Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of three-dimensional tongue shape from midsagittal tongue contour using regression models

In this study, we investigated methods to estimate the tongue contours of the outer sagittal planes from a midsagittal tongue contour using linear and machine-learning-based regression models. To validate the method, tongue contours were extracted from each frame of a 3D MRI movie of the three sagittal planes, including the midsagittal plane, recorded while a male adult speaker produced the Japanese vowel sequence /aiueo/. The extracted tongue contours were then converted into harmonic amplitude profiles (HAPs) using a 2D Fourier transformation to reduce the number of dimensions. Finally, we calculated the coefficients of a multiple regression model and trained a random forest regression model to map the HAP of the midsagittal plane to those of the outer sagittal planes. In comparison with the multiple regression model, the random forest regression model demonstrated a higher estimation accuracy and exhibited an average distance error of less than 2.0 mm in a 10-fold cross-validation test.
BIROn - Birkbeck Institutional Research Online

Interferon Gamma Release Assays; Isonicotinoyl hydrazide Latent Infection; Multidrug NAAT, Nucleic Acid Ampliﬁcation Test; Arylamine N -acetyltransferase; Non-Steroidal Anti-Inﬂammatory Drugs; Tuberculosis Control Program; Polymerase Chain Reaction; PZA, Pyrazinamide; Random Blood Glucose; RIF, Rifampicin; an disease; the malady and its cause, Mycobacterium tuberculosis have remained an enigma with many questions that remain unanswered. The ability of the pathogen to survive and switch between varied physiological states necessitates a protracted therapeutic regimen that exerts an excessive strain on low-resource countries. To complicate things further, there has been a signi ﬁ cant rise of antimicrobial resistance. Existing control measures, including treatment regimens have remained fairly uniform globally for at least half a century and require reinvention. Overcoming the societal and scienti ﬁ c challenges requires an increase in dialog to identify key regions that need attention and effective partners with whom successful collaborations can be fostered. In this report, we explore the discussions held at the International TB Summit 2015 hosted by EuroSciCon, which served as an excellent platform for researchers to share their recent ﬁ ndings. Ground-breaking results require outreach to affect policy design, governance and control of the disease. Hence, we feel it is important that meetings such as these reach a wider, global audience.
Implementation of Static and 3-round Group Key Agreement with RLWE Assumption

With the development of communication technology, the demand for group key agreement protocols is growing. However, most of the group key agreement protocols are based on discrete logarithm problem, which is vulnerable to a quantum adversary. To solve this problem, various quantum-resistant cryptosystems have been proposed. RLWE problem is a kind of problems used in lattice-based cryptography, one of the promising post quantum cryptography. Several group key agreement protocols have been proposed based on RLWE problem. To the best of our knowledge, RLWE-based group key agreement protocol has never been implemented. In this thesis, we instantiate a static and 3-round group key agreement with RLWE assumption and implement the protocol on the arbiter-aided network. We also quantitatively analyze the performance of the implemented protocol.
A NEW METHOD FOR ANALYZING DEFECTS IN VENEER IMAGES: HYPOTHESIS TESTING BASED ON GAUSSIAN MIXTURE DECISION FUNCTION

Accurate detection of defects plays a vital role in wood industry due to the direct relation between quality and price of wood products. To this aim, in this paper we introduce a new method in which we first use a hypothesis testing to distinguish between wood defects and clear wood. In the proposed scheme, firstly the natural pattern of veneer is removed by applying morphological enhancement, and in the second step the probable defects are estimated by a decision function based on Gaussian mixture concept. The performance of the proposed algorithm is evaluated on a data set of veneer images containing several types of surface defects. The results demonstrate that the proposed method extracts the defects approximately 8.2% better than its alternatives, in parallel with decreasing false detections by approximately 7.3%. The results obtained also show the considerable improvements in Accuracy and Precision of the proposed method compared to other examined methods, especially when a high detection rate (i.e. at least 90%) is desired.
SMALL ALPHAHERPESVIRUS LATENCY-ASSOCIATED PROMOTERS DRIVE EFFICIENT AND LONG-TERM TRANSGENE EXPRESSION IN THE CENTRAL NERVOUS SYSTEM

Recombinant adeno-associated viral vectors (rAAV) are used as gene therapy vectors to treat central nervous system (CNS) diseases. Despite their safety and broad tropism, important issues need to be corrected such as the limited payload capacity and the lack of small gene promoters providing long-term, pan-neuronal transgene expression in the CNS. Commonly used gene promoters are relatively large and can be repressed a few months after CNS transduction, risking the long-term performance of single-dose gene therapy applications. We used a whole-CNS screening approach based on systemic delivery of AAV-PHP.eB, iDisco+ tissue-clearing and light-sheet microscopy, to identify three small latency-associated promoters (LAP) from the herpesvirus pseudorabies virus (PRV). These promoters are LAP1 (404bp), LAP2 (498bp) and LAP1_2 (880bp). They drive chronic transcription of the virus encoded latency-associated transcript (LAT) during productive and latent phases of PRV infection. We observed stable, pan-neuronal transgene transcription and translation from AAV-LAP in the CNS for six months post AAV transduction. In several CNS areas, the number of cells expressing the transgene was higher for LAP2 than the large conventional EF1α promoter (1264bp). Our data suggests that the LAP are suitable candidates for viral vector-based CNS gene therapies requiring chronic transgene expression after one-time viral-vector administration.
Are we looking at the same thing? Multiple methods to frame 'occupancy' of a library building

The user occupancy patterns of a building are an important factor influencing the performance of that building. The occupancy patterns influence the technological performance of a building such as its energy use, indoor air quality and thermal comfort. The occupancy patterns are also linked to the socio-material practices in which a building is embroiled. A range of different research methods can be utilised to study a building’s occupancy. However, the influence of different methods in framing the conception of occupancy is under-articulated. This paper draws from a multi-method research on occupancy patterns of the 50-year old University of Reading’s library building. Methods used include archival method, ethnographic method and ‘sweeping’ method. A comparison is also made with the published findings of this building’s occupancy pattern using Wi-fi-based indoor positioning method. The data and findings from different methods are analysed using the concepts of 'method assemblage' and 'ontological politics'. The findings reveal that the 'occupancy' of library building is framed differently by each method. Moreover, these framings do not suggest that the conception of occupancy is either singular (i.e. methods framing occupancy from different perspectives) or plural (i.e. methods framing occupancy in a mutually exclusive way). Rather, the framing of occupancy is multiple, such that different versions are linked to one another in a variety of ways. The findings also allude to the ontological politics of choosing one version over the other and what is at stake when making such decisions. The paper concludes that the research methods are not a set of technical procedures. But the methods are performative as they are a process of crafting and enacting the research object.
Multimodal Molecular Imaging Strategies using Functionalized Nano Probes

Cancer prognosis was mainly done by imaging and biopsy with histological analysis of solid tumours. Identifying tumour margins and staging of the tumour are critical for choosing appropriate treatments so that the risk of reoccurrence could be minimized. Imaging modalities have their strengths and limitations. Hence multimodality imaging that takes advantage of strengths from two or more imaging techniques may serve improved diagnostic and therapeutic monitoring abilities. Radiolabelled small molecules have been used as contrast agents to detect tumour for prognosticating therapeutic interventions. However, these probes lack tissue specificity and stability for optimal usage. Quantum dots (QDs) are fluorescent probes for optical imaging, which are capable of tunable optical properties, ability to target tumours when their surface-functionalized with high stability. Recently, a wide application of biocompatible surface-functionalized QDs is evidenced as multimodal imaging probes. However, their pathway to clinical translation is not yet fully explored. In this review, different biocompatible functionalized nanoprobes, their in-vivo application in animal model followed by their future possible clinical applications, recent developments of optical fluorescence imaging probes and its integration with other imaging modalities such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT) and ultrasonography (US) are discussed.
Vision-based Automated Visual Inspection of Large-scale Bridges *

Visual inspection of bridges is extensively used to identify faults such as crack, corrosion, or deformation. However, current methods conducted by human inspectors demand long inspection time to cover large and difficult to access structures, and highly rely on inspector’s subjective or empirical knowledge. To address these limitations, a vision based visual inspection technique is proposed by automatically processing and analyzing images collected by aerial cameras. First, aerial cameras gather sequential images of exterior of bridges with high resolution. Next, the object areas, which are susceptible to damage such as a bolt hole or joint, are detected from all collected images. Then, images of object areas are matched and grouped across images so that damage near objects can be inspected from many different angles. Finally, damage initiated from objects, is detected by processing those images. All these processes are designed to be fully automated. The effectiveness of the propose technique is investigated and validated through a large beam with array of bolts and artificial cracks.
Towards Fine-Grained Data Access Control Through Active Peer Probing

Data is routinely being shared online by peers, for instance in business transactions, social activities and others. This data, in turn, is often transferred, processed and combined through complex querying and analytics. This raises questions such as the following: who owns the derived data? With whom and for what purpose may it be published? If consent is required for its dissemination, whose consent should be obtained? The related topics of data sharing, privacy and access control have been extensively studied, but uniquely our focus here is not on data management with known policies but rather on the active probing of peers to ask for their consent. Active probing has the potential to allow finer-grained access control, where it is unreasonable to expect data owners to publish their full policies, defined for all possible sharing scenarios. They may not even have a clear view of their own policies, before asked whether they are willing to share data with a specific third party. This short paper informally introduces and motivates this new problem. It further identifies interesting connections to two distinct areas: data provenance, which captures the way output data are derived from inputs, and Boolean evaluation, which focuses on effective strategies to probe hidden Boolean values for evaluating a formula. As we shall demonstrate, the composition of these two areas in the context of this problem yields intriguing avenues for further research.
Type C Personality and Depression Among Newly Diagnosed Breast Cancer Patients: The Mediating Role of Sense of Coherence

Purpose: This study aims to explore the mediating role of sense of coherence in the relationship of type C personality and depression among newly diagnosed breast cancer patients. Methods: A descriptive and correlational survey was conducted in 600 breast cancer patients aged ≥ 18 years from September 2018 to March 2019 in Zhengzhou, China. The demographic questionnaire, Cancer Behavior Scale, Sense of Coherence Scale and Hamilton Depression Scale were included in this study. Data analysis was performed by correlation analysis, multiple linear regression analysis, and structural equation modeling. Results: The valid questionnaires were 575 (effective response rate: 95.8%). The score of sense of coherence was negatively correlated with type C personality and depression ( r = − 0.41, P < 0.01; r = − 0.58, P < 0.01); the score of type C personality and depression were positively correlated ( r = 0.51, P < 0.01). The results of multiple linear regression analysis revealed that the effect of type C personality on depression was partly mediated by sense of coherence, which was con ﬁ rmed by structural equation modeling. The mediation effect accounts for 45.2% (0.269/0.594) of the total effect. Conclusion: The results of this study revealed that sense of coherence as a partial mediating role is essential for reducing the in ﬂ uence of type C personality on depression. Early and targeted psychological interventions on sense of coherence are needed to alleviate the symptom of depression in newly diagnosed breast cancer patients.
Reinforcement Learning Guided Software Debloating∗

Modern software engineering practices encourage software bloat. Libraries provide more APIs than any one client needs; programs have many command line and configuration options, with most never used by the user and remaining set to their default values. The result is software systems that are bloated, slow, and, most importantly, vulnerable to attacks. Software debloating is an emerging technique to address this problem by automatically trimming (or specializing) a program to a user-defined execution environment (e.g., command line options, configuration files, common usage scenarios, etc.). Since deciding on what to specialize is computationally expensive, current state-of-the-art software debloaters rely on hand-crafted heuristics. This is problematic because these heuristics are not always effective, and, more importantly, difficult to adjust to different debloating metrics. In this paper, we propose a reinforcement learning based approach to automatically learn a good debloating policy. We have implemented the approach as a tool called DEEPOCCAM, and evaluated it on debloating several C programs. Our results show that the technique outperforms the base-line policy of a state-of-the-art debloater OCCAM, while being easily adaptable to a variety of debloating metrics.
Comparison between LTE and WiMAX

One of the most dynamic researches in the field of medical image processing is using an "MRI" which stands for magnetic resonance image for the detection of a brain tumor. Identification of the tumor is the primary target of the system. Detection plays a critical role in biomedical imaging. In this research, "MRI" brain image is used to utilized to tumor recognition processThis system includes test the brain image process, image filtering, segmentation, classification and delineation of the tumor area, the detailed procedures are implemented using “MATLAB”. The proposed strategy removes the tumor region precisely from the "MRI" brain image. The test results show that the proposed technique proficiently recognized the tumor region from the brain image. Afterwards, the condition of the tumor region in the system is viably applied in any shape of the tumor region.
Blockchain Technology as a Means for Brand Trust Repair - Empirical Evidence from a Digital Transgression

Though much discussion in the realm of blockchain revolves around the concept of trust, research examining blockchain technology as a means for brand trust repair is still at an initial stage. This study conducts an experiment that analyzes blockchain technology as a substantive response to a data breach within a global business-to-consumer information systems application. Thereby, the present study expands trust repair theories to the context of blockchain and branding. Research results indicate that the use of blockchain technology as a reaction to a digital transgression may be able to reinstate brand trust, having a superior impact compared to an approach that uses a centrally managed information systems platform to restore brand trust. Overall, study results suggest that the use of blockchain technology can be an effective component of brand trust repair strategies in the digital space.
Visualizing Syscalls using Self-organizing Maps for System Intrusion Detection

: Monitoring syscall logs provides a detailed view on almost all processes running on a system. Existing approaches therefore analyze sequences of executed syscall types for system behavior modeling and anomaly detection in cyber security. However, failures and attacks that do not manifest themselves as type sequences violations remain undetected. In this paper we therefore propose to incorporate syscall parameter values with the objective of enriching analysis and detection with execution context information. Our approach thereby ﬁrst selects and encodes syscall log parameters and then visualizes the resulting high-dimensional data using self-organizing maps to enable complex analysis. We thereby display syscall occurrence frequencies and transitions of consecutively executed syscalls. We employ a sliding window approach to detect changes of the system behavior as anomalies in the SOM mappings. In addition, we use SOMs to cluster aggregated syscall data for classiﬁcation of normal and anomalous system behavior states. Finally, we validate our approach on a real syscall data set collected from an Apache web server. Our experiments show that all injected attacks are represented as changes in the SOMs, thus enabling visual or semi-automatic anomaly detection.
SOCIO-ECONOMIC DETERMINANTS OF CRIME: FURTHER EVIDENCE FROM NIGERIA

This study examines the socioeconomic and demographic determinants of crime in Nigeria, using error-correction modelling approach for 1981-2015. The results indicate that the twin macroeconomic problems of inflation and unemployment positively impact on crime rate in Nigeria. The level of education was also found to be significant and negatively related to crime rates, suggesting that education not only make people risk averse, but also indirectly alters individual’s decision to adopt criminal behaviour. Also, the one-period lagged value of average income was significant and negatively related to crime rate, suggesting that, appreciable increase in per capita income tends to reduce the incentive to commit crime. Accordingly, the study recommends, among others, the need for the relevant authorities to design and implement policy measures aimed at combating the twin evil of inflation and unemployment on the one hand, and increasing the level of education and schooling, especially among the indigent.
Hydrophobically modified nanocomposite hydrogels with self‐healing ability

Several strategies have been developed in the past two decades to increase the mechanical performance of the hydrogels, and to generate self-healing function within the polymer network. Here, we combine two of these strategies to create hydrophobically modified nanocomposite (NC) hydrogels with high mechanical strength and self-healing efficiency. The hydrogels were prepared by in situ copolymerization of N,N-dimethylacrylamide and n-octadecyl acrylate (C18A) in the presence of 2 w/v % Laponite clay nanoparticles in an aqueous solution of worm-like sodium dodecyl sulfate micelles. Incorporation of hydrophobic C18A segments into the gel network significantly increases both the storage and loss moduli of NC hydrogels indicating increasing elasticity and energy dissipation. An improvement in the mechanical performance and self-recoverability of NC hydrogels was also observed after hydrophobic modification. The compressive fracture stress and Young’s modulus increase with increasing amount of C18A, and they become 9 1 MPa and 30 2 kPa, respectively, at 4 mol % C18A. Incorporation of hydrophobic segments also provides a larger energy dissipation under large strain as compared to the traditional NC hydrogels providing a self-healing efficiency of 90 10% in mechanically strong NC hydrogels. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 137, 48853.
Sentiment Analysis of Text Documents

By popularizing Web 2.0 technologies, the internet has become a rich source of user-generated content. Sentiment analysis collects and processes people's opinions and attitudes toward products, services, politics, social events, marketing campaigns and company strategies. In the sentiment analysis, unstructured text is processed, and it brings numerous problems in computer processing. In order to overcome problems, different methods and techniques are used. This paper gives a literature overview in the field of sentiment analysis focusing on current research approaches machine learning, lexiconbased approach, dataless classification and deep learning. Emotion mining, a relatively new subfield within sentiment analysis, is also discussed in this
Improvement of Establishing a Myocardial Ischemic Reperfusion Model in Rats

Objective To improve the method of establishing model in myocardial ischemic reperfusion rat.Methods 32 SD male rats were divided into four groups randomly(Group A:sham group,Group B:myocardial ischemic,Group C:ischemic 30 min and reperfusion,Group D:ischemic 60 min and reperfusion). Inspected the changing of ECG in preparation /in-operation and rats breathing were finished under the control of artificial respirator in the period of MI.After 72 h of the operation,the levels of myocardial zymogram were determined and the left ventricular pressure was traced.Results Survival rate was 91% after operation.Myocardium zymogram content in the group B,C,D was significantly higher than that of group A(P 0.05);Myocardium zymogram content in group C、D was lower than that of group B(P 0.05). The left ventricular pressure in group B,C,D was lower than that of group A(P 0.05);The left ventricular pressure in group C、D was higher than that of group B(P 0.05). Conclusion The improving method is convenient,higher survival rate,and the evaluation was according with clinical procedures.
Judgments on Exclusion of a Biracial Peer

The study of the development of social reasoning requires the analysis of the complex phenomenon in social interactions.1) One of the principal ways to understand the social reasoning of children and adolescents is to study how they think, feel, and judge about critical issues in their peer relationships such as friendship, exclusion, identity, and victimization. Especially, analyses on the judgments of children and adolescents on peer exclusion, based on social-domain approach,2) have revealed multifaceted features of social and