Reviewerly/exHarmony huggingface dataset:
Autonomous Obstacle Avoidance Scheme Using Monocular Vision Applied to Mobile Robots

Abstract Mobile robots cannot move in an unknown environment with static or slow-moving obstacles effectively. We present an enhanced obstacle avoidance strategy using monocular vision to solve this problem. First, we combine Canny and Otsu to extract the barrier feature and find the critical pixel position of obstacles by the monocular vision. Then the image depth estimation algorithm is used to estimate the gaps. With these parameters of barriers, an improved bug algorithm is proposed to avoid the obstacles autonomously. The experiments show that the proposed obstacles avoidance strategy can effectively make a small mobile robot avoid different kinds of obstacles.
A cheaper Rectified-Nearest-Feature-Line-Segment classifier based on safe points

The Rectified Nearest Feature Line Segment (RN-FLS) classifier is an improved version of the Nearest Feature Line (NFL) classification rule. RNFLS corrects two drawbacks of NFL, namely the interpolation and extrapolation inaccuracies, by applying two consecutive processes-segmentation and rectification - to the initial set of feature lines. The main drawbacks of this technique, occurring in both training and test phases, are the high computational cost of the rectification procedure and the exponential explosion of the number of lines. We propose a cheaper version of RNFLS, based on a characterization of the points that should form good lines. The characterization relies on a recent neighborhood-based principle that categorizes objects into four types: safe, borderline, rare and outliers, depending on the position of each point with respect to the other classes. The proposed approach represents a variant of RNFLS in the sense that it only considers lines between safe points. This allows a drastic reduction in the computational burden imposed by RNFLS. We carried out an empirical and thorough analysis based on different public data sets, showing that our proposed approach, in general, is not significantly different from RNFLS, but cheaper since the consideration of likely irrelevant feature line segments is avoided.
Tighter Bounds on MultiParty Coin Flipping via Augmented Weak Martingales and Differentially Private Sampling

In his seminal work, Cleve [Proceedings of the 18th Annual ACM Symposium on Theory of Computing, 1986, pp. 364--369] has proved that any $r$-round coin-flipping protocol can be efficiently biased by $\Theta(1/r)$. This lower bound was met for the two-party case by Moran, Naor, and Segev [J. Cryptology, 29 (2016), pp. 491--513] and the three-party case (up to a ${polylog}$ factor) by Haitner and Tsfadia [SIAM J. Comput., 46 (2017), pp. 479--542] and was approached for $n$-party protocols when $n< {loglog} r$ by Buchbinder et al. [Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms, 2017, pp. 2580--2600]. For $n > {loglog} r$, however, the best bias for $n$-party coin-flipping protocols remains $O(n/\sqrt{r})$ achieved by the majority protocol of Awerbuch et al. [How to implement Bracha's ${O}(\log n)$ Byzantine Agreement Algorithm, manuscript, 1985]. Our main result is a tighter lower bound on the bias of coin-flipping protocols, showing that, for every constant $\varepsilon >0$, an $r^{\varepsilon}$-party $r$-round coin-flipping protocol can be efficiently biased by $\widetilde{\Omega}(1/\sqrt{r})$. As far as we know, this is the first improvement of Cleve's bound and is only $n=r^{\varepsilon}$ (multiplicative) far from the aforementioned upper bound of Awerbuch et al. We prove the above bound using two new results that we believe are of independent interest. The first result is that a sequence of (``augmented'') weak martingales have large gap: with constant probability there exists two adjacent variables whose gap is at least the ratio between the gap between the first and last variables and the square root of the number of variables. This generalizes over the result of Cleve and Impagliazzo [Martingales, Collective Coin Flipping and Discrete Control Processes (Extended Abstract), http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.1797, 1993], who showed that the above holds for strong martingales, and allows in some setting to exploit this gap by efficient algorithms. We prove the above using a novel argument that does not follow the more complicated approach of R. Cleve and R. Impagliazzo. The second result is a new sampling algorithm that uses a differentially private mechanism to minimize the effect of data divergence.
Semi-Supervised Learning With Label Proportion

The scarcity of labels is common and great challenge in traditional supervised learning. Semi-supervised learning (SSL) leverages unlabeled samples to alleviate the absence of label information. Similar with annotation, label proportion is another type of prior information and plays a significant role in classification tasks. Compared with the acquisition of labels, label proportion can be obtained more easily. For example, only a small number of patients have been diagnosed with or not with cancers in hospital database, while the proportion with cancer can be generally estimated by historical records. How to incorporate such prior information of label proportion is crucial but rarely studied in literature. Traditional SSL methods often ignore this prior information and will lead to performance degradation inevitably. To solve this problem, we propose a novel SSL with Label Proportion (SSLLP). Our approach encourages to preserve label consistency and label proportion by imposing the cardinality bound constraints. Our formulated problem equals to a mixed-integer constrained submodular minimization and it is difficult to be solved directly. Therefore, we transformed the original problem into a convex one by Lov <inline-formula><tex-math notation="LaTeX">$\acute{\text{a}}$</tex-math></inline-formula> sz extension and designed an efficient solving algorithm. Extensive experimental results present the improved performance of our method over several state-of-the-art methods.
Semi-supervised Learning for Medical Image Segmentation

Semi-supervised learning is a combination of conventional supervised methods with weakly supervised learning. A recent development in neural networks allows to achieve high-quality results but the training requires a large amount of annotated examples. This hinders the applicability of deep learning to some problems, especially medical imaging. In this paper, we present a semi-supervised learning approach based on convolutional neural networks (CNN) for medical image segmentation. A network is trained on a combination of fully labeled samples that have segmentation masks available and weakly labeled samples that only have class labels. We performed experiments that compare the results of the semi-supervised model with the baseline supervised method. Experiment results show the superiority of suggested methods on a low amount of fully annotated samples for lung nodules CT images.
Use Text Mining for Financial Reports Analysis: Long Text to Image Converter

In this study, we propose a novel article analysis method. This method converts the article classification problem into image classification problem by projecting texts into images and then applying CNN models for classification. We call the method the Long Text to Image Converter (LTIC). The features are extract automatically from the generate images, hence there is no need of any explicit step of embedding the words or characters into numeric vector representations.
A novel krill herd algorithm with orthogonality and its application to data clustering

Krill herd algorithm (KHA) is an emerging nature-inspired approach that has been successfully applied to optimization. However, KHA may get stuck into local optima owing to its poor exploitation. In this paper, the orthogonal learning (OL) mechanism is incorporated to enhance the performance of KHA for the first time, then an improved method named orthogonal krill herd algorithm (OKHA) is obtained. Compared with the existing hybridizations of KHA, OKHA could discover more useful information from historical data and construct a more promising solution. The proposed algorithm is applied to solve CEC2017 numerical problems, and its robustness is verified based on the simulation results. Moreover, OKHA is applied to tackle data clustering problems selected from the UCI Machine Learning Repository. The experimental results illustrate that OKHA is superior to or at least competitive with other representative clustering techniques.
Overview of Research on Image Super-Resolution Reconstruction

Image super-resolution reconstruction refers to reconstructing a high-resolution image from a low-resolution image through a corresponding method, that is, to generate a clearer image. Throughout the development of this technology, we can see the results of previous efforts, such as image super-resolution reconstruction based on interpolation, image super-resolution reconstruction based on reconstruction, super-resolution reconstruction based on learning, and image super-resolution reconstruction technology based on deep learning. Nowadays, the idea of deep learning is very popular. It is an important branch of machine learning and is widely concerned and favored by researchers. Combining deep learning ideas into the research of image super-resolution reconstruction can achieve very satisfying effects. In this paper, we mainly describe various algorithms of image super-resolution reconstruction based on deep learning, compare their advantages and disadvantages, and discuss their future development directions.
Gesture Detection and Recognition Based on Pyramid Frequency Feature Fusion Module and Multiscale Attention in Human-Computer Interaction

Aiming at the problem of the absence of detail texture and other high-frequency features in the feature extraction process of the deep network employing the upsampling operation, the accuracy of gesture recognition is seriously affected in complex scenes. This study integrates object detection and gesture recognition into one model and proposes a gesture detection and recognition based on the pyramid frequency feature fusion module and multiscale attention in human-computer interaction. Pyramid fusion module is used to perform efficient feature fusion and is proposed to obtain feature layers with rich details and semantic information, which is helpful to improve the efficiency and accuracy of gesture recognition. In addition, the multiscale attention module is further adopted to adaptively mine important and effective feature information from both temporal and spatial channels and embedded into the detection layer. Finally, our proposed network realizes the enhancement of the effective information and the suppression of the invalid information of the detection layer. Experimental results show that our proposed model makes full use of the high-low frequency feature fusion module without replacing the basic backbone network, which can greatly reduce the computational overhead while improving the detection accuracy.
A Novel Cloud Data Integrity Verification Scheme Based on Dynamic Array Multi-branch Tree

With the continuous development of cloud storage technology, more and more users choose to store their data to the cloud server to save local storage costs, but at the same time, they also lose direct control over their data, so ensuring data integrity becomes a challenge. A cloud storage data integrity verification scheme based on Dynamic Array Multi-branch Tree (DA-MBT) is proposed. The leaf node is set as an array structure, which reduces the height of the tree, improves the utilization rate of nodes, simplifies the process of dynamic update, reduces the query time of the data block, and improves the verification efficiency. Finally, it can be concluded that, the proposed DA-MBT scheme supports full dynamic operation, realizes the privacy protection of users, can effectively verify the integrity of a large number of data, and can effectively reduce the communication and computing overhead during the verification process.
Generalized Fibonacci Search Method in One-Dimensional Unconstrained Non-Linear Optimization

In this paper, we develop a generalized Fibonacci search method for one-dimensional unconstrained non-linear optimization of unimodal functions. This method uses the idea of the “ratio length of 1” from the golden section search. Our method takes successive lower Fibonacci numbers as the initial ratio and does not specify beforehand, the number of iterations to be used. We evaluated the method using Microsoft Excel with nine one-dimensional benchmark functions. We found that our generalized Fibonacci search method out-performed the golden section and other Fibonacci-type search methods such as the Fibonacci, Lucas and Pell approaches.
Attribute-Based Privacy-Preserving Data Sharing for Dynamic Groups in Cloud Computing

Cloud computing is recognized as an alternative to traditional information technology due to its intrinsic resource sharing and low maintenance characteristics. Cloud computing provides an economical and efficient solution for sharing group resource among cloud users. Unfortunately, when sharing the data in a group while preserving data, identity privacy is still a challenging issue due to frequent change in membership. In overcome this problem, a secure data sharing scheme for dynamic groups is proposed so that any user within a group can share the data in a secure manner by leveraging both the group signature and dynamic broadcast encryption techniques. It should enable any cloud user to anonymously share data with others within the group and support efficient member revocation. The storage overhead and encryption computation cost are dependent on the number of revoked users.
Construction of Alliance System Model Based on Blockchain Technology-Taking the Catering Industry as an Example

As the market demand and size of the industry in China's F&B industry continues to expand, companies rely on technology for effective supply chain management and digital application reform in the F&B industry to drive brand enhancement in the face of the three fastest rising costs: raw materials, rent and human resources. This thesis proposes an alliance system model based on blockchain technology and theory. The model introduces decentralisation, consensus and trusted traceability features to the current state of restaurant market operations, facilitating regulation and reducing costs. The model uses the competition and cooperation formed between shops as an application case to simulate the relationship between international multilateral trading economies, providing new ideas for exploring new models of international trade relations.
A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data

In this paper, a methodology for design of fuzzy Kalman filter, using interval type-2 fuzzy models, in discrete time domain, via spectral decomposition of experimental data, is proposed. The adopted methodology consists of recursive parametric estimation of local state space linear submodels of interval type-2 fuzzy Kalman filter for tracking and forecasting of the dynamics inherited to experimental data, using an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm. The partitioning of the experimental data is performed by interval type-2 fuzzy Gustafson–Kessel clustering algorithm. The interval Kalman gains in the consequent proposition of interval type-2 fuzzy Kalman filter are updated according to unobservable components computed by recursive spectral decomposition of experimental data. Computational results illustrate the efficiency of proposed methodology for filtering and tracking the time delayed state variables of Chen’s chaotic attractor in a noisy environment, and experimental results illustrate its applicability for adaptive and real time forecasting the dynamic spread behavior of novel Coronavirus 2019 (COVID-19) outbreak in Brazil.
An Analysis of Regularized Approaches for Constrained Machine Learning

Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced (see e.g. [, , , ]) to improve a predictive model via domain knowledge. Given the recent interest in ethical and trustworthy AI, however, several works are resorting to these approaches for enforcing desired properties over a ML model (e.g. fairness [, ]).
Research of Incremental Learning Algorithm for SVM Based on Class Center Diameter

Abstract In the learning process based on ISVM, how to effectively retain the history information and selectively discard some new training data, so as to maintain the classification accuracy and save the storage space after adding new samples every time, which is the key of the current ISVM classification algorithm. This thesis proposes a new incremental learning algorithm, that is, SVM incremental learning algorithm based on cluster diameter (CD-ISVM). This algorithm firstly calculates the two centers of the positive and negative samples, and then to build the boundary vector set by the coordinates of the two class centers. Moreover, KKT conditions are combined to filter incremental data and boundary vector set, and feedback information is provided to adjust the position of the boundary vector set. Then, the union of SV set and boundary vector set is taken as the increment set. After several increments, a classifier with strong generalization is finally trained.
Comparison of Stacking-based Classifier Ensembles using Euclidean and Riemannian Geometries

This paper considers three different classifier stacking algorithms: simple stacking, cascades of classifier ensembles and nonlinear version of classifier stacking based on classifier interactions. Classifier interactions can be expressed using classifier prediction pairwise matrix (CPPM). As a meta-learner for the last algorithm Convolutional Neural Networks (CNNs) and two other classifier stacking algorithms (simple classifier stacking and cascades of classifier ensembles) have been applied. This allows applying classical stacking and cascade-based recursive stacking in the Euclidean and the Riemannian geometries. The cascades of random forests (RFs) and extra trees (ETs) are considered as a forest-based alternative to deep neural networks [1]. Our goal is to compare accuracies of the cascades of RFs and CNN-based stacking or deep multi-layer perceptrons (MLPs) for different classifications problems. We use gesture phase dataset from UCI repository [2] to compare and analyze cascades of RFs and extra trees (ETs) in both geometries and CNN-based version of classifier stacking. This data set was selected because generally motion is considered as a nonlinear process (patterns do no lie in Euclidean vector space) in computer vision applications. Thus we can assess how good are forest-based deep learning and the Riemannian manifolds (R-manifolds) when applied to nonlinear processes. Some more datasets from UCI repository were used to compare the aforementioned algorithms to some other well-known classifiers and their stacking-based versions in both geometries. Experimental results show that classifier stacking algorithms in Riemannian geometry (R-geometry) are less dependent on some properties of individual classifiers (e.g. depth of decision trees in RFs or ETs) in comparison to Euclidean geometry. More independent individual classifiers allow to obtain R-manifolds with better properties for classification. Generally, accuracy of classification using classifier stacking in R-geometry is higher than in Euclidean one.
Improving FM-GAN through Mixup Manifold Regularization

Due to their ability to model the manifold of natural images, generative adversarial networks (GANs) have been adapted to semi-supervised learning and shown promising results. Using this property of GANs, manifold regularization has been incorporated into feature-matching GANs (FM-GAN) [14] and shown improvement over FM-GAN. In this work, we present a novel approach for performing manifold regularization in semi- supervised classification using the FM-GAN framework. Our approach, mixup manifold regularization (MUMR), regularizes the networks to favor linear behavior so that linear interpolations of noise vectors lead to linear interpolations of the associated predictions. We show that our approach is able to achieve state-of-the-art results when compared to other GANs- and perturbation-based approaches.
ConfigRand: A Moving Target Defense Framework against the Shared Kernel Information Leakages for Container-based Cloud

Lightweight virtualization represented by container technology provides a virtual environment for cloud services with more flexibility and efficiency due to the kernel-sharing property. However, the shared kernel also means that the system isolation mechanisms are incomplete. Attackers can scan the shared system configuration files to explore vulnerabilities for launching attacks. Previous works mainly eliminate the problem by fixing operating systems or using access control policies, but these methods require significant modifications and cannot meet the security needs of individual containers accurately. In this paper, we present ConfigRand, a moving target defense framework to prevent the information leakages due to the shared kernel in the container-based cloud. The ConfigRand deploys deceptive system configurations for each container, bounding the scan of attackers aimed at the shared kernel. In design of ConfigRand, we (1) propose a framework applying the moving target defense philosophy to periodically generate, distribute, and deploy the deceptive system configurations in the container-based cloud; (2) establish a model to formalize these configurations and quantify their heterogeneity; (3) present a configuration movement strategy to evaluate and optimize the variation of configurations. The results show that ConfigRand can effectively prevent the information leakages due to the shared kernel and apply to typical container applications with minimal system modification and performance degradation.
Design of LSTM–CNN with Feature Map Merge for Crime Scene Detection in CCTV Footage

Nowadays, circuited televisions are installed in many places, and it is been monitored by the authorities to detect the crimes. The authorities carelessness may prone to miss the important crimes that are been recorded. There are two existing architectures for detecting crimes and human activities such as 3D CNN and two-stream CNN. The drawbacks of the existing techniques are that it is unable to capture the complete local features and the accuracy is less. In order to overcome these problems, this paper proposes long short-term memory based convolutional neural network with feature map merge block to capture the complete features of the scene. The proposed architecture detects the weapons such as guns and knives, robberies, and if anybody is fighting. The experiments are conducted by using 500 crime scene videos collected from Internet. The experimental results shows that proposed architecture yields 90% of accuracy. Further, the results are compared with existing architectures.
Bengali Handwritten Character Transformation: Basic to Compound and Compound to Basic Using Convolutional Neural Network

Bengali handwritten character recognization technique faces numerous complexities because of the unusual shape and stroke of the letters. In Bengali scripts, two or more basic characters construct a compound character. So it is challenging for the children to understand how these compound characters have been structured. Here, we recommend a system that can turn these basic characters into a compound character. Also, transfer a compound character into the basic characters by combining deep learning and image processing principles. The identification is begun by preprocessing the image using several image processing techniques, and then a convolutional neural network model is used for the transformation of the characters. Furthermore, the MapReduce technique is introduced for finding the appropriate character. This experiment was done on a dataset of more than 300,000 images and received an 89.20% accuracy rate.