Reviewerly/exHarmony huggingface dataset:
On the two conceptualizations of information experience as an object of study: a response to Yu and Liu

Purpose The purpose of this paper is to further the scholarly discussion of information experience as an object of study, including how this research area fits into library and information studies more broadly. Design/methodology/approach A conceptual discussion of certain issues raised in Yu and Liu's recent paper in this journal. Findings While Yu and Liu seem to suggest that only a priori information experience research belong in library and information science (LIS), this paper suggests that a priori and a posteriori research have a synergistic relationship and both have a home in LIS. Originality/value This paper clarifies how the two conceptualizations of information experience as an object of study relate to each other, as well as how these relate to information experience as a research approach, and how all this fits within the metadisciplinary field of LIS.
Exp-SoftLexicon Lattice Model Integrating Radical-Level Features for Chinese NER

The Lattice series model using potential words information has been proved to be effective in Chinese Named Entity Recognition (NER).The recently proposed Simplified Lattice not only brings new baseline results, but also improves the inference speed of Lattice models.However, the Simplified Lattice fails to fully explore the rich information contained in the radical-level features of the character sequences.Moreover, the performance of the Simplified Lattice decreases dramatically as the length of entity increases.In this paper, we propose the SLRL-NER model that integrates word, character, and radical-level information to alleviate the above problems.Specifically, text Convolutional Neural Network (CNN) is used to extract the radical-level features.The original SoftLexicon set is expanded to refine the relative position information of characters in the candidate words to cope with the challenge of increasing entity length.Experiments on three datasets show SLRL-NER outperforms the state-of-the-art comparison methods.
An Enhanced Data Augmentation Approach to Support Multi-Class Code Readability Classification

Context: Code readability plays a critical role in software maintenance and evolvement, where a metric for classifying code readability levels is both applicable and desired.However, most prior research has treated code readability classification as a binary classification task due to the lack of labeled data.Objective: To support the training of multi-class code readability classification models, we propose an enhanced data augmentation approach.Method: The approach includes the use of domainspecific data transformation and GAN-based data augmentation.By virtue of this augmentation approach, we could generate sufficient readability data and well train a multi-class code readability model.Result: A series of experiments are conducted to evaluate our augmentation approach.The experimental results show that a state-of-the-art multi-class code readability classification accuracy of 68.0% is reached with a significant improvement of 6.3% compared to only using the original data.Conclusion: As an innovative work of proposing multi-class code readability classification and an enhanced code readability data augmentation approach, our method is proved to be effective.
DeepController: Feedback-Directed Fuzzing for Deep Learning Systems

Deep learning (DL) systems are increasingly adopted in various fields, while fatal failures are still inevitable in them.One mainstream testing approach for DL is fuzzing, which can generate a large amount of semi-random yet syntactically valid test cases.Previous studies on fuzzing are mainly focused on selecting "quality" seeds or using "good" mutation strategies.In this paper, we attempt to improve the performance of fuzzing from a different perspective.A new fuzzer, namely DeepController, is accordingly developed, which makes use of the feedback information obtained in the test execution process to dynamically select seeds and mutation strategies.DeepController is evaluated through empirical studies on three datasets and eight DL models.The experimental results show that, with the same number of seeds, DeepController can generate more adversarial inputs and achieve higher neuron coverage than the state-of-the-art testing techniques for DL systems.
Comparison of Academic Achievement Between Classroom Learning and Untact Online Learning－Focusing on the Inference and Argumentation classes at University B

Since the onset of the COVID-19 pandemic, education has been severely impacted, and this impact will have repercussions that go beyond our survival as a society. In the early stages of the COVID-19 pandemic, most schools and colleges in Korea changed their teaching methods from live classes to “untact”, online classes. Even though there was a degree of distrust regarding the educational effectiveness and learning efficacy of online learning, The COVID-19 pandemic did not give us a choice. However, at this point in the third year of the COVID-19 pandemic, most professors and students have adapted to online learning and are still looking for various ways to increase learning effectiveness. Moreover, even after the COVID-19 pandemic is over, online learning is highly likely to become a substitute for traditional classroom learning, or at the very least, it will run concurrently with traditional classes. Either way, in order for online learning to continue, the actual learning effect of these sorts of classes must be confirmed.&lt;/br&gt;As a result of the operation of the “Inference and Argumentation” class, contrary to concerns, research from 2021 showed that online learning can have as much of a learning effect for students as classroom learning. However, this judgment was based on the questionnaires of the learners and the qualitative feelings of the professor. But in order to clearly show that untact learning can have as much of an effect as classroom learning, it is necessary to compare the learning results of classroom learning with those of untact learning. That is the focus of this study. In order to secure the reliability of the data, the same conditions were maintained as much as possible, except for the difference in class operation according to untact learning and classroom learning. As a result of this study, we will come to see whether untact learning has the same learning effect for students as classroom learning.
AuthROS: Secure Data Sharing Among Robot Operating Systems Based on Ethereum

Abstract The Robot Operating System (ROS) streamlines human processes, increasing the efficiency of various production tasks. However, the security of data transfer operations in ROS is still in its immaturity. Securing data exchange between several robots is a significant problem. This paper proposes \textit{AuthROS}, an Ethereum blockchain-based secure data sharing method, for robot communication. It is a ROS node authorization system capable of ensuring the immutability and security of private data flow between ROS nodes of any size. To ensure data security, AuthROS employs the smart contract for permission granting and identification, SM2-based key exchange, and SM4-based plaintext encryption techniques. In addition, we deploy a data digest upload technique to optimize data query and upload performance. Finally, the experimental findings reveal that AuthROS has strong security, time performance, and node forging in cases where data should be recorded and robots need to remain immobile.
Enhancement of Design Level Class Decomposition using Evaluation Process

Refactoring on the design level artifact such as the class diagram was already done using the threshold-based agglomerative hierarchical clustering method, specifically class decomposition.The approach produced a better cluster based on the label name similarity of attribute and method.But, some problems emerge from the experiment result.The negative Silhouettes element still exist in the cluster.And, there is an unusable cluster that only consists of one attribute element.This paper has proposed the evaluation process to optimize the result of clustering.This evaluation process is an additional process that aims to move the negative Silhouettes element to the other cluster.The movement is also to get the better value of element Silhouettes value.The evaluation process can produce a better result for clusters.The clusters produced from the evaluation process have higher Silhouettes values.The average Silhouettes value is increased by about 40%.Ultimately, the result shows no unusable cluster as mentioned in the previous research.
The new role of the technological component in the social reality of the digital transition era

The article explicates a new factor that is relevant for researchers of social processes occurring at a current time. For the first time in the history of mankind, activity at any given moment must be correlated with unpredictable revolutionary development of the technological environment, the result of which, during one human life, repeatedly transforms the socioeconomic environment. In the past history of mankind, when studying social dynamics at any particular moment in time, the factor of the development of the technological environment could be neglected, despite the fact that the state of the technological environment always predetermined the state of the social environment. This action was possible because, within the limits of human life, the technological environment could be perceived as static (constant scenery for the action of the social). Modernity, characterized by a specific pace of development of the digital environment, brings fundamental transformations to this picture. The radical acceleration of the pace of technological development, combined with the focus of this development on the field of information technology, determines the unprecedented originality of the digital era that has come. It is shown that under conditions of the critical speed of transformation processes, the horizons of scientific foresight are sharply narrowed. The author defines the fundamental need for a concept of “techno”. Thus, contemporary social theories require an inextricable connection between the concepts of “socio-technological”.
Evaulation of Homomorphic Encryption Implementation on Iot Device

An encryption scheme is homomorphic if it supports operations on encrypted data. Homomorphic encryption allows a device to perform arbitrary computations on encrypted data without user secret key. Recently it is introduced new homomorphic encryption schemes with improved performance that can be implemented in IoT device in production environments. The IoT concept encompasses devices, sensors, and services existing within an interconnected infrastructure with an efficient access to sample computational facilities. In this paper we evaluated features of exact arithmetic homomorphic encryption mechanisms: BFV and BGV and approximate homomorphic encryption scheme: CKKS. In the paper we measured performances of operations of homomorphic encryption schemes: BGV, BFV and CKKS that are implemented in Raspberry Pi 4 IoT device.
Digital Communication Based on Image Security using Grasshopper Optimization and Chaotic Map

Encryption is very important to protect sensitive data, especially images, from any illegal access and infringement. This research is presented to provide an image encryption optimization method for communication based on image security. This method uses the grasshopper optimization algorithm to perform optimal encryption and irregular logical mapping. Initially, this approach creates multiple encrypted images and a chaotic map, in which the session key for the initial conditions of the map depends on a simple suspended image. After that, the encrypted images work as an initial and particles set for optimization through the grasshopper optimization algorithm. The optimized encoded image with the correlation coefficient of the continuous pixels is expressed as a function of proportion. The results from Matlab simulation of the proposed encoding method show that the encrypted images are the same, and the adjacent pixels are highly correlated with other outstanding encoding rows, such as planar histogram entropy and effective pixel rate of change average correction strength.
Viewport History as a Heuristic for Quality Enhancement and Quality Variation Control in Viewport-Aware Tile-Based 360-Degree Video Streaming

Despite the growing popularity of Virtual Reality (VR), 360-degree videos are often regarded as challenging to stream due to their large bandwidth requirement.As a solution, the 360-degree video content is spatially divided into tiles, and the quality level for each tile is selected based on the user's network environment and viewport information.To determine the high quality tiles, viewport prediction and viewport history methods are used to estimate the user's viewport.However, due to the unpredictability of user head movements, generating accurate viewport estimates are difficult, which can severely degrade the Quality of Experience (QoE) for the user.In this paper, to sustain high user QoE, we detail a novel tile quality selection algorithm that employs viewport prediction, viewport history, viewport extensions, and a viewport tile count limit.In addition, we also include comparison analysis on six 360-degree videos that vary in content pace.Based from simulations, using viewport history as a heuristic for tile quality selection demonstrated a significant increase in the perceived quality while suppressing quality variation inside the viewport and across segments compared to eight reference methods; and secondly, 360-degree videos slow in content pace tended to result in lower viewport prediction accuracy, QoE performance, and weaker viewport history trends compared to 360-degree videos fast in content pace.
Random Partition Based Adaptive Distributed Kernelized SVM for Big Data

In this paper, we present a distributed classification technique for big data by efficiently using distributed storage architecture and data processing units of a cluster.While handling such large data, the existing approaches consider specific data partitioning techniques which demand complete data be processed before partitioning.This leads to an excessive overhead of high computation and data communication.The proposed method does not require any pre-structured data partitioning technique and is also adaptive to big data mining tools.We hypothesize that an effective aggregation of the information generated from data partitions by subprocesses of the complete learning process can lead to accurate prediction results while reducing the overall time complexity.We build three SVM based classifiers, namely one phase voting SVM (1PVSVM), two phase voting SVM (2PVSVM), and similarity based SVM (SIMSVM).Each of these classifiers utilizes the support vectors as the local information to construct the synthesized learner for efficiently reducing the training time and ensuring minimal communication between processing units.In this context, an extensive empirical analysis demonstrates the effectiveness of our classifiers when compared to other existing approaches on several benchmark datasets.However, among existing methods and three of our proposed (1PVSVM, 2PVSIM, and SIMSVM) methods, SIMSVM is the most efficient.Considering MNIST dataset, SIMSVM achieves an average speedup ratio of 0.78 and minimum scalability of 73% when the data size is scaled up to 10 times.It also retains high accuracy (99%) similar to centralized approaches.
Sequence Video and Artificial Intelligence Assisted Basketball Injury Risk Early Warning Method

Basketball sport is a comprehensive nonperiodic collective sport, in which sports injuries of various parts are also prone to occur. Its higher exercise intensity not only effectively enhances the physical fitness of the athlete but also causes physical damage due to a series of actions such as frequent take-offs and landings, resulting in certain sports injuries. Therefore, the early warning of injury in basketball is very necessary, and it is very useful to protect the safety of players. In this paper, we investigate how to use the sequence video and artificial intelligence method for the basketball injury risk early warning in order to protect the player and better assist the player to improve their efficiency of training. First, we preprocess the video sequence. We convert the video sequence into image data and perform noise reduction and transformation operations on the image. Second, for the processed image data, we designed a convolutional neural network model to determine the damaged area. Third, we use the neural network model to take the image data with the detection area as the input, perform feature extraction on the data, and finally obtain the early warning value of basketball sports injury. The experimental results prove that the method proposed in this paper has good evaluation performance.
A study on the Korean self and learning motivation of Chinese learners - Focusing on vision and visual style -

This study aims to examine the relationship between Korean learners’ possible selves comprising their ‘ideal Korean self’ and ‘ought-to Korean self’, visual styles, vision, and their motivation to learn Korean. 243 Chinese students replied to a self-report questionnaire that was used to obtain the data. Through Structural Equation Modeling, Confirmatory Factor Analysis and path analysis were employed to investigate possible relationships between the aforementioned 5 latent variables. Both ‘ideal Korean self’ and ‘ought-to Korean self’ were found to predict motivated Korean learning behaviors; however, the former was found to be stronger than and significantly impacted by the latter. On the other hand, learners who preferred visual styles were proven to have a stronger vision for the future, which emerged as a strong predictor of the two types of possible selves.
Improved learning outcomes of CNC programming through Augmented Reality jobsheet learning media

This study aims to determine: (1) the differences in learning outcomes of students who are taught using Augmented Reality (AR) jobsheet learning media with students taught using conventional learning media, (2) differences in learning outcomes of highly motivated students and low motivated students, and (3) the interaction between Project Based Learning learning methods and learning motivation in influencing learning outcomes. This research is a quasi-experimental research with a population of 64 people, the sample was taken as many as 32 people using the cluster random sampling method. Data were collected using a questionnaire for learning motivation and tests for learning outcomes. The data analysis technique used is descriptive analysis and two-way analysis of variance (Two Ways Anova). The research results found: 1) there are differences in student learning outcomes taught by the Project Based Learning learning method compared to student learning outcomes taught conventionally with Fcount of 7.98 (2) there are differences in learning outcomes of highly motivated students with low motivated students with Fcount 34.40, and (3) there is no learning interaction between learning methods and learning motivation in influencing student learning outcomes with Fcount 3.19
Exploring the Barriers to the Use of Blackboard Learning System at Arar University College in Saudi Arabia

The success of lecturers in adopting innovation in this age of digitization has a direct impact on their student's academic performance and, ultimately, institutional goals. However, many teachers have not integrated technological resources into their classrooms, such as the Blackboard learning system. As a result, this article explored lecturers' perspectives on potential obstacles to the use of the Blackboard in teaching. The study was carried out at Arar University College in the Kingdom of Saudi Arabia in the 2021-2022 academic session. Semi-structured interviews were conducted with nine lecturers. Findings indicate that technological, institutional, and cultural barriers as the main themes that emerged from the lecturers’ interviews. Thus, for effective utilization of the blackboard system, there is a need for technological advancement in both hardware and software infrastructures, continuous technical support, training, and institutional commitments of the government of Saudi Arabia and Arar university college in particular. Moreover, the design and development of software should bridge gender gaps and retain the cultural norms of the kingdom of Saudi Arabia.
Identifikasi Wilayah Resiko Kerusakan Lahan Terbangun Sebagai Dampak Tsunami Berdasarkan Analisis Building Indices

Indonesia has a very large water area and there is the territory that is the confluence of the earth's slabs. It can allowing the occurrence of tsunami natural disasters. The study aims to find out which areas have risks the highest and the lowest land damage. The data used in this study were satellite images taken from 2014-2021 with coverage area in Kulon Progo Regency which consists of 12 sub-districts. This study used indexes vegetation UI, NDBI, IBI, EBBI. With an ANN algorithm get results which is quite accurate with an accuracy rate of 94.50%. Predictions states that the area has a high risk of damage due to the tsunami, namely the villages of Jingkaran, Sindutan, Palihan, Glagah, Karangwuluh, Janten, Temon Kulon, Kedundang, Sogan, Kalidengen, Ngestiharjo, Depok, Kanoman, Panjatan, Wahyuharjo, Pandowan, Nomporejo, and, Kranggan.
Fast Detection of Defective Insulator Based on Improved YOLOv5s

Defective insulator detection is an essential part of transmission line inspections based on unmanned aerial vehicles. It can timely discover insulator defects and repair them to avoid a power transmission accident. The detection speed of defective insulators based on artificial intelligence directly affects inspection efficiency. To improve the detection speed of defective insulators based on YOLOv5s, an improved detection method with faster detection speed and acceptable precision is proposed. First, a new ResNet unit with three branches is designed based on depthwise separable convolution with kernel three and average pooling. To reduce parameters, the new ResNet unit is used to replace the original ResNet unit used in the CSP1_X module in YOLOv5s. Besides, we also introduce channel shuffle in the CSP1_X module to facilitate the flow of feature information from different channels. Second, a new residual CBL module is designed based on depthwise separable and standard convolution. The new residual CBL module is used to replace the two CBL modules used in the CSP2_X module in YOLOv5s to reduce parameters and extract more useful features. Third, we design a separate, coordinated attention module by introducing location information into channel attention. The new attention module is added to the end of the CSP2_X module to improve the ability to extract insulator location information. Besides, we also use convolution to replace the focus model to reduce computation. Compared with defective insulator detection methods, the proposed method has smaller parameters, floating-point operations per second, and higher frames per second. Although it has lower mean precision, it has a faster detection speed. Besides, the increase in detection speed is greater than the decrease in mean precision.
THE MENTOR-EMPLOYER BY THE EYES OF THE MENTEE-STUDENT

5.8.4 (13.00.04), 5.8.5 (13.00.04), 5.8.6 (13.00.04), 5.8.7)