Reviewerly/exHarmony huggingface dataset:
Edge Aware Turbidity Restoration of Single Shallow Coastal Water Image

Abstract The blur and low contrast of underwater images are indicative of the high noise, intense scattering and ultimately the low quality of such images. Image Enhancement and Restoration are imperative pre processing steps for single images from shallow coastal areas. Pre processing of underwater images is hence a prerequisite to processes like classification, object detection and computer vision. The paper presents an effective edge aware restoration models that goals turbid water images. The dark channel based restoration method with a rolling guidance filter gives a more edge aware restored and denoised version of the heavily blurred, low contrast shallow coastal image. This is evident from the subjective and objective projections. Moreover the edge preserving nature projects 13.05% and 5.53% more than UHP (which is in close) in terms of number of edges and UCIQE scores in comparisons with promising algorithms over the decade.
Is Virtual Currency a Standard Financial Bubble? Blockchain Industry Research

Blockchain is an innovative application model. Blockchain technology is considered to be a disruptive innovation in computing mode after the mainframe, personal computer and the Internet. Catalyzed by multiple forces and factors, blockchain may have begun a period of rapid development. Hence, the rapid development of blockchain isn’t about everything. It’s about opportunities and risks, impetus and obstacles. In particular, it lacks mature applications outside the financial field, which is a reality of blockchain. More importantly, a series of recent security incidents have shown that blockchain technology is still facing security risks. It's not technology, It's blindness and conformity. There is no actual use value, the actual total amount is unlimited and uncontrolled. All the determinants of the price do not come from supply and demand, nor from government regulations, but only from the next investor’s virtual currency. In the long run, it may become the next classic financial bubble.
Visualization Analysis of Marine Fish Data (2014-2019 in China)

In recent years, China's mariculture industry has made rapid development. Based on the official Fisheries Yearbook data, we analyzed the marine fish production and economic situation in 2014-2019 with a visual method. Through the analysis results, we can see the following points clearly: The output of marine fishing is decreasing year by year, while that of mariculture is increasing year by year; the output of marine fish is still far greater than that of mariculture fish, but the output value of cultured fish is far greater than that of fishing fish, which indicates that the value of mariculture fish is relatively high; the area of aquaculture water body is decreasing year by year, while the output is increasing year by year, which indicates that the output per unit area is increasing year by year. These situations reflect the improvement of cultivation level, the enhancement of intensive cultivation, and the improvement of industrialization and intelligence of cultivation. This paper also calculates the linear formula for the change of annual fishing yield and annual aquaculture yield, which has certain reference significance for the prediction of next year's yield.
A Framework For Scalable Similarity Evaluation in Text Graphs

Graphs and graph databases are applicable over a wide range of domains, including text mining and web mining. Using graphs to represent relationships between entities provides enriched models for emerging tasks of web search and information retrieval. Natural language processing algorithms use graphs to model structural relationships of texts efficiently, resulting in improved performance. However, the need to increase the accuracy of graph construction and weight allocation remains a fundamental challenge. Existing methods for these tasks provide limited efficiency and lack scalability for large graphs. In this study, we propose a novel graph-based method for text modeling and running a query to evaluate the similarity of text segments. In this method, the graph corresponding to the text is first created by modeling words and named entities by the state-of-the-art pre-trained BERT model. Graph nodes are then weighted in two stages. In the first stage, the nodes with more generalization obtain higher weights. The second weighting stage is done by the graph obtained from the query text. In this weighting step, nodes are considered important if they are specifically related to the query text. After determining the important nodes in the graph, the semantic similarity between the query text and the texts in the database is measured. The whole process of this framework uses a natural language processing pipeline in Apache Spark scalable platform. The efficiency of the model was evaluated for both distributed and non-distributed configuration and its scalability on a Spark cluster. Evaluation of the accuracy using the Pearson correlation coefficient shows that the proposed method performs higher performance than its competitors.
Formation of professional competencies of future subject teachers in the field of creating digital educational resources

Modern society is developing under the influence of various factors, among which new information technologies occupy an important place, which make any person’s access to a huge flow of information simply instantaneous. Today, reliable information is one of the important products of society, and a thinking person is the main strategic resource of this society. Therefore, in the information society, the intellectual development of each of its members becomes a priority. In this regard, it is necessary to look for ways to optimize the process of training teachers who are able to organize the educational process in such a way that students develop the knowledge, skills and abilities that will be in demand in their future life and professional activity in the digital economy. Almost all improvements in the educational process are already unthinkable without the use of information and communication technologies, which have colossal didactic capabilities. They can be used in the educational process in two ways: as a powerful teaching tool and as an object of study, which allows you to solve educational problems and achieve set goals in the process of teaching, upbringing and development of students. One of the most effective and productive tasks that arouses great interest and is associated with the use of information technologies in both ways is the task of designing and creating digital educational resources, as well as their application in the educational process. The article provides an example of the implementation of a task related to the development of a digital educational resource for preparation for the Basis State Examination in informatics of ninth-grade secondary school students, which allows future subject teachers to form the professional competencies they need in the field of creating educational resources with their further inclusion in the educational process.
Feature Extraction and Classification of Colon Cancer Using a Hybrid Approach of Supervised and Unsupervised Learning

The demand for research in the medical field is growing at a very rapid rate which can be seen in the unprecedented progress of COVID19 medication and detection development which take a lot of time and effort. But due to recent advancement of Machine Learning and Artificial Intelligence (i.e. ML/AI) in the medical field, it has been observed that the time frame for the development of such medications and detection has reduced a lot. This chapter paves the way for such demand by combining two basic ML techniques involving unsupervised and supervised learning. Since the occurrence of Colon Cancer is somewhat rare and complex, available Colon Cancer data sets are still underdeveloped and the Colon Cancer data set that is present is complex. The data set also has very less number of samples. To resolve this problem of fewer data and to get the most relevant information from the available data set, our research involves an oversampling method on the original data with preserving of the basic nature of the data set which is followed by the clustering of the data using K-Medoid clustering, Girvan-Newman clustering and Mahalanobis distance-based clustering for multivariate data. Once the same set of samples are predicted using the clustering, we are extrapolating the data based on the clusters formed using a statistical approach. To reduce complex processing and remove the irrelevant features with the intent of cleaning the data set, we are applying a correlation-based approach for feature selection and then Principal Component Analysis (PCA) has been applied for dimensionality reduction. The final set of features thus obtained is then fed into an Artificial Neural Network for the classification of the Colon Cancer. The result of the experiment shows that the methodology proposed has a better accuracy than the available approaches which proves the effectiveness of our proposed method.
Determinants of Professional Self-motivation and Educational Motivation of Students

The article deals with the problem of professional self-determination of young people under the influence of social and personal factors. The classification of these factors based on the purposeful impact on the process of professional self-determination is considered. The following types of influence were identified: external social determinants, internal social determinants, and personal determinants of professional self-determination. External social determinants represent all kinds of career guidance technologies: advertising, campaigning, involvement in clubs, and so on. Internal social determinants are various forms of influence of parents and relatives on the decision of young people. Personal determinants are conscious, independent, and informed choices based on personal motives, interests, and abilities. As a result of analysis of numerous studies, scientific knowledge about the nature of motive and motivation, depending on the educational motive of professional self-determination, has been identified and insufficiently studied problem of the interdependence of the determinants of professional identity and learning motivation of University students. The conducted empirical research revealed some regularities in the development of educational motivation of students whose professional self-determination was influenced by specific determinants.
Calligraphy Teaching System in Primary and Secondary Schools Based on Artificial Intelligence

Aiming at the demand of artificial intelligence popularization education, this paper designs and implements a web-based a-algorithm visual assistant teaching system. The system realizes the functions of algorithm visualization teaching, adaptive test, learning navigation based on knowledge map, knowledge recommendation and so on. This paper introduces the system structure design, algorithm visualization process, adaptive test based on learner model and knowledge map, knowledge recommendation and other key issues. Calligraphy is one of the quintessence of Chinese excellent traditional culture, and the writing level is the concentrated embodiment of one's comprehensive quality. The 2017 edition of the new curriculum standard strongly advocates the integration of excellent traditional culture into all aspects of student education. But in recent years, with the rapid development of artificial intelligence technology, calligraphy teaching in primary schools has encountered unprecedented challenges and opportunities.
«Ubbie frivole e fantastiche». Appunti linguistici su una lettera di Arrigo Boito

The paper proposes a historical-linguistic analysis of Arrigo Boito’s controversial letter A sua Ecc. il Ministro della Istruzione publica. Lettera in quattro paragrafi, published in Il Pungolo on May 21, 1868 and addressed to the Minister Emilio Broglio. This study contextualizes the letter and shows that the caricature of Broglio’s linguistic policy reveals not only Boito’s distance from Alessandro Manzoni’s theory of use, but also some misunderstandings. Then, the paper explores and underlines the socio-cultural reasons of Boito’s “antifiorentinism”. Eventually, the letter appears to be a significant example of “antimanzonism”: a position which, beyond the writer’s personal intolerance, arose in those years against a linguistic policy perceived as imposed by force, overbearing and inadequate to the real needs of the country.
Design and Implementation of A Vulnerability-Tolerant Reverse Proxy Based on Moving Target Defense for E-Government Application

The digital transformation is injecting energy into economic growth and governance improvement for the China government. Digital governance and e-government services are playing a more and more important role in public management and social governance. Meanwhile, cyber-attacks and threats become the major challenges for e-government application systems. In this paper, we proposed a novel dynamic access entry scheme for web application, which provide a rapidly-changing defender-controlled attack surface based on Moving Target Defense (MTD) technology. The scheme can turn the static keywords of Uniform Resource Locator (URL) into the dynamic and random ones, which significantly increase the cost to adversaries attack. We present the prototype of the proposed scheme and evaluate the feasibility and effectiveness. The experimental results demonstrated the scheme is practical and effective.
Query Auto-Completion Using Graphs

Search engines have a complete dependency on the query auto-completion. Query auto-completion is a process that suggests a group of words for every click dynamically. Query suggestions help in formulating the query and improve the quality of the search. Graphs are data structures that are universal and extensively used in computer science and related fields. The graph machine learning approach is growing rapidly with applications such as friendship recommendation, social network, and information retrieval. The main goal is to represent the data into a graph that is nodes and edges. A supervised logistic regression model is proposed to compute the accuracy. Node2vec algorithm learns the feature representation of nodes in a graph. It is derived by word embedding algorithm Word2vec. The evaluation metric is the semantic similarity score and the area under the receiver operating characteristic curve (ROC_AUC). Semantic similarity between the two nodes helps in query auto-completion. Areas under curve (AUC) help in measuring the prediction accuracy and to know the outcomes of completion.
An improved 3D human model reconstruction technique based on Cascade MVSNet

In order to improve the capability of feature point extraction and matching in 3D human model reconstruction and enhance the accuracy of it, this paper improves a multi-view-based 3D reconstruction method of the human body model. We first take dozens of photos of a person at the angle of a circle around him, and then perform instance segmentation on the images to remove the background. Next, we introduce Laplace operator in extracting image feature points to sharpen the edges of the images and use KNN algorithm to match feature points on the images. When filtering the matching points, we introduce the Adaptive Locally-Affine Matching (AdaLAM) algorithm to optimize the matching results. Finally, we use incremental SFM and Cascade MVSNet to reconstruct sparse and dense point clouds, respectively, and obtain better reconstruction results. The results show that, compared with the original 3D reconstruction algorithm, the improved method in this paper effectively improves the capability of feature point extraction and matching, enhances the accuracy of the generated human dense point clouds. As a result, the number of point clouds obtained increased by 17.8%.
Ramon Huerta-Valera: Convicted felon suspected of leading troopers on a high-speed chase ending in a crash

King County Sheriff's detectives need your help locating a convicted felon who police say keeps getting his hands on guns - when he shouldn't have them at all.
Heterogeneous Sensor Fusion with GMPHD for Environmentally Adaptable Obstacle Detection in Mobility Systems

Obstacle detection is an essential process in consumer's autonomous mobility systems such as autonomous vehicles inside the dedicated lane to acquire the location of obstacles, and it has become a popular topic in this decade with the blooming of various object detection algorithms and the enhancement of sensor quality. To maintain high accuracy of obstacles' detection in mobility systems outdoor, a sensor fusion system is required to essentially support environmental influence such as lousy weather as well as high moving speeds and adaptably deal with clutter and miss detection based on the incoming measurements from heterogenous sensors with Camera, LiDAR and Radar. Since no current literature about Gaussian mixture probability hypothesis density (GMPHD) handles the above low accuracy fusion problem due to environmental influence for heterogeneous sensors, we propose the concept of integrating GMPHD to heterogeneous sensor fusion with three architectures, Track-to-Track-Fusion (T2TF), Measurement-to-Track-Fusion (M2TF) and Track-to-Association-Fusion (T2AF) and further evaluate their performances respectively in terms of their fusion improvement abilities to determine their practicalities for mobility systems by using the simulation datasets which reproduce ordinary and poorer conditions with the degradation of sensors' performance in the assumption of environmental influences.
Research on Household Waste Detection System Based on Deep Learning

Household waste is threatening the urban environment increasingly day by day for people’s material needs increasing with the acceleration of urbanization. In this paper, a new waste sorting model is proposed to solve the problems of waste sorting. The style transfer was used to increase the data set to make some objects be sorted well. Then the rotational attention mechanism model was used to increase the accuracy of waste sorting of the blocked objects. The representation vector extraction module in the target tracking algorithm Deep Sort was replaced with Siamese network to make the network more lightweight. As a result, this paper effectively solves the current waste sorting tasks.
Appraising the Impact of Kenya’s Cyber-Harassment Law on the Freedom of Expression

Kenya’s Computer Misuse and Cybercrimes Act makes it an offence, in Section 27, for a person to communicate with another a message that they know or ought to know would cause the recipient fear; is indecent or offensive in nature; or would detrimentally affect the recipient. This offence carries a penalty of either a 20 million shilling fine or a 10-year term of imprisonment or—discretionarily—both. While the offence is termed ‘cyber-harassment’, its wording appears to exclude a number of offences that would count as cyber-harassment such as cyber-stalking, doxing or impersonation. In fact, its wording is vague and overbroad, using undefined terms such as ‘detrimentally affect’ which require subjective interpretation. Cyber-harassment laws constitute a limitation on the freedom of expression and as such, ought to conform to the limitations of human rights test as provided in Article 24 of the Constitution. Where the aim sought is legitimate in a democratic society and other conditions such as legality are met, this limitation is valid. This paper reviews Kenya’s law that was recently upheld by the High Court in Bloggers Association of Kenya (BAKE) v Attorney General &amp; Three others; Article 19 East Africa &amp; another and finds that it fails to meet the limitations test prescribed under Article 24 of the Constitution. It argues that Section 27 of the Computer Misuse and Cybercrimes Act is therefore overbroad and has the potential to be used as a tool for the unconstitutional suppression of legitimate criticism.
Secure Storage of Electronic Health Records on Cloud Using Integrity Verification Auditing

Health care facilities have tried to keep sensitive patient information safe. Health information is important in identifying any stage of treatment. However, such information should be kept confidential and only available at health care facilities. To ensure data availability, health care data is now stored in the cloud and accessible online. But, this approach poses many threats due to the possibility of a patient data to be accessed by unauthorized personnel. Moreover, the standard data access control mechanisms are insufficient to ensure integrity of data due to numerous users. The constant adjustment of privileges also affected confidentiality. This paper proposes a novel approach in which the sensitive patient data in Electronic Health Records is hidden and stored more securely in the cloud. It uses a sanitization technique to detect sensitive data in the EHR and make use of identity based shared data integrity auditing to allow authorized access to the data. The web based application which uses the proposed technique is developed and tested to demonstrate its effectiveness.
Variable Neighborhood Programming as a Tool of Machine Learning

Automatic programming is an efficient technique that has contributed to an important development in the artificial intelligence and machine learning fields. In this chapter, we introduce the technique called Variable Neighborhood Programming (VNP) that was inspired by the principle of the Variable Neighborhood Search (VNS) algorithm. VNP starts from a single solution presented by a program, and the search for a good quality global solution (program) continues by exploring different neighborhoods. The goal of our algorithm is to generate a good representative program adequate to a selected problem. VNP takes the advantages of the systematic change of neighborhood structures randomly or within a local search algorithm to diversify or intensify search through the solution space. To show its efficiency and usefulness, the VNP method is applied first for solving the symbolic regression problem (VNP-SRP) and tested and compared on usual test instances from the literature. In addition, the VNP-SRP method is tested in finding formulas for life expectancy as a function of some health care economic factors in 18 Russian districts. Finally, the VNP is implemented on prediction and classification problems and tested on real-life maintenance railway problems from the US railway system.
Improving students’ problem-solving ability through the ‘information system security’ project guided by the theory of inventive problem solving (TIPS)

To improve the practical effect of an information system security course for students majoring in information security, a teaching method based on the theory of inventive problem solving (TIPS) that combines case-based teaching and project practice was introduced in the class. Adopting innovative thinking methods, we instructed students on the projects and goals of curriculum design. Using TIPS tools, the students completed complex projects. To test the teaching results, we examined 121 students who received case-based teaching using the TIPS method to design innovative projects. Quantitative data were collected using a statistically analysed anonymised questionnaire, while qualitative data collected from student reflective reports were thematically analysed. Through the in-depth development involved in the teaching of this course, their thinking and problem-solving ability significantly improved. The teaching effect evaluations indicated that this teaching method effectively improved students’ practical abilities. This method was found to be conducive to planning students’ future careers.