Reviewerly/exHarmony huggingface dataset:
Graph Representation Learning of Banking Transaction Network with Edge Weight-Enhanced Attention and Textual Information

In this paper, we propose a novel approach to capture inter-company relationships from banking transaction data using graph neural networks with a special attention mechanism and textual industry or sector information. Transaction data owned by financial institutions can be an alternative source of information to comprehend real-time corporate activities. Such transaction data can be applied to predict stock price and miscellaneous macroeconomic indicators as well as to sophisticate credit and customer relationship management. Although the inter-company relationship is important, traditional methods for extracting information have not captured that enough. With the recent advances in deep learning on graphs, we can expect better extraction of inter-company information from banking transaction data. Especially, we analyze common issues that arise when we represent banking transactions as a network and propose an efficient solution to such problems by introducing a novel edge weight-enhanced attention mechanism, using textual information, and designing an efficient combination of existing graph neural networks.
Learning from Unlabeled 3D Environments for Vision-and-Language Navigation

In vision-and-language navigation (VLN), an embodied agent is required to navigate in realistic 3D environments following natural language instructions. One major bottleneck for existing VLN approaches is the lack of sufficient training data, resulting in unsatisfactory generalization to unseen environments. While VLN data is typically collected manually, such an approach is expensive and prevents scalability. In this work, we address the data scarcity issue by proposing to automatically create a large-scale VLN dataset from 900 unlabeled 3D buildings from HM3D [45]. We generate a navigation graph for each building and transfer object predictions from 2D to generate pseudo 3D object labels by cross-view consistency. We then fine-tune a pretrained language model using pseudo object labels as prompts to alleviate the cross-modal gap in instruction generation. Our resulting HM3D-AutoVLN dataset is an order of magnitude larger than existing VLN datasets in terms of navigation environments and instructions. We experimentally demonstrate that HM3D-AutoVLN significantly increases the generalization ability of resulting VLN models. On the SPL metric, our approach improves over state of the art by 7.1% and 8.1% on the unseen validation splits of REVERIE and SOON datasets respectively.
Individual Diversity Preference Aware Neural Collaborative Filtering

The diversified recommendation of recommender systems enriches user experiences by diversifying recommendation lists. However, the conventional post-processing strategy, which re-ranking the recommendation lists by diversity measurement, could cause a trade-off between accuracy and diversity. An improvement of one performance usually sacrifices the other one. Thus, we propose an in-processing method named Individual Diversity Preference Aware Neural Collaborative Filtering that takes accuracy and diversity together as optimal direction during the recommendation process. It consists of two filtering processes: collaborative filtering and diversity filtering. The items with high accuracy and high diversity to users could be on the recommendation lists. Besides, to meet different diverse needs of users, we model the diversity preference of an individual user by the items he/she has interacted with. As the front preparation of our method, we pre-train the item embeddings by applying a graph embedding technique on the item-linking knowledge graph. Since the pre-training is independent of recommendation, the pre-trained item embeddings could be used for objective diversity comparison among different recommendation models. To validate our proposed method, we conduct extensive experiments on Movielens datasets. Experimental results suggest that our approach has achieved high accuracy and high diversity of recommendations.
Understanding IMF Decision-Making with Sentiment Analysis

With the advances in information technologies, the amount of available data on web sources where people express their opinions increases continually. Sentiment analysis is one of the effective tools for decision-makers to gain insights from massive heaps of data. The field of International Organizations, which produces big data in the form of large documents, has significant potential to benefit from sentiment analysis in decision-making. In this paper, we evaluate the effectiveness of different sentiment analysis tools in classifying the sentiments of the International Monetary Fund&#x2019;s (IMF) Executive Board members regarding the design of IMF programs. We introduce a novel dataset, Executive Board meeting minutes of the IMF, in which the sentences are labelled as positive, neutral, or negative. The experimental results demonstrate that sentiment classification with state-of-the-art language models yields high performance on this dataset when trained with domain-specific data.
Developing a Tutorial for Improving Usability and User Skills in an Immersive Virtual Reality Experience

The fast development and progressive price reduction of Virtual Reality (VR) devices open a broad range of VR applications. Especially interesting are those applications focused on educational objectives. However, before these VR applications can be extensively presented in the educational system, some main issues to optimize their efficiency in the student’s autonomous learning process should be solved. While in non-VR games designers have consistently developed introductory tutorials to prepare new players for the game’s mechanics, in the case of VR, the design of these tutorials is still an open issue. This research presents a tutorial for VR educational applications to help the users to become familiar with the virtual environment and to learn the use of the interaction devices and the different mechanics within the experiences. In addition, the usability of this tutorial was tested with final users to assure its effectiveness.
Photovoltaic Output Prediction Method Based on Weather Forecast and Machine Learning

Abstract This paper conducts prediction research based on the historical power generation data and meteorological data of a photovoltaic power station. The obtained data is firstly classified by season, and the correlation analysis of meteorological factors is carried out. The data closely related to PV generation is selected for prediction, which improves the prediction efficiency. Then the Kmeans algorithm is used to cluster the historical data, and data are divided into four types: sunny, partly cloudy, cloudy, and rainy, further each type of data is separately trained to obtain four training models. The classified data is decomposed by EEMD, and a set of frequency-distributed IMF components and a monotonic distribution of residual components are obtained. Use support vector machine to predict the dimension-reduced components, and reconstruct the result to obtain the final predicted value. After obtaining the future weather forecast data, select the same type of historical data set for model training. The results show that the prediction accuracy will be significantly improved after signal processing in cloudy and rainy weather.
Digital Transformation Security Challenges

Digital transformation has become one of the most popular strategies for information systems (IS). Developing and implementing a digital strategy is a mandatory task for any organization that relies on information systems. In this digital age, an IS/IT strategy without security considerations could lead to serious data breaches. Despite all countermeasures, security remains a major concern in digital transformation, as digitization is misjudged and brings with it major security concerns. The digitization of manual goods, processes and services, as well as their use, leads to various security issues in different organizations. The aim of this study is to identify and analyze elements that are typical barriers to digital innovation in organizations. This study addresses common elements that impact the security of digital transformation through a literature review and a case study.
Improving Adoption of Immersive Technologies at a Norwegian University

Adoption of immersive technologies in higher education can potentially offer novel and educative experiences. Despite the increasing amount of available XR content for education, adoption of XR for regular teaching at higher institutions appears to be still lagging. Some reasons for hesitancy in integrating the technologies in university courses are barriers such as lack of content, cost of equipment or expectations on technical knowledge required to use the technology. We run an immersive tech lab to support innovation in education. The lab supports educators with equipment and expertise to include immersive technologies in their courses. In this paper we present the results of a thematic analysis of interviews with lectures at a Norwegian higher education institution. Our contribution is to give further insight into factors the educators consider when adopting immersive technologies in their educational practices.
A model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter

Summary A dynamic motion primitive (DMP) is a robust framework that generates obstacle avoidance trajectories by introducing perturbative terms. The perturbative term is usually constructed with an artificial potential field (APF) method. Dynamic obstacle avoidance is rarely considered with this approach; furthermore, even when dynamic obstacles are considered, only the velocity and position information of the current state are incorporated into the obstacle avoidance framework. However, if the position of an obstacle changes suddenly, a robot may be placed in a dangerous position close to the obstacle, resulting in large obstacle avoidance accelerations, sharp trajectories, or even obstacle avoidance failure. Therefore, we present a model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter. This method has three main components: Dynamic motion primitives are used to generate the desired trajectory and introduce perturbations to achieve obstacle avoidance; the Kalman filter method is adopted to estimate the future positions of the obstacles; and model predictive control is employed to optimize the repulsive force generated by the APF while minimizing the defined cost function, thus guaranteeing the safety and flexibility of the method. We validate the presented method with 2D and 3D obstacle avoidance simulations. The method is also verified with a real robot: the‐Kinova MOVO. The simulation and experimental results show that the proposed method not only avoids dynamic obstacles but also tracks the desired trajectory more smoothly and precisely.
Mutual Information Neural Estimation for Unsupervised Multi-Modal Registration of Brain Images

Many applications in image-guided surgery and therapy require fast and reliable non-linear, multi-modal image registration. Recently proposed unsupervised deep learning-based registration methods have demonstrated superior per-formance compared to iterative methods in just a fraction of the time. Most of the learning-based methods have focused on mono-modal image registration. The extension to multi-modal registration depends on the use of an appropriate similarity function, such as the mutual information (MI). We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network. Our results show that a small, 2-layer network produces competitive results in both mono- and multi-modal registration, with sub-second run-times. Comparisons to both iterative and deep learning-based methods show that our MI-based method produces topologically and qualitatively superior results with an extremely low rate of non-diffeomorphic transformations. Real-time clinical application will benefit from a better visual matching of anatomical structures and less registration failures/outliers.
Memorization and generalization in neural code intelligence models

Deep Neural Networks (DNNs) are increasingly being used in software engineering and code intelligence tasks. These are powerful tools that are capable of learning highly generalizable patterns from large datasets through millions of parameters. At the same time, their large capacity can render them prone to memorizing data points. Recent work suggests that the memorization risk manifests especially strongly when the training dataset is noisy, involving many ambiguous or questionable samples, and memorization is the only recourse. The goal of this paper is to evaluate and compare the extent of memorization and generalization in neural code intelligence models. It aims to provide insights on how memorization may impact the learning behavior of neural models in code intelligence systems. To observe the extent of memorization in models, we add random noise to the original training dataset and use various metrics to quantify the impact of noise on various aspects of training and testing. We evaluate several state-of-the-art neural code intelligence models and benchmarks based on Java, Python, and Ruby codebases. Our results highlight important risks: millions of trainable parameters allow the neural networks to memorize anything, including noisy data, and provide a false sense of generalization. We observed all models manifest some forms of memorization. This can be potentially troublesome in most code intelligence tasks where they rely on rather noise-prone and repetitive data sources, such as code from GitHub. To the best of our knowledge, we provide the first study to quantify memorization effects in the domain of software engineering and code intelligence systems. This work raises awareness and provides new insights into important issues of training neural models in code intelligence systems that are usually overlooked by software engineering researchers.
Increasing Accuracy and Explainability in Fuzzy Regression Trees: An Experimental Analysis

Regression Trees (RTs) have been widely used in the last decades in various domains, also thanks to their inherent explainability. Fuzzy RTs (FRTs) extend RTs by using fuzzy sets and have proven to be particularly suitable for dealing with noisy and/or uncertain environments. The modelling capability of FRTs depends, among other factors, on the model used in the leaves for determining the output, and on the inference strategy. Nevertheless, the impact of such factors on FRTs accuracy and explainability has not been adequately investigated.In this paper, we extend a recently proposed learning scheme for FRTs by employing both linear models in the leaves and the maximum matching inference strategy. The former extension aims to increase accuracy, and the latter to improve explainability. We carried out an extensive experimental analysis by comparing the four FRT versions corresponding to any possible combination of the two extensions introduced in the paper. The results show that the best trade-off between accuracy and explainability is obtained by employing both of them.
Investigate Financial Crime Patterns Using Graph Databases

Law enforcement, legal authorities, financial fraud, and financial investigators seek evidence of financial crimes, and graph technologies provide a unique opportunity to uncover financial criminals by reviewing all-inclusive entities involved, their relationships to identify suspicious connections with graph patterns. This article presents the investigation of circular money transfers using graph technology as an efficient, effective, and new-age technical alternative to existing techniques that use manual methods.
Hunting with the Ju/’hoansi in the Kalahari: A co-created VR gesture application

In collaboration with members of a Ju/’Hoansi community in Namibia, we have co-created a gesture-controlled VR application of a hunting story, as told by an elder in the village, demonstrated in the field by an elder and youth, as well as 3D models and scenes described and designed by community members and interpreted by the researchers. The story has been implemented in VR using the Unity game engine. The application consists of a number of scenes, where the user progress in the story, either through object interactions, or through hand gesture interactions with the Non-player characters. The application was validated by the community youth and elders, in regards to expectation of cultural appropriate representation, gesture interactions and narrative immersion.
Delving Deeper Into Mask Utilization in Video Object Segmentation

This paper focuses on the mask utilization of video object segmentation (VOS). The mask here mains the reference masks in the memory bank, i.e., several chosen high-quality predicted masks, which are usually used with the reference frames together. The reference masks depict the edge and contour features of the target object and indicate the boundary of the target against the background, while the reference frames contain the raw RGB information of the whole image. It is obvious that the reference masks could play a significant role in the VOS, but this is not well explored yet. To tackle this, we propose to investigate the mask advantages of both the encoder and the matcher. For the encoder, we provide a unified codebase to integrate and compare eight different mask-fused encoders. Half of them are inherited or summarized from existing methods, and the other half are devised by ourselves. We find the best configuration from our design and give valuable observations from the comparison. Then, we propose a new mask-enhanced matcher to reduce the background distraction and enhance the locality of the matching process. Combining the mask-fused encoder, mask-enhanced matcher and a standard decoder, we formulate a new architecture named MaskVOS, which sufficiently exploits the mask benefits for VOS. Qualitative and quantitative results demonstrate the effectiveness of our method. We hope our exploration could raise the attention of mask utilization in VOS.
Long-distance laser Doppler water flow velocimetry method based on adaptive Gaussian weighted integration

Velocity measurement has a high application value in hydrological monitoring and flood disaster warning. The long-distance laser Doppler water flow velocimetry technology has the advantage of strong anti-interference ability and high spatial resolution, and it can realize the high-precision measurement of water flow velocity. Because water flow has low reflectance characteristics, how to extract Doppler frequency from weak non-stationary coherent signals is a crucial problem to be solved to realize long-distance water flow velocity measurement. However, the classical method requires the time domain signal to have high stationarity and is not suitable for processing the coherent signal in the water flow velocity measurement. Aiming at this problem, we proposed a water flow velocimetry method based on adaptive Gaussian weighted integral (AGWI). First, the spectral characteristics of the coherent signal are analyzed in detail, and a statistical model of weak non-stationary signals is established. A second-order Kaiser self-multiplication window (KSMW) is designed to suppress spectral leakage for the asynchronously sampled data. Then, an adaptive homogenization power spectral subtraction (AHPSS) is designed to reduce system noise. Finally, the Doppler spectrum reconstruction and Doppler frequency estimation are performed using the AGWI method to obtain the Doppler frequency, which is further processed to get the water flow velocity. The experimental results show that the method proposed in this paper can achieve accurate and stable measurement of river surface velocity under long-distance conditions.
Mutually activated residual linear modeling GAN for pose-guided person image generation

Translating a pose of a given person to another desired pose is popular in computer vision applications. However, previous works usually directly utilized pose information to guide appearance information for the generation without deep consideration of the interaction between these two kinds of information. Moreover, the global long-range relation that exists in both kinds of data has not been well modeled due to the physical design of convolutional filters. In this paper, a novel Mutually Activated Residual Linear Modeling Generative Adversarial Network (MARLM-GAN) is proposed to address these two challenges. The MARLM-GAN consists of T cascaded MARLM modules for learning the latent transformation progressively from both appearance and pose codes. In each MARLM module, there are two mutually-activated residual linear modeling blocks for both appearance and pose pathways. In addition, an information update strategy is also developed, which makes the latent appearance and pose representations benefit each other interactively. Our experiments on two challenging datasets demonstrate that the proposed MARLM-GAN can achieve competitive results in terms of objective evaluation metrics and subjective visual realness compared with recent state-of-the-art methods.
Blockchain-driven Smart Healthcare System: Challenges, Technologies and Future Research

Healthcare service is one of the most important pillars of the existence of life on the planet. It requires frequent updates in its existing methodology; it can be in terms of resources, manpower and technology. Many researchers have proposed various types of technologies (i.e., Internet of Healthcare Things (IoHT)) and ways to enhance the capabilities of the healthcare services, which also contributed in the real world scenario. However, IoHT suffers from various security and privacy-related issues. Because of them, the sensitive healthcare data may be disclosed or altered in the unauthorized way. Therefore, we need some security scheme to secure the communication of IoHT. The security issues of IoHT can be resolved by the blockchain-driven mechanisms. In this paper, we discuss the issues and challenges of blockchain-driven smart healthcare system. We also provide the details of the “generic architecture of the blockchain-driven smart healthcare system”. We then provide the practical implementation of the presented system. After that, we provide the details of various tools and technologies of the smart healthcare system. Finally, the work is finished with some concluding remarks and future research directions, which should be handled by the researchers working in the smart healthcare domain.