personal policy generated by big data analysis. By dynamically scheduling the apps among three states, QoE of a low-end Android device is improved. At the same time, with the help of an app state recovery mechanism, the user can directly access a large number of apps provided by the cloud with consistent app view. Experimental results on a low-end smartphone, i.e., Samsung Galaxy ON5, and a smart watch based on Newton2_Plus wearable development board show the benefits of the proposed MAS schema.
RGB Color Measuringc Tool Of Simple Colorimetric Detection For High Throughput Antioxidant Capacity Assay On Well Plate

High throughput screening (HTS) assays is routinely used in basic biochemical, cell biology and drug discovery research. A system called Simple Colorimetric Detection (SCD) for HST of antioxidant capacity was presented. It performs on colorimetric method that uses observable colors of chemical reaction to estimate the antioxidant concentration. Previously, manual detecting the color intensity by using an image editor tool was used. It takes a lot of time and workforce. In this work, we proposes an automatic color measuring method for the SCD system. The proposed method consists of four steps. It obtains a color image of well plate as an input image. In the first step, region of well plate is d etermined. Next, we apply an edge detection and morphological techniques to obtain all objects on the well plate in the second step. The obtaining objects on the well plate are classified into spot or non-spot. The radius and eccentricity of each object are used for this purpose. In the third step, all spot locations are estimated by using the information of detected spots. Finally, color intensities of each spot is measured. The experimental results show that the proposed method perfectly detected all spots on well plate images generated from the SCD instrument. Moreover, it performed well in measuring color intensity values with acceptable precision. We additionally demonstrated how it works in practical with real dataset obtained from the SCD system.
A systematic review of risk factors for cataract in type 2 diabetes

Type 2 diabetes (T2D) is a risk factor for cataract development. With T2D prevalence increasing, the burden of cataract‐associated vision loss will also increase. We aimed to characterise cataract diabetes‐specific risk factors to assist prevention and management strategies. As part of a systematic review, two investigators independently searched online electronic databases according to a predetermined protocol for relevant published data to end‐March 2018. Studies were included if they were longitudinal with ≥100 participants, diabetes was defined, a description of cataract assessment was provided, data were from humans, and the reports were in English. Study quality was assessed using the Newcastle Ottawa Scale and GRADE. Of 5255 publications identified, 19 from 13 study populations were included. The overall risk of bias was low. There was between‐study variability. Age and glycaemic control were consistently associated with cataract development in T2D, but blood pressure, diabetes duration, sex, and aspirin use were not. Serum lipids and smoking remain possible risk factors, but available data are inconclusive. Glycaemia is the only consistent modifiable risk factor amongst a range of candidate variables. Due to the lack of consistency of the available evidence, and since mortality associated with T2D is declining with the likelihood of increased cataract‐associated vision loss, additional well‐conducted longitudinal studies are needed to identify modifiable risk factors that could prevent or delay cataract formation.
A Study on Social Graph Analysis Using Beacon Bluetooth Radio Transmitter

Social graph analysis using Bluetooth radio transmitters called "Beacon" is discussed in this paper. Each person carries the beacon and a smart phone; the smart phone is performed for a receiver. Someone's smart phone can recognize another person's beacon and the distance between the two persons. As the result, our social graph can be generated using those data. Graph pruning is necessary, and we discuss how to determine the two threshold parameters for the distance between two persons and the received signal reception frequency. We show one guideline to determine the two threshold. A system simulation is shown in our experiments.