Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic and Virulent Difference Between Pigmented and Non-pigmented Staphylococcus aureus

Staphyloxanthin (STX), a golden carotenoid pigment produced by Staphylococcus aureus, is suggested to act as an important virulence factor due to its antioxidant properties. Restraining biosynthesis of STX was considered as an indicator of virulence decline in pigmented S. aureus isolates. However, it is not clear whether natural non-pigmented S. aureus isolates have less virulence than pigmented ones. In this study, it is aimed to compare the pigmented and non-pigmented S. aureus isolates to clarify the genetic and virulent differences between the two groups. Here, 132 S. aureus isolates were divided into two phenotype groups depending on the absorbance (OD450) of the extracted carotenoids. Then, all isolates were subjected to spa typing and multilocus sequence typing (MLST), and then the detection of presence of 30 virulence factors and the gene integrity of crtN and crtM. Furthermore, 24 typical S. aureus isolates and 4 S. argenteus strains were selected for the murine infection assay of in vivo virulence, in which the histological observation and enumeration of CFUs were carried out. These isolates were distributed in 26 sequence types (STs) and 49 spa types. The pigmented isolates were scattered in 25 STs, while the non-pigmented isolates were more centralized, which mainly belonged to ST20 (59%) and ST25 (13%). Among the 54 non-pigmented isolates, about 20% carried intact crtN and crtM genes. The in vivo assay suggested that comparing with pigmented S. aureus, non-pigmented S. aureus and S. argenteus strains did not show a reduced virulence in murine sepsis models. Therefore, it suggested that there were no significant genetic and virulent differences between pigmented and non-pigmented S. aureus.
Delay-Aware LTE WLAN Aggregation in Heterogeneous Wireless Network

The scarcity of spectrum turns the next generation wireless network increasingly heterogeneous. Recently, much attention has been focused on exploiting the unlicensed spectrum to leverage traffic burden from the cellular network. Specified in 3GPP Release 13, long term evolution (LTE) wireless local area networks (WLAN) aggregation (LWA) promises an effective approach for the LTE WLAN interworking and unlicensed spectrum utilization. The LWA could improve the data rate by the licensed and unlicensed carriers aggregation. In contrast to the previous studies, where the LWA is always chosen to boost the peak data rate without considering the user payment, we jointly consider the LWA with Wi-Fi offloading in this paper, aiming to strike the balance between user payment and quality of service (QoS) requirement. We formulate the multi-slot modes selection as a finite-horizon Markov decision problem and propose a Delay-Aware LTE WLAN Aggregation (DLWA) algorithm to obtain the optimal transmission modes strategy. Due to the computational complexity of sequential decision problem, we exhibit the threshold structure of LTE usage and develop a low complexity algorithm based on this structure. Moreover, imperfection in LWA backhaul is considered and analyzed in this paper. Simulation results show the DLWA algorithm and threshold-based DLWA algorithm could guarantee the QoS requirement with lower user payment compared with current LTE WLAN interworking schemes.
Enterprise contextual awareness from news and social media — A framework

This paper proposes a formal framework for capturing context for enterprise tasks with the aim to provide contextual awareness about business-critical events to enterprise decision-makers at the point of decision-making. Contextual awareness can enhance business intelligence by providing critical insights about why questions around enterprise data. It exploits enterprise social intelligence to gradually improve the quality of recommendations.
Influence of Compaction Pressure and Particle Content on Thermal and Mechanical Behavior of Artificial Marbles with Marble Waste and Unsaturated Polyester

Compact artificial stones are widely used in buildings around the world, and the expected demand to the next years is increasing. In this study, stone fragments from marble processing are recycled as raw materials for produce compact artificial stones using compaction in a vacuum environment (100 mm Hg). Crushed marble waste (80 and 85%wt) are mixed with unsaturated polyester resin as binder. Materials were processed under compaction pressures of 1 and 10 MPa, for 20 minutes at 90 °C. Artificial marble with flexural ultimate strength of 25 MPa, water absorption below 0.2% and homogeneous particles distribution were obtained to materials produced with 80%wt of marble particles. To artificial marbles with 85%wt of particles, lower flexural ultimate strength and higher water absorption were observed (18 MPa and 0.4% respectively). In addition, the greater compaction pressure used improves slightly values obtained in 3 points bending test. Furthermore, tangent delta peaks at 115 °C were observed to evaluated artificial marbles.
Detection of shoe sole features using DNN

Shoe prints belong among the most common types of evidence at crime scenes. To determine the brand or manufacturer of a shoe, it must be matched with known collection of shoe print samples, or with shoe sole pictures. Comparing the shoe print to the shoe sole picture is difficult task, because of different nature of data. Therefore it is more convenient to compare shoe sole features instead of raw image data. In this paper we focus on detection of the shoe sole features in picture of shoe sole using Deep Neural Network.
Analyzing Relationship of Words Using Biased LexRank from Geotagged Tweets

A place is one important factor that affects the relevance between two words. For example, in places where tourists can view Tokyo Tower and cherry blossoms at the same time, it is considered that these words are related. However, in other places, these words would not be considered related. In this research, we propose a method to extract the relationship between words in an area based on this hypothesis, using geotagged texts obtained from Twitter. To extract the relevant words of a word posted at each place, our approach uses propagation of words co-occurring with co-occurring words in texts. We apply Biased LexRank, adapted from PageRank, to a graph constructed from co-occurrence relationships in each area. We also determined the places where there are two characteristic objects based on the relationship of the words. We visualize and discuss such places based on the relationship, such as places where you can appreciate "Tokyo Tower" and "cherry blossom" together.
A software tool of digital tomosynthesis application for patient positioning in radiotherapy

Digital Tomosynthesis (DTS) is an image modality in reconstructing tomographic images from two‐dimensional kV projections covering a narrow scan angles. Comparing with conventional cone‐beam CT (CBCT), it requires less time and radiation dose in data acquisition. It is feasible to apply this technique in patient positioning in radiotherapy. To facilitate its clinical application, a software tool was developed and the reconstruction processes were accelerated by graphic processing unit (GPU). Two reconstruction and two registration processes are required for DTS application which is different from conventional CBCT application which requires one image reconstruction process and one image registration process. The reconstruction stage consists of productions of two types of DTS. One type of DTS is reconstructed from cone‐beam (CB) projections covering a narrow scan angle and is named onboard DTS (ODTS), which represents the real patient position in treatment room. Another type of DTS is reconstructed from digitally reconstructed radiography (DRR) and is named reference DTS (RDTS), which represents the ideal patient position in treatment room. Prior to the reconstruction of RDTS, The DRRs are reconstructed from planning CT using the same acquisition setting of CB projections. The registration stage consists of two matching processes between ODTS and RDTS. The target shift in lateral and longitudinal axes are obtained from the matching between ODTS and RDTS in coronal view, while the target shift in longitudinal and vertical axes are obtained from the matching between ODTS and RDTS in sagittal view. In this software, both DRR and DTS reconstruction algorithms were implemented on GPU environments for acceleration purpose. The comprehensive evaluation of this software tool was performed including geometric accuracy, image quality, registration accuracy, and reconstruction efficiency. The average correlation coefficient between DRR/DTS generated by GPU‐based algorithm and CPU‐based algorithm is 0.99. Based on the measurements of cube phantom on DTS, the geometric errors are within 0.5 mm in three axes. For both cube phantom and pelvic phantom, the registration errors are within 0.5 mm in three axes. Compared with reconstruction performance of CPU‐based algorithms, the performances of DRR and DTS reconstructions are improved by a factor of 15 to 20. A GPU‐based software tool was developed for DTS application for patient positioning of radiotherapy. The geometric and registration accuracy met the clinical requirement in patient setup of radiotherapy. The high performance of DRR and DTS reconstruction algorithms was achieved by the GPU‐based computation environments. It is a useful software tool for researcher and clinician in evaluating DTS application in patient positioning of radiotherapy. PACS number(s): 87.57. nf
The Journal of the Acoustical Society of America The role of phase synchronisation between low frequency amplitude modulations in child phonology and morphology speech tasks

22 23 Recent models of the neural encoding of speech suggest a core role for amplitude modulation 24 (AM) structure, particularly regarding AM phase alignment. Accordingly, speech tasks that 25 measure linguistic development in children may exhibit systematic properties regarding AM 26 structure. Here the acoustic structure of spoken items in child phonological and 27 morphological tasks, phoneme deletion and plural elicitation, was investigated. The phase 28 synchronisation index (PSI), reflecting the degree of phase alignment between pairs of AMs, 29 was computed for 3 AM bands (delta, theta, beta/low gamma; 0.9-2.5 Hz, 2.5-12 Hz, 12-40 30 Hz respectively), for five spectral bands covering 100 – 7250 Hz. For phoneme deletion, data 31 from 94 child participants with and without dyslexia was used to relate AM structure to 32 behavioural performance. Results revealed that a significant change in magnitude of the 33 phase synchronisation index (∆PSI) of slower AMs (delta-theta) systematically accompanied 34 both phoneme deletion and plural elicitation. Further, children with dyslexia made more 35 linguistic errors as the delta-theta ∆PSI increased. Accordingly, ∆PSI between slower 36 temporal modulations in the speech signal systematically distinguished test items from 37 accurate responses and predicted task performance. This may suggest that sensitivity to 38 slower AM information in speech is a core aspect of phonological and morphological 39 development. 40 41
Phone Application for Indoor Localization Based on Ble Signal Fingerprint

Indoor position of devices and persons is a very valuable information for context-aware devices and human activity recognition. The need of such information grows bigger by the advancement of IoT technologies. In this paper, we discuss existing indoor positioning approaches and their applications, give specific interest to the signal strength fingerprint approaches, to which belongs the presented paper. Bluetooth Low Energy Beacons are used as the source of the signal, and the Radial Basis Functions method is employed to create a model that relates to signal strength and position. The proposed approach allows the possibility of making all the computation on a smartphone application, namely, offline data collection and model computation, and online position estimation. Besides, it allows updating the model later.
PhishBox: An Approach for Phishing Validation and Detection

In this paper, we propose an approach, called PhishBox, to effectively collect phishing data and generate models for phishing validation and detection. The proposed approach integrates the phishing websites collection, validation and detection into an on-line tool, which can monitor the blacklist of PhishTank and validate and detect phishing websites in real-time. Due to the short life time of phishing websites, the proposed approach uses a two-stage detection model to ensure the performance. First, we design an ensemble model to validate the phishing data and apply active learning for reducing the cost of manual labeling. The result shows that our ensemble validation model can achieve high performance with 95% accuracy and 3.9% false-positive rate. Next, the validated phishing data will be used to train a detection model. Comparing with the original dataset, the false-positive rate of phishing detection is dropped by 43.7% in average. After participating the voting procedure on PhishTank, the result shows that our two-stage model is effective to verify phishing websites. Finally, we monitor the blacklist and found that the blacklist contains lots of invalid data. According to our experiment, we can remove five times more than regularly update after one week.
Tunable Schiffman phase shifter for continuous beam steering antenna

This paper proposes a compact and tunable phase shifter based on Schiffman topology for beam steering antenna in Ku-band. The achievement of the tunability is carried out by placing capacitors on the phase shifter to control the phase shift variation. For this, two configurations have been developed and optimized with ADS software simulator, the first used two capacitors and the second used three capacitors. By adjusting the capacitance, the phase shifters provide a continuously tunable phase shift of 70.66° and 94.4° at 12.7 GHz in two and three compensated capacitors structure, respectively. The phase shifter compensated with two and three capacitances is able to achieve a beam steering angle of 14° and 20°, respectively. Simulations results show a good performance with excellent phase shift characteristics over a wide bandwidth.
Joint analysis of left‐censored longitudinal biomarker and binary outcome via latent class modeling

Joint latent class modeling is an appealing approach for evaluating the association between a longitudinal biomarker and clinical outcome when the study population is heterogeneous. The link between the biomarker trajectory and the risk of event is reflected by the latent classes, which accommodate the underlying population heterogeneity. The estimation of joint latent class models may be complicated by the censored data in the biomarker measurements due to detection limits. We propose a modified likelihood function under the parametric assumption of biomarker distribution and develop a Monte Carlo expectation‐maximization algorithm for joint analysis of a biomarker and a binary outcome. We conduct simulation studies to demonstrate the satisfactory performance of our Monte Carlo expectation‐maximization algorithm and the superiority of our method to the naive imputation method for handling censored biomarker data. In addition, we apply our method to the Genetic and Inflammatory Markers of Sepsis study to investigate the role of inflammatory biomarker profile in predicting 90‐day mortality for patients hospitalized with community‐acquired pneumonia.
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension

Reading comprehension has been widely studied. One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is already comparable with human. On the other hand, accessing large collections of multimedia or spoken content is much more difficult and time-consuming than plain text content for humans. It's therefore highly attractive to develop machines which can automatically understand spoken content. In this paper, we propose a new listening comprehension task - Spoken SQuAD. On the new task, we found that speech recognition errors have catastrophic impact on machine comprehension, and several approaches are proposed to mitigate the impact.
Elckerlyc Goes Mobile Enabling Natural Interaction in Mobile User Interfaces

The fast growth of computational resources and speech technology available on mobile devices makes it possible to entertain users of these devices in having a natural dialogue with service systems. These systems are sometimes perceived as social agents and this can be supported by presenting them on the interface by means of an animated embodied conversational agent. To take the full advantage of the power of embodied conversational agents in service systems it is important to support real-time, online and responsive interaction with the system through the embodied conversational agent. The design of responsive animated conversational agents is a daunting task. Elckerlyc is a model-based platform for the speciﬁcation and animation of synchronised multi-modal responsive animated agents. This paper presents a new light-weight PictureEngine that allows to run this platform in mobile applications. We describe the integration of the PictureEngine in the user interface of two different coaching applications and discuss the ﬁndings from user evaluations. We also conducted a study to evaluate an editing tool for the speciﬁcation of the agent’s communicative behaviour. Twenty one participants had to specify the behaviour of an embodied conversational agent using the PictureEngine. We may conclude that this new lightweight back-end engine for the Elckerlyc platform makes it easier to build embodied conversational interfaces for mobile devices.
Optimization of LevelDB by Separating Key and Value

LevelDB uses the merger mechanism for data integration during the writing process. During this process, the value will move together with the key, causing a lot of unnecessary data rewriting. This paper presents a structure that the key stored separately from the value. And the value is stored in a separate file (we call it Value File), with value offset in the file and length information stored in LevelDB. Test results show that the optimized LevelDBs sequential write performance is reduced by about 40%. But random write and overwrite performances improve more than 200%. And with the increase of the number of tests records, the improvement becomes more and more obvious. The amount of rewriting data and the number of merging files, depending on the length of every different record, reduce about 80% averagely, which significantly improves the performance of the original program.
Expression Recognition Using Sparse Selection of log-Gabor Facial Features

Automated expression recognition is a contemporary research field estimating human expressions from image or video data using computer algorithms combined with machine learning. This work proposes an algorithm for expression recognition including a feature extraction algorithm, consisting of log- Gabor filters followed by a feature selection based on sparse approximation of graph embedding. The classification is done on the selected features and is implemented using the Support Vector Machines classifier with radial basis kernel function. The algorithm is tested on the posed facial expressions image database Cohn-Kanade and provides competitive results compared to the state of the art.
Determination of leaf respiration in the light: comparison between an isotopic disequilibrium method and the Laisk method.

Quantification of leaf respiration is important for understanding plant physiology and ecosystem biogeochemical processes. Leaf respiration continues in the light (RL ) but supposedly at a lower rate than in the dark (RDk ). However, there is no method for direct measurement of RL and the available methods require nonphysiological measurement conditions. A method based on isotopic disequilibrium quantified RL (RL13C ) and mesophyll conductance of young and old fully expanded leaves of six species. RL13C was compared to RL determined by the Laisk method (RL Laisk ) on the very same leaves with a minimum time lag. RL 13C and RL Laisk were generally lower than RDk , and were not significantly affected by leaf ageing. RL Laisk and RL 13C were positively correlated (r2 = 0.35), and both were positively correlated with RDk (r2 ≥ 0.6). RL Laisk was systematically lower than RL 13C by 0.4 μmol m-2 s-1 . Using A/Cc instead of A/Ci curves, a higher photocompensation point Γ* (by 5 μmol mol-1 ) was found but no influence on RL Laisk estimates was observed. The results imply that the Laisk method underestimates actual RL significantly, probably related to the measurement condition of low CO2 and irradiance. The isotopic disequilibrium method is useful for assessing responses of RL to irradiance and CO2 , improving our mechanistic understanding of RL .
A Bayesian simulation approach for supply chain synchronization

While simulation has been used extensively to model supply chain processes, the use of a Bayesian approach has been limited. However, Bayesian modeling brings key advantages, especially in cases of uncertainty. In this paper, we develop a data informatics model that could be used to realize a digital synchronized supply chain. To realize this model, we take a hybrid approach that combines Bayesian modeling with discrete-event simulation and apply it to the supply chain process of a Procter & Gamble (P&G) manufacturing and distribution facility. We use approximately one year of transactional data to inform our model, including information on customer orders, production, raw materials, inventory, and shipments. A driving force for creating this model is to better understand and manage the balance between inventory, profit, and service.
Patient and physician perspectives on the use of cyclosporine ophthalmic emulsion 0.05% for the management of chronic dry eye

Dry eye disease (DED) is a multifactorial disease of the ocular surface and is one of the most common reasons for patients to visit an eye care provider. Cyclosporine A (CsA) is an immune modulating drug that was approved in the US for topical use in the treatment of DED in 2003, which led to a paradigm change in our understanding and treatment of DED, turning attention to control of inflammation for treatment. This review summarizes the literature to date regarding the impact of CsA on the treatment of DED. A special focus is given to the patient and physician perspectives of CsA, including dry eye symptom improvement, medication side effects, and overall patient satisfaction. Studies evaluating CsA in DED have considerable heterogeneity making generalized conclusions about the effect of CsA difficult. However, most studies have demonstrated improvement in at least some symptoms of dry eye in CsA-treated patients. Side effects, most commonly ocular burning on administration of CsA, are common. The literature is sparse regarding long-term follow-up of patients treated with CsA, optimal duration of treatment, and identifying which patients may receive the most benefit from CsA.