Miracle-dz/UniFAR-FTUs huggingface dataset:
Pengaruh Kualitas Sistem Informasi Cyber Campus Terhadap Kepuasan dan Loyalitas Mahasiswa STIKOM Surabaya

Kemajuan Teknologi Informasi yang terjadi memberikan berbagai sarana bagi perguruan tinggi untuk meningkatkan kualitas pelayanan terhadap sivitas akademikanya. Salah satu hal yang dilakukan untuk menunjang sistem informasi yang ada di lingkungan kampus yaitu dengan membuat Website. Sehingga pada penelitian ini dikhususkan pada penggunaan sistem informasi akademik berbasis web di Institut Bisnis dan Informatika Stikom Surabaya yang dikenal dengan sebutan Sistem Informasi Cyber Campus (SICYCA). Melalui SICYCA, mahasiswa dapat dengan mudah mendapatkan data mengenai berbagai macam layanan antara lain Akademik, Keuangan, PPTA, Perpustakaan, E-Resource, Email, Komunitas. Dalam penggunaannya SICYCA menghadapi beberapa kendala, akan tetapi SICYCA tetap digunakan oleh mahasiswa untuk kepentingan berlangsungnya proses pembelajaran. Sehingga dalam penelitian ini perlu dilakukan pengukuran pengaruh kualitas SICYCA terhadap kepuasan dan loyalitas mahasiswa Stikom Surabaya untuk mengetahui hal apa saja yang mempengaruhi kepuasan mahasiswa terhadap SICYCA. Penelitian ini menggunakan metode Webqual 4.0 yang dimodifikasi dengan menambahkan dimensi kualitas antarmuka pengguna. Pengambilan data dilakukan dengan menggunakan kuisioner, kemudian dilakukan deskripsi pada data yang diperoleh dan dilakukan pengolahan data dengan menggunakan analisis Structural Equation Modelling. Hasil yang diperoleh dari analisis menunjukkan bahwa variabel yang berpengaruh signifikan terhadap kepuasan mahasiswa yaitu kualitas kegunaan dan kualitas desain antarmuka pengguna, sedangkan kepuasan mahasiswa sangat berpengaruh signifikan terhadap loyalitas mahasiswa.
γ -Ray hit-location in large monolithic crystals with SiPM readout: analytical versus neural-network algorithms

— The main objective of this research was to achieve an excellent gamma-ray position reconstruction with very large monolithic scintillation crystals coupled to pixelated silicon photosensors. Analyzing the scintillation-light distribution measured along the SiPM-pixels one can get a sub-pixel accuracy for the position reconstruction. The quality of the latter depends on both the characteristics of the crystal-photosensor assembly, and the goodness of the algorithm used to reconstruct the gamma-ray hit location. We have carried out a systematic study for 511 keV gamma-rays using three different crystal thicknesses of 10 mm, 20 mm and 30 mm, all of them with planar geometry and a base size of 50x50 mm 2 . To our knowledge, these are the largest monolithic crystals with SiPM readout aimed at gamma-ray imaging reported in the literature thus far. We have optimized state-of-the-art 3D position-reconstruction methods based on the fit of an analytical model for the propagation of the scintillation light distribution, as well as methods based on artificial neural-networks. In all cases the experimental data-set used was a matrix of 35x35 collimated-source positions measured across the transversal xy-plane on a pitch of 1.5 mm. In terms of spatial resolution, a superior performance is obtained with the analytical-fit methods for the 10 mm and 20 mm thick crystals, with results of 1-2 mm FWHM on average across the full field-of-view of nearly 25 cm 2 . On the other hand, NN-algorithms perform better for thick crystals (30mm), with average position resolutions of 3 mm FWHM and significantly better S/N-ratio and FoV than analytical approaches. This research is intended for the development of a total-energy detector with gamma-ray imaging capability, so-called i-TED, which is aimed at the measurement of neutron-capture cross sections using the time-of-flight technique. The results reported here are of interest
Anti-nucleocapsid antibodies following SARS-CoV-2 infection in the blinded phase of the mRNA-1273 Covid-19 vaccine efficacy clinical trial

Importance: The performance of immunoassays for determining past SARS-CoV-2 infection, which were developed in unvaccinated individuals, has not been assessed in vaccinated individuals. Objective: To evaluate anti-nucleocapsid antibody (anti-N Ab) seropositivity in mRNA-1273 vaccine efficacy trial participants after SARS-CoV-2 infection during the trial's blinded phase. Design: Nested analysis in a Phase 3 randomized, placebo-controlled vaccine efficacy trial. Nasopharyngeal swabs for SARS-CoV-2 PCR testing were taken from all participants on Day 1 and Day 29 (vaccination days), and during symptom-prompted illness visits. Serum samples from Days 1, 29, 57, and the Participant Decision Visit (PDV, when participants were informed of treatment assignment, median day 149) were tested for anti-N Abs. Setting: Multicenter, randomized, double-blind, placebo-controlled trial at 99 sites in the US. Participants: Trial participants were [≥] 18 years old with no known history of SARS-CoV-2 infection and at appreciable risk of SARS-CoV-2 infection and/or high risk of severe Covid-19. Nested sub-study consists of participants with SARS-CoV-2 infection during the blinded phase of the trial. Intervention: Two mRNA-1273 (Moderna) or Placebo injections, 28 days apart. Main Outcome and Measure: Detection of serum anti-N Abs by the Elecsys (Roche) immunoassay in samples taken at the PDV from participants with SARS-CoV-2 infection during the blinded phase. The hypothesis tested was that mRNA-1273 recipients have different anti-N Ab seroconversion and/or seroreversion profiles after SARS-CoV-2 infection, compared to placebo recipients. The hypothesis was formed during data collection; all main analyses were pre-specified before being conducted. Results: We analyzed data from 1,789 participants (1,298 placebo recipients and 491 vaccine recipients) with SARS-CoV-2 infection during the blinded phase (through March 2021). Among participants with PCR-confirmed Covid-19 illness, seroconversion to anti-N Abs at a median follow up of 53 days post diagnosis occurred in 21/52 (40%) of the mRNA-1273 vaccine recipients vs. 605/648 (93%) of the placebo recipients (p < 0.001). Higher SARS-CoV-2 viral copies at diagnosis was associated with a higher likelihood of anti-N Ab seropositivity (odds ratio 1.90 per 1-log increase; 95% confidence interval 1.59, 2.28). Conclusions and Relevance: As a marker of recent infection, anti-N Abs may have lower sensitivity in mRNA-1273-vaccinated persons who become infected. Vaccination status should be considered when interpreting seroprevalence and seropositivity data based solely on anti-N Ab testing Trial Registration: ClinicalTrials.gov NCT04470427
On the hip abduction moment required to balance on one leg: an experimental study

Although the time a patient can stand on one leg is a common clinical test of balance in those prone to fall, a surprising knowledge gap is how much hip abduction muscle strength is required. This is important because hip abduction strength has been shown to be important for compensating for impairments in diabetic neuropathy, for example. As a start we tested the hypothesis that maximum hip abduction muscle endurance time at 50% effort would be longer than the time that 18 young and 17 older healthy adults can stand on one leg. First, maximum hip abduction endurance time at 50% effort as well as maximum abduction strength were measured in the gravity-free plane. Then subjects were asked to balance on their left foot for as long as they could while body segment kinematics and ground reaction data were measured. The results showed that the mean intensity of the hip abduction moment required to stand on one leg exceeded 50% of the maximum hip abduction strength for all four groups (young women and men 53% and 55%, and older women and men 94% and 72% respectively). However, unipedal stance times were not limited by hip abduction 50% effort endurance time (p = 0.9). Therefore a significant portion of the hip abduction moment required to stand on one leg must be carried by passive tissues. The underlying mechanism remains to be explained.
Development of a Highly Sensitive Neurofilament Light Chain Assay on an Automated Immunoassay Platform

Background Neurofilament light chain (NfL) is an axonal cytoskeletal protein that is released into the extracellular space following neuronal or axonal injury associated with neurological conditions such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), and other diseases. NfL is detectable in the cerebrospinal fluid (CSF) and blood. Numerous studies on MS have demonstrated that NfL is correlated with disease activity, predicts disease progression, and is reduced by treatment with MS disease-modifying drugs, making NfL an attractive candidate to supplement existing clinical and imaging measures in MS. However, for NfL to achieve its potential as a clinically useful biomarker for clinical decision-making or drug development, a standardized, practical, and widely accessible assay is needed. Our objective was to develop a novel NfL assay on an automated, globally available immunoassay platform and validate its performance. Methods A prototype NfL assay was first developed and evaluated on the ADVIA Centaur® XP immunoassay system from Siemens Healthineers. The lower limit of quantitation (LLoQ), within-lab precision, assay range, cross-reactivity with neurofilament medium and heavy chains, and effect of interfering substances were determined. NfL assay values in serum and CSF were compared with radiological and clinical disease activity measures in patients with MS and ALS, respectively. This assay was further optimized to utilize serum, plasma, and CSF sample types on the Atellica® IM system and transferred to Siemens' CLIA laboratory where it was analytically validated as a laboratory-developed test (LDT). Results In this study, an LLoQ of 1.85 pg/mL, within-lab precision <6%, and an assay range of up to 646 pg/mL were demonstrated with the serum prototype assay. Cross-reactivity of <0.7% with the neurofilament medium and heavy chains was observed. Serum and CSF NfL assay values were associated with radiological and clinical disease activity measures in patients with MS and ALS, respectively. The optimized version of the NfL assay demonstrated specimen equivalence with additional plasma tube types and was analytically validated as an LDT. Conclusion The analytical performance of the NfL assay fulfilled all acceptance criteria; therefore, we suggest that the assay is acceptable for use in both research and clinical practice settings to determine elevated NfL levels in patients.
A mixed methods study evaluating acceptability of a daily COVID-19 testing regimen with a mobile-app connected, at-home, rapid antigen test: Implications for current and future pandemics

Introduction. Widespread use of at-home rapid COVID-19 antigen tests has been proposed as an important public health intervention to interrupt chains of transmission. Antigen tests may be preferred over PCR because they provide on-demand results for relatively low cost and can identify people when they are most likely to be infectious, particularly when used daily. Yet the extent to which a frequent antigen testing intervention will result in a positive public health impact for COVID-19 will depend on high acceptability and high adherence to such regimens. Methods. We conducted a mixed-methods study assessing acceptability of and adherence to a daily at-home mobile-app connected rapid antigen testing regimen among employees of a US-based media company. Acceptability was assessed across seven domains of the Theoretical Framework of Acceptability. Results. Among 31 study participants, acceptability of the daily testing intervention was generally high, with participants reporting high perceived effectiveness, intervention coherence, and self-efficacy; positive affective attitude; acceptable degree of burden and opportunity cost; and assessing the intervention as ethical. 71% reported a preference to test daily using an at-home antigen test than weekly employment-based PCR. Mean adherence to the 21-day testing regimen was 88% with 43% of participants achieving 100% adherence, 48% testing at least every other day, and 10% testing less than every other day. Conclusions. Despite overall high acceptability and adherence, we identified three implementation challenges that must be addressed for frequent serial testing for COVID-19 to be implemented at scale and have a positive public health impact. First, users need guidance on how and when to adapt testing frequencies to different epidemiological conditions. Second, users and institutions need guidelines for how to safely store and share test results. Third, implementation of serial testing strategies must prioritize health equity and protect those most vulnerable to COVID-19.
DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning

Coreset selection, which aims to select a subset of the most informative training samples, is a long-standing learning problem that can benefit many downstream tasks such as data-efficient learning, continual learning, neural architecture search, active learning, etc. However, many existing coreset selection methods are not designed for deep learning, which may have high complexity and poor generalization performance. In addition, the recently proposed methods are evaluated on models, datasets, and settings of different complexities. To advance the research of coreset selection in deep learning, we contribute a comprehensive code library, namely DeepCore, and provide an empirical study on popular coreset selection methods on CIFAR10 and ImageNet datasets. Extensive experiments on CIFAR10 and ImageNet datasets verify that, although various methods have advantages in certain experiment settings, random selection is still a strong baseline.
Missingness Bias in Model Debugging

Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools. However, in computer vision, pixels cannot simply be removed from an image. One thus tends to resort to heuristics such as blacking out pixels, which may in turn introduce bias into the debugging process. We study such biases and, in particular, show how transformer-based architectures can enable a more natural implementation of missingness, which side-steps these issues and improves the reliability of model debugging in practice. Our code is available at https://github.com/madrylab/missingness
On the Influence of Explainable AI on Automation Bias

Artificial intelligence (AI) is gaining momentum, and its importance for the future of work in many areas, such as medicine and banking, is continuously rising. However, insights on the effective collaboration of humans and AI are still rare. Typically, AI supports humans in decision-making by addressing human limitations. However, it may also evoke human bias, especially in the form of automation bias as an over-reliance on AI advice. We aim to shed light on the potential to influence automation bias by explainable AI (XAI). In this pre-test, we derive a research model and describe our study design. Subsequentially, we conduct an online experiment with regard to hotel review classifications and discuss first results. We expect our research to contribute to the design and development of safe hybrid intelligence systems.
Probing for the Usage of Grammatical Number

A central quest of probing is to uncover how pre-trained models encode a linguistic property within their representations. An encoding, however, might be spurious—i.e., the model might not rely on it when making predictions. In this paper, we try to find an encoding that the model actually uses, introducing a usage-based probing setup. We first choose a behavioral task which cannot be solved without using the linguistic property. Then, we attempt to remove the property by intervening on the model’s representations. We contend that, if an encoding is used by the model, its removal should harm the performance on the chosen behavioral task. As a case study, we focus on how BERT encodes grammatical number, and on how it uses this encoding to solve the number agreement task. Experimentally, we find that BERT relies on a linear encoding of grammatical number to produce the correct behavioral output. We also find that BERT uses a separate encoding of grammatical number for nouns and verbs. Finally, we identify in which layers information about grammatical number is transferred from a noun to its head verb.
Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views. Specifically, we consider the user-item graph as a collaborative view, the item-entity graph as a semantic view, and the user-item-entity graph as a structural view. MCCLK hence performs contrastive learning across three views on both local and global levels, mining comprehensive graph feature and structure information in a self-supervised manner. Besides, in semantic view, a k-Nearest-Neighbor (k NN) item-item semantic graph construction module is proposed, to capture the important item-item semantic relation which is usually ignored by previous work. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/MCCLK.
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. To tackle this, we build ELEVATER (Evaluation of Language-augmented Visual Task-level Transfer), the first benchmark and toolkit for evaluating(pre-trained) language-augmented visual models. ELEVATER is composed of three components. (i) Datasets. As downstream evaluation suites, it consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. (ii) Toolkit. An automatic hyper-parameter tuning toolkit is developed to facilitate model evaluation on downstream tasks. (iii) Metrics. A variety of evaluation metrics are used to measure sample-efficiency (zero-shot and few-shot) and parameter-efficiency (linear probing and full model fine-tuning). ELEVATER is a platform for Computer Vision in the Wild (CVinW), and is publicly released at at https://computer-vision-in-the-wild.github. io/ELEVATER/
Active Learning Helps Pretrained Models Learn the Intended Task

Models can fail in unpredictable ways during deployment due to task ambiguity, when multiple behaviors are consistent with the provided training data. An example is an object classifier trained on red squares and blue circles: when encountering blue squares, the intended behavior is undefined. We investigate whether pretrained models are better active learners, capable of disambiguating between the possible tasks a user may be trying to specify. Intriguingly, we find that better active learning is an emergent property of the pretraining process: pretrained models require up to 5 times fewer labels when using uncertainty-based active learning, while non-pretrained models see no or even negative benefit. We find these gains come from an ability to select examples with attributes that disambiguate the intended behavior, such as rare product categories or atypical backgrounds. These attributes are far more linearly separable in pretrained model's representation spaces vs non-pretrained models, suggesting a possible mechanism for this behavior.
Particle creation and entanglement in a dispersive model with step velocity profile

We investigate particle creation and entanglement structure in a dispersive model with subliminal dispersion relation. Assuming the step function spatial velocity profile of the background flow, mode functions for a massless scalar field is exactly obtained by the matching method. Power spectrums of created particles are calculated for the subsonic and the transsonic flow cases. For the transsonic case, the sonic horizon exists and created particles show the Planckian distribution for low frequency region but the thermal property disappears for high frequency region near the cutoff frequency introduced by the non-linear dispersion. For the subsonic case, although the sonic horizon does not exist, the effective group velocity horizon appears due to the non-linear dispersion for high frequency region and approximate thermal property of the power spectrum arises. Relation between particle creation and entanglement between each mode is also discussed.
Geometry-dependent skin effects in reciprocal photonic crystals

Abstract Skin effect that all eigenmodes within a frequency range become edge states is dictated by the topological properties of complex eigenvalues unique in non-Hermitian systems. The prevailing attempts to realize such a fascinating effect are confined to either one-dimensional or nonreciprocal systems exhibiting asymmetric couplings. Here, inspired by a recent model Hamiltonian theory, we propose a realistic reciprocal two-dimensional (2D) photonic crystal (PhC) system that shows the desired skin effect. Specifically, we establish a routine for designing such non-Hermitian systems via revealing the inherent connections between the nontrivial eigenvalue topology of order-2 exceptional points (EPs) and the skin effects. Guided by the proposed strategy, we successfully design a 2D PhC that possesses the EPs with nonzero eigenvalue winding numbers. The spectral area along a specific wavevector direction is then formed by leveraging the symmetry of the macroscopic geometry and the unit cell. The projected-band-structure calculations are performed to demonstrate that the desired skin effect exists at the specific crystalline interfaces. We finally employ time-domain simulations to vividly illustrate this phenomenon by exciting a pulse at the center of a finite-sized PhC. Our results form a solid basis for further experimental confirmations and applications of the skin effect.