Miracle-dz/UniFAR-FTUs huggingface dataset:
When Value Co-Creation Turns to Co-Destruction: Users' Experiences of Augmented Reality Mobile Games

Service-dominant logic (SDL) provides a well-established lens for understanding services as value co-creation processes. However, also value co-destruction can occur in service processes, but the literature on value co-destruction remains scattered and more studies are called for. We address this research gap with a classification of users’ subjective reasoning for value co-destructive experiences while playing the augmented reality (AR) mobile game Pokémon GO. We conduct laddering interviews with Pokémon GO players to uncover their value co-destruction experiences. Employing clustering analysis, we investigate users’ reasonin g for value co-destruction experiences, and argue that value co-destruction may occur due to seven types of reasoning: value contradiction, unmet expectations, technical challenges, personal or social norm conflict, effect of constant mobile use, absence or loss of resources, and insufficient perceived value. The study contributes to research and practice with new insights to the unfavorable side of value co-creation and users’ co -destructive service experiences.
Accurate PPCC-Based DoA Estimation Using Multiple Calibration Planes for WSN Nodes Equipped with ESPAR Antennas

In this paper, we have introduced a new direction-of-arrival (DoA) estimation method, which relies on received signal strength (RSS) values measured at the output port of electronically steerable parasitic array radiator (ESPAR) antenna and uses the power pattern cross-correlation (PPCC) estimator. In the method, we have successfully incorporated measurements of ESPAR antenna’s radiation patterns performed at multiple calibration planes within the PPCC estimator in a way easily implementable in wireless sensor network (WSN) nodes equipped with ESPAR antennas. Performed anechoic chamber measurements of our ESPAR antenna prototype indicate that the proposed approach provides much lower DoA estimation errors in a wide span of incoming signal elevation angles than other methods currently available in the literature, which makes the concept applicable in practical WSN deployment scenarios.
Eye movement desensitization and reprocessing (EMDR) in children and adolescents with subthreshold PTSD after medically related trauma: design of a randomized controlled trial

ABSTRACT Background: Three in every 10 children and adolescents admitted to a hospital or undergoing medical treatment develop subthreshold symptoms of posttraumatic stress disorder (PTSD). When untreated, subthreshold PTSD can have a serious impact on psychosocial functioning, quality of life and long-term psychopathology. However, research investigating subthreshold PTSD and its treatment following paediatric medical interventions and/or hospitalization is scarce. Eye Movement Desensitization and Reprocessing (EMDR) is a fast and non-invasive psychosocial treatment for posttraumatic stress complaints. However, the effectiveness of EMDR in paediatric patients with subthreshold PTSD has not previously been systematically investigated. Objective: Describing the design of a randomized controlled trial (RCT) set up to evaluate the effectiveness of EMDR in children with subthreshold PTSD after hospitalization. Method: Children aged 4–15 years who have undergone a one-time (trauma type I) or repeated (trauma type II) hospitalization up to five years ago will be included. Participating children will be first screened with a standardized questionnaire for PTSD-symptoms. Subsequently, children with subthreshold PTSD will be randomly assigned to (1) approximately six sessions of standardized EMDR or (2) care as usual (CAU). Children with full diagnostic PTSD do not participate in the RCT, but are referred for direct treatment. Follow-up measurements will take place after eight weeks and eight months. Discussion: Considering the scarce evidence for the effectiveness of EMDR in children with medically related trauma, clinicans, researchers and children treated in hospitals can benefit from this study. Potential strengths and limitations of this study are discussed. Trial Registration: Netherlands Trial Register NTR5801
Multimodal Depression Detection: Fusion Analysis of Paralinguistic, Head Pose and Eye Gaze Behaviors

An estimated 350 million people worldwide are affected by depression. Using affective sensing technology, our long-term goal is to develop an objective multimodal system that augments clinical opinion during the diagnosis and monitoring of clinical depression. This paper steps towards developing a classification system-oriented approach, where feature selection, classification and fusion-based experiments are conducted to infer which types of behaviour (verbal and nonverbal) and behaviour combinations can best discriminate between depression and non-depression. Using statistical features extracted from speaking behaviour, eye activity, and head pose, we characterise the behaviour associated with major depression and examine the performance of the classification of individual modalities and when fused. Using a real-world, clinically validated dataset of 30 severely depressed patients and 30 healthy control subjects, a Support Vector Machine is used for classification with several feature selection techniques. Given the statistical nature of the extracted features, feature selection based on T-tests performed better than other methods. Individual modality classification results were considerably higher than chance level (83 percent for speech, 73 percent for eye, and 63 percent for head). Fusing all modalities shows a remarkable improvement compared to unimodal systems, which demonstrates the complementary nature of the modalities. Among the different fusion approaches used here, feature fusion performed best with up to 88 percent average accuracy. We believe that is due to the compatible nature of the extracted statistical features.
Outsiders No More? An Empirical Investigation of the Effect of Digital Institutional Pressure on Corporate IT

Despite the growing relevance of IT for business success, IT departments have largely remained in an outsider role associated with low status and power. However, changes in the external environment resulting from ongoing digitalization heighten the relevance of resources, capabilities, and knowledge residing within corporate IT. By combining institutional theory with resource dependency arguments, our research model investigates the relationship of digital institutional pressure on the power of IT departments and resulting impacts on digital innovation performance. We argue that digital institutional pressure increases internal power of corporate IT and that firms with a powerful IT department in terms of organizational support and decision rights exhibit greater levels of digital innovation performance. Based on survey data from 181 IT managers, we find empirical support for our hypotheses. These findings provide implications for IS research relating to digital innovation, institutional theory, and the IT function, as well as for managerial practice.
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

Federated learning, i.e., a mobile edge computing framework for deep learning, is a recent advance in privacy-preserving machine learning, where the model is trained in a decentralized manner by the clients, i.e., data curators, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. This paper gives the first attempt to explore user-level privacy leakage against the federated learning by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works that tend to interfere the training process of the federated learning, the proposed method works “invisibly” on the server side. The experimental results demonstrate the effectiveness of the proposed attacking approach and the superior to the state-of-the-art.
Abnormal Behavior Detection in Crowded Scenes Using Density Heatmaps and Optical Flow

Crowd behavior analysis is an arduous task due to scale, light and crowd density variations. This paper aims to develop a new method that can precisely detect and classify abnormal behavior in dense crowds. A two-stream network is proposed that uses crowd density heat-maps and optical flow information to classify abnormal events. Work on this network has highlighted the lack of large scale relevant datasets due to the fact that dealing and annotating such kind of data is a highly time consuming and demanding task. Therefore, a new synthetic dataset has been created using the Grand Theft Auto V engine which offers highly detailed simulated crowd abnormal behaviors.
Towards Spectral Estimation from a Single RGB Image in the Wild

In contrast to the current literature, we address the problem of estimating the spectrum from a single common trichromatic RGB image obtained under unconstrained settings (e.g. unknown camera parameters, unknown scene radiance, unknown scene contents). For this we use a reference spectrum as provided by a hyperspectral image camera, and propose efficient deep learning solutions for sensitivity function estimation and spectral reconstruction from a single RGB image. We further expand the concept of spectral reconstruction such that to work for RGB images taken in the wild and propose a solution based on a convolutional network conditioned on the estimated sensitivity function. Besides the proposed solutions, we study also generic and sensitivity specialized models and discuss their limitations. We achieve state-of-the-art competitive results on the standard example-based spectral reconstruction benchmarks: ICVL, CAVE and NUS. Moreover, our experiments show that, for the first time, accurate spectral estimation from a single RGB image in the wild is within our reach.
XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets

X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions. We apply a machine learning approach to this problem, presenting an end-to-end solution which results in robust and efficient inference. Since medical institutions often do not have the resources to process and label the large quantity of X-Ray images usually needed for neural network training, we design an end-to-end solution for small datasets, while achieving state-of-the-art results. Our implementation produces an overall accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical image processing techniques, such as clustering and entropy based methods, while improving upon the output of existing neural networks used for segmentation in non-medical contexts. The code used for this project is available online.1
Bifacial interdigitated-back-contact (IBC) crystalline silicon solar cell: fabrication and evaluation by internal quantum efficiency mapping

We present a bifacial interdigitated-back-contact (IBC) silicon solar cell with high bifaciality. Screen-printing and firing technology were used to form electrodes. Al-Ag paste and Ag paste were used for the p- and n-electrodes. The cell efficiencies were 20.5 % (cell area: $10.2\,\mathrm{cm}^{\mathrm {2}}$) and 19.8 % (cell area: $156.25\,\mathrm{cm}^{{2}}$). From the cell analysis by internal quantum efficiency mapping, it was found that the short-circuit current decreased above the screen printed electrodes and back surface field region. To increase the bifacial factor, it is necessary to improve the passivation properties at the BSF region and optimize the electrode design.
Simulation of Solar Cells Employing 2 Dimensional Transition Metal Dichalcogenide – Silicon Front Surfaces

Transition metal dichalcogenides are a promising avenue for fabricating high performance silicon in a simplified manner, by exploiting their two dimensional properties. This paper looks at simulating monolayers of four such materials on silicon, namely molybdenum disulfide and diselenide, and tungsten disulfide and diselenide. Materials parameters determined by density functional theory were used in a semiconductor device simulator, TCAD, to assess the potential performances for solar cells using these layers as a front surface contact layer. Results are presented for molybdenum disulfide on both n and p type silicon, showing a kink in the current-voltage curve already reported in the literature. A potential efficiency of 19.2% is identified for an n type molybdenum disulfide on p type silicon device.
Distributed Optimization for Energy-Efficient Fog Computing in the Tactile Internet

Tactile Internet is an emerging concept that focuses on supporting high-fidelity, ultra-responsive, and widely available human-to-machine interactions. To reduce the transmission latency and alleviate Internet congestion, fog computing has been advocated as an important component of the Tactile Internet. In this paper, we focus on an energy-efficient design of fog computing networks that support low-latency Tactile Internet applications. We investigate two performance metrics: Service response time of end-users and power usage efficiency of fog nodes. We quantify the fundamental tradeoff between these two metrics and then extend our analysis to fog computing networks involving cooperation between fog nodes. We introduce a novel cooperative fog computing concept, referred to as offload forwarding, in which a set of fog nodes with different computing and energy resources can cooperate with each other. The objective of this cooperation is to balance the workload processed by different fog nodes, further reduce the service response time, and improve the efficiency of power usage. We develop a distributed optimization framework based on dual decomposition to achieve the optimal tradeoff. Our framework does not require fog nodes to disclose their private information nor conduct back-and-forth negotiations with each other. Two distributed optimization algorithms are proposed. One is based on the subgradient method with dual decomposition and the other is based on distributed alternating direction method of multipliers via variable splitting. We prove that both algorithms can achieve the optimal workload allocation that minimizes the response time under the given power efficiency constraints of fog nodes. Finally, to evaluate the performance of our proposed concept, we simulate a possible implementation of a city-wide self-driving bus system supported by fog computing in the city of Dublin. The fog computing network topology is set based on a real cellular network infrastructure involving 200 base stations deployed by a major cellular operator in Ireland. Numerical results show that our proposed framework can balance the power usage efficiency among fog nodes and reduce the service latency for users by around 50% in urban scenarios.
Development and Evaluation of the Dutch Clinician-Administered PTSD Scale for DSM-5 (CAPS-5)

ABSTRACT Background: In 2013, the Clinician-Administered PTSD Scale, the golden standard to assess PTSD, was adapted to the DSM-5 (CAPS-5). Objective: This project aimed to develop a clinically relevant Dutch translation of the CAPS-5 and to investigate its psychometric properties. Method: We conducted a stepped translation including Delphi rounds with a crowd of 44 Dutch psychotrauma experts and five senior psychotrauma experts. Using partial crowd-translations, two professional translations and the official Dutch translation of the DSM-5, each senior expert aggregated one independent translation. Consensus was reached plenary. After back-translation, comparison with the original CAPS-5 and field testing, a last round with the senior experts resulted in the final version. After implementation clinicians conducted CAPS-5 interviews with 669 trauma-exposed individuals referred for specialized diagnostic assessment. Reliability of the Dutch CAPS-5 was investigated through internal consistency and interrater reliability analyses, and construct validity through confirmatory factor analysis (CFA). Results: CAPS-5 total severity score showed high internal consistency (α = .90) and interrater reliability (ICC = .98, 95% CI: .94–.99). CAPS-5 diagnosis showed modest interrater reliability (kappa = .59, 95% CI: .20–.98). CFA with alternative PTSD models revealed adequate support for the DSM-5 four-factor model, but a six-factor (Anhedonia) model fit the data best. Conclusions: The Dutch CAPS-5 is a carefully translated instrument with adequate psychometric properties. Current results add to the growing support for more refined (six and seven) factor models for DSM-5 PTSD indicating that the validity and clinical implications of these models should be objective of further research.
The Neutrophil Nucleus: An Important Influence on Neutrophil Migration and Function

Neutrophil nuclear morphology has historically been used in haematology for neutrophil identification and characterisation, but its exact role in neutrophil function has remained enigmatic. During maturation, segmentation of the neutrophil nucleus into its mature, multi-lobulated shape is accompanied by distinct changes in nuclear envelope composition, resulting in a unique nucleus that is believed to be imbued with extraordinary nuclear flexibility. As a rate-limiting factor for cell migration, nuclear morphology and biomechanics are particularly important in the context of neutrophil migration during immune responses. Being an extremely plastic and fast migrating cell type, it is to be expected that neutrophils have an especially deformable nucleus. However, many questions still surround the dynamic capacities of the neutrophil nucleus, and which nuclear and cytoskeletal elements determine these dynamics. The biomechanics of the neutrophil nucleus should also be considered for their influences on the production of neutrophil extracellular traps (NETs), given this process sees the release of chromatin “nets” from nucleoplasm to extracellular space. Although past studies have investigated neutrophil nuclear composition and shape, in a new era of more sophisticated biomechanical and genetic techniques, 3D migration studies, and higher resolution microscopy we now have the ability to further investigate and understand neutrophil nuclear plasticity at an unprecedented level. This review addresses what is currently understood about neutrophil nuclear structure and its role in migration and the release of NETs, whilst highlighting open questions surrounding neutrophil nuclear dynamics.
Notch inhibitor can attenuate apparent diffusion coefficient and improve neurological function through downregulating NOX2-ROS in severe traumatic brain injury

Introduction Secondary brain injury is a major factor that affects the prognosis and outcome of traumatic brain injury (TBI) patients. Secondary brain edema is considered to be an initiating factor in secondary brain injury after TBI. A previous study has indicated that Notch signaling activation contributes to neuron death in mice affected by stroke; however, its role in neuronal oxidation stress for brain edema after TBI is not well established. Apparent diffusion coefficient (ADC) values can represent the brain edema after TBI. Methods We established a rat model of acute craniocerebral injury, using functional MRI to evaluate the ADC and cerebral blood flow values. The present study was designed to determine the effect of Notch inhibitor DAPT upon oxidation stress for brain edema after TBI. Rats were randomly distributed into five groups, control group, severe TBI group, severe TBI + vehicle group, severe TBI + DAPT group, and severe TBI + DPI group. All rats were sacrificed at 24 hours after TBI. Results Our data indicated that Notch signaling inhibitor DAPT significantly reduced the ADC values and improved the neurological function after TBI. In addition, DAPT decreased NOX2 levels and the ROS levels. Furthermore, DPI can decrease NOX2 levels and ROS levels. Conclusion This study indicated that DAPT Notch signal inhibitors can inhibit NOX2-ROS generation, reduce the ADC values, relieve cerebral edema, and improve nerve function.
An Extensive Study of Cycle-Consistent Generative Networks for Image-to-Image Translation

Image-to-image translation between different domains has been an important research direction, with the aim of arbitrarily manipulating the source image content to become similar to a target image. Recently, cycle-consistent generative network (CycleGAN) has become a fundamental approach for general-purpose image-to-image translation, while almost no work has examined what factors may influence its performance. To provide more insights, we propose two new models roughly based on CycleGAN, namely Long CycleGAN and Nest CycleGAN. First, Long CycleGAN cascades several generators to perform the domain translation in a long cycle. It shows the benefit of stacking more generators on the generation quality. In addition to the long cycle, Nest CycleGAN develops new inner cycles to bridge intermediate generators directly, which can help constrain the unsupervised mappings. In the experiments, we conduct qualitative and quantitative comparisons for tasks including photo<-> label, photo<->sketch, and photo colorization. The quantitative and qualitative results demonstrate the effectiveness of our two proposed models.
Intranasal Insulin Administration Does Not Affect LH Concentrations in Men with Diabetes

A quarter of men with obesity or type 2 diabetes have hypogonadotropic hypogonadism. Animal studies and in vitro data have shown that insulin action and insulin responsiveness in the brain are necessary for the maintenance of the functional integrity of the hypothalamo-hypophyseal-gonadal axis. We conducted a randomized, placebo-controlled trial to evaluate the effect of one dose of intranasal insulin (40 IU of regular insulin) or saline on LH concentrations in 14 men (8 with type 2 diabetes and 6 healthy lean men). Insulin or saline was administered intranasally on two different occasions, at least one week apart. Blood samples were collected to measure LH concentrations every 15 minutes for 5 hours. Study drug was administered intranasally after a 2-hour baseline sampling period. Patients remained fasting throughout the procedure. The primary endpoint of the study was to compare the change in LH concentrations after intranasal insulin as compared to placebo (intranasal saline). Change was defined as the difference between baseline LH concentrations (average of the 9 samples collected in two hours prior to drug administration) and average LH concentrations following drug administration (average of the 12 samples collected in 3 hours). There was no change in LH concentrations following insulin administration as compared to placebo in men with diabetes or in lean men. We conclude that one dose of 40 IU of regular insulin administered intranasally does not change LH concentrations acutely in men.