Reviewerly/exHarmony huggingface dataset:
Deep Learning for Differentiation of Breast Masses Detected by Screening Ultrasound Elastography

Recently, deep learning using convolutional neural networks (CNNs) has yielded consistent results in image-pattern recognition. This study was aimed at investigating the effectiveness of deep learning using CNNs to differentiate benign and malignant breast masses identified by elastography on ultrasound screening. A data set of the elastography images of 245 breast masses (146 benign, 99 malignant) in 239 consecutive patients was retrospectively obtained. The data set was randomly split into training (55%), validation (25%) and test (20%) cohorts. A deep learning model predicting the probability of malignancy was constructed using GoogLeNet architectures (pre-trained by ImageNet) with 50 epochs. The model was then applied to the test data, and the results were compared with those obtained by evaluating the fat-to-lesion ratio (FLR) and by a 5-point visual color assessment (elasticity score). The receiver operating characteristic (ROC) curve was calculated to evaluate the performance of the model. The DeLong test was used to compare the areas under the ROC curve (AUCs). The CNN, FLR and elasticity score had a sensitivity of 0.800, 0.800 and 0.350; specificity of 0.966, 0.586 and 0.931; accuracy of 0.898, 0.673 and 0.694; positive predictive value of 0.941, 0.571 and 0.778; negative predictive value of 0.875, 0.810 and 0.675; and AUC of 0.895, 0.693 and 0.641, respectively. The AUC of the CNN was significantly higher than that of the FLR or elasticity score (p < 0.001). A CNN-based deep learning model for predicting benign or malignant breast masses revealed better diagnostic performance than did FLR or elasticity score-based estimations on ultrasound elastography. The CNN-based model also increased the positive predictive value from 57%-78% to 94%. Therefore, this model may reduce unnecessary biopsy recommendations for masses detected on breast ultrasound screening.
3D geometrical total variation regularized low-rank matrix factorization for hyperspectral image denoising

The total variation (TV) regularization has been widely used in hyperspectral image (HSI) denoising owing to its powerful capabilities in terms of structure preservation. However, existing TV terms ignore the geometrical structures, such as edges and textures, in HSIs. To this end, we present a novel 3D geometrical total variation (3D GTV) regularizer to capture the local information in HSIs. 3D GTV can preserve the spatial and spectral information in 3D cubes effectively due to the introduction of geometrical structures. By incorporating this term, a 3D GTV regularized low-rank matrix factorization (3DGTVLR) method is proposed for the noise removal of noisy HSIs. In the proposed method, different norms are adopted to model the structures in 3D cubes. More specifically, the L1 norm is employed to enhance the structural information in the 3D cubes containing edges or textures, while the L2 norm is considered to ensure the information in flat 3D cubes. The local structures in HSIs are represented more compatibly by the combination of L1 and L2 norms. Then, we derive an efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the optimization problem. Experiments are performed on synthetic and real HSI datasets and the effectiveness of the proposed method is demonstrated when compared with the state-of-the-art methods. Our code is available at https://github.com/RSMagneto/3DGTVLR.
Single-Motor and Dual-Axis Solar Tracking System for Micro Photovoltaic Power Plants

Abstract Photovoltaic (PV) panels convert solar radiation into electrical energy in a clean and cost-effective way. PV panels are positioned against the Sun using fixed or solar tracking systems to generate electricity at maximum efficiency. Although solar tracking systems work with higher power efficiency than fixed solar systems, they do not attract commercial attention due to their high investment and maintenance costs. In this study, a single-motor and dual-axis solar tracking system called asymmetric solar tracker (AST) was designed. The most significant innovation of AST is the adjustable asymmetrical stand that carries the PV panels. Thanks to its asymmetrical stand, AST does not need concrete or heavy metal construction to carry PV panels, as in traditional solar tracking systems. In addition, AST can track the Sun on the dual axis by moving on a single axis owing to its asymmetrical stand. These features make AST approximately as cost-effective as fixed solar systems and as efficient as dual-axis solar tracking systems. As an experimental study, an AST for two PV panels was fabricated and compared with a fixed solar system under different weather conditions. A microcontroller was employed to control the AST and light-dependent resistor (LDR) sensors were used to track the instant position of the Sun. Experimental results reveal that, depending on the weather conditions, AST increases the daily electrical energy produced by PV panels between 25% and 38% compared to the fixed solar system.
Tensor Implementation of Monte-Carlo Tree Search for Model-Based Reinforcement Learning

Monte-Carlo tree search (MCTS) is a widely used heuristic search algorithm. In model-based reinforcement learning, MCTS is often utilized to improve action selection process. However, model-based reinforcement learning methods need to process large number of observations during the training. If MCTS is involved, it is necessary to run one instance of MCTS for each observation in every iteration of training. Therefore, there is a need for efficient method to process multiple instances of MCTS. We propose a MCTS implementation that can process batch of observations in fully parallel fashion on a single GPU using tensor operations. We demonstrate efficiency of the proposed approach on a MuZero reinforcement learning algorithm. Empirical results have shown that our method outperforms other approaches and scale well with increasing number of observations and simulations.
Exploring uplift modeling with high class imbalance

Abstract Uplift modeling refers to individual level causal inference. Existing research on the topic ignores one prevalent and important aspect: high class imbalance. For instance in online environments uplift modeling is used to optimally target ads and discounts, but very few users ever end up clicking an ad or buying. One common approach to deal with imbalance in classification is by undersampling the dataset. In this work, we show how undersampling can be extended to uplift modeling. We propose four undersampling methods for uplift modeling. We compare the proposed methods empirically and show when some methods have a tendency to break down. One key observation is that accounting for the imbalance is particularly important for uplift random forests, which explains the poor performance of the model in earlier works. Undersampling is also crucial for class-variable transformation based models.
Usefulness of Plane-Based Augmented Geovisualization—Case of “The Crown of Polish Mountains 3D”

In this article, we suggest the introduction of a new method of generating AR content, which we propose to call plane-based augmented geovisualizations (PAGs). This method concerns cases in which AR geovisualizations are embedded directly on any plane detected by the AR device, as in the case of the investigated “Crown of Polish Mountains 3D” application. The study on the usefulness of the AR solution against a classic solution was conducted as part of an online survey of people from various age and social groups. The application in the monitor version showing 3D models of mountain peaks (without AR mode) was tested by the respondents themselves. The use of the application in the AR mode, which requires a smartphone with the appropriate module, was tested by the respondents based on a prepared video demonstrating its operation. The results of the research on three age groups show that the AR mode was preferred among users against all compared criteria, but some differences between age groups were clearly visible. In the case of the criterion of ease of use of the AR mode, the result was not so unambiguous, which is why further research is necessary. The research results show the potential of the AR mode in presenting 3D terrain models.
Learning Functional Descriptors Based on the Bernstein Polynomials – Preliminary Studies

We propose a new method of constructing and estimating descriptors for classifying functional data. These descriptors are based on Bernstein polynomials and their estimation is based on noisy samples of a function (signal) to be classified. The next step is to select an appropriate classifier, well suited to these descriptors. Although the result can be case dependent, we provide the methodology of running comparisons. As a vehicle for presenting the results, we choose benchmark data published in [32]. They represent shocks and vibrations of the operator’s cabin in a large mechanical structure.
Robust Chinese Named Entity Recognition Based on Fusion Graph Embedding

Named entity recognition is an important basic task in the field of natural language processing. The current mainstream named entity recognition methods are mainly based on the deep neural network model. The vulnerability of the deep neural network itself leads to a significant decline in the accuracy of named entity recognition when there is adversarial text in the text. In order to improve the robustness of named entity recognition under adversarial conditions, this paper proposes a Chinese named entity recognition model based on fusion graph embedding. Firstly, the model encodes and represents the phonetic and glyph information of the input text through graph learning and integrates above-multimodal knowledge into the model, thus enhancing the robustness of the model. Secondly, we use the Bi-LSTM to further obtain the context information of the text. Finally, conditional random field is used to decode and label entities. The experimental results on OntoNotes4.0, MSRA, Weibo, and Resume datasets show that the F1 values of this model increased by 3.76%, 3.93%, 4.16%, and 6.49%, respectively, in the presence of adversarial text, which verifies the effectiveness of this model.
Writing Virtual Reality Teaching Resources

Simulations can provide opportunities for engaged exploration in physics teaching and learning. Beyond the two-dimensional world of screen-based simulations, abstract concepts like vectors (for example, of electric fields) can frequently be visualized better in a three-dimensional virtual reality (VR) environment. These visualizations can be immersive, where the user is able to walk around, look around, and intuitively interact with objects in virtual space. Finally, it has been shown that this bodily acting out of physics scenarios (“embodiment”) can lead to even better learning results of particularly basic mechanics concepts.
A unified RGB-T crowd counting learning framework

In this paper, a novel Unified RGB-T Crowd Counting Learning Framework (UCCF) is proposed, which utilizes an image fusion network architecture and a crowd counting network architecture to estimate the density map and count results simultaneously. Since there are few deep learning methods for the RGB-T crowd counting task, current research lacks unified learning frameworks to solve the image fusion and crowd counting problems synchronously. To fill this gap, our framework aims to fuse two modalities, visible and thermal infrared images, that exploit the complementary information to accurately count the dense population and construct the end-to-end training process. To this end, we first propose the unified RGB-T crowd counting learning framework to complete the image fusion and crowd counting tasks simultaneously by redesigning the unified training loss function. Also, to further narrow the gap between the two models and simplify the end-to-end training process, we design the Assisted Learning Module (ALM) by merging the density map feature into the image fusion encoding process. Meanwhile, we propose an Extensive Context Extraction Module (ECEM) and apply the Multi-domain Attention Block (MAB) to further improve the counting accuracy. The experimental results show that our method outperforms all single-modal input methods (26.9% improvements over the best RGB-input approaches and 3% improvements over the best thermal infrared input methods for MAE) and is at the forefront of multi-modal input methods, which demonstrates the robustness and effectiveness of our framework.
The effects of graphical encodings on reading performance of the data chart displayed on the periphery of attention

The accuracy and efficiency of estimating the true data value in visualizations is critical to the user interface design for large displays. As a variable, the attention engagement has not been the focus of existing related studies. In the current study, we intended to measure the performance users estimate proportions from a visualization under the low-attention condition. On this purpose, the dual-task paradigm was employed to simulate the scenario that data charts were read simultaneously with searching and memorizing information on the display. We evaluated eight data charts with three graphical encodings (shape, orientation and area) for the proportion value. The experimental results show that the vertical stacked chart is relatively more suitable for quantitative reading under the situation of temporary distraction. This is reflected by the high accuracy of estimation of this chart with relative less time consumption. When data charts are difficult to read, the users tend to pay less attention to information processing and make more inaccurate inferences of this primary task. Graphical encoding and the task time have a significant effect on task performances overall, while the reading accuracy seems not to vary with the difference of the primary task. The present study can be a supplement to the understanding of graphical perception and provide implications for the design of data visualization in display-human interfaces.
Benchmarking the Second Edition of the Omnidirectional Media Format Standard

Omnidirectional MediA Format (OMAF) is the first worldwide virtual reality (VR) standard to store and distribute immersive media, completed in 2019. Later, in 2021, the second edition of this standard (OMAF v2) was published. The second edition kept all the features defined in the first OMAF edition while introducing some new ones, such as overlays and multi-viewpoints. OMAF v2’s Tile Index Segments that contain metadata to track fragment data per segment and quality levels create a bandwidth overhead. During the OMAF v2 standardization, multiple methods for the track fragment run representation were studied to deal with this overhead. This paper presents the implementation of one of these methods, the compressed box method using the DEFLATE algorithm (OMAF v2*). It also provides comprehensive test results of OMAF v1, OMAF v2 and OMAF v2* with various combinations of three tile grids (6x4, 8x6 and 12x8), three segment durations (300 ms, 900 ms and 3 s), two videos (RollerCoaster and Timelapse), two bitrate groups (each group with four different bitrates) and two HTTP versions (HTTP/1.1 and H2).
An Efficient Dynamic Sampling Policy for Monte Carlo Tree Search

We consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of finite-horizon Markov decision process. We propose a dynamic sampling tree policy that efficiently allocates limited computational budget to maximize the probability of correct selection of the best action at the root node of the tree. Experimental results on Tic-Tac-Toe and Gomoku show that the proposed tree policy is more efficient than other competing methods.
Label Noise Detection in IoT Security based on Decision Tree and Active Learning

Classification is an essential and important task in Machine Learning (ML). The existence of label noise in training datasets can negatively impact the performance of supervised classification. Due to the growing interest in the data-centric AI that aims at improving the quality of training data without enhancing the complexity of models, there have been a variety of research works to tackle the label noise problem. However, few works have investigated this problem in the IoT network intrusion detection domain. This paper addresses the issue of label noise in the intrusion detection domain by presenting a framework to detect the samples with noisy labels. The decision tree classification algorithm and active learning are the main components of the proposed framework. The use of the framework is composed of two steps: making a decision tree robust against the label noise in a dataset and then using this robust model with the help of active learning with uncertainty sampling to detect noisy samples effectively. In this way, the inherent resiliency of the decision tree algorithm against label noise is utilized to tackle this issue in datasets. Based on the results of our experiments, the proposed framework can detect a considerable number of noisy samples in the training dataset, with up to 98% noise reduction.
Jointing Recurrent Across-Channel and Spatial Attention for Multi-Object Tracking With Block-Erasing Data Augmentation

Although deep-learning-based multi-object tracking (MOT) approaches have achieved remarkable performances in terms of accuracy and efficiency, the issue of object occlusions remains an open challenge for most one-shot MOT methods. To address the problem of object occlusions, in this paper we present a recurrent across-channel and spatial attention-based one-shot multi-object tracking method with block-erasing data augmentation. First, we construct a multiattention feature learning module, named RASFL, that combines recurrent across -channel attention with spatial attention. The RASFL extracts both the correlations of the feature channels and the differences of the spatial locations to improve the accuracy of the re-identification (Re-ID) task. Second, we adopt a block-erasing data augmentation strategy to handle object occlusions by using random pixel blocks to simulate occlusion cases during the network training process. This block-erasing data augmentation assists the network to be more robust under object occlusions. By integrating the proposed RASFL module and the block-erasing data augmentation strategy into a one-shot online MOT system, we build an accurate and robust MOT model called DcMOT. Finally, we run our method on the MOT16, MOT17 and MOT20 datasets to conduct a comprehensive comparison with some of the state-of-the-art MOT methods. The experimental results demonstrate that the proposed DcMOT model achieves a competitive performance in terms of both accuracy and efficiency; with especially good performances in the occlusion cases.
Secure and Efficient Stigmergy-Empowered Blockchain Framework for Heterogeneous Collaborative Services in the Internet of Vehicles

The Internet of Vehicles (IoV) is expected to address the significant problems of modern transportation through collaboration among various entities. It is crucial to establish a collaborative mechanism for untrusted entities to achieve the full potential of IoV. Blockchain is a promising solution for building a credible environment for entities. However, because of the difference and complexity of services, using a classical blockchain system to support heterogeneous collaborative services in IoV causes some challenges in smart contract support, system security, and computational efficiency. In this article, we propose a stigmergy-empowered blockchain framework called SEB, which enables untrusted IoV entities to perform heterogeneous collaborative services conveniently, securely, and efficiently. Specifically, we first explore the characteristics of collaborative services and analyze the challenges of existing blockchain systems. Furthermore, we introduce the stigmergy of swarm intelligence into blockchain and integrate the stigmergy into SEB by designing a new transaction data structure, a digital pheromone and transaction selection rules, and a new transaction selection algorithm. Simulation experiments demonstrate that compared with IOTA, SEB reduces smart contract transaction sorting searches by approximately 56%, increases the average chain length by up to approximately 87%, and decreases the computation time of the transaction selection algorithm by up to approximately 98%.
Generalized Secure and Dynamic Decentralized Reputation System With a Dishonest Majority

Reputation management systems are essential for establishing trust among network users. They are tools for reinforcing cooperation and sanctioning malicious behavior. This importance becomes a requirement in decentralized environments such as mobile ad-hoc networks (MANETs), peer-to-peer systems (P2P), wireless sensor networks (WSNs), or decentralized social networks (DSNs) where there is no trusted third party to monitor and enforce good behavior among users. In this paper, we propose a dynamic decentralized reputation system that fits such network characteristics, namely decentralization, dynamism, and openness, without conceding on security. The novel system is a general-purpose system that uses blockchain to gather and supply global reputation information while remaining fully decentralized. Unlike previous works on decentralized reputation systems where reputation information is inconsistent and limited to users’ direct experience and recommendations from peers (neighbors), our system gathers feedback from all over the network and stores reputation information on a distributed ledger fully accessible to all users. In terms of security, the proposed method achieves privacy utilizing secure multiparty computation, a cryptographic primitive that preserves feedback privacy even with a dishonest majority reaching <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n-2$ </tex-math></inline-formula> malicious parties while requiring only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> messages. The employed techniques enable the system to achieve unique characteristics like consistency, conservation, and verifiability in addition to privacy. The security analysis we provide confirms these properties, and the performed simulation shows the protocol’s effectiveness.
Kaleidoscope: Physical Backdoor Attacks Against Deep Neural Networks With RGB Filters

Recent research has shown that deep neural networks are vulnerable to backdoor attacks. A carefully-designed backdoor trigger will mislead the victim model to misclassify any sample with the trigger to the target label. Nevertheless, existing works usually utilize visible triggers, such as a white square at the corner of the image, which are easily detected by human inspections. Current efforts on developing invisible triggers yield low attack success in the physical domain. In this paper, we propose Kaleidoscope, an RGB (red, green, and blue) filter-based backdoor attack method, which utilizes RGB filter operations as the backdoor trigger. To enhance the attack success rate, we design a novel model-dependent filter trigger generation algorithm. We also introduce two constraints in the loss function to make the backdoored samples more natural and less distorted. Extensive experiments on CIFAR-10, CIFAR-100, ImageNette, and VGG-Flower have demonstrated that RGB filter-processed samples not only achieve high attack success rate but also are unnoticeable to humans. It is shown that Kaleidoscope can reach an attack success rate of more than 84% in the physical world under different lighting intensities and shooting angles. Kaleidoscope is also shown to be robust to state-of-the-art backdoor defenses, such as spectral signature, STRIP, and MNTD.