Miracle-dz/UniFAR-FTUs huggingface dataset:
Gray Matter Atrophy Is Associated With Cognitive Impairment in Patients With Presbycusis: A Comprehensive Morphometric Study

Presbycusis (PC) is characterized by bilateral sensorineural hearing loss at high frequencies and speech-perception difficulties in noisy environments and has a strikingly detrimental impact on cognitive function. As the neural consequences of PC may involve the whole brain, we hypothesized that patients with PC would show structural alterations not only in the auditory cortex but also in the cortexes involved in cognitive function. The purpose of this study was to use surface-based morphometry (SBM) analysis to elucidate whole-brain structural differences between patients with PC and age-matched normal hearing controls. Three-dimensional T1-weighted MR images of 26 patients with mild PC and 26 age-, sex- and education-matched healthy controls (HCs) were acquired. All participants underwent a battery of neuropsychological tests. Our results revealed gray matter atrophy in several auditory cortical areas, nodes of the default mode network (DMN), including the bilateral precuneus and inferior parietal lobule, the right posterior cingulate cortex (PCC), and the right insula of patients with PC compared to that in the HCs. Our findings also revealed that hearing loss was associated with reduced gray matter volume in the right primary auditory cortex of patients with PC. Moreover, structural alterations in the nodes of the DMN were associated with cognitive impairments in PC patients. Additionally, this study provides evidence that a thicker right insula is associated with better speech perception in patients with PC. Based on these findings, we argue that the onset of PC seems to trigger its own cascade of conditions, including a need for increased cognitive resources during speech comprehension, which might lead to auditory and cognition-related cortical reorganization.
SLAYER: Spike Layer Error Reassignment in Time

Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation. However, the spike generation function is non-differentiable and therefore not directly compatible with the standard error backpropagation algorithm. In this paper, we introduce a new general backpropagation mechanism for learning synaptic weights and axonal delays which overcomes the problem of non-differentiability of the spike function and uses a temporal credit assignment policy for backpropagating error to preceding layers. We describe and release a GPU accelerated software implementation of our method which allows training both fully connected and convolutional neural network (CNN) architectures. Using our software, we compare our method against existing SNN based learning approaches and standard ANN to SNN conversion techniques and show that our method achieves state of the art performance for an SNN on the MNIST, NMNIST, DVS Gesture, and TIDIGITS datasets.
The factor structure of a short acculturation scale for Filipino Americans in an adult U.S.‐born sample

Abstract The influx of non‐European immigrants since 1965 ushered the development and use of acculturation measures in immigrant health studies. A Short Acculturation Scale for Filipino Americans (ASASFA) represents a validated, unidirectional ethnic‐specific measure used with first‐generation FAs. ASASFA's psychometric properties with adult U.S.‐born children—the second generation—remain unexplored. This study determined (a) the factor structure of ASASFA with adult U.S.‐born FAs and (b) the predictors of their acculturation scores. A secondary analysis was conducted on ASASFA data from a mental health survey of 116 U.S.‐born FAs. Exploratory factor and parallel analyses showed a two‐factor solution: language use and preference (Factor 1) and ethnic social relations (Factor 2). Ordinary least squares regression indicated gender and ethnic self‐identification predict Factor 1 scores; self‐identification solely predicts Factor 2 scores. Results demonstrate ASASFA's validity and parsimony, supporting its use in FA health studies when lengthy bidirectional acculturation measures become impractical.
Play to Learn: Conducting a Playtest Session for Improving an Educational Game

The integration of game elements in education is a widespread trend to increase learner’s engagement and motivation. Nevertheless, many game-based learning applications failure because they are not fun. Game design processes that are used to iteratively develop commercial games include playtests during the entire design process to make sure that the desired gaming experience is achieved. However, playtests are seldom used in the development process of game-based learning applications. For this reason, we conduct and evaluate a playtest session for a digital prototype of a serious game for learning. Generally, the results show that the playtesters positively assessed the serious game but there are some aspects for improvement as well.
How Prior Users' Helpfulness Votes on a Review Influence Subsequent Users' Trust of the Review and Corresponding Product Evaluations in E-commerce Context

We focus on how two numeric characteristics of prior users’ votes influence user’s attitude towards the product/service reviewed. These characteristics are 1) vote ratio and 2) vote magnitude. The former is the ratio of prior viewers who believe that a review is helpful, and the latter is the total number of prior viewers who vote the review. We draw on social influence theory and propose vote ratio influential across positive and negative reviews but vote magnitude influential only for negative reviews. We conduct two experiments to test the research model. Our research finds that regardless of the valence of reviews, vote ratio enhances trustworthiness and guide corresponding product evaluation. By contrast, vote magnitude is significantly influential only for the negative review. These findings contribute to review helpfulness literature and extend social influence theory. Our research also provides practical implications for online voting system providers, general participatory sites, and online retailers.
Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments

This paper introduces PyDCI, a new implementation of Distributional Correspondence Indexing (DCI) written in Python. DCI is a transfer learning method for cross-domain and cross-lingual text classification for which we had provided an implementation (here called JaDCI) built on top of JaTeCS, a Java framework for text classification. PyDCI is a stand-alone version of DCI that exploits scikit-learn and the SciPy stack. We here report on new experiments that we have carried out in order to test PyDCI, and in which we use as baselines new high-performing methods that have appeared after DCI was originally proposed. These experiments show that, thanks to a few subtle ways in which we have improved DCI, PyDCI outperforms both JaDCI and the above-mentioned high-performing methods, and delivers the best known results on the two popular benchmarks on which we had tested DCI, i.e., MultiDomainSentiment (a.k.a. MDS -- for cross-domain adaptation) and Webis-CLS-10 (for cross-lingual adaptation). PyDCI, together with the code allowing to replicate our experiments, is available at this https URL .
Gradient target propagation

We report a learning rule for neural networks that computes how much each neuron should contribute to minimize a giving cost function via the estimation of its target value. By theoretical analysis, we show that this learning rule contains backpropagation, Hebian learning, and additional terms. We also give a general technique for weights initialization. Our results are at least as good as those obtained with backpropagation. The neural networks are trained and tested in three problems: MNIST, MNIST-Fashion, and CIFAR-10 datasets. The associated code is available at this https URL.
A review of the IS strategic alignment literature: A replication study

Using the Chen, Mocker, Preston, and Teubner (2010) study titled Information Systems Strategy: Reconceptualization, Measurement, and Implications as a guide, we investigate if any published literature since 2010 utilize the measures, the typology, and the conceptions identified in Chen et al., (2010). We seek to identify IS Strategy literature from leading IS journals and explain the impact from Chen et al. (2010) that is found. Our results demonstrate that much of the discourse postulated in the Chen et al. (2010) study remains in the information systems (IS) strategy research domain and that indeed there are several studies that extend and/or utilized various findings originating from the Chen et al. (2010) study.
Taking Advantage of Using Professionally-Oriented Social Network Sites: The Role of Users' Actions and Profiles

The main objective of this research is to propose a model that explains the process by which individuals develop social capital through using Professionally-Oriented Social Network Sites (P-SNSs) such as LinkedIn. The theoretical framework of the proposed research draws upon the extant literature in social network analysis and social capital theory. The proposed research model aims to explain how people’s investment on their social networks by building their profiles and actively participating on P-SNSs can lead to developing sources of social capital which, in turn, can provide them valuable benefits. Our research results show that profile disclosure and active participation positively affect perceived social connectedness. However, only profile disclosure positively affects network size, and there is no association between active participation and network size. This study differs from previous studies in this field in that it highlights the role of one’s profile in the social capital formation process on P-SNSs.
Modeling Composite Labels for Neural Morphological Tagging

Neural morphological tagging has been regarded as an extension to POS tagging task, treating each morphological tag as a monolithic label and ignoring its internal structure. We propose to view morphological tags as composite labels and explicitly model their internal structure in a neural sequence tagger. For this, we explore three different neural architectures and compare their performance with both CRF and simple neural multiclass baselines. We evaluate our models on 49 languages and show that the neural architecture that models the morphological labels as sequences of morphological category values performs significantly better than both baselines establishing state-of-the-art results in morphological tagging for most languages.
Private Information Retrieval over Networks

In this paper, the problem of providing privacy to users requesting data over a network from a distributed storage system (DSS) is considered. The DSS, which is considered as the multi-terminal destination of the network from the user's perspective, is encoded by a maximum rank distance (MRD) code to store the data on these multiple servers. A private information retrieval (PIR) scheme ensures that a user can request a file without revealing any information on which file is being requested to any of the servers. In this paper, a novel PIR scheme is proposed, allowing the user to recover a file from a storage system with low communication cost, while allowing some servers in the system to collude in the quest of revealing the identity of the requested file. The network is modeled as in random linear network coding (RLNC), i.e., all nodes of the network forward random (unknown) linear combinations of incoming packets. Both error-free and erroneous RLNC networks are considered.
The Role of Cerebral Embolic Protection Devices During Transcatheter Aortic Valve Replacement

Transcatheter aortic valve replacement is the therapy of choice for patients with severe aortic stenosis who have prohibitive or high surgical risk. However, the benefit of TAVR is attenuated by the occurrence of major disabling stroke which is associated with increased mortality and early-reduced quality of life. Despite advances in TAVR technology, stroke remains a serious complication that is associated with significant negative outcomes. The majority of these occur in the acute phase following TAVR where cerebral embolic events are frequent. Cerebral embolic protection devices (CEPD) have been developed to minimize the risk of peri-procedural ischemic stroke during TAVR. CEPD have the potential to reduce intraprocedural burden of new silent ischemic injury. In this review we outline the etiology and incidence of stroke in TAVR population, and systematically review current evidence for cerebral embolic protection devices.
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification

Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-attention hierarchical recurrent neural network for DA classification. Our model is partially inspired by the observation that conversational utterances are normally associated with both a DA and a topic, where the former captures the social act and the latter describes the subject matter. However, such a dependency between DAs and topics has not been utilised by most existing systems for DA classification. With a novel dual task-specific attention mechanism, our model is able, for utterances, to capture information about both DAs and topics, as well as information about the interactions between them. Experimental results show that by modelling topic as an auxiliary task, our model can significantly improve DA classification, yielding better or comparable performance to the state-of-the-art method on three public datasets.
Genomic Network-Based Analysis Reveals Pancreatic Adenocarcinoma Up-Regulating Factor-Related Prognostic Markers in Cervical Carcinoma

We previously showed that PAUF is involved in tumor development and metastases in cervical cancer. This study was conducted to discover novel molecular markers linked with PAUF in cervical cancer using genomic network analysis and to assess their prognostic value in cervical cancer. Three PAUF-related genes were identified using in-silico network-based analysis of the open genome datasets. To assess the expression of these genes and their relationship to the outcome of cervical cancer, immunohistochemical analysis was performed using cervical cancer TMA. The associations of the identified proteins with clinicopathologic characteristics and prognosis were examined. AGR2, BRD7, and POM121 were identified as interconnected with PAUF through in-silico network-based analysis. AGR2 (r = 0.213, p < 0.001) and POM121 (r = 0.135, p = 0.013) protein expression were positively correlated with PAUF. BRD7High and AGR2Low were significantly associated with favorable disease-free survival (DFS) (p = 0.009 and p < 0.001, respectively), and in combination with PAUFHigh, even more significantly favorable DFS observed (p < 0.001 for both). In multivariate analysis, AGR2High (HR = 3.16, p = 0.01) and BRD7High (HR = 0.5, p = 0.025) showed independent prognostic value for DFS. In a random survival forest (RSF) model, the combined clinical and molecular variable model predicted DFS with significantly improved power compared with that of the clinical variable model (C-index of 0.79 vs. 0.75, p < 0.001). In conclusion, AGR2 and BRD7 expression have prognostic significance in cervical cancer and provide opportunities for improved treatment options. Genomic network-based approaches using the cBioPortal may facilitate the discovery of additional biomarkers for the prognosis of cervical cancer and may provide new insights into the biology of cervical carcinogenesis.
Costumes in League of Legends - Replicating Findings from Cyworld and Habbo

A danger in contemporary academic research is the occurrence of the publication bias effect – a scientific contamination resulting from the implicit demand in academic outlets for publications to report significant results. One possibility to address this phenomenon relies in strengthening replication research, which allows scientific knowledge to rigorously grow. We use an original study from Kim et al. (2012) which explained the purchase of digital items in the two virtual communities Cyworld and Habbo with the theory of self-presentation, and try to conceptually replicate their findings in the context of League of Legends. Therefore, we conducted an online survey (n=209). Although our results empirically support most of our hypotheses, the overall test of the postulated original model indicated an insufficient fit. In response, we illustrate the derivation of an adjusted model, compare the original and the adjusted model, and discuss arising implications.
Unsupervised Detection of Anomalous Sound Based on Deep Learning and the Neyman–Pearson Lemma

This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of the unsupervised-ADS is to detect unknown anomalous sounds without training data of anomalous sounds. The use of an AE as a normal model is a state-of-the-art technique for the unsupervised-ADS. To decrease the false positive rate (FPR), the AE is trained to minimize the reconstruction error of normal sounds, and the anomaly score is calculated as the reconstruction error of the observed sound. Unfortunately, since this training procedure does not take into account the anomaly score for anomalous sounds, the true positive rate (TPR) does not necessarily increase. In this study, we define an objective function based on the Neyman–Pearson lemma by considering the ADS as a statistical hypothesis test. The proposed objective function trains the AE to maximize the TPR under an arbitrary low FPR condition. To calculate the TPR in the objective function, we consider that the set of anomalous sounds is the complementary set of normal sounds and simulate anomalous sounds by using a rejection sampling algorithm. Through experiments using synthetic data, we found that the proposed method improved the performance measures of the ADS under low FPR conditions. In addition, we confirmed that the proposed method could detect anomalous sounds in real environments.
How ECS Improve Creative Use of Employees' Knowledge?

Recently, organizations are using crowdsourcing systems (CSs) to collect innovative ideas from their employees harnessing their insights of companies’ products, processes, customers, and competitors. While crowd workers in third-party CSs are a diverse and multifaceted population with a range of motives and experience, and yet few researchers have grappled with the facilitators of the employees’ behavior comprising the creative application of their knowledge using enterprise CSs. This study develops a theoretical framework to identify enterprise CSs role and to provide the way how CSs are related to creative behavior via knowledge sharing. In this research, we used a survey to collect data from organizational employees and conducted data analysis to understand how enterprise CSs affect employees’ creative knowledge application. The findings of this study can help organization refine their ECSs and innovative initiatives.
Contributions of Three Starch Branching Enzyme Isozymes to the Fine Structure of Amylopectin in Rice Endosperm

Three starch branching enzyme (BE) isozymes, BEI, BEIIa, and BEIIb, are involved in starch biosynthesis in rice endosperm. Past in vivo and in vitro studies have suggested that each BE isozyme plays a distinct role in forming the fine structure of amylopectin. To elucidate more details of their roles, we prepared DNA constructs in which all the possible combinations of the expressions of these three isozymes were suppressed in developing rice endosperm. Analysis of the chain-length distributions of amylopectin produced under these various conditions confirmed the contributions of the individual BE isozymes to the fine structure of amylopectin in rice endosperm. Among these isozymes, the impact of loss of BEIIb activity on amylopectin fine structure was most remarkable and indicated that it plays a specific role in the synthesis of short chains with a 6–13 degree of polymerization (DP). The contribution of BEI to the amylopectin synthesis was unclear when only BEI activity was reduced. It was clear, however, when both BEI and BEIIb activities were substantially inhibited. The DP11-22 intermediate chains were markedly reduced in the ΔBEI/BEIIb line compared with the ΔBEIIb line, indicating that BEI plays a distinct role in the synthesis of these intermediate chains. Although no substantial change in amylopectin chain profile was detected in the ΔBEIIa line, the role of BEIIa could be deciphered by analyzing amylopectin fine structure from the ΔBEI/BEIIa/BEIIb line in comparison to that from ΔBEI/BEIIb line. This strongly suggests that BEIIa compensates for the role of BEI, rather than that of BEIIb, by forming intermediate chains of DP11-22. In addition, the new possibility that BEIIa is involved in the formation of starch granules in rice endosperm was suggested because the onset temperature for gelatinization of starch granules in the ΔBEIIa/BEIIb line was significantly higher than that in the ΔBEIIb line. In summary, the present study highlights the distinct roles of BEI, BEIIa, and BEIIb in the synthesis of amylopectin in developing rice endosperm.
The Role of mHealth for Equitable Access to Healthcare for Rural Residents

While Mobile Health (mHealth), and medical applications offer the possibility of pervasive and improved delivery of healthcare services, their adoption in rural communities remains limited. Following an overview of the potential of mHealth applications to support improved access to equitable healthcare by rural residents, this paper offers an analysis of perceptions held by rural residents relating to their use technologies in a rural Midwestern community. Using data from a sample of 1,667 rural residents, a descriptive statistics analysis of rural residents’ use of mobile applications to support equitable access to healthcare is carried out. The findings suggest that mHealth applications can support equitable access to healthcare for rural residents by offering specific functionalities for communicating with providers, searching for health information and tools for addressing common symptoms. The contribution of this paper is in offering key concepts relating to equitable healthcare provision through the use of internet enabled mobile devices.
Conceptualizing IT Service Management as a Management Control System for Business-IT Alignment

The present paper advances a new conceptualization of IT Service Management (ITSM) as a Management Control System. The paper develops a conceptual model on the organizational impacts of ITSM. The model argues that reaching the organizational objectives of ITSM implementations, that is, increased IT function productivity, customer satisfaction and IT function responsiveness, requires the fit (conceptualized as profile deviation) of five ITSM controls which include: 1) values, 2) planning, 3) rewards and compensation, 4) cybernetic systems and 5) administrative controls. This paper helps unpack the black-box of ITSM by theorizing on the mechanisms that generate outcomes for organization.