Reviewerly/exHarmony huggingface dataset:
An adaptively multi-correlations aggregation network for skeleton-based motion recognition

Previous work based on Graph Convolutional Networks (GCNs) has shown promising performance in 3D skeleton-based motion recognition. We believe that the 3D skeleton-based motion recognition problem can be explained as a modeling task of dynamic skeleton-based graph construction. However, existing methods fail to model human poses with dynamic correlations between human joints, ignoring the information contained in the skeleton structure of the non-connected relationship during human motion modeling. In this paper, we propose an Adaptively Multi-correlations Aggregation Network(AMANet) to capture dynamic joint dependencies embedded in skeleton graphs, which includes three key modules: the Spatial Feature Extraction Module (SFEM), Temporal Feature Extraction Module (TFEM), and Spatio-Temporal Feature Extraction Module (STFEM). In addition, we deploy the relative coordinates of the joints of various parts of the human body via moving frames of Differential Geometry. On this basis, we design a Data Preprocessing Module (DP), enriching the characteristics of the original skeleton data. Extensive experiments are conducted on three public datasets(NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics-Skeleton 400), demonstrating our proposed method's effectiveness.
Lightning Network Economics: Channels

Compared with existing payment systems, Bitcoin’s throughput is low. Designed to address Bitcoin’s scalability challenge, the Lightning Network (LN) is a protocol allowing two parties to secure bitcoin payments and escrow holdings between them. In a lightning channel, each party commits collateral toward future payments to the counterparty and payments are cryptographically secured updates of collaterals. The network of channels increases transaction speed and reduces blockchain congestion. This paper (i) identifies conditions for two parties to optimally establish a channel, (ii) finds explicit formulas for channel costs, (iii) obtains the optimal collaterals and savings entailed, and (iv) derives the implied reduction in congestion of the blockchain. Unidirectional channels costs grow with the square-root of payment rates, while symmetric bidirectional channels with their cubic root. Asymmetric bidirectional channels are akin to unidirectional when payment rates are significantly different, otherwise to symmetric bidirectional. This paper was accepted by Joshua Gans, business strategy. Funding: Partially supported by SFI (16/IA/4443, 16/SPP/3347), Columbia-IBM Center for Blockchain and Data Transparency, Chaire Fintech at University Paris Dauphine—PSL, Algorand Foundation, and Simons Institute.
Improving School Disaster Preparedness Through Teacher, Cadre Training and Disaster Simulation for Students at Public Elementary School

Based on its geographical location, Public Elementary School (PES) 1 Banyuroto, Sawangan, Magelang, Central Java is located in an area prone to volcanic eruptions and earthquakes. Because in general disaster hazards can occur at any time, it is very important for the school environment to improve its preparedness in disaster preparedness. One of the efforts that are considered the most strategic is through training and disaster simulation for cadres, school teachers and students to improve their ability to respond to disasters through disaster preparedness schools. The purpose of this activity is to realize the implementation of disaster management training activities and disaster simulation at PES 1 Banyuroto. The method used is a direct instruction to increase partners' understanding related to disaster and disaster mitigation materials, Forum Discussion Groups to develop disaster learning modules and simulations to improve the skills of disaster response partners. This activity was carried out at PES 1 Banguroto, Sawangan, Magelang, Central Java. The output targets resulting from this activity are the formation of evacuation route maps and their explanations and the preparation of disaster learning modules that can be used in the learning process and implementation of earthquake disaster simulations at PES 1 Banyuroto, Magelang, Central Java. The development of a disaster preparedness school program will effectively contribute to the early prevention of disaster hazards.
A Framework for Developing Public Data-map Using Similarity between Meta-data and Graph: The Case of Public Data from Seoul

The South Korean government is actively working to make data available to the public. However, as data from different departments is integrated and made accessible, efficient search algorithms for big data have become a major issue. This paper proposes a framework for developing a public data-map that uses metadata similarity and graph concepts to suggest ways to visualize and search related data. Additionally, to improve the performance of measuring similarity, we develop the domain-specific data pre-processing for public data and incorporate the step into the framework. To validate the framework, an empirical study was conducted using the case of the Seoul Metropolitan Government’s Big Data Division. The results show that this framework can significantly improve the usability of public data and facilitate its open access.
“PROGRAM INDONESIA PINTAR” IMPLEMENTATION FOR UPPER SECONDARY EDUCATION IN MOJOKERTO

Education is one of the keys to success in effort to prepare quality resources for progress of the nation in the future. However, economic factor is one of the reasons for the high dropout rate in Indonesia. Therefore, the Government issued “Program Indonesia Pintar” Policy which aims to provide the widest opportunity for students from underpivilliged families to have access for education. The purpose of this study is to describe the inhibiting factors as well as the efforts made to overcome the problems of “Program Indonesia Pintar” implementation for the upper secondary education in Mojokerto. The results of this study indicate that the implementation of “Program Indonesia Pintar” at upper secondary level in Mojokerto encountered some obstacles seen from the policy implementation model of Van metter dan van Horn, namely the aspects of policy standards and objectives, policy sources, inter-organizational communication, characteristics of implementing agencies, socio-political economic conditios, executive dispositions. Then, the implementing agencies of “Program Indonesia Pintar” for upper secondary education in Mojokerto has sought various alternative solutions to deal with these obstacles.
Face Detection, Tracking, and Classification from Large-Scale News Archives for Analysis of Key Political Figures

Abstract Analyzing the appearances of political figures in large-scale news archives is increasingly important with the growing availability of large-scale news archives and developments in computer vision. We present a deep learning-based method combining face detection, tracking, and classification, which is particularly unique because it does not require any re-training when targeting new individuals. Users can feed only a few images of target individuals to reliably detect, track, and classify them. Extensive validation of prominent political figures in two news archives spanning 10 to 20 years, one containing three U.S. cable news and the other including two major Japanese news programs, consistently shows high performance and flexibility of the proposed method. The codes are made readily available to the public.
MOVIN: Real‐time Motion Capture using a Single LiDAR

Abstract Recent advancements in technology have brought forth new forms of interactive applications, such as the social metaverse, where end users interact with each other through their virtual avatars. In such applications, precise full‐body tracking is essential for an immersive experience and a sense of embodiment with the virtual avatar. However, current motion capture systems are not easily accessible to end users due to their high cost, the requirement for special skills to operate them, or the discomfort associated with wearable devices. In this paper, we present MOVIN, the data‐driven generative method for real‐time motion capture with global tracking, using a single LiDAR sensor. Our autoregressive conditional variational autoencoder (CVAE) model learns the distribution of pose variations conditioned on the given 3D point cloud from LiDAR. As a central factor for high‐accuracy motion capture, we propose a novel feature encoder to learn the correlation between the historical 3D point cloud data and global, local pose features, resulting in effective learning of the pose prior. Global pose features include root translation, rotation, and foot contacts, while local features comprise joint positions and rotations. Subsequently, a pose generator takes into account the sampled latent variable along with the features from the previous frame to generate a plausible current pose. Our framework accurately predicts the performer's 3D global information and local joint details while effectively considering temporally coherent movements across frames. We demonstrate the effectiveness of our architecture through quantitative and qualitative evaluations, comparing it against state‐of‐the‐art methods. Additionally, we implement a real‐time application to showcase our method in real‐world scenarios. MOVIN dataset is available at https://movin3d.github.io/movin_pg2023/https://movin3d.github.io/movin_pg2023/"&gt;https://movin3d.github.io/movin_pg2023/ .
Enhancing Low‐Light Images: A Variation‐based Retinex with Modified Bilateral Total Variation and Tensor Sparse Coding

Abstract Low‐light conditions often result in the presence of significant noise and artifacts in captured images, which can be further exacerbated during the image enhancement process, leading to a decrease in visual quality. This paper aims to present an effective low‐light image enhancement model based on the variation Retinex model that successfully suppresses noise and artifacts while preserving image details. To achieve this, we propose a modified Bilateral Total Variation to better smooth out fine textures in the illuminance component while maintaining weak structures. Additionally, tensor sparse coding is employed as a regularization term to remove noise and artifacts from the reflectance component. Experimental results on extensive and challenging datasets demonstrate the effectiveness of the proposed method, exhibiting superior or comparable performance compared to state‐of‐the‐art approaches. Code, dataset and experimental results are available at https://github.com/YangWeipengscut/BTRetinex .
Continuous Layout Editing of Single Images with Diffusion Models

Abstract Recent advancements in large‐scale text‐to‐image diffusion models have enabled many applications in image editing. However, none of these methods have been able to edit the layout of single existing images. To address this gap, we propose the first framework for layout editing of a single image while preserving its visual properties, thus allowing for continuous editing on a single image. Our approach is achieved through two key modules. First, to preserve the characteristics of multiple objects within an image, we disentangle the concepts of different objects and embed them into separate textual tokens using a novel method called masked textual inversion. Next, we propose a training‐free optimization method to perform layout control for a pre‐trained diffusion model, which allows us to regenerate images with learned concepts and align them with user‐specified layouts. As the first framework to edit the layout of existing images, we demonstrate that our method is effective and outperforms other baselines that were modified to support this task. Code is available at our project page.
Mapping the Soil Chemical Properties of the Sarigkiol Basin, Western Macedonia, Greece, in View of the Transition to the Post-Lignite Era

The chemical properties of soils in the Sarigkiol Basin, Western Macedonia, Greece, were studied to investigate the potential for new land uses in post-mining areas. An extensive dataset of chemical parameters was analyzed in a total of 34 representative soil samples from the Sarigkiol Basin. The data were processed using statistical methods (Spearman’s correlation coefficient (CC), factor analysis (FA), and hierarchical cluster analysis (HCA)) and thematic maps. Soil properties were assessed using different geoenvironmental indices (e.g., pollution load index (PLI), enrichment factor (EF), and geoaccumulation index (Igeo)). The results show that geogenic factors are crucial for soil chemical properties and that agricultural activities play an important role in soil contamination. The mapping of soil properties in such a mining area is of great interest for the next step of the transition to the post-lignite era and new land uses for sustainable rehabilitation.
Fine Back Surfaces Oriented Human Reconstruction for Single RGB‐D Images

Abstract Current single RGB‐D image based human surface reconstruction methods generally take both the RGB images and the captured frontal depth maps together so that the 3D cues from the frontal surfaces can help infer the full surface geometries. However, we observe that the back surfaces can often be quite different from the frontal surfaces and, therefore, current methods can mess the recovery process by adopting such 3D cues, especially for the unseen back surfaces. We need to do the back surface inference without the frontal depth map. Consequently, a novel human reconstruction framework is proposed, so that human models with fine geometric details, especially for the back surfaces, can be obtained. In this approach, a progressive estimation method is introduced to effectively recover the unseen back depth maps. The coarse back depth maps are recovered by the parametric models of the subjects, with the fine ones further obtained by the normal‐maps conditioned GAN. This framework also includes a cross‐attention based denoising method for the frontal depth maps. This method adopts the cross attention between the features of the last two layers encoded from the frontal depth maps and thus suppresses the noise for fine depth maps by the attentions of features from the low‐noise and globally‐structured highest layer. Experimental results show the efficacies of the proposed ideas.
Interactive Authoring of Terrain using Diffusion Models

Abstract Generating heightfield terrains is a necessary precursor to the depiction of computer‐generated natural scenes in a variety of applications. Authoring such terrains is made challenging by the need for interactive feedback, effective user control, and perceptually realistic output encompassing a range of landforms. We address these challenges by developing a terrain‐authoring framework underpinned by an adaptation of diffusion models for conditional image synthesis, trained on real‐world elevation data. This framework supports automated cleaning of the training set; authoring control through style selection and feature sketches; the ability to import and freely edit pre‐existing terrains, and resolution amplification up to the limits of the source data. Our framework improves on previous machine‐learning approaches by: expanding landform variety beyond mountainous terrain to encompass cliffs, canyons, and plains; providing a better balance between terseness and specificity in user control, and improving the fidelity of global terrain structure and perceptual realism. This is demonstrated through drainage simulations and a user study testing the perceived realism for different classes of terrain. The full source code, blender add‐on, and pre‐trained models are available.
Controllable Garment Image Synthesis Integrated with Frequency Domain Features

Abstract Using sketches and textures to synthesize garment images is able to conveniently display the realistic visual effect in the design phase, which greatly increases the efficiency of fashion design. Existing garment image synthesis methods from a sketch and a texture tend to fail in working on complex textures, especially those with periodic patterns. We propose a controllable garment image synthesis framework that takes as inputs an outline sketch and a texture patch and generates garment images with complicated and diverse texture patterns. To improve the performance of global texture expansion, we exploit the frequency domain features in the generative process, which are from a Fast Fourier Transform (FFT) and able to represent the periodic information of the patterns. We also introduce a perceptual loss in the frequency domain to measure the similarity of two texture pattern patches in terms of their intrinsic periodicity and regularity. Comparisons with existing approaches and sufficient ablation studies demonstrate the effectiveness of our method that is capable of synthesizing impressive garment images with diverse texture patterns while guaranteeing proper texture expansion and pattern consistency.
Graphene Oxide: A Key Solution for Future: Recent Achievements as A New Adsorbent for Water Treatment Applications: Review

Polluted water has been considered a critical issue nowadays, threatening the environment and lives of living creatures. Because of technological and industrial advancements, as well as increased social activities of humans in various countries, pollution sources have multiplied. To reduce the impact of this problem, many techniques have been developed in order to reach zero discharge pollution. In the last decade, graphene oxide (GO) - a member of the graphene nanomaterials family, has been the focus of many research efforts in the water treatment sector because of its extraordinary properties. This review highlights the research efforts conducted to investigate GO as a novel adsorbent for water treatment applications and recent fulfilments in the last 3 years. The synthesis techniques, properties, and efficiency of this material in water treatment will be explained. All results confirm the future role of GO as an efficient absorbent to solve wastewater purification challenges, but the big challenge is to reduce time and simplify the complicated extent of synthesis stages, besides reducing the high cost of production methods. According to the review, Iraqi researchers' efforts to use this nanomaterial in water purification are still in their early stages.
A Smishing Detection Method Based on SMS Contents Analysis and URL Inspection Using Google Engine and VirusTotal

Smishing is the delivery of phishing content to mobile users via a short message service (SMS). SMS allows cybercriminals to reach out to mobile end users in a new way, attempting to deliver phishing messages, mobile malware, and online scams that appear to be from a trusted brand. This paper proposes a new method for detecting smishing by combining two detection methods. The first method is uniform resource locators (URL) analysis, which employs a novel combination of the Google engine and VirusTotal. The second method involves examining SMS content to extract efficient features and classify messages as ham or smishing based on keywords contained within them using four well-known classifiers: support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The best results of the proposed method were 98.5%, 96.9%, 93.1%, and 95.05% in terms of accuracy, precision, detection rate, and F1-score, respectively. Furthermore, the evaluation results of the proposed method outperformed the state-of-the-art and showed that the proposed method is effective in detecting smishing messages.
Use of service robots in hospitality: An observational study in terms of technology acceptance model

This study aims to explore hospitality customers’ behavioral intentions and reactions toward service robots from the perspective of the Technology Acceptance Model. Kastamonu University Scientific Research Projects Coordination Department has supported this research. In this context, the service robot was leased. The service robot has been shown to customers in the lobbies, restaurants, rest areas, and cafes of accommodation establishments. Customers were enabled to interact with the robot. The participant observation method was adopted in the study. Unstructured interviews were conducted. The data divided into categories from the perspective of the Technology Acceptance Model were processed using the MAXQDA analysis tool. The density map was drawn from the “Document Portfolio.” The data were obtained as a result of participant observation. It is divided into two categories. These; are “Behavioral Responses” and “Cognitive and Emotional Responses.” The most prominent themes within the categories are video/photo taking, speaking, touching (behavioral), excitement, liking, and having fun (cognitive and emotional). The user rate of these reactions is 7/10 on average. It was observed that the participants enjoyed, were satisfied, and wanted to interact with the service robot. These findings reveal that behavioral intentions related to technology acceptance may occur against service robots.
Noise reduction and characteristic analysis of fluid signal in the jet impact‐negative pressure deamination reactor based on wavelet transform

Abstract The jet impact‐negative pressure deamination reactor (JI‐NPDR) is a new type of continuous and efficient deamination equipment. The study of the random flow pattern of porous jet impingement in the reactor under negative pressure conditions is an important issue. In this work, the signal processing method based on wavelet transform is used to analyze the characteristics of random flow signals in the reactor. Meanwhile, an analog similar signal is built and three sets of Gaussian white noise with various signal‐to‐noise ratios are employed via the MATLAB platform. Based on the adjustment of threshold function, threshold, decomposition level and other parameters of wavelet transform, the noise ratio (SNR) and mean squared error (MSE) are used to evaluate the wavelet denoising effect. And then, the optimal denoising scheme for the obtained signal will be applied in processing the vacuum flow signal collected inside the deamination reactor. Subsequently, the 8‐layer wavelet decomposition is investigated by using sym7 as the wavelet basis, soft threshold function, and heursure threshold for signal denoising. Then, the analog signal is fed back through the results of the actual signal denoising, and the number of wavelet decomposition layers is adjusted from 8 to 9 layers to optimize the original wavelet denoising combination. By analyzing the low‐frequency and high‐frequency parts of the signal spectrum before and after denoising, it was found that wavelet transform can effectively denoise the fluid signal in the reactor.