Reviewerly/exHarmony huggingface dataset:
Research Article Introduction: from Structure to Functions

Introduction. A research article (RA) is considered to be a tool for conceptualizing new knowledge. The research article introduction is intended to demonstrate the motivation of the author to conduct the study and justify its necessity and social significance. The inability of the author(s) to write a high-quality Introductio n, replacing it with a description of the literature, reduces the interest of readers and hinders the promotion of the article in databases. And we do not mean only its structure being correct, but also the way each of its structural components performs its function. Writing an Introduction, it is also necessary to follow the rules of academic writing, since they make it possible to achieve analyticity and transparency of the description. The purpose of the editorial is to describe the structure and functional content of the RA Introduction through the prism of the content of an already published article. Materials and Methods . Based on the analysis of the traditional model of writing the Introduction and the options for its presentation in various subject areas, the structure of the RA Introduction was described. Further, the authors, using the example of the RA Introduction on the problems of food processing, commented on the declared structure. Results. A description of the rhetorical structure of the RA is presented. Linguistic features and strategies for describing a gap in knowledge are described. The comments of the editors are given regarding the typical shortcomings of the RA Introduction section. Conclusions . The material of this editorial will help the authors to optimize the introductions of their research articles and will enhance their visibility in scientometric databases and increase their citation.
Fault Diagnosis of Power Grid Based on Convolutional Neural Network

Due to the operation of regional networking, the scale of the power grid is becoming larger and larger, and a fault in the power grid needs to be located in the fault area timely and accurately. The models and structures of BP neural network and convolution neural network are analyzed. The training and test samples are constructed for a power grid model, and the BP neural network and convolution neural network are used for simulation verification respectively. The simulation results show that the convolutional neural network based grid fault diagnosis method has higher accuracy and fault tolerance.
Machine Learning enabled Missing Measurement Data Detection and Recovery of Electricity Grids

This paper proposes a machine learning enabled missing data detection and recovery of electrical measurements based on the improved CPCAE. The proposed solution firstly accurately models the missing generation process to generate the missing mask and then combines the absolute difference sequence and the linear correlation as criteria to detect the possible missing segments under different signal-noise ratios (SNR). The solution divides the detected missing mask into different grades and reshapes the origin of one-dimensional data and mask into two-dimensional matrices as a kind of data enhancement. Then we intuitively turn to the deep learning technologies on image processing and design an improved CPCAE model to repair the damaged images. The proposed machine learning-enabled missing data detection and recovery solution are assessed through simulations and the numerical results confirmed its effectiveness for different missing situations.
Research and Implementation of Curriculum Cluster Modern Teaching Model Based on Multi-objective Optimization Algorithm

Under the background of engineering education certification, cultivating students' innovation, competition and employment competitiveness have become the key to teaching. Based on the idea of multi-objective optimization and modern educational technology, this paper suggests the Zongheng group teaching model of curriculum cluster, which runs software projects through multiple courses to achieve a satisfactory teaching effect that the level of students' academic knowledge is balanced, multiple course resources are integrated, and a knowledge system is formed, and reaching the multi-objective goal of cultivating students' innovation, competition, and employment competitiveness. To test the effectiveness of multi-objective optimization of information-based teaching model, we also use software engineering major of the College of Computer and Information Technology of Mudanjiang Normal University as an example to carry out teaching practice. In a sense, this research provides a reference for follow-up research of curriculum reform and teaching construction of new engineering.
Design of Intelligent Recognition English Translation System Based on Machine Learning Algorithm

In recent years, with the continuous innovation and development of communication technology, different countries have become more closely connected, especially under the system of trade globalization, communication between different countries is more frequent, among which English is the One of the international common languages, the use of English language as an international communication reflects the importance of English translation (ET). This paper focuses on the research on translation systems that can only recognize English. ML (machine learning) algorithms can make feasible and effective results for large data sources in a relatively short period of time. Therefore, in the translation process, it is necessary to call internal and external resources, and use various translation strategies to understand the original text and output the translation. The ability to verify data plays an important role in this understanding and output process. This paper can greatly improve the efficiency and accuracy of ET through ML algorithms. After extracting a large number of translation rules, it is necessary to establish a probability model for the translation rules. The general methods include counting-based maximum likelihood estimation, EM method, discriminant training and other methods. The final results of the research show that the ML algorithm scores 91.7, 93.6, and 94.5 in terms of recognition speed, recognition accuracy, and update ability, respectively. Compared with the GLA algorithm, the ML algorithm has obvious advantages. The accuracy, and the ability to speed up the update are better.
Multi-population Quantum Genetic Algorithm Based on Optimal Computational Distribution

Ordinary quantum genetic algorithm is easy to fall into local optimum in the process of complex function optimization, especially in the case of random sampling in which it is difficult to find a solution close to the true value. Therefore, a multi-group quantum genetic algorithm combining the idea of order optimization is proposed, and the number of simulations is reasonably allocated through the optimal calculation allocation technology, which further improves the convergence speed and reliability of the algorithm. Experiments show that a solution close to the true value by this proposed algorithm can be found efficiently.
ANALISIS PLTS ATAP 25 KWP ON GRID KANTOR DPRD PROVINSI BALI

Development to new renewable energy still lower than Indonesia's target of 23% in 2025. For this reason, alternative solutions are needed, namely the use of renewable energy, one of which is from the solar energy sector, namely solar PV. Solar energy has the highest potential compared to other renewable energy sectors of 207.9 GW. Based on that, in 2020 the Province of Bali received a Rooftop Solar PV grant through the Ministry of Energy and Mineral Resources, one of which is located at the Bali Provincial DPRD Government Office with an installed power of 25 kWp using the On Grid system. Based on this, it is necessary to conduct a detailed study of the potential, operational systems, savings and the proper management model of Rooftop Solar PV at the Bali Provincial DPRD Office. The potential of Solar PV electricity production on the roof of the Bali Provincial DPRD Office was analyzed by simulating the orientation and tilt angle of the Solar PV to get the optimum Solar PV electrical energy production results using HelioScope software. The results showed that the results of electrical energy production in the simulations of the two scenarios tended to be higher with the total difference in scenario 1 and scenario 2 being 21.75% and 19.66%, respectively. For savings that can be obtained for 6 months of Rp. 36,055,301 with a percentage of 22.60%. The PLTS management model in the future is located in the general section, precisely in the Sub Coordinator of the Equipment, Assets and Household Substance Unit.
STUDI ANALISA PENGHEMATAN ENERGI DI GEDUNG DEWAN PERWAKILAN RAKYAT DAERAH KABUPATEN BADUNG

Today, excessive use of electrical energy will certainly cause energy wastage, given the energy crisis that has hit all countries, the government must have made efforts to save energy use. In 2019, the energy consumption of the Badung Regency DPRD Building was 41.8 kWh/m2 month, when compared to the Minister of Energy and Mineral Resources Regulation No. 13 on an Air-conditioned Building, which was 14 kWh/m2 month, the DPRD Building was included in the wasteful category. Based on these problems, efforts are needed to evaluate the use of electrical energy in the DPRD Building by means of energy conservation. The purpose of the study was to identify the potential for saving electrical energy that can be applied to the Badung Regency DPRD Building. The research method uses a descriptive quantitative approach where the results of calculations and measurements are then described and compared with Indonesian national standards on energy conservation. The results of the research show that the potential for saving electrical energy can be done by replacing the standard Split AC into a low watt AC where the electrical energy savings are obtained by 11.62% or a budget savings of Rp. 21,068,468.-
Accelerator-Aware Training for Transducer-Based Speech Recognition

Machine learning model weights and activations are represented in full-precision during training. This leads to performance degradation in runtime when deployed on neural network accelerator (NNA) chips, which leverage highly parallelized fixed-point arithmetic to improve runtime memory and latency. In this work, we replicate the NNA operators during the training phase, accounting for the degradation due to low-precision inference on the NNA in back-propagation. Our proposed method efficiently emulates NNA operations, thus foregoing the need to transfer quantization error-prone data to the Central Processing Unit (CPU), ultimately reducing the user perceived latency (UPL). We apply our approach to Recurrent Neural Network-Transducer (RNN-T), an attractive architecture for on-device streaming speech recognition tasks. We train and evaluate models on 270K hours of English data and show a 5-7% improvement in engine latency while saving up to 10% relative degradation in WER.
Research on Career Guidance Course System based on Apriori Algorithm and Computer Big Data

Aiming at the problems of unsmooth information and difficulty in statistical analysis of information in the employment guidance work of college students, the article uses Visual Fox-Pro to program, designs a data verification program, and realizes the function of data intelligent proofreading. Secondly, the article applies the Apriori algorithm in data mining technology, takes university employment history data as the research object, and takes the student employment guidance as the research purpose. Through the mining of university employment data, it finds the internal factors that affect the employment of students, and discovers the education attributes and education attributes of students. The correlation between employment attributes. Through the actual test of the optimization algorithm, the generation of a large number of meaningless association rules can be avoided and the mining efficiency can be improved.
Evaluation Method of Multidimensional Scientific Research Value of Power Grid Enterprises Based on Fuzzy Analytic Hierarchy Process

Because the traditional scientific research value evaluation method is not comprehensive enough for the analysis of relevant indicators, resulting in the final evaluation results and professional evaluation results of the degree of the fitting is low, this paper proposes a multi-dimensional scientific research value evaluation method for power grid enterprises based on fuzzy analytic hierarchy process. The Evaluation index system of scientific research value is constructed from the dimensions of economic benefit, social benefit, scientific and technological value, environmental benefit, operational capacity, the scope of application, and effective time domain, on the basis of which a fuzzy judgment matrix includes membership degree. The non-membership degree and hesitation degree are constructed. A single evaluation index of scientific research value is taken as a level. The method of consistency check is used to analyze and calculate the information distance in the fuzzy judgment matrix, which is used as the value evaluation result of the corresponding dimension. In the example analysis results, the evaluation results of the design method and the expert evaluation results have a high degree of the fitting.
Inovasi pelayanan era new normal di Dinas Perpustakaan dan Kearsipan Kota Cirebon

The presence of the New Normal era is not only a solution to reducing the tension of the Covid-19 pandemic and creating a new order of life but also a challenge for various sectors that must continue. The closure of the library building should not be a reason for libraries to stop providing information services to the public. The Cirebon City Library and Archive Service (Dispusip) continues to adapt to changes and strive to implement services to keep running. This study aimed to find out the form of Cirebon City Dispusip's innovation and obstacles during the Covid-19 pandemic. This study used a descriptive qualitative approach. Data collection techniques were carried out through field observations, direct interviews, and literature studies. The informants selected in this study were two employees at the Cirebon City Dispusip. The study results indicated that in the implementation of library services during the Covid-19 pandemic, the Cirebon City Office of Education had made innovations in the form of online services. They included several types of services, namely offline visiting services with limited quotas, online circulation services, online membership services, digital library application services, as well as digital-based creative literacy programs. The obstacle faced by the Cirebon City Dispusip in implementing online service innovations was the problem of system/server errors. The existence of online service innovations that have been implemented has enabled services at the Cirebon City Dispusip to continue running even in a pandemic.
Comparison of Port Security Switch Layer 2 MAC Address Dynamic With MAC Address Static Sticky

Security and stability in a network service is a top priority for a network administrator. The slightest security vulnerability can make a very big threat in the stability of network services. The rise of cybercrime that intercepts access to a network service by performing ARP spoofing to imitate a client who has the right to access the network, with this happening it can be detrimental and disrupt network services. The application of MAC Address filtering to access network services is able to minimize the occurrence of cybercrime in the network. The filtering technique used is by registering the MAC address of each network service user who will connect to the network. This technique is able to recap the MAC Address on each device in the MAC Address table and is able to block access to clients whose MAC addresses are not registered. The test results obtained for the comparison of MAC Address filtering security using Dynamic with sticky MAC addresses are that the implementation of port security static sticky is considered better than the implementation of dynamic port security, where if there is a new client trying to access the network and the client's mac address is not registered then the client absolutely does not get access to network services.
Detecting biased user-product ratings for online products using opinion mining

Abstract Collaborative filtering recommender system (CFRS) plays a vital role in today’s e-commerce industry. CFRSs collect ratings from the users and predict recommendations for the targeted product. Conventionally, CFRS uses the user-product ratings to make recommendations. Often these user-product ratings are biased. The higher ratings are called push ratings (PRs) and the lower ratings are called nuke ratings (NRs). PRs and NRs are injected by factitious users with an intention either to aggravate or degrade the recommendations of a product. Hence, it is necessary to investigate PRs or NRs and discard them. In this work, opinion mining approach is applied on textual reviews that are given by users for a product to detect the PRs and NRs. The work also examines the effect of PRs and NRs on the performance of CFRS by evaluating various measures such as precision, recall, F -measure and accuracy.
Token Turing Machines

We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is inspired by the seminal Neural Turing Machine, and has an external memory consisting of a set of tokens which summarise the previous history (i.e., frames). This memory is efficiently addressed, read and written using a Transformer as the processing unit/controller at each step. The model's memory module ensures that a new observation will only be processed with the contents of the memory (and not the entire history), meaning that it can efficiently process long sequences with a bounded computational cost at each step. We show that TTM outperforms other alternatives, such as other Transformer models designed for long sequences and recurrent neural networks, on two real-world sequential visual understanding tasks: online temporal activity detection from videos and vision-based robot action policy learning. Code is publicly available at: https://github.com/google-research/scenic/tree/main/scenic/projects/token_turing
Experiences of a virtual think tank. New ways of working, knowledge development and Virtual Reality events

Virtual Reality (VR) as a paradigm for casual communication and socialising is seeing increasingly high adoption rates, but since the pandemic it also sees consideration as an alternative environment for (remote) knowledge work. This study explores VR as it relates to knowledge development at scale (i.e., conferences/events), to identify barriers for adoption. The study consists of observations of a series of VR and videoconferencing events, interviews with organisers and participants, and a survey of participants. Elements of Extended Adaptive Structuration Theory were used to examine results, focusing on technological, social, and performance dimensions. Results show no clear technological obstructions for events’ outcome quality compared to conventional forms of remote collaboration. VR provides practical advantages involving non-verbal communication and immersion/presence over conventional alternatives, but lacks in other aspects (e.g., facial expressions). Organisers are still learning to work around the practical limitations of VR. Issues raised primarily relate to technology habituation, or to social interactions and the cultural coordination gaps stemming from a lack of (communication) agreements. Emphasising the advantages of VR while further developing technologies and thoughtful social conventions to alleviate the objections will further open the door to VR as a viable alternative for remote work.
An Analysis of College Students’ Behavior Based on Positive and Negative Association Rules

Positive association rule (PAR) mining has been often used in campus data analysis, while negative association rule (NAR) mining has not, which will result in a lot of valuable information missing. This paper collects real campus data that contains the student’s academic performance, e-card consumption behavior, book lending records and mental health status and analyzes them with NAR and PAR techniques. We first preprocess the data to obtain a suitable format. Then we use a method called Positive and Negative Association Rules on Correlation (PNARC) to mine NARs and PARs from these data. Finally, we obtain a lot of valuable information, for example, there is a strong negative correlation between depression and academic performance. These results are very helpful for educators to improve college students’ performance.
Research on FPGA Accelerator Optimization Based on Graph Neural Network

In this paper, Field Programmable Gate Array (FPGA) hardware structure is designed to accelerate the graph neural network model, and suitable hardware structure is designed for different computing modules to accelerate it, so that the function of the whole system can be improved. The performance of computing intensive applications can be improved by using heterogeneous systems, which are composed of various processor architectures, such as FPGA. Graph Neural Network (GNN) is a framework that uses deep learning to learn graph structure data directly in recent years. Its excellent performance has attracted scholars’ high attention and in-depth exploration. By formulating certain strategies on the nodes and edges of the graph, the graph neural network converts the graph structure data into a standard representation, and inputs it into a variety of different neural networks for training. Good results have been achieved in the task of node classification, edge information dissemination and graph clustering.
Tower Building Technique on Elliptic Curve with Embedding Degree 72

Pairing based cryptography is one of the newest security solution that attract a lot of attention, because we can work with efficient and faster pairing to make the security a lot practical, also the working with extension finite field of the form F p k is more useful and secure with k ≥ 12 the implementation become more important.In this paper, we will presents cases studies of improving pairing arithmetic calculation on curves with embedding degree 72.We use the tower building technique, and study the case when using a degree 2 or 3 twist to carry out most operations in F p 4 , F p 6 , F p 8 , F p 9 , F p 12 , F p 18 , F p 24 , F p 36 and F p 72 .