Reviewerly/exHarmony huggingface dataset:
INTEGRATION OF GEOSPATIAL AND CITIZEN PARTICIPATION USING GEOGRAPHIC INFORMATION SYSTEM FOR SMART CITY: A STUDY OF PRIORITY VILLAGES PROGRAM IN JAKARTA, INDONESIA

Abstract. Jakarta, the capital city of Indonesia, is the city with the largest population in Indonesia with a population of 10,609,681. In line with the rapid growth and the increment of the population in Jakarta lead to the increment in the number of buildings in Jakarta. However, the building increment in Jakarta leads to the emergence of buildings ownership problems. Meanwhile, the increasing urbanization promotes the need for a system that accommodates the smart city concepts which require complex interactions between governments and citizens. Therefore, the Jakarta Provincial Government established a breakthrough program called Priority Villages, a program that integrates geospatial and citizen participation using the Geographic Information System (GIS) for the smart city planning model. This program focused on the distribution acceleration of building establishment decision letters. This research aims to visualize the concept and advantages of the Priority Village program as an analysis-oriented application for the development of intelligent city concepts. The outputs of the Priority Villages program are the issuances of 112 building establishment decision letters per region and 7,534 building establishment decision letters per individual building. The integration of geospatial and citizen participation using Geographic Information System (GIS) in Priority Villages program strongly supports the sustainability of the Jakarta Smart City program, shortening the Estimated Time of Arrival (ETA) of the building establishment decision letter making from 16 days to 7 days and give the positive impact on the broader citizen in Jakarta.
Nursing XR – a VR application to teach decision making to student nurses

This paper validates an approach to the design and development of VR applications that are integrated into the curricula and address fundamental student needs. To accomplish this, a case study describing the process undertaken to create Nursing XR, a wound dressing scenario where the patient is discharged home and requires follow up care and treatment by a nurse. The aim of the VR application is to support nursing students in developing their communication, risk assessment, holistic assessment, and person-centred clinical decision-making skills.&#x0D; To design Nursing XR, needs and initial requirements were collected via a workshop with student nurses. The workshop, which involved 10 student nurses and two lecturers in nursing from two Universities (Co-Is) and was led by the PI, supported by the learning technologist and the head developer of the company used for development of the software. Results from the workshop identified two major needs for the students: the need to undertake practical applications of the procedures learned in the lectures and the need to build confidence in the skills required of a nursing student. These needs were the foundations for the design process, which followed an artefact-based approach. The artefacts generated during the design were also used to elicit additional interaction and software requirements from the nursing lecturers. An iterative lean development process was followed by the company for the software implementation. Throughout the development, students and lecturers were involved as user testes ensuring that the user experience of the application was satisfactory, and the application fit for purpose.In this paper, we describe the high-level design and development process followed by the multidisciplinary team to develop Nursing XR and report initial qualitative findings from the workshop focus group.
KAJIAN SOSIOLINGUISTIK MASYARAKAT TRANSMIGRAN DI RIAU DAN RELEVANSINYA TERHADAP PEMBELAJARAN KETERAMPILAN BERBICARA BAHASA ARAB

This Research explains about Used of language in Sari galuh Village Society. Researcher uses descriptive qualitative research with observation method, interview method, and documentation method.&#x0D; Results of this reaserch are firts, There is Multilanguage. Because, a lot of people came in Sari galuh Village for Transmigration programe. They were from Medan, Java, Bukittinggi, and the original people in Riau. The multilanguage process is caused transmigration programe or migration in Sociolinguistic. The second result shows that Javanese and bahasa are most dominant languages in Sari Galuh village. Third, there is incfluences environment for language process in Sari Galuh Village. The succesfully in speaking learning skills can be approved with making arabic domination dan arabic environment in the class.&#x0D; Keywords: Sociolinguistic, Language Domination, Speaking Skills
New Archaeological Discoveries along the Mole, Desoi and Baran Nian/ Rain Water Nalla District Jamshoro Sindh, Pakistan

The area on the western edge of the district is culturally rich and it is investigated by various historians and archaeologists for a long time. The current study focusses to analyze the effects of Darawat Dam construction on the archeological sites due to the merging of the Mole, Desai, and Baran Nian. The researcher conducted a field survey and used the Global Positioning System (GPS) to unexpose the archeologist sites around the dam and discover fresh archeological sites also. The cultural material found on the surface was analyzed to find out the accurate date and time depth of the sites. During the survey and analysis of the material, it was found that 27 archaeological sites have been badly affected due to the construction of the dam and some of these will be extinct. The study suggests the local authority or Government organizations protect these archeological sites scientifically.
Few-shot learning for modeling cyber physical systems in non-stationary environments

Abstract This paper proposes a modeling scheme for cyber physical systems operating in non-stationary, small data environments. Unlike the traditional modeling logic, we introduce the few-shot learning paradigm, the operation of which is based on quantifying both similarities and dissimilarities. As such, we designed a suitable change detection mechanism able to reveal previously unknown operational states, which are incorporated in the dictionary online. We elaborate on spectrograms extracted from high-resolution ultrasound depth sensor timeseries, while the backbone of the proposed method is a Siamese Neural Network. The experimental scenario considers data representing liquid containers for fuel/water when the following five operational states are present: normal , accident , breakdown , sabotage , and cyber-attack . Thorough experiments were carried out assessing every aspect of the present framework and demonstrating its efficacy even when very few samples per class are available. In addition, we propose a probabilistic data selection scheme facilitating one-shot learning. Last but not least, responding to the wide requirement for interpretable AI, we explain the obtained predictions by examining the layer-wise activation maps.
Impact of Calibrating Public Accountability (CPA) Model and Utilization of Quality of Management Accounting Information Systems on School Organizational Performance: Survey on Private Schools in Cimahi City

Education is important for the progress and prosperity of the nation and state. Through education, competent and reliable human resources will be born. Minister of Education and Culture Research and Technology Nadiem Makarim in November 2019 mentioned the importance of character education in the midst of the large role of technology today. Character education will be the foundation for the nation's successors to survive and thrive in the current era of digitalization. Continuing education must be supported by safe conditions, situations and conditions. But now, especially in the past few years, the pandemic has had another effect on the world of education. The effect of this pandemic also has an impact on the achievement of performance in schools, especially schools with private status. This decline in the performance of private schools can be caused by accountability in presenting their financial statements and the use of accounting information systems. The purpose of this study was to find out how the description of accountability in private schools using accountability measurements based on Calibrating Public Accountability, description of the use of management accounting information systems, descriptions of school organizational performance and how accountability and utilization of management accounting information systems influence school organizational performance, either partially or partially. simultaneous. Based on the results of the study, it shows that the accountability variable is in very good condition. The variable utilization of accounting information systems in private schools in Cimahi City is in good condition.
Manipulated Faces Detection with Adaptive Filter

With the progress of face manipulation techniques, synthesized faces are spreading on the Internet, which raises concerns about potential threats. To prevent these techniques’ abuse, various detection algorithms have been proposed. In this paper, we consider the image’s frequency information, then propose an adaptive filtering algorithm named spatial and adaptive filtering (SAF) Network. SAF is a dual-stream network that considers spatial and frequency domains. In the frequency domain, wavelet transform is used to divide the image into different frequency bands, then an adaptive filter is introduced, which aims to capture more decisive information by giving different weights to different frequencies. To fuse spatial and frequency features, spatial pyramid pooling fusion (SPPF) is proposed, which solves the mismatch of feature maps, and considers the relationship between different patches by attention mechanism. Experiment results show that the performance of SAF is better than the comparison algorithm.
Gradient Boosting Performs Gaussian Process Inference

This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Ridge Regression problem. Thus, we obtain the convergence to a Gaussian Process' posterior mean, which, in turn, allows us to easily transform gradient boosting into a sampler from the posterior to provide better knowledge uncertainty estimates through Monte-Carlo estimation of the posterior variance. We show that the proposed sampler allows for better knowledge uncertainty estimates leading to improved out-of-domain detection.
Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features

Hateful memes are a growing menace on social media. While the image and its corresponding text in a meme are related, they do not necessarily convey the same meaning when viewed individually. Hence, detecting hateful memes requires careful consideration of both visual and textual information. Multimodal pre-training can be beneficial for this task because it effectively captures the relationship between the image and the text by representing them in a similar feature space. Furthermore, it is essential to model the interactions between the image and text features through intermediate fusion. Most existing methods either employ multimodal pre-training or intermediate fusion, but not both. In this work, we propose the Hate-CLIPper architecture, which explicitly models the cross-modal interactions between the image and text representations obtained using Contrastive Language-Image Pre-training (CLIP) encoders via a feature interaction matrix (FIM). A simple classifier based on the FIM representation is able to achieve state-of-the-art performance on the Hateful Memes Challenge (HMC) dataset with an AUROC of 85.8, which even surpasses the human performance of 82.65. Experiments on other meme datasets such as Propaganda Memes and TamilMemes also demonstrate the generalizability of the proposed approach. Finally, we analyze the interpretability of the FIM representation and show that cross-modal interactions can indeed facilitate the learning of meaningful concepts. The code for this work is available at https://github.com/gokulkarthik/hateclipper.
Pre-trained convolution neural networks models for content-based medical image retrieval

Content-based image retrieval (CBIR) is a recent method used to retrieve different types of images from repositories. The traditional content-based medical image retrieval (CBMIR) methods commonly used low-level image representation features extracted from color, texture, and shape image descriptors. Since most of these CBMIR systems depend mainly on the extracted features, the methods used in the feature extraction phase are more important. Features extraction methods, which generate inaccurate features, lead to very poor performance retrieval because of semantic gap widening. Hence, there is high demand for independent domain knowledge features extraction methods, which have automatic learning capabilities from input images. Pre-trained deep convolution neural networks (CNNs), the recent generation of deep learning neural networks, could be used to extract expressive and accurate features. The main advantage of these pre-trained CNNs models is the pre-training process for huge image data of thousands of different classes, and their knowledge after the training process could easily be transferred. There are many successful models of pre-trained CNNs models used in the area of medical image retrieval, image classification, and object recognition. This study utilizes two of the most known pre-trained CNNs models; ResNet18 and SqueezeNet for the offline feature extraction stage. Additionally, the highly accurate features extracted from medical images are used for the CBMIR method of medical image retrieval. This study uses two popular medical image datasets; Kvasir and PH2 to show that the proposed methods have good retrieval results. The retrieval performance evaluation measures of our proposed method have average precision of 97.75% and 83.33% for Kvasir and PH2 medical images respectively, and outperform some of the state-of-the-art methods in this field of study because these pre-trained CNNs have well trained layers among a huge number of image types. Finally, intensive statistical analysis shows that the proposed ResNet18-based retrieval method has the best performance for enhancing both recall and precision measures for both medical images.
Geospatial Assessment and Mapping of Suitable Sites for a Utility-scale Solar PV Farm

Geospatial and multi-criteria decision-making techniques are applied to process and analyse datasets for determining suitable areas for multiple utilityscale solar photovoltaic farms in the city of Akure, Ondo State, southwestern Nigeria. Data processed include local electric power distribution system data, Shuttle Radar Topographic Mission elevation data, Landsat 8 and solar global horizontal irradiance. Multi-criteria decision-making techniques adopted are the analytical hierarchy process, reclassification, and overlay. These techniques were carried out considering criteria for siting solar photovoltaic farms. Criteria considered in this study are climate, topography, economic, environmental impact operational and technical while sub-criteria are solar global horizontal irradiance, slope, proximity and land cover. The outcome of the study shows that the study area covering a total extent of ~33,200 ha, 15%, 8%, 13% and 64% are highly suitable, suitable, moderately suitable, and unsuitable respectively for siting utility-scale solar photovoltaic farms within the study area. The study reveals the potential of multiple utility-scale solar photovoltaic farms in the study area. However, the proportions of areas suitable for solar photovoltaic farms are quite lower compared to findings from similar studies conducted in northwestern Nigeria. The study recommends solar photovoltaic sources as an alternative energy source in and around the study area.
Leveraging the Graph Structure of Neural Network Training Dynamics

Understanding the training dynamics of deep neural networks (DNNs) is important as it can lead to improved training efficiency and task performance. Recent works have demonstrated that representing the wirings of neurons in feedforward DNNs as graphs is an effective strategy for understanding how architectural choices can affect performance. However, these approaches fail to model training dynamics since a single, static graph cannot capture how DNNs change over the course of training. Thus, in this work, we propose a compact, expressive temporal graph framework that effectively captures the dynamics of many workhorse architectures in computer vision. Specifically, our framework extracts an informative summary of graph properties (e.g., degree, eigenvector centrality) over a sequence of DNN graphs obtained during training. We demonstrate that the proposed framework captures useful dynamics by accurately predicting trained, task performance when using a summary over early training epochs (<5) across four different architectures and two image datasets. Moreover, by using a novel, highly-scalable DNN graph representation, we further demonstrate that the proposed framework captures generalizable dynamics as summaries extracted from smaller-width networks are effective when evaluated on larger widths.
Long-tail Mixup for Extreme Multi-label Classification

Extreme multi-label classification (XMC) aims at finding multiple relevant labels for a given sample from a huge label set at the industrial scale. The XMC problem inherently poses two challenges: scalability and label sparsity - the number of labels is too large, and labels follow the long-tail distribution. To resolve these problems, we propose a novel Mixup-based augmentation method for long-tail labels, called TailMix. Building upon the partition-based model, TailMix utilizes the context vectors generated from the label attention layer. It first selectively chooses two context vectors using the inverse propensity score of labels and the label proximity graph representing the co-occurrence of labels. Using two context vectors, it augments new samples with the long-tail label to improve the accuracy of long-tail labels. Despite its simplicity, experimental results show that TailMix consistently outperforms other augmentation methods on three benchmark datasets, especially for long-tail labels in terms of two metrics, [email protected] and [email protected]
A Hyperbolic-to-Hyperbolic User Representation with Multi-aspect for Social Recommendation

Social recommender systems play a key role in solving the problem of information overload. In order to better extract latent hierarchical property in the data, they usually explore the user-user connections and user-item interactions in hyperbolic space. Existing methods resort tangent spaces to realize some operations (e.g., matrix multiplication) on hyperbolic manifolds. However, frequently projecting between the hyperbolic space and the tangent space will destroy the global structure of the manifold and reduce the accuracy of predictions. Besides, decisions made by users are often influenced by multi-aspect potential preferences, which are usually represented as a vector for each user. To this end, we design a novel hyperbolic-to-hyperbolic user representation with multi-aspect social recommender system, namely H2HMSR, which directly works in hyperbolic space. Extensive experiments on three public datasets demonstrate that our model can adequately extract social information of users with multi-aspect preferences and outperforms hyperbolic and Euclidean counterparts.
Federated Data Preparation, Learning, and Debugging in Apache SystemDS

Federated learning allows training machine learning (ML) models without central consolidation of the raw data. Variants of such federated learning systems enable privacy-preserving ML, and address data ownership and/or sharing constraints. However, existing work mostly adopt data-parallel parameter-server architectures for mini-batch training, require manual construction of federated runtime plans, and largely ignore the broad variety of data preparation, ML algorithms, and model debugging. Over the last years, we extended Apache SystemDS by an additional federated runtime backend for federated linear-algebra programs, federated parameter servers, and federated data preparation. In this paper, we share the system-level compiler and runtime integration, new features such as multi-tenant federated learning, selected federated primitives, multi-key homomorphic encryption, and our monitoring infrastructure. Our demonstrator showcases how composite ML pipelines can be compiled into federated runtime plans with low overhead.
Scalable Graph Representation Learning via Locality-Sensitive Hashing

A massive amount of research on graph representation learning has been carried out to learn dense features as graph embedding for information networks, thereby capturing the semantics in complex networks and benefiting a variety of downstream tasks. Most of the existing studies focus on structural properties, such as distances and neighborhood proximity between nodes. However, real-world information networks are dominated by the low-degree nodes because they are not only sparse but also subject to the Power law form. Due to the sparsity, proximity-based methods are incapable of deriving satisfactory representations for these tail nodes. To address this challenge, we propose a novel approach, Content-Preserving Locality-Sensitive Hashing~(CP-LSH), by incorporating the content information for representation learning. Specifically, we aim at preserving LSH-based content similarity between nodes to leverage the knowledge from popular nodes to long-tail nodes. We also propose a novel hashing trick to reduce the redundant space consumption so that CP-LSH is capable of tackling industry-scale data. Extensive offline experiments have been conducted on three large-scale public datasets. We also deploy CP-LSH to real-world recommendation systems in one of the largest e-commerce platforms for online experiments. Experimental results demonstrate that CP-LSH outperforms competitive baseline methods in node classification and link prediction tasks. Besides, the results of online experiments also indicate that CP-LSH is practical and robust for real-world production systems.
Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification

Graph Neural Networks (GNNs) have proven to be powerful in many graph-based applications. However, they fail to generalize well under heterophilic setups, where neighbor nodes have different labels. To address this challenge, we employ a confidence ratio as a hyper-parameter, assuming that some of the edges are disassortative (heterophilic). Here, we propose a two-phased algorithm. Firstly, we determine edge coefficients through subgraph matching using a supplementary module. Then, we apply GNNs with a modified label propagation mechanism to utilize the edge coefficients effectively. Specifically, our supplementary module identifies a certain proportion of task-irrelevant edges based on a given confidence ratio. Using the remaining edges, we employ the widely used optimal transport to measure the similarity between two nodes with their subgraphs. Finally, using the coefficients as supplementary information on GNNs, we improve the label propagation mechanism which can prevent two nodes with smaller weights from being closer. The experiments on benchmark datasets show that our model alleviates over-smoothing and improves performance.