Reviewerly/exHarmony huggingface dataset:
Hydraulic performance of mini polder water management in flood management in Tabalong District, South Kalimantan

Abstract Flood disaster is a common problem, especially in densely populated urban, coastal, and basin areas, respectively. This leads to the development of the Mini Polder design, which is used to solve the problem of flooding in agricultural land. The design is projected in lebak swamp in Ampukung village, Tabalong Regency, Indonesia, covering an area of 393 Ha. Therefore, this study aims to determine the hydraulic performance of mini polders in flood management, within Tabalong District, South Kalimantan, Indonesia. Using Hec-RAS in the planning stage, the results showed that a dike was produced with an elevation and a width of +10.30 and 4 m, based on the Q25th flood discharge of 514.37 m 3 /s. Furthermore, the construction of a channel around the land was planned as a replacement for the retention pond, due to the efficiency of excavation and stockpiling. The channel also functioned as a flood reducer, which approached water sources to the rice fields or community plantations. By excavating the existing outlet and rehabilitating the door building, the channel normalization was subsequently carried out. For emergency conditions, a portable water pump was needed for water supply and disposal of the Tabalong river, during the arrival of the dry season.
Editing Compressed High‐resolution Voxel Scenes with Attributes

Abstract Sparse Voxel Directed Acyclic Graphs (SVDAGs) are an efficient solution for storing high‐resolution voxel geometry. Recently, algorithms for the interactive modification of SVDAGs have been proposed that maintain the compressed geometric representation. Nevertheless, voxel attributes, such as colours, require an uncompressed storage, which can result in high memory usage over the course of the application. The reason is the high cost of existing attribute‐compression schemes which remain unfit for interactive applications. In this paper, we introduce two attribute compression methods (lossless and lossy), which enable the interactive editing of compressed high‐resolution voxel scenes including attributes.
CubeGAN: Omnidirectional Image Synthesis Using Generative Adversarial Networks

Abstract We propose a framework to create projectively‐correct and seam‐free cube‐map images using generative adversarial learning. Deep generation of cube‐maps that contain the correct projection of the environment onto its faces is not straightforward as has been recognized in prior work. Our approach extends an existing framework, StyleGAN3, to produce cube‐maps instead of planar images. In addition to reshaping the output, we include a cube‐specific volumetric initialization component, a projective resampling component, and a modification of augmentation operations to the spherical domain. Our results demonstrate the network's generation capabilities trained on imagery from various 3D environments. Additionally, we show the power and quality of our GAN design in an inversion task, combined with navigation capabilities, to perform novel view synthesis.
Periocular Biometric Recognition for Masked Faces

Since the outbreak of Coronavirus Disease 2019 (COVID-19), people are recommended to wear facial masks to limit the spread of the virus. Under the circumstances, traditional face recognition technologies cannot achieve satisfactory results. In this paper, we propose a face recognition algorithm that combines the traditional features and deep features of masked faces. For traditional features, we extract Local Binary Pattern (LBP), Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG) features from the periocular region, and use the Support Vector Machines (SVM) classifier to perform personal identification. We also propose an improved Convolutional Neural Network (CNN) model Angular Visual Geometry Group Network (A-VGG) to learn deep features. Then we use the decision-level fusion to combine the four features. Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces, including frontal and side faces taken at different angles. Images with motion blur were also tested to evaluate the robustness of the algorithm. Besides, the experiment of matching a masked face with the corresponding full face is accomplished. The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition, and the periocular region has rich biological features and high discrimination.
Fundamental Graphical User Interface Design of an Educational Android Application

A graphical user interface (GUI) design of an application presents a user-friendly, elegant, concise, and attractive mechanism for interacting with visual components. Android is a powerful Linux-based mobile operating system, and these days android devices are becoming the most sought-after and widely used devices than other types of mobile devices in the mobile application market. Nowadays, android applications (herein as ‘android apps’) have also become popular in the educational sector because of their compatible, efficient, and effective interface to access the content. In android apps, graphical designs are built from GUI components which is an object with which mobile user interacts via various forms of input. This paper presents the basic structured GUI design of an educational android application. These days, the GUI design of any android is an increasingly important factor in deciding whether your app will be accepted by mobile users to achieve success in the market and it also builds a connection between users and applications. In addition, users have more expectations and demands for good quality GUI design. GUI design contains three phases: informational architecture, interaction, and visual design. The aim of this paper helps to explain how to create a basic GUI design of an educational android application by using an integrated development environment (Android Studio) and programming languages such as Extensible Markup Language (XML) with different graphical components such as visual layouts and widgets. To assess user experience, this study has carried the survey using a questionnaire set. The main motive (purpose) of the survey was to assess (evaluate) the effectiveness and the user experience of the interface design.
Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice

Aiming at the problem that the fully homomorphic encryption scheme based on single identity cannot satisfy the homomorphic operation of ciphertext under different identities, as well as the inefficiency of trapdoor function and the complexity of sampling algorithm, an improved lattice MIBFHE scheme was proposed. Firstly, we combined MP12 trapdoor function with dual LWE algorithm to construct a new IBE scheme under the standard model, and prove that the scheme is IND-sID-CPA security under the selective identity. Secondly, we used the eigenvector method to eliminate the evaluation key, and transform the above efficient IBE scheme into a single identity IBFHE scheme to satisfy the homomorphic operation. Finally, we improved the ciphertext extension method of CM15 and constructed a new Link-mask system that supports the transformation of IBFHE scheme under the standard model, and then, converted the above IBFHE scheme into MIBFHE scheme based on this system. The comparative analysis results showed that the efficiency of this scheme is improved compared with similar schemes in the trapdoor generation and preimage sampling, and the dimension of lattice and ciphertext size are significantly shortened.
Performance models of data parallel DAG workflows for large scale data analytics

Abstract Directed Acyclic Graph (DAG) workflows are widely used for large-scale data analytics in cluster-based distributed computing systems. The performance model for a DAG on data-parallel frameworks (e.g., MapReduce) is a research challenge because the allocation of preemptable system resources among parallel jobs may dynamically vary during execution. This resource allocation variation during execution makes it difficult to accurately estimate the execution time. In this paper, we tackle this challenge by proposing a new cost model, called Bottleneck Oriented Estimation (BOE), to estimate the allocation of preemptable resources by identifying the bottleneck to accurately predict task execution time. For a DAG workflow, we propose a state-based approach to iteratively use the resource allocation property among stages to estimate the overall execution plan. Furthermore, to handle the skewness of various jobs, we refine the model with the order statistics theory to improve estimation accuracy. Extensive experiments were performed to validate these cost models with HiBench and TPC-H workloads. The BOE model outperforms the state-of-the-art models by a factor of five for task execution time estimation. For the refined skew-aware model, the average prediction error is under $$3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> when estimating the execution time of 51 hybrid analytics (HiBench) and query (TPC-H) DAG workflows.
Variational Pose Prediction with Dynamic Sample Selection from Sparse Tracking Signals

Abstract We propose a learning‐based approach for full‐body pose reconstruction from extremely sparse upper body tracking data, obtained from a virtual reality (VR) device. We leverage a conditional variational autoencoder with gated recurrent units to synthesize plausible and temporally coherent motions from 4‐point tracking (head, hands, and waist positions and orientations). To avoid synthesizing implausible poses, we propose a novel sample selection and interpolation strategy along with an anomaly detection algorithm. Specifically, we monitor the quality of our generated poses using the anomaly detection algorithm and smoothly transition to better samples when the quality falls below a statistically defined threshold. Moreover, we demonstrate that our sample selection and interpolation method can be used for other applications, such as target hitting and collision avoidance, where the generated motions should adhere to the constraints of the virtual environment. Our system is lightweight, operates in real‐time, and is able to produce temporally coherent and realistic motions.
A Framework for Cybersecurity Requirements Management in the Automotive Domain

The rapid development of intelligent connected vehicles has increased the attack surface of vehicles and made the complexity of vehicle systems unprecedented. Original equipment manufacturers (OEMs) need to accurately represent and identify threats and match corresponding security requirements. Meanwhile, the fast iteration cycle of modern vehicles requires development engineers to quickly obtain cybersecurity requirements for new features in their developed systems in order to develop system code that meets cybersecurity requirements. However, existing threat identification and cybersecurity requirement methods in the automotive domain cannot accurately describe and identify threats for a new feature while also quickly matching appropriate cybersecurity requirements. This article proposes a cybersecurity requirements management system (CRMS) framework to assist OEM security experts in conducting comprehensive automated threat analysis and risk assessment and to help development engineers identify security requirements prior to software development. The proposed CRMS framework enables development engineers to quickly model their systems using the UML-based (i.e., capable of describing systems using UML) Eclipse Modeling Framework and security experts to integrate their security experience into a threat library and security requirement library expressed in Alloy formal language. In order to ensure accurate matching between the two, a middleware communication framework called the component channel messaging and interface (CCMI) framework, specifically designed for the automotive domain, is proposed. The CCMI communication framework enables the fast model of development engineers to match with the formal model of security experts for threat and security requirement matching, achieving accurate and automated threat and risk identification and security requirement matching. To validate our work, we conducted experiments on the proposed framework and compared the results with the HEAVENS approach. The results showed that the proposed framework is superior in terms of threat detection rates and coverage rates of security requirements. Moreover, it also saves analysis time for large and complex systems, and the cost-saving effect becomes more pronounced with increasing system complexity.
Investigating the influence of agent modality and expression on agent-mediated fairness behaviours

Abstract With technological developments, individuals are increasingly able to delegate tasks to autonomous agents that act on their behalf. This may cause individuals to behave more fairly, as involving an agent representative encourages individuals to strategise ahead and therefore adhere to social norms of fairness. Research suggests that an audio smiling agent may further promote fairness as it provides a signal of honesty and trust. What is still unclear is whether presenting a multimodal smiling agent (by using visual and auditory cues) rather than a unimodal smiling agent as normally available commercially (using only an auditory cue e.g., Siri) could amplify the impact of smiles. In the present study, participants ( N = 86) played an ultimatum game either directly with another player (control), through a smiling multimodal and unimodal agent or through a neutral multimodal and unimodal agent. Participants’ task was to offer a number of tickets to the other player from a fixed amount. Results showed that when playing the ultimatum game through a smiling multimodal agent, participants offered more tickets to the other player compared to the control condition and the other agent conditions. Hence, exploiting multisensory perception to enhance an agent’s expression may be key for increasing individuals' pro-social behaviour when interacting through such an agent.
An Analysis of Different Noise Removal Techniques in Medical Images

In medical application like diagnosis of diseases, image processing plays crucial role. The process of segmenting the medical images is followed by careful analyzation to detect the ailments ranging from minor to serious. The presence of noise hinders the accurate diagnosis, and denoising becomes important part of image processing in medical images. The denoising approach can be built upon understanding the noise models and using denoising algorithm accordingly. This research aims at studying different medical images, noises and noise removal methods followed by experimental and comparative analysis of the prominent ones.
Analysis of the Development Needs for Interactive Web-Based E-book on Pressure Topic for Differentiated Science Learning

This research aims to describe the needs analysis results conducted at SMP Negeri 19 Singkawang as the basis for developing web-based interactive e-book teaching materials on pressure for differentiated science learning. This study is a Research and Development study. The researcher employed a qualitative descriptive research method. Data collection techniques included non-test instruments such as questionnaires, interviews, observations, and documentation. Data analysis was conducted using a qualitative approach. The development of web-based interactive e-books utilized the 4D development model, consisting of four stages: define, design, develop, and disseminate. This study served as a preliminary study in the define stage, where the researcher ensured that the developed product would meet the needs of teachers and students. Based on the findings, students highly demanded web-based interactive e-book media for the topic of pressure. The researcher concluded that students require engaging, interactive teaching materials that can be easily accessed through their frequently used smartphones and contain diverse content with various media formats that cater to their learning styles. Teachers require interactive e-books to enhance the quality of science teaching on the topic of pressure. Differentiated science learning strategies integrated into interactive e-book are necessary for teachers to provide appropriate teaching materials and media that meet the student's learning needs. The availability of supporting facilities and adequate internet connection allows the utilization of web-based interactive e-books on the topic of pressure by students at SMP Negeri 19 Singkawang.
Boosting Adversarial Transferability with Shallow-Feature Attack on SAR Images

Adversarial example generation on Synthetic Aperture Radar (SAR) images is an important research area that could have significant impacts on security and environmental monitoring. However, most current adversarial attack methods on SAR images are designed for white-box situations by end-to-end means, which are often difficult to achieve in real-world situations. This article proposes a novel black-box targeted attack method, called Shallow-Feature Attack (SFA). Specifically, SFA assumes that the shallow features of the model are more capable of reflecting spatial and semantic information such as target contours and textures in the image. The proposed SFA generates ghost data packages for input images and generates critical features by extracting gradients and feature maps at shallow layers of the model. The feature-level loss is then constructed using the critical features from both clean images and target images, which is combined with the end-to-end loss to form a hybrid loss function. By fitting the critical features of the input image at specific shallow layers of the neural network to the target critical features, our attack method generates more powerful and transferable adversarial examples. Experimental results show that the adversarial examples generated by the SFA attack method improved the success rate of single-model attack under a black-box scenario by an average of 3.73%, and 4.61% after combining them with ensemble-model attack without victim models.
Pre-trained Language Models

Abstract This chapter presents the main architecture types of attention-based language models, which describe the distribution of tokens in texts: Autoencoders similar to BERT receive an input text and produce a contextual embedding for each token. Autoregressive language models similar to GPT receive a subsequence of tokens as input. They produce a contextual embedding for each token and predict the next token. In this way, all tokens of a text can successively be generated. Transformer Encoder-Decoders have the task to translate an input sequence to another sequence, e.g. for language translation. First they generate a contextual embedding for each input token by an autoencoder. Then these embeddings are used as input to an autoregressive language model, which sequentially generates the output sequence tokens. These models are usually pre-trained on a large general training set and often fine-tuned for a specific task. Therefore, they are collectively called Pre-trained Language Models (PLM). When the number of parameters of these models gets large, they often can be instructed by prompts and are called Foundation Models. In further sections we described details on optimization and regularization methods used for training. Finally, we analyze the uncertainty of model predictions and how predictions may be explained.
Subpixel Deblurring of Anti‐Aliased Raster Clip‐Art

Abstract Artist generated clip‐art images typically consist of a small number of distinct, uniformly colored regions with clear boundaries. Legacy artist created images are often stored in low‐resolution (100x100px or less) anti‐aliased raster form. Compared to anti‐aliasing free rasterization, anti‐aliasing blurs inter‐region boundaries and obscures the artist's intended region topology and color palette; at the same time, it better preserves subpixel details. Recovering the underlying artist‐intended images from their low‐resolution anti‐aliased rasterizations can facilitate resolution independent rendering, lossless vectorization, and other image processing applications. Unfortunately, while human observers can mentally deblur these low‐resolution images and reconstruct region topology, color and subpixel details, existing algorithms applicable to this task fail to produce outputs consistent with human expectations when presented with such images. We recover these viewer perceived blur‐free images at subpixel resolution, producing outputs where each input pixel is replaced by four corresponding (sub)pixels. Performing this task requires computing the size of the output image color palette, generating the palette itself, and associating each pixel in the output with one of the colors in the palette. We obtain these desired output components by leveraging a combination of perceptual and domain priors, and real world data. We use readily available data to train a network that predicts, for each anti‐aliased image, a low‐blur approximation of the blur‐free double‐resolution outputs we seek. The images obtained at this stage are perceptually closer to the desired outputs but typically still have hundreds of redundant differently colored regions with fuzzy boundaries. We convert these low‐blur intermediate images into blur‐free outputs consistent with viewer expectations using a discrete partitioning procedure guided by the characteristic properties of clip‐art images, observations about the antialiasing process, and human perception of anti‐aliased clip‐art. This step dramatically reduces the size of the output color palettes, and the region counts bringing them in line with viewer expectations and enabling the image processing applications we target. We demonstrate the utility of our method by using our outputs for a number of image processing tasks, and validate it via extensive comparisons to prior art. In our comparative study, participants preferred our deblurred outputs over those produced by the best‐performing alternative by a ratio of 75 to 8.5.
Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction

Abstract Clean energy is a major trend. The importance of photovoltaic power generation is also growing. Photovoltaic power generation is mainly affected by the weather. It is full of uncertainties. Previous work has relied chiefly on historical photovoltaics data for time series forecasts. However, unforeseen weather conditions can sometimes skew. Consequently, a spatial‐temporal‐meteorological‐long short‐term memory prediction model (STM‐LSTM) is proposed to compensate for the shortage of photovoltaic prediction models for uncertainties. This model can simultaneously process satellite image data, historical meteorological data, and historical power generation data. In this way, historical patterns and meteorological change information are extracted to improve the accuracy of photovoltaic prediction. STM‐LSTM processes raw satellite data to obtain cloud image data. It can extract cloud motion information using the dense optical flow method. First, the cloud images are processed to extract cloud position information. By adaptive attentive learning of images in different bands, a better representation for subsequent tasks can be obtained. Second, it is important to process historical meteorological data to learn meteorological change patterns. Last but not least, the historical photovoltaic power generation sequences are combined to obtain the final photovoltaic prediction results. After a series of experimental validation, the performance of the proposed STM‐LSTM model has a good improvement compared with the baseline model.