Reviewerly/exHarmony huggingface dataset:
Perception Action Aware-Based Autonomous Drone Race in a Photorealistic Environment

The development of autonomous, fast, agile small Unmanned Aerial Vehicles (UAVs) brings up fundamental challenges in dynamic environments with fast and agile maneuvers, unreliable state estimation, imperfect sensing, coupling action, and perception in real-time under severe resource constraints. However, autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control. To bridge this, we propose an approach in the context of autonomous, perception-action aware vision-based drone racing in a photorealistic environment. Our approach integrates a deep convolutional neural network (CNN) with state-of-the-art path planning, state estimation, and control algorithms. The developed deep learning method is based on computer vision approaches to detecting the gates and estimating the flyable area. The planner and controller then use this information to generate a short, minimum-snap trajectory segment and send corresponding motor commands to reach the desired goal. A thorough evaluation of our proposed methodology has been carried out using the Gazebo and FlightGoggles (photorealistic sensor) environments. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art methods and flies the drone more consistently than many human pilots. Moreover, we demonstrated that our proposed system successfully guided the drone through tight race courses, reaching speeds up to 7m/s of the 2019 AlphaPilot Challenge.
Patching Your Clothes: Semantic-Aware Learning for Cloth-Changed Person Re-Identification

Great advances have been observed in conventional person re-identification (Re-ID), which heavily relies on the assumption that the cloth remains unchanged. However, this dramatically limits their applicability in practical cloth-changed scenarios, leading to dramatic performance drop. Existing cloth-changed methods mainly exploit the body shape information, ignoring the relation between different clothes of the same identity. In this paper, we present a powerful semantic-aware patching strategy for clothes augmentation. It greatly enriches the cloth styles by randomly assembling the semantic cloth patches, simulating the appearances of the same person under different clothes. This augmentation strategy has two primary advantages: 1) It significantly reinforces the robustness against clothes variations without additional cloth collection. 2) It does not damage semantic structure, fitting well with cloth-unchanged scenarios. To further address the uncertainty in cloth changed, a Semantic Part-aware Feature Learning scheme is incorporated to mine fine-grained granularities, addressing the misalignment issue under changed clothes. Extensive experiments conducted on both clothing-changed and cloth-unchanged tasks demonstrate our proposed method’s superiority, consistently improving the performance over various baselines.
Efficient Sensitive File Encryption Strategy with Access Control and Integrity Auditing

Cloud storage performs a significant role in data sharing and data storage. The sharing of data is accessible over the internet. Protection has gotten to be a noticeable issue once the applying of the cloud data unit significantly developing in cloud computing in cloud computing. The benefits of the execution of these rising technologies have moved forward or changed administration models and progress application exhibitions from a different viewpoint. In any case, the exceptional developing volume of data sizes had to come about in numerous challenges in hones. The execution time of the data encryption is one of all the firm issues all through preparing and transmissions. A few current applications forsake data encryption to realize a versatile execution level companioning with protection issues. In this framework, we concentrate on efficient data protection. For that, a novel sensitive encryption approach is proposed with a sensitive file encryption strategy (SFES). Our proposed approach points to scramble the records which have more delicate data and utilize a sensitive file classification strategy. This methodology is intended to expand the security assurance scope by employing a sensitive encryption procedure that diminishes the time and distributed cloud storage space.
The query complexity of certification

We study the problem of certification: given queries to a function f : {0,1}n → {0,1} with certificate complexity ≤ k and an input x⋆, output a size-k certificate for f’s value on x⋆.
Performance Comparison of ECDH and ECDSA

Elliptic curve cryptography is the current standard for public key cryptography and it is also promoted by the National Institute of Standards and Technology (NIST) as the best way to ensure private communication between parties. It can be used to create faster, smaller, and more efficient encryption keys. It allows to transfer keys using elliptic curve mathematics. Smaller key sizes mean smaller requirements for hardware units performing arithmetic operations on the required cryptographic protocols. The content of this paper is a comparison of time intervals of key processes in the creation of ECDH (Elliptic Curve Diffie-Hellman) and ECDSA (Elliptic Curve Digital Signature Algorithm) algorithms.
Efficient Deep Learning Modalities for Object Detection from Infrared Images

For military warfare purposes, it is necessary to identify the type of a certain weapon through video stream tracking based on infrared (IR) video frames.Computer vision is a visual search trend that is used to identify objects in images or video frames.For military applications, drones take a main role in surveillance tasks, but they cannot be confident for longtime missions.So, there is a need for such a system, which provides a continuous surveillance task to support the drone mission.Such a system can be called a Hybrid Surveillance System (HSS).This system is based on a distributed network of wireless sensors for continuous surveillance.In addition, it includes one or more drones to make short-time missions, if the sensors detect a suspicious event.This paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks (CNNs) and Convolutional Long Short-Term Memory (ConvLSTM).Based on initial results, the importance of video frame enhancement is obvious to improve the visibility of objects in video streams.The accuracy of the proposed methods reach 99%, which reflects the effectiveness of the presented solution.In addition, the experimental results prove that the proposed methods provide superior performance compared to traditional ones.
An Improved ${k}$-Means Algorithm with Spatial Constraints for Image Segmentation

The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${k}$</tex> -means is sensitive to the initial center and noise in image segmentation. To reduce the sensitivity of k-means to the initial center, we use the equidistant strategy to segment the cumulative sum of histogram to initialize centers. Then we introduce local spatial information into objective function of k-means to reduce the impact of noise in image segmentation and improve the robustness of segmentation algorithm. We combine the equidistant segmentation strategy and k-means with spatial constraints to propose an improved k-means algorithm (I- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${k}$</tex> -means_S). I- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${k}$</tex> -means_S solves the problem that traditional <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${k}$</tex> -means is easy to be affected by initial center and noise in image segmentation. To test the performance of I- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${k}$</tex> -means, we create two synthetic images and add Gaussian and Salt & Pepper noises to the two images. We compare I- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${k}$</tex> -means_S with 5 classical cluster algorithms on the two noisy images. From the results, we know that the proposed algorithm is more robust than the other algorithms. Meanwhile, we apply I- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${k}$</tex> -means_S to natural image processing. The results show that it still has high robustness.
Applying digital technology to linguistic education: a connectivism-based intelligent learning system

The advent of a highly interactive and networked digital era of Web 2.0 has greatly changed the way we view knowledge and the learning process. Knowledge is more reflected as an emerging dynamic network, rather than an attainable objective state. The learning process, as a result, is a process of network formation instead of knowledge transferring. With the acknowledgement of these transformations, this study sheds light on how to apply digital technology to linguistic education by matching up the digital learning theory of connectivism with the dynamic and emerging features of linguistics sources. By aggregating large-scale corpus into linguistic learning network, the study observes and evaluates how data extracting, model exploring, verifying and falsifying are interacted in students' linguistic learning process. It is demonstrated that this corpus-assisted digital learning approach, can not only promote students' acquisition in linguistics, but can also enhance students' learning abilities in general, such as problem-solving ability, data-mining ability, and online learning ability.
Word-Level Quality Estimation for Korean-English Neural Machine Translation

Quality estimation (QE) task aims to predict the machine translation (MT) quality well by referring to the source sentence and its MT output. The various applicability of QE proves the importance of QE research, but the enormous human labor to construct the QE dataset remains a challenge. This study proposes three automatic word-level pseudo-QE data construction strategies using a monolingual or parallel corpus and an external machine translator without human labor. We utilize these individual pseudo-QE datasets to finetune multilingual pretrained language models such as cross-lingual language models (XLM), XLM-RoBERTa, and multilingual BART and comparatively analyze the results. Considering the synthetic dataset creation setup, we attempt to validate the objectivity of the QE model by leveraging four test sets translated by external translators from Google, Amazon, Microsoft, and Systran. As a result, XLM-R-large shows the best performance among mPLMs. We also verify the reliability of the QE model through the close performance gaps between different test sets. To the best of our knowledge, this is the first study to experiment with word-level Korean-English QE.
A study of two-way authentication for decentralized network identities

The rapid development of the Internet has posed a more serious challenge to communication. The widely used authentication scheme is now a centralized authentication scheme, which obviously cannot meet the demand in the era of explosive information growth, and the development of blockchain has provided the impetus for the development of decentralization, so the design of decentralized authentication scheme has become a hot topic, and the use of blockchain and asymmetric encryption technology can solve the authentication problem. However, due to the limitations of blockchain, it cannot meet the conditions of non-Internet. A two-way authentication needs to be designed to solve the trust problem between the client and the service node. In this paper, we propose a two-way authentication scheme based on digital signature using elliptic encryption algorithm to achieve reliable authentication of identity.
Mix-Up Self-Supervised Learning for Contrast-Agnostic Applications

Contrastive self-supervised learning has attracted significant research attention recently. It learns effective visual represen-tations from unlabeled data by embedding augmented views of the same image close to each other while pushing away embeddings of different images. Despite its great success on ImageNet classification, COCO object detection, etc., its performance degrades on contrast-agnostic applications, e.g., medical image classification, where all images are visually similar to each other. This creates difficulties in optimizing the embedding space as the distance between images is rather small. To solve this issue, we present the first mix-up self-supervised learning framework for contrast-agnostic applications. We address the low variance across images based on cross-domain mix-up and build the pretext task based on two synergistic objectives: image reconstruction and transparency prediction. Experimental results on two benchmark datasets validate the effectiveness of our method, where an improve-ment of 2.5% ~ 7.4% in top-1 accuracy was obtained compared to existing self-supervised learning methods.
Service Portability and Information Discovery in Building Operating Systems using Semantic Modeling

To achieve cost-efficient IoT based Building Operating Systems (BOS), portable building services are needed. Most previous work has gone into hardware abstraction, while service abstraction has been neglected. This paper presents an information discovery mechanism for BOSs that is based on an ontology which integrates with other semantic models used in the space. The built environment is characterized by extreme heterogeneity; no buildings are entirely alike. Equipment is replaced or updated, control systems and building functionality evolve, as applications, models, forecasters, and controllers improve. For services deployed in such settings to operate at a scale, they must be robust to change. Describing service interfaces using a semantic model, together with the physical context in a building, enables applications to query for their service dependencies. Applications then depend on an abstract query, instead of specific services. For evaluation, nine services running on models of the service ecosystems of three concrete buildings, are implemented, demonstrated, and discussed. Results show services have successfully been made portable and adapts to the changing environments of buildings. Merging and splitting services without code changes to depending services also work as intended, as well as increasing system resilience, by arbitrating similar services.
Fine-grained Fashion Clothing Image Classification and Recommendation

Fashion has been an important part of society and culture bringing different people together to celebrate their own individuality. The sheer volume of the different kinds of clothing available today brings to the foreground the issue of searching and finding products that match the likings of a customer in this massive proverbial haystack of products. To resolve this issue, we propose a solution to use transfer learning techniques to match products against a clothing image of the user's liking and provide links to these relevant products. The user is allowed to input an image of a clothing apparel and search for related links without having to enter text input.
Extracting Descriptive Words from Untranscribed Handwritten Images

Extracting descriptive text from manuscripts to be included in the manuscript metadata is an important task that is generally performed in archives and libraries by experts with a wealth of knowledge on the manuscripts contents. Unfortunately, many manuscript collections are so vast that it is not feasible to rely solely on experts to perform this task. To our knowledge, this is the first work aiming at automatic extraction of descriptive text from untranscribed text images. To attempt dealing with such a task, a first step would be to transcribe the handwritten images into text – but achieving sufficiently accurate transcripts is generally unfeasible for large sets of historical manuscripts. We propose new approaches to automatically extract descriptive words which do not rely on any explicit image transcripts. They are based on "probabilistic indexing", a relatively novel technology which allows to effectively represent the intrinsic word-level uncertainty generally exhibited by handwritten text images. We assess the performance of this approach on samples of a large collection of complex manuscripts from the Spanish Archivo General de Indias. Since no standard metrics exist for the novel task considered in this work, we propose two new evaluation measures which aim at measuring the quality of the detected descriptive words in terms close to practical usage of these words. Using these metrics we report promising preliminary results.
Predicting Emotional-Social Competence Based on Academic Engagement, Self-efficacy and Perception of School Climate in High School Students

Predicting Emotional-Social Competence Based on Academic Engagement, Self-efficacy and Perception of School Climate in High School Students
The Status Quo of English Educational Apps Based on Computer Information Technology

With the development of computer information technology, network education technology is widely used in English teaching. The application of information education technology to English teaching has become a common phenomenon in the field of education. At present, many scholars and scholars have begun to study the teaching methods of applying information education technology to foreign language teaching and teachers' attitudes towards information education technology. How to promote the effective use of English apps by English teachers in teaching has become an important research topic in English teaching today. In order to investigate the use of an English education app in English teaching, this article mainly uses the questionnaire method to study the situation of 400 students in a university's English audiovisual course using English education apps. The survey results show that most students use English Apps in English learning, and the application of information education technology to college English teaching has become very common. However, there are still some problems in English teaching, such as poor self-control of students and many English education App advertisements, which can easily distract English learners. In order to solve the above-mentioned problems, the author puts forward the following suggestions to improve the construction of information education technology hardware and software and reduce spam advertisements. Cultivate students' ability to use English education apps for autonomous learning.
Construction of Performance Evaluation System for Intelligent Simulation of University Research Under Big Data

Performance evaluation of scientific research can effectively improve the overall performance of construction projects and the core competitiveness of scientific research enterprises. Considering the financial situation, customer satisfaction, internal operation ability, agility, learning and growth of scientific research, this paper establishes a scientific research performance evaluation index system including 5 first level indicators and 15 s level indicators, and puts forward a BP neural network model to evaluate the scientific research performance. Taking the data of a construction enterprise in Qingdao in December 2017 as the sample, the performance of the enterprise in January and February 2018 is predicted after learning, and the results prove the feasibility of the method.
Experience in the implementation of the electronic information and educational environment in the practice of a scientific and pedagogical worker of a university

Currently, educational institutions are actively transitioning educational activities to the use of an electronic information and educational environment through the introduction of a wide range of information and telecommunication technologies in the field of higher education. This leads to global changes in the conditions for the functioning of higher educational institutions, which makes it necessary to revise traditional approaches to the organization of the educational process. The basis of this process is not only a change in the educational process, but also the improvement of the management of this process and the university as a whole through the creation of an electronic information and educational environment. The problem of the research is to reveal the experience of implementing the electronic information and educational environment in the practice of a scientific and pedagogical worker of a university. The purpose of the research is to substantiate the effectiveness of using the electronic information and educational environment of a university in the educational process. The research methods are analysis, survey, generalization, description, forecasting. The research results are the concept of "electronic information and educational environment" has been disclosed; the basic resources of the electronic information and educational environment of higher educational institutions have been described; the experience of using the Moodle platform in the educational process in the direction of preparation 44.03.02 Psychological and pedagogical education, focus: "Psychology and Pedagogy of Primary Education" has been presented; the results of an online survey of students on the advisability of using an electronic information and educational environment in the educational process have been shown. Key conclusions: the electronic information and educational environment of the university is a set of electronic resources that ensure the synchronous interaction of all participants in the educational process of the educational institution; the basic resources of the electronic information and educational environment of higher educational institutions are the official website of the educational organization, online learning platforms, automated testing systems, portals / teams of deans, departments, curators, teachers, students, etc.; An online survey of students has revealed the interest of students in the use of electronic resources in the educational process.
Daughters, devices and doorkeeping: how gender and class shape adolescent mobile phone access in Mumbai, India

How do gender and class work together to shape adolescent girls’ unequal access to mobile phones within the family in Mumbai, India? What are the everyday practices and cultural logics upon which these inequalities are built? This paper addresses these questions by using a mixed-methods study of 59 group interviews and 268 surveys with adolescents aged 13–15 in Mumbai. Taking an intersectional analytical framework, the findings show how gender and class together create varying standards of ‘respectable femininity’ and class distinction that families aspire to and cultivate in adolescent girls. The mobile phone can be seen as both a threat and a necessity to the maintenance of these standards of respectability, resulting in families variously enabling or constraining access to mobile phones by girls. Rather than interpreting the findings through binaries of low-income/high-income or empowered/constrained, I instead consider how classed ideals of ‘respectable femininity’ create different aspirational conditions for girls belonging to each class group, and form the cultural frames of everyday life.