Reviewerly/exHarmony huggingface dataset:
Matting by Classification

Natural image matting, since its born, has been regarded as a regression problem. Some previous works show the classification attributes in image matting are of great potential. Here we further explore the attributes to solve the matting problem in a pure classification way, which outperforms its regressive counterpart. We delved into the characteristics of $\alpha$ on alpha mattes. Then we chop the continuous alpha value from 0 to 1 into 21 segments, each of which accounts for one class. The 21 classes are encoded by a simple number from 0 to 20 and each class represents a corresponding alpha value. Thus each pixel in image belongs to one class. Our model can be trained with both classification loss and regression loss. The latter loss is implemented with soft-argmax, which makes the selection of maximum index differentiable. Through softargmax, our model can be aware of the distance relationship among all classes. Moreover, we find this strategy enables the two kinds of losses to promote each other during training, which allows our model converge further.
UHRCS: A High-Throughput Platform for Real-time Cameras-Sampling based on UE4

Nowadays, simulation environments are widely used in different research fields. As the complexity of the problem increases, Cameras-Sampling in a simulation environment is important since it can provide highly realistic training data for online machine learning and reinforcement learning, etc. This paper attempts to improve the throughput and efficiency when multiple camera sampling are performed simultaneously. We implement a high-throughput platform for Real-time Cameras-Sampling based on Unreal Engine 4. The platform uses multi-graphics queues to support multi-cameras sampling in parallel. We construct a virtual desert scene to verify the correctness and effectiveness of the proposed platform. The experiments show that the proposed platform can generate eight 960x640 pixels pictures at a frequency of 30Hz. The throughput and GPU utilization have been increased by 2.57x and 1.43x, respectively, compared with Unreal Engine4.
Attention Auxiliary Spatial Fusion for Pedestrian Attribute Recognition

Pedestrian Attribute Recognition (PAR) is an indispensable topic in smart video analysis. The recognition of fine-grained attributes is challenging work as they are indistinguishable in surveillance images. In this study, we propose an Attention Auxiliary Spatial Fusion (AASF) model to improve the performance of PAR from the following two aspects: (1) We employ an Embedded Attention (EA) module to encode position information into channel information so that it can aggregate features in two different spatial directions with the small-scale visual clues. (2) We propose a Feature Pyramid Adaptive Fusion (FPAF) module to adaptively select useful features for multiple attributes from different levels with contradictory information. Extensive experiments conducted on two large public indoor and outdoor PAR datasets demonstrate that our model achieves state-of-the-art results, especially obtaining better performances on fine-grained attributes.
Construction Methods for Error Correcting Output Codes Using Constructive Coding and Their System Evaluations

Consider M-valued (M$\geq$3) classification systems realized by combination of N(N$\geq\lceil\log_{2}$M$\rceil$) binary classifiers. Such a construction method is called an Error Correcting Output Code (ECOC). First, focusing on a Reed-Muller (RM) code, we derive a modified RM (mRM) code to make it suitable for the ECOC. Using the mRM code and the Hadamard matrix, we introduce a simplex code which is one of the powerful equidistant codes. Next, from the viewpoint of system evaluation model, we evaluate the ECOC by using constructive coding described above. We show that they have desirable properties such as Flexible, Elastic, and Effective Elastic as M becomes large, by employing analytical formulas and experiments.
FedGosp: A Novel Framework of Gossip Federated Learning for Data Heterogeneity

Federated learning (FL) provides the possibility to solve the problem of data privacy, but it suffers much from the data heterogeneity among different participants. Currently, some promising FL algorithms improve the effectiveness of learning under the non independent-and-identically-distributed (Non-IID) data settings. However, they require a large number of communication rounds between the server and clients for an acceptable accuracy. Inspired by the training paradigm of gossip learning, this paper proposes a new FL framework, named FedGosp. It first classifies the clients into different categories based on the model weights trained by the locally stored data. Then FedGosp utilizes the communication not only between clients and the server, but also between different classes of clients themselves. This training process enables instilling knowledge about various data distributions in the passed models. We evaluate the performance of FedGosp in multiple Non-IID settings on CIFAR10 and MNIST datasets, and compare it with the recently popular algorithms such as SCAFFOLD, FedAvg and FedProx. The experimental results show that FedGosp can improve the model accuracy by 6.53% and save 5.6 × communication costs at best compared to the second-ranked baseline.
Evolutionary Algorithms with Heuristic Gradient-based Repair for Constrained Optimization

Gradient-based repair aims to repair infeasible solutions to feasible ones using the gradient information of the constraints. As an effective constraint handling method, gradientbased repair has received extensive attention and has been applied in various evolutionary algorithms (EAs). Nevertheless, due to the complexity of constraints in practical problems, a single infeasible solution often needs to be repaired multiple times until it becomes a feasible solution or reaches the maximum number of repairs. As far as we know, existing related research on gradient-based repair mainly applies this method directly to EAs, while there is little work in the evolutionary computing community on how to improve gradient-based repair. Currently, the multiple repairs for a single individual are independent. That is, the current repair does not consider the previous repair experience. However, only using gradient information to repair infeasible individuals may result in oscillations in the search process. Therefore, in this paper, we propose a heuristic gradient-based repair method (HGR) which exploits the previous repair information of an individual to alleviate this issue. Experimental results on several benchmarks demonstrate the effectiveness of the proposed method. The source code is available at https://github.com/DMiC-Lab-HFUT/HGR-SMC2022.
DF-SBMPO: A Direct and Computationally Efficient Trajectory Planner for Dynamic Environments

In this paper we introduce a novel optimal trajectory planning method for robots in static and dynamic environments while accounting for the robot’s kinematic and dynamic constraints. The proposed method is called Danger Field Sampling Based Model Predictive optimization (DF-SBMPO). This new method includes the advantages of SBMPO such as dealing with non-linear constraints and non-invertible models and planning in the input space while improving its computational efficiency in local minima situations. By introducing a new cost function and combining the Danger Field method in the collision checking process, our method can efficiently find a near time-optimal and smooth trajectory to the goal. We verify the method in simulation using a car-like robot model in an environment containing both static and moving obstacles. By comparing it systematically to other sampling-based path planning algorithms and the Time-Scaling time-optimal trajectory planning method, the results show that our method generally produces collision-free, smoother, near time-optimal trajectories that satisfy constraints with less computation time and fewer samples.
Spatial Consistency and Feature Diversity Regularization in Transfer Learning for Fine-Grained Visual Categorization

Fine-grained visual categorization is challenged by limited training data by localizing discriminative regions and learning diverse features. We propose an effective regularization method that simultaneously imposes spatial consistency and feature diversity on CNN feature maps from a unified perspective. The former guides different feature map channels to concentrate collaboratively on the discriminative areas while the latter ensures that the feature maps are diverse. The proposed method does not require additional supervision, and leverages the covariance matrix of multi-channel feature maps to regularize the loss at the last convolutional layer where the semantic information is the richest. This allows the influence to be backpropagated to update all convolutional layers. We perform experiments using four network architectures for transfer learning from two source domains to three target domains, and demonstrate that our regularization method improves accuracy in all different settings. The proposed regularization method achieves state-of-the-art performance on CUB-200-2011, Stanford-Cars, and Stanford-Dogs datasets with 89.8%, 94.6%, and 88.5% accuracy, respectively.
An Improved Novel View Synthesis Approach Based on Feature Fusion and Channel Attention

Single image novel view synthesis allows the generation of target images with different views from a single input image. Pixel generation methods are one of the main approaches for novel view synthesis, with previous methods typically using the input images to infer the target image in the new view. However, only features from input images in the source view might not be sufficient to generate a good target image, especially when only a single input image is available. In this paper, we present a deep learning-based novel view synthesis approach that fuses features from an input and a warped image to collaboratively generate pixels in the new view. The warped image here is an intermediate output generated by projecting pixels of the input image onto the target view via an estimated depth. Since the estimated depth and the generated warped image are not perfect, errors will be introduced when generating target pixels. To alleviate these and to ensure better channel information between the features from input and warped image, channel attention blocks are employed. Experimental results on standard benchmark datasets show that our method produces excellent view synthesis results and outperforms other state-of-the-art methods.
Regression models based in optimized Ensemble of Extreme Learning Machine Networks

Predicting the failure rate in various educational contexts is an approach that can provide useful information about factors that can negatively influence the students’ interaction with the academic environment. Data mining provides a set of tools that can be used to approach this type of problem. It addresses educational data, searching for extracting knowledge and identifying patterns, featuring the process known as educational data mining. The current work proposes four different models using optimization algorithms, specifically artificial bee colony and genetic algorithm, combined with Extreme Learning Machine networks and ensemble learning to predict the failure rate of schools from Pernambuco, Brazil, in the elementary degree. The results’ consistency is ratified by applying the models to different benchmark datasets from UCI. After obtaining the results related to the prediction error of each model, hypothesis tests were performed to ratify the results. The results and tests confirmed that the proposed models are statistically better at predicting school failure rates in the specified context.
Infinitesimal Confidence Residuals-based Image Authentication

In this work, we propose an image authentication method based on image matting. We demonstrate that an infinitesimal confidence interval computed from an $\alpha$-matte of a reference image is able to expose manipulated images at the pixel level. The corresponding critical bounds are infinitesimally conditional on the spatial and intensity image affinity which is determined by a constrained soft matting. A single reconstruction parameter is embedded in the image rather than embedding a watermark which is known to cause image distortions. The test images showed a high visual quality achieving a distinctive peak signal-to-noise ratio. The results indicated also a higher sensitivity of the reconstructed image residuals regarding the bounds of the infinitesimal confidence interval due to the broken affinity. Moreover, the approach implements a visual assessment for image authentication.
Vehicle Detection based on Positive Samples Selection with Low Threshold

Vehicle detection is a challenging task in computer vision. Since the differences among different vehicle shapes are small and not easy to recognize, the common object detection models usually do not work well for vehicle recognition. To select positive samples, the existing anchor-based detection methods usually use high-quality anchor boxes which are very close to the shapes of the vehicles. Such methods often make the prediction strongly dependent on high-quality anchor boxes. At present, the ATSS (Adaptive Training Sample Selection) method has also been used to select high-quality anchor boxes. The main purpose of this method is to automatically set the shapes of the anchor boxes according to the training datasets. However, for vehicle detection, the shapes of the vehicles are usually similar while the scales of the vehicles are quite different, which may influence the performance of the ATSS based vehicle detection. To solve the above problems, we propose a vehicle detection model PSLTNet (Positives Selection with Low Threshold Network). To reduce the prediction instability due to relying on high-quality anchor boxes, PSLTNet applies the positive samples selection with low threshold to enhance the prediction ability for different quality anchor boxes. To improve the identification ability of different vehicle types, PSLTNet adopts a pyramid structure and cascades dilated convolutions at each level. In addition, in order to make the bounding box regression loss be independent of scales, PSLTNet uses GIOU (Generalized Intersection Over Union) to supervise the position regression task of candidate bounding boxes. Finally, to reduce the loss of a large number of simple negatives, PSLTNet uses Focal Loss to supervise the classification task of the background candidate bounding boxes. The Experimental results upon the BIT-Vehicle dataset show that the proposed method can obtain better performance than that of the existing methods.
A Novel Blockchain-Driven Framework for Deterring Fraud in Supply Chain Finance

Frauds in supply chain finance not only result in substantial loss for financial institutions (e.g., banks, trust company, private funds), but also are detrimental to the reputation of the ecosystem. However, such frauds are hard to detect due to the complexity of the operating environment in supply chain finance such as involvement of multiple parties under different agreements. Traditional instruments of financial institutions are time-consuming yet insufficient in countering fraudulent supply chain financing. In this study, we propose a novel blockchain-driven framework for deterring fraud in supply chain finance. Specifically, we use inventory financing in jewelry supply chain as an illustrative scenario. The blockchain technology enables secure and trusted data sharing among multiple parties due to its characteristics of immutability and traceability. Consequently, information on manufacturing, brand license, and warehouse status are available to financial institutions in real time. Moreover, we develop a novel rule-based fraud check module to automatically detect suspicious fraud cases by auditing documents shared by multiple parties through a blockchain network. To validate the effectiveness of the proposed framework, we employ agent-based modeling and simulation. Experimental results show that our proposed framework can effectively deter fraudulent supply chain financing as well as improve operational efficiency.
CNN Based survivability prediction Using Pathological Image of Soft Tissue Tumor

The number of patients of malignant soft tissue tumor is about 3,000 every year in Japan. In the treatment procedure of soft tissue tumor, survivability of patient must be predicted based on experience. The miss-prediction causes the unnecessary treatment or patient death, an objective decision making system based on pathological image is required. This paper proposes a convolutional neural network (CNN) based survivability and survival time prediction method using pathological image of soft tissue tumor. The proposed method trained Inception v3 and ResNetl4-based modified CNN model using 47 pathological images of 26 subjects. As the result of 4-fold cross validation test, the image-wise survivability, image-wise non-survivability, subject-wise survivability, and subject-wise non-survivability were predicted in F-measure of 0.847, 0.743, 0.909 and 0.824, respectively. Additional experiment using non-survival patients showed that the survival time was predicted in 4.57 months error.
Task Detection in Continual Learning via Familiarity Autoencoders

Continual learning requires the ability to reliably transfer previously learned knowledge to new tasks without disrupting established competencies. Methods such as Progressive Neural Network [1] accomplish high-quality transfer learning while nullifying the insidious problem of catastrophic forgetting. However, most module-based continual learning systems require task labels during operation – a constraint that limits their application in many real-world conditions where task indicators are opaque. This paper proposes a task detector neural algorithm to acquire task information while maintaining immunity to forgetting. Our proposed task detector allows progressive neural networks (and many similar systems) to operate without task labels during test time. Our task detector is built from familiarity autoencoders which recognize the nature of the required task from input data. We demonstrate the generality and effectiveness of our approach through experiments in video game playing and automated image repair. Our results show near-perfect task recognition in all domains (>.99 F1), rewards above published single-task scores in MinAtar, and realistic image repairs on damaged human face pictures. The performance of our integrated method is nearly identical to the progressive systems equipped with ground-truth task labels <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
SEAH: Semantic Preserving Asymmetric Hashing for Efficient Cross-Media retrieval

Cross-modal hashing utilize the advantages of hash codes to enable flexible retrieval across different modalities, greatly improving the retrieval efficiency of heterogeneous modes. However, most existing approach do not fully take modal intrinsic semantic properties and semantic category structure into consideration during learning the latent subspace. In addition, previous theory works commonly focus on binary pairwise similarity, without investigating the rich semantic contained in the label matrix. To alleviate these problems, in this study, we present a novel cross-media retrieval approach, termed SEmantic preserving Asymmetric discrete Hashing (SEAH), which constructs an asymmetric scheme to learn the binary codes from the common representation to maintain the similarity. Specially, we incorporate label matrix and hash codes into a mutual mapping framework, the learned hash codes are more discriminative. Moreover, we introduce the Augmented Lagrange Multiplier (ALM) algorithm for optimization, which make it easier to solve the objective functions. Comprehensive systematically experiments on two benchmark datasets demonstrate that our approach achieves promising performance gain and outperforms the several state-of-the-art works.
Batch Learning Growing Neural Gas for Sequential Point Cloud Processing

This papers describes a learning algorithm for growing neural gas to construct a topology-preserving map from a 3D point cloud whose topology can change dynamically. Growing Neural Gas with Utility Factor (GNG-U) has been presented as a method for learning the topology of a 3D space environment and applying it to non-stationary or dynamic data distribution. However, when a node is added to an existing network after several errors with sampling data have accumulated, it is difficult for a standard GNG-U to considerably boost learning speed. As a result, we propose a revolutionary growth strategy that dramatically accelerates learning and convergence. This method immediately adds a sample of data as a new node to an existing network based on the likelihood of node addition estimated by the distance to the third closest node and the first and second closest nodes at maximum. Experiment findings show that the proposed algorithm’s network can quickly adapt to represent the topology of non-stationary input distributions.
Compound Facial Expression Recognition with Multi-Domain Fusion Expression based on Adversarial Learning

The emotion of human beings tends to be complex in real conditions, generating compound expressions in human faces. Compound expression recognition is an important challenge for the assessment of human complex emotion. The recognition system based on six basic expressions cannot meet the demand of compound expressions recognition. The recognition performance of models learned from the basic expressions is poor due to the small number of compound expression datasets with highly accurate labels and insufficient sample diversity. Making full use of domains outside of compound expressions in small sample datasets will help promote diversity. We propose the Multi-Domain Fusion Generative Adversarial Network (MDFGAN), which innovatively fuses the face domain, compound expression domain and basic expression domain to obtain rich expression generation capability and high accuracy recognition. Pairing the face domain and the contour-unrelated compound expression domain in the generator will expand the sample diversity. The contour-related compound expression domain and the basic expression domain will jointly improve the expression recognition accuracy of the discriminator. Finally, we conducted comprehensive experiments on CFEE-26, CFEE-7 and CK+. In the experiments, the results of MDFGAN improved 6.79% on UF1 and 8.5% on UAR.
GateKeeper: Operator-centric Trusted App Management Framework on ARM TrustZone

Employing Trusted Execution Environment (TEE) technology such as ARM TrustZone to deploy sensitive security modules and credentials for secure, authenticated access is the go-to solution to address integrity and confidentiality challenges in untrusted devices. While it has been attracting attention as an effective building block for secure enterprise IT systems (e.g., BYOD), these secure operating systems are often not open-source, and thus system operators and developers have to largely depend on mobile platform vendors to deploy their applications in the secure world on TEE. Our solution, called GateKeeper, addresses the primary obstacle for system operators to adopt ARM TrustZone TEE to deploy their own, in-house security systems, by enabling the operators more control and flexibility on Trusted App (TA) installation and update procedure without mandating involvement of the mobile platform vendors at each iteration. In this paper, we first formulate an ecosystem for enabling such operator-centric TA management, and then discuss the design of GateKeeper, which is a comprehensive framework to enable operator-centric TA management on top of GlobalPlatform specification. We further present a proof-of-concept implementation using OP-TEE open-source secure OS to demonstrate the feasibility and practical resource consumption (less than 1000 lines of code and 500 KBytes on memory).