Miracle-dz/UniFAR-FTUs huggingface dataset:
Translating Natural Language into Source Code Via Tree Transduction

Semantic parsing is the task of transforming a natural language sentence into a computer executable complete meaning representation. It is also the basis of natural language understanding, which is one of the oldest and most studied problems in the field of artificial intelligence. This thesis describes a semantic parser that translates a natural language sentence into source code based on a tree-to-tree transducer. The core of the method is the extraction of a synchronous tree substitution grammar that translates an elementary tree in the source language into in the target language. Although the method focuses on translating a natural language specification into source code, it can be used to perform the opposite task (generating language from source code) as well. Moreover, the very same methodology can be applied to perform syntax based machine translation from one language to another but it requires a parser for both languages. Lastly, the method is compared and evaluated against two baselines on two different datasets, one based on Excel commands, and one based on queries for a geography database. The main contributions are that this is the first method to perform tree-to-tree translation, as previous methods were based on tree-to-string translation, it is based on a generative model, whilst previous approaches employ a generative model, and lastly, it is the first statistical method to perform experiments on the Excel dataset. Finally, ideas for extensions or modifications that could improved the method are proposed.
A cross-sectional study on patient satisfaction in an Urban Health Care Centre of Siliguri Municipal Corporation, Darjeeling, West Bengal

Background: Patient satisfaction can be explored which may draw attention of the health care providers, administrators, and health policy makers, in order to monitor the performance, determine patients' needs, plan the development of services, and provide evidence to support the applications for financial support and expenditure. Aims and Objectives: To assess the level of satisfaction of patients regarding the different aspects of health care in an Urban Health Care Centre of Siliguri Municipal Corporation, Darjeeling, West Bengal and to identify the reasons of dissatisfaction among patients in that urban health care center. Materials and Methods: A cross-sectional survey on 102 patients with a Patient Satisfaction Questionnaire-18 was done. Systematic random sampling was adopted to draw samples. Results: Overall satisfaction was 73.1% with a mean value of 3.655. Highest satisfaction was in general satisfaction, and the lowest was in time spent with doctors Conclusion: Mean score and percentage of the patient satisfaction was high in that Urban Health Centre. Only matter of concern was time spent with the doctor. Doctor's time, lack of facility and poor quality of services were the major reasons for dissatisfaction among a few dissatisfied patients.
Drifting codes within a stable coding scheme for 1 working memory 2 3

1. Department Experimental Psychology, University of Oxford, Oxford, OX2 6GG, 5 United Kingdom 6 2. Department Experimental Psychology, University of Groningen, Groningen, 9712 TS, 7 The Netherlands 8 3. Oxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, 9 United Kingdom 10 4. Princeton Neuroscience Institute and Department of Psychology, Princeton 11 University, Princeton, NJ 08540, USA 12
IMPACT OF POLYCYCLIC AROMATIC HYDROCARBONS ON MALE REPRODUCTIVE HEALTH IN COAL TAR WORKERS

Introduction: It’s well known that exposure to polycyclic aromatic hydrocarbons (PAHs) increasesthe risk of cancer; however less is known about their adverse effects on male reproductive health. Aim of work: To assess the effects of occupational exposure to PAHs on male reproductive health in a company for coke production. Materials and methods: A cross sectional comparative study was performed on an exposed group of coal tar workers (No=45) and a matched control group (No=45). Personal interview with specially designed questionnaire and standardized self-completed questionnaire was done to assess the erectile dysfunction. Urinary 1-hydroxypyrene (1-OH Pyrene) and serum malondialdehyde (MDA) in addition to reproductive hormones and semen analysis were assessed. Results: The study revealed a statistically significant increase in the level of 1-OHpyrene,MDA and estradiol ratio among the exposed group with Mean ±SD 8.42±3.61, 11.95±7.29, 7.89±3.45 vs 3.14±3.51, 2.44±1.61, 6.59±2.21 respectively among the controls with p 0.05). Conclusion and recommendations: The adversehealth effect on the male reproductive system in the current study might be one of the hazards that can be attributed to occupational exposure to PAHs in coal tar workers. Regular wearing of good quality personal protective equipment, especially masks and gloves is highly recommended to decreaseexposure to PAH in addition to screening with urinary 1-OH pyrene and serum MDA levels.
Determinants and Outcomes of IT Governance in Manufacturing SMEs: A Strategic IT Management Perspective

The need to strategically manage IT resources such that they enhance the business value of firms makes IT governance (ITG), in conjunction with IT alignment, an ongoing issue for IS researchers and practitioners. In addressing this issue, the present study aims to validate a research model that relates, within a strategic IT management framework, the firm’s ITG and IT alignment capabilities to its IT performance. To do so, a survey of 223 manufacturing SMEs (small and medium-sized enterprises) was realized. Results confirm the hypothesized relationships between the firm’s environmental uncertainty, strategic IT orientation, IT governance and IT alignment capabilities. They constitute a solid validation of the impact of the strategic management of IT resources and governance of IT on IT performance in the specific context of SMEs.
Molecular Detection of Plasmid Mediated Quinolone Resistant Genes in Uropathogenic E. coli from Tertiary Referral Hospital in Tehran Iran

Ali Badamchi1, Shima Javadinia2, Reza Farahani3, Hamid Solgi4, Azardokht Tabatabaei2* 1Children’s Medical Center Hospital, Tehran University of Medical Sciences, Tehran, Iran 2Department of Pediatric Infectious Disease, Research Center of Pediatric Infectious Diseases, Iran University of Medical Sciences, Tehran, Iran 3Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran 4Department of Microbiology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran *Correspondence should be addressed to Azardokht Tabatabaei; azardokht _tabatabaei@yahoo.com
Vegetation secondary succession in response to time since last fire in a broad-leaved savanna in central Namibia

Savanna ecosystems developed as a response to frequent fires, and to date, fire plays an important role in shaping the vegetation structure of these ecosystems. Yet, little research on the effect of fire on Namibian savanna, especially at the more arid end of the rainfall gradient, has been done. This study was conducted at the Waterberg Plateau National Park, which experiences a range of fire frequencies, with the fire return period and time since last fire relatively well-known since 1976. Four fire blocks last burned 1, 2, 14 and 24 years ago prior to the study (2014) were surveyed, with the objective to assess the secondary succession after fire of perennial grasses and woody plants in terms of density, cover and species composition in a broad-leaved savanna on dystrophic sandy soils. Findings revealed that fire positively affects the grass component by increasing grass density and grass productivity (p 0.05) but led to a steady increase in woody canopy cover and height (p<0.05), thus reducing the amount of browse available for small to medium sized browsers. Overall plant species composition did not significantly change with time after fire, although when grass species were grouped into grazing value categories, species with high grazing value declined with time after fire. Recent fires thus improve grazing and browsing opportunities, without significant plant species successional changes, but rather with significant plant structural succession.
Angular-Domain Channel Estimation for One-Bit Massive MIMO Systems: Performance Bounds and Algorithms

We consider angular-domain channel estimation in massive MIMO systems using one-bit analog-to-digital converters (ADCs) with various thresholding schemes at the receivers. We first derive the performance bounds for estimating angular-domain channel parameters, including the angles-of-arrival (AoA), angles-of-departure (AoD) and the associated path gains. Specifically, we derive 1) the deterministic Cramér-Rao bound (CRB) when all of the angular-domain channel parameters are treated as deterministic unknowns; 2) the hybrid CRB when some parameters have known prior probability density functions (pdfs) while the rest are assumed to be deterministic unknowns; 3) the Bayesian CRB when all of them have known prior pdfs. We also consider using the maximum likelihood (ML) method for channel estimation and a computationally efficient relaxation based cyclic algorithm (referred to as 1bRELAX) to obtain the ML estimates. When the prior information is available, the maximum a posteriori (MAP) and joint ML-MAP (JML-MAP) estimators are derived. We also use the one-bit Bayesian information criterion (1bBIC) to determine the number of scattering paths. Numerical examples are provided to verify the derived performance bounds with different thresholding schemes and demonstrate the performance of the proposed channel estimation algorithms.
Safety & Security in the Context of Autonomous Driving

With the advent of cypher-physical (systems of) systems, new challenges for safety and security arise. Especially in the context of autonomous driving we are currently facing a complex environment, where security problems can easily result in safety-relevant issues, and vice versa. There have been multiple approaches in the past to combine the approaches from safety and security best practices into a combined view, all with their individual challenges. We propose a fully integrated approach, combining safety with security and modelling their complex interactions. In this work we start by giving a thorough definition of the basic terms and concepts used in safety and security, in order to identify similarities and differences. We then propose and outline a combined view on the safety and security causal chains and define their interdependencies.
A Driver Fatigue Recognition Algorithm Based on Spatio-Temporal Feature Sequence

Researches show that fatigue driving is one of the important causes of road traffic accidents, so it is of great significance to study the driver fatigue recognition algorithm to improve road traffic safety. In recent years, with the development of deep learning, the field of pattern recognition has made great development. This paper designs a real-time fatigue state recognition algorithm based on spatio-temporal feature sequence, which can be mainly applied to the scene of fatigue driving recognition. The algorithm is divided into three task networks: face detection network, facial landmark detection and head pose estimation network, fatigue recognition network. Experiments show that the algorithm has the advantages of small volume, high speed and high accuracy.
Low-Complexity Sound Event Classification Based on Graph Signal in Noisy Environments

To solve the problem of high computational cost in sound event classification using graph signal, in this paper, a low complexity algorithm based on dynamic selection of Time-Frequency (TF) matrix was proposed. There are four stages of sound classification in this paper, including preprocessing, dynamic resizing of the spectrogram, acoustic feature extraction based on graph signal, and SVM-based classification. In the resizing stage, the method to determine the proper resolution of frequency and time axis was proposed. Thus, the spectrogram is resized into smaller TF matrix while maintaining the key information of the original spectrogram. Experiments were conducted on the Sound Scene Database of Real Word Computing Partnership (RWCP) group and NOISEX-92 noise database. It is shown that the proposed algorithm achieves an average classification accuracy of 99.6% for 10 classes of sounds, and 93.15% for 63 classes of sounds under the SNR of -5dB.
Online Manhattan Keyframe-based Dense Reconstruction from Indoor Depth Sequences

We present an online framework for dense 3D reconstruction of indoor scenes using sequential Manhattan keyframes. We take advantage of the global geometry of indoor scenes extracted by Manhattan frames and pose optimization to enhance the accuracy and robustness of the reconstructed models. During sequential reconstruction, a Manhattan frame is extracted for each keyframe after surface normal adjustment, and used for a Manhattan keyframe-based planar alignment to initialize the surface registration while a pose graph optimization is used to refine camera poses. The final model is created by integrating the Manhattan keyframes into the unified volumetric model using refined pose estimations. Experimental results demonstrate the advantage of our geometry-based approach to reduce the cumulative registration error and overall geometric drift.
Scratch and Arduino for Effectively Developing Programming and Computing-Electronic Competences in Primary School Children

Programming and designing computing-electronic solutions imply complex competences, which usually demand to restructure previous thinking and problem-solving knowledge. Both programming and computing-electronic design solutions ask for step-by-step thinking, and also being cautious regarding involved hardware items for the second one. Designing computing-electronic solutions is a non-trivial task since it unifies both worlds, even though platforms such as Arduino notably simplify that development. This article describes experiences of programming and computing-electronic teaching to a group of primary school children in Valparaiso-Chile by the use of Scratch and Tinkercad for the goal of developing programming and Arduino-based computing-electronic competences in school children. The obtained results demonstrate that children can effectively develop programming and computing-electronic theoretical and practical skills. These results also show that students can get abilities and enthusiasm to know more advanced skills and applications concerning sensing systems. Thus, programming and electronics can motivate the learning process in children. (Abstract)
Field Measurements of a Cryogenic Permanent Magnet Undulator at the TPS

A PrFeB-based cryogenic permanent-magnet undulator (CPMU) with a period length of 15 mm (CU15) is being constructed to provide high brilliant X-rays for the Taiwan Photon Source (TPS) nano-probe beamline. Field measurements of this undulator will be performed with a Hall probe in a vacuum environment. The in-situ Hall probe measurement system (Yang et al., 2014) was improved (Yang et al., 2016) to allow measurement and tuning of the CU15 magnetic field. A field strength and temperature dependent calibration of the Hall probe was done to obtain realistic field distributions. By tuning the differential adjusters and spring modules, we can correct magnetic field errors caused by assembly tolerances and temperature gradients along the magnetic arrays, thus lowering phase errors to reach our design goals. This paper describes details of improvements in the measurement system, field tuning methods, and measurement results at cryogenic temperatures.
Approximate NoC and Memory Controller Architectures for GPGPU Accelerators

High interconnect bandwidth is crucial for achieving better performance in many-core GPGPU architectures that execute highly data parallel applications. The parallel warps of threads running on shader cores generate a high volume of read requests to the main memory due to the limited size of data caches at the shader cores. This leads to a scenarios with rapid arrival of an even larger volume of reply data from the DRAM, which creates a bottleneck at memory controllers (MCs) that send reply packets back to the requesting cores over the network-on-chip (NoC). Coping with such high volumes of data requires intelligent memory scheduling and innovative NoC architectures. To mitigate memory bottlenecks in GPGPUs, we first propose a novel approximate memory controller architecture (AMC) that reduces the DRAM latency by opportunistically exploiting row buffer locality and bank level parallelism in memory request scheduling, and leverages approximability of the reply data from DRAM, to reduce the number of reply packets injected into the NoC. To further realize high throughput and low energy communication in GPGPUs, we propose a low power, approximate NoC architecture (Dapper) that increases the utilization of the available network bandwidth by using single cycle overlay circuits for the reply traffic between MCs and shader cores. Experimental results show that Dapper and AMC together increase NoC throughput by up to 21 percent; and reduce NoC latency by up to 45.5 percent and energy consumed by the NoC and MC by up to 38.3 percent, with minimal impact on output accuracy, compared to state-of-the-art approximate NoC/MC architectures.
License Plate Character Segmentation Using Key Character Location and Projection Analysis

Character segmentation forms a link between license plate detection and character recognition. This paper presents a two-stage character segmentation framework. Firstly, a cascade classifier is trained by AdaBoost algorithm to locate key character which represents an administrative area. Then, based on key character location information, the rest of characters can be predicted or estimated. For the neighbor character of key character, it can be predicted by connected components analysis. For other characters that are far from the key character, a vertical projection strategy without hyphens is proposed to segment them. This strategy is promising to address the issue of touching characters. To illustrate the effectiveness of character segmentation, broad learning system is adopted to classify segmented characters. Experimental results performed on Macau license plates demonstrate the proposed method’s effectiveness in comparison with some state-of-the-art approaches. It is expected to apply the designed techniques to license plate character segmentation of other regions or countries that have similar situations with Macau’s.
Forecast Application of Time Series Model Based on BLS in Port Cargo Throughput

During the last decade, there was a dramatic increasing of container throughput in China, especially in server harbor cities such as Shanghai and Shenzhen. It is a necessary and crucial task to enhance the ability of port throughput. In the existing studies, time-series model is one of the most powerful methods to solve this problem, which can predict the container cargo throughput accurately and effectively. Based on this technical background, this paper employs a novel algorithm to design a new type of time-series model for predicting port throughput. In the experiments, we firstly use the Matlab to pursue the statistical analyses on the throughput data. Secondly, we apply our method to predict the changing rate of container throughput, and compare the results with several classic time-series models. Finally, the experimental results show that our method was optimized based on the training data, and outperformed other time-series models in the prediction of 10 months throughput.
Fast QTMT Partition Decision Algorithm in VVC Intra Coding based on Variance and Gradient

Quadtree with nested multi-type tree (QTMT) partition structure in Versatile Video Coding (VVC) contributes to superior encoding performance compared to the basic quad-tree (QT) structure in High Efficiency Video Coding (HEVC). However, the improvement of performance leads to an un-avoidable increase of computational complexity. To achieve a balance between coding efficiency and compression quality, we propose a fast intra partition algorithm based on variance and gradient to solve the rectangular partition problem in VVC. First, further splitting of smooth areas is terminated. Then, QT partition is chosen depending on the gradient features extracted by Sobel operator. Finally, one partition from five possible QTMT partitions is directly chosen by computing the variance of variance of sub-CUs. The theoretical basis of our method is that a homogeneous area tends to be predicted with a larger coding unit (CU), and sub-parts of a split CU are prone to have different textures from each other. To our knowledge, this is the first attempt to apply traditional method to accelerating the rectangular partition problem in VVC intra prediction. Experimental results show that the proposed method can save averagely 53.17% encoding time with only 1.62% BDBR increase and 0.09dB BDPSNR loss compared to anchor VTM4.0.
UAV-Enhanced Intelligent Offloading for Internet of Things at the Edge

With the explosive growth of diverse Internet of Things (IoT) applications, mobile edge computing (MEC) has been brought to settle the conflict between computation-intensive applications and resource-limited IoT mobile devices (IMDs). Note that the assistance of unmanned aerial vehicles (UAVs) is of great importance in providing reliable connectivity in areas with limited or no available communication infrastructure. To cope with the surging demands for Big Data processing from UAV-aided IoT applications, combining UAV-aided communication and MEC has been envisioned to be a promising paradigm, which gives rise to the so-called UAV-enhanced edge. In consideration of IMDs’ limited battery capacity and UAV energy budget, in this article we study the energy reduction problem in UAV-enhanced edge by smartly making offloading decisions, allocating transmitted bits in both uplink and downlink, as well as designing UAV trajectory. This joint optimization problem is formulated as a mix-integer nonconvex optimization problem, and an alternative optimization algorithm based on block coordinate descent and successive convex approximation techniques is proposed as our solution. Extensive numerical results demonstrate that the overall energy consumption for accomplishing the tasks can be effectively reduced by adopting our joint optimization scheme, and the necessity of task offloading, UAV trajectory design, and bit allocation during transmission is validated.
Analysis and Initial Observations on Varying Penetration Rates of Automated Vehicles in Mixed Traffic Flow utilizing SUMO

Understanding the effects of having automated vehicles in the future traffic scenarios is an important research topic that attracts a great deal of attention currently. The difficulty in studying this problem is the fact that real life measurement and testing of these scenarios can not be made as there are still a very small fraction of automated vehicles in the traffic. So analyzing and understanding the effects of mixed traffic requires extensive simulative analysis. In this paper we analyze this problem using real traffic data in combination with the open-source SUMO traffic simulation software. The traffic flow is modeled based on the measurement data from a section of the Austrian A2 motorway, while the effects of automated vehicles at various penetration rates is simulated and consequently some observation are made.