Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning for the Beginning of Starcraft II Game

Starcraft II is a popular real-time strategy game that is welcomed by many young people. This game is really complicated to play due to the long game timeline, various society/buildings/units, a large number of actions or selections, different constraints ( e.g. , population limit), and the partially observed environments. In this project, we plan to develop a reinforcement learning model for the beginning of Starcraft II game, instead of the full-length game. The beginning of the game is essential for the further economy, population increase, and technology development. Our project is based on the SC2LE (StarCraft II Learning Environment) platform. We build a feasible pipeline for training reinforcement learning models and design random, scripted, and our actor-critic based agents. Experiments show that our actor-critic based agents can learn valuable knowledge in this task. The video has been publicly available. 1
PrendoSim: Proxy-Hand-Based Robot Grasp Generator

The synthesis of realistic robot grasps in a simulated environment is pivotal in generating datasets that support sim-to-real transfer learning. In a step toward achieving this goal, we propose PrendoSim, an open-source grasp generator based on a proxy-hand simulation that employs NVIDIA’s physics engine (PhysX) and the recently released articulated-body objects developed by Unity (https://prendosim.github.io). We present the implementation details, the method used to generate grasps, the approach to operationally evaluate stability of the generated grasps, and examples of grasps obtained with two different grippers (a parallel jaw gripper and a three-finger hand) grasping three objects selected from the YCB dataset (a pair of scissors, a hammer, and a screwdriver). Compared to simulators proposed in the literature, PrendoSim balances grasp realism and ease of use, displaying an intuitive interface and enabling the user to produce a large and varied dataset of stable
A Privacy-Preservation Framework Based on Biometrics Blockchain (BBC) to Prevent Attacks in VANET

In the near future, intelligent vehicles will be part of the Internet of Things (IoT) and will offer valuable services and opportunities that could revolutionise human life in smart cities. The Vehicular Ad-hoc Network (VANET) is the core structure of intelligent vehicles. It ensures the accuracy and security of communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes to enhance road safety and decrease traffic congestion. However, VANET is subject to security vulnerabilities such as denial-of-service (DoS), replay attacks and Sybil attacks that may undermine the security and privacy of the network. Such issues may lead to the transmission of incorrect information from a malicious node to other nodes in the network. In this paper, we present a biometrics blockchain (BBC) framework to secure data sharing among vehicles in VANET and to retain statuary data in a conventional and trusted system. In the proposed framework, we take advantage of biometric information to keep a record of the genuine identity of the message sender, thus preserving privacy. Therefore, the proposed BBC scheme establishes security and trust between vehicles in VANET alongside the capacity to trace identities whenever required. Simulations in OMNeT++, veins and SUMO were carried out to demonstrate the viability of the proposed framework using the urban mobility model. The performance of the framework is evaluated in terms of packet delivery rate, packet loss rate and computational cost. The results show that our novel model is superior to existing approaches.
Improved Transformer Net for Hyperspectral Image Classification

In recent years, deep learning has been successfully applied to hyperspectral image classification (HSI) problems, with several convolutional neural network (CNN) based models achieving an appealing classification performance. However, due to the multi-band nature and the data redundancy of the hyperspectral data, the CNN model underperforms in such a continuous data domain. Thus, in this article, we propose an end-to-end transformer model entitled SAT Net that is appropriate for HSI classification and relies on the self-attention mechanism. The proposed model uses the spectral attention mechanism and the self-attention mechanism to extract the spectral–spatial features of the HSI image, respectively. Initially, the original HSI data are remapped into multiple vectors containing a series of planar 2D patches after passing through the spectral attention module. On each vector, we perform linear transformation compression to obtain the sequence vector length. During this process, we add the position–coding vector and the learnable–embedding vector to manage capturing the continuous spectrum relationship in the HSI at a long distance. Then, we employ several multiple multi-head self-attention modules to extract the image features and complete the proposed network with a residual network structure to solve the gradient dispersion and over-fitting problems. Finally, we employ a multilayer perceptron for the HSI classification. We evaluate SAT Net on three publicly available hyperspectral datasets and challenge our classification performance against five current classification methods employing several metrics, i.e., overall and average classification accuracy and Kappa coefficient. Our trials demonstrate that SAT Net attains a competitive classification highlighting that a Self-Attention Transformer network and is appealing for HSI classification.
A STUDY ON THE CUSTOMER’S BEHAVIOUR TOWARDS ONLINE SHOPPING IN BHOPAL

In present era it has become one of the popular approaches for business and customer to perform trade over the internet. Businesses have been coming up with creative ways to promote their product via online. This study titled “A Study on Customer Behaviour towards Online Shopping in Bhopal City” has been conducted to understand and analyze the conditions which affects customers’ online purchase decisions and how these factors affects customer satisfaction. Descriptive Research Design has been used for this study. A well-defined questionnaire was designed to collect samples data across Bhopal City. This requires variety of respondents who already have experience of online purchasing , so convinced sampling technique was chosen. The questionnaire filled by a sample size of 100 respondents. After collecting the data, the data was classified and , tabulated . Necessary statistical tools such as Percentage Analysis and statistical tests has been used to interpret the data. Results of this work may be useful to business mans, who are seeking options to expand their business into online shopping environment.
Support needs and barriers to accessing support: Baseline results of a mixed-methods national survey of people bereaved during the COVID-19 pandemic

Background: The COVID-19 pandemic is a mass bereavement event which has profoundly disrupted grief experiences. Understanding support needs and access to support among people bereaved at this time is crucial to ensuring appropriate bereavement support infrastructure. Aim: To investigate grief experiences, support needs and use of formal and informal bereavement support among people bereaved during the pandemic. Design: Baseline results from a longitudinal survey. Support needs and experiences of accessing support are reported using descriptive statistics and thematic analysis of free-text data. Setting/participants: 711 adults bereaved in the UK between March and December 2020, recruited via media, social media, national associations and community/charitable organisations. Results: High-level needs for emotional support were identified. Most participants had not sought support from bereavement services (59%, n = 422) or their General-Practitioner (60%, n = 428). Of participants who had sought such support, over half experienced difficulties accessing bereavement services (56%, n = 149)/General-Practitioner support (52%, n = 135). About 51% reported high/severe vulnerability in grief; among these, 74% were not accessing bereavement or mental-health services. Barriers included limited availability, lack of appropriate support, discomfort asking for help and not knowing how to access services. About 39% (n = 279) experienced difficulties getting support from family/friends, including relational challenges, little face-to-face contact and disrupted collective mourning. The perceived uniqueness of pandemic bereavement and wider societal strains exacerbated their isolation. Conclusions: People bereaved during the pandemic have high levels of support needs alongside difficulties accessing support. We recommend increased provision and tailoring of bereavement services, improved information on support options and social/educational initiatives to bolster informal support and ameliorate isolation.
ASCERTAINING THE SLEEP APNEA DISORDER IN CHILDREN AGE UNDER 15

The main purpose of this project is to detect the sleep apnea that is respiratory problem while sleeping. However, most minute-by-minute analysis techniques have difficulty detecting accurate event start/end positions. Furthermore, they require hand-engineered feature extraction and selection processes. In this project, we are going to approach for real-time apnea event detection using neural networks. This objective is used to predict the sleeping disorder for children by oxygen rate, sleep/wake status, noice in breathing by using neural network system and to bring about the ahi level in both boy and girl child who are affected by sleep hypnea.
Resource Allocation Method for Network Slicing Using Constrained Reinforcement Learning

With the proliferation of mobile networks, we face strong diversification of services, demanding the network to be more flexible. To satisfy this dire need, network slicing is embraced as a promising solution for resource utilization in 5G and future networks. However, this process is complicated that the traditional approaches cannot effectively perform resource orchestration due to the lack of accurate models and the existence of dynamic hidden structures. We formulate the resource allocation problem as a Constrained Markov Decision Process and solve it using constrained reinforcement learning. Specifically, we use the adaptive interior-point policy optimization and projection layers to handle cumulative and instantaneous constraints. Our evaluations show that our method is effective in resource allocation and outperforms baselines.
Polymer Applications In Biomedical Fields

Polymers are macromolecules with a wide range of structures, characteristics, and composition. They can be used in a variety of domains, including biophysics, medicine, and electronics. Biomedical polymers stand out among these polymers because of their low toxicity in vivo, ease of processing and sterilization, longer shelf life, small weight, as well as unique characteristics suitable for different appliances. This work investigates the different concentrations of biomedical polymers in the area of medicine.
Sequence analysis Prediction of histone post-translational modifications using deep learning

Motivation: Histone post-translational modiﬁcations (PTMs) are involved in a variety of essential regulatory processes in the cell, including transcription control. Recent studies have shown that histone PTMs can be accurately predicted from the knowledge of transcription factor binding or DNase hypersensitivity data. Similarly, it has been shown that one can predict PTMs from the underlying DNA primary sequence. Results: In this study, we introduce a deep learning architecture called DeepPTM for predicting histone PTMs from transcription factor binding data and the primary DNA sequence. Extensive experimental results show that our deep learning model outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS 2014) and DeepHistone (BMC Genomics 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classiﬁcation framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type-speciﬁc) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data.
Bioclimatic drivers of fire severity across the Australian geographical range of giant Eucalyptus forests

The relationships between productivity, fire frequency and fire severity shape the distribution of plant communities globally. Dry forests are expected to burn frequently and wet forests to burn infrequently. However, the effect of productivity on intensity and severity of wildfire is less consistent and poorly understood. One productive ecosystem where this is especially true is the Australian tall wet Eucalyptus‐dominated forest (TWEF), which spans wet areas across the continent. This study aims to characterise how climate shapes the likelihood of low‐ and high‐severity wildfire across Australian TWEF. We performed a continental‐scale analysis of fuels in 48 permanent plots in early‐mature stage TWEF across four climate regions in Australia. We estimated fuel loads and measured understorey microclimate. We then obtained historical fire‐weather observations from nearby meteorological stations and used fuel moisture and fire behaviour equations to predict the historical frequency with which TWEF could burn and what fire severities were expected. We investigated how this varies across the different TWEF climate regions. Lastly, we validated our approach by remeasuring eight plots that burned unexpectedly post‐measurement. We found that surface fuels in cooler, moister regions were available to burn 1–16 days per year historically, with only low‐severity, surface fire possible most of these days: high‐severity fire was only possible under rare, extreme fire‐weather conditions. However, in warmer, drier regions, fuels were available to burn 23–35 days annually, and high‐severity fire was more likely than low‐severity fire. Validation showed that we slightly overestimated flame heights, inflating high‐severity risk estimates. If we used elevated fuel loads to predict flame heights, however, high‐severity fire was more likely than low‐severity fire everywhere. Lastly, the likelihood of high‐severity fire increased with increasing temperature and worsening fire weather. Synthesis. Fire activity in early‐mature TWEF is limited by climatic constraints on fire weather and availability to burn, with high‐severity fire more likely in warmer, drier regions than in cooler, wetter ones. This indicates a particularly worrisome vulnerability to climate change, given TWEF's diminished ability to recover from disturbance in a warmer world. The occurrence of both low‐ and high‐severity fire means the fire regimes of TWEF are best described as mixed severity.
A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images

Building segmentation is widely used in urban planning, disaster prevention, human flow monitoring and environmental monitoring. However, due to the complex landscapes and highdensity settlements, automatically characterizing building in the urban village or cities using remote sensing images is very challenging. Inspired by the rencent deep learning methods, this paper proposed a novel end-to-end building segmentation network for segmenting buildings from remote sensing images. The network includes two branches: one branch uses Widely Adaptive Spatial Pyramid (WASP) structure to extract multi-scale features, and the other branch uses a deep residual network combined with a sub-pixel up-sampling structure to enhance the detail of building boundaries. We compared our proposed method with three state-of-the-art networks: DeepLabv3+, ENet, ESPNet. Experiments were performed using the publicly available Inria Aerial Image Labelling dataset (Inria aerial dataset) and the Satellite dataset II(East Asia). The results showed that our method outperformed the other networks in the experiments, with Pixel Accuracy reaching 0.8421 and 0.8738, respectively and with mIoU reaching 0.9034 and 0.8936 respectively. Compared with the basic network, it has increased by about 25% or more. It can not only extract building footprints, but also especially small building objects.
SD-QA: Spoken Dialectal Question Answering for the Real World

Question answering (QA) systems are now available through numerous commercial applications for a wide variety of domains, serving millions of users that interact with them via speech interfaces. However, current benchmarks in QA research do not account for the errors that speech recognition models might introduce, nor do they consider the language variations (dialects) of the users. To address this gap, we augment an existing QA dataset to construct a multi-dialect, spoken QA benchmark on five languages (Arabic, Bengali, English, Kiswahili, Korean) with more than 68k audio prompts in 24 dialects from 255 speakers. We provide baseline results showcasing the real-world performance of QA systems and analyze the effect of language variety and other sensitive speaker attributes on downstream performance. Last, we study the fairness of the ASR and QA models with respect to the underlying user populations. The dataset, model outputs, and code for reproducing all our experiments are available: https://github.com/ffaisal93/SD-QA.
An Adaptive Block Bregman Proximal Gradient Method for Computing Stationary States of Multicomponent Phase-Field Crystal Model

In this paper, we compute the stationary states of the multicomponent phase-field crystal model by formulating it as a block constrained minimization problem. The original infinite-dimensional non-convex minimization problem is approximated by a finite-dimensional constrained non-convex minimization problem after an appropriate spatial discretization. To efficiently solve the above optimization problem, we propose a so-called adaptive block Bregman proximal gradient (AB-BPG) algorithm that fully exploits the problem's block structure. The proposed method updates each order parameter alternatively, and the update order of blocks can be chosen in a deterministic or random manner. Besides, we choose the step size by developing a practical linear search approach such that the generated sequence either keeps energy dissipation or has a controllable subsequence with energy dissipation. The convergence property of the proposed method is established without the requirement of global Lipschitz continuity of the derivative of the bulk energy part by using the Bregman divergence. The numerical results on computing stationary ordered structures in binary, ternary, and quinary component coupled-mode Swift-Hohenberg models have shown a significant acceleration over many existing methods.
Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review

Background. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches.Methods. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided.Results. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes.Discussion. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues.Conclusion. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.
Model Explainability in Deep Learning Based Natural Language Processing

Machine learning (ML) model explainability has received growing attention, especially in the area related to model risk and regulations. In this paper, we reviewed and compared some popular ML model explainability methodologies, especially those related to Natural Language Processing (NLP) models. We then applied one of the NLP explainability methods Layer-wise Relevance Propagation (LRP) to a NLP classification model. We used the LRP method to derive a relevance score for each word in an instance, which is a local explainability. The relevance scores are then aggregated together to achieve global variable importance of the model. Through the case study, we also demonstrated how to apply the local explainability method to false positive and false negative instances to discover the weakness of a NLP model. These analysis can help us to understand NLP models better and reduce the risk due to the black-box nature of NLP models. We also identified some common issues due to the special natures of NLP models and discussed how explainability analysis can act as a control to detect these issues after the model has been trained.
Representative Functional Connectivity Learning for Multiple Clinical Groups in Alzheimer's Disease

Mild cognitive impairment (MCI) is a high-risk dementia condition which progresses to probable Alzheimer’s disease (AD) at approximately 10% to 15% per year. Characterization of group-level differences between two subtypes of MCI – stable MCI (sMCI) and progressive MCI (pMCI) is the key step to understand the mechanisms of MCI progression and enable possible delay of transition from MCI to AD. Functional connectivity (FC) is considered as a promising way to study MCI progression since which may show alterations even in preclinical stages and provide substrates for AD progression. However, the representative FC patterns during AD development for different clinical groups, especially for sMCI and pMCI, have been understudied. In this work, we integrated autoencoder and multi-class classification into a single deep model and successfully learned a set of clinical group related feature vectors. Specifically, we trained two non-linear mappings which realized the mutual transformations between the original FC space and the feature space. By mapping the learned clinical group related feature vectors to the original FC space, representative FCs were constructed for each group. Moreover, based on these feature vectors, our model achieves a high classification accuracy – 68% for multi-class classification (NC vs SMC vs sMCI vs pMCI vs AD). Code has been released1.