Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction and Application of Ship Data Mining Platform Based on Spark

With the explosive growth of ship information data brought by the intellectualization and digitization of waterborne traffic, it has become more and more important to process and apply massive ship data quickly and accurately. In view of the growing maturity and wide application of Spark large data technology in data mining field, this paper, against the characteristics of ship AIS data, chooses a series of large data technologies based on Spark to build the AIS data mining platform. According to the basic process of mining and processing, the overall framework of the platform is divided into three modules: database, Spark computing and mining, visualization. The AIS data of Sunda Strait with high ship density is used to process and mine the actual data to verify the availability of the platform. The results show that the platform works well and the three modules can work normally. And the AIS data of the Strait are processed and mined quickly and accurately. According to different requirements, it can display bar charts, polyline charts, histogram, trajectory charts and thermal charts and other commonly used data analysis graphics to achieve the effect of navigation assistance.
Speech Synthesis Evaluation — State-of-the-Art Assessment and Suggestion for a Novel Research Program

Speech synthesis applications have become an ubiquity, in navigation systems, digital assistants or as screen or audio book readers. Despite their impact on the acceptability of the systems in which they are embedded, and despite the fact that different applications probably need different types of TTS voices, TTS evaluation is still largely treated as an isolated problem. Even though there is strong agreement among researchers that the mainstream approaches to Text-to-Speech (TTS) evaluation are often insufﬁcient and may even be misleading, there exist few clear-cut suggestions as to (1) how TTS evaluations may be realistically improved on a large scale, and (2) how such improvements may lead to an informed feedback for system developers and, ultimately, better systems relying on TTS. This paper reviews the current state-of-the-art in TTS evaluation, and suggests a novel user-centered research program for this area.
Analyse the Convergence of Multi-Domain Research Opportunities in Software Engineering

:- Software Engineering (SE) is taking part in an important role in the software industry as a result of specific software is required in virtually every industry, in each business, and for each operate. There are completely different levels are available in software engineering and they serve several functions throughout the application lifecycle. At the early stage, the industry developed an application mostly for personal use and small scale industries. In that time, the SDLC processes are very marginal and moderated, and even the accounting data is also moderated. The revolution of software industry demands the amount of multifaceted applications and obviously, it leads to the high quality of SDLC. The human-driven era of software development meant writing rule-based code that solved deterministic problems using logic however it needs a transition to the requirement of large scale software developments. It’s going to be a replacement proposal of SDLC or cross-industry standards like data mining, Machine Learning, etc. This paper argues the future opportunities and challenges of software Engineering and highlights how actually the SE wants cross-industry standards to improve the quality of large scale software developments.
External Monetary Constraints Imposed by Developed Economies on Developing Economies: Empirical Evidence from Pakistan

The problem with the most of the developing economies is that their monetary policy is constrained by external shocks and developed economies central banks especially FED and ECB. In this research, we examine the effects of major external shocks like global oil price shocks, foreign interest rate shocks and global food price shocks on the major macro variables of Pakistan which creating hurdles for the SBP to achieve its monetary policy objectives independently. The results stating that the global oil price and global food price shocks have direct impacts on the major macro variables of Pakistan and put inflationary pressure on the Pakistan economy which making difficult for the central bank to achieve its predetermined dual objective of monetary policy i.e. full employment and stable inflation. Additionally, we examine how the changes in US monetary policy effects Pakistan economy and find that positive foreign interest rate shock has minor impacts on the major macro variables of Pakistan except for the exchange rate and domestic inflation rates which is also imposing an external constraint on the monetary policymaking process of Pakistan central bank. In a nutshell, all these external shocks are creating hurdles and imposing monetary constraints on the Pakistan which making difficulties for Pakistani central bank to achieve its monetary policy objectives.
The role of PTEN in cardioprotection against ischaemia-reperfusion injury

Activation of the PI3K/AKT pathway protects the heart from ischaemia-reperfusion injury. Phosphatase and Tensin Homolog deleted on Chromosome10 (PTEN) is a negative regulator of this pathway. The hypothesis on which this thesis was based stated that inhibition of PTEN would confer protection against ischaemia-reperfusion injury. PTEN was reduced using: 1) a PTEN inhibitor, bpV(HOpic), 2) a mouse model of PTEN haploinsufficiency and 3) PTEN siRNA. The effects of PTEN reduction on ischaemia-reperfusion injury were investigated by using: 1) an isolated perfused heart model of ischaemia-reperfusion injury, 2) an isolated cardiomyocyte model of ROS induced mitochondria damage and 3) a cellular model of hypoxia-reoxygenation injury. No protection against ischaemia-reperfusion was observed in isolated perfused myocardium from C57BL/J6 mice, which were perfused with bpV(HOpic), or from PTEN+/-mice. Likewise, no protection against ROS induced mitochondrial damage was observed in isolated cardiomyocytes from the PTEN+/- mice. In these models an increase in AKT activity was recorded, however, this was not sufficient to confer cardioprotection. Similarly, H9c2 rat myoblast cells, silenced for PTEN expression using siRNA, were not protected against hypoxia-reoxygenation injury. Nevertheless, in isolated C57BL/J6 hearts perfused with bpV(HOpic) and in myocardium from PTEN+/- mice, when the PI3K/AKT pathway was stimulated by the cardioprotective intervention of ischaemic preconditioning a reduced threshold for protection was achieved. To conclude, the level of PTEN inhibition achieved in this study was not sufficient to bestow protection against simulated ischaemiareperfusion injury. However, it appears that reductions in PTEN can increase the sensitivity towards cardioprotection.
Electron Properties and Thermal Decomposition Behaviors for HMX/HTPB Plastic-Bonded Explosives

We study the microscopic structure properties and early thermal decomposition of octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX)/hydroxyl-terminated polybutadiene (HTPB) composites on the ba.
Subspace tracking from missing and corrupted data using NORST and its heuristic extensions

We study the problem of subspace tracking (ST) in the presence of missing data (ST-miss). In recent work, we have studied the Robust ST (RST) problem. In this work, we show that a simple modiﬁcation of our solution approach for RST also provably solves ST-miss under weaker assumptions. To our knowledge, our result is the ﬁrst complete guarantee for ST-miss. This means we are able to show that, under assumptions on only the algorithm inputs (input data and/or initialization), the output subspace estimates are close to the true data subspaces at all times. Our guarantees hold under mild and easily interpretable assumptions and handle time-varying subspaces (unlike all previous work). We also show that our algorithm and its extensions are fast and have competitive experimental performance when compared with existing methods. Finally, our solution can be interpreted as a provably correct mini-batch and memory-eﬃcient solution to low rank Matrix Completion (MC).
Gauge the Trade-off Effects between Social Media and Traditional Platform in the Consumer Purchasing Funnel

Targeted display advertising for individual consumers has become pervasive on social media platform and other online websites (traditional platform). Yet, the effectiveness of targeted advertising across online platforms is not well understood. Moreover, such advertising effect may be different for different types of consumers, i.e. consumers in the early stage and those in the late stage, relative to the final purchase stage. This paper aims at assessing the effectiveness of targeted advertising across online platforms on consumers' final conversion (purchase). In addition, we measure the complementarity and substitutability of online platforms for targeted advertising for upper funnel (early-stage) consumers and lower funnel (latestage) consumers. We use machine learning techniques to form case-control designs analyzed employing regularized discrete choice models to select relevant features explaining the final conversion. The empirical analysis shows that (1) targeting across platforms is positively associated with the final conversion for the lower funnel consumers, but there is no measurable synergistic effect for the upper funnel consumers; (2) the main effect of targeting on social media is positively related to the final conversion for consumers in the upper funnel but has no significant impact for lower funnel consumers. We leverage upon these findings to discuss actionable managerial prescriptions.
Homography estimation with deep convolutional neural networks by random color transformations

Most classic approaches to homography estimation are based on the ﬁltering of out-liers by means of the RANSAC method. New proposals include deep convolutional neural networks. Here a new method for homography estimation is presented, which supplies a deep neural homography estimator with color perturbated versions of the original image pair. The obtained outputs are combined in order to obtain a more robust estimation of the underlying homography. Experimental results are shown, which demonstrate the adequate performance of our approach, both in quantitative and qualitative terms.
Crystal structure of catena-poly[[[bis(3-oxo-1,3-diphenylprop-1-enolato-κ2 O,O′)zinc(II)]-μ2-tris[4-(pyridin-3-yl)phenyl]amine-κ2 N:N′] tetrahydrofuran monosolvate]

The synthesis and crystal structure of a coordination polymer composed of β-diketonato zinc(II) [Zn(dbm)2] and triarylamine-based bridging ligands (T3PyA) is reported. The asymmetric unit in the crystal consists of two independent halves of Zn(dbm)2, one T3PyA and one solvate THF. Each ZnII atom is located on an inversion centre and adopts an elongated octahedral coordination geometry. In the crystal, the coordination polymer chains are linked via C—H⋯π interactions into a three-dimensional framework.
Towards a New Monetary Theory of Exchange Rate Determination

We study exchange rate determination in a 2-country model where domestic banks create each economy’s supply of domestic and foreign currency. The model combines the UIP-based and monetary theories of exchange rate determination, but the latter with a focus on private rather than public money creation. The model features an endogenous monetary spread or excess return in the UIP condition. This spread experiences sizeable changes when shocks affect the relative supplies (of bank loans) or demands (for bank deposits) of the two currencies. Under such shocks, monetary effects dominate traditional UIP effects in the determination of exchange rates and allocations, and this becomes stronger as domestic and foreign currencies become more imperfect substitutes. With these shocks, the model successfully addresses the UIP puzzle, and it is also consistent with the Meese-Rogoff and PPP puzzles.
Methods for Studying Endoplasmic Reticulum Stress

The endoplasmic reticulum (ER) comprises a network of tubules and vesicles that constitutes the largest organelle of the eukaryotic cell. Being the location where most proteins are synthesized and folded, it is crucial for the upkeep of cellular homeostasis. In addition, it is the largest ionic calcium reservoir in cells, tightly regulating the levels of this second messenger according to cellular necessities. Disturbed ER homeostasis triggers the activation of an intricate and conserved molecular machinery, termed the unfolded protein response (UPR). Given the impact of this signaling network upon an extensive list of cellular processes, ER stress is involved in the onset and progression of multiple diseases, including cancer and neurodegenerative disorders. There is, for this reason, an increasing number of publications focused on characterizing and/or modulating ER stress, which have resulted in a wide array of techniques employed to study ER-related molecular events. This review aims to sum up the tools available design a study of this nature.
Evaluation of models for predicting relative viscosity of ultrasound-assisted synthetic water-in-oil emulsions of Brazilian crude oil

Abstract Emulsions of water-in-oil (W/O) is a very common occurrence in the oil and gas industries. The viscosity and rheological properties of the emulsions are of great interest to the petroleum industry, especially during oil exploitation and transportation. In this study, emulsion dynamic viscosities were measured (20 °C–50 °C), and rheological profiles were modeled according to Ronningsen, ASTM D-341, Farah, and other viscosity models . Also, a methodology for producing water-in-oil emulsions using ultrasound energy was proposed, and the generated emulsion properties were compared with relevant literature. Emulsions were prepared with 22.3° API crude oil without surfactants. A 400 W ultrasound generator was used for sonication at 24 kHz, and the effects of sonication time and ultrasound amplitude on dynamic viscosity and droplet size distribution were investigated. The emulsions generated by ultrasound sonication showed stability and homogeneity, and droplet size distribution of D 0.5=0.10 µm and D 4.3=0.37 µm. The rheological profiles strongly depended on emulsion viscosity and water volumetric fraction. The water volumetric fraction was shown to be the most important factor, the viscosity increased as volumetric fraction increase, while temperature increase led to a viscosity decrease. The experimental results were well fitted by the model proposed by Farah with a 5.5% error. Graphical Abstract
Daily edge deformation prediction using an unsupervised convolutional neural network model for low dose prior contour based total variation CBCT reconstruction (PCTV-CNN)

Purpose: Previously we developed a PCTV method to enhance the edge sharpness for low-dose CBCT reconstruction. However, the iterative deformable registration method used for deforming edges from planning-CT to on-board CBCT is time-consuming and user-dependent. This study aims to automate and accelerate PCTV reconstruction by developing an unsupervised CNN model to bypass the conventional deformable registration. Methods: The new method uses unsupervised CNN model for deformation prediction and PCTV reconstruction. An unsupervised CNN model with a u-net structure was used to predict deformation vector fields (DVF) to generate on-board contours for PCTV reconstruction. Paired 3D image volumes of prior CT and on-board CBCT are inputs and DVF are predicted without the need of ground truths. The model was initially trained on brain MRI images, and then fine-tuned using our lung SBRT data. This method was evaluated using lung SBRT patient data. In the intra-patient study, the first n−1 day’s CBCTs are used for CNN training to predict nth day edge information (n = 2, 3, 4, 5). 45 half-fan projections covering 360˚ from nth day CBCT is used for reconstruction. In the inter-patient study, the 10 patient images including CT and first day’s CBCT are used for training. Results from Edge-preserving (EPTV), PCTV and PCTV-CNN are compared. Results: The cross-correlations of the predicted edge map and the ground truth were on average 0.88 for both intra-patient and inter-patient studies. PCTV-CNN achieved comparable image quality as PCTV while automating the registration process and reducing the registration time from 1–2 min to 1.4 s. Conclusion: It is feasible to use an unsupervised CNN to predict daily deformation of on-board edge information for PCTV based low-dose CBCT reconstruction. PCTV-CNN has a great potential for enhancing the edge sharpness with high efficiency for low-dose CBCT to improve the precision of on-board target localization and adaptive radiotherapy.
A Saliency-Based Attention LSTM Model for Cognitive Load Classification from Speech

Cognitive Load (CL) refers to the amount of mental demand that a given task imposes on an individual’s cognitive system and it can affect his/her productivity in very high load situations. In this paper, we propose an automatic system capable of classifying the CL level of a speaker by analyzing his/her voice. Our research on this topic goes into two main directions. In the ﬁrst one, we focus on the use of Long Short-Term Memory (LSTM) networks with different weighted pooling strategies for CL level classiﬁcation. In the second contribution, for overcoming the need of a large amount of training data, we pro-pose a novel attention mechanism that uses the Kalinli’s auditory saliency model. Experiments show that our proposal out-performs signiﬁcantly both, a baseline system based on Support Vector Machines (SVM) and a LSTM-based system with logistic regression attention model.
Modeling Interstation Travel Speed of Hybrid Bus Rapid Transit within A Bayesian Framework

Interstation travel speed is an important indicator of the running state of hybrid Bus Rapid Transit and passenger experience. Due to the influence of road traffic, traffic lights and other factors, the interstation travel speeds are often some kind of multi-peak and it is difficult to use a single distribution to model them. In this paper, a Gaussian mixture model charactizing the interstation travel speed of hybrid BRT under a Bayesian framework is established. The parameters of the model are inferred using the Reversible-Jump Markov Chain Monte Carlo approach (RJMCMC), including the number of model components and the weight, mean and variance of each component. Then the model is applied to Guangzhou BRT, a kind of hybrid BRT. From the results, it can be observed that the model can very effectively describe the heterogeneous speed data among different inter-stations, and provide richer information usually not available from the traditional models, and the model also produces an excellent fit to each multimodal speed distribution curve of the inter-stations. The causes of different speed distribution can be identified through investigating the Internet map of GBRT, they are big road traffic and long traffic lights respectively, which always contribute to a main road crossing. So, the BRT lane should be elevated through the main road to decrease the complexity of the running state.
Human Annotation and Interpretation of Public Sentiments about Jio Coin marked in Social Networks using Machine Learning Algorithms

The basic errand in Sentiment Analysis is to categorize the orientation of a given review and subsequently identifying whether the sentiment implied is positive, negative or fair. In this article the authors present the following lines of experimentation and outcomes. One is related to human annotation of Tweets and assessment of their quality and dataset properties. Another is about training sentiment classifiers, their performance and comparisons. The authors' presents a comprehensive assessment about various supervised machine learning techniques to interpret the public sentiments about ‘Jio Coin’ marked in social networks. Various evaluation measures like Precision, Recall, F-Score, Matthews Correlation Coefficient, Jaccard Index and Kappa statistics depicts the efficiency rate of the models in the datasets. The learning time and the predicting time taken by various classifiers depicted in the article helps to choose the classifier that suits well if time is a constraint.
Survival Analysis of Complex Featured Data with Measurement Error

Survival analysis plays an important role in many fields, such as cancer research, clinical trials, epidemiological studies, actuarial science, and so on. A large body of methods on analyzing survival data have been developed. However, many important problems have still not been fully explored. In this thesis, we focus on the analysis of survival data with complex features. In Chapter 1, we review relevant topics including survival analysis, the measurement error model, the graphical model, and variable selection. Graphical models are useful in characterizing the dependence structure of variables. They have been commonly used for analysis of high-dimensional data, including genetic data and data with network structures. Many estimation procedures have been developed under various graphical models with a stringent assumption that the associated variables must be measured precisely. In applications, this assumption, however, is often unrealistic and mismeasurement in variables is usually presented in data. In Chapter 2, we investigate the high-dimensional graphical model with error-prone variables. We propose valid estimation procedures to account for measurement error effects. Theoretical results are established for the proposed methods and numerical studies are reported to assess the performance of our proposed methods. In Chapter 3, we consider survival analysis with network structures and measurement error in covariates. In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High-dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error-contaminated. To hand such kinds of survival data, we propose graphical proportional hazards measurement error models, and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods. In Chapter 4, we focus on sufficient dimension reduction for high-dimensional survival data with covariate measurement error. Sufficient dimension reduction (SDR) is an important tool in regression analysis which reduces the dimension of covariates without losing
Face Recognition Using MATLAB

This project dives into the topic of facial recognition and facial detection in a digital communications system. Face recognition is a technology that is widely used today which brings various benefits to society. Facial recognition differs from facial detection in the aspect that facial detection only finds and detects the present face/s in an image whereas, in facial recognition, the computer finds the face/s present in a subject and is able to distinguish the face from a sample of different faces. This research focuses on the implementation of both a facial recognition system and a facial detection system in MATLAB. This research would use the different imaging toolboxes available in the program and would be judged on its ability to accurately detect and recognize a sample in a given database. Additionally, this system should be able to create and to read a database of different faces.