Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Learning Networks

Graph Neural Networks (GNNs) are an architecture naturally suited to graph structured data. In this work, we consider datasets without an explicitly deﬁned graph, but which contain some implicit relational structure. To apply GNNs to such data one must ﬁrst construct a graph. We propose Graph Learning Networks (GLNs), which extend the GNN architecture by learning task-speciﬁc graphs, and consider the generic problem of assigning unique labels to each point of a point-set. We apply GLNs to labelling the cell nuclei of C. elegans nematodes from their 3D positions, and show how this outperforms previous approaches. Furthermore, we conduct experiments that show that graph learning in GLN is key to solving hard synthetic labelling problems with an implicit and non-trivial relational structure.
Influence of Spatial Variability on Consolidation of Unsaturated Soils

Reliable prediction of consolidation rates in soil deposits is difficult due to highly spatial variability of in situ soil properties. In this paper, a numerical model for coupled consolidation of unsaturated soils is established combined with the random field method to investigate the influence of spatial variability on coupled consolidation of unsaturated soils. A fast and accurate of random field generation method, the Fast Fourier Transform method, is adopted to generate realizations of random field, which are then mapped onto the finite-element mesh of the numerical model. Parametric studies are conducted to investigate the effects of the coefficient of variation and the correlation length of saturated permeability ks on the dissipation of excess pore water pressure and variation of settlement. The COV of ks is greater or the correlation length is smaller means the spatial variability of saturated permeability is more significant. It is found that when the COV of ks is greater, the mean settlement of the unsaturated soil is smaller while the COV of settlement is larger. When the correlation distance of ks decreases, the mean settlement of the unsaturated soil decreases while the COV of settlement increases. Comparing with the results for the effect of the COV of ks, the effect of the correlation distance is less significant. It is also found that when the spatial variability is more significant, the mean value and standard deviation of the generated excess pore water pressure are greater. The results imply that when the spatial variability of unsaturated soils is greater, the dissipation of excess pore water pressure is slower and hence the settlement of the soil is smaller.
Induction of Direct Shoot Organogenesis from Shoot Tip Explants of an Ornamental Aquatic Plant, Cryptocoryne wendtii

Cryptocoryne wendtii is an important amphibious species with a wide range of foliage colors. Although it has a high market demand, the natural propagation of its aquatic species is limited due to the limited production on the number of plants with a long cultivation period, disease, and the requirement for a large space for propagation. Thus, we studied the effects of the plant growth regulators and their concentrations on the induction of direct shoot organogenesis from shoot tip explants of Cryptocoryne wendtii. The shoot tips were sterilized on its surface using 8 % Clorox® (5.25 % sodium hypochlorite, NaOCl) for 15 min followed by rinsing them three times with sterile distilled water. They were again sterilized on the surface for another 4 % Clorox® (5.25 % sodium hypochlorite, NaOCl) for 5 min. Cytokinins played a crucial role in direct shoot organogenesis. Direct shoot organogenesis from shoot tip explants was promoted by incubating these explants on Murashige and Skoog (MS) medium [1] in the presence of two different cytokinins [6-benzyl-aminopurine (BAP) or Kinetin (Kin)], each provided at four different levels. Direct shoot organogenesis was induced in both explants. Shooting occurred 100 % from in vitro shoot tip explants, which was cultured on MS medium and supplemented with 3.0 mg/l BAP. This was significantly different from the other treatments with the highest number of 16.20 shoots per explant and number of leaves at 72.40 leaves per explant after 60 days of culture. Individual shoots, aseptically excised, which produced normal roots within 45 days on the MS medium supplemented with α-Naphthaleneacetic acid (NAA). The highest number of roots per shoot and the longest roots were obtained on MS medium supplemented with 1.0 mg/l NAA (100 % rooting, which was an average of 36 roots per plantlet and root length at 26.02 mm). Rooted plantlets were successfully hardened and established in pots containing a mixture of organic soil and sand (1:1) overlaid with tap water under greenhouse conditions at 90 % survival. This complete study has successfully outlined a rapid, high frequency direct shoot organogenesis induction of an ornamental aquatic plant, Cryptocoryne wendtii from shoot tip explants inclusive of shoot proliferation, rooting and acclimatization. The present in vitro propagation protocol would facilitate an alternative method for rapid, large-scale production and germplasm preservation of this important endangered species C. wendtii.
Event-Related Exchange-Rate Forecasts Combining Information from Betting Quotes and Option Prices

Betting quotes provide valuable information on market-implied probabilities for outcomes of events such as elections or referendums, which may have an impact on exchange rates. We generate exchange-rate forecasts around such events based on a model that combines risk-neutral event probabilities implied from betting quotes with risk-neutral exchange-rate densities extracted from currency option prices. Its application to predict exchange rates around the Brexit referendum and the U.S. presidential elections shows that these forecasts, conditional on the respective outcomes, were accurate, and markets were able to separate their views on the likelihood and the impact of these events.
Computational Framework for Web Metrics towards Automated Web Design

: The discipline of engineering of software has evolved from software engineering to web engineering, and now to cloud engineering, the last of which develops required resources & environment for cloud computing. Apart from the technical characteristics of each, software engineering has been first product-oriented and then also process-oriented; web engineering has client-orientation and now cloud engineering is oriented toward open-endedness. Because of changes in the orientations and other concerns during the evolution, from time to time, the sets of web metrics required have also ever been evolving. Thus, any approach/ framework for (knowledge-base of) metrics must be flexible. The proposed framework is flexible enough to incorporate changes with minimum efforts. The framework assumes LISP-like environment and assumes single abstract data type (ADT), viz. list (or recursive list). The ADT is not only a data type, but basic program construct also, with the advantage that even programs may be treated as data and can be given as inputs to any program including itself.
Occurrence and histopathology of Ascocotyle tenuicollis metacercaria in gill of platyfish (Xiphophorus maculatus) imported to Iran.

The objective of this study was to determine the prevalence of metacercarial infection in plathyfish imported to Iran. A total of 100 imported live common platyfish (Xiphophorus maculatus) from approximately 24 shipments were collected from a quarantine facility in Iran in 2007 and examined in our veterinary laboratory. Among 800 examined gill arches about 1300 cysts were found in different locations of gill filaments and were identified as a heterophyid parasite metacercaria; Ascocotyle (Ascocotyle) tenuicollis Price, 1935. The prevalence of this parasite was 58%, the maximum and minimum quantity of metacercarial cysts per infected platyfish were 70 and 10, respectively. The adult worms are nonpathogenic and are intestinal parasites of piscivorous birds (e.g. Heron and Egret) and mammals. The first intermediate hosts are snails and the second intermediate hosts are centrarchiid, cyprinodontid, mugilid, and poeciliid fishes. Histopathologic examination of the infected fish gills revealed cartilage proliferation around metacercarial cysts, hyperplasia, hypertrophy and fusion in the affected gill filaments. To our knowledge, this is the first report of infection with Ascocotyle tenuicollis in the imported platyfish in Iran.
FOREIGN DIRECT INVESTMENT AND EMPLOYMENT NEXUS: EVIDENCE FROM PAKISTAN

Being a developing country, Pakistan is experiencing a severe problem of increased unemployment and shortage of capital. Because of this, Pakistan has to depend on capital inflows from abroad to increase the level of employment. Along with Pakistan’s current proactive policy and increasing economic globalization, the scale of attracting foreign investment has grown over the past few years. Foreign Direct Investment (FDI) is considered as the best supplement for domestic resources and can play a crucial role in promoting employment. This study evaluates the significance of FDI for increasing employment level in Pakistan and uncovers whether FDI alone is the best supplement to comparable domestic resources. Johansen Cointegration technique and Vector Error Correction Model (VECM) have been used on time series data ranging from 19802014 to assess the relationship in the short and long term between the three variables: FDI, Gross Domestic Product (GDP) and Employment Level. The results suggest that a strong negative long-run relation exists between employment and FDI, whereas GDP positively and significantly affects the employment level. Therefore, to maximize the benefits from increased FDI, there is a need to complement the policies for enhancing FDI inflow with those that increase the employment level.
Lightweight detection of Android-specific code smells: The aDoctor project

Code smells are symptoms of poor design solutions applied by programmers during the development of software systems. While the research community devoted a lot of effort to studying and devising approaches for detecting the traditional code smells defined by Fowler, little knowledge and support is available for an emerging category of Mobile app code smells. Recently, Reimann et al. proposed a new catalogue of Android-specific code smells that may be a threat for the maintainability and the efficiency of Android applications. However, current tools working in the context of Mobile apps provide limited support and, more importantly, are not available for developers interested in monitoring the quality of their apps. To overcome these limitations, we propose a fully automated tool, coined ADOCTOR, able to identify 15 Android-specific code smells from the catalogue by Reimann et al. An empirical study conducted on the source code of 18 Android applications reveals that the proposed tool reaches, on average, 98% of precision and 98% of recall. We made ADOCTOR publicly available.
DETECTION AND SEGMENTATION OF ENDOSCOPIC ARTEFACTS AND DISEASES USING DEEP ARCHITECTURES

We describe in this paper our deep learning-based approach for the EndoCV2020 challenge, which aims to detect and segment either artefacts or diseases in endoscopic images. For the detection task, we propose to train and optimize EfficientDet-a state-of-the-art detector-with different EfficientNet backbones using Focal loss. By ensembling multiple detectors, we obtain a mean average precision (mAP) of 0.2524 on EDD2020 and 0.2202 on EAD2020. For the segmentation task, two different architectures are proposed: UNet with EfficientNet-B3 encoder and Feature Pyramid Network (FPN) with dilated ResNet-50 encoder. Each of them is trained with an auxiliary classification branch. Our model ensemble reports an sscore of 0.5972 on EAD2020 and 0.701 on EDD2020, which were among the top submitters of both challenges.
Optimising word embeddings with search-based approaches

Word embeddings have rapidly become an all-purpose tool for a diverse range of real world applications. This development is nurtured by the availability and applicability of pre-trained models. However, their usage faces the risk of being inaccurate when used in domains different from the ones they were trained on. In this paper, we formulate the adaptation of word embeddings as a vector multiplication problem, which enables us to apply search methods to explore potential word embedding adaptations with respect to their semantic correctness. To assess the effectiveness of our proposal, we empirically investigate the use of both local and global search-based approaches (i.e. Hill Climbing, Tabu Search and Genetic Algorithm) in order to maximise the semantic correctness of a popular Word2Vec pre-trained model (namely GoogleNews) when applied to another domain (i.e. the MEN dataset). The results of our study reveal that Hill Climbing, Tabu Search and Genetic Algorithm perform equally well and all outperform the original GoogleNews model as well as a baseline model based on Random Search. This shows that optimising word embeddings with search-based approaches is possible and effective.
Heart Rate Sensing with a Robot Mounted mmWave Radar

Heart rate monitoring at home is a useful metric for assessing health e.g. of the elderly or patients in post-operative recovery. Although non-contact heart rate monitoring has been widely explored, typically using a static, wall-mounted device, measurements are limited to a single room and sensitive to user orientation and position. In this work, we propose mBeats, a robot mounted millimeter wave (mmWave) radar system that provide periodic heart rate measurements under different user poses, without interfering in a users daily activities. mBeats contains a mmWave servoing module that adaptively adjusts the sensor angle to the best reflection pro le. Furthermore, mBeats features a deep neural network predictor, which can estimate heart rate from the lower leg and additionally provides estimation uncertainty. Through extensive experiments, we demonstrate accurate and robust operation of mBeats in a range of scenarios. We believe by integrating mobility and adaptability, mBeats can empower many down-stream healthcare applications at home, such as palliative care, post-operative rehabilitation and telemedicine.
Small-Footprint Highway Deep Neural Networks for Speech Recognition

State-of-the-art speech recognition systems typically employ neural network acoustic models. However, compared to Gaussian mixture models, deep neural network (DNN) based acoustic models often have many more model parameters, making it challenging for them to be deployed on resource-constrained platforms, such as mobile devices. In this paper, we study the application of the recently proposed highway deep neural network (HDNN) for training small-footprint acoustic models. HDNNs are a depth-gated feedforward neural network, which include two types of gate functions to facilitate the information flow through different layers. Our study demonstrates that HDNNs are more compact than regular DNNs for acoustic modeling, i.e., they can achieve comparable recognition accuracy with many fewer model parameters. Furthermore, HDNNs are more controllable than DNNs: The gate functions of an HDNN can control the behavior of the whole network using a very small number of model parameters. Finally, we show that HDNNs are more adaptable than DNNs. For example, simply updating the gate functions using adaptation data can result in considerable gains in accuracy. We demonstrate these aspects by experiments using the publicly available AMI corpus, which has around 80 h of training data.
Parametric Study of magnetic abraSive finiShing of unS c26000 flat braSS Plate

Magnetic abrasive machining is a nontraditional machining process and is often applied as a final finishing process which uses flexible multipoint cutting brush to obtain the desired level of surface finish. The knowledge of different significant factors is essential to understand the nature of surface finish produced. In the present examination work the effect of boron carbide abrasives (B4C) on surface finishing of flat brass plate was examined using magnetic abrasive finishing process. Four input process parameters were taken in this work which are rotational speed, quantity of magnetic abrasives, mesh number and machining time. The work material selected was UNS C26000. The experiments were conducted as per Two Level Full factorial technique. The analysis of variance (ANOVA) was investigated using statistical software to find optimal conditions for better surface roughness. Regression models has been developed by using MINITAB-17 statistical software to analyze surface roughness. Experimental results showed that rotational speed was the most significant parameter on change in surface roughness. The minimum surface roughness (Ra) achieved was 0.061 mm for initial value of 0.544 mm.
An autoregressive recurrent mixture density network for parametric speech synthesis

Neural-network-based generative models, such as mixture density networks, are potential solutions for speech synthesis. In this paper we follow this path and propose a recurrent mixture density network that incorporates a trainable autoregressive model. An advantage of incorporating an autoregressive model is that the time dependency within acoustic feature trajectories can be modeled without using the conventional dynamic features. More interestingly, experiments show that this autoregressive model learns to be a filter that emphasizes the high frequency components of the target acoustic feature trajectories in the training stage. In the synthesis stage, it boosts the low frequency components of the generated feature trajectories and hence increases their global variance. Experimental results show that the proposed model achieved higher likelihood on the training data and generated speech with better quality than other models when dynamic features were not utilized in any model.
A kinodynamic steering-method for legged multi-contact locomotion

We present a novel method for synthesizing collision-free, dynamic locomotion behaviors for legged robots, including jumping, going down a very steep slope, or recovering from a push using the arms of the robot. The approach is automatic and generic: non-gaited motions, comprising arbitrary contact postures can be generated along any environment. At the core of our framework is a new steering method that generates trajectories connecting two states of the robot. These trajectories account for the state-dependent, centroidal dynamic constraints inherent to legged robots. The method, of low dimension, formulated as a Linear Program, is really efficient to compute, and can find an application in various problems related to legged locomotion. By incorporating this steering method into an existing sampling-based contact planner, we propose the first kinodynamic contact planner for legged robots.
An Efficient New PDE-based Characters Reconstruction after Graphics Removal

The separation between texts and graphics when they are overlapped is a challenging problem for digitization companies. In a previous work [1], we presented the first unsupervised fully automatic segmentation system adapted for colour business document with significant colour complexity and dithered background. The system achieves several operations to segment automatically colour images, separate text from noise and graphics and provides colour information about text colour. After split overlapped characters and separates characters from graphics, characters are broken. The OCR system becomes unable to recognize successfully broken characters and its efficiency is thus seriously affected. This paper presents the first Character Reconstruction System through a new PDE (Partial Differential Equation)-based approach. Our approach takes benefit of the combination of the anisotropic morphology proposed by Breuß and the Weickert Coherence enhancing shock filter diffusion. We introduce and present a continuous anisotropic morphology method driven by the main direction of the first order tensors applied in the neighborhood of the missing part left by the separation between text and graphics. It reconstructs the missing part even when the left area is larger than the strokes width. The coherency of the orientation of the tensors around missing parts overcomes the problem of image noises. The application of the ABBY FineReader OCR engine proves an important reduction in OCR errors. Our experiments show that our proposition compared to the existing state of the art requires no training steps and outperforms both of anisotropic morphology and the Weickert Coherence enhancing shock filter diffusion applied separately.
Numerical description of discharge characteristics of the plasma needle

The plasma needle is a small atmospheric, nonthermal, radio-frequency discharge, generated at the tip of a needle, which can be used for localized disinfection of biological tissues. Although several experiments have characterized various qualities of the plasma needle, discharge characteristics and electrical properties are still not well known. In order to provide initial estimates on electrical properties and quantities such as particle densities, we employed a two-dimensional, time-dependent fluid model to describe the plasma needle. In this model the balance equation is solved in the drift-diffusion approach for various species and the electron energy, as well as Poisson’s equation. We found that the plasma production occurs in the sheath region and results in a steady flux of reactive species outwards. Even at small (<0.1%) admixtures of N2 to the He background, N2+ is the dominant ion. The electron density is typically 1011cm−3 and the dissipated power is in the order of 10mW. These results are con.
Best Arm Identification in Multi-Armed Bandits

We consider the problem of finding the best arm in a stochastic multi-armed bandit game. The regret of a forecaster is here defined by the gap between the mean reward of the optimal arm and the mean reward of the ultimately chosen arm. We propose a highly exploring UCB policy and a new algorithm based on successive rejects. We show that these algorithms are essentially optimal since their regret decreases exponentially at a rate which is, up to a logarithmic factor, the best possible. However, while the UCB policy needs the tuning of a parameter depending on the unobservable hardness of the task, the successive rejects policy benefits from being parameter-free, and also independent of the scaling of the rewards. As a by-product of our analysis, we show that identifying the best arm (when it is unique) requires a number of samples of order (up to a log(K) factor) Σ i 1/Δ2i, where the sum is on the suboptimal arms andΔi represents the difference between the mean reward of the best arm and the one of arm i. This generalizes the well-known fact that one needs of order of 1/Δ2 samples to differentiate the means of two distributions with gap Δ.
Some applications of coding theory in cryptography

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