Reviewerly/exHarmony huggingface dataset:
Evolving Neo-Fuzzy System for Distorted Data Online Processing

The problem of evolving system of computational intelligence synthesis for online processing of stochastic nonstationary sequences distorted by missing values in the data is considered when these missing values can be present both in the input signals and in the reference one. The evolving neo-fuzzy system, that in the process of operation restores the missing values in data, adjusting its parameters (synaptic weights) and architecture is proposed. The elements of the system are nonlinear synapses of the neo-fuzzy neuron, where the traditional membership functions are replaced by orthogonal polynomials. Using of orthogonal polynomials allows to rebuild the architecture of the system without changing the already formed membership-activation functions and the corresponding synaptic weights. For the tuning of system parameters we use either the recurrent least squares method or the multistep gradient learning algorithm with the sliding window both. The neo-fuzzy system under consideration is designed to solve the Data Stream Mining tasks including prediction, adaptive identification and control of nonlinear stochastic and chaotic objects.
Graft: Static Analysis of Java Bytecode with Graph Databases

This paper proposes a static analysis tool for finding security vulnerabilities in Java programs. Security vulnerabilities are an ever-present concern for developers and researchers alike. Even a large-scale system can be breached or even rendered nonoperational if a security vulnerability is found and exploited by an attacker. As computer systems grow ever larger and more complex, the need for robust and precise tools for finding such vulnerabilities only grows as well. Most security vulnerabilities stem from untrusted input making its way to sensitive portions of the system, which can have serious consequences ranging from exposure of sensitive data to memory corruption and denial of service.
Industrial Robot Training Platform Based on Virtual Reality and Mixed Reality Technology

With the steady implementation of the national strategy of "Made in China 2025", industrial robot education has achieved phased development. Many universities have opened majors in robot engineering. The problem that accompanies it is that there are many industrial robot brands in the industry. There are many model differences in the integration industry, and the investment in purchasing a stable industrial robot is enormous. At the same time, improper operations during industrial training may cause serious accidents. We have designed a set of industrial robot training platforms based on VR technology and MR technology. And it solved the problem of the shortage of teaching resources caused by the high purchase cost of industrial robot training equipment and potential safety hazards.
SCW-SGD: Stochastically Confidence-Weighted SGD

As the field of deep learning has been rapidly growing, the optimization methods (optimizers) gain keen attention for efficiently training neural networks. While SGD exhibits practically favorable performance on various tasks, adaptive methods, such as ADAM, are also formulated to equip the gradient-based updating with adaptive scaling in a sophisticated way. In this paper, we propose a novel optimizer to integrate those two approaches of the adaptive method and SGD through assigning stochastic confidence weights to the gradient-based updating. We define statistical uncertainty of the gradients which is implicitly embedded in the adaptive scaling of ADAM, and then based on the uncertainty, naturally incorporate stochasticity into the optimizer as a bridge between SGD and ADAM. Thereby, the proposed optimizer, SCWSGD, endows the parameter updating with two types of stochasticity regarding multiplicative scaling for the gradient and mini-batch sampling to compute the gradient, for improving generalization performance. In the experiments on image classification using various CNNs, the proposed optimizer produces favorable performance in comparison to the other optimizers.
Deep Reinforcement Learning with Successive Over-Relaxation and its Application in Autoscaling Cloud Resources

We present a new deep reinforcement learning algorithm using the technique of successive over-relaxation (SOR) in Deep Q-networks (DQNs). The new algorithm, named SOR-DQN, uses modified targets in the DQN framework with the aim of accelerating training. This work is motivated by the problem of auto-scaling resources for cloud applications, for which existing algorithms suffer from issues such as slow convergence, poor performance during the training phase and non-scalability. For the above problem, SOR-DQN achieves significant improvements over DQN on both synthetic and real datasets. We also study the generalization ability of the algorithm to multiple tasks by using it to train agents playing Atari video games.
Multidimensional Decision Tree Splits to Improve Interpretability

We revisit the binary splitting functionality used in decision trees to handle numerical attributes. Even if the true relationship between the class label and a few numerical attributes can be expressed directly (using a Boolean expression), resulting decision trees may appear quite large and complicated. In cases where interpretability is important, an increased computational effort on the splitting criteria that offers more compact trees might be worthwhile. We propose and empirically evaluate multidimensional splits, where a tree node may test for inclusion in a low-dimensional bounding box.
Constant-Time BCH Error-Correcting Code

Error-correcting codes can be useful in reducing decryption failure rate of several lattice-based and code-based public-key encryption schemes. Two schemes, namely LAC and HQC, in NIST's round 2 phase of its post-quantum cryptography standardisation project use the strong error-correcting BCH code. However, direct application of the BCH code in decryption algorithms of public-key schemes could open new avenues to the attacks. For example, a recent attack [1] exploited non-constant-time execution of BCH code to reduce the security of LAC. In this paper we analyse the BCH error-correcting code, identify computation steps that cause timing variations and design a constant-time BCH implementation. We implement our algorithm in software and evaluate its resistance against timing attacks by performing leakage detection tests. To study the computational overhead of the countermeasures, we integrated our constant-time BCH code in the reference and optimised implementations of the LAC scheme as a case study, and observed nearly 1.1 and 1.4 factor slowdown respectively for the CCA-secure primitives.
Shrinkage as Activation for Learned Image Compression

With recent advances in learned entropy and context models, the rate-distortion performance of deep learned image compression methods reached or surpassed those of conventional codecs. However, learned image compression is currently more complex and slower than conventional image compression. Learned image and video compression methods almost exclusively employ the generalized divisive normalization (GDN) activation function. This paper investigates the effect of activation function on the performance of image compression in terms of both objective and subjective criteria as well as runtime. In particular, we show that the distribution of latents produced by hard shrinkage fits a Laplacian better, and it is possible to achieve similar rate-distortion and better visual performance using hard shrinkage with lower complexity.
Explicit analyses of proof/refutation interaction for constructible falsity and Heyting–Brouwer logic

Abstract Nelson’s logic of constructible falsity $\textsf{N}$ and Rauszer’s Heyting–Brouwer logic $\textsf{HB}$ are well-known cases of extensions of intuitionistic logic $\textsf{Int}$ enriched with novel connectives. Wansing has suggested that Gödel’s provability interpretation of $\textsf{Int}$ can be extended to these systems by pairing the category of formal proofs with a distinct category of formal refutations. In this paper, we extend the framework of Artemov’s justification logic to provide explicit analyses of $\textsf{N}$ and $\textsf{HB}$ (and the dual-intuitionistic logic $\textsf{DualInt}$) that respect a distinction between proofs and refutations. The application distinguishes the categories by reinterpreting the agents of multiple-agent justification logic as devices that operate exclusively on one or the other category. The analyses reveal that differences between $\textsf{N}$ and $\textsf{HB}$ can be reduced to competing interaction principles characterizing the coordination between proofs and refutations. We conclude by reappraising some of the unusual features of $\textsf{HB}$ in light of the explicit analysis of $\textsf{HB}$.
3D Human Pose Estimation based on Center of Gravity

In this paper, we propose a method about 3D human pose estimation with only 2D joints as input. Previous methods generally lift 2D poses to 3D space through a single mapping function, in which case some large-pose samples far away from the majority distribution may not be well concerned. To address the issue above, we design a multi-branch network based on the human center of gravity (COG) to enhance the robustness of the model to large-pose samples. Specifically, noticing the correspondence between the COG and human pose, by clustering the COG, we separate the large-pose samples from the normal ones in an unsupervised pattern, and lift them with separate branch network. In addition, we introduce a global loss function to regularize the integrality of 3D joints. Extensive experiments on the largest publicly available dataset demonstrate the validity and efficiency of our method.
Statistical and Geometrical Alignment using Metric Learning in Domain Adaptation

Domain adapted machine learning is driven by the possibilities of learning from source data distribution to understand different target data distributions. An assumption is made that one application (source) domain always has enough labeled information, but the other related application (target) may contain information that is partially labeled or completely unlabeled. Therefore, it is necessary to train the target domain classifier using enough labeled information of the source domain. However, contrary to primitive assumptions, the source domain and target domain data need not have the same distribution. Therefore, we can't directly use data of source domain to train classifier for data of target domain. Existing approaches can be deprived of one or more objectives: perform geometric diffusion on the manifold, align the cross-domain distributions, preserve the discriminative information using metric learning. Here, we have proposed a novel framework that aims to meet all such objectives. In this framework, we proposed two methods, statistical and geometrical alignment using metric learning with pseudo labels (SGA-MDAP) and without pseudo labels (SGA-MDA) in visual domain adaptation. It has been demonstrated through various experiments that our framework outperforms various state-of-the-art methods over four different real-world cross-domain visual identification datasets such as PIE face, ORL face, Yale face, and Office Caltech.
Functionality Assessment of Software System using Fuzzy Approach

The acceptance or rejection of any software depends on the quality of the software. Numerous factors have an impact on its quality. Functionality of the software is one of them which lead to significant impact on the software quality. To evaluate the functionality of any software system is a tedious job. It incurs the cost, takes efforts as well as time. This research paper proposes a fuzzy model to evaluate the functionality of the system. Fuzzy aspect handles the uncertainty and complex decision problems very well. An object oriented system has been chosen for experimental purpose in this research paper.
Modeling and Simulation of Perching With a Quadrotor Aerial Robot With Passive Bio-inspired Legs and Feet

Abstract Perching in unmanned aerial vehicles is appealing for reconnaissance, monitoring, communications, and charging. This paper focuses on modeling, simulation, and control of bioinspired perching in unmanned aerial vehicles on cylindrical objects, which will be used for future planning and control research. A modular approach is taken where the quadrotor, legs, feet, and toes are modeled separately and then integrated to form a complete simulation system. New models of these components consider kinematics and dynamics of each element and their coupling through tendons that provide actuation. The integrated model is assembled to simulate a physical prototype and then validated based upon physical experiments to provide calibration. Simulation results evaluate the validated model performing perching with different gripper-perch alignments. The simulation environment developed in this research provides a foundation to research control approaches for use with the discussed passive perching mechanism. The simulation was validated to capture the dynamics of the real perching mechanism. This platform will be used in future work to develop a control approach that will be implemented in a quadrotor system to land and take-off from a perch in a reliable manner.
User Alignment with Jumping Seed Alignment Information Propagation

User Alignment is to find users belonging to a same real person on different social networks and has become a fundamental task for many sequent applications such as cross-network recommendation systems. When matching users in multiple social networks, existing approaches always know some correctly matched users, which can be called seeds. Then, existing methods strongly depend on the neighboring users of each user to propagate alignment information from seeds and align probable matching users implicitly. However, the completeness and validity of original alignment information among seeds cannot be fully preserved when learning and aligning multiple user spaces. In this paper, we propose a unified framework named Jumping Seed Alignment Information Propagation (JSAIP) to flexibly leverage, for each user, complete and correct alignment information from seeds. Specifically, JSAIP learns a reasonable user space for each social network by preserving enough original network and label information. Then, JSAIP ensures the correct alignment among seeds and shared labels to reduce the diversity between different user spaces. Finally, JSAIP constructs jumping links from seeds to each user in each social network and ultilizes original seed alignment information to enhance or rectify the alignment information propagated from neighbors. Experiments on real world datasets demonstrate the effectiveness of our proposed JSAIP method compared to several state-of-the-art methods.
A composed technical debt identification methodology to predict software vulnerabilities

Technical debt (TD), its impact on development and its consequences such as defects and vulnerabilities, are of common interest and great importance to software researchers and practitioners. Although there exist many studies investigating TD, the majority of them focuses on identifying and detecting TD from a single stage of development. There are also studies that analyze vulnerabilities focusing on some phases of the life cycle. Moreover, several approaches have investigated the relationship between TD and vulnerabilities, however, the generalizability and validity of findings are limited due to small dataset. In this study, we aim to identify TD through multiple phases of development, and to automatically measure it through data and text mining techniques to form a comprehensive feature model. We plan to utilize neural network based classifiers that will incorporate evolutionary changes on TD measures into predicting vulnerabilities. Our approach will be empirically assessed on open source and industrial projects.
Impulse Noise Filtering using MLMVN

In this paper, we consider how a complex-valued neural network, the multilayer neural network with multi-valued neurons (MLMVN), can be efficiently used for impulse noise filtering. It is shown that MLMVN with only a single hidden layer of neurons can restore an image corrupted by random-valued impulse noise while carefully preserving edges and boundaries. MLMVN processes overlapping patches, which to a noisy image should be broken down. A network shall be trained using a robust learning set created from patches randomly picked up from many images. Then, the trained network should be used for actual filtering. Final intensities in pixels of a resulting image are obtained by averaging the intensities over all overlapping patches. This approach becomes highly efficient for images corrupted by random impulse noise with a low corruption rate of 5-10%. It makes it possible to preserve the smallest image details very carefully and avoid the smoothing of edges. MLMVN outperforms sophisticated filters with detectors of impulse noise for a lower corruption rate and shows comparable results for a higher corruption rate.
An Optimization of Deep Sensor Fusion Based on Generalized Intersection over Union

3D object detection is a major topic in unmanned driving and robotics, which is suffering from the low accuracy recently. We found that the loss function of 3D object detection network is the main cause to the low accuracy. For this, we proposed an optimized realization of deep sensor fusion network model (DSFN) based on Generalized Intersection over Union (GIoU). In DSFN, the designed backbone network is used to fuse point cloud features and image features, making full use of heterogeneous sensor information. Specifically, we introduced the GIoU as the loss function of the backbone network. We evaluated our model on KITTI dataset which is resulted from a LIDAR-camera setup. Compared with similar models, our model shows a higher accuracy.
Object Detection and Localization Using Stereo Cameras

Camera systems have become increasingly popular because cameras are cheap and easy to deploy. Compared with other depth cameras, the stereo camera is small, and it is easily carried by subjects. Through a fixed baseline, the stereo camera is able to compute depth information. However, the traditional stereo matching algorithm can not compute the depth information on the edge of the image. Meanwhile, due to the large amount in the 3D point cloud, there is no specific numerical relationship between semantic information and depth information. In order to solve this problem, estimating depth and semantic information in an accurate way is required. A deep neural network model is used to predict semantic information and depth at the same time. Further, we propose a robust method to deal with variation brightness and improve performance under actual conditions.
Space-Time Guided Association Learning For Unsupervised Person Re-Identification

Person re-identification (Re-ID) aims to match images of the same person across distinct camera views. In this paper, we propose the Space-Time Guided Association Learning (STGAL) for unsupervised Re-ID without ground truth identity nor image correspondence observed during training. By exploiting the spatial-temporal information presented in pedestrian data, our STGAL is able to identify positive and negative image pairs for learning Re-ID feature representations. Experiments on a variety of datasets confirm the effectiveness of our approach, which achieves promising performance when comparing to the state-of-the-art methods.
Computerized Logo Synthesis with Wavelets-Enhanced Adversarial Learning

While logo design requires creative thoughts from artistic side, computerized logo synthesis could provide significant assistance in terms of workload reduction and productivity improvements. By applying wavelet transform to decompose the input logo images into four frequency bands, we introduce two new regularization terms into the GAN-based adversarial learning towards improved logo synthesis. As the LL band of images preserve the primary content information, we apply clustering to these LL bands to generate supervisory labels to regulate the logo generation and hence the logo synthesis can be predominated by a label-guided theme. To create varieties and diversities for the synthesized logos, we further establish a second regularization term out of the HH-band and enable the learning process to simulate the creativity illustrated by logo designers. Extensive experiments are carried out and, compared with the existing state of the arts, the results show that our proposed achieves overwhelmingly better performances in terms of the inception scores.
Deep Learning Algorithms for Diagnosis of Breast Cancer with Maximum Likelihood Estimation

Machine Learning (ML) and particularly Deep Learning (DL) continue to advance rapidly, attracting the attention of the health imaging community to apply these techniques to increase the precision of cancer screening. The most common cancer in women is breast cancer that affects more than 2 million women each year and causes the largest number of deaths from cancer in the female population.This work provides state-of-the-art research on the contributions and new applications of DL for early diagnosis of breast cancer. Also, it emphasizes on how and which major applications of DL algorithms are going to be benefitted for early diagnosis of breast cancer for which CNNs, one of the DL architectures, will be used. In this study, a DL method to be used for diagnostic and prognostic analysis using the X-ray breast image dataset for breast cancer is studied. Based on the dataset, it is aimed to diagnose breast cancer at an early stage. Thus, it may take place before a clinical diagnosis. For the testing probability of the disease, 21400 X-ray breast images, both normal and cancer, were taken from USF mammography datasets. From these images, 70% is used as the training step, while 30% of images are benefitted for the testing step. After the implementation of the architecture, VGG16 has achieved an overall accuracy of 96.77%, with 97.04% sensitivity and 96.74% as AUC, while Inception-v4 has an overall accuracy of 96.67%, with 96.03% sensitivity and 99.88% as AUC. These results show the high value of using DL for early diagnosis of breast cancer. The results are promising.
Underwater Object Detection using Transfer Learning with Deep Learning

In recent years, Deep learning based methods have proven their excellent performance in generic object detection. However, underwater object detection is still a challenge, especially from underwater optical images. In contrast to generic datasets, underwater images usually have color shift and low contrast, underwater detection datasets are scarce and the objects in the available underwater datasets and real applications are usually small. To address these issues, we propose a underwater object detection algorithm which choose Yolov3-tiny network as backbone and pre-train on Pascal VOC datasets. Experiments show that our proposed method improves the performance of region-based object detectors on CHINAMM2019 datasets, compared with SSD algorithm and Yolov3.