Reviewerly/exHarmony huggingface dataset:
Adaptive Feature Extractor of Global Representation and Local Semantics for Text Classification

A hybrid model of Convolutional Neural Network (CNN) and self-attention has achieved remarkable results in text classification fields. In previous researches, text local semantics(captured by CNN) and global representation (extracted by self-attention) play equally important roles for each input. However, the importance of the two varies greatly with complex linguistic backgrounds. In this paper, we take an adaptive approach to automatically determine the contribution degree of each model to classification, according to specific structure and grammar information of the text. This strategy can make the most of two models. To better extract variable-size features of a word, multi-scale feature attention is introduced into our hybrid model. The attention focus on assigning larger weights to those multi-scale features that are important to a word. In addition, for fine-grained emotion classification tasks, a new type of loss function is also established. Experiment results show that, the proposed model makes noticeable improvements over hybrid models. Metrics are 0.3 to 1.5 percentages higher than previous methods. And results also prove that the new loss function further improves the performance of our model in all fine-grained emotion classification datasets.
Evaluating privacy during the COVID-19 public health emergency

The present article aims to discuss how governments have turned to biometric technologies to fight the spread of COVID-19, mainly through the adoption of facial recognition technologies, and the risks to people's privacy of inadequate measures to protect their personal data. We have identified seven systems from different countries (i.e., China, France, Israel, Poland, Singapore, South Korea, and Russia) that present some form of facial recognition during their operation and pointed out their functionalities and released information on safeguards for data protection. The data collected so far has shown that, in most countries, the necessary safeguards to protect people's privacy and their personal data in the short-term and long-term, are not receiving sufficient considerations.
Occlusion Detection for Dynamic Adaptation

Occlusion is a common issue for object detection and tracking applications using a remote sensor platform, especially in complex urban environments where occlusions from buildings, bridges, and trees are frequent events. While occlusions are unavoidable, the events can be predicted to occur before the object of interest is obscured if there is prior knowledge of the observed environment. To aid in object detection and tracking tasks, we create an environment to map terrain and find obscured regions in the scene which helps with re-detecting objects once they are no longer obscured. We propose a dynamic data driven applications systems (DDDAS) framework for detecting occluded regions in an imaged scene by integrating streams of real data with a physics-based simulation model that updates based on the most recent images.
Application of Cloud Class in Comprehensive English Teaching in the Context of Internet Plus

The development of information technology in college education is constantly changing the learning style of college students. The teaching mode based on internet plus background integrates two ways of Online Self-learning and Face-to-face Teaching Approach. It is the central research topic in the reform of professional English teaching. This paper will focus on how the cloud class education platform can be applied in Comprehensive English Teaching under the background of Internet plus. The results show that the model can motivate the learning enthusiasm of students, develop their self-study skills, and reinforce teachers’ role of guidance in teaching.
On the Complexity of Learning Description Logic Ontologies

Ontologies are a popular way of representing domain knowledge, in particular, knowledge in domains related to life sciences. (Semi-) automating the process of building an ontology has attracted researchers from different communities into a field called “Ontology Learning”. We provide a formal specification of the exact and the probably approximately correct learning models from computational learning theory. Then, we recall from the literature complexity results for learning lightweight description logic (DL) ontologies in these models. Finally, we highlight other approaches proposed in the literature for learning DL ontologies.
The flexible resource management in optical data center networks based on machine learning and SDON

Based on software defined optical network and machine learning, the flexible resource management mechanism (ML-FRM) is proposed, which meets the resource requirements of different services in the optical data center networks. The machine learning is integrated to the SDON controller, which accomplishes the resource allocation algorithms according to the classification and clustering results. ML-FRM firstly utilizes unsupervised learning K-means algorithm to cluster traffic flows, and uses supervised learning support vector machine (SVM) algorithm to realize hierarchical classification of channel qualities. Fragmentation-Function-Fit algorithm is proposed to reduce the blocking probability, the results show that it has the lower blocking probability than First-Fit and Exact-First-Fit algorithms. ML-FRM allocates the required resources through different algorithms based on different traffic flow clustering results, and uses different modulation methods for different channel qualities. The analysis results show that ML-FRM has lower blocking probability, acceptable complexity level, and higher spectrum resource utilization efficiency than other algorithms under different offered load level.
Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases

The recent developments and growing interest in neural-symbolic models has shown that hybrid approaches can offer richer models for Artificial Intelligence. The integration of effective relational learning and reasoning methods is one of the key challenges in this direction, as neural learning and symbolic reasoning offer complementary characteristics that can benefit the development of AI systems. Relational labelling or link prediction on knowledge graphs has become one of the main problems in deep learning-based natural language processing research. Moreover, other fields which make use of neural-symbolic techniques may also benefit from such research endeavours. There have been several efforts towards the identification of missing facts from existing ones in knowledge graphs. Two lines of research try and predict knowledge relations between two entities by considering all known facts connecting them or several paths of facts connecting them. We propose a neural-symbolic graph neural network which applies learning over all the paths by feeding the model with the embedding of the minimal subset of the knowledge graph containing such paths. By learning to produce representations for entities and facts corresponding to word embeddings, we show how the model can be trained end-to-end to decode these representations and infer relations between entities in a multitask approach. Our contribution is two-fold: a neural-symbolic methodology leverages the resolution of relational inference in large graphs, and we also demonstrate that such neural-symbolic model is shown more effective than path-based approaches.
Development Status and Trend of Indoor VR Technology Design Under the Background of Big Data

Abstract With the development of economy and society, virtual reality technology has been paid more and more attention and gradually applied to all walks of life. Due to its interactive, immersive and real-time nature, virtual reality has a broad prospect in the field of architecture, especially in interior decoration and design. The use of virtual reality interior design allows designers and users to experience effect of what they see is what they get, but also allows users to feel as if they are in the design of the virtual scene, immersive mood. The purpose of this paper is to study the development status and trend of indoor VR technology design under the background of big data. After reviewing the development background of virtual reality technology and its application status in interior design, this paper introduces the technology related to big data, studies the global lighting rendering technology, and then analyzes the interior design system based on big data and VR technology. The experimental results show that the interior design system designed in this paper is accurate. By comparing the detection results of the digital model with the real results, the error of the digital model is not more than 0.5.
Fashion Recommender Systems in Cold Start

With the rapid growth of online market for clothing, footwear, hairstyle, and makeup, consumers are getting increasingly overwhelmed with the volume, velocity and variety of production. Fashion Recommender Systems can tackle choice overload by suggesting the most interesting products to the users. However, recommender systems are unable to generate recommendation unless some information is collected from users. Indeed, there are situations where a recommender system is requested for recommendation while no or little information is provided by users (Cold Start problem). In this book chapter, we investigate the different scenarios where fashion recommender systems may encounter cold start problem and review approaches that have been proposed to deal with this problem. We further elaborate potential solutions that can be applied to mitigate moderate and severe cases of cold start problem.
Food Commodity Price Prediction in East Java Using Extreme Learning Machine (ELM) Method

Fluctuations in food commodities price in East Java Province cause various negative impacts when there are significant changes. To avoid this problem, it is necessary to predict food commodities prices to prevent high price increases. This study aims to apply the Extreme Learning Machine (ELM) method to predict the price of staple food commodities in East Java Province and measure the performance of the ELM in predicting staple food commodities price. The ELM is a method develop from feedforward Artificial Neural Networks (ANN) with one hidden layer or commonly called Single Hidden Layer Feedforward Neural Networks (SLFNs). The prediction process of staple food commodities is carried out using 3 data features, 7 neurons, and composition of training and testing data is 80%: 20%. The results showed that the average level of prediction accuracy for all staple food commodities was 98.79%. This shows that the prediction error is very low, ie the predicted results approach the actual value.
An ANN-Based Model to Artificially Transform a Floating Vessel into a Wave Monitoring Buoy

Abstract Floating platform offshore operations, such as load transfer from a supply boat and hydrocarbon offloading operations, are often limited by sea conditions. Access to sea condition information from a nearby wave/environmental monitoring buoy is not always available or there may be delays in data transmission. In the absence of reliable and accurate data from a nearby wave buoy, the motions of a floating platform/vessel may be used to estimate the sea conditions. This paper presents a method in using Artificial Neural Network (ANN) models to map the motions of a floating vessel to the wave elevations and to eventually estimate the significant wave heights of the sea the floating vessel is in. The ANN models can be trained using either: (1) measured data in the form of measured vessel motions and data from a nearby wave buoy, or (2) simulated data, i.e. vessel motions computed using numerical simulations. In this paper, demonstration of the ANN method uses simulated data under various sea conditions. The trained ANN models are tested for sea conditions that are not part of the training data, and the ANN predictions are found to be very close to the results calculated using numerical simulations. This is an important step to show that the trained ANN models have learned the presented information and can generalize the knowledge. The methodology presented in this paper may be used to establish an ANN model for estimation of the significant wave heights based on the measured motions of a floating vessel.
Dynamic Software Project Scheduling Problem with PSO and Dynamic Strategies Based on Memory

The Software Project Scheduling Problem (SPSP) aims to allocate employees to tasks in the development of a software project, such that the cost and duration, two conflicting goals, are minimized. The dynamic model of SPSP, called DSPSP, considers that some unpredictable events may occur during the project life cycle, like the arrival of new tasks, which implies on schedule updating along the project. In the context of Search-Based Software Engineering, this work proposes the use of dynamic optimization strategies, based on memory, together with the particle swarm optimization algorithm (PSO) to solve the DSPSP. The results suggest that the addition of these dynamic strategies improves the quality of the solutions in comparison with the application of the PSO algorithm only.
Research on Remote Measurement System Based on Computer Network

Abstract Rapid development in computer network and automated measurement technologies makes it possible to build a remote measurement system. This paper starts with the requirement of this system. Meanwhile, several different software implementation schemes and network security schemes, which are based on identity authentication and encryption technology, are proposed. In addition, a remote measurement system applying LXI and VPN technologies is implemented and tested. The results indicate that the requirements of both bandwidth and transmission latency can be met easily. The application of this technique is very promising considering the flexibility, cost reduction and other benefits it brings.
Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform

Recently, the development and utilization of industrial heritage resources by using big data has gradually attracted attention. This paper proposes a real-time visualization optimization management simulation of an industrial heritage cloud platform, which realizes the high reliability and diversified storage and utilization of industrial big data by the cloud data distributed storage subsystem. The big data prediction model of the GRU neural network based on a spark distributed framework is constructed to realize the prediction of industrial genetic data. Finally, visualization technology can provide information supporting for industrial production by displaying effective information intuitively. The model’s effectiveness and reliability are verified by simulation.
Automating Software Re-engineering

Software Engineering as a discipline and, in particular, as a research field within Computer Science, is still mainly focused on methods, techniques, processes, and tools to develop software from scratch. In reality, however, greenfield scenarios are not the most common ones. It is important to realize that dynamic evolution of software became a much more common and relevant issue in recent times, and its importance keeps growing. Software refactoring, parallelization, adaptation, therefore, become central activities in the value chain: automating them can realize huge gains. Formal approaches to software modeling and analysis are poised to make a substantial contribution to software re-engineering, because they are fundamentally concerned with automation and correctness. This potential, however, is far from being realized. Formal methods tend to aim at software development ab ovo or look at some piece of given software as a static object. This state of affairs motivated a track on Automating Software Re-Engineering, where we invited a group of leading researchers with an active interest in the automation of software re-engineering to discuss the state of the art.
Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions

Distance weighted discrimination (DWD) is an appealing classification method that is capable of overcoming data piling problems in high-dimensional settings. Especially when various sparsity structures are assumed in these settings, variable selection in multicategory classification poses great challenges. In this paper, we propose a multicategory generalized DWD (MgDWD) method that maintains intrinsic variable group structures during selection using a sparse group lasso penalty. Theoretically, we derive minimizer uniqueness for the penalized MgDWD loss function and consistency properties for the proposed classifier. We further develop an efficient algorithm based on the proximal operator to solve the optimization problem. The performance of MgDWD is evaluated using finite sample simulations and miRNA data from an HIV study.
Cashless Payment System Using RFID with NRF24L01 Communication

The cashless payment system has become one of the people's favorite choices because of the various benefits and conveniences that are obtained. One of the media that is often used today for transactions is an RFID card that contains several data. With only one media RFID card, we can do a lot of things as long as the data is centralized. This research aims to combine Internet of Things technology with payment applications that are generally used on the cashier side of shopping centers. The results of this research show that the system built is quite reliable to be used in communication lines with a range of fewer than 12 meters in a walled room and a distance of fewer than 60 meters outdoorsFile size after encrypting
Exploring Tangible VR as a Tool for Workplace Design

In this paper, we present a demonstrator that combines elements of physical prototyping and Virtual Reality. Our goal is to integrate VR in the design process of workplaces, not as a replacement of physical prototyping, but complementary to it. We call this approach Tangible Virtual Reality. We discuss our own background in embodied interaction, and present the context for our research: an industrial work cell for human-robot interaction, proposed by Audi Brussels, Kuka Belgium and FRS Robotics. We explain and illustrate how we used 3D CAD geometry to create a VR model and a physical prototype, and how we mapped them over one another. The result is a demonstrator that offers a VR experience, enhanced with real tactile information, and channeled by the natural limitations of the physical world. We suggest where the benefits of this physical/virtual design approach lay, and discuss how it could be operationalized in workplace design practice.
CommunityClick

Despite the lack of inclusive participation from attendees and civic organizers struggle to capture their feedback in reports, local governments continue to depend on traditional methods such as town halls for community consultation. We present CommunityClick, a community-sourcing system that uses modified iClickers to enable silent attendees' to provide real-time feedback and records meeting audio to capture vocal attendees' feedback. These feedbacks are combined to generate an augmented meeting transcript and feedback-weighted summary, incorporated into an interactive tool for organizers to author reports. Our field deployment at a town hall and interviews with 8 organizers demonstrate CommunityClick's utility in improving inclusivity and authoring more comprehensive reports.
Design of College English Culture Teaching Network Platform under the Background of the Differences between Chinese and Western Tea Culture

Abstract Chinese tea culture spread from the east to the west through the Western caravan in the early sixteenth century, and improved by the westerners for countless generations, forming a Western tea culture which is completely different from traditional Chinese tea culture today. Although there are great differences between the two cultures, each has its own irresistible charm. Based on the differences between Chinese and Western tea cultures, this article aims to study the design of a network platform for college English culture teaching under the background of the differences between Chinese and Western tea cultures. It aims to integrate the two into daily teaching so as to truly help students better understand the charm of English. On this basis, students can truly learn college English courses well. At the same time, they can appreciate the charm brought by the impact of the differences between Chinese and Western cultures and form their own positive cognition.
Research Review of Design Pattern Mining

Design pattern is widely used in the field of object oriented development. Mining design pattern examples in the source code is helpful to deepen the understanding of the program and facilitate later maintenance. Therefore, design pattern mining has become a hot topic in reverse engineering. In recent years, new research methods have emerged in the direction of design pattern mining, and the mining accuracy and time complexity have been improved. This paper classifies and summarizes the existing design pattern mining research work, analyzes and discusses the different methods, points out the advantages and disadvantages of the corresponding research, and prospects the future research. This review provides the latest research trends.
Twitter Comment Predictions on Dues Changes BPJS Health In 2020

The Social Security Administering Body (BPJS) is a facility established by The government in providing services to citizens in The field of health welfare. The Spirit of cooperation in the utilization of health services which is very much currently a constraint in the budget is still insufficient in covering health services as a whole. For this reason, government policy is following with PERPRES No. 75 in 2019, the Government officially raised the BPJS Health contributions for 2020. The increase in BPJS Health contributions certainly caused a lot of comments. Namely Twitter, one of the social media that is used by the public to express disapproval or support for this government policy. This study, testing was carried out related to the prediction of comments from social media on community responses to the increase in BPJS Health contributions taken by the government. In the test carried out 3 (three) input algorithms. For every single algorithm including getting results through the K-NN method with an accuracy of 71.83% and AUC value of 0812, for the Naïve Bayes method produces an accuracy of 81.63% and AUC value of 0586. As for the C 4.5 method, the accuracy is 65.37% and the AUC value is 0628. While testing conducted through the Ensembles Vote method which combines the 3 algorithms above gives the best results with an accuracy of 80.10% and AUC value is 0871 for Twitter comment predictions.
FLAVA

Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems. These LiDAR-based solutions are typically data hungry, requiring a large amount of data to be labeled for training and evaluation. However, annotating this kind of data is very challenging due to the sparsity and irregularity of point clouds and more complex interaction involved in this procedure. To tackle this problem, we propose FLAVA, a systematic approach to minimizing human interaction in the annotation process. Specifically, we divide the annotation pipeline into four parts: find, localize, adjust and verify. In addition, we carefully design the UI for different stages of the annotation procedure, thus keeping the annotators to focus on the aspects that are most important to each stage. Furthermore, our system also greatly reduces the amount of interaction by introducing a lightweight yet effective mechanism to propagate the annotation results. Experimental results show that our method can remarkably accelerate the procedure and improve the annotation quality.