Reviewerly/exHarmony huggingface dataset:
FLOOD VULNERABILITY MAPPING OF RIVER NGADDA USING GEOSPATIAL AND REMOTE SENSING TECHNIQUES MAIDUGURI METROPOLIS, BORNO STATE

Flood disaster has been threating to animal, plants, land, human and non-human resources over the years. This have increased concerns on the need to investigate various vulnerable areas to flood for decision making. As a mitigative measures to loss of lives, properties, and livelihood which could arise from flood disaster, understanding zones at risk to flooding is vital to support all stages of disaster management including early warning and planning. River Ngadda is a major river within Maiduguri metropolis. Its plains and banks are known to have different activities and resources. When the river overflows its shores, neighboring lands could suffer destruction, there could be loss of lives and properties. This project aims to investigate flood vulnerability along River Ngadda identifying the shores, plains, and banks of the river at risk. The research adopts ground truth information, the use of Aster DEM (30m) and Sentinel 2 (10m) Satellite Imagery dataset through Geospatial and Remote Sensing techniques to assess undulating nature of the area under study, identify areas liable to flood and analyzed the flood plain. Further analysis was carried out to obtain the Land use and Landcover (LULC) within the study area using a supervised classification and areas at various flood risk were also identified. This research results presented the river plains, flood vulnerable impact on LULC, it was found that 13.17 hectares of built-up areas fall within the very high flood risk zone, over sixty six percent (66%) of the study area appears fall within the moderate to very high vulnerable flood risk zone and the production of the flood plains, LULC, risk and hazard map of the study area was presented. This result could aid early warning, planning and decision making for River Ngadda and its environs.
A Super-Resolution Algorithm Based on Hybrid Network for Multi-Channel Remote Sensing Images

In recent years, the development of super-resolution (SR) algorithms based on convolutional neural networks has become an important topic in enhancing the resolution of multi-channel remote sensing images. However, most of the existing SR models suffer from the insufficient utilization of spectral information, limiting their SR performance. Here, we derive a novel hybrid SR network (HSRN) which facilitates the acquisition of joint spatial–spectral information to enhance the spatial resolution of multi-channel remote sensing images. The main contributions of this paper are three-fold: (1) in order to sufficiently extract the spatial–spectral information of multi-channel remote sensing images, we designed a hybrid three-dimensional (3D) and two-dimensional (2D) convolution module which can distill the nonlinear spectral and spatial information simultaneously; (2) to enhance the discriminative learning ability, we designed the attention structure, including channel attention, before the upsampling block and spatial attention after the upsampling block, to weigh and rescale the spectral and spatial features; and (3) to acquire fine quality and clear texture for reconstructed SR images, we introduced a multi-scale structural similarity index into our loss function to constrain the HSRN model. The qualitative and quantitative comparisons were carried out in comparison with other SR methods on public remote sensing datasets. It is demonstrated that our HSRN outperforms state-of-the-art methods on multi-channel remote sensing images.
A Human-Centered Design Process for Developing Non-Humanoid Social Robotic (NH-SR) Work and Exercise Environment

The iWEE, is a non-humanoid social robotic system that recognizes office workers' limited movement, e.g., sitting for more than an hour, combines this information with their meditation and/or exercise preferences, and in response prompts them to engage in periodic whole-body movement activity breaks. The iWEE' robotic systems sense users' movements, assess the users' states, and reconfigure to encourage exercise by actuating its different spatial sensory cues, i.e., form, sound and color. The iWEE also employs architectural conventions, providing different configurations to encourage movement of different muscle groups. iWEE's users interact with the robotic system using for example touch, proximity and/or infrared sensors, or by using push buttons located at the iWEE's spatial interface. The cyber physical system are iteratively designed, prototyped, and empirically evaluated at different scales.
SPoW: A solution to the problem of centralization of computing power

As the underlying data structure of encrypted digital currency, blockchain technology has become the key technology of current research because of its decentralization characteristics. Engineers and scholars from industry and academia have also extensively discussed the opportunities that the combination of blockchain technology and traditional industries may bring to the development of the industry. However, the consensus algorithm used by the current blockchain technology has the problem of energy waste caused by the centralization of computing power. In response to this problem, after in-depth research on the mechanism of traditional consensus algorithms and the process of reaching consensus, this paper proposes an SPoW algorithm that separates computation and verification to solve the problem of centralization of computing power. At the same time, in order to ensure decentralization, this paper combines the economic Kznets ratio to measure the degree of decentralization and introduces deep reinforcement learning to dynamically balance the number of node tokens.
Mutual information maximization and feature space separation and bi-bimodal mo-dality fusion for multimodal sentiment analysis

In recent years multimodal sentiment analysis (MSA) has been devoted to developing effective fusion mechanisms and has made advances, however, there are several challenges that have not been addressed adequately: the models make insufficient use of important information (inter-modal relevance and independence information) resulting in additional noise, and the traditional ternary symmetric architecture cannot well solve the problem of uneven distribution of task-related information among modalities. Thus, we propose Mutual Information Maximization and Feature Space Separation and Bi-Bimodal Modality Fusion (MFSBF)framework which effectively alleviates these problems. To alleviate the problem of underutilization of important information among modalities, a mutual information maximization module and a feature space separation module have been designed. The mutual information module maximizes the mutual information between two modalities to retain more relevance (modality-invariant) information, while the feature separation module separates fusion features to prevent the loss of independence(modality-specific) information during the fusion process. As different modalities contribute differently to the model, a bimodal fusion architecture is used, which involves the fusion of two bimodal pairs. The architecture focuses more on the modality that contains more task-ralated information and alleviates the problem of uneven distribution of useful information among modalities. The experiment results of our model on two publicly available datasets (CUM-MOSI and CUM-MOSEI) achieved better or comparable results than previous models, which demonstrate the efficacy of our method.
Neural Sequence Labeling Based Sentence Segmentation for Myanmar Language

In the informal Myanmar language, for which most NLP applications are used, there is no predefined rule to mark the end of the sentence. Therefore, in this paper, we contributed the first Myanmar sentence segmentation corpus and systematically experimented with twelve neural sequence labeling architectures trained and tested on both sentence and sentence+paragraph data. The word LSTM + Softmax achieved the highest accuracy of 99.95% while trained and tested on sentence-only data and 97.40% while trained and tested on sentence + paragraph data.
An investigation of the formation and pragmatic strategies of “xx-<i>zi</i>”

Abstract With the stride of technology, new media and online communication bring innovative language phenomena to the fore, in particular, the spread of internet buzzwords. In 2021, the internet buzzword juejuezi gathered momentum on the internet. Enlightened by this online catchword, this paper investigates the popularity behind “xx- zi ” and its three types of word-formation, then takes juejuezi derived from “adj.+ zi ” as the example, conducting a qualitative encoding and analysis of the posts sourced from Weibo based on NVivo 20. Additionally, the study examines the specific pragmatic strategies, such as, praise, compliant and irony, embedded in the posts for further explicating how individuals employ juejuezi to express a chain of connotations in online communication. Notably, the internet buzzword provides channels to mirror the discourse ecology of the internet era, the public’s attitudes and behaviors regarding to shifts of politics, economy and culture change, thus providing a supplement for further research on internet buzzwords.
Three-Dimensional Measurement of Full Profile of Steel Rail Cross-Section Based on Line-Structured Light

The wear condition of steel rails directly affects the safety of railway operations. Line-structured-light visual measurement technology is used for online measurement of rail wear due to its ability to achieve high-precision dynamic measurements. However, in dynamic measurements, the random deviation of the measurement plane caused by the vibration of the railcar results in changes in the actual measured rail profile relative to its cross-sectional profile, ultimately leading to measurement deviations. To address these issues, this paper proposes a method for three-dimensional measurement of steel rail cross-sectional profiles based on binocular line-structured light. Firstly, calibrated dual cameras are used to simultaneously capture the profiles of both sides of the steel rail in the same world coordinate system, forming the complete rail profile. Then, considering that the wear at the rail waist is zero in actual operation, the coordinate of the circle center on both sides of the rail waist are connected to form feature vectors. The measured steel rail profile is aligned with the corresponding feature vectors of the standard steel rail model to achieve initial registration; next, the rail profile that has completed the preliminary matching is accurately matched with the target model based on the iterative closest point (ICP) algorithm. Finally, by comparing the projected complete rail profile onto the rail cross-sectional plane with the standard 3D rail model, the amount of wear on the railhead can be obtained. The experimental results indicate that the proposed line-structured-light measurement method for the complete rail profile, when compared to the measurements obtained from the rail wear gauge, exhibits smaller mean absolute deviation (MAD) and root mean square error (RMSE) for both the vertical and lateral dimensions. The MAD values for the vertical and lateral measurements are 0.009 mm and 0.039 mm, respectively, while the RMSE values are 0.011 mm and 0.048 mm. The MAD and RMSE values for the vertical and lateral wear measurements are lower than those obtained using the standard two-dimensional rail profile measurement method. Furthermore, it effectively eliminates the impact of vibrations during the dynamic measurement process, showcasing its practical engineering application value.
Bayesian network model structure based on binary evolutionary algorithm

With the continuous development of new technologies, the scale of training data is also expanding. Machine learning algorithms are gradually beginning to be studied and applied in places where the scale of data is relatively large. Because the current structure of learning algorithms only focus on the identification of dependencies and ignores the direction of dependencies, it causes multiple labeled samples not to identify categories. Multiple labels need to be classified using techniques such as machine learning and then applied to solve the problem. In the environment of more training data, it is very meaningful to explore the structure extension to identify the dependencies between attributes and take into account the direction of dependencies. In this article, Bayesian network structure learning, analysis of the shortcomings of traditional algorithms, and binary evolutionary algorithm are applied to the randomized algorithm to generate the initial population. In the optimization process of the algorithm, it uses a Bayesian network to do a local search and uses a depth-first algorithm to break the loop. Finally, it finds a higher score for the network structure. In the simulation experiment, the classic data sets, ALARM and INSURANCE, are introduced to verify the effectiveness of the algorithm. Compared with NOTEARS and the Expectation-Maximization (EM) algorithm, the weight evaluation index of this article was 4.5% and 7.3% better than other schemes. The clustering effect was improved by 13.5% and 15.2%. The smallest error and the highest accuracy are also better than other schemes. The discussion of Bayesian reasoning in this article has very important theoretical and practical significance. This article further improves the Bayesian network structure and optimizes the performance of the classifier, which plays a very important role in promoting the expansion of the network structure and provides innovative thinking.
Ambient Awareness of Who Knows What: Spontaneous Inferences of Domain Expertise

Being aware of others' expertise is essential for knowledge exchange. Browsing social media has been shown to foster such awareness, often referred to as "ambient awareness". However, the underlying psychological mechanism has not yet been established. In a series of three preregistered experiments (total N = 445), we tested the notions that individuals make inferences about others' domains of expertise when being exposed to domain-implying social media posts, and that they do so spontaneously – that is, unintentionally and efficiently. Regardless of whether individuals were carefully reading, browsing through, or memorizing domain-implying posts, and whether they had an explicit goal to form an impression or not, we found consistent evidence that they made inferences about others' expertise domains, as evidenced by responses in a (false) probe recognition paradigm and explicit domain identification (Experiments 1 − 3). We also showed that they did so unintentionally (Experiment 2) and efficiently (Experiment 3). Spontaneous domain inferences are a plausible psychological mechanism for how ambient awareness of who knows what is achieved through regular social media use. Our findings provide causal evidence for the proposed mechanism underlying the link between social media use and expertise localization and highlight the role of social media posts in this process.
Private Timestamps and Selective Verification of Notarised Data on a Blockchain

In this paper, we present a novel method for timestamping and data notarisation on a distributed ledger. The problem with on-chain hashes is that a cryptographic hash is a deterministic function that it allows the blockchain be used as an oracle that confirms whether potentially leaked data is authentic (timestamped or notarised by the user). Instead, we suggest using on-chain Pedersen commitments and off-chain zero-knowledge proofs (ZKP) for designated verifiers to prove the link between the data and the on-chain commitment. Our technique maintains the privacy of the data, and retains control of who can access it and when they can access it. This holds true even on a public blockchain, and even if the data is leaked by authorised parties. Indeed, an authorised data consumer (a designated-verifier for the ZKP), who discloses the data publicly, cannot convince anyone about the legitimacy of the data (in the sense that it is consistent with the information uploaded to the blockchain), because the ZKP proof is valid only for them. Our techniques can be used in scenarios where it is required to audit highly-sensitive data (e.g. application logs) by specific third parties, or to provide on-demand data certification by notaries.
Towards Accessible Augmented Reality Learning Authoring Tool: A Case of MirageXR

Due to the improvement of information and communication technology, especially how information technology is integrated into teaching and learning in formal and informal learning, augmented reality has penetrated and grabbed the attention of many educators, experts, and accessibility advocators that wants to shape learning activities. In shaping the learning activities, the augmented reality authoring tools are paramount as they offer different affordances to non-technical educators that help them to author augmented reality learning scenarios. Unfortunately, the existing Augmented reality learning authoring tools are not accessible, removing the rightful benefits of users with disabilities. This research, therefore, used task scenarios, think-aloud protocol, system usability scale, and questionnaire to evaluate the accessibility of augmented reality learning authoring tool and identify the missing accessibility features for users with vision or hearing impairments. The evaluation results indicate that the development of an accessible authoring tool with the capacity to support users with disabilities is required.
Prediction With Visual Evidence: Sketch Classification Explanation via Stroke-Level Attributions

Sketch classification models have been extensively investigated by designing a task-driven deep neural network. Despite their successful performances, few works have attempted to explain the prediction of sketch classifiers. To explain the prediction of classifiers, an intuitive way is to visualize the activation maps via computing the gradients. However, visualization based explanations are constrained by several factors when directly applying them to interpret the sketch classifiers: (i) low-semantic visualization regions for human understanding. and (ii) neglecting of the inter-class correlations among distinct categories. To address these issues, we introduce a novel explanation method to interpret the decision of sketch classifiers with stroke-level evidences. Specifically, to achieve stroke-level semantic regions, we first develop a sketch parser that parses the sketch into strokes while preserving their geometric structures. Then, we design a counterfactual map generator to discover the stroke-level principal components for a specific category. Finally, based on the counterfactual feature maps, our model could explain the question of "why the sketch is classified as X" by providing positive and negative semantic explanation evidences. Experiments conducted on two public sketch benchmarks, Sketchy-COCO and TU-Berlin, demonstrate the effectiveness of our proposed model. Furthermore, our model could provide more discriminative and human understandable explanations compared with these existing works.
Cybersecurity-Related Behavior of Personnel in the Norwegian Industry

Information security policies are formalized rules and regulations that employees should follow to avoid unwanted cyber incidents. This paper reports on the findings of a survey among personnel employed in the Norwegian industrial sector. The survey measured how the respondents self-assess their risky behavior and cognitive awareness regarding the importance and likelihood of cyber security events. A modified version of the Behavioral Cognitive Internet Security Questionnaire was used as the survey instrument. The results indicate that the employees in the target group have a low level of risky behavior and a high level of cognitive awareness and that minimal discrepancy between how respondents self-assess and act in the simulation exists. The result should be of interest to practitioners in the field of cybersecurity since training is attributed as the main driver of the obtained results. Furthermore, strong indications exist that the selected literature and theory do not hold true for the Norwegian industry sector.
State of the Art in Telepresence (Part 2)

course Share on Opportunities for AI-Mediated 3D Telepresence Author: David Luebke VP Graphics Research VP Graphics ResearchView Profile Authors Info & Claims SIGGRAPH '23: ACM SIGGRAPH 2023 CoursesJuly 2023Article No.: 18Pages 1–220https://doi.org/10.1145/3587423.3595489Published:24 July 2023Publication History 0citation93DownloadsMetricsTotal Citations0Total Downloads93Last 12 Months93Last 6 weeks85 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Comparative Study of Wrapper Feature Selection Techniques in Software Fault Prediction

Software fault prediction aims to classify whether the module is defective or not-defective. In software systems, there are some software metrics may contain irrelevant or redundant information that leads to negative impact on the performance of the fault prediction model. Therefore, feature selection is an method that several studies have addressed to reduce computation time, improve prediction performance and provide a better understanding of data in machine learning. Additionally, the presence of imbalanced classes is one of the most challenge in software fault prediction. In this study, we examined the effectiveness of six different wrapper feature selection including Genetic Algorithm, Particle Swarm Optimization, Whale Optimization Algorithm, Cuckoo Search, Mayfly Algorithm and Binary Bat Algorithm for selecting the optimal subset of features. Then, we applied VanilaGAN to train the dataset with optimal features for handling the imbalanced problem. Subsequently, these generated training dataset and the testing dataset are fed to the machine learning techniques. Experimental validation has been done on five dataset collected from Promise repository and Precision, Recall, F1-score, and AUC are evaluation performance measurements.
SIGGRAPH 2023 Course on Diffusion Models

Diffusion models have been successfully used in various applications such as text-to-image generation, 3D assets generation, controllable image editing, video generation, natural language generation, audio synthesis, and motion generation. The rate of progress on diffusion models is astonishing. In the year 2022 alone, diffusion models have been applied to many large-scale text-to-image foundation models, such as DALL-E 2 [Ramesh et al. 2022], Imagen [Saharia et al. 2022], Stable Diffusion [Rombach et al. 2022], and eDiff-I [Balaji et al. 2022]; video generation models such as Imagen Video [Ho et al. 2022] and Make-a-video [Singer et al. 2022]; 3D asset generation models such as Magic3D [Lin et al. 2022] and DreamFusion [Poole et al. 2022]. This course covers the advances in diffusion models over the last few years and will be tailored to the computer graphics community. We will first cover the fundamental machine learning and deep learning techniques relevant to diffusion models. Next, we will present state-of-the-art techniques for the application of diffusion models to high-fidelity image synthesis, controllable image generation, compositional representation learning, and 3D asset generation. Finally, we will conclude with a discussion on the future application of this technology, societal impact and open research problems. After the course, the attendees will learn basic knowledge about diffusion models and how such models can be applied to different applications such as image generation, image editing, and 3D asset generation.
To the centenary of the birth of Oleg Ksenofontovich Skobelkin

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