Reviewerly/exHarmony huggingface dataset:
Reshaping the full body illusion through visuo-electro-tactile sensations

The physical boundaries of our body do not define what we perceive as self. This malleable representation arises from the neural integration of sensory information coming from the environment. Manipulating the visual and haptic cues produces changes in body perception, inducing the Full Body Illusion (FBI), a vastly used approach to exploring humans' perception. After pioneering FBI demonstrations, issues arose regarding its setup, using experimenter-based touch and pre-recorded videos. Moreover, its outcome measures are based mainly on subjective reports, leading to biased results, or on heterogeneous objective ones giving poor consensus on their validity. To address these limitations, we developed and tested a multisensory platform allowing highly controlled experimental conditions, thanks to the leveraged use of innovative technologies: Virtual Reality (VR) and Transcutaneous Electrical Nerve Stimulation (TENS). This enabled a high spatial and temporal precision of the visual and haptic cues, efficiently eliciting FBI. While it matched the classic approach in subjective measures, our setup resulted also in significant results for all objective measurements. Importantly, FBI was elicited when all 4 limbs were multimodally stimulated but also in a single limb condition. Our results behoove the adoption of a comprehensive set of measures, introducing a new neuroscientific platform to investigate body representations.
HyMap imagery for copper and manganese prospecting in the east of Ameln valley shear zone (Kerdous inlier, western Anti-Atlas, Morocco)

Eastern Kerdous demonstrates a polymetallic hydrothermal activity due its association with a paleorelief area. This study focuses on generating a Mineral Potential Map based on Fuzzy Logic Modelling and HyMap-Based input layers. The Relative Absorption Band Depth, Directed Principal Component Analysis, Line Density, and Mixture Tuned Matched Filtering techniques were implemented to perform 20 thematic layers. As a result, three new prospective zones were identified. Fieldwork, petrography, and XRD analysis verified the remote sensing results. Finally, this investigation highlights the significant potential of HyMap imagery for mineral exploration in inaccessible and remote parts of the western Anti-Atlas belt.
Kernel-based Substructure Exploration for Next POI Recommendation

Point-of-Interest (POI) recommendation, which benefits from the proliferation of GPS-enabled devices and location-based social networks (LBSNs), plays an increasingly important role in recommender systems. It aims to provide users with the convenience to discover their interested places to visit based on previous visits and current status. Most existing methods usually merely leverage recurrent neural networks (RNNs) to explore sequential influences for recommendation. Despite the effectiveness, these methods not only neglect topological geographical influences among POIs, but also fail to model high-order sequential substructures. To tackle the above issues, we propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences in a collaborative way. KBGNN consists of a geographical module and a sequential module. On the one hand, we construct a geographical graph and leverage a message passing neural network to capture the topological geographical influences. On the other hand, we explore high-order sequential substructures in the user-aware sequential graph using a graph kernel neural network to capture user preferences. Finally, a consistency learning framework is introduced to jointly incorporate geographical and sequential information extracted from two separate graphs. In this way, the two modules effectively exchange knowledge to mutually enhance each other. Extensive experiments conducted on two real-world LBSN datasets demonstrate the superior performance of our proposed method over the state-of-the-arts. Our codes are available at https://github.com/ ang6ang/KBGNN.
Mobile money, ICT, financial inclusion and growth: How different is Africa?

We investigate the contributions of fixed and mobile telecommunications (ICT) and mobile money to economic growth and financial inclusion in a 22-year panel of 146 countries. We extend the Solow growth model to include human capital, money, ICT, and mobile money, splitting the sample into sub-Saharan Africa (SSA) and the rest of the world (RoW) in addition to the whole sample analysis. We find mobile money affects economic growth through direct and indirect channels. Mobile money has a significant overall positive impact on growth, especially in countries with better mobile phone penetration and more dispersed populations. But its total quantitative effect is not large. Mobile money also tends to improve financial inclusion which in turn promotes growth. There are important differences between the SSA and RoW parameters, implying that the quantitative determinants of growth are different to some extent as between SSA and RoW.
Reversible data hiding based on prediction-error value ordering and multiple-embedding

Prediction-error expansion (PEE) is the most widely investigated reversible data hiding (RDH) framework. However, the performance of PEE can be further improved since the image redundancy has not fully exploited yet. To enlarge the embedding capacity and optimize the embedding performance of RDH, in this paper, a novel high-fidelity reversible embedding scheme using prediction-error value ordering and multiple embedding is proposed. Incorporating with PEE, the inter-correlation of prediction-errors is exploited by ordering the prediction-errors values, and multiple to-be-modified prediction-errors within each image block are adaptive determined. Specifically, firstly, a newly defined prediction-error is computed by considering the difference between the top two maximum (minimum) prediction-errors within each image block. Then, these prediction-errors are modified for expansion embedding. Moreover, a multiple-embedding mechanism is introduced to adaptive determine the number of modified prediction-errors. Finally, extensive experiments are conducted, and it is experimentally verified that the proposed method is superior to than some state-of-the-art RDH works. A benchmark result is that, for the standard Lena image, a PSNR as high as 62.01 dB can be derived for an embedding capacity of 10,000 bits.
Real Estate App Development Based on AI/VR Technologies

This paper deals with an investigation centered on developing a real estate app on the basis of Artificial Intelligence and Virtual Reality technologies. The study explores the advantages and disadvantages of using Artificial Intelligence and Virtual Reality technologies in real estate. The main focus of the study was on AI/VR applications that have importance for the real estate industry. This paper explains how AI and VR technologies can benefit the real estate market. VR and AI technologies have had a long history in the academic world since the middle of the last century, but not at the same level, due to the lack of large amounts of data and computational power required for both technologies. In recent years, the expansion of IT technologies has helped to remove the technical obstacles, which is why the interest in VR and AI technologies has acutely increased in society and the public over the past several years. Not only the research and abstract ideas of the virtual world but also the feasibility of companies from different industries are becoming more and more relevant. In particular, when it comes to virtual reality, the focus is on 360° images. With special cameras, the entire environment can be captured in a three-dimensional space and then cut together in such a way that the viewer can actually look around in this room and monitor events from his perspective. This opens the possibility of presenting different content in a completely new way. Technical shortcomings currently hamper the feeling of true immersion in virtual worlds. A detailed literature review provides the necessary theoretical basis for artificial intelligence and virtual reality with a particular emphasis on its use in the real estate industry.
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs

Graph-based semi-supervised node classification (GraphSSC) has wide applications, ranging from networking and security to data mining and machine learning, etc. However, existing centralized GraphSSC methods are impractical to solve many real-world graph-based problems, as collecting the entire graph and labeling a reasonable number of labels is time-consuming and costly, and data privacy may be also violated. Federated learning (FL) is an emerging learning paradigm that enables collaborative learning among multiple clients, which can mitigate the issue of label scarcity and protect data privacy as well. Therefore, performing GraphSSC under the FL setting is a promising solution to solve real-world graph-based problems. However, existing FL methods 1) perform poorly when data across clients are non independent identically distributed (nonIID), 2) cannot handle data with new label domains, and 3) cannot leverage unlabeled data, while all these issues naturally happen in real-world graph-based problems. To address the above issues, we propose the first FL framework, namely GraphFL, for semi-supervised node classification on graphs. Our framework is motivated by meta-learning methods. Specifically, we propose two GraphFL methods to respectively address the non-IID issue in graph data and handle the tasks with new label domains. Furthermore, we design a self-training method to leverage unlabeled graph data. We adopt representative graph neural networks as GraphSSC methods and evaluate GraphFL on multiple graph datasets. Experimental results on various benchmark datasets demonstrate that GraphFL significantly outperforms the compared FL baseline, GraphFL can handle data with new label domains, and GraphFL with selftraining can obtain better performance. Source code is available at https://github.com/binghuiivang/GraphFL.
Listless Block Cube Tree Coding for Low Resource Hyperspectral Image Compression Sensors

Hyperspectral (HS) image has rich spectral information content, which facilitates multiple applications including remote sensing. Due to the big data size of the HS image, compression is a required process for the efficiency of image storage and transmission. However, the complexity of the compression algorithms turns real-time compression into a very challenging task. A novel listless set partitioned hyperspectral image compression algorithm is proposed. The proposed compression algorithm uses zero block cube tree structure to exploit the inter and intra sub-band correlation to achieve the compression. From the result, it has been clear that the proposed compression algorithm has low coding complexity with at-par coding efficiency. Thus, it can be a suitable contender for low-resource hyperspectral image sensors.
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation

Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due to the discrete and high-dimensional nature of graphs, as well as permutation invariance to node orderings in underlying graph distributions. Current leading autoregressive models fail to capture the permutation invariance nature of graphs for the reliance on generation ordering and have high time complexity. Here, we propose a continuous-time generative diffusion process for permutation invariant graph generation to mitigate these issues. Specifically, we first construct a forward diffusion process defined by a stochastic differential equation (SDE), which smoothly converts graphs within the complex distribution to random graphs that follow a known edge probability. Solving the corresponding reverse-time SDE, graphs can be generated from newly sampled random graphs. To facilitate the reverse-time SDE, we newly design a position-enhanced graph score network, capturing the evolving structure and position information from perturbed graphs for permutation equivariant score estimation. Under the evaluation of comprehensive metrics, our proposed generative diffusion process achieves competitive performance in graph distribution learning. Experimental results also show that GraphGDP can generate high-quality graphs in only 24 function evaluations, much faster than previous autoregressive models.
THOR: Self-Supervised Temporal Knowledge Graph Embedding via Three-Tower Graph Convolutional Networks

The goal of temporal knowledge graph embedding (TKGE) is to represent the entities and relations in a given temporal knowledge graph (TKG) as low-dimensional vectors (i.e., embeddings), which preserve both semantic information and temporal dynamics of the factual information. In this paper, we posit that the intrinsic difficulty of existing TKGE methods lies in the lack of information in KG snapshots with timestamps, each of which contains the facts that co-occur at a specific timestamp. To address this challenge, we propose a novel self-supervised TKGE approach, THOR (Three-tower grapH cOnvolution netwoRks (GCNs)), which extracts latent knowledge from TKGs by jointly leveraging both temporal and atemporal dependencies between entities and the structural dependency between relations. THOR learns the embeddings of entities and relations Our experiments on three real-world datasets demonstrate that THOR significantly outperforms 13 competitors in terms of TKG completion tasks. The codebase of THOR is available at https://github.com/EJHyun/THOR.
Trustworthy Fault Diagnosis With Uncertainty Estimation Through Evidential Convolutional Neural Networks

Deep neural networks (DNNs) have been widely used for intelligent fault diagnosis under the closed-world assumption that any testing data are within classes of the training data. However, in reality, out-of-distribution (OOD) cases, such as new fault conditions, can happen after the original trained model is deployed. Most of the current DNNs are deterministic, which can misclassify with high confidence in the open-world scenario. This overconfident behavior would not guarantee the reliability and robustness of fault diagnosis results in practice. Therefore, trustworthy intelligent fault diagnosis with uncertainty estimation is crucial for real applications. In this article, we develop a novel convolutional neural network integrating evidence theory to achieve fault classifications with prediction uncertainty estimation. The estimated prediction uncertainty can identify potential OOD samples. This approach allows a minimal modification of the state-of-the-art DNN model by using a risk-calibrated evidential loss function and Dirichlet distribution that replaces the classification probabilities. The experimental results show that the proposed approach can not only achieve accurate classification of known classes but also detect unknown classes effectively. The proposed method shows significant potential in detecting OOD patterns and provides trustworthy fault diagnosis in open and nonstationary environments.
Genetic brake-net: Deep learning based brake light detection for collision avoidance using genetic algorithm

Automobiles are the primary means of transportation and increased traffic leads to the emphasis on techniques for safe transportation. Vehicle brake light detection is essential to avoid collisions among vehicles. Even though motorcycles are a common mode of transportation in many developing countries, little research has been done on motorcycle brake light detection. The effectiveness of Deep Neural Network (DNN) models has led to their adoption in different domains. The efficiency of the manually designed DNN architecture is dependent on the expert's insight on optimality, which may not lead to an optimal model. Recently, Neural Architecture Search (NAS) has emerged as a method for automatically generating a task-specific backbone for object detection and classification tasks. In this work, we propose a genetic algorithm based NAS approach to construct a Mask R-CNN based object detection model. We designed the search space to include the architecture of the backbone in Mask R-CNN along with attributes used in training the object detection model. Genetic algorithm is used to explore the search space to find the optimal backbone architecture and training attributes. We achieved a mean accuracy of 97.14% and 89.44% for detecting brake light status for two-wheelers (on NITW-MBS dataset) and four-wheelers (on CaltechGraz dataset) respectively. The experimental study suggests that the architecture obtained using the proposed approach exhibits superior performance compared to existing models.
Deconfounded recommendation via causal intervention

Traditional recommenders suffer from hidden confounding factors, leading to the spurious correlations between user/item profiles and user preference prediction, i.e., the confounding bias issue. Most works resort to only one confounding bias, which greatly block their applications on recommendations with mixture confounder, i.e., more than one bias. It is therefore of practical demand to empower the recommender with the capability of debiasing different biases from data. Moreover, the positive effect of bias is neglected in most previous works. We argue that confounding bias is actually beneficial for capturing users’ preferences in some recommendation scenarios. In this paper, we propose a novel deconfounded causal learning method called GCRec (Graph Causal Recommendetion) to debias two confounders: social network confounder and item group confounder. We employ Graph Neural Networks (GNNs) to aggregate user-user connections for social networks and user-item interactions for item groups in order to learn high-order representations that can efficiently debias these two confounders from a causal view. In the inference stage, we use symmetric Kullback–Leibler divergence to measure the user preference drift. If the divergence is large, we perform the causal intervention to alleviate the bias amplification caused by confounders on user preferences. Otherwise, we incorporate the user preferences that can potentially deliver a positive effect on favoring recommendation performance. Extensive experiments are conducted on two benchmark datasets to verify that GCRec outperforms state-of-the-art methods and achieves robust recommendations.
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes

Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by 'contextual explanations' that let the practitioner connect system inferences to their context of use. However, their importance in improving model usage and understanding has not been extensively studied. Hence, we consider a comorbidity risk prediction scenario and focus on contexts regarding the patients' clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions. We explore how relevant information for such dimensions can be extracted from Medical guidelines to answer typical questions from clinical practitioners. We identify this as a question answering (QA) task and employ several state-of-the-art Large Language Models (LLM) to present contexts around risk prediction model inferences and evaluate their acceptability. Finally, we study the benefits of contextual explanations by building an end-to-end AI pipeline including data cohorting, AI risk modeling, post-hoc model explanations, and prototyped a visual dashboard to present the combined insights from different context dimensions and data sources, while predicting and identifying the drivers of risk of Chronic Kidney Disease (CKD) - a common type-2 diabetes (T2DM) comorbidity. All of these steps were performed in deep engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel. We show that LLMs, in particular BERT and SciBERT, can be readily deployed to extract some relevant explanations to support clinical usage. To understand the value-add of the contextual explanations, the expert panel evaluated these regarding actionable insights in the relevant clinical setting. Overall, our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case. Our findings can help improve clinicians' usage of AI models.
UAV aerial image target detection based on BLUR-YOLO

With the development of unmanned aerial vehicle (UAV) remote sensing technology, target detection based on UAV images has increasingly become a hot spot for research. Aiming at the problems of many small target instances, complex backgrounds and difficult feature extraction in UAV images, we propose a UAV aerial image target detection algorithm called BLUR-YOLO. First, the h-swish activation function is used in the backbone network and the neck network to increase the expressiveness of the model. Second, an attention mechanism (CoordAttention) is added to the bottleneck layer of the backbone network, thereby increasing the weight of valid information and suppressing background noise interference. Finally, by removing redundant nodes of the path aggregation network (PANet), adding additional connections, and using BlurPool instead of the downsampling method, a feature pyramid network (Blur-PANet) is proposed to effectively fuse multilayer features. Experimental results on the VisDrone public dataset show that the proposed drone image object detection model (BLUR-YOLO) is 1.2% better than the YOLOv4 algorithm, which proves the effectiveness of the method.
We are meeting on Microsoft Teams: Forensic analysis in Windows, Android, and <scp>iOS</scp> operating systems

Abstract Microsoft released a new communication platform, Microsoft Teams, in 2017. Due in part to COVID‐19, the popularity of communication platforms, like Microsoft Teams, increased exponentially. Given its user base and increased popularity, it seems likely that digital forensic investigators will encounter cases where Microsoft Teams is a relevant component. However, because Microsoft Teams is a relatively new application, there is limited forensic research on the application particularly focusing on mobile operating systems. To address this gap, an analysis of data stored at rest by Microsoft Teams was conducted on the Windows 10 operating system as well as on Android and Apple iOS mobile operating systems. Basic functionalities, such as messaging, sharing files, participating in video conferences, and other functionalities that Teams provides, were performed in an isolated testing environment. Cellebrite UFED Physical Analyzer and Magnet AXIOM Examine tools were used to analyze the mobile devices and the Windows device, respectively. Manual or non‐automated investigation recovered, at least partially, the majority of artifacts across all three operating systems. In this study, a total of 77.6% of the populated artifacts were partially or fully recovered in the manual investigation. On the other hand, forensic tools used did not automatically recover many of the artifacts found with the manual investigation. Only 13.8% of artifacts were partially or fully recovered by the forensic tools across all three devices. These discovered artifacts and the results of the investigations are presented in order to aid digital forensic investigations.