Reviewerly/exHarmony huggingface dataset:
Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

Hajj and Umrah are two main religious duties for Muslims. To help faithfuls to perform their religious duties comfortably in overcrowded areas, a crowd management system is a must to control the entering and exiting for each place. Since the number of people is very high, an intelligent crowd management system can be developed to reduce human effort and accelerate the management process. In this work, we propose a crowd management process based on detecting, tracking, and counting human faces using Artificial Intelligence techniques. Human detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security purposes. The proposed crowd management system is composed form three main parts which are: (1) detecting human faces, (2) assigning each detected face with a numerical identifier, (3) storing the identity of each face in a database for further identification and tracking. The main contribution of this work focuses on the detection and tracking model which is based on an improved object detection model. The improved Yolo v4 was used for face detection and tracking. It has been very effective in detecting small objects in high-resolution images. The novelty contained in this method was the integration of the adaptive attention mechanism to improve the performance of the model for the desired task. Channel wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building blocks. The main idea from the adaptive attention mechanisms is to make the model focus more on the target and ignore false positive proposals. We demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available dataset. The wider faces dataset was used for the train and the evaluation of the proposed detection and tracking model. The proposed model has achieved good results with 91.2% of mAP and a processing speed of 18 FPS on the Nvidia GTX 960 GPU.
HER-PDQN: A Reinforcement Learning Approach for UAV Navigation with Hybrid Action Spaces and Sparse Rewards

Reinforcement learning (RL) equipped with neural networks has recently led to a wide range of successes in learning policies for unmanned aerial vehicle (UAV) navigation and control problems.The success of RL relies on two human-designed heuristics: appropriate action space definition and reward function engineering.The commonly used fully continuous or fully discrete action spaces in optimal control and decision making problems may lack control authority and remove the inherent problem structure, which can negatively affect learning performance.Besides, reward engineering requires a lot of human effort and may lead to unwanted behavior.In this paper, we address these challenges by proposing a new off-policy RL algorithm called HER-PDQN which incorporates Hindsight Experience Replay (HER) with Parameterized Deep Q-Networks (P-DQN).In simulation experiments, HER-PDQN is used to train an agent to fulfill a UAV navigation task in a 2-dimensional environment.The results indicate the effectiveness of P-DQN algorithm in dealing both with the hybrid action space and sparse rewards.This paper can be considered as the first attempt at applying RL in sparse reward setting for UAV navigation with hybrid action spaces.
A Deep Generative Approach to Conditional Sampling

We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. The proposed approach aims at learning a conditional generator, so that a random sample from the target conditional distribution can be obtained by transforming a sample drawn from a reference distribution. The conditional generator is estimated nonparametrically with neural networks by matching appropriate joint distributions using the Kullback-Liebler divergence. An appealing aspect of our method is that it allows either of or both the predictor and the response to be high-dimensional and can handle both continuous and discrete type predictors and responses. We show that the proposed method is consistent in the sense that the conditional generator converges in distribution to the underlying conditional distribution under mild conditions. Our numerical experiments with simulated and benchmark image data validate the proposed method and demonstrate that it outperforms several existing conditional density estimation methods. Supplementary materials for this article are available online.
An Adaptive, Context-Aware, and Stacked Attention Network-Based Recommendation System to Capture Users’ Temporal Preference

Recommendation systems have become more important since the widespread use of the Internet. The large amount of information means that it is difficult for users to discover what they really need. However, users’ preferences can change over time because of the age and the impact of social networks. Recommendation systems must capture users’ preferences and recommend suitable items, which is a difficult challenge. This study proposes a novel recommendation system that adapts to the changing preferences of users. By learning and adapting to users’ changing preferences better recommendations are provided. This study uses context factors as additional information to model users’ preferences more accurately. For the proposed recommendation system, the context is based on users’ most recent interaction items. This study proposes two novel attention mechanisms for the recommendation system. The contextual item attention module captures contextual information, changing pattern in users’ preference and the importance of items. The multi-head attention module extends the diversity of users’ preferences and adapts to changing preferences. The recommendation performance is improved using additional item's temporal information to model the contextual item's representation. Experiments compare the proposed algorithm with several state-of-the-art recommendation methods using three real-world datasets. The experimental results demonstrate that the proposed context-aware recommendation model outperforms traditional methods and demonstrate the effectiveness with which contextual information is captured by the attention mechanism.
EYEDi: Graphical Authentication Scheme of Estimating Your Encodable Distorted Images to Prevent Screenshot Attacks

Graphical authentication schemes have the advantage of being more memorable than conventional passwords. Although some image distortion methods have been proposed to prevent the risks of over-the-shoulder attacks (OSAs), these methods cannot prevent camera recording attacks, as the key images are the same each time. In this study, we propose a graphical authentication scheme that generates various distorted images, named Estimating Your Encodable Distorted images (EYEDi). EYEDi generates distorted images by applying several image processing filters to the original images. Moreover, EYEDi estimates the appropriate image processing filter strength based on the authentication data. To measure attack resistance, twenty participants performed three types of attacks (OSA, camera recording attack, and screenshot) 300 times, each using existing methods and EYEDi. The classification error rate of all three types of attacks showed that EYEDi had a lower classification error rate between the legitimate user and attackers. Especially for the screenshot attack, which is the most severe threat model, the existing method was completely broken through, while EYEDi prevented the attacks with a classification error rate of 10&#x0025;. This result shows that EYEDi can eliminate the screenshot attacker by using the difference in authentication times and a simple improvement in defense performance.
Utilizing Link Prediction Approach to Predict Express-Related Counterfeit Cigarette Crime Cases

In this paper, an approach for express-related counterfeit cigarette criminality analysis based on link prediction approach and historical express big data is proposed. In the pre-processing stage, data integration, data cleaning and data structured processing methods are utilized to process the original express data. Then time series and graph topological features of express data are computed. The former can be computed based on package delivery and receiving frequency during a period of time, which can be regarded as time series data, by using time domain statistics. The latter can be calculated on a directed graph built with historical express big data. Next, based on this feature pool, the classifier model was trained and optimized to predict the probability of a package containing counterfeit cigarettes. Preliminary experimental results demonstrate feasibility, effectiveness and encouraging discriminative power of the proposed approach.
Interpersonal Competences and Attitude to Online Collaborative Learning (OCL) among Future Pedagogues and Educators—A Polish and Portuguese Perspective

Interpersonal competences in relationship building, collaboration and teamwork are essential both in F2F education and in distance or hybrid styles of education. In the face of the COVID-19 crisis, as well as previous trends, some or all of the higher education didactics were transferred to the online environment. In this context, the Online Collaborative Learning (OCL) model, now known as collaborativism, is gaining importance. This research aims to diagnose and compare the level of interpersonal competences and attitude in the OCL model among students preparing for the profession of an educator, pedagogue or teacher in the autonomous region of Madeira and in Poland. Quantitative studies were conducted using the Interpersonal Competence Questionnaire by D. Buhrmester, W. Furman, M.T. Wittenberg and H.T. Reis—as adapted by Poland and Portugal. In addition, the respondents expressed their views on OCL in the self-written questionnaire. According to the research findings, the highest level of competence in the Portuguese group was achieved by the respondents in terms of: Emotional Support (EW), Conflict Management and Resolution (RK) and Negative Assertion—Asserting Influence (AO). In the Polish group, EW was the lowest. The highest level of interpersonal competences was observed for Conflict Management and Resolution (RK), Disclosure (US) and Initiation (IR). The largest group of the surveyed students in both countries expressed a positive attitude towards implementing OCL in their courses of study and declared their willingness to be a member of a group that learns online together.
SoManyConflicts: Resolve Many Merge Conflicts Interactively and Systematically

Code merging plays an important role in collaborative software development. However, it is often tedious and error-prone for developers to manually resolve merge conflicts, especially when there are many conflicts after merging long-lived branches or parallel versions. In this paper, we present SoManyConflicts, a language-agnostic approach to help developers resolve merge conflicts systematically, by utilizing their interrelations (e.g., dependency, similarity, etc.). SoManyConflicts employs a graph representation to model these interrelations and provides 3 major features: 1) cluster and order related conflict based on the graph connectivity; 2) suggest related conflicts of one focused conflict based on the topological sorting, 3) suggest resolution strategies for unresolved conflicts based already resolved ones. We have implemented SoManyConflicts as a Visual Studio Code extension that supports multiple languages (Java, JavaScript, and TypeScript, etc.), which is briefly introduced in the video: https://youtu.be/asWhj1KTU. The source code is publicly available at: https://github.com/Symbolk/somanyconflicts.
Neural networks and deep learning

In this chapter, we will provide the general and fundamental background related to neural networks and deep learning techniques. Specifically, we divide the fundamentals of deep learning in three parts; the first one introduces deep feed forward networks and the main training algorithms in the context of optimization. The second part covers convolutional neural networks (CNN) and discusses their main advantages and shortcomings for different scenarios and variants of CNNs. Finally, the third part presents neural networks for sequence modeling, in particular recurrent neural networks (RNN), gated recurrent units (GRU), long short-term memory (LSTM) and attention mechanisms. The description of the latter models is made in the context of different applications that allows explaining in a better way the details of each particular kind of neural network.
ISAR images tracking for extended small vehicles using cubature Kalman MB filter

This work provides a new approach for extended drone targets (EDTs) tracking from inverse synthetic aperture radar (ISAR) images based on robust extended cubature Kalman multi-Bernoulli track-before-detect (ECK-MB-TBD) algorithm that handling of the non-linear ISAR observations with Doppler effect. During this work, we present the drone's extension as multiple ellipses with a time-varying orientation angle and solve the resulting inference problem, which involves data association between the nonlinear measurements and sub-ellipses or sub-random matrices model (RMM). In addition, we give a new implementation of the proposed robust ECK-Sub-RMM-MB-TBD filter to estimate the change in the nonlinear orientation of target's shape based on ECK-Beta Gaussian inverse Wishart. Tracking performance is compared to state-of-the-art algorithms on EDTs such as sequential Monte Carlo (SMC)-Sub-RMM-MB-TBD and histogram probabilistic multi-hypothesis (H-PMHT)-TBD. The simulation results confirm the effectiveness and robustness of the proposed algorithm. From the results, the SMC-Sub-RMM-MB-TBD algorithm take significantly more time to compute than the proposed algorithm, because the first algorithm needs a large number of the particles for EDTs. The average running times were 88.63 s for SMC-Sub-RMM-MB-TBD, 38.54 s for ECK-Sub-RMM-MB-TBD and 55.34 s for H-PMHT-TBD.
Network Calibration by Class-based Temperature Scaling

It is well known that modern neural networks are poorly calibrated. They tend to overestimate or underestimate probabilities when compared to the expected accuracy. This results in misleading reliability and corrupting our decision policy. We show that the amount of calibration error differs across the classes. As a result, we propose to calibrate each class separately. We apply this class-level calibration paradigm to the concept of temperature scaling and describe an optimization method that finds the suitable temperature scaling for each class. We report extensive experiments on a variety of image datasets, and a wide variety of network architectures, and show that our approach achieves state-of-the-art calibration without compromising on accuracy in almost all cases.
Interannual variation of measured atmospheric solar radiation extinction levels

Solar radiation loss by attenuation throughout the heliostat-receiver pathway in concentrating solar power plants is a daily factor to consider in order to know the real power production efficiency and performance of this kind of plants. A direct real-time extinction measurement system has been developed at Plataforma Solar de Almería. The system is running from June 2017 and could be installed in other plants. The system is used as a reference system for other extinction methodologies with which they are compared to validate results. Therefore, two years of measurement have been analysed to verify the reliability of the system and to conclude if it can be used as a reference. The study clearly shows that the extinction has a seasonal behaviour and a high dependency on meteorological parameters, episodes of humidity, aerosol and particle concentration. It has also been proven that the system is capable of detecting extinction variations due to the presence of meteorological phenomena. The annual extinction levels measured are coincident in both years analysed, within the error range of the measurement system (≈2%). The measured extinction coincides with the extinction obtained with other methodologies using aerosol data and typical meteorological years.
Privacy-Preserved Distributed Learning With Zeroth-Order Optimization

We develop a privacy-preserving distributed algorithm to minimize a regularized empirical risk function when the first-order information is not available and data is distributed over a multi-agent network. We employ a zeroth-order method to minimize the associated augmented Lagrangian function in the primal domain using the alternating direction method of multipliers (ADMM). We show that the proposed algorithm, named distributed zeroth-order ADMM (D-ZOA), has intrinsic privacy-preserving properties. Most existing privacy-preserving distributed optimization/estimation algorithms exploit some perturbation mechanism to preserve privacy, which comes at the cost of reduced accuracy. Contrarily, by analyzing the inherent randomness due to the use of a zeroth-order method, we show that D-ZOA is intrinsically endowed with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\epsilon,\delta)-$ </tex-math></inline-formula> differential privacy. In addition, we employ the moments accountant method to show that the total privacy leakage of D-ZOA grows sublinearly with the number of ADMM iterations. D-ZOA outperforms the existing differentially-private approaches in terms of accuracy while yielding similar privacy guarantee. We prove that D-ZOA reaches a neighborhood of the optimal solution whose size depends on the privacy parameter. The convergence analysis also reveals a practically important trade-off between privacy and accuracy. Simulation results verify the desirable privacy-preserving properties of D-ZOA and its superiority over the state-of-the-art algorithms as well as its network-wide convergence.
Early breast cancer diagnosis using cogent activation function‐based deep learning implementation on screened mammograms

Abstract Breast cancer is detected in one out of eight females worldwide. Principally biomedical image processing techniques work with images captured by a microscope and then analyzed with the help of different algorithms and methods. Instead of microscopic image diagnosis, machine learning algorithms are now incorporated to detect and diagnose therapeutic imagery. Computer‐aided mechanisms are used for better efficiency and reliability compared with manual pathological detection systems. Machine learning algorithms detect tumors by extracting features through a convolutional neural network (CNN) and then classifying them using a fully connected network. As Machine learning does not require prior expertise, it is profoundly used in biomedical imaging. This article has customized a convolutional neural network by mathematical modeling of a proposed activation function. We have obtained an appreciable prediction accuracy of up to 99%, along with a precision of 0.97.
A Robot 3D Grasping Application Based on Binocular Vision System

3D vision system plays an essential role in robot grasping applications in complex environments. Six-degree-of-freedom pose estimation for weakly textured targets is a current research hotspot in the field of machine vision. This paper proposes a method based on feature region distribution to optimize the feature matching point pairs. This paper presents a global feature constraint-based target pose estimation method, enabling the visual system to filter the target to grasp complex backgrounds and stacking situations. Finally, we validate the effectiveness of the proposed approach through robot grasping experiments.
Neural String Diagrams: A Universal Modelling Language for Categorical Deep Learning

Category theory has been successfully applied to compositional modelling of diverse systems, including computational systems, logical systems, and physical systems, paving the way for a new kind of general system science (or rather general process science). It has been particularly successful in quantum computing and natural language processing recently; traditionally, it has played major rôles in compositional semantics of programming languages and symbolic reasoning systems. Building upon them, we propose the mathematical system of neural string diagrams as a universal modelling language for categorical deep learning, which allows us to turn informal neural network architecture pictures into formally explainable, mathematically verifiable, and systematically composable entities in their own right. We give, in particular, a neural string diagram account of CNN and Transformer (which has never been achieved before). Neural string diagrams can be computed with DisCoPy, Quantomatic, and their extensions. Categorically formalised neural networks can be instantiated for both ordinary vector spaces and other monoidal categorical structures, allowing for generalisations of deep learning (e.g., deep learning on relational structures, deep learning on graph and other network structures, etc.). We envisage that the category theory approach to artificial intelligence ultimately contributes to the development of artificial general intelligence, giving a universal modelling language for intelligent systems and agents.
The Application of Agricultural Resource Management Information System based on Internet of Things and Data Mining

To promote the progression of information management of agricultural resources in China, the present situation of agricultural management model in China is introduced, and an information agricultural tracking model based on Internet of Things (IoT) is proposed according to the characteristics of production flow of agricultural products.Then, the progression level of agricultural mechanization in China is analyzed based on data mining, and some suggestions are given.The research results show that in the four links of production, processing, transportation, and sales, the agricultural products tracking system in this study can simultaneously deal with more than 800 events per second when conducting information query.When the number of users reaches 20, the response time is 0.021 seconds.When processing messages sent by 100 users simultaneously, the response time is only about 0.12 seconds.When 160 users are sent to the system at the same time, the response time is less than 0.2 seconds.As the number of users sending messages at the same time increases, the response time remains within 0.3 seconds, although delayed.This system can effectively complete the agricultural commodity information tracking, with good performance, query speed, and high accuracy.The agricultural products tracking model in this study is very powerful to complete the information tracking of agricultural commodities, with good performance, fast query speed, and high accuracy.In summary, IoT and data mining are applied to agricultural resource information management, and the analysis and experiment are carried out.The results provide some ideas and references for the research of related technologies in agricultural management and progression.