Reviewerly/exHarmony huggingface dataset:
CPQN: Central Product Quantization Network for Semi-supervised Image Retrieval

The hash method or product quantization based on deep learning has achieved great success in image retrieval. But most deep hash methods are designed for supervised scenes. They only use semantic similarity information and ignore the underlying data structure. Moreover, a large amount of manual label information is expensive and time-consuming, which is not in line with the actual application scenario. In order to tackle this problem, we propose a novel quantization-based semi-supervised image retrieval network: Central Product Quantization Network (CPQN). We design a novel central similarity strategy to preserve the semantic similarity and underlying data structure in labeled data, and generalize it to unlabeled data through consistent regularization to tap the potential of unlabeled data. We also propose a novel semi-supervised loss algorithm to achieve effective hashing by reducing quantization noise and minimizing the empirical error of labeled data and the embedding error of unlabeled data. Experiments on public benchmark dataset clearly show that our proposed method is superior to the most advanced hash method.
Random coordinate descent methods for non-separable composite optimization

In this paper we consider composite optimization problems having the objective function formed as a sum of two terms, one has Lipschitz continuous gradient and another is differentiable but non-separable. Under these settings we design two random coordinate descent methods, one takes into account the composite form of the objective function, while the other algorithm disregards the composite form of the objective function yielding a random coordinate gradient descent scheme with a novel stepsize rule. Our methods are able to tackle both convex and non-convex problems. For these algorithms we also show that they are descent methods in expectation. Preliminary numerical results confirm the efficiency of our two algorithms.
Privacy in Cloud-Based Computing

Cloud computing, internet of things (IoT), edge computing, and fog computing are gaining attention as emerging research topics and computing approaches in recent years. These computing approaches are rather conceptual and contextual strategies rather than being computing technologies themselves, and in practice, they often overlap. For example, an IoT architecture may incorporate cloud computing and fog computing. Cloud computing is a significant concept in contemporary computing and being adopted in almost every means of computing. All computing architectures incorporating cloud computing are termed as cloud-based computing (CbC) in general. However, cloud computing itself is the basis of CbC because it significantly depends on resources that are remote, and the remote resources are often under third-party ownership where the privacy of sensitive data is a big concern. This chapter investigates various privacy issues associated with CbC. The data privacy issues and possible solutions within the context of cloud computing, IoT, edge computing, and fog computing are also explored.
Crowd Analysis of Almasjid Alnabawi using convolutional neural networks of CCTV footage

In recent months, crowd management has become more important than ever, given the spread of contagious diseases such as COVID-19. The Hajj, in Saudi Arabia, is one of the largest gatherings in the world; it happens annually and is getting bigger every year. The development of radio-frequency identification (RFID) and mobile apps has been investigated to help estimate crowd movements in and among the holy sites. However, network-based technologies require large infrastructures and are therefore very costly. In this paper, a system is proposed to use existing closed-circuit television (CCTV) to accurately visualize the movements of crowds in the Almasjid Alnabawi, also known as The Prophet's Mosque. The proposed neural network is trained with large datasets of crowd images to produce estimates of the number of pilgrims in an image. Images are then integrated to produce crowd level models throughout the building. The system has been tested on two instances and showed high performance.
An Improved Multi-target Tracking Trajectory Association Algorithm in Dense Clutter Environment

In order to solve the trajectory association of multi-target tracking in dense clutter environment, we propose a joint probabilistic data association algorithm based on Sequential Probability Ratio Test with the simplified association probability(S-SPRT-JPDA). First, we use the sequential probability ratio test to introduce a target tracking management system, to solve the problem of the trajectory initiation and termination in dense clutter environment; and we redefine the confirmation matrix in the joint probability data association algorithm to simplify the calculation of the association probability when the target and the measurement are interconnected. The theoretical analysis and simulation experimental results show that the algorithm runs faster by 55% under the condition of ensuring the accuracy of association, which greatly reduces the calculation amount of trajectory association algorithm in the process of multi-target tracking and effectively improves the real-time performance of multi-target tracking data association algorithm in dense clutter environment.
Two Model-Free Schemes for Solving Kinematic Tracking Control of Redundant Robot Manipulators Based on Cmac Networks

Kinematic tracking control of robotic manipulators is a basic and vital issue in robotics. Existing studies show that cerebellum, a pivotal region of brain, plays a major role in human motion control, which prompts us to devise two model-free CMAC-based (MFCB) schemes for the kinematic tracking control of redundant manipulators in this article. The first MFCB scheme is based on a CMAC network and a GND algorithm. The CMAC network is utilized to generate approximate joint angle commands for the manipulator. By using the Jacobian matrix of the manipulator which is estimated by the GND algorithm, the task space error is transformed into joint space error, which is taken as the teaching signal for training the CMAC. For the second scheme, another CMAC network replaces the GND algorithm to achieve the estimation of the Jacobian matrix of the manipulator. The effectiveness and robustness of the proposed control schemes are verified by both simulations and physical experiments.
Khanh Thien gold-ore-magmatic cluster (North Vietnam)

Abstract New data on the gold mineralization related of the Late Paleozoic alkaline complexes of North Vietnam are presented. Two types of gold mineralization associated with syenites and granosyenites have been identified: gold only and gold-polymetallic. Geochemical characteristics are given for both types and the first data on the sulfur isotopic composition of ore minerals.
Correlated Binary Data for Machine Learning

Data sets containing instances that are assigned values by an ensemble of annotators of unknown accuracy are becoming increasingly common. Binary, potentially correlated data are frequent in a number of disciplines, and thus eligible to be exploited by ensemble meta-learners. A prior key step is testing the meta-learners with synthetic data sets featuring realistic correlation patterns, which is the main scope of this work. To achieve this goal, two challenges are faced: (i) finding out a new correlated pattern to model Bernoulli random variables, and (ii) obtaining a process to generate realistic synthetic data sets. A comparative analysis and performance results are provided for two methods of artificial data generation. The methods are also tested using two state-of-the-art binary ensemble meta-learners that consider inter-classifier dependencies.
A probabilistic perspective on nearest neighbor for implicit recommendation

Over the past years, the recommender systems community invented several novel approaches that reached better and better prediction accuracy. Remarkably, however, the classic k nearest neighbor algorithm appears to remain competitive, even when compared to much more sophisticated methods. In this paper, we attempt to explain the inner workings of the nearest neighbor using probabilistic tools, treating similarity as conditional probability and presenting a novel model for explaining and removing popularity bias.First, we provide a probabilistic formulation of similarity and the classic prediction formula. Second, by modeling user behavior as a combination of personal preference and global influence, we are able to explain the presence of popularity bias in the predictions. Finally, we utilize Bayesian inference to construct a theoretically grounded variant of the widely used inverse frequency scaling, which we use to mitigate the effect of popularity bias in the predictions.By replacing the formerly ad hoc choices of nearest neighbor with probabilistically founded counterparts, we are able to improve prediction accuracy over a variety of data sets and gain an increased understanding of the theory behind the method.
AI safety and quality management method of autonomous mobile robots using deep reinforcement learning

In services using statistical machine learning, safety quality management is important to reduce accidents and economic losses due to misjudgment and miscontrol by Artificial Intelligence (AI). However, research and development has just begun, and the management method has not yet been established. Moreover, the target of the guidelines published to date for quality management of AI-based systems are limited to supervised learning. In this paper, I develop AI safety and quality management methods for autonomous mobile robots that makes decisions and controls using deep reinforcement learning, which is not covered by the AI quality management guidelines. These methods focus on the quality characteristics and record quality assessment as evidence for analysis, understanding, and quantitative quality explanation. As a result, they enable understanding and sharing the achieved quality among stakeholders, and give concrete explanations to society. It is also useful for clarifying ordering conditions, identifying problems, and presenting added value.
Quantum harmonic oscillators with nonlinear effective masses in the weak density approximation

Abstract We study the eigen-energy and eigen-function of a quantum particle acquiring the probability density-dependent effective mass (DDEM) in harmonic oscillators. Instead of discrete eigen-energies, continuous energy spectra are revealed due to the introduction of a nonlinear effective mass. Analytically, we map this problem into an infinite discrete dynamical system and obtain the stationary solutions in the weak density approximation, along with the proof on the monotonicity in the perturbed eigen-energies. Numerical results not only give agreement to the asymptotic solutions stemmed from the expansion of Hermite-Gaussian functions, but also unveil a family of peakon-like solutions without linear counterparts. As nonlinear Schrödinger wave equation has served as an important model equation in various sub-fields in physics, our proposed generalized quantum harmonic oscillator opens an unexplored area for quantum particles with nonlinear effective masses.
An Edge Based Multi-Agent Model for Improving Hospital Bed Management

Today, and due to the population growth, the healthcare field recognizes many difficulties and challenges in many countries, especially in the underdeveloped ones. The spread of COVID-19 has shown these difficulties such as the lack of equipment and the failing of patient admission process. Therefore, healthcare systems require quick, newer and more sophisticated solutions that match the speed of technology and the citizens’ aspirations. In this context, we propose in this paper a model for improving and helping hospital managers to orchestrate the patient admission process. The proposed model deals with the hospital bed management in order to ensure a quick patient transfer to the nearest and the most appropriate hospital. The proposed model is based on the coupling of multi-agent an edge computing system. Multi-agent systems represent a powerful choice to develop complex, distributed, dynamic and interactive systems, which represent typically our case. In addition, edge computing ensures a real-time collection of data about the bed’s availability in the neighboring hospitals in order to transfer the patient to the adequate one as quickly as possible.
Nonconvex Optimization

In the last few chapters, we have discussed a few stochastic gradient descent type methods and established their convergence rates for solving different convex optimization problems. Note however that convexity has played an important role in our analysis. In this chapter, we focus on stochastic optimization problems which are not necessarily convex. We first introduce some new stochastic optimization algorithms, including the randomized stochastic gradient and stochastic accelerated gradient descent methods, for solving these nonconvex problems. We establish the complexity of these methods for computing an approximate stationary point of a nonlinear programming problem. We will also discuss variants of these methods for solving a class of simulation-based optimization problems in which only stochastic zeroth-order information is available. In addition, we study variance-reduced mirror descent methods for solving nonconvex finite-sum problems, as well as indirect acceleration schemes through proximal point methods for solving nonconvex finite-sum and multi-block problems.
Efficient Near-Optimal t-Closeness With Low Information Loss

The t-closeness technique is one of the most popular techniques for preserving individual privacy in data. It involves generalizing and suppressing some attributes of a given table, after partitioning the set of all records of that table into equivalence classes that satisfy a certain constraint. We present a new method to partition the set of records of a table into such equivalence classes. Our method has several advantages over the existing methods for this task. Firstly, the classes generated by our method are near-optimal, in that they satisfy the t-closeness constraint for even the “smallest” t value for which t-closeness is achievable and useful for the given table, thereby providing the highest amount of privacy. Secondly, our method generates classes of approximately the same size, resulting in low information loss caused by generalization of attribute values. Lastly, while generating classes with minimum information loss is known to be NP-hard, our classes with reasonably low information loss can be generated in just polynomial time.
Secml: Secure and Explainable Machine Learning in Python

We present secml, an open-source Python library for secure and explainable machine learning. It implements the most popular attacks against machine learning, including not only test-time evasion attacks to generate adversarial examples against deep neural networks, but also training-time poisoning attacks against support vector machines and many other algorithms. These attacks enable evaluating the security of learning algorithms and of the corresponding defenses under both white-box and black-box threat models. To this end, secml provides built-in functions to compute security evaluation curves, showing how quickly classification performance decreases against increasing adversarial perturbations of the input data. secml also includes explainability methods to help understand why adversarial attacks succeed against a given model, by visualizing the most influential features and training prototypes contributing to each decision. It is distributed under the Apache License 2.0, and hosted at https://gitlab.com/pralab/secml.
Weighted Least Square - Support Vector Machine

In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM), Twin Support Vector Machine (TSVM), or Least Square Twin Support Vector Machine (LSTSVM) cannot sufficiently exploit structural information with cluster granularity of the data, cause limitation in the ability to detect data trends. Structural Twin Support Vector Machine (S-TSVM) sufficiently exploits structural information with cluster granularity for learning a represented hyperplane. Therefore, the ability to describe the data of S-TSVM is better than that of TSVM and LSTSVM. However, for the datasets where each class consists of clusters of different trends, the S-TSVM’s ability to describe data seems restricted. Besides, the training time of S-TSVM has not been improved compared to TSVM. This paper proposes a new Weighted Least Square - Support Vector Machine (called WLS-SVM) for binary classification problems with a clusters-vs-class strategy. Experimental results show that WLS-SVM could describe the tendency of the distribution of cluster information. Furthermore, the WLS-SVM training time is faster than that of S-TSVM and TSVM, and the WLS-SVM accuracy is better than LSTSVM and TSVM in most cases.
Facial Recognition based on Deep Learning

Human beings are the inquisitive creatures, who are fascinated by several things. One of the things that captivate their interest more than anything else is humanity itself. One of the primary tasks in the investigation of ethnic gathering recognition is facial component disclosure. Exceptionally, with the new innovation of profound learning techniques, significant research progress has been made in computer-based facial recognition. This paper has proposed a deep learning model for identity recognition by considering the facial features. According to the recent research studies, due to the distribution of identity/race in preparing datasets, biometric calculations suffer from cross-race impact, where their display is better on the subjects closer to the “country of origin.” To begin, this research work has compiled a dataset of various identities/races and further prepared and examined four state-of-the-art convolutional neural networks on the issue of race and ethnicity characterization. The purpose of performing such analysis is to illuminate activity, assemble proof for hypotheses, and incorporate it to create information in the field of profound learning. These tests and an acknowledgment pace of 82.0% on the human race arrangement issue that exhibit the adequacy of proposed arrangement.
Real-time Violence Activity Detection Using Deep Neural Networks in a CCTV camera

In surveillance systems one of the most difficult and critical tasks is detecting violence, most video surveillance systems are faced with the challenges of false alarms and working in a real-time environment. This paper introduces a lightweight system that can be employed in real-time systems. The system proposed uses OpenPose for multi-person 2D pose estimation, YoloV3 for person detection, and a CNN to classify the violent action. OpenPose unlike other systems uses a bottom-up approach which decouples the running time from the number of people in the frame, YoloV3 deployed for person detection has a very fast processing time and the CNN used was trained on the skeleton dataset that was generated and had very good accuracy. We also propose an skeleton image dataset of three action categories, namely kicking, punching, and non-violent.
Adaptive vision-based system for landing an autonomous hexacopter drone on a specific landing platform

Today, autonomous flight and precise landing of drones are crucial for many applications such as object detection, and delivery of services. The focus of this research is to present an adaptive vision-based system for landing an autonomous hexacopter unmanned aerial vehicle (UAV) on a specific landing platform. The system is based on a colour-based algorithm to position a visual marker and then land the drone in environments where GPS signal is weak or unavailable. Four cameras and one marker are used in the suggested approach. The attitude and altitude of the drone are adjusted automatically based on the information obtained from the vision system. To verify the performance of the proposed system, a practical test bench was developed. Simulation studies and real experiments were conducted under different scenarios. The results show that the proposed system is highly reliable and robust in detecting the landing area and landing the drone.
Application of QR Code Recognition Technology in Modern Power Logistics Management

Modern power logistics management highly depends on the collection, analysis, processing and real-time update of a large number of data and information. One of the decisive factors for the rapid development of a series of information technology is the continuous development demand of modern power logistics enterprises, including graphic identification technology. This paper introduces the basic principle of image recognition technology, explains the whole process of bar code recognition, and mainly studies the application of the graphic identification technology in modern power logistics enterprises, especially in each link. This paper analyzes the application of the technology in modern power logistics enterprises, and points out that the bar code automatic identification technology can solve the problems existing in procurement management, production management, sales management and warehouse management. Through the application of information technology such as two-dimensional code, we can improve the efficiency of management, enhance economic benefits, and better provide technical support for the power sector and enterprises to achieve management objectives.
The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

Current weather-related research only focuses on weather prediction based on raw data and the factors used are generally 4 factors: average temperature, solar radiation, air pressure, and wind. In this research, monthly weather prediction is done using 5 factors where the additional factor used is rainfall in the previous time. In contrast to previous prediction research, the prediction process carried out in this study emphasizes the modeling of training data according to the desired prediction model.. These two things distinguish this research from previous studies. The prediction model used in this study is a classification-based prediction model that is the Artificial Neural Network (ANN) method combined with the backpropagation algorithm for calculating the weight of the ANN network. The data used are meteorological data from 2010 to 2018 in the Bogor area, where data from 2010 to 2016 are used as training data, and data from 2017 to 2018 are used as test data. The results of this study indicate that the design of the model with the use of data for 6 years with feature data of 5 factors has an accuracy rate of 83.33%.
Library Reader Behavior Based on Apriori Association Algorithm

The key to library modernization is to use readers’ borrowing materials to extract relevant laws of reader behavior and predict readers’ reading needs. This article aims to study the borrowing behavior of library readers based on Apriori association rule algorithm. This paper proposes a library reader behavior analysis algorithm based on association rules. Based on the analysis of the library’s reader system, the reader’s historical borrowing record is expected to be completed, the division of its “common user” customer group is completed, and the library is provided with suggested reading services. In addition, a knowledge team and service support has been constructed. community. The experimental results of this paper show that the borrowing behavior of library readers based on the Apriori correlation algorithm has a great relationship with regions. Among them, the borrowing volume of most regions is 10 volumes and 1 volume.