Reviewerly/exHarmony huggingface dataset:
Learning Adaptively Context-Weight-Aware Correlation Filters for UAV Tracking with Robust Spatial-Temporal Regularization

Recently, Discriminative Correlation Filter (DCF) based methods have been widely applied in tracking for unmanned aerial vehicles (UAVs) because of their promising performance and efficiency. However, boundary effect, filter corruption, lack of context information and the poor representation of the object lead to the decrease in discriminability. In this paper, a novel learning adaptively context-weight-aware correlation filters with robust spatial-temporal regularization method (ACRST) is proposed. Both convolutional features and hand-crafted features are employed to improve representations for object appearances. Then the ACRST tracker extracts context samples around the object to help the filter be aware of the background information and adaptively learns the weights of these context patches. Thus, the tracker can improve the robustness against background noises especially for similar samples. Meanwhile, the tracker merges a robust spatial-temporal regularization to prevent the filter corruption and boundary effect. We design a center-attention spatial regularizer to focus on the valid information of the object better and we propose a method to obtain the value of the parameter of the temporal regularization adaptively. Extensive experiments have been conducted on 123 challenging UAV tracking sequences. The results prove that our tracker performs better than other state-of-the-art trackers.
Hybrid Unsupervised Extreme Learning Machine Applied to Facies Identification

Predictive models for classifying the distributionSaporetti, Camila M. of heterogeneities and quality in hydrocarbon reservoirs are fundamental for exploring and optimizing oil and gas field production.Rosa, Iago G. L. Determining heterogeneities manually through facies is generally a time-consuming task;Carvalho, Ruan M. thus, computational intelligence,Pereira, Egberto such as clustering techniques, appears as an alternative.Da Fonseca, Leonardo G. This work aims to apply the UnSupervised Extreme Learning Machine, also known as US-ELM, to cluster petrographic data collected from the Paraná Basin, Brazil. We propose a hybrid approach to tune the internal parameters for US-ELM. We use Principal Component Analysis to remove redundant attributes. The results show that the hybrid US-ELM hit higher average results for accuracy, silhouette metrics, and adjusted rand score than the methods commonly used in the literature.
CLE against SOA with Better Data Security Storage to Cloud 5G

Cloud 5G and Cloud 6G technologies are strong backbone infrastructures to provide high data rate and data storage with low latency for preserving QoS (Quality of Service) and QoE (Quality of Experience) in applications such as driverless vehicles, drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial-intelligence-based personalized assistants. There are many techniques to support the aforementioned applications, but for privacy preservation of Cloud 5G, the existing methods are still not sufficient. Public key encryption (PKE) scheme is an important means to protect user data privacy in Cloud 5G. Currently, the most common PKE used in Cloud 5G is CCA or CPA secure ones. However, its security level maybe not enough. SOA security is a stronger security standard than CPA and CCA. Roughly speaking, PKE with SOA security means that the adversary is allowed to open a subset of challenger ciphertexts and obtains the corresponding encrypted messages and randomness, but the unopended messages and randomness remain secure in the rest of the challenger ciphertexts. Security against SOA in PKEs has been a research hotspot, especially with the wide discussion in Cloud 5G. We revisited the SOA-CLE and proposed a new security proof, which is more concise and user friendly to understand privacy preservation in Cloud 5G applications.
A Linear-Time <i>n</i> <sup>0.4</sup> -Approximation for Longest Common Subsequence

We consider the classic problem of computing the Longest Common Subsequence (LCS) of two strings of length n . The 40-year-old quadratic-time dynamic programming algorithm has recently been shown to be near-optimal by Abboud, Backurs, and Vassilevska Williams [FOCS’15] and Bringmann and Künnemann [FOCS’15] assuming the Strong Exponential Time Hypothesis. This has led the community to look for subquadratic approximation algorithms for the problem. Yet, unlike the edit distance problem for which a constant-factor approximation in almost-linear time is known, very little progress has been made on LCS, making it a notoriously difficult problem also in the realm of approximation. For the general setting, only a naive O ( n ɛ /2-approximation algorithm with running time OŠ ( n 2-ɛ has been known, for any constant 0 &lt; ɛ ≤ 1. Recently, a breakthrough result by Hajiaghayi, Seddighin, Seddighin, and Sun [SODA’19] provided a linear-time algorithm that yields a O ( n 0.497956 -approximation in expectation; improving upon the naive \(O(\sqrt {n})\) -approximation for the first time. In this paper, we provide an algorithm that in time O ( n 2-ɛ ) computes an OŠ ( n 2ɛ/5 -approximation with high probability, for any 0 &lt; ɛ ≤ 1. Our result (1) gives an OŠ ( n 0.4 -approximation in linear time, improving upon the bound of Hajiaghayi, Seddighin, Seddighin, and Sun, (2) provides an algorithm whose approximation scales with any subquadratic running time O ( n 2-ɛ ), improving upon the naive bound of O ( n ɛ/2 ) for any ɛ, and (3) instead of only in expectation, succeeds with high probability.
Payment Systems in Digital Economy

Digital technologies are changing business models and business process management systems in banks. The fintech phenomenon—a combination of technology and innovation—is creating a completely new competitive environment in which credit institutions, as traditional financial intermediaries, face virtual cryptocurrency emission centres that also account for the movement of payment and settlement means. The payment system does not just enter the virtual space but acquires a new quality. The chapter compares traditional payment systems with virtual ones. The object of the research is payment and settlement systems, the subject of the research is the features of the payment systems that make payments and settlements in cyberspace and are based on the blockchain technology.
Research Guides: OBVU100 / 200, University Seminar / Transfer Seminar: Interlibrary loan services

This guide is designed to introduce new students to the services and collections of the Buena Vista University Library
Digitalization of customs control of cultural properties in international trad

The development of international trade and the growing influence of visual images on the consciousness of modern society have led to a steadily growing demand for art objects. However, the art market is a gold mine for professional thieves. Every month, more than a thousand works of art are stolen in the world. The criminal art business ranks third after drug trafficking and the arms trade. The level of detection of crimes related to the theft of art objects is still negligibly low (only 1.5 % of court cases end in favor of the owners), the amount of money is colossal (according to general estimates, the global value of stolen works is 3.7 billion pounds), and the interest of the internal authorities of different countries in training specialized search personnel is scanty. The relevance of the presented topic lies in the fact that the globalization of international trade in cultural values and the high corruption component require improving customs administration and improving the quality of customs activities. Today, innovative technologies are able to automate the process of control over cultural values, speed up and simplify the process of interaction between foreign trade participants and customs authorities, as well as ensure complete safety of transportation of cultural heritage objects. Objective: to develop proposals for improving customs control over the movement of cultural property, in particular, by creating an information interdepartmental system for monitoring the movement of cultural property using blockchain technologies and the Internet of Things, which can provide greater transparency in the movement of art objects. Object of research: ensuring customs control over the movement of cultural heritage objects. Subject of research: cutting-edge technologies as a means of improving the effectiveness of customs control over the movement of cultural property.
On the Current Situation and New Development Direction of Student Management in Chinese Universities Based on Network Information Technology

Abstract With the development of network information technology, its application in the teaching industry has become more extensive and mature. The network platform greatly expands students’ learning resources and learning horizons. Of course, while the Internet has improved the education, it also has a negative impact on students. Some unconscious students are addicted to the Internet, which makes their learning plummet. College student management needs to adapt to the requirements of the network age, network information technology must be applied in student management.
The Impact of Mobile Learning on Students' Self-Test Behavior in MOOCs

Students can use personal mobile devices to access Massive Open Online Courses (MOOCs) in addition to desktop computers. However, user interfaces are often only scaled to smaller screen sizes and interaction patterns of a desktop learning experience do not always fit well with the characteristics of mobile devices. Adequate solutions for answering self-test questions on mobile devices often do not exist. In this paper, we explore the currently shown interaction patterns of MOOC learners when answering self-tests to make an informed decision about the requirements for the appropriate solution on mobile devices. The students' context was categorized into Desktop Web, Mobile Web, and Mobile Application. In an observational case study, the interaction events of two courses were analyzed regarding these device groups. Desktop Web is the most used environment. No practical differences between device groups were identified for subsequent attempts. Learners stick to a single device group and often only participate once in a self-test. Also, learners using mobile applications spend more time submitting self-tests.
Protecting Personal and Business Data During a Pandemic - Lessons from Research on Online Victimization -

The world has been undergoing an unprecedented time of crisis since the outbreak of the Corona Virus 19 (COVID-19) pandemic. Among the notable changes brought by the pandemic are social distancing measures and stay-at-home directives, which have led to temporary closure of non-essential businesses as well as people working remotely in their homes. Moreover, increased numbers of the pandemic-themed phishing and fraud cases have been reported in different parts of the world. With the current work-at-home trend expected to continue, it is important to mitigate the threats posed by motivated offenders in cyberspace. Based on existing research evidence, this paper discusses strategies to prevent online victimization and protect business data during a pandemic.
Language and Communication Problems in Formalization: A Natural Language Approach

"The bride is dressed in red and the groom in white." Sometimes someone cannot believe their own ears, thinking they have misunderstood, and instead the communication is clear and exact, Egon actually got married in white and Donatella was in red. Some other times someone believe they have understood and instead the message is ambiguous and unclear.
The FLOOD DISASTER MANAGEMENT BASED ON EXTREME TROPICAL RAINFALL IN DECADES OF CLIMATE CHANGE IN INDONESIA

Indonesia's climate classification is divided into three rainfall patterns. The three patterns are Seasonal Pattern, Equatorial Pattern, and Local Pattern (Anti Seasonal). Flood Disaster Management based on extreme rainfall is very much needed, as the analysis was taken as a case study on January 22, 2019, a flood disaster occurred in South Sulawesi. The flood event indicated that there was heavy rain that flushed the South Sulawesi region for several days, which is classified as monsoonal rainfall. This study aims to analyze the characteristics of heavy rain with atmospheric anomalies during these events by calculating rainfall intensity to determine future flooding patterns and using the WRF model to analyze cloud distribution patterns and rainfall distribution. The method used in this research is Mononobe and Weather Research and Forecasting (WRF) using the Fabric Fritsch cumulus parameterization scheme. The analysis showed that the intensity and duration of rainfall of 2, 5 10, 25, and 50 years were obtained from the Mononobe model, as well as from the atmospheric dynamics data, there was rain for 3 consecutive days caused by cumulonimbus type rain clouds. Based on the WRF model, it can be seen that the CAPE value before the onset of rain is quite significant, thus supporting the growth of rain clouds as an important variable in flood disaster management in the South Sulawesi region in particular and the tropical zone in. general.&#x0D; Motivation/Background: Indonesia is included in a tropical climate where extreme rainfall is important to analyze. The majority of flood disasters in the tropics occur in decades of extreme atmosphere, this is an important reason in this study.&#x0D; Method: The Mononobe method can be used to calculate the distribution pattern of rainfall intensity throughout 2, 5, 10, 25, and 50 years, as a prediction of future rainfall intensity patterns. The WRF model is used to calculate the cloud distribution pattern and the spatial distribution of rainfall.&#x0D; Results: The results of this study obtained patterns of rainfall intensity and duration of 2, 5, 10, 25, and 50 years from the Mononobe model, as well as from the atmospheric dynamics data, there was rain for 3 consecutive days caused by cumulonimbus rain clouds. The pattern of cloud distribution and rainfall at the time of the incident at the WRF model research location.&#x0D; Conclusions: Analysis of the distribution pattern of rainfall intensity for the periods of 2, 5, 10, 25, and 50 years, as well as the distribution pattern of clouds and rainfall, is very necessary for disaster identification, especially hydrometeorology. This is very important as a variable in flood disaster management, especially in the tropics&#x0D;
Evolutionary Game and Computer Data Optimization Control for Multi-Valued Logical Dynamic Systems

Abstract Evolutionary game theory specifically refers to the fact that today we no longer regard the traditional human model as a super rational player, but ascribe the outcome of human beings reaching the game balance to the fact that human beings seek the right method from the wrong through continuous trial and error. In the traditional control theory and the modern control theory, it is very difficult to establish the model for some systems which are described and summarized by the laws of physics or mathematics, and which have many influencing factors and are coupled with each other. With the continuous progress of science and technology, the application of computer data control system in many fields is more and more extensive, and based on the continuous advancement of modernization, the dependence on computer data control system is also constantly improving. Optimizing the computer data control system can not only greatly save time, but also improve the working efficiency of the group, thus increasing the benefits of the enterprise. In this paper, from the optimization control of computer data system as the analysis goal, through the study of evolutionary game theory and the analysis of multi-valued logic dynamic system, and then combined with the optimization control of computer data, this problem is discussed and analyzed from multiple aspects.
Analysis of Medical Image Segmentation

At present segmentation of medical images is an important guarantee for treatment. Especially, accurate segmentation of medical images is of great significance for doctors to diagnose and analyze the cause of disease. However, there are many problems in medical image which can’t effectively improve the segmentation accuracy of medical image. For this purpose the basic flow of medical image processing is analyzed, and the segmentation of medical image is described mathematically in this paper. Three main methods of medical image analysis are discussed, including image segmentation based on region, edge and specific theory. The main ideas of Threshold analysis, edge detection method and watershed algorithm are elaborated in detail.
Small Object Recognition Using a Spatio-Temporal Neural Network

Object recognition at different scales has been a fundamental problem in computer vision. In particular, small object recognition attracts increasing attention recently. However, because of working on a single frame only, many recognizers’ performances become unacceptable in many practical application scenarios: very low resolutions, invisible small targets, extremely similar appearances etc. Motivated by the way humans deal with these challenging scenarios of object recognition, this paper introduces frame sequence and attention mechanism to compensate for mutilated information. Specifically, this paper proposes a spatiotemporal neural network (dubbed STNet) for small object recognition. STNet fixes the regions of interest with a super-resolution module, and focuses on the discriminative region with a spatio-temporal attention module. In addition, STNet applies a double layer long short-term memory subnet to make full use of the inter-frame information. Furthermore, this paper presents a challenging air-target recognition dataset ATSETC4 for evaluating the performance of each method in identifying small targets. Our model outperforms many state-of-the-art models on ATSETC4, including MobileNetV2 and SENet. In particular, STNet surpasses VGG11 at an average of 3.67%, even reaches 87.50% and 82.50% on 28 scale and 14 scale on AT-SETC4 respectively.
mRNA Synthesis in Real World using AR

Augmented Reality provides an interactive experience of a real-world environment where the objects that reside in real world are superimposed by computer-generated perceptual information. This can provide an engaging, immersive experience for students of all ages, and allows them to view and understand subjects and topics through personal experience. Concepts such as nucleotide sequence, genome analysis, protein synthesis are pretty complex to understand and the minute details of the concepts are just left to the student’s imagination. Objective of the work is to use Augmented Reality to create an interface in such a way that when the device’s camera is pointed at a picture of DNA molecule the recognition of the human DNA molecule happens, and projection of known and unknown DNA nucleotide sequence will appear where in identification of the mRNA sequence and genome analysis is done. This environment works on marker-based Augmented Reality where the Target 2D image will be uploaded to Vuforia. The 3D models required in the project are imported as assets and are organized on the scene in Unity. The nucleotide sequence is collected from NCBI (National Center of Biotechnology Information), The EMBOSS tools is used for sequence analysis where it uses Needleman–Wunsch algorithm to align protein or nucleotide sequences. The expected outcome is an AR driven learning model along with identification of the mRNA sequence, demonstrate transcription translation and also protein synthesis.
Design and analysis security architecture virtualization OpenVz

Abstract OpenVZ is a capsule-based virtualization technology for OS Linux which allows administrators to deploy multiple Operating Systems with different virtual hardware specifications, called containers, virtual environments, or Virtual Private Servers. In this paper, we propose new security architecture for OpenVZ depends on the type of attacks that commonly happen in servers. This security technic called OpenVzSec. The server is attacked by the client using OS windows and Ubuntu OS which is equipped with an attacker code program based on python language. Type of attacks used in this research: SSH vulnerability, SYN Flood attacks, Attack on the Rootkit vulnerability, and checksum spoofing. Some server attacks on a server and containers able to detected and anticipate by OpenVZSec. The OpenVZSec security model does not decrease performance of the server.
2BEDM: Brightness-Based Enhancement for Disparity Maps

Stereo vision is a technique used to recover depth information from stereo (two) images that capture the view from different perspectives. The stereo-matching/stereo-correspondence process presents an essential role in stereo vision. Much research has been done on this process. However, the accuracy of stereo-matching/stereo-correspondence algorithms is affected by many characteristics related to the scene, such as the lack of discriminative image features, repetitive patterns, specular reflections, and regions of uniform texture. In this paper, we propose a pre-processing schema called brightness-based enhancement for disparity maps (2BEDM). 2BEDM is used to enhance the accuracy of local stereo-matching/stereo-correspondence algorithms (which are affected by the aforementioned characteristics related to the scene in stereo images) and to enhance the output-disparity map of stereo-matching/stereo-correspondence algorithms. It is based on the brightness of each pixel in the stereo images (left image and right image) rather than on the pixel itself. 2BEDM works on the basis of the hypothesis that the brightness of each block of stereo images of the same views is equal. The experimental results illustrate the enhancement provided by the proposed 2BEDM schema.
Personalized Route Finding System Using Genetic Algorithm

The significant improvement of population and multiple types of vehicles causes traffic congestion and environment problem. The economic prosperity and continuing urbanization have led to quick raise in travel demand in urban areas. Rapid urbanization and motorization in cities cause congestion and an increase in vehicular emission and also increase the accident. Travelers face the practical problem of identifying the most efficient route on their daily journey. Fuel consumption, emission, long travel time, and accident can be direct and indirect consequences of vehicle traffic congestion and rough driving pattern. Personalized driving system needs some specific real-time data like vehicle volume, weather condition of the area, road condition, carbon emission factors, and safety measures. Hence in this work, genetic algorithm (GA) is proposed to find the optimal route for the drivers based on their requirement. In this work, optimal path will be identified from several paths. Route maps of Madurai City, Delhi City, Northern Utah City are considered. The GA result is compared to Bellman–Ford algorithm. The GA finds the best route for 94.6% of accurately, and Bellman–Ford algorithm finds the shortest path for 92.4% of accurately. The results have proven that the proposed GA works better for the personalized route finding system.