Reviewerly/exHarmony huggingface dataset:
Ant colony optimization algorithm and its application

Ant Colony Optimization (ACO) algorithm is a bionic algorithm simulating ant colony behavior. Ant colony can find the shortest path by sensing pheromone when looking for food. Ant colony algorithm plays an important role in solving a variety of planning problems such as traveling salesman problem (TSP). This paper mainly introduces the background knowledge of ACO algorithm in entomology and computer and uses python implementation to solve an example of traveling salesman problem. This work focuses on the ACO algorithm, which was inspired by the ant colony system. Starting with pheromone direction, this study concentrates on the properties and principles of the ACO algorithm, and then applies the ACO algorithm to solve the TSP issue successfully. Pheromone is used by the entire ant colony as an indirect communication method to address difficult challenges.
Adaptive Superpixel-Based Disparity Estimation Algorithm Using Plane Information and Disparity Refining Mechanism in Stereo Matching

The motivation of this paper is to address the limitations of the conventional keypoint-based disparity estimation methods. Conventionally, disparity estimation is usually based on the local information of keypoints. However, keypoints may distribute sparsely in the smooth region, and keypoints with the same descriptors may appear in a symmetric pattern. Therefore, conventional keypoint-based disparity estimation methods may have limited performance in smooth and symmetric regions. The proposed algorithm is superpixel-based. Instead of performing keypoint matching, both keypoint and semiglobal information are applied to determine the disparity in the proposed algorithm. Since the local information of keypoints and the semi-global information of the superpixel are both applied, the accuracy of disparity estimation can be improved, especially for smooth and symmetric regions. Moreover, to address the non-uniform distribution problem of keypoints, a disparity refining mechanism based on the similarity and the distance of neighboring superpixels is applied to correct the disparity of the superpixel with no or few keypoints. The experiments show that the disparity map generated by the proposed algorithm has a lower matching error rate than that generated by other methods.
Parallel Batch-Dynamic Minimum Spanning Forest and the Efficiency of Dynamic Agglomerative Graph Clustering

Hierarchical agglomerative clustering (HAC) is a popular algorithm for clustering data, but despite its importance, no dynamic algorithms for HAC with good theoretical guarantees exist. In this paper, we study dynamic HAC on edge-weighted graphs. As single-linkage HAC reduces to computing a minimum spanning forest (MSF), our first result is a parallel batch-dynamic algorithm for maintaining MSFs. On a batch of k edge insertions or deletions, our batch-dynamic MSF algorithm runs in O(k log6 n) expected amortized work and O(log4 n) span with high probability. It is the first fully dynamic MSF algorithm handling batches of edge updates with polylogarithmic work per update and polylogarithmic span. Using our MSF algorithm, we obtain a parallel batch-dynamic algorithm that can answer queries about single-linkage graph HAC clusters.
Entry Screening Device for Public Transport During Pandemic Period

Social distancing is the one of the preferred ways to get out of the current situation and it has become difficult to follow it. Most people use public transport as a medium to travel. It becomes very difficult to maintain an effective social distance in public transports. So, we have planned to give a solution to address this problem by ensuring safeness in buses. Here, we kept a fixed limit for persons entering into the bus and before that they had to pass the temperature check and mask detection checks. The device consists of a Raspberry pi Module which acts as a system, a Camera module, PIR Sensors, Infrared Temperature Sensors, LCD Monitor. LCD Monitor shows the details of the number of persons that can be accommodated in a particular bus and the number of people present inside the bus. Temperature sensors, such as infrared temperature sensors, are used to detect the temperature of people entering the bus. It is captured and determined whether or not the person is wearing their mask in the entrance by the Camera Module. PIR Sensors are used to monitor the count of the persons entering or exiting the bus which is displayed in the LCD monitor. So, these work together and make the entry screening while entering a transport system.
Detection of Phishing Website Using Machine Learning

Phishing is a common method of tricking people int disclosing their entire personal information by using fake websites. Phishing records process tool URLs are used to steal personal information such as customer names, passwords, and online banking activities. Phishers (assailants) employ websites with rectangular diplomas that are visually and semantically similar to the real ones. As the century progressed, phishing strategies advanced swiftly, and this might be avoided by employing anti-phishing technologies to detect phishing. A strong gadget that is frequently utilized in the direction of phishing attacks is machine planning to apprehend. The capabilities used for detection and detection strategies by using Machine Learning have also been investigated in the suggested system.
A new Gaussian–Student's <i>t</i> mixing distribution‐based Kalman filter with unknown measurement random delay rate

Summary In this article, a new Gaussian–Student's t mixing distribution‐based Kalman filter is presented to investigate the filtering issue for linear stochastic system with unknown measurement random delay rate and non‐stationary heavy‐tailed measurement noise. Firstly, by employing a Bernoulli distributed variable and introducing system state extension method, the form of measurement likelihood function of double measurement noise distributions is converted from the weighted sum to an exponential product. Secondly, the non‐stationary heavy‐tailed measurement noises of current time and last time are modeled as Gaussian–Student's t mixing distributions by introducing extra Bernoulli distributed variables. Thirdly, the variational Bayesian technique is utilized to jointly infer the system state, the Bernoulli distributed variables of measurement noises, distribution mixing probabilities, the intermediate random variables, the Bernoulli distributed variable of measurement delay and the unknown measurement random delay rate. Finally, the effectiveness of the proposed Kalman filter is demonstrated by a target tracking simulation experiment.
Application of Fine Motion Capture Method for Tai Chi Chuan Assistant Training

Tai Chi Chuan is an important part of Chinese wushu and is the product of the Chinese civilization; it contains abundant philosophy sense. Tai Chi Chuan has experienced a long historical process of development; in the process of development and change numerous for brief, to the rough, now widely process of Tai Chi Chuan for its style is easy to learn and has a strong role of strengthening the body while widely popular among the people of all ages. The existing Tai Chi Chuan training methods have not satisfied people’s pursuit of spiritual life that has more and more high request, therefore urgently needs to study new and efficient Tai Chi Chuan training method. In this paper, the characteristics and development history of the Tai Chi Chuan development were analyzed. In order to overcome difficulties in training, at present, the research on application of AI technology with fine motion capture method auxiliary taijiquan training is aimed at, which can further improve the social mass to understand Tai Chi Chuan, and guide people to exercise more scientifically, improve the body quality, training effect, and enrich the spiritual and cultural life.
BCube Connected Crossbar and GBC3 Network Architecture: an Overview

Traditional tree network topologies with their technological infrastructure do not allow to grow, expand, and scale because they cannot offer sufficient bandwidth and constant latency performance due to the thousands of servers connected to the network. This incident is a challenging issue related to being able to provide better service to its customers. The trend of the data center network structure is towards managing large volumes of data and anticipating growth (scalability). Then, the network of the data center appeared, which had a greater impact on society, economy, and life. In this way, the network continues to evolve, and a new network model emerges at the historical moment. The information that exists regarding the data center network is important to consider for future projects in this area. That is why the article addresses data center network structures related to BCube Connected Crossbar (BCCC) and GBC3, since both provide good operation and performance by making use of low-cost products available in the networking industry such as switches and servers. This issue is very important for researchers because it allows them to study server-centric data network topologies.
Vision-based aerial image mosaicking algorithm with object detection

Aerial image sequence mosaicking is one of the challenging research fields in computer vision. To obtain large-scale orthophoto maps with object detection information, we propose a vision-based image mosaicking algorithm without any extra location data. According to object detection results, we define a complexity factor to describe the importance of each input Image and dynamically optimize the feature extraction process. The feature points extraction and matching processes are mainly guided by the speeded-up robust features (SURF) and the grid motion statistic (GMS) algorithm respectively. A robust reference frame selection method is proposed to eliminate the transformation distortion by searching for the center area based on overlaps. Besides, the sparse Levenberg-Marquardt (LM) algorithm and the heavy occluded frames removal method are Applied to reduce accumulated errors and further improve the mosaicking performance. The proposed algorithm is performed by using multithreading and graphics processing unit (GPU) acceleration on several aerial image datasets. Extensive experiment results demonstrate that our algorithm outperforms most of the existing aerial image mosaicking methods in visual quality while guaranteeing a high calculation speed.
Study on eye-gaze input interface based on deep learning using images obtained by multiple cameras

In this research, an eye-gaze input interface with features of high accuracy, simplicity, low cost, and almost no possibilities in causing illness or damages to user's eyes is proposed using commercial web cameras in natural light environment. The system employs the Haar-like function in OpenCV library to detect the user's face and eyes. Following the preprocessing of grayscale and histogram equalization, the processed eye images of the user are applied to train the proposed Convolutional Neural Network (CNN) for the estimation of user's eye-gaze direction. Six types of camera layouts have been investigated in this research. The images from different camera layouts are merged and processed for the CNN training to estimate eye-gaze direction. According to experimental study using the proposed method, the improvement in accuracy confirmed. Meanwhile, the layout with highest accuracy and effectiveness among the proposed six layouts was confirmed.
A Reliability Concern on Photonic Neural Networks

Emerging integrated photonic neural networks have experimentally proved to achieve an ultra-high speedup of deep neural network training and inference in the optical domain. However, photonic devices suffer from the inherent crosstalk noise and loss, inevitably leading to reliability concerns. This paper systematically analyzes the impacts of crosstalk and loss on photonic computing systems. We propose a crosstalk-aware model for reliability estimation and find out the worst-case bounds as we increase the footprints and scales of the photonic chips. Our evaluations show that −30dB crosstalk noise can cause maximal photonic chip integration to a sharp drop by 109x. To facilitate very-large-scale photonic integration for future computing, we further propose multiple heterogeneous bijou photonic-cores to address the crosstalk-aware reliability concern.
Constrained Bayesian optimization and spatio-temporal surveillance for sensor network design in the presence of measurement errors

The optimal placement of sensors is studied to construct a surveillance sensor network for a complicated stochastic system with random measurement errors. The problem is formulated as a joint problem of constrained black-box optimization for the fast detection of an anomaly event and spatio-temporal change-point detection for a low false alarm rate. An algorithm is proposed called Confidence-Set based Constrained Bayesian Optimization (CSCBO) that models performance measures as Gaussian Processes (GPs) and provides a flexible and easy-to-implement framework for handling noisy black-box constraints. As the decision variables of this problem are high-dimensional binary variables, the Wasserstein similarity metric is introduced as a distance measure among different solutions to capture the similarity among solutions properly. Finally, a newly proposed detection statistic for spatio-temporal surveillance is combined with CSCBO to identify the optimal sensor placement while controlling the false alarm rate. The combined procedure is applied to the Altamaha River.
Data‐driven Q‐matrix learning based on Boolean matrix factorization in cognitive diagnostic assessment

Attributes and the Q-matrix are the central components for cognitive diagnostic assessment, and are usually defined by domain experts. However, it is challenging and time consuming for experts to specify the attributes and Q-matrix manually. Thus, there is an urgent need for an automatic and intelligent means to address this concern. This paper presents a new data-driven approach for learning the Q-matrix from response data. By constructing a statistical index and a heuristic algorithm based on Boolean matrix factorization, the response matrix is decomposed into the Boolean product of the Q-matrix and the attribute mastery patterns. The feasibility of the proposed approach is evaluated using simulated data generated under various conditions. A real data example is also presented to demonstrate the usefulness of the proposed approach.
Interactive English Online Teaching System Based on B/S Model

In order to improve the effect of online interactive teaching of English, this paper analyzes the online interactive teaching data of English in combination with the generation of confrontation network and simulates the human interaction process to perform simulation. Moreover, this paper introduces the strategy gradient of reinforcement learning to the generator of the generation confrontation network to solve the problem that the generation confrontation network is difficult to be used for dialogue generation. In addition, this paper combines intelligent algorithms to construct an online interactive teaching system for English. The experimental research results show that the interactive English online teaching system based on the B/S model proposed in this paper has a greater advantage than the traditional online English teaching method.
Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography

Breast cancer accounts for the largest number of patients among all cancers in the world. Intervention treatment for early breast cancer can dramatically extend a woman's 5-year survival rate. However, the lack of public available breast mammography databases in the field of Computer-aided Diagnosis and the insufficient feature extraction ability from breast mammography limit the diagnostic performance of breast cancer. In this paper, A novel classification algorithm based on Convolutional Neural Network (CNN) is proposed to improve the diagnostic performance for breast cancer on mammography. A multi-view network is designed to extract the complementary information between the Craniocaudal (CC) and Mediolateral Oblique (MLO) mammographic views of a breast mass. For the different predictions of the features extracted from the CC view and MLO view of the same breast mass, the proposed algorithm forces the network to extract the consistent features from the two views by the cross-entropy function with an added consistent penalty term. To exploit the discriminative features from the insufficient mammographic images, the authors learnt an encoder in the classification model to learn the invariable representations from the mammographic breast mass by Supervised Contrastive Learning (SCL) to weaken the side effect of colour jitter and illumination of mammographic breast mass on image quality degradation. The experimental results of all the classification algorithms mentioned in this paper on Digital Database for Screening Mammography (DDSM) illustrate that the proposed algorithm greatly improves the classification performance and diagnostic speed of mammographic breast mass, which is of great significance for breast cancer diagnosis.
Leading through the COVID-19 crisis: a study of the public sector in the Arab Middle East

This study explores responses of public sector leaders to the COVID-19 crisis and their perceptions regarding the impact of the pandemic. Situated in the under-researched Middle Eastern cultural context, the paper draws on data from qualitative, in-depth semi-structured interviews with public sector leaders in Saudi Arabia and Oman. The qualitative analysis revealed that leaders in both countries responded to the crisis by displaying certain behaviours and actions, including sharing power, finding ways to maintain productivity in a virtual work environment, prioritizing employee well-being, and using effective virtual communication strategies. This study provides evidence that, even with complex challenges and negative effects of the crisis, there is a window of opportunity to reform the workplace and to strengthen group cohesion and solidarity within public organizations. The paper also offers novel insights into leaders’ experiences, contributing to theory and practice in this area. The findings identify approaches for human resource development practitioners and government sectors in embracing collective leadership styles and implementing group cohesion strategies to strengthen organizational culture and ultimately to overcome any future crisis.
Project Portfolio Reliability: A Bayesian Approach for LeAgile Projects

We propo se an applied Bayesian learning approach for continuous planning and evolution of information system projects and portfolios. Unlike traditional project management approaches for information system, the proposed approach considers the cumulative effect of all past experiences to achieve continuous performance and reliability prediction. The results of quantitative comparisons with other common estimation approaches, such as non-learning point estimates and traditional Bayesian approach, using real case data indicate that the proposed approach can generate a more realistic metric to continuously plan and measure the performance of evolving LeAgile projects or portfolios. This study can support decision makers, engineering teams, and management by supplying a practical and scalable project performance prediction tool for continuous planning and system evolution.
Apparently identical verbs can be represented differently: comparing L1–L2 inflection with contingency-based measure ΔP

We propose a method that models unidirectional, contingency-based association scale ΔP in order to analyse the different degrees of morpheme productivity in apparently identical L1–L2 inflected pairs. The method has the potential to uncover differences in how in L1–L2 inflected items are represented by L2 learners and native speakers. Such differences are at risk of remaining invisible if one considers only frequency, distribution and rank of predicates.
Parallel Bidirectionally Pretrained Taggers as Feature Generators

In a setting where multiple automatic annotation approaches coexist and advance separately but none completely solve a specific problem, the key might be in their combination and integration. This paper outlines a scalable architecture for Part-of-Speech tagging using multiple standalone annotation systems as feature generators for a stacked classifier. It also explores automatic resource expansion via dataset augmentation and bidirectional training in order to increase the number of taggers and to maximize the impact of the composite system, which is especially viable for low-resource languages. We demonstrate the approach on a preannotated dataset for Serbian using nested cross-validation to test and compare standalone and composite taggers. Based on the results, we conclude that given a limited training dataset, there is a payoff from cutting a percentage of the initial training set and using it to fine-tune a machine-learning-based stacked classifier, especially if it is trained bidirectionally. Moreover, we found a measurable impact on the usage of multiple tagsets to scale-up the architecture further through transfer learning methods.
Numerical methods for differential equations as encryption key

Several cryptographic systems rely on the generation of pseudo-random sequences to conceal messages, chaotic attractors with intrinsic characteristics such as high sensitivity to initial conditions and the randomness of generated signals, have become able to replace their pseudo-random generator. In a crypto-chaotic system, one chooses one of the temporal functions resulting from the dynamic law characterizing the attractor so that it delivers stream-keys. The attractor plays the role of the generator (Ali-Pacha Generator) of the random data and by applying to it a sampling and then a quantification and finally an encoding, it is xored to the data. Thus it satisfies the condition of the sensitivity to the initial conditions and then it is added to the data to be secured.The majority of the chaotic attractors are defined by a system of differential equations (DE). A differential equation is an equality relation linking a function y with one or even several of its derivatives. When the differential equation is completed by an initial condition, that is, by the knowledge of the image of a particular real, we say that we have to solve a Cauchy problem. They do not have analytical solutions except approximations by numerical methods. Indeed, there are several types of differential equations. Each type requires a particular resolution method.The question then arises, “How to choose the numerical method that best approximates from a cryptographic point of view the attractor that delivers a chaotic stream (streamkeys), which will be added to the data to be secured”.Obtained results show that all of the different numerical analysis methods approximate globally the same differential equation. However, they do not give the same results as locally for all the points. This difference can be exploited by cryptography system. In other words, the name of the numeric method can be a parameter of the encryption key. So, the result found in this work poses another condition for the extraction of the data coming from a coordinate of a given attractor which are the same for the encryption and the decryption. Therefore, we must use the same numerical analysis technique to find solutions of the differential equations of the attractor for encryption and for decryption. In other words, the name of the numeric analysis method can be a parameter of the encryption key.