Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Tree Structure in Context Clustering Process of Hidden Markov Model-Based Thai Speech Synthesis

Problem statement: In HMM-based Thai speech synthesis, the tone degradation due to the imbalance of training data of all tones. Some distortion of syllable duration is obviously noticeable when the system is trained with a small amount of data. These problems cause the decrement in naturalness and intelligibility of the synthesized speech. Approach: This study proposes an approach to improve the correctness of tone of the synthesized speech which is generated by an HMM-based Thai speech synthesis system. In the tree-based context clustering process, tone groups and tone types are used to design four different structures of decision tree including a single binary tree structure, a simple tone-separated tree structure, a constancy-based-tone-separated tree structure and a trend-based-tone-separated tree structure. Results: A subjective evaluation of tone correctness is conducted by using tone perception of eight Thai listeners. The simple tone-separated tree structure gives the highest level of tone correctness, while the single binary tree structure gives the lowest level of tone correctness. The additional contextual tone information which is applied to all structures of the decision tree achieves a significant improvement of tone correctness. Finally, the evaluation of syllable duration distortion among the four structures shows that the constancy-based-tone-separated and the trend-based-tone-separated tree structures can alleviate the distortions that appear when using the simple tone-separated tree structure. Conclusion: The appropriate structure of tree in context clustering process with the additional contextual tone information can improve the correctness of tones, while the constancy-based-tone-separated and the trend-based-tone-separated tree structures can alleviate the syllable duration distortions.
Homomorphic Sensing

A recent line of research termed unlabeled sensing and shuffled linear regression has been exploring under great generality the recovery of signals from subsampled and permuted measurements; a challenging problem in diverse fields of data science and machine learning. In this paper we introduce an abstraction of this problem which we call homomorphic sensing. Given a linear subspace and a finite set of linear transformations we develop an algebraic theory which establishes conditions guaranteeing that points in the subspace are uniquely determined from their homomorphic image under some transformation in the set. As a special case, we recover known conditions for unlabeled sensing, as well as new results and extensions. On the algorithmic level we exhibit two dynamic programming based algorithms, which to the best of our knowledge are the first working solutions for the unlabeled sensing problem for small dimensions. One of them, additionally based on branch-and-bound, when applied to image registration under affine transformations, performs on par with or outperforms state-of-the-art methods on benchmark datasets.
AStERISK: Auction-based Shared Economy ResolutIon System for blocKchain

Recent developments in blockchains and edge computing allows to deploy decentralized shared economy with utility tokens, where altcoins secure and reward useful work. However, the majority of the systems being developed, does not provide mechanisms to pair workers and clients, or rely on manual and insecure resolution. AStERISK bridges this gap allowing to perform sealed-bid auctions on blockchains, automatically determine the most optimal price for services, and assign clients to the most suitable workers. AStERISK allows workers to specify a minimal price for their work, and hide submitted bids as well the identity of the bidders without relying on any centralized party at any point. We provide a smart contract implementation of AStERISK and show how to deploy it within the Filecoin network, and perform an initial benchmark on Chainspace.
Submodular Maximization under Fading Model: Building Online Quizzes for Better Customer Segmentation

E-Commerce personalization aims to provide individualized offers, product recommendations, and other content to customers based on their interests. The foundation of any personalization effort is customer segmentation. The idea of customer segmentation is to group customers together according to identifiable segmentation attributes including geolocation, gender, age, and interests. Personality quiz turns out to be a powerful tool that enables costumer segmentation by actively asking them questions, and marketers are using it as an effective method of generating leads and increasing e-commerce sales. In this paper, we study the problem of how to select and sequence a group of quiz questions so as to optimize the quality of customer segmentation. In particular, we use conditional entropy to measure the utility of a given group of quiz questions. We model the user behavior when interacting with a sequence of quiz questions as a Markov process. Then we develop a series of question allocation strategies with provable performance bound.
Expansional Retrofitting for Word Vector Enrichment

Retrofitting techniques, which inject external resources into word representations, have compensated the weakness of distributed representations in semantic and relational knowledge between words. Implicitly retrofitting word vectors by expansional technique outperforms retrofitting in word similarity tasks with word vector generalization. In this paper, we propose unsupervised extrofitting: expansional retrofitting (extrofitting) without external semantic lexicons. We also propose deep extrofitting: in-depth stacking of extrofitting and further combinations of extrofitting with retrofitting. When experimenting with GloVe, we show that our methods outperform the previous methods on most of word similarity tasks while requiring only synonyms as an external resource. Lastly, we show the effect of word vector enrichment on text classification task, as a downstream task.
CTCModel: a Keras Model for Connectionist Temporal Classification

We report an extension of a Keras Model, called CTCModel, to perform the Connection-ist Temporal Classification (CTC) in a transparent way. Combined with Recurrent Neural Networks, the Connectionist Temporal Classification is the reference method for dealing with unsegmented input sequences, i.e. with data that are a couple of observation and label sequences where each label is related to a subset of observation frames. CTCModel makes use of the CTC implementation in the Tensorflow backend for training and predicting sequences of labels using Keras. It consists of three branches made of Keras models: one for training, computing the CTC loss function; one for predicting, providing sequences of labels; and one for evaluating that returns standard metrics for analyzing sequences of predictions.
Construction of 1-Bit Transmit-Signal Vectors for Downlink MU-MISO Systems With PSK Signaling

We consider a downlink multi-user multiple-input single-output system where the base station has a large number of antennas with cost-effective 1-b digital-to-analog converters. For this system, we first identify that assigning the zero-power to some transmit signals (in short, zero-power allocation) can yield a non-trivial symbol-error-rate performance gain by alleviating an error-floor problem. Likewise the related works on 1-b precoding, finding an optimal transmit-signal vector (encompassing precoding and zero-power allocation) requires an exhaustive search due to its combinatorial nature. Motivated by this, we propose a low-complexity two-stage algorithm to construct such transmit-signal vector directly. In the first stage, we derive a feasible transmit-signal vector via iterative-soft-thresholding algorithm whose output ensures that each user's noiseless observation belong to a desired region (called decision region). In the second stage, a bit-flipping algorithm is performed to refine the feasible vector so that each user's received signal is more robust to additive Gaussian noises. Simulation results demonstrate that the proposed method can yield a more elegant performance-complexity tradeoff than the existing 1-b precoding methods.
Incremental Principal Component Analysis Exact implementation and continuity corrections

This paper describes some applications of an incremental implementation of the principal component analysis (PCA). The algorithm updates the transformation coefficients matrix on-line for each new sample, without the need to keep all the samples in memory. The algorithm is formally equivalent to the usual batch version, in the sense that given a sample set the transformation coefficients at the end of the process are the same. The implications of applying the PCA in real time are discussed with the help of data analysis examples. In particular we focus on the problem of the continuity of the PCs during an on-line analysis.
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-frequency channel feature (SFCF) by jointly considering the rotation-invariant channel features constructed in the frequency domain and the original spatial channel features (e.g., color channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.
Hamiltonian Monte-Carlo for Orthogonal Matrices

We consider the problem of sampling from posterior distributions for Bayesian models where some parameters are restricted to be orthogonal matrices. Such matrices are sometimes used in neural networks models for reasons of regularization and stabilization of training procedures, and also can parameterize matrices of bounded rank, positive-definite matrices and others. In \citet{byrne2013geodesic} authors have already considered sampling from distributions over manifolds using exact geodesic flows in a scheme similar to Hamiltonian Monte Carlo (HMC). We propose new sampling scheme for a set of orthogonal matrices that is based on the same approach, uses ideas of Riemannian optimization and does not require exact computation of geodesic flows. The method is theoretically justified by proof of symplecticity for the proposed iteration. In experiments we show that the new scheme is comparable or faster in time per iteration and more sample-efficient comparing to conventional HMC with explicit orthogonal parameterization and Geodesic Monte-Carlo. We also provide promising results of Bayesian ensembling for orthogonal neural networks and low-rank matrix factorization.
Programmable Neural Network Trojan for Pre-Trained Feature Extractor

Neural network (NN) trojaning attack is an emerging and important attack model that can broadly damage the system deployed with NN models. Existing studies have explored the outsourced training attack scenario and transfer learning attack scenario in some small datasets for specific domains, with limited numbers of fixed target classes. In this paper, we propose a more powerful trojaning attack method for both outsourced training attack and transfer learning attack, which outperforms existing studies in the capability, generality, and stealthiness. First, The attack is programmable that the malicious misclassification target is not fixed and can be generated on demand even after the victim's deployment. Second, our trojan attack is not limited in a small domain; one trojaned model on a large-scale dataset can affect applications of different domains that reuse its general features. Thirdly, our trojan design is hard to be detected or eliminated even if the victims fine-tune the whole model.
Unique Information and Secret Key Decompositions

The unique information (UI) is an information measure that quantifies a deviation from the Blackwell order. We have recently shown that this quantity is an upper bound on the one-way secret key rate. In this paper, we prove a triangle inequality for the UI, which implies that the UI is never greater than one of the best known upper bounds on the two-way secret key rate. We conjecture that the UI lower bounds the two-way rate and discuss implications of the conjecture.
A Survey of Optimization Approaches for Wireless Physical Layer Security

Due to the malicious attacks in wireless networks, physical layer security has attracted increasing concerns from both academia and industry. The research on physical layer security mainly focuses either on the secrecy capacity/achievable secrecy rate/capacity-equivocation region from the perspective of information theory, or on the security designs from the viewpoints of optimization and signal processing. Because of its importance in security designs, the latter research direction is surveyed in a comprehensive way in this paper. The survey begins with typical wiretap channel models to cover common scenarios and systems. The topics on physical-layer security designs are then summarized from resource allocation, beamforming/precoding, and antenna/node selection and cooperation. Based on the aforementioned schemes, the performance metrics and fundamental optimization problems are discussed, which are generally adopted in security designs. Thereafter, the state of the art of optimization approaches on each research topic of physical layer security is reviewed from four categories of optimization problems, such as secrecy rate maximization, secrecy outrage probability minimization, power consumption minimization, and secure energy efficiency maximization. Furthermore, the impacts of channel state information on optimization and design are discussed. Finally, the survey concludes with the observations on potential future directions and open challenges.
CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model

Background: Widespread adoption of electronic health records (EHRs) has enabled secondary use of EHR data for clinical research and healthcare delivery. Natural language processing (NLP) techniques have shown promise in their capability to extract the embedded information in unstructured clinical data, and information retrieval (IR) techniques provide flexible and scalable solutions that can augment the NLP systems for retrieving and ranking relevant records. Methods: In this paper, we present the implementation of Cohort Retrieval Enhanced by Analysis of Text from EHRs (CREATE), a cohort retrieval system that can execute textual cohort selection queries on both structured and unstructured EHR data. CREATE is a proof-of-concept system that leverages a combination of structured queries and IR techniques on NLP results to improve cohort retrieval performance while adopting the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to enhance model portability. The NLP component empowered by cTAKES is used to extract CDM concepts from textual queries. We design a hierarchical index in Elasticsearch to support CDM concept search utilizing IR techniques and frameworks. Results: Our case study on 5 cohort identification queries evaluated using the IR metric, P@5 (Precision at 5) at both the patient-level and document-level, demonstrates that CREATE achieves an average P@5 of 0.90, which outperforms systems using only structured data or only unstructured data with average P@5s of 0.54 and 0.74, respectively.
Instinctive Assistive Indoor Navigation using Distributed Intelligence

Cyber-physical systems (CPS) and the Internet of Things (IoT) offer a significant potential to improve the effectiveness of assistive technologies for those with physical disabilities. Practical assistive technologies should minimize the number of inputs from users to reduce their cognitive and physical effort. This article presents an energy-efficient framework and algorithm for assistive indoor navigation with multi-modal user input. The goal of the proposed framework is to simplify the navigation tasks and make them more instinctive for the user. Our framework automates indoor navigation using only a few user commands captured through a wearable device. The proposed methodology is evaluated using both a virtual smart building and a prototype. The evaluations for three different floorplans show one order of magnitude reduction in user effort and communication energy required for navigation, when compared to conventional navigation methodologies that require continuous user inputs.
Perchlorate Removal in Microbial Electrochemical Systems With Iron/Carbon Electrodes

Perchlorate removal was tested in the cathode chamber of microbial electrochemical systems (MESs). Dual-chambers MESs were constructed and operated in batch mode with four kinds of cathode materials including Fe/C particles (Fe/C), zero valent iron particles (ZVI), blank carbon felt (CF), and active carbon (AC). Without external energy supply or perchlorate-reducing microbial pre-enrichment, perchlorate (ClO4-) removal could be achieved in the cathode chambers of MESs at different efficiencies. The highest ClO4- removal rates in these reactors were 18.96 (Fe/C, 100 Ω, 2 days), 15.84 (ZVI, 100 Ω, 2 days), 14.37 (CF, 100 Ω, 3 days), and 19.78 mg/L/day (AC, 100 Ω, 2 days). ClO4- degradation products were mainly Cl− and ClO3-, and the total chlorine in the products was lower than the theoretical input. The non-conservation of the total chlorine may be caused by the adsorption and co-precipitation related to the electrode materials. Coulombs and coulombic efficiency calculation showed that electron provided by MESs was partially responsible for ClO4- reduction, for the Fe/C cathode reactors, about a quarter of electron was provided by MESs.
A Simplified Approach to Operational InSAR Monitoring of Volcano Deformation in Low- and Middle-Income Countries: Case Study of Rabaul Caldera, Papua New Guinea

The primary goal of operational volcano monitoring is the timely identification of volcanic unrest. This provides critical information to decision makers tasked with mitigating the societal impacts of volcanic eruptions. Volcano deformation is recognized as a key indicator of unrest at many active volcanoes and can be used to provide insight into the depth and geometry of the magma source. Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique that has detected deformation at many volcanoes globally, but most often with hindsight. To date, the use of InSAR for operational volcano monitoring has been limited to a few cases and only in high income countries. Yet a vast number of active volcanoes are located in low- and middle-income countries, where resources for operational monitoring are constrained. In these countries, InSAR could provide deformation monitoring at many active volcanoes, including those that have no existing ground monitoring infrastructure. Several barriers combine to make uptake of InSAR into operational volcano monitoring difficult in most countries, but particularly in resource-constrained environments. To overcome some of these limiting factors, we propose a simplified processing chain to better incorporate InSAR and Global Navigation Satellite Systems (GNSS) data into the decision-making process at volcano observatories. To combine the InSAR and GNSS data we use a joint modelling procedure that infers volume changes of a spherical source beneath the volcano. The benefits of our approach for operational use include that the algorithm is computationally lightweight and can be run quickly on a standard desktop or laptop PC. This enables a volcano observatory to interpret geodetic data in a timely fashion, and use the information as part of frequent reporting procedures. To demonstrate our approach we combine ALOS-PALSAR InSAR data and continuous GNSS data from the Rabaul Caldera, Papua New Guinea between 2007 and 2011. Joint inversion of the two datasets indicates volume loss of ~1x107 m3 (deflation) occurring between February 2008 and November 2009, followed by volume gain of ~2.5x106 m3 (inflation) until February 2011 in a magma body situated ~1.5 km beneath the caldera.
Exosomes Derived From Bone Mesenchymal Stem Cells Ameliorate Early Inflammatory Responses Following Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of mortality and disability worldwide. Although treatment guidelines have been developed, no best treatment option or medicine for this condition exists. Recently, mesenchymal stem cells (MSCs)-derived exosomes have shown lots of promise for the treatment of brain disorders, with some results highlighting the neuroprotective effects through neurogenesis and angiogenesis after TBI. However, studies focusing on the role of exosomes in the early stages of neuroinflammation post-TBI are not sufficient. In this study, we investigated the role of bone mesenchymal stem cells (BMSCs)-exosomes in attenuating neuroinflammation at an early stage post-TBI and explored the potential regulatory neuroprotective mechanism. We administered 30 μg protein of BMSCs-exosomes or an equal volume of phosphate-buffered saline (PBS) via the retro-orbital route into C57BL/6 male mice 15 min after controlled cortical impact (CCI)-induced TBI. The results showed that the administration of BMSCs-exosomes reduced the lesion size and improved the neurobehavioral performance assessed by modified Neurological Severity Score (mNSS) and rotarod test. In addition, BMSCs-exosomes inhibited the expression of proapoptosis protein Bcl-2-associated X protein (BAX) and proinflammation cytokines, tumor necrosis factor-α (TNF-α) and interleukin (IL)-1β, while enhancing the expression of the anti-apoptosis protein B-cell lymphoma 2 (BCL-2). Furthermore, BMSCs-exosomes modulated microglia/macrophage polarization by downregulating the expression of inducible nitric oxide synthase (INOS) and upregulating the expression of clusters of differentiation 206 (CD206) and arginase-1 (Arg1). In summary, our result shows that BMSCs-exosomes serve a neuroprotective function by inhibiting early neuroinflammation in TBI mice through modulating the polarization of microglia/macrophages. Further research into this may serve as a potential therapeutic strategy for the future treatment of TBI.