Reviewerly/exHarmony huggingface dataset:
PIDKG: Propagating Interaction Influence on the Dynamic Knowledge Graph for Recommendation

Modelling the dynamic interactions between users and items on knowledge graphs is very crucial for improving the accuracy of recommendation. Although existing methods have made great progress in modelling the dynamic knowledge graphs for recommendation, they usually only consider the mutual influence between users and items involved in the interactions, and ignore the influence propagation from the interacting nodes ( i.e. , users and items) on dynamic knowledge graphs. In this paper, we propose an influence propagation-enhanced deep coevolutionary method for recommendation, which can capture not only the direct mutual influence between interacting users and items but also influence propagation from multiple interacting nodes to their high-order neighbors at the same time on the dynamic knowledge graph. Specifically, the proposed model consists of two main components: direct mutual influence component and influence propagation component. The former captures direct interaction influence between the interacting users and items to generate the effective representations for them. The latter refines their representations via aggregating the interaction influence propagated from multiple interacting nodes. In this process, a neighbor selection mechanism is designed for selecting more effective propagation influence, which can significantly reduce the computational cost and accelerate the training. Finally, the refined representations of users and items are used to predict which item the user is most likely to interact with. The experimental results on three real-world datasets illustrate that the effectiveness and robustness of PIDKG outperform all the state-of-the-art baselines and the efficiency of it is faster than most of comparative baselines.
A Cross sectional Study on Association of Human Papilloma Virus Infection in Cervical Malignancy: Record based Regression Analysis

Papilloma viruses are small, non-enveloped, epitheliotropic, double stranded deoxyribonucleic acid (DNA) viruses that infect mucosal and cutaneous epithelia in a wide variety of higher vertebrates in a species-specific manner and induce cellular proliferation.The study was conducted to observe the association & prevalence of Human Papilloma Virus (HPV) infection in suspected (pre-malignant lesion) and known cases of cervical malignancy and to study the risk factor of HPV infection.Study design is based on observational recordbased hospital study via cross-sectional survey and review, and conclusion was drawn on primary data collection and analysis.Patients (Women) coming to out-patient department (OPD) and emergency department of Obstetrics and Gynecology at Rajendra Institute of Medical Sciences (RIMS), Ranchi, Jharkhand, India from March 2018 to February 2019 with cervical growth and unhealthy Cervix with abnormal uterine bleeding (AUB) or history of contact bleeding were considered as population.Sample size of 177 patients were considered for this study.Written and informed consent was obtained from all the participants after brief explanation of the procedure.Menstrual and obstetric history was taken in relation to the presenting complaints and then these patients were examined.Observational parameters recorded from each patient were age, socio-demography, age at first intercourse, age at first childbirth, reproductive history, marital history, tobacco consumption, type of cancer, type of HPV and clinical staging.It was found that association of HPV with unhealthy cervix which were either diagnosed as cancer or premalignant lesion (low grade squamous intraepithelial lesion or high grade squamous intraepithelial lesion) was found to be 94%.In the study, prevalence of HPV was found high in women in mid age group, women belonging to low socio-economic strata, women who were married at young age and women with high parity.
TBNet: Stereo Image Super-Resolution with Multi-Scale Attention

With 3D products being widely applied, more attention has focused on studying stereo image super-resolution (SR). Current stereo image SR studies mainly aim to improve the performance by the additional information from a pair of low-resolution stereo images. However, it is challenging for stereo image SR to fully exploit self-similarity information from its own image and parallax information between stereo image pairs. In line with these challenges, this paper presents a Two-Branch Network (TBNet) to integrate self-similarity information and parallax information for SR. In the TBnet, a stereo parallax transfer module with an encoder–decoder structure was first proposed to sufficiently transfer multi-scale parallax information and preserve the stereo consistency between stereo images. This paper further presented a residual pyramid self-attention module to employ self-similarity information to take advantage of self-predictive power. Finally, extensive experiments demonstrate the superiority of our model over the state-of-the-art performance in terms of objective and perceptual quality and the accuracy of disparity estimation.
SIGN LANGUAGE RECOGNITION USING MEDIA PIPE

Sign language is a form of communication used by millions of people worldwide who are deaf or hard of hearing.In recent years, there has been a growing interest in developing automated sign language recognition systems to improve communication between hearing and non-hearing individuals.The aim of this project is to develop an automated sign language recognition system that can improve communication between hearing and non-hearing individuals.This paper discusses the development of an automated sign language recognition system using MediaPipe, a computer vision framework.The proposed method involves capturing hand movements through a camera and processing them using Media Pipe's hand tracking and pose estimation algorithms.A convolutional neural network (CNN) is trained on a dataset of 10,000 grayscale images of American Sign Language (ASL) gestures to classify the signs.The system achieved an accuracy of 92.3% on the testing set, demonstrating the effectiveness of using MediaPipe and machine learning techniques in improving communication accessibility for the deaf and hard of hearing community.
ADOPTING TECHNOLOGIES IN DEVELOPING COMMUNICATION SKILLS IN JOBLAB ENGLISH TRAINING COURSE FOR GRADUATES

In the digital society variety of technological tools became available that bring the new tutoring student-centred ecosystem enhancing professional English L2 teaching/learning skills and abilities. The work aims at adopting technology in education (EdTech) by comparing the advantages and limitations and addressing experimental evidence on the effectiveness of technology-based approaches by developing English communication skills for graduates. The pre-experimental phase recognised and mapped the most effective and supportive approaches in topic-based presentation skills development. In an experimental 8-week lasting professional English language course, 50 students of economics from University Zilina formed an experimental group (N=25) with EdTech (educational technology) approach and a control group (N=25) with traditional teaching approach. The primary research method, the pedagogical experiment, is set to determine the correlation between students' attitudes to EdTech tools. The study is designed to explore the impact of EdTech on students' motivation, engagement, and development of speaking skills. In the post-experimental phase, pre/post-test results were compared based on the student's self-assessment questionnaire (5-Linkert scale, open-ended questions). Data was used to measure the differences in connection with the experiment. Both qualitative and quantitative research methods were implemented (mean, median, standard deviation) via SPSS software, and open-ended questions were coded and thematically analysed. The experimental group showed increased verbal and non-verbal skills and more strong retention, motivation, and engagement. Discussion leads expectation that with safe EdTech adoption, the future of education will look smart in terms of virtual reality, augmented reality, and artificial intelligence support enhancing real-world needs and core professional English skills development.
New Student Registration Platform Innovation Based Interactive Virtual Customer Relationship Management (vCRM)

The development of information technology in the field of registration must be supported by the existence of information that supports learning activities, facilities, without having difficulty getting information prospective new students can carry out online consultations via video calls in real time with Rangers 2.0 (Marketing) Raharja University. Usually a university only uses the chat method with whatsapp for prospective students to conduct consultations. Therefore, the purpose of implementing this web service is to produce a web-based application that can be used by prospective new students to register and consult directly with the campus. This new service system is expected to have a positive impact resulting in: 1) Providing the best service for prospective new student relations with the campus 2) Efficiency for both parties. The method used in this study is Interactive vCRM (Virtual Customer Relationship Management), which is to provide better services and approaches virtually. In this expert system service system, prospective new students can make video calls to consult with the marketing department and send the documents needed for registration. The end result of this study is to optimize the use of Rangers 2.0 to measure the quality improvement obtained. Improving the quality of the online registration process on a campus is expected to continue to improve the quality in the eyes of the public.
Mining Method of Power Grid Monitoring Alarm Association Rules Based on Improved FP-growth Algorithm

The core of current grid monitoring eventization technology is the discrimination method based on the alarm-event correlation rule base. However, with the continuous expansion of the grid scale, the traditional artificially refined rules can no longer meet the needs of monitoring eventization. Based on an improved FP-growth algorithm, this paper proposes a mining method for alarm association rules in power grid monitoring. First, the DBSCAN clustering model is used to replace the traditional sliding time window model to divide the original alarm siganls. Then, based on the FP-growth algorithm, a signal filter is added to eliminate the known signal pairs with strong correlation in the alarm cluster, and the signal is weighted according to the importance of the alarm signal. Next, according to the improved FP-growth algorithm, the association rules of the divided alarm clusters are mined. Finally, through the analysis and comparison of data from the power grid, the effectiveness of this method is verified, and the efficiency of rule mining is further improved, which has a certain reference value for the maintenance and improvement of the alarm-event correlation rule base.
Research on Blockchain Privacy Protection Method Based on Improved Schnorr Ring Signature

The stable operation of the power grid depends on the security and reliability of data. The characteristics of decentralization, openness, transparency, and traceability of blockchain not only improve efficiency and reduce costs but also face serious privacy leakage problems. Therefore, this paper attempts to propose an improved Schnorr signature algorithm, which improves the traditional Schnorr signature. Firstly, the principle of Schnorr signature is introduced and the flow of Schnorr signature is elaborated; secondly, the ring signature generation function Sign and the ring signature verification function Verify in Schnorr signature are improved, and the algorithm implementation of the improved Schnorr signature is given. Finally, the numerical simulation shows that the improved Schnorr signature is better than the traditional Schnorr signature in terms of signature efficiency, and further improves the level of blockchain privacy protection to achieve higher security.
Analisis Keterampilan Menulis Deskripsi Siswa Kelas VI SDN 2 Worawari

This study aims to determine: (1) the descriptive writis skill of grade VI students in SDN 2 Worawari; (2) the obstacles faced by students in writing descriptive reset. This type of the research was descriptive qualitative. This research war conducted in the second semester of the 2019/2020 in academic year of subjects were 23 grade VI students of SDN 2 Worawari. Data collection used test methods, and interviews. The validity of the data used technical triangulation. The data analysis technique in this study used Miles and Hubberman.The results of this study indicate that the writing skills of grade VI students of SDN 2 Worawari were 26% in the high category, 39% in the medium category, and 34% in the low category. The obstacles faced by students in writing descriptive were students still found difficulties to put ideas into words or sentences, and in correct spelling and punctuation.
Application of blockchain technology in virtual power plant considering distributed photovoltaic generation

Since 2014, Shanghai has built the virtual power plant operation system which includes commercial buildings, energy storage system, distributed photovoltaic and any other resources. However, there are also some problems during the construction of virtual power plant. On the one hand, the characteristics of distributed energy resources are different, so it is difficult to achieve optimal scheduling; On the other hand, data trusted sharing and intelligent transaction are imperfect, so it is difficult to realize an efficient transaction mechanism based on user trust. Therefore, it is necessary to use blockchain technology to promote the collaborative interaction ability of "source network load storage" resources and to optimize the intelligent transaction. In order to verify the demand response function of virtual power plant based on blockchain technology, this paper constructs an integrated monitoring platform based on the micro-grid in State Grid Shanghai Electrical Power Research Institude. The platform takes the distributed photovoltaic, charging pile, energy storage, air conditioning and any other adjustable loads as trading users. It can analyze and evaluate the power supply and power demand of micro-grid through way of power forecasting and load forecasting; When there is a major gap between power supply and power demand, the platform will automatically organize the various trading users to participate in the transaction through the smart contract technology of blockchain; Formulate the scheduling of adjustable load according to the transaction results; Write the settlement rules into the smart contract to realize the automation of transaction in virtual power plant. Smart contract can not only improve the credibility of transaction data certificate of virtual power plant, but also simplify the transaction process of massive load resources. In addition, it can reduce the transaction delay and improve the internal management efficiency of virtual power plant. The whole business process is stored on the chain and all data are obtained on the chain to ensure the safety of data during the transaction process. Generally the platform can improve the degree of automation through smart contract and can promote trusted transactions through the whole process data saving in the blockchain. The platform can be applied in other virtual power plants composed by distributed resources. It also can improve the development of new energy industry and the construction of power trading market in Shanghai.
Demystify Artificial Intelligence

Artificial intelligence (AI) marks a paradigm shift in every sector of the tech industry. AI-powered tools (e.g., robots) and applications (e.g., recommendation systems) have progressively permeated in almost every individual and business arena on different levels and been repeatedly hyped, particularly in Asia. Southeast Asian countries have undertaken a 200% higher adoption rate of robots than Europe or North America. China is overtaking Europe and North America in research investments on AI. Supported by government policies and a strong incentive plan, China is planning to become the leader in AI innovations by 2030. This trend is partially attributed to AI’s ability to augment human intelligence with unprecedented analytics and pattern prediction capabilities to improve the quality, effectiveness, and creativity of human decisions. The amount of data that is generated by machines far outpaces humans’ ability to absorb and interpret. In business, AI-powered tools or machines can uncover gaps and opportunities in the market more quickly, helping the firm introduce new products, services, channels and business models rapidly with a high level of quality. AI-powered robots can function just like or more than humans. Stephen Hawking warned that developments and advancements of AI-powered robots might be disastrous for mankind. AI experts made predictions of AI superseding or replacing humans with significant time variations: the timeline stretches between a 50% chance within 45 years and a 9% chance within the next 9 years (Grace et al, in J Artif Intell Res 62:729–754, 2018, p. 729); 30 years from Asia and 74 years by North American respondents. This chapter is intended to demystify AI by revealing the evolution of AI, what it is, how it functions, and where it can be applied. The positioning of AI as a commercial service is highlighted in this chapter.
Exploration and Application of Loose Leaf Teaching Material Reverse Engineering and Rapid Prototyping Technology under the Background of "Three Education" Reform

Based on the background of the "three education" reform, this project explores the application of loose leaf teaching materials of Reverse Engineering and Rapid Prototyping Technology, fully implements the National Vocational Education Reform Implementation Plan, closely combines the four in one talent training goal of "values+knowledge+skills+innovation", and aims at the teaching materials in the "three education" reform task.In the classroom construction research of Reverse Engineering and Rapid Prototyping Technology, combined with the specific situation of the professional students and their own teaching experience, this paper puts forward the application and exploration of teaching materials for the course of Reverse Engineering and Rapid Prototyping Technology.In view of the characteristics of vocational education, one is to change the proportion of theory and practice in the content of teaching materials, strengthen the connection with production and life, highlight the integration of theory and practice, and pay attention to the renewal and change of learning methods by technological development.The second is to improve the form of teaching materials, supplement the classical paper teaching materials with loose leaf teaching materials to new technologies and processes, build new teaching materials, and break through the status quo of traditional teaching materials, to build a complete curriculum teaching system by constantly improving the digital and information resources of curriculum teaching.In addition, through the improvement of loose leaf teaching materials and innovative research on the teaching mode of combining online and offline courses, we can achieve complementary advantages and promote the optimization of curriculum teaching.
Globally Consistent Vertical Federated Graph Autoencoder for Privacy-Preserving Community Detection

Community detection is a trendy area in research on complex network analysis and has a wide range of real-world applications, like advertising. As people become increasingly concerned about privacy, protecting participants’ privacy in distributed community detection has become a new challenge. When applied to not identically and independently distributed (Non-IID) data, most federated graph algorithms suffer from the weight divergence problem caused by diversified training of local and global models, resulting in accuracy degradation. Furthermore, the privacy protection approaches based on anonymization, such as differential privacy (DP), and cryptography, such as homomorphic encryption (HE), incur accuracy loss and high time consumption, respectively. In this paper, we propose a globally consistent vertical federated graph autoencoder (GCVFGAE) algorithm, which builds a globally consistent model among the coordinator and all participants to solve the Non-IID graph data problem. As well, an attribute blinding strategy based on security aggregation is developed to protect the network privacy of each participant without losing accuracy. Both real-world and artificial networks’ experiments show that our algorithm reaches higher accuracy than the existing vertical federated graph neural networks (GNNs) and the simple distributed graph autoencoder without federated learning and detects communities identical to those found by the standard graph autoencoder (GAE).
Representation of Chinese-Vietnamese Bilingual News Topics Based on Heterogeneous Graph

The Chinese-Vietnamese bilingual news topic representations are generated from Chinese-Vietnamese bilingual news texts describing the same topic into concise Chinese sentences that can correctly describe the topic. However, there is a semantic gap between Chinese and Vietnamese, and the association relationship between multiple documents in multiple languages is complicated, which makes it challenging to generate concise and correct topic representations. In this paper, we propose a cross-language topic representation method based on heterogeneous graphs. The method first uses a heterogeneous graph containing sentences and entity nodes to represent bilingual Chinese-Vietnamese news texts and effectively models the complex association relationships between multiple texts in multiple languages through graph attention networks (GAT). The topic encoder is then used to encode topic words into cues for topic representation generation, and the decoder side constraints are incorporated to generate the correct topic representation. The experimental results show that the proposed method improves the ROUGE value by up to 3.5 compared with the baseline method.
Study on the Function Positioning and Operation Model of Teacher Education College of Zhangjiakou University

There are different types and characteristics of teacher education colleges; the functional positioning and operation mode of the Teacher Education College of Zhangjiakou University is a unique type with two attributes of business management and service to teachers and students, and is an important department in the integration of teacher education in the university.
A Study on the Level of Teachers' Well-Being and the Influencing Factors under Double Reduction—A Case of Teachers in Compulsory Education in Guangxi

Based on the PERMA well-being model proposed by Martin E.P. Seligman, this study examined the current situation and factors influencing teachers' well-being in Guangxi's compulsory education from five dimensions: positive emotions, meaning, achievement, commitment, and relationships in the context of the double reduction policy.The results show that teachers' well-being is generally at an intermediate level, with uneven development of each dimension; individual factors such as teachers' professional competence, organizational factors such as students and families, and social factors such as teachers' treatment and policy implementation all influence the level of teachers' well-being.This paper then goes on to construct pathways to enhance teachers' happiness at the micro, meso, and macro levels