Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Cerebral Tissue Oximeters Using Multilayered Dynamic Head Models

Cerebral tissue oximetry (CTO) based on near-infrared spectroscopy provides clinically relevant information on tissue perfusion and has been used specifically in infant monitoring and during cardiac surgeries. With different sensor designs in the selection and configuration of light sources and detectors and implemented analysis algorithms in tissue oxygen saturation (StO<sub>2</sub>) extraction, substantial differences were observed between different commercially available CTO device measurements. Tissue models (phantoms) mimicking a human head both optically and anatomically provide the controllable, safe, universally agreeable, and reproducible environment for the evaluation of CTO device performances. In this article, we implemented our realistic and dynamic multilayer mixed solid and liquid phantom design with controlled and repeatable test procedures in the evaluation of multiple commercially available CTO devices. Performances were evaluated for each CTO device within themselves, across each other and a reference device simultaneously under continuously changing oxygen saturation conditions. Results indicated the feasibility of our phantoms in CTO device performance testing and suggested that in general, CTO device measurements were very precise (<inline-formula> <tex-math notation="LaTeX">$S_{\mathrm {res}} < 1.48$ </tex-math></inline-formula>%) and highly correlated (<inline-formula> <tex-math notation="LaTeX">$R^{2} > 0.89$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$p < 0.0001$ </tex-math></inline-formula>) but had varying levels of accuracy, sensitivity, static, and proportional bias (<inline-formula> <tex-math notation="LaTeX">$15.4{\%}< A_{\mathrm {rms}} < 21.1$ </tex-math></inline-formula>%, 0.31 < sensitivity (regression slope) <0.43, and 35.94< bias (regression intercept) <49.74). In addition, gauge repeatability and reproducibility (Gauge R&R) analysis provided that most of the CTO devices were within acceptable ranges with precision-to-tolerance (P/T) ratio <10%, a number of distinct categories (NDC) >5, and intraclass correlation coefficient (ICC) >80%.
Domain Generalization with Triplet Network for Cross-Corpus Speech Emotion Recognition

Domain generalization is a major challenge for cross-corpus speech emotion recognition. The recognition performance built on "seen" source corpora is inevitably degraded when the systems are tested against "unseen" target corpora that have different speakers, channels, and languages. We present a novel framework based on a triplet network to learn more generalized features of emotional speech that are invariant across multiple corpora. To reduce the intrinsic discrepancies between source and target corpora, an explicit feature transformation based on the triplet network is implemented as a preprocessing step. Extensive comparison experiments are carried out on three emotional speech corpora; two English corpora, and one Japanese corpus. Remarkable improvements of up-to 35.61% are achieved for all cross-corpus speech emotion recognition, and we show that the proposed framework using the triplet network is effective for obtaining more generalized features across multiple emotional speech corpora.
Asynchronous Time-Sensitive Networking for 5G Backhauling

Fifth Generation (5G) phase 2 rollouts are around the corner to make mobile ultra-reliable and low-latency services a reality. However, to realize that scenario, besides the new 5G built-in Ultra-Reliable Low-Latency Communication (URLLC) capabilities, it is required to provide a substrate network with deterministic Qual-ity-of-Service support for interconnecting the different 5G network functions and services. Time-Sensitive Networking (TSN) appears as an appealing network technology to meet the 5G connectivity needs in many scenarios involving critical services and their coexistence with Mobile Broadband traffic. In this article, we delve into the adoption of asynchronous TSN for 5G backhauling and some of the relevant related aspects. We start motivating TSN and introducing its mainstays. Then, we provide a comprehensive overview of the architecture and operation of the Asynchronous Traffic Shaper (ATS), the building block of asynchronous TSN. Next, a management framework based on ETSI Zero-touch network and Service Management (ZSM) and Abstraction and Control of Traffic Engineered Networks (ACTN) reference models is presented for enabling the TSN transport network slicing and its interworking with Fifth Generation (5G) for backhauling. Then we cover the flow allocation problem in asynchronous TSNs and the importance of Machine Learning techniques for assisting it. Last, we present a simulation-based proof-of-concept (PoC) to assess the capacity of ATS-based forwarding planes for accommodating 5G data flows.
Polarization-Reconfigurable Antenna Using Combination of Circular Polarized Modes

This paper presents a novel polarization-reconfigurable antenna based on the combination of circularly polarized (CP) modes, which is capable of changing its polarization states among arbitrary linear polarizations (LPs), left-handed circular polarization (LHCP) and right-handed circular polarization (RHCP). A truncated square patch with two isolated aperture-coupling feeds is used as radiation element operating at 2.45 GHz, which can operate in two orthogonal CP modes with low crosstalk. By exciting one of the two feeding microstrip lines, LHCP or RHCP mode can be independently generated. Meanwhile, by simultaneously exciting the two feeding microstrip lines with identical amplitudes but with different initial phases, LP mode with polarization plane oriented in any desired azimuth direction can be obtained. The simulated and measured peak gain of the proposed antenna remains at about 6.8 dBi at 2.45 GHz and the impedance matching bandwidth ranging from 2.25 to 2.60 GHz is fully overlapped for all the polarization states. Experimental results show a good performance of the proposed antenna.
Teachers Falling off the Cliff Affordances and Constraints of Social Media in School

In a digitalized society our work environment is highly integrated with our home environment making work boundless both in terms of time and space. The digital work environment risks increased stress. Based on a case study in Swedish schools we investigate how teachers experience the use of social media for work-related purposes. We do so by using the Technology Affordance and Constraints Theory to capture the affordances as well as constraints of this use. Findings show that affordances of social media in schools were increased opportunities for learning, transparency and community building. Constraints were distractions from learning, increased isolation, stress and, above all, lack of guidance in how and when to use social media. We end the paper arguing that lack of policies and guidelines governing the use of social media at work is risking an increase in boundary blurring and potentially more stress.
Truth or Dare? - How can we Influence the Adoption of Artificial Intelligence in Municipalities?

Artificial intelligence (AI) is becoming an increasingly important factor of everyday life. The progress of AI adoption continues to accelerate with increasing investments in AI techniques and applications worldwide. However, the use of AI is still not present in employee’s daily life of German municipalities. Since this technology has a promising potential that German municipalities can also take advantage of, it is important to facilitate the transition of municipalities to AI. For this reason, we have conducted semi-structured expert interviews in twelve German municipalities to examine perceived challenges of AI adoption from employee’s perspective. Using methods from Grounded Theory and Gioia we extended research regarding the Technology-Organization-Environment (TOE) framework. Our results proof six and identified four additional perceived challenges of AI adoption in municipalities. With these results, we are able to extend literature on the use of AI in the public sector introducing perceived challenges of AI adoption from employee’s perspective in municipalities extending the
Surface tension of undercooled liquid cobalt

This paper provides the results on experimentally measured and numerically predicted surface tensions of undercooled liquid cobalt. The experiments were performed by using the oscillation drop technique combined with electromagnetic levitation. The simulations are carried out with the Monte Carlo (MC) method, where the surface tension is predicted through calculations of the work of cohesion, and the interatomic interaction is described with an embedded-atom method. The maximum undercooling of the liquid cobalt is reached at 231 K (0.13Tm) in the experiment and 268 K (0.17Tm) in the simulation. The surface tension and its relationship with temperature obtained in the experiment and simulation are σexp = 1.93 − 0.000 33 (T − T m) N m−1 and σcal = 2.26 − 0.000 32 (T − T m) N m−1 respectively. The temperature dependence of the surface tension calculated from the MC simulation is in reasonable agreement with that measured in the experiment.
Sensitive to the Digital Touch? Exploring Sensory Processing Sensitivity and Its Impact on Anthropomorphized Products in E-Commerce

Individual characteristics have a strong impact on decision-making behavior in virtual environments, including interacting with virtual agents, avatars, or animated objects. Prior IS research on individual characteristics has mostly focused on constructs such as the Big Five personality traits, emotional intelligence, and social sensitivity to understand their effects on behavior in virtual environments. However, we believe how individuals receive, process, and react to sensory information at the basic level is critical for developing perceptions that influence online behavior, including towards humanized objects through the process of anthropomorphism. This study explores sensory processing sensitivity as an inherent individual characteristic and its impact on individuals’ bidding decision towards humanized products in online auctions. Results show that sensory processing sensitivity has a positive impact on perceived anthropomorphism, which in turn strongly affects willingness to pay.
Identifying Vaccine Hesitant Communities on Twitter and their Geolocations: A Network Approach

Vaccine misinformation online may contribute to the increase of anti-vaccine sentiment and vaccine-hesitant behaviors. Social network data was used to identify Twitter vaccine influencers, their online twitter communities, and their geolocations to determine provaccine and vaccine-hesitant online communities. We explored 139,433 tweets and identified 420 vaccine Twitter influencers—opinion leaders and assessed 13,487 of their tweets and 7,731 of their connections. Semantic network analysis was employed to determine twitter conversation themes. Results suggest that locating social media influencers is an efficient way to identify and target vaccine-hesitant communities online. We discuss the implications of using this process for public health education and disease management.
VULNERLIZER: Cross-analysis Between Vulnerabilities and Software Libraries

The identification of vulnerabilities is a continuous challenge in software projects. This is due to the evolution of methods that attackers employ as well as the constant updates to the software, which reveal additional issues. As a result, new and innovative approaches for the identification of vulnerable software are needed. In this paper, we present VULNERLIZER, which is a novel framework for cross-analysis between vulnerabilities and software libraries. It uses CVE and software library data together with clustering algorithms to generate links between vulnerabilities and libraries. In addition, the training of the model is conducted in order to reevaluate the generated associations. This is achieved by updating the assigned weights. Finally, the approach is then evaluated by making the predictions using the CVE data from the test set. The results show that the VULNERLIZER has a great potential in being able to predict future vulnerable libraries based on an initial input CVE entry or a software library. The trained model reaches a prediction accuracy of 75% or higher.
Fitness First or Safety First? Examining Adverse Consequences of Privacy Seals in the Event of a Data Breach

Data breaches are increasing, and fitness trackers have proven to be an ideal target, as they collect highly sensitive personal health data and are not governed by strict security guidelines. Nevertheless, companies encourage their customers to share data with the fitness tracker using privacy seals, gaining their trust without ensuring security. Since companies cannot guarantee security, the question arises on how privacy seals work after not keeping the security promise. This study examines the possibilities to mitigate the consequences of data breaches in advance to maintain the continuance intention. Expectation-confirmation theory (ECT) and privacy assurance statements as a shaping of privacy seals are used to influence customer expectations regarding the data security of fitness trackers in the run-up to a data breach. Results show that the use of privacy assurance statements leads to high-security expectations, and failure to meet these has a negative impact on satisfaction and thus continuance intention.
Identifying Expertise Gaps in Cyber Incident Response: Cyber Defender Needs vs. Technological Development

Incident response is an area within cyber defense that is responsible for detecting, mitigating, and preventing threats within a given network. Like other areas of cyber security, incident response is experiencing a shortage of qualified workers which has led to technological development aimed at alleviating labor-related pressures on organizations. A cognitive task analysis was conducted with incident response experts to capture expertise requirements and used an existing construct to help prioritize development of new technology. Findings indicated that current software development incorporates factors such as analyst efficiency and consistency. Gaps were identified regarding communication and team navigation that are inherent to dynamic team environments. This research identified which expertise areas are needed at lower-tier levels of incident response and which of those areas current automation platforms are addressing. These gaps help focus future studies by bridging expertise research to development efforts.
Too Busy to Monitor? Board Busyness and the Occurrence of Reported Information Security Incidents

This paper investigates the association between board busyness (i.e., directors with multiple positions) and the occurrence of reported information security incidents. Building on prior studies of board busyness, this paper argues that directors holding multiple board seats may fail to commit the time and effort necessary to ensure the appropriate information security strategy or investment plans are in place. Our results demonstrate that board busyness is positively associated with reported information security incidents. This effect is larger when independent directors are busy, thus suggesting the importance of the governance role played by independent directors in managing information security risks. The board of directors’ role has been emphasized in anecdotal evidence and IT governance frameworks, but our study empirically demonstrates the board’s relevance in information security strategy and management.
Requirements for a Control Instrument of Intraorganizational Online Collaboration - A Maturity Model Analysis

Intraorganizational online collaboration (IOC) can be designed in various ways but there is still a backlog in how to control IOC and deriving corresponding actions. This paper aims to find and analyze approaches for an evaluation model of intraorganizational online collaboration. By using interview data, of in-depth interview with field experts the importance of an organizational control instrument for IOC is elaborated and a requirement catalog for such instruments is deduced. This catalog is applied in an initial analysis of maturity models (MM) as one identified approach of a control instrument for IOC. The findings show that the analyzed MMs fulfil the catalog of requirements in different degrees and that suitable approaches exist. However, all MMs do have disadvantages and further developments of the models are required.
What Your Radiologist Might be Missing: Using Machine Learning to Identify Mislabeled Instances of X-ray Images

Label quality is an important and common problem in contemporary supervised machine learning research. Mislabeled instances in a data set might not only impact the performance of machine learning models negatively but also make it more difﬁcult to explain, and thus trust, the predictions of those models. While extant research has especially focused on the ex-ante improvement of label quality by proposing improvements to the labeling process, more recent research has started to investigate the use of machine learning-based approaches to identify mislabeled instances in training data sets automatically. In this study, we propose a two-staged pipeline for the automatic detection of potentially mislabeled instances in a large medical data set. Our results show that our pipeline successfully detects mislabeled instances, helping us to identify 7.4% of mislabeled instances of Cardiomegaly in the data set. With our research, we contribute to ongoing efforts regarding data quality in machine learning. heuristic approach we follow to identify potentially mislabled instances.
Expert vs. Peer: The Role of Situational Characteristics in Electronic Networks of Practice

Individuals turn to online forums sponsored by Electronic Networks of Practice (ENPs) to acquire information on a range of topics; yet, quality and relevancy of such information can vary greatly. To assist information seekers, many ENP forums incorporate contextual cues that provide signals originating from both expert and lay forum users about the quality of solutions. While extant research has explored how these cue sources influence information filtering on ENP forums, results on their relative impact are fragmented and often contradictory. This paper advances research in this domain by employing situation theory to examine how six situational characteristics influence individuals’ propensity to rely on peer vs. expert recommendations. Results demonstrate that users rely more on expert recommendations when seeking information for situations that they perceive as stressful, task-oriented, or requiring greater cognitive processing, while peer recommendations are preferred for situations perceived as fun. Implications for research and practice are discussed.
Bioconcentration of gaseous organic chemicals in plant leaves: Comparison of experimental data with model predictions

This literature study is performed to support the implementation of two models in a risk assessment system for the evaluation of chemicals and their risk for human health and the environment (Uniform System for the Evaluation of Substances [USES] 1.0). One of the exposure pathways for humans and cattle is the uptake of chemicals by plants. In this risk assessment system the transfer of gaseous organic substances from air to plants modeled by Riederer is included. A similar model with a more refined approach, including dilution by growth, is proposed by Trapp and Matthies, which was implemented in the European version of this risk assessment system (EUSES). In this study both models are evaluated by comparison with experimental data on leaf/air partition coefficients (bioconcentration factors [BCFs]) found in the literature. For herbaceous plants both models give good estimations for the leaf/air partition coefficient up to 107, with deviations for most substances within a factor of five. For the azalea and spruce group the fit between experimental BCF values and the calculated model values is less adequate. For substances for which Riederer estimates a leaf/air partition coefficient above 107, the approach of Trapp and Matthies seems more adequate; however, few data were available.
Detection of schizophrenia: A machine learning algorithm for potential early detection and prevention based on event-related potentials

We show that event-related potentials can be used to detect schizophrenia with a high degree of precision. With our machine learning algorithm we achieve a balanced accuracy of 96.4%, which exceeds all results with comparable approaches. For this we use additional sensors on the left and right hemisphere in addition to the common central sensors. The experimental design when recording the data takes into account the dysfunction of the schizophrenic efference copy. Due to its serious consequences, schizophrenia is a social issue in which early detection and prevention plays a central role. In the future, machine learning could be used to support early interventions. When the ﬁrst symptoms appear, potential patients could be tested for the dysfunction typical for schizophrenia. In this way, risk groups and potential patients could be adequately treated before the onset of psychosis.
Perceptions of Information Systems Security Compliance: An Empirical Study in Higher Education Setting

Ensuring information systems security policy compliance is an integral part of the security program of any organization. This paper investigated the perceptions of different stakeholder groups towards information security policy compliance constructs of the Unified Model of Information Security Compliance (UMISPC) [1] in a higher education environment. The research findings showed that faculty/staff generally has higher tendency towards security policy compliance comparing to students in a higher education institution. In addition, students with security knowledge are more incline to have security policy compliance activities. Our finding not only added to the knowledge base of information systems security compliance research, but also offers practical implications.
Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study

This paper explores the factors that impact the adoption of a process methodology for managing and coordinating data science projects. Specifically, by conducting semi-structured interviews from data scientists and managers across 14 organizations, eight factors were identified that influence the adoption of a data science project management methodology. Two were technical factors (Exploratory Data Analysis, Data Collection and Cleaning). Three were organizational factors (Receptiveness to Methodology, Team Size, Knowledge and Experience), and three were environmental factors (Business Requirements Clarity, Documentation Requirements, Release Cadence Expectations). The research presented in this paper extends recognized factors for IT process adoption by bringing together influential factors that apply to data science. Teams can use the developed process adoption model to make a more informed decision when selecting their data science project management process methodology.
ICTs as Challenges to Enacting IS Project Control: An Interpretive Case Study of an ERP Implementation

This study investigates challenges posed by Information and Communication Technologies (ICTs) in the enactment of Information Systems (IS) project control. Using the control perspective developed by the sociologist John Law, we conducted an interpretive study of an Enterprise Resource Planning System implementation at a large public university system. From our investigation, four salient insights emerge. These insights are accompanied by corresponding assertions that demonstrate how ICTs, through varied forms of involvement, may challenge IS project control enactment. We integrate these insights for deeper illumination and conclude with contributions and implications of this study.