Reviewerly/exHarmony huggingface dataset:
USING THE ONTOLOGICAL MODEL OF THE STEM–CENTER IN THE EDUCATIONAL PROCESS

The article is devoted to the practical aspects of using the ontological model of the STEM Center, which is built on the basis of cognitive information technology “Polyhedron”, in educational and research processes. Tools created using Microsoft and Google Spreadsheets are described. The application of the system of virtual museum Laboratories, a database of microorganisms, an ontological STEM classifier, a virtual laboratory of scientific publica­tions, ontological virtual laboratories of expeditions, and a cognitive system for selecting research papers is analyzed. All tools of the CIT “Polyhedron” can be represented as a taxonomy, object-oriented, or prism. Approaches to data processing using ranking and auditing, which are elements of the CIT “Polyhedron”, have been developed. This ap­proach is used in the selection of data by teachers and students, as well as in the processing of ontological knowledge bases, which is shown on the example of the microbial selection system. The database of a centralized information web-oriented educational environment consists of various types of educational programs. The ontologized program differs from the classical approaches to the interpretation of educational programs by a higher level of structured material and interactivity. Students and teachers can interact with structured curricula. Internal and external search tools are used to search for contexts based on the program’s terminology field. The ontological system of virtu­al museum laboratories provides a high level of structuring of information about objects and their virtualization. This allows you to use Museum ontologies during sightseeing classes. The database of microorganisms in the CIT “Polyhedron” system provided systematization of research knowledge in the educational process. CIT “Polyhedron” made it possible to identify the characteristics of research papers and systematize their processing in accordance with the user’s request.
High Performance Computing of Possible Minds

It has been shown that the space of possible minds is vast, actually infinite. Intellectology is a new field of study, which examines in more detail features of possible minds. Among the many open and unexplored questions in this field is the following: “Which activities can minds perform during their lifetime?” This question is very broad, thus our contribution here addresses the sub-question “Which non-boring activities can minds perform?” This issue is ethically relevant for human minds if the predicted significant extension of our lifetime materializes and we are then potentially challenged how to spend this additional time. The space of potential non-boring activities has been called “fun space.” We analyze the relation between various types of minds and the portion of the fun space, which is accessible for them. As a novel result, we demonstrate that human minds can experience two types of fun when transforming information to knowledge: novelty fun and process fun.
Objects

To a computer scientist, an object is something that can send and receive a set of standardized digital messages. Your maps app is an object
Reviewing the Effectiveness and Learning Outcomes of a 3D Virtual Museum

This comparative descriptive mixed methods pilot study reports results of a joint research pilot study between the University of North Texas (UNT) and the Autonomous University of the State of Mexico (UAEM). Data was collected and analyzed for effectiveness, usability, and knowledge acquisition in the Leopoldo Flores Museum located in Mexico, and its online 3dimensional replica. The primary results of this research show a) students using the virtual environment first and then visiting the museum exhibited better knowledge acquisition about the museum and had higher level of discourse when on the guided tour, and b) the virtual museum experience, when used alone, was a comparable experience to the actual museum guided tour in both knowledge gained and satisfaction.
Detection and Mapping of an Uncooperative Spinning Target under Low-light Illumination Condition

This paper investigates the simultaneous detection and mapping problem for inspecting an unknown and uncooperative target that is spinning in space. First, we apply a new unsupervised generative adversarial network (GAN) to enhance the low contrast and poor visibility images which are captured in low-light space illumination conditions. Second, due to the captured low-resolution (LR) images of the target contain small size of key-components, so that we propose a new small object detection network that combines a GAN-based super-resolution (SR) network and a FRCNN-based detection network to locate these objects. The SR network was used to reconstruct super-resolved images from the original LR images. Third, we utilize a SLAM-based algorithm to map and estimate the pose of the spinning target based on previous image enhancement. In summary, the integrated architecture has three components: a low-light enhancement GAN, a small object detection network, and a real-time SLAM system. The experimental results show that the integrated architecture achieves better visual quality and improves the awareness of an uncooperative spinning target.
Twin Analysis: A Closer Look at the Fraudulent Data of the Field Experiment in Shu, Mazar, Gino, Ariely, and Bazerman (2012)

The Data Colada blog post 98 presents evidence that the data in the paper in question were fabricated in two ways: the miles driven were generated as random numbers from a uniform distribution between 0 and 50,000, and the initial datasets in font ‘Calibri’ were duplicated in font ‘Cambria’ with baseline miles increased by random numbers drawn from a uniform distribution between 0 and 1,000. These manipulations alone do not create the desired effect (which consists of greater values of miles driven for persons in the Top-sign condition). Quite the opposite: randomizing the core variable in this way practically guarantees not finding the large effect shown in the original paper (t-stat > 10). Therefore, it must have been built into the data separately. To understand how this was done, this short note analyzes the duplicates (“twins”) that can be uniquely identified. The analysis suggests that for a subgroup of twins, the Top-sign and Bottom-sign conditions were changed in line with the desired effect.
Mutual sentiment

Sentiment analysis is a modern task in natural language processing and linguistics. Also referred to as opinion mining , it deals with different kinds of affective states: opinion, emotions, stance and evaluations. Sentiment itself is the polarity of these affective states. Taking analytical articles as source material for the study, several problems should be considered. Firstly, these texts broaden the understanding of the subject of opinion, because it does not coincide with the author of the text in the majority of cases. Secondly, subjects and objects of opinion are entities – words or word combinations with strictly denoted referent. In the paper only Named Entities, that are normally expressed by proper nouns, are considered. This kind of sentiment analysis requires deeper research of possible sentiment relations between entities and of lexical and grammatical influence on these relations. The paper is devoted to the study of the influence of the group of lexemes on opinion structure. The research shows that mutual sentiment can be presented as stable patterns.
Finding Predictors

In this chapter we consider methods of constructing predictors for class labels or numeric target attributes. However, in contrast to Chap. 8 , where we discussed methods for basically the same purpose, the methods in this chapter yield models that do not help much to explain the data or even dispense with models altogether. Nevertheless, they can be useful, namely if the main goal is good prediction accuracy rather than an intuitive and interpretable model. Especially artificial neural networks and support vector machines, which we study in Sects. 9.2 and 9.4, are known to outperform other methods w.r.t. accuracy in many tasks. However, due to the abstract mathematical structure of the prediction procedure, which is usually difficult to map to the application domain, the models they yield are basically “black boxes” and almost impossible to interpret in terms of the application domain. Hence they should be considered only if a comprehensible model that can easily be checked for plausibility is not required, and high accuracy is the main concern.
Research on the monitoring technology for geological disasters by the intelligent video recognition technology based on the optical flow

research-article Research on the monitoring technology for geological disasters by the intelligent video recognition technology based on the optical flow Share on Authors: Qingjia Meng Innovation Centre of Geological Environment Monitoring Engineering Technology, MNR, China and Center for Hydrogeology and Environmental Geology Survey, CGS, China Innovation Centre of Geological Environment Monitoring Engineering Technology, MNR, China and Center for Hydrogeology and Environmental Geology Survey, CGS, ChinaSearch about this author , Chenhui Wang Innovation Centre of Geological Environment Monitoring Engineering Technology, MNR, China and Center for Hydrogeology and Environmental Geology Survey, CGS, China Innovation Centre of Geological Environment Monitoring Engineering Technology, MNR, China and Center for Hydrogeology and Environmental Geology Survey, CGS, ChinaSearch about this author , Wei Guo Innovation Centre of Geological Environment Monitoring Engineering Technology, MNR, China and Center for Hydrogeology and Environmental Geology Survey, CGS, China Innovation Centre of Geological Environment Monitoring Engineering Technology, MNR, China and Center for Hydrogeology and Environmental Geology Survey, CGS, ChinaSearch about this author Authors Info & Claims ICSCC 2021: 2021 6th International Conference on Systems, Control and Communications (ICSCC)October 2021 Pages 54–59https://doi.org/10.1145/3510362.3510372Online:17 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Intelligent Techniques for Providing Effective Security to Cloud Databases

Cloud databases have been used in a spate of web-based applications in recent years owing to their capacity to store big data efficiently. In such a scenario, access control techniques implemented in relational databases are so modified as to suit cloud databases. The querying features of cloud databases are designed with facilities to retrieve encrypted data. The performance with respect to retrieval and security needs further improvements to ensure a secured retrieval process. In order to provide an efficient secured retrieval mechanism, a rule- and agent-based intelligent secured retrieval model has been proposed in this paper that analyzes the user, query and contents to be retrieved so as to effect rapid retrieval with decryption from the cloud databases. The major advantage of this retrieval model is in terms of its improved query response time and enhanced security of the storage and retrieval system. From the experiments conducted in this work, proposed model increased storage and access time and, in addition, intensified the security of the data stored in cloud databases.
Big Data

In his description of a “Universal Library” Lasswitz asks us to try and make sense of an almost unimaginable amount of information. Even in our increasingly digital world we don’t have to deal with anything remotely resembling a Universal Library. Nevertheless, as citizens navigating our connected world, we are all of us creating ever-increasing amounts of digital data. Algorithms are now routinely mining big data sets and finding hitherto unknown relationships. It’s a field of immense promise, but also one of great threat.
A Semantic-based Replacement for Event Image Privacy

Thanks to the continuous development of deep learning and the updating of deep neural networks, the accuracy of various computer vision tasks continue improving. On the one hand, the accuracy of image recognition is significantly improved. On the other hand, it also poses a higher challenge of image-based privacy preservation. Although traditional privacy protection methods such as cryptography methods can provide a good privacy protection, they are extremely inconvenient to use and cannot provide good image utility. In order to obtain a balance between image privacy and utility, we propose a privacy-preserving model based on image semantic replacement. We perform semantic replacement or obfuscation to multiple information. Taking the human figures as an example, the information includes faces, scenes, and dressing style. As that information contributes the most to the recognition, we define those items as the privacy of the original image. We replace the event information of the original image, so that the figures in the image can no longer be recognized. With this strategy, the image can still be detected by various detection networks, such as scene detection, which ensures utility. The framework consists of three parts: detection network, scene replacement network, and clothing replacement network. A comprehensive and quantitative experiment set proves the effectiveness of the proposed model.
DIEF: An Autopsy Module for Distributed Identification of E-mail Files from Disk Images

Hard drives are a predominant source of residual data in today’s digital environments. However, the increasing size of hard disks compounds the challenge of digital forensics, particularly in time-poor and resource-constrained forensic laboratories (e.g., due to the time required to recover residual data), using conventional digital investigation techniques. There are various types of information in the disk images such as e-mail data (a key source of information in disk images). However, it is time-consuming to identify and extract e-mail files from the disk images, when the hard disk size is big and contains large number of (other) files. We posit the importance of improving existing processes, for example by incorporating efficient and scalable processing mechanisms, to handle increasing volume of data. This study investigates the development of an Autopsy module for Distributed Identification of E-mail Files (DIEF). Initial experiments on the benchmark dataset show that DIEF achieves an average of 52% performance improvement in the process method, as compared to a traditional approach that does not employ distributed processing.
Stratified Radial Based Oversampling for Imbalanced Data Classification

The imbalanced learning methods aim to learn the unbiased classification models from massive class-imbalanced datasets. However, due to the data distributions affected by noise and borderline samples, many conventional resampling methods based on heuristic assumptions suffer from the lack of practicability and accuracy for real-world classification tasks. In order to effectively cope with these issues, in this work, we propose a novel Stratified Radial Based Oversampling (SRBO) oversampling method for imbalanced massive data classification. Starting from the data level, we first divide minority samples into safe points, dangerous points, and noise points, and the number of new points is allocated according to the number of the three types of samples, and then over-sampling is performed based on the Radial Based Oversampling (RBO). Compared with two state-of-the-art resampling classification methods 10 public imbalanced datasets, our method yields comparable or better results on the effectiveness and accuracy.
Secure Big Data Computing Based on Trusted Computing and Key Management

MapReduce system is widely used under the cloud computing architecture, and plays an important role in finance, medical health, scientific research, transportation and energy. Big data computing shows its incomparable convenience and quickness. But at the same time, more and more people begin to care about the security of big data platform, and worry about their own privacy security. Due to the sensitivity of the data involved in the above fields, users are exposed to great threats to privacy and security all the time. Big data platform may cause huge disaster due to system intrusion, data replacement and wrong result of calculation. The current solutions mainly focus on the encryption process before data transmission and storage, and the decryption process during data calculation, but fundamentally, these solutions can not prevent user data from being leaked by big data processing program, nor can they guarantee the accuracy of calculation results. A security oriented MapReduce architecture is proposed to provide a secure and reliable computing architecture by integrating big data processing framework, key management system and trusted computing module. Big data processing architecture controls the whole life cycle of cloud computing platform from creation, operation to resource recovery, and is the cornerstone of the whole architecture. The key management system ensures the security of data encryption and decryption by providing a complete set of key verification and authority authentication. Trusted computing architecture is the ultimate scheme to provide security guarantee for the whole architecture. Through the module authentication at the operating system level and the user-defined white list authority control, the security authentication of the whole system module is carried out. Through the above three modules, user data can be encrypted and decrypted before the big data platform is processed. Trusted computing can protect the security status of the big data platform in real time. The security oriented MapReduce architecture provides continuous and strong security guarantee for user data and calculation results.
Language Theory and Semantics

There are two key parts to any programming language, and these are its syntax and semantics.
Mining expertise of developers from software repositories

This paper presents a technique to mine the developers' contributions to explore their expertise in open source projects. The technique analyses the keywords that appear in the textual content of commits. It is a lightweight technique since the text in commits is analysed without making any syntactic code differencing. Each developer is linked with a list of keywords, with their frequencies, that appeared in his commits. Based on these keywords, three types of expertise are defined: unique, common and frequent. The identified expertise can help in identifying topics or issues that are handled by specific or group of developers. A tool is developed to automatically mine and analyse committed code changes to support expertise identification. A case study is presented on three open source projects to show how the proposed techniques can be applied. The observations of the study showed that frequent terms provide useful information about developers' expertise.
Endless loops

We present an algorithm for producing a seamless animated loop from a single image. The algorithm detects periodic structures, such as the windows of a building or the steps of a staircase, and generates a non-trivial displacement vector field that maps each segment of the structure onto a neighboring segment along a user- or auto-selected main direction of motion. This displacement field is used, together with suitable temporal and spatial smoothing, to warp the image and produce the frames of a continuous animation loop. Our cinemagraphs are created in under a second on a mobile device. Over 140,000 users downloaded our app and exported over 350,000 cinemagraphs. Moreover, we conducted two user studies that show that users prefer our method for creating surreal and structured cinemagraphs compared to more manual approaches and compared to previous methods.
Theory Driven Modeling as the Core of Software Development

Some experts opine that software is built in a primitive way. The role of modeling as a treatment for the weakness of software engineering became more important when the principles of Model Driven Architecture (MDA) appeared. Its main advantage is architectural separation of concerns. It showed the necessity of modeling and opened the way for software development to become an engineering discipline. However, this principle does not demonstrate its whole potential power in practice because of lack of mathematical accuracy in the very initial steps of software development. The sufficiency of modeling in software development is still disputable. The authors believe that software development in general (and modeling in particular) based on mathematical formalism in all of its stages and together with the implemented principle of architectural separation of concerns can become an important part of software engineering in its real sense. They propose the formalism by topological modeling of system functioning as the first step towards engineering.
Information Graphics

Information graphics include a wide variety of static and dynamic visual representations of information such as diagrams, statistical graphics, and maps. These displays take different forms depending on the purpose for which the graphic is being constructed (e.g., for thinking or communication), characteristics of the data, potential visual display forms, and whether or not the information graphic is interactive. Choosing an appropriate method for representing data requires a basic understanding of the perceptual and cognitive processing that occurs in the human visual system. Information graphics can be constructed using a wide array of tools, but are increasingly constructed using computer code and distributed through the internet.
Introduction

Abstract This chapter introduces the topic of the book: the philosophical foundations of AI, and in particular the powerful contemporary AI that guides our lives and receives so much attention in the media. It introduces the idea of a neural network, and poses the central puzzle of the book: when a given system’s outputs take the form of what appear to be acts of verbal communication, what is going on? When an AI system indicates, for example, that we are not creditworthy, is it saying that we are not? Can it speak? Along with posing this central question, the chapter also makes clear what the book is not about—ethics, for example—and areas in which the book might have relevance for extra-philosophical debates, such as the need for explainable AI.