Reviewerly/exHarmony huggingface dataset:
Chapter 10. Semantic role labeling

We investigate the feasibility of automatic semantic role labeling (SRL) using Swedish FrameNet (SweFN). In the first part of the chapter, we describe a baseline system using a traditional division into segmentation and labeling steps. These subsystems are implemented as separate machine learning models, and we explore a wide range of syntactic and lexical features for these models. In the second part, we turn to the question of how the frame-to-frame relations defined in FrameNet allow us to use the annotated examples more effectively. The cross-frame generalization methods reduce the number of errors made by the labeling classifier by 27%. For previously unseen frames, the reduction is even more significant: 50%.
Automatic Generation of Work Support Behavior with Smart Glasses based on the Deep Neural Network Corresponding to Encrypted Training Data

In Japan, which has a labor shortage problem, Smart Glasses can be a powerful tool for realizing work support. However, depending on the industry field, the types of work to be supported are enormous, and designing each one is a heavy burden for the designer. Therefore, in this research, we have investigated a method for automatically generating the support behavior of the system. Here, the method by using deep learning technology that enables end-to-end learning has been proposed so that the support behavior of the system can be obtained without specialized knowledge. However, in order to use deep learning technology, it is necessary to prepare an expensive computer environment. On the other hand, many companies have started services that provide deep learning environments that can be used in the cloud. When these services are used, training data must be sent to cloud. There are cases when this action may lead to an information leak and, from this perspective, many companies are resistant to using the cloud. Therefore, in this paper, we verify the effectiveness of the learning data encryption process for the proposed support behavior modeling method. Here, the pixel-based image encryption method proposed in the previous research is applied. As a result of the experiment, it was confirmed that the obtained support behavior model has same model structure and support behavior regardless of the presence or absence of encryption processing.
Research on the Influence of Dehazing Algorithm on YOLOv3 Target Recognition

The YOLOv3 target recognition algorithm has a wide range of applications in various fields. Under the haze conditions, the recognition effect of the YOLOv3 algorithm is affected, and its mAP value decreases significantly. To solve this problem, it can be improved by adding a dehazing algorithm before the recognition algorithm. In this paper, three commonly used dehazing algorithms are studied, and they are combined with YOLOv3 algorithm to perform target recognition experiments on hazing images. Solve their mAP values separately and compare them. The results show that all the three dehazing algorithms can improve the target recognition ability, and the Retinex algorithm works best.
Q-Learning-Based Obstacle Avoidance Path Planning with Local Optimal Points

Conventional obstacle avoidance path planning based on Q-learning often results in the agent falling into the local optimal point (LOP). Although such conventional path planning methods have been used in a variety of maps, complications that prevent successful navigation around obstacles occur. Thus, to improve the success rate of obstacle avoidance path planning, this paper introduces the detection of the LOP in the navigation process. Two auxiliary methods based on changing the temporary goal of the agent are proposed for escaping the LOPs. Using the proposed methods, an improved obstacle avoidance path planning algorithm is simulated, resulting in a better outcome.
The Content And Practical Significance Of The Principles Of Academic Independence In The Management Process Of Higher Education

The article analyzes the content and practical significance of the principles of academic independence in the management of higher education and the socio-economic significance of improving the quality and activity of services in the market of educational services based on the results of marketing research. In general, the concept of marketing research has the same meaning as the term “market research”. That is, it includes the state of the market, the assessment of goods and services, the analysis of key trends, stages of development, the study of local and regional markets.
Framework for Real-Time Parallel and Distributed Natural Language Processing

In this paper, we present a new framework for parallel and distributed processing of real-time text streams capable for executing NLP-Natural Language Processing algorithms. The focus is set on acceleration based on attention for building the topology, and not on the individual NLP algorithms. We elaborate the configuration of our specific use case and discuss the reduction of the time required for system configuration in order to use the benefits of virtualization and containers. Research hypothesis: We can process more text tuples per unit time using the newly developed framework for an algorithm that divides the sequential algorithm into smaller jobs and tasks including tokenization, part of speech tagging, stopwords, sentiment analysis, where each of these individual jobs are specific nodes in the Apache Storm-based topology. We have conducted an experimental proof-of-concept and found the optimal configuration confirming the validity of the hypothesis.
Nonderived environment blocking and input-oriented computation

Abstract This paper presents a computational account of nonderived environment blocking (NDEB) that indicates the challenges it has posed for phonological theory do not stem from any inherent complexity of the patterns themselves. Specifically, it makes use of input strictly local (ISL) functions, which are among the most restrictive (i.e., lowest computational complexity) classes of functions in the subregular hierarchy ( Heinz 2018 ) and shows that NDEB is ISL provided the derived and nonderived environments correspond to unique substrings in the input structure. Using three classic examples of NDEB from Finnish, Polish, and Turkish, it is shown that the distinction between derived and nonderived sequences is fully determined by the input structure and can be achieved without serial derivation or intermediate representations. This result reveals that such cases of NDEB are computationally unexceptional and lends support to proposals in rule- and constraint-based theories that make use of its input-oriented nature.
Clustering The Quality Level of Lecturer Teaching Using The K-Means Method

Universities require faculty who have the quality in the process of learning (PBM). Therefore, it is necessary to level the quality of teaching faculty grouping which aims to assist the academic authorities in determining the quality of teaching faculty decision. Material treated in this study were 104 faculty assessment in the form of questionnaires in the academic year 2018/2019 are sourced from the student and processed using the k-means and then implemented using rapid miner software. The results of testing the accuracy of clustering quality of faculty performance against this method is 100%. The results can be concluded from this study is very precise k-means is used to classify the level of quality of teaching faculty.
A DWT and Coefficient Exchange Block Based DCT Image Watermarking and their Comparative Assessment

In the cyber world to protect any product of human intellect from the unauthorized user one of the most widely used security technique i.e., digital watermarking comes into play. This paper produces a detailed comparative analysis between DCT and wavelet-based watermarking technique based on different watermarking evaluation parameter. The exhaustive comparison begins with embedding a watermark into all three different level frequency coefficients of DCT technique based on the coefficient exchange method and evaluates their performance against many common attacks. All four sub-bands of 1 level of DWT are considered for embedding a watermark. The paper also emphasized JPEG based comparison between selected bands of two approaches on various quality factors (QF). The best band are selected from these two techniques for further contrastive analysis using measuring matrices like PSNR and NCC. Experimental results and simulation of the system are based on the MATLAB tool. Different graphs and observation tables are also explained in this process to clearly visualize the comparison results.
Building scalable variational circuit training for machine learning tasks

Parameterized quantum circuits (PQC) have emerged as a quantum analogue of deep neural networks and can be trained for discriminative or generative tasks and can be trained with gradient-based optimization on near-term quantum devices [1], [2], [3]. In the current era of quantum computing, known as the noisy intermediate scale quantum (NISQ) era [4], these devices contain a moderate number of qubits (< 100), and algorithmic performance is strongly impacted by hardware noise. Additionally, the training of PQCs are hybrid algorithms, in which the computational workflow is split between quantum and classical computing platforms.
Uso de Moodle para fortalecer competencias ciudadanas en estudiantes de fisioterapia: un diseño pretest-postest

espanolIntroduccion: Moodle es una tecnologia de informacion y comunicacion de aprendizaje ampliamente utilizada en todo el mundo y se ha utilizado poco para fortalecer las competencias ciudadanas. Objetivo: Mejorar las competencias ciudadanas a traves de una estrategia pedagogica en la plataforma Moodle en estudiantes registrados en los cursos formativos de practica clinica/administrativa de fisioterapia de la Universidad de Santander, Bucaramanga, Colombia. Metodos: Se realizo un diseno pretest-postest. La intervencion educativa consistio en una actividad pedagogica disponible en linea las 24 horas del dia, en Moodle durante 10 semanas. Una muestra de 41 estudiantes cumplio con un cuestionario antes y despues de la intervencion educativa, que consistio en 25 preguntas distribuidas en cinco dimensiones: constitucion politica, multiperpectivismo, pensamiento sistemico y reflexivo, argumentacion y etica. El puntaje total por dimension oscilo entre 0 a 100. Resultados: El puntaje promedio de conocimiento fue 60,8 ± 12,9 en la prueba previa y 65,1 ± 15,4 en la prueba posterior (p = 0,0665). Despues de la intervencion educativa, hubo cambios positivos en ocho de las 25 preguntas (p EnglishSummary. Introduction: Moodle is a learning information and communication technology widely used worldwide and has been little used to strengthen citizenship competencies. Objective: To improve citizenship competencies through a pedagogical strategy in the Moodle platform in students registered in the training courses of clinical/administrative physiotherapy practice at the University of Santander, Bucaramanga, Colombia. Methods: A pretest-posttest design was carried out. The educational intervention consisted of a pedagogical activity available online 24 hours a day in Moodle for ten weeks. A sample of 41 students completed a questionnaire before and after the educational intervention, which consisted of 25 questions distributed in five dimensions: political constitution, multi-perspective, systemic and reflective thinking, argumentation, and ethics. The total score per dimension ranged from 0 to 100. Results: The average knowledge score was 60.8 ± 12.9 in the pretest and 65.1 ± 15.4 in the posttest (p = 0.0665). After the educational intervention, there were positive changes in eight of the 25 questions (p
Formation of Design Skills of Schoolchildren As a Meta-Subject Learning Outcome

In the article, design skills are considered as one of the important meta-subject results of the chemical education of schoolchildren. On the example of designing devices, the main stages of their formation in the process of performing educational and research activities are revealed.
Histogram Publishing Method based on Geometric Mechanism

Histogram publishing method based on differential privacy has been widely concerned. However, most histogram publishing methods are based on the Laplace mechanism, which can not effectively avoid floating-point approximation attack. To address this problem, this paper proposes a histogram publishing method based on the geometric mechanism, and each bin of histogram is counted by an integer. Besides, the dynamic programming grouping strategy is adopted to effectively reduce the histogram publishing error in long queries.
Underwater Image Enhancement Method Based On Color Correction and Dark Channel Prior

Abstract Concerning to the problem in the distortion of color and the low contrast of underwater image, the image enhancement method in the underwater environment based on color correction and dark channel prior was proposed. When dealing with the color bias problem, the blue channel standard ratio is firstly calculated based on the blue channel, and the red and green channels of the underwater image are compensated to remove the blue and green background colors of the underwater image. In light of the problem in the low contrast of image in underwater environment, the dark channel prior (DCP) method based on the super pixel was used to enhance the corrected underwater image. Finally, the underwater object detection dataset images are tested, and the algorithm proposed in terms of the quality is made the comparison with six advanced image enhancement method in underwater environment. The experimental results show that the proposed algorithm earned the highest score in underwater quality evaluation index (UIQM) compared with the above algorithm.
Sensor Based Intelligent Measurement and Blockchain in Food Quality Management

Today’s information and communication technologies (ICT) which include interdisciplinary science integration, which meets applications in food manufacturing, are used for production processes and quality management. Food production and quality management are ever-developing together with the development of metrology, sensor-based measuring instruments, and ICT technology which is used to design for the evaluation. Quality management is starting from measuring the accurate and reliable data from the production. And it continues with the transfer/store of the collected data in a secure chain. The data that collected from the food production process transform an information by the processing operations that contain artificial intelligence or statistical prediction system which helps the decision makers in food production management. This information shared with the experts and quality managers for the monitoring the system. This sharing process need to be done in secure way to keep safe the system from foreign/internal intervention. Blockchain is a secured and distributed database solution that provides decentralized management of transaction data. Blockchain application structure focuses on three different structures: Blockchain ledger, Blockchain network and stakeholders. With the help of these structures, Blockchain applications have security, privacy, efficiency, performance, usability, data integrity and scalability features. With this study, we aim to further consolidate the quality processes by aiming to include the Blockchain technology, which has the specified features, with the quality processes. Our designed model encompasses; wireless sensor network (WSN) which using for collecting, processing the sensor-based measuring data of the condition of a food production facility, and distributing the information using by blockchain technology. This approach targets; higher secured monitoring system, progress further reliable quality management and efficiency.
Automatic Flash Tool for Mobile Devices

Operational Systems (OS) can be roughly seen as a bunch of code that is usually integrated to become a system image, vendors must be careful with the integration process to ensure quality control. To achieve that, they also need to provide an efficient management system, one of the most popular tools available on the market is the JIRA. Besides management, the size of an OS must be taken into consideration, in particular, OS images from mobile devices are significantly large to compile from a local machine, one way to overcome this matter would be investing in remote build systems such as QuickBuild(QB) which provides a fast and distributed build platform. With the built final OS image is necessary to perform tests on the target device. This paper focuses on Android Mobile Devices OS images and proposes an automated flash tool that will acquire a task, search for related task OS image, download the target OS image and finally flash it to the mobile device. This tool will handle the mentioned management tool and also the QuickBuild remote build system, to automate several manual tasks done by a user. We successfully made this tool work in the proposed scenario which reduced time spent on manual tasks and also contributes to team task's time effectiveness.
Work system method as an e-learning design framework in private universities

Abstract The development of information and communication technology, especially the internet provides opportunities to improve the ability of lecturers to deliver courses to students without being limited by distance and time. This can be done by utilizing e-learning which will accelerate the transfer of knowledge and skills. E-learning is distance education that utilizes various digital tools and platforms. These tools and platform are used to support interaction, lectures and delivery of instructions between lecturers and students. This study will discuss the use of work system methods and the use of e-learning technology in a business entity. The work system methods are used to design information systems. Meanwhile e-learning technology in a business entity serves lecturers and students, conduct lectures at private universities in increasing knowledge dissemination, educational affordability and the use of technology and information as its support. The result of this research is the works system method model which refers to the analysis of data from the internet accessibility survey of students, analysis of its components, the system development cycle, the revision of the vision, mission and resources in private universities and recommendations of opportunities for the development of higher quality e-learning implementation.
The Construction the Intelligent Classroom Teaching Mode based on Effective Teaching Interaction

In the intelligent classroom, some advanced information technology is used to create a smart classroom teaching environment. In recent years, in this new form of classroom, how to achieve effective teaching interaction between teachers and learners, teaching resources and teaching environment has attracted more and more attentions. Based on the construction theory of intelligent classroom and the application of effective teaching interaction in higher vocational colleges, this paper analyzes the problems existing in the process of daily teaching based on intelligent classroom in higher vocational colleges. On this basis, this paper puts forward the general solutions to achieve effective teaching interaction in the intelligent classroom scene. Furthermore, the information technology and teaching interaction are deeply integrated, and a intelligent classroom mode based on effective teaching interaction is designed to promote effective learning of students in higher vocational colleges.
Post COVID-19 Work Transformation Behavior for Optimum Performance in the Public Sector in the Kingdom of Bahrain: A Design Thinking Approach

The COVID-19 pandemic has brought changes in the administrative routine in the public sector. New norms at workplace have taken place. A portion of employees are required to work from home (WFH) due to the precautionary measures. WFH implied that communication was heavily relying on technological mediated platforms. The new working setup have posed a new challenge to supervisors in distributing tasks to employees. This study aims to investigate the challenges in maintaining and optimizing the performance of public sector during this new norm. Six online semi-structured interviews were conducted using Microsoft Teams with supervisors and employees from public sector to have a better understanding of this challenge. The analysis showed that organizational factors, supervisor factors, peer factors and infrastructure factors played a significant role in determining the efficiency of task distribution. Based on the findings, it is recommended to set up a detailed WFH policy as well as utilizing Microsoft Office Planner to address the problem.
Software Redocumentation Using Distributed Data Processing Technique to Support Program Understanding for Legacy System: A Proposed Approach

Source code is the most updated source among all the available software artifacts. The majority of existing software redocumentation approaches relied on source code to extract the necessary information for program comprehension in order to support software maintenance tasks. However, performing Extract, Transform and Load (ETL) using a parser from the source code becoming a challenging task. The traditional approach is no longer able to handle the ETL efficiently due to the effect of the analysis efficiency, especially for large source code. This paper proposed to use distributed data processing technique to extract legacy source code components to generate detailed designed or technical software documentation at source code level to support program understanding. The objective of this paper is to apply the distributed data processing technique to the parser by using Hadoop Distributed File System and Apache Spark. Legacy java source code used as a case study to apply our proposed approach to extract the source code components and generate the technical software documentation.
Understanding the Energy vs. Adversarial Robustness Trade-Off in Deep Neural Networks

Adversarial examples, which are crafted by adding small inconspicuous perturbations to typical inputs in order to fool the prediction of a deep neural network (DNN), can pose a threat to security-critical applications, and robustness against adversarial examples is becoming an important factor for designing a DNN. In this work, we first examine the methodology for evaluating adversarial robustness that uses the first-order attack methods, and analyze three cases when this evaluation methodology overestimates robustness: 1) numerical saturation of cross-entropy loss, 2) non-differentiable functions in DNNs, and 3) ineffective initialization of the attack methods. For each case, we propose compensation methods that can be easily combined with the existing attack methods, thus provide a more precise evaluation methodology for robustness. Second, we benchmark the relationship between adversarial robustness and inference-time energy at an embedded hardware platform using our proposed evaluation methodology, and demonstrate that this relationship can be obscured by the three cases behind overestimation. Overall, our work shows that the robustness-energy trade-off has differences from the conventional accuracy-energy trade-off, and highlights importance of the precise evaluation methodology for robustness.
DAGNet-based ANN development for image enhancement

The article is devoted to the development of a system for increasing the resolution of a raster image based on a deep artificial neural network. The article presents and describes a step-by-step plan for the development of a deep artificial neural network that solves the problem of increasing the resolution of a raster image. Examples of pairs of images from the training dataset, the developed topology of a deep artificial network, the loss function, and the training graph of the network are given. At the end of the article, an overview of the effectiveness of the developed system is provided.