Reviewerly/exHarmony huggingface dataset:
Difficulties of Elementary School Students in Writing Poetry

This study aims to describe the difficulties of elementary school students in writing poetry and to describe the factors of the difficulties of elementary school students in writing poetry. This study describes descriptively the difficulties of students in writing poetry. The method of data collection in the core research is carried out by means of a literature study method that has been studied and analyzed by the researchers themselves. The results obtained from this study are that students have difficulty determining the title/idea or theme in writing poetry, students have difficulty making rhythmic words, students have difficulty making imaginative sentences, students have difficulty making diction words or sentences. Meanwhile, the factors that make it difficult for students to write poetry are external factors, namely the teacher has not used innovative learning strategies, approaches, models and methods, and, the teacher has not used innovative learning media.
Privacy Preserving OpenPGP Public Key Distribution with Spamming Resistance

OpenPGP public key distribution via Synchronizing KeyServers (SKS) is facing the challenges of user privacy leakage caused by keyword search behaviors and service unavailability of OpenPGP software caused by OpenPGP certificate spamming attack (CVE-2019-13050). Most existing solutions to the problem dispense with the necessary features and functions of SKS or Web of Trust (WoT) for attack mitigation. In this paper, we put forward a solution which is privacy-preserving and spamming-resistant, while maintaining the functionalities of SKS and WoT. Considering the characteristics of our scenario, we protect user privacy by introducing a third-party server, and propose a specific third party-based private set intersection protocol to improve usability of OpenPGP software. Our protocol helps users filter out the required key data by intersection computation between unbalanced sets of keywords. We also propose an enhanced scheme for multi-key query and further privacy protection. We evaluate the usability and privacy of our schemes. Experimental results show that our scheme can largely reduce unnecessary data download with appropriate filter parameters. The proposed solution relies on the security of Elliptic Curve Diffie–Hellman, HMAC-based Key Derivation Function, Bloom filter and symmetric cryptographic encryption to defend against semi-honest adversaries.
Comparative analysis of UCP and SLIM methods to estimate the development of software products

This study is devoted to analysis different methods to estimate the software products development. The following most popular methods were selected for the study: Use Case Points and SLIM. The considered methods are algorithmic and are used in case of insufficient experience of the project team to use empirical calculation methods. A comparative analysis of these methods was also carried out based on calculating the forecasting error for IT projects for various platforms, such as a mobile device and a personal computer. The disadvantages of using the considered methods for developing applications for mobile platforms were identified. Based on the research results, it was concluded that today algorithmic methods for assessing labor intensity cannot give the best results and must be changed to adapt them to mobile software, considering its specific specifics, which makes it advisable to continue researching this industry in direction of mobile development.
Shared Component Cross Punctuation Clauses Recognition in Chinese

NT (Naming-telling) Clause Complex Framework defines the clause complex structures through component sharing and logic-semantic relationships. In this paper, we formalize component sharing recognition as a multi-span extraction problem in machine learning. And we propose a model with mask strategy to recognize the shared components of cross punctuation clauses based on pre-training models. Furthermore, we present a Chinese Long-distance Shared Component Recognition Dataset (LSCR) with four domains, including 43k texts and 156k shared components that need to be predicted. Experimental results and analysis show that our model outperforms previous methods in large margin. All the codes and dataset are available at https://github.com/smiletm/LSCR.
Interactive Games for Improving Pre-school Children's Linguistic Awareness: Design and Field Application

Learning with game-based interactive strategies becomes a popular trend in this mobile era. Especially for young children to acquire linguistic awareness, practicing through games can be more light-hearted and engaging. This study investigates the effectiveness of hand-eye coordination training with game-based interactive learning and multi-touch technology on orthographic awareness in an online study environment for preschool children. Field experiments in real online teaching scenarios were conducted with a large sample size of 1712 K5 preschool students in total. Our results showed a significant improvement in the orthographic awareness performance for the group which participated in the hand-eye coordination training. We conclude that the practice of visual-motor integration skills with the interactive game approach has a positive effect on orthographic awareness cultivation.
Electronic Patient Records as a Substrate for Collaboration for Distributed Care in Low-Resource Contexts

Collaboration to provide patient care in low-resource contexts has been a challenge due to heavy patient load, limited connectivity, and knowledge-gap between primary and tertiary care. Through the design, development, and implementation of a private social network-connected, large-scale hospital information system, which has scaled to several zonal and district hospitals in a small hilly country in South East Asia, we present the case study of a system that has enabled collaboration. Using coordination mechanisms as a theoretical framework, we discuss some methods of collaboration. In the paper, we present electronic patient records (EPR) as the substrate that enables collaboration between providers, departments, developers throughout the health systems. In our analysis, we present useful learnings of collaboration between provider-provider, developer-developer, provider-patient, implementer-provider, and how the balance of these is a necessary condition to create a useful substrate for collaboration.
Effects of Students Learning Styles on Adaptive e-Learning Design

The paper presents the results of the research aimed at evaluating the effects of using branching scenario in the online learning course of Russian as a foreign language. The course was designed according to the distribution of the learners according to their learning styles and preferences. The aim of the research was to show how the structure of the course can be tailored around the individual learning styles. The paper gives a brief explanation of how learning styles and adaptive learning are defined in modern educational environment. The terms “learning styles” and “adaptive learning” are not new in the pedagogical research, but they have become popular recently with the emergence of distance learning. 178 students from different countries were interviewed using VARK questionnaire to get information about their learning preferences and cognitive characteristics. On the bases of the received data the e-learning Russian course was designed with adaptive content, sequencing and assessment. The survey of students' satisfaction after the study showed that adaptive learning is beneficial for training language skills and can be widely used in the situation when the students and teachers can communicate only through electronic media.
DOREP 2.0: An Upgraded Version of Robot Control Teaching Experimental Platform with Reinforcement Learning and Visual Analysis

The Deep Open Robot Experiment Platform (DOREP) is an experimental system for general robot control. It includes a robot toolbox, a Linux based real-time controller and corresponding environment deployment tools. It is compatible with ROKAE robots, Universal robots, ABB robots, AUBO robots and other 6-DOF small low load general robots. It aims to provide users with a direct, high-level, more open and comprehensive programming interface. The toolbox of the original version of DOREP system contains more than 30 functions, including forward and inverse kinematics calculation, point-to-point joint and Cartesian control, trajectory generation, graphic display, 3D animation and diagnosis. On the basis of the original version, DOREP 2.0 system adds some new functional modules such as reinforcement learning and visual analysis, which further improves the performance of DOREP system. Taking the newly added module as an example, this paper expounds the functions of reinforcement learning module and visual analysis module, and applies them to simulation and experiment successfully.
Featured Games

Feature-based analysis of software product lines and family-based model checking have seen rapid development. Many model checking problems can be reduced to two-player games on finite graphs. A prominent example is mu-calculus model checking, which is generally done by translating to parity games, but also many quantitative model-checking problems can be reduced to (quantitative) games. As part of a program to make game-based model checking available for software product lines, we introduce featured reachability games, featured minimum reachability games, featured discounted games, featured energy games, and featured parity games. We show that all these admit optimal featured strategies, which project to optimal strategies for any product, and how to compute winners and values of such games in a family-based manner.
The Effect of Using an Animation Video in Writing Recount Text at SMK Antartika 1 Sidoarjo

The objectives of this research are to find out whether there is any effect and significant difference achievement of students in writing recount text between before and after being taught by using an animation video and how the students’ respon toward the use of an animation video in teaching writing recount text. The research was conducted at SMK Antartika 1 Sidoarjo. Sample of this study is students in first grade TPM 6 class. The method that is used in this research is quantitative method with pre-experimental design. To get the data, the researcher gave pre-test and post-test to the students. The researcher used an animation video in teaching writing recount text. An essay is given by the researcher to the students to know the effect of the students after used an animation video. Based on the result of the research, there are some research findings that can be represented. First, there is significant difference achievement of students in writing recount text. Second, the students’ understanding give a positive respon toward the use of an animation video. In conclusion, the use of holiday animation video gives a significant difference on the students’ achievement in writing recount text and the students’ respon of the use of an animation video in teaching writing recount text are positive.
Filter Pruning Using Expectation Value of Feature Map’s Summation

With the widespread application of artificial intelligence, many scenarios require fewer parameters network. Mobile devices require networks with faster inference speed. However, current networks are becoming more and more complex. Network pruning is an important way to solve this contradiction. Current data dependent network pruning methods use a large amount of data to guide the pruning. Such a pruning strategy is undoubtedly time-consuming and computing resource-consuming. In order to guide pruning process with less computing resource and time, this paper proposes a new pruning method. The proposed method comes from a phenomenon observed in experiments: the expectation value of feature map’s summation of a certain filter is relatively stable, and the number of input images has little effect on this value. Therefore, we use a small amount of images to abtain and sort these values to guide pruning. We prune filters corresponding to smaller expectation value according to a certain pruning rate. Using the proposed method, we perform experiments on public datasets. Experiments show that our method can reach Top-1 accuracy of 93.72\({\%}\) on CIFAR-10 on VGG-16-BN under 83.3\({\%}\) compression ratio of parameters. In order to verify the proposed pruning strategy on medical image analysis, we compare the accuracy of landmarks detection on the DDH (Developmental Dysplaisa of the Hips) dataset. The experimental results show that after network pruning, the landmark detection accuracy can also be well maintained.
Exploiting Invariance of Mining Facial Landmarks

In this paper, we propose an invariant learning method for facial landmark mining in a self-supervised manner. The conventional methods mostly train with raw data of paired facial appearances and landmarks, assuming that they are evenly distributed. However, assumptions like this tend to lead to failures in challenging cases even undergo costly training since they usually don't hold in real-world scenarios. To address this issue, our model achieves to be invariant to facial biases by learning through the landmark-anchored distributions. Specifically, we generate faces from these distributions, then group them based on the appearance sources and the probe facial landmarks into intra-identities and intra-landmarks classes, respectively. Thus, we construct intra-class invariance losses to disentangle the spatial structures from appearances. In addition, we adopt a reconstruction loss to produce more realistic faces with probe landmarks. Extensive experimental results on four standard facial landmark datasets demonstrate that our method achieves compelling performance compared with supervised and unsupervised methods.
An attention-based RGBD dual-branch gesture recognition network

In this work, we use a hierarchical architecture based on detector-classifier for gesture recognition task. During the operation of the architecture, the detector, which is essentially the switch of the classifier, is always running. When the output of the detector is true, then the classifier is activated and returns a classification label for the input video stream. Our work focuses on the improvement of detectors and classifiers. In the detector, we introduce an attention mechanism to guide the network to focus on the space and channel where the gesture is located. For the classifier, based on the RGB information stream, we use an independent branch to extract the features of the depth stream, and finally merge the two branches. Because gestures move in a three-dimensional space, depth information can make up for the lack of RGB information. Experiments show that on the Egogesture test set, our detector achieves 98.86% accuracy on RGB input, while the classifier achieves 93.85% accuracy. At the same time, our gesture recognition architecture can fully meet the real-time requirements.
Pengembangan Aplikasi E-Commerce Usaha Mikro, Kecil dan Menengah Berbasis Web (Studi Kasus : Rumah Lemon Pekanbaru)

Rumah Lemon Pekanbaru is one of the UMKM in the Riau area that sell products from organic lemons processed products such as beverages, laundry soap, skin care and others.  Based on an interview with the owner of the Rumah Lemon Pekanbaru obtained the information that there are some problems. The first problem is the difficulty in marketing the product, because the product can only be found in stores or stalls.             The second problem is the difficulty UMKM in recording customer orders made through social media is happening is not efficient and effective for orders data was not recorded properly. Therefore, we need a solution that is an e-commerce web. The system is built by implementing the method of User Centered Design (UCD) in which these techniques focus on users. This system was also tested using Usability, User Acceptance Test (UAT) and User Experience Questionnaire (UEQ). Based on Usability testing that has been conducted shows that 90% of systems that have been built already received users and testing UEQ obtained assert scale of efficiency, dependability, novelty, stimulation and attractiveness on the website Home Lemon Pekanbaru on a scale user experience is excellent. As for the average scale of perspicuity value is good. So e-commerce web which has been developed in accordance with user needs. Keywords: E-Commerce, Website, User Centered Design, Usability, User Acceptance Test, User Experience Questionnaire, UMKM
Mining Social Media to Understand User Opinions on IoT Security and Privacy

Internet of Things (IoT), as an emerging technology, has attracted lots of attentions in recent years, but also faced criticism regarding its security and privacy. Some user interviews have been done in the past to understand the security and privacy concerns of end users on IoT devices, but they were limited in the number of participants. To better understand user opinions on IoT security and privacy, we conducted a larger user study using Twitter and Reddit data. We collected more than 6 million tweets and reddit submissions using query search keywords such as drone security, smart camera privacy and etc and labeled them using flair pre-trained en-sentiment model. We performed sentiment analysis using BERT, a transformer-based model, and a neural network classifier. Our sentiment analysis results have shown that Twitter users are more positive towards IoT security and privacy, whereas Reddit users are more negative. On certain categories such as smart refrigerator, smart TV, drone, speaker, voice assistant, fitness tracker, and smartwatch, both users express negative sentiment. We also conducted a longitudinal study to understand how user opinions change over time. A continuous upward trend has been identified for both positive and negative sentiments on Reddit users. We further analyzed users’ specific concerns through topic modeling particularly related to smart lock, smart camera, and drone.
An Effective Feature Generation and Selection Approach for Lymph Disease Recognition

Health care data mining is noteworthy in disease diagnosis and recognition procedures. There exist several potentials to further improve the performance of machine learning based-classification methods in healthcare data analysis. The selection of a substantial subset of features is one of the feasible approaches to achieve improved recognition results of classification methods in disease diagnosis prediction. In the present study, a novel combined approach of feature generation using latent semantic analysis (LSA) and selection using ranker search (RAS) has been proposed to improve the performance of classification methods in lymph disease diagnosis prediction. The performance of the proposed combined approach (LSA-RAS) for feature generation and selection is validated using three function-based and two tree-based classification methods. The performance of the LSA-RAS selected features is compared with the original attributes and other subsets of attributes and features chosen by nine different attributes and features selection approaches in the analysis of a most widely used benchmark and open access lymph disease dataset. The LSA-RAS selected features improve the recognition accuracy of the classification methods significantly in the diagnosis prediction of the lymph disease. The tree-based classification methods have better recognition accuracy than the function-based classification methods. The best performance (recognition accuracy of 93.91%) is achieved for the logistic model tree (LMT) classification method using the feature subset generated by the proposed combined approach (LSA-RAS).
The Use of Virtual Reality Learning Materials in Nursing Seminar and Its Possibility

This research aims to develop VR learning materials using smart phones, propose to use it in Nursing Seminar which prepares a variety of situations, and look into the possibility of effective learning methods. We filmed the seminar of Livelihood Support Skills on Home Care Nursing in June 2016 in which 54 third-year students, teachers, and nurses attended with a 360-degree camera and a wearable camera. We conducted a self-administered questionnaire survey of 134 students in a nursing college. They were asked to fill out 10 question items (five-point Likert scales for each item) on the use of VR learning materials and learning effects. A t-test was conducted to compare men and women's self-evaluation of learning effects. Pearson's correlation coefficient was also used to compute the correlation between question items for examining the content of learning effects. Results showed that on the learning effects of VR learning materials the average score of nine questions was over 4.0 and Cronbach's alpha was 0.828, showing high internal consistency. It showed that except for the question 8, which asked about whether a subject knows how to use VR learning materials, respondents tended to answer questions positively. VR learning materials helped students to learn their own skills objectively and repeatedly from a holistic view by realizing actual situations through watching videos. In this study we examined the evaluation of learning effects of VR learning materials from learners' subjective answers. In further study, we need to measure them in a more objective way for improving them.
Research on Energy Saving of Air Conditioning System in Data Center

With the development of the Internet today, there are more and more small and medium data center computer rooms, the energy consumption of data centers has also become the focus of attention. Air conditioning system is a big energy consumer in data centers, approximately 40% of the total energy consumption of the data center. So reducing the energy consumption of air conditioners has become the top priority of reducing the energy consumption of data centers. This paper studies the air distribution generated by different air-conditioning methods, building a computer room model and uses the simulation software 6sigma to simulate the different air conditioning modes, thereby reducing the energy consumption of the computer room.
Supply Chain Flows and the Possible Benefits of Blockchain: The Case of Felpharma S.R.L.

This paper introduces a case study of Felpharma S.r.l. The company renovated some of its Supply Chain Flows and used blockchain technology to achieve the targets. Hence, the paper explores the main operational challenges and the key benefits that blockchain provides. The paper ends with some managerial recommendations.
Research on Online Autonomous Learning Support System of Advanced Mathematics Based on Feature Extraction Algorithm

Abstract One of the key problems in researching and developing online autonomous learning support system of advanced mathematics which adapts to learners’ personality characteristics is to acquire learners’ characteristics. According to the theory of teaching structure and network teaching mode, based on analyzing the syllabus and teaching materials of higher mathematics, this paper puts forward the overall goal and design principle of the system, redesigns the teaching expression form, and designs an online autonomous learning support system of higher mathematics based on feature extraction algorithm. In the design of the system, the teaching idea of taking teachers as the leading factor and students as the main body runs through, so that students can arrange their own study plans, actively explore knowledge, actively participate in interaction and get feedback in time, thus improving the learning effect.