Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Representation and Phonological Rules for the Automatic Transliteration of Balinese Script on Palm Leaf Manuscript

Balinese ancient palm leaf manuscripts record many important knowledges about world civilization histories. They vary from ordinary texts to Bali’s most sacred writings. In reality, the majority of Balinese can not read it because of language obstacles as well as tradition which perceived them as a sacrilege. Palm leaf manuscripts attract the historians, philologists, and archaeologists to discover more about the ancient ways of life. But unfortunately, there is only a limited access to the content of these manuscripts, because of the linguistic difficulties. The Balinese palm leaf manuscripts were written in Balinese script in Balinese language, in the ancient literary texts composed in the old Javanese language of Kawi and Sanskrit. Balinese script is considered to be one of the most complex scripts from Southeast Asia. A transliteration engine for transliterating the Balinese script of palm leaf manuscript to the Latin-based script is one of the most demanding systems which has to be developed for the collection of palm leaf manuscript images. In this paper, we present an implementation of knowledge representation and phonological rules for the automatic transliteration of Balinese script on palm leaf manuscript. In this system, a rule-based engine for performing transliterations is proposed. Our model is based on phonetics which is based on traditional linguistic study of Balinese transliteration. This automatic transliteration system is needed to complete the optical character recognition (OCR) process on the palm leaf manuscript images, to make the manuscripts more accessible and readable to a wider audience.
Processing Technique and Property Evaluations of Chitosan Dressings

This study uses acetic acid as the solvent to make two different deacetylated chitosans into chitosan solutions (CS1 and CS2). A specified amount of chitosan solutions are evaporated at 55 °C for 30, 60, and 90 minutes, immersed in NaOH-NaHCO3(aq), and then freeze-dried to form six membrane types, CS1-30, CS1-60, CS1-90 ,CS2-30, CS2-60 and CS2-90. Scanning electron microscopic (SEM) observation, a swelling ratio test, and an antibacterial assay are performed to evaluate the influence of differing evaporation times on the CS membranes. The test results show that membrane CS1 types have good properties. In particular, CS1-60 possesses an optimal swelling property and a maximum inhibition zone.
Recent Advances of Computational Modeling for Predicting Drug Metabolism: A Perspective.

BACKGROUND Absorption, Distribution, Metabolism, Excretion (ADME) properties along with drug induced adverse effects are the major reasons for the late stage failure of drug candidates as well as the cause for the expensive withdrawal of many approved drugs from the market. Considering the adverse effects of drugs, metabolism factor has great importance in medicinal chemistry and clinical pharmacology because it influences the deactivation, activation, detoxification and toxification of drugs. METHODS Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and metabolism followed by adverse effects, as they serve the integration of information on several levels to enhance the reliability of outcomes. RESULTS AND DISCUSSION In silico profiling of drug metabolism can help progress only those molecules along the discovery chain that is less likely to fail later in the drug discovery process. This positively impacts the very high costs of drug discovery and development. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true influence on drug discovery at different levels. If applied in a scientifically consequential way, computational tools may improve the capability to identify and evaluate potential drug molecules considering pharmacokinetic properties of drugs. CONCLUSION Herein, current trends in computational modeling for predicting drug metabolism are reviewed highlighting new computational tools for drug metabolism prediction followed by reporting large and integrated databases of approved drugs associated with diverse metabolism issues.
Wireless energy harvesting in interference alignment networks with adversarial jammers

Anti-jamming interference alignment (IA) algorithm is an effective method to battle with adversarial jammers. Nevertheless, the abundant power from the jammers and interferences can be harvested by the legitimate users as a natural power supply. In this paper, we propose a novel anti-jamming IA scheme with wireless energy harvesting (EH). In the scheme, the power partition coefficient and transmit power are jointly optimized to minimize the total transmit power of the network, with the requirements of sum rate and harvested energy guaranteed. To further reduce the complexity of the joint optimization, a suboptimal algorithm is also developed with lower complexity. Extensive simulation results are presented to show the effectiveness of the proposed anti-jamming IA scheme with wireless EH.
Binary hashing using siamese neural networks

With the growth in multimedia data, it is the need of the hour to have methods for efficient storage and quick retrieval. In this work, we propose an approach for learning binary codes for fast image retrieval. We use a siamese architecture with two parallel feed forward branches but with a shared weight for the generation of binary codes. The training data is divided into similar and dissimilar pairs. The network tries to learn the weights such that it minimizes the distance between similar image pairs and maximizes the distance between dissimilar image pairs. The binary codes are formed by squashing the neural network output through a sigmoid activation function. The training with sigmoid hashing constrains the output of each node in the final fully connected layer to either 0 or 1. We have compared the retrieval performance of our approach with other state-of-the-art hashing methods and our method shows significant improvement.
Automatic estimation of deterioration level on transmission towers via deep extreme learning machine based on local receptive field

This paper presents an automatic estimation method of deterioration levels on transmission towers via Deep Extreme Learning Machine based on Local Receptive Field (DELM-LRF). Although Convolutional Neural Network (CNN) requires a large number of training images, it is difficult to prepare a sufficient number of training images of transmission towers. Thus, we generate a novel estimation method which enables training from a small number of training images. Specifically, we automatically extract image features based on Local Receptive Field (LRF) which combines convolution and pooling without using hand-craft features and estimate deterioration levels via Deep Extreme Learning Machine (DELM), which is a part of efficient deep learning methods. The derivation of DELM-LRF is the biggest contribution of this paper, and it can be trained from less training images compared to CNN. Experimental results show the effectiveness of DELM-LRF for the estimation of deterioration levels on transmission towers. Consequently, the proposed method makes it possible to approach challenging tasks with high expertise having difficulty in preparing enough images.
Robust Histogram Shape-Based Method for Image Watermarking

Cropping and random bending are two common attacks in image watermarking. In this paper we propose a novel image-watermarking method to deal with these attacks, as well as other common attacks. In the embedding process, we first preprocess the host image by a Gaussian low-pass filter. Then, a secret key is used to randomly select a number of gray levels and the histogram of the filtered image with respect to these selected gray levels is constructed. After that, a histogram-shape-related index is introduced to choose the pixel groups with the highest number of pixels and a safe band is built between the chosen and nonchosen pixel groups. A watermark-embedding scheme is proposed to insert watermarks into the chosen pixel groups. The usage of the histogram-shape-related index and safe band results in good robustness. Moreover, a novel high-frequency component modification mechanism is also utilized in the embedding scheme to further improve robustness. At the decoding end, based on the available secret key, the watermarked pixel groups are identified and watermarks are extracted from them. The effectiveness of the proposed image-watermarking method is demonstrated by simulation examples.
Transferring CNNS to multi-instance multi-label classification on small datasets

Image tagging is a well known challenge in image processing. It is typically addressed through multi-instance multi-label (MIML) classification methodologies. Convolutional Neural Networks (CNNs) possess great potential to perform well on MIML tasks, since multi-level convolution and max pooling coincide with the multi-instance setting and the sharing of hidden representation may benefit multi-label modeling. However, CNNs usually require a large amount of carefully labeled data for training, which is hard to obtain in many real applications. In this paper, we propose a new approach for transferring pre-trained deep networks such as VGG16 on Imagenet to small MIML tasks. We extract features from each group of the network layers and apply multiple binary classifiers to them for multi-label prediction. Moreover, we adopt an L1-norm regularized Logistic Regression (L1LR) to find the most effective features for learning the multi-label classifiers. The experiment results on two most-widely used and relatively small benchmark MIML image datasets demonstrate that the proposed approach can substantially outperform the state-of-the-art algorithms, in terms of all popular performance metrics.
Cell Therapy with Embryonic Stem Cell-Derived Cardiomyocytes Encapsulated in Injectable Nanomatrix Gel Enhances Cell Engraftment and Promotes Cardiac Repair

A significant barrier to the therapeutic use of stem cells is poor cell retention in vivo. Here, we evaluate the therapeutic potential and long-term engraftment of cardiomyocytes (CMs) derived from mouse embryonic stem cells (mESCs) encapsulated in an injectable nanomatrix gel consisting of peptide amphiphiles incorporating cell adhesive ligand Arg-Gly-Asp-Ser (PA-RGDS) in experimental myocardial infarction (MI). We cultured rat neonatal CMs in PA-RGDS for 7 days and found that more than 90% of the CMs survived. Next, we intramyocardially injected mouse CM cell line HL-1 CMs with or without PA-RGDS into uninjured hearts. Histologic examination and flow cytometry analysis of digested heart tissues showed approximately 3-fold higher engraftment in the mice that received CMs with PA-RGDS compared to those without PA-RGDS. We further investigated the therapeutic effects and long-term engraftment of mESC-CMs with PA-RGDS on MI in comparison with PBS control, CM-only, and PA-RGDS only. Echocardiography demonstrated that the CM-only and CM+PA-RGDS groups showed higher cardiac function at week 2 compared to other groups. However, from 3 weeks, higher cardiac function was maintained only in the CM+PA-RGDS group; this was sustained for 12 weeks. Confocal microscopic examination of the cardiac tissues harvested at 14 weeks demonstrated sustained engraftment and integration of mESC-CMs into host myocardium in the CM+PA-RGDS group only. This study for the first time demonstrated that PA-RGDS encapsulation can enhance survival of mESC-derived CMs and improve cardiac function post-MI. This nanomatrix gel-mediated stem cell therapy can be a promising option for treating MI.
On the Etiology of Listening Difficulties in Noise Despite Clinically Normal Audiograms

Many people with difficulties following conversations in noisy settings have “clinically normal” audiograms, that is, tone thresholds better than 20 dB HL from 0.1 to 8 kHz. This review summarizes the possible causes of such difficulties, and examines established as well as promising new psychoacoustic and electrophysiologic approaches to differentiate between them. Deficits at the level of the auditory periphery are possible even if thresholds remain around 0 dB HL, and become probable when they reach 10 to 20 dB HL. Extending the audiogram beyond 8 kHz can identify early signs of noise-induced trauma to the vulnerable basal turn of the cochlea, and might point to “hidden” losses at lower frequencies that could compromise speech reception in noise. Listening difficulties can also be a consequence of impaired central auditory processing, resulting from lesions affecting the auditory brainstem or cortex, or from abnormal patterns of sound input during developmental sensitive periods and even in adulthood. Such auditory processing disorders should be distinguished from (cognitive) linguistic deficits, and from problems with attention or working memory that may not be specific to the auditory modality. Improved diagnosis of the causes of listening difficulties in noise should lead to better treatment outcomes, by optimizing auditory training procedures to the specific deficits of individual patients, for example.
Retrain-free fully connected layer optimization using matrix factorization

The complexity of Deep Neural Networks (DNNs) hinders their implementation on embedded system with limited hardware resources. To deal with this issue, this paper presents a novel optimization algorithm based on semi-Nonnegative Matrix Factorization for Fully Connected layers (semi-NMF-based FC optimization). Compared with previous network surgery techniques, our proposed method optimizes network structure in a more implementation-friendly manner with full controllability and no requirement on retraining using training data. Simulations using AlexNet and VGG-16 on ImageNet task show the effectiveness and efficiency of semi-NMF-based FC optimization.
Energy cost of running instability evaluated with wearable trunk accelerometry.

Maintaining stability under dynamic conditions is an inherent challenge to bipedal running. This challenge may impose an energetic cost (Ec) thus hampering endurance running performance, yet the underlying mechanisms are not clear. Wireless triaxial trunk accelerometry is a simple tool that could be used to unobtrusively evaluate these mechanisms. Here, we test a cost of instability hypothesis by examining the contribution of trunk accelerometry-based measures (triaxial root mean square, step and stride regularity, and sample entropy) to interindividual variance in Ec (J/m) during treadmill running. Accelerometry and indirect calorimetry data were collected concurrently from 30 recreational runners (16 men; 14 women) running at their highest steady-state running speed (80.65 ± 5.99% V̇o2max). After reducing dimensionality with factor analysis, the effect of dynamic stability features on Ec was evaluated using hierarchical multiple regression analysis. Three accelerometry-based measures could explain an additional 10.4% of interindividual variance in Ec after controlling for body mass, attributed to anteroposterior stride regularity (5.2%), anteroposterior root mean square ratio (3.2%), and mediolateral sample entropy (2.0%). Our results lend support to a cost of instability hypothesis, with trunk acceleration waveform signals that are 1) more consistent between strides anteroposterioly, 2) larger in amplitude variability anteroposterioly, and 3) more complex mediolaterally and are energetically advantageous to endurance running performance. This study shows that wearable trunk accelerometry is a useful tool for understanding the Ec of running and that running stability is important for economy in recreational runners. NEW & NOTEWORTHY This study evaluates and more directly lends support to a cost of instability hypothesis between runners. Moreover, this hypothesis was tested using a minimalist setup including a single triaxial trunk mounted accelerometer, with potential transferability to biomechanical and performance analyses in typical outdoor settings.
Deep network for image super-resolution with a dictionary learning layer

The aim of single image super-resolution (SR) is to generate a high-resolution (HR) image from a low-resolution (LR) observable image. In this paper, we address this task by integrating sparse coding and dictionary learning schemes into an end-to-end deep architecture. More specifically, we propose a new non-linear dictionary learning layer composed of a finite number of recurrent units to solve the sparse codes and also to yield the relevant gradients to update the dictionary. In addition, we present a new deep network architecture using the proposed non-linear layers, where two separate parallel dictionaries are adopted to represent the LR and HR images respectively. The whole network is optimized by back propagation, constraining not only reconstruction errors between the restored and the ground truth HR images but also between the sparse codes of the LR and HR image pairs. Various datasets are used to evaluate the performance of the proposed approach and it is shown to outperform many state-of-the-art single image super-resolution algorithms.
Novel Statistical Post Processing to Improve Blood Pressure Estimation from Smartphone Photoplethysmogram

Blood pressure (BP) is considered to be an important biomarker for cardiac risk estimation. This paper deals with a non-conventional way of estimating BP using smartphone captured Photoplethysmogram (PPG) that enables unobtrusive health monitoring at home for possible alert generation. We have proposed a set of features that are independent to the inbuilt sensor of the capturing device. It is also observed that, BP estimated from a typical smartphone PPG signal fluctuates in successive cardiac cycles due to poor signal quality compared to a medical grade device. Hence, a novel post processing block is introduced, that rejects data depending on the BP distribution over all cardiac cycles in a session. Finally, Half Range Mode is used as a statistical average for the accepted sessions. This post processing methodology outperforms standard statistical averages in providing a better representative BP per session. The methodology yields mean absolute errors of 7.4% and 9.1% for predicting systolic and diastolic pressure respectively when validated over a dataset with a wide variation of BP.
The real gain of cross-layer routing in wireless mesh networks

Despite the large amount of research performed on wireless networking, test-beds are still limited in number and size. This paper shows the results of our efforts in building and deploying a test-bed, using the Wireless Mesh Net-work (WMN) architecture. This platform is composed of MeshDVboxes our custom Wireless Mesh Router (WMR), built up from off-the-shelf hardware and open source software. As a routing protocol we use two versions (with and without cross-layer metrics) of MeshDV a simple routing protocol expressly designed for WMNs. Our tests show that in cases where traditional hop-count metric compromises network connectivity, cross-layer routing has still non-negligible performance.
Multi-objective decision-making framework for effective waste collection in smart cities

There are metropolitan areas in smart cities that are experiencing waste collection challenges through ineffective methods of waste collection in resource constrained environments. This paper identified an opportunity to investigate efficient decision-making ways that will make use of data generated by IoT-enabled objects, taking into account the multi-objective goals in a smart city through addressing data loss challenge. Having the list of decision-making algorithms is one thing but choosing which algorithm to use requires intelligence. There is a need for decision-making algorithms that will be sufficiently dynamic to address different levels of data loss inherent in IoT data collection. This paper presents the framework that will enhance the smarter decisions in the smart city.
Light-field image compression based on variational disparity estimation and motion-compensated wavelet decomposition

This paper presents a compression framework for light-field images. The main idea of our approach is exploiting the similarity across sub-aperture images extracted from light-field data to improve encoding performance. For this purpose we propose a variational optimisation approach to estimate the disparity map from light-field images and then apply it to a motion-compensated wavelet lifting scheme. Making use of JPEG2000 for coding all high-/low-pass sub-band views as well as disparity map, our approach can therefore support both lossless and lossy compression. The coding framework is tested with both synthetic and real-world light-field dataset. The experimental results demonstrate that our approach outperforms JPEG-LS and the direct application of JPEG2000 in both lossless and lossy compression scenarios.
The association between prostatitis and prostate cancer. Systematic review and meta-analysis.

OBJECTIVE The main outcome of this review was the association between a history of clinical chronic prostatitis (NIH category II or III) and a histologically confirmed diagnosis of prostate cancer. MATERIALS AND METHODS Crude odds ratios and 95% confidence intervals (CI) were calculated to analyze dichotomous data. For analysis of pooled data we adopted a random-effects model and the inverse variance weighing method. Heterogeneity was assessed by calculating the I2 value. RESULTS Out of 2794 screened records, we retrieved 16 full-text articles written in English, reporting the data of 15 case-control studies, involving 422.943 patients. Pooled analysis resulted in a significant crude odds ratio of 1.83 (95% CI: 1.43 to 2.35; P < 0.00001). The total set of data showed considerable heterogeneity (I2 = 91%). Both the Egger's test and the Begg's test for funnel plot asymmetry did not reach statistical significance. The 'trim and fill' method applied to the funnel plot imputed 3 missing studies and the resulting adjusted estimate of the odds ratio was 2.12 (95% CI: 1.38 to 3.22). According to GRADE criteria, the overall quality of the meta-analysis data is low, mainly due to the presence of bias, confounders and extreme effect size outliers. Five among the included studies reported data assessed in 8015 African-American subjects. Pooled analysis resulted in a non-significant crude odds ratio of 1.59 (95% CI: 0.71 to 3.57; P = 0.26), and considerable heterogeneity (I2 = 90%). CONCLUSIONS Meta-analysis of 15 case-control studies shows that a history of clinical chronic prostatitis can significantly increase the odds for prostate cancer in the general population, whereas such association in African-American individuals remains uncertain.
Investigations on Nanolaminated TiZrN/CrN as a Tribological PVD Hard Coating for Incremental Sheet Forming Tools

PVD coated metal forming tools may enormously reduce tool and work piece wear, friction and forming capacities. A PVD deposited TiZrN/CrN + CrN material system is presented for application on incremental sheet forming tools. This work reports on coating process development and tribological investigations leading to a clear friction reduction and wear protection.
Constructing a linear discrete system in Kernel space as a supervised classifier

The pattern recognition techniques involve feature extraction from the data, dimensionality reduction (like PCA, LDA, K-LDA, etc) and constructing a classifier (NN, NM, SVM, etc.) using the training set and validating the constructed classifier using the testing set. The usage of digital signal processing (DSP) techniques in pattern recognition is always limited to the feature extraction stage such as collecting the Fourier, wavelet co-efficients, HMM, GMM, etc. In this paper we explore the usage of classical DSP techniques like convolution, FIR filter to construct the classifier and compare it with the state of the art techniques. The proposed technique paves the alternative way to construct a classifier that is helpful for Big data analysis.