Reviewerly/exHarmony huggingface dataset:
3D Image reconstruction using C-dual attention network from multi-view images

3D image reconstruction using multi-view imaging is widely utilized in several application domains: construction field, disaster management, urban planning, etc. The 3D reconstruction from the multi-view image is still challenging due to the high freedom and inaccurate reconstruction. This research introduces the hybrid deep learning technique for reconstructing the 3D image, in which the C-dual attention layer is proposed for generating the feature map to support the image reconstruction. The proposed 3D image reconstruction uses the encoder–decoder–refiner which is utilized for reconstruction. Initially, the features are extracted from the AlexNet and ResNet-50 features automatically. Then, the proposed C-dual attention layer is utilized for generating the inter-channel and inter-spatial relationship among the features to obtain enhanced reconstruction accuracy. The inter-channel relationship is evaluated using the channel attention layer, and the inter-spatial relationship is evaluated using the spatial attention layer of the encoder module. Here, the features generated by the spatial attention layer are combined to form the feature map in a 2D map. The proposed C-dual attention encoder provides enhanced features that help to acquire enhanced 3D image reconstruction. The proposed method is evaluated based on loss, IoU_3D, and IoU_2D, and acquired the values of 0.0721, 1.25 and 1.37, respectively.
Multivariate Mixture Model for Small Area Estimation of Poverty Indicators

Abstract When disaggregation of national estimates in several domains or areas is required, direct survey estimators, which use only the domain-specific survey data, are usually design-unbiased even under complex survey designs (at least approximately) and require no model assumptions. Nevertheless, they are appropriate only for domains or areas with sufficiently large sample size. For example, when estimating poverty in a domain with a small sample size (small area), the volatility of a direct estimator might make that area seems like very poor in one period and very rich in the next one. Small area (or indirect) estimators have been developed in order to avoid such undesired instability. Small area estimators borrow strength from the other areas so as to improve the precision and therefore obtain much more stable estimators. However, the usual model-based assumptions, which include some kind of area homogeneity, may not hold in real applications. A more flexible model based on multivariate mixtures of normal distributions that generalises the usual nested error linear regression model is proposed for estimation of general parameters in small areas. This flexibility makes the model adaptable to more general situations, where there may be areas with a different behaviour from the other ones, making the model less restrictive (hence, more close to nonparametric) and more robust to outlying areas. An expectation-maximisation (E-M) method is designed for fitting the proposed mixture model. Under the proposed mixture model, two different new predictors of general small area indicators are proposed. A parametric bootstrap method is used to estimate the mean squared errors of the proposed predictors. Small sample properties of the new predictors and of the bootstrap procedure are analysed by simulation studies and the new methodology is illustrated with an application to poverty mapping in Palestine.
Establishing a co-design framework for disaster mitigation agenda in the urban context. A case study: SIBAT Solo

Abstract One of the impacts of climate change on the urban environment is the flood cycle which is significantly difficult to predict. Along with using engineering methods, one more effort that can be conducted is to prepare communities for flood catastrophes in order to address the problem of unpredictable floods in urban settings. The correct technique and approach should be employed to involve residents in a disaster mitigation system. The objective of this study is to provide a co-design framework to assist the agenda for reducing flood disasters in urban settings. The co-design technique is being utilized as a participatory strategy to perform the work objectives of the community-based disaster preparedness community in Solo, Indonesia, according to the preliminary study findings reported in this paper. This study employed a case study method of flood disaster mitigation practices by the SIBAT community. Data analysis administered the interactive model through three stages: data reduction, data display, and conclusion drawing. Preliminary results obtained indicate that the local social and cultural context is essential in developing a co-design framework. Therefore, these findings will later become the basis for building a co-design framework for implementing the flood disaster mitigation agenda in Solo
Video art as environmental communication media for the problem of plastic waste in Solo

Abstract Garbage is a big problem in the city of Solo. It can be seen in the Putri Cempo Landfill (TPA), which is the place where the final processing of waste from all areas of Surakarta City takes place. The waste management activities of the Putri Cempo harm the safety and comfort aspects of the environment as well as location and accessibility but also become an opportunity in the economic part. To campaign for Solo as a city that supports and cares about environmentally friendly issues, what art actors can do is disseminate the crisis that occurs through various media so that the public more easily understands it. One of the most effective ways to attract people today is through a video. They are supported by music that can foster empathy for the audience. Research methods, creation, and presentation are based on research and experimentation. The process of creating works consists of several stages, including the exploration stage, experimental experiment stage, improvisation stage, forming step, and finishing stage.
Professional Competences in the Context of Inclusive Education: A Model Design

&lt;p style="text-align:justify"&gt;The most crucial factor influencing inclusive education is the preparation of educators for their professional activities. The aim of this study was to create and present a model for developing the professional competences of pre-service social educators in the context of inclusive education. The model reflected its main components: aim, objectives, methodological approaches, principles, technologies, study conditions, forms of training and stages of implementation (preliminary, main and final). The model introduced for professional activity in the conditions of inclusive education in the university's study process has the following characteristics: expediency, integrity, internal unity and consistency. The distinctiveness of the presented model lies in the possible application of taught subjects, such as the Inclusion in the Educational Environment module developed by the authors, educational internships and research work of students (RWS). Furthermore, the model is functional, based on a systemic and student-centred approaches and the principle of continuity of learning, i.e., lifelong learning (LLL).&lt;/p&gt;
Analytic solution of the exact Daum–Huang flow equation for particle filters

State estimation for nonlinear systems, especially in high dimensions, is a generally intractable problem, despite the ever-increasing computing power. Efficient algorithms usually apply a finite-dimensional model for approximating the probability density of the state vector or treat the estimation problem numerically. In 2007 Daum and Huang introduced a novel particle filter approach that uses a homotopy-induced particle flow for the Bayesian update step. Multiple types of particle flows were derived since with different properties. The exact flow considered in this work is a first-order linear ordinary time-varying inhomogeneous differential equation for the particle motion. An analytic solution in the interval [0,1] is derived for the scalar measurement case, which enables significantly faster computation of the Bayesian update step for particle filters.
Real-Time Facial Animation Generation on Face Mask

In light of the COVID-19 pandemic, wearing a mask is crucial to avoid contracting infectious diseases. However, wearing a mask is known to impair communication functions. This study aims to address the communication difficulties caused by wearing a mask and provide a strategy for aiding in understanding the speaker’s speech through facial animation. Facial animation is generated in real-time, and upper facial information is processed to detect the speaker’s emotions, generating a lower facial expression. In addition, the system detects the mask’s shape and enables accurate registration in the proper position. This technology can improve communication and alleviate challenges associated with communication between persons wearing face masks.
Implementation of Blockchain in the Banking Sector

Blockchain technology is transformative, and is expected to have a massive economic impact similar to the one the Internet has had in the past few decades.With the increasing innovation in technology for modernizing our lives, new ways to make day to day life effortless and quicker have been introduced. Banking industry is one such field that has been majorly impacted by the digital transformations with the aim of providing quicker transactions for enhanced customer services, cost efficiency and security. Banks have been used as the intermediary third party for carrying financial transactions from early history. With the advancements in technology, the banking industry has adopted one such technique to overcome this third-party intervention. Blockchain is a digital technology that offers decentralization, security and transparency of transaction records. Bank transactions are often considered non-transparent, inefficient and expensive, blockchain is a data structure that ensures that all of the above problems are taken care of. Blockchain gained its popularity after the introduction of the first cryptocurrency which is bitcoin. Almost every sector and domain have been trying to work with this technology to solve their complications. And this paper is a brief study of how blockchain works and can be used to revolutionize the banking industry.
Clinical Artificial Intelligence

Clinical artificial intelligence (AI)/machine learning (ML) is anticipated to offer new abilities in clinical decision support, diagnostic reasoning, precision medicine, clinical operational support, and clinical research, but careful concern is needed to ensure these technologies work effectively in the clinic. Here, we detail the clinical ML/AI design process, identifying several key questions and detailing several common forms of issues that arise with ML tools, as motivated by real-world examples, such that clinicians and researchers can better anticipate and correct for such issues in their own use of ML/AI techniques.
Domain Knowledge Based Small Sample Ship Target Detection Method

Synthetic Aperture Radar (SAR) is widely used in ship target detection because of its ability to operate under various weather conditions. However, some target images are difficult to obtain and label, resulting in a small sample size, which limits the development of target detection. Aiming at the problem of ship target detection in small sample SAR images, domain knowledge is used to revise and enhance the basic model in this paper. Firstly, a lightweight convolutional neural network model more suitable for small-sample SAR image ship target classification is proposed, involving target pixel and aspect ratio as domain knowledge to correct the classification results. Then, for ship target detection in SAR images, the acquisition method of scale class domain knowledge is improved. Moreover, texture-related domain knowledge based on the gray level co-occurrence matrix is extracted and used as classification features for the model to revise the model. Finally, Marine Targets Classification Dataset (MTCD) and Marine Targets Detection Dataset (MTDD) are briefly introduced and adjusted to meet the requirements of this research through screening and modification. The test results on balanced MTDD and small-sample datasets demonstrate the effectiveness of domain knowledge in improving network performance.
Vehicle detection algorithm based on lightweight YOLOX

Nowadays, accurate and fast vehicle detection technology is of great significance for constructing intelligent transportation systems in the context of the era of big data. This paper proposes an improved lightweight YOLOX real-time vehicle detection algorithm. Compared with the original network, the detection speed and accuracy of the new algorithm have been improved with fewer parameters. First, referring to the GhostNet, we make a lightweight design of the backbone extraction network, which significantly reduces the network parameters, training cost, and inference time. Furthermore, by introducing the α-CIoU loss function, the regression accuracy of the bounding box (bbox) is improved, while the convergence speed of the model is also accelerated. The experimental results show that the mAP of the improved algorithm on the BIT-Vehicle dataset can reach up to 99.21% with 41.2% fewer network parameters and 12.7% higher FPS than the original network and demonstrate the effectiveness of our proposed method.
A Novel Method for Constructing Knowledge Graph of Railway Safety Risk

Accurately and quickly constructing a high-quality knowledge graph of railway safety is an important challenge in the field of knowledge engineering. This paper adopts the "four-step method" to construct an accurate and efficient knowledge graph in the field of railway safety risk, including domain ontology construction, crowdsourcing semi-automatic semantic annotation, external data completion and knowledge acquisition. Then the sub-graph of public works safety risk is analyzed, and the application is discussed. Experiments show that the "four-step method" can quickly and accurately acquire knowledge in the field of railway safety, providing a powerful tool for integrating multi-source and heterogeneous railway safety risk knowledge, and making the use of railway safety risk-related information resources more convenient.
Scientific Knowledge Graph-driven Research Profiling

Due to the growing expansion of scientific literature and knowledge, access to scholarly contents is becoming more challenging. This paper presents a research profiling framework driven by scientific knowledge graphs (SKGs), which aims at achieving the deep fusion and thorough disclosure of scientific resources and domain knowledge, as well as satisfying the needs of researchers for knowledge overview and acquisition in a reasonable period of time using fine-grained and multi-dimensional profiling scenarios. Further, we conduct the experiment of the SKG construction task on a specific domain by combining scientific literature from Web Of Science and an open knowledge base, and develop a prototype system for user's single entity search to validate the approach, with profile presentations of overall graph view, literature profile, hotness topics list, high-impact experts, and providing a service of profile viewing and download.
Psychophysiological Approach for Measuring Social Presence in A Team-Based Activity: A Comparison Between Real and Virtual Environments

Teamwork is becoming increasingly important in today's engineering and product design professions. In an effort to enhance teamwork and collaboration, our approach relies on adopting a psychophysiological approach for determining how participants' feelings of social presence in a virtual reality environment in comparison to the “real” world. Using a combination of ECG data and participant feedback from an anonymous questionnaire, our comparative analysis shows that participants feel more relaxed and have a greater sense of participation in the virtual environment. That could provide a stronger theoretical basis for the development of virtual collaborative projects in the future.
ALGANs: Enhancing membership inference attacks in federated learning with GANs and active learning

Federated learning has received a lot of attention in recent years due to its privacy protection features. However, federated learning is susceptible to various inference attacks. Membership inference attack aims to determine whether the target data is a member or non-member of the target federated learning model, which poses a serious threat to the privacy of the training data set. Membership inference method in federated learning is dissatisfied due to a lack of attack data. Recent work shows that generative adversarial networks(GANs) can effectively enrich attack data. However, data generated by GANs lacks labels. Previous work labels data by inputting it to the target classifier model, which may be imprecise when the target model outputs ambiguous results. In this paper, to overcome the lack of attack data and the lack of labels for GANs, we propose ALGANs. ALGANs increases data diversity using GANs while applies active learning to label data generated by GANs. Membership inference attack enhanced by ALGANs has a high attack accuracy due to applying active learning to label data and extensive experimental results prove our point. We performed experiments to show that ALGAN makes membership inference attacks more threatening in federated learning.
Modified Blowfish Algorithm Based on Improved Lorenz Attractor

Image security becomes important topic because of increasing image usage in communication besides assures information security which is unseen in these images such as military and medical images. Blowfish is a superb symmetric cryptography that ensures a high degree of resistance to attacks. The proposed system modifies Blowfish algorithm by substituting the function in blowfish round with light weight function to save memory and resources of the platforms and Using 3-D chaotic system (Improved Lorenz) that work as a key timetable for creating Blowfish sub keys in order to increasing randomness of the encrypted image. Because no hardware multiplier is required, this technology may be employed in high-speed applications. Both 3-D chaotic circuit and modified blowfish encryption/decryption schemes are designed using VHDL and simulated by Xilinx ISE 14.7. The proposed system has been embedded in an FPGA device XC5VLX50T to examine modification of Blowfish encryption algorithm efficiency, the system is examined by MATLAB and passes NIST statistical suite test. This system only used 6 % of the FPGA Virtex 5 kit’s slices, with an operating frequency of 351MHz.
A Dynamically Reconfigurable Column Streaming-based Convolution Engine for Edge AI Accelerators

Edge AI accelerators are being targeted for numerous emerging applications due to their compact size and low power consumption. However, these applications require reliability, fault-tolerance, and flexibility to overcome the defect rate caused by radiation or manufacturing defects for hard-to-reach applied environments such as space or nuclear power stations. As a result, this paper proposes a dynamically reconfigurable structure for the column streaming-based convolution engine in order to enhance the edge AI accelerator's reliability and flexibility. The high-level description code synthesized from Xilinx Vivado software shows that the design offers more mapping methods in terms of flexibility with 17.15% powerless and [0.43%,3.18%] area overhead as a trade-off compared to the commercial streaming-based CNN accelerator.
Learning Chemistry with Interactive Simulations: Augmented Reality as Teaching Aid

Augmented Reality (AR) has been identified by educational scientists as a technology with significant potential to improve emotional and cognitive learning outcomes. However, very few papers highlighted the technical process of creating AR applications reserved for education. The following paper proposes a method and framework for how to set up an AR application to teach primary school children the basic forms and shapes of atoms, molecules, and DNA. This framework uses the Unity 3D game engine (GE) with Vuforia SDK (Software Development Kit) packages combined with phone devices or tablets to create an interactive App for AR environments, to enhance the student's vision and understanding of basic chemistry models. We also point out some difficulties in practice. As for those difficulties mentioned, a series of solutions plus further development orientation are put forth.
An End-To-End Explainable AI System for Analyzing Breast Cancer Prediction Models

Deep learning-based predictive models, for identifying malign tumor data in various cancer types, emerged as a hot research topic in the Internet of Medical Things (IoT). Such models can be deployed onto biomedical machines acting as IoMT devices to provide highly accurate breast cancer screening. While there has been a significant advancement in deep learning models for classifying cancer imaging data, a key shortcoming exists in terms of their use as blackbox algorithms rendering them unexplainable or non-interpretable. Caregivers, such as oncologists and radiologists, however, need to understand the nature of the model outcome. We address this in this paper by providing an end-to-end explainable AI framework for analyzing breast cancer prediction models based on a publicly available mammography dataset. In addition, we demonstrate how the various methods in such an end-to-end system can be effectively evaluated with appropriate performance measures.
Hybrid deep learning based POS tagger for Roman Urdu

Parts-of-Speech (POS) tagging is a highly encouraged research topic in the field of Natural Language Processing. POS entails numerous practical applications such as text indexing, information retrieval, corpus tagging for research, and linguistic work. This paper outlines multiple methods for part-of-speech tagging in Roman Urdu. Sufficient work and relevant required corpora are not available for Roman Urdu. We have identified that there are several parts-of-speech classes in the Urdu Language, with limited access to a well-annotated corpus. A manually verified corpus has been used to evaluate and report multiple methods for the said task. Our experiments deal with twenty-three unique parts-of-speech classes based on the contextual requirements of the Urdu Language. Our experiments include several methods built upon artificial neural networks, based on approaches such as multi-layered neural networks, feedback recurrent networks, and self-attention models. The corpus we used is not domain specific and covers several topics of Pakistani interest. Our experiments varied to a certain degree in the success demonstrated and outperformed numerous baseline models of machine learning and deep learning.