Reviewerly/exHarmony huggingface dataset:
Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities

Explainable artificial intelligence (xAI) is seen as a solution to making AI systems less of a "black box". It is essential to ensure transparency, fairness, and accountability – which are especially paramount in the financial sector. The aim of this study was a preliminary investigation of the perspectives of supervisory authorities and regulated entities regarding the application of xAI in the financial sector. Three use cases (consumer credit, credit risk, and anti-money laundering) were examined using semi-structured interviews at three banks and two supervisory authorities in the Netherlands. We found that for the investigated use cases a disparity exists between supervisory authorities and banks regarding the desired scope of explainability of AI systems. We argue that the financial sector could benefit from clear differentiation between technical AI (model) explainability requirements and explainability requirements of the broader AI system in relation to applicable laws and regulations.
Privacy-preserving in the Internet of Things based on steganography and chaotic functions

Nowadays, with the spread of the Internet and its applications, the use of the Internet of Things is becoming increasingly widespread. One of the requirements of the Internet of Things is privacy-preserving, which protects the personal and confidential information of individuals. In this paper, to preserve the privacy of personal and confidential information, a combination of chaos functions and a new steganography method based on image blocking is proposed. In this paper, block centers are used to generate the initial key of the chaos function, then randomly selected secret data bits are hidden in the pixels of randomly selected blocks. The simulation results not only show the quality of the stego_image but also the better performance of the proposed method compared to the existing methods.
Local search driven periodic scheduling for workflows with random task runtime in clouds

When data processing applications from various fields are deployed in cloud computing, workflow scheduling is vital in satisfying users’ requirements and improving cloud platforms’ performance. Up to the present, some works have put forward heuristic methods to handle dynamic and uncertain factors when scheduling workflows in cloud platforms. Although these heuristics can generate feasible schedules quickly, their performance can be further improved. It is noteworthy that not all the workflow tasks can be executed immediately due to their data dependencies. Then, their waiting time can be utilized to further optimize schedules generated by heuristic methods. Motivated by the above fact, this paper proposes a Local Search driven Periodic Scheduling, LSPS, for workflows having deadlines and random task runtime. Specifically, in each scheduling period, the LSPS only schedule the tasks to start running, thus shortening the length of local waiting queues on resources to alleviate the negative effects of dynamics and uncertainty. Moreover, we design a problem-specific local search strategy for LSPS to fully use the scheduling period to improve the quality of schedules iteratively. At last, in the context of real cloud platforms, four groups of compared experiments are carried out to measure the effectiveness of the proposed LSPS concerning monetary cost and resource efficiency.
Boosting Scene Graph Generation with Visual Relation Saliency

The scene graph is a symbolic data structure that comprehensively describes the objects and visual relations in a visual scene, while ignoring the inherent perceptual saliency of each visual relation (i.e., relation saliency). However, humans often quickly allocate attention to important/salient visual relations in a scene. To align with such human perception of a scene, we explicitly model the perceptual saliency of visual relation in scene graph by upgrading each graph edge (i.e., visual relation) with an attribute of relation saliency. We present a new design, named as Saliency-guided Message Passing (SMP), that boosts the generation of such scene graph structure with the guidance from the visual relation saliency. Technically, an object interaction encoder is first utilized to strengthen object relation representations by jointly exploiting the appearance, semantic, and spatial relations in between. A branch is further leveraged to estimate the relation saliency of each visual relation by ordinal regression. Next, conditioned on the object and relation features (coupled with the estimated relation saliency), our SMP enhances scene graph generation by performing message passing over the objects and the most salient relations. Extensive experiments on VG-KR and VG150 datasets demonstrate the superiority of SMP for the scene graph generation. Moreover, we empirically validate the compelling generalizability of the learned scene graphs via SMP on downstream tasks like cross-model retrieval and image captioning.
Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

Modern machine learning techniques commonly rely on complex, high-dimensional embedding representations to capture underlying structure in the data and improve performance. In order to characterize model flaws and choose a desirable representation, model builders often need to compare across multiple embedding spaces, a challenging analytical task supported by few existing tools. We first interviewed nine embedding experts in a variety of fields to characterize the diverse challenges they face and techniques they use when analyzing embedding spaces. Informed by these perspectives, we developed a novel system called Emblaze that integrates embedding space comparison within a computational notebook environment. Emblaze uses an animated, interactive scatter plot with a novel Star Trail augmentation to enable visual comparison. It also employs novel neighborhood analysis and clustering procedures to dynamically suggest groups of points with interesting changes between spaces. Through a series of case studies with ML experts, we demonstrate how interactive comparison with Emblaze can help gain new insights into embedding space structure.
Entanglement, nonlocal features, quantum teleportation of two-mode squeezed vacuum states with superposition of photon-pair addition and subtraction operations

In this paper, we introduce new nonclassical states called two-mode squeezed vacuum states with superposition of photon-pair addition and subtraction operations (TMSVSs-SPPASOs) by adding and subtracting pairs of photons to a two-mode squeezed vacuum state (TMSVS). It is shown that the superposition operations of photon-pair added and subtracted can enhance the degree of entanglement of the TMSVSs-SPPASOs by using the linear entropy criterion. Besides, the EPR correlation and the EPR steering in the TMSVSs-SPPASOs can be improved compared with the original TMSVS. In particular, the manifestation of these features becomes more obvious in the small region of the squeezing parameter r in the TMSVSs-SPPASOs. By using the TMSVSs-SPPASOs as entanglement resources, the quantum teleportation process of a qubit-type single-mode state is studied in detail. Based on the average fidelity Fav, it indicates that the quantum teleportation process is ideally successful as Fav approaches the unit when r is high. More importantly, the average fidelity Fav can be enhanced by increasing the number of photon pairs added or subtracted. Finally, we propose experimental schemes for generating these states by using quantum optical devices.
Potential of a virtual reality environment based on very-high-resolution satellite imagery for structural geology measurements of lava flows

Combining very-high-spatial resolution (<1 m) and the ability to provide stereo-pairs for 3D topographic reconstructions, Pleiades satellites offer great potential for large-scale and high-resolution studies in geosciences. In parallel, research-dedicated Virtual Reality (VR) applications are emerging based on high-quality data. We examined the potential of a VR application derived from very-high-resolution Pleiades imagery to infer the orientation of individual flows from kilometers-thick lava piles in the eastern Afar rift area (Stratoïd Formation, Djibouti). Based on Pleiades Digital Elevation Models and orthoimages, we compared two approaches: i) a “classical” method in “Mouse Mode” and ii) a method using the VR application. Both methods provide similar results in facet orientation measurements over the studied transects. Nevertheless, VR enables operator immersion in a very realistic virtual environment combining aerial synoptic and ground-level views. Our studied case shows the potential of those methods to get high-quality and unexpected structural information from an otherwise inaccessible environment.
Multilingual multi-aspect explainability analyses on machine reading comprehension models

<h2>Summary</h2> Achieving human-level performance on some of the machine reading comprehension (MRC) datasets is no longer challenging with the help of powerful pre-trained language models (PLMs). However, the internal mechanism of these artifacts remains unclear, placing an obstacle to further understand these models. This paper focuses on conducting a series of analytical experiments to examine the relations between the multi-head self-attention and the final MRC system performance, revealing the potential explainability in PLM-based MRC models. To ensure the robustness of the analyses, we perform our experiments in a multilingual way on top of various PLMs. We discover that passage-to-question and passage understanding attentions are the most important ones in the question answering process, showing strong correlations to the final performance than other parts. Through comprehensive visualizations and case studies, we also observe several general findings on the attention maps, which can be helpful to understand how these models solve the questions.
Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine

Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crude oil by employing a hybrid approach based on an extreme learning machine (ELM) as a regressor and the improved grey wolf optimizer (IGWO) for prophesying the crude oil rate for West Texas Intermediate (WTI) and Brent crude oil datasets. The datasets are augmented using technical indicators (TIs) and statistical measures (SMs) to obtain better insight into the forecasting ability of this proposed model. The differential evolution (DE) strategy has been used for evolution and the survival of the fittest (SOF) principle has been used for elimination while implementing the GWO to achieve better convergence rate and accuracy. Whereas, the algorithmic simplicity, use of less parameters, and easy implementation of DE efficiently decide the evolutionary patterns of wolves in GWO and the SOF principle updates the wolf pack based on the fitness value of each wolf, thereby ensuring the algorithm does not fall into local optimum. Furthermore, the comparison and analysis of the proposed model with other models, such as ELM–DE, ELM–Particle Swarm Optimization (ELM–PSO), and ELM–GWO shows that the predictability evidence obtained substantially achieves better performance for ELM–IGWO with respect to faster error convergence rate and mean square error (MSE) during training and testing phases. The sensitivity study of the proposed ELM–IGWO provides better results in terms of the performance measures, such as Theil’s U, mean absolute error (MAE), average relative variance (ARV), mean average percentage error (MAPE), and minimal computational time.
Colorful 3D reconstruction at high resolution using multi-view representation

High-quality 3D models should contain accurate shapes, as well as other correct attributes, such as realistic surface color. However, current researches were mostly focused on the reconstruction of shapes. We present a method to reconstruct high-resolution colorful 3D models from single images. Shapes and colors are learned separately, using a coarse-to-fine strategy in which the 3D color is expressed as 3-channel volumes. Colorful volumes share the same spatial dimension with generated shape volumes. We propose orthographic colorful maps to retain and recover projected coordinates and corresponding color for 3D surface points. To achieve a fine granularity increase in the quality of maps from low-resolution to high-resolution, we introduce 2D super resolution during reconstructing 3D shapes and color volumes. Models are carved by utilizing predicted high-resolution silhouette, depth and color details. Experimental results in a subset of the ShapeNet dataset and the Colorful Human dataset show the effectiveness of our method.
Retargeted multi-view classification via structured sparse learning

Multi-view classification is an essential issue in machine learning. Many multi-view classification methods have been proposed by fusing complementary information of multiple views. However, most of them ignore the expectation of a large margin between different classes as they adopt the fixed label matrix for classification. In addition, these methods concatenate views into long vectors to build models, which are of high complexity. To tackle these problems, in this study, we propose a novel multi-view classification model to satisfy the margin maximization criterion and reduce time complexity. In the proposed model, a retargeted constraint is introduced to increase the margin between different classes by relaxing the target matrix, and structured sparse learning is applied to remove redundant features. Moreover, an optimization algorithm is designed to divide the proposed model into several small-scale problems which could be efficiently solved with low complexity. Compared with three classical and six advanced methods, the proposed method achieves superior classification performance on 11 multi-view public datasets. Statistically, the proposed method is significantly better than the six comparison methods (SVM, simpleMKL, LDA, ROMS, LRRR, and LHFS). The visualization results on the MF and Caltech20 datasets also indicate that the proposed method certainly increases the distance between classes.
Assessing the influence of celebrity and government endorsements on bitcoin’s price volatility

The global market capitalisation of bitcoin has exponentially increased in recent years and there are concerns that the current prices of bitcoin do not reflect the true and fair underlying value of this particular type of digital asset. Applying Cue utilisation theory and signalling theory, and using a panel data on bitcoin prices from Bloomberg between 1st November 2019 and 31st May 2021, we examine the association between celebrity and government endorsements and volatility in bitcoin prices. We find that positive celebrity tweets and positive government sentiments towards bitcoin are significantly positively associated with positive changes in its prices. Our findings imply that although celebrity endorsements may cause a temporary ‘exponential rise’ in bitcoin prices, investors need to carefully diversify their portfolio to maximise their risk–return relationship.
Interaction augmented transformer with decoupled decoding for video captioning

Transformer-based architectures achieve competitive performances in video captioning. However, their applicability still has many issues: (1) Existing methods only consider the correlation of query and key modalities when calculating the attention weights, and ignore their interaction with other modalities. (2) Deep stacked cross-modal encoding blocks make the different modalities assimilative and lose their preliminary discriminative properties. (3) The decoder usually employs the output of the last encoding block, which is not a comprehensive representation. Based on these concerns, we propose a novel method called Interaction Augmented Transformer (IAT) with discriminative encoding and decoupled decoding for video captioning. Concretely, by concatenating “[CLS]” tokens to multimodal features, we perform reconstructive contrastive constraints for the encoded results. Based on the conclusive information carried by these tokens, we first introduce the global-gated interaction into multi-head attention, where the conclusive information mentioned above is transformed into multiple interaction augmented functions. Additionally, the dot-product operation is replaced by the tucker-fused operation to better capture the query-to-key correlation. Furthermore, we employ fine-grained layer-wise decoding for multi-layer multi-modal features from the encoder with decoupled strategy. We conduct extensive quantitative, qualitative, and ablation experiments on the benchmark datasets and the experimental results show that IAT outperforms the state-of-the-art methods under most of the metrics.
Local-boundary-information-dependent control design for interval type-2 fuzzy systems under self-triggered scheme

This work investigates the self-triggered control issue for interval type-2 (IT2) fuzzy systems. The transmission of the system state is determined via a self-triggered scheme. Instead of continuously monitoring the prespecified triggering condition, the self-triggered scheme can calculate the next triggering instant by the latest triggered state information. Then, by means of the triggered state, a self-triggered IT2 fuzzy controller is designed, whose fuzzy rules and membership functions (MFs) can be flexibly selected independent of the controlled fuzzy system. Under the constraint of imperfectly premise matching, the relationship between the triggered state and the system state is explicitly established, and the boundaries of MFs in the local operation region are given. Meanwhile, the local boundary information is utilized to obtain the relaxed feasible conditions. Finally, the simulation examples are give and the comparison with other triggered schemes are also made.
Two-stage segmentation network with feature aggregation and multi-level attention mechanism for multi-modality heart images

Accurate segmentation of cardiac substructures in multi-modality heart images is an important prerequisite for the diagnosis and treatment of cardiovascular diseases. However, the segmentation of cardiac images remains a challenging task due to (1) the interference of multiple targets, (2) the imbalance of sample size. Therefore, in this paper, we propose a novel two-stage segmentation network with feature aggregation and multi-level attention mechanism (TSFM-Net) to comprehensively solve these challenges. Firstly, in order to improve the effectiveness of multi-target features, we adopt the encoder-decoder structure as the backbone segmentation framework and design a feature aggregation module (FAM) to realize the multi-level feature representation (Stage1). Secondly, because the segmentation results obtained from Stage1 are limited to the decoding of single scale feature maps, we design a multi-level attention mechanism (MLAM) to assign more attention to the multiple targets, so as to get multi-level attention maps. We fuse these attention maps and concatenate the output of Stage1 to carry out the second segmentation to get the final segmentation result (Stage2). The proposed method has better segmentation performance and balance on 2017 MM-WHS multi-modality whole heart images than the state-of-the-art methods, which demonstrates the feasibility of TSFM-Net for accurate segmentation of heart images.
A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine

Abstract Sensor fault diagnosis and performance degradation estimation (SFDPDE) plays a critical role in the operation and maintenance of aero-engines. In this study, a modified fusion model driven by sensor measurements is proposed to overcome the drawbacks of the single data-driven and single model-based methods. Two types of on-board models are established based on augmented state space equations, and a data-driven model based on an extreme learning machine (ELM) is constructed for residual correction of the on-board model. A bidirectional information transmission algorithm is designed in the SFDPDE framework in order to include the function coordination. The Kalman filter is employed as the optimal algorithm in the SFDPDE framework, containing a standardized sensor parameter selection process. The experimental results indicate that the proposed fusion model improves the accuracy of sensor fault diagnosis and reduces the mean square error of health parameter estimations, while the information sharing module expands the application scope of SFDPDE and improves its accuracy as well as stability.
Single-pass inline pipeline 3D reconstruction using depth camera array

A novel inline inspection (ILI) approach using depth cameras array (DCA) is introduced to create high-fidelity, dense 3D pipeline models. A new camera calibration method is introduced to register the color and the depth information of the cameras into a unified pipe model. By incorporating the calibration outcomes into a robust camera motion estimation approach, dense and complete 3D pipe surface reconstruction is achieved by using only the inline image data collected by a self-powered ILI rover in a single pass through a straight pipeline. The outcomes of the laboratory experiments demonstrate one-millimeter geometrical accuracy and 0.1-pixel photometric accuracy. In the reconstructed model of a longer pipeline, the proposed method generates the dense 3D surface reconstruction model at the millimeter level accuracy with less than 0.5% distance error. The achieved performance highlights its potential as a useful tool for efficient in-line, non-destructive evaluation of pipeline assets.
Exploring the Effects of Machine Learning Literacy Interventions on Laypeople’s Reliance on Machine Learning Models

Today, machine learning (ML) technologies have penetrated almost every aspect of people's lives, yet public understandings of these technologies are often limited. This highlights the urgent need of designing effective methods to increase people's machine learning literacy, as the lack of relevant knowledge may result in people's inappropriate usage of machine learning technologies. In this paper, we focus on an ML-assisted decision-making setting and conduct a human-subject randomized experiment to explore how providing different types of user tutorials as the machine learning literacy interventions can influence laypeople's reliance on ML models, on both in-distribution and out-of-distribution examples. We vary the existence, interactivity and scope of the user tutorial across different treatments in our experiment. Our results show that user tutorials, when presented in appropriate forms, can help some people rely on ML models more appropriately. For example, for those individuals who have relatively high ability in solving the decision-making task themselves, receiving a user tutorial that is interactive and addresses the specific ML model to be used allows them to reduce their over-reliance on the ML model when they could outperform the model. In contrast, low-performing individuals' reliance on the ML model is not affected by the presence or the type of user tutorial. Finally, we also find that people perceive the interactive tutorial to be more understandable and slightly more useful. We conclude by discussing the design implications of our study.