Reviewerly/exHarmony huggingface dataset:
Practice and Application of Wiki Open Source Document Platform Based on Vue

China education informatization has developed rapidly, and has experienced the development and construction process from “digital campus” to “smart campus”. In order to solve the practical problems of teachers and students accessing campus VPN, and improve the ability of smart campus services. The article proposes a Wiki open source document platform based on Vue, which mainly relies on MVVM, VuePress and Vue Router to build the entire service platform. According to the principle of project development, the system is divided into front end and back end according to functional modules, which improves the stability and scalability of the system. With the Vue framework, greatly improve development efficiency, shorten the development cycle, and provide important experience for other system development.
A Superconducting Quantum Chip Architecture Design Method for Quantum Programs

In the field of superconducting quantum computing, chip architecture design is an important research direction. Currently, most chips are designed for general-purpose architectures, and the fixed connection structure is difficult to ensure the flexibility of mapping, adding extra gate operations and affecting the execution efficiency of different quantum programs. To this end, this paper proposes a superconducting quantum chip architecture design method for quantum programs. The method aims at reducing the number of gates after mapping, and generates a chip architecture with higher fitness by combining the two-qubit gate information of quantum programs through an improved genetic algorithm. The experimental results show that the chip architecture generated by this method outperforms two 2 * 8 scale square lattice generic architectures in 33 test samples, with an average reduction of 35.3 % and 17.9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> in the total number of gates optimized, respectively, which verifies the effectiveness of this method.
Design Thinking Capabilities in the STEAM Education: A bibliometric Analysis

This paper aims to explore the scientific production and the thematic evolution of design thinking applied to education in STEAM. A bibliometric approach was used to quantify the impact, under the analysis of scientific mapping within a longitudinal framework with SciMAT. The results show a stability index of the research field of 58%, showing its gradual consolidation. This field emphasizes the topics of computational thinking, curriculum, educational innovation, human behavior in design, libraries, problem-solving, requirement engineering, system design thinking, systems engineering competencies, and user-centered design. Human behavior in design, and requirement engineering are the driving themes of the specialty for the period 2019-2022. In the same way, computational thinking and curriculum appears as a basic and transversal theme for this period and is very fundamental in the STEAM areas.Therefore, the identified themes simultaneously focus on conscious human behavior, interaction, and emotional involvement.Meanwhile, there is still limited discussion about human behavior related to STEAM areas.
Aggression and Hate in Spanish Text Messages, Identification Using a Pre-Trained Transformer Model.

Nowadays, social networks have given rise to the free expression of opinions and thoughts in real time, however, this can also lead to negative interactions, such as bullying, discrimination and other aggressive and hateful behaviour. To address this issue, different Natural Language Processing (NLP) methods and techniques exist. In this paper, a quasi-experimental investigation was carried out using the pre-trained Pysentimiento Transformer model to detect the presence of aggression and hate in Spanish text messages on the social network Twitter. Data extraction and processing tools were used to ensure the quality of the data before it was run through the model. A web interface was also designed to present the information obtained through graphs and tables, allowing a clear assessment of the detection of aggressive and hateful content in text messages through various analysis criteria. It is shown that it is possible to detect aggression and hatred in text messages using a Transformer model pre-trained for the task and use it to create systems or applications that detect and quantify these symptoms in messages written by people.
Improved Fashion Classification Method Base on GooLeNet

Traditional Image Classification algorithms have problems of high recognition error rate and low efficiency in fashion classification. So, the research proposes an improved model based on GooLeNet model.At first, we created a new inception network module which called inception-improved module.Compared with original inception module,Inception-improved module reduced the computational cost and improved the efficiency of the network.Secondly, we used Inception-improved modules to build the new model which named GooLeNet-improved and trained the model with Fashion image dataset. The experimental results show that the improved model GooLeNet-improved can obviously reduce the error rate of the fashion classification and improve the computational efficiency. The GooleNet-improved model achieved the accuracy of 87.1% on fashion image dataset, and the accuracy is respectively 0.8% higher than the original GooLeNet model.
SMOTE-TOMEK: A Hybrid Sampling-Based Ensemble Learning Approach for Sepsis Prediction

Sepsis is a lethal condition that requires early detection and intervention to improve patient outcomes. Machine learning algorithms have shown promise in detecting sepsis using electronic health records. This Study evaluates the performance of an ensemble based machine learning model for prediction of sepsis earlier using six vital signs. The research validates the tool and compares it to existing methodologies. The results suggest that the technique can accurately forecast sepsis up to 48 hours in advance. The method had high sensitivity and specificity, and it was resistant to missing data. Early prediction is critical for improving outcomes of sepsis patients, and delay in treatment increases the risk of patients. Therefore, machine learning algorithms can play a crucial role in early sepsis detection and prediction, leading to better patient outcomes.
Research on the construction of knowledge graph in the field of high speed rail vehicle equipment faults

In the daily maintenance and fault diagnosis of high-speed rail on-board equipment, a large amount of equipment fault data analysis has been accumulated, but the information of these fault analysis is too scattered to be effectively used by staff. In this paper, the construction of knowledge map in the field of high-speed rail vehicle equipment fault is preliminarily explored, and the knowledge map of high-speed rail vehicle equipment fault is constructed by combining top-down and bottom-up methods. In the process of entity extraction, this paper uses BERT+BiLSTM-CRF entity recognition model to extract entities. Through comparative analysis, BERT+BiLSTM-CRF model has a good effect of entity identification, and can identify and extract fault entities. Finally, a certain scale of vehicle-mounted equipment fault knowledge map is constructed according to the fault data of high-speed rail vehicle, which can effectively realize the structured storage and visual display of fault data and facilitate the guidance of vehicle-mounted equipment fault maintenance.
A Novel Hybrid Feature Extraction Technique and Spam Review Detection using Ensemble Machine Learning Algorithm by Web Scrapping

Objectives: To develop a novel hybrid method for feature generation and a novel dataset for experimenting and extracting the features for numerical representation.Methods: In the pursuit of the best spam review detection model, a four-stage process was undertaken.Initially, a dataset 'Fake reviews' was collected from Flipkart, containing 9926 samples from the home and kitchen products domain.Next, the data underwent pre-processing using the Natural Language Toolkit (NLTK) library.A novel Hybrid Feature Generator (HFG) was then developed, extracting informative features based on parameters like TF-IDF (Term Frequency -Inverse Document Frequency), sentiment analysis scores, and syntactic patterns.Finally, the model was trained on these generated features using Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), and Bernoulli Naïve Bayes (BNB) algorithms.Performance evaluation was conducted using metrics such as accuracy, precision, recall, and F1-score, comparing the model's results to gold standard or known spam reviews.Findings: The feature generation technique was implemented on three different models, and the models were trained using 70% of the available data.The results of these experiments showed that GNB, NB, and NB achieved testing accuracies of 99.7%, 96.4%, and 99%, respectively.The performance of these models was compared with and without the inclusion of extracted product review features.The results demonstrated that the GNB algorithm outperformed the other methods in terms of accuracy and precision.Novelty: This study presents a novel HFG for feature extraction from review-text and a novel dataset that outperforms hitherto reported https://www.indjst.org/approaches.
An analysis of griefs and griefing factors

We consider griefing factors, a system for measurement of the cost-effectiveness of sabotage, which have been used to analyze the attack resistance of blockchain systems. Attackers are said to “grief” if they accept economic harm to themselves in order to harm others; the griefing factor is the ratio of the harm done to the victim to the harm taken on by the attacker. In this work, we study the mathematical properties of this notion, particularly focusing on how the presence of players willing to engage in griefs at varying griefing factors impacts the equilibria present in games.
CONTRIBUTION OF SPACE REMOTE SENSING AND NEW GIS TOOLS FOR MAPPING GEOLOGICAL STRUCTURES IN THE MEKKAM REGION OF NORTHEAST MOROCCO

The Paleozoic buttonhole of Mekkam is located in the SE of the town of Taourirt, North Eastern Morocco.The geological framework of this buttonhole is characterized by the presence of terrains of different ages and lithologies.The oldest terrain forms a monotonous schistose bedrock with a flyshoid character of Devonian age unconformably underlain by volcanic and sedimentary formations of probable Upper Visean age, which have not been the subject of detailed structural studies.The objective of this work is to create a structural map based on satellite image processing techniques from space remote sensing.Our methodology uses remote sensing data composed of LANDSAT 8 Oli satellite imagery, taken under favorable weather conditions.Principal Component Analysis (PCA) and directional filtering were applied specifically.Thus, a map was produced using ArcGIS and Rockworks16 software.The application of these techniques allowed us to highlight the presence of lineaments with four main directions: N-S, NE-SW, E-W and NW-SE with a dominance of NW-SE and E-W directions.The interpretation of the obtained map was verified by field missions (measurements of faults, veins, and dykes).The NW-SE and E-W directions correspond in the field to mineralized quartz dykes and veins.The highlighted lineaments are concentrated in the Devonian-aged terrains where the geological material is represented by schists and volcanic rocks.The terrain is characterized by steep and rugged relief, sometimes difficult to access, such as the Sidi Lahcen massifs, the present study will initially help guide mining exploration by determining the geological lineaments in the region Thus, it confirms that remote sensing is an effective and less costly means of structural mapping in arid and semi-arid areas.
Effect of Psychological Factors on the Learning Outcomes of Saudi University Students According to the Application of Online Learning During the Corona Pandemic

The study aimed to investigate some psychological factors such as the attitudes towards e-learning and coping strategies considering the continuing Corona pandemic on the cognitive learning outcomes of Saudi university students.The study relied on the descriptive correlative approach to verify the objectives of the study.An available sample of university students was used, with sample size was 128 male students.The fear of corona, attitude towards corona, and coping strategies scales were used, and the study prepared a cognitive learning outcomes scale.EFA and CFA techniques performed the structural validity of the scales.Then, structural equation modeling was performed to test the relationships between study variables.The results revealed positive causal pathways from the coping strategies and attitudes towards e-learning to the cognitive learning outcomes.In addition, a negative causal pathway from fear of corona to cognitive learning outcomes.The study suffers from some limitations, which conducted on males without females.The study recommends improving the learner's attitudes towards e-learning and thus improving the technological self-efficacy of the academically poor, in addition to enhance the coping strategy based on problem-solving rather than focusing on those strategies based on actions.
Crop modeling frameworks interoperability through bidirectional source code transformation

Recently, we proposed Crop2ML, an open-source modeling framework for exchanging and reusing crop model components between modeling platforms. Here, we present an approach based on reverse engineering to automatically extract and transform meta-information and algorithms of existing crop biophysical models into a platform-independent model component. A search algorithm using Crop2ML concepts, and a many-to-one transformation system were used for producing high-level models. The system consists of parsing the codebase of model components written in different languages using the ANother Tool for Language Recognition (ANTLR) parser generator and processing the generated syntax trees to produce various model implementations. The system was evaluated for three crop model components provided by the BioMA, SIMPLACE, and DSSAT platforms. We demonstrated the extensibility of our approach with the STICS, OpenAlea, and SiriusQuality modeling platforms. CyMLTx is a significant contribution towards the interoperability of crop modeling platforms and the reuse of model components beyond programming languages.
High-performance Data Hybrid Encryption Scheme Based on Mimic Defense

Cryptography algorithms are crucial technologies in today's network security, playing important roles in areas such as cloud data, digital currencies, and blockchain. In this paper, based on the FPGA platform, we propose a high-performance hybrid encryption scheme by integrating mimic defense with multiple encryption algorithms. Firstly, we optimize the encryption algorithms 3DES, ZUC, and ChaCha20 using a pipelining approach and incorporate them as heterogeneous execution units in the mimic defense architecture, achieving high-speed hybrid encryption of data. Secondly, we generate random keys using pseudo-random number generation and employ the “one-time-one-key” encryption method to enhance resistance against attacks. Lastly, we integrate control mechanisms such as decision-making and feedback to enhance the security of the encryption defense system. Experimental results demonstrate that compared to other schemes, the throughput of the three algorithms in this scheme is improved by 67.3%, 357.1%, and 13.7%, respectively. By balancing efficiency and security, the proposed scheme enhances encryption speed and the reliability of encrypted data.
Protection Route Optimization for Multimedia Services

Ant colony algorithm finds better solution through positive feedback learning between ants and has good adaptability in different networks. This paper proposes a protection routing optimization algorithm based on ant colony optimization algorithm (PROA), which can provide differentiated protection for different multimedia services and carry out network load balancing to adapt to different network environments. The optimization objective of this algorithm includes multiple indexes, and the main challenge is how to find a better protection path based on feedback dynamic updating under complex indexes. Experimental results show that this method can effectively improve the service protection capability and load balancing degree of multimedia network.
Prospective Motion Correction and Data Re-acquisition at 7 Tesla using E/M Trackers

We evaluated the utility of electro-magnetic (E/M) based tracking to correct for motion at 7 Tesla. A 3D Turbo-FLASH sequence was modified to include prospective field-of-view (FOV) steering and track motion events. Custom software provided FOV updates to the TFL sequence from E/M sensor location measurements. Motion events recorded by the software indicated time-periods when acquired data was likely corrupted due to motion. Logic within the sequence re-acquired motion-corrupted k-space data segments to yield high-quality high-spatial-resolution images. Results demonstrate the viability of E/M tracking at 7T.
Enhance E2E Online Shopping System Using Secure Fingerprint Parcel Delivering Method

Nowadays, with the rapid growth of online marketing or online purchasing is increasing day by day as customers tend to purchase or buy goods online rather than physical shopping. This was due to the recent Covid-19 outbreak, which lead to a massive or vast of online shopping businesses or franchises to have emerged. However, with this vast growth of online businesses with massive online purchases being done every day, questions are being raised by customers as were these online businesses secure enough when comes to parcel delivery. With regards to that, a survey was conducted to gather customer’s feedback on the current online shopping method, whether was it secure enough or not and this gathered data was the main reason or input that provided purpose to initiate this research, where surprisingly, majority of the customers agreed that the current online shopping is not secured in terms of parcel delivery, in which there is no customer authentication process being done upon parcel delivered. Besides that, no designated parcel lockers are being built for unattended customers and even though there are parcel lockers, these lockers have low-security levels [1]. Therefore, the customer's concerns and the issues highlighted by previous researchers, explain the purpose of this research which was conducted to help curb the issues by implementing an alphanumeric sequence for customer verification and fingerprint sensing for customer confirmation during parcel delivery. Finally, this method will also be implemented for parcel lockers to enhance the security levels upon parcel collection.
The Impact of Environmental Disclosure on Market Performance: An Empirical Study of Jordanian Listed Industrial Companies

The world is currently grappling with the daunting environmental issue of global warming and climate change, which pose a significant threat to the future of our planet from various perspectives. Increased public awareness and concern over the detrimental effects of these problems have resulted in the creation of new environmental regulations. One important aspect of corporate governance that contributes to addressing this challenge is environmental accounting. By embracing the potential of environmental accounting, businesses can work towards achieving sustainable growth and development. The study aimed to investigate the impact of environmental disclosure on market performance in Jordanian industrial companies listed on the Amman Stock Exchange. The descriptive analytical method was relied on to achieve the study’s objectives. The data was collected through annual reports in addition to the financial statements of a group of industrial companies listed on the Amman Stock Exchange, which met the requirements of environmental disclosure, which were (43) industrial companies during the period (2017-2021). The special data in the study sample were analyzed using the statistical analysis software E-views. The arithmetic means standard deviation, and each of the upper and lower values was extracted in addition to using multiple linear regression analysis. The study concluded that there is a statistically significant effect of the environmental disclosure in both the market value and the stock turnover rate, in addition to Tobin’s Q ratio in the industrial companies surveyed. According to the findings, industrial companies in Jordan should be encouraged to become more interested in and open about their environmental activities. This is because being open about these activities helps them get more investments, start their financing and investment operations, increase their market value, and increase their investment market share.
P‐8.5: An Efficient Algorithm for Single Image Super‐Resolution

In order to solve the problem of insufficient key information and serious damage of detail texture in the feature extraction process of single image super‐resolution reconstruction algorithm when processing low‐resolution image, we propose a high‐performance feature information dependence algorithm (HPDA) based on a Prior Preserving Block (PPB) and Residual Feature Holding Block (RFHB) to obtain feature information dependence between image blocks. To better focus key information in the original image, We introduced an improved Enhanced Spatial Attention module (ESA) to ensure the overall integrity and accuracy of the image reconstruction. The reconstruction quality detail and reconstruction speed of different scales have achieved a good balance.
PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer

Personalized dialogue agents (DAs) powered by large pre-trained language models (PLMs) often rely on explicit persona descriptions to maintain personality consistency.However, such descriptions may not always be available or may pose privacy concerns.To tackle this bottleneck, we introduce PersonaPKT, a lightweight transfer learning approach that can build persona-consistent dialogue models without explicit persona descriptions.By representing each persona as a continuous vector, PersonaPKT learns implicit persona-specific features directly from a small number of dialogue samples produced by the same persona, adding less than 0.1% trainable parameters for each persona on top of the PLM backbone.Empirical results demonstrate that PersonaPKT effectively builds personalized DAs with high storage efficiency, outperforming various baselines in terms of persona consistency while maintaining good response generation quality.In addition, it enhances privacy protection by avoiding explicit persona descriptions.Overall, PersonaPKT is an effective solution for creating personalized DAs that respect user privacy.
Research and Application Status of Text Generation Tasks Based on Generative Adversarial Network

In recent years, in the field of natural language processing, significant progress has been made in text generation. Text generation has gained widespread popularity in many fields such as abstract extraction, poetry creation, and response to social network comments. Given the excellent generative capabilities of Generative Adversarial Networks (GAN), it is often used as main model for text generation with remarkable results. This review aims to provide the core tasks of generative adversarial network text generation and the architecture used to deal with these tasks, and draw attention to the challenges in text generation with generative adversarial network. Firstly, we outline the mainstream text generation models, and then introduce datasets, advanced models and challenges of text generation tasks in detail. Finally, we discuss the prospects and challenges of the fusion of generative adversarial networks and text generation tasks in the future.