Reviewerly/exHarmony huggingface dataset:
An Efficient Hybrid Planning Framework for In-Station Train Dispatching

In-station train dispatching is the problem of optimising the effective utilisation of available railway infrastructures for mitigating incidents and delays. This is a fundamental problem for the whole railway network efficiency, and in turn for the transportation of goods and passengers, given that stations are among the most critical points in networks since a high number of interconnections of trains’ routes holds therein . Despite such importance, nowadays in-station train dispatching is mainly managed manually by human operators. In this paper we present a framework for solving in-station train dispatching problems, to support human operators in dealing with such task. We employ automated planning languages and tools for solving the task: PDDL+ for the specification of the problem, and the ENHSP planning engine, enhanced by domain-specific techniques, for solving the problem. We carry out a in-depth analysis using real data of a station of the North West of Italy, that shows the effectiveness of our approach and the contribution that domain-specific techniques may have in efficiently solving the various instances of the problem. Finally, we also present a visualisation tool for graphically inspecting the generated plans.
Object Detection of Remote Sensing Image Based on Multi-level Domain Adaption

Due to the limited amount of labeled data, remote sensing object detection is faced with great difficulties. The problem of insufficient labeled data usually can be solved by domain adaption. However, current methods mostly focus on feature alignment, without paying attention to the context information or discussing the level of features, which makes it impossible to effectively apply them to remote sensing images. In this paper, we construct our method based on Faster-RCNN model, and design three domain adaptive components for remote sensing object detection at image-level, instance-level, and pixel-level. Image-level alignment enhances global recognition ability by image weight redistribution. Instance-level alignment makes global awareness possible by combining context information. Pixel-level alignment reduces local differences between domains by focusing on small features and enhancing semantic information. Moreover, we collect domain adaption dataset to verify the proposed method, and the experimental results show that our method is superior to other current methods.
Code Buddy: A Machine Learning-Based Automatic Source Code Quality Reviewing System

The growing complexity of software and associated code makes it difficult for software developers to produce high-quality code in a timely fashion. However, this process of assessing code quality can be automated with the help of software code metrics, which is a quantitative measure of code properties. The software metrics consist of several attributes, which describe the source code and this includes lines of code, program length, the effort required, the difficulty involved, cyclomatic complexity, volume, vocabulary, intelligence count, and so on. With the help of these features, code can be classified as a well-written code or badly-written code. This study focuses on evaluating the performance of main classification algorithms: Naïve Bayes, K-nearest neighbors (KNN), logistic regression, stochastic gradient descent (SGD) classifier, support vector machine (SVM), and decision tree (D-Tree) with thirteen of NASA metrics data program (MDP) dataset. The research work also focuses on understanding the math and working of each of the classifiers and the quality of each dataset. The comparison measure for the evaluating classifiers includes confusion matrix and other derived measures, namely F-measure, recall, precision, accuracy, and Matthews correlation coefficient (MCC). The best model is chosen along with the appropriate dataset. In order to allow the developers to use the trained model, we created Code Buddy a SharePoint web-portal; which allows the developers either assess the code quality by sending the review request to any of the colleagues or assess the code automatically using a trained model, which will predict whether the code is well written or badly written. Moreover, if the developer is not satisfied with the results, he/she can send a review request to any fellow colleague who can review the code and provide the review comment on the same.
New Bit Level Positional Encryption Algorithm (NBPLEA - Ver 2)

In the age of the digital world, cyber security and protecting the digital assets of a person and organization are of the utmost importance. Various types of attackers and foreign entities attempt to break in and steal or modify records and valuable assets for their personal gain which is why designing efficient and unbreakable cryptographic algorithms is a very crucial field of Computer Science.[2] In this project, we develop a cryptographic algorithm that is able to encrypt small texts like passwords or pins.
Multispectral Image Compression Based on Multiscale Features

We propose a compression framework based on multiscale features using deep learning technique for complex features of multispectral image. The multiscale feature extraction module is taken as the basic block of the framework to constitute the encoder and the decoder. The encoder is used to extract the dominant features of the input multispectral images and the decoder, which is symmetrical to the encoder, is used to reconstruct multispectral images. The balance of the bit rates and the distortion is implemented by the rate-distortion optimizer in loss function. The encoder and the decoder are trained jointly based on minimizing the mean squared error (MSE). Our proposed framework is compared with JPEG2000 in terms of PSNR and the experimental results validate that our proposed framework outperforms JPEG2000.
A Method of Network Topology Discovery Based on Knowledge Reasoning : Based on more than heterogeneous data sources

At present, the network has gradually become the foundation of information construction. People have more demands for network topology management. The increasing scale of network topology and the heterogeneity between devices and protocols have brought great difficulties to network topology discovery. This paper proposes a knowledge-based reasoning Network topology discovery method. This method uses multiple heterogeneous data sources, uses computer network prior knowledge to construct a knowledge base from top to bottom, uses inference learning to establish reasoning rules, and then constructs a multi-level network topology structure.
Research on User Demand-Driven Service Matching Methodology

Services have seen a spike, and thus, how to accurately identify the needs of the users and match them with the appropriate services becomes the key to the research of service matching. Service matching involves a lot of work, of which the existing methods to complete the matching mainly depend on semantic similarity while the information of the service function is rarely taken into consideration. To solve the problem above, a user demand-driven method will be introduced. Based on the original service model, the user demand model is established, which aims at the actual needs of users. To cope with incomplete and inaccurate expressions of users' demands, the UserCF is applied to matching the similar needs of users so that the tag set is generated, making the user service tag information complete and modified; the LMIA is recommended to match the classified demands of users level by level with the purpose of weighted fusion. In the analysis of living examples, it is suggested that the method is more feasible than workable than the existing methods of service matching, and meanwhile, an 5.3% higher precision are obtained.
Moral education – a fundamental criterion of educational activity, in the direction of the formation and development of the moral consciousness of the individual

The essence of moral education is concentrated axiomatically at the level of the first principle, which highlights the necessary pedagogical correspondence between moral theory and moral practice. The objective dimension of moral education of an axiological and sociological nature is expressed, on the one hand, by moral norms and values, and, on the other hand, by moral traditions; the subjective one, of a psychological nature, is achieved by moral actions supported affectively and motivationally.
More harm than good? The perils of regulatoring online content

Online communication and sharing of user generated content have become part of everyday life, but have long worried governments, which have sought to monitor and gain access for reasons of security and crime prevention. Digital platforms used to be considered socially beneficial, and legal measures were passed to facilitate the hosting of user generated content, but governments are increasingly worried about what they consider "harmful" content that is widely available through digital platforms. It has been claimed that democratic processes have been subverted by online disinformation and misinformation, and that children and adults are at risk of psychological harm and exploitation from offensive or inappropriate material. Measures are being pursued to counter these perceived harms, including the EU's Code of Practice on Disinformation and the UK government's forthcoming Online Safety Bill. This paper considers the need for such measures and the risks of unintended consequences.
Non-Adversarial Novelty Detection with Generative Latent Nearest Neighbors

Novelty detection is the task of identifying whether a new data point is considered to be an inlier or an outlier. Generative Adversarial Networks (GAN)-based methods suffer from mode dropping and unstable training issue, which poses the greatest threat to learn the target class distribution. To solve mode dropping issues, the nearest neighbor generator is designed to ensure that for every training image there exists a candidate generated image that is near to it at optimality. The generator considers the entire distribution of training data without mode dropping. To avoid the instability training issue, we consider capturing the distribution of the target class by non-adversarial strategy. In addition, to provide great image priors and fully diversity candidate samples for the generator, we also design a two-step mapping process. Finally, Experiments show that our model has clear superiority over cutting-edge novelty detectors and achieves state-of-the-art results on the datasets.
Conducting Educational Research

This chapter is designed to introduce my journey into research, walk you through the process I have found successful in conducting educational research in the classroom, and give examples of what facets of this process look like in our classrooms. A step-by-step process of ideating and designing an educational research study is detailed, along with methods for collecting and analyzing data and sharing findings. Through educational research, periphery professional development and a real sense of professionalism within the teaching field can be achieved.
Bildsuche: Erfahrungen zur Erkennung von Emblemen und zur automatischen Annotation von Segmenten

The search for images with specific content using keywords or verbal descriptions is one of the great challenges in information retrieval and machine learning. This kind of search usually requires that a human being interprets the images and annotates them with a controlled vocabulary. Working with digital image corpora – e. g. in illustrated children’s and youth literature or emblems – is therefore often associated with a high manual effort: Each individual image must be carefully examined and manually classified. On the other hand, there have been major advances in the field of computer vision in recent years, which could make it possible in the future to interpret and annotate images using algorithms. In addition to an introduction to the basics of computer vision, this article uses practical examples to explore the question of the extent to which the current state of research allows the computer-aided analysis and search of historical image corpora. In doing so, we will particularly address the specific requirements of illustrations and discuss possible solutions.
Numerical Correlation Analysis of Power Grid Construction Project Based on Apriori Algorithm

Power grid construction projects are proceeded on the basis of the multi-department collaboration. Besides, the whole process of project management involves a huge amount of data, which is difficult to disclose its inside correlation. Currently, the higher standards for power grid construction project management poses challenges on data management. Thus, it is necessary to further explore the inside data relationship in the process of project management, so as to address the data inaccuracy issue. In this paper, a correlation analysis method is proposed based on a modified apriori algorithm to study the correlation among the key indicators in the process of power grid construction project management. Specifically, the modified apriori algorithm by deploying dichotomizing search, can successfully decrease high computation demand while guaranteeing the analyzing accuracy. By establishing a data mining tool to analyze the indexes from the power grid construction projects in recent years, the pre-control and in-process control methods are proposed for the project management. In this regard, the paper offers a brand-new way to for the data management in the power grid construction projects.
한국의 고교학점제 정책 동향분석

The high school credit is a that allows students to choose their own subjects and allows them to complete their courses, thus allowing them to graduate with cumulative credits. To settle the high school credit system, our country is implementing various policies. In that way, it will be embodied in research schools, designation and operation of leading schools, operation of roadmaps for the promotion of high school credit system, promotion of various support projects, and policy forums for high school credit system. The Ministry of Education said in a press release that it will establish a Korean-style high school credit system based on advanced cases in major foreign countries such as the United States, Finland and Singapore. If discussions and deliberation in various and wide areas are conducted at the central-Education-Unit School level through forums, research and seminars, it will be able to achieve flexible and individualized education centered on student growth.
How to Solve Unconventional Mathematical Problems with the Help of Electronic Visualisation

The article discusses the necessity and possibility of how to use virtual digital environments while solving unconventional mathematical problems. It also compares a few ways of solution to the Bertrand paradox problem with traditional visual methods and the MathKit virtual environment. The support of interactive modelling platforms allows students to form holistic and integrated knowledge on the subject.
Email Common Weaknesses and Enumeration Through Software Customer Perspective

Ontology represents a best tool for knowledge management, concepts definitions and semantic search. It is considered a successful style work to organize and manage the knowledge in a single given domain. This paper is an attempt to establish knowledge base for email weaknesses using ontology, which represents a best method for illustrating relationships among Common Weakness Enumeration (CWE) email entries as defined by MITRE Corporation Weakness List Dictionary. The ontology usage is demonstrated with sufficient samples of queries by analyzing email software products weaknesses according to software customer point of view. This work is based on the MITRE community effort CWE List, Version 3.1 - Research Concept view CWE-1000, the effort in this work is limited by software customer perspective.
Modeling Brain-like Association Among Focal Visual Objects by a Bipartite Mesh

The challenge of traditional visual recognition tasks has long fallen on the segmentation of objects in two-dimensional images, whereas it is less an issue in human visual learning with the help of stereo vision and physical touches. In this kind of configuration, object classification and landmark matching are fundamentally based on the semantic similarity from inputs to conceptual prototypes in memory. Here we propose a brain-inspired cognition model that deals with visual learning tasks after the focal objects have been distinguished from their backgrounds. We designed a bipartite mesh to implement visual cognition on human faces. This mesh resolves facial landmarks into point clouds in a unique semantic space, where facial characteristics can be perceived and classified through the comparison with prototypes in the memorized ontology. These face prototypes are updatable online, and landmark matching between prototypes in the vicinity is feasible through a direct mapping between relative positions within their point clouds. Besides, the association between distant prototypes in the semantic space can be realized by a sequence of matching processes on intermediaries in memory. Our findings suggest a concise framework for simulating human visual learning mechanisms that well execute one-shot learning, online learning, and analogical reasoning, at the same time subject to certain brain-like constraints such as oblivion and lack of analogical cues between two dissimilar concepts.
Cyberinfrastructure for Social Good: Ensuring That No Homeless Individual Stays Behind

We present a prototype decentralized transactional platform designed to improve the transparency of homeless serving organizations and facilitate their accountability and oversight. In the proposed system, the complete history of transactions between organizations offering homelessness services (e.g., shelters, transitional housing) and individuals seeking such services is stored in a distributed ledger. Using smart contracts, the proposed tamper-proof framework can automate the exchange of information between clients, organizations and government agencies, and allow government agencies audit organizations without violating the privacy of homeless individuals. We begin by describing the goals and concepts, the stakeholders’ requirements and the corresponding desirable system properties, and identified challenges. We continue with an in-depth description of the overall architecture of the proposed system designed to achieve these goals, and lessons learned towards transitioning this system to the real-world.
Recommender Systems Evaluator: A Framework for Evaluating the Performance of Recommender Systems

Recommender systems are filters that suggest products of interest to customers, which may positively impact sales. Nowadays, there is a multitude of algorithms for recommender systems, and their performance varies widely. So it is crucial to choose the most suitable option given a situation, but it is not a trivial task. In this context, we propose the Recommender Systems Evaluator (RSE): a framework aimed to accomplish an offline performance evaluation of recommender systems. We argue that the usage of a proper methodology is crucial when evaluating the available options. However, it is frequently overlooked, leading to inconsistent results. To help appraisers draw reliable conclusions, RSE is based on statistical concepts and displays results intuitively. A comparative study of classical recommendation algorithms is presented as an evaluation, highlighting RSE’s critical features.
Research on Stable Motion Control Method of Underwater Robot Based on Collaborative Filtering Technology

Abstract As countries in the world continue to deepen marine research and development and the need for national defense construction, unmanned underwater robots are playing an increasingly important role in scientific experiments, seabed oil and gas exploration and testing, and military affairs. The motion stability of underwater robots is the key to ensure the safe and reliable operation of underwater robots, and it is the main content of stability control research to make underwater robots return to the initial state of motion after being disturbed. Therefore, it becomes very important to study stability control methods. It’s necessary. This thesis first analyzes the motion characteristics of collaborative filtering technology in water, and establishes a dynamic model of an underwater robot system composed of main thrusters, double vertical thrusters and double-sided thrusters, and performs basic fluid flow for underwater robots.
Data Mining for Badminton On-the-Spot Tactics Analysis System

Badminton is one of the traditional competitive sports in China. However, with more and more information science and technology being applied in the field of sports, especially the application of decision support system based on data mining technology is becoming more and more popular, which poses a new challenge to whether our badminton can continue to defend its title in the international high-level competitions. At present, the information level of badminton in China is limited, and the analysis of a large number of game related data is only limited in the field of statistics. Therefore, it is necessary to introduce association rule mining to conduct in-depth analysis of the existing data, and find the rules related to the winning factors of athletes. At the same time, because of the disadvantages of traditional association rule algorithm, such as time-consuming, redundant rules and so on, it can not adapt to the fast-paced badminton game in efficiency. Aiming at this problem, this paper makes an exploratory research on the application of data mining technology in badminton field. This paper is selected from the research project of General Administration of sport of China, which is “the development and application of the software for monitoring the quality of badminton technical training and statistics of on-the-spot tactics based on tablet computer". It designs and implements the on-the-spot tactics analysis system of badminton, including data collection, data preprocessing and data mining, which can provide auxiliary support for coaches to make targeted training plans and on-the-spot tactics decisions hold on.
Türkiye’de Online Kitap Satışlarının Veri Madenciliği Yöntemiyle Analizi

Online sales networks occupy a greater space in our lives, day by day.The increasing competition and access to wide product range with just one click bring online book sales to the forefront.During current pandemic, online sales has been one of the top areas in terms of profitability.The research has a major importance in terms of increasing reading habit in Turkey and developing accurate strategies due to reader preferences.In the context of this research, best-selling books data between 2014-2019, obtained from popular web-sites, involving parameters such as name, author, genre and publisher of 508 books have been analyzed by data mining method (clustering -K Means algorithm) using RapidMiner.The findings within the scope of the parameters indicated the superiority of both native Turkish and foreign novels, while some thematic books declined.The results have been deeply evaluated particularly to guide authors at the idea stage and to determine a road map for publishers while bearing the costs.