Miracle-dz/UniFAR-FTUs huggingface dataset:
Intellectual structure and subject themes in information systems research: A journal cocitation study

Information Systems (IS) is a discipline in which research and practice are closely intertwined. IS is also closely related to and overlapping several other disciplines, including Information Science. Thus, IS provides an excellent case for examining the interplay of research and practice in a rapidly changing discipline. We explore the intellectual structure and subject themes in Information Systems (IS) research for 1990 to 1999 through the identification and analysis of the field's core journal literature. A core journal list of 100 titles was created and examined with journal cocitation analysis (JSA). JSA demonstrates that IS is a coherent discipline with research ranging from technology-oriented software and hardware to the application of IS in business and organizations. Journals are grouped into seven subject clusters: computer science, computer networking, computer engineering, information science, software engineering, human-computer interaction, and management information systems. Information Science journals occupy a bridging position between technically oriented and application-focused clusters. ASIST publications, JASIST, ARIST, and PASIS, figure prominently in the Information Science cluster.
A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement

Many real-world applications of speech enhancement, such as hearing aids and cochlear implants, desire real-time processing, with no or low latency. In this paper, we propose a novel convolutional recurrent network (CRN) to address real-time monaural speech enhancement. We incorporate a convolutional encoder-decoder (CED) and long short-term memory (LSTM) into the CRN architecture, which leads to a causal system that is naturally suitable for real-time processing. Moreover, the proposed model is noise-and speaker-independent, i.e. noise types and speakers can be different between training and test. Our experiments suggest that the CRN leads to consistently better objec-tive intelligibility and perceptual quality than an existing LSTM based model. Moreover, the CRN has much fewer trainable parameters.
Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling

In this study, children’s exposure to extremely low frequency magnetic fields (ELF-MF, 40–800 Hz) is investigated. The interest in this thematic has grown due to a possible correlation between the increased risk of childhood leukemia and a daily average exposure above 0.4 µT, although the causal relationship is still uncertain. The aim of this paper was to present a new method of characterizing the children’s exposure to ELF-MF starting from personal measurements using a stochastic approach based on segmentation (and to apply it to the personal measurements themselves) of two previous projects: the ARIMMORA project and the EXPERS project. The stochastic model consisted in (i) splitting the 24 h recordings into stationary events and (ii) characterizing each event with four parameters that are easily interpretable: the duration of the event, the mean value, the dispersion of the magnetic field over the event, and a final parameter characterizing the variation speed. Afterward, the data from the two databases were divided in subgroups based on a characteristic (i.e., children’s age, number of inhabitants in the area, etc.). For every subgroup, the kernel density estimation (KDE) of each parameter was calculated and the p-value histogram of the parameters together was obtained, in order to compare the subgroups and to extract information about the children’s exposure. In conclusion, this new stochastic approach allows for the identification of the parameters that most affect the level of children’s exposure.
Dysarthric Speech Recognition Using Convolutional LSTM Neural Network

Dysarthria is a motor speech disorder that impedes the physical production of speech. Speech in patients with dysarthria is generally characterized by poor articulation, breathy voice, and monotonic intonation. Therefore, modeling the spectral and temporal characteristics of dysarthric speech is critical for better performance in dysarthric speech recognition. Convolutional long short-term memory recurrent neural networks (CLSTM-RNNs) have recently successfully been used in normal speech recognition, but have rarely been used in dysarthric speech recognition. We hypothesized CLSTM-RNNs have the potential to capture the distinct characteristics of dysarthric speech, taking advantage of convolutional neural networks (CNNs) for extracting effective local features and LSTM-RNNs for modeling temporal dependencies of the features. In this paper, we investigate the use of CLSTM-RNNs for dysarthric speech recognition. Experimental evaluation on a database collected from nine dysarthric patients showed that our approach provides substantial improvement over both standard CNN and LSTM-RNN based speech recognizers.
Associations between Flavonoid Intakes and Gut Microbiota in a Group of Adults with Cystic Fibrosis

Dietary flavonoid intakes can influence gut microbiota (GM), which in turn can affect immune function and host metabolism, both vital considerations in cystic fibrosis (CF) management. In CF, GM may be altered and link to CF respiratory events. This study explored the relationship between flavonoid intakes and GM in free-living adults with CF. Associations between the overall GM variations (unweighted and weighted UniFrac distances between pyrosequencing results of bacterial 16-ss rDNA from frozen faecal samples of sixteen CF adults) and standardised dietary flavonoid intakes (a validated flavonoid-specific food frequency questionnaire) were analysed using adonis tests. Flavonoid intakes that were significant at a false discovery rate (FDR) < 0.3 were subjected to Spearman correlation tests with standardised bacterial relative abundances (FDR < 0.3). Gallocatechin intakes (p = 0.047, q = 0.285) were associated with unweighted UniFrac distances. Intakes of apigenin (p = 0.028, q = 0.227) and kaempferol (p = 0.029, q = 0.227), and % flavonoid intake as flavones (p = 0.013, q = 0.227) and flavonols (p = 0.016, q = 0.227) (both excluding contribution of tea) were associated with weighted UniFrac distances. Among these, gallocatechin correlated with the genus Actinomyces and family Actinomycetaceae (Actinobacteria). Gallocatechin correlated negatively with class Coriobacteriia (Actinobacteria). Intakes of some flavonoids may be associated with GM variations with potential consequences for metabolism, immune function, and inflammation, which are important in CF lung disease and co-morbidity management.
Deep Discriminative Embeddings for Duration Robust Speaker Verification

The embedding-based deep convolution neural networks (C-NNs) have demonstrated effective for text-independent speaker veriﬁcation systems with short utterances. However, the duration robustness of the existing deep CNNs based algorithms has not been investigated when dealing with utterances of arbitrary duration. To improve robustness of embedding-based deep C-NNs for longer duration utterances, we propose a novel algo-rithm to learn more discriminative utterance-level embeddings based on the Inception-ResNet speaker classiﬁer. Speciﬁcal-ly, the discriminability of embeddings is enhanced by reducing intra-speaker variation with center loss, and simultaneously increasing inter-speaker discrepancy with softmax loss. To further improve system performance when long utterances are available, at test stage long utterances are segmented into shorter ones, where utterance-level speaker embeddings are extracted by an average pooling layer. Experimental results show that when cosine distance is employed as the measure of similarity for a trial, the proposed method outperforms ivector/PLDA framework for short utterances and is effective for long utterances.
On the Difference Between Numerosity Processing and Number Processing

The ANS theory on the processing of non-symbolic numerosities and the ANS mapping account on the processing of symbolic numbers have been the most popular theories on numerosity and number processing, respectively, in the last 20 years. Recently, both the ANS theory and the ANS mapping account have been questioned. In the current study, we examined two main assumptions of both the ANS theory and the ANS mapping account. ERPs were measured in 21 participants during four same-different match-to-sample tasks, involving non-symbolic stimuli, symbolic stimuli, or a combination of symbolic and non-symbolic stimuli (i.e., mapping tasks). We strictly controlled the visual features in the non-symbolic stimuli. Based on the ANS theory, one would expect an early distance effect for numerosity in the non-symbolic task. However, the results show no distance effect for numerosity. When analyzing the stimuli based on visual properties, an early distance effect for area subtended by the convex hull was found. This finding is in line with recent claims that the processing of non-symbolic stimuli may be dependent on the processing of visual properties instead of on numerosity (only). With regards to the processing of symbolic numbers, the ANS mapping account states that symbolic numbers are first mapped onto their non-symbolic representations before further processing, since the non-symbolic representation is at the basis of processing the symbolic number. If the non-symbolic format is the basic format of processing, one would expect that the processing of non-symbolic numerosities would not differ between purely non-symbolic tasks and mapping tasks, resulting in similar ERP waveforms for both tasks. Our results show that the processing of non-symbolic numerosities does differ between the tasks, indicating that processing of non-symbolic number is dependent on task format. This provides evidence against the ANS mapping account. Alternative theories for both the processing of non-symbolic numerosities and symbolic numbers are discussed.
Effect of Unilateral Iatrogenic Torsion onContralateral Testis inRat, Prepubertal and Postpubertal.

PURPOSE The present study was conducted to investigate the influence of hemicastration and age at hemicastration on the subsequent contralateral testis. MATERIALS AND METHODS 64 Wistar-derived male rats divided randomly in 4 groups. Group 1 named immature intervention, group 2 immature control, group 3 mature intervention and group 4mature control. In group 1, rats hemicastrated at 30 days of age (prepubertal). In group 2 sham surgery (midscrotal incision) was done at same age. In group 3 rats hemicastrated at 70 days of age (postpubertal) and in group 4 sham surgery was done at same age. 20 days after first surgery, contralateral orchiectomy was donein intervention groups and random orchiectomy (left or right) was donein control groups. Blood sampling for evaluation of serum testosterone was performed just before second surgery. RESULTS Testis weight (1692 ± 26.7 in Group 1 vs 1375 ± 39.7 in Group 2; P< 0.001 and 1760 ± 26.6 in Group 3 vs 1425 ± 44.9 in Group 4; P< 0.001) and the mean testicular weight (mg) per 100 g of body weight (735.8 ± 82.3 in Group 1 vs 634.8 ± 84.8 in Group 2; P = 0.005 and 652.4 ± 61.4 in Group 3 vs 572.6 ± 97.7 in Group 4; P = 0.03) was greater in hemicastrated rats. These parameters was greater in prepubertal group than postpubertal hemicastrated rats. There was no appreciable difference in serum testosterone levels in 4 groups (P = 0.77). CONCLUSION Our research demonstrated that hemicastration resulted in compensatory hypertrophy of the remaining testis and it decreased as the animals aged. Hemicastration does not lead to reduction in serum testosterone levels and remaining testis can retrieve a normal serum testosterone level.
Biocatalysis of Platycoside E and Platycodin D3 Using Fungal Extracellular β-Glucosidase Responsible for Rapid Platycodin D Production

Platycodi radix (i.e., Platycodon grandiflorum root) products (e.g., tea, cosmetics, and herbal supplements) are popular in East Asian nutraceutical markets due to their reported health benefits and positive consumer perceptions. Platycosides are the key drivers of Platycodi radixes’ biofunctional effects; their nutraceutical and pharmaceutical activities are primarily related to the number and varieties of sugar side-chains. Among the various platycosides, platycodin D is a major saponin that demonstrates various nutraceutical activities. Therefore, the development of a novel technology to increase the total platycodin D content in Platycodi radix extract is important, not only for consumers’ health benefits but also producers’ commercial applications and manufacturing cost reduction. It has been reported that hydrolysis of platycoside sugar moieties significantly modifies the compound’s biofunctionality. Platycodi radix extract naturally contains two major platycodin D precursors (platycoside E and platycodin D3) which can be enzymatically converted to platycodin D via β-d-glucosidase hydrolysis. Despite evidence that platycodin D precursors can be changed to platycodin D in the Platycodi radix plant, there is little research on increasing platycodin D concentrations during processing. In this work, platycodin D levels in Platycodi radix extracts were significantly increased via extracellular Aspergillus usamii β-d-glucosidase (n = 3, p < 0.001). To increase the extracellular β-d-glucosidase activity, A. usamii was cultivated in a culture media containing cellobiose as its major carbon source. The optimal pH and temperature of the fungal β-d-glucosidase were 6.0 and 40.0 °C, respectively. Extracellular A. usamii β-d-glucosidase successfully converted more than 99.9% (w/v, n = 3, p < 0.001) of platycoside E and platycodin D3 into platycodin D within 2 h under optimal conditions. The maximum level of platycodin D was 0.4 mM. Following the biotransformation process, the platycodin D was recovered using preparatory High Performance Liquid Chromatography (HPLC) and applied to in vitro assays to evaluate its quality. Platycodin D separated from the Platycodi radix immediately following the bioconversion process showed significant anti-inflammatory effects from the Lipopolysaccharide (LPS)-induced macrophage inflammatory responses with decreased nitrite and IL-6 production (n = 3, p < 0.001). Taken together, these results provide evidence that biocatalysis of Platycodi radix extracts with A. usamii may be used as an efficient method of platycodin D-enriched extract production and novel Platycodi radix products may thereby be created.
End-To-End Audio Replay Attack Detection Using Deep Convolutional Networks with Attention

With automatic speaker veriﬁcation (ASV) systems becoming increasingly popular, the development of robust countermeasures against spooﬁng is needed. Replay attacks pose a signiﬁcant threat to the reliability of ASV systems because of the relative difﬁculty in detecting replayed speech and the ease with which such attacks can be mounted. In this paper, we pro-pose an end-to-end deep learning framework for audio replay attack detection. Our proposed approach uses a novel visual attention mechanism on time-frequency representations of utterances based on group delay features, via deep residual learning (an adaptation of ResNet-18 architecture). Using a single model system, we achieve a perfect Equal Error Rate (EER) of 0% on both the development as well as the evaluation set of the ASVspoof 2017 dataset, against a previous best of 0.12% on the development set and 2.76% on the evaluation set reported in the literature. This highlights the efﬁcacy of our feature representation and attention-based architecture in tackling the challenging task of audio replay attack detection.
LSTBM: A Novel Sequence Representation of Speech Spectra Using Restricted Boltzmann Machine with Long Short-Term Memory

In this paper, we propose a novel probabilistic model, namely long short-term Boltzmann memory (LSTBM), to represent sequential data like speech spectra. The LSTBM is an extension of a restricted Boltzmann machine (RBM) that has generative long short-term memory (LSTM) units. The original RBM automatically learns relationships between visible and hidden units and is widely used as a feature extractor, a generator, a classiﬁer, a pre-training method of deep neural networks, etc. However, the RBM is not sufﬁcient to represent sequential data because it assumes that each frame from sequential data is completely independent of the others. Unlike conventional RBMs, the LSTBM has connections over time via LSTM units and represents time dependencies in sequential data. Our speech coding experiments demonstrated that the proposed LSTBM out-performed the other conventional methods: an RBM and a temporal RBM.
miR‐214‐5p targets KLF5 and suppresses proliferation of human hepatocellular carcinoma cells

MicroRNAs (miRNAs) are small endogenous conserved RNAs regulating genes expression through base pairing with the 3′‐untranslated region (3′‐UTR) of target messenger RNAs. MiR‐214‐5p is a newly identified miRNA with its biological role largely unknown. In this study, we explored miR‐214‐5p expression status in 78 paired tumor and nontumor tissues obtained from patients with hepatocellular carcinoma (HCC) by RT‐qPCR. The effects of miR‐214‐5p expression on HCC cell proliferation, cell cycle progression, and cell migration were measured by CCK‐8 assay, flow cytometry, and wound‐healing assay. A dual‐luciferase activity assay was performed to identify whether KLF5 was a target of miR‐214‐5p. Kaplan‐Meier curve and log‐rank test were used to investigate the effects of miR‐214‐5p and KLF5 on overall survival and disease‐free survival of patients with HCC. We found miR‐214‐5p expression was sharply reduced in HCC tissues and cell lines compared with the normal tissues and cell lines. Functional assay revealed that miR‐214‐5p overexpression could downregulate cell proliferation, cell migration, and arrested cell cycle at G0/G1 phase. Further, we validated Krüppel‐like factor 5 (KLF5) as a direct target of miR‐214‐5p, and was upregulated in HCC and inversely correlated with the expression of miR‐214‐5p. Moreover, we found the low expression of miR‐214‐5p and high expression of KLF5 were correlated with tumor size, tumor stage, and poorer 5‐year overall survival and disease‐free survival of patients with HCC. In conclusion, our results suggested miR‐214‐5p functions as a tumor suppressor through targeting KLF5 in HCC. Also, miR‐214‐5p and KLF5 were identified as potential prognostic markers and might be therapeutic targets in HCC.
Detecting Signs of Dementia Using Word Vector Representations

Recent approaches to word vector representations, e.g., ‘w2vec’ and ‘GloVe’, have been shown to be powerful methods for capturing the semantics and syntax of words in a text. The approaches model the co-occurrences of words and recent successful applications on written text have shown how the vector representations and their interrelations represent the meaning or sentiment in the text. Most applications have targeted written language, however, in this paper, we investigate how these models port to the spoken language domain where the text is the result of (erroneous) automatic speech transcription. In particular, we are interested in the task of detecting signs of dementia in a person’s spoken language. This is motivated by the fact that early signs of dementia are known to affect a person’s ability to express meaning articulately for example when they engage in a conversation – something which is known to be cognitively very demanding. We analyse conversations designed to probe people’s short and long-term memory and propose three different methods for how word vectors may be used in a classiﬁcation setup. We show that it is possible to identify dementia from the output of a speech recogniser despite a high occurrence of recognition errors.
Ultrasound Grade of Liver Steatosis Is Independently Associated with the Risk of Metabolic Syndrome

The aim of the study was to explore (a) prevalence and grade of nonalcoholic fatty liver (NAFL) among outpatients referred for abdominal ultrasound (US) examination and (b) relationship between the presence and severity of liver steatosis and metabolic syndrome (MS). This was a retrospective analysis of patients without history of liver disease examined by abdominal US in the University hospital setting. US was used to detect and semiquantitatively grade (0-3) liver steatosis. Data on patients' age, gender, body mass index (BMI), impaired glucose metabolism (IGM), atherogenic dyslipidaemia (AD), raised blood pressure (RBP), transaminases, and platelet counts were obtained from medical records. MS was defined as having at least 3 of the following components: obesity, IGM, AD, and RBP. Of the 631 patients (median age 60 years, median BMI 27.4 kg/m2, and 57.4% females) 71.5% were overweight and 48.5% had NAFL. In the subgroup of 159 patients with available data on the components of MS, patients with higher US grade of steatosis had significantly higher BMI and increased prevalence of obesity, IGM, AD, RBP, and accordingly more frequently had MS, whereas they did not differ in terms of age and gender. NAFL was independently associated with the risk of having MS in a multivariate model adjusted for age, gender, BMI, and IGM. The grade of liver steatosis did not correlate with the presence of liver fibrosis. We demonstrated worrisome prevalence of obesity and NAFL in the outpatient population from our geographic region. NAFL is independently associated with the risk of having MS implying worse prognosis.
Learning Local Distortion Visibility from Image Quality

Accurate prediction of local distortion visibility thresholds is critical in many image and video processing applications. Existing methods require an accurate modeling of the human visual system, and are derived through pshycophysical experiments with simple, artificial stimuli. These approaches, however, are difficult to generalize to natural images with complex types of distortion. In this paper, we explore a different perspective, and we investigate whether it is possible to learn local distortion visibility from image quality scores. We propose a convolutional neural network based optimization framework to infer local detection thresholds in a distorted image. Our model is trained on multiple quality datasets, and the results are correlated with empirical visibility thresholds collected on complex stimuli in a recent study. Our results are comparable to state-of-the-art mathematical models that were trained on phsycovisual data directly. This suggests that it is possible to predict psychophysical phenomena from visibility information embedded in image quality scores.
Unusually Small Thermal Expansion of Ordered Perovskite Oxide CaCu3Ru4O12 with High Conductivity

We measured the coefficient of thermal expansion (CTE) of conducting composite ceramics 30 vol.% CuO-mixed CaCu3Ru4O12 together with CaCu3Ru4O12 and CuO. Although conducting ceramics tend to show higher CTE values than insulators, and its CTE value does not match with other ceramic materials, the CTE of CaCu3Ru4O12 (7–9 × 10−6/K) was as small as those of insulators such as CuO (9 × 10−6/K), alumina (8 × 10−6/K), and other insulating perovskite oxides. We propose that the thermal expansion of CaCu3Ru4O12 was suppressed by the Cu-O bond at the A-site due to the Jahn–Teller effect. This unusually small CTE of CaCu3Ru4O12 compared to other conducting oxides plays a vital role enabling successful coating of 30 vol.% CuO-mixed CaCu3Ru4O12 thick films on alumina substrates, as demonstrated in our previous study.