Miracle-dz/UniFAR-FTUs huggingface dataset:
A Geometry-Based Band Selection Approach for Hyperspectral Image Analysis

Band selection (BS) is a special case of the feature selection problem, and it tries to remove redundant bands and select a few informative and distinctive bands to represent the whole image cube. The maximum ellipsoid volume (MEV) method regards the band subset with the maximum volume as the optimal band combination. However, the MEV method cannot be directly applied for hyperspectral imagery due to the high dimensionality of the data sets. Therefore, we first combine MEV with the sequential forward search (SFS) and propose a new unsupervised BS method called MEV-SFS. Furthermore, a subtle relationship between the ellipsoid volume of the band set and the orthogonal projections (OPs) of the candidate bands is observed. Based on this relationship, we propose another equivalent method, namely, the OP-based BS (OPBS) method. OPBS is the fast version of MEV-SFS, and it has a better computational efficiency and the potential to determine the number of bands to be selected. We specifically explain the rationality of the MEV-based methods (MEV-SFS and OPBS) and illustrate their theoretical significance and physical meaning from different aspects. Theoretical analysis also demonstrates that OPBS can be regarded as a model or framework for BS, and thus, we further propose a third novel BS method named the OPBS-information divergence (OPBS-ID) method, which is a variant of OPBS. OPBS-ID can achieve a better classification performance than OPBS in many cases. Experimental results on different hyperspectral data sets demonstrate that the proposed methods have high computational efficiency, and the selected bands can achieve satisfactory classification performances.
Privacy Preservation of e-Governance Data Using Local Beam Search

E-governance is mandated to extend various digital services to the citizens in a transparent and easy-to-access manner. This requires an enormous amount of data acquisition, analysis, and processing. Normally, such databases contain references to private information of different stakeholders. It is the responsibility of the government to protect the data against privacy disclosure which can be maneuvered through direct or indirect inference by linking different databases. Though governments have adopted policies to control privacy but still there are technical issues and even the data analyst may turn out to be the one responsible for privacy disclosure. k-anonymity is one of the frequently used approaches to ensure privacy preservation in such scenarios by transforming a database to a sanitize database such that the probability of disclosure is minimized. However, this may lead to information loss and thus reduce the utility for data analysis. Therefore, it is always a challenge that while providing privacy of data through k-anonymity one must ensure minimum information loss without hampering the utility of data. Towards this end, we analyze the efficiency of two existing greedy heuristic based techniques, viz., Datafly and Improved Greedy heuristic and we propose two Local Beam Search based algorithms: Sequential Local Beam Search (SLBS) and Binary search based Sequential Local Beam Search (BSLBS). A comparative analysis of the algorithms is done with the help of Information Loss metric Info.Loss. It is observed that BSLBS outperforms the existing greedy based heuristic methods.
Tai Chi for Stroke Rehabilitation: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Stroke is a major cause of poor health and has numerous complications. Tai Chi (TC) may have positive effects on the rehabilitation of stroke survivors, but recent clinical findings have not been included in previously published reviews. Objectives: We conducted this systematic review and meta-analysis to determine the effectiveness of all types of TC vs. conventional rehabilitation therapy for all aspects of stroke survivors' rehabilitation that have been studied. Method: We searched seven electronic literature databases (three in English, four in Chinese) and one clinical registry platform using established strategies to identify randomized controlled trials performed up to October 2017. Screening, quality assessment, and data collection were performed by two researchers separately, using the same standard. The results were analyzed using RevMan 5.3. 0. The quality of evidence was evaluated with GRADEpro. Results: A total of 21 studies with 1,293 stroke survivors met inclusion criteria; 14 were included in the quantitative synthesis to evaluate four aspects and five outcomes. Nine studies indicated that TC was able to improve independent activities of daily living (ADL), especially TC vs. conventional rehabilitation therapy [mean difference (MD) [95% confidence interval (CI)] = 9.92 [6.82, 13.02], P < 0.00001]. Five studies reported significant effects of TC plus conventional rehabilitation therapy in increasing scores on the Fugl–Meyer Assessment for the upper limb [MD (95%CI) = 8.27 [4.69, 11.84], P < 0.0001], lower limb [MD (95%CI) = 2.75 [0.95, 4.56], P = 0.003], and overall [MD (95%CI) = 4.49 [1.92, 7.06], P = 0.0006]. The Berg Balance Scale revealed significant improvements according to pooled estimates for TC vs. conventional rehabilitation therapy [MD (95%CI) = 5.23 [3.42, 7.05], P < 0.00001]. TC plus conventional rehabilitation therapy also improved walking ability as measured by the Holden scale [MD (95%CI) = 0.61 [0.38, 0.85], P < 0.00001] and up-and-go time [MD (95%CI) = 2.59 [1.76, 3.43], P < 0.00001]. Conclusion: TC has an overall beneficial effect on ADL, balance, limb motor function, and walking ability among stroke survivors, based on very low-quality evidence, and may also improve sleep quality, mood, mental health, and other motor function. Well-designed, higher-quality trials with longer-term follow-up periods are needed to develop better-quality evidence.
How to Train a Data Scientist for the Global South?

The extensive use of mobile devices and modern technologies has led to the expansion of amount and quality of data in the Global South. With the help of professional data scientists, developing countries could benefit from big data effectively and leapfrog from traditional methods of data generation to modern data handling methods without going through the same process as in wealthier regions. The sky-high demand for data scientists is due to the enormous business value that they can bring to the table, deriving deep learning from the big data. In this paper, we give one solution how to train a data scientist for the Global South where data scientists face partly different challenges as their colleagues in the Global North. Based on more than ten years of collaboration in ICT education with various African partners, we developed the general framework for an innovative BSc degree programme in computer science with specialisation in data science including two essential threads - learning-by-being and learning-by-doing. Only by combining learning to both doing and being, we can train data scientists that are able to change the target context in a profound way. A built-in research program on the data science training increase Global South – North collaboration, which in turn combined with open collaboration and flexible way of overcoming hierarchical boundaries most probably leads to the innovative joint ventures.
Model-based Clustering of Short Text Streams

Short text stream clustering has become an increasingly important problem due to the explosive growth of short text in diverse social medias. In this paper, we propose a model-based short text stream clustering algorithm (MStream) which can deal with the concept drift problem and sparsity problem naturally. The MStream algorithm can achieve state-of-the-art performance with only one pass of the stream, and can have even better performance when we allow multiple iterations of each batch. We further propose an improved algorithm of MStream with forgetting rules called MStreamF, which can efficiently delete outdated documents by deleting clusters of outdated batches. Our extensive experimental study shows that MStream and MStreamF can achieve better performance than three baselines on several real datasets.
Introducing Traceability Information Models in Connected Health Projects

In software-intensive products, evidences of regulation conformance and failure propagation mitigation are required. The use of Traceability Information Model (TIM) is an important practice to support system engineering artifacts tracing and links, becoming part of a project traceability planning. Despite the relevance of traceability in terms of guaranteeing quality attributes and certification of these products, improvements creating a link between safety and systems engineering artifacts have not been focus of attention in the connected health industry. The lack of reference practices for adequately applying traceability has hindered product certification. In this work, we move towards introducing a TIM practice for integrated functional and safety specifications in connected health projects. To achieve this, we performed a case study involving an infusion control system. Evidences show that considerable advantages can be obtained by introducing a TIM into the modeling process. Therefore, the main contribution of this paper is to present an approach for the connected health industry to start dealing with traceability challenges.
Clusterin Is Required for β-Amyloid Toxicity in Human iPSC-Derived Neurons

Our understanding of the molecular processes underlying Alzheimer’s disease (AD) is still limited, hindering the development of effective treatments, and highlighting the need for human-specific models. Advances in identifying components of the amyloid cascade are progressing, including the role of the protein clusterin in mediating β-amyloid (Aβ) toxicity. Mutations in the clusterin gene (CLU), a major genetic AD risk factor, are known to have important roles in Aβ processing. Here we investigate how CLU mediates Aβ-driven neurodegeneration in human induced pluripotent stem cell (iPSC)-derived neurons. We generated a novel CLU-knockout iPSC line by CRISPR/Cas9-mediated gene editing to investigate Aβ-mediated neurodegeneration in cortical neurons differentiated from wild type and CLU knockout iPSCs. We measured response to Aβ using an imaging assay and measured changes in gene expression using qPCR and RNA sequencing. In wild type neurons imaging indicated that neuronal processes degenerate following treatment with Aβ25-35 peptides and Aβ1-42 oligomers, in a dose dependent manner, and that intracellular levels of clusterin are increased following Aβ treatment. However, in CLU knockout neurons Aβ exposure did not affect neurite length, suggesting that clusterin is an important component of the amyloid cascade. Transcriptomic data were analyzed to elucidate the pathways responsible for the altered response to Aβ in neurons with the CLU deletion. Four of the five genes previously identified as downstream to Aβ and Dickkopf-1 (DKK1) proteins in an Aβ-driven neurotoxic pathway in rodent cells were also dysregulated in human neurons with the CLU deletion. AD and lysosome pathways were the most significantly dysregulated pathways in the CLU knockout neurons, and pathways relating to cytoskeletal processes were most dysregulated in Aβ treated neurons. The absence of neurodegeneration in the CLU knockout neurons in response to Aβ compared to the wild type neurons supports the role of clusterin in Aβ-mediated AD pathogenesis.
Distance based heterogeneous volume modelling.

Natural objects, such as bones and watermelons, often have a heterogeneous composition and complex internal structures. Material properties inside the object can change abruptly or gradually, and representing such changes digitally can be problematic. Attribute functions represent physical properties distribution in the volumetric object. Modelling complex attributes within a volume is a complex task. There are several approaches to modelling attributes, but distance functions have gained popularity for heterogeneous object modelling because, in addition to their usefulness, they lead to predictability and intuitiveness. In this thesis, we consider a unified framework for heterogeneous volume modelling, specifically using distance fields. In particular, we tackle various issues associated with them such as the interpolation of volumetric attributes through time for shape transformation and intuitive and predictable interpolation of attributes inside a shape. To achieve these results, we rely on smooth approximate distance fields and interior distances. This thesis deals with outstanding issues in heterogeneous object modelling, and more specifically in modelling functionally graded materials and structures using different types of distances and approximation thereof. We demonstrate the benefits of heterogeneous volume modelling using smooth approximate distance fields with various applications, such as adaptive microstructures, morphological shape generation, shape driven interpolation of material properties through time and shape conforming interpolation of properties. Distance based modelling of attributes allows us to have a better parametrization of the object volume and design gradient properties across an object. This becomes more important nowadays with the growing interest in rapid prototyping and digital fabrication of heterogeneous objects and can find practical applications in different industries.
Finding Groups in Obstructive Sleep Apnea Patients: A Categorical Cluster Analysis

Obstructive sleep apnea (OSA) is a significant sleep problem with various clinical presentations that have not been formally characterized. This poses critical challenges for its recognition, resulting in missed or delayed diagnosis. Recently, cluster analysis has been used in different clinical domains, particularly within numeric variables. We applied an extension of k-means to be used in categorical variables: k-modes, to identify groups of OSA patients. Demographic, physical examination, clinical history, and comorbidities characterization variables (n=46) were collected from 318 patients; missing values were all imputed with k-nearest neighbors (k-NN). Feature selection, through Chi-square test, was executed and 17 variables were inserted in cluster analysis, resulting in three clusters. Cluster 1 having an age between 65 and 90 years (54%), 78% of males, with the presence of diabetes and gastroesophageal reflux, and high OSA prevalence; Cluster 2 presented a lower percentage of OSA (46%), with middle-aged women without comorbidities, but with gastroesophageal reflux; and Cluster 3 was very similar to cluster 1, only differing in age (45-64) and comorbidities were not present. Our results suggest that there are different groups of OSA patients, creating the need to rethink the baseline characteristics of these patients before being sent to perform polysomnography (gold standard exam for diagnosis).
Importance Sampling of Interval Markov Chains

In real-world systems, rare events often characterize critical situations like the probability that a system fails within some time bound and they are used to model some potentially harmful scenarios in dependability of safety-critical systems. Probabilistic Model Checking has been used to verify dependability properties in various types of systems but is limited by the state space explosion problem. An alternative is the recourse to Statistical Model Checking (SMC) that relies on Monte Carlo simulations and provides estimates within predefined error and confidence bounds. However, rare properties require a large number of simulations before occurring at least once. To tackle the problem, Importance Sampling, a rare event simulation technique, has been proposed in SMC for different types of probabilistic systems. Importance Sampling requires the full knowledge of probabilistic measure of the system, e.g. Markov chains. In practice, however, we often have models with some uncertainty, e.g., Interval Markov Chains. In this work, we propose a method to apply importance sampling to Interval Markov Chains. We show promising results in applying our method to multiple case studies.
A Generalized Minor Component Extraction Algorithm and Its Analysis

Generalized minor component analysis (GMCA) is of great use in modern signal processing. The GMCA algorithms can be simplified to extract the minor generalized eigenvector of the autocorrelation input matrices pencil. In contrast to batching methods, the Hebbian-rule-based algorithm can extract the minor generalized eigenvector online. Few Hebbian-rule-based GMCA algorithms have been reported in the literature, and most of them are not self-stabilizing. Thus, a novel algorithm for GMCA, which is advantageous in terms of good convergence speed, self-stabilizing property, and multiple generalized minor component extraction in sequence, is proposed in this paper. A theoretical analysis verifies these properties via matrix theory and the deterministic discrete-time method. Numerical simulations are conducted to further demonstrate the advantages of the proposed algorithm.
A Multi-level Dataset of Linux Kernel Patchwork

In many open source software projects (e.g., the Linux kernel), people contribute by sending code patches to the community. The community evaluates these contributions and decides whether to integrate the changes. To improve the efficiency of code contributions, substantial effort has been devoted to analyzing how patches are submitted and processed. Patch data are critical for this type of analysis, while retrieving and cleaning the data is a non-trivial job. To facilitate these studies, we share a multi-level dataset of a Linux kernel patchwork covering a nine-year history of patches and related discussion recorded by the Linux kernel mailing list (LKML). The data and scripts are provided at: https://zenodo.org/record/1165576
Practical Implementation of Link Adaptation with Dual Polarized Modulation

The use of dual polarization in mobile satellite systems is very promising for increasing the channel capacity. Polarized Modulation is proposed in this paper for use in practical systems, by providing simple equations for computing its capacity and featuring a link adaptation algorithm. This scheme shows remarkable gains in the spectral efficiency when compared with single polarization and other multi-antenna techniques such as V-BLAST. Polarized Modulation is a particular instance of more general Index Modulations, which are being considered for 5G networks. Thus, the proposed link adaptation algorithm could find synergies with current activities for future terrestrial networks.
Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data

Kernel methods are powerful supervised machine learning models for their strong generalization ability, especially on limited data to effectively generalize on unseen data. However, most kernel methods, including the state-of-the-art LIBSVM, are vulnerable to the curse of kernelization, making them infeasible to apply to large-scale datasets. This issue is exacerbated when kernel methods are used in conjunction with a grid search to tune their kernel parameters and hyperparameters which brings in the question of model robustness when applied to real datasets. In this paper, we propose a robust Bayesian Kernel Machine (BKM) - a Bayesian kernel machine that exploits the strengths of both the Bayesian modelling and kernel methods. A key challenge for such a formulation is the need for an efficient learning algorithm. To this end, we successfully extended the recent Stein variational theory for Bayesian inference for our proposed model, resulting in fast and efficient learning and prediction algorithms. Importantly our proposed BKM is resilient to the curse of kernelization, hence making it applicable to large-scale datasets and robust to parameter tuning, avoiding the associated expense and potential pitfalls with current practice of parameter tuning. Our extensive experimental results on 12 benchmark datasets show that our BKM without tuning any parameter can achieve comparable predictive performance with the state-of-the-art LIBSVM and significantly outperforms other baselines, while obtaining significantly speedup in terms of the total training time compared with its rivals
Automatic Classification of Software Artifacts in Open-Source Applications

With the increasing popularity of open-source software development, there is a tremendous growth of software artifacts that provide insight into how people build software. Researchers are always looking for large-scale and representative software artifacts to produce systematic and unbiased validation of novel and existing techniques. For example, in the domain of software requirements traceability, researchers often use software applications with multiple types of artifacts, such as requirements, system elements, verifications, or tasks to develop and evaluate their traceability analysis techniques. However, the manual identification of rich software artifacts is very labor-intensive. In this work, we first conduct a large-scale study to identify which types of software artifacts are produced by a wide variety of open-source projects at different levels of granularity. Then we propose an automated approach based on Machine Learning techniques to identify various types of software artifacts. Through a set of experiments, we report and compare the performance of these algorithms when applied to software artifacts.
Calibrated Multi-Task Learning

This paper proposes a novel algorithm, named Non-Convex Calibrated Multi-Task Learning (NC-CMTL), for learning multiple related regression tasks jointly. Instead of utilizing the nuclear norm, NC-CMTL adopts a non-convex low rank regularizer to explore the shared information among different tasks. In addition, considering that the regularization parameter for each regression task desponds on its noise level, we replace the least squares loss function by square-root loss function. Computationally, as proposed model has a nonsmooth loss function and a non-convex regularization term, we construct an efcient re-weighted method to optimize it. Theoretically, we frst present the convergence analysis of constructed method, and then prove that the derived solution is a stationary point of original problem. Particularly, the regularizer and optimization method used in this paper are also suitable for other rank minimization problems. Numerical experiments on both synthetic and real data illustrate the advantages of NC-CMTL over several state-of-the-art methods.
Deep Sparse Tensor Filtering Network for Synthetic Aperture Radar Images Classification

Recognizing scenes from synthetic aperture radar (SAR) images has been a challenging task due to the increasing resolution of SAR data. Extracting discriminative features from SAR images is extremely difficult for their sensitivity to target aspect. Considering the intractability of the available deep neural networks in practical implementations, in this brief, we propose a simple and efficient deep sparse tensor filtering network (DSTFN) for SAR image classification. An SAR image is first organized into a data tensor by an overlapped partition. Then, a set of dimension-inseparable geometric filters is developed from a least squares support vector machine, followed by a learned sparse filtering of tensors. Finally, the constructed sparse tensor filters are cascaded to a deep network to automatically extract the discriminative features of the image for accurate classification. Simulations are carried out to verify the effectiveness of the proposed DSTFN.