Reviewerly/exHarmony huggingface dataset:
UAV Trajectory Planning Based on PH Curve Improved by Particle Swarm Optimization and Quasi-Newton Method

Trajectory planning technology is an important foundation for the development of modern military intelligence games. This paper, based on the particle swarm optimization (PSO) and quasi-Newton method, aims to study the optimal trajectory planning of UAVs under threats such as obstacles and radar. Firstly, the five-order PH path curve in the complex number domain is established to determine the decision parameters. Secondly, in view of the shortcomings of traditional PH trajectory planning algorithms, the particle swarm optimization with directionality in the optimization process is introduced, which can iterate to a better solution at a faster rate. However, the PSO has a slower convergence speed in the later iteration, and then the quasi-Newton method is introduced to overcome its shortcomings and iterate to the optimal solution quickly. By organically combining the two algorithms, a new trajectory planning algorithm that can solve the defects of the basic PH algorithm is designed, which overcomes the shortcomings of the traditional path planning algorithm and the particle swarm algorithm. Meanwhile, the performance of the algorithm can be effectively improved. Finally, the effectiveness of our algorithm is verified by some simulation experiments.
Supervised Learning

This chapter and the next concern the mathematical modelling of data that is the essential core of data science. We can call this statistics, or we can call it machine learning. At its heart, it is the same thing. It is all about extracting information out of data.
Application of Big Data Technology in Equipment System Simulation Experiment

Abstract In order to solve the problem of single utility between system data in simulation experiment, the simulation experiment method and experimental framework of equipment system development are analyzed. This paper constructs the experimental framework of big data technology in equipment system simulation, uses big data analysis technology, analyzes the application process in equipment system simulation experiment, and puts forward the shortcomings and difficulties of applying big data technology in equipment system simulation experiment. By introducing big data technology, it provides a reference basis for weapon equipment system development demonstration.
Mining Fork-Including Software Development Traces

Open-source software development is a common practice that encourages collaborative development and reuse across projects. Forking is a way to make a copy of an existing project and explore it for different purposes. Two types of forks are commonly mentioned in the literature: contributing forks which continue the development lines of the forked projects and aim at merging the contribution back to the forked projects; and independently developed forks which open new lines of development deviating from the forked projects. In this study, we aim to explore characteristics of fork-involving traces for better understanding collaboration and reuse considerations in software development. Analyzing 880 Java projects and their related action and observation events, with process mining and statistical techniques, we found that the occurrence of certain event types may predict the fork type, while the creation of certain fork types increase the involvement of users in the forked projects.
Protecting Location Privacy by Multiquery: A Dynamic Bayesian Game Theoretic Approach

When using location-based services (LBSs), a user obtains points-of-interest (PoI) information by providing the LBS platform with his current geo-location. Such a search leads to potential privacy leakage if an adversary has access to his geo-data. Traditional <i>k</i>-anonymity mechanisms instruct a user to bear the overhead to report his current location together with <i>k</i> - 1 dummy locations to confuse the adversary, which only work well given a large number <i>k</i>. Aware of the common practices that a user is actually flexible in service requirement (e.g., as long as the searched PoIs are within his walking distance), we propose a novel approach to help the user gain location privacy from service flexibility for the challenging case of a small number <i>k</i>. By analyzing the strategic interaction between the user and the adversary in a dynamic Bayesian game, we prove that the user&#x2019;s equilibrium strategy depends on the adversary&#x2019;s capability of accessing geo-data. Take <i>k</i> = 2 for example, we find that if the adversary is not likely to access both geo-data, the user should report the two dummy locations at two different directions of his real location, and otherwise at the same direction. Perhaps surprisingly, the user may benefit from the adversary&#x2019;s access to more geo-data. Furthermore, we extend the game-theoretic approach for multi-query and arbitrary user location distributions. Numerical results show that our approach obviously outperforms <i>k</i>-anonymity mechanisms especially under a small number <i>k</i>.
Enabling Multi-Factor Authentication and Verification in Searchable Encryption

Data management with third-party involvement is often considered a severe concern in the new normal. The cloud environment has become the current trend in the modern internet era, but anonymity, maintenance, and data secrecy have become more concerned. Trusting third-party that store confidential data on the cloud may cause entities irreparable damage. As a result, the data should be kept protected to sustain secrecy. Searchable Encryption (SE) solves the problem by browsing through encrypted data. Previous studies have mainly focused on encrypting, searching, and decrypting data using searchable encryption schemes. However, most of the schemes failed to include multi-factor authentication and verification, which adds an extra layer of security to data access with SE. We introduced a multi-factor authentication and verification scheme called the TOTPV-KASE scheme, i.e., time-based one-time password and verification in key-aggregate searchable encryption. We present a summary of various searchable encryption schemes and implement the time-based one-time password generated by the owner of the data and verify it using an Inter Planetary File System (IPFS).
PECULIARITIES OF USING MEL-CEPSTRAL COEFFICIENTS IN SPEECH RECOGNITION

One of the main forms of human interaction is speech. Nowadays, there are enough useful programs for speech recognition of people who have certain limitations. These programs translate the text that was spoken aloud into the text, to clearly understand what the person wants to say. The main purpose of speech recognition methods is to obtain information as an input voice signal for further clear translation. Modern methods of speech recognition use an important part - language modeling and acoustic modeling. Nowadays, there are quite a number of methods for translating a voice into a test. The best method of recognition is bas on hidden Markov models. Statistical models are using to display sequences of characters. Therefore, the method based on hidden Markov models is one of the best for solving such problems. The article investigates the methods of speech recognition of people with speech disorders in a short dictionary using mel-keptral coefficients. The proposed method applies the criterion used for unbiased estimation of the logarithmic spectrum to the spectral model represented by MEL coefficients. To solve the nonlinear minimization problem involved in the method, they provide an iterative algorithm whose convergence is guaranteed. Examples of speech analysis and results of an isolated word recognition experiment.
Big Data Parallelization Analysis Method Based on Hierarchical Clustering Algorithm

Group is an important part of pattern recognition. According to the specified similarity measure, the data is classified into different types to make the same kind of data more similar and different kinds of data less similar. Therefore, the hidden rules and related information in the data can be found. Although great achievements have been made in the research of classification algorithm, the big data processing is limited by time, space, processor and other resources, and the classification efficiency is reduced. Therefore, we study the big data parallel analysis method based on hierarchical classification algorithm.
Augmenting Context Representation with Triggers Knowledge for Relation Extraction

Relation Extraction (RE) requires the model to classify the correct relation from a set of relation candidates given the corresponding sentence and two entities. Recent work mainly studies how to utilize more data or incorporate extra context information especially with Pre-trained Language Models (PLMs). However, these models still face with the challenges of avoiding being affected by irrelevant or misleading words. In this paper, we propose a novel model to help alleviate such deficiency. Specifically, our model automatically mines the triggers of the sentence iteratively with the sentence itself from the previous iteration, and augment the semantics of the context representation from BERT with both entity pair and triggers skillfully. We conduct extensive experiments to evaluate the proposed model and effectively obtain empirical improvement in TACRED.
Programa psicoeducativo para pessoas com diabetes (PROPSID): intervenção em grupo on-line para ansiedade e depressão

Introduction: diabetes Mellitus (DM) is a chronic disease characterized by the presence of hyperglycemia due to impaired secretion and/or use of insulin by the body.Studies indicate that the presence of anxiety in people with DM is related to the complications of the disease, unfavorable behaviors to self-care and a higher risk of mortality.In addition, there is an association between depression and anxiety with the worsening of quality of life (QoL) in this population.Objective: to investigate the effects of an online intervention group focused on the levels of anxiety and depression in people with diabetes.Methods: clinical, quasi-experimental study of repeated measures, using the pre-and post-test design, with a sample of 11 patients, 2 men and 9 women (81.8%), aged between 26 and 65 years (M=42.81;SD=12.24).The intervention was held in 8 weekly group meetings, where themes about anxiety, depression, medication, self-care and coping strategies were addressed.Results: there was a reduction in anxiety, stress and depression scores in the post-test.The magnitude of the effect of the intervention was considered strong for anxiety, depression and stress.Conclusions: it was concluded that there was a significant reduction in symptoms of anxiety, depression and stress, and clinically perceived improvement of self-care and QoL.
Computation of 2D Discrete Geometric Moments Through Inclusion-Exclusion

We propose a new formula for computing discrete geometric moments on 2D binary images. The new formula is based on the inclusion-exclusion principle, and is especially tailored for images coming from computer art, characterized by a prevalence of horizontal and vertical lines. On the target class of images, our formula reduces the number of pixels where calculations are to be performed.
Heuristic Evaluation of Public Service Chatbots

In recent years, chatbots have been adopted in business contexts and also for public services at a growing rate. Chatbots provide dialogue interfaces combining visual elements with natural conversation. Good Conversational Design in this context covers the topics of Natural-Language Processing (NLP) and Dialogue Management (DM). Few attention has been paid to the usability evaluation of conversational interfaces (Höhn &amp; Bongard-Blanchy, 2021). The present paper builds upon the work by Höhn &amp; Bongard-Blanchy by applying their framework of conversational heuristics to evaluate a set of public service chatbots operated in the federal state of Schleswig-Holstein. Thus, for each public service chatbot, a usability score is established and typical characteristics of public service chatbots in general are summarized. We discuss results by comparing the overall scores, weaknesses and strengths of each chatbot. In addition, we reflect on our experience in the application of the framework as well as highlight possible optimization potentials. Concludingly, this paper collects UX recommendations for public service chatbots.
Wearable Design for Epilepsy Patients: Human-Centered Design and Speculative Design for a Positive User Experience

Despite encouraging indicators that epilepsy cases can be prevented with low-cost, efficient drugs, a significant number of patients are resistant to treatment. Constant monitoring is one technique to get around this problem. Wearables are a suitable alternative, but they must deliver a positive user experience; hence, they must consider “material” and functional factors. Human-Centred Design (HCD) is an effective method to achieve that because it focuses on users’ needs. However, HCD is concerned with the potential problem space but overlooks some essential features of technological use. Speculative design is a method for investigating potential design outcomes and possibilities that may arise in the future (i.e., problems concerning privacy). In this sense, the paper argues that speculative design can extend HCD to create wearables for epilepsy patients who require constant monitoring.Keywords: Epilepsy, Human-Centered Design, Privacy, Speculative Design, User Experience, Wearables
Travel Mutual Information Classification Based on Convolution Neural Network

Affected by human cognitive and emotional factors, there are errors, conflicts and redundant contents in travel mutual information, so it is necessary to distinguish and classify travel mutual information. Based on the analysis of mutual information characteristics, this paper constructs mutual information dataset by road experiment, and selects the convolutional neural network to classify and identify it. The python simulation results show that compared with SVM and fully connected network, convolutional neural network has higher recognition degree for mutual aid information classification.
Are DVDs Dinosaurs? The Impact, and Inequities, of Streaming

When the main branch of the San Francisco Public Library (SFPL) re-opened in August 2020 after being shuttered to the public for six months, it launched a soft reopening with SFPL To Go . Patrons could return materials and put holds on physical items at the branch’s entrance, but the public health order prohibited the public from entering the building; this restriction would not be lifted until May 2021. At first, the main branch received all returns that had accumulated during the closure for a system of twenty-seven branches and a bookmobile. Two media formats, DVDs and Blu-rays—belong to a floating collection. With only two locations open, the main branch was inundated with these returns.
Fine Gold Grains Inside the Limonite in the S Upergene Shangmanggang Gold Deposit, Sw China: Implications for Gold Mobilization and Mineral Exploration

Supergene Au deposits have attracted economical attention because of their increased grades and more environmentally leaching. The study on supergene processes responsible for the Au mobilization in the surface environments has the potential in determining hypogene sources, a potential concealed Au deposit. In this study, we recognized and sampled three Au-rich spatial zones in the Shangmanggang deposit in southwestern China: the hypogene, semi-oxidized, and red clay zones. The microscopic and chemical analyses of Au-bearing minerals show that (i) gold is mostly present as lattice-bound Au(I) in the pyrite within the hypogene zone and (ii) numerous fine Au grains, mostly 100 × n nm to 1 × n μm in size, with a granular, spindle, or irregular shape, have been widely observed in the semi-oxidized and red clay zones, mainly attached inside the fissures and pores of limonite. When exposed to an oxidizing environment, primary pyrite is gradually oxidized into limonite. With S diffusing out of the limonite, the Au in pyrite becomes rapidly activated and also diffuses into the environment as Au(S2O3)23– with a neutral or weakly alkaline pH in karst terrains. When meteoric water containing Au(S2O3)23– infiltrated the regolith profile, Au(I) was reduced to elemental Au(0) and precipitated in the limonite pores and fissures due to their difference in the pH with the soil solution, which is a new insight into the formation of Au grains in the regolith. These results could provide a reference to better understand Au mobilization in the immature laterite in SW China, as well as the exploration of a potential hypogene Au deposit covered by the lateritic profile.
Problems and forms of education of students in the framework of economic and management fields

The article examines the role of active methods and forms of education in conducting classes within the framework of basic educational programs that implement economic disciplines, the problems of their use, and their role in the formation of economic thinking in the preparation of professionals. The material of the article reflects the main concept of the Federal Law of July 31, 2020 No. 304-FZ "On Amendments to the Federal Law "On Education in the Russian Federation" on Students’ Education".
Intelligent Building Management and Control System Based on Intelligent Video Analysis Algorithm for Behavior Recognition

Today’s world is an era of big data. Intelligent video analysis plays an important role. As one of the key technologies in intelligent video analysis, target tracking has great academic research value and broad application prospects. This paper provides a smart building management and control system based on intelligent video analysis algorithm to realize behavior recognition. Through the video analysis component to identify the emergencies in the monitoring video and generate early warning information, the corresponding emergency plan is called through the emergency service component, and the external access equipment is handled according to the emergency plan through the data acquisition monitoring component, To a certain extent, it can solve the problems that the monitoring and management system can not achieve unattended, missed and misjudged emergencies, and can not quickly and automatically implement response measures after emergencies.
An Overview of Pedestrian Detection Based on LiDAR for Advanced Driving Assistance System

Nowadays, cars have been becoming more and more essential as means of transport in our daily life. With the unstoppable development of science and technology, self-driving cars were developed as a smart transportation solution, which can solve a lot of problems such as car accidents, congestion, parking lots, and so on. Pedestrian detection is a part of object detection, plays an important foundation in advanced driving assistance systems, especially autonomous vehicles. Pedestrian detection using LiDAR signals is has attracted great attention from researchers because of its ability to provide accurate 3D information about the environment and operate in low-light conditions, which improves disadvantages when using a Camera. Pedestrian detection for autonomous vehicles not only needs to ensure real-time perception but also to ensure high accuracy because it is directly related to safety and eventually human life. Hence, this article will summarize and analyze the currently LiDAR-based detection methods namely grid-based and point-based approaches to apply the detection of pedestrians for autonomous vehicles. Then, we analyze and compare the results between the highly regarded algorithms using this approach and their applicability to autonomous vehicles, target finally is pedestrian. The results show us that there are advantages and disadvantages of the two grid-based and point-based approaches. The point-based approach stores information better, but requires higher computational resources than the grid-based method. And the combination of both methods yields an overall improvement in detection accuracy. Finally, we look at a few challenges that need to be overcome for greater detection accuracy.
Task Scheduling Based on Improved Particle Swarm Optimization for Cloud Computing

Particle Swarm Optimization (PSO) algorithm is widely used in cloud computing task scheduling. As PSO algorithm tends to fall into local optimum and has poor convergence accuracy in the later stage, as a result, in this paper i integrates Simulated Annealing algorithm (SA) into PSO algorithm and adopts a strategy of mixing random and nonlinear decreasing inertia weight. At the same time, the idea of “accept the bad solution with a certain probability” in simulated annealing is used to improve the global search ability of the algorithm. Finally, the chaotic disturbance strategy is added to make the algorithm search for a better solution as far as possible, so as to improve the convergence accuracy of the algorithm in the later stage. The improved PSOSA algorithm and the standard particle swarm optimization algorithm are applied to the task scheduling test in the cloud environment through the Cloudsim simulation platform. The results show that the improved algorithm can achieve better scheduling results and faster convergence speed.
Optical Visual Encryption Using Focused Beams and Convolutional Neural Networks

The target of this paper is to implement an optically-based visual encryption system able to work with a large set of optical codes. The optical setup comprises a holographic system designed to generate spirally-polarized highly focused fields and an imaging module able to perform polarimetric analysis. In a previous stage, the optical system is numerically simulated in order to produce synthetic polarimetric distributions that are used to train a convolutional neural network. Interestingly, the way the network is trained depends on the selected state of polarization. Then, secret codes are split in two XOR-connected ones that are optically processed. The corresponding experimental polarimetric distribution is obtained and transmitted to the corresponding recipients, that can recover the code by interrogating the neural network. Finally, combining the two pieces of information, the encrypted message can be decoded.
AI Technologies and Firefly Algorithms

A survey on application of artificial intelligence technologies in firefly algorithm, which tries to model the natural working phenomenon of the firefly, is presented in this research. Firefly algorithms have been used in a variety of applications for different domains like communication, electrical power consumption, image and video processing, medical image processing, geo-informatics, etc., where optimal solution is a necessity. Artificial intelligence techniques have also been used with firefly algorithms in many applications in improving the performance. This paper tries to survey the applications of artificial intelligence techniques with firefly algorithms and the attempts made by the researchers toward that.