Reviewerly/exHarmony huggingface dataset:
Research on the Risk of Social Stability of Enterprise Credit Supervision Mechanism Based on Big Data

The study aims to establish a platform-based enterprise credit supervision mechanism, and combined with big data, accurately evaluate the credit assets of enterprises under the influence of social stability risk, and improve the ability of enterprises to deal with risks. Using descriptive statistical methods, the study shows that most local enterprises exist in the form of micro loans, which promotes the development of local economy to a certain extent, but it is a vicious cycle of economic development; The overall prediction accuracy of the single enterprise risk assessment model under the influence of social stability risk is 65%. Compared with the single algorithm, the prediction accuracy of the integrated algorithm model is significantly improved, and the prediction accuracy can reach 83.5%, the standard deviation of data prediction is small, and the stability of the model is high.
Dual-Task Mutual Learning for Semi-supervised Medical Image Segmentation

The success of deep learning methods in medical image segmentation tasks usually requires a large amount of labeled data. However, obtaining reliable annotations is expensive and time-consuming. Semi-supervised learning has attracted much attention in medical image segmentation by taking the advantage of unlabeled data which is much easier to acquire. In this paper, we propose a novel dual-task mutual learning framework for semi-supervised medical image segmentation. Our framework can be formulated as an integration of two individual segmentation networks based on two tasks: learning region-based shape constraint and learning boundary-based surface mismatch. Different from the one-way transfer between teacher and student networks, an ensemble of dual-task students can learn collaboratively and implicitly explore useful knowledge from each other during the training process. By jointly learning the segmentation probability maps and signed distance maps of targets, our framework can enforce the geometric shape constraint and learn more reliable information. Experimental results demonstrate that our method achieves performance gains by leveraging unlabeled data and outperforms the state-of-the-art semi-supervised segmentation methods.
Deep Representation Learning for Affective Speech Signal Analysis and Processing: Preventing unwanted signal disparities

Speech emotion recognition (SER) is an important research area, with direct impacts in applications of our daily lives, spanning education, health care, security and defense, entertainment, and human–computer interaction. The advances in many other speech signal modeling tasks, such as automatic speech recognition, text-to-speech synthesis, and speaker identification, have led to the current proliferation of speech-based technology. Incorporating SER solutions into existing and future systems can take these voice-based solutions to the next level. Speech is a highly nonstationary signal, with dynamically evolving spatial-temporal patterns. It often requires a sophisticated representation modeling framework to develop algorithms capable of handling real-life complexities.
FCL-Net: Towards accurate edge detection via Fine-scale Corrective Learning

Integrating multi-scale predictions has become a mainstream paradigm in edge detection. However, most existing methods mainly focus on effective feature extraction and multi-scale feature fusion while ignoring the low learning capacity in fine-level branches, limiting the overall fusion performance. In light of this, we propose a novel Fine-scale Corrective Learning Net (FCL-Net) that exploits semantic information from deep layers to facilitate fine-scale feature learning. FCL-Net mainly consists of a Top-down Attentional Guiding (TAG) and a Pixel-level Weighting (PW) module. TAG module adopts semantic attentional cues from coarse-scale prediction into guiding the fine-scale branches by learning a top-down LSTM. PW module treats the contribution of each spatial location independently and promote fine-level branches to detect detailed edges with high confidence. Experiments on three benchmark datasets, i.e., BSDS500, Multicue, and BIPED, show that our approach significantly outperforms the baseline and achieves a competitive ODS F-measure of 0.826 on the BSDS500 benchmark. The source code and models are publicly available at https://github.com/DREAMXFAR/FCL-Net.
SHOWAR

Microservices architecture have been widely adopted in designing distributed cloud applications where the application is decoupled into multiple small components (i.e. "microservices"). One of the challenges in deploying microservices is finding the optimal amount of resources (i.e. size) and the number of instances (i.e. replicas) for each microservice in order to maintain a good performance as well as prevent resource wastage and under-utilization which is not cost-effective. This paper presents SHOWAR, a framework that configures the resources by determining the number of replicas (horizontal scaling) and the amount of CPU and Memory for each microservice (vertical scaling). For vertical scaling, SHOWAR uses empirical variance in the historical resource usage to find the optimal size and mitigate resource wastage. For horizontal scaling, SHOWAR uses basic ideas from control theory along with kernel level performance metrics. Additionally, once the size for each microservice is found, SHOWAR bridges the gap between optimal resource allocation and scheduling by generating affinity rules (i.e. hints) for the scheduler to further improve the performance. Our experiments, using a variety of microservice applications and real-world workloads, show that, compared to the state-of-the-art autoscaling and scheduling systems, SHOWAR on average improves the resource allocation by up to 22% while improving the 99th percentile end-to-end user request latency by 20%.
The <i>c</i>-Differential Uniformity and Boomerang Uniformity of Two Classes of Permutation Polynomials

The Difference Distribution Table (DDT) and the differential uniformity play a major role for the design of substitution boxes in block ciphers, since they indicate the function’s resistance against differential cryptanalysis. This concept was extended recently to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> -DDT and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> -differential uniformity, which have the potential of extending differential cryptanalysis. Recently, a new theoretical tool, the Boomerang Connectivity Table (BCT) and the corresponding boomerang uniformity were introduced to quantify the resistance of a block cipher against boomerang-style attacks. Here we concentrate on two classes (introduced recently) of permutation polynomials over finite fields of even characteristic. For one of these, which is an involution used to construct a 4-uniform permutation, we explicitly determine the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> -DDT entries and BCT entries. For the second type of function, which is a differentially 4-uniform function, we give bounds for its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> -differential and boomerang uniformities.
Privacy-preserving task allocation for edge computing-based mobile crowdsensing

In the era of big data, edge computing has coped greatly with the increase in data. Recently, edge computing has been incorporated into mobile crowdsensing (MCS) to collect large-scale data, but existing edge computing-based MCS (EC-MCS) ideally assumes that edge servers are trusted. In this paper, a novel mechanism is proposed that we use semi-honest entities to securely and efficiently complete task assignment in large-scale crowdsensing. Firstly, homomorphic encryption is used to encrypt users’ location information, and the collaboration between edge servers is used to complete task allocation under cipher-text. Then, the optimal users are selected to complete tasks and upload the encrypted sensing data. Moreover, a secure payment mechanism is proposed to avoid fraud problems in semi-honest edge servers. Finally, we analyze the security of our scheme theoretically and conduct a multi-dimensional simulation experiment to prove the effectiveness and availability of the proposed scheme. • Allocate task and collect data while protecting for data privacy of users. • Protect users’ location by a homomorphic encryption task allocation mechanism. • Avoid the fraud problem of edge servers by secure payment mechanism.
BETULA: Fast clustering of large data with improved BIRCH CF-Trees

BIRCH clustering is a widely known approach for clustering that has influenced much subsequent research and commercial products. The key contribution of BIRCH is the Clustering Feature tree (CF-Tree), which is a compressed representation of the input data. As new data arrives, the tree is eventually rebuilt to increase the compression. Afterward, the leaves of the tree are used for clustering. Because of the data compression, this method is very scalable. The idea has been adopted, for example, for k-means, data stream, and density-based clustering. Clustering features used by BIRCH are simple summary statistics that can easily be updated with new data: the number of points, the linear sums, and the sum of squared values. Unfortunately, how the sum of squares is then used in BIRCH is prone to catastrophic cancellation. We introduce a replacement cluster feature that does not have this numeric problem, that is not much more expensive to maintain, and which makes many computations simpler and, hence, more efficient. These cluster features can also easily be used in other work derived from BIRCH such as algorithms for streaming data. In the experiments, we demonstrate the numerical problem and compare the performance of the original algorithm compared to the improved cluster features. We furthermore explain how to improve clustering with different algorithms such as hierarchical clustering, k-means, k-means++ and Gaussian mixture modeling by using the variance information stored in the cluster features to obtain high-quality result approximations in a substantially reduced runtime (up to 500× faster in our experiments).
Confidence-Based Out-of-Distribution Detection: A Comparative Study and Analysis

Image classification models deployed in the real world may receive inputs outside the intended data distribution. For critical applications such as clinical decision making, it is important that a model can detect such out-of-distribution (OOD) inputs and express its uncertainty. In this work, we assess the capability of various state-of-the-art approaches for confidence-based OOD detection through a comparative study and in-depth analysis. First, we leverage a computer vision benchmark to reproduce and compare multiple OOD detection methods. We then evaluate their capabilities on the challenging task of disease classification using chest X-rays. Our study shows that high performance in a computer vision task does not directly translate to accuracy in a medical imaging task. We analyse factors that affect performance of the methods between the two tasks. Our results provide useful insights for developing the next generation of OOD detection methods.
Improved Harris hawks optimization algorithm based on random unscented sigma point mutation strategy

The Harris hawks optimization algorithm (HHO) is a new swarm intelligence algorithm for simulating the capture and attack of the hawks which has good global exploration and local exploitation capabilities. In order to further improve the optimization performance of the algorithm, quasi-opposite learning and quasi-reflection learning strategies performed according to probability are involved in the attack phase to enhance the diversity of the population and accelerate the convergence rate of HHO while a logarithmic nonlinear convergence factor is designed to balance the ability of global search and local optimization of the algorithm. Furthermore, in order to avoid the algorithm falling into a local optimum, using the characteristics of the unscented transform (UT) to estimate the mean and variance of a random variable function can achieve second-order accuracy, a new strategy for generating random symmetric sigma points is designed to mutate the current best individual in the visible range, at last, an improved Harris hawk algorithm (IHHO) based on random unscented sigma point mutation is proposed. The new stochastic UT ensures the random exploitation of the algorithm and has a certain theoretical support, which overcomes the theoretical deficiency of the stochastic optimization algorithm to some extent. The numerical optimization ability of IHHO is verified by CEC2017 benchmark functions, CEC2020 benchmark function and fifteen standard test functions. Finally, the practicality and effectiveness of the IHHO algorithm are verified by three engineering constraint optimization and the discounted {0-1} knapsack problem.
A blind color image watermarking algorithm using the energy concentration principle of Hadamard matrix

The convenience of the network has brought the copyright crisis of some multimedia works. Therefore, a secure digital watermarking algorithm is urgently needed to realize the copyright maintenance of digital works. A blind digital watermarking algorithm using approximate Hadamard transform is presented in this paper. Since Hadamard transform has a certain energy aggregation effect, in this paper, the principle of energy concentration of Hadamard transform is explored, and the Hadamard transform is slightly treated, so that the multiple increase of large energy coefficient is realized. This modified approximate Hadamard transform is applied to the process of changing from spatial domain to transform domain in the embedding and extraction stage of our proposed watermarking algorithm. In this algorithm, the color image digital watermark is hided in the maximum energy coefficient of color host image in transform domain by using the idea of quantization. In the extraction stage, the watermark image can be extracted completely without the participation of any original information. In order to check on the feasibility of the algorithm, a large number of simulation experiments have been done. The results of simulation experiments show that the proposed algorithm has excellent effect in various performance index.
Brain Tumor Segmentation From Multi-Modal MR Images via Ensembling UNets

Glioma is a type of severe brain tumor, and its accurate segmentation is useful in surgery planning and progression evaluation. Based on different biological properties, the glioma can be divided into three partially-overlapping regions of interest, including whole tumor (WT), tumor core (TC), and enhancing tumor (ET). Recently, UNet has identified its effectiveness in automatically segmenting brain tumor from multi-modal magnetic resonance (MR) images. In this work, instead of network architecture, we focus on making use of prior knowledge (brain parcellation), training and testing strategy (joint 3D+2D), ensemble and post-processing to improve the brain tumor segmentation performance. We explore the accuracy of three UNets with different inputs, and then ensemble the corresponding three outputs, followed by post-processing to achieve the final segmentation. Similar to most existing works, the first UNet uses 3D patches of multi-modal MR images as the input. The second UNet uses brain parcellation as an additional input. And the third UNet is inputted by 2D slices of multi-modal MR images, brain parcellation, and probability maps of WT, TC, and ET obtained from the second UNet. Then, we sequentially unify the WT segmentation from the third UNet and the fused TC and ET segmentation from the first and the second UNets as the complete tumor segmentation. Finally, we adopt a post-processing strategy by labeling small ET as non-enhancing tumor to correct some false-positive ET segmentation. On one publicly-available challenge validation dataset (BraTS2018), the proposed segmentation pipeline yielded average Dice scores of 91.03/86.44/80.58% and average 95% Hausdorff distances of 3.76/6.73/2.51 mm for WT/TC/ET, exhibiting superior segmentation performance over other state-of-the-art methods. We then evaluated the proposed method on the BraTS2020 training data through five-fold cross validation, with similar performance having also been observed. The proposed method was finally evaluated on 10 in-house data, the effectiveness of which has been established qualitatively by professional radiologists.
Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

Most existing aspect-based sentiment analysis (ABSA) research efforts are devoted to extracting the aspect-dependent sentiment features from the sentence towards the given aspect. However, it is observed that about 60% of the testing aspects in commonly used public datasets are unknown to the training set. That is, some sentiment features carry the same polarity regardless of the aspects they are associated with (aspect-invariant sentiment), which props up the high accuracy of existing ABSA models when inevitably inferring sentiment polarities for those unknown testing aspects. Therefore, in this paper, we revisit ABSA from a novel perspective by deploying a novel supervised contrastive learning framework to leverage the correlation and difference among different sentiment polarities and between different sentiment patterns (aspect-invariant/-dependent). This allows improving the sentiment prediction for (unknown) testing aspects in the light of distinguishing the roles of valuable sentiment features. Experimental results on 5 benchmark datasets show that our proposed approach substantially outperforms state-of-the-art baselines in ABSA. We further extend existing neural network-based ABSA models with our proposed framework and achieve improved performance.
Computer vision assisted human computer interaction for logistics management using deep learning

Human-Computer Interaction is the secret to technological advancement in the area of logistics and supply chain. The key challenges are the degree of energy transferred to devices, like automated vehicles and robotic equipment, and lack of belief in intelligent decision-making, which may overrule the system in the event of misperceptions of automated decisions. This paper presents an efficient Logistics Management Framework Using Deep Learning (eLMF-DL) to implement the computer vision-assisted Human-Computer Interaction (HCI) in the logistic management sector. With a hybrid CNN-LSTM network, eLMF-DL implements a single-stage or one-step convergence optimum decision-support design model that intelligently combines production maximization and demand forecasting. The architecture with the integration of convolutional neural network and long short-term memory network models the machine dynamics and relationships in assorted diverse logistics services demand. To determine uncertainties through dynamic delivery and optimal decisions on allocating logistical service power, the eLMF-DL results in the highest performance.
Uncertainty for Identifying Open-Set Errors in Visual Object Detection

Deployed into an open world, object detectors are prone to open-set errors, false positive detections of object classes not present in the training dataset. We propose GMM-Det, a real-time method for extracting epistemic uncertainty from object detectors to identify and reject open-set errors. GMM-Det trains the detector to produce a structured logit space that is modelled with class-specific Gaussian Mixture Models. At test time, open-set errors are identified by their low log-probability under all Gaussian Mixture Models. We test two common detector architectures, Faster R-CNN and RetinaNet, across three varied datasets spanning robotics and computer vision. Our results show that GMM-Det consistently outperforms existing uncertainty techniques for identifying and rejecting open-set detections, especially at the low-error-rate operating point required for safety-critical applications. GMM-Det maintains object detection performance, and introduces only minimal computational overhead. We also introduce a methodology for converting existing object detection datasets into specific open-set datasets to evaluate open-set performance in object detection.
OPTDP: Towards optimal personalized trajectory differential privacy for trajectory data publishing

With the development of location-based applications, more and more trajectory data are collected. Trajectory data often contains users' sensitive information, and direct release it may pose a threat to users' privacy. Differential privacy, as a privacy preserving method with solid mathematical foundation, has been widely used in trajectory data publishing. However, current trajectory data publishing methods based on differential privacy cannot fully realize the personalized privacy protection. In this paper, an optimal personalized trajectory differential privacy mechanism is proposed. Firstly, by establishing the probabilistic mobility model of trajectories, we cluster the locations to achieve semantic location matching between different trajectories. Based on the semantic similarity, we identify the templet trajectory, and propose a privacy level allocation method based on stay-points and frequent sub-trajectories. Then, according to the location matching results, we can automatically identify the privacy level of all locations. Combined with the optimal location differential privacy mechanism, we disturb the location points on the user's trajectory before publishing, where different location privacy levels correspond to different privacy budgets. Experiment results on real-world datasets show that our mechanism provides a better tradeoff between privacy protection and data utility compared with traditional differential privacy methods.
Deep Frequency Re-calibration U-Net for Medical Image Segmentation

The human visual cortex is biased towards shape components while CNNs produce texture biased features. This fact may explain why the performance of CNN significantly degrades with low-labeled input data scenarios. In this paper, we propose a frequency re-calibration U-Net (FRCU-Net) for medical image segmentation. Representing an object in terms of frequency may reduce the effect of texture bias, resulting in better generalization for a low data regime. To do so, we apply the Laplacian pyramid in the bottleneck layer of the U-shaped structure. The Laplacian pyramid represents the object proposal in different frequency domains, where the high frequencies are responsible for the texture information and lower frequencies might be related to the shape. Adaptively re-calibrating these frequency representations can produce a more discriminative representation for describing the object of interest. To this end, we first propose to use a channel-wise attention mechanism to capture the relationship between the channels of a set of feature maps in one layer of the frequency pyramid. Second, the extracted features of each level of the pyramid are then combined through a non-linear function based on their impact on the final segmentation output. The proposed FRCU-Net is evaluated on five datasets ISIC 2017, ISIC 2018, the PH <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , lung segmentation, and SegPC 2021 challenge datasets and compared to existing alternatives, achieving state-of-the-art results.