Reviewerly/exHarmony huggingface dataset:
Realization of English Instructional Resources Clusters Reconstruction System Using the Machine Learning Model

Based on ML algorithm, this paper puts forward a method that can search instructional resources through keyword indexing technology, and then cluster and recombine the related results and present them centrally. In this paper, the semantic processing of user query based on the subject index of educational resources is adopted to make up for the deficiency of query semantics, solve the problem of mismatch between query words and document words, and improve the recall and precision of resource retrieval. It is proposed to select the category feature items manually and establish the category feature model. In the environment of small sample set, the weight of category feature items is trained by ML method. The research shows that the user rating of this system is ideal, reaching 93.21% at the highest. In addition, the stability of the system can still reach 89.31% under the condition of relatively large usage, and its performance is excellent. This system can effectively solve the problem of scattered distribution of English instructional resources and make the presentation of knowledge more in line with the needs of users, thereby further improving the utilization rate of English instructional resources and users’ satisfaction.
Information Dissemination Model in Rural Live Broadcasting under Blockchain in the Era of Artificial Intelligence

This work aims to improve the information dissemination atmosphere of rural live broadcasting and ensure its long-term development. First, it studies and analyzes the current situation and characteristics of rural live broadcasting. Then, it discusses the blockchain (BC) technology and information dissemination mode in the era of artificial intelligence (AI). Finally, the characteristics of BC technology (BT) and the information dissemination model are discussed. A BT-optimized susceptible exposed infected recovered (SEIR) information dissemination model is proposed. The results show that the BT-optimized SEIR model has no effect on the traditional information transmission characteristics and can ensure the conventional transmission of all information. Additionally, BT can effectively improve the overall transmission efficiency of the SEIR model for false information and accurate information and effectively shorten the duration. Specifically, the BT-optimized SEIR model's maximum false information transmission proportion is reduced by about 13%. The duration is reduced by 15 days over the traditional model. By comparison, in positive information dissemination, the maximum transmission proportion of the proposed BT-optimized SEIR model is about 19% higher than the traditional model. The duration is about 30 days longer. The research provides technical support for controlling and improving the current situation and atmosphere of rural live broadcasting information dissemination.
MediaEval multimedia evaluation benchmark

MediaEval is a benchmarking initiative that offers challenges in multimedia retrieval, analysis and exploration. The tasks offered by MediaEval concentrate specifically on the human and social aspects of multimedia. They encourage researchers to bring together multiple modalities (visual, text, audio) and to think in terms of systems that serve users. Our larger aim is to promote reproducible research that makes multimedia a positive force for society. In order to provide an impression of the topical scope of MediaEval, we describe a few examples of typical tasks.
How bitcoin makes burning fossil fuels more profitable than ever

Bitcoin mining is a lucrative but energy-intensive activity. To keep costs low, miners are finding new ways to make their own electricity, often derived from fossil fuels. Mining companies are buying struggling coal, coal waste, and gas power plants and using the electricity they generate for their mining operations. Existing utility companies and power plants are pivoting to mining to make up for declining demand. Oil and gas companies are also buying mobile generators and installing them at well sites, mining right there at the oil or gas well, or selling the gas to third-party miners. The fossil fuel-to-bitcoin pipeline is getting shorter and shorter, and associated greenhouse gas emissions are climbing.
Awareness-Raising Program of Anemia Using Comics and Social Media for Teenage Girls

Nutritional problems that teenagers often experience are malnutrition, overweight, obesity and anemia. Risk of anemia in teenage girls is higher than in boys because of the increased demand of iron during the menstruation period. However this also becomes problematic because girls often limit their food intake. Blood Boost Formula is often prescribed to prevent anemia in teenage girls. The purpose of this community dedication activity is to find out about the compliance with consuming Blood Boost Formula, anemia prevalence and knowledge improvement of the audience after the program. This community dedication activity used comics and social media such as WhatsApp group. The target of this activity is 47 teenage girls of SMK Budi Nasional Tebing Tinggi students. Measured parameters for evaluation are medication compliance with consuming Blood Boost Formula, the degrees of awareness in anemia using questionnaire and anemia prevalence through Hemoglobin test using digital test. The results are showing that 69% of the students are not complying with the medication. There are 34% anemia prevalences. The pretest score was 20 points at the lowest and 60 points at the highest. While the post-test score was 27 points at the lowest and 67 points at the highest. There is an increase of mean value between pretest and post-test by about 10 points.
A Text Classification for Vietnamese Feedback via PhoBERT-Based Deep Learning

With the rapid development of social media platforms as well as the current pandemic, the majority of activities are performed online. The user comments obtained from the digital channels are crucial in order that the agencies or organizations can improve and develop their brand. Thus, an automatic system is necessary to analyze the sentiment of a customer feedback. Recently, the well-known pre-trained language models for Vietnamese (PhoBERT) have achieved high performance in comparison with other approaches. However, this method may not focus on the local information in the sentiment like phrases or fragments. In this paper, we propose a PhoBERT-based convolutional neural networks (CNN) for text classification. The output of contextualized embeddings of the PhoBERT’s last four layers is fed into the CNN. This makes the network capable of obtaining more local information from the text. Besides, the PhoBERT output is also given to the transformer encoder layers in order to employ the self-attention technique, and this also makes the model more focused on the important information of the text segments. The experimental results demonstrate that the proposed approach gives competitive performance compared to the existing studies on three public datasets with Vietnamese texts.
The Improved Estimation of the Least Upper Bound to Search for RSA’s Private key

RSA is known as one of the best techniques for securing secret information across an unsecured network.The private key which is one of private parameters is the aim for attackers.However, it is exceedingly impossible to derive this value without disclosing all unknown parameters.In fact, many methods to recover the private key were proposed, the performance of each algorithm is acceptable for the different cases.For example, Wiener's attack is extremely efficient when the private key is very small.On the other hand, Fermat's factoring can quickly break RSA when the difference between two large prime factors of the modulus is relatively small.In general, if all private parameters are not disclosed, attackers will be able to confirm that the private key is unquestionably inside the scope [3, n -2], where n is the modulus.However, this scope has already been reduced by increasing the greatest lower bound to [dil, n -2], where dil ≥ 3. The aim of this paper is to decrease the least upper bound to narrow the scope that the private key will remain within this boundary.After finishing the proposed method, the new scope of the private key can be allocated as [dil, dir], where dir ≤ n -2.In fact, if the private key is extremely close to the new greatest lower bound, it can be retrieved quickly by performing a brute force attack, in which dir is decreased until it is equal to the private key.The experimental results indicate that the proposed method is extremely effective when the difference between prime factors is close to each other and one of two following requirement holds: the first condition is that the multiplier of Euler totient function is very close to the public key's small value whereas the second condition is that the public key should be large whenever the multiplier is far enough.
Social Impact in Design Education

Social design has become a significant part of design education system. The paper takes transformation of design education as background and focuses on in-depth analysis of social design related degree programs, curriculums, methodologies, and intuitional influence of organizations towards social design education shift. The paper addresses that how social impact affects the development direction of related disciplines and its relationship, discusses its complexity and extension, and actively integrates social resources to develop new forms of design practice. The pedagogy for social design employs a collaborative, systems-oriented approach to design and social thought through emphasizing design-led research. It helps students understand their role, define their engagement, and develop creative practices to address emerging and complex challenges by considering social, economic, political, and environmental issues from multiple perspectives and scales to generate transformative multimedia strategies. The paper brings a new perspective for future social design learning and demonstrates the social impact in design education in a general framework, and which help emerging designers understand the role they play in design practice worldwide, as well as foster an understanding of civic engagement and contrasts it with the idea of design as service.
Fine Grained Categorization of Drug Usage Tweets

Drug misuse and overdose has plagued the United States over the past decades and has severely impacted several communities and families. Often, it is difficult for drug users to get the assistance they need and thus many usage cases remain undetected until it is too late. With the booming age of social media, many users often prefer to discuss their emotions through virtual environments where they can also meet others dealing with similar problems. The widespread use of social media sites creates interesting new opportunities to apply NLP techniques to analyze content and potentially help those drug users (e.g., early detection and intervention). To tap into such opportunities, we study categorization of tweets about drug usage into fine-grained categories. To facilitate the study of the proposed new problem, we create a new dataset and use this data to study the effectiveness of multiple representative categorization methods. We further analyze errors made by these methods and explore new features to improve them. We find that a new feature based on tweet tone is quite useful in improving classification scores. We further explore possible downstream applications based on this classification system and provide a set of preliminary findings.
Study on IoT Big Data Direction in Civil Aviation

“Big data” is a hot word in the IT industry in recent years. Under the new normal, big data is changing all walks of life, including the civil aviation industry. In the context of the increasingly close relationship between the aviation industry and people’s daily travel, the application of big data in the civil aviation field continues to increase, so that the civil aviation industry better conforms to the development trend of the times. This article mainly talks about in-depth exploration of the IoT big data direction in the field of civil aviation.
Preprocessing Requirements Documents for Automatic UML Modelling

Current approaches to natural language processing of requirements documents restrict their input to documents that are relevant to specific types of models only, such as domain- or process-focused models. Such input texts do not reflect real-world requirements documents. To address this issue, we propose a pipeline for preprocessing such requirements documents at the conceptual level, for subsequent automatic generation of class, activity, and use case models in the Unified Modelling Language (UML) downstream. Our pipeline consists of three steps. Firstly, we implement entity-based extractive summarization of the raw text to enable highlighting certain parts of the requirements that are of interest to the modelling goal. Secondly, we develop a rule-based bucketing method for selecting sentences into a range of ‘buckets’ for transformation into their corresponding UML models. Finally, to prove the effectiveness of supervised machine learning models on requirements texts, a sequence labelling model is applied to the text specific for class modelling to distinguish classes and attributes in the running text. In order to enable this step of our pipeline, we address the lack of available annotated data by labelling the widely used PURE requirements dataset on a word level by tagging classes and attributes within the texts. We validate our findings using this extended dataset.
A comparison study of MABC, ACO, ABC and PSO load balancing algorithms

This paper shows a comparison study of dynamic load balancing algorithms using a CloudSim simulator. As we know technology is growing rapidly and every software requires some kind of information or data, and other resources as input, these resources might not be present locally. The resources are provided to various users using cloud computing and virtual data centres all over the world. Many organizations have migrated to cloud to provide their services on a global level, thus becoming cloud service providers. When they become global they receive huge number of requests from user all over the world and to handle those requests they need to manage their data bases efficiently and effectively. And here comes the concept of load balancing, which is used by these service providers. Load balancing comes under the NP-hard which is further regarded as optimization problem. Many researchers tried to propose load balancing algorithms based on different theories, logics, strategies and approaches. Aiming to minimize makespan time which in turn helps in reducing the overall response time, the paper provides detailed approach for a comparison study to determine the algorithm that best fits for our requirement. The algorithm improves the fitness function by minimizing the makespan time. The proposed comparison study compares modified algorithm with different algorithms already present and the results also show that the algorithm performs with lower response time in a decentralized environment with dynamic tasks.
Text summarization using NLTK with GUI interface

In today&apos;s digital world everything is revolving around data. Now every individual require everything to be in a precise manner and wants the work to be done as fast as possible. So, our project is mainly based on “Summarization of Text”, which is a process of deflating the text to help people save their time. If the data generated is enormous every individual do not have time and patience to read and understand the given data. In this project we provide solution for the problem faced by user to get an idea about huge data by summarizing it. Text summarization is the process of automatically extracting a compressed version of the document by preserving its information. There are two types of summarization extractive and abstractive here we go with the extractive method which simplify the problem by considering the subsets of the sentence and its aim is to cover the important sentence for understanding the document. We developed this by considering text rank algorithm which follows by tokenization and vectorization. The experiment results shows that our suggested approach provides quality in summarization by maintaining its information.
The Impact of Asset Management Efficiency on the Financial Leverage and Return on Investment in Small Production Enterprises in View of the Covid-19 Pandemic. Case Study: The Jordanian Ministry of Social Development - Zarqa Governorate

The study aimed to determine the impact of the asset management efficiency of small production enterprises on financial leverage and the return on investment in view of the Covid-19 pandemic. The Jordanian Ministry of Social Development - Zarqa Governorate was selected as a case study. The descriptive-analytical approach was used to achieve the study objectives. The data related to the study variables were collected from the statements of Social Development Directorates in Zarqa Governorate, as these directorates are the case study of the study population represented by 40 directorates of the Ministry of Social Development in Jordan. The study concluded that there is a statistically significant negative impact of the asset management efficiency on the financial leverage in small production enterprises in the Ministry of Social Development - Zarqa Governorate. Also, there is a statistically significant positive impact of the asset management efficiency on the return on investment in small production enterprises in the Ministry of Social Development in Zarqa Governorate. The study recommended providing training courses for these enterprises’ ownership to empower them to face economic crises, especially in view of global economic crises such as the Covid-19 pandemic and increase the ability to reduce financial leverage and raise the return on investment for these enterprises.
Self-Study System Evaluation of Oral English Based on Limit Theory

With the globalization of the contemporary social economy and its ever-increasing influence, it is particularly important to master a common language in the world at this stage English. For the subjects who have the opportunity to learn English, their listening, reading, writing, and other abilities are relatively good, but the speaking ability is relatively lacking. Therefore, the self-study of spoken English is particularly important for the improvement of the practical application ability of English, and an efficient self-study system will also play a better role in the self-study process. The efficiency of a system depends on the maximum and minimum magnitudes that the system can carry. Therefore, for the self-learning system of spoken English in this paper, it is necessary to calculate the maximum and minimum carrying capacity. Therefore, it is necessary to deal with the limit thinking of the maximum and minimum carrying capacity of the system. The evaluation based on limit theory in this paper is a kind of evaluation of the maximum and minimum carrying capacity of the English oral self-learning system by using the convergence and dispersion characteristics of limit thinking. In order to evaluate the performance of the oral English self-learning system, this paper implements the dynamic evaluator of the oral English self-learning system, and constructs a hardware system for the self-learning oral English. This paper also uses the hidden Markov model in the self-learning system and the application of the central limit theory. The corresponding experimental results show that the mean value of the spoken English test data is 25.1337, and the standard deviation is 2.01385, which is in line with the normal distribution. It shows that the evaluation based on the limit theory can make the operation of the spoken English self-learning system stable.
Blockchain Based Scalable Model for Secure Dynamic Spectrum Access

Conventional Cooperative spectrum sensing techniques either suffer from single point of failure attack or lack in providing incentives to users which makes them incompatible for Wireless Service Provider (WSP). We propose a dynamic spectrum access framework for WSP which gives prominence to automated sensing and sharing with the use of blockchain. In this system, the opportunity of spectrum access is first examined by sensor nodes and the access right is then allocated to the users when their transactions to WSP are authenticated in a decentralized manner. Apart from using blockchain as a reliable platform for automatic enforcement of spectrum sensing, we propose a novel mechanism for securing our network from the threats designed primarily for Cognitive Radio Networks. In addition to this, our proposed approach enhances the scalability of blockchain networks by using the sidechains for storing data and checkpointing it onto main chain after periodic intervals of time. Extensive simulations in Octave indicate superior performance offered by our proposed model.
Three dimensional image encryption algorithm based on quantum random walk and multidimensional chaos

With the development of computer network technology, people’s requirements for information security is increasing day by day. However, the classical encryption technology has the defects of small key space and easy crack. The problems of image encryption technology in protecting image information security and private content need solving urgently. As a new type of quantum key generator, quantum random walk has a large key space. Compared with the classical random walk, the computing speed and security are significantly improved. This paper presents a three-dimensional image encryption algorithm that is based on quantum random walk and involves Lorenz and Rossler multidimensional chaos. Firstly, Gaussian pyramid is used to segment the image. Secondly, the Hamming distances of several sub images are calculated by using the random sequence generated by quantum random walk and the random sequence generated by Lorenz chaotic system in multi-dimensional chaos, and then synthesized, and the Euclidean distances between the three RGB channels of the image are calculated. Finally, the sequence value obtained from the remainder of Hamming distance and Euclidean distance, as an initial value is input into the Rossler system in multi-dimensional chaos to generate a random sequence which is used as the key to XOR the RGB channel of the image so as to create an encrypted image. The corresponding decryption scheme is the inverse process of the encryption process. In addition, in terms of transmission security, this paper uses a blind watermark embedding algorithm based on DCT and SVD to embed the watermark information into the encrypted image, so that the receiver can extract the watermark and judge whether the image is damaged by the attack in the transmission process according to the integrity of the watermark information. If it is not attacked maliciously, the image will be decrypted. This operation further improves the protection of image information security.The experimental results show that the peak signal-to-noise ratio of the encrypted image is stable between 7 and 9 and the encryption effect is good, the GVD score is close to 1, the correlation of the encrypted image is uniformly distributed, and the correlation coefficient is close to 0, and the key space is 2&lt;sup&gt;128&lt;/sup&gt; in size and the encrypted histogram is evenly distributed, showing a high ability to resist statistical analysis attacks.