Reviewerly/exHarmony huggingface dataset:
The Absolute Phase Retrieval Based on the Rotation of Phase-Shifting Sequence

The absolute phase retrieval has been the focus of developments in three-dimensional (3D) shape measurement. The traditional absolute phase retrievals based on phase-shifting algorithm mainly utilize extra patterns to generate fringe order map, which decreases the measuring speed and income more phase shift error. In this paper, the cue of fringe order is directly embedded into phase-shifting patterns to minimize the number of projecting patterns. Fringe order is encoded by rotating the sequence of phase shift amounts, causing the 2&#x03C0; discontinuity to be extended into &#x00B1;2&#x03C0;/<i>N</i>, &#x00B1;4&#x03C0;/<i>N</i>,..., &#x00B1;2(<i>N</i>-1)&#x03C0;/<i>N</i>, &#x00B1;2&#x03C0;. Due to the coding periodicity, the regional wrapped phase without 2&#x03C0; ambiguity can be extracted from different wrapped phase maps. When decoding, the coding phase jump points can be located and identified from one or multiple wrapped phase maps, and then used to determine fringe order by the bidirectional data processing. Experimental results have proved that the proposed method without extra coding patterns not only provides high robustness to shadow and considerable accuracy closed to that of traditional phase-coded method, but also inherits the performance to measure the complex and colorful object using traditional sinusoidal phase-shifting patterns.
Event-Event Relation Extraction using Probabilistic Box Embedding

To understand a story with multiple events, it is important to capture the proper relations across these events.However, existing event relation extraction (ERE) frameworks regard it as a multi-class classification task, and do not guarantee any coherence between different relation types.For instance, if a phone line died after storm, then it is evident that the storm happened before the died.Current frameworks of event relation extraction do not guarantee this anti-symmetry and thus enforce it via a constraint loss function (Wang et al., 2020).In this work, we propose to modify the underlying ERE model to guarantee coherence by representing each event as a box representation (BERE) without applying explicit constraints.Our experiments show that BERE has stronger conjunctive constraint satisfaction while performing on par or better in terms of F 1 compared to previous models with constraint injection. 1
Side-Pull Maneuver: A Novel Control Strategy for Dragging a Cable-Tethered Load of Unknown Weight Using a UAV

This work presents an approach for dealing with suspended-cable load transportation using unmanned aerial vehicles (UAVs), specifically when the cargo overcomes the lifting capacity. Herein, this approach is referred to as the Side-Pull Maneuver (SPM). This maneuver is an alternative and viable strategy for cases where there is no impediment or restriction to dragging the load along a surface, such as with pastures or marine environments. The proposal is based on a joint observation of the thrust and altitude of the UAV. To make this possible, the high-level rigid-body dynamics model is described and represented as an underactuated system. Its altitude-rate control input is then analyzed during flight. A flight state supervisor decides whether the cargo should be carried by lifting or by side-pulling, or whether it should be labeled as non-transportable. Comparative real experiments validate the proposal according to which maneuver (lifting or dragging) is performed for transport.
Multi-Robot Object Transport Motion Planning With a Deformable Sheet

Using a deformable sheet to handle objects is convenient and found in many practical applications. For object manipulation through a deformable sheet that is held by multiple mobile robots, it is a challenging task to model the object-sheet interactions. We present a computational model and algorithm to capture the object position on the deformable sheet with changing robotic team formations. A virtual variable cables model (VVCM) is proposed to simplify the modeling of the robot-sheet-object system. With the VVCM, we further present a motion planner for the robotic team to transport the object in a three-dimensional (3D) cluttered environment. Simulation and experimental results with different robot team sizes show the effectiveness and versatility of the proposed VVCM. We also compare and demonstrate the planning results to avoid the obstacle in 3D space with the other benchmark planner.
Continuous-variable quantum key distribution with low-complexity information reconciliation

Information reconciliation is an essential step of continuous-variable quantum key distribution (CV-QKD) because its efficiency affects both the maximum transmission distance and the final secret key rate of the whole system. Slice reconciliation can achieve high secret key rates for the CV-QKD systems under high signal-to-noise ratios (SNRs). However, it suffers from the high designing cost of the error-correcting codes in the multi-level coding and multi-stage decoding step. To reconcile one set of data with fixed SNR, multiple error-correcting codes with different code rates need to be designed. If the channel environment changed, the code rates need to change along to maintain high reconciliation efficiency, making it complicated and costly. In this paper, we introduce polar codes into slice reconciliation and proposed a scheme that is simply constructed, effectively performed, and well adapted to the systems with wide-range and variational SNRs. Simulation results show that the proposed scheme offers reconciliation efficiency over 95% for the systems with SNRs range from 1 to 10, which can support the systems with high secret key rates. The decrease of complexity and the increase of secret key rate have great significance for a practical CV-QKD system.
An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems

An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.
Hyperspectral image denoising by low-rank models with hyper-Laplacian total variation prior

The total variation (TV) regularized low-rank models have emerged as a powerful tool for hyperspectral image (HSI) denoising. TV, defined by the ℓ1-norm of gradients, is assumed that gradients obey the Laplacian distribution from the statistics point of view. By investigating the histogram of HSI’s gradients, we find that gradients in real HSIs are actually distributed as the hyper-Laplacian distribution with the power parameter q=1/2. Taking this prior into account, a hyper-Laplacian spectral-spatial total variation (HTV), defined by the ℓ1/2-norm of gradients, is proposed for HSI denoising. Furthermore, by incorporating HTV as the regularizer, a low-rank matrix model and a low-rank tensor model are proposed. The two models can be solved by the augmented Lagrange multiplier algorithm. To validate the effectiveness of HTV, we formulate baseline models by replacing HTV with ℓ1-norm and ℓ0-norm based TV regularizations, and it is revealed that our proposed HTV outperforms them. Furthermore, compared with several popular HSI denoising algorithms, the experiments conducted on both the simulated and real data demonstrate the superiority of proposed models.
TransDSSL: Transformer Based Depth Estimation via Self-Supervised Learning

Recently, transformers have been widely adopted for various computer vision tasks and show promising results due to their ability to encode long-range spatial dependencies in an image effectively. However, very few studies on adopting transformers in self-supervised depth estimation have been conducted. When replacing the CNN architecture with the transformer in self-supervised learning of depth, we encounter several problems such as problematic multi-scale photometric loss function when used with transformers and, insufficient ability to capture local details. In this letter, we propose an attention-based decoder module, Pixel-Wise Skip Attention (PWSA), to enhance fine details in feature maps while keeping global context from transformers. In addition, we propose utilizing self-distillation loss with single-scale photometric loss to alleviate the instability of transformer training by using correct training signals. We demonstrate that the proposed model performs accurate predictions on large objects and thin structures that require global context and local details. Our model achieves state-of-the-art performance among the self-supervised monocular depth estimation methods on KITTI and DDAD benchmarks.
Impact of blockchain-enabled analytics as a tool to revolutionize the banking industry

&lt;abstract&gt; &lt;p&gt;Blockchain technology is disrupting the financial services industry and leading to extended big data applications in the banking sectors. Using blockchain and big data technology, banking industries can greatly improve decision-making, efficiency, and transparency. Nevertheless, there is a gap in research on the use of blockchain and big data technologies in banking systems from an academic viewpoint. To address the gap, we present a thorough overview of the impact of blockchain and big data technologies on banking systems. Although some banks have started blockchain development in small groups or isolation, this study was designed as a comprehensive exploration into a few facets of banking with blockchain technology to tackle the difficulties currently impeding the adoption of such technologies into banking systems throughout the world. This study shows that implementing big data and blockchain technology can significantly impact the security, speed and cost of transactions for banks. Further research could be conducted over a long-time span to capture the longitudinal impact of blockchain and big data technologies on banking in terms of the operating costs, profitability and scalability.&lt;/p&gt; &lt;/abstract&gt;
An adaptive Laplacian weight random forest imputation for imbalance and mixed-type data

As the application of information technology in the medical field is resulting in a large amount of medical data. As early withdrawal and refusal of participants, there are a lot of missing values in medical data. Although various processing methods for missing values have been proposed, few methods for those medical data with characteristics of imbalance and mixed-type data. In this work, we proposed an adaptive Laplacian weight random forest, called ALWRF. In ALWRF, feature weights were adjusted dynamically when model constructing, which increases selection probabilities of features with low Laplacian score and high importance. Meanwhile, a random operator is introduced to increase the diversity of trees. Furthermore, we proposed an imputation method based on SMOTE-NC oversampling technology and the ALWRF method for imbalanced and mixed-type data, called SncALWRFI. Meanwhile, Bayesian optimization and cross-validation were employed to search optimal parameters. The experimental results showed that the ALWRF method outperforms random forest and Bayesian optimized random forest in terms of classification and regression accuracy. Further, in the experiment for missing values, the SncALWRFI showed the best imputation accuracy, and it performed high imputation effectiveness in public datasets with characteristics of imbalanced and mixed-type.
SATr: Slice Attention with Transformer for Universal Lesion Detection

Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by multi-slice-input detection approaches which model 3D context from multiple adjacent CT slices, but such methods still experience difficulty in obtaining a global representation among different slices and within each individual slice since they only use convolution-based fusion operations. In this paper, we propose a novel Slice Attention Transformer (SATr) block which can be easily plugged into convolution-based ULD backbones to form hybrid network structures. Such newly formed hybrid backbones can better model long-distance feature dependency via the cascaded self-attention modules in the Transformer block while still holding a strong power of modeling local features with the convolutional operations in the original backbone. Experiments with five state-of-the-art methods show that the proposed SATr block can provide an almost free boost to lesion detection accuracy without extra hyperparameters or unique network designs. Code: https://github.com/MIRACLE-Center/A3D_SATr .
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI

Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by leveraging important multi-scale image-based information from adjacent slices, but current methods do not fully learn and exploit such cross-slice information. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn cross-slice information at multiple scales. The module can be utilized in any existing deep-learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture cross-slice information significant for prostate zonal segmentation in order to improve the performance of current state-of-the-art methods. Cross-slice attention improves segmentation accuracy in the peripheral zones, such that segmentation results are consistent across all the prostate slices (apex, mid-gland, and base). The code for the proposed model is available at https://bit.ly/CAT-Net.
Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity

There has been a growing interest in developing image super-resolution (SR) algorithms that convert low-resolution (LR) to higher resolution images, but automatically evaluating the visual quality of super-resolved images remains a challenging problem. Here we look at the problem of SR image quality assessment (SR IQA) in a two-dimensional (2D) space of deterministic fidelity (DF) versus statistical fidelity (SF). This allows us to better understand the advantages and disadvantages of existing SR algorithms, which produce images at different clusters in the 2D space of (DF, SF). Specifically, we observe an interesting trend from more traditional SR algorithms that are typically inclined to optimize for DF while losing SF, to more recent generative adversarial network (GAN) based approaches that by contrast exhibit strong advantages in achieving high SF but sometimes appear weak at maintaining DF. Furthermore, we propose an uncertainty weighting scheme based on content-dependent sharpness and texture assessment that merges the two fidelity measures into an overall quality prediction named the Super Resolution Image Fidelity (SRIF) index, which demonstrates superior performance against state-of-the-art IQA models when tested on subject-rated datasets.
Dynamical properties of neuromorphic Josephson junctions

Neuromorphic computing exploits the dynamical analogy between many physical systems and neuron biophysics. Superconductor systems, in particular, are excellent candidates for neuromorphic devices due to their capacity to operate at great speeds and with low energy dissipation compared to their silicon counterparts. In this paper, we revisit a prior work on Josephson Junction-based neurons to identify the exact dynamical mechanisms underlying the system's neuronlike properties and reveal complex behaviors which are relevant for neurocomputation and the design of superconducting neuromorphic devices. Our paper lies at the intersection of superconducting physics and theoretical neuroscience, both viewed under a common framework---that of nonlinear dynamics theory.
On the User Behavior Leakage from Recommender System Exposure

Modern recommender systems are trained to predict users’ potential future interactions from users’ historical behavior data. During the interaction process, despite the data coming from the user side, recommender systems also generate exposure data to provide users with personalized recommendation slates. Compared with the sparse user behavior data, the system exposure data are much larger in volume since only very few exposed items would be clicked by the user. In addition, user historical behavior data are privacy sensitive and commonly protected with careful access authorization. However, the large volume of recommender exposure data generated by the service provider itself usually receives less attention and could be accessed within a relatively larger scope of various information seekers or even potential adversaries. In this article, we investigate the problem of user behavior data leakage in the field of recommender systems. We show that the privacy-sensitive user past behavior data can be inferred through the modeling of system exposure. In other words, one can infer which items the user has clicked just from the observation of current system exposure for this user . Given the fact that system exposure data could be widely accessed from a relatively larger scope, we believe that user past behavior privacy has a high risk of leakage in recommender systems. More precisely, we conduct an attack model whose input is the current recommended item slate (i.e., system exposure) for the user while the output is the user’s historical behavior. Specifically, we exploit an encoder-decoder structure to construct the attack model and apply different encoding and decoding strategies to verify attack performance. Experimental results on two real-world datasets indicate a great danger of user behavior data leakage. To address the risk, we propose a two-stage privacy-protection mechanism that first selects a subset of items from the exposure slate and then replaces the selected items with uniform or popularity-based exposure. Experimental evaluation reveals a trade-off effect between the recommendation accuracy and the privacy disclosure risk, which is an interesting and important topic for privacy concerns in recommender systems.
Quantitative Assessment and Spatial Analysis of Metals and Metalloids in Soil Using the Geo-Accumulation Index in the Capital Town of Romblon Province, Philippines

The municipality of Romblon in the Philippines is an island known for its marble industry. The subsurface of the Philippines is known for its limestone. The production of marble into slab, tiles, and novelty items requires heavy equipment to cut rocks and boulders. The finishing of marble requires polishing to smoothen the surface. During the manufacturing process, massive amounts of particulates and slurry are produced, and with a lack of technology and human expertise, the environment can be adversely affected. Hence, this study assessed and monitored the environmental conditions in the municipality of Romblon, particularly the soils and sediments, which were affected due to uncontrolled discharges and particulates deposition. A total of fifty-six soil and twenty-three sediment samples were collected and used to estimate the metal and metalloid (MM) concentrations in the whole area using a neural network-particle swarm optimization inverse distance weighting model (NN-PSO). There were nine MMs; e.g., As, Cr, Ni, Pb, Cu, Ba, Mn, Zn and Fe, with significant concentrations detected in the area in both soils and sediments. The geo-accumulation index was computed to assess the level of contamination in the area, and only the soil exhibited contamination with zinc, while others were still on a safe level. Nemerow’s pollution index (NPI) was calculated for the samples collected, and soil was evaluated and seen to have a light pollution level, while sediment was considered as “clean”. Furthermore, the single ecological risk (Er) index for both soil and sediment samples was considered to be a low pollution risk because all values of Er were less than 40.
APT-Net: Adaptive encoding and parallel decoding transformer for medical image segmentation

There are limitations in the study of transformer-based medical image segmentation networks for token position encoding and decoding of images. The position encoding module cannot encode the position information adequately, and the serial decoder cannot utilize the contextual information efficiently. In this paper, we propose a new CNN-transformer hybrid structure for the medical image segmentation network APT-Net based on the encoder-decoder architecture. The network introduces an adaptive position encoding module for the fusion of position information of a multi-receptive field to provide more adequate position information for the token sequences in the transformer. In addition, the dual-path parallel decoder's basic and guide information paths simultaneously process multiscale feature maps to efficiently utilize contextual information. We conducted extensive experiments and reported a number of important metrics from multiple perspectives on seven datasets containing skin lesions, polyps, and glands. The IoU reached 0.783 and 0.851 on the ISIC2017 and Glas datasets, respectively. To the best of our knowledge, APT-Net achieves state-of-the-art performance on the Glas dataset and polyp segmentation tasks. Ablation experiments validate the effectiveness of the proposed adaptive position encoding module and the dual-path parallel decoder. Comparative experiments with state-of-the-art methods demonstrate the high accuracy and portability of APT-Net.