Reviewerly/exHarmony huggingface dataset:
Human Vs. Machine Eye for Chart Interpretation

Recent advances in machine learning have enabled the creation of models that can read, parse, and to a certain extent, reason about charts. These models are yet to reach up to human interpretation, but given the fast progressive technologies, these models can analyze, interpret and visualize chart images to some extent. Many people are acquainted with data visualization paradigms; still, estimating a users' capability to read complex charts or graphs is not easy. For designing an interpretation and visualization system for the non-specialist user, it becomes necessary to separate the potential efficiency of the system and the comprehension capability of the user. Complex data interpretation requires extraction, analysis, and manipulation of the data repeatedly, with the influence of multiple perceptions leveraging multiple interpretation tools. Moreover, interpreting charts requires the extraction of textual and graphical encodings from chart images. With the complex layout, the retrieval system fails to interpret data relationships. The paper discusses human and machine perception for understanding charts, and the societal need for chart interpretation. Visually impaired users rely more on summaries to understand images. However, neither images are described by the assistive readers, nor chart images are accompanied by any proper contextual messages. To overcome this issue, the paper also proposes a style-invariant automated encoding system that uses inter and intra-relationship of chart elements to interpret data encodings from various types of chart images.
Online Learning of Basics of Graph Theory

As a result of COVID-19 pandemic, the methods of teaching at Slovak universities have significantly changed. Before 2019, majority of subjects was taught by face-to-face instruction. Despite positive experience with utilization of modern technologies in education, only a little number of subjects was taught bye-learning or blended learning. In the paper, we summarize our experience with teaching the subject Basics of Graph Theory. In the winter term of the academic year 2021/2022, due to restrictions caused by COVID-19, full-time students had to be taught by a combination of pre-recorded video lessons, synchronous online lessons, and e-learning course, while part-time students were taught only by video lessons and e-learning course. A comparison of students' results in the final test reveals that online learning is a suitable method to teach Basics of Graph Theory, as there is no significant difference compared to the results of the students taught by blended learning two years ago. Further, this method is equally suitable for both full-time and part-time students, since there is no significant gap in the level of their knowledge. Moreover, answers of students in a questionnaire reveal other benefits of this method.
Effectiveness Assessment of the Maintenance Program Using As-Found in the Nuclear Power Plant

In this paper, firstly, an overview of the As-Found development and implementation process in domestic nuclear power plants is given, and then a systematic study on the effectiveness assessment of the maintenance program using As-Found is conducted from 4 aspects, namely, state monitoring, real-time assessment, failure alarm, and data support, and the corresponding judging criteria and concerns are formulated. In order to apply As-Found data in all aspects, the weak links of As-Found data analysis in domestic nuclear power plants have been enhanced, and the effectiveness of the maintenance program has been improved, reducing the corrective maintenance of critical equipment and ensuring the safe and reliable operation of system equipment in nuclear power plants.
The Effect of Different Feedback Strategy on Learning Outcome inMicro-video Course Learning

The outbreak of COVID-19 promotes the development of online learning. Micro-video course has become the mainstream learning resource of online learning because of its concise theme, strong pertinence and vivid content. However, there are few researches on feedback strategy of micro-video course in basic education. Based on the principles of multi-media design and ADDIE instructional design model, a micro-video course of robot knowledge was designed in this study. Different feedback strategies(i.e., descriptive feedback (DF), evaluative feedback (EF), suggestive feedback(SF), guiding feedback (GF)) were embedded in the micro-video course. The effects of four feedback strategies on students' learning outcome, cognitive load, and learning motivation were explored through experiment. The results provide some suggestions for the production of basic education micro-video course.
PCPS: Personalized Care through Patient Similarity

Electronic Health Records (EHR) contain rich information about patients that can be used to improve decision support systems that assist Health Care workers. Personalized care is a concept that is gaining much acceleration as it talks about tailoring the treatment of an individual by proactively identifying possible risks. One approach used in personalized care is to identify similar patients to plan customized treatment. This paper reports work done to identify patient similarity from EHR records to form clusters of similar patients. Paper discusses the proof of concept that uses Jaccard Similarity and the Sorensen-Dice Similarity Index to find patient similarity. The similarity score calculated is used to build clusters that were optimized using the Elbow Method and Silhouette Index. The quantitative analysis of metrics suggests that Jaccard Index with Silhouette Index and Sorensen-Dice Similarity Index with the Elbow Method gave better clusters. The EHR of the patients in a cluster can be used to derive insights that can guide personalized care.
A Methodological Survey on Fake Data Generation in Multimedia Forensics

Manipulation of real-world photos/ videos through various computer-aided softwares and android applications has received significant attention. Even though most of the people across the community use these softwares for their personal entertainment, it is malicious if the people use these softwares for hiding/ concealing certain contents in images/ videos from criminal activity (i.e., forgers). Therefore, there is a huge demand for developing computer-aided systems for identifying and locating these forged regions in the images/ videos. In the present scope of this paper, we provided an extensive survey on procedures and tools for fake/ forged dataset generation. It also presents a survey on various benchmark datasets used by the researchers for fake/ forgery detection. The presented survey can be a useful contribution for the research community to develop a new method/ model for forgery detection thereby overcoming the limitations of the state-of-the-art methods.
Evaluation of a Web-based Teaching Platform with EduTools and Adapted Indicators

With the popularity and application of information technology in China's education industry, the demand for education and teaching resources based on the Internet is also increasing, and the trend of intelligent teaching platforms is becoming more and more obvious. Most of the mainstream teaching platforms appearing at home and abroad have similar characteristics, and it is not possible to fully understand the differences as well as their advantages and disadvantages in-depth just through the user manuals provided by each intelligent teaching platform. Based on the EduTools evaluation system, this study conducts statistical and comparative research based on a comprehensive survey of intelligent teaching platform tools.
Hot Data Identification for Dynamic Workload using Parallel setup

The use of Semantic Web applications is increasing day by day and so is the volume of Resource Description Framework (RDF) data. To manage the huge volume of RDF data, Hot Data identification for Dynamic Workload using parallel setup, HD2W is proposed. It exploits the skewness of the query workload. Data can be identified based on the frequency and recentness of the usage. It consists of two phases: Phase 1 generates the log and shuffles the data using randomized ranks and Phase 2 implements improved caching algorithm. The technique has been demonstrated using weather observation data collected by sensor networks. It uses a physical cluster for parallel implementation. The algorithm execution time increases linearly with workload. The experiments report 43% Query Execution Time (QET) gain over the original configuration. QET accelerates further for repeated occurrences. This work will help in developing interactive applications for the weather observation domain.
Multiple Choice Question Answering Using Attention Based Ranking and Transfer Learning

The multiple choice question answering is still considered as an challenging task in Natural Language Processing. In this paper, we have tried to solve the problem of answering multiple choice questions where supporting documents corresponding to each question are not explicitly provided. Context retrieval is the strategy, which focuses on both reasoning and retrieving better supporting contexts. We present a improvised version of attention based deep neural network that eventually learns to order documents according to their relevance in relation to a given topic, all while achieving the goal of predicting the correct response. The top documents retrieved are considered more relevant context for given question answer pair. To achieve more accurate results transformer based pre-trained models are used in the implementation. We have used the concept of transfer learning which is related to learning and adapting knowledge by fine tuning model on other datasets. The reasoning challenge dataset by Allen institute is used to test the approach and SQuAD 2.0 and RACE datasets are used to fine tune the transformer based models. The accuracy of proposed model on ARC easy dataset is 89.51% and on ARC challenge dataset is 62.53%.
SplitGlass: A Splitting Based Deep Network for Efficient Human Pose Estimation

Recent trends of performing all the image-related tasks on mobile devices results in developing a scalable and efficient model. The current state-of-the-art(SOTA) methods are able to achieve high accuracy by using deep neural networks. However such performance is achieved by compromising the computational complexity. To overcome this, we propose a novel deep network that aims to find the perfect equilibrium between accuracy and efficiency. We use the most common benchmark, the hourglass network as our backbone and propose a splitting-based network in which individual modules within the network focuses on estimating the keypoints for particular regions of the body such as the upper body, lower body, leg, hand, etc. The splitting is done in a hierarchical manner based on the different types of semantic features at each stage. The network with this type of splitting needs a lesser number of parameters to achieve the task of human pose estimation and thereby reducing the computational complexity. The approach to increase efficiency using semantic features based splitting is more generic and can be applied to any deep network. The results of our proposed network is evaluated on the MPII human pose dataset and it achieves a significant reduction in the network parameters as compared to the current SOTA models with similar accuracy.
Study of Different Types of Evaluation Methods in Classification and Regression

Machine learning is used to analyze the data and predict the results depending upon the features. As per the problem statement, selecting the best machine learning technique is a challenge. This paper lists and compares various evaluation methods in machine learning which can help us to find the best technique according to our required application. List of errors are presented in tree diagrams which are usually used for regression and classification. Depending on the model proposed, we can choose a technique to evaluate. The paper discusses evaluation techniques in detail which can help us to choose the most efficient machine learning model.
A PageRank-based Collaborative Design Team Member Influence Study

Aiming at the lack of evaluation indicators of the influence of collaborative design team members, this paper proposes a collaborative design team member influence algorithm DesignRank that combines collaborative design behavior characteristics with PageRank. The DesignRank algorithm takes into account the two factors of self-design level and interactor's design level, and calculates the influence of group members in the collaborative design platform. The experimental results show that the DesignRank algorithm is a more comprehensive and complex method.
Design and Application of Piano Micro-teaching Mode under the Environment of “Internet +”

In the era of “Internet +”, it is a trend to apply various information technology and information teaching modes to piano teaching. The sudden outbreak of COVID-19 in 2020 has provided a new opportunity for the construction of a real school curriculum teaching model in the era of “Internet +”. With the help of the thinking and technology in the era of “Internet +”, this paper studies the design principle and teaching mode of piano micro lessons in universities. This paper analyzes the influence of the learning data such as watching times, watching hours and talking times of teaching video on the online platform on the students' piano performance level, and studies the effect of this effect to find out the relationship between them. In order to open up new ways, new links and new processes of piano micro-course teaching, enrich and expand the content of piano teaching, break through and innovate the traditional piano teaching method, realize the upgrade, optimization and reform of piano course resources, and achieve the expected teaching effect.
Investigation on inter- and intra-class ambiguity between handwritten case-sensitive characters using customized MobileNetv2

The unconstrained writing styles and pattern variety in handwriting cause confusion among characters of the same class (intra-class) and characters of different classes (inter-class). The developers designed handwritten character recognition (HCR) model, which is specific to language script and dictionary. The ambiguity of alphabets has received very little attention, despite evidence that certain characters are more easily misinterpreted, and only a few people have explored the open-source deep architectures for HCR using transfer learning. Previous works focused on the accuracy of the HCR model, not explaining the reasons for the reduction in performance, and letters, digits recognized separately. Experiments on two widely used datasets, EMNIST and NIST SD19, demonstrate that the original database size information is required to distinguish case-sensitive (C-c, F-f, I-I, J-j, K-k, M-m, O-o, P-p, S-s, U-u, V-v, W-w, X-x, Y-y, Z-z) characters having similar pattern. The performance of MobileNetv2 on (i) EMNIST (By-class: accuracy (AR) – 87.88%), NIST SD19 (By-class: AR – 90.65%) and (ii) customized Mo-bileNetv2 EMNIST (By-class: accuracy (AR) – 87.12%), NIST SD19 (By-class: AR – 89.55 %). The results of the modified MobileNetv2 with NIST SD19 are likewise comparable to state-of-the-art and earlier results.
Multi-Behavior Sequential Recommendation with Low-Rank Decomposed Self-Attention

Sequential recommendation seeks to simulate the changing preferences of users over time by using the sequential item information of user interactions. Most of the current sequential recommendation methods focus on a single row for sequential recommendation. In practical recommendation scenarios, user interactions are diverse, and it is necessary to mine multiple types of interaction data to predict user preferences over time. To this end, we propose a multi-behavior sequence recommendation method based on low-rank self-attentive decomposition networks. The dynamic interests of users are captured by exploring the behavioral relationships within and between multi-behavior sequences. Experiments on publicly available datasets show that our proposed approach consistently outperforms state-of-the-art recommendation models.
Intelligent Technology Empowering the Design and Implementation of Primary School English Project-based Learning

Promoting the integration innovation of intelligent technology and classroom teaching is an inevitable choice for current primary and secondary schools to implement precise burden reduction and efficiency improvement. In view of the practical needs of classroom reform under the background of “Double Reduction” policy, this study explores the design method and implementation steps of intelligent technology empowering primary school English project-based learning. Firstly, this study clears the intelligent technology empowering primary school English project-based learning is: collaborate with digital intelligence determine project themes, immersive environment realizes the reality of language situation, exploration tools facilitate visualization of project activities and intelligent evaluation drives project evaluation diversification; Then, the “theme-implementation-evaluation” design framework and method are constructed. The teaching practice is carried out with the help of intelligent tools such as “Baidu Translation” and “Fun Dubbing”, and the implementation steps and operating procedures are summarized. Finally, results show that it can improve students' comprehensive application ability and the core literacy of English, and cultivate students' collaborative problem-solving ability.
Research on exploration and operation of military microgrid based on blockchain technology

This paper takes blockchain technology in the field of electric power and energy as the research object, and conducts feasibility studies on the application scenarios and application modes of the integration and application of new technologies, and explores the optimization methods of operational reliability and invulnerability of military microgrids based on blockchain., to improve the availability of military microgrids in the event of battle damage, partial destruction, etc. Through the decentralization of the consensus algorithm, the microgrid information processing process is optimized, the decision-making tasks are divided, and the demand for data processing equipment is reduced. Through smart contracts, automatic decision-making of the system is made, which reduces the cost of manual intervention.
Sparsity Based Framework for Discrimination between Confusion Pairs

Sparse Representation (SR) has successful applications in vision and classification problems. SR methods work by transforming the data into a low-rank latent space to find sparse representations for the data. Handwritten Character Recognition (HWCR) is a challenging classification task. Its difficulty arises from the high intraclass variances of the writing. HWCR for Latin scripts is better studied, but a few challenges remain. For Indic scripts, HWCR is not very mature and much research is needed. Indic scripts reflect the shallow orthography and are quite syllabic. Telugu is one such Indic script with more than 80 million native speakers. Literature available on Telugu HWCR is less. In this paper, we propose a framework for Telugu HWCR using sparse representation techniques focusing on confusion pairs. The proposed method is evaluated on confusion pairs from the standard HPL isolated Telugu character data set.
Identification and Analysis of Breast Cancer Disease using Swarm and Evolutionary Algorithm

Medical data analysis is an important field of research, among which analysis of disease detection or patient's survival after a specific treatment is of prime importance. The recent survey suggests that breast cancer is the most common life-threatening disease among women in the age group (35–50). Therefore, the focus of this study aims on breast cancer disease detection and survival prediction using UCI machine learning standard benchmark breast cancer datasets. The research is conducted through simulation work that includes five meta-heuristic-based clustering models. To access the performances of each models accuracy, specificity, Precision, F-score and MCC performance measures are used. To check superiority among each clustering model, Duncan's multiple range test is conducted with a 95 % significance level. In addition, the Friedman and Nymenyi hypothesis test is applied to access the ranks and verify the stability of each model for breast cancer disease analysis.
FedPruNet: Federated Learning Using Pruning Neural Network

Federated Learning (FL) is a distributed form of training the machine learning and deep learning models on the data spread over heterogeneous edge devices. The global model at the server learns by aggregating local models sent by the edge devices, maintaining data privacy, and lowering communication costs by just communicating model updates. The edge devices on which the model gets trained usually will have limitations towards power resource, storage, computations to train the model. This paper address the computation overhead issue on the edge devices by presenting a new method named FedPruNet, which trains the model in edge devices using the neural network model pruning method. The proposed method successfully reduced the computation overhead on edge devices by pruning the model. Experimental results show that for the fixed number of communication rounds, the model parameters are pruned up to 41.35% and 65% on MNIST and CIFAR-10 datasets, respectively, without compromising the accuracy compared to training FL edge devices without pruning.
Research on the Influence of Collaborative Mind Mapping Strategy in Smart Classroom on College Students' Scientific Research Problem-Solving Ability and Learning Anxiety

The ability to solve scientific research problems is an important ability that undergraduates need to have, and the learning methods in the smart classroom environment are also more intelligent and diversified. This study proposes a collaborative mind mapping strategy based on interactive intelligent panels, including 1. mind map support teaching; 2. collaborative mind map drawing; 3. group report presentation. In order to explore the application effect of this strategy in the course “Research Methods in Educational Technology ”., this study selected a sophomore class to conduct a one-group pre-test-posttest experiment. The research results show that the application of a collaborative mind mapping strategy can significantly improve college students' scientific research problem-solving ability and academic achievement, and can also reduce college students' learning anxiety in the learning process.
Current Status and Issues of University Education-related Data in TOKYO OPEN DATA

In this report, the necessary challenges for the openness of educational data in universities are described regarding "TOKYO OPEN DATA". It pointed out the lack of textual data and that data exist but are not open in universities. In addition, the report expressed how to solve these problems. The recommendations also included issues related to open data and data science. &nbsp