Reviewerly/exHarmony huggingface dataset:
An Unified Model by Combining Multiple Methods for Integrating Constituent into Chinese Semantic Role Labeling

Recently, Chinese Syntax-aware Semantic Role Labeling (SRL) has attracted the attention of many researchers because syntax information is related to semantic role labeling intuitively. In this paper, we integrate constituency representation into SRL by combining multiple methods into a unified model. Specifically, our proposed model combine three methods for integrating constituent into SRL, including pipeline (use syntax structure), hard parameter (share encoder), implicit representation integration (not share encoder). We verify the effect of our model on Chinese Proposition Treebank (CPB) 1.0 dataset and conduct some ablation experiments to verify the impact of various parts of the model.
Improving the Quality of Requirements Engineering Process in Software Development with Agile Methods: a Case Study Telemedicine Startup XYZ

XYZ is a telemedicine startup company that uses Scrum, an Agile methodology, as a software development method. The company uses Scrum to deliver good products and services to customers on time, even with limited requirements. However, the company faced problems in product development, so the company could not release the product on time. Based on interviews with internal experts, the company has issues determining the priority scale of requirements and change requirement management, where both problems are part of requirement engineering. This study discusses the problems of the company XYZ regarding the requirement engineering process, especially the problem of determining the priority scale of requirements and change requirement management. This research uses the CMMI-Dev 1.3 specific process area to assess the requirement engineering process in the company using sub-specific practices in the Requirement Development (RD) and Requirement Management (REQM) process areas. Researchers conducted interviews with product managers to appraise sub-specific RD and REQM practices with current activities in the company. The results of this study are recommendations to improve the quality of the requirement engineering process by using Objective Key Result (OKR) and Action Priority Matrix (APM).
Engaging with the IoT Sandbox - Designing with Systemic Openness

This studio revolves around designing with systemic openness and introduces the IoT Sandbox as a tool to design with systemic openness through multi-activity and multi-person scenarios. The studio knows three learning goals: (1) how to characterize and facilitate a design challenge with systemic openness, (2) how to design with multi-activity and multi-person scenarios, and (3) how to design for diverse interaction styles in a systems context. In a hands-on manner the fundamental concepts and challenges are introduced and experienced through a series of short design sprints. The studio ends with a reflective discussion on the propositions created by the participants, in relation to the learning goals as well as the studio and the IoT Sandbox platform.
Graph Wasserstein Autoencoder-Based Asymptotically Optimal Motion Planning With Kinematic Constraints for Robotic Manipulation

This paper presents a learning based motion planning method for robotic manipulation, aiming to solve the asymptotically-optimal motion planning problem with nonlinear kinematics in a complex environment. The core of the proposed method is based on a novel neural network model, i.e., graph wasserstein autoencoder (GraphWAE) network, which is used to represent the implicit sampling distributions of the configuration space (C-space) for sampling-based planning algorithms. Through learning the implicit distributions, we can guide the planning process to search or extend in the desired region to reduce the collision checks dramatically for fast and high-quality motion planning. The theoretical analysis and proofs are given to demonstrate the probabilistic completeness and asymptotic optimality of the proposed method. Numerical simulations and experiments are conducted to validate the effectiveness of the proposed method through a series of planning problems from 2D, 6D and 12D robot C-spaces in the challenging scenes. Results indicate that the proposed method can achieve better planning performance than the state-of-the-art planning algorithms. Note to Practitioners—The motivation of this work is to develop a fast and high-quality asymptotically optimal motion planning method for practical applications such as autonomous driving, robotic manipulation and others. Due to the time consumption caused by collision detection, current planning algorithms usually take much time to converge to the optimal motion path especially in the complicated environment. In this paper, we present a neural network model based on GraphWAE to learn the biasing sampling distributions as the sample generation source to further reduce or avoid collision checks of sampling-based planning algorithms. The proposed method is general and can be also deployed in other sampling-based planning algorithms for improving planning performance in different robot applications.
BERTina: A BERT-powered HIV/AIDS Question Answering Tool

The HIV/AIDS (Human Immunodeficiency Virus/Acquired Immuno-Deficiency Syndrome) pandemic is still prominent in the global South. However, the proper messaging concerning this pandemic is still a challenge; especially in resource-limited countries. Fortunately, the use of Information and Communication Technology (ICT) can assist in resolving this problem. In this paper, we propose a Natural Language Processing (NLP) tool called BERTina, which can be used as an HIV/AIDS question answering tool. The utility of the BERTina tool is underlined by the robustness of this tool against adversarial questions.
A Novel Contrast Operator for Robust Object Searching

In this paper, a novel operator called contrast value operator <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\left(cvo\right)}$</tex> was proposed for robust object searching. The proposed operator can effectively extract the texture information from images and represent finer structures of object searching, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${cvo}$</tex> can be used in template matching algorithms. For example, in Normalized Cross-Correlation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\left(NCC\right)}$</tex> , <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${cvo}$</tex> can significantly improve the robustness of the algorithm. The structure and realization of cvo is effective and can be used in a variety of industrial applications such as defect detection, object searching, work-piece recognition, etc. The properties of the work were verified under the datasets with different challenges, and the experimental results proved the success of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${cvo}$</tex> .
A Simulation of Smart Pelican Crossing with Python

We develop a virtual simulation system of a smart pelican crossing on PC. First, a snapshot of the specified monitoring area is taken with a webcam on PC frame by frame. In order to detect motion, next, the following image-processing is done for each frame by Python-OpenCV package: frame difference, grayscale conversion, Gaussian blur, image segmentation, image dilation, finding counters, and drawing rectangles. Then, traffic and pedestrian lights change on PC if motion is detected. Also, we measure the execution time for one frame by the use of Python time library. It is 0.02041 seconds with the microprocessor Intel Core i3 on Dell Latitude 3580, which means that the load of the developed simulation system is not so heavy on PC. Therefore, the simulation runs smoothly.
Chapter 15. Exploration of the Rhapsodie corpus

This chapter describes the data structure of the Rhapsodie Treebank and discusses methodological issues stemming from the complexity of this structure, articulated around three independent, non-aligned, hierarchies: Microsyntactic, macrosyntactic and prosodic, and the challenging questions to be resolved in this context. It discusses the specific problems posed by the simultaneous processing of the phonological stream (prosodic level) and the orthographic stream (syntactic level), which are often far from being isomorphic in French, and the related problem of the processing of disfluent and/or overlapped strings, which have not the same representation in the syntactic and the prosodic hierarchy. Then, it presents the formats adopted to encode prosodic and syntactic annotations and query them simultaneously, given that the prosodic architecture is a non-recursive time-aligned representation while the syntactic one is a recursive tree-based representation.
Some aspects of the university education crisis, the need for demarginalization of humanities and social sciences and the renewal of the role of intellectuals today

The paper compares some aspects of the crisis of education and science in modern times, with special reference to the reform called the "Bologna Process". The author points out negative tendencies of excessive partialization of the higher education system, in the form of disciplinary chaos and marginalization of humanities and social sciences, instead of its integration in the era of globalization. Criticizing the emergence of "distorted modernization" and instrumental rationality in modern civilization, the author presents his commitment for the demarginalization of humanities and social sciences, and the reaffirmation of the vocation of the intellectual. In this context, the author supports the emergence of a new journal "Social Horizons" in our country, expecting that it will grow into a strong theoretical body for renewing critical thinking and developing civic consciousness in the service of strengthening participatory democracy, sustainable development and progress of the Serbian society.
Moving Target Depth Information Extraction Based on Nonlinear Strategy Network

To improve the effect of depth information extraction of moving targets in the network, a nonlinear strategy-oriented method is proposed. With the advancement of science and technology, especially in wireless networks, a large amount of data is provided to people every hour of every day. Hence, it can increase the demand for data analysis tools. Nonlinear system modeling by using rough set theory to extract valuable information from large amounts of information, and then through the analytic hierarchy process (ahp) to determine the effect of input factors, then use particle swarm optimization algorithm (PSO) to find the accurate function, and USES the adaptive and population catastrophe and vaccine algorithm to make it to the local optimum, to achieve the aim of the complex. The experimental results show that, compared with M2 and M1 for 30 groups of samples, the model obtained by using M2 has a better fitting effect on the actual curve. The error of M2 is within ±3%, and the error of M1 is within ±6%, and the error is relatively large. The accuracy of the proposed method is higher than that of the neural network method, which proves that the nonlinear strategy is effective in the actual target depth information extraction.
Research and Application Demonstration of Predictive Maintenance Cloud Platform for Building Intelligent Perception Equipment

At present, Chongqing is vigorously promoting the construction of a new smart city, and continuously promulgating new documents to promote the orderly construction of the Internet of Things in urban infrastructure, and provide key basic data support for the Chongqing City Information Modeling (CIM platform). After years of development, the city&#x0027;s urban infrastructure has achieved good results in terms of quantity and quality. However, for the important sensing equipment (elevators, security, temperature and humidity sensors, biogas concentration, etc.) that are related to the society and people&#x0027;s livelihood in the building, and There is no unified collection and monitoring, but to promote the smart construction of the community, the collection and utilization of public equipment data are important evaluation indicators. We will not only establish a Chongqing smart community management platform, but also display the statistical information of important equipment data online, The real-time status of equipment, historical data recording, utilization of equipment data and other functions must also be actively improved, so as to vigorously support the development and use of the CIM platform.
Mobile Learning and Indigenous Education in Canada

M-Learning holds great potential for supporting the positive educational outcomes of underserved Indigenous communities in the Candian North, and even in urban centers, that are at risk of exclusion from affordable, high-quality learning experiences. The technical advantages of having mobile technology to deliver educational curricula and assess outcomes, however, must not overshadow the continuing need for culturally relevant teaching modalities that work for Indigenous learners. When used innovatively, mobile learning can be integrated successfully into a context of existing practices, beliefs, experiences, and values related to Indigenous epistemologies and pedagogies. These mobile technologies are not only helping Indigenous learners to develop new media aptitudes, they are providing an opportunity for learners and instructors to develop stronger links between formal and informal learning opportunities, building on the inherently mobile and contextual traditions of Indigenous peoples.
Optimizing Off-Chip Memory Access for Deep Neural Network Accelerator

Off-chip memory, such as DRAM, its access energy cost is orders of magnitude higher than other operations such as multiply and accumulate, thereby dominating the system energy consumption. Therefore, optimizing the access of the off-chip memory is crucial to further improve the energy efficiency of the deep neural network (DNN) accelerator. Towards this, this brief proposed an adaptive scheduling algorithm to minimize the DRAM access. Compared with the previous works, it can not only dynamically determine the data partition and the data type that will be reused, but also considered the constraints between adjacent layer, that is, if the output feature map of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> th layer is divided into N parts, the output feature map of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i+1$ </tex-math></inline-formula> th layer can only be divided into N parts or write back to the off-chip memory. Therefore, a minimize and realizable memory access solution can be obtained. Choosing 3 popular networks UNet, VGG-16 and MobileNet as benchmarks, the experiment results show that our scheduling algorithm can achieve a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$34.46\% \sim 93.42\%$ </tex-math></inline-formula> reduction in energy consumption of DRAM access and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$34.34\% \sim 93.37\%$ </tex-math></inline-formula> reduction in DRAM access latency when compared to previous works.
UI Analysis and Redesign of A Simpul Seribu Website Using Cognitive Walkthrough Method

Simpul Seribu is a website that is managing by the Thousand Islands Marine National Park Authority. The Simpul Seribu has been used to apply for SIMAKSI since 2018. More than 245 SIMAKSI have been an issue. The purpose of this research is: 1) to identify and analyze problems for SIMAKSI users, and 2) to provide recommendations on the website interface to make it more user-friendly. The research method used is Cognitive Walkthrough to test usability by performing a series of task scenarios. The usability test component includes five aspects as 1) effectiveness, 2) some errors, 3) task time, 4) overall relative efficiency, and 5) task-level satisfaction to test the evaluation of data processing results based on respondents. This study analyzes the interface design on the Simpul Seribu website using the cognitive walkthrough (CW) method. Based on the results of the research tests, there are no respondents who could complete all of the given task scenarios, especially for task scenario 2. There was the highest number of errors of 114 errors and the lowest efficiency level of 22%. The results of this study are in the form of recommendations for improvements and mock-up designs to improve the quality of the Simpul Seribu website.
IoT Sensor Systems Mapping Quality Parameters Using Cloud Computing

Internet of Things (IoT) is an emerging field with a worldwide system of multiple smart devices with the ability to sense and connect with the surroundings while interacting with the users and other systems. To get reading from a wide geographical area, IoT sensors are best suited for this task. IoT sensor systems have made their way into many daily life technologies. The cloud Service helps in creating excessive communication between the inexpensive sensors in the IoT helping in achieving even greater connectivity. The focus of the research has been on the role of cloud computing with that of Internet of Things. A framework is proposed to analyse the quality parameters. The data gathered from the IoT devices is passed on to the cloud platform. The cloud platform in the proposed research will focus on using it as a service. Machine learning technique is implemented by taking data stored in the cloud. The dataset of diabetes is considered for prediction analysis by applying multiple machine learning techniques, to generate the probability of the onset of diabetes.
Introduction

Digital technologies and their role in the everyday life and learning of present-day teenagers are the main topic of this book. The content is based primarily on the results of a research project conducted over several years in which this topic was the central focus. We introduce the research design and project methodology in more detail in this chapter. In the course of writing of this book, we also built on our previous studies concerning digital technologies in education. These include studies on digital technologies in the life of primary schools and in the work of principals and teachers in primary schools (Hrtoňová et al., 2015; Zounek, 2006; Zounek & Šeďová, 2008; Zounek & Šeďová, 2009) and studies focused on the problems of e-learning in general (Zounek et al., 2016). We also followed up on studies focused on the options for researching learning in various online educational environments (Juhaňák & Zounek, 2016, 2019). This book can therefore be understood as a further research path for our focus, as we have focused on digital technologies only in connection to formal education in our previous studies. In this respect, this research represents not only a follow-up to our existing line of research projects and topics, but also, in many aspects, an expansion of the research field.
Two Test Assembly Methods With Two Statistical Targets

In educational measurement, exploring the method of generating multiple high-quality parallel tests has become a research hotspot. One purpose of this research is to construct parallel forms item by item according to a seed test, using two proposed item selection heuristic methods [minimum parameters-information-distance method (MPID) and minimum information-parameters-distance method (MIPD)]. Moreover, previous research addressing test assembly issues has been limited mainly to situations in which the information curve of the item pool or seed test has a normal or skewed distribution. However, in practice, the distributions of information curves for tests are diverse. These include multimodal distributions, the most common type of which is the bimodal distribution. Therefore, another main aim of this article is to extend the information curves of unimodal distributions to bimodal distributions. Thus, this study adopts simulation research to compare the results of two item, response, theory (IRT)-based item matching methods (MPID and MIPD) using different information curve distributions for item pools or seed tests. The results show that the MPID and MIPD methods yield rather good performance in terms of both two statistical targets when the information curve has a unimodal distribution, and two new methods yield better performance than two existing methods in terms of test information functions target when the information curve has a bimodal distribution.
Gradual Control Method for Program File Placement in Hierarchical Cloud-Edge Platform

This paper proposes a gradual program file placement control method using intermediate solutions output by an optimization solver. The computation resources in the platform can be efficiently utilized by reconfiguring the program file placement following the intermediate solutions. We present two policies, which differ on when to reconfigure the platform using intermediate solutions. Simulation results show that the proposed method achieves less cumulative number of program files placed in the platform, in exchange for the increase in the reconfiguration cost.
HarDNet-PSA: A High Precision Polyp Detection Neural Network with Attention Module

Colorectal cancer is a common malignant tumor in the gastrointestinal tract. Regular colonoscopy to identify and remove polyps can help reduce the risk of the disease. With the development of computer image processing technology, the way of identifying intestinal polyps has developed from manual examination to machine recognition, with higher accuracy. Based on a convolutional neural network HarDNet-MSEG with low memory flow and high precision, a network HarDNet68 with high inference performance is used as the backbone, and three Receptive Filed Blocks and three Polarized Self-Attention modules are used for decoding in the U-shaped encoder-decoder architecture. By maintaining a relatively high resolution in the space and channel dimensions and combining the nonlinear functions Softmax and Sigmoid, the model will obtain better performance on pixel-wise task. The model is trained with Kvasir data set. Through experimental verification, the index Mean Dice of HarDNet-PSA on Kvasir is 0.911. In addition, HarDNet-PSA has excellent performance in meanIoU, MAE, Sm and meanEm.