Reviewerly/exHarmony huggingface dataset:
A Case Study of Object Recognition from Drone Videos

To study a potential autonomous drone's object recognition and reaction, we created a convolutional neural network (CNN) and used it to detect and count the empty parking spots in a parking lot taken from drone video footage. We first trained the network through supervised learning with snapshots of individual parking spots, from a previous drone footage, to correctly classify the spots as empty or occupied. Then we store the model to be used for detection and labeling of objects in new drone videos such as empty vs. occupied spots, as well as cars moving in and out of spots. We invented a video object referencing (VOR) to estimate object dimensions. After many rounds of tuning, we eventually achieve close to a hundred percent of accuracy. We concluded that adjusting batch size and epoch number could improve object recognition. We hope this research will contribute to tuning CNN for object recognition from drone videos to help with eventual autonomous drones.
Multistage Fusion of Face Matchers

Multistage, or serial, fusion refers to the algorithms sequentially fusing an increased number of matching results at each step and making decisions about accepting or rejecting the match hypothesis, or going to the next step. Such fusion methods are beneficial in the situations where running additional matching algorithms needed for later stages is time consuming or expensive. The construction of multistage fusion methods is challenging, since it requires both learning fusion functions and finding optimal decision thresholds for each stage. In this paper, we propose the use of single neural network for learning the multistage fusion. In addition we discuss the choices for the performance measurements of the trained algorithms and for the selection of network training optimization criteria. We perform the experiments using three face matching algorithms and IJB-A and IJB-C databases.
There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It

Abstract Limerick generation exemplifies some of the most difficult challenges faced in poetry generation, as the poems must tell a story in only five lines, with constraints on rhyme, stress, and meter. To address these challenges, we introduce LimGen, a novel and fully automated system for limerick generation that outperforms state-of-the-art neural network-based poetry models, as well as prior rule-based poetry models. LimGen consists of three important pieces: the Adaptive Multi-Templated Constraint algorithm that constrains our search to the space of realistic poems, the Multi-Templated Beam Search algorithm which searches efficiently through the space, and the probabilistic Storyline algorithm that provides coherent storylines related to a user-provided prompt word. The resulting limericks satisfy poetic constraints and have thematically coherent storylines, which are sometimes even funny (when we are lucky).
Digital Technologies in Preschool Education: Risks and Prospect

The paper aims to analyze one of the new problems of preschool education: the problem of digital education. On the one hand, digitalization has embraced all levels of the Russian education system and represents a powerful trend that radically changes the educational process, and on the other hand, there are certain risks, fears of teachers and parents about the negative impact of digital education technologies on the formation and development of a child's personality. Research in this area is still very discreet, nevertheless, it allows us to see the outlines of the educational process, organized with regard to the use of digital technologies.
Rendering Tree Roots Outdoors: A Comparison Between Optical See Through Glasses and Smartphone Modules for Underground Augmented Reality Visualization

In this paper we propose augmented reality (AR) modes for showing virtual tree roots in the nature. The main question that we want to answer is “How is it possible to convincingly represent virtual 3D models of tree roots as being located in the forest soil, underground, while using the AR technology?”. We present different rendering and occlusion modes and how they were implemented on two different types of hardware. Two user studies, that we performed to compare the AR visualization for optical see-through glasses (HoloLens head mounted display) and for mobile devices (smartphone), are described. We specifically focus on depth perception and situated visualization (the merging of real and virtual environment and actions). After discussing the experiences collected during outdoor user tests and the results of a questionnaire, we give some directions for the future use of AR in the nature and as a part of an environmental educational setting. The central result of the study is that while supporting depth perception with additional depth cues, specifically occlusion, is very beneficial in the mobile device setting, this support does not change depth perception significantly in the stereo HMD setting.
Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.
A Novel Approach to Detect Anomalies in Business Process Event Logs Using Deep Learning Algorithm

Enterprises whose businesses are driven by web-based or cloud-based applications contain thousands of business processes involved. Due to the dynamic runtime environments and distributed nature of business processes and dependencies, there is possibility of noise and anomalies. Moreover, naturally, businesses are interested in finding anomalies in business processes and rectify them for improving quality of service (QoS). Especially, as part of process mining, anomaly detection has become an important research area in the contemporary era. Many anomaly detection methods came into existence based on machine learning techniques. There are attempts made using autoencoders for business process anomaly detection. However, from the literature, it is understood that there is need for a deep learning-based autoencoder with unsupervised learning approach for efficient detection of anomalies by analysing business process event logs. Towards this end, in this paper, we proposed a methodology and defined an algorithm known as deep learning encoder-based anomaly detection (DLE-AD) for enhancing the ability of anomaly detection. From the experiments, it is revealed that deep learning-based anomaly detection showed better performance over the traditional approaches. The proposed algorithm is evaluated against state of the art and found that it outperforms the existing methods.
Comparative Analysis of Singular Value Decomposition and Eigen Value Decomposition Based Principal Component Analysis for Earth and Lunar Hyperspectral Image

Data dimensionality reduction is a sensitive issue in the hyperspectral image processing. Hyperspectral imageries with hundreds of contiguous bands, have lot of redundant information. Data dimensionality reduction techniques used for hyperspectral imageries varies in the terms of decomposition method used. This paper discusses about a comparative analysis of Singular Value Decomposition (SVD) and Eigen Value Decomposition (EVD) based principal component analysis for PRISMA and Moon Mineralogy Mapper hyperspectral imageries. It is found that both methods have their own advantages. PCA with Eigen value Decomposition and lower correlation between different bands, can be used for deriving various band ratios, which facilitates a better mineral mapping. The newly launched PRISMA datasets shows sufficient results for minerals when compared to lithological map.
Deep Learning-Based Nuclear Lobe Count Method for Differential Count of Neutrophils

Differentiating neutrophils based on the count of nuclear lobulation is useful for diagnosing various hematological disorders, including megaloblastic anemia, myelodysplastic syndrome, and sepsis. It has been reported that one-fifth of sepsis-infected patients worldwide died between 1990 and 2017. Notably, fewer nuclear-lobed and stab-formed neutrophils develop in the peripheral blood during sepsis. This abnormality can serve as an early diagnostic criterion. However, testing this feature is a complex and time-consuming task that is rife with human error. For this reason, we apply deep learning to automatically differentiate neutrophil and nuclear lobulation counts and report the world's first small-scale pilot. Blood films are prepared using venous peripheral blood taken from four healthy volunteers and are stained with May-Grünwald Giemsa stain. Six-hundred 360 × 363-pixel images of neutrophils having five different nuclear lobulations are automatically captured by Cellavision DM-96, an automatic digital microscope camera. Images are input to an original architecture with five convolutional layers built on a deep learning neural-network platform by Sony, Neural Network Console. The deep learning system distinguishes the four groups (i.e., band-formed, two-, three-, and four- and five- segmented) of neutrophils with up to 99% accuracy, suggesting that neutrophils can be automatically differentiated based on their count of segmented nuclei using deep learning.
Product-Based Market Analysis Using Deep Learning

Product Market Analysis understands how the market reacts to a product manufactured by a company. In this paper, a deep-learning-based model is created. The model can understand how a customer feels about a particular product. The dataset used is “fer2013” (Ref. Kaggle Dataset) and is famous for creating “Sentiment Analysis.” The model developed is a self-made model giving a training accuracy of 68.61 and 65.92% test accuracy. The self-made model is a 27-layer deep convolutional neural network consisting of 8 convoluting layers, three max-pooling layers, and two fully connected layers. The model is created using Keras, which is a framework built on Tensorflow, a machine learning library. A total of 427,319 parameters are used to develop the proposed model. Out of these parameters, 426,839 are trainable, and 480 are non-trainable.
A Similarity Measure of Histopathology Images by Deep Embeddings

Histopathology digital scans are large-size images that contain valuable information at the pixel level. Contentbased comparison of these images is a challenging task. This study proposes a content-based similarity measure for highresolution gigapixel histopathology images. The proposed similarity measure is an expansion of cosine vector similarity to a matrix. Each image is divided into same-size patches with a meaningful amount of information (i.e., contained enough tissue). The similarity is measured by the extraction of patchlevel deep embeddings of the last pooling layer of a pre-trained deep model at four different magnification levels, namely, 1x, 2.5x, 5x, and 10x magnifications. In addition, for faster measurement, embedding reduction is investigated. Finally, to assess the proposed method, an image search method is implemented. Results show that the similarity measure represents the slide labels with a maximum accuracy of 93.18% for top-5 search at 5x magnification.
Cuckoo Optimization Algorithm Visual Positioning System Based on Particle Swarm Algorithm

In view of the problem of cuckoo optimization algorithm, an improved cuckoo optimization visual positioning algorithm based on particle group optimization (cuckoo) is proposed. First of all, using MATLAB’s powerful computational function, the image is decomposed in matrix, and the target function is easy to fall into local optimality and difficult to break away when the cuckoo algorithm is executed. The algorithm abandons the gradient drop method, which is extremely sensitive to the selection of initial values in the Chesig method, and uses the cuckoo dynamic Levy flight and random probability mechanism to adjust the size and orientation of particle population updates at poor locations. The main advantage of this method is that the position of the particle group algorithm is not updated repeatedly, and when a more suitable position is found, the cuckoo algorithm is used to adjust the position to varying degrees. Overcomes the strong randomness of cuckoo algorithm. In addition, the algorithm can introduce three-dimensional space from the two-dimensional space level. The experimental results show that the algorithm is faster and more stable than the gradient-based method when applied to the visual positioning of particle groups.
Reachability-based Safe Planning for Multi-Vehicle Systems with Multiple Targets

Recently there have been a lot of interests in introducing UAV s for a wide range of applications, making ensuring safety of multi-vehicle systems a highly crucial problem. Hamilton-Jacobi (HJ) reachability is a promising tool for analyzing safety of vehicles for low-dimensional systems. However, reachability suffers from the curse of dimensionality, making its direct application to more than two vehicles intractable. Recent works have made it tractable to guarantee safety for 3 and 4 vehicles with reachability. However, the number of vehicles safety can be guaranteed for remains small. In this paper, we propose a novel reachability-based approach that guarantees safety for any number of vehicles while vehicles complete their objectives of visiting multiple targets efficiently, given any K-vehicle collision avoidance algorithm where K can in general be a small number. We achieve this by developing an approach to group vehicles into clusters efficiently and a control strategy that guarantees safety for any in-cluster and cross-cluster pair of vehicles for all time. Our proposed method is scalable to large number of vehicles with little computation overhead. We demonstrate our proposed approach with a simulation on 15 vehicles. In addition, we contribute a more general solution to the 3-vehicle collision avoidance problem from a past recent work, show that the prior work is a special case of our proposed generalization, and prove its validity.
Unveiling unexpected training data in internet video

Using clever video curation and processing practices to extract video training signals automatically.
Comparison of PV System Energy Output Between with and without Solar Tracking: Simulation in a Mountain Area in Indonesia

One of the key performances of a PV system is the amount of solar irradiation received by PV modules. Solar irradiation will be maximum received by solar modules when the angle of incidence is kept 0° using a solar tracker system. This paper studies comparison of the energy output of PV systems between with and without solar tracking in a mountain area of Bromo, Pasuruan, East Java, Indonesia. The studies are carried out using SolarGIS PV Planner simulation. It found that specific energy output PV system with two axes solar tracking system is 2060 kWh/kWp per year, while for the system without solar tracking (fixed optimized), the specific energy production is 1657 kWh/kWp.
Investigating the Information Security Awareness at Taibah University (TU)

Information is one of the critical assets in every organization. Maintaining these assets and ensuring their security is a challenge, since it is a responsibility for every organization member. One of these challenges is employee, faculty, and student lack of security awareness that could affect the organization's assets. Therefore, this research paper investigates and examines the information security policy awareness in Taibah University's committee and their overall understanding of security. A qualitatively method is used to gain in depth data about TU committee’s awareness. An online survey was conducted to measure students’, faculty, and other employees’ awareness of three topics including, Taibah University security policies, information security crimes, and basic security actions. The findings of the survey shown a lack of security awareness and mostly between bachelor’s degree students. Therefore, TU institute should improve security education and provide training courses, workshops, and online libraries to raise students' awareness of security. A list of recommendations are proposed to enhance this awareness. This survey will help practitioners understand security awareness challenges and explore new and promising research opportunities in this field. To the best of our knowledge, this paper constitutes the first survey focusing on security policies awareness in TU.
Holistic Requirements Analysis for Specifying New Systems for 3D Media Production and Promotion

This paper presents a requirements engineering process for driving the design of new systems that will allow for enhancing 3D media productivity, for lowering the entry barrier in 3D media creation, and for innovative media forms across many media types. This work has been carried out with the perspective of enhancing recovery and transformation as the pandemic has driven many professionals in culture to zero income. Toward this goal, we perform a requirements engineering process based on the IEEE 830 standard for requirements specification. It allows us to elucidate system requirements through existing (AS-IS) and envisioned (TO-BE) scenarios affected by the latest trends on design methodologies and content promotion in social media. A total of 30 tools for content creation, promotion, and monetization are reviewed and 10 TO-BE scenarios were engineered and validated. The validation was performed through a survey of 24 statements on a 5 Likert scale by 47 individuals from the domains of Media, Fine arts, Architecture, and Informatics. Useful evaluation results and comments have been collected that can be useful for future systems design.
LPDBN: A Privacy Preserving Scheme for Deep Belief Network

In recent years, the successful applications of deep learning technology bring serious privacy issues. Current countermeasures can achieve privacy protection by introducing differential privacy mechanisms to convolutional deep belief network, which will inevitably bring huge computational complexity in convolutional kernels. In this paper, we focus on designing a lightweight security privacy protection framework LPDBN, a novel Local differential Privacy binary pattern Deep Belief Network. We use the local binary pattern to extract texture information from the images instead of convolutional kernels, which greatly reduces the time complexity and data dimension. Meanwhile, the proposed framework can improve recognition performance under the same privacy protection intensity. The theorem analysis and experiments show the security and efficiency respectively.
3D Trajectory Planning of UAV Based on DPGA

The goal of trajectory planning is to shorten the flight distance as much as possible on the premise of ensuring the safety of UAV in flight. Therefore, the research of trajectory planning has broad prospects and great significance. As the key technology of trajectory planning, optimization algorithm has increasingly become one of the focuses of scholars at home and abroad. The dynamic programming algorithm is characterized by high computational efficiency and global optimization in trajectory planning. In 3D trajectory planning, as the spatial search space expands, the number of grid points increases faster, and time complexity of the dynamic programming algorithm is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). It often leads to a “Curse of Dimension” phenomenon, which lowers its computational efficiency drastically. To solve this problem, this paper divides the entire planning space into stages based on Bellman's optimality principle. A dynamic programming-genetic algorithm(DPGA) is proposed by using genetic algorithm(GA) in each stage for optimization, while using dynamic programming algorithm(DP) in global planning. The global optimization ability of the algorithm is verified through convergence analysis. Moreover, based on a series of simulation experiments, it shows that the improved algorithm proposed in this paper is more efficient than the dynamic programming algorithm and genetic algorithm alone in global optimization.
Formation of digital competencies in higher education as the basis for realization of sustainable development goals

The formation of digital competencies of citizens is currently a strategic goal for the Russian education system from the perspective of realization of sustainable development goals. The article considers the level model of digital competencies within higher education programmes at the SPbPU, the key feature of which is a universal core that allows to facilitate sequential growth of competencies and implement flexible educational trajectories of students within the “2+2+2” model.
Epsilon: A Microservices based distributed scheduler for Kubernetes Cluster

Kubernetes is a popular container orchestration platform designed to simplify the deployment of containers in a compute cluster. Kubernetes provides a monolithic cluster scheduler, which is responsible for allocating resources in the cluster. We developed a Kubernetes cluster scheduler using microservices as its foundation called Epsilon. Epsilon is designed as a proof of concept to examine the viability of using microservices to develop a cluster scheduler. On top of the basic scheduler microservice, additional microservices are developed to support the scheduler operations. e.g. The Retry service handles unsuccessful deployment of pods and reschedules them after a period of time. In our experiments, we deployed Epsilon in a Kubernetes cluster using AWS Cloud services. The results shows that the scheduling throughput of Epsilon is comparable to the default Kubernetes scheduler. Epsilon also shows even distribution of pods among the nodes. Furthermore, the distributed nature of the Epsilon's microservices decrease code complexity when compared with the Kubernetes default scheduler.
Novel Approach of Video Tracking System Using Learning-Based Mechanism over Crowded Environment

Video tracking is an essential operation in the area of the surveillance system. The model must possess an efficient object detection and recognition capability to facilitate a better video tracking form. Existing video tracking approaches show that they emphasize accuracy without considering the larger proportion of dynamics in mobile objects’ movement patterns. Therefore, this work presents a novel learning-based approach that facilitates video tracking by differentiating mobile objects specific dynamics from other mobile objects. The approach extracts significant cells from the video feed. It subjects them to a learning-based approach in contextual information of all moving objects and dictionaries to extract dominant features. A study outcome shows higher accuracy and faster tracking in contrast to the frequently used approach.