Reviewerly/exHarmony huggingface dataset:
Self-adapting Industrial Augmented Reality Applications with Proactive Dynamic Software Product Lines

Industrial Augmented Reality (IAR) is a key enabling technology for Industry 4.0. However, its adoption poses several challenges because it requires the execution of computing-intensive tasks in devices with poor computational resources, which contributes to a faster draining of the device batteries. Proactive self-adaptation techniques could overcome these problems that affect the quality of experience by optimizing computational resources and minimizing user disturbance. In this work, we propose to apply ProDSPL, a proactive Dynamic Software Product Line, for the self-adaptation of IAR applications to satisfy the quality requirements. PRODSPL is compared against MODAGAME, a multi-objective DSPL approach that uses a genetic algorithm to generate quasi-optimal feature model configurations at runtime. The evaluation with randomly generated feature models running on mobile devices shows that PRODSPL gives results closer to the Pareto optimal than MODAGAME.
IBPO: Solving 3D Strategy Game with the Intrinsic Reward

Li, Huale Cao, Rui Hou, Xiaohan Wang, Xuan Tang, Linlin Zhang, Jiajia Qi, ShuhanIn recent years, deep reinforcement learning achieves great success in many fields, especially in the field of games, such as AlphaGo, AlphaZero and AlphaStar. However, reward sparsity is still a problem in the 3D strategy games with a higher dimension of state space and more complex game scenarios. To solve this problem, in this paper, we propose an intrinsic-based policy optimization algorithm (IBPO) for reward sparsity. The IBPO incorporates the intrinsic reward into the traditional policy, which composed by the differential fusion mechanism and the modified value network. The experimental results show our method can obtain better performance than the previous methods on the VizDoom.
Hybrid Software Defect Prediction Based on LSTM (Long Short Term Memory) and Word Embedding

Defects often occur in software so it can cause various problems for users. The running system may have defects, although they are not immediately apparent. Defects can be UI or display defects or defects in logic code. In predicting software defects several methods have been developed focusing on software metrics (Line of Code, Cyclomatic Complexity, etc.). But in capturing program syntax and semantics and the ability to build accurate predictive models, software metrics often fail. We propose a method that combines deep learning methods and word embedding to predict software defects. We will map tokens using an abstract syntax tree from the source code and then build an LSTM network to predict software defects. The dataset used is taken from an open source Java project, namely the Apache Project Repository. The evaluation results show that the LSTM method combined with word embedding can get accuracy 98%, precision 89%, recall 92% and f1-score 90%. This is greater than other software defect prediction methods.
Adaptive Calibration for Camera and Stitching of Images

In machine vision, cameras obtain the geometric data of the 3D object. The calibration of the camera has become most significant computer vision component. This allows us to acquire the camera intrinsic and extrinsic parameters. The image pixel coordinates are connected with the respective coordinates in the reference frame of camera by intrinsic parameters. Extrinsic parameters describe the camera reference frame's place and alignment in relation to a well- known reference frame of the world. Currently, calibration of camera is constantly crucial part of photogrammetric extent, with a fundamental and regularly implemented procedure of self-calibration. This paper summarizes the present approaches such as calibration using MATLAB, homography, kruppas equation, and fundamental matrix adopted for calibration in the field of computer vision. The purpose of image calibration is to diminish the distinctions among an optimal lens model and the amalgamation of camera lens that has been utilized, resulting in image merging. Image stitching is the method of integrating several scene pictures to create a novel high-resolution solitary image. Computer software usually performs image stitching. For obtaining better outcomes, most approaches involve precise overlap between image and exposures. The innovative and easy approach for combining the images is suggested here. Detection and matching of features are performed using FAST and RANSAC methods which assist efficiently in stitching of the images.
Coordinated path planning for Multi-UAVs based on critical track points

Multi-UAVs cooperative trajectory planning (MUCTP) refers to planning a number of safe, reliable and non-collision from each UAV starting point to the target point in known, partially known or unknown environment. In the planning process, it is necessary to consider the constraints of the UAV itself and the synergistic restriction relationship. Therefore, in order to improve the efficiency of collaborative path planning, a multi-UAVs collaborative path planning algorithm based on key path points is proposed in this paper. In this algorithm, the gene location representation method of individual population was defined, the feasible domain of three-dimensional space was set, and the objective function was constructed by combining the constraint conditions. The experimental results show that the algorithm proposed in this paper has fast convergence speed and strong synergistic ability in multi-UAVs cooperative path planning, which makes the planned track group more reasonable.
A Methodological Framework for Creating Large-Scale Corpus for Natural Language Processing Models

Currently, there is a boom in introducing Machine Learning models to various aspects of everyday life. A relevant field consists of Natural Language Processing (NLP) that seeks to model human language. A key and basic component for these models to learn properly consists of the data. This article proposes a methodological framework for constructing a large-scale corpus to feed NLP models. The development of this framework emerges from the problem of finding inputs in languages other than English to feed NLP models. With an approach focused on producing a high-quality resource, the construction phases were designed along with the considerations that must be taken. The stages implemented consist of the corpus characterization to be obtained, collecting documents, cleaning, translation, storage, and evaluation. The proposed approach implemented automatic translators to take advantage of the vast amount of English literature and implemented through non-cost libraries. Finally, a case study was developed, resulting in a corpus in Spanish with more than 170,000 documents within a specific domain, i.e., opinions on textile products. Through the evaluations carried out, it is established that the proposed framework can build a large-scale and high-quality corpus.
State Evaluation of Long-term Storage Product Based on Cloud Model and TFN-AHP

State is an important characteristic of a product. The strategy of condition based maintenance mainly depends on accurate evaluation of state. Long-term storage products, such as missiles, are usually tested with less number of times. That results in few information for constructing a state evaluation model. State of a product is the comprehensive reflection of multi-components status level. In this paper, the triangular fuzzy number (TFN) was utilized in analytic hierarchy process (AHP) to overcome the defects of subjective fuzziness in weight distribution. And the cloud model was introduced to describe qualitative information for state evaluation. A numerical example with a product in storage is introduced to illustrate engineering applicability of the method proposed in this paper.
The Research on the User Experience of Consultation Designed by China’s Medical Mobile Media Platforms Under the Background of COVID-19

At the beginning of 2020, the outbreak of COVID-19, a black swan event, brought a huge impact and change to people's normal life. Online consultation has become the best choice for users to consult and seek medical treatment. This paper starts from the problem of the experience of User Consultation of the Medical Mobile Media Platforms under the Backdrop of COVID-19, method based on an online survey from of 167 valid questionnaires, we studied the influence and role of medical APP on the society and the rigid medical demand. Based on the research purposes, the content of the Mobile Medical App User Consultation, the Use Effect of Mobile Medical App User Consultation are studied on field research. Research shows that the consultation design is not perfect, the application effect varies due to the differences in age, region, condition, cost and other aspects of the patients. But we can be predicted that in addition to its unique diagnostic and therapeutic advantages in the face of epidemic diseases such as COVID-19, it can also play a role in the diagnosis and treatment of common diseases and chronic diseases.
Health and Safety Innovations to Reduce the Spread of Contagious Disease

In this paper, we report on a series of design innovation projects conducted by student teams at the University of Colorado at Denver in collaboration with research scientists at the US Air Force Research Lab. The COVID-19 pandemic has made apparent the risks of disease transmission through multiple vectors in public spaces that could potentially benefit from further protection and safety measures. Utilizing formal ideation and innovation methods from the 4D Design Innovation (DI) process, the teams studied what specific problems to solve, explored potential solution ideas, and developed some early-concept prototypes for feasibility demonstration. Design Innovation (DI) provides both an overall process for product design and also methods for each step in the process. This paper details the correlation between the steps in the DI process and the results produced by the teams. The teams primarily focused on minimizing the risk of disease transmission through reducing the physical touching of common public items; additional solutions relating to contact tracing and social distancing were also explored. Initial functional prototypes were developed and demonstrated for: (a) touchless elevator control systems using voice commands, foot pedals, gestures, and touchless buttons; (b) an automated desk usage hygienic management system; (c) an automated remote video person tracker for monitoring compliance with indoor social distancing and room capacity limits; and (d) a contact tracing app. We discuss the prototyped solutions in detail, and describe promising ways forward for developing these technologies, with the aims of combatting the spread of disease and ensuring overall public health, safety, and security.
The Knowledge Description Language

A knowledge base with a notional model of a subject domain is considered. The notional model consists of a notional structure and a content of notions included in it. The notional structure describes each notion as a result of the generalization or association of other notions. Enumerable and solvable sets of entities define the content of the notions. The notional structure contains the knowledge inference rules and is considered a formal theory of the subject domain that preserves the truth of all the consequences deduced in it. Declarative knowledge is represented by the notions with an enumeration of the entities belonging to them. Procedural knowledge is defined using the knowledge description language, which describes the notions with the solvable sets of the entities and facts and queries to the knowledge base. The fact is a true statement with the logical connectives, parentheses, and elementary expressions: unary predicates of belonging to an entity to the notion, functors for extracting the entities from the composite entities, and relations between the entities. A query is expressed as a fact with free occurrences of an entity variable. The execution of the query creates the notion with the entities for which the fact is satisfiable. The knowledge language provides operations for creating, deleting, and changing the entities and conditional and loop operators. The use of the knowledge description language allows you to increase the expressiveness of presentation and the efficiency of processing “large” knowledge.
The Design and Implementation Strategy of Online Music Live Teaching System in Post-epidemic Era Based on Network Information Dissemination

With the rapid development of the Internet and information technology, the mode of information dissemination and its research paradigm have undergone tremendous changes. Based on network information dissemination technology, this paper analyzes the advantages of music online live teaching, and designs the music online live teaching system, including system architecture design, real-time message transmission protocol interaction process design, and live server subsystem design. The live server subsystem is used to receive the user's video service request, provide a real-time content stream in a suitable format, and implement real-time streaming control according to the user's request. In order to adapt to the online live music teaching in the post-epidemic era, an implementation strategy for online music live teaching is proposed to give full play to the functions of the direct teaching system.
ECUL-Miner: Efficiently mining high utility closed itemsets

High utility itemset mining (HUIM) provides more information, which can help manager to make better decisions. However, the results of HUIM are often huge sets, especially in dense datasets or small threshold. To solve this problem, this paper proposed an approach for mining high utility closed itemsets (HUCIs) efficiently with no loss of information, whose number of elements is significantly smaller than HUIM. This approach includes two phases: (1) proposing an extended utility list structure for storing itemset information in a compact form. (2) The proposed algorithms called ECUL-Miner utilize efficient data structures and backward and forward checking techniques to combine candidates. It also makes use of merging similar transactions to reduce storage space. We carry out experiments to show the efficiency of the proposed method. The experimental results show that this new approach is more efficient in than existing methods for both dense and sparse datasets.
WITHDRAWN: An ensemble practical method for text and Image-based steganography with the aid of AES encryption cryptography

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Teaching Reform of Network Courses Aiming at Student Management in Higher Vocational Colleges Based on Information Technology

With the continuous advancement of science and technology, network technology has developed rapidly and has penetrated its tentacles into every corner of life. In education technology, the development and application of network education technology and network education platform will not only help improve the teaching level accelerates the reform of the education system, and can make up for the shortcomings of current education. The purpose of this article is to study the reform of the online course teaching of student management in higher vocational colleges. This article firstly analyzes the core technology, database, model architecture, etc. of the network course based on theory, and then results in the current situation of modern network course teaching in our country, to carry out the network course teaching of student management in higher vocational colleges. Applied research, and explored its course teaching reform path. This article systematically expounds the principles, advantages and disadvantages of current online course teaching, and conducts experimental research on the improvement of online course teaching that informs college student management through research methods such as questionnaire surveys and logical analysis. The research shows that the reformed higher vocational colleges Student-managed online courses are more efficient and more feasible.
Improving Semantic Image Segmentation via Label Fusion in Semantically Textured Meshes

Models for semantic segmentation require a large amount of hand-labeled training data which is costly and time-consuming to produce.For this purpose, we present a label fusion framework that is capable of improving semantic pixel labels of video sequences in an unsupervised manner.We make use of a 3D mesh representation of the environment and fuse the predictions of different frames into a consistent representation using semantic mesh textures.Rendering the semantic mesh using the original intrinsic and extrinsic camera parameters yields a set of improved semantic segmentation images.Due to our optimized CUDA implementation, we are able to exploit the entire c-dimensional probability distribution of annotations over c classes in an uncertaintyaware manner.We evaluate our method on the Scannet dataset where we improve annotations produced by the state-of-the-art segmentation network ESANet from 52.05% to 58.25% pixel accuracy.We publish the source code of our framework online to foster future research in this area (https://github.com/fferflo/semantic-meshes).To the best of our knowledge, this is the first publicly available label fusion framework for semantic image segmentation based on meshes with semantic textures.
A Reverse Turing Like Test for Quad-copters

An important piece of information that anti-drone systems seek is whether a drone is autonomous or being operated by a human. In this work, we develop a reverse Turing test to find the answer. A reverse Turing test is when the roles of human and computer are reversed, with respect to the original Turing test. We develop a model using a neural network and features extracted from flights executed by an autonomous drone or controlled by a human operator. Every flight path taken requires the drone to avoid an obstacle that manifests certain attributes. Features from the flight are extracted to distinguish between an autonomous drone and a human operator. Our method achieves 97.8% accuracy, 91% precision and 72% recall, on an imbalanced data used for training and testing the model.
Depth-based fusion network for human attention prediction in 3D light field

The human visual attention mechanism promotes human to acquire the most important cues from large amount of information. However, most methods of simulating human visual attention now focus on 2D display, people know little about how human assign their visual attention under a 3D display. This paper firstly produced a saliency dataset consisting of human eye-fixation data for different 3D scenes under human quick glance, which demonstrated the human visual attention distribution. By analyzing the dataset, an approach based on convolutional neural network for human visual attention prediction under 3D light field display was proposed. The network is composed of three parts which are two-way feature extraction, feature fusion and prediction output. Comparing with saliency prediction models under 2D display devices, our proposed model can predict the distribution of human visual attention under 3D light field more accurately. This research promote further investigation of 3D applications such as 3D device evaluation and 3D content production.
Banknote Dirty Degree Identification Method Based on Texture Features of Banknote Images and Multi-layer Support Vector Machines

The dirty degree of banknots determines whether them can continue to circulate to some extent. In this paper, a banknote dirty degree identification method based on texture features of banknote images and multi-layer support vector machines (MLSVMs) was proposed. Based on the contact image sensor, the double-sided reflection images of banknotes in red, green, blue and infrared light, as well as images in green and infrared light transmission are collected. By analyzing the images formed by different dirty grades of banknotes under various light sources, the green reflection positive and negative images, blue reflection positive and negative images, green light transmission images and infrared light transmission images are finally selected for image texture feature extraction. Based on the gray level co-occurrence matrix(GLCM), the texture features such as energy, entropy and inertia were extracted to describe the visual features of banknotes dirty degree. Finally, MLSVMs was used to identify the dirty degree of banknotes, and simulation experiments results show the effectiveness of the proposed method.
Recent Advances and Challenges of Arabic Why Question Answering Systems

Arabic Question Answering Systems have gaining a remarkable significance through the increasing amount of the Arabic contents in the Internet and the growing demand for precise answers which cannot be offered by the regular information retrieval techniques. As the amount of research in Arabic Question Answering system is behind when it is compared to other languages, and handling non-factoid QASs is the not-trivial task in Natural Language Processing (NLP), it's not surprising that few researches built Arabic why question answering (whyQA) systems. This paper addresses the main challenges and gaps analysis for Arabic whyQA systems, and some future trends have been mentioned for the guidance of the new research in that area.
Multi-scale Mobile Phone Playing Behavior Recognition Based on Temporal Enhancement and Interaction

Analyzing students' classroom behavior is an important part of evaluating classroom teaching effects in the education field, and the behavior of using mobile phones is an important manifestation of students' learning status. Therefore, the status of using mobile phones in the classroom can reflect the effect of classroom teaching to a certain extent. This article establishes a video-based classroom student behavior data set, and divides the behavior categories in the data into two categories: mobile phone playing and other; analysis of students playing mobile phone and other behaviors reveals that there are subtle movements in student behavior and a certain visual tempo. The resolution is low, and the occlusion is serious. In response to the above problems, this paper proposes a multi-scale mobile phone playing behavior recognition method based on temporal information enhancement and interaction. First, use the motion enhancement module to enhance the motion information between two frames to improve the recognition ability of subtle actions; secondly, add the temporal pyramid to extract the multi-scale features of the action, and then obtain the visual tempo information of the video; finally add the temporal information interaction module to enhance the temporal dimension information interaction , To further model the temporal information. The experimental results on the self-made student action dataset StudentAction show that compared with the existing methods, the algorithm has significantly improved recognition accuracy and better solves the problem of low accuracy in the recognition of subtle actions. Good performance have shown on the public datasets HMDB51 and UCF101, indicating that the method has strong generalization ability and can adapt to the recognition problems of different scene actions.