Reviewerly/exHarmony huggingface dataset:
China’s policy similarity evaluation using LDA model: An experimental analysis in Hebei province

This article proposes a combination model, which is composed of latent Dirichlet allocation model, TF-IDF feature extraction algorithm and Euclidean distance measurement method, to identify and judge whether the similarities between multiple policy texts exist or not. With the help of actual data result, this will drive the relevant government agencies to figure out problems in a timely manner and provide a decision-making basis for them to formulate and optimise appropriate economic policies. To this end, this article analyses and studies the four types of economic texts that are classified as Insurance, Banking, Tax and Finance from the Central Government of Hebei province and Shijiazhuang city levels. Also, we consider Beijing, Shanghai and Guangdong. Experimental results show that (1) the combination model can quickly and effectively recognise and determine whether there are similarities between multiple economic policy texts; (2) similarities exist or not between the central, provincial and municipal level policy texts depending on the comparison of the distance values across them; (3) the smaller the distance value between economic policy texts of the same kind, the higher the similarity in them; and (4) the distance values between the six policy texts in Finance, Insurance, Bank and Tax categories are ranked from low to high. In terms of similarity, the Finance category is the highest, followed by Insurance and Bank, and the Tax category is the lowest.
Evaluation of Mathematical Models for Teaching Precision in the Framework of Big Data

With the continuous development and application of data computing in the whole society in the construction of digital campus and intelligent campus of each higher education institution. In the environment of education, universities use these data well, which not only affects the orderly operation of higher education but also will become an inexhaustible power to help higher education promote the reform and innovation of education and teaching system. In this paper, we focus on the teaching operation and students’ independent learning by taking students’ evaluation data and students’ online learning data of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>Y</mi> </math> school as the research objects. We conducted a preliminary analysis and transformation of students’ evaluation data of a university, eliminated the abnormal evaluation data by using the improved cosine phase dissimilarity algorithm, standardized the evaluation data by using the normalization method, and used the traditional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>K</mi> </math> -modes algorithm. Based on these three problems, the traditional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>K</mi> </math> -modes algorithm was improved in three aspects, including the determination of the number of clustering families, the determination of the measurement of clustering distances, and the experimental results showed that the improved algorithm was more reasonable and effective.
Computational authorship analysis of the homeric poems

Abstract Natural language modeling is used to predict or generate the next word or character of modern languages. Furthermore, statistical character-based language models have been found useful in authorship attribution analyses by studying the linguistic proximity of excerpts unknown to the model. In prior work, we modeled Homeric language and provided empirical findings regarding the authorship nature of the 48 Iliad and Odyssey books. Following this line of work, and considering the current philological views and trends, we break down the two poems further into smaller portions. By employing language modeling we identify outlying passages, indicating reduced linguistic affinity with the main body of the two works and, by extension, potentially different authorship. Our results show that some of the passages isolated as outliers by the language models were also identified as such by human researchers. We further test our methodology and models on texts of similar language and genre created by other authors, namely Hesiod’s “Theogony” and “Work and Days”.
The Construction and Application of E-Learning Curricula Evaluation Metrics for Competency-Based Teacher Professional Development

Today, students at universities in advanced countries typically enroll in colleges, such as the College of Education, which offer interdisciplinary programs for undergraduates in their first and second years, allowing them to explore personal interests, experience educational research fields, complete their integrated curricula, and then choose a major in their third year. To cooperate with the government’s epidemic prevention policies and measures in the post-COVID-19 era, the trend of e-learning and distance teaching has accelerated the establishment of integrated online curricula with interdisciplinary programs for undergraduates in the College of Education to facilitate effective future teacher professional development (TPD). Therefore, it is very important to construct e-learning curricula evaluation metrics for competency-based teacher professional development (CB-TPD) and to implement them in teaching practice. This research used social network analysis (SNA) methods, approaches, and theoretical concepts, such as affiliation networks and bipartite graphs comprised of educational occupational titles and common professional competencies (i.e., Element Name and ID), as well as knowledge, skills, abilities, and other characteristics (KSAOs), from the U.S. occupational information network (O*NET) 26.1 OnLine database, to collect data on the occupations of educational professionals. This study also used Gephi network analysis and visualization software to carry out descriptive statistics of keyword co-occurrences to measure their centrality metrics, including weighted degree centrality, degree centrality, betweenness centrality, and closeness centrality, and to verify their importance and ranking in professional competency in eight categories of educational professionals (i.e., three categories of special education teachers and five categories of teachers, except special education). The analysis of the centrality metrics identified the educational common professional competency (ECPC) keyword co-occurrences, which were then used to design, develop, and apply e-learning curricula evaluation metrics for CB-TPD. The results of this study can be used as a reference for conducting related academic research and cultivating educational professionals’ online curricula, including ECPC keywords, integrated curricula design and the development of transdisciplinary programs, and teacher education, as well as to facilitate the construction and application of future e-learning curricula evaluation metrics for CB-TPD.
Numerical Demonstration of the Transmission of Low Frequency Fluctuation Dynamics Generated by a Semiconductor Laser with Optical Feedback

In this paper, the transmission mechanism of the spike information embedded in the low frequency fluctuation (LFF) dynamic in a cascaded laser system is numerically demonstrated. In the cascaded laser system, the LFF waveform is first generated by a drive laser with optical feedback and is then injected into a response laser. The range of crucial system parameters that can make the response laser generate the LFF dynamic is studied, and the effect of parameter mismatch on the transmission of LFF dynamics is explored through a method of symbolic time-series analysis and the index, such as the spike rate and the cross-correlation coefficient. The results show that the mismatch of the pump current has a more significant influence on the transmission of LFF waveforms than that of the internal physical parameter of the laser, such as the linewidth enhancement factor. Moreover, increasing the injection strength can enhance the robustness of LFF transmission. As spikes of the LFF dynamic generated by lasers with optical feedback is similar to the spike of neurons, the results of this paper can help understanding the information transporting and processing inside the photonic neurons.
Multi-view hybrid recommendation model based on deep learning

With the rapid development of technologies such as cloud computing, big data, and the Internet of Things, the scale of data continues to grow. The recommendation system has become one of the important intelligent software to help users make decisions. The recommendation model based on user rating data is widely studied and applied, but the data sparsity problem and the cold start problem seriously affect the recommendation quality. In this paper, Multi-view Hybrid Recommendation Model (MHRM) based on deep learning is proposed. First, we use WLDA (an improved Latent Dirichlet Allocation method) to extract the vector representation of user review text, and then apply LSTM to contextual semantic level user review sentiment analysis. At the same time, the emotion fusion method based on user score embedding is proposed. The problems such as deviations between the user score and actual interest preference, and unbalanced distribution of the score level are solved. This paper has been tested on Amazon product data and compared with various classic recommendation algorithms, using Mean Absolute Error (MAE), hit rate and standardized discount cumulative return for performance evaluation. The experimental results show that the prediction of the MHRM proposed in this paper on the 7 recommendation data and the TopN recommendation index have been significantly improved.
Lightweight fine-grained classification for scientific paper

The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model.
Information Security of the Digital State in the Quantum Era

The article provides an overview of the digital technology quantum communication and its place among end-to-end digital technologies. Also the problem of quantum threat to the information security of the Russian Federation is considered. The cryptography used today is based on discrete logarithms or factorization, which will lose their cryptographic strength after the implementation of a quantum computer. All countries have started to analyze and develop new information security technologies which are capable to ensure its confidentiality in the quantum era.The article analyzes the experience of building a new information security system in several countries (Great Britain, the European Union, the USA, China). The analysis made it possible to evaluate Russian actions in this area, as well as to identify shortcomings in the regulatory framework governing quantum cryptography, which include quantum key distribution, quantum random number generator, post-quantum cryptography.The author substantiates the need for the introduction of an experimental legal regime and the creation of the «cryptographic agility». The implementation of these proposals will help to prepare a regulatory framework for data migration to an environment protected by cryptographic ciphers resistant to quantum computing. The quantum communication is a new way to protect information in a digital state, where data plays a key role, and their confidentiality and integrity become the key to national security.
A state lossless scheduling strategy in distributed stream computing systems

Stateful scheduling is of critical importance for the performance of a distributed stream computing system. In such a system, inappropriate task deployment lowers the resource utilization of cluster and introduces more communication between compute nodes. Also an online adjustment to task deployment scheme suffers slow state recovery during task restart. To address these issues, we propose a state lossless scheduling strategy (Sl-Stream) to optimize the task deployment and state recovery process. This paper discusses this strategy from the following aspects: (1) A stream application model and a resource model are constructed, together with the formalization of problems including subgraph partitioning, task deployment and stateful scheduling. (2) A multi-factor topology partitioning method is proposed using a quantum particle swarm algorithm. The assignment between tasks and nodes is optimized using a bipartite graph minimum matching algorithm. (3) A hierarchical local topology migration is performed when an online scheduling is triggered, which ensures the processing sustainability of data streams. (4) A fragment loss-tolerant jerasure tool is used to divide the state data into fragments and periodically save them in upstream vertex instances, which ensures the available fragments be able to reconstruct the whole state in parallel. (5) Metrics including latency, throughput and state recovery time are evaluated in a real distributed stream computing environment. With a comprehensive evaluation of variable-rate input scenarios, the proposed Sl-Stream system provides promising improvements on throughput, latency and state recovery time compared to the existing Storm’s scheduling strategies.
An Innovative Facial Emotion Recognition Model Enabled by Optimal Feature Selection Using Firefly Plus Jaya Algorithm

This paper intents to develop an intelligent facial emotion recognition model by following four major processes like (a) Face detection (b) Feature extraction (c) Optimal feature selection and (d) Classification. In the face detection model, the face of the human is detected using the viola-Jones method. Then, the resultant face detected image is subjected to feature extraction via (a) LBP (b) DWT (c) GLCM. Further, the length of the features is large in size and hence it is essential to choose the most relevant features from the extracted image. The optimally chosen features are classified using NN. The outcome of NN portrays the type of emotions like Normal, disgust, fear, angry, smile, surprise or sad. As a novelty, this research work enhances the classification accuracy of the facial emotions by selecting the optimal features as well as optimizing the weight of NN. These both tasks are accomplished by hybridizing the concept of FF and JA together referred as MF-JFF. The resultant of NN is the accurate recognized facial emotion and the whole model is simply referred as MF-JFF-NN.
Dynamic and static feature fusion for increased accuracy in signature verification

The success rate in offline signature verification studies has reached high and limiting levels recently. However, any increase in this performance is and will be highly valuable in terms of fraud detection. This study assesses the impact of the sound arising from the friction of pen and paper on handwritten signature verification. A dataset was built containing static data from the signature image and dynamic data from the signature sound by taking samples from 75 participants according to different combinations of pen, paper types, and mobile phone models for recording the sounds of the signatures with their internal microphones. It was aimed to increase verification success by fusing dynamic and static features. From the static data, the features are extracted by the LBP and SIFT algorithms. For dynamic data, spectral flux onset envelopes and spectral centroids of audio signals are plotted and converted to image files. Thus, the dynamic data of the signature sound signal became static data and as in the static image of the signature, feature extraction was performed with the LBP and SIFT algorithms. Classification is performed with the OC-SVM algorithm. Moreover, instead of LBP and SIFT features, another verification method with the deep features obtained with a CNN-based model was also proposed and comparatively analyzed. Test results indicate that the aforementioned fusion of these two traits leads to increased signature verification success rates (statistical significance test results are provided), without incurring large costs, considering the sensor availability and acquisition times.
Toward a big data analysis system for historical newspaper collections research

The availability and generation of digitized newspaper collections have provided researchers in several domains with a powerful tool to advance their research. More specifically, digitized historical newspapers give us a magnifying glass into the past. In this paper, we propose a scalable and customizable big data analysis system that enables researchers to study complex questions about our society as depicted in news media for the past few centuries by applying cutting-edge text analysis tools to large historical newspaper collections. We discuss our experience with building a preliminary version of such a system, including how we have addressed the following challenges: processing millions of digitized newspaper pages from various publications worldwide, which amount to hundreds of terabytes of data; applying article segmentation and Optical Character Recognition (OCR) to historical newspapers, which vary between and within publications over time; retrieving relevant information to answer research questions from such data collections by applying human-in-the-loop machine learning; and enabling users to analyze topic evolution and semantic dynamics with multiple compatible analysis operators. We also present some preliminary results of using the proposed system to study the social construction of juvenile delinquency in the United States and discuss important remaining challenges to be tackled in the future.
Indoor Location Monitoring and Navigation System for Hospitals

As large buildings, hospitals offer many situations where patients can get lost or medical personnel has trouble locating a patient inside a hospital or around its premises. Lost patients can lead to increased cost from wasting hospital employee time but more importantly to situations that can be dangerous or life threatening for some patients. The paper studies the use of a ZigBee network for patient location detection as well as indoor navigation within a hospital building. The system employs hospital mapping based on graph theory, a distinct three-technology network and a patient device that both measures the patient temperature and is also part of the location subsystem. The system obtained has lower power consumption and an average cost lower than other similar solutions.
Security Scenarios Automation and Deployment in Virtual Environment using Ansible

Cyber security is a 21st-century challenge doubled by the risk introduced by the lack of cyber security workforce. To bridge this workforce gap, cyber security learning environment are necessary. The Cyber range is cyber virtual environment for security training for cyber security professionals. This study presents the next-generation tools for developing Cyber ranges in the cloud and integrates automation with Ansible hence, orchestrating the virtual environment for the efficacy of security testsbeds, equipping the user with cyber security readiness to defend against real-life cyber-attacks. The proposed automation methodology can be also incorporated in continuous integration queues, defining the Infrastructure as Code (IaC) DevSecOps strategy. The developed scalable and reusable platform allows malicious cyber activities to be simulated through automation. The exercises and the tools employed gives security professionals a good understanding of adversary's operations. while the vulnerabilities are mapped to MITRE ATT &CK knowledge base. Our results show that, the developed Cyber range platform is suitable and effective for cyber security training. Notwithstanding, we propose future studies be carried out to look into cyber security problematic in virtual reality (VR) platforms.
Abstractive Text Summarization Using Hybrid Technique of Summarization

Abstractive text summarization focuses at compress the document into a shorter form while keeping the connotation intact. The extractive summary can select chunks of sentences that are very related to the document, on the other hand, an abstractive summary can generate a summary based on extracted keywords. This research proposed an abstractive text summarization model, it gets data from source data (e.g., Daily Mail/CNN) or other documents and two summaries of this are generated. one from a philologist and the other by proposed model. The summary generated by the philologist kept as a model to compare with the machine-generated summary. The proposed model increased the accuracy and the readability of the summary.
Detecting Amazon Bot Reviewers Using Unsupervised and Supervised Learning

Customers of e-commerce platforms often rely on other customers' reviews to make purchasing decisions. On these e-commerce platforms, such as Amazon, sellers will sometimes use fake reviews, often created by bots, to boost ratings on their products. This can negatively affect customer purchase satisfaction. This research employs unsupervised learning algorithms K-Means, DBSCAN, and OPTICS to discover clusters of potential bot reviewer feature values. These clusters will then be analyzed and used for training multiple classifier networks to attempt to be able to detect bot reviewers using machine learning. The results of this research article point towards a direction that this is a possible future path given additional research and domain expertise.
Major threats to the continued adoption of Artificial Intelligence in today's hyperconnected world

From the golden era of science fiction which dates to the late 1930s, scientific and technological advances in artificial intelligence (AI), along with one of its key subsets, machine learning (ML) have been growing significantly, especially in recent years. In 2021 alone, notable feats included an AI program capable of creating images from seen or previously unseen textual captions, an AI model that effectively integrates computer vision and natural language processing, and a novel AI framework for diagnosing dementia in 24 hours with real-world feasibility underway amongst a host of other fascinating breakthroughs. This paper briefly delves into AI/ML and recaps some key essentials, covering AI and ML subfields, ML methods, industries where AI/ML finds relevance, key stages and the common technical challenges in ML development. Importantly, the paper shifts attention from the latter to underscore the duo of transparency and ethics in AI, highlighting specifically what these are and why they are important, subsequently positing a PESTEL (Political, Economic, Social, Technological, Environmental and Legal) framework for AI design, build and implementation. It is anticipated that the upshot of this would be the facilitation of continuous adoption and long-term sustainability of AI/ML.