Reviewerly/exHarmony huggingface dataset:
Construction of English smart classroom learning model based on big data technology

This paper specifically analyzes the constructive role and practical application function of big data technology in English smart classroom, and this paper fully improves the classroom model and designs the data model based on big data technology, and puts forward the function analysis of relevant smart classroom modules. The big data smart classroom system is mainly composed of three parts: English learning resource database, data model analysis, data storage and data evaluation. There is a large amount of data information in the application process of smart classroom. Therefore, using data information processing and data mining technology, we can develop new model functions based on the original functions of smart classroom, so as to further improve the technical framework for the teaching system. With the support of data information processing technology and data mining technology, English smart classroom learning system continues to carry out technological innovation, providing technical support for English classroom innovation.
Ensemble Method for User Activity classification in Ambient Assisted Living

Artificial Intelligence(AI) has become a global plat-form that allows objects in IoT to Interact and perform computations. The wide range of application areas of IoT are Smart Cities, Smart grids, Smart Supply chain and Ambient Assisted Living(AAL). These applications have challenges like tolerance to uncertainty,adaptiveness to the changing environment and improved trust among users. Thus, machine learning algorithms improve the performance of smart objects in various environment. The AAL environment deploys heterogeneous devices and sensors to capture various activities carried out through the daily by the individuals who resides in the smart home. In this work, an ensemble method using k-Nearest Neighbor(KNN), Decision Tree(DT) and Logistic Regression(LR)is proposed by investigating the performance of existing conventional supervised machine learning algorithms and selecting best model by considering the sensors features and improves the performance metrics. The work is evaluated using the benchmark ARAS (Activity Recognition with Ambient Sensing) dataset. The results are analysed using different parameters. The comparative analysis show that the proposed ensemble method gives accuracy of 76.28%.
Feature Mutual Feedback Network for Salient Object Detection

Most existing SOD works have achieved remarkable results by designing efficient feature fusion strategies. However, many works generally neglected the integrity of the original features in the spatial structure and the feature dilution during the transmission of deep features, making them hard to distinguish the foreground from the background in a complex environment. To address the above problems, we present a Feature Mutual Feedback Network, which is composed of the Semantic Supplement Module (SSM), cascaded Progressive Mutual Feedback Groups (PMFGs) and the Global Guidance Module (GGM). SSM enriches the feature information through different receptive fields. Cascaded PMFGs refine features through mutual feedback of contextual features. GGM provides global information of salient objects for each layer of features. Furthermore, we propose a Structural Foreground Enhancement loss to supervise the proposed model. Experimental results on 5 benchmark datasets demonstrate that the proposed method has superior performance compared with 18 state-of-the-art methods.
Classification of Damage of House Images Based on CNN Model

Our goal in this research is to deal with the problem of classifying images for buildings on whether they are damaged. If the target is reached successfully, it would significantly reduce the cost of the initial stage of rescue and disaster relief. The current method has the problem of low accuracy, which strongly limits the effect of automatic image analysis. The problem is caused by the low quality of images taken by satellite. To deal with it, I used the retreatment of images to overcome the problem. Besides this, I choose to use deep learning to build the program, which is Convolutional Neural Network. It works better on image analyzing, especially on extracting image features than other kinds of image-analyze models. After experimenting with different data several times, I constantly increased the likelihood of correct justification successfully. The research shows that the idea of taking retreatment of images before analyzing to strengthen the classification model is effective and useful.
Towards Efficient Co-audit of Privacy-Preserving Data on Consortium Blockchain via Group Key Agreement

Blockchain is well known for its storage consistency, decentralization and tamper-proof, but the privacy disclosure and difficulty in auditing discourage the innovative application of blockchain technology. As compared to public blockchain and private blockchain, consortium blockchain is widely used across different industries and use cases due to its privacy-preserving ability, auditability and high transaction rate. However, the present co-audit of privacy-preserving data on consortium blockchain is inefficient. Private data is usually encrypted by a session key before being published on a consortium blockchain for privacy preservation. The session key is shared with transaction parties and auditors for their access. For decentralizing auditorial power, multiple auditors on the consortium blockchain jointly undertake the responsibility of auditing. The distribution of the session key to an auditor requires individually encrypting the session key with the public key of the auditor. The transaction initiator needs to be online when each auditor asks for the session key, and one encryption of the session key for each auditor consumes resources. This work proposes GAChain and applies group key agreement technology to efficiently co-audit privacy-preserving data on consortium blockchain. Multiple auditors on the consortium blockchain form a group and utilize the blockchain to generate a shared group encryption key and their respective group decryption keys. The session key is encrypted only once by the group encryption key and stored on the consortium blockchain together with the encrypted private data. Auditors then obtain the encrypted session key from the chain and decrypt it with their respective group decryption key for co-auditing. The group key generation is involved only when the group forms or group membership changes, which happens very infrequently on the consortium blockchain. We implement the prototype of GAChain based on Hyperledger Fabric framework. Our experimental studies demonstrate that GAChain improves the co-audit efficiency of transactions containing private data on Fabric, and its incurred overhead is moderate.
Driving Risk-Aversive Motion Planning in Off-Road Environment

Incremental sampling-based motion-planning algorithms, such as the rapidly-exploring random tree (RRT), are popular in motion planning because of their ability to proffer rapid solutions. Driving safety is the ability of a vehicle to prevent overturning and collision. However, the RRT algorithms neither involve any safety mechanism, nor consider any terrain measurement information of the environment, there is no assurance on the driving safety of the solutions. Therefore, obtaining an efficient trajectory while considering driving safety is a dilemma for intelligent vehicles in unstructured off-road environments. This study presents a potential-field based RRT* motion-planning algorithm for vehicle risk aversion. The algorithm constructs a configuration space using the potential field, which identifies risk range around obstacles and off-road terrains. A random-exploring tree grows through sampling inside the potential-field space with the consideration of nonholonomic constraints of the vehicles. A cost function that trades off driving safety and efficiency is used as a priority-sequence mechanism to get an optimized planning solution. A heuristic sampling method is employed to obtain a fast solution to the initial trajectory, and the trajectory can be optimized by decreasing the cost in the following sampling process thereafter. We tested the algorithm on scenarios to verify its effectiveness. Simulation results, under different scenarios, demonstrate that the proposed algorithm improves vehiclesâ€™ motion planning performance.
What Affects the Performance of Models? Sensitivity Analysis of Knowledge Graph Embedding

Knowledge graph (KG) embedding aims to embed entities and relations into a low-dimensional vector space, which has been an active research topic for knowledge base completion (KGC). Recent researchers improve existing models in terms of knowledge representation space, scoring function, encoding method, etc., have achieved progressive improvements. However, the theoretical mechanism behind them has always been ignored. There are few works on sensitivity analysis of embedded models, which is extremely challenging. The diversity of KGE models makes it difficult to consider them uniformly and compare them fairly. In this paper, we first study the internal connections and mutual transformation methods of different KGE models from the generic group perspective, and further propose a unified KGE learning framework. Then, we conduct an in-depth sensitivity analysis on the factors that affect the objective of embedding learning. Specifically, in addition to the impact of the embedding algorithm itself, this article also considers the structural features of the dataset and the strategies of the training method. After a comprehensive experiment and analysis, we can conclude that the Head-to-Tail rate of datasets, the definition of model metric function, the number of negative samples and the selection of regularization methods have a greater impact on the final performance.
Methodical support of the project activities of senior preschoolers in kindergarten

The relevance of the research topic is due to the contradiction between the high educational potential of project activities for the intellectual, creative, social, personal development of pre-schoolers and the poor organization of methodical support for project activities in preschool institutions, as a result, project activity acquires a formal character, its essence as a developing means of education and upbringing, productive interaction between teachers, preschoolers and parents is distorted. Purpose: to substantiate the need to organize methodical support of project activities in preschool institutions and develop a structural and functional model of methodological support for project activities of older preschoolers in kindergarten. Research methods: as research methods, an analytical review of scientific sources on the problem of organizing project activities of preschoolers and a modelling method were chosen. Results: methodical support is reviewed from the position of system-organized interaction of the subjects of the educational process – participants in project activities (teachers, children, parents), aimed at achieving the planned project activities, the intellectual and creative development of preschoolers, the development of psychological and pedagogical competencies of parents, mastering their pedagogical experience of interaction with children, improving the professional competence of teachers. A structural-functional model of methodological support for the project activities of older preschoolers is proposed, including three interrelated modules that reflect the logic and functions of the process of methodological support (information-analytical, content-organizational, evaluative-reflexive). It is concluded that a necessary condition for ensuring the effectiveness of the project activities of older preschoolers in kindergarten is building relationships of social and pedagogical partnership between parents (families) of pupils and kindergarten teachers in order to integrate partners’ resources and coordinate joint actions to successfully solve pedagogical problems and achieving predicted results.
Towards Real Time Interpretable Object Detection for UAV Platform by Saliency Maps

On-board object detection is an important requirement for Unmanned Aerial Vehicles (UAVs) when carrying out a variety of tasks such as obstacle avoidance, search and rescue, and automatic target recognition. One of the main difficulties of conducting object detection for a UAV is that, because the objects of interest are observed from altitude, this causes them to appear very small in images acquired from an onboard camera. In this work, we attempt to provide a solution to this difficulty, while also seeking to meet two other criteria that are important to the deployment of Artificial Intelligence with UAVs: firstly, the capability to operate in real-time; and secondly, the ability of the user to be able to trust the predictions it makes. To meet the challenge of small object detection we present the use of an image Tile Loader to enhance the capability of Deep Neural Network (DNN) style detectors, while minimising the processing time costs. Furthermore, we also introduce the practice of using Grad-CAM to provide better insights into a detection style architecture as a means to enhance trustworthiness.
Learning restrictions on admissible switching signals for switched systems

The knowledge of restrictions on the set of admissible switching signals is important for the design of control strategies for switched systems. We propose an algorithm that learns these restrictions by collecting data from a gray-box simulation model of the switched system. Our learning technique is a modified version of the well-known L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> -algorithm from machine learning literature. Examples are presented to demonstrate our learning algorithm.
Graphical Enhancements for Effective Exemplar Identification in Contextual Data Visualizations

An exemplar is an entity that represents a desirable instance in a multi-attribute configuration space. It offers certain strengths in some of its attributes without unduly compromising the strengths in other attributes. Exemplars are frequently sought after in real life applications, such as systems engineering, investment banking, drug advisory, product marketing and many others. We study a specific method for the visualization of multi-attribute configuration spaces, the Data Context Map (DCM), for its capacity in enabling users to identify proper exemplars. The DCM produces a 2D embedding where users can view the data objects in the context of the data attributes. We ask whether certain graphical enhancements can aid users to gain a better understanding of the attribute-wise tradeoffs and so select better exemplar sets. We conducted several user studies for three different graphical designs, namely iso-contour, value-shaded topographic rendering and terrain topographic rendering, and compare these with a baseline DCM display. As a benchmark we use an exemplar set generated via Pareto optimization which has similar goals but unlike humans can operate in the native high-dimensional data space. Our study finds that the two topographic maps are statistically superior to both the iso-contour and the DCM baseline display.
Research on Interactive Object Generation in VR Games Based on Grid Deformation

In order to make the objects in VR games more diverse and the interaction more immersive, this paper investigates the interaction process of object generation in the VR environment of head-mounted devices. Based on Laplacian grid deformation and Loop subdivision methods, interactive handles are used to generate 3D objects in the VR environment. Variable game objects are implemented in the VR game, which simulates the plant growth. Players can control the morphological changes of any object through the handle, so that the user's self-made content is presented on the user's retina in a way that meets the requirements of game precision. In the experiment of this paper, the deformation and growth process of plants are simulated, and the user interaction is freer and the experience has more realistic immersion.
Probabilistic Embedding for Collaborative Reasoning

Collaborative Reasoning (CR) aims to view users’ historical behaviors as logical expressions to generate recommendations. Existing neural CR models embed users and items as points in a latent space and define each logical operator with one neural network constrained by many logical regularizers, which causes difficulties in modelling uncertainty and training networks. To address these shortcomings, this paper develops a probabilistic embedding based CR model BeCR for recommendation tasks. In BeCR, we encode each user or item as a Beta embedding (each element follows a Beta distribution) and then define probabilistic logical negation and conjunction operators on these embeddings. The proposed model can naturally characterize the uncertainty and satisfy basic logical rules without any logical regularizer. With our logical operators, the embedding of a user is capable of filling the gap between reasoning and personalization. Based on it, BeCR is extended to a personalized recommendation model BeCR4p. Experimental results on three benchmark datasets demonstrate that our models achieve competitive performance against eight state-of-the-art baselines.
Unsupervised Federated Adversarial Domain Adaptation for Heterogeneous Internet of Things

Federated learning, as a novel machine learning paradigm, aims to collaboratively train a global model while keeping the training data on local devices, which protects data privacy and security of distributed devices. However, the model cannot generalize to new devices because of domain shift caused by the statistical difference between the labeled data and unlabeled data collected by different devices in heterogeneous internet of things networks. In this paper, we propose a method named Unsupervised Federated Adversarial Domain Adaptation with Controller Modules (UFADACM), which aims to reduce the distribution difference between source nodes with labeled data and target nodes with unlabeled data, and reduce the parameter cost and communication overhead while achieving a comparable performance. We also conduct extensive experiments to demonstrate the effectiveness of the proposed method.
Remote Network Pharmacology Course Reform through Artificial Intelligence and Cloud Platform

Pharmacology is a main course in higher vocational colleges, and it is also a required course for medical and pharmacy majors. According to the characteristics of the students in higher vocational colleges, the classroom teaching and practical teaching of pharmacology are reformed, and the practicality is more prominent by combining the innovation of teaching methods with concrete cases, promoting the cultivation of technical and technical talents in higher vocational colleges.
Contextual Profiling of Stack Overflow Java Code Security Vulnerabilities Initial Insights from a Pilot Study

Stack Overflow is noteworthy in the value it provides to the practitioner community. However, concerns have also been expressed around the quality of content that is included on this website. Researchers have thus studied Stack Overflow code snippet quality, with particular focus on security issues. However, there has been less effort aimed at providing concrete solutions and demonstrations of the debilitating effects (vulnerabilities) of Stack Overflow security issues. In this pilot study, we bridge this gap by using inductive analyses to study the scale of security faults on Stack Overflow, and contributors' awareness of these faults and their fixes. We also examine the potential debilitating effects of Stack Overflow code snippets security faults and mechanisms for cataloguing security faults for practitioners. Our outcomes reveal that most Stack Overflow code snippets do not possess security faults. However, some code snippets with faults are accepted by the community. We note that the security faults observed in Stack Overflow code snippets are likely to result in various levels of security breaches and ensuing consequences. Thus, our attempt at providing a preliminary catalogue could be useful and encouraging for the community to further develop. While this is an initial step, ongoing efforts aimed at investigating and enhancing the code on online portals are noteworthy for helping the community to deliver secure software.
2D Pose Estimation Based on Deep Learning for Mobile System

Abstract Pose estimation is the detection of human action poses, which is an important task of computer vision. The requirements of tasks such as unmanned driving and intelligent surveillance have also contributed to the development of human pose estimation. In this context, deep learning (DL) has a remarkable impact on human pose estimation. In this study, we investigate a pose estimation algorithm called convolutional pose machines (CPM) and implement it on a mobile platform (i.e., Android). By combining the classical CPM and the mobile deep learning framework Mobile AI Compute Engine (MACE), the model is deployed to the mobile platform to estimate the human pose directly and locally on the mobile phone without relying on the Internet. Thus, the proposed method can achieve effective performance while protecting users’ personal data. Experimental results on real-world data validate that this system has achieved the expected results of accurate recognition of common actions with a simple interface.
ECopy-Paste: An Effective Data Augmentation Method for Object Detection of UAV

Deep learning-based target models require a large amount of data for training in order to achieve good generalization. But the amount of data in some widely used datasets cannot meet this requirement, while the data set also has the problems of brightness imbalance, small targets and data imbalance. These will lead to poor robustness of the model. In this paper, we propose the ECopy-Paste method to solve the above problems. Our method consists of three parts: brightness transformation, over large scale jittering and copy-paste. Brightness transformation can balance the brightness of datasets, Oversize large scale jittering can expand the size of small targets and Copy-paste can relieve the problem of data imbalance and enlarge the size of datasets. Our method tested on VisDrone2019-DET by using Faster R-CNN, and the final score obtained is 17.6%, which is about 3.1% improvement over the benchmark of VisDrone2019-DET.
Virtual Learning Object (VLO), a Digital Resource to Enhance the Development of Algebraic Thinking

Mathematics is a fundamental area in the curricula of international and national curricula in Colombia. Its teaching objective is the development of mathematical thinking. In our project, we intended to approach its teaching by leveraging the benefits offered by technology. Thus, we could respond to the need to teach mathematics in its contexts and other sciences. Specifically, this work addresses Mathematics teaching considering the function of a Virtual Learning Object (VLO) using the GeoGebra Online platform as a cognitive partner. It aims to answer the research question: What is the effectiveness of a VLO, based on the Theory of Didactic Situations, in developing Algebraic Thinking in ninth-grade students attending the Vicente Azuero Technical School in Floridablanca, Colombia? Methodologically, this work incorporates the holistic understanding of science, a holistic research method, evaluative research, and a quasi-experimental research design applied to a population of ninth-grade students. Holistic research is understood as a global, evolutionary, integrative, concatenated, and organized process passing through different stages and phases of the holistic spiral of science. It begins with the descriptive stage, using a survey with the Algebraic Thinking Scale instrument. It ends with the evaluative stage comparing the before and after of the survey with Wilcoxon's T analysis technique. This paper reports the first results corresponding to the analysis of the Pre-test application.