Reviewerly/exHarmony huggingface dataset:
Agent-based Modelling of Strategic behavior in PoW Protocols

In blockchain-based systems, such as Bitcoin&#x2019;s Proof-of-Work (PoW) protocol, it is expected that a miner&#x2019;s share of total block revenue is proportional to their share of the network&#x2019;s total hashing power. However, a deviation to this behavior is the selfish mining strategy, an attack vector discovered by Eyal and Sirer in 2014. This strategy may lead to a miner earning more than their &#x201C;fair share As a result, Bitcoin&#x2019;s security assumption of an honest majority may not be sufficient. In this paper, in order to verify whether selfish mining is indeed a profitable strategy in PoW systems, we introduce an agent-based model to simulate the dynamics of selfish mining behavior. The model is by design minimalistic allowing us to analyze the effect of network latency, hashing power distribution, and network topology on relative revenue of selfish miners. We find that for high levels of latency, selfish mining is always a relatively more profitable strategy, and the results turn out to be very robust to changes in the network topology. In addition, we find that the hashing power distribution following power laws, as found empirically, can make it harder for selfish miners to be profitable. Our analysis confirms the main observations that selfish mining is always relatively more profitable for hashing powers representing more than one third of the total computing power. Further, it also confirms that selfish mining behavior could cause a statistically significant high probability of contiguously mined blocks.
DIPONet: Dual-information progressive optimization network for salient object detection

With the rise of fully convolutional networks (FCN), in recent years, more and more models of salient object detection based on deep learning have used or modified FCN to perform pixel-level saliency prediction. However, many existing algorithms lack accurate saliency edges due to insufficient use of edge information. In this paper, we solve this problem by focusing on the complementarity between salient object information and salient edge information. Because salient edge features contain more details, the boundary of salient object will be more accurate when fused with edge information. Salient object features can help edge features to better locate the foreground object and filter out background interference. Accordingly, we propose a dual-information progressive optimization network (DIPONet) for salient object detection. In the first step, we extract salient edge features from the low-level of VGG or ResNet backbone and salient object features from the subsequent layers separately, and send them to group optimization fusion modules (GOFMs) which aim to achieve self-optimization of these two kinds of features. In the second step, we design a model named dual-stream information optimization (DSIO) module based on the logical relationship between salient object information and salient edge information to jointly optimize these two kinds of features in a progressive fusion way. Benefiting from the rich edge information in salient edge features and location information in salient object features, the fused features can help detecting salient objects with more accurate boundaries. Excessive experimental results conducted on five benchmark datasets show that our proposed model surpasses the state-of-the-art models and can produce accurate salient object predictions with sharp details.
CrowdQuake+: Data-driven Earthquake Early Warning via IoT and Deep Learning

In recent years, a low-cost micro-electro-mechanical systems (MEMS) acceleration sensor has been widely used for earthquake early warning (EEW). In our previous work, we introduced a networked earthquake detection system, CrowdQuake with three-hundred smartphones’ acceleration sensors and a deep-learning based earthquake detection model. For one year’s operation, CrowdQuake detected a series of earthquakes and collected various earthquake and non-earthquake data. Based on the successful operation of CrowdQuake, in this paper, we discuss how it can be expanded across the country by addressing the following challenges: (1) sensor deployments for highly dense network, (2) earthquake detection performance using a deep learning model, and (3) high performance and scalable system design for big data processing. The improved system is CrowdQuake+ which can deal with acceleration data sent from 8,000 IoT sensors and detect an earthquake in few seconds using a newly proposed detection model. Moreover, CrowdQuake+ stores all acceleration data sent from sensors and assesses their qualities by calculating noise levels. Then, the collected data are used for deep learning model training, so that its detection performance becomes more accurate.
Quantum Annealing Formulation for Binary Neural Networks

Quantum annealing is a promising paradigm for building practical quantum computers. Compared to other approaches, quantum annealing technology has been scaled up to a larger number of qubits. On the other hand, deep learning has been profoundly successful in pushing the boundaries of AI. It is thus natural to investigate potentially game changing technologies such as quantum annealers to augment the capabilities of deep learning. In this work, we explore binary neural networks, which are lightweight yet powerful models typically intended for resource constrained devices. Departing from current training regimes for binary networks that smooth/approximate the activation functions to make the network differentiable, we devise a quadratic unconstrained binary optimization formulation for the training problem. While the problem is intractable, i.e., the cost to estimate the binary weights scales exponentially with network size, we show how the problem can be optimized directly on a quantum annealer, thereby opening up to the potential gains of quantum computing. We experimentally validated our formulation via simulation and testing on an actual quantum annealer (D-Wave Advantage), the latter to the extent allowable by the capacity of current technology.
Thermal Infrared Single-Pedestrian Tracking for Advanced Driver Assistance System

Tracking algorithms with low computational complexity and reliable performance are important in developing advanced driver assistance systems (DAS). This paper proposes a method of single-pedestrian tracking using thermal infrared cameras to meet the needs of DAS operating in nighttime and low-visibility conditions. The proposed algorithm uses the background-aware correlation filter (BACF) as the basic tracking framework. In order to address the problem that directly introducing the convolutional features leads to tracking performance degradation in the BACF framework, this paper proposes a fusion scheme to integrate handcrafted and convolutional features to make full use of the advantages of both the features. The proposed scheme combines response maps from convolutional and handcrafted features through fusion coefficients to improve the performance of the trackers based on the single features. In order to calculate fusion coefficients, a novel approach of searching the main peak and interference peaks of a response map is proposed by using local binary pattern values of the response map to locate all local maximum points. Experimental results show that the proposed algorithm outperforms the existing 9 competing tracking algorithms and can be used in vehicle platforms as a module of DAS to improve the safe level of driving in nighttime.
SIECP: Neural Network Channel Pruning based on Sequential Interval Estimation

Pruning is widely regarded as an effective neural network compression and acceleration method, which can significantly reduce model parameters and speed up inference. This paper proposes a novel network Channel Pruning method based on Sequential Interval Estimation (SIECP). Our method mainly solves the problem that existing methods need to sample and evaluate a large number of sub-structures or introduce a large number of parameters, which leads to slow search. Specifically, we divide the entire channel number search process into multiple stages. In each stage, we divide the channel range that needs to be estimated into multiple channel intervals by grouping and use gradient descent to optimize the number of reserved channels. At the same time, the distribution of the number of channels is counted in each interval. At the end of each search stage, we select the channel interval with the highest frequency for further search to gradually narrow the search range until the final number of channels is determined. Then, the unpruned network is pruned according to the number of channels in each layer of the network to obtain the pruned network. Extensive experiments using ResNet and MobileNet V2 as backbones on CIFAR10, CIAFR100 and Tiny-ImageNet datasets are conducted to demonstrate the effectiveness of our method.
Learning to Drop Points for LiDAR Scan Synthesis

3D laser scanning by LiDAR sensors plays an important role for mobile robots to understand their surroundings. Nevertheless, not all systems have high resolution and accuracy due to hardware limitations, weather conditions, and so on. Generative modeling of LiDAR data as scene priors is one of the promising solutions to compensate for unreliable or incomplete observations. In this paper, we propose a novel generative model for learning LiDAR data based on generative adversarial networks. As in the related studies, we process LiDAR data as a compact yet lossless representation, a cylindrical depth map. However, despite the smoothness of real-world objects, many points on the depth map are dropped out through the laser measurement, which causes learning difficulty on generative models. To circumvent this issue, we introduce measurement uncertainty into the generation process, which allows the model to learn a disentangled representation of the underlying shape and the dropout noises from a collection of real LiDAR data. To simulate the lossy measurement, we adopt a differentiable sampling framework to drop points based on the learned uncertainty. We demonstrate the effectiveness of our method on synthesis and reconstruction tasks using two datasets. We further showcase potential applications by restoring LiDAR data with various types of corruption.
SoFTNet: A concept-controlled deep learning architecture for interpretable image classification

Interpreting deep learning (DL)-based computer vision models is challenging due to the complexity of internal representations. Most recent techniques for rendering DL learning outcomes interpretable operate on low-level features rather than high-level concepts. Methods that explicitly incorporate high-level concepts do so through a determination of the relevancy of user-defined concepts or else concepts extracted directly from the data. However, they do not leverage the potential of concepts to explain model predictions. To overcome this challenge, we introduce a novel DL architecture – the Slow/Fast Thinking Network (SoFTNet) – enabling users to define/control high-level features and utilize them to perform image classification predicatively. We draw inspiration from the dual-process theory of human thought processes, decoupling low-level, fast & non-transparent processing from high-level, slow & transparent processing. SoFTNet hence uses a shallow convolutional neural network for low-level processing in conjunction with a memory network for high-level concept-based reasoning. We conduct experiments on the CUB-200-2011 and STL-10 datasets and also present a novel concept-based deep K-nearest neighbor approach for baseline comparisons. Our experiments show that SoFTNet achieves comparable performance to state-of-art non-interpretable models and outperforms comparable interpretative methods.
Learning Lightweight Low-Light Enhancement Network Using Pseudo Well-Exposed Images

Recently, there has been growing attention on deep learning-based low-light image enhancement algorithms. With this interest, various synthetic low-light image datasets have been released publicly. However, real-world low-light and well-exposed image pair datasets are still lacking. In this paper, we propose a real-world low-light image dataset and a practical lightweight low-light image enhancement network. In order to construct a large-scale real-world low-light dataset, we have not only captured under-exposed images by ourselves but also collected under-exposed images from the Internet. Then, we produce pseudo well-exposed images for each low-light image. Using pairs of a real-world low-light image and a pseudo well-exposed image, we present a lightweight deep CNN model through knowledge distillation. Experimental results demonstrate the effectiveness and practicality of the proposed method on various datasets.
ECT-NAS: Searching Efficient CNN-Transformers Architecture for Medical Image Segmentation

The combination of convolution and Transformer applied to medical image segmentation has achieved great success. However, it still cannot reach extremely accurate segmentation on complex and low-contrast anatomical structures under lower calculation. To solve this problem, we propose ECT-NAS method to automatically search Efficient CNN-Transformers architecture for medical image segmentation, which featured with multi-scale search space. To better extract the global context in the search space of ECT-NAS, we carefully design a light transformer with local-global attention. Last, we proposed an efficient resource constrained search strategy that simultaneously optimizes the accuracy and cost (Params/FLOP) of the model. We evaluate ECT-NAS by conducting extensive experiments on synapse multi-organ, Chaos and ACDC datasets, showing that this approach achieves competitive performance over other segmentation methods, with fewer parameters and lower FLOPs.
Application of Multimedia Technology in Middle School Education Management

&lt;p&gt;In today’s era, the connection between science of technology and all aspects of social life is increasingly in-depth. Multimedia technology, particularly in the sphere of education and teaching, demonstrates its tremendous advantages, such as vast teaching resources and the ability to move between various teaching modalities at any moment. To a large extent, all of these have aided the successful execution of China’s education reform. Teachers and students were continually adjusting to the dispersion of conventional roles, resulting in an increase in instructional efficacy. How to improve the significance of multimedia in middle school curriculum development and achieve our academic objectives of &amp;quot;fostering individuals&amp;quot; has now become a contentious issue among academicians. Aiming at the current situation of the development of middle school education management in China, this paper adopts the method of combining questionnaire survey with comparative experiment, proving the advanced nature of multimedia teaching mode. Meanwhile, with the help of questionnaire analysis, the author also understands some middle school students’ real ideas about the application of multimedia technology to the classroom. The experimental results show that multimedia technology is very effective in middle school education management. The classroom quality has been effectively improved, and the enthusiasm of students in class has been improved by about 20%. Furthermore, such an improvement was obtained in only one month of testing, which is quite encouraging. Finally, the researcher provides a thorough review of the research, arguing that using multimedia technology should not only enhance the material’s effectiveness, but also prevent overwhelming the viewers.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Fast Human Pose Estimation in Compressed Videos

Current approaches for human pose estimation in videos can be categorized into per-frame and warping-based methods. Both approaches have their pros and cons. For example, per-frame methods are generally more accurate, but they are often slow. Warping-based approaches are more efficient, but the performance is usually not good. To bridge the gap, in this paper, we propose a novel fast framework for human pose estimation to meet the real-time inference with controllable accuracy degradation in compressed video domain. Our approach takes advantage of the motion representation (called “motion vector”) that is readily available in a compressed video. Pose joints in a frame are obtained by directly warping the pose joints from the previous frame using the motion vectors. We also propose modules to correct possible errors introduced by the pose warping when needed. Extensive experimental results demonstrate the effectiveness of our proposed framework for accelerating the speed of top-down human pose estimation in videos.
A Novel Graphical Password Authentication Scheme with Improved Usability

Pictures are remembered better than text by humans, motivated by the proposition that graphical authentication passwords are the possible alternatives for text-based passwords. The main objective of this study is to examine the usability attributes of the recognition-based graphical passwords, concerning ISO standard and general usability attributes. Then, the usability attributes and sub features were compared to get the new ones are considered and implemented in the novel proposed graphical password scheme. The proposed scheme was performed as a prototype and a usability evaluation towards the proposed scheme was conducted to measure its usability and practicality as the alternative user authentication scheme. Based on the questionnaire survey result and user's feedback concerning the whole system and the usability attributes of the proposed scheme, all the percentage of findings is described to be very good which from the usability point of view indicates that the new graphical password scheme is highly acceptable.
Super Learner Ensemble for Anomaly Detection and Cyber-Risk Quantification in Industrial Control Systems

Industrial control systems (ICSs) are integral parts of smart cities and critical to modern societies. Despite indisputable opportunities introduced by disruptor technologies, they proliferate the cybersecurity threat landscape, which is increasingly more hostile. The quantum of sensors utilized by ICS aided by artificial intelligence (AI) enables data collection capabilities to facilitate automation, process streamlining, and cost reduction. However, apart from the operational use, the sensors generated data combined with AI can be innovatively utilized to model anomalous behavior as part of layered security to increase resilience to cyberattacks. We introduce a framework to profile anomalous behavior in ICS and derive a cyber-risk score. A novel super learner ensemble for one-class classification is developed, using overlapping rolling windows with stratified, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -fold, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -repeat cross-validation applied to each base learner followed by majority voting to derive the best learner. Our approach is demonstrated on a liquid distribution sensor data set. The experimental results reveal that the proposed technique achieves an overall <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score of 99.13%, an anomalous recall score of 99% detecting anomalies lasting only 17 s. The key strength of the framework is the low computational complexity and error rate. The framework is modular, generic, applicable to other ICS, and transferable to other smart city sectors.
Consistent Anomaly Detection and Localization of Multivariate Time Series via Cross-Correlation Graph-Based Encoder–Decoder GAN

Multivariate time series is widely derived from industrial facilities, such as power plants, manufacturing machines, spacecraft, digital devices, and so on, and anomaly detection and location is of great importance to industrial preventive maintenance. However, anomalies in multivariate time series always result from their unusual change of temporal or correlative features, and it is challenging to capture these complex characteristics. Besides, achieving consistent anomaly detection and location performance is also a tricky issue. In this article, a novel anomaly detection and location framework that combines generative adversarial networks and autoencoder is proposed to capture time dependent and correlation features of multivariate time series with the need of anomalous sequences. First, multitime scale correlation computation is utilized to encode multivariate time series into multiple cross correlation graphs, which can be fed into the proposed deep architecture for extracting more distinguishable features. On this basis, a robust cost function with multiple loss issues is designed, and reconstruction matrix deviation from original space of encoder–encoder structure is utilized to detect and locate abnormal time series, ensuring the consistency of detection and location tasks and the framework reliability. Extensive experiments on five industrial datasets are conducted to indicate our model is a generic and excellent framework for anomaly detection and location of multivariate time series.
Joint Discriminative Latent Subspace Learning for Image Classification

Latent subspace learning aims to produce a latent representation for better reconstruction and classification from high-dimensional data through exploiting the optimal subspace. Current latent subspace learning methods commonly have three problems: 1) The discriminative property is ignored when learning the latent subspace, 2) The redundancy exists between the latent subspace and the prediction space, 3) There is no unified latent subspace that exploits knowledge jointly from the raw space, latent subspace, and label space. In this paper, we formulate the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</b> oint <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> iscriminative <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> atent <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ubspace <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> earning (JDLSL) problem to address these issues, and provide its optimization solution. JDLSL learns image representation from two aspects: a) the joint learning of latent subspaces for data reconstruction and prediction, b) the joint learning of label space and latent subspace for data reconstruction. To integrate knowledge from the joint learning, we organize the sparsity-induced latent subspace, where row-sparsity and column sparsity are simultaneously imposed. We provide the theoretical proof for the discriminativity learning ability of the sparsity-induced latent subspace. Extensive experiments and comparisons with the state-of-the-art showed that the proposed method has better performance. JDLSL shows a competitive performance with deep features compared to deep learning architectures, reflecting it potential integrating with deep learning.